title: Model-induced term-weighting schemes for text classification

abstract: The bag-of-words representation of text data is very popular for document classification. In the recent literature, it has been shown that properly weighting the term feature vector can improve the classification performance significantly beyond the original term-frequency based features. In this paper we demystify the success of the recent term-weighting strategies as well as provide possibly more reasonable modifications. We then propose novel term-weighting schemes that can be induced from the well-known document probabilistic models such as the Naive Bayes and the multinomial term model. Interestingly, some of the intuition-based term-weighting schemes coincide exactly with the proposed derivations. Our term-weighting schemes are tested on large-scale text classification problems/datasets where we demonstrate improved prediction performance over existing approaches.


title: Beyond the hype: Big data concepts, methods, and analytics

abstract: We define what is meant by big data.We review analytics techniques for text, audio, video, and social media data.We make the case for new statistical techniques for big data.We highlight the expected future developments in big data analytics. Size is the first, and at times, the only dimension that leaps out at the mention of big data. This paper attempts to offer a broader definition of big data that captures its other unique and defining characteristics. The rapid evolution and adoption of big data by industry has leapfrogged the discourse to popular outlets, forcing the academic press to catch up. Academic journals in numerous disciplines, which will benefit from a relevant discussion of big data, have yet to cover the topic. This paper presents a consolidated description of big data by integrating definitions from practitioners and academics. The paper's primary focus is on the analytic methods used for big data. A particular distinguishing feature of this paper is its focus on analytics related to unstructured data, which constitute 95% of big data. This paper highlights the need to develop appropriate and efficient analytical methods to leverage massive volumes of heterogeneous data in unstructured text, audio, and video formats. This paper also reinforces the need to devise new tools for predictive analytics for structured big data. The statistical methods in practice were devised to infer from sample data. The heterogeneity, noise, and the massive size of structured big data calls for developing computationally efficient algorithms that may avoid big data pitfalls, such as spurious correlation.


title: A novel line-to-line back EMF calculation for sensorless brushless DC motor drives

abstract: This paper proposes a novel line-to-line back EMF detection method for sensorless brushless DC (BLDC) motor drives. The actual phase back EMF signal can be indirectly obtained from the line-to-line back EMF. F function constructed based on line-to-line back EMF is calculated for commutation estimation. Compared to the traditional back EMF method, this scheme is simple to implement without the need to sense or reconstruct the motor neutral, and good motor performance is achieved from near zero to high speed. The results of simulation and experiment verify the validity of the proposed method with sufficient accuracy in both steady state and dynamic operation.


title: SQUINKY! A Corpus of Sentence-level Formality, Informativeness, and Implicature

abstract: We introduce a corpus of 7,032 sentences rated by human annotators for formality, informativeness, and implicature on a 1-7 scale. The corpus was annotated using Amazon Mechanical Turk. Reliability in the obtained judgments was examined by comparing mean ratings across two MTurk experiments, and correlation with pilot annotations (on sentence formality) conducted in a more controlled setting. Despite the subjectivity and inherent difficulty of the annotation task, correlations between mean ratings were quite encouraging, especially on formality and informativeness. We further explored correlation between the three linguistic variables, genre-wise variation of ratings and correlations within genres, compatibility with automatic stylistic scoring, and sentential make-up of a document in terms of style. To date, our corpus is the largest sentence-level annotated corpus released for formality, informativeness, and implicature.


title: Immersive Virtual Video Game Play and Presence: Influences on Aggressive Feelings and Behavior

abstract: Immersive virtual environment technology (IVET) allows developers to create simulated environments that can engage users in context relevant behaviors and that can produce relatively intense user experiences for purposes such as entertainment (e.g., video games), phobia desensitization, and training. We predicted that playing a violent video game using an IVET platform would lead to increased presence and aggressive feelings and behavior compared to playing on a less immersive desktop platform. The results of two experiments supported this hypothesis. The data suggest that presence mediated the relationship between playing platform and aggressive feelings but not the relationship between playing platform and aggressive behavior. Finally, we explored the utility of using cardiovascular measures within this research paradigm.


Two Novel Fully Complementary Self-Biased CMOS Differential Amplifiers


title: PEAKS - A system for the automatic evaluation of voice and speech disorders

abstract: We present a novel system for the automatic evaluation of speech and voice disorders. The system can be accessed via the internet platform-independently. The patient reads a text or names pictures. His or her speech is then analyzed by automatic speech recognition and prosodic analysis. For patients who had their larynx removed due to cancer and for children with cleft lip and palate we show that we can achieve significant correlations between the automatic analysis and the judgment of human experts in a leave-one-out experiment (p<.001). A correlation of .90 for the evaluation of the laryngectomees and .87 for the evaluation of the children's data was obtained. This is comparable to human inter-rater correlations.


title: Integrating procedural generation and manual editing of virtual worlds

abstract: Because of the increasing detail and size of virtual worlds, designers are more and more urged to consider employing procedural methods to alleviate part of their modeling work. However, such methods are often unintuitive to use, difficult to integrate, and provide little user control, making their application far from straightforward. In our declarative modeling approach, designers are provided with a more productive and simplified virtual world modeling workflow that matches better with their iterative way of working. Using interactive procedural sketching, they can quickly layout a virtual world, while having proper user control at the level of large terrain features. However, in practice, designers require a finer level of control. Integrating procedural techniques with manual editing in an iterative modeling workflow is an important topic that has remained relatively unaddressed until now. This paper identifies challenges of this integration and discusses approaches to combine these methods in such a way that designers can freely mix them, while the virtual world model is kept consistent during all modifications. We conclude that overcoming the challenges mentioned, for example in a declarative modeling context, is instrumental to achieve the much desired adoption of procedural modeling in mainstream virtual world modeling.


title: Automatic Segmentation of Hair in Images

abstract: Based on a multi-step process, an automatic hair segmentation method is created and tested on a database of 115 manually segmented hair images. By extracting various information components from an image, including background color, face position, hair color, skin color, and skin mask, a heuristic-based method is created for the detection and segmentation of hair that can detect hair with an accuracy of approximately 75% and with a false hair overestimation error of 34%. Furthermore, it is shown that downsampling the image down to a face width of 25px results in a 73% reduction in computation time with insignificant change in detection accuracy.


title: Distractor Efficiency in an Item Pool for a Statistics Classroom Exam: Assessing Its Relation With Item Cognitive Level Classified According to Bloom’s Taxonomy

abstract: Multiple-choice items are one of the most commonly used tools for evaluating students’ knowledge and skills. A key aspect in this type of assessment is the presence of functioning distractors, i.e., wrong alternatives intended to be plausibly correct among those students with lower achievement. To our knowledge, no work has investigated the relationship between distractor performance and the complexity of the cognitive task required to give the correct answer. The aim of this study was to investigate this relation, employing the first three levels of Bloom’s Taxonomy. Specifically, it was hypothesized that items classified into a higher level of Bloom’s classification would show a greater number of functioning distractors. The study involved 174 items administered to a sample of 848 undergraduate psychology students during their Statistics exam. Each student received 30 items randomly selected from the 174-item pool. Bivariate results mainly supported the authors’ hypothesis. Effect size was small (2=.024) but relevant. When the relationship was controlled for other item features, it lost statistical significance, partly because of a confounding effect with items’ difficulty.


title: Adaptive Grey-Box Fuzz-Testing with Thompson Sampling

abstract: Fuzz testing, or "fuzzing," refers to a widely deployed class of techniques for testing programs by generating a set of inputs for the express purpose of finding bugs and identifying security flaws. Grey-box fuzzing, the most popular fuzzing strategy, combines light program instrumentation with a data driven process to generate new program inputs. In this work, we present a machine learning approach that builds on AFL, the preeminent grey-box fuzzer, by adaptively learning a probability distribution over its mutation operators on a program-specific basis. These operators, which are selected uniformly at random in AFL and mutational fuzzers in general, dictate how new inputs are generated, a core part of the fuzzer's efficacy. Our main contributions are two-fold: First, we show that a sampling distribution over mutation operators estimated from training programs can significantly improve performance of AFL. Second, we introduce a Thompson Sampling, bandit-based optimization approach that fine-tunes the mutator distribution adaptively, during the course of fuzzing an individual program and outperforms offline training. A set of experiments across complex programs demonstrates that tuning the mutational operator distribution generates sets of inputs that yield significantly higher code coverage and finds more crashes faster and more reliably than both baseline versions of AFL as well as other AFL-based learning approaches.


title: Change Detection in Feature Space Using Local Binary Similarity Patterns

abstract: In general, the problem of change detection is studied in color space. Most proposed methods aim at dynamically finding the best color thresholds to detect moving objects against a background model. Background models are often complex to handle noise affecting pixels. Because the pixels are considered individually, some changes cannot be detected because it involves groups of pixels and some individual pixels may have the same appearance as the background. To solve this problem, we propose to formulate the problem of background subtraction in feature space. Instead of comparing the color of pixels in the current image with colors in a background model, features in the current image are compared with features in the background model. The use of a feature at each pixel position allows accounting for change affecting groups of pixels, and at the same time adds robustness to local perturbations. With the advent of binary feature descriptors such as BRISK or FREAK, it is now possible to use features in various applications at low computational cost. We thus propose to perform background subtraction with a small binary descriptor that we named Local Binary Similarity Patterns (LBSP). We show that this descriptor outperforms color, and that a simple background subtractor using LBSP outperforms many sophisticated state of the art methods in baseline scenarios.


