Question: A fresh viewpoint on port supply chain management based on the service-dominant logic.

This paper delves into the critical examination of the role and implications of information technology (IT) in supply chain management (SCM), a key strategy for enhancing organizational competitiveness in the global landscape of the 21st century. Faced with the imperative to boost flexibility, responsiveness, and ultimately, competitiveness, companies are increasingly adopting SCM principles and IT innovations. Despite the growing importance of IT in SCM, a comprehensive and critical analysis of the existing literature to extract significant insights and factors has been lacking. To address this gap, this paper meticulously categorizes and reviews pertinent literature on IT in SCM, employing a systematic framework. This review yields valuable recommendations for the practical application of IT in SCM, along with suggestions for future research directions, contributing to a deeper understanding of the integration of IT and SCM. Copyright 2003 Elsevier B.V. All rights reserved.

**Question**: The Effects of Different Types of Training on the Performance of Employees in a Multinational Company

**Documents**: The impact of training on employee performance: A study of selected manufacturing firms in Nigeria
This study examined the effect of training on employee performance in selected manufacturing firms in Nigeria. The study was a survey research design and the population comprised of all employees in the selected manufacturing firms. A sample size of 384 was selected using the Krejcie and Morgan table. A structured questionnaire was used to collect data from the respondents. Data collected were analyzed using descriptive statistics and inferential statistics (Pearson Product Moment Correlation and Multiple Regression Analysis). The results revealed that there is a significant relationship between training and employee performance. The study also revealed that training has a significant effect on employee performance in the manufacturing firms. The study recommended that management should ensure regular and continuous training of employees in order to enhance their performance.

**Similar Documents**: This research investigates the influence of training on employee performance within a selection of manufacturing companies operating in Nigeria. Employing a survey design, the study encompassed all employees across the chosen manufacturing enterprises. A sample size of 384 participants was determined using the Krejcie and Morgan table, facilitating a structured questionnaire for data collection. The analysis of the collected data was conducted using descriptive statistics and inferential statistics, specifically Pearson Product Moment Correlation and Multiple Regression Analysis. The findings confirmed a significant correlation between training and employee performance, underscoring the substantial impact of training on enhancing employee performance within the manufacturing sector. Based on these insights, the study advocates for the consistent and ongoing training of employees by management to bolster performance levels.

How effectively business deals with the challenges of sustainability will define its success for decades to come. Current sustainability strategies have three major deficiencies: they do not directly focus on the customer, they do not recognize the looming threats from rising global over-consumption, and they do not take a holistic approach. We present a framework for a customer-centric approach to sustainability. This approach recasts the sustainability metric to emphasize the outcomes of business actions measured holistically in term of environmental, personal and economic well-being of the consumer. We introduce the concept of mindful consumption (MC) as the guiding principle in this approach. MC is premised on a consumer mindset of caring for self, for community, and for nature, that translates behaviorally into tempering the selfdefeating excesses associated with acquisitive, repetitive and aspirational consumption. We also make the business case for fostering mindful consumption, and illustrate how the marketing function can be harnessed to successfully implement the customer-centric approach to sustainability.

Editor’s note The authors of this paper presented an All-academy session at the 2008 Academy of Management annual meeting in Anaheim, California. We were excited by the dynamic nature of the debate and felt that it related closely to critical issues in the areas of operations management, strategy, product development and international business. We thus invited the authors to write an article offering their individual and joint views on the productivity dilemma. We trust you will find it to be stimulating and thought provoking. We invite you to add your voice to the discussion by commenting on this article at the Operations and Supply Chain (OSM) Forum at http://www.journaloperationsmanagement.org/OSM.asp. – Kenneth K. Boyer and Morgan L. Swink

Kaizen refers to the philosophy or practices that focus upon continuous improvement of processes in manufacturing, engineering and business management. The improvements are usually accomplished at little or no expense and without sophisticated techniques. The purpose of this paper is to present the results of implementing the Kaizen principles at Port Installed Options Center in Toyota Saudi Arabia. Using relevant Kaizen tools including Toyota Production System (TPS), 5S and the seven Muda enabled us to understand the system and identify the most critical problem areas. The Kaizen target was set after analyzing the problems and identifying the gap between the existing system and the TPS. The main results of the study can be summarized as: using the same facilities it was possible to be more efficient by reducing the manpower needed by 26.9% (from 349 to 275 installers) and more effective by increasing the annual output by 13% (from 188000 to 212400 vehicles); improve the Associates Engagement Index (Q12) by 6.4% (from 2.91 to 3.11); potential for inventory reduction due to the use of Kanban system with just-in-time production; avoid investing in new facilities needed to meet the increase in the demand in the market. All these resulted in improving the production line productivity which were appreciated and acknowledged by Toyota Motor Corporation.

The paper focuses on the analysis and optimization of production warehouses, proposing a novel approach to reduce inefficiencies which employs three lean manufacturing tools in an integrated and iterative framework. The proposed approach integrates the Unified Modeling Language (UML) – providing a detailed description of the warehouse logistics – the Value Stream Mapping (VSM) tool – identifying non-value adding activities – and a mathematical formulation of the so-called Genba Shikumi philosophy – ranking such system anomalies and assessing how they affect the warehouse. The subsequent reapplication of the VSM produces a complete picture of the reengineered warehouse, and using the UML tool allows describing in detail the updated system. By applying the presented methodology to the warehouse of an Italian interior design producer, we show that it represents a useful tool to systematically and dynamically improve the warehouse management. Indeed, the application of the approach to the company leads to an innovative proposal for the warehouse analysis and optimization: a warehouse management system that leads to increased profitability and quality as well as to reduced errors. 2014 Elsevier B.V. All rights reserved.

Three core imperatives are essential for modern businesses and organizations: seamless integration of customer and operational processes, agility, and the ability to change. These imperatives are thus relevant in view of successfully executing strategic choices, but all too often not satisfied. Businesses and organizations can be viewed as complex adaptive socio-technical systems. Two fundamentally different perspectives on systems play a role: the functional (black-box) perspective and the constructional (white-box) perspective. Management and governance of businesses and organizations regard the functional, black-box perspective, which is inherently ill-suited for addressing the imperatives mentioned. Rather, establishing system integration, agility and change requires a focus on the system’s design, hence entails the constructional perspective. Both perspectives are relevant. The concept of architecture plays a fundamental role for operationalizing the constructional perspective. Next to the more familiar notion of technology architecture, the concepts of business and organizational architecture are formally introduced and elucidated. In view of the important role of information in customer and operational processes, the notion of information architecture will be additionally discussed, specifically the relationships with other architectures. Various domains within these architectures will be highlighted, whereby the importance of coherence and consistency is stressed, especially in view of the ability to change. Collectively, the four architectures are labeled Enterprise Architecture. Finally, enterprise architecture will be positioned as a crucial means for linking strategy development and execution. The functional and constructional perspective Function and construction A system may be defined as an identifiable bounded set of functionally and/or methodically related elements or principles with a certain operational purpose. Within this definition, a plethora of systems can be mentioned: a payment system, an accounting system, a transport

This paper reports some findings of an on-going research into ERP implementation issues. A part of the research, which this paper reports, consists of reviewing several cases of successful implementations of ERP systems. The analysis of all these cases revealed that the critical factors for successful implementation of ERP systems fall into two main categories: technological and organizational factors. However, the organizational factors seem to be more important than the technological ones as far as the successful implementation of ERP systems under SCM is concerned.

Customer Relationship Management (CRM) is increasingly found at the top of corporate agendas. Online companies in particular are embracing CRM as a major element of corporate strategy, because online technological applications permit a precise segmentation, profiling and targeting of customers, and the competitive pressures of the digital markets require a customer-centric corporate culture. The implementation of CRM systems in online organisation determines a complex restructuring of all organisational elements and processes. The strategic planning process will have to adapt to new customer-centric procedures. The present paper analyses the implementation process of a CRM system in online retail businesses and develops a model of the strategic planning function in a customer-centric context.


