title: Detected motion classification with a double-background and a neighborhood-based difference

abstract: This paper describes a new method to detect moving objects in a dynamic scene based on background subtraction. The main goal of the method is to obtain and keep a stable background image to cope with variations on environmental changing conditions. In this way, we use a double background (long-term background and short-term background) to deal with temporal stability and fast changes. In addition, this method computes the temporal changes in the video sequence by a local convolution mask taking into account the information of the pixel neighborhood, being less sensitive to noise. Besides, the method classifies the regions of change in moving and static blobs. The first ones represent real moving objects, and the second are related to illumination changes and noise. Finally, experimental results and a performance measure establishing the confidence of the method are presented.


title: Reinforcement Learning for Spoken Dialogue Systems

abstract: Recently, a number of authors have proposed treating dialogue systems as Markov decision processes (MDPs). However, the practical application of MDP algorithms to dialogue systems faces a number of severe technical challenges. We have built a general software tool (RLDS, for Reinforcement Learning for Dialogue Systems) based on the MDP framework, and have applied it to dialogue corpora gathered from two dialogue systems built at AT&T Labs. Our experiments demonstrate that RLDS holds promise as a tool for "browsing" and understanding correlations in complex, temporally dependent dialogue corpora.


title: A systematic review of the antipsychotic properties of cannabidiol in humans

abstract: Despite extensive study over the past decades, available treatments for schizophrenia are only modestly effective and cause serious metabolic and neurological side effects. Therefore, there is an urgent need for novel therapeutic targets for the treatment of schizophrenia. A highly promising new pharmacological target in the context of schizophrenia is the endocannabinoid system. Modulation of this system by the main psychoactive component in cannabis, Δ9-tetrahydrocannabinol (THC), induces acute psychotic effects and cognitive impairment. However, the non-psychotropic, plant-derived cannabinoid agent cannabidiol (CBD) may have antipsychotic properties, and thus may be a promising new agent in the treatment of schizophrenia. Here we review studies that investigated the antipsychotic properties of CBD in human subjects. Results show the ability of CBD to counteract psychotic symptoms and cognitive impairment associated with cannabis use as well as with acute THC administration. In addition, CBD may lower the risk for developing psychosis that is related to cannabis use. These effects are possibly mediated by opposite effects of CBD and THC on brain activity patterns in key regions implicated in the pathophysiology of schizophrenia, such as the striatum, hippocampus and prefrontal cortex. The first small-scale clinical studies with CBD treatment of patients with psychotic symptoms further confirm the potential of CBD as an effective, safe and well-tolerated antipsychotic compound, although large randomised clinical trials will be needed before this novel therapy can be introduced into clinical practice.


title: Multi-level Attention Model for Weakly Supervised Audio Classification

abstract: In this paper, we propose a multi-level attention model to solve the weakly labelled audio classification problem. The objective of audio classification is to predict the presence or absence of audio events in an audio clip. Recently, Google published a large scale weakly labelled dataset called Audio Set, where each audio clip contains only the presence or absence of the audio events, without the onset and offset time of the audio events. Our multi-level attention model is an extension to the previously proposed single-level attention model. It consists of several attention modules applied on intermediate neural network layers. The output of these attention modules are concatenated to a vector followed by a multi-label classifier to make the final prediction of each class. Experiments shown that our model achieves a mean average precision (mAP) of 0.360, outperforms the state-of-the-art single-level attention model of 0.327 and Google baseline of 0.314.


title: Learning to Compute Word Embeddings On the Fly

abstract: Words in natural language follow a Zipfian distribution whereby some words are frequent but most are rare. Learning representations for words in the ``long tail'' of this distribution requires enormous amounts of data. Representations of rare words trained directly on end tasks are usually poor, requiring us to pre-train embeddings on external data, or treat all rare words as out-of-vocabulary words with a unique representation. We provide a method for predicting embeddings of rare words on the fly from small amounts of auxiliary data with a network trained end-to-end for the downstream task. We show that this improves results against baselines where embeddings are trained on the end task for reading comprehension, recognizing textual entailment and language modeling.


A Review of Data Mining in Sports


title: Neurobiological Mechanisms of the Onset of Puberty in Primates *

abstract: An increase in pulsatile release of LHRH is essential for the onset of puberty. However, the mechanism controlling the pubertal increase in LHRH release is still unclear. In primates the LHRH neurosecretory system is already active during the neonatal period but subsequently enters a dormant state in the juvenile/prepubertal period. Neither gonadal steroid hormones nor the absence of facilitatory neuronal inputs to LHRH neurons is responsible for the low levels of LHRH release before the onset of puberty in primates. Recent studies suggest that during the prepubertal period an inhibitory neuronal system suppresses LHRH release and that during the subsequent maturation of the hypothalamus this prepubertal inhibition is removed, allowing the adult pattern of pulsatile LHRH release. In fact,γ -aminobutyric acid (GABA) appears to be an inhibitory neurotransmitter responsible for restricting LHRH release before the onset of puberty in female rhesus monkeys. In addition, it appears that the reduction in tonic G...


title: Wireless Network Information Flow: A Deterministic Approach

abstract: In a wireless network with a single source and a single destination and an arbitrary number of relay nodes, what is the maximum rate of information flow achievable? We make progress on this long standing problem through a two-step approach. First, we propose a deterministic channel model which captures the key wireless properties of signal strength, broadcast and superposition. We obtain an exact characterization of the capacity of a network with nodes connected by such deterministic channels. This result is a natural generalization of the celebrated max-flow min-cut theorem for wired networks. Second, we use the insights obtained from the deterministic analysis to design a new quantize-map-and-forward scheme for Gaussian networks. In this scheme, each relay quantizes the received signal at the noise level and maps it to a random Gaussian codeword for forwarding, and the final destination decodes the source's message based on the received signal. We show that, in contrast to existing schemes, this scheme can achieve the cut-set upper bound to within a gap which is independent of the channel parameters. In the case of the relay channel with a single relay as well as the two-relay Gaussian diamond network, the gap is 1 bit/s/Hz. Moreover, the scheme is universal in the sense that the relays need no knowledge of the values of the channel parameters to (approximately) achieve the rate supportable by the network. We also present extensions of the results to multicast networks, half-duplex networks, and ergodic networks.


title: Detecting differences across multiple instances of code clones

abstract: Clone detectors find similar code fragments (i.e., instances of code clones) and report large numbers of them for industrial systems. To maintain or manage code clones, developers often have to investigate differences of multiple cloned code fragments. However,existing program differencing techniques compare only two code fragments at a time. Developers then have to manually combine several pairwise differencing results. In this paper, we present an approach to automatically detecting differences across multiple clone instances. We have implemented our approach as an Eclipse plugin and evaluated its accuracy with three Java software systems. Our evaluation shows that our algorithm has precision over 97.66% and recall over 95.63% in three open source Java projects. We also conducted a user study of 18 developers to evaluate the usefulness of our approach for eight clone-related refactoring tasks. Our study shows that our approach can significantly improve developers’performance in refactoring decisions, refactoring details, and task completion time on clone-related refactoring tasks. Automatically detecting differences across multiple clone instances also opens opportunities for building practical applications of code clones in software maintenance, such as auto-generation of application skeleton, intelligent simultaneous code editing.


title: Supplemental Poverty Measure: A Comparison of Geographic Adjustments with Regional Price Parities vs. Median Rents from the American Community Survey

abstract: One of the innovations of the Supplemental Poverty Measure is to make adjustments in the official poverty threshold to account for geographic price level differences, particularly for differences in the cost of shelter as measured by rents. A more recent initiative is to estimate thresholds that include price differences for goods and services other than rents. The focus in this paper is to compare two types of geographic adjustments: one based on the ACS median rent index (MRI), and one based on a recently published set of state and metropolitan regional price parities (RPPs). The RPPs are of two types: an all item index that includes a broad group of expenditure classes and another that is more narrowly focused on just food, clothing and rents. The differences between the MRI and the all item RPPs are significant for most states, resulting in higher poverty rates for 15 states and lower rates for 26 states. When the narrower RPPs are used, poverty estimates are higher than the MRI poverty rates in 20 states, lower in 22 states and not statistically different in 9 states. In metropolitan areas, the overall RPPs lower the poverty rates when compared to the MRI, because differences in the combined price level of goods and services are generally not as large as differences in rents. When the RPPs are constrained to food, clothing and rents, the poverty rates in metropolitan areas are greater than the MRI poverty rates.


