title: The Andersson lesion in ankylosing spondylitis: DISTINGUISHING BETWEEN THE INFLAMMATORY AND TRAUMATIC SUBTYPES

abstract: A number of causes have been advanced to explain the destructive discovertebral (Andersson) lesions that occur in ankylosing spondylitis, and various treatments have been proposed, depending on the presumed cause. The purpose of this study was to identify the causes of these lesions by defining their clinical and radiological characteristics. ::: ::: We retrospectively reviewed 622 patients with ankylosing spondylitis. In all, 33 patients (5.3%) had these lesions, affecting 100 spinal segments. Inflammatory lesions were found in 91 segments of 24 patients (3.9%) and traumatic lesions in nine segments of nine patients (1.4%). The inflammatory lesions were associated with recent-onset disease; a low modified Stoke ankylosing spondylitis spine score (mSASSS) due to incomplete bony ankylosis between vertebral bodies; multiple lesions; inflammatory changes on MRI; reversal of the inflammatory changes and central bony ankylosis at follow-up; and a good response to anti-inflammatory drugs. Traumatic lesions were associated with prolonged disease duration; a high mSASSS due to complete bony ankylosis between vertebral bodies; a previous history of trauma; single lesions; nonunion of fractures of the posterior column; acute kyphoscoliotic deformity with the lesion at the apex; instability, and the need for operative treatment due to that instability. ::: ::: It is essential to distinguish between inflammatory and traumatic Andersson lesions, as the former respond to medical treatment whereas the latter require surgery.


title: High Performance Transactions via Early Write Visibility

abstract: In order to guarantee recoverable transaction execution, database systems permit a transaction's writes to be observable only at the end of its execution. As a consequence, there is generally a delay between the time a transaction performs a write and the time later transactions are permitted to read it. This delayed write visibility can significantly impact the performance of serializable database systems by reducing concurrency among conflicting transactions. ::: ::: This paper makes the observation that delayed write visibility stems from the fact that database systems can arbitrarily abort transactions at any point during their execution. Accordingly, we make the case for database systems which only abort transactions under a restricted set of conditions, thereby enabling a new recoverability mechanism, early write visibility, which safely makes transactions' writes visible prior to the end of their execution. We design a new serializable concurrency control protocol, piece-wise visibility (PWV), with the explicit goal of enabling early write visibility. We evaluate PWV against state-of-the-art serializable protocols and a highly optimized implementation of read committed, and find that PWV can outperform serializable protocols by an order of magnitude and read committed by 3X on high contention workloads.


title: Combining Power and Communication Network Simulation for Cost-Effective Smart Grid Analysis

abstract: Today's electricity grid is transitioning to a so-called smart grid. The associated challenges and funding initiatives have spurred great efforts from the research community to propose innovative smart grid solutions. To assess the performance of possible solutions, simulation tools offer a cost effective and safe approach. In this paper we will provide a comprehensive overview of various tools and their characteristics, applicable in smart grid research: we will cover both the communication and associated ICT infrastructure, on top of the power grid. First, we discuss the motivation for the development of smart grid simulators, as well as their associated research questions and design challenges. Next, we discuss three types of simulators in the smart grid area: power system simulators, communication network simulators, and combined power and communication simulators. To summarize the findings from this survey, we classify the different simulators according to targeted use cases, simulation model level of detail, and architecture. To conclude, we discuss the use of standards and multi-agent based modeling in smart grid simulation.


title: Autonomic Computing: Applications of Self-Healing Systems

abstract: Self -- Management systems are the main objective of Autonomic Computing (AC), and it is needed to increase the running system's reliability, stability, and performance. This field needs to investigate some issues related to complex systems such as, self-awareness system, when and where an error state occurs, knowledge for system stabilization, analyze the problem, healing plan with different solutions for adaptation without the need for human intervention. This paper focuses on self-healing which is the most important component of Autonomic Computing. Self-healing is a technique that aims to detect, analyze, and repair existing faults within the system. All of these phases are accomplished in real-time system. In this approach, the system is capable of performing a reconfiguration action in order to recover from a permanent fault. Moreover, self-healing system should have the ability to modify its own behavior in response to changes within the environment. Recursive neural network has been proposed and used to solve the main challenges of self-healing, such as monitoring, interpretation, resolution, and adaptation.


title: Variational Inference for Data-Efficient Model Learning in POMDPs

abstract: Partially observable Markov decision processes (POMDPs) are a powerful abstraction for tasks that require decision making under uncertainty, and capture a wide range of real world tasks. Today, effective planning approaches exist that generate effective strategies given black-box models of a POMDP task. Yet, an open question is how to acquire accurate models for complex domains. In this paper we propose DELIP, an approach to model learning for POMDPs that utilizes amortized structured variational inference. We empirically show that our model leads to effective control strategies when coupled with state-of-the-art planners. Intuitively, model-based approaches should be particularly beneficial in environments with changing reward structures, or where rewards are initially unknown. Our experiments confirm that DELIP is particularly effective in this setting.


title: MUCH-SWIFT: A High-Throughput Multi-Core HW/SW Co-design K-means Clustering Architecture

abstract: K-mean clustering is an essential tool for many big data applications including data mining, predictive analysis, forecasting studies, and machine learning. However, due to large size (volume) of Big-Data, and large dimensionality of its data points, even the application of a simple k-mean clustering may become extremely time and resource demanding. In this paper, we propose a two-level filtering algorithm based on binary kd-tree structure, which considerably decreases the time of convergence in K-means algorithm for large datasets. The proposed modification to the classification algorithm, evolves the SW to naturally divide the classification into smaller data sets, based on the number of available cores and size of logic available in an FPGA. The empirical results show that on a multi-core FPGA, provides 330x speed-up compared to a conventional SW-only solution.


title: Dimensionality Reduction of Massive Sparse Datasets Using Coresets

abstract: In this paper we present a practical solution with performance guarantees to the problem of dimensionality reduction for very large scale sparse matrices. We show applications of our approach to computing the Principle Component Analysis (PCA) of any $n\times d$ matrix, using one pass over the stream of its rows. Our solution uses coresets: a scaled subset of the $n$ rows that approximates their sum of squared distances to \emph{every} $k$-dimensional \emph{affine} subspace. An open theoretical problem has been to compute such a coreset that is independent of both $n$ and $d$. An open practical problem has been to compute a non-trivial approximation to the PCA of very large but sparse databases such as the Wikipedia document-term matrix in a reasonable time. We answer both of these questions affirmatively. Our main technical result is a new framework for deterministic coreset constructions based on a reduction to the problem of counting items in a stream.


title: Kinematics and design of a 5 DoF exoskeleton for the rehabilitation of the upper limb

abstract: This paper aims at presenting the mechanical design, kinematics and workspace analysis of a 5 DoF exoskeleton, which is a new powered exoskeleton for the upper limb rehabilitation that allows the movements of the shoulder rotations at the shoulder joint as well as the elbow movements. The exoskeleton structure is a simple light grounded device designed to face both shoulder and the elbow rhythms. the exoskeleton is provided with an extra joints used to adjust the exoskeleton lengths to make an ideal match between human joint axes and the exoskeleton axe in addition to, a large exploitable range of motion. Due to these feature, that are provided by the exoskeleton, a confortable rehabilitation therapies and activities of daily living assistance can be delivered.


On the Peripheral Venous Murmurs of the Circulatory Hyperkinetic Syndrome


title: 3D Building Roof Modeling by Optimizing Primitive’s Parameters Using Constraints from LiDAR Data and Aerial Imagery

abstract: In this paper, a primitive-based 3D building roof modeling method, by integrating LiDAR data and aerial imagery, is proposed. The novelty of the proposed modeling method is to represent building roofs by geometric primitives and to construct a cost function by using constraints from both LiDAR data and aerial imagery simultaneously, so that the accuracy potential of the different sensors can be tightly integrated for the building model generation by an integrated primitive’s parameter optimization procedure. To verify the proposed modeling method, both simulated data and real data with simple buildings provided by ISPRS (International Society for Photogrammetry and Remote Sensing), were used in this study. The experimental results were evaluated by the ISPRS, which demonstrate the proposed modeling method can integrate LiDAR data and aerial imagery to generate 3D building models with high accuracy in both the horizontal and vertical directions. The experimental results also show that by adding a component, such as a dormer, to the primitive, a variant of the simple primitive is constructed, and the proposed method can generate a building model with some details.


title: Unleashing the Power of Mobile Cloud Computing using ThinkAir

abstract: Smartphones have exploded in popularity in recent years, becoming ever more sophisticated and capable. As a result, developers worldwide are building increasingly complex applications that require ever increasing amounts of computational power and energy. In this paper we propose ThinkAir, a framework that makes it simple for developers to migrate their smartphone applications to the cloud. ThinkAir exploits the concept of smartphone virtualization in the cloud and provides method level computation offloading. Advancing on previous works, it focuses on the elasticity and scalability of the server side and enhances the power of mobile cloud computing by parallelizing method execution using multiple Virtual Machine (VM) images. We evaluate the system using a range of benchmarks starting from simple micro-benchmarks to more complex applications. First, we show that the execution time and energy consumption decrease two orders of magnitude for the N-queens puzzle and one order of magnitude for a face detection and a virus scan application, using cloud offloading. We then show that if a task is parallelizable, the user can request more than one VM to execute it, and these VMs will be provided dynamically. In fact, by exploiting parallelization, we achieve a greater reduction on the execution time and energy consumption for the previous applications. Finally, we use a memory-hungry image combiner tool to demonstrate that applications can dynamically request VMs with more computational power in order to meet their computational requirements.


