title: Vehicle routing with multiple deliverymen: Modeling and heuristic approaches for the VRPTW

abstract: In real life distribution of goods, relatively long service times may make it difficult to serve all requests during regular working hours. These difficulties are even greater if the beginning of the service in each demand site must occur within a time window and violations of routing time restrictions are particularly undesirable. We address this situation by considering a variant of the vehicle routing problem with time windows for which, besides routing and scheduling decisions, a number of extra deliverymen can be assigned to each route in order to reduce service times. This problem appears, for example, in the distribution of beverage and tobacco in highly dense Brazilian urban areas. We present a mathematical programming formulation for the problem, as well as a tabu search and an ant colony optimization heuristics for obtaining minimum cost routes. The performance of the model and the heuristic approaches are evaluated using instances generated from a set of classic examples from the literature.


title: The Structure(s) Of Particle Verbs

abstract: This paper argues that particle verb combinations do not display a uniform structure but are represented either as a small clause structure or a complex V’-structure. The central claim is that particle verb combinations fall into two classes semantically— transparent vs. idiomatic particle verb combinations—and that this interpretive difference is reflected in the syntactic structure of particle verb combinations. It is argued that transparent particle verb combinations involve a small clause structure whereas idiomatic particle verb combinations involve a complex V’-structure. The arguments for different structures come from a number of syntactic and semantic properties (in particular, thematic properties of the two kinds of particle verb combinations, predication contexts, topicalization, verb second, and verb (projection) raising contexts) that consistently differentiate between transparent and idiomatic particle verb combinations. The analysis proposed does not only bear on the structure of particle verb combinations but also has consequences for the theoretical status of PF-movement.


title: Methods for infield user calibration of an inertial measurement unit without external equipment

abstract: This paper presents methods to calibrate and compensate for non-zero biases, non-unit scale factors, axis misalignments and cross-axis sensitivities of both the tri-axial accelerometer and gyroscopic setups in a micro-electro-mechanical systems (MEMS) based inertial measurement unit (IMU). These methods depend on the Earth's gravity as a stable physical calibration standard. Specifically, the calibration of gyroscopes is significantly improved by comparing the outputs of the accelerometer and the IMU orientation integration algorithm, after arbitrary motions. The derived property and proposed cost function allow the gyroscopes to be calibrated without external equipment, such as a turntable, or requiring precise maneuvers. Both factors allow the IMU to be easily calibrated by the user in the field so that it can function as an accurate orientation sensor. A custom-made prototype IMU is used to demonstrate the effectiveness of the proposed methods, with data that are carefully obtained using prescribed motions, as well as those less rigorously collected from the IMU when it is mounted on the head of a user or held in hands with brief random movements. With calibration, the observed average static angular error is less than a quarter of a degree and the dynamic angular error is reduced by a factor of 2 to 5.


title: From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis

abstract: This review paper is to give a full picture of fault detection and diagnosis (FDD) in complex systems from the perspective of data processing. As a matter of fact, an FDD system is a data-processing system on the basis of information redundancy, in which the data and human's understanding of the data are two fundamental elements. Human's understanding may be an explicit input-output model representing the relationship among the system's variables. It may also be represented as knowledge implicitly (e.g., the connection weights of a neural network). Therefore, FDD is done through some kind of modeling, signal processing, and intelligence computation. In this paper, a variety of FDD techniques are reviewed within the unified data-processing framework to give a full picture of FDD and achieve a new level of understanding. According to the types of data and how the data are processed, the FDD methods are classified into three categories: model-based online data-driven methods, signal-based methods, and knowledge-based history data-driven methods. An outlook to the possible evolution of FDD in industrial automation, including the hybrid FDD and the emerging networked FDD, are also presented to reveal the future development direction in this field.


title: Social Influence: Compliance and Conformity

abstract: This review covers recent developments in the social influence literature, focusing primarily on compliance and conformity research published between 1997 and 2002. The principles and processes underlying a target's susceptibility to outside influences are considered in light of three goals fundamental to rewarding human functioning. Specifically, targets are motivated to form accurate perceptions of reality and react accordingly, to develop and preserve meaningful social relationships, and to maintain a favorable self-concept. Consistent with the current movement in compliance and conformity research, this review emphasizes the ways in which these goals interact with external forces to engender social influence processes that are subtle, indirect, and outside of awareness.


title: Ultrasound Evaluation of Gynecologic Causes of Pelvic Pain

abstract: Ultrasound should be considered the first-line imaging modality of choice in women presenting with acute or chronic pelvic pain of suspected gynecologic or obstetric origin because many, if not most, gynecologic/obstetric causes of pelvic pain are easily diagnosed on ultrasound examination. Since the clinical presentation of gynecologic causes of pelvic pain overlaps with gastrointestinal and genitourinary pathology, referral to CT or MRI, especially in pregnant patients, should be considered if the US examination is nondiagnostic.


title: Quality-Guided Fusion-Based Co-Saliency Estimation for Image Co-Segmentation and Colocalization

abstract: Despite the advantage of exploiting interimage information by performing joint processing of images for co-saliency, co-segmentation, or co-localization, it introduces a few drawbacks: 1) its necessity in scenarios where the joint processing might not perform better than individual image processing; 2) increased complexity over individual image processing; and 3) complex parameter tuning. In this paper, we propose a simple cosaliency estimation method where we fuse saliency maps of different images using the dense correspondence technique. More important, the co-saliency estimation is guided by our proposed quality measurement that helps decide whether the saliency fusion really improves the quality of the saliency map or not. Our basic idea for developing the quality metric is that a high-quality saliency map should have well-separated foreground and background, as well as a concentrated foreground like ground-truths. Extensive experiments on several benchmark datasets including the large-scale dataset, ImageNet, for the applications of foreground co-segmentation and co-localization show that our proposed framework is able to achieve very competitive results.


title: Minimizing Electricity Cost: Optimization of Distributed Internet Data Centers in a Multi-Electricity-Market Environment

abstract: The study of Cyber-Physical System (CPS) has been an active area of research. Internet Data Center (IDC) is an important emerging Cyber-Physical System. As the demand on Internet services drastically increases in recent years, the power used by IDCs has been skyrocketing. While most existing research focuses on reducing power consumptions of IDCs, the power management problem for minimizing the total electricity cost has been overlooked. This is an important problem faced by service providers, especially in the current multi-electricity market, where the price of electricity may exhibit time and location diversities. Further, for these service providers, guaranteeing quality of service (i.e. service level objectives-SLO) such as service delay guarantees to the end users is of paramount importance. This paper studies the problem of minimizing the total electricity cost under multiple electricity markets environment while guaranteeing quality of service geared to the location diversity and time diversity of electricity price. We model the problem as a constrained mixed-integer programming and propose an efficient solution method. Extensive evaluations based on real-life electricity price data for multiple IDC locations illustrate the efficiency and efficacy of our approach.


title: Do students like the flipped classroom? An investigation of student reaction to a flipped undergraduate IT course

abstract: The flipped classroom pedagogy has achieved significant mention in academic circles in recent years. "Flipping" involves the reinvention of a traditional course so that students engage with learning materials via recorded lectures and interactive exercises prior to attending class and then use class time for more interactive activities. Proper implementation of a flipped classroom is difficult to gauge, but combines successful techniques for distance education with constructivist learning theory in the classroom. While flipped classrooms are not a novel concept, technological advances and increased comfort with distance learning have made the tools to produce and consume course materials more pervasive. Flipped classroom experiments have had both positive and less-positive results and are generally measured by a significant improvement in learning outcomes. This study, however, analyzes the opinions of students in a flipped sophomore-level information technology course by using a combination of surveys and reflective statements. The author demonstrates that at the outset students are new — and somewhat receptive — to the concept of the flipped classroom. By the conclusion of the course satisfaction with the pedagogy is significant. Finally, student feedback is provided in an effort to inform instructors in the development of their own flipped classrooms.


title: Recent Progress on PZT Based Piezoelectric Energy Harvesting Technologies

abstract: Energy harvesting is the most effective way to respond to the energy shortage and to produce sustainable power sources from the surrounding environment. The energy harvesting technology enables scavenging electrical energy from wasted energy sources, which always exist everywhere, such as in heat, fluids, vibrations, etc. In particular, piezoelectric energy harvesting, which uses a direct energy conversion from vibrations and mechanical deformation to the electrical energy, is a promising technique to supply power sources in unattended electronic devices, wireless sensor nodes, micro-electronic devices, etc., since it has higher energy conversion efficiency and a simple structure. Up to now, various technologies, such as advanced materials, micro- and macro-mechanics, and electric circuit design, have been investigated and emerged to improve performance and conversion efficiency of the piezoelectric energy harvesters. In this paper, we focus on recent progress of piezoelectric energy harvesting technologies based on PbZrxTi1-xO3 (PZT) materials, which have the most outstanding piezoelectric properties. The advanced piezoelectric energy harvesting technologies included materials, fabrications, unique designs, and properties are introduced to understand current technical levels and suggest the future directions of piezoelectric energy harvesting.


title: FRESCO: Modular Composable Security Services for SoftwareDefined Networks

abstract: OpenFlow is an open standard that has gained tremendous interest in the last few years within the network community. It is an embodiment of the software-defined networking paradigm, in which higher-level flow routing decisions are derived from a control layer that, unlike classic network switch implementations, is separated from the data handling layer. The central attraction to this paradigm is that by decoupling the control logic from the closed and proprietary implementations of traditional network switch infrastructure, researchers can more easily design and distribute innovative flow handling and network control algorithms. Indeed, we also believe that OpenFlow can, in time, prove to be one of the more impactful technologies to drive a variety of innovations in network security. OpenFlow could offer a dramatic simplification to the way we design and integrate complex network security applications into large networks. However, to date there remains a stark paucity of compelling OpenFlow security applications. In this paper, we introduce FRESCO, an OpenFlow security application development framework designed to facilitate the rapid design, and modular composition of OF-enabled detection and mitigation modules. FRESCO, which is itself an OpenFlow application, offers a Click-inspired [19] programming framework that enables security researchers to implement, share, and compose together, many different security detection and mitigation modules. We demonstrate the utility of FRESCO through the implementation of several well-known security defenses as OpenFlow security services, and use them to examine various performance and efficiency aspects of our proposed framework.


