title: Image Haze Removal via Reference Retrieval and Scene Prior

abstract: Photography of hazy scene typically suffers from low-contrast which degrades the visibility of the scene. The performance of single-image dehazing methods is limited by the priors or constraints. In this paper, we present an effective method for haze removal, which utilizes its retrieved correlated haze-free images as external information. The correlated haze-free images are with scene prior offering scene structure and local high frequency information for dehazing, although variations in viewpoints, scales, and illumination conditions exist. To utilize those reference more effectively, global geometric registration and local block matching toward the hazy input are performed to reinforce the spatial correlations. Based on the registration, different kinds of external information are estimated. In addition, we combine that additional external information with internal constraint and regularization for estimating scene transmission map. Experiments demonstrate that our approach can produce dehazing results with better visual quality compared with other state-of-the-art methods.


title: Native Italian speakers’ perception and production of English vowels

abstract: This study examined the production and perception of English vowels by highly experienced native Italian speakers of English. The subjects were selected on the basis of the age at which they arrived in Canada and began to learn English, and how much they continued to use Italian. Vowel production accuracy was assessed through an intelligibility test in which native English-speaking listeners attempted to identify vowels spoken by the native Italian subjects. Vowel perception was assessed using a categorial discrimination test. The later in life the native Italian subjects began to learn English, the less accurately they produced and perceived English vowels. Neither of two groups of early Italian/English bilinguals differed significantly from native speakers of English either for production or perception. This finding is consistent with the hypothesis of the speech learning model [Flege, in Speech Perception and Linguistic Experience: Theoretical and Methodological Issues (York, Timonium, MD, 1995)] that early bilinguals establish new categories for vowels found in the second language (L2). The significant correlation observed to exist between the measures of L2 vowel production and perception is consistent with another hypothesis of the speech learning model, viz., that the accuracy with which L2 vowels are produced is limited by how accurately they are perceived.


title: Pole-zero analysis of multi-stage amplifiers: A tutorial overview

abstract: Analyzing pole-zero locations of an amplifier is essential to 1) understand the characteristics of a circuit in the frequency domain, and 2) choose appropriate frequency compensation techniques to guarantee the stability of a circuit over a specified range of load resistance and capacitance. The objective of this paper is to provide tutorial treatment of the steps for analyzing poles and zeros in multi-stage amplifiers. These techniques can be equally applied for the analysis of power management circuits such as low-dropout voltage regulators (LDOs) and controllers for DC-DC converters.


title: Hybrid passive and communications-based methods for islanding detection in medium and low voltage smart grids

abstract: Islanding condition takes place when a distributed generator (DG) continues to power a part of the grid even if power from electric utility is no longer present. Adverse effects of islanding are power quality deterioration, grid-protection interference, equipment damage, and personnel safety hazards. For these reasons, DG systems must detect an islanding condition and immediately disconnect from the grid (anti-islanding protection). This paper proposes an hybrid solution, for both low voltage (LV) and medium voltage (MV) applications, which makes use of passive and communication-based methods for islanding detection. Among the existing communication technologies, the study is focused on power line communications (PLCs). An interface device for DGs is presented, which implements the proposed method. An analysis of the proposed solution is carried out and its feasibility is showed, by means of simulation and experimental results.


title: Geometric and force errors compensation in a 3-axis CNC milling machine

abstract: This paper proposes a new off line error compensation model by taking into accounting of geometric and cutting force induced errors in a 3-axis CNC milling machine. Geometric error of a 3-axis milling machine composes of 21 components, which can be measured by laser interferometer within the working volume. Geometric error estimation determined by back-propagation neural network is proposed and used separately in the geometric error compensation model. Likewise, cutting force induced error estimation by back-propagation neural network determined based on a flat end mill behavior observation is proposed and used separately in the cutting force induced error compensation model. Various experiments over a wide range of cutting conditions are carried out to investigate cutting force and machine error relation. Finally, the combination of geometric and cutting force induced errors is modeled by the combined back-propagation neural network. This unique model is used to compensate both geometric and cutting force induced errors simultaneously by a single model. Experimental tests have been carried out in order to validate the performance of geometric and cutting force induced errors compensation model.


title: Wanted: Collaborative intelligence

abstract: Abstract Although the original vision for artificial intelligence was the simulation of (implicitly human) intelligence, research has gradually shifted to autonomous systems that compete with people. The resultant popular attitude toward artificial intelligence, we argue here, is by turns disdain, grudging acceptance, and fear. That attitude not only limits our work's potential, but also imperils its support. This paper proposes a constructive alternative: the development of collaborative intelligence. As envisioned here, a collaborative intelligence does not require encyclopedic command and need not be limited to a single problem. The necessary components of a collaborative intelligence are nearly at hand, and the key issues readily identified. As a first step, this paper proposes three challenging but accessible problems that would both change the public perception of artificial intelligence and spur substantive research to advance our science.


Flow-based benchmark data sets for intrusion detection


title: A Flexible, Low Noise Reflective PPG Sensor Platform for Ear-Worn Heart Rate Monitoring

abstract: This paper presents a novel ear-worn reflective photoplethysmography (PPG) sensor that addresses the mechanical complexities of coupling the sensor to the surface of the skin and a detection circuit that minimises ambient noise artefacts. The flexible optoelectronic transducer structure can adapt to a variety of skin surface contours. Light emitting diode (LED) modulation and a unique integrating photocurrent demodulator reduce susceptibility to wideband noise and allow subtraction of ambient light from the desired PPG signal. Experimental results demonstrate that the sensitivity is robust to sensor location and application pressure variations. Simulations also show that the photodetection method is resilient against high levels of wideband noise.


title: OPTIMIZATION OF GEOMETRICAL PARAMETERS OF EQUAL CHANNEL MULTI-ANGULAR PRESSING PROCESS

abstract: ARTICLE INFORMATION ABSTRACT Original Research Paper Received 12 November 2015 Accepted 27 December 2015 Available Online 15 February 2016 Equal channel angular pressing (ECAP) is one of the most efficient techniques among severe plastic deformation (SPD) methods that enhance the mechanical properties of polycrystalline metals by refining subjected grains. In this article, equal channel multi angular pressing (ECMAP) process as one of the effective ECAP methods on production of ultra-fine grained (UFG) Al5754 strips is studied. Route C is considered as a multi pressing route and grey relational analysis is used as the optimization method. Geometrical parameters were taken as input variables and strain inhomogeneity index, equivalent plastic strain and required process load were taken as the objectives. The suggested tests by full factorial method were simulated by FEM. Finite element simulations were done by ABAQUS commercial software and obtained results were validated by analytical methods and experimental tests. Then considering all input and output parameters, optimization was done and optimum values of input parameters were elicited. Also, the effectiveness of each parameter on the objective parameters was obtained. It is concluded that, among geometrical parameters of route C, die channel angle ( _2) and die corner angle ( _1) have the maximum and minimum effectiveness respectively..


title: Timeline trees: visualizing sequences of transactions in information hierarchies

abstract: In many applications transactions between the elements of an information hierarchy occur over time. For example, the product offers of a department store can be organized into product groups and subgroups to form an information hierarchy. A market basket consisting of the products bought by a customer forms a transaction. Market baskets of one or more customers can be ordered by time into a sequence of transactions. Each item in a transaction is associated with a measure, for example, the amount paid for a product. In this paper we present a novel method for visualizing sequences of these kinds of transactions in information hierarchies. It uses a tree layout to draw the hierarchy and a timeline to represent progression of transactions in the hierarchy. We have developed several interaction techniques that allow the users to explore the data. Smooth animations help them to track the transitions between views. The usefulness of the approach is illustrated by examples from several very different application domains.


title: Film Theory: An Introduction

abstract: Foreword Introduction: Film theory: A retrospect Part one 1. Politics 2. Semiotics 3. Psychoanalysis Part two 4. Authorship 5. Narrative 6. Realism 7. The avant-garde Afterword Notes Bibliography Index.


title: Block-Sparse Recurrent Neural Networks

abstract: Recurrent Neural Networks (RNNs) are used in state-of-the-art models in domains such as speech recognition, machine translation, and language modelling. Sparsity is a technique to reduce compute and memory requirements of deep learning models. Sparse RNNs are easier to deploy on devices and high-end server processors. Even though sparse operations need less compute and memory relative to their dense counterparts, the speed-up observed by using sparse operations is less than expected on different hardware platforms. In order to address this issue, we investigate two different approaches to induce block sparsity in RNNs: pruning blocks of weights in a layer and using group lasso regularization with pruning to create blocks of weights with zeros. Using these techniques, we can create block-sparse RNNs with sparsity ranging from 80% to 90% with a small loss in accuracy. This technique allows us to reduce the model size by roughly 10x. Additionally, we can prune a larger dense network to recover this loss in accuracy while maintaining high block sparsity and reducing the overall parameter count. Our technique works with a variety of block sizes up to 32x32. Block-sparse RNNs eliminate overheads related to data storage and irregular memory accesses while increasing hardware efficiency compared to unstructured sparsity.