Question: Anatomical study of online social networks: Investigating Facebook and WhatsApp within cellular networks.

This paper delves into the transformation of Internet inter-domain traffic demands and interconnection policies, offering a comprehensive analysis. We scrutinize over 200 Exabytes of commercial Internet traffic data, collected over a two-year timeframe, by instrumenting 110 major and geographically dispersed cable operators, international transit backbones, regional networks, and content providers. Our findings reveal notable shifts in inter-AS traffic dynamics and the evolution of provider peering strategies. Specifically, we observe that the bulk of inter-domain traffic volume is now directly exchanged between major content providers, data centers/CDNs, and consumer networks. Additionally, we highlight significant alterations in Internet application consumption, such as a worldwide decrease in P2P usage juxtaposed with a substantial increase in video traffic. Our analysis culminates with estimations of the current scale of the Internet in terms of inter-domain traffic volume and the rate of annualized inter-domain traffic expansion.

**Question**: The Influence of Online Social Networks on Cellular Networks: A Study of Facebook and WhatsApp

**Documents**: The Anatomy of Online Social Networks: A Case Study of Facebook and WhatsApp in Cellular Networks

In this study, we dissect the impact of online social networks (OSNs) on cellular networks, focusing on two dominant platforms: Facebook and WhatsApp. We analyze a large dataset of network traffic from a major cellular operator to understand the patterns and effects of OSN usage on network performance. Our findings reveal that Facebook and WhatsApp exhibit distinct traffic characteristics, with Facebook consuming more data due to its rich media content and complex web structure, while WhatsApp's traffic is more consistent and predictable, driven by its messaging and voice call functionalities. We also observe that the usage of these OSNs is heavily skewed, with a small percentage of users generating a disproportionate amount of traffic. Moreover, we identify temporal patterns in OSN usage, with peak activity during off-peak hours, which can have significant implications for network congestion management. Our analysis provides insights into the challenges and opportunities presented by OSNs to cellular network operators, highlighting the need for tailored traffic management policies and infrastructure optimization to accommodate the growing demand for OSN services.

**Similar Documents**: This investigation explores the repercussions of online social networks (OSNs) on cellular networks, with a particular emphasis on Facebook and WhatsApp. Utilizing extensive network traffic data from a significant cellular service provider, we aim to elucidate the usage patterns and impacts of these OSNs on network performance. Our results demonstrate that Facebook and WhatsApp present unique traffic profiles. Facebook's data consumption is notably higher due to its

How do social networks affect the spread of behavior? A popular hypothesis states that networks with many clustered ties and a high degree of separation will be less effective for behavioral diffusion than networks in which locally redundant ties are rewired to provide shortcuts across the social space. A competing hypothesis argues that when behaviors require social reinforcement, a network with more clustering may be more advantageous, even if the network as a whole has a larger diameter. I investigated the effects of network structure on diffusion by studying the spread of health behavior through artificially structured online communities. Individual adoption was much more likely when participants received social reinforcement from multiple neighbors in the social network. The behavior spread farther and faster across clustered-lattice networks than across corresponding random networks.

We report the results of a field experiment where we sent to over 1200 university students an email or a Facebook message with a link to (non-existing) party pictures from a non-existing person, and later asked them about the reasons for their link clicking behavior. We registered a significant difference in clicking rates: 20% of email versus 42.5% of Facebook recipients clicked. The most frequently reported reason for clicking was curiosity (34%), followed by the explanations that the message fit recipient’s expectations (27%). Moreover, 16% thought that they might know the sender. These results show that people’s decisional heuristics are relatively easy to misuse in a targeted attack, making defense especially challenging.

Instagram is a popular social networking application that allows users to express themselves through the uploaded content and the different filters they can apply. In this study we look at the relationship between the content of the uploaded Instagram pictures and the personality traits of users. To collect data, we conducted an online survey where we asked participants to fill in a personality questionnaire, and grant us access to their Instagram account through the Instagram API. We gathered 54,962 pictures of 193 Instagram users. Through the Google Vision API, we analyzed the pictures on their content and clustered the returned labels with the k-means clustering approach. With a total of 17 clusters, we analyzed the relationship with users’ personality traits. Our findings suggest a relationship between personality traits and picture content. This allow for new ways to extract personality traits from social media trails, and new ways to facilitate personalized systems. Author

The small-world phenomenon — the principle that most of us are linked by short chains of acquaintances — was first investigated as a question in sociology and is a feature of a range of networks arising in nature and technology. Experimental study of the phenomenon revealed that it has two fundamental components: first, such short chains are ubiquitous, and second, individuals operating with purely local information are very adept at finding these chains. The first issue has been analysed, and here I investigate the second by modelling how individuals can find short chains in a large social network.

URLs: Self-disclosure on Facebook among female users and its relationship to feelings of loneliness Author(s): Al-Saggaf, Y.M. ; Nielsen, S.G. Computers in Human Behavior

Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays,, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them ‘small-world’ networks, by analogy with the small-world phenomenon, (popularly known as six degrees of separation). The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.

Using detailed traces from 255 users, we conduct a comprehensive study of smartphone use. We characterize intentional user activities -- interactions with the device and the applications used -- and the impact of those activities on network and energy usage. We find immense diversity among users. Along all aspects that we study, users differ by one or more orders of magnitude. For instance, the average number of interactions per day varies from 10 to 200, and the average amount of data received per day varies from 1 to 1000 MB. This level of diversity suggests that mechanisms to improve user experience or energy consumption will be more effective if they learn and adapt to user behavior. We find that qualitative similarities exist among users that facilitate the task of learning user behavior. For instance, the relative application popularity for can be modeled using an exponential distribution, with different distribution parameters for different users. We demonstrate the value of adapting to user behavior in the context of a mechanism to predict future energy drain. The 90th percentile error with adaptation is less than half compared to predictions based on average behavior across users.

During our digital social life, we share terabytes of information that can potentially reveal private facts and personality traits to unexpected strangers. Despite the research efforts aiming at providing efficient solutions for the anonymization of huge databases (including networked data), in online social networks the most powerful privacy protection is in the hands of the users. However, most users are not aware of the risks derived by the indiscriminate disclosure of their personal data. With the aim of fostering their awareness on private data leakage risk, some measures have been proposed that quantify the privacy risk of each user. However, these measures do not capture the objective risk of users since they assume that all user’s direct social connections are close (thus trustworthy) friends. Since this assumption is too strong, in this paper we propose an alternative approach: each user decides which friends are allowed to see each profile item/post and our privacy score is defined accordingly. We show that it can be easily computed with minimal user intervention by leveraging an active learning approach. Finally, we validate our measure on a set of real Facebook users.

Mobile livestreaming is now well into its third wave. From early systems such as Bambuser and Qik, to more popular apps Meerkat and Periscope, to today's integrated social streaming features in Facebook and Instagram, both technology and usage have changed dramatically. In this latest phase of livestreaming, cameras turn inward to focus on the streamer, instead of outwards on the surroundings. Teens are increasingly using these platforms to entertain friends, meet new people, and connect with others on shared interests. We studied teens' livestreaming behaviors and motivations on these new platforms through a survey completed by 2,247 American livestreamers and interviews with 20 teens, highlighting changing practices, teens' differences from the broader population, and implications for designing new livestreaming services.