A Software System Development Life Cycle Model for Improved Students Communication and Collaboration


title: A physical implementation of the self-reconfiguring crystalline robot

abstract: We discuss a physical implementation of the crystalline robot system. Crystalline robots consist of modules that can aggregate together to form distributed robot systems. Crystalline modules are actuated by expanding and contracting each unit. This actuation mechanism permits automated shape metamorphosis. We describe the crystalline module concept and a physical implementation of a robot system with ten units. We describe experiments with this robot.


title: New Classification for Correction of Alar Retraction Using the Alar Spreader Graft

abstract: Background ::: Identifying the cause of alar retraction is essential for proper correction of this deformity. In secondary surgery, aimed primarily at cephalic orientation and medialization of the lateral crus, corrections involving spreading and lateralization of the lateral crus can achieve a more horizontal orientation. In their clinic, the authors have practiced the use of an alar spreader graft to support the spread of the lateral crus. For the lateral crus to move freely without any resistance, it is critical to release the nasal hinge and pyriform ligament. A frontal view of the alar notching and the direction of the lateral crus are highly important factors needed to determine the cause of alar retraction. This report describes a new classification system for alar retractions viewed from the front to aid in determining the cause of the retraction and the surgical management.


title: Dimming the Internet: Detecting Throttling as a Mechanism of Censorship in Iran

abstract: In the days immediately following the contested June 2009 Presidential election, Iranians attempting to reach news content and social media platforms were subject to unprecedented levels of the degradation, blocking and jamming of communications channels. Rather than shut down networks, which would draw attention and controversy, the government was rumored to have slowed connection speeds to rates that would render the Internet nearly unusable, especially for the consumption and distribution of multimedia content. Since, political upheavals elsewhere have been associated with headlines such as "High usage slows down Internet in Bahrain" and "Syrian Internet slows during Friday protests once again," with further rumors linking poor connectivity with political instability in Myanmar and Tibet. For governments threatened by public expression, the throttling of Internet connectivity appears to be an increasingly preferred and less detectable method of stifling the free flow of information. In order to assess this perceived trend and begin to create systems of accountability and transparency on such practices, we attempt to outline an initial strategy for utilizing a ubiquitious set of network measurements as a monitoring service, then apply such methodology to shed light on the recent history of censorship in Iran.


title: Improved Probabilistic Inference as a General Learning Mechanism with Action Video Games

abstract: Action video game play benefits performance in an array of sensory, perceptual, and attentional tasks that go well beyond the specifics of game play [1-9]. That a training regimen may induce improvements in so many different skills is notable because the majority of studies on training-induced learning report improvements on the trained task but limited transfer to other, even closely related, tasks ([10], but see also [11-13]). Here we ask whether improved probabilistic inference may explain such broad transfer. By using a visual perceptual decision making task [14, 15], the present study shows for the first time that action video game experience does indeed improve probabilistic inference. A neural model of this task [16] establishes how changing a single parameter, namely the strength of the connections between the neural layer providing the momentary evidence and the layer integrating the evidence over time, captures improvements in action-gamers behavior. These results were established in a visual, but also in a novel auditory, task, indicating generalization across modalities. Thus, improved probabilistic inference provides a general mechanism for why action video game playing enhances performance in a wide variety of tasks. In addition, this mechanism may serve as a signature of training regimens that are likely to produce transfer of learning.


Surgical site infection prevention: a global priority


title: Conformal bluetooth antenna for the watch-type wireless communication device application

abstract: A promising conformal Bluetooth monopole antenna in the watch-type wireless device has been proposed. The proposed antenna is short-circuited to an upper ground plane mounted above the system ground plane. Results have indicated that, with the proposed arrangement, the radiation efficiency can reach about 50% over the band with the presence of the user's forearm. In addition, the antenna shows a low profile of 4 mm only, and the thickness of the wireless device can be about 10 mm only, which makes it very promising and attractive to be worn around the user's wrist or forearm for practical applications.


title: Survey of SCADA security challenges and potential attack vectors

abstract: SCADA systems run silently in the background of our power generation plants, manufacturing facilities, and many other critical infrastructure resources. Even though these SCADA systems are critical in nature, the IT community often poorly understands them. As a result, security of SCADA systems has been neglected, even though companies have improved their security posture to meet regulatory demands. This paper will explore some of the weaknesses in SCADA based system security and the effectiveness of current security techniques. In addition, new methods and tools will be proposed to augment SCADA system security strategies.


title: Hyper-parameter selection in deep neural networks using parallel particle swarm optimization

abstract: The need of manual hyper-parameter selection can seriously hamper the model optimization of Deep Neural Networks (DNNs). Conventional automated approaches tackling this problem suffer from poor scalability or fail in certain scenarios. In this paper, we introduce a parallel method that applies Particle Swarm Optimization (PSO) for the hyper-parameter selection in DNNs. To estimate the best hyper-parameters, a population of particles is evolved, with their fitness calculated in parallel. The experimental results demonstrate very desirable scalability properties for different DNNs. We show that the parallel PSO can further optimize existent models designed by experts in an affordable amount of time.


title: DCCast: Efficient Point to Multipoint Transfers Across Datacenters

abstract: Using multiple datacenters allows for higher availability, load balancing and reduced latency to customers of cloud services. To distribute multiple copies of data, cloud providers depend on inter-datacenter WANs that ought to be used efficiently considering their limited capacity and the ever-increasing data demands. In this paper, we focus on applications that transfer objects from one datacenter to several datacenters over dedicated inter-datacenter networks. We present DCCast, a centralized Point to Multi-Point (P2MP) algorithm that uses forwarding trees to efficiently deliver an object from a source datacenter to required destination datacenters. With low computational overhead, DCCast selects forwarding trees that minimize bandwidth usage and balance load across all links. With simulation experiments on Google's GScale network, we show that DCCast can reduce total bandwidth usage and tail Transfer Completion Times (TCT) by up to $50\%$ compared to delivering the same objects via independent point-to-point (P2P) transfers.


title: A Survey of 2D Face Recognition Techniques

abstract: Despite the existence of various biometric techniques, like fingerprints, iris scan, as well as hand geometry, the most efficient and more widely-used one is face recognition. This is because it is inexpensive, non-intrusive and natural. Therefore, researchers have developed dozens of face recognition techniques over the last few years. These techniques can generally be divided into three categories, based on the face data processing methodology. There are methods that use the entire face as input data for the proposed recognition system, methods that do not consider the whole face, but only some features or areas of the face and methods that use global and local face characteristics simultaneously. In this paper, we present an overview of some well-known methods in each of these categories. First, we expose the benefits of, as well as the challenges to the use of face recognition as a biometric tool. Then, we present a detailed survey of the well-known methods by expressing each method’s principle. After that, a comparison between the three categories of face recognition techniques is provided. Furthermore, the databases used in face recognition are mentioned, and some results of the applications of these methods on face recognition databases are presented. Finally, we highlight some new promising research directions that have recently appeared.


title: FiberMesh: designing freeform surfaces with 3D curves

abstract: This paper presents a system for designing freeform surfaces with a collection of 3D curves. The user first creates a rough 3D model by using a sketching interface. Unlike previous sketching systems, the user-drawn strokes stay on the model surface and serve as handles for controlling the geometry. The user can add, remove, and deform these control curves easily, as if working with a 2D line drawing. The curves can have arbitrary topology; they need not be connected to each other. For a given set of curves, the system automatically constructs a smooth surface embedding by applying functional optimization. Our system provides real-time algorithms for both control curve deformation and the subsequent surface optimization. We show that one can create sophisticated models using this system, which have not yet been seen in previous sketching or functional optimization systems.


title: Cloud-assisted channel selection for wireless mesh networks

abstract: This paper proposes a cloud-assisted channel selection method for multi-node wireless networks, in particular wireless mesh networks. Periodic channel idle airtime measurements of the access points (APs) that constitute the mesh network are accumulated at a remote controller that runs various forecasting methods on the aggregated data, and finds the best fitting method for each AP-channel pair. By using the selected forecasting method, for every channel and every AP, expected idle airtime in the near future is calculated. Channel assignment is carried out by the remote controller, based on the forecasted idle airtime of each AP. While performing the channel assignment, the remote controller takes into account the available frequency bands in the network, in particular, the band used by the wireless backbone of the mesh network, e.g. 5 GHz band, and the other frequency band, e.g., 2.4 GHz band. The proposed method is implemented and tested in a real test bed that comprises dual-band concurrent mesh APs, and a remote controller that runs in a cloud server. The results show that the proposed method accurately forecasts the idle airtime, and yields successful channel assignment for each frequency band the mesh network is operating in.


