title: Functional neuroanatomy of face and object processing : a positron emission tomography study

abstract: Studies of brain-damaged patients have revealed the existence of a selective impairment of face processing, prosopagnosia, resulting from lesions at different loci in the occipital and temporal lobes. The results of such studies have led to the identification of several cortical areas underlying the processing of faces, but it remains unclear what functional aspects of face processing are served by these areas and whether they are uniquely devoted to the processing of faces. The present study addresses these questions in a positron emission tomography (PET) study of regional cerebral blood flow in normal adults, using the 15 oxygen water bolus technique. The subjects participated in six tasks (with gratings, faces and objects), and the resulting level of cerebral activation was mapped on images of the subjects' cerebral structures obtained through magnetic resonance and was compared between tasks using the subtraction method. Compared with a fixation condition, regional cerebral blood flow (rCBF) changes were found in the striate and extrastriate cortex when subjects had to decide on the orientation of sine-wave gratings. A face-gender categorization resulted in activation changes in the right extrastriate cortex, and a face-identity condition produced additional activation of the fusiform gyrus and anterior temporal cortex of both hemispheres, and of the right parahippocampal gyrus and adjacent areas. Cerebral activation during an object-recognition task occurred essentially in the left occipito-temporal cortex and did not involve the right hemisphere regions specifically activated during the face-identity task. The results provide the first empirical evidence from normal subjects regarding the crucial role of the ventro-medial region of the right hemisphere in face recognition, and they offer new information about the dissociation between face and object processing.


title: The `Star Wars' botnet with>350k Twitter bots

abstract: A large number of Twitter users are bots. They can send spam, manipulate public opinion, and contaminate the Twitter API stream that underline so many research works. One of the major challenges of research on Twitter bots is the lack of ground truth data. Here we report our discovery of the Star Wars botnet with more than 350k bots. We show these bots were generated and centrally controlled by a botmaster. These bots exhibit a number of unique features, which reveal profound limitations of existing bot detection methods. Our work has significant implications for cybersecurity, not only because the size of the botnet is larger than those analysed before, but also because it has been well hidden since its creation in 2013. We argue that more research is needed to fully understand the potential security risks that a large, hidden botnet can pose to the Twitter environment, and research in general.


title: Building web applications on top of encrypted data using Mylar

abstract: Using a Web application for confidential data requires the user to trust the server to protect the data from unauthorized disclosures. This trust is often misplaced, however, because there are many ways in which confidential data could leak from a server. For example, attackers could exploit a vulnerability in the server software to break in, a curious administrator could peek at the data on the server, or the server operator may be compelled to disclose data by law. How can one build Web applications that protect data confidentiality against attackers with full access to servers?


title: Medical Student Mental Health 3.0: Improving Student Wellness Through Curricular Changes

abstract: Medical education can have significant negative effects on the well-being of medical students. To date, efforts to improve student mental health have focused largely on improving access to mental health providers, reducing the stigma and other barriers to mental health treatment, and implementing ancillary wellness programs. Still, new and innovative models that build on these efforts by directly addressing the root causes of stress that lie within the curriculum itself are needed to properly promote student wellness. In this article, the authors present a new paradigm for improving medical student mental health, by describing an integrated, multifaceted, preclinical curricular change program implemented through the Office of Curricular Affairs at the Saint Louis University School of Medicine starting in the 2009-2010 academic year. The authors found that significant but efficient changes to course content, contact hours, scheduling, grading, electives, learning communities, and required resilience/mindfulness experiences were associated with significantly lower levels of depression symptoms, anxiety symptoms, and stress, and significantly higher levels of community cohesion, in medical students who participated in the expanded wellness program compared with those who preceded its implementation. The authors discuss the utility and relevance of such curricular changes as an overlooked component of change models for improving medical student mental health.


Explainable and Interpretable Models in Computer Vision and Machine Learning


State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications


title: An Energy Efficiency Perspective on Rate Adaptation for 802.11n NIC

abstract: Rate adaptation (RA) has been traditionally used to achieve high goodput. In this work, we design RA for 802.11n NICs from an energy-efficiency perspective. We show that current MIMO RA algorithms are not energy efficient for NICs despite ensuring high throughput. The fundamental problem is that, the high-throughput setting is not equivalent to the energy-efficient one. Marginal throughput gain may be realized at high energy cost. We then propose EERA and EERA+, two energy-based RA schemes that trade off goodput for energy savings at NICs. EERA applies multidimensional ternary search and simultaneous pruning to speed up its runtime convergence in single-client operations, and uses fair airtime sharing to handle multiple-client operations. EERA+ further searches for multiple, staged rates to yield more energy savings over EERA. Our experiments have confirmed their effectiveness in various scenarios.


title: A Stable and Effective Learning Strategy for Trainable Greedy Decoding

abstract: Beam search is a widely used approximate search strategy for neural network decoders, and it generally outperforms simple greedy decoding on tasks like machine translation. However, this improvement comes at substantial computational cost. In this paper, we propose a flexible new method that allows us to reap nearly the full benefits of beam search with nearly no additional computational cost. The method revolves around a small neural network actor that is trained to observe and manipulate the hidden state of a previously-trained decoder. To train this actor network, we introduce the use of a pseudo-parallel corpus built using the output of beam search on a base model, ranked by a target quality metric like BLEU. Our method is inspired by earlier work on this problem, but requires no reinforcement learning, and can be trained reliably on a range of models. Experiments on three parallel corpora and three architectures show that the method yields substantial improvements in translation quality and speed over each base system.


title: Optimal Myopic Sensing and Dynamic Spectrum Access in Cognitive Radio Networks with Low-Complexity Implementations

abstract: Cognitive radio techniques allow secondary users (SU's) to opportunistically access underutilized primary channels that are licensed to primary users. We consider a group of SU's with limited spectrum sensing capabilities working cooperatively to find primary channel spectrum holes. The objective is to design the optimal sensing and access policies that maximize the total secondary throughput on primary channels accrued over time. Although the problem can be formulated as a Partially Observable Markov Decision Process (POMDP), the optimal solutions are intractable. Instead, we find the optimal sensing policy within the class of myopic policies. Compared to other existing approaches, our policy is more realistic because it explicitly assigns SU's to sense specific primary channels by taking into account spatial and temporal variations of primary channels. Contributions: (1) formulation of a centralized spectrum sensing/access architecture that allows exploitation of all available primary spectrum holes; and (2) proposing sub-optimal myopic sensing policies with low-complexity implementations and performance close to the myopic policy. We show that our proposed sensing/access policy is close to the optimal POMDP solution and outperforms other proposed strategies. We also propose a Hidden Markov Model based algorithm to estimate the parameters of primary channel Markov models with a linear complexity.


title: Livestock husbandry as a tool for carnivore conservation in Africa’s community rangelands: a case–control study

abstract: Conflict between people and wildlife is a major issue in both wildlife conservation and rural development. In African rangelands, species such as African wild dogs (Lycaon pictus), cheetahs (Acinonyx jubatus), lions (Panthera leo), leopards (Panthera pardus), and spotted hyaenas (Crocuta crocuta) may kill livestock and are therefore themselves killed by local pastoralists. Such conflict has led to the extirpation of these species from many areas, and also impacts the livelihoods of local livestock farmers. To investigate the possibilities for coexistence of people, livestock, and large predators in community rangelands, we measured the effectiveness of traditional livestock husbandry in reducing depredation by wild carnivores, using a case–control approach. Different measures were effective against different predator species but, overall, the risk of predator attack by day was lowest for small herds, accompanied by herd dogs as well as human herders, grazing in open habitat. By night, the risk of attack was lowest for herds held in enclosures (‘bomas’) with dense walls, pierced by few gates, where both men and domestic dogs were present. Unexpectedly, the presence of scarecrows increased the risks of attack on bomas. Our findings suggest that improvements to livestock husbandry can contribute to the conservation and recovery of large carnivores in community rangelands, although other measures such as prey conservation and control of domestic dog diseases are also likely to be necessary for some species.


title: Left superior vena cava: revisited.

abstract: The persistence of a left-sided superior vena cava is the most common variant of systemic venous drainage. Increased utility of cardiac imaging, in particular cross-sectional techniques such as computed tomography and magnetic resonance (MR), will result in increased detection of the anomaly and its variants. Whilst in the typical form it is often haemodynamically insignificant, its discovery may have clinical significance nonetheless, and its mimics require exclusion. During cardiac development the anomaly results from a failure of the left anterior cardinal vein to obliterate. Recognized anatomical variants include the absence of the right superior vena cava and of an innominate bridging vein. Typical drainage is to the coronary sinus, dilatation of which may be the first hint to the anomaly. Clinical implications with respect to vascular access and arrhythmia are well described. A significant minority drain into the left atrium, potentially creating a haemodynamically significant lesion. Additionally, differentiation from anomalous left upper pulmonary venous drainage via a vertical vein is mandatory. A newly discovered variant runs an intra-atrial course with subsequent typical drainage, and if not recognized as such, may be confused with a left atrial mass. The use of 3D contrast-enhanced MR venography has proven extremely helpful in characterizing anomalous vasculature, and we demonstrate how such techniques can help delineate the anomaly and differentiate from its mimics.