Impact of the Ear and Head Shapes on the Near Fields Radiated by Mobile Phones


title: Detecting Depression Severity from Vocal Prosody

abstract: To investigate the relation between vocal prosody and change in depression severity over time, 57 participants from a clinical trial for treatment of depression were evaluated at seven-week intervals using a semistructured clinical interview for depression severity (Hamilton Rating Scale for Depression (HRSD)). All participants met criteria for major depressive disorder (MDD) at week one. Using both perceptual judgments by naive listeners and quantitative analyses of vocal timing and fundamental frequency, three hypotheses were tested: 1) Naive listeners can perceive the severity of depression from vocal recordings of depressed participants and interviewers. 2) Quantitative features of vocal prosody in depressed participants reveal change in symptom severity over the course of depression. 3) Interpersonal effects occur as well; such that vocal prosody in interviewers shows corresponding effects. These hypotheses were strongly supported. Together, participants' and interviewers' vocal prosody accounted for about 60 percent of variation in depression scores, and detected ordinal range of depression severity (low, mild, and moderate-to-severe) in 69 percent of cases (kappa = 0.53). These findings suggest that analysis of vocal prosody could be a powerful tool to assist in depression screening and monitoring over the course of depressive disorder and recovery.


title: A Closer Look at the Self-Correcting Crowd: Examining Corrections in Online Rumors

abstract: This paper examines how users of social media correct online rumors during crisis events. Focusing on Twitter, we identify different patterns of information correcting behaviors and describe the actions, motivations, rationalizations and experiences of people who exhibited them. To do this, we analyze digital traces across two separate crisis events and interviews of fifteen individuals who generated some of those traces. Salient themes ensuing from this work help us describe: 1) different mechanisms of corrective action with respect to who gets corrected and how; 2) how responsibility is positioned for verifying and correcting information; and 3) how users' imagined audience influences their corrective strategy. We synthesize these three components into a preliminary model and explore the role of imagined audiences-both who those audiences are and how they react to and interact with shared information-in shaping users' decisions about whether and how to correct rumors.


title: What Bugs Live in the Cloud? A Study of 3000+ Issues in Cloud Systems

abstract: We conduct a comprehensive study of development and deployment issues of six popular and important cloud systems (Hadoop MapReduce, HDFS, HBase, Cassandra, ZooKeeper and Flume). From the bug repositories, we review in total 21,399 submitted issues within a three-year period (2011-2014). Among these issues, we perform a deep analysis of 3655 "vital" issues (i.e., real issues affecting deployments) with a set of detailed classifications. We name the product of our one-year study Cloud Bug Study database (CbsDB) [9], with which we derive numerous interesting insights unique to cloud systems. To the best of our knowledge, our work is the largest bug study for cloud systems to date.


title: Fast ray-tracing of human eye optics on Graphics Processing Units

abstract: We present a new technique for simulating retinal image formation by tracing a large number of rays from objects in three dimensions as they pass through the optic apparatus of the eye to objects. Simulating human optics is useful for understanding basic questions of vision science and for studying vision defects and their corrections. Because of the complexity of computing such simulations accurately, most previous efforts used simplified analytical models of the normal eye. This makes them less effective in modeling vision disorders associated with abnormal shapes of the ocular structures which are hard to be precisely represented by analytical surfaces. We have developed a computer simulator that can simulate ocular structures of arbitrary shapes, for instance represented by polygon meshes. Topographic and geometric measurements of the cornea, lens, and retina from keratometer or medical imaging data can be integrated for individualized examination. We utilize parallel processing using modern Graphics Processing Units (GPUs) to efficiently compute retinal images by tracing millions of rays. A stable retinal image can be generated within minutes. We simulated depth-of-field, accommodation, chromatic aberrations, as well as astigmatism and correction. We also show application of the technique in patient specific vision correction by incorporating geometric models of the orbit reconstructed from clinical medical images.


ON THE ASYMPTOTIC DISTRIBUTION OF U-STATISTICS by


title: Tunneling Transistors Based on Graphene and 2-D Crystals

abstract: As conventional transistors become smaller and thinner in the quest for higher performance, a number of hurdles are encountered. The discovery of electronic-grade 2-D crystals has added a new “layer” to the list of conventional semiconductors used for transistors. This paper discusses the properties of 2-D crystals by comparing them with their 3-D counterparts. Their suitability for electronic devices is discussed. In particular, the use of graphene and other 2-D crystals for interband tunneling transistors is discussed for low-power logic applications. Since tunneling phenomenon in reduced dimensions is not conventionally covered in texts, the physics is developed explicitly before applying it to transistors. Though we are in an early stage of learning to design devices with 2-D crystals, they have already been the motivation behind a list of truly novel ideas. This paper reviews a number of such ideas.


title: Handbook of Black Librarianship

abstract: E. J. Josey and Marva DeLoach have compiled a treasure trove of information about black librarianship. This volume includes history, statistics, and documentation of contemporary issues related both to African American participation in librarianship and to the organizations that they built to provide information resources for their people. Of interest to all librarians, bibliophiles, bibliographers, and students of American culture, this handbook fills a niche in American cultural history.


Path Optimization with Artificial Bee Colony Algorithm in WSN


title: Primary radio user activity models for cognitive radio networks: A survey

abstract: Abstract Cognitive Radio Networks have been emerged as a promising solution for solving the problem of spectrum scarcity and improving spectrum utilization by opportunistic use of spectrum. Cognitive radio networks utilize the spectrum which is licensed to primary radio users when they are not utilizing it, i.e., when the spectrum is idle. Thus, the performance of cognitive radio networks is highly dependent upon the activity of primary radio users. Hence, it is very important to model primary radio users activity in cognitive radio networks. By keeping this in mind, several models in the literature have been proposed for modeling primary radio users activity. But there is not any source which consolidate all these models into single platform. Therefore, this paper combines all the primary radio user activity models for cognitive radio networks at a single place. The goal of this paper is to provide a single source in the form of survey paper by which a reader can get an idea about which primary radio user activity models have been used in the literature for cognitive radio networks and how the modeling is performed. Furthermore, we also discuss issues, challenges and future directions for primary radio activity models. In fact, in this paper, different primary radio user activity models have been presented along with their classification. This paper also discusses those approaches which performed real implementation for spectrum occupancy along with spectrum bands on which the implementation is performed and location where implementation is carried out. In summary, this paper provides up-to-date survey of primary radio user activity models for cognitive radio networks.


title: Counterfeit Currency Detection Technique Using Image Processing, Polarization Principle and Holographic Technique

abstract: The growing menace of fake or counterfeit currency is one of the biggest threat to any nations economy across the globe and to ameliorate this threat is a far cry, so through this paper titled DIGITIZED CURRENCY we provide an innovative method to identify the counterfeit currency, (i.e) it encompasses polarization concept, Image processing technique, and Holographic Detection methods to detect counterfeit currency based on the physical properties of the currency rather than the existing methodology that relay on its chemical property. This is achieved by providing mathematical model, experimental verification and Lab view based simulation and also through automation to enhance reliability and accuracy in a cheaper, efficient and dynamic way in detecting the counterfeit currency.


title: Estimation of IMU and MARG orientation using a gradient descent algorithm

abstract: This paper presents a novel orientation algorithm designed to support a computationally efficient, wearable inertial human motion tracking system for rehabilitation applications. It is applicable to inertial measurement units (IMUs) consisting of tri-axis gyroscopes and accelerometers, and magnetic angular rate and gravity (MARG) sensor arrays that also include tri-axis magnetometers. The MARG implementation incorporates magnetic distortion compensation. The algorithm uses a quaternion representation, allowing accelerometer and magnetometer data to be used in an analytically derived and optimised gradient descent algorithm to compute the direction of the gyroscope measurement error as a quaternion derivative. Performance has been evaluated empirically using a commercially available orientation sensor and reference measurements of orientation obtained using an optical measurement system. Performance was also benchmarked against the propriety Kalman-based algorithm of orientation sensor. Results indicate the algorithm achieves levels of accuracy matching that of the Kalman based algorithm; < 0.8° static RMS error, < 1.7° dynamic RMS error. The implications of the low computational load and ability to operate at small sampling rates significantly reduces the hardware and power necessary for wearable inertial movement tracking, enabling the creation of lightweight, inexpensive systems capable of functioning for extended periods of time.


Acute Myeloid Leukemia


title: Autominder: an intelligent cognitive orthotic system for people with memory impairment

abstract: The world’s population is aging at a phenomenal rate. Certain types of cognitive decline, in particular some forms of memory impairment, occur much more frequently in the elderly. This paper describes Autominder, a cognitive orthotic system intended to help older adults adapt to cognitive decline and continue the satisfactory performance of routine activities, thereby potentially enabling them to remain in their own homes longer. Autominder achieves this goal by providing adaptive, personalized reminders of (basic, instrumental, and extended) activities of daily living. Cognitive orthotic systems on the market today mainly provide alarms for prescribed activities at fixed times that are specified in advance. In contrast, Autominder uses a range of AI techniques to model an individual’s daily plans, observe and reason about the execution of those plans, and make decisions about whether and when it is most appropriate to issue reminders. Autominder is currently deployed on a mobile robot, and is being developed as part of the Initiative on Personal Robotic Assistants for the Elderly (the Nursebot project).


title: Context Trees: Crowdsourcing Global Understanding from Local Views

abstract: Crowdsourcing struggles when workers must see all of the pieces of input to make an accurate judgment. For example, to find the most important scenes in a novel or movie, each worker must spend hours consuming the entire plot to acquire a global understanding and then apply that understanding to each local scene. To enable the crowdsourcing of large-scale goals with only local views, we introduce context trees, a crowdsourcing workflow for creating global summaries of a large input. Context trees recursively combine elements through written summaries to form a tree. Workers can then ground their local decisions by applying those summaries back down to the leaf nodes. In the case of scale ratings such as scene importance, we introduce a weighting process that percolates ratings downwards through the tree so that important nodes in unimportant branches are not overweighted. When using context trees to rate the importance of scenes in a 4000-word story and a 100-minute movie, workers’ ratings are nearly as accurate as those who saw the entire input, and much improved over the traditional approach of splitting the input into independent segments. To explore whether context trees enable crowdsourcing to undertake new classes of goals, we also crowdsource the solution to a large hierarchical puzzle of 462,000 interlocking pieces.