Some of the most frequently used online applications are Facebook, WhatsApp, and Twitter. These applications allow individuals to communicate with other users, to share information or pictures, and to stay in contact with friends all over the world. However, a growing number of users suffer from negative consequences due to their excessive use of these applications, which can be referred to as Internet-communication disorder. The frequent use and easy access of these applications may also trigger the individual's fear of missing out on content when not accessing these applications. Using a sample of 270 participants, a structural equation model was analyzed to investigate the role of psychopathological symptoms and the fear of missing out on expectancies towards Internet-communication applications in the development of symptoms of an Internet-communication disorder. The results suggest that psychopathological symptoms predict higher fear of missing out on the individual's Internet-communication applications and higher expectancies to use these applications as a helpful tool to escape from negative feelings. These specific cognitions mediate the effect of psychopathological symptoms on Internet-communication disorder. Our results are in line with the theoretical model by Brand et al. (2016) as they show how Internet-related cognitive bias mediates the relationship between a person's core characteristics (e.g., psychopathological symptoms) and Internet-communication disorder. However, further studies should investigate the role of the fear of missing out as a specific predisposition, as well as specific cognition in the online context.


Question: Anatomical study of online social networks: Investigating Facebook and WhatsApp within cellular networks.

This study delves into the complexities and challenges associated with privacy management within the context of online social networking platforms, focusing on Facebook. Online social networks have revolutionized the way individuals establish and sustain connections, often involving extensive sharing of personal details. This widespread disclosure of personal information has sparked significant privacy concerns. Building upon earlier research that highlighted the underutilization of privacy settings by users, our investigation explores the current tactics employed by individuals to safeguard their privacy on Facebook, as well as the limitations and failures of these strategies. The findings reveal a critical demand for tools that enhance users' awareness of the privacy implications of their routine online activities, underscoring the need for more effective privacy management mechanisms within social networking environments.

People spend most of their time at a few key locations, such as home and work. Being able to identify how the movements of people cluster around these “important places” is crucial for a range of technology and policy decisions in areas such as telecommunications and transportation infrastructure deployment. In this paper, we propose new techniques based on clustering and regression for analyzing anonymized cellular network data to identify generally important locations, and to discern semantically meaningful locations such as home and work. Starting with temporally sparse and spatially coarse location information, we propose a new algorithm to identify important locations. We test this algorithm on arbitrary cellphone users, including those with low call rates, and find that we are within 3 miles of ground truth for 88% of volunteer users. Further, after locating home and work, we achieve commute distance estimates that are within 1 mile of equivalent estimates derived from government census data. Finally, we perform carbon footprint analyses on hundreds of thousands of anonymous users as an example of how our data and algorithms can form an accurate and efficient underpinning for policy and infrastructure studies.

Mobile instant messaging (e.g., via SMS or WhatsApp) often goes along with an expectation of high attentiveness, i.e., that the receiver will notice and read the message within a few minutes. Hence, existing instant messaging services for mobile phones share indicators of availability, such as the last time the user has been online. However, in this paper we not only provide evidence that these cues create social pressure, but that they are also weak predictors of attentiveness. As remedy, we propose to share a machine-computed prediction of whether the user will view a message within the next few minutes or not. For two weeks, we collected behavioral data from 24 users of mobile instant messaging services. By the means of machine-learning techniques, we identified that simple features extracted from the phone, such as the user's interaction with the notification center, the screen activity, the proximity sensor, and the ringer mode, are strong predictors of how quickly the user will attend to the messages. With seven automatically selected features our model predicts whether a phone user will view a message within a few minutes with 70.6% accuracy and a precision for fast attendance of 81.2%

In this paper we propose a new two-phase algorithm for overlapping community detection (OCD) in social networks. In the first phase, called disassortative degree mixing, we identify nodes with high degrees through a random walk process on the row-normalized disassortative matrix representation of the network. In the second phase, we calculate how closely each node of the network is bound to the leaders via a cascading process called network coordination game. We implemented the algorithm and four additional ones as a Web service on a federated peer-to-peer infrastructure. Comparative test results for small and big real world networks demonstrated the correct identification of leaders, high precision and good time complexity. The Web service is available as open source software.

The increased use of the Internet as a new tool in communication has changed the way people interact. This fact is even more evident in the recent development and use of friend-networking sites. However, no research has evaluated these sites and their impact on college students. Therefore, the present study was conducted to evaluate: (a) why people use these friend-networking sites, (b) what the characteristics are of the typical college user, and (c) what uses and gratifications are met by using these sites. Results indicated that the vast majority of college students are using these friend-networking sites for a significant portion of their day for reasons such as making new friends and locating old friends. Additionally, both men and women of traditional college age are equally engaging in this form of online communication with this result holding true for nearly all ethnic groups. Finally, results showed that many uses and gratifications are met by users (e.g., "keeping in touch with friends"). Results are discussed in light of the impact that friend-networking sites have on communication and social needs of college students.

We analyse a large mobile phone activity dataset provided by Telecom Italia for the Telecom Big Data Challenge contest. The dataset reports the international country codes of every call/SMS made and received by mobile phone users in Milan, Italy, between November and December 2013, with a spatial resolution of about 200 meters. We first show that the observed spatial distribution of international codes well matches the distribution of international communities reported by official statistics, confirming the value of mobile phone data for demographic research. Next, we define an entropy function to measure the heterogeneity of the international phone activity in space and time. By comparing the entropy function to empirical data, we show that it can be used to identify the city’s hotspots, defined by the presence of points of interests. Eventually, we use the entropy function to characterize the spatial distribution of international communities in the city. Adopting a topological data analysis approach, we find that international mobile phone users exhibit some robust clustering patterns that correlate with basic socio-economic variables. Our results suggest that mobile phone records can be used in conjunction with topological data analysis tools to study the geography of migrant communities in a global city.

Purpose – The purpose of this paper is to evaluate and assess the awareness and extent of the use of social networking sites (SNSs) by the students and research scholars of universities of North India. Design/methodology/approach – The study is a questionnaire-based survey on the usage of SNSs among the students and research scholars of the universities of North India. The data of the study were collected through questionnaires, which were personally distributed to the identified population, i.e. undergraduate students, postgraduate students and research scholars, by the authors. The survey was based on a sample of 610 questionnaires; of which, 486 questionnaires were received, having a response rate of 79.67 per cent. Findings – The study showed that all the respondents were found to be aware of and making use of such applications in their academic affairs. It was revealed from the study that Facebook is the most popular SNS by all categories of respondents. To determine the purpose of SNSs, it emerged that SNSs are mostly used for entertainment and communication. The study also found that the majority of respondents were aware about the security aspects of SNSs. It signifies that excessive time consumption and fear of misusing personal information were the major hurdles in the way of accessing SNSs. Research limitations/implications – The study covers the students and research scholars of select universities of North India. It also signifies the use of SNSs in their research and academic environment. Originality/value – The paper provides reliable and authentic data. The study is worth, justifiable and enlightens the salient findings on the topic, which will be very useful for researchers in this area.

Online social networks (OSNs) are immensely popular, with some claiming over 200 million users. Users share private content, such as personal information or photographs, using OSN applications. Users must trust the OSN service to protect personal information even as the OSN provider benefits from examining and sharing that information. We present Persona, an OSN where users dictate who may access their information. Persona hides user data with attribute-based encryption (ABE), allowing users to apply fine-grained policies over who may view their data. Persona provides an effective means of creating applications in which users, not the OSN, define policy over access to private data. We demonstrate new cryptographic mechanisms that enhance the general applicability of ABE. We show how Persona provides the functionality of existing online social networks with additional privacy benefits. We describe an implementation of Persona that replicates Facebook applications and show that Persona provides acceptable performance when browsing privacy-enhanced web pages, even on mobile devices.

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].

Knowing the users' personality can be a strategic advantage for the design of adaptive and personalized user interfaces. In this paper, we present the results of a first trial conducted with the aim of inferring people's personality traits based on their mobile phone call behavior. Initial findings corroborate the efficacy of using call detail records (CDR) and Social Network Analysis (SNA) of the call graph to infer the Big Five personality factors. On-going work includes a large-scale study that shall refine the accuracy of the models with a reduced margin of error.