title: Defining the insider threat

abstract: Many diverse groups have studied the insider threat problem, including government organizations such as the Secret Service, federally-funded research organizations such as RAND and CERT, and university researchers. In addition, many industry participants are interested in the problem, such as those in the financial sector. However, despite this interest, no consistent definition of an insider has emerged.


title: A Study on Internet of Things based Applications

abstract: This paper gives a detail analysis of various applications based on Internet of Thing (IoT)s. This explains about how internet of things evolved from mobile computing and ubiquitous computing. It emphasises the fact that objects are connected over the internet rather than people. The properties of Internet of Things (IOT) are product information, electronic tag, standard expressed and uploading information. It utilises the Radio Frequency Identification (RFID) technology and wireless sensor networks (WSN). IOT applications are used in domains such as healthcare, supply chain management, defence and agriculture. Lastly the paper focuses on issues involved in IOT. Though it is a boon, IOT faces certain crucial issues like privacy and security.


title: An approach to conceptual modelling of ETL processes

abstract: In this paper, we propose a new conceptual model based on the visualization of data flow showing transformations of records accompanied with attribute transformations. A data warehouse development relies on the development of Extract, Transform, and Load (ETL) processes responsible for extracting data from transactional systems, data quality checks, transformation and conversion and loading data to target tables or cubes in a data warehouse. During the planning and design phases for data warehouse, the ETL conceptual model should be developed not only to show an overview of the whole process, but also to map sources, targets and necessary transformations, verify transformation logic, ensure that requirements are met and the needed structures and content exist. Once created, the conceptual model can be used for development of an ETL process, maintenance/optimization and redesign of a process due to new business requirements and database schema changes, all of which makes it a valuable addendum to data warehouse documentation.


title: Children of depressed parents : an integrative review

abstract: This article reviews the litterature on the adjustment of children of depressed parents, difficulties in parenting and parent-child interactions in these families, and contextual factors that may play a role in child adjustment and parent depression.


title: Underwater object detection and tracking based on multi-beam sonar image processing

abstract: A new framework capable of analyzing multi-beam sonar images for detecting and tracking underwater object using a BlueView (BV) Sonar is presented in this paper. This framework is applied to the design of an obstacle avoidance system for Unmanned Measurement Boat. The real-time sonar data flow collected by multi-beam sonar is expressed as an image and pre-processed by the system. According to the characteristics of sonar images, an improved Otsu method has been carried out to detect the object combining with the contour detection algorithm, with which the foreground object can be separated from background successfully. Then the object is tracked by a particle filter tracking method based on multi-feature adaptive fusion. Results obtained on real sonar data show that the proposed framework can detect and track the object accurately and robustly.


title: Recognizing Facial Expressions Automatically from Video

abstract: Facial expressions, resulting from movements of the facial muscles, are the face changes in response to a person’s internal emotional states, intentions, or social communications. There is a considerable history associated with the study on facial expressions. Darwin [22] was the first to describe in details the specific facial expressions associated with emotions in animals and humans, who argued that all mammals show emotions reliably in their faces. Since that, facial expression analysis has been a area of great research interest for behavioral scientists [27]. Psychological studies [48, 3] suggest that facial expressions, as the main mode for nonverbal communication, play a vital role in human face-to-face communication. For illustration, we show some examples of facial expressions in Fig. 1.


title: Bandwidth Enhancement of Microstrip Patch Antenna Using Jerusalem Cross-Shaped Frequency Selective Surfaces by Invasive Weed Optimization Approach

abstract: In this paper, we present a novel approach for improving the bandwidth of a microstrip patch antenna using Jerusalem cross- shaped frequency selective surfaces (JC-FSSs) as an artiflcial magnetic ground plane. The invasive weed optimization (IWO) algorithm is employed to derive optimal dimensions of the patch antenna and JC-FSS element in order for the whole structure to work at 5.8GHz with consideration of gain. For the most e-cient design, the antenna and FSS ground plane are optimized together, rather than as separate components. Simulation results demonstrate that this optimum conflguration (the microstrip patch antenna over the artiflcial magnetic ground plane) have a broad bandwidth of about 10.44%. This wide bandwidth is obtained while the thickness of the whole structure is limited to 0.1‚. Further more desirable radiation characteristics have been successfully realized for this structure. The radiation e-ciency of the AMC antenna conflguration was found to be greater than 85% over the entire bandwidth. In general by introducing this novel Jerusalem cross artiflcial magnetic conductor (JC-AMC) in lieu of the conventional perfect electric conductor (PEC) ground plane, the bandwidth enhancement of about 67% and a thinner and lighter weight design has been obtained. Sample antenna and EBG layer are also fabricated and tested, to verify the designs. It is shown that the simulation data in general agree with the measurement results for the patch antennas implemented with FSS ground plane.


title: Towards fabrication of 3D printed medical devices to prevent biofilm formation.

abstract: The use of three-dimensional (3D) printing technologies is transforming the way that materials are turned into functional devices. We demonstrate in the current study the incorporation of anti-microbial nitrofurantoin in a polymer carrier material and subsequent 3D printing of a model structure, which resulted in an inhibition of biofilm colonization. The approach taken is very promising and can open up new avenues to manufacture functional medical devices in the future.


title: Consumer Behavior towards Online Shopping: An Empirical Study from Delhi

abstract: In today's era of internet proliferation, the prevalence of online shopping has increased. Internet has made the transactions smoother, quicker, faster and easier and both the buyers and sellers get benefited by his technology. The growing use of internet in India has created a basis for tremendous prospects for online retailers; knowledge of factors affecting consumer buying behavior can help retailers develop their marketing strategies to convert their potential consumers into active one. In this study four dimensions of online shopping as perceived by consumers in India are identified and the different demographic factors are also studied which are the primary basis of market segmentation for retailers. The main objective of this research is to study the impact of Perceived Risk, Perceived enjoyment, Perceived usefulness and Perceived ease of use factors on online shopping behavior of consumers in Delhi. The population of this research consisted of online shoppers from Delhi. The sample of the study comprised of 160 online shoppers. Respondents belonged to different age groups, income groups and occupations and have a prior experience in online shopping. A pre - structured questionnaire was used with a 5 point Likert rating scale to measure the factors influencing the respondent's behavior to shop online. The empirical results revealed that only one factor, namely Perceived Risk significantly affected online shopping behavior of consumers in Delhi while Perceived enjoyment, Perceived ease of use and Perceived enjoyment hinders the consumers to shop online so the researchers suggests that the companies must focus on Perceived risks to develop consumers trust towards online shopping. The recommendations presented in this research may help foster growth of Indian online retailing in future. The research findings revealed that perceived risk negatively impact consumers attitude towards online shopping while perceived usefulness, perceived ease of use and perceived enjoyment has no impact on consumers' attitude towards online shopping.


title: Feature selection using hybrid Taguchi genetic algorithm and Fuzzy Support Vector Machine

abstract: The noises or the outliers in data points and the irrelevent or redundant features often reduce classification accuracy. This paper presents a novel approach of hybridizing two conventional machine learning algorithms for feature selection. Taguchi genetic algorithm (TGA) and Fuzzy Support Vector Machine (FSVM) with a new fuzzy membership function were combined in this hybrid method. The Taguchi method is an experimental design method, which is inserted between the crossover and the mutation operations of a GA to enhance the genetic algorithm so that better potential offspring can be generated. The TGA searches for the best feature set using principles of evolutionary process, after which these optimal features are then passed to the FSVM to calculate classification accuracy. Experimental results show that the presented approach is able to produce good performance on reducing the effects of the outliers and the noises and significantly improves the classification accuracy.


title: Strategic self-presentation online: A cross-cultural study

abstract: Contemporary social networking sites (SNSs) make idealized self-presentation and image maintenance difficult because users' audiences are becoming more diverse and individual users must negotiate often unanticipated other-provided information in the form of text posts and digital images on their profile pages. This cross-cultural study examines how audience-related variables affect a range of strategic self-presentation and image management behaviors online. Results from samples of Singaporean and American SNS users (N=411) show that while Americans update their profiles with text-based wall posts more frequently, Singaporeans share significantly more photos. Audience diversity is positively associated with active management of other-provided information, and females share more photos and actively manage unwanted photo tagging. Cultural identity and the tendency to 'friend' unknown others interact on managing other-provided wall posts; individualistic cultural identity exhibited positive relationships with these reactions for those less likely to friend unknown others but negative ones for those more likely to friend unknown others. Implications for the theoretical understanding of and practical suggestions about self-presentation online are discussed.


title: Pestiviral infection of llamas and alpacas

abstract: Abstract This review summarizes the literature pertaining to pestiviral infections of members of the camelid family. The exact nature of pestiviral infections, in particular bovine viral diarrhea virus (BVDV), is the subject of active investigation especially in llamas and alpacas. Earlier reports based on serology-detected pestiviral (BVDV) antibodies in members of the camelid group ranging from a low 4% to a high of 53%. These studies indicate that members of the camelid group are susceptible to infection and do seroconvert. Over the past decade, clinical reports have documented disease conditions in llamas, alpacas and more recently, camels. These conditions range from respiratory and enteric diseases to chronic wasting and in utero infections resulting in stillbirths, and abortion. The review brings together some thoughts on whether infections of the camelid group are due to interspecies transmission and/or the potential that members of this group have their own unique pestiviral infections.


