Miracle-dz/UniFAR-FTUs huggingface dataset:
Elements of News Literacy

Focus groups with teenagers (ages 15–18) were conducted to understand how they define news; what motivates them to consume news; what news sources they use; and how much knowledge about the news media industry, content, and effects they bring to the task of consuming and thinking critically about the news. Findings suggested exposure to news came largely incidentally via social media and/or parents; participants expressed the sense that news would find them. These teens saw news as depressing, conflict-ridden, and something that, although important, was of less value to them than to adults. Considered in light of a media literacy model adapted for news, these focus group participants exhibited a basic sense of news literacy but lacked the kind of knowledge about news industries, content, and effects that could better direct their own exposure, understanding, and subsequent civic engagement.
3D Animation framework for sign language

Abstract A critical part of animating a sign language using virtual avatar is to display a sign gesture having multiple rotational arm poses to identify a word instead of a single static arm pose. Sequencing a group of gestures related to a sentence requires each gesture in the middle of a sentence to be animated using different initial arm positions. Sequencing pre-captured arm videos, ordering preset animations compiled by 3D animations, and ordering motion capture data are the widely used techniques used by sign language animators presently. The transition from one word to another is not smooth as the initial and the terminating positions of each animation is not the same. This paper presents a technique with smooth transitions between gestures to animate a sign language. A sequencing technique is also presented to animate known words using gestures that are already defined and also to animate unknown words using character-to-character sign animation. New sentences are dynamically added in real-time and the system will adjust the animation list automatically by appending the required animations of the words in the new sentence to the end of the playlist. Results indicate an average distance of 3.81 pixels for 27 static pose finger spelling characters.
An Efficient Adaptive Transfer Neural Network for Social-aware Recommendation

Many previous studies attempt to utilize information from other domains to achieve better performance of recommendation. Recently, social information has been shown effective in improving recommendation results with transfer learning frameworks, and the transfer part helps to learn users' preferences from both item domain and social domain. However, two vital issues have not been well-considered in existing methods: 1) Usually, a static transfer scheme is adopted to share a user's common preference between item and social domains, which is not robust in real life where the degrees of sharing and information richness are varied for different users. Hence a non-personalized transfer scheme may be insufficient and unsuccessful. 2) Most previous neural recommendation methods rely on negative sampling in training to increase computational efficiency, which makes them highly sensitive to sampling strategies and hence difficult to achieve optimal results in practical applications. To address the above problems, we propose an Efficient Adaptive Transfer Neural Network (EATNN). By introducing attention mechanisms, the proposed model automatically assign a personalized transfer scheme for each user. Moreover, we devise an efficient optimization method to learn from the whole training set without negative sampling, and further extend it to support multi-task learning. Extensive experiments on three real-world public datasets indicate that our EATNN method consistently outperforms the state-of-the-art methods on Top-K recommendation task, especially for cold-start users who have few item interactions. Remarkably, EATNN shows significant advantages in training efficiency, which makes it more practical to be applied in real E-commerce scenarios. The code is available at (https://github.com/chenchongthu/EATNN).
Teach Machine How to Read: Reading Behavior Inspired Relevance Estimation

Retrieval models aim to estimate the relevance of a document to a certain query. Although existing retrieval models have gained much success in both deepening our understanding of information seeking behavior and constructing practical retrieval systems (e.g. Web search engines), we have to admit that the models work in a rather different manner than how humans make relevance judgments. In this paper, we aim to reexamine the existing models as well as to propose new ones based on the findings in how human read documents during relevance judgment. First, we summarize a number of reading heuristics from practical user behavior patterns, which are categorized into implicit and explicit heuristics. By reviewing a variety of existing retrieval models, we find that most of them only satisfy a part of these reading heuristics. To evaluate the effectiveness of each heuristic, we conduct an ablation study and find that most heuristics have positive impacts on retrieval performance. We further integrate all the effective heuristics into a new retrieval model named Reading Inspired Model (RIM). Specifically, implicit reading heuristics are incorporated into the model framework and explicit reading heuristics are modeled as a Markov Decision Process and learned by reinforcement learning. Experimental results on a large-scale public available benchmark dataset and two test sets from NTCIR WWW tasks show that RIM outperforms most existing models, which illustrates the effectiveness of the reading heuristics. We believe that this work contributes to constructing retrieval models with both higher retrieval performance and better explainability.
Why do Users Issue Good Queries?: Neural Correlates of Term Specificity

