title: Investigating interactions of trust and interest similarity

abstract: Online communities that allow their users to express their personal preferences, such as the members they trust and the products they appreciate, are becoming increasingly popular. Exploiting these communities as playgrounds for sociological research we present two frameworks for analyzing the correlation between interpersonal trust and interest similarity. We obtain empirical results from applying the two frameworks on two real, operational communities, that suggest there is a strong correlation between both trust and interest similarity. We believe our findings particularly relevant for ongoing research in recommender systems and collaborative filtering, where people are suggested products based on their similarity with other customers, and propose ways in which trust models can be integrated into these systems.


title: 2-D Beam-Steerable Integrated Lens Antenna System for 5G $E$ -Band Access and Backhaul

abstract: The new services available through smart devices require very high cellular network capacity. The capacity requirement is expected to increase exponentially with the forthcoming 5G networks. The only available spectrum for truly wideband communication (>1 GHz) is at millimeter wavelengths. The high free space loss can be overcome by using the directive and beam-steerable antennas. This paper describes a design and the measurement results for a lens antenna system for $E$ -band having 2-D beam-steering capability. Continuous beam-switching range of about $\pm 4 {^{\circ }} \times \pm 17^{\circ }$ is demonstrated with the lens having the maximum measured directivity of 36.7 dB. Link budget calculation for backhaul application using the presented lens antenna system is presented and compared with the measurement results of the implemented demo system.


title: Explorative Learning and Functional Inferences on a Five-Step Means-Means-End Problem in Goffin’s Cockatoos ( Cacatua goffini )

abstract: To investigate cognitive operations underlying sequential problem solving, we confronted ten Goffin’s cockatoos with a baited box locked by five different inter-locking devices. Subjects were either naive or had watched a conspecific demonstration, and either faced all devices at once or incrementally. One naive subject solved the problem without demonstration and with all locks present within the first five sessions (each consisting of one trial of up to 20 minutes), while five others did so after social demonstrations or incremental experience. Performance was aided by species-specific traits including neophilia, a haptic modality and persistence. Most birds showed a ratchet-like progress, rarely failing to solve a stage once they had done it once. In most transfer tests subjects reacted flexibly and sensitively to alterations of the locks’ sequencing and functionality, as expected from the presence of predictive inferences about mechanical interactions between the locks.


title: Semiotic and Cognitive Aspects of Professional Discourse Study

abstract: The authors focus on some cognitive and semiotic aspects of professional discourse study. Different approaches towards the definition of professional discourse are highlighted. From the cognitive perspective professional world view is analyzed. It is said that terminology constitutes the core of professional world view; some means of categorization and conceptualization in various types of professional discourse are presented. The role of conceptual metaphor and metonymy as means of world categorization and conceptualization in professional communication is revealed. Describing the structure of professional consciousness the authors suggest an ethnocultural component. The background of semiotic research of professional discourse is analyzed on the level of metalanguage and professional speech communication. The sign character of professional discourse on the metalanguage level is represented by the language for specific purposes. The systematic character of professional speech communication is witnessed in the organization of speech genres, functioning of intertextual incorporations and precedent texts, communicative situations typical for the professional activity, etc. Some perspectives of cognitive and semiotic approach towards professional discourse study are suggested. DOI: 10.5901/mjss.2015.v6n6s5p30


title: The peculiarities and paradoxes of Plasmodium heme metabolism.

abstract: For over a century, heme metabolism has been recognized to play a central role during intraerythrocytic infection by Plasmodium parasites, the causative agent of malaria. Parasites liberate vast quantities of potentially cytotoxic heme as a by-product of hemoglobin catabolism within the digestive vacuole, where heme is predominantly sequestered as inert crystalline hemozoin. Plasmodium spp. also utilize heme as a metabolic cofactor. Despite access to abundant host-derived heme, parasites paradoxically maintain a biosynthetic pathway. This pathway has been assumed to produce the heme incorporated into mitochondrial cytochromes that support electron transport. In this review, we assess our current understanding of the love-hate relationship between Plasmodium parasites and heme, we discuss recent studies that clarify several long-standing riddles about heme production and utilization by parasites, and we consider remaining challenges and opportunities for understanding and targeting heme metabolism within parasites.


title: Bayesian Optimization Using Domain Knowledge on the ATRIAS Biped

abstract: Robotics controllers often consist of expert-designed heuristics, which can be hard to tune in higher dimensions. Simulation can aid in optimizing these controllers if parameters learned in simulation transfer to hardware. Unfortunately, this is often not the case in legged locomotion, necessitating learning directly on hardware. This motivates using data-efficient learning techniques like Bayesian Optimization (BO) to minimize collecting expensive data samples. BO is a black-box data-efficient optimization scheme, though its performance typically degrades in higher dimensions. We aim to overcome this problem by incorporating domain knowledge, with a focus on bipedal locomotion. In our previous work, we proposed a feature transformation that projected a 16-dimensional locomotion controller to a 1-dimensional space using knowledge of human walking. When optimizing a human-inspired neuromuscular controller in simulation, this feature transformation enhanced sample efficiency of BO over traditional BO with a Squared Exponential kernel. In this paper, we present a generalized feature transform applicable to non-humanoid robot morphologies and evaluate it on the ATRIAS bipedal robot, in both simulation and hardware. We present three different walking controllers and two are evaluated on the real robot. Our results show that this feature transform captures important aspects of walking and accelerates learning on hardware and simulation, as compared to traditional BO.


title: Comparison of antioxidant and antimicrobial properties for Ginkgo biloba and Rosemary (Rosmarinus officinalis L.) from Egypt.

abstract: The widespread use of medicinal plants for health purposes has increased dramatically due to their great importance to the public health. In this study levels of phenolic, flavonoid contents of Ginkgo biloba and Rosmarinus officinalis from Egypt were determined. HPLC was used to identify and quantify the phenolic compounds in selected plants. The plant extracts were evaluated for their antioxidant activities using various antioxidant methodologies, (i) scavenging of free radicals using 2, 2-diphenyl-1-picrylhydrazyl, (ii) metal ion chelating capacity, and (iii) scavenging of superoxide anion radical. The antimicrobial activity of both plantâ€™s extracts were evaluated against a panel of microorganisms by using agar disc diffusion method. The total phenolic content (75.30 and 98.31 mg/g dry weight in G. biloba and R. officinalis, respectively) was significantly (p<0.05) different. Among the identified phenolic compounds, quercetin, kaempferol and caffeic acid were the predominant phenolic compounds in Ginkgo biloba, whereas carnosic acid, rosmarinic acid, narinigen and hispidulin were the predominant phenolic compound in Rosmarinus officinalis leaves. The antioxidant activity increased with the concentration increase. The R. officinalis was more active than G. biloba extract against Gram-negative bacteria. This study reveals that the consumption of these plants would exert several beneficial effects by virtue of their antioxidant and antimicrobial activities.


title: Efficient feature extraction and classification of chromosomes

abstract: Karryogram is a preliminary procedure to detect the most characteristic signs of a disorder that may require for further investigation of medical applications mainly for cancerous. Diagnosis of karryogram is generally very complex, eroding and a time consuming operation. As of now it requires fussy attention to details and calls for meritoriously and trained personnel. Normally chromosomes are essential genomic information carriers which contains 23 pairs. This paper suggests a efficient classifier Support Vector Machine (SVM) for classifying chromosomes in comparison to the already existing methods such as support vector machine based medial axis and density profiles. The features are extracted based on GLCM (Gray level co-occurrence) feature extraction algorithm which is very well known for its high accuracy. First order features and GLCM features of chromosomes are extracted from the segmented image. As a prerequisite, image segmentation needs to be done by using Fuzzy-C Mean (FCM) procedure to obtain efficient features in coordination with SVM which is used to classify the chromosomes from the available pairs of 23 chromoseomes. Using this methodology increased the accuracy of classification results. Simulation results are carried out in MATLAB to support the analysis.


title: Kernelized Locality-Sensitive Hashing

abstract: Fast retrieval methods are critical for many large-scale and data-driven vision applications. Recent work has explored ways to embed high-dimensional features or complex distance functions into a low-dimensional Hamming space where items can be efficiently searched. However, existing methods do not apply for high-dimensional kernelized data when the underlying feature embedding for the kernel is unknown. We show how to generalize locality-sensitive hashing to accommodate arbitrary kernel functions, making it possible to preserve the algorithm's sublinear time similarity search guarantees for a wide class of useful similarity functions. Since a number of successful image-based kernels have unknown or incomputable embeddings, this is especially valuable for image retrieval tasks. We validate our technique on several data sets, and show that it enables accurate and fast performance for several vision problems, including example-based object classification, local feature matching, and content-based retrieval.


title: Fast address lookups using controlled prefix expansion

abstract: Internet (IP) address lookup is a major bottleneck in high-performance routers. IP address lookup is challenging because it requires a longest matching prefix lookup. It is compounded by increasing routing table sizes, increased traffic, higher-speed links, and the migration to 128-bit IPv6 addresses. We describe how IP lookups and updates can be made faster using a set of of transformation techniques. Our main technique, controlled prefix expansion , transforms a set of prefixes into an equivalent set with fewer prefix lengths. In addition, we use optimization techniques based on dynamic programming, and local transformations of data structures to improve cache behavior. When applied to trie search, our techniques provide a range of algorithms ( Expanded Tries ) whose performance can be tuned. For example, using a processor with 1MB of L2 cache, search of the MaeEast database containing 38000 prefixes can be done in 3 L2 cache accesses. On a 300MHz Pentium II which takes 4 cycles for accessing the first word of the L2 cacheline, this algorithm has a worst-case search time of 180 nsec., a worst-case insert/delete time of 2.5 msec., and an average insert/delete time of 4 usec. Expanded tries provide faster search and faster insert/delete times than earlier lookup algirthms. When applied to Binary Search on Levels, our techniques improve worst-case search times by nearly a factor of 2 (using twice as much storage) for the MaeEast database. Our approach to algorithm design is based on measurements using the VTune tool on a Pentium to obtain dynamic clock cycle counts. Our techniques also apply to similar address lookup problems in other network protocols.


