title: A Taxonomy and Future Directions for Sustainable Cloud Computing: 360 Degree View

abstract: The cloud computing paradigm offers on-demand services over the Internet and supports a wide variety of applications. With the recent growth of Internet of Things (IoT) based applications the usage of cloud services is increasing exponentially. The next generation of cloud computing must be energy-efficient and sustainable to fulfil the end-user requirements which are changing dynamically. Presently, cloud providers are facing challenges to ensure the energy efficiency and sustainability of their services. The usage of large number of cloud datacenters increases cost as well as carbon footprints, which further effects the sustainability of cloud services. In this paper, we propose a comprehensive taxonomy of sustainable cloud computing. The taxonomy is used to investigate the existing techniques for sustainability that need careful attention and investigation as proposed by several academic and industry groups. Further, the current research on sustainable cloud computing is organized into several categories: application design, sustainability metrics, capacity planning, energy management, virtualization, thermal-aware scheduling, cooling management, renewable energy and waste heat utilization. The existing techniques have been compared and categorized based on the common characteristics and properties. A conceptual model for sustainable cloud computing has been proposed along with discussion on future research directions.


title: Optimization of partially shaded PV array using fuzzy MPPT

abstract: Solar photovoltaic (PV) array shows non-linear P-V characteristic when the array is illuminated under uniform solar irradiance. In this circumstance, the P-V characteristic consists only one maximum power point (MPP) along the functional operating voltage. However, the P-V characteristic has changed and become more complex with multiple MPP when the array is exposed under partially shaded conditions. This circumstance has caused maximum power point tracking (MPPT) algorithm faces difficulty to allocate the exact optimal operating voltage for PV array to generate maximum power. In this paper, initial voltage tracking function (IVTF) has been introduced to assign new initial voltage for tracking the absolute MPP when the PV array is at shaded conditions. Nevertheless, MPPT approach deals with the challenge of tracking the MPP in the faster mode while maintaining the power stability of the PV array toward the MPP. Therefore, fuzzy logic is embedded in the conventional MPPT to adapt the size of the perturbed voltage. The obtained results show that fuzzy logic based MPPT with IVTF is able to track the absolute MPP faster while minimizing power lost due to the power fluctuations.


title: Security analysis of blowfish algorithm

abstract: Blowfish algorithm (BA) is a symmetric block cipher with a 64-bit block size and variable key lengths from 32 bits up to a maximum of 448 bits. In order to measure the degree of security of blowfish algorithm, some cryptographic tests must be applied such as randomness test, avalanche criteria and correlation coefficient. In this paper we attempt to analyze the security of blowfish using avalanche criteria and correlation coefficient. We analyzed the randomness of the Blowfish output in an earlier paper titled “Randomness Analysis on Blowfish Block Cipher using ECB and CBC Modes”. The results obtained from the analysis of correlation coefficient showed that Blowfish algorithm gives a good nonlinear relation between plaintext and ciphertext while the results of avalanche effect indicate that the algorithm presents good avalanche effect from the second round. C++ is used in the implementation of the blowfish algorithm; MATLAB programming (Mathworks, R., 2012a) is used in the implementation of avalanche effect and correlation coefficient.


title: Hierarchical Active Transfer Learning.

abstract: We describe a unified active transfer learning framework called Hierarchical Active Transfer Learning (HATL). HATL exploits cluster structure shared between different data domains to perform transfer learning by imputing labels for unlabeled target data and to generate effective label queries during active learning. The resulting framework is flexible enough to perform not only adaptive transfer learning and accelerated active learning but also unsupervised and semi-supervised transfer learning. We derive an intuitive and useful upper bound on HATL’s error when used to infer labels for unlabeled target points. We also present results on synthetic data that confirm both intuition and our analysis. Finally, we demonstrate HATL’s empirical effectiveness on a benchmark data set for sentiment classification.


title: Does Physical Activity Increase Life Expectancy? A Review of the Literature

abstract: Physical activity reduces many major mortality risk factors including arterial hypertension, diabetes mellitus type 2, dyslipidemia, coronary heart disease, stroke, and cancer. All-cause mortality is decreased by about 30% to 35% in physically active as compared to inactive subjects. The purpose of this paper was to synthesize the literature on life expectancy in relation to physical activity. A systematic PubMed search on life expectancy in physically active and inactive individuals was performed. In addition, articles comparing life expectancy of athletes compared to that of nonathletes were reviewed. Results of 13 studies describing eight different cohorts suggest that regular physical activity is associated with an increase of life expectancy by 0.4 to 6.9 years. Eleven studies included confounding risk factors for mortality and revealed an increase in life expectancy by 0.4 to 4.2 years with regular physical activity. Eleven case control studies on life expectancy in former athletes revealed consistently greater life expectancy in aerobic endurance athletes but inconsistent results for other athletes. None of these studies considered confounding risk factors for mortality. In conclusion, while regular physical activity increases life expectancy, it remains unclear if high-intensity sports activities further increase life expectancy.


title: Teaching case: applying gamification techniques and virtual reality for learning building engineering 3D arts

abstract: In this paper the researchers describe the use of gamification and virtual reality-enhanced learning in university engineering and architecture classes. Our goal is to increase student motivation and engagement through various technologies and learning methodologies based on game mechanics, called gamification, and the use of virtual reality. Gamification is used to engage students in the learning process and stretch their retention of the knowledge and skills received beyond a single lecture. Engagement is the main objective in applying gamification. The use of gamification and new technologies for enhancing the learning process will boost achievement in 3D architectural subjects for design, and convey and validate any architectural project. The researchers used virtual reality and software like Sketchfab, Unity, and Oculus Rift VR to enter virtual and immersive worlds that students used to enhance their architectural designs. To connect the technologies, the researchers used Unity for programming all the viewer behaviors and the virtual world interactivity. The researchers used Oculus Rift, a heads-up display for virtual reality with a high immersive presence, to design a pavilion and create a virtual island from scratch.


title: Learning to rank for robust question answering

abstract: This paper aims to solve the problem of improving the ranking of answer candidates for factoid based questions in a state-of-the-art Question Answering system. We first provide an extensive comparison of 5 ranking algorithms on two datasets -- from the Jeopardy quiz show and a medical domain. We then show the effectiveness of a cascading approach, where the ranking produced by one ranker is used as input to the next stage. The cascading approach shows sizeable gains on both datasets. We finally evaluate several rank aggregation techniques to combine these algorithms, and find that Supervised Kemeny aggregation is a robust technique that always beats the baseline ranking approach used by Watson for the Jeopardy competition. We further corroborate our results on TREC Question Answering datasets.


title: Obstacle avoidance and path planning for humanoid robots using stereo vision

abstract: This work presents methods for path planning and obstacle avoidance for the humanoid robot QRIO, allowing the robot to autonomously walk around in a home environment. For an autonomous robot, obstacle detection and localization as well as representing them in a map are crucial tasks for the success of the robot. Our approach is based on plane extraction from data captured by a stereo-vision system that has been developed specifically for QRIO. We briefly overview the general software architecture composed of perception, short and long term memory, behavior control, and motion control, and emphasize on our methods for obstacle detection by plane extraction, occupancy grid mapping, and path planning. Experimental results complete the description of our system.


title: Patterning of thick electroplated CoPt magnets using SU-8 micromoulds

abstract: Recently, electroplated cobalt platinum (CoPt) magnetic films have been developed that afford thick (up to ∼100 μm), high-energy-density (up to ∼100 kJ/m3) permanent-magnet microstructures. However, the aggressive electroplating conditions have shown to be incompatible with most thick-film photoresist mould materials, making patterning these films an unanswered technical challenge. In this work, it is reported a complete process flow of fabricating and removing SU-8 photoresist moulds to enable the ultra-thick patterned CoPt magnets on silicon wafers. The implementation of thick SU-8 2050 electroplating moulds makes it feasible to deposit 100 µm thick CoPt magnets in 4 h. A 25 nm thick sputtered titanium layer is used as a sacrificial layer to completely release the SU-8 mould in hydrofluoric acid after electrodeposition. Compared to prior research, significantly improved pattern definition and constant magnetic performance are observed and characterised by optical profilometer, scanning electron microscope, and vibrating sample magnetometer.


title: Design and characterization of corporate feed rectangular microstrip patch array antenna

abstract: The modern mobile communication systems requires high gain, large bandwidth and minimal size antenna's that are capable of providing better performance over a wide range of frequency spectrum. This requirement leads to the design of Microstrip patch antenna. This paper proposes the design of 4-Element microstrip patch antenna array which uses the corporate feed technique for excitation. Low dielectric constant substrates are generally preferred for maximum radiation. Thus it prefers Taconic as a dielectric substrate. Desired patch antenna design is initially simulated by using high frequency simulation software SONNET and FEKO and patch antenna is designed as per requirements. Antenna dimensions such as Length (L), Width (W) and substrate Dielectric Constant (er) and parameters like Return Loss, Gain and Impedance are calculated using high frequency simulation software. The antenna has been designed for the range 9-11 GHz. Hence this antenna is highly suitable for X-band applications.


title: Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question Answering

abstract: Open-domain question answering remains a challenging task as it requires models that are capable of understanding questions and answers, collecting useful information, and reasoning over evidence. Previous work typically formulates this task as a reading comprehension or entailment problem given evidence retrieved from search engines. However, existing techniques struggle to retrieve indirectly related evidence when no directly related evidence is provided, especially for complex questions where it is hard to parse precisely what the question asks. In this paper we propose a retriever-reader model that learns to attend on essential terms during the question answering process. We build (1) an essential term selector which first identifies the most important words in a question, then reformulates the query and searches for related evidence; and (2) an enhanced reader that distinguishes between essential terms and distracting words to predict the answer. We evaluate our model on multiple open-domain QA datasets where it outperforms the existing state-of-the-art, notably leading to a relative improvement of 8.1% on the AI2 Reasoning Challenge (ARC) dataset.


title: From senses to texts : An all-in-one graph-based approach for measuring semantic similarity

abstract: Quantifying semantic similarity between linguistic items lies at the core of many applications in Natural Language Processing and Artificial Intelligence. It has therefore received a considerable amount of research interest, which in its turn has led to a wide range of approaches for measuring semantic similarity. However, these measures are usually limited to handling specific types of linguistic item, e.g., single word senses or entire sentences. Hence, for a downstream application to handle various types of input, multiple measures of semantic similarity are needed, measures that often use different internal representations or have different output scales. In this article we present a unified graph-based approach for measuring semantic similarity which enables effective comparison of linguistic items at multiple levels, from word senses to full texts. Our method first leverages the structural properties of a semantic network in order to model arbitrary linguistic items through a unified probabilistic representation, and then compares the linguistic items in terms of their representations. We report state-of-the-art performance on multiple datasets pertaining to three different levels: senses, words, and texts.


