Query: Evaluation Datasets for Twitter Sentiment Analysis: A survey and a new dataset, the STS-Gold

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Characterizing debate performance via aggregated twitter sentiment <=SEP=> Television broadcasters are beginning to combine social micro-blogging systems such as Twitter with television to create social video experiences around events. We looked at one such event, the first U.S. presidential debate in 2008, in conjunction with aggregated ratings of message sentiment from Twitter. We begin to develop an analytical methodology and visual representations that could help a journalist or public affairs person better understand the temporal dynamics of sentiment in reaction to the debate video. We demonstrate visuals and metrics that can be used to detect sentiment pulse, anomalies in that pulse, and indications of controversial topics that can be used to inform the design of visual analytic systems for social media events.

Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments <=SEP=> We address the problem of part-of-speech tagging for English data from the popular microblogging service Twitter. We develop a tagset, annotate data, develop features, and report tagging results nearing 90% accuracy. The data and tools have been made available to the research community with the goal of enabling richer text analysis of Twitter and related social media data sets.

Semantic Sentiment Analysis of Twitter <=SEP=> Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics’ feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. “Apple product”) as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment. We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification.

Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews <=SEP=> This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g., “subtle nuances”) and a negative semantic orientation when it has bad associations (e.g., “very cavalier”). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word “excellent” minus the mutual information between the given phrase and the word “poor”. A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews.

From bias to opinion: a transfer-learning approach to real-time sentiment analysis <=SEP=> Real-time interaction, which enables live discussions, has become a key feature of most Web applications. In such an environment, the ability to automatically analyze user opinions and sentiments as discussions develop is a powerful resource known as real time sentiment analysis. However, this task comes with several challenges, including the need to deal with highly dynamic textual content that is characterized by changes in vocabulary and its subjective meaning and the lack of labeled data needed to support supervised classifiers. In this paper, we propose a transfer learning strategy to perform real time sentiment analysis. We identify a task - opinion holder bias prediction - which is strongly related to the sentiment analysis task; however, in constrast to sentiment analysis, it builds accurate models since the underlying relational data follows a stationary distribution.
 Instead of learning textual models to predict content polarity (i.e., the traditional sentiment analysis approach), we first measure the bias of social media users toward a topic, by solving a relational learning task over a network of users connected by endorsements (e.g., retweets in Twitter). We then analyze sentiments by transferring user biases to textual features. This approach works because while new terms may arise and old terms may change their meaning, user bias tends to be more consistent over time as a basic property of human behavior. Thus, we adopted user bias as the basis for building accurate classification models. We applied our model to posts collected from Twitter on two topics: the 2010 Brazilian Presidential Elections and the 2010 season of Brazilian Soccer League. Our results show that knowing the bias of only 10% of users generates an F1 accuracy level ranging from 80% to 90% in predicting user sentiment in tweets.

PKLot - A robust dataset for parking lot classification <=SEP=> http://dx.doi.org/10.1016/j.eswa.2015.02.009 0957-4174/ 2015 Elsevier Ltd. All rights reserved. ⇑ Corresponding author. E-mail addresses: prlalmeida@inf.ufpr.br (P.R.L. de Almeida), lesoliveira@inf. ufpr.br (L.S. Oliveira), alceu@ppgia.pucpr.br (A.S. Britto Jr.), eunelson@ppgia.pucpr. br (E.J. Silva Jr.), alessandro.koerich@etsmtl.ca (A.L. Koerich). Paulo R.L. de Almeida , Luiz S. Oliveira , Alceu S. Britto Jr. b,⇑, Eunelson J. Silva Jr. b Alessandro L. Koerich b,c

Confidence sets for persistence diagrams <=SEP=> Persistent homology is a method for probing topological properties of point clouds and functions. The method involves tracking the birth and death of topological features as one varies a tuning parameter. Features with short lifetimes are informally considered to be “topological noise,” and those with a long lifetime are considered to be “topological signal.” In this paper, we bring some statistical ideas to persistent homology. In particular, we derive confidence sets that allow us to separate topological signal from topological noise.

