Question: Assessing the impact of geo-social factors in location-based social networking platforms.

This paper introduces SimRank, a novel measure for assessing the structural-context similarity between objects within any domain that features object-to-object relationships. Unlike domain-specific similarity metrics that might focus on text matching across documents or item-set overlap, SimRank offers a more general approach. It evaluates the similarity of the structural context in which objects are situated, based on their relationships with other objects. Essentially, SimRank posits that "two objects are similar if they are related to similar objects," utilizing a straightforward and intuitive graph-theoretic model to compute this similarity measure. In a given domain, SimRank can be combined with other domain-specific similarity measures to enhance its applicability. The paper also outlines efficient computation techniques for SimRank scores and provides empirical evidence from two application domains, demonstrating both the computational feasibility and the effectiveness of the proposed approach.

**Question**: A review of machine learning techniques for image classification

**Documents**: A survey on deep learning methods for medical image analysis
Medical image analysis is an important task in the medical field, which can help doctors to diagnose diseases more accurately and efficiently. In recent years, deep learning methods have shown great potential in medical image analysis, achieving state-of-the-art performance in various tasks such as image classification, segmentation, and registration. This paper provides a comprehensive survey of deep learning methods for medical image analysis, covering the most popular architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs). We also discuss the challenges and future trends in this field, including the lack of annotated data, the need for explainable AI, and the potential of transfer learning.

**Similar Documents**: This paper offers a thorough review of the application of deep learning techniques in the realm of medical image analysis, an area critical for enhancing the precision and efficiency of disease diagnosis by healthcare professionals. Over the past few years, deep learning has emerged as a powerful tool in medical image analysis, consistently delivering state-of-the-art outcomes across a range of tasks including image classification, segmentation, and registration. Our survey encompasses a broad spectrum of deep learning architectures, with a focus on the most prevalent models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs). Additionally, we delve into the current challenges and prospective future directions within this field, encompassing issues such as the scarcity of annotated data, the demand for AI explainability, and the promise of transfer learning methodologies.

Here we present a review of PetaJakarta.org, a system designed to harness social media use in Jakarta for the purpose of relaying information about flood locations from citizen to citizen and from citizens and the city’s emergency management agency. The project aimed to produce an open, real-time situational overview of flood conditions and provide decision support for the management agency, as well as offering the government a data source for post-event analysis. As such, the platform was designed as a socio-technological system and developed as a civic co-management tool to enable climate adaptation and community resilience in Jakarta, a delta megacity suffering enormous infrastructural instability due to a troubled confluence of environmental factors—the city’s rapid urbanization, its unique geographic limitations, and increasing sea-levels and monsoon rainfalls resulting from climate change. The chapter concludes with a discussion of future research in open source platform and their role in infrastructure and disaster management. From now on there is an interconnection, an intertwining, even a symbiosis of technologies, exchanges, movements, which makes it so that a flood—for instance—wherever it may occur, must necessarily involve relationships with any number of technical, social, economic, political intricacies that keep us from regarding it simply as a misadventure or a misfortune whose consequences can be more or less easily circumscribed. — Jean-Luc Nancy (2015, 3–4) * E. Turpin & T. Holderness SMART Infrastructure Facility, University of Wollongong, Australia e-mail: eturpin@uow.edu.au | tomas@uow.edu.au Chapter 6 in Social Media for Government Services, Springer 2016 (preprint version)

We empirically examine and quantify network effects on a large online dating platform in Brazil. We exploit a natural experiment, wherein a focal platform acquired its Brazilian competitor and subsequently imported the competitor’s base of 150,000+ users over a 3-day period; a large exogenous shock to the composition of the purchasing platform. Our study context and the natural experiment provide two unique sources of identification: i) accounts purchased from the competitor were almost exclusively heterosexual users, even though the purchasing platform also played host to homosexual users, and ii) the treatment varied across cities, in that the “value” of new users to the existing user base differed. because purchased users differed from existing users in terms of their average characteristics (e.g., location within the city). We leverage the former to estimate a difference-in-differences specification, treating homosexual enrollment and exit rates as a plausible control for those of the heterosexual population, whereas the latter provides us with an opportunity to explore the importance of local market structure in the manifestation of network effects. We find that the treatment increased both rates of enrollment and rates of exit, amongst both genders, with a net positive effect that translated to a 17% increase in short-term revenue for the platform. We find clear evidence of local network effects; in cities where the average spatial distance between new users and existing users was larger, the treatment effect was significantly weaker. We discuss the implications for the literature and practice, and we suggest a number of avenues for future work in this space. ∗Assistant Professor, Information Systems Department, Carlson School of Management, University of Minnesota. gburtch@umn.edu. †Associate Professor, Information Systems Department, Desautels Faculty of Management, McGill University. jui.ramaprasad@mcgill.ca.

Context -aware computing often involves tracking peoples’ location. Many studies and applications highlight the importance of keeping people’s location information private. We discuss two types of locationbased services; location-tracking services that are based on other parties tracking the user’s location and position-aware services that rely on the device’s knowledge of its own location. We present an experimental case study that examines people’s concern for location privacy and compare this to the use of location-based services. We find that even though the perceived usefulness of the two different types of services is the same, locationtracking services generate more concern for privacy than posit ion-aware services. We conclude that development emphasis should be given to position-aware services but that location-tracking services have a potential for success if users are given a simple option for turning the location-tracking off.

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.

Social networking communities have become an important communications platform, but the popularity of these communities has also made them targets for a new breed of social spammers. Unfortunately, little is known about these social spammers, their level of sophistication, or their strategies and tactics. Thus, in this paper, we provide the first characterization of social spammers and their behaviors. Concretely, we make two contributions: (1) we introduce social honeypots for tracking and monitoring social spam, and (2) we report the results of an analysis performed on spam data that was harvested by our social honeypots. Based on our analysis, we find that the behaviors of social spammers exhibit recognizable temporal and geographic patterns and that social spam content contains various distinguishing characteristics. These results are quite promising and suggest that our analysis techniques may be used to automatically identify social spam.

How do two people become friends? What role does homophily play in bringing two people closer to help them forge friendship? Is the similarity between two friends different from the similarity between any two people? How does the similarity between a friend of a friend compare to similarity between direct friends? In this work, our goal is to answer these questions. We study the relationship between semantic similarity of user profile entries and the social network topology. A user profile in an on-line social network is characterized by its profile entries. The entries are termed as user keywords. We develop a model to relate keywords based on their semantic relationship and define similarity functions to quantify the similarity between a pair of users. First, we present a ‘forest model’ to categorize keywords across multiple categorization trees and define the notion of distance between keywords. Second, we use the keyword distance to define similarity functions between a pair of users. Third, we analyze a set of Facebook data according to the model to determine the effect of homophily in on-line social networks. Based on our evaluations, we conclude that direct friends are more similar than any other user pair. However, the more striking observation is that except for direct friends, similarities between users are approximately equal, irrespective of the topological distance between them.

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 recommendation, which aims to systematically leverage the social relationships between users as well as their past behaviors for automatic recommendation, attract much attention recently. The belief is that users linked with each other in social networks tend to share certain common interests or have similar tastes (homophily principle); such similarity is expected to help improve the recommendation accuracy and quality. There have been a few studies on social recommendations; however, they almost completely ignored the heterogeneity and diversity of the social relationship.
 In this paper, we develop a joint personal and social latent factor (PSLF) model for social recommendation. Specifically, it combines the state-of-the-art collaborative filtering and the social network modeling approaches for social recommendation. Especially, the PSLF extracts the social factor vectors for each user based on the state-of-the-art mixture membership stochastic blockmodel, which can explicitly express the varieties of the social relationship. To optimize the PSLF model, we develop a scalable expectation-maximization (EM) algorithm, which utilizes a novel approximate mean-field technique for fast expectation computation. We compare our approach with the latest social recommendation approaches on two real datasets, Flixter and Douban (both with large social networks). With similar training cost, our approach has shown a significant improvement in terms of prediction accuracy criteria over the existing approaches.

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.

The rapid growth of contemporary social network sites (SNSs) has coincided with an increasing concern over personal privacy. College students and adolescents routinely provide personal information on profiles that can be viewed by large numbers of unknown people and potentially used in harmful ways. SNSs like Facebook and MySpace allow users to control the privacy level of their profile, thus limiting access to this information. In this paper, we take the preference for privacy itself as our unit of analysis, and analyze the factors that are predictive of a student having a private versus public profile. Drawing upon a new social network dataset based on Facebook, we argue that privacy behavior is an upshot of both social influences and personal incentives. Students are more likely to have a private profile if their friends and roommates have them; women are more likely to have private profiles than are men; and having a private profile is associated with a higher level of online activity. Finally, students who have private versus public profiles are characterized by a unique set of cultural preferences—of which the ‘‘taste for privacy’’ may be only a small but integral part.


