title: Home Health Care (HHC) Managers Perceptions About Challenges and Obstacles that Hinder HHC Services in Jordan

abstract: Home care aims at supporting people with various degrees of dependency to remain at home rather than use residential, long-term, or institutional-based nursing care. Demographic, epidemiological, social, and cultural trends in Jordan as in other countries are changing the traditional patterns of care with growing emphasis on home care. The purpose of this study is to highlight the most common challenges related to home health care (HHC) services in Jordan as perceived by the managers of HHC agencies. Methods: a descriptive qualitative design that depends on focus group discussions has been used to collect data from a sample of 18 managers who met the selection criteria and who are willing to participate, the study found that, the main challenges of HHC services as perceived by managers were: shortage of female staff, lack of governance and regulation, poor management, unethical practices, lack of referral systems, and low accessibility of the poor and less privileged as HHC services are not included in health insurance schemes, it concludes also that the home health care industry in Jordan is facing many challenges and problems that may have negative effects on the effectiveness, efficiency, equity and quality of services and should be addressed by health policy makers.


Functions of Bounded k th p-Variation and Continuity Modulus


title: A Survey on Hardware and Software Support for Thread Level Parallelism

abstract: To support growing massive parallelism, functional components and also the capabilities of current processors are changing and continue to do so. Todays computers are built upon multiple processing cores and run applications consisting of a large number of threads, making runtime thread management a complex process. Further, each core can support multiple, concurrent thread execution. Hence, hardware and software support for threads is more and more needed to improve peak-performance capacity, overall system throughput, and has therefore been the subject of much research. This paper surveys, many of the proposed or currently available solutions for executing, distributing and managing threads both in hardware and software. The nature of current applications is diverse. To increase the system performance, all programming models may not be suitable to harness the built-in massive parallelism of multicore processors. Due to the heterogeneity in hardware, hybrid programming model (which combines the features of shared and distributed model) currently has become very promising. In this paper, first, we have given an overview of threads, threading mechanisms and its management issues during execution. Next, we discuss about different parallel programming models considering to their explicit thread support. We also review the programming models with respect to their support to shared-memory, distributed-memory and heterogeneity. Hardware support at execution time is very crucial to the performance of the system, thus different types of hardware support for threads also exist or have been proposed, primarily based on widely used programming models. We also further discuss on software support for threads, to mainly increase the deterministic behavior during runtime. Finally, we conclude the paper by discussing some common issues related to the thread management.


title: An Analysis of ISO 26262: Using Machine Learning Safely in Automotive Software

abstract: Machine learning (ML) plays an ever-increasing role in advanced automotive functionality for driver assistance and autonomous operation; however, its adequacy from the perspective of safety certification remains controversial. In this paper, we analyze the impacts that the use of ML as an implementation approach has on ISO 26262 safety lifecycle and ask what could be done to address them. We then provide a set of recommendations on how to adapt the standard to accommodate ML.


title: Integration trends in monolithic power ICs: Application and technology challenges

abstract: This paper highlights the general trend towards further monolithic integration in power applications by enabling power management and interfacing solutions in advanced CMOS nodes. The need to combine high-density digital circuits, power-management circuits, and robust interfaces in a single technology platform requires the development of additional process options on top of baseline CMOS. Examples include high-voltage devices, devices to enable area-efficient ESD protection, and integrated capacitors and inductors with high quality factors. The use of bipolar devices in these technologies for protection and control purposes in power applications is also addressed.


title: Reviews on Various Inertial Measurement Unit (IMU) Sensor Applications

abstract: Inertial Measurement Unit (IMU) sensors are used widely in many different movable applications. Across many years, the improvements and applications of IMU have increased through various areas such as manufacturing, navigation, and robotics. This paper presents a literature review of several current IMU categories and applications. A few considerations on choosing an IMU for different applications are summarized and current methods being used to improve the accuracy of the output from IMU are also presented to avoid the errors that latest IMU is facing. Improvement methods include the control algorithms and type of filters for the sensor. Pros and cons of the types and algorithms used are also discussed in relation to different applications. 


title: EXPLORING THE CONSUMERS' ACCEPTANCE OF ELECTRONIC RETAILING USING TECHNOLOGY ACCEPTANCE MODEL

abstract: Following the approach of Pavlou (2003), we investigate key drivers for consumers` electronic retailing (e-tailing) acceptance that are integrated into TAM (technology acceptance model). TAM is one of the better known models for explaining the intention to use technology and it integrates two perceptions: usefulness and ease of use. The main purpose of this paper is to explore the relationship between perceived ease of use and perceived usefulness in accepting e-tailing among Croatian consumers. In particular, the focus of the paper is on the areas which influenced customer commitment and loyalty in e-tailing. The results of the quantitative study among Croatian consumers show that electronic retailing, e.g. purchasing via the Internet, would enhance consumers` effectiveness in getting product information and their effectiveness in purchasing products.


title: Recursive Bayesian Recurrent Neural Networks for Time-Series Modeling

abstract: This paper develops a probabilistic approach to recursive second-order training of recurrent neural networks (RNNs) for improved time-series modeling. A general recursive Bayesian Levenberg-Marquardt algorithm is derived to sequentially update the weights and the covariance (Hessian) matrix. The main strengths of the approach are a principled handling of the regularization hyperparameters that leads to better generalization, and stable numerical performance. The framework involves the adaptation of a noise hyperparameter and local weight prior hyperparameters, which represent the noise in the data and the uncertainties in the model parameters. Experimental investigations using artificial and real-world data sets show that RNNs equipped with the proposed approach outperform standard real-time recurrent learning and extended Kalman training algorithms for recurrent networks, as well as other contemporary nonlinear neural models, on time-series modeling.


title: High-Sensitivity Software-Configurable 5.8-GHz Radar Sensor Receiver Chip in 0.13-$\mu$ m CMOS for Noncontact Vital Sign Detection

abstract: In this paper, analyses on sensitivity and link budget have been presented to guide the design of high-sensitivity noncontact vital sign detector. Important design issues such as flicker noise, baseband bandwidth, and gain budget have been discussed with practical considerations of analog-to-digital interface and signal processing methods in noncontact vital sign detection. Based on the analyses, a direct-conversion 5.8-GHz radar sensor chip with 1-GHz bandwidth was designed and fabricated. This radar sensor chip is software configurable to set the operation point and detection range for optimal performance. It integrates all the analog functions on-chip so that the output can be directly sampled for digital signal processing. Measurement results show that the fabricated chip has a sensitivity of better than -101 dBm for ideal detection in the absence of random body movement. Experiments have been performed successfully in laboratory environment to detect the vital signs of human subjects.


title: Direct and indirect pathways of basal ganglia: a critical reappraisal

abstract: The basal ganglia are subcortical nuclei controlling voluntary actions and have been implicated in Parkinson's disease (PD). The prevailing model of basal ganglia function states that two circuits, the direct and indirect pathways, originate from distinct populations of striatal medium spiny neurons (MSNs) and project to different output structures. These circuits are believed to have opposite effects on movement. Specifically, the activity of direct pathway MSNs is postulated to promote movement, whereas the activation of indirect pathway MSNs is hypothesized to inhibit it. Recent findings have revealed that this model might not fully account for the concurrent activation of both pathways during movement. Accordingly, we propose a model in which intrastriatal connections are critical and the two pathways are structurally and functionally intertwined. Thus, all MSNs might either facilitate or inhibit movement depending on the form of synaptic plasticity expressed at a certain moment. In PD, alterations of dopamine-dependent synaptic plasticity could alter this coordinated activity.


title: High-inductive short-circuit Type IV in multi-level converter protection schemes

abstract: To build a redundant medium-voltage converter, the semiconductors must be able to turn OFF different short circuits. The most challenging one is a hard turn OFF of a diode which is called short-circuit type IV. Without any protection measures this short circuit destroys the high-voltage diode. Therefore, a novel three-level converter with an increased short-circuit inductance is used. In this paper several short-circuit measurements on a 6.5 kV diode are presented which explain the effect of the protection measures. Moreover, the limits of the protection scheme are presented.


title: FALCON gravity gradiometer technology

abstract: BHP Billiton's FALCON airborne gravity gradiometer is a derivative of the Gravity Gradient Instrument (GGI) developed by Bell Aerospace (now Lockheed Martin) between 1975 and 1990. The basis of the GGI design is an accelerometer complement consisting of four accelerometers equi-spaced on a circle with their sensitive axes tangential to the circle. This configuration rejects both common mode acceleration and rotations about the axis perpendicular to the plane of the complement. The complement remains intrinsically sensitive to rotation rates about axes in the plane of the complement and is sensitive to the acceleration environment to the extent that there is imbalance in the accelerometer sensitivities. Rotation of the complement about the perpendicular axis moves the gradient signal to twice the rotation frequency, away from the effects of low frequency accelerometer bias changes. The GGI is mounted in a high-performance inertial stabilised platform to reduce rotation of the instrument so that its sensitivity to this motion does not represent a significant noise source. The GGI accelerometers are designed for very low noise, requiring hard evacuation, high pendulosity, low spring constant and attention to the constrainment loop. Accelerometer pairs are aligned with precision and their sensitivities and frequency responses are matched. The scale factor (sensitivity) and alignment of the sensitive axis of each accelerometer are adjusted by compensation feedback loops to minimise accelerometer imbalance by monitoring the response of the system to specific stimuli. The requirements of survey operations were taken into account during development of the system and the result is an instrument which requires limited preparation, is largely automated during surveys, places few restrictions on flight planning and has been operated in harsh ambient conditions. Data processing is streamlined and data quality can be checked immediately after a flight.


