title: MATLAB program for simulation and control of the continuous stirred tank reactor

abstract: The paper shows advantages of the computer technology for the simulation of the technological system's behaviour and control. The technological process here is represented by the Continuous Stirred Tank Reactor (CSTR) as a typical member of a nonlinear lumped-parameters system widely used in the industry. The computer simulation has great importance nowadays with the decreasing value of the computer components together with the growing computer speed. The adaptive control used here fulfils all basic control requirements and it can be used for the systems with nonlinear behaviour. The benefit of this paper can be found in the simulation program made in mathematical software MATLAB with the use of Graphical User Interface (GUI) that provides user possibilities to examine simulations without changing of the program code.


title: Real-time Semi-Global Matching on the CPU

abstract: Among the top-performing stereo algorithms on the Middlebury Stereo Database, Semi-Global Matching (SGM) is commonly regarded as the most efficient algorithm. Consequently, real-time implementations of the algorithm for graphics hardware (GPU) and reconfigurable hardware (FPGA) exist. However, the computation time on general purpose PCs is still more than a second.


Improved Word Embeddings with Implicit Structure Information


title: Fog at the Edge: Experiences Building an Edge Computing Platform

abstract: Technology advancement has pushed computation to the network edge, paving the way for a class of IoT applications that leverage CPU, storage and communications in edge devices. Building these new IoT applications is not an easy task however. Two key challenges are supporting the dynamic nature of the edge network and the context-dependent characteristics of application logic. In this paper we report our experience in building an edge computing platform called Distributed Node-RED (DNR) that uses a distributed data flow programming model based on the popular open source Node-RED tool. We describe some of the challenges we faced as well as some novel solutions that were implemented in our platform. A new approach in applying the concept of exogenous coordination is also presented and shown to be necessary in building large-scale IoT applications across the edge, fog and cloud.


title: Quantum generalisation of feedforward neural networks

abstract: We propose a quantum generalisation of a classical neural network. The classical neurons are firstly rendered reversible by adding ancillary bits. Then they are generalised to being quantum reversible, i.e., unitary (the classical networks we generalise are called feedforward, and have step-function activation functions). The quantum network can be trained efficiently using gradient descent on a cost function to perform quantum generalisations of classical tasks. We demonstrate numerically that it can: (i) compress quantum states onto a minimal number of qubits, creating a quantum autoencoder, and (ii) discover quantum communication protocols such as teleportation. Our general recipe is theoretical and implementation-independent. The quantum neuron module can naturally be implemented photonically. We often want computers to tell us something about the input data, e.g. if a given image corresponds to a cat or a dog. It seems the human brain learns this by looking at examples whilst getting feedback from a teacher, rather than being given an algorithm. Such an approach to programming is now revolutionising the ability of machines to learn. The approach uses simplified models of the brain: neural nets. Quantum information processing devices are now emerging as the next generation of information processors. One may hope that the neural net approach will be similarly powerful there. We therefore designed quantum neural nets, processing quantum superpositions. The nets work well in two example tasks: compressing data stored in superpositions, and rediscovering a protocol known as quantum teleportation.


title: A Frequency Selective Radome With Wideband Absorbing Properties

abstract: A frequency selective radome is presented, acting as a pass band filter at a given frequency band, while behaving as an absorber above the transmission band. The pass band behavior is obtained by a metallic FSS realized through a compact interdigitated Jerusalem cross element characterized by a very large rejection band. The metallic FSS is used as the ground plane of a thin wideband absorber based on resistive high-impedance surfaces within the total reflection band. The outer absorber reduces the signature of the antenna system when the radome is illuminated by out of band signals. The resistive FSS which comprises the absorber is designed so to minimize losses within the transmitting band of the radome. The composite structure is thoroughly analyzed by an efficient equivalent circuit approach and by full-wave numerical simulations.


title: Text-to-Text Semantic Similarity for Automatic Short Answer Grading

abstract: In this paper, we explore unsupervised techniques for the task of automatic short answer grading. We compare a number of knowledge-based and corpus-based measures of text similarity, evaluate the effect of domain and size on the corpus-based measures, and also introduce a novel technique to improve the performance of the system by integrating automatic feedback from the student answers. Overall, our system significantly and consistently outperforms other unsupervised methods for short answer grading that have been proposed in the past.


title: Inferring Character From Faces A Developmental Study

abstract: Human adults attribute character traits to faces readily and with high consensus. In two experiments investigating the development of face-to-trait inference, adults and children ages 3 through 10 attributed trustworthiness, dominance, and competence to pairs of faces. In Experiment 1, the attributions of 3- to 4-year-olds converged with those of adults, and 5- to 6-year-olds' attributions were at adult levels of consistency. Children ages 3 and above consistently attributed the basic mean/nice evaluation not only to faces varying in trustworthiness (Experiment 1) but also to faces varying in dominance and competence (Experiment 2). This research suggests that the predisposition to judge others using scant facial information appears in adultlike forms early in childhood and does not require prolonged social experience.


Providing Streaming Joins as a Service at Facebook


title: On the feasibility of wearable exotendon networks for whole-hand movement patterns in stroke patients

abstract: Fully wearable hand rehabilitation and assistive devices could extend training and improve quality of life for patients affected by hand impairments. However, such devices must deliver meaningful manipulation capabilities in a small and lightweight package. In this context, this paper investigates the capability of single-actuator devices to assist whole-hand movement patterns through a network of exotendons. Our prototypes combine a single linear actuator (mounted on a forearm splint) with a network of exotendons (routed on the surface of a soft glove). We investigated two possible tendon network configurations: one that produces full finger extension (overcoming flexor spasticity), and one that combines proximal flexion with distal extension at each finger. In experiments with stroke survivors, we measured the force levels needed to overcome various levels of spasticity and open the hand for grasping using the first of these configurations, and qualitatively demonstrated the ability to execute fingertip grasps using the second. Our results support the feasibility of developing future wearable devices able to assist a range of manipulation tasks.


title: An Overview of Massive MIMO Technology Components in METIS

abstract: As the standardization of full-dimension MIMO systems in the Third Generation Partnership Project progresses, the research community has started to explore the potential of very large arrays as an enabler technology for meeting the requirements of fifth generation systems. Indeed, in its final deliverable, the European 5G project METIS identifies massive MIMO as a key 5G enabler and proposes specific technology components that will allow the cost-efficient deployment of cellular systems taking advantage of hundreds of antennas at cellular base stations. These technology components include handling the inherent pilot-data resource allocation trade-off in a near optimal fashion, a novel random access scheme supporting a large number of users, coded channel state information for sparse channels in frequency- division duplexing systems, managing user grouping and multi-user beamforming, and a decentralized coordinated transceiver design. The aggregate effect of these components enables massive MIMO to contribute to the METIS objectives of delivering very high data rates and managing dense populations.


title: Brainstem processing of vestibular sensory exafference: implications for motion sickness etiology

abstract: The origin of the internal “sensory conflict” stimulus causing motion sickness has been debated for more than four decades. Recent studies show a subclass of neurons in the vestibular nuclei and deep cerebellar nuclei that respond preferentially to passive head movements. During active movement, the semicircular canal and otolith input (“reafference”) to these neurons are canceled by a mechanism comparing the expected consequences of self-generated movement (estimated with an internal model—presumably located in the cerebellum) with the actual sensory feedback. The un-canceled component (“exafference”) resulting from passive movement normally helps compensate for unexpected postural disturbances. Notably, the existence of such vestibular “sensory conflict” neurons had been postulated as early as 1982, but their existence and putative role in posture control and motion sickness have been long debated. Here, we review the development of “sensory conflict” theories in relation to recent evidence for brainstem and cerebellar reafference cancelation, and identify some open research questions. We propose that conditions producing persistent activity of these neurons, or their targets, stimulate nearby brainstem emetic centers—via an as yet unidentified mechanism. We discuss how such a mechanism is consistent with the notable difference in motion sickness susceptibility of drivers as opposed to passengers, human immunity to normal self-generated movement and why head restraint or lying horizontal confers relative immunity. Finally, we propose that fuller characterization of these mechanisms and their potential role in motion sickness could lead to more effective, scientifically based prevention and treatment for motion sickness.


title: A descriptive study of Microsoft’s threat modeling technique

abstract: Microsoft's STRIDE is a popular threat modeling technique commonly used to discover the security weaknesses of a software system. In turn, discovered weaknesses are a major driver for incepting security requirements. Despite its successful adoption, to date no empirical study has been carried out to quantify the cost and effectiveness of STRIDE. The contribution of this paper is the evaluation of STRIDE via a descriptive study that involved 57 students in their last master year in computer science. The study addresses three research questions. First, it assesses how many valid threats per hour are produced on average. Second, it evaluates the correctness of the analysis results by looking at the average number of false positives, i.e., the incorrect threats. Finally, it determines the completeness of the analysis results by looking at the average number of false negatives, i.e., the overlooked threats.


