title: Visual–Tactile Fusion for Object Recognition

abstract: The camera provides rich visual information regarding objects and becomes one of the most mainstream sensors in the automation community. However, it is often difficult to be applicable when the objects are not visually distinguished. On the other hand, tactile sensors can be used to capture multiple object properties, such as textures, roughness, spatial features, compliance, and friction, and therefore provide another important modality for the perception. Nevertheless, effective combination of the visual and tactile modalities is still a challenging problem. In this paper, we develop a visual–tactile fusion framework for object recognition tasks. This paper uses the multivariate-time-series model to represent the tactile sequence and the covariance descriptor to characterize the image. Further, we design a joint group kernel sparse coding (JGKSC) method to tackle the intrinsically weak pairing problem in visual–tactile data samples. Finally, we develop a visual–tactile data set, composed of 18 household objects for validation. The experimental results show that considering both visual and tactile inputs is beneficial and the proposed method indeed provides an effective strategy for fusion.


title: Sentence Similarity based on Relevance

abstract: The issue about sentence similarity is essential to many areas of artificial intelligence. Although there are related studies on determining text similarity, fewer publications are about the similarity between short texts especially about the similarity between sentence pairs. In this paper, a novel method is proposed to estimate the sentence similarity with consideration of direct relevance and indirect relevance between sentence pairs. Analysis of experiment provides insights into the strengths and weakness of our proposed method.


title: EmotionCheck: leveraging bodily signals and false feedback to regulate our emotions

abstract: In this paper we demonstrate that it is possible to help individuals regulate their emotions with mobile interventions that leverage the way we naturally react to our bodily signals. Previous studies demonstrate that the awareness of our bodily signals, such as our heart rate, directly influences the way we feel. By leveraging these findings we designed a wearable device to regulate user's anxiety by providing a false feedback of a slow heart rate. The results of an experiment with 67 participants show that the device kept the anxiety of the individuals in low levels when compared to the control group and the other conditions. We discuss the implications of our findings and present some promising directions for designing and developing this type of intervention for emotion regulation.


title: A survey of variability modeling in industrial practice

abstract: Over more than two decades, numerous variability modeling techniques have been introduced in academia and industry. However, little is known about the actual use of these techniques. While dozens of experience reports on software product line engineering exist, only very few focus on variability modeling. This lack of empirical data threatens the validity of existing techniques, and hinders their improvement. As part of our effort to improve empirical understanding of variability modeling, we present the results of a survey questionnaire distributed to industrial practitioners. These results provide insights into application scenarios and perceived benefits of variability modeling, the notations and tools used, the scale of industrial models, and experienced challenges and mitigation strategies.


title: Autonomy and machine intelligence in complex systems: A tutorial

abstract: This tutorial paper will discuss the development of novel state-of-the-art control approaches and theory for complex systems based on machine intelligence in order to enable full autonomy. Given the presence of modeling uncertainties, the unavailability of the model, the possibility of cooperative/non-cooperative goals and malicious attacks compromising the security of teams of complex systems, there is a need for approaches that respond to situations not programmed or anticipated in design. Unfortunately, existing schemes for complex systems do not take into account recent advances of machine intelligence. We shall discuss on how to be inspired by the human brain and combine interdisciplinary ideas from different fields, i.e. computational intelligence, game theory, control theory, and information theory to develop new self-configuring algorithms for decision and control given the unavailability of model, the presence of enemy components and the possibility of network attacks. Due to the adaptive nature of the algorithms, the complex systems will be capable of breaking or splitting into parts that are themselves autonomous and resilient. The algorithms discussed will be characterized by strong abilities of learning and adaptivity. As a result, the complex systems will be fully autonomous, and tolerant to communication failures.


title: Want a Good Answer? Ask a Good Question First!

abstract: Question Answering (CQA) websites have become valu- able repositories which host a massive volume of human knowl- edge. To maximize the utility of such knowledge, it is essential to evaluate the quality of an existing question or answer, especially soon after it is posted on the CQA website. In this paper, we study the problem of inferring the quality of ques- tions and answers through a case study of a software CQA (Stack Overflow). Our key finding is that the quality of an answer is strongly positively correlated with that of its question. A rmed with this observation, we propose a family of algorithms to jointly pre- dict the quality of questions and answers, for both quantifying nu- merical quality scores and differentiating the high-quali ty ques- tions/answers from those of low quality. We conduct extensive experimental evaluations to demonstrate the effectiveness and ef- ficiency of our methods.


Effects of sewage pollution on coral-reef communities


title: Classification of methods in transcranial Electrical Stimulation (tES) and evolving strategy from historical approaches to contemporary innovations

abstract: Transcranial Electrical Stimulation (tES) encompasses all methods of non-invasive current application to the brain used in research and clinical practice. We present the first comprehensive and technical review, explaining the evolution of tES in both terminology and dosage over the past 100 years of research to present day. Current transcranial Pulsed Current Stimulation (tPCS) approaches such as Cranial Electrotherapy Stimulation (CES) descended from Electrosleep (ES) through Cranial Electro-stimulation Therapy (CET), Transcerebral Electrotherapy (TCET), and NeuroElectric Therapy (NET) while others like Transcutaneous Cranial Electrical Stimulation (TCES) descended from Electroanesthesia (EA) through Limoge, and Interferential Stimulation. Prior to a contemporary resurgence in interest, variations of trans-cranial Direct Current Stimulation were explored intermittently, including Polarizing current, Galvanic Vestibular Stimulation (GVS), and Transcranial Micropolarization. The development of these approaches alongside Electroconvulsive Therapy (ECT) and pharmacological developments are considered. Both the roots and unique features of contemporary approaches such as transcranial Alternating Current Stimulation (tACS) and transcranial Random Noise Stimulation (tRNS) are discussed. Trends and incremental developments in electrode montage and waveform spanning decades are presented leading to the present day. Commercial devices, seminal conferences, and regulatory decisions are noted. We conclude with six rules on how increasing medical and technological sophistication may now be leveraged for broader success and adoption of tES.


title: MEMS-loaded millimeter wave frequency reconfigurable quasi-Yagi dipole antenna

abstract: A new millimeter-wave frequency reconfigurable quasi-Yagi antenna is presented. The quasi-Yagi antenna is printed on a quartz substrate integrated with RF MEMS switches. It consists of one driven dipole, two dipole directors, and one truncated ground plane as reflector. By controlling the actuation of the RF MEMS loaded on the driven and director dipole elements, the antenna operation frequency is switchable in the millimeter wave wireless personal area network (WPAN) band (57–66 GHz) and E-band (71–86 GHz). The end-fire pattern of the Yagi-antenna is maintained in both two-bands. Theoretical results show that the antenna gain varies from 5.5 to 6.7 dBi in the lower band and from 6.5 dBi to 8.1 dBi in the higher band respectively. A high resistive biasing line configuration is presented and its effect on the antenna reflection coefficient is investigated.


title: Autism spectrum disorder: neuropathology and animal models

abstract: Autism spectrum disorder (ASD) has a major impact on the development and social integration of affected individuals and is the most heritable of psychiatric disorders. An increase in the incidence of ASD cases has prompted a surge in research efforts on the underlying neuropathologic processes. We present an overview of current findings in neuropathology studies of ASD using two investigational approaches, postmortem human brains and ASD animal models, and discuss the overlap, limitations, and significance of each. Postmortem examination of ASD brains has revealed global changes including disorganized gray and white matter, increased number of neurons, decreased volume of neuronal soma, and increased neuropil, the last reflecting changes in densities of dendritic spines, cerebral vasculature and glia. Both cortical and non-cortical areas show region-specific abnormalities in neuronal morphology and cytoarchitectural organization, with consistent findings reported from the prefrontal cortex, fusiform gyrus, frontoinsular cortex, cingulate cortex, hippocampus, amygdala, cerebellum and brainstem. The paucity of postmortem human studies linking neuropathology to the underlying etiology has been partly addressed using animal models to explore the impact of genetic and non-genetic factors clinically relevant for the ASD phenotype. Genetically modified models include those based on well-studied monogenic ASD genes (NLGN3, NLGN4, NRXN1, CNTNAP2, SHANK3, MECP2, FMR1, TSC1/2), emerging risk genes (CHD8, SCN2A, SYNGAP1, ARID1B, GRIN2B, DSCAM, TBR1), and copy number variants (15q11-q13 deletion, 15q13.3 microdeletion, 15q11-13 duplication, 16p11.2 deletion and duplication, 22q11.2 deletion). Models of idiopathic ASD include inbred rodent strains that mimic ASD behaviors as well as models developed by environmental interventions such as prenatal exposure to sodium valproate, maternal autoantibodies, and maternal immune activation. In addition to replicating some of the neuropathologic features seen in postmortem studies, a common finding in several animal models of ASD is altered density of dendritic spines, with the direction of the change depending on the specific genetic modification, age and brain region. Overall, postmortem neuropathologic studies with larger sample sizes representative of the various ASD risk genes and diverse clinical phenotypes are warranted to clarify putative etiopathogenic pathways further and to promote the emergence of clinically relevant diagnostic and therapeutic tools. In addition, as genetic alterations may render certain individuals more vulnerable to developing the pathological changes at the synapse underlying the behavioral manifestations of ASD, neuropathologic investigation using genetically modified animal models will help to improve our understanding of the disease mechanisms and enhance the development of targeted treatments.


