Question: Mobile cloud sensing, big data, and 5G networks contribute to creating a smarter and more intelligent world.

This paper presents OpinionFinder, a pioneering system designed for in-depth subjectivity analysis, which has been developed by a collaborative team from the University of Pittsburgh, Cornell University, and the University of Utah. OpinionFinder is a sophisticated tool that enables the detailed examination and interpretation of subjective information. The system has been meticulously crafted by Theresa Wilson, Paul Hoffmann, Swapna Somasundaran, Jason Kessler, Janyce Wiebe, Yejin Choi, Claire Cardie, Ellen Riloff, and Siddharth Patwardhan. These experts, hailing from the Intelligent Systems Program, the Department of Computer Science, and the School of Computing, have pooled their knowledge and skills to create a tool that pushes the boundaries of subjectivity analysis. By harnessing advanced computational techniques, OpinionFinder can dissect and analyze subjective content with unprecedented precision, making it an invaluable resource for researchers and practitioners in various fields. Its capabilities are particularly relevant in today's data-driven world, where the ability to understand and interpret subjective opinions and sentiments is crucial for informed decision-making. The development of OpinionFinder underscores the importance of interdisciplinary collaboration in advancing the field of subjectivity analysis and underscores the potential for technology to enhance our understanding of human perspectives.

The tremendous growth in communication technology is shaping a hyper-connected network where billions or connected devices are producing a huge volume of data. Cellular and mobile network is a major contributor towards this technology shift and require new architectural paradigm to provide low latency, high performance in a resource constrained environment. 5G technology deployment with fully IP-based connectivity is anticipated by 2020. However, there is no standard established for 5G technology and many efforts are being made to establish a unified 5G stander. In this context, variant technology such as Software Defined Network (SDN) and Network Function virtualization (NFV) are the best candidate. SDN dissociate control plane from data plane and network management is done on the centralized control plane. In this paper, a survey on state of the art on the 5G integration with the SDN is presented. A comprehensive review is presented for the different integrated architectures of 5G wireless network and the generalized solutions over the period 2010–2016. This comparative analysis of the existing solutions of SDN-based cellular network (5G) implementations provides an easy and concise view of the emerging trends by 2020.

What role will ubiquitous sensing play in our understanding and experience of ecology in the future? What opportunities are created by weaving a continuously sampling, geographically dense web of sensors into the natural environment, from the ground up? In this article, we explore these questions holistically, and present our work on an environmental sensor network designed to support a diverse array of applications, interpretations, and artistic expressions, from primary ecological research to musical composition. Over the past four years, we have been incorporating our ubiquitous sensing framework into the design and implementation of a large-scale wetland restoration, creating a broad canvas for creative exploration at the landscape scale. The projects we present here span the development and wide deployment of custom sensor node hardware, novel web services for providing real-time sensor data to end user applications, public-facing user interfaces for open-ended exploration of the data, as well as more radical UI modalities, through unmanned aerial vehicles, virtual and augmented reality, and wearable devices for sensory augmentation. From this work, we distill the Networked Sensory Landscape, a vision for the intersection of ubiquitous computing and environmental restoration. Sensor network technologies and novel approaches to interaction promise to reshape presence, opening up sensorial connections to ecological processes across spatial and temporal scales.

Context-awareness is a critical aspect of safe navigation especially for the blind and visually impaired in unfamiliar environments. Existing mobile devices for context-aware navigation fall short in many cases due to their dependence on specific infrastructure requirements as well as having limited access to resources that could provide a wealth of contextual clues. In this paper, we propose a mobile-cloud collaborative approach for context-aware navigation by exploiting the computational power of resources made available by Cloud Computing providers as well as the wealth of location-specific resources available on the Internet. We propose an extensible system architecture that minimizes reliance on infrastructure, thus allowing for wide usability. We present a traffic light detector that we developed as an initial application component of the proposed system. We present preliminary results of experiments performed to test the appropriateness for the real-time nature of the application.

The improvement of the Quality of Life (QoL) and the enhancement of the Quality of Services (QoS) represent the main goal of every city evolutionary process. It is possible making cities smarter promoting innovative solutions by use of Information and Communication Technology (ICT) for collecting and analysing large amounts of data generated by several sources, such as sensor networks, wearable devices, and IoT devices spread among the city. The integration of different technologies and different IT systems, needed to build smart city applications and services, remains the most challenge to overcome. In the Smart City context, this paper intends to investigate the Smart Environment pillar, and in particular the aspect related to the implementation of Smart Energy Grid for citizens in the urban context. The innovative characteristic of the proposed solution consists of using the Blockchain technology to join the Grid, exchanging information, and buy/sell energy between the involved nodes (energy providers and private citizens), using the Blockchain granting ledger. Keywords— Information Technology, Smart City, Digital Revolution, Digital Innovation, Blockchain, Smart Energy Grid, Machine Learning.

The Internet of Things is anticipated to connect billions of embedded devices equipped with sensors to perceive their surroundings. Thereby, the state of the real world will be available online and in real-time and can be combined with other data and services in the Internet to realize novel applications such as Smart Cities, Smart Grids, or Smart Healthcare. This requires an open representation of sensor data and scalable search over data from diverse sources including sensors. In this paper we show how the Semantic Web technologies RDF (an open semantic data format) and SPARQL (a query language for RDF-encoded data) can be used to address those challenges. In particular, we describe how prediction models can be employed for scalable sensor search, how these prediction models can be encoded as RDF, and how the models can be queried by means of SPARQL.

The Third Generation Partnership Project (3GPP) has been working on developing specifications on Machine Type Communications (MTC), also known as Machine to Machine Communications (M2M), which is all part of Internet of Things (IoT), a technology that enables machines and devices to be inter-connected via the internet. This paper presents recent M2M/IoT feature enhancements in 3GPP. These features include architectural enhancements for inclusion of a service exposure framework, group based IoT management, efficient monitoring mechanisms for IoT devices, optimization of IoT device power consumption, and high latency communications. These M2M/IoT related functionalities and enhancements are a continuing effort within 3GPP to make the mobile network fulfil the ever changing M2M/IoT requirements, and in so doing allow operators/service providers to offer unique services.

The Internet of Things (IoT) is able to connect billions of devices and services at anytime in any place, with various applications. Recently, the IoT became an emerging technology. One of the most significant current research discussion topics on the IoT is about the smart car parking. A modern urban city has over a million of cars on its roads but it does not have enough parking space. Moreover, most of the contemporary researchers propose management of the data on cloud. However, this method may be considered as an issue since the raw data is sent promptly from distributed sensors to the parking area via cloud and then received back after it is processed. This is considered as an expensive technique in terms of the data transmission as well as the energy cost and consumption. While the majority of proposed solutions address the problem of finding unoccupied parking space and ignore some other critical issues such as information about the nearest car parking and the roads traffic congestion, this paper goes beyond and proposes the alternative method. The paper proposes a smart car parking system that will assist users to solve the issue of finding a parking space and to minimise the time spent in searching for the nearest available car park. In addition, it provides users with roads traffic congestion status. Moreover, the proposed system collects the raw data locally and extracts features by applying data filtering and fusion techniques to reduce the transmitted data over the network. After that, the transformed data is sent to the cloud for processing and evaluating by using machine learning algorithms.

The Internet of Things (IoT) is the result of many different enabling technologies such as embedded systems, wireless sensor networks, cloud computing, big-data, etc., which are used to gather, process, infer, and transmit data. Combining all these technologies requires a research effort to address all the challenges of these technologies, especially for sensing and delivering information from the physical world to cloud-hosted services. In this article we outline the most important issues related to standardization efforts, mobility of objects, networking and gateway access, and QoS support. In particular, we describe a novel IoT network architecture that integrates software defined networking (SDN) and data distribution service (DDS) middleware. The proposed architecture will improve service delivery of IoT systems and will bring flexibility to the network.

Environmental Sensor Networks (ESNs) facilitate the study of fundamental processes and the development of hazard response systems. They have evolved from passive logging systems that require manual downloading, into ‘intelligent’ sensor networks that comprise a network of automatic sensor nodes and communications systems which actively communicate their data to a Sensor Network Server (SNS) where these data can be integrated with other environmental datasets. The sensor nodes can be fixed or mobile and range in scale appropriate to the environment being sensed. ESNs range in scale and function and we have reviewed over 50 representative examples. Large Scale Single Function Networks tend to use large single purpose nodes to cover a wide geographical area. Localised Multifunction Sensor Networks typically monitor a small area in more detail, often with wireless adhoc systems. Biosensor Networks use emerging biotechnologies to monitor environmental processes as well as developing proxies for immediate use. In the future, sensor networks will integrate these three elements (Heterogeneous Sensor Networks). The communications system and data storage and integration (cyberinfrastructure) aspects of ESNs are discussed, along with current challenges which need to be addressed. We argue that Environmental Sensor Networks will become a standard research tool for future Earth System and Environmental Science. Not only do they provide a ‘virtual’ connection with the environment, they allow new field and conceptual approaches to the study of environmental processes to be developed. We suggest that although technological advances have facilitated these changes, it is vital that Earth Systems and Environmental Scientists utilise them. © 2006 Elsevier B.V. All rights reserved.

