title: The Role of Emotion Regulation in Autism Spectrum Disorder

abstract: Objective Autism spectrum disorder (ASD) is associated with amplified emotional responses and poor emotional control, but little is known about the underlying mechanisms. This article provides a conceptual and methodologic framework for understanding compromised emotion regulation (ER) in ASD. Method After defining ER and related constructs, methods to study ER were reviewed with special consideration on how to apply these approaches to ASD. Against the backdrop of cognitive characteristics in ASD and existing ER theories, available research was examined to identify likely contributors to emotional dysregulation in ASD. Results Little is currently known about ER in youth with ASD. Some mechanisms that contribute to poor ER in ASD may be shared with other clinical populations (e.g., physiologic arousal, degree of negative and positive affect, alterations in the amygdala and prefrontal cortex), whereas other mechanisms may be more unique to ASD (e.g., differences in information processing/perception, cognitive factors [e.g., rigidity], less goal-directed behavior and more disorganized emotion in ASD). Conclusions Although assignment of concomitant psychiatric diagnoses is warranted in some cases, poor ER may be inherent in ASD and may provide a more parsimonious conceptualization for the many associated socioemotional and behavioral problems in this population. Further study of ER in youth with ASD may identify meaningful subgroups of patients and lead to more effective individualized treatments.


title: The pupillary light response reflects eye-movement preparation.

abstract: When the eyes are exposed to an increased influx of light, the pupils constrict. The pupillary light response (PLR) is traditionally believed to be purely reflexive and not susceptible to cognitive influences. In contrast to this traditional view, we report that preparation of a PLR occurs in parallel with preparation of a saccadic eye movement toward a bright (or dark) stimulus, even before the eyes set in motion. Participants fixated a central gray area and made a saccade toward a peripheral target. Using gaze-contingent display changes, we manipulated whether or not the brightness of the target background was the same during and after saccade preparation. More specifically, on some trials we changed the brightness of the target background during the saccade, thus dissociating the preparatory PLR (i.e., to the brightness of the target background before the saccade) from the regular PLR (i.e., to the brightness after the saccade). We show that preparation triggers a pupillary response to the brightness of a to-be-fixated target background already before the eyes have landed on it. We link our findings to the presaccadic shift of attention: The pupil prepares to adjust its size to the brightness of a to-be-fixated stimulus as soon as attention covertly shifts toward that stimulus. Our findings illustrate that the PLR is a dynamic movement that is tightly linked to visual attention and eye-movement preparation. (PsycINFO Database Record (c) 2015 APA, all rights reserved). Language: en


title: Delaware School Climate Survey--Student: Its Factor Structure, Concurrent Validity, and Reliability.

abstract: Abstract The Delaware School Climate Survey—Student (DSCS—S) was developed to provide schools with a brief and psychometrically sound student survey for assessing school climate, particularly the dimensions of social support and structure. Confirmatory factor analyses, conducted on a sample of 11,780 students in 85 schools, showed that a bifactor model consisting of five specific factors and one general factor (School Climate) best represented the data. Those five factors are represented in five subscales of the DSCS—S: Teacher–Student Relations, Student–Student Relations, Fairness of Rules, Liking of School, and School Safety. The factor structure was shown to be stable across grade levels (i.e., elementary, middle, and high school), racial–ethnic groups (i.e., Caucasian, African American, and Hispanic), and gender. As evidence of the survey's concurrent validity, scores for each of the five subscales and the total scale correlated moderately, across groups and at the school level, with academic achievement and suspensions and expulsions.


title: Chatbot as an Intelligent Personal Assistant for Mobile Language Learning

abstract: The application of automatic conversational system (chatbot) in learning foreign language is still limited. In this study, we built a chatbot dedicated to English learners. The system is named English Practice is installed on the mobile devices to interact with users through a window chat. Chatbot is able to automatically remind learners to study and suggest some answers to multiple choice questions. It also has the ability to help users in learning vocabulary and new lessons. The result shows that most of the basic functions of the system are used by the users and this this promises to be applied widely in the future.


title: Besov Regularity of Stochastic Integrals with Respect to the Fractional Brownian Motion with Parameter H > 1/2

abstract: Let {B t ,t∈[0,1]} be a fractional Brownian motion with Hurst parameter H > 1/2. Using the techniques of the Malliavin calculus we show that the trajectories of the indefinite divergence integral ∫ t 0u s δB s belong to the Besov space ℬ p,q α for all \(q \geqslant 1,\frac{1}{p} < \alpha < H\), provided the integrand u belongs to the space \(\mathbb{L}^{p,1} \). Moreover, if u is bounded and belongs to \(\mathbb{L}^{\delta ,2} \) for some even integer p≥2 and for some δ large enough, then the trajectories of the indefinite divergence integral ∫ t 0u s δB s belong to the Besov space ℬ p,∞ H .


title: A Survey on Food Computing

abstract: Food is essential for human life and it is fundamental to the human experience. Food-related study may support multifarious applications and services, such as guiding human behavior, improving human health, and understanding the culinary culture. With the rapid development of social networks, mobile networks, and Internet of Things (IoT), people commonly upload, share, and record food images, recipes, cooking videos, and food diaries, leading to large-scale food data. Large-scale food data offers rich knowledge about food and can help tackle many central issues of human society. Therefore, it is time to group several disparate issues related to food computing. Food computing acquires and analyzes heterogenous food data from different sources for perception, recognition, retrieval, recommendation, and monitoring of food. In food computing, computational approaches are applied to address food-related issues in medicine, biology, gastronomy, and agronomy. Both large-scale food data and recent breakthroughs in computer science are transforming the way we analyze food data. Therefore, a series of works has been conducted in the food area, targeting different food-oriented tasks and applications. However, there are very few systematic reviews that shape this area well and provide a comprehensive and in-depth summary of current efforts or detail open problems in this area. In this article, we formalize food computing and present such a comprehensive overview of various emerging concepts, methods, and tasks. We summarize key challenges and future directions ahead for food computing. This is the first comprehensive survey that targets the study of computing technology for the food area and also offers a collection of research studies and technologies to benefit researchers and practitioners working in different food-related fields.


title: Hardware software co-design in Haskell

abstract: We present a library in Haskell for programming Field Programmable Gate Arrays (FPGAs), including hardware software co-design. Code for software (in C) and hardware (in VHDL) is generated from a single program, along with the code to support communication between hardware and software. We present type-based techniques for the simultaneous implementation of more than one embedded domain specific language (EDSL). We build upon a generic representation of imperative programs that is loosely coupled to instruction and expression types, allowing the individual parts to be developed and improved separately. Code generation is implemented as a series of translations between progressively smaller, typed EDSLs, safeguarding against errors that arise in untyped translations. Initial case studies show promising performance.


title: Sentiment analysis using Konstanz Information Miner in social networks

abstract: The sentiment analysis process that gives this work its name is the main theme of the work. Since the beginning of 2000, sentiment analysis has become one of the most active research areas by researchers working on natural language processing and social networking analysis. In addition, data mining, web mining, and text mining are also studied extensively. Moreover, the method of sentiment analysis has spread over many fields, from computer science to management science, from social science to economics, due to the importance given to the business world as a whole and the collectivity. In this study, Konstanz Information Miner (KNIME), which is a powerful data mining tool with its richest features and many visualization tools, was used on twitter data. Ten thousand Twitter data were used in this study. The sentiment analysis study, which is in fact a classification study, was conducted using machine learning algorithms on Twitter data. The results of the study were interpreted by carrying out an accuracy analysis. It is anticipated that the use of the KNIME, which has rich visualization tools, will be widespread in sentiment analysis studies to make these works both easier and more reliable.


title: Laser treatment of atrophoderma vermiculata.

abstract: Atrophoderma vermiculata is a rare genodermatosis with usual onset in childhood, characterized by a "honey-combed" reticular atrophy of the cheeks. The course is generally slow, with progressive worsening. We report successful treatment of 2 patients by means of the carbon dioxide and 585 nm pulsed dye lasers.


title: An improved corpus of disease mentions in PubMed citations

abstract: The latest discoveries on diseases and their diagnosis/treatment are mostly disseminated in the form of scientific publications. However, with the rapid growth of the biomedical literature and a high level of variation and ambiguity in disease names, the task of retrieving disease-related articles becomes increasingly challenging using the traditional keyword-based approach. An important first step for any disease-related information extraction task in the biomedical literature is the disease mention recognition task. However, despite the strong interest, there has not been enough work done on disease name identification, perhaps because of the difficulty in obtaining adequate corpora. Towards this aim, we created a large-scale disease corpus consisting of 6900 disease mentions in 793 PubMed citations, derived from an earlier corpus. Our corpus contains rich annotations, was developed by a team of 12 annotators (two people per annotation) and covers all sentences in a PubMed abstract. Disease mentions are categorized into Specific Disease, Disease Class, Composite Mention and Modifier categories. When used as the gold standard data for a state-of-the-art machine-learning approach, significantly higher performance can be found on our corpus than the previous one. Such characteristics make this disease name corpus a valuable resource for mining disease-related information from biomedical text. The NCBI corpus is available for download at http://www.ncbi.nlm.nih.gov/CBBresearch/Fellows/Dogan/disease.html.


title: Inter-alpha-trypsin inhibitor heavy chain H4 as a diagnostic and prognostic indicator in patients with hepatitis B virus-associated hepatocellular carcinoma.