title: Emotional Feedback Generation for Physical Therapy

abstract: Consider a personal assistant who lives with physical therapy patients, reminding them to perform various exercises and giving patients feedback about how well they have enacted such exercises. The assistant has learned, through the patient's interactions with his or her physical therapist, to personalize its commands and responses to the patient. Having such an assistant would decrease the time it takes for patients to recover via a more regimented exercise routine and would give physical therapists a much more detailed account of the activities of their patients. Here I demonstrate a system that, given an encoding of a patient's exercise, generates emotionally-manipulated sentence feedback about how to better perform the exercise. In essence, the system is composed of two primary modules: a reasoning module and a natural language generation module. For my results, I present a technique to generating instances of emotionallyaltered exercise feedback on a stern-nurturing axis. I show a method for relating feedback words to deficiencies in exercise performance. Finally, I demonstrate a proof of concept technique for generating feedback given real-time skeleton mapping software. In future work, the system will be integrated into a physical therapy social avatar, which will interact as an assistant to patients within their home.


title: Piezoelectric polymer oxide semiconductor field effect transistor (POSFET) devices for touch sensing

abstract: This work presents the POSFET based touch sensing devices and their experimental evaluation. POSFET touch sensing devices presented here are primarily developed for the robotic applications. The design of these devices is inspired from human sense of touch. These devices are implemented by spin coating thin (∼2.5 µm) piezoelectric polymer (PVDF-TrFE) film, directly on to the gate area of MOS transistor. The polymer film is processed in situ. The POSFET device represents an integral “sensotronic” unit comprising of transducer and the transistor and are thus capable of ‘sensing and processing at same site’. The POSFET touch sensing devices are tested in a wide range (0.15-5N) of dynamic normal forces and at different frequencies.


title: Automatically Early Detection of Skin Cancer: Study Based on Nueral Netwok Classification

abstract: In this paper, an automatically skin cancer classification system is developed and the relationship of skin cancer image across different type of neural network are studied with different types of preprocessing.. The collected images are feed into the system, and across different image processing procedure to enhance the image properties. Then the normal skin is removed from the skin affected area and the cancer cell is left in the image. Useful information can be extracted from these images and pass to the classification system for training and testing. Recognition accuracy of the 3-layers back-propagation neural network classifier is 89.9% and auto-associative neural network is 80.8% in the image database that include dermoscopy photo and digital photo


title: Topology Study of Photovoltaic Interface for Maximum Power Point Tracking

abstract: This paper looks at the performance of photovoltaic modules in nonideal conditions and proposes topologies to minimize the degradation of performance caused by these conditions. It is found that the peak power point of a module is significantly decreased due to only the slightest shading of the module, and that this effect is propagated through other nonshaded modules connected in series with the shaded one. Based on this result, two topologies for parallel module connections have been outlined. In addition, dc/dc converter technologies, which are necessary to the design, are compared by way of their dynamic models, frequency characteristics, and component cost. Out of this comparison, a recommendation has been made


title: Reliable, Built-in, High-Accuracy Force Sensing for Legged Robots

abstract: An approach for achieving reliable, built-in, high-accuracy force sensing for legged robots is presented, based on direct exploitation of the properties of a robot’s mechanical structure. The proposed methodology relies on taking account of force-sensing requirements at the robot design stage, with a view to embedding force-sensing capability within the mechanical structure of the robot itself. The test case is ROBOCLIMBER, a bulky, quadruped climbing and walking machine whose weighty legs enable it to carry out heavy-duty drilling operations. The paper shows that, with finite-element analysis of ROBOCLIMBER’s mechanical configuration during the design stage, candidate positions can be selected for the placement of force transducers to measure indirectly the contact forces between the feet and the ground. Force sensors are then installed at the theoretically best positions on the mechanical structure, and several experiments are carried out to calibrate all sensors within their operational range of interest. After calibration, the built-in sensors are subjected to experimental performance evaluation, and the final best sensor option is found. The built-in force-sensing capability thus implemented is subjected to its first test of usability when it is employed to compute the actual centre of gravity of ROBOCLIMBER. The method is shown to be useful for determining variation during a gait (due to the non-negligible weight of the legs). Afterwards the force sensors are shown to be useful for controlling foot-ground interaction, and several illustrative experiments confirm the high sensitivity, reliability and accuracy of the selected approach. Lastly, the built-in sensors are used to measure ground-reaction forces and to compute the zero-moment point for ROBOCLIMBER in real time, both while standing and while executing a dynamically balanced gait.


title: Stroke risk profile: adjustment for antihypertensive medication. The Framingham Study

abstract: BACKGROUND AND PURPOSE ::: We sought to modify existing sex-specific health risk appraisal functions (profile functions) for the prediction of first stroke that better assess the effects of the use of antihypertensive medication. ::: ::: ::: METHODS ::: Health risk appraisal functions were previously developed from the Framingham Study cohort. These functions were Cox proportional hazards regression models relating age, systolic blood pressure, diabetes mellitus, cigarette smoking, prior cardiovascular disease, atrial fibrillation, left ventricular hypertrophy by electrocardiogram, and the use of antihypertensive medication to the occurrence of stroke. Closer examination of the data indicated that antihypertensive therapy effect is present only for systolic blood pressures between 110 and 200 mm Hg. Adjustments to the regressions to better fit the observed data were developed and tested for statistical significance and goodness-of-fit of the model residuals. ::: ::: ::: RESULTS ::: Modified functions more consistent with the data were developed, and, from these, tables to evaluate 10-year risk of first stroke were computed. ::: ::: ::: CONCLUSIONS ::: The stroke profile can be used for evaluation of the risk of stroke and suggestion of risk factor modification to reduce risk. The effect of antihypertensive therapy in the evaluation of stroke risk can now be better evaluated.


title: A statistical method for 3D object detection applied to faces and cars

abstract: In this paper, we describe a statistical method for 3D object detection. We represent the statistics of both object appearance and "non-object" appearance using a product of histograms. Each histogram represents the joint statistics of a subset of wavelet coefficients and their position on the object. Our approach is to use many such histograms representing a wide variety of visual attributes. Using this method, we have developed the first algorithm that can reliably detect human faces with out-of-plane rotation and the first algorithm that can reliably detect passenger cars over a wide range of viewpoints.


title: Classification of complete blood count and haemoglobin typing data by a C4.5 decision tree, a naïve Bayes classifier and a multilayer perceptron for thalassaemia screening

abstract: Abstract This article presents the classification of blood characteristics by a C4.5 decision tree, a naive Bayes classifier and a multilayer perceptron for thalassaemia screening. The aim is to classify eighteen classes of thalassaemia abnormality, which have a high prevalence in Thailand, and one control class by inspecting data characterised by a complete blood count (CBC) and haemoglobin typing. Two indices namely a haemoglobin concentration (HB) and a mean corpuscular volume (MCV) are the chosen CBC attributes. On the other hand, known types of haemoglobin from six ranges of retention time identified via high performance liquid chromatography (HPLC) are the chosen haemoglobin typing attributes. The stratified 10-fold cross-validation results indicate that the best classification performance with average accuracy of 93.23% (standard deviation = 1.67%) and 92.60% (standard deviation = 1.75%) is achieved when the naive Bayes classifier and the multilayer perceptron are respectively applied to samples which have been pre-processed by attribute discretisation. The results also suggest that the HB attribute is redundant. Moreover, the achieved classification performance is significantly higher than that obtained using only haemoglobin typing attributes as classifier inputs. Subsequently, the naive Bayes classifier and the multilayer perceptron are applied to an additional data set in a clinical trial which respectively results in accuracy of 99.39% and 99.71%. These results suggest that a combination of CBC and haemoglobin typing analysis with a naive Bayes classifier or a multilayer perceptron is highly suitable for automatic thalassaemia screening.


Latent Variable Analysis and Signal Separation


title: State of the art in statistical methods for language and speech processing

abstract: HighlightsThe purpose of this contribution is to review the state of the art in both areas, statistical methods and speech processing.Point out the top trends in statistical modelling across a wide range of problems and identify their most salient characteristics.The paper concludes with some prognostications regarding the likely impact on the field going forward. Recent years have seen rapid growth in the deployment of statistical methods for computational language and speech processing. The current popularity of such methods can be traced to the convergence of several factors, including the increasing amount of data now accessible, sustained advances in computing power and storage capabilities, and ongoing improvements in machine learning algorithms. The purpose of this contribution is to review the state of the art in both areas, point out the top trends in statistical modelling across a wide range of problems, and identify their most salient characteristics. The paper concludes with some prognostications regarding the likely impact on the field going forward.


title: Image Compression: A Survey

abstract: Image Compression is a demanding field in this era of communication. There is a need to study and analyze the literature for image compression, as the demand for images, video sequences and computer animation has increased at very high rate so that the increment is drastically over the years. Multimedia data whether graphics, audio, video data which is uncompress requires considerable transmission bandwidth and storage capacity. So this leads to the need of compression of images and all multimedia applications to save storage and transmission time. In this study we discuss different compression algorithms used to reduce size of images without quality reduction.


