Query: NLANGP at SemEval-2016 Task 5: Improving Aspect Based Sentiment Analysis using Neural Network Features

Relevant passages:
Glove: Global Vectors for Word Representation <=SEP=> Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition.

Linguistic Regularities in Continuous Space Word Representations <=SEP=> • Neural network language model and distributed representation for words (Vector representation) • Capture syntactic and remantic regularities in language • Outperform state-of-the-art

NLANGP: Supervised Machine Learning System for Aspect Category Classification and Opinion Target Extraction <=SEP=> This paper describes our system used in the Aspect Based Sentiment Analysis Task 12 of SemEval-2015. Our system is based on two supervised machine learning algorithms: sigmoidal feedforward network to train binary classifiers for aspect category classification (Slot 1), and Conditional Random Fields to train classifiers for opinion target extraction (Slot 2). We extract a variety of lexicon and syntactic features, as well as cluster features induced from unlabeled data. Our system achieves state-of-the-art performances, ranking 1st for three of the evaluations (Slot 1 for both restaurant and laptop domains, and Slot 1 & 2) and 2nd for Slot 2 evaluation.

SemEval-2016 Task 5: Aspect Based Sentiment Analysis <=SEP=> This paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis (ABSA), a continuation of the respective tasks of 2014 and 2015. In its third year, the task provided 19 training and 20 testing datasets for 8 languages and 7 domains, as well as a common evaluation procedure. From these datasets, 25 were for sentence-level and 14 for text-level ABSA; the latter was introduced for the first time as a subtask in SemEval. The task attracted 245 submissions from 29 teams.

A Logic Programming Approach to Aspect Extraction in Opinion Mining <=SEP=> Aspect extraction aims to extract fine-grained opinion targets from opinion texts. Recent work has shown that the syntactical approach performs well. In this paper, we show that Logic Programming, particularly Answer Set Programming (ASP), can be used to elegantly and efficiently implement the key components of syntax based aspect extraction. Specifically, the well known double propagation (DP) method is implemented using 8 ASP rules that naturally model all key ideas in the DP method. Our experiment on a widely used data set also shows that the ASP implementation is much faster than a Java-based implementation. Syntactical approach has its limitation too. To further improve the performance of syntactical approach, we identify a set of general words from Word Net that have little chance to be an aspect and prune them when extracting aspects. The concept of general words and their pruning are concisely captured by 10 new ASP rules, and a natural extension of the 8 rules for the original DP method. Experimental results show a major improvement in precision with almost no drop in recall compared with those reported in the existing work on a typical benchmark data set. Logic Programming provides a convenient and effective tool to encode and thus test knowledge needed to improve the aspect extraction methods so that the researchers can focus on the identification and discovery of new knowledge to improve aspect extraction.

Cortical demyelination and diffuse white matter injury in multiple sclerosis. <=SEP=> Focal demyelinated plaques in white matter, which are the hallmark of multiple sclerosis pathology, only partially explain the patient's clinical deficits. We thus analysed global brain pathology in multiple sclerosis, focusing on the normal-appearing white matter (NAWM) and the cortex. Autopsy tissue from 52 multiple sclerosis patients (acute, relapsing-remitting, primary and secondary progressive multiple sclerosis) and from 30 controls was analysed using quantitative morphological techniques. New and active focal inflammatory demyelinating lesions in the white matter were mainly present in patients with acute and relapsing multiple sclerosis, while diffuse injury of the NAWM and cortical demyelination were characteristic hallmarks of primary and secondary progressive multiple sclerosis. Cortical demyelination and injury of the NAWM, reflected by diffuse axonal injury with profound microglia activation, occurred on the background of a global inflammatory response in the whole brain and meninges. There was only a marginal correlation between focal lesion load in the white matter and diffuse white matter injury, or cortical pathology, respectively. Our data suggest that multiple sclerosis starts as a focal inflammatory disease of the CNS, which gives rise to circumscribed demyelinated plaques in the white matter. With chronicity, diffuse inflammation accumulates throughout the whole brain, and is associated with slowly progressive axonal injury in the NAWM and cortical demyelination.

