Search

Twitter, a microblogging platform, has developed into an increasingly invaluable information source, where millions of users post a great quantity of tweets with various topics per day. Heterogeneous information networks consisting of multi-type objects and relations are becoming more and more prevalent as an organization form of knowledge and information. The task of linking an entity mention in a tweet with its corresponding entity in a heterogeneous information network is of great importance, for the purpose of enriching heterogeneous information networks with the abundant and fresh knowledge embedded in tweets. However, the entity mention is ambiguous. Additionally, tweets are short and informal, making it difficult to mine enough information from a single tweet for entity linking. In this paper, we propose an unsupervised iterative clustering framework TELHIN to link multiple similar tweets with a heterogeneous information network jointly. Our framework takes three dimensions of tweet similarity into consideration: (1) content similarity, (2) temporal similarity, and (3) user similarity. The appropriate weights of different similarity dimensions for each entity mention are learned iteratively based on the metric learning algorithm by leveraging the pairwise constraints generated automatically. Experiments on real data demonstrate the effectiveness of our framework in comparison with the baselines. [ABSTRACT FROM AUTHOR]

Many datasets including social media data and bibliographic data can be modeled as graphs. Clustering such graphs is able to provide useful insights into the structure of the data. To improve the quality of clustering, node attributes can be taken into account, resulting in attributed graphs. Existing attributed graph clustering methods generally consider attribute similarity and structural similarity separately. In this paper, we represent attributed graphs as star-schema heterogeneous graphs, where attributes are modeled as different types of graph nodes. This enables the use of personalized pagerank (PPR) as a unified distance measure that captures both structural and attribute similarities. We employ DBSCAN for clustering, and we update edge weights iteratively to balance the importance of different attributes. The rapidly growing volume of data nowadays challenges traditional clustering algorithms, and thus, a distributed method is required. Hence, we adopt a popular distributed graph computing system Blogel, based on which, we develop four exact and approximate approaches that enable efficient PPR score computation when edge weights are updated. To improve the effectiveness of the clustering, we propose a simple yet effective edge weight update strategy based on entropy. In addition, we present a game theory based method that enables trading efficiency for result quality. Extensive experiments on real-life datasets offer insights into the effectiveness and efficiency of our proposals. [ABSTRACT FROM AUTHOR]

Fake news detection attracts many researchers’ attention due to the negative impacts on the society. Most existing fake news detection approaches mainly focus on semantic analysis of news’ contents. However, the detection performance will dramatically decrease when the content of news is short. In this paper, we propose a novel fake news detection multi-task learning (FDML) model based on the following observations: 1) some certain topics have higher percentages of fake news; and 2) some certain news authors have higher intentions to publish fake news. FDML model investigates the impact of topic labels for the fake news and introduce contextual information of news at the same time to boost the detection performance on the short fake news. Specifically, the FDML model consists of representation learning and multi-task learning parts to train the fake news detection task and the news topic classification task, simultaneously. As far as we know, this is the first fake news detection work that integrates the above two tasks. The experiment results show that the FDML model outperforms state-of-the-art methods on real-world fake news dataset. [ABSTRACT FROM AUTHOR]

The number of “hits” has been widely regarded as the lifeblood of many web systems, e.g., e-commerce systems, advertising systems and multimedia consumption systems. However, users would not hit an item if they cannot see it, or they are not interested in the item. Recommender system plays a critical role of discovering interesting items from near-infinite inventory and exhibiting them to potential users. Yet, two issues are crippling the recommender systems. One is “how to handle new users”, and the other is “how to surprise users”. The former is well-known as cold-start recommendation. In this paper, we show that the latter can be investigated as long-tail recommendation. We also exploit the benefits of jointly challenging both cold-start and long-tail recommendation, and propose a novel approach which can simultaneously handle both of them in a unified objective. For the cold-start problem, we learn from side information, e.g., user attributes, user social relationships, etc. Then, we transfer the learned knowledge to new users. For the long-tail recommendation, we decompose the overall interesting items into two parts: a low-rank part for short-head items and a sparse part for long-tail items. The two parts are independently revealed in the training stage, and transfered into the final recommendation for new users. Furthermore, we effectively formulate the two problems into a unified objective and present an iterative optimization algorithm. A fast extension of the method is proposed to reduce the complexity, and extensive theoretical analysis are provided to proof the bounds of our approach. At last, experiments of social recommendation on various real-world datasets, e.g., images, blogs, videos and musics, verify the superiority of our approach compared with the state-of-the-art work. [ABSTRACT FROM AUTHOR]

This paper proposes a collaborative approach to enhancing the student learning experience based on Web 2.0 principles. Specifically, wiki Web sites are used by students for collaboration and for publication of course assignments, which are then shared with the class. Web 2.0 principles include: the Web as platform, harnessing collective intelligence, data are the next Intel Inside, and rich user experiences. Based on a case study in a junior-level undergraduate class, this paper studies a set of six factors with comprehensive grading and evaluation criteria that are critical to make this approach successful. The six factors are knowledge base, motivation, research, social aspects, presentation, and feedback and support. The data collected show that most of the students who participated feel that this approach is exciting and rewarding, and that even some undergraduate students are able to produce original and innovative concepts. The data also show other interesting phenomena with respect to motivation, undergraduate research, and social aspects. Finally, the paper proposes a methodology of conducting a wiki project in a university class using a cyclic constant improvement process. [ABSTRACT FROM AUTHOR]

This paper studies electrical engineering learners’ preferences for learning methods with various degrees of activity. Less active learning methods such as homework and peer reviews are investigated, as well as a newly introduced very active (constructive) learning method called “slectures,” and some others. The results suggest that graduate students’ perception of the usefulness of the activity increases with its level of activity. For undergraduate students, an increased perception of the usefulness of the activity was observed for lightly active but structured learning methods. Group-based analysis focusing on two types of learners, defined as “instructor-dependent” and “instructor-independent” according to their perception of the usefulness of the classroom lectures, was also performed. The results suggest that instructor-independent learners may benefit more from active learning methods than instructor-dependent learners. For example, instructor-independent undergraduate learners were found to perceive the homework assignment as being more useful than the lectures. Such a preference was not seen in the average group data. In fact, no learning method was found to be perceived as more useful than the lectures, on average. Thus this paper illustrates the pertinence of group-based data analysis. [ABSTRACT FROM AUTHOR]

