title: 43 Visual Data-Mining Techniques*

abstract: Never before in history has data been generated at such high volumes as it is today. Exploring and analyzing the vast volumes of data has become increasingly dicult. Information visualization and visual data mining can help to deal with the flood of information. The advan- tage of visual data exploration is that the user is directly involved in the data mining process. There are a large number of information visualiza- tion techniques that have been developed over the last two decades to support the exploration of large data sets. In this paper, we propose a classification of information visualization and visual data mining tech- niques based on the data type to be visualized, the visualization technique, and the interaction technique. We illustrate the classification using a few examples, and indicate some directions for future work.


title: Preliminary experimental study on the sensing performance of a capacitive tactile sensor array mounted on curved surfaces

abstract: In real applications, the capacitive tactile sensor array usually needs to be mounted on curved surfaces. To study the effect of curved surfaces on the sensing performance of the sensor array, we have fabricated the sensor array with 4 × 4 sensor units. Four types of rigid and flexible curved surfaces are selected to mount the sensor array, with radius of curvature of +∞, 40 mm, 30 mm, and 20 mm. The initial capacitances of the sensor array increase as the radius of the surfaces decrease. By applying normal and shear forces to the sensor unit mounted on the curved surfaces, we have found that as the radius of the surface decrease, the capacitance increases faster when the sensor is mounted on 40 mm curved surfaces, and increases slower on 30 mm and 20 mm curved surfaces. The shear forces in the x- and y-axes cause symmetric and asymmetric capacitance changes to the sensor unit, respectively. The flexible curved surfaces can induce larger capacitance changes to the sensor unit, compared with the rigid ones.


ATtention Spanned: Comprehensive Vulnerability Analysis of {AT} Commands Within the Android Ecosystem


title: Deep Convolutional Neural Networks and Learning ECG Features for Screening Paroxysmal Atrial Fibrillation Patients

abstract: In this paper, a novel computationally intelligent-based electrocardiogram (ECG) signal classification methodology using a deep learning (DL) machine is developed. The focus is on patient screening and identifying patients with paroxysmal atrial fibrillation (PAF), which represents a life threatening cardiac arrhythmia. The proposed approach operates with a large volume of raw ECG time-series data as inputs to a deep convolutional neural networks (CNN). It autonomously learns representative and key features of the PAF to be used by a classification module. The features are therefore learned directly from the large time domain ECG signals by using a CNN with one fully connected layer. The learned features can effectively replace the traditional ad hoc and time-consuming user’s hand-crafted features. Our experimental results verify and validate the effectiveness and capabilities of the learned features for PAF patient screening. The main advantages of our proposed approach are to simplify the feature extraction process corresponding to different cardiac arrhythmias and to remove the need for using a human expert to define appropriate and critical features working with a large time-series data set. The extensive simulations and case studies conducted indicate that combining the learned features with other classifiers will significantly improve the performance of the patient screening system as compared to an end-to-end CNN classifier. The effectiveness and capabilities of our proposed ECG DL classification machine is demonstrated and quantitative comparisons with several conventional machine learning classifiers are also provided.


title: A novel P300-based brain–computer interface stimulus presentation paradigm: Moving beyond rows and columns

abstract: Abstract Objective An electroencephalographic brain–computer interface (BCI) can provide a non-muscular means of communication for people with amyotrophic lateral sclerosis (ALS) or other neuromuscular disorders. We present a novel P300-based BCI stimulus presentation – the checkerboard paradigm (CBP). CBP performance is compared to that of the standard row/column paradigm (RCP) introduced by Farwell and Donchin (1988) . Methods Using an 8 × 9 matrix of alphanumeric characters and keyboard commands, 18 participants used the CBP and RCP in counter-balanced fashion. With approximately 9–12 min of calibration data, we used a stepwise linear discriminant analysis for online classification of subsequent data. Results Mean online accuracy was significantly higher for the CBP, 92%, than for the RCP, 77%. Correcting for extra selections due to errors, mean bit rate was also significantly higher for the CBP, 23 bits/min, than for the RCP, 17 bits/min. Moreover, the two paradigms produced significantly different waveforms. Initial tests with three advanced ALS participants produced similar results. Furthermore, these individuals preferred the CBP to the RCP. Conclusions These results suggest that the CBP is markedly superior to the RCP in performance and user acceptability. Significance The CBP has the potential to provide a substantially more effective BCI than the RCP. This is especially important for people with severe neuromuscular disabilities.


title: Optimal coordination of overcurrent and distance relays with hybrid genetic algorithm

abstract: To achieve better protection, it is common to use both distance and overcurrent relays as main and backup relays respectively, in power transmission protection scheme. In this situation it is necessary to coordinate these two types of relays simultaneously that makes the problem harder to find a global optimal point. To overcome the complexity, it is a good idea to use an optimization algorithm like (GA) to perform coordination between distance and overcurrent relays. In this paper, a hybrid algorithm based on GA and linear programming (LP) is proposed to obtain the relay settings which are Time Multiplier Setting (TMS) and I set for overcurrent relays and Second Zone Time (T z2 ) for distance relays. By this assumption, new variables are added to the problem and size of search space will become bigger, thus finding the global optimal point will be harder. It is shown that the proposed hybrid GA can find a better solution in comparing to the conventional methods.


title: Group-Lasso on Splines for Spectrum Cartography

abstract: The unceasing demand for continuous situational awareness calls for innovative and large-scale signal processing algorithms, complemented by collaborative and adaptive sensing platforms to accomplish the objectives of layered sensing and control. Towards this goal, the present paper develops a spline-based approach to field estimation, which relies on a basis expansion model of the field of interest. The model entails known bases, weighted by generic functions estimated from the field's noisy samples. A novel field estimator is developed based on a regularized variational least-squares (LS) criterion that yields finite-dimensional (function) estimates spanned by thin-plate splines. Robustness considerations motivate well the adoption of an overcomplete set of (possibly overlapping) basis functions, while a sparsifying regularizer augmenting the LS cost endows the estimator with the ability to select a few of these bases that “better” explain the data. This parsimonious field representation becomes possible, because the sparsity-aware spline-based method of this paper induces a group-Lasso estimator for the coefficients of the thin-plate spline expansions per basis. A distributed algorithm is also developed to obtain the group-Lasso estimator using a network of wireless sensors, or, using multiple processors to balance the load of a single computational unit. The novel spline-based approach is motivated by a spectrum cartography application, in which a set of sensing cognitive radios collaborate to estimate the distribution of RF power in space and frequency. Computer simulations and tests on real data corroborate that the estimated power spectrum density atlas yields the desired RF state awareness, since the maps reveal spatial locations where idle frequency bands can be reused for transmission, even when fading and shadowing effects are pronounced.


title: Workload-aware database monitoring and consolidation

abstract: In most enterprises, databases are deployed on dedicated database servers. Often, these servers are underutilized much of the time. For example, in traces from almost 200 production servers from different organizations, we see an average CPU utilization of less than 4%. This unused capacity can be potentially harnessed to consolidate multiple databases on fewer machines, reducing hardware and operational costs. Virtual machine (VM) technology is one popular way to approach this problem. However, as we demonstrate in this paper, VMs fail to adequately support database consolidation, because databases place a unique and challenging set of demands on hardware resources, which are not well-suited to the assumptions made by VM-based consolidation. Instead, our system for database consolidation, named Kairos, uses novel techniques to measure the hardware requirements of database workloads, as well as models to predict the combined resource utilization of those workloads. We formalize the consolidation problem as a non-linear optimization program, aiming to minimize the number of servers and balance load, while achieving near-zero performance degradation. We compare Kairos against virtual machines, showing up to a factor of 12× higher throughput on a TPC-C-like benchmark. We also tested the effectiveness of our approach on real-world data collected from production servers at Wikia.com, Wikipedia, Second Life, and MIT CSAIL, showing absolute consolidation ratios ranging between 5.5:1 and 17:1.


title: Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks

abstract: Automated melanoma recognition in dermoscopy images is a very challenging task due to the low contrast of skin lesions, the huge intraclass variation of melanomas, the high degree of visual similarity between melanoma and non-melanoma lesions, and the existence of many artifacts in the image. In order to meet these challenges, we propose a novel method for melanoma recognition by leveraging very deep convolutional neural networks (CNNs). Compared with existing methods employing either low-level hand-crafted features or CNNs with shallower architectures, our substantially deeper networks (more than 50 layers) can acquire richer and more discriminative features for more accurate recognition. To take full advantage of very deep networks, we propose a set of schemes to ensure effective training and learning under limited training data. First, we apply the residual learning to cope with the degradation and overfitting problems when a network goes deeper. This technique can ensure that our networks benefit from the performance gains achieved by increasing network depth. Then, we construct a fully convolutional residual network (FCRN) for accurate skin lesion segmentation, and further enhance its capability by incorporating a multi-scale contextual information integration scheme. Finally, we seamlessly integrate the proposed FCRN (for segmentation) and other very deep residual networks (for classification) to form a two-stage framework. This framework enables the classification network to extract more representative and specific features based on segmented results instead of the whole dermoscopy images, further alleviating the insufficiency of training data. The proposed framework is extensively evaluated on ISBI 2016 Skin Lesion Analysis Towards Melanoma Detection Challenge dataset. Experimental results demonstrate the significant performance gains of the proposed framework, ranking the first in classification and the second in segmentation among 25 teams and 28 teams, respectively. This study corroborates that very deep CNNs with effective training mechanisms can be employed to solve complicated medical image analysis tasks, even with limited training data.