title: Real-Time Pedestrian Detection and Tracking at Nighttime for Driver-Assistance Systems

abstract: Pedestrian detection is one of the most important components in driver-assistance systems. In this paper, we propose a monocular vision system for real-time pedestrian detection and tracking during nighttime driving with a near-infrared (NIR) camera. Three modules (region-of-interest (ROI) generation, object classification, and tracking) are integrated in a cascade, and each utilizes complementary visual features to distinguish the objects from the cluttered background in the range of 20-80 m. Based on the common fact that the objects appear brighter than the nearby background in nighttime NIR images, efficient ROI generation is done based on the dual-threshold segmentation algorithm. As there is large intraclass variability in the pedestrian class, a tree-structured, two-stage detector is proposed to tackle the problem through training separate classifiers on disjoint subsets of different image sizes and arranging the classifiers based on Haar-like and histogram-of-oriented-gradients (HOG) features in a coarse-to-fine manner. To suppress the false alarms and fill the detection gaps, template-matching-based tracking is adopted, and multiframe validation is used to obtain the final results. Results from extensive tests on both urban and suburban videos indicate that the algorithm can produce a detection rate of more than 90% at the cost of about 10 false alarms/h and perform as fast as the frame rate (30 frames/s) on a Pentium IV 3.0-GHz personal computer, which also demonstrates that the proposed system is feasible for practical applications and enjoys the advantage of low implementation cost.


title: Protein-Silicone Oil Interactions: Comparative Effect of Nonionic Surfactants on the Interfacial Behavior of a Fusion Protein

abstract: PURPOSE ::: To study the effect of three nonionic surfactants on the protein-silicone oil interactions. ::: ::: ::: METHODS ::: The adsorption of Tween® 80, Pluronic® F68 and Tween® 20 at the silicone oil/water interface (using shifts in frequency (ΔF) and resistance (ΔR) with quartz crystal microbalance) was compared to the adsorption at air/water interface (using surface tension). Effect of surfactants on protein adsorption to the silicone oil/water interface was studied in sequential- and co-adsorption modes. Protein-surfactant binding in the bulk was measured using dynamic surface tension method. ::: ::: ::: RESULTS ::: Saturation of air/water and silicone oil/water interfaces by surfactants was observed at similar bulk concentrations. ΔF due to protein adsorption to the interface decreased only when surfactant was present as a pre-adsorbed species. Insignificant differences in the dynamic surface tension values of surfactant solutions were observed in the presence of protein. ::: ::: ::: CONCLUSIONS ::: Similar hydrophobic forces were responsible for driving the surfactant adsorption at both air/water and silicone oil/water interfaces. Surfactants were effective in reducing the protein adsorption to the silicone oil only when introduced before or along with the protein. No significant binding between the protein and surfactants was observed in the bulk.


title: Queueing Theory for Semiconductor Manufacturing Systems: A Survey and Open Problems

abstract: This paper surveys applications of queueing theory for semiconductor manufacturing systems (SMSs). Due to sophisticated tool specifications and process flows in semiconductor manufacturing, queueing models can be very complicated. Research efforts have been on the improvement of model assumptions and model input, mainly in the first moment (averages) and the second moment (variations). However, practices show that implementation of classical queueing theory in semiconductor industry has been unsatisfactory. In this paper, open problems on queueing modeling of SMS are discussed. A potential solution is also proposed by relaxing the independent assumptions in the classical queueing theory. Cycle time reduction has constantly been a key focus of semiconductor manufacturing. Compared with simulation, queueing theory-based analytical modeling is much faster in estimating manufacturing system performance and providing more insights for performance improvement. Therefore, queueing modeling attracts generous semiconductor research grants. Unfortunately, existing queueing models focus on simple extensions of the classical queueing theory and fail to question its applicability to the complicated SMS. Hence, related researches have not been employed widely in the semiconductor industry. In this paper, we conduct a survey on the important works and also present some open problems. We also propose a novel solution by relaxing a key assumption in the classical queueing theory. We are currently funded by Intel to explore this potential solution, and we hope it can foster an interesting research field for the years to come.


title: Energy Efficient Application Controlled Multi Radio PAN over Optical Network

abstract: A distance based power control scheme is proposed and developed for application-controlled handover (ACH) in wireless personal area network (WPAN). In this paper, a power efficient communications in the wired wireless synergy network environment that consists of 3G (UMTS) + WiMAX + WLAN + WPAN (UWB) and optical network has been developed. This paper presents an enhanced power controlled transmission mechanism, which maintains the channel and the seamless transmission from mobile device to the remote optical fibre network, to provide real time service continuity for multimedia traffic with minimum power consumption. The results have proven the system performance improvements in the network throughput, the average data drop has been reduced 40% and the end-to-end network power consumption is reduced 10% when the proposed approach is applied on ACH based WPAN.


title: Effect of various domestic processing methods on antinutrients and in vitro protein and starch digestibility of two indigenous varieties of Indian tribal pulse, Mucuna pruriens Var. utilis.

abstract: The effect of various domestic processing methods on antinutrients and starch fractions and in vitro protein and starch digestibilities of white and black varieties of Mucuna pruriens var. utilis was studied. Cooking or autoclaving of both raw seeds and presoaked seeds in different solutions (water, tamarind extract, sodium bicarbonate, and citric acid) significantly (p < 0.05) reduced the content of total phenolics, phytic acid, trypsin inhibitor and chymotrypsin inhibitor activities, and L-dopa compared to soaking or dry heating techniques. The germination processes (24 and 48 h) were also effective in the reduction of various antinutrients, although this reduction appeared to be more pronounced in a prolonged period of germination (72 h). Water soaking followed by dehusking was found to be ineffective in the reduction of trypsin and chymotrypsin inhibitor activities in both varieties. All of the treatments were effective in significantly (p < 0.05) reducing the resistant starch content in the presently investigated samples. Cooking as well as autoclaving brought about a more significant (p < 0.05) improvement in the digestibility of protein and starch compared to germination and dry heat treatment. Moreover, among the different processing techniques, soaking in sodium bicarbonate solution followed by cooking (29.6-34.8%) or autoclaving (33.0-37.2%) seemed to be the best method for improving starch digestibility.


title: Role of Matrix Factorization Model in Collaborative Filtering Algorithm: A Survey

abstract: Recommendation Systems apply Information Retrieval techniques to select the online information relevant to a given user. Collaborative Filtering is currently most widely used approach to build Recommendation System. CF techniques uses the user behavior in form of user item ratings as their information source for prediction. There are major challenges like sparsity of rating matrix and growing nature of data which is faced by CF algorithms. These challenges are been well taken care by Matrix Factorization. In this paper we attempt to present an overview on the role of different MF model to address the challenges of CF algorithms, which can be served as a roadmap for research in this area.


title: Current Status of Grafts and Implants in Rhinoplasty: Part I. Autologous Grafts

abstract: LEARNING OBJECTIVES ::: After reading this article, the participant should be able to: 1. Understand the challenges in restoring volume and structural integrity in rhinoplasty. 2. Identify the appropriate uses of various autografts in aesthetic and reconstructive rhinoplasty (septal cartilage, auricular cartilage, costal cartilage, calvarial and nasal bone, and olecranon process of the ulna). 3. Identify the advantages and disadvantages of each of these autografts. ::: ::: ::: SUMMARY ::: This review specifically addresses the use of autologous grafts in rhinoplasty. Autologous materials remain the preferred graft material for use in rhinoplasty because of their high biocompatibility and low risk of infection and extrusion. However, these advantages should be counterbalanced with the concerns of donor-site morbidity, graft availability, and graft resorption.


title: Air Mounted Eyepiece: Optical See-Through HMD Design with Aerial Optical Functions

abstract: We propose a novel method of implementing an optical see-through (OST) head-mounted display (HMD) with a wide viewing angle and high resolution for augmented reality, called an Air Mounted Eyepiece (AME). In past years, many optical elements, such as transmissive liquid-crystal display (LCD), half-mirror, and waveguide have been adopted for OST-HMD. To achieve the AME design, we employ an off-the-shelf HMD and Transmissive Mirror Device (TMD), which is used in aerial real-imaging systems, instead of conventional optical elements. In the proposed method, we present 'Virtual lens," which has the same function as the HMD lens in front of the eyes. By using TMD, it is possible to shorten the optical length between the virtual lens and the eye. Therefore, the aerial lens provides an immersive image with see-through capability. In this paper, we describe a detailed design method of TMD-based HMD, and compare it to previous half mirror-based HMD and convex mirror-based HMD. Then, we construct a fabricated prototype of the OST-HMD using TMD. We aim to contribute to the field of human-computer interaction and the research on eyepiece interfaces by discussing the advantages and the limitations through simulations and experiments.


