title: The Robustness of ＂Connecting Characters Together＂ CAPTCHAs

abstract: CAPTCHA is now commonly used as standard security technology to tell computers and humans apart. The most widely deployed CAPTCHAs are text-based schemes. In this paper, we document how we have broken such a text-based scheme which uses the ”connecting characters together (CCT)” principle. CAPTCHAs of this type can be classified into three types: CAPTCHA with overlap but no noise arcs; CAPTCHA with noise arcs but no overlap; and CAPTCHA without noise arcs and overlap. Yahoo!, Baidu CAPTCHA and reCAPTCHA were selected as representatives of the three types. The CCT CAPTCHA is effectively resistant to segmentation and recognition in the early attacks. In contrast to early works that recognized the text after segmentation, we combined recognition with segmentation to break the CCT CAPTCHA. Our method segments the text by extracting the recognized characters. The experiments show that our extraction and segmentation attack on Yahoo! CAPTCHA achieved a success rate of 78% and an overall (individual character recognition with OCR) success rate of 55%. The segmentation and recognition success rate of Baidu CAPTCHA was 34%. On reCAPTCHA we achieved 34% success rate.


title: Behavioral Intention of Using Virtual Reality in Learning

abstract: This study integrated the unified theory of acceptance and use of technology (UTAUT) and the four stages of Kolb's learning style-concrete experience, reflective observation, abstract conception, and active experimentation to investigate the factors affecting students' behavioral intention to use a virtual reality headset (VRH) in learning. The research model, constructed using structural equation modeling, included the four constructs of the UTAUT, namely performance expectancy, effort expectancy, social influence, and facilitating condition. Hypotheses on whether the four stages of Kolb's learning style and the four constructs of the UTAUT significantly affect behavioral intention to use VRHs in learning were proposed and tested through inference analysis. The results show that only the concrete experience stage of Kolb's learning style has a positive and significant effect on users' behavioral intention to use VRHs in learning, whereas all four constructs of the UTAUT do. These findings should be applied by educational institutions to increase VRH use in learning.


title: Fast Support Vector Data Description Using K-Means Clustering

abstract: Support Vector Data Description (SVDD) has a limitation for dealing with a large data set in which computational load drastically increases as training data size becomes large. To handle this problem, we propose a new fast SVDD method using K-means clustering method. Our method uses divide-and-conquerstrategy; trains each decomposed sub-problems to get support vectors and retrains with the support vectors to find a global data description of a whole target class. The proposed method has a similar result to the original SVDD and reduces computational cost. Through experiments, we show efficiency of our method.


title: Microscopic evolution of social networks

abstract: We present a detailed study of network evolution by analyzing four large online social networks with full temporal information about node and edge arrivals. For the first time at such a large scale, we study individual node arrival and edge creation processes that collectively lead to macroscopic properties of networks. Using a methodology based on the maximum-likelihood principle, we investigate a wide variety of network formation strategies, and show that edge locality plays a critical role in evolution of networks. Our findings supplement earlier network models based on the inherently non-local preferential attachment. Based on our observations, we develop a complete model of network evolution, where nodes arrive at a prespecified rate and select their lifetimes. Each node then independently initiates edges according to a "gap" process, selecting a destination for each edge according to a simple triangle-closing model free of any parameters. We show analytically that the combination of the gap distribution with the node lifetime leads to a power law out-degree distribution that accurately reflects the true network in all four cases. Finally, we give model parameter settings that allow automatic evolution and generation of realistic synthetic networks of arbitrary scale.


title: A sparse grid stochastic collocation method for partial differential equations with random input data

abstract: This work proposes and analyzes a Smolyak-type sparse grid stochastic collocation method for the approximation of statistical quantities related to the solution of partial differential equations with random coefficients and forcing terms (input data of the model). To compute solution statistics, the sparse grid stochastic collocation method uses approximate solutions, produced here by finite elements, corresponding to a deterministic set of points in the random input space. This naturally requires solving uncoupled deterministic problems as in the Monte Carlo method. If the number of random variables needed to describe the input data is moderately large, full tensor product spaces are computationally expensive to use due to the curse of dimensionality. In this case the sparse grid approach is still expected to be competitive with the classical Monte Carlo method. Therefore, it is of major practical relevance to understand in which situations the sparse grid stochastic collocation method is more efficient than Monte Carlo. This work provides error estimates for the fully discrete solution using $L^q$ norms and analyzes the computational efficiency of the proposed method. In particular, it demonstrates algebraic convergence with respect to the total number of collocation points and quantifies the effect of the dimension of the problem (number of input random variables) in the final estimates. The derived estimates are then used to compare the method with Monte Carlo, indicating for which problems the former is more efficient than the latter. Computational evidence complements the present theory and shows the effectiveness of the sparse grid stochastic collocation method compared to full tensor and Monte Carlo approaches.


