title: UAV-Aided Offloading for Cellular Hotspot

abstract: In conventional terrestrial cellular networks, mobile terminals (MTs) at the cell edge often pose a performance bottleneck due to their long distances from the serving ground base station (GBS), especially in the hotspot period when the GBS is heavily loaded. This paper proposes a new hybrid network architecture that leverages use of unmanned aerial vehicle (UAV) as an aerial mobile base station, which flies cyclically along the cell edge to offload data traffic for cell-edge MTs. We aim to maximize the minimum throughput of all MTs by jointly optimizing the UAV’s trajectory, bandwidth allocation, and user partitioning. We first consider orthogonal spectrum sharing between the UAV and GBS, and then extend to spectrum reuse where the total bandwidth is shared by both the GBS and UAV with their mutual interference effectively avoided. Numerical results show that the proposed hybrid network with optimized spectrum sharing and cyclical multiple access design significantly improves the spatial throughput over the conventional GBS-only network; while the spectrum reuse scheme provides further throughput gains at the cost of slightly higher complexity for interference control. Moreover, compared with the conventional small-cell offloading scheme, the proposed UAV offloading scheme is shown to outperform in terms of throughput, besides saving the infrastructure cost.


title: Multi-dimensional students' evaluation of e-learning systems in the higher education context: An empirical investigation

abstract: There has been little research on assessment of learning management systems (LMS) within educational organizations as both a web-based learning system for e-learning and as a supportive tool for blended learning environments. This study proposes a conceptual e-learning assessment model, hexagonal e-learning assessment model (HELAM) suggesting a multi-dimensional approach for LMS evaluation via six dimensions: (1) system quality, (2) service quality, (3) content quality, (4) learner perspective, (5) instructor attitudes, and (6) supportive issues. A survey instrument based on HELAM has been developed and applied to 84 learners. This sample consists of students at both undergraduate and graduate levels who are users of a web-based learning management system, U-Link, at Brunel University, UK. The survey instrument has been tested for content validity, reliability, and criterion-based predictive validity. The analytical results strongly support the appropriateness of the proposed model in evaluating LMSs through learners' satisfaction. The explanatory factor analysis showed that each of the six dimensions of the proposed model had a significant effect on the learners' perceived satisfaction. Findings of this research will be valuable for both academics and practitioners of e-learning systems.


title: High-Performance Circularly Polarized Antenna Array Using Metamaterial-Line Based Feed Network

abstract: This communication presents a 2 × 2 circularly polarized (CP) antenna array composed of H-shaped patches. The uniqueness of this CP array is its feeding network, where state-of-the-art metamaterial transmission lines (MMLs) are built to obtain the required quadrature phases. Experimental results show that the feed network has excellent power distribution and progressive phases over a wide bandwidth. As a consequence, the MML-based array exhibits excellent performance, viz. 85% impedance bandwidth (SWR ≤ 2), 89% axial-ratio (3-dB) bandwidth, 80% efficiency at a center frequency of 2.5 GHz and 55% pattern bandwidth with a criterion of 8-dBic gain level. The axial-ratio bandwidth is validated with cross-polar discriminations obtained from radiation patterns.


title: Using ARM trustzone to build a trusted language runtime for mobile applications

abstract: This paper presents the design, implementation, and evaluation of the Trusted Language Runtime (TLR), a system that protects the confidentiality and integrity of .NET mobile applications from OS security breaches. TLR enables separating an application's security-sensitive logic from the rest of the application, and isolates it from the OS and other apps. TLR provides runtime support for the secure component based on a .NET implementation for embedded devices. TLR reduces the TCB of an open source .NET implementation by a factor of $78$ with a tolerable performance cost. The main benefit of the TLR is to bring the developer benefits of managed code to trusted computing. With the TLR, developers can build their trusted components with the productivity benefits of modern high level languages, such as strong typing and garbage collection.


Genetic diversity of donkeys in Croatia.


title: Prevalence of disorders of the autism spectrum in a population cohort of children in South Thames: the Special Needs and Autism Project (SNAP)

abstract: BACKGROUND ::: Recent reports have suggested that the prevalence of autism and related spectrum disorders (ASDs) is substantially higher than previously recognised. We sought to quantify prevalence of ASDs in children in South Thames, UK. ::: ::: ::: METHODS ::: Within a total population cohort of 56 946 children aged 9-10 years, we screened all those with a current clinical diagnosis of ASD (n=255) or those judged to be at risk for being an undetected case (n=1515). A stratified subsample (n=255) received a comprehensive diagnostic assessment, including standardised clinical observation, and parent interview assessments of autistic symptoms, language, and intelligence quotient (IQ). Clinical consensus diagnoses of childhood autism and other ASDs were derived. We used a sample weighting procedure to estimate prevalence. ::: ::: ::: FINDINGS ::: The prevalence of childhood autism was 38.9 per 10,000 (95% CI 29.9-47.8) and that of other ASDs was 77.2 per 10,000 (52.1-102.3), making the total prevalence of all ASDs 116.1 per 10,000 (90.4-141.8). A narrower definition of childhood autism, which combined clinical consensus with instrument criteria for past and current presentation, provided a prevalence of 24.8 per 10,000 (17.6-32.0). The rate of previous local identification was lowest for children of less educated parents. ::: ::: ::: INTERPRETATION ::: Prevalence of autism and related ASDs is substantially greater than previously recognised. Whether the increase is due to better ascertainment, broadening diagnostic criteria, or increased incidence is unclear. Services in health, education, and social care will need to recognise the needs of children with some form of ASD, who constitute 1% of the child population.


title: Discovering Business Rules in Knowledge-Intensive Processes Through Decision Mining: An Experimental Study

abstract: Decision mining allows discovering rules that constraint the paths that the instances of a business process may follow during its execution. In Knowledge-intensive Processes (KiP), the discovery of such rules is a great challenge because they lack structure. In this context, this experimental study applies a decision mining technique in an event log of a real company that provides ICT infrastructure services. The log comprises structured data (ticket events) and non-structured data (messages exchanged among team members). The goal was to discover tacit decisions that could be potentially declared as business rules for the company. In addition to mining the decision points, we validated the discovered rule with the company w.r.t. their meaning.


title: Gender Variance and Dysphoria in Children and Adolescents.

abstract: Gender variant and transgender youth are seeking medical care at younger ages. Pediatricians and other primary care physicians are often the first professionals who encounter such youth and their families. The goals of this article are to provide information on the epidemiology and natural history of gender variant and transgender youth, current clinical practice guidelines regarding the use of puberty blockers and cross-sex hormones for transgender youth, and limitations and challenges to optimal care.


title: Guidance method using laser pointer and gestures for librarian robot

abstract: This paper presents a guidance method using a laser pointer attached to a librarian robot and gestures consist of body movements. Our librarian robot, that is fixed in an experimental environment, is able to say greeting properly based on estimation results of human behavior using a laser range finder, talk with a library user with a natural language, search books depending on a user's request. When the robot explains where a requested book is in the library, it points out a target position using a laser spot while the body and head are also turned around to the direction of the target. This makes detailed oral explanations unnecessary. However, the robot cannot always point out a target position directly using the laser pointer. So we design three types of guidance using the laser pointer and the gestures depending on the library environment. Some experimental results reveal the validity and effectiveness of our proposed guidance method using the laser pointer and some gestures for our librarian robot.


title: Extended-connectivity fingerprints.

abstract: Extended-connectivity fingerprints (ECFPs) are a novel class of topological fingerprints for molecular characterization. Historically, topological fingerprints were developed for substructure and similarity searching. ECFPs were developed specifically for structure−activity modeling. ECFPs are circular fingerprints with a number of useful qualities: they can be very rapidly calculated; they are not predefined and can represent an essentially infinite number of different molecular features (including stereochemical information); their features represent the presence of particular substructures, allowing easier interpretation of analysis results; and the ECFP algorithm can be tailored to generate different types of circular fingerprints, optimized for different uses. While the use of ECFPs has been widely adopted and validated, a description of their implementation has not previously been presented in the literature.


title: Gendered voice and robot entities: Perceptions and reactions of male and female subjects

abstract: There is recent evidence that males and females view robots differently, from the way robots are conceptualized, to the way humans respond when they interact with them. In this paper, we further explore gender-based differences in human-robot interaction. Moreover, we provide the first available evidence for sex-related differences in reactions to gendered synthetic voices that are either disembodied or physically embodied within a robot. Results indicate that physical embodiment and perceived entity gender may interact with human sex-related characteristics and pre-experimental attitudes in determining how people respond to artificial entities.


title: A sequential dictionary learning algorithm with enforced sparsity

abstract: Dictionary learning algorithms have received widespread acceptance when it comes to data analysis and signal representations problems. These algorithms alternate between two stages: the sparse coding stage and dictionary update stage. In all existing dictionary learning algorithms the use of sparsity has been limited to the sparse coding stage while presenting differences in the dictionary update stage which can be achieved sequentially or in parallel. The singular value decomposition (SVD) has been successfully used for sequential dictionary update. In this paper we propose a dictionary learning algorithm that include a sparsity constraint also in the dictionary update stage. The cost function used to include sparsity in the dictionary update stage is derived using the link between SVD and rank one matrix approximation. The effectiveness of the proposed dictionary learning method is tested on synthetic data and an image processing application. The results reveal that including a sparsity constraint in the dictionary update stage is not a bad idea.