abstract: Abstract Objectives Inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4) is associated with various diseases. We evaluated the diagnostic and prognostic significance of serum ITIH4 levels in healthy controls and patients with chronic hepatitis B (CHB), hepatitis B virus (HBV)-related liver cirrhosis, and HBV-related hepatocellular carcinoma (HCC). Design and methods The study enrolled 300 individuals (50 healthy controls, 50 with CHB, 100 with HBV-associated cirrhosis, and 100 with HBV-associated HCC). Serum ITIH4 levels were determined by western blot analysis and expressed in densitometry units (DU). Results ITIH4 levels were higher in CHB (mean: 252.96 DU) and liver cirrhosis (mean: 206.43 DU) patients than in healthy controls (mean: 75.92 DU) and HCC patients (mean: 92.86 DU) ( P P = 0.017). A Kaplan–Meier survival analysis showed significantly worse survival among HCC patients with both low ITIH4 ( P 200 ng/mL) and low ITIH4 compared to that among other HCC patients ( P = 0.041). Conclusions Serum ITIH4 levels are reduced in HCC patients compared to that in CHB and cirrhosis patients, and low serum ITIH4 levels are associated with shorter survival in HBV-associated HCC patients.


title: Sensitivity and diagnosticity of NASA-TLX and simplified SWAT to assess the mental workload associated with operating an agricultural sprayer.

abstract: The objectives of the present study were: a) to investigate three continuous variants of the NASA-Task Load Index (TLX) (standard NASA (CNASA), average NASA (C1NASA) and principal component NASA (PCNASA)) and five different variants of the simplified subjective workload assessment technique (SSWAT) (continuous standard SSWAT (CSSWAT), continuous average SSWAT (C1SSWAT), continuous principal component SSWAT (PCSSWAT), discrete event-based SSWAT (D1SSWAT) and discrete standard SSWAT (DSSWAT)) in terms of their sensitivity and diagnosticity to assess the mental workload associated with agricultural spraying; b) to compare and select the best variants of NASA-TLX and SSWAT for future mental workload research in the agricultural domain. A total of 16 male university students (mean 30.4 +/- 12.5 years) participated in this study. All the participants were trained to drive an agricultural spraying simulator. Sensitivity was assessed by the ability of the scales to report the maximum change in workload ratings due to the change in illumination and difficulty levels. In addition, the factor loading method was used to quantify sensitivity. The diagnosticity was assessed by the ability of the scale to diagnose the change in task levels from single to dual. Among all the variants of NASA-TLX and SSWAT, PCNASA and discrete variants of SSWAT showed the highest sensitivity and diagnosticity. Moreover, among all the variants of NASA and SSWAT, the discrete variants of SSWAT showed the highest sensitivity and diagnosticity but also high between-subject variability. The continuous variants of both scales had relatively low sensitivity and diagnosticity and also low between-subject variability. Hence, when selecting a scale for future mental workload research in the agricultural domain, a researcher should decide what to compromise: 1) between-subject variability or 2) sensitivity and diagnosticity. STATEMENT OF RELEVANCE: The use of subjective workload scales is very popular in mental workload research. The present study investigated the different variants of two popular workload rating scales (i.e. NASA-TLX and SSWAT) in terms of their sensitivity and diagnositicity and selected the best variants of each scale for future mental workload research.


Ideal Ratio Mask Estimation Using Deep Neural Networks for Monaural Speech Segregation in Noisy Reverberant Conditions


A Methodology for Increasing Business Process Maturity in Public Sector


title: Automatic face caricatures synthesis and exaggeration

abstract: Face caricatures are widely used in political cartoons and generating caricatures from images has become a popular research topic recently. The main challenge lies in achieving nice artistic effect and capturing face characteristics by exaggerating the most featured parts while keeping the resemblance to the original image. In this paper, a sketch-based face caricature synthesis framework is proposed to generate and exaggerate the face caricature from a single near-frontal picture. We first present an effective and robust face component rendering method using Adaptive Thresholding to eliminate the influence of illumination by separating face components into layers. Then, we propose an automatic exaggeration method, in which face component features are trained using Support Vector Machine (SVM) and then amplified using image processing techniques to make the caricature more hilarious and thus more impressive. After that, a hair rendering method is presented, which synthesizes hair in the same caricature style using edge-detection techniques. Practical results show that the synthesized face caricatures are of great artistic effect and well characterized, and our method is robust and efficient even under unfavorable lighting conditions.


title: Postural stability of a human riding a unicycle and its emulation by a robot

abstract: A unicycle is an inherently unstable system in three dimensions. As shown by a human riding a unicycle, the rider's complicated successive dynamic control actions are needed for the postural stability control of the unicycle, and the rider's actions for stability are quite nonlinear and jerky. By emulating the performance of a human riding unicycle, we can do tests of different classic and modern control methods used in 3-D inherently unstable systems, where a theoretical analytical methodology for the stability of this unstable system is expected to be developed. Because the longitudinal and lateral stability are coupled to each other in this system, and no actuator can be used directly for the control of unicycle's posture in 3-D (pitch, roll, and yaw angles), it is not easy for us to emulate the performance of a human riding a unicycle. In this paper, a unique unicycle robot is developed. The postural stability control of a human riding a unicycle in 3-D is realized by this robot.


title: Background subtraction and human detection in outdoor videos using fuzzy logic

abstract: This paper presents a scale and view invariant approach for human detection in the presence of various other objects like animals, vehicles, etc. Human detection is one of the essential steps in applications like activity recognition, gait recognition, human centric surveillance etc. Inaccurate detection of humans in such applications may increase the number of false alarms. In the proposed work, fuzzy logic has been used to model a robust background for object detection. Three different features are extracted from the contours of the detected objects. These features are aggregated using fuzzy inference system. Then human contour is identified using template matching. The proposed method consists of four main steps; Moving Object Detection, Feature Extraction, Feature Aggregation, and Human Contour Detection.


title: Lactobacillus helveticus Lafti L10 supplementation reduces respiratory infection duration in a cohort of elite athletes: a randomized, double-blind, placebo-controlled trial.

abstract: A randomized, double-blind, placebo-controlled study was conducted to evaluate if Lactobacillus helveticus Lafti L10 (Lallemand Health Solutions, Montreal, Que., Canada) supplementation during 14 weeks in winter can influence the duration, severity, and incidence of upper respiratory tract illness (URTI), as well as to monitor different immune parameters in the population of elite athletes. Before and after the treatment, cardiopulmonary testing and self-rated state of moods evaluation (by Profile of Mood States questionnaire) were performed and blood samples were collected. Thirty-nine elite athletes were randomized either to the placebo (n = 19) or the probiotic (n = 20) group. The probiotic group received L. helveticus Lafti L10, 2 × 10(10) Colony Forming Units. Lafti L10 significantly shortened the URTI episode duration (7.25 ± 2.90 vs. 10.64 ± 4.67 days, p = 0.047) and decreased the number of symptoms in the probiotic group (4.92 ± 1.96 vs. 6.91 ± 1.22, p = 0.035). Severity and incidence of URTI did not differ between the treatments. There were no significant changes in leukocyte subpopulation abundance, transforming growth factor-β serum levels, level of interleukin-10 secreted from peptidoglican stimulated peripheral blood mononuclear cells (PBMCs), interferon-γ level secreted from concanavalin A-stimulated PBMCs or viability/proliferation of PBMCs upon antigen stimulation. Group effect for CD4+/CD8+ ratio was significant (F[1,37] = 6.99, p = 0.020, η(2) = 0.350); this difference was not significant at baseline, but was evident after 14 weeks (p = 0.02). A significant interaction effect was noted for self-rated sense of vigor (F[1,37] = 11.76, p = 0.009, η(2) = 0.595). Self-rated sense of vigor increased in the probiotic group (18.5 ± 4.1 vs. 21.0 ± 2.6, p = 0.012). Probiotic strain Lafti L10 can be a beneficial nutritional supplement for the reduction of URTI length in elite athletes.


title: Study of Molecular Interactions of Binary Mixture of Mandelic Acid in Different Solvents Using Ultrasonic, Acoustical Parameters, Dielectric Constant and FTIR Measurements

abstract: Mandelic Acid (MA) is a polar non-centero symmetric chiral molecule, having high polarisability. Solute -solvent interactions of binary liquid mixtures is carried out for MA in different solvents. Polar protic solvent methanol, polar aprotic solvent DMSO and non-polar solvent chloroform are used for the study. Ultrasonic velocity (U), viscosity (ɳ), density (ρ) are measured and theoretical calculations are carried out to determine the adiabatic compressibility (β), relative association (R A), inter molecular free length (L f) and acoustic impedance (Z). The excess values are calculated for the above parameters. Compared the solute- solvent interactions in the title compound due to their polarizability behaviour, inter and intra molecular hydrogen bonding in the above solvents, are confirmed by FTIR measurements and their dielectric constant values. The results obtained show the molecular interactions of the title compound with the respective solvents and the nature of hydrogen bonding.


title: Hyperbolic Attention Networks

abstract: We introduce hyperbolic attention networks to endow neural networks with enough capacity to match the complexity of data with hierarchical and power-law structure. A few recent approaches have successfully demonstrated the benefits of imposing hyperbolic geometry on the parameters of shallow networks. We extend this line of work by imposing hyperbolic geometry on the activations of neural networks. This allows us to exploit hyperbolic geometry to reason about embeddings produced by deep networks. We achieve this by re-expressing the ubiquitous mechanism of soft attention in terms of operations defined for hyperboloid and Klein models. Our method shows improvements in terms of generalization on neural machine translation, learning on graphs and visual question answering tasks while keeping the neural representations compact.