title: Hunting the Ethereum Smart Contract: Color-inspired Inspection of Potential Attacks

abstract: Blockchain and Cryptocurrencies are gaining unprecedented popularity and understanding. Meanwhile, Ethereum is gaining a significant popularity in the blockchain community, mainly due to the fact that it is designed in a way that enables developers to write smart contract and decentralized applications (Dapps). This new paradigm of applications opens the door to many possibilities and opportunities. However, the security of Ethereum smart contracts has not received much attention; several Ethereum smart contracts malfunctioning have recently been reported. Unlike many previous works that have applied static and dynamic analyses to find bugs in smart contracts, we do not attempt to define and extract any features; instead we focus on reducing the expert's labor costs. We first present a new in-depth analysis of potential attacks methodology and then translate the bytecode of solidity into RGB color code. After that, we transform them to a fixed-sized encoded image. Finally, the encoded image is fed to convolutional neural network (CNN) for automatic feature extraction and learning, detecting compiler bugs of Ethereum smart contract.


title: Generating Synthetic but Plausible Healthcare Record Datasets

abstract: Generating datasets that"look like"given real ones is an interesting tasks for healthcare applications of ML and many other fields of science and engineering. In this paper we propose a new method of general application to binary datasets based on a method for learning the parameters of a latent variable moment that we have previously used for clustering patient datasets. We compare our method with a recent proposal (MedGan) based on generative adversarial methods and find that the synthetic datasets we generate are globally more realistic in at least two senses: real and synthetic instances are harder to tell apart by Random Forests, and the MMD statistic. The most likely explanation is that our method does not suffer from the"mode collapse"which is an admitted problem of GANs. Additionally, the generative models we generate are easy to interpret, unlike the rather obscure GANs. Our experiments are performed on two patient datasets containing ICD-9 diagnostic codes: the publicly available MIMIC-III dataset and a dataset containing admissions for congestive heart failure during 7 years at Hospital de Sant Pau in Barcelona.


title: Neonatal Pain Expression Recognition Using Transfer Learning

abstract: Transfer learning using pre-trained Convolutional Neural Networks (CNNs) has been successfully applied to images for different classification tasks. In this paper, we propose a new pipeline for pain expression recognition in neonates using transfer learning. Specifically, we propose to exploit a pre-trained CNN that was originally trained on a relatively similar dataset for face recognition (VGG Face) as well as CNNs that were pre-trained on a relatively different dataset for image classification (iVGG F,M, and S) to extract deep features from neonates' faces. In the final stage, several supervised machine learning classifiers are trained to classify neonates' facial expression into pain or no pain expression. The proposed pipeline achieved, on a testing dataset, 0.841 AUC and 90.34 accuracy, which is approx. 7 higher than the accuracy of handcrafted traditional features. We also propose to combine deep features with traditional features and hypothesize that the mixed features would improve pain classification performance. Combining deep features with traditional features achieved 92.71 accuracy and 0.948 AUC. These results show that transfer learning, which is a faster and more practical option than training CNN from the scratch, can be used to extract useful features for pain expression recognition in neonates. It also shows that combining deep features with traditional handcrafted features is a good practice to improve the performance of pain expression recognition and possibly the performance of similar applications.


title: Low cost and efficient ECG measurement system using PIC18F4550 microcontroller

abstract: This paper demonstrates a simple, efficient and low cost ECG and heart beat measurement system using PIC18F4550 microcontroller. The design includes LT1028 OP AMP IC based signal conditioning unit. Detected ECG signal inside microcontroller is send to a Lab VIEW based GUI platform with USB CDC protocol and also tested with RS-232 protocol. Signal plotting and heart beat calculation is done inside LabVIEW program. ECG signal can also be viewed from PWM port of PIC18F4550 microcontroller via a low pass filtering stage. The whole design is simulated successfully with Proteus circuit simulator. The ECG signal is extracted in audio format (.WAV) from LabVIEW biomedicai toolkit for ECG simulator keeping 1.2 mV upper voltage limit and −0.4 mV lower voltage limit. The audio file is given as the input to the circuit in Proteus. USB simulation is done with the help of Proteus Virtual USB and Microchip USB CDC driver. RS-232 virtual serial port is created using Eltima virtual serial port software. MPLAB IDE is used as microcontroller program compiler.


title: Real-Time Myoprocessors for a Neural Controlled Powered Exoskeleton Arm

abstract: Exoskeleton robots are promising assistive/rehabilitative devices that can help people with force deficits or allow the recovery of patients who have suffered from pathologies such as stroke. The key component that allows the user to control the exoskeleton is the human machine interface (HMI). Setting the HMI at the neuro-muscular level may lead to seamless integration and intuitive control of the exoskeleton arm as a natural extension of the human body. At the core of the exoskeleton HMI there is a model of the human muscle, the "myoprocessor," running in real-time and in parallel to the physiological muscle, that predicts joint torques as a function of the joint kinematics and neural activation levels. This paper presents the development of myoprocessors for the upper limb based on the Hill phenomenological muscle model. Genetic algorithms are used to optimize the internal parameters of the myoprocessors utilizing an experimental database that provides inputs to the model and allows for performance assessment. The results indicate high correlation between joint moment predictions of the model and the measured data. Consequently, the myoprocessor seems an adequate model, sufficiently robust for further integration into the exoskeleton control system


title: Shape Synthesis from Sketches via Procedural Models and Convolutional Networks

abstract: Procedural modeling techniques can produce high quality visual content through complex rule sets. However, controlling the outputs of these techniques for design purposes is often notoriously difficult for users due to the large number of parameters involved in these rule sets and also their non-linear relationship to the resulting content. To circumvent this problem, we present a sketch-based approach to procedural modeling. Given an approximate and abstract hand-drawn 2D sketch provided by a user, our algorithm automatically computes a set of procedural model parameters, which in turn yield multiple, detailed output shapes that resemble the user's input sketch. The user can then select an output shape, or further modify the sketch to explore alternative ones. At the heart of our approach is a deep Convolutional Neural Network (CNN) that is trained to map sketches to procedural model parameters. The network is trained by large amounts of automatically generated synthetic line drawings. By using an intuitive medium, i.e., freehand sketching as input, users are set free from manually adjusting procedural model parameters, yet they are still able to create high quality content. We demonstrate the accuracy and efficacy of our method in a variety of procedural modeling scenarios including design of man-made and organic shapes.


MODIFIED ANT COLONY OPTIMIZATION ALGORITHM FOR BRAIN MRI IMAGE SEGMENTATION AND BRAIN TUMOR DIAGNOSIS


title: A Process Mining Based Model for Customer Journey Mapping

abstract: Customer journey maps (CJMs) are used to understand customers’ behavior, and ultimately to better serve them. This new approach is used in numerous disciplines for different purposes. As a response, several software applications have emerged. Although they provide interfaces to understand CJMs, they lack measures to assist in decision making. We contribute by proposing a CJM model. We show its potential by using it with process mining, a data analytics technique that we leverage to assess the impact of the journey’s duration on the customer experience. The model brings data scientists and customer journey planners closer together, the first step in gaining a better understanding of customer behavior. This study also highlights the prospective value of process mining for CJM analysis.


title: Naming the Pain in Requirements Engineering: Contemporary Problems, Causes, and Effects in Practice

abstract: Requirements Engineering (RE) has received much attention in research and practice due to its importance to software project success. Its interdisciplinary nature, the dependency to the customer, and its inherent uncertainty still render the discipline difficult to investigate. This results in a lack of empirical data. These are necessary, however, to demonstrate which practically relevant RE problems exist and to what extent they matter. Motivated by this situation, we initiated the Naming the Pain in Requirements Engineering (NaPiRE) initiative which constitutes a globally distributed, bi-yearly replicated family of surveys on the status quo and problems in practical RE. In this article, we report on the qualitative analysis of data obtained from 228 companies working in 10 countries in various domains and we reveal which contemporary problems practitioners encounter. To this end, we analyse 21 problems derived from the literature with respect to their relevance and criticality in dependency to their context, and we complement this picture with a cause-effect analysis showing the causes and effects surrounding the most critical problems. Our results give us a better understanding of which problems exist and how they manifest themselves in practical environments. Thus, we provide a first step to ground contributions to RE on empirical observations which, until now, were dominated by conventional wisdom only.