title: Application of machine learning to predict the recurrence-proneness for cervical cancer

abstract: This study applied advanced machine learning techniques, widely considered as the most successful method to produce objective to an inferential problem of recurrent cervical cancer. Traditionally, clinical diagnosis of recurrent cervical cancer was based on physician’s clinical experience with various risk factors. Since the risk factors are broad categories, years of clinical study and experience have tried to identify key risk factors for recurrence. In this study, three machine learning approaches including support vector machine, C5.0 and extreme learning machine were considered to find important risk factors to predict the recurrence-proneness for cervical cancer. The medical records and pathology were accessible by the Chung Shan Medical University Hospital Tumor Registry. Experimental results illustrate that C5.0 model is the most useful approach to the discovery of recurrence-proneness factors. Our findings suggest that four most important recurrence-proneness factors were Pathologic Stage, Pathologic T, Cell Type and RT Target Summary. In particular, Pathologic Stage and Pathologic T were important and independent prognostic factor. To study the benefit of adjuvant therapy, clinical trials should randomize patients stratified by these prognostic factors, and to improve surveillance after treatment might lead to earlier detection of relapse, and precise assessment of recurrent status could improve outcome.


title: Grain-oriented segmentation of scanning electron microscope images

abstract: Quantitative analysis of nanostructures from scanning electron microscope (SEM) images requires a clear segmentation of grains and their boundaries. This is not provided by active contour models, which also require user guidance. Our automatic technique creates a rough representation of grain boundaries by adaptive thresholding. It then performs raycasting from a rectangular grid of seed points to ensure that the grain shapes are convex, and selects the best result for each grain. The whole process can be repeated several times to improve the segmentation. We present results for images of titanium foil, which show that our approach compares favorably in terms of speed and segmentation quality with four competing techniques.


title: A novel gate control strategy for 3.4kV cascade SiC MOSFETs stack

abstract: Several topologies of high voltage input DC/DC power supply are investigated firstly in this paper and then a novel gate control strategy for cascade SiC MOSFET stack using a single standard gate driver is proposed. Thereafter, operation principles including both static sate and switching transitions are analyzed. The LTspice simulation results validate the analysis and show the availability of the proposed topology. At last 3.4KV cascade SiC MOSFET stack prototype has been designed and fabricated, the experimental results under double-pulse-test validate the proposed gate driver strategy. The proposed gate driver strategy is suitable to not only the SiC device but also the Si device.


title: Generation of Pencil Sketch Drawing

abstract: This paper proposes a pencil sketch generating system that can create various pencil sketching styles for images, to satisfy different needs of users. The pencil sketch generating process includes process of three parts: line drawing, tone adjustment, and texture rendering. Some operations needed are first proposed, and various pencil sketching styles that generated by the proposed system are illustrated.


title: Redescription of Petasiger exaeretus Dietz, 1909 and P. phalacrocoracis (Yamaguti, 1939) (Trematoda: Echinostomatidae), parasites of cormorants

abstract: Two echinostome trematodes of the genus Petasiger, P. exaeretus Dietz, 1909 and P. phalacrocoracis (Yamaguti, 1939), parasitising cormorants (Phalacrocoracidae), were found in Phalacrocorax carbo L. from the Czech Republic. Both taxa are redescribed and literary data on their occurence in cormorants are reviewed. The species P. exaeretus appears to be a rather rare species, hitherto recorded in Phalacrocorax carbo in Europe, Kirghizia and Azerbaidzhan. P. baschkirovi Ablassov & Iksanov, 1959 is synonymized with P. exaeretus. P. phalacrocoracis (syn. P. hospitale (Mendheim, 1940) and P. exaeretus auct. nec Dietz, 1909), which has previously been misidentified and erroneously designated as P. exaeretus, appears to be the commonest species of the genus, parasitising cormorants in the Palaearctic Region (Europe, CIS [USSR], Japan) and in Australia. Differential diagnoses of both the above mentioned species are presented.


Sub-word information in pre-trained biomedical word representations: evaluation and hyper-parameter optimization


title: Sinhala Speech Recognition for Interactive Voice Response Systems Accessed Through Mobile Phones

abstract: This paper presents the development of a Sinhala Speech Recognition System to be deployed in an Interactive Voice Response (IVR) system of a telecommunication service provider. The main objectives are to recognize Sinhala digits and names of Sinhala songs to be set up as ringback tones. Sinhala being a phonetic language, its features are studied to develop a list of 47 phonemes. A continuous speech recognition system is developed based on Hidden Markov Model (HMM). The acoustic model is trained using the voice through mobile phone. The outcome is a speaker independent speech recognition system which is capable of recognizing 10 digits and 50 Sinhala songs. A word error rate (WER) of 11.2% using a speech corpus of 0.862 hours and a sentence error rate (SER) of 5.7% using a speech corpus of 1.388 hours are achieved for digits and songs respectively.


title: Continuous Integration and Continuous Delivery Pipeline Automation for Agile Software Project Management

abstract: Agile practices with Continuous Integration and Continuous Delivery (CICD) pipeline approach has increased the efficiency of projects. In agile, new features are introduced to the system in each sprint delivery, and although it may be well developed, the delivery failures are possible due to performance issues. By considering delivery timeline, moving for system scaling is common solution in such situations. But, how much system should be scaled? System scale requires current system benchmark status and expected system status. Benchmarking the production is a critical task, as it interrupts the live system. The new version should go through a load test to measure expected system status. The traditional load test methods are unable to identify production performance behavior due to simulated traffic patterns are highly deviated from production. To overcome those issues, this approach has extended CICD pipeline to have three automation phases named benchmark, load test and scaling. It minimizes the system interruption by using test bench approach when system benchmarking and it uses the production traffic for load testing which gives more accurate results. Once benchmark and load test phases are completed, system scaling can be evaluated. Initially, the pipeline was developed using Jenkins CI server, Git repository and Nexus repository with Ansible automation. Then GoReplay is used for traffic duplication from production to test bench environment. Nagios monitoring is used to analyze the system behavior in each phase and the result of test bench has proven that scaling is capable to handle the same load while changing the application software, but it doesn't optimize response time of application at significant level and it helps to reduce the risk of application deployment by integrating this three phase approach as CICD automation extended feature. Thereby the research provides effective way to manage Agile based CICD projects.


title: Scheduling Aircraft Using Constraint Satisfaction

abstract: Abstract In this paper, an airport departure scheduling tool for aircraft is presented based on constraint satisfaction techniques. Airports are getting more and more congested with the available runway configuration as one of the most constraining factors. A possibility to alleviate this congestion is to assist controllers in the planning and scheduling process of aircraft. The prototype presented here is aimed to offer such assistance in the establishment of an optimal departure schedule and the planning of initial climb phases for departing aircraft. This goal is accomplished by modelling the scheduling problem as a constraint satisfaction problem, using ILOG Solver and Scheduler as an implementation environment.


ERP SYSTEMS AND INTERNAL AUDIT


title: A compact cavity filter with novel TM mode dielectric resonator structure

abstract: A novel transverse magnetic (TM) mode dielectric resonator and its filter realization are proposed for mobile communication system miniaturization applications. Single dielectric resonator and an eight-pole filter with three-transmission-zeros are modeled and designed for desired specification by using efficient optimization technique. About 30% horizontal dimensions are saved comparing with the common coaxial filter. Excellent filter responses are obtained and measured.


title: Percutaneous Pedicle Screw Fixation for Thoracolumbar Fractures

abstract: Introduction Percutaneous surgery has been gaining popularity in the past 2 decades due to its advantages of less muscle dissection, faster recovery and less complication rates. In parallel to its application to degenerative spine disease it is also a treatment option for thoracolumbar fractures. We present our experience with percutaneous pedicle screw fixation (PPDF) in the treatment of a variety of thoracolumbar fractures.


title: Paragraph: Thwarting Signature Learning by Training Maliciously

abstract: Defending a server against Internet worms and defending a user's email inbox against spam bear certain similarities. In both cases, a stream of samples arrives, and a classifier must automatically determine whether each sample falls into a malicious target class (e.g., worm network traffic, or spam email). A learner typically generates a classifier automatically by analyzing two labeled training pools: one of innocuous samples, and one of samples that fall in the malicious target class. ::: ::: Learning techniques have previously found success in settings where the content of the labeled samples used in training is either random, or even constructed by a helpful teacher, who aims to speed learning of an accurate classifier. In the case of learning classifiers for worms and spam, however, an adversary controls the content of the labeled samples to a great extent. In this paper, we describe practical attacks against learning, in which an adversary constructs labeled samples that, when used to train a learner, prevent or severely delay generation of an accurate classifier. We show that even a delusive adversary, whose samples are all correctly labeled, can obstruct learning. We simulate and implement highly effective instances of these attacks against the Polygraph [15] automatic polymorphic worm signature generation algorithms.