title: Social Effects of Robot Therapy in a Care House - Change of Social Network of the Residents for Two Months

abstract: Robot therapy for elderly residents in a care house has been conducted since June 2005. Two therapeutic seal robots were introduced and activated for over 9 hours every day to interact with the residents. This paper presents a progress report of this experiment. In order to investigate the psychological and social effects of the robots, each subject was interviewed by using the free pile sort method, and their subjective social network was analysed. In addition, the activities of the residents in public areas were recorded by video cameras during daytime hours (8:30-18:00) for over 2 months. Then, their social network was analysed from the video data objectively. The results showed that the density of the social networks was increased through interaction with the robots subjectively and objectively.


title: FaceMatch: Real-World Face Image Retrieval

abstract: We researched and developed a practical methodology for face and image retrieval (FIR) based on optimally weighted image descriptor ensemble. We describe a single-image-per-person (SIPP) face image retrieval system for real-world applications that include large photo collection search, person location in disaster scenarios, semi-automatic image data annotation, etc. Our system provides efficient means for face detection, matching and annotation, working with unconstrained digital photos of variable quality, requiring no time-consuming training, yet showing a commercial performance level at its sub-tasks. Our system benefits public by providing practical FIR technology, annotated image data and web-services to a real-world family reunification system.


title: Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid and local binary patterns in texture and shape encoding

abstract: We address a novel problem domain in the analysis of optical coherence tomography (OCT) images: the diagnosis of multiple macular pathologies in retinal OCT images. The goal is to identify the presence of normal macula and each of three types of macular pathologies, namely, macular edema, macular hole, and age-related macular degeneration, in the OCT slice centered at the fovea. We use a machine learning approach based on global image descriptors formed from a multi-scale spatial pyramid. Our local features are dimension-reduced local binary pattern histograms, which are capable of encoding texture and shape information in retinal OCT images and their edge maps, respectively. Our representation operates at multiple spatial scales and granularities, leading to robust performance. We use 2-class support vector machine classifiers to identify the presence of normal macula and each of the three pathologies. To further discriminate sub-types within a pathology, we also build a classifier to differentiate full-thickness holes from pseudo-holes within the macular hole category. We conduct extensive experiments on a large dataset of 326 OCT scans from 136 subjects. The results show that the proposed method is very effective (all AUC > 0.93).


title: Collective Opinion Target Extraction in Chinese Microblogs

abstract: Microblog messages pose severe challenges for current sentiment analysis techniques due to some inherent characteristics such as the length limit and informal writing style. In this paper, we study the problem of extracting opinion targets of Chinese microblog messages. Such fine-grained word-level task has not been well investigated in microblogs yet. We propose an unsupervised label propagation algorithm to address the problem. The opinion targets of all messages in a topic are collectively extracted based on the assumption that similar messages may focus on similar opinion targets. Topics in microblogs are identified by hashtags or using clustering algorithms. Experimental results on Chinese microblogs show the effectiveness of our framework and algorithms.


Coloration neighborhood structures for general graph coloring


title: Differentiating what is humorous from what is not.

abstract: After 2.5 millennia of philosophical deliberation and psychological experimentation, most scholars have concluded that humor arises from incongruity. We highlight 2 limitations of incongruity theories of humor. First, incongruity is not consistently defined. The literature describes incongruity in at least 4 ways: surprise, juxtaposition, atypicality, and a violation. Second, regardless of definition, incongruity alone does not adequately differentiate humorous from nonhumorous experiences. We suggest revising incongruity theory by proposing that humor arises from a benign violation: something that threatens a person’s well-being, identity, or normative belief structure but that simultaneously seems okay. Six studies, which use entertainment, consumer products, and social interaction as stimuli, reveal that the benign violation hypothesis better differentiates humorous from nonhumorous experiences than common conceptualizations of incongruity. A benign violation conceptualization of humor improves accuracy by reducing the likelihood that joyous, amazing, and tragic situations are inaccurately predicted to be humorous. (PsycINFO Database Record (c) 2016 APA, all rights reserved)


title: Frequency doublers with 10.2/5.2 dBm peak power at 100/202 GHz in 45nm SOI CMOS

abstract: This paper presents frequency doublers with high output power for millimeter-wave applications. The circuits are fabricated using a 45nm SOI CMOS technology. A new circuit topology, combining a push-push doubler core with a cascaded stacked amplifier, has been implemented to increase the output power. The first doubler delivers 10.2 dBm peak power at 100 GHz output, with a 3-dB bandwidth from 88 to 104 GHz and DC-RF efficiency of 4.1%, while the second doubler has 5.2 dBm peak power at 202 GHz, with a 3-dB bandwidth from 180 to 212 GHz and DC-RF efficiency of 3.3%. To the authors' knowledge, these are the highest powers reported for silicon frequency doublers in similar frequency ranges to date. The 200 GHz doubler also provides the highest on-chip power from a single-element signal generation circuit without power combining.


title: Measuring problem video game playing in adolescents

abstract: Aims Some researchers suggest that for some people, video game playing is an addictive behaviour similar to substance dependence. Our aim was to design and validate a scale to measure the problems associated with the apparently addictive use of all types of video games and video game systems, because there is no instrument at the present time that can be used for this purpose. Design We reviewed the DSM-IV criteria for substance dependence and for pathological gambling, as well as the literature on the addictions in order to design a short scale (PVP; problem video game playing) that is quick and easy to apply. Participants The scale was administered to 223 Spanish adolescents aged between 13 and 18 years. The study was carried out in Granada and Algeciras, Spain. Findings Psychometric analyses show that the PVP seems to be unidimensional and has acceptable internal consistency (Cronbach's alpha) at 0.69. The pattern of associations between the scale scores and alternative measures of problem play supports its construct validity (higher total scores in the scale were associated with higher frequency of play, mean and longest times per session, self and parents' perception of playing to excess, and scores in the Severity of Dependence Scale). Conclusions Our results confirm that the excessive use of video games is associated with a number of problems which resemble a dependence syndrome, and the PVP appears as a useful instrument for the measurement of such problems.


title: Development of ultra low noise VHF OCXO with excellent temperature stability

abstract: This paper presents development of ultra low noise VHF oven controlled crystal oscillator (OCXO) with excellent temperature stability. The OCXO had the low phase noise less than -170 dBc/Hz at 10 kHz Fourier frequency and the temperature stability within +/-20 ppb in the range of -20 to +70deg.C has been achieved.


title: Small-signal analysis for designing a stable voltage-doubler PFC system

abstract: In this paper, a stable operating Voltage-Doubler (VD) PFC converter is designed and verified in real-time environment. The performance of the converter for transient condition is examined. The proposed converter-control system maintains sinusoidal current at input and regulated voltage at output end. A complete set of small-signal transfer functions are derived for all common operating modes of the converter. The right half s-plane poles and zeros are filtered out by set point filter method and a stable VD PFC converter-control system is designed. For the real-time validation, a 390 V DC , 1.5 kW VD PFC hardware model is designed and its performance under transient loading condition is examined.


title: Event-based Sensing for Space Situational Awareness

abstract: A revolutionary type of imaging device, known as a silicon retina or event-based sensor, has recently been developed and is gaining in popularity in the field of artificial vision systems. These devices are inspired by a biological retina and operate in a significantly different way to traditional CCD-based imaging sensors. While a CCD produces frames of pixel intensities, an event-based sensor produces a continuous stream of events, each of which is generated when a pixel detects a change in log light intensity. These pixels operate asynchronously and independently, producing an event-based output with high temporal resolution. There are also no fixed exposure times, allowing these devices to offer a very high dynamic range independently for each pixel. Additionally, these devices offer high-speed, low power operation and a sparse spatio-temporal output. As a consequence, the data from these sensors must be interpreted in a significantly different way to traditional imaging sensors and this paper explores the advantages this technology provides for space imaging. The applicability and capabilities of event-based sensors for SSA applications are demonstrated through telescope field trials. Trial results have confirmed that the devices are capable of observing resident space objects from LEO through to GEO orbital regimes. Significantly, observations of RSOs were made during both day-time and night-time (terminator) conditions without modification to the camera or optics. The event based sensor’s ability to image stars and satellites during day-time hours offers a dramatic capability increase for terrestrial optical sensors. This paper shows the field testing and validation of two different architectures of event-based imaging sensors. An event-based sensor’s asynchronous output has an intrinsically low data-rate. In addition to low-bandwidth communications requirements, the low weight, low-power and high-speed make them ideally suitable to meeting the demanding challenges required by space-based SSA systems. Results from these experiments and the systems developed highlight the applicability of event-based sensors to ground and space-based SSA tasks.