Received: 20 July 2012 Revised: 18 February 2013 2nd Revision: 28 June 2013 3rd Revision: 20 September 2013 4th Revision: 7 November 2013 Accepted: 1 February 2014 Abstract As the number of messages and social relationships embedded in social networking sites (SNS) increases, the amount of social information demanding a reaction from individuals increases as well. We observe that, as a consequence, SNS users feel they are giving too much social support to other SNS users. Drawing on social support theory (SST), we call this negative association with SNS usage ‘social overload’ and develop a latent variable to measure it. We then identify the theoretical antecedents and consequences of social overload and evaluate the social overload model empirically using interviews with 12 and a survey of 571 Facebook users. The results show that extent of usage, number of friends, subjective social support norms, and type of relationship (online-only vs offline friends) are factors that directly contribute to social overload while age has only an indirect effect. The psychological and behavioral consequences of social overload include feelings of SNS exhaustion by users, low levels of user satisfaction, and a high intention to reduce or even stop using SNS. The resulting theoretical implications for SST and SNS acceptance research are discussed and practical implications for organizations, SNS providers, and SNS users are drawn. European Journal of Information Systems advance online publication, 4 March 2014; doi:10.1057/ejis.2014.3; corrected online 11 March 2014


Question: An examination of the structure of online social networks: Analyzing Facebook and WhatsApp within cellular networks.

This paper delves into the complexities of photo privacy in the context of tagged images on social network platforms, with a particular emphasis on features like those found on Facebook and WhatsApp. Photo tagging, a prevalent function on these sites, enables users to associate uploaded pictures directly with the profiles of the individuals depicted, thereby creating explicit connections between the photo and the person. This study investigates the privacy concerns and management strategies associated with such tagged images. Through a series of focus groups, we explored the perspectives and apprehensions of users regarding photo tagging, leading to the formulation of a set of design principles aimed at enhancing tagged photo privacy. Subsequently, we developed a privacy-enhancing mechanism grounded in our research findings, which we rigorously evaluated using a combination of qualitative and quantitative methods. Our findings reveal the social dynamics and tensions that arise from photo tagging, as well as the critical need for privacy tools that can effectively manage the social ramifications of photo privacy in an increasingly interconnected digital landscape.

This study was conducted to answer this simple question: ‘Can cultural values explain global social media use?’ Along with cultural dimensions introduced by past studies we also added several demographic, socio-economic and personality variables into this study that generated quite interesting findings. We found that there are low levels of suicide, more happiness and more corruption in societies that use social media heavily. We also observed that GDP per capita and median age are negatively related with social media use. Self-esteem stood out as important variable related to social media use intensity along with emotional expressiveness, openness and conscientiousness. Contrary to the common view, nation-level social capital achievement was negatively related with social media use and there was absolutely no relationship between high-context and low-context communication characteristics and local social media use. Some other findings also indicated that conservative and collectivistic countries use social media more often than do individualistic and developed countries. Schwartz’s cultural dimensions and the results of the GLOBE study accounted for a considerable amount of variation in country-level social media use where Hofstede and Trompenaars’ cultural dimensions were insignificant. Since most of the cultural values failed to explain the intensity of social media use, we also developed a cross-cultural online communication framework called cross-cultural self and others’ worth.

We report results from an exploratory analysis examining “last-minute” self-censorship, or content that is filtered after being written, on Facebook. We collected data from 3.9 million users over 17 days and associate self-censorship behavior with features describing users, their social graph, and the interactions between them. Our results indicate that 71% of users exhibited some level of last-minute self-censorship in the time period, and provide specific evidence supporting the theory that a user’s “perceived audience” lies at the heart of the issue: posts are censored more frequently than comments, with status updates and posts directed at groups censored most frequently of all sharing use cases investigated. Furthermore, we find that: people with more boundaries to regulate censor more; males censor more posts than females and censor even more posts with mostly male friends than do females, but censor no more comments than females; people who exercise more control over their audience censor more content; and, users with more politically and age diverse friends censor less, in general.

In this study we investigate how social media shape the networked public sphere and facilitate communication between communities with different political orientations. We examine two networks of political communication on Twitter, comprised of more than 250,000 tweets from the six weeks leading up to the 2010 U.S. congressional midterm elections. Using a combination of network clustering algorithms and manually-annotated data we demonstrate that the network of political retweets exhibits a highly segregated partisan structure, with extremely limited connectivity between leftand right-leaning users. Surprisingly this is not the case for the user-to-user mention network, which is dominated by a single politically heterogeneous cluster of users in which ideologically-opposed individuals interact at a much higher rate compared to the network of retweets. To explain the distinct topologies of the retweet and mention networks we conjecture that politically motivated individuals provoke interaction by injecting partisan content into information streams whose primary audience consists of ideologically-opposed users. We conclude with statistical evidence in support of this hypothesis.

The ability to mine data represented as a graph has become important in several domains for detecting various structural patterns. One important area of data mining is anomaly detection, but little work has been done in terms of detecting anomalies in graph-based data. In this paper we present graph-based approaches to uncovering anomalies in applications containing information representing possible insider threat activity: e-mail, cell-phone calls, and order processing.

Many problems in industry — and in the social, natural, information, and medical sciences — involve discrete data and benefit from approaches from subjects such as network science, information theory, optimization, probability, and statistics. Because the study of networks is concerned explicitly with connectivity between different entities, it has become very prominent in industrial settings, and this importance has been accentuated further amidst the modern data deluge. In this commentary, we discuss the role of network analysis in industrial and applied mathematics, and we give several examples of network science in industry. We focus, in particular, on discussing a physical-applied-mathematics approach to the study of networks. 1 ar X iv :1 70 3. 06 84 3v 2 [ cs .S I] 1 4 Se p 20 17 The Role of Network Analysis in Industrial and Applied Mathematics: A Physical-Applied-Mathematics Perspective Mason A. Porter and Sam D. Howison Department of Mathematics, University of California, Los Angeles, Los Angeles, California 90095, USA Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK CABDyN Complexity Centre, University of Oxford, Oxford OX1 1HP, UK

We present NodeXL, an extendible toolkit for network overview, discovery and exploration implemented as an add-in to the Microsoft Excel 2007 spreadsheet software. We demonstrate NodeXL data analysis and visualization features with a social media data sample drawn from an enterprise intranet social network. A sequence of NodeXL operations from data import to computation of network statistics and refinement of network visualization through sorting, filtering, and clustering functions is described. These operations reveal sociologically relevant differences in the patterns of interconnection among employee participants in the social media space. The tool and method can be broadly applied.

Drawing on social network theories and previous studies, this research examines the dynamics of social network structures in open source software (OSS) teams. Three projects were selected from SourceForge.net in terms of their similarities as well as their differences. Monthly data were extracted from the bug tracking systems in order to achieve a longitudinal view of the interaction pattern of each project. Social network analysis was used to generate the indices of social structure. The finding suggests that the interaction pattern of OSS projects evolves from a single hub at the beginning to a core/periphery model as the projects move forward. INtrODUctION The information system development arena has seen many revolutions and evolutions. We have witnessed the movement from structured development to object-oriented (OO) development. Modeling methods, such as data flow diagram and entity relationship diagram, are facing new OO modeling languages, such as the unified modeling language (UML) (see Siau & Cao, 2001; Siau, Erickson, & Lee, 2005; Siau & Loo, 2006) and OO methodologies, such as unified process (UP). The latest development includes agile modeling (see Erickson, Lyytinen, & Siau, 2005), extreme programming, and OSS development. While many of these changes are related to systems development paradigms, methodologies, methods, and techniques, the phenomenon of OSS development entails a different structure for software development teams. Chapter 2.17 Social Network Structures in Open Source Software Development Teams Yuan Long Colorado State University-Pueblo, USA Keng Siau University of Nebraska-Lincoln, USA Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited. 646 Social Network Structures in Open Source Software Development Teams Unlike conventional software projects, the participants of OSS projects are volunteers. They are self-selected based on their interests and capability to contribute to the projects (Raymond, 2000). In addition, the developers of OSS projects are distributed all around the world. They communicate and collaborate with each other through the Internet, using e-mails or discussion boards. Therefore, effective and efficient communication and collaboration are critical to OSS success. However, little empirical research has been conducted to study the underlying interaction pattern of OSS teams, especially the dynamics of the social network structures in OSS development teams. To fill this gap, this study examines the evolvement of social structure in OSS teams. The study contributes to the enhancement of the understanding of OSS development, and provides foundation for future studies to analyze the antecedents and consequences of social networks in the OSS context. The remainder of the paper is structured as follows. First, prior studies on social network structures in OSS teams are reviewed. Second, theories related to social structure and social network theory are discussed. Third, the research methodology is presented, and the research results are reported. Next, discussions of the results, the limitations, and the implications are provided. The paper concludes with suggestions for future research.

