Query: A multi-level encoder for text summarization

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Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond <=SEP=> ive Text Summarization using Sequence-to-sequence RNNs and Beyond Ramesh Nallapati IBM Watson nallapati@us.ibm.com Bowen Zhou IBM Watson zhou@us.ibm.com Cicero dos Santos IBM Watson

Language as a Latent Variable: Discrete Generative Models for Sentence Compression <=SEP=> In this work we explore deep generative models of text in which the latent representation of a document is itself drawn from a discrete language model distribution. We formulate a variational auto-encoder for inference in this model and apply it to the task of compressing sentences. In this application the generative model first draws a latent summary sentence from a background language model, and then subsequently draws the observed sentence conditioned on this latent summary. In our empirical evaluation we show that generative formulations of both abstractive and extractive compression yield state-of-the-art results when trained on a large amount of supervised data. Further, we explore semi-supervised compression scenarios where we show that it is possible to achieve performance competitive with previously proposed supervised models while training on a fraction of the supervised data.

Abstractive Sentence Summarization with Attentive Recurrent Neural Networks <=SEP=> Abstractive Sentence Summarization generates a shorter version of a given sentence while attempting to preserve its meaning. We introduce a conditional recurrent neural network (RNN) which generates a summary of an input sentence. The conditioning is provided by a novel convolutional attention-based encoder which ensures that the decoder focuses on the appropriate input words at each step of generation. Our model relies only on learned features and is easy to train in an end-to-end fashion on large data sets. Our experiments show that the model significantly outperforms the recently proposed state-of-the-art method on the Gigaword corpus while performing competitively on the DUC-2004 shared task.ive Sentence Summarization generates a shorter version of a given sentence while attempting to preserve its meaning. We introduce a conditional recurrent neural network (RNN) which generates a summary of an input sentence. The conditioning is provided by a novel convolutional attention-based encoder which ensures that the decoder focuses on the appropriate input words at each step of generation. Our model relies only on learned features and is easy to train in an end-to-end fashion on large data sets. Our experiments show that the model significantly outperforms the recently proposed state-of-the-art method on the Gigaword corpus while performing competitively on the DUC-2004 shared task.

Selective Encoding for Abstractive Sentence Summarization <=SEP=> We propose a selective encoding model to extend the sequence-to-sequence framework for abstractive sentence summarization. It consists of a sentence encoder, a selective gate network, and an attention equipped decoder. The sentence encoder and decoder are built with recurrent neural networks. The selective gate network constructs a second level sentence representation by controlling the information flow from encoder to decoder. The second level representation is tailored for sentence summarization task, which leads to better performance. We evaluate our model on the English Gigaword, DUC 2004 and MSR abstractive sentence summarization datasets. The experimental results show that the proposed selective encoding model outperforms the state-ofthe-art baseline models.

Recurrent Neural Network Regularization <=SEP=> We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs. In this paper, we show how to correctly apply dropout to LSTMs, and show that it substantially reduces overfitting on a variety of tasks. These tasks include language modeling, speech recognition, and machine translation.

Simulating crowd movement in agent-based model of large-scale flood <=SEP=> Crowd movement during natural disasters and major accidents can affect the success of evacuation procedure. Thus to develop an effective evacuation plan, a wide-range of scenarios causing different routes and patterns of crowd movement should be considered. Recent researches in agent-based simulation have achieved techniques to simulate crowd movement in emergency scenarios at city scale. However simulating crowd movement at a macroscopic level for disasters which might affect several cities, like large-scale flood, is still a challenge. This paper addresses this issue and makes three contributions. First, the development of an agent-based layered model to simulate large-scale flood using GIS is demonstrated. Second, the simulation of crowd agents' movement on available roads is presented. Third, the preliminary experiments running on private Cloud server is reported. The experiments cover case studies illustrated the movement of crowd agents around Thailand while several parts of the country were inundated. The 2D animation depict the movement of crowd; and the simulation results show the status of the agents and the amount of individuals which required shelters. To simulate one day events, the simulator took 4-9 hours execution time depending on the severity of floods and available facilities.