title: Large-Scale Image Clustering Based on Camera Fingerprints

abstract: Practical applications of digital forensics are often faced with the challenge of grouping large-scale suspicious images into a vast number of clusters, each containing images taken by the same camera. This task can be approached by resorting to the use of sensor pattern noise (SPN), which serves as the fingerprint of the camera. The challenges of large-scale image clustering come from the sheer volume of the image set and the high dimensionality of each image. The difficulties can be further aggravated when the number of classes (i.e., the number of cameras) is much higher than the average size of class (i.e., the number of images acquired by each camera). We refer to this as the $NC\gg SC$ problem, which is not uncommon in many practical scenarios. In this paper, we propose a novel clustering framework that is capable of addressing the $NC\gg SC$ problem without a training process. The proposed clustering framework was evaluated on the Dresden image database and compared with the state-of-the-art SPN-based image clustering algorithms. Experimental results show that the proposed clustering framework is much faster than the state-of-the-art algorithms while maintaining a high level of clustering quality.


title: Two Decades of SCADA Exploitation: A Brief History

abstract: Since the early 1960, industrial process control has been applied by electric systems. In the mid 1970's, the term SCADA emerged, describing the automated control and data acquisition. Since most industrial and automation networks were physically isolated, security was not an issue. This changed, when in the early 2000's industrial networks were opened to the public internet. The reasons were manifold. Increased interconnectivity led to more productivity, simplicity and ease of use. It decreased the configuration overhead and downtimes for system adjustments. However, it also led to an abundance of new attack vectors. In recent time, there has been a remarkable amount of attacks on industrial companies and infrastructures. In this paper, known attacks on industrial systems are analysed. This is done by investigating the exploits that are available on public sources. The different types of attacks and their points of entry are reviewed in this paper. Trends in exploitation as well as targeted attack campaigns against industrial enterprises are introduced.


title: Students Acceptance of Mobile Learning for Higher Education in Saudi Arabia

abstract: Mobile learning is the next step in the development of distance learning. Widespread access to mobile devices and the opportunity to learn regardless of time and place make the mobile learning an important tool for lifelong learning. The research objectives are to examine the possibility of acceptance in mobile learning (m-Learning) and study main factors that affect using m- Learning that focus on higher education students in Saudi Arabia. The researcher used a quantitative approach survey of 80 students. The modified acceptance framework that based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model is adopted to determine the factors that influence the students’ intention to use m-Learning. The results from statistical analysis show that the acceptance level of students on m-Learning is in the high level.


title: A stochastic neuronal model predicts random search behaviors at multiple spatial scales in C. elegans

abstract: An animal’s ability to rapidly and efficiently locate new sources of food in its environment can mean the difference between life and death. As a result, animals have evolved foraging strategies that are adapted to the distribution and detectability of food sources. Organisms ranging from bacteria to humans use one such strategy, called random search, to locate food that cannot be detected at a distance and that is randomly distributed in their surroundings. The biological mechanisms that underpin random search are relatively well understood in single-cell organisms such as bacteria, but this information tells us little about the mechanisms that are used by animals, which use their nervous system to control their foraging behavior. Roberts et al. have now investigated the biological basis for random search behavior in a tiny roundworm called Caenorhabditis elegans. This worm forages for pockets of bacteria in decaying plant matter and has a simple and well-understood nervous system. Roberts et al. used information on how the cells in this worm’s nervous system connect together into so-called “neural circuits” to generate a mathematical model of random searching. The model revealed that the worm’s neural circuitry for random searching can be understood in terms of two groups of neuron-like components that switch randomly between “ON” and “OFF” states. While one group promotes forward movement, the other promotes backward movement, which is associated with a change in search direction. These two groups inhibit each other so that only one group usually is active at a given time. By adjusting this model to reproduce the behavioral records of real worms searching for food, Roberts et al. could predict the key neuronal connections involved. These predictions were then confirmed by taking electrical recordings from neurons. The model could also account for the unexpected behavioral effects that are seen when a neuron in one of these groups was destroyed or altered by a genetic mutation. These findings thus reveal a biological mechanism for random search behavior in worms that might operate in other animals as well. The findings might also provide future insight into the neural circuits involved in sleep and wakefulness in mammals, which is organized in a similar way.


title: Intrinsic t-Stochastic Neighbor Embedding for Visualization and Outlier Detection

abstract: Analyzing high-dimensional data poses many challenges due to the “curse of dimensionality”. Not all high-dimensional data exhibit these characteristics because many data sets have correlations, which led to the notion of intrinsic dimensionality. Intrinsic dimensionality describes the local behavior of data on a low-dimensional manifold within the higher dimensional space.


title: GFP-X: A parallel approach to massive graph comparison using spark

abstract: The problem of how to compare empirical graphs is an area of great interest within the field of network science. The ability to accurately but efficiently compare graphs has a significant impact in such areas as temporal graph evolution, anomaly detection and protein comparison. The comparison problem is compounded when working with massive graphs containing millions of vertices and edges. This paper introduces a parallel feature extraction based approach for the efficient comparison of large unlabelled graph datasets using Apache Spark. The approach acts by producing a ‘Graph Fingerprint’ which represents both vertex level and global level topological features from a graph. By using Spark we are able to efficiently compare graphs considered unmanageably large to other approaches. The runtime of the approach is shown to scale sub-linearly with the size and complexity of the graphs being fingerprinted. Importantly, the approach is shown to not only be comparable to existing approaches, but on when comparing topology and size, more sensitive at detecting variation between graphs.


Automatic Detection and Tracking of Abandoned Objects


title: Hand Dynamics for Behavioral User Authentication

abstract: We propose and evaluate a method to authenticate individuals by their unique hand dynamics, based on measurements from wearable sensors. Our approach utilises individual characteristics of hand movement when opening a door. We implement a sensor-fusion machine learning algorithm to classify individuals based on their hand movement and conduct a lab study with 20 participants to test the feasibility of the concept in the context of accessing physical doors as found in office buildings. Our results show that our approach yields an accuracy of 92% in classifying an individual and thus highlights the potential for behavioral hand dynamics for authentication.


title: 'Lost and found': some logical and methodological limitations of self-report questionnaires as tools to study cognitive ageing.

abstract: Laboratory tests show a decline in memory efficiency with age, but may not reflect highly practiced everyday memory skills. Self-rating questionnaires probe memory efficiency in everyday life, but seven logical and methodological problems ambiguate interpretation of the data they yield. To explore these problems, 442 volunteers aged from 50 to 85 years were given the AH 4 IQ tests, Mill Hill Vocabulary Tests, Beck Depression Inventory (BDI), Broadbent Cognitive Failures Questionnaire (CFQ), Sunderland & Harris Memory Failure Questionnaire (MFQ), the 'Lost and Found' Questionnaire (LF, a new domain-specific probe of the variety, frequency and aetiologies of losses of possessions) and two laboratory memory tests. All self-rating questionnaire scores correlated modestly with each other. Counter-intuitively, individuals over 60 years old reported fewer lapses on the LF and on the CFQ. No SRQ scores correlated with performance on laboratory tasks. CFQ scores did not correlate with IQ test scores but high IQ test scorers reported greater recent decline in memory efficiency (MFQ) and greater current frequency of losses of objects (LF). CFQ and LF scores correlated positively with Beck Depression Inventory scores, suggesting some common loading for poor self-regard. The results suggest that subjective self-ratings cannot reflect absolute levels of everyday competence, but only the relative success of individuals' adaptations to idiosyncratic environments which, as they age, may change faster than their abilities. However, besides exposing the limitations of self-report techniques, these analyses also suggest practical ways of adapting and using SRQs which may allow us to gain better insights into changes and individual differences in functional efficiency in everyday life.


Low-resource eclipse attacks on Ethereum’s peer-to-peer network


title: Animal Identification in Low Quality Camera-Trap Images Using Very Deep Convolutional Neural Networks and Confidence Thresholds

abstract: Monitoring animals in the wild without disturbing them is possible using camera trapping framework. Automatic triggered cameras, which take a burst of images of animals in their habitat, produce great volumes of data, but often result in low image quality. This high volume data must be classified by a human expert. In this work a two step classification is proposed to get closer to an automatic and trustfully camera-trap classification system in low quality images. Very deep convolutional neural networks were used to distinguish images, firstly between birds and mammals, secondly between mammals sets. The method reached \(97.5\%\) and \(90.35\%\) in each task. An alleviation mode using a confidence threshold of automatic classification is proposed, allowing the system to reach \(100\%\) of performance traded with human work.


title: Nonlinear optimal control for autonomous navigation of a truck and trailer system

abstract: A solution to the problem of autonomous navigation of a truck and trailer system is developed using a nonlinear optimal control method. The kinematic model of the autonomous vehicle undergoes linearization through Taylor series expansion. The linearization is performed at a temporary operating point (equilibrium) that is re-computed at each time instant using the present value of the state vector and the last value of the control inputs vector. The linearization is based on the Jacobian matrices of the model. For the linearized equivalent model of the truck and trailer system an H-infinity feedback controller is designed. The feedback control gain is obtained from the solution of an algebraic Riccati equation at each iteration of the control algorithm. The stability of the control loop is proven with Lyapunov analysis. It is demonstrated that the control loop exhibits the H-infinity tracking performance which signifies elevated robustness against modelling errors and external disturbances. Moreover, under moderate conditions the global asymptotic stability of the control loop is assured. Finally, to implement state estimation-based control for the autonomous vehicle, through the processing of a small number of sensor measurements, the H-infinity Kalman Filter is proposed.