With numerous nodes on online heterogeneous networks, how to reach and extract target nodes of our specific interests is a pressing problem. In this paper, we propose a novel heterogeneous network crawler, MCrawl. It addresses the problem via iterative online heterogeneous network crawling by navigating its available APIs, starting from a set of target nodes, i.e., seed nodes. We are facing two challenges towards addressing the problem. First, to navigate within a vast network, how do we start from a small set of target nodes? In other words, which nodes in the “current frontier” and which direction shall we expand, to reach promising target nodes quickly? We propose motif-based crawling to exploit the complex structures and rich semantics of heterogeneous networks. Second, in many scenarios, we do not have a classifier to assess the quality of the harvested nodes and thus the motifs to expand. We develop a probabilistic inference framework to estimate the yield and harvest rates of motifs, achieving principled bootstrapping for crawling. Our experiment on real networks of MCrawl achieves significant margins over baselines. [ABSTRACT FROM AUTHOR]

In entropy coding, universal coding of integers (UCI) is a binary universal prefix code, such that the ratio of the expected codeword length to $\max \{1, H(P)\}$ is less than or equal to a constant expansion factor $K_{\mathcal {C}}$ for any probability distribution $P$ , where $H(P)$ is the Shannon entropy of $P$. $K_{\mathcal {C}}^{*}$ is the infimum of the set of expansion factors. The optimal UCI is defined as a class of UCI possessing the smallest $K_{\mathcal {C}}^{*}$. Based on prior research, the range of $K_{\mathcal {C}}^{*}$ for the optimal UCI is $2\leq K_{\mathcal {C}}^{*}\leq 2.75$. Currently, the code constructions achieve $K_{\mathcal {C}}=2.75$ for UCI and $K_{\mathcal {C}}=3.5$ for asymptotically optimal UCI. In this paper, we construct a class of UCI, termed $\iota $ code, to achieve $K_{\mathcal {C}}=2.5$. This further narrows the range of $K_{\mathcal {C}}^{*}$ to $2\leq K_{\mathcal {C}}^{*}\leq 2.5$. Next, a family of asymptotically optimal UCIs is presented, where their expansion factor infinitely approaches 2.5. Then, tighter upper bounds of the expansion factors are derived for several classic UCIs. Finally, we prove that the length of the first codeword of optimal UCI is one. [ABSTRACT FROM AUTHOR]

Travelers in vehicles often have a strong willingness to share their travel experience and exchange information to each other through social networks, such as Facebook and Twitter. This, however, can be costly due to the limited connections on the road to the Internet. In this paper, we develop Verse to facilitate the social communications among vehicle travelers on highways. Verse enables passengers onboard vehicles to share the content information, such as travel blogs with pictures, among each other using impromptu wireless intervehicle communications. Unlike traditional online social networks, which are built upon reliable internet protocol (IP) networks, vehicular social networks face fundamental challenges in that 1) users are anonymous and strangers to each other and that it is hard to identify potential friends of shared interests and that 2) users communicate through intermittent and unreliable intervehicle connections. On addressing the two challenges, Verse implements a friend recommendation function, which helps passengers efficiently identify potential social friends with both shared interests and relatively reliable wireless connections. In addition, Verse is equipped with a social-aware rate control scheme toward efficient utilization of network bandwidth. Using extensive simulations, we show that the friend recommendation function of Verse can effectively predict the mobility of vehicles to assist in social communication and that the social-aware rate control scheme quickly and efficiently adapts to the vehicle's transmission rate according to their social impact. [ABSTRACT FROM PUBLISHER]

Hashtag is an important advertising tool and a must-have feature for social media nowadays. In the past, many hashtag recommendation methods have been proposed from different perspectives of images, texts, and users. However, most previous works consider neither the mutual influence between multi-modalities, nor the visual similarity between images. In this paper, we devise a novel model, named Triplet-Attention Graph Network (TAGNet). The rationale behind our method is that visually similar images share some common hashtags. Therefore, we build an image graph, and apply a new Aggregated Graph Convolution (AGC) module to propagate information in a collective way. Furthermore, it is noted that text and user also have rich content information within posts, and we hence propose a Triplet Attention (TA) module to incorporate multi-modalities into node features. Experiments on the large-scale dataset collected from Instagram show that TAGNet achieved significant improvement in recall rate over the best state-of-the-art method. [ABSTRACT FROM AUTHOR]

Universal coding of integers (UCI) is a prefix coding suitable for probability distributions without prior knowledge. UCI has the property that the expected codeword length is no more than constant $K_{C}$ times $\max \{1,H(P)\}$ , where $K_{C}$ is called the expansion factor and $H(P)$ is the entropy of source $P$. A class of UCI $C$ with a smaller $K_{C}$ is preferred, but the minimum value of $K_{C}$ (as well as its code construction) is still unknown. Thus, this paper provides a range of the minimum values of $K_{C}$. First, we show that $K_{C}\geq 2$ for each UCI $C$. Then, for the upper bound, we provide a class of UCIs, termed $\eta $ code, to achieve $K_{C}=2.75$. This approach improves the prior result $K_{C}=3$ achieved by Elias $\gamma $ coding. Next, we propose an asymptotically optimal UCI, termed $\theta $ code, to achieve $K_{C}=3.5$. Finally, we compare the range of the minimum value of $K_{C}$ of $\eta $ code, $\theta $ code and some other UCIs. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of short text summarization on the comment stream of a specific message from social network services (SNS). Due to the high popularity of SNS, the quantity of comments may increase at a high rate right after a social message is published. Motivated by the fact that users may desire to get a brief understanding of a comment stream without reading the whole comment list, we attempt to group comments with similar content together and generate a concise opinion summary for this message. Since distinct users will request the summary at any moment, existing clustering methods cannot be directly applied and cannot meet the real-time need of this application. In this paper, we model a novel incremental clustering problem for comment stream summarization on SNS. Moreover, we propose IncreSTS algorithm that can incrementally update clustering results with latest incoming comments in real time. Furthermore, we design an at-a-glance visualization interface to help users easily and rapidly get an overview summary. From extensive experimental results and a real case demonstration, we verify that IncreSTS possesses the advantages of high efficiency, high scalability, and better handling outliers, which justifies the practicability of IncreSTS on the target problem. [ABSTRACT FROM PUBLISHER]