Brain cortex regeneration affected by scaffold architectures Laboratory investigation


title: Design of electronic shelf label systems based on ZigBee

abstract: In order to solve the problems of low efficiency, lag and changing price tags frequently with paper labels, a low power electronic shelf label system was designed based on ZigBee technology, making computers controlling and display screen updating information in time come true. The system overcame the disadvantages of large power consumption, low transmission speed and high cost, having a high commercial value.


title: Distributed multi-robot task assignment and formation control

abstract: Distributed task assignment for multiple agents raises fundamental and novel problems in control theory and robotics. A new challenge is the development of distributed algorithms that dynamically assign tasks to multiple agents, not relying on a priori assignment information. We address this challenge using market-based coordination protocols where the agents are able to bid for task assignment with the assumption that every agent has knowledge of the maximum number of agents that any given task can accommodate. We show that our approach always achieves the desired assignment of agents to tasks after exploring at most a polynomial number of assignments, dramatically reducing the combinatorial nature of discrete assignment problems. We verify our algorithm through both simulation and experimentation on a team of non-holonomic robots performing distributed formation stabilization and group splitting and merging.


title: An Encoder-Decoder Based Convolution Neural Network (CNN) for Future Advanced Driver Assistance System (ADAS)

abstract: We propose a practical Convolution Neural Network (CNN) model termed the CNN for Semantic Segmentation for driver Assistance system (CSSA). It is a novel semantic segmentation model for probabilistic pixel-wise segmentation, which is able to predict pixel-wise class labels of a given input image. Recently, scene understanding has turned out to be one of the emerging areas of research, and pixel-wise semantic segmentation is a key tool for visual scene understanding. Among future intelligent systems, the Advanced Driver Assistance System (ADAS) is one of the most favorite research topic. The CSSA is a road scene understanding CNN that could be a useful constituent of the ADAS toolkit. The proposed CNN network is an encoder-decoder model, which is built on convolutional encoder layers adopted from the Visual Geometry Group’s VGG-16 net, whereas the decoder is inspired by segmentation network (SegNet). The proposed architecture mitigates the limitations of the existing methods based on state-of-the-art encoder-decoder design. The encoder performs convolution, while the decoder is responsible for deconvolution and un-pooling/up-sampling to predict pixel-wise class labels. The key idea is to apply the up-sampling decoder network, which maps the low-resolution encoder feature maps. This architecture substantially reduces the number of trainable parameters and reuses the encoder’s pooling indices to up-sample to map pixel-wise classification and segmentation. We have experimented with different activation functions, pooling methods, dropout units and architectures to design an efficient CNN architecture. The proposed network offers a significant improvement in performance in segmentation results while reducing the number of trainable parameters. Moreover, there is a considerable improvement in performance in comparison to the benchmark results over PASCAL VOC-12 and the CamVid.


19p13.2 Microdeletion including NFIX associated with overgrowth and intellectual disability suggestive of Malan syndrome


title: Streaming spectral clustering

abstract: Clustering is a classical data mining task used for discovering interrelated pattern of similarities in the data. In many modern day domains, data is getting continuously generated as a stream. For scalability reasons, clustering the points in a data stream requires designing single pass, limited memory streaming clustering algorithms. However, the performance of the known streaming clustering algorithms, that typically use K-means (or its variants) on the original feature space, tend to suffer when the feature space is high-dimensional. To overcome this problem, we propose a streaming spectral clustering algorithm. Our algorithm maintains an approximation of the normalized Laplacian of the data stream over time and efficiently updates the changing eigenvectors of this Laplacian in a streaming fashion. It requires just one pass over the data, consumes limited memory, and is stable to the ordering of the data stream. We provide a theoretical analysis of our streaming spectral clustering algorithm and our experimental results show that while gaining in scalability, its performance compares well with other known batch/streaming clustering approaches.


title: The Big-Five Trait Taxonomy: History, Measurement, and Theoretical Perspectives

abstract: Barbecuing apparatus having a plurality of broiling trays arranged to rotate about a common axis. The trays are circumferentially spaced about a rotary shaft, extend radially with respect thereto, and are releasably mounted on disks and arms attached to the disks. The disks are mounted on and driven by the rotary shaft. Below the trays a bed for charcoal is located which heats the food in the trays. The charcoal fire is controlled by water directed at the fire through a plurality of quenching nozzles. Smoke rising from the trays is scrubbed by a high-velocity water mist supplied by a nozzle assembly above a fine-mesh screen through which the smoke passes.


title: A review of joint attention and social‐cognitive brain systems in typical development and autism spectrum disorder

abstract: This article provides a review of the increasingly detailed literature on the neurodevelopment of joint attention. Many findings from this literature support and inform the hypothesis that the neurodevelopment of joint attention contributes to the functional development of neural systems for human social cognition. Joint attention begins to develop by 5 months of age and is tantamount to the ability to adopt a common perspective with another person. It involves a whole-brain system with nodes in the: (a) dorsal and medial frontal cortex, (b) orbital frontal/insula cortex, (c) anterior/ posterior cingulate cortex, (d) superior temporal cortex, (e) precuneus/parietal cortex, and (f) amygdala and striatum. This system integrates triadic information processing about: (a) self-attention/action, (b) information about others’ attention/action during social interactions that involve, (c) coordinated attention as well as processing a common referent in space. The results of this new imaging literature have the potential to advance current models of social cognition and the social brain, which rarely consider the contribution of the cognitive neurodevelopment of joint attention. The new neuroscience of joint attention is also extremely valuable for clinical research on social-cognitive neurodevelopmental disorders. This is most clearly the case for autism spectrum disorder (ASD) because it is consistent with the hypothesis of substantial functional neurodevelopmental continuity between the preschool impairments of joint attention, and childhood theory of mind ability that characterize the development of ASD. ::: ::: This article is protected by copyright. All rights reserved.


title: Motivating creativity in organizations: On doing what you love and loving what you do

abstract: Creativity in all fields, including business, flourishes under intrinsic motivation—the drive to do something because it is interesting, involving, exciting, satisfying, or personally challenging. This article presents the Componential Theory of Organizational Creativity and Innovation, which defines the factors—including intrinsic motivation—that determine a person's creativity. This article also shows how the work environment can influence individual creativity.


title: Tuning of neural networks using particle swarm optimization to model MIG welding process

abstract: Abstract Particle swarm optimization technique has been used for tuning of neural networks utilized for carrying out both forward and reverse mappings of metal inert gas (MIG) welding process. Four approaches have been developed and their performances are compared to solve the said problems. The first and second approaches deal with tuning of multi-layer feed-forward neural network and radial basis function neural network, respectively. In the third and fourth approaches, a back-propagation algorithm has been used along with particle swarm optimization to tune radial basis function neural network. Moreover, in these two approaches, two different clustering algorithms have been utilized to decide the structure of the network. The performances of hybrid approaches (that is, the third and fourth approaches) are found to be better than that of the other two.


title: Topic themes for multi-document summarization

abstract: The problem of using topic representations for multi-document summarization (MDS) has received considerable attention recently. In this paper, we describe five different topic representations and introduce a novel representation of topics based on topic themes. We present eight different methods of generating MDS and evaluate each of these methods on a large set of topics used in past DUC workshops. Our evaluation results show a significant improvement in the quality of summaries based on topic themes over MDS methods that use other alternative topic representations.


title: TaskMe: multi-task allocation in mobile crowd sensing

abstract: Task allocation or participant selection is a key issue in Mobile Crowd Sensing (MCS). While previous participant selection approaches mainly focus on selecting a proper subset of users for a single MCS task, multi-task-oriented participant selection is essential and useful for the efficiency of large-scale MCS platforms. This paper proposes TaskMe, a participant selection framework for multi-task MCS environments. In particular, two typical multi-task allocation situations with bi-objective optimization goals are studied: (1) For FPMT (few participants, more tasks), each participant is required to complete multiple tasks and the optimization goal is to maximize the total number of accomplished tasks while minimizing the total movement distance. (2) For MPFT (more participants, few tasks), each participant is selected to perform one task based on pre-registered working areas in view of privacy, and the optimization objective is to minimize total incentive payments while minimizing the total traveling distance. Two optimal algorithms based on the Minimum Cost Maximum Flow theory are proposed for FPMT, and two algorithms based on the multi-objective optimization theory are proposed for MPFT. Experiments verify that the proposed algorithms outperform baselines based on a large-scale real-word dataset under different experiment settings (the number of tasks, various task distributions, etc.).


