Training Products of Experts by Minimizing Contrastive Divergence

It is possible to combine multiple latent-variable models of the same data by multiplying their probability distributions together and then renormalizing. This way of combining individual expert models makes it hard to generate samples from the combined model but easy to infer the values of the latent variables of each expert, because the combination rule ensures that the latent variables of different experts are conditionally independent when given the data. A product of experts (PoE) is therefore an interesting candidate for a perceptual system in which rapid inference is vital and generation is unnecessary. Training a PoE by maximizing the likelihood of the data is difficult because it is hard even to approximate the derivatives of the renormalization term in the combination rule. Fortunately, a PoE can be trained using a different objective function called contrastive divergence whose derivatives with regard to the parameters can be approximated accurately and efficiently. Examples are presented of contrastive divergence learning using several types of expert on several types of data.


Artificial General Intelligence

Against the backdrop of increasing progresses in AI research paired with a rise of AI applications in decision-making processes, security-critical domains as well as in ethically relevant frames, a largescale debate on possible safety measures encompassing corresponding long-term and short-term issues has emerged across different disciplines. One pertinent topic in this context which has been addressed by various AI Safety researchers is e.g. the AI alignment problem for which no final consensus has been achieved yet. In this paper, we present a multidisciplinary toolkit of AI Safety strategies combining considerations from AI and Systems Engineering as well as from Cognitive Science with a security mindset as often relevant in Cybersecurity. We elaborate on how AGI “Self-awareness” could complement different AI Safety measures in a framework extended by a jointly performed Human Enhancement procedure. Our analysis suggests that this hybrid framework could contribute to undertake the AI alignment problem from a new holistic perspective through security-building synergetic effects emerging thereof and could help to increase the odds of a possible safe future transition towards superintelligent systems.


Spectrum Sensing Methods and Dynamic Spectrum Sharing in Cognitive Radio Networks: A Survey

Dynamic spectrum access and cognitive radio (CR) are emerging technologies to utilize the scarce frequency spectrum in an efficient and opportunistic manner. They have been receiving tremendous amount of well-deserved attention in the literature. One of the challenging problems in dynamic spectrum access for cognitive radios is to sense the spectrum in wide band regime of heterogeneous networks to identify the spectrum opportunities. Our main goal in this paper is to present the state-of-the-art research results in signal processing techniques for spectrum sensing in CR network to make automatic and wise decision by unlicensed secondary users to adapt their transmission parameters according to their operating RF environment and respect the primary users. Specifically, we present dynamic spectrum sharing models for spectrum access and signal processing techniques for spectrum sensing by categorizing them into five basic groups. We also present the comparison of different signal processing techniques from different perspectives including networking metrics for the evaluation of the methods e.g. how accurate and how fast can sense spectrum utilization given the various methods and how this will affect networking functionality, and pros and cons of all the methods in the context of networking systems. We also present the status, research challenges and perspectives of signal processing/detection techniques for spectrum sensing in cognitive radio networks.


Signal Processing in Cognitive Radio

Cognitive radio allows for usage of licensed frequency bands by unlicensed users. However, these unlicensed (cognitive) users need to monitor the spectrum continuously to avoid possible interference with the licensed (primary) users. Apart from this, cognitive radio is expected to learn from its surroundings and perform functions that best serve its users. Such an adaptive technology naturally presents unique signal-processing challenges. In this paper, we describe the fundamental signal-processing aspects involved in developing a fully functional cognitive radio network, including spectrum sensing and spectrum sculpting.


Cognitive radio: brain-empowered wireless communications

Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: /spl middot/ highly reliable communication whenever and wherever needed; /spl middot/ efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radio-scene analysis. 2) Channel-state estimation and predictive modeling. 3) Transmit-power control and dynamic spectrum management. This work also discusses the emergent behavior of cognitive radio.


Cooperative Sensing among Cognitive Radios

Cognitive Radios have been advanced as a technology for the opportunistic use of under-utilized spectrum since they are able to sense the spectrum and use frequency bands if no Primary user is detected. However, the required sensitivity is very demanding since any individual radio might face a deep fade. We propose light-weight cooperation in sensing based on hard decisions to mitigate the sensitivity requirements on individual radios. We show that the "link budget" that system designers have to reserve for fading is a significant function of the required probability of detection. Even a few cooperating users (~10-20) facing independent fades are enough to achieve practical threshold levels by drastically reducing individual detection requirements. Hard decisions perform almost as well as soft decisions in achieving these gains. Cooperative gains in a environment where shadowing is correlated, is limited by the cooperation footprint (area in which users cooperate). In essence, a few independent users are more robust than many correlated users. Unfortunately, cooperative gain is very sensitive to adversarial/failing Cognitive Radios. Radios that fail in a known way (always report the presence/absence of a Primary user) can be compensated for by censoring them. On the other hand, radios that fail in unmodeled ways or may be malicious, introduce a bound on achievable sensitivity reductions. As a rule of thumb, if we believe that 1/N users can fail in an unknown way, then the cooperation gains are limited to what is possible with N trusted users.


An intrusion detection system against malicious attacks on the communication network of driverless cars

Vehicular ad hoc networking (VANET) have become a significant technology in the current years because of the emerging generation of self-driving cars such as Google driverless cars. VANET have more vulnerabilities compared to other networks such as wired networks, because these networks are an autonomous collection of mobile vehicles and there is no fixed security infrastructure, no high dynamic topology and the open wireless medium makes them more vulnerable to attacks. It is important to design new approaches and mechanisms to rise the security these networks and protect them from attacks. In this paper, we design an intrusion detection mechanism for the VANETs using Artificial Neural Networks (ANNs) to detect Denial of Service (DoS) attacks. The main role of IDS is to detect the attack using a data generated from the network behavior such as a trace file. The IDSs use the features extracted from the trace file as auditable data. In this paper, we propose anomaly and misuse detection to detect the malicious attack.


Modelling and testing consumer trust dimensions in e-commerce

Prior research has found trust to play a significant role in shaping purchase intentions of a consumer. However there has been limited research where consumer trust dimensions have been empirically defined and tested. In this paper we empirically test a path model such that Internet vendors would have adequate solutions to increase trust. The path model presented in this paper measures the three main dimensions of trust, i.e. competence, integrity, and benevolence. And assesses the influence of overall trust of consumers. The paper also analyses how various sources of trust, i.e. consumer characteristics, firm characteristic, website infrastructure and interactions with consumers, influence dimensions of trust. The model is tested using 365 valid responses. Findings suggest that consumers with high overall trust demonstrate a higher intention to purchase online. © 2017 Elsevier Ltd. All rights reserved.


Time Series Data Classification Using Recurrent Neural Network with Ensemble Learning

In statistics and signal processing, a time series is a sequence of data points, measured typically at successive times, spaced apart at uniform time intervals. Time series prediction is the use of a model to predict future events based on known past events; to predict future data points before they are measured. Solutions in such cases can be provided by non-parametric regression methods, of which each neural network based predictor is a class. As a learning method of time series data with neural network, Elman type Recurrent Neural Network has been known. In this paper, we propose the multi RNN. In order to verify the effectiveness of our proposed method, we experimented by the simple artificial data and the heart pulse wave data.


Evidence-based strategies in weight-loss mobile apps.

BACKGROUND
Physicians have limited time for weight-loss counseling, and there is a lack of resources to which they can refer patients for assistance with weight loss. Weight-loss mobile applications (apps) have the potential to be a helpful tool, but the extent to which they include the behavioral strategies included in evidence-based interventions is unknown.

PURPOSE
The primary aims of the study were to determine the degree to which commercial weight-loss mobile apps include the behavioral strategies included in evidence-based weight-loss interventions, and to identify features that enhance behavioral strategies via technology.

METHODS
Thirty weight-loss mobile apps, available on iPhone and/or Android platforms, were coded for whether they included any of 20 behavioral strategies derived from an evidence-based weight-loss program (i.e., Diabetes Prevention Program). Data on available apps were collected in January 2012; data were analyzed in June 2012.

RESULTS
The apps included on average 18.83% (SD=13.24; range=0%-65%) of the 20 strategies. Seven of the strategies were not found in any app. The most common technology-enhanced features were barcode scanners (56.7%) and a social network (46.7%).

CONCLUSIONS
Weight-loss mobile apps typically included only a minority of the behavioral strategies found in evidence-based weight-loss interventions. Behavioral strategies that help improve motivation, reduce stress, and assist with problem solving were missing across apps. Inclusion of additional strategies could make apps more helpful to users who have motivational challenges.


Pipsqueak: Lean Lambdas with Large Libraries

Microservices are usually fast to deploy because each microservice is small, and thus each can be installed and started quickly. Unfortunately, lean microservices that depend on large libraries will start slowly and harm elasticity. In this paper, we explore the challenges of lean microservices that rely on large libraries in the context of Python packages and the OpenLambda serverless computing platform. We analyze the package types and compressibility of libraries distributed via the Python Package Index and propose PipBench, a new tool for evaluating package support. We also propose Pipsqueak, a package-aware compute platform based on OpenLambda.