title: Accurate mobile malware detection and classification in the cloud

abstract: AbstractAs the dominator of the Smartphone operating system market, consequently android has attracted the attention of s ::: malware authors and researcher alike. The number of types of android malware is increasing rapidly regardless of the considerable number of proposed malware analysis systems. In this paper, by taking advantages of low false-positive rate of misuse detection and the ability of anomaly detection to detect zero-day malware, we propose a novel hybrid detection system based on a new open-source framework CuckooDroid, which enables the use of Cuckoo Sandbox’s features to analyze Android malware through dynamic and static analysis. Our proposed system mainly consists of two parts: anomaly detection engine performing abnormal apps detection through dynamic analysis; signature detection engine performing known malware detection and classification with the combination of static and dynamic analysis. We evaluate our system using 5560 malware samples and 6000 benign samples. Experiments show that our anomaly detection engine with dynamic analysis is capable of detecting zero-day malware with a low false negative rate (1.16 %) and acceptable false positive rate (1.30 %); it is worth noting that our signature detection engine with hybrid analysis can accurately classify malware samples with an average positive rate 98.94 %. Considering the intensive computing resources required by the static and dynamic analysis, our proposed detection system should be deployed off-device, such as in the Cloud. The app store markets and the ordinary users can access our detection system for malware detection through cloud service.


title: Two Stage DC/DC Converter for Photovoltaic Generation

abstract: Solar cell is one of the most important new renewable energy for future energy generation. This paper presents a novel two stage DC/DC converter topology for PV PCSs. The proposed converter consists of an interleaved boost converter and a two-tank LLC resonant converter which is connected in parallel in primary and series in secondary. The main idea of this topology is that the system can achieve either unilateral or bilateral operations due to the input voltage level of the PV module, which leads to a better performance. The operating schemes on the proposed converter are analyzed and described. A 2.2kW prototype product is built, tested and verified.


title: Modeling Induction Motors for System Studies

abstract: Deriving induction motor equivalent circuit parameters from manufacturer's response curves is a difficult and time consuming task. Parameters for locked rotor and running conditions are supplied with the curves, but neither accurately describes motor response between these conditions. This paper presents a convenient way to derive a set of parameters suitable for the entire operating range and, therefore, suitable for use in induction motor-starting studies. An accurate model is essential to predict available starting torques.


title: Algorithm 799: revolve: an implementation of checkpointing for the reverse or adjoint mode of computational differentiation

abstract: In its basic form, the reverse mode of computational differentiation yields the gradient of a scalar-valued function at a cost that is a small multiple of the computational work needed to evaluate the function itself. However, the corresponding memory requirement is proportional to the run-time of the evaluation program. Therefore, the practical applicability of the reverse mode in its original formulation is limited despite the availability of ever larger memory systems. This observation leads to the development of checkpointing schedules to reduce the storage requirements. This article presents the function revolve, which generates checkpointing schedules that are provably optimal with regard to a primary and a secondary criterion. This routine is intended to be used as an explicit “controller” for running a time-dependent applications program.


title: All your contacts are belong to us: automated identity theft attacks on social networks

abstract: Social networking sites have been increasingly gaining popularity. Well-known sites such as Facebook have been reporting growth rates as high as 3% per week. Many social networking sites have millions of registered users who use these sites to share photographs, contact long-lost friends, establish new business contacts and to keep in touch. In this paper, we investigate how easy it would be for a potential attacker to launch automated crawling and identity theft attacks against a number of popular social networking sites in order to gain access to a large volume of personal user information. The first attack we present is the automated identity theft of existing user profiles and sending of friend requests to the contacts of the cloned victim. The hope, from the attacker's point of view, is that the contacted users simply trust and accept the friend request. By establishing a friendship relationship with the contacts of a victim, the attacker is able to access the sensitive personal information provided by them. In the second, more advanced attack we present, we show that it is effective and feasible to launch an automated, cross-site profile cloning attack. In this attack, we are able to automatically create a forged profile in a network where the victim is not registered yet and contact the victim's friends who are registered on both networks. Our experimental results with real users show that the automated attacks we present are effective and feasible in practice.


title: Kronecker-factored Curvature Approximations for Recurrent Neural Networks

abstract: Kronecker-factor Approximate Curvature (Martens & Grosse, 2015) (K-FAC) is a 2nd-order optimization method which has been shown to give state-of-the-art performance on large-scale neural network optimization tasks (Ba et al., 2017). It is based on an approximation to the Fisher information matrix (FIM) that makes assumptions about the particular structure of the network and the way it is parameterized. The original K-FAC method was applicable only to fully-connected networks, although it has been recently extended by Grosse & Martens (2016) to handle convolutional networks as well. In this work we extend the method to handle RNNs by introducing a novel approximation to the FIM for RNNs. This approximation works by modelling the covariance structure between the gradient contributions at different time-steps using a chain-structured linear Gaussian graphical model, summing the various cross-covariances, and computing the inverse in closed form. We demonstrate in experiments that our method significantly outperforms general purpose state-of-the-art optimizers like SGD with momentum and Adam on several challenging RNN training tasks.