title: OctopusFS: A Distributed File System with Tiered Storage Management

abstract: The ever-growing data storage and I/O demands of modern large-scale data analytics are challenging the current distributed storage systems. A promising trend is to exploit the recent improvements in memory, storage media, and networks for sustaining high performance and low cost. While past work explores using memory or SSDs as local storage or combine local with network-attached storage in cluster computing, this work focuses on managing multiple storage tiers in a distributed setting. We present OctopusFS, a novel distributed file system that is aware of heterogeneous storage media (e.g., memory, SSDs, HDDs, NAS) with different capacities and performance characteristics. The system offers a variety of pluggable policies for automating data management across the storage tiers and cluster nodes. The policies employ multi-objective optimization techniques for making intelligent data management decisions based on the requirements of fault tolerance, data and load balancing, and throughput maximization. At the same time, the storage media are explicitly exposed to users and applications, allowing them to choose the distribution and placement of replicas in the cluster based on their own performance and fault tolerance requirements. Our extensive evaluation shows the immediate benefits of using OctopusFS with data-intensive processing systems, such as Hadoop and Spark, in terms of both increased performance and better cluster utilization.


title: Translating Questions into Answers using DBPedia n-triples

abstract: In this paper we present a question answering system using a neural network to interpret questions learned from the DBpedia repository. We train a sequence-to-sequence neural network model with n-triples extracted from the DBpedia Infobox Properties. Since these properties do not represent the natural language, we further used question-answer dialogues from movie subtitles. Although the automatic evaluation shows a low overlap of the generated answers compared to the gold standard set, a manual inspection of the showed promising outcomes from the experiment for further work.


title: Updating the 97% identity threshold for 16S ribosomal RNA OTUs

abstract: Motivation ::: The 16S ribosomal RNA (rRNA) gene is widely used to survey microbial communities. Sequences are often clustered into Operational Taxonomic Units (OTUs) as proxies for species. The canonical clustering threshold is 97% identity, which was proposed in 1994 when few 16S rRNA sequences were available, motivating a reassessment on current data. ::: ::: ::: Results ::: Using a large set of high-quality 16S rRNA sequences from finished genomes, I assessed the correspondence of OTUs to species for five representative clustering algorithms using four accuracy metrics. All algorithms had comparable accuracy when tuned to a given metric. Optimal identity thresholds were ∼99% for full-length sequences and ∼100% for the V4 hypervariable region. ::: ::: ::: Availability and implementation ::: Reference sequences and source code are provided in the Supplementary Material. ::: ::: ::: Supplementary information ::: Supplementary data are available at Bioinformatics online.


Emotion and the autonomic nervous system: A prospectus for research on autonomic specificity.


title: Movie Question Answering: Remembering the Textual Cues for Layered Visual Contents

abstract: Movies provide us with a mass of visual content as well as attracting stories. Existing methods have illustrated that understanding movie stories through only visual content is still a hard problem. In this paper, for answering questions about movies, we put forward a Layered Memory Network (LMN) that represents frame-level and clip-level movie content by the Static Word Memory module and the Dynamic Subtitle Memory module, respectively. Particularly, we firstly extract words and sentences from the training movie subtitles. Then the hierarchically formed movie representations, which are learned from LMN, not only encode the correspondence between words and visual content inside frames, but also encode the temporal alignment between sentences and frames inside movie clips. We also extend our LMN model into three variant frameworks to illustrate the good extendable capabilities. We conduct extensive experiments on the MovieQA dataset. With only visual content as inputs, LMN with frame-level representation obtains a large performance improvement. When incorporating subtitles into LMN to form the clip-level representation, we achieve the state-of-the-art performance on the online evaluation task of 'Video+Subtitles'. The good performance successfully demonstrates that the proposed framework of LMN is effective and the hierarchically formed movie representations have good potential for the applications of movie question answering.


title: Hemi-cylinder unwrapping algorithm of fish-eye image based on equidistant projection model

abstract: This paper presents a novel fish-eye image unwrapping algorithm based on equidistant projection mode. We discuss a framework to estimate the image distortion center using vanishing points extraction. Then we propose a fish-eye image unwrapping algorithm using hemi-cylinder projection in 3D space. Experimental results show that our algorithm is efficient and effective. In particular, the hemi-cylinder unwrapping results do not reduce the horizontal field of view which is very useful for panoramic surveillance with applications in important sites safety compared with other fish-eye image correction methods.


title: The Granular Tabu Search and Its Application to the Vehicle-Routing Problem

abstract: We describe a new variant, calledgranular tabu search, of the well-known tabu-search approach. The method uses an effective intensification/diversification tool that can be successfully applied to a wide class of graph-theoretic and combinatorial-optimization problems. Granular tabu search is based on the use of drastically restricted neighborhoods, not containing the moves that involve only elements that are not likely to belong to good feasible solutions. These restricted neighborhoods are calledgranular, and may be seen as an efficient implementation of candidate-list strategies proposed for tabu-search algorithms. Results of computational testing of the proposed approach on the well-known symmetric capacitated and distance-constrained vehicle-routing problem are discussed, showing that the approach is able to determine very good solutions within short computing times.


Fast, Automatic, Topology-Preserving Gradient Mesh Generation for Image Vectorization


title: Mental Contrasting and Transfer of Energization

abstract: Mental contrasting a desired future with present reality is a self-regulation strategy that fosters energization in line with a person’s expectations of successfully attaining the desired future. We investigated whether physiological energization (measured by systolic blood pressure) elicited by mental contrasting a desired future of solving a given task transfers to effort in an unrelated task. As predicted, mental contrasting a desired future of excelling in an intelligence test (Study 1) and of writing an excellent essay (Study 2) triggered changes in energization that translated into physical effort in squeezing a handgrip (Study 1) and translated into mental effort in writing a get-well letter (Study 2). Results suggest that mental contrasting of solving one task triggers energization that may fuel effort for performing an unrelated task. Implications for intervention research are discussed.


abstract: The NavChair Assistive Wheelchair Navigation System is being developed to reduce the cognitive and physical requirements of operating a power wheelchair for people with wide ranging impairments that limit their access to powered mobility. The NavChair is based on a commercial wheelchair system with the addition of a DOS-based computer system, ultrasonic sensors, and an interface module interposed between the joystick and power module of the wheelchair. The obstacle avoidance routines used by the NavChair in conjunction with the ultrasonic sensors are modifications of methods originally used in mobile robotics research. The NavChair currently employs three operating modes: general obstacle avoidance, door passage, and automatic wall following. Results from performance testing of these three operating modes demonstrate their functionality. In additional to advancing the technology of smart wheelchairs, the NavChair has application to the development and testing of "shared control" systems where a human and machine share control of a system and the machine can automatically adapt to human behaviors.


title: Antenna array enhancement using mushroom-like electromagnetic band gap (EBG)

abstract: A double elements micro-strip antenna with mushroom-like electromagnetic band gap (EBG) inserted between elements have been investigated. The performances of the antenna before and after the EBG are inserted between the micro-strip patches are analyzed. FR4 substrate with dielectric constant of 4.7 and 1.6mm thickness was simulated at operating frequency of 5.8GHz. The results shown that the performance of the array changes if EBG parameters such as EBG size, spacing between EBG, number of column of the EBG inserted between the elements and also the thickness of the substrate are changed. The simulation results shows that by inserting (3×3) mm2 mushroom-like EBG with spacing of 0.6mm between EBG elements, the side-lobe (SL) improved from −19.dB to −24dB with 0.3dB gain increment and back radiation pattern reduced significantly. Meanwhile, by inserting (3×4.5) mm2 mushroom-like EBG the mutual coupling improved from −30.87dB to −52.78dB. Overall, this paper provides significant guidelines in enhancing the performance of the antenna array integrated with mushroom-like EBG.


title: The dynamics of solar sails with a non-point source of radiation pressure

abstract: The form of the solar radiation pressure on a heliocentric orbiting solar sail is obtained for a finite angular sized and limb darkened solar disk by the use of the radiation pressure tensor. It is found that the usual inverse square variation of the solar radiation pressure is modified by the finite angular size, and to a lesser extent by the solar limb darkening. The actual magnitude of the modification is in itself small, except at close heliocentric distances. However, its existence has implications for the dynamical stability of solar sails both in parked and circular orbital configurations and for the accuracy of trajectory calculations, particularly for sails in the inner solar system.


title: Comparative analysis of five machine learning algorithms for IP traffic classification

abstract: With rapid increase in internet traffic over last few years due to the use of variety of internet applications, the area of IP traffic classification becomes very significant from the point of view of various internet service providers and other governmental and private organizations. Now days, traditional IP traffic classification techniques such as port number based and payload based direct packet inspection techniques are seldom used because of use of dynamic port number instead of well-known port number in packet headers and various encryption techniques which inhibit inspection of packet payload. Current trends are use of machine learning (ML) techniques for this classification. In this research paper, real time internet traffic dataset has been developed using packet capturing tool and then using attribute selection algorithms, a reduced feature dataset has been developed. After that, five ML algorithms MLP, RBF, C4.5, Bayes Net and Naive Bayes are used for IP traffic classification with these datasets. This experimental analysis shows that Bayes Net and C4.5 are effective ML techniques for IP traffic classification with accuracy in the range of 94 %.