title: Use of Social Media for Disaster Management: A Prescriptive Framework

abstract: Social media is emerging as an important information-based communication tool for disaster management. Yet there are many relief organizations that are not able to develop strategies and allocate resources to effectively use social media for disaster management. The reason behind this inability may be a lack of understanding regarding the different functionalities of social media. In this paper, we examine the literature using content analysis to understand the current usage of social media in disaster management. We draw on the honeycomb framework and the results of our content analysis to suggest a new framework that can help in utilizing social media more effectively during the different phases of disaster management. We also discuss the implications of our study.


title: Recognition of Nutrition Intake Using Time-Frequency Decomposition in a Wearable Necklace Using a Piezoelectric Sensor

abstract: Food intake levels, hydration, ingestion rate, and dietary choices are all factors known to impact the risk of obesity. This paper presents a novel wearable system in the form of a necklace, which aggregates data from an embedded piezoelectric sensor capable of detecting skin motion in the lower trachea during ingestion. The skin motion produces an output voltage with varying frequencies over time. As a result, we propose an algorithm based on time-frequency decomposition, spectrogram analysis of piezoelectric sensor signals, to accurately distinguish between food types, such as liquid and solid, hot and cold drinks, and hard and soft foods. The necklace transmits data to a smartphone, which performs the processing of the signals, classifies the food type, and provides visual feedback to the user to assist the user in monitoring their eating habits over time. We compare our spectrogram analysis with other time-frequency features, such as matching pursuit and wavelets. Experimental results demonstrate promise in using time-frequency features, with high accuracy of distinguishing between food categories using spectrogram analysis and extracting key features representative of the unique swallow patterns of various foods.


title: Managing Medical and Insurance Information Through a Smart-Card-Based Information System

abstract: The continuously increased mobility of patients and doctors, in conjunction with the existence of medical groups consisting of private doctors, general practitioners, hospitals, medical centers, and insurance companies, pose significant difficulties on the management of patients' medical data. Inevitably this affects the quality of the health care services provided. The evolving smart card technology can be utilized for the implementation of a secure portable electronic medical record, carried by the patient herself/himself. In addition to the medical data, insurance information can be stored in the smart card thus facilitating the creation of an “intelligent system” supporting the efficient management of patient's data. In this paper we present the main architectural and functional characteristics of such a system. We also highlight how the security features offered by smart cards can be exploited in order to ensure confidentiality and integrity of the medical data stored in the patient cards.


KnightCap: A chess program that learns by combining TD(lambda) with minimax search


Temporal Evolution of Weighted Signed Networks


A kinetic framework for computational geometry


title: Impacts of High Penetration of DFIG Wind Turbines on Rotor Angle Stability of Power Systems

abstract: With the integration of wind power into power systems continues to increase, the impact of high penetration of wind power on power system stability becomes a very important issue. This paper investigates the impact of doubly fed induction generator (DFIG) control and operation on rotor angle stability. Acontrol strategy for both the rotor-side converter (RSC) and grid-side converter (GSC) of the DFIG is proposed to mitigate DFIGs impacts on the system stability. DFIG-GSC is utilized to be controlled as static synchronous compensator (STATCOM) to provide reactive power support during grid faults. In addition, a power system stabilizer (PSS) is implemented in the reactive power control loop of DFIG-RSC. The proposed approaches are validated on a realistic Western System Coordinating Council (WSCC) power system under both small and large disturbances. The simulation results show the effectiveness and robustness of both DFIG-GSC control strategy and PSS to enhance rotor angle stability of power system.


title: English shellcode

abstract: History indicates that the security community commonly takes a divide-and-conquer approach to battling malware threats: identify the essential and inalienable components of an attack, then develop detection and prevention techniques that directly target one or more of the essential components. This abstraction is evident in much of the literature for buffer overflow attacks including, for instance, stack protection and NOP sled detection. It comes as no surprise then that we approach shellcode detection and prevention in a similar fashion. However, the common belief that components of polymorphic shellcode (e.g., the decoder) cannot reliably be hidden suggests a more implicit and broader assumption that continues to drive contemporary research: namely, that valid and complete representations of shellcode are fundamentally different in structure than benign payloads. While the first tenet of this assumption is philosophically undeniable (i.e., a string of bytes is either shellcode or it is not), truth of the latter claim is less obvious if there exist encoding techniques capable of producing shellcode with features nearly indistinguishable from non-executable content. In this paper, we challenge the assumption that shellcode must conform to superficial and discernible representations. Specifically, we demonstrate a technique for automatically producing English Shellcode, transforming arbitrary shellcode into a representation that is superficially similar to English prose. The shellcode is completely self-contained---i.e., it does not require an external loader and executes as valid IA32 code)---and can typically be generated in under an hour on commodity hardware. Our primary objective in this paper is to promote discussion and stimulate new ideas for thinking ahead about preventive measures for tackling evolutions in code-injection attacks.


title: Detecting Factual and Non-Factual Content in News Articles

abstract: News articles are a major source of facts about the current state and events of our surrounding world. However, not all news articles are equally rich in presenting the facts. In this paper, we consider the problem of detecting factual and non-factual parts in news articles. We present a comprehensive survey on the existing literature on fact classification on news articles as well as a related and more widely studied problem of subjectivity vs objectivity classification of statements. Combining these techniques and some new features we design a framework for classifying facts and non-facts in news articles. We present extensive experiments on this task using several features and combinations of those on two datasets, one of which was used for subjectivity classification in previous works. We show that standard textual dataset dependent features such as n-grams produce good results on both datasets, but more general features such as part of speech tags and entity types produce inconsistent results. We analyze the results based on the nature of the datasets to present insights on the usefulness of the features and their applicability in the classification task we are considering.


title: A survey on advanced metering infrastructure and its application in Smart Grids

abstract: This survey paper is an excerpt of a more comprehensive study on Smart Grid (SG) and the role of Advanced Metering Infrastructure (AMI) in SG. The survey was carried out as part of a feasibility study for the creation of a Net-Zero community in a city in Ontario, Canada. SG is not a single technology; rather it is a combination of different areas of engineering, communication and management. This paper intends to focus on AMI, which is responsible for collecting all the data and information from loads and consumers, as the foundation for SG. AMI is also responsible for implementing control signals and commands to perform necessary control actions, including Demand Side Management (DSM). In this paper we introduce SG and its features, establish the relation between SG and AMI, explain three main subsystems of AMI and discuss related security issues.


title: Indexing million of packets per second using GPUs

abstract: Network traffic recorders are devices that record massive volumes of network traffic for security applications, like retrospective forensic investigations. When deployed over very high-speed networks, traffic recorders must process and store millions of packets per second. To enable interactive explorations of such large traffic archives, packet indexing mechanisms are required. Indexing packets at wire rates (10 Gbps and above) on commodity hardware imposes unparalleled requirements for high throughput index creation. Such indexing throughputs are presently untenable with modern indexing technologies and current processor architectures. In this work, we propose to intelligently offload indexing to commodity General Processing Units (GPUs). We introduce algorithms for building compressed bitmap indexes in real time on GPUs and show that we can achieve indexing throughputs of up to 185 millions records per second, which is an improvement by one order of magnitude compared to the state-of-the-art. This shows that indexing network traffic at multi-10-Gbps rates is well within reach.


title: Relational data factorization

abstract: Motivated by an analogy with matrix factorization, we introduce the problem of factorizing relational data. In matrix factorization, one is given a matrix and has to factorize it as a product of other matrices. In relational data factorization, the task is to factorize a given relation as a conjunctive query over other relations, i.e., as a combination of natural join operations. Given a conjunctive query and the input relation, the problem is to compute the extensions of the relations used in the query. Thus, relational data factorization is a relational analog of matrix factorization; it is also a form of inverse querying as one has to compute the relations in the query from the result of the query. The result of relational data factorization is neither necessarily unique nor required to be a lossless decomposition of the original relation. Therefore, constraints can be imposed on the desired factorization and a scoring function is used to determine its quality (often similarity to the original data). Relational data factorization is thus a constraint satisfaction and optimization problem. We show how answer set programming can be used for solving relational data factorization problems.


title: Environmental Practices as Requirements for Supplier Evaluation and Selection in the Automotive Supply Chain

abstract: Sustainable supply chains are now growing in importance as shareholders, employees and consumers question whether the company and its associate suppliers are socially and environmentally responsible. This fact has been seriously considered in the auto industry supply chain where companies seem to be concerned with the environment performance of their vendors. In order to verify to what extent the environmental requirements have been imposed by the auto industry companies to their suppliers, this paper examines the case of a first tier multinational Brazilian auto parts manufacturer. The case analysis was able to demonstrate that environmental requirements are of key importance in the evaluation, selection and maintenance of a supplier as part of the respective supply chain.


title: Health promotion from the perspective of social cognitive theory

abstract: Abstract This article examines health promotion and disease prevention from the perspective of social cognitive theory. The areas of overlap with some of the most widely applied psychosocial models of health are identified. The models of health promotion and disease prevention have undergone several generational changes. We have shifted from trying to scare people into health, to rewarding them into health, to equipping them with self-regulatory skills to manage their health habits, to shoring up their habit changes with dependable social supports. These transformations have evolved a multifaceted approach that addresses the reciprocal interplay between self-regulatory and environmental determinants of health behavior. Social cognitive theory addresses the socio structural determinants of health as well as the personal determinants. A comprehensive approach to health promotion requires changing the practices of social systems that have widespread detrimental effects on health rather than solely changing t...