title: Climate change impacts on crop yield, crop water productivity and food security - A review

abstract: This paper provides a comprehensive review of literature related to the assessment of climate change impacts on crop productivity using climate, water and crop yield models. The existing studies present that climate change models with higher spatial resolution can be a way forward for future climate projections. Meanwhile, stochastic projections of more than one climate model are necessary for providing insights into model uncertainties as well as to develop risk management strategies. It is projected that water availability will increase in some parts of the world, which will have its own effect on water use efficiency and water allocation. Crop production can increase if irrigated areas are expanded or irrigation is intensified, but these may increase the rate of environmental degradation. Since climate change impacts on soil water balance will lead to changes of soil evaporation and plant transpiration, consequently, the crop growth period may shorten in the future impacting on water productivity. Crop yields affected by climate change are projected to be different in various areas, in some areas crop yields will increase, and for other areas it will decrease depending on the latitude of the area and irrigation application. Existing modelling results show that an increase in precipitation will increase crop yield, and what is more, crop yield is more sensitive to the precipitation than temperature. If water availability is reduced in the future, soils of high water holding capacity will be better to reduce the impact of drought while maintaining crop yield. With the temperature increasing and precipitation fluctuations, water availability and crop production are likely to decrease in the future. If the irrigated areas are expanded, the total crop production will increase; however, food and environmental quality may degrade. 2009 National Natural Science Foundation of China and Chinese Academy of Sciences. Published by Elsevier Limited and Science in China Press. All rights reserved.


title: Waking and dreaming: Related but structurally independent. Dream reports of congenitally paraplegic and deaf-mute persons

abstract: Models of dream analysis either assume a continuum of waking and dreaming or the existence of two dissociated realities. Both approaches rely on different methodology. Whereas continuity models are based on content analysis, discontinuity models use a structural approach. In our study, we applied both methods to test specific hypotheses about continuity or discontinuity. We contrasted dream reports of congenitally deaf-mute and congenitally paraplegic individuals with those of non-handicapped controls. Continuity theory would predict that either the deficit itself or compensatory experiences would surface in the dream narrative. We found that dream form and content of sensorially limited persons was indifferent from those of non-handicapped controls. Surprisingly, perceptual representations, even of modalities not experienced during waking, were quite common in the dream reports of our handicapped subjects. Results are discussed with respect to feedforward mechanisms and protoconsciousness theory of dreaming.


title: Reverse Engineering in the Semiconductor Industry

abstract: The intent of this paper is to give an overview of the place of reverse engineering (RE) in the semiconductor industry, and the techniques used to obtain information from semiconductor products. The continuous drive of Moore's law to increase the integration level of silicon chips has presented major challenges to the reverse engineer, obsolescing simple teardowns and demanding the adopted of new and more sophisticated technology to analyse chips. This trend is continuing; the 2006 update of the International Technology Roadmap for Semiconductors is predicting the shrinkage of transistor gates from the current 65-nm generation to 16 nm at the turn of the decade, and the usage of over 1.5 billion transistors in high-volume microprocessor chips. The paper covers product teardowns, and discusses the techniques used for system-level analysis, both hardware and software; circuit extraction, taking the chip down to the transistor level and working back up through the interconnects to create schematics; and process analysis, looking at how a chip is made, and what it is made of. Examples are also given of each type of RE.


Picolo: A simple python framework for introducing component principles


title: Class Diagram Extraction from Textual Requirements Using Natural Language Processing (NLP) Techniques

abstract: The automation of class generation from natural language requirements is highly challenging. This paper proposes a method and a tool to facilitate requirements analysis process and class diagram extraction from textual requirements supporting natural language processing NLP and Domain Ontology techniques. Requirements engineers analyze requirements manually to come out with analysis artifacts such as class diagram. The time spent on the analysis and the low quality of human analysis proved the need of automated support. A "Requirements Analysis and Class Diagram Extraction (RACE)" is a desktop instrument to assist requirements analysts and SE students in analyzing textual requirements, finding core concepts and its relationships, and step by step extraction of the class diagram. The evaluation of RACE system is in the process and will be conducted using two forms of evaluation, experimental and expert evaluation.


title: The Survey of Real Time Operating System: RTOS

abstract: The paper discusses the literature survey of RTOS (Real Time Operating Systems) and its contributions to the embedded world. RTOS is defined as a system in which the correctness of the system does not depend only on the logical results of computation but also on the time at which the results are produced. It has to perform critical tasks on priority basis keeping the context switching time minimum. It is often associated with few misconceptions & we have tried to throw some light on it. Since last 20 years, RTOS is undergoing continuous evolution and has resulted into development of many commercial RTOS products. We have selected few commercial RTOS of different categories of real-time applications and have discussed its real-time features. A comparison of the commercial RTOSs' is presented. We conclude by discussing the results of the survey and comparing the RTOS based on performance parameters.


title: 3DMAX-Net: A Multi-Scale Spatial Contextual Network for 3D Point Cloud Semantic Segmentation

abstract: Semantic segmentation of 3D scenes is a fundamental problem in 3D computer vision. In this paper, we propose a deep neural network for 3D semantic segmentation of raw point clouds. A multi-scale feature learning block is first introduced to obtain informative contextual features in 3D point clouds. A global and local feature aggregation block is then extended to improve the feature learning ability of the network. Based on these strategies, a powerful architecture named 3DMAX-Net is finally provided for semantic segmentation in raw 3D point clouds. Experiments have been conducted on the Stanford large-scale 3D Indoor Spaces Dataset using only geometry information. Experimental results have clearly shown the superiority of the proposed network.


title: Perceptual linear predictive (PLP) analysis of speech

abstract: A new technique for the analysis of speech, the perceptual linear predictive (PLP) technique, is presented and examined. This technique uses three concepts from the psychophysics of hearing to derive an estimate of the auditory spectrum: (1) the critical-band spectral resolution, (2) the equal-loudness curve, and (3) the intensity-loudness power law. The auditory spectrum is then approximated by an autoregressive all-pole model. A 5th-order all-pole model is effective in suppressing speaker-dependent details of the auditory spectrum. In comparison with conventional linear predictive (LP) analysis, PLP analysis is more consistent with human hearing. The effective second formant F2' and the 3.5-Bark spectral-peak integration theories of vowel perception are well accounted for. PLP analysis is computationally efficient and yields a low-dimensional representation of speech. These properties are found to be useful in speaker-independent automatic-speech recognition.


title: Factored MDPs for detecting topics of user sessions

abstract: Recommender systems aim to capture interests of users to provide tailored recommendations. User interests are however often unique and depend on many unobservable factors including a user's mood and the local weather. We take a contextual session-based approach and propose a sequential framework using factored Markov decision processes (fMDPs) to detect the user's goal (the topic) of a session. We show that an independence assumption on the attributes of items leads to a set of independent models that can be optimised efficiently. Our approach results in interpretable topics that can be effectively turned into recommendations. Empirical results on a real world click log from a large e-commerce company exhibit highly accurate topic prediction rates of about 90%. Translating our approach into a topic-driven recommender system outperforms several baseline competitors.


title: A Fuzzy Logic Approach for Opinion Mining on Large Scale Twitter Data

abstract: Recently, some efforts have been made to mine social media for the analysis of public sentiment. By means of a literature review on early works related to social media analytics especially on opinion mining, it was recognized that in the real life social media environment, the structure of the data is commonly not clear and it does not directly generate enough information to fully represent any selected target. However, most of these works were unable to accurately extract clear indications of general public opinion from the ambiguous social media data. They also lacked the capacity to summarize multi-characteristics from the scattered mass of social data and use it to compile useful models, also lacked any efficient mechanism for managing the big data. Motivated by these research problems, this paper proposes a novel matrix-based fuzzy algorithm, called the FMM system, to mine the defined multi-layered Twitter data. Through sets of comparable experiments applied on Twitter data, the proposed FMM system achieved an excellent performance, with both fast processing speeds and high predictive accuracy.


title: Trust fraud: A crucial challenge for China's e-commerce market

abstract: Currently, China's e-commerce market is growing at an unprecedented pace, however, it is faced with many challenges, among which the trust fraud problem is the biggest issue. In this article, we use Taobao as an example and conduct a thorough investigation of the trust fraud phenomenon in China's e-commerce market. We present the development history of trust fraud, summarize its unique characteristics, and explore the reasons why so many sellers commit fraud. We further propose a dynamic time decay trust model that aims to deter trust fraud by raising its cost and promote the growth of small and medium-sized sellers. The model utilizes detailed seller ratings as the data source, and incorporates a transaction amount weight, a time decay coefficient, and three trust factors in the calculation of trust. We test the model on real transaction data from Taobao, and the experimental results verify its effectiveness. Our proposed trust model yields a practical approach to online trust management not only in the Taobao market but also for other e-commerce platforms.


title: Classification Of Diabetes Disease Using Support Vector Machine

abstract: Diabetes mellitus is one of the most serious health challenges in both developing and developed countries. According to the International Diabetes Federation, there are 285 million diabetic people worldwide. This total is expected to rise to 380 million within 20 years. Due to its importance, a design of classifier for the detection of Diabetes disease with optimal cost and better performance is the need of the age. The Pima Indian diabetic database at the UCI machine learning laboratory has become a standard for testing data mining algorithms to see their prediction accuracy in diabetes data classification. The proposed method uses Support Vector Machine (SVM), a machine learning method as the classifier for diagnosis of diabetes. The machine learning method focus on classifying diabetes disease from high dimensional medical dataset. The experimental results obtained show that support vector machine can be successfully used for diagnosing diabetes disease.