title: Probability density function estimation based over-sampling for imbalanced two-class problems

abstract: A novel probability density function (PDF) estimation based over-sampling approach is proposed for two-class imbalanced classification problems. The Parzen-window kernel function is applied to estimate the PDF of the positive class, from which synthetic instances are generated as additional training data to re-balance the class distribution. Utilising the re-balanced over-sampled training data, a radial basis function (RBF) classifier is constructed by applying an orthogonal forward regression, in which the classifier's structure and the parameters of RBF kernels are determined using a particle swarm optimisation algorithm based on the criterion of minimising the leave-one-out misclassification rate. The effectiveness of the proposed approach is demonstrated by an empirical study on several imbalanced data sets.


title: Docker container security via heuristics-based multilateral security-conceptual and pragmatic study

abstract: In this paper we attempt to provide unified security and privacy multilateral security architecture for cloud services stack, using key latest technology via LxC in general and Docker containers in specific. Docker is one of the first to use LxC at such a large scale, but LxC is not a very new concept, it is around for quite some time. Docker container service is a key Platform as a Service (PaaS) based engine. Unified layered cloud computing multilateral security implementation addresses the potential risks associated with cloud computing environment. It also specifies when, at what time, where, and how to apply security controls. Unified Design includes the structured connection between the components of cloud ecosystem for basket and bucket of unified cloud-based hybrid services using micro service oriented venture architectural services realized in terms of PAAS using Docker container as a service. We surveyed various industry/academic professionals and based on our experience we tried to understand the challenges they faced while using the cloud system. We tried to understand the security challenges, incidents, story pieces, accidents, experience they faced while designing, implementing and using the cloud services/resources virtually via online. This work helps to assess the security design and architecture quality using multilateral security framework for Docker container. We try to present the architecture using OSI/TCP/IP stack model with reference to Cloud service stack model and deployment stack model and try to propose the balanced multilateral security prototype with reference to Docker Container security architecture.


Enterprise Architecture Analysis and Application - An Exploratory Study


title: Outsourcing to an unknown workforce: exploring opensourcing as a global sourcing strategy

abstract: This paper presents a psychological contract perspective on the use of the open source development model as a global sourcing strategy-opensourcing, as we term it here-whereby commercial companies and open source communities collaborate on development of software of commercial interest to the company. Building on previous research on information systems outsourcing, a theoretical framework for exploring the opensourcing phenomenon is derived. The first phase of the research concerned qualitative case studies involving three commercial organizations (IONA Technologies, ::: ::: Philips Medical Systems, and Telefonica) that had "liberated" what had hitherto been proprietary software and sought to grow a global open source community around their product. We followed this with a large-scale survey involving additional exemplars of the phenomenon. The study identifies a number of symmetrical and complementary customer and community obligations that are associated with opensourcing success. We also identify a number of tension points on which customer and community perceptions tend to vary. Overall the key watchwords for opensourcing are openness, trust, tact, professionalism, transparency, and complementariness: The customer and community need to establish a trusted partnership of shared responsibility in building an overall opensourcing ecosystem. The study reveals an ongoing shift from OSS as a community of individual developers to OSS as a community of commercial organizations, primarily small to medium-sized enterprises. It also reveals that opensourcing provides ample opportunity for companies to headhunt top developers, hence moving from outsourcing to a largely unknown OSS workforce toward recruitment of developers from a global open source community whose talents have become known as a result of the opensourcing experience.


title: The knowledge model of Protege-2000: combining interoperability and flexibility

abstract: Knowledge-based systems have become ubiquitous in recent years. Knowledge-base developers need to be able to share and reuse knowledge bases that they build. Therefore, interoperability among different knowledge-representation systems is essential. The Open Knowledge-Base Connectivity protocol (OKBC) is a common query and construction interface for frame-based systems that facilitates this interoperability. ProtEgE-2000 is an OKBC-compatible knowledge-base-editing environment developed in our laboratory. We describe ProtEgE-2000 knowledge model that makes the import and export of knowledge bases from and to other knowledge-base servers easy. We discuss how the requirements of being a usable and configurable knowledge-acquisition tool affected our decisions in the knowledge-model design. ProtEgE-2000 also has a flexible metaclass architecture which provides configurable templates for new classes in the knowledge base. The use of metaclasses makes ProtEgE-2000 easily extensible and enables its use with other knowledge models. We demonstrate that we can resolve many of the differences between the knowledge models of ProtEgE-2000 and Resource Description Framework (RDF)--a system for annotating Web pages with knowledge elements--by defining a new metaclass set. Resolving the differences between the knowledge models in declarative way enables easy adaptation of ProtEgE-2000 as an editor for other knowledge-representation systems.


title: Geometric data perturbation for privacy preserving outsourced data mining

abstract: Data perturbation is a popular technique in privacy-preserving data mining. A major challenge in data perturbation is to balance privacy protection and data utility, which are normally considered as a pair of conflicting factors. We argue that selectively preserving the task/model specific information in perturbation will help achieve better privacy guarantee and better data utility. One type of such information is the multidimensional geometric information, which is implicitly utilized by many data-mining models. To preserve this information in data perturbation, we propose the Geometric Data Perturbation (GDP) method. In this paper, we describe several aspects of the GDP method. First, we show that several types of well-known data-mining models will deliver a comparable level of model quality over the geometrically perturbed data set as over the original data set. Second, we discuss the intuition behind the GDP method and compare it with other multidimensional perturbation methods such as random projection perturbation. Third, we propose a multi-column privacy evaluation framework for evaluating the effectiveness of geometric data perturbation with respect to different level of attacks. Finally, we use this evaluation framework to study a few attacks to geometrically perturbed data sets. Our experimental study also shows that geometric data perturbation can not only provide satisfactory privacy guarantee but also preserve modeling accuracy well.


title: Visceral and serum lysozyme activities in some freshwater fish (three catfish and two carps)

abstract: The lysozyme activities were investigated in three catfish: hybrid catfish [Clarias macrocephalus (Gunther 1864) x Clarias gariepinus (Burchell 1822)], Hemibagrus filamentus (Fang and Chaux 1949), and Kryptopterus apogon (Bleeker 1851) and two carps: Oreochromis niloticus (Linnaeus 1758) and Channa striata (Bloch 1793). Fish samples used in this study were from two sites of Chiang Mai province; C. macrocephalus x C. gariepinus and O. niloticus were from Ton Payom Market, Amphoe Muang. H. filamentus, K. apogon, and C. striata were from Doi Tao Lake, Amphoe Doi Tao. The serum, brain, liver, and kidney of the catfish and carps from both sites were spectrophotometrically measured at 450 nm by ELISA reader. C. macrocephalus x C. gariepinus and H. filamentus showed the highest lysozyme activity in the serum, followed by the kidney, brain, and liver, respectively, while the highest lysozyme activity was observed in the brain of K. apogon, and the least one was observed in the liver. Among the catfish, lysozyme activities of K. apogon were significantly higher than those of the other fish (P < 0.01). However, no significant difference in lysozyme activity from the three investigated visceral organs was found. In carps, O. niloticus had the highest lysozyme activity in the serum, followed by the kidney, brain, and liver, respectively. Meanwhile, the highest lysozyme activity in C. striata was recorded in the serum, followed by the liver, brain, and kidney. However, there was no significant difference in lysozyme activity from the serum and the internal organs in these two fish. In conclusion, the innate immunity arms in these five fish species encompassed the similar function pattern, although they were differently present in their organs. The data will be used as reference ranks for these species to evaluate health status when they are exposed to some pathogens or chemical contamination in streams and reservoirs.


title: Similarity Metric Learning for Face Recognition

abstract: Recently, there is a considerable amount of efforts devoted to the problem of unconstrained face verification, where the task is to predict whether pairs of images are from the same person or not. This problem is challenging and difficult due to the large variations in face images. In this paper, we develop a novel regularization framework to learn similarity metrics for unconstrained face verification. We formulate its objective function by incorporating the robustness to the large intra-personal variations and the discriminative power of novel similarity metrics. In addition, our formulation is a convex optimization problem which guarantees the existence of its global solution. Experiments show that our proposed method achieves the state-of-the-art results on the challenging Labeled Faces in the Wild (LFW) database [10].


title: Computationally efficient leakage inductance calculation for a high-frequency core-type transformer

abstract: Leakage inductance is a critical design attribute of high-frequency transformers. In this paper, a new method to estimate core-type transformer leakage inductance is proposed. The magnetic analysis uses a combination of analytical and numerical analysis. A two-dimensional magnetic analysis using Biot-Savart law and the method of images is introduced. Using the same field analysis, expressions for mean squared field intensity values necessary for estimating proximity effect loss in the windings are also derived.


title: A simple algorithm for Boolean operations on polygons

abstract: In this paper a simple and efficient algorithm for computing Boolean operations on polygons is presented. The algorithm works with almost any kind of input polygons: concave polygons, polygons with holes, several contours and self-intersecting edges. Important topological information, as the holes of the result polygon, is computed.


title: W-Band, Broadband 2W GaN MMIC

abstract: This paper reports the first high-power GaN MMIC covering 70 percent of W-band (75-110 GHz). Using an on-chip traveling-wave power combiner circuit, it achieves power levels of greater than 1 W CW over the 80 to 100 GHz band and a peak power of 2 W CW at 84 GHz. Operating in a pulsed mode (10% duty), this MMIC produces a peak power of 3.2 W at 86 GHz. This work represents the first application of an on-chip combining network to simultaneously achieve high power levels and wide bandwidths at W-band frequencies.