title: Gunshot detection in noisy environments

abstract: Gunshot detection finds application in the fields of law enforcement, forensic science, and defense (military applications). The first task of a sniper detector, aiming to estimate the direction of arrival of a given gunshot, is to detect automatically the presence of this audio event. Since it is a typical on-line application where a fast response is of paramount importance, a non (computationally) expensive procedure is needed. In a recent work, a simple procedure based on the correlation of the audio signal against a template has proved its efficiency as a gunshot detection algorithm. In this paper, we extend its evaluation to a noisy environment and assess its performance, in gunshot recognition and gunshot detection tasks, comparing it to other more complex methods. Keywords— gunshot detection; gunshot classification; impul- sive signals; Hidden Markov Models; Mel-frequency cepstral coefficients; stable distributions; linear predictive coding.


title: Internet of Things to Smart IoT Through Semantic, Cognitive, and Perceptual Computing

abstract: Rapid growth in the Internet of Things (IoT) has resulted in a massive growth of data generated by these devices and sensors put on the Internet. Physical-cyber-social (PCS) big data consist of this IoT data, complemented by relevant Web-based and social data of various modalities. Smart data is about exploiting this PCS big data to get deep insights and make it actionable, and making it possible to facilitate building intelligent systems and applications. This article discusses key AI research in semantic computing, cognitive computing, and perceptual computing. Their synergistic use is expected to power future progress in building intelligent systems and applications for rapidly expanding markets in multiple industries. Over the next two years, this column on IoT will explore many challenges and technologies on intelligent use and applications of IoT data.


title: Driver torque and road reaction force estimation of an Electric Power Assisted Steering using sliding mode observer with unknown inputs

abstract: Driver torque and steering reaction force are important information in the designing of EPAS (Electric Power Assisted Steering) system. The driver torque determines the amount of assisted torque and the reaction force can be used to improve the steering feeling and the vehicle stability or introduce a new assist torque control. Since, in practice, they are not usually measureable; a sliding mode observer with unknown inputs is introduced to estimate the driver torque and the road reaction force of an EPAS system by using other measured signals which are normally available. A state space formulation for EPAS system with brushed motor is used for the synthesis of observer with unknown inputs. The proposed observer is applied to state and unknown input estimation of EPAS. Simulation results demonstrate the effectiveness of the proposed observer which improves the EPAS system performance and also reduces system cost and complexity.


title: Feedforward semantic segmentation with zoom-out features

abstract: We introduce a purely feed-forward architecture for semantic segmentation. We map small image elements (superpixels) to rich feature representations extracted from a sequence of nested regions of increasing extent. These regions are obtained by “zooming out” from the superpixel all the way to scene-level resolution. This approach exploits statistical structure in the image and in the label space without setting up explicit structured prediction mechanisms, and thus avoids complex and expensive inference. Instead superpixels are classified by a feedforward multilayer network. Our architecture achieves 69.6% average accuracy on the PASCAL VOC 2012 test set.


title: Improved Control Strategy for DFIG Wind Turbines for Low Voltage Ride Through

abstract: This paper presents an improved control strategy for both the rotor side converter (RSC) and grid side converter (GSC) of a doubly fed induction generator (DFIG)-based wind turbine (WT) system to enhance the low voltage ride through (LVRT) capability. Within the proposed control strategy, the RSC control introduces transient feed-forward compensation terms to mitigate the high frequency harmonic components and reduce the surge in the rotor currents. The proposed GSC control scheme also introduces a compensation term reflecting the instantaneous variation of the output power of the rotor side converter with consideration of the instantaneous power of grid filter impendence to keep the dc-link voltage nearly constant during the grid faults. To provide precise control, non-ideal proportional resonant (PR) controllers for both the RSC and GSC current regulation are employed to further improve dynamic performance. Simulations performed in Matlab/Simulink verify the effectiveness of the proposed control strategy.


title: Tracking Multiple Occluding People by Localizing on Multiple Scene Planes

abstract: Occlusion and lack of visibility in crowded and cluttered scenes make it difficult to track individual people correctly and consistently, particularly in a single view. We present a multi-view approach to solving this problem. In our approach we neither detect nor track objects from any single camera or camera pair; rather evidence is gathered from all the cameras into a synergistic framework and detection and tracking results are propagated back to each view. Unlike other multi-view approaches that require fully calibrated views our approach is purely image-based and uses only 2D constructs. To this end we develop a planar homographic occupancy constraint that fuses foreground likelihood information from multiple views, to resolve occlusions and localize people on a reference scene plane. For greater robustness this process is extended to multiple planes parallel to the reference plane in the framework of plane to plane homologies. Our fusion methodology also models scene clutter using the Schmieder and Weathersby clutter measure, which acts as a confidence prior, to assign higher fusion weight to views with lesser clutter. Detection and tracking are performed simultaneously by graph cuts segmentation of tracks in the space-time occupancy likelihood data. Experimental results with detailed qualitative and quantitative analysis, are demonstrated in challenging multi-view, crowded scenes.


title: Virtual reality for the treatment of autism

abstract: A process for the manufacture of aliphatic isocyanates by thermal decomposition of aliphatic carbamic acid halides. The decomposition is carried out in stages and the hydrogen halide liberated is expelled, in the first stage, without passing inert gas through the reaction mixture, while in the second stage it is expelled while passing an inert gas, which is recycled, through the reaction mixture.


title: SCRIBE: The design of a large-scale event notification infrastructure

abstract: This paper presents Scribe, a large-scale event notification infrastructure for topic-based publish-subscribe applications. Scribe supports large numbers of topics, with a potentially large number of subscribers per topic. Scribe is built on top of Pastry, a generic peer-to-peer object location and routing substrate overlayed on the Internet, and leverages Pastry's reliability, self-organization and locality properties. Pastryi s used to create a topic (group) and to build an efficient multicast tree for the dissemination of events to the topic's subscribers (members). Scribe provides weak reliability guarantees, but we outline how an application can extend Scribe to provide stronger ones.


title: Bayesian Active Remote Sensing Image Classification

abstract: In recent years, kernel methods, in particular support vector machines (SVMs), have been successfully introduced to remote sensing image classification. Their properties make them appropriate for dealing with a high number of image features and a low number of available labeled spectra. The introduction of alternative approaches based on (parametric) Bayesian inference has been quite scarce in the more recent years. Assuming a particular prior data distribution may lead to poor results in remote sensing problems because of the specificities and complexity of the data. In this context, the emerging field of nonparametric Bayesian methods constitutes a proper theoretical framework to tackle the remote sensing image classification problem. This paper exploits the Bayesian modeling and inference paradigm to tackle the problem of kernel-based remote sensing image classification. This Bayesian methodology is appropriate for both finite- and infinite-dimensional feature spaces. The particular problem of active learning is addressed by proposing an incremental/active learning approach based on three different approaches: 1) the maximum differential of entropies; 2) the minimum distance to decision boundary; and 3) the minimum normalized distance. Parameters are estimated by using the evidence Bayesian approach, the kernel trick, and the marginal distribution of the observations instead of the posterior distribution of the adaptive parameters. This approach allows us to deal with infinite-dimensional feature spaces. The proposed approach is tested on the challenging problem of urban monitoring from multispectral and synthetic aperture radar data and in multiclass land cover classification of hyperspectral images, in both purely supervised and active learning settings. Similar results are obtained when compared to SVMs in the supervised mode, with the advantage of providing posterior estimates for classification and automatic parameter learning. Comparison with random sampling as well as standard active learning methods such as margin sampling and entropy-query-by-bagging reveals a systematic overall accuracy gain and faster convergence with the number of queries.


title: Adversarial Vision Challenge

abstract: The NIPS 2018 Adversarial Vision Challenge is a competition to facilitate measurable progress towards robust machine vision models and more generally applicable adversarial attacks. This document is an updated version of our competition proposal that was accepted in the competition track of 32nd Conference on Neural Information Processing Systems (NIPS 2018).


title: Impaired self-agency in functional movement disorders: A resting-state fMRI study

abstract: Objective:To investigate the neural mechanisms underlying impaired self-agency in patients with functional movement disorders using resting-state functional MRI (fMRI). Methods:We obtained resting-state fMRI on 35 patients with clinically definite functional movement disorders and 35 age- and sex-matched healthy controls. Between-group differences in functional connectivity from the right temporo-parietal junction (TPJ), a region previously demonstrated to play a critical role in self-agency by comparing internal predictions of movement with actual external events, were assessed using t tests. All participants were screened for psychiatric diagnoses using a structured clinical interview and completed the Beck Depression Inventory and Childhood Trauma Questionnaire. Results:Compared to the healthy controls, patients with functional movement disorders showed decreased functional connectivity between the right TPJ and the right sensorimotor cortex, cerebellar vermis, bilateral supplementary motor area, and right insula. These findings were independent of depression, anxiety, and childhood trauma scores included in our assessment as covariates. Conclusions:The decreased functional connectivity between the right TPJ and bilateral sensorimotor regions observed in patients with functional movement disorders supports a model whereby impaired motor feed-forward together with altered sensory feedback from sensorimotor regions and areas of sensorimotor integration to the right TPJ contributes to patients' impaired sense of self-agency.


title: An Analysis and Survey of the Development of Mutation Testing

abstract: Mutation Testing is a fault-based software testing technique that has been widely studied for over three decades. The literature on Mutation Testing has contributed a set of approaches, tools, developments, and empirical results. This paper provides a comprehensive analysis and survey of Mutation Testing. The paper also presents the results of several development trend analyses. These analyses provide evidence that Mutation Testing techniques and tools are reaching a state of maturity and applicability, while the topic of Mutation Testing itself is the subject of increasing interest.