title: A computational approach to body mass index prediction from face images

abstract: Human faces encode plenty of useful information. Recent studies in psychology and human perception have found that facial features have relations to human weight or body mass index (BMI). These studies focus on finding the correlations between facial features and the BMI. Motivated by the recent psychology studies, we develop a computational method to predict the BMI from face images automatically. We formulate the BMI prediction from facial features as a machine vision problem, and evaluate our approach on a large database with more than 14,500 face images. A promising result has been obtained, which demonstrates the feasibility of developing a computational system for BMI prediction from face images at a large scale.


title: A Further Note on Inductive Generalization

abstract: In this paper, we develop the algorithm, given in Plotkin (1970), for finding the least generalization of two clauses, into a theory of inductive generalization.. The types of hypothesis which can be formed are very simple. They all have the form: (x)PX=:l Qx.. We have been guided by ideas from the philosophy of science, following Buchanan (1966). There is' no search for infallible methods of .generating true hypotheses. Instead we define (in terms of first-order predicate calculus) the notions of data and evidence for the data. Next, some formal criteria are set up for a sentence to be a descriptive hypothesis which is a goodexplanation of the data, given the evidence. We can then look for the best such hypothesis. Although this problem is insoluble in general, some soluble subcases can be distinguished. We programmed one of these and tried some examples.


title: Semi-Paired Discrete Hashing: Learning Latent Hash Codes for Semi-Paired Cross-View Retrieval

abstract: Due to the significant reduction in computational cost and storage, hashing techniques have gained increasing interests in facilitating large-scale cross-view retrieval tasks. Most cross-view hashing methods are developed by assuming that data from different views are well paired, e.g., text-image pairs. In real-world applications, however, this fully-paired multiview setting may not be practical. The more practical yet challenging semi-paired cross-view retrieval problem, where pairwise correspondences are only partially provided, has less been studied. In this paper, we propose an unsupervised hashing method for semi-paired cross-view retrieval, dubbed semi-paired discrete hashing (SPDH). In specific, SPDH explores the underlying structure of the constructed common latent subspace, where both paired and unpaired samples are well aligned. To effectively preserve the similarities of semi-paired data in the latent subspace, we construct the cross-view similarity graph with the help of anchor data pairs. SPDH jointly learns the latent features and hash codes with a factorization-based coding scheme. For the formulated objective function, we devise an efficient alternating optimization algorithm, where the key binary code learning problem is solved in a bit-by-bit manner with each bit generated with a closed-form solution. The proposed method is extensively evaluated on four benchmark datasets with both fully-paired and semi-paired settings and the results demonstrate the superiority of SPDH over several other state-of-the-art methods in term of both accuracy and scalability.


title: Individual ball possession in soccer

abstract: This paper describes models for detecting individual and team ball possession in soccer based on position data. The types of ball possession are classified as Individual Ball Possession (IBC), Individual Ball Action (IBA), Individual Ball Control (IBC), Team Ball Possession (TBP), Team Ball Control (TBC) und Team Playmaking (TPM) according to different starting points and endpoints and the type of ball control involved. The machine learning approach used is able to determine how long the ball spends in the sphere of influence of a player based on the distance between the players and the ball together with their direction of motion, speed and the acceleration of the ball. The degree of ball control exhibited during this phase is classified based on the spatio-temporal configuration of the player controlling the ball, the ball itself and opposing players using a Bayesian network. The evaluation and application of this approach uses data from 60 matches in the German Bundesliga season of 2013/14, including 69,667 IBA intervals. The identification rate was F = .88 for IBA and F = .83 for IBP, and the classification rate for IBC was κ = .67. Match analysis showed the following mean values per match: TBP 56:04 ± 5:12 min, TPM 50:01 ± 7:05 min and TBC 17:49 ± 8:13 min. There were 836 ± 424 IBC intervals per match and their number was significantly reduced by -5.1% from the 1st to 2nd half. The analysis of ball possession at the player level indicates shortest accumulated IBC times for the central forwards (0:49 ± 0:43 min) and the longest for goalkeepers (1:38 ± 0:58 min), central defenders (1:38 ± 1:09 min) and central midfielders (1:27 ± 1:08 min). The results could improve performance analysis in soccer, help to detect match events automatically, and allow discernment of higher value tactical structures, which is based on individual ball possession.


title: Gait-Based Continuous Authentication Using Multimodal Learning

abstract: The ever-growing threats of security and privacy loss from unauthorized access to mobile devices has led to the development of various biometric authentication methods for easier and safer data access. In this work we present a gait-based continuous authentication method using accelerometer and ground contact force data recorded from a pair of smart socks. Multi-modal learning and auto-encoders are used for feature extraction and a multi-task learning approach is used for classification. The effectiveness of the proposed approach has been demonstrated through preliminary experiments on a dataset of 8 subjects.


title: Performance analysis and odometry improvement of an omnidirectional mobile robot for outdoor terrain

abstract: In this paper an omnidirectional mobile robot that possesses high mobility in rough terrain is presented. The omnidirectional robot has four active split offset caster (ASOC) modules, enabling the robot to move in any planar direction. It also possesses passive suspension articulation, allowing the robot to conform to uneven terrain. The agility of the robot is experimentally evaluated in various configurations. In addition, an odometry method that mitigates position estimation error due to wheel slippage is proposed. A key aspect of the proposed method is to utilize sensory data of wheel velocity, and turning rate around each ASOC pivot shaft, along with kinematic constraints of the robot configuration. Experimental odometry tests with different maneuvers in rough terrain are presented that confirm the utility of the proposed method.


title: Development and preliminary evaluation of a caregiver's manual for robot therapy using the therapeutic seal robot Paro

abstract: Robot therapy is expected to have psychological, physiological and social effects similar to animal therapy. The use of a therapeutic seal robot, Paro, in various facilities for the elderly is spreading around the world. However, caregivers use Paro freely, and the ways in which they use Paro differ among them. Therefore, the effects are influenced by their skills. A manual that shows effective ways to use Paro is needed. In this paper, such a manual that shows effective methods for robot therapy is developed and its effectiveness is evaluated in a preliminary study with one caregiver and two patients.


title: Dynamic stability of off-road vehicles: Quasi-3D analysis

abstract: This paper presents a method to determine the stability of off-road vehicles moving on rough terrain. The measures of stability are defined as the maximum speed and acceleration under which the vehicle does not slide or tip over. To compute these stability measures, we propose a quasi 3D analysis by decomposing the vehicle dynamics into three separate planes: the yaw, pitch, and roll planes. In each plane, we compute the set of admissible speeds and acceleration for the planar vehicle model, contact model, and ground force constraints. The intersection of the admissible sets provides the total range of feasible speeds and accelerations along the vehicle's path, from which we obtain the stability margins. Numerical results for a vehicle traversing a simulated terrain demonstrate the effectiveness of the approach.


title: Responses of Hydroponically Grown Sorghum (Sorghum bicolor L.M) to Zinc (Zn) Stress

abstract: Abiotic stress especially due heavy metals is one of the major environmental problems that threatens food security and pose greater risks to human health worldwide. In this research, greenhouse hydroponic experiments were carried out to study the morphological and biochemical responses of Sorghum bicolor L.M to different Zinc (Zn) levels. Two-week-old seedlings transplanted in hydroponic solutions were treated with different doses of Zn in the concentration ranges of 5, 25, 50, 100 and 200 mg/L supplied as ZnSO4. 5H2O. After 21 day of culture, the plants were harvested, blotted to dryness and separated into roots and shoots. The root and shoot lengths, dry weights and non-enzymatic biochemical parameters such as proline, Chlorophyll a, b, Carotenoids (pigments) were determined. The results indicate that Zn applications significantly (PPsorghum bicolor L.M should not be grown beyond Zn concentration of above 3.2 mg/L.


title: An automated vehicle parking monitoring and management system using ANPR cameras

abstract: Car parking has become a serious problem of everyday occurrence for educational institutions with the decreasing parking supply, increasing enrollments and high percentage of vehicle ownership, in result causing congestion, time and money wastage. This problem is getting worse and more frustrating in Jazan University due to the fact that majority of students, faculty and staff members own cars and drive through them to the University. The most common problem is to find out people (evidence) who are responsible for the damages (hitting, scraping, scratching and dents) to other cars. Another problem is the blockage of car due to wrong car parking which takes much time to locate the owner of the car. Moreover, another difficulty that is often faced by the students/faculty is to locate their cars on forgetting their car park location. The existing cameras located at the parking lots are only for video surveillance and cannot help in such situations as there is a lack of proper car parking management and guidance system. To remedy the above mentioned problems and to ensure a better parking experience by accommodating increasing number of vehicles in a proper convenient manner, we propose an automated car parking management and monitoring system (CPMMS) which employs Automatic Number Plate Recognition (ANPR) cameras to efficiently manage, monitor and protect the parking facilities of the University. We have also conducted a survey to analyze the parking problems around the University campus faced by the students, faculty and staff members.