An adapted lesk algorithm for word sense disambiguation using wordnet


title: Innovation in Analogical Design: A Model-Based Approach

abstract: Analogical reasoning, it is commonly accepted, plays an important role in innovation and creativity. Since design often is innovative, and can be creative, the design task provides a good context for exploring the role of analogy in innovation and creativity. In the IDEAL project, we are exploring the processes of innovation and creativity in the context of conceptual (or preliminary, qualitative) design of physical devices. In particular, we are developing a model-based approach to innovation and creativity in analogical design: our core hypothesis is that innovative analogical design involves both reasoning from past experiences in designing devices (in the form of analogs or cases) and comprehension of how those devices work (in the form of device models or theories). In this paper, we focus on the issues of understanding feedback on the performance of a design, discovery of new design constraints, and reformulation of the design problem, and describe how the model-based approach addresses these issues.


title: Steady-state VEP-based brain-computer interface control in an immersive 3D gaming environment

abstract: This paper presents the application of an effective EEG-based brain-computer interface design for binary control in a visually elaborate immersive 3D game. The BCI uses the steady-state visual evoked potential (SSVEP) generated in response to phase-reversing checkerboard patterns. Two power-spectrum estimation methods were employed for feature extraction in a series of offline classification tests. Both methods were also implemented during real-time game play. The performance of the BCI was found to be robust to distracting visual stimulation in the game and relatively consistent across six subjects, with 41 of 48 games successfully completed. For the best performing feature extraction method, the average real-time control accuracy across subjects was 89%. The feasibility of obtaining reliable control in such a visually rich environment using SSVEPs is thus demonstrated and the impact of this result is discussed.


title: The recognition of multi-finger prehensile postures using LDA

abstract: Abstract The prehensile hand gestures play an important role in daily living for seizing or holding subjects stably. In order to realize the accurate recognition of eight prehensile hand gestures with a minimal number of electrodes, an off-line myoelectric control system with only two electrodes is developed. We choose Mean Absolute Value, Variance, the fourth-order Autoregressive Coefficient, Zero Crossings, Mean Frequency and Middle Frequency as original electromyography feature set and utilize the linear discriminant analysis to reduce the dimension and complete classification. The extent of dimension reduction is investigated and on the premise of it, the average accuracy can achieve 97.46% in the recognition of six hand gestures. The optimal feature set based on the original feature set is determined to be Mean Absolute Value, Variance, and the fourth-order Autoregressive Coefficient, which yields an average accuracy of 95.94% in the recognition of eight hand gestures. An average method is proposed to improve the accuracy further, resulting in the average accuracy in eight gestures being 98.12% and the best individual accuracy of some hand gestures being 100%.


title: AN HMM-BASED SPEECH SYNTHESIS SYSTEM APPLIED TO ENGLISH

abstract: This paper describes an HMM-based speech synthesis system (HTS), in which speech waveform is generated from HMMs themselves, and applies it to English speech synthesis using the general speech synthesis architecture of Festival. Similarly to other datadriven speech synthesis approaches, HTS has a compact language dependent module: a list of contextual factors. Thus, it could easily be extended to other languages, though the first version of HTS was implemented for Japanese. The resulting run-time engine of HTS has the advantage of being small: less than 1 M bytes, excluding text analysis part. Furthermore, HTS can easily change voice characteristics of synthesized speech by using a speaker adaptation technique developed for speech recognition. The relation between the HMM-based approach and other unit selection approaches is also discussed.


title: On training the recurrent neural network encoder-decoder for large vocabulary end-to-end speech recognition

abstract: Recently, there has been an increasing interest in end-to-end speech recognition using neural networks, with no reliance on hidden Markov models (HMMs) for sequence modelling as in the standard hybrid framework. The recurrent neural network (RNN) encoderdecoder is such a model, performing sequence to sequence mapping without any predefined alignment. This model first transforms the input sequence into a fixed length vector representation, from which the decoder recovers the output sequence. In this paper, we extend our previous work on this model for large vocabulary end-to-end speech recognition. We first present a more effective stochastic gradient decent (SGD) learning rate schedule that can significantly improve the recognition accuracy. We then extend the decoder with long memory by introducing another recurrent layer that performs implicit language modelling. Finally, we demonstrate that using multiple recurrent layers in the encoder can reduce the word error rate. Our experiments were carried out on the Switchboard corpus using a training set of around 300 hours of transcribed audio data, and we have achieved significantly higher recognition accuracy, thereby reduced the gap compared to the hybrid baseline.


title: A geometry-based investigation of the tool path generation for zigzag pocket machining

abstract: We present a detailed description of a zigzag algorithm for pocket machining. The algorithm is capable of computing correct zigzag tool paths for multiply-connected planar areas (“pockets”) bounded by a wide class of curves. It features a number of optimizations with respect to geometrical and technological objectives. In particular, a near-optimum inclination of the tool path is automatically determined. The underlying geometric principles are simple enough to allow the algorithm to be included in a numerical control computer.


Discriminative Information Retrieval for Question Answering Sentence Selection


title: Dual Marching Squares: Description and analysis

abstract: A new variant on the Marching Squares isocontouring method is introduced. This variant, the Dual Marching Squares, is the 2D analogue of the Dual Marching Cubes. It is the dual of Marching Squares, taking as its contour segment vertices the midpoints of the Marching Squares isocontour. Experimental analyses of the Dual Marching Squares are also presented. The analyses focus on examining its shape-preserving properties, especially in comparison to the standard Marching Squares.


title: Efficacy of psychotherapeutic interventions to promote forgiveness: A meta-analysis.

abstract: OBJECTIVE ::: This meta-analysis addressed the efficacy of psychotherapeutic interventions to help people forgive others and to examine moderators of treatment effects. ::: ::: ::: METHOD ::: Eligible studies reported quantitative data on forgiveness of a specific hurt following treatment by a professional with an intervention designed explicitly to promote forgiveness. Random effects meta-analyses were conducted using k = 53 posttreatment effect sizes (N = 2,323) and k = 41 follow-up effect sizes (N = 1,716) from a total of 54 published and unpublished research reports. ::: ::: ::: RESULTS ::: Participants receiving explicit forgiveness treatments reported significantly greater forgiveness than participants not receiving treatment (Δ+ = 0.56 [0.43, 0.68]) and participants, receiving alternative treatments (Δ+ = 0.45 [0.21, 0.69]). Also, forgiveness treatments resulted in greater changes in depression, anxiety, and hope than no-treatment conditions. Moderators of treatment efficacy included treatment dosage, offense severity, treatment model, and treatment modality. Multimoderator analyses indicated that treatment dosage (i.e., longer interventions) and modality (individual > group) uniquely predicted change in forgiveness compared with no-treatment controls. Compared with alternative treatment conditions, both modality (individual > group) and offense severity were marginally predictive (ps < .10) of treatment effects. ::: ::: ::: CONCLUSIONS ::: It appears that using theoretically grounded forgiveness interventions is a sound choice for helping clients to deal with past offenses and helping them achieve resolution in the form of forgiveness. Differences between treatment approaches disappeared when controlling for other significant moderators; the advantage for individual interventions was most clearly demonstrated for Enright-model interventions, as there have been no studies of individual interventions using the Worthington model.


title: BFAN: A Bloom Filter-Based Authentication in Wireless Sensor Networks

abstract: Wireless Sensor Networks are often deployed in unattended environments and thus an adversary could inject misleading information in the network and even fool other nodes, making them believe it is an authentic device. Different approaches in the literature have addressed this problem while taking into account the performance requirements of sensors (QoS, Memory, CPU). The most promising approaches are based on key disclosure. However, these approaches introduce delay in authentication and are thus susceptible to denial-of-service (DoS) attacks. In addition, delayed key disclosure schemes do not minimize the energy consumption overhead. In this paper, we propose an efficient authentication mechanism which integrates a Bloom Filter in order to authenticate sensors. We demonstrate that the use of multiple MACs in delayed key disclosure schemes, with the integration of Bloom filter, mitigates the risk of DoS attacks. We assess the feasibility of our protocol with a set of scenarios. Our results confirm that our authentication protocol is reducing the false positive rate of a Bloom Filter.


title: Learning Vision-based Cohesive Flight in Drone Swarms

abstract: This paper presents a data-driven approach to learning vision-based collective behavior from a simple flocking algorithm. We simulate a swarm of quadrotor drones and formulate the controller as a regression problem in which we generate 3D velocity commands directly from raw camera images. The dataset is created by simultaneously acquiring omnidirectional images and computing the corresponding control command from the flocking algorithm. We show that a convolutional neural network trained on the visual inputs of the drone can learn not only robust collision avoidance but also coherence of the flock in a sample-efficient manner. The neural controller effectively learns to localize other agents in the visual input, which we show by visualizing the regions with the most influence on the motion of an agent. This weakly supervised saliency map can be computed efficiently and may be used as a prior for subsequent detection and relative localization of other agents. We remove the dependence on sharing positions among flock members by taking only local visual information into account for control. Our work can therefore be seen as the first step towards a fully decentralized, vision-based flock without the need for communication or visual markers to aid detection of other agents.


title: MTNT: A Testbed for Machine Translation of Noisy Text

abstract: Noisy or non-standard input text can cause disastrous mistranslations in most modern Machine Translation (MT) systems, and there has been growing research interest in creating noise-robust MT systems. However, as of yet there are no publicly available parallel corpora of with naturally occurring noisy inputs and translations, and thus previous work has resorted to evaluating on synthetically created datasets. In this paper, we propose a benchmark dataset for Machine Translation of Noisy Text (MTNT), consisting of noisy comments on Reddit (www.reddit.com) and professionally sourced translations. We commissioned translations of English comments into French and Japanese, as well as French and Japanese comments into English, on the order of 7k-37k sentences per language pair. We qualitatively and quantitatively examine the types of noise included in this dataset, then demonstrate that existing MT models fail badly on a number of noise-related phenomena, even after performing adaptation on a small training set of in-domain data. This indicates that this dataset can provide an attractive testbed for methods tailored to handling noisy text in MT. The data is publicly available at www.cs.cmu.edu/~pmichel1/mtnt/.