How GoGet CarShare's Product-Service System is Facilitating Collaborative Consumption


title: Analysis of DNA methylation in cancer: location revisited

abstract: Changes in DNA methylation in cancer have been heralded as promising targets for the development of powerful diagnostic, prognostic, and predictive biomarkers. Despite the existence of more than 14,000 scientific publications describing DNA methylation-based biomarkers and their clinical associations in cancer, only 14 of these biomarkers have been translated into a commercially available clinical test. Methodological and experimental obstacles are both major causes of this disparity, but the genomic location of a DNA methylation-based biomarker is an intrinsic and essential property that also has an important and often overlooked role. Here, we examine the importance of the location of DNA methylation for the development of cancer biomarkers, and take a detailed look at the genomic location and other relevant characteristics of the various biomarkers with commercially available tests. We also emphasize the value of publicly available databases for the development of DNA methylation-based biomarkers and the importance of accurate reporting of the full methodological details of research findings.


title: Introducing the GASP Scale: A New Measure of Guilt and Shame Proneness

abstract: Although scholars agree that moral emotions are critical for deterring unethical and antisocial behavior, there is disagreement about how two prototypical moral emotions — guilt and shame — should be defined, differentiated, and measured. We addressed these issues by developing a new assessment — the Guilt And Shame Proneness scale (GASP) — that measures individual differences in the propensity to experience guilt and shame across a range of personal transgressions. The GASP contains two guilt subscales that assess negative behavior-evaluations (NBEs) and repair action tendencies following private transgressions and two shame subscales that assess negative self-evaluations (NSEs) and withdrawal action tendencies following publically-exposed transgressions. Both guilt subscales were highly correlated with one another and negatively correlated with unethical decision making. Although both shame subscales were associated with relatively poor psychological functioning (e.g., neuroticism, personal distress, low self-esteem), they were only weakly correlated with one another and their relationships with unethical decision making diverged. Whereas shame-NSE constrained unethical decision making, shame-withdraw did not. Our findings suggest that differentiating the tendency to make negative self-evaluations following publically-exposed transgressions from the tendency to hide or withdraw from public is critically important for understanding and measuring dispositional shame proneness. The GASP’s ability to distinguish these two classes of responses represents an important advantage of the scale over existing assessments. Although further validation research is required, the present studies are promising in that they suggest the GASP has the potential to be an important measurement tool for detecting individuals susceptible to corruption and unethical behavior.


title: fMRI activation of the fusiform gyrus and amygdala to cartoon characters but not to faces in a boy with autism

abstract: Abstract Abnormal hypoactivation in the amygdala and fusiform gyrus, brain areas that participate in face processing and social cognition, has consistently been demonstrated in persons with autism. We investigated activity in these areas in a boy with autism, DD, who had a special interest in “Digimon” cartoon characters. DD individuates Digimon faster than familiar faces and objects, but he individuates familiar faces no faster than objects. In contrast, a typically developing boy with an interest in “Pokemon” cartoon characters is equally fast at individuating faces and Pokemon and faster at individuating faces and Pokemon than objects and Digimon. In addition, using functional magnetic resonance imaging (fMRI), we show that DD activates his amygdala and fusiform gyrus for perceptual discriminations involving Digimon but not for those involving familiar or unfamiliar faces. This pattern of activation is not seen in the typically developing control with an interest in Pokemon or in a second comparison case who has autism but no interest in Digimon. These results have important implications for our understanding of autism, cortical face specialization, and the possible role of the amygdala in the development of perceptual expertise.


title: No Reciprocity in "Liking" Photos: Analyzing Like Activities in Instagram

abstract: In social media, people often press a "Like" button to indicate their shared interest in a particular content or to acknowledge the user who posted the content. Such activities form relationships and networks among people, raising interesting questions about their unique characteristics and implications. However, little research has investigated such Likes as a main study focus. To address this lack of understanding, based on a theoretical framework, we present an analysis of the structural, influential, and contextual aspects of Like activities from the test datasets of 20 million users and their 2 billion Like activities in Instagram. Our study results first highlight that Like activities and networks increase exponentially, and are formed and developed by one's friends and many random users. Second, we observe that five other essential Instagram elements influence the number of Likes to different extents, but following others will not necessarily increase the number of Likes that one receives. Third, we explore the relationship between LDA-based topics and Likes, characterize two user groups-specialists and generalists-and show that specialists tend to receive more Likes and promote themselves more than generalists. We finally discuss theoretical and practical implications and future research directions.


title: Facial expression recognition with PCA and LBP features extracting from active facial patches

abstract: Facial expression recognition is an important part of Natural User Interface (NUI). Feature extraction is one important step which could contribute to fast and accurate expression recognition. In order to extract more effective features from the static images, this paper proposes an algorithm based on the combination of gray pixel value and Local Binary Patterns (LBP) features. Principal component analysis (PCA) is used to reduce dimensions of the features which are combined by the gray pixel value and Local Binary Patterns (LBP) features. All the features are extracted from the active facial patches. The active facial patches are these face regions which undergo a major change during different expressions. Softmax regression classifier is used to classify the six basic facial expressions, the experimental results on extended Cohn-Kanade (CK+) database gain an average recognition rate of 96.3% under leave-one-out cross validation method which validates every subject in the database.


The Roman Mind


title: Learning to Match Aerial Images with Deep Attentive Architectures

abstract: Image matching is a fundamental problem in Computer Vision. In the context of feature-based matching, SIFT and its variants have long excelled in a wide array of applications. However, for ultra-wide baselines, as in the case of aerial images captured under large camera rotations, the appearance variation goes beyond the reach of SIFT and RANSAC. In this paper we propose a data-driven, deep learning-based approach that sidesteps local correspondence by framing the problem as a classification task. Furthermore, we demonstrate that local correspondences can still be useful. To do so we incorporate an attention mechanism to produce a set of probable matches, which allows us to further increase performance. We train our models on a dataset of urban aerial imagery consisting of 'same' and 'different' pairs, collected for this purpose, and characterize the problem via a human study with annotations from Amazon Mechanical Turk. We demonstrate that our models outperform the state-of-the-art on ultra-wide baseline matching and approach human accuracy.


title: A System for Generating and Injecting Indistinguishable Network Decoys

abstract: We propose a novel trap-based architecture for detecting passive, “silent”, attackers who are eavesdropping on enterprise networks. Motivated by the increasing number of incidents where attackers sniff the local network for interesting information, such as credit card numbers, account credentials, and passwords, we introduce a methodology for building a trap-based network that is designed to maximize the realism of bait-laced traffic. Our proposal relies on a “record, modify, replay” paradigm that can be easily adapted to different networked environments. The primary contributions of our architecture are the ease of automatically injecting large amounts of believable bait, and the integration of different detection mechanisms in the back-end. We demonstrate our methodology in a prototype platform that uses our decoy injection API to dynamically create and dispense network traps on a subset of our campus wireless network. Our network traps consist of several types of monitored passwords, authentication cookies, credit cards and documents containing beacons to alarm when opened. The efficacy of our decoys against a model attack program is also discussed, along with results obtained from experiments in the field. In addition, we present a user study that demonstrates the believability of our decoy traffic, and finally, we provide experimental results to show that our solution causes only negligible interference to ordinary users.


title: Implication of anchor placement on performance of UWB real-time locating system

abstract: Recently, real-time locating system (RTLS) with centimeter accuracy using radio frequency signal is available for commercial deployment with the use of ultra-wideband (UWB) technology. One of the commercial UWB real time locating systems in the market now is DecaWave's DW1000 IC which utilizes two-way-ranging (TWR) mechanism to measure accurate distance between an anchor and a tag. Then, a trilateration algorithm with distances from three or more anchors is used to calculate the tag's location. It is recommended that, to improve the accuracy of the system, a clear view between all anchors must be provided. This work presents a performance study of commercial UWB RTLS evaluation kits in two different anchor placement scenarios. In the first scenario, each anchor was provided a clear communication path between each other while in the latter some direct communication paths between the anchors were obstructed by concrete pillars. The evaluation kits were configured to allow maximum range and data rate. Intuitively, the first scenario should perform much better than the latter. However, the experiments showed the contrary in some situations. Moreover, the overall performance, in terms of error distances and R95 values, of the first scenario did not outperform significantly as expected.


title: An Analysis of Machine Learning Intelligence

abstract: Deep neural networks (DNNs) have set state of the art results in many machine learning and NLP tasks. However, we do not have a strong understanding of what DNN models learn. In this paper, we examine learning in DNNs through analysis of their outputs. We compare DNN performance directly to a human population, and use characteristics of individual data points such as difficulty to see how well models perform on easy and hard examples. We investigate how training size and the incorporation of noise affect a DNN's ability to generalize and learn. Our experiments show that unlike traditional machine learning models (e.g., Naive Bayes, Decision Trees), DNNs exhibit human-like learning properties. As they are trained with more data, they are more able to distinguish between easy and difficult items, and performance on easy items improves at a higher rate than difficult items. We find that different DNN models exhibit different strengths in learning and are robust to noise in training data.


title: Feature Coding in Image Classification: A Comprehensive Study

abstract: Image classification is a hot topic in computer vision and pattern recognition. Feature coding, as a key component of image classification, has been widely studied over the past several years, and a number of coding algorithms have been proposed. However, there is no comprehensive study concerning the connections between different coding methods, especially how they have evolved. In this paper, we first make a survey on various feature coding methods, including their motivations and mathematical representations, and then exploit their relations, based on which a taxonomy is proposed to reveal their evolution. Further, we summarize the main characteristics of current algorithms, each of which is shared by several coding strategies. Finally, we choose several representatives from different kinds of coding approaches and empirically evaluate them with respect to the size of the codebook and the number of training samples on several widely used databases (15-Scenes, Caltech-256, PASCAL VOC07, and SUN397). Experimental findings firmly justify our theoretical analysis, which is expected to benefit both practical applications and future research.


title: Privacy and Security in Internet of Things and Wearable Devices

abstract: Enter the nascent era of Internet of Things (IoT) and wearable devices, where small embedded devices loaded with sensors collect information from its surroundings, process it, and relay it to remote locations for further analysis. Albeit looking harmless, these nascent technologies raise security and privacy concerns. We pose the question of the possibility and effects of compromising such devices. Concentrating on the design flow of IoT and wearable devices, we discuss some common design practices and their implications on security and privacy. Two representatives from each category, the Google Nest Thermostat and the Nike+ Fuelband, are selected as examples on how current industry practices of security as an afterthought or an add-on affect the resulting device and the potential consequences to the user's security and privacy. We then discuss design flow enhancements, through which security mechanisms can efficiently be added into a device, vastly differing from traditional practices.