title: Layout analysis and path planning of a robot palletizing production line

abstract: This paper deals with facility layout analysis and path planning of a robot palletizing production line using a 4-DOF partial closed-loop palletizing robot and two carton conveyors. The layout of the production line is arranged by the reachable workspace of the robot with a goal to achieve the minimum footprint. A straight-line and arc based motion planning approach is proposed under the constraints of joint velocity and acceleration to achieve higher productivity and easier implementation. An example is given to demonstrate the effective of this method.


title: A Pilot Qualitative Study of “Conflicts of Interests and/or Conflicting Interests” Among Canadian Bioethicists. Part 2: Defining and Managing Conflicts

abstract: This paper examines one aspect of professional practice for bioethicists: managing conflicts of interest. Drawing from our qualitative study and descriptive analysis of the experiences of conflicts of interest and/or conflicting interests (COI) of 13 Canadian clinical bioethicists (Frolic and Chidwick 2010), this paper examines how bioethicists define their roles, the nature of COIs in their roles, how their COIs relate to conventional definitions of conflicts of interest, and how COIs can be most effectively managed.


title: The universal motor: A classic machine with evergreen challenges in design and modeling

abstract: The universal motor has been used for more than one century in a variety of small rating utilizations, mainly in household appliances, thanks to its features of robustness, cheapness and simplicity of feeding and speed regulation. Nevertheless, even if it can be considered a mature component, both the increasing technological and performance requirements and the need to refine its model for operation analysis and design purposes has leaded to deepen its study till nowadays. In this paper, a comprehensive overview of the different involved aspects will be presented, among which: structures, steady state operation analysis, evaluation of equivalent circuit parameters, models for the calculation of instantaneous quantities and related harmonic distortion, efficiency figures of merit, commutation waveforms and estimation of brush wear, electronic converter feeding topologies, design methods.


title: Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks

abstract: Motivated by the need to automate medical information extraction from free-text radiological reports, we present a bi-directional long short-term memory (BiLSTM) neural network architecture for modelling radiological language. The model has been used to address two NLP tasks: medical named-entity recognition (NER) and negation detection. We investigate whether learning several types of word embeddings improves BiLSTM's performance on those tasks. Using a large dataset of chest x-ray reports, we compare the proposed model to a baseline dictionary-based NER system and a negation detection system that leverages the hand-crafted rules of the NegEx algorithm and the grammatical relations obtained from the Stanford Dependency Parser. Compared to these more traditional rule-based systems, we argue that BiLSTM offers a strong alternative for both our tasks.


title: Data Acquisition and Processing of 3-D Fingerprints

abstract: To solve the problems associated with conventional 2-D fingerprint scanners such as skin deformation and print smearing, in this paper we introduce a noncontact fingerprint scanner employing structured light illumination to generate high-resolution albedo images as well as 3-D ridge scans. The question to be answered in this research is whether or not ridge depth information improves the quality and matching capability of acquired fingerprints? For evaluation of this question, we use the National Institute of Standards and Technology fingerprint image quality metrics. These metrics require the 3-D prints to be flattened. We present a complete and detailed flattening algorithm based upon unfolding an elastic tube fit to the peaks and valleys of ridges identified within the scan. Further improvement of the flattened print is achieved through the incorporation of ridge information extracted from the albedo image with the depth and albedo ridge information fused together according to local scan quality. Our study compares image quality between the flattened 3-D prints and ink rolled prints. Most significantly, the matching performance of 3-D flattened to 3-D flattened prints is evaluated based on ridge depth only, albedo only, and depth with albedo fusion.


title: The dark side of smartphone usage: Psychological traits, compulsive behavior and technostress

abstract: Smartphones have become necessities in people' lives. Along with its obvious benefits, however, the smartphone has other effects that are not all that glorious. This study investigates the dark side of the smartphone trend. We examine the link between psychological traits and the compulsive behaviors of smartphone users, and look further into the stress caused by those compulsive behaviors. We conducted an empirical study consisting of 325 participants and compared Structural Equation Modeling with competing models. The results suggest that compulsive usage of smartphone and technostress are positively related to psychological traits including locus of control, social interaction anxiety, materialism and the need for touch. Gender differences are also found in the aforementioned relationships. The results have practical implications to user-oriented smartphone design and operation companies as well as government agencies as they combat the social ills brought on by smartphones.


Citation Handling: Processing Citation Texts in Scientific Documents


abstract: User alignment between different social networks is a fundamental issue for many applications, such as information diffusion and recommendation. In actuality, the observed anchor users are normally sparse due to the expensiveness of labeling data. Hence how to make the best use of these sparse anchor information is an important open issue. To this end, we proposed a User Alignment via Structural Interaction and Propagation (UASIP) model to capture the structural information interaction across two social networks, which exploits deep structural infor- mation to enhance the representations of users. UASIP learns vector representation by automatically keeping the consistency between this additional structural information and intrinsic structural information of the two social networks. Experiments on real-world social network datasets demonstrate the effectiveness of UASIP compared with several state-of-the-art methods.


title: A survey of computational location privacy

abstract: This is a literature survey of computational location privacy, meaning computation-based privacy mechanisms that treat location data as geometric information. This definition includes privacy-preserving algorithms like anonymity and obfuscation as well as privacy-breaking algorithms that exploit the geometric nature of the data. The survey omits non-computational techniques like manually inspecting geotagged photos, and it omits techniques like encryption or access control that treat location data as general symbols. The paper reviews studies of peoples' attitudes about location privacy, computational threats on leaked location data, and computational countermeasures for mitigating these threats.


Investigation of Wear Mechanism in Quenched and Tempered Medium Carbon-High Chromium Martensitic Steel Using Dry Sand/Rubber Wheel


title: Kinematics analysis and application of 5-DOF manipulator with special joint

abstract: An improved method based on the D-H method is proposed for a 5-DOF manipulator with special structure which is difficult to establish joint transformation by using normal D-H method. The forward kinematics of this manipulator with special structure is established by adding virtual joint. The method is suitable for manipulators with rotating joints, and the coordinate transformation must be achieved by transform along the Z axis. The process of inverse kinematics of the manipulator is analyzed. The conditions for the existence of inverse solution and the number of inverse solutions are also analyzed. Finally, the simulation of the proposed scheme is carried out to verify its correctness. And it has been applied to the physical mechanical arm.


title: Using a Hybrid Bayes Classifier/Evolutionary Algorithm

abstract: A key element of bioinformatics research is the extraction of meaningful information from large experimental data sets. Various approaches, including statistical and graph theoretical methods, data mining, and computational pattern recognition, have been applied to this task with varying degrees of success. Using a novel classifier based on the Bayes discriminant function, we present a hybrid algorithm that employs feature selection and extraction to isolate salient features from large medical and other biological data sets. We have previously shown that a genetic algorithm coupled with a k-nearest-neighbors classifier performs well in extracting information about protein-water binding from X-ray crystallographic protein structure data. The effectiveness of the hybrid EC-Bayes classifier is demonstrated to distinguish the features of this data set that are the most statistically relevant and to weight these features appropriately to aid in the prediction of solvation sites.


title: Understanding sequence and reply relationships within email conversations: a mixed-model visualization

abstract: It has been proposed that email clients could be improved if they presented messages grouped into conversations. An email conversation is the tree of related messages that arises from the use of the reply operation. We propose two models of conversation. The first model characterizes a conversation as a chronological sequence of messages; the second as a tree based on the reply relationship. We show how existing email clients and prior research projects implicitly support each model to a greater or lesser degree depending on their design, but none fully supports both models simultaneously. We present a mixed-model visualization that simultaneously presents sequence and reply relationships among the messages of a conversation, making both visible at a glance. We describe the integration of the visualization into a working prototype email client. A usability study indicates that the system meets our usability goals and verifies that the visualization fully conveys both types of relationships within the messages of an email conversation.


title: Security perception in e-commerce: Conflict between customer and organizational perspectives

abstract: Security is one of the principal and continuing concerns that restrict customers and organizations engaging with e-commerce. The aim of this paper is to explore the perception of security in e-commerce B2C and C2C websites from both customer and organisational perspectives. It explores factors that influence customers- perceptions of security. It also highlights conflicts between customer concerns with respect to security and those of an organization; existing research has not highlighted this issue greatly. This research provides a better understanding of customer needs and priorities on the subject of security, and enriches the currently available security perception literature, by providing new insights from empirical research conducted in Jordan. A qualitative research approach was adopted, since the research seeks an understanding of human (i.e., customer and organisational employee) perceptions.


