title: Multi-Bernoulli sample consensus for simultaneous robust fitting of multiple structures in machine vision

abstract: In many image processing applications, such as parametric range and motion segmentation, multiple instances of a model are fitted to data points. The most common robust fitting method, RANSAC, and its extensions are normally devised to segment the structures sequentially, treating the points belonging to other structures as outliers. Thus, the ratio of inliers is small and successful fitting requires a very large number of random samples, incurring cumbrous computation. This paper presents a new method to simultaneously fit multiple structures to data points in a single run. We model the parameters of multiple structures as a random finite set with multi-Bernoulli distribution. Simultaneous search for all structure parameters is performed by Bayesian update of the multi-Bernoulli parameters. Experiments involving segmentation of numerous structures show that our method outperforms well-known methods in terms of estimation error and computational cost. The fast convergence and high accuracy of our method make it an excellent choice for real-time estimation and segmentation of multiple structures in image processing applications.


title: A Maximum Entropy Approach to Adaptive Statistical Language Modeling

abstract: An adaptive statistical language model is described, which successfully integrates long distance linguistic information with other knowledge sources. Most existing statistical language models exploit only the immediate history of a text. To extract information from further back in the document's history, we propose and usetrigger pairsas the basic information bearing elements. This allows the model to adapt its expectations to the topic of discourse. Next, statistical evidence from multiple sources must be combined. Traditionally, linear interpolation and its variants have been used, but these are shown here to be seriously deficient. Instead, we apply the principle of Maximum Entropy (ME). Each information source gives rise to a set of constraints, to be imposed on the combined estimate. The intersection of these constraints is the set of probability functions which are consistent with all the information sources. The function with the highest entropy within that set is the ME solution. Given consistent statistical evidence, a unique ME solution is guaranteed to exist, and an iterative algorithm exists which is guaranteed to converge to it. The ME framework is extremely general: any phenomenon that can be described in terms of statistics of the text can be readily incorporated. An adaptive language model based on the ME approach was trained on theWall Street Journalcorpus, and showed a 32–39% perplexity reduction over the baseline. When interfaced to SPHINX-II, Carnegie Mellon's speech recognizer, it reduced its error rate by 10–14%. This thus illustrates the feasibility of incorporating many diverse knowledge sources in a single, unified statistical framework.


title: Watson: Beyond Jeopardy!

abstract: This paper presents a vision for applying the Watson technology to health care and describes the steps needed to adapt and improve performance in a new domain. Specifically, it elaborates upon a vision for an evidence-based clinical decision support system, based on the DeepQA technology, that affords exploration of a broad range of hypotheses and their associated evidence, as well as uncovers missing information that can be used in mixed-initiative dialog. It describes the research challenges, the adaptation approach, and finally reports results on the first steps we have taken toward this goal.


Selective Data Acquisition for Machine Learning


title: Notice of Violation of IEEE Publication PrinciplesA greedy inverse kinematics algorithm for animating 3D signing avatars

abstract: The Inverse Kinematicss (IK) is a method for calculating the postures, in order to satisfy a given task by the user, based on degree of freedom of each joint. This method plays an important role in the animation and simulation of virtual characters to improve and facilitate the use of interfaces. In this paper, we present the description of an animation solver that allows the automatic moving of the different parts of a signing avatar body based on a greedy algorithm. The objective is to obtain a realistic animation of the virtual character that enables deaf person to visualize realistic gestures. Our approach is based on a thorough study of sign language and gestures classification. The challenge of this project is to find a good compromise between computational time and realistic representation that should be closer to real-time generation signs.


title: Replicating the Manchester Baby: motives, methods, and messages from the past

abstract: The University of Manchester's Small-Scale Experimental Machine (SSEM), known as the Baby, was rebuilt as a replica to celebrate, in June 1998, the 50th anniversary of the running of the world's first stored program. This article explains the background of the original Baby, and why and how a replica of it was built. The article concludes with some of the lessons learned from the project.


title: Modular analytics management architecture for interoperability and decision support

abstract: The Dual Node Decision Wheels (DNDW) architecture is a new approach to information fusion and decision support systems. By combining cognitive systems engineering organizational analysis tools, such as decision trees, with the Dual Node Network (DNN) technical architecture for information fusion, the DNDW can align relevant data and information products with an organization’s decision-making processes. In this paper, we present the Compositional Inference and Machine Learning Environment (CIMLE), a prototype framework based on the principles of the DNDW architecture. CIMLE provides a flexible environment so heterogeneous data sources, messaging frameworks, and analytic processes can interoperate to provide the specific information required for situation understanding and decision making. It was designed to support the creation of modular, distributed solutions over large monolithic systems. With CIMLE, users can repurpose individual analytics to address evolving decision-making requirements or to adapt to new mission contexts; CIMLE’s modular design simplifies integration with new host operating environments. CIMLE’s configurable system design enables model developers to build analytical systems that closely align with organizational structures and processes and support the organization’s information needs.


title: Superelastic and pH-Responsive Degradable Dendrimer Cryogels Prepared by Cryo-aza-Michael Addition Reaction

abstract: Dendrimers exhibit super atomistic features by virtue of their well-defined discrete quantized nanoscale structures. Here, we show that hyperbranched amine-terminated polyamidoamine (PAMAM) dendrimer G4.0 reacts with linear polyethylene glycol (PEG) diacrylate (575 g/mol) via the aza-Michael addition reaction at a subzero temperature (−20 °C), namely cryo-aza-Michael addition, to form a macroporous superelastic network, i.e., dendrimer cryogel. Dendrimer cryogels exhibit biologically relevant Young’s modulus, high compression elasticity and super resilience at ambient temperature. Furthermore, the dendrimer cryogels exhibit excellent rebound performance and do not show significant stress relaxation under cyclic deformation over a wide temperature range (−80 to 100 °C). The obtained dendrimer cryogels are stable at acidic pH but degrade quickly at physiological pH through self-triggered degradation. Taken together, dendrimer cryogels represent a new class of scaffolds with properties suitable for biomedical applications.


title: An optimization model for collaborative recommendation using a covariance-based regularizer

abstract: This paper suggests a convex regularized optimization model to produce recommendations, which is adaptable, fast, and scalable—while remaining very competitive to state-of-the-art methods in terms of accuracy. We introduce a regularizer based on the covariance matrix such that the model minimizes two measures ensuring that the recommendations provided to a user are guided by both the preferences of the other users in the system and the known preferences of the user being processed. It is adaptable since (1) it can be viewed from both user and item perspectives (allowing to choose, depending on the task, the formulation with fewer decision variables) and (2) multiple constraints depending on the context (and not only based on the accuracy, but also on the utility of personalized recommendations) can easily be added, as shown in this paper through two examples. Since our regularizer is based on the covariance matrix, this paper also describes how to improve computational and space complexities by using matrix factorization techniques in the optimization model, leading to a fast and scalable model. To illustrate all these concepts, experiments were conducted on four real datasets of different sizes (i.e., FilmTrust, Ciao, MovieLens, and Netflix) and comparisons with state-of-the-art methods are provided, showing that our context-sensitive approach is very competitive in terms of accuracy.


title: Using Wireless Sensor Networks for Fire Rescue Applications: Requirements and Challenges

abstract: Research in wireless sensor networks has attracted a lot of attention in recent years. Real applications, such as habitat monitoring, environmental and structural monitoring, start to work ni practical. In this paper, we argue that wirreless sensor network is a very promising technology for fire rescue applications. First, we abstract four specific requirements of this application, including accountability of firefighters, real-time monitoring, intelligent scheduling and resource allocation, and web-enabled service and integration. To meet these requirements, we propose FireNet, a wireless sensor network architecture for this specific type of application. Based on these requirements and the characteristics of wireless sensor networks, several research challenges in terms of new protocols as well as hardware and software support are examined. Finally, we conclude that wireless sensor network is a very powerful and suitable tool to be applied in this application.


title: A BL-CSC Converter-Fed BLDC Motor Drive With Power Factor Correction

abstract: This paper presents a power factor correction (PFC)-based bridgeless canonical switching cell (BL-CSC) converter-fed brushless dc (BLDC) motor drive. The proposed BL-CSC converter operating in a discontinuous inductor current mode is used to achieve a unity power factor at the ac mains using a single voltage sensor. The speed of the BLDC motor is controlled by varying the dc bus voltage of the voltage source inverter (VSI) feeding the BLDC motor via a PFC converter. Therefore, the BLDC motor is electronically commutated such that the VSI operates in fundamental frequency switching for reduced switching losses. Moreover, the bridgeless configuration of the CSC converter offers low conduction losses due to partial elimination of diode bridge rectifier at the front end. The proposed configuration shows a considerable increase in efficiency as compared with the conventional scheme. The performance of the proposed drive is validated through experimental results obtained on a developed prototype. Improved power quality is achieved at the ac mains for a wide range of control speeds and supply voltages. The obtained power quality indices are within the acceptable limits of IEC 61000-3-2.


title: Automatic visual inspection and defect detection on variable data prints

abstract: We present a system for automatic, on-line visual inspection and print defect detection for variable data printing (VDP). This system can be used to automatically stop the printing process and alert the operator to problems. We lay out the components required for constructing a vision-based inspection system and show that our approach is novel for the high-speed detection of defects on variable data. When implemented in a high-speed digital printing press, the system allows a single skilled operator to monitor and maintain several presses, reducing the number of operators required to run a shop floor of presses as well as reduce wasted consumables when a defect goes undetected.