True Inline Cross-Coupled Coaxial Cavity Filters

In this paper, a novel true inline configuration for cross-coupled coaxial cavity filters is presented, which is characterized by a simple and compact structure, improved performance, and good tunability. Instead of using folded structures, dedicated coupling probes, or extra cavities, as required by conventional techniques, cross coupling is realized by changing the orientation of selected resonators. Sequential coupling between adjacent resonators and cross coupling between nonadjacent resonators are effectively controlled by introducing small metal plates at different locations. A six-pole bandpass filter with two transmission zeros was built and tested. The measurement and simulation results agree very well, demonstrating feasibility of the inline filter configuration. It enables compact design with improved resonator Q compared to conventional combline filters. Furthermore, cross coupling can be readily adjusted using tuning elements.


Bridgeless half-bridge AC-DC converter with series-connected two transformers

This paper proposes a bridgeless half-bridge ac-dc converter with series-connected two transformers. The proposed converter integrates the operation of the bridgeless power factor correction (PFC) boost rectifier and the asymmetrical pulse-width modulation half-bridge dc-dc converter. The proposed converter provides an isolated dc output voltage without any full-bridge diode rectifier. Conduction losses are lowered by eliminating the full-bridge diode rectifier. Switching losses are reduced by achieving zero-voltage switching of the power switches. To improve the power efficiency of the bridgeless half-bridge ac-dc converter, this paper presents the study results about the bridgeless half-bridge ac-dc converter with the series-connected two transformers. By using series-connected two transformers, the proposed convert has a simpler power circuit. The proposed converter gives a high efficiency, high power factor, and low cost. The proposed converter achieves a high efficiency of 93.5 % for 250 W (50 V / 5 A) output power at 90 Vrms line voltage.


Phase Current Sensor and Short-Circuit Detection based on Rogowski Coils Integrated on Gate Driver for 1.2 kV SiC MOSFET Half-Bridge Module

Silicon-carbide (SiC) MOSFETs are enabling electrical vehicle motor drives to meet the demands of higher power density, efficiency, and lower system cost. Hence, this paper seeks to explore the benefits that a gate-driver-level intelligence can contribute to SiC-based power inverters. The intelligence is brought by PCB-embedded Rogowski switch-current sensors (RSCS) integrated on the gate driver of a 1.2 kV, 300 A SiC MOSFET half-bridge module. They collect two MOSFET switch currents in a manner of high magnitude, high bandwidth, and solid signal isolation. The switch-current signals are used for short-circuit detection under various fault impedances, as well as for phase-current reconstruction by subtracting one switch current from another. The fundamentals and noise-immunity design of the gate driver containing the RSCS are presented in the paper and can be applied to any half-bridge power module. A three-phase inverter prototype has been built and operated in continuous PWM mode. On this setup, the performance and limitations of the short-circuit detection and phase-current reconstruction are experimentally validated by comparing with commercial current probes and Hall sensors.


Design of a CMOS bandgap Reference Circuit with a Wide temperature Range, High Precision and Low temperature coefficient

This paper presents an approach to the design of a high-precision CMOS voltage reference. The proposed circuit is designed for TSMC 0.35 m standard CMOS process. We design the ̄rstorder temperature compensation bandgap voltage reference circuit. The proposed post-simulated circuit delivers an output voltage of 0.596V and achieves the reported temperature coe±cient (TC) of 3.96 ppm/ C within the temperature range from 60 C to 130 C when the supply voltage is 1.8V. When simulated in a smaller temperature range from 40 C to 80 C, the circuit achieves the lowest reported TC of 2.09 ppm/ C. The reference current is 16.586 A. This circuit provides good performances in a wide range of temperature with very small TC.


A brief survey on deep belief networks and introducing a new object oriented MATLAB toolbox (DeeBNet)

Nowadays, this is very popular to use the deep architectures in machine learning. Deep Belief Networks (DBNs) are deep architectures that use stack of Restricted Boltzmann Machines (RBM) to create a powerful generative model using training data. DBNs have many ability like feature extraction and classification that are used in many applications like image processing, speech processing and etc. This paper introduces a new object oriented MATLAB toolbox with most of abilities needed for the implementation of DBNs. According to the results of the experiments conducted on MNIST (image), ISOLET (speech), and 20 Newsgroups (text) datasets, it was shown that the toolbox can learn automatically a good representation of the input from unlabeled data with better discrimination between different classes. Also on all datasets, the obtained classification errors are comparable to those of state of the art classifiers. In addition, the toolbox supports different sampling methods (e.g. Gibbs, CD, PCD and our new FEPCD method), different sparsity methods (quadratic, rate distortion and our new normal method), different RBM types (generative and discriminative), using GPU, etc. The toolbox is a user-friendly open source software and is freely available on the website http://ceit.aut.ac.ir/~keyvanrad/DeeBNet%20Toolbox.html .


Detection and Visualization of Android Malware Behavior

Malware analysts still need to manually inspect malware samples that are considered suspicious by heuristic rules. They dissect software pieces and look for malware evidence in the code. The increasing number of malicious applications targeting Android devices raises the demand for analyzing them to find where the malcode is triggered when user interacts with them. In this paper a framework to monitor and visualize Android applications’ anomalous function calls is described. Our approach includes platformindependent application instrumentation, introducing hooks in order to trace restricted API functions used at runtime of the application. These function calls are collected at a central server where the application behavior filtering and a visualization take place. This can help Android malware analysts in visually inspecting what the application under study does, easily identifying such malicious functions.


Discriminatively trained recurrent neural networks for single-channel speech separation

This paper describes an in-depth investigation of training criteria, network architectures and feature representations for regression-based single-channel speech separation with deep neural networks (DNNs). We use a generic discriminative training criterion corresponding to optimal source reconstruction from time-frequency masks, and introduce its application to speech separation in a reduced feature space (Mel domain). A comparative evaluation of time-frequency mask estimation by DNNs, recurrent DNNs and non-negative matrix factorization on the 2nd CHiME Speech Separation and Recognition Challenge shows consistent improvements by discriminative training, whereas long short-term memory recurrent DNNs obtain the overall best results. Furthermore, our results confirm the importance of fine-tuning the feature representation for DNN training.


Accurate Whole Human Genome Sequencing using Reversible Terminator Chemistry

DNA sequence information underpins genetic research, enabling discoveries of important biological or medical benefit. Sequencing projects have traditionally used long (400–800 base pair) reads, but the existence of reference sequences for the human and many other genomes makes it possible to develop new, fast approaches to re-sequencing, whereby shorter reads are compared to a reference to identify intraspecies genetic variation. Here we report an approach that generates several billion bases of accurate nucleotide sequence per experiment at low cost. Single molecules of DNA are attached to a flat surface, amplified in situ and used as templates for synthetic sequencing with fluorescent reversible terminator deoxyribonucleotides. Images of the surface are analysed to generate high-quality sequence. We demonstrate application of this approach to human genome sequencing on flow-sorted X chromosomes and then scale the approach to determine the genome sequence of a male Yoruba from Ibadan, Nigeria. We build an accurate consensus sequence from >30× average depth of paired 35-base reads. We characterize four million single-nucleotide polymorphisms and four hundred thousand structural variants, many of which were previously unknown. Our approach is effective for accurate, rapid and economical whole-genome re-sequencing and many other biomedical applications.


Pleasure and orgasm in women with Female Genital Mutilation/Cutting (FGM/C).

INTRODUCTION
Female genital mutilation/cutting (FGM/C) violates human rights. FGM/C women's sexuality is not well known and often it is neglected by gynecologists, urologists, and sexologists. In mutilated/cut women, some fundamental structures for orgasm have not been excised.

AIM
The aim of this report is to describe and analyze the results of four investigations on sexual functioning in different groups of cut women.

MAIN OUTCOME MEASURE

INSTRUMENTS
semistructured interviews and the Female Sexual Function Index (FSFI).

METHODS

SAMPLE
137 adult women affected by different types of FGM/C; 58 young FGM/C ladies living in the West; 57 infibulated women; 15 infibulated women after the operation of defibulation.

RESULTS
The group of 137 women, affected by different types of FGM/C, reported orgasm in almost 86%, always 69.23%; 58 mutilated young women reported orgasm in 91.43%, always 8.57%; after defibulation 14 out of 15 infibulated women reported orgasm; the group of 57 infibulated women investigated with the FSFI questionnaire showed significant differences between group of study and an equivalent group of control in desire, arousal, orgasm, and satisfaction with mean scores higher in the group of mutilated women. No significant differences were observed between the two groups in lubrication and pain.

CONCLUSION
Embryology, anatomy, and physiology of female erectile organs are neglected in specialist textbooks. In infibulated women, some erectile structures fundamental for orgasm have not been excised. Cultural influence can change the perception of pleasure, as well as social acceptance. Every woman has the right to have sexual health and to feel sexual pleasure for full psychophysical well-being of the person. In accordance with other research, the present study reports that FGM/C women can also have the possibility of reaching an orgasm. Therefore, FGM/C women with sexual dysfunctions can and must be cured; they have the right to have an appropriate sexual therapy.


Midpalatal implants vs headgear for orthodontic anchorage--a randomized clinical trial: cephalometric results.

INTRODUCTION
The purpose of this study was to compare the clinical effectiveness of the midpalatal implant as a method of reinforcing anchorage during orthodontic treatment with that of conventional extraoral anchorage. This was a prospective, randomized, clinical trial at Chesterfield and North Derbyshire Royal Hospital NHS Trust and the Charles Clifford Dental Hospital, Sheffield, in the United Kingdom.