title: Image Quality Assessment Techniques Show Improved Training and Evaluation of Autoencoder Generative Adversarial Networks

abstract: We propose a training and evaluation approach for autoencoder Generative Adversarial Networks (GANs), specifically the Boundary Equilibrium Generative Adversarial Network (BEGAN), based on methods from the image quality assessment literature. Our approach explores a multidimensional evaluation criterion that utilizes three distance functions: an $l_1$ score, the Gradient Magnitude Similarity Mean (GMSM) score, and a chrominance score. We show that each of the different distance functions captures a slightly different set of properties in image space and, consequently, requires its own evaluation criterion to properly assess whether the relevant property has been adequately learned. We show that models using the new distance functions are able to produce better images than the original BEGAN model in predicted ways.


title: Optimal design methodology of a three phase rotary transformer for doubly fed induction generator application

abstract: Rotary transformers can be used to provide magnetic coupling to the rotor circuit of a doubly fed induction generator. This topology eliminates the need for slip ring and brush assemblies thus reducing the maintenance required. This paper presents an analytical method for determining an optimal design of a rotary transformer. The methodology is used to design a 3 kVA three phase transformer which is validated using a FEM simulation package. Also, a study of suitability in terms of size at different power levels is conducted.


title: Dual band (28/38 GHz) CPW slot directive antenna for future 5G cellular applications

abstract: A design and analysis of a 28/38 GHz dual-band CPW slot directive antenna are presented. To obtain a dual band at 28/38 GHz that covers 5G applications, chambered rectangular patches are printed on a low permittivity “er2=2.2” grounded substrates with a 0.254-mm-thick where it is fed by a 50Ω microstrip line on the backside through a staircase rectangular apertures etched in the ground plane. The simulated results show that the designed antenna has dual band at 28 and 38 GHz for 5G system. The antenna provides almost directional patterns with acceptable gain values at both frequency band of operation.


title: AN ANALYSIS OF ZMP CONTROL PROBLEM OF HUMANOID ROBOT WITH COMPLIANCES IN SOLE OF THE FOOT

abstract: Abstract This paper describes an analysis of balance control of humanoid robot based on ZMP feedback control. The purpose of this paper is to make clear understanding of ZMP control problem which is composed by inverted pendulum control and ZMP compensation control. This paper shows that this problem can be formulated as high gain output feedback and the existence of zero dynamics in closed loop system. This paper also shows stability condition of zero dynamics, and then stability of zero dynamics and closed-loop systems are analyzed using root locus. From the root locus analysis, the guidelines on finding state and output feedback gain which achieve good performance of control system, are shown.


title: Psychological Predictors of Cyber Bullying in Early Adulthood

abstract: Objectives: The aim of the present research was to investigate the possible psychological predictors of cyber bullying behavior in early adulthood. It was hypothesized that lack of empathy and emotional-behavioral problems would be related to the cyber bullying as well as will prove significant predictors of cyber bullying behavior in early adulthood. ::: Design: It was a co-relational study and cross-sectional research design was followed. ::: Duration and place of study: It took six months to collect data from three sites of Lahore, Pakistan; that were colleges, universities and net cafes. ::: Sample and method: Purposive Sample of 150 young adults including 78 men and 72 women with age range 18-25 was drawn Assessment measures were Toronto Empathy Questionnaire 1 and Cyber Bullying Scale2. For emotional problems Depression, Anxiety and Stress 23 and for behavioral problems Aggression Questionnaire 4 was used. ::: Results: Results revealed that there was a significant inverse relationship between empathy and cyber bullying, whereas significant positive relationship between emotional-behavioral problems and cyber bullying. Multiple Hierarchical regression analysis revealed that lack of empathy and emotional problems were significant predictors of cyber bullying. All the three groups including young adults from universities, colleges and net cafes perform significantly different on all study variables. The present research findings will give new directions for future studies in the field of cyber crimes as well as in making therapeutic intervention plans to treat emotionalbehavioural problems in youth.


title: Iterative localization refinement in convolutional neural networks for improved object detection

abstract: Accurate region proposals are of importance to facilitate object localization in the existing convolutional neural network (CNN)-based object detection methods. This paper presents a novel iterative localization refinement (ILR) method which, undertaken at a mid-layer of a CNN architecture, iteratively refines region proposals in order to match as much ground-truth as possible. The search for the desired bounding box in each iteration is first formulated as a statistical hypothesis testing problem and then solved by a divide-and-conquer paradigm. The proposed ILR is not only data-driven, free of learning, but also compatible with a variety of CNNs. Furthermore, to reduce complexity, an approximate variant based on a refined sampling strategy using linear interpolation is addressed. Simulations show that the proposed method improves the main state-of-the-art works on the PASCAL VOC 2007 dataset.


AutoTutor Improves Deep Learning of Computer Literacy : Is it the Dialog or the Talking Head ?


title: Flowing ConvNets for Human Pose Estimation in Videos

abstract: The objective of this work is human pose estimation in videos, where multiple frames are available. We investigate a ConvNet architecture that is able to benefit from temporal context by combining information across the multiple frames using optical flow. To this end we propose a network architecture with the following novelties: (i) a deeper network than previously investigated for regressing heatmaps, (ii) spatial fusion layers that learn an implicit spatial model, (iii) optical flow is used to align heatmap predictions from neighbouring frames, and (iv) a final parametric pooling layer which learns to combine the aligned heatmaps into a pooled confidence map. We show that this architecture outperforms a number of others, including one that uses optical flow solely at the input layers, one that regresses joint coordinates directly, and one that predicts heatmaps without spatial fusion. The new architecture outperforms the state of the art by a large margin on three video pose estimation datasets, including the very challenging Poses in the Wild dataset, and outperforms other deep methods that don't use a graphical model on the single-image FLIC benchmark (and also [5, 35] in the high precision region).


title: Magnetoencephalography for brain electrophysiology and imaging

abstract: We review the aspects that uniquely characterize magnetoencephalography (MEG) among the techniques available to explore and resolve brain function and dysfunction. While emphasizing its specific strengths in terms of millisecond source imaging, we also identify and discuss current practical challenges, in particular in signal extraction and interpretation. We also take issue with some perceived disadvantages of MEG, including the misconception that the technique is redundant with electroencephalography. Overall, MEG contributes uniquely to our deeper comprehension of both regional and large-scale brain dynamics: from the functions of neural oscillations and the nature of event-related brain activation, to the mechanisms of functional connectivity between regions and the emergence of modes of network communication in brain systems. We expect MEG to play an increasing and pivotal role in the elucidation of these grand mechanistic principles of cognitive, systems and clinical neuroscience.


title: Developing a Comprehensive Engagement Framework of Gamification for Reflective Learning

abstract: Engagement is a key reason for introducing gamification to learning and thus serves as an important measurement of its effectiveness. Based on a literature review and meta-synthesis, this paper proposes a comprehensive framework of engagement in gamification for learning. The framework sketches out the connections among gamification strategies, dimensions of engagement, and the ultimate learning outcome. It also elicits other task - and user - related factors that may potentially impact the effect of gamification on learner engagement. To verify and further strengthen the framework, we conducted a user study to demonstrate that: 1) different gamification strategies can trigger different facets of engagement; 2) the three dimensions of engagement have varying effects on skill acquisition and transfer; and 3) task nature and learner characteristics that were overlooked in previous studies can influence the engagement process. Our framework provides an in-depth understanding of the mechanism of gamification for learning, and can serve as a theoretical foundation for future research and design.


title: Computational Creativity

abstract: Creative thinking is one of the hallmarks of human-level competence. Although it is still a poorly understood subject speculative ideas about brain processes involved in creative thinking may be implemented in computational models. A review of different approaches to creativity, insight and intuition is presented. Two factors are essential for creativity: imagination and selection or filtering. Imagination should be constrained by experience, while filtering in the case of creative use of words may be based on semantic and phonological associations. Analysis of brain processes involved in invention of new words leads to practical algorithms that create many interesting and novel names associated with a set of keywords.


title: The Presence of Consciousness in Absence Seizures

abstract: This paper examines three respects in which the study of epileptic absence seizures promises to inform our understanding of consciousness. Firstly, it has the potential to bear on debates concerning the behavioural and cognitive functions associated with consciousness. Secondly, it has the potential to illuminate the relationship between background states (or ‘levels’) of consciousness and the contents of consciousness. Thirdly, it has the potential to bear on our understanding of the unity of consciousness.


title: An amoeboid modular robot that exhibits real-time adaptive reconfiguration

abstract: This paper discusses experimental verifications of a two-dimensional modular robot called “Slimebot”, consisting of many identical modules, each of which has simple motile functions. We have so far investigated a fully decentralized algorithm able to control the morphology of the modular robot in real-time according to the environment encountered. One of the most significant features of our approach is that we explicitly exploit “emergent phenomena” stemming from the interplay between control and mechanical systems. In order to verify our proposed control scheme, we have constructed real physical Slimebot. Experiments with 10 modules suggest that this robot exhibits significant abilities, i.e., adaptivity, scalability, and fault tolerance.