A Diagnostic Tool for Magnesium Nutrition in Maize Based on Image Analysis of Different Leaf Sections


title: Biometric authentication on a mobile device: a study of user effort, error and task disruption

abstract: We examine three biometric authentication modalities -- voice, face and gesture -- as well as password entry, on a mobile device, to explore the relative demands on user time, effort, error and task disruption. Our laboratory study provided observations of user actions, strategies, and reactions to the authentication methods. Face and voice biometrics conditions were faster than password entry. Speaking a PIN was the fastest for biometric sample entry, but short-term memory recall was better in the face verification condition. None of the authentication conditions were considered very usable. In conditions that combined two biometric entry methods, the time to acquire the biometric samples was shorter than if acquired separately but they were very unpopular and had high memory task error rates. These quantitative results demonstrate cognitive and motor differences between biometric authentication modalities, and inform policy decisions in selecting authentication methods.


title: Full-dimension MIMO (FD-MIMO) for next generation cellular technology

abstract: This article considers a practical implementation of massive MIMO systems [1]. Although the best performance can be achieved when a large number of active antennas are placed only in the horizontal domain, BS form factor limitation often makes horizontal array placement infeasible. To cope with this limitation, this article introduces full-dimension MIMO (FD-MIMO) cellular wireless communication system, where active antennas are placed in a 2D grid at BSs. For analysis of the FD-MIMO systems, a 3D spatial channel model is introduced, on which system-level simulations are conducted. The simulation results show that the proposed FD-MIMO system with 32 antenna ports achieves 2-3.6 times cell average throughput gain and 1.5-5 times cell edge throughput gain compared to the 4G LTE system of two antenna ports at the BS.


title: Promoting Access Through Complementary eHealth Technologies: Recommendations for VA’s Home Telehealth and Personal Health Record Programs

abstract: BACKGROUND ::: Many healthcare organizations have embraced eHealth technologies in their efforts to promote patient-centered care, increase access to services, and improve outcomes.


title: Long short-term memory RNN for biomedical named entity recognition

abstract: BackgroundBiomedical named entity recognition(BNER) is a crucial initial step of information extraction in biomedical domain. The task is typically modeled as a sequence labeling problem. Various machine learning algorithms, such as Conditional Random Fields (CRFs), have been successfully used for this task. However, these state-of-the-art BNER systems largely depend on hand-crafted features.ResultsWe present a recurrent neural network (RNN) framework based on word embeddings and character representation. On top of the neural network architecture, we use a CRF layer to jointly decode labels for the whole sentence. In our approach, contextual information from both directions and long-range dependencies in the sequence, which is useful for this task, can be well modeled by bidirectional variation and long short-term memory (LSTM) unit, respectively. Although our models use word embeddings and character embeddings as the only features, the bidirectional LSTM-RNN (BLSTM-RNN) model achieves state-of-the-art performance — 86.55% F1 on BioCreative II gene mention (GM) corpus and 73.79% F1 on JNLPBA 2004 corpus.ConclusionsOur neural network architecture can be successfully used for BNER without any manual feature engineering. Experimental results show that domain-specific pre-trained word embeddings and character-level representation can improve the performance of the LSTM-RNN models. On the GM corpus, we achieve comparable performance compared with other systems using complex hand-crafted features. Considering the JNLPBA corpus, our model achieves the best results, outperforming the previously top performing systems. The source code of our method is freely available under GPL at https://github.com/lvchen1989/BNER.


title: Semantic Blockchain to Improve Scalability in the Internet of Things

abstract: Generally scarce computational and memory resource availability is a well known problem for the IoT, whose intrinsic volatility makes complex applications unfeasible. Noteworthy efforts in overcoming unpredictability (particularly in case of large dimensions) are the ones integrating Knowledge Representation technologies to build the so-called Semantic Web of Things (SWoT). In spite of allowed advanced discovery features, transactions in the SWoT still suffer from not viable trust management strategies. Given its intrinsic characteristics, blockchain technology appears as interesting from this perspective: a semantic resource/service discovery layer built upon a basic blockchain infrastructure gains a consensus validation. This paper proposes a novel Service-Oriented Architecture (SOA) based on a semantic blockchain for registration, discovery, selection and payment. Such operations are implemented as smart contracts, allowing distributed execution and trust. Reported experiments early assess the sustainability of the proposal.


title: Filtered channel features for pedestrian detection

abstract: This paper starts from the observation that multiple top performing pedestrian detectors can be modelled by using an intermediate layer filtering low-level features in combination with a boosted decision forest. Based on this observation we propose a unifying framework and experimentally explore different filter families. We report extensive results enabling a systematic analysis. Using filtered channel features we obtain top performance on the challenging Caltech and KITTI datasets, while using only HOG+LUV as low-level features. When adding optical flow features we further improve detection quality and report the best known results on the Caltech dataset, reaching 93% recall at 1 FPPI.


title: Boosted Learning in Dynamic Bayesian Networks for Multimodal Speaker Detection

abstract: Bayesian network models provide an attractive framework for multimodal sensor fusion. They combine an intuitive graphical representation with efficient algorithms for inference and learning. However, the unsupervised nature of standard parameter learning algorithms for Bayesian networks can lead to poor performance in classification tasks. We have developed a supervised learning framework for Bayesian networks, which is based on the Adaboost algorithm of Schapire and Freund. Our framework covers static and dynamic Bayesian networks with both discrete and continuous states. We have tested our framework in the context of a novel multimodal HCI application: a speech-based command and control interface for a Smart Kiosk. We provide experimental evidence for the utility of our boosted learning approach.


title: A Continuous Relaxation of Beam Search for End-to-end Training of Neural Sequence Models

abstract: Beam search is a desirable choice of test-time decoding algorithm for neural sequence models because it potentially avoids search errors made by simpler greedy methods. However, typical cross entropy training procedures for these models do not directly consider the behaviour of the final decoding method. As a result, for cross-entropy trained models, beam decoding can sometimes yield reduced test performance when compared with greedy decoding. In order to train models that can more effectively make use of beam search, we propose a new training procedure that focuses on the final loss metric (e.g. Hamming loss) evaluated on the output of beam search. While well-defined, this "direct loss" objective is itself discontinuous and thus difficult to optimize. Hence, in our approach, we form a sub-differentiable surrogate objective by introducing a novel continuous approximation of the beam search decoding procedure. In experiments, we show that optimizing this new training objective yields substantially better results on two sequence tasks (Named Entity Recognition and CCG Supertagging) when compared with both cross entropy trained greedy decoding and cross entropy trained beam decoding baselines.


A Novel Hypothesis of Visual Loss Secondary to Cosmetic Facial Filler Injection


title: A systematic literature review of stakeholder identification methods in requirements elicitation

abstract: This paper presents a systematic review of relevant published studies related to topics in Requirements Engineering, specifically, concerning stakeholder identification methods in requirements elicitation, dated from 1984 to 2011. Addressing four specific research questions, this systematic literature review shows the following evidence gathered from these studies: current status of stakeholder identification in software requirement elicitation, the best practices recommended for its performance, consequences of incorrect identification in requirements quality, and, aspects which need to be improved. Our findings suggest that the analyzed approaches still have serious limitations in terms of covering all aspects of stakeholder identification as an important part of requirements elicitation. However, through correctly identifying and understanding the stakeholders, it is possible to develop high quality software.


title: Feature selection with test cost constraint

abstract: Feature selection is an important preprocessing step in machine learning and data mining. In real-world applications, costs, including money, time and other resources, are required to acquire the features. In some cases, there is a test cost constraint due to limited resources. We shall deliberately select an informative and cheap feature subset for classification. This paper proposes the feature selection with test cost constraint problem for this issue. The new problem has a simple form while described as a constraint satisfaction problem (CSP). Backtracking is a general algorithm for CSP, and it is efficient in solving the new problem on medium-sized data. As the backtracking algorithm is not scalable to large datasets, a heuristic algorithm is also developed. Experimental results show that the heuristic algorithm can find the optimal solution in most cases. We also redefine some existing feature selection problems in rough sets, especially in decision-theoretic rough sets, from the viewpoint of CSP. These new definitions provide insight to some new research directions.


title: A survey on multiple object tracking algorithm

abstract: Visual Multiple Object Tracking (VMOT) is an important computer vision task which has gained increasing attention due to its academic and commercial potential. There are many different approaches have been proposed to solve the problem. Compared with single object tracking which focuses on appearance model, motion model and other factors, multiple object tracking shares these common challenges, and has some more challenging tasks to be tackled, such as identifying among multiple objects, frequent occlusion due to the crowd, initializing and terminating of the object tracking, et al, which making it difficult to get a comprehensive understanding of this problem. In this paper, we try to make systematic review on VMOT. We review the recent advances in various aspects and introduce the classification of each aspect in detail. The main aspects include observation model and tracking algorithm. In each aspect, the existing methods are classified into different groups, and each group is discussed in details for their principles, advantages and drawbacks. This review work can provide researchers, especially new comers to the topic of VMOT, a general understanding of the state-of-the-arts, and help them to comprehend the aspects of a VMOT system and the inter-connection of these aspects.


title: QuickSpot: a video analytics solution for on-street vacant parking spot detection

abstract: Vehicles searching for a vacant parking spot on the street can amount to as much as 40 % of the traffic in certain city areas, thus largely affecting mobility in urban environments. For this reason, it would be desirable to create integrated smart traffic management systems capable of providing real-time information to drivers about the location of available vacant parking spots. A scalable solution would consist in exploiting the existing and widely-deployed video surveillance camera networks, which requires the development of computer vision algorithms for detecting vacant parking spots. Following this idea, this work introduces QuickSpot, a car-driven video analytics solution for on-street vacant parking spot detection designed as a motion detection, object tracking and visual recognition pipeline. One of the main features of QuickSpot is its simplified setup, as it can be trained on external databases to learn the appearances of the objects it is capable of recognizing (pedestrians and vehicles). To test its performance under different daytime lighting conditions, we have recorded, edited, annotated and made available to the research community the QuickSpotDB video database for the vacant parking spot detection problem. In the conducted experiments, we have evaluated the trade-off between the accuracy and the computational complexity of QuickSpot with an eye to its practical applicability. The results show that QuickSpot detects parking spot status with an average accuracy close to 99 % at a 1-second rate regardless of the illumination conditions, outperforming in an indirect comparison the other car-driven approaches reported in the literature.