title: Is it possible to explain complex mental disorders at the biological level

abstract: The Forum which appears in this issue of World Psychiatry and the one we published in the previous issue 1,2,3,4,5,6,7 aim to help practising clinicians to orient themselves in the huge mass of data which have accumulated in the past decades concerning the pathophysiology of two major mental disorders, schizophrenia and depression. ::: ::: It is a fact that the gap between the restricted circle of researchers working in this area and the large population of clinicians dealing with patients worldwide has been constantly increasing over the years. The average psychiatrist does not follow the progress of biological research with the same attention and confidence as the average clinician in the other branches of medicine. He either does not believe at all in that research, or does not expect that research to produce in the near future anything which may be of practical utility for his daily practice. Furthermore, he does not perceive the gradual accumulation of “evidence” as an indication of a continuing increase of “knowledge”, but rather as a sign of uncertainty and confusion. ::: ::: Is there anything, in the mass of biological data on schizophrenia and depression, which promises to become in the foreseeable future of any usefulness for everyday clinical practice? This is the question that the two Forums were expected to address, and the reader will see that the views expressed by the participants are quite different. They can be schematically reconducted to two main positions. ::: ::: The first position is that we are on the wrong track. This may be because the current characterization of the phenotypes (schizophrenia and depression) is inadequate, or because these conditions are very heterogeneous and largely overlap with each other, or because the biological level at which our current research efforts are being displayed (e.g., neuronal circuits, neurotransmitters) is very far from the one at which a convincing explanation of the disorders is likely to be found; or because brain dysfunctions can only account for a vulnerability to something which emerges at the interface between the brain and the world of interpersonal relationships, so that many different brain dysfunctions can be found in patients with schizophrenia or with depression, but the essence of these disorders cannot be delineated at the biological level. ::: ::: The second position is that we are on the right track, but we are dealing with conditions that are very complex, much more than those which are the subject of investigation of the other branches of medicine. The functions which are perturbed in schizophrenia and in depression are the most complex of human beings. Most of them involve an interaction between such a composite organ as the brain and the even more composite world of interpersonal relationships in which all of us are immersed. It is not surprising that research is progressing so slowly and that many alternative avenues are being pursued. Our current technology and modeling may not be adequate to address that complexity, and the future may bring out very important advances in this respect. Furthermore, we cannot expect a single model to explain all the constituents of the complex picture of schizophrenia or depression: “decomposing” these disorders in their various elements may be very helpful. On the other hand, the many models which are currently proposed should not be regarded as mutually exclusive: they may address different levels of the complexity and may turn out to be consistent with each other. ::: ::: Time will tell whether it is possible to explain complex mental disorders at the biological level, or whether biological dysfunctions can only account for various pathways of vulnerability to those disorders, whose identity emerges at a higher level. Meanwhile, we hope these Forums will offer to our readers a clear and accessible picture of the ongoing research and of the hypotheses which are currently put forward.


title: Determining Functional Groups of Commercially Available Ink-Jet Printing Reactive Dyes Using Infrared Spectroscopy

abstract: An infrared (IR) spectroscopic technique was used to determine the possible functional groups and chemical compounds present in commercially available ink-jet printing reactive dyes of four primary colours, i.e. Cyan, Magenta, Yellow, and Black. Although other instrumental analytical methods can help to determine the actual chemical composition of reactive dyes, the IR technique alone can still provide important structural information about the commercially available reactive dyes. Experimental results revealed that the reactive dyes under determination contained the same functional groups and chemical compounds as the reference reactive dyes.


title: RFID Antennas for Body-Area Applications: From Wearables to Implants

abstract: Radio-frequency identification (RFID) technology has recently attracted significant interest in the realm of body-area applications, including both wearables and implants. The presence of the human body in close proximity to the RFID device creates several challenges in terms of design, fabrication, and testing, while also ushering in a whole new realm of opportunities for health care and other body-area applications. With these factors in mind, this article provides a holistic and critical review of design challenges associated with body-area RFID technologies, including operation frequencies, influence of the surrounding biological tissues, antenna design and miniaturization, and conformance to international safety guidelines. Concurrently, a number of fabrication methods are discussed for realizing flexible, conformal, and robust RFID device prototypes. The article concludes by reviewing transformative RFID-based solutions for wearable and implantable applications and discussing the future opportunities and challenges raised. Notably, this is the first time that a comprehensive review has been presented in the area of RFID antennas for body-area applications, addressing challenges specific to on-/in-body RFID operation and spanning a wide range of aspects that include design, fabrication, testing, and, eventually, applications and future directions. As such, the utmost aim of this article is to be a unique point of reference for experts and nonexperts in the field.


Intelligent Monitoring, Control, and Security of Critical Infrastructure Systems


title: Making friends on the fly: Cooperating with new teammates

abstract: Robots are being deployed in an increasing variety of environments for longer periods of time. As the number of robots grows, they will increasingly need to interact with other robots. Additionally, the number of companies and research laboratories producing these robots is increasing, leading to the situation where these robots may not share a common communication or coordination protocol. While standards for coordination and communication may be created, we expect that robots will need to additionally reason intelligently about their teammates with limited information. This problem motivates the area of ad hoc teamwork in which an agent may potentially cooperate with a variety of teammates in order to achieve a shared goal. This article focuses on a limited version of the ad hoc teamwork problem in which an agent knows the environmental dynamics and has had past experiences with other teammates, though these experiences may not be representative of the current teammates. To tackle this problem, this article introduces a new general-purpose algorithm, PLASTIC, that reuses knowledge learned from previous teammates or provided by experts to quickly adapt to new teammates. This algorithm is instantiated in two forms: 1) PLASTIC-Model - which builds models of previous teammates' behaviors and plans behaviors online using these models and 2) PLASTIC-Policy - which learns policies for cooperating with previous teammates and selects among these policies online. We evaluate PLASTIC on two benchmark tasks: the pursuit domain and robot soccer in the RoboCup 2D simulation domain. Recognizing that a key requirement of ad hoc teamwork is adaptability to previously unseen agents, the tests use more than 40 previously unknown teams on the first task and 7 previously unknown teams on the second. While PLASTIC assumes that there is some degree of similarity between the current and past teammates' behaviors, no steps are taken in the experimental setup to make sure this assumption holds. The teammates were created by a variety of independent developers and were not designed to share any similarities. Nonetheless, the results show that PLASTIC was able to identify and exploit similarities between its current and past teammates' behaviors, allowing it to quickly adapt to new teammates.


title: TL-OSR, a low cost mobile robot with open-source technology based on sensor fusion

abstract: This paper presents the state-of-the-art for building a low cost mobile robot with open-source technology based on sensor fusion. Designing open-source educational robots has a great impact on empowering engineers and those interested in mobile robots. In the last decade, different robots have been designed and built for this purpose. In this project, with the help of sensors available on a 9-axis open-source IMU and a robot based on open-source hardware and control software, a graphical user interface has been implemented to view the condition of motion variables in a real-time manner and combine sensor data. Moreover, by implementing different filter programs, such as low-pass filter, complementary filter and by using KF implementation and deliberation this mobile robot becomes suitable for improving the accuracy of the robot in extracting parameters required for localization, mapping and robot navigation. Features of the developed robot, the so-called TL-OSR, can be regarded as: using open-source hardware and software with real-time performance, low price equipment, presenting a solution based on sensor fusion for better accuracy in robot navigation and using different types of sensors. This robot can be used as an suitable open-source platform as a new class of mobile robots for educational and research purposes in mechatronics and robotics.


title: Clinic Overbooking to Improve Patient Access and Increase Provider Productivity

abstract: The problem of patient no-shows (patients who do not arrive for scheduled appointments) is significant in many health care settings, where no-show rates can vary widely. No-shows reduce provider productivity and clinic efficiency, increase health care costs, and limit the ability of a clinic to serve its client population by reducing its effective capacity. In this article, we examine the problem of no-shows and propose appointment overbooking as one means of reducing the negative impact of no-shows. We find that patient access and provider productivity are significantly improved with overbooking, but that overbooking causes increases in both patient wait times and provider overtime. We develop a new clinic utility function to capture the trade-offs between these benefits and costs, and we show that the relative values that a clinic assigns to serving additional patients, minimizing patient waiting times, and minimizing clinic overtime will determine whether overbooking is warranted. From the results of a series of simulation experiments, we determine that overbooking provides greater utility when clinics serve larger numbers of patients, no-show rates are higher, and service variability is lower. Even with highly variable service times, many clinics will achieve positive net results with overbooking. Our analysis provides valuable guidance to clinic administrators about the use of appointment overbooking to improve patient access, provider productivity, and overall clinic performance.


title: Predicting smartphone brand loyalty: Consumer value and consumer-brand identification perspectives

abstract: We explore the determinants of smartphone brand loyalty based on the perspectives of consumer value and consumer-brand identification.Functional value, emotional value, social value, and brand identification have a positive influence on smartphone brand loyalty.Age enhances the emotional value-brand loyalty and social value-brand loyalty linkages but weakens the brand identification-brand loyalty relationship. With the growth and competition of the smartphone industry, developing a better understanding of what drives consumers' loyalty to smartphone brands has become an important issue for academics and practitioners. This study hypothesizes four determinants of smartphone brand loyalty based on the perspectives of consumer value and consumer-brand identification. Furthermore, this study also explores the moderating effects of age and gender differences on the determination process of smartphone brand loyalty. Data collected from 157 respondents was tested against the research model using a partial least squares (PLS) approach. The results indicate that functional value, emotional value, social value, and brand identification have a positive influence on smartphone brand loyalty. Of the two moderators, results show that age enhances the emotional value-brand loyalty and social value-brand loyalty linkages but weakens the brand identification-brand loyalty relationship. However, gender does not play a moderating role in the determination of smartphone brand loyalty. The results of this study provide several important theoretical and practical implications for smartphone brand management.