title: Classification of the Dubins set

abstract: Abstract Given two points in a plane, each with a prescribed direction of motion in it, the question being asked is to find the shortest smooth path of bounded curvature that joins them. The classical 1957 result by Dubins gives a sufficient set of paths (each consisting of circular arcs and straight line segments) which always contains the shortest path. The latter is then found by explicitly computing all paths on the list and then comparing them. This may become a problem in applications where computation time is critical, such as in real-time robot motion planning. Instead, the logical classification scheme considered in this work allows one to extract the shortest path from the Dubins set directly, without explicitly calculating the candidate paths. The approach is demonstrated on one of two possible cases that appear here — when the distance between the two points is relatively large (the case with short distances can be treated similarly). Besides computational savings, this result sheds light on the nature of factors affecting the length of paths in the Dubins problem, and is useful for further extensions, e.g. for finding the shortest path between a point and a manifold in the corresponding configuration space.


title: A study on the relationship between supply chain integration, disintegration and organizational efficiency

abstract: Supply chain integration (SCI) which is a supply chain management (SCM) activity is progressively playing a critical capacity in the organization. An efficient supply chain is the key determinant factor for any process industry to stay competitive. The study suggests that organizations are opting for integrated supply chain system for seamless information flow among the stakeholders without analyzing the degree of integration beneficial for supply chain efficiency (SCE). Using data, primarily from a large oil & gas industry in Malaysia that has an integrated environment, this research studied how SCI and selective disintegration (DIS) affects supply chain efficiency (SCE) and organization performance (PER). In particular, this research has examined the benefits of disintegration of selective business processes from core supply chain system, which in-turn improves the efficiency of the chain. The study has incorporated multiple data collection and analysis techniques, combining qualitative and quantitative approaches and partial least square (PLS) structural equation modeling (SEM) has been employed to test the theoretical framework and hypotheses. The findings reveal that, almost 59% of PER and 76% SCE is improved as a result of the DIS. The main managerial lesson is that, in contrast to what has been written in many books and popular publications, efficient supply chain system is a cycle of integration and selective disintegration.


title: Local Large-Margin Multi-Metric Learning for Face and Kinship Verification

abstract: Metric learning has attracted wide attention in face and kinship verification, and a number of such algorithms have been presented over the past few years. However, most existing metric learning methods learn only one Mahalanobis distance metric from a single feature representation for each face image and cannot make use of multiple feature representations directly. In many face-related tasks, we can easily extract multiple features for a face image to extract more complementary information, and it is desirable to learn distance metrics from these multiple features, so that more discriminative information can be exploited than those learned from individual features. To achieve this, we present a large-margin multi-metric learning (LM3L) method for face and kinship verification, which jointly learns multiple global distance metrics under which the correlations of different feature representations of each sample are maximized, and the distance of each positive pair is less than a low threshold and that of each negative pair is greater than a high threshold. To better exploit the local structures of face images, we also propose a local metric learning and local LM3Lmethods to learn a set of local metrics. Experimental results on three face data sets show that the proposed methods achieve very competitive results compared with the state-of-the-art methods.


title: Reduction of plastic carrier bag use: An analysis of alternatives in Israel.

abstract: Plastic carrier bags have been drawing the attention of the public and politicians. Different policy measures to reduce the environmental burden of these bags have been implemented and more are planned. The research analyzed the actual environmental aspects of consumption and use of plastic carrier bags and assessed the effectiveness of the proposed regulation in Israel. Since plastic bags are provided free of charge, people have a tendency to use these bags excessively, therefore a rigorous educational program should address this trend. However, the environmental load imposed by the bags is more a politically correct issue than an actual environmental hazard, and therefore the means for reducing their use should not include a high levy or total elimination of these bags.


A Context-Aware Approach for Detecting Worth-Checking Claims in Political Debates


title: Modular Deep Q Networks for Sim-to-real Transfer of Visuo-motor Policies

abstract: While deep learning has had significant successes in computer vision thanks to the abundance of visual data, collecting sufficiently large real-world datasets for robot learning can be costly. To increase the practicality of these techniques on real robots, we propose a modular deep reinforcement learning method capable of transferring models trained in simulation to a real-world robotic task. We introduce a bottleneck between perception and control, enabling the networks to be trained independently, but then merged and fine-tuned in an end-to-end manner to further improve hand-eye coordination. On a canonical, planar visually-guided robot reaching task a fine-tuned accuracy of 1.6 pixels is achieved, a significant improvement over naive transfer (17.5 pixels), showing the potential for more complicated and broader applications. Our method provides a technique for more efficient learning and transfer of visuo-motor policies for real robotic systems without relying entirely on large real-world robot datasets.


title: Mechanical response of Ti–6Al–4V octet-truss lattice structures

abstract: Abstract A simple snap-fit and vacuum brazing method has been developed to fabricate three dimensional space filling octet-truss lattice structures from Ti–6Al–4V alloy sheets. Using strut lengths of 7–25 mm resulted in a relative density of the lattices ranging from 2% to 16%. The lattice elastic stiffness constants and strengths have been characterized under through-thickness compression and in-plane shear as a function of their relative density, and are shown to be well predicted by previously proposed micromechanical models adapted to account for the increased nodal mass and strut separations of the snap-fit lattice design. The Ti–6Al–4V octet-truss lattices exhibit excellent mechanical properties compared to other cellular material – cell topology combinations, and appear to be promising candidates for high temperature applications where a robust mechanical performance is required.


title: A Silicon-Based Low-Power Broadband Transimpedance Amplifier

abstract: The analysis, design, and implementation of a 50-Gb/s transimpedance amplifier (TIA) in a 0.13- $\mu \text{m}$ SiGe BiCMOS process are presented. The proposed TIA, designed for use in a single-channel optical communication network, is comprised of three stages including: 1) a shunt-peaked, shunt-series feedback stage incorporating a transformer-based positive feedback; 2) an $RC$ -degenerated common-emitter stage; and 3) an inductively degenerated emitter follower. The TIA chip integrates an on-chip 100-fF input capacitor to emulate the photo-detector junction capacitor, and achieves a measured transimpedance gain of 41 dB $\Omega $ and an input-referred current-noise spectral density of 39.8 pA/ $\sqrt {Hz}$ over a 50-GHz bandwidth. The TIA achieves an open eye at 50 Gbps with random jitter of 2.3-ps rms (including the jitter contribution of the test fixture). The prototype chip occupies 0.58 mm2 (including pads) of die area and dissipates 24 mW of dc power from a 2-V supply voltage (i.e., less than 0.5 pJ/bit).


title: Partner selection in green supply chains using PSO – a practical approach

abstract: AbstractPartner selection is crucial to green supply chain management as the focal firm is responsible for the environmental performance of the whole supply chain. The construction of appropriate selection criteria is an essential, but often neglected pre-requisite in the partner selection process. This paper proposes a three-stage model that combines Dempster–Shafer belief acceptability theory and particle swarm optimisation technique for the first time in this application. This enables optimisation of both effectiveness, in its consideration of the inter-dependence of a broad range of quantitative and qualitative selection criteria, and efficiency in its use of scarce resources during the criteria construction process to be achieved simultaneously. This also enables both operational and strategic attributes that can be selected at different levels of hierarchy criteria in different decision-making environments. The practical efficacy of the model is demonstrated by an application in Company ABC, a large...


title: Using 4D modelling in a university-museum research partnership

abstract: In the field of virtual heritage, scientific 4D modelling brings together interdisciplinary expertise in an iterative process to create and revise digital models that also reflect the passage of time. Our work focusses on making this type of model serve the needs of museologists by linking various documentary sources (both text-based and iconographic) and testimonials to the 4D model. Our case study involves a former industrial site. Over the decades, various buildings in this old factory complex have been the subject of construction, demolition and redevelopment. Under a university-museum research partnership, our 4D model will be part of an interactive digital environment designed for a museum exhibit. Our goal is to maximize the flexibility of the model, which was based on i) research to find new documentary sources, ii) oral testimonies to complete the documentary record and iii) testing to make the user experience more cognitively enriching. This approach has strengthened the partnership between the museum and the university, made university research available to a wider audience and provided a modest museum with a new tool.


Statistical and Structural Approaches to Texture


title: Towards an ontological infrastructure for chemical batch process management

abstract: A crucial step for batch process improvement and optimization is to develop information structures that streamline data gathering and, above all, are capable of integrating transactional data into a system using the analytical tools that are developed. Current trends in electronics, computer science, artificial intelligence and control system technology are providing technical capability that greatly facilitates the development of multilevel decision-making support. In this paper, we present the batch process ontology (BaPrOn), wherein different concepts regarding batch processes are categorized and the relationships between them are examined and structured in accordance with ANSI/ISA-88 standards, which provide a solid and transparent framework for integrating batch-related information. This paper also focuses on systematic integration of different actors within the control process. The proposed approach bases the conceptualization through the ANSI/ISA-88 representation, providing the advantage of establishing a more general conceptualization of the batch process domain. The capabilities of the envisaged ontological framework were assessed in a test bed PROCEL pilot plant: scheduling-monitoring and control-rescheduling was closed, information quality was accessed by knowledge description, and an optimum decision-making task was performed. The ontological structure can be extended in the future to incorporate other hierarchical levels and their respective modeling knowledge.