Hedonic and instrumental motives in anger regulation. <=SEP=> What motivates individuals to regulate their emotions? One answer, which has been highlighted in emotion-regulation research, is that individuals are motivated by short-term hedonic goals (e.g., the motivation to feel pleasure). Another answer, however, is that individuals are motivated by instrumental goals (e.g., the motivation to perform certain behaviors). We suggest that both answers have merit. To demonstrate the role instrumental goals may play in emotion regulation, we pitted short-term hedonic motives and instrumental motives against each other, by testing whether individuals were motivated to experience a potentially useful, albeit unpleasant, emotion. We found that (a) individuals preferred activities that would increase their level of anger (but not their level of excitement) when they were anticipating confrontational, but not nonconfrontational, tasks and that (b) anger improved performance in a confrontational, but not a nonconfrontational, task. These findings support a functional view of emotion regulation, and demonstrate that in certain contexts, individuals may choose to experience emotions that are instrumental, despite short-term hedonic costs.

Building Poker Agent Using Reinforcement Learning with Neural Networks <=SEP=> Poker is a game with incomplete and imperfect information. The ability to estimate opponent and interpret its actions makes a player as a world class player. Finding optimal game strategy is not enough to win poker game. As in real life as in online poker game, the most time of it consists of opponent analysis. This paper illustrates a development of poker agent using reinforcement learning with neural networks. 1 STAGE OF THE RESEARCH Poker is a game with incomplete and imperfect information. The ability to estimate opponent and interpret its actions makes a player as a world class player. Finding optimal game strategy is not enough to win poker game. As in real life as in online poker game, the most time of it consists of opponent analysis. Author is working on development of poker agent that would find optimal game strategy using reinforcement learning (RL) in combination with artificial neural network (ANN) for value function approximation. 2 OUTLINE OF OBJECTIVES This paper illustrates a development of poker agent using reinforcement learning with neural networks. Complete poker agent should have an ability to create optimal game strategy that makes decisions based on information:  Hand strength/potential estimation;  Table card estimation;  Opponent hand strength prediction;  Opponent classification (tight/loose passive/ aggressive);  Current state evaluation using neural network. AI Poker agent should be able to find an optimal strategy by itself (unsupervised learning) using information given above. It also should be able to adapt opponent play style and change its strategy during the game.

MATLAB Based BackPropagation Neural Network for Automatic Speech Recognition <=SEP=> Speech interface to computer is the next big step that the technology needs to take for general users. Automatic speech recognition (ASR) will play an important role in taking technology to the people. There are numerous applications of speech recognition such as direct voice input in aircraft, data entry, speech-to-text processing, voice user interfaces such as voice dialling. ASR system can be divided into two different parts, namely feature extraction and feature recognition. In this paper we present MATLAB based feature recognition using backpropagation neural network for ASR. The objective of this research is to explore how neural networks can be employed to recognize isolated-word speech as an alternative to the traditional methodologies. The general techniques developed here can be further extended to other applications such as sonar target recognition, missile tracking and classification of underwater acoustic signals. Back-propagation neural network algorithm uses input training samples and their respective desired output values to learn to recognize specific patterns, by modifying the activation values of its nodes and weights of the links connecting its nodes. Such a trained network is later used for feature recognition in ASR systems.

Automated Fingerprint Identification and Imaging Systems <=SEP=> More than a century has passed since Alphonse Bertillon first conceived and then industriously practiced the idea of using body measurements for solving crimes [1]. Just as his idea was gaining popularity, it faded into relative obscurity by a far more significant and pract ic l discovery of the uniqueness of the human fingerprints 1. Soon after this discovery, many major law enforcement departments embraced the idea of first “booking” the fingerprints of criminals, so tha t their records are readily available and later using leftover fingerprint smudges (latent s), the identity of criminals can be determined. These agencies sponsored a rigorous study of fingerprints, developed s cientific 1In 1893, the Home Ministry Office, UK, accepted that no two ind ividuals have the same fingerprints.

Global Linear Neighborhoods for Efficient Label Propagation <=SEP=> Graph-based semi-supervised learning improves classification by combining labeled and unlabeled data through label propagation. It was shown that the sparse representation of graph by weighted local neighbors provides a better similarity measure between data points for label propagation. However, selecting local neighbors can lead to disjoint components and incorrect neighbors in graph, and thus, fail to capture the underlying global structure. In this paper, we propose to learn a nonnegative low-rank graph to capture global linear neighborhoods, under the assumption that each data point can be linearly reconstructed from weighted combinations of its direct neighbors and reachable indirect neighbors. The global linear neighborhoods utilize information from both direct and indirect neighbors to preserve the global cluster structures, while the low-rank property retains a compressed representation of the graph. An efficient algorithm based on a multiplicative update rule is designed to learn a nonnegative low-rank factorization matrix minimizing the neighborhood reconstruction error. Large scale simulations and experiments on UCI datasets and high-dimensional gene expression datasets showed that label propagation based on global linear neighborhoods captures the global cluster structures better and achieved more accurate classification results.