Secure routing for internet of things: A survey <=SEP=> The Internet of Things (IoT) could be described as the pervasive and global network which aids and provides a system for the monitoring and control of the physical world through the collection, processing and analysis of generated data by IoT sensor devices. It is projected that by 2020 the number of connected devices is estimated to grow exponentially to 50 billion. The main drivers for this growth are our everyday devices such as cars, refrigerators, fans, lights, mobile phones and other operational technologies including the manufacturing infrastructures which are now becoming connected systems across the world. It is apparent that security will pose a fundamental enabling factor for the successful deployment and use of most IoT applications and in particular secure routing among IoT sensor nodes thus, mechanisms need to be designed to provide secure routing communications for devices enabled by the IoT technology. This survey analyzes existing routing protocols and mechanisms to secure routing communications in IoT, as well as the open research issues. We further analyze how existing approaches ensure secure routing in IoT, their weaknesses, threats to secure routing in IoT and the open challenges and strategies for future research work for a better secure IoT routing.

Practical Identity-Based Encryption Without Random Oracles <=SEP=> We present an Identity Based Encryption (IBE) system that is fully secure in the standard model and has several advantages over previous such systems – namely, computational efficiency, shorter public parameters, and a “tight” security reduction, albeit to a stronger assumption that depends on the number of private key generation queries made by the adversary. Our assumption is a variant of Boneh et al.’s decisional Bilinear Diffie-Hellman Exponent assumption, which has been used to construct efficient hierarchical IBE and broadcast encryption systems. The construction is remarkably simple. It also provides recipient anonymity automatically, providing a second (and more efficient) solution to the problem of achieving anonymous IBE without random oracles. Finally, our proof of CCA2 security, which has more in common with the security proof for the Cramer-Shoup encryption scheme than with security proofs for other IBE systems, may be of independent interest.

Empirical Evaluation of User Experience in two Adaptive Mobile Application Prototypes <=SEP=> Today’s applications such as ubiquitous systems are more and more aware of user’s habits and the context of use. The features of products and the context of use will affect the human’s experiences and preferences about the use of device. Thus, user experience in user-product interaction has been regarded as an important research topic in the mobile application design area. The purpose of this paper is to clarify how user experience can be evaluated in adaptive mobile applications. The user experience evaluations were performed through interviews and observation while test users were using PDA-based adaptive mobile application prototypes. As a result, this paper presents the analysis of the test methods for further user experience evaluations. CR Categories: J.m [Computer Applications]: Miscellaneous; Experimentation; Human Factors

Changes in adult attachment styles in American college students over time: a meta-analysis. <=SEP=> The current article examines changes over time in a commonly used measure of adult attachment style. A cross-temporal meta-analysis was conducted on 94 samples of American college students (total N = 25,243, between 1988 and 2011) who chose the most representative description of four possible attachment styles (Secure, Dismissing, Preoccupied, and Fearful) on the Relationship Questionnaire. The percentage of students with Secure attachment styles has decreased in recent years (1988: 48.98%; 2011: 41.62%), whereas the percentage of students with Insecure attachment styles (sum of Dismissing, Preoccupied, Fearful) has increased in recent years (1988: 51.02%; 2011: 58.38%). The percentage of students with Dismissing attachment styles has increased over time (1988: 11.93%; 2011: 18.62%), even after controlling for age, gender, race, and publication status. Positive views of others have declined across the same time period. We discuss possible implications and explanations for these changes.

Zero moment point-measurements from a human walker wearing robot feet as shoes <=SEP=> The anthropomorphic biped robot Bip is equipped with sensors for measuring the ground/feet forces in order to localize the center of pressure (CoP) and zero moment point (ZMP). This paper focuses on experimental results regarding the evolution of the ground contact forces, obtained from a human walker wearing the robot feet as shoes. First, one determines the influence of heavy and rigid metallic shoes on the gait of the subject, during experiments carried out on flat ground. Second, the evolution of the contact forces is studied while walking on parallel planes with different elevations (stairs), then one addresses the case where the feet are supported by two nonparallel planes (uneven terrain). The corresponding analysis is founded on the concepts of virtual supporting surface and pseudo-CoP-ZMP introduced in a companion paper, discussing the theoretical aspects of CoP and ZMP (Sardain and Bessonnet, 2004). Beyond the academic contribution, the data analyzed in this paper could constitute an incitement to design truly anthropomorphic feet, for Bip as well as for other biped robots, because all the current robot feet are functionally rather poor.

Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms <=SEP=> Find the secret to improve the quality of life by reading this evolutionary algorithms in theory and practice evolution strategies evolutionary programming genetic algorithms. This is a kind of book that you need now. Besides, it can be your favorite book to read after having this book. Do you ask why? Well, this is a book that has different characteristic with others. You may not need to know who the author is, how well-known the work is. As wise word, never judge the words from who speaks, but make the words as your good value to your life.