title: Broadband circularly polarized rectangular loop antenna with impedance matching

abstract: A broadband circularly polarized rectangular loop antenna is developed. The broadband performance is achieved by adding a pair of parasitic loops inside the rectangular loop. Good impedance matching is obtained by introducing a broadband balun. The proposed rectangular loop has a bandwidth of 46% for axial ratioles2dB and 50% for VSWRles2 with a gain of around 8dBi


title: The use of Thompson sampling to increase estimation precision.

abstract: In this article, we consider a sequential sampling scheme for efficient estimation of the difference between the means of two independent treatments when the population variances are unequal across groups. The sampling scheme proposed is based on a solution to bandit problems called Thompson sampling. While this approach is most often used to maximize the cumulative payoff over competing treatments, we show that the same method can also be used to balance exploration and exploitation when the aim of the experimenter is to efficiently increase estimation precision. We introduce this novel design optimization method and, by simulation, show its effectiveness.


title: Machine-to-Machine (M2M) communications: A survey

abstract: Machine-to-Machine (M2M) communication is a promising technology for next generation communication systems. This communication paradigm facilitates ubiquitous communications with full mechanical automation, where a large number of intelligent devices connected by wired/wireless links, interact with each other without direct human intervention. As a result, M2M communication finds applications in wide areas such as smart grids, e-healthcare, home area networks, intelligent transportation systems, environmental monitoring, smart cities, and industrial automation. However, distinctive features in M2M communications form different challenges from those in human-to-human communications. These challenges need to be addressed, or otherwise it is not easy for this paradigm to gain trust of people. To understand M2M communications deeply, this paper presents a comprehensive review of M2M communication technology in terms of its system model architecture proposed by different standards developing organizations. This mainly includes 3GPP, ETSI, and oneM2M. Further, we have investigated distinctive features of various M2M applications and their supporting attributes, the M2M data traffic and their characterization, various M2M standardization bodies and their unique tasks, and potential M2M communication challenges and their proposed state-of-the-art solutions, followed by future research directions.


title: Low Data Drug Discovery with One-Shot Learning

abstract: Recent advances in machine learning have made significant contributions to drug discovery. Deep neural networks in particular have been demonstrated to provide significant boosts in predictive power when inferring the properties and activities of small-molecule compounds (Ma, J. et al. J. Chem. Inf. Model. 2015, 55, 263–274). However, the applicability of these techniques has been limited by the requirement for large amounts of training data. In this work, we demonstrate how one-shot learning can be used to significantly lower the amounts of data required to make meaningful predictions in drug discovery applications. We introduce a new architecture, the iterative refinement long short-term memory, that, when combined with graph convolutional neural networks, significantly improves learning of meaningful distance metrics over small-molecules. We open source all models introduced in this work as part of DeepChem, an open-source framework for deep-learning in drug discovery (Ramsundar, B. deepchem.io. https:...


title: A spark gap model for LTspice and similar circuit simulation software

abstract: In the past, the simulation of pulsed power circuits used specifically designed programs such as Sandia's SCREAMER, NRL's BERTHA, or the industry developed TL Code. These codes incorporated specialized models for many pulsed power components which captured key manifestations of the physical phenomena. Now, the simulation of pulsed power circuits is likely to be accomplished with the ubiquitous SPICE-based software. Of the available software, LTspice, a program developed by semiconductor manufacturer Linear Technology Corp, has become very prevalent due to its ease of use, continuous improvement, and free availability. However, LTspice — like all SPICE programs — does not include realistic models for key pulsed power circuit devices — including the spark gap switch. While simple switch models do exist in LTspice and other SPICE programs, these can only crudely approximate the behavior of a spark gap. This effort focuses on developing an LTspice circuit model for a gas-filled spark gap switch that is physically realistic while being simple enough as to permit simulations to run in reasonable times on typical personal computers. Efforts are made to create a model that is portable between different SPICE programs with minimal modification.


title: Application of the Finite Element Method in Steady-State Diffusion Equation

abstract: Here, the steady-state neutron diffusion equation is solved to obtain the effective multiplication factor and the neutron flux distribution of a nuclear core reactor. Two different implementations are applied to this problem. In one implementation a classic formalism of finite difference method is used whereas in the other one, a more modern formalism of finite element method which mesh is automatically generated using an error indicator based in the continuity of the neutron current. The method is evaluated in two geometric core configurations: the one-dimensional and the two-dimensional case. In the second case the core homogenization is performed in two ways: in a fuel element level (macro cell) and in a rod element level (micro cell) using two groups of energy. The finite element results are compared with the ones generated by the finite difference method, taken as a reference. The case of one-dimensional slab core reactor, described for the BSS-6-A1 numerical benchmark, and the Ulchin1 two-dimensional core reactor had been tested. A good agreement between the methods can be verified.


title: Accessible photo album: enhancing the photo sharing experience for people with visual impairment

abstract: While a photograph is a visual artifact, studies reveal that a number of people with visual impairments are also interested in being able to share their memories and experiences with their sighted counterparts in the form of a photograph. We conducted an online survey to better understand the challenges faced by people with visual impairments in sharing and organizing photos, and reviewed existing tools and their limitations. Based on our analysis, we developed an accessible mobile application that enables a visually impaired user to capture photos along with audio recordings for the ambient sound and memo description and to browse through them eyes-free. Five visually impaired participants took part in a study in which they used our app to take photographs in naturalistic settings and to share them later with a sighted viewer. The participants were able to use our app to identify each photograph on their own during the photo sharing session, and reported high satisfaction in having been able to take the initiative during the process.


title: Towards a framework for attribute retrieval

abstract: In this paper, we propose an attribute retrieval approach which extracts and ranks attributes from HTML tables. We distinguish between class attribute retrieval and instance attribute retrieval. On one hand, given an instance (e.g. University of Strathclyde) we retrieve from the Web its attributes (e.g. principal, location, number of students). On the other hand, given a class (e.g. universities) represented by a set of instances, we retrieve common attributes of its instances. Furthermore, we show we can reinforce instance attribute retrieval if similar instances are available. Our approach uses HTML tables which are probably the largest source for attribute retrieval. Three recall oriented filters are applied over tables to check the following three properties: (i) is the table relational, (ii) has the table a header, and (iii) the conformity of its attributes and values. Candidate attributes are extracted from tables and ranked with a combination of relevance features. Our approach is shown to have a high recall and a reasonable precision. Moreover, it outperforms state of the art techniques.


title: CMOS, Circuit Design, Layout, and Simulation

abstract: The Third Edition of CMOS Circuit Design, Layout, and Simulation continues to cover the practical design of both analog and digital integrated circuits, offering a vital, contemporary view of a wide range of analog/digital circuit blocks including: phase-locked-loops, delta-sigma sensing circuits, voltage/current references, op-amps, the design of data converters, and much more. Regardless of one's integrated circuit (IC) design skill level, this book allows readers to experience both the theory behind, and the hands-on implementation of, complementary metal oxide semiconductor (CMOS) IC design via detailed derivations, discussions, and hundreds of design, layout, and simulation examples.


title: Machine Learning Methods for Disease Prediction with Claims Data

abstract: One of the primary challenges of healthcare delivery is aggregating disparate, asynchronous data sources into meaningful indicators of individual health. We combine natural language word embedding and network modeling techniques to learn meaningful representations of medical concepts by using the weighted network adjacency matrix in the GloVe algorithm, which we call Code2Vec. We demonstrate that using our learned embeddings improve neural network performance for disease prediction. However, we also demonstrate that popular deep learning models for disease prediction are not meaningfully better than simpler, more interpretable classifiers such as XGBoost. Additionally, our work adds to the current literature by providing a comprehensive survey of various machine learning algorithms on disease prediction tasks.


title: You Are What Apps You Use: Demographic Prediction Based on User's Apps

abstract: Understanding the demographics of app users is crucial, for example, for app developers, who wish to target their advertisements more effectively. Our work addresses this need by studying the predictability of user demographics based on the list of a user's apps which is readily available to many app developers. We extend previous work on the problem on three frontiers: (1) We predict new demographics (age, race, and income) and analyze the most informative apps for four demographic attributes included in our analysis. The most predictable attribute is gender (82.3 % accuracy), whereas the hardest to predict is income (60.3 % accuracy). (2) We compare several dimensionality reduction methods for high-dimensional app data, finding out that an unsupervised method yields superior results compared to aggregating the apps at the app category level, but the best results are obtained simply by the raw list of apps. (3) We look into the effect of the training set size and the number of apps on the predictability and show that both of these factors have a large impact on the prediction accuracy. The predictability increases, or in other words, a user's privacy decreases, the more apps the user has used, but somewhat surprisingly, after 100 apps, the prediction accuracy starts to decrease.


title: Gibberellin-regulated phosphatidylcholine biosynthesis in suspension cultures of Apium graveolens

abstract: Recent studies with mammalian tissues have shown thatphosphatidylcholine (PC) plays a key role in signal transduction mechanismsinvolved in cell proliferation and differentiation. This investigation attemptsto show if PC has a role in gibberellin-stimulated plant signal transduction.With respect to cell differentiation, GA3-treatment caused an overallincrease in the elongation ratio exhibited by the cells. The effects ofGA3 on the biosynthetic route of PC were investigated. Carbon fluxthrough the CDP-base pathway was significantly reduced upon GA3treatment. Whereas, radiolabelling studies with tritiated methionine showed aGA3 concentration-dependent increase in the flux of intermediatesthrough the methyltransferase pathway indicating a possible link betweenmethyltransferase-derived PC and cell differentiation.