Automatic Linguistic Annotation of Large Scale L 2 Databases : The EF-Cambridge Open Language Database ( EFCamDat ) <=SEP=> ∗Naturalistic learner productions are an important empirical resource for SLA research. Some pioneering works have produced valuable second language (L2) resources supporting SLA research.1 One common limitation of these resources is the absence of individual longitudinal data for numerous speakers with different backgrounds across the proficiency spectrum, which is vital for understanding the nature of individual variation in longitudinal development.2 A second limitation is the relatively restricted amounts of data annotated with linguistic information (e.g., lexical, morphosyntactic, semantic features, etc.) to support investigation of SLA hypotheses and obtain patterns of development for different linguistic phenomena. Where available, annotations tend to be manually obtained, a situation posing immediate limitations to the quantity of data that could be annotated with reasonable human resources and within reasonable time. Natural Language Processing (NLP) tools can provide automatic annotations for parts-of-speech (POS) and syntactic structure and are indeed increasingly applied to learner language in various contexts. Systems in computer-assisted language learning (CALL) have used a parser and other NLP tools to automatically detect learner errors and provide feedback accordingly.3 Some work aimed at adapting annotations provided by parsing tools to accurately describe learner syntax (Dickinson & Lee, 2009) or evaluated parser performance on learner language and the effect of learner errors on the parser. Krivanek and Meurers (2011) compared two parsing methods, one using a hand-crafted lexicon and one trained on a corpus. They found that the former is more successful in recovering the main grammatical dependency relations whereas the latter is more successful in recovering optional, adjunction relations. Ott and Ziai (2010) evaluated the performance of a dependency parser trained on native German (MaltParser; Nivre et al., 2007) on 106 learner answers to a comprehension task in L2 German. Their study indicates that while some errors can be problematic for the parser (e.g., omission of finite verbs) many others (e.g., wrong word order) can be parsed robustly, resulting in overall high performance scores. In this paper we have two goals. First, we introduce a new English L2 database, the EF Cambridge Open Language Database, henceforth EFCAMDAT. EFCAMDAT was developed by the Department of Theoretical and Applied Linguistics at the University of Cambridge in collaboration with EF Education First, an international educational organization. It contains writings submitted to Englishtown, the

A Radius and Ulna TW3 Bone Age Assessment System <=SEP=> An end-to-end system to automate the well-known Tanner-Whitehouse (TW3) clinical procedure to estimate the skeletal age in childhood is proposed. The system comprises the detailed analysis of the two most important bones in TW3: the radius and ulna wrist bones. First, a modified version of an adaptive clustering segmentation algorithm is presented to properly semi-automatically segment the contour of the bones. Second, up to 89 features are defined and extracted from bone contours and gray scale information inside the contour, followed by some well- founded feature selection mathematical criteria, based on the ideas of maximizing the classes' separability. Third, bone age is estimated with the help of a generalized softmax perceptron (GSP) neural network (NN) that, after supervised learning and optimal complexity estimation via the application of the recently developed posterior probability model selection (PPMS) algorithm, is able to accurately predict the different development stages in both radius and ulna from which and with the help of the TW3 methodology, we are able to conveniently score and estimate the bone age of a patient in years, in what can be understood as a multiple- class (multiple stages) pattern recognition approach with posterior probability estimation. Finally, numerical results are presented to evaluate the system performance in predicting the bone stages and the final patient bone age over a private hand image database, with the help of the pediatricians and the radiologists expert diagnoses.

An Approach to Data Privacy in Smart Home using Blockchain Technology <=SEP=> Nowadays, numerous applications of smart home systems provide recommendations for users, including reducing their energy consumption, warnings of defective devices, selecting reliable devices and software, diagnoses, etc [1]. The internet connected, dynamic and heterogeneous nature of the smart home environment creates new security, authentication, and privacy challenges [2]. To solve those challenges, an approach to data privacy in smart home using blockchain technology, which is called smart home based the IoT-Blockchain (SHIB), is proposed in this paper. In order to demonstrate the proposed architecture, an experimental scenario using Ganache, Remix, and web3. js is built among the user, service provider, and smart home to evaluate the performance of the smart contract in the SHIB. Based on the experiment results, the SHIB architecture brings the advantages like data privacy, trust access control, and high extension ability. In addition, the comparison between the proposed architecture and existing models in different parameters such as smart contract, the privacy of data, usage of tokens, updating the policies, and misbehavior judging are performed.

