title: Hierarchical Gaussian Descriptor for Person Re-identification

abstract: Describing the color and textural information of a person image is one of the most crucial aspects of person re-identification. In this paper, we present a novel descriptor based on a hierarchical distribution of pixel features. A hierarchical covariance descriptor has been successfully applied for image classification. However, the mean information of pixel features, which is absent in covariance, tends to be major discriminative information of person images. To solve this problem, we describe a local region in an image via hierarchical Gaussian distribution in which both means and covariances are included in their parameters. More specifically, we model the region as a set of multiple Gaussian distributions in which each Gaussian represents the appearance of a local patch. The characteristics of the set of Gaussians are again described by another Gaussian distribution. In both steps, unlike the hierarchical covariance descriptor, the proposed descriptor can model both the mean and the covariance information of pixel features properly. The results of experiments conducted on five databases indicate that the proposed descriptor exhibits re-markably high performance which outperforms the state-of-the-art descriptors for person re-identification.


title: An X-band 250W solid-state power amplifier using GaN power HEMTs

abstract: More than 250 W of peak output power (pulse width 64 mus, duty cycle 7%) is achieved with newly developed X-band solid-state power amplifier. The final stage of the SSPA consists of four 80 W class GaN power HEMTs and a low loss 4-way combiner. Compared with the conventional klystron tubes, our newly developed SSPAs are smaller, more reliable and require less amounts of occupied frequency bandwidth.


title: Robust hair capture using simulated examples

abstract: We introduce a data-driven hair capture framework based on example strands generated through hair simulation. Our method can robustly reconstruct faithful 3D hair models from unprocessed input point clouds with large amounts of outliers. Current state-of-the-art techniques use geometrically-inspired heuristics to derive global hair strand structures, which can yield implausible hair strands for hairstyles involving large occlusions, multiple layers, or wisps of varying lengths. We address this problem using a voting-based fitting algorithm to discover structurally plausible configurations among the locally grown hair segments from a database of simulated examples. To generate these examples, we exhaustively sample the simulation configurations within the feasible parameter space constrained by the current input hairstyle. The number of necessary simulations can be further reduced by leveraging symmetry and constrained initial conditions. The final hairstyle can then be structurally represented by a limited number of examples. To handle constrained hairstyles such as a ponytail of which realistic simulations are more difficult, we allow the user to sketch a few strokes to generate strand examples through an intuitive interface. Our approach focuses on robustness and generality. Since our method is structurally plausible by construction, we ensure an improved control during hair digitization and avoid implausible hair synthesis for a wide range of hairstyles.


title: Dynamic vs. static-stretching warm up: the effect on power and agility performance.

abstract: McMillian, D.J., J.H. Moore, B.S. Hatler, and D.C. Taylor. Dynamic vs. static-stretching warm up: The effect on power and agility performance. J. Strength Cond. Res. 20(3):492- 499. 2006.—The purpose of this study was to compare the effect of a dynamic warm up (DWU) with a static-stretching warm up (SWU) on selected measures of power and agility. Thirty cadets at the United States Military Academy completed the study (14 women and 16 men, ages 18-24 years). On 3 consecutive days, subjects performed 1 of the 2 warm up routines (DWU or SWU) or performed no warm up (NWU). The 3 warm up protocols last- ed 10 minutes each and were counterbalanced to avoid carryover effects. After 1-2 minutes of recovery, subjects performed 3 tests of power or agility. The order of the performance tests (T-shuttle run, underhand medicine ball throw for distance, and 5-step jump) also was counterbalanced. Repeated measures analysis of variance revealed better performance scores after the DWU for all 3 performance tests (p 0.01), relative to the SWU and NWU. There were no significant differences between the SWU and NWU for the medicine ball throw and the T-shuttle run, but the SWU was associated with better scores on the 5-step jump (p 0.01). Because the results of this study indicate a relative performance enhancement with the DWU, the utility of warm up routines that use static stretching as a stand-alone activity should be reassessed.


title: Positive-Unlabeled Learning with Non-Negative Risk Estimator

abstract: From only positive (P) and unlabeled (U) data, a binary classifier could be trained with PU learning, in which the state of the art is unbiased PU learning. However, if its model is very flexible, empirical risks on training data will go negative, and we will suffer from serious overfitting. In this paper, we propose a non-negative risk estimator for PU learning: when getting minimized, it is more robust against overfitting, and thus we are able to use very flexible models (such as deep neural networks) given limited P data. Moreover, we analyze the bias, consistency, and mean-squared-error reduction of the proposed risk estimator, and bound the estimation error of the resulting empirical risk minimizer. Experiments demonstrate that our risk estimator fixes the overfitting problem of its unbiased counterparts.


title: CircleView: a new approach for visualizing time-related multidimensional data sets

abstract: This paper introduces a new approach for visualizing multidimensional time-referenced data sets, called Circle View. The Circle View technique is a combination of hierarchical visualization techniques, such as treemaps [6], and circular layout techniques such as Pie Charts and Circle Segments [2]. The main goal is to compare continuous data changing their characteristics over time in order to identify patterns, exceptions and similarities in the data.To achieve this goal Circle View is a intuitive and easy to understand visualization interface to enable the user very fast to acquire the information needed. This is an important feature for fast changing visualization caused by time related data streams. Circle View supports the visualization of the changing characteristics over time, to allow the user the observation of changes in the data. Additionally it provides user interaction and drill down mechanism depending on user demands for a effective exploratory data analysis. There is also the capability of exploring correlations and exceptions in the data by using similarity and ordering algorithms.


title: LEARNING TO LAUGH (AUTOMATICALLY): COMPUTATIONAL MODELS FOR HUMOR RECOGNITION

abstract: Humor is one of the most interesting and puzzling aspects of human behavior. Despite the attention it has received in fields such as philosophy, linguistics, and psychology, there have been only few attempts to create computational models for humor recognition or generation. In this article, we bring empirical evidence that computational approaches can be successfully applied to the task of humor recognition. Through experiments performed on very large data sets, we show that automatic classification techniques can be effectively used to distinguish between humorous and non-humorous texts, with significant improvements observed over a priori known baselines.


title: InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services

abstract: Cloud computing providers have setup several data centers at different geographical locations over the Internet in order to optimally serve needs of their customers around the world However, existing systems do not support mechanisms and policies for dynamically coordinating load distribution among different Cloud-based data centers in order to determine optimal location for hosting application services to achieve reasonable QoS levels Further, the Cloud computing providers are unable to predict geographic distribution of users consuming their services, hence the load coordination must happen automatically, and distribution of services must change in response to changes in the load To counter this problem, we advocate creation of federated Cloud computing environment (InterCloud) that facilitates just-in-time, opportunistic, and scalable provisioning of application services, consistently achieving QoS targets under variable workload, resource and network conditions The overall goal is to create a computing environment that supports dynamic expansion or contraction of capabilities (VMs, services, storage, and database) for handling sudden variations in service demands. ::: ::: This paper presents vision, challenges, and architectural elements of InterCloud for utility-oriented federation of Cloud computing environments The proposed InterCloud environment supports scaling of applications across multiple vendor clouds We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit The results demonstrate that federated Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.


title: Interactive Visualization Tools for Exploring the Semantic Graph of Large Knowledge Spaces

abstract: While the amount of available information on the Web is increasing rapidly, the problem of managing it becomes more difficult. We present two applications, Thinkbase and Thinkpedia, which aim to make Web content more accessible and usable by utilizing visualizations of the semantic graph as a means to navigate and explore large knowledge repositories. Both of our applications implement a similar concept: They extract semantically enriched contents from a large knowledge spaces (Freebase and Wikipedia respectively), create an interactive graph-based representation out of it, and combine them into one interface together with the original text based content. We describe the design and implementation of our applications, and provide a discussion based on an informal evaluation.


Improving Covariate Balancing Propensity Score : A Doubly Robust and Efficient Approach ∗


title: Statistical Pattern Recognition and Built-in Reliability Test for Feature Extraction and Health Monitoring of Electronics under Shock Loads

abstract: The built-in stress test (BIST) is extensively used for diagnostics or identification of failure. The current version of BIST approach is focused on reactive failure detection and provides limited insight into reliability and residual life. A new approach has been developed to monitor product-level damage during shock and vibration. The approach focuses on the pre- failure space and methodologies for quantification of failure in electronic equipment subject to shock and vibration loads using the dynamic response of the electronic equipment. The presented methodologies are applicable at the system level for the identification of impending failures to trigger repair or replacement significantly prior to failure. Leading indicators of shock-damage have been developed to correlate with the damage initiation and progression in shock and drop of electronic assemblies. Three methodologies have been investigated for feature extraction and health monitoring including development of a new solder- interconnect built-in reliability test, FFT-based statistical-pattern recognition, and time-frequency moments based statistical pattern recognition. The solder-joint built-in reliability test has been developed for detecting high resistance and intermittent faults in operational, fully programmed field programmable gate arrays. Frequency band energy is computed using FFT and utilized as the classification feature to check for damage and failure in the assembly. In addition, the time-frequency analysis has been used to study the energy densities of the signal in both time and frequency domains, and provide information about the time evolution of frequency content of transient- strain signal. Closed-form models and explicit finite-element models have been developed for the eigen frequencies, mode shapes, and transient response of electronic assemblies with various boundary conditions and component placement configurations. Model predictions have been validated with experimental data from modal analysis. Pristine configurations have been perturbed to quantify the degradation in confidence values with progression of damage. Sensitivity of leading indicators of shock damage to subtle changes in boundary conditions,effective flexural rigidity, and transient strain response has been quantified.