title: Lemmatization Technique in Bahasa: Indonesian Language

abstract: Many researches and inventions have been made in the field of linguistics and technology. Even so, the integration between linguistics and technology is not always reliable to all language. Every language is unique in its linguistic nature and rules. In this paper, a lemmatization technique in Bahasa (Indonesian language) is presented. It has achieved good precision by using The Indonesian Dictionary and a set of rules to remove affixes. The lemmatization technique is developed based on the previous algorithm, Indonesian stemmer. Both Indonesian stemming and lemmatization method have the same characteristics but a little bit different in its implementation. The way to reach its own goal/purpose is defined as a core difference and therefore possible to modify. The result shows that the algorithm achieved roughly 98% precision on a collection consisting 57,261 valid words with 7,839 unique valid words gathered from Kompas.com, an Indonesian online news article.


title: Using Mobile-Assisted Exercises to Support Students' Vocabulary Skill Development.

abstract: The use of mobile phones for learning has become well-known and is widely adopted in many language classes. The use of SMS for transmitting short messages is a fast way of helping students to learn vocabulary. To address this issue, this study was conducted to examine the effects of mobile-assisted vocabulary exercises on vocabulary acquisition of first-year students. Eighty students from two sections enrolled in a fundamental English course participated in the study. Each section consisted of 40 students. One of the groups was chosen as the experimental group, and the other as the control group. All students received the same amount of new words and dictation in class. Then only the students in the experimental group did vocabulary exercises on mobile phones via SMS. Those in the control group received paper-based exercises to be done in class. The instruments were pre-and post-vocabulary tests and a questionnaire surveying the students’ attitudes toward mobile-assisted exercises. The findings revealed that vocabulary knowledge of students in the experimental group outperformed the control group. They used and learned target vocabulary better than those in the control group. Mobileassisted vocabulary exercises had a significant effect on vocabulary ability of the students. The results of the questionnaire also illustrated their positive attitudes toward doing mobile-assisted exercises as a whole. It can be concluded that using mobile phones as a learning tool contributes to the success of students meanwhile increases their learning motivation.


title: Finding approximate competitive equilibria: Efficient and fair course allocation

abstract: In the course allocation problem, a university administrator seeks to efficiently and fairly allocate schedules of over-demanded courses to students with heterogeneous preferences. We investigate how to computationally implement a recently-proposed theoretical solution to this problem (Budish, 2009) which uses approximate competitive equilibria to balance notions of efficiency, fairness, and incentives. Despite the apparent similarity to the well-known combinatorial auction problem we show that no polynomial-size mixed-integer program (MIP) can solve our problem. Instead, we develop a two-level search process: at the master level, the center uses tabu search over the union of two distinct neighborhoods to suggest prices; at the agent level, we use MIPs to solve for student demands in parallel at the current prices. Our method scales near-optimally in the number of processors used and is able to solve realistic-size problems fast enough to be usable in practice.


ITB Journal A Survey of Procedural Techniques for City Generation


title: ZipPy on truffle: a fast and simple implementation of python

abstract: Building high-performance virtual machines is a complex and expensive undertaking; many popular languages still have low-performance implementations. We present a new approach to virtual machine (VM) construction that amortizes much of the effort in initial construction by allowing new languages to be implemented with modest additional effort. The approach (named Truffle) relies on tree-rewriting abstract syntax tree (AST) interpretation, together with an optimizing compiler (named Graal) that exploits the structure of the interpreter. The compiler uses speculative assumptions and deoptimization in order to produce efficient machine code. We illustrate the concepts of Truffle by the means of our open-source implementation of Python (named ZipPy). To show that Truffle supports a variety of programming language paradigms, we also present prototype implementations of other languages.


title: Mining infrequent patterns in data stream

abstract: In recent years researches are focused towards mining infrequent patterns rather than frequent patterns. Mining infrequent pattern plays vital role in detecting any abnormal event. In this paper, an algorithm named Infrequent Pattern Miner for Data Streams (IPM-DS) is proposed for mining nonzero infrequent patterns from data streams. The proposed algorithm adopts the FP-growth based approach for generating all infrequent patterns. The proposed algorithm (IPM-DS) is evaluated using health data set collected from wearable physiological sensors that measure vital parameters such as Heart Rate (HR), Breathing Rate (BR), Oxygen Saturation (SPO2) and Blood pressure (BP) and also with two publically available data sets such as e-coli and Wine from UCI repository. The experimental results show that the proposed algorithm generates all possible infrequent patterns in less time.


title: DSP-based arrhythmia classification using wavelet transform and probabilistic neural network

abstract: Abstract A large part of the biomedical research spectrum is dedicated to develop electrocardiogram (ECG) signal processing techniques to contribute to early diagnosis. However, it is common to find that ECG analysis methods reported are confined to off-line PC host operation. The authors present an arrhythmia classification method implemented on a Digital Signal Processing (DSP) platform intended for on-line, real-time ambulatory operation to classify eight heartbeat conditions: normal sinus rhythm (N), auricular fibrillation (AF), premature atrial contraction (PAC), left bundle branch block (LBBB), right bundle branch block (RBBB), premature ventricular contraction (PVC), sinoauricular heart block (SHB) and supraventricular tachycardia (SVT). The algorithm uses a wavelet transform process based on quadratic wavelets for identifying individual ECG waves and obtain a fiducial marker array. Classification is conducted by means of a Probabilistic Neural Network. The algorithm is tested with 17 ECG records obtained from the PhysioNet repository. The proposed classification procedure was tested initially on MATLAB and the results where compared with the equivalent analogue data fed to a DSP-based ECG data acquisition prototype through an arbitrary waveform generator. The results derived from confusion matrix tests yielded on-line classification accuracy of 92.69% (AF), 97.15% (N), 76.82% (PAC), 91.06% (LBBB), 87.5% (RBBB), 71.04% (PVC), 91.94% (SHB) and 95.45% (SVT), overall classification rate of 92.746% and 100% agreement between the MATLAB and on-line DSP implementations. The results suggest that the method and prototype presented may be suitable for being implemented on wearable sensing applications auxiliary for on-line, real-time diagnosis.


title: Data Driven Analytics for Personalized Healthcare

abstract: The concept of Learning Health Systems (LHS) is gaining momentum as more and more electronic healthcare data becomes increasingly accessible. The core idea is to enable learning from the collective experience of a care delivery network as recorded in the observational data, to iteratively improve care quality as care is being provided in a real world setting. In line with this vision, much recent research effort has been devoted to exploring machine learning, data mining and data visualization methodologies that can be used to derive real world evidence from diverse sources of healthcare data to provide personalized decision support for care delivery and care management. In this chapter, we will give an overview of a wide range of analytics and visualization components we have developed, examples of clinical insights reached from these components, and some new directions we are taking.


title: Modeling and simulation of photovoltaic cell/module/array with two-diode model

abstract: The purpose of this paper is to propose a MATLAB/ Simulink simulators for PV cell/module/array based on the Two-diode model of a PV cell.This model is known to have better accuracy at low irradiance levels which allows for more accurate prediction of PV systems performance.To reduce computational time , the input parameters are reduced as the values of Rs and Rp are estimated by an efficient iteration method. Furthermore ,all of the inputs to the simulators are information available on a standard PV module datasheet. The present paper present first abrief introduction to the behavior and functioning of a PV device and write the basic equation of the two-diode model,without the intention of providing an indepth analysis of the photovoltaic phenomena and the semicondutor physics. The introduction on PV devices is followed by the modeling and simulation of PV cell/PV module/PV array, which is the main subject of this paper. A MATLAB Simulik based simulation study of PV cell/PV module/PV array is carried out and presented .The simulation model makes use of the two-diode model basic circuit equations of PV solar cell, taking the effect of sunlight irradiance and cell temperature into consideration on the output current I-V characteristic and output power P-V characteristic . A particular typical 50W solar panel was used for model evaluation. The simulation results , compared with points taken directly from the data sheet and curves pubblished by the manufacturers, show excellent correspondance to the model.