title: Improved single-channel noise reduction method of speech by blind source separation

abstract: An improved method of single-channel noise reduction by blind source separation (BSS) is discussed in this paper. A method of and a system for noise suppression, consisting of two adaptive filters for the first noise-reduced speech and the estimated noise-dominant signal, are developed. Initially, we reduce the noise level by the weighted noise subtraction (WNS) method and obtain the first noise-reduced speech. We consider the square of the complement of the estimated noise degree as a weighting factor during the subtraction. The least-mean-squares (LMS) algorithm that is based on the steepest descent method is implemented in adaptive filtering. The method addresses the situations in which the input signal-to-noise ratio (SNR) varies substantially and performing the specified number of iterations of the LMS algorithm for each SNR is time-consuming. Therefore, we propose a function that can be used to estimate the number of iterations required for a given value of the noise degree. The proposed iteration number reduces the computational time and minimizes the signal regeneration problem. Moreover, good efficiency of the algorithm is achieved by appropriate block length processing. The experimental results confirm the improved performance of the proposed WNS+BSS method.


title: The reseach on PCCP risk management based on wireless sensor network

abstract: During the recent years, numerous utilities have experienced catastrophic rupture of critical Prestressing Concrete Cylinder Pipe (PCCP) lines throughout the world. Because PCCP is usually used throughout many water and wastewater utilities as critical mains with high flow rates, sudden failures can have significant negative consequences. Much attention has been focused on reliably assessing the condition of PCCP mains. Some techniques, such as visual inspections, electromagnetic inspections, acoustic monitoring and fiber-optic monitoring, are used to assess the condition of a pipeline now. Each of these techniques has capabilities and limitations that are important to understand when assessing the condition of a main. This can lead to the adoption of inadequate or over-conservative mitigation strategies. In this paper the combination of continuous acoustic monitoring and comprehensive dynamic risk management modeling is proposed. It provides an assessment of remaining time to failure for each pipe segment. This strategy can provide the opportunity to identify problematic pipe sections and repair the pipe prior to failure, and also can assess the presence and extent of deterioration in these large-diameter water and wastewater pipelines.


Dialogue Response Generation using Neural Networks with Attention and Background Knowledge


title: A flexible new technique for camera calibration

abstract: We propose a flexible technique to easily calibrate a camera. It only requires the camera to observe a planar pattern shown at a few (at least two) different orientations. Either the camera or the planar pattern can be freely moved. The motion need not be known. Radial lens distortion is modeled. The proposed procedure consists of a closed-form solution, followed by a nonlinear refinement based on the maximum likelihood criterion. Both computer simulation and real data have been used to test the proposed technique and very good results have been obtained. Compared with classical techniques which use expensive equipment such as two or three orthogonal planes, the proposed technique is easy to use and flexible. It advances 3D computer vision one more step from laboratory environments to real world use.


title: Graph-based approaches to insider threat detection

abstract: This work presents the use of graph-based approaches to discovering anomalous instances of structural patterns in data that represent entities, relationships and actions. Using the minimum description length (MDL) principle to first identify the normative pattern, the algorithms presented in this paper identify the three possible changes to a graph: modifications, insertions and deletions. Each algorithm discovers those substructures that match the closest to the normative pattern without matching exactly. As a result, this proposed approach searches for those activities that appear to match normal (or legitimate) transactions, but in fact are structurally different. After briefly presenting the three algorithms, we then show the usefulness of applying these graph theoretic approaches to discovering illegal activity for a simulated insider threat within a passport processing scenario.


title: A comparison of algorithms and humans for mitosis detection

abstract: We consider the problem of detecting mitotic figures in breast cancer histology slides. We investigate whether the performance of stateof-the-art detection algorithms is comparable to the performance of humans, when they are compared under fair conditions: our test subjects were not previously exposed to the task, and were required to learn their own classification criteria solely by studying the same training set available to algorithms. We designed and implemented a standardized web-based test based on the publicly-available MITOS dataset, and compared results with the performance of the 6 topscoring algorithms in the ICPR 2012 Mitosis Detection Contest. The problem is presented as a classification task on a balanced dataset. 45 different test subjects produced a total of 3009 classifications. The best individual (accuracy = 0.859 ± 0.012), is outperformed by the most accurate algorithm (accuracy = 0.873 ± 0.004). This suggests that state-of-the-art detection algorithms are likely limited by the size of the training set, rather than by lack of generalization ability.


title: The MinMax k-Means clustering algorithm

abstract: Applying k-Means to minimize the sum of the intra-cluster variances is the most popular clustering approach. However, after a bad initialization, poor local optima can be easily obtained. To tackle the initialization problem of k-Means, we propose the MinMax k-Means algorithm, a method that assigns weights to the clusters relative to their variance and optimizes a weighted version of the k-Means objective. Weights are learned together with the cluster assignments, through an iterative procedure. The proposed weighting scheme limits the emergence of large variance clusters and allows high quality solutions to be systematically uncovered, irrespective of the initialization. Experiments verify the effectiveness of our approach and its robustness over bad initializations, as it compares favorably to both k-Means and other methods from the literature that consider the k-Means initialization problem. & 2014 Elsevier Ltd. All rights reserved.


title: Muscle fascia and force transmission.

abstract: SUMMARY ::: This paper reviews the major intramuscular extracellular matrix (IM-ECM) structures (endomysium, perimysium and epimysium) and their possible mechanical contributions to muscle functions. The endomysium appears to provide an efficient mechanism for transmission of contractile forces from adjacent muscle fibres within fascicles. This coordinates forces and deformations within the fascicle, protects damaged areas of fibres against over-extension, and provides a mechanism whereby myofibrils can be interrupted to add new sarcomeres during muscle growth without loss of contractile functionality of the whole column. Good experimental evidence shows that perimysium and epimysium are capable in some circumstances to act as pathways for myofascial force transmission. However, an alternative role for perimysium is reviewed, which involves the definition of slip planes between muscle fascicles which can slide past each other to allow large shear displacements due to shape changes in the whole muscle during contraction. As IM-ECM is continually remodelled so as to be mechanically adapted for its roles in developing and growing muscles, control of the processes governing IM-ECM turnover and repair may be an important avenue to explore in the reduction of fibrosis following muscle injury.


title: AVSS: Airborne Video Surveillance System

abstract: Most surveillance systems only contain CCTVs. CCTVs, however, provide only limited maneuverability against dynamic targets and are inefficient for short term surveillance. Such limitations do not raise much concern in some cases, but for the scenario in which traditional surveillance systems do not suffice, adopting a fleet of UAVs can help overcoming the limitations. In this paper, we present a surveillance system implemented with a fleet of unmanned aerial vehicles (UAVs). A surveillance system implemented with a fleet of UAVs is easy to deploy and maintain. A UAV fleet requires little time to deploy and set up, and removing the surveillance is also virtually instant. The system we propose deploys UAVs to the target area for installation and perform surveillance operations. The camera mounted UAVs act as surveillance probes, the server provides overall control of the surveillance system, and the fleet platform provides fleet-wise control of the UAVs. In the proposed system, the UAVs establish a network and enable multi-hop communication, which allows the system to widen its coverage area. The operator of the system can control the fleet of UAVs via the fleet platform and receive surveillance information gathered by the UAVs. The proposed system is described in detail along with the algorithm for effective placement of the UAVs. The prototype of the system is presented, and the experiment carried out shows that the system can successfully perform surveillance over an area set by the system.


title: A study of finger lengths, semen quality and sex hormones in 360 young men from the general Danish population

abstract: BACKGROUND: It has been suggested that finger length may correlate with function or disorders of the male reproductive system. This is based on the HOXA and HOXD genes’ common embryological control of finger development and differentiation of the genital bud. Th e objective of this study was to expl ore the association between the ratio of 2nd to 4th finger length (2D:4D ratio) and testis function in a sample of yo ung Danish men from the general population. METHODS: Semen samples and finger measurements were obtained from a total of 360 young Danish men in addition to blood samples for sex hormone analysis to describe the possible association between 2D:4D and semen and sex-hormone parameters. RESULTS: A statistically significant inverse association with the 2D:4D was found only in relation to hormone levels of FSH in the group of young men with a 2D:4D >1 (P = 0.036) and a direct association with the total sperm count in the group of young men with a 2D:4D 1 (P = 0.045). CONCLUSION: The statistically significant results may be ‘false positive s’ (type I error) rather than represen ting true associations. This relatively large study of young, normal Danish men shows no reliable association between 2D:4D finger ratio and testicular function. Measurements of finger lengths do not have th e power to predict the testicular function of adult men.


title: A Novel Autologous Scaffold for Diced-cartilage Grafts in Dorsal Augmentation Rhinoplasty

abstract: Background ::: Diced-cartilage grafts have been used for dorsal nasal augmentation for several years with good results. However, compounds such as Surgicel and temporalis fascia used as a wrap have inherent problems associated with them, predominantly inflammation and graft resorption. An autologous carrier could provide stabilization of cartilage grafts while avoiding the complications seen with earlier techniques.


title: Standardization versus adaptation of international marketing strategy: an integrative assessment of the empirical research

abstract: Despite 40 years of debate on international marketing strategy standardization vs adaptation, extant empirical research is too fragmented to yield clear insights. Based on an integrative analysis of 36 studies centering around strategy standardization/adaptation, its antecedents, and performance outcomes, this stream of research was found to be characterized by non-significant, contradictory, and, to some extent, confusing findings attributable to inappropriate conceptualizations, inadequate research designs, and weak analytical techniques. The central conclusion that stems from this analysis is that the decision whether to standardize or adapt the marketing strategy to achieve superior business performance will largely depend on the set of circumstances that a firm is confronted by within a particular foreign market at a specific period of time.