title: Datalog+/-: A Family of Logical Knowledge Representation and Query Languages for New Applications

abstract: This paper summarizes results on a recently introduced family of Datalog-based languages, called Datalog+/-, which is a new framework for tractable ontology querying, and for a variety of other applications. Datalog+/- extends plain Datalog by features such as existentially quantified rule heads and, at the same time, restricts the rule syntax so as to achieve decidability and tractability. In particular, we discuss three paradigms ensuring decidability: chase termination, guardedness, and stickiness.


title: Anomaly Detection in the Bitcoin System - A Network Perspective

abstract: The problem of anomaly detection has been studied for a long time, and many Network Analysis techniques have been proposed as solutions. Although some results appear to be quite promising, no method is clearly to be superior to the rest. In this paper, we particularly consider anomaly detection in the Bitcoin transaction network. Our goal is to detect which users and transactions are the most suspicious; in this case, anomalous behavior is a proxy for suspicious behavior. To this end, we use the laws of power degree and densification and local outlier factor (LOF) method (which is proceeded by k-means clustering method) on two graphs generated by the Bitcoin transaction network: one graph has users as nodes, and the other has transactions as nodes. ::: We remark that the methods used here can be applied to any type of setting with an inherent graph structure, including, but not limited to, computer networks, telecommunications networks, auction networks, security networks, social networks, Web networks, or any financial networks. We use the Bitcoin transaction network in this paper due to the availability, size, and attractiveness of the data set.


title: Addressing cold-start in app recommendation: latent user models constructed from twitter followers

abstract: As a tremendous number of mobile applications (apps) are readily available, users have difficulty in identifying apps that are relevant to their interests. Recommender systems that depend on previous user ratings (i.e., collaborative filtering, or CF) can address this problem for apps that have sufficient ratings from past users. But for apps that are newly released, CF does not have any user ratings to base recommendations on, which leads to the cold-start problem. In this paper, we describe a method that accounts for nascent information culled from Twitter to provide relevant recommendation in such cold-start situations. We use Twitter handles to access an app's Twitter account and extract the IDs of their Twitter-followers. We create pseudo-documents that contain the IDs of Twitter users interested in an app and then apply latent Dirichlet allocation to generate latent groups. At test time, a target user seeking recommendations is mapped to these latent groups. By using the transitive relationship of latent groups to apps, we estimate the probability of the user liking the app. We show that by incorporating information from Twitter, our approach overcomes the difficulty of cold-start app recommendation and significantly outperforms other state-of-the-art recommendation techniques by up to 33%.


title: #Smiling, #venting, or both? Adolescents' social sharing of emotions on social media

abstract: Abstract Although social media offer adolescents new possibilities for emotion regulation, little is known about how adolescents use different platforms to this end. This study adds to the emotion regulation literature and affordances-of-technologies perspective by describing whether and how adolescents use different social media platforms (Facebook, Instagram, Snapchat, Twitter, Tumblr, YouTube, 9Gag, and blogs) for emotion sharing and how technical, social, and contextual factors influence these practices. In-depth interviews with 22 adolescents aged 14 to 18 show that adolescents share emotions on multiple of these platforms. Although the different platforms have similar affordances, their social norms clearly differ and influence adolescents’ online behavior. Facebook statuses, Instagram, and Snapchat are mostly used for sharing positive emotions, if emotions are shared at all. Twitter and Messenger, on the other hand, are also used for sharing negative emotions, albeit for different reasons.


title: Design of 45 degree microstrip phase shifter for beam forming network application using parallel coupled lines

abstract: A 45 degree differential microstrip phase shifter operating in the frequency band ranging from 1.4 GHz to 2.7 GHz is presented in this paper. It is designed by adopting the model of double parallel Schiffman phase shifter and simulated on an FR4 substrate by using two electromagnetic simulation software, ADS from Agilent technologies and CST Microwave Studio. The proposed phase shift circuit offers good results in terms of return loss, insertion loss and phase shift. Good agreement between simulated results using ADS and CST Microwave Studio is obtained.


title: Memory Encryption for General-Purpose Processors

abstract: Intel's Software Guard Extensions allows general-purpose computing platforms to run software in a trustworthy manner and securely handle encrypted data. To satisfy the technology's security goals, the external system memory must be cryptographically protected. A new hardware unit added to the processor's memory controller--the Memory Encryption Engine (MEE)--was recently developed to protect the confidentiality, integrity, and freshness of this external memory traffic, against eavesdropping and tampering. The MEE is a successful feat of real-world cryptographic engineering: it's the first time such cryptographic memory protection has been added to a widely deployed general-purpose processor.


title: Secret and public key image watermarking schemes for image authentication and ownership verification

abstract: We describe a watermarking scheme for ownership verification and authentication. Depending on the desire of the user, the watermark can be either visible or invisible. The scheme can detect any modification made to the image and indicate the specific locations that have been modified. If the correct key is specified in the watermark extraction procedure, then an output image is returned showing a proper watermark, indicating the image is authentic and has not been changed since the insertion of the watermark. Any modification would be reflected in a corresponding error in the watermark. If the key is incorrect, or if the image was not watermarked, or if the watermarked image is cropped, the watermark extraction algorithm will return an image that resembles random noise. Since it requires a user key during both the insertion and the extraction procedures, it is not possible for an unauthorized user to insert a new watermark or alter the existing watermark so that the resulting image will pass the test. We present secret key and public key versions of the technique.


title: High dimensional model representations

abstract: In the chemical sciences, many laboratory experiments, environmental and industrial processes, as well as modeling exercises, are characterized by large numbers of input variables. A general objective in such cases is an exploration of the high-dimensional input variable space as thoroughly as possible for its impact on observable system behavior, often with either optimization in mind or simply for achieving a better understanding of the phenomena involved. An important concern when undertaking these explorations is the number of experiments or modeling excursions necessary to effectively learn the system input → output behavior, which is typically a nonlinear relationship. Although simple logic suggests that the number of runs could grow exponentially with the number of input variables, broadscale evidence indicates that the required effort often scales far more comfortably. This paper considers an emerging family of high dimensional model representation concepts and techniques capable of dealing with s...


title: A three-stage approach for aircraft line maintenance personnel rostering using MIP, discrete event simulation and DEA

abstract: Personnel scheduling problems need to cope with personnel preferences, coverage constraints, legal restrictions, and many other constraints. We present a three-stage methodology that can be used to select personnel rosters. In the first stage we generate multiple personnel rosters with a mathematical programming model. In the second stage, the performance of the rosters regarding a number of service criteria is evaluated through discrete event simulation. In the third stage, a ranking is made using data envelopment analysis. The methodology is tested on a personnel scheduling problem for aircraft line maintenance.


title: BODIES IN CONTEXT: POWER POSES AS A COMPUTATION OF ACTION POSSIBILITY

abstract: One perspective on embodiment proposes that bodily states exert direct, context-free effects on psychological states, as in the research on “power poses.” We propose instead that bodily states influence psychology by providing information about what actions are possible. If such an assessment is to be effective, however, it must consider the body as it exists in context, as context provides essential information in defining action possibility. In Study 1, expansive and constrictive poses influenced power only when held in an interpersonal context, which provides action-relevant meaning to these poses as dominance and submissiveness. In Study 2, poses had no effect on power when roles provided more important information about potential action (e.g., being frisked by police while holding an expansive pose resulted in less powerful behavior). If the function of cognition is to prepare the body for effective action, then cognitive processes cannot be insensitive to the current context.


title: Analysis and taxonomy of column header categories for web tables

abstract: We describe a component of a document analysis system for constructing ontologies for domain-specific web tables imported into Excel. This component automates extraction of the Wang Notation for the column header of a table. Using column-header specific rules for XY cutting we convert the geometric structure of the column header to a linear string denoting cell attributes and directions of cuts. The string representation is parsed by a context-free grammar and the parse tree is further processed to produce an abstract data-type representation (the Wang notation tree) of each column category. Experiments were carried out to evaluate this scheme on the original and edited column headers of Excel tables drawn from a collection of 200 used in our earlier work. The transformed headers were obtained by editing the original column headers to conform to the format targeted by our grammar. Forty-four original headers and their reformatted versions were submitted as input to our software system. Our grammar was able to parse and the extract Wang notation tree for all the edited headers, but for only four of the original headers. We suggest extensions to our table grammar that would enable processing a larger fraction of headers without manual editing.


title: An upper limb exoskeleton with an optimized 4R spherical wrist mechanism for the shoulder joint

abstract: This paper presents an upper limb rehabilitation exoskeleton with an optimized 4R spherical wrist mechanism for the shoulder joint. Traditional designs of shoulder exoskeletons use 3R mechanisms to replicate the spherical motion of the shoulder. However, due to the exceptionally large range of motion of the human shoulder, the 3R mechanism is required to operate at a singular configuration at some point in the shoulder workspace. In this configuration, the 3R mechanism loses the ability to rotate the shoulder about one DOF. To overcome this problem, the use of a kinematically redundant 4R mechanism is proposed. The 4R mechanism has been optimized in a previous work and is used in the design of the presented exoskeleton prototype. This new shoulder mechanism allows the exoskeleton to achieve the entire human shoulder workspace without mechanical interference and while operating well away from singular configurations. Numerous features have also been included in the exoskeleton design to ensure it is safe, comfortable and easy to use. The shoulder mechanism of the exoskeleton is analyzed to demonstrate the capabilities of the 4R mechanism.