title: Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping

abstract: BackgroundLaserscanning recently has become a powerful and common method for plant parameterization and plant growth observation on nearly every scale range. However, 3D measurements with high accuracy, spatial resolution and speed result in a multitude of points that require processing and analysis. The primary objective of this research has been to establish a reliable and fast technique for high throughput phenotyping using differentiation, segmentation and classification of single plants by a fully automated system. In this report, we introduce a technique for automated classification of point clouds of plants and present the applicability for plant parameterization.ResultsA surface feature histogram based approach from the field of robotics was adapted to close-up laserscans of plants. Local geometric point features describe class characteristics, which were used to distinguish among different plant organs. This approach has been proven and tested on several plant species. Grapevine stems and leaves were classified with an accuracy of up to 98%. The proposed method was successfully transferred to 3D-laserscans of wheat plants for yield estimation. Wheat ears were separated with an accuracy of 96% from other plant organs. Subsequently, the ear volume was calculated and correlated to the ear weight, the kernel weights and the number of kernels. Furthermore the impact of the data resolution was evaluated considering point to point distances between 0.3 and 4.0 mm with respect to the classification accuracy.ConclusionWe introduced an approach using surface feature histograms for automated plant organ parameterization. Highly reliable classification results of about 96% for the separation of grapevine and wheat organs have been obtained. This approach was found to be independent of the point to point distance and applicable to multiple plant species. Its reliability, flexibility and its high order of automation make this method well suited for the demands of high throughput phenotyping.Highlights• Automatic classification of plant organs using geometrical surface information• Transfer of analysis methods for low resolution point clouds to close-up laser measurements of plants• Analysis of 3D-data requirements for automated plant organ classification


title: Cloud and Cache-Aided Wireless Networks: Fundamental Latency Trade-Offs

abstract: A cloud and cache-aided wireless network architecture is studied in which edge-nodes (ENs), such as base stations, are connected to a cloud processor via dedicated fronthaul links, while also being endowed with caches. Cloud processing enables the centralized implementation of cooperative transmission strategies at the ENs, albeit at the cost of an increased latency due to fronthaul transfer. In contrast, the proactive caching of popular content at the ENs allows for the low-latency delivery of the cached files, but with generally limited opportunities for cooperative transmission among the ENs. The interplay between cloud processing and edge caching is addressed from an information-theoretic viewpoint by investigating the fundamental limits of a high Signal-to-Noise-Ratio (SNR) metric, termed normalized delivery time (NDT), which captures the worst-case latency for delivering any requested content to the users. The NDT is defined under the assumption of either serial or pipelined fronthaul-edge transmissions, and is studied as a function of fronthaul and cache capacity constraints. Transmission policies that encompass the caching phase as well as the transmission phase across both fronthaul and wireless, or edge, segments are proposed, with the aim of minimizing the NDT for given fronthaul and cache capacity constraints. Informationtheoretic lower bounds on the NDT are also derived. Achievability arguments and lower bounds are leveraged to characterize the minimal NDT in a number of important special cases, including systems with no caching capability, as well as to prove that the proposed schemes achieve optimality within a constant multiplicative factor of 2 for all values of the problem parameters.


title: A least-norm approach to flattenable mesh surface processing

abstract: Following the definition of developable surface in differential geometry, the flattenable mesh surface, a special type of piecewise- linear surface, inherits the good property of developable surface about having an isometric map from its 3D shape to a corresponding planar region. Different from the developable surfaces, a flattenable mesh surface is more flexible to model objects with complex shapes (e.g., cramped paper or warped leather with wrinkles). Modelling a flattenable mesh from a given input mesh surface can be completed under a constrained nonlinear optimization framework. In this paper, we reformulate the problem in terms of estimation error. Therefore, the shape of a flattenable mesh can be computed by the least-norm solutions faster. Moreover, the method for adding shape constraints to the modelling of flattenable mesh surfaces has been exploited. We show that the proposed method can compute flattenable mesh surfaces from input piecewise linear surfaces successfully and efficiently.


title: Attribute-based Crowd Entity Resolution: Technical Report

abstract: We study the problem of using the crowd to perform entity resolution (ER) on a set of records. For many types of records, especially those involving images, such a task can be difficult for machines, but relatively easy for humans. Typical crowd-based ER approaches ask workers for pairwise judgments between records, which quickly becomes prohibitively expensive even for moderate numbers of records. In this paper, we reduce the cost of pairwise crowd ER approaches by soliciting the crowd for attribute labels on records, and then asking for pairwise judgments only between records with similar sets of attribute labels. However, due to errors induced by crowd-based attribute labeling, a naive attribute-based approach becomes extremely inaccurate even with few attributes. To combat these errors, we use error mitigation strategies which allow us to control the accuracy of our results while maintaining significant cost reductions. We develop a probabilistic model which allows us to determine the optimal, lowest-cost combination of error mitigation strategies needed to achieve a minimum desired accuracy. We test our approach with actual crowdworkers on a dataset of celebrity images, and find that our results yield crowd ER strategies which achieve high accuracy yet are significantly lower cost than pairwise-only approaches.


title: Agile Teams in Large-Scale Distributed Context: Isolated or Connected?

abstract: Background: Agile development heavily relies on effective communication and coordination. Large-scale software development, where many teams are involved in developing often complex projects - even more so. As projects grow more complex, there is a finite limit to the amount of information that one person or a team within that project can possess at a given time. When we have one or two agile teams in a project, achieving good communication and coordination is easy. In fact, agile teams can be self-going. However, when there are many teams, should they be governed and supported or autonomous? Can they work in isolation? Aim: We aim to understand how isolated or connected are the teams in large-scale software development and answer a number of questions: Who supports the teams in their daily work? How often do teams communicate and coordinate with external contacts? Are supporting roles available? Method: We use the data from 10 teams from two large-scale distributed development projects. Quantitative data was collected through a survey and qualitative data was obtained from interviews and focus groups. We analyzed teams' knowledge networks using social network analysis techniques. Results: In this paper, we identify a number of governance roles supporting the teams, and discuss their importance, knowledge exchange frequency and contact availability in the context of large-scale distributed projects. Conclusion: We conclude that teams interact with a large number of supporting roles grouped into four categories. Although the frequency of interaction differed between the two projects, the amount of interaction was quite high. We thus conclude that agile teams in large-scale development depend on the knowledge and information that resides outside of the team, which means that they are not fully self-going and autonomous. We also found that there are larger differences between networking behavior of teams working in the same project than across the two projects. And finally, our results suggest that distribution and distance introduces barriers to networking across remote locations.


title: Spatial Pyramid Pooling Mechanism in 3D Convolutional Network for Sentence-Level Classification

abstract: In this paper, we investigate the usage of the convolutional neural network (CNN) to propose a novel end-to-end language processing structure to model textual data for this task. In particular, we propose a 3D CNN structure for the task, which is featured by spatial pyramid pooling (SPP). To our knowledge, it is the first time that 3D convolution and SPP structure are applied together in language processing issues. Compared with methods of 2D CNNs, the proposed method can effectively and efficiently capture the complicated internal relations in sentences. Furthermore, in previous work, the issue of sentence length variety is usually addressed by padding zero to make all sentences vectors to a fixed length, which causes too much redundant and useless noise. Inspired by the SPP structure for object detection in image processing, this issue can be well handled with the SPP, which divides the sentences into several length sections for respective pooling processing. Experiments are conducted for the task of sentence classification as well as relation classification. Experiments on Stanford Treebank, TREC, subj, and Yelp datasets demonstrate that our proposed method can outperform other state-of-the-art models, with respect to classification accuracy. Auxiliary attempts to leverage our method to SemEval-2010 Task 8 dataset further substantiate the model's capability of extracting features efficiently.


title: Learning FRAME Models Using CNN Filters

abstract: The convolutional neural network (ConvNet or CNN) has proven to be very successful in many tasks such as those in computer vision. In this conceptual paper, we study the generative perspective of the discriminative CNN. In particular, we propose to learn the generative FRAME (Filters, Random field, And Maximum Entropy) model using the highly expressive filters pre-learned by the CNN at the convolutional layers. We show that the learning algorithm can generate realistic and rich object and texture patterns in natural scenes. We explain that each learned model corresponds to a new CNN unit at a layer above the layer of filters employed by the model. We further show that it is possible to learn a new layer of CNN units using a generative CNN model, which is a product of experts model, and the learning algorithm admits an EM interpretation with binary latent variables.


title: An Efficient Sentence-based Sentiment Analysis for Expressive Text-to-speech using Fuzzy Neural Network

abstract: In recent years, speech processing has become an active research area in the field of signal processing due to the usage of automated systems for spoken language interface. In developed countries, the customer service with automated system in speech synthesis has been the recent trend. The existing automated speech synthesis systems have certain problems during the real time implementation such as lack of naturalness in output speech, lack of emotions and so on. In this study, the novel Text to Speech system is introduced along with the sentiment analysis in Tamil language. The input text is first classified into the positive, negative and neutral based on the emotions in the sentence then the text is converted into speech with emotions during TTS conversion. Existing approaches used neural network based classifiers for classification. But, neural networks have certain drawbacks in real time training. So, this research study uses Fuzzy Neural Network (FNN) to classify the sentence based on the emotions. The text to speech with sentiment analysis effective scheme which is evaluated using Doordarshan news Tamil dataset. The proposed scheme is implemented using MATLAB. This TTS system has several social applications, especially in railway stations where the announcements can be made through expressive speech.


title: Aten: A Dispatcher for Big Data Applications in Heterogeneous Systems

abstract: Stream Processing Engines (SPEs) have to support high data ingestion to ensure the quality and efficiency for the end-user or a system administrator. The data flow processed by SPE fluctuates over time, and requires real-time or near real-time resource pool adjustments (network, memory, CPU and other). This scenario leads to the problem known as skewed data production caused by the non-uniform incoming flow at specific points on the environment, resulting in slow down of applications caused by network bottlenecks and inefficient load balance. This work proposes Aten as a solution to overcome unbalanced data flows processed by Big Data Stream applications in heterogeneous systems. Aten manages data aggregation and data streams within message queues, assuming different algorithms as strategies to partition data flow over all the available computational resources. The paper presents preliminary results indicating that is possible to maximize the throughput and also provide low latency levels for SPEs.