title: An Accurate and Robust Artificial Marker Based on Cyclic Codes

abstract: Artificial markers are successfully adopted to solve several vision tasks, ranging from tracking to calibration. While most designs share the same working principles, many specialized approaches exist to address specific application domains. Some are specially crafted to boost pose recovery accuracy. Others are made robust to occlusion or easy to detect with minimal computational resources. The sheer amount of approaches available in recent literature is indeed a statement to the fact that no silver bullet exists. Furthermore, this is also a hint to the level of scholarly interest that still characterizes this research topic. With this paper we try to add a novel option to the offer, by introducing a general purpose fiducial marker which exhibits many useful properties while being easy to implement and fast to detect. The key ideas underlying our approach are three. The first one is to exploit the projective invariance of conics to jointly find the marker and set a reading frame for it. Moreover, the tag identity is assessed by a redundant cyclic coded sequence implemented using the same circular features used for detection. Finally, the specific design and feature organization of the marker are well suited for several practical tasks, ranging from camera calibration to information payload delivery.


title: Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet

abstract: Recent results suggest that state-of-the-art saliency models perform far from optimal in predicting fixations. This lack in performance has been attributed to an inability to model the influence of high-level image features such as objects. Recent seminal advances in applying deep neural networks to tasks like object recognition suggests that they are able to capture this kind of structure. However, the enormous amount of training data necessary to train these networks makes them difficult to apply directly to saliency prediction. We present a novel way of reusing existing neural networks that have been pretrained on the task of object recognition in models of fixation prediction. Using the well-known network of Krizhevsky et al. (2012), we come up with a new saliency model that significantly outperforms all state-of-the-art models on the MIT Saliency Benchmark. We show that the structure of this network allows new insights in the psychophysics of fixation selection and potentially their neural implementation. To train our network, we build on recent work on the modeling of saliency as point processes.


title: Minding the Money: A Growing Responsibility for Informal Carers

abstract: Managing the assets of older people is a common and potentially complex task of informal care with legal, financial, cultural, political and family dimensions. Older people are increasingly recognised -as having significant assets, but the family, the state, service providers and the market have competing interests in their use. Increased policy interest in self-provision and user-charges for services underline the importance of asset management in protecting the current and future health, care and accommodation choices of older people. Although 'minding the money' has generally been included as an informal care-giving task, there is limited recognition of either its growing importance and complexity or of care-givers' involvement. The focus of both policy and practice have been primarily on substitute decision-making and abuse. This paper reports an Australian national survey and semi-structured interviews that have explored the prevalence of non-professional involvement in asset management. The findings reveal the nature and extent of involvement, the tasks that informal carers take on, the management processes that they use, and that 'minding the money' is a common informal care task and mostly undertaken in the private sphere using some risky practices. Assisting informal care-givers with asset management and protecting older people from financial risks and abuse require various strategic policy and practice responses that extend beyond substitute decision-making legislation. Policies and programmes are required: to increase the awareness of the tasks, tensions and practices surrounding asset management; to improve the financial literacy of older people, their informal care-givers and service providers; to ensure access to information, advice and support services; and to develop better accountability practices.


title: High-Performance OCR for Printed English and Fraktur Using LSTM Networks

abstract: Long Short-Term Memory (LSTM) networks have yielded excellent results on handwriting recognition. This paper describes an application of bidirectional LSTM networks to the problem of machine-printed Latin and Fraktur recognition. Latin and Fraktur recognition differs significantly from handwriting recognition in both the statistical properties of the data, as well as in the required, much higher levels of accuracy. Applications of LSTM networks to handwriting recognition use two-dimensional recurrent networks, since the exact position and baseline of handwritten characters is variable. In contrast, for printed OCR, we used a one-dimensional recurrent network combined with a novel algorithm for baseline and x-height normalization. A number of databases were used for training and testing, including the UW3 database, artificially generated and degraded Fraktur text and scanned pages from a book digitization project. The LSTM architecture achieved 0.6% character-level test-set error on English text. When the artificially degraded Fraktur data set is divided into training and test sets, the system achieves an error rate of 1.64%. On specific books printed in Fraktur (not part of the training set), the system achieves error rates of 0.15% (Fontane) and 1.47% (Ersch-Gruber). These recognition accuracies were found without using any language modelling or any other post-processing techniques.


title: Towards autonomous vehicular clouds

abstract: The dawn of the 21st century has seen a growing interest in vehicular networking and its myriad potential applications. The initial view of practitioners and researchers was that radio-equipped vehicles could keep the drivers informed about potential safety risks and increase their awareness of road conditions. The view then expanded to include access to the Internet and associated services. This position paper proposes and promotes a novel and more comprehensive vision namely, that advances in vehicular networks, embedded devices, and cloud computing will enable the formation of autonomous clouds of vehicular computing, communication, sensing, power and physical resources. Hence, we coin the term, Autonomous Vehicular Clouds (AVCs). A key features distinguishing AVCs from conventional cloud computing is that mobile AVC resources can be pooled dynamically to serve authorized users and to enable autonomy in real-time service sharing and management on terrestrial, aerial, or aquatic pathways or theatres of operations. In addition to general-purpose AVCs, we also envision the emergence of specialized AVCs such as mobile analytics laboratories. Furthermore, we envision that the integration of AVCs with ubiquitous smart infrastructures including intelligent transportation systems, smart cities, and smart electric power grids, will have an enormous societal impact enabling ubiquitous utility cyber-physical services at the right place, right time, and with right-sized resources.


title: On the Origin of CRISPR-Cas Technology: From Prokaryotes to Mammals

abstract: Clustered regularly-interspaced short palindromic repeat (CRISPR) sequences cooperate with CRISPR-associated (Cas) proteins to form the basis of CRISPR-Cas adaptive immune systems in prokaryotes. For more than 20 years, these systems were of interest only to specialists, mainly molecular microbiologists, who tried to understand the properties of this unique defense mechanism. In 2012, the potential of CRISPR-Cas systems was uncovered and these were presented as genome-editing tools with an outstanding capacity to trigger targeted genetic modifications that can be applied to virtually any organism. Shortly thereafter, in early 2013, these tools were shown to efficiently drive specific modification of mammalian genomes. This review attempts to summarize, in a comprehensive manner, the key events and milestones that brought CRISPR-Cas technology from prokaryotes to mammals.


title: Cryptanalysis of SPA Protocol

abstract: In this paper, we analyze the security vulnerabilities of SPA, a Strong and lightweight RFID private authentication protocol proposed by Li Lu et al. We present an effective de-synchronization attack against the protocol, which permanently disables the authentication capability of a RFID tag by destroying synchronization between the tag and the reader. The de-synchronization attack can be carried out in just two rounds of interaction in SPA. Then we improved their scheme to protect against DoS attack.


title: Emotion recognition based on physiological signals using valence-arousal model

abstract: This paper considers two dimensional valence-arousal model. Pictorial stimuli of International Affective Picture Systems were chosen for emotion elicitation. Physiological signals like, Galvanic Skin Response, Heart Rate, Respiration Rate and Skin Temperature were measured for accessing emotional responses. The experimental procedure uses non-invasive sensors for signal collection. A group of healthy volunteers was shown four types of emotional stimuli categorized as High Valence High Arousal, High Valence Low Arousal, Low Valence High Arousal and Low Valence Low Arousal for around thirty minutes for emotion elicitation. Linear and Quadratic Discriminant Analysis are used and compared to the emotional class classification. Classification of stimuli into one of the four classes has been attempted on the basis of measurements on responses of experimental subjects. If classification is restricted within the responses of a specific individual, the classification results show high accuracy. However, if the problem is extended to entire population, the accuracy drops significantly.


title: Nanoscale FinFET Based SRAM Cell Design: Analysis of Performance Metric, Process Variation, Underlapped FinFET, and Temperature Effect

abstract: In this paper the analysis of SNM, RNM, WNM and static power variation with width of access, load and driver have been carried out for nanoscale FinFET based SRAM cell. FinFET based SRAM design has been proposed as an alternative solution to the bulk devices. It can be inferred from the results that with increase in the width of driver FinFET, the high SNM reduces and low SNM increases. This is due the fact that the leakage current is considerably reduced due to increased control of the FinFET device structure, resulting relatively in highIon/Ioffratio. Further, the effect of process variation on the SRAM cell performance was analyzed using Monte Carlo simulation on HSPICE. The Monte Carlo simulation results for RNM and WNM to quantify the effect of process variation arising due to variation in FinFET's widths. The simulation was carried out for 1000 values, assuming 3σ equal to 10% of the mean value. Two structures of the FinFET viz. the standard PTM model and an underlapped FinFET have been also used for the simulations. It was identified that while the relative levels of the noise margins were lower for the underlapped case, the standard deviation was considerably lower too. In this work we also analyze the effect of temperature on noise margins and static power for FinFET based SRAM cell. FinFET is suitable for future nanoscale memory circuits design due to its reduced Short Channel Effects (SCE) and leakage current.


title: Moving rigid objects segmentation in 3D dynamic traffic scenes using a stereovision system

abstract: This paper proposes a novel method for detecting the moving vehicles in dynamic urban traffic scenes using a stereo camera. Relying on the fact that a set of feature points on a rigid 3D scene object are staying in a rigid 3D configuration, we propose to compute the relative motion between the camera and a moving object with an algorithm that follows from the visual odometry based motion estimation methods. Subtracting the camera motion we obtain the absolute object motion. Additionally we create a compact representation of the scene using superpixels computed from intensity and depth information. A graph-like structure is built, having superpixels as nodes and indicating neighboring relationships between adjacent superpixels. Objects are segmented using a fast region growing algorithm that considers as seeds the features used to compute the object motion.