title: Learning The Discriminative Power-Invariance Trade-Off

abstract: We investigate the problem of learning optimal descriptors for a given classification task. Many hand-crafted descriptors have been proposed in the literature for measuring visual similarity. Looking past initial differences, what really distinguishes one descriptor from another is the tradeoff that it achieves between discriminative power and invariance. Since this trade-off must vary from task to task, no single descriptor can be optimal in all situations. Our focus, in this paper, is on learning the optimal tradeoff for classification given a particular training set and prior constraints. The problem is posed in the kernel learning framework. We learn the optimal, domain-specific kernel as a combination of base kernels corresponding to base features which achieve different levels of trade-off (such as no invariance, rotation invariance, scale invariance, affine invariance, etc.) This leads to a convex optimisation problem with a unique global optimum which can be solved for efficiently. The method is shown to achieve state-of-the-art performance on the UIUC textures, Oxford flowers and Cal- tech 101 datasets.


title: Interposer Technologies for High-Performance Applications

abstract: This paper explores the current state of the art in silicon, organic, and glass interposer technologies and their high-performance applications. Issues and challenges broadly encompassing electrical, mechanical, and thermal properties of these interposer technologies are discussed along with the proven and under research solutions pertaining to these challenges. An evaluation of high-performance applications for these three technologies provides a useful insight into the role of interposers for such applications. This paper is an effort to evaluate and compare the viability of silicon, organic, and glass interposer technologies for high-performance applications. This paper also discusses the future trends, promising advancements, and market requirements with special emphasis on glass interposer technologies as evaluated to be the most viable option for the future high-performance applications.


title: Printing soft matter in three dimensions

abstract: Light- and ink-based three-dimensional (3D) printing methods allow the rapid design and fabrication of materials without the need for expensive tooling, dies or lithographic masks. They have led to an era of manufacturing in which computers can control the fabrication of soft matter that has tunable mechanical, electrical and other functional properties. The expanding range of printable materials, coupled with the ability to programmably control their composition and architecture across various length scales, is driving innovation in myriad applications. This is illustrated by examples of biologically inspired composites, shape-morphing systems, soft sensors and robotics that only additive manufacturing can produce.


Audio-Visual Speech Recognition Using Bimodal-Trained Bottleneck Features for a Person with Severe Hearing Loss


title: High performance IPDs (Integrated passive devices) and TGV (Through glass via) interposer technology using the photosensitive glass

abstract: In this paper, a new TGV interposer technology is introduced in which the through via could be made by using a photolithography and chemical wet-etching. To make fine and accuracy via-holes, etching properties of the glass are studied in various UV-exposure times. It is possible to make TGVs from 60 μm to 20 μm with a 4:1 aspect ratio. Based on the TGV process, a high-aspect-ratio metal is made and high-Q spiral inductors can be realized by using the technology. The fabricated inductor has a very thick signal height more than 80 μm and its Q factor is more than 30 at 2 GHz. To demonstrate the process technology, a 0.9 GHz LPF, which is can be used for the RF front-end of the GSM band, is designed and fabricated using the inductor. The realized LPF has very low insertion loss of only 0.31 dB at the pass band, and this value is an improvement of more than 32% compared with the normal LPF.


title: Learning technology research in practice: Reflections from the field.

abstract: Research into the effectiveness of new learning technologies is needed. However education research is complex and challenging. In this paper we explore problems associated with different methodological approaches. In particular the value of controlled experiments in this context will be analysed and the use of interpretativist approaches to illuminate particular situations discussed. This is based on a critical reflection of our own experiences of doing educational research into student learning with interactive multimedia. These reflections will be useful to others embarking on similar projects.


title: Automatic Discovery of Behavioral Models From Software Execution Data

abstract: During the execution of a software system, tremendous amounts of data are recorded, and such data provide valuable information on software runtime behavior analysis. This paper presents an approach on how to utilize process mining as an enabler to discover software behavioral models. To achieve this, we formally define the software event log and its transformation from original software execution data. Essentially, a software event log consists of a set of cases that each is a manifestation of an independent software run. A case is represented as an ordered sequence of events that each refers to a method call. Given the observation that software usually has a hierarchical structure, we first propose an approach to construct a hierarchical software event log from the original flat one by the recursively applying method calling relation detection. Next, using extended process discovery techniques, we discover a software behavioral model, which is represented as a kind of hierarchical Petri net with components, from the hierarchical software event log. We have implemented the proposed approach in the open source process mining toolkit ProM . By using two synthetic software event logs, we show that our approach can deal with infrequent behavior. Moreover, a validation with one real-life software event log shows that our approach can help visualize actual software runtime behavior in an easy-to-understand manner. Note to Practitioners —Software data analysis techniques play an increasingly important role for understanding the real behavior of software systems. This paper addresses the issue of discovering behavioral models from real-life software execution data. A kind of hierarchical Petri nets is defined to represent the discovered software behavior. All proposed concepts and algorithms are supported by open source tools, and experiments over both synthetic and real-life software execution data have shown their applicability. The proposed methodology is readily applicable to industrial-size software behavior discovery problems.


title: A High Frame Rate Wearable EIT System Using Active Electrode ASICs for Lung Respiration and Heart Rate Monitoring

abstract: A high specification, wearable, electrical impedance tomography (EIT) system with 32 active electrodes is presented. Each electrode has an application specific integrated circuit (ASIC) mounted on a flexible printed circuit board, which is then wrapped inside a disposable fabric cover containing silver-coated electrodes to form the wearable belt. It is connected to a central hub that operates all the 32 ASICs. Each ASIC comprises a high-performance current driver capable of up to 6 mAp-p output, a voltage buffer for EIT and heart rate signal recording as well as contact impedance monitoring, and a sensor buffer that provides multi-parameter sensing. The ASIC was designed in a CMOS 0.35- $\mu \text{m}$ high-voltage process technology. It operates from ±9 V power supplies and occupies a total die area of 3.9 mm2. The EIT system has a bandwidth of 500 kHz and employs two parallel data acquisition channels to achieve a frame rate of 107 frames/s, the fastest wearable EIT system reported to date. Measured results show that the system has a measurement accuracy of 98.88% and a minimum EIT detectability of 0.86 $\Omega $ /frame. Its successful operation in capturing EIT lung respiration and heart rate biosignals from a volunteer is demonstrated.


title: A Survey on Intrusion Detection Techniques in MANET

abstract: The mobile ad-hoc network (MANET) is a new wireless technology, having features like dynamic topology and self-configuring ability of nodes. The self configuring ability of nodes in MANET made it popular among the critical situation such as military use and emergency recovery. But due to open medium and broad distribution of nodes make MANET vulnerable to different attacks. So to protect MANET from various attacks, it is important to develop an efficient and secure system for MANET. Intrusion means any set of actions that attempt to compromise the integrity, confidentiality, or availability of a resource. Intrusion Prevention is the primary defense because it is the first step to make the systems secure from attacks by using passwords, biometrics etc. Even if intrusion prevention methods are used, the system may be subjected to some vulnerability. So we need a second wall of defense known as Intrusion Detection Systems (IDSs), to detect and produce responses whenever necessary. In this article we present a survey of various intrusion detection schemes available for ad hoc networks. We have also described some of the basic attacks present in ad hoc network and discussed their available solution.


Revisiting NARX Recurrent Neural Networks for Long-Term Dependencies.


title: Domain Dependent and Independent Data Cleansing Techniques

abstract: Data warehousing is emerging as the cornerstone of an organization’s information Infrastructure. Today every business organization needs accurate and large amount of information to make proper decisions. For taking the business decisions the data should be of good quality. To improve the data quality data cleansing is needed. Data cleansing is fundamental to warehouse data reliability, and to data warehousing success. There are various methods for data cleansing. We classify the methods in two categories domain dependent and domain independent. This paper presents a survey and review of data cleansing methods, classification of existing methods and comparison between them.


title: Crop/Weed gene flow:Cucurbita argyrosperma Huber andC. fraterna L. H. Bailey (Cucurbitaceae)

abstract: A mixed population of Cucurbita at Vado El Mow in northern Tamaulipas, Mexico showed an anomalous pattern of fruit bitterness. Some domesticated plants (C. argyrosperma andC. moschata) expressed cucurbitacin bitterness whereas some sympatric free-living plants produced non-bitter fruits. This reversal of typical cucurbitacin expression suggested gene flow between crop and weed at the site. Isozyme analysis provided little insight as to taxa involved in gene exchange, although progeny from a single free-living plant carried IDH allozymes that are associated with Mexican landraces ofC. pepo. Synthetic hybridization revealed that fertile F, hybrids are produced from crosses involvingC. fraterna as the pistillate parent andC. argyrosperma as the staminate parent. Interspecific crop/weed hybrids can produce viable progeny upon self-pollination or backcrossingto either parent, andF2 families display normal allozyme segregation. Hybrid fertility, as indicated by pollen stainability, increases in progeny produced by backcrossingfrom theC. argyrosperma parent. Interspecific hybridfertility represents a potential for crop/weed gene flow that would be realized under natural conditions if pollen flow occurs betweenC. fraterna andC. argyrosperma in the fields of Tamaulipas. Oligolectic “squash bees” (Teponapis), efficientCucurbita pollen vectors, are present at the site. Thus, it is likely that natural interspecific crop/weed hybridization has occurred at Vado El Moro and this might at least partially explain the anomalous distribution of fruit bitterness among extant populations at the site.


title: A robust and high-throughput Cre reporting and characterization system for the whole mouse brain

abstract: This Resource paper describes a set of five new reporter mice, derived from Rosa26, driving Cre-dependent strong and ubiquitous expression of fluorescent proteins. In particular, the new mice show clear and specific expression patterns in the adult brain. The mice are available through Jackson Laboratories, and growing expression datasets can be accessed at http://transgenicmouse.alleninstitute.org/.