Replication plays an essential role for in-memory distributed transactional platforms, given that it represents the primary means to ensure data durability. Unfortunately, no single replication technique can ensure optimal performance across a wide range of workloads and system configurations. This paper tackles this problem by presenting MorphR , a framework that allows to automatically adapt the replication protocol of in-memory transactional platforms according to the current operational conditions. MorphR presents two key innovative aspects. On one hand, it allows to plug in, in a modular fashion, specialized algorithms to regulate the switching between arbitrary replication protocols. On the other hand, MorphR relies on state of the art machine learning techniques to autonomously determine the best replication in face of varying workloads. We integrated MorphR in an open-source in-memory NoSQL data grid, and evaluated it by means of an extensive experimental study. The results highlight that MorphR is accurate in identifying the best replication strategy in presence of complex realistic workloads, and does so with minimal overhead. [ABSTRACT FROM PUBLISHER]

Internet users can be classified as two types: (a) active users—actively contribute to blogs, publish opinions, write comments in Youtube, tweet messages, etc. and (b) passive consumers who only consume Internet information without contributing to it. While the majority of current social media research deals with active user analysis, there is very little work in understanding the dynamics of passive consumers and their influence. Our global scale Internet measurement of user access patterns of a diverse set of Internet media services indicates conclusively that majority of consumers are passive. In this paper, we develop a spatio-temporal mathematical model and the corresponding stochastic analysis to understand the passive consumer dynamics. Both discrete and continuous time analysis are presented. We also show how the analysis can be used to identify spatial points of influence, i.e., spatial locations that have maximum expertise or influence on a topic. The analysis takes into account, the initial level of consumption at each spatial location and the influence of different passive consumers at different geographic locations on each other. The effect of information noise is taken into account to derive fundamental limits of passive information consumption. Theoretical results are verified using real Internet measurement data. The large scale data have been made available for other researchers. [ABSTRACT FROM AUTHOR]

Identifying which text corpus leads in the context of a topic presents a great challenge of considerable interest to researchers. Recent research into lead-lag analysis has mainly focused on estimating the overall leads and lags between two corpora. However, real-world applications have a dire need to understand lead-lag patterns both globally and locally. In this paper, we introduce TextPioneer, an interactive visual analytics tool for investigating lead-lag across corpora from the global level to the local level. In particular, we extend an existing lead-lag analysis approach to derive two-level results. To convey multiple perspectives of the results, we have designed two visualizations, a novel hybrid tree visualization that couples a radial space-filling tree with a node-link diagram and a twisted-ladder-like visualization. We have applied our method to several corpora and the evaluation shows promise, especially in support of text comparison at different levels of detail. [ABSTRACT FROM PUBLISHER]

Erasure list decoding was introduced to correct a larger number of erasures by outputting a list of possible candidates. In this paper, we consider both random linear codes and algebraic geometry codes for list decoding from erasures. The contributions of this paper are twofold. First, for arbitrary \(00\) ( \(R\) and \(\epsilon \) are independent), we show that with high probability a \(q\) -ary random linear code of rate \(R\) is an erasure list-decodable code with constant list size \(q^{O(1/\epsilon )}\) that can correct a fraction \(1-R-\epsilon \) of erasures, i.e., a random linear code achieves the information-theoretic optimal tradeoff between information rate and fraction of erasures. Second, we show that algebraic geometry codes are good erasure list-decodable codes. Precisely speaking, a \(q\) -ary algebraic geometry code of rate \(R\) from the Garcia-Stichtenoth tower can correct \(1-R-({1}/{\sqrt {q}-1})+({1}/{q})-\epsilon \) fraction of erasures with list size \(O(1/\epsilon )\) . This improves the Johnson bound for erasures applied to algebraic geometry codes. Furthermore, list decoding of these algebraic geometry codes can be implemented in polynomial time. Note that the code alphabet size \(q\) in this paper is constant and independent of \(\epsilon \) . [ABSTRACT FROM PUBLISHER]

We consider the single-source shortest path (SSSP) problem: given an undirected graph with integer edge weights and a source vertex v , find the shortest paths from v to all other vertices. In this paper, we introduce a novel parallel algorithm, derived from the Bellman-Ford and Delta-stepping algorithms. We employ various pruning techniques, such as edge classification and direction-optimization, to dramatically reduce inter-node communication traffic, and we propose load balancing strategies to handle higher-degree vertices. These techniques are particularly effective on power-law graphs, as demonstrated by our extensive performance analysis. In the largest tested configuration, an R-MAT graph with 2^38 vertices and 2^42 edges on 32,768 Blue Gene/Q nodes, we have achieved a processing rate of three Trillion Edges Per Second (TTEPS), a four orders of magnitude improvement over the best published results. [ABSTRACT FROM AUTHOR]

The ideal of self-paced learning, which was introduced nearly 50 years ago by Keller in his Personalized System of Instruction (PSI), has not yet been widely adopted. In spite of its perceived promise of helping students to learn at the speed aligned to their individual backgrounds, motivation, and skills, PSI has been challenging to implement. University teaching practice with weekly plans means that instructors expect students to learn at the same pace. Against this backdrop, this paper reports experiences from deploying PSI in multiple offerings of an introductory programming course at a Scandinavian university over five years. These include variations, such as a buddy system, rightsizing modules, encouraging pacing, alternative targets, intermediate milestones, active coaching, and time management, all primarily aimed at overcoming a key concern associated with PSI, namely, procrastination. The lessons associated with each are described with a view to providing fellow instructors with strategies and options that they can consider for implementing PSI into introductory programming courses. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the problem of mining social emotions from text. Recently, with the fast development of web 2.0, more and more documents are assigned by social users with emotion labels such as happiness, sadness, and surprise. Such emotions can provide a new aspect for document categorization, and therefore help online users to select related documents based on their emotional preferences. Useful as it is, the ratio with manual emotion labels is still very tiny comparing to the huge amount of web/enterprise documents. In this paper, we aim to discover the connections between social emotions and affective terms and based on which predict the social emotion from text content automatically. More specifically, we propose a joint emotion-topic model by augmenting Latent Dirichlet Allocation with an additional layer for emotion modeling. It first generates a set of latent topics from emotions, followed by generating affective terms from each topic. Experimental results on an online news collection show that the proposed model can effectively identify meaningful latent topics for each emotion. Evaluation on emotion prediction further verifies the effectiveness of the proposed model. [ABSTRACT FROM PUBLISHER]