title: Recoding, storage, rehearsal and grouping in verbal short-term memory: an fMRI study

abstract: Functional magnetic resonance imaging (fMRI) of healthy volunteers is used to localise the processes involved in verbal shortterm memory (VSTM) for sequences of visual stimuli. Specifically, the brain areas underlying (i) recoding, (ii) storage, (iii) rehearsal and (iv) temporal grouping are investigated. Successive subtraction of images obtained from five tasks revealed a network of left-lateralised areas, including posterior temporal regions, supramarginal gyri, Broca’s area and dorsolateral premotor cortex. The results are discussed in relation to neuropsychological distinctions between recoding and rehearsal, previous neuroimaging studies of storage and rehearsal, and, in particular, a recent connectionist model of VSTM that makes explicit assumptions about the temporal organisation of rehearsal. The functional modules of this model are tentatively mapped onto the brain in light of the imaging results. Our findings are consistent with the representation of verbal item information in left posterior temporal areas and short-term storage of phonological information in left supramarginal gyrus. They also suggest that left dorsolateral premotor cortex is involved in the maintenance of temporal order, possibly as the location of a timing signal used in the rhythmic organisation of rehearsal, whereas Broca’s area supports the articulatory processes required for phonological recoding of visual stimuli. # 2000 Elsevier Science Ltd. All rights reserved.


title: Evaluation Of Spoken Language Systems: The ATIS Domain

abstract: Progress can be measured and encouraged via standards for comparison and evaluation. Though qualitative assessments can be useful in initial stages, quantifiable measures of systems under the same conditions are essential for comparing results and assessing claims. This paper will address the emerging standards for evaluation of spoken language systems.


title: Towards a Semantic Administrative Shell for Industry 4.0 Components

abstract: In the engineering and manufacturing domain, there is currently an atmosphere of departure to a new era of digitized production. In different regions, initiatives in these directions are known under different names, such as industrie du futur in France, industrial internet in the US or Industrie 4.0 in Germany. While the vision of digitizing production and manufacturing gained much traction lately, it is still relatively unclear how this vision can actually be implemented with concrete standards and technologies. Within the German Industry 4.0 initiative, the concept of an Administrative Shell was devised to respond to these requirements. The Administrative Shell is planned to provide a digital representation of all information being available about and from an object which can be a hardware system or a software platform. In this paper, we present an approach to developing such a digital representation based on semantic knowledge representation formalisms such as RDF, RDF Schema and OWL. We present our concept of a Semantic I4.0 Component which addresses the communication and comprehension challenges in Industry 4.0 scenarios using semantic technologies. Our approach is illustrated with a concrete example showing its benefits in a real-world use case.


title: What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models.

abstract: Objective:Statistical models, such as linear or logistic regression or survival analysis, are frequently used as a means to answer scientific questions in psychosomatic research. Many who use these techniques, however, apparently fail to appreciate fully the problem of overfitting, ie, capitalizing


title: Real-time foreground segmentation via range and color imaging

abstract: This paper describes a real-time method for foreground/background segmentation of a color video sequence based primarily on range data of a time-of-flight sensor. This method uses depth information of a TOF-sensor paired with a high resolution color video camera to efficiently segment foreground from background in a two-step process. First a trimap is produced using only range data: areas are located in each frame that have a high probability of being background or foreground, respectively. Pixels which cannot be definitively classified as foreground or background, typically about 1-2% of the frame, are assigned alpha-matte values using a cross bilateral filtering, applied directly to an estimate of the alpha-matte.


title: A Novel Self-Reference Technique for STT-RAM Read and Write Reliability Enhancement

abstract: Spin-transfer torque random access memory (STT-RAM) has demonstrated great potential in embedded and stand-alone applications. However, process variations and thermal fluctuations greatly influence the operation reliability of STT-RAM and limit its scalability. In this paper, we propose a new field-assisted access scheme to improve the read/write reliability and performance of STT-RAM. During read operations, an external magnetic field is applied to a magnetic tunneling junction (MTJ) device, generating a resistive sense signal without referring to other devices. Such a self-reference scheme offers a very promising alternative approach to overcome the severe cell-to-cell variations at highly scaled technology node. Furthermore, the external magnetic field can be used to assist the MTJ switching during write operations without introducing extra hardware overhead.


title: Performance improvement of a double stage switch mode AC voltage regulator

abstract: Widespread use of sensitive loads in industries has required the need for high efficiency voltage regulators in order to provide continuous and fast dynamic adjustable AC voltage. This paper introduces a new control strategy to improve the response time and performance of a double stage switch mode regulator. The proposed strategy responds to input voltage variations quickly and improves the efficiency and performance of the circuit. Results of some simulations are presented and analyzed. Experimental tests on a laboratory prototype verify the excellent performance of the proposed system.


title: A fast algorithm for YCbCr to perception color model conversion based on fixed-point DSP

abstract: Real-time video processing heavily relies on the color space conversion. Due to the real-time requirement, the traditional conversion methods often suffer from the moderate conversion speed, inaccuracy and low video quality. In order to meet the efficiency requirement of color space conversion in real-time video systems, we present a fast algorithm for color space YCbCr to perception color model conversion, which is based on the simplified shift and look-up table (SSLUT). The approach can be divided into two steps. At first, the simplified fixed-point shift method is used to convert YCbCr to RGB. Then look-up table assists with the YCbCr to perception color model conversion, such as HSV, HSI and HSL. To validate the proposed fast algorithm, the conversion speed and accuracy are compared with the traditional methods based on Code Composer Studio (CCS) test platform. Moreover, we make the video quality evaluation by using the peak signal-to-ratio (PSNR) and the structural similarity (SSIM). Experimental results illustrate the real-time, robustness and accuracy of the fast algorithm.


title: An Efficient Method for Skew Correction of License Plate

abstract: Character segmentation and recognition are important steps in the automatic license plate recognition (ALPR) system. The skew license plate has a great influence on the accurate character segmentation and recognition. To solve the problem, an efficient approach for skew correction of license plate is proposed. First, the plate image is divided into a set of 5×5 non-overlapping blocks. The local orientation of each black is estimated by gradients of pixels in the block. The horizontal incline angle of license plate is detected by the local maximum of the direction angle histogram. The plate image is rotated according to this angle. Then, the vertical distortion of license plate image was corrected by the single-character projection method. The experimental results demonstrate the great robustness and efficiency of proposed method.


title: A framework for mining process models from emails logs

abstract: Due to its wide use in personal, but most importantly, professional contexts, email represents a valuable source of information that can be harvested for understanding, reengineering and repurposing undocumented business processes of companies and institutions. Towards this aim, a few researchers investigated the problem of extracting process oriented information from email logs in order to take benefit of the many available process mining techniques and tools. In this paper we go further in this direction, by proposing a new method for mining process models from email logs that leverage unsupervised machine learning techniques with little human involvement. Moreover, our method allows to semi-automatically label emails with activity names, that can be used for activity recognition in new incoming emails. A use case demonstrates the usefulness of the proposed solution using a modest in size, yet real-world, dataset containing emails that belong to two different process models


title: Tutorial on advanced design patterns in event processing

abstract: We introduce a reference architecture for event processing, as defined by the EPTS reference architecture (RA) working group. An event processing reference architecture allows users to quickly create event processing solutions that adhere to known stakeholder requirements and architectural qualities. The focus in this paper is on the EPTS reference architecture description of the functional view which is supported by a mapping of its functions into design patterns as means to derive and prove these architectural descriptions to be usable solutions for recurring best practice implementations in common CEP languages.


title: Accurate rough terrain estimation with space-carving kernels

abstract: Accurate terrain estimation is critical for autonomous offroad navigation. Reconstruction of a 3D surface allows rough and hilly ground to be represented, yielding faster driving and better planning and control. However, data from a 3D sensor samples the terrain unevenly, quickly becoming sparse at longer ranges and containing large voids because of occlusions and inclines. The proposed approach uses online kernel-based learning to estimate a continuous surface over the area of interest while providing upper and lower bounds on that surface. Unlike other approaches, visibility information is exploited to constrain the terrain surface and increase precision, and an efficient gradient-based optimization allows for realtime implementation.


title: Multiway broadband substrate integrated waveguide (SIW) power divider

abstract: A planar integrated multi-way broadband SIW power divider is proposed. It can be combined by the fundamental modules of T-type or Y-type two-way power dividers and an SIW bend directly. A sixteen way SIW power divider prototype was designed, fabricated and measured. The whole structure is made by various metallic-vias on the same substrate. Hence, it can be easily fabricated and conveniently integrated into microwave and millimeter-wave integrated circuits for mass production with low cost and small size.