title: Myers-Briggs Type Indicator (MBTI): Some Psychometric Limitations

abstract: The present paper critically reviews the psychometric adequacy of the Myers-Briggs Type Indicator (MBTI). Although the instrument is extremely popular in applied settings, there is an urgent need for the development of valid and comprehensive local norms, in order to increase its predictive validity and utility within the Australian context. In addition, there is a number of psychometric limitations pertaining to the reliability and validity of the MBTI, which raise concerns about its use by practitioners. In view of these seri- ous limitations, routine use of the MBTI is not recommended, and psychologists should be cautious as to its likely misuse in various organisational and occupational settings.


title: Integrating Deep Learning Based Perception with Probabilistic Logic via Frequent Pattern Mining

abstract: The bridging of the gap between 1) subsymbolic pattern recognition and learning algorithms and 2) symbolic reasoning algorithms, has been a major issue for AI since the early days of the field. One class of approaches involves integrating subsymbolic and symbolic systems, but this raises the question of how to effectively translate between the very different languages involved. In the approach described here, a frequent subtree mining algorithm is used to identify recurrent patterns in the state of a hierarchical deep learning system (DeSTIN) that is exposed to visual stimuli. The relationships between state-subtrees and percepts are then input to a probabilistic logic system (OpenCog's Probabilistic Logic Networks), which conducts uncertain inferences using them as axioms. The core conceptual idea is to use patterns in the states inferred by a perceptual hierarchy, as inputs to an uncertain logic system. Simple illustrative examples are presented based on the presentation of images of typed letters to DeSTIN. This work forms a component of a larger project to integrate perceptual, motoric and cognitive processing within the integrative OpenCog cognitive architecture.


title: Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning

abstract: Visual language grounding is widely studied in modern neural image captioning systems, which typically adopts an encoder-decoder framework consisting of two principal components: a convolutional neural network (CNN) for image feature extraction and a recurrent neural network (RNN) for language caption generation. To study the robustness of language grounding to adversarial perturbations in machine vision and perception, we propose Show-and-Fool, a novel algorithm for crafting adversarial examples in neural image captioning. The proposed algorithm provides two evaluation approaches, which check whether neural image captioning systems can be mislead to output some randomly chosen captions or keywords. Our extensive experiments show that our algorithm can successfully craft visually-similar adversarial examples with randomly targeted captions or keywords, and the adversarial examples can be made highly transferable to other image captioning systems. Consequently, our approach leads to new robustness implications of neural image captioning and novel insights in visual language grounding.


title: Intention to use new mobile payment systems: a comparative analysis of SMS and NFC payments

abstract: AbstractThe rapid growth of mobile technology among the world’s population has led many companies to attempt to exploit mobile devices as an additional tool in the business of sales. In this sense, the main objective of our study resides in comparing the factors that determine the acceptance by consumers of the SMS (Short Message Service) and NFC (Near Field Communication) mobile payment systems as examples of means of future payment. The model used in our research applies the classic variables of the Technology Acceptance Model, as well as that of Perceived Security, a model deriving from the review of the major relevant recent literature. The results achieved in this study demonstrate that there are differences in the factors that determine the acceptance in each of the systems, as well as the level of the Intention to Use. Finally, we highlight the main implications for management and cite some strategies to reinforce this new business in the context of new technical developments.


title: Predicting Failures of Vision Systems

abstract: Computer vision systems today fail frequently. They also fail abruptly without warning or explanation. Alleviating the former has been the primary focus of the community. In this work, we hope to draw the community's attention to the latter, which is arguably equally problematic for real applications. We promote two metrics to evaluate failure prediction. We show that a surprisingly straightforward and general approach, that we call ALERT, can predict the likely accuracy (or failure) of a variety of computer vision systems--semantic segmentation, vanishing point and camera parameter estimation, and image memorability prediction--on individual input images. We also explore attribute prediction, where classifiers are typically meant to generalize to new unseen categories. We show that ALERT can be useful in predicting failures of this transfer. Finally, we leverage ALERT to improve the performance of a downstream application of attribute prediction: zero-shot learning. We show that ALERT can outperform several strong baselines for zero-shot learning on four datasets.


title: Utilization of stored elastic energy in leg extensor muscles by men and women.

abstract: An alternating cycle of eccentric-concentric contractions in locomotion represents a sequence when storage and utilization of elastic energy takes place. It is possible that this storage capacity and its utilization depends on the imposed stretch loads in activated muscles, and that sex differences may be present in these phenomena. To investigate these assumed differences, subjects from both sexes and of good physical condition performed vertical jumps on the force-platform from the following experimental conditions: squatting jump (SJ) from a static starting position; counter-movement jump (CMJ) from a free standing position and with a preparatory counter-movement; drop jumps (DJ) from the various heights (20 to 100 cm) on to the platform followed immediately by a vertical jump. In all subjects the SJ, in which condition no appreciable storage of elastic energy takes place, produced the lowest height of rise of the whole body center of gravity (C.G.). The stretch load (drop height) influenced the performance so that height of rise of C. of G. increased when the drop height increased from 26 up to 62 cm (males) and from 20 to 50 cm (females). In all jumping conditions the men jumped higher than the women. However, examination of the utilization of elastic energy indicated that in CMJ the female subjects were able to utilize most (congruent to 90%) of the energy produced in the prestretching phase. Similarly, in DJ the overall change in positive energy over SJ condition was higher in women as compared to men. Thus the results suggest that although the leg extensor muscles of the men subjects could sustain much higher stretch loads, the females may be able to utilize a greater portion of the stored elastic energy in jumping activities.


title: An Experiment in Teaching Cognitive Systems Online

abstract: In Fall 2014 we offered an online course CS 7637 Knowledge-Based Artificial Intelligence: Cognitive Systems (KBAI) to about 200 students as part of the Georgia Tech Online MS in CS program. We incorporated lessons from learning science into the design of the project-based online KBAI course. We embedded ~150 microexercises and ~100 AI nanotutors into the online videos. As a quasi-experiment, we ran a typical inperson class with 75 students in parallel, with the same course syllabus, structure, assignments, projects and examinations. Based on the feedback of the students in the online KBAI class, and comparison of their performance with the students in the inperson class, the online course appears to have been a success. In this paper, we describe the design, development and delivery of the online KBAI class. We also discuss the evaluation of the course.


title: Identifying Framing Bias in Online News

abstract: It has been observed that different media outlets exert bias in the way they report the news, which seamlessly influences the way that readers’ knowledge is built through filtering what we read. Therefore, understanding bias in news media is fundamental for obtaining a holistic view of a news story. Traditional work has focused on biases in terms of “agenda setting,” where more attention is allocated to stories that fit their biased narrative. The corresponding method is straightforward, since the bias can be detected through counting the occurrences of different stories/themes within the documents. However, these methods are not applicable to biases which are implicit in wording, namely, “framing” bias. According to framing theory, biased communicators will select and emphasize certain facts and interpretations over others when telling their story. By focusing on facts and interpretations that conform to their bias, they can tell the story in a way that suits their narrative. Automatic detection of framing bias is challenging since nuances in the wording can change the interpretation of the story. In this work, we aim to investigate how the subtle pattern hidden in language use of a news agency can be discovered and further leveraged to detect frames. In particular, we aim to identify the type and polarity of frame in a sentence. Extensive experiments are conducted on real-world data from different countries. A case study is further provided to reveal possible applications of the proposed method.


title: Smart city architecture for community level services through the internet of things

abstract: Today, more than half of the world's population spend their lives in cities, and this number will jump to 70 percent by 2050. Increasing population density in urban environments demands adequate provision of services and infrastructure. This explosion in city population will present major challenges including air pollution, traffic congestion, health concerns, energy and waste management. Solution to these challenges might require the integration of various Information and Communication Technologies into the artifact of the city. This paper presents an architecture for smart cities, where city management, community service providers and citizens have access to real time data which has been gathered using various sensory mechanisms in order to analyze and make decisions for future planning.


title: Dependency Parsing with LSTMs: An Empirical Evaluation

abstract: We propose a transition-based dependency parser using Recurrent Neural Networks with Long Short-Term Memory (LSTM) units. This extends the feedforward neural network parser of Chen and Manning (2014) and enables modelling of entire sequences of shift/reduce transition decisions. On the Google Web Treebank, our LSTM parser is competitive with the best feedforward parser on overall accuracy and notably achieves more than 3% improvement for long-range dependencies, which has proved difficult for previous transition-based parsers due to error propagation and limited context information. Our findings additionally suggest that dropout regularisation on the embedding layer is crucial to improve the LSTM's generalisation.


title: Design of 60 GHz Vector Modulator Based Active Phase Shifter

abstract: This paper presents a design of 60 GHz active phase shifter for next generation mm-Wave WPAN using 90 nm TSMC CMOS technology. The vector modulator based phase shifter is chosen as a prototype because it provides accurate resolution over 360 degree. The phase shifter is controlled by 5 bit digitally controlled by binary current weighted DAC. The miniaturized Marchand balun and 2 stage poly phase filter are used to generate differential I/Q signal from single signal. The three CS amplifiers are implemented for providing gain to the proposed circuit. The measured phase resolution of the circuit is 11.250 with maximum 30 phase error. The peak gain from the S-parameter measurement is 9 dB while drawing 50 mA from a 1.2 V supply.