title: Regular Linear Temporal Logic ⋆

abstract: We present regular linear temporal logic (RLTL), a logic that generalizes linear temporal logic with the ability to use regular expressions arbitrarily as sub-expressions. Every LTL operator can be defined as a context in regular linear temporal logic. This implies that there is a (linear) translation from LTL to RLTL. ::: ::: Unlike LTL, regular linear temporal logic can define all ω-regular languages, while still keeping the satisfiability problem in PSPACE. Unlike the extended temporal logics ETL*, RLTL is defined with an algebraic signature. In contrast to the linear time µ-calculus, RLTL does not depend on fix-points in its syntax.


title: Evaluation and selection of open-source EMR software packages based on integrated AHP and TOPSIS

abstract: Display Omitted Outlined 13 active open-source EMR software packages in the healthcare area.Evaluate the set of metrics based hands-on study.The open-source EMR software packages was scored based on integrated AHP-TOPSIS. Evaluating and selecting software packages that meet the requirements of an organization are difficult aspects of software engineering process. Selecting the wrong open-source EMR software package can be costly and may adversely affect business processes and functioning of the organization. This study aims to evaluate and select open-source EMR software packages based on multi-criteria decision-making. A hands-on study was performed and a set of open-source EMR software packages were implemented locally on separate virtual machines to examine the systems more closely. Several measures as evaluation basis were specified, and the systems were selected based a set of metric outcomes using Integrated Analytic Hierarchy Process (AHP) and TOPSIS. The experimental results showed that GNUmed and OpenEMR software can provide better basis on ranking score records than other open-source EMR software packages.


title: The Relationship Between Training Load and Injury, Illness and Soreness: A Systematic and Literature Review

abstract: BACKGROUND ::: Clinically it is understood that rapid increases in training loads expose an athlete to an increased risk of injury; however, there are no systematic reviews to qualify this statement. ::: ::: ::: OBJECTIVE ::: The aim of this systematic review was to determine training and competition loads, and the relationship between injury, illness and soreness. ::: ::: ::: METHODS ::: The MEDLINE, SPORTDiscus, CINAHL and EMBASE databases were searched using a predefined search strategy. Studies were included if they analysed the relationship between training or competition loads and injury or illness, and were published prior to October 2015. Participants were athletes of any age or level of competition. The quality of the studies included in the review was evaluated using the Newcastle-Ottawa Scale (NOS). The level of evidence was defined as strong, 'consistent findings among multiple high-quality randomised controlled trials (RCTs)'; moderate, 'consistent findings among multiple low-quality RCTs and/or non-randomised controlled trials (CCTs) and/or one high-quality RCT'; limited, 'one low-quality RCT and/or CCTs, conflicting evidence'; conflicting, 'inconsistent findings among multiple trials (RCTs and/or CCTs)'; or no evidence, 'no RCTs or CCTs'. ::: ::: ::: RESULTS ::: A total of 799 studies were identified; 23 studies met the inclusion criteria, and a further 12 studies that were not identified in the search but met the inclusion criteria were subsequently added to the review. The largest number of studies evaluated the relationship between injuries and training load in rugby league players (n = 9) followed by cricket (n = 5), football (n = 3), Australian Football (n = 3), rugby union (n = 2),volleyball (n = 2), baseball (n = 2), water polo (n = 1), rowing (n = 1), basketball (n = 1), swimming (n = 1), middle-distance runners (n = 1) and various sports combined (n = 1). Moderate evidence for a significant relationship was observed between training loads and injury incidence in the majority of studies (n = 27, 93 %). In addition, moderate evidence exists for a significant relationship between training loads and illness incidence (n = 6, 75 %). Training loads were reported to have a protective effect against injury (n = 9, 31 %) and illness (n = 1, 13 %). The median (range) NOS score for injury and illness was 8 (5-9) and 6 (5-9), respectively. ::: ::: ::: LIMITATIONS ::: A limitation of this systematic review was the a priori search strategy. Twelve further studies were included that were not identified in the search strategy, thus potentially introducing bias. The quality assessment was completed by only one author. ::: ::: ::: CONCLUSIONS ::: The results of this systematic review highlight that there is emerging moderate evidence for the relationship between the training load applied to an athlete and the occurrence of injury and illness. ::: ::: ::: IMPLICATIONS ::: The training load applied to an athlete appears to be related to their risk of injury and/or illness. Sports science and medicine professionals working with athletes should monitor this load and avoid acute spikes in loads. It is recommended that internal load as the product of the rate of perceived exertion (10-point modified Borg) and duration be used when determining injury risk in team-based sports. External loads measured as throw counts should also be monitored and collected across a season to determine injury risk in throwing populations. Global positioning system-derived distances should be utilised in team sports, and injury monitoring should occur for at least 4 weeks after spikes in loads.


title: Bipolar Weighted Argumentation Graphs

abstract: This paper discusses the semantics of weighted argumentation graphs that are biplor, i.e. contain both attacks and support graphs. The work builds on previous work by Amgoud, Ben-Naim et. al., which presents and compares several semantics for argumentation graphs that contain only supports or only attacks relationships, respectively.


K-Means Clustering Approach to Analyze NSL-KDD Intrusion Detection Dataset


title: Urban planning and building smart cities based on the Internet of Things using Big Data analytics

abstract: The rapid growth in the population density in urban cities demands that services and an infrastructure be provided to meet the needs of city inhabitants. Thus, there has been an increase in the request for embedded devices, such as sensors, actuators, and smartphones, leading to considerable business potential for the new era of the Internet of Things (IoT), in which all devices are capable of interconnecting and communicating with each other over the Internet. Thus, Internet technologies provide a way of integrating and sharing a common communication medium. With this knowledge, in this paper, we propose a combined IoT-based system for smart city development and urban planning using Big Data analytics. We propose a complete system consisting of various types of sensor deployment, including smart home sensors, vehicular networking, weather and water sensors, smart parking sensors, and surveillance objects. A four-tier architecture is proposed that includes 1) Bottom tier-1, which is responsible for IoT sources and data generation and collection, 2) Intermediate tier-1, which is responsible for all types of communication between, for instance, sensors, relays, base stations, and the Internet, 3) Intermediate tier 2, which is responsible for data management and processing using a Hadoop framework, and 4) Top tier, which is responsible for application and usage of the data analysis and the results generated. The system implementation consists of various steps that begin with data generation and move to collection, aggregation, filtration, classification, preprocessing, computing and decision making. The proposed system is implemented using Hadoop with Spark, voltDB, Storm or S4 for real time processing of the IoT data to generate results to establish the smart city. For urban planning or city future development, the offline historical data are analyzed with Hadoop using MapReduce programming. IoT datasets generated by smart homes, smart parking weather, pollution, and vehicle data sets are used for analysis and evaluation. This type of system with full functionality does not currently exist. Similarly, the results demonstrate that the proposed system is more scalable and efficient than existing systems. Moreover, system efficiency is measured in terms of throughput and processing time.


title: A "Never-Loose" Strategy to Play the Game of Tic-Tac-Toe

abstract: In much of the literature available to solve the tic-tac-toe board game, the common approaches, such as, co evolution, neural networks, evolutionary programming and genetic algorithm are used. In the present work we present a deterministic approach for playing tic-tac-toe game, in which 9 objective functions are defined to decide player's best move. For choosing the best from the solutions generated, by combination of mutating zero as 1, certain axioms are defined. The beauty of this method lies in the fact that if the player decides a move with this method, he never loses the match whenever the player begins the game. We suspect that functions can be defined on the similar grounds for other existing board games and some of this work is in progress and will be reported elsewhere. Some implications on these lines have been made as recommendations in this paper.


title: Collect, Decompile, Extract, Stats, and Diff: Mining Design Pattern Changes in Android Apps

abstract: Mobile user interface design patterns have been widely used across different mobile platforms. UI design patterns have evolved and changed significantly as new trends emerge and fade at different times. This paper presents a data-mining approach to analyzing design pattern changes in Android apps. Over a period of 18 months, we tracked 24,436 apps and collected their versions. In total, our sample consists of 56,349 unique app versions, more than 5 million source files, and more than 25 million UI elements. We developed a dedicated infrastructure based on modern big data technologies to support our differential analyses regarding design pattern changes. Some highlights of our findings include a) some apps would switch to a design pattern even after it was deprecated, b) the adoption rate of newly introduced design patterns (e.g., Fragment) is relatively low, c) some apps would update their listing details to reflect changes in design patterns.


title: Activity-Conditioned Continuous Human Pose Estimation for Performance Analysis of Athletes Using the Example of Swimming

abstract: In this paper we consider the problem of human pose estimation in real-world videos of swimmers. Swimming channels allow filming swimmers simultaneously above and below the water surface with a single stationary camera. These recordings can be used to quantitatively assess the athletes' performance. The quantitative evaluation, so far, requires manual annotations of body parts in each video frame. We therefore apply the concept of CNNs in order to automatically infer the required pose information. Starting with an off-the-shelf architecture, we develop extensions to leverage activity information - in our case the swimming style of an athlete - and the continuous nature of the video recordings. Our main contributions are threefold: (a) We apply and evaluate a fine-tuned Convolutional Pose Machine architecture as a baseline in our very challenging aquatic environment and discuss its error modes, (b) we propose an extension to input swimming style information into the fully convolutional architecture and (c) modify the architecture for continuous pose estimation in videos. With these additions we achieve reliable pose estimates with up to +16% more correct body joint detections compared to the baseline architecture.