Semi-supervised Density-Based Clustering <=SEP=> Most of the effort in the semi-supervised clustering literature was devoted to variations of the K-means algorithm. In this paper we show how background knowledge can be used to bias a partitional density-based clustering algorithm. Our work describes how labeled objects can be used to help the algorithm detecting suitable density parameters for the algorithm to extract density-based clusters in specific parts of the feature space. Considering the set of constraints estabilished by the labeled dataset we show that our algorithm, called SSDBSCAN, automatically finds density parameters for each natural cluster in a dataset. Four of the most interesting characteristics of SSDBSCAN are that (1) it only requires a single, robust input parameter, (2) it does not need any user intervention, (3) it automaticaly finds the noise objects according to the density of the natural clusters and (4) it is able to find the natural cluster structure even when the density among clusters vary widely. The algorithm presented in this paper is evaluated with artificial and real-world datasets, demonstrating better results when compared to other unsupervised and semi-supervised density-based approaches.

Shake-your-head: revisiting walking-in-place for desktop virtual reality <=SEP=> The Walking-In-Place interaction technique was introduced to navigate infinitely in 3D virtual worlds by walking in place in the real world. The technique has been initially developed for users standing in immersive setups and was built upon sophisticated visual displays and tracking equipments.
 In this paper, we propose to revisit the whole pipeline of the Walking-In-Place technique to match a larger set of configurations and apply it notably to the context of desktop Virtual Reality. With our novel "Shake-Your-Head" technique, the user is left with the possibility to sit down, and to use small screens and standard input devices such as a basic webcam for tracking. The locomotion simulation can compute various motions such as turning, jumping and crawling, using as sole input the head movements of the user. We also introduce the use of additional visual feedback based on camera motions to enhance the walking sensations.
 An experiment was conducted to compare our technique with classical input devices used for navigating in desktop VR. Interestingly, the results showed that our technique could even allow faster navigations when sitting, after a short learning. Our technique was also perceived as more fun and increasing presence, and was generally more appreciated for VR navigation.

Should one compute the Temporal Difference fix point or minimize the Bellman Residual? The unified oblique projection view <=SEP=> We investigate projection methods, for evaluating a linear approximation of the value function of a policy in a Markov Decision Process context. We consider two popular approaches, the one-step Temporal Difference fix-point computation (TD(0)) and the Bellman Residual (BR) minimization. We describe examples, where each method outperforms the other. We highlight a simple relation between the objective function they minimize, and show that while BR enjoys a performance guarantee, TD(0) does not in general. We then propose a unified view in terms of oblique projections of the Bellman equation, which substantially simplifies and extends the characterization of Schoknecht (2002) and the recent analysis of Yu & Bertsekas (2008). Eventually, we describe some simulations that suggest that if the TD(0) solution is usually slightly better than the BR solution, its inherent numerical instability makes it very bad in some cases, and thus worse on average.

Analysis Enhanced Particle-based Flow Visualization <=SEP=> Particle-based fluid simulation (PFS), such as Smoothed Particle Hydrodynamics (SPH) and Position-based Fluid (PBF), is a mesh-free method that has been widely used in various fields, including astrophysics, mechanical engineering, and biomedical engineering for the study of liquid behaviors under different circumstances. Due to its meshless nature, most analysis techniques that are developed for mesh-based data need to be adapted for the analysis of PFS data. In this work, we study a number of flow analysis techniques and their extension for PFS data analysis, including the FTLE approach, Jacobian analysis, and an attribute accumlation framework. In particular, we apply these analysis techniques to free surface fluids. We demonstrate that these analyses can reveal some interesting underlying flow patterns that would be hard to see otherwise via a number of PFS simulated flows with different parameters and boundary settings. In addition, we point out that an in-situ analysis framework that performs these analyses can potentially be used to guide the adaptive PFS to allocate the computation and storage power to the regions of interest during the simulation.