With the advent of Web 2.0, searching and publishing became two major forms of online activities for web users. When an event happens, web users would search for the latest information about the event as well as publish blog posts to discuss the event. Both the queries from users and the blog posts published give strong indications of the real-world events of users' concern. In this paper, we propose to study query-guided event detection from two parallel document streams (i.e., news and blog). Our goal is to group user queries, news articles, and blog posts into events to which they are related. The evolution of an event is reflected by the changes in both the query keywords and news/blogs content during the event happening period. We propose a two-stage real-time event detection framework consisting of event fragment detection and event detection. The proposed framework integrates queries, news articles, and blog posts through the notion of query profile. In our experiments, we evaluate the proposed framework using real-world data collected from Technorati and Google News. [ABSTRACT FROM PUBLISHER]

In a blog network, there are special users who induce other users to actively utilize blogs. Identifying such influential users is important when establishing business policy and business models for the blog network. This paper defines the users whose contents exhibit significant influence over other users as content power users (CPUs) and proposes a method of identifying them. We analyze the performance of the proposed method by applying it to an actual blog network and comparing its results with those of preexisting methods for determining power users. The experimental results demonstrate that the definition of CPUs is adequate to address the dynamic nature of the blogosphere and the main concerns of the blog industry. We also discuss the business models based on CPUs that could be used to stimulate user activities in a blog network. [ABSTRACT FROM PUBLISHER]

The rapid growth of Android malware results in a large body of approaches devoted to malware analysis by leveraging machine learning algorithms. However, the effectiveness of these approaches primarily depends on the manual feature engineering process, which is time-consuming and labor-intensive based on expert knowledge and intuition. In this paper, we propose an automatic approach that engineers informative features from a corpus of Android malware related technical blogs, which are written in a way that mirrors the human feature engineering process. However, there are two main challenges. First, it is difficult to recognize useful knowledge in the magnanimity information of thousands of blogs. To this end, we leverage natural language processing techniques to process the blogs and extract a set of sensitive behaviors that might do harmful activities to users potentially. Second, there exists a semantic gap between the extracted sensitive behaviors and the programming language. To this end, we propose two semantic matching rules to match the behaviors with concrete code snippets such that the apps can be tested experimentally. We design and implement a system called CTDroid for malware analysis, including malware detection (MD) and familial classification (FC). After the evaluation of CTDroid on a large scale of real malware and benign apps, the experimental results demonstrate that CTDroid can achieve 95.8% true positive rate with only 1% false positive rate for MD and 97.9% accuracy for FC. Furthermore, our proposed features are more informative than those of state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

This paper focuses on the balancedness of symmetric Boolean functions. We prove a conjecture presented by Canteaut and Videau, which states that the balanced symmetric Boolean functions of fixed algebraic degree are trivially balanced when the number of variables is large enough. Denoted by \sigma n,d , the n -variable elementary symmetric Boolean function of degree d . As an application of this result to elementary symmetric Boolean functions, we show that all the trivially balanced elementary symmetric Boolean functions are of the form \sigma 2^{t+1l-1,2^{t}} , where t and $l$ are any positive integers. It implies that Cusick et al.’s conjecture, which claims that \sigma _{2^{t+1}l-1,2^{t}} is the only nonlinear balanced elementary symmetric Boolean functions, is equivalent to the conjecture that all the balanced elementary symmetric Boolean functions are trivially balanced. [ABSTRACT FROM PUBLISHER]

Driven by real-life applications in geo-social networks, we study the problem of computing radius-bounded $k$ k -cores (RB- $k$ k -cores) that aims to find communities satisfying both social and spatial constraints. In particular, the model $k$ k -core (i.e., the subgraph where each vertex has at least $k$ k neighbors) is used to ensure the social cohesiveness, and a radius-bounded circle is used to restrict the locations of users in an RB- $k$ k -core. We explore several algorithmic paradigms to compute RB- $k$ k -cores, including a triple-vertex-based paradigm, a binary-vertex-based paradigm, and a paradigm utilizing the concept of rotating circles. The rotating-circle-based paradigm is further enhanced by several pruning techniques to achieve better efficiency. In addition, to find representative RB- $k$ k -cores, we study the diversified radius-bounded $k$ k -core search problem, which finds $t$ t RB- $k$ k -cores to cover the most number of vertices. We first propose a baseline algorithm that identifies the distinctive RB- $k$ k -cores after finding all the RB- $k$ k -cores. Beyond this, we design algorithms that can efficiently maintain the top- $t$ t candidate RB- $k$ k -cores and also achieve a guaranteed approximation ratio. Experimental studies on both real and synthetic datasets demonstrate that our proposed techniques can efficiently compute (diversified) RB- $k$ k -cores. Moreover, our techniques can be used to compute the minimum-circle-bounded $k$ k -core and significantly outperform the existing techniques. [ABSTRACT FROM AUTHOR]

This paper reports on the implementation of a learner identification system developed to facilitate real-time interaction in an international distance course. To increase the teacher immediacy and social presence, it proposes an integrative system for incorporating technology to improve the learning involvement, teaching effectiveness, and quality of interaction in the context of distance education. After the system was implemented in a formal international distance course involving universities in Japan and Taiwan for a semester, the results of the formative evaluation suggested that a perceived integrative classroom environment was achieved. The instructors and learners agreed that the system effectively and seamlessly integrated synchronous and asynchronous learning through the computing components of face and speech recognition. It was also found that the improved interaction led to a better sense of learning community and improved performance. This study concludes that the system could be useful in improving interactions and reducing students’ feelings of isolation in distance courses by increasing their social presence. Suggestions for future studies and system development are also provided. [ABSTRACT FROM PUBLISHER]