title: Modeling of EXG (ECG, EMG and EEG) non-idealities using MATLAB

abstract: EXG [Electrocardiography (ECG), Electromyography (EMG) and Electroencephalogram (EEG)] signals are of very low amplitude and frequency with immense diagnostic value. These signals acquire noise while travelling through electrodes, printed circuit board (PCB), integrated circuits (ICs), etc. and thus their detection and interpretation is a challenge in biomedical engineering. This paper presents MATLAB Simulink environment for an integrated EXG electronic measuring system that allows designers to analyze the time/frequency domain behavior of sensing electrodes, programmable instrumentation amplifiers (PIA), programmable band pass filter (BPF) and second order sigma delta analog to digital converter (SD-ADC). The Simulink model provides non-idealities such as sensing electrodes noise, programmable gain amplifier parameters (thermal noise, flicker noise, resistor mismatch, settling time (slew rate), dc gain, bandwidth, and voltage dynamic ranges), band pass filter parameters (noise and bandwidth), and sigma delta ADC parameters (clock jitter, capacitor mismatch, noise, settling time (slew rate), dc gain, bandwidth, and voltage dynamic ranges). A detailed simulation and implementation (Using CMOS, 180nm technology) results of the complete system is presented for both the ideal and actual performance parameters.


title: Sarcasm Detection on Twitter: A Behavioral Modeling Approach

abstract: Sarcasm is a nuanced form of language in which individuals state the opposite of what is implied. With this intentional ambiguity, sarcasm detection has always been a challenging task, even for humans. Current approaches to automatic sarcasm detection rely primarily on lexical and linguistic cues. This paper aims to address the difficult task of sarcasm detection on Twitter by leveraging behavioral traits intrinsic to users expressing sarcasm. We identify such traits using the user's past tweets. We employ theories from behavioral and psychological studies to construct a behavioral modeling framework tuned for detecting sarcasm. We evaluate our framework and demonstrate its efficiency in identifying sarcastic tweets.


title: Affective image classification using features inspired by psychology and art theory

abstract: Images can affect people on an emotional level. Since the emotions that arise in the viewer of an image are highly subjective, they are rarely indexed. However there are situations when it would be helpful if images could be retrieved based on their emotional content. We investigate and develop methods to extract and combine low-level features that represent the emotional content of an image, and use these for image emotion classification. Specifically, we exploit theoretical and empirical concepts from psychology and art theory to extract image features that are specific to the domain of artworks with emotional expression. For testing and training, we use three data sets: the International Affective Picture System (IAPS); a set of artistic photography from a photo sharing site (to investigate whether the conscious use of colors and textures displayed by the artists improves the classification); and a set of peer rated abstract paintings to investigate the influence of the features and ratings on pictures without contextual content. Improved classification results are obtained on the International Affective Picture System (IAPS), compared to state of the art work.


title: Integrating process and ontology to support supply chain modelling

abstract: Many researchers have recognised a lack of common framework to support supply chain modelling and analysis and proposed their solutions accordingly. Majority of the approaches proposed are more concerned with building an object model of a supply chain than identifying processes that realistically describe a supply chain. Although object models provide means or building blocks necessary to model and analyse different elements of a supply chain, absence of supply chain processes promotes a ‘black box’ view on the supply chain. This study proposes an ontology model specifically developed to support supply chain process modelling and analysis. It is based on a premise that prior identification of processes the ontology is supposed to support facilitates the ontology development and validation. This study introduces development, validation and application of supply chain ontology to support supply chain process modelling and analysis.


title: Vigilantism in Science: The Need and the Risks

abstract: Science is in a constant state of flux. In recent years, science and scientists have been coming under attack, and scrutiny, by internal and external skeptics and critics, as well as a potentially anti-science movement. Despite this fairly negative portrayal, the expected outcome is that science – and its players – will become more robust, more critical and thus more reliable and accountable, i.e., that there will be a positive light at the end of a tumultuous period of challenge, although we are still far from reaching that point. Part of this process involves individuals or groups, known or anonymous, who are actively seeking out faults or errors, to demonstrate that science and scientists, and journals and publishers, are flawed and that something needs to be done to rectify this. Very rarely are such individuals referred to as vigilantes, but in essence, this is in fact what they are: individuals who have taken the reigns of quality control where it has failed, or where it has perceived to have failed. Vigilantism, which tends to conjure images of mob squads or self-appointed policing figures, involves taking on community style group awareness and implementation of their own rules, morals and values, as they see fit. Vigilantism, in the era of post-publication peer review, has reached science. This paper examines the positive and negative role that vigilantism plays, or can play, in science. DOI: 10.5901/ajis.2016.v5n3p9


title: Two-Phase SVPWM Modulation Method and Its Application in Stepper Motor

abstract: Two-phase space vector pulse-width modulation (SVPWM) algorithm is proposed in this paper based on the SVPWM analysis. Formulas of space voltage vector pulse width modulation (SVPWM) is calculated. Two-phase SVPWM is applied on the current most widely used two-phase hybrid stepping motor in this paper and simulated by Matlab/Simulink. Simulation results show that the method is feasible.


title: Neural shape codes for 3D model retrieval

abstract: A practical way for applying deep learning to the 3D model retrieval task.The activations of different layers of CNN are shown to be complementary.State-of-the-art performances on many 3D shape benchmark datasets. The paradigm of Convolutional Neural Network (CNN) has already shown its potential for many challenging applications of computer vision, such as image classification, object detection and action recognition. In this paper, the task of 3D model retrieval is addressed by exploiting such promising paradigm. However, 3D models are usually represented with a collection of orderless points, lines and surfaces in a three dimensional space, which makes it difficult to involve the operation of convolution, pooling, etc. Yet, we propose a practical and effective way for applying CNN to 3D model retrieval, by training the network with the depth projections of 3D model. This CNN is regarded as a generic feature extractor for depth image. With large amounts of training data, the learned feature, which is called Neural Shape Codes, can handle various deformation changes that exist in shape analysis. The reported experimental results on several 3D shape benchmark datasets show the superior performance of the proposed method.


title: Attentional Modulation of Unconscious Automatic Processes: Evidence from Event-related Potentials in a Masked Priming Paradigm

abstract: Automatic processes are usually thought to occur independently of any cognitive resources. This traditional view has been recently challenged by showing that temporal attention to a target stimulus is a prerequisite for "automatic" response priming. The event-related potential (ERP) study reported here extends this research by pursuing a somewhat different approach. In two experiments, it was investigated whether masked semantic priming effects can be modulated by temporal attention to the prime using a cueing procedure. We hypothesized that masked priming is amplified when attention is directed to the stimulus stream in the time window of masked prime presentation, even in the absence of any prime awareness. ERPs were recorded while subjects performed a primed lexical decision task. Target words were preceded by semantically related or unrelated masked prime words, which were not consciously identified. A cue stimulus prompted subjects to direct their attention to the stimulus stream either shortly before the masked prime (short cue interval) or a long time interval before. Priming affected the amplitude of the N400 ERP component, an electrophysiological index of semantic processing. Unrelated prime-target pairs elicited a larger N400 than related pairs (N400 priming effect). Most importantly, this masked N400 priming effect was strongest when the cue interval and the stimulus onset asynchronies were short. The present results show that temporal attention to the prime is a prerequisite for obtaining masked N400 priming effects. They also demonstrate that unconscious automatic processes are susceptible to attentional modulation.


title: Assumptions of Decision-Making Models in AGI

abstract: This paper analyzes the assumptions of the decision making models in the context of artificial general intelligence AGI. It is argued that the traditional approaches, exemplified by decision theory and reinforcement learning, are inappropriate for AGI, because their fundamental assumptions on available knowledge and resource cannot be satisfied here. The decision making process in the AGI system NARS is introduced and compared with the traditional approaches. It is concluded that realistic decision-making models must acknowledge the insufficiency of knowledge and resources, and make assumptions accordingly.


title: Managing knee osteoarthritis with yoga or aerobic/strengthening exercise programs in older adults: a pilot randomized controlled trial

abstract: Although exercise is often recommended for managing osteoarthritis (OA), limited evidence-based exercise options are available for older adults with OA. This study compared the effects of Hatha yoga (HY) and aerobic/strengthening exercises (ASE) on knee OA. Randomized controlled trial with three arms design was used: HY, ASE, and education control. Both HY and ASE groups involved 8 weekly 45-min group classes with 2-4 days/week home practice sessions. Control group received OA education brochures and weekly phone calls from study staff. Standardized instruments were used to measure OA symptoms, physical function, mood, spiritual health, fear of falling, and quality of life at baseline, 4 and 8 weeks. HY/ASE adherences were assessed weekly using class attendance records and home practice video recordings. Primary analysis of the difference in the change from baseline was based on intent-to-treat and adjusted for baseline values. Eight-three adults with symptomatic knee OA completed the study (84% female; mean age 71.6 ± 8.0 years; mean BMI 29.0 ± 7.0 kg/m2). Retention rate was 82%. Compared to the ASE group at 8 weeks, participants in the HY group had a significant improvement from baseline in perception of OA symptoms (-9.6 [95% CI -15.3, -4]; p = .001), anxiety (-1.4 [95% CI -2.7, -0]; p = .04), and fear of falling (-4.6 [-7.5, -1.7]; p = .002). There were no differences in class/home practice adherence between HY and ASE. Three non-serious adverse events were reported from the ASE group. Both HY and ASE improved symptoms and function but HY may have superior benefits for older adults with knee OA. Trial registration The full trial protocol is available at clinicaltrials.gov (NCT02525341).