Toroidal Solitons in Nicole-type Models


- Protected Post Hoc Procedure F Scheffé ' s More Powerful


title: Social influences on secure development tool adoption: why security tools spread

abstract: Security tools can help developers build more secure software systems by helping developers detect or fix security vulnerabilities in source code. However, developers do not always use these tools. In this paper, we investigate a number of social factors that impact developers' adoption decisions, based on a multidisciplinary field of research called diffusion of innovations. We conducted 42 one-on-one interviews with professional software developers, and our results suggest a number of ways in which security tool adoption depends on developers' social environments and on the channels through which information about tools is communicated. For example, some participants trusted developers with strong reputations on the Internet as much as they trust their colleagues for information about security tools.


title: Regaining Trust in VLSI Design: Design-for-Trust Techniques

abstract: Designers use third-party intellectual property (IP) cores and outsource various steps in their integrated circuit (IC) design flow, including fabrication. As a result, security vulnerabilities have been emerging, forcing IC designers and end-users to reevaluate their trust in hardware. If an attacker gets hold of an unprotected design, attacks such as reverse engineering, insertion of malicious circuits, and IP piracy are possible. In this paper, we shed light on the vulnerabilities in very large scale integration (VLSI) design and fabrication flow, and survey design-for-trust (DfTr) techniques that aim at regaining trust in IC design. We elaborate on four DfTr techniques: logic encryption, split manufacturing, IC camouflaging, and Trojan activation. These techniques have been developed by reusing VLSI test principles.


title: Classification Techniques for Speech Recognition: A Review

abstract: Speech Processing is emerged as one of the important application area of digital signal processing. Various fields for research in speech processing are speech recognition, speaker recognition, speech synthesis, speech coding etc. Speech recognition is the process of automatically recognizing the spoken words of person based on information content in speech signal. This paper introduces a brief survey on Automatic Speech Recognition and discusses the various classification techniques that have been accomplished in this wide area of speech processing. The objective of this review paper is to summarize some of the well-known methods that are widely used in several stages of speech recognition system.


title: Artistic Rendering of Portraits

abstract: This chapter presents an overview of the latest computerized artistic portrait rendering techniques. Artistic rendering of portraits can be viewed as an image generating process controlled by two factors. The first one is face fidelity, meaning a rendered portrait image should preserve a certain amount of the original face’s information. The second factor is the artistic style chosen to simulate different media such as sketch and painting. In the literature, portrait rendering algorithms either adopt different models and data structures to represent the facial information or use different graphical elements and compositional methods to simulate various media and styles. These two factors essentially reveal the two principles in portraiture, namely the pursuits of likeness and aesthetic.


title: An Introduction to Logistic Regression Analysis and Reporting

abstract: Abstract The purpose of this article is to provide researchers, editors, and readers with a set of guidelines for what to expect in an article using logistic regression techniques. Tables, figures, and charts that should be included to comprehensively assess the results and assumptions to be verified are discussed. This article demonstrates the preferred pattern for the application of logistic methods with an illustration of logistic regression applied to a data set in testing a research hypothesis. Recommendations are also offered for appropriate reporting formats of logistic regression results and the minimum observation-to-predictor ratio. The authors evaluated the use and interpretation of logistic regression presented in 8 articles published in The Journal of Educational Research between 1990 and 2000. They found that all 8 studies met or exceeded recommended criteria.


Neural Networks: A Comprehensive Foundation


title: Scalable Collaborative Filtering Approaches for Large Recommender Systems

abstract: The collaborative filtering (CF) using known user ratings of items has proved to be effective for predicting user preferences in item selection. This thriving subfield of machine learning became popular in the late 1990s with the spread of online services that use recommender systems, such as Amazon, Yahoo! Music, and Netflix. CF approaches are usually designed to work on very large data sets. Therefore the scalability of the methods is crucial. In this work, we propose various scalable solutions that are validated against the Netflix Prize data set, currently the largest publicly available collection. First, we propose various matrix factorization (MF) based techniques. Second, a neighbor correction method for MF is outlined, which alloys the global perspective of MF and the localized property of neighbor based approaches efficiently. In the experimentation section, we first report on some implementation issues, and we suggest on how parameter optimization can be performed efficiently for MFs. We then show that the proposed scalable approaches compare favorably with existing ones in terms of prediction accuracy and/or required training time. Finally, we report on some experiments performed on MovieLens and Jester data sets.


title: Statistical Themes and Lessons for Data Mining

abstract: Data mining is on the interface of Computer Science andStatistics, utilizing advances in both disciplines to make progressin extracting information from large databases. It is an emergingfield that has attracted much attention in a very short period oftime. This article highlights some statistical themes and lessonsthat are directly relevant to data mining and attempts to identifyopportunities where close cooperation between the statistical andcomputational communities might reasonably provide synergy forfurther progress in data analysis.


title: On the behavior of the Sierpinski multiband fractal antenna

abstract: The multiband behavior of the fractal Sierpinski antenna is described. Due to its mainly triangular shape, the antenna is compared to the well-known single-band bow-tie antenna. Both experimental and numerical results show that the self-similarity properties of the fractal shape are translated into its electromagnetic behavior. A deeper physical insight on such a behavior is achieved by means of the computed current densities over the antenna surface, which also display some similarity properties through the bands.


title: RGB-based 3D Hand Pose Estimation via Privileged Learning with Depth Images

abstract: This paper proposes a method for hand pose estimation from RGB images that uses both external large-scale depth image datasets and paired depth and RGB images as privileged information at training time. We show that providing depth information during training significantly improves performance of pose estimation from RGB images during testing. We explore different ways of using this privileged information: (1) using depth data to initially train a depth-based network, (2) using the features from the depth-based network of the paired depth images to constrain mid-level RGB network weights, and (3) using the foreground mask, obtained from the depth data, to suppress the responses from the background area. By using paired RGB and depth images, we are able to supervise the RGB-based network to learn middle layer features that mimic that of the corresponding depth-based network, which is trained on large-scale, accurately annotated depth data. During testing, when only an RGB image is available, our method produces accurate 3D hand pose predictions. Our method is also tested on 2D hand pose estimation. Experiments on three public datasets show that the method outperforms the state-of-the-art methods for hand pose estimation using RGB image input.


title: noWorkflow: Capturing and Analyzing Provenance of Scripts

abstract: We propose noWorkflow, a tool that transparently captures provenance of scripts and enables reproducibility. Unlike existing approaches, noWorkflow is non-intrusive and does not require users to change the way they work --- users need not wrap their experiments in scientific workflow systems, install version control systems, or instrument their scripts. The tool leverages Software Engineering techniques, such as abstract syntax tree analysis, reflection, and profiling, to collect different types of provenance, including detailed information about the underlying libraries. We describe how noWorkflow captures multiple kinds of provenance and the different classes of analyses it supports: graph-based visualization; differencing over provenance trails; and inference queries.


title: Towards Understanding Man-In-The-Middle Attacks on IEC 60870-5-104 SCADA Networks

abstract: This paper investigates cyber attacks on ICS which rely on IEC 60870-5-104 for telecontrol communications. The main focus of the paper is on man-in-the-middle attacks, covering modification and injection of commands, it also details capture and replay attacks. An initial set of attacks are preformed on a local software simulated laboratory. Final experiments and validation of a man-in-the-middle attack are performed in a comprehensive testbed environment in conjunction with an electricity distribution operator.


title: Genetic and environmental influences on sexual orientation and its correlates in an Australian twin sample.

abstract: We recruited twins systematically from the Australian Twin Registry and assessed their sexual orientation and 2 related traits: childhood gender nonconformity and continuous gender identity. Men and women differed in their distributions of sexual orientation, with women more likely to have slight-tomoderate degrees of homosexual attraction, and men more likely to have high degrees of homosexual attraction. Twin concordances for nonheterosexual orientation were lower than in prior studies. Univariate analyses showed that familial factors were important for all traits, but were less successful in distinguishing genetic from shared environmental influences. Only childhood gender nonconformity was significantly heritable for both men and women. Multivariate analyses suggested that the causal architecture differed between men and women, and, for women, provided significant evidence for the importance of genetic factors to the traits' covariation.


title: A systematic mapping study of mobile application testing techniques

abstract: We performed a systematic mapping study on mobile application testing techniques.79 empirical studies are mapped to a classification schema.Classification scheme includes, structure the topic, evaluation type and contribution facets.Several research gaps in current literature are revealed and discussed. The importance of mobile application specific testing techniques and methods has been attracting much attention of software engineers over the past few years. This is due to the fact that mobile applications are different than traditional web and desktop applications, and more and more they are moving to being used in critical domains. Mobile applications require a different approach to application quality and dependability and require an effective testing approach to build high quality and more reliable software. We performed a systematic mapping study to categorize and to structure the research evidence that has been published in the area of mobile application testing techniques and challenges that they have reported. Seventy nine (79) empirical studies are mapped to a classification schema. Several research gaps are identified and specific key testing issues for practitioners are identified: there is a need for eliciting testing requirements early during development process; the need to conduct research in real-world development environments; specific testing techniques targeting application life-cycle conformance and mobile services testing; and comparative studies for security and usability testing.


title: YASA: Yet Another Time Series Segmentation Algorithm for Anomaly Detection in Big Data Problems

abstract: Time series patterns analysis had recently attracted the attention of the research community for real-world applications. Petroleum industry is one of the application contexts where these problems are present, for instance for anomaly detection. Offshore petroleum platforms rely on heavy turbomachines for its extraction, pumping and generation operations. Frequently, these machines are intensively monitored by hundreds of sensors each, which send measurements with a high frequency to a concentration hub. Handling these data calls for a holistic approach, as sensor data is frequently noisy, unreliable, inconsistent with a priori problem axioms, and of a massive amount. For the anomalies detection problems in turbomachinery, it is essential to segment the dataset available in order to automatically discover the operational regime of the machine in the recent past. In this paper we propose a novel time series segmentation algorithm adaptable to big data problems and that is capable of handling the high volume of data involved in problem contexts. As part of the paper we describe our proposal, analyzing its computational complexity. We also perform empirical studies comparing our algorithm with similar approaches when applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection.