title: Grandiose and Vulnerable Narcissism and the DSM–5 Pathological Personality Trait Model

abstract: The Diagnostic and Statistical Manual of Personality Disorders (4th ed., American Psychiatric Association, 2000) personality disorders (PDs) that will be included in the DSM–5 will be diagnosed in an entirely different manner; the explicit criterion sets will be replaced with impairments in self and interpersonal functioning and personality traits from a 25-trait dimensional model of personality pathology. From a trait perspective, narcissistic personality disorder (NPD), the focus of this study, is assessed using 2 specific traits: grandiosity and attention seeking. Using a sample collected online from Amazon's Mechanical Turk (MTurk; N = 306), we examined the relations among traits from a new measure of DSM–5's trait model—the Personality Inventory for DSM–5 (PID5; Krueger, Derringer, Markon, Watson, & Skodol, in press)—and grandiose and vulnerable narcissism. The 25 traits from PID5 captured a significant portion of the variance in grandiose and vulnerable factors, although the 2 specific facets design...


title: WhatsApp, Doc? A First Look at WhatsApp Public Group Data

abstract: In this dataset paper we describe our work on the collection and analysis of public WhatsApp group data. Our primary goal is to explore the feasibility of collecting and using WhatsApp data for social science research. We therefore present a generalisable data collection methodology, and a publicly available dataset for use by other researchers. To provide context, we perform statistical exploration to allow researchers to understand what public WhatsApp group data can be collected and how this data can be used. Given the widespread use of WhatsApp, our techniques to obtain public data and potential applications are important for the community.


title: Paracingulate sulcus morphology and hallucinations in clinical and non-clinical groups

abstract: Hallucinations are a characteristic symptom of psychotic mental health conditions that are also experienced by many individuals without a clinical diagnosis. Research has linked the experience of hallucinations in schizophrenia to differences in the length of the paracingulate sulcus (PCS), a structure in the medial prefrontal cortex of the brain which has previously been associated with the ability to differentiate perceived and imagined information. We investigated whether this notion of a specific morphological basis for hallucinations in the paracingulate cortex extends to individuals without a clinical diagnosis by testing the hypothesis that non-clinical individuals with hallucinations have shorter PCS than non-clinical individuals without hallucinations. Structural MRI scans were examined from three demographically matched groups of individuals: 50 patients with psychotic diagnoses who experienced auditory verbal hallucinations, 50 non-clinical individuals with auditory verbal hallucinations, and 50 healthy control subjects with no life-time history of hallucinations. Measurements of paracingulate sulcal length were compared between the groups and the results verified using automated data-driven gyrification analyses. Patients with hallucinations had shorter PCS than both healthy controls and non-clinical individuals with hallucinations, with no difference between non-clinical individuals with hallucinations and healthy controls. These findings suggest that the association of shorter PCS length with hallucinations is specific to patients with a psychotic disorder. This presents challenges for continuum models of psychosis and suggests possible differences in the mechanisms underlying hallucinations in clinical and non-clinical groups.


title: Dynamic Impact of Online Word-of-Mouth and Advertising on Supply Chain Performance

abstract: Cooperative (co-op) advertising investments benefit brand goodwill and further improve supply chain performance. Meanwhile, online word-of-mouth (OWOM) can also play an important role in supply chain performance. On the basis of co-op advertising, this paper considers a single supply chain structure led by a manufacturer and examines a fundamental issue concerning the impact of OWOM on supply chain performance. Firstly, by the method of differential game, this paper analyzes the dynamic impact of OWOM and advertising on supply chain performance (i.e., brand goodwill, sales, and profits) under three different supply chain decisions (i.e., only advertising, and manufacturers with and without sharing cost of OWOM with retailers). We compare and analyze the optimal strategies of advertising and OWOM under the above different supply chain decisions. Secondly, the system dynamics model is established to reflect the dynamic impact of OWOM and advertising on supply chain performance. Finally, three supply chain decisions under two scenarios, strong brand and weak brand, are analyzed through the system dynamics simulation. The results show that the input of OWOM can enhance brand goodwill and improve earnings. It further promotes the OWOM reputation and improves the supply chain performance if manufacturers share the cost of OWOM with retailers. Then, in order to eliminate the retailers from word-of-mouth fraud and establish a fair competition mechanism, the third parties (i.e., regulators or e-commerce platforms) should take appropriate punitive measures against retailers. Furthermore, the effect of OWOM on supply chain performance under a strong brand differed from those under a weak brand. Last but not least, if OWOM is improved, there would be more remarkable performance for the weak brand than that for the strong brand in the supply chain.


title: A W-Band Low-Loss and Wideband LTCC Transition From Waveguide to Microstrip

abstract: A broadband transition structure from rectangular waveguide (RWG) to microstrip line (MSL) is presented for the realization of the low-loss packaging module using Low-temperature co-fired ceramic (LTCC) technology at W-band. In this transition, a cavity structure is buried in LTCC layers, which provides the wide bandwidth, and a laminated waveguide (LWG) transition is designed, which provides the low-loss performance, as it reduces the radiation loss of conventional direct transition between RWG and MSL. The design procedure is also given. The measured results show that the insertion loss of better than 0.7 dB from 86 to 97 GHz can be achieved.


title: A New Quadratic Classifier Applied to Biometric Recognition

abstract: In biometric recognition applications, the number of training examples per class is limited and consequently the conventional quadratic classifier either performs poorly or cannot be calculated. Other non-conventional quadratic classifiers have been used in limited sample and high dimensional classification problems. In this paper, a new quadratic classifier called Maximum Entropy Covariance Selection (MECS) is presented. This classifier combines the sample group covariance matrices and the pooled covariance matrix under the principle of maximum entropy. This approach is a direct method that not only deals with the singularity and instability of the maximum likelihood covariance estimator, but also does not require an optimisation procedure. In order to evaluate the MECS effectiveness, experiments on face and fingerprint recognition were carried out and compared with other similar classifiers, including the Reguralized Discriminant Analysis (RDA), the Leave-One-Out Covariance estimator (LOOC) and the Simplified Quadratic Discriminant Function (SQDF). In both applications, using the publicly released databases FERET and NIST-4, the MECS classifier achieved the lowest classification error.


title: NS3 Based HDFS Data Placement Algorithm Evaluation Framework

abstract: Big data analytics based data exploration and utilization holds immense prospects for the future of businesses. However, as the name suggests, processing such a huge amount of data is challenging. Hadoop with its parallel processing solutions, assists in processing big data in reasonable time. The heart of Hadoop is its distributed File System; and indeed how data is placed in the file system dictates the speed of the data processing. Hence, over the years efficient data placement algorithms has been one of the key research area in big data analytics. Evaluation of such algorithms traditionally requires deploying HDFS on hardware clusters and implementing the data placement algorithm on it. It is often difficult for researchers to acquire required hardware and build a hardware clusters. Even when such clusters are available, scalability becomes an issue. Moreover, real life data center like cluster is not available to many researchers. Simulation provides low cost alternative to evaluation of big data placement algorithms on HDFS. One of the key metrices that is optimized in data placement algorithms is to minimize communication costs and latency. Thus a network simulation based simulation framework would fit the role perfectly. NS3 is one of the most prominent network simulation tool available for researchers. However, full HDFS support for data placement research is still not implemented. This work proposes to extend the NS3 simulation environment for HDFS support and eventual use for data placement algorithm evaluation.


title: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Using Double-Oracle Method and Serialized Alpha-Beta Search for Pruning in Simultaneous Move Games

abstract: We focus on solving two-player zero-sum extensive-form games with perfect information and simultaneous moves. In these games, both players fully observe the current state of the game where they simultaneously make a move determining the next state of the game. We solve these games by a novel algorithm that relies on two components: (1) it iteratively solves the games that correspond to a single simultaneous move using a double-oracle method, and (2) it prunes the states of the game using bounds on the sub-game values obtained by the classical Alpha-Beta search on a serialized variant of the game. We experimentally evaluate our algorithm on the Goofspiel card game, a pursuit-evasion game, and randomly generated games. The results show that our novel algorithm typically provides significant running-time improvements and reduction in the number of evaluated nodes compared to the full search algorithm.


title: Mobility Models for UAV Group Reconnaissance Applications

abstract: In MANET research the mobility of the nodes is often described using standard synthetic models. Given a particular application, e.g. networks of unmanned aerial vehicles (UAVs) performing a cooperative task, the use of a standard synthetic mobility model can result in incorrect conclusions, as the movement pattern can impact the networking performance of the system. In this paper we present the criteria that characterize desirable mobility properties for the movement of UAVs in a reconnaissance scenario, and provide two mobility models for the scenario. In the first mobility model the UAVs move independently and randomly, and in the second model pheromones guide their movement. The random model is very simple but it achieves mediocre results. The pheromone model has very good scanning properties, but it has problems with respect to network connectivity. The study shows that there in an inherent conflict between the two goals of maximum coverage by frequent rescanning and adequate communication connectivity.