Genetic Evidence That Carbohydrate-Stimulated Insulin Secretion Leads to Obesity. <=SEP=> BACKGROUND
A fundamental precept of the carbohydrate-insulin model of obesity is that insulin secretion drives weight gain. However, fasting hyperinsulinemia can also be driven by obesity-induced insulin resistance. We used genetic variation to isolate and estimate the potentially causal effect of insulin secretion on body weight.


METHODS
Genetic instruments of variation of insulin secretion [assessed as insulin concentration 30 min after oral glucose (insulin-30)] were used to estimate the causal relationship between increased insulin secretion and body mass index (BMI), using bidirectional Mendelian randomization analysis of genome-wide association studies. Data sources included summary results from the largest published metaanalyses of predominantly European ancestry for insulin secretion (n = 26037) and BMI (n = 322154), as well as individual-level data from the UK Biobank (n = 138541). Data from the Cardiology and Metabolic Patient Cohort study at Massachusetts General Hospital (n = 1675) were used to validate genetic associations with insulin secretion and to test the observational association of insulin secretion and BMI.


RESULTS
Higher genetically determined insulin-30 was strongly associated with higher BMI (β = 0.098, P = 2.2 × 10-21), consistent with a causal role in obesity. Similar positive associations were noted in sensitivity analyses using other genetic variants as instrumental variables. By contrast, higher genetically determined BMI was not associated with insulin-30.


CONCLUSIONS
Mendelian randomization analyses provide evidence for a causal relationship of glucose-stimulated insulin secretion on body weight, consistent with the carbohydrate-insulin model of obesity.

Quality Metrics in optical modulation analysis: EVM and its relation to Q-factor, OSNR, and BER <=SEP=> The quality of optical signals is a very important parameter in optical communications. Several metrics are in common use, like optical signal-to-noise power ratio (OSNR), Q-factor, error vector magnitude (EVM) and bit error ratio (BER). A measured raw BER is not necessarily useful to predict the final BER after soft-decision forward error correction (FEC), if the statistics of the noise leading to errors is unknown. In this respect the EVM is superior, as it allows an estimation of the error statistics. We compare various metrics analytically, by simulation, and through experiments. We employ six quadrature amplitude modulation (QAM) formats at symbol rates of 20 GBd and 25 GBd. The signals were generated by a software-defined transmitter. We conclude that for optical channels with additive Gaussian noise the EVM metric is a reliable quality measure. For nondata-aided QAM reception, BER in the range 10-6...10-2 can be reliably estimated from measured EVM.

Deep Marching Cubes: Learning Explicit Surface Representations <=SEP=> Existing learning based solutions to 3D surface prediction cannot be trained end-to-end as they operate on intermediate representations (e.g., TSDF) from which 3D surface meshes must be extracted in a post-processing step (e.g., via the marching cubes algorithm). In this paper, we investigate the problem of end-to-end 3D surface prediction. We first demonstrate that the marching cubes algorithm is not differentiable and propose an alternative differentiable formulation which we insert as a final layer into a 3D convolutional neural network. We further propose a set of loss functions which allow for training our model with sparse point supervision. Our experiments demonstrate that the model allows for predicting sub-voxel accurate 3D shapes of arbitrary topology. Additionally, it learns to complete shapes and to separate an object's inside from its outside even in the presence of sparse and incomplete ground truth. We investigate the benefits of our approach on the task of inferring shapes from 3D point clouds. Our model is flexible and can be combined with a variety of shape encoder and shape inference techniques.

Causal Generative Domain Adaptation Networks <=SEP=> An essential problem in domain adaptation is to understand and make use of distribution changes across domains. For this purpose, we first propose a flexible Generative Domain Adaptation Network (G-DAN) with specific latent variables to capture changes in the generating process of features across domains. By explicitly modeling the changes, one can even generate data in new domains using the generating process with new values for the latent variables in G-DAN. In practice, the process to generate all features together may involve high-dimensional latent variables, requiring dealing with distributions in high dimensions and making it difficult to learn domain changes from few source domains. Interestingly, by further making use of the causal representation of joint distributions, we then decompose the joint distribution into separate modules, each of which involves different low-dimensional latent variables and can be learned separately, leading to a Causal G-DAN (CG-DAN). This improves both statistical and computational efficiency of the learning procedure. Finally, by matching the feature distribution in the target domain, we can recover the target-domain joint distribution and derive the learning machine for the target domain. We demonstrate the efficacy of both G-DAN and CG-DAN in domain generation and cross-domain prediction on both synthetic and real data experiments.