title: Is seeing believing?: how recommender system interfaces affect users' opinions

abstract: Recommender systems use people's opinions about items in an information domain to help people choose other items. These systems have succeeded in domains as diverse as movies, news articles, Web pages, and wines. The psychological literature on conformity suggests that in the course of helping people make choices, these systems probably affect users' opinions of the items. If opinions are influenced by recommendations, they might be less valuable for making recommendations for other users. Further, manipulators who seek to make the system generate artificially high or low recommendations might benefit if their efforts influence users to change the opinions they contribute to the recommender. We study two aspects of recommender system interfaces that may affect users' opinions: the rating scale and the display of predictions at the time users rate items. We find that users rate fairly consistently across rating scales. Users can be manipulated, though, tending to rate toward the prediction the system shows, whether the prediction is accurate or not. However, users can detect systems that manipulate predictions. We discuss how designers of recommender systems might react to these findings.


title: Procedural zelda: a PCG environment for player experience research

abstract: To contribute to the domain of player experience research, this paper presents a new PCG environment with a relatively wide expressive range that builds upon the iconic The Legend of Zelda: A Link to the Past action-RPG game; it contributes by providing the openly-available Procedural Zelda environment for gaming research. The paper presents the design goals and design context of the research environment, and provides a detailed overview of the procedural capabilities of Procedural Zelda, together with its capabilities for data logging, to benefit, e.g., player modelling investigations.


title: A cost-effective recommender system for taxi drivers

abstract: The GPS technology and new forms of urban geography have changed the paradigm for mobile services. As such, the abundant availability of GPS traces has enabled new ways of doing taxi business. Indeed, recent efforts have been made on developing mobile recommender systems for taxi drivers using Taxi GPS traces. These systems can recommend a sequence of pick-up points for the purpose of maximizing the probability of identifying a customer with the shortest driving distance. However, in the real world, the income of taxi drivers is strongly correlated with the effective driving hours. In other words, it is more critical for taxi drivers to know the actual driving routes to minimize the driving time before finding a customer. To this end, in this paper, we propose to develop a cost-effective recommender system for taxi drivers. The design goal is to maximize their profits when following the recommended routes for finding passengers. Specifically, we first design a net profit objective function for evaluating the potential profits of the driving routes. Then, we develop a graph representation of road networks by mining the historical taxi GPS traces and provide a Brute-Force strategy to generate optimal driving route for recommendation. However, a critical challenge along this line is the high computational cost of the graph based approach. Therefore, we develop a novel recursion strategy based on the special form of the net profit function for searching optimal candidate routes efficiently. Particularly, instead of recommending a sequence of pick-up points and letting the driver decide how to get to those points, our recommender system is capable of providing an entire driving route, and the drivers are able to find a customer for the largest potential profit by following the recommendations. This makes our recommender system more practical and profitable than other existing recommender systems. Finally, we carry out extensive experiments on a real-world data set collected from the San Francisco Bay area and the experimental results clearly validate the effectiveness of the proposed recommender system.


title: A framework for unsupervised spam detection in social networking sites

abstract: Social networking sites offer users the option to submit user spam reports for a given message, indicating this message is inappropriate. In this paper we present a framework that uses these user spam reports for spam detection. The framework is based on the HITS web link analysis framework and is instantiated in three models. The models subsequently introduce propagation between messages reported by the same user, messages authored by the same user, and messages with similar content. Each of the models can also be converted to a simple semi-supervised scheme. We test our models on data from a popular social network and compare the models to two baselines, based on message content and raw report counts. We find that our models outperform both baselines and that each of the additions (reporters, authors, and similar messages) further improves the performance of the framework.


title: POSTER: Towards Highly Interactive Honeypots for Industrial Control Systems

abstract: Honeypots are a common tool to set intrusion alarms and to study attacks against computer systems. In order to be convincing, honeypots attempt to resemble actual systems that are in active use. Recently, researchers have begun to develop honeypots for programmable logic controllers (PLCs). The tools of which we are aware have limited functionality compared to genuine devices. Particularly, they do not support running actual PLC programs. In order to improve upon the interactive capabilities of PLC honeypots we set out to develop a simulator for Siemens S7-300 series PLCs. Our current prototype XPOT supports PLC program compilation and interpretation, the proprietary S7comm protocol and SNMP. While the supported feature set is not yet comprehensive, it is possible to program it using standard IDEs such as Siemens' TIA portal. Additionally, we emulate the characteristics of the network stack of our reference PLC in order to resist OS fingerprinting attempts using tools such as Nmap. Initial experiments with students whom we trained in PLC programming indicate that XPOT may resist cursory inspection but still fails against knowledgeable and suspicious adversaries. We conclude that high-interactive PLC honeypots need to support a fairly complete feature set of the genuine, simulated PLC.


title: EROS: Ensemble rough subspaces

abstract: Ensemble learning is attracting much attention from pattern recognition and machine learning domains for good generalization. Both theoretical and experimental researches show that combining a set of accurate and diverse classifiers will lead to a powerful classification system. An algorithm, called FS-PP-EROS, for selective ensemble of rough subspaces is proposed in this paper. Rough set-based attribute reduction is introduced to generate a set of reducts, and then each reduct is used to train a base classifier. We introduce an accuracy-guided forward search and post-pruning strategy to select part of the base classifiers for constructing an efficient and effective ensemble system. The experiments show that classification accuracies of ensemble systems with accuracy-guided forward search strategy will increase at first, arrive at a maximal value, then decrease in sequentially adding the base classifiers. We delete the base classifiers added after the maximal accuracy. The experimental results show that the proposed ensemble systems outperform bagging and random subspace methods in terms of accuracy and size of ensemble systems. FS-PP-EROS can keep or improve the classification accuracy with very few base classifiers, which leads to a powerful and compact classification system.


title: Music recommendation by unified hypergraph: combining social media information and music content

abstract: Acoustic-based music recommender systems have received increasing interest in recent years. Due to the semantic gap between low level acoustic features and high level music concepts, many researchers have explored collaborative filtering techniques in music recommender systems. Traditional collaborative filtering music recommendation methods only focus on user rating information. However, there are various kinds of social media information, including different types of objects and relations among these objects, in music social communities such as Last.fm and Pandora. This information is valuable for music recommendation. However, there are two challenges to exploit this rich social media information: (a) There are many different types of objects and relations in music social communities, which makes it difficult to develop a unified framework taking into account all objects and relations. (b) In these communities, some relations are much more sophisticated than pairwise relation, and thus cannot be simply modeled by a graph. In this paper, we propose a novel music recommendation algorithm by using both multiple kinds of social media information and music acoustic-based content. Instead of graph, we use hypergraph to model the various objects and relations, and consider music recommendation as a ranking problem on this hypergraph. While an edge of an ordinary graph connects only two objects, a hyperedge represents a set of objects. In this way, hypergraph can be naturally used to model high-order relations. Experiments on a data set collected from the music social community Last.fm have demonstrated the effectiveness of our proposed algorithm.


title: Approach, avoidance, and coping with stress.

abstract: The study of stress and coping points to two concepts central to an understanding of the response to trauma: approach and avoidance. This pair of concepts refers to two basic modes of coping with stress. Approach and avoidance are simply metaphors for cognitive and emotional activity that is oriented either toward or away from threat. An approach-avoidance model of coping is presented in the context of contemporary theoretical ap- proaches to coping. The research literature on coping ef- fectiveness, including evidence from our laboratory, is dis- cussed, and speculations are made about the implications for future research. The study of stress and coping has become quite popular in recent years, particularly in regard to traumatic life events. Although the area is broad and the coping process is complex, there is a striking coherence in much of the literature. This coherence is based on two concepts central to an understanding of coping with trauma: approach and avoidance. In its simplest form, this pair of concepts refers to two basic orientations toward stressful infor- mation, or two basic modes of coping with stress. Ap- proach and avoidance are shorthand terms for the cog- nitive and emotional activity that is oriented either toward or away from threat. In this article we will present the case for utilizing the concepts of approach and avoidance to provide a co- herent theoretical structure to our understanding of cop- ing with stress. Several different formulations of the ap- proach-avoidance dimension will be reviewed, followed by a brief review of the coping effectiveness literature. Several studies from our laboratory will be used to illus- trate the relationship between coping and outcome. Fi- nally, a general approach-avoidance model of coping will be presented, with suggestions for further research to cor- roborate or extend the theory. The study of coping with stress has been split into two areas: anticipation of future stressful events and re- covery from trauma. These areas have been kept re- markably distinct in both theory and research on coping. Although there are clearly important differences between the two cases, we have chosen not to emphasize this dis- tinction. For any given stress, anticipation and recovery are not always clearly separable; dealing with a trauma involves coming to terms with the event itself and with the threat of recurrence in the future. More important, Correspondence concerning this article should be sent to Susan


title: Estimating the number of citations using author reputation

abstract: We study the problem of predicting the popularity of items in a dynamic environment in which authors post continuously new items and provide feedback on existing items. This problem can be applied to predict popularity of blog posts, rank photographs in a photo-sharing system, or predict the citations of a scientific article using author information and monitoring the items of interest for a short period of time after their creation. As a case study, we show how to estimate the number of citations for an academic paper using information about past articles written by the same author(s) of the paper. If we use only the citation information over a short period of time, we obtain a predicted value that has a correlation of r = 0.57 with the actual value. This is our baseline prediction. Our best-performing system can improve that prediction by adding features extracted from the past publishing history of its authors, increasing the correlation between the actual and the predicted values to r = 0.81.