title: Performance analysis of a cylindric dielectric lens antenna for 77 GHz automotive radar

abstract: This paper introduces a novel dielectric lens antenna configuration for automotive radar applications. A cylindric lens fed by a uniform linear array (ULA) accomplishes elevation beam shaping and simultaneously allows for the application of array signal processing in the azimuth plane. Measured and simulated radiation patterns show a superior performance compared to a conventional antenna concept without lens.


title: Graph evolution: Densification and shrinking diameters

abstract: How do real graphs evolve over time? What are ``normal'' growth patterns in social, technological, and information networks? Many studies have discovered patterns in static graphs, identifying properties in a single snapshot of a large network, or in a very small number of snapshots; these include heavy tails for in- and out-degree distributions, communities, small-world phenomena, and others. However, given the lack of information about network evolution over long periods, it has been hard to convert these findings into statements about trends over time. ::: Here we study a wide range of real graphs, and we observe some surprising phenomena. First, most of these graphs densify over time, with the number of edges growing super-linearly in the number of nodes. Second, the average distance between nodes often shrinks over time, in contrast to the conventional wisdom that such distance parameters should increase slowly as a function of the number of nodes (like O(log n) or O(log(log n)). ::: Existing graph generation models do not exhibit these types of behavior, even at a qualitative level. We provide a new graph generator, based on a ``forest fire'' spreading process, that has a simple, intuitive justification, requires very few parameters (like the ``flammability'' of nodes), and produces graphs exhibiting the full range of properties observed both in prior work and in the present study. ::: We also notice that the ``forest fire'' model exhibits a sharp transition between sparse graphs and graphs that are densifying. Graphs with decreasing distance between the nodes are generated around this transition point.


title: Effectiveness of computer-generated (virtual reality) graded exposure in the treatment of acrophobia

abstract: Objective: The authors' goal was to examine the efficacy of computer-generated (virtual reality) graded exposure in the treatment of acrophobia (fear of heights). Method: Twenty college students with acrophobia were randomly assigned to virtual reality graded exposure treatment (N=12) or to a waiting-list comparison group (N=8). Seventeen students completed the study. Sessions were conducted individually over 8 weeks. Outcome was assessed by using measures of anxiety, avoidance, attitudes, and distress associated with exposure to heights before and after treatment. Results: Significant differences between the students who completed the virtual reality treatment (N=10) and those on the waiting list (N=7) were found on all measures. The treatment group was significantly improved after 8 weeks, but the comparison group was unchanged. Conclusions: The authors conclude that treatment with virtual reality graded exposure was successful in reducing fear of heights


title: Dynamic MRI reconstruction using low rank plus sparse tensor decomposition

abstract: In this paper, we introduce a multi-dimensional approach to the problem of reconstruction of MR image sequences that are highly undersampled in k-space. By formulating the reconstruction as a high-order low-rank plus sparse tensor decomposition problem, we propose an efficient numerical algorithm based on the alternating direction method of multipliers (ADMM) to solve the optimization. Through extensive experimental results we show that our proposed method achieves superior reconstruction quality, compared to the state-of-the-art reconstruction methods.


title: THE RFID TECHNOLOGY AND ITS CURRENT APPLICATIONS

abstract: The paper gives an overview of the current state of the art in the radio frequency identification (RFID) technology. Aside from a brief introduction to the principles of the technology, a survey is given on major classes of RFID tags and readers, commonly used frequencies and identifier systems, current and envisaged fields of application, as well as advantages, concerns and limitations of use.


title: A 32 kb 10T Sub-Threshold SRAM Array With Bit-Interleaving and Differential Read Scheme in 90 nm CMOS

abstract: Ultra-low voltage operation of memory cells has become a topic of much interest due to its applications in very low energy computing and communications. However, due to parameter variations in scaled technologies, stable operation of SRAMs is critical for the success of low-voltage SRAMs. It has been shown that conventional 6T SRAMs fail to achieve reliable subthreshold operation. Hence, researchers have considered different configuration SRAMs for subthreshold operations having single-ended 8T or 10T bit-cells for improved stability. While these bit-cells improve SRAM stability in subthreshold region significantly, the single-ended sensing methods suffer from reduced bit-line swing due to bit-line leakage noise. In addition, efficient bit-interleaving in column may not be possible and hence, the multiple-bit soft errors can be a real issue. In this paper, we propose a differential 10T bit-cell that effectively separates read and write operations, thereby achieving high cell stability. The proposed bit-cell also provides efficient bit-interleaving structure to achieve soft-error tolerance with conventional Error Correcting Codes (ECC). For read access, we employ dynamic DCVSL scheme to compensate bitline leakage noise, thereby improving bitline swing. To verify the proposed techniques, a 32 kb array of the proposed 10T bit-cell is fabricated in 90 nm CMOS technology. The hardware measurement results demonstrate that this bit-cell array successfully operates down to 160 mV. For leakage power comparison, we also fabricated 49 kb arrays of the 6T and the proposed 10T bit-cells. Measurement results show that the leakage power of the proposed bit-cell is close to that of the 6T (between 0.96x and 1.22x of 6T).


title: Classification of annotation semirings over containment of conjunctive queries

abstract: We study the problem of query containment of conjunctive queries over annotated databases. Annotations are typically attached to tuples and represent metadata, such as probability, multiplicity, comments, or provenance. It is usually assumed that annotations are drawn from a commutative semiring. Such databases pose new challenges in query optimization, since many related fundamental tasks, such as query containment, have to be reconsidered in the presence of propagation of annotations. We axiomatize several classes of semirings for each of which containment of conjunctive queries is equivalent to existence of a particular type of homomorphism. For each of these types, we also specify all semirings for which existence of a corresponding homomorphism is a sufficient (or necessary) condition for the containment. We develop new decision procedures for containment for some semirings which are not in any of these classes. This generalizes and systematizes previous approaches.


title: On value and value co-creation : A service systems and service logic perspective

abstract: Summary The creation of value is the core purpose and central process of economic exchange. Traditional models of value creation focus on the firm's output and price. We present an alternative perspective, one representing the intersection of two growing streams of thought, service science and service-dominant (S-D) logic. We take the view that (1) service, the application of competences (such as knowledge and skills) by one party for the benefit of another, is the underlying basis of exchange; (2) the proper unit of analysis for service-for-service exchange is the service system, which is a configuration of resources (including people, information, and technology) connected to other systems by value propositions; and (3) service science is the study of service systems and of the co-creation of value within complex configurations of resources. We argue that value is fundamentally derived and determined in use - the integration and application of resources in a specific context - rather than in exchange - embedded in firm output and captured by price. Service systems interact through mutual service exchange relationships, improving the adaptability and survivability of all service systems engaged in exchange, by allowing integration of resources that are mutually beneficial. This argument has implications for advancing service science by identifying research questions regarding configurations and processes of value co-creation and measurements of value-in-use, and by developing its ties with economics and other service-oriented disciplines.


title: How robust are prediction effects in language comprehension?: Failure to replicate article-elicited N400 effects

abstract: ABSTRACTCurrent psycholinguistic theory proffers prediction as a central, explanatory mechanism in language processing. However, widely-replicated prediction effects may not mean that prediction is necessary in language processing. As a case in point, C. D. Martin et al. [2013. Bilinguals reading in their second language do not predict upcoming words as native readers do. Journal of Memory and Language, 69(4), 574–588. doi:10.1016/j.jml.2013.08.001] reported ERP evidence for prediction in native- but not in non-native speakers. Articles mismatching an expected noun elicited larger negativity in the N400 time window compared to articles matching the expected noun in native speakers only. We attempted to replicate these findings, but found no evidence for prediction irrespective of language nativeness. We argue that pre-activation of phonological form of upcoming nouns, as evidenced in article-elicited effects, may not be a robust phenomenon. A view of prediction as a necessary computation in language compr...


title: An effective log mining approach for database intrusion detection

abstract: Organizations spend a significant amount of resources securing their servers and network perimeters. However, these mechanisms are not sufficient for protecting databases. In this paper, we present a new technique for identifying malicious database transactions. Compared to many existing approaches which profile SQL query structures and database user activities to detect intrusions, the novelty of this approach is the automatic discovery and use of essential data dependencies, namely, multi-dimensional and multi-level data dependencies, for identifying anomalous database transactions. Since essential data dependencies reflect semantic relationships among data items and are less likely to change than SQL query structures or database user behaviors, they are ideal for profiling data correlations for identifying malicious database activities.1


title: Fundamental instability: Why telecom is becoming a cyclical and oligopolistic industry

abstract: Abstract This essay analyzes the long-term lessons of the recent upturn and downturn in the telecommunications industry. It concludes that volatility and cyclicality will be an inherent part of the telecom sector in the future. To deal with such instabilities, companies and investors seek consolidation and cooperation. Government, too, is likely to stress stability more than before. Hence, an oligopoly is likely to emerge as the equilibrium market structure, and with it some regulation.


title: Modeling Survival Data Extending The Cox Model

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title: Downsides of robot-assisted laparoscopic radical prostatectomy: limitations and complications.