title: Orthogonal AMP for compressed sensing with unitarily-invariant matrices

abstract: Approximate message passing (AMP) is a low-cost iterative signal recovery algorithm for compressed sensing. For sensing matrices with independent identically distributed (IID) Gaussian entries, the performance of AMP can be asymptotically characterized by a simple scaler recursion called state evolution (SE). SE analysis shows that AMP can potentially approach the optimal minimum mean squared-error (MMSE) limit. However, SE may become unreliable for other matrix ensembles, especially for ill-conditioned ones. In this paper, we propose an orthogonal AMP (OAMP) algorithm based on de-correlated linear estimation (LE) and divergence-free non-linear estimation (NLE). The Onsager term in standard AMP vanishes as a result of the divergence-free constraint on NLE. We develop an SE procedure for OAMP and show numerically that the SE for OAMP is accurate for a wide range of sensing matrices, including IID Gaussian matrices, partial orthogonal matrices, and general unitarily-invariant matrices. We further derive optimized options for OAMP and show that the corresponding SE fixed point coincides with the optimal performance obtained via the replica method.


title: Distributed Deep Learning for Question Answering

abstract: This paper is an empirical study of the distributed deep learning for question answering subtasks: answer selection and question classification. Comparison studies of SGD, MSGD, ADADELTA, ADAGRAD, ADAM/ADAMAX, RMSPROP, DOWNPOUR and EASGD/EAMSGD algorithms have been presented. Experimental results show that the distributed framework based on the message passing interface can accelerate the convergence speed at a sublinear scale. This paper demonstrates the importance of distributed training. For example, with 48 workers, a 24x speedup is achievable for the answer selection task and running time is decreased from 138.2 hours to 5.81 hours, which will increase the productivity significantly.


title: Multiple impacts: A state transition diagram approach

abstract: Impact happens when two or more bodies collide, generating very large impulsive forces in a very short period of time during which kinetic energy is first absorbed and then released after some loss. This paper introduces a state transition diagram to model a frictionless multibody collision. Each state describes a different topology of the collision characterized by the set of instantaneously active contacts. A change of state happens when a contact disappears at the end of restitution, or when a disappeared contact reappears as the relative motion of two bodies goes from separation into penetration. Within a state, (normal) impulses are coupled differentially subject to relative stiffnesses at the active contact points and the strain energies stored there. Such coupling may cause restart of compression from restitution during a single impact. Impulses grow along a bounded curve with first-order continuity, and converge during the state transitions. To solve a multibody collision problem with friction and tangential compliance, the above impact model is integrated with a compliant impact model. The paper compares model predictions to a physical experiment for the masse shot, which is a difficult trick in billiards, with a good result.


title: A Kernel Fuzzy c-Means Clustering-Based Fuzzy Support Vector Machine Algorithm for Classification Problems With Outliers or Noises

abstract: The support vector machine (SVM) has provided higher performance than traditional learning machines and has been widely applied in real-world classification problems and nonlinear function estimation problems. Unfortunately, the training process of the SVM is sensitive to the outliers or noises in the training set. In this paper, a common misunderstanding of Gaussian-function-based kernel fuzzy clustering is corrected, and a kernel fuzzy c-means clustering-based fuzzy SVM algorithm (KFCM-FSVM) is developed to deal with the classification problems with outliers or noises. In the KFCM-FSVM algorithm, we first use the FCM clustering to cluster each of two classes from the training set in the high-dimensional feature space. The farthest pair of clusters, where one cluster comes from the positive class and the other from the negative class, is then searched and forms one new training set with membership degrees. Finally, we adopt FSVM to induce the final classification results on this new training set. The computational complexity of the KFCM-FSVM algorithm is analyzed. A set of experiments is conducted on six benchmarking datasets and four artificial datasets for testing the generalization performance of the KFCM-FSVM algorithm. The results indicate that the KFCM-FSVM algorithm is robust for classification problems with outliers or noises.