title: Energy-Efficient Resource Allocation and User Scheduling for Collaborative Mobile Clouds With Hybrid Receivers

abstract: In this paper, we study the resource allocation and user scheduling algorithm for minimizing the energy cost of data transmission in the context of OFDMA collaborative mobile cloud (CMC) with simultaneous wireless information and power transfer (SWIPT) receivers. The CMC, which consists of several collaborating MTs offers one potential solution for downlink con- tent distribution and for the energy consumption reduction at the terminal side. Meanwhile, as RF signal can carry both informa- tion and energy simultaneously, the induced SWIPT has gained much attention for energy efficiency design of mobile nodes. Previous work on the design of CMC system mainly focused on the cloud formulation or energy efficiency investigation, while how to allocate the radio resource and schedule user transmission lacks attention. With the objective to minimize the system energy consumption, an optimization problem which jointly considers subchannel assignment, power allocation and user scheduling for a group of SWIPT receivers has been presented. The formulated problem is addressed through the convex optimization technique. Simulation results demonstrate that the proposed user scheduling and resource allocation algorithms can achieve significant energy saving performance.


title: Diagnostic value of the soluble triggering receptor expressed on myeloid cells-1 in bacterial infection: a meta-analysis

abstract: Objective ::: To evaluate the accuracy of the soluble triggering receptor expressed on myeloid cells-1 (sTREM-1) as a diagnostic test for bacterial infection.


title: Imperfect shadow maps for efficient computation of indirect illumination

abstract: We present a method for interactive computation of indirect illumination in large and fully dynamic scenes based on approximate visibility queries. While the high-frequency nature of direct lighting requires accurate visibility, indirect illumination mostly consists of smooth gradations, which tend to mask errors due to incorrect visibility. We exploit this by approximating visibility for indirect illumination with imperfect shadow maps---low-resolution shadow maps rendered from a crude point-based representation of the scene. These are used in conjunction with a global illumination algorithm based on virtual point lights enabling indirect illumination of dynamic scenes at real-time frame rates. We demonstrate that imperfect shadow maps are a valid approximation to visibility, which makes the simulation of global illumination an order of magnitude faster than using accurate visibility.


title: Remote heart rate measurement using low-cost RGB face video: a technical literature review

abstract: Remote photoplethysmography (rPPG) allows remote measurement of the heart rate using low-cost RGB imaging equipment. In this study, we review the development of the field of rPPG since its emergence in 2008. We also classify existing rPPG approaches and derive a framework that provides an overview of modular steps. Based on this framework, practitioners can use our classification to design algorithms for an rPPG approach that suits their specific needs. Researchers can use the reviewed and classified algorithms as a starting point to improve particular features of an rPPG algorithm.


title: Partial power DC-DC converter for photovoltaic two-stage string inverters

abstract: In order to increase the conversion efficiency in photovoltaic (PV) systems, different configurations and topologies were developed. Depending on the application, the converters used for grid connection are built using one or two conversion stages. The advantages of the converters with a DC-stage are mainly the distributed maximum power point tracking algorithm per PV string, a wider range of operation, higher energy yield and, when required for grid connection, the possibility of voltage regulation. However, the conversion efficiency is lower than configurations with a single stage as the central inverter. Therefore, the proposed work presents a Partial Power DC-DC converter (PPC) which process part of the entire system power, and the surplus power is directly supplied to the output side. A topology is proposed and the details of its operation are explained based on the operating principle. Simulations are performed in order to evaluate the converter performance.


title: Bayesian visual analytics: BaVA

abstract: Leman et al. and Endert et al. develop an interactive data visualization framework called visual to parametric interaction V2PI. With V2PI, experts may explore data visually assess multiple data visualizations based on their judgments and an underlying data analytic method. Specifically, V2PI offers a deterministic procedure to quantify expert judgments and update analytical parameters to create new data visualizations. In this article, we explain V2PI from a probabilistic perspective and develop Bayesian visual analytics BaVA. We model data probabilistically, develop parallels between quantifying expert judgments and eliciting prior distributions from experts, and justify how we update parameters using Bayesian sequential updating. We apply BaVA using two linear projections methods to assess simulated and real-world datasets.


title: A 90–100-GHz 4 $\times$ 4 SiGe BiCMOS Polarimetric Transmit/Receive Phased Array With Simultaneous Receive-Beams Capabilities

abstract: This paper presents a 4 × 4 transmit/receive (T/R) SiGe BiCMOS phased-array chip at 90-100 GHz with vertical and horizontal polarization capabilities, 3-bit gain control (9 dB), and 4-bit phase control. The 4 × 4 phased array fits into a 1.6×1.5 mm2 grid, which is required at 94 GHz for wide scan-angle designs. The chip has simultaneous receive (Rx) beam capabilities (V and H) and this is accomplished using dual-nested 16:1 Wilkinson combiners/divider with high isolation. The phase shifter is based on a vector modulator with optimized design between circuit level and electromagnetic simulation and results in 1 dB and gain and phase error, respectively, at 85-110 GHz. The behavior of the vector modulator phase distortion versus input power level is investigated and measured, and design guidelines are given for proper operation in a transmit (Tx) chain. The V and H Rx paths result in a gain of 22 and 25 dB, respectively, a noise figure of 9-9.5 (max. gain), and 11 dB (min. gain) measured without the T/R switch, and an input P1 dB of -31 to -26 dBm over the gain control range. The measured output Psat is ~ -5 dBm per channel, limited by the T/R switch loss. Measurements show ±0.6- and ±0.75-dB variation between the 4 × 4 array elements in the Tx mode (Psat) and Rx mode, respectively, and 40-dB coupling between the different channels on the chip. The chip consumes 1100 mA from a 2-V supply in both the Tx and Rx modes. The design can be scaled to >10 000 elements using polyimide redistribution layers on top of the chip and the application areas are in W-band radars for landing systems.


title: Plant leaf identification via a growing convolution neural network with progressive sample learning

abstract: Plant identification is an important problem for ecologists, amateur botanists, educators, and so on. Leaf, which can be easily obtained, is usually one of the important factors of plants. In this paper, we propose a growing convolution neural network (GCNN) for plant leaf identification and report the promising results on the ImageCLEF2012 Plant Identification database. The GCNN owns a growing structure which starts training from a simple structure of a single convolution kernel and is gradually added new convolution neurons to. Simultaneously, the growing connection weights are modified until the squared-error achieves the desired result. Moreover, we propose a progressive learning method to determine the number of learning samples, which can further improve the recognition rate. Experiments and analyses show that our proposed GCNN outperforms other state-of-the-art algorithms such as the traditional CNN and the hand-crafted features with SVM classifiers.


title: Hypervision Across Worlds: Real-time Kernel Protection from the ARM TrustZone Secure World

abstract: TrustZone-based Real-time Kernel Protection (TZ-RKP) is a novel system that provides real-time protection of the OS kernel using the ARM TrustZone secure world. TZ-RKP is more secure than current approaches that use hypervisors to host kernel protection tools. Although hypervisors provide privilege and isolation, they face fundamental security challenges due to their growing complexity and code size. TZ-RKP puts its security monitor, which represents its entire Trusted Computing Base (TCB), in the TrustZone secure world; a safe isolated environment that is dedicated to security services. Hence, the security monitor is safe from attacks that can potentially compromise the kernel, which runs in the normal world. Using the secure world for kernel protection has been crippled by the lack of control over targets that run in the normal world. TZ-RKP solves this prominent challenge using novel techniques that deprive the normal world from the ability to control certain privileged system functions. These functions are forced to route through the secure world for inspection and approval before being executed. TZ-RKP's control of the normal world is non-bypassable. It can effectively stop attacks that aim at modifying or injecting kernel binaries. It can also stop attacks that involve modifying the system memory layout, e.g, through memory double mapping. This paper presents the implementation and evaluation of TZ-RKP, which has gone through rigorous and thorough evaluation of effectiveness and performance. It is currently deployed on the latest models of the Samsung Galaxy series smart phones and tablets, which clearly demonstrates that it is a practical real-world system.


title: Progress and Tradeoffs in Neural Language Models

abstract: In recent years, we have witnessed a dramatic shift towards techniques driven by neural networks for a variety of NLP tasks. Undoubtedly, neural language models (NLMs) have reduced perplexity by impressive amounts. This progress, however, comes at a substantial cost in performance, in terms of inference latency and energy consumption, which is particularly of concern in deployments on mobile devices. This paper, which examines the quality-performance tradeoff of various language modeling techniques, represents to our knowledge the first to make this observation. We compare state-of-the-art NLMs with "classic" Kneser-Ney (KN) LMs in terms of energy usage, latency, perplexity, and prediction accuracy using two standard benchmarks. On a Raspberry Pi, we find that orders of increase in latency and energy usage correspond to less change in perplexity, while the difference is much less pronounced on a desktop.


title: Grid Interface of Wind Power With Large Split-Winding Alternator Using Cascaded Multilevel Inverter

abstract: In this paper, a wind energy conversion system based on a cascaded H-bridge multilevel inverter (CHBMLI) topology has been proposed to be used for the grid interface of large split-winding alternators (SWAs). A new method has been suggested for the generation of reference currents for the voltage source inverter (VSI) depending upon the available wind power. The CHBMLI has been used as a VSI and operated in a current control mode in order to achieve the objectives of real power injection and load compensation (power factor correction, load balancing, and harmonic compensation) based on the proposed reference-generation scheme. The field excitation control of SWA provides a single means to vary the dc-link voltages of all the CHBs simultaneously and proportionately. The equal distribution of switching stress and power losses among H-bridge cells, reduction in their power ratings, and high quality of inverter output makes the proposed arrangement more efficient. The closed-loop performance of the VSI has been shown to be adequate and fully achieves the control objectives, i.e., grid interface of distributed energy resource (wind) in addition to load compensation. The simulation studies on the proposed scheme have been obtained through the PSCAD/EMTDC software.