title: A CMOS Low-Dropout Regulator With Dominant-Pole Substitution

abstract: A dominant-pole substitution (DPS) technique for low-dropout regulator (LDO) is proposed in this paper. The DPS technique involves signal-current feedforward and amplification such that an ultralow-frequency zero is generated to cancel the dominant pole of LDO, while a higher frequency pole substitutes in and becomes the new dominant pole. With DPS, the loop bandwidth of the proposed LDO can be significantly extended, while a standard value and large output capacitor for transient purpose can still be used. The resultant LDO benefits from both the fast response time due to the wide loop bandwidth and the large charge reservoir from the output capacitor to achieve the significant enhancement in the dynamic performances. Implemented with a commercial 0.18-μm CMOS technology, the proposed LDO with DPS is validated to be capable of delivering 100 mA at 1.0-V output from a 1.2-V supply, with current efficiency of 99.86%. Experimental results also show that the error voltage at the output undergoing 100 mA of load transient in 10-ns edge time is about 25 mV. Line transient responses reveal that no more than 20-mV instantaneous changes at the output when the supply voltage swings between 1.2 and 1.8 V in 100 ns. The power-supply rejection ratio at 3 MHz is −47 dB.


title: A dynamic bandwidth allocation algorithm in mobile networks with big data of users and networks

abstract: Data collection has become easy due to the rapid development of both mobile devices and wireless networks. In each second, numerous data are generated by user devices and collected through wireless networks. These data, carrying user and network related information, are invaluable for network management. However, they were seldom employed to improve network performance in existing research work. In this article we propose a bandwidth allocation algorithm to increase the throughput of cellular network users by exploring user and network data collected from user devices. With the aid of these data, users can be categorized into clusters and share bandwidth to improve the resource utilization of the network. Simulation results indicate that the proposed scheme is able to rationally form clusters among mobile users and thus significantly increase the throughput and bandwidth efficiency of the network.


Machine Learning Applied to Weather Forecasting


title: Anti-inflammatory and Surgical Therapy of Olfactory Disorders Related to Sino-nasal Disease

abstract: Olfactory loss may be caused by mechanical obstruction or inflammation of the olfactory epithelium due to allergic/non-allergic rhinitis and chronic sinusitis with or without polyps. Treatment of olfactory loss related to sino-nasal disease is possible. Apart from surgical approaches and/or treatment with antibiotics, both systemic and topical steroids are effectively used in the therapy of olfactory loss related to sino-nasal disease. In most cases improvement of olfactory function appears to relate to the anti-inflammatory actions of the steroids used. While some details of therapeutic effect and dose regimen are not clear, systemic steroids are often helpful even in patients without nasal obstruction due to polyps or obvious inflammatory changes.


title: A method for cartoon-style rendering of liquid animations

abstract: In this paper we present a visually compelling and informative cartoon rendering style for liquid animations. Our style is inspired by animations such as Futurama,1 The Little Mermaid,2 and Bambi2. We take as input a liquid surface obtained from a three-dimensional physically based liquid simulation system and output animations that evoke a cartoon style and convey liquid movement. Our method is based on four cues that emphasize properties of the liquid's shape and motion. We use bold outlines to emphasize depth discontinuities, patches of constant color to highlight near-silhouettes and areas of thinness, and, optionally place temporally coherent oriented textures on the liquid surface to help convey motion.


title: Retrospective on Aurora

abstract: This experience paper summarizes the key lessons we learned throughout the design and implementation of the Aurora stream-processing engine. For the past 2 years, we have built five stream-based applications using Aurora. We first describe in detail these applications and their implementation in Aurora. We then reflect on the design of Aurora based on this experience. Finally, we discuss our initial ideas on a follow-on project, called Borealis, whose goal is to eliminate the limitations of Aurora as well as to address new key challenges and applications in the stream-processing domain.


title: Toxic Peripheral Neuropathies

abstract: Peripheral neuropathies caused by toxin exposure are rare. The majority of cases are individual, sporadic intoxications, but outbreaks such as the Minamata Bay mercury disaster garner a disproportionate amount of attention. The sporadic occurrence of toxic peripheral neuropathies (TxPNs) makes diagnosis very difficult. Therefore, a high index of suspicion is needed to detect the unusual cases of suicidal or homicidal ingestion and rare small-scale occupational exposures.


title: Color image database TID2013: Peculiarities and preliminary results

abstract: Visual quality of color images is an important aspect in various applications of digital image processing and multimedia. A large number of visual quality metrics (indices) has been proposed recently. In order to assess their reliability, several databases of color images with various sets of distortions have been exploited. Here we present a new database called TID2013 that contains a larger number of images. Compared to its predecessor TID2008, seven new types and one more level of distortions are included. The need for considering these new types of distortions is briefly described. Besides, preliminary results of experiments with a large number of volunteers for determining the mean opinion score (MOS) are presented. Spearman and Kendall rank order correlation factors between MOS and a set of popular metrics are calculated and presented. Their analysis shows that adequateness of the existing metrics is worth improving. Special attention is to be paid to accounting for color information and observers focus of attention to locally active areas in images.


title: Study on Self-Q-switched Er-doped Fiber Laser Based on SBS

abstract: Self-Q-switched Er-doped fiber laser has been studied in this paper. Er-doped fiber is applied as gain medium, which is pumped by CW 980 nm LD. A fiber grating is used as the wavelength selector and cavity reflector. The stable self-Q-switched laser output is realized based on the nonlinearity of SM fiber—stimulated Brillouin scattering. The repetition rate about 64 5 MHz, and the full width at half maximum (FWHM) about 2 2 ns are obtained.


title: Millimeter-Wave Microstrip Comb-Line Antenna Using Reflection-Canceling Slit Structure

abstract: A microstrip comb-line antenna is developed in the millimeter-wave band. When the element spacing is one guide wavelength for the broadside beam in the traveling-wave excitation, reflections from all the radiating elements are synthesized in phase. Therefore, the return loss increases significantly. Furthermore, re-radiation from elements due to the reflection wave degrades the design accuracy for the required radiation pattern. We propose the way to improve the reflection characteristic of the antenna with arbitrary beam directions including strictly a broadside direction. To suppress the reflection, we propose a reflection-canceling slit structure installed on the feeding line around each radiating element. A 27-element linear array antenna with a broadside beam is developed at 76.5 GHz. To confirm the feasibility of the simple design procedure, the performance is evaluated through the measurement in the millimeter-wave band.


title: Blind path obstacle detector using smartphone camera and line laser emitter

abstract: Visually impaired people find navigating within unfamiliar environments challenging. Many smart systems have been proposed to help blind people in these difficult, often dangerous, situations. However, some of them are uncomfortable, difficult to obtain or simply too expensive. In this paper, a low-cost wearable system for visually impaired people was implemented which allows them to detect and locate obstacles in their locality. The proposed system consists of two main hardware components, a laser pointer ($12) and an android smart phone, making our system relatively cheap and accessible. The collision avoidance algorithm uses image processing to measure distances to objects in the environment. This is based on laser light triangulation. This obstacle detection is enhanced by edge detection within the captured image. An additional feature of the system is to recognize and warn the user when stairs are present in the camera's field of view. Obstacles are brought to the user's attention using an acoustic signal. Our system was shown to be robust, with only 5 % false alarm rate and a sensitivity of 90 % for 1 cm wide obstacles.


Semantic Parsing of Pre-university Math Problems


title: Traffic prediction for mobile network using Holt-Winter’s exponential smoothing

abstract: This paper presents a technique for evaluation of future radio traffic of circuit switched services in Erlang for near-term outlook using Holt-Winter's exponential smoothing. The proposed traffic prediction technique relies on analysis of traffic data on cells. We propose how to classify traffic data for prediction. The result from a trial in history data of commercial GSM/GPRS network is presented. It shows that predicted traffic is good fitted with original one within our classification. The technique should be used in automated overload warning and express estimation of future traffic for capacity planning.


title: RC-NVM: Enabling Symmetric Row and Column Memory Accesses for In-memory Databases

abstract: Ever increasing DRAM capacity has fostered the development of in-memory databases (IMDB). The massive performance improvements provided by IMDBs have enabled transactions and analytics on the same database. In other words, the integration of OLTP (on-line transactional processing) and OLAP (on-line analytical processing) systems is becoming a general trend. However, conventional DRAM-based main memory is optimized for row-oriented accesses generated by OLTP workloads in row-based databases. OLAP queries scanning on specified columns cause so-called strided accesses and result in poor memory performance. Since memory access latency dominates in IMDB processing time, it can degrade overall performance significantly. To overcome this problem, we propose a dual-addressable memory architecture based on non-volatile memory, called RC-NVM, to support both row-oriented and column-oriented accesses. We first present circuit-level analysis to prove that such a dual-addressable architecture is only practical with RC-NVM rather than DRAM technology. Then, we rethink the addressing schemes, data layouts, cache synonym, and coherence issues of RC-NVM in architectural level to make it applicable for IMDBs. Finally, we propose a group caching technique that combines the IMDB knowledge with the memory architecture to further optimize the system. Experimental results show that the memory access performance can be improved up to 14.5X with only 15% area overhead.