title: Mining Social Media: A Brief Introduction

abstract: The pervasive use of social media has generated unprecedented amounts of social data. Social media provides easily an accessible platform for users to share informa- tion. Mining social media has its potential to extract actionable patterns that can be benecial for business, users, and consumers. Social media data are vast, noisy, unstructured, and dynamic in nature, and thus novel challenges arise. This tutorial reviews the basics of data mining and social media, introduces representative research problems of mining social media, illustrates the application of data mining to social media using examples, and describes some projects of mining social media for human- itarian assistance and disaster relief for real-world applications.


title: Aerodynamic Design Optimization Studies of a Blended-Wing-Body Aircraft

abstract: The blended wing body is an aircraft configuration that has the potential to be more efficient than conventional large transport aircraft configurations with the same capability. However, the design of the blended wing is challenging due to the tight coupling between aerodynamic performance, trim, and stability. Other design challenges include the nature and number of the design variables involved, and the transonic flow conditions. To address these issues, a series of aerodynamic shape optimization studies using Reynolds-averaged Navier–Stokes computational fluid dynamics with a Spalart–Allmaras turbulence model is performed. A gradient-based optimization algorithm is used in conjunction with a discrete adjoint method that computes the derivatives of the aerodynamic forces. A total of 273 design variables—twist, airfoil shape, sweep, chord, and span—are considered. The drag coefficient at the cruise condition is minimized subject to lift, trim, static margin, and center plane bending moment constraints. ...


title: Seeing through water

abstract: We consider the problem of recovering an underwater image distorted by surface waves. A large amount of video data of the distorted image is acquired. The problem is posed in terms of finding an undistorted image patch at each spatial location. This challenging reconstruction task can be formulated as a manifold learning problem, such that the center of the manifold is the image of the undistorted patch. To compute the center, we present a new technique to estimate global distances on the manifold. Our technique achieves robustness through convex flow computations and solves the "leakage" problem inherent in recent manifold embedding techniques.


Augmented reality in neurosurgery


title: What a tangled web we weave: lying backfires in location-sharing social media

abstract: Prior research shows that a root cause of many privacy concerns in location-sharing social media is people's desire to preserve offline relationship boundaries. Other literature recognizes lying as an everyday phenomenon that preserves such relationship boundaries by facilitating smooth social interactions. Combining these strands of research, one might hypothesize that people with a predisposition to lie would generally have lower privacy concerns since lying is a means to preserve relationship boundaries. We tested this hypothesis using structural equation modeling on data from a survey administered nationwide (N=1532), and found that for location-sharing, people with a high propensity to lie actually have increased boundary preservation concerns as well as increased privacy concerns. We explain these findings using results from semi-structured interviews.


title: More Algorithms for Provable Dictionary Learning

abstract: In dictionary learning, also known as sparse coding, the algorithm is given samples of the form $y = Ax$ where $x\in \mathbb{R}^m$ is an unknown random sparse vector and $A$ is an unknown dictionary matrix in $\mathbb{R}^{n\times m}$ (usually $m>n$, which is the overcomplete case). The goal is to learn $A$ and $x$. This problem has been studied in neuroscience, machine learning, visions, and image processing. In practice it is solved by heuristic algorithms and provable algorithms seemed hard to find. Recently, provable algorithms were found that work if the unknown feature vector $x$ is $\sqrt{n}$-sparse or even sparser. Spielman et al. \cite{DBLP:journals/jmlr/SpielmanWW12} did this for dictionaries where $m=n$; Arora et al. \cite{AGM} gave an algorithm for overcomplete ($m>n$) and incoherent matrices $A$; and Agarwal et al. \cite{DBLP:journals/corr/AgarwalAN13} handled a similar case but with weaker guarantees. This raised the problem of designing provable algorithms that allow sparsity $\gg \sqrt{n}$ in the hidden vector $x$. The current paper designs algorithms that allow sparsity up to $n/poly(\log n)$. It works for a class of matrices where features are individually recoverable, a new notion identified in this paper that may motivate further work. The algorithm runs in quasipolynomial time because they use limited enumeration.


Construction of a high-density genetic map by specific locus amplified fragment sequencing (SLAF-seq) and its application to Quantitative Trait Loci (QTL) analysis for boll weight in upland cotton (Gossypium hirsutum.)


title: Ultrasensitive Flexible Temperature-Mechanical Dual-Parameter Sensor Based on Vanadium Dioxide Films

abstract: Flexible temperature and mechanical sensing capabilities are of great importance in the artificial intelligence. Here, we report a skin conformable, high sensitive temperature-mechanical dual-parameter sensor based on a PET/vanadium dioxide (VO2)/PDMS multilayer structure fabricated using the transfer printing technique. The ultrahigh mechanical sensitivity to strains in the range of 0%–0.1% with a gauge factor over 400 was attributed to the disconnection-reconnection process of the ziplike nanoscale cracks under strain or vibration. And the enhanced temperature sensing performance with a resolution of 0.1 K was attributed to the high temperature coefficient of resistance of VO2 material. The temperature and the mechanical signals simultaneously recorded in the electrical resistance signal were separated via fast Fourier transform (FFT) and inverse FFT. The flexibility and ultra-sensitivity to temperature and vibration make the sensor a promising application in advanced artificial intelligence.


title: Smart port: Design and perspectives

abstract: This work aims to define the concept of smart logistics and determine the major challenges that have led researchers to think about the smart port. On the other hand, it presents a modeling of the smart port concept by determining its main pillars as well as the essential components for the success of each pillar.


title: Research and development of new generation robotic exoskeleton for over ground walking in individuals with mobility disorders (Novel design and control)

abstract: The potential of improving ambulation and enhancing mobility for individuals with different types of neurological disabilities is possible through the use of robotic exoskeletons. Yet, current robotic technology, when used for rehabilitation or as assistive devices for over ground walking, should consider user intended movements to achieve more biologically similar gait patterns. Our robotic exoskeleton, the New Jersey Trekker, includes sufficient degrees of freedom, joint torque and sensing capabilities to permit the generation of a more natural gait controlled in real-time by the user's intended movements. The primary control strategy of the Trekker employs admittance control to convey user hand movements to control the exoskeleton's leg movement. We expect that this novel interface will transform user-exoskeleton control strategies and enable the user to independently ambulate in the real-world, and enhance community independence.


title: Enabling IoT for In-Home Rehabilitation: Accelerometer Signals Classification Methods for Activity and Movement Recognition

abstract: Rehabilitation and elderly monitoring for active aging can benefit from Internet of Things (IoT) capabilities in particular for in-home treatments. In this paper, we consider two functions useful for such treatments: 1) activity recognition (AR) and 2) movement recognition (MR). The former is aimed at detecting if a patient is idle, still, walking, running, going up/down the stairs, or cycling; the latter individuates specific movements often required for physical rehabilitation, such as arm circles, arm presses, arm twist, curls, seaweed, and shoulder rolls. Smartphones are the reference platforms being equipped with an accelerometer sensor and elements of the IoT. The work surveys and compares accelerometer signals classification methods to enable IoT for the aforementioned functions. The considered methods are support vector machines (SVMs), decision trees, and dynamic time warping. A comparison of the methods has been proposed to highlight their performance: all the techniques have good recognition accuracies and, among them, the SVM-based approaches show an accuracy above 90% in the case of AR and above 99% in the case of MR.


title: Knowledge management in software engineering

abstract: Software organizations' main assets are not plants, buildings, or expensive machines. A software organization's main asset is its intellectual capital, as it is in sectors such asconsulting, law, investment banking, and advertising. The major problem with intellectual capital is that it has legs and walks home every day. At the same rate experience walks out the door, inexperience walks in the door. Whether or not many software organizations admit it, they face the challenge ofsustaining the level of competence needed to win contracts and fulfill undertakings.


title: FastRunner: A fast, efficient and robust bipedal robot. Concept and planar simulation

abstract: Bipedal robots are currently either slow, energetically inefficient and/or require a lot of control to maintain their stability. This paper introduces the FastRunner, a bipedal robot based on a new leg architecture. Simulation results of a Planar FastRunner demonstrate that legged robots can run fast, be energy efficient and inherently stable. The simulated FastRunner has a cost of transport of 1.4 and requires only a local feedback of the hip position to reach 35.4 kph from stop in simulation.


title: Stateful intrusion detection for high-speed network's

abstract: As networks become faster there is an emerging need for security, analysis techniques that can keep up with the increased network throughput. Existing network-based intrusion detection sensors can barely, keep up with bandwidths of a few hundred Mbps. Analysis tools that can deal with higher throughput are unable to maintain state between different steps of an attack or they are limited to the analysis of packet headers. We propose a partitioning approach to network security, analysis that supports in-depth, stateful intrusion detection on high-speed links. The approach is centered around a slicing mechanism that divides the overall network traffic into subsets of manageable size. The traffic partitioning is done so that a single slice contains all the evidence necessary to detect a specific attack, making sensor-to-sensor interactions unnecessary. This paper describes the approach and presents a first experimental evaluation of its effectiveness.