Stakeholder engagement, social auditing and corporate sustainability <=SEP=> Purpose – To identify the applicability of social auditing as an approach of engaging stakeholders in assessing and reporting on corporate sustainability and its performance. Design/methodology/approach – Drawing upon the framework of AA1000 and the social auditing studies, this paper links stakeholder engagement, social auditing and corporate sustainability with a view to applying dialogue-based social auditing to address corporate sustainability. Findings – This paper identifies a “match” between corporate sustainability and social auditing, as both aim at improving the social, environmental and economic performance of an organisation, considering the well-being of a wider range of stakeholders and requiring the engagement of stakeholders in the process. This paper suggests that social auditing through engaging stakeholders via dialogue could be applied to build trusts, identify commitment and promote co-operation amongst stakeholders and corporations. Research limitations/implications – This research requires further empirical research into the practicality of social auditing in addressing corporate sustainability and the determination of the limitations of dialogue-based social auditing. Practical implications – Social auditing has been identified as a useful mechanism of balancing differing interests among stakeholders and corporations in a democratic business society. The application of social auditing in developing and achieving corporate sustainability has apparently practical implications. Originality/value – This paper examines the applicability of dialogue-based social auditing in helping business to move towards sustainability. Social auditing as a process of assessing and reporting on corporate social and environmental performance through engaging stakeholders via dialogue could be applied to build trusts, identify commitment and promote cooperation amongst stakeholders and corporations.

Migrating code with statistical machine translation <=SEP=> In the era of mobile computing, developers often need to migrate code written for one platform in a programming language to another language for a different platform, e.g., from Java for Android to C# for Windows Phone. The migration process is often performed manually or semi-automatically, in which developers are required to manually define translation rules and API mappings. This paper presents semSMT, an automatic tool to migrate code written in Java to C#. semSMT utilizes statistical machine translation to automatically infer translation rules from existing migrated code, thus, requires no manual defining of rules. The video demonstration on semSMT can be found on YouTube at http://www.youtube.com/watch?v=aRSnl5-7vNo.

A Faster and More Realistic Flush+Reload Attack on AES <=SEP=> Cloud’s unrivaled cost effectiveness and on the fly operation versatility is attractive to enterprise and personal users. However, the cloud inherits a dangerous behavior from virtualization systems that poses a serious security risk: resource sharing. This work exploits a shared resource optimization technique called memory deduplication to mount a powerful known-ciphertext only cache side-channel attack on a popular OpenSSL implementation of AES. In contrast to the other cross-VM cache attacks, our attack does not require synchronization with the target server and is fully asynchronous, working in a more realistic scenario with much weaker assumption. Also, our attack succeeds in just 15 seconds working across cores in the cross-VM setting. Our results show that there is strong information leakage through cache in virtualized systems and the memory deduplication should be approached with caution.

Enhanced ARP: preventing ARP poisoning-based man-in-the-middle attacks <=SEP=> In this letter, an enhanced version of Address Resolution Protocol (ARP) is proposed to prevent ARP poisoning-based Man-in-the-Middle (MITM) attacks. The proposed mechanism is based on the following concept. When a node knows the correct Media Access Control (MAC) address for a given IP address, if it retains the IP/MAC address mapping while that machine is alive, then MITM attack is impossible for that IP address. In order to prevent MITM attacks even for a new IP address, a voting-based resolution mechanism is proposed. The proposed scheme is backward compatible with existing ARP and incrementally deployable.

Knowledge sharing behavior of physicians in hospitals <=SEP=> Sharing knowledge of Physicians within hospitals can realize potential gains enormously and is critical to be successful and survive in competitive environments. There is a need for empirical research to identify the factors that determine physician's behavior to share knowledge. This study investigates the factors that determine the physician's individual knowledge sharing behavior in his/her department. The purpose of this study is to examine empirically the physicians' knowledge sharing behavior The research models under investigation are the Theory of Reasoned Action (TRA) model and the Theory of Planned Behavior (TPBI model. These models are empirically examined and compared, using the survey results on physicians' knowledge sharing behavior collected from 286 physicians practicing in 28 departments in 13 tertiary hospitals in Korea. TPB model exhibited a good fit with the data and appeared to be superior to TRA in explaining physicians' intentions to share knowledge. Amended TPB model provided an important improvement In fit over that of original TPB model. In amended TPB model, subjective norms were found to have the strongest total effects on behavioral intentions to share knowledge of physicians through direct and indirect path by attitude. Attitude was found to be the second important factor influencing physicians' intentions. Perceived behavioral control was also found to have effect on the intentions to share knowledge though it was weaker than that of subjective norms or attitude. The implications for physician's knowledge sharing activities are discussed