title: Modeling of the Cost of EV Battery Wear Due to V2G Application in Power Systems

abstract: This paper presents an analysis of the cost of utilizing battery electric vehicle (BEV) batteries as energy storage in power grids [also known as vehicle-to-grid (V2G)] associated with lessening battery cycle life due to more frequent charging and discharging activities and utilization in elevated ambient temperature. Comparison is made between V2G in the U.K., where annual electricity peak demand is reached in winter, and in China, where peak demand is reached in summer due to the air conditioning load. This paper presents mathematical correlations between charging-discharging, ambient temperature, depth of discharge (DoD), and the degradation of electric vehicle batteries based on manufacturer's data. Simulation studies were carried out for V2G in both the U.K. and China. Numerical results show that ambient temperature and DoD of a BEV battery play a crucial role in the cost of battery wear. Lead-acid and NiMH battery powered BEVs are not cost effective in V2G use due to the present electricity tariff. Under the present electricity tariff structure, no vehicles would be cost effective for the peak power sources in China. However, lithium-ion battery powered BEVs are cost effective in the U.K. due to a much longer cycle life.


title: Internet of Thing based healthcare monitoring system

abstract: Advances in the information and communication technologies have led to the emergence of Internet of Thing (IoT). IoT allows many physical devices to capture transmit data, through the internet, providing more data interoperability methods. Nowadays IoT plays an important role not only in communication, but also in monitoring, recording, storage and display. Hence the latest trend in Healthcare communication method using IoT is adapted. Monitored on a continual basis, aggregated and effectively analyzed-such information can bring about a massive positive transformation in the field of healthcare. Our matter of concern in this project is to focus on the development and implementation of an effective healthcare monitoring system based on IoT. The proposed system monitors the vital health parameters and transmits the data through a wireless communication, which is further transferred to a network via a Wi-Fi module. The data can be accessed anytime promoting the reception of the current status of the patient. In case any abnormal behavior or any vital signs are recognized, the caretaker, as well as the doctors are notified immediately through a message service or an audio signaling device (buzzer). In order to design an efficient remote monitoring system, security plays an important part. Cloud computing and password protected Wi-Fi module handles authentication, privacy and security of patient details by allowing restricted access to the database. Hence the system provides quality healthcare to all. This paper is a review of Healthcare Monitoring system using IoT.


title: An integrated conceptual digital forensic framework for cloud computing

abstract: Increasing interest in and use of cloud computing services presents both opportunities for criminal exploitation and challenges for law enforcement agencies (LEAs). For example, it is becoming easier for criminals to store incriminating files in the cloud computing environment but it may be extremely difficult for LEAs to seize these files as the latter could potentially be stored overseas. Two of the most widely used and accepted forensic frameworks – McKemmish (1999) and NIST (Kent et al., 2006) – are then reviewed to identify the required changes to current forensic practices needed to successfully conduct cloud computing investigations. We propose an integrated (iterative) conceptual digital forensic framework (based on McKemmish and NIST), which emphasises the differences in the preservation of forensic data and the collection of cloud computing data for forensic purposes. Cloud computing digital forensic issues are discussed within the context of this framework. Finally suggestions for future research are made to further examine this field and provide a library of digital forensic methodologies for the various cloud platforms and deployment models.


title: DeepTag: inferring diagnoses from veterinary clinical notes

abstract: Large scale veterinary clinical records can become a powerful resource for patient care and research. However, clinicians lack the time and resource to annotate patient records with standard medical diagnostic codes and most veterinary visits are captured in free-text notes. The lack of standard coding makes it challenging to use the clinical data to improve patient care. It is also a major impediment to cross-species translational research, which relies on the ability to accurately identify patient cohorts with specific diagnostic criteria in humans and animals. In order to reduce the coding burden for veterinary clinical practice and aid translational research, we have developed a deep learning algorithm, DeepTag, which automatically infers diagnostic codes from veterinary free-text notes. DeepTag is trained on a newly curated dataset of 112,558 veterinary notes manually annotated by experts. DeepTag extends multitask LSTM with an improved hierarchical objective that captures the semantic structures between diseases. To foster human-machine collaboration, DeepTag also learns to abstain in examples when it is uncertain and defers them to human experts, resulting in improved performance. DeepTag accurately infers disease codes from free-text even in challenging cross-hospital settings where the text comes from different clinical settings than the ones used for training. It enables automated disease annotation across a broad range of clinical diagnoses with minimal preprocessing. The technical framework in this work can be applied in other medical domains that currently lack medical coding resources.


Student engagement at school : a sense of belonging and participation : results from PISA 2000


title: AROMA: ambient awareness through olfaction in a messaging application

abstract: This work explores the properties of different output modalities as notification mechanisms in the context of messaging. In particular, the olfactory (smell) modality is introduced as a potential alternative to visual and auditory modalities for providing messaging notifications. An experiment was performed to compare these modalities as secondary display mechanisms used to deliver notifications to users working on a cognitively engaging primary task. It was verified that the disruptiveness and effectiveness of notifications varied with the notification modality. The olfactory modality was shown to be less effective in delivering notifications than the other modalities, but produced a less disruptive effect on user engagement in the primary task. Our results serve as a starting point for future research into the use of olfactory notification in messaging systems.


title: "Law and Politics in the European Union: The Europeanization of Market Regulation and Its Discontents"

abstract: This paper examines the impact of decisions by the European Court of Justice (ECJ) on processes of market liberalization in the European Union (EU). Research on national responses to European legal obligations in the telecommunications and electricity sectors concentrates on France, Germany, and the United Kingdom. I argue that political responses to parallel legal obligations resulted in distinct regulatory outcomes. Traditionally regulated on a national level as public services, telecommunications and electricity markets offered different opportunities and costs. Distinct patterns of political mobilization among concentrated, intensely interested actors encouraged a wide-ranging program of telecommunications liberalization but only a limited competitive regime for electricity.


title: Qualitative and quantitative composition of essential oils: A literature-based database on contact allergens used for safety assessment

abstract: Abstract The risks related to the use of essential oils are difficult to ascertain at present, due in part to the large number of different oils available on the market, making it difficult for the risk assessor. Essential oils may contain skin allergens in significant amounts, and could thus pose a risk to the consumer. The aim of our study was to collect as much qualitative and quantitative data as possible on allergens present in essential oils. 11 types of essential oils, with 25 respective subspecies, were taken into account based on a previous survey. Based on the literature, 517 dosages were recorded from 112 publications, providing precious information for probabilistic exposure assessment purposes. 22 substances recognized as established allergens were found in the essential oils we included. Of these, 11 are also found in cosmetics as fragrance components. These results are of major importance regarding co-exposure to fragrance allergens. Moreover, this could lead to regulatory measures for essential oils in the future, as it is the case for cosmetic products, in order to better protect consumers against skin allergy.


title: Prenatal exposure to diazepam and alprazolam, but not to zolpidem, affects behavioural stress reactivity in handling-naı̈ve and handling-habituated adult male rat progeny

abstract: A gentle long-lasting handling produces persistent neurochemical and behavioural changes and attenuates the impairment in the behavioural reactivity to novelty induced by the prenatal exposure to diazepam (DZ) in adult male rat progeny. This study investigated the consequences of a late prenatal treatment with three GABA/BDZ R agonists (DZ) alprazolam (ALP) and zolpidem (ZOLP)), on different stress-related behavioural patterns, in non-handled (NH), short-lasting handled (SLH) and long-lasting handled (LLH) adult male rats exposed to forced swim test (FST), acoustic startle reflex (ASR) and Vogel test (VT). The effects on motor activity were evaluated in the open field and in the Skinner box. The seizure sensitivity to picrotoxin (PTX) was investigated as an index of the functional state of GABA/BDZ Rs. A single daily s.c. injection of DZ (1.25-2.50 mg/kg) and ALP (0.125-0.250 mg/kg) over gestational days 14-20 induced a decrease in immobility time in the FST in NH rats, no change in SLH rats and an increase in LLH rats; DZ induced an increase in the peak amplitude of the ASR in NH rats, no change in SLH rats and a reduction in LLH rats; ALP was ineffective in all groups. DZ and ALP reduced the number of punished licks in the VT in NH, SLH and LLH rats while the unpunished licks were not modified. DZ decreased locomotion and the lever pressing responses while ALP increased them. DZ and ALP increased the seizure sensitivity to PTX (2.5-4.0 mg/kg i.p.). These findings indicate a convergence on anxiety-related behaviours in the effects of prenatal exposure to DZ and ALP and a differentiation on motor activity. Long-lasting handling was able to overcompensate the increased behavioural stress reactivity induced by the prenatal exposure to DZ and ALP.


title: Design optimal built-in snubber in trench field plate power MOSFET for superior EMI and efficiency performance

abstract: A 40V field plate trench MOSFET with optimal built-in snubber has been evaluated in Sentaurus TCAD, showing superior EMI and efficiency performance in the application of power sync-buck and power brick. We developed an advanced TCAD mixed-mode simulation to simulate switching performance of the field plate trench MOSFET with different built-in snubber resistance. Simulation demonstrates Vds damping effects through different snubber designs and an optimal range has been indicated. Physical insight has also been obtained through simulation by visualizing electrical field propagation and current distribution. In-circuit efficiency simulation demonstrates that with an optimize snubber design one can maximize the self-damping without penalization on the switching power losses or the product cost.