Question: A hierarchical method for text generation and planning in strategic dialogue scenarios.

This paper explores the development of open-domain, conversational dialogue systems using generative models and large dialogue datasets. The focus is on generative models that can autonomously produce system responses word-by-word, thereby enabling more realistic and adaptable interactions. To achieve this, we adapt the recently introduced hierarchical recurrent encoder-decoder neural network model to the dialogue context. Our experiments confirm that this model can rival the performance of leading neural language models and backoff n-gram models. We also delve into the challenges associated with such models and propose methods to enhance their effectiveness. Specifically, we illustrate how performance can be boosted through the use of a larger question-answer corpus for bootstrapping the learning process and by leveraging pretrained word embeddings. These enhancements contribute to the overall goal of creating more sophisticated and human-like dialogue systems.

Neural probabilistic language models (NPLMs) have been shown to be competitive with and occasionally superior to the widely-used n-gram language models. The main drawback of NPLMs is their extremely long training and testing times. Morin and Bengio have proposed a hierarchical language model built around a binary tree of words, which was two orders of magnitude faster than the nonhierarchical model it was based on. However, it performed considerably worse than its non-hierarchical counterpart in spite of using a word tree created using expert knowledge. We introduce a fast hierarchical language model along with a simple feature-based algorithm for automatic construction of word trees from the data. We then show that the resulting models can outperform non-hierarchical neural models as well as the best n-gram models.

This paper presents the HDG learning algorithm, which uses a hierarchical decomposition of the state space to make learning to achieve goals more efficient with a small penalty in path quality. Special care must be taken when performing hierarchical planning and learning in stochastic domains, because macro-operators cannot be executed ballistically. The HDG algorithm, which is a descendent of Watkins’ Q-learning algorithm, is described here and preliminary empirical results are presented.

The field of computational narratology has produced many efforts aimed at generating narrative by computational means. In recent times, a number of such efforts have considered the task of modelling how a reader might consume the story. Whereas all these approaches are clearly different aspects of the task of generating narrative, so far the efforts to model them have occurred as separate and disjoint initiatives. There is an enormous potential for improvement if a way was found to combine results from these initiatives with one another. The present position paper provides a breakdown of the activity of creating stories into five stages that are conceptually different from a computational point of view and represent important aspects of the overall process as observed either in humans or in existing systems. These stages include a feedback loop that builds interpretations of an ongoing composition and provides feedback based on these to inform the composition process. This model provides a theoretical framework that can be employed first to understand how the various aspects of the task of generating narrative relate to one another, second to identify which of these aspects are being addressed by the different existing research efforts, and finally to point the way towards possible integrations of these aspects within progressively more complex systems.

Intelligent computer agents are both the original goal and the ultimate goal of artificial intelligence research. In striving toward that goal, our community has followed a practical research strategy of "divide-and-conquer," with different sub-communities attacking important component functions of intelligence, such as planning, search, knowledge representation, vision, and natural language. This strategy has been almost too successful, yielding both challenging theoretical problems that have come to dominate the inquiries of many researchers and a spate of practical techniques that have enabled other researchers to move into profitable commercial enterprises. While each of these pursuits is worthy in its own right, together they have had the unfortunate sideeffect of fragmenting the field and almost completely diverting it from efforts to build intelligent agents.

Conversational machine comprehension requires a deep understanding of the conversation history. To enable traditional, single-turn models to encode the history comprehensively, we introduce FLOW, a mechanism that can incorporate intermediate representations generated during the process of answering previous questions, through an alternating parallel processing structure. Compared to shallow approaches that concatenate previous questions/answers as input, FLOW integrates the latent semantics of the conversation history more deeply. Our model, FLOWQA, shows superior performance on two recently proposed conversational challenges (+7.2% F1 on CoQA and +4.0% on QuAC). The effectiveness of FLOW also shows in other tasks. By reducing sequential instruction understanding to conversational machine comprehension, FLOWQA outperforms the best models on all three domains in SCONE, with +1.8% to +4.4% improvement in accuracy.

This paper summarizes our ongoing project on developing an architecture for a robot that can acquire new words and their meanings while engaging in multidomain dialogues. These two functions are crucial in making conversational service robots work in real tasks in the real world. Household robots and office robots need to be able to work in multiple task domains and they also need to engage in dialogues in multiple domains corresponding to those task domains. Lexical acquisition is necessary because speech understanding cannot be done without enough knowledge on words that are possibly spoken in the task domain. Our architecture is based on a multi-expert model in which multiple domain experts are employed and one of them is selected based on the user utterance and the situation to engage in the control of the dialogue and physical behaviors. We incorporate experts that have an ability to acquire new lexical entries and their meanings grounded on the physical world through spoken interactions. By appropriately selecting those experts, lexical acquisition in multi-domain dialogues becomes possible. An example robotic system based on this architecture that can acquire object names and location names demonstrates the viability of the architecture.

We study automatic question generation for sentences from text passages in reading comprehension. We introduce an attention-based sequence learning model for the task and investigate the effect of encoding sentencevs. paragraph-level information. In contrast to all previous work, our model does not rely on hand-crafted rules or a sophisticated NLP pipeline; it is instead trainable end-to-end via sequenceto-sequence learning. Automatic evaluation results show that our system significantly outperforms the state-of-the-art rule-based system. In human evaluations, questions generated by our system are also rated as being more natural (i.e., grammaticality, fluency) and as more difficult to answer (in terms of syntactic and lexical divergence from the original text and reasoning needed to answer).

This paper illustrates a web-based infrastructure of an architecture for conversational agents equipped with a modular knowledge base. This solution has the advantage to allow the building of specific modules that deal with particular features of a conversation (ranging from its topic to the manner of reasoning of the chatbot). This enhances the agent interaction capabilities. The approach simplifies the chatbot knowledge base design process: extending, generalizing or even restricting the chatbot knowledge base in order to suit it to manage specific dialoguing tasks as much as possible.

Many language generation tasks require the production of text conditioned on both structured and unstructured inputs. We present a novel neural network architecture which generates an output sequence conditioned on an arbitrary number of input functions. Crucially, our approach allows both the choice of conditioning context and the granularity of generation, for example characters or tokens, to be marginalised, thus permitting scalable and effective training. Using this framework, we address the problem of generating programming code from a mixed natural language and structured specification. We create two new data sets for this paradigm derived from the collectible trading card games Magic the Gathering and Hearthstone. On these, and a third preexisting corpus, we demonstrate that marginalising multiple predictors allows our model to outperform strong benchmarks.

In human interactions, language is used to project and maintain a social identity over time. The way people speak with others and revisit language across repeated interactions helps to create rapport and develop a feeling of coordination between conversational partners. Memory of past conversations is the main mechanism that allows us to exploit and explore ways of speaking, given knowledge acquired in previous encounters. As such, we introduce an agent that uses its conversational memory to revisit shared history with users to maintain a coherent social relationship over time. In this paper, we describe the dialog management mechanisms to achieve these goals when applied to a robot that engages in social chit-chat. In a study lasting 14 days with 28 users, totaling 474 interactions, we find that it is difficult to leverage the shared history with individual users and to also accommodate to expected conversational coordination patterns. We discuss the implications of this finding for long-term human-agent interaction. In particular, we highlight the importance of topic modeling and signaling explicit recall of previous episodes. Moreover, the way that users contribute to interactions requires additional adaptation, indicating a significant challenge for language interaction designers.


Question: A stratified text creation and planning method for tactical conversation.

This paper introduces the Dialog State Tracking Challenge (DSTC), a crucial initiative aimed at advancing research in spoken dialog systems. Dialog state tracking involves the system's ability to deduce and maintain information about the user's intent throughout the dialog, synthesizing data from dialog acts and external sources. The lack of standardized test environments and evaluation metrics has hindered comparative analysis and progress in this field. To address these limitations, the DSTC offers a diverse corpus of 15,000 human-computer dialogs in a standardized format, accompanied by 11 performance metrics for evaluation. The challenge attracted 27 submissions from 9 research teams. The outcomes revealed that performance metrics can be grouped into four categories, and that systems with less accurate speech recognition confidence scores benefit most from state tracking. Notably, generalization remains a significant challenge, as in two out of four test sets, less than half of the entries outperformed basic baselines. In commercial systems, dialog state tracking is typically handled through heuristic methods, selecting the spoken language understanding (SLU) result with the highest confidence score. However, statistical approaches offer an alternative by evaluating multiple hypotheses for the dialog state, leveraging correlations between dialog turns and external data to mitigate SLU errors. Various techniques for state tracking have been proposed, ranging from heuristic scoring and Bayesian networks to kernel density estimators and discriminative models. These methods have been successfully applied to large-scale dialog problems in real-time scenarios. The DSTC seeks to provide a unified platform for evaluating and comparing these techniques, thereby accelerating advancements in dialog state tracking and enhancing the overall performance of spoken dialog systems.