As of January 2009, the online social networking application Facebook registered more than 175 million active users. To put that number in perspective, this is only slightly less than the population of Brazil (190 million) and over twice the population of Germany (80 million)! At the same time, every minute, 10 hours of content were uploaded to the video sharing platform YouTube. And, the image hosting site Flickr provided access to over 3 billion photographs, making the world-famous Louvre Museum’s collection of 300,000 objects seem tiny in comparison. According to Forrester Research, 75% of Internet surfers used ‘‘Social Media’’ in the second quarter of 2008 by joining social networks, reading blogs, or contributing reviews to shopping sites; this represents a significant rise from 56% in 2007. The growth is not limited to teenagers, either; members of Generation X, now 35—44 years old, increasingly populate the ranks of joiners, spectators, and critics. It is therefore reasonable to say that Social Media represent a revolutionary new trend that should be of interest to companies operating in online space–—or any space, for that matter. Yet, not overly many firms seem to act comfortably in a world where consumers can speak so freely Business Horizons (2010) 53, 59—68

Models of Network Growth All networks, whether they are social, technological, or biological, are the result of a growth process. Many of these networks continue to grow for prolonged periods of time, continually modifying their connectivity structure throughout their entire existence. For example, the World Wide Web has grown from a small number of cross-linked documents in the early 1 990s to an estimated 30 billion indexed web pages in 2009.3 The extraordinary growth of the Web continues unabated and has occurred without any top-down design, yet the topology of its hyperlink structure exhibits characteristic statistical patterns (Pastor-Satorras and Vespig nani, 2004). Other technological networks such as the power grid, global transportation networks, or mobile communication networks continue to grow and evolve, each displaying characteristic patterns of expansion and elaboration. Growth and change in social and organizational

Online relationships are less valuable than offline ones. Indeed, their net benefit depends on whether they supplement or substitute for offline social relationships.


Question: Investigating the structure of online social networks: A study of Facebook and WhatsApp in mobile networks.

This paper explores the intricacies of information disclosure and privacy concerns within online social networks, focusing on the behaviors of over 4,000 Carnegie Mellon University students who are active on a prominent college-oriented social networking platform. The study delves into the extent of personal data that users willingly share and examines their engagement with the site's privacy controls. It is observed that users often expose significant amounts of personal details, which can potentially lead to privacy breaches. The research underscores the vulnerabilities in privacy settings, noting that a mere fraction of the user base modifies the default, often lax, privacy configurations. This highlights the need for greater awareness and more robust privacy management tools within social networking environments, particularly those that cater to academic communities. The findings suggest that while social networking sites offer valuable platforms for connection and communication, they also present significant risks to user privacy that are often overlooked or underestimated by the majority of users.

Many problems in industry — and in the social, natural, information, and medical sciences — involve discrete data and benefit from approaches from subjects such as network science, information theory, optimization, probability, and statistics. Because the study of networks is concerned explicitly with connectivity between different entities, it has become very prominent in industrial settings, and this importance has been accentuated further amidst the modern data deluge. In this commentary, we discuss the role of network analysis in industrial and applied mathematics, and we give several examples of network science in industry. We focus, in particular, on discussing a physical-applied-mathematics approach to the study of networks. 1 ar X iv :1 70 3. 06 84 3v 2 [ cs .S I] 1 4 Se p 20 17 The Role of Network Analysis in Industrial and Applied Mathematics: A Physical-Applied-Mathematics Perspective Mason A. Porter and Sam D. Howison Department of Mathematics, University of California, Los Angeles, Los Angeles, California 90095, USA Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK CABDyN Complexity Centre, University of Oxford, Oxford OX1 1HP, UK

In many online social systems, social ties between users play an important role in dictating their behavior. One of the ways this can happen is through social influence, the phenomenon that the actions of a user can induce his/her friends to behave in a similar way. In systems where social influence exists, ideas, modes of behavior, or new technologies can diffuse through the network like an epidemic. Therefore, identifying and understanding social influence is of tremendous interest from both analysis and design points of view.
 This is a difficult task in general, since there are factors such as homophily or unobserved confounding variables that can induce statistical correlation between the actions of friends in a social network. Distinguishing influence from these is essentially the problem of distinguishing correlation from causality, a notoriously hard statistical problem.
 In this paper we study this problem systematically. We define fairly general models that replicate the aforementioned sources of social correlation. We then propose two simple tests that can identify influence as a source of social correlation when the time series of user actions is available.
 We give a theoretical justification of one of the tests by proving that with high probability it succeeds in ruling out influence in a rather general model of social correlation. We also simulate our tests on a number of examples designed by randomly generating actions of nodes on a real social network (from Flickr) according to one of several models. Simulation results confirm that our test performs well on these data. Finally, we apply them to real tagging data on Flickr, exhibiting that while there is significant social correlation in tagging behavior on this system, this correlation cannot be attributed to social influence.

Social media is a popular method for communication amongst university students in Saudi Arabia. However excessive social media use can raise questions about whether academic performance is affected. This research explores this question by conducting a survey on university students in Saudi Arabia in regards to social media usage and their academic performance. The survey also explored which social network is the most popular amongst Saudi students, what students thought about their social media usage and factors besides social media usage which negatively affect academic performance. The survey received 108 responses and descriptive statistics including normality tests i.e. scatter plots were used to examine the relationship between the average number of hours students spent of social media a week and GPA scores of the students. The results demonstrated that there was no linear relationship between social media usage in a week and GPA score. Students highlighted that besides social media use, time management is a factor which affects students ‘studies negatively. The findings of the paper can be used to propose the effective plans for improving the academic performance of the students in such a way that a balance in the leisure, information exchange and academic performance can be maintained. 2014 Elsevier Ltd. All rights reserved.

Online social networks have become an effective and important social platform for communication, opinions exchange, and information sharing. However, they also make it possible for rapid and wide misinformation diffusion, which may lead to pernicious influences on individuals or society. Hence, it is extremely important and necessary to detect the misinformation propagation by placing monitors.
 In this article, we first define a general misinformation-detection problem for the case where the knowledge about misinformation sources is lacking, and show its equivalence to the influence-maximization problem in the reverse graph. Furthermore, considering node vulnerability, we aim to detect the misinformation reaching to a specific user. Therefore, we study a τ-Monitor Placement problem for cases where partial knowledge of misinformation sources is available and prove its #P complexity. We formulate a corresponding integer program, tackle exponential constraints, and propose a Minimum Monitor Set Construction (MMSC) algorithm, in which the cut-set2 has been exploited in the estimation of reachability of node pairs. Moreover, we generalize the problem from a single target to multiple central nodes and propose another algorithm based on a Monte Carlo sampling technique. Extensive experiments on real-world networks show the effectiveness of proposed algorithms with respect to minimizing the number of monitors.

People share personal content online with varied audiences, as part of tasks ranging from conversational-style content sharing to collaborative activities. We use an interview- and diary-based study to explore: 1) what factors impact channel choice for sharing with particular audiences; and 2) what behavioral patterns emerge from the ability to combine or switch between channels. We find that in the context of different tasks, participants match channel features to selective-sharing and other task-based needs, shaped by recipient attributes and communication dynamics. Participants also combine multiple channels to create composite sharing features or reach broader audiences when one channel is insufficient. We discuss design implications of these channel dynamics.