title: Towards Cloud-Based Analytics-as-a-Service (CLAaaS) for Big Data Analytics in the Cloud

abstract: Data Analytics has proven its importance in knowledge discovery and decision support in different data and application domains. Big data analytics poses a serious challenge in terms of the necessary hardware and software resources. The cloud technology today offers a promising solution to this challenge by enabling ubiquitous and scalable provisioning of the computing resources. However, there are further challenges that remain to be addressed such as the availability of the required analytic software for various application domains, estimation and subscription of necessary resources for the analytic job or workflow, management of data in the cloud, and design, verification and execution of analytic workflows. We present a taxonomy for analytic workflow systems to highlight the important features in existing systems. Based on the taxonomy and a study of the existing analytic software and systems, we propose the conceptual architecture of CLoud-based Analytics-as-a-Service (CLAaaS), a big data analytics service provisioning platform, in the cloud. We outline the features that are important for CLAaaS as a service provisioning system such as user and domain specific customization and assistance, collaboration, modular architecture for scalable deployment and Service Level Agreement.


title: Effective Approaches to Attention-based Neural Machine Translation

abstract: An attentional mechanism has lately been used to improve neural machine translation (NMT) by selectively focusing on parts of the source sentence during translation. However, there has been little work exploring useful architectures for attention-based NMT. This paper examines two simple and effective classes of attentional mechanism: a global approach which always attends to all source words and a local one that only looks at a subset of source words at a time. We demonstrate the effectiveness of both approaches on the WMT translation tasks between English and German in both directions. With local attention, we achieve a significant gain of 5.0 BLEU points over non-attentional systems that already incorporate known techniques such as dropout. Our ensemble model using different attention architectures yields a new state-of-the-art result in the WMT’15 English to German translation task with 25.9 BLEU points, an improvement of 1.0 BLEU points over the existing best system backed by NMT and an n-gram reranker. 1


title: CrowdRecruiter: selecting participants for piggyback crowdsensing under probabilistic coverage constraint

abstract: This paper proposes a novel participant selection framework, named CrowdRecruiter, for mobile crowdsensing. CrowdRecruiter operates on top of energy-efficient Piggyback Crowdsensing (PCS) task model and minimizes incentive payments by selecting a small number of participants while still satisfying probabilistic coverage constraint. In order to achieve the objective when piggybacking crowdsensing tasks with phone calls, CrowdRecruiter first predicts the call and coverage probability of each mobile user based on historical records. It then efficiently computes the joint coverage probability of multiple users as a combined set and selects the near-minimal set of participants, which meets coverage ratio requirement in each sensing cycle of the PCS task. We evaluated CrowdRecruiter extensively using a large-scale real-world dataset and the results show that the proposed solution significantly outperforms three baseline algorithms by selecting 10.0% -- 73.5% fewer participants on average under the same probabilistic coverage constraint.


Novel Subband Autoencoder Features for Non-Intrusive Quality Assessment of Noise Suppressed Speech.


title: Applications of Microalgal Biotechnology for Disease Control in Aquaculture

abstract: Aquaculture industries, and in particular the farming of fish and crustaceans, are major contributors to the economy of many countries and an increasingly important component in global food supply. However, the severe impact of aquatic microbial diseases on production performance remains a challenge to these industries. This article considers the potential applications of microalgal technology in the control of such diseases. At the simplest level, microalgae offer health-promoting benefits as a nutritional supplement in feed meal because of their digestibility and high content of proteins, lipids and essential nutrients. Furthermore, some microalgal species possess natural anti-microbial compounds or contain biomolecules that can serve as immunostimulants. In addition, emerging genetic engineering technologies in microalgae offer the possibility of producing ‘functional feed additives’ in which novel and specific bioactives, such as fish growth hormones, anti-bacterials, subunit vaccines, and virus-targeted interfering RNAs, are components of the algal supplement. The evaluation of such technologies for farm applications is an important step in the future development of sustainable aquaculture.


Erector Spinae Plane Block for Different Laparoscopic Abdominal Surgeries: Case Series


title: A survey: Several technologies of non-orthogonal transmission for 5G

abstract: One key advantage of 4G OFDM system is the relatively simple receiver implementation due to the orthogonal resource allocation. However, from sum-capacity and spectral efficiency points of view, orthogonal systems are never the achieving schemes. With the rapid development of mobile communication systems, a novel concept of non-orthogonal transmission for 5G mobile communications has attracted researches all around the world. In this trend, many new multiple access schemes and waveform modulation technologies were proposed. In this paper, some promising ones of them were discussed which include Non-orthogonal Multiple Access (NOMA), Sparse Code Multiple Access (SCMA), Multi-user Shared Access (MUSA), Pattern Division Multiple Access (PDMA) and some main new waveforms including Filter-bank based Multicarrier (FBMC), Universal Filtered Multi-Carrier (UFMC), Generalized Frequency Division Multiplexing (GFDM). By analyzing and comparing features of these technologies, a research direction of guiding on future 5G multiple access and waveform are given.


title: Anti-proliferative and anti-estrogenic effects of ICI 164,384 and ICI 182,780 in 4-OH-tamoxifen-resistant human breast-cancer cells

abstract: The effects of the anti-estrogens 4-hydroxytamoxifen (OHTam), ICI 164,384 and ICI 182,780 were tested on the MCF-7/LCC2 breast-carcinoma cell line, which grows significantly in the presence of OHTam and serves as a model for studying anti-estrogen resistance of estrogen-receptor-positive breast cancer. Cell proliferation and cathepsin-D secretion were strongly inhibited by either ICI 182,780 or ICI 164,384 alone or ICI 164,384 in combination with 17-beta-estradiol (E2) or OHTam. ICI 164,384 alone did not affect the cathepsin-D and pS2 mRNA levels, but antagonized the stimulatory effects of E2 or OHTam on these 2 mRNAs. OHTam was more effective than E2 in increasing cathepsin-D mRNA levels, supporting the idea that anti-estrogen-resistant breast cancer continues to overexpress cathepsin-D. These data show that the steroidal anti-estrogens ICI 164,384 and ICI 182,780 retain their ability to inhibit cell proliferation and the estrogen-responsiveness of cathepsin-D and pS2 genes in the OHTam-resistant MCF-7/LCC2 cell lines. These pure anti-estrogens may thus be efficient second-line treatments of some Tamoxifen-resistant tumors.


title: Connectionist Speech Recognition of Broadcast News

abstract: Abstract This paper describes connectionist techniques for recognition of Broadcast News. The fundamental difference between connectionist systems and more conventional mixture-of-Gaussian systems is that connectionist models directly estimate posterior probabilities as opposed to likelihoods. Access to posterior probabilities has enabled us to develop a number of novel approaches to confidence estimation, pronunciation modelling and search. In addition we have investigated a new feature extraction technique based on the modulation-filtered spectrogram (MSG), and methods for combining multiple information sources. We have incorporated all of these techniques into a system for the transcription of Broadcast News, and we present results on the 1998 DARPA Hub-4E Broadcast News evaluation data.


title: Overview on electrostatic discharge protection designs for mixed-voltage I/O interfaces: design concept and circuit implementations

abstract: Electrostatic discharge (ESD) protection design for mixed-voltage I/O interfaces has been one of the key challenges of system-on-a-chip (SOC) implementation in nano-scale CMOS processes. The on-chip ESD protection circuit for mixed-voltage I/O interfaces should meet the gate-oxide reliability constraints and prevent the undesired leakage current paths. This paper presents an overview on the design concept and circuit implementations of the ESD protection designs for mixed-voltage I/O interfaces without using the additional thick gate-oxide process. The ESD design constraints in mixed-voltage I/O interfaces, the classification and analysis of ESD protection designs for mixed-voltage I/O interfaces, and the designs of high-voltage-tolerant power-rail ESD clamp circuit are presented and discussed.


title: Fast asynchronous parallel stochastic gradient descent: a lock-free approach with convergence guarantee

abstract: Stochastic gradient descent (SGD) and its variants have become more and more popular in machine learning due to their efficiency and effectiveness. To handle large-scale problems, researchers have recently proposed several parallel SGD methods for multicore systems. However, existing parallel SGD methods cannot achieve satisfactory performance in real applications. In this paper, we propose a fast asynchronous parallel SGD method, called AsySVRG, by designing an asynchronous strategy to parallelize the recently proposed SGD variant called stochastic variance reduced gradient (SVRG). AsySVRG adopts a lock-free strategy which is more efficient than other strategies with locks. Furthermore, we theoretically prove that AsySVRG is convergent with a linear convergence rate. Both theoretical and empirical results show that AsySVRG can outperform existing state-of-the-art parallel SGD methods like Hogwild! in terms of convergence rate and computation cost.


title: The loss function of sensorimotor learning

abstract: Motor learning can be defined as changing performance so as to optimize some function of the task, such as accuracy. The measure of accuracy that is optimized is called a loss function and specifies how the CNS rates the relative success or cost of a particular movement outcome. Models of pointing in sensorimotor control and learning usually assume a quadratic loss function in which the mean squared error is minimized. Here we develop a technique for measuring the loss associated with errors. Subjects were required to perform a task while we experimentally controlled the skewness of the distribution of errors they experienced. Based on the change in the subjects' average performance, we infer the loss function. We show that people use a loss function in which the cost increases approximately quadratically with error for small errors and significantly less than quadratically for large errors. The system is thus robust to outliers. This suggests that models of sensorimotor control and learning that have assumed minimizing squared error are a good approximation but tend to penalize large errors excessively.