Improving Event Causality Recognition with Multiple Background Knowledge Sources Using Multi-Column Convolutional Neural Networks.


title: Serious Games – An Overview

abstract: This report discusses some issues concerning serious games, that is, (digital) games used for purposes other than mere entertainment. The starting point is the serious games concept itself, and wha ...


title: Moving object tracking using optical flow and motion vector estimation

abstract: Moving object detection and tracking is an evolving research field due to its wide applications in traffic surveillance, 3D reconstruction, motion analysis (human and non-human), activity recognition, medical imaging etc. However real time object tracking is a challenging task due to dynamic tacking environment and different limiting parameters like view point, anthropometric variation, dimensions of an object, cluttered background, camera motions, occlusion etc. In this paper, we have developed new object detection and tracking algorithm which makes use of optical flow in conjunction with motion vector estimation for object detection and tracking in a sequence of frames. The optical flow gives valuable information about the object movement even if no quantitative parameters are computed. The motion vector estimation technique can provide an estimation of object position from consecutive frames which increases the accuracy of this algorithm and helps to provide robust result irrespective of image blur and cluttered background. The use of median filter with this algorithm makes it more robust in the presence of noise. The developed algorithm is applied to wide range of standard and real time datasets with different illumination (indoor and outdoor), object speed etc. The obtained results indicates that the developed algorithm over performs over conventional methods and state of art methods of object tracking.


Improved Functional Mappings via Product Preservation


title: A Retrospective Analysis of the Fake News Challenge Stance Detection Task

abstract: The 2017 Fake News Challenge Stage 1 (FNC-1) shared task addressed a stance classification task as a crucial first step towards detecting fake news. To date, there is no in-depth analysis paper to critically discuss FNC-1's experimental setup, reproduce the results, and draw conclusions for next-generation stance classification methods. In this paper, we provide such an in-depth analysis for the three top-performing systems. We first find that FNC-1's proposed evaluation metric favors the majority class, which can be easily classified, and thus overestimates the true discriminative power of the methods. Therefore, we propose a new F1-based metric yielding a changed system ranking. Next, we compare the features and architectures used, which leads to a novel feature-rich stacked LSTM model that performs on par with the best systems, but is superior in predicting minority classes. To understand the methods' ability to generalize, we derive a new dataset and perform both in-domain and cross-domain experiments. Our qualitative and quantitative study helps interpreting the original FNC-1 scores and understand which features help improving performance and why. Our new dataset and all source code used during the reproduction study are publicly available for future research.


title: FPGA based system for blood glucose sensing using photoplethysmography and online motion artifact correction using adaline

abstract: This paper proposes a non invasive blood glucose sensing system using photoplethysmography (PPG). Neural network based adaptive noise cancellation (adaline) is employed to reduce the motion artifact. Also artificial neural network is used to create the predictive model which estimates the glucose levels based on PPG signals. Error in estimating glucose levels came out to be 5.48 mg/dl using ANN on MATLAB. This predictive model created by ANN has been implemented on FPGA. Error in estimating glucose levels by the ANN model implemented on FPGA, came out to be 7.23mg/dl. The results have been validated by performing Clarke error grid analysis.


title: PhishAri: Automatic realtime phishing detection on twitter

abstract: With the advent of online social media, phishers have started using social networks like Twitter, Facebook, and Foursquare to spread phishing scams. Twitter is an immensely popular micro-blogging network where people post short messages of 140 characters called tweets. It has over 100 million active users who post about 200 million tweets everyday. Phishers have started using Twitter as a medium to spread phishing because of this vast information dissemination. Further, it is difficult to detect phishing on Twitter unlike emails because of the quick spread of phishing links in the network, short size of the content, and use of URL obfuscation to shorten the URL. Our technique, PhishAri, detects phishing on Twitter in realtime. We use Twitter specific features along with URL features to detect whether a tweet posted with a URL is phishing or not. Some of the Twitter specific features we use are tweet content and its characteristics like length, hashtags, and mentions. Other Twitter features used are the characteristics of the Twitter user posting the tweet such as age of the account, number of tweets, and the follower-followee ratio. These twitter specific features coupled with URL based features prove to be a strong mechanism to detect phishing tweets. We use machine learning classification techniques and detect phishing tweets with an accuracy of 92.52%. We have deployed our system for end-users by providing an easy to use Chrome browser extension. The extension works in realtime and classifies a tweet as phishing or safe. In this research, we show that we are able to detect phishing tweets at zero hour with high accuracy which is much faster than public blacklists and as well as Twitter's own defense mechanism to detect malicious content. We also performed a quick user evaluation of PhishAri in a laboratory study to evaluate the usability and effectiveness of PhishAri and showed that users like and find it convenient to use PhishAri in real-world. To the best of our knowledge, this is the first realtime, comprehensive and usable system to detect phishing on Twitter.


title: Impeding Malware Analysis Using Conditional Code Obfuscation

abstract: Malware programs that incorporate trigger-based behavior initiate malicious activities based on conditions satisfied only by specific inputs. State-of-the-art malware analyzers discover code guarded by triggers via multiple path exploration, symbolic execution, or forced conditional execution, all without knowing the trigger inputs. We present a malware obfuscation technique that automatically conceals specific trigger-based behavior from these malware analyzers. Our technique automatically transforms a program by encrypting code that is conditionally dependent on an input value with a key derived from the input and then removing the key from the program. We have implemented a compiler-level tool that takes a malware source program and automatically generates an obfuscated binary. Experiments on various existing malware samples show that our tool can hide a significant portion of trigger based code. We provide insight into the strengths, weaknesses, and possible ways to strengthen current analysis approaches in order to defeat this malware obfuscation technique.


A Scrum Framework for Requirement Engineering Practices


Spirit of Berlin : An Autonomous Car for the DARPA Urban Challenge Hardware and Software Architecture


title: E-GOVERNMENT EVALUATION FACTORS: CITIZEN’S PERSPECTIVE

abstract: The e-government field is growing to a considerable size, both in its contents and position with respect to other research fields. The government to citizen segment of egovernment is taking the lead in terms of its impor tance and size. Like the evaluation of all other information system s initiatives, the evaluation of egovernments in both theory and practice has proved to be important but complex. The complexity of evaluation is mostly due to the multi ple perspectives involved, the difficulties of quantifying benefits, and the socia l and technical context of use. The importance of e-government evaluation is due to the enormous investment of governments on delivering e-government services, and to the considerable pace of growing in the e-government field. However, despite the importance of the evaluation of e-government services, literature shows that e-gove rnment evaluation is still an immature area in terms of development and management. This work is part of a research effort that aims to develop a holistic evaluation framework for e-government systems. The main aim of this paper is to investigate the citizen’ perspective in evaluating e-government services, an d present a set of evaluating factors that influence citizens’ utilization of e-governmen t services. These evaluation factors can serve as part of an e-government evaluation framework. Moreover, the evaluation factors can also be used as means of providing valuable fee dback for the planning of future egovernment initiatives.


title: NScale: Neighborhood-centric Analytics on Large Graphs

abstract: There is an increasing interest in executing rich and complex analysis tasks over large-scale graphs, many of which require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph. Examples of such tasks include ego network analysis, motif counting in biological networks, finding social circles, personalized recommendations, link prediction, anomaly detection, analyzing influence cascades, and so on. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high communication, scheduling, and memory overheads in executing such tasks. Further, most existing graph processing frameworks also typically ignore the challenges in extracting the relevant portions of the graph that an analysis task is interested in, and loading it onto distributed memory. ::: ::: In this demonstration proposal, we describe NScale, a novel end-to-end graph processing framework that enables the distributed execution of complex neighborhood-centric analytics over large-scale graphs in the cloud. NScale enables users to write programs at the level of neighborhoods or subgraphs. NScale uses Apache YARN for efficient and fault-tolerant distribution of data and computation; it features GEL, a novel graph extraction and loading phase, that extracts the relevant portions of the graph and loads them into distributed memory using as few machines as possible. NScale utilizes novel techniques for the distributed execution of user computation that minimize memory consumption by exploiting overlap among the neighborhoods of interest. A comprehensive experimental evaluation shows orders-of-magnitude improvements in performance and total cost over vertex-centric approaches.