title: The human amygdala and the emotional evaluation of sensory stimuli

abstract: A wealth of animal data implicates the amygdala in aspects of emotional processing. In recent years, functional neuroimaging and neuropsychological studies have begun to refine our understanding of the functions of the amygdala in humans. This literature offers insights into the types of stimuli that engage the amygdala and the functional consequences that result from this engagement. Specific conclusions and hypotheses include: (1) the amygdala activates during exposure to aversive stimuli from multiple sensory modalities; (2) the amygdala responds to positively valenced stimuli, but these responses are less consistent than those induced by aversive stimuli; (3) amygdala responses are modulated by the arousal level, hedonic strength or current motivational value of stimuli; (4) amygdala responses are subject to rapid habituation; (5) the temporal characteristics of amygdala responses vary across stimulus categories and subject populations; (6) emotionally valenced stimuli need not reach conscious awareness to engage amygdala processing; (7) conscious hedonic appraisals do not require amygdala activation; (8) activation of the amygdala is associated with modulation of motor readiness, autonomic functions, and cognitive processes including attention and memory; (9) amygdala activations do not conform to traditional models of the lateralization of emotion; and (10) the extent and laterality of amygdala activations are related to factors including psychiatric status, gender and personality. The strengths and weakness of these hypotheses and conclusions are discussed with reference to the animal literature.


title: A Bridgeless PFC Boost Rectifier With Optimized Magnetic Utilization

abstract: The implementation of a bridgeless power factor correction (PFC) boost rectifier with low common-mode noise is presented in this paper. The proposed implementation employs a unique multiple-winding, multicore inductor to increase the utilization of the magnetic material. The operation and performance of the circuit were verified on a 750-W, universal-line experimental prototype operating at 110 kHz.


MID03782 Mental Toughness: Is the Mental Toughness Test Tough Enough?


title: A Survey on Gait Recognition

abstract: Recognizing people by their gait has become more and more popular nowadays due to the following reasons. First, gait recognition can work well remotely. Second, gait recognition can be done from low-resolution videos and with simple instrumentation. Third, gait recognition can be done without the cooperation of individuals. Fourth, gait recognition can work well while other features such as faces and fingerprints are hidden. Finally, gait features are typically difficult to be impersonated. ::: ::: Recent ubiquity of smartphones that capture gait patterns through accelerometers and gyroscope and advances in machine learning have opened new research directions and applications in gait recognition. A timely survey that addresses current advances is missing. ::: ::: In this article, we survey research works in gait recognition. In addition to recognition based on video, we address new modalities, such as recognition based on floor sensors, radars, and accelerometers; new approaches that include machine learning methods; and examine challenges and vulnerabilities in this field. In addition, we propose a set of future research directions. Our review reveals the current state-of-art and can be helpful to both experts and newcomers of gait recognition. Moreover, it lists future works and publicly available databases in gait recognition for researchers.


Sexuality (and Lack Thereof) in Adolescence and Early Adulthood: A Review of the Literature


title: Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction

abstract: The cerebral cortex predicts visual motion to adapt human behaviour to surrounding objects moving in real time. Although the underlying mechanisms are still unknown, predictive coding is one of the leading theories. Predictive coding assumes that the brain’s internal models (which are acquired through learning) predict the visual world at all times and that errors between the prediction and the actual sensory input further refine the internal models. In the past year, deep neural networks based on predictive coding were reported for a video prediction machine called PredNet. If the theory substantially reproduces the visual information processing of the cerebral cortex, then PredNet can be expected to represent the human visual perception of motion. In this study, PredNet was trained with natural scene videos of the self-motion of the viewer, and the motion prediction ability of the obtained computer model was verified using unlearned videos. We found that the computer model accurately predicted the magnitude and direction of motion of a rotating propeller in unlearned videos. Surprisingly, it also represented the rotational motion for illusion images that were not moving physically, much like human visual perception. While the trained network accurately reproduced the direction of illusory rotation, it did not detect motion components in negative control pictures wherein people do not perceive illusory motion. This research supports the exciting idea that the mechanism assumed by the predictive coding theory is one of basis of motion illusion generation. Using sensory illusions as indicators of human perception, deep neural networks are expected to contribute significantly to the development of brain research.


title: Self-tuned deep super resolution

abstract: Deep learning has been successfully applied to image super resolution (SR). In this paper, we propose a deep joint super resolution (DJSR) model to exploit both external and self similarities for SR. A Stacked Denoising Convolutional Auto Encoder (SDCAE) is first pre-trained on external examples with proper data augmentations. It is then fine-tuned with multi-scale self examples from each input, where the reliability of self examples is explicitly taken into account. We also enhance the model performance by sub-model training and selection. The DJSR model is extensively evaluated and compared with state-of-the-arts, and show noticeable performance improvements both quantitatively and perceptually on a wide range of images.


title: Sleep posture analysis using a dense pressure sensitive bedsheet

abstract: Sleep posture affects the quality of our sleep and is especially important for such medical conditions as sleep apnea and pressure ulcers. In this paper, we propose a design for a dense pressure-sensitive bedsheet along with an algorithmic framework to recognize and monitor sleeping posture. The bedsheet system uses comfortable textile sensors that produces high-resolution pressure maps. We develop a novel framework for pressure image analysis to monitor sleep postures, including a set of geometrical features for sleep posture characterization and three sparse classifiers for posture recognition. In demonstrating this system, we run 2 pilot studies: one evaluates the performance of our methods with 14 subjects to analyze 6 common postures; the other is a series of overnight studies to verify continuous performance. The experimental results show that our proposed method enables reliable sleep posture recognition and offers better overall performance than traditional methods, achieving up to 83.0% precision and 83.2% recall on average.


title: Efficiently inferring community structure in bipartite networks

abstract: Bipartite networks are a common type of network data in which there are two types of vertices, and only vertices of different types can be connected. While bipartite networks exhibit community structure like their unipartite counterparts, existing approaches to bipartite community detection have drawbacks, including implicit parameter choices, loss of information through one-mode projections, and lack of interpretability. Here we solve the community detection problem for bipartite networks by formulating a bipartite stochastic block model, which explicitly includes vertex type information and may be trivially extended to k-partite networks. This bipartite stochastic block model yields a projection-free and statistically principled method for community detection that makes clear assumptions and parameter choices and yields interpretable results. We demonstrate this model's ability to efficiently and accurately find community structure in synthetic bipartite networks with known structure and in real-world bipartite networks with unknown structure, and we characterize its performance in practical contexts.


title: Cluster Definition by the Optimization of Simple Measures

abstract: We adopt the following measures of clustering based on simple edge counts in an undirected loop-free graph. Let S be a subset of the points of the graph. The compactness of S is measured by the number of edges connecting points in S to other points in S. The isolation or separation of S is measured by the negative of the number of edges connecting points in S to points not in S. The subset S is a cluster if it is compact and isolated. We study the cluster search problem: find a subset S which maximizes a linear combination of the compactness and (negative) isolation edge counts. We show that a closely related decision problem is NP-complete. We develop a pruned search tree algorithm which is much faster than complete search, especially for graphs which are derived from points embedded in a space of low dimensionality.


title: The information in option volume for future stock prices

abstract: We present strong evidence that option trading volume contains information about future stock prices. Taking advantage of a unique data set, we construct put-call ratios from option volume initiated by buyers to open new positions. Stocks with low put-call ratios outperform stocks with high put-call ratios by more than 40 basis points on the next day and more than 1% over the next week. Partitioning our option signals into components that are publicly and nonpublicly observable, we find that the economic source of this predictability is nonpublic information possessed by option traders rather than market inefficiency. We also find greater predictability for stocks with higher concentrations of informed traders and from option contracts with greater leverage. Copyright 2006, Oxford University Press.


title: Visual Design Thinking: Understanding the Role of Knowledge Visualization in the Design Thinking Process

abstract: This paper sheds light on the role of visualization methods within the design thinking process. It provides a conceptual framework showing illustrative examples of visualization for each phase indicating its functions and benefits. Based on a thorough understanding of the functions and benefits of knowledge visualization in general, this paper seeks to provide an overview of the use of visualization in design thinking informing and supporting practitioners and researcher for more conscious selections of visualization methods in their design thinking efforts.


title: Generate Public Randomness Based on Blockchain

abstract: We design a protocol based on blockchain, which produces unpredictable, unbiased and publicly verifiable randomness. We use the properties of blockchain to enlarge the scale of participation and output public randomness without trusted third party. And we restructure a timed commitment to make the commitments can be verified, and to ensure that no one can control or bias the value of randomness and it can be verified quickly. Everyone who just need to check the content recorded in blockchain can quickly verify that the randomness produced by our protocol is computationally indistinguishable from uniform distribution and unknown before the end of protocol. And not like the previous works that generate public randomness based on PoW, our protocol does not depend on the cost of mining to guarantee security, so our protocol can generate public randomness based on PoS and other protocols with low consumption.


title: Disguised executable files in spear-phishing emails: detecting the point of entry in advanced persistent threat

abstract: In recent years, cyber attacks have caused substantial financial losses and been able to stop fundamental public services. Among the serious attacks, Advanced Persistent Threat (APT) has emerged as a big challenge to the cyber security hitting selected companies and organisations. The main objectives of APT are data exfiltration and intelligence appropriation. As part of the APT life cycle, an attacker creates a Point of Entry (PoE) to the target network. This is usually achieved by installing malware on the targeted machine to leave a back-door open for future access. A common technique employed to breach into the network, which involves the use of social engineering, is the spear phishing email. These phishing emails may contain disguised executable files. This paper presents the disguised executable file detection (DeFD) module, which aims at detecting disguised exe files transferred over the network connections. The detection is based on a comparison between the MIME type of the transferred file and the file name extension. This module was experimentally evaluated and the results show a successful detection of disguised executable files.