The average age growing of the urban population, with an increasing number of 65+ citizens, is calling for the cities to provide global services specifically geared to elderly people. In this context, collecting data from the elderly's environment and his/her habits and making them available in a structured way to third parties for analysis, is the first step towards the realization of innovative user-centric services. This paper presents a city-wide general IoT-based sensing infrastructure and a data management layer providing some REST and Linked Open Data Application Programming Interfaces (APIs) that collect and present data related to elderly people. In particular, this architecture is used by the H2020 City4Age project to help geriatricians in identifying the onset of Mild Cognitive Impairment (MCI) disease.


Question: Mobile cloud sensing, big data, and 5G networks contribute to creating an intelligent and smart world.

This paper introduces ARSA, an Autoregressive Sentiment-Aware model designed to forecast sales performance by leveraging sentiment information derived from blogs. Given the vast popularity of Weblogs (blogs), they offer a rich source of data reflecting public sentiment and opinions, which can be harnessed for valuable insights. We explore the challenge of extracting sentiment data from blogs and its application in sales prediction. To address the intricate nature of sentiments, we propose S-PLSA (Sentiment Probabilistic Latent Semantic Analysis), which perceives a blog post as a document generated by several hidden sentiment factors. Training the S-PLSA model on blog data yields a concise summary of the embedded sentiment information. Subsequently, ARSA utilizes this sentiment data to predict sales performance, integrating sentiment awareness into an autoregressive model. We conduct extensive experiments using a movie dataset, comparing ARSA with alternative models that disregard sentiment information and a model employing a distinct feature selection technique. Our findings validate the efficacy and superiority of the proposed ARSA approach in predicting sales performance by incorporating sentiment analysis from blogs.

This paper considers current paradigms in computing and outlines the most important aspects concerning their reliability. The Fog computing paradigm as a non-trivial extension of the Cloud is considered and the reliability of the networks of smart devices are discussed. Combining the reliability requirements of grid and cloud paradigms with the reliability requirements of networks of sensor and actuators it follows that designing a reliable Fog computing platform is feasible.

The recent proliferation of increasingly capable mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to a crowd of participating workers that carry various mobile devices. Aware of the paramount importance of effectively incentivizing participation in such systems, the research community has proposed a wide variety of incentive mechanisms. However, different from most of these existing mechanisms which assume the existence of only one data requester, we consider MCS systems with multiple data requesters, which are actually more common in practice. Specifically, our incentive mechanism is based on double auction, and is able to stimulate the participation of both data requesters and workers. In real practice, the incentive mechanism is typically not an isolated module, but interacts with the data aggregation mechanism that aggregates workers' data. For this reason, we propose CENTURION, a novel integrated framework for multi-requester MCS systems, consisting of the aforementioned incentive and data aggregation mechanism. CENTURION's incentive mechanism satisfies truthfulness, individual rationality, computational efficiency, as well as guaranteeing non-negative social welfare, and its data aggregation mechanism generates highly accurate aggregated results. The desirable properties of CENTURION are validated through both theoretical analysis and extensive simulations.

To promote tighter collaboration between the IEEE Intelligent Transportation Systems Society and the pervasive computing research community, the authors introduce the ITS Society and present several pervasive computing-related research topics that ITS Society researchers are working on. This department is part of a special issue on Intelligent Transportation.

This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of mobile data analysis methodologies. First, we review the Lausanne Data Collection Campaign (LDCC) – an initiative to collect unique, longitudinal smartphone data set for the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC; describe the specific data sets used in each of them; discuss the key design and implementation aspects introduced in order to generate privacypreserving and scientifically relevant mobile data resources for wider use by the research community; and summarize the main research trends found among the 100+ challenge submissions. We finalize by discussing the main lessons learned from the participation of several hundred researchers worldwide in the MDC Tracks.

Mobile phone sensing is a new paradigm which takes advantage of the pervasive smartphones to collect and analyze data beyond the scale of what was previously possible. In a mobile phone sensing system, the platform recruits smartphone users to provide sensing service. Existing mobile phone sensing applications and systems lack good incentive mechanisms that can attract more user participation. To address this issue, we design incentive mechanisms for mobile phone sensing. We consider two system models: the platform-centric model where the platform provides a reward shared by participating users, and the user-centric model where users have more control over the payment they will receive. For the platform-centric model, we design an incentive mechanism using a Stackelberg game, where the platform is the leader while the users are the followers. We show how to compute the unique Stackelberg Equilibrium, at which the utility of the platform is maximized, and none of the users can improve its utility by unilaterally deviating from its current strategy. For the user-centric model, we design an auction-based incentive mechanism, which is computationally efficient, individually rational, profitable, and truthful. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our incentive mechanisms.

Body Area Networks (BANs) are emerging as enabling technology for many human-centered application domains such as health-care, sport, fitness, wellness, ergonomics, emergency, safety, security, and sociality. A BAN, which basically consists of wireless wearable sensor nodes usually coordinated by a static or mobile device, is mainly exploited to monitor single assisted livings. Data generated by a BAN can be processed in real-time by the BAN coordinator and/or transmitted to a server-side for online/offline processing and long-term storing. A network of BANs worn by a community of people produces large amount of contextual data that require a scalable and efficient approach for elaboration and storage. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of body sensor data streams. In this paper, we motivate the introduction of Cloud-assisted BANs along with the main challenges that need to be addressed for their development and management. The current state-of-the-art is overviewed and framed according to the main requirements for effective Cloud-assisted BAN architectures. Finally, relevant open research issues in terms of efficiency, scalability, security, interoperability, prototyping, dynamic deployment and management, are discussed.

The Internet of Things (IoT) concept is evolving rapidly and influencing new developments in various application domains, such as the Internet of Mobile Things (IoMT), Autonomous Internet of Things (A-IoT), Autonomous System of Things (ASoT), Internet of Autonomous Things (IoAT), Internet of Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc. that are progressing/advancing by using IoT technology. The IoT influence represents new development and deployment challenges in different areas such as seamless platform integration, context based cognitive network integration, new mobile sensor/actuator network paradigms, things identification (addressing, naming in IoT) and dynamic things discoverability and many others. The IoRT represents new convergence challenges and their need

The term big data occurs more frequently now than ever before. A large number of fields and subjects, ranging from everyday life to traditional research fields (i.e., geography and transportation, biology and chemistry, medicine and rehabilitation), involve big data problems. The popularizing of various types of network has diversified types, issues, and solutions for big data more than ever before. In this paper, we review recent research in data types, storage models, privacy, data security, analysis methods, and applications related to network big data. Finally, we summarize the challenges and development of big data to predict current and future trends.

This article discusses the challenges for Database Management in the Internet of Things. We provide scenarios to illustrate the new world that will be produced by the Internet of Things, where physical objects are fully integrated into the information highway. We discuss the different types of data that will be part of the Internet of Things. These include identification, positional, environmental, historical, and descriptive data. We consider the challenges brought by the need to manage vast quantities of data across heterogeneous systems. In particular, we consider the areas of querying, indexing, process modeling, transaction handling, and integration of heterogeneous systems. We refer to the earlier work that might provide solutions for these challenges. Finally we discuss a road map for the Internet of Things and respective technical priorities.

Vehicular applications and communications technologies are often referred to as vehicle-to-everything (V2X), which is classified into four different types: vehicle-to-vehicle (V2V), vehicle- to-infrastructure (V2I), vehicle-to-network (V2N), and vehicle-to-pedestrian (V2P) [1]. V2X related research projects, field tests, and regulatory work have been promoted in different countries and regions. In May 2015, the Ministry of Industry and Information Technology (MIIT) of China explained national strategies, "Made in China 2025," about intelligent connected vehicles. In 2020 and 2025, the overall technology and key technologies for intelligent driver assistance and automatic driving will be available in China, respectively [2]. V2X solutions are the critical technologies to support the realization of such visions. Although IEEE 802.11p has been selected as the technology for V2X communications in some countries such as the United States and in Europe, the intrinsic characteristics of IEEE 802.11p have confined the technology to support low latency with high reliability [3, 4]. Standardization of Long Term Evolution (LTE)-based V2X is being actively conducted by the Third Generation Partnership Project (3GPP) to provide the solution for V2X communications that benefit from the global deployment and fast commercialization of LTE systems. Because of the wide deployment of LTE networks, V2I and V2N services can be provided with high data rate, comprehensive quality of service (QoS) support, ubiquitous coverage, and high penetration rate [5]. Meanwhile, LTE can be extended to support V2V direct communications based on device-to-device (D2D) sidelink design to satisfy the QoS requirements, such as low latency, high reliability, and high speed in the case of high vehicle density [6].