title: Nasal CPAP or Intubation at Birth for Very Preterm Infants

abstract: BACKGROUND ::: Bronchopulmonary dysplasia is associated with ventilation and oxygen treatment. This randomized trial investigated whether nasal continuous positive airway pressure (CPAP), rather than intubation and ventilation, shortly after birth would reduce the rate of death or bronchopulmonary dysplasia in very preterm infants. ::: ::: ::: METHODS ::: We randomly assigned 610 infants who were born at 25-to-28-weeks' gestation to CPAP or intubation and ventilation at 5 minutes after birth. We assessed outcomes at 28 days of age, at 36 weeks' gestational age, and before discharge. ::: ::: ::: RESULTS ::: At 36 weeks' gestational age, 33.9% of 307 infants who were assigned to receive CPAP had died or had bronchopulmonary dysplasia, as compared with 38.9% of 303 infants who were assigned to receive intubation (odds ratio favoring CPAP, 0.80; 95% confidence interval [CI], 0.58 to 1.12; P=0.19). At 28 days, there was a lower risk of death or need for oxygen therapy in the CPAP group than in the intubation group (odds ratio, 0.63; 95% CI, 0.46 to 0.88; P=0.006). There was little difference in overall mortality. In the CPAP group, 46% of infants were intubated during the first 5 days, and the use of surfactant was halved. The incidence of pneumothorax was 9% in the CPAP group, as compared with 3% in the intubation group (P<0.001). There were no other serious adverse events. The CPAP group had fewer days of ventilation. ::: ::: ::: CONCLUSIONS ::: In infants born at 25-to-28-weeks' gestation, early nasal CPAP did not significantly reduce the rate of death or bronchopulmonary dysplasia, as compared with intubation. Even though the CPAP group had more incidences of pneumothorax, fewer infants received oxygen at 28 days, and they had fewer days of ventilation. (Australian New Zealand Clinical Trials Registry number, 12606000258550.).


title: Collaboration theory and tourism practice in protected areas: stakeholders, structuring and sustainability

abstract: A growing body of research is emerging on tourism and partnerships in protected areas, but conceptual development remains fragmented. This paper tackles this challenge by focusing on three aspects important for sustainability: (1) complexity (nested systems of biophysical environments, tourism and park management structures, community–resident systems, local–global systems and use–conservation gap; (2) scale, structure and scope of collaborations (including community involvement and control) and (3) challenges of implementation and long term structuring (for sustainability and success). Some related questions are: How does the tourism system fit with the protected area system? Who represents “Nature” in negotiations over conservation and use? How can plans and programmes be effectively enacted at the local level for long term success? A theoretical discussion plus an example of community-based collaboration for conservation and economic development in Bolivia (Chalalan Ecolodge) are provided to explore th...


title: A Horse of a Different Color: Specifying With Precision Infants’ Mappings of Novel Nouns and Adjectives

abstract: A precisely controlled automated procedure confirms a developmental decalage: Infants acquiring English link count nouns to object categories well before they link adjectives to properties. Fourteen- and 18-month-olds (n= 48 at each age) extended novel words presented as count nouns based on category membership rather than shared properties. When the same words were presented as adjectives, infants revealed no preference for either category- or property-based extensions. The convergence between performance in this automated procedure and in more interactive tasks is striking. Perhaps more importantly, the automated task provides a methodological foundation for (a) exploring the development of form–meaning links in infants acquiring languages other than English and (b) investigating the time course underlying infants’ mapping of novel words to meaning.


title: A Exploratory Study of @-Mention in GitHub's Pull-Requests

abstract: Pull-request mechanism is an outstanding social development method in Git Hub. @-mention is a social media tool that deeply integrated with pull-request mechanism. Recently, many research results show that social media tools can promote the collaborative software development, but few work focuses on the impacts of @-mention. In this paper, we conduct an exploratory study of @-mention in pull-request based software development, including its current situation and benefits. We obtain some interesting findings which indicate that @-mention is beneficial to the processing of pull-request. Our work also proposes some possible research directions and problems of the @-mention. It helps the developers and researchers notice the significance of @-mention in the pull-request based software development.


title: Advances in the care of patients with mucinous colorectal cancer

abstract: The majority of colorectal cancers (CRCs) are classified as adenocarcinoma not otherwise specified (AC). Mucinous carcinoma (MC) is a distinct form of CRC and is found in 10-15% of patients with CRC. MC differs from AC in terms of both clinical and histopathological characteristics, and has long been associated with an inferior response to treatment compared with AC. The debate concerning the prognostic implications of MC in patients with CRC is ongoing and MC is still considered an unfavourable and unfamiliar subtype of the disease. Nevertheless, in the past few years epidemiological and clinical studies have shed new light on the treatment and management of patients with MC. Use of a multidisciplinary approach, including input from surgeons, pathologists, oncologists and radiologists, is beginning to lead to more-tailored approaches to patient management, on an individualized basis. In this Review, the authors provide insight into advances that have been made in the care of patients with MC. The prognostic implications for patients with colon or rectal MC are described separately; moreover, the predictive implications of MC regarding responses to commonly used therapies for CRC, such as chemotherapy, radiotherapy and chemoradiotherapy, and the potential for, and severity of, metastasis are also described.


title: Single-Document Summarization as a Tree Knapsack Problem

abstract: Recent studies on extractive text summarization formulate it as a combinatorial optimization problem such as a Knapsack Problem, a Maximum Coverage Problem or a Budgeted Median Problem. These methods successfully improved summarization quality, but they did not consider the rhetorical relations between the textual units of a source document. Thus, summaries generated by these methods may lack logical coherence. This paper proposes a single document summarization method based on the trimming of a discourse tree. This is a two-fold process. First, we propose rules for transforming a rhetorical structure theorybased discourse tree into a dependency-based discourse tree, which allows us to take a treetrimming approach to summarization. Second, we formulate the problem of trimming a dependency-based discourse tree as a Tree Knapsack Problem, then solve it with integer linear programming (ILP). Evaluation results showed that our method improved ROUGE scores.


title: Social Networks IM Forensics: Encryption Analysis

abstract: Abstract—In most regards, the twenty-first century may not bring revolutionary changes in electronic messaging technology in terms of applications or protocols. Security issues that have long been a concern in messaging application are finally being solved using a variety of products. Web-based messaging systems are rapidly evolving the text-based conversation. The users have the right to protect their privacy from the eavesdropper, or other parties which interferes the privacy of the users for such purpose. The chatters most probably use the instant messages to chat with others for personal issue; in which no one has the right eavesdrop the conversation channel and interfere this privacy. This is considered as a non-ethical manner and the privacy of the users should be protected. The author seeks to identify the security features for most public instant messaging services used over the internet and suggest some solutions in order to encrypt the instant messaging over the conversation channel. The aim of this research is to investigate through forensics and sniffing techniques, the possibilities of hiding communication using encryption to protect the integrity of messages exchanged. Authors used different tools and methods to run the investigations. Such tools include Wireshark packet sniffer, Forensics Tool Kit (FTK) and viaForensic mobile forensic toolkit. Finally, authors will report their findings on the level of security that encryption could provide to instant messaging services.


The Value of User Participation in E-Commerce Systems Development


title: A structured approach to anomaly detection for in-vehicle networks

abstract: The complexity and connectivity of modern vehicles has constantly increased over the past years. Within the scope of this development the security risk for the in-vehicle network and its components has risen massively. Apart from threats for comfort and confidentiality, these attacks can also affect safety critical systems of the vehicle and therefore endanger the driver and other road users. In this paper the introduction of anomaly detection systems to the automotive in-vehicle network is discussed. Based on properties of typical vehicular networks, like the Controller Area Network (CAN), a set of anomaly detection sensors is introduced which allow the recognition of attacks during the operation of the vehicle without causing false positives. Moreover, important design and application criteria for a vehicular attack detection system are explained and discussed.


title: Conservation of Mass in Collision

abstract: The conservation of mass in inertial reference frame is a property of the conservation law of momentum. The inelastic collision between two identical objects shows that the total momentum is zero in the COM frame (center of mass). In another inertial reference frame, the rest frame of one object before collision, the total momentum before the collision is equal to the total momentum after the collision. This conservation of momentum shows that the mass of an object is also conserved in all inertial reference frames. The mass of a moving object is independent of its velocity.


title: Mixed-Initiative Real-Time Topic Modeling & Visualization for Crisis Counseling

abstract: Text-based counseling and support systems have seen an increasing proliferation in the past decade. We present Fathom, a natural language interface to help crisis counselors on Crisis Text Line, a new 911-like crisis hotline that takes calls via text messaging rather than voice. Text messaging opens up the opportunity for software to read the messages as well as people, and to provide assistance for human counselors who give clients emotional and practical support. Crisis counseling is a tough job that requires dealing with emotionally stressed people in possibly life-critical situations, under time constraints. Fathom is a system that provides topic modeling of calls and graphical visualization of topic distributions, updated in real time. We develop a mixed-initiative paradigm to train coherent topic and word distributions and use them to power real-time visualizations aimed at reducing counselor cognitive overload. We believe Fathom to be the first real-time computational framework to assist in crisis counseling.


title: Soft computing and fuzzy logic

abstract: Discusses soft computing, a collection of methodologies that aim to exploit the tolerance for imprecision and uncertainty to achieve tractability, robustness, and low solution cost. Its principal constituents are fuzzy logic, neurocomputing, and probabilistic reasoning. Soft computing is likely to play an increasingly important role in many application areas, including software engineering. The role model for soft computing is the human mind. >


title: Learning to Answer Questions From Image using Convolutional Neural Network

abstract: In this paper, we propose to employ the convolutional neural network (CNN) for the image question answering (QA). Our proposed CNN provides an end-to-end framework with convolutional architectures for learning not only the image and question representations, but also their inter-modal interactions to produce the answer. More specifically, our model consists of three CNNs: one image CNN to encode the image content, one sentence CNN to compose the words of the question, and one multimodal convolution layer to learn their joint representation for the classification in the space of candidate answer words. We demonstrate the efficacy of our proposed model on the DAQUAR and COCO-QA datasets, which are two benchmark datasets for the image QA, with the performances significantly outperforming the state-of-the-art.