METHODS
Fifty-one orthodontic patients between the ages of 12 and 39, with Class II Division 1 malocclusion and absolute anchorage requirements, were randomly allocated to receive either a midpalatal implant or headgear to reinforce orthodontic anchorage. The main outcome was to compare the mesial movement of the molars and the incisors of the 2 treatment groups between the start and the end of anchorage reinforcement as measured from cephalometric radiographs.

RESULTS
The reproducibility of the measuring technique was acceptable. There were significant differences between T1 and T2 in the implant group for the positions of the maxillary central incisor (P <.001), the maxillary molar (P = .009), and the mandibular molar (P <.001). There were significant differences between T1 and T2 in the headgear group for the positions of the mandibular central incisor (P <.045), the maxillary molar (P <.001), and the mandibular molar (P <.001). All skeletal and dental points moved mesially more in the headgear group during treatment than in the implant group. These ranged from an average of 0.5 mm more mesially for the mandibular permanent molar to 1.5 mm more mesially for the maxillary molar and the mandibular base. No treatment changes between the groups were statistically significant.

CONCLUSIONS
Midpalatal implants are an acceptable technique for reinforcing anchorage in orthodontic patients.


Electronic Game Play and School Performance of Adolescents in Southern Thailand

Increasing exposure of children and adolescents to electronic media is a worldwide phenomenon, including in Thailand. To date, few studies examine electronic game play in Thai adolescents. Our research describes the prevalence of electronic game play and examines associations between the time spent engaged in electronic game play and school performance of adolescents in Hat-Yai municipality. A cross-sectional study was conducted among 1,492 adolescents from four secondary schools and one commercial college from January through March 2007, using questionnaires for collecting information about demographic data, school grades, and electronic game play behaviors. The prevalence of electronic game play was 75% in boys and 59% in girls. Twenty-two percent of boys and 8.7% of girls played electronic games every day with more than 2 hours per session. The two most common places of game play were at game shops (71%), followed by at their own home (70%). Using linear regression analysis, the "low user or less than 2 hours per session" game players and females were less likely to have school grades below 3.00 with adjusted odds ratios of 0.44 (95% CI 0.25-0.80, p = 0.004) and 0.49 (95% CI 0.30-0.76, p = 0.005) respectively. This study finds that excessive playing of electronic games is associated with school grades below 3.00.


Implant Removal after Percutaneous Short Segment Fixation for Thoracolumbar Burst Fracture : Does It Preserve Motion?

OBJECTIVE
The purpose of this study was to evaluate the efficacy of implant removal of percutaneous short segment fixation after vertebral fracture consolidation in terms of motion preservation.

METHODS
Between May 2007 and January 2011, 44 patients underwent percutaneous short segment screw fixation due to a thoracolumbar burst fracture. Sixteen of these patients, who underwent implant removal 12 months after screw fixation, were enrolled in this study. Motor power was intact in all patients, despite significant vertebral height loss and canal compromise. The patients were divided into two groups by degree of osteoporosis : Group A (n=8), the non-osteoporotic group, and Group B (n=8), the osteoporotic group. Imaging and clinical findings including vertebral height loss, kyphotic angle, range of motion (ROM), and complications were analyzed.

RESULTS
Significant pain relief was achieved in both groups at final follow-up versus preoperative values. In terms of vertebral height loss, both groups showed significant improvement at 12 months after screw fixation and restored vertebral height was maintained to final follow-up in spite of some correction loss. ROM (measured using Cobb's method) in flexion and extension in Group A was 10.5° (19.5/9.0°) at last follow-up, and in Group B was 10.2° (18.8/8.6°) at last follow-up. Both groups showed marked improvement in ROM as compared with the screw fixation state, which was considered motionless.

CONCLUSION
Removal of percutaneous implants after vertebral fracture consolidation can be an effective treatment to preserve motion regardless of osteoporosis for thoracolumbar burst fractures.


The effects of monetary and social rewards on task performance in children and adolescents: liking is not enough.

The current study compared the effects of reward anticipation on task performance in children and adolescents (8-16 years old) using monetary and various social rewards. Eighty-five typically developing children undertook the Monetary Incentive Delay (MID) task. Of these 44 also undertook the Social Incentive Delay (SID-basic) task where social reward was operationalized as a smiling face and spoken compliments. Forty-one children participated in the SID-plus where points were added to a pictogram with written compliments. In a preparatory validation study participants were asked howmuch they liked the SID-basic rewards.Results showed that there was an effect of reward size on accuracy and RT in both the MID task and SID-plus, but not SID-basic. Subjective value of the SID-basic rewards was rated higher with hypothesized increasing reward intensity. In conclusion, although the social rewards in SID-basic were liked by children andadolescents in the validation study, they had no effect on the behaviour. Only when points were added (SID-plus), anticipated social reward affected task performance. Thus our results highlight (i) the difference between likeability andreinforcing quality and (ii) the need for a quantifiable element to rewards for themto be reinforcing for children.


Subgoal-labeled instructional material improves performance and transfer in learning to develop mobile applications

Mental models are mental representations of how an action changes a problem state. Creating a mental model early in the learning process is a strong predictor of success in computer science classes. One major problem in computer science education, however, is that novices have difficulty creating mental models perhaps because of the cognitive overload caused by traditional teaching methods. The present study employed subgoal-labeled instructional materials to promote the creation of mental models when teaching novices to program in Android App Inventor. Utilizing this and other well-established educational tools, such as scaffolding, to reduce cognitive load in computer science education improved the performance of participants on novel tasks when learning to develop mobile applications.


Predicting Knowledge Sharing Intention Based on Theory of Reasoned Action Framework : An Empirical Study on Higher Education Institution

Based on theory of reasoned action (TRA) theoretical framework, this study try to predict individual knowledge sharing intention on higher education institution context. This study also integrate some potential factors (channel richness, psychological forces and organizational climate factors) which supporting individuals' knowledge-sharing intentions Through a field survey of 242 lecturer on public and privateuniversity at Purwokerto and Yogyakarta city, this study confirm our hypothesis that attitudes toward knowledge sharing and subjective norms with regard to knowledge sharing as well as organizational climate affect individuals' intentions to share knowledge. This study showed thatPerceived Reciprocal Benefits, Perceived Enjoyment in Helping Others, Perceived Reputation Enhancement and channel richness affect individuals'attitudes toward knowledge sharing while organizational climate affect subjective norms. Overall, the results of the study advance prior research in the area of knowledge sharing by shedding light on the determinants of knowledge sharing behaviors of knowledge workers. These insights could be used by university in developing work environments that are conducive for knowledge worker to share their knowledge. Keyword: Knowledge sharing intention, TRA, Knowledge worker, attitude, university.


Gradient Domain Guided Image Filtering

Guided image filter (GIF) is a well-known local filter for its edge-preserving property and low computational complexity. Unfortunately, the GIF may suffer from halo artifacts, because the local linear model used in the GIF cannot represent the image well near some edges. In this paper, a gradient domain GIF is proposed by incorporating an explicit first-order edge-aware constraint. The edge-aware constraint makes edges be preserved better. To illustrate the efficiency of the proposed filter, the proposed gradient domain GIF is applied for single-image detail enhancement, tone mapping of high dynamic range images and image saliency detection. Both theoretical analysis and experimental results prove that the proposed gradient domain GIF can produce better resultant images, especially near the edges, where halos appear in the original GIF.


Dead Code Detection Method Based on Program Slicing

Malicious code writers often insert dead code into the source code to prevent reverse engineer analysis. There are few methods for detecting dead code used in conjunction with opaque predicates. In this paper, a method based on program slicing for dead code detection is presented, which combines static slicing analysis and dynamic slicing analysis. Further, we design a dead code detection framework in the LLVM compiler infrastructure. The proposed method is used to detect the dead code inserted in some benchmark programs. The experimental results show that the proposed method has high detection rate.


Face poser: Interactive modeling of 3D facial expressions using facial priors

This article presents an intuitive and easy-to-use system for interactively posing 3D facial expressions. The user can model and edit facial expressions by drawing freeform strokes, by specifying distances between facial points, by incrementally editing curves on the face, or by directly dragging facial points in 2D screen space. Designing such an interface for 3D facial modeling and editing is challenging because many unnatural facial expressions might be consistent with the user's input. We formulate the problem in a maximum a posteriori framework by combining the user's input with priors embedded in a large set of facial expression data. Maximizing the posteriori allows us to generate an optimal and natural facial expression that achieves the goal specified by the user. We evaluate the performance of our system by conducting a thorough comparison of our method with alternative facial modeling techniques. To demonstrate the usability of our system, we also perform a user study of our system and compare with state-of-the-art facial expression modeling software (Poser 7).