title: Results for Named Entity Recognition on Chinese Social Media with an Updated Dataset

abstract: Named entity recognition, and other information extraction tasks, frequently use linguistic features such as part of speech tags or chunkings. For languages where word boundaries are not readily identified in text, word segmentation is a key first step to generating features for an NER system. While using word boundary tags as features are helpful, the signals that aid in identifying these boundaries may provide richer information for an NER system. New state-of-the-art word segmentation systems use neural models to learn representations for predicting word boundaries. We show that these same representations, jointly trained with an NER system, yield significant improvements in NER for Chinese social media. In our experiments, jointly training NER and word segmentation with an LSTM-CRF model yields nearly 5% absolute improvement over previously published results.


title: An ensemble approach for multi-label classification of item click sequences

abstract: In this paper, we describe our approach to RecSys 2015 challenge problem. Given a dataset of item click sessions, the problem is to predict whether a session results in a purchase and which items are purchased if the answer is yes. We define a simpler analogous problem where given an item and its session, we try to predict the probability of purchase for the given item. For each session, the predictions result in a set of purchased items or often an empty set. We apply monthly time windows over the dataset. For each item in a session, we engineer features regarding the session, the item properties, and the time window. Then, a balanced random forest classifier is trained to perform predictions on the test set. The dataset is particularly challenging due to privacy-preserving definition of a session, the class imbalance problem, and the volume of data. We report our findings with respect to feature engineering, the choice of sampling schemes, and classifier ensembles. Experimental results together with benefits and shortcomings of the proposed approach are discussed. The solution is efficient and practical in commodity computers.


title: Machine Learning for E-mail Spam Filtering: Review,Techniques and Trends

abstract: We present a comprehensive review of the most effective content-based e-mail spam filtering techniques. We focus primarily on Machine Learning-based spam filters and their variants, and report on a broad review ranging from surveying the relevant ideas, efforts, effectiveness, and the current progress. The initial exposition of the background examines the basics of e-mail spam filtering, the evolving nature of spam, spammers playing cat-and-mouse with e-mail service providers (ESPs), and the Machine Learning front in fighting spam. We conclude by measuring the impact of Machine Learning-based filters and explore the promising offshoots of latest developments.


Global Range Encoding for Efficient Partition Elimination


title: Covert retrieval practice benefits retention as much as overt retrieval practice.

abstract: Many experiments provide evidence that practicing retrieval benefits retention relative to conditions of no retrieval practice. Nearly all prior research has employed retrieval practice requiring overt responses, but a few experiments have shown that covert retrieval also produces retention advantages relative to control conditions. However, direct comparisons between overt and covert retrieval are scarce: Does covert retrieval—thinking of but not producing responses—on a first test produce the same benefit as overt retrieval on a criterial test given later? We report 4 experiments that address this issue by comparing retention on a second test following overt or covert retrieval on a first test. In Experiment 1 we used a procedure designed to ensure that subjects would retrieve on covert as well as overt test trials and found equivalent testing effects in the 2 cases. In Experiment 2 we replicated these effects using a procedure that more closely mirrored natural retrieval processes. In Experiment 3 we showed that overt and covert retrieval produced equivalent testing effects after a 2-day delay. Finally, in Experiment 4 we showed that covert retrieval benefits retention more than restudying. We conclude that covert retrieval practice is as effective as overt retrieval practice, a conclusion that contravenes hypotheses in the literature proposing that overt responding is better. This outcome has an important educational implication: Students can learn as much from covert self-testing as they would from overt responding.


title: Deep learning enhanced mobile-phone microscopy

abstract: Mobile-phones have facilitated the creation of field-portable, cost-effective imaging and sensing technologies that approach laboratory-grade instrument performance. However, the optical imaging interfaces of mobile-phones are not designed for microscopy and produce spatial and spectral distortions in imaging microscopic specimens. Here, we report on the use of deep learning to correct such distortions introduced by mobile-phone-based microscopes, facilitating the production of high-resolution, denoised and colour-corrected images, matching the performance of benchtop microscopes with high-end objective lenses, also extending their limited depth-of-field. After training a convolutional neural network, we successfully imaged various samples, including blood smears, histopathology tissue sections, and parasites, where the recorded images were highly compressed to ease storage and transmission for telemedicine applications. This method is applicable to other low-cost, aberrated imaging systems, and could offer alternatives for costly and bulky microscopes, while also providing a framework for standardization of optical images for clinical and biomedical applications.


title: Wearable Button Antenna for Dual-Band WLAN Applications With Combined on and off-Body Radiation Patterns

abstract: A new type of wearable button antenna for wireless local area network (WLAN) applications is proposed. The antenna is composed of a button with a diameter of circa 16 mm incorporating a patch on top of a dielectric disc. The button is located on top of a textile substrate and a conductive textile ground that are to be incorporated in clothing. The main characteristic feature of this antenna is that it shows two different types of radiation patterns, a monopole type pattern in the 2.4 GHz band for on-body communications and a broadside type pattern in the 5 GHz band for off-body communications. A very high efficiency of about 90% is obtained, which is much higher than similar full textile solutions in the literature. A prototype has been fabricated and measured. The effect of several real-life situations such as a tilted button and bending of the textile ground have been studied. Measurements agree very well with simulations.


title: Implicit learning and tacit knowledge

abstract: I examine the phenomenon of implicit learning, the process by which knowledge about the ralegoverned complexities of the stimulus environment is acquired independently of conscious attempts to do so. Our research with the two, seemingly disparate experimental paradigms of synthetic grammar learning and probability learning is reviewed and integrated with other approaches to the general problem of unconscious cognition. The conclusions reached are as follows: (a) Implicit learning produces a tacit knowledge base that is abstract and representative of the structure of the environment; (b) such knowledge is optimally acquired independently of conscious efforts to learn; and (c) it can be used implicitly to solve problems and make accurate decisions about novel stimulus circumstances. Various epistemological issues and related prob1 lems such as intuition, neuroclinical disorders of learning and memory, and the relationship of evolutionary processes to cognitive science are also discussed. Some two decades ago the term implicit learning was first used to characterize how one develops intuitive knowledge about the underlying structure of a complex stimulus environment (Reber, 1965, 1967). In those early writings, I argued that implicit learning is characterized by two critical features: (a) It is an unconscious process and (b) it yields abstract knowledge. Implicit knowledge results from the induction of an abstract representation of the structure that the stimulus environment displays, and this knowledge is acquired in the absence of conscious, reflective strategies to learn. Since then, the evidence in support of this theory has been abundant, and many of the details of the process have been sharpened. This article is an overview of this evidence and an attempt to extend the general concepts to provide some insight into a variety of related processes such as arriving at intuitive judgments, complex decision making, and, in a broad sense, learning about the complex covariations among events that characterize the environment. Put simply, this is an article about learning. It seems curious, given the pattern of psychological investigation of the middle decades of this century, that the topic of learning should be so poorly represented in the contemporary literature in cognitive psychology. The energies of cognitive scientists have been invested largely in the analysis and modeling of existing knowledge rather than in investigations of how it was acquired. For example, in an important recent article on the general topic of unconscious memorial systems, Schacter (1987) never came to grips with the distinction between implicit learning and implicit memory. The latter, the focus of his review, was dealt with historically, characterized, out


Quality Dimensions Trend Analysis in the Context of Evaluating E-government Services


title: Computed tomography (CT)-compatible remote center of motion needle steering robot: Fusing CT images and electromagnetic sensor data.

abstract: Lung cancer is the most common cause of cancer-related death, and early detection can reduce the mortality rate. Patients with lung nodules greater than 10 mm usually undergo a computed tomography (CT)-guided biopsy. However, aligning the needle with the target is difficult and the needle tends to deflect from a straight path. In this work, we present a CT-compatible robotic system, which can both position the needle at the puncture point and also insert and rotate the needle. The robot has a remote-center-of-motion arm which is achieved through a parallel mechanism. A new needle steering scheme is also developed where CT images are fused with electromagnetic (EM) sensor data using an unscented Kalman filter. The data fusion allows us to steer the needle using the real-time EM tracker data. The robot design and the steering scheme are validated using three experimental cases. Experimental Case I and II evaluate the accuracy and CT-compatibility of the robot arm, respectively. In experimental Case III, the needle is steered towards 5 real targets embedded in an anthropomorphic gelatin phantom of the thorax. The mean targeting error for the 5 experiments is 1.78 ± 0.70 mm. The proposed robotic system is shown to be CT-compatible with low targeting error. Small nodule size and large needle diameter are two risk factors that can lead to complications in lung biopsy. Our results suggest that nodules larger than 5 mm in diameter can be targeted using our method which may result in lower complication rate.