The development of pre-service teachers ' TPACK in the use of digital tools


title: Model-Driven Software Engineering in Practice

abstract: This book discusses how model-based approaches can improve the daily practice of software professionals. This is known as Model-Driven Software Engineering (MDSE) or, simply, Model-Driven Engineering (MDE). MDSE practices have proved to increase efficiency and effectiveness in software development, as demonstrated by various quantitative and qualitative studies. MDSE adoption in the software industry is foreseen to grow exponentially in the near future, e.g., due to the convergence of software development and business analysis. The aim of this book is to provide you with an agile and flexible tool to introduce you to the MDSE world, thus allowing you to quickly understand its basic principles and techniques and to choose the right set of MDSE instruments for your needs so that you can start to benefit from MDSE right away. The book is organized into two main parts. The first part discusses the foundations of MDSE in terms of basic concepts (i.e., models and transformations), driving principles, application scenarios and current standards, like the well-known MDA initiative proposed by OMG (Object Management Group) as well as the practices on how to integrate MDSE in existing development processes. The second part deals with the technical aspects of MDSE, spanning from the basics on when and how to build a domain-specific modeling language, to the description of Model-to-Text and Model-to-Model transformations, and the tools that support the management of MDSE projects. The book is targeted to a diverse set of readers, spanning: professionals, CTOs, CIOs, and team managers that need to have a bird's eye vision on the matter, so as to take the appropriate decisions when it comes to choosing the best development techniques for their company or team; software analysts, developers, or designers that expect to use MDSE for improving everyday work productivity, either by applying the basic modeling techniques and notations or by defining new domain-specific modeling languages and applying end-to-end MDSE practices in the software factory; and academic teachers and students to address undergrad and postgrad courses on MDSE. In addition to the contents of the book, more resources are provided on the book's website http://www.mdse-book.com/, including the examples presented in the book. Table of Contents: Introduction / MDSE Principles / MDSE Use Cases / Model-Driven Architecture (MDA) / Integration of MDSE in your Development Process / Modeling Languages at a Glance / Developing your Own Modeling Language / Model-to-Model Transformations / Model-to-Text Transformations / Managing Models / Summary


title: WaveNet: A Generative Model for Raw Audio

abstract: This paper introduces WaveNet, a deep neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with the predictive distribution for each audio sample conditioned on all previous ones; nonetheless we show that it can be efficiently trained on data with tens of thousands of samples per second of audio. When applied to text-to-speech, it yields state-of-the-art performance, with human listeners rating it as significantly more natural sounding than the best parametric and concatenative systems for both English and Mandarin. A single WaveNet can capture the characteristics of many different speakers with equal fidelity, and can switch between them by conditioning on the speaker identity. When trained to model music, we find that it generates novel and often highly realistic musical fragments. We also show that it can be employed as a discriminative model, returning promising results for phoneme recognition.


title: Matching 3D models with shape distributions

abstract: Measuring the similarity between 3D shapes is a fundamental problem, with applications in computer vision, molecular biology, computer graphics, and a variety of other fields. A challenging aspect of this problem is to find a suitable shape signature that can be constructed and compared quickly, while still discriminating between similar and dissimilar shapes. In this paper, we propose and analyze a method for computing shape signatures for arbitrary (possibly degenerate) 3D polygonal models. The key idea is to represent the signature of an object as a shape distribution sampled from a shape function measuring the global geometric properties of an object. The primary motivation for this approach is to reduce the shape matching problem to the comparison of probability distributions, which is simpler than traditional shape matching methods that require pose registration, feature correspondence or model fitting. We find that the dissimilarities between sampled distributions of simple shape functions (e.g. the distance between two random points on a surface) provide a robust method for discriminating between classes of objects (e.g. cars versus airplanes) in a moderately sized database, despite the presence of arbitrary translations, rotations, scales, reflections, tessellations, simplifications and model degeneracies. They can be evaluated quickly, and thus the proposed method could be applied as a pre-classifier in an object recognition system or in an interactive content-based retrieval application.


title: Restenosis After Carotid Endarterectomy Performed With Routine Intraoperative Duplex Ultrasonography and Arterial Patch Closure: A Contemporary Series

abstract: The restenosis rates of 5% to 15% have been reported after carotid endarterectomy (CEA). We undertook this investigation to determine whether the routine practice of carotid artery patch closure and intraoperative completion duplex ultrasonography would result in lower rates of carotid restenosis after CEA. All consecutive carotid endarterectomies performed between 2000 and 2004 at a single institution were reviewed retrospectively. Patients underwent CEA using a longitudinal arteriotomy, followed by routine patching and intraoperative completion duplex ultrasonography. Only patients with at least one postoperative duplex scan performed at a minimum of 180 days after CEA were included. During the 5-year study period, 407 consecutive carotid endarterectomies were performed, with a combined 30-day stroke and mortality rate of 2.5%; 217 patients (53%) had one or more duplex ultrasound examinations performed at least 180 days after CEA. The mean follow-up duration was 692 days. Of the patients who underwent intraoperative intervention based on the results of the completion duplex study, none experienced restenosis, stroke, or death. CEA that is performed with routine patching and intraoperative duplex completion ultrasonography is a safe, durable operation with restenosis rates below those commonly reported.


title: Functional brain architecture is associated with the rate of tau accumulation in Alzheimer’s disease

abstract: In Alzheimer’s diseases (AD), tau pathology is strongly associated with cognitive decline. Preclinical evidence suggests that tau spreads across connected neurons in an activity-dependent manner. Supporting this, cross-sectional AD studies show that tau deposition patterns resemble functional brain networks. However, whether higher functional connectivity is associated with higher rates of tau accumulation is unclear. Here, we combine resting-state fMRI with longitudinal tau-PET in two independent samples including 53 (ADNI) and 41 (BioFINDER) amyloid-biomarker defined AD subjects and 28 (ADNI) vs. 16 (BioFINDER) amyloid-negative healthy controls. In both samples, AD subjects show faster tau accumulation than controls. Second, in AD, higher fMRI-assessed connectivity between 400 regions of interest (ROIs) is associated with correlated tau-PET accumulation in corresponding ROIs. Third, we show that a model including baseline connectivity and tau-PET is associated with future tau-PET accumulation. Together, connectivity is associated with tau spread in AD, supporting the view of transneuronal tau propagation. Tau accumulation is associated with disease progression in Alzheimer’s disease. Here the authors use resting state fMRI and tau-PET to demonstrate that baseline connectivity in Alzheimer's disease is associated with tau spreading.


title: Exploring the Role of Prior Beliefs for Argument Persuasion

abstract: Public debate forums provide a common platform for exchanging opinions on a topic of interest. While recent studies in natural language processing (NLP) have provided empirical evidence that the language of the debaters and their patterns of interaction play a key role in changing the mind of a reader, research in psychology has shown that prior beliefs can affect our interpretation of an argument and could therefore constitute a competing alternative explanation for resistance to changing one's stance. To study the actual effect of language use vs. prior beliefs on persuasion, we provide a new dataset and propose a controlled setting that takes into consideration two reader level factors: political and religious ideology. We find that prior beliefs affected by these reader level factors play a more important role than language use effects and argue that it is important to account for them in NLP studies of persuasion.


Intra-arterial thrombolysis in acute ischemic stroke.


title: Container-as-a-Service at the Edge: Trade-off between Energy Efficiency and Service Availability at Fog Nano Data Centers

abstract: In the last few years, we have witnessed the huge popularity of one of the most promising technologies of the modern era: the Internet of Things. In IoT, various smart objects (smart sensors, embedded devices, PDAs, and smartphones) share their data with one another irrespective of their geographical locations using the Internet. The amount of data generated by these connected smart objects will be on the order of zettabytes in the coming years. This huge amount of data creates challenges with respect to storage and analytics given the resource constraints of these smart devices. Additionally, to process the large volume of information generated, the traditional cloud-based infrastructure may lead to long response time and higher bandwidth consumption. To cope up with these challenges, a new powerful technology, edge computing, promises to support data processing and service availability to end users at the edge of the network. However, the integration of IoT and edge computing is still in its infancy. Task scheduling will play a pivotal role in this integrated architecture. To handle all the above mentioned issues, we present a novel architecture for task selection and scheduling at the edge of the network using container-as-a-service (CoaaS). We solve the problem of task selection and scheduling by using cooperative game theory. For this purpose, we developed a multi-objective function in order to reduce the energy consumption and makespan by considering different constraints such as memory, CPU, and the user's budget. We also present a real-time internal and external container migration technique for minimizing the energy consumption. For task selection and scheduling, we have used lightweight containers instead of the conventional virtual machines to reduce the overhead and response time as well as the overall energy consumption of fog devices, that is, nano data centers (nDCs). Our empirical results demonstrate that the proposed scheme reduces the energy consumption and the average number of SLA violations by 21.75 and 11.82 percent, respectively.


title: Temporal video segmentation: A survey

abstract: Temporal video segmentation is the first step towards automatic annotation of digital video for browsing and retrieval. This article gives an overview of existing techniques for video segmentation that operate on both uncompressed and compressed video stream. The performance, relative merits and limitations of each of the approaches are comprehensively discussed and contrasted. The gradual development of the techniques and how the uncompressed domain methods were tailored and applied into compressed domain are considered. In addition to the algorithms for shot boundaries detection, the related topic of camera operation recognition is also reviewed.


title: Calcific discitis in an adult patient with intravertebral migration and spontaneous remission

abstract: Symptomatic disc calcifications have been reported, especially in the pediatric population, and remain of unknown etiology. Such a condition has been very rarely reported in adults. The aim of this paper is to present a case report of calcific discitis in an adult patient with intravertebral migration and spontaneous calcification resorption. The clinical presentation was that of back pain with an abrupt onset, not related to trauma or to physical activity. No fever or neurological deficits were present. Blood count, erythrocyte sedimentation rate, routine urine, and urine culture were negative. The pain regressed in 20 days with analgesic therapy. Findings of thoracic calcific discitis are illustrated with X-rays, CT, MRI, and bone scintigraphy.