Cognitive Mapping Based on Conjunctive Representations of Space and Movement


title: Technical Section: Exploring the use of handheld AR for outdoor navigation

abstract: Recently, mobile-phone based outdoor augmented reality (AR) systems have become readily available. One of the most popular applications are AR browsers that show virtual points of interest (POIs) overlaid on top of the phone's camera view. These virtual cues can be used to guide people to the POIs. However, the usefulness of AR systems for guiding users to POI has not yet been evaluated, especially when compared to map interfaces. In this paper we present results of a user study comparing navigation with information typically provided by currently available handheld AR browsers, to navigation with a digital map, and a combined map and AR condition. We found no overall difference in task completion time, but found evidence that AR browsers are less useful for navigation in some environment conditions. We also found that navigation performance differed significantly with gender for the Map and AR+Map interfaces, but is very similar across gender for the AR interface. Users preferred the combined AR+Map condition, and felt that there were significant problems with using the AR view alone for navigation.


Translation Inference across Dictionaries via a Combination of Graph-based Methods and Co-occurrence Statistics


title: Generalization of Deep Neural Networks for Chest Pathology Classification in X-Rays Using Generative Adversarial Networks

abstract: Medical datasets are often highly imbalanced, over representing common medical problems, and sparsely representing rare problems. We propose simulation of pathology in images to overcome the above limitations. Using chest Xrays as a model medical image, we implement a generative adversarial network (GAN) to create artificial images based upon a modest sized labeled dataset. We employ a combination of real and artificial images to train a deep convolutional neural network (DCNN) to detect pathology across five classes of disease. We furthermore demonstrate that augmenting the original imbalanced dataset with GAN generated images improves performance of chest pathology classification using the proposed DCNN in comparison to the same DCNN trained with the original dataset alone. This improved performance is largely attributed to balancing of the dataset using GAN generated images, where image classes that are lacking in example images are preferentially augmented.


title: A pneumatic tactile alerting system for the driving environment

abstract: Sensory overloaded environments present an opportunity for innovative design in the area of Human-Machine Interaction. In this paper we study the usefulness of a tactile display in the automobile environment. Our approach uses a simple pneumatic pump to produce pulsations of varying frequencies on the driver's hands through a car steering wheel fitted with inflatable pads. The goal of the project is to evaluate the effectiveness of such a system in alerting the driver of a possible problem, when it is used to augment the visual display presently used in automobiles. A steering wheel that provides haptic feedback using pneumatic pockets was developed to test our hypothesis. The steering wheel can pulsate at different frequencies. The system was tested in a simple multitasking paradigm on several subjects and their reaction times to different stimuli were measured and analyzed. For these experiments, we found that using a tactile feedback device lowers reaction time significantly and that modulating frequency of vibration provides extra information that can reduce the time necessary to identify a problem.


title: Indexing by Latent Dirichlet Allocation and Ensemble Model

abstract: The contribution of this article is twofold. First, we present Indexing by latent Dirichlet allocationLDI, an automatic document indexing method. Many ad hoc applications, or their variants with smoothing techniques suggested in LDA-based language modeling, can result in unsatisfactory performance as the document representations do not accurately reflect concept space. To improve document retrieval performance, we introduce a new definition of document probability vectors in the context of LDA and present a novel scheme for automatic document indexing based on LDA. Second, we propose an Ensemble Model EnM for document retrieval. EnM combines basic indexing models by assigning different weights and attempts to uncover the optimal weights to maximize the mean average precision. To solve the optimization problem, we propose an algorithm, which is derived based on the boosting method. The results of our computational experiments on benchmark data sets indicate that both the proposed approaches are viable options for document retrieval.


title: The effect of organizational support on ERP implementation

abstract: Purpose – The purpose of this paper is to propose a model to examine the impact of organizational support on behavioral intention (BI) regarding enterprise resource planning (ERP) implementation based on the technology acceptance model (TAM).Design/methodology/approach – A research model is proposed which describes the effects of organizational support, both formal and informal, on factors of TAM. A survey questionnaire is developed to test the proposed model. A total of 700 of questionnaires are distributed to users in small and medium enterprises that have implemented ERP systems in Korea and 209 responses are used for analyses. Structural equation modeling is employed to test the research hypotheses.Findings – The results indicate that the organizational support is an important factor for perceived usefulness (PU) and perceived ease of use (PEOU). PU and PEOU seem to lead to a higher level of interest in the ERP system and BI to use the system. The most notable finding of our study is that organization...


title: Attitudes and knowledge about genetic testing before and after finding the disease-causing mutation among individuals at high risk for familial, early-onset Alzheimer's disease.

abstract: This study was designed to investigate the influence of finding a disease-causing mutation for an early-onset form of Alzheimer's disease, with the intention of taking a presymptomatic genetic test. First-degree relatives of patients with Alzheimer's disease from a large Cuban family with a newly described mutation in presenilin 1 gene were interviewed before and after molecular studies. Significant differences were observed regarding the knowledge of the disease (p=0.0004), interest in presymptomatic testing (p=0.000), and possible reproductive behavior (p=0.0087) in the same individuals in two different periods, 1997 and 2007. Our results show that there is a marked difference in the attitudes concerning genetic testing when individuals gain more knowledge about the disease and when there is more certainty about the test.


title: A printed inverted-F antenna for circular polarization

abstract: In this paper, a printed inverted-F antenna for circular polarization is proposed. A rectangular element is loaded at the feeding element perpendicularly. The circular polarized wave is radiated in the frequency range between the first resonant frequency and the second resonant frequency produced by the rectangular element. The relationships between the geometrical parameters of the loaded rectangular element and the antenna characteristics (return loss and axial ratio) are investigated.


FACETS: Adaptive Local Exploration of Large Graphs


title: OpenRec: A Modular Framework for Extensible and Adaptable Recommendation Algorithms

abstract: With the increasing demand for deeper understanding of users» preferences, recommender systems have gone beyond simple user-item filtering and are increasingly sophisticated, comprised of multiple components for analyzing and fusing diverse information. Unfortunately, existing frameworks do not adequately support extensibility and adaptability and consequently pose significant challenges to rapid, iterative, and systematic, experimentation. In this work, we propose OpenRec, an open and modular Python framework that supports extensible and adaptable research in recommender systems. Each recommender is modeled as a computational graph that consists of a structured ensemble of reusable modules connected through a set of well-defined interfaces. We present the architecture of OpenRec and demonstrate that OpenRec provides adaptability, modularity and reusability while maintaining training efficiency and recommendation accuracy. Our case study illustrates how OpenRec can support an efficient design process to prototype and benchmark alternative approaches with inter-changeable modules and enable development and evaluation of new algorithms.


title: Introduction to mediation analysis with structural equation modeling

abstract: Conflict of Interest: The authors report no conflict of interest related to this manuscript. ::: ::: ::: ::: Funding: Financial support for this study was provided by a grant from NIH/NCRR CTSA KL2TR000440. The funding agreement ensured the authors' independence in designing the study, interpreting the data, writing, and publishing the report.


title: A low cost, multithreaded processing-in-memory system

abstract: This paper discusses die cost vs. performance tradeoffs for a PIM system that could serve as the memory system of a host processor. For an increase of less than twice the cost of a commodity DRAM part, it is possible to realize a performance speedup of nearly a factor of 4 on irregular applications. This cost efficiency derives from developing a custom multithreaded processor architecture and implementation style that is well-suited for embedding in a memory. Specifically, it takes advantage of the low latency and high row bandwidth to both simplify processor design --- reducing area --- as well as to improve processing throughput. To support our claims of cost and performance, we have used simulation, analysis of existing chips, and also designed and fully implemented a prototype chip, PIM Lite.


title: Landmark-based data location verification in the cloud: review of approaches and challenges

abstract: Data storage on the cloud is growing every day and many companies, administrations, and individuals are now outsourcing storage of their data to large-scale Cloud Service Providers (CSP). However, because of today’s cloud infrastructure virtualization, data owners cannot easily know the location where their data are stored. Even in case of the establishment of a strong Service-Level Agreement, which includes an initial guarantee regarding data location, the CSP may then move data to another location, like another country, in order to cut storage costs or for any other reasons, including backup mistakes and fraudulent use of data. Data location verification is required due to legal, privacy, and performance constraints. Recently “Where are my data located in the cloud?” has become a challenge and solutions have been proposed to verify data location, under given assumptions regarding CSP behavior. The objective of this paper is twofold: propose a comprehensive classification of the location verification approaches and discuss their vulnerabilities regarding malicious CSP attacks. Location verification solutions may be Framework-based, Hardware-based or Landmark-based. This paper addresses only landmark-based approaches for their deployment flexibility and low cost.


title: Can internet blogs be used as an effective advertising tool? The role of product blog type and brand awareness

abstract: Purpose – As blogs have become an important information communication medium, selecting blog types that are appropriate and effective for the products they are to promote is now an important issue in corporate advertising. However, the impact of different blog types on consumers and their advertising effectiveness are issues seldom addressed in the existing literature. The purpose of this paper is to view product information that consumers post on blogs as advertisements and compares advertising effectiveness between different blog types to help enterprises properly employ blogs in their marketing campaigns. Design/methodology/approach – An experimental design was adopted, and 12 experimental contexts were designed. A valid sample of over 2,000 responses was collected to study between-group differences in advertising effectiveness. Findings – The analytical results showed between-group difference in advertising effectiveness, which indicated that blog advertising effectiveness varies with different combin...