title: Algorithms for Lipschitz Learning on Graphs

abstract: We develop fast algorithms for solving regression problems on graphs where one is given the value of a function at some vertices, and must find its smoothest possible extension to all vertices. The extension we compute is the absolutely minimal Lipschitz extension, and is the limit for large $p$ of $p$-Laplacian regularization. We present an algorithm that computes a minimal Lipschitz extension in expected linear time, and an algorithm that computes an absolutely minimal Lipschitz extension in expected time $\widetilde{O} (m n)$. The latter algorithm has variants that seem to run much faster in practice. These extensions are particularly amenable to regularization: we can perform $l_{0}$-regularization on the given values in polynomial time and $l_{1}$-regularization on the initial function values and on graph edge weights in time $\widetilde{O} (m^{3/2})$.


title: FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis

abstract: Convolutional neural networks (CNNs) have massively impacted visual recognition in 2D images, and are now ubiquitous in state-of-the-art approaches. CNNs do not easily extend, however, to data that are not represented by regular grids, such as 3D shape meshes or other graph-structured data, to which traditional local convolution operators do not directly apply. To address this problem, we propose a novel graph-convolution operator to establish correspondences between filter weights and graph neighborhoods with arbitrary connectivity. The key novelty of our approach is that these correspondences are dynamically computed from features learned by the network, rather than relying on predefined static coordinates over the graph as in previous work. We obtain excellent experimental results that significantly improve over previous state-of-the-art shape correspondence results. This shows that our approach can learn effective shape representations from raw input coordinates, without relying on shape descriptors.


title: Hybridization capture reveals microbial diversity missed using current profiling methods

abstract: BACKGROUND ::: Microorganisms comprise the majority of living organisms on our planet. For many years, exploration of the composition of microbial communities has been performed through the PCR-based study of the small subunit rRNA gene due to its high conservation across the domains of life. The application of this method has resulted in the discovery of many unexpected evolutionary lineages. However, amplicon sequencing is subject to numerous biases, with some taxa being missed, and is limited by the read length of second-generation sequencing platforms, which drastically reduces the phylogenetic resolution. ::: ::: ::: RESULTS ::: Here, we describe a hybridization capture strategy that allows the enrichment of 16S rRNA genes from metagenomic samples and enables an exhaustive identification and a complete reconstruction of the biomarker. Applying this approach to a microbial mock community and a soil sample, we demonstrated that hybridization capture is able to reveal greater microbial diversity than 16S rDNA amplicon sequencing and shotgun sequencing. The reconstruction of full-length 16S rRNA genes facilitated the improvement of phylogenetic resolution and the discovery of novel prokaryotic taxa. ::: ::: ::: CONCLUSIONS ::: Our results demonstrate that hybridization capture can lead to major breakthroughs in our understanding of microbial diversity, overcoming the limitations of conventional 16S rRNA gene studies. If applied to a broad range of environmental samples, this innovative approach could reveal the undescribed diversity of the still underexplored microbial communities and could provide a better understanding of ecosystem function.


title: Modeling and protection of electric vehicle charging station

abstract: An Electric Vehicle Charging Station (EVCS) for fast DC charging of multiple Electric Vehicles (EVs) simultaneously is studied in this paper. A brief insight is put into the modeling aspects of EVCS in order to explain the control and stability of DC bus voltage in spite of varying load. Possible faults that generally occur on DC side are discussed. Faults of various fault impedances have been introduced at various fault locations of EVCS and corresponding effects on EVs are analyzed. Short comparison has been carried out distinguishing the advantages and disadvantages of DC bus over AC counterpart. An appropriate protection strategy has been suggested and put to use in order to alleviate the faults, mainly to ensure safe charging of EVs connected. All the simulations are performed in MATLAB®/Simulink and results show feasibility of proposed model. A scope for further improving the proposed model has been discussed for research purpose.


title: Open semantic annotation of scientific publications using DOMEO

abstract: BackgroundOur group has developed a useful shared software framework for performing, versioning, sharing and viewing Web annotations of a number of kinds, using an open representation model.MethodsThe Domeo Annotation Tool was developed in tandem with this open model, the Annotation Ontology (AO). Development of both the Annotation Framework and the open model was driven by requirements of several different types of alpha users, including bench scientists and biomedical curators from university research labs, online scientific communities, publishing and pharmaceutical companies.Several use cases were incrementally implemented by the toolkit. These use cases in biomedical communications include personal note-taking, group document annotation, semantic tagging, claim-evidence-context extraction, reagent tagging, and curation of textmining results from entity extraction algorithms.ResultsWe report on the Domeo user interface here. Domeo has been deployed in beta release as part of the NIH Neuroscience Information Framework (NIF, http://www.neuinfo.org) and is scheduled for production deployment in the NIF’s next full release.Future papers will describe other aspects of this work in detail, including Annotation Framework Services and components for integrating with external textmining services, such as the NCBO Annotator web service, and with other textmining applications using the Apache UIMA framework.


title: 3D Visualization of Multiscale Video Key Frames

abstract: In this paper, an innovative 3D visualization tool is proposed to facilitate the quickly browsing and understanding of the video sequence for users. Taking advantage of the major windows, our tool presents the multiscale key frames and the video content clearly and effectively. Namely, KF View provides a wonderful navigation of the video key frames with different levels of details. Frame View presents an interesting view of the whole video content. SIM View allows an expressive exploration of the similarities between key frames and also between key frames and the video frames. Importantly, together with many convenient and attractive interactions, this tool is quite efficient to help users grasp the video information soundly.


title: Inverse Depth Parametrization for Monocular SLAM

abstract: We present a new parametrization for point features within monocular simultaneous localization and mapping (SLAM) that permits efficient and accurate representation of uncertainty during undelayed initialization and beyond, all within the standard extended Kalman filter (EKF). The key concept is direct parametrization of the inverse depth of features relative to the camera locations from which they were first viewed, which produces measurement equations with a high degree of linearity. Importantly, our parametrization can cope with features over a huge range of depths, even those that are so far from the camera that they present little parallax during motion---maintaining sufficient representative uncertainty that these points retain the opportunity to "come in'' smoothly from infinity if the camera makes larger movements. Feature initialization is undelayed in the sense that even distant features are immediately used to improve camera motion estimates, acting initially as bearing references but not permanently labeled as such. The inverse depth parametrization remains well behaved for features at all stages of SLAM processing, but has the drawback in computational terms that each point is represented by a 6-D state vector as opposed to the standard three of a Euclidean XYZ representation. We show that once the depth estimate of a feature is sufficiently accurate, its representation can safely be converted to the Euclidean XYZ form, and propose a linearity index that allows automatic detection and conversion to maintain maximum efficiency---only low parallax features need be maintained in inverse depth form for long periods. We present a real-time implementation at 30 Hz, where the parametrization is validated in a fully automatic 3-D SLAM system featuring a handheld single camera with no additional sensing. Experiments show robust operation in challenging indoor and outdoor environments with a very large ranges of scene depth, varied motion, and also real time 360deg loop closing.


title: Treebank-Based Multilingual Unification-Grammar Development

abstract: Broad-coverage, deep unification grammar development is time-consuming and costly. This problem can be exacerbated ::: in multilingual grammar development scenarios. Recently (Cahill et al., 2002) presented a treebank-based methodology ::: to semi-automatically create broadcoverage, deep, unification grammar resources for English. In this paper we ::: present a project which adapts this model to a multilingual grammar development scenario to obtain robust, wide-coverage, probabilistic Lexical-Functional Grammars ::: (LFGs) for English and German via automatic f-structure annotation algorithms based on the Penn-II and TIGER ::: treebanks. We outline our method used to extract a probabilistic LFG from the TIGER treebank and report on the quality of the f-structures produced. We achieve an f-score of 66.23 on the evaluation of 100 random sentences against a manually constructed gold standard.


title: Malware detection based on opcode frequency

abstract: Malware is a computer program or a piece of software that is designed to penetrate and detriment computers without owner's permission. There are different malware types such as viruses, rootkits, keyloggers, worms, trojans, spywares, ransomware, backdoors, bots, logic bomb, etc. Volume, Variant and speed of propagation of malwares are increasing every year. Antivirus companies are receiving thousands of malwares on the daily basis, so detection of malwares is complex and time consuming task. There are many malwares detection techniques like signature based detection, behavior based detection and machine learning based techniques, etc. The signatures based detection system fails for new unknown malware. In case of behavior based detection, if the antivirus program identify attempt to change or alter a file or communication over internet then it will generate alarm signal, but still there is a chance of false positive rate. Also the obfuscation and polymorphism techniques are hinderers the malware detection process. In this paper we propose new method to detect malwares based on the frequency of opcodes in the portable executable file. This research applied machine learning algorithm to find false positives, false negatives, true positives and true negatives for malwares and got 96.67 per cent success rate.


title: A Survey on Data Quality: Classifying Poor Data

abstract: Data is part of our everyday life and an essential asset in numerous businesses and organizations. The quality of the data, i.e., the degree to which the data characteristics fulfill requirements, can have a tremendous impact on the businesses themselves, the companies, or even in human lives. In fact, research and industry reports show that huge amounts of capital are spent to improve the quality of the data being used in many systems, sometimes even only to understand the quality of the information in use. Considering the variety of dimensions, characteristics, business views, or simply the specificities of the systems being evaluated, understanding how to measure data quality can be an extremely difficult task. In this paper we survey the state of the art in classification of poor data, including the definition of dimensions and specific data problems, we identify frequently used dimensions and map data quality problems to the identified dimensions. The huge variety of terms and definitions found suggests that further standardization efforts are required. Also, data quality research on Big Data appears to be in its initial steps, leaving open space for further research.