Conformity is a type of social influence involving a change in opinion or behavior in order to fit in with a group. Employing several social networks as the source for our experimental data, we study how the effect of conformity plays a role in changing users' online behavior. We formally define several major types of conformity in individual, peer, and group levels. We propose Confluence model to formalize the effects of social conformity into a probabilistic model. Confluence can distinguish and quantify the effects of the different types of conformities. To scale up to large social networks, we propose a distributed learning method that can construct the Confluence model efficiently with near-linear speedup. Our experimental results on four different types of large social networks, i.e., Flickr, Gowalla, Weibo and Co-Author, verify the existence of the conformity phenomena. Leveraging the conformity information, Confluence can accurately predict actions of users. Our experiments show that Confluence significantly improves the prediction accuracy by up to 5-10% compared with several alternative methods.

Using detailed traces from 255 users, we conduct a comprehensive study of smartphone use. We characterize intentional user activities -- interactions with the device and the applications used -- and the impact of those activities on network and energy usage. We find immense diversity among users. Along all aspects that we study, users differ by one or more orders of magnitude. For instance, the average number of interactions per day varies from 10 to 200, and the average amount of data received per day varies from 1 to 1000 MB. This level of diversity suggests that mechanisms to improve user experience or energy consumption will be more effective if they learn and adapt to user behavior. We find that qualitative similarities exist among users that facilitate the task of learning user behavior. For instance, the relative application popularity for can be modeled using an exponential distribution, with different distribution parameters for different users. We demonstrate the value of adapting to user behavior in the context of a mechanism to predict future energy drain. The 90th percentile error with adaptation is less than half compared to predictions based on average behavior across users.

Mobile instant messaging (e.g., via SMS or WhatsApp) often goes along with an expectation of high attentiveness, i.e., that the receiver will notice and read the message within a few minutes. Hence, existing instant messaging services for mobile phones share indicators of availability, such as the last time the user has been online. However, in this paper we not only provide evidence that these cues create social pressure, but that they are also weak predictors of attentiveness. As remedy, we propose to share a machine-computed prediction of whether the user will view a message within the next few minutes or not. For two weeks, we collected behavioral data from 24 users of mobile instant messaging services. By the means of machine-learning techniques, we identified that simple features extracted from the phone, such as the user's interaction with the notification center, the screen activity, the proximity sensor, and the ringer mode, are strong predictors of how quickly the user will attend to the messages. With seven automatically selected features our model predicts whether a phone user will view a message within a few minutes with 70.6% accuracy and a precision for fast attendance of 81.2%

URLs: Self-disclosure on Facebook among female users and its relationship to feelings of loneliness Author(s): Al-Saggaf, Y.M. ; Nielsen, S.G. Computers in Human Behavior

In recent years, mobile devices (e.g., smartphones and tablets) have met an increasing commercial success and have become a fundamental element of the everyday life for billions of people all around the world. Mobile devices are used not only for traditional communication activities (e.g., voice calls and messages) but also for more advanced tasks made possible by an enormous amount of multi-purpose applications (e.g., finance, gaming, and shopping). As a result, those devices generate a significant network traffic (a consistent part of the overall Internet traffic). For this reason, the research community has been investigating security and privacy issues that are related to the network traffic generated by mobile devices, which could be analyzed to obtain information useful for a variety of goals (ranging from fine-grained user profiling to device security and network optimization). In this paper, we review the works that contributed to the state of the art of network traffic analysis targeting mobile devices. In particular, we present a systematic classification of the works in the literature according to three criteria: 1) the goal of the analysis; 2) the point where the network traffic is captured; and 3) the targeted mobile platforms. In this survey, we consider points of capturing such as Wi-Fi access points, software simulation, and inside real mobile devices or emulators. For the surveyed works, we review and compare analysis techniques, validation methods, and achieved results. We also discuss possible countermeasures, challenges, and possible directions for future research on mobile traffic analysis and other emerging domains (e.g., Internet of Things). We believe our survey will be a reference work for researchers and practitioners in this research field.


Question: Investigating the structure of online social networks: A study of Facebook and WhatsApp in mobile networks.

This paper introduces Persona, an innovative online social network (OSN) platform that empowers users with unprecedented control over their privacy. Given the staggering popularity of OSNs, with some boasting over 200 million active users, the management and protection of personal data have become critical concerns. Traditionally, users have had to place their trust in OSN providers to safeguard their information, even as these providers often analyze and share user data for various benefits. Persona addresses this issue by placing the power in the hands of users, allowing them to specify who can access their personal content. By leveraging attribute-based encryption (ABE), Persona ensures that user data remains secure, enabling individuals to apply nuanced policies regarding who can view their information. This approach not only enhances privacy but also facilitates the development of applications where privacy policies are defined by users, rather than the OSN itself. The paper highlights new cryptographic techniques that broaden the applicability of ABE, showcasing how Persona can deliver the functionalities of established OSNs like Facebook, but with enhanced privacy features. An implementation of Persona that mirrors Facebook applications is described, demonstrating that the platform can offer satisfactory performance when navigating privacy-enhanced web pages, even on mobile devices, without compromising user experience.

The emergence of mobile application software (App) has explosively grown in conjunction with the worldwide use of smartphones in recent years. Among numerous categories of mobile Apps, social Apps were one of those with the greatest growth in 2013. Despite abundant research on users’ behavior intention of mobile App usage, few studies have focused on investigating key determinants of users’ continuance intention regarding social Apps. To fill this gap, we integrated customer value perspectives to explore the influential factors in the continuance intention of social App use. Moreover, users’ satisfaction and habit from both the marketing and psychology literature were also incorporated into the research model. A total of 378 valid questionnaires were collected by survey method, and structural equation modeling was employed in the subsequent data analysis. The results indicate that the continuance usage of social Apps is driven by users’ satisfaction, tight connection with others, and hedonic motivation to use the Apps. In addition, full mediation effects of satisfaction and habit were found between perceived usefulness and intention to continue use. These findings extend our understanding of users’ continuance intention in the context of social Apps. Discussion and implications are provided. 2015 Elsevier Ltd. All rights reserved.

As the use and influence of social networking continues to grow, researchers have begun to explore its consequences for psychological well-being. Some research suggests that Facebook use can have negative consequences for well-being. Instagram, a photo-sharing social network created in 2010, has particular characteristics that may make users susceptible to negative consequences. This study tested a theoretically grounded moderated meditation model of the association between Instagram use and depressive symptoms through the mechanism of negative social comparison, and moderation by amount of strangers one follows. One hundred and seventeen 18-29 year olds completed online questionnaires containing demographics, frequency of Instagram use, amount of strangers followed on Instagram, the Center for Epidemiological Resources Scale for Depression, and the Social Comparison Rating Scale. Instagram use was marginally positively associated with depressive symptoms, and positive social comparison was significantly negatively associated with depressive symptoms. Amount of strangers followed moderated the associations of Instagram use with social comparison (significantly) and depressive symptoms (marginally), and further significantly moderated the indirect association of Instagram use with depressive symptoms through social comparison. Findings generally suggest that more frequent Instagram use has negative associations for people who follow more strangers, but positive associations for people who follow fewer strangers, with social comparison and depressive symptoms. Implications of negative associations of social networking for people who follow strangers and the need for more research on Instagram use given its increasing popularity are explored.