"Work Extrinsic and Intrinsic Motivation Scale: Its value for organizational psychology research": Correction to Tremblay et al (2009).


title: Can You See the Real Me? Activation and Expression of the "True Self" on the Internet

abstract: Those who feel better able to express their “true selves” in Internet rather than face-to-face interaction settings are more likely to form close relationships with people met on the Internet (McKenna, Green, & Gleason, this issue). Building on these correlational findings from survey data, we conducted three laboratory experiments to directly test the hypothesized causal role of differential self-expression in Internet relationship formation. Experiments 1 and 2, using a reaction time task, found that for university undergraduates, the true-self concept is more accessible in memory during Internet interactions, and the actual self more accessible during face-to-face interactions. Experiment 3 confirmed that people randomly assigned to interact over the Internet (vs. face to face) were better able to express their true-self qualities to their partners.


title: Data-Driven Color Augmentation Techniques for Deep Skin Image Analysis

abstract: Dermoscopic skin images are often obtained with different imaging devices, under varying acquisition conditions. In this work, instead of attempting to perform intensity and color normalization, we propose to leverage computational color constancy techniques to build an artificial data augmentation technique suitable for this kind of images. Specifically, we apply the \emph{shades of gray} color constancy technique to color-normalize the entire training set of images, while retaining the estimated illuminants. We then draw one sample from the distribution of training set illuminants and apply it on the normalized image. We employ this technique for training two deep convolutional neural networks for the tasks of skin lesion segmentation and skin lesion classification, in the context of the ISIC 2017 challenge and without using any external dermatologic image set. Our results on the validation set are promising, and will be supplemented with extended results on the hidden test set when available.


title: Predictive Mechanisms in Idiom Comprehension

abstract: Prediction is pervasive in human cognition and plays a central role in language comprehension. At an electrophysiological level, this cognitive function contributes substantially in determining the amplitude of the N400. In fact, the amplitude of the N400 to words within a sentence has been shown to depend on how predictable those words are: The more predictable a word, the smaller the N400 elicited. However, predictive processing can be based on different sources of information that allow anticipation of upcoming constituents and integration in context. In this study, we investigated the ERPs elicited during the comprehension of idioms, that is, prefabricated multiword strings stored in semantic memory. When a reader recognizes a string of words as an idiom before the idiom ends, she or he can develop expectations concerning the incoming idiomatic constituents. We hypothesized that the expectations driven by the activation of an idiom might differ from those driven by discourse-based constraints. To this aim, we compared the ERP waveforms elicited by idioms and two literal control conditions. The results showed that, in both cases, the literal conditions exhibited a more negative potential than the idiomatic condition. Our analyses suggest that before idiom recognition the effect is due to modulation of the N400 amplitude, whereas after idiom recognition a P300 for the idiomatic sentence has a fundamental role in the composition of the effect. These results suggest that two distinct predictive mechanisms are at work during language comprehension, based respectively on probabilistic information and on categorical template matching.


title: Real-time approaching vehicle detection in blind-spot area

abstract: Detecting objects (including all kinds of vehicles, bicycles, and pedestrians) accurately and efficiently is an essential issue in blind-spot information system (BLIS). To meet these requirements, this paper presents an image-based method to detect approaching objects in blind-spot area and proposes a verification method by using the recorded video database from real traffic environment. By taking video frames and converting the images into one dimensional information, the image entropy of the road scene in the near lane are estimated. Thus, by analysis the lane information, an object will be detected and located in a constant time. This idea has been realized and implemented on low-cost DSP platform developed by Automotive Research and Testing Center (ARTC, Taiwan). The accurate rate of this blind-spot detection system (BDS) is 91% and the frame rate is more than 20 frames per sec (fps), in day and night and all weather conditions. The BDS has been applied for general vehicles and heavy truck vehicles nowadays.


title: Can Cascades be Predicted?

abstract: On many social networking web sites such as Facebook and Twitter, resharing or reposting functionality allows users to share others' content with their own friends or followers. As content is reshared from user to user, large cascades of reshares can form. While a growing body of research has focused on analyzing and characterizing such cascades, a recent, parallel line of work has argued that the future trajectory of a cascade may be inherently unpredictable. In this work, we develop a framework for addressing cascade prediction problems. On a large sample of photo reshare cascades on Facebook, we find strong performance in predicting whether a cascade will continue to grow in the future. We find that the relative growth of a cascade becomes more predictable as we observe more of its reshares, that temporal and structural features are key predictors of cascade size, and that initially, breadth, rather than depth in a cascade is a better indicator of larger cascades. This prediction performance is robust in the sense that multiple distinct classes of features all achieve similar performance. We also discover that temporal features are predictive of a cascade's eventual shape. Observing independent cascades of the same content, we find that while these cascades differ greatly in size, we are still able to predict which ends up the largest.


SignalP 4.0: discriminating signal peptides from transmembrane regions


title: Data mining in IoT: data analysis for a new paradigm on the internet

abstract: This paper provides an overview on Data Mining (DM) technologies for the Internet of Things (IoT). IoT has become an active area of research, since IoT promises among other to improve quality of live and safety in Smart Cities, to make resource supply and waste management more efficient, and optimize traffic. DM is highly domain specific and depends on what is being mined for. For instance, if IoT is used to optimize traffic in a Smart City to reduce traffic jams and to find parking spaces quicker, different types of data needs to be collected and analysed from an eHealth solution, where IoT is used in a Smart Home to monitor the well being of patients or elderly people. IoT connects things that can collect numeric data from smart sensors, streaming data from cameras or route information on maps. Depending on the type of data, different techniques need to be adopted to analyse them. Also, many IoT applications analyse data from different devices and correlate them to make predictions about possible machine failures in production sites or looming emergency situations in Smart Buildings in a home security application. DM techniques need to handle the heterogeneity of IoT data, the large volumes of data and the speed at which they are produced. This paper explores the state of the art DM techniques for IoT.


title: The impact of European institutionalisation on professional services: the case of business consultants

abstract: The original signatory states to the Treaty of Rome had formally committed themselves to policies of liberalisation by implementing mutual recognition in the sector of professional services by the end of the twelve year transition period. In fact, the process took much longer and the freedom of services implemented in the early 1990s affected professional services in various forms and different degrees. ::: Business consulting is an industry with a tradition of rather low degree of market regulation in all European countries. However, it is indirectly affected by a high level of regulation in neighbouring fields of business services and in some of the fields where they have developed special consulting expertise. As a consequence re-regulations of these branches do have an impact on the consultants' business as well. ::: There is a wide agreement in the political and scientific debates over the shift of decision making arenas and powers from the national to the supranational and from the public to the private sector. We argue that business asso-ciations and large companies can be regarded as co-responsible for the increasing impact of supranational institutions on the sectoral governance. ::: Based on the analysis of documents and on expert interviews in Brussels and in Germany (with representatives of associations and companies) we will highlight the need for differentiation among professions (and would be-professions) and concerning groups of organisations (associations and companies).


title: Optimized rate matching architecture for a LTE-Advanced FPGA-based PHY

abstract: In this paper we present an optimized rate matching architecture for a LTE-Advanced FPGA-based physical layer. Since LTE-Advanced can reach up to rates of 1 Gbps in downlink, and since rate matching is in that critical path, it is very important that the design of the hardware architecture be efficient enough to allow this high data rate with little resources as possible. If not well planned, implementations on FPGAs can be quite challenging, limiting the choices of speed grades and FPGAs sizes capable of supporting such requirements. We propose efficient hardware architecture for the LTE-Advanced rate matching generic procedure; occupying only 218 slices and 9 block RAMs and performing in frequencies greater than 400 MHz in a FPGA-based solution.


title: Example-based 3D inpainting of point clouds using metric tensor and Christoffel symbols

abstract: In this paper, we address the problem of 3D inpainting using example-based methods for point cloud data. 3D inpainting is a process of filling holes or missing regions in the reconstructed 3D models. Typically inpainting methods addressed in the literature fill missing regions due to occlusions or inaccurate scanning of 3D models. However, we focus on scenarios involving naturally existing damaged models which are partly broken or incomplete in artifacts at cultural heritage sites. We propose two example-based inpainting techniques, namely region of interest (ROI)-based and patch-based methods, to inpaint the missing regions of the damaged model. For both the methods, we represent the 3D model as a set of Riemannian manifolds in Euclidean space, to capture the inherent geometry using metric tensor and Christoffel symbols as geometric features and decompose into basic shape (such as spherical, conical and cylindrical) regions using decomposition algorithm derived from supervised learning. In ROI-based method, instead of using single similar example for inpainting, we select the most relevant regions that best-fit the missing region from the set of basic shape regions derived from n similar examples. And in patch-based method, we not only select the most relevant regions but cluster the regions into a set of patches. The best corresponding patches that match the missing region to be inpainted are considered to be the most relevant best-fit patches that cover the complete missing region. We demonstrate the performance of proposed inpainting methods on cultural heritage artifacts with varying complexities and sizes for both synthetically generated holes and real missing regions.