Classification of Liver Cancer using Artificial Neural Network and Support Vector Machine


title: [Retroperitoneal, mediastinal and subcutaneous emphysema with pneumothorax after colonoscopy].

abstract: HISTORY ::: Severe ulcerative colitis had been diagnosed in a 25-year-old man six years ago. That same year he underwent laparoscopy for mechanical ileus. Since then he has been on a maintenance dose of sulphasalazine without any further symptoms. Because of a known pseudopolyposis coli, repeat coloscopy was performed. EXAMINATIONS AND FINDINGS: The endoscope was smoothly introduced and the examination was without pain. No premedication had been given. A biopsy was obtained during withdrawal of the endoscope. Suddenly the patient noticed marked scrotal swelling. The examination was terminated abruptly. No lesions of the wall of the distal colon were seen when the endoscope was pulled out immediately. Quickly, cutaneous emphysema developed over the right side of the abdomen and thorax up to the neck. The patient could no more properly hear his own voice. Radiology demonstrated bilateral pneumoretroperitoneum, mediastinal emphysema and pneumothorax. ::: ::: ::: TREATMENT AND COURSE ::: The patient was put on parenteral nutrition for 4 days and prophylactically received amoxycillin, calvulanic acid and metronidazole. The air was gradually absorbed and the patient discharged symptom-free after 7 days. ::: ::: ::: CONCLUSION ::: In contrast to intraperitoneal perforation, which usually causes acute symptoms of peritonitis and requires urgent surgical treatment, the course of the much rarer retroperitoneal perforation is more benign with few symptoms and can be managed conservatively.


title: Effects of strength training on endurance capacity in top-level endurance athletes

abstract: The effect of concurrent strength (S) and endurance (E) training on adaptive changes in aerobic capacity, endurance performance, maximal muscle strength and muscle morphology is equivocal. Some data suggest an attenuated cardiovascular and musculoskeletal response to combined E and S training, while other data show unimpaired or even superior adaptation compared with either training regime alone. However, the effect of concurrent S and E training only rarely has been examined in top-level endurance athletes. This review describes the effect of concurrent SE training on short-term and long-term endurance performance in endurance-trained subjects, ranging from moderately trained individuals to elite top-level athletes. It is concluded that strength training can lead to enhanced long-term (>30 min) and short-term (<15 min) endurance capacity both in well-trained individuals and highly trained top-level endurance athletes, especially with the use of high-volume, heavy-resistance strength training protocols. The enhancement in endurance capacity appears to involve training-induced increases in the proportion of type IIA muscle fibers as well as gains in maximal muscle strength (MVC) and rapid force characteristics (rate of force development), while likely also involving enhancements in neuromuscular function.


title: A bibliometric analysis on rural studies in human geography and related disciplines

abstract: Although the world has experienced rapid urbanization, rural areas have always been and are still an important research field in human geography. This paper performed a bibliometric analysis on rural geography studies based on the peer-reviewed articles concerning rural geography published in the SSCI-listed journals from 1990 to 2012. Our analysis examines publication patterns (document types and publishing languages, article outputs and their categories, major journals and their publication, most productive authors, geographic distribution and international collaboration) and demonstrates the evolution of intellectual development of rural geography by studying highly cited papers and their citation networks and temporal evolution of keywords. Our research findings include: The article number has been increasing since the 1900s, and went through three phases, and the rural geography research is dominated in size by UK and USA. The USA is the most productive in rural geography, but the UK had more impact than other countries in the terms of the average citation of articles. Three distinct but loosely linked research streams of rural geography were identified and predominated by the UK rural geographers. The keywords frequencies evolved according to contexts of rural development and academic advances of human geography, but they were loosely and scattered since the rural researches in different regions or different systems faced with different problems.


title: Cleaneval: a Competition for Cleaning Web Pages

abstract: Cleaneval is a shared task and competitive evaluation on the topic of cleaning arbitrary web pages, with the goal of preparing web data for use as a corpus for linguistic and language technology research and development. The first exercise took place in 2007. We describe how it was set up, results, and lessons learnt.


title: Classification , subtype discovery , and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling

abstract: Treatment of pediatric acute lymphoblastic leukemia (ALL) is based on the concept of tailoring the intensity of therapy to a patient's risk of relapse. To determine whether gene expression profiling could enhance risk assignment, we used oligonucleotide microarrays to analyze the pattern of genes expressed in leukemic blasts from 360 pediatric ALL patients. Distinct expression profiles identified each of the prognostically important leukemia subtypes, including T-ALL, E2A-PBX1, BCR-ABL, TEL-AML1, MLL rearrangement, and hyperdiploid >50 chromosomes. In addition, another ALL subgroup was identified based on its unique expression profile. Examination of the genes comprising the expression signatures provided important insights into the biology of these leukemia subgroups. Further, within some genetic subgroups, expression profiles identified those patients that would eventually fail therapy. Thus, the single platform of expression profiling should enhance the accurate risk stratification of pediatric ALL patients.


title: Effectiveness of Neuromuscular Electrical Stimulation on Auxiliary Respiratory Muscles in Patients with Chronic Obstructive Pulmonary Disease Treated in the Intensive Care Unit

abstract: Objective: Chronic obstructive pulmonary disease is a major public health problem. In the present study, we aimed to investigate the possible effects of upper extremity exercises and neuromuscular electrical stimulation therapy applied to auxiliary respiratory muscles on arterial blood gases, blood pressure, heart rate values, and quality of life in patients with chronic obstructive pulmonary disease. Material and Methods: Thirty patients who were diagnosed with stage 4 severe chronic obstructive pulmonary disease by a pulmonologist and who needed intensive care were included in the study. The patients were equally allocated into two groups containing 15 patients each: control and intervention. Both groups were given an unassisted upper extremity exercise program for four weeks, and only one group was given neuromuscular electrical stimulation on auxiliary respiratory muscles for 10 days. Arterial blood gas measurements, peak heart rate, and breathing frequency were recorded. St. George’s Respiratory Questionnaire and short form-36 were performed for the evaluation of the quality of life. Functional capacity was determined by functional independent measurement. Evaluations of the patients were performed on the 1 st , 8 th , 15 th , and 30 th days of the study. Results: There were statistically significant improvements in peak heart rate, breathing frequency per minute, and functional independency scores in the group where exercise and neuromuscular electrical stimulation had been concomitantly applied (p<0.05). Conclusion: This study revealed positive effects of neuromuscular electrical stimulation in addition with therapeutic exercises on the cardiorespiratory system in the short run.


title: Incomplete Follow-up Question Resolution using Retrieval based Sequence to Sequence Learning

abstract: Intelligent personal assistants (IPAs) and interactive question answering (IQA) systems frequently encounter incomplete follow-up questions. The incomplete follow-up questions only make sense when seen in conjunction with the conversation context: the previous question and answer. Thus, IQA and IPA systems need to utilize the conversation context in order to handle the incomplete follow-up questions and generate an appropriate response. In this work, we present a retrieval based sequence to sequence learning system that can generate the complete (or intended) question for an incomplete follow-up question (given the conversation context). We can train our system using only a small labeled dataset (with only a few thousand conversations), by decomposing the original problem into two simpler and independent problems. The first problem focuses solely on selecting the candidate complete questions from a library of question templates (built offline using the small labeled conversations dataset). In the second problem, we re-rank the selected candidate questions using a neural language model (trained on millions of unlabelled questions independently). Our system can achieve a BLEU score of 42.91, as compared to 29.11 using an existing generation based approach. We further demonstrate the utility of our system as a plug-in module to an existing QA pipeline. Our system when added as a plug-in module, enables Siri to achieve an improvement of 131.57% in answering incomplete follow-up questions.


title: Shared Fun Is Doubled Fun: Player Enjoyment as a Function of Social Setting

abstract: Although the social relevance of digital gaming has been discussed and investigated in cultural and sociological readings, social context has been largely neglected in terms of in-game player experience. In this paper we present a study in which player experience is empirically tested comprehen-sively, with the focus on the interplay between player enjoyment and social context. Self reports on the Game Experience Questionnaire and an aggression state questionnaire were explored for three play configurations: virtual, mediated, and co-located co-play. Additionally, effects of familiarity and winning vs. losing were tested. Results indicate that a co-located co-player significantly adds to the fun, challenge, and perceived competence in the game. Aggression was influenced by familiarity and performance. The effect of social context was mediated by the level of social presence. These findings illustrate that social context is an important determinant of player enjoyment and should be incorporated in models of player experience.


title: The Relative Importance of Performance Factors in Korean Archery

abstract: This study explored the factors affecting archery performance by calculating their relative importance in Korean archery. This study used the Delphi technique and the analytic hierarchy process (AHP). After reviewing the literature and collecting data on performance factors in archery, the importance of factors affecting archery performance was calculated by holding meetings with experts (20 archery experts) and conducting confirmatory factor analysis (463 archers) and the AHP (36 archery experts). Performance factors were divided into mental, skill, and fitness categories. Fitness factors affecting performance included “drawing a bow without an arrow,” “lower-body weight training,” and “upper-body weight training.” Skill factors affecting performance included “extending by maintaining left and right shoulder balance during aiming,” “shooting skill over a regular clicker time,” “maintaining pace and direction at release,” and “drawing skill by maintaining left and right shoulder balance.” Mental factors affecting performance were “confidence,” “concentration,” “emotion control,” and “positive thinking.” “Confidence” was identified as the most important factor among the 11 subfactors. The performance factors identified in this study and their relative importance in determining successful performance can be used in training for optimal archery performance worldwide.