Explicit Document Modeling through Weighted Multiple-Instance Learning


title: IDUTC: an intelligent decision-making system for urban traffic-control applications

abstract: The design of systems for intelligent control of urban traffic is important in providing a safe environment for pedestrians and motorists. Artificial neural networks (ANNs) (learning systems) and expert systems (knowledge-based systems) have been extensively explored as approaches for decision-making. While the ANNs compute decisions by learning from successfully solved examples, the expert systems rely on a knowledge base developed by human reasoning for decision making. It is possible to integrate the learning abilities of an ANN and the knowledge-based decision-making ability of the expert system. This paper presents a real-time intelligent decision-making system, IDUTC, for urban traffic control applications. The system integrates a backpropagation-based ANN that can learn and adapt to the dynamically changing environment and a fuzzy expert system for decision-making. The performance of the proposed intelligent decision-making system is evaluated by mapping the adaptable traffic light control problem. The application is implemented using the ANN approach, the FES approach, and the proposed integrated system approach. The results of extensive simulations using the three approaches indicate that the integrated system provides better performance and leads to a more efficient implementation than the other two approaches.


Adaptive Distance Estimation Based on RSSI in 802 . 15 . 4 Network


title: Daily activity recognition with inertial ring and bracelet: An unsupervised approach

abstract: Daily activity recognition can help people to maintain a healthy lifestyle and robot to better interact with users. Robots could therefore use the information coming from the activities performed by users to give them some custom hints to improve lifestyle and daily routine. The pervasiveness of smart things together with advances in cloud robotics can help the robot to perceive and collect more information about the users and the environment. In particular thanks to the miniaturization and low cost of Inertial Measurement Units, in the last years, body-worn activity recognition has gained popularity. In this work, we investigated the performances with an unsupervised approach to recognize eight different gestures performed in daily living wearing a system composed of two inertial sensors placed on the hand and on the wrist. In this context our aim is to evaluate whether the system is able to recognize the gestures in more realistic applications, where is not possible to have a training set. The classification problem was analyzed using two unsupervised approaches (K-Mean and Gaussian Mixture Model), with an intra-subject and an inter-subject analysis, and two supervised approaches (Support Vector Machine and Random Forest), with a 10-fold cross validation analysis and with a Leave-One-Subject-Out analysis to compare the results. The outcomes show that even in an unsupervised context the system is able to recognize the gestures with an averaged accuracy of 0.917 in the K-Mean inter-subject approach and 0.796 in the Gaussian Mixture Model inter-subject one.


title: Effect of withdrawal of antihypertensive drug on depressive mood

abstract: To evaluate the effects on depressive mood of withdrawal of antihypertensive medication, we conducted a randomized, multi-center study with clinical centers at the Albert Einstein College of Medicine, Bronx, NY, University of Alabama School of Medicine, Birmingham, and the University of Mississippi School of Medicine in Jackson. Patients were formerly active participants in the Hypertension Detection and Follow-Up Program (HDFP) whose blood pressure was controlled with drugs for a period of 5 years. Of 496 patients, 431 had both baseline and «1-year» mood scores


title: Effects of Teachers’ Experience and Training on Implementation of Information Communication Technology in Public Secondary Schools in Nyeri, Central District, Kenya

abstract: The purpose of the study was to investigate the effects of teachers’ demographic variables on implementation of Information Communication Technology in public secondary schools in Nyeri Central district, Kenya. The dependent variable was implementation of ICT and the independent variables were teachers’ teaching experience and training. The research design used was descriptive survey design. The target population was 275 teachers working in 15 public secondary schools in Nyeri Central district. The sampling design was stratified random sampling and sample size was 82 teachers. The study targeted 15 principals of the schools in Nyeri Central district. The data collection tools were questionnaires, interview schedule and observation schedule. Data were analyzed quantitatively and qualitatively. Teachers’ training in ICT and teaching experience are not consistent in affecting ICT implementation. Many schools especially in rural areas had not embraced ICT mainly because teachers lacked adequate training, had lower levels of education, and had negative attitude towards ICT implementation. This has led to schools facing major challenges in ICT implementation. The researcher recommends that Public secondary schools should find a way to purchase more ICT facilities and support teachers’ training on the use of ICT. The government needs to give more financial support through free education programme and donations to enhance ICT implementation in public secondary schools. The teachers should change their attitude towards the use and implementation of ICT in the schools so as to create information technology culture in all aspects of teaching and learning. Wachiuri Reuben Nguyo


title: Countersniper system for urban warfare

abstract: An ad-hoc wireless sensor network-based system is presented that detects and accurately locates shooters even in urban environments. The localization accuracy of the system in open terrain is competitive with that of existing centralized countersniper systems. However, the presented sensor network-based solution surpasses the traditional approach because it can mitigate acoustic multipath effects prevalent in urban areas and it can also resolve multiple simultaneous shots. These unique characteristics of the system are made possible by employing novel sensor fusion techniques that utilize the spatial and temporal diversity of multiple detections. In this article, in addition to the overall system architecture, the middleware services and the unique sensor fusion algorithms are described. An analysis of the experimental data gathered during field trials at US military facilities is also presented.