We conducted a laboratory-based observational study where pairs of people performed search tasks communicating verbally. Examination of the discourse allowed commonly used interactions to be identified for Spoken Conversational Search (SCS). We compared the interactions to existing models of search behaviour. We find that SCS is more complex and interactive than traditional search. This work enhances our understanding of different search behaviours and proposes research opportunities for an audio-only search system. Future work will focus on creating models of search behaviour for SCS and evaluating these against actual SCS systems.

Recent approaches based on artificial neural networks (ANNs) have shown promising results for short-text classification. However, many short texts occur in sequences (e.g., sentences in a document or utterances in a dialog), and most existing ANN-based systems do not leverage the preceding short texts when classifying a subsequent one. In this work, we present a model based on recurrent neural networks and convolutional neural networks that incorporates the preceding short texts. Our model achieves state-of-the-art results on three different datasets for dialog act prediction.

The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains.

Approach • Social information independent of physical analog s – Text (e.g., emote) – abstract graphical representations (e.g., chat cir cles) • Interested in Abstract Approach a. Creates and deploys working systems b. Lack of attention Babble Prototype

Task planning for mobile robots usually relies solely on spatial information and on shallow domain knowledge, like labels attached to objects and places. Although spatial information is necessary for performing basic robot operations (navigation, localization, and obstacle avoidance), the use of deeper domain knowledge is pivotal to endow a robot with higher degrees of autonomy and intelligence. In this paper, we focus on semantic knowlege, and show how this type of knowledge can be profitably used for robot task planning. We start by defining a specific type of semantic maps, which integrate hierarchical spatial information and semantic knowledge. We then proceed to describe how these semantic maps can improve task planning in three ways: enriching the planning domain, relaxing unachievable goals, and improving the efficiency of the planner in large domains. Finally, we show several exepriments that demonstrate the effectiveness of our solutions in a doman involving robot navigation in a domestic environment.

In this paper we develop a story generator that leverages knowledge inherent in corpora without requiring extensive manual involvement. A key feature in our approach is the reliance on a story planner which we acquire automatically by recording events, their participants, and their precedence relationships in a training corpus. Contrary to previous work our system does not follow a generate-and-rank architecture. Instead, we employ evolutionary search techniques to explore the space of possible stories which we argue are well suited to the story generation task. Experiments on generating simple children’s stories show that our system outperforms previous data-driven approaches.

This paper investigates the problem of bootstrapping a statistical dialogue manager without access to training data and proposes a new probabilistic agenda-based method for simulating user behaviour. In experiments with a statistical POMDP dialogue system, the simulator was realistic enough to successfully test the prototype system and train a dialogue policy. An extensive study with human subjects showed that the learned policy was highly competitive, with task completion rates above 90%. 1 Background and Introduction 1.1 Bootstrapping Statistical Dialogue Managers One of the key advantages of a statistical approach to Dialogue Manager (DM) design is the ability to formalise design criteria as objective reward functions and to learn an optimal dialogue policy from real dialogue data. In cases where a system is designed from scratch, however, it is often the case that no suitable in-domain data is available for training the DM. Collecting dialogue data without a working prototype is problematic, leaving the developer with a classic chicken-and-egg problem. Wizard-of-Oz (WoZ) experiments can be carried out to record dialogues, but they are often time-consuming and the recorded data may show characteristics of humanhuman conversation rather than typical human-computer dialogue. Alternatively, human-computer dialogues can be recorded with a handcrafted DM prototype but neither of these two methods enables the system designer to test the implementation of the statistical DM and the learning algorithm. Moreover, the size of the recorded corpus (typically 10 dialogues) usually falls short of the requirements for training a statistical DM (typically 10 dialogues). 1.2 User Simulation-Based Training In recent years, a number of research groups have investigated the use of a two-stage simulation-based setup. A statistical user model is first trained on a limited amount of dialogue data and the model is then used to simulate dialogues with the interactively learning DM (see Schatzmann et al. (2006) for a literature review). The simulation-based approach assumes the presence of a small corpus of suitably annotated in-domain dialogues or out-of-domain dialogues with a matching dialogue format (Lemon et al., 2006). In cases when no such data is available, handcrafted values can be assigned to the model parameters given that the model is sufficiently simple (Levin et al., 2000; Pietquin and Dutoit, 2005) but the performance of dialogue policies learned this way has not been evaluated using real users.

Most current chatbot engines are designed to reply to user utterances based on existing utterance-response (or Q-R)1 pairs. In this paper, we present DocChat, a novel information retrieval approach for chatbot engines that can leverage unstructured documents, instead of Q-R pairs, to respond to utterances. A learning to rank model with features designed at different levels of granularity is proposed to measure the relevance between utterances and responses directly. We evaluate our proposed approach in both English and Chinese: (i) For English, we evaluate DocChat on WikiQA and QASent, two answer sentence selection tasks, and compare it with state-of-the-art methods. Reasonable improvements and good adaptability are observed. (ii) For Chinese, we compare DocChat with XiaoIce2, a famous chitchat engine in China, and side-by-side evaluation shows that DocChat is a perfect complement for chatbot engines using Q-R pairs as main source of responses.

One long-term goal of machine learning research is to produce methods that are applicable to reasoning and natural language, in particular building an intelligent dialogue agent. To measure progress towards that goal, we argue for the usefulness of a set of proxy tasks that evaluate reading comprehension via question answering. Our tasks measure understanding in several ways: whether a system is able to answer questions via chaining facts, simple induction, deduction and many more. The tasks are designed to be prerequisites for any system that aims to be capable of conversing with a human. We believe many existing learning systems can currently not solve them, and hence our aim is to classify these tasks into skill sets, so that researchers can identify (and then rectify) the failings of their systems. We also extend and improve the recently introduced Memory Networks model, and show it is able to solve some, but not all, of the tasks.

Users interact with mobile apps with certain intents such as finding a restaurant. Some intents and their corresponding activities are complex and may involve multiple apps; for example, a restaurant app, a messenger app and a calendar app may be needed to plan a dinner with friends. However, activities may be quite personal and third-party developers would not be building apps to specifically handle complex intents (e.g., a DinnerPlanner). Instead we want our intelligent agent to actively learn to understand these intents and provide assistance when needed. This paper proposes a framework to enable the agent to learn an inventory of intents from a small set of task-oriented user utterances. The experiments show that on previously unseen user activities, the agent is able to reliably recognize user intents using graph-based semi-supervised learning methods. The dataset, models, and the system outputs are available to research community.


Question: A hierarchical approach to text generation and planning for strategic dialogue.

This paper introduces a novel dialogue generation framework designed to address the issue of monotonous responses often produced by deterministic models. By incorporating latent variables, our model explicitly accounts for the inherent variability in potential responses to any given input. This approach not only mitigates the 'boring output' problem but also leads to a more diverse set of generated responses. Our experimental results demonstrate that, compared to baseline models, our framework yields significantly more diverse outputs while maintaining a high level of acceptability in the generated dialogue. This is a marked improvement over deterministic encoder-decoder models, where increased diversity is often achieved at the cost of coherence and acceptability.

This paper describes Movie-DiC a Movie Dialogue Corpus recently collected for research and development purposes. The collected dataset comprises 132,229 dialogues containing a total of 764,146 turns that have been extracted from 753 movies. Details on how the data collection has been created and how it is structured are provided along with its main statistics and characteristics.

The functions of social dialogue between people in the context of performing a task is discussed, as well as approaches to modelling such dialogue in embodied conversational agents. A study of an agent’s use of social dialogue is presented, comparing embodied interactions with similar interactions conducted over the phone, assessing the impact these media have on a wide range of behavioural, task and subjective measures. Results indicate that subjects’ perceptions of the agent are sensitive to both interaction style (social vs. task-only dialogue) and

This system demonstration paper presents IRIS (Informal Response Interactive System), a chat-oriented dialogue system based on the vector space model framework. The system belongs to the class of examplebased dialogue systems and builds its chat capabilities on a dual search strategy over a large collection of dialogue samples. Additional strategies allowing for system adaptation and learning implemented over the same vector model space framework are also described and discussed.

Generating production-quality plans is an essential element in transforming planners from research tools into real-world applications. However most of the work to date on learning planning control knowledge has been aimed at improving the efficiency of planning; this work has been termed “speed-up learning”. This paper focuses on learning control knowledge to guide a planner towards better solutions, i.e. to improve the quality of the plans produced by the planner, as its problem solving experience increases. We motivate the use of quality-enhancing search control knowledge and its automated acquisition from problem solving experience. We introduce an implemented mechanism for learning such control knowledge and some of our preliminary results in a process planning domain.