title: Using Artificial Bee Colony Algorithm for MLP Training on Earthquake Time Series Data Prediction

abstract: Nowadays, computer scientists have shown the interest in the study of social insect’s behaviour in neural networks area forsolving different combinatorial and statistical problems. Chief among these is the Artificial Bee Colony (ABC) algorithm. This paper investigates the use of ABC algorithm that simulates the intelligent foraging behaviour of a honey bee swarm. Multilayer Perceptron(MLP) trained with the standard back propagation algorithm normally utilises computationally intensive training algorithms. One of thecrucial problems with the backpropagation (BP) algorithm is that it can sometimes yield the networks with suboptimal weights becauseof the presence of many local optima in the solution space. To overcome ABC algorithm used in this work to train MLP learning thecomplex behaviour of earthquake time series data trained by BP, the performance of MLP-ABC is benchmarked against MLP trainingwith the standard BP. The experimental result shows that MLP-ABC performance is better than MLP-BP for time series data.


title: Autofocus Layer for Semantic Segmentation

abstract: We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing. Autofocus layers adaptively change the size of the effective receptive field based on the processed context to generate more powerful features. This is achieved by parallelising multiple convolutional layers with different dilation rates, combined by an attention mechanism that learns to focus on the optimal scales driven by context. By sharing the weights of the parallel convolutions we make the network scale-invariant, with only a modest increase in the number of parameters. The proposed autofocus layer can be easily integrated into existing networks to improve a model’s representational power. We evaluate our mod els on the challenging tasks of multi-organ segmentation in pelvic CT and brain tumor segmentation in MRI and achieve very promising performance.


title: Evaluation of Modulation-MFCC Features and DNN Classification for Acoustic Event Detection

abstract: Acoustic event detection, i.e., the task of assigning a human interpretable label to a segment of audio, has only recently attracted increased interest in the research community. Driven by the DCASE challenges and the availability of large-scale audio datasets, the state-of-the-art has progressed rapidly with deep-learning-based classi- fiers dominating the field. Because several potential use cases favor a realization on distributed sensor nodes, e.g. ambient assisted living applications, habitat monitoring or surveillance, we are concerned with two issues here. Firstly the classification performance of such systems and secondly the computing resources required to achieve a certain performance considering node level feature extraction. In this contribution we look at the balance between the two criteria by employing traditional techniques and different deep learning architectures, including convolutional and recurrent models in the context of real life everyday audio recordings in realistic, however challenging, multisource conditions.


title: Training Augmentation with Adversarial Examples for Robust Speech Recognition

abstract: This paper explores the use of adversarial examples in training speech recognition systems to increase robustness of deep neural network acoustic models. During training, the fast gradient sign method is used to generate adversarial examples augmenting the original training data. Different from conventional data augmentation based on data transformations, the examples are dynamically generated based on current acoustic model parameters. We assess the impact of adversarial data augmentation in experiments on the Aurora-4 and CHiME-4 single-channel tasks, showing improved robustness against noise and channel variation. Further improvement is obtained when combining adversarial examples with teacher/student training, leading to a 23% relative word error rate reduction on Aurora-4.


title: HMM-based Indic Handwritten Word Recognition using Zone Segmentation

abstract: This paper presents a novel approach towards Indic handwritten word recognition using zone-wise information. Because of complex nature due to compound characters, modifiers, overlapping and touching, etc., character segmentation and recognition is a tedious job in Indic scripts (e.g. Devanagari, Bangla, Gurumukhi, and other similar scripts). To avoid character segmentation in such scripts, HMM-based sequence modeling has been used earlier in holistic way. This paper proposes an efficient word recognition framework by segmenting the handwritten word images horizontally into three zones (upper, middle and lower) and then recognize the corresponding zones. The main aim of this zone segmentation approach is to reduce the number of distinct component classes compared to the total number of classes in Indic scripts. As a result, use of this zone segmentation approach enhances the recognition performance of the system. The components in middle zone, where characters are mostly touching, are recognized using HMM. After the recognition of middle zone, HMM based Viterbi forced alignment is applied to mark the left and right boundaries of the characters in the middle zone. Next, the residue components, if any, in upper and lower zones are obtained in a character boundary then the components are combined with the character to achieve the final word level recognition. Water reservoir-based properties have been integrated in this framework to improve the zone segmentation and character boundary detection defects while segmentation. A novel sliding window-based feature, called Pyramid Histogram of Oriented Gradient (PHOG) is proposed for middle zone recognition. PHOG features have been compared with other existing features and found robust for Indic script recognition. An exhaustive experiment is performed on two Indic scripts namely, Bangla and Devanagari for the performance evaluation. From the experiment, it has been noted that proposed zone-wise recognition improves accuracy with respect to the traditional way of Indic word recognition. A novel approach of Indic handwritten word recognition using zone segmentation.Efficient PHOG features developed to improve the performance of HMM based middle zone recognition.Integration of water reservoir concept for better character alignment in a word image.A detailed study of experimental results in Bangla and Devanagari scripts has been performed.The proposed framework outperforms traditional without-zone-segmentation based recognition systems.


title: Comparative Analysis of Sequence Clustering Methods for Deduplication of Biological Databases

abstract: The massive volumes of data in biological sequence databases provide a remarkable resource for large-scale biological studies. However, the underlying data quality of these resources is a critical concern. A particular challenge is duplication, in which multiple records have similar sequences, creating a high level of redundancy that impacts database storage, curation, and search. Biological database deduplication has two direct applications: for database curation, where detected duplicates are removed to improve curation efficiency, and for database search, where detected duplicate sequences may be flagged but remain available to support analysis. ::: ::: Clustering methods have been widely applied to biological sequences for database deduplication. Since an exhaustive all-by-all pairwise comparison of sequences cannot scale for a high volume of data, heuristic approaches have been recruited, such as the use of simple similarity thresholds. In this article, we present a comparison between CD-HIT and UCLUST, the two best-known clustering tools for sequence database deduplication. Our contributions include a detailed assessment of the redundancy remaining after deduplication, application of standard clustering evaluation metrics to quantify the cohesion and separation of the clusters generated by each method, and a biological case study that assesses intracluster function annotation consistency to demonstrate the impact of these factors on a practical application of the sequence clustering methods. Our results show that the trade-off between efficiency and accuracy becomes acute when low threshold values are used and when cluster sizes are large. This evaluation leads to practical recommendations for users for more effective uses of the sequence clustering tools for deduplication.


title: Optimization of the Jaccard index for image segmentation with the Lov\'asz hinge

abstract: The Jaccard loss, commonly referred to as the intersection-over-union loss, is commonly employed in the evaluation of segmentation quality due to its better perceptual quality and scale invariance, which lends appropriate relevance to small objects compared with per-pixel losses. We present a method for direct optimization of the per-image intersection-over-union loss in neural networks, in the context of semantic image segmentation, based on a convex surrogate: the Lov\'asz hinge. The loss is shown to perform better with respect to the Jaccard index measure than other losses traditionally used in the context of semantic segmentation; such as cross-entropy. We develop a specialized optimization method, based on an efficient computation of the proximal operator of the Lov\'asz hinge, yielding reliably faster and more stable optimization than alternatives. We demonstrate the effectiveness of the method by showing substantially improved intersection-overunion segmentation scores on the Pascal VOC dataset using a state-of-the-art deep learning segmentation architecture.


title: A Novel Technique for Improving Hardware Trojan Detection and Reducing Trojan Activation Time

abstract: Fabless semiconductor industry and government agencies have raised serious concerns about tampering with inserting hardware Trojans in an integrated circuit supply chain in recent years. Most of the recently proposed Trojan detection methods are based on Trojan activation to observe either a faulty output or measurable abnormality on side-channel signals. Time to activate a hardware Trojan circuit is a major concern from the authentication standpoint. This paper analyzes time to generate a transition in functional Trojans. Transition is modeled by geometric distribution and the number of clock cycles required to generate a transition is estimated. Furthermore, a dummy scan flip-flop insertion procedure is proposed aiming at decreasing transition generation time. The procedure increases transition probabilities of nets beyond a specific threshold. The relation between circuit topology, authentication time, and the threshold is carefully studied. The simulation results on s38417 benchmark circuit demonstrate that, with a negligible area overhead, our proposed method can significantly increase Trojan activity and reduce Trojan activation time.


title: Chronic, multisite, multielectrode recordings in macaque monkeys

abstract: A paradigm is described for recording the activity of single cortical neurons from awake, behaving macaque monkeys. Its unique features include high-density microwire arrays and multichannel instrumentation. Three adult rhesus monkeys received microwire array implants, totaling 96-704 microwires per subject, in up to five cortical areas, sometimes bilaterally. Recordings 3-4 weeks after implantation yielded 421 single neurons with a mean peak-to-peak voltage of 115 +/- 3 microV and a signal-to-noise ratio of better than 5:1. As many as 247 cortical neurons were recorded in one session, and at least 58 neurons were isolated from one subject 18 months after implantation. This method should benefit neurophysiological investigation of learning, perception, and sensorimotor integration in primates and the development of neuroprosthetic devices.


title: Does Language Shape Thought? Mandarin and English Speakers' Conceptions of Time

abstract: Abstract Does the language you speak affect how you think about the world? This question is taken up in three experiments. English and Mandarin talk about time differently—English predominantly talks about time as if it were horizontal, while Mandarin also commonly describes time as vertical. This difference between the two languages is reflected in the way their speakers think about time. In one study, Mandarin speakers tended to think about time vertically even when they were thinking for English (Mandarin speakers were faster to confirm that March comes earlier than April if they had just seen a vertical array of objects than if they had just seen a horizontal array, and the reverse was true for English speakers). Another study showed that the extent to which Mandarin–English bilinguals think about time vertically is related to how old they were when they first began to learn English. In another experiment native English speakers were taught to talk about time using vertical spatial terms in a way similar to Mandarin. On a subsequent test, this group of English speakers showed the same bias to think about time vertically as was observed with Mandarin speakers. It is concluded that (1) language is a powerful tool in shaping thought about abstract domains and (2) one's native language plays an important role in shaping habitual thought (e.g., how one tends to think about time) but does not entirely determine one's thinking in the strong Whorfian sense.