Mental Health Nursing: Dimensions of Praxis


title: Solving Travelling Salesman Problem by Using Optimization Algorithms

abstract: This paper presents the performances of different types of optimization techniques used in artificial intelligence (AI), these are Ant Colony Optimization (ACO), Improved Particle Swarm Optimization with a new operator (IPSO), Shuffled Frog Leaping Algorithms (SFLA) and modified shuffled frog leaping algorithm by using a crossover and mutation operators. They were used to solve the traveling salesman problem (TSP) which is one of the popular and classical route planning problems of research and it is considered as one of the widely known of combinatorial optimization. Combinatorial optimization problems are usually simple to state but very difficult to solve. ACO, PSO, and SFLA are intelligent meta-heuristic optimization algorithms with strong ability to analyze the optimization problems and find the optimal solution. They were tested on benchmark problems from TSPLIB and the test results were compared with each other. Keywords : Ant colony optimization, shuffled frog leaping algorithms, travelling salesman problem, improved particle swarm optimization


title: Insight into Structure and Assembly of the Nuclear Pore Complex by Utilizing the Genome of a Eukaryotic Thermophile

abstract: Despite decades of research, the structure and assembly of the nuclear pore complex (NPC), which is composed of ∼30 nucleoporins (Nups), remain elusive. Here, we report the genome of the thermophilic fungus Chaetomium thermophilum (ct) and identify the complete repertoire of Nups therein. The thermophilic proteins show improved properties for structural and biochemical studies compared to their mesophilic counterparts, and purified ctNups enabled the reconstitution of the inner pore ring module that spans the width of the NPC from the anchoring membrane to the central transport channel. This module is composed of two large Nups, Nup192 and Nup170, which are flexibly bridged by short linear motifs made up of linker Nups, Nic96 and Nup53. This assembly illustrates how Nup interactions can generate structural plasticity within the NPC scaffold. Our findings therefore demonstrate the utility of the genome of a thermophilic eukaryote for studying complex molecular machines.


title: Epilepsy: new advances

abstract: Summary Epilepsy affects 65 million people worldwide and entails a major burden in seizure-related disability, mortality, comorbidities, stigma, and costs. In the past decade, important advances have been made in the understanding of the pathophysiological mechanisms of the disease and factors affecting its prognosis. These advances have translated into new conceptual and operational definitions of epilepsy in addition to revised criteria and terminology for its diagnosis and classification. Although the number of available antiepileptic drugs has increased substantially during the past 20 years, about a third of patients remain resistant to medical treatment. Despite improved effectiveness of surgical procedures, with more than half of operated patients achieving long-term freedom from seizures, epilepsy surgery is still done in a small subset of drug-resistant patients. The lives of most people with epilepsy continue to be adversely affected by gaps in knowledge, diagnosis, treatment, advocacy, education, legislation, and research. Concerted actions to address these challenges are urgently needed.


SHREC ’ 17 Track Large-Scale 3 D Shape Retrieval from ShapeNet Core 55


title: Towards a walking, turning, and jumping quadruped robot with compliant mechanisms

abstract: In this paper, we present a small-sized quadruped robot by using compliant mechanisms to accomplish walking, turning, and jumping motions. The design of cable-driven legs with a tension spring on the knee joint can provide robust, stable, and energy efficient gaits for quadruped robots. The quadruped robot can produce a trot-like walking motion, based on the proposed foot trajectory, and the turning movement can be achieved by regulating the step length during the walking motion. Additionally, an energy storing mechanism is designed for the quadruped robot to be able to jump with different height and distance. The proposed system is validated via simulations and experiments to show the efficacy of the quadruped robot in walking, turning, and jumping motions.


title: Matching Islamic patterns in Kufic images

abstract: In this study, we address the problem of matching patterns in Kufic calligraphy images. Being used as a decorative element, Kufic images have been designed in a way that makes it difficult to be read by non-experts. Therefore, available methods for handwriting recognition are not easily applicable to the recognition of Kufic patterns. In this study, we propose two new methods for Kufic pattern matching. The first method approximates the contours of connected components into lines and then utilizes chain code representation. Sequence matching techniques with a penalty for gaps are exploited for handling the variations between different instances of sub-patterns. In the second method, skeletons of connected components are represented as a graph where junction and end points are considered as nodes. Graph isomorphism techniques are then relaxed for partial graph matching. Methods are evaluated over a collection of 270 square Kufic images with 8,941 sub-patterns. Experimental results indicate that, besides retrieval and indexing of known patterns, our method also allows the discovery of new patterns.


title: On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data.

abstract: For modern evidence-based medicine, a well thought-out risk scoring system for predicting the occurrence of a clinical event plays an important role in selecting prevention and treatment strategies. Such an index system is often established based on the subject's 'baseline' genetic or clinical markers via a working parametric or semi-parametric model. To evaluate the adequacy of such a system, C-statistics are routinely used in the medical literature to quantify the capacity of the estimated risk score in discriminating among subjects with different event times. The C-statistic provides a global assessment of a fitted survival model for the continuous event time rather than focussing on the prediction of bit-year survival for a fixed time. When the event time is possibly censored, however, the population parameters corresponding to the commonly used C-statistics may depend on the study-specific censoring distribution. In this article, we present a simple C-statistic without this shortcoming. The new procedure consistently estimates a conventional concordance measure which is free of censoring. We provide a large sample approximation to the distribution of this estimator for making inferences about the concordance measure. Results from numerical studies suggest that the new procedure performs well in finite sample.


title: Cryptocurrencies, smart contracts, and artificial intelligence

abstract: Recent developments in "cryptocurrencies" and "smart contracts" are creating new opportunities for applying AI techniques. These economic technologies would benefit from greater real world knowledge and reasoning as they become integrated with everyday commerce. Cryptocurrencies and smart contracts may also provide an infrastructure for ensuring that AI systems follow specified legal and safety regulations as they become more integrated into human society.


title: BGSA: binary gravitational search algorithm

abstract: Gravitational search algorithm is one of the new optimization algorithms that is based on the law of gravity and mass interactions. In this algorithm, the searcher agents are a collection of masses, and their interactions are based on the Newtonian laws of gravity and motion. In this article, a binary version of the algorithm is introduced. To evaluate the performances of the proposed algorithm, several experiments are performed. The experimental results confirm the efficiency of the BGSA in solving various nonlinear benchmark functions.


title: TSPLIB—A Traveling Salesman Problem Library

abstract: This paper contains the description of a traveling salesman problem library (TSPLIB) which is meant to provide researchers with a broad set of test problems from various sources and with various properties. For every problem a short description is given along with known lower and upper bounds. Several references to computational tests on some of the problems are given. INFORMS Journal on Computing , ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.


title: Driving Behavior Analysis for Smartphone-based Insurance Telematics

abstract: Insurance telematics programs are continuously gaining market shares in the automotive insurance industry. By recording data on drivers' behavior, the information asymmetry between the policyholder and the insurer is reduced, enabling a granular risk differentiation based on the true risk levels of the drivers. However, the growth of the insurance telematics industry is being held up by large logistic costs associated with the process of acquiring data. As a result, several market participants have started looking towards smartphone-based solutions, which have the potential of easing and improving the data collection process for both policyholders and insurers. In this paper, we present a unified framework highlighting the challenges of smartphone-based driver behavior analysis. Since all driver behavior analysis relies on access to accurate navigation data, we first address the intermediate step of smartphone-based automotive navigation. The considered topics include estimation of the smartphone's orientation with respect to the vehicle, classification of the smartphone owner as a passenger or driver, and navigation in GNSS-challenged areas. Once a driver-specific high-performance navigation solution has been obtained, it can be used to extract information on the driver's behavior. We review the most commonly employed driving events, and discuss some of the difficulties inherent in detecting these events.


title: Forensic Data Carving

abstract: File or data carving is a term used in the field of Cyber forensics. Cyber forensics is the process of acquisition, authentication, analysis and documentation of evidence extracted from and/or contained in a computer system, computer network and digital media. Extracting data (file) out of undifferentiated blocks (raw data) is called as carving. Identifying and recovering files based on analysis of file formats is known as file carving. In Cyber Forensics, carving is a helpful technique in finding hidden or deleted files from digital media. A file can be hidden in areas like lost clusters, unallocated clusters and slack space of the disk or digital media. To use this method of extraction, a file should have a standard file signature called a file header (start of the file). A search is performed to locate the file header and continued until the file footer (end of the file) is reached. The data between these two points will be extracted and analyzed to validate the file. The extraction algorithm uses different methods of carving depending on the file formats.


title: Gender classification using face recognition

abstract: This paper addresses the issue of gender classification using the method of Principal Component Analysis (PCA) for face recognition and classification of human faces. The use of the PCA algorithm has a maximum success rate of 82%. The gender classification system is then improved by using the Linear Discriminant Analysis (LDA. This algorithm has a machine-learning framework by which it trains on a database and using this trained environment to predict the outcome of other images. The classification is restricted to two classes - male and female. Upon using LDA, the success rate increased to approximately 85%. The database used in this paper for the training and testing of images is called the FERET database.


Dissecting cellulitis of the scalp treated with tumour necrosis factor-α inhibitors: experience with two agents


title: Design of planar dual-band multi-way power dividers

abstract: This paper presents planar multi-way bagley polygon (BP) power dividers (PDs) that can operate at two arbitrary frequencies. Design equations are derived mathematically, and practical issues in microstrip manufacture are discussed. 3-way and 5-way dual-band BP PDs are fabricated and measured for verification purpose. Good agreement between simulation and measurement results proves the validity of the design method. The presented PDs exhibit wide bandwidths and 180° phase shifts between output ports. Furthermore, simple layouts lead to convenient design procedure and easy fabrication.


title: Ice melting simulation with water flow handling

abstract: In this paper, we propose a new approach based on a particle-based model for ice melting simulation. Each particle has an attribute called virtual water. The amount of the virtual water of an ice particle indicates the amount of water surrounding the ice particle. The transfer of the virtual water is performed between the exterior ice particles so as to simulate the thin layer of water flow on the surface. Our approach also handles the transition between the virtual water and the water particles. We compute the isosurface of a density field defined by the ice particles and the virtual water. A simple ray tracing method is adopted for rendering the objects. We report the experimental results of several ice melting simulations with water flow and water drops.


title: Student Evaluations of Faculty Grading Methods.

abstract: Abstract Considerable literature exists concerning grading, its purposes, its best practices, and its reliability. In this research, the authors investigated students' complaints about how faculty members grade them on both essay assignments and quantitative problems. The two major problems cited by students were lack of “fairness in grading” and too little feedback from their instructors. The authors explain how strategies such as setting clear assignment objectives and using matrices and rubrics would help to reduce these negative student comments.