Look into Person: Joint Body Parsing & Pose Estimation Network and a New Benchmark <=SEP=> Human parsing and pose estimation have recently received considerable interest due to their substantial application potentials. However, the existing datasets have limited numbers of images and annotations and lack a variety of human appearances and coverage of challenging cases in unconstrained environments. In this paper, we introduce a new benchmark named “Look into Person (LIP)” that provides a significant advancement in terms of scalability, diversity, and difficulty, which are crucial for future developments in human-centric analysis. This comprehensive dataset contains over 50,000 elaborately annotated images with 19 semantic part labels and 16 body joints, which are captured from a broad range of viewpoints, occlusions, and background complexities. Using these rich annotations, we perform detailed analyses of the leading human parsing and pose estimation approaches, thereby obtaining insights into the successes and failures of these methods. To further explore and take advantage of the semantic correlation of these two tasks, we propose a novel joint human parsing and pose estimation network to explore efficient context modeling, which can simultaneously predict parsing and pose with extremely high quality. Furthermore, we simplify the network to solve human parsing by exploring a novel self-supervised structure-sensitive learning approach, which imposes human pose structures into the parsing results without resorting to extra supervision. The datasets, code and models are available at http://www.sysu-hcp.net/lip/.

OctoPocus: a dynamic guide for learning gesture-based command sets <=SEP=> We describe OctoPocus, an example of a dynamic guide that combines on-screen feedforward and feedback to help users learn, execute and remember gesture sets. OctoPocus can be applied to a wide range of single-stroke gestures and recognition algorithms and helps users progress smoothly from novice to expert performance. We provide an analysis of the design space and describe the results of two experi-ments that show that OctoPocus is significantly faster and improves learning of arbitrary gestures, compared to con-ventional Help menus. It can also be adapted to a mark-based gesture set, significantly improving input time compared to a two-level, four-item Hierarchical Marking menu.

Controversies surrounding nonspeech oral motor exercises for childhood speech disorders. <=SEP=> The use of nonspeech oral motor exercises (NSOMEs) for influencing children’s speech sound productions is a common therapeutic practice used by speech-language pathologists (SLPs) in the United States, Canada, and the United Kingdom. Reports from these countries have documented that between 71.5% and 85% of practicing clinicians use some type of NSOMEs in therapy to change children’s speech productions. NSOMEs can be defined as any therapy technique that does not require the child to produce a speech sound but is used to influence the development of speaking abilities. The National Center for Evidence-Based Practice in Communication Disorders (NCEP) of the American Speech-Language-Hearing Association (ASHA) developed this definition: ‘‘Oral-motor exercises are activities that involve sensory stimulation to or actions of the lips, jaw, tongue, soft palate, larynx, and respiratory muscles which are intended to influence the physiologic underpinnings of the oropharyngeal mechanism and thus improve its functions; oral-motor exercises may include active muscle exercise, muscle stretching, passive exercise, and sensory stimulation.’’ The term ‘‘oral motor,’’ which relates to movements and placements of the oral musculature, is established in the field of SLP. Although the existence and importance of the oral-motor aspects of speech production is well understood, the use and effectiveness of nonspeech oral-motor activities is disputed because of the lack of theoretical and empirical support. To understand more about the use of NSOMEs for changing disordered productions of speech, a colleague and I conducted a nationwide survey of 537 practicing clinicians from 48 states. We found that SLPs frequently used the exercises of blowing, tongue wagging and pushups, cheek puffing, the alternating movement of pucker-smile, ‘‘big smile,’’ and tongue-to-nose-to-chin. The clinicians believed these NSOMEs would help their clients obtain tongue elevation, protrusion, and lateralization; increase their tongue and lip strength; become more aware of their articulators; stabilize the jaw; control drooling; and increase velopharyngeal and sucking abilities. The respondents to the survey reported using these techniques for children with a wide variety of different speech disorders stemming from a wide variety of etiologies: dysarthria, childhood apraxia of speech (CAS), Down syndrome, late talkers, phonological impairment, functional misarticulations, and hearing impairment. It makes one curious why clinicians would select these nonspeech therapeutic techniques because they lack adequate theoretical underpinnings for their use. Additionally, the research studies that have evaluated treatment efficacy using the NSOME techniques, although admittedly scant and not at the highest levels of scientific rigor, do not show therapeutic effectiveness. Not only are these nonspeech tasks lacking in theoretical and data support for their use, their application to improve speech intelligibility also often defies logical reasoning. So why do clinicians use these techniques? As I have previously pointed out, SLPs have several possible reasons for using NSOMEs. Some of these reasons may be that