title: Rectification of images of QR codes posted on cylinders by conic segmentation

abstract: Powered by the pervasive penetration of inexpensive mobile devices and relevant APP's into our daily life, QR codes have found a wide range of applications in recent years. Therefore, the processing of the images of QR codes (also referred to as QR images) is an important task. In this paper, we try to deal with the scenario wherein the QR code is printed or pasted (altogether also referred to as "posted") on the surface of a cylinder. In the scenario considered in this work, the posting need not be aligned to the axis of the cylinder. When a picture is taken on such a QR code, the picture can be seriously distorted, and thus suffers a high probability of decoding failure. In this paper, a scheme called rectification by conic segmentation (CS) is proposed to tackle this kind of problems. Experiments show that the proposed scheme is effective, in the sense that many failed trials at QR decoding become successful after the rectification.


title: Time and sample efficient discovery of Markov blankets and direct causal relations

abstract: Data Mining with Bayesian Network learning has two important characteristics: under conditions learned edges between variables correspond to casual influences, and second, for every variable T in the network a special subset (Markov Blanket) identifiable by the network is the minimal variable set required to predict T. However, all known algorithms learning a complete BN do not scale up beyond a few hundred variables. On the other hand, all known sound algorithms learning a local region of the network require an exponential number of training instances to the size of the learned region.The contribution of this paper is two-fold. We introduce a novel local algorithm that returns all variables with direct edges to and from a target variable T as well as a local algorithm that returns the Markov Blanket of T. Both algorithms (i) are sound, (ii) can be run efficiently in datasets with thousands of variables, and (iii) significantly outperform in terms of approximating the true neighborhood previous state-of-the-art algorithms using only a fraction of the training size required by the existing methods. A fundamental difference between our approach and existing ones is that the required sample depends on the generating graph connectivity and not the size of the local region; this yields up to exponential savings in sample relative to previously known algorithms. The results presented here are promising not only for discovery of local causal structure, and variable selection for classification, but also for the induction of complete BNs.


title: ROC curve equivalence using the Kolmogorov-Smirnov test

abstract: This paper describes a simple, non-parametric and generic test of the equivalence of receiver operating characteristic (ROC) curves based on a modified Kolmogorov-Smirnov (KS) test. The test is described in relation to the commonly used techniques such as the area under the ROC curve (AUC) and the Neyman-Pearson method. We first review how the KS test is used to test the null hypotheses that the class labels predicted by a classifier are no better than random. We then propose an interval mapping technique that allows us to use two KS tests to test the null hypothesis that two classifiers have ROC curves that are equivalent. We demonstrate that this test discriminates different ROC curves both when one curve dominates another and when the curves cross and so are not discriminated by AUC. The interval mapping technique is then used to demonstrate that, although AUC has its limitations, it can be a model-independent and coherent measure of classifier performance.


title: A Feasible Nonconvex Relaxation Approach to Feature Selection

abstract: Variable selection problems are typically addressed under a penalized optimization framework. Nonconvex penalties such as the minimax concave plus (MCP) and smoothly clipped absolute deviation (SCAD), have been demonstrated to have the properties of sparsity practically and theoretically. In this paper we propose a new nonconvex penalty that we call exponential-type penalty. The exponential-type penalty is characterized by a positive parameter, which establishes a connection with the l0 and l1 penalties. We apply this new penalty to sparse supervised learning problems. To solve to resulting optimization problem, we resort to a reweighted l1 minimization method. Moreover, we devise an efficient method for the adaptive update of the tuning parameter. Our experimental results are encouraging. They show that the exponential-type penalty is competitive with MCP and SCAD.


title: The Neurobiology of Consolidations, Or, How Stable is the Engram?

abstract: Consolidation is the progressive postacquisition stabilization of long-term memory. The term is commonly used to refer to two types of processes: synaptic consolidation, which is accomplished within the first minutes to hours after learning and occurs in all memory systems studied so far; and system consolidation, which takes much longer, and in which memories that are initially dependent upon the hippocampus undergo reorganization and may become hippocampal-independent. The textbook account of consolidation is that for any item in memory, consolidation starts and ends just once. Recently, a heated debate has been revitalized on whether this is indeed the case, or, alternatively, whether memories become labile and must undergo some form of renewed consolidation every time they are activated. This debate focuses attention on fundamental issues concerning the nature of the memory trace, its maturation, persistence, retrievability, and modifiability.


title: Integrating Grid-Based and Topological Maps for Mobile Robot Navigation

abstract: Research on mobile robot navigation has produced two major paradigms for mapping indoor environments: grid-based and topological. While grid-based methods produce accurate metric maps, their complexity often prohibits efficient planning and problem solving in large-scale indoor environments. Topological maps, on the other hand, can be used much more efficiently, yet accurate and consistent topological maps are considerably difficult to learn in large-scale environments. ::: ::: This paper describes an approach that integrates both paradigms: grid-based and topological. Grid-based maps are learned using artificial neural networks and Bayesian integration. Topological maps are generated on top of the grid-based maps, by partitioning the latter into coherent regions. By combining both paradigms--grid-based and topological--, the approach presented here gains the best of both worlds: accuracy/consistency and efficiency. The paper gives results for autonomously operating a mobile robot equipped with sonar sensors in populated multi-room environments.


title: The Role of Chatbots in Formal Education

abstract: Chatbots appeared in large numbers at the beginning of the current decade. Interactive technology, often combined with artificial intelligence, has rapidly invaded and occupied the world of online chat. Chatbots are not just elements of virtual assistants, but are used by organizations and governments on websites, in applications, and instant messaging platforms to promote products, ideas or services. In this paper, the authors firstly present a theoretical and historical background, then discuss the issues of using chatbots as educational assistants, and finally describe the basic steps and challenges of programming a bot.


The Ontology of Christopher Langan ' s Psychical Physics : The Neuropsychology of the Atemporal Recursive Processes – – an empirical framework


title: Direct Output Connection for a High-Rank Language Model

abstract: This paper proposes a state-of-the-art recurrent neural network (RNN) language model that combines probability distributions computed not only from a final RNN layer but also from middle layers. Our proposed method raises the expressive power of a language model based on the matrix factorization interpretation of language modeling introduced by Yang et al. (2018). The proposed method improves the current state-of-the-art language model and achieves the best score on the Penn Treebank and WikiText-2, which are the standard benchmark datasets. Moreover, we indicate our proposed method contributes to two application tasks: machine translation and headline generation. Our code is publicly available at: this https URL.


title: Multi-task Correlation Particle Filter for Robust Object Tracking

abstract: In this paper, we propose a multi-task correlation particle filter (MCPF) for robust visual tracking. We first present the multi-task correlation filter (MCF) that takes the interdependencies among different features into account to learn correlation filters jointly. The proposed MCPF is designed to exploit and complement the strength of a MCF and a particle filter. Compared with existing tracking methods based on correlation filters and particle filters, the proposed tracker has several advantages. First, it can shepherd the sampled particles toward the modes of the target state distribution via the MCF, thereby resulting in robust tracking performance. Second, it can effectively handle large-scale variation via a particle sampling strategy. Third, it can effectively maintain multiple modes in the posterior density using fewer particles than conventional particle filters, thereby lowering the computational cost. Extensive experimental results on three benchmark datasets demonstrate that the proposed MCPF performs favorably against the state-of-the-art methods.


title: Hybrid Client Side Phishing Websites Detection Approach

abstract: Phishing tricks to steal personal or credential information by entering victims into a forged website similar to the original site, and urging them to enter their information believing that this site is legitimate. The number of internet users who are becoming victims of phishing attacks is increasing beside that phishing attacks have become more sophisticated. In this paper we propose a client-side solution to protect against phishing attacks which is a Firefox extension integrated as a toolbar that is responsible for checking whether recipient website is trusted or not by inspecting URLs of each requested webpage. If the site is suspicious the toolbar is going to block it. Every URL is evaluated corresponding to features extracted from it. Three heuristics (primary domain, sub domain, and path) and Naive Bayes classification using four lexical features combined with page ranking received from two different services (Alexa, and Google page rank) used to classify URL. The proposed method requires no server changes and will prevent internet users from fraudulent sites especially from phishing attacks based on deceptive URLs. Experimental results show that our approach can achieve 48% accuracy ratio using a test set of 246 URL, and 87.5% accuracy ratio by excluding NB addition tested over 162 URL.


title: The Risk Need Responsivity Model of Offender Rehabilitation: Is There Really a Need for a Paradigm Shift?.

abstract: The current paper critically reviews the Risk-Need-Responsivity (RNR) and Good Lives Model (GLM) approaches to correctional treatment. Research, or the lack thereof, is discussed in terms of whether there is a need for a new model of offender rehabilitation. We argue that although there is a wealth of research in support of RNR approaches, there is presently very little available research demonstrating the efficacy of the GLM in terms of the impact that programs based on this model of rehabilitation have on observed rates of recidivism among offender populations. Additionally, the emphasis of the GLM approach on the principles and techniques of positive psychology is untested in the area of forensic psychology. Evidence with reference to the assessment and treatment of sexual offenders is discussed as this is a particular focus of the GLM approach. We conclude, in agreement with the developers of the GLM approach, that the RNR model needs to be adapted in order to incorporate recent research related to the factors associated with recidivism among sexual offenders. However, we argue that the GLM is largely an empirically untested model, and further offers little in terms of adding to or replacing the RNR model. We recognize that a revised version of the RNR based approach is necessary, incorporating an integrated approach to treatment, and we introduce and briefly describe the RNR-I (Integrated)), a model developed by the authors and supported by a variety of empirical research, including a number of outcome studies produced by our team and others.