Essential communication practices for Extreme Programming in a global software development team <=SEP=> We conducted an industrial case study of a distributed team in the USA and the Czech Republic that used Extreme Programming. Our goal was to understand how this globally-distributed team created a successful project in a new problem domain using a methodology that is dependent on informal, face-to-face communication. We collected quantitative and qualitative data and used grounded theory to identify four key factors for communication in globally-distributed XP teams working within a new problem domain. Our study suggests that, if these critical enabling factors are addressed, methodologies dependent on informal communication can be used on global software development projects.

The application of virtual reality to (chemical engineering) education <=SEP=> Virtual reality, VR, offers many benefits to technical education, including the delivery of information through multiple active channels, the addressing of different learning styles, and experiential-based learning. This poster presents work performed by the authors to apply VR to engineering education, in three broad project areas: virtual chemical plants, virtual laboratory accidents, and a virtual UIC campus. The first area provides guided exploration of domains otherwise inaccessible, such as the interior of operating reactors and microscopic reaction mechanisms. The second promotes safety by demonstrating the consequences of not following proper lab safety procedures. And the third provides valuable guidance for (foreign) visitors. All programs developed are available on the Web, for free download to any interested parties.

Deep EndoVO: A Recurrent Convolutional Neural Network (RCNN) based Visual Odometry Approach for Endoscopic Capsule Robots <=SEP=> Ingestible wireless capsule endoscopy is an emerging minimally invasive diagnostic technology for inspection of the GI tract and diagnosis of a wide range of diseases and pathologies. Medical device companies and many research groups have recently made substantial progresses in converting passive capsule endoscopes to active capsule robots, enabling more accurate, precise, and intuitive detection of the location and size of the diseased areas. Since a reliable real time pose estimation functionality is crucial for actively controlled endoscopic capsule robots, in this study, we propose a monocular visual odometry (VO) method for endoscopic capsule robot operations. Our method lies on the application of the deep Recurrent Convolutional Neural Networks (RCNNs) for the visual odometry task, where Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are used for the feature extraction and inference of dynamics across the frames, respectively. Detailed analyses and evaluations made on a real pig stomach dataset proves that our system achieves high translational and rotational accuracies for different types of endoscopic capsule robot trajectories.

Pondering the Concept of Abstraction in (Illustrative) Visualization <=SEP=> We explore the concept of abstraction as it is used in visualization, with the ultimate goal of understanding and formally defining it. Researchers so far have used the concept of abstraction largely by intuition without a precise meaning. This lack of specificity left questions on the characteristics of abstraction, its variants, its control, or its ultimate potential for visualization and, in particular, illustrative visualization mostly unanswered. In this paper we thus provide a first formalization of the abstraction concept and discuss how this formalization affects the application of abstraction in a variety of visualization scenarios. Based on this discussion, we derive a number of open questions still waiting to be answered, thus formulating a research agenda for the use of abstraction for the visual representation and exploration of data. This paper, therefore, is intended to provide a contribution to the discussion of the theoretical foundations of our field, rather than attempting to provide a completed and final theory.

Haraka - Efficient Short-Input Hashing for Post-Quantum Applications <=SEP=> Recently, many efficient cryptographic hash function design strategies have been explored, not least because of the SHA-3 competition. These designs are, almost exclusively, geared towards high performance on long inputs. However, various applications exist where the performance on short (fixed length) inputs matters more. Such hash functions are the bottleneck in hash-based signature schemes like SPHINCS or XMSS, which is currently under standardization. Secure functions specifically designed for such applications are scarce. We attend to this gap by proposing two short-input hash functions (or rather simply compression functions). By utilizing AES instructions on modern CPUs, our proposals are the fastest on such platforms, reaching throughputs below one cycle per hashed byte even for short inputs, while still having a very low latency of less than 60 cycles. Under the hood, this results comes with several innovations. First, we study whether the number of rounds for our hash functions can be reduced, if only second-preimage resistance (and not collision resistance) is required. The conclusion is: only a little. Second, since their inception, AES-like designs allow for supportive security arguments by means of counting and bounding the number of active S-boxes. However, this ignores powerful attack vectors using truncated differentials, including the powerful rebound attacks. We develop a general tool-based method to include arguments against attack vectors using truncated differentials.