Understanding susceptibility to phishing emails: Assessing the impact of individual differences and culture.


title: Knowledge Discovery from Road Traffic Accident Data in Ethiopia: Data Quality, Ensembling and Trend Analysis for Improving Road Safety

abstract: a sel y Abstract: Descriptive analysis of the magnitude and situation of road safety in general and road accidents in particular is important, but understanding of data quality, factors related with dangerous situations and various interesting patterns in data is of even greater importance. Under the umbrella of information architec- ture research for road safety in developing countries, the objective of this machine learning experimental research is to explore data quality issues, analyze trends and predict the role of road users on possible injury risks. The research employed TreeNet, Classification and Adaptive Regression Trees (CART), Random Forest (RF) and hybrid ensemble approach. To identify relevant patterns and illustrate the performance of the techniques for the road safety domain, road accident data collected from Addis Ababa Traffic Office is subject to several analyses. Empiri- cal results illustrate that data quality is a major problem that needs architectural guideline and the prototype models could classify accidents with promising accu- racy. In addition, an ensemble technique proves to be better in terms of predictive accuracy in the domain under study.


title: Hierarchical Semantic Labeling for Task-Relevant RGB-D Perception

abstract: Semantic labeling of RGB-D scenes is very important in enabling robots to perform mobile manipulation tasks, but different tasks may require entirely different sets of labels. For example, when navigating to an object, we may need only a single label denoting its class, but to manipulate it, we might need to identify individual parts. In this work, we present an algorithm that produces hierarchical labelings of a scene, following is-part-of and is-type-of relationships. Our model is based on a Conditional Random Field that relates pixel-wise and pair-wise observations to labels. We encode hierarchical labeling constraints into the model while keeping inference tractable. Our model thus predicts different specificities in labeling based on its confidence—if it is not sure whether an object is Pepsi or Sprite, it will predict soda rather than making an arbitrary choice. In extensive experiments, both offline on standard datasets as well as in online robotic experiments, we show that our model outperforms other stateof-the-art methods in labeling performance as well as in success rate for robotic tasks.


title: Layered Light Field Reconstruction for Defocus Blur

abstract: We present a novel algorithm for reconstructing high-quality defocus blur from a sparsely sampled light field. Our algorithm builds upon recent developments in the area of sheared reconstruction filters and significantly improves reconstruction quality and performance. While previous filtering techniques can be ineffective in regions with complex occlusion, our algorithm handles such scenarios well by partitioning the input samples into depth layers. These depth layers are filtered independently and then combined together, taking into account inter-layer visibility. We also introduce a new separable formulation of sheared reconstruction filters that achieves real-time preformance on a modern GPU and is more than two orders of magnitude faster than previously published techniques.


title: Online movement adaptation based on previous sensor experiences

abstract: Personal robots can only become widespread if they are capable of safely operating among humans. In uncertain and highly dynamic environments such as human households, robots need to be able to instantly adapt their behavior to unforseen events. In this paper, we propose a general framework to achieve very contact-reactive motions for robotic grasping and manipulation. Associating stereotypical movements to particular tasks enables our system to use previous sensor experiences as a predictive model for subsequent task executions. We use dynamical systems, named Dynamic Movement Primitives (DMPs), to learn goal-directed behaviors from demonstration. We exploit their dynamic properties by coupling them with the measured and predicted sensor traces. This feedback loop allows for online adaptation of the movement plan. Our system can create a rich set of possible motions that account for external perturbations and perception uncertainty to generate truly robust behaviors. As an example, we present an application to grasping with the WAM robot arm.


title: Iris Recognition Using Possibilistic Fuzzy Matching on Local Features

abstract: In this paper, we propose a novel possibilistic fuzzy matching strategy with invariant properties, which can provide a robust and effective matching scheme for two sets of iris feature points. In addition, the nonlinear normalization model is adopted to provide more accurate position before matching. Moreover, an effective iris segmentation method is proposed to refine the detected inner and outer boundaries to smooth curves. For feature extraction, the Gabor filters are adopted to detect the local feature points from the segmented iris image in the Cartesian coordinate system and to generate a rotation-invariant descriptor for each detected point. After that, the proposed matching algorithm is used to compute a similarity score for two sets of feature points from a pair of iris images. The experimental results show that the performance of our system is better than those of the systems based on the local features and is comparable to those of the typical systems.


title: Cytotoxic T lymphocyte antigen-4 Ala17 polymorphism is a genetic marker of autoimmune adrenal insufficiency: Italian association study and meta-analysis of European studies

abstract: Objective: Cytotoxic T lymphocyte antigen-4 (CTLA4) gene polymorphism has been associated with human autoimmune diseases, but discordant data are available on its association with autoimmune Addison’s disease (AAD). We tested the human leukocyte antigen (HLA)-independent association of CTLA4C49 (A/G) (Ala 17) and/or CTLA4 CT60 (A/G) polymorphism with AAD. Design: DNA samples from 180 AAD patients and 394 healthy control subjects from continental Italy were analyzed, and association statistical analyses and meta-analysis of published studies were performed. Methods: TaqMan minor groove binder chemistry assays and PCR fragment length polymorphism assays were used. Results: Frequency of allele G of CTLA4C49 was significantly increased among AAD patients (40% alleles) than among healthy controls (27% alleles; P!0.0001). CTLA4 CT60 polymorphism was associated with AAD only in the heterozygous A/G individuals. The frequency of C49 AGCGG genotypes was significantly higher among AAD patients than among healthy control subjects, in both a co-dominant (P!0.0001) and G dominant model (P!0.0001). CTLA4C49 allele G was significantly associated with disease risk in both patients with isolated AAD and in patients with autoimmune polyendocrine syndrome. Multivariate logistic regression analysis showed that CTLA4 C49 allele G was positively associated with AAD (P!0.0001, odds ratio (OR)Z2.43, 95% confidence intervalZ1.54–3.86) also after correction for DRB1*03-DQA1*0501-DQB1*0201, DRB1*04DQA1*0301-DQB1*0302, and sex. Meta-analysis of five studies revealed a significant association of CTLA4C49 allele G with AAD (P!0.0001) with an overall OR of 1.48 (1.28–1.71). Conclusions: The CTLA4C49 polymorphism is strongly associated with genetic risk for AAD, independently from the well-known association with HLA class II genes.


title: Surgical management for a huge presacral teratoma and a meningocele in an adult with Currarino triad: a case report

abstract: The Currarino triad is a rare hereditary syndrome comprising anorectal malformation, sacral bony defect, and presacral mass. Most of the patients are diagnosed during infancy. A 44-year-old man was diagnosed with Currarino triad, with a huge presacral teratoma and meningocele. One-stage surgery via posterior approach was successful. Treatment of the presacral mass in the Currarino triad, diagnosed in adulthood, is challenging. Multidisciplinary management and detailed planning before surgery are important for a satisfactory outcome.


title: Addressing Organisational Pressures as Drivers towards Sustainability in Manufacturing Projects and Project Management Methodologies

abstract: The concept of sustainability continues to rapidly grow in interest from disparate academic and industrial fields. This research aims to elucidate further the implications of the sustainability drivers upon project management methodological approaches specifically in the manufacturing industry. This paper studies the three prevalent dialogues in the field of sustainability, relevant to the environmental and social aspects of the Triple Bottom Line, and utilises Institutional Theory to propose organisational pressures as affecting sustainability efforts in industrial manufacturing project management. Furthermore, the literature bodies of Lean and Life Cycle Analysis in manufacturing project management guided our reflection that the various drivers of sustainability put forward that do not consider the distinctive organisational pressures fail to address institutional and systemic project management issues holistically. The authors further conduct and draw on a systematic literature review on the constructs of sustainability in the manufacturing industry and their adopted methodologies, evaluating academic articles published from the year 2001 to 2017. The findings indicate that normative pressures prevail over coercive and mimetic pressures and are seen as the main drivers of sustainability in the manufacturing industry. In an incremental reductionist approach, project management knowledge areas are analysed, and the study posits that Stakeholder and Communications Management are two of the knowledge areas that need to integrate the above pressures to achieve cohesive sustainable industrial results. The principle contribution is to offer a new conceptual perspective on integrating project management knowledge areas with Institutional Theory pressures for more sustainable project management methodologies.


Emotion Detection Through Facial Feature Recognition


title: Functional Assessment of Problem Behavior: Dispelling Myths, Overcoming Implementation Obstacles, and Developing New Lore

abstract: Hundreds of studies have shown the efficacy of treatments for problem behavior based on an understanding of its function. Assertions regarding the legitimacy of different types of functional assessment vary substantially across published articles, and best practices regarding the functional assessment process are sometimes difficult to cull from the empirical literature or from published discussions of the behavioral assessment process. A number of myths regarding the functional assessment process, which appear to be pervasive within different behavior-analytic research and practice communities, will be reviewed in the context of an attempt to develop new lore regarding the functional assessment process. Frequently described obstacles to implementing a critical aspect of the functional assessment process, the functional analysis, will be reviewed in the context of solutions for overcoming them. Finally, the aspects of the functional assessment process that should be exported to others versus those features that should remain the sole technological property of behavior analysts will be discussed.


title: Self-organization, free energy minimization, and optimal grip on a field of affordances

abstract: In this paper, we set out to develop a theoretical and conceptual framework for the new field of Radical Embodied Cognitive Neuroscience. This framework should be able to integrate insights from several relevant disciplines: theory on embodied cognition, ecological psychology, phenomenology, dynamical systems theory, and neurodynamics. We suggest that the main task of Radical Embodied Cognitive Neuroscience is to investigate the phenomenon of skilled intentionality from the perspective of the self-organization of the brain-body-environment system, while doing justice to the phenomenology of skilled action. In previous work, we have characterized skilled intentionality as the organism’s tendency towards an optimal grip on multiple relevant affordances simultaneously. Affordances are possibilities for action provided by the environment. In the first part of this paper, we introduce the notion of skilled intentionality and the phenomenon of responsiveness to a field of relevant affordances. Second, we use Friston’s work on neurodynamics, but embed a very minimal version of his Free Energy Principle in the ecological niche of the animal. Thus amended, this principle is helpful for understanding the embeddedness of neurodynamics within the dynamics of the brain-body-environment system. Next, we show how we can use this adjusted principle to understand the neurodynamics of selective openness to the environment: interacting action-readiness patterns at multiple timescales contribute to the organism’s selective openness to relevant affordances. In the final part of the paper, we emphasize the important role of metastable dynamics in both the brain and the brain-body-environment system for adequate affordance-responsiveness. We exemplify our integrative approach by presenting research on the impact of Deep Brain Stimulation on affordance responsiveness of OCD patients.