Time-Varying Graphs and Social Network Analysis: Temporal Indicators and Metrics <=SEP=> Most instruments formalisms, concepts, and metrics for social networks analysis fail to capture their dynamics. Typical systems exhibit different scales of dynamics, ranging from the fine-grain dynamics of interactions (which recently led researchers to consider temporal versions of distance, connectivity, and related indicators), to the evolution of network properties over longer periods of time. This paper proposes a general approach to study that evolution for both atemporal and temporal indicators, based respectively on sequences of static graphs and sequences of time-varying graphs that cover successive time-windows. All the concepts and indicators, some of which are new, are expressed using a time-varying graph formalism recently proposed in [10].

Speech Synthesis for Mixed-Language Navigation Instructions <=SEP=> Text-to-Speech (TTS) systems that can read navigation instructions are one of the most widely used speech interfaces today. Text in the navigation domain may contain named entities such as location names that are not in the language that the TTS database is recorded in. Moreover, named entities can be compound words where individual lexical items belong to different languages. These named entities may be transliterated into the script that the TTS system is trained on. This may result in incorrect pronunciation rules being used for such words. We describe experiments to extend our previous work in generating code-mixed speech to synthesize navigation instructions, with a mixed-lingual TTS system. We conduct subjective listening tests with two sets of users, one being students who are native speakers of an Indian language and very proficient in English, and the other being drivers with low English literacy, but familiarity with location names. We find that in both sets of users, there is a significant preference for our proposed system over a baseline system that synthesizes instructions in English.

Deep-learning: A potential method for tuberculosis detection using chest radiography <=SEP=> Tuberculosis (TB) is a major health threat in the developing countries. Many patients die every year due to lack of treatment and error in diagnosis. Developing a computer-aided diagnosis (CAD) system for TB detection can help in early diagnosis and containing the disease. Most of the current CAD systems use handcrafted features, however, lately there is a shift towards deep-learning-based automatic feature extractors. In this paper, we present a potential method for tuberculosis detection using deep-learning which classifies CXR images into two categories, that is, normal and abnormal. We have used CNN architecture with 7 convolutional layers and 3 fully connected layers. The performance of three different optimizers has been compared. Out of these, Adam optimizer with an overall accuracy of 94.73% and validation accuracy of 82.09% performed best amongst them. All the results are obtained using Montgomery and Shenzhen datasets which are available in public domain.

MARKETING AND THE INTERNET : A RESEARCH REVIEW <=SEP=> This paper reviews the research to-date on how the Internet is impacting marketing. It focuses mainly on academic research published in US journals, but includes selected material from other sources. It covers internet adoption and usage; online purchasing behavior; internet advertising; internet economics and pricing; channels and intermediaries and online marketing strategy. The final section gives overall conclusions and briefly lists some opportunities for future research. Contrary to earlier hype, the Internet does not change the fundamental principles of marketing. Nor has its impact to-date – e.g. on consumer behavior, advertising, pricing, channels, intermediaries, strategy and globalization – been anything like as dramatic as predicted. Nevertheless, it has already had some effect on all of these areas. For instance, it is emerging as a flexible, fast-growing advertising medium and as a significant direct channel in many markets (PCs, books, music, travel, and even cars). It has led to some reduction in average prices and in price dispersion. It is bringing into the mainstream previously exotic business models such as auctions, electronic markets, and brand communities. Most of its successes have come from established businesses with ‘bricks and clicks’ strategies which use the Internet to supplement existing activities but rarely to generate much direct revenue We expect its impact to increase steadily over the next ten years, as the technology improves, its availability and usage continue to grow, and businesses learn better how to use it within their wider marketing strategy. Earlier expectations about the Internet were so overblown that the reality was bound to disappoint. But by any normal standards, it is a fast-growing young medium with wide-ranging effects across the whole marketing spectrum.