title: Design of series-parallel combined resonant circuit with rotary transformer used for ultrasonic spindle drive

abstract: The ultrasonic spindle is well-known as one of the processing equipment and has high-machining performances. An ultrasonic transducer used for the ultrasonic spindle drive is usually driven by high-frequency sinusoidal voltage. To supply this sinusoidal voltage to the ultrasonic transducer attached to the rotary axis, a rotary transformer, instead of the voltage transfer method using a brush and a slip ring is utilized to realize maintenance-free. However, the rotary transformer cannot be transferred the required voltage to drive the ultrasonic transducer because the coupling coefficient of the rotary transformer is not sufficiently high. Therefore, the leakage inductances in the rotary transformer are needed to cancel equivalently by utilizing LC cancellation techniques. This paper proposes two series-parallel combined resonant circuits. The one is a circuit which a series resonant circuit is buried in a parallel-resonant circuit, on the primary side of the transformer. The other is a circuit which a parallel-resonant circuit is buried in a series resonant circuit. In addition, the design methods for both circuits are proposed, and these circuits are compared from theoretical analysis and simulation point of views.


title: Video copy-move forgery detection and localization based on Tamura texture features

abstract: Copy-move forgery is one of the most common types of video forgeries. This paper proposed a new algorithm to detect and locate the copy-move forgery based on Tamura texture features. In the algorithm, the eigenvector matrix of the video is made up by extracting the Tamura texture features of every video frames. Then the eigenvector matrix of video is sorted by dictionary ordering and computing the differences between the feature vector and the adjacent feature vectors. And when the differences between two feature vectors less than the threshold, compare their distance of the serial number with the threshold, record the pairs of the serial numbers which is greater than the distance threshold. Finally, locate the copy-move sequences. The experimental results show that the precision of this method is higher than the previous relevant study.


title: Online Experiences and Virtual Goods Purchase Intention

abstract: Purpose – The purpose of this paper is to examine the effects of interactive and social features on users' online experiences and their purchase intention of virtual goods from a social network site.Design/methodology/approach – A banner with a hyperlink that connected to the author's web survey was posted on the homepage of Facebook. Of the 258 responses returned, 176 were fully completed. Measurement items were adapted from previous literature. Structural equation modeling (SEM) was used to evaluate the research model and hypotheses testing.Findings – The results of an empirical study supported the use of the stimuli‐organism‐response (S‐O‐R) model in a social networking site and showed how environmental features should be incorporated to enhance users' online experiences and purchase intentions. Specifically, social identity showed the strongest influence on involvement and flow. More specifically, affective involvement showed the greatest influence on purchase intention compared to flow and cognitive ...


title: Fingerprint-Quality Index Using Gradient Components

abstract: Fingerprint image-quality checking is one of the most important issues in fingerprint recognition because recognition is largely affected by the quality of fingerprint images. In the past, many related fingerprint-quality checking methods have typically considered the condition of input images. However, when using the preprocessing algorithm, ridge orientation may sometimes be extracted incorrectly. Unwanted false minutiae can be generated or some true minutiae may be ignored, which can also affect recognition performance directly. Therefore, in this paper, we propose a novel quality-checking algorithm which considers the condition of the input fingerprints and orientation estimation errors. In the experiments, the 2-D gradients of the fingerprint images were first separated into two sets of 1-D gradients. Then, the shapes of the probability density functions of these gradients were measured in order to determine fingerprint quality. We used the FVC2002 database and synthetic fingerprint images to evaluate the proposed method in three ways: 1) estimation ability of quality; 2) separability between good and bad regions; and 3) verification performance. Experimental results showed that the proposed method yielded a reasonable quality index in terms of the degree of quality degradation. Also, the proposed method proved superior to existing methods in terms of separability and verification performance.


title: Universal Reasoning, Rational Argumentation and Human-Machine Interaction

abstract: Classical higher-order logic, when utilized as a meta-logic in which various other (classical and non-classical) logics can be shallowly embedded, is well suited for realising a universal logic reasoning approach. Universal logic reasoning in turn, as envisioned already by Leibniz, may support the rigorous formalisation and deep logical analysis of rational arguments within machines. A respective universal logic reasoning framework is described and a range of exemplary applications are discussed. In the future, universal logic reasoning in combination with appropriate, controlled forms of rational argumentation may serve as a communication layer between humans and intelligent machines.


title: SDN Based Architecture to Improve Video Streaming in Home Networks

abstract: Nowadays, Internet video is the dominant internet traffic. DASH is an adaptive video streaming technique introduced to enable high quality video delivery over HTTP. In home networks, multiple video streams will compete for bandwidth, thus leading to poor performance and impacting the received quality of experience. In this paper we introduce a new technique to address this issue at the home network gateway without modifying neither the client player nor the video server. We design our framework NAVS (Network Assisted Video Streaming) relies on the deployment of Software Defined Networking (SDN). NAVS performs a dynamic traffic shaping based on the collected network traffic statistics and monitoring of video flows. NAVS dynamically allocates bandwidth for each video flow in real time. NAVS scheme has been evaluated over several metrics: bandwidth utilization, instability of players as well as the average video quality received by the clients. Our results demonstrate an improvement for all these parameters.


title: Performance Evaluation of NB-IoT Coverage

abstract: Narrowband Internet of Things (NB-IoT) is a new radio access technology, recently standardized in 3GPP to enable support for IoT devices. NB-IoT offers a range of flexible deployment options and provides improved coverage and support for a massive number of devices within a cell. In this paper, we provide a detailed evaluation of the coverage performance of NBIoT and show that it achieves a coverage enhancement of up to 20 dB when compared with existing LTE technology.


title: Dynamic analysis of Clavel's Delta parallel robot

abstract: Some iterative matrix relations for the geometric, kinematic and dynamic analysis of a Delta parallel robot are established in this paper. The prototype of this manipulator is a three degree of freedom spatial mechanism, which consists of a system of parallel chains. Supposing that the position and the translation motion of the platform are known, an inverse dynamic problem is solved using the virtual powers method. Finally, some recursive matrix relations and some graphs for the moments and the powers of the three active couples are determined.


title: Analyzing Sentiment in Classical Chinese Poetry

abstract: Although sentiment analysis in Chinese social media has attracted a lot of interest in recent years, it has been less explored in traditional Chinese literature (e.g., classical Chinese poetry) due to the lack of sentiment lexicon resources. In this paper, we propose a weakly supervised approach based on Weighted Personalized PageRank (WPPR) to create a sentiment lexicon for classical Chinese poetry. We evaluate our lexicon intrinsically and extrinsically. We show that our graphbased approach outperforms a previous well-known PMI-based approach (Turney and Littman, 2003) on both evaluation settings. On the basis of our sentiment lexicon, we analyze sentiment in the Complete Anthology of Tang Poetry. We extract topics associated with positive (negative) sentiment using a position-aware sentimenttopic model. We further compare sentiment among different poets in Tang Dynasty (AD 618 ‐ 907).


title: Motion estimation with non-local total variation regularization

abstract: State-of-the-art motion estimation algorithms suffer from three major problems: Poorly textured regions, occlusions and small scale image structures. Based on the Gestalt principles of grouping we propose to incorporate a low level image segmentation process in order to tackle these problems. Our new motion estimation algorithm is based on non-local total variation regularization which allows us to integrate the low level image segmentation process in a unified variational framework. Numerical results on the Mid-dlebury optical flow benchmark data set demonstrate that we can cope with the aforementioned problems.


title: DBpedia based Ontological Concepts Driven Information Extraction from Unstructured Text

abstract: In this paper a knowledge base concept driven named entity recognition (NER) approach is presented. The technique is used for information extraction from news articles and linking it with background concepts in knowledge base. The work specifically focuses on extracting entity mentions from unstructured articles. The extraction of entity mentions from articles is based on the existing concepts from DBPedia ontology, representing the knowledge associated with the concepts present in Wikipedia knowledge base. A collection of the Wikipedia concepts through structured DBpedia ontology has been extracted and developed. For processing of unstructured text, Dawn news articles have been scrapped, preprocessed and thereby a corpus has been built. The proposed knowledge base driven system shows that given an article, the system identifies the entity mentions in the text article and how they can automatically be linked with the concepts to the corresponding entity mentions representing their respective pages on Wikipedia. The system is evaluated on three test collections of news articles on politics, sports and entertainment domains. The experimental results in respect of entity mentions are reported. The results are presented as precision, recall and f-measure, where the precision of extraction of relevant entity mentions identified yields the best results with a little variation in percent recall and f-measures. Additionally, facts associated with the extracted entity mentions both in form of sentences and Resource Description Framework (RDF) triples are presented so as to enhance the user’s understanding of the related facts presented in the article.


title: Trajectory analysis and semantic region modeling using a nonparametric Bayesian model

abstract: We propose a novel nonparametric Bayesian model, Dual Hierarchical Dirichlet Processes (Dual-HDP), for trajectory analysis and semantic region modeling in surveillance settings, in an unsupervised way. In our approach, trajectories are treated as documents and observations of an object on a trajectory are treated as words in a document. Trajectories are clustered into different activities. Abnormal trajectories are detected as samples with low likelihoods. The semantic regions, which are intersections of paths commonly taken by objects, related to activities in the scene are also modeled. Dual-HDP advances the existing Hierarchical Dirichlet Processes (HDP) language model. HDP only clusters co-occurring words from documents into topics and automatically decides the number of topics. Dual-HDP co-clusters both words and documents. It learns both the numbers of word topics and document clusters from data. Under our problem settings, HDP only clusters observations of objects, while Dual-HDP clusters both observations and trajectories. Experiments are evaluated on two data sets, radar tracks collected from a maritime port and visual tracks collected from a parking lot.