High-quality passive facial performance capture using anchor frames

We present a new technique for passive and markerless facial performance capture based on anchor frames. Our method starts with high resolution per-frame geometry acquisition using state-of-the-art stereo reconstruction, and proceeds to establish a single triangle mesh that is propagated through the entire performance. Leveraging the fact that facial performances often contain repetitive subsequences, we identify anchor frames as those which contain similar facial expressions to a manually chosen reference expression. Anchor frames are automatically computed over one or even multiple performances. We introduce a robust image-space tracking method that computes pixel matches directly from the reference frame to all anchor frames, and thereby to the remaining frames in the sequence via sequential matching. This allows us to propagate one reconstructed frame to an entire sequence in parallel, in contrast to previous sequential methods. Our anchored reconstruction approach also limits tracker drift and robustly handles occlusions and motion blur. The parallel tracking and mesh propagation offer low computation times. Our technique will even automatically match anchor frames across different sequences captured on different occasions, propagating a single mesh to all performances.


Hierarchical Model-Based Motion Estimation

This paper describes a hierarchical estimation framework for the computation of diverse representations of motion information. The key features of the resulting framework (or family of algorithms) a,re a global model that constrains the overall structure of the motion estimated, a local rnodel that is used in the estimation process, and a coa,rse-fine refinement strategy. Four specific motion models: affine flow, planar surface flow, rigid body motion, and general optical flow, are described along with their application to specific examPles.


Detailed Real-Time Urban 3D Reconstruction from Video

The paper presents a system for automatic, geo-registered, real-time 3D reconstruction from video of urban scenes. The system collects video streams, as well as GPS and inertia measurements in order to place the reconstructed models in geo-registered coordinates. It is designed using current state of the art real-time modules for all processing steps. It employs commodity graphics hardware and standard CPU’s to achieve real-time performance. We present the main considerations in designing the system and the steps of the processing pipeline. Our system extends existing algorithms to meet the robustness and variability necessary to operate out of the lab. To account for the large dynamic range of outdoor videos the processing pipeline estimates global camera gain changes in the feature tracking stage and efficiently compensates for these in stereo estimation without impacting the real-time performance. The required accuracy for many applications is achieved with a two-step stereo reconstruction process exploiting the redundancy across frames. We show results on real video sequences comprising hundreds of thousands of frames.


Establishment of the Diagnosis of Follicular Occlusion Tetrad

Background: Follicular occlusion tetrad (FOT) is a clinical syndrome consisting of suppurative hidradenitis, acne conglobata, dissecting cellulitis of the scalp, and pilonidal sinus. FOT is a rare case that is mostly found in severe conditions resulting in resistance to therapy. Diagnostic accuracy and therapy for all components of FOT is extremely important. Case: A 43-year old male presented with small, painful blisters and bumps filled with pus that had been ongoing for five years. We found pustules and erythematous nodules and multiple abscess with atrophic scars on the scalp, anterior and posterior trunks, abdomen, groin and gluteal regions on the facial region, we found multiple atrophic scars. The pus culture showed the presence of Escherichia coli, and fistulography examination revealed multiple cutaneous fistules and enterocutaneous sinus. The histopathological examination indicated rupture of hair follicles, follicular plugs, and infiltration of heavy-mixed infiltrate cells. Discussion: The FOT diagnosis was established through medical history recording, physical and histopathological examinations, and fistulography. Upon medical history recording and physical examination, we found pustules and nodules in several hairy areas. The fistulographic examination showed some fistules and sinus tracts, and the histopathological examination showed adnexal tissue damage caused by occlusion of hair follicles and inflammation due to accumulation of keratin and debris. All these findings led to the FOT diagnosis. ISSN 2476-2415


A Case Study of Automated Face Recognition: The Boston Marathon Bombings Suspects

While face recognition can be a useful tool, helping authorities to narrow leads and confirm a criminal suspect's identity, the media tended to highlight the technology's limitations in the Boston Marathon bombings investigation. Consequently, we were motivated to reexamine the efficacy of unconstrained face recognition using this incident as a case study. We chose two automated face recognition systems known for their superior unconstrained matching performance based on tests conducted by the US National Institute of Standards and Technology: NEC's NeoFace v3.1 and Google-owned Pittsburgh Pattern Recognition v5.2.2. To conduct the experiment, we added six reference images of the Tsarnaevs taken from press releases and news articles following their identification to a background database of 1 million mug shots. Against this database, we searched for matches of the five face images of the brothers extracted by the FBI from surveillance, smartphone, or point-and-shoot camera footage before their identification.


Using link structure to infer opinions in social networks

The emergence of online social networks in the past few years has generated an enormous amount of information about potentially any subject. Valuable data containing users' opinions and thoughts are available on those repositories and several sentiment analysis techniques have been proposed that address the problem of understanding the opinion orientation of the users' postings. In this paper, we take a different perspective to the problem through a user centric approach, which uses a graph to model users (and their postings) and applies link mining techniques to infer opinions of users. Preliminary experiments on a Twitter corpus have shown promising results.


Political Facebook groups: Micro-activism and the digital front stage

The growth of social networking sites (SNS), like Facebook, has caused many to rethink how we understand political activism and citizen engagement. In 2010, 43% of Internet users reported using social networking sites " several times a day, " a sizeable increase from 2009 where only 34% reported using social networking at the same rate (Pew 2010). Research on digital activism has emphasized the use of websites, listservs, and other forums by formal organizations to raise awareness for their specific causes (McCaughey and Ayers 2003; Van de Donk 2004). Information communication technologies (ICT's) have been regarded as particularly effective tools in mobilizing disparate, resource-poor counter-spheres in collective off-line activity (Van de Donk 2004). In recent years, scholars have moved away from viewing information communication technologies (ICT's) as simply a tool for formal activist organizations. Breindl (2010) argues that on-line digital activism has largely been examined through a classic " social movement paradigm. " She calls for scholarship that looks at how the meaning and structure of activism has been transformed by ICT's. One key way it has been transformed is by allowing for small scale, many-to-many forms of politically oriented communication, what I conceptualize as a new term: microactivism. Examples include the formation of political Facebook groups, re-tweeting of articles of political interest and sharing politically relevant videos on YouTube. These acts reflect micro-level intentions and are not necessarily geared towards mobilization like more traditional forms of digital activism. This microactivism has helped bring about a radical reformulation of the political arena. One major change that emerges from the ease of many-to-many communication brought about by SMS is the reduced incentive to join social movements (Earl and Schussman 2003). These new applications encourage movement entrepreneurs (Garrett 2006) not affiliated with formal social movements but interested in fomenting social change. The ease with which individuals can create content and connect with one another to share content is viewed by others as a harbinger of a more democratic and egalitarian society (Benkler 2006, Jenkins 2007, Shirky 2010). Salter (2005) argues that sites like SNS' provide new radical public spheres that provide additional spaces for voice cultivation and political citizenship formation. More importantly, in his view information technologies help shift political discourse to more informal venues that are less subject to elite control (Dahlberg 2004). Social critics like Morozov (2009) suggest, however, that microactivism might do more harm than good. Morozov (2009) refers …


Classification of Model Transformation Approaches

While the current OMG standards such as the Meta Object Facility (MOF) [MOF] and the UML [UML] provide a well-established foundation for defining PIMs and PSMs, no such wellestablished foundation exists for transforming PIMs into PSMs [GLR+02]. In 2002, in its effort to change this situation, the OMG initiated a standardization process by issuing a Request for Proposal (RFP) on Query / Views / Transformations (QVT) [QVT]. This process will eventually lead to an OMG standard for defining model transformations, which will be of interest not only for PIM-to-PSM transformations, but also for defining views on models and synchronization between models. Driven by practical needs and the OMG’s request, a large number of approaches to model transformation have recently been proposed.


Slant: A Blackboard System to Generate Plot, Figuration, and Narrative Discourse Aspects of Stories

We introduce Slant, a system that integrates more than a decade of research into computational creativity, and specifically story generation, by connecting subsystems that deal with plot, figuration, and the narrative discourse using a blackboard. The process of integrating these systems highlights differences in the representation of story and has led to a better understanding of how story can be usefully abstracted. The plot generator MEXICA and a component of Curveship are used with little modification in Slant, while the figuration subsystem Fig-S and the template generator GRIOT-Gen, inspired by GRIOT, are also components. The development of the new subsystem Verso, which deals with genre, shows how different genres can be computationally modeled and applied to in-development stories to generate results that are surprising in terms of their connections and valuable in terms of their relationship to cultural questions. Example stories are discussed, as is the potential of the system to allow for broader collaboration, the empirical testing of how subsystems interrelate, and possible contributions in literary and artistic contexts.


Probabilistic Horn Abduction and Bayesian Networks

This paper presents a simple framework for Horn clause abduc tion with probabilities associated with hypotheses The framework incorporates assumptions about the rule base and independence as sumptions amongst hypotheses It is shown how any probabilistic knowledge representable in a discrete Bayesian belief network can be represented in this framework The main contribution is in nding a relationship between logical and probabilistic notions of evidential reasoning This provides a useful representation language in its own right providing a compromise between heuristic and epistemic ad equacy It also shows how Bayesian networks can be extended be yond a propositional language This paper also shows how a language with only unconditionally independent hypotheses can represent any probabilistic knowledge and argues that it is better to invent new hy potheses to explain dependence rather than having to worry about dependence in the language Scholar Canadian Institute for Advanced Research Probabilistic Horn abduction and Bayesian networks


Locking the Elbow: Improved Antibody Fab Fragments as Chaperones for Structure Determination.