title: Automatic facial attribute analysis via adaptive sparse representation of random patches

abstract: A new general algorithm that is able to recognize a wide range of facial attributes.It has been evaluated in the recognition of expressions, gender, race, disguise and beard.It obtained a performance at least comparable with that achieved by state-of-art techniques.It proposes a new representation for the gallery images based on random patches.It proposes a new representation for the testing image using sparse representations. Display Omitted It is well known that some facial attributes -like soft biometric traits- can increase the performance of traditional biometric systems and help recognition based on human descriptions. In addition, other facial attributes, such as facial expressions, can be used in human-computer interfaces, image retrieval, talking heads and human emotion analysis. This paper addresses the problem of automated recognition of facial attributes by proposing a new general approach called Adaptive Sparse Representation of Random Patches (ASR+). The proposed method consists of two stages: in the learning stage, random patches are extracted from representative face images of each class (e.g., in gender recognition -a two-class problem-, images of females/males) in order to construct representative dictionaries. A stop list is used to remove very common words of the dictionaries. In the testing stage, random test patches of the query image are extracted, and for each non-stopped test patch a dictionary is built concatenating the 'best' representative dictionary of each class. Using this adapted dictionary, each non-stopped test patch is classified following the Sparse Representation Classification (SRC) methodology. Finally, the query image is classified by patch voting. Thus, our approach is able to learn a model for each recognition task dealing with a larger degree of variability in ambient lighting, pose, expression, occlusion, face size and distance from the camera. Experiments were carried out on eight face databases in order to recognize facial expression, gender, race, disguise and beard. Results show that ASR+ deals well with unconstrained conditions, outperforming various representative methods in the literature in many complex scenarios.


title: Effects of silver nanoparticles in combination with antibiotics on the resistant bacteria Acinetobacter baumannii

abstract: Acinetobacter baumannii resistance to carbapenem antibiotics is a serious clinical challenge. As a newly developed technology, silver nanoparticles (AgNPs) show some excellent characteristics compared to older treatments, and are a candidate for combating A. baumannii infection. However, its mechanism of action remains unclear. In this study, we combined AgNPs with antibiotics to treat carbapenem-resistant A. baumannii (aba1604). Our results showed that single AgNPs completely inhibited A. baumannii growth at 2.5 μg/mL. AgNP treatment also showed synergistic effects with the antibiotics polymixin B and rifampicin, and an additive effect with tigecyline. In vivo, we found that AgNPs–antibiotic combinations led to better survival ratios in A. baumannii-infected mouse peritonitis models than that by single drug treatment. Finally, we employed different antisense RNA-targeted Escherichia coli strains to elucidate the synergistic mechanism involved in bacterial responses to AgNPs and antibiotics.


title: Diamond-shaped Ge and Ge0.9Si0.1 gate-all-around nanowire FETs with four {111} facets by dry etch technology

abstract: We propose a feasible pathway to scale the Ge MOSFET technology by using a novel diamond-shaped Ge and Ge09Si01 gate-all-around (GAA) nanowire (NW) FETs with four {111} facets. The device fabrication requires only simple top-down dry etching and blanket Ge epitaxy techniques readily available in mass production. The proposed dry etching process involves three isotropic/anisotropic etching steps with different Cl2/HBr ratios for forming the suspended diamond-shaped channel. Taking advantages of the GAA configuration, favorable carrier mobility of the {111} surface, nearly defect-free suspended channel, and improved dopant activation by incorporating Si, nFET and pFET with excellent performance have been demonstrated, including an Ion/Ioff ratio exceeding 108, the highest ever reported for Ge-based pFETs.


title: Informed Recommender: Basing Recommendations on Consumer Product Reviews

abstract: Recommender systems attempt to predict items in which a user might be interested, given some information about the user's and items' profiles. Most existing recommender systems use content-based or collaborative filtering methods or hybrid methods that combine both techniques (see the sidebar for more details). We created Informed Recommender to address the problem of using consumer opinion about products, expressed online in free-form text, to generate product recommendations. Informed recommender uses prioritized consumer product reviews to make recommendations. Using text-mining techniques, it maps each piece of each review comment automatically into an ontology.


title: Multi-label automatic GrabCut for image segmentation

abstract: This paper presents a multi-label automatic GrabCut technique for the problem of image segmentation. GrabCut is considered as one of the binary-label segmentation techniques because it is based on the famous s/t graph cut minimization technique for image segmentation. This paper extends the automatic binary-label GrabCut to a multi-label technique that can segment a given image into its natural segments without user intervention. Since multi-label segmentation is an NP-hard problem, the proposed algorithm converts the segmentation problem into multiple iterative piecewise binary label GrabCut segmentations. This implies separating one segment from the image, under consideration, per iteration. In this way, the proposed algorithm maintains the powerful advantage of the GrabCut to get the optimal solution for the segmentation problem. Evaluation of the segmentation results was carried out using different accuracy metrics from the literature. The evaluations were conducted with human ground truth segmentations from Berkeley benchmark dataset of natural images. Although human segmentations are semantically more meaningful, experiments showed that the proposed multi-label GrabCut provided matching segmentation results to that of individual humans with acceptable accuracy.


title: The Drosophila Imd Signaling Pathway

abstract: The fruit fly, Drosophila melanogaster, has helped us to understand how innate immunity is activated. In addition to the Toll receptor and the Toll signaling pathway, the Drosophila immune response is regulated by another evolutionarily conserved signaling cascade, the immune deficiency (Imd) pathway, which activates NF-κB. In fact, the Imd pathway controls the expression of most of the antimicrobial peptides in Drosophila; thus, it is indispensable for normal immunity in flies. In this article, we review the current literature on the Drosophila Imd pathway, with special emphasis on its role in the (patho)physiology of different organs. We discuss the systemic response, as well as local responses, in the epithelial and mucosal surfaces and the nervous system.


Use of tubulization (nerve conduits) in repairing nerve defects in children


title: Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification

abstract: Multi-label classification is the task of predicting a set of labels for a given input instance. Classifier chains are a state-of-the-art method for tackling such problems, which essentially converts this problem into a sequential prediction problem, where the labels are first ordered in an arbitrary fashion, and the task is to predict a sequence of binary values for these labels. In this paper, we replace classifier chains with recurrent neural networks, a sequence-to-sequence prediction algorithm which has recently been successfully applied to sequential prediction tasks in many domains. The key advantage of this approach is that it allows to focus on the prediction of the positive labels only, a much smaller set than the full set of possible labels. Moreover, parameter sharing across all classifiers allows to better exploit information of previous decisions. As both, classifier chains and recurrent neural networks depend on a fixed ordering of the labels, which is typically not part of a multi-label problem specification, we also compare different ways of ordering the label set, and give some recommendations on suitable ordering strategies.


title: A Verilog Model of Adaptable Traffic Control System Using Mealy State Machines

abstract: In this paper an efficient traffic control system is designed using Mealy finite state machines. The effects of state encoding schemes like sequential encoding, gray encoding and One-Hot encoding are compared on the basis of size of synthesized circuit. Coding of the design is done in Verilog HDL and the design is tested and simulated on Spartan-3 xc3s400 FPGA development kit. The sensors are added as input to the controller for emergency conditions like ambulance etc. This system is also capable to change the timings of traffic signals according to the density of vehicles on the roads. The design has several benefits over ordinary traffic light controllers built with microcontrollers or Programmable logic controller such as simple structure, high reliability, low costs, ease in installation and maintenance. The design of adaptable traffic control system is carried out for a chowk consisting of five roads. Each road is divided into main road (for straight movement) and cross road (for crossing). To some extent, traffic jam, unreasonably latency time of stoppage of vehicles, emergency vehicles like ambulances, VIP movement or forcibly passing is solved. So, this system carries a broader application prospect.


title: Stack4Things: Integrating IoT with OpenStack in a Smart City context

abstract: As the adoption of embedded systems, mobiles and other smart devices keeps rising, and the scope of their involvement broadens, for instance in the enablement of Smart City-like scenarios, a pressing need emerges to tame such complexity and reuse as much tooling as possible without resorting to vertical ad-hoc solutions, while at the same time taking into account valid options with regards to infrastructure management, and other more advanced functionalities. In this sense, a widely used and competitive framework for Infrastructure as a Service such as OpenStack, with its breadth in terms of feature coverage and expanded scope, looks like fitting the bill. This work thus describes rationale, efforts, and results so far achieved, for an integration of IoT paradigms and resource ecosystems with such a kind of Cloud-oriented environment, by focusing on a Smart City scenario, and featuring data collection and visualization as example use cases of such integration.


title: Development of a case-based reasoning cognitive engine for IEEE 802.22 WRAN applications

abstract: On Nov. 4 2008, the Federal Communications Commission adopted rules for unlicensed use of television white spaces. The IEEE 802.22 Wireless Regional Area Networks (WRAN) standard is the first IEEE standard utilizing cognitive radio (CR) technology to exploit the television white space. A decision engine that is able to respond to the changes in the radio environment is necessary to efficiently exploit underutilized spectrum resources and avoid interfering with the licensed systems (e.g., TV services). This paper discusses the development of a case-based reasoning cognitive engine (CBR-CE) for the IEEE 802.22 WRAN applications. The performance of the CBR-CE is evaluated under various radio scenarios and compared to that of several multi objective search based algorithms, including the hill climbing search (HCS) and the genetic algorithm (GA). The simulation results show that the developed CBR-CE can achieve comparable utility with faster adaptation than the search based cognitive engines after appropriate training / learning. The learning process of the CBR is also simulated and discussed.