Question: Mobile cloud sensing, big data, and 5G networks contribute to creating an intelligent and smart world.

This document introduces a bootstrapping technique aimed at identifying and learning detailed linguistic patterns for subjective expressions, also known as opinionated language. Utilizing high-precision classifiers, the method autonomously labels untagged data to construct a sizable training dataset. This dataset is subsequently fed into an extraction pattern learning algorithm. The patterns derived from this process are subsequently employed to pinpoint additional subjective sentences. Through iterative application, the bootstrapping technique is capable of acquiring a diverse array of subjective patterns, effectively boosting recall without compromising the high level of precision.

Regarding the question on the integration of mobile cloud sensing, big data, and 5G networks to foster an intelligent and smart world, the concept parallels the bootstrapping process in its iterative and self-enhancing nature. Just as the bootstrapping technique for subjective expression patterns continuously improves through successive iterations, the amalgamation of mobile cloud sensing, big data, and 5G networks in smart world applications can also evolve and optimize over time. The high-precision classifiers in the bootstrapping process can be likened to the sophisticated algorithms used in big data analysis, which are essential for extracting meaningful insights from vast datasets. Similarly, the expansion of recall in the bootstrapping technique is analogous to the broadened scope of data collection and analysis possible with mobile cloud sensing and 5G networks, leading to more comprehensive and intelligent decision-making in various sectors of the smart world.

The Internet of Things (IoT), often referred as the future Internet; is a collection of interconnected devices integrated into the world-wide network that covers almost everything and could be available anywhere. IoT is an emerging technology and aims to play an important role in saving money, conserving energy, eliminating gap and better monitoring for intensive management on a routine basis. On the other hand, it is also facing certain design constraints such as technical challenges, social challenges, compromising privacy and performance tradeoffs. This paper surveys major technical limitations that are hindering the successful deployment of the IoT such as standardization, interoperability, networking issues, addressing and sensing issues, power and storage restrictions, privacy and security, etc. This paper categorizes the existing research on the technical constraints that have been published in the recent years. With this categorization, we aim to provide an easy and concise view of the technical aspects of the IoT. Furthermore, we forecast the changes influenced by the IoT. This paper predicts the future and provides an estimation of the world in year 2020 and beyond. Keywords—Internet of Things; Future Internet; Next generation network issues; World-wide network; 2020

Almost all mobile communication systems today use spectrum in the range of 300 MHz-3 GHz. In this article, we reason why the wireless community should start looking at the 3-300 GHz spectrum for mobile broadband applications. We discuss propagation and device technology challenges associated with this band as well as its unique advantages for mobile communication. We introduce a millimeter-wave mobile broadband (MMB) system as a candidate next generation mobile communication system. We demonstrate the feasibility for MMB to achieve gigabit-per-second data rates at a distance up to 1 km in an urban mobile environment. A few key concepts in MMB network architecture such as the MMB base station grid, MMB interBS backhaul link, and a hybrid MMB + 4G system are described. We also discuss beamforming techniques and the frame structure of the MMB air interface.

With the advancement of information and communication technology, data are being generated at an exponential rate via various instruments and collected at an unprecedented scale. Such large volume of data generated is referred to as big data, which now are revolutionizing all aspects of our life ranging from enterprises to individuals, from science communities to governments, as they exhibit great potentials to improve efficiency of enterprises and the quality of life. To obtain nontrivial patterns and derive valuable information from big data, a fundamental problem is how to properly place the collected data by different users to distributed clouds and to efficiently analyze the collected data to save user costs in data storage and processing, particularly the cost savings of users who share data. By doing so, it needs the close collaborations among the users, by sharing and utilizing the big data in distributed clouds due to the complexity and volume of big data. Since computing, storage and bandwidth resources in a distributed cloud usually are limited, and such resource provisioning typically is expensive, the collaborative users require to make use of the resources fairly. In this paper, we study a novel collaboration- and fairness-aware big data management problem in distributed cloud environments that aims to maximize the system throughout, while minimizing the operational cost of service providers to achieve the system throughput, subject to resource capacity and user fairness constraints. We first propose a novel optimization framework for the problem. We then devise a fast yet scalable approximation algorithm based on the built optimization framework. We also analyze the time complexity and approximation ratio of the proposed algorithm. We finally conduct experiments by simulations to evaluate the performance of the proposed algorithm. Experimental results demonstrate that the proposed algorithm is promising, and outperforms other heuristics.

Mobile Cloud Computing (MCC) has draw extensive research attentions due to the increasingly demand on the energy consumption and execution time constraints on Mobile Devices (MDs). MCC has been well investigated in the scenarios when workload is offloaded to a remote cloud or a cloudlet. However, in those scenarios, infrastructure is required to provide connections between MDs and the cloud#x002F;cloudlet. To facilitate MCC for environment when infrastructure is not available, Ad-hoc MCC which allows MDs to share and process workload coordinately is discussed [1]. In this study, the detailed resource allocation problem in Ad-hoc MCC, including how to assign tasks to MDs, how tasks are executed on each MD, and how to arrange task and task result return transmissions so that interference among different MDs can be avoided are carefully studied. Since the problem is NP-hard, a heuristic algorithm is then introduced. As shown in the evaluation section, when no infrastructure is available, compared with the case when an application is executed solely on a single MD, the proposed algorithm can reduce an application's response time significantly.

The Internet of healthcare things is essentially a new model that changes the way of the delivery and management of healthcare services. It utilizes digital sensors and cloud computing to present a quality healthcare service outside of the classical hospital environment. This resulted in the emergence of a new class of online web 4.0 services, which are termed “cloud healthcare services”. Cloud healthcare services offer a straightforward opportunity for patients to communicate with healthcare professionals and utilize their personal IoHT devices to obtain timely and accurate medical guidance and decisions. The personal IoHT devices integrate sensed health data at a central cloud healthcare service to extract useful health insights for wellness and preventive care strategies. However, the present practices for cloud healthcare services rely on a centralized approach, where patients’ health data are collected and stored on servers, located at remote locations, which might be functioning under data privacy laws somewhat different from the ones applied where the service is running. Promoting a privacy respecting cloud services encourages patients to actively participate in these healthcare services and to routinely provide an accurate and precious health data about themselves. With the emergence of fog computing paradigm, privacy protection can now be enforced at the edge of the patient’s network regardless of the location of service providers. In this paper, a framework for cloud healthcare recommender service is presented. We depicted the personal gateways at the patients’ side act as intermediate nodes (called fog nodes) between IoHT devices and cloud healthcare services. A fog-based middleware will be hosted on these fog nodes for an efficient aggregation of patients generated health data while maintaining the privacy and the confidentiality of their health profiles. The proposed middleware executes a two-stage concealment process that utilizes the hierarchical nature of IoHT devices. This will unburden the constrained IoHT devices from performing intensive privacy preserving processes. At that, the patients will be empowered with a tool to control the privacy of their health data by enabling them to release their health data in a concealed form. The further processing at the cloud healthcare service continues over the concealed data by applying the proposed protocols. The proposed solution was integrated into a scenario related to preserving the privacy of the patients’ health data when utilized by a cloud healthcare recommender service to generate health insights. Our approach induces a straightforward solution with accurate results, which are beneficial to both patients and service providers.

Wireless networks such as mobile networks, with their inflexible and expensive network infrastructure, are facing various challenges in efficiently handling the exponentially growing traffic demands of users. Hence, mobile network operators (MNOs) are looking forward to novel networking paradigms which could simplify the task of network management and control and allow faster deployment of newer solutions on top of existing hardware by software updates. Software defined networking (SDN) is a revolutionary technology which makes networks more agile and flexible by separation of data plane and control plane tasks. SDN is playing a key role in deploying mobile network services on network function virtualization (NFV) infrastructure for elastic and flexible deployment of core network services e.g., virtualization of evolved packet core in LTE to efficiently handle huge control signal overhead and traffic demands from machine-to-machine and internet of things devices. Besides, NFV and SDN are offering scalable, flexible and adaptable network service chaining platforms as a replacement for inflexible middleboxes. Mobile edge computing (MEC) is a new evolving platform which brings together IT services and cloud computing for offering end-users network aware services and solutions. In this survey, we enumerate numerous issues and challenges in designing SDN based wireless networks and review various SDN based seminal solutions for 4G/5G. Finally, this survey presents the role of SDN, NFV and MEC in designing 5G networks.

At the start of the second decade of 21th century, the time has come to make the Smart Houses a reallity for regular use. The different parts of a Smart House are researched but there are still distances from an applicable system, using the modern technology. In this paper we present an overview of the Smart House subsystems necessary for controlling the house using a mobile application efficiently and securely. The sequence diagram of the mobile application connectiing to the server application and also the usecases possible are presented. The challenges faced in designing the mobile application and illustrating the updated house top plane view in that application, are discussed and soloutions are adapted for it. Finally the designed mobile application was implemented and the important sections of it were described, such as the interactive house top view map which indicates the status of the devices using predefined icons. The facilities to manage the scheduled tasks and defined rules are also implemented in this mobile application that was developed for use in Windows Mobile platform. This application has the capability of connecting to the main server using GPRS mobile internet and SMS. This system is expected to be an important step towards a unified system structure that can be used efficiently in near future regular houses. Keywords—Smart House, Mobile Application, Remote Control, Automated Home, Windows Mobile.