title: A Lightweight Message Authentication Scheme for Smart Grid Communications

abstract: Smart grid (SG) communication has recently received significant attentions to facilitate intelligent and distributed electric power transmission systems. However, communication trust and security issues still present practical concerns to the deployment of SG. In this paper, to cope with these challenging concerns, we propose a lightweight message authentication scheme features as a basic yet crucial component for secure SG communication framework. Specifically, in the proposed scheme, the smart meters which are distributed at different hierarchical networks of the SG can first achieve mutual authentication and establish the shared session key with Diffie-Hellman exchange protocol. Then, with the shared session key between smart meters and hash-based authentication code technique, the subsequent messages can be authenticated in a lightweight way. Detailed security analysis shows that the proposed scheme can satisfy the desirable security requirements of SG communications. In addition, extensive simulations have also been conducted to demonstrate the effectiveness of the proposed scheme in terms of low latency and few signal message exchanges.


title: A simplified design of quasi-Yagi antennas using the new microstrip-to-CPS transitions

abstract: A new, systematic, simplified design procedure for quasi-Yagi antennas is presented. The design is based on the simple impedance matching among antenna components: i.e., transition, feed, and antenna. This new antenna design is possible due to the newly developed ultra-wideband transition. As design examples, wideband quasi- Yagi antennas are successfully designed and implemented in Ku- and Ka-bands with frequency bandwidths of 53.2% and 29.1%, and antenna gains of 4-5 dBi and 5.2-5.8 dBi, respectively. The design method can be applied to other balanced antennas and their arrays.


Survey on Hand Gesture Recognition Approaches


title: Online Defects Inspection Method for Velcro Based on Image Processing

abstract: Quality control is a crucial issue in producing velcro, and defects existing in velcro can dramatically downgrade velcro level. Manual inspection can not meet the requirements of production efficiency, so an online feasible inspection method is referred to control the surface quality of the velcro. The original algorithm for the edge detection has been improved, and the flaws are extracted according to the first-order characteristic value. And these defects are classified according to the spectrum. Finally, the experiments have indicated that the various defects can be detected by proposed algorithm accurately, and the defects inspection method has been efficient and of great significance.


AESTHETIC DATA VISUALIZATION AS A RESOURCE FOR EDUCATING CREATIVE DESIGN From Information Visualization over Ambient Display to Data Art


title: Double Microstrip-Slot Transitions for Broadband ${\pm} 90^{\circ}$ Microstrip Phase Shifters

abstract: The letter describes double microstrip-slot transitions for use in planar ± 90° phase shifters. The described devices exhibit broadband performance and offer compatibility with ordinary microstrip circuits. Full-wave EM simulation results show a phase shift of ± 90° ± 7° over the frequency band of 3.1-12.0 GHz when compared with a suitably chosen section of microstripline. The observed differential phase shift is accompanied by return losses of not less than 14 dB and insertion losses between 0.7 to 1.5 dB in the band 3.1-11.0 GHz. The simulated performance is confirmed by experimental results of ± 90° ± 8° phase shift, return loss not less than 14 dB and insertion loss between 0.5 and 1.8 dB in the frequency band of 3.1-11.0 GHz.


title: Controlling Physically Based Virtual Musical Instruments Using The Gloves

abstract: In this paper we propose an empirical method to develop mapping strategies between a gestural-based interface (the Gloves) and physically based sound synthesis models. An experiment was conducted to investigate which gestures listeners associate with sounds synthesised using physical models, corresponding to three categories of sound: sustained, iterative and impulsive. The results of the experiment show that listeners perform similar gestures when controlling sounds from the different categories. We used such gestures in order to create the mapping strategy between the Gloves and the physically based synthesis engine.


title: WallBots: Interactive Wall-Crawling Robots In the Hands of Public Artists and Political Activists

abstract: Street art and political activism have a rich history of shaping urban landscapes. Our work explores the processes by which public artists and political activists contribute to public spaces, introducing opportunities for HCI researchers to engage with the people who shape the aesthetic feel of our cities. We present WallBots-autonomous, wall-crawling robots as a research probe for public expression across a wide range of surfaces and hard-to-reach places, including bus stops, whiteboards, streetpoles, trashcans, moving vehicles and building walls. We evaluate WallBots as a low-cost DIY authoring tool for public artists and activists. Our study of six individuals who extensively contribute to public spaces offers insights into the materials and practices behind grassroots public expression. We then leverage feedback from participants, among them a graffiti artist, light painter, political activists, and street musician, to evaluate interaction techniques for manipulating WallBots as a medium for public expression across a range of surfaces. Our findings expose a research space for technological interventions in the context of street art, and we conclude with design insights for magnetic kinetic systems as an approach for supporting engagement, expression and creativity in public spaces.