title: Reconstructing High Quality Face-Surfaces using Model Based Stereo

abstract: We present a novel model based stereo system, which accurately extracts the 3D shape and pose of faces from multiple images taken simultaneously. Extracting the 3D shape from images is important in areas such as pose-invariant face recognition and image manipulation. The method is based on a 3D morphable face model learned from a database of facial scans. The use of a strong face prior allows us to extract high precision surfaces from stereo data of faces, where traditional correlation based stereo methods fail because of the mostly textureless input images. The method uses two or more uncalibrated images of arbitrary baseline, estimating calibration and shape simultaneously. Results using two and three input images are presented. We replace the lighting and albedo estimation of a monocular method with the use of stereo information, making the system more accurate and robust. We evaluate the method using ground truth data and the standard PIE image dataset. A comparison with the state of the art monocular system shows that the new method has a significantly higher accuracy.


title: On Subversive Miner Strategies and Block Withholding Attack in Bitcoin Digital Currency

abstract: Bitcoin is a "crypto currency", a decentralized electronic payment scheme based on cryptography. Bitcoin economy grows at an incredibly fast rate and is now worth some 10 billions of dollars. Bitcoin mining is an activity which consists of creating (minting) the new coins which are later put into circulation. Miners spend electricity on solving cryptographic puzzles and they are also gatekeepers which validate bitcoin transactions of other people. Miners are expected to be honest and have some incentives to behave well. However. In this paper we look at the miner strategies with particular attention paid to subversive and dishonest strategies or those which could put bitcoin and its reputation in danger. We study in details several recent attacks in which dishonest miners obtain a higher reward than their relative contribution to the network. In particular we revisit the concept of block withholding attacks and propose a new concrete and practical block withholding attack which we show to maximize the advantage gained by rogue miners. ::: RECENT EVENTS: it seems that the attack was recently executed, see Section XI-A.


title: Photometric Stereo in the Wild

abstract: Conventional photometric stereo requires to capture images or videos in a dark room to obstruct complex environment light as much as possible. This paper presents a new method that capitalizes on environment light to avail geometry reconstruction, thus bringing photometric stereo to the wild, such as an outdoor scene, with uncontrolled lighting. We do not make restrictive assumption, and only use simple capture equipments, which include a mirror sphere and a video camera. Qualitative and quantitative experiments indicate the potential and practicality of our system to generalize existing frameworks.


title: Detection of cyberbullying on social media messages in Turkish

abstract: The increased use of the Internet and the ease of access to online communities like social media have provided an avenue for cybercrimes. Cyberbullying, which is a kind of cybercrime, is defined as an aggressive, intentional action against a defenseless person by using the Internet, social media, or other electronic contents. Researchers have found that many of the bullying cases have tragically ended in suicides; hence automatic detection of cyberbullying has become important. The aim of this study is to detect cyberbullying on social media messages written in Turkish. To our knowledge, this is the first study which makes cyberbully detection on Turkish texts. We prepare a dataset from Instagram and Twitter messages written in Turkish and then we applied machine learning techniques that are Support Vector Machines (SVM), decision tree (C4.5), Naive Bayes Multinomial, and k Nearest Neighbors (kNN) classifiers to detect cyberbullying. We also apply information gain and chi-square feature selection methods to improve the accuracy of classifiers. We observe that when both words and emoticons in the text messages are taken into account as features, cyberbully detection improves. Among the classifiers, Naive Bayes Multinomial is the most successful one in terms both classification accuracy and running time. When feature selection is applied classification accuracy improves up to 84% for the dataset used.


title: Behavior based learning in identifying High Frequency Trading strategies

abstract: Electronic markets have emerged as popular venues for the trading of a wide variety of financial assets, and computer based algorithmic trading has also asserted itself as a dominant force in financial markets across the world. Identifying and understanding the impact of algorithmic trading on financial markets has become a critical issue for market operators and regulators. We propose to characterize traders' behavior in terms of the reward functions most likely to have given rise to the observed trading actions. Our approach is to model trading decisions as a Markov Decision Process (MDP), and use observations of an optimal decision policy to find the reward function. This is known as Inverse Reinforcement Learning (IRL), and a variety of approaches for this problem are known. Our IRL-based approach to characterizing trader behavior strikes a balance between two desirable features in that it captures key empirical properties of order book dynamics and yet remains computationally tractable. Using an IRL algorithm based on linear programming, we are able to achieve more than 90% classification accuracy in distinguishing High Frequency Trading from other trading strategies in experiments on a simulated E-Mini S&P 500 futures market. The results of these empirical tests suggest that High Frequency Trading strategies can be accurately identified and profiled based on observations of individual trading actions.


title: Men and things, women and people: A meta-analysis of sex differences in interests.

abstract: The magnitude and variability of sex differences in vocational interests were examined in the present meta-analysis for Holland’s (1959, 1997) categories (Realistic, Investigative, Artistic, Social, Enterprising, and Conventional), Prediger’s (1982) Things–People and Data–Ideas dimensions, and the STEM (science, technology, engineering, and mathematics) interest areas. Technical manuals for 47 interest inventories were used, yielding 503,188 respondents. Results showed that men prefer working with things and women prefer working with people, producing a large effect size (d 0.93) on the Things–People dimension. Men showed stronger Realistic (d 0.84) and Investigative (d 0.26) interests, and women showed stronger Artistic (d 0.35), Social (d 0.68), and Conventional (d 0.33) interests. Sex differences favoring men were also found for more specific measures of engineering (d 1.11), science (d 0.36), and mathematics (d 0.34) interests. Average effect sizes varied across interest inventories, ranging from 0.08 to 0.79. The quality of interest inventories, based on professional reputation, was not differentially related to the magnitude of sex differences. Moderators of the effect sizes included interest inventory item development strategy, scoring method, theoretical framework, and sample variables of age and cohort. Application of some item development strategies can substantially reduce sex differences. The present study suggests that interests may play a critical role in gendered occupational choices and gender disparity in the STEM fields.


title: Modeling and Analysis of Cellular Networks using Stochastic Geometry: A Tutorial

abstract: This paper presents a tutorial on stochastic geometry (SG)-based analysis for cellular networks. This tutorial is distinguished by its depth with respect to wireless communication details and its focus on cellular networks. This paper starts by modeling and analyzing the baseband interference in a baseline single-tier downlink cellular network with single antenna base stations and universal frequency reuse. Then, it characterizes signal-to-interference-plus-noise-ratio and its related performance metrics. In particular, a unified approach to conduct error probability, outage probability, and transmission rate analysis is presented. Although the main focus of this paper is on cellular networks, the presented unified approach applies for other types of wireless networks that impose interference protection around receivers. This paper then extends the unified approach to capture cellular network characteristics (e.g., frequency reuse, multiple antenna, power control, etc.). It also presents numerical examples associated with demonstrations and discussions. To this end, this paper highlights the state-of-the-art research and points out future research directions.


title: Local neighbourhood extension of SMOTE for mining imbalanced data

abstract: In this paper we discuss problems of inducing classifiers from imbalanced data and improving recognition of minority class using focused resampling techniques. We are particularly interested in SMOTE over-sampling method that generates new synthetic examples from the minority class between the closest neighbours from this class. However, SMOTE could also overgeneralize the minority class region as it does not consider distribution of other neighbours from the majority classes. Therefore, we introduce a new generalization of SMOTE, called LN-SMOTE, which exploits more precisely information about the local neighbourhood of the considered examples. In the experiments we compare this method with original SMOTE and its two, the most related, other generalizations Borderline and Safe-Level SMOTE. All these pre-processing methods are applied together with either decision tree or Naive Bayes classifiers. The results show that the new LN-SMOTE method improves evaluation measures for the minority class.


title: Adaptive Security for Multi-layer Ad-hoc Networks

abstract: Secure communication is critical in military environments where the network infrastructure is vulnerable to various attacks and compromises. A conventional centralized solution breaks down when the security servers are destroyed by the enemies. In this paper we design and evaluate a security framework for multi-layer ad-hoc wireless networks with unmanned aerial vehicles (UAVs). In battlefields, the framework adapts to the contingent damages on the network infrastructure. Depending on the availability of the network infrastructure, our design is composed of two modes. In infrastructure mode, security services, specifically the authentication services, are implemented on UAVs that feature low overhead and flexible managements. When the UAVs fail or are destroyed, our system seamlessly switches to infrastructureless mode, a backup mechanism that maintains comparable security services among the surviving units. In the infrastructureless mode, the security services are localized to each node’s vicinity to comply with the ad-hoc communication mechanism in the scenario. We study the instantiation of these two modes and the transitions between them. Our implementation and simulation measurements confirm the effectiveness of our design.


title: Depth of Presence in Virtual Environments

abstract: This paper describes a study to assess the influence of a variety of factors on reported level of presence in immersive virtual environments. It introduces the idea of “stacking depth,” that is, where a participant can simulate the process of entering the virtual environment while already in such an environment, which can be repeated to several levels of depth. An experimental study including 24 subjects was carried out. Half of the subjects were transported between environments by using virtual head-mounted displays, and the other half by going through doors. Three other binary factors were whether or not gravity operated, whether or not the subject experienced a virtual precipice, and whether or not the subject was followed around by a virtual actor. Visual, auditory, and kinesthetic representation systems and egocentric/exocentric perceptual positions were assessed by a preexperiment questionnaire. Presence was assessed by the subjects as their sense of “being there,” the extent to which they experienced the virtual environments as more the presenting reality than the real world in which the experiment was taking place, and the extent to which the subject experienced the virtual environments as places visited rather than images seen. A logistic regression analysis revealed that subjective reporting of presence was significantly positively associated with visual and kinesthetic representation systems, and negatively with the auditory system. This was not surprising since the virtual reality system used was primarily visual. The analysis also showed a significant and positive association with stacking level depth for those who were transported between environments by using the virtual HMD, and a negative association for those who were transported through doors. Finally, four of the subjects moved their real left arm to match movement of the left arm of the virtual body displayed by the system. These four scored significantly higher on the kinesthetic representation system than the remainder of the subjects.