title: A web-based appointment system to reduce waiting for outpatients: A retrospective study

abstract: BackgroundLong waiting times for registration to see a doctor is problematic in China, especially in tertiary hospitals. To address this issue, a web-based appointment system was developed for the Xijing hospital. The aim of this study was to investigate the efficacy of the web-based appointment system in the registration service for outpatients.MethodsData from the web-based appointment system in Xijing hospital from January to December 2010 were collected using a stratified random sampling method, from which participants were randomly selected for a telephone interview asking for detailed information on using the system. Patients who registered through registration windows were randomly selected as a comparison group, and completed a questionnaire on-site.ResultsA total of 5641 patients using the online booking service were available for data analysis. Of them, 500 were randomly selected, and 369 (73.8%) completed a telephone interview. Of the 500 patients using the usual queuing method who were randomly selected for inclusion in the study, responses were obtained from 463, a response rate of 92.6%. Between the two registration methods, there were significant differences in age, degree of satisfaction, and total waiting time (P < 0.001). However, gender, urban residence, and valid waiting time showed no significant differences (P > 0.05). Being ignorant of online registration, not trusting the internet, and a lack of ability to use a computer were three main reasons given for not using the web-based appointment system. The overall proportion of non-attendance was 14.4% for those using the web-based appointment system, and the non-attendance rate was significantly different among different hospital departments, day of the week, and time of the day (P < 0.001).ConclusionCompared to the usual queuing method, the web-based appointment system could significantly increase patient's satisfaction with registration and reduce total waiting time effectively. However, further improvements are needed for broad use of the system.


title: Menu Design for Computers and Cell Phones: Review and Reappraisal

abstract: Menu systems have been key components in modern graphical user interfaces, and there has been a lot of research about menu design. Menu design features play a significant role from the perspective of customer satisfaction. Therefore, researchers have investigated various features in menu design. 3D menus have been investigated because these can display more items and provide a natural and intuitive interface. Small screens of the type used on mobile phones are limited in the amount of available space, and thus it might be more beneficial to use 3D menus in cell phones. A review of previous menu design studies for human–computer interaction suggests that menu design guidelines for computers and mobile phones need to be reappraised, especially toward the use of 3D interfaces in cell phones. The main objective of this article is to propose an overall framework for 3D menu interfaces in cell phones. The second objective is to propose guidelines for 2D and 3D menu design in computers and cell phones based on a...


The Mathieu groups


title: A gender recognition system from facial images using SURF based BoW method

abstract: Gender recognition from facial images has become one of challenging research problem in computer vision, security, verbal-nonverbal communication and human computer interaction applications nowadays. Because facial images include many information such as gender, facial expressions, age, ethnic origin in computer-aided applications, the success rate of the gender recognition depends on quality of facial images. In this paper, it is proposed a new gender recognition method combining Speed Up Robust Features (SURF) based Bags of Visual Words (BoW) and Support Vector Machine (SVM) algorithm unlike previous work. The method is tested on realistic frontal, left and right face images from modern gender recognition FERET dataset with 3560 samples to see efficiency of the proposed method. Experimental results show that the proposed method can obtain better gender recognition performance on FERET database and the accuracy level of on left and right face images is a bit lower than the average accuracy level of frontal ones.


title: Demand interprocedural dataflow analysis

abstract: An exhaustive dataflow analysis algorithm associates with each point in a program a set of “dataflow facts” that are guaranteed to hold whenever that point is reached during program execution. By contrast, a demand dataflow analysis algorithm determines whether a single given dataflow fact holds at a single given point. This paper presents a new demand algorithm for interprocedural dataflow analysis. The new algorithm has three important properties: It provides precise (meet over all interprocedurally valid paths) solutions to a large class of problems. It has a polynomial worst-case cost for both a single demand and a sequence of all possible demands. The worst-case total cost of the sequence of all possible demands is no worse than the worst-case cost of a single run of the current best exhaustive algorithm.


title: Music Feature Maps with Convolutional Neural Networks for Music Genre Classification

abstract: Nowadays, deep learning is more and more used for Music Genre Classification: particularly Convolutional Neural Networks (CNN) taking as entry a spectrogram considered as an image on which are sought different types of structure. But, facing the criticism relating to the difficulty in understanding the underlying relationships that neural networks learn in presence of a spectrogram, we propose to use, as entries of a CNN, a small set of eight music features chosen along three main music dimensions: dynamics, timbre and tonality. With CNNs trained in such a way that filter dimensions are interpretable in time and frequency, results show that only eight music features are more efficient than 513 frequency bins of a spectrogram and that late score fusion between systems based on both feature types reaches 91% accuracy on the GTZAN database.


title: Food color is in the eye of the beholder: the role of human trichromatic vision in food evaluation

abstract: Non-human primates evaluate food quality based on brightness of red and green shades of color, with red signaling higher energy or greater protein content in fruits and leafs. Despite the strong association between food and other sensory modalities, humans, too, estimate critical food features, such as calorie content, from vision. Previous research primarily focused on the effects of color on taste/flavor identification and intensity judgments. However, whether evaluation of perceived calorie content and arousal in humans are biased by color has received comparatively less attention. In this study we showed that color content of food images predicts arousal and perceived calorie content reported when viewing food even when confounding variables were controlled for. Specifically, arousal positively co-varied with red-brightness, while green-brightness was negatively associated with arousal and perceived calorie content. This result holds for a large array of food comprising of natural food - where color likely predicts calorie content - and of transformed food where, instead, color is poorly diagnostic of energy content. Importantly, this pattern does not emerged with nonfood items. We conclude that in humans visual inspection of food is central to its evaluation and seems to partially engage the same basic system as non-human primates.


title: Human Motion Prediction via Spatio-Temporal Inpainting

abstract: We propose a Generative Adversarial Network (GAN) to forecast 3D human motion given a sequence of past 3D skeleton poses. While recent GANs have shown promising results, they can only forecast plausible motion over relatively short periods of time (few hundred milliseconds) and typically ignore the absolute position of the skeleton w.r.t. the camera. Our scheme provides long term predictions (two seconds or more) for both the body pose and its absolute position. Our approach builds upon three main contributions. First, we represent the data using a spatio-temporal tensor of 3D skeleton coordinates which allows formulating the prediction problem as an inpainting one, for which GANs work particularly well. Secondly, we design an architecture to learn the joint distribution of body poses and global motion, capable to hypothesize large chunks of the input 3D tensor with missing data. And finally, we argue that the L2 metric, considered so far by most approaches, fails to capture the actual distribution of long-term human motion. We propose two alternative metrics, based on the distribution of frequencies, that are able to capture more realistic motion patterns. Extensive experiments demonstrate our approach to significantly improve the state of the art, while also handling situations in which past observations are corrupted by occlusions, noise and missing frames.


title: Human activity recognition using smartphone sensors with two-stage continuous hidden Markov models

abstract: Recognizing human activities from temporal streams of sensory data observations is a very important task on a wide variety of applications in context recognition. Especially for time-series sensory data, a method that takes into account the inherent sequential characteristics of the data is needed. Moreover, activities are hierarchical in nature, in as much that complex activities can be decomposed to a number of simpler ones. In this paper, we propose a two-stage continuous hidden Markov model (CHMM) approach for the task of activity recognition using accelerometer and gyroscope sensory data gathered from a smartphone. The proposed method consists of first-level CHMMs for coarse classification, which separates stationary and moving activities, and second-level CHMMs for fine classification, which classifies the data into their corresponding activity classes. Random Forests (RF) variable importance measures are exploited to determine the optimal feature subsets for both coarse and fine classification. Experiments show that with the use of a significantly reduced number of features, the proposed method shows competitive performance in comparison to other classification algorithms, achieving an over-all accuracy of 91.76%.


title: A CMOS beta multiplier voltage reference with improved temperature performance and silicon tunability

abstract: A new implementation has been proposed for the beta multiplier voltage reference to improve its performance with regard to process variations. The scope for silicon tunability on the proposed circuit is also discussed. The circuit was implemented in a 0.18 /spl mu/ process and was found to have a temperature sensitivity of less than 500 ppm/C in the virgin die without trimming.


title: Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation

abstract: Growing interest in eXplainable Artificial Intelligence (XAI) aims to make AI and machine learning more understandable to human users. However, most existing work focuses on new algorithms, and not on usability, practical interpretability and efficacy on real users. In this vision paper, we propose a new research area of eXplainable AI for Designers (XAID), specifically for game designers. By focusing on a specific user group, their needs and tasks, we propose a human-centered approach for facilitating game designers to co-create with AI/ML techniques through XAID. We illustrate our initial XAID framework through three use cases, which require an understanding both of the innate properties of the AI techniques and users’ needs, and we identify key open challenges.