One of the biggest challenges of endto-end language generation from meaning representations in dialogue systems is making the outputs more natural and varied. Here we take a large corpus of 50K crowd-sourced utterances in the restaurant domain and develop text analysis methods that systematically characterize types of sentences in the training data. We then automatically label the training data to allow us to conduct two kinds of experiments with a neural generator. First, we test the effect of training the system with different stylistic partitions and quantify the effect of smaller, but more stylistically controlled training data. Second, we propose a method of labeling the style variants during training, and show that we can modify the style of the generated utterances using our stylistic labels. We contrast and compare these methods that can be used with any existing large corpus, showing how they vary in terms of semantic quality and

This paper describes the results of an implemented computational model that cashes out the belief that metaphor interpretation is grounded in embodied primitives. The speci c task addressed is the interpretation of simple causal narratives in the domains of Politics and Economics. The stories are taken from newspaper articles in these domains. When presented with a preparsed version of these narratives as input, the system described is able to generate commonsense inferences consistent with the input.

Endowing a dialogue system with particular personality traits is essential to deliver more human-like conversations. However, due to the challenge of embodying personality via language expression and the lack of large-scale persona-labeled dialogue data, this research problem is still far from well-studied. In this paper, we investigate the problem of incorporating explicit personality traits in dialogue generation to deliver personalized dialogues. To this end, firstly, we construct PersonalDialog, a large-scale multi-turn dialogue dataset containing various traits from a large number of speakers. The dataset consists of 20.83M sessions and 56.25M utterances from 8.47M speakers. Each utterance is associated with a speaker who is marked with traits like Age, Gender, Location, Interest Tags, etc. Several anonymization schemes are designed to protect the privacy of each speaker. This large-scale dataset will facilitate not only the study of personalized dialogue generation, but also other researches on sociolinguistics or social science. Secondly, to study how personality traits can be captured and addressed in dialogue generation, we propose persona-aware dialogue generation models within the sequence to sequence learning framework. Explicit personality traits (structured by key-value pairs) are embedded using a trait fusion module. During the decoding process, two techniques, namely persona-aware attention and persona-aware bias, are devised to capture and address trait-related information. Experiments demonstrate that our model is able to address proper traits in different contexts. Case studies also show interesting results for this challenging research problem.

Recent approaches based on artificial neural networks (ANNs) have shown promising results for short-text classification. However, many short texts occur in sequences (e.g., sentences in a document or utterances in a dialog), and most existing ANN-based systems do not leverage the preceding short texts when classifying a subsequent one. In this work, we present a model based on recurrent neural networks and convolutional neural networks that incorporates the preceding short texts. Our model achieves state-of-the-art results on three different datasets for dialog act prediction.

The paper describes how a high-level abstract world model can be used to support the decision-making process of an autonomous driving system. The approach uses a hierarchical world model and distinguishes between a low-level model for the trajectory planning and a high-level model for solving the traffic coordination problem. The abstract world model used in the CyberCars-2 project is presented. It is based on a topological lane segmentation and introduces relations to represent the semantic context of the traffic scenario. This makes it much easier to realize a consistent and complete driving control system, and to analyze, evaluate and simulate such a system.

In this paper, we apply reinforcement learning for automatically learning cooperative persuasive dialogue system policies using framing, the use of emotionally charged statements common in persuasive dialogue between humans. In order to apply reinforcement learning, we describe a method to construct user simulators and reward functions specifically tailored to persuasive dialogue based on a corpus of persuasive dialogues between human interlocutors. Then, we evaluate the learned policy and the effect of framing through experiments both with a user simulator and with real users. The experimental evaluation indicates that applying reinforcement learning is effective for construction of cooperative persuasive dialogue systems which use framing.


Question: A stratified text creation and planning method for tactical conversation.

In the realm of spoken dialog systems, the challenge of action selection in uncertain environments is paramount, given the unreliability of automatic speech recognition. This uncertainty complicates the accurate assessment of the conversation's state. Research in this area has explored multiple strategies to address this issue, including parallel dialog state hypotheses, the localized application of confidence scores, and automated planning. Despite their individual merits, these approaches have not been fully integrated into a comprehensive statistical framework capable of global optimization. This paper proposes the adoption of partially observable Markov decision processes (POMDPs) as a unifying framework for spoken dialog systems. This approach not only consolidates existing techniques but also extends their capabilities, offering a principled framework for action selection. Through illustrative examples, we highlight the qualitative advantages of POMDPs over conventional methods. Furthermore, we present empirical evidence from dialog simulations that showcases substantial quantitative improvements. The paper concludes with a discussion of the primary obstacles to advancing this methodology, notably scalability, and suggests potential avenues for future research. Copyright 2006 Elsevier Ltd. All rights reserved.

This paper describes a novel Bayesian approach to unsupervised topic segmentation. Unsupervised systems for this task are driven by lexical cohesion: the tendency of wellformed segments to induce a compact and consistent lexical distribution. We show that lexical cohesion can be placed in a Bayesian context by modeling the words in each topic segment as draws from a multinomial language model associated with the segment; maximizing the observation likelihood in such a model yields a lexically-cohesive segmentation. This contrasts with previous approaches, which relied on hand-crafted cohesion metrics. The Bayesian framework provides a principled way to incorporate additional features such as cue phrases, a powerful indicator of discourse structure that has not been previously used in unsupervised segmentation systems. Our model yields consistent improvements over an array of state-of-the-art systems on both text and speech datasets. We also show that both an entropy-based analysis and a well-known previous technique can be derived as special cases of the Bayesian framework.1

This paper proposes a generic methodology and architecture for developing a novel conversational intelligent tutoring system (CITS) called Oscar that leads a tutoring conversation and dynamically predicts and adapts to a student’s learning style. Oscar aims to mimic a human tutor by implicitly modelling the learning style during tutoring, and personalising the tutorial to boost confidence and improve the effectiveness of the learning experience. Learners can intuitively explore and discuss topics in natural language, helping to establish a deeper understanding of the topic. The Oscar CITS methodology and architecture are independent of the learning styles model and tutoring subject domain. Oscar CITS was implemented using the Index of Learning Styles (ILS) model (Felder & Silverman 1988) to deliver an SQL tutorial. Empirical studies involving real students have validated the prediction of learning styles in a real-world teaching/learning environment. The results showed that all learning styles in the ILS model were successfully predicted from a natural language tutoring conversation, with an accuracy of 61-100%. Participants also found Oscar’s tutoring helpful and achieved an average learning gain of 13%.

Intelligent computer agents are both the original goal and the ultimate goal of artificial intelligence research. In striving toward that goal, our community has followed a practical research strategy of "divide-and-conquer," with different sub-communities attacking important component functions of intelligence, such as planning, search, knowledge representation, vision, and natural language. This strategy has been almost too successful, yielding both challenging theoretical problems that have come to dominate the inquiries of many researchers and a spate of practical techniques that have enabled other researchers to move into profitable commercial enterprises. While each of these pursuits is worthy in its own right, together they have had the unfortunate sideeffect of fragmenting the field and almost completely diverting it from efforts to build intelligent agents.

TacAir-Soar is an intelligent, rule-based system that generates believable “human-like” behavior for large scale, distributed military simulations. The innovation of the application is primarily a matter of scale and integration. The system is capable of executing most of the airborne missions that the United States military flies in fixed-wing aircraft. It accomplishes this by integrating a wide variety of intelligent capabilities, including real-time hierarchical execution of complex goals and plans, communication and coordination with humans and simulated entities, maintenance of situational awareness, and the ability to accept and respond to new orders while in flight. The system is currently deployed at the Oceana Naval Air Station WISSARD Lab and the Air Force Research Laboratory in Mesa, AZ. Its most dramatic use was in the Synthetic Theater of War 1997, which was an operational training exercise that ran for 48 continuous hours during which TacAir-Soar flew all U.S. fixed-wing aircraft.

In order for reinforcement learning systems to learn quickly in vast action spaces such as the space of all possible pieces of text or the space of all images, leveraging human intuition and creativity is key. However, a human-designed action space is likely to be initially imperfect and limited; furthermore, humans may improve at creating useful actions with practice or new information. Therefore, we propose a framework in which a human adds actions to a reinforcement learning system over time to boost performance. In this setting, however, it is key that we use human effort as efficiently as possible, and one significant danger is that humans waste effort adding actions at places (states) that aren’t very important. Therefore, we propose Expected Local Improvement (ELI), an automated method which selects states at which to query humans for a new action. We evaluate ELI on a variety of simulated domains adapted from the literature, including domains with over a million actions and domains where the simulated experts change over time. We find ELI demonstrates excellent empirical performance, even in settings where the synthetic “experts” are quite poor.