title: The Monte Carlo technique applied to radiative transfer

abstract: Abstract A general review is given of the application of the Monte Carlo method to the numerical calculations of non-equilibrium radiation transport. With the aid of a general flow chart, the various steps required in such Monte Carlo calculations are detailed. Advanced techniques which improve efficiency of calculations such as weighting and biasing are also discussed. Although specific results are not given in the text, references to recent Monte Carlo calculations of radiation transport are included.


title: The Separability of Working Memory Resources for Spatial Thinking and Language Processing: An Individual Differences Approach

abstract: The current study demonstrates the separability of spatial and verbal working memory resources among college students. In Experiment 1, we developed a spatial span task that taxes both the processing and storage components of spatial working memory. This measure correlates with spatial ability (spatial visualization) measures, but not with verbal ability measures. In contrast, the reading span test, a common test of verbal working memory, correlates with verbal ability measures, but not with spatial ability measures. Experiment 2, which uses an interference paradigm to cross the processing and storage demands of span tasks, replicates this dissociation and further demonstrates that both the processing and storage components of working memory tasks are important for predicting performance on spatial thinking and language processing tasks.


title: A simple and efficient connected components labeling algorithm

abstract: We describe a two-scan algorithm for labeling connected components in binary images in raster format. Unlike the classical two-scan approach, our algorithm processes equivalences during the first scan by merging equivalence classes as soon as a new equivalence is found. We show that this significantly improves the efficiency of the labeling process with respect to the classical approach. The data structure used to support the handling of equivalences is a 1D-array. This renders the more frequent operation of finding class identifiers very fast, while the less-frequent class-merging operation has a relatively high computational cost. Nonetheless, it is possible to reduce significantly the merging cost by two slight modifications to the algorithm's basic structure. The idea of merging equivalence classes is present also in Samet's general labeling algorithm. However when considering the case of binary images in raster format this algorithm is much more complex than the one we describe in this paper.


title: Roll-to-Roll Fabrication of Solution Processed Electronics

abstract: The production of electronic devices using solution based (“wet”) deposition technologies has some decisive technical and commercial advantages compared to competing approaches like vacuum based (“dry”) manufacturing. Particularly, the potential to scale up production processes to large areas and high volumes by introducing continuous roll-to-roll (R2R) methods on flexible substrates has been the topic of intense studies from both applied research institutes and industry already for some years. Decisive steps forward have been achieved during that time, resulting in the dawn of commercial applications for a number of processes, while additional development work is still needed in some other fields. This review summarizes the work published during the last few years on the R2R printing and wet coating of electronic devices. An overview is presented of the basic operational principles for the most commonly used R2R printing and coating methods and techniques for proper web handling in R2R lines. Then, the most commonly used types of flexible substrate materials are introduced, followed by a review of the work published in the application areas of transparent conductor materials, printed electric connections, light emitting devices, photovoltaic energy generation, printed logic, and sensing.


THE FALL AND RISE OF STRATEGIC PLANNING1: A META-ANALYSIS OF THE ENVIRONMENT-PLANNING-OUTCOME LINK


title: Parameter estimation for single-phase induction motors using test measurement data

abstract: This paper presents a methodology to estimate the parameters of a single-phase induction motor (SPIM) equivalent circuit using a dc test, a locked-rotor test, and a no-load test. By neglecting the core-loss resistance, the SPIM parameters can be first directly calculated using a simplified equivalent circuit. However, an equivalent circuit using the resultant parameter values fail to match the power input measured at the motor terminals, especially for the active power under the no-load test. We include the core-loss resistance into the parameter estimation framework and use the Newton-Raphson (N-R) algorithm to improve the estimates obtained by the direct calculation method. Experimental results on a laboratory SPIM demonstrate the effectiveness of our proposed N-R based parameter estimation scheme, in terms of excellent match with the active and reactive power measurement data from the aforementioned tests, and mismatch reduction at other operating conditions.


title: Electrochemical array microsystem with integrated potentiostat

abstract: This paper presents a single-chip electrochemical array system with a high performance CMOS potentiostat suitable for the implementation of a fully integrated instrument-on-chip microsystem. The chip contains a highly sensitive, range-programmable, amperometric readout amplifier that supports cyclic voltammetry assay techniques. Through post-CMOS fabrication, the surface of the chip provides an Au counter electrode, an Ag/AgCl reference electrode, and 3 times 3 array of individually addressable Au working electrodes. The microsystem is configurable and implements self-correction techniques to provide an adaptive interface for variety of electrochemical and current-mode sensors. The chip supports amperometric output from 10pA to 10muA and provides good noise performance to achieve sub-pA resolution


title: Aggregation, Transformation, and the Design of Benthos Sampling Programs

abstract: Solutions are offered to the problems of data transformation and the design of efficient programs for sampling the benthos of lakes and large rivers. All types of benthic animals from many types of substrate, sampled with diverse sampling gear, are aggregated in a similar fashion. Aggregation can be indexed by the unbiased exponent of the power relationship between density and variance. A single variance stabilizing transformation can be used for all macrobenthos population data since the relationship of sample variance to mean density is similar in all taxa of benthic animals. Stabilized variance in population data satisfies one of the main assumptions of the analysis of variance and allows use of normal statistics provided that the other assumptions are met. The fourth-root transformation stabilized the variance in all macrobenthos samples while either the commonly used square root or logarithmic transformations did not. Sampling programs can be optimized empirically. Standard deviation (s) is predictab...


title: The Secular Dynamics of TNOs and Planet Nine Interactions

abstract: The existence of Planet Nine has been suggested to explain the pericenter clustering of extreme trans-Neptunian objects (TNOs). However, the underlying dynamics involving Planet Nine, test particles and Neptune is rich, and it remains unclear which dynamical processes lead to the alignment and how they depends on the properties of Planet Nine. Here, we investigate the secular interactions between an eccentric outer perturber and TNOs starting in a near-coplanar configuration. We find that a large number of TNOs could survive outside of mean motion resonances at 4Gyr, which differs from previous results obtained in the exact coplanar case with Neptune being treated as a quadrupole potential. In addition, secular dynamics leads to the orbital clustering seen in N-body simulations. We find that a near coplanar Planet Nine can flip TNO orbital planes, and when this happens, the geometrical longitudes of pericenter of the TNOs librate around $180^\circ$ during the flip. Orbital precession caused by the inner giant planets can suppress the flips while keeping the longitude of pericenter librating when $30\lesssim r_p \lesssim 80$ AU $\&$ $a\gtrsim 250$ AU. This results in the alignment of the pericenter of the low inclination TNOs ($i \lesssim 40^\circ$). We find the anti-aligned population and the flipped orbits could be produced by an eccentric ($e_9 \gtrsim 0.4$) outer planet of $\sim 10M_{\oplus}$ in a wide $a_9 \gtrsim 400 \sim 800$ AU orbit. Future surveys on the high inclination TNOs will help further constrain the properties of possible outer planets.


title: Why are infertile patients not using psychosocial counselling

abstract: The purpose of this study was to examine the sources of support that infertile patients relied on when distressed, and the factors that prevented them from using psychosocial counselling. The sample consisted of 143 infertile patients (49 couples plus 45 additional women) who were in their mid-thirties and had been infertile for approximately 6 years. Before their clinic appointment, participants completed (anonymously) a short questionnaire concerning various sources of support and factors related to the uptake of counselling. The results showed that patients relied primarily on their spouse and family when distressed, rather than on formal support resources such as psychosocial counselling. The principal reason preventing patients from using counselling varied as a function of current distress level. Less distressed patients reported that the coping resources available to them were sufficient to cope with the strains of infertility, while the more distressed patients failed to initiate contact with a counsellor because of practical concerns such as knowing who to contact and/or the cost of counselling. The findings of this study indicate that alternative ways of intervening with infertile patients would need to be developed, as few use the type of psychosocial services most frequently offered.


title: Wireless power harvesting with planar rectennas for 2.45 GHz RFIDs

abstract: This paper presents a rectenna (rectifier + antenna) design to harvest electrical energy for powering RFIDs from ambient electromagnetic radiation at the 2.45 GHz ISM band (WiFi, Bluetooth, RFID, etc.). The rectenna structure is formed by a miniaturize 2nd iteration Koch fractal patch antenna and two stage Dickson charge pump voltage-doubler rectifier circuit. The proposed rectenna achieves a small size with relatively high realized gain (4 dBi) and good RF to DC conversion efficiency (up to 70%). As a result, the proposed rectenna harvests enough energy from a commercial RFID interrogator 3.1 meters away (4W EIRP at 2.45 GHz ISM band) to power up a 1.6 V LED, enough voltage to enable some RFID chips.