title: Understanding the Complexity of Lifted Inference and Asymmetric Weighted Model Counting

abstract: In this paper we study lifted inference for the Weighted First-Order Model Counting problem (WFOMC), which counts the assignments that satisfy a given sentence in first-order logic (FOL); it has applications in Statistical Relational Learning (SRL) and Probabilistic Databases (PDB). We present several results. First, we describe a lifted inference algorithm that generalizes prior approaches in SRL and PDB. Second, we provide a novel dichotomy result for a non-trivial fragment of FO CNF sentences, showing that for each sentence the WFOMC problem is either in PTIME or #P-hard in the size of the input domain; we prove that, in the first case our algorithm solves the WFOMC problem in PTIME, and in the second case it fails. Third, we present several properties of the algorithm. Finally, we discuss limitations of lifted inference for symmetric probabilistic databases (where the weights of ground literals depend only on the relation name, and not on the constants of the domain), and prove the impossibility of a dichotomy result for the complexity of probabilistic inference for the entire language FOL.


title: Outlier detection in heart rate signal using activity information

abstract: During exercise, heart rate will increase and its distribution will be different from that in stationary statement. Moreover, activity introduces several outliers into heart rate series. Heart rate variability analysis under exercise conditions can't be conducted identically to the traditional methods. A heart rate distribution analysis method is proposed to fuse the activity information and heart rate signals which will be used to make heart rate analysis under exercise conditions. Firstly we use Gaussian function to fit RR intervals under various intensity activities, and then establish dynamic model for the parameter μ and σ which is changed with activity intensity. With the distribution of heart rate determined, outliers in RR interval can be detected and replaced according to possibility distribution. In the last, the validity of outlier detection algorithm and the influence of outliers to HRV are verified.


title: Methodologies for Agile Product Line Engineering: A Survey and Evaluation.

abstract: Agile Product Line Engineering (APLE) is a relatively new approach which has emerged as the result of combining two successful approaches: Software Product Line Engineering and Agile Software Development. The goal of this combined approach is to cover the weaknesses of each of the two approaches while maximizing the advantages of both. Several methodologies exist which provide a practical process for applying APLE in organizations. In this paper, these APLE methodologies will be evaluated using a criteria-based approach. Results of this evaluation show each methodology’s strengths and weaknesses, and can be helpful in selecting, comparing, and modifying APLE methodologies. The evaluation framework and the results can also be used for developing bespoke APLE methodologies, tailored to fit the specific needs of organizations and individual projects.


To appear in: Evolutionary Psychology and the Emotions Handbook of Emotions, 2nd Edition


title: Gregex: GPU Based High Speed Regular Expression Matching Engine

abstract: Regular expression matching engine is a crucial infrastructure which is widely used in network security systems, like IDS. We propose Gregex, a Graphics Processing Unit (GPU) based regular expression matching engine for deep packet inspection (DPI). Gregex leverages the computational power and high memory bandwidth of GPUs by storing data in proper GPU memory space and executing massive GPU thread concurrently to process lots of packets in parallel. Three optimization techniques, ATP, CAB, and CAT are proposed to significantly improve the performance of Gregex. On a GTX260 GPU, Gregex achieves a regular matching throughput of 126.8 Gbps, which is a speedup of 210% over traditional CPU-based implementation and a speedup of 7.9% over the state-of-the-art GPU based regular expression engine.


title: Robust estimation of camera translation between two images using a camera with a 3D orientation sensor

abstract: It is still a difficult problem to establish correspondences of feature points and to estimate view relations for multiple images of a static scene, if the images have large disparities. In this paper we explore the possibility of applying a cheap and general-purpose 3D orientation sensor to improve the robustness of matching such two images. We attach a 3D orientation sensor to a camera and use the system to acquire the images. The camera orientation is obtained from the sensor. Assuming known intrinsic parameters of the camera, we are to estimate only the camera translation between the two views. Owing to the small number of parameters needed to be estimated, it becomes possible to apply a voting method. We show that the method by voting is more robust than the methods based on random sampling, especially for difficult pair of images to make correspondences. In addition, using the known camera orientation, the images can be rectified so that it is as if they were taken by parallel cameras, before the candidate matches are searched for. This helps finding as many correct matches as possible for pairs of images that include rotation around the camera axis. Experimental results for synthetic images as well as real images are shown.


title: Psychopathological rating scales for diagnostic use in adults with attention-deficit/hyperactivity disorder (ADHD)

abstract: * The authors thank Dr. Frederick Reimherr, University of Utah, Salt Lake City, USA for critical advice.


title: Androgen Deprivation Therapy And Morbid Obesity: Do They Share Cardiovascular Risk Through Metabolic Syndrome?

abstract: Abstract Background Although the use of androgen deprivation therapy (ADT) has resulted in improved survival in men with advanced prostate cancer, the resulting hypogonadism is associated with profound adverse effects comparable to those found in morbid obesity, being cardiovascular risk among the most lethal. Objectives Evaluate metabolic syndrome, metabolic abnormalities and cardiovascular risk in patients with prostate cancer under ADT, not under ADT and morbid obese men. Methods This is a cross-sectional study that involves 79 men presenting prostate cancer, of whom 54 under ADT and 25 not under ADT and 91 morbidly obese patients paired by sex and age. To define metabolic syndrome, we used the International Diabetes Federation (IDF) criteria. Metabolic abnormalities, metabolic markers and Framingham score to predict the ten year coronary heart disease risk were compared among patients under ADT, not under ADT and morbid obese. Results Patients under ADT presented significantly greater occurrence of diabetes and central obesity and higher levels of total cholesterol and low density lipoprotein (LDL) compared to eugonadal men. The mean cardiovascular risk was significantly higher in patients under ADT (39.97 ± 12.53% vs. 26.09 ± 14.80%; p = 0.021). Morbidly obese subjects had increased ten year coronary heart disease risk; comparable to patients under ADT ( p = 0.054). Conclusion This study suggests that patients under ADT show higher prevalence of metabolic abnormalities and cardiovascular risk similar to those found in morbidly obese subjects. It is possible that both processes share cardiovascular risk through metabolic syndrome.


title: Instructor Competencies: Standards for Face-to-Face, Online, and Blended Settings

abstract: Dedication. The ibstpi Board. Acknowledgements. Author Biographical Sketches. Foreword. Preface. Chapter 1: An Introduction to Instructor Competencies Overview. The Evolution of Instructor Competence. Traditional Conceptualizations of Instruction. New Learning Paradigms. New Educational Technologies. New Roles and Settings for Instructors. Face-to Face Settings. Online Settings. Blended Settings. Conclusion. Chapter 2: The ibstpi Competency Development Model. Overview. What is a Competency?. The Competency Development Model. Applying the Model to Instructor Competencies. Conclusion. Chapter 3: The ibstpi Instructor Competencies. Chapter 4: Instructor Competencies: Discussion and Rationale. Overview. Professional Foundations. Planning and Preparation, Instructional Methods and Strategies, Assessment and Evaluation, Management. Conclusion. Chapter 5: The Uses of the ibstpi Instructor Competencies. Overview. Individual Uses. Organizational Uses. Instructor Competencies and Certification. Conclusion. Chapter 6 Competency Validation Study. Overview. Foundation of the ibstpi Instructor Competencies. Worldwide Validation Study. Conclusion. Epilogue. References. Appendices. A. The 1993 ibstpi Instructor Competencies and Performance Statements. B. The ibstpi Code of Ethics for Instructors. C. Additional Resources for Instructors. D. Glossary of Terms. Index.


title: A Word-Character Convolutional Neural Network for Language-Agnostic Twitter Sentiment Analysis

abstract: Convolutional Neural Networks (CNN) have been widely used for text classification. Both word-based CNNs and character-based CNNs have shown good performance for Twitter sentiment classification. Most research on CNNs is towards English Twitter sentiment analysis and language-independent sentiment classification is still a challenging task due to the lack of non-English resources. Recently there are several works using character-based CNNs for tackling the language-independence challenge. Motivated by the intuition that the word-level and character-level deep features contain complimentary information, we propose a hybrid word-character CNN for language-agnostic Twitter sentiment classification. Word-character CNN comprises two convolutional channels, one for word-level convolution and one for character-level convolution, and a merge layer is included in our model for combining features from two convolutional channels. Moreover, our model does not require language identification and do not use unsupervised embeddings or other external resources. Our proposed model can achieve more superior performance on language-agnostic Twitter sentiment classification than word-based CNNs and character-based CNNs.


title: Fast gradient-descent methods for temporal-difference learning with linear function approximation

abstract: Sutton, Szepesvari and Maei (2009) recently introduced the first temporal-difference learning algorithm compatible with both linear function approximation and off-policy training, and whose complexity scales only linearly in the size of the function approximator. Although their gradient temporal difference (GTD) algorithm converges reliably, it can be very slow compared to conventional linear TD (on on-policy problems where TD is convergent), calling into question its practical utility. In this paper we introduce two new related algorithms with better convergence rates. The first algorithm, GTD2, is derived and proved convergent just as GTD was, but uses a different objective function and converges significantly faster (but still not as fast as conventional TD). The second new algorithm, linear TD with gradient correction, or TDC, uses the same update rule as conventional TD except for an additional term which is initially zero. In our experiments on small test problems and in a Computer Go application with a million features, the learning rate of this algorithm was comparable to that of conventional TD. This algorithm appears to extend linear TD to off-policy learning with no penalty in performance while only doubling computational requirements.