title: Retrieve-and-Read: Multi-task Learning of Information Retrieval and Reading Comprehension

abstract: This study considers the task of machine reading at scale (MRS) wherein, given a question, a system first performs the information retrieval (IR) task of finding relevant passages in a knowledge source and then carries out the reading comprehension (RC) task of extracting an answer span from the passages. Previous MRS studies, in which the IR component was trained without considering answer spans, struggled to accurately find a small number of relevant passages from a large set of passages. In this paper, we propose a simple and effective approach that incorporates the IR and RC tasks by using supervised multi-task learning in order that the IR component can be trained by considering answer spans. Experimental results on the standard benchmark, answering SQuAD questions using the full Wikipedia as the knowledge source, showed that our model achieved state-of-the-art performance. Moreover, we thoroughly evaluated the individual contributions of our model components with our new Japanese dataset and SQuAD. The results showed significant improvements in the IR task and provided a new perspective on IR for RC: it is effective to teach which part of the passage answers the question rather than to give only a relevance score to the whole passage.


title: A Practical Approach to the Use of Conventional Synthetic, Biologic and Targeted Synthetic Disease Modifying Anti-Rheumatic Drugs for the Treatment of Inflammatory Arthritis in Patients with a History of Malignancy

abstract: Purpose of Review ::: Conventional synthetic disease modifying anti-rheumatic drugs (csDMARDs) have been used in the treatment of inflammatory arthritis (IA) for many years. More recently, biologic (bDMARDs) and targeted synthetic (tsDMARDs) DMARDs have further improved treatment. Due to increased patient longevity and effective oncology treatment, rheumatologists often encounter patients with IA and previous malignancy. The immunosuppressive effect of DMARDs causes concern regarding impaired tumour surveillance with a potential increased risk of malignancy. We reviewed the literature regarding the risk of malignancy in patients on cs-/b-/tsDMARDS and sought to provide practical advice regarding use of these drugs in patients with previous malignancy.


title: Communication skills of specialist doctors: Assessment with SKAF scale a new tool

abstract: Objective: To evaluate the standards and uses of communication skills of specialist doctors during the doctor-patient interactions among a sample of specialists belonging to various specialities and based at different hospitals of Lahore. Methodology: One hundred specialists were asked to participate in the prospective, questionnaire based survey in Lahore. These volunteers were asked to fill a form keeping in view their current practices. The scores thus achieved were measured using a proposed SKAF score. The results were graded into four parts depending upon the score achieved by the subjects. Results: Twelve people never returned the survey instrument. Of those who did majority scored as good or excellent. Females showed a better score than males. Those who had previously been instructed formally did better on the SKAF scale. Conclusions: The need of formal hands on training workshops on Communication Skills in both under graduate as well as post graduate trainees cannot be reiterated.


title: Artificial Intelligence as Structural Estimation: Economic Interpretations of Deep Blue, Bonanza, and AlphaGo

abstract: Artificial intelligence (AI) has achieved superhuman performance in a growing number of tasks, but understanding and explaining AI remain challenging. This paper clarifies the connections between machine-learning algorithms to develop AIs and the econometrics of dynamic structural models through the case studies of three famous game AIs. Chess-playing Deep Blue is a calibrated value function, whereas shogi-playing Bonanza is an estimated value function via Rust's (1987) nested fixed-point method. AlphaGo's "supervised-learning policy network" is a deep neural network implementation of Hotz and Miller's (1993) conditional choice probability estimation; its "reinforcement-learning value network" is equivalent to Hotz, Miller, Sanders, and Smith's (1994) conditional choice simulation method. Relaxing these AIs' implicit econometric assumptions would improve their structural interpretability.


title: Female competition and its evolutionary consequences in mammals

abstract: Following Darwin's original insights regarding sexual selection, studies of intrasexual competition have mainly focused on male competition for mates; by contrast, female reproductive competition has received less attention. Here, we review evidence that female mammals compete for both resources and mates in order to secure reproductive benefits. We describe how females compete for resources such as food, nest sites, and protection by means of dominance relationships, territoriality and inter-group aggression, and by inhibiting the reproduction of other females. We also describe evidence that female mammals compete for mates and consider the ultimate causes of such behaviour, including competition for access to resources provided by mates, sperm limitation and prevention of future resource competition. Our review reveals female competition to be a potentially widespread and significant evolutionary selection pressure among mammals, particularly competition for resources among social species for which most evidence is currently available. ::: ::: We report that female competition is associated with many diverse adaptations, from overtly aggressive behaviour, weaponry, and conspicuous sexual signals to subtle and often complex social behaviour involving olfactory signalling, alliance formation, altruism and spite, and even cases where individuals appear to inhibit their own reproduction. Overall, despite some obvious parallels with male phenotypic traits favoured under sexual selection, it appears that fundamental differences in the reproductive strategies of the sexes (ultimately related to parental investment) commonly lead to contrasting competitive goals and adaptations. Because female adaptations for intrasexual competition are often less conspicuous than those of males, they are generally more challenging to study. In particular, since females often employ competitive strategies that directly influence not only the number but also the quality (survival and reproductive success) of their own offspring, as well as the relative reproductive success of others, a multigenerational view ideally is required to quantify the full extent of variation in female fitness resulting from intrasexual competition. Nonetheless, current evidence indicates that the reproductive success of female mammals can also be highly variable over shorter time scales, with significant reproductive skew related to competitive ability. ::: ::: Whether we choose to describe the outcome of female reproductive competition (competition for mates, for mates controlling resources, or for resources per se) as sexual selection depends on how sexual selection is defined. Considering sexual selection strictly as resulting from differential mating or fertilisation success, the role of female competition for the sperm of preferred (or competitively successful) males appears particularly worthy of more detailed investigation. Broader definitions of sexual selection have recently been proposed to encompass the impact on reproduction of competition for resources other than mates. Although the merits of such definitions are a matter of ongoing debate, our review highlights that understanding the evolutionary causes and consequences of female reproductive competition indeed requires a broader perspective than has traditionally been assumed. We conclude that future research in this field offers much exciting potential to address new and fundamentally important questions relating to social and mating-system evolution.


title: Electromagnetic Two-Dimensional Scanner Using Radial Magnetic Field

abstract: In this paper, we present the design, fabrication, and measurement results of a two-dimensional electromagnetic scanning micromirror actuated by radial magnetic field. The scanner is realized by combining a gimbaled single-crystal-silicon micromirror with a single turn electroplated metal coil, with a concentric permanent magnet assembly composed of two concentric permanent magnets and an iron yoke. The proposed scanner utilizes the radial magnetic field rather than using a lateral magnetic field oriented 45deg to the horizontal and vertical scan axes to achieve a biaxial magnetic actuation. The single turn coil fabricated with electroplated copper achieves a nominal resistance of 1.2 Omega. A two-dimensional scanner with mirror size of 1.5 mm in diameter was fabricated. Maximum optical scan angle of 8.8deg in horizontal direction and 8.3deg in vertical direction were achieved. Forced actuation of the gimbal at 60 Hz and resonant actuation of the micromirror at 19.1-19.7 kHz provide slow vertical scan and fast horizontal scan, respectively. The proposed scanner can be used in raster scanning laser display systems and other scanner applications.


title: Comparison of the response of doubly fed and fixed-speed induction generator wind turbines to changes in network frequency

abstract: Synchronous and fixed-speed induction generators release the kinetic energy of their rotating mass when the power system frequency is reduced. In the case of doubly fed induction generator (DFIG)-based wind turbines, their control system operates to apply a restraining torque to the rotor according to a predetermined curve with respect to the rotor speed. This control system is not based on the power system frequency and there is negligible contribution to the inertia of the power system. A DFIG control system was modified to introduce inertia response to the DFIG wind turbine. Simulations were used to show that with the proposed control system, the DFIG wind turbine can supply considerably greater kinetic energy than a fixed-speed wind turbine.


title: Collecting and Analyzing the Presence of Terrorists on the Web: A Case Study of Jihad Websites

abstract: The Internet which has enabled global businesses to flourish has become the very same channel for mushrooming 'terrorist news networks.' Terrorist organizations and their sympathizers have found a cost-effective resource to advance their courses by posting high-impact Websites with short shelf-lives. Because of their evanescent nature, terrorism research communities require unrestrained access to digitally archived Websites to mine their contents and pursue various types of analyses. However, organizations that specialize in capturing, archiving, and analyzing Jihad terrorist Websites employ different, manual-based analyses techniques that are inefficient and not scalable. This study proposes the development of automated or semi-automated procedures and systematic methodologies for capturing Jihad terrorist Website data and its subsequent analyses. By analyzing the content of hyperlinked terrorist Websites and constructing visual social network maps, our study is able to generate an integrated approach to the study of Jihad terrorism, their network structure, component clusters, and cluster affinity.


title: Single depth image super resolution and denoising via coupled dictionary learning with local constraints and shock filtering

abstract: Recently, consumer depth cameras have gained significant popularity due to their affordable cost. However, the limited resolution and quality of the depth map generated by these cameras are still problems for several applications. In this paper, we propose a new algorithm for depth image super resolution using a single depth image as input. We reconstruct the corresponding high resolution depth map through a robust coupled dictionary learning algorithm with local coordinate constraints. The local constraints remove the prediction uncertainty and prevent the dictionary from over-fitting. We also incorporate an adaptively regularized Shock filter to simultaneously reduce the noise and sharpen the edges. Experimental results demonstrate the effectiveness of our proposed algorithm compared to previously reported methods.