Spatial as Deep: Spatial CNN for Traffic Scene Understanding <=SEP=> Convolutional neural networks (CNNs) are usually built by stacking convolutional operations layer-by-layer. Although CNN has shown strong capability to extract semantics from raw pixels, its capacity to capture spatial relationships of pixels across rows and columns of an image is not fully explored. These relationships are important to learn semantic objects with strong shape priors but weak appearance coherences, such as traffic lanes, which are often occluded or not even painted on the road surface as shown in Fig. 1 (a). In this paper, we propose Spatial CNN (SCNN), which generalizes traditional deep layer-by-layer convolutions to slice-byslice convolutions within feature maps, thus enabling message passings between pixels across rows and columns in a layer. Such SCNN is particular suitable for long continuous shape structure or large objects, with strong spatial relationship but less appearance clues, such as traffic lanes, poles, and wall. We apply SCNN on a newly released very challenging traffic lane detection dataset and Cityscapse dataset. The results show that SCNN could learn the spatial relationship for structure output and significantly improves the performance. We show that SCNN outperforms the recurrent neural network (RNN) based ReNet and MRF+CNN (MRFNet) in the lane detection dataset by 8.7% and 4.6% respectively. Moreover, our SCNN won the 1st place on the TuSimple Benchmark Lane Detection Challenge, with an accuracy of 96.53%.

Multi-modality web video categorization <=SEP=> This paper reports a first comprehensive study and large-scale test on web video (so-called user generated video or micro video) categorization. Observing that web videos are characterized by a much higher diversity of quality, subject, style, and genres compared with traditional video programs, we focus on studying the effectiveness of different modalities in dealing with such high variation. Specifically, we propose two novel modalities including a semantic modality and a surrounding text modality, as effective complements to most commonly used low-level features. The semantic modality includes three feature representations, i.e., concept histogram, visual word vector model and visual word Latent Semantic Analysis (LSA), while text modality includes the titles, descriptions and tags of web videos. We conduct a set of comprehensive experiments for evaluating the effectiveness of the proposed feature representations over different classifiers such as Support Vector Machine (SVM), Gaussian Mixture Model (GMM) and Manifold Ranking (MR). Our experiments on a large-scale dataset with 11k web videos (nearly 450 hours in total) demonstrate that (1) the proposed multimodal feature representation is effective for web video categorization, and (2) SVM outperforms GMM and MR on nearly all the modalities.

A Fast-Response Pseudo-PWM Buck Converter With PLL-Based Hysteresis Control <=SEP=> Hysteresis voltage-mode control is a simple and fast control scheme for switched-mode power converters. However, it is well-known that the switching frequency of a switched-mode power converter with hysteresis control depends on many factors such as loading current and delay of the controller which vary from time to time. It results in a wide noise spectrum and leads to difficulty in shielding electro-magnetic interference. In this work, a phase-lock loop (PLL) is utilized to control the hysteresis level of the comparator used in the controller, while not interfering with the intrinsic behavior of the hysteresis controller. Some design techniques are used to solve the integration problem and to improve the settling speed of the PLL. Moreover, different control modes are implemented. A buck converter with proposed control scheme is fabricated using a commercial 0.35-μ m CMOS technology. The chip area is 1900 μm × 2200 μm. The switching frequency is locked to 1 MHz, and the measured frequency deviation is within ±1%. The measured load transient response between 160 and 360 mA is 5 μ s only.