title: Paddy rice mapping in red river delta region using landsat 8 images: Preliminary results

abstract: Paddy rice, an important crop in Vietnam, need to be monitored and managed for food security, water resource management and Greenhouse gas (GHG) emission. In this study, we present preliminary results of rice mapping in Red River Delta (RRD) region, one of two largest rice planting area in Vietnam. We employ all available Landsat 8 acquired over RRD, regardless of cloud coverage, to establish yearly rice maps from 2013 to 2016. A time-series classification scheme is proposed which includes two main stages. At first, all Landsat 8 images in classification year are used to create monthly composite images. Then, time-series features are extracted to feed an eXtreme Gradient Boosting (XGBoost) classifier which is able to work with sparse features. Validation against test data show promising results with overall accuracy of 89.42–91.53%, kappa of 0.76–0.79 and F1 score of 0.90–0.92. Map validation against statistical data at provincial levels shows high correlations with R2 ranging from 0.96–0.98 and mapped rice areas difference from statistical rice areas is from 7.06%–15.42%. The results highlighted effect of our approach in which Landsat 8 images are only used but rice maps are still created with fine spatial resolution, high accuracy and large coverage over cloud-prone areas.


title: Voting models in random networks

abstract: A crucial problem of Social Sciences is under what conditions agreement, or disagreement, emerge in a network of interacting agents. This topic has application in many contexts, including business and marketing decisions, with potential impact on information and technological networks. In this paper we consider a particular model of interaction between a group of individuals connected through a network of acquaintances. In the first model, a node waits an exponentially time with parameter one, and when it expires it chooses one of its neighbors' at random and adopts its decision. In the second one, the node chooses the opinion which is the most adopted by its neighbors (hence, majority rule). We show how different updating rule of the agent' state lead to different emerging patterns, namely, agreement and disagreement. In addition, in the case of agreement, we provide bounds on the time to convergence for various types of graphs.


title: Analysis of splitting attacks on Bitcoin and GHOST consensus protocols

abstract: We propose new mathematical models of splitting attacks targeted at two major consensus protocols applied in cryptocurrencies. Obtained upper bounds of probability of successful fork maintenance by an attacker allows to get estimates on block expectation number for securing the system against splitting attacks.


title: Big Five Audits and Accounting Fraud*

abstract: Empirical studies of earnings management, audit pricing, and audit reporting provide extensive evidence that the Big Five public accounting firms are associated with higher quality financial statements (Francis 2004). Nevertheless, the recent high-profile financial reporting failures that roiled the U.S. capital markets cast doubt on whether this proposition remains valid. Indeed, many commentators interpret the steep upward trend during the late 1990s and early 2000s in accounting misstatements by companies with Big Five auditors as almost conclusive evidence that their assurance services have deteriorated over time (e.g., Coffee 2002; Imhoff 2003; Zeff 2003). Measuring trends in absolute audit quality is typically infeasible, so we more narrowly focus on discriminating between the relative performance of the Big Five and non-Big Five audit firms in preventing companies from orchestrating accounting fraud. Contrary to recent criticisms of the Big Five firms, we provide compelling evidence that they were consistently associated with a lower incidence of accounting fraud, even in the years shortly prior to the sweeping corporate governance reforms. Moreover, this finding is robust to controlling for the endogenous effects of screening by audit firms and selection by their clients.


title: Virtual remote inspection — A new concept for virtual reality enhanced real-time maintenance

abstract: Virtual Reality (VR) technology has found its way from entertainment and gaming applications into industrial environments. Only few approaches discuss the application of VR in the inspection and maintenance domain. Having reliable maintenance processes is however of significant importance for the economic success of manufacturing companies. In order to optimize manual maintenance processes, we developed a concept for visual remote inspections of manufacturing machines based on Virtual Reality technology. The approach focusses on the usage of real-world recordings that enable the viewer to virtually move to another place. We realized and demonstrated the developed concept in a prototypical implementation.


title: Apple disease classification using color, texture and shape features from images

abstract: The presence of diseases in several kinds of fruits is the major factor of production and the economic degradation of the agricultural industry worldwide. An approach for the apple disease classification using color-, texture- and shape-based features is investigated and experimentally verified in this paper. The primary steps of the introduced image processing-based method are as follows: (1) infected fruit part detection is done with the help of K-means clustering method, (2) color-, texture- and shape-based features are computed over the segmented image and combined to form the single descriptor, and (3) multi-class support vector machine is used to classify the apples into one of the infected or healthy categories. Apple fruit is taken as the test case in this study with three categories of diseases, namely blotch, rot and scab as well as healthy apples. The experimentation points out that the introduced method is better as compared to the individual features. It also points out that shape feature is not better suited for this purpose.


title: Pairwise preference regression for cold-start recommendation

abstract: Recommender systems are widely used in online e-commerce applications to improve user engagement and then to increase revenue. A key challenge for recommender systems is providing high quality recommendation to users in ``cold-start" situations. We consider three types of cold-start problems: 1) recommendation on existing items for new users; 2) recommendation on new items for existing users; 3) recommendation on new items for new users. We propose predictive feature-based regression models that leverage all available information of users and items, such as user demographic information and item content features, to tackle cold-start problems. The resulting algorithms scale efficiently as a linear function of the number of observations. We verify the usefulness of our approach in three cold-start settings on the MovieLens and EachMovie datasets, by comparing with five alternatives including random, most popular, segmented most popular, and two variations of Vibes affinity algorithm widely used at Yahoo! for recommendation.


title: Adventures in level design: generating missions and spaces for action adventure games

abstract: This paper investigates strategies to generate levels for action adventure games. This genre relies more strongly on well-designed levels than rule-driven genres such as strategy or roleplaying games for which procedural level generation has been successful in the past. The approach outlined by this paper distinguishes between missions and spaces as two separate structures that need to be generated in two individual steps. It discusses the merits of different types of generative grammars for each individual step in the process.


title: Meta-Learning for Low-Resource Neural Machine Translation

abstract: In this paper, we propose to extend the recently introduced model-agnostic meta-learning algorithm (MAML) for low-resource neural machine translation (NMT). We frame low-resource translation as a meta-learning problem, and we learn to adapt to low-resource languages based on multilingual high-resource language tasks. We use the universal lexical representation~\citep{gu2018universal} to overcome the input-output mismatch across different languages. We evaluate the proposed meta-learning strategy using eighteen European languages (Bg, Cs, Da, De, El, Es, Et, Fr, Hu, It, Lt, Nl, Pl, Pt, Sk, Sl, Sv and Ru) as source tasks and five diverse languages (Ro, Lv, Fi, Tr and Ko) as target tasks. We show that the proposed approach significantly outperforms the multilingual, transfer learning based approach~\citep{zoph2016transfer} and enables us to train a competitive NMT system with only a fraction of training examples. For instance, the proposed approach can achieve as high as 22.04 BLEU on Romanian-English WMT'16 by seeing only 16,000 translated words (~600 parallel sentences).


title: A Comparison of Two Treatment Regimens for Lateral Epicondylitis: A Randomized Trial of Clinical Interventions

abstract: Eighteen subjects participated in a randomized controlled clinical trial to compare the effectiveness of two physical therapy treatments for tennis elbow. The subjects were divided into two groups: In the neural tension group (NTG), the head of the radius was mobilized and specific physical therapy mobilizations were used to address hypomobility of the radial nerve. The standard treatment group (STG) received ultrasound, transverse friction massage, and stretching and strengthening exercises for the extensors of the wrist. All subjects were treated twice weekly for 6 to 8 weeks. Follow-up data were obtained at 3 months post-treatment. Subjects who received radial head mobilization improved over time (p < .05), while those who did not receive radial head mobilization did not improve. Results of the NTG treatment were linked to the radial head treatment, and isolated effects of the NTG treatment could not be determined. There were no long-term positive results in the STG.


title: Ontology matching: A literature review

abstract: We present a literature review regarding articles on ontology matching published in the last decade.It serves the purpose of offering an up-to-date review of the field and showing its evolution trends.Over 1600 papers have been sorted according to a classification framework that we have defined.This framework helps in identifying the distribution of the load work in the last decade.Practitioners have been consulted to contrast and validate the results of the review. The amount of research papers published nowadays related to ontology matching is remarkable and we believe that reflects the growing interest of the research community. However, for new practitioners that approach the field, this amount of information might seem overwhelming. Therefore, the purpose of this work is to help in guiding new practitioners get a general idea on the state of the field and to determine possible research lines.To do so, we first perform a literature review of the field in the last decade by means of an online search. The articles retrieved are sorted using a classification framework that we propose, and the different categories are revised and analyzed. The information in this review is extended and supported by the results obtained by a survey that we have designed and conducted among the practitioners.


title: Minimizing GUI event traces

abstract: GUI input generation tools for Android apps, such as Android's Monkey, are useful for automatically producing test inputs, but these tests are generally orders of magnitude larger than necessary, making them difficult for humans to understand. We present a technique for minimizing the output of such tools. Our technique accounts for the non-deterministic behavior of mobile apps, producing small event traces that reach a desired activity with high probability. We propose a variant of delta debugging, augmented to handle non-determinism, to solve the problem of trace minimization. We evaluate our algorithm on two sets of commercial and open-source Android applications, showing that we can minimize large event traces reaching a particular application activity, producing traces that are, on average, less than 2% the size of the original traces.


title: Recognizing Emotions Induced by Affective Sounds through Heart Rate Variability

abstract: This paper reports on how emotional states elicited by affective sounds can be effectively recognized by means of estimates of Autonomic Nervous System (ANS) dynamics. Specifically, emotional states are modeled as a combination of arousal and valence dimensions according to the well-known circumplex model of affect, whereas the ANS dynamics is estimated through standard and nonlinear analysis of Heart rate variability (HRV) exclusively, which is derived from the electrocardiogram (ECG). In addition, Lagged Poincare Plots of the HRV series were also taken into account. The affective sounds were gathered from the International Affective Digitized Sound System and grouped into four different levels of arousal (intensity) and two levels of valence (unpleasant and pleasant). A group of 27 healthy volunteers were administered with these standardized stimuli while ECG signals were continuously recorded. Then, those HRV features showing significant changes (p $ 0.05 from statistical tests) between the arousal and valence dimensions were used as input of an automatic classification system for the recognition of the four classes of arousal and two classes of valence. Experimental results demonstrated that a quadratic discriminant classifier, tested through Leave-One-Subject-Out procedure, was able to achieve a recognition accuracy of 84.72 percent on the valence dimension, and 84.26 percent on the arousal dimension.