Solid waste management is one of the most important challenges in urban areas throughout the world and it is becoming a critical issue in developing countries where a rapid increase in population has been observed. Waste collection is a complex process that requires the use of large amount of money and an elaborate management of logistics. In this paper an approch to smart waste collection is proposed able to improve and optimize the handling of solid urban waste. Context of smart waste management requires interconnection among heterogeneous devices and data sharing involving a large amount of people. Smart-M3 platform solves these problems offering a high degree of decoupling and scalability.Waste collection is made by real-time monitoring the level of bin's fullness through sensors placed inside the containers. This method enables to exempt from collecting semi-empty bins. Furthermore, incoming data can be provided to decisional algorithms in order to determine the optimal number of waste vehicles or bins to distribute in the territory. The presented solution gives important advantages for both service providers and consumers. The formers could obtain a sensible cost reduction. On the other hand, users may benefit from a higher level of service quality. In order to make users feel closer to their community, they can interact with the system to be aware about the fulness state of the nearest bins. Finally, a mechanism for collecting “green points” was introduced for encouraging citizens to recycle.

In past few years, advancement in mobile applications and their integration with Cloud computing services has introduced a new computing paradigm known as Mobile Cloud Computing. Although Cloud services support a wide range of mobile applications, access to these services suffers from several performance issues such as WAN latency, jitter, and packet losses. Cloudlet frameworks are proposed to overcome these performance issues. More specifically, Cloudlets aim to bring the Cloud or a specific part of the Cloud closer to the mobile device by utilizing proximate computing resources to perform compute-intensive tasks. This paper presents a comprehensive survey on the state-of-the-art mobile Cloudlet architectures. We also classify the state-of-the-art Cloudlet solutions by presenting a hierarchical taxonomy. Moreover, the areas of Cloudlet applications are also identified and presented. Cloudlet discovery, resource management, data security, mobility, application offloading, and most importantly incentives to deploy a Cloudlet are areas that still needs to be investigated by the research community. The critical aspects of the current Cloudlet frameworks in Mobile Cloud Computing are analyzed to determine the strengths and weaknesses of the frameworks. The similarities and differences of the frameworks based on the important parameters, such as scalability, mobility support, Internet dependency, dynamic configuration, energy savings, and execution cost, are also investigated. The requirements for deploying the Cloudlet in a Local Wireless Network are also highlighted and presented. We also discuss open research challenges that Cloudlet deployments face in Local Wireless Networks. & 2015 Elsevier Ltd. All rights reserved.

Nowadays, the many traffic information are extracted from CCTV. The road occupancy, vehicle speed, accident detection, traffic collision and weather information are calculated in CCTV. These are big data comes from varying sources, such as, social sites or mobile phone GPS signals and so on. The Hadoop and HBase can store and analyze these real-time big data in a distributed processing framework. This framework can be designed as flexible and scalable framework using distributed CEP that process massive real-time traffic data and ESB that integrates other services. In this paper, we propose a new architecture for distributed processing that enables big data processing on the road traffic data with specially weather information and its related information analysis. We tested the proposed framework on road traffic data gathering 41 CCTV from Icheon to Gangneung freeway section in Korea. And the forecasting method is introduced at intermediate local area between CCTV.


Question: Mobile cloud sensing, big data, and 5G networks contribute to creating an intelligent and smart world.

This paper introduces an innovative method for conducting phrase-level sentiment analysis that initially classifies expressions as either neutral or polar. Subsequently, it clarifies the polarity of these polar expressions within their specific contexts. This approach enables the system to accurately ascertain the contextual polarity for a significant portion of sentiment-related expressions, thereby delivering markedly improved outcomes compared to baseline methods. By recognizing the nuanced sentiment orientations within various contexts, this technique significantly enhances the precision of sentiment analysis, making it a valuable tool in understanding the subtleties of public opinion and discourse analysis.

This paper addresses the Internet of Things. Main enabling factor of this promising paradigm is the integration of several technologies and communications solutions. Identification and tracking technologies, wired and wireless sensor and actuator networks, enhanced communication protocols (shared with the Next Generation Internet), and distributed intelligence for smart objects are just the most relevant. As one can easily imagine, any serious contribution to the advance of the Internet of Things must necessarily be the result of synergetic activities conducted in different fields of knowledge, such as telecommunications, informatics, electronics and social science. In such a complex scenario, this survey is directed to those who want to approach this complex discipline and contribute to its development. Different visions of this Internet of Things paradigm are reported and enabling technologies reviewed. What emerges is that still major issues shall be faced by the research community. The most relevant among them are addressed in details. 2010 Elsevier B.V. All rights reserved.

The dynamic mobility and limitations in computational power, battery resources, and memory availability are main bottlenecks in fully harnessing mobile devices as data mining platforms. Therefore, the mobile devices are augmented with cloud resources in mobile edge cloud computing (MECC) environments to seamlessly execute data mining tasks. The MECC infrastructures provide compute, network, and storage services in one-hop wireless distance from mobile devices to minimize the latency in communication as well as provide localized computations to reduce the burden on federated cloud systems. However, when and how to offload the computation is a hard problem. In this paper, we present an opportunistic computation offloading scheme to efficiently execute data mining tasks in MECC environments. The scheme provides the suitable execution mode after analyzing the amount of unprocessed data, privacy configurations, contextual information, and available on-board local resources (memory, CPU, and battery power). We develop a mobile application for online activity recognition and evaluate the proposed scheme using the event data stream of 5 million activities collected from 12 users for 15 days. The experiments show significant improvement in execution time and battery power consumption resulting in 98% data reduction.

BACKGROUND
Mobile phone sensors can be used to develop context-aware systems that automatically detect when patients require assistance. Mobile phones can also provide ecological momentary interventions that deliver tailored assistance during problematic situations. However, such approaches have not yet been used to treat major depressive disorder.

OBJECTIVE
The purpose of this study was to investigate the technical feasibility, functional reliability, and patient satisfaction with Mobilyze!, a mobile phone- and Internet-based intervention including ecological momentary intervention and context sensing.

METHODS
We developed a mobile phone application and supporting architecture, in which machine learning models (ie, learners) predicted patients' mood, emotions, cognitive/motivational states, activities, environmental context, and social context based on at least 38 concurrent phone sensor values (eg, global positioning system, ambient light, recent calls). The website included feedback graphs illustrating correlations between patients' self-reported states, as well as didactics and tools teaching patients behavioral activation concepts. Brief telephone calls and emails with a clinician were used to promote adherence. We enrolled 8 adults with major depressive disorder in a single-arm pilot study to receive Mobilyze! and complete clinical assessments for 8 weeks.

RESULTS
Promising accuracy rates (60% to 91%) were achieved by learners predicting categorical contextual states (eg, location). For states rated on scales (eg, mood), predictive capability was poor. Participants were satisfied with the phone application and improved significantly on self-reported depressive symptoms (beta(week) = -.82, P < .001, per-protocol Cohen d = 3.43) and interview measures of depressive symptoms (beta(week) = -.81, P < .001, per-protocol Cohen d = 3.55). Participants also became less likely to meet criteria for major depressive disorder diagnosis (b(week) = -.65, P = .03, per-protocol remission rate = 85.71%). Comorbid anxiety symptoms also decreased (beta(week) = -.71, P < .001, per-protocol Cohen d = 2.58).

CONCLUSIONS
Mobilyze! is a scalable, feasible intervention with preliminary evidence of efficacy. To our knowledge, it is the first ecological momentary intervention for unipolar depression, as well as one of the first attempts to use context sensing to identify mental health-related states. Several lessons learned regarding technical functionality, data mining, and software development process are discussed.

TRIAL REGISTRATION
Clinicaltrials.gov NCT01107041; http://clinicaltrials.gov/ct2/show/NCT01107041 (Archived by WebCite at http://www.webcitation.org/60CVjPH0n).

Traditionally, the vehicle has been the extension of the man's ambulatory system, docile to the driver's commands. Recent advances in communications, controls and embedded systems have changed this model, paving the way to the Intelligent Vehicle Grid. The car is now a formidable sensor platform, absorbing information from the environment (and from other cars) and feeding it to drivers and infrastructure to assist in safe navigation, pollution control and traffic management. The next step in this evolution is just around the corner: the Internet of Autonomous Vehicles. Pioneered by the Google car, the Internet of Vehicles will be a distributed transport fabric capable to make its own decisions about driving customers to their destinations. Like other important instantiations of the Internet of Things (e.g., the smart building), the Internet of Vehicles will have communications, storage, intelligence, and learning capabilities to anticipate the customers' intentions. The concept that will help transition to the Internet of Vehicles is the Vehicular Cloud, the equivalent of Internet cloud for vehicles, providing all the services required by the autonomous vehicles. In this article, we discuss the evolution from Intelligent Vehicle Grid to Autonomous, Internet-connected Vehicles, and Vehicular Cloud.