title: Smart contracts vulnerabilities: a call for blockchain software engineering?

abstract: Smart Contracts have gained tremendous popularity in the past few years, to the point that billions of US Dollars are currently exchanged every day through such technology. However, since the release of the Frontier network of Ethereum in 2015, there have been many cases in which the execution of Smart Contracts managing Ether coins has led to problems or conflicts. Compared to traditional Software Engineering, a discipline of Smart Contract and Blockchain programming, with standardized best practices that can help solve the mentioned problems and conflicts, is not yet sufficiently developed. Furthermore, Smart Contracts rely on a non-standard software life-cycle, according to which, for instance, delivered applications can hardly be updated or bugs resolved by releasing a new version of the software. In this paper we advocate the need for a discipline of Blockchain Software Engineering, addressing the issues posed by smart contract programming and other applications running on blockchains.We analyse a case of study where a bug discovered in a Smart Contract library, and perhaps "unsafe" programming, allowed an attack on Parity, a wallet application, causing the freezing of about 500K Ethers (about 150M USD, in November 2017). In this study we analyze the source code of Parity and the library, and discuss how recognised best practices could mitigate, if adopted and adapted, such detrimental software misbehavior. We also reflect on the specificity of Smart Contract software development, which makes some of the existing approaches insufficient, and call for the definition of a specific Blockchain Software Engineering.


Logo Image Based Approach for Phishing Detection


title: Survey of Medical Image Registration

abstract: Computerized Image Registration approaches can offer automatic and accurate image alignments without extensive user involvement and provide tools for visualizing combined images. The aim of this survey is to present a review of publications related to Medical Image Registration. This paper paints a comprehensive picture of image registration methods and their applications. This paper is an introduction for those new to the ﬁeld, an overview for those working in the ﬁeld and a reference for those searching for literature on a speciﬁc application. Methods are classiﬁed according to the diﬀerent aspects of Medical Image Registration.


title: EMC compliant LIN transceiver

abstract: This paper introduces an integrated Local Interconnect Network (LIN) transceiver which sets a new performance benchmark in terms of electromagnetic compatibility (EMC). The proposed topology succeeds in an extraordinary high robustness against RF disturbances which are injected into the BUS and in very low electromagnetic emissions (EMEs) radiated by the LIN network without adding any external components for filtering. In order to evaluate the circuits superior EMC performance, it was designed using a HV-BiCMOS technology for automotive applications, the EMC behavior was measured and the results were compared with a state of the art topology.


title: Deconstructing the seductive allure of neuroscience explanations

abstract: Previous work showed that people find explanations more satisfying when they contain irrelevant neuroscience information. The current studies investigate why this effect happens. In Study 1 (N=322), subjects judged psychology explanations that did or did not contain irrelevant neuroscience information. Longer explanations were judged more satisfying, as were explanations containing neuroscience information, but these two factors made independent contributions. In Study 2 (N=255), subjects directly compared good and bad explanations. Subjects were generally successful at selecting the good explanation except when the bad explanation contained neuroscience and the good one did not. Study 3 (N=159) tested whether neuroscience jargon was necessary for the effect, or whether it would obtain with any reference to the brain. Responses to these two conditions did not differ. These results confirm that neuroscience information exerts a seductive effect on people's judgments, which may explain the appeal of neuroscience information within the public sphere.


title: Face Authentication With Makeup Changes

abstract: Recent studies have shown that facial cosmetics have an impact on face recognition. To develop a face recognition system that is robust to facial makeup, we propose performing correlation mapping between makeup and nonmakeup faces on features extracted from local patches. Three methods are explored to learn the correlations. We also study the problem of makeup detection. Four categories of features are proposed to characterize cosmetics, including skin color tone, skin smoothness, texture, and highlight. A patch selection scheme and discriminative mapping are presented to enhance the performance of makeup detection. A complete system is then developed for face verification utilizing the makeup detection result. Experimental results show that our system is robust to cosmetics in face authentication. An accuracy of about 80.0% can be achieved on a database of about 500 pairs of makeup and nonmakeup face images.


Barriers to ecommerce in developing countries


title: Novel semi-reactively-matched multistage broadband power amplifier architecture for monolithic ICs in GaN technology

abstract: This paper reports on a novel broadband amplifier architecture suitable for wide bandwidth applications such as electronic warfare systems. The proposed topology comprises a distributed active power splitter to function as a driver stage for a reactively-matched power amplifier. As a result, a purely resistive interstage impedance is obtained and therefore the proposed architecture allows wider bandwidth operation as compared to the conventional reactively-matched multistage topology. Due to the traveling wave amplifier character of the driver stage, the presented novel amplifier topology is named semi-reactively-matched amplifier. A 6GHz to 20GHz dualstage high power amplifier MMIC is designed and realized in order to demonstrate the capabilities of the new concept. The MMIC is based on a 0.25μm gate length AlGaN/GaN HEMT microstrip transmission line technology. It shows a measured small-signal gain of (18 ± 4)dB over the entire frequency range. The saturated output power is higher than 4.5W at 20GHz in continuous wave operation.


title: Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration

abstract: We present a parameter free approach that utilizes multiple cues for image segmentation. Beginning with an image, we execute a sequence of bottom-up aggregation steps in which pixels are gradually merged to produce larger and larger regions. In each step we consider pairs of adjacent regions and provide a probability measure to assess whether or not they should be included in the same segment. Our probabilistic formulation takes into account intensity and texture distributions in a local area around each region. It further incorporates priors based on the geometry of the regions. Finally, posteriors based on intensity and texture cues are combined using a mixture of experts formulation. This probabilistic approach is integrated into a graph coarsening scheme providing a complete hierarchical segmentation of the image. The algorithm complexity is linear in the number of the image pixels and it requires almost no user-tuned parameters. We test our method on a variety of gray scale images and compare our results to several existing segmentation algorithms.


title: 2D Human Pose Estimation: New Benchmark and State of the Art Analysis

abstract: Human pose estimation has made significant progress during the last years. However current datasets are limited in their coverage of the overall pose estimation challenges. Still these serve as the common sources to evaluate, train and compare different models on. In this paper we introduce a novel benchmark "MPII Human Pose" that makes a significant advance in terms of diversity and difficulty, a contribution that we feel is required for future developments in human body models. This comprehensive dataset was collected using an established taxonomy of over 800 human activities [1]. The collected images cover a wider variety of human activities than previous datasets including various recreational, occupational and householding activities, and capture people from a wider range of viewpoints. We provide a rich set of labels including positions of body joints, full 3D torso and head orientation, occlusion labels for joints and body parts, and activity labels. For each image we provide adjacent video frames to facilitate the use of motion information. Given these rich annotations we perform a detailed analysis of leading human pose estimation approaches and gaining insights for the success and failures of these methods.


title: Improving localization accuracy of android's Fused Location Provider API using Kalman Filter

abstract: This paper intends to improve the location accuracy of Google's Fused Location Provider API, for android handheld device using Kalman Filter. Since the Fused Location Provider was built for managing the battery and accuracy tradeoff between GPS provider and Network Provider, the estimate is likely to be noisy and the track obtained contains jumps. So by using Kalman filter, the jumps can be devoid, and a smooth track can be obtained. A real time experiment is carried out to check the improvements. The results show that the proposed location path is smoother than the path travelled using the conventional Fused Location Provider API.


title: Modeling driver behavior at roundabouts: Results from a field study

abstract: Advanced Driving Assistance Systems could improve driving safety and comfort by supporting drivers in their driving task. To realize intelligent assistance, driver behavior prediction and recognition is an important challenge. Therefore, the aim of this study is to develop a method to predict whether a vehicle, having entered a roundabout, will choose an upcoming exit or stay within the roundabout. A field study has been conducted to collect driving behavior data for analyzing and modeling human driver behavior in interaction with roundabouts. Support vector machines proved to be a robust and efficient classification method for the roundabout leaving/ staying pattern recognition problem. From the experimental results the vehicles position can be estimated, for which the prediction becomes reliable. The steering wheel angle and angle velocity also proved to be able to provide sufficient information to predict the driver behavior at the investigated roundabouts.


title: A 3x9 Gb/s Shared, All-Digital CDR for High-Speed, High-Density I/O

abstract: This paper presents a novel all-digital CDR scheme in 90 nm CMOS. Two independently adjustable clock phases are generated from a delay line calibrated to 2 UI. One clock phase is placed in the middle of the eye to recover the data (“data clock”) and the other is swept across the delay line (“search clock”). As the search clock is swept, its samples are compared against the data samples to generate eye information. This information is used to determine the best phase for data recovery. After placing the search clock at this phase, search and data functions are traded between clocks and eye monitoring repeats. By trading functions, infinite delay range is realized using only a calibrated delay line, instead of a PLL or DLL. Since each clock generates its own alignment information, mismatches in clock distribution can be tolerated. The scheme's generalized sampling and retiming architecture is used in an efficient sharing technique that reduces the number of clocks required, saving power and area in high-density interconnect. The shared CDR is implemented using static CMOS logic in a 90 nm bulk process, occupying 0.15 mm2. It operates from 6 to 9 Gb/s, and consumes 2.5 mW/Gb/s of power at 6 Gb/s and 3.8 mW/Gb/s at 9 Gb/s.