We describe a method for identifying and performing functional analysis of structured regions that are embedded in natural language documents, such as tables or key-value lists. Such regions often encode information according to ad hoc schemas and avail themselves of visual cues in place of natural language grammar, presenting problems for standard information extraction algorithms. Unlike previous work in table extraction, which assumes a relatively noiseless two-dimensional layout, our aim is to accommodate a wide variety of naturally occurring structure types. Our approach has three main parts. First, we collect and annotate a a diverse sample of “naturally” occurring structures from several sources. Second, we use probabilistic text segmentation techniques, featurized by skip bigrams over spatial and token category cues, to automatically identify contiguous regions of structured text that share a common schema. Finally, we identify the records and fields within each structured region using a combination of distributional similarity and sequence alignment methods, guided by minimal supervision in the form of a single annotated record. We evaluate the last two components individually, and conclude with a discussion of further work.

This paper presents an overview of the intelligent decision-making capabilities of the CLARAty robotic architecture for autonomy. CLARAty is a two layered architecture where the top Decision Layer contains techniques for autonomously creating a plan of robot commands and the bottom Functional Layer provides standard robot capabilities that interface to system hardware. This paper focuses on the Decision Layer organization and capabilities. Specifically, the Decision Layer provides a framework for utilizing AI planning and executive techniques, which provide onboard, autonomous command generation and replanning for planetary rovers. The Decision Layer also provides a flexible interface to the Functional Layer, which can be tailored based on user preferences and domain features. This architecture is currently being tested on several JPL rovers.

The utility of additional semantic information for the task of next utterance selection in an automated dialogue system is the focus of study in this paper. In particular, we show that additional information available in the form of dialogue acts –when used along with context given in the form of dialogue history– improves the performance irrespective of the underlying model being generative or discriminative. In order to show the model agnostic behavior of dialogue acts, we experiment with several well-known models such as sequence-to-sequence encoder-decoder model, hierarchical encoder-decoder model, and Siamese-based models with and without hierarchy; and show that in all models, incorporating dialogue acts improves the performance by a significant margin. We, furthermore, propose a novel way of encoding dialogue act information, and use it along with hierarchical encoder to build a model that can use the sequential dialogue act information in a natural way. Our proposed model achieves an MRR of about 84.8% for the task of next utterance selection on a newly introduced DailyDialog dataset, and outperform the baseline models. We also provide a detailed analysis of results including key insights that explain the improvement in MRR because of dialogue act information.

We describe our Alexa prize system (called ‘Alana’) which consists of an ensemble of bots, combining rule-based and machine learning systems, and using a contextual ranking mechanism to choose system responses. This paper reports on the version of the system developed and evaluated in the semi-finals of the competition (i.e. up to 15 August 2017), but not on subsequent enhancements. The ranker for this system was trained on real user feedback received during the competition, where we address the problem of how to train on the noisy and sparse feedback obtained during the competition. In order to avoid initial problems of inappropriate and boring utterances coming from big datasets such as Reddit and Twitter, we later focussed on ‘clean’ data sources such as news and facts. We report on experiments with different ranking functions and versions of our NewsBot. We find that a multiturn news strategy is beneficial, and that a ranker trained on the ratings feedback from users is also effective. Our system continuously improved using the data gathered over the course over the competition (1 July – 15 August) . Our final user score (averaged user rating over the whole semi-finals period) was 3.12, and we achieved 3.3 for the averaged user rating over the last week of the semi-finals (8-15 August 2017). We were also able to achieve long dialogues (average 10.7 turns) during the competition period. In subsequent weeks, after the end of the semi-final competition, we have achieved our highest scores of 3.52 (daily average, 18th October), 3.45 (weekly average on 23 and 24 October), and average dialogue lengths of 14.6 turns (1 October), and median dialogue length of 2.25 minutes (average for 7 days on 10th October).

Task planning for mobile robots usually relies solely on spatial information and on shallow domain knowledge, like labels attached to objects and places. Although spatial information is necessary for performing basic robot operations (navigation, localization, and obstacle avoidance), the use of deeper domain knowledge is pivotal to endow a robot with higher degrees of autonomy and intelligence. In this paper, we focus on semantic knowlege, and show how this type of knowledge can be profitably used for robot task planning. We start by defining a specific type of semantic maps, which integrate hierarchical spatial information and semantic knowledge. We then proceed to describe how these semantic maps can improve task planning in three ways: enriching the planning domain, relaxing unachievable goals, and improving the efficiency of the planner in large domains. Finally, we show several exepriments that demonstrate the effectiveness of our solutions in a doman involving robot navigation in a domestic environment.


Question: A stratified text creation and planning method for tactical conversation.

This paper presents a novel neural network framework, the Dynamic Memory Network (DMN), designed to tackle a wide range of tasks in natural language processing by framing them as question answering (QA) problems. The DMN is a comprehensive system that can process textual inputs and questions, establish semantic and episodic memories, and produce contextually relevant responses. A unique feature of the DMN is its iterative attention mechanism, which allows the model to refine its focus on specific parts of the input based on the outcomes of previous attention iterations. The information gathered through these iterations is then processed through a hierarchical recurrent sequence model for reasoning, enabling the generation of accurate answers. The DMN can be trained in an end-to-end manner and has achieved state-of-the-art performance on various benchmarks, including question answering on Facebook's bAbI dataset, part-of-speech tagging on the WSJ-PTB corpus, coreference resolution on the Quizbowl dataset, and sentiment analysis on the Stanford Sentiment Treebank. Remarkably, the DMN operates solely on learned word vector representations, eliminating the need for string matching or handcrafted features.

**Question**: A Direct Search Method to solve Economic Dispatch Problem with Valve-Point Effect

**Documents**: An Adaptive Evolutionary Algorithm for the Economic Load Dispatch Problem
This paper presents an adaptive evolutionary algorithm for solving the economic load dispatch problem (ELDP) in power systems. The proposed algorithm, called Adaptive Evolutionary Algorithm (AEA), is designed to efficiently handle the non-convex characteristics of the ELDP, which arise due to the valve-point effect. The AEA incorporates an adaptive mutation operator and a local search mechanism to enhance its exploration and exploitation capabilities. The adaptive mutation operator adjusts the mutation rate based on the search progress, allowing for a more effective exploration of the solution space. The local search mechanism is employed to refine the solutions, ensuring a fine-grained exploitation of the search space. The performance of the AEA is evaluated on several benchmark problems with varying numbers of generating units and valve-point effects. The results demonstrate that the AEA outperforms other state-of-the-art algorithms in terms of solution quality and convergence speed. The AEA's ability to find near-optimal solutions to the ELDP with the valve-point effect highlights its effectiveness in solving complex power system optimization problems.

**Similar Documents**: This work introduces an Adaptive Evolutionary Algorithm (AEA) tailored for addressing the Economic Load Dispatch Problem (ELDP) in electrical power systems, particularly focusing on overcoming the challenges posed by the valve-point effect. The AEA

Existing research on response generation for chatbot focuses on First Response Generation which aims to teach the chatbot to say the first response (e.g. a sentence) appropriate to the conversation context (e.g. the user’s query). In this paper, we introduce a new task Second Response Generation, termed as Improv chat, which aims to teach the chatbot to say the second response after saying the first response with respect the conversation context, so as to lighten the burden on the user to keep the conversation going. Specifically, we propose a general learning based framework and develop a retrieval based system which can generate the second responses with the users’ query and the chatbot’s first response as input. We present the approach to building the conversation corpus for Improv chat from public forums and social networks, as well as the neural networks based models for response matching and ranking. We include the preliminary experiments and results in this paper. This work could be further advanced with better deep matching models for retrieval base systems or generative models for generation based systems as well as extensive evaluations in real-life applications.