Texas 3D Face Recognition Database <=SEP=> We make the Texas 3D Face Recognition Database available to researchers in three dimensional (3D) face recognition and other related areas. This database contains 1149 pairs of high resolution, pose normalized, preprocessed, and perfectly aligned color and range images of 118 adult human subjects acquired using a stereo camera. The images are accompanied with information about the subjects' gender, ethnicity, facial expression, and the locations of 25 manually located anthropometric facial fiducial points. Specific partitions of the data for developing and evaluating 3D face recognition algorithms are also included.

Maximum Entropy Deep Inverse Reinforcement Learning <=SEP=> This paper presents a general framework for employing deep architectures in particular neural networks to solve the inverse reinforcement learning (IRL) problem. Specifically, we propose to exploit the representational capacity and favourable computational complexity of deep networks to approximate complex, nonlinear reward functions. We show that the Maximum Entropy paradigm for IRL lends itself naturally to the efficient training of deep architectures. At test time, the approach leads to a computational complexity independent of the number of demonstrations. This makes it especially well-suited for applications in life-long learning scenarios commonly encountered in robotics. We demonstrate that our approach achieves performance commensurate to the state-of-the-art on existing benchmarks already with simple, comparatively shallow network architectures while significantly outperforming the stateof-the-art on an alternative benchmark based on more complex, highly varying reward structures representing strong interactions between features. Furthermore, we extend the approach to include convolutional layers in order to eliminate the dependency on precomputed features of current algorithms and to underline the substantial gain in flexibility in framing IRL in the context of deep learning.

Distributed Secret Sharing Approach With Cheater Prevention Based on QR Code <=SEP=> QR barcodes are used extensively due to their beneficial properties, including small tag, large data capacity, reliability, and high-speed scanning. However, the private data of the QR barcode lacks adequate security protection. In this article, we design a secret QR sharing approach to protect the private QR data with a secure and reliable distributed system. The proposed approach differs from related QR code schemes in which it uses the QR characteristics to achieve secret sharing and can resist the print-and-scan operation. The secret can be split and conveyed with QR tags in the distribution application, and the system can retrieve the lossless secret when authorized participants cooperate. General browsers can read the original data from the marked QR tag via a barcode reader, and this helps reduce the security risk of the secret. Based on our experiments, the new approach is feasible and provides content readability, cheater detectability, and an adjustable secret payload of the QR barcode.

Big Data analytics. Three use cases with R, Python and Spark <=SEP=> Management and analysis of big data are systematically associated with a data distributed architecture in the Hadoop and now Spark frameworks. This article offers an introduction for statisticians to these technologies by comparing the performance obtained by the direct use of three reference environments : R, Python Scikit-learn, ∗Université de Toulouse – INSA, Institut de Mathématiques, UMR CNRS 5219 †Institut de Mathématiques, UMR CNRS 5219 ‡Université de Toulouse – UT3, Institut de Mathématiques, UMR CNRS 5219 1 ar X iv :1 60 9. 09 61 9v 1 [ st at .A P] 3 0 Se p 20 16 Spark MLlib on three public use cases : character recognition, recommending films, categorizing products. As main result, it appears that, if Spark is very efficient for data munging and recommendation by collaborative filtering (non-negative factorization), current implementations of conventional learning methods (logistic regression, random forests) in MLlib or SparkML do not ou poorly compete habitual use of these methods (R, Python Scikit-learn) in an integrated or undistributed architecture.