The Challenges of Multi-dimensional Sentiment Analysis Across Languages


title: Exposure to childhood sexual and physical abuse and adjustment in early adulthood

abstract: Abstract Objective This research examined linkages between exposure to childhood sexual abuse (CSA) and childhood physical punishment/abuse (CPA) and mental health issues in early adulthood. Method The investigation analyzed data from a birth cohort of over 1,000 New Zealand young adults studied to the age of 25. Results Exposure to CSA and CPA was associated with increased risks of later mental disorders including depression, anxiety disorder, conduct/anti-social personality disorder, substance dependence, suicidal ideation, and suicide attempts at ages 16–25. Control for social, family, and individual factors reduced the associations between CPA and mental health outcomes to the point of statistical non-significance. However, there was a consistent finding for CSA to remain associated with increased risks of later mental health problems. After adjustment, those exposed to CSA including attempted or completed sexual penetration had rates of disorder that were 2.4 times higher than those not exposed to CSA. Those exposed to harsh or abusive physical punishment had rates of disorder that were 1.5 times higher than those exposed to no or occasional physical punishment. It was estimated that exposure to CSA accounted for approximately 13% of the mental health problems experienced by the cohort. Findings showed that exposure to CPA had only weak effects on later mental health. It was estimated that exposure to CPA accounted for approximately 5% of the mental health problems experienced by the cohort. Conclusions Exposure to CSA was associated with consistent increases in risks of later mental health problems. Exposure to CPA had weaker and less consistent effects on later mental health. These findings suggest that much of the association between CPA and later mental health reflects the general family context in which CPA occurs, whereas this is less the case for CSA.


title: Coverage path planning for mobile robots using genetic algorithm with energy optimization

abstract: Coverage path planning (CPP) is one of the current researches for mobile robots. This study presents a new approach for solving CPP. In this approach the coverage area is divided into small squares where the squares diagonal is the size of the robot tool. Four fundamental motions in a square are defined, which are straight, left turn, right turn and U-turn. A cost function is taken in which a fixed cost for each fundamental motion in a square is used and costs for all moves are summed up to get an approximated cost for a path. This function makes it possible to find the better path between two paths with equal repetitive visits. Furthermore genetic algorithm (GA) is used to find the best path to cover an area. Via crossover, selection and mutation GA improves current paths which leads to optimal, near optimal solutions. Simulation results are taken with reasonable areas.


Multi-Path Transport for RDMA in Datacenters.


title: A Comparative Study That Measures Ball Mill Load Parameters Through Different Single-Scale and Multiscale Frequency Spectra-Based Approaches

abstract: Data-driven modeling based on the shell vibration and acoustic signals of ball mills is normally applied to overcome the subjective errors of human inference. Many previously proposed selective ensemble (SEN) modeling approaches are based on “the manipulation of input features” from the multiinformation fusion perspective, which cannot selectively and jointly fuse the information hidden in multiscale spectral features and under several operating conditions (training samples). Therefore, this study suggests a new soft measuring procedure based on ensemble empirical mode decomposition (EEMD) and SEN. An improved kernel partial least-squares algorithm for SEN that is based on “subsample training samples” is utilized to construct a soft measuring model with the selected features and training samples. This study compares such data-driven soft measuring methods. The comparative results of bootstrap-based prediction performance estimation show that different methods have specific advantages in terms of simplicity, prediction accuracy, and interpretability. The industrial application of the EEMD-SEN method is discussed in this paper, and a new virtual sample generation method is proposed to address the modeling problem based on small sample spectral data.


title: Twitter volume spikes: analysis and application in stock trading

abstract: Stock is a popular topic in Twitter. The number of tweets concerning a stock varies over days, and sometimes exhibits a significant spike. In this paper, we investigate Twitter volume spikes related to S&P 500 stocks, and whether they are useful for stock trading. Through correlation analysis, we provide insight on when Twitter volume spikes occur and possible causes of these spikes. We further explore whether these spikes are surprises to market participants by comparing the implied volatility of a stock before and after a Twitter volume spike. Moreover, we develop a Bayesian classifier that uses Twitter volume spikes to assist stock trading, and show that it can provide substantial profit. We further develop an enhanced strategy that combines the Bayesian classifier and a stock bottom picking method, and demonstrate that it can achieve significant gain in a short amount of time. Simulation over a half year's stock market data indicates that it achieves on average 8.6% gain in 27 trading days and 15.0% gain in 55 trading days. Statistical tests show that the gain is statistically significant, and the enhanced strategy significantly outperforms the strategy that only uses the Bayesian classifier as well as a bottom picking method that uses trading volume spikes.


title: A hybrid SoC interconnect with dynamic TDMA-based transaction-less buses and on-chip networks

abstract: The two dominant architectural choices for implementing efficient communication fabrics for SoC's have been transaction-based buses and packet-based networks-on-chip (NoC). Both implementations have some inherent disadvantages - the former resulting from poor scalability and the transactional character of their operation, and the latter from inconsistent access times and deterioration of performance at high injection rates. In this paper, we propose a transaction-less, time-division-based bus architecture, which dynamically allocates timeslots on-the-fly - the dTDMA bus. This architecture addresses the contention issues of current bus architectures, while avoiding the multi-hop overhead of NoC's. It is compared to traditional bus architectures and NoC's and shown to outperform both for configurations with fewer than 10 PE's. In order to exploit the advantages of the dTDMA bus for smaller configurations, and the scalability of NoC's, we propose a new hybrid SoC interconnect combining the two, showing significant improvement in both latency and power consumption.


IEEE 802 . 11 ad : Directional 60 GHz Communication for Multi-Gbps


title: Defining Privacy for Data Mining

abstract: Privacy preserving data mining – getting valid data mining results without learning the underlying data values – has been receiving attention in the research community and beyond. It is unclear what privacy preserving means. This paper provides a framework and metrics for discussing the meaning of privacy preserving data mining, as a foundation for further research in this field.


title: A Framework for Sequential Planning in Multi-Agent Settings

abstract: This paper extends the framework of partially observable Markov decision processes (POMDPs) to multi-agent settings by incorporating the notion of agent models into the state space. Agents maintain beliefs over physical states of the environment and over models of other agents, and they use Bayesian updates to maintain their beliefs over time. The solutions map belief states to actions. Models of other agents may include their belief states and are related to agent types considered in games of incomplete information. We express the agents autonomy by postulating that their models are not directly manipulable or observable by other agents. We show that important properties of POMDPs, such as convergence of value iteration, the rate of convergence, and piece-wise linearity and convexity of the value functions carry over to our framework. Our approach complements a more traditional approach to interactive settings which uses Nash equilibria as a solution paradigm. We seek to avoid some of the drawbacks of equilibria which may be non-unique and do not capture off-equilibrium behaviors. We do so at the cost of having to represent, process and continuously revise models of other agents. Since the agents beliefs may be arbitrarily nested, the optimal solutions to decision making problems are only asymptotically computable. However, approximate belief updates and approximately optimal plans are computable. We illustrate our framework using a simple application domain, and we show examples of belief updates and value functions.


title: A 1-µW 10-bit 200-kS/s SAR ADC With a Bypass Window for Biomedical Applications

abstract: This paper presents an energy efficient successive-approximation-register (SAR) analog-to-digital converter (ADC) for biomedical applications. To reduce energy consumption, a bypass window technique is used to select switching sequences to skip several conversion steps when the signal is within a predefined small window. The power consumptions of the capacitive digital-to-analog converter (DAC), latch comparator, and digital control circuit of the proposed ADC are lower than those of a conventional SAR ADC. The proposed bypass window tolerates the DAC settling error and comparator voltage offset in the first four phases and suppresses the peak DNL and INL values. A proof-of-concept prototype was fabricated in 0.18-μm 1P6M CMOS technology. At a 0.6-V supply voltage and a 200-kS/s sampling rate, the ADC achieves a signal-to-noise and distortion ratio of 57.97 dB and consumes 1.04 μW, resulting in a figure of merit of 8.03 fJ/conversion-step. The ADC core occupies an active area of only 0.082 mm2.


title: FloWaveNet : A Generative Flow for Raw Audio

abstract: Most of modern text-to-speech architectures use a WaveNet vocoder for synthesizing a high-fidelity waveform audio, but there has been a limitation for practical applications due to its slow autoregressive sampling scheme. A recently suggested Parallel WaveNet has achieved a real-time audio synthesis by incorporating Inverse Autogressive Flow (IAF) for parallel sampling. However, the Parallel WaveNet requires a two-stage training pipeline with a well-trained teacher network and is prone to mode collapsing if using a probability distillation training only. We propose FloWaveNet, a flow-based generative model for raw audio synthesis. FloWaveNet requires only a single maximum likelihood loss without any additional auxiliary terms and is inherently parallel due to the flow-based transformation. The model can efficiently sample the raw audio in real-time with a clarity comparable to the original WaveNet and ClariNet. Codes and samples for all models including our FloWaveNet is available via GitHub: this https URL


title: Multimodal One-shot Learning of Speech and Images

abstract: Imagine a robot is shown new concepts visually together with spoken tags, e.g. "milk", "eggs", "butter". After seeing one paired audiovisual example per class, it is shown a new set of unseen instances of these objects, and asked to pick the "milk". Without receiving any hard labels, could it learn to match the new continuous speech input to the correct visual instance? Although unimodal one-shot learning has been studied, where one labelled example in a single modality is given per class, this example motivates multimodal one-shot learning. Our main contribution is to formally define this task, and to propose several baseline and advanced models. We use a dataset of paired spoken and visual digits to specifically investigate recent advances in Siamese convolutional neural networks. Our best Siamese model achieves twice the accuracy of a nearest neighbour model using pixel-distance over images and dynamic time warping over speech in 11-way cross-modal matching.