title: Anomaly Detection in Transportation Corridors using Manifold Embedding ABSTRACT

abstract: The formation of secure transportation corridors, where cargoes and shipments from points of entry can be dispatched safely to highly sensitive and secure locations, is a high national priority. One of the key tasks of the program is the detection of anomalous cargo based on sensor readings in truck weigh stations. Due to the high variability, dimensionality, and/or noise content of sensor data in transportation corridors, appropriate feature representation is crucial to the success of anomaly detection methods in this domain. In this paper, we empirically investigate the usefulness of manifold embedding methods for feature representation in anomaly detection problems in the domain of transportation corridors. We focus on both linear methods, such as multi-dimensional scaling (MDS), as well as nonlinear methods, such as locally linear embedding (LLE) and isometric feature mapping (ISOMAP). Our study indicates that such embedding methods provide a natural mechanism for keeping anomalous points away from the dense/normal regions in the embedding of the data. We illustrate the efficacy of manifold embedding methods for anomaly detection through experiments on simulated data as well as real truck data from weigh stations.


title: A Thermal Energy Harvesting Power Supply With an Internal Startup Circuit for Pacemakers

abstract: A complete thermal energy harvesting power supply for implantable pacemakers is presented in this paper. The designed power supply includes an internal startup and does not need any external reference voltage. The startup circuit includes a prestartup charge pump (CP) and a startup boost converter. The prestartup CP consists of an ultralow-voltage oscillator followed by a high-efficiency modified Dickson. Forward body biasing is used to effectively reduce the MOS threshold voltages as well as the supply voltage in oscillator and CP. The steady-state circuit includes a high-efficiency boost converter that utilizes a modified maximum powerpoint tracking scheme. The system is designed so that no failure occurs under overload conditions. Using this approach, a thermal energy harvesting power supply has been designed using 180-nm CMOS technology. According to HSPICE simulation results, the circuit operates from input voltages as low as 40 mV provided from a thermoelectric generator and generates output voltages up to 3 V. A maximum power of 130 $\mu $ W can be obtained from the output of the boost converter, which means that its efficiency is 60%. A minimum voltage of 60 mV and a maximum time of 400 ms are needed for the circuit to start up.


title: Persistent problems and practices in information systems development

abstract: Abstract. This paper identifies and discusses the persistent problems and development practices of information systems development (ISD). A critical examination and comparison of past times’‘traditional’ and present-day ‘web-based’ development shows that contemporary ISD can be seen as an accentuated evolution – rather than a revolution – of well-known challenges and solutions. On this basis, (1) diversity; (2) knowledge; and (3) structure are identified as inherent and interrelated problems, while the practices for coping with these three challenges are (a) organization and specialization; (b) constant verbal communication and negotiation; and (c) pragmatic application of certain development methods and methodical concepts. We conclude that more research on the occurrence and interaction of problems and practices at, and between, different contextual levels (e.g. the business environment, company, project, team and individual levels) is needed to understand and assess (the gap between) ‘observed practice’ and ‘good practice’ across the many types of Web and non-Web ISD projects conducted today. We outline a possible research agenda to investigate these issues.


Biosurfactant Production by Pseudomonas Aeruginosa Strains on 2 ml of Inoculum Size


title: Customer Satisfaction Modeling in Hotel Industry: A Case Study of Kish Island in Iran

abstract: Purpose- Hotel Industry is one of the most substantial factors for Tourism Industry. It could provide the necessary substructures for Tourism. Therefore the growth in Tourism industry depends on growth in Hotel industry. Hotel industry had a major growth in recent years in Iran; therefore the purpose of this paper is to explore the customer satisfaction in hotel industry. This paper aims to identify the relation between customer satisfactions as dependent variable with other independent variables such image, customer expectation, service quality, perceived value, locality, and complaining behavior. The second goal of this study was to propose a customer satisfaction model for hotel industry in Kish Island (IRAN). Therefore, the findings of this study will contribute to both hotel management and tourism industry improvement in Iran. Design/Methodology/approach- A proportional simple random sampling of Iranian visitor to Kish Island were drawn. In this study the researcher had used two different kinds of samples. The first sample was customers of three different hotels of 3 stars, 4 stars, 5 stars which are immeasurable and unlimited. The total number of hotels with the rating of 3 stars, 4 stars, and 5 stars were 22. Findings- The correlation of image, customer expectation, service quality, perceived value, and customer loyalty are significant enough to show the correlation with customer satisfaction in hotel industry in Iran, furthermore the correlation of complaining behavior and customer was weak and negative, complaint behavior and customer satisfaction is negatively related, which means the lower complaint behavior was, the higher customer satisfaction obtained. Originality/Value- From a theoretical point of view the results of this study illuminate the relationship between customer satisfaction with variety of factors in Iranian hotel industry. From practical point of view, it explains why certain customer has more trust, experience, loyalty in particular hotels. It is hypothesized and demonstrated empirically that customer satisfaction.


title: Testing the School-to-Prison Pipeline

abstract: The School-to-Prison Pipeline is a social phenomenon where students become formally involved with the criminal justice system as a result of school policies that use law enforcement, rather than discipline, to address behavioral problems. A potentially important part of the School-to-Prison Pipeline is the use of sworn School Resource Officers (SROs), but there is little research on the causal effect of hiring these officers on school crime or arrests. Using credibly exogenous variation in the use of SROs generated by federal hiring grants specifically to place law enforcement in schools, I find evidence that law enforcement agencies learn about more crimes in schools upon receipt of a grant, and are more likely to make arrests for those crimes. This primarily affects children under the age of 15. However, I also find evidence that SROs increase school safety, and help law enforcement agencies make arrests for drug crimes occurring on and off school grounds.


title: Rhinoplasty: The Nasal Bones - Anatomy and Analysis

abstract: Background The analysis of nasal anatomy, and especially the nasal bones including the osseocartilaginous vault, is significant for functional and aesthetic reasons. ::: ::: Objectives The objective was to understand the anatomy of the nasal bones by establishing new descriptions, terms, and definitions because the existing parameters were insufficient. Adequate terminology was employed to harmonize the anthropometric and clinical measurements. ::: ::: Methods A two-part harvest technique consisting of resecting the specimen and then creating a replica of the skull was performed on 44 cadavers to obtain specific measurements. ::: ::: Results The nasal bones have an irregular, variable shape, and three distinct angles can be found along the dorsal profile line beginning with the nasion angle (NA), the dorsal profile angulation (DPA) and the kyphion angulation (KA). In 12% of cases, the caudal portion of the nasal bones was straight and without angulation resulting in a “V-shape” configuration. In 88% of cases, the caudal portion of the bone was angulated, which resulted in an “S-shape” nasal bone configuration. The intervening cephalic bone, nasion to sellion (N-S), represents the radix while the caudal bone, sellion to r (S-R), represents the bony dorsum. ::: ::: Conclusions By standardizing and measuring existing nasal landmarks and understanding the different anatomic configurations of the nasal bones, rhinoplasty surgeons can better plan their operations within the radix and bony and osseocartilaginous vaults.


title: Epidermoid cyst of the temporal region

abstract: Epidermoid cysts are rare, slow-growing, benign, developmental cysts that are derived from abnormally situated ectodermal tissue. Epidermoid cysts may grow anywhere on the body and about 7% of them are located in the head and neck. In literature, very few epidermoid cysts have been reported in the temporal region. Histopathologically, they are lined with plain stratified keratin-producing squamous epithelium, although in some cases part of the lining is made up of mucous secreting and ciliated epithelium. This may suggest an endodermal rather than an ectodermal origin. We present and discuss the management of a patient presenting a posttraumatic epidermoid cyst of the temporal region.


title: Connectivity-based garbage collection

abstract: We introduce a new family of connectivity-based garbage collectors (Cbgc) that are based on potential object-connectivity properties. The key feature of these collectors is that the placement of objects into partitions is determined by performing one of several forms of connectivity analyses on the program. This enables partial garbage collections, as in generational collectors, but without the need for any write barrier.The contributions of this paper are 1) a novel family of garbage collection algorithms based on object connectivity; 2) a detailed description of an instance of this family; and 3) an empirical evaluation of Cbgc using simulations. Simulations help explore a broad range of possibilities for Cbgc, ranging from simplistic ones that determine connectivity based on type information to oracular ones that use run-time information to determine connectivity. Our experiments with the oracular Cbgc configurations give an indication of the potential for Cbgc and also identify weaknesses in the realistic configurations. We found that even the simplistic implementations beat state-of-the-art generational collectors with respect to some metrics (pause times and memory footprint).


title: Channel Coded Distribution Field Tracking for Thermal Infrared Imagery

abstract: We address short-term, single-object tracking, a topic that is currently seeing fast progress for visual video, for the case of thermal infrared (TIR) imagery. The fast progress has been possible thanks to the development of new template-based tracking methods with online template updates, methods which have not been explored for TIR tracking. Instead, tracking methods used for TIR are often subject to a number of constraints, e.g., warm objects, low spatial resolution, and static camera. As TIR cameras become less noisy and get higher resolution these constraints are less relevant, and for emerging civilian applications, e.g., surveillance and automotive safety, new tracking methods are needed. Due to the special characteristics of TIR imagery, we argue that template-based trackers based on distribution fields should have an advantage over trackers based on spatial structure features. In this paper, we propose a templatebased tracking method (ABCD) designed specifically for TIR and not being restricted by any of the constraints above. In order to avoid background contamination of the object template, we propose to exploit background information for the online template update and to adaptively select the object region used for tracking. Moreover, we propose a novel method for estimating object scale change. The proposed tracker is evaluated on the VOT-TIR2015 and VOT2015 datasets using the VOT evaluation toolkit and a comparison of relative ranking of all common participating trackers in the challenges is provided. Further, the proposed tracker, ABCD, and the VOT-TIR2015 winner SRDCFir are evaluated on maritime data. Experimental results show that the ABCD tracker performs particularly well on thermal infrared sequences.