TALE-SPIN, the laat major AI attempt at atory generation, approached the problem of making up atoriea primarily from the perspective of an impartial world simulator. AUTHOR ia a program (under development) which generates atoriea as a creative reaaoner in purauit of her own narrative goals. It is thus intended to aimulate an author's mind aa ahe makes up a story, rather than the world as things happen in it. The four major forces driving the atory generation process, according to the AUTHOR model, are (1) author intentionality, (2) conceptual reformulation, (3) reminding, and (4) the opportunity enhancement metagoal. 1. World Simulation Based Story Generation TALE-SPIN [Meehan, 1976] is a program which made up Aesop-fable-like conceptual atoriea, which it then expressed in natural language. Each atory TALE-SPIN made up waa based on a model of the storyworld, including the characters in it and their goala, peraonalities, and interperaonal relatione. Storiea were apun by working out consequences of the model. Thus, central to TALE-SPIN waa the idea of world simulation. The three levels TALE-SPIN was most concerned with simulating were: 1. character intentionality-e.g., if Joe Bear was hungry, he would try to get something to eat 2. social conatrainta-e.g., if Joe Bear believed Irving Bird to be hia friend, he could aak an outright favor; if Joe Bear believed their relation to be a bit cooler, he would feel obliged to offer something in return (auch aa a worm) 3. phyaical cauaality-e.g., if Irving Bird ate a worm, the worm would cease to exist; after "fooling around", Aa oppoaed to Sheldon Klein's ayetem which only produced atory texts and which ia incapable, in principle, of understanding the atoriea it "makes up" [Klein et el., 1976]. characters felt "wiped out" World simulation, however, fails to reflect the proctaa that an author goes through in making up a atory-storyworlds are developed by authors aa needed, frequently as post hoc justification for eventa that the author has already decided ahe wants as part of the story. As illustration, let us consider the question of why, in the Star Wars aequel, "The Empire Strikes Back", Princess Leia and Solo go to visit Landeau. If one asks a reader (or viewer) this question, the anawer will be along the lines of, "They needed a place to hide from imperial stormtroopers until they got the hyperdrive fixed, and Landeau was an old buddy of Solo's, and ..." • …

Current dialogue systems generally operate in a pipelined, modular fashion on one complete utterance at a time. Evidence from human language understanding shows that human understanding operates incrementally and makes use of multiple sources of information during the parsing process, including traditionally “later” components such as pragmatics. In this paper we describe a spoken dialogue system that understands language incrementally, provides visual feedback on possible referents during the course of the user’s utterance, and allows for overlapping speech and actions. We further present findings from an empirical study showing that the resulting dialogue system is faster overall than its nonincremental counterpart. Furthermore, the incremental system is preferred to its nonincremental counterpart – beyond what is accounted for by factors such as speed and accuracy. These results indicate that successful incremental understanding systems will improve both performance and usability.

SUMMARIST is an attempt to create a robust automated text summarization system, based on the ‘equation’: summarization = topic identification + interpretation + generation. Each of these stages contains several independent modules, many of them trained on large corpora of text. We describe the system’s architecture and provide details of some of its modules.

This paper describes how language is grounded by a comprehension system called Lucia within a robotic agent called Rosie that can manipulate objects and navigate indoors. The whole system is built within the Soar cognitive architecture and uses Embodied Construction Grammar (ECG) as a formalism for describing linguistic knowledge. Grounding is performed using knowledge from the grammar itself, from the linguistic context, from the agent's perception, and from an ontology of long-term knowledge about object categories and properties and actions the agent can perform. The paper also describes a benchmark corpus of 200 sentences in this domain, along with test versions of the world model and ontology, and gold-standard meanings for each of the sentences. The benchmark is contained in the supplemental materials.

Inquiry is fundamental to communication, and machines cannot effectively collaborate with humans unless they can ask questions. In this thesis work, we explore how can we teach machines to ask clarification questions when faced with uncertainty, a goal of increasing importance in today’s automated society. We do a preliminary study using data from StackExchange, a plentiful online resource where people routinely ask clarifying questions to posts so that they can better offer assistance to the original poster. We build neural network models inspired by the idea of the expected value of perfect information: a good question is one whose expected answer is going to be most useful. To build generalizable systems, we propose two future research directions: a template-based model and a sequence-to-sequence based neural generative model.

Chat-bots are computer programs coded to have a textual or verbal conversation which is logical or intelligent. Chat-bots are designed to make humans believe that they are talking to a human; but instead they are in fact talking to a machine. Taking advantage of this transparency property of chat-bot, an artificial character and personality can be given to a chat-bot which acts like a person of a specific profession. This paper describes an approach to the idea of implementing web-based artificially intelligent chat-bot as a personal assistant of the user, which stimulates setting and initiating meetings of user with his clients. The exchange of information happens through email conversations whereas its evaluation happens through natural language procession and natural language generation and AIML files.

We propose a methodology for a novel type of discourse annotation whose model is tuned to the analysis of a text as narrative. This is intended to be the basis of a “story bank” resource that would facilitate the automatic analysis of narrative structure and content. The methodology calls for annotators to construct propositions that approximate a reference text, by selecting predicates and arguments from among controlled vocabularies drawn from resources such as WordNet and VerbNet. Annotators then integrate the propositions into a conceptual graph that maps out the entire discourse; the edges represent temporal, causal and other relationships at the level of story content. Because annotators must identify the recurring objects and themes that appear in the text, they also perform coreference resolution and word sense disambiguation as they encode propositions. We describe a collection experiment and a method for determining inter-annotator agreement when multiple annotators encode the same short story. Finally, we describe ongoing work toward extending the method to integrate the annotator’s interpretations of character agency (the goals, plans and beliefs that are relevant, yet not explictly stated in the text).

This article presents a deep analysis of the influence of context information on dialogue act recognition. We performed experiments on the annotated subsets of three different corpora: the widely explored Switchboard Dialog Act Corpus, as well as the unexplored LEGO and Cambridge Restaurant corpora. In contrast with previous work, especially in what concerns the Switchboard corpus, we used an event-based classification approach, using Support Vector Machines (SVMs), instead of the more common sequential approaches, such as Hidden Markov Models (HMMs). We opted for such an approach so that we could control the amount of provided context information and, thus, explore its range of influence. Our base features consist of n-grams, punctuation, and whwords. Context information is obtained from previous utterances and provided in three ways – n-grams, n-grams tagged with relative position, and dialogue act classifications. A comparative study was conducted to evaluate the performance of the three approaches. From it, we were able to assess the importance of context information on dialogue act recognition, as well as its range of influence for each of the three selected representations. In addition to the conclusions originated by the analysis, this work also produced results that advance the state-of-the-art, especially considering previous work on the Switchboard corpus. Furthermore, since, to our knowledge, the Email addresses: eugenio.ribeiro@l2f.inesc-id.pt (Eugénio Ribeiro), ricardo.ribeiro@inesc-id.pt (Ricardo Ribeiro), david.matos@inesc-id.pt (David Martins de Matos) Preprint submitted to Computer Speech and Language June 3, 2015 ar X iv :1 50 6. 00 83 9v 1 [ cs .C L ] 2 J un 2 01 5 remaining datasets had not been previously explored for this task, our experiments can be used as baselines for future work on those corpora.

STRAND (Resnik, 1998) is a languageindependent system for automatic discovery of text in parallel translation on the World Wide Web. This paper extends the preliminary STRAND results by adding automatic language identification, scaling up by orders of magnitude, and formally evaluating performance. The most recent end-product is an automatically acquired parallel corpus comprising 2491 English-French document pairs, approximately 1.5 million words per language. 1 I n t r o d u c t i o n Text in parallel translation is a valuable resource in natural language processing. Statistical methods in machine translation (e.g. (Brown et al., 1990)) typically rely on large quantities of bilingual text aligned at the document or sentence level, and a number of approaches in the burgeoning field of crosslanguage information retrieval exploit parallel corpora either in place of or in addition to mappings between languages based on information from bilingual dictionaries (Davis and Dunning, 1995; Landauer and Littman, 1990; Hull and Oard, 1997; Oard, 1997). Despite the utility of such data, however, sources of bilingual text are subject to such limitations as licensing restrictions, usage fees, restricted domains or genres, and dated text (such as 1980's Canadian politics); or such sources simply may not exist for * This work was supported by Department of Defense contract MDA90496C1250, DARPA/ITO Contract N66001-97-C-8540, and a research grant from Sun Microsystems Laboratories. The author gratefully acknowledges the comments of the anonymous reviewers, helpful discussions with Dan Melamed and Doug Oard, and the assistance of Jeff Allen in the French-English experimental evaluation. language pairs of interest. Although the majority of Web content is in English, it also shows great promise as a source of multilingual content. Using figures from the Babel survey of multilinguality on the Web (h tZp : / /www. i s o c . o r g / ) , it is possible to estimate that as of June, 1997, there were on the order of 63000 primarily non-English Web servers, ranging over 14 languages. Moreover, a followup investigation of the non-English servers suggests that nearly a third contain some useful cross-language data, such as parallel English on the page or links to parallel English pages the follow-up also found pages in five languages not identified by the Babel study (Catalan, Chinese, Hungarian, Icelandic, and Arabic; Michael Littman, personal communication). Given the continued explosive increase in the size of the Web, the trend toward business organizations that cross national boundaries, and high levels of competition for consumers in a global marketplace, it seems impossible not to view multilingual content on the Web as an expanding resource. Moreover, it is a dynamic resource, changing in content as the world changes. For example, Diekema et al., in a presentation at the 1998 TREC-7 conference (Voorhees and Harman, 1998), observed that the performance of their cross-language information retrieval was hurt by lexical gaps such as Bosnia/Bosniethis illustrates a highly topical missing pair in their static lexical resource (which was based on WordNet 1.5). And Gey et al., also at TREC-7, observed that in doing cross-language retrieval using commercial machine translation systems, gaps in the lexicon (their example was acupuncture/Akupunktur) could make the difference between precision of 0.08 and precision of 0.83 on individual queries. ttesnik (1998) presented an algorithm called


Question: Compact circularly polarized patch antenna with reduced rearward radiation for GPS satellite communications.