title: Automatic Definition of Modular Neural Networks

abstract: This article illustrates an artificial developmental system that is a computationally efficient technique for the automatic generation of complex artificial neural networks (ANNs). The artificial developmental system can develop a graph grammar into a modular ANN made of a combination of simpler subnetworks. A genetic algorithm is used to evolve coded grammars that generate ANNs for controlling six-legged robot locomotion. A mechanism for the automatic definition of neural subnetworks is incorporated Using this mechanism, the genetic algorithm can automatically decompose a problem into subproblems, generate a subANN for solving the subproblem, and instantiate copies of this subANN to build a higher-level ANN that solves the problem. We report some simulation results showing that the same problem cannot be solved if the mechanism for automatic definition of subnetworks is suppressed. We support our argument with pictures that describe the steps of development, how ANN structures are evolved, and how the AN...


title: Alternating Directions Dual Decomposition

abstract: We propose AD3, a new algorithm for approximate maximum a posteriori (MAP) inference on factor graphs based on the alternating directions method of multipliers. Like dual decomposition algorithms, AD3 uses worker nodes to iteratively solve local subproblems and a controller node to combine these local solutions into a global update. The key characteristic of AD3 is that each local subproblem has a quadratic regularizer, leading to a faster consensus than subgradient-based dual decomposition, both theoretically and in practice. We provide closed-form solutions for these AD3 subproblems for binary pairwise factors and factors imposing first-order logic constraints. For arbitrary factors (large or combinatorial), we introduce an active set method which requires only an oracle for computing a local MAP configuration, making AD3 applicable to a wide range of problems. Experiments on synthetic and realworld problems show that AD3 compares favorably with the state-of-the-art.


title: Accounting for interrupt handling costs in dynamic priority task systems

abstract: In order to apply the results of formal studies of real-time task models, a practitioner must account for the effects of phenomena present in the implementation but not present in the formal model. We study the feasibility and schedulability problems for periodic tasks that must compete for the processor with interrupt handlers - tasks that are assumed to always have priority over application tasks. The emphasis in the analysis is on deadline driven scheduling methods. We develop conditions that solve the feasibility and schedulability problems and demonstrate that our solutions are computationally feasible. Lastly, we compare our analysis with others developed for static priority task systems. >


title: SVM Optimization for Sentiment Analysis

abstract: Exponential growth in mobile technology and mini computing devices has led to a massive increment in social media users, who are continuously posting their views and comments about certain products and services, which are in their use. These views and comments can be extremely beneficial for the companies which are interested to know about the public opinion regarding their offered products or services. This type of public opinion otherwise can be obtained via questionnaires and surveys, which is no doubt a difficult and complex task. So, the valuable information in the form of comments and posts from micro-blogging sites can be used by the companies to eliminate the flaws and to improve the products or services according to customer needs. However, extracting a general opinion out of a staggering number of users’ comments manually cannot be feasible. A solution to this is to use an automatic method for sentiment mining. Support Vector Machine (SVM) is one of the widely used classification techniques for polarity detection from textual data. This study proposes a technique to tune the SVM performance by using grid search method for sentiment analysis. In this paper, three datasets are used for the experiment and performance of proposed technique is evaluated using three information retrieval metrics: precision, recall and f-measure.


title: A Fan-Based Smart Selective Trap for Flying Insects

abstract: For several years, insect-borne diseases have been a major global concern. Countries with warmer climates are these disease vectors primary victims. However, due to rising global temperatures, countries with no history of insect-borne diseases have presented alarming statistics on the mosquitoes population lack control. Due to the importance of this subject, this paper proposes a smart and selective fan-based trap to capture the Aedes aegypti mosquito, main vector of diseases such as Yellow Fever, Dengue Fever, Zika, and Chikungunya. The central purpose of this study is the development of an electronic trap integrable to variable approaches of flying insects sensors. The trap is made up of a microcontroller that operates two DC fans arranged at the ends of an aerodynamic structure. The construction of a prototype validates the project through results evaluation and discussion.


title: Big Data Mobile Services for New York City Taxi Riders and Drivers

abstract: In recent years, taxis in New York City have been equipped with GPS sensors to record the start and end locations of every trip, in conjunction with fare collection equipment recording trip time, fare, and distance. This paper analyzes this vast dataset with big data technologies. We use MapReduce and Hive in order to understand traffic and travel patterns, discover relationships among the data, and make predictions on the taxi network. We also propose a service model combining the results of our data analysis with smartphone applications to offer users various taxi-related services based on results of our big data analysis.


title: A comparison of feature detectors and descriptors for object class matching

abstract: Solid protocols to benchmark local feature detectors and descriptors were introduced by Mikolajczyk et al. 1,2. The detectors and the descriptors are popular tools in object class matching, but the wide baseline setting in the benchmarks does not correspond to class-level matching where appearance variation can be large. We extend the benchmarks to the class matching setting and evaluate state-of-the-art detectors and descriptors with Caltech and ImageNet classes. Our experiments provide important findings with regard to object class matching: (1) the original SIFT is still the best descriptor; (2) dense sampling outperforms interest point detectors with a clear margin; (3) detectors perform moderately well, but descriptors' performance collapses; (4) using multiple, even a few, best matches instead of the single best has significant effect on the performance; (5) object pose variation degrades dense sampling performance while the best detector (Hessian-affine) is unaffected. The performance of the best detector-descriptor pair is verified in the application of unsupervised visual class alignment where state-of-the-art results are achieved. The findings help to improve the existing detectors and descriptors for which the framework provides an automatic validation tool.


title: Multimodal Word Distributions

abstract: Word embeddings provide point representations of words containing useful semantic information. We introduce multimodal word distributions formed from Gaussian mixtures, for multiple word meanings, entailment, and rich uncertainty information. To learn these distributions, we propose an energy-based max-margin objective. We show that the resulting approach captures uniquely expressive semantic information, and outperforms alternatives, such as word2vec skip-grams, and Gaussian embeddings, on benchmark datasets such as word similarity and entailment.


AN 1477 Digital Compensator Design for LLC Resonant Converter


title: Pulga, a tiny open-source MQTT broker for flexible and secure IoT deployments

abstract: The Eyes of Things (EoT) EU H2020 project envisages a computer vision platform that can be used both standalone and embedded into more complex artifacts, particularly for wearable applications, robotics, home products, surveillance etc. The core hardware will be based on a number of technologies and components that have been designed for maximum performance of the always-demanding vision applications while keeping the lowest energy consumption. An important functionality is to be able to communicate with other devices that we use everyday (say, configuring and controlling the EoT device from a tablet). Apart from low-power hardware components, an efficient protocol is necessary. Text-oriented protocols like HTTP are not appropriate in this context. Instead, the lightweight publish/subscribe MQTT protocol was selected. Still, the typical scenario is that of a device that sends/receives messages, the messages being forwarded by a cloud-based message broker. In this paper we propose a novel approach in which each EoT device acts as an MQTT broker instead of the typical cloud-based architecture. This eliminates the need for an external Internet server, which not only makes the whole deployment more affordable and simpler but also more secure by default.


title: Protecting Military Avionics Platforms from Attacks on MIL-STD-1553 Communication Bus

abstract: MIL-STD-1553 is a military standard that defines the physical and logical layers, and a command/response time division multiplexing of a communication bus used in military and aerospace avionic platforms for more than 40 years. As a legacy platform, MIL-STD-1553 was designed for high level of fault tolerance while less attention was taken with regard to security. Recent studies already addressed the impact of successful cyber attacks on aerospace vehicles that are implementing MIL-STD-1553. In this study we present a security analysis of MIL-STD-1553. In addition, we present a method for anomaly detection in MIL-STD-1553 communication bus and its performance in the presence of several attack scenarios implemented in a testbed, as well as results on real system data. Moreover, we propose a general approach towards an intrusion detection system (IDS) for a MIL-STD-1553 communication bus.


title: A modified serpenoid equation for snake robots

abstract: This paper presents a modified set of serpenoid equations to navigate snake robots more efficiently. Serpentine gait is recognized as the most efficient gait for snakes in terms of energy, motor torques and friction forces. However, the conventional serpentine model is based on fixed parameters. Changing parameters to reach an optimal motion or avoid bumping to obstacles causes the robot parts slip. Using serpenoid curve is therefore no longer optimal due to the side slipping; the modified serpenoid equations presented here develops a serpenoid curve with variable parameters such that the motion remains optimal even when the parameters are changed. The results of simulations conducted here, representing motor torques and friction forces, show the efficiency of applying this method to a real robot.