title: Programming grid: a computer-aided education system for programming courses based on online judge

abstract: Based on POJ (PKU Judge Online), an online judge system for ACM/ICPC in Peking University, we develop a system called Programming Grid (PG), which is aimed at computer-aided education (CAE) for programming courses. PG is a course-centered system for CAE, while POJ is a problem-centered system for programming training. PG system designs many resources to support programming courses: problem, problem list, digest, course, etc. Based on those resources, PG supports many computer-aided education methods. The teachers and the students can use PG to solve problems, submit source codes and check their answers, download the courseware, compare scores with others, ask questions, and communicate with each others for education aim. Our practice on the course "Introduction to Computing" in PKU proves that PG is useful for computer-aided education for computer programming courses.


title: Long-Term Trends in the Public Perception of Artificial Intelligence

abstract: Analyses of text corpora over time can reveal trends in beliefs, interest, and sentiment about a topic. We focus on views expressed about artificial intelligence (AI) in the New York Times over a 30-year period. General interest, awareness, and discussion about AI has waxed and waned since the field was founded in 1956. We present a set of measures that captures levels of engagement, measures of pessimism and optimism, the prevalence of specific hopes and concerns, and topics that are linked to discussions about AI over decades. We find that discussion of AI has increased sharply since 2009, and that these discussions have been consistently more optimistic than pessimistic. However, when we examine specific concerns, we find that worries of loss of control of AI, ethical concerns for AI, and the negative impact of AI on work have grown in recent years. We also find that hopes for AI in healthcare and education have increased over time.


title: MobiSteer: using steerable beam directional antenna for vehicular network access

abstract: In this work, we investigate the use of directional antennas and beam steering techniques to improve performance of 802.11 links in the context of communication between amoving vehicle and roadside APs. To this end, we develop a framework called MobiSteer that provides practical approaches to perform beam steering. MobiSteer can operate in two modes - cached mode - where it uses prior radiosurvey data collected during "idle" drives, and online mode, where it uses probing. The goal is to select the best AP and beam combination at each point along the drive given the available information, so that the throughput can be maximized. For the cached mode, an optimal algorithm for AP and beam selection is developed that factors in all overheads. We provide extensive experimental results using a commercially available eight element phased-array antenna. In the experiments, we use controlled scenarios with our own APs, in two different multipath environments, as well as in situ scenarios, where we use APs already deployed in an urban region - to demonstrate the performance advantage of using MobiSteer over using an equivalent omni-directional antenna. We show that MobiSteer improves the connectivity duration as well as PHY-layer data rate due to better SNR provisioning. In particular, MobiSteer improves the throughput in the controlled experiments by a factor of 2 - 4. In in situ experiments, it improves the connectivity duration by more than a factor of 2 and average SNR by about 15 dB.


title: Internet of Things based Expert System for Smart Agriculture

abstract: Agriculture sector is evolving with the advent of the information and communication technology. Efforts are being made to enhance the productivity and reduce losses by using the state of the art technology and equipment. As most of the farmers are unaware of the technology and latest practices, many expert systems have been developed in the world to facilitate the farmers. However, these expert systems rely on the stored knowledge base. We propose an expert system based on the Internet of Things (IoT) that will use the input data collected in real time. It will help to take proactive and preventive actions to minimize the losses due to diseases and insects/pests.


title: The flexural behavior of fibrous plaster sheets

abstract: Abstract Tests on fibrous plaster sheet, reinforced with sisal and glass fibres in the bulk density range 56 to 72.5 lb/ft 3 (W/P = 0.9 to 0.6), show that sisal fibre decreases the strength of the gypsum composite by approximately 30 to 50% and glass fibre by approximately 0 to 30%. The effect on compressive strength shown by the two fibres can be largely explained on the basis of differing volumes and stiffness. Variation of the fibre content in the plaster sheet increases the tensile strength with increasing fibre content much as would be expected. A theory based on a non-linear stress-strain curve is proposed and the predictions of this theory are found to be in reasonable agreement with the test results. However, it is concluded that variations in the ultimate flexural strength, where sisal is used as the reinforcing fibre, are of such magnitude that it is impossible to predict flexural strengths with any degree of accuracy. Until the source of this variability can be found it is not possible to make any design improvements to the strength of the fibrous plaster sheet. The outlook is more hopeful for the use of glass fibre as a reinforcing material but further work is required to confirm this.


title: New horizons in the study of language

abstract: The study of language is one of the oldest branches of systematic inquiry, tracing back to classical India and Greece, with a rich and fruitful history of achievement. From a different point of view, it is quite young. The major research enterprises of today took shape only about 40 years ago, when some of the leading ideas of the tradition were revived and reconstructed, opening the way to what has proven to be very productive inquiry. That language should have exercised such fascination over the years is not surprising. The human faculty of language seems to be a true “species property,” varying little among humans and without significant analogue elsewhere. Probably the closest analogues are found in insects, at an evolutionary distance of a billion years. There is no serious reason today to challenge the Cartesian view that the ability to use linguistic signs to express freely-formed thoughts marks “the true distinction between man and animal” or machine, whether by “machine” we mean the automata that captured the imagination of the seventeenth and eighteenth century, or those that are providing a stimulus to thought and imagination today. Furthermore, the faculty of language enters crucially into every aspect of human life, thought, and interaction. It is largely responsible for the fact that alone in the biological world, humans have a history, cultural evolution and diversity of any complexity and richness, even biological success in the technical sense that their numbers are huge.


title: Blockchain Beyond Bitcoin: Block Maturity Level Consensus Protocol

abstract: This paper enlightens the stipulation of blockchain beyond the bitcoin and identifies the key challenges in the implementation of blockchain on a small scale or newly implemented projects based on the literature review, furthermore it also introduces Block maturity level (BML) as a Consensus protocol, which eliminates the 51% attack, the hardfork problem and small-scale problem by introducing sub-blocks as a defense mechanism against attackers. This technique can be used in the implementation of blockchain where the hash power and difficulty is lower and easy to match-up. This proposed consensus protocol would immensely secure the implementation of the blockchain on different projects.


title: Temperature prediction of Soil-Pipe-Air Heat Exchanger using neural networks

abstract: In this paper, we use the concept of neural networks to propose an intelligent tool that can we help to evaluate any aspect of earth-to-air heat exchanger. The present study focusesmostly on those aspects related to the passive heating or cooling performance of the building. Two models have been developed for this purpose, namely theoretical and intelligent. The theoretical model is developed by analyzing the energy balance equation in ground whereas the intelligent model is a development of data driven artificial neural networks model. Seven variables influencing the thermal performance of the Soil-Pipe-Air Heat Exchanger (SPAHE) which are taken into account. Both models are validated against other published model.


title: Comparison of the mucosal immune response in dogs vaccinated with either an intranasal avirulent live culture or a subcutaneous antigen extract vaccine of Bordetella bronchiseptica.

abstract: Healthy dogs with low antibody titer to Bordetella bronchiseptica were vaccinated intranasally with an avirulent live vaccine, subcutaneously with an antigen extract vaccine, or subcutaneously and intranasally with a placebo. Intranasally vaccinated dogs developed B. bronchiseptica-specific IgA titers in nasal secretions that remained at high levels until the end of the study; dogs vaccinated subcutaneously with the antigen extract or placebo did not develop measurable antigen-specific IgA titers in nasal secretions. Dogs were challenged with virulent live B. bronchiseptica 63 days after vaccination. Intranasally vaccinated dogs had significantly lower cough scores (P < or =.0058) and shed significantly fewer challenge organisms (P <.0001) than dogs in either of the other groups. Cough scores of subcutaneously vaccinated dogs were not significantly different from placebo-vaccinated dogs.


title: A Phase-shifted Full-Bridge ZVS DC/DC Converter for Wireless Charging of Electric Vehicles

abstract: This paper describes a Phase-shifted Full-Bridge (PSFB) Zero Voltage Switching (ZVS) DC/DC Converter for Wireless Charging of Electric Vehicles. Wireless charging obviates the need for plug in chargers while providing convenient charging in parking lots and residential locations. A prototype charger is presented with a full working circuit. The magnetic fields around the charger as well as the mutual coupling are also described. Full experimental results are provided.


title: Strengthening the health information system in Mozambique through malaria incidence prediction

abstract: Malaria is one of the principal health problems in Mozambique, affecting mostly children. The prediction of accurate future incidence cases is crucial for the implementation of appropriate policies of intervention and disease control in order to strengthen the health system. We propose a model based on support vector machines (SVM) for predicting yearly malaria incidence cases for children 0-4 years of age in the Maputo province, Mozambique. The predictive model is trained on two years of historical malaria data in combination with climatic and malaria control factors. A grid optimization parameter tuning procedure was firstly employed to detect the best parameters and select the kernel. In order to determine the most influential factors, variable importance was calculated through estimating the impact of permuting feature values on the predictive performance. The most important malaria incidence predictors turned out to be temperature variation, followed by Matutuine (district), April (month) and Namaacha (district).