title: Factors affecting Finnish consumers' intention to receive SMS marketing: a conceptual model and an empirical study

abstract: The study examines factors that affect consumers' intentions to participate in mobile marketing and explores the relationship between consumers' intentions to participate in mobile marketing and their experience with mobile marketing. The authors develop and test a research model with a sample of 4062 mobile phone users. The empirical model highlights that Perceived Utility (PU), context, credibility and Perceived Social Utility (PSU) are the main contributors to consumers' intentions to participate in mobile-marketing initiatives. The paper provides several contributions to both theory and practice.


title: Phantom-limb pain as a perceptual correlate of cortical reorganization following arm amputation

abstract: Although phantom-limb pain is a frequent consequence of the amputation of an extremity, little is known about its origin. On the basis of the demonstration of substantial plasticity of the somatosensory cortex after amputation or somatosensory deafferentation in adult monkeys, it has been suggested that cortical reorganization could account for some non-painful phantom-limb phenomena in amputees and that cortical reorganization has an adaptive (that is, pain-preventing) function. Theoretical and empirical work on chronic back pain has revealed a positive relationship between the amount of cortical alteration and the magnitude of pain, so we predicted that cortical reorganization and phantom-limb pain should be positively related. Using non-invasive neuromagnetic imaging techniques to determine cortical reorganization in humans, we report a very strong direct relationship (r = 0.93) between the amount of cortical reorganization and the magnitude of phantom limb pain (but not non-painful phantom phenomena) experienced after arm amputation. These data indicate that phantom-limb pain is related to, and may be a consequence of, plastic changes in primary somatosensory cortex.


title: Action unit selective feature maps in deep networks for facial expression recognition

abstract: Facial expression recognizers based on handcrafted features have achieved satisfactory performance on many databases. Recently, deep neural networks, e. g. deep convolutional neural networks (CNNs) have been shown to boost performance on vision tasks. However, the mechanisms exploited by CNNs are not well established. In this paper, we establish the existence and utility of feature maps selective to action units in a deep CNN trained by transfer learning. We transfer a network pre-trained on the Image-Net dataset to the facial expression recognition task using the Karolinska Directed Emotional Faces (KDEF), Radboud Faces Database(RaFD) and extended Cohn-Kanade (CK+) database. We demonstrate that higher convolutional layers of the deep CNN trained on generic images are selective to facial action units. We also show that feature selection is critical in achieving robustness, with action unit selective feature maps being more critical in the facial expression recognition task. These results support the hypothesis that both human and deeply learned CNNs use similar mechanisms for recognizing facial expressions.


title: A novel approach for automated detection of focal EEG signals using empirical wavelet transform

abstract: The determination of epileptogenic area is a prime task in presurgical evaluation. The seizure activity can be prevented by operating the affected areas by clinical surgery. In this paper, an automatic approach has been presented to detect electroencephalogram (EEG) signals of non-focal and focal groups. The proposed approach can be used to determine the area linked to the focal epilepsy. In our method, the EEG signal is decomposed into rhythms using empirical wavelet transform technique. The two-dimensional (2D) projections of the reconstructed phase space (RPS) have been obtained for the rhythms. Area measures for various RPS plots are estimated using central tendency measure (CTM) parameter. The area parameters are used with least-squares support vector machine (LS-SVM) classifier to classify the focal and non-focal classes of EEG signals. In this work, we have achieved a maximum classification accuracy of 90%, sensitivity and specificity of 88 and 92%, respectively, using 50 pairs of focal and non-focal EEG signals. The same method has achieved maximum classification accuracy, sensitivity and specificity of 82.53, 81.60 and 83.46%, respectively, with 750 pairs of signals. The developed prototype can be used for the epileptic patients and aid the clinicians to confirm diagnosis.


title: Social media self-efficacy and information evaluation online

abstract: We introduce the concept of social media self-efficacy (SMSE).We examine this concept using survey data (N=3568) of Internet users.Users with higher SMSE find online social information to be more trustworthy.They also use others' opinions and social media when evaluating information. This study introduces the concept of social media self-efficacy, or a person's perceived ability to reach desired outcomes in the social media environment, and examines the relationship between social media self-efficacy and how people evaluate information found online. Results of a survey of a representative sample of adult Internet users in the United States (N=3568) indicate that users with higher social media self-efficacy find information shared via social media to be more trustworthy than do those lower in social media self-efficacy. These self-efficacious social media users also rely more both on the opinions of others and on social media specifically when evaluating or verifying the information they find online, suggesting that they may be more prone to seek out and be influenced by input from others. Practical and theoretical implications of these findings are explored.


title: Misleading Health-Related Information Promoted Through Video-Based Social Media : Anorexia on YouTube

abstract: INTRODUCTION ::: The amount of information being uploaded onto social video platforms, such as YouTube, Vimeo, and Veoh, continues to spiral, making it increasingly difficult to discern reliable health information from misleading content. There are thousands of YouTube videos promoting misleading information about anorexia (eg, anorexia as a healthy lifestyle). ::: ::: ::: OBJECTIVE ::: The aim of this study was to investigate anorexia-related misinformation disseminated through YouTube videos. ::: ::: ::: METHODS ::: We retrieved YouTube videos related to anorexia using the keywords anorexia, anorexia nervosa, proana, and thinspo on October 10, 2011.Three doctors reviewed 140 videos with approximately 11 hours of video content, classifying them as informative, pro-anorexia, or others. By informative we mean content describing the health consequences of anorexia and advice on how to recover from it; by pro-anorexia we mean videos promoting anorexia as a fashion, a source of beauty, and that share tips and methods for becoming and remaining anorexic. The 40 most-viewed videos (20 informative and 20 pro-anorexia videos) were assessed to gauge viewer behavior. ::: ::: ::: RESULTS ::: The interrater agreement of classification was moderate (Fleiss' kappa=0.5), with 29.3% (n=41) being rated as pro-anorexia, 55.7% (n=78) as informative, and 15.0% (n=21) as others. Pro-anorexia videos were favored 3 times more than informative videos (odds ratio [OR] 3.3, 95% CI 3.3-3.4, P<.001). ::: ::: ::: CONCLUSIONS ::: Pro-anorexia information was identified in 29.3% of anorexia-related videos. Pro-anorexia videos are less common than informative videos; however, in proportional terms, pro-anorexia content is more highly favored and rated by its viewers. Efforts should focus on raising awareness, particularly among teenagers, about the trustworthiness of online information about beauty and healthy lifestyles. Health authorities producing videos to combat anorexia should consider involving celebrities and models to reach a wider audience. More research is needed to study the characteristics of pro-anorexia videos in order to develop algorithms that will automatically detect and filter those videos before they become popular.


title: The generation of handwriting with delta-lognormal synergies

abstract: This paper presents a handwriting generation model that takes advantage of the asymptotic impulse response of neuromuscular networks to produce and control complex two-dimensional synergistic movements. A parametric definition of a ballistic stroke in the context of the kinematic theory of rapid human movements is given. Two types of parameters are used: command and system parameters. The first group provides a representation of the action plan while the second takes into account the temporal properties of the neuromuscular systems executing that plan. Handwriting is described as the time superimposition of basic discontinuous strokes that results in a continuous summation of delta-lognormal velocity vectors. The model leads to trajectory reconstruction, both in the spatial and in the kinematic domain. According to this new paradigm, the angular velocity does not have to be controlled independently and continuously; it naturally emerges from the vectorial summation process. Several psychophysical phenomena related to two-dimensional movements are explained and analyzed in the context of the model: the speed/accuracy trade-offs, spatial scaling, the isochrony principle, the two-thirds power law, effector independence, etc. The overall approach also shows how basic handwriting characteristics (dimension, slant, baseline, shape, etc.) are affected and controlled using an action plan made up of virtual targets fed into a neuromuscular synergy that is governed by a delta-lognormal law.


title: The competition–performance relation: A meta-analytic review and test of the opposing processes model of competition and performance.

abstract: What is the relation between competition and performance? The present research addresses this important multidisciplinary question by conducting a meta-analysis of existing empirical work and by proposing a new conceptual model—the opposing processes model of competition and performance. This model was tested by conducting an additional meta-analysis and 3 new empirical studies. The first meta-analysis revealed that there is no noteworthy relation between competition and performance. The second meta-analysis showed, in accord with the opposing processes model, that the absence of a direct effect is the result of inconsistent mediation via achievement goals: Competition prompts performance-approach goals which, in turn, facilitate performance; and competition also prompts performance-avoidance goals which, in turn, undermine performance. These same direct and mediational findings were also observed in the 3 new empirical studies (using 3 different conceptualizations of competition and attending to numerous control variables). Our findings provide both interpretational clarity regarding past research and conceptual guidance regarding future research on the competition–performance relation.


title: Hybrid of wavelet and MFCC features for speaker verification

abstract: In this paper Wavelet Based Mel Frequency Cepstral Coefficient (WMFCC) features are proposed for speaker verification. The performance of WMFCC features is evaluated and compared with the performance of Mel Frequency Cepstral Coefficient (MFCC) features. A database of ten Hindi digits of sixteen speakers is used during simulation of results. Gaussian Mixture Models (GMMs) are used for maximum log likelihood calculation during verification. The proposed features have shown an increment of 1.18% in performance over MFCC features for text dependent speaker verification system.