title: The Digital Future is Now: A Call to Action for the Humanities

abstract: Author(s): Borgman, Christine L. | Abstract: The digital humanities are at a critical moment in the transition from a specialty area to a full-fledged community with a common set of methods, sources of evidence, and infrastructure – all of which are necessary for achieving academic recognition. As budgets are slashed and marginal programs are eliminated in the current economic crisis, only the most articulate and productive will survive. Digital collections are proliferating, but most remain difficult to use, and digital scholarship remains a backwater in most humanities departments with respect to hiring, promotion, and teaching practices. Only the scholars themselves are in a position to move the field forward. Experiences of the sciences in their initiatives for cyberinfrastructure and eScience offer valuable lessons. Information- and data-intensive, distributed, collaborative, and multi-disciplinary research is now the norm in the sciences, while remaining experimental in the humanities. Discussed here are six factors for comparison, selected for their implications for the future of digital scholarship in the humanities: publication practices, data, research methods, collaboration, incentives, and learning. Drawing upon lessons gleaned from these comparisons, humanities scholars are “called to action” with five questions to address as a community: What are data? What are the infrastructure requirements? Where are the social studies of digital humanities? What is the humanities laboratory of the 21st century? What is the value proposition for digital humanities in an era of declining budgets?


title: Multi-threaded Sparse Matrix-Matrix Multiplication for Many-Core and GPU Architectures

abstract: Abstract Sparse matrix-matrix multiplication is a key kernel that has applications in several domains such as scientific computing and graph analysis. Several algorithms have been studied in the past for this foundational kernel. In this paper, we develop parallel algorithms for sparse matrix-matrix multiplication with a focus on performance portability across different high performance computing architectures. The performance of these algorithms depend on the data structures used in them. We compare different types of accumulators in these algorithms and demonstrate the performance difference between these data structures. Furthermore, we develop a meta-algorithm, kkSpGEMM , to choose the right algorithm and data structure based on the characteristics of the problem. We show performance comparisons on three architectures and demonstrate the need for the community to develop two phase sparse matrix-matrix multiplication implementations for efficient reuse of the data structures involved.


title: Sliding mode control design principles and applications to electric drives

abstract: The basic concepts, mathematics, and design aspects of variable-structure systems as well as those with sliding modes as a principle operation mode are treated. The main arguments in favor of sliding-mode control are order reduction, decoupling design procedure, disturbance rejection, insensitivity to parameter variations, and simple implementation by means of power converters. The control algorithms and data processing used in variable structure systems are analyzed. The potential of sliding mode control methodology is demonstrated for versatility of electric drives and functional goals of control. >


title: Large-scale physiological waveform retrieval via locality-sensitive hashing

abstract: We propose a fast, scalable locality-sensitive hashing method for the problem of retrieving similar physiological waveform time series. When compared to the naive k-nearest neighbor search, the method vastly speeds up the retrieval time of similar physiological waveforms without sacrificing significant accuracy. Our result shows that we can achieve 95% retrieval accuracy or better with up to an order of magnitude of speed-up. The extra time required in advance to create the optimal data structure is recovered when query quantity equals 15% of the repository, while the method incurs a trivial additional memory cost. We demonstrate the effectiveness of this method on an arterial blood pressure time series dataset extracted from the ICU physiological waveform repository of the MIMIC-II database.


title: Improving the Light-Load Regulation Capability of LLC Series Resonant Converter Using Impedance Analysis

abstract: Generally, an LLC series resonant converter ( LLC SRC) is an attractive topology for applications, which require wide input variation and high conversion efficiency because of its wide gain capability and soft-switching capability. However, there is a regulation problem in which the output voltage increases as the load current decreases. In this paper, Bode plot and impedance asymptote analysis were conducted to obtain an intuitive sense of the regulation characteristic of LLC SRC under the light-load condition. Moreover, to improve the regulation capability, a new resonant tank with an additional capacitor is proposed. Its design guidelines were determined by Bode plot and impedance asymptote analysis. Therefore, the proposed LLC SRC achieves very light load regulation, while it maintains the advantages of typical LLC SRCs.


title: A Cryptographic Analysis of the TLS 1.3 draft-10 Full and Pre-shared Key Handshake Protocol

abstract: We analyze the handshake protocol of TLS 1.3 draft-ietf-tls-tls13-10 (published October 2015). This continues and extends our previous analysis (CCS 2015, Cryptology ePrint Archive 2015) of former TLS 1.3 drafts (draft-ietf-tls-tls13-05 and draft-ietf-tls-tls13-dh-based). Here we show that the full (EC)DHE Diffie–Hellman-based handshake of draft-10 is also secure in the multi-stage key exchange framework of Fischlin and Gunther which captures classical Bellare–Rogaway key secrecy for key exchange protocols that derive multiple keys. We also note that a recent protocol change—the introduction of a NewSessionTicket message for resumption, encrypted under the application traffic key—impairs the protocol modularity and hence our compositional guarantees that ideally would allow an independent analysis of the record protocol. We additionally analyze the pre-shared key modes (with and without ephemeral Diffie–Hellman key), and fit them into the composability framework, addressing composability with the input resumption secret from a previous handshake and of the output session keys.


title: BRIEF REPORTS Exploring the Validity of the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) with Established Emotions Measures

abstract: Emotions measures represent an important means of obtaining construct validity evidence for emotional intelligence (EI) tests because they have the same theoretical underpinnings. Additionally, the extent to which both emotions and EI measures relate to intelligence is poorly understood. The current study was designed to address these issues. Participants (N 138) completed the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT), two emotions measures, as well as four intelligence tests. Results provide mixed support for the model hypothesized to underlie the MSCEIT, with emotions research and EI measures failing to load on the same factor. The emotions measures loaded on the same factor as intelligence measures. The validity of certain EI components (in particular, Emotion Perception), as currently assessed, appears equivocal.


title: Cracking Intel Sandy Bridge's Cache Hash Function

abstract: On Intel Sandy Bridge processor, last level cache (LLC) is divided into cache slices and all physical addresses are distributed across the cache slices using an hash function. With this undocumented hash function existing, it is impossible to implement cache partition based on page coloring. This article cracks the hash functions on two types of Intel Sandy processors by converting the problem of cracking the hash function to the problem of classifying data blocks into different groups based on eviction relationship existing between data blocks that are mapped to the same cache set. Based on the cracking result, this article proves that it's possible to implement cache partition based on page coloring on cache indexed by hashing.


3 D ACTION RECOGNITION USING DATA VISUALIZATION AND CONVOLUTIONAL NEURAL NETWORKS


Expressing emotion through posture and gesture Introduction


title: Mapping brain maturation and cognitive development during adolescence

abstract: Non-invasive mapping of brain structure and function with magnetic resonance imaging (MRI) has opened up unprecedented opportunities for studying the neural substrates underlying cognitive development. There is an emerging consensus of a continuous increase throughout adolescence in the volume of white matter, both global and local. There is less agreement on the meaning of asynchronous age-related decreases in the volume of grey matter in different cortical regions; these might equally represent loss (‘pruning’) or gain (intra-cortical myelination) of tissue. Functional MRI studies have so far focused mostly on executive functions, such as working memory and behavioural inhibition, with very few addressing questions regarding the maturation of social cognition. Future directions for research in this area are discussed in the context of processing biological motion and matching perceptions and actions.


title: Face Re-Identification for Digital Signage Applications

abstract: The estimation of soft biometric features related to a person standing in front an advertising screen plays a key role in digital signage applications. Information such as gender, age, and emotions of the user can help to trigger dedicated advertising campaigns to the target user as well as it can be useful to measure the type of audience attending a store. Among the technologies useful to monitor the customers in this context, there are the ones that aim to answer the following question: is a specific subject back to the advertising screen within a time slot? This information can have an high impact on the automatic selection of the advertising campaigns to be shown when a new user or a re-identified one appears in front the smart screen. This paper points out, through a set of experiments, that the re-identification of users appearing in front a screen is possible with a good accuracy. Specifically, we describe a framework employing frontal face detection technology and re-identification mechanism, based on similarity between sets of faces learned within a time slot (i.e., the models to be re-identified) and the set of face patches collected when a user appears in front a screen. Faces are pre-processed to remove geometric and photometric variability and are represented as spatial histograms of Locally Ternary Pattern for re-identification purpose. A dataset composed by different presentation sessions of customers to the proposed system has been acquired for testing purpose. Data have been collected to guarantee realistic variabilities. The experiments have been conducted with a leave-one-out validation method to estimate the performances of the system in three different working scenarios: one sample per presentation session for both testing and training (one-to-one), one sample per presentation session for testing and many for training (one-to-many), as well as considering many samples per presentation sessions for both testing and training (many-to-many). Experimental results on the considered dataset show that an accuracy of 88.73 % with 5 % of false positive can be achieved by using a many-to-many re-identification approach which considers few faces samples in both training and testing.


title: SNAP, Small-world Network Analysis and Partitioning: An open-source parallel graph framework for the exploration of large-scale networks

abstract: We present SNAP (small-world network analysis and partitioning), an open-source graph framework for exploratory study and partitioning of large-scale networks. To illustrate the capability of SNAP, we discuss the design, implementation, and performance of three novel parallel community detection algorithms that optimize modularity, a popular measure for clustering quality in social network analysis. In order to achieve scalable parallel performance, we exploit typical network characteristics of small-world networks, such as the low graph diameter, sparse connectivity, and skewed degree distribution. We conduct an extensive experimental study on real-world graph instances and demonstrate that our parallel schemes, coupled with aggressive algorithm engineering for small-world networks, give significant running time improvements over existing modularity-based clustering heuristics, with little or no loss in clustering quality. For instance, our divisive clustering approach based on approximate edge betweenness centrality is more than two orders of magnitude faster than a competing greedy approach, for a variety of large graph instances on the Sun Fire T2000 multicore system. SNAP also contains parallel implementations of fundamental graph-theoretic kernels and topological analysis metrics (e.g., breadth-first search, connected components, vertex and edge centrality) that are optimized for small- world networks. The SNAP framework is extensible; the graph kernels are modular, portable across shared memory multicore and symmetric multiprocessor systems, and simplify the design of high-level domain-specific applications.