title: KDD, SEMMA AND CRISP-DM: A PARALLEL OVERVIEW

abstract: In the last years there has been a huge growth and consolidation of the Data Mining field. Some efforts are being done that seek the establishment of standards in the area. Included on these efforts there can be enumerated SEMMA and CRISP-DM. Both grow as industrial standards and define a set of sequential steps that pretends to guide the implementation of data mining applications. The question of the existence of substantial differences between them and the traditional KDD process arose. In this paper, is pretended to establish a parallel between these and the KDD process as well as an understanding of the similarities between them.


abstract: An EEG/ERP study of efficient versus inefficient visual search Steven Phillips (steve@ni.aist.go.jp) Neuroscience Research Institute (AIST), Tsukuba Central 2, 1-1-1 Umezono, Tsukuba, Ibaraki 305-8568 Japan Yuji Takeda (yuji-takeda@aist.go.jp) Institute for Human Science and Biomedical Engineering (AIST), Tsukuba Central 6, 1-1-1 Higashi, Tsukuba, Ibaraki 305-8568 Japan Abstract (inefficient search). The way in which bottom-up (possibly parallel) and top-down (possibly serial) processes interact to determine search efficiency, though, remains unclear despite decades of intensive research (see Wolfe, 2003, for a review). EEG/ERP affords a non-invasive alternative for discern- ing visual search subprocesses (e.g., Luck & Hillyard, 1990; Wolber & Wascher, 2003). Although there are well- developed theories of visual search at the behavioural level, relatively little neuroimaging work has provided the neces- sary bridging laws that afford rigourous testing via corre- spondences to the neurophysiological level (Coltheart, 2006). Hence, our approach is primarily exploratory. That is, to map out and contrast the temporal relationships between pu- tative EEG/ERP markers of cognitive processes under ef- ficient and inefficient search conditions, which motivates a more hypothesis-driven approach for future studies. Visual search involves perception, attention, memory, and re- sponse selection. In this EEG/ERP study, we investigate how these components change with the type of search. Participants identified the location of a target item (coloured, oriented rect- angular bar) in search displays also containing non-target dis- tractors. In the condition yielding efficient search, where re- sponse time was independent of set size (two or four items), distractors had no feature in common with the target. In the condition yielding inefficient search, where response time in- creased with set size, distractors shared one feature (either colour, or orientation) with the target. Stimulus- and response- locked event-related potentials (P1, N1, P2, N2, P3, and rLRP) were analyzed. P3, an indicator of working memory engage- ment, showed no significant difference in onset latency be- tween efficient and inefficient conditions. Although P3 peak amplitude was smaller, activity over time was greater for in- efficient search. The duration of response-locked lateralized readiness potential (rLRP), indicating response selection, was also longer in the inefficient condition. By contrast, indicators of selective attention, P1 peak amplitude and N1 peak latency, were sensitive to number of items; and P2 peak amplitude to number of non-target features. (N2 results were inconclusive.) These results suggest a difference between efficient and ineffi- cient search that lies not in the time required to select items as potential targets of search (selective attention), but in the time required to confirm which of those items is indeed the target (target confirmation). Keywords: Visual search; attention; similarity; EEG; ERP; P3; rLRP; synchrony; phase-locking Methods Participants Eleven Japanese university students (aged 24 ± 3.4 years; male; one left handed) with normal, or corrected-to-normal vision, were paid to participate in the experiment. Apparatus and stimuli A standard desktop computer was used to present stimuli. Stimuli were rectangular bars, either red, green, blue, or yel- low; and at an orientation of 0 ◦ , 45 ◦ , 90 ◦ , or 135 ◦ degrees from horizontal. The background color for all trials was grey. The display was divided into four equal quadrants by invisi- ble horizontal and vertical centerlines. A quadrant contained at most one stimulus item, jittered about its center so that the location of the target was clearly identifiable by the contain- ing quadrant. Electrical potentials were collected using a dig- ital electroencephalograph system (Nihon Kohden Neurofax EEG-1100) with an Ag/AgCl electrode cap. Introduction Visual search is generally conceptualized as involving an in- teraction between bottom-up perceptual information process- ing versus top-down processing of contextual information by working memory (see Corbetta & Shulman, 2002; Desimone & Duncan, 1995, for reviews). Reflecting this distinction, some models of visual search include two types of compu- tation based on (1) the relationships between items in the search display independent of the target item; and (2) the re- lationships between display items and the currently searched for target (see, e.g., Duncan & Humphreys, 1989; Wolfe, Cave, & Franzel, 1989). Such models have accounted for the well-established effects of target-distractor and distractor- distractor similarity on search efficiency. Respectively, they are the increase and decrease in search time per display item (i.e., search slope, or conversely efficiency) with more items sharing the same features (e.g., colour, or orientation). When the target is uniquely identifiable by a single feature, search time is typically independent of the number of display items (efficient search), else it increases linearly with item number Conditions and procedure There were two search display type conditions: efficient, where distractors shared no feature with the target so that response times are independent of search set size; and inef- ficient, where distractors shared one feature (either colour, or orientation) with the target so that response times increase with size. Set size was two or four items. The display type- size conditions are labeled, Eff-2, Eff-4, Ineff-2, and Ineff- 4 for efficient and inefficient, two and four item conditions,


title: Performance of fast frequency hopping OFDM under Doppler spread

abstract: The performances of fast frequency hopping OFDM (FFH/OFDM) and conventional OFDM signals are evaluated and compared numerically over Doppler spread flat Rayleigh fading channels with an equal channel gain for all subcarriers. The considered receiver algorithms in this paper are Zero Forcing (ZF) and Minimum Mean Square Error (MMSE). The Doppler Effect is modeled by an improved Jakes Channel Model with M=16 sinusoids. In numerical calculations, four Phase Shift Keying (4-PSK) modulation is employed. The numerical results show that both systems have a similar Bit Error Rate (BER) performance. On the other hand, Doppler spread constraints the performance of the both systems and the BER cannot reduce after a certain value, although signal power rises.


title: Romantic Relationships among Young Adults: An Attachment Perspective

abstract: The present study used adult attachment as a theoretical framework to investigate individual differences in young adults’ romantic relationships. Emphasis was put on studying the relational aspects of the self and coping strategies in an attempt to better understand how romantic partners feel about themselves and how they subsequently behave in periods of conflict. The sample comprised undergraduate university students (N=377) who responded to a self report questionnaire in English. Most findings were consistent with the proposed hypotheses. Results indicated that anxious individuals tend to report an ambivalent coping style and that fearful individuals were most vulnerable to health risks such as depression. Gender differences were also explored and results showed for instance that women generally tend to seek more support, and men might be more dismissing than women.


espaçamento simples , century 16 < Espaço < espaçamento simples , century 16 Printed circuit boards : its characteristics and its potential for recycling spaço > espaçamento simples , century 11


title: Distribution of Eigenvalues for 2x2 MIMO Channel Capacity Based on Indoor Measurements

abstract: Based on the non line-of-sight (NLOS) indoor measurements performed in the corridors on the third floor of the Electronics and Telecommunications Research Institute (ETRI) building in Daejeon city, Republic of Korea, we investigated the distribution of the eigenvalues of HH*, where H denotes a 2×2 multiple-input multiple-output (MIMO) channel matrix. Using the observation that the distribution of the measured eigenvalues matches well with the Gamma distribution, we propose a model of eigenvalues as Gamma distributed random variables that prominently feature both transmitting and receiving correlations. Using the model with positive integer k_i, i=1, 2, which is the shape parameter of a Gamma distribution, we derive the closed-form ergodic capacity of the 2×2 MIMO channel. Validation results show that the proposed model enables the evaluation of the outage and ergodic capacities of the correlated 2×2 MIMO channel in the NLOS indoor corridor environment.


title: Developing a bioinspired pole climbing robot

abstract: This paper proposes a pole climbing robot that has an ability to climb pipes. The novelty of this design is that it uses no motors to climb on the pole. Till now many robots have been fabricated with the ability to climb pipes but most of them solely depend on DC motors. The use of DC motor induces the risk of loosening the grip of the robot in case of power failure that may lead to disastrous situations if robot is working on high altitudes. This could be easily avoidable by the use of electro-pneumatics and by using self-locking circuits. We used two pairs of pneumatic cylinders as linear actuators for gripping the pole i.e. one pair for one claw. Besides this two more cylinders are used for climbing purpose. Unlike other mechanisms, installation is extremely easy. It is like controlling a radio controlled car. This mechanism can be easily modified for different payload capacities by simply using the appropriate diameter of the pneumatic cylinders. The robot can be controlled in tele-operated mode as well as autonomous mode which is a closed loop system. It has a high accuracy with 4mm error per cycle that has a cycle time period of 5 seconds. This robot can be very useful in oil industries for inspection of pipes and power poles in electric industries. It can serve of great help in cases of emergency situation caused during fire accidents.