title: Semantic Web Services

abstract: The authors propose the markup of Web services in the DAML family of Semantic Web markup languages. This markup enables a wide variety of agent technologies for automated Web service discovery, execution, composition and interoperation. The authors present one such technology for automated Web service composition.


title: RDB2Graph: A Generic Framework for Modeling Relational Databases as Graphs

abstract: Graph data mining is highly versatile, as it applies not only to graph data but to relational data, as long as it can be represented as pairs of relationships. However, modeling RDBs as graphs using existing methods is limited in describing semantics of the relational data. In this paper, we propose a two-phased graph-modeling framework that converts any RDB to a directed graph with richer semantics than previously allowed. We implemented the framework and used it for analyzing medical records of diabetes patients.


title: Graphicionado: A high-performance and energy-efficient accelerator for graph analytics

abstract: Graphs are one of the key data structures for many real-world computing applications and the importance of graph analytics is ever-growing. While existing software graph processing frameworks improve programmability of graph analytics, underlying general purpose processors still limit the performance and energy efficiency of graph analytics. We architect a domain-specific accelerator, Graphicionado, for high-performance, energy-efficient processing of graph analytics workloads. For efficient graph analytics processing, Graphicionado exploits not only data structure-centric datapath specialization, but also memory subsystem specialization, all the while taking advantage of the parallelism inherent in this domain. Graphicionado augments the vertex programming paradigm, allowing different graph analytics applications to be mapped to the same accelerator framework, while maintaining flexibility through a small set of reconfigurable blocks. This paper describes Graphicionado pipeline design choices in detail and gives insights on how Graphicionado combats application execution inefficiencies on general-purpose CPUs. Our results show that Graphicionado achieves a 1.76 − 6.54x speedup while consuming 50 − 100x less energy compared to a state-of-the-art software graph analytics processing framework executing 32 threads on a 16-core Haswell Xeon processor.


title: Generating Sentence Planning Variations for Story Telling

abstract: There has been a recent explosion in applications for dialogue interaction ranging from direction-giving and tourist information to interactive story systems. Yet the natural language generation (NLG) component for many of these systems remains largely handcrafted. This limitation greatly restricts the range of applications; it also means that it is impossible to take advantage of recent work in expressive and statistical language generation that can dynamically and automatically produce a large number of variations of given content. We propose that a solution to this problem lies in new methods for developing language generation resources. We describe the ES-TRANSLATOR, a computational language generator that has previously been applied only to fables, and quantitatively evaluate the domain independence of the EST by applying it to personal narratives from weblogs. We then take advantage of recent work on language generation to create a parameterized sentence planner for story generation that provides aggregation operations, variations in discourse and in point of view. Finally, we present a user evaluation of different personal narrative retellings.


title: Scale coding bag of deep features for human attribute and action recognition

abstract: Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a single, scale-invariant encoding. Both in bag-of-words and the recently popular representations based on convolutional neural networks, local features are computed at multiple scales. However, these multi-scale convolutional features are pooled into a single scale-invariant representation. We argue that entirely scale-invariant image representations are sub-optimal and investigate approaches to scale coding within a bag of deep features framework. Our approach encodes multi-scale information explicitly during the image encoding stage. We propose two strategies to encode multi-scale information explicitly in the final image representation. We validate our two scale coding techniques on five datasets: Willow, PASCAL VOC 2010, PASCAL VOC 2012, Stanford-40 and Human Attributes (HAT-27). On all datasets, the proposed scale coding approaches outperform both the scale-invariant method and the standard deep features of the same network. Further, combining our scale coding approaches with standard deep features leads to consistent improvement over the state of the art.


title: Face Detection by Orientation Map Matching

abstract: This paper describes a fast and accurate face detection method based on edge orientation map matching of colour images. Face detection speed and accuracy is crucial to realtime face recognition applications but still remains a problem even though much research effort has been devoted to face detection algorithms. What make our method different from existing works are that only simple computation is involved so that faces can be detected in less than 1 second, and both face shape and skin colour information is taken into account whereby false detection is reduced. In addition our algorithm is robust to noise as edge orientation maps are computed for each image block, rather than for each individual pixel. Experimental results demonstrate the advantages of our approach.


Investigation of parametric rectified linear units for noise robust speech recognition.


title: The Malmo platform for artificial intelligence experimentation

abstract: We present Project Malmo - an AI experimentation platform built on top of the popular computer game Minecraft, and designed to support fundamental research in artificial intelligence. As the AI research community pushes for artificial general intelligence (AGI), experimentation platforms are needed that support the development of flexible agents that learn to solve diverse tasks in complex environments. Minecraft is an ideal foundation for such a platform, as it exposes agents to complex 3D worlds, coupled with infinitely varied game-play. ::: ::: Project Malmo provides a sophisticated abstraction layer on top of Minecraft that supports a wide range of experimentation scenarios, ranging from navigation and survival to collaboration and problem solving tasks. In this demo we present the Malmo platform and its capabilities. The platform is publicly released as open source software at IJCAI, to support openness and collaboration in AI research.


title: Shared waypoints and social tagging to support collaboration in software development

abstract: This paper presents the conceptual design of TagSEA, a collaborative tool to support asynchronous software development. Our goal is to develop a lightweight source code annotation tool that enhances navigation, coordination, and capture of knowledge relevant to a software development team. Our design is inspired by combining "waypoints" from geographical navigation with "social tagging" from social bookmarking software to support coordination and communication among software developers. We describe the motivation behind this work, walk through the design and implementation, and report early feedback on how this lightweight tool supports collaborative software engineering activities. Finally, we suggest a number of new research directions that this topic exposes.


title: Opportunities for silicon at mmWave and Terahertz frequencies

abstract: This paper summarizes opportunities for silicon process technologies at mmWave and Terahertz frequencies and demonstrates key building blocks for 94-GHz and 600-GHz imaging arrays. It reviews potential applications and summa- rizes state-of-the-art terahertz technologies. Terahertz focal-plane arrays (FPAs) for video-rate imaging applications have been fabricated in commercially available CMOS and SiGe process technologies respectively. The 3×5 arrays achieve a responsivity of up to 50 kV/W with a minimum NEP of 400 pW/ p Hz at 600 GHz. Images of postal envelopes are presented which demonstrate the potential of silicon integrate 600-GHz terahertz FPAs for future low-cost terahertz camera systems. I. INTRODUCTION The terahertz region of the electromagnetic spectrum is defined as the sub-mmWave band spanning from 300 GHz through 3 THz. Historically, THz radiation was generated by optical means with the help of femto-second laser pulses and was primarily used for spectroscopic purposes in the scientific community (1). This band is often referred to as the THz gap, loosely describing the lack of adequate technologies to effectively bridge this transition region between microwaves and optics. The use of either electronic or photonic systems lead to the subdivison in THz Electronic and THz Photonic bands with frequencies spanning from 100 GHz through 10 THz as illustrated in Fig 1.


title: A Single-Stage Photovoltaic System for a Dual-Inverter-Fed Open-End Winding Induction Motor Drive for Pumping Applications

abstract: This paper presents an integrated solution for a photovoltaic (PV)-fed water-pump drive system, which uses an open-end winding induction motor (OEWIM). The dual-inverter-fed OEWIM drive achieves the functionality of a three-level inverter and requires low value dc-bus voltage. This helps in an optimal arrangement of PV modules, which could avoid large strings and helps in improving the PV performance with wide bandwidth of operating voltage. It also reduces the voltage rating of the dc-link capacitors and switching devices used in the system. The proposed control strategy achieves an integration of both maximum power point tracking and V/f control for the efficient utilization of the PV panels and the motor. The proposed control scheme requires the sensing of PV voltage and current only. Thus, the system requires less number of sensors. All the analytical, simulation, and experimental results of this work under different environmental conditions are presented in this paper.


title: A Comparison between Different Error Modeling of MEMS Applied to GPS/INS Integrated Systems

abstract: Advances in the development of micro-electromechanical systems (MEMS) have made possible the fabrication of cheap and small dimension accelerometers and gyroscopes, which are being used in many applications where the global positioning system (GPS) and the inertial navigation system (INS) integration is carried out, i.e., identifying track defects, terrestrial and pedestrian navigation, unmanned aerial vehicles (UAVs), stabilization of many platforms, etc. Although these MEMS sensors are low-cost, they present different errors, which degrade the accuracy of the navigation systems in a short period of time. Therefore, a suitable modeling of these errors is necessary in order to minimize them and, consequently, improve the system performance. In this work, the most used techniques currently to analyze the stochastic errors that affect these sensors are shown and compared: we examine in detail the autocorrelation, the Allan variance (AV) and the power spectral density (PSD) techniques. Subsequently, an analysis and modeling of the inertial sensors, which combines autoregressive (AR) filters and wavelet de-noising, is also achieved. Since a low-cost INS (MEMS grade) presents error sources with short-term (high-frequency) and long-term (low-frequency) components, we introduce a method that compensates for these error terms by doing a complete analysis of Allan variance, wavelet de-nosing and the selection of the level of decomposition for a suitable combination between these techniques. Eventually, in order to assess the stochastic models obtained with these techniques, the Extended Kalman Filter (EKF) of a loosely-coupled GPS/INS integration strategy is augmented with different states. Results show a comparison between the proposed method and the traditional sensor error models under GPS signal blockages using real data collected in urban roadways.


title: AV@CAR: A Spanish Multichannel Multimodal Corpus for In-Vehicle Automatic Audio-Visual Speech Recognition

abstract: This paper describes the acquisition of the multichannel multimodal database AV@CAR for automatic audio-visual speech recognition in cars. Automatic speech recognition (ASR) plays an important role inside vehicles to keep the driver away from distraction. It is also known that visual information (lip-reading) can improve accuracy in ASR under adverse conditions as those within a car. The corpus described here is intended to provide training and testing material for several classes of audiovisual speech recognizers including isolated word system, word-spotting systems, vocabulary independent systems, and speaker dependent or speaker independent systems for a wide range of applications. The audio database is composed of seven audio channels including, clean speech (captured using a close talk microphone), noisy speech from several microphones placed on the overhead of the cabin, noise only signal coming from the engine compartment and information about the speed of the car. For the video database, a small video camera sensible to the visible and the near infrared bands is placed on the windscreen and used to capture the face of the driver. This is done under different light conditions both during the day and at night. Additionally, the same individuals are recorded in laboratory, under controlled environment conditions to obtain noise free speech signals, 2D images and 3D + texture face models.