Analysis and Simulation of Theme Park Queuing System <=SEP=> It has been an important issue to improve customers' satisfaction in theme parks for which become a major role of recreation in our daily life. Waiting for rides has been identified as a factor decreasing satisfaction. A previous study indicated that a virtual queuing system can reduce the total waiting time so the customer's satisfaction is improved. The results from a simulation tool Arena show that an index Satisfaction Value (SV) increases when the queuing system is introduced. In this study, a more complex scenario of theme park queuing system (TPQS) is first designed, followed by comparison of a number of combinations of the rides with various waiting time and distribution factors. Analysis is also carried out.

Effectiveness Evaluation of Rule Based Classifiers for the Classification of Iris Data Set <=SEP=> ISSN 2250 – 1061 | © 2011 Bonfring Abstract--In machine learning, classification refers to a step by step procedure for designating a given piece of input data into any one of the given categories. There are many classification problem occurs and need to be solved. Different types are classification algorithms like tree-based, rule-based, etc are widely used. This work studies the effectiveness of Rule-Based classifiers for classification by taking a sample data set from UCI machine learning repository using the open source machine learning tool. A comparison of different rulebased classifiers used in Data Mining and a practical guideline for selecting the most suited algorithm for a classification is presented and some empirical criteria for describing and evaluating the classifiers are given.

Comparison of high-speed switched reluctance machines with conventional and toroidal windings <=SEP=> This paper presents designs of 50,000 rpm 6/4 switched reluctance motors (SRM's) with a focus of the comparative study on conventional and toroidal windings. There are four different machines compared in this paper, while the first is a conventional SRM, and the other three are toroidal winding machines. The first toroidal SRM (TSRM1) employs the conventional asymmetric convert and the same switching sequence as conventional SRM (CSRM). Therefore, an equivalent magnetic performance is observed. The second toroidal SRM (TSRM2) introduces a 12-switch converter topology. With a proper coil connection and switching sequence, all the coils are active and contribute to the flux and torque generation at the same time. The analysis shows that for the same amount of copper losses, TSRM2 yields a 50% higher output torque and power at rated speed than CSRM, while TSRM1 only generates half the torque of CSRM. The third toroidal SRM is a resized TSRM2, which is presented with the same envelope dimension of CSRM (same volumetric comparison). The comparison shows it's competitive to CSRM, especially as toroidal-winding can achieve higher filling factor during the manufacture process of winding.

Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition <=SEP=> Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state-of-the-art performance on some speech recognition tasks. To achieve a further performance improvement, in this research, deep extensions on LSTM are investigated considering that deep hierarchical model has turned out to be more efficient than a shallow one. Motivated by previous research on constructing deep recurrent neural networks (RNNs), alternative deep LSTM architectures are proposed and empirically evaluated on a large vocabulary conversational telephone speech recognition task. Meanwhile, regarding to multi-GPU devices, the training process for LSTM networks is introduced and discussed. Experimental results demonstrate that the deep LSTM networks benefit from the depth and yield the state-of-the-art performance on this task.

Efficient Architecture Search by Network Transformation <=SEP=> Techniques for automatically designing deep neural network architectures such as reinforcement learning based approaches have recently shown promising results. However, their success is based on vast computational resources (e.g. hundreds of GPUs), making them difficult to be widely used. A noticeable limitation is that they still design and train each network from scratch during the exploration of the architecture space, which is highly inefficient. In this paper, we propose a new framework toward efficient architecture search by exploring the architecture space based on the current network and reusing its weights. We employ a reinforcement learning agent as the meta-controller, whose action is to grow the network depth or layer width with function-preserving transformations. As such, the previously validated networks can be reused for further exploration, thus saves a large amount of computational cost. We apply our method to explore the architecture space of the plain convolutional neural networks (no skip-connections, branching etc.) on image benchmark datasets (CIFAR-10, SVHN) with restricted computational resources (5 GPUs). Our method can design highly competitive networks that outperform existing networks using the same design scheme. On CIFAR-10, our model without skip-connections achieves 4.23% test error rate, exceeding a vast majority of modern architectures and approaching DenseNet. Furthermore, by applying our method to explore the DenseNet architecture space, we are able to achieve more accurate networks with fewer parameters.