title: A Practical Algorithm for Solving the Incoherence Problem of Topic Models In Industrial Applications

abstract: Topic models are often applied in industrial settings to discover user profiles from activity logs where documents correspond to users and words to complex objects such as web sites and installed apps. Standard topic models ignore the content-based similarity structure between these objects largely because of the inability of the Dirichlet prior to capture such side information of word-word correlation. Several approaches were proposed to replace the Dirichlet prior with more expressive alternatives. However, this added expressivity comes with a heavy premium: inference becomes intractable and sparsity is lost which renders these alternatives not suitable for industrial scale applications. In this paper we take a radically different approach to incorporating word-word correlation in topic models by applying this side information at the posterior level rather than at the prior level. We show that this choice preserves sparsity and results in a graph-based sampler for LDA whose computational complexity is asymptotically on bar with the state of the art Alias base sampler for LDA \cite{aliasLDA}. We illustrate the efficacy of our approach over real industrial datasets that span up to billion of users, tens of millions of words and thousands of topics. To the best of our knowledge, our approach provides the first practical and scalable solution to this important problem.


title: A survey on extractive text summarization

abstract: Text Summarization is the process of obtaining salient information from an authentic text document. In this technique, the extracted information is achieved as a summarized report and conferred as a concise summary to the user. It is very crucial for humans to understand and to describe the content of the text. Text Summarization techniques are classified into abstractive and extractive summarization. The extractive summarization technique focuses on choosing how paragraphs, important sentences, etc produces the original documents in precise form. The implication of sentences is determined based on linguistic and statistical features. In this work, a comprehensive review of extractive text summarization process methods has been ascertained. In this paper, the various techniques, populous benchmarking datasets and challenges of extractive summarization have been reviewed. This paper interprets extractive text summarization methods with a less redundant summary, highly adhesive, coherent and depth information.


title: Cooperative Intelligent Transport Systems: 5.9-GHz Field Trials

abstract: The mobile outdoor radio environment is challenging for vehicular communications. Although multipath propagation offers diversity and benefits in non-line-of-sight (NLOS) conditions, simultaneous multipath and mobility results in a doubly-selective fading channel. In practice, this means that the channel parameters vary significantly in both time and frequency within the bandwidth and typical packet durations used in 802.11p/WAVE standards for short-range vehicular communications. This paper presents the results of extensive field trial campaigns conducted in several countries, totaling over 1100 km. These field trials are scenario based, focusing on challenging low-latency, high-reliability vehicle-to-vehicle (V2V) safety applications including intersection collision warn- ing, turn across path, emergency electronic brake light, do not pass warning, and precrash sensing. Vehicle-to-infrastructure (V2I) applications are also considered. The field trials compared the performance of off-the-shelf WiFi-based radio equipment with a more advanced 802.11p compliant radio employing more sophisticated channel estimation and tracking. Field trial results demonstrate significantly improved performance using the advanced radio, translating into greatly increased driver warning times and stopping distances. In fact the results show that off-the-shelf WiFi equipment fails to provide sufficient stopping distance to avert accidents in some cases. During the field trials, channel sounding data were also captured. Analysis of these channel measurements reveals the critical importance of accurate channel estimation, tracking the channel in both time and frequency within each packet. Delay spread and Doppler spread statistics computed from the channel measure- ments validate previously reported results in the literature. The results in this paper, however, provide the first instance of channel measurements performed simultaneously to applica- tion performance evaluation. The objective is to firmly establish the link between radio channel characteristics and the performance of critical V2V safety applications.


title: A Reinforcement Learning Approach to Sensor Planning for 3D Models.

abstract: We introduce a novel, fully automated solution method for sensor planning problem for 3D models. By modeling the human approach to the problem first, we put the problem into a reinforcement learning (RL) framework and successfully solve it using the well-known RL algorithms with function approximation. We compare our method with the greedy algorithm in various test cases and show that we can out-perform the baseline greedy algorithm in all cases.


title: Development of An Android Application for Object Detection Based on Color, Shape, or Local Features

abstract: Object detection and recognition is an important task in many computer vision applications. In this paper an Android application was developed using Eclipse IDE and OpenCV3 Library. This application is able to detect objects in an image that is loaded from the mobile gallery, based on its color, shape, or local features. The image is processed in the HSV color domain for better color detection. Circular shapes are detected using Circular Hough Transform and other shapes are detected using Douglas-Peucker algorithm. BRISK (binary robust invariant scalable keypoints) local features were applied in the developed Android application for matching an object image in another scene image. The steps of the proposed detection algorithms are described, and the interfaces of the application are illustrated. The application is ported and tested on Galaxy S3, S6, and Note1 Smartphones. Based on the experimental results, the application is capable of detecting eleven different colors, detecting two dimensional geometrical shapes including circles, rectangles, triangles, and squares, and correctly match local features of object and scene images for different conditions. The application could be used as a standalone application, or as a part of another application such as Robot systems, traffic systems, e-learning applications, information retrieval and many others.


title: The Role of Culture in Health Literacy and Chronic Disease Screening and Management

abstract: Cultural and language differences and socioeconomic status interact with and contribute to low health literacy, defined as the inability to understand or act on medical/therapeutic instructions. Health literacy is increasingly recognized as an important factor in patient compliance, cancer screening utilization, and chronic disease outcomes. Commendable efforts have been initiated by the American Medical Association and other organizations to address low health literacy among patients. Less work has been done, however, to place health literacy in the broader context of socioeconomic and cultural differences among patients and providers that hinder communication and compliance. This review examines cultural influences on health literacy, cancer screening and chronic disease outcomes. We argue that cultural beliefs around health and illness contribute to an individual’s ability to understand and act on a health care provider’s instructions. This paper proposes key aspects of the intersection between health literacy and culturally varying beliefs about health which merit further exploration.


title: Ferrite-magnet spoke-type IPMSM with W-shaped magnet placement

abstract: Rare earth magnets that have a large energy product are generally used in high-efficiency interior permanent-magnet synchronous motors (IPMSMs). However, efficient rare-earth-free motors are needed because of problems such as the high prices of and export restrictions on rare earth materials. This paper introduces a ferrite-magnet spoke-type IPMSM that employs a W-shaped magnet placement in order to achieve high efficiency. The effectiveness of the W-shaped magnet placement is verified from the results of two-dimensional finite element analysis and experiments on a prototype.


title: The Emergence of Edge Computing

abstract: Industry investment and research interest in edge computing, in which computing and storage nodes are placed at the Internet's edge in close proximity to mobile devices or sensors, have grown dramatically in recent years. This emerging technology promises to deliver highly responsive cloud services for mobile computing, scalability and privacy-policy enforcement for the Internet of Things, and the ability to mask transient cloud outages. The web extra at www.youtube.com/playlist?list=PLmrZVvFtthdP3fwHPy_4d61oDvQY_RBgS includes a five-video playlist demonstrating proof-of-concept implementations for three tasks: assembling 2D Lego models, freehand sketching, and playing Ping-Pong.


title: Privacy-preserving pattern matching over encrypted genetic data in cloud computing

abstract: Personalized medicine performs diagnoses and treatments according to the DNA information of the patients. The new paradigm will change the health care model in the future. A doctor will perform the DNA sequence matching instead of the regular clinical laboratory tests to diagnose and medicate the diseases. Additionally, with the help of the affordable personal genomics services such as 23andMe, personalized medicine will be applied to a great population. Cloud computing will be the perfect computing model as the volume of the DNA data and the computation over it are often immense. However, due to the sensitivity, the DNA data should be encrypted before being outsourced into the cloud. In this paper, we start from a practical system model of the personalize medicine and present a solution for the secure DNA sequence matching problem in cloud computing. Comparing with the existing solutions, our scheme protects the DNA data privacy as well as the search pattern to provide a better privacy guarantee. We have proved that our scheme is secure under the well-defined cryptographic assumption, i.e., the sub-group decision assumption over a bilinear group. Unlike the existing interactive schemes, our scheme requires only one round of communication, which is critical in practical application scenarios. We also carry out a simulation study using the real-world DNA data to evaluate the performance of our scheme. The simulation results show that the computation overhead for real world problems is practical, and the communication cost is small. Furthermore, our scheme is not limited to the genome matching problem but it applies to general privacy preserving pattern matching problems which is widely used in real world.


title: Sentiment analysis and affective computing for depression monitoring

abstract: Depression is one of the most common and disabling mental disorders that has a relevant impact on society. Semiautomatic and/or automatic health monitoring systems could be crucial and important to improve depression detection and follow-up. Sentiment Analysis refers to the use of natural language processing and text mining methodologies aiming to identify opinion or sentiment. Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects. Sentiment Analysis and Affective Computing methodologies could provide effective tools and systems for an objective assessment and monitoring of psychological disorders and, in particular, of depression. In this paper, the application of sentiment analysis and affective computing methodologies to depression detection and monitoring are presented and discussed. Moreover, a preliminary design of an integrated multimodal system for depression monitoring, that includes sentiment analysis and affective computing techniques, is proposed. Specifically, the paper outlines the main issues and challenges relative to the design of such a system.


title: Wideband planar monopole antenna for LTE, GSM, Bluetooth, WiMAX, DCS, PCS, and GPS mobile terminals

abstract: A simple planar antenna of high gain for long term evolution (LTE), global system for mobile communications (GSM), Bluetooth, worldwide interoperability for microwave access (WiMAX), digital cellular services (DCS), personal communication services (PCS), wireless local area network (WLAN), and global positioning system (GPS) mobile terminals is presented. The antenna has wide −6 dB reflection coefficient bandwidth of 2.17 GHz (1.33 − 3.5 GHz). The antenna has a compact size of 44.9 mm ×35.5 mm × 0.87 mm placed one side of the substrate of dimension 35.5 mm × 75.5 mm. The antenna has high gain of 3.85 dBi at 2.7 GHz, maximum directivity is 5.04 dBi at 2.7 GHz and total efficiency of the antenna varies between 61% and 95% within the wide impedance bandwidth. In addition, the antenna gain and efficiency is almost constant in individual operating band of operation. Dimensions of the antenna are optimized for wide impedance band. Antenna radiation patterns are presented at 1.5, 2, 2.5, and 3 GHz.