Design Considerations for an RTSP-Based Prefix-Caching Proxy for Multimedia Streams


title: The Moderating Role of Autonomy

abstract: The self-regulatory strength model maintains that all acts of self-regulation, self-control, and choice result in a state of fatigue called ego-depletion. Self-determination theory differentiates between autonomous regulation and controlled regulation. Because making decisions represents one instance of self-regulation, the authors also differentiate between autonomous choice and controlled choice. Three experiments support the hypothesis that whereas conditions representing controlled choice would be egodepleting, conditions that represented autonomous choice would not. In Experiment 3, the authors found significant mediation by perceived self-determination of the relation between the choice condition (autonomous vs. controlled) and ego-depletion as measured by performance.


title: Comparing the Results of Support Vector Machines with Traditional Data Mining Algorithms

abstract: This paper presents the results of a series of analyses performed on direct mail data. A total of 577 features were used to identify individuals who were most likely to respond to a life insurance mailing. The tools used for these analyses were support vector machines (SVMs), logistic regression, and a series of algorithms employed automatically using the integrated “Affinium Model” © data mining software package produced by the Unica Corporation. The data mining software included linear regression, logistic regression, classification and regression trees (CART) and neural networks. Results indicated that SVMs performed slightly better than logistic regression, and logistic regression performed slightly better than the data mining tool. The results indicated that SVMs can be used with very unbalanced and noisy data, and that SVMs can be used to rank observations based on likelihood.


title: Towards relation based Argumentation Mining

abstract: We advocate a relation based approach to Argumentation Mining. Our focus lies on the extraction of argumentative relations instead of the identification of arguments, themselves. By classifying pairs of sentences according to the relation that holds between them we are able to identify sentences that may be factual when considered in isolation, but carry argumentative meaning when read in context. We describe scenarios in which this is useful, as well as a corpus of annotated sentence pairs we are developing to provide a testbed for this approach.


title: Queer diagnoses revisited: The past and future of homosexuality and gender diagnoses in DSM and ICD

abstract: The American Psychiatric Association (APA) recently completed a several year process of revising the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). During that time, there were objections raised to retaining DSM's gender identity disorder diagnoses and calls to remove them, just as homosexuality had been removed from DSM-II in 1973. At the conclusion of the DSM-5 revision process, the gender diagnoses were retained, albeit in altered form and bearing the new name of ‘gender dysphoria’. The author of this paper was a member of the DSM-5 Workgroup on Sexual and Gender Identity Disorders and presently serves on the WHO Working Group on Sexual Disorders and Sexual Health. Both groups faced similar tasks: reconciling patients’ needs for access to care with the stigma of being given a psychiatric diagnosis. The differing nature of the two diagnostic manuals led to two different outcomes. As background, this paper updates the history of homosexuality and the gender diagnoses ...


title: A Robust Indoor Scene Recognition Method based on Sparse Representation

abstract: In this paper, we present a robust method for scene recognition, which leverages Convolutional Neural Networks (CNNs) features and Sparse Coding setting by creating a new representation of indoor scenes. Although CNNs highly benefited the fields of computer vision and pattern recognition, convolutional layers adjust weights on a global-approach, which might lead to losing important local details such as objects and small structures. Our proposed scene representation relies on both: global features that mostly refers to environment’s structure, and local features that are sparsely combined to capture characteristics of common objects of a given scene. This new representation is based on fragments of the scene and leverages features extracted by CNNs. The experimental evaluation shows that the resulting representation outperforms previous scene recognition methods on Scene15 and MIT67 datasets, and performs competitively on SUN397, while being highly robust to perturbations in the input image such as noise and occlusion.


title: An edge-region force guided active shape approach for automatic lung field detection in chest radiographs

abstract: Abstract Automatic and accurate lung field segmentation is an essential step for developing an automated computer-aided diagnosis system for chest radiographs. Although active shape model (ASM) has been useful in many medical imaging applications, lung field segmentation remains a challenge due to the superimposed anatomical structures. We propose an automatic lung field segmentation technique to address the inadequacy of ASM in lung field extraction. Experimental results using both normal and abnormal chest radiographs show that the proposed technique provides better performance and can achieve 3–6% improvement on accuracy, sensitivity and specificity compared to traditional ASM techniques.


title: Automated verification of safety properties of declarative networking programs

abstract: Networks are complex systems that unfortunately are ridden with errors. Such errors can lead to disruption of services, which may have grave consequences. Verification of networks is key to eliminating errors and building robust networks. In this paper, we propose an approach to verify networks using declarative networking, where networks are specified in NDlog, a declarative language. We focus on analyzing safety properties. We develop a technique to statically analyze NDlog programs: first, we build a dependency graph of the predicates of NDlog programs; then, we build a summary data structure called a derivation pool to represent all possible derivations and their associated constraints for predicates in the program; finally, properties specified in first-order logic are checked on the data structure with the help of the SMT solver Z3. We build a prototype tool and demonstrate the effectiveness of the tool in validating and debugging several SDN applications.


title: Coherent spatiotemporal filtering, upsampling and rendering of RGBZ videos. Computer Graphics Forum (Proc

abstract: Sophisticated video processing effects require both image and geometry information. We explore the possibility to augment a video camera with a recent infrared time-of-flight depth camera, to capture high-resolution RGB and low-resolution, noisy depth at video frame rates. To turn such a setup into a practical RGBZ video camera, we develop efficient data filtering techniques that are tailored to the noise characteristics of IR depth cameras. We first remove typical artefacts in the RGBZ data and then apply an efficient spatiotemporal denoising and upsampling scheme. This allows us to record temporally coherent RGBZ videos at interactive frame rates and to use them to render a variety of effects in unprecedented quality. We show effects such as video relighting, geometry-based abstraction and stylisation, background segmentation and rendering in stereoscopic 3D. © 2012 Wiley Periodicals, Inc.


title: Automatic Photo Adjustment Using Deep Neural Networks

abstract: Photo retouching enables photographers to invoke dramatic visual impressions by artistically enhancing their photos through stylistic color and tone adjustments. However, it is also a time-consuming and challenging task that requires advanced skills beyond the abilities of casual photographers. Using an automated algorithm is an appealing alternative to manual work, but such an algorithm faces many hurdles. Many photographic styles rely on subtle adjustments that depend on the image content and even its semantics. Further, these adjustments are often spatially varying. Existing automatic algorithms are still limited and cover only a subset of these challenges. Recently, deep learning has shown unique abilities to address hard problems. This motivated us to explore the use of deep neural networks (DNNs) in the context of photo editing. In this article, we formulate automatic photo adjustment in a manner suitable for this approach. We also introduce an image descriptor accounting for the local semantics of an image. Our experiments demonstrate that training DNNs using these descriptors successfully capture sophisticated photographic styles. In particular and unlike previous techniques, it can model local adjustments that depend on image semantics. We show that this yields results that are qualitatively and quantitatively better than previous work.


title: Halogen Bond: Its Role beyond Drug-Target Binding Affinity for Drug Discovery and Development

abstract: Halogen bond has attracted a great deal of attention in the past years for hit-to-lead-to-candidate optimization aiming at improving drug-target binding affinity. In general, heavy organohalogens (i.e., organochlorines, organobromines, and organoiodines) are capable of forming halogen bonds while organofluorines are not. In order to explore the possible roles that halogen bonds could play beyond improving binding affinity, we performed a detailed database survey and quantum chemistry calculation with close attention paid to (1) the change of the ratio of heavy organohalogens to organofluorines along the drug discovery and development process and (2) the halogen bonds between organohalogens and nonbiopolymers or nontarget biopolymers. Our database survey revealed that (1) an obviously increasing trend of the ratio of heavy organohalogens to organofluorines was observed along the drug discovery and development process, illustrating that more organofluorines are worn and eliminated than heavy organohalogens ...


title: Dilute acid pretreatment of rye straw and bermudagrass for ethanol production.

abstract: Ethanol production from lignocellulosic materials provides an alternative energy production system. Rye and bermudagrass that are used in hog farms for nutrient uptake from swine wastewater have the potential for fuel ethanol production because they have a relative high cellulose and hemicellulose content. Dilute sulfuric acid pretreatment of rye straw and bermudagrass before enzymatic hydrolysis of cellulose was investigated in this study. The biomass at a solid loading rate of 10% was pretreated at 121 degrees C with different sulfuric acid concentrations (0.6, 0.9, 1.2 and 1.5%, w/w) and residence times (30, 60, and 90 min). Total reducing sugars, arabinose, galactose, glucose, and xylose in the prehydrolyzate were analyzed. In addition, the solid residues were hydrolyzed by cellulases to investigate the enzymatic digestibility. With the increasing acid concentration and residence time, the amount of arabinose and galactose in the filtrates increased. The glucose concentration in the prehydrolyzate of rye straw was not significantly influenced by the sulfuric acid concentration and residence time, but it increased in the prehydrolyzate of bermudagrass with the increase of pretreatment severity. The xylose concentration in the filtrates increased with the increase of sulfuric acid concentration and residence time. Most of the arabinan, galactan and xylan in the biomass were hydrolyzed during the acid pretreatment. Cellulose remaining in the pretreated feedstock was highly digestible by cellulases from Trichoderma reesei.


title: Comparison of the effect of two interdental cleaning devices around implants on the reduction of bleeding: a 30-day randomized clinical trial.

abstract: OBJECTIVE ::: To determine the effectiveness of a water flosser in reducing the bleeding on probing (BOP) index around dental implants as compared to flossing. ::: ::: ::: METHODS AND MATERIALS ::: Patients with implants were randomly assigned to one of two groups in this examiner-masked, single-center study. The study compared the efficacy of a manual toothbrush paired with either traditional string floss or a water flosser. ::: ::: ::: RESULTS ::: The primary outcome was the reduction in the incidence of BOP after 30 days. There were no differences in the percent of bleeding sites between the groups at baseline. At 30 days, 18 of the 22 (81.8%) implants in the water flosser group showed a reduction in BOP compared to 6 of the 18 (33.3%) in the floss group (P=0.0018). ::: ::: ::: CONCLUSIONS ::: These results demonstrate that the water flosser group had statistically significantly greater bleeding reduction than the string floss group. The authors concluded that water flossing may be a useful adjuvant for implant hygiene maintenance.


title: An Adaptive Fuzzy Sliding Mode Control Scheme for Robotic Systems

abstract: In this article, an adaptive fuzzy sliding mode control (AFSMC) scheme is derived for robotic systems. In the AFSMC design, the sliding mode control (SMC) concept is combined with fuzzy control strategy to obtain a model-free fuzzy sliding mode control. The equivalent controller has been substituted for by a fuzzy system and the uncertainties are estimated on-line. The approach of the AFSMC has the learning ability to generate the fuzzy control actions and adaptively compensates for the uncertainties. Despite the high nonlinearity and coupling effects, the control input of the proposed control algorithm has been decoupled leading to a simplified control mechanism for robotic systems. Simulations have been carried out on a two link planar robot. Results show the effectiveness of the proposed control system.


title: Comparison of Thermal Detector Arrays for Off-Axis THz Holography and Real-Time THz Imaging

abstract: In terahertz (THz) materials science, imaging by scanning prevails when low power THz sources are used. However, the application of array detectors operating with high power THz sources is increasingly reported. We compare the imaging properties of four different array detectors that are able to record THz radiation directly. Two micro-bolometer arrays are designed for infrared imaging in the 8–14 μm wavelength range, but are based on different absorber materials (i) vanadium oxide; (ii) amorphous silicon; (iii) a micro-bolometer array optimized for recording THz radiation based on silicon nitride; and (iv) a pyroelectric array detector for THz beam profile measurements. THz wavelengths of 96.5 μm, 118.8 μm, and 393.6 μm from a powerful far infrared laser were used to assess the technical performance in terms of signal to noise ratio, detector response and detectivity. The usefulness of the detectors for beam profiling and digital holography is assessed. Finally, the potential and limitation for real-time digital holography are discussed.