title: Boosted Generative Models

abstract: We propose a novel approach for using unsupervised boosting to create an ensemble of generative models, where models are trained in sequence to correct earlier mistakes. Our meta-algorithmic framework can leverage any existing base learner that permits likelihood evaluation, including recent deep expressive models. Further, our approach allows the ensemble to include discriminative models trained to distinguish real data from model-generated data. We show theoretical conditions under which incorporating a new model in the ensemble will improve the fit and empirically demonstrate the effectiveness of our black-box boosting algorithms on density estimation, classification, and sample generation on benchmark datasets for a wide range of generative models.


title: Detection of the eye blinks for human's fatigue monitoring

abstract: This paper presents a non-intrusive vision based system for eye blinks detection and fatigue level monitoring. It uses a web camera positioned in front of the face. A cascade of boosted classifiers based on Haar-like features is used for fast detection of the eyes region. The frames differencing in combination with the thresholding are applied to detect the eyes closure and opening. The frame processing algorithm is pointed out in order to distinguish the involuntary blinks from the voluntary ones. Experimental tests are shown that validate the proposed system.


title: The impact of social and conventional media on firm equity value: A sentiment analysis approach

abstract: This study aims to investigate the effect of social media and conventional media, their relative importance, and their interrelatedness on short term firm stock market performances. We use a novel and large-scale dataset that features daily media content across various conventional media and social media outlets for 824 public traded firms across 6 industries. Social media outlets include blogs, forums, and Twitter. Conventional media includes major newspapers, television broadcasting companies, and business magazines. We apply the advanced sentiment analysis technique that goes beyond the number of mentions (counts) to analyze the overall sentiment of each media resource toward a specific company on the daily basis. We use stock return and risk as the indicators of companies' short-term performances. Our findings suggest that overall social media has a stronger relationship with firm stock performance than conventional media while social and conventional media have a strong interaction effect on stock performance. More interestingly, we find that the impact of different types of social media varies significantly. Different types of social media also interrelate with conventional media to influence stock movement in various directions and degrees. Our study is among the first to examine the effect of multiple sources of social media along with the effect of conventional media and to investigate their relative importance and their interrelatedness. Our findings suggest the importance for firms to differentiate and leverage the unique impact of various sources of media outlets in implementing their social media marketing strategies.


title: Impact of Social Media Marketing Strategies used by Micro Small and Medium Enterprises (MSMEs) on Customer acquisition and retention

abstract: Purpose and objectives of the study: The primary objective of the researchers is to study and understand the concept of social media marketing and its role in the Micro Small and Medium Enterprise (MSME) sector. It is also intended to study the correlation between the sales obtained by a successful marketer and the time spent on social media marketing. Research Methodology: Data was collected from 50 Micro Small and Medium Enterprises (MSMEs) using judgement sampling. Exploratory research is used to study the social media marketing strategies used by these MSMEs for customer acquisition and retention. Results: It is found that social media participation has a strong influence on brand awareness and brand trust, which in turn, have a strong influence on customer acquisition and customer retention. Also, there is a strong positive correlation between sales and amount of time spent on social media. Social media marketing strategies have a positive influence on customer acquisition and retention and to increase in market share. Managerial implications: This research will be helpful for MSME’s as they will be able to target customers with appropriate social media marketing strategies. The research will also uncover factors contributing to increase in sales through social media marketing. The MSME’s will be able to choose social media channels, social tactics and create an effective social media marketing strategy to acquire and retain customers based on the findings and conclusion of the research.


title: Optimization of the crushing characteristics of triangulated aluminum beverage cans

abstract: In this study, a cylindrical shell body of the aluminum cans is triangulated and optimized for being folded down easily and safely for recycling. The triangulated cylindrical shell is constructed by a family of triangular surfaces based on one set of helical strips and circles lying on a cylindrical side surface. The intersections of helical strips and circles are used as the vertexes of the triangular surfaces. By changing the helical angle of the strips, the number of the strips, and the number of the circles, various triangulated cylindrical shells with different crushing characteristics can be developed. On the basis of the numerical simulation, the minimization problem of the crushing force of the triangulated cylindrical shells is solved using the crashworthiness maximization technique for tubular structures that combines the techniques of design of experiment, response surface approximation as well as usual mathematical programming.


title: Monte carlo simulation for molecular gas dynamics

abstract: The dynamics of low-density flows is governed by the Boltzmann equation of the kinetic theory of gases. This is a nonlinear integro-differential equation and, in general, numerical methods must be used to obtain its solution. The present paper, after a brief review of Direct Simulation Monte Carlo (DSMC) methods due to Bird, and Belotserkovskii and Yanitskii, studies the details of theDSMC method of Deshpande for mono as well as multicomponent gases. The present method is a statistical particle-in-cell method and is based upon the Kac-Prigogine master equation which reduces to the Boltzmann equation under the hypothesis of molecular chaos. The proposed Markoff model simulating the collisions uses a Poisson distribution for the number of collisions allowed in cells into which the physical space is divided. The model is then extended to a binary mixture of gases and it is shown that it is necessary to perform the collisions in a certain sequence to obtain unbiased simulation.


title: Detecting Memorization in ReLU Networks

abstract: We propose a new notion of `non-linearity' of a network layer with respect to an input batch that is based on its proximity to a linear system, which is reflected in the non-negative rank of the activation matrix. We measure this non-linearity by applying non-negative factorization to the activation matrix. Considering batches of similar samples, we find that high non-linearity in deep layers is indicative of memorization. Furthermore, by applying our approach layer-by-layer, we find that the mechanism for memorization consists of distinct phases. We perform experiments on fully-connected and convolutional neural networks trained on several image and audio datasets. Our results demonstrate that as an indicator for memorization, our technique can be used to perform early stopping.


title: Efficient technique for ellipse detection using restricted randomized Hough transform

abstract: This paper proposes a new efficient method to detect ellipses in binary or gray-scale images, called Restricted Randomized Hough Transform (RRHT). The key of RRHT is to restrict the scope of selected points when detecting ellipses by prior image processing, from which the information of curves can be obtained and further work focuses on detection and validation of each curve orderly. Artificial images and natural gray images are employed to test this method and the results show that RRHT spends less time to detect ellipses more successfully compared with that of conventional Randomized Hough Transform (RHT).


title: Design scheme of seeker equivalent device of Missile-Borne Synthetic Aperture Radar

abstract: Synthetic Aperture Radar (SAR) is an active microwave remote sensor, which could form real-time image around the target. Traditionally, the simulation of SAR imaging algorithm is based on Matlab which is totally software simulation and the final performance is difficult to guarantee. In this paper, the design of the seeker Equivalent Device of Missile-Borne Synthetic Aperture Radar is a device in hardware-in loop simulation of echo signal and imaging algorithm. Based on this device the target echo signal is simulated in a real environment which provides us a more reliable simulation platform for the echo signal and imaging algorithms. In practical testing, this device performs well in point target imaging simulation.


title: Cube-CNN-SVM: A Novel Hyperspectral Image Classification Method

abstract: CNNs (convolutional neural networks) have been proved to be efficient deep learning models that can directly extract high level features from raw data. In this paper, a novel CCS (Cube-CNN-SVM) method is proposed for hyperspectral image classification, which is a spectral-spatial feature based hybrid model of CNN and SVM (support vector machine). Different from most of traditional methods that only take spectral information into consideration, a target pixel and the spectral information of its neighbors are organized into a spectral-spatial multi-feature cube used in hyperspectral image classification. It is a straightforward but valid spatial strategy that can easily improve classification accuracy without extra modification of deep CNN's structure except the size of input layer and convolutional kernel. Our deep CNN consists of the input layer, convolutional layer, max pooling layer, full connection layer and output layer. To further improve hyperspectral image classification accuracy, SVM is trained as hyperspectral image classifier with the features extracted by deep CNN from spectral-spatial fusion information. Three hyperspectral image datasets such as the KSC (Kennedy Space Center), PU (Pavia University Scene) and Indian Pines are used to evaluate the performance of CCS method. Experimental results indicate that the hyperspectral image classification can be improved efficiently with the spectral-spatial fusion strategy and CCS method. Firstly, it is easy to implement the spatial strategy to improve classification accuracy about 4% compared with only spectral information used for classification, in which 98.49% is gained on the KSC dataset. Secondly, CCS method can further improve classification accuracy about 1%~3% compared to the best performance of deep CNN, in which 99.45% is gained on the PU dataset.


title: Data, Information, Knowledge, Wisdom (DIKW): A Semiotic Theoretical and Empirical Exploration of the Hierarchy and its Quality Dimension

abstract: What exactly is the difference between data and information? What is the difference between data quality and information quality; is there any difference between the two? And, what are knowledge and wisdom? Are there such things as knowledge quality and wisdom quality? As these primitives are the most basic axioms of information systems research, it is somewhat surprising that consensus on exact definitions seems to be lacking. This paper presents a theoretical and empirical exploration of the sometimes directly quoted, and often implied Data, Information, Knowledge, Wisdom (DIKW) hierarchy and its quality dimension. We first review relevant literature from a range of perspectives and develop and contextualise a theoretical DIKW framework through semiotics. The literature review identifies definitional commonalities and divergences from a scholarly perspective; the theoretical discussion contextualises the terms and their relationships within a semiotic framework and proposes relevant definitions grounded in that framework. Next, rooted in Wittgenstein’s ordinary language philosophy, we analyse 20 online news articles for their uses of the terms and present the results of an online focus group discussion comprising 16 information systems experts. The empirical exploration identifies a range of definitional ambiguities from a practical perspective.