title: Single disperser design for coded aperture snapshot spectral imaging

abstract: We present a single disperser spectral imager that exploits recent theoretical work in the area of compressed sensing to achieve snapshot spectral imaging. ::: An experimental prototype is used to capture the spatiospectral information of a scene that consists of two balls illuminated by different light sources. ::: An iterative algorithm is used to reconstruct the data cube. The average spectral resolution is 3.6 nm per spectral channel. The accuracy of the instrument is demonstrated by comparison of the spectra acquired with the proposed system with the spectra acquired by a nonimaging reference spectrometer.


title: Mining Text Enriched Heterogeneous Citation Networks

abstract: The paper presents an approach to mining text enriched heterogeneous information networks, applied to a task of categorizing papers from a large citation network of scientific publications in the field of psychology. The methodology performs network propositionalization by calculating structural context vectors from homogeneous networks, extracted from the original network. The classifier is constructed from a table of structural context vectors, enriched with the bag-of-words vectors calculated from individual paper abstracts. A series of experiments was performed to examine the impact of increasing the number of publications in the network, and adding different types of structural context vectors. The results indicate that increasing the network size and combining both types of information is beneficial for improving the accuracy of paper categorization.


title: On a Future for Power Electronics

abstract: This paper presents a historical and philosophical perspective on a possible future for power electronics. Technologies have specific life cycles that are driven by internal innovation, subsequently reaching maturity. Power electronics appears to be a much more complex case, functioning as an enabling technology spanning an enormous range of power levels, functions and applications. Power electronics is also divided into many constituent technologies. Till now, the development of power electronics has been driven chiefly by internal semiconductor technology and converter circuit technology, approaching maturity in its internally set metrics, such as efficiency. This paper examines critically the fundamental functions found in electronic energy processing, the constituent technologies comprising power electronics, and the power electronics technology space in light of the internal driving philosophy of power electronics and its historical development. It is finally concluded that, although approaching the limits of its internal metrics indicates internal maturity, the external constituent technologies of packaging, manufacturing, electromagnetic and physical impact, and converter control technology still present remarkable opportunities for development. As power electronics is an enabling technology, its development, together with internal developments, such as wide bandgap semiconductors, will be driven externally by applications in the future.


title: Diagnostic value of four-dimensional CT angiography in arterial erectile dysfunction using 320-detector row dynamic volume CT

abstract: The present study aims to evaluate the diagnostic value of four-dimensional CT angiography (4D-CTA) in the diagnosis of arterial erectile dysfunction (ED) using 320-detector row dynamic volume CT. Arterial ED patients attributed to arterial insufficiency were enrolled. To induce penile erection, an intracavernous injection (ICI) of corpus cavernosum with a vasoactive drug was administered. Patients were assigned into the erection hardness score (EHS) 1/2 group or EHS 3/4 group. Color duplex Doppler ultrasound (CDDU) was used to analyze blood flow spectrum. Each patient was examined using 4D-CTA. Receiver operating characteristic (ROC) curve was plotted to evaluate the diagnostic value of 4D-CTA in arterial ED. According to Irwin Goldstein, the EHS 3/4 group (n=38) had a shorter course of ED and low proportion with history of hypertension, hyperlipidemia, and diabetes than the EHS 1/2 group (n=35). The peak systolic velocity (PSV), end diastolic velocity (EDV), and resistant index (RI) in the EHS 3/4 group were lower than those of the EHS 1/2 group. 4D-CTA showed there were a total of 35 cases in the EHS 1/2 group (two cases missed) and 38 cases in the EHS 3/4 group (seven cases misdiagnosed). Using 4D-CTA to diagnose arterial ED, the area under the ROC curve yielded a value of 0.879, with a specificity of 93.9% and a sensitivity of 82.5%. These findings indicated that 4D-CTA using 320-detector row dynamic volume CT is a promising and reliable utility in diagnosing arterial ED.


title: From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions

abstract: We propose to use the visual denotations of linguistic expressions (i.e. the set of images they describe) to define novel denotational similarity metrics , which we show to be at least as beneficial as distributional similarities for two tasks that require semantic inference. To compute these denotational similarities, we construct a denotation graph , i.e. a subsumption hierarchy over constituents and their denotations, based on a large corpus of 30K images and 150K descriptive captions.


title: Spectrophotometric assay of d-isoleucine using an artificially created d-amino acid dehydrogenase

abstract: D-isoleucine (D-Ile) can be assayed using chiral chromatography but the availability of that method is limited by the necessity for special expertise and expensive equipment. We therefore developed a simple and specific colorimetric assay system for D-Ile determination using an artificially created NADP(+)-dependent D-amino acid dehydrogenase (DAADH). The system consists of two reaction steps: the first is the quantitative conversion of D-Ile to (3R)-2-oxo-3-methyl valerate by DAADH in which NADP(+) is converted to NADPH, while the second is chemical conversion of NADPH to reduced water-soluble Tetrazolium-3 via a redox mediator. D-Ile was determined from 1 to 50 µM, and the assay was unaffected by the presence of any of three other isomers (100 µM), alcohol and organic acids.


title: Helios: a hybrid electrical/optical switch architecture for modular data centers

abstract: The basic building block of ever larger data centers has shifted from a rack to a modular container with hundreds or even thousands of servers. Delivering scalable bandwidth among such containers is a challenge. A number of recent efforts promise full bisection bandwidth between all servers, though with significant cost, complexity, and power consumption. We present Helios, a hybrid electrical/optical switch architecture that can deliver significant reductions in the number of switching elements, cabling, cost, and power consumption relative to recently proposed data center network architectures. We explore architectural trade offs and challenges associated with realizing these benefits through the evaluation of a fully functional Helios prototype.


SybilLimit: A Near-Optimal Social Network Defense Against Sybil Attacks


title: Sea-sky-line detection based on local Otsu segmentation and Hough transform

abstract: Due to the interference such as sea waves, ships and light, it is difficult to accurately detect the sea-sky-line of the visible light maritime image. To improve the detection accuracy and robustness, a sea-sky-line detection method based on local Otsu segmentation and Hough transform is proposed. Firstly, high-frequency noise such as light spot in the gray image is rapidly suppressed by longitudinal median filter. Then, according to the image features, the gray image is divided into image blocks in longitudinal to compensate for inhomogeneity of illumination and limit the interference scope of ships to some image blocks. Afterwards, local Otsu segmentation is performed on the gray image to obtain the binary image where edge pixels are extracted, which suppresses the interference of waves. Finally, Hough transform is used to fit edge pixels to complete the sea-sky-line detection. Experimental results show that the proposed method is relatively accurate, robust and real-time. The detection accuracy of the proposed method is 93.0%, which is significantly higher than that of three representative sea-sky-line detection methods.


title: PLEdestrians: a least-effort approach to crowd simulation

abstract: We present a new algorithm for simulating large-scale crowds at interactive rates based on the Principle of Least Effort. Our approach uses an optimization method to compute a biomechanically energy-efficient, collision-free trajectory that minimizes the amount of effort for each heterogeneous agent in a large crowd. Moreover, the algorithm can automatically generate many emergent phenomena such as lane formation, crowd compression, edge and wake effects ant others. We compare the results from our simulations to data collected from prior studies in pedestrian and crowd dynamics, and provide visual comparisons with real-world video. In practice, our approach can interactively simulate large crowds with thousands of agents on a desktop PC and naturally generates a diverse set of emergent behaviors.


title: Cross-lingual Text Classification via Model Translation with Limited Dictionaries

abstract: Cross-lingual text classification (CLTC) refers to the task of classifying documents in different languages into the same taxonomy of categories. An open challenge in CLTC is to classify documents for the languages where labeled training data are not available. Existing approaches rely on the availability of either high-quality machine translation of documents (to the languages where massively training data are available), or rich bilingual dictionaries for effective translation of trained classification models (to the languages where labeled training data are lacking). This paper studies the CLTC challenge under the assumption that neither condition is met. That is, we focus on the problem of translating classification models with highly incomplete bilingual dictionaries. Specifically, we propose two new approaches that combines unsupervised word embedding in different languages, supervised mapping of embedded words across languages, and probabilistic translation of classification models. The approaches show significant performance improvement in CLTC on a benchmark corpus of Reuters news stories (RCV1/RCV2) in English, Spanish, German, French and Chinese and an internal dataset in Uzbek, compared to representative baseline methods using conventional bilingual dictionaries or highly incomplete ones.