Integrating BIM and GIS for large scale (building) asset management: a critical review


title: Prefrontal space ratio in second- and third-trimester screening for trisomy 21

abstract: Objective To evaluate the prefrontal space ratio (PFSR) in second- and third-trimester euploid fetuses and fetuses with trisomy 21. Methods This was a retrospective study utilizing stored mid-sagittal two-dimensional images of second- and thirdtrimester fetal faces that were recorded during prenatal ultrasound examinations at the Department of Prenatal Medicine at the University of Tuebingen, Germany and at a private center for prenatal medicine in Nuremberg, Germany. For the normal range, 279 euploid pregnancies between 15 and 40 weeks’ gestation were included. The results were compared with 91 cases with trisomy 21 between 15 and 40 weeks. For the ratio measurement, a line was drawn between the leading edge of the mandible and the maxilla (MM line) and extended in front of the forehead. The ratio of the distance between the leading edge of the skull and the leading edge of the skin (d1) to the distance between the skin and the point where the MM line was intercepted (d2) was calculated. The PFSR was determined by dividing d2 by d1. Results In the euploid and trisomy 21 groups, the median gestational age at the time of ultrasound examination was 21.1 (range, 15.0–40.0) and 21.4 (range, 15.0–40.3) weeks, respectively. Multiple regression analysis showed that PFSR was independent of maternal and gestational age. In the euploid group, the mean PFSR was 0.97 ± 0.29. In fetuses with trisomy 21, the mean PFSR was 0.2 ± 0.38 (P < 0.0001). The PFSR was below the 5 th centile in 14 (5.0%) euploid fetuses and in 72 (79.1%) fetuses with trisomy 21. Conclusion The PFSR is a simple and effective marker in second- and third-trimester screening for trisomy 21. Copyright  2012 ISUOG. Published by John Wiley & Sons, Ltd.


title: Music Influences Ratings of the Affect of Visual Stimuli

abstract: This review provides an overview of recent studies that have examined how music influences the judgment of emotional stimuli, including affective pictures and film clips. The relevant findings are incorporated within a broader theory of music and emotion, and suggestions for future research are offered. Music is important in our daily lives, and one of its primary uses by listeners is the active regulation of one's mood. Despite this widespread use as a regulator of mood and its general pervasiveness in our society, the number of studies investigating the issue of whether, and how, music affects mood and emotional behaviour is limited however. Experiments investigating the effects of music have generally focused on how the emotional valence of background music impacts how affective pictures and/or film clips are evaluated. These studies have demonstrated strong effects of music on the emotional judgment of such stimuli. Most studies have reported concurrent background music to enhance the emotional valence when music and pictures are emotionally congruent. On the other hand, when music and pictures are emotionally incongruent, the ratings of the affect of the pictures will in- or decrease depending on the emotional valence of the background music. These results appear to be consistent in studies investigating the effects of (background) music.


title: Delivering on the promise of universal memory for spin-transfer torque RAM (STT-RAM)

abstract: Spin-Transfer Torque RAM (STT-RAM) has emerged as a potential candidate for Universal memory. However, there are two challenges to using STT-RAM in memory system design: (1) the intrinsic variation in the storage element, the Magnetic Tunnel Junction (MTJ), and (2) the high write energy. In this paper, we present a physically based thermal noise model for simulating the statistical variations of MTJs. We have implemented it in HSPICE and validated it against analytical results. We demonstrate its use in setting the write pulse width for a given write error rate. We then propose two write-energy reduction techniques. At the device level, we propose the use of a low-M S ferromagnetic material that can reduce the write energy without sacrificing retention time. At the architecture level, we show that Invert Coding provides a 7% average reduction in the total write energy for the SPEC CPU2006 benchmark suite without any performance overhead.


title: Scalable stacking and learning for building deep architectures

abstract: Deep Neural Networks (DNNs) have shown remarkable success in pattern recognition tasks. However, parallelizing DNN training across computers has been difficult. We present the Deep Stacking Network (DSN), which overcomes the problem of parallelizing learning algorithms for deep architectures. The DSN provides a method of stacking simple processing modules in buiding deep architectures, with a convex learning problem in each module. Additional fine tuning further improves the DSN, while introducing minor non-convexity. Full learning in the DSN is batch-mode, making it amenable to parallel training over many machines and thus be scalable over the potentially huge size of the training data. Experimental results on both the MNIST (image) and TIMIT (speech) classification tasks demonstrate that the DSN learning algorithm developed in this work is not only parallelizable in implementation but it also attains higher classification accuracy than the DNN.


title: A Stable Marriage Requires Communication

abstract: The Gale-Shapley algorithm for the Stable Marriage Problem is known to take $\Theta(n^2)$ steps to find a stable marriage in the worst case, but only $\Theta(n \log n)$ steps in the average case (with $n$ women and $n$ men). In 1976, Knuth asked whether the worst-case running time can be improved in a model of computation that does not require sequential access to the whole input. A partial negative answer was given by Ng and Hirschberg, who showed that $\Theta(n^2)$ queries are required in a model that allows certain natural random-access queries to the participants' preferences. A significantly more general - albeit slightly weaker - lower bound follows from Segal's elaborate analysis of communication complexity, namely that $\Omega(n^2)$ Boolean queries are required in order to find a stable marriage, regardless of the set of allowed Boolean queries. Using a reduction to the communication complexity of the disjointness problem, we give a far simpler, yet significantly more powerful argument showing that $\Omega(n^2)$ Boolean queries of any type are indeed required. Notably, unlike Segal's lower bound, our lower bound generalizes also to (A) randomized algorithms, (B) finding approximately-stable marriages (C) verifying the stability (or the approximate stability) of a proposed marriage, (D) allowing arbitrary separate preprocessing of the women's preferences profile and of the men's preferences profile, and (E) several variants of the basic problem, such as whether a given pair is married in every/some stable marriage.


title: Multi-cue onboard pedestrian detection

abstract: Various powerful people detection methods exist. Surprisingly, most approaches rely on static image features only despite the obvious potential of motion information for people detection. This paper systematically evaluates different features and classifiers in a sliding-window framework. First, our experiments indicate that incorporating motion information improves detection performance significantly. Second, the combination of multiple and complementary feature types can also help improve performance. And third, the choice of the classifier-feature combination and several implementation details are crucial to reach best performance. In contrast to many recent papers experimental results are reported for four different datasets rather than using a single one. Three of them are taken from the literature allowing for direct comparison. The fourth dataset is newly recorded using an onboard camera driving through urban environment. Consequently this dataset is more realistic and more challenging than any currently available dataset.


title: Combining cell phones and WSNs for preventing accidents in smart-homes with disabled people

abstract: Smart homes provide automatic services through a combination of wireless sensor networks (WSNs) and wireless devices that allow monitoring operations in the home. These smart homes have been increasingly studied not only by the academic community but also by industry on account of the great potential they have, especially in applications that can help people with some type of physical disability such as visual or hearing impairment. This kind of application can give them independence and make their lives safer and more comfortable. Thus, in this paper we propose a Health Smart Home system for disabled people that can warn them about possible accidents that can occur in a particular location of their house. In other words, the system anticipates possible accidents that may occur somewhere in the house. We implemented a preliminary prototype that monitors rooms and issues a warning whenever it detects a potential hazard. Our proposal relies on low-cost cutting-edge technology and includes the following: Raspberry Pi boards, motion sensors, cameras and a central server. The server will handle the data that has been captured by sensors and send warnings through smartphones to people with physical impairments.


title: Knowledge sharing practices as a facilitating factor for improving organizational performance through human capital: A preliminary test

abstract: Organizational knowledge sharing, argued to be able to improve organizational performance and achieve competitive advantage, is often not induced successfully. How organizations should encourage and facilitate knowledge sharing to improve organizational performance is still an important research question. This study proposes and examines a model of organizational knowledge sharing that improves organizational performance. Organizational knowledge sharing practices are argued to be able to encourage and facilitate knowledge sharing, and are hypothesized to have a positive relationship with organizational human capital (employee competencies), which is hypothesized to have a positive relationship with organizational performance. Two organizational antecedents (innovation strategy and top management knowledge values) are hypothesized to lead to the implementation of organizational knowledge sharing practices. The hypotheses were examined with data collected from 256 companies in Taiwan. All the hypotheses are supported. This study has both theoretical and practical implications.