title: Metabolism of metalaxyl in cell suspension cultures of Lactuca sativa L. and Vitis vinifera L

abstract: Abstract The metabolic fate of metalaxyl in suspension cultures of Lactuca sativa has been investigated as a function of explant source, culture age, and nutrient medium quality. In all cases, the fungicide was the subject of diverse metabolic attack yielding products which arose from O- and N-dealkylation, alkyl and aryl hydroxylation, and ester hydrolysis. Substantial amounts of metabolites were found in the culture filtrates although products arising from aryl hydroxylation and combined O-/N-dealkylation accumulated exclusively as cellular conjugates. Cultures newly isolated from the cotyledon, hypocotyl, and root of seedlings displayed very little variation in the rate and qualitative nature of breakdown and in the relative distribution of metabolites among the polar conjugate fraction, unconjugated cellular fraction, and culture filtrate. Similarly, metabolism proceeded independently of changes in nutrient medium composition. A suspension culture initiated from 4-year-old hypocotyl callus produced less identifiable metabolites and had an impaired ability to perform alkyl hydroxylation, O-dealkylation, and ester hydrolysis. A greater proportion of metabolites occurred as polar conjugates in the 4-year-old hypocotyl cells than that in the newly isolated hypocotyl line. The metabolic profile seen in Vitis vinifera was similar to that in L. sativa and a time-course study indicated some interconversion of metabolites and a change in the ratio of two atropisomers formed by alkyl hydroxylation.


title: ICT enabling technologies for smart cities

abstract: A smart city adjusts its social, business, and natural needs, improving the assets it has accessible. Information and Communications Technology (ICT) for shrewd urban areas is to give city answers for encourage an improvement and manageability of a city for the advantage of its population, its economy, and the greater ecosystem in the city. It is to gauge a keen city as far as the enhancements in personal satisfaction and monetary prosperity that are accomplished through applying ICT innovations to design, outline, fabricate, and work the city foundation. In smart city applications, the initial phase in the information's voyage through the application is its gathering by the diverse advancements conveyed all through the city. This paper surveys data acquisition technologies such as Sensor Networks, MANETs, Unmanned Aerial Vehicles (UAVs), Vehicular Ad hoc Networks (VANETs), Internet of Things (IoT), Software-Defined Networking (SDN), Network Functions Virtualization (NFV), 5G. Next, it demonstrates information processing technologies, for example, Cloud Platform, IoT Platform, Big Data Platform, Machine Learning, Deep Learning, and IoT Analytics. Encouraging data spread between various nodes is vital to savvy city acknowledgment. Last, because of the presence of various types of end users (e.g., residents, organizations, government offices, and so forth.) requiring distinctive levels of nature of management, the paper exhibits a proposed testbed solution and recent associated experiments.


title: Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function

abstract: In this paper we develop a randomized block-coordinate descent method for minimizing the sum of a smooth and a simple nonsmooth block-separable convex function and prove that it obtains an $\epsilon$-accurate solution with probability at least $1-\rho$ in at most $O(\tfrac{n}{\epsilon} \log \tfrac{1}{\rho})$ iterations, where $n$ is the number of blocks. For strongly convex functions the method converges linearly. This extends recent results of Nesterov [Efficiency of coordinate descent methods on huge-scale optimization problems, CORE Discussion Paper #2010/2], which cover the smooth case, to composite minimization, while at the same time improving the complexity by the factor of 4 and removing $\epsilon$ from the logarithmic term. More importantly, in contrast with the aforementioned work in which the author achieves the results by applying the method to a regularized version of the objective function with an unknown scaling factor, we show that this is not necessary, thus achieving true iteration complexity bounds. In the smooth case we also allow for arbitrary probability vectors and non-Euclidean norms. Finally, we demonstrate numerically that the algorithm is able to solve huge-scale $\ell_1$-regularized least squares and support vector machine problems with a billion variables.


Market Reaction to Announcement of the Corporate Governance “Comply or Explain” Regulation and Determinants of Compliance Level


title: User-Constrained Multimodal Route Planning

abstract: In the multimodal route planning problem, we are given multiple transportation networks (e.g., pedestrian, road, public transit) and ask for a best integrated journey between two points. The main challenge is that a seemingly optimal journey may have changes between networks that do not reflect the user’s modal preferences. In fact, quickly computing reasonable multimodal routes remains a challenging problem: previous approaches either suffer from poor query performance or their available choices of modal preferences during query time is limited. In this work, we focus on computing exact multimodal journeys that can be restricted by specifying arbitrary modal sequences at query time. For example, a user can say whether he or she wants to only use public transit, prefers to also use a taxi or walking at the beginning or end of the journey, or has no restrictions at all. By carefully adapting node contraction, a common ingredient to many speedup techniques on road networks, we are able to compute point-to-point queries on a continental network combined of cars, railroads, and flights several orders of magnitude faster than Dijkstra’s algorithm. Thereby, we require little space overhead and obtain fast preprocessing times.


title: Feedforward technique for offset cancellation in broadband differential amplifiers

abstract: This paper presents a new feedforward offset cancellation technique for broadband amplifiers which are used in high speed communication systems. Unlike other conventional techniques, such as feedback cancellation or AC coupling, the proposed technique eliminates the need for larger area passive components and avoids the potential stability issue by eliminating the feedback path. Implementing in 90nm CMOS, the broadband amplifier achieves 52.3 dB gain and 2.7 GHz 3-dB bandwidth with more than 50 dB DC offset rejection.


title: ISD-WiFi: An intelligent SDN based solution for enterprise WLANs

abstract: With the development of SDN(Software-Defined Networking) and WiFi, it is very important and useful to integrate them under one unified architecture. SDN controller can manage routers, switches and WiFi's APs with standard OpenFlow protocol and provide end-to-end control, by which flexible management strategies can be achieved to satisfy various requirements of future network. In this paper, we propose an intelligent SDN based solution for enterprise WLANs and put forward an unified management architecture in the solution. New mobility and interference management schemes are designed under this architecture. Simulation results show that our solutions perform better than current solutions in mobility and interference management.


title: Dynamic Compressive Wide-Band Spectrum Sensing Based on Channel Energy Reconstruction in Cognitive Internet of Things

abstract: For wireless networks in the Internet of Things (IoT), cognitive radio (CR) is a promising way to obtain the available spectrum for objects. Wide-band spectrum sensing plays an important role in building such CR networks of IoT. In this paper, we propose a novel dynamic compressive wide-band spectrum sensing method based on channel energy reconstruction. After a bank of wide-band random filters is employed to measure the channel energy, rather than to recover all the channel energy in the whole spectrum, only the channel energy with a changing occupancy status in consecutive time slots is recovered. Furthermore, it is unnecessary to use reconstruction algorithm unless there are two or more channels changing their occupancy status. Compared to the existing methods, our proposed schemes bear significant improvements in the probability of detection and reduction of probability of false alarms. Simulation results also show its fast speed and robustness to noise.


title: Real-time pixel luminance optimization for dynamic multi-projection mapping

abstract: Using projection mapping enables us to bring virtual worlds into shared physical spaces. In this paper, we present a novel, adaptable and real-time projection mapping system, which supports multiple projectors and high quality rendering of dynamic content on surfaces of complex geometrical shape. Our system allows for smooth blending across multiple projectors using a new optimization framework that simulates the diffuse direct light transport of the physical world to continuously adapt the color output of each projector pixel. We present a real-time solution to this optimization problem using off-the-shelf graphics hardware, depth cameras and projectors. Our approach enables us to move projectors, depth camera or objects while maintaining the correct illumination, in realtime, without the need for markers on the object. It also allows for projectors to be removed or dynamically added, and provides compelling results with only commodity hardware.


title: Analyzing Aggregated Semantics-enabled User Modeling on Google+ and Twitter for Personalized Link Recommendations

abstract: In this paper, we study if reusing Google+ profiles can provide reliable recommendations on Twitter to resolve the cold start problem. Next, we investigate the impact of giving different weights for aggregating user profiles from two OSNs and present that giving a higher weight to the targeted OSN profile for aggregation allows the best performance in the context of a personalized link recommender system. Finally, we propose a user modeling strategy which combines entity-and category-based user profiles using with a discounting strategy. Results show that our proposed strategy improves the quality of user modeling significantly compared to the baseline method.


title: Consumer Acceptance and Use of Instagram

abstract: This study extends the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model to investigate factors affecting the acceptance and use of a social networking service (SNS) called Instagram. The UTAUT2 model is modified to better suit the context of SNSs by replacing the price value construct with self-congruence. Furthermore, we explore the effects of behavioral intention and use behavior on "user indegree" defined as the number of people who follow an SNS user. The results of the survey study largely support the hypothesized model in the context of Instagram. The findings contribute to previous knowledge by demonstrating the important roles of hedonic motivation and habit in consumer acceptance and use of SNSs, and by providing novel insights into how users can attract followers within the social networks.


title: Data Mining and Soft Computing using Support Vector Machine: A Survey

abstract: In this paper, the basic concepts and survey of the available literature on Support Vector Machines (SVM) in data mining and soft computing research area is provided. While at the time of survey several new methods were found related to SVM like as Support Vector Representation and Discrimination Machine (SVRDM), Recursive SVM (RSVM), On-line Independent SVM (OISVM), Pruning SVM, Fast Nearest Neighbor Condensation classifier (FCNNSVM), Improved SV Clustering (iSVC), Cost-sensitive SVM (2v-SVM), 2C-SVM, Profile SVM (PSVM), Twin SVM (TWSVM), Twin Bounded SVM (TBSVM), Parametricmargin n-SVM (par-n-SVM), Twin Parametric-Margin SVM (TPMSVM), Structural Twin SVM (S-TWSVM), Hierarchical Linear SVM (H-LSVM), Bio-SVM, FuzzySVMCIL, Kernel Fuzzy C-Means clustering-based Fuzzy SVM (KFCM-FSVM), Multi-Class Instance Selection (MCIS). After studied these methods a comparative and analytical survey upon those methods are presented here. Also a large future scope is available on several techniques and they are discussed in this paper.