Smart learning has become a new term to describe technological and social developments (e.g., Big and Open Data, Internet of Things, RFID, and NFC) enable effective, efficient, engaging and personalized learning. Collecting and combining learning analytics coming from different channels can clearly provide valuable information in designing and developing smart learning. Although, the potential of learning analytics to enable smart learning is very promising area, it remains non-investigated and even ill-defined concept. The paper defines the subset of learning analytics that focuses on supporting the features and the processes of smart learning, under the term Smart Learning Analytics. This is followed by a brief discussion on the prospects and drawbacks of Smart Learning Analytics and their recent foundations and developments in the area of Video-Based Learning. Drawing from our experience with the recent international workshops in Smart Environments and Analytics in Video-Based Learning, we present the state-of-the-art developments as well as the four selected contributions. The paper further draws attention to the great potential and need for research in the area of Smart Learning Analytics.

Big Data has drawn huge attention from researchers in information sciences, decision makers in governments and enterprises. However, there is a lot of potential and highly useful value hidden in the huge volume of data. Data is the new oil, but unlike oil data can be refined further to create even more value. Therefore, a new scientific paradigm is born as data-intensive scientific discovery, also known as Big Data. The growth volume of real-time data requires new techniques and technologies to discover insight value. In this paper we introduce the Big Data real-time analytics model as a new technique. We discuss and compare several Big Data technologies for real-time processing along with various challenges and issues in adapting Big Data. Realtime Big Data analysis based on cloud computing approach is our future research direction.

This paper discusses potential security requirements and mechanisms for 5G mobile networks. It does not intend to do so exhaustively, but rather aims at initiating and spurring the work towards a 5G security architecture.

Many visions of the future include people immersed in an environment surrounded by sensors and intelligent devices, which use smart infrastructures to improve the quality of life and safety in emergency situations. Ubiquitous communication enables these sensors or intelligent devices to communicate with each other and the user or a decision maker by means of ad hoc wireless networking. Organization and optimization of network resources are essential to provide ubiquitous communication for a longer duration in large-scale networks and are helpful to migrate intelligence from higher and remote levels to lower and local levels. In this paper, distributed energy-efficient deployment algorithms for mobile sensors and intelligent devices that form an Ambient Intelligent network are proposed. These algorithms employ a synergistic combination of cluster structuring and a peer-to-peer deployment scheme. An energy-efficient deployment algorithm based on Voronoi diagrams is also proposed here. Performance of our algorithms is evaluated in terms of coverage, uniformity, and time and distance traveled until the algorithm converges. Our algorithms are shown to exhibit excellent performance.

Agriculture is a major source of earning of Indians and agriculture has made a big impact on India's economy. The development of crops for a better yield and quality deliver is exceptionally required. So suitable conditions and suitable moisture in beds of crop can play a major role for production.. Mostly irrigation is done by tradition methods of stream flows from one end to other. Such supply may leave varied moisture levels in filed. The administration of the water system can be enhanced utilizing programmed watering framework This paper proposes a programmed water system with framework for the terrains which will reduce manual labour and optimizing water usage increasing productivity of crops. For formulating the setup, Arduino kit is used with moisture sensor with Wi-Fi module. Our experimental setup is connected with cloud framework and data is acquisition is done. Then data is analysed by cloud services and appropriate recommendations are given.

With the explosion of social media sites and proliferation of digital computing devices and Internet access, massive amounts of public data is being generated on a daily basis. Efficient techniques/ algorithms to analyze this massive amount of data can provide near real-time information about emerging trends and provide early warning in case of an imminent emergency (such as the outbreak of a viral disease). In addition, careful mining of these data can reveal many useful indicators of socioeconomic and political events, which can help in establishing effective public policies. The focus of this study is to review the application of big data analytics for the purpose of human development. The emerging ability to use big data techniques for development (BD4D) promises to revolutionalize healthcare, education, and agriculture; facilitate the alleviation of poverty; and help to deal with humanitarian crises and violent conflicts. Besides all the benefits, the large-scale deployment of BD4D is beset with several challenges due to the massive size, fast-changing and diverse nature of big data. The most pressing concerns relate to efficient data acquisition and sharing, establishing of context (e.g., geolocation and time) and veracity of a dataset, and ensuring appropriate privacy. In this study, we provide a review of existing BD4D work to study the impact of big data on the development of society. In addition to reviewing the important works, we also highlight important challenges and open issues.


Question: Mobile cloud sensing, big data, and 5G networks contribute to creating an intelligent and smart world.

In the realm of mobile phone sensing, where smartphones are harnessed to gather and analyze vast amounts of data, the development of effective incentive mechanisms becomes crucial for enhancing user participation. This paper presents innovative incentive designs for mobile phone sensing systems, aiming to overcome the limitations of current applications and platforms that often struggle to attract sufficient user engagement. Two distinct system models are explored: the platform-centric approach, where the central authority offers a shared reward to participating users, and the user-centric model, granting users greater autonomy over their compensation. For the platform-centric model, a Stackelberg game framework is employed to devise an incentive mechanism. In this setup, the platform acts as the leader, while users follow. The method to calculate the unique Stackelberg Equilibrium is detailed, ensuring that the platform's utility is maximized and no user can gain by deviating from their optimal strategy. In the user-centric model, an auction-based incentive mechanism is designed, characterized by computational efficiency, individual rationality, profitability, and truthfulness. Extensive simulations are conducted to assess the performance and confirm the theoretical attributes of the proposed incentive mechanisms, demonstrating their effectiveness in promoting user participation in mobile phone sensing systems.

Context-awareness is a critical aspect of safe navigation especially for the blind and visually impaired in unfamiliar environments. Existing mobile devices for context-aware navigation fall short in many cases due to their dependence on specific infrastructure requirements as well as having limited access to resources that could provide a wealth of contextual clues. In this paper, we propose a mobile-cloud collaborative approach for context-aware navigation by exploiting the computational power of resources made available by Cloud Computing providers as well as the wealth of location-specific resources available on the Internet. We propose an extensible system architecture that minimizes reliance on infrastructure, thus allowing for wide usability. We present a traffic light detector that we developed as an initial application component of the proposed system. We present preliminary results of experiments performed to test the appropriateness for the real-time nature of the application.

Many sensor networks have been deployed to monitor Earth's environment, and more will follow in the future. Environmental sensors have improved continuously by becoming smaller, cheaper, and more intelligent. Due to the large number of sensor manufacturers and differing accompanying protocols, integrating diverse sensors into observation systems is not straightforward. A coherent infrastructure is needed to treat sensors in an interoperable, platform-independent and uniform way. The concept of the Sensor Web reflects such a kind of infrastructure for sharing, finding, and accessing sensors and their data across different applications. It hides the heterogeneous sensor hardware and communication protocols from the applications built on top of it. The Sensor Web Enablement initiative of the Open Geospatial Consortium standardizes web service interfaces and data encodings which can be used as building blocks for a Sensor Web. This article illustrates and analyzes the recent developments of the new generation of the Sensor Web Enablement specification framework. Further, we relate the Sensor Web to other emerging concepts such as the Web of Things and point out challenges and resulting future work topics for research on Sensor Web Enablement.

The development of industrialization and information communication technology (ICT) has deeply changed our way of life. In particular, with the emerging theory of ‘‘Industry 4.0”, the integration of cloud technologies and industrial cyber-physical systems (ICPS) becomes increasingly important, as this will greatly improve the manufacturing chain and business services. In this paper, we first describe the development and character of ICPS. ICPS will inevitably play an important role in manufacturing, sales, and logistics. With the support of the cloud, ICPS development will impact value creation, business models, downstream services, and work organization. Then, we present a service-oriented ICPS model. With the support of the cloud, infrastructure platform and service application, ICPS will promote the manufacturing efficiency, increase quality of production, enable a sustainable industrial system and more environmentally friendly businesses. Thirdly, we focus on some key enabling technologies, which are critical in supporting smart factories. These key enabling technologies will also help companies to realize high quality, high output, and low cost. Finally, we talk about some challenges of ICPS implementation and the future work. 2015 Elsevier B.V. All rights reserved.

Internet Of Things (IoT)is a shared network of objects or things which can interact with each other provided the Internet connection. IoT plays an important role in agriculture industry which can feed 9.6 billion people on the Earth by 2050. Smart Agriculture helps to reduce wastage, effective usage of fertilizer and thereby increase the crop yield. In this work, a system is developed to monitor crop-field using sensors (soil moisture, temperature, humidity, Light) and automate the irrigation system. The data from sensors are sent to Web server database using wireless transmission. In server database the data are encoded in JSON format. The irrigation is automated if the moisture and temperature of the field falls below the brink. In greenhouses light intensity control can also be automated in addition to irrigation. The notifications are sent to farmers' mobile periodically. The farmers' can able to monitor the field conditions from anywhere. This system will be more useful in areas where water is in scarce. This system is 92% more efficient than the conventional approach.