title: Clinical evaluation of removable partial dentures 10 years after insertion: success rates, hygienic problems, and technical failures

abstract: The aim of this retrospective study was to examine the condition and success rate of various designs of removable partial dentures 10 years after placement. Seventy-four patients treated with 101 mainly conical crown-retained dentures (CCRDs), clasp-retained removable partial dentures (RPDs), or a combination of conical crown and clasp-retained dentures (CRPDs) were reexamined. Prosthetic findings were compared to baseline values at insertion and checked for factors that may affect them. Overall, 36.6% of the prostheses were regarded as successes, 23.8% as partial successes, and 39.6% as failures. Also, only one-third of the prostheses showed neither hygienic problems nor technical failures. The statistical analysis identified no single parameter as significantly affecting the success rate of the partial dentures. Prostheses supported by several abutment teeth had improved success, and RPDs had a higher failure rate (66.7%) than CCRDs (33.3%) and CRPDs (44.8%); however, because of the limited number of RPDs these differences were not statistically significant. Although the prostheses were unsupervised in service, the 60.4% overall success rate of the dentures was somewhat higher than in other studies on precision-retained prostheses.


title: Extending and Improving Wordnet via Unsupervised Word Embeddings

abstract: This work presents an unsupervised approach for improving WordNet that builds upon recent advances in document and sense representation via distributional semantics. We apply our methods to construct Wordnets in French and Russian, languages which both lack good manual constructions.1 These are evaluated on two new 600-word test sets for word-to-synset matching and found to improve greatly upon synset recall, outperforming the best automated Wordnets in F-score. Our methods require very few linguistic resources, thus being applicable for Wordnet construction in low-resources languages, and may further be applied to sense clustering and other Wordnet improvements.


title: MarrNet: 3D Shape Reconstruction via 2.5D Sketches

abstract: 3D object reconstruction from a single image is a highly under-determined problem, requiring strong prior knowledge of plausible 3D shapes. This introduces challenges for learning-based approaches, as 3D object annotations are scarce in real images. Previous work chose to train on synthetic data with ground truth 3D information, but suffered from domain adaptation when tested on real data. In this work, we propose MarrNet, an end-to-end trainable model that sequentially estimates 2.5D sketches and 3D object shape. Our disentangled, two-step formulation has three advantages. First, compared to full 3D shape, 2.5D sketches are much easier to be recovered from a 2D image; models that recover 2.5D sketches are also more likely to transfer from synthetic to real data. Second, for 3D reconstruction from 2.5D sketches, systems can learn purely from synthetic data. This is because we can easily render realistic 2.5D sketches without modeling object appearance variations in real images, including lighting, texture, etc. This further relieves the domain adaptation problem. Third, we derive differentiable projective functions from 3D shape to 2.5D sketches; the framework is therefore end-to-end trainable on real images, requiring no human annotations. Our model achieves state-of-the-art performance on 3D shape reconstruction.


title: Raman study of surface optical phonons in ZnO(Co) nanoparticles prepared by hydrothermal method

abstract: On Raman scattering spectra of nanocrystaline samples ZnO(Co) prepared by use ::: of the microwave assisted hydrothermal synthesis, surface optical phonons ::: (SOP) where observed in the range of 519 - 572 cm-1. The mean crystalline ::: size (33 - 300 nm) as well as the phase composition of obtained samples (ZnO, ::: ZnCo2O4) where determinate with X - ray diffraction measurements. These ::: measurements aloud us to study the change of SOP modes position with ::: crystalline size and how change in concentration of doping element CoO ::: influence the change of SOP modes intensity.


title: Kinetic and Optimization Studies on Ethanol Production from Corn Flour

abstract: Studies on Simultaneous Saccharification and Fermentation (SSF) of corn flour, a major agricultural product as the substrate using starch digesting glucoamylase enzyme derived from Aspergillus niger and non starch digesting and sugar fermenting Saccharomyces cerevisiae in a batch fermentation. Experiments based on Central Composite Design (CCD) were conducted to study the effect of substrate concentration, pH, temperature, enzyme concentration on Ethanol Concentration and the above parameters were optimized using Response Surface Methodology (RSM). The optimum values of substrate concentration, pH, temperature and enzyme concentration were found to be 160 g/l, 5.5, 30°C and 50 IU respectively. The effect of inoculums age on ethanol concentration was also investigated. The corn flour solution equivalent to 16% initial starch concentration gave the highest ethanol concentration of 63.04 g/l after 48 h of fermentation at optimum conditions of pH and temperature. Monod model and Logistic model were used for growth kinetics and Leudeking - Piret model was used for product formation kinetics.


title: Whole-Body Motion Generation Integrating Operator's Intention and Robot's Autonomy in Controlling Humanoid Robots

abstract: This paper introduces a framework for whole-body motion generation integrating operator's control and robot's autonomous functions during online control of humanoid robots. Humanoid robots are biped machines that usually possess multiple degrees of freedom (DOF). The complexity of their structure and the difficulty in maintaining postural stability make the whole-body control of humanoid robots fundamentally different from fixed-base manipulators. Getting hints from human conscious and subconscious motion generations, the authors propose a method of generating whole-body motions that integrates the operator's command input and the robot's autonomous functions. Instead of giving commands to all the joints all the time, the operator selects only the necessary points of the humanoid robot's body for manipulation. This paper first explains the concept of the system and the framework for integrating operator's command and autonomous functions in whole-body motion generation. Using the framework, autonomous functions were constructed for maintaining postural stability constraint while satisfying the desired trajectory of operation points, including the feet, while interacting with the environment. Finally, this paper reports on the implementation of the proposed method to teleoperate two 30-DOF humanoid robots, HRP-1S and HRP-2, by using only two 3-DOF joysticks. Experiments teleoperating the two robots are reported to verify the effectiveness of the proposed method.


title: Corporate performance : a ratio-based approach to country and industry analyses

abstract: Corporate performance interpretation and measuring problems.- Country and industry factors as determinants of corporate performance.- Analysis of the corporate performance diversity in the selected European Union countries.- Cross-industry and cross-country comparative cluster analysis.- Factors affecting corporate performance in countries and industries.


title: G-thinker: Big Graph Mining Made Easier and Faster

abstract: This paper proposes a general system for compute-intensive graph mining tasks that find from a big graph all subgraphs that satisfy certain requirements (e.g., graph matching and community detection). Due to the broad range of applications of such tasks, many single-threaded algorithms have been proposed. However, graphs such as online social networks and knowledge graphs often have billions of vertices and edges, which require distributed processing in order to scale. Unfortunately, existing distributed graph processing systems such as Pregel and GraphLab are designed for data-intensive analytics, and are inefficient for compute-intensive graph mining tasks since computation over any data is coupled with the data's access that involves network transmission. We propose a distributed graph mining framework, called G-thinker, which is designed for compute-intensive graph mining workloads. G-thinker provides an intuitive graph-exploration API for the convenient implementation of various graph mining algorithms, and the runtime engine provides efficient execution with bounded memory consumption, light network communication, and parallelism between computation and communication. Extensive experiments were conducted, which demonstrate that G-thinker is orders of magnitude faster than existing solution, and can scale to graphs that are two orders of magnitude larger given the same hardware resources.


An Investigation on Organisation Cyber Resilience


title: Phased Array for UAV Communications at 5.5 GHz

abstract: This letter presents the design and manufacturing of a phased array at 5.5-GHz band. The antenna application is the communications with unmanned aerial vehicles (UAVs) in a wildfire. The antenna is controlled through a WiMAX station. The vertical radiation pattern is a shaped beam. This shaped beam has the maximum at broadside and a smooth gain decay in positive θ. This way, the differences in the free-space losses of the UAVs are compensated. The antenna is steered in the horizontal plane through phase shifters. It also includes amplifiers and digital attenuators to calibrate the antenna performance. A calibration method is also proposed in the letter.


title: The blocker tag: selective blocking of RFID tags for consumer privacy

abstract: We propose the use of "selective blocking" by "blocker tags" as a way of protecting consumers from unwanted scanning of RFID tags attached to items they may be carrying or wearing.While an ordinary RFID tag is a simple, cheap (e.g. five-cent) passive device intended as an "electronic bar-code" for use in supply-chain management, a blocker tag is a cheap passive RFID device that can simulate many ordinary RFID tags simultaneously. When carried by a consumer, a blocker tag thus "blocks" RFID readers. It can do so universally by simulating all possible RFID tags. Or a blocker tag can block selectively by simulating only selected subsets of ID codes, such as those by a particular manufacturer, or those in a designated "privacy zone.We believe that this approach, when used with appropriate care, provides a very attractive alternative for addressing privacy concerns raised by the potential (and likely) widespread use of RFID tags in consumer products.We also discuss possible abuses arising from blocker tags, and means for detecting and dealing with them.


title: Informational vs. Controlling Gamification: A Study Design

abstract: Recent research suggests that gamification has the potential to increase intrinsic motivation, as well as decrease users' intrinsic motivation. However, the understanding of why gamification sometimes is successful and other times not, is still not fully understood. One reason for this is that applied research has been lacking a theoretical foundation. Therefore, we are currently designing a study in which we examine the underlying psychological mechanisms on how gamification works. Based on self determination theory, in our approach we compare how autonomy, competence and intrinsic motivation differ between an informational and a controlling condition.