abstract: CONTEXT ::: Robot-assisted laparoscopic radical prostatectomy (RALP) using the da Vinci Surgical System (Intuitive Surgical, Sunnyvale, CA, USA) is now in widespread use for the management of localised prostate cancer (PCa). Many reports of the safety and efficacy of this procedure have been published. However, there are few specific reports of the limitations and complications of RALP. ::: ::: ::: OBJECTIVE ::: The primary purpose of this review is to ascertain the downsides of RALP by focusing on complications and limitations of this approach. ::: ::: ::: EVIDENCE ACQUISITION ::: A Medline search of the English-language literature was performed to identify all papers published since 2001 relating to RALP. Papers providing data on technical failures, complications, learning curve, or other downsides of RALP were considered. Of 412 papers identified, 68 were selected for review based on their relevance to the objective of this paper. ::: ::: ::: EVIDENCE SYNTHESIS ::: RALP has the following principal downsides: (1) device failure occurs in 0.2-0.4% of cases; (2) assessment of functional outcome is unsatisfactory because of nonstandardised assessment techniques; (3) overall complication rates of RALP are low, although higher rates are noted when complications are reported using a standardised system; (4) long-term oncologic data and data on high-risk PCa are limited; (5) a steep learning curve exists, and although acceptable operative times can be achieved in <20 cases, positive surgical margin (PSM) rates may require experience with >80 cases before a plateau is achieved; (6) robotic assistance does not reduce the difficulty associated with obese patients and those with large prostates, middle lobes, or previous surgery, in whom outcomes are less satisfactory than in patients without such factors; (7) economic barriers prevent uniform dissemination of robotic technology. ::: ::: ::: CONCLUSIONS ::: Many of the downsides of RALP identified in this paper can be addressed with longer-term data and more widespread adoption of standardised reporting measures. The significant learning curve should not be understated, and the expense of this technology continues to restrict access for many patients.


title: Deep neural network architecture for sentiment analysis and emotion identification of Twitter messages

abstract: In the work presented in this paper, we showcase a deep learning system for sentiment analysis and emotion identification in Twitter messages. The system consists of a convolutional neural network used for extracting features from textual data and a classifier for which we experiment with several different classifying algorithms. We train the network using pre-trained word embeddings obtained by unsupervised learning on large text corpora and compare the effectiveness of the different word vectors for this task. We evaluate our system on 3-class sentiment analysis with datasets provided by the Sentiment analysis in Twitter task from the SemEval competition. Additionally, we explore the effectiveness of our approach for emotion identification, by using an automatically annotated dataset with 7 distinct emotions. Our architecture achieves comparable performances to state-of-the-art techniques in the field of sentiment analysis and improves results in the field of emotion identification on the test we use in our evaluation. Moreover, the paper presents several use case scenarios, depicting real-world usage of our architecture.


How E-Commerce Improves the Brick and Mortar Shopping Experience: Explaining the Post-2002 Slowdown


title: Credit card fraud detection using Naïve Bayes model based and KNN classifier

abstract: Machine Learning is the technology, in which algorithms which are capable of learning from previous cases and past experiences are designed. It is implemented using various algorithms which reiterate over the same data repeatedly to analyze the pattern of data. The techniques of data mining are no far behind and are widely used to extract data from large databases to discover some patterns making decisions. This paper presents the Naive Bayes improved K-Nearest Neighbor method (NBKNN) for Fraud Detection of Credit Card. Experimental results illustrate that both classifiers work differently for the same dataset. The purpose is to enhance the accuracy and enhance the flexibility of the algorithm.


title: Fast Singular Value Thresholding without Singular Value Decomposition

abstract: We are interested in solving the following minimization problem Dτ (Y ) := arg min X∈Rm×n 1 2 ∥Y −X∥F + τ∥X∥∗, where Y ∈ Rm×n is a given matrix, and ∥ ⋅ ∥F is the Frobenius norm and ∥ ⋅ ∥∗ the nuclear norm. This problem serves as a basic subroutine in many popular numerical schemes for nuclear norm minimization problems, which arise from low rank matrix recovery such as matrix completion. As Dτ (Y ) has an explicit expression which shrinks the singular values of Y and keeps the singular vectors, Dτ is referred to singular value thresholding (SVT) operator in the literature. Conventional approaches for Dτ (Y ) first find the singular value decomposition (SVD) of Y and then shrink the singular values. However, such approaches are time consuming under some circumstances, especially when the rank of Dτ (Y ) is not low compared to the matrix dimension or is completely unpredictable. In this paper, we propose a fast algorithm for directly computing Dτ (Y ) without using SVDs. Numerical experiments show that the proposed algorithm is much more efficient than the approach using the full SVD.


title: DeepSecure: Scalable Provably-Secure Deep Learning

abstract: This paper presents DeepSecure, the an scalable and provably secure Deep Learning (DL) framework that is built upon automated design, efficient logic synthesis, and optimization methodologies. DeepSecure targets scenarios in which neither of the involved parties including the cloud servers that hold the DL model parameters or the delegating clients who own the data is willing to reveal their information. Our framework is the first to empower accurate and scalable DL analysis of data generated by distributed clients without sacrificing the security to maintain efficiency. The secure DL computation in DeepSecure is performed using Yao's Garbled Circuit (GC) protocol. We devise GC-optimized realization of various components used in DL. Our optimized implementation achieves up to 58-fold higher throughput per sample compared with the best prior solution. In addition to the optimized GC realization, we introduce a set of novel low-overhead pre-processing techniques which further reduce the GC overall runtime in the context of DL. Our extensive evaluations demonstrate up to two orders-of-magnitude additional runtime improvement achieved as a result of our pre-processing methodology.


title: Studying the implementation of iterative impedance control for assistive hand rehabilitation using an exoskeleton

abstract: A positive training synergy can be obtained when two individuals attempt to learn the same motor task while mechanically coupled to one another. In this paper, we have studied how mimicking this interaction through impedance control can be exploited to improve assistance delivered by hand exoskeleton devices during rehabilitation. In this context, the machine and user take complementary roles akin to two coupled individuals. We present the derivation of a dynamic model of the human hand for the purpose of controller development for new hand exoskeleton platforms. Using this model, we have simulated the behavior of an iterative impedance controller programmed for rehabilitative training. The controller interacts with cylindrical objects to be grasped by means of an inverse kinematic mapping and tuning of mechanical impedance characteristic of the finger joints. Through fusion of concepts from motor control theory, muscle impedance and task oriented control, the controller is capable of iteratively learn to accomplish simple tasks involving grasping and lifting while cooperating with a user. The controller is also capable of adapting to more complex dynamics for more dexterous tasks, such as pulling on a hand-bar or loosening the cap of a jar. We believe the human-robot synergy established in this investigation has benefits to therapy. It can be combined with a broad range of training exercises and represents an incremental step towards mimicking natural human motor responses.


title: The Slow Oscillation in Cortical and Thalamic Networks: Mechanisms and Functions

abstract: During even the most quiescent behavioral periods, the cortex and thalamus express rich spontaneous activity in the form of slow (<1 Hz), synchronous network state transitions. Throughout this so-called slow oscillation, cortical and thalamic neurons fluctuate between periods of intense synaptic activity (Up states) and almost complete silence (Down states). The two decades since the original characterization of the slow oscillation in the cortex and thalamus have seen considerable advances in deciphering the cellular and network mechanisms associated with this pervasive phenomenon. There are, nevertheless, many questions regarding the slow oscillation that await more thorough illumination, particularly the mechanisms by which Up states initiate and terminate, the functional role of the rhythmic activity cycles in unconscious or minimally conscious states, and the precise relation between Up states and the activated states associated with waking behavior. Given the substantial advances in multineuronal recording and imaging methods in both in vivo and in vitro preparations, the time is ripe to take stock of our current understanding of the slow oscillation and pave the way for future investigations of its mechanisms and functions. My aim in this Review is to provide a comprehensive account of the mechanisms and functions of the slow oscillation, and to suggest avenues for further exploration.


title: Kernel-level ARINC 653 partitioning for Linux

abstract: The Integrated Modular Avionics (IMA) architecture has been suggested for the next-generation avionics systems. ARINC 653 is the standard for application programming interfaces (APIs) of avionics software for IMA architecture. There are several researches on design and implementation of ARINC 653 but legacy operating systems have not been considered much for a base operating system of ARINC 653. Though the legacy operating systems may not be initially developed for avionics systems, some of them including Linux recently show high potential of providing software platform for avionics systems. In this paper, we suggest a kernel-level design to support partitioning and hierarchical real-time scheduling of ARINC 653 for Linux. We believe that our suggestion can provide a very valuable reference for extending an existing operating system for ARINC 653 especially due to the complexity of the Linux kernel. We show that the overhead and jitter of the proposed design is significantly low compared with a user-level design.