title: Technology Acceptance Model 3 and a Research Agenda on Interventions

abstract: Prior research has provided valuable insights into how and why employees make a decision about the adoption and use of information technologies (ITs) in the workplace. From an organizational point of view, however, the more important issue is how managers make informed decisions about interventions that can lead to greater acceptance and effective utilization of IT. There is limited research in the IT implementation literature that deals with the role of interventions to aid such managerial decision making. Particularly, there is a need to understand how various interventions can influence the known determinants of IT adoption and use. To address this gap in the literature, we draw from the vast body of research on the technology acceptance model (TAM), particularly the work on the determinants of perceived usefulness and perceived ease of use, and: (i) develop a comprehensive nomological network (integrated model) of the determinants of individual level (IT) adoption and use; (ii) empirically test the proposed integrated model; and (iii) present a research agenda focused on potential pre- and postimplementation interventions that can enhance employees' adoption and use of IT. Our findings and research agenda have important implications for managerial decision making on IT implementation in organizations.


title: Economically Efficient Virtualization over Cloud Using Docker Containers

abstract: In computing era, a virtual version of a stratagem or resource, such as computer network resources, server, storage device, or a combination of these usually refers as virtualization where the framework segregates the resources into one or more execution instances. A Virtual Machine (VM) created on the host hardware as a software is called a hypervisor or a VM manager. Nowadays, hypervisor based virtualization is the most prevailing technology for virtual environment. It is supple and performs for equivalently any guest operating system (OS) but it leads to overload on memory and CPU. Nevertheless, container based virtualization (or OS virtualization) technique which allows various different VMs employed on top of running kernel. Unalike the existing virtualization approach, a containerization does not execute a VM as a complete OS instance but runs as partial instances of the OS which reduces the CPU and memory overload and also provides the portability due to small size of containers. Docker is a tool to make Linux Containers (LXC) easier to use. Docker containers could be specifically deployed for the applications and systems deployment using lightweight containerization technique which results reducing economical overload instead of VMs over Cloud.


title: Moving Vehicle Detection for Measuring Traffic Count Using OpenCV

abstract: System in this paper is designed and implemented with Visual C++ software with Intel's OpenCV video stream processing system to realize the real-time automatic vehicle detection and vehicle counting. Expressways, highways and roads are getting overcrowded due to increase in number of vehicles. Vehicle detection, tracking, classification and counting is very important for military, civilian and government applications, such as highway monitoring, traffic planning, toll collection and traffic flow. For the traffic management, vehicles detection is the critical step. Computer Vision based techniques are more suitable because these systems do not disturb traffic while installation and they are easy to modify. In this paper we present inexpensive, portable and Computer Vision based system for moving vehicle detection and counting. Image from video sequence are taken to detect moving vehicles, so that background is extracted from the images. The extracted background is used in subsequent analysis to detect and classify moving vehicles as light vehicles, heavy vehicles and motorcycle. The system is implemented using OpenCV image development kits and experimental results are demonstrated from real-time video taken from single camera. We tested this system on a laptop powered by an Intel Core Duo (1.83 GHZ) CPU and 2GB RAM. This highway traffic counting process has been developed by background subtraction, image filtering, image binary and segmentation methods are used. This system is also capable of counting moving vehicles from pre-recorded videos. 


title: Integration of updated DeLone & McLean success model, KANO model and QFD to analyze quality of an information system

abstract: Studies on information system success and evaluation have been done for several decades. However, an evaluation should be followed by system improvement to give practical contribution to organization. This study proposes an integrated step to evaluate an IS success and analyze its improvement opportunities based on user requirements. To aim this objective, DeLone & McLean success model, KANO model and QFD concept are utilized. A case study of Electronic Disposition and Mailing System (EDMS) in Ministry of State Secretariat Indonesia is presented to illustrate the use of proposed method. The results indicate that priorities of needs are different before and after the integration of Kano's Model in QFD.


title: Adaptive hierarchical RBF interpolation for creating smooth digital elevation models

abstract: This paper presents a fast algorithm for smooth digital elevation model interpolation and approximation from scattered elevation data. The global surface is reconstructed by subdividing it into overlapping local subdomains using a perfectly balanced binary tree. In each tree leaf, a smooth local surface is reconstructed using radial basis functions. Finally a hierarchical blending is done to create the final C1-continuous surface using a family of functions called Partition of Unity. We present two terrain data sets and show that our method is robust since the number of data points in the Partition of Unity blending areas is explicitly specified.


title: Introduction to Space-Time Wireless Communications

abstract: Wireless networks are under constant pressure to provide ever-higher data rates to increasing numbers of users with greater reliability. This book is an accessible introduction to every fundamental aspect of space-time wireless communications. Space-time processing technology is a powerful tool for improving system performance that already features in the UMTS and CDMA2000 mobile standards. The ideal volume for graduate students and professionals, it features homework problems and other supporting material on a companion website.


title: Two Billion Devices and Counting

abstract: Mobile computing has grown drastically over the past decade. Despite the rapid pace of advancements, mobile device understanding, benchmarking, and evaluation are still in their infancies, both in industry and academia. This article presents an industry perspective on the challenges facing mobile computer architecture, specifically involving mobile workloads, benchmarking, and experimental methodology, with the hope of fostering new research within the community to address pending problems. These challenges pose a threat to the systematic development of future mobile systems, which, if addressed, can elevate the entire mobile ecosystem to the next level.


title: pClock: an arrival curve based approach for QoS guarantees in shared storage systems

abstract: Storage consolidation is becoming an attractive paradigm for data organization because of the economies of sharing and the ease of centralized management. However, sharing of resources is viable only if applications can be isolated from each other. This work targets the problem of providing performance guarantees to an application irrespective of the behavior of other workloads. Application requirements are represented in terms of the average throughput, latency and maximum burst size. Most earlier schemes only do weighted bandwidth allocation; schemes that provide control of latency either cannot handle bursts or penalize applications for their own prior behavior, such as using spare capacity. Our algorithm pClock is based on arrival curves that intuitively capture the bandwidth and burst requirements of applications. We show analytically that an application following its arrival curve never misses its deadline. We have implemented pClock both in DiskSim and as a module in the Linux kernel 2.6. Our evaluation shows three important features of pClock: (1) benefits over existing algorithms; (2) efficient performance isolation and burst handling; and (3) the ability to allocate spare capacity to either speed up some applications or to a background utility, such as backup. pClock can be efficiently implemented in a system without much overhead.


title: A support vector machine based technique for online detection of outliers in transient time series

abstract: This paper deals with online detection and accommodation of outliers in transient time series by appealing to a machine learning technique. The methodology is based on a Least Squares Support Vector Machine technique together with a sliding window-based learning algorithm. A modification to this method is proposed so as to extend its application to transient raw data collected from transmitters attached to a Wireless Sensor Network. The performance of two approaches are compared on a particular controlled data set.


The Object Image-Schema and Other Dependent Schemas


title: REVIEW Drug Discovery: A Historical Perspective

abstract: Driven by chemistry but increasingly guided by pharmacology and the clinical sciences, drug research has contributed more to the progress of medicine during the past century than any other scientific factor. The advent of molecular biology and, in particular, of genomic sciences is having a deep impact on drug discovery. Recombinant proteins and monoclonal antibodies have greatly enriched our therapeutic armamentarium. Genome sciences, combined with bioinformatic tools, allow us to dissect the genetic basis of multifactorial diseases and to determine the most suitable points of attack for future medicines, thereby increasing the number of treatment options. The dramatic increase in the complexity of drug research is enforcing changes in the institutional basis of this interdisciplinary endeavor. The biotech industry is establishing itself as the discovery arm of the pharmaceutical industry. In bridging the gap between academia and large pharmaceutical companies, the biotech firms have been effective instruments of technology transfer.


title: Deep learning with shallow architecture for image classification

abstract: This paper presents a new scheme for image classification. The proposed scheme depicts a shallow architecture of Convolutional Neural Network (CNN) providing deep learning: For each image, we calculated the connection weights between the input layer and the hidden layer based on MultiResolution Analysis (MRA) at different levels of abstraction. Then, we selected the best features, representing well each class of images, with their corresponding weights using Adaboost algorithm. These weights are used as the connection weights between the hidden layer and the output layer, and will be used in the test phase to classify a given query image. The proposed approach was tested on different datasets and the obtained results prove the efficiency and the speed of the proposed approach.


FPGA Implementation of Mixed Radix CORDIC FFT Izhar


title: Range-Based Localization in Wireless Networks Using Density-Based Outlier Detection

abstract: Node localization is commonly employed in wireless networks. For example, it is used to improve routing and enhance security. Localization algorithms can be classified as range-free or range-based. Range-based algorithms use location metrics such as ToA, TDoA, RSS, and AoA to estimate the distance between two nodes. Proximity sensing between nodes is typically the basis for range-free algorithms. A tradeoff exists since range-based algorithms are more accurate but also more complex. However, in applications such as target tracking, localization accuracy is very important. In this paper, we propose a new range-based algorithm which is based on the density-based outlier detection algorithm (DBOD) from data mining. It requires selection of the K-nearest neighbours (KNN). DBOD assigns density values to each point used in the location estimation. The mean of these densities is calculated and those points having a density larger than the mean are kept as candidate points. Different performance measures are used to compare our approach with the linear least squares (LLS) and weighted linear least squares based on singular value decomposition (WLS-SVD) algorithms. It is shown that the proposed algorithm performs better than these algorithms even when the anchor geometry about an unlocalized node is poor.