This paper presents the development of a compact, dual-band, circularly polarized microstrip antenna (CPMA) specifically tailored for integration into satellite mobile devices. The antenna's design employs two miniature radiation elements, each featuring a folded structure, which are vertically stacked to enable dual-band operation. Each of these elements exhibits left-handed circular polarization, a critical characteristic for satellite communications. The dual-band CPMA boasts a compact size of 46 mm (length) x 24 mm (width) x 13 mm (height), making it highly suitable for space-constrained applications. To ensure compatibility with satellite mobile devices, the entire antenna assembly is mounted on a phone-shaped ground plane, allowing for seamless integration within the device's internal structure. This innovative design not only achieves the necessary performance for GPS satellite communications but also minimizes back radiation, a common issue in traditional antenna designs. The antenna's reduced dimensions and optimized performance make it an ideal choice for modern satellite communication systems, where size and efficiency are paramount.

This paper presents phase compensated techniques for 60 GHz communication. A phase compensation method for antenna embedded in a mobile device and a planar lens for base station antenna are introduced. For the Line-of Sight (LoS) scenario, end-fire antenna arrays are adapted for mobile device. 8×1 end-fire patch antenna arrays are designed in Low temperature co-fired ceramic (LTCC) substrate. Simulated max gain of the antenna arrays is 13.03 dBi. When the antenna module is mounted, scattering problem is occurred and its max gain is severely degraded to 6.4 dBi. By using phase compensated technique, max gain of the antenna arrays are enhanced to 12.5 dBi. The beam steered gain of the arrays is also simulated and good coverage can be achieved. A planar lens antenna is also studied for high gain of the base station antenna.

A 2 times 1 dual-polarized L-probe stacked patch antenna array is presented. It has employed a novel technique to achieve high isolation between the two input ports. The proposed antenna has a 14-dB return loss bandwidth of 19.8%, which is ranged from 0.808 to 0.986 GHz, for both ports. Also, it has an input port isolation of more than 30 dB and an average gain of 10.5 dBi over this bandwidth. Moreover, its radiation patterns in the two principal planes have cross-polarization level of less than -15 dB within the 3-dB beamwidths across the passband. Due to these features, this antenna array is highly suitable for the outdoor base station that is required to cover the operating bandwidths of both CDMA800 and GSM900 mobile communication systems.

In this letter, a dual-band circular patch antenna is introduced. The antenna consists of a circular patch with a direct feed through microstrip feedline designed to radiate at 2.45 GHz with fractional bandwidth of 4.5%. A circular slot is inserted into the ground plane that radiates by capacitive coupling between the patch and the ground plane. This slot radiates at 1.95 GHz with fractional impedance bandwidth of 5%. The antenna achieves good radiation characteristics by inserting a reflecting plane at optimum position behind it. The antenna has gain of 8.3 and 7.8 dBi at 1.95 and 2.45 GHz, respectively. This antenna is proposed for the rectenna; then it is designed to direct the main beam in a certain direction by increasing front-to-back (F/B) ratio with low cross-polarization levels by using defected reflector structure in the reflecting plane. The equivalent circuit of the proposed antenna is introduced to model the electrical behavior of the antenna. The proposed antenna can be used to harvest the energy from Wi-Fi and widely spread mobile networks. The proposed antenna was designed using CST Microwave Studio. The simulated and measured results show good agreement.

A sidelobe suppression method for a series slot array antenna which radiates 45° -inclined linear polarization is proposed. Axial displacements are employed to create arbitrary excitation coefficients for individual centered-inclined radiating slots along the center line of a broad wall. To verify the proposed design method, we design two types of center-fed linear slot array antennas with a Dolph-Chebyshev distribution for -20 dB and -26 dB sidelobe levels (SLLs) in the Ka band. Furthermore, a cross-validation process involving an equivalent circuit model analysis and electromagnetic full-wave simulation using CST MWS is utilized. The entire structure of the proposed series slot array antenna is fabricated on printed circuit boards (PCBs), including drilling and chemical etching, to secure advantages of miniaturization and cost reduction. The measured realized gains are 15.17 and 15.95 dBi and SLLs are -18.7 and -22.5 dB respectively for two types of fabricated antennas. It demonstrates the validity of the proposed sidelobe suppression method.

A low radar cross section (RCS) and low profile antenna co-designed with absorbent frequency selective radome (AFSR) is investigated. A pair of circular slot resonators is embedded on surface of the AFSR to realize a transmission window in the vertical polarization, while the wide absorption band is still maintained in the horizontal polarization. When a patch antenna is etched within the AFSR, where the metal grounds of the patch antenna and AFSR are co-used, the co-designed antenna with low RCS and low profile is thus realized. For demonstration, an AFSR is designed with its transmission window has a minimal insertion loss of 0.45 dB at 8.9 GHz, and two separate absorption bands (a lower absorption band from 4.8 to 7.5 GHz and an upper absorption band from 10 to 13 GHz) in the vertical polarization, a wide absorption band (from 4.5 to 12.5 GHz) in the horizontal polarization. A patch antenna etched within the AFSR is optimized to operate at 8.9 GHz, then it is simulated and fabricated. The measured results demonstrate that the proposed antenna not only has good radiation patterns, but also obtains significant RCS reduction.

Two novel Defected Ground Structures (DGS) were first proposed, which have better results than that of the dumbbell (published shape). Using the general model of DGS, its equivalent parameters were extracted. The two new proposed shapes of DGS were then used to design a novel compact spider microstrip antenna to minimize its area. The size of the developed antenna was reduced to about 90.5% of that of the conventional one. This antenna with two different novel shapes of DGS was designed and simulated by using the ready-made software package Zeland-IE3D. Finally, it was fabricated by using thin film and photolithographic technique and measured by using vector network analyzer. Good agreements were found between the simulated and measured results.

The antenna characteristics are described of a single-arm spiral (SAS) antenna. A balun circuit, required for a conventional two-arm balanced-mode spiral (TAS), is not necessary for feeding this SAS. First, the radiation pattern of an SAS having a disc/ground-plane is investigated. When the radius of the disc is smaller than the radius of the first-mode active region on the spiral and the spacing between the disc and spiral plane is small, the SAS is found to radiate a circularly polarized (CP) bidirectional beam, whose radiation pattern is almost symmetric with respect to the antenna axis normal to the spiral plane. Second, from a practical standpoint, the CP bidirectional beam is transformed into a CP unidirectional beam by placing a conducting cavity behind an SAS with a disc. It is revealed that the SAS has a good VSWR (less than 2) and a good axial ratio (less than 3 dB) over a design frequency range of 3 GHz to 10 GHz, where a cavity/antenna height of 7 mm (0.07 wavelength at the lowest design frequency) is chosen. The frequency response of the gain for the SAS is found to be similar to that for a conventional TAS, and the radiation efficiency for the SAS is slightly larger than that for the TAS. It is concluded that the SAS with a small disc backed by a cavity realizes a circularly polarized, low-profile, wideband antenna with a simple feed system that does not require a balun circuit.

A Complementary Electric Field Coupled (CELC) resonator, loaded in the ground plane of a conventional patch antenna to achieve dual band operation is presented here. Two antenna designs have been presented. In the first design, one CELC is etched out from the ground plane of patch antenna which leads to generation of a lower order mode in addition to the conventional patch mode. In order to achieve better impedance bandwidth for the lower order resonant mode two CELC resonators with closely spaced resonant frequencies are etched out from the ground plane of patch antenna in the second design. The proposed antennas can be used in WLAN (Wireless Local Area Network) and WiMAX (Worldwide Interoperability for Microwave Access) applications.

A shark-fin rooftop multiband antenna for the automotive industry is proposed. The antenna system receives Right Hand Circularly Polarized (RHCP) satellite signals for the Global Positioning System's (GPS) L1-Band and Vertically Linearly Polarized (VLP) signals for the Long Term Evolution (LTE), the cellular frequencies, the Universal Mobile Telecommunications Systems (UMTS) and the Dedicated Short Range Communications (DSRC) system. A ceramic patch antenna element was used for the GPS signals and a printed monopole utilizing printed ground sleeve technique for the cellular and LTE signals. A study of four potential DSRC antenna elements to determine the appropriate antenna element location within the antenna cover to obtain optimum performance is also presented and discussed in detail. The antenna module was measured on a 1-meter diameter ground plane and on a vehicle roof model. The design and measurements results are presented and discussed below.