title: Comparative analysis of DC-DC converter topologies for fuel cell based application

abstract: Power electronic converters play an important role in integrating renewable energy sources in to the electrical grid. A power conditioning unit (PCU) which comprises of different power electronic converters is essential to process the power of renewable energy sources and make it useable for stand-alone or grid-tied application. DC-DC converters are used in the primary stage of PCU to step up the voltage of renewable energy source to a desired level and also to improve the conversion efficiency. In this paper, a comprehensive analysis is carried out to provide details about different non-isolated, isolated and soft switched converters used in PCU. The performance of these converters are analyzed according to their conversion efficiency and number of components.


title: Network Security Vulnerabilities in SCADA and EMS

abstract: Power system deregulation brings broader reliance on information systems and telecommunication network to share the critical and non-critical data. In par with the network system expansion in power system, network security vulnerabilities have increased tremendously. Utilities are currently more vulnerable for external attacks due to integration of cooperate network and supervisory control and data acquisition (SCADA) system at modern utilities. The potential impact of security breaches are operational disruptions, lose of public confidence and corporate reputation. Regular vulnerability assessments, expert information security architecture design and managing security are the major steps that every electric power company should undertake to minimize the number and impact of security breaches. This paper classifies key information security vulnerabilities in SCADA and energy management system (EMS) and elaborates the potential impacts of each. Several remedial strategies to cerate an efficient information security of SCADA and EMS have also been presented in this paper


title: Stiffness analysis for a 3-PUU parallel kinematic machine

abstract: Abstract This paper presents the stiffness characteristics of a three-prismatic-universal–universal (3-PUU) translational parallel kinematic machine (PKM). The stiffness matrix is derived intuitively based upon an alternative approach considering actuations and constraints, and the compliances subject to both actuators and legs are involved in the stiffness model. The stiffness performance of the manipulator is evaluated by utilizing the extremum stiffness values, and the influences of design parameters on the stiffness properties are presented, which will be valuable for the architecture design of a 3-PUU PKM. Moreover, the stiffness behavior of the PKM is investigated via the eigenscrew decomposition of the stiffness matrix, which provides a physical interpretation of the PKM stiffness and allows the identification of the stiffness center and compliant axis.


title: Wideband Millimeter-Wave Surface Micromachined Tapered Slot Antenna

abstract: A millimeter-wave surface micromachined tapered slot antenna (TSA) fed by an air-filled rectangular coaxial line is proposed. Detailed parametric study is conducted to determine the effects of the TSA's key structural features on its VSWR and gain. Selected exponential taper with determined growth rate and corrugation length resulted in an antenna with VSWR 2.75 dBi, and stable patterns over a 43-140-GHz range. The TSA is fabricated, and good correlation between modeling and W-band measurements confirms its wideband performance.


title: 16-bit vs. 32-bit instructions for pipelined microprocessors

abstract: In any stored-program computer system, information is constantly transferred between the memory and the instruction processor. Machine instructions are a major portion of this traffic. Since transfer bandwidth is a limited resource, inefficiency in the encoding of instruction information (low code density) can have definite hardware and performance costs. Starting with a parameterized baseline RISC design, we compare performance for two instruction encodings for the same instruction processing core. One is a variant of DLX, a typical 32-bit RISC instruction set. The other is a 16-bit format which sacrifices some expressive power while retaining essential RISC features. Using optimizing compilers and software simulation, we measure code density and path length for a suite of benchmark programs, relating performance differences to specific instruction set features. We measure time to completion performance while varying memory latency and instruction cache size parameters. The 16-bit format is shown to have significant cost-performance advantages over the 32-bit format under typical memory system performance constraints.


title: Stability of delayed neural networks with time-varying impulses

abstract: This paper addresses the stability problem of a class of delayed neural networks with time-varying impulses. One important feature of the time-varying impulses is that both the stabilizing and destabilizing impulses are considered simultaneously. Based on the comparison principle, the stability of delayed neural networks with time-varying impulses is investigated. Finally, the simulation results demonstrate the effectiveness of the results.


title: The effects of television and Internet food advertising on parents and children

abstract: Objective The current study examined the impact of television and Internet food advertising on Australian parents and children. Design Parents and their children aged 8 to 14 years were exposed to a television advertisement, an Internet advertisement or a control picture for four commonly advertised energy-dense, nutrient-poor foods. Setting Online web panel survey, Australia. Subjects Parents ( n 1302) and their children aged 8 to 14 years ( n 1302). Results After a single exposure to each advertisement, parent respondents in the two exposure conditions evaluated the products more favourably, had a greater desire to consume the products and thought the product could be consumed more frequently than those in the control condition. Similar trends were observed among children, although the differences were statistically significant only for the frequency of food consumption in the Internet advertisement condition and the evaluation of one product. Conclusions The results have implications for assumptions of adults’ immunity to advertising. This is of particular importance in efforts to address child obesity and the reliance on parents to mediate the effects of food advertising.


title: Database technologies in the world of big data

abstract: Now we have a number of database technologies called usually NoSQL, like key-value, column-oriented, and document stores as well as search engines and graph databases. Whereas SQL software vendors offer advanced products with the capability to handle highly complex queries and transactions, NoSQL databases share rather characteristics concerning scaling and performance, as e.g. auto-sharding, distributed query support, and integrated caching. Their drawbacks can be a lack of schema or data consistency, difficulty in testing and maintaining, and absence of a higher query language. Complex data modelling and the SQL language as the only access tool to data are missing here. On the other hand, last studies show that both SQL and NoSQL databases have value for both for transactional and analytical Big Data. Top databases providers offer rearchitected database technologies combining row data stores with columnar in-memory compression enabling processing large data sets and analytical querying, often over massive, continuous data streams. The technological progress led to development of massively parallel processing analytic databases. The paper presents some details of current database technologies, their pros and cons in different application environments, and emerging trends in this area.


title: An Improved Binary Image Representation Method by Using Non-symmetry and Anti-packing Model with Triangles and Squares

abstract: Inspired by the optimization idea of the triangle and square packing problems, in this paper we propose an improved binary image representation method by using the non-symmetry and anti-packing model with triangles and squares (NAMTS). Also, we propose an algorithm of the NAMTS for binary images and analyze the total data amount of the algorithm. By comparing the representation algorithm of the NAMTS with those of the latest triangle non-symmetry and anti-packing model (TNAM) and the popular linear quadtree, the theoretical and experimental results presented in this paper show that the former can greatly reduce the numbers of subpatterns or nodes and simultaneously save the data storage much more effectively than the latter, and therefore it is a better method to represent binary images.


title: Business Trend Analysis by Simulation

abstract: Business processes are constantly affected by the environment in which they execute. The environment can change due to seasonal and financial trends. For organisations it is crucial to understand their processes and to be able to estimate the effects of these trends on the processes. Business process simulation is a way to investigate the performance of a business process and to analyse the process response to injected trends. However, existing simulation approaches assume a steady state situation. Until now correlations and dependencies in the process have not been considered in simulation models, which can lead to wrong estimations of the performance. In this work we define an adaptive simulation model with a history-dependent mechanism that can be used to propagate changes in the environment through the model. In addition we focus on the detection of dependencies in the process based on the executions of the past. We demonstrate the application of adaptive simulation models by means of an experiment.


title: VisReduce: Fast and responsive incremental information visualization of large datasets

abstract: Performance and responsiveness of visual analytics sytems for exploratory data analysis of large datasets has been a long standing problem. We propose a method for incrementally computing visualizations in a distributed fashion by combining a modified MapReduce-style algorithm with a compressed columnar data store, resulting in significant improvements in performance and responsiveness for constructing commonly encountered information visualizations, e.g. bar charts, scatterplots, heat maps, cartograms and parallel coordinate plots. We compare our method with one that queries three other readily available database and data warehouse systems - PostgreSQL, Cloudera Impala and the MapReduce-based Apache Hive - in order to build visualizations. We show that our end-to-end approach allows for greater speed and guaranteed end-user responsiveness, even in the face of large, long-running queries.


title: An evaluation of e-readiness assessment tools with respect to information access: Towards an integrated information rich tool

abstract: This paper is an offshoot of the findings of a doctoral thesis carried out at the University of Johannesburg (formerly Rand Afrikaans University) in South Africa on the assessment of e-readiness tools with respect to information access. The paper argues that information is a key component in the e-readiness equation and yet it is not given much emphasis in the e-readiness extant tools, but is instead subsumed under Information and Communication Technology (ICT). The paper notes that the various major segments of e-readiness as synthesised from existing tools are scattered in different tools making their use painstakingly cumbersome. The paper proposes a new e-readiness integrated tool that emphasises information access and also collocates the various segments of organizational, ICT, human resources, and external readiness into one single tool.


title: Automatic Generation of Grounded Visual Questions

abstract: In this paper, we propose a new task and solution for vision and language: generation of grounded visual questions. Visual question answering (VQA) is an emerging topic which links textual questions with visual input. To the best of our knowledge, it lacks automatic method to generate reasonable and versatile questions. So far, almost all the textual questions are generated manually, as well as the corresponding answers. To this end, we propose a system that automatically generates visually grounded questions . First, visual input is analyzed with deep caption model. Second, the captions along with VGG-16 features are used as input for our proposed question generator to generate visually grounded questions. Finally, to enable generating of versatile questions, a question type selection module is provided which selects reasonable question types and provide them as parameters for question generation. This is done using a hybrid LSTM with both visual and answer input. Our system is trained using VQA and Visual7W dataset and shows reasonable results on automatically generating of new visual questions. We also propose a quantitative metric for automatic evaluation of the question quality.


title: Face recognition using HOG-EBGM

abstract: This paper presents a new face recognition algorithm based on the well-known EBGM which replaces Gabor features by HOG descriptors. The recognition results show a better performance of our approach compared to other face recognition approaches using public available databases. This better performance is explained by the properties of HOG descriptors which are more robust to changes in illumination, rotation and small displacements, and to the higher accuracy of the face graphs obtained compared to classical Gabor-EBGM ones.