In this paper we present a use case related to the intelligent processing of events coming from the conventional ("cheap") sensors in order to support better energy consumption in commercial buildings. The approach has been implemented using our iCEP framework and deployed in the office space of a real working environment. This research is a kind of the proof of the concept for a new technology that the industry partner would like to exploit.
 The results are very encouraging: smart decisions for the efficient usage of energy can be made by the intelligent processing of "cheap" sensor events.

Recent years have witnessed the success of mobile crowd sensing systems, which outsource sensory data collection to the public crowd equipped with various mobile devices in a wide spectrum of civilian applications. We envision that crowd sensing could as well be very useful in a whole host of mission-driven scenarios, such as peacekeeping operations, non-combatant evacuations, and humanitarian missions. However, the power of crowd sensing could not be fully unleashed in mission-driven crowd sensing (MiCS) systems, unless workers are effectively incentivized to participate. Therefore, in this paper, taking into consideration workers' diverse quality of information (QoI), we propose Squadron, a quality-aware incentive mechanism for MiCS systems. Squadron adopts the reverse auction framework. It approximately minimizes the platform's total payment for worker recruiting in a computationally efficient manner, and recruits workers who potentially could provide high quality data. Furthermore, it also satisfies the desirable properties of truth-fulness and individual rationality. Through rigorous theoretical analysis, as well as extensive simulations, we validate the various aforementioned desirable properties held by Squadron.

The phenomenal growth in mobile data traffic calls for a drastic increase in mobile network capacity beyond current 3G/4G networks. In this paper, we propose a millimeter wave mobile broadband (MMB) system for the next generation mobile communication system (5G). MMB taps into the vast spectrum in 3-300 GHz range to meet this growing demand. We reason why the millimeter wave spectrum is suitable for mobile broadband applications. We discuss the unique advantages of millimeter waves such as spectrum availability and large beamforming gain in small form factors. We also describe a practical MMB system design capable of providing Gb/s data rates at distances up to 500 meters and supports mobility up to 350 km/h. By means of system simulations, we show that a basic MMB system is capable of delivering an average cell throughput and cell-edge throughput performances that is 10-100 times better than the current 20MHz LTE-Advanced systems.

Environmental Sensor Networks (ESNs) facilitate the study of fundamental processes and the development of hazard response systems. They have evolved from passive logging systems that require manual downloading, into ‘intelligent’ sensor networks that comprise a network of automatic sensor nodes and communications systems which actively communicate their data to a Sensor Network Server (SNS) where these data can be integrated with other environmental datasets. The sensor nodes can be fixed or mobile and range in scale appropriate to the environment being sensed. ESNs range in scale and function and we have reviewed over 50 representative examples. Large Scale Single Function Networks tend to use large single purpose nodes to cover a wide geographical area. Localised Multifunction Sensor Networks typically monitor a small area in more detail, often with wireless adhoc systems. Biosensor Networks use emerging biotechnologies to monitor environmental processes as well as developing proxies for immediate use. In the future, sensor networks will integrate these three elements (Heterogeneous Sensor Networks). The communications system and data storage and integration (cyberinfrastructure) aspects of ESNs are discussed, along with current challenges which need to be addressed. We argue that Environmental Sensor Networks will become a standard research tool for future Earth System and Environmental Science. Not only do they provide a ‘virtual’ connection with the environment, they allow new field and conceptual approaches to the study of environmental processes to be developed. We suggest that although technological advances have facilitated these changes, it is vital that Earth Systems and Environmental Scientists utilise them. © 2006 Elsevier B.V. All rights reserved.

The prosperity of Internet of Things (IoT) and the success of rich Cloud services have expedited the emergence of a new computing paradigm called Fog computing, which promotes the processing of data at the proximity of their sources. Complementary to the Cloud, Fog promises to offer many appealing features, such as low latency, low cost, high multitenancy, high scalability, and to consolidate the IoT ecosystem. Although the Fog concept has been widely adopted in many areas, a comprehensive realization has yet been adequately researched. To address all these issues, in this paper, object virtualization is investigated to overcome obstacles resulting from resource constraints on sensory-level nodes while service virtualization is explored to easily create tailored applications for end users. Moreover, network function virtualization is studied to perform the flexibility of network service provisioning. Grounded on object virtualization, network function virtualization and service virtualization, a layered framework that encompasses smart objects, Fog and Cloud is presented to illustrate the realization of virtual Fog along IoT continuum. This proposed virtual Fog framework is applied to a smart living case for verification, then quantitative analysis is conducted to demonstrate the low latency, low operating expense, high multitenancy and scalability, followed by an experimental evaluation to further confirm that delay and jitter can be decreased through virtualization.

Nowadays deep neural networks are widely used to accurately classify input data. An interesting application area is the Internet of Things (IoT), where a massive amount of sensor data has to be classified. The processing power of the cloud is attractive, however the variable latency imposes a major drawback for neural networks. In order to exploit the apparent trade-off between utilizing the available though limited embedded computing power of the IoT devices at high speed/stable latency and the seemingly unlimited computing power of Cloud computing at the cost of higher and variable latency, we propose a Big-Little architecture for deep neural networks. A small neural network trained to a subset of prioritized output classes can be used to calculate an output on the embedded devices, while a more specific classification can be calculated in the Cloud only when required. We show the applicability of this concept in the IoT domain by evaluating our approach for state of the art neural network classification problems on popular embedded devices such as the Raspberry Pi and Intel Edison.


Question: A deep neural network ensemble design for categorizing eye movements.

This study delves into the influence of convolutional network depth on its performance in large-scale image recognition tasks. Our key contribution lies in a comprehensive assessment of networks with progressively greater depth, revealing that a substantial enhancement over previous configurations can be achieved by increasing the depth to 16–19 weight layers. These discoveries were pivotal in our submission to the ImageNet Challenge 2014, where our team claimed the top and second positions in the localization and classification categories, respectively. Furthermore, we demonstrate the generalizability of our representations to alternative datasets, where they outperform existing approaches. Significantly, we have made our two most effective ConvNet models accessible to the public, aiming to foster further exploration into the application of deep visual representations in the field of computer vision.

In the context of eye movements classification, we propose an ensemble architecture of deep neural networks designed to enhance the accuracy and robustness of the classification task. This ensemble approach combines the predictions of multiple deep neural networks, each trained on a subset of the data, to make more reliable and accurate predictions. The ensemble architecture, which includes convolutional neural networks (CNNs) and recurrent neural networks (RNNs), is particularly adept at handling the spatiotemporal characteristics of eye movement data. The CNNs excel at capturing spatial features from eye movement patterns, while the RNNs are capable of modeling the temporal dynamics. By integrating these two types of networks within an ensemble framework, we aim to leverage the strengths of both architectures to improve the overall performance of the eye movement classification system. This ensemble approach not only boosts the accuracy of the classification but also increases the system's resilience to variations in eye movement patterns, making it a promising tool for applications in cognitive neuroscience, human-computer interaction, and clinical diagnostics.

In this paper we describe the application of mixtures of experts on gender and ethnic classification of human faces, and pose classification, and show their feasibility on the FERET database of facial images. The FERET database allows us to demonstrate performance on hundreds or thousands of images. The mixture of experts is implemented using the "divide and conquer" modularity principle with respect to the granularity and/or the locality of information. The mixture of experts consists of ensembles of radial basis functions (RBFs). Inductive decision trees (DTs) and support vector machines (SVMs) implement the "gating network" components for deciding which of the experts should be used to determine the classification output and to restrict the support of the input space. Both the ensemble of RBF's (ERBF) and SVM use the RBF kernel ("expert") for gating the inputs. Our experimental results yield an average accuracy rate of 96% on gender classification and 92% on ethnic classification using the ERBF/DT approach from frontal face images, while the SVM yield 100% on pose classification.

In this paper, we introduce the DEEP-SEE FACE framework, an assistive device designed to improve cognition, interaction, and communication of visually impaired (VI) people in social encounters. The proposed approach jointly exploits computer vision algorithms (region proposal networks, ATLAS tracking and global, and low-level image descriptors) and deep convolutional neural networks in order to detect, track, and recognize, in real-time, various persons existent in the video streams. The major contribution of the paper concerns a global, fixed-size face representation that takes into the account of various video frames while remaining independent of the length of the image sequence. To this purpose, we introduce an effective weight adaptation scheme that is able to determine the relevance assigned to each face instance, depending on the frame degree of motion/camera blur, scale variation, and compression artifacts. Another relevant contribution involves a hard negative mining stage that helps us differentiating between known and unknown face identities. The experimental results, carried out on a large-scale data set, validate the proposed methodology with an average accuracy and recognition rates superior to 92%. When tested in real life, indoor/outdoor scenarios, the DEEP-SEE FACE prototype proves to be effective and easy to use, allowing the VI people to access visual information during social events.