Methodology for Penetration Testing


title: Accurate prediction of ferrite core loss with nonsinusoidal waveforms using only Steinmetz parameters

abstract: An improved calculation of ferrite core loss for nonsinusoidal waveforms separates a flux trajectory into major and minor loops via a new recursive algorithm. It is highly accurate and outperforms two previous methods for our measured data. The only characteristics of the material required are the standard Steinmetz-equation parameters.


title: Bumpless Automatic Transfer for a Switched-Doubly-Fed-Machine Propulsion Drive

abstract: Variable-speed drives (VSDs) combined with doubly-fed machines (DFMs) offer interesting flexibility for power electronic drive design. These also provide opportunities for controlling interactions with an ac grid. Design options are most versatile for the VSD DFM when the machine stator can be operated from an ac or a dc source, selected as appropriate. This paper presents a silicon-controlled rectifier (SCR)-based transfer switch that can connect the stator of the DFM either to an ac source or a dc source “on-the-fly.” Current commutations of the SCRs and a “bumpless” transition in shaft behavior are both controlled from the rotor. Experimental results demonstrate wide-speed-range and four-quadrant operation of the drive achieved with a rotor converter rating that is one third of the DFM power rating.


A Survey on the Amount of the Ardabil Movement Towards Sustainability by Assessing the Ecological Footprint


Robust Training under Linguistic Adversity


title: Multi-Site Calibration of the SWAT Model for Hydrologic Modeling

abstract: The growing popularity of applying complex, semi-physically based distributed hydrologic models to solve water resource problems poses important issues that must be addressed related to the use of spatial data to calibrate and validate such models. In this study, a single-objective optimization method (GA) and a multi-objective optimization algorithm (SPEA2) were applied to optimize the parameters of the Soil and Water Assessment Tool (SWAT) using observed streamflow data at three monitoring sites within the Reynolds Creek Experimental Watershed, Idaho. Results indicated that different optimization schemes can lead to substantially different objective function values, parameter solutions, and corresponding simulated hydrographs. Thus, the selection of an optimization scheme can potentially impact modeled streamflow. Parameters estimated by optimizing the objective function at three monitoring sites consistently produced better goodness-of-fit than those obtained by optimization at a single monitoring site. This stresses the importance of collecting detailed, spatially distributed data to conduct simultaneous multi-site calibrations. When applied with multi-site data, the single-objective (GA) method better identified parameter solutions in the calibration period, but the multi-objective (SPEA2) method performed better in the validation period. Overall, the application of different optimization schemes in the Reynolds Creek Experimental Watershed demonstrated that the single-objective (GA) and the multi-objective (SPEA2) optimization methods can provide promising results for multi-site calibration and validation of the SWAT model. These results are expected to help the users of SWAT and other distributed hydrologic models understand the sensitivity of distributed hydrologic simulation to different calibration methods and to demonstrate the advantages and disadvantages of single-objective and multi-objective parameter estimation methods.


title: Magnetoresistive sensors for nondestructive evaluation (Invited Paper)

abstract: New high-sensitivity solid-state magnetoresistive (MR) sensor technologies offer significant advantages in nondestructive evaluation (NDE) systems. A key advantage of MR sensors is a flat frequency response extending from dc to hundreds of MHz, making them particularly attractive for low-frequency and multi-frequency eddy current detection for deep-flaw detection and depth profiling. MR sensors are mass produced by thin film processing techniques similar to integrated circuit manufacturing, dramatically reducing the cost per sensor. The fabrication process is compatible with silicon circuit technology, allowing integration of sensors with on-chip signal processing. MR sensors can easily be produced in dense arrays for rapid, single-pass scanning of large areas. The small size and low power consumption of these solid-state magnetic sensors enable the assembly of compact arrays of sensors on a variety of substrates as well as on-chip sensor arrays. Arrays have been fabricated with sensor spacing as small as 5 μm. This paper presents a review of the state of the art in MR sensors and applications in NDE. The physical principles, manufacturing process, and performance characteristics of the three main types of MR devices, anisotropic magnetoresistance (AMR), giant magnetoresistance (GMR) and tunneling magnetoresistance (TMR) are discussed. Their performance is compared to other magnetic sensor technologies for NDE applications. Finally, we provide a comprehensive review of the literature on NDE applications of MR sensors.


Switching Linear Dynamical Systems For Speech Recognition


title: Tactile remapping: from coordinate transformation to integration in sensorimotor processing

abstract: Tactile localization entails the transformation of the initial skin-based location into an external reference frame that accounts for body posture and subsequent flexible integration of these two reference frames. The mechanisms underlying this tactile remapping are not well understood. Notably, there is a gap between the principles uncovered by psychophysical research and the mechanistic explanations offered by neuroscientific studies. We suggest that spatial localization is best viewed as a process of integrating multiple concurrently active spatial representations rather than a sequential transformation process. To achieve integration, large-scale interactions are required that link these different representations. Coordinated oscillatory activity may be a suitable mechanism that allows parallel representation of multiple spatial formats and the formation of an integrated location estimate.


A differentiable approach to inductive logic programming


title: Microcystic Adnexal Carcinoma Misdiagnosed as Desmoplastic Trichoepithelioma on Preoperative Biopsy

abstract: Microcystic adnexal carcinoma is a rare type of tumor, with about 300 cases reported globally. Due to its similar histology with other tumors, it is occasionally misdiagnosed as desmoplastic trichoepithelioma, basal cell carcinoma, syringoma, and so on. We present a patient with a mass on the perioral area who was preoperatively diagnosed with trichoepithelioma. Microcystic adnexal carcinoma was diagnosed after excisional biopsy and a wide excision. Defects were reconstructed with a mucosal advancement flap. There was no recurrence and there were no significant complications during the 18-month follow-up period. Because superficial punch biopsy has limitations in width and depth, surgeons should always consider the possibility of malignancy of a mass even if a biopsy shows a benign result.


title: Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition

abstract: Display Omitted Past approaches to health-domain NER have mainly used manual features and conventional classifiers.In this paper, we explore a neural network approach (B-LSTM-CRF) that can learn the features automatically.In addition, initializing the features with pre-trained embeddings can lead to higher accuracy.We pre-train the features using a critical care database (MIMIC-III).Experiments have been carried out over three contemporary datasets for health-domain NER, outperforming past systems. BackgroundPrevious state-of-the-art systems on Drug Name Recognition (DNR) and Clinical Concept Extraction (CCE) have focused on a combination of text feature engineering and conventional machine learning algorithms such as conditional random fields and support vector machines. However, developing good features is inherently heavily time-consuming. Conversely, more modern machine learning approaches such as recurrent neural networks (RNNs) have proved capable of automatically learning effective features from either random assignments or automated word embeddings. Objectives(i) To create a highly accurate DNR and CCE system that avoids conventional, time-consuming feature engineering. (ii) To create richer, more specialized word embeddings by using health domain datasets such as MIMIC-III. (iii) To evaluate our systems over three contemporary datasets. MethodsTwo deep learning methods, namely the Bidirectional LSTM and the Bidirectional LSTM-CRF, are evaluated. A CRF model is set as the baseline to compare the deep learning systems to a traditional machine learning approach. The same features are used for all the models. ResultsWe have obtained the best results with the Bidirectional LSTM-CRF model, which has outperformed all previously proposed systems. The specialized embeddings have helped to cover unusual words in DrugBank and MedLine, but not in the i2b2/VA dataset. ConclusionsWe present a state-of-the-art system for DNR and CCE. Automated word embeddings has allowed us to avoid costly feature engineering and achieve higher accuracy. Nevertheless, the embeddings need to be retrained over datasets that are adequate for the domain, in order to adequately cover the domain-specific vocabulary.


title: Integrating 3D Channel Model and Grid of Beams for 5G mMIMO System Level Simulations

abstract: Massive MIMO (mMIMO) antenna array and Grid of Beams (GoB) are one of the key components for 5G to achieve high spectral efficiency and coverage. System level simulations are necessary to optimally intergrate the components of 5G system. Simulations with mMIMO antenna array involve handling large matrices, which results in increased simulation time typically upto several days depending on the size of the antenna array. However, GoB are fixed wideband beams and the receiver sees only the effective channel which is a combination of the channel and the GoB. Therefore, for system level simulations it is sufficient to general only the effective channel. In this paper, we propose a simulation model to directly generate the effective channel based on the 3GPP 3D channel model. It is shown that for the simulation of urban macro cells scenario specified in TR 38.913, the computational effort can be reduced by a factor of 1000. Furthermore, simulations show that very high SINR and user throughput values are achievable through GoB.