Despite advances in the past few decades in studying what kind of queries users input to search engines and how to suggest queries for the users, the fundamental question of what makes human cognition able to estimate goodness of query terms is largely unanswered. For example, a person searching information about "cats'' is able to choose query terms, such as "housecat'', "feline'', or "animal'' and avoid terms like "similar'', "variety'', and "distinguish''. We investigated the association between the specificity of terms occurring in documents and human brain activity measured via electroencephalography (EEG). We analyzed the brain activity data of fifteen participants, recorded in response to reading terms from Wikipedia documents. Term specificity was shown to be associated with the amplitude of evoked brain responses. The results indicate that by being able to determine which terms carry maximal information about, and can best discriminate between, documents, people have the capability to enter good query terms. Moreover, our results suggest that the effective query term selection process, often observed in practical search behavior studies, has a neural basis. We believe our findings constitute an important step in revealing the cognitive processing behind query formulation and evaluating informativeness of language in general.
Effects of Temporal Self-Comparisons on the Pursuit of Improvement

Effects of Temporal Self-Comparisons on the Pursuit of Improvement Sokiente Watariye Dagogo-Jack Chair of the Supervisory Committee: Professor Mark R. Forehand Department of Marketing and International Business This dissertation consists of three chapters. Chapter one reviews the current state of knowledge on temporal self-comparisons and distills different theories and findings into three key principles that can guide future research on the role of temporal comparisons in consumer behavior. Chapters two and three are empirical investigations intended to stand alone as submissions to peer-reviewed journals. Chapter two examines the effects of temporal self-comparisons on product upgrade behavior (i.e., the pursuit of improvement external to the self). Four experiments show a “temporal egotism” process whereby consumers project their own selfimprovement perceptions onto self-connected brands, which subsequently increases product upgrade likelihood. Chapter three explores the interactive effects of temporal and social comparisons on self-improvement pursuit (i.e., the pursuit of improvement internal to the self). Five experiments demonstrate that temporal decline in the self increases consumers’ interest in self-improvement products and remedial behaviors when they are of relatively high social standing, but not when they are of lower social standing.
Effects of User Negative Experience in Mobile News Streaming

Online news streaming services have been one of the major information acquisition resources for mobile users. In many cases, users click an article but find it cannot satisfy or even annoy them. Intuitively, these negative experiences will affect users' behaviors and satisfaction, but such effects have not been well understood. In this work, a retrospective analysis is conducted using real users' log data, containing user's explicit feedback of negative experiences, from a commercial news streaming application. Through multiple intra-session comparison experiments, we find that in current session, users will spend less time reading the content, lose activeness and leave sooner after having negative experiences. Later return and significant changes of user behaviors in the next session are also observed, which demonstrates the existence of inter-session effects of negative experiences. Since users' negative experiences are generally implicit, we further investigate the possibility and the approach to automatically identify them. Results show that using changes of both users' intra-session and inter-session behaviors achieves significant improvement. Besides the effects on user behaviors, we also explore the effects on user satisfaction by incorporating a laboratory user study. Results show that negative experiences reduce user satisfaction in the current session, and the impact will last to the next session. Moreover, we demonstrate users' negative feedback helps on the meta-evaluation of online metrics. Our research has comprehensively analyzed the impacts of users' item-level negative experiences, and shed light on the understanding of user behaviors and satisfaction.
Personalized Fashion Recommendation with Visual Explanations based on Multimodal Attention Network: Towards Visually Explainable Recommendation

Fashion recommendation has attracted increasing attention from both industry and academic communities. This paper proposes a novel neural architecture for fashion recommendation based on both image region-level features and user review information. Our basic intuition is that: for a fashion image, not all the regions are equally important for the users, i.e., people usually care about a few parts of the fashion image. To model such human sense, we learn an attention model over many pre-segmented image regions, based on which we can understand where a user is really interested in on the image, and correspondingly, represent the image in a more accurate manner. In addition, by discovering such fine-grained visual preference, we can visually explain a recommendation by highlighting some regions of its image. For better learning the attention model, we also introduce user review information as a weak supervision signal to collect more comprehensive user preference. In our final framework, the visual and textual features are seamlessly coupled by a multimodal attention network. Based on this architecture, we can not only provide accurate recommendation, but also can accompany each recommended item with novel visual explanations. We conduct extensive experiments to demonstrate the superiority of our proposed model in terms of Top-N recommendation, and also we build a collectively labeled dataset for evaluating our provided visual explanations in a quantitative manner.
Document Gated Reader for Open-Domain Question Answering