Final Project : Entangled Photons and Quantum Teleportation


title: Automating the generation of coordinated multimedia explanations

abstract: An explanation system developed to overcome the disadvantages of conventional authoring in multimedia applications is presented. This experimental test bed for the automated generation of multimedia explanations is called COMET (coordinated multimedia explanation testbed), and has as its goal the coordinated, interactive generation of explanations that combine text and three-dimensional graphics, all of which is generated on the fly. In response to a user request for an explanation, COMET dynamically determines the explanation's content using constraints based on the type of request, the information available in a set of underlying knowledge bases, and information about the user's background and goals. Having determined what to say, COMET also determines how to express it at the time of generation. A brief overview of COMET's domain and architecture is followed by a description of the specific ways in which COMET can coordinate its text and graphics. The current status and limitations of COMET are discussed. >


title: Parallel field alignment for cross media retrieval

abstract: Cross media retrieval systems have received increasing interest in recent years. Due to the semantic gap between low-level features and high-level semantic concepts of multimedia data, many researchers have explored joint-model techniques in cross media retrieval systems. Previous joint-model approaches usually focus on two traditional ways to design cross media retrieval systems: (a) fusing features from different media data; (b) learning different models for different media data and fusing their outputs. However, the process of fusing features or outputs will lose both low- and high-level abstraction information of media data. Hence, both ways do not really reveal the semantic correlations among the heterogeneous multimedia data. In this paper, we introduce a novel method for the cross media retrieval task, named Parallel Field Alignment Retrieval (PFAR), which integrates a manifold alignment framework from the perspective of vector fields. Instead of fusing original features or outputs, we consider the cross media retrieval as a manifold alignment problem using parallel fields. The proposed manifold alignment algorithm can effectively preserve the metric of data manifolds, model heterogeneous media data and project their relationship into intermediate latent semantic spaces during the process of manifold alignment. After the alignment, the semantic correlations are also determined. In this way, the cross media retrieval task can be resolved by the determined semantic correlations. Comprehensive experimental results have demonstrated the effectiveness of our approach.


title: Antidepressant dose, age, and the risk of deliberate self-harm.

abstract: IMPORTANCE ::: A comprehensive meta-analysis of randomized trial data suggests that suicidal behavior is twice as likely when children and young adults are randomized to antidepressants compared with when they are randomized to placebo. Drug-related risk was not elevated for adults older than 24 years. To our knowledge, no study to date has examined whether the risk of suicidal behavior is related to antidepressant dose, and if so, whether risk depends on a patient's age. ::: ::: ::: OBJECTIVE ::: To assess the risk of deliberate self-harm by antidepressant dose, by age group. ::: ::: ::: DESIGN, SETTING, AND PARTICIPANTS ::: This was a propensity score-matched cohort study using population-based health care utilization data from 162,625 US residents with depression ages 10 to 64 years who initiated antidepressant therapy with selective serotonin reuptake inhibitors at modal or at higher than modal doses from January 1, 1998, through December 31, 2010. ::: ::: ::: MAIN OUTCOMES AND MEASURES ::: International Classification of Diseases, Ninth Revision (ICD-9) external cause of injury codes E950.x-E958.x (deliberate self-harm). ::: ::: ::: RESULTS ::: The rate of deliberate self-harm among children and adults 24 years of age or younger who initiated high-dose therapy was approximately twice as high as among matched patients initiating modal-dose therapy (hazard ratio [HR], 2.2 [95% CI, 1.6-3.0]), corresponding to approximately 1 additional event for every 150 such patients treated with high-dose (instead of modal-dose) therapy. For adults 25 to 64 years of age, the absolute risk of suicidal behavior was far lower and the effective risk difference null (HR, 1.2 [95% CI, 0.8-1.9]). ::: ::: ::: CONCLUSIONS AND RELEVANCE ::: Children and young adults initiating therapy with antidepressants at high-therapeutic (rather than modal-therapeutic) doses seem to be at heightened risk of deliberate self-harm. Considered in light of recent meta-analyses concluding that the efficacy of antidepressant therapy for youth seems to be modest, and separate evidence that antidepressant dose is generally unrelated to therapeutic efficacy, our findings offer clinicians an additional incentive to avoid initiating pharmacotherapy at high-therapeutic doses and to closely monitor patients starting antidepressants, especially youth, for several months.


title: A Hybrid Model to Detect Malicious Executables

abstract: We present a hybrid data mining approach to detect malicious executables. In this approach we identify important features of the malicious and benign executables. These features are used by a classifier to learn a classification model that can distinguish between malicious and benign executables. We construct a novel combination of three different kinds of features: binary n-grams, assembly n-grams, and library function calls. Binary features are extracted from the binary executables, whereas assembly features are extracted from the disassembled executables. The function call features are extracted from the program headers. We also propose an efficient and scalable feature extraction technique. We apply our model on a large corpus of real benign and malicious executables. We extract the above mentioned features from the data and train a classifier using support vector machine. This classifier achieves a very high accuracy and low false positive rate in detecting malicious executables. Our model is compared with other feature-based approaches, and found to be more efficient in terms of detection accuracy and false alarm rate.


Analysis of Intrusion Detection Systems in Industrial Ecosystems


title: Potentiality of Bacterial Cellulose as the Scaffold of Tissue Engineering of Cornea

abstract: The bacterial cellulose (BC) secreted by Gluconacetobacter xylinus was explored as a novel scaffold material due to its unusual biocompatibility, light transmittance and material properties. The specific surface area of the frozen- dried BC sheet based on BET isotherm was 22.886 m 2 /g, and the porosity was around 90%. It is known by SEM graphs that significant difference in porosity and pore size exists in the two sides of air-dried BC sheets. The width of cellulose ribbons was 10 nm to 100 nm known by AFM image. The examination of the growth of human corneal stromal cells on BC demonstrated that the material supported the growth and proliferation of human corneal stromal cells. The ingrowth of corneal stromal cells into the scaffold was verified by Laser Scanning Confocal Microscope. The results suggest the potentiality for this biomaterial as a scaffold for tissue engineering of artificial cornea.


title: Slot-Coupled Multisection Quadrature Hybrid for UWB Applications

abstract: We present a three-section quadrature hybrid based on slot-coupled directional couplers, which operates over a bandwidth from 3.1 to 10.6 GHz. A prototype has been fabricated which exhibits a return loss better than 20 dB, isolation around 20 dB, an amplitude imbalance between output ports of less than plusmn0.75 dB and a phase imbalance between +1deg and -3deg across the 3.1-10.6 GHz band. This design outperforms previously reported results for ultra wide band operation.


title: Cyber Threats Facing Autonomous and Connected Vehicles: Future Challenges

abstract: Vehicles are currently being developed and sold with increasing levels of connectivity and automation. As with all networked computing devices, increased connectivity often results in a heightened risk of a cyber security attack. Furthermore, increased automation exacerbates any risk by increasing the opportunities for the adversary to implement a successful attack. In this paper, a large volume of publicly accessible literature is reviewed and compartmentalized based on the vulnerabilities identified and mitigation techniques developed. This review highlighted that the majority of studies are reactive and vulnerabilities are often discovered by friendly adversaries (white-hat hackers). Many gaps in the knowledge base were identified. Priority should be given to address these knowledge gaps to minimize future cyber security risks in the connected and autonomous vehicle sector.


title: A differential evolution algorithm with self-adapting strategy and control parameters

abstract: This paper presents a Differential Evolution algorithm with self-adaptive trial vector generation strategy and control parameters (SspDE) for global numerical optimization over continuous space. In the SspDE algorithm, each target individual has an associated strategy list (SL), a mutation scaling factor F list (FL), and a crossover rate CR list (CRL). During the evolution, a trial individual is generated by using a strategy, F, and CR taken from the lists associated with the target vector. If the obtained trial individual is better than the target vector, the used strategy, F, and CR will enter a winning strategy list (wSL), a winning F list (wFL), and a winning CR list (wCRL), respectively. After a given number of iterations, the FL, CRL or SL will be refilled at a high probability by selecting elements from wFL, wCRL and wSL or randomly generated values. In this way, both the trial vector generation strategy and its associated parameters can be gradually self-adapted to match different phases of evolution by learning from their previous successful experience. Extensive computational simulations and comparisons are carried out by employing a set of 19 benchmark problems from the literature. The computational results show that overall the SspDE algorithm performs better than the state-of-the-art differential evolution variants.


title: An efficient denoising algorithm for global illumination

abstract: We propose a hybrid ray-tracing/rasterization strategy for realtime rendering enabled by a fast new denoising method. We factor global illumination into direct light at rasterized primary surfaces and two indirect lighting terms, each estimated with one path-traced sample per pixel. Our factorization enables efficient (biased) reconstruction by denoising light without blurring materials. We demonstrate denoising in under 10 ms per 1280×720 frame, compare results against the leading offline denoising methods, and include a supplement with source code, video, and data.


title: Brain computer interface for gesture control of a social robot: An offline study

abstract: Brain computer interface (BCI) provides promising applications in neuroprosthesis and neurorehabilitation by controlling computers and robotic devices based on the patient's intentions. Here, we have developed a novel BCI platform that controls a personalized social robot using noninvasively acquired brain signals. Scalp electroencephalogram (EEG) signals are collected from a user in real-time during tasks of imaginary movements. The imagined body kinematics are decoded using a regression model to calculate the user-intended velocity. Then, the decoded kinematic information is mapped to control the gestures of a social robot. The platform here may be utilized as a human-robot-interaction framework by combining with neurofeedback mechanisms to enhance the cognitive capability of persons with dementia.