Antibody Fab fragments have been exploited with significant success to facilitate the structure determination of challenging macromolecules as crystallization chaperones and as molecular fiducial marks for single particle cryo-electron microscopy approaches. However, the inherent flexibility of the "elbow" regions, which link the constant and variable domains of the Fab, can introduce disorder and thus diminish their effectiveness. We have developed a phage display engineering strategy to generate synthetic Fab variants that significantly reduces elbow flexibility, while maintaining their high affinity and stability. This strategy was validated using previously recalcitrant Fab-antigen complexes where introduction of an engineered elbow region enhanced crystallization and diffraction resolution. Furthermore, incorporation of the mutations appears to be generally portable to other synthetic antibodies and may serve as a universal strategy to enhance the success rates of Fabs as structure determination chaperones.


Double Pole Four Throw switch design with CMOS inverter

To avoid the uses of multiple RF chain associated with the multiple antennas, RF switch is most essential component. Multiple antennas systems are used to replace traditional single antennas circuitry in the radio transceiver system in order to improve the transmission capability and reliability. The desired switching system must have low cost and simple structure, yet still can capture all the advantage of Multiple Inputs Multiple Output (MIMO) systems. In this paper, we have proposed a Double Pole Four Throw (DP4T) RF switch using Complementary Metal Oxide Semiconductor (CMOS) technology with inverter property. This switch is low in cost, and capable of selecting data streams to or from the two antennas for transmitting or receiving processes, respectively.


Trellis: Privilege Separation for Multi-user Applications Made Easy

Operating systems provide a wide variety of resource isolation and access control mechanisms, ranging from traditional user-based security models to fine-grained permission systems as found in modern mobile operating systems. However, comparatively little assistance is available for defining and enforcing access control policies within multiuser applications. These applications, often found in enterprise environments, allow multiple users to operate at different privilege levels in terms of exercising application functionality and accessing data. Developers of such applications bear a heavy burden in ensuring that security policies over code and data in this setting are properly expressed and enforced. We present Trellis, an approach for expressing hierarchical access control policies in applications and enforcing these policies during execution. The approach enhances the development toolchain to allow programmers to partially annotate code and data with simple privilege level tags, and uses a static analysis to infer suitable tags for the entire application. At runtime, policies are extracted from the resulting binaries and are enforced by a modified operating system kernel. Our evaluation demonstrates that this approach effectively supports the development of secure multi-user applications with modest runtime performance overhead.


Markov Chains for Exploring Posterior Distributions

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White matter lesion extension to automatic brain tissue segmentation on MRI

A fully automated brain tissue segmentation method is optimized and extended with white matter lesion segmentation. Cerebrospinal fluid (CSF), gray matter (GM) and white matter (WM) are segmented by an atlas-based k-nearest neighbor classifier on multi-modal magnetic resonance imaging data. This classifier is trained by registering brain atlases to the subject. The resulting GM segmentation is used to automatically find a white matter lesion (WML) threshold in a fluid-attenuated inversion recovery scan. False positive lesions are removed by ensuring that the lesions are within the white matter. The method was visually validated on a set of 209 subjects. No segmentation errors were found in 98% of the brain tissue segmentations and 97% of the WML segmentations. A quantitative evaluation using manual segmentations was performed on a subset of 6 subjects for CSF, GM and WM segmentation and an additional 14 for the WML segmentations. The results indicated that the automatic segmentation accuracy is close to the interobserver variability of manual segmentations.


T-bar reconstruction of constricted ears and a new classification.

For the correction of constricted ears, many techniques are described in the literature, the majority based on Tanzer's classification of 1975. The improvements in ear reconstruction published by Brent, Nagata, Firmin and Park make better outcomes possible. It is therefore that a new classification for constricted ears is proposed, together with an alternative technique for correction of group IIA and IIB deformities, using a T-strut of costal cartilage to reconstruct the underdeveloped or missing superior crus of the antihelix.


Penetration testing in a box

Network and application vulnerability assessments have a tendency to be difficult and costly; however, failing to have an assessment done and fixing security loopholes may result in a security breach by malicious attackers. A security breach can cost an organization time and money remediating the damage, such as lost confidential business information, which far exceeds the cost of a security assessment. Our solution for this problem is a semi-automated system that can help a penetration tester, security professional, or systems administrator, scan and report on the vulnerabilities of a network and services running on the network. It is also able to perform some simulated attacks. This system relies on a miniaturized computer to host the necessary components to conduct a security assessment. This system has been done in an open source manner to allow others to contribute and benefit from a community effort in the name of security.


X-Band GaN SPDT MMIC with over 25 Watt Linear Power Handling

Single pole double throw (SPDT) switches are becoming more and more key components in phased-array radar transmit/receive modules. An SPDT switch must be able to handle the output power of a high power amplifier and must provide enough isolation to protect the low noise amplifier in the receive chain when the T/R module is transmitting. Therefore gallium nitride technology seems to become a key technology for high power SPDT switch design. The technology shows good performance on microwave frequencies and is able to handle high power. An X-band SPDT switch, with a linear power handling of over 25 W, has been designed, measured and evaluated. The circuit is designed in the coplanar waveguide AlGaN/GaN technology established at QinetiQ.


The nature of procrastination: a meta-analytic and theoretical review of quintessential self-regulatory failure.

Procrastination is a prevalent and pernicious form of self-regulatory failure that is not entirely understood. Hence, the relevant conceptual, theoretical, and empirical work is reviewed, drawing upon correlational, experimental, and qualitative findings. A meta-analysis of procrastination's possible causes and effects, based on 691 correlations, reveals that neuroticism, rebelliousness, and sensation seeking show only a weak connection. Strong and consistent predictors of procrastination were task aversiveness, task delay, self-efficacy, and impulsiveness, as well as conscientiousness and its facets of self-control, distractibility, organization, and achievement motivation. These effects prove consistent with temporal motivation theory, an integrative hybrid of expectancy theory and hyperbolic discounting. Continued research into procrastination should not be delayed, especially because its prevalence appears to be growing.


Morpheo: Traceable Machine Learning on Hidden data

Morpheo is a transparent and secure machine learning platform collecting and analysing large datasets. It aims at building state-of-the art prediction models in various fields where data are sensitive. Indeed, it offers strong privacy of data and algorithm, by preventing anyone to read the data, apart from the owner and the chosen algorithms. Computations in Morpheo are orchestrated by a blockchain infrastructure, thus offering total traceability of operations. Morpheo aims at building an attractive economic ecosystem around data prediction by channelling cryptomoney from prediction requests to useful data and algorithms providers. Morpheo is designed to handle multiple data sources in a transfer learning approach in order to mutualize knowledge acquired from large datasets for applications with smaller but similar datasets. Document purpose: This is a white-paper: an introduction to the technology powering Morpheo. It is evolving with the project to reflect the current vision and development status of Morpheo. It introduces Morpheo as a data-agnostic platform, although Morpheo is originally developed for a medical application, namely sleep data analysis. For technical details about Morpheo, see the online documentation and source code.


A Method for Refocusing Photos using Depth from Defocus

This paper will describe a method for refocusing photos taken with a regular camera. By capturing a series of images from the same viewpoint with varying focal depth, a depth metric for the scene can be calculated and used to artificially blur the photo in a realistic way to emulate a new focal depth.


ROUTERLESS NETWORKS-ON-CHIP

Traditional bus-based interconnects are simple and easy to implement, but the scalability is greatly limited. While router-based networks-on-chip (NoCs) offer superior scalability, they also incur significant power and area overhead due to complex router structures. In this paper, we explore a new class of on-chip networks, referred to as Routerless NoCs, where routers are completely eliminated. We propose a novel design that utilizes on-chip wiring resources smartly to achieve comparable hop count and scalability as router-based NoCs. Several effective techniques are also proposed that significantly reduce the resource requirement to avoid new network abnormalities in routerless NoC designs. Evaluation results show that, compared with a conventional mesh, the proposed routerless NoC achieves 9.5X reduction in power, 7.2X reduction in area, 2.5X reduction in zero-load packet latency, and 1.7X increase in throughput. Compared with a state-of-the-art low-cost NoC design, the proposed approach achieves 7.7X reduction in power, 3.3X reduction in area, 1.3X reduction in zero-load packet latency, and 1.6X increase


Transferable Semi-Supervised Semantic Segmentation

The performance of deep learning based semantic segmentation models heavily depends on sufficient data with careful annotations. However, even the largest public datasets only provide samples with pixel-level annotations for rather limited semantic categories. Such data scarcity critically limits scalability and applicability of semantic segmentation models in real applications. In this paper, we propose a novel transferable semi-supervised semantic segmentation model that can transfer the learned segmentation knowledge from a few strong categories with pixel-level annotations to unseen weak categories with only image-level annotations, significantly broadening the applicable territory of deep segmentation models. In particular, the proposed model consists of two complementary and learnable components: a Label transfer Network (L-Net) and a Prediction transfer Network (PNet). The L-Net learns to transfer the segmentation knowledge from strong categories to the images in the weak categories and produces coarse pixel-level semantic maps, by effectively exploiting the similar appearance shared across categories. Meanwhile, the P-Net tailors the transferred knowledge through a carefully designed adversarial learning strategy and produces refined segmentation results with better details. Integrating the L-Net and P-Net achieves 96.5% and 89.4% performance of the fully-supervised baseline using 50% and 0% categories with pixel-level annotations respectively on PASCAL VOC 2012. With such a novel transfer mechanism, our proposed model is easily generalizable to a variety of new categories, only requiring image-level annotations, and offers appealing scalability in real applications.


FraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent Itemset Mining

This paper proposes an intelligent credit card fraud detection model for detecting fraud from highly imbalanced and anonymous credit card transaction datasets. The class imbalance problem is handled by finding legal as well as fraud transaction patterns for each customer by using frequent itemset mining. A matching algorithm is also proposed to find to which pattern (legal or fraud) the incoming transaction of a particular customer is closer and a decision is made accordingly. In order to handle the anonymous nature of the data, no preference is given to any of the attributes and each attribute is considered equally for finding the patterns. The performance evaluation of the proposed model is done on UCSD Data Mining Contest 2009 Dataset (anonymous and imbalanced) and it is found that the proposed model has very high fraud detection rate, balanced classification rate, Matthews correlation coefficient, and very less false alarm rate than other state-of-the-art classifiers.


A decision support based on data mining in e-banking

The use of data mining techniques in banking domain is suitable due to the nature and sensitivity of bank data and due to the real time complex decision processes. The main concern for a bank's manager is to take good decisions in order to minimize the risks level associated to bank's activities. It is very important for a bank to have knowledge of causes which generate the financial crises or imbalances. Lending is one of the most risky activities in banking area and adequate methods to support the decision making process are necessary. In this paper the authors present a prototype decision support system based on data mining techniques used in lending process. The proposed system was designed to assist a customer who applies for a credit and it may represent an extension for e-banking activities.


A Survey on Outlier Detection Techniques for Credit Card Fraud Detection

Credit card fraud detection is an important application of outlier detection. Due to drastic increase in digital frauds, there is a loss of billions dollars and therefore various techniques are evolved for fraud detection and applied to diverse business fields. The traditional fraud detection schemes use data analysis methods that require knowledge about different domains such as financial, economics, law and business practices. The current fraud detection techniques may be offline or online, and may use neural networks, clustering, genetic algorithm, decision tree etc. There are various outlier detection techniques are available such as statistical based, density based, clustering based and so on. This paper projected to find credit card fraud by using appropriate outlier detection technique, which is suitable for online applications where large scale data is involved. The method should also work efficiently for applications where memory and computation limitations are present. Here we have discussed one such unsupervised method Principal Component Analysis(PCA) to detect an outlier.


Anomaly detection: A survey

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.


Smart Home Mobile RFID-Based Internet-of-Things Systems and Services

Smart homes can apply new Internet-Of-Things concepts along with RFID technologies for creating ubiquitous services. This paper introduces a novel read-out method for a hierarchical wireless master-slave RFID reader architecture of multi standard NFC (Near Field Communication) and UHF (Ultra High Frequency) technologies to build a smart home service system that benefits in terms of cost, energy consumption and complexity. Various smart home service use cases such as washing programs, cooking, shopping and elderly health care are described as examples that make use of this system.


Handwritten document image segmentation into text lines and words

Article history: Received 22 July 2008 Received in revised form 23 February 2009 Accepted 14 May 2009


A hybrid dragonfly algorithm with extreme learning machine for prediction

In this work, a proposed hybrid dragonfly algorithm (DA) with extreme learning machine (ELM) system for prediction problem is presented. ELM model is considered a promising method for data regression and classification problems. It has fast training advantage, but it always requires a huge number of nodes in the hidden layer. The usage of a large number of nodes in the hidden layer increases the test/evaluation time of ELM. Also, there is no guarantee of optimality of weights and biases settings on the hidden layer. DA is a recently promising optimization algorithm that mimics the moving behavior of moths. DA is exploited here to select less number of nodes in the hidden layer to speed up the performance of the ELM. It also is used to choose the optimal hidden layer weights and biases. A set of assessment indicators is used to evaluate the proposed and compared methods over ten regression data sets from the UCI repository. Results prove the capability of the proposed DA-ELM model in searching for optimal feature combinations in feature space to enhance ELM generalization ability and prediction accuracy. The proposed model was compared against the set of commonly used optimizers and regression systems. These optimizers are namely, particle swarm optimization (PSO) and genetic algorithm (GA). The proposed DA-ELM model proved an advance overall compared methods in both accuracy and generalization ability.


A comparative study of vein pattern recognition for biometrie authentication

In informational age there are many information crucial to a individual. To protect the data from the unwanted use we proposed the biometric system of vein recognition as today's world demand for a easy and accurate biometric system to protect our data. The biometric system is accurate and easy to use as every individual has a unique vein pattern. In this paper we have studied various method used by different people to achieve the goal to secure world and give the reader a brief prospective of this technology.


The 2009 Mario AI Competition

This paper describes the 2009 Mario AI Competition, which was run in association with the IEEE Games Innovation Conference and the IEEE Symposium on Computational Intelligence and Games. The focus of the competition was on developing controllers that could play a version of Super Mario Bros as well as possible. We describe the motivations for holding this competition, the challenges associated with developing artificial intelligence for platform games, the software and API developed for the competition, the competition rules and organization, the submitted controllers and the results. We conclude the paper by discussing what the outcomes of the competition can teach us both about developing platform game AI and about organizing game AI competitions. The first two authors are the organizers of the competition, while the third author is the winner of the competition.


Characterization of evoked tactile sensation in forearm amputees with transcutaneous electrical nerve stimulation.

OBJECTIVE
The goal of this study is to characterize the phenomenon of evoked tactile sensation (ETS) on the stump skin of forearm amputees using transcutaneous electrical nerve stimulation (TENS).

APPROACH
We identified the projected finger map (PFM) of ETS on the stump skin in 11 forearm amputees, and compared perceptual attributes of the ETS in nine forearm amputees and eight able-bodied subjects using TENS. The profile of perceptual thresholds at the most sensitive points (MSPs) in each finger-projected area was obtained by modulating current amplitude, pulse width, and frequency of the biphasic, rectangular current stimulus. The long-term stability of the PFM and the perceptual threshold of the ETS were monitored in five forearm amputees for a period of 11 months.

MAIN RESULTS
Five finger-specific projection areas can be independently identified on the stump skin of forearm amputees with a relatively long residual stump length. The shape of the PFM was progressively similar to that of the hand with more distal amputation. Similar sensory modalities of touch, pressure, buzz, vibration, and numb below pain sensation could be evoked both in the PFM of the stump skin of amputees and in the normal skin of able-bodied subjects. Sensory thresholds in the normal skin of able-bodied subjects were generally lower than those in the stump skin of forearm amputees, however, both were linearly modulated by current amplitude and pulse width. The variation of the MSPs in the PFM was confined to a small elliptical area with 95% confidence. The perceptual thresholds of thumb-projected areas were found to vary less than 0.99 × 10(-2) mA cm(-2).

SIGNIFICANCE
The stable PFM and sensory thresholds of ETS are desirable for a non-invasive neural interface that can feed back finger-specific tactile information from the prosthetic hand to forearm amputees.


Evaluation of Text Summarization in a Cross-lingual Information Retrieval Framework

We report on research in multi-document summarization and on evaluation of summarization in the framework of cross-lingual information retrieval. This work was carried out during a summer workshop on Language Engineering held at Johns Hopkins University by a team of nine researchers from seven universities. The goals of the research were as follows: (1) to develop a toolkit for evaluation of single-document and multi-document summarizers, (2) to develop a modular multi-document summarizer, called MEAD, that works in both English and Chinese, and (3) to perform a meta-evaluation of four automatic summarizers, including MEAD, using several types of evaluation measures: some currently used by summarization researchers and a couple of novel techniques. Central to the experiments in this workshop was the cross-lingual experimental setup based on a large-scale Chinese and English parallel corpus. An extensive set of human judgments were specifically prepared by the Linguistic Data Consortium for our research. These human judgments include a) which documents are relevant to a certain query and b) which sentences in the relevant documents are most relevant to the query and which therefore constitute a good summary of the cluster. These judgments were used to construct variable-length multiand single document summaries as model summaries. Since one of the novel evaluation metrics that we used, Relevance Correlation, is based on the premise that good summaries preserve query relevance both within a language and across languages, we made use of a cross-lingual Information Retrieval (IR) engine. We evaluated the quality of the automatic summaries using co-selection and content-based evaluation, two established techniques. A relatively new metric, relative utility, was also extensively tested. Part of the new scientific contribution is the measurement of relevance correlation, which we introduced and systematically examined in this workshop. Relevance correlation measures the quality of summaries in comparison to the entire documents as a function of how much document relevance drops if summaries are indexed instead of documents. Our results show that this measure is sensible, in that it correlates with more established evaluation measures. Another contribution is the cross-lingual setup which allows us to automatically translate English queries into Chinese, perform Chinese IR with or without summarization. This allows us to calculate relevance correlation for English and for Chinese in parallel (i.e., for the same queries) and to take direct cross-lingual comparisons of evaluations. Additionally, an alternative way of constructing Chinese model summaries from English ones was implemented which relies on the sentence alignment of English and Chinese documents. The results of our large-scale meta-evaluation are numerous, but some of the highlights are the following: (1) All evaluation measures rank human summaries first, which is an appropriate and expected property of such measures, (2) Both relevance correlation and the content-based measures place leading sentence extracts ahead of the more sophisticated summarizers, (3) Relative utility ranks our system, MEAD, as the best summarizer for shorter summaries, although for longer summaries, lead-based summaries outperform MEAD, (4) Co-selection measurements show overall low agreement amongst humans (above chance), whereas relative utility reports higher numbers on the same data (but does not normalize for chance). The deliverable resources and software include: (1) a turn-key extractive multi-document summarizer, MEAD, which allows users to add their own features based on single sentences or pairs of sentences, (2) a large corpus of summaries produced by several automatic methods, including baseline and random summaries, (3) a collection of manual summaries produced by the Linguistic Data Consortium (LDC), (4) a battery of evaluation routines, (5) a collection of IR queries in English and Chinese and the corresponding relevance judgments from the Hong Kong news collection, (6) SMART relevance outputs for both full documents and summaries, (7) XML tools for processing of documents and summaries. JHU 2001 Summer workshop final report Evaluation of Text Summarization