Open-domain question answering focuses on using diverse information resources to answer any types of question. Recent years, with the development of large-scale data set and various deep neural networks models, some recent advances in open domain question answering system first utilize the distantly supervised dataset as the knowledge resource, then apply deep learning based machine comprehension techniques to generate the right answers, which achieves impressive results compared with traditional feature-based pipeline methods. However, these deep learning based methods suffer from the inferior quality of the distantly supervised data, and the answer score is un-normalized among multiple documents. Furthermore, unlike previous open-domain question answering system, they process each document independently which may ignore the valuable information in the context. In this paper, we propose a document gated reader to generate the right answer from multiple documents. We propose a document-level gate operation to determine the question-document relevance and embed it into the answer generation process, and optimize it with the global normalization objective. We also develop a bootstrapping based data generation scheme to obtain high-quality training data. Experimental results on several question answering datasets show the advantage of the proposed methods.
Design of a multi-channel medium access control protocol for ad-hoc wireless networks

Typically, a device in an ad hoc wireless network has a tunable radio allowing it to listen or transmit on one channel at a time. To ensure maximum connectivity, all devices tune their radio to the same channel. However, as node density increases, the interference worsens; resulting in lower throughput per device. Some of the interference is unnecessary. Modern radios can switch channels rapidly (i.e., tune to different frequencies quickly). Given multiple available channels, devices in a neighborhood can potentially transmit on different channels simultaneously to increase the network throughput. This dissertation explores whether it is possible to exploit the existence of multiple channels and the ability of radios to switch between them quickly to increase network throughput without a central coordinator. The thesis starts by showing that if a radio can modulate the entire available spectrum, splitting it into smaller channels does not increase capacity. However, since there is always an engineering limit as to how wide a channel can be, multi-channel MAC should be viewed as a practical way to increase throughput when more spectrum is available, without hardware changes. We first classify existing multi-channel MAC protocols into four categories based on their operation principle: Dedicated Control Channel, Split-phase, Common Hopping, and Parallel Rendezvous. Then, we compare these four approaches through both analysis and simulation. All approaches apart from Parallel Rendezvous suffer from control channel congestion when channels are numerous and communication tend to be short. Using the best performing approach, Parallel Rendezvous, as a skeleton, we designed our own multi-channel MAC protocol called McMAC. To prove that McMAC is practical and to discover any hidden issue, we implemented the salient features of McMAC on an experimental hardware platform and evaluated its performance. In summary, through analysis, simulation, and experiments, we show that it is possible to increase throughput of an ad hoc wireless network by using multiple channels efficiently even though each node can only use one channel at a time.
Information Nutritional Label and Word Embedding to Estimate Information Check-Worthiness

Automatic fact-checking is an important challenge nowadays since anyone can write about anything and spread it in social media, no matter the information quality. In this paper, we revisit the information check-worthiness problem and propose a method that combines the "information nutritional label" features with POS-tags and word-embedding representations. To predict the information check-worthy claim, we train a machine learning model based on these features. We experiment and evaluate the proposed approach on the CheckThat! CLEF 2018 collection. The experimental result shows that our model that combines information nutritional label and word-embedding features outperforms the baselines and the official participants' runs of CheckThat! 2018 challenge.
Leveraging Emotional Signals for Credibility Detection

The spread of false information on the Web is one of the main problems of our society. Automatic detection of fake news posts is a hard task since they are intentionally written to mislead the readers and to trigger intense emotions to them in an attempt to be disseminated in the social networks. Even though recent studies have explored different linguistic patterns of false claims, the role of emotional signals has not yet been explored. In this paper, we study the role of emotional signals in fake news detection. In particular, we propose an LSTM model that incorporates emotional signals extracted from the text of the claims to differentiate between credible and non-credible ones. Experiments on real world datasets show the importance of emotional signals for credibility assessment.
Subspace neural physics: fast data-driven interactive simulation