title: Laplacian surface editing

abstract: Surface editing operations commonly require geometric details of the surface to be preserved as much as possible. We argue that geometric detail is an intrinsic property of a surface and that, consequently, surface editing is best performed by operating over an intrinsic surface representation. We provide such a representation of a surface, based on the Laplacian of the mesh, by encoding each vertex relative to its neighborhood. The Laplacian of the mesh is enhanced to be invariant to locally linearized rigid transformations and scaling. Based on this Laplacian representation, we develop useful editing operations: interactive free-form deformation in a region of interest based on the transformation of a handle, transfer and mixing of geometric details between two surfaces, and transplanting of a partial surface mesh onto another surface. The main computation involved in all operations is the solution of a sparse linear system, which can be done at interactive rates. We demonstrate the effectiveness of our approach in several examples, showing that the editing operations change the shape while respecting the structural geometric detail.


title: EigenRank: a ranking-oriented approach to collaborative filtering

abstract: A recommender system must be able to suggest items that are likely to be preferred by the user. In most systems, the degree of preference is represented by a rating score. Given a database of users' past ratings on a set of items, traditional collaborative filtering algorithms are based on predicting the potential ratings that a user would assign to the unrated items so that they can be ranked by the predicted ratings to produce a list of recommended items. In this paper, we propose a collaborative filtering approach that addresses the item ranking problem directly by modeling user preferences derived from the ratings. We measure the similarity between users based on the correlation between their rankings of the items rather than the rating values and propose new collaborative filtering algorithms for ranking items based on the preferences of similar users. Experimental results on real world movie rating data sets show that the proposed approach outperforms traditional collaborative filtering algorithms significantly on the NDCG measure for evaluating ranked results.


title: Comprehensive database for facial expression analysis

abstract: Within the past decade, significant effort has occurred in developing methods of facial expression analysis. Because most investigators have used relatively limited data sets, the generalizability of these various methods remains unknown. We describe the problem space for facial expression analysis, which includes level of description, transitions among expressions, eliciting conditions, reliability and validity of training and test data, individual differences in subjects, head orientation and scene complexity image characteristics, and relation to non-verbal behavior. We then present the CMU-Pittsburgh AU-Coded Face Expression Image Database, which currently includes 2105 digitized image sequences from 182 adult subjects of varying ethnicity, performing multiple tokens of most primary FACS action units. This database is the most comprehensive testbed to date for comparative studies of facial expression analysis.


A Dataset of Hindi-English Code-Mixed Social Media Text for Hate Speech Detection


title: Imaginary Bodies: Ethics, Power and Corporeality

abstract: Moira Gatens investigates the ways in which differently sexed bodies can occupy the same social or political space. Representations of sexual difference have unacknowledged philosophical roots which cannot be dismissed as a superficial bias on the part of the philosopher, nor removed without destroying the coherence of the philosophical system concerned. The deep structural bias against women extends beyond metaphysics and its effects are felt in epistemology, moral, social and political theory. The idea of sexual difference is contextualised in Imaginary Bodies and traced through the history of philosophy. Using her work on Spinoza, Gatens develops alternative conceptions of power, new ways of conceiving women's embodiment and their legal, political and ethical status.


title: NLRP3 inflammasome activation: the convergence of multiple signalling pathways on ROS production?

abstract: The NLR family, pyrin domain-containing 3 (NLRP3) inflammasome is a multiprotein complex that activates caspase 1, leading to the processing and secretion of the pro-inflammatory cytokines interleukin-1β (IL-1β) and IL-18. The NLRP3 inflammasome is activated by a wide range of danger signals that derive not only from microorganisms but also from metabolic dysregulation. It is unclear how these highly varied stress signals can be detected by a single inflammasome. In this Opinion article, we review the different signalling pathways that have been proposed to engage the NLRP3 inflammasome and suggest a model in which one of the crucial elements for NLRP3 activation is the generation of reactive oxygen species (ROS).


title: Multi-level Wavelet-CNN for Image Restoration

abstract: The tradeoff between receptive field size and efficiency is a crucial issue in low level vision. Plain convolutional networks (CNNs) generally enlarge the receptive field at the expense of computational cost. Recently, dilated filtering has been adopted to address this issue. But it suffers from gridding effect, and the resulting receptive field is only a sparse sampling of input image with checkerboard patterns. In this paper, we present a novel multi-level wavelet CNN (MWCNN) model for better tradeoff between receptive field size and computational efficiency. With the modified U-Net architecture, wavelet transform is introduced to reduce the size of feature maps in the contracting subnetwork. Furthermore, another convolutional layer is further used to decrease the channels of feature maps. In the expanding subnetwork, inverse wavelet transform is then deployed to reconstruct the high resolution feature maps. Our MWCNN can also be explained as the generalization of dilated filtering and subsampling, and can be applied to many image restoration tasks. The experimental results clearly show the effectiveness of MWCNN for image denoising, single image super-resolution, and JPEG image artifacts removal.


title: Beacon based indoor positioning system using weighted centroid localization approach

abstract: There has been an upward trend in the requirement of indoor positioning systems using bluetooth low energy (BLE), Wi-Fi, and visible light communication. In order to realize the indoor positioning with these communication systems, techniques such as fingerprinting, trilateration, and triangulation have been widely studied. Even though fingerprinting has been chosen as a representative approach in many literatures, it is known as tedious and time consuming method due to the long-time location learning phase. Therefore, the fingerprinting is expected to be integrated with other methods to enhance the location accuracy and reduce the location estimation procedures. In this work, we discuss one of these methods used for indoor positioning, i.e., weighted centroid localization (WCL) using received signal strength indicator (RSSI) observed from neighboring BLE beacons. The WCL is evaluated in our testbed building and analyzed to configure its parameters for indoor positioning.


title: Professional Learning Interventionn in Mathematics: A Case of Developing Portfolio Assessment

abstract: This paper reports findings from a Fijian study that engaged secondary mathematics teachers in a two-day professional development workshop on the use of portfolios as an alternative means of assessing student learning in mathematics. Utilising an action research approach with a view to involve teachers as key ::: stakeholders in mathematics education, the professional development prompted a small group of secondary mathematics teachers to think about the mathematics content and processes which could not be assessed using written tests, and to develop assessments that would ::: reflect higher-order thinking in mathematics. While the majority of the teachers came up with ideas that were well suited for portfolio assessment, some participants faced minor difficulties in relating to real-world experiences and developing higher-order tasks. Teachers ::: were able to come up with teaching and learning activities that could be used for portfolio assessment. At the end of the workshop ten of the 12 teachers were interviewed to gain an insight into their experiences. ::: All of them stated explicitly that they had found the workshop very useful and rewarding because portfolios could provide an alternative way to assess student learning, and could possibly influence the way they taught mathematics.


title: Adversarial-Playground: A visualization suite showing how adversarial examples fool deep learning

abstract: Recent studies have shown that attackers can force deep learning models to misclassify so-called "adversarial examples": maliciously generated images formed by making imperceptible modifications to pixel values. With growing interest in deep learning for security applications, it is important for security experts and users of machine learning to recognize how learning systems may be attacked. Due to the complex nature of deep learning, it is challenging to understand how deep models can be fooled by adversarial examples. Thus, we present a web-based visualization tool, Adversarial-Playground, to demonstrate the efficacy of common adversarial methods against a convolutional neural network (CNN) system. Adversarial-Playground is educational, modular and interactive. (1) It enables non-experts to compare examples visually and to understand why an adversarial example can fool a CNN-based image classifier. (2) It can help security experts explore more vulnerability of deep learning as a software module. (3) Building an interactive visualization is challenging in this domain due to the large feature space of image classification (generating adversarial examples is slow in general and visualizing images are costly). Through multiple novel design choices, our tool can provide fast and accurate responses to user requests. Empirically, we find that our client-server division strategy reduced the response time by an average of 1.5 seconds per sample. Our other innovation, a faster variant of JSMA evasion algorithm, empirically performed twice as fast as JSMA and yet maintains a comparable evasion rate. ::: Project source code and data from our experiments available at: this https URL


title: Hardware accelerated ambient occlusion techniques on GPUs

abstract: We introduce a visually pleasant ambient occlusion approximation running on real-time graphics hardware. Our method is a multi-pass algorithm that separates the ambient occlusion problem into high-frequency, detailed ambient occlusion and low-frequency, distant ambient occlusion domains, both capable of running independently and in parallel. The high-frequency detailed approach uses an image-space method to approximate the ambient occlusion due to nearby occluders caused by high surface detail. The low-frequency approach uses the intrinsic properties of a modern GPU to greatly reduce the search area for large and distant occluders with the help of a low-detail approximated version of the occluder geometry. Our method utilizes the highly parallel, stream processors (GPUs) to perform real-time visually pleasant ambient occlusion. We show that our ambient occlusion approximation works on a wide variety of applications such as molecular data visualization, dynamic deformable animated models, highly detailed geometry. Our algorithm demonstrates scalability and is well-suited for the current and upcoming graphics hardware.