title: Can beyond-CMOS devices illuminate dark silicon?

abstract: Throughout the last decade, the microprocessor industry has been struggling to preserve the benefits of Moore's Law scaling. The persistent scaling of CMOS technology no longer yields exponential performance gains due in part to the growth of dark silicon. With each subsequent technology node generation, power constraints resulting from factors such as sub-threshold leakage currents are projected to further limit the number of transistors that can be simultaneously powered. To overcome the limits of CMOS devices, researchers are working to develop “beyond-CMOS” device technologies. To determine the most promising beyond-CMOS devices, it is necessary to benchmark them against CMOS. In this paper, we present the design and validation of an analytical benchmarking model that evaluates CMOS and beyond-CMOS devices at the architectural-level. Our model is built from the device to the architectural/application-level. Our target architecture is a symmetric multi-core processor executing highly parallel applications (i.e., PARSEC). As a case study, we select one class of promising beyond-CMOS devices, tunneling field-effect transistors, to evaluate against CMOS.


title: The value of online surveys

abstract: Purpose – To provide a thorough analysis of the role of the internet in survey research and to discuss the implications of online surveys becoming such a major force in research.Design/methodology/approach – The paper is divided into four major sections: an analysis of the strengths and potential weaknesses of online surveys; a comparison of online surveys with other survey formats; a discussion on the best uses for online surveys and how their potential weaknesses may be moderated; and an overview of the online survey services being offered by the world's largest research firms.Findings – If conducted properly, online surveys have significant advantages over other formats. However, it is imperative that the potential weaknesses of online surveys be mitigated and that online surveys only be used when appropriate. Outsourcing of online survey functions is growing in popularity.Practical implications – The paper provides a very useful source of information and impartial advice for any professional who is co...


title: Complex trauma in children and adolescents: evidence-based practice in clinical settings

abstract: Complex trauma (CT) results from exposure to severe stressors that occur within the caregiver system or with another presumably responsible adult, are repetitive, and begin in childhood or adolescence. As a result, many of these children and adolescents experience lifelong difficulties related to self-regulation, relationships, psychological symptoms, alterations in attention and consciousness, self-injury, identity, and cognitive distortions. The aims of this article include the following: (a) to examine several representative approaches identified as treatments for children and adolescents exposed to CT with respect to similarities and differences; (b) to examine representative evidence of model effectiveness; (c) to discuss how these approaches are and/or could be implemented in clinical practice; and (d) to suggest research designs that would facilitate greater translation of effective treatment into clinical settings. Language: en


title: Improving Models of Slipping, Guessing, and Moment-By-Moment Learning with Estimates of Skill Difficulty

abstract: Over the past several years, several extensions to Bayesian knowledge tracing have been proposed in order to improve predictions of students’ in-tutor and post-test performance. One such extension is Contextual Guess and Slip, which incorporates machine-learned models of students’ guess and slip behaviors in order to enhance the overall model’s predictive performance [Baker et al. 2008a]. Similar machine learning approaches have been introduced in order to detect specific problem-solving steps during which students most likely learned particular skills [Baker, Goldstein, and Heffernan in press]. However, one important class of features that have not been considered in machine learning models used in these two techniques is metrics of item and skill difficulty, a key type of feature in other assessment frameworks [e.g Hambleton, Swaminathan, & Rogers, 1991; Pavlik, Cen, & Koedinger 2009]. In this paper, a set of engineered features that quantify skill difficulty and related skill-level constructs are investigated in terms of their ability to improve models of guessing, slipping, and detecting moment-by-moment learning. Supervised machine learning models that have been trained using the new skill-difficulty features are compared to models from the original contextual guess and slip and moment-by-moment learning detector work. This includes performance comparisons for predicting students’ in-tutor responses, as well as post-test responses, for a pair of Cognitive Tutor data sets.


Exploring Optimizations to Paragraph Vectors


title: Finding and counting given length cycles

abstract: We present an assortment of methods for finding and counting simple cycles of a given length in directed and undirected graphs. Most of the bounds obtained depend solely on the number of edges in the graph in question, and not on the number of vertices. The bounds obtained improve upon various previously known results.


title: Design and Implementation of a Direct AC–DC Boost Converter for Low-Voltage Energy Harvesting

abstract: In this paper, a direct ac-dc power electronic converter topology is proposed for efficient and optimum energy harvesting from low-voltage microgenerators. The converter utilizes the bidirectional current-conduction capability of MOSFETs to avoid the use of a front-end bridge rectifier. It is operated in discontinuous conduction mode and offers a resistive load to the microgenerator. Detailed analysis and modeling of the converter is presented. In such low-power applications, the power consumption of gate drive and control circuits should be minimal. In this paper, they are specifically designed to consume very low power. A suitable startup circuit and auxiliary dc supply circuit is proposed for the implementation of the converter. A low-voltage microgenerator is used to verify the performance and operation of the converter and the gate drive circuits.


title: Utilizing Purchase Intervals in Latent Clusters for Product Recommendation

abstract: Collaborative filtering have become increasingly important with the development of Web 2.0. Online shopping service providers aim to provide users with quality list of recommended items that will enhance user satisfaction and loyalty. Matrix factorization approaches have become the dominant method as they can reduce the dimension of the data set and alleviate the sparsity problem. However, matrix factorization approaches are limited because they depict each user as one preference vector. In practice, we observe that users may have different preferences when purchasing different subsets of items, and the periods between purchases also vary from one user to another. In this work, we propose a probabilistic approach to learn latent clusters in the large user-item matrix, and incorporate temporal information into the recommendation process. Experimental results on a real world dataset demonstrate that our approach significantly improves the conversion rate, precision and recall of state-of-the-art methods.


title: Tapping into knowledge base for concept feedback: leveraging conceptnet to improve search results for difficult queries

abstract: Query expansion is an important and commonly used technique for improving Web search results. Existing methods for query expansion have mostly relied on global or local analysis of document collection, click-through data, or simple ontologies such as WordNet. In this paper, we present the results of a systematic study of the methods leveraging the ConceptNet knowledge base, an emerging new Web resource, for query expansion. Specifically, we focus on the methods leveraging ConceptNet to improve the search results for poorly performing (or difficult) queries. Unlike other lexico-semantic resources, such as WordNet and Wikipedia, which have been extensively studied in the past, ConceptNet features a graph-based representation model of commonsense knowledge, in which the terms are conceptually related through rich relational ontology. Such representation structure enables complex, multi-step inferences between the concepts, which can be applied to query expansion. We first demonstrate through simulation experiments that expanding queries with the related concepts from ConceptNet has great potential for improving the search results for difficult queries. We then propose and study several supervised and unsupervised methods for selecting the concepts from ConceptNet for automatic query expansion. The experimental results on multiple data sets indicate that the proposed methods can effectively leverage ConceptNet to improve the retrieval performance of difficult queries both when used in isolation as well as in combination with pseudo-relevance feedback.


title: Fast image alignment in the Fourier domain

abstract: In this paper we propose a framework for gradient descent image alignment in the Fourier domain. Specifically, we propose an extension to the classical Lucas & Kanade (LK) algorithm where we represent the source and template image's intensity pixels in the complex 2D Fourier domain rather than in the 2D spatial domain. We refer to this approach as the Fourier LK (FLK) algorithm. The FLK formulation is especially advantageous, over traditional LK, when it comes to pre-processing the source and template images with a bank of filters (e.g., Gabor filters) as: (i) it can handle substantial illumination variations, (ii) the inefficient pre-processing filter bank step can be subsumed within the FLK algorithm as a sparse diagonal weighting matrix, (iii) unlike traditional LK the computational cost is invariant to the number of filters and as a result far more efficient, (iv) this approach can be extended to the inverse compositional form of the LK algorithm where nearly all steps (including Fourier transform and filter bank pre-processing) can be pre-computed leading to an extremely efficient and robust approach to gradient descent image matching. We demonstrate robust image matching performance on a variety of objects in the presence of substantial illumination differences with exactly the same computational overhead as that of traditional inverse compositional LK during fitting.


title: Requirement Analysis Method of E-Commerce Websites Development for Small-Medium Enterprises, Case Study: Indonesia

abstract: Along with the growth of the Internet, the trend shows that e-commerce have been growing significantly in the last several years. This means business opportunities for small-medium enterprises (SMEs), which are recognized as the backbone of the economy. SMEs may develop and run small to medium size of particular e-commerce websites as the solution of specific business opportunities. Certainly, the websites should be developed accordingly to support business success. In developing the websites, key elements of e-commerce business model that are necessary to ensure the success should be resolved at the requirement stage of the development. In this paper, we propose an enhancement of requirement analysis method found in literatures such that it includes activities to resolve the key elements. The method has been applied in three case studies based on Indonesia situations and we conclude that it is suitable to be adopted by SMEs.