title: Tracing Function Dependencies across Clouds

abstract: In this paper, we present Lowgo, a crosscloud tracing tool for capturing causal relationships in serverless applications. To do so, Lowgo records dependencies between functions, through cloud services, and across regions to facilitate debugging and reasoning about highly concurrent, multi-cloud applications. We empirically evaluate Lowgo using microbenchmarks and multi-function and multi-cloud applications. We find that Lowgo is able to capture causal dependencies with overhead that ranges from 2-12%, which is less than half that of the best-performing, cloud-specific approach.


title: The burke dysphagia screening test: validation of its use in patients with stroke

abstract: Abstract The objective of this study was to validate a dysphagia screening test to identify patients in the rehabilitation phase post stroke at risk for pneumonia, recurrent upper airway obstruction, and death. The setting was an inpatient stroke rehabilitation unit. One hundred thirty-nine consecutive patients met the following criteria: stroke confirmed by clinical history and neurological exam with compatible computed tomography (CT) or magnetic resonance imaging (MRI) scan; ages 20 to 90 years inclusive; and no known history of significant oral or pharyngeal anomaly. The main outcome measures were pneumonia, recurrent upper airway obstruction, and death. The Burke Dysphagia Screening Test (BDST) identified 11 of 12 patients who subsequently developed pneumonia, recurrent upper airway obstruction, or death (Fisher's exact test: p = .03). The relative risk for the occurrence of any of these complications was 7.65 times greater for those failing versus passing the BDST. The BDST identified 9 of 9 patients who developed pneumonia (Fisher's exact test: p = .01). We concluded that the BDST is of value in identifying patients in the rehabilitation phase poststroke at risk for pneumonia, recurrent upper airway obstruction, and death.


title: Portfolio theory of information retrieval

abstract: This paper studies document ranking under uncertainty. It is tackled in a general situation where the relevance predictions of individual documents have uncertainty, and are dependent between each other. Inspired by the Modern Portfolio Theory, an economic theory dealing with investment in financial markets, we argue that ranking under uncertainty is not just about picking individual relevant documents, but about choosing the right combination of relevant documents. This motivates us to quantify a ranked list of documents on the basis of its expected overall relevance (mean) and its variance; the latter serves as a measure of risk, which was rarely studied for document ranking in the past. Through the analysis of the mean and variance, we show that an optimal rank order is the one that balancing the overall relevance (mean) of the ranked list against its risk level (variance). Based on this principle, we then derive an efficient document ranking algorithm. It generalizes the well-known probability ranking principle (PRP) by considering both the uncertainty of relevance predictions and correlations between retrieved documents. Moreover, the benefit of diversification is mathematically quantified; we show that diversifying documents is an effective way to reduce the risk of document ranking. Experimental results in text retrieval confirm performance.


title: It's who you know: graph mining using recursive structural features

abstract: Given a graph, how can we extract good features for the nodes? For example, given two large graphs from the same domain, how can we use information in one to do classification in the other (i.e., perform across-network classification or transfer learning on graphs)? Also, if one of the graphs is anonymized, how can we use information in one to de-anonymize the other? The key step in all such graph mining tasks is to find effective node features. We propose ReFeX (Recursive Feature eXtraction), a novel algorithm, that recursively combines local (node-based) features with neighborhood (egonet-based) features; and outputs regional features -- capturing "behavioral" information. We demonstrate how these powerful regional features can be used in within-network and across-network classification and de-anonymization tasks -- without relying on homophily, or the availability of class labels. The contributions of our work are as follows: (a) ReFeX is scalable and (b) it is effective, capturing regional ("behavioral") information in large graphs. We report experiments on real graphs from various domains with over 1M edges, where ReFeX outperforms its competitors on typical graph mining tasks like network classification and de-anonymization.


title: Smooth sensitivity and sampling in private data analysis

abstract: We introduce a new, generic framework for private data analysis.The goal of private data analysis is to release aggregate information about a data set while protecting the privacy of the individuals whose information the data set contains.Our framework allows one to release functions f of the data withinstance-based additive noise. That is, the noise magnitude is determined not only by the function we want to release, but also bythe database itself. One of the challenges is to ensure that the noise magnitude does not leak information about the database. To address that, we calibrate the noise magnitude to the smoothsensitivity of f on the database x --- a measure of variabilityof f in the neighborhood of the instance x. The new frameworkgreatly expands the applicability of output perturbation, a technique for protecting individuals' privacy by adding a smallamount of random noise to the released statistics. To our knowledge, this is the first formal analysis of the effect of instance-basednoise in the context of data privacy. Our framework raises many interesting algorithmic questions. Namely,to apply the framework one must compute or approximate the smoothsensitivity of f on x. We show how to do this efficiently for several different functions, including the median and the cost ofthe minimum spanning tree. We also give a generic procedure based on sampling that allows one to release f(x) accurately on manydatabases x. This procedure is applicable even when no efficient algorithm for approximating smooth sensitivity of f is known orwhen f is given as a black box. We illustrate the procedure by applying it to k-SED (k-means) clustering and learning mixtures of Gaussians.


title: On the Design of a Recumbent Bicycle With a Perspective on Handling Qualities

abstract: A novel approach to bicycle design for handling qualities is presented. The design method is introduced through a case study in which a new front-wheel drive recumbent bicycle is developed. Since there exists no proper definition nor assessment for bicycle handling qualities, design process is based on comparing the uncontrolled dynamics of the new concepts to an existing design, known to handle well. A prototype was built and road test were conducted to compare the handling before being taken into production. The new design shows comparable handling.Copyright © 2012 by ASME


title: Nearest Neighbor Classification for High-Speed Big Data Streams Using Spark

abstract: Mining massive and high-speed data streams among the main contemporary challenges in machine learning. This calls for methods displaying a high computational efficacy, with ability to continuously update their structure and handle ever-arriving big number of instances. In this paper, we present a new incremental and distributed classifier based on the popular nearest neighbor algorithm, adapted to such a demanding scenario. This method, implemented in Apache Spark, includes a distributed metric-space ordering to perform faster searches. Additionally, we propose an efficient incremental instance selection method for massive data streams that continuously update and remove outdated examples from the case-base. This alleviates the high computational requirements of the original classifier, thus making it suitable for the considered problem. Experimental study conducted on a set of real-life massive data streams proves the usefulness of the proposed solution and shows that we are able to provide the first efficient nearest neighbor solution for high-speed big and streaming data.


title: Progenitor Cell Homing in the Postischemic Myocardium: Just an Unmotivated Pitstop in the Microcirculation?

abstract: OBJECTIVE ::: We developed a model for direct assessment of BMC sequestration in the postischemic murine myocardium after direct antegrade intracoronary injection. ::: ::: ::: METHODS ::: Modified syngeneic heterotopic heart transplantation was used as a basic model for global myocardial I/R injury in a total of n = 29 animals. IVM was employed to analyze the right ventricular subepicardial coronary microcirculation and for tracking fluorescently labeled BMCs. ::: ::: ::: RESULTS ::: IVM allowed monitoring all segments of the coronary microcirculation including feeding arterioles, nutritive capillaries, and postcapillary venules. WI and generalized atherosclerosis induced profound reperfusion failure, particularly in nutritive myocardial capillaries. BMCs were found to exclusively sequester in myocardial capillaries, but not in coronary arterioles or postcapillary venules. The sequestration of BMCs in coronary capillaries occurred independent of WI, generalized atherosclerosis, or adhesion molecule function. ::: ::: ::: CONCLUSIONS ::: This is the first study allowing direct assessment of BMC homing to the postischemic myocardium. Heterotopic heart transplantation and IVM are proper means to study the myocardial sequestration of BMCs after direct antegrade intracoronary injection in vivo. We show for the first time that intracoronarily injected BMCs sequester exclusively in nutritive myocardial capillaries.


title: Thanatophoric dysplasia: case report of an autopsy complemented by postmortem computed tomographic study

abstract: Thanatophoric dysplasia (TD) is one of the most common lethal skeletal dysplasias, which was first designated as thanatophoric dwarfism and described in 1967. The authors report a case of a Caucasian girl with TD, born to a 31-year-old woman without comorbidities. The newborn presented respiratory distress immediately after delivery, progressing to death in less than 2 hours. An autopsy was carried out after postmortem tomographic examination. The autopsy findings depicted extensive malformations of the skeletal system and the brain. The aim of this report is to discuss the pathogenesis and correlate the morphologic features of TD that were disclosed at the tomography and the autopsy.


A Neural Network Approach for Knowledge-Driven Response Generation


title: A Performance Analysis of DES and RSA Cryptography

abstract: Security is playing a vital role in the field of communication system and Internet. Data encryption standard (DES) and the Rivest-Shamir-Adleman (RSA) algorithms are the two popular encryption algorithms that vouch confidentiality and authenticity over an insecure communication network and Internet. There has been paltry cryptanalytic progress against these two algorithms since their advent. This paper presents the comparison between the DES private key based Algorithm and RSA public key based algorithm. The main feature that specifies and differentiate one algorithm from another are the ability to the speed of encryption and decryption of the input plain text. It also includes several computational issues as well as the analysis of DES algorithm and RSA algorithm like the encryption throughput and decryption throughput. The recipe of finding the encryption throughput and decryption throughput is discovered.