AutoHair: fully automatic hair modeling from a single image

We introduce AutoHair, the first fully automatic method for 3D hair modeling from a single portrait image, with no user interaction or parameter tuning. Our method efficiently generates complete and high-quality hair geometries, which are comparable to those generated by the state-of-the-art methods, where user interaction is required. The core components of our method are: a novel hierarchical deep neural network for automatic hair segmentation and hair growth direction estimation, trained over an annotated hair image database; and an efficient and automatic data-driven hair matching and modeling algorithm, based on a large set of 3D hair exemplars. We demonstrate the efficacy and robustness of our method on Internet photos, resulting in a database of around 50K 3D hair models and a corresponding hairstyle space that covers a wide variety of real-world hairstyles. We also show novel applications enabled by our method, including 3D hairstyle space navigation and hair-aware image retrieval.


A Dual-Loop Antenna Design for Hepta-Band WWAN/LTE Metal-Rimmed Smartphone Applications

A simple direct-fed dual-loop antenna capable of providing hepta-band WWAN/LTE operation under surroundings of an unbroken metal rim in smartphone applications is proposed. The greatest highlight of this proposed antenna is that it provides a simple and effective multiband antenna solution for an unbroken metal-rimmed smartphone. The unbroken metal rim with 5 mm in height embraces the system circuit board of 130 × 70 mm2. Two no-ground portions of 10 × 70 mm2 and 5 × 70 mm2 are set on the top and bottom edge of the system circuit board, respectively. In-between the two separate no-ground portions, there is a system ground of 115 × 70 mm2 connected with the unbroken metal rim via a small grounded patch which divides the unbroken metal rim into two strips. Finally, a dual-loop antenna is formed by combining the inner system ground and two strips. This proposed dual-loop antenna is capable of covering GSM850/900/DCS/PCS/UMTS2100/LTE 2300/2500 operating bands. Detailed design considerations of the proposed antenna are described and both experimental and simulation results are also presented and discussed.


Kalman filtering with state constraints : a survey of linear and nonlinear algorithms

The Kalman filter is the minimum-variance state estimator for linear dynamic systems with Gaussian noise. Even if the noise is non-Gaussian, the Kalman filter is the best linear estimator. For nonlinear systems it is not possible, in general, to derive the optimal state estimator in closed form, but various modifications of the Kalman filter can be used to estimate the state. These modifications include the extended Kalman filter, the unscented Kalman filter, and the particle filter. Although the Kalman filter and its modifications are powerful tools for state estimation, we might have information about a system that the Kalman filter does not incorporate. For example, we may know that the states satisfy equality or inequality constraints. In this case we can modify the Kalman filter to exploit this additional information and get better filtering performance than the Kalman filter provides. This paper provides an overview of various ways to incorporate state constraints in the Kalman filter and its nonlinear modifications. If both the system and state constraints are linear, then all of these different approaches result in the same state estimate, which is the optimal constrained linear state estimate. If either the system or constraints are nonlinear, then constrained filtering is, in general, not optimal, and different approaches give different results.


Feature line extraction from unorganized noisy point clouds using truncated Fourier series

The detection of feature lines is important for representing and understanding geometric features of 3D models. In this paper, we introduce a new and robust method for extracting feature lines from unorganized point clouds. We use a one-dimensional truncated Fourier series for detecting feature points. Each point and its neighbors are approximated along the principal directions by using the truncated Fourier series, and the curvature of the point is computed from the approximated curves. The Fourier coefficients are computed by Fast Fourier Transform (FFT). We apply low-pass filtering to remove noise and to compute the curvature of the point robustly. For extracting feature points from the detected potential feature points, the potential feature points are thinned using a curvature weighted Laplacian-like smoothing method. The feature lines are constructed through growing extracted points and then projected onto the original point cloud. The efficiency and robustness of our approach is illustrated by several experimental results.


An analysis of facial expression recognition under partial facial image occlusion

In this paper, an analysis of the effect of partial occlusion on facial expression recognition is investigated. The classification from partially occluded images in one of the six basic facial expressions is performed using a method based on Gabor wavelets texture information extraction, a supervised image decomposition method based on Discriminant Non-negative Matrix Factorization and a shape-based method that exploits the geometrical displacement of certain facial features. We demonstrate how partial occlusion affects the above mentioned methods in the classification of the six basic facial expressions, and indicate the way partial occlusion affects human observers when recognizing facial expressions. An attempt to specify which part of the face (left, right, lower or upper region) contains more discriminant information for each facial expression, is also made and conclusions regarding the pairs of facial expressions misclassifications that each type of occlusion introduces, are drawn. 2008 Published by Elsevier B.V.


Otologic and Audiology Concerns of Microtia Repair.

Microtia is a congenital auricular deformity that commonly presents with associated congenital aural atresia. The most acute concern in these patients is concomitant hearing loss at birth. A team-based approach by plastic surgeons and otologists is necessary to address both the otologic and audiologic concerns of microtia and atresia. Hearing rehabilitation is imperative; yet it should not compromise the aesthetic components of reconstruction and vice versa. Here, the authors propose a framework to evaluate and manage patients with microtia and atresia with the goal of optimizing functional and cosmetic outcomes.


Building a smart home environment for service robots based on RFID and sensor networks

This paper is concerned with constructing a prototype smart home environment which has been built in the research building of Korea Institute of Industrial Technology (KITECH) to demonstrate the practicability of a robot-assisted future home environment. Key functionalities that a home service robot must provide are localization, navigation, object recognition and object handling. A considerable amount of research has been conducted to make the service robot perform these operations with its own sensors, actuators and a knowledge database. With all heavy sensors, actuators and a database, the robot could have performed the given tasks in a limited environment or showed the limited capabilities in a natural environment. We initiated a smart home environment project for light-weight service robots to provide reliable services by interacting with the environment through the wireless sensor networks. This environment consists of the following three main components: smart objects with an radio frequency identification (RFID) tag and smart appliances with sensor network functionality; the home server that connects smart devices as well as maintains information for reliable services; and the service robots that perform tasks in collaboration with the environment. In this paper, we introduce various types of smart devices which are developed for assisting the robot by providing sensing and actuation, and present our approach on the integration of these devices to construct the smart home environment. Finally, we discuss the future directions of our project.


Natural Language Interface to Relational Database (NLI-RDB) Through Object Relational Mapping (ORM)

This paper proposes a novel approach for building a Natural Language Interface to a Relational Database (NLI-RDB) using Conversational Agent (CA), Information Extraction (IE) and Object Relational Mapping (ORM) framework. The CA will help in disambiguating the user’s queries and guiding the user interaction. IE will play an important role in named entities extraction in order to map Natural Language queries into database queries. The ORM framework i.e. the Hibernate framework resolves the impedance mismatch between the Object Oriented Paradigms (OOP) and Relational Databases (RDBs) i.e. OOP concepts differ from RDB concepts, thus it reduces the complexity in generating SQL statements. Also, by utilizing ORM framework, the RDBs entities are mapped into real world objects, which bring the RDBs a step closer to the user. In addition, the ORM framework simplify the interaction between OOP and RDBs. The developed NLI-RDB system allows the user to interact with objects directly in natural language and through navigation, rather than by using SQL statements. This direct interaction tends to be easier and more acceptable for humans whom are nor technically orientated and have no SQL knowledge. The NLI-RDB system also offers friendly and interactive user interface in order to refine the query generated automatically. The NLI-RDB system has been evaluated by a group of participants through a combination of qualitative and quantitative measures. The experimental results show good performance of the prototype and excellent user’s satisfaction.


Artificial intelligence applications in the intensive care unit.

OBJECTIVE
To review the history and current applications of artificial intelligence in the intensive care unit.

DATA SOURCES
The MEDLINE database, bibliographies of selected articles, and current texts on the subject.

STUDY SELECTION
The studies that were selected for review used artificial intelligence tools for a variety of intensive care applications, including direct patient care and retrospective database analysis.

DATA EXTRACTION
All literature relevant to the topic was reviewed.

DATA SYNTHESIS
Although some of the earliest artificial intelligence (AI) applications were medically oriented, AI has not been widely accepted in medicine. Despite this, patient demographic, clinical, and billing data are increasingly available in an electronic format and therefore susceptible to analysis by intelligent software. Individual AI tools are specifically suited to different tasks, such as waveform analysis or device control.