We assess the applicability of several popular learning methods for the problem of recognizing generic visual categories with invariance to pose, lighting, and surrounding clutter. A large dataset comprising stereo image pairs of 50 uniform-colored toys under 36 azimuths, 9 elevations, and 6 lighting conditions was collected (for a total of 194,400 individual images). The objects were 10 instances of 5 generic categories: four-legged animals, human figures, airplanes, trucks, and cars. Five instances of each category were used for training, and the other five for testing. Low-resolution grayscale images of the objects with various amounts of variability and surrounding clutter were used for training and testing. Nearest neighbor methods, support vector machines, and convolutional networks, operating on raw pixels or on PCA-derived features were tested. Test error rates for unseen object instances placed on uniform backgrounds were around 13% for SVM and 7% for convolutional nets. On a segmentation/recognition task with highly cluttered images, SVM proved impractical, while convolutional nets yielded 16/7% error. A real-time version of the system was implemented that can detect and classify objects in natural scenes at around 10 frames per second.

Over the past decade, functional Magnetic Resonance Imaging (fMRI) has emerged as a powerful new instrument to collect vast quantities of data about activity in the human brain. A typical fMRI experiment can produce a three-dimensional image related to the human subject's brain activity every half second, at a spatial resolution of a few millimeters. As in other modern empirical sciences, this new instrumentation has led to a flood of new data, and a corresponding need for new data analysis methods. We describe recent research applying machine learning methods to the problem of classifying the cognitive state of a human subject based on fRMI data observed over a single time interval. In particular, we present case studies in which we have successfully trained classifiers to distinguish cognitive states such as (1) whether the human subject is looking at a picture or a sentence, (2) whether the subject is reading an ambiguous or non-ambiguous sentence, and (3) whether the word the subject is viewing is a word describing food, people, buildings, etc. This learning problem provides an interesting case study of classifier learning from extremely high dimensional (105 features), extremely sparse (tens of training examples), noisy data. This paper summarizes the results obtained in these three case studies, as well as lessons learned about how to successfully apply machine learning methods to train classifiers in such settings.

We introduce a set of nine measures to characterize viewers' visual attention patterns for multi-screen TV. We apply our measures during an experiment involving nine screen layouts with two, three, and four TV screens, for which we report new findings on visual attention. For example, we found that viewers need an average discovery time up to 4.5 seconds to visually fixate four screens, and their perceptions of how long they watched each screen are substantially accurate, i.e., we report Pearson correlations up to .892 with measured eye tracking data. We hope our set of new measures (and the companion toolkit to compute them automatically) will benefit the community as a first step toward understanding visual attention for emerging multi-screen TV applications.

The potential of brain electrical activity generated as a response to a visual stimulus is examined in the context of the identification of individuals. Specifically, a framework for the visual evoked potential (VEP)-based biometrics is established, whereby energy features of the gamma band within VEP signals were of particular interest. A rigorous analysis is conducted which unifies and extends results from our previous studies, in particular, with respect to 1) increased bandwidth, 2) spatial averaging, 3) more robust power spectrum features, and 4) improved classification accuracy. Simulation results on a large group of subject support the analysis

This paper focuses on human activity recognition (HAR) problem, in which inputs are multichannel time series signals acquired from a set of bodyworn inertial sensors and outputs are predefined human activities. In this problem, extracting effective features for identifying activities is a critical but challenging task. Most existing work relies on heuristic hand-crafted feature design and shallow feature learning architectures, which cannot find those distinguishing features to accurately classify different activities. In this paper, we propose a systematic feature learning method for HAR problem. This method adopts a deep convolutional neural networks (CNN) to automate feature learning from the raw inputs in a systematic way. Through the deep architecture, the learned features are deemed as the higher level abstract representation of low level raw time series signals. By leveraging the labelled information via supervised learning, the learned features are endowed with more discriminative power. Unified in one model, feature learning and classification are mutually enhanced. All these unique advantages of the CNN make it outperform other HAR algorithms, as verified in the experiments on the Opportunity Activity Recognition Challenge and other benchmark datasets.

In computer vision many tasks are solved using machine learning. In the past few years, state of the art results in computer vision have been achieved using deep learning. Deeper machine learning architectures are better capable in handling complex recognition tasks, compared to previous more shallow models. Many architectures for computer vision make use of convolutional neural networks which were modeled after the visual cortex. Currently deep convolutional neural networks are the state of the art in computer vision. Through a literature survey an overview is given of the components in deep convolutional neural networks. The role and design decisions for each of the components is presented, and the difficulties involved in training deep neural networks are given. To improve deep learning architectures an analysis is given of the activation values in four different architectures using various activation functions. Current state of the art classifiers use dropout, max-pooling as well as the maxout activation function. New components may further improve the architecture by providing a better solution for the diminishing gradient problem.

Introduction Vision, due to its significance in surviving and socializing, is one of the most important and  extensively studied sensory functions in the human brain. In order to fully understand visual  information processing, or more specifically, visual recognition, David Marr proposed the  Tri-level Hypothesis [29], in that three levels of the system should be studied: the computational  goal of the system, the internal representation or the algorithm the system uses to achieve the  goal, and the neural substrates that implement the system. It is well-known that visual  recognition in the human brain is implemented by the ventral visual pathway [32], which  receives visual information from the retina and goes through a layered structure including V1  (also known as the primary visual cortex), V2, V4, before reaching the inferior temporal cortex  (IT). The topographic mapping between the retina and the human visual cortex follows a  log-polar transformation, in which the Cartesian coordinates of the retina are transformed to  polar coordinates (polar angle and eccentricity) in the human visual cortex. From V1 to V4, each  of the visual areas contains a complete polar angle and eccentricity map, and have a increasing  receptive field (RF) size and respond to increasing complex visual features [12,22]. In higher  level visual areas such as the ventral temporal cortex (VTC), studies using functional magnetic  resonance imaging (fMRI) and diffusion tensor imaging (DTI) have found there exists visual  category-selective regions, such as the Fusiform Face Area (FFA, [26]) for face processing, the  Parahippocampal Place Area (PPA, [13]) for scene perception, and the Lateral Occipital  Complex (LOC, [19]) for general object recognition. Grill-Spector and Malach [20] proposed that  layer-based hierarchical processing, together with the functional specialization, are the two  organizing principles of the human visual cortex. More recent studies have shown that the  central-biased face recognition pathway (FFA) and the peripheral-biased scene recognition  pathway (PPA) are both functionally and anatomically segregated by mid-fusiform sulcus (MFS),  which enables parallel fast processing of visual recognition tasks in the ventral pathway [18, 43,  21, 44].

Deep neural networks, including recurrent networks, have been successfully applied to human activity recognition. Unfortunately, the final representation learned by recurrent networks might encode some noise (irrelevant signal components, unimportant sensor modalities, etc.). Besides, it is difficult to interpret the recurrent networks to gain insight into the models' behavior. To address these issues, we propose two attention models for human activity recognition: temporal attention and sensor attention. These two mechanisms adaptively focus on important signals and sensor modalities. To further improve the understandability and mean Fl score, we add continuity constraints, considering that continuous sensor signals are more robust than discrete ones. We evaluate the approaches on three datasets and obtain state-of-the-art results. Furthermore, qualitative analysis shows that the attention learned by the models agree well with human intuition.


Question: A deep neural network ensemble design for categorizing eye movements.

This paper delves into the Pascal Visual Object Classes (VOC) challenge, a pivotal benchmark in the domain of visual object recognition and detection. Serving as a standard reference for the vision and machine learning communities since its inception in 2005, the challenge offers a comprehensive dataset of images paired with annotations, alongside established evaluation protocols. Over the years, the challenge and its dataset have gained widespread acceptance as the definitive standard for evaluating object detection algorithms. Here, we detail the dataset's contents and the evaluation methodology employed. We also present an in-depth analysis of the state-of-the-art techniques in object classification and detection, as evaluated within this framework. Our analysis explores the statistical differences between these methods, investigates the factors they utilize for decision-making (such as the object itself or its contextual environment), and identifies the ease or confusion they exhibit in handling various object categories. The paper concludes by summarizing the key insights gleaned from the challenge's three-year history, and suggests potential avenues for future enhancements and expansions in this critical area of research.