Understanding how much two individuals are alike in their interests (i.e., interest similarity) has become virtually essential for many applications and services in Online Social Networks (OSNs). Since users do not always explicitly elaborate their interests in OSNs like Facebook, how to determine users’ interest similarity without fully knowing their interests is a practical problem. In this paper, we investigate how users’ interest similarity relates to various social features (e.g. geographic distance); and accordingly infer whether the interests of two users are alike or unalike where one of the users’ interests are unknown. Relying on a large Facebook dataset, which contains 479, 048 users and 5, 263, 351 user-generated interests, we present comprehensive empirical studies and verify the homophily of interest similarity across three interest domains (movies, music and TV shows). The homophily reveals that people tend to exhibit more similar tastes if they have similar demographic information (e.g., age, location), or if they are friends. It also shows that the individuals with a higher interest entropy usually share more interests with others. Based on these results, we provide a practical prediction model under a real OSN environment. For a given user with no interest information, this model can select some individuals who not only exhibit many interests but also probably achieve high interest similarities with the given user. Eventually, we illustrate a use case to demonstrate that the proposed prediction model could facilitate decision-making for OSN applications and services.

Social network analysis has attracted much attention in recent years. Community mining is one of the major directions in social network analysis. Most of the existing methods on community mining assume that there is only one kind of relation in the network, and moreover, the mining results are independent of the users' needs or preferences. However, in reality, there exist multiple, heterogeneous social networks, each representing a particular kind of relationship, and each kind of relationship may play a distinct role in a particular task. Thus mining networks by assuming only one kind of relation may miss a lot of valuable hidden community information and may not be adaptable to the diverse information needs from different users.In this paper, we systematically analyze the problem of mining hidden communities on heterogeneous social networks. Based on the observation that different relations have different importance with respect to a certain query, we propose a new method for learning an optimal linear combination of these relations which can best meet the user's expectation. With the obtained relation, better performance can be achieved for community mining. Our approach to social network analysis and community mining represents a major shift in methodology from the traditional one, a shift from single-network, user-independent analysis to multi-network, user-dependant, and query-based analysis. Experimental results on Iris data set and DBLP data set demonstrate the effectiveness of our method.

People have long sought answers to questions online, typically using either anonymous or pseudonymous forums or social network platforms that primarily use real names. Systems that allow anonymous communication afford freedom to explore identity and discuss taboo topics, but can result in negative disinhibited behavior such as cyberbullying. Identifiable communication systems allows one to reach a known audience and avoid negative disinhibition, but can constrain behavior with concerns about privacy and reputation. One persistent design issue is understanding how to leverage the benefits of anonymity without suffering its drawbacks. This paper presents a case study analysis of question asking on Facebook confession boards (FCBs), a tool popular on some college campuses. FCBs present a unique configuration in which members of an offline community (e.g., a university) anonymously submit content to a moderator who posts it to a Facebook page where others in the community can view it and respond. Response is via identifiable Facebook comments and likes. Our results show users asking about taboo and stigmatized topics with local others, and receiving relevant responses with little cyberbullying or negativity.

With the rapidly growing development of cellular networks and powerful smartphones, botnets have invaded the mobile domain. Social media, like Twitter, Facebook, and YouTube have created a new communication channel for attackers. Recently, bot masters started to exploit social media for different malicious activity, such as sending spam, recruitment of new bots, and botnet command and control. In this paper we propose a detection technique for social mediabased mobile botnets using Twitter. The proposed method combines the correlation between tweeting and user activity, such as clicks or taps, and an Artificial Immune System detector, to detect tweets caused by bots and differentiate them from tweets generated by user or by user-approved applications. This detector creates a signature of the tweet and compares it with a dynamically updated signature library of bot behavior signatures. The proposed system has been fully implemented on Android platform and tested under several sets of generated tweets. The test results show that the proposed method has a very high accuracy in detecting bot tweets with about 95% detection ratio.

In recent years, Online Social Networks (OSNs) have essentially become an integral part of our daily lives. There are hundreds of OSNs, each with its own focus and offers for particular services and functionalities. To take advantage of the full range of services and functionalities that OSNs offer, users often create several accounts on various OSNs using the same or different personal information. Retrieving all available data about an individual from several OSNs and merging it into one profile can be useful for many purposes. In this paper, we present a method for solving the Entity Resolution (ER), problem for matching user profiles across multiple OSNs. Our algorithm is able to match two user profiles from two different OSNs based on machine learning techniques, which uses features extracted from each one of the user profiles. Using supervised learning techniques and extracted features, we constructed different classifiers, which were then trained and used to rank the probability that two user profiles from two different OSNs belong to the same individual. These classifiers utilized 27 features of mainly three types: name based features (i.e., the Soundex value of two names), general user info based features (i.e., the cosine similarity between two user profiles), and social network topological based features (i.e., the number of mutual friends between two users' friends list). This experimental study uses real-life data collected from two popular OSNs, Facebook and Xing. The proposed algorithm was evaluated and its classification performance measured by AUC was 0.982 in identifying user profiles across two OSNs.

■ Abstract Similarity breeds connection. This principle—the homophily principle—structures network ties of every type, including marriage, friendship, work, advice, support, information transfer, exchange, comembership, and other types of relationship. The result is that people’s personal networks are homogeneous with regard to many sociodemographic, behavioral, and intrapersonal characteristics. Homophily limits people’s social worlds in a way that has powerful implications for the information they receive, the attitudes they form, and the interactions they experience. Homophily in race and ethnicity creates the strongest divides in our personal environments, with age, religion, education, occupation, and gender following in roughly that order. Geographic propinquity, families, organizations, and isomorphic positions in social systems all create contexts in which homophilous relations form. Ties between nonsimilar individuals also dissolve at a higher rate, which sets the stage for the formation of niches (localized positions) within social space. We argue for more research on: (a) the basic ecological processes that link organizations, associations, cultural communities, social movements, and many other social forms; (b) the impact of multiplex ties on the patterns of homophily; and (c) the dynamics of network change over time through which networks and other social entities co-evolve.

Many complex systems in nature, society and technologyfrom the online social networks to the internet from the nervous system to power gridscan be represented as a graph of vertices interconnected by edges. Small world network, Scale free network, Community detection are fundamental properties of complex networks. Community is a sub graph with densely connected nodes typically reveals topological relations, network structure, organizational and functional characteristics of the underlying network, e.g., friendships on Facebook, Followers of a VIP account on Twitter, and interaction with business professionals on LinkedIn. Online social networks are dynamic in nature, they evolve in size and space rapidly. In this paper we are detecting incremental disjoint communities using dyanamic multi label propagation algorithm. It tackles the temporal event changes, i.e., addition, deletion of an edge or vertex in a sub graph for every timestamp. The experimental results on Enron real network dataset shows that the proposed method is quite well in identifying communities.

WeChat was published in China by Tencent company since Jan. 21st 2011. After 100 million users in Mar. 2012, Tencent announced that the number of WeChat's users reached 300 million in Jan 15th 2013. WeChat has become one of the most heated topics within the last 2 years and its every move is attracting attention from various academia, industries, and enterprises. This study selected 50 university graduates as object and analyzed their We Chat using patterns through investigation and survey with statistic methods. The author designed the methods of user study and invited users to participate in the user study at first. Second, by using the collected data from university graduates from 2013 to Mar 2014, the author analyzed the quantities and composition of their contacts in We Chat, figured out the source, type and goal of the public platform they are following, recorded the time, type, form and time relativity of their first 100 posts in the "Moments" function. Through analyzing the data, the author intended to study the real situation in which university graduates virtually socialize and communicate with each other using WeChat and the popularity of WeChat on building social network with strangers. Also, this paper has certain contribution to how to improve the function and service of WeChat. What's more, it analyzed both positive and negative influence of We Chat on students' socialization.


Question: Stochastic deep kernel learning using variational methods.