title: SIMPSON'S PARADOX IN ARTIFICIAL INTELLIGENCE AND IN REAL LIFE

abstract: “Simpson's paradox,” first described nearly a century ago, is an anomaly that sometimes arises from pooling data. Dramatic instances of the paradox have occurred in real life in the domains of epidemiology and admissions policies. Many writers have recently described hypothetical examples of the paradox arising in other areas of life and it seems possible that the paradox may occur frequently in mundane domains but with less serious implications. Thus, it is not surprising that the paradox should arise in commonsense reasoning, that subarea of artificial intelligence that seeks to axiomatize reasoning in such mundane domains. It arises as the problem “approximate proof by cases” and the question of whether to accept it may well depend on whether we wish to construct performance or competence models of reasoning. This article gives a brief history of the paradox and discusses its occurrence in our own discipline. It argues that if the paradox occurs frequently but undramatically in real life, every uncertain reasoning system will have to deal with the problem in some way.


title: Cloud-based smart waste management for smart cities

abstract: With the ever increasing population, urbanization, migration issues, and change in lifestyle, municipal solid waste generation levels are increasing significantly. Hence, waste management becomes a challenge faced not only by the developing nations, but also the developed and advanced countries. The overall waste management involves three main types of entities: 1) users who generate waste, 2) waste collectors/city admin., 3) stakeholders. Waste management directly effects the lifestyle, healthcare, environment, recycling and disposal, and several other industries. Current waste management trends are not sophisticated enough to achieve a robust and efficient waste management mechanism. It is very important to have a smart way of managing waste, so that not only the waste status is notified in-time when to be collected, but also, all the stakeholders are made aware in timely fashion that what type of waste in what quantity is coming up at what particular time. This will not only help in attracting and identifying stakeholders, but also aids in creating more effective ways of recycling and minimizing waste also making the overall waste management more efficient and environment friendly. Keeping all this in mind, we propose a cloud-based smart waste management mechanism in which the waste bins are equipped with sensors, capable of notifying their waste level status and upload the status to the cloud. The stakeholders are able to access the desired data from the cloud. Moreover, for city administration and waste management, it will be possible to do route optimization and select path for waste collection according to the statuses of waste bins in a metropolis, helping in fuel and time efficiency.


title: Optimization Algorithms and Applications for Speech and Language Processing

abstract: Optimization techniques have been used for many years in the formulation and solution of computational problems arising in speech and language processing. Such techniques are found in the Baum-Welch, extended Baum-Welch (EBW), Rprop, and GIS algorithms, for example. Additionally, the use of regularization terms has been seen in other applications of sparse optimization. This paper outlines a range of problems in which optimization formulations and algorithms play a role, giving some additional details on certain application problems in machine translation, speaker/language recognition, and automatic speech recognition. Several approaches developed in the speech and language processing communities are described in a way that makes them more recognizable as optimization procedures. Our survey is not exhaustive and is complemented by other papers in this volume.


title: Online multimodal deep similarity learning with application to image retrieval

abstract: Recent years have witnessed extensive studies on distance metric learning (DML) for improving similarity search in multimedia information retrieval tasks. Despite their successes, most existing DML methods suffer from two critical limitations: (i) they typically attempt to learn a linear distance function on the input feature space, in which the assumption of linearity limits their capacity of measuring the similarity on complex patterns in real-world applications; (ii) they are often designed for learning distance metrics on uni-modal data, which may not effectively handle the similarity measures for multimedia objects with multimodal representations. To address these limitations, in this paper, we propose a novel framework of online multimodal deep similarity learning (OMDSL), which aims to optimally integrate multiple deep neural networks pretrained with stacked denoising autoencoder. In particular, the proposed framework explores a unified two-stage online learning scheme that consists of (i) learning a flexible nonlinear transformation function for each individual modality, and (ii) learning to find the optimal combination of multiple diverse modalities simultaneously in a coherent process. We conduct an extensive set of experiments to evaluate the performance of the proposed algorithms for multimodal image retrieval tasks, in which the encouraging results validate the effectiveness of the proposed technique.


title: Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks

abstract: In this paper we propose a deep learning solution to age estimation from a single face image without the use of facial landmarks and introduce the IMDB-WIKI dataset, the largest public dataset of face images with age and gender labels. If the real age estimation research spans over decades, the study of apparent age estimation or the age as perceived by other humans from a face image is a recent endeavor. We tackle both tasks with our convolutional neural networks (CNNs) of VGG-16 architecture which are pre-trained on ImageNet for image classification. We pose the age estimation problem as a deep classification problem followed by a softmax expected value refinement. The key factors of our solution are: deep learned models from large data, robust face alignment, and expected value formulation for age regression. We validate our methods on standard benchmarks and achieve state-of-the-art results for both real and apparent age estimation.


title: WEKA: a machine learning workbench

abstract: WEKA is a workbench for machine learning that is intended to aid in the application of machine learning techniques to a variety of real-world problems, in particular, those arising from agricultural and horticultural domains. Unlike other machine learning projects, the emphasis is on providing a working environment for the domain specialist rather than the machine learning expert. Lessons learned include the necessity of providing a wealth of interactive tools for data manipulation, result visualization, database linkage, and cross-validation and comparison of rule sets, to complement the basic machine learning tools. >


title: Low side-lobe substrate integrated cavity antenna array using unequal microstrip-ridge gap waveguide feeding network at 94 GHz

abstract: In this paper, a 4×32 low side-lobe slot antenna array using unequal microstrip-ridge gap waveguide (GWG) feeding network at 94 GHz is presented. The slot antenna array is built by two double-sided printed circuit boards (PCBs). The top one employs SIW-based 2×2 subarrays and the bottom one is a broadband 2×16 way unequal GWG feeding network. Applying Taylor amplitude weighting in the 16 way GWG feeding network, low side-lobe performance is achieved. The unequal T-junction dividers with phase compensation are proposed and designed for various output ratios. Simulated results show that the antenna array achieves 5 GHz bandwidth with a peak gain of 26.3 dBi at 94 GHz. Across the entire band, low side lobe level (SLL) below −20 dB is realized.


title: Extracting N-gram terms collocation from tagged Arabic corpus

abstract: Information Extraction (IE) is one of the most important Natural Language Processing (NLP) applications, which extracts information such as Named-Entities (NE) and collocation of terms from the corpus. Collocation is a sequence of terms that co-occur together in the corpus. In Arabic Information Extraction, there are many problems because of the complex of Arabic's grammar and ambiguity. In general, in linguistics research, the more efficient corpus is the one annotated by Part of Speech Tagging (POST). In this paper, we propose a prototype that extracts collocation of N-gram words (from 2–6 gram) based on the sequence of POST from Arabic Quran corpus. This approach extracts the collocation of N-gram words by matching the input structured pattern of Arabic language versus the Part of Speech Tagging of Quran corpus. The system enables users to select a sequence of tags (2–6 gram) and scope of the corpus source (whole Quran Corpus or specific Surah). To show how the system is beneficial for linguistic research, a set of experiments has been conducted in different scenarios.


title: AprilTag: A robust and flexible visual fiducial system

abstract: While the use of naturally-occurring features is a central focus of machine perception, artificial features (fiducials) play an important role in creating controllable experiments, ground truthing, and in simplifying the development of systems where perception is not the central objective. We describe a new visual fiducial system that uses a 2D bar code style “tag”, allowing full 6 DOF localization of features from a single image. Our system improves upon previous systems, incorporating a fast and robust line detection system, a stronger digital coding system, and greater robustness to occlusion, warping, and lens distortion. While similar in concept to the ARTag system, our method is fully open and the algorithms are documented in detail.


title: Rich Short Text Conversation Using Semantic-Key-Controlled Sequence Generation

abstract: With the recent advances of the sequence-to-sequence framework, generation approaches for the short text conversation STC become attractive. The traditional sequence-to-sequence approaches for the STC often suffer from poor diversity and general reply without substantiality. It is also hard to control the topic or semantics of the selected reply from multiple generated candidates. In this paper, a novel external-memory-driven sequence-to-sequence learning approach is proposed to address these problems. A tensor of the external memory is constructed to represent interpretable topics or semantics. During generation, a controllable memory trigger is extracted given the input sequence, and a reply is then generated using the memory trigger as well as the sequence-to-sequence model. Experiments show that the proposed approach can generate much richer diversity than the traditional sequence-to-sequence training with attention. Meanwhile, it achieves better quality score in human evaluation. It is also observed that by manually manipulating the memory trigger, it is possible to interpretably guide the topics or semantics of the reply.


title: AutoTutor : A simulation of a human tutor Action

abstract: AutoTutor is a computer tutor that simulates the discourse patterns and pedagogical strategies of a typical human tutor. AutoTutor is designed to assist college students in learning the fundamentals of hardware, operating systems, and the Internet in an introductory computer literacy course. Most tutors in school systems are not highly trained in tutoring techniques and have only a modest expertise on the tutoring topic, but they are surprisingly effective in producing learning gains in students. We have dissected the discourse and pedagogical strategies these unskilled tutors exhibit by analyzing approximately 100 hours of naturalistic tutoring sessions. These mechanisms are implemented in AutoTutor. AutoTutor presents questions and problems from a curriculum script, attempts to comprehend learner contributions that are entered by keyboard, formulates dialog moves that are sensitive to the learner's contributions (such as short feedback, pumps, prompts, elaborations, corrections, and hints), and delivers the dialog moves with a talking head. AutoTutor has seven modules: a curriculum script, language extraction, speech act classification, latent semantic analysis, topic selection, dialog move generation, and a talking head.