This paper presents a study of perturbations of symmetric Boolean functions. In particular, it establishes a connection between exponential sums of these perturbations and Diophantine equations of the form \sum l=0^{n} \binom {n}{l} xl=0 , where xj belongs to some fixed bounded subset $\Gamma $ of \mathbb {Z} . The concepts of trivially balanced symmetric Boolean function and sporadic balanced Boolean function are extended to this type of perturbations. An observation made by Canteaut and Videau for symmetric Boolean functions of fixed degree is extended. To be specific, it is proved that, excluding the trivial cases, balanced perturbations of fixed degree do not exist when the number of variables grows. Some sporadic balanced perturbations are presented. Finally, a beautiful but unexpected identity between exponential sums for perturbations of two different symmetric Boolean functions is also included in this work. [ABSTRACT FROM AUTHOR]

Teaching problem: Students' written assignments show that they tend to list ideas rather than provide evidence-based arguments. This might be because they do not have a framework to base their arguments on. Research question: Does the communication model framework help students to write evidence-based arguments when evaluating the communicative effectiveness in corporate blogs? Situating the case: The ability to engage in argument from evidence is one of the Next Generation Science Standards for scientific and engineering practices. Thus, it is important for engineering students to know how to present evidence-based arguments. The communication model framework was introduced to provide students with a framework to base their arguments on. This framework builds on the genre-based and academic literacies approaches to teaching writing. More companies are now using corporate blogs (an open, participatory, and globally networked social media tool) to engage stakeholders directly across multiple contexts. The framework is useful in analyzing evolving genres like corporate blogs because it is not only structured but also flexible. About the case: This teaching case describes the use of the communication model framework as the basis for students' arguments. The framework was used in a general writing course for engineering students. Working in groups, the students used the framework for their oral practice critique and their critique assignment on a given piece of academic writing or corporate blog. They also had to write a reflection paper individually at the end of the course. Results: Overall, the mixed groups and international students groups made a stronger attempt to apply the framework compared to the Singaporean student groups. The students' educational backgrounds, the group dynamics within the group, and the nature of the discussions affected the level of adoption of the framework in their writing. Conclusions: This teaching case reflects the value of mixed group, face-to-face discussions, and personal reflection in teaching students evidence-based writing, and calls for more research on flexible frameworks as genres evolve. [ABSTRACT FROM PUBLISHER]

More and more women are participating in and exchanging opinions through community-based online social media. Questions concerning gender differences in the new media have been raised. This paper proposes a feature-based text classification framework to examine online gender differences between Web forum posters by analyzing writing styles and topics of interest. Our experiment on an Islamic women's political forum shows that feature sets containing both content-free and content-specific features perform significantly better than those consisting of only content-free features, feature selection can improve the classification results significantly, and female and male participants have significantly different topics of interest. [ABSTRACT FROM AUTHOR]

NAND flash memory is the major storage media for both mobile storage cards and enterprise Solid-State Drives (SSDs). Log-block-based Flash Translation Layer (FTL) schemes have been widely used to manage NAND flash memory storage systems in industry. In log-block-based FTLs, a few physical blocks called log blocks are used to hold all page updates from a large amount of data blocks. Frequent page updates in log blocks introduce big overhead so log blocks become the system bottleneck. To address this problem, this paper presents BLog, a block-level log-block management scheme for MLC NAND flash memory storage system. In BLog, with block-level management, the update pages of a data block can be collected together and put into the same log block as much as possible; therefore, we can effectively reduce the associativities of log blocks so as to reduce the garbage collection overhead. We also propose a novel partial merge operation strategy called reduced-order merge by which we can effectively postpone the garbage collection of log blocks so as to maximally utilize valid pages and reduce unnecessary erase operations in log blocks. Based on BLog, we design an FTL called BLogFTL for Multi-Level Cell (MLC) NAND flash. We conduct a set of experiments on a real hardware platform. Both representative FTL schemes and the proposed BLogFTL have been implemented in the hardware evaluation board. The experimental results show that our scheme can effectively reduce the garbage collection operations and reduce the system response time compared to the previous log-block-based FTLs for MLC NAND flash. [ABSTRACT FROM PUBLISHER]

The large-scale aggregation and analysis of user opinions is becoming increasingly relevant to a variety of applications, from detecting social mood on some political topics to tracking their sentiment changes related to events. The analysis of diverse sentiments is another important application, which becomes possible based on the ability of modern methods to capture sentiment polarity on various topics with high precision and on the ever-growing scale. Therefore, there is a need for a scalable way of sentiment aggregation with respect to the time dimension, which stores enough information to preserve diversity, and which allows statistically accurate analysis of sentiment trends and opinion shifts. In this paper, we are focusing on the novel problem of aggregating diverse sentiments at a large scale, based on data sources that are continuously updated. First, we develop a theoretical framework that models sentiment diversity (contradiction) and defines two types of contradictions, depending on the distribution of sentiments over time. Second, we introduce novel measures that capture sentiment diversity from aggregated sentiment statistics. Third, we develop robust and scalable indexing and storage methods for diverse sentiments. Finally, we propose an adaptive approach for identifying contradictions at different time scales. The experimental evaluation demonstrates the effectiveness of the proposed method of capturing contradictions and its superiority over relational databases in real-world scenarios. [ABSTRACT FROM AUTHOR]

Sentiment analysis of microblog texts has drawn lots of attention in both the academic and industrial fields. However, most of the current work only focuses on polarity classification. In this paper, we present an opinion mining system for Chinese microblogs called CMiner. Instead of polarity classification, CMiner focuses on more complicated opinion mining tasks - opinion target extraction and opinion summarization. Novel algorithms are developed for the two tasks and integrated into the end-to-end system. CMiner can help to effectively understand the users’ opinion towards different opinion targets in a microblog topic. Specially, we develop an unsupervised label propagation algorithm for opinion target extraction. The opinion targets of all messages in a topic are collectively extracted based on the assumption that similar messages may focus on similar opinion targets. In addition, we build an aspect-based opinion summarization framework for microblog topics. After getting the opinion targets of all the microblog messages in a topic, we cluster the opinion targets into several groups and extract representative targets and summaries for each group. A co-ranking algorithm is proposed to rank both the opinion targets and microblog sentences simultaneously. Experimental results on a benchmark dataset show the effectiveness of our system and the algorithms. [ABSTRACT FROM PUBLISHER]