title: Making CNC machine tools more open, interoperable and intelligent - a review of the technologies

abstract: The aim of the next generation of computer numerically controlled (CNC) machines is to be portable, interoperable and adaptable. Over the years, G-codes (ISO 6983) have been extensively used by the CNC machine tools for part programming and are now considered as a bottleneck for developing next generation of CNC machines. A new standard known as STEP-NC is being developed as the data model for a new breed of CNC machine tools. The data model represents a common standard specifically aimed at the intelligent CNC manufacturing workstation, making the goal of a standardised CNC controller and NC code generation facility a reality. It is believed that CNC machines implementing STEP-NC will be the basis for a more open and adaptable architecture. This paper outlines a futuristic view of STEP-NC to support distributed interoperable intelligent manufacturing through global networking with autonomous manufacturing workstations with STEP compliant data interpretation, intelligent part program generation, diagnostics and maintenance, monitoring and job production scheduling.


title: Tree Stem Diameter Estimation from Mobile Laser Scanning Using Line-Wise Intensity-Based Clustering

abstract: Diameter at breast height has been estimated from mobile laser scanning using a new set of methods. A 2D laser scanner was mounted facing forward, tilted nine degrees downwards, on a car. The trajectory was recorded using inertial navigation and visual SLAM (simultaneous localization and mapping). The laser scanner data, the trajectory and the orientation were used to calculate a 3D point cloud. Clusters representing trees were extracted line-wise to reduce the effects of uncertainty in the positioning system. The intensity of the laser echoes was used to filter out unreliable echoes only grazing a stem. The movement was used to obtain measurements from a larger part of the stem, and multiple lines from different views were used for the circle fit. Two trigonometric methods and two circle fit methods were tested. The best results with bias 2.3% (6 mm) and root mean squared error 14% (37 mm) were acquired with the circle fit on multiple 2D projected clusters. The method was evaluated compared to field data at five test areas with approximately 300 caliper-measured trees within a 10-m working range. The results show that this method is viable for stem measurements from a moving vehicle, for example a forest harvester.


title: Fog Computing for the Internet of Mobile Things: Issues and Challenges

abstract: The Internet of Things (IoT) conceives a world where everyday objects are able to join the Internet and exchange data as well as process, store, collect them from the surrounding environment, and actively intervene on it. An unprecedented number of services may be envisioned by exploiting the Internet of Things. Fog Computing, which is also known as Edge Computing, was proposed in 2012 as the ideal paradigm to support the resource-constrained IoT devices in data processing and information delivery. Indeed, the Fog, which does not replace the centralized Cloud but cooperates with it, distributes Cloud Computing technologies and principles anywhere along the Cloud-to-Things continuum and particularly at the network edge. The Fog proximity to the IoT devices allows for several advantages that must be continuously guaranteed, also when end devices move from one place to another. In this paper, we aim at examining in depth what it means to provide mobility support in a Fog environment and at investigating what are the main challenges to be faced. Besides, in order to highlight the importance of this topic in everyday life, we provide the reader with three scenarios where there is an integration between the IoT and Fog Computing, and in which mobility support is essential. We finally point out the main research directions in the field.


Therapeutic Effects of Roasted Lentil Flour from the Viewpoint of Iranian Islamic Resources : A Review


title: Modeling and control of temperature dynamics in induction furnace system

abstract: This paper explains modeling and control of temperature dynamics on induction furnace. Induction furnace is used for melting metal to process raw material scrap into steel ferrit rate 0.22 wt % carbons. The dynamics response of the induction furnace temperature affects the resulting product. Therefore, the controller of temperature dynamics is required to produce the desired process response. The induction furnace systems consist of electrical and thermal system dynamics. The dynamics of the electrical system represents the induction furnace system in the form of an electric circuit i.e. fed current inverter with a parallel resonant circuit. Meanwhile, the thermal system dynamics represents the thermal energy transfer process, which is developed with the principle of energy balance, including heat generated energy and heat loss. Induction furnace system dynamics is modelled in an order 2 system, with a time constant coil 1000 times faster than time constant temperature. Thus, by ignoring the time constant coil, induction furnace system dynamic model can be transformed into a first order nonlinear system. Then, linear system can be obtained by making a replacement variable. Induction furnace temperature control has been implemented by adjusting the PWM to control the input power to the induction furnace. PI controller is designed in three cases, namely linear model with linear PI controller, saturated linear model with linear PI controller, and saturated linear model with anti-windup PI controller. Each case is simulated by Simulink. To get the appropriate specifications, the temperature should be controlled at 912 Celsius degree. The best results can be achieved with maximum overshoot is 7% and the rise time is 2.9 seconds.


title: The effectiveness of a computer-assisted math learning program

abstract: Computer-assisted instruction CAI programs are considered as a way to improve learning outcomes of students. However, little is known on the schools who implement such programs as well as on the effectiveness of similar information and communication technology programs. We provide a literature review that pays special attention to the existing causal evidence of computer-assisted programs on learning outcomes. The paper relies on a rich data set consisting of i pupil-level information on the use of a Dutch computer-assisted program and ii detailed school-level information on, among others, outcomes on national exams. The results suggest that schools with lower educational attainments use more frequently CAI programs. This suggests that they use CAI programs to catch up on learning outcomes. Moreover, using an instrumental variable design, we argue that given the participation in the CAI program, making more exercises leads to higher test results. Working with a CAI program seems therefore effective.