title: 3D Object Modeling and Recognition Using Local Affine-Invariant Image Descriptors and Multi-View Spatial Constraints

abstract: This article introduces a novel representation for three-dimensional (3D) objects in terms of local affine-invariant descriptors of their images and the spatial relationships between the corresponding surface patches. Geometric constraints associated with different views of the same patches under affine projection are combined with a normalized representation of their appearance to guide matching and reconstruction, allowing the acquisition of true 3D affine and Euclidean models from multiple unregistered images, as well as their recognition in photographs taken from arbitrary viewpoints. The proposed approach does not require a separate segmentation stage, and it is applicable to highly cluttered scenes. Modeling and recognition results are presented.


title: Targeting protein aggregation for the treatment of degenerative diseases

abstract: The misfolding and aggregation of specific proteins — a process known as amyloidogenesis — seem to underlie a range of degenerative disorders. Here, Kelly and colleagues discuss the current understanding of the process and pathological role of protein aggregation, focusing on emerging disease-modifying strategies to ameliorate aggregation-associated degenerative disorders.


title: Permanent-Magnet Temperature Estimation in PMSMs Using Pulsating High-Frequency Current Injection

abstract: The injection of a high-frequency signal in the stator via inverter has been shown to be a viable option to estimate the magnet temperature in permanent-magnet synchronous machines (PMSMs). The variation of the magnet resistance with temperature is reflected in the stator high-frequency resistance, which can be measured from the resulting current when a high-frequency voltage is injected. However, this method is sensitive to $d$ - and $q$ -axis inductance ( $L_{d}$ and $L_{q} $ ) variations, as well as to the machine speed. In addition, it is only suitable for surface PMSMs (SPMSMs) and inadequate for interior PMSMs (IPMSMs) . In this paper, the use of a pulsating high-frequency current injection in the $d$ -axis of the machine for temperature estimation purposes is proposed. The proposed method will be shown to be insensitive to the speed, $L_{q}$ , and $L_{d} $ variations. Furthermore, it can be used with both SPMSMs and IPMSMs.


title: EasyEnsemble and Feature Selection for Imbalance Data Sets

abstract: There are many labeled data sets which have an unbalancedrepresentation among the classes in them. When the imbalance islarge, classification accuracy on the smaller class tends to belower. In particular, when a class is of great interest but occursrelatively rarely such as cases of fraud, instances of disease, andso on, it is important to accurately identify it. Here we propose a novel algorithm named MIEE(Mutual Information based feature selection for EasyEnsemble) totreat this problem and improve generalization performance of theEasyEnsemble classifier. Experimental results on the UCI data setsshow that MIEE obtain better performance, compared with theasymmetric bagging and EasyEnsemble.


title: Antioxidant, Anti-inflammatory, and Antiulcer Potential of Manuka Honey against Gastric Ulcer in Rats

abstract: Gastric ulcers are among the most common diseases affecting humans. This study aimed at investigating the gastroprotective effects of manuka honey against ethanol-induced gastric ulcers in rats. The mechanism by which honey exerts its antiulcer potential was elucidated. Four groups of rats were used: control, ethanol (ulcer), omeprazole, and manuka honey. Stomachs were examined macroscopically for hemorrhagic lesions in the glandular mucosa, histopathological changes, and glycoprotein detection. The effects of oxidative stress were investigated using the following indicators: gastric mucosal nitric oxide (NO), reduced glutathione (GSH), lipid peroxide (MDA, measured as malondialdehyde) glutathione peroxidase (GPx), superoxide dismutase (SOD), and catalase. Plasma tumour necrosis factor-α, interleukin-1β, and IL-6 were also measured. Manuka honey significantly decreased the ulcer index, completely protected the mucosa from lesions, and preserved gastric mucosal glycoprotein. It significantly increased gastric mucosal levels of NO, GSH, GPx, and SOD. Manuka honey also decreased gastric mucosal MDA and plasma TNF-α, IL-1β, and IL-6 concentrations. In conclusion, manuka honey likely exerted its antiulcer, effect by keeping enzymatic (GPx and SOD) and nonenzymatic (GSH and NO) antioxidants as well as inflammatory cytokines (TNF-α, IL-1β, and IL-6) in a reduced form, inhibited lipid peroxidation (MDA), and preserved mucous glycoproteins levels.


title: Optimization-Based Autonomous Racing of 1:43 Scale RC Cars

abstract: Summary ::: ::: This paper describes autonomous racing of RC race cars based on mathematical optimization. Using a dynamical model of the vehicle, control inputs are computed by receding horizon based controllers, where the objective is to maximize progress on the track subject to the requirement of staying on the track and avoiding opponents. Two different control formulations are presented. The first controller employs a two-level structure, consisting of a path planner and a nonlinear model predictive controller (NMPC) for tracking. The second controller combines both tasks in one nonlinear optimization problem (NLP) following the ideas of contouring control. Linear time varying models obtained by linearization are used to build local approximations of the control NLPs in the form of convex quadratic programs (QPs) at each sampling time. The resulting QPs have a typical MPC structure and can be solved in the range of milliseconds by recent structure exploiting solvers, which is key to the real-time feasibility of the overall control scheme. Obstacle avoidance is incorporated by means of a high-level corridor planner based on dynamic programming, which generates convex constraints for the controllers according to the current position of opponents and the track layout. The control performance is investigated experimentally using 1:43 scale RC race cars, driven at speeds of more than 3 m/s and in operating regions with saturated rear tire forces (drifting). The algorithms run at 50 Hz sampling rate on embedded computing platforms, demonstrating the real-time feasibility and high performance of optimization-based approaches for autonomous racing. Copyright © 2014 John Wiley & Sons, Ltd.


title: Generalized fisheye views

abstract: In many contexts, humans often represent their own “neighborhood” in great detail, yet only major landmarks further away. This suggests that such views (“fisheye views”) might be useful for the computer display of large information structures like programs, data bases, online text, etc. This paper explores fisheye views presenting, in turn, naturalistic studies, a general formalism, a specific instantiation, a resulting computer program, example displays and an evaluation.


title: A wireless sensor network compatible wearable u-healthcare monitoring system using integrated ECG, accelerometer and SpO2

abstract: This paper presents the design and development of a wearable ubiquitous healthcare monitoring system using integrated electrocardiogram (ECG), accelerometer and oxygen saturation (SpO(2)) sensors. In this design, non-intrusive healthcare system was designed based on wireless sensor network (WSN) for wide area coverage with minimum battery power to support RF transmission. We have developed various devices such as wearable ubiquitous sensor network (USN) node, wearable chest sensor belt and wrist pulse oximeter for this system. Low power ECG, accelerometer and SpO(2) sensors board was integrated to the wearable USN node for user's health monitoring. The wearable ubiquitous healthcare monitoring system allows physiological data to be transmitted in wireless sensor network using IEEE 802.15.4 from on-body wearable sensor devices to a base-station which is connected to a server PC. Physiological data can be displayed and stored in the server PC continuously.


title: Development and characterization of new generation panel fan-out (P-FO) packaging technology

abstract: Wafer Level Packaging (WLP) is a packaging technology focusing on integrated circuit (IC) packaging at wafer level instead of die level. WLP essentially consists of IC foundry fabrication process and subsequent device interconnection and back-end passivation process. The general wafer level packages (WLPs) are designed for fan-in chip scale packaging but the shrinkage of pad pitch and size at the chip to package interface is much faster than the shrinkage at the package to board interface. The embedded package technologies are developed to provide larger package size in order to offer a sufficient area to accommodate the redistribution interconnection with standard pitches at the packaging. Embedded technology (FO-WLP / eWLB and embedded die) are unique technological breakthroughs enabling further growth of next-generation packaging modules and brings several benefits such as small form factor of packages, electrical and thermal performance improvement and cost reduction for IDMs. Compare to FO-WLP, panel level packaging (PLP) has the advantage of using large panel processing compare with FO-WLP while 12" is commonly used. Hence panel fan-out package (P-FO) has a better cost down potential than FO-WLP. To demonstrate the new generation panel fan-out package conception, the several hetero-technologies are integrated into the packaging which composed of PCB, LCD, Bumping and FOWLP has substantially broadened the spectrum of the well established wafer level packaging (WLP) technologies. The new generation panel fan-out packaging combines the different infrastructures from several fields to realize the conception. The achieved results for the package, such as the large panel size process and precise die alignment capability point to the outstanding potential of this novel hetero-system. In this paper, mixing PCB, semiconductor back-end, semiconductor WLP and LCD Gen 2.5 (370×470mm) size processing technologies combined with innovation as well as integration of P-FO techniques are proposed, including high accuracy die bonding and die shift compensation at film lamination, lower warpage sheet form film lamination, good copper trace plating uniformity control at large panel area and also precise photolithographic technique. In addition to technology elaboration and process depiction, relevant experimental data and final reliability test results on board level have also been thoroughly demonstrated and discussed.


title: Lateral power MOSFET for megahertz-frequency, high-density DC/DC converters

abstract: DC/DC converters to power future CPU cores mandate low-voltage power metal-oxide semiconductor field-effect transistors (MOSFETs) with ultra low on-resistance and gate charge. Conventional vertical trench MOSFETs cannot meet the challenge. In this paper, we introduce an alternative device solution, the large-area lateral power MOSFET with a unique metal interconnect scheme and a chip-scale package. We have designed and fabricated a family of lateral power MOSFETs including a sub-10 V class power MOSFET with a record-low R/sub DS(ON)/ of 1m/spl Omega/ at a gate voltage of 6V, approximately 50% of the lowest R/sub DS(ON)/ previously reported. The new device has a total gate charge Q/sub g/ of 22nC at 4.5V and a performance figures of merit of less than 30m/spl Omega/-nC, a 3/spl times/ improvement over the state of the art trench MOSFETs. This new MOSFET was used in a 100-W dc/dc converter as the synchronous rectifiers to achieve a 3.5-MHz pulse-width modulation switching frequency, 97%-99% efficiency, and a power density of 970W/in/sup 3/. The new lateral MOSEFT technology offers a viable solution for the next-generation, multimegahertz, high-density dc/dc converters for future CPU cores and many other high-performance power management applications.