title: An evaluation of alternative architectures for transaction processing in the cloud

abstract: Cloud computing promises a number of advantages for the deployment of data-intensive applications. One important promise is reduced cost with a pay-as-you-go business model. Another promise is (virtually) unlimited throughput by adding servers if the workload increases. This paper lists alternative architectures to effect cloud computing for database applications and reports on the results of a comprehensive evaluation of existing commercial cloud services that have adopted these architectures. The focus of this work is on transaction processing (i.e., read and update workloads), rather than analytics or OLAP workloads, which have recently gained a great deal of attention. The results are surprising in several ways. Most importantly, it seems that all major vendors have adopted a different architecture for their cloud services. As a result, the cost and performance of the services vary significantly depending on the workload.


title: Design of a mathematical expression understanding system

abstract: Abstract A scientific document usually consists of text and mathematical expressions. In this paper, we present a system for segmenting and understanding text and mathematical expressions in a document. The system can be divided into six stages: page segmentation and labeling, character segmentation, feature extraction, character recognition, expression formation, and error correction and expression extraction. After we extract all text lines in a document, we separate all symbols in each text line and calculate direction-feature vectors and aspect ratios for those symbols. Then, a nearest-neighbor algorithm recognizes characters. In the expression formation stage, we build a symbol relation tree for each text line that represents the relationships among the symbols in the text line. Each text line is decomposed into a collection of primitive tokens: operands, operators and separators. Heuristic rules based on these primitive tokens are used to correct text recognition errors. Finally, we extract all mathematical expressions according to basic expression forms. Several pages of documents were scanned to test the method. All mathematical expressions are understood. In the expressions generated, a few symbols are misrecognized. The average recognition rate was 96.16%.


title: Control of Single-Phase-to-Three-Phase AC/DC/AC PWM Converters for Induction Motor Drives

abstract: This paper proposes a novel control scheme of single-phase-to-three-phase pulsewidth-modulation (PWM) converters for low-power three-phase induction motor drives, where a single-phase half-bridge PWM rectifier and a two-leg inverter are used. With this converter topology, the number of switching devices is reduced to six from ten in the case of full-bridge rectifier and three-leg inverter systems. In addition, the source voltage sensor is eliminated with a state observer, which controls the deviation between the model current and the system current to be zero. A simple scalar voltage modulation method is used for a two-leg inverter, and a new technique to eliminate the effect of the dc-link voltage ripple on the inverter output current is proposed. Although the converter topology itself is of lower cost than the conventional one, it retains the same functions such as sinusoidal input current, unity power factor, dc-link voltage control, bidirectional power flow, and variable-voltage and variable-frequency output voltage. The experimental results for the V/f control of 3-hp induction motor drives controlled by a digital signal processor TMS320C31 chip have verified the effectiveness of the proposed scheme


Conditional Generative Adversarial Networks for Commonsense Machine Comprehension


title: Artificial Intelligence in Cyber Threats Intelligence

abstract: In the field of Cyber Security there has been a transition from the stage of Cyber Criminality to the stage of Cyber War over the last few years. According to the new challenges, the expert community has two main approaches: to adopt the philosophy and methods of Military Intelligence, and to use Artificial Intelligence methods for counteraction of Cyber Attacks. Тhis paper describes some of the results obtained at Technical University of Sofia in the implementation of project related to the application of intelligent methods for increasing the security in computer networks. The analysis of the feasibility of various Artificial Intelligence methods has shown that a method that is equally effective for all stages of the Cyber Intelligence cannot be identified. While for Tactical Cyber Threats Intelligence has been selected and experimented a Multi-Agent System, the Recurrent Neural Networks are offered for the needs of Operational Cyber Threats Intelligence.


title: Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems

abstract: Automatic machine learning systems can inadvertently accentuate and perpetuate inappropriate human biases. Past work on examining inappropriate biases has largely focused on just individual systems. Further, there is no benchmark dataset for examining inappropriate biases in systems. Here for the first time, we present the Equity Evaluation Corpus (EEC), which consists of 8,640 English sentences carefully chosen to tease out biases towards certain races and genders. We use the dataset to examine 219 automatic sentiment analysis systems that took part in a recent shared task, SemEval-2018 Task 1 'Affect in Tweets'. We find that several of the systems show statistically significant bias; that is, they consistently provide slightly higher sentiment intensity predictions for one race or one gender. We make the EEC freely available.


title: An Energy-Efficient Programmable Manycore Accelerator for Personalized Biomedical Applications

abstract: Wearable personalized health monitoring systems can offer a cost-effective solution for human health care. These systems must constantly monitor patients’ physiological signals and provide highly accurate, and quick processing and delivery of the vast amount of data within a limited power and area footprint. These personalized biomedical applications require sampling and processing multiple streams of physiological signals with a varying number of channels and sampling rates. The processing typically consists of feature extraction, data fusion, and classification stages that require a large number of digital signal processing (DSP) and machine learning (ML) kernels. In response to these requirements, in this paper, a tiny, energy-efficient, and domain-specific manycore accelerator referred to as power-efficient nanoclusters (PENC) is proposed to map and execute the kernels of these applications. Simulation results show that the PENC is able to reduce energy consumption by up to 80% and 25% for DSP and ML kernels, respectively, when optimally parallelized. In addition, we fully implemented three compute-intensive personalized biomedical applications, namely, multichannel seizure detection, multiphysiological stress detection, and standalone tongue drive system (sTDS), to evaluate the proposed manycore performance relative to commodity embedded CPU, graphical processing unit (GPU), and field-programmable gate array (FPGA)-based implementations. For these three case studies, the energy consumption and the performance of the proposed PENC manycore, when acting as an accelerator along with an Intel Atom processor as a host, are compared with the existing commercial off-the-shelf general-purpose, customizable, and programmable embedded platforms, including Intel Atom, Xilinx Artix-7 FPGA, and NVIDIA TK1 advanced RISC machine -A15 and K1 GPU system on a chip. For these applications, the PENC manycore is able to significantly improve throughput and energy efficiency by up to $1872{\times}$ and $276{\times} $ , respectively. For the most computational intensive application of seizure detection, the PENC manycore is able to achieve a throughput of 15.22 giga-operations-per-second (GOPs), which is a $14{\times} $ improvement in throughput over custom FPGA solution. For stress detection, the PENC achieves a throughput of 21.36 GOPs and an energy efficiency of 4.23 GOP/J, which is $14.87{\times} $ and $2.28{\times} $ better over FPGA implementation, respectively. For the sTDS application, the PENC improves a throughput by $5.45{\times} $ and an energy efficiency by $2.37{\times} $ over FPGA implementation.


title: State Aware Imitation Learning

abstract: Imitation learning is the study of learning how to act given a set of demonstrations provided by a human expert. It is intuitively apparent that learning to take optimal actions is a simpler undertaking in situations that are similar to the ones shown by the teacher. However, imitation learning approaches do not tend to use this insight directly. In this paper, we introduce State Aware Imitation Learning (SAIL), an imitation learning algorithm that allows an agent to learn how to remain in states where it can confidently take the correct action and how to recover if it is lead astray. Key to this algorithm is a gradient learned using a temporal difference update rule which leads the agent to prefer states similar to the demonstrated states. We show that estimating a linear approximation of this gradient yields similar theoretical guarantees to online temporal difference learning approaches and empirically show that SAIL can effectively be used for imitation learning in continuous domains with non-linear function approximators used for both the policy representation and the gradient estimate.


title: Recognizing People by Their Gait: The Shape of Motion

abstract: Videre: Journal of Computer Vision Research (ISSN 1089-2788) is a quarterly journal published electronically on the Internet by The MIT Press, Cambridge, Massachusetts, 02142. Subscriptions and address changes should be addressed to MIT Press Journals, Five Cambridge Center, Cambridge, MA 02142; phone: (617) 253-2889; fax: (617) 577-1545; e-mail: journals-orders@mit.edu. Subscription rates are: Individuals $30.00, Institutions $125.00. Canadians add additional 7% GST. Prices subject to change without notice.


title: Edge Weight Prediction in Weighted Signed Networks

abstract: Weighted signed networks (WSNs) are networks in which edges are labeled with positive and negative weights. WSNs can capture like/dislike, trust/distrust, and other social relationships between people. In this paper, we consider the problem of predicting the weights of edges in such networks. We propose two novel measures of node behavior: the goodness of a node intuitively captures how much this node is liked/trusted by other nodes, while the fairness of a node captures how fair the node is in rating other nodes' likeability or trust level. We provide axioms that these two notions need to satisfy and show that past work does not meet these requirements for WSNs. We provide a mutually recursive definition of these two concepts and prove that they converge to a unique solution in linear time. We use the two measures to predict the edge weight in WSNs. Furthermore, we show that when compared against several individual algorithms from both the signed and unsigned social network literature, our fairness and goodness metrics almost always have the best predictive power. We then use these as features in different multiple regression models and show that we can predict edge weights on 2 Bitcoin WSNs, an Epinions WSN, 2 WSNs derived from Wikipedia, and a WSN derived from Twitter with more accurate results than past work. Moreover, fairness and goodness metrics form the most significant feature for prediction in most (but not all) cases.