title: miRWalk - Database: Prediction of possible miRNA binding sites by walking the genes of three genomes

abstract: MicroRNAs are small, non-coding RNA molecules that can complementarily bind to the mRNA 3'-UTR region to regulate the gene expression by transcriptional repression or induction of mRNA degradation. Increasing evidence suggests a new mechanism by which miRNAs may regulate target gene expression by binding in promoter and amino acid coding regions. Most of the existing databases on miRNAs are restricted to mRNA 3'-UTR region. To address this issue, we present miRWalk, a comprehensive database on miRNAs, which hosts predicted as well as validated miRNA binding sites, information on all known genes of human, mouse and rat. All mRNAs, mitochondrial genes and 10kb upstream flanking regions of all known genes of human, mouse and rat were analyzed by using a newly developed algorithm named 'miRWalk' as well as with eight already established programs for putative miRNA binding sites. An automated and extensive text-mining search was performed on PubMed database to extract validated information on miRNAs. Combined information was put into a MySQL database. miRWalk presents predicted and validated information on miRNA-target interaction. Such a resource enables researchers to validate new targets of miRNA not only on 3'-UTR, but also on the other regions of all known genes. The 'Validated Target module' is updated every month and the 'Predicted Target module' is updated every 6months. miRWalk is freely available at http://mirwalk.uni-hd.de/.


title: Coded rolling shutter photography: Flexible space-time sampling

abstract: We propose a novel readout architecture called coded rolling shutter for complementary metal-oxide semiconductor (CMOS) image sensors. Rolling shutter has traditionally been considered as a disadvantage to image quality since it often introduces skew artifact. In this paper, we show that by controlling the readout timing and the exposure length for each row, the row-wise exposure discrepancy in rolling shutter can be exploited to flexibly sample the 3D space-time volume of scene appearance, and can thus be advantageous for computational photography. The required controls can be readily implemented in standard CMOS sensors by altering the logic of the control unit. We propose several coding schemes and applications: (1) coded readout allows us to better sample time dimension for high-speed photography and optical flow based applications; and (2) row-wise control enables capturing motion-blur free high dynamic range images from a single shot. While a prototype chip is currently in development, we demonstrate the benefits of coded rolling shutter via simulation using images of real scenes.


title: Applying support vector machines to imbalanced datasets

abstract: Support Vector Machines (SVM) have been extensively studied and have shown remarkable success in many applications. However the success of SVM is very limited when it is applied to the problem of learning from imbalanced datasets in which negative instances heavily outnumber the positive instances (e.g. in gene profiling and detecting credit card fraud). This paper discusses the factors behind this failure and explains why the common strategy of undersampling the training data may not be the best choice for SVM. We then propose an algorithm for overcoming these problems which is based on a variant of the SMOTE algorithm by Chawla et al, combined with Veropoulos et al's different error costs algorithm. We compare the performance of our algorithm against these two algorithms, along with undersampling and regular SVM and show that our algorithm outperforms all of them.


title: Modelling flow enhancement in nanochannels: Viscosity and slippage

abstract: With a large number of experimental and modelling papers reporting higher than expected liquid flow rates in both hydrophobic and hydrophilic nanochannels published in the last few years, there is a need to develop a coherent theoretical framework to explain these phenomena. In this work we will introduce a complete modelling and present a comparison between experimental data and predicted flows, showing good agreement.


Shaping Global Classrooms.


title: Better cross company defect prediction

abstract: How can we find data for quality prediction? Early in the life cycle, projects may lack the data needed to build such predictors. Prior work assumed that relevant training data was found nearest to the local project. But is this the best approach? This paper introduces the Peters filter which is based on the following conjecture: When local data is scarce, more information exists in other projects. Accordingly, this filter selects training data via the structure of other projects. To assess the performance of the Peters filter, we compare it with two other approaches for quality prediction. Within-company learning and cross-company learning with the Burak filter (the state-of-the-art relevancy filter). This paper finds that: 1) within-company predictors are weak for small data-sets; 2) the Peters filter+cross-company builds better predictors than both within-company and the Burak filter+cross-company; and 3) the Peters filter builds 64% more useful predictors than both within-company and the Burak filter+cross-company approaches. Hence, we recommend the Peters filter for cross-company learning.


title: Design Issues and Challenges in Wireless Sensor Networks

abstract: Sensor Networks (WSNs) are composed self- organized wireless ad hoc networks which comprise of a large number of resource constrained sensor nodes. The major areas of research in WSN is going on hardware, and operating system of WSN, deployment, architecture, localization, synchronization, programming models, data aggregation and dissemination, database querying, architecture, middleware, quality of service and security. This paper study highlights ongoing research activities and issues that affect the design and performance of Wireless Sensor Network.


title: Cross-domain collaboration recommendation

abstract: Interdisciplinary collaborations have generated huge impact to society. However, it is often hard for researchers to establish such cross-domain collaborations. What are the patterns of cross-domain collaborations? How do those collaborations form? Can we predict this type of collaborations? Cross-domain collaborations exhibit very different patterns compared to traditional collaborations in the same domain: 1) sparse connection: cross-domain collaborations are rare; 2) complementary expertise: cross-domain collaborators often have different expertise and interest; 3) topic skewness: cross-domain collaboration topics are focused on a subset of topics. All these patterns violate fundamental assumptions of traditional recommendation systems. In this paper, we analyze the cross-domain collaboration data from research publications and confirm the above patterns. We propose the Cross-domain Topic Learning (CTL) model to address these challenges. For handling sparse connections, CTL consolidates the existing cross-domain collaborations through topic layers instead of at author layers, which alleviates the sparseness issue. For handling complementary expertise, CTL models topic distributions from source and target domains separately, as well as the correlation across domains. For handling topic skewness, CTL only models relevant topics to the cross-domain collaboration. We compare CTL with several baseline approaches on large publication datasets from different domains. CTL outperforms baselines significantly on multiple recommendation metrics. Beyond accurate recommendation performance, CTL is also insensitive to parameter tuning as confirmed in the sensitivity analysis.


title: Face Recognition: A Novel Multi-Level Taxonomy based Survey

abstract: In a world where security issues have been gaining growing importance, face recognition systems have attracted increasing attention in multiple application areas, ranging from forensics and surveillance to commerce and entertainment. To help to understand the landscape and abstraction levels relevant for face recognition systems, face recognition taxonomies allow a deeper dissection and comparison of the existing solutions. This study proposes a new, more encompassing and richer multi-level face recognition taxonomy, facilitating the organisation and categorisation of available and emerging face recognition solutions; this taxonomy may also guide researchers in the development of more efficient face recognition solutions. The proposed multi-level taxonomy considers levels related to the face structure, feature support, and feature extraction approach. Following the proposed taxonomy, a comprehensive survey of representative face recognition solutions is presented. This study concludes with a discussion on current algorithmic and application related challenges, which may define future research directions for face recognition.


title: Automated Essay Scoring Using Bayes' Theorum

abstract: Two Bayesian models for text classification from the information science field were extended and applied to student produced essays. Both models were calibrated using 462 essays with two score points. The calibrated systems were applied to 80 new, pre-scored essays with 40 essays in each score group. Manipulated variables included the two models; the use of words, phrases and arguments; two approaches to trimming; stemming; and the use of stopwords. While the text classification literature suggests the need to calibrate on thousands of cases per score group, accuracy of over 80% was achieved with the sparse dataset used in this study.


title: SPIRIT: Sequential Pattern Mining with Regular Expression Constraints

abstract: Discovering sequential patterns is an important problem in data mining with a host of application domains including medicine, telecommunications, and the World Wide Web. Conventional mining systems provide users with only a very restricted mechanism (based on minimum support) for specifying patterns of interest. In this paper, we propose the use of Regular Expressions (REs) as a flexible constraint specification tool that enables user-controlled focus to be incorporated into the pattern mining process. We develop a family of novel algorithms (termed SPIRIT ‐ Sequential Pattern mIning with Regular expressIon consTraints) for mining frequent sequential patterns that also satisfy user-specified RE constraints. The main distinguishing factor among the proposed schemes is the degree to which the RE constraints are enforced to prune the search space of patterns during computation. Our solutions provide valuable insights into the tradeoffs that arise when constraints that do not subscribe to nice properties (like anti-monotonicity) are integrated into the mining process. A quantitative exploration of these tradeoffs is conducted through an extensive experimental study on synthetic and real-life data sets.