title: The job satisfaction-job performance relationship: a qualitative and quantitative review.

abstract: A qualitative and quantitative review of the relationship between job satisfaction and job performance is provided. The qualitative review is organized around 7 models that characterize past research on the relationship between job satisfaction and job performance. Although some models have received more support than have others, research has not provided conclusive confirmation or disconfirmation of any model, partly because of a lack of assimilation and integration in the literature. Research devoted to testing these models waned following 2 meta-analyses of the job satisfaction-job performance relationship. Because of limitations in these prior analyses and the misinterpretation of their findings, a new meta-analysis was conducted on 312 samples with a combined N of 54,417. The mean true correlation between overall job satisfaction and job performance was estimated to be .30. In light of these results and the qualitative review, an agenda for future research on the satisfaction-performance relationship is provided.


title: D.M.: Unfriendly: multi-party privacy risks in social networks

abstract: As the popularity of social networks expands, the information users expose to the public has potentially dangerous implications for individual privacy. While social networks allow users to restrict access to their personal data, there is currently no mechanism to enforce privacy concerns over content uploaded by other users. As group photos and stories are shared by friends and family, personal privacy goes beyond the discretion of what a user uploads about himself and becomes an issue of what every network participant reveals. In this paper, we examine how the lack of joint privacy controls over content can inadvertently reveal sensitive information about a user including preferences, relationships, conversations, and photos. Specifically, we analyze Facebook to identify scenarios where conflicting privacy settings between friends will reveal information that at least one user intended remain private. By aggregating the information exposed in this manner, we demonstrate how a user's private attributes can be inferred from simply being listed as a friend or mentioned in a story. To mitigate this threat, we show how Facebook's privacy model can be adapted to enforce multi-party privacy. We present a proof of concept application built into Facebook that automatically ensures mutually acceptable privacy restrictions are enforced on group content.


title: Design and Dynamic Modeling of Humanoid Biped Robot e-Robot

abstract: This paper describes the method of the dynamic simulation of the humanoid biped robot e-Robot using SimMechanics of MATLAB toolbox and Solid Works. When we built a humanoid biped robot, we need to make the design of each component, i.e., obtain the necessary torque in each motor, visualize the comportment of humanoid robot, and obtain the mathematical model of each part. To get these parameters are the hardest task, by these reasons we can use SimMechanics to obtain more easily these parameters, where the mechanical systems are represented by connected block diagrams. So, each block represents physical components with geometric and kinematic relationships. This is not only more intuitive, but it also saves time and effort to derive differential equations of motion. The paper also describes the humanoid biped robot e-Robot to show how a dynamic model created from Solid Works assembly can be exported to SimMechanics and make its dynamic simulation of waist and left leg.


title: QoS-Aware Scheduling of Services-Oriented Internet of Things

abstract: The Internet of Things (IoT) contains a large number of different devices and heterogeneous networks, which make it difficult to satisfy different quality of service (QoS) requirements and achieve rapid services composition and deployment. In addition, some services in service-oriented IoT are required to be reconfigurable and composable for QoS-aware services. This paper proposed a three-layer QoS scheduling model for service-oriented IoT. At application layer, the QoS schedule scheme explores optimal QoS-aware services composition by using the knowledge of each component service. At network layer, the model aims at dealing with scheduling of heterogeneous networks environment; at sensing layer, it deals with the information acquisition and resource allocation scheduling for different services. The proposed QoS-aware scheduling for service-oriented IoT architecture is able to optimize the scheduling performance of IoT network and minimize the resource costs.


title: Symbol Recognition: Current Advances and Perspectives

abstract: The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content.


title: Grey wolf optimizer (GWO) for automated offshore crane design

abstract: In this paper, a new meta-heuristic optimization algorithm called Grey Wolf Optimizer (GWO) is applied to offshore crane design. An offshore crane is a pedestal-mounted elevating and rotating lifting device used to transfer materials or personnel to or from marine vessels, barges and structures whereby the load can be moved horizontally in one or more directions and vertically. Designing and building offshore cranes is a very complex process. It depends on the configuration of a large set of design parameters and is characterized by increased workability and functionality for the owner and cost effectiveness in the total cost of ownership. In an attempt to reduce time and cost involved in the design process, this paper defines a best set of design parameters and uses GWO for the automatic configuration of this set of parameters in a manner that increases the maximum safe working load of the crane and reduces its total weight. Results are verified by a comparative study with other Evolutionary Algorithms (EAs). Results show that the GWO algorithm is able to provide very competitive results compared to these well-known meta-heuristics.


title: Technical Report 2014-001 An Approach to Stack Overflow Counter-Measures Using Kernel Properties

abstract: Our work contributes to the investigation of buffer overflow s by finding a more accurate way to prevent the exploitation of buffer overflows. Our working platform is the GNU/Linux family of operating systems. Our work is at the highest privilege levels and in the safest part of a GNU/Linux system, namely the kernel. We provide a system that allows the kernel to detect overflows and prevent their exploitation. Our system allows the kernel to inject a t launch time some (minimal) code into the binary being run, and subsequently use this code to monitor the execution of that program with respect to its stack use, thus detecting stack overflows. The system stands alone in the sense that it does not need any hardware support; it also works on any program, no matter how that program was conceived or compiled. Beside the theoretical concepts we also present a proof-of-concept patch to the kernel supporting our idea. Overall we effectively show that guarding against buffer overflows at run time is not only possible but also feasible, and we also take the first steps toward implementing such a defense.


title: Weighted Nuclear Norm Minimization with Application to Image Denoising

abstract: As a convex relaxation of the low rank matrix factorization problem, the nuclear norm minimization has been attracting significant research interest in recent years. The standard nuclear norm minimization regularizes each singular value equally to pursue the convexity of the objective function. However, this greatly restricts its capability and flexibility in dealing with many practical problems (e.g., denoising), where the singular values have clear physical meanings and should be treated differently. In this paper we study the weighted nuclear norm minimization (WNNM) problem, where the singular values are assigned different weights. The solutions of the WNNM problem are analyzed under different weighting conditions. We then apply the proposed WNNM algorithm to image denoising by exploiting the image nonlocal self-similarity. Experimental results clearly show that the proposed WNNM algorithm outperforms many state-of-the-art denoising algorithms such as BM3D in terms of both quantitative measure and visual perception quality.


The Riskit Method for Software Risk Management, version 1.00


title: Semantic Clustering and Convolutional Neural Network for Short Text Categorization

abstract: Short texts usually encounter data sparsity and ambiguity problems in representations for their lack of context. In this paper, we propose a novel method to model short texts based on semantic clustering and convolutional neural network. Particularly, we first discover semantic cliques in embedding spaces by a fast clustering algorithm. Then, multi-scale semantic units are detected under the supervision of semantic cliques, which introduce useful external knowledge for short texts. These meaningful semantic units are combined and fed into convolutional layer, followed by max-pooling operation. Experimental results on two open benchmarks validate the effectiveness of the proposed method.


title: Adversarial Generation of Training Examples: Applications to Moving Vehicle License Plate Recognition

abstract: Generative Adversarial Networks (GAN) have attracted much research attention recently, leading to impressive results for natural image generation. However, to date little success was observed in using GAN generated images for improving classification tasks. Here we attempt to explore, in the context of car license plate recognition, whether it is possible to generate synthetic training data using GAN to improve recognition accuracy. With a carefully-designed pipeline, we show that the answer is affirmative. First, a large-scale image set is generated using the generator of GAN, without manual annotation. Then, these images are fed to a deep convolutional neural network (DCNN) followed by a bidirectional recurrent neural network (BRNN) with long short-term memory (LSTM), which performs the feature learning and sequence labelling. Finally, the pre-trained model is fine-tuned on real images. Our experimental results on a few data sets demonstrate the effectiveness of using GAN images: an improvement of 7.5% over a strong baseline with moderate-sized real data being available. We show that the proposed framework achieves competitive recognition accuracy on challenging test datasets. We also leverage the depthwise separate convolution to construct a lightweight convolutional RNN, which is about half size and 2x faster on CPU. Combining this framework and the proposed pipeline, we make progress in performing accurate recognition on mobile and embedded devices.


title: Explicit Pose Deformation Learning for Tracking Human Poses.

abstract: We present a method for human pose tracking that learns explicitly about the dynamic effects of human motion on joint appearance. In contrast to previous techniques which employ generic tools such as dense optical flow or spatio-temporal smoothness constraints to pass pose inference cues between frames, our system instead learns to predict joint displacements from the previous frame to the current frame based on the possibly changing appearance of relevant pixels surrounding the corresponding joints in the previous frame. This explicit learning of pose deformations is formulated by incorporating concepts from human pose estimation into an optical flow-like framework. With this approach, state-of-the-art performance is achieved on standard benchmarks for various pose tracking tasks including 3D body pose tracking in RGB video, 3D hand pose tracking in depth sequences, and 3D hand gesture tracking in RGB video.