title: Computational Decomposition of Style for Controllable and Enhanced Style Transfer

abstract: Neural style transfer has been demonstrated to be powerful in creating artistic images with help of Convolutional Neural Networks (CNN), but continuously controllable transfer is still a challenging task. This paper provides a computational decomposition of the style into basic factors, which aim to be factorized, interpretable representations of the artistic styles. We propose to decompose the style by not only spectrum based methods including Fast Fourier Transform and Discrete Cosine Transform, but also latent variable models such as Principal Component Analysis, Independent Component Analysis, and so on. Such decomposition induces various ways of controlling the style factors to generate enhanced, diversified styled images. We mix or intervene the style basis from more than one styles so that compound style or new style could be generated to produce styled images. To implement our method, we derive a simple, effective computational module, which can be embedded into state-of-the-art style transfer algorithms. Experiments demonstrate the effectiveness of our method on not only painting style transfer but also other possible applications such as picture-to-sketch problems.


Design and Development of an Infant Incubator for Controlling Multiple Parameters


title: Forest fire detection using spiking neural networks

abstract: Forest fires is one of the main causes of environmental degradation and its detection and forecasting is challenging. A novel method of forest fire detection based on spiking neural networks is proposed in this paper. Data obtained from controlled experiments are used as input training samples and a detection model is established by considering the factors of temperature, humidity, carbon monoxide concentration, wind speed and wind direction. Experimental results show that the spiking neural network can achieve a detection accuracy of ∼91%, and therefore provides a better power/accuracy trade-off against existing approaches.


title: Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking

abstract: With efficient appearance learning models, discriminative correlation filter (DCF) has been proven to be very successful in recent video object tracking benchmarks and competitions. However, the existing DCF paradigm suffers from two major issues, i.e., spatial boundary effect and temporal filter degradation. To mitigate these challenges, we propose a new DCF-based tracking method. The key innovations of the proposed method include adaptive spatial feature selection and temporal consistent constraints, with which the new tracker enables joint spatial-temporal filter learning in a lower dimensional discriminative manifold. More specifically, we apply structured spatial sparsity constraints to multi-channel filters. Consequently, the process of learning spatial filters can be approximated by the lasso regularization. To encourage temporal consistency, the filter model is restricted to lie around its historical value and updated locally to preserve the global structure in the manifold. Last, a unified optimization framework is proposed to jointly select temporal consistency preserving spatial features and learn discriminative filters with the augmented Lagrangian method. Qualitative and quantitative evaluations have been conducted on a number of well-known benchmarking datasets such as OTB2013, OTB50, OTB100, Temple-Colour, UAV123, and VOT2018. The experimental results demonstrate the superiority of the proposed method over the state-of-the-art approaches.


Denoising Distantly Supervised Open-Domain Question Answering


title: Slim-DP: A Multi-Agent System for Communication-Efficient Distributed Deep Learning

abstract: To afford the huge computational cost, large-scale deep neural networks (DNN) are usually trained on the distributed system, especially the widely-used parameter server architecture, consisting of a parameter server as well as multiple local workers with powerful GPU cards. During the training, local workers frequently pull the global model and push their computed gradients from/to the parameter server. Due to the limited bandwidth, such frequent communication will cause severe bottleneck for the training acceleration. As recent attempts to address this problem, quantization methods have been proposed to compress the gradients for efficient communication. However, such methods overlook the effects of compression on the model performance such that they either suffer from a low compression ratio or an accuracy drop. In this paper, to better address this problem, we investigate the distributed deep learning as a multi-agent system (MAS) problem. Specifically, 1) local workers and the parameter server are separate agents in the system; 2) the objective of these agents is to maximize the efficacy of the learned model through their cooperative interactions; 3) the strategy of the agents describes how they take actions, i.e. communicate their computed gradients or the global model; 4) rational agents always select the best-response strategy with the optimal utility. Inspired by this, we design a MAS approach for distributed training of DNN. In our method, the agents first estimate the utility (i.e., the benefit to help improve the model) of each action (i.e., transferring a subset of the gradients or the global model), and then take the best-response strategy based on their estimated utilities mixed with e-random exploration. We call our new method Slim-DP as it, being different from the standard data-parallelism, only communicates a subset of the gradient or the global model. Our experimental results demonstrate that our proposed Slim-DP can reduce more communication cost and achieve better speedup without loss of accuracy than the standard data parallelism and its quantization version.