title: A case study of trajectory transfer through non-rigid registration for a simplified suturing scenario

abstract: Suturing is an important yet time-consuming part of surgery. A fast and robust autonomous procedure could reduce surgeon fatigue, and shorten operation times. It could also be of particular importance for suturing in remote tele-surgery settings where latency can complicate the master-slave mode control that is the current practice for robotic surgery with systems like the da Vinci®. We study the applicability of the trajectory transfer algorithm proposed in [12] to the automation of suturing. The core idea of this procedure is to first use non-rigid registration to find a 3D warping function which maps the demonstration scene onto the test scene, then use this warping function to transform the robot end-effector trajectory. Finally a robot joint trajectory is generated by solving a trajectory optimization problem that attempts to find the closest feasible trajectory, accounting for external constraints, such as joint limits and obstacles. Our experiments investigate generalization from a single demonstration to differing initial conditions. A first set of experiments considers the problem of having a simulated Raven II system [5] suture two flaps of tissue together. A second set of experiments considers a PR2 robot performing sutures in a scaled-up experimental setup. The simulation experiments were fully autonomous. For the real-world experiments we provided human input to assist with the detection of landmarks to be fed into the registration algorithm. The success rate for learning from a single demonstration is high for moderate perturbations from the demonstration's initial conditions, and it gradually decreases for larger perturbations.


title: Technology scaling challenges and opportunities of memory devices

abstract: Challenges in scaling of semiconductor memory technologies are reviewed with the focus on DRAM and NAND Flash while demands for memory improvement in the ICT industry are increasing. This paper introduces evolutionary and revolutionary paths to overcome scaling challenges of current and emerging memory technologies along with some promising solutions.


title: Web Media and Stock Markets : A Survey and Future Directions from a Big Data Perspective

abstract: Stock market volatility is influenced by information release, dissemination, and public acceptance. With the increasing volume and speed of social media, the effects of Web information on stock markets are becoming increasingly salient. However, studies of the effects of Web media on stock markets lack both depth and breadth due to the challenges in automatically acquiring and analyzing massive amounts of relevant information. In this study, we systematically reviewed 229 research articles on quantifying the interplay between Web media and stock markets from the fields of Finance, Management Information Systems, and Computer Science. In particular, we first categorized the representative works in terms of media type and then summarized the core techniques for converting textual information into machine-friendly forms. Finally, we compared the analysis models used to capture the hidden relationships between Web media and stock movements. Our goal is to clarify current cutting-edge research and its possible future directions to fully understand the mechanisms of Web information percolation and its impact on stock markets from the perspectives of investors cognitive behaviors, corporate governance, and stock market regulation.


title: Real-time continuous identification system using ECG signals

abstract: This paper presents a prototype biometric system that performs real time human identification using the ECG signal. Cardiac signals have been lately suggested for biometric deployment, since they carry subject specific information and at the same time allow for inherent liveness detection. The HeartID is the first system to continuously authenticate individuals who are conveniently wearing a portable, wireless ECG sensor. The AC/LDA algorithm is used for biomet-ric template design because of its computational and performance advantages. The performance of the prototype testbed was evaluated over 10 subjects at the University of Toronto with very promising results.


title: Online Communication and Adolescent Well-Being: Testing the Stimulation Versus the Displacement Hypothesis

abstract: The aim of this study was to contrast the validity of two opposing explanatory hypotheses about the effect of online communication on adolescents’ well-being. The displacement hypothesis predicts that online communication reduces adolescents’ well-being because it displaces time spent with existing friends, thereby reducing the quality of these friendships. In contrast, the stimulation hypothesis states that online communication stimulates well-being via its positive effect on time spent with existing friends and the quality of these friendships. We conducted an online survey among 1,210 Dutch teenagers between 10 and 17 years of age. Using mediation analyses, we found support for the stimulation hypothesis but not for the displacement hypothesis. We also found a moderating effect of type of online communication on adolescents’ well-being: Instant messaging, which was mostly used to communicate with existing friends, positively predicted well-being via the mediating variables (a) time spent with existing friends and (b) the quality of these friendships. Chat in a public chatroom, which was relatively often used to talk with strangers, had no effect on adolescents’ well-being via the mediating variables.


title: Balancing customer and network value in business models for mobile services

abstract: Designing business models for mobile services is complex. A business model can be seen as a blueprint of four interrelated components: service offering, technical architecture, and organisational and financial arrangements. In this paper the connections among these components are explored by analysing the critical design issues in business models for mobile services, e.g., targeting and branding in the service domain, security and quality of service in the technology domain, network governance in the organisation domain, and revenue sharing in the finance domain. A causal framework is developed linking these critical design issues to expected customer value and expected network value, and hence, to business model viability.


title: Spoken language understanding using long short-term memory neural networks

abstract: Neural network based approaches have recently produced record-setting performances in natural language understanding tasks such as word labeling. In the word labeling task, a tagger is used to assign a label to each word in an input sequence. Specifically, simple recurrent neural networks (RNNs) and convolutional neural networks (CNNs) have shown to significantly outperform the previous state-of-the-art - conditional random fields (CRFs). This paper investigates using long short-term memory (LSTM) neural networks, which contain input, output and forgetting gates and are more advanced than simple RNN, for the word labeling task. To explicitly model output-label dependence, we propose a regression model on top of the LSTM un-normalized scores. We also propose to apply deep LSTM to the task. We investigated the relative importance of each gate in the LSTM by setting other gates to a constant and only learning particular gates. Experiments on the ATIS dataset validated the effectiveness of the proposed models.


title: Detection of Aedes Aegypti mosquito by digital image processing techniques and support vector machine

abstract: In this paper, it uses digital image processing and support vector machine (SVM) to detect aedes aegypti mosquito. Aedes aegypti mosquito is a carrier of dengue virus which causes the problem that is called dengue hemorrhagic fever. As of today there is still no particular medicine for the treatment of such. As a result the study proposed a way on how to detect if a mosquito is an aedes aegypti or not using SVM in Matlab. First, it was trained by feeding images of aedes aegypti and not aedes aegypti such as aedes albopictus and culex that were taken at different views, highlighting their unique features. In the image processing, the features were extracted and then classified to aedes aegypti or not aedes aegypti. Confusion matrix was used to show the accuracy of the system in detecting the Aedes Aegypti mosquito.


title: Resilient Cell-Based Architecture for Time-to-Digital Converter

abstract: This paper proposes a resilient Time-to-Digital Converter (TDC) that lends itself to cell-based design automation. We adopt a shrinking-based architecture with a number of distinctive techniques. First of all, a specialized on-chip re-calibration scheme is developed so that the real-time transfer function of the TDC in silicon (which maps an input pulse-width to its corresponding output code) can be derived on the chip and thereby the absolute value (instead of just a relative code) of an input pulse-width under measurement can be reported. Secondly, the sampling errors stemming from the jitters of training clocks used in the calibration scheme are mitigated by the principle of multi sampling. Thirdly, a flexible coarse-shrinking block is adopted and an automatic adjustment scheme is employed so that the coarse-shrinking block can adjust itself when operated under different input pulse-width ranges.


title: Handwriting Recognition by Attribute Embedding and Recurrent Neural Networks

abstract: Handwriting recognition consists in obtaining the transcription of a text image. Recent word spotting methods based on attribute embedding have shown good performance when recognizing words. However, they are holistic methods in the sense that they recognize the word as a whole (i.e. they find the closest word in the lexicon to the word image). Consequently, these kinds of approaches are not able to deal with out of vocabulary words, which are common in historical manuscripts. Also, they cannot be extended to recognize text lines. In order to address these issues, in this paper we propose a handwriting recognition method that adapts the attribute embedding to sequence learning. Concretely, the method learns the attribute embedding of patches of word images with a convolutional neural network. Then, these embeddings are presented as a sequence to a recurrent neural network that produces the transcription. We obtain promising results even without the use of any kind of dictionary or language model.


Lake Sammamish boat launch and parking improvement project


title: Robust Poisson Surface Reconstruction

abstract: We propose a method to reconstruct surfaces from oriented point clouds with non-uniform sampling and noise by formulating the problem as a convex minimization that reconstructs the indicator function of the surface’s interior. Compared to previous models, our reconstruction is robust to noise and outliers because it substitutes the least-squares fidelity term by a robust Huber penalty; this allows to recover sharp corners and avoids the shrinking bias of least squares. We choose an implicit parametrization to reconstruct surfaces of unknown topology and close large gaps in the point cloud. For an efficient representation, we approximate the implicit function by a hierarchy of locally supported basis elements adapted to the geometry of the surface. Unlike ad-hoc bases over an octree, our hierarchical B-splines from isogeometric analysis locally adapt the mesh and degree of the splines during reconstruction. The hierarchical structure of the basis speeds-up the minimization and efficiently represents clustered data. We also advocate for convex optimization, instead isogeometric finite-element techniques, to efficiently solve the minimization and allow for non-differentiable functionals. Experiments show state-of-the-art performance within a more flexible framework.