This letter presents a compact eight-band monopole antenna whose size is 70 mm × 7 mm × 6 mm for LTE metal-frame mobile phones. The mainly radiating part of the proposed antenna is the metal frame. There are three gaps with the same size of 1 mm in the frame, and these gaps divide the antenna into two parts. Besides, a match circuit is used to enlarge the bandwidth and make the lower band matched. The advantage of the proposed antenna is that eight bands can be covered with a metal frame under the condition of only a 7 mm ground clearance. A prototype is manufactured and tested. The measured –6 dB impedance bandwidths are 375 MHz (0.675–1.05 GHz) and 1.2 GHz (1.6–2.8 GHz). The LTE700, GSM850, GSM900, GSM1800, GSM1900, UMTS, LTE2300, and LTE2500 bands are covered. The measured efficiencies are 52.7%–78.7% at the lower band and 45.6%–81% at the higher band. The measured gains are 0–2.1 dBi at the lower band and 1.6–3.6 dBi at the higher band.


Question: Compact circularly polarized patch antenna with reduced back radiation for GPS satellite communications.

This article presents an innovative, compact design for an integrated Global Positioning System (GPS) and Satellite Digital Audio Radio Service (SDARS) antenna, tailored to meet the stringent size constraints of automotive applications. The GPS component utilizes a conventional miniature ceramic patch antenna, while the SDARS element is ingeniously configured to coexist within the limited space without compromising the radiation characteristics or frequency band performance of the GPS antenna. The SDARS antenna is based on a square-ring microstrip design, operating within the 2.320-2.345 GHz range with left-hand circular polarization (LHCP), seamlessly integrated around the GPS patch, which receives right-hand circular polarization (RHCP) signals at 1.575 GHz. The total volume of this advanced integrated antenna solution is an impressively compact 30 x 30 x 7.6 mm3, making it a highly desirable option for automotive integration.

A Complementary Electric Field Coupled (CELC) resonator, loaded in the ground plane of a conventional patch antenna to achieve dual band operation is presented here. Two antenna designs have been presented. In the first design, one CELC is etched out from the ground plane of patch antenna which leads to generation of a lower order mode in addition to the conventional patch mode. In order to achieve better impedance bandwidth for the lower order resonant mode two CELC resonators with closely spaced resonant frequencies are etched out from the ground plane of patch antenna in the second design. The proposed antennas can be used in WLAN (Wireless Local Area Network) and WiMAX (Worldwide Interoperability for Microwave Access) applications.

A compact printed log-periodic dipole antenna (LPDA) with distributed inductive load has been presented in this paper. By adding a stub on the top of each element, the dimension of the LPAD can be reduced by 60%. The antenna has obtained an impedance bandwidth of 10GHz (8GHz-18GHz). According to the simulation results, the designed structure achieves stable radiation patterns throughout the operating frequency band.

This paper presents a high gain dual-band microstrip antenna. The antenna operates in 9.25 and 11 GHz (X-band). High gain of antenna is achieved by employing a FSS as superstrate which is formed by the 5 × 5 array of square loops. The square loop FSS resonance is varied and its influence on the antenna behavior is analyzed. Analyze of the simulated antenna gain and return loss are shown. The gain improvement depends on the resonance of the FSS superstrate. The higher gain obtained is 6 dB at 11 GHz while maintaining a good matching level for the first resonance.

A novel resonator-loaded wideband dual-polarized antenna is proposed for LTE700/GSM850/GSM900 applications. Four single-polarized miniaturized antenna elements are placed with square arrangement upon the reflector for dual polarization. One pair of parallel elements is for +45° polarization, and the other pair is for –45° polarization. The impedance bandwidth is greatly improved loaded with a resonator. Experimentally, the antenna exhibits a wide impedance bandwidth of 37.5% ranging from 0.67 to 0.98 GHz for |<inline-formula> <tex-math notation="LaTeX">$S$</tex-math></inline-formula>11|<–15 dB, high isolation of more than 40 dB, and stable gain of around 9.5 dBi over the whole operating band.

This paper proposed a design scheme of wide band monopulse antenna system for the passive radar seeker (PRS) of anti-radiation missile (ARM). To save the installation space of the PRS, the conical conformal log periodic antennas (LPDA) are employed to form the monopulse antenna system. The specific implementation scheme of PRS based on the conically conformal monopulse antennas was designed and analyzed. A practical monopulse antenna system composed of two log periodic antennas was designed and fabricated over the operating frequency range of 1GHz to 8GHz. A loaded resistor with impedance equal to 50Ω is used to reduce the return loss at low frequency. The experimental results demonstrate that acceptable wide band impedance matching and port isolation for each fabricated antenna was achieved. Furthermore, the wide band sum and difference radiation patterns were formed, which validates the proposed conformal monopulse antenna design scheme for the PRS application.

Theoretical and experimental results of a wide-band planar antenna are presented. This antenna can achieve a wide bandwidth, low cross-polarization levels, and low backward radiation levels. For wide bandwidth and easy integration with active circuits, it uses aperture-coupled stacked square patches. The coupling aperture is an H-shaped aperture. Based on the finite-difference time-domain method, a parametric study of the input impedance of the antenna is presented, and effects of each parameter on the antenna impedance are illustrated. One antenna is also designed, fabricated, and measured. The measured return loss exhibits an impedance bandwidth of 21.7%. The cross-polarization levels in both and planes are better than 23 dB. The front-to-back ratio of the antenna radiation pattern is better than 22 dB. Both theoretical and experimental results of parameters and radiation patterns are presented and discussed.

New designs for compact reconfigurable antennas are introduced for mobile communication devices. The uniqueness of the antenna designs are that they allow various groups of their operating frequency bands to be selected electronically. In particular, each group of frequency bands, or mode, can be made to serve several different communication systems simultaneously. These systems may include various combinations of GSM, DCS, PCS, UMTS, Bluetooth, and wireless local-area network (LAN). Therefore, by electronically selecting different antenna modes, a variety of communication systems can be conveniently served by only one antenna. One advantage is that through the different operational modes, the total antenna volume can be reused, and therefore the overall antenna can be made compact. In these designs, the selection of the different modes is achieved by either i) switching different feeding locations of the antenna (switched feed) or ii) switching or breaking of the antenna's connection to the ground (switched ground). This paper demonstrates these two designs. For the first design of switched feed, it can support GSM, DCS, PCS, and UMTS. In the second design, the antenna makes use of a switched-ground technique, which can cover GSM, DCS, PCS, UMTS, Bluetooth, and 2.4 GHz wireless LAN. The designs are investigated when ideal switches and also various realistic active switches based on PIN diodes, GaAs field effect transistor, and MEMs configurations. The designs are verified through both numerical simulations and measurement of an experimental prototype. The results confirm good performance of the two multiband reconfigurable antenna designs.

Abstract—In this paper a dual band circularly polarized antenna is designed, fabricated, and measured. Based on the concept of composite right and left handed (CRLH) metamaterials, the same phase constants and current distributions on the ring antenna are achieved at two frequency bands. Thus, the antenna has similar radiation patterns at both bands. The circular polarization is implemented by feeding two vertical ports from power dividers that provide equal magnitudes and quadrature phase excitations.

A novel pattern-reconfigurable compact planar ultra-wideband monopole antenna is presented. By the incorporation of four p-i-n diode switches and two parasitic elements, the antenna's radiation patterns can be shaped to concentrate energy in specific directions while minimising the gain in other unwanted directions without significantly affecting the impedance bandwidth of the antenna. A fully functional prototype has been developed and tested. The measured results of the return loss, radiation patterns, and realised gain verify the effectiveness of the proposed antenna configuration. The antenna switches its radiation patterns between an omni-directional mode and two directional modes with opposite directions in the operating range from 3 to 6 GHz. The proposed antenna could be a suitable candidate for advanced and smart radio applications such as cognitive radio (CR) as it can enhance the radio front-end flexibility and performance by adding the benefits of pattern diversity, specifically in multipath environments.

A circularly polarized slot radiator in millimetre band is presented. Low reflection and good polarization performance is obtained in comparison with state of art slot elements. A high gain array prototype has been implemented in substrate integrated waveguides (SIW) technology, in order to check the new element performance. A cosecant amplitude coverage in the elevation plane, as well as a 6° tilted monopulse pattern in the azimuth plane are obtained. A gain peak value of 28.6 dBi and 79% of efficiency at 36.7 GHz have been obtained in the specified operation band (36.7 – 37 GHz) for a manufactured antenna prototype. Likewise, the top measured axial ratio is 1.95 dB at 36.85 GHz.