Iron Loss Reduction in an Interior PM Automotive Alternator <=SEP=> This paper examines the iron loss characteristics of a high-flux interior permanent-magnet machine. The machine was designed as a concept demonstrator for a 6-kW automotive alternator and has a wide field-weakening range. Initial experimental results revealed a high iron loss during field-weakening operation. Finite-element analysis was used to investigate the cause of the high iron losses and to predict their magnitude as a function of speed. The effects of changes in the machine design were examined in order to reduce iron losses and hence improve the machine performance

Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions <=SEP=> Deep autoregressive models have shown state-of-the-art performance in density estimation for natural images on large-scale datasets such as ImageNet. However, such models require many thousands of gradient-based weight updates and unique image examples for training. Ideally, the models would rapidly learn visual concepts from only a handful of examples, similar to the manner in which humans learns across many vision tasks. In this paper, we show how 1) neural attention and 2) meta learning techniques can be used in combination with autoregressive models to enable effective few-shot density estimation. Our proposed modifications to PixelCNN result in state-of-the art few-shot density estimation on the Omniglot dataset. Furthermore, we visualize the learned attention policy and find that it learns intuitive algorithms for simple tasks such as image mirroring on ImageNet and handwriting on Omniglot without supervision. Finally, we extend the model to natural images and demonstrate few-shot image generation on the Stanford Online Products dataset.

A Strategic Analysis of Electronic Marketplaces <=SEP=> 

On the Convergence of Adam and Beyond <=SEP=> 

New Fast and Accurate Jacobi SVD Algorithm. I <=SEP=> This paper is the result of contrived efforts to break the barrier between numerical accuracy and run time efficiency in computing the fundamental decomposition of numerical linear algebra – the singular value decomposition (SVD) of a general dense matrix. It is an unfortunate fact that the numerically most accurate one–sided Jacobi SVD algorithm is several times slower than generally less accurate bidiagonalization based methods such as the QR or the divide and conquer algorithm. Despite its sound numerical qualities, the Jacobi SVD is not included in the state of the art matrix computation libraries and it is even considered obsolete by some leading researches. Our quest for a highly accurate and efficient SVD algorithm has led us to a new, superior variant of the Jacobi algorithm. The new algorithm has inherited all good high accuracy properties, and it outperforms not only the best implementations of the one–sided Jacobi algorithm but also the QR algorithm. Moreover, it seems that the potential of the new approach is yet to be fully exploited.

Predictive Control in Power Electronics and Drives <=SEP=> Predictive control is a very wide class of controllers that have found rather recent application in the control of power converters. Research on this topic has been increased in the last years due to the possibilities of today's microprocessors used for the control. This paper presents the application of different predictive control methods to power electronics and drives. A simple classification of the most important types of predictive control is introduced, and each one of them is explained including some application examples. Predictive control presents several advantages that make it suitable for the control of power converters and drives. The different control schemes and applications presented in this paper illustrate the effectiveness and flexibility of predictive control.