Quadcopter Flight Dynamics


title: Parallel Optimal Pairwise Biological Sequence Comparison: Algorithms, Platforms, and Classification

abstract: Many bioinformatics applications, such as the optimal pairwise biological sequence comparison, demand a great quantity of computing resource, thus are excellent candidates to run in high-performance computing (HPC) platforms. In the last two decades, a large number of HPC-based solutions were proposed for this problem that run in different platforms, targeting different types of comparisons with slightly different algorithms and making the comparative analysis of these approaches very difficult. This article proposes a classification of parallel optimal pairwise sequence comparison solutions, in order to highlight their main characteristics in a unified way. We then discuss several HPC-based solutions, including clusters of multicores and accelerators such as Cell Broadband Engines (CellBEs), Field-Programmable Gate Arrays (FPGAs), Graphics Processing Units (GPUs) and Intel Xeon Phi, as well as hybrid solutions, which combine two or more platforms, providing the actual landscape of the main proposals in this area. Finally, we present open questions and perspectives in this research field.


title: CATS: A Color and Thermal Stereo Benchmark

abstract: Stereo matching is a well researched area using visibleband color cameras. Thermal images are typically lower resolution, have less texture, and are noisier compared to their visible-band counterparts and are more challenging for stereo matching algorithms. Previous benchmarks for stereo matching either focus entirely on visible-band cameras or contain only a single thermal camera. We present the Color And Thermal Stereo (CATS) benchmark, a dataset consisting of stereo thermal, stereo color, and cross-modality image pairs with high accuracy ground truth (


title: Using quality function deployment to conduct vendor assessment and supplier recommendation for business-intelligence systems

abstract: A QFD (quality function deployment) based framework is presented.Evaluation criteria consist of marketing requirements and technical attributes.Various fuzzy MCDM (multi-criteria decision making) schemes are fused.Vendor assessment and supplier recommendation are accomplished.Supplier recommendation is conducted in an unsupervised way.BI (business intelligence) vendors are visualized to generate managerial insights Business intelligence (BI) has been recognized as an important enterprise information system to help decision makers achieve performance measurement and management. Generally, typical BI users consist of financial analysts, marketing planners, and general managers. However, most of them are not familiar with BI's core technologies. In order to help corporate executives better assess BI vendors, evaluation criteria are separated into marketing requirements (MRs) and technical attributes (TAs), respectively. In particular, a fuzzy MCDM (multi-criteria decision making) based QFD (quality function deployment) is proposed as follows: (1) fuzzy Delphi is used to aggregate the performance scores of BI vendors, (2) fuzzy DEMATEL (decision making and trial laboratory) is conducted to recognize the causalities between MRs and TAs, and (3) fuzzy AHP (analytical hierarchy process) is employed to recommend optimal BI systems. For better benchmarking, the strengths and weaknesses of three competitive BI vendors (i.e. SAP, SAS, and Microsoft) are concurrently visualized through displaying a line diagram (in terms of TAs) and a radar diagram (in terms of MRs). More importantly, experimental results demonstrate that supplier assessment and supplier recommendation have been successfully accomplished.


title: Intrinsic frames of reference and egocentric viewpoints in scene recognition

abstract: Three experiments investigated the roles of intrinsic directions of a scene and observer's viewing direction in recognizing the scene. Participants learned the locations of seven objects along an intrinsic direction that was different from their viewing direction and then recognized spatial arrangements of three or six of these objects from different viewpoints. The results showed that triplets with two objects along the intrinsic direction (intrinsic triplets) were easier to recognize than triplets with two objects along the study viewing direction (non-intrinsic triplets), even when the intrinsic triplets were presented at a novel test viewpoint and the non-intrinsic triplets were presented at the familiar test viewpoint. The results also showed that configurations with the same three or six objects were easier to recognize at the familiar test viewpoint than other viewpoints. These results support and develop the model of spatial memory and navigation proposed by Mou, McNamara, Valiquette, and Rump [Mou, W., McNamara, T. P., Valiquiette C. M., & Rump, B. (2004). Allocentric and egocentric updating of spatial memories. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 142-157].


title: AdaBoost-Based Face Detection in Color Images with Low False Alarm

abstract: In this paper, we have proposed a new face detection method which combines the AdaBoost algorithm with skin color information and support vector machine (SVM). First, a cascade classifier based on AdaBoost is used to detect faces in images. Due to noise and illumination changes some nonfaces might be detected too, therefore we have used a skin color model in the YCbCr color space to remove some of the detected nonfaces. Finally, we have utilized SVM to detect faces more accurately. Experimental results show that the performance of the proposed method is higher than the basic AdaBoost in the sense of detecting fewer nonfaces.


title: Modeling and simulation of a DC motor control system with digital PID controller and encoder in FPGA using Xilinx system generator

abstract: This paper presents a novel method for design and simulation of a DC motor closed loop control system in Field Programmable Gate Array device (FPGA). The digital designed PID controller is more advanced, beneficial and therefore produces a better response, as compared to the analog PID with velocity measurement. It can be implemented on system-on-chip devices easily. MATLAB Xilinx system generator toolbox based on Fixed-Point Arithmetic is used to design the digital PID controller using DSP architecture, plot the responses of the control system and generate the VHDL source code. The control system consists of a digital PID, the model of a real DC motor, a real and new incremental optical shaft encoder model, encoder to rpm and position blocks. Results show that the proposed system leads to lower the steady state and transient error.


title: Clustering scientific documents with topic modeling

abstract: Topic modeling is a type of statistical model for discovering the latent "topics" that occur in a collection of documents through machine learning. Currently, latent Dirichlet allocation (LDA) is a popular and common modeling approach. In this paper, we investigate methods, including LDA and its extensions, for separating a set of scientific publications into several clusters. To evaluate the results, we generate a collection of documents that contain academic papers from several different fields and see whether papers in the same field will be clustered together. We explore potential scientometric applications of such text analysis capabilities.


title: Controversy is Marketing: Mining Sentiments in Social Media

abstract: We propose a set of novel quantitative measures for controversy derived from statistical properties of textual social media. We empirically establish strong positive correlations between social media controversy and sales performance across multiple datasets. The power of the newly proposed measures is further illustrated in a linear regression model for predicting product sales.


Unsupervised Learning of Edit Distance Weights for Retrieving Historical Spelling Variations


title: A High-Swing, High-Impedance MOS Cascode Circuit

abstract: A simple cascode with the gate voltage of the cascode transistor being controlled by a feedback amplifier called a regulated cascode is presented. In comparison to the standard cascode circuit, the minimum output voltage is lower by about 30 to 60% while the output conductance and the feedback capacitance are lower by about 100 times. An analytical large-signal, small-signal, and noise analysis is carried out. Some applications like current mirrors and voltage amplifiers are discussed. Experimental results confirming the theory are presented. >


title: Ground penetrating radar hyperbola detection using Scale-Invariant Feature Transform

abstract: Ground-penetrating radar (GPR) is a geophysical method used for subsurface targets identification. However, a sophisticated processing of GPR data is necessary to extract targets signature.


title: Delay attacks — Implication on NTP and PTP time synchronization

abstract: In this paper specific variants of delay attacks are examined. First, the Network Time Protocol is regarded. Second, delay attacks and implications on the time synchronization of the Precise Time Protocol are described. In particular, consequences for the offset calculation O prox are regarded. At the end of the paper possible countermeasurements are described.


Drug-target interaction prediction via class imbalance-aware ensemble learning


title: Genetic merit for fertility traits in Holstein cows: III. Hepatic expression of somatotropic axis genes during pregnancy and lactation

abstract: Abstract The objective of this study was to characterize the circulating concentrations of insulin-like growth factor-I (IGF-I) and the hepatic expression of key genes regulating the somatotropic axis in cows divergent in genetic merit for fertility traits but with similar genetic merit for milk production traits. A total of 11 cows with good genetic merit for fertility (Fert+) and 12 cows with poor genetic merit for fertility (Fert−) underwent liver biopsy by percutaneous punch technique on d 20 (±6.7 d) prepartum and on d 2 (±1.5 d), d 58 (±3.7 d), d 145 (±13 d), and d 245 (±17.1 d) postpartum. Total RNA was isolated and the mRNA expression of growth hormone receptor ( GHR 1A and GHRtot ), IGF-I , janus tyrosine kinase 2 ( JAK2 ), signal transducer and activator of transcription 5B ( STAT5B ), suppressor of cytokine signaling 3 ( SOCS-3 ), acid-labile subunit ( ALS ), and IGF-binding proteins ( IGFBP1 to IGFBP6 ) were measured by real-time quantitative PCR. During lactation, the circulating concentrations of IGF-I were 34% greater in Fert+ cows. The Fert+ cows had increased mean expression of IGF-I mRNA during the study; however, the difference in IGF-I mRNA abundance between Fert+ and Fert− cows was most pronounced at d 145 and 245. The expression of IGFBP3 and ALS transcript was similar in Fert+ and Fert− cows for the duration of the study. The Fert− cows, however, had greater expression of IGFBP2 , IGFBP4 , IGFBP5 , and IGFBP6 . Genotype had no effect on mRNA abundance of GHR 1A , STAT5B , JAK2 , or SOCS-3 . Genetic merit for fertility traits affects hepatic expression of key genes of the somatotropic axis regulating the synthesis, bioavailability, and stability of circulating IGF-I.


title: Gender recognition from face images with local WLD descriptor

abstract: In various biometric applications, gender recognition from facial images plays an important role. In this paper, we investigate Weber's Local Descriptor (WLD) for gender recognition. WLD is a texture descriptor that performs better than other similar descriptors but it is holistic due to its very construction. We extend it by introducing local spatial information; divide an image into a number of blocks, calculate WLD descriptor for each block and concatenate them. This spatial WLD descriptor has better discriminatory power. Spatial WLD descriptor has three parameters. Through a large number of experiments performed on FERET database, we report the best combination of these parameters and that our proposed spatial WLD descriptor with simplest classifier gives much better accuracy i.e. 99.08% with lesser algorithmic complexity than state-of-the-art gender recognition approaches.