title: Predicting movie Box-office revenues by exploiting large-scale social media content

abstract: Predicting the box-office revenue of a movie before its theatrical release is an important but challenging problem that requires a high level of Artificial Intelligence. Nowadays, social media has shown its predictive power in various domains, which motivates us to exploit social media content to predict box-office revenues. In this study, we employ both linear and non-linear regression models, which are based on the crowd wisdom of social media, especially the posts of users, to predict movie box-office revenues. More specifically, the attention and popularity of the movie, purchase intention of users, and comments of users are automatically mined from social media data. In our model, the use of Linear Regression and Support Vector Regression in predicting the box-office revenue of a movie before its theatrical release is explored. To evaluate the effectiveness of the proposed approach, a cross-validation experiment is conducted. The experimental results show that large-scale social media content is correlated with movie box-office revenues and that the purchase intention of users can lead to more accurate movie box-office revenue predictions. Both the linear and non-linear prediction models have the advantage of predicting movie grosses in our experiments.


title: Review: The use of pervasive sensing for behaviour profiling - a survey

abstract: With the maturity of sensing and pervasive computing techniques, extensive research is being carried out in using different sensing techniques for understanding human behaviour. An introduction to key modalities of pervasive sensing is presented. Behaviour modelling is then highlighted with a focus on probabilistic models. The survey discusses discriminative approaches as well as relevant work on behaviour pattern clustering and variability. The influence of interacting with people and objects in the environment is also discussed. Finally, challenges and new research opportunities are highlighted.


title: A continuous linear optimal transport approach for pattern analysis in image datasets

abstract: We present a new approach to facilitate the application of the optimal transport metric to pattern recognition on image databases. The method is based on a linearized version of the optimal transport metric, which provides a linear embedding for the images. Hence, it enables shape and appearance modeling using linear geometric analysis techniques in the embedded space. In contrast to previous work, we use Monge's formulation of the optimal transport problem, which allows for reasonably fast computation of the linearized optimal transport embedding for large images. We demonstrate the application of the method to recover and visualize meaningful variations in a supervised-learning setting on several image datasets, including chromatin distribution in the nuclei of cells, galaxy morphologies, facial expressions, and bird species identification. We show that the new approach allows for high-resolution construction of modes of variations and discrimination and can enhance classification accuracy in a variety of image discrimination problems. HighlightsA continuous version of the LOT framework is described that bypasses many of the difficulties associated with the discrete formulation.Using continuous transport maps, a forward and inverse transform operation is defined for images.An improved reference/average image estimation algorithm is proposed.The range within which points in LOT space are invertible according to the continuous formulation is calculated.We show that our method significantly speeds up the computation of the LOT embedding.


title: Paparazzi: surface editing by way of multi-view image processing

abstract: The image processing pipeline boasts a wide variety of complex filters and effects. Translating an individual effect to operate on 3D surface geometry inevitably results in a bespoke algorithm. Instead, we propose a general-purpose back-end optimization that allows users to edit an input 3D surface by simply selecting an off-the-shelf image processing filter. We achieve this by constructing a differentiable triangle mesh renderer, with which we can back propagate changes in the image domain to the 3D mesh vertex positions. The given image processing technique is applied to the entire shape via stochastic snapshots of the shape: hence, we call our method Paparazzi. We provide simple yet important design considerations to construct the Paparazzi renderer and optimization algorithms. The power of this rendering-based surface editing is demonstrated via the variety of image processing filters we apply. Each application uses an off-the-shelf implementation of an image processing method without requiring modification to the core Paparazzi algorithm.


title: Towards Safer Smart Contracts: A Survey of Languages and Verification Methods

abstract: With a market capitalisation of over USD 205 billion in just under ten years, public distributed ledgers have experienced significant adoption. Apart from novel consensus mechanisms, their success is also accountable to smart contracts. These programs allow distrusting parties to enter agreements that are executed autonomously. However, implementation issues in smart contracts caused severe losses to the users of such contracts. Significant efforts are taken to improve their security by introducing new programming languages and advance verification methods. We provide a survey of those efforts in two parts. First, we introduce several smart contract languages focussing on security features. To that end, we present an overview concerning paradigm, type, instruction set, semantics, and metering. Second, we examine verification tools and methods for smart contract and distributed ledgers. Accordingly, we introduce their verification approach, level of automation, coverage, and supported languages. Last, we present future research directions including formal semantics, verified compilers, and automated verification.


Malware normalization


title: FreePDK15: An Open-Source Predictive Process Design Kit for 15nm FinFET Technology

abstract: This paper discusses design rules and layout guidelines for an open source predictive process design kit (PDK) for multi-gate 15nm FinFET devices. Additional design rules are introduced considering process variability, and challenges involved in fabrication beyond 20nm. Particularly, double patterning lithography is assumed and a unique set of design rules are developed for critical dimensions. In order to improve the FinFET layout density, Middle-of-line local interconnect layers are implemented for the FinFET layout. The rules are further validated by running Calibre design-rule checks on Virtuoso layout of an Inverter and NAND4 cells. As part of the validation process, the area of a FreePDK15 inverter was compared to the area of an inverter in 45nm bulk MOS process and the ratio was found to be 1:6. This kit primarily aims to support introduction of sub-20nm FinFET devices into research and universities.


title: Autonomous operation of hybrid microgrid with AC and DC sub-grids

abstract: This paper investigates on the active and reactive power sharing of an autonomous hybrid microgrid. Unlike existing microgrids which are purely ac, the hybrid microgrid studied here comprises dc and ac sub-grids, interconnected by power electronic interfaces. The main challenge here is to manage the power flow among all the sources distributed throughout the two types of sub-grids, which certainly is tougher than previous efforts developed for only either ac or dc microgrid. This wider scope of control has not yet been investigated, and would certainly rely on the coordinated operation of dc sources, ac sources and interlinking converters. Suitable control and normalization schemes are therefore developed for controlling them with results presented for showing the overall performance of the hybrid microgrid.


IOT Based Smart Gas Monitoring System


title: WSRec: A Collaborative Filtering Based Web Service Recommender System

abstract: As the abundance of Web services on the World Wide Web increase,designing effective approaches for Web service selection and recommendation has become more and more important. In this paper, we present WSRec, a Web service recommender system, to attack this crucial problem. WSRec includes a user-contribution mechanism for Web service QoS information collection and an effective and novel hybrid collaborative filtering algorithm for Web service QoS value prediction. WSRec is implemented by Java language and deployed to the real-world environment. To study the prediction performance, A total of 21,197 public Web services are obtained from the Internet and a large-scale real-world experiment is conducted, where more than 1.5 millions test results are collected from 150 service users in different countries on 100 publicly available Web services located all over the world. The comprehensive experimental analysis shows that WSRec achieves better prediction accuracy than other approaches.


title: Advertising power consumption of bluetooth low energy systems

abstract: Bluetooth Low Energy (BLE) is a 2.4 GHz wireless technology which has been widely adopted in consumer devices. Available in most modern smartphones, it is a popular choice for connecting battery powered devices like sensors or beacons to mobile handsets. A low energy consumption, while being beneficial for all BLE applications, becomes a key requirement for applications containing larger numbers of BLE devices like smart home sensors or beacons in indoor localization due to the high cost of battery replacement. In this paper, we investigate the energy consumption of different BLE System on Chips (SoCs) which provide the foundation to most BLE devices. Based on extensive measurements, we present a vendor-independent model for BLE power consumption which allows energy consumption prediction from key design parameters. This helps in the tradeoff between engergy consumption and BLE performance. We concentrate on advertising mode which governs the energy consumption in popular applications like beacons.


title: The paradoxical success of fuzzy logic

abstract: This paper investigates the question of which aspects of fuzzy logic are essential to its practical usefulness. We show that as a formal system, a standard version of fuzzy logic collapses mathematically to two-valued logic, while empirically, fuzzy logic is not adequate for reasoning about uncertain evidence in expert systems. Nevertheless, applications of fuzzy logic in heuristic control have been highly successful. We argue that the inconsistencies of fuzzy logic have not been harmful in practice because current fuzzy controllers are far simpler than other knowledge-based systems. In the future, the technical limitations of fuzzy logic can be expected to become important in practice, and work on fuzzy controllers will also encounter several problems of scale already known for other knowledge-based systems.


title: DNN-based enhancement of noisy and reverberant speech

abstract: In the real world, speech is usually distorted by both reverberation and background noise. In such conditions, speech intelligibility is degraded substantially, especially for hearing-impaired (HI) listeners. As a consequence, it is essential to enhance speech in the noisy and reverberant environment. Recently, deep neural networks have been introduced to learn a spectral mapping to enhance corrupted speech, and shown significant improvements in objective metrics and automatic speech recognition score. However, listening tests have not yet shown any speech intelligibility benefit. In this paper, we propose to enhance the noisy and reverberant speech by learning a mapping to reverberant target speech rather than anechoic target speech. A preliminary listening test was conducted, and the results show that the proposed algorithm is able to improve speech intelligibility of HI listeners in some conditions. Moreover, we develop a masking-based method for denoising and compare it with the spectral mapping method. Evaluation results show that the masking-based method outperforms the mapping-based method.


title: Controllability of Multi-Agent Systems from a Graph-Theoretic Perspective

abstract: In this work, we consider the controlled agreement problem for multi-agent networks, where a collection of agents take on leader roles while the remaining agents execute local, consensus-like protocols. Our aim is to identify reflections of graph-theoretic notions on system-theoretic properties of such systems. In particular, we show how the symmetry structure of the network, characterized in terms of its automorphism group, directly relates to the controllability of the corresponding multi-agent system. Moreover, we introduce network equitable partitions as a means by which such controllability characterizations can be extended to the multileader setting.


title: DeBot: Twitter Bot Detection via Warped Correlation

abstract: We develop a warped correlation finder to identify correlated user accounts in social media websites such as Twitter. The key observation is that humans cannot be highly synchronous for a long duration, thus, highly synchronous user accounts are most likely bots. Existing bot detection methods are mostly supervised, which requires a large amount of labeled data to train, and do not consider cross-user features. In contrast, our bot detection system works on activity correlation without requiring labeled data. We develop a novel lag-sensitive hashing technique to cluster user accounts into correlated sets in near real-time. Our method, named DeBot, detects thousands of bots per day with a 94% precision and generates reports online everyday. In September 2016, DeBot has accumulated about 544,868 unique bots in the previous one year. We compare our detection technique with per-user techniques and with Twitter's suspension system. We observe that some bots can avoid Twitter's suspension mechanism and remain active for months, and, more alarmingly, we show that DeBot detects bots at a rate higher than the rate Twitter is suspending them.