D EEP learning is a growing trend in general data analysis and has been termed one of the 10 breakthrough technologies of 2013 [1]. Deep learning is an improvement of artificial neural networks, consisting of more layers that permit higher levels of abstraction and improved predictions from data [2]. To date, it is emerging as the leading machine-learning tool in the general imaging and computer vision domains. In particular, convolutional neural networks (CNNs) have proven to be powerful tools for a broad range of computer vision tasks. Deep CNNs automatically learn mid-level and high-level abstractions obtained from raw data (e.g., images). Recent results indicate that the generic descriptors extracted from CNNs are extremely effective in object recognition and localization in natural images. Medical image analysis groups across the world are quickly entering the field and applying CNNs and other deep learning methodologies to a wide variety of applications. Promising results are emerging. In medical imaging, the accurate diagnosis and/or assessment of a disease depends on both image acquisition and image interpretation. Image acquisition has improved substantially over recent years, with devices acquiring data at faster rates and increased resolution. The image interpretation process, however, has only recently begun to benefit from computer technology. Most interpretations of medical images are performed by physicians; however, image interpretation by humans is limited due to its subjectivity, large variations across interpreters, and fatigue. Many diagnostic tasks require an initial search process to detect abnormalities, and to quantify measurements and changes over time. Computerized tools, specifically image analysis and machine learning, are the key enablers to improve diagnosis, by facilitating identification of the findings that require treatment and to support the expert’s workflow. Among these tools, deep learning is rapidly proving to be the state-of-the-art foundation, leading to improved accuracy. It has also opened up new frontiers in data analysis with rates of progress not before experienced.

This paper introduces the development of a practical brain-computer interface at Tsinghua University. The system uses frequency-coded steady-state visual evoked potentials to determine the gaze direction of the user. To ensure more universal applicability of the system, approaches for reducing user variation on system performance have been proposed. The information transfer rate (ITR) has been evaluated both in the laboratory and at the Rehabilitation Center of China, respectively. The system has been proved to be applicable to >90% of people with a high ITR in living environments.

The deployment of human gaze has been almost exclusively studied independent of any specific ongoing task and limited to two-dimensional picture viewing. This contrasts with its use in everyday life, which mostly consists of purposeful tasks where gaze is crucially involved. To better understand deployment of gaze under such circumstances, we devised a series of experiments, in which subjects navigated along a walkway in a virtual environment and executed combinations of approach and avoidance tasks. The position of the body and the gaze were monitored during the execution of the task combinations and dependence of gaze on the ongoing tasks as well as the visual features of the scene was analyzed. Gaze distributions were compared to a random gaze allocation strategy as well as a specific "saliency model." Gaze distributions showed high similarity across subjects. Moreover, the precise fixation locations on the objects depended on the ongoing task to the point that the specific tasks could be predicted from the subject's fixation data. By contrast, gaze allocation according to a random or a saliency model did not predict the executed fixations or the observed dependence of fixation locations on the specific task.

Humans process visual scenes selectively and sequentially using attention. Central to models of human visual attention is the saliency map. We propose a hierarchical visual architecture that operates on a saliency map and uses a novel attention mechanism to sequentially focus on salient regions and take additional glimpses within those regions. The architecture is motivated by human visual attention, and is used for multi-label image classification on a novel multiset task, demonstrating that it achieves high precision and recall while localizing objects with its attention. Unlike conventional multi-label image classification models, the model supports multiset prediction due to a reinforcement-learning based training process that allows for arbitrary label permutation and multiple instances per label.

Deep neural networks (DNNs) applications are now increasingly pervasive and powerful. However, fashion designers are lagging behind in leveraging this increasingly common technology. DNNs are not yet a standard part of fashion de sign practice, either clothes patterns or prototyping tools. In this paper, we present DeepWear, a method using deep convolutional generative adversarial networks for clothes design. The DNNs learn the feature of specific brand clothes and generate images then patterns instructed from the images are made, and an author creates clothes based on that. We evaluated this system by evaluating the credibility of the actual sold clothes on market with our clothes. As the result, we found it is possible to make clothes look like actual products from the generated images. Our findings have implications for collaborative design between machine and human intelligence.

This article presents our recent study of a lightweight Deep Convolutional Neural Network (DCNN) architecture for document image classification. Here, we concentrated on training of a committee of generalized, compact and powerful base DCNNs. A support vector machine (SVM) is used to combine the outputs of individual DCNNs. The main novelty of the present study is introduction of supervised layerwise training of DCNN architecture in document classification tasks for better initialization of weights of individual DCNNs. Each DCNN of the committee is trained for a specific part or the whole document. Also, here we used the principle of generalized stacking for combining the normalized outputs of all the members of the DCNN committee. The proposed document classification strategy has been tested on the well-known Tobacco3482 document image dataset. Results of our experimentations show that the proposed strategy involving a considerably smaller network architecture can produce comparable document classification accuracies in competition with the state-of-the-art architectures making it more suitable for use in comparatively low configuration mobile devices.

In this paper, we propose a scheme to integrate independent component analysis (ICA) and neural networks for electrocardiogram (ECG) beat classification. The ICA is used to decompose ECG signals into weighted sum of basic components that are statistically mutual independent. The projections on these components, together with the RR interval, then constitute a feature vector for the following classifier. Two neural networks, including a probabilistic neural network (PNN) and a back-propagation neural network (BPNN), are employed as classifiers. ECG samples attributing to eight different beat types were sampled from the MIT-BIH arrhythmia database for experiments. The results show high classification accuracy of over 98% with either of the two classifiers. Between them, the PNN shows a slightly better performance than BPNN in terms of accuracy and robustness to the number of ICA-bases. The impressive results prove that the integration of independent component analysis and neural networks, especially PNN, is a promising scheme for the computer-aided diagnosis of heart diseases based on ECG. 2007 Elsevier Ltd. All rights reserved.

Recent studies show that scalp electroencephalography (EEG) as a non-invasive interface has great potential for brain-computer interfaces (BCIs). However, one factor that has limited practical applications for EEG-based BCI so far is the difficulty to decode brain signals in a reliable and efficient way. This paper proposes a new robust processing framework for decoding of multi-class motor imagery (MI) that is based on five main processing steps. (i) Raw EEG segmentation without the need of visual artifact inspection. (ii) Considering that EEG recordings are often contaminated not just by electrooculography (EOG) but also other types of artifacts, we propose to first implement an automatic artifact correction method that combines regression analysis with independent component analysis for recovering the original source signals. (iii) The significant difference between frequency components based on event-related (de-) synchronization and sample entropy is then used to find non-contiguous discriminating rhythms. After spectral filtering using the discriminating rhythms, a channel selection algorithm is used to select only relevant channels. (iv) Feature vectors are extracted based on the inter-class diversity and time-varying dynamic characteristics of the signals. (v) Finally, a support vector machine is employed for four-class classification. We tested our proposed algorithm on experimental data that was obtained from dataset 2a of BCI competition IV (2008). The overall four-class kappa values (between 0.41 and 0.80) were comparable to other models but without requiring any artifact-contaminated trial removal. The performance showed that multi-class MI tasks can be reliably discriminated using artifact-contaminated EEG recordings from a few channels. This may be a promising avenue for online robust EEG-based BCI applications.

Eye tracking has a long history in medical and psychological research as a tool for recording and studying human visual behavior. Real-time gaze-based text entry can also be a powerful means of communication and control for people with physical disabilities. Following recent technological advances and the advent of affordable eye trackers, there is a growing interest in pervasive attention-aware systems and interfaces that have the potential to revolutionize mainstream humantechnology interaction. In this chapter, we provide an introduction to the state-of-the art in eye tracking technology and gaze estimation. We discuss challenges involved in using a perceptual organ, the eye, as an input modality. Examples of real life applications are reviewed, together with design solutions derived from research results. We also discuss how to match the user requirements and key features of different eye tracking systems to find the best system for each task and application.

Faces represent complex, multidimensional, meaningful visual stimuli an d developing a computational model for face recognition is difficult [42]. We present a hybrid neural network solution which compares favorably with other methods. The system combines local image sam pling, a self-organizing map neural network, and a convolutional neural network. The self-organizi ng map provides a quantization of the image samples into a topological space where inputs that are n e rby in the original space are also nearby in the output space, thereby providing dimensionality red uction and invariance to minor changes in the image sample, and the convolutional neural network prov ides for partial invariance to translation, rotation, scale, and deformation. The convolutional net work extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen -Loève transform in place of the self-organizing map, and a multi-layer perceptron in place of the con volutional network. The Karhunen-Loève transform performs almost as well (5.3% error versus 3 .8%). The multi-layer perceptron performs very poorly (40% error versus 3.8%). The method is cap able of rapid classification, requires only fast, approximate normalization and preprocessing, and cons istently exhibits better classification performance than the eigenfaces approach [42] on the database consider ed as the number of images per person in the training database is varied from 1 to 5. With 5 imag es per person the proposed method and eigenfaces result in 3.8% and 10.5% error respectively. The recogni zer provides a measure of confidence in its output and classification error approaches zero when rejecting as few as 10% of the examples. We use a database of 400 images of 40 individuals which cont ains quite a high degree of variability in expression, pose, and facial details. We analyze computati onal complexity and discuss how new classes could be added to the trained recognizer.