Data-driven methods for physical simulation are an attractive option for interactive applications due to their ability to trade precomputation and memory footprint in exchange for improved runtime performance. Yet, existing data-driven methods fall short of the extreme memory and performance constraints imposed by modern interactive applications like AAA games and virtual reality. Here, performance budgets for physics simulation range from tens to hundreds of micro-seconds per frame, per object. We present a data-driven physical simulation method that meets these constraints. Our method combines subspace simulation techniques with machine learning which, when coupled, enables a very efficient subspace-only physics simulation that supports interactions with external objects - a longstanding challenge for existing subspace techniques. We also present an interpretation of our method as a special case of subspace Verlet integration, where we apply machine learning to efficiently approximate the physical forces of the system directly in the subspace. We propose several practical solutions required to make effective use of such a model, including a novel training methodology required for prediction stability, and a GPU-friendly subspace decompression algorithm to accelerate rendering.
CROSS: Cross-platform Recommendation for Social E-Commerce

Social e-commerce, as a new concept of e-commerce, uses social media as a new prevalent platform for online shopping. Users are now able to view, add to cart, and buy products within a single social media app. In this paper, we address the problem of cross-platform recommendation for social e-commerce, i.e., recommending products to users when they are shopping through social media. To the best of our knowledge, this is a new and important problem for all e-commerce companies (e.g. Amazon, Alibaba), but has never been studied before. Existing cross-platform and social related recommendation methods cannot be applied directly for this problem since they do not co-consider the social information and the cross-platform characteristics together. To study this problem, we first investigate the heterogeneous shopping behaviors between traditional e-commerce app and social media. Based on these observations from data, we propose CROSS (Cross-platform Recommendation for Online Shopping in Social Media), a recommendation model utilizing not only user-item interaction data on both platforms, but also social relation data on social media. Extensive experiments on real-world online shopping dataset demonstrate that our proposed CROSS significantly outperforms existing state-of-the-art methods.
VISEN: a video interactive retrieval engine based on semantic network in large video collections

Following technological advances carried out recently, there has been an explosion in the quantity of videos available and their accessibility. This is largely justified by the fall of the prices of acquisition and the increase of the capacity of the memory supports, which made the storage of the large document video in computer system possible. To allow an effective exploitation of the collections, it is necessary to install tools facilitating the access to the documents and handle them. In this context, we propose a multimedia retrieval approach that puts the user at the center of the retrieval process starting from a text query. The new aspects of our proposal is as follows: (i) concerning the indexation part, we propose a new approach allowing a multilevel and semantic classification of videos, (ii) regarding the retrieval part, the inclusion of query expansion mechanism helps the user to formulate the query and the relevance feedback mechanism which helps improve the results considering the user's feedback. Our contribution at the experimental level consists in the implementation of prototype VISEN. In fact the technique proposed have been integrated in system seeks by the contents to evaluate the contribution in terms of effectiveness and precision. After carrying out a set of tests on 2700 videos and 62838 images, the experimental results showed that the proposed algorithm performs well.
A Lightweight Representation of News Events on Social Media

The sheer amount of newsworthy information published by users in social media platforms makes it necessary to have efficient and effective methods to filter and organize content. In this scenario, off-the-shelf methods fail to process large amounts of data, which is usually approached by adding more computational resources. Simple data aggregations can help to cope with space and time constraints, while at the same time improve the effectiveness of certain applications, such as topic detection or summarization. We propose a lightweight representation of newsworthy social media data. The proposed representation leverages microblog features, such as redundancy and re-sharing capabilities, by using surrogate texts from shared URLs and word embeddings. Our representation allows us to achieve comparable clustering results to those obtained by using the complete data, while reducing running time and required memory. This is useful when dealing with noisy and raw user-generated social media data.
Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs

Direct answering of questions that involve multiple entities and relations is a challenge for text-based QA. This problem is most pronounced when answers can be found only by joining evidence from multiple documents. Curated knowledge graphs (KGs) may yield good answers, but are limited by their inherent incompleteness and potential staleness. This paper presents QUEST, a method that can answer complex questions directly from textual sources on-the-fly, by computing similarity joins over partial results from different documents. Our method is completely unsupervised, avoiding training-data bottlenecks and being able to cope with rapidly evolving ad hoc topics and formulation style in user questions. QUEST builds a noisy quasi KG with node and edge weights, consisting of dynamically retrieved entity names and relational phrases. It augments this graph with types and semantic alignments, and computes the best answers by an algorithm for Group Steiner Trees. We evaluate QUEST on benchmarks of complex questions, and show that it substantially outperforms state-of-the-art baselines.