Social-Aware Movie Recommendation via Multimodal Network Learning <=SEP=> With the rapid development of Internet movie industry social-aware movie recommendation systems (SMRs) have become a popular online web service that provide relevant movie recommendations to users. In this effort many existing movie recommendation approaches learn a user ranking model from user feedback with respect to the movie's content. Unfortunately this approach suffers from the sparsity problem inherent in SMR data. In the present work we address the sparsity problem by learning a multimodal network representation for ranking movie recommendations. We develop a heterogeneous SMR network for movie recommendation that exploits the textual description and movie-poster image of each movie as well as user ratings and social relationships. With this multimodal data we then present a heterogeneous information network learning framework called SMR-multimodal network representation learning (MNRL) for movie recommendation. To learn a ranking metric from the heterogeneous information network we also developed a multimodal neural network model. We evaluated this model on a large-scale dataset from a real world SMR Web site and we find that SMR-MNRL achieves better performance than other state-of-the-art solutions to the problem.

A hyper-heuristic evolutionary algorithm for automatically designing decision-tree algorithms <=SEP=> Decision tree induction is one of the most employed methods to extract knowledge from data, since the representation of knowledge is very intuitive and easily understandable by humans. The most successful strategy for inducing decision trees, the greedy top-down approach, has been continuously improved by researchers over the years. This work, following recent breakthroughs in the automatic design of machine learning algorithms, proposes a hyper-heuristic evolutionary algorithm for automatically generating decision-tree induction algorithms, named HEAD-DT. We perform extensive experiments in 20 public data sets to assess the performance of HEAD-DT, and we compare it to traditional decision-tree algorithms such as C4.5 and CART. Results show that HEAD-DT can generate algorithms that significantly outperform C4.5 and CART regarding predictive accuracy and F-Measure.

On the tradeoff between privacy and utility in data publishing <=SEP=> In data publishing, anonymization techniques such as generalization and bucketization have been designed to provide privacy protection. In the meanwhile, they reduce the utility of the data. It is important to consider the tradeoff between privacy and utility. In a paper that appeared in KDD 2008, Brickell and Shmatikov proposed an evaluation methodology by comparing privacy gain with utility gain resulted from anonymizing the data, and concluded that "even modest privacy gains require almost complete destruction of the data-mining utility". This conclusion seems to undermine existing work on data anonymization. In this paper, we analyze the fundamental characteristics of privacy and utility, and show that it is inappropriate to directly compare privacy with utility. We then observe that the privacy-utility tradeoff in data publishing is similar to the risk-return tradeoff in financial investment, and propose an integrated framework for considering privacy-utility tradeoff, borrowing concepts from the Modern Portfolio Theory for financial investment. Finally, we evaluate our methodology on the Adult dataset from the UCI machine learning repository. Our results clarify several common misconceptions about data utility and provide data publishers useful guidelines on choosing the right tradeoff between privacy and utility.

Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM <=SEP=> Sentiment analysis on large-scale social media data is important to bridge the gaps between social media contents and real world activities including political election prediction, individual and public emotional status monitoring and analysis, and so on. Although textual sentiment analysis has been well studied based on platforms such as Twitter and Instagram, analysis of the role of extensive emoji uses in sentiment analysis remains light. In this paper, we propose a novel scheme for Twitter sentiment analysis with extra attention on emojis. We first learn bi-sense emoji embeddings under positive and negative sentimental tweets individually, and then train a sentiment classifier by attending on these bi-sense emoji embeddings with an attention-based long short-term memory network (LSTM). Our experiments show that the bi-sense embedding is effective for extracting sentiment-aware embeddings of emojis and outperforms the state-of-the-art models. We also visualize the attentions to show that the bi-sense emoji embedding provides better guidance on the attention mechanism to obtain a more robust understanding of the semantics and sentiments.

Wideband Tapered-Slot Antenna with Corrugated Edges for GPR Applications <=SEP=> A novel Wideband tapered-slot antenna for GPR applications is proposed. A conventional approach to reduce unwanted interactions between the antenna and the ground surface requires extensive microwave absorbers treatment. For the same purpose we propose to use corrugations along the antenna edges. The corrugations improve directivity and wideband matching of the antenna with free space retaining also good radiation efficiency. Further improvement of the antenna performance has been achieved by patching the corrugated areas of the antenna with the resistance card strips. The elimination of the foam-type absorbers makes the antenna more robust and easier in fabrication.