Effects of Lectures with PowerPoint or Prezi Presentations on Cognitive Load, Recall, and Conceptual Learning


title: NETWORK INTRUSION DETECTION SYSTEM BASED ON MACHINE LEARNING ALGORITHMS

abstract: Network and system security is of paramount importance in the present data communication environment. Hackers and intruders can create many successful attempts to cause the crash of the networks and web services by unauthorized intrusion. New threats and associated solutions to prevent these threats are emerging together with the secured system evolution. Intrusion Detection Systems (IDS) are one of these solutions. The main function of Intrusion Detection System is to protect the resources from threats. It analyzes and predicts the behaviours of users, and then these behaviours will be considered an attack or a normal behaviour. We use Rough Set Theory (RST) and Support Vector Machine (SVM) to detect network intrusions. First, packets are captured from the network, RST is used to pre-process the data and reduce the dimensions. The features selected by RST will be sent to SVM model to learn and test respectively. The method is effective to decrease the space density of data. The experiments compare the results with Principal Component Analysis (PCA) and show RST and SVM schema could reduce the false positive rate and increase the accuracy.


Design of Wireless Home automation and security system using PIC Microcontroller


title: Modeling Musical Context with Word2vec

abstract: We present a semantic vector space model for capturing complex polyphonic musical context. A word2vec model based on a skip-gram representation with negative sampling was used to model slices of music from a dataset of Beethoven's piano sonatas. A visualization of the reduced vector space using t-distributed stochastic neighbor embedding shows that the resulting embedded vector space captures tonal relationships, even without any explicit information about the musical contents of the slices. Secondly, an excerpt of the Moonlight Sonata from Beethoven was altered by replacing slices based on context similarity. The resulting music shows that the selected slice based on similar word2vec context also has a relatively short tonal distance from the original slice.


title: A novel voltage clamp circuit for the measurement of transistor dynamic on-resistance

abstract: For determining the dynamic on-resistance R dyn,on of a power transistor, the voltage and current waveforms have to be measured during the switching operation. In measurements of voltage waveforms, using an oscilloscope, the characteristics of an amplifier inside the oscilloscope are distorted when the range of the measurement channel is not set wide enough to measure both on-state and off-state voltage, resulting in failure to accurately measure the voltage waveforms. A novel voltage clamp circuit improving the accuracy of the transistor on-state voltage measurement is presented. The measurement accuracy is improved by clamping the off-state voltage across the transistor to a lower voltage that is still greater than the on-state voltage. Unlike traditional clamping circuit, the presented voltage clamp circuit does not introduce delay caused by RC time constants keeping the voltage waveform clear even during state transitions of the evaluated semiconductor device for frequencies up to 1MHz.


title: A systematic review of the survival and complication rates of implant-supported fixed dental prostheses (FDPs) after a mean observation period of at least 5 years.

abstract: OBJECTIVES ::: The objective of this systematic review was to assess the 5- and 10-year survival of implant-supported fixed dental prostheses (FDPs) and to describe the incidence of biological and technical complications. ::: ::: ::: METHODS ::: An electronic Medline search complemented by manual searching was conducted to identify prospective and retrospective cohort studies and case series on FDPs with a mean follow-up time of at least 5 years. Patients had to have been examined clinically at the follow-up visit. Failure and complication rates were analyzed using standard and random-effects Poisson regression models to obtain summary estimates of 5-year and 10-year survival and complication rates. ::: ::: ::: RESULTS ::: The updated search provided 979 titles and 257 abstracts. Full-text analysis was performed for 90 articles resulting in a total 32 studies that met the inclusion criteria. Meta-analysis of these studies indicated an estimated survival of implants supporting FDPs of 95.6% after 5 years and 93.1% after 10 years. When machined surface implants were excluded from the analysis and only rough surface implants included, the survival rate increased to 97.2% after 5 years. The survival rate of implant-supported FDPs was 95.4% after 5 years and 80.1% after 10 years of function. When the analysis was done exclusively for metal-ceramic FDPs, hence the old gold-acrylic FDPs were excluded, the survival rate increased significantly. The survival rate of metal-ceramic implant-supported FDPs was 96.4% after 5 years and 93.9% after 10 years. Only 66.4% of the patients were free of any complications after 5 years. The most frequent complications over the 5-year observation period were fractures of the veneering material (13.5%), peri-implantitis and soft tissue complications (8.5%), loss of access hole restoration (5.4%), abutment or screw loosening (5.3%), and loss of retention of cemented FDPs (4.7%). ::: ::: ::: CONCLUSION ::: It may be concluded that implant-supported fixed dental prostheses (FDPs) are a safe and predictable treatment method with high survival rates. However, biological and technical complications were frequent (33.6%). To minimize the incidence of complications, dental professionals should make great effort in choosing reliable components and materials for implant-supported FDPs and the patients should be placed in well-structured maintenance system after treatment.


title: Generalizing database forensics

abstract: In this article we present refinements on previously proposed approaches to forensic analysis of database tampering. We significantly generalize the basic structure of these algorithms to admit new characterizations of the “where” axis of the corruption diagram. Specifically, we introduce page-based partitioning as well as attribute-based partitioning along with their associated corruption diagrams. We compare the structure of all the forensic analysis algorithms and discuss the various design choices available with respect to forensic analysis. We characterize the forensic cost of the newly introduced algorithms, compare their forensic cost, and give our recommendations. We then introduce a comprehensive taxonomy of the types of possible corruption events, along with an associated forensic analysis protocol that consolidates all extant forensic algorithms and the corresponding type(s) of corruption events they detect. The result is a generalization of these algorithms and an overarching characterization of the process of database forensic analysis, thus providing a context within the overall operation of a DBMS for all existing forensic analysis algorithms.


title: 1d-SAX: A Novel Symbolic Representation for Time Series

abstract: SAX Symbolic Aggregate approXimation is one of the main symbolization techniques for time series. A well-known limitation of SAX is that trends are not taken into account in the symbolization. This paper proposes 1d-SAX a method to represent a time series as a sequence of symbols that each contain information about the average and the trend of the series on a segment. We compare the efficiency of SAX and 1d-SAX in terms of goodness-of-fit, retrieval and classification performance for querying a time series database with an asymmetric scheme. The results show that 1d-SAX improves performance using equal quantity of information, especially when the compression rate increases.


title: A Comparative Evaluation of Approximate Probabilistic Simulation and Deep Neural Networks as Accounts of Human Physical Scene Understanding

abstract: Humans demonstrate remarkable abilities to predict physical events in complex scenes. Two classes of models for physical scene understanding have recently been proposed: "Intuitive Physics Engines", or IPEs, which posit that people make predictions by running approximate probabilistic simulations in causal mental models similar in nature to video-game physics engines, and memory-based models, which make judgments based on analogies to stored experiences of previously encountered scenes and physical outcomes. Versions of the latter have recently been instantiated in convolutional neural network (CNN) architectures. Here we report four experiments that, to our knowledge, are the first rigorous comparisons of simulation-based and CNN-based models, where both approaches are concretely instantiated in algorithms that can run on raw image inputs and produce as outputs physical judgments such as whether a stack of blocks will fall. Both approaches can achieve super-human accuracy levels and can quantitatively predict human judgments to a similar degree, but only the simulation-based models generalize to novel situations in ways that people do, and are qualitatively consistent with systematic perceptual illusions and judgment asymmetries that people show.


title: Hierarchical Theme and Topic Modeling

abstract: Considering the hierarchical data groupings in text corpus, e.g., words, sentences, and documents, we conduct the structural learning and infer the latent themes and topics for sentences and words from a collection of documents, respectively. The relation between themes and topics under different data groupings is explored through an unsupervised procedure without limiting the number of clusters. A tree stick-breaking process is presented to draw theme proportions for different sentences. We build a hierarchical theme and topic model, which flexibly represents the heterogeneous documents using Bayesian nonparametrics. Thematic sentences and topical words are extracted. In the experiments, the proposed method is evaluated to be effective to build semantic tree structure for sentences and the corresponding words. The superiority of using tree model for selection of expressive sentences for document summarization is illustrated.