The Residue Logarithmic Number System (RLNS) offers fast multiplication and division, but poses challenges for implementing addition and subtraction because the underlying integer Residue Number System (RNS) has slow sign detection. The conventional Binary Logarithmic Number Systems (BLNS) has benefited from interpolation and cotransformation. We propose a dual-path ALU that speculates about the sign detection to adapt interpolation and cotransformation to the limitations of RLNS. Synthesis shows for the same precision and technology, the area of the proposed RLNS circuit is similar to BLNS and much smaller than prior RLNS methods. We also compare against Floating Point (FP). [ABSTRACT FROM AUTHOR]

In this paper, we study a generalization of addition chains where $k$ previous values are summed together on each step instead of only two values as in traditional addition chains. Such chains are called $k$ -chains and we show that they have applications in finding efficient parallelizations in problems that are known to be difficult to parallelize. In particular, 3-chains improve computations of inversions in finite fields using hybrid-double multipliers. Recently, it was shown that this operation can be efficiently computed using a ternary algorithm but we show that 3-chains provide a significantly more efficient solution. [ABSTRACT FROM PUBLISHER]

Despite the extensive application of Monte Carlo analytic hierarchy process (MCAHP) in various fields of decision making, its performance has not been compared with the classic analytic hierarchy process (AHP). Both of these methods are heavily affected by individual or group preferences and thus provide subjective rankings. Since the mere difference between their results does not necessarily warrant the superiority of one against the other, a reliable and robust ranking of alternatives should be available as a comparison basis so that the results of these two methods can be evaluated. In this paper, we use a simulation approach to compare the results of AHP with MCAHP under different levels of uncertainty. We validate our simulation results by comparing the performance of these two alternatives against a real world and reliable ranking of blogs. Our simulation results show that as long as the variation in different pairwise comparisons is less than 0.24, the performance of AHP is not statistically different from the performance of MCAHP. When the uncertainty in terms of variation grows beyond 0.24, MCAHP provides more precise rankings. The findings of this research add to the current body of knowledge in the multicriteria decision analysis as well as Information Systems literature and provide insights for managerial applications of these techniques. [ABSTRACT FROM PUBLISHER]

System testing of software applications with a graphical-user interface (GUI) front-end requires that sequences of GUI events, that sample the application’s input space, be generated and executed as test cases on the GUI. However, the context-sensitive behavior of the GUI of most of today’s non-trivial software applications makes it practically impossible to fully determine the software’s input space. Consequently, GUI testers—both automated and manual—working with undetermined input spaces are, in some sense, blindly navigating the GUI, unknowingly missing allowable event sequences, and failing to realize that the GUI implementation may allow the execution of some disallowed sequences. In this paper, we develop a new paradigm for GUI testing, one that we call Observe-Model-Exercise* (OME*) to tackle the challenges of testing context-sensitive GUIs with undetermined input spaces. Starting with an incomplete model of the GUI’s input space, a set of coverage elements to test, and test cases, OME* iteratively observes the existence of new events during execution of the test cases, expands the model of the GUI’s input space, computes new coverage elements, and obtains new test cases to exercise the new elements. Our experiment with 8 open-source software subjects, more than 500,000 test cases running for almost 1,100 machine-days, shows that OME* is able to expand the test space on average by 464.11 percent; it detected 34 faults that had never been detected before. [ABSTRACT FROM PUBLISHER]

We present some upper bounds on the size of nonlinear codes and their restriction to systematic codes and linear codes. These bounds are independent of other known theoretical bounds, e.g., the Griesmer bound, the Johnson bound, the Plotkin bound, and of linear programming bounds. One of the new bound is actually an improvement of a bound by Zinoviev, Litsyn, and Laihonen. Our experiments show that in the linear case our bounds provide the best value in a wide range, compared with all other closed-formula upper bounds. In the nonlinear case, we also compare our bound with the linear programming bound and with some improvements on it, show that there are cases where we beat these bounds. In particular, we obtain a new bound in Brouwer's table for A3(16,3). [ABSTRACT FROM AUTHOR]

Social tagging in online communities has become an important method for reflecting classified thoughts of individual users. A number of social Web sites provide tagging functionalities and also offer folksonomies within or across the sites. However, it is practically not easy to find users' interests based on such folksonomies. In this paper, we provide a novel approach for clustering user-centric interests by analyzing tagging practices of individual users. To do this, we collect Really Simple Syndication data from blogosphere, find conceptual clusters using formal concept analysis, and then evaluate the significance of these clusters. The results of the empirical evaluation show that we can effectively recommend different collections of tags to an individual or a set of users. [ABSTRACT FROM AUTHOR]

In the increasingly competitive global economy, corporations throughout the world must take advantage of all the marketing and communication tools available to them, including blogging. Blogs allow corporations to connect with their stakeholders in a more personal way and, thus, strengthen their image, brand, and customer loyalty. Instant feedback is available through comments posted on the corporate blog, saving organizations large sums of money otherwise spent on market research. However, entering the blogosphere poses a number of risks for a corporation, such as potential damage to the corporate reputation and customer loyalty as well as legal liability. Conflicts still exist between the rights of bloggers and a corporation's interests. Blogs may be restricted by legal and ethical boundaries, which may differ across countries. This paper presents the benefits and risks associated with corporate blogging around the world and provides some interesting success stories as well as lessons learned. It also offers a compilation of guidelines for effective blogging and suggests topics for future research. [ABSTRACT FROM AUTHOR]