title: Adaptive User Interfaces in Augmented Reality

abstract: In this position paper, we consider the issue of developing a flexible software architecture which will allow multiple interaction techniques to operate simultaneously and in parallel with one another. We describe a set of interaction techniques, identify their architectural requirements, and consider methods for prioritizing different techniques and arbitrating between them when they have conflicting objectives. The discussion is made within the context of a mobile AR system for urban situation awareness.


title: Advances in the conceptualization and measurement of religion and spirituality: Implications for physical and mental health research.

abstract: Empirical studies have identified significant links between religion and spirituality and health. The reasons for these associations, however, are unclear. Typically, religion and spirituality have been measured by global indices (e.g., frequency of church attendance, self-rated religiousness and spirituality) that do not specify how or why religion and spirituality affect health. The authors highlight recent advances in the delineation of religion and spirituality concepts and measures theoretically and functionally connected to health. They also point to areas for areas for growth in religion and spirituality conceptualization and measurement. Through measures of religion and spirituality more conceptually related to physical and mental health (e.g., closeness to God, religious orientation and motivation, religious support, religious struggle), psychologists are discovering more about the distinctive contributions of religiousness and spirituality to health and well-being.


title: Stability analysis of water movement in unsaturated porous materials: 1. Theoretical considerations

abstract: The occurrence of wetting front instability during water movement in unsaturated porous materials under certain conditions is discussed and a brief review presented of relevant stability studies. The principles of hydrodynamic stability analysis are summarized and then used in developing a linear perturbation equation for vertical water movement with nonsharp fronts. A numerical approach for determining the stability parameters involving the eigenvalues of a matrix solution is outlined, and the numerical results are checked against an analytical solution for a profile exhibiting a uniform distribution of capillary potential.


title: A Table-Based Algorithm for Pipelined CRC Calculation

abstract: In this paper, we present a fast cyclic redundancy check (CRC) algorithm that performs CRC computation for any length of message in parallel. For a given message with any length, the algorithm first chunks the message into blocks, each of which has a fixed size equal to the degree of the generator polynomial. Then it performs CRC computation using only lookup tables among the chunked blocks in parallel and the results are combined together by XOR operations. It was feedback in the traditional implementation that makes pipelining problematic. In the proposed algorithm, we solve this problem and implement a pipelined calculation of 32-bit CRC in SMIC 0.13μm CMOS technology. Our algorithm allows calculation over data that is not the full width of the input. Furthermore, the pipeline latency is very short in our algorithm, and this method allows easy scaling of the parallelism while only slightly affecting timing. The simulation results show that our proposed pipelined CRC is more efficient than the current CRC implementations.


title: Towards suicide prevention: early detection of depression on social media

abstract: The statistics presented by the World Health Organization inform that 90% of the suicides can be attributed to mental illnesses in high-income countries. Besides, previous studies concluded that people with mental illnesses tend to reveal their mental condition on social media, as a way of relief. Thus, the main objective of this work is the analysis of the messages that a user posts online, sequentially through a time period, and detect as soon as possible if this user is at risk of depression. This paper is a preliminary attempt to minimize measures that penalize the delay in detecting positive cases. Our experiments underline the importance of an exhaustive sentiment analysis and a combination of learning algorithms to detect early symptoms of depression.


title: Benchmarking Single-Image Dehazing and Beyond

abstract: We present a comprehensive study and evaluation of existing single-image dehazing algorithms, using a new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single-Image DEhazing (RESIDE). RESIDE highlights diverse data sources and image contents, and is divided into five subsets, each serving different training or evaluation purposes. We further provide a rich variety of criteria for dehazing algorithm evaluation, ranging from full-reference metrics to no-reference metrics and to subjective evaluation, and the novel task-driven evaluation. Experiments on RESIDE shed light on the comparisons and limitations of the state-of-the-art dehazing algorithms, and suggest promising future directions.


title: Control of Grid Connected PV Array Using P&O MPPT Algorithm

abstract: The renewable energy is becoming moremainstream and accessible. This has been made possible due toan increase in environmental awareness coupled with thepopular demand to cut back on the greenhouse emissions. We inthis project propose a grid connected PV system. The aim of theproject is to implement a complete distributed energy resourcesystem (DER). The project will feature a PV module, which willbe controlled and optimized by means of a maximum powerpoint tracking (MPPT) algorithm. A boost converter along witha single phase grid tie inverter will be used to increase theoutput voltage and to convert it to AC. A phase locked loopcircuit will be used to integrate the single phase inverter withthe grid. A control methodology consisting of PI controllers isemployed for operating the PV at the MPPT point bycontrolling the switching of the boost converter and also for theoperation of the single phase inverter and its integration withthe grid. The parameters of these controllers are tuned to givethe best results. This will be followed by a detailed mathematicaland engineering analysis for the simulated results. The validityof the proposed scheme will be verified by simulation using thePSIM software.


title: Generating smooth surface meshes from multi‐region medical images

abstract: SUMMARY ::: ::: Thanks to advances in medical imaging technologies and numerical methods, patient-specific modelling is more and more used to improve diagnosis and to estimate the outcome of surgical interventions. It requires the extraction of the domain of interest from the medical scans of the patient, as well as the discretisation of this geometry. However, extracting smooth multi-material meshes that conform to the tissue boundaries described in the segmented image is still an active field of research. We propose to solve this issue by combining an implicit surface reconstruction method with a multi-region mesh extraction scheme. The surface reconstruction algorithm is based on multi-level partition of unity implicit surfaces, which we extended to the multi-material case. The mesh generation algorithm consists in a novel multi-domain version of the marching tetrahedra. It generates multi-region meshes as a set of triangular surface patches consistently joining each other at material junctions. This paper presents this original meshing strategy, starting from boundary points extraction from the segmented data to heterogeneous implicit surface definition, multi-region surface triangulation and mesh adaptation. Results indicate that the proposed approach produces smooth and high-quality triangular meshes with a reasonable geometric accuracy. Hence, the proposed method is well suited for subsequent volume mesh generation and finite element simulations. Copyright © 2011 John Wiley & Sons, Ltd.


A Unified Syntax-aware Framework for Semantic Role Labeling


Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text Generation


title: Empirical Analysis of Crypto Currencies

abstract: Analysis of the currency networks is not easy as the transactions are not centralized but rather take place over a large number of banks and commercial entities. Digital crypto currencies, however, require a public ledger to work and provide an opportunity for analysis of currency transactions. A crypto currency is a medium of exchange using cryptography to secure the transactions and to control the creation of new units. In this paper, we analyze two of the popular crypto currencies, i.e., Bitcoin and Litecoin. We construct network of transactions from public transaction ledger. We investigate the structure of currency transaction network by measuring the network characteristics.


title: Knowledge management in hybrid supply channels: a case study

abstract: In this paper, we present a theoretical framework for information technology supported knowledge management in hybrid supply channels. The framework has been applied to IBM in an exploratory case study. The study supports the framework. The framework characterises the competitive environment of companies and describes the resulting need for hybrid channelling and effective partnering. For hybrid channelling three types of knowledge are important: contextual, operational and functional knowledge. For partners situated upstream in the channel, contextual knowledge is the hardest to acquire. One needs its partners situated downstream in the channel to provide this knowledge. These partners on the other hand need their upstream partners to provide functional and operational knowledge. Information technology is a powerful tool for knowledge management in supply chains by providing four functions: adviser, assistant, librarian and teacher.


title: Cat-inspired mechanical design of self-adaptive toes for a legged robot

abstract: Cats have protractible claws to fold their tips to keep them sharp. They protract claws while hunting and pawing on slippery surfaces. Protracted claws by tendons and muscles of toes can help cats anchoring themselves steady while their locomotion trends to slip and releasing the hold while they retract claws intentionally. This research proposes a kind of modularized self-adaptive toe mechanism inspired by cat claws to improve the extremities' contact performance for legged robot. The mechanism is constructed with four-bar linkage actuated by contact reaction force and retracted by applied spring tension. A feasible mechanical design based on several essential parameters is introduced and an integrated Sole-Toe prototype is built for experimental evaluation. Mechanical self-adaption and actual contact performance on specific surface have been evaluated respectively on a biped walking platform and a bench-top mechanical testing.


title: A Comparison Between Five Models Of Software Engineering

abstract: This research deals with a vital and important issue in computer world. It is concerned with the software management processes that examine the area of software development through the development models, which are known as software development life cycle. It represents five of the development models namely, waterfall, Iteration, V-shaped, spiral and Extreme programming. These models have advantages and disadvantages as well. Therefore, the main objective of this research is to represent different models of software development and make a comparison between them to show the features and defects of each model.