Introducing Supervised Manifold Gaussian Processes for Enhanced Regression
Gaussian Processes (GPs) are a popular choice for modeling functions due to their flexibility and probabilistic nature. However, standard covariance functions often impose smoothness assumptions that may not align with the complexity or discontinuities present in real-world data. To address this limitation, we present Supervised Manifold Gaussian Processes (Manifold GPs), a method designed to simultaneously learn a transformation of the input data into a more suitable feature space and perform Gaussian Process regression from this feature space to the output space. Unlike unsupervised feature learning methods that may not optimize for the regression task, Manifold GPs learn data representations that are explicitly tailored to improve the regression performance. This supervised approach ensures that the learned feature space is beneficial for the overall modeling objective. To demonstrate the efficacy of our method, we apply Manifold GPs to challenging regression tasks involving non-smooth functions, such as step functions and robotics problems with contact dynamics, where traditional GPs struggle. Our results highlight the superiority of Manifold GPs in handling such complex scenarios by effectively learning representations that facilitate accurate regression.

Neuromorphic hardware tends to pose limits on the connectivity of deep networks that one can run on them. But also generic hardware and software implementations of deep learning run more efficiently on sparse networks. Several methods exist for pruning connections of a neural network after it was trained without connectivity constraints. We present an algorithm, DEEP R, that enables us to train directly a sparsely connected neural network. DEEP R automatically rewires the network during supervised training so that connections are there where they are most needed for the task, while its total number is all the time strictly bounded. We demonstrate that DEEP R can be used to train very sparse feedforward and recurrent neural networks on standard benchmark tasks with just a minor loss in performance. DEEP R is based on a rigorous theoretical foundation that views rewiring as stochastic sampling of network configurations from a posterior.

Deep generative models can perform dramatically better than traditional graphical models in a number of machine learning tasks. However, training such models remains challenging because their latent variables typically do not have an analytical posterior distribution, largely due to the nonlinear activation nodes. We present a deep convolutional factor analyser (DCFA) for multivariate time series modeling. Our network is constructed in a way that bottom layer nodes are independent. Through a process of up-sampling and convolution, higher layer nodes gain more temporal dependency. Our model can thus give a time series different representations at different depths. DCFA only consists of linear Gaussian nodes. Therefore, the posterior distributions of latent variables are also Gaussian and can be estimated easily using standard variational Bayes algorithm. We show that even without nonlinearity the proposed deep model can achieve state-of-the-art results in anomaly detection, classification and clustering using both synthetic and real-world datasets.

In many applications of black-box optimization, one can evaluate multiple points simultaneously, e.g. when evaluating the performances of several different neural networks in a parallel computing environment. In this paper, we develop a novel batch Bayesian optimization algorithm — the parallel knowledge gradient method. By construction, this method provides the one-step Bayes optimal batch of points to sample. We provide an efficient strategy for computing this Bayes-optimal batch of points, and we demonstrate that the parallel knowledge gradient method finds global optima significantly faster than previous batch Bayesian optimization algorithms on both synthetic test functions and when tuning hyperparameters of practical machine learning algorithms, especially when function evaluations are noisy.

We show how the success of deep learning could depend not only on mathematics but also on physics: although well-known mathematical theorems guarantee that neural networks can approximate arbitrary functions well, the class of functions of practical interest can frequently be approximated through “cheap learning” with exponentially fewer parameters than generic ones. We explore how properties frequently encountered in physics such as symmetry, locality, compositionality, and polynomial log-probability translate into exceptionally simple neural networks. We further argue that when the statistical process generating the data is of a certain hierarchical form prevalent in physics and machine-learning, a deep neural network can be more efficient than a shallow one. We formalize these claims using information theory and discuss the relation to the renormalization group. We prove various “no-flattening theorems” showing when efficient linear deep networks cannot be accurately approximated by shallow ones without efficiency loss; for example, we show that n variables cannot be multiplied using fewer than 2 neurons in a single hidden layer.

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space—classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semi-definite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm— using the labelled part of the data one can learn an embedding also for the unlabelled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method to learn the 2-norm soft margin parameter in support vector machines, solving another important open problem. Finally, the novel approach presented in the paper is supported by positive empirical results.

High-dimensional data can be converted to low-dimensional codes by training a multilayer neural network with a small central layer to reconstruct high-dimensional input vectors. Gradient descent can be used for fine-tuning the weights in such "autoencoder" networks, but this works well only if the initial weights are close to a good solution. We describe an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data.

Deep and recurrent neural networks (DNNs and RNNs respectively) are powerful models that were considered to be almost impossible to train using stochastic gradient descent with momentum. In this paper, we show that when stochastic gradient descent with momentum uses a well-designed random initialization and a particular type of slowly increasing schedule for the momentum parameter, it can train both DNNs and RNNs (on datasets with long-term dependencies) to levels of performance that were previously achievable only with Hessian-Free optimization. We find that both the initialization and the momentum are crucial since poorly initialized networks cannot be trained with momentum and well-initialized networks perform markedly worse when the momentum is absent or poorly tuned. Our success training these models suggests that previous attempts to train deep and recurrent neural networks from random initializations have likely failed due to poor initialization schemes. Furthermore, carefully tuned momentum methods su ce for dealing with the curvature issues in deep and recurrent network training objectives without the need for sophisticated second-order methods.

Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the “coding criterion” to build program vector representations, which are the premise of deep learning for program analysis. Our representation learning approach directly makes deep learning a reality in this new field. We evaluate the learned vector representations both qualitatively and quantitatively. We conclude, based on the experiments, the coding criterion is successful in building program representations. To evaluate whether deep learning is beneficial for program analysis, we feed the representations to deep neural networks, and achieve higher accuracy in the program classification task than “shallow” methods, such as logistic regression and the support vector machine. This result confirms the feasibility of deep learning to analyze programs. It also gives primary evidence of its success in this new field. We believe deep learning will become an outstanding technique for program analysis in the near future.

The applicability of machine learning methods is often limi ted by the amount of available labeled data, and by the ability (or inability) of the de signer to produce good internal representations and good similarity measures for the input data vectors. The aim of this thesis is to alleviate these two limitations by proposing al gorithms tolearngood internal representations, and invariant feature hierarchies from u nlabeled data. These methods go beyond traditional supervised learning algorithms, and rely on unsupervised, and semi-supervised learning. In particular, this work focuses on “deep learning” methods , a et of techniques and principles to train hierarchical models. Hierarchical mod els produce feature hierarchies that can capture complex non-linear dependencies among the observed data variables in a concise and efficient manner. After training, these mode ls can be employed in real-time systems because they compute the representation by a very fast forward propagation of the input through a sequence of non-linear transf ormations. When the paucity of labeled data does not allow the use of traditional supervi s d algorithms, each layer of the hierarchy can be trained in sequence starting at the bott om by using unsupervised or semi-supervised algorithms. Once each layer has been train ed, the whole system can be fine-tuned in an end-to-end fashion. We propose several unsu pervised algorithms that can be used as building block to train such feature hierarchi es. We investigate algorithms that produce sparse overcomplete representations a nd fe tures that are invariant to known and learned transformations. These algorithms are designed using the Energy-

Abstract Gaussian Process (GP) regression models typically assume that residuals are Gaussian and have the same variance for all observations. However, applications with input-dependent noise (heteroscedastic residuals) frequently arise in practice, as do applications in which the residuals do not have a Gaussian distribution. In this paper, we propose a GP Regression model with a latent variable that serves as an additional unobserved covariate for the regression. This model (which we call GPLC) allows for heteroscedasticity since it allows the function to have a changing partial derivative with respect to this unobserved covariate. With a suitable covariance function, our GPLC model can handle (a) Gaussian residuals with input-dependent variance, or (b) nonGaussian residuals with input-dependent variance, or (c) Gaussian residuals with constant variance. We compare our model, using synthetic datasets, with a model proposed by Goldberg, Williams and Bishop (1998), which we refer to as GPLV, which only deals with case (a), as well as a standard GP model which can handle only case (c). Markov Chain Monte Carlo methods are developed for both modelsl. Experiments show that when the data is heteroscedastic, both GPLC and GPLV give better results (smaller mean squared error and negative log-probability density) than standard GP regression. In addition, when the residual are Gaussian, our GPLC model is generally nearly as good as GPLV, while when the residuals are non-Gaussian, our GPLC model is better than GPLV.