Web logs, or "blogs," are fast developing in diverse social and business contexts as influential sources of discourse, knowledge, and community development. In this paper, we investigate an aspect of blogging highly relevant to professional communication: the fast-developing world of "tech blogging." Tech blogs are blogs that focus on information technology innovation and the high-tech industry. We examine nine months of blog entries gathered by an internet aggregator site dedicated to technology news and commentary. Our analysis provides insights on the discourse of tech bloggers and an elite subgroup ("A-list bloggers"), on the discursive practices of this virtual community and on issues of identity and legitimacy. Our findings hold implications for tech bloggers as well as for managers who need to navigate the expanding blogosphere and for technical communicators who can benefit from using the information that tech bloggers produce. [ABSTRACT FROM AUTHOR]

Evaluation is prevalent in real life. With the advent of Web 2.0, online evaluation has become an important feature in many applications that involve information (e.g., video, photo, and audio) sharing and social networking (e.g., blogging). In these evaluation settings, a set of reviewers assign scores to a set of objects. As part of the evaluation analysis, we want to obtain fair reviews for all the given objects. However, the reality is that reviewers may deviate in their scores assigned to the same object, due to the potential "bias" of reviewers or "controversy" of objects. The statistical approach of averaging deviations to determine bias and controversy assumes that all reviewers and objects should be given equal weight. In this paper, we look beyond this assumption and propose an approach based on the following observations: 1) evaluation is "subjective," as reviewers and objects have varying bias and controversy, respectively, and 2) bias and controversy are mutually dependent. These observations underlie our proposed reinforcement-based model to determine bias and controversy simultaneously. Our approach also quantifies "evidence," which reveals the degree of confidence with which bias and controversy have been derived. This model is shown to be effective by experiments on real-life and synthetic data sets. [ABSTRACT FROM AUTHOR]

Researchers have recently discovered several interesting, self-organized regularities from the World Wide Web, ranging from the structure and growth of the Web to the access patterns in Web surfing. What remains to be a great challenge in Web log mining is how to explain user behavior underlying observed Web usage regularities. In this paper, we will address the issue of how to characterize the strong regularities in Web surfing in terms of user navigation strategies, and present an information foraging agent- based approach to describing user behavior. By experimenting with the agent-based decision models of Web surfing, we aim to explain how some Web design factors as well as user cognitive factors may affect the overall behavioral patterns in Web usage. [ABSTRACT FROM AUTHOR]

Mining social media messages such as tweets, blogs, and Facebook posts for health and drug related information has received significant interest in pharmacovigilance research. Social media sites (e.g., Twitter), have been used for monitoring drug abuse, adverse reactions to drug usage, and analyzing expression of sentiments related to drugs. Most of these studies are based on aggregated results from a large population rather than specific sets of individuals. In order to conduct studies at an individual level or specific groups of people, identifying posts mentioning intake of medicine by the user is necessary. Toward this objective we develop a classifier for identifying mentions of personal intake of medicine in tweets. We train a stacked ensemble of shallow convolutional neural network (CNN) models on an annotated dataset. We use random search for tuning the hyper-parameters of the CNN models and present an ensemble of best models for the prediction task. Our system produces state-of-the-art results, with a micro-averaged F-score of 0.693. We believe that the developed classifier has direct uses in the areas of psychology, health informatics, pharmacovigilance, and affective computing for tracking moods, emotions, and sentiments of patients expressing intake of medicine in social media. [ABSTRACT FROM AUTHOR]

Background: Building a reputable network on Twitter is viewed as impactful in several scholarly disciplines, but little is known about the professional and interdisciplinary human-computer interaction (HCI) community. This study combined two approaches from scholarly communication and technical communication to capture the static and dynamic features of the HCI scholar Twitter network. Literature review: Related studies that described the scholarly reputation built through Twitter and social networking in the field of HCI were reviewed and discussed. Research questions: 1. In which countries are HCI scholars more likely to follow their peers in the same country? 2. What are the characteristics (country, reputation) and actions (reciprocity) of HCI scholars who are more likely to build HCI scholarly network profiles on Twitter? Research methodology: The network analysis method of the exponential random graph model (ERGM) was adopted to trace and visualize current follower networks on Twitter. Results and discussion: We found that 22.9% of HCI scholars use Twitter and that reciprocity and country of current employment best drive the Twitter connections of scholars. Characteristics of HCI scholars’ tie formation online are also illustrated and discussed. Implications for practice: This study contributes to field studies of professional networks by identifying the structural properties and factors that influence scholars’ search for professional development on Twitter. The empirical findings should be a helpful reference for HCI professional societies and individual scholars in operating online professional networks. [ABSTRACT FROM AUTHOR]

Contribution: This work contributes to computer-supported collaborative learning (CSCL) offering support to the teaching presence among the enormous volume of interactions to provide a more active classroom posture in online environments. Background: Tracking interactions on collaborative learning environments (CLEs) with an increasing number of collaborative tools, offering real-time support for students, is one of the most intense and exhaustive challenges online teachers face. In this work context, social presence indicates a level of awareness of the other in the CLE, and it is necessary to improve and promote online social interactions. Research Questions: Aiming to answer the research question if the support of the teaching presence through Amadeus-SIMM helps to promote the learners’ social presence in a distance learning course, an experiment was conducted through a Python Programming distance course with Computer Science and Electrical Engineering learners. Methodology: The research methods adopted were a survey and the reports generated by Amadeus-SIMM. The statistical models used were the difference between means calculated in two moments on the same sample and the chi-square test with two criteria. Findings: The conclusion is that the significant differences in six of the eight survey-independent questions related to the dependent question indicate an increase in learners’ perception of social presence. The statistical analysis verified that the difference between the sample means in the two measurements of social behavior visibility, engagement, information intermediation, and prestige is statistically significant to confirm that Amadeus-SIMM supports teachers in promoting learners’ social presence. [ABSTRACT FROM AUTHOR]