CONCLUSIONS
The intensive care environment is particularly suited to the implementation of AI tools because of the wealth of available data and the inherent opportunities for increased efficiency in inpatient care. A variety of new AI tools have become available in recent years that can function as intelligent assistants to clinicians, constantly monitoring electronic data streams for important trends, or adjusting the settings of bedside devices. The integration of these tools into the intensive care unit can be expected to reduce costs and improve patient outcomes.


Design of a Wideband Waveguide Slot Array Antenna and its Decoupling Method for Synthetic Aperture Radar

For the synthetic aperture radar systems, there is an increasing demand for achieving resolutions of 0.2 mtimes0.2 m. As the range resolution and system bandwidth are inversely proportional, the system bandwidth is expected to be greater than 1 GHz. Thus an antenna with wider band needs to be developed. Waveguide slot antennas have been implemented on several SAR satellites due to its inherent advantages such as high efficiency and power handling capacity, but its bandwidth is quite limited. To avoid the manufacture difficulties of the ridge waveguide, which is capable of broadening the bandwidth of slot antennas, a novel antenna element with conventional waveguide is designed. The bandwidth of VSWR les1.5 is greater than 1 GHz at X-band. To reduce the mutual coupling of closely placed antenna elements, a decoupling method with cavity-like walls inserted between adjacent elements is adopted and their effects on the performance of the antenna are summarized.


Identification of a DNA-binding site for the transcription factor Haa1, required for Saccharomyces cerevisiae response to acetic acid stress

The transcription factor Haa1 is the main player in reprogramming yeast genomic expression in response to acetic acid stress. Mapping of the promoter region of one of the Haa1-activated genes, TPO3, allowed the identification of an acetic acid responsive element (ACRE) to which Haa1 binds in vivo. The in silico analysis of the promoter regions of the genes of the Haa1-regulon led to the identification of an Haa1-responsive element (HRE) 5'-GNN(G/C)(A/C)(A/G)G(A/G/C)G-3'. Using surface plasmon resonance experiments and electrophoretic mobility shift assays it is demonstrated that Haa1 interacts with high affinity (K(D) of 2 nM) with the HRE motif present in the ACRE region of TPO3 promoter. No significant interaction was found between Haa1 and HRE motifs having adenine nucleotides at positions 6 and 8 (K(D) of 396 and 6780 nM, respectively) suggesting that Haa1p does not recognize these motifs in vivo. A lower affinity of Haa1 toward HRE motifs having mutations in the guanine nucleotides at position 7 and 9 (K(D) of 21 and 119 nM, respectively) was also observed. Altogether, the results obtained indicate that the minimal functional binding site of Haa1 is 5'-(G/C)(A/C)GG(G/C)G-3'. The Haa1-dependent transcriptional regulatory network active in yeast response to acetic acid stress is proposed.


Analyzing spatial user behavior in computer games using geographic information systems

An important aspect of the production of digital games is user-oriented testing. A central problem facing practitioners is however the increasing complexity of user-game interaction in modern games, which places challenges on the evaluation of interaction using traditional user-oriented approaches. Gameplay metrics are instrumentation data which detail user behavior within the virtual environment of digital games, forming accurate and detailed datasets about user behavior that supplement existing user-testing methods such as playtesting and usability testing. In this paper existing work on gameplay metrics is reviewed, and spatial analysis of gameplay metrics introduced as a new approach in the toolbox of user-experience testing and research. Furthermore, Geographic Information Systems (GIS) are introduced as a tool for performing spatial analysis. A case study is presented with Tomb Raider: Underworld, showcasing the merger of GIS with gameplay metrics analysis and its application to game testing and design.


Nanoseconds Switching for High Voltage Circuit Using Avalanche Transistors

The avalanche transistor is suitable for switching high voltage in kilovolts region. In this paper, a simple switching circuit consists of avalanche transistor is demonstrated. Sixteen of ZTX 415 avalanche transistors were used to switch a high voltage circuit up to 4.5 kV. A PIC16F84 microcontroller was utilized as control unit to generate input trigger. The result shows that the developed circuit was able to switch applied voltage up-to 4.5 kV with an average falling time is 2.89 ns.


Signal Processing and Machine Learning with Differential Privacy: Algorithms and Challenges for Continuous Data

Private companies, government entities, and institutions such as hospitals routinely gather vast amounts of digitized personal information about the individuals who are their customers, clients, or patients. Much of this information is private or sensitive, and a key technological challenge for the future is how to design systems and processing techniques for drawing inferences from this large-scale data while maintaining the privacy and security of the data and individual identities. Individuals are often willing to share data, especially for purposes such as public health, but they expect that their identity or the fact of their participation will not be disclosed. In recent years, there have been a number of privacy models and privacy-preserving data analysis algorithms to answer these challenges. In this article, we will describe the progress made on differentially private machine learning and signal processing.


Overview of the ImageCLEF 2016 Medical Task

ImageCLEF is the image retrieval task of the Conference and Labs of the Evaluation Forum (CLEF). ImageCLEF has historically focused on the multimodal and language–independent retrieval of images. Many tasks are related to image classification and the annotation of image data as well. The medical task has focused more on image retrieval in the beginning and then retrieval and classification tasks in subsequent years. In 2016 a main focus was the creation of meta data for a collection of medical images taken from articles of the the biomedical scientific literature. In total 8 teams participated in the four tasks and 69 runs were submitted. No team participated in the caption prediction task, a totally new task. Deep learning has now been used for several of the ImageCLEF tasks and by many of the participants obtaining very good results. A majority of runs was submitting using deep learning and this follows general trends in machine learning. In several of the tasks multimodal approaches clearly led to best results.


Principal Component Analysis


Recognizing independent and joint activities among multiple residents in smart environments

The pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. A primary challenge that needs to be tackled to meet this need is the ability to recognize and track functional activities that people perform in their own homes and everyday settings. In this paper, we look at approaches to perform real-time recognition of Activities of Daily Living. We enhance other related research efforts to develop approaches that are effective when activities are interrupted and interleaved. To evaluate the accuracy of our recognition algorithms we assess them using real data collected from participants performing activities in our on-campus smart apartment testbed.


Definition Modeling: Learning to define word embeddings in natural language

Distributed representations of words have been shown to capture lexical semantics, as demonstrated by their effectiveness in word similarity and analogical relation tasks. But, these tasks only evaluate lexical semantics indirectly. In this paper, we study whether it is possible to utilize distributed representations to generate dictionary definitions of words, as a more direct and transparent representation of the embeddings’ semantics. We introduce definition modeling, the task of generating a definition for a given word and its embedding. We present several definition model architectures based on recurrent neural networks, and experiment with the models over multiple data sets. Our results show that a model that controls dependencies between the word being defined and the definition words performs significantly better, and that a characterlevel convolution layer designed to leverage morphology can complement word-level embeddings. Finally, an error analysis suggests that the errors made by a definition model may provide insight into the shortcomings of word embeddings.


Message Passing Inference for Large Scale Graphical Models with High Order Potentials

To keep up with the Big Data challenge, parallelized algorithms based on dual decomposition have been proposed to perform inference in Markov random fields. Despite this parallelization, current algorithms struggle when the energy has high order terms and the graph is densely connected. In this paper we propose a partitioning strategy followed by a message passing algorithm which is able to exploit pre-computations. It only updates the high-order factors when passing messages across machines. We demonstrate the effectiveness of our approach on the task of joint layout and semantic segmentation estimation from single images, and show that our approach is orders of magnitude faster than current methods.


Increasing emitter efficiency in 3.3-kV enhanced trench IGBTs for higher short-circuit capability

In this paper, a 3.3-kV Enhanced Trench IGBT has been designed with a high emitter efficiency, for improving its short-circuit robustness. The carrier distribution profile has been shaped in a way that it is possible to increase the electric field at the surface of the IGBT, and thereby, counteract the Kirk Effect onset. This design approach is beneficial for mitigating high-frequency oscillations, typically observed in IGBTs under short-circuit conditions. The effectiveness of the proposed design rule is validated by means of mixed-mode device simulations. Then, two IGBTs have been fabricated with different emitter efficiencies and tested under short circuit, validating that the high-frequency oscillations can be mitigated, with higher emitter efficiency IGBT designs.


Transparent authentication systems for mobile device security: A review

Sensitive data such as text messages, contact lists, and personal information are stored on mobile devices. This makes authentication of paramount importance. More security is needed on mobile devices since, after point-of-entry authentication, the user can perform almost all tasks without having to re-authenticate. For this reason, many authentication methods have been suggested to improve the security of mobile devices in a transparent and continuous manner, providing a basis for convenient and secure user re-authentication. This paper presents a comprehensive analysis and literature review on transparent authentication systems for mobile device security. This review indicates a need to investigate when to authenticate the mobile user by focusing on the sensitivity level of the application, and understanding whether a certain application may require a protection or not.


Cayuga : A High-Performance Event Processing Engine ∗ [ Demonstration Paper ]

We propose a demonstration of Cayuga, a complex event monitoring system for high speed data streams. Our demonstration will show Cayuga applied to monitoring Web feeds; the demo will illustrate the expressiveness of the Cayuga query language, the scalability of its query processing engine to high stream rates, and a visualization of the internals of the query processing engine.