title: Molecular characterization of an influenza A virus (H4N2) isolated from waterfowl habitats in the State of Mexico

abstract: Wild waterfowl and their habitats are the main reservoirs of influenza A virus (IAV) mainly during the breeding season and prior to migration. This study describes the molecular characterization of an IAV isolated from 240 water samples of a small wetland during non-breeding season of migratory wild ducks in the State of Mexico, Mexico. The results showed that the virus belongs to the H4N2 subtype and each of its eight segments of the viral genome has similarity to IAV isolated from ducks in North America. This study suggests that IAV can be isolated from small wetland during non-breeding season of migrating waterfowl.


title: IOFlow: a software-defined storage architecture

abstract: In data centers, the IO path to storage is long and complex. It comprises many layers or "stages" with opaque interfaces between them. This makes it hard to enforce end-to-end policies that dictate a storage IO flow's performance (e.g., guarantee a tenant's IO bandwidth) and routing (e.g., route an untrusted VM's traffic through a sanitization middlebox). These policies require IO differentiation along the flow path and global visibility at the control plane. We design IOFlow, an architecture that uses a logically centralized control plane to enable high-level flow policies. IOFlow adds a queuing abstraction at data-plane stages and exposes this to the controller. The controller can then translate policies into queuing rules at individual stages. It can also choose among multiple stages for policy enforcement. We have built the queue and control functionality at two key OS stages-- the storage drivers in the hypervisor and the storage server. IOFlow does not require application or VM changes, a key strength for deployability. We have deployed a prototype across a small testbed with a 40 Gbps network and storage devices. We have built control applications that enable a broad class of multi-point flow policies that are hard to achieve today.


title: Kambin's Triangle Approach of Lumbar Transforaminal Epidural Injection with Spinal Stenosis

abstract: Objective ::: To compare the short-term effect and advantage of transforaminal epidural steroid injection (TFESI) performed using the Kambin's triangle and subpedicular approaches.


title: Spatial Generalization and Aggregation of Massive Movement Data

abstract: Movement data (trajectories of moving agents) are hard to visualize: numerous intersections and overlapping between trajectories make the display heavily cluttered and illegible. It is necessary to use appropriate data abstraction methods. We suggest a method for spatial generalization and aggregation of movement data, which transforms trajectories into aggregate flows between areas. It is assumed that no predefined areas are given. We have devised a special method for partitioning the underlying territory into appropriate areas. The method is based on extracting significant points from the trajectories. The resulting abstraction conveys essential characteristics of the movement. The degree of abstraction can be controlled through the parameters of the method. We introduce local and global numeric measures of the quality of the generalization, and suggest an approach to improve the quality in selected parts of the territory where this is deemed necessary. The suggested method can be used in interactive visual exploration of movement data and for creating legible flow maps for presentation purposes.


title: FFT-based full-search block matching using overlap-add method

abstract: One category of fast full-search block matching algorithms (BMAs) is based on the fast Fourier transformation (FFT). In conventional methods in this category, the macroblock size must be adjusted to the search window size by zero-padding. In these methods, the memory consumption and computational complexity heavily depend on the size difference between the macroblock and the search window. Thus, we propose a novel FFT-based BMA to solve this problem. The proposed method divides the search window into multiple sub search windows to versatilely control the difference between the macroblock and the search window sizes. Simulation results show the effectiveness of the proposed method.


title: Random subspace method for multivariate feature selection

abstract: In a growing number of domains data captured encapsulates as many features as possible. This poses a challenge to classical pattern recognition techniques, since the number of samples often still is limited with respect to the number of features. Classical pattern recognition methods suffer from the small sample size, and robust classification techniques are needed. In order to reduce the dimensionality of the feature space, the selection of informative features becomes an essential step towards the classification task. The relevance of the features can be evaluated either individually (univariate approaches), or in a multivariate manner. Univariate approaches are simple and fast, therefore appealing. However, possible correlation and dependencies between the features are not considered. Therefore, multivariate search techniques may be helpful. Several limitations restrict the use of multivariate searches. First, they are prone to overtraining, especially in [email protected]?n (many features and few samples) settings. Secondly, they can be computationally too expensive when dealing with a large feature space. We introduce a new multivariate search technique, that is less sensitive to the noise in the data and computationally feasible as well. We compare our approach with several multivariate and univariate feature selection techniques, on an artificial dataset which provides us with ground truth information, and on a real dataset. The results show the importance of multivariate search techniques and the robustness and reliability of our novel multivariate feature selection method.


title: Comparable TNF-alpha, IFN-gamma and GM-CSF production by purified normal marrow CD3 cells in response to horse anti-lymphocyte and rabbit antithymocyte globulin

abstract: In vitro priming of T cell with horse antilymphocyte globulin (HALG) results in cytokine release, and this has been associated with its clinical efficacy in patients with severe aplastic anaemia (SAA). Rabbit antithymocyte globulin (RATG) has been studied less extensively. In this study we compare the in vitro priming effect of HALG and RATG on purified normal marrow T cells: end-points of the study were 1) levels of TNF-alpha (TNF-alpha), IFN-gamma (IFN-gamma) GM-CSF in T cell supernatants, and 2) effect of T cell supernatants on colony formation with or without exogenous GM-CSF TNF-alpha, IFN-gamma and GM-CSF levels were comparable for HALG, RATG and phytohaemagglutinin (PHA). T cell supernatants showed comparable enhancement of colony formation in the presence of recombinant human GM-CSF (rhGM-CSF) and supported colony forming unit granulomacrophage (CFU-GM) growth in the absence of growth factor. This study shows that horse and rabbit derived ALG/ATG and PHA have a comparable in vitro priming effect on T cells: both agents should probably be tested for their clinical efficacy in SAA patients.


title: Artwork creation by a cognitive architecture integrating computational creativity and dual process approaches

abstract: Abstract The paper proposes a novel cognitive architecture (CA) for computational creativity based on the Psi model and on the mechanisms inspired by dual process theories of reasoning and rationality. In recent years, many cognitive models have focused on dual process theories to better describe and implement complex cognitive skills in artificial agents, but creativity has been approached only at a descriptive level. In previous works we have described various modules of the cognitive architecture that allows a robot to execute creative paintings. By means of dual process theories we refine some relevant mechanisms to obtain artworks, and in particular we explain details about resolution level of the CA dealing with different strategies of access to the Long Term Memory (LTM) and managing the interaction between S1 and S2 processes of the dual process theory. The creative process involves both divergent and convergent processes in either implicit or explicit manner. This leads to four activities (exploratory, reflective, tacit, and analytic) that, triggered by urges and motivations, generate creative acts. These creative acts exploit both the LTM and the WM in order to make novel substitutions to a perceived image by properly mixing parts of pictures coming from different domains. The paper highlights the role of the interaction between S1 and S2 processes, modulated by the resolution level which focuses the attention of the creative agent by broadening or narrowing the exploration of novel solutions, or even drawing the solution from a set of already made associations. An example of artificial painter is described in some experimentations by using a robotic platform.


title: Pharmacokinetic-Pharmacodynamic Modeling of Antibacterial Drugs

abstract: Pharmacokinetic-pharmacodynamic (PKPD) modeling and simulation has evolved as an important tool for rational drug development and drug use, where developed models characterize both the typical trends in the data and quantify the variability in relationships between dose, concentration, and desired effects and side effects. In parallel, rapid emergence of antibiotic-resistant bacteria imposes new challenges on modern health care. Models that can characterize bacterial growth, bacterial killing by antibiotics and immune system, and selection of resistance can provide valuable information on the interactions between antibiotics, bacteria, and host. Simulations from developed models allow for outcome predictions of untested scenarios, improved study designs, and optimized dosing regimens. Today, much quantitative information on antibiotic PKPD is thrown away by summarizing data into variables with limited possibilities for extrapolation to different dosing regimens and study populations. In vitro studies allow for flexible study designs and valuable information on time courses of antibiotic drug action. Such experiments have formed the basis for development of a variety of PKPD models that primarily differ in how antibiotic drug exposure induces amplification of resistant bacteria. The models have shown promise for efficacy predictions in patients, but few PKPD models describe time courses of antibiotic drug effects in animals and patients. We promote more extensive use of modeling and simulation to speed up development of new antibiotics and promising antibiotic drug combinations. This review summarizes the value of PKPD modeling and provides an overview of the characteristics of available PKPD models of antibiotics based on in vitro, animal, and patient data.


title: Identifying Sentiment-Based Contradictions

abstract: Contradiction Analysis is a relatively new multidisciplinary and complex area with the main goal of identifying contradictory pieces of text. It can be addressed from the perspectives of different research areas such as Natural Language Processing, Opinion Mining, Information Retrieval, and Information Extraction. This article focuses on the problem of detecting sentiment-based contradictions which occur in the sentences of a given review text. Unlike other types of contradictions, the detection of sentiment-based contradictions can be tackled as a post-processing step in the traditional sentiment analysis task. In this context, we adapted and extended an existing contradiction analysis framework by filtering its results to remove the reviews that are erroneously labeled as contradictory. The filtering method is based on two simple term similarity algorithms which relies on sets of known positive and negative words. An experimental evaluation on real product reviews has shown proportional improvements of up to 30% in classification accuracy and 26% in the precision of contradiction detection by considering an manual selection of positive and negative words. When the sets of positive and negative words are automatically selected, the improvements are of up to 22% in classification accuracy and 37% in the precision of contradiction detection.