In social networks, one of the most significant challenges is how to estimate the tweet popularity. Prior studies focus on leveraging different aspects of just a single tweet, while ignoring the impact of historical tweets. In this article, we propose to leverage such historical information and rethink the problem of tweet popularity estimation. From historical information, there are two important factors that can be extracted: (1) user reputation feature, which can represent coarse-grained level of tweet popularity and (2) tweet related features, which can represent fine-grained level of tweet popularity. To incorporate these two factors from historical information, we design a novel deep neural architecture, a Bi-layered LSTM for tweet popUlarity Estimation, called DeepBlue. Specifically, we first propose a user-reputation aware mechanism to combine coarse-grained and fine-grained level estimation into a united LSTM model. We also design a content attention mechanism to consider different impacts of historical tweets in terms of content similarity. We then propose a time aware mechanism to address the time interval irregularity issue. Finally, we apply the Poisson regression model to obtain the overall loss for tweet popularity estimation. Extensive experiments demonstrate the superiority of our proposed approach to other state-of-the-arts in terms of MAE and SRC. [ABSTRACT FROM AUTHOR]

In a shared-memory multicore system, the intrinsic irregular data structure of graphs leads to poor cache utilization, and therefore deteriorates the performance of graph analytics. To address the problem, prior works have proposed a variety of lightweight reordering methods with focus on the optimization of cache locality. However, there is a compromise between cache locality and workload balance. Little insight has been devoted into the issue of workload imbalance for the underlying multicore system, which degrades the effectiveness of parallel graph processing. In this work, a measurement approach is proposed to quantify the imbalance incurred by the concentration of vertices. Inspired by it, we present Cache-aware Reorder (Corder), a lightweight reordering method exploiting the cache hierarchy of multicore systems. At the shared-memory level, Corder promotes even distribution of computation loads amongst multicores. At the private-cache level, Corder facilitates cache efficiency by applying further refinement to local vertex order. Comprehensive performance evaluation of Corder is conducted on various graph applications and datasets. Experimental results show that Corder yields speedup of up to $2.59\times$ 2. 59 × and on average $1.45\times$ 1. 45 × , which significantly outperforms existing lightweight reordering methods. To identify the root causes of performance boost delivered by Corder, multicore activities are investigated in terms of thread behavior, cache efficiency, and memory utilization. Statistical analysis demonstrates that the issue of imbalanced thread execution time dominates other factors in determining the overall graph processing time. Moreover, Corder achieves remarkable advantages in cross-platform scalability and reordering overhead. [ABSTRACT FROM AUTHOR]

External search for knowledge and foresight have become strategically important activities for firms in an increasingly uncertain and complex business environment. Novel methods to monitor development are therefore essential for both firms and scholars. This article illustrates how firms can apply one such novel method called Social Media Analytics, a multiplatform approach incorporating multiple external sources drawn from Web 2.0, that enable external search for knowledge but simultaneously avoid information overload. To illustrate the potential of the method, this article draws upon a dataset spanning 36 months from August 2016 to August 2019 and 100 283 publicly posted user-generated contents concerning Tesla to analyze their autopilot and the controversies surrounding autonomous driving. The results show that indications of the regulatory scrutiny Tesla's driverless technology faced in 2019 could be seen in the data across several platforms at an early point and that these signals became stronger over time, especially on blogs and Facebook which exhibited strong indications of future regulatory scrutiny in contrast to Twitter and Instagram. Our results underscore the potential of the Social Media Analytics for external search for knowledge and open foresight that enable firms to tune in to weak signals and scan the periphery. [ABSTRACT FROM AUTHOR]

This article explains the main mechanisms influencing the knowledge collaboration dynamics occurring across an online business community. Prior research study had conceived firms as autonomous entities competing against each other to reach the competitive advantage. Nonetheless, online business social networks (BSNs) can be seen as networks of organizations that interact within digital structures, providing an optimal locus to foster external knowledge flows, enabling participants to share knowledge across organizational boundaries, and eventually leading to open innovation outcomes. We analyze how virtual networks of business relationships influence the form of knowledge collaboration firms can be engaged in. We analyze the entire three-year collaborative history of a BSN. After a promising launch of the BSN, it declined and failed. By analyzing front- and backstage combined with organizational and technological features of the online business community, we identify and discuss five categories of mechanisms affecting the knowledge collaboration over time and reducing the firms’ uncertainty in exchanging information: first, orchestrators’ skills and competencies in running the online platform; second, internal firm capabilities allocating proper organizational resources; third, usability of the digital platform; four, interconnections between frontstage and backstage combined with technological and organizational features; and fifth, perceived virtual copresence. [ABSTRACT FROM AUTHOR]

Twitter has received much attention recently. An important characteristic of Twitter is its real-time nature. We investigate the real-time interaction of events such as earthquakes in Twitter and propose an algorithm to monitor tweets and to detect a target event. To detect a target event, we devise a classifier of tweets based on features such as the keywords in a tweet, the number of words, and their context. Subsequently, we produce a probabilistic spatiotemporal model for the target event that can find the center of the event location. We regard each Twitter user as a sensor and apply particle filtering, which are widely used for location estimation. The particle filter works better than other comparable methods for estimating the locations of target events. As an application, we develop an earthquake reporting system for use in Japan. Because of the numerous earthquakes and the large number of Twitter users throughout the country, we can detect an earthquake with high probability (93 percent of earthquakes of Japan Meteorological Agency (JMA) seismic intensity scale 3 or more are detected) merely by monitoring tweets. Our system detects earthquakes promptly and notification is delivered much faster than JMA broadcast announcements. [ABSTRACT FROM AUTHOR]

We present a Monte Carlo approach for epipolar geometry estimation that efficiently searches for minimal sets of inlier correspondences in the presence of many outliers in the putative correspondence set, a condition that is prevalent when we have wide baselines, significant scale changes, rotations in depth, occlusion, and repeated patterns. The proposed Monte Carlo algorithm uses Balanced LOcal and Global Search (BLOGS) to find the best minimal set of correspondences. The local search is a diffusion process using Joint Feature Distributions that captures the dependencies among the correspondences. And, the global search is a hopping search process across the minimal set space controlled by photometric properties. Using a novel experimental protocol that involves computing errors for manually marked ground truth points and images with outlier rates as high as 90 percent, we find that BLOGS is better than related approaches such as MAPSAC [1], NAPSAC [2], and BEEM [3]. BLOGS results are of similar quality as other approaches, but BLOGS generate them in 10 times fewer iterations. The time per iteration for BLOGS is also the lowest among the ones we studied. [ABSTRACT FROM AUTHOR]