title: Multi-strategy monarch butterfly optimization algorithm for discounted {0-1} knapsack problem

abstract: As an expanded classical 0-1 knapsack problem (0-1 KP), the discounted {0-1} knapsack problem (DKP) is proposed based on the concept of discount in the commercial world. The DKP contains a set of item groups where each group includes three items, whereas no more than one item in each group can be packed in the knapsack, which makes it more complex and challenging than 0-1 KP. At present, the main two algorithms for solving the DKP include exact algorithms and approximate algorithms. However, there are some topics which need to be further discussed, i.e., the improvement of the solution quality. In this paper, a novel multi-strategy monarch butterfly optimization (MMBO) algorithm for DKP is proposed. In MMBO, two effective strategies, neighborhood mutation with crowding and Gaussian perturbation, are introduced into MMBO. Experimental analyses show that the first strategy can enhance the global search ability, while the second strategy can strengthen local search ability and prevent premature convergence during the evolution process. Based on this, MBO is combined with each strategy, denoted as NCMBO and GMMBO, respectively. We compared MMBO with other six methods, including NCMBO, GMMBO, MBO, FirEGA, SecEGA and elephant herding optimization. The experimental results on three types of large-scale DKP instances show that NCMBO, GMMBO and MMBO are all suitable for solving DKP. In addition, MMBO outperforms other six methods and can achieve a good approximate solution with its approximation ratio close to 1 on almost all the DKP instances.


title: Frequency of arrival-based interference localization using a single satellite

abstract: Intentional and unintentional interferences are an increasing threat for the satellite communications industry. In this paper, we aim to localize an interference with unknown location using frequency of arrival (FoA) technique by only relying on the measurements obtained through a single satellite. In each time instance, the satellite samples the interference and forwards it to the gateway to estimate its frequency. Since the satellite moves, each estimated frequency includes a Doppler shift, which is related to the location of the unknown interferer. The satellite's position, velocity, oscillator frequency, and the interference frequency are used at the gateway to build a location-related equation between the estimated frequency and the location of the unknown interference. Simultaneously with the interference signal, the satellite samples a reference signal to calibrate the estimated frequency and compensate for the mismatches between the available and real values of the satellite's position, velocity, and oscillator frequency. Multiple location-related equations obtained based on the FoA measurements, (at least two), along with the equation of the earth surface are used to localize the unknown interference. Simulations show that increasing the number of these equations, and the satellite velocity can improve the localization accuracy by 80% and 95%, respectively.


title: Agroforestry — a last hope for conservation in Haiti?

abstract: Haiti is the poorest country in the western hemisphere and one of the 25 poorest in the world. Its economy is marked by severe structural problems, and there is an extreme and widening gap between rich and poor. Per capita income in 1983 was US $315, and only about $50 in the countryside where many people live on the edge of starvation. Indices such as illiteracy, infant mortality and life expectancy are the worst in the hemisphere. Prolonged drought conditions since the mid-1970s have increased the country's external dependence, and much of the population depends on food aid.


title: Structure extraction from texture via relative total variation

abstract: It is ubiquitous that meaningful structures are formed by or appear over textured surfaces. Extracting them under the complication of texture patterns, which could be regular, near-regular, or irregular, is very challenging, but of great practical importance. We propose new inherent variation and relative total variation measures, which capture the essential difference of these two types of visual forms, and develop an efficient optimization system to extract main structures. The new variation measures are validated on millions of sample patches. Our approach finds a number of new applications to manipulate, render, and reuse the immense number of "structure with texture" images and drawings that were traditionally difficult to be edited properly.


title: A High Step-Up Three-Port DC–DC Converter for Stand-Alone PV/Battery Power Systems

abstract: A three-port dc–dc converter integrating photovoltaic (PV) and battery power for high step-up applications is proposed in this paper. The topology includes five power switches, two coupled inductors, and two active-clamp circuits. The coupled inductors are used to achieve high step-up voltage gain and to reduce the voltage stress of input side switches. Two sets of active-clamp circuits are used to recycle the energy stored in the leakage inductors and to improve the system efficiency. The operation mode does not need to be changed when a transition between charging and discharging occurs. Moreover, tracking maximum power point of the PV source and regulating the output voltage can be operated simultaneously during charging/discharging transitions. As long as the sun irradiation level is not too low, the maximum power point tracking (MPPT) algorithm will be disabled only when the battery charging voltage is too high. Therefore, the control scheme of the proposed converter provides maximum utilization of PV power most of the time. As a result, the proposed converter has merits of high boosting level, reduced number of devices, and simple control strategy. Experimental results of a 200-W laboratory prototype are presented to verify the performance of the proposed three-port converter.


Artificial Intelligence With Deep Learning Technology Looks Into Diabetic Retinopathy Screening


title: Efficient hierarchical graph-based video segmentation

abstract: We present an efficient and scalable technique for spatiotemporal segmentation of long video sequences using a hierarchical graph-based algorithm. We begin by over-segmenting a volumetric video graph into space-time regions grouped by appearance. We then construct a “region graph” over the obtained segmentation and iteratively repeat this process over multiple levels to create a tree of spatio-temporal segmentations. This hierarchical approach generates high quality segmentations, which are temporally coherent with stable region boundaries, and allows subsequent applications to choose from varying levels of granularity. We further improve segmentation quality by using dense optical flow to guide temporal connections in the initial graph. We also propose two novel approaches to improve the scalability of our technique: (a) a parallel out-of-core algorithm that can process volumes much larger than an in-core algorithm, and (b) a clip-based processing algorithm that divides the video into overlapping clips in time, and segments them successively while enforcing consistency. We demonstrate hierarchical segmentations on video shots as long as 40 seconds, and even support a streaming mode for arbitrarily long videos, albeit without the ability to process them hierarchically.


title: Online Tracker Optimization for Multi-Pedestrian Tracking Using a Moving Vehicle Camera

abstract: Multi-pedestrian tracking (MPT) on the road is closely related to a reduction in the possibility of pedestrian–vehicle collisions when using advanced driver assistance systems. Therefore, this paper focuses on MPT on real roads using a moving camera. Although convolutional neural network (CNN)-based single object tracking methods have recently been proposed, the online learning of CNN for MPT is a significant burden in terms of real-time processing. However, because the features extracted from CNN are a good representation and achieve a high generalization capability, to reduce the learning time, a shallow pre-trained CNN was applied in the present study as a feature extractor instead of an object tracker As an online tracker, this study uses random ferns (RF) with pre-trained CNN output feature, which can make the problem computationally tractable and robustness. However, a large RF requires significant amount of memory and computational complexity, and is still a burden in terms of online learning in cases of multi-pedestrian tracking. Therefore, we introduce a teacher–student model compression algorithm for selecting a few optimal ferns for each tracker, thereby, reducing the online learning time. The online learning of a student-RF tracker has an advantage in that it can adaptively update the tracker to achieve robustness against various changes, such as in pose and illumination, and partial occlusions within a limited period of time. The proposed algorithm was successfully applied to benchmark video sequences captured from a moving camera that include multiple pedestrians in various poses. Specifically, the proposed algorithm yields a more accurate tracking performance than the other state-of-the-art methods.


title: Assembling short reads from jumping libraries with large insert sizes

abstract: Motivation: Advances in NGS technologies and sample preparation recently enabled generation of high-quality jumping libraries that have a potential to significantly improve short read assemblies. However, assembly algorithms have to catch up with experimental innovations in order to benefit from them and to produce high-quality assemblies. Results: We present a new algorithm that extends recently described EXSPANDER universal repeat resolution approach to enable its applications to several challenging data types, including jumping libraries generated by the recently developed Illumina Nextera Mate Pair protocol. We demonstrate that, with these improvements, bacterial genomes often can be assembled in a few contigs using only a single Nextera Mate Pair library of short reads.


title: Whose Culture Matters? Near-Market Knowledge and Its Impact on Foreign Market Entry Timing

abstract: Abstract Market entry decisions are some of a firm’s most important strategic choices. Although some recent studies have begun to consider the impact of learning and experience on foreign market entry, no study has examined the impact of a firm’s own operations in similar markets on subsequent entry decisions. In this study, the authors introduce the concept of near-market knowledge to reflect the knowledge firms generate by operating in markets that are culturally and economically similar. The authors compile extensive data on the complete foreign market entries of 19 multinational firms. They use a hazard model on 722 entry observations to evaluate the impact of the dynamic near-market knowledge measures and other economic and cultural variables on foreign market entry timing. In contrast with much previous research, the authors find that cultural distance from the domestic market is not a significant factor. However, the authors find significant effects for the new measures of near-market cultural and ...


title: SymPy: symbolic computing in Python

abstract: SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become the standard symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select domain specific submodules. The supplementary materials provide additional examples and further outline details of the architecture and features of SymPy.


title: Multi-label informed feature selection

abstract: Multi-label learning has been extensively studied in the area of bioinformatics, information retrieval, multimedia annotation, etc. In multi-label learning, each instance is associated with multiple interdependent class labels, the label information can be noisy and incomplete. In addition, multilabeled data often has noisy, irrelevant and redundant features of high dimensionality. As an effective data preprocessing step, feature selection has shown its effectiveness to prepare high-dimensional data for numerous data mining and machine learning tasks. Most of existing multi-label feature selection algorithms either boil down to solving multiple single-labeled feature selection problems or directly make use of imperfect labels. Therefore, they may not be able to find discriminative features that are shared by multiple labels. In this paper, we propose a novel multi-label informed feature selection framework MIFS, which exploits label correlations to select discriminative features across multiple labels. Specifically, to reduce the negative effects of imperfect label information in finding label correlations, we decompose the multi-label information into a low-dimensional space and then employ the reduced space to steer the feature selection process. Empirical studies on real-world datasets demonstrate the effectiveness and efficiency of the proposed framework.