Battery Modeling


title: Critical Success Factors in Enterprise Resource Planning (ERP) System Implementation Stages: An Exploratory Study in Oman

abstract: Many studies in literature have shown the importance of ERP system in companies' effectiveness, and this is because ERP system have become one of the main prerequisites, a price of entry, and a strong and integrated IT infrastructure for many companies enabling them to compete in the local and global marketplace, and ensuring them to gain a competitive advantage in the global economy particularly with the current e-business era. This study will investigate the critical success factors (CSFs) that can play a role during the implementation process in Omani organizations and will identify the critical success factors that affecting ERP system implementation success during the three implementation stages which are pre-implementation stage, implementation stage and post- implementation stage.


title: Examining the Effectiveness of an Outpatient Clinic–Based Social Skills Group for High-Functioning Children with Autism

abstract: Although social skills group interventions for children with autism are common in outpatient clinic settings, little research has been conducted to determine the efficacy of such treatments. This study examined the effectiveness of an outpatient clinic–based social skills group intervention with four high-functioning elementary-aged children with autism. The group was designed to teach specific social skills, including greeting, conversation, and play skills in a brief therapy format (eight sessions total). At the end of each skills-training session, children with autism were observed in play sessions with typical peers. Typical peers received peer education about ways to interact with children with autism. Results indicate that a social skills group implemented in an outpatient clinic setting was effective in improving greeting and play skills, with less clear improvements noted in conversation skills. In addition, children with autism reported increased feelings of social support from classmates at school following participation in the group. However, parent report data of greeting, conversation, and play skills outside of the clinic setting indicated significant improvements in only greeting skills. Thus, although the clinic-based intervention led to improvements in social skills, fewer changes were noted in the generalization to nonclinic settings.


title: gHRV: Heart rate variability analysis made easy

abstract: a b s t r a c t In this paper, the gHRV software tool is presented. It is a simple, free and portable tool developed in python for analysing heart rate variability. It includes a graphical user interface and it can import files in multiple formats, analyse time intervals in the signal, test statistical significance and export the results. This paper also contains, as an example of use, a clinical analysis performed with the gHRV tool, namely to determine whether the heart rate variability indexes change across different stages of sleep. Results from tests completed by researchers who have tried gHRV are also explained: in general the application was positively valued and results reflect a high level of satisfaction. gHRV is in continuous development and new versions will include suggestions made by testers.


title: Segmentation-Based Image Copy-Move Forgery Detection Scheme

abstract: In this paper, we propose a scheme to detect the copy-move forgery in an image, mainly by extracting the keypoints for comparison. The main difference to the traditional methods is that the proposed scheme first segments the test image into semantically independent patches prior to keypoint extraction. As a result, the copy-move regions can be detected by matching between these patches. The matching process consists of two stages. In the first stage, we find the suspicious pairs of patches that may contain copy-move forgery regions, and we roughly estimate an affine transform matrix. In the second stage, an Expectation-Maximization-based algorithm is designed to refine the estimated matrix and to confirm the existence of copy-move forgery. Experimental results prove the good performance of the proposed scheme via comparing it with the state-of-the-art schemes on the public databases.


title: Geometric and Mathematical Proportions and their Relations to Maxillary Anterior Teeth

abstract: AIM ::: One of the critical aspects of esthetic dentistry is creating geometric or mathematical proportions to relate the successive widths of the anterior teeth. The golden proportion, the recurring esthetic dental (RED) proportion, and the golden percentage are theories introduced in this field. The aim of this study was to investigate the existence of the golden proportion, RED proportion, and the golden percentage between the widths of the maxillary anterior teeth in individuals with natural dentition. ::: ::: ::: METHODS AND MATERIALS ::: Standardized frontal images of 376 dental student smiles were captured. The images were transferred to a personal computer, the widths of the maxillary anterior teeth were measured, and calculations were made according to each of the above mentioned theories. The data were statistically analyzed using paired student T-test (level of significance P<0.05). ::: ::: ::: RESULTS ::: The golden proportion was found to be accurate between the width of the right central and lateral incisors in 31.3% of men and 27.1% of women. The values of the RED proportion were not constant, and the farther the one moves distally from the midline the higher the values. Furthermore, the results revealed the golden percentage was rather constant in terms of relative tooth width. The width of the central incisor represents 23%, the lateral incisor 15%, and the canine 12% of the width of the six maxillary anterior teeth as viewed from the front. ::: ::: ::: CONCLUSIONS ::: Both the golden proportion and the RED proportion are unsuitable methods to relate the successive widths of the maxillary anterior teeth. However, the golden percentage theory seems to be applicable to relate the successive widths of the maxillary anterior teeth if percentages are adjusted taking into consideration the ethnicity of the population.


Evaluation and Retraining of the Intrinsic Foot Muscles for Pain Syndromes Related to Abnormal Control of Pronation


title: Medical Cannabis Laws and Opioid Analgesic Overdose Mortality in the United States, 1999-2010

abstract: Importance Opioid analgesic overdose mortality continues to rise in the United States, driven by increases in prescribing for chronic pain. Because chronic pain is a major indication for medical cannabis, laws that establish access to medical cannabis may change overdose mortality related to opioid analgesics in states that have enacted them. Objective To determine the association between the presence of state medical cannabis laws and opioid analgesic overdose mortality. Design, Setting, and Participants A time-series analysis was conducted of medical cannabis laws and state-level death certificate data in the United States from 1999 to 2010; all 50 states were included. Exposures Presence of a law establishing a medical cannabis program in the state. Main Outcomes and Measures Age-adjusted opioid analgesic overdose death rate per 100 000 population in each state. Regression models were developed including state and year fixed effects, the presence of 3 different policies regarding opioid analgesics, and the state-specific unemployment rate. Results Three states (California, Oregon, and Washington) had medical cannabis laws effective prior to 1999. Ten states (Alaska, Colorado, Hawaii, Maine, Michigan, Montana, Nevada, New Mexico, Rhode Island, and Vermont) enacted medical cannabis laws between 1999 and 2010. States with medical cannabis laws had a 24.8% lower mean annual opioid overdose mortality rate (95% CI, −37.5% to −9.5%; P = .003) compared with states without medical cannabis laws. Examination of the association between medical cannabis laws and opioid analgesic overdose mortality in each year after implementation of the law showed that such laws were associated with a lower rate of overdose mortality that generally strengthened over time: year 1 (−19.9%; 95% CI, −30.6% to −7.7%; P = .002), year 2 (−25.2%; 95% CI, −40.6% to −5.9%; P = .01), year 3 (−23.6%; 95% CI, −41.1% to −1.0%; P = .04), year 4 (−20.2%; 95% CI, −33.6% to −4.0%; P = .02), year 5 (−33.7%; 95% CI, −50.9% to −10.4%; P = .008), and year 6 (−33.3%; 95% CI, −44.7% to −19.6%; P Conclusions and Relevance Medical cannabis laws are associated with significantly lower state-level opioid overdose mortality rates. Further investigation is required to determine how medical cannabis laws may interact with policies aimed at preventing opioid analgesic overdose.


title: Heterogeneity-aware data regeneration in distributed storage systems

abstract: Distributed storage systems provide large-scale re- liable data storage services by spreading redundancy across a large group of storage nodes. In such big systems, node failures take place on a regular basis. When a node fails or leaves the system, to maintain the same level of redundancy, it is expected to regenerate the redundant data at a replacement node as soon as possible. Previous studies aim to minimize the network traffic in the regeneration process, but in practical networks, where link capacities vary in a wide range, minimizing network traffic does not always mean minimizing regeneration time. Considering the heterogeneous link capacities, Li et al. proposed a tree-structured regeneration scheme, called RCTREE, to bypass the low-capacitated link encountered in direct transmissions. However, we find that RCTREE may rapidly lose data integrity after several regenerations. In this paper, we reconsider the problem of minimizing regeneration time in networks with heterogeneous link capacities. We derive the minimum amount of data to be transmitted through each link to preserve data integrity. We prove that building an optimal regeneration tree is NP-complete and propose a heuristic algorithm for a near-optimal solution. We further introduce a flexible regeneration scheme, which allows providers to generate different amount of coded data. Simulation results show that the flexible tree-structured regeneration scheme can reduce the regeneration time significantly.


title: Brainstem responses to speech syllables

abstract: Objective: To establish reliable procedures and normative values to quantify brainstem encoding of speech sounds. Methods: Auditory brainstem responses to speech syllables presented in quiet and in background noise were obtained from 38 normal children. Brainstem responses consist of transient and sustained, periodic components—much like the speech signal itself. Transient peak responses were analyzed with measures of latency, amplitude, area, and slope. Magnitude of sustained, periodic frequency-following responses was assessed with root mean square, fundamental frequency, and first formant amplitudes; timing was assessed by stimulus-toresponse and quiet-to-noise inter-response correlations. Results: Measures of transient and sustained components of the brainstem response to speech syllables were reliably obtained with high test –retest stability and low variability across subjects. All components of the brainstem response were robust in quiet. Background noise disrupted the transient responses whereas the sustained response was more resistant to the deleterious effects of noise. Conclusions: The speech-evoked brainstem response faithfully reflects many acoustic properties of the speech signal. Procedures to quantitatively describe it have been developed. Significance: Accurate and precise manifestation of stimulus timing at the auditory brainstem is a hallmark of the normal perceptual system. The brainstem response to speech sounds provides a mechanism for understanding the neural bases of normal and deficient attentionindependent auditory function. q 2004 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.


title: Detection of exudates in fundus photographs using convolutional neural networks

abstract: Diabetic retinopathy is one of the leading causes of preventable blindness in the developed world. Early diagnosis of diabetic retinopathy enables timely treatment and in order to achieve it a major effort will have to be invested into screening programs and especially into automated screening programs. Detection of exudates is very important for early diagnosis of diabetic retinopathy. Deep neural networks have proven to be a very promising machine learning technique, and have shown excellent results in different compute vision problems. In this paper we show that convolutional neural networks can be effectively used in order to detect exudates in color fundus photographs.