Idealism versus Realism in Student and Practitioner Attitude toward Teaching


title: Lightly-supervised utterance-level emotion identification using latent topic modeling of multimodal words

abstract: Research on multimodal emotion recognition has drawn much attention recently in diverse disciplines. With the increasing amount of multimodal data, unsupervised or semi-supervised learning has become highly desirable to automatically discover expression of emotion patterns in behavioral data. We present a novel approach for multimodal emotion learning using only a small amount of labels. Our approach is hinging on probabilistic latent semantic analysis (pLSA) that defines the latent variable as the emotion class, motivated by the conceptualization that human emotion acts as a latent control variable that regulates the external behavior manifestations, such as through speech and body gesture. In our approach, we represent the audio-visual information in an utterance as a bag of multimodal words. To exploit the interrelation between speech and gesture modalities, we propose a canonical correlation analysis (CCA) based vocabulary of multimodal words. Our approach has achieved promising experimental results. We have also demonstrated the superiority of the CCA-based multimodal words over those derived directly from the original cues.


title: Stem Cell Factor (SCF) is a putative biomarker of antidepressant response

abstract: Growth factors involved in neurogenesis and neuroplasticity could play a role in biological processes that drive depression recovery. Combined total sleep deprivation and morning light therapy (TSD + LT) can acutely reverse depressive symptoms, thus allowing to investigate the neurobiological correlates of antidepressant response. We tested if changes on plasma levels of Brain Derived Neurotrophic Factor (BDNF), S100 calcium binding protein B (S100-B), Stem Cell Factor (SCF), Insulin-like Growth Factor-Binding Protein 2 (IGFBP-2), Epidermal Growth Factor (EGF), Platelet-Derived Growth Factor-BB (PDGF-BB), and Vascular Endothelial Growth Factor (VEGF) are associated with response to TSD + LT in 26 inpatients affected by a major depressive episode in the course of bipolar disorder. Regional grey matter (GM) volumes were assessed at baseline, and BOLD fMRI neural responses to a moral valence decision task were recorded before and after treatment. 61.5 % of patients responded to treatment. SCF plasma levels increased significantly more in responders, and correlated with GM volumes in frontal and parietal cortical areas. The pattern of change of SCF also associated with both GM volumes and changes of BOLD fMRI neural responses in the anterior cingulate and medial prefrontal cortex. SCF is both a hematopoietic growth factor and a neurotrophic factor, involved in neuron-neuron and neuron-(micro) glia interactions, fostering neuronal growth and an anti-inflammatory milieu. We correlated SCF levels with antidepressant response and with functional and structural MRI measures in cortical areas that are involved in the cognitive generation and control of affect. SCF may be a candidate growth factor that contributes to neurotrophic and immune effects that are involved in the process of remission/recovery from depression.


title: Indoor target tracking using high doppler resolution passive Wi-Fi radar

abstract: This paper describes two Doppler only indoor passive Wi-Fi tracking methods based on high Doppler resolution passive radar. Two filters are investigated in this paper, the extended Kalman filter and the sequential importance resampling (SIR) particle filter. Experimental results for these two tracking filters are presented using results from software defined passive Wi-Fi radar using a standard 802.11 access point as an illuminator. The experimental results show that the SIR particle filter performs well using Wi-Fi signals for indoor tracking with a high degree of accuracy. Proposals for simplifying the SIR particle and application to multiple target tracking are also discussed.


abstract: We introduce the "Energy-based Generative Adversarial Network" model (EBGAN) which views the discriminator as an energy function that attributes low energies to the regions near the data manifold and higher energies to other regions. Similar to the probabilistic GANs, a generator is seen as being trained to produce contrastive samples with minimal energies, while the discriminator is trained to assign high energies to these generated samples. Viewing the discriminator as an energy function allows to use a wide variety of architectures and loss functionals in addition to the usual binary classifier with logistic output. Among them, we show one instantiation of EBGAN framework as using an auto-encoder architecture, with the energy being the reconstruction error, in place of the discriminator. We show that this form of EBGAN exhibits more stable behavior than regular GANs during training. We also show that a single-scale architecture can be trained to generate high-resolution images.


title: Robust Spectral Learning for Unsupervised Feature Selection

abstract: In this paper, we consider the problem of unsupervised feature selection. Recently, spectral feature selection algorithms, which leverage both graph Laplacian and spectral regression, have received increasing attention. However, existing spectral feature selection algorithms suffer from two major problems: 1) since the graph Laplacian is constructed from the original feature space, noisy and irrelevant features may have adverse effect on the estimated graph Laplacian and hence degenerate the quality of the induced graph embedding, 2) since the cluster labels are discrete in natural, relaxing and approximating these labels into a continuous embedding can inevitably introduce noise into the estimated cluster labels. Without considering the noise in the cluster labels, the feature selection process may be misguided. In this paper, we propose a Robust Spectral learning framework for unsupervised Feature Selection (RSFS), which jointly improves the robustness of graph embedding and sparse spectral regression. Compared with existing methods which are sensitive to noisy features, our proposed method utilizes a robust local learning method to construct the graph Laplacian and a robust spectral regression method to handle the noise on the learned cluster labels. In order to solve the proposed optimization problem, an efficient iterative algorithm is proposed. We also show the close connection between the proposed robust spectral regression and robust Huber M-estimator. Experimental results on different datasets show the superiority of RSFS.


title: The advisor visual surveillance system

abstract: ADVISOR is an automated visual surveillance system for metro stations which was developed as part of the project ADVISOR, involving 3 academic and 3 industrial project partners. The ADVISOR system aims at making public transport safer by automatically detecting at an early stage dangerous situations which may lead to accidents, violence or vandalism. In order to achieve this people are tracked across the station and their behaviours analysed. Additional measurements on crowd density and movement are also obtained. Warnings are generated and displayed to human operators for possible intervention. The article explores the main difficulties encountered during the design and implementation of ADVISOR and describes the ways in which they were solved. A prototype system has been built and extensively tested, proving the feasibility of automated visual surveillance systems. An analysis of test runs at a metro station in Barcelona and several individual experiments show that the system copes with many difficult image analysis problems. The analysis also points the way for future development and ways of deployment of the techniques used in the system.


title: Large-scale item categorization for e-commerce

abstract: This paper studies the problem of leveraging computationally intensive classification algorithms for large scale text categorization problems. We propose a hierarchical approach which decomposes the classification problem into a coarse level task and a fine level task. A simple yet scalable classifier is applied to perform the coarse level classification while a more sophisticated model is used to separate classes at the fine level. However, instead of relying on a human-defined hierarchy to decompose the problem, we we use a graph algorithm to discover automatically groups of highly similar classes. As an illustrative example, we apply our approach to real-world industrial data from eBay, a major e-commerce site where the goal is to classify live items into a large taxonomy of categories. In such industrial setting, classification is very challenging due to the number of classes, the amount of training data, the size of the feature space and the real-world requirements on the response time. We demonstrate through extensive experimental evaluation that (1) the proposed hierarchical approach is superior to flat models, and (2) the data-driven extraction of latent groups works significantly better than the existing human-defined hierarchy.


Mnemotechnics in Second-Language Learning


title: Ranging RFID Tags With Ultrasound

abstract: Indoor localization and tracking of persons and assets with centimeter-level accuracy for inventory, security, medical monitoring, and training, as well as gesture interfaces for domotics, is highly desirable in the framework of the emerging IoT paradigm. Low cost, tiny, battery, or batteryless operated position sensors are required. 3-D localization can be computed by combining three or more distance measurements between sensor and reference points. Our aim is to give the capability of measuring the distance from a reference point to radio frequency identification (RFID) tags. The main challenge is in the estimation of the distances with millimeter accuracy in presence of both size and power supply strict constraints, and thus with very limited computational power. An accurate ranging technique using cross-correlation and small RFID-based sensors is proposed. Its originality resides in moving the main computational efforts from the sensor to an external processing unit with sufficient computational and supply power, thus overcoming the sensor limits. The system is composed of a beacon that emits ultrasound chirps and RF sync signals, a RFID-based distance sensor, a commercial RFID reader, and a processing unit. Main advantages are the high miniaturization and low power consumption of the remote sensor, and its compatibility with existing RFID standards.


title: Probing into code analysis tools: A comparison of C# supporting static code analyzers

abstract: Essence of a software system lies in the quality of source code and the degree to which it follows the underlying coding standard. The widely-used code analysis techniques focus upon examining the programs without actually executing them. The major purpose is the detection of complex code constructs and the potential defects that result in the decrement in quality of the code. The code complexity is often assumed to inflate the maintenance cost and lead to the unexpected system behaviour. A number of code analyzing tools are available and currently many researches are being conducted to improve software quality, bring down software complexity without affecting its external behaviour. A brief review of the existing code analyzers is presented in this paper; however the major focus is upon the tools that analyze the source code written in C#. Researchers round the globe have identified that the potential problems in the source code are: code smells, code clones, anti-detect pattern etc that often lead to the increase in system complexity and hence amplification in the system response time. The code analyzing tools have their own significance depending upon the domain where they are being applied. Code analyzers are of extreme value especially in the mission critical systems, where the system efficiency is one of the factors for mission success. The methodology adopted for the purpose of review of the existing tools is that first of all, the purpose of tool is described, secondly, the personal findings, along with reviews from the clients and technology analysts regarding the tool are taken into account, thirdly, a sample C# application is chosen and the outputs from the tools for this sample are taken for analysis and comparison. Upon the basis of analysis, reviews and comparison, the relatively best tool among the 8 analyzed tools has been figured out.


title: IDEL: In-Database Entity Linking with Neural Embeddings

abstract: We present a novel architecture, In-Database Entity Linking (IDEL), in which we integrate the analytics-optimized RDBMS MonetDB with neural text mining abilities. Our system design abstracts core tasks of most neural entity linking systems for MonetDB. To the best of our knowledge, this is the first defacto implemented system integrating entity-linking in a database. We leverage the ability of MonetDB to support in-database-analytics with user defined functions (UDFs) implemented in Python. These functions call machine learning libraries for neural text mining, such as TensorFlow. The system achieves zero cost for data shipping and transformation by utilizing MonetDB's ability to embed Python processes in the database kernel and exchange data in NumPy arrays. IDEL represents text and relational data in a joint vector space with neural embeddings and can compensate errors with ambiguous entity representations. For detecting matching entities, we propose a novel similarity function based on joint neural embeddings which are learned via minimizing pairwise contrastive ranking loss. This function utilizes a high dimensional index structures for fast retrieval of matching entities. Our first implementation and experiments using the WebNLG corpus show the effectiveness and the potentials of IDEL.