Mining Structures from Massive Text Data: A Data-Driven Approach


title: An Optimized Circuit of 8:1 Multiplexer Circuit using Reversible Logic Gates

abstract: Designing of reversible circuit has become the promising area for researchers. The designing of digital circuits using reversible logic should have zero power loss in ideal conditions. However in practical aspect, it does not occur. This paper illustrates an optimized 8:1 multiplexer circuit grounded on reversible logic using a combination of available reversible logic gates. The multiplexer is optimized on the basis of two parameters namely total number of reversible gates used in the design of the circuits and total garbage outputs generated. This circuit is more advantageous for further designing of any digital circuit with low power loss. The devices designed through this circuit would have better performance as compared to the existing circuits. General Terms Low loss digital circuit design.


title: Toward a Science of Cyber – Physical System Integration This paper focuses on the design phase of the system lifecycle and proposes a passivity-based approach to decouple system stability from cyber timing uncertainties

abstract: System integration is the elephant in the china store of large-scale cyber-physical system (CPS) design. It would be hard to find any other technology that is more undervalued scientifically and at the same time has bigger impact on the presence and future of engineered systems. The unique challenges in CPS integration emerge from the heterogeneity of components and interactions. This heterogeneity drives the need for modeling and analyzing cross-domain interactions among physical and computational/networking domains and demands deep understanding of the effects of heterogeneous abstraction layers in the design flow. To address the challenges of CPS integration, significant progress needs to be made toward a new science and technology foundation that is model based, precise, and predictable. This paper presents a theory of composition for heterogeneous systems focusing on stability. Specifically, the paper presents a passivity-based design approach that decouples stability from timing uncertainties caused by networking and computation. In addition, the paper describes cross-domain abstractions that provide effective solution for model-based fully automated software synthesis and high-fidelity performance analysis. The design objectives demonstrated using the techniques presented in the paper are group coordination for networked unmanned air vehicles (UAVs) and high-confidence embedded control software design for a quadrotor UAV. Open problems in the area are also discussed, including the extension of the theory of compositional design to guarantee properties beyond stability, such as safety and performance.


Private Sector Cyber Security Investment: An Empirical Analysis


title: Pill Identification with Imprints Using a Neural Network

abstract: Since there are more than ten thousand different types of pills commonly used in hospitals in Thailand, they are not easily recognized by an inexperienced pharmacist or even an experienced pharmacist. Thus, our long term goals are to develop a commonly-used pill database in Thailand and to build a system to assist pharmacists to identify unknown pills in real-time. In this paper, we focused only on using imprints to identify pills that are almost identical in color and shape. We proposed a new algorithm to extract the feature vector from the imprints. The extracted feature vector was invariance to image rotation and was fed into the neural network. The neural network was used to identify the type of an unknown pill. Although the number of pill types was limited to six in this work, the results were promising. The percent accuracy was about 94.4%.


Multi-Task Learning For Parsing The Alexa Meaning Representation Language


title: Importance Weighted Autoencoders

abstract: The variational autoencoder (VAE; Kingma, Welling (2014)) is a recently proposed generative model pairing a top-down generative network with a bottom-up recognition network which approximates posterior inference. It typically makes strong assumptions about posterior inference, for instance that the posterior distribution is approximately factorial, and that its parameters can be approximated with nonlinear regression from the observations. As we show empirically, the VAE objective can lead to overly simplified representations which fail to use the network's entire modeling capacity. We present the importance weighted autoencoder (IWAE), a generative model with the same architecture as the VAE, but which uses a strictly tighter log-likelihood lower bound derived from importance weighting. In the IWAE, the recognition network uses multiple samples to approximate the posterior, giving it increased flexibility to model complex posteriors which do not fit the VAE modeling assumptions. We show empirically that IWAEs learn richer latent space representations than VAEs, leading to improved test log-likelihood on density estimation benchmarks.


title: Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA)

abstract: This paper describes a novel inpainting algorithm that is capable of filling in holes in overlapping texture and cartoon image layers. This algorithm is a direct extension of a recently developed sparse-representation-based image decomposition method called MCA (morphological component analysis), designed for the separation of linearly combined texture and cartoon layers in a given image (see [J.-L. Starck, M. Elad, D.L. Donoho, Image decomposition via the combination of sparse representations and a variational approach, IEEE Trans. Image Process. (2004), in press] and [J.-L. Starck, M. Elad, D.L. Donoho, Redundant multiscale transforms and their application for morphological component analysis, Adv. Imag. Electron Phys. (2004) 132]). In this extension, missing pixels fit naturally into the separation framework, producing separate layers as a by-product of the inpainting process. As opposed to the inpainting system proposed by Bertalmio et al., where image decomposition and filling-in stages were separated as two blocks in an overall system, the new approach considers separation, hole-filling, and denoising as one unified task. We demonstrate the performance of the new approach via several examples.


title: Segmenting Consumers Based on Luxury Value Perceptions

abstract: 2 Abstract: This paper seeks to discover consumer segments by using a multidimensional concept of luxury by encompassing functional, individual and social components in the luxury market. Survey data was collected from 1097 consumers in Iran. Eight luxury factors were indentified through an exploratory factor analysis. These factors are used for segmenting these consumers with the K-means method. Cluster analysis of the data resulted in four different behavioral style segments namely: non-luxury consumers, rational consumers, social seeker consumers and materialistic consumers. Each segment shows the importance of luxury value dimensions differently. This paper sheds light on the differences between consumers' perception about luxury value, which helps marketers to choose their marketing strategies more consistently with the consumers'viewpoint.


title: Exercise, protein metabolism, and muscle growth.

abstract: Exercise has a profound effect on muscle growth, which can occur only if muscle protein synthesis exceeds muscle protein breakdown; there must be a positive muscle protein balance. Resistance exercise improves muscle protein balance, but, in the absence of food intake, the balance remains negative (i.e., catabolic). The response of muscle protein metabolism to a resistance exercise bout lasts for 24-48 hours; thus, the interaction between protein metabolism and any meals consumed in this period will determine the impact of the diet on muscle hypertrophy. Amino acid availability is an important regulator of muscle protein metabolism. The interaction of postexercise metabolic processes and increased amino acid availability maximizes the stimulation of muscle protein synthesis and results in even greater muscle anabolism than when dietary amino acids are not present. Hormones, especially insulin and testosterone, have important roles as regulators of muscle protein synthesis and muscle hypertrophy. Following exercise, insulin has only a permissive role on muscle protein synthesis, but it appears to inhibit the increase in muscle protein breakdown. Ingestion of only small amounts of amino acids, combined with carbohydrates, can transiently increase muscle protein anabolism, but it has yet to be determined if these transient responses translate into an appreciable increase in muscle mass over a prolonged training period.


title: CONNECT: CONtextual NamE disCovery for blockchain-based services in the IoT

abstract: The Internet of Things is gaining momentum thanks to the provided vision of seamlessly interconnected devices. However, a unified way to discover and to interact with the surrounding smart environment is missing. As an outcome, we have been assisting to the development of heterogeneous ecosystems, where each service provider adopts its own protocol— thus preventing IoT devices from interacting when belonging to different providers. And, the same is happening again for the blockchain technology which provides a robust and trusted way to accomplish tasks —unfortunately not providing interoperability thus creating the same heterogeneous ecosystems above highlighted. In this context, the fundamental research question we address is how do we find things or services in the Internet of Things. In this paper, we propose the first IoT discovery approach which provides an answer to the above question by exploiting hierarchical and universal multi-layered blockchains. Our approach does neither define new standards nor force service providers to change their own protocol. On the contrary, it leverages the existing and publicly available information obtained from each single blockchain to have a better knowledge of the surrounding environment. The proposed approach is detailed and discussed with the support of relevant use cases.


title: Japanese character (Kana) pattern recognition application using neural network

abstract: Japanese language has complex writing systems, Kanji and Kana (Katakana and Hiragana). Each one has different style of writing. One simple way to differentiate is Kanji have more strokes than Kana. Meanwhile, it needs a lot of effort to remember characters of Katakana and Hiragana, thus it will be very difficult to distinguish handwritten Katakana and Hiragana, since there are a lot of similar characters. This is the reason why we need pattern recognition.


title: Outcome for Children Receiving the Early Start Denver Model Before and After 48 Months

abstract: The Early Start Denver Model (ESDM) is an intervention program recommended for pre-schoolers with autism ages 12–48 months. The rationale for this recommendation is the potential for intervention to affect developmental trajectories during early sensitive periods. We investigated outcomes of 32 children aged 18–48 months and 28 children aged 48–62 months receiving the ESDM for one year (approximately 20 h per week). Younger children achieved superior verbal DQ gains compared to their older counterparts. There were no group differences with respect to non-verbal DQ and adaptive behavior (with both age-groups undergoing significant change), or ASD severity (with neither age-group showing improvements on the ADOS). The association between verbal DQ gains and age at intake was moderated by baseline verbal level.