title: Could the Laws of Nature Change?*

abstract: After reviewing several failed arguments that laws cannot change, I use the laws’ special relation to counterfactuals to show how temporary laws would have to differ from eternal but time‐dependent laws. Then I argue that temporary laws are impossible and that neither Lewis’s nor Armstrong’s analyses of law nicely accounts for the laws’ immutability.


title: Visualization of omics data for systems biology

abstract: High-throughput studies of biological systems are rapidly accumulating a wealth of 'omics'-scale data. Visualization is a key aspect of both the analysis and understanding of these data, and users now have many visualization methods and tools to choose from. The challenge is to create clear, meaningful and integrated visualizations that give biological insight, without being overwhelmed by the intrinsic complexity of the data. In this review, we discuss how visualization tools are being used to help interpret protein interaction, gene expression and metabolic profile data, and we highlight emerging new directions.


title: Technology Aware Training in Memristive Neuromorphic Systems based on non-ideal Synaptic Crossbars

abstract: The advances in the field of machine learning using neuromorphic systems have paved the pathway for extensive research on possibilities of hardware implementations of neural networks. Various memristive technologies such as oxide-based devices, spintronics, and phase change materials have been explored to implement the core functional units of neuromorphic systems, namely the synaptic network, and the neuronal functionality, in a fast and energy efficient manner. However, various nonidealities in the crossbar implementations of the synaptic arrays can significantly degrade performance of neural networks, and hence, impose restrictions on feasible crossbar sizes. In this paper, we build mathematical models of various nonidealities that occur in crossbar implementations such as source resistance, neuron resistance, and chip-to-chip device variations and analyze their impact on the classification accuracy of a fully connected network (FCN) and convolutional neural network (CNN) trained with Backpropagation algorithm. We show that a network trained under ideal conditions can suffer accuracy degradation as large as 59.84% for FCNs and 62.4% for CNNs when implemented on nonideal crossbars for relevant nonideality ranges. This severely constrains the sizes for crossbars. As a solution, we propose a technology aware training algorithm, which incorporates the mathematical models of the nonidealities in the backpropagation algorithm. We demonstrate that our proposed methodology achieves significant recovery of testing accuracy within 1.9% of the ideal accuracy for FCNs and 1.5% for CNNs. We further show that our proposed training algorithm can potentially allow the use of significantly larger crossbar arrays of sizes 784 × 500 for FCNs and 4096 × 512 for CNNs with a minor or no tradeoff in accuracy.


title: Warehousing manufacturing data: a holistic process warehouse for advanced manufacturing analytics

abstract: Strong competition in the manufacturing industry makes efficient and effective manufacturing processes a critical success factor. However, existing warehousing and analytics approaches in manufacturing are coined by substantial shortcomings, significantly preventing comprehensive process improvement. Especially, they miss a holistic data base integrating operational and process data, e. g., from Manufacturing Execution and Enterprise Resource Planning systems. To address this challenge, we introduce the Manufacturing Warehouse, a concept for a holistic manufacturing-specific process warehouse as central part of the overall Advanced Manufacturing Analytics Platform. We define a manufacturing process meta model and deduce a universal warehouse model. In addition, we develop a procedure for its instantiation and the integration of concrete source data. Finally, we describe a first proof of concept based on a prototypical implementation.


title: Scikit-ribo: Accurate estimation and robust modeling of translation dynamics at codon resolution

abstract: Ribosome profiling (Riboseq) is a powerful technique for measuring protein translation, however, sampling errors and biological biases are prevalent and poorly understand. Addressing these issues, we present Scikit-ribo (https://github.com/hanfang/scikit-ribo), the first open-source software for accurate genome-wide A-site prediction and translation efficiency (TE) estimation from Riboseq and RNAseq data. Scikit-ribo accurately identifies A-site locations and reproduces codon elongation rates using several digestion protocols (r = 0.99). Next we show commonly used RPKM-derived TE estimation is prone to biases, especially for low-abundance genes. Scikit-ribo introduces a codon-level generalized linear model with ridge penalty that correctly estimates TE while accommodating variable codon elongation rates and mRNA secondary structure. This corrects the TE errors for over 2000 genes in S. cerevisiae, which we validate using mass spectrometry of protein abundances (r = 0.81) and allows us to determine the Kozak-like sequence directly from Riboseq. We conclude with an analysis of coverage requirements needed for robust codon-level analysis, and quantify the artifacts that can occur from cycloheximide treatment.


title: Design of injected grid current regulator and capacitor-current-feedback active-damping for LCL-type grid-connected inverter

abstract: The injected grid current regulator and active damping of the LCL filter are essential to the control of LCL-type grid-connected inverters. Generally speaking, the current regulator guarantees the quality of the injected grid current, and the active damping reduces the resonance peak caused by the LCL filter and makes it easier to stabilize the whole system. However, the frequency responses of the current regulator and active damping interact with each other, making it difficult to design the proper controller parameters and investigate the effect of the controller parameters. Taking proportional-integral (PI) compensator together with capacitor-current-feedback active-damping as an instance, this paper proposes a step-by-step design method of the current regulator and capacitor current feedback coefficient in both analog control and digital control. By carefully dealing with the interaction between the current regulator and active damping, the satisfactory regions of the controller parameters are obtained based on stability margin and steady-state error checking, making it more convenient and explicit to optimize the performance of the system. The convenient trial-and-error procedures are successfully reduced. Experimental results verify the effectiveness of the proposed design method.


title: Front and rear wheel independent drive type electric vehicles (FRID EVs) with outstanding running performance suitable for next-generation electric vehicles

abstract: The safety and running performance of front and rear wheel independent drive type electric vehicles (FRID EVs) for use as advanced, next-generation electric vehicles (EVs) is clarified under severe running conditions, with particular focus on energy saving control performance and braking and cornering performance on low-friction coefficient (n) roads. The running performance of FRID EVs, which could not be obtained from the propulsion power generating systems of the EVs developed until now, is produced from propulsion power generating systems that can mutually or independently control the driving and braking forces of the front and rear wheels. The first energy saving control performance was obtained by applying the driving forces (torques) corresponding to the friction coefficient between the tire and road surface to the front and rear wheels while estimating the friction coefficient and the load movement occurring during acceleration. Outstanding braking and cornering performance is obtained by optimizing the relationship between the longitudinal and lateral forces acting on the tires so as to prevent an imbalanced torque between the left and right wheels from occurring, based on the friction circle principle. These outstanding functions were verified by undertaking tests performed under various running conditions using a prototype FRID EV.


title: Marquardt’s Phi Mask: Pitfalls of Relying on Fashion Models and the Golden Ratio to Describe a Beautiful Face

abstract: BACKGROUND ::: Stephen Marquardt has derived a mask from the golden ratio that he claims represents the "ideal" facial archetype. Many have found his mask convincing, including cosmetic surgeons. However, Marquardt's mask is associated with numerous problems. The method used to examine goodness of fit with the proportions in the mask is faulty. The mask is ill-suited for non-European populations, especially sub-Saharan Africans and East Asians. The mask also appears to approximate the face shape of masculinized European women. Given that the general public strongly and overwhelmingly prefers above average facial femininity in women, white women seeking aesthetic facial surgery would be ill-advised to aim toward a better fit with Marquardt's mask. This article aims to show the proper way of assessing goodness of fit with Marquardt's mask, to address the shape of the mask as it pertains to masculinity-femininity, and to discuss the broader issue of an objective assessment of facial attractiveness. ::: ::: ::: METHODS ::: Generalized Procrustes analysis is used to show how goodness of fit with Marquardt's mask can be assessed. Thin-plate spline analysis is used to illustrate visually how sample faces, including northwestern European averages, differ from Marquardt's mask. ::: ::: ::: RESULTS ::: Marquardt's mask best describes the facial proportions of masculinized white women as seen in fashion models. ::: ::: ::: CONCLUSIONS ::: Marquardt's mask does not appear to describe "ideal" face shape even for white women because its proportions are inconsistent with the optimal preferences of most people, especially with regard to femininity.


title: Automatic detection and rapid determination of earthquake magnitude by wavelet multiscale analysis of the primary arrival

abstract: Earthquake early warning systems must save lives. It is of great importance that networked systems of seismometers be equipped with reliable tools to make rapid determinations of earthquake magnitude in the few to tens of seconds before the damaging ground motion occurs. A new fully automated algorithm based on the discrete wavelet transform detects as well as analyzes the incoming first arrival with great accuracy and precision, estimating the final magnitude to within a single unit from the first few seconds of the P wave.


title: Design of distributed optical-fiber raman amplifiers using multi-objective particle swarm optimization

abstract: A novel method is presented to design the configuration of pumping lasers of Raman amplifiers using a multi-objective particle swarm optimizer. The goal is to obtain the pump laser wavelengths and powers that maximize the amplifier on-off gain, while maintaining the flatness of the gain over the used bandwidth. We used an algorithm called Multiple Objective Particle Swarm Optimization with Crowding Distance and Roulette Wheel to generate the non-dominated solutions, considering the average on-off gain and the ripple of the amplifier over the transmission bandwidth as the objectives in the optimization process. We designed amplifiers using three, four and five pump lasers. The experimental results showed that our proposal was able to design Raman amplifiers with a gain ripple lower than 0.2 dB and with an average on-off gain around 16.7 dB, when 20 signal channels and a total pump power of 1 W were considered. Moreover, we demonstrated that it is possible to allow the decision maker to choose among many possible non-dominated solutions depending on the application requirements.