title: Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection

abstract: Deep learning has received significant attention recently as a promising solution to many problems in the area of artificial intelligence. Among several deep learning architectures, convolutional neural networks (CNNs) demonstrate superior performance when compared to other machine learning methods in the applications of object detection and recognition. We use a CNN for image enhancement and the detection of driving lanes on motorways. In general, the process of lane detection consists of edge extraction and line detection. A CNN can be used to enhance the input images before lane detection by excluding noise and obstacles that are irrelevant to the edge detection result. However, training conventional CNNs requires considerable computation and a big dataset. Therefore, we suggest a new learning algorithm for CNNs using an extreme learning machine (ELM). The ELM is a fast learning method used to calculate network weights between output and hidden layers in a single iteration and thus, can dramatically reduce learning time while producing accurate results with minimal training data. A conventional ELM can be applied to networks with a single hidden layer; as such, we propose a stacked ELM architecture in the CNN framework. Further, we modify the backpropagation algorithm to find the targets of hidden layers and effectively learn network weights while maintaining performance. Experimental results confirm that the proposed method is effective in reducing learning time and improving performance.


title: Analysis of a Novel Design of a Three-Degree of Freedom Hip Exoskeleton Based on Biomimetic Parallel Structure

abstract: This research develops and analyzes a new three degree of freedom parallel robotic structured hip exoskeleton. Compared with a typical exoskeleton designed as a serial mechanism for the entire leg or entire body, the parallel mechanism is a structure only for the hip. The parallel manipulator is more stable, stronger and provides more accurate movement than existing manipulators. A kinematic analysis is performed and an exhaustive mobility analysis and inverse kinematic solution are derived in closed form.


title: The Vulnerability of Public Spaces: Challenges for UK hospitals under the ‘new’ terrorist threat

abstract: This article considers the challenges for hospitals in the United Kingdom that arise from the threats of mass-casualty terrorism. Whilst much has been written about the role of health care as a rescuer in terrorist attacks and other mass-casualty crises, little has been written about health care as a victim within a mass-emergency setting. Yet, health care is a key component of any nation's contingency planning and an erosion of its capabilities would have a significant impact on the generation of a wider crisis following a mass-casualty event. This article seeks to highlight the nature of the challenges facing elements of UK health care, with a focus on hospitals both as essential contingency responders under the United Kingdom's civil contingencies legislation and as potential victims of terrorism. It seeks to explore the potential gaps that exist between the task demands facing hospitals and the vulnerabilities that exist within them.


title: Wireless Sensor and Actor ∗ Networks: Research Challenges

abstract: Abstract Wireless sensor and actor networks (WSANs) refer to a group of sensors and actors linked by wireless medium to perform distributed sensing and acting tasks. The realization of wireless sensor and actor networks (WSANs) needs to satisfy the requirements introduced by the coexistence of sensors and actors. In WSANs, sensors gather information about the physical world, while actors take decisions and then perform appropriate actions upon the environment, which allows a user to effectively sense and act from a distance. In order to provide effective sensing and acting, coordination mechanisms are required among sensors and actors. Moreover, to perform right and timely actions, sensor data must be valid at the time of acting. This paper explores sensor-actor and actor-actor coordination and describes research challenges for coordination and communication problems.


title: Computing and Information FINGERPRINT ORIENTATION FIELD INTERPOLATION BASED ON THE CONSTRAINED DELAUNAY TRIANGULATION

abstract: A direction map or an orientation field is widely used in automatic fingerprint verification and recognition systems. In most existing methods, the block-wise orientation field is computed from the squared gradients. Since a fingerprint image can contain areas that may be smeared or scarcely distributed, faulty orientations can be produced during the process of orientation field estimation. In this paper, we propose a constrained Delaunay triangulation (CDT) based orientation field interpolation method. In this method, the orientation coherence is used to segment an image into foreground and background, valid regions and invalid regions, and the contours of the invalid regions are used to construct the CDT that is used to interpolate the orientations of the invalid regions. Compared with the commonly used filter and diffusion based methods, the advantages of our method include that no pre-defined neighborhood is needed, that no iteration is needed, and that the orientations still conform well to the fingerprints’ ridges and valleys. Our experiment results show the efficiency of the proposed approach.


Designing a robotic assistant for healthcare applications


title: A Survey of Multi-Objective Sequential Decision-Making

abstract: Sequential decision-making problems with multiple objectives arise naturally in practice and pose unique challenges for research in decision-theoretic planning and learning, which has largely focused on single-objective settings. This article surveys algorithms designed for sequential decision-making problems with multiple objectives. Though there is a growing body of literature on this subject, little of it makes explicit under what circumstances special methods are needed to solve multi-objective problems. Therefore, we identify three distinct scenarios in which converting such a problem to a single-objective one is impossible, infeasible, or undesirable. Furthermore, we propose a taxonomy that classifies multi-objective methods according to the applicable scenario, the nature of the scalarization function (which projects multi-objective values to scalar ones), and the type of policies considered. We show how these factors determine the nature of an optimal solution, which can be a single policy, a convex hull, or a Pareto front. Using this taxonomy, we survey the literature on multi-objective methods for planning and learning. Finally, we discuss key applications of such methods and outline opportunities for future work.


title: A bilingual advantage for episodic memory in older adults

abstract: The ability to remember events—referred to as episodic memory—is typically subject to decline in older adulthood. Episodic memory decline has been attributed in part to less successful executive functioning, which may hinder an older adult's ability to implement controlled encoding and retrieval processes. Since bilingual older adults often show more successful executive functioning than monolinguals, they may be better able to maintain episodic memory. To examine this hypothesis, we compared bilingual and monolingual older adults on a picture scene recall task (assessing episodic memory) and a Simon task (assessing executive functioning). Bilinguals exhibited better episodic memory than their monolingual peers, recalling significantly more items overall. Within the bilingual group, earlier second language acquisition and more years speaking two languages were associated with better recall. Bilinguals also demonstrated higher executive functioning, and there was evidence that level of executive functionin...


title: Tracking free-weight exercises

abstract: Weight training, in addition to aerobic exercises, is an important component of a balanced exercise program. However, mechanisms for tracking free weight exercises have not yet been explored. In this paper, we study methods that automatically recognize what type of exercise you are doing and how many repetitions you have done so far. We incorporated a three-axis accelerometer into a workout glove to track hand movements and put another accelerometer on a user's waist to track body posture. To recognize types of exercises, we tried two methods: a Naive Bayes Classifier and Hidden Markov Models. To count repetitions developed and tested two algorithms: a peak counting algorithm and a method using the Viterbi algorithm with a Hidden Markov Model. Our experimental results showed overall recognition accuracy of around 90% over nine different exercises, and overall miscount rate of around 5%. We believe that the promising results will potentially contribute to the vision of a digital personal trainer, create a new experience for exercising, and enable physical and psychological well-being.


title: Battery Management System: An Overview of Its Application in the Smart Grid and Electric Vehicles

abstract: With the rapidly evolving technology of the smart grid and electric vehicles (EVs), the battery has emerged as the most prominent energy storage device, attracting a significant amount of attention. The very recent discussions about the performance of lithium-ion (Li-ion) batteries in the Boeing 787 have confirmed so far that, while battery technology is growing very quickly, developing cells with higher power and energy densities, it is equally important to improve the performance of the battery management system (BMS) to make the battery a safe, reliable, and cost-efficient solution. The specific characteristics and needs of the smart grid and EVs, such as deep charge/discharge protection and accurate state-of-charge (SOC) and state-of-health (SOH) estimation, intensify the need for a more efficient BMS. The BMS should contain accurate algorithms to measure and estimate the functional status of the battery and, at the same time, be equipped with state-of-the-art mechanisms to protect the battery from hazardous and inefficient operating conditions.


title: Dissolvable films of silk fibroin for ultrathin conformal bio-integrated electronics

abstract: Electronics that are capable of intimate integration with the surfaces of biological tissues create opportunities for improving animal/machine interfaces. A bio-interfaced system of ultrathin electronics supported by bioresorbable silk-fibroin substrates is now presented. Mounting such devices on tissue and then allowing the silk to dissolve initiates a conformal wrapping process that is driven by capillary forces.


title: Test of Memory Malingering and Word Memory Test: A new comparison of failure concordance rates

abstract: Two commonly used symptom validity tests are the Test of Memory Malingering (TOMM) and Word Memory Test (WMT). After examining TOMM–WMT failure concordance rates, Green [Green, P. (2007). Making comparisons between forced-choice effort tests. In K. B. Boone (Ed.), Assessment of feigned cognitive impairment (pp. 50–77). New York: Guilford] urged widespread adoption of the WMT, arguing the TOMM is insensitive to feigned impairment. But Green (2007) used a skewed concordance method that favored WMT (one TOMM subtest vs. three WMT subtests). In the present study we compare pass/fail agreement rates with different combinations of TOMM and WMT subtests in 473 persons seeking compensation for predominately mild neurological trauma. We replicated Green (2007) using his asymmetrical method, but otherwise we found the WMT and TOMM produce comparable failure rates in samples at-risk for exaggeration with balanced comparison (three TOMM subtests vs. three WMT). Further work is necessary to compare WMT and TOMM specificities, as failure concordance designs establish reliability but are insufficient for proving validity.


title: Visible Machine Learning for Biomedicine

abstract: A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Here, we argue for “visible” approaches that guide model structure with experimental biology.