Anomaly Detection using Autoencoders in High Performance Computing Systems <=SEP=> Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components. The current state of the art for automated anomaly detection employs Machine Learning methods or statistical regression models in a supervised fashion, meaning that the detection tool is trained to distinguish among a fixed set of behaviour classes (healthy and unhealthy states). We propose a novel approach for anomaly detection in High Performance Computing systems based on a Machine (Deep) Learning technique, namely a type of neural network called autoencoder. The key idea is to train a set of autoencoders to learn the normal (healthy) behaviour of the supercomputer nodes and, after training, use them to identify abnormal conditions. This is different from previous approaches which where based on learning the abnormal condition, for which there are much smaller datasets (since it is very hard to identify them to begin with). We test our approach on a real supercomputer equipped with a fine-grained, scalable monitoring infrastructure that can provide large amount of data to characterize the system behaviour. The results are extremely promising: after the training phase to learn the normal system behaviour, our method is capable of detecting anomalies that have never been seen before with a very good accuracy (values ranging between 88% and 96%).

Real-Time Standard Scan Plane Detection and Localisation in Fetal Ultrasound Using Fully Convolutional Neural Networks <=SEP=> Fetal mid-pregnancy scans are typically carried out according to fixed protocols. Accurate detection of abnormalities and correct biometric measurements hinge on the correct acquisition of clearly defined standard scan planes. Locating these standard planes requires a high level of expertise. However, there is a worldwide shortage of expert sonographers. In this paper, we consider a fully automated system based on convolutional neural networks which can detect twelve standard scan planes as defined by the UK fetal abnormality screening programme. The network design allows real-time inference and can be naturally extended to provide an approximate localisation of the fetal anatomy in the image. Such a framework can be used to automate or assist with scan plane selection, or for the retrospective retrieval of scan planes from recorded videos. The method is evaluated on a large database of 1003 volunteer mid-pregnancy scans. We show that standard planes acquired in a clinical scenario are robustly detected with a precision and recall of 69% and 80%, which is superior to the current state-of-the-art. Furthermore, we show that it can retrospectively retrieve correct scan planes with an accuracy of 71% for cardiac views and 81% for non-cardiac views.

Data Security and Privacy Protection Issues in Cloud Computing <=SEP=> It is well-known that cloud computing has many potential advantages and many enterprise applications and data are migrating to public or hybrid cloud. But regarding some business-critical applications, the organizations, especially large enterprises, still wouldn't move them to cloud. The market size the cloud computing shared is still far behind the one expected. From the consumers' perspective, cloud computing security concerns, especially data security and privacy protection issues, remain the primary inhibitor for adoption of cloud computing services. This paper provides a concise but all-round analysis on data security and privacy protection issues associated with cloud computing across all stages of data life cycle. Then this paper discusses some current solutions. Finally, this paper describes future research work about data security and privacy protection issues in cloud.

INTEGRATING MOOCs IN BLENDED COURSES <=SEP=> Recent studies appreciate that "MOOCs bring an impetus of reform, research and innovation to the Academy" [9]. Even though MOOCs are usually developed and delivered as independent online courses, experiments to wrap formal university courses around existing MOOCs are reported by teachers and researchers in different articles [3], [4], [8], [17]. This paper describes a new approach, in which the participation of students in different MOOCs was integrated in a blended course run on a social mobile LMS. The topics of MOOCs delivered on specific platforms and having particular characteristics were connected with the Fall 2013 undergraduate course of Web Programming, at University Politehnica Timisoara, Romania, facilitated by the first author, the co-authors providing a shadow/peer facilitation, for tailoring the course scenario. The main parts of this study deal with: a) The reasons to integrate MOOCs in the university course. b) How the course was designed, how the students’ activities on different MOOC platforms were assessed and integrated in the course scenario. c) The results of a survey that evaluates students’ experiences related to MOOCs: a number of MOOC features were assessed [14]; answers to a few problems are also analysed: Did the participation in MOOCs support students to clarify and expand the course issues? What are students' suggestions for a more active participation in MOOCs? By comparing the learning scenarios of MOOCs with the Web Programming blended course, how can the course and its virtual space be improved? Do students consider that the MOOCs phenomenon is important for professional and personal development? The conclusions of the paper can be used by other teachers/instructors for integrating MOOCs in the courses they deliver/facilitate.