Memristor-The Missing Circuit Element LEON 0 <=SEP=> A new two-terminal circuit element-called the memrirtorcharacterized by a relationship between the charge q(t) s St% i(7J d7 and the flux-linkage (p(t) = J-‘-m vfrj d T is introduced os the fourth boric circuit element. An electromagnetic field interpretation of this relationship in terms of a quasi-static expansion of Maxwell’s equations is presented. Many circuit~theoretic properties of memdstorr are derived. It is shown that this element exhibiis some peculiar behavior different from that exhibited by resistors, inductors, or capacitors. These properties lead to a number of unique applications which cannot be realized with RLC networks alone. I + ” -3 nl Although a physical memristor device without internal power supply has not yet been discovered, operational laboratory models have been built with the help of active circuits. Experimental results ore presented to demonstrate the properties and potential applications of memristors. (a) I. 1NTR00~cnoN I + Y -3 T HIS PAPER presents the logical and scientific basis for the existence of a new two-terminal circuit element called the memristor (a contraction for memory (b) resistor) which has every right to be as basic as the three classical circuit elements already in existence, namely, the resistor, inductor, and capacitor. Although the existence of a memristor in the form of a physical device without internal power supply has not yet been discovered, its laboratory realization in the form of active circuits will be presented in Section II.’ Many interesting circuit-theoretic properties possessed by the memristor, the most important of which is perhaps the passivity property which provides the circuit-theoretic basis for its physical realizability, will be derived in Section III. An electromagnetic field interpretation of the memristor characterization will be presented in Section IV with the help of a quasi-static expansion of Maxwell’s equations. Finally, some novel applications of memristors will be presented in Section V. 1 + ” -3

Millimeter-wave antennas for radio access and backhaul in 5G heterogeneous mobile networks <=SEP=> Millimeter-wave communications are expected to play a key role in future 5G mobile networks to overcome the dramatic traffic growth expected over the next decade. Such systems will severely challenge antenna technologies used at mobile terminal, access point or backhaul/fronthaul levels. This paper provides an overview of the authors' recent achievements in the design of integrated antennas, antenna arrays and high-directivity quasi-optical antennas for high data-rate 60-GHz communications.

Frequency compensation of high-speed, low-voltage CMOS multistage amplifiers <=SEP=> This paper presents the frequency compensation of high-speed, low-voltage multistage amplifiers. Two frequency compensation techniques, the Nested Miller Compensation with Nulling Resistors (NMCNR) and Reversed Nested Indirect Compensation (RNIC), are discussed and employed on two multistage amplifier architectures. A four-stage pseudo-differential amplifier with CMFF and CMFB is designed in a 1.2 V, 65-nm CMOS process. With NMCNR, it achieves a phase margin (PM) of 59° with a DC gain of 75 dB and unity-gain frequency (fug) of 712 MHz. With RNIC, the same four-stage amplifier achieves a phase margin of 84°, DC gain of 76 dB and fug of 2 GHz. Further, a three-stage single-ended amplifier is designed in a 1.1-V, 40-nm CMOS process. The three-stage OTA with RNIC achieves PM of 81°, DC gain of 80 dB and fug of 770 MHz. The same OTA achieves PM of 59° with NMCNR, while maintaining a DC gain of 75 dB and fug of 262 MHz. Pole-splitting, to achieve increased stability, is illustrated for both compensation schemes. Simulations illustrate that the RNIC scheme achieves much higher PM and fug for lower values of compensation capacitance compared to NMCNR, despite the growing number of low voltage amplifier stages.

Time-Frequency Masking in the Complex Domain for Speech Dereverberation and Denoising <=SEP=> In real-world situations, speech is masked by both background noise and reverberation, which negatively affect perceptual quality and intelligibility. In this paper, we address monaural speech separation in reverberant and noisy environments. We perform dereverberation and denoising using supervised learning with a deep neural network. Specifically, we enhance the magnitude and phase by performing separation with an estimate of the complex ideal ratio mask. We define the complex ideal ratio mask so that direct speech results after the mask is applied to reverberant and noisy speech. Our approach is evaluated using simulated and real room impulse responses, and with background noises. The proposed approach improves objective speech quality and intelligibility significantly. Evaluations and comparisons show that it outperforms related methods in many reverberant and noisy environments.

Depth Images-based Human Detection , Tracking and Activity Recognition Using Spatiotemporal Features and Modified HMM <=SEP=> Human activity recognition using depth information is an emerging and challenging technology in computer vision due to its considerable attention by many practical applications such as smart home/office system, personal health care and 3D video games. This paper presents a novel framework of 3D human body detection, tracking and recognition from depth video sequences using spatiotemporal features and modified HMM. To detect human silhouette, raw depth data is examined to extract human silhouette by considering spatial continuity and constraints of human motion information. While, frame differentiation is used to track human movements. Features extraction mechanism consists of spatial depth shape features and temporal joints features are used to improve classification performance. Both of these features are fused together to recognize different activities using the modified hidden Markov model (M-HMM). The proposed approach is evaluated on two challenging depth video datasets. Moreover, our system has significant abilities to handle subject's body parts rotation and body parts missing which provide major contributions in human activity recognition.