title: Visual Analysis of Public Utility Service Problems in a Metropolis

abstract: Issues about city utility services reported by citizens can provide unprecedented insights into the various aspects of such services. Analysis of these issues can improve living quality through evidence-based decision making. However, these issues are complex, because of the involvement of spatial and temporal components, in addition to having multi-dimensional and multivariate natures. Consequently, exploring utility service problems and creating visual representations are difficult. To analyze these issues, we propose a visual analytics process based on the main tasks of utility service management. We also propose an aggregate method that transforms numerous issues into legible events and provide visualizations for events. In addition, we provide a set of tools and interaction techniques to explore such issues. Our approach enables administrators to make more informed decisions.


title: Learning Spatiotemporal Features Using 3DCNN and Convolutional LSTM for Gesture Recognition

abstract: Gesture recognition aims at understanding the ongoing human gestures. In this paper, we present a deep architecture to learn spatiotemporal features for gesture recognition. The deep architecture first learns 2D spatiotemporal feature maps using 3D convolutional neural networks (3DCNN) and bidirectional convolutional long-short-term-memory networks (ConvLSTM). The learnt 2D feature maps can encode the global temporal information and local spatial information simultaneously. Then, 2DCNN is utilized further to learn the higher-level spatiotemporal features from the 2D feature maps for the final gesture recognition. The spatiotemporal correlation information is kept through the whole process of feature learning. This makes the deep architecture an effective spatiotemporal feature learner. Experiments on the ChaLearn LAP large-scale isolated gesture dataset (IsoGD) and the Sheffield Kinect Gesture (SKIG) dataset demonstrate the superiority of the proposed deep architecture.


title: Classification of Multiple-Sentence Questions

abstract: Conventional QA systems cannot answer to the questions composed of two or more sentences. Therefore, we aim to construct a QA system that can answer such multiple-sentence questions. As the first stage, we propose a method for classifying multiple-sentence questions into question types. Specifically, we first extract the core sentence from a given question text. We use the core sentence and its question focus in question classification. The result of experiments shows that the proposed method improves F-measure by 8.8% and accuracy by 4.4%.


title: Wikipedia entity expansion and attribute extraction from the web using semi-supervised learning

abstract: We develop a new framework to achieve the goal of Wikipedia entity expansion and attribute extraction from the Web. Our framework takes a few existing entities that are automatically collected from a particular Wikipedia category as seed input and explores their attribute infoboxes to obtain clues for the discovery of more entities for this category and the attribute content of the newly discovered entities. One characteristic of our framework is to conduct discovery and extraction from desirable semi-structured data record sets which are automatically collected from the Web. A semi-supervised learning model with Conditional Random Fields is developed to deal with the issues of extraction learning and limited number of labeled examples derived from the seed entities. We make use of a proximate record graph to guide the semi-supervised learning process. The graph captures alignment similarity among data records. Then the semi-supervised learning process can leverage the unlabeled data in the record set by controlling the label regularization under the guidance of the proximate record graph. Extensive experiments on different domains have been conducted to demonstrate its superiority for discovering new entities and extracting attribute content.


title: Research Article INS Assisted Fuzzy Tracking Loop for GPS-Guided Missiles and Vehicular Applications

abstract: Autonomous Navigation Systems used in missiles and other high dynamic platforms are mostly dependent on the Global Positioning System (GPS). GPS users face limitations in terms of missile high dynamics and signal interference. Receiver’s tracking loops bandwidth requirements to avoid these problems are conflicting. The paper presents a novel signal frequency and phase tracking algorithm for very high dynamic conditions, which mitigates the conflicting choice of bandwidths and reduces tracking loop measurement noise. It exploits the flexibility of fuzzy control systems for directly generating the required Numerically Controlled Oscillator (NCO) tuning frequency using phase and frequency discriminators information and is labeled Fuzzy Frequency Phase Lock Loop (FFPLL). Because Fuzzy Systems can be computationally demanding and an Inertial Navigation System (INS) is often onboard the vehicle, an assisted INS Doppler version has been designed and is also proposed. Assessment of the new GPS tracking method is performed with both simulated and experimental data under jamming conditions. The main enhancements of the proposed system consist in reduced processing time, improved tracking continuity and faster reacquisition time.


A Markerless Augmented Reality Approach Based on Real-Time 3 D Reconstruction using Kinect


title: Three-dimensional bipedal walking control using Divergent Component of Motion

abstract: In this paper, we extend the Divergent Component of Motion (DCM, also called `Capture Point') to 3D. We introduce the “Enhanced Centroidal Moment Pivot point” (eCMP) and the “Virtual Repellent Point” (VRP), which allow for the encoding of both direction and magnitude of the external (e.g. leg) forces and the total force (i.e. external forces plus gravity) acting on the robot. Based on eCMP, VRP and DCM, we present a method for real-time planning and control of DCM trajectories in 3D. We address the problem of underactuation and propose methods to guarantee feasibility of the finally commanded forces. The capabilities of the proposed control framework are verified in simulations.


title: Simultaneous Localization and Mapping (SLAM) using RTAB-MAP

abstract: This paper implements Simultaneous Localization and Mapping (SLAM) technique to construct a map of a given environment. A Real Time Appearance Based Mapping (RTAB-Map) approach was taken for accomplishing this task. Initially, a 2d occupancy grid and 3d octomap was created from a provided simulated environment. Next, a personal simulated environment was created for mapping as well. In this appearance based method, a process called Loop Closure is used to determine whether a robot has seen a location before or not. In this paper, it is seen that RTAB-Map is optimized for large scale and long term SLAM by using multiple strategies to allow for loop closure to be done in real time and the results depict that it can be an excellent solution for SLAM to develop robots that can map an environment in both 2d and 3d.


title: Visual Relationship Prediction via Label Clustering and Incorporation of Depth Information

abstract: In this paper, we investigate the use of an unsupervised label clustering technique and demonstrate that it enables substantial improvements in visual relationship prediction accuracy on the Person in Context (PIC) dataset. We propose to group object labels with similar patterns of relationship distribution in the dataset into fewer categories. Label clustering not only mitigates both the large classification space and class imbalance issues, but also potentially increases data samples for each clustered category. We further propose to incorporate depth information as an additional feature into the instance segmentation model. The additional depth prediction path supplements the relationship prediction model in a way that bounding boxes or segmentation masks are unable to deliver. We have rigorously evaluated the proposed techniques and performed various ablation analysis to validate the benefits of them.


title: A model explanation system

abstract: We propose a new methodology for explaining the predictions of black box classifiers. We use the motivating paradigm that predictive performance is of primary importance but human analysts (e.g., in fraud detection) desire a classifier's predictions to be augmented with useful explanations. To be truly general and principled, we derive a scoring system for finding explanations based on formal requirements. In this system, the explanations are assumed to take the form of simple logical statements. We derive an efficient Monte Carlo algorithm to find explanations for black box classifiers with finite sample guarantees. The methodology is then applied to interesting examples in facial recognition and credit data.


title: Hand grasp and motion for intent expression in mid-air virtual pottery

abstract: We describe the design and evaluation of a geometric interaction technique for bare-hand mid-air virtual pottery. We model the shaping of a pot as a gradual and progressive convergence of the pot-profile to the shape of the user's hand represented as a point-cloud (PCL). Our pottery-inspired application served as a platform for systematically revealing how users use their hands to express the intent of deformation during a pot shaping process. Through our approach, we address two specific problems: (a) determining start and end of deformation without explicit clutching and declutching, and (b) identifying user's intent by characterizing grasp and motion of the hand on the pot. We evaluated our approach's performance in terms of intent classification, users' behavior, and users' perception of controllability. We found that the expressive capability of hand articulation can be effectively harnessed for controllable shaping by organizing the deformation process in broad classes of intended operations such as pulling, pushing and fairing. After minimal practice with the pottery application, users could figure out their own strategy for reaching, grasping and deforming the pot. Further, the use of PCL as mid-air input allows for using common physical objects as tools for pot deformation. Users particularly enjoyed this aspect of our method for shaping pots.


title: Learning high dimensional correspondences from low dimensional manifolds

abstract: Many different high dimensional data sets are characterized by the same underlying modes of variability. When these modes of variability are continuous and few in number, they can be viewed as parameterizing a low dimensional manifold. The manifold provides a compact shared representation of the data, suggesting correspondences between the high dimensional examples from different data sets. These correspondences, though naturally induced by the underlying manifold, are difficult to learn using traditional methods in supervised learning. In this paper, we generalize three methods in unsupervised learning—principal components analysis, factor analysis, and locally linear embedding— to discover subspaces and manifolds that provide common low dimensional representations of different high dimensional data sets. We use the shared representations discovered by these algorithms to put high dimensional examples from different data sets into correspondence. Finally, we show that a notion of “self-correspondence” between examples in the same data set can be used to improve the performance of these algorithms on small data sets. The algorithms are demonstrated on images and text.


title: Planning algorithms for interactive storytelling

abstract: Interactive storytelling (IS) is an important research topic in the broader area of interactive digital entertainment. Research in IS focuses in the search for techniques that allow the creation of systems that can generate stories that change as a consequence of a player's actions. There have been numerous articles attempting to reach this goal by employing a planning algorithm as part of the solution, but they typically do not discuss in detail why a given planning algorithm was used. Given this reality, this article makes two main contributions. First, it proposes an initial set of criteria that can be used to evaluate how well a given planning algorithm fits IS systems. Second, on the basis of these criteria, it presents an analysis using available planning systems.