Linear codes over F2+uF2+vF2+uvF2


title: Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis

abstract: Abstract The advent of the Social Web has enabled anyone with an Internet connection to easily create and share their ideas, opinions and content with millions of other people around the world. In pace with a global deluge of videos from billions of computers, smartphones, tablets, university projectors and security cameras, the amount of multimodal content on the Web has been growing exponentially, and with that comes the need for decoding such information into useful knowledge. In this paper, a multimodal affective data analysis framework is proposed to extract user opinion and emotions from video content. In particular, multiple kernel learning is used to combine visual, audio and textual modalities. The proposed framework outperforms the state-of-the-art model in multimodal sentiment analysis research with a margin of 10–13% and 3–5% accuracy on polarity detection and emotion recognition, respectively. The paper also proposes an extensive study on decision-level fusion.


title: Hybrid Whale Optimization Algorithm with Simulated Annealing for Feature Selection

abstract: Hybrid metaheuristics are of the most interesting recent trends in optimization and memetic algorithms. In this paper, two hybridization models are used to design different feature selection techniques based on Whale Optimization Algorithm (WOA). In the first model, Simulated Annealing (SA) algorithm is embedded in WOA algorithm, while it is used to improve the best solution found after each iteration of WOA algorithm in the second model. The goal of using SA here is to enhance the exploitation by searching the most promising regions located by WOA algorithm. The performance of the proposed approaches is evaluated on 18 standard benchmark datasets from UCI repository and compared with three well-known wrapper feature selection methods in the literature. The experimental results confirm the efficiency of the proposed approaches in improving the classification accuracy compared to other wrapper-based algorithms, which insures the ability of WOA algorithm in searching the feature space and selecting the most informative attributes for classification tasks.


title: Planar square quadrifilar spiral antenna for mobile RFID reader

abstract: A compact planar square quadrifilar spiral antenna (QSA) for passive UHF radio frequency identification (RFID) reader is proposed and experimentally investigated. The presented RFID reader antenna consists of four spiral antennas which are fed by microstrip feed network and the 4-port antenna matching technique for QSA is used to improve radiation efficiency. Experimental results shows the proposed antenna has peak gain of 0.06 dBic, axial ratio under 3 dB in wanted frequency band, 3-dB beamwidth of 124° with good right hand circular polarization (RHCP) characteristics.


title: Partnering with community-based professionals to improve substance abuse screening and intervention for youth and young adults.

abstract: Abstract The paper describes the process by which a team conducted a needs assessment and developed training programs to address service gaps in substance abuse prevention for young people.


title: Automatic 3D reconstruction of manifold meshes via delaunay triangulation and mesh sweeping

abstract: In this paper we propose a new approach to incrementally initialize a manifold surface for automatic 3D reconstruction from images. More precisely we focus on the automatic initialization of a 3D mesh as close as possible to the final solution; indeed many approaches require a good initial solution for further refinement via multi-view stereo techniques. Our novel algorithm automatically estimates an initial manifold mesh for surface evolving multi-view stereo algorithms, where the manifold property needs to be enforced. It bootstraps from 3D points extracted via Structure from Motion, then iterates between a state-of-the-art manifold reconstruction step and a novel mesh sweeping algorithm that looks for new 3D points in the neighborhood of the reconstructed manifold to be added in the manifold reconstruction. The experimental results show quantitatively that the mesh sweeping improves the resolution and the accuracy of the manifold reconstruction, allowing a better convergence of state-of-the-art surface evolution multi-view stereo algorithms.


Asynchronous event-based high speed vision for microparticle tracking


title: Test-cost optimization in a scan-compression architecture using support-vector regression

abstract: Scan compression is widely used in high-volume testing of complex integrated circuits. With an increase in design complexity, the increased density of unknown (X) values from output responses reduces compression efficiency. In order to effectively block X values and maximize the effectiveness of test compression, a scan-compression architecture has recently been proposed, in which deterministic test patterns can be loaded into selected scan cells by controlling the initial state of the pseudo-random pattern generator (PRPG). A careful selection of the PRPG length is however essential to reduce test cost. We propose an optimization method based on support-vector regression to determine the PRPG length for test-cost reduction in a given scan-compression architecture. A correlation-based feature selection methodology is also proposed to reduce the amount of data needed for the accurate selection of the PRPG length. Experimental results on industrial designs highlight the effectiveness of the proposed method.


title: Visual Analysis of Large Graphs

abstract: The analysis of large graphs plays a prominent role in various fields of research and is relevant in many important application areas. Effective visual analysis of graphs requires appropriate visual presentations in combination with respective user interaction facilities and algorithmic graph analysis methods. How to design appropriate graph analysis systems depends on many factors, including the type of graph describing the data, the analytical task at hand, and the applicability of graph analysis methods. The most recent surveys of graph visualization and navigation techniques were presented by Herman et al. [HMM00] and Diaz [DPS02]. The first work surveyed the main techniques for visualization of hierarchies and graphs in general that had been introduced until 2000. The second work concentrated on graph layouts introduced until 2002. Recently, new techniques have been developed covering a broader range of graph types, such as time-varying graphs. Also, in accordance with ever growing amounts of graph-structured data becoming available, the inclusion of algorithmic graph analysis and interaction techniques becomes increasingly important. In this State-of-the-Art Report, we survey available techniques for the visual analysis of large graphs. Our review firstly considers graph visualization techniques according to the type of graphs supported. The visualization techniques form the basis for the presentation of interaction approaches suitable for visual graph exploration. As an important component of visual graph analysis, we discuss various graph algorithmic aspects useful for the different stages of the visual graph analysis process.


title: Multi-Level Surface Maps for Outdoor Terrain Mapping and Loop Closing

abstract: To operate outdoors or on non-flat surfaces, mobile robots need appropriate data structures that provide a compact representation of the environment and at the same time support important tasks such as path planning and localization. One such representation that has been frequently used in the past are elevation maps which store in each cell of a discrete grid the height of the surface in the corresponding area. Whereas elevation maps provide a compact representation, they lack the ability to represent vertical structures or even multiple levels. In this paper, we propose a new representation denoted as multi-level surface maps (MLS maps). Our approach allows to store multiple surfaces in each cell of the grid. This enables a mobile robot to model environments with structures like bridges, underpasses, buildings or mines. Additionally, they allow to represent vertical structures. Throughout this paper we present algorithms for updating these maps based on sensory input, to match maps calculated from two different scans, and to solve the loop-closing problem given such maps. Experiments carried out with a real robot in an outdoor environment demonstrate that our approach is well-suited for representing large-scale outdoor environments.


title: Memory of viral infections by CRISPR-Cas adaptive immune systems: acquisition of new information.

abstract: Multiple organisms face the threat of viral infections. To combat phage invasion, bacteria and archaea have evolved an adaptive mechanism of protection against exogenic mobile genetic elements, called CRISPR-Cas. In this defense strategy, phage infection is memorized via acquisition of a short invader sequence, called a spacer, into the CRISPR locus of the host genome. Upon repeated infection, the 'vaccinated' host expresses the spacer as a precursor RNA, which is processed into a mature CRISPR RNA (crRNA) that guides an endonuclease to the matching invader for its ultimate destruction. Recent efforts have uncovered molecular details underlying the crRNA biogenesis and interference steps. However, until recently the step of adaptation had remained largely uninvestigated. In this minireview, we focus on recent publications that have begun to reveal molecular insights into the adaptive step of CRISPR-Cas immunity, which is required for the development of the heritable memory of the host against viruses.


title: A systematic review of physical interventions for patellofemoral pain syndrome

abstract: ObjectivePhysical interventions (nonpharmacological and nonsurgical) are the mainstay of treatment for patellofemoral pain syndrome (PFPS). Physiotherapy is the most common of all physical interventions and includes specific vastus medialis obliquus or general quadriceps strengthening and/or realign


title: A Novel Permanent Magnet Vernier Machine With Halbach Array Magnets in Stator Slot Opening

abstract: Permanent-magnet vernier (PMV) machines are attracting more and more attention due to their high torque density and simple mechanical structure. In this paper, a novel PMV machine with an improved permanent-magnet circuit is proposed. Compared with the regular PMV machine, half of the permanent magnets (PMs) in the rotor are moved to the stator slot opening, and Halbach array PMs are employed in the stator while the consequent pole is employed in the rotor. The analysis of this machine indicates that the back electromotive-force amplitude of the proposed PMV machine could be two times that of the regular PMV machine with similar magnet usage, and for the same torque density, viz., 23.1 kNm/m3, the power factor of the proposed machine can reach 0.89, while this value is only 0.65 in the regular PMV machine.