title: An Optimal Fuzzy PID Controller

abstract: This paper introduces an optimal fuzzy proportional-integral-derivative (PID) controller. The fuzzy PID controller is a discrete-time version of the conventional PID controller, which preserves the same linear structure of the proportional, integral, and derivative parts but has constant coefficient yet self-tuned control gains. Fuzzy logic is employed only for the design; the resulting controller does not need to execute any fuzzy rule base, and is actually a conventional PID controller with analytical formulae. The main improvement is in endowing the classical controller with a certain adaptive control capability. The constant PID control gains are optimized by using the multiobjective genetic algorithm (MOGA), thereby yielding an optimal fuzzy PID controller. Computer simulations are shown to demonstrate its improvement over the fuzzy PID controller without MOGA optimization.


title: Assessment of website security by penetration testing using Wireshark

abstract: Evolving technology has created an inevitable threat of expose of data that is shared online. Wireshark tool enables the ethical hacker to reveal the flaws in the system security at the user authentication level. This approach of identifying vulnerabilities is deemed fit as the strategy involved in this testing is rapid and provides good success in identifying vulnerabilities. The usage of Wireshark also ensures that the procedure followed is up to the required standards. This paper discussed about the need to utilize penetration testing, the benefits of using Wireshark for the same and goes on to illustrate one method of using the tool to perform penetration testing. Most areas of a network are highly susceptible to security attacks by adversaries. This paper focuses on solving the aforementioned issue by surveying various tools available for penetration testing. This also provides a sample of basic penetration testing using Wireshark.


title: Implants in the Esthetic Zone

abstract: To achieve a successful esthetic result and good patient satisfaction,implant placement in the esthetic zone demands a thorough under-standing of anatomic, biologic, surgical, and prosthetic principles. The ability to achieve harmonious, indistinguishable prosthesis from adjacent natural teeth in the esthetic zone is sometimes challenging. Placement of dental implants in the esthetic zone is a technique-sensitive procedure with little room for error. Guidelines are presented for ideal implant positioning and for a variety of therapeutic modalities that can be implemented for addressing different clinical situations involving replacement of missing teeth in the esthetic zone.


title: Making processes from best practice frameworks actionable

abstract: Best-practice frameworks provide guidance for organizing work in business. They enable reuse of experience within a domain. However, best practice frameworks are general and usually cover broad domains. Their guidance thus is often offered at an abstract level rather than as details of actionable tasks and processes to accomplish work. This paper presents an approach to bridge the gap between the abstractions available in best practice framework and actions that have to be performed by people or systems in a repeatable manner. We identify knowledge from best practices frameworks, categorize it and represent it in the form of reusable, interpretable templates. Template interpretation guides the refinement process from general concepts of best practices frameworks into actionable concepts such as specific tasks to be performed by assigned roles. A prototype implemented to validate the approach is also described.


title: Emerging Security Threats and Countermeasures in IoT

abstract: IoT (Internet of Things) diversifies the future Internet, and has drawn much attention. As more and more gadgets (i.e. Things) connected to the Internet, the huge amount of data exchanged has reached an unprecedented level. As sensitive and private information exchanged between things, privacy becomes a major concern. Among many important issues, scalability, transparency, and reliability are considered as new challenges that differentiate IoT from the conventional Internet. In this paper, we enumerate the IoT communication scenarios and investigate the threats to the large-scale, unreliable, pervasive computing environment. To cope with these new challenges, the conventional security architecture will be revisited. In particular, various authentication schemes will be evaluated to ensure the confidentiality and integrity of the exchanged data.


title: A conformal, helmet-mounted microphone array for auditory situational awareness and hearing protection

abstract: Hearing protectors often are considered detrimental to auditory situational awareness due in part to the distortions they impose on localization cues. The addition of a helmet or other head-worn equipment can exacerbate this problem by covering the ears and effectively changing the shape of the head. In an effort to design a hearing-protection system that maintains natural auditory localization performance, we have built a prototype conformal microphone array integrated into a helmet. The system involves beamforming for spatial selectivity, filtering with head-related transfer functions to reintroduce spatial auditory cues to the beamformed data, dynamic range compression to attenuate potentially damaging sound levels, and insert earphones to deliver binaural audio and provide additional hearing protection. All directions are auralized simultaneously to provide full spatial coverage around the wearer. In this paper we describe the design, implementation, and testing of a prototype 32-channel system, including 3D modeling and simulations, signal-processing approaches, real-time processing, and localization performance.


title: Implementation of Slotted Meander-Line Resonators for Isolation Enhancement in Microstrip Patch Antenna Arrays

abstract: A new approach to enhance the isolation in microstrip patch antenna arrays is described in this letter. The implementation of a slotted meander-line resonator (SMLR) is done by creating defect in the microstrip structure particularly designed for band-notch function. The resonator is designed to block the surface current at the resonant frequency of the two patch antennas coupled along H-plane and operating at a frequency of 4.8 GHz. The interelement isolation before and after the implementation of SMLR has been investigated. The proposed configuration provides an improvement in isolation by 16 dB (measured value) with a reduced edge-to-edge spacing of 7 mm $(\lambda_{o}/9)$ . The configuration has been designed, simulated, and validated experimentally.


title: Mobile CPU's rise to power: Quantifying the impact of generational mobile CPU design trends on performance, energy, and user satisfaction

abstract: In this paper, we assess the past, present, and future of mobile CPU design. We study how mobile CPU designs trends have impacted the end-user, hardware design, and the holistic mobile device. We analyze the evolution often cutting-edge mobile CPU designs released over the past seven years. Specifically, we report measured performance, power, energy and user satisfaction trends across mobile CPU generations. A key contribution of our work is that we contextualize the mobile CPU's evolution in terms of user satisfaction, which has largely been absent from prior mobile hardware studies. To bridge the gap between mobile CPU design and user satisfaction, we construct and conduct a novel crowdsourcing study that spans over 25,000 survey participants using the Amazon Mechanical Turk service. Our methodology allows us to identify what mobile CPU design techniques provide the most benefit to the end-user's quality of user experience. Our results quantitatively demonstrate that CPUs play a crucial role in modern mobile system-on-chips (SoCs). Over the last seven years, both single-and multicore performance improvements have contributed to end-user satisfaction by reducing user-critical application response latencies. Mobile CPUs aggressively adopted many power-hungry desktop-oriented design techniques to reach these performance levels. Unlike other smartphone components (e.g. display and radio) whose peak power consumption has decreased over time, the mobile CPU's peak power consumption has steadily increased. As the limits of technology scaling restrict the ability of desktop-like scaling to continue for mobile CPUs, specialized accelerators appear to be a promising alternative that can help sustain the power, performance, and energy improvements that mobile computing necessitates. Such a paradigm shift will redefine the role of the CPU within future SoCs, which merit several design considerations based on our findings.


title: TaggerOne: joint named entity recognition and normalization with semi-Markov Models.

abstract: MOTIVATION ::: Text mining is increasingly used to manage the accelerating pace of the biomedical literature. Many text mining applications depend on accurate named entity recognition (NER) and normalization (grounding). While high performing machine learning methods trainable for many entity types exist for NER, normalization methods are usually specialized to a single entity type. NER and normalization systems are also typically used in a serial pipeline, causing cascading errors and limiting the ability of the NER system to directly exploit the lexical information provided by the normalization. ::: ::: ::: METHODS ::: We propose the first machine learning model for joint NER and normalization during both training and prediction. The model is trainable for arbitrary entity types and consists of a semi-Markov structured linear classifier, with a rich feature approach for NER and supervised semantic indexing for normalization. We also introduce TaggerOne, a Java implementation of our model as a general toolkit for joint NER and normalization. TaggerOne is not specific to any entity type, requiring only annotated training data and a corresponding lexicon, and has been optimized for high throughput. ::: ::: ::: RESULTS ::: We validated TaggerOne with multiple gold-standard corpora containing both mention- and concept-level annotations. Benchmarking results show that TaggerOne achieves high performance on diseases (NCBI Disease corpus, NER f-score: 0.829, normalization f-score: 0.807) and chemicals (BioCreative 5 CDR corpus, NER f-score: 0.914, normalization f-score 0.895). These results compare favorably to the previous state of the art, notwithstanding the greater flexibility of the model. We conclude that jointly modeling NER and normalization greatly improves performance. ::: ::: ::: AVAILABILITY AND IMPLEMENTATION ::: The TaggerOne source code and an online demonstration are available at: http://www.ncbi.nlm.nih.gov/bionlp/taggerone ::: ::: ::: CONTACT ::: zhiyong.lu@nih.gov ::: ::: ::: SUPPLEMENTARY INFORMATION ::: Supplementary data are available at Bioinformatics online.


A Survey on Mobile Edge Computing


title: Towards Automated Swimming Analytics Using Deep Neural Networks

abstract: Methods for creating a system to automate the collection of swimming analytics on a pool-wide scale are considered in this paper. There has not been much work on swimmer tracking or the creation of a swimmer database for machine learning purposes. Consequently, methods for collecting swimmer data from videos of swim competitions are explored and analyzed. The result is a guide to the creation of a comprehensive collection of swimming data suitable for training swimmer detection and tracking systems. With this database in place, systems can then be created to automate the collection of swimming analytics.


A Novel Highly Thermostable Multifunctional Beta-Glycosidase from Crenarchaeon Acidilobus saccharovorans