title: The Operational Role of Security Information and Event Management Systems

abstract: An integral part of an enterprise computer security incident response team (CSIRT), the security operations center (SOC) is a centralized unit tasked with real-time monitoring and identification of security incidents. Security information and event management (SIEM) systems are an important tool used in SOCs; they collect security events from many diverse sources in enterprise networks, normalize the events to a common format, store the normalized events for forensic analysis, and correlate the events to identify malicious activities in real time. In this article, the authors discuss the critical role SIEM systems play SOCs, highlight the current operational challenges in effectively using SIEM systems, and describe future technical challenges that SIEM systems must overcome to remain relevant.


title: “I Still Feel Isolated and Disposable”: Perceptions of Professional Development for Part-time Teachers in HE

abstract: Part-time teachers form an increasingly large part of the workforce within the Academy, in the UK and internationally. They can be employed on sessional or hourly-paid lecturer contracts, and as casual employees are not always able to access professional or academic development and support that is available for other employees. In 2013/14, there has been extensive coverage in the national and higher education press about ‘zero-hours’ contracts. Although some part-time teachers are also graduate students and able to access development through graduate schools and the like, it is likely that many hourly-paid lecturers are left without support. A survey of hourly-paid lecturers at one University in the UK provided data on how these individuals perceived the support and development opportunities available to them. Accessing the hourly-paid lecturers was challenging. Unsurprisingly, given the difficulties in communicating with them as a cohesive group, 60% (n=78) reported that they were unable to access or unaware of any development opportunities. In addition, this group of UK part-time teachers reported feeling isolated and lacking in support, as has been reported by casual academics in Australia.


title: Statistical Machine Translation with Rule based Machine Translation

abstract: We have evaluated the two-stage machine translation (MT) system. The first stage is a state-of-the-art trial rule-based machine translation system. The second stage is a normal statistical machine translation system. For Japanese-English machine translation, first, we used a Japanese-English rule-based MT, and we obtained "ENGLISH" sentences from Japanese sentences. Second, we used a standard statistical machine translation. This means that we translated "ENGLISH" to English machine translation. This method has an advantages that it produces grammatically correct sentences. From the results of experiments in the JE task, we obtained a BLEU score of 0.1996 using our proposed method. In contrast, we obtained a BLEU score of 0.1436 using a standard method. And for the EJ task, we obtained a BLEU score of 0.2775 using our proposed method. In contrast, we obtained a BLEU score of 0.0831 using a standard method. This means that our proposed method was effective for the JE and EJ task. However, there is a problem. The BLEU score was not so effective to measure the translation quality.


Lifelike Computer Characters : the Persona project at Microsoft Research


Using Fully Homomorphic Encryption for Statistical Analysis of Categorical, Ordinal and Numerical Data


title: Appropriate emotional labelling of non-acted speech using basic emotions, geneva emotion wheel and self assessment manikins

abstract: In emotion recognition from speech, a good transcription and annotation of given material is crucial. Moreover, the question of how to find good emotional labels for new data material is a basic issue. It is not only the question of which emotion labels to choose, it is also a matter of how labellers can cope with annotation methods. In this paper, we present our investigations for emotional labelling with three different methods (Basic Emotions, Geneva Emotion Wheel and Self Assessment Manikins) and compare them in terms of emotion coverage and usability. We show that emotion labels derived from Geneva Emotion Wheel or Self Assessment Manikins fulfill our requirements, but Basic Emotions are not feasible for emotion labelling from spontaneous speech.


Analysis of Language Style Self-Presentational and Relational Concerns Revealed Through the Managing Impressions and Relationships on Facebook :


title: Ultrawideband Antennas for Multiband Satellite Communications at UHF–Ku Frequencies

abstract: In space-borne applications, reduction of size, weight, and power can be critical. Pursuant to this goal, we present an ultrawideband, tightly coupled dipole array (TCDA) capable of supporting numerous satellite communication bands, simultaneously. Such antennas enable weight reduction by replacing multiple antennas. In addition, it provides spectral efficiency by reusing intermediate frequencies for inter-satellite communication. For ease of fabrication, the array is initially designed for operation across the UHF, L, S, and lower-C bands (0.6–3.6 GHz), with emphasis on dual-linear polarization, and wide-angle scanning. The array achieves a minimum 6:1 bandwidth for VSWR less than 1.8, 2.4, and 3.1 for 0°, 45°, and 60° scans, respectively. The presented design represents the first practical realization of dual polarizations using a TCDA topology. This is accomplished through a dual-offset, split unit cell with minimized inter-feed coupling. Array simulations are verified with measured results of an ${\bf{8}}\times {\bf{8}}$ prototype, exhibiting very low cross polarization and near-theoretical gain across the band. Further, we present a TCDA design operating across the upper-S, C, X, and Ku bands (3–18 GHz). The array achieves this 6:1 bandwidth for VSWR $ at broadside, and VSWR $ at 45°. A discussion on design and fabrication for low-cost arrays operating at these frequencies is included.


title: Attitude Determination from Vector Observations: Quaternion Estimation

abstract: Two recursive estimation algorithms, which use pairs of measured vectors to yield minimum variance estimates of the quaternion of rotation, are presented. The nonlinear relations between the direction cosine matrix and the quaternion are linearized, and a variant of the extended Kalman filter is used to estimate the difference between the quaternion and its estimate. With each measurement this estimate is updated and added to the whole quaternion estimate. This operation constitutes a full state reset in the estimation process. Filter tuning is needed to obtain a converging filter. The second algorithm presented uses the normality property of the quaternion of rotation to obtain, in a straightforward design, a filter which converges, with a smaller error, to a normal quaternion. This algorithm changes the state but not the covariance computation of the original algorithm and implies only a partial reset. Results of Monte-Carlo simulation runs are presented which demonstrate the superiority of the normalized quaternion.


title: Transfer learning approach for learning of unstructured data from structured data in medical domain

abstract: Transfer learning is a most important research area within information retrieval. As we know, there are different types of data available everywhere and among those, dealing with unstructured data is quite difficult. This paper focuses on dealing with unstructured data. Social challenge is, any nonmedical background person also uses this system for prediction of patient disease. This paper utilizes a bisecting k-means algorithm for the purpose of disease prediction. We have proposed a model for identifying more relevant disease using readings mentioned in patient's pathology lab test report. Our model is influenced by clustering and unsupervised transfer learning. We demonstrate the effectiveness of our model using patient pathology lab report dataset and dataset used for storing different test names (hemoglobin, sugar, etc.) of four diseases (Diabetes, Lipid profile cholesterol, Liver profile and Kidney profile). Our basic aim is to improve performance of the system by transferring knowledge, learned in one or multiple source tasks and use the same to improve learning in a related target task.


title: Towards Deep Neural Network Architectures Robust to Adversarial Examples

abstract: Recent work has shown deep neural networks (DNNs) to be highly susceptible to well-designed, small perturbations at the input layer, or so-called adversarial examples. Taking images as an example, such distortions are often imperceptible, but can result in 100% mis-classification for a state of the art DNN. We study the structure of adversarial examples and explore network topology, pre-processing and training strategies to improve the robustness of DNNs. We perform various experiments to assess the removability of adversarial examples by corrupting with additional noise and pre-processing with denoising autoencoders (DAEs). We find that DAEs can remove substantial amounts of the adversarial noise. How- ever, when stacking the DAE with the original DNN, the resulting network can again be attacked by new adversarial examples with even smaller distortion. As a solution, we propose Deep Contractive Network, a model with a new end-to-end training procedure that includes a smoothness penalty inspired by the contractive autoencoder (CAE). This increases the network robustness to adversarial examples, without a significant performance penalty.


title: A real-time automated system for the recognition of human facial expressions

abstract: A fully automated, multistage system for real-time recognition of facial expression is presented. The system uses facial motion to characterize monochrome frontal views of facial expressions and is able to operate effectively in cluttered and dynamic scenes, recognizing the six emotions universally associated with unique facial expressions, namely happiness, sadness, disgust, surprise, fear, and anger. Faces are located using a spatial ratio template tracker algorithm. Optical flow of the face is subsequently determined using a real-time implementation of a robust gradient model. The expression recognition system then averages facial velocity information over identified regions of the face and cancels out rigid head motion by taking ratios of this averaged motion. The motion signatures produced are then classified using Support Vector Machines as either nonexpressive or as one of the six basic emotions. The completed system is demonstrated in two simple affective computing applications that respond in real-time to the facial expressions of the user, thereby providing the potential for improvements in the interaction between a computer user and technology.


title: Astrometry with Hubble Space Telescope Fine Guidance Sensors - A Review

abstract: Over the last 20 years Hubble Space Telescope Fine Guidance Sensor interferometric astrometry has produced precise and accurate parallaxes of astrophysical interesting stars and mass estimates for stellar companions. We review parallax results, and binary star and exoplanet mass determinations, and compare a subset of these parallaxes with preliminary Gaia results. The approach to single-field relative astrometry described herein may continue to have value for targets fainter than the Gaia limit in the coming era of 20-30m telescopes.


Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images


A software law primer.


title: Large Scale Urban Scene Modeling from MVS Meshes

abstract: In this paper we present an efficient modeling framework for large scale urban scenes. Taking surface meshes derived from multi-view-stereo systems as input, our algorithm outputs simplified models with semantics at different levels of detail (LODs). Our key observation is that urban building is usually composed of planar roof tops connected with vertical walls. There are two major steps in our framework: segmentation and building modeling. The scene is first segmented into four classes with a Markov random field combining height and image features. In the following modeling step, various 2D line segments sketching the roof boundaries are detected and slice the plane into faces. Through assigning each face with a roof plane, the final model is constructed by extruding the faces to the corresponding planes. By combining geometric and appearance cues together, the proposed method is robust and fast compared to the state-of-the-art algorithms.


title: User Behavior Aware Cell Association in Heterogeneous Cellular Networks

abstract: In heterogeneous cellular networks (HetNets), cell association of User Equipment (UE) affects UE transmit rate and network throughput. Conventional cell association rules are usually based on UE received Signal-to-Interference-and-Noise-Ratio (SINR) without taking into account user behaviors, which can indeed be exploited for improving network performance. In this paper, we investigate UE cell association in HetNets based on individual user behavior characteristics with aim to maximize long- term expected system throughput. We model the problem as a stochastic optimization model Restless Multi-Armed Bandit (RMAB). As it is a PSPACE-hard problem, we develop a primal-dual heuristic index algorithm and the solution specifies the rule that determines which arms in the RMAB model to be selected at each decision time. According to the solution of RMAB, we propose a new cell association strategy called Index Enabled Association (IDEA). We also conduct simulation experiments to compare IDEA with conventional max-SINR cell association strategy and an existing game-based RAT selection scheme. Numerical results demonstrate the advantages of IDEA in typical scenarios.


title: Protein microarrays and proteomics

abstract: The system-wide study of proteins presents an exciting challenge in this information-rich age of whole-genome biology. Although traditional investigations have yielded abundant information about individual proteins, they have been less successful at providing us with an integrated understanding of biological systems. The promise of proteomics is that, by studying many components simultaneously, we will learn how proteins interact with each other, as well as with non-proteinaceous molecules, to control complex processes in cells, tissues and even whole organisms. Here, I discuss the role of microarray technology in this burgeoning area.


NONDESTRUCTIVE METHODS OF EVALUATING QUALITY OF FRESH FRUITS AND VEGETABLES


title: A 5-GHz, 30-dBm, 0.9-dB insertion loss single-pole double-throw T/R switch in 90nm CMOS

abstract: A 5 GHz, 30-dBm CMOS T/R switch implemented in 90 nm CMOS is reported. A body isolation technique is employed and optimized for power handling capability. Inductors are employed with the transistor switches for parallel resonance to improve isolation. Thick oxide NMOS transistors are used for the switching transistors and placed inside the inductors to reduce the active chip area to approximately 0.2 mm2. 0.9-dB insertion loss for both TX and RX modes is achieved with a 5-V control voltage.


title: Computing and Applying Trust in Web-based Social Networks

abstract: The proliferation of web-based social networks has lead to new innovations in social networking, particularly by allowing users to describe their relationships beyond a basic connection. In this dissertation, I look specifically at trust in web-based social networks, how it can be computed, and how it can be used in applications. I begin with a definition of trust and a description of several properties that affect how it is used in algorithms. This is complemented by a survey of web-based social networks to gain an understanding of their scope, the types of relationship information available, and the current state of trust. ::: The computational problem of trust is to determine how much one person in the network should trust another person to whom they are not connected. I present two sets of algorithms for calculating these trust inferences: one for networks with binary trust ratings, and one for continuous ratings. For each rating scheme, the algorithms are built upon the defined notions of trust. Each is then analyzed theoretically and with respect to simulated and actual trust networks to determine how accurately they calculate the opinions of people in the system. I show that in both rating schemes the algorithms presented can be expected to be quite accurate. ::: These calculations are then put to use in two applications. FilmTrust is a website that combines trust, social networks, and movie ratings and reviews. Trust is used to personalize the website for each user, displaying recommended movie ratings, and ordering reviews by relevance. I show that, in the case where the user's opinion is divergent from the average, the trust-based recommended ratings are more accurate than several other common collaborative filtering techniques. The second application is TrustMail, an email client that uses the trust rating of each sender as a score for the message. Users can then sort messages according to their trust value. ::: I conclude with a description of other applications where trust inferences can be used, and how the lessons from this dissertation can be applied to infer information about relationships in other complex systems.


title: Enterprise architecture modelling-the issue of integration

abstract: The problem of aligning and integrating business and IT is hampering many companies in their strategic and tactical development. Constructing integrated architecture models contributes to tackling this problem. Unfortunately, no enterprise architecture description language currently exists that fully enables integrated enterprise modelling. A variety of architectural domains are commonly distinguished for which architects use their own modelling techniques and concepts, tool support, visualisation techniques, etc. In this paper we outline such an integrated language and describe concepts that relate architectural domains. This language also serves as a bridge between other existing modelling languages. Furthermore, we present the design of a workbench for enterprise architecture that serves as a modelling tool and a tool integration environment at the same time: it supports both the integration of models in existing modelling languages and the integration of existing modelling tools.


An Approach For Designing A Universal Asynchronous Receiver Transmitter (UART)


title: COVERAGE — A novel database for copy-move forgery detection

abstract: We present COVERAGE — a novel database containing copy-move forged images and their originals with similar but genuine objects. COVERAGE is designed to highlight and address tamper detection ambiguity of popular methods, caused by self-similarity within natural images. In COVERAGE, forged-original pairs are annotated with (i) the duplicated and forged region masks, and (ii) the tampering factor/similarity metric. For benchmarking, forgery quality is evaluated using (i) computer vision-based methods, and (ii) human detection performance. We also propose a novel sparsity-based metric for efficiently estimating forgery quality. Experimental results show that (a) popular forgery detection methods perform poorly over COVERAGE, and (b) the proposed sparsity based metric best correlates with human detection performance. We release the COVERAGE database to the research community.


title: Effective supply chain management: theory and practice

abstract: This paper describes an attempt to build an analytical tool/model of the supply chain which can assist companies managing their supply chain. Some new techniques of representing the supply chain are presented. The paper also reports the results of testing the new model in a small company’s supply chain.


title: Whispered speech recognition using deep denoising autoencoder

abstract: Abstract Recently Deep Denoising Autoencoders (DDAE) have shown state-of-the-art performance on various machine learning tasks. In this paper, the authors extended this approach to whispered speech recognition which is one of the most challenging problems in Automatic Speech Recognition (ASR). Namely, due to the profound differences between acoustic characteristics of neutral and whispered speech, the performance of traditional ASR systems trained on neutral speech degrades significantly when whisper is applied. This mismatch between training and testing is successfully alleviated with the new proposed system based on deep learning, where DDAE is applied for generating whisper-robust cepstral features. This system was tested and compared in terms of word recognition accuracy with conventional Hidden Markov Model (HMM) speech recognizer in an isolated word recognition task with a real database of whispered speech (WhiSpe). Three types of cepstral coefficients were used in the experiments: MFCC (Mel-Frequency Cepstral Coefficients), TECC (Teager-Energy Cepstral Coefficients) and TEMFCC (Teager-based Mel-Frequency Cepstral Coefficients). The experimental results showed that the proposed system significantly improves whisper recognition accuracy and outperforms traditional HMM-MFCC baseline, resulting in an absolute 31% improvement of whisper recognition accuracy. The highest word recognition rate of 92.81% in whispered speech was achieved with TECC feature.


title: A Question Answer System for Math Word Problems

abstract: Solving word problems is an important part in school education in primary as well as in high school. Although, the equations that are given by a word problem could be solved by most computer algebra programs without problems, there are just few systems that are able to solve word problems. In this paper we present the ongoing work on a system, that is able to solve word problems from german primary school math books.


Design and Construction of Rescue Robot and Pipeline Inspection Using Zigbee


title: The Micronutrient Indole-3-Carbinol: Implications for Disease and Chemoprevention

abstract: This review provides a historical perspective for the development of indole-3-carbinol (I-3-C) as a chemopreventive or therapeutic agent. Early experiments in animal models clearly showed that feeding cruciferous vegetables reduced the incidence of chemical carcinogenesis. Excitement was generated by the finding that these vegetables contained a high content of indole-containing compounds, and I-3-C could by itself inhibit neoplasia. The mechanism of action was linked primarily to the ability of I-3-C and derived substances to induce mixed-function oxidases and phase II antioxidant enzymes by binding and activating the aryl hydrocarbon receptor. Most of the literature on chemoprotection by dietary indole compounds relates to this mechanism of action. Other mechanisms, however, are notable for this class of compounds, including their ability to act as radical and electrophile scavengers; the various ascorbate conjugates of I-3-C (ascorbigens) may be important in this regard. Exciting recent findings have demonstrated that I-3-C and its reaction products can affect cellular signaling pathways, regulate the cell cycle, and decrease tumor cell properties related to metastasis. It does not appear that I-3-C per se is the primary active compound in chemoprotection or chemoprevention. Rather, I-3-C and ascorbate provide the parent compounds for the formation of a myriad of nonenzymatic reaction products that have strong biological potency. We conclude with our thoughts regarding the current status and future directions for the use of I-3-C and related compounds.


title: Proximal support vector machine classifiers

abstract: Instead of a standard support vector machine (SVM) that classifies points by assigning them to one of two disjoint half-spaces, points are classified by assigning them to the closest of two parallel planes (in input or feature space) that are pushed apart as far as possible. This formulation, which can also be interpreted as regularized least squares and considered in the much more general context of regularized networks [8, 9], leads to an extremely fast and simple algorithm for generating a linear or nonlinear classifier that merely requires the solution of a single system of linear equations. In contrast, standard SVMs solve a quadratic or a linear program that require considerably longer computational time. Computational results on publicly available datasets indicate that the proposed proximal SVM classifier has comparable test set correctness to that of standard SVM classifiers, but with considerably faster computational time that can be an order of magnitude faster. The linear proximal SVM can easily handle large datasets as indicated by the classification of a 2 million point 10-attribute set in 20.8 seconds. All computational results are based on 6 lines of MATLAB code.