Decolorization, Degradation and Subsequent Toxicity Assessment of Reactive Red 35 by Enterococcus gallinarum


title: A Blind CNN Denoising Model for Random-Valued Impulse Noise

abstract: Denoising convolutional neural networks (DnCNNs), initially developed for Gaussian noise removal, are powerful nonlinear mapping models in image processing. After changes in training data, they can be used for suppression of random-valued impulse noise (RVIN) with excellent results. To achieve favorable denoising performance, however, it is necessary to have an accurate perception of the noise ratio so that the most suitable DnCNN can be chosen for denoising. Thus, this model is severely limited in flexibility. To address this problem, we propose a blind CNN model for RVIN denoising with a flexible noise ratio predictor (NRP) as an indicator. Some patches are randomly selected from the RVIN-corrupted test image, and feature vectors that indicate whether the center pixel is contaminated or not are extracted by the predictor. These feature vectors are composed of multiple statistics, namely, the multiple rank-ordered absolute differences (ROADs), the clean pixel median deviation (CPMD), and the edge pixel difference (EPD). They are rapidly mapped to noise/clean (1 for noise, 0 for clean) labels by the pre-trained noise detector (the key component of our NRP). According to the ratio of the obtained noisy labels to the total number of selected patches, the predictor provides the noise ratio of the whole image. From the output of the NRP, i.e., the predicted noise ratio, the most appropriate DnCNN specifically trained for this noise ratio is exploited for denoising. Under the guidance of the NRP, the proposed method has the ability to handle unknown noise ratios. Simulation results indicate that our blind denoising CNN model achieves state-of-the-art performance in terms of both execution efficiency and restoration results.


title: Exploiting Facial Landmarks for Emotion Recognition in the Wild

abstract: In this paper, we describe an entry to the third Emotion Recognition in the Wild Challenge, EmotiW2015. We detail the associated experiments and show that, through more accurately locating the facial landmarks, and considering only the distances between them, we can achieve a surprising level of performance. The resulting system is not only more accurate than the challenge baseline, but also much simpler.


title: eWOM source credibility, perceived risk and food product customer's information adoption

abstract: This study examined the relations between source credibility of eWOM (electronic word of mouth), perceived risk and food products customer's information adoption mediated by argument quality and information usefulness. eWOM has been commonly used to refer the customers during decision-making process for food commodities. Based on this study, we used Elaboration Likelihood Model of information adoption presented by Sussman and Siegal (2003) to check the willingness to buy. Non-probability purposive samples of 300 active participants were taken through questionnaire from several regions of the Republic of China and analyzed the data through structural equation modeling (SEM) accordingly. We discussed that whether eWOM source credibility and perceived risk would impact the degree of information adoption through argument quality and information usefulness. It reveals that eWOM has positively influenced on perceived risk by source credibility to the extent of information adoption and, for this, customers use eWOM for the reduction of the potential hazards when decision making. Companies can make their marketing strategies according to their target towards loyal clients' needs through online food- product forums review sites.


Diagnosis of canine atopic dermatitis (including food allergy)


title: 3D metal printed Ku/Ka Band modified turnstile junction Orthomode Transducer

abstract: Orthomode Transducers (OMTs) are key components in dual-polarized satellite communication systems. Turnstile junction (TJ) OMTs are widely popular since their structural symmetry suppresses the undesired higher order modes, which degrade impedance matching and isolation of the OMT. Several works on TJ OMTs operating in single (or narrow) frequency band have been published. In contrast, this paper presents a modified TJ OMT designed for multiple frequency bands: Ku-Band (10.7 GHz–12.75 GHz), lower Ka-Band (19.5 GHz–20.5 GHz) and upper Ka-Band (29.2 GHz–30.2 GHz). Unlike ordinary TJ OMT, the modified OMT besides discriminating the orthogonal polarization signals, also acts as a frequency diplexer. The OMT was manufactured using cost-effective 3D metal printing. The simulated and measured S-parameters were evaluated in Ku, lower and upper Ka bands. The OMT functionality was verified through field test measurement.


title: A Feature Based Approach for Sentiment Analysis by Using Support Vector Machine

abstract: In this modern era of globalization, e-commerce has become one of the most convenient ways to shop. Every day people buy many products through online and post their reviews about the product which they have used. These reviews play a vital role in determining how far a product has been placed in consumers' psyche. So that the manufacturer can modify the features of the product as required and on the other hand these will also help the new consumers to decide on whether to buy the product or not. However, it would be a tedious task to manually extract overall opinion out of enormous unstructured data. This problem can be addressed by an automated system called 'Sentiment Analysis and Opinion Mining' that can analyze and extract the users' perception in the whole reviews. In our work we have developed an overall process of 'Aspect or Feature based Sentiment Analysis' by using a classifier called Support Vector Machine (SVM) in a novel approach. It is proved to be one of the most effective ways to analyze and extract the overall users' view about the particular feature and whole product as well.


title: Evaluation of Docker as Edge computing platform

abstract: High latency, network congestion and network bottleneck are some of problems in cloud computing. Moving from centralized to decentralized paradigm, Edge computing could offload the processing to the edge which indirectly reduces application response time and improves overall user experience. This paper evaluate Docker, a container based technology as a platform for Edge Computing. 4 fundamental criteria were evaluated 1) deployment and termination, 2) resource & service management, 3) fault tolerance and 4) caching. Based on our evaluation and experiment Docker provides fast deployment, small footprint and good performance which make it potentially a viable Edge Computing platform.


title: Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for Codeine Therapy in the Context of Cytochrome P450 2D6 (CYP2D6) Genotype

abstract: Codeine is bioactivated to morphine, a strong opioid agonist, by the hepatic cytochrome P450 2D6 (CYP2D6); hence, the efficacy and safety of codeine as an analgesic are governed by CYP2D6 polymorphisms. Codeine has little therapeutic effect in patients who are CYP2D6 poor metabolizers, whereas the risk of morphine toxicity is higher in ultrarapid metabolizers. The purpose of this guideline (periodically updated at http://www.pharmgkb.org) is to provide information relating to the interpretation of CYP2D6 genotype test results to guide the dosing of codeine.


title: A new drain-current injection technique for the measurement of off-state breakdown voltage in FETs

abstract: A simple three-terminal technique for measuring the off-state breakdown voltage of FETs is presented. With the source grounded, current is injected into the drain of the on-state device. The gate is then ramped down to shut the device off. In this process, the drain-source voltage rises to a peak and then drops. This peak represents an unambiguous definition of three-terminal breakdown voltage. In the same scan, a measurement of the two-terminal gate-drain breakdown voltage is also obtained. The method offers potential for use in a manufacturing environment, as it is fully automatable. It also enables easy measurement of breakdown voltage in unstable and fragile devices. >


SeiSIM : Structural Similarity Evaluation for Seismic Data Retrieval


title: Design and analysis of a non-backdrivable series elastic actuator

abstract: The compliance of series elastic actuators makes them robust to environmental perturbations, and their ability to use impedance control may improve their performance in open-loop environments such as conventional prosthetic control. Series elastic actuators have historically required a backdrivable transmission to ensure adequate bandwidth. The authors have created a non-backdrivable series elastic actuator through the inclusion of a harmonic drive transmission, which contains no backlash; the dominant feature in limiting frequency resolution. The actuator has an acceptable speed and stall torque and an adequate frequency range, and would be used in the future to implement impedance control of prosthetics.


title: A clinical and genetic study of the Say/Barber/Biesecker/Young-Simpson type of Ohdo syndrome

abstract: We report a series of eight patients with the Say/Barber/Biesecker/Young-Simpson (SBBYS) type of Ohdo syndrome, which is the largest cohort described to date. We expand on the type, frequency and severity of the clinical characteristics in this condition; comment on the natural history of Ohdo syndrome and further refine previously published diagnostic criteria. Cytogenetic investigations and microarray CGH analysis undertaken in this cohort of patients failed to identify a chromosomal aetiology. It remains possible that this rare condition is heterogeneous and therefore caution must be undertaken during counselling until the underlying genetic mechanism(s) is (are) identified.