Advertising, Learning, and Consumer Choice in Experience Good Markets: A Structural Examination


title: Variable separated ODE method--A powerful tool for testing traveling wave solutions of nonlinear equations

abstract: The variable separated ODE method is extended by choosing the additional variable separated equation as the general elliptic equation. More exact traveling wave solutions of nonlinear equations are obtained by using the method of comparison of coefficients and the known solutions of the auxiliary equation.


title: A systems architecture for ubiquitous video

abstract: Realityflythrough is a telepresence/tele-reality system that works in the dynamic, uncalibrated environments typically associated with ubiquitous computing. By harnessing networked mobile video cameras, it allows a user to remotely and immersively explore a physical space. RealityFlythrough creates the illusion of complete live camera coverage in a physical environment. This paper describes the architecture of RealityFlythrough, and evaluates it along three dimensions: (1) its support of the abstractions for infinite camera coverage, (2) its scalability, and (3) its robustness to changing user requirements.


title: The varied nature of women's sexuality: Unresolved issues and a theoretical approach

abstract: During the 20th century there were clear indications that the socio-cultural suppression of women's sexuality had lessened, revealing a marked variability of women's sexual expression. In this article we review the recent literature to explore explanations for this variability. It is clear that we know little about the nature of sexual desire, and in particular, what it is that is desired. There is also now substantial evidence that vaginal response, as measured by vaginal pulse amplitude, is a relatively automatic response to perception of sexual stimuli, regardless of whether these stimuli are perceived positively or result in subjective arousal. This is considered as a possible mechanism that allows vaginal intercourse without pain, even when the woman is not sexually aroused. The roles of androgens and estrogen in women's sexuality remain uncertain. The evidence is, however, consistent with there being a testosterone-dependent component of women's sexuality that is more important for some women than others. Finally, a new theoretical model is presented that aims to resolve these uncertainties and that proposes different types of women's sexuality. Once we have a better understanding of “normal” female sexuality, in its various forms, our ability to develop effective treatments for women's sexual problems should improve.


title: Genomic selection for wheat traits and trait stability

abstract: KEY MESSAGE ::: Based on the estimates of accuracy, genomic selection would be useful for selecting for improved trait values and trait stability for agronomic and quality traits in wheat. Trait values and trait stability estimated by two methods were generally independent indicating a breeder could select for both simultaneously. Genomic selection (GS) is a new marker-assisted selection tool for breeders to achieve higher genetic gain faster and cheaper. Breeders face challenges posed by genotype by environment interaction (GEI) pattern and selecting for trait stability. Obtaining trait stability is costly, as it requires data from multiple environments. There are few studies that evaluate the efficacy of GS for predicting trait stability. A soft winter wheat population of 273 lines was genotyped with 90 K single nucleotide polymorphism markers and phenotyped for four agronomic and seven quality traits. Additive main effect and multiplicative interaction (AMMI) model and Eberhart and Russell regression (ERR) were used to estimate trait stability. Significant GEI variation was observed and stable lines were identified for all traits in this study. The accuracy of GS ranged from 0.33 to 0.67 for most traits and trait stability. Accuracy of trait stability was greater than trait itself for yield (0.44 using AMMI versus 0.33) and heading date (0.65 using ERR versus 0.56). The opposite trend was observed for the other traits. GS did not predict the stability of the quality traits except for flour protein, lactic acid and softness equivalent. Significant GS accuracy for some trait stability indicated that stability was under genetic control for these traits. The magnitude of GS accuracies for all the traits and most of the trait stability index suggests the possibility of rapid selection for these trait and trait stability in wheat breeding.


title: Improving Word Sense Disambiguation in Neural Machine Translation with Sense Embeddings

abstract: Word sense disambiguation is necessary in translation because different word senses often have different translations. Neural machine translation models learn different senses of words as part of an end-to-end translation task, and their capability to perform word sense disambiguation has so far not been quantified. We exploit the fact that neural translation models can score arbitrary translations to design a novel cross-lingual word sense disambiguation task that is tailored towards evaluating neural machine translation models. We present a test set of 7,200 lexical ambiguities for German → English, and 6,700 for German → French, and report baseline results. With 70% of lexical ambiguities correctly disambiguated, we find that word sense disambiguation remains a challenging problem for neural machine translation, especially for rare word senses. To improve word sense disambiguation in neural machine translation, we experiment with two methods to integrate sense embeddings. In a first approach we pass sense embeddings as additional input to the neural machine translation system. For the second experiment, we extract lexical chains based on sense embeddings from the document and integrate this information into the NMT model. While a baseline NMT system disambiguates frequent word senses quite reliably, the annotation with both sense labels and lexical chains improves the neural models’ performance on rare word senses.


title: Preschool children's development in classic Montessori, supplemented Montessori, and conventional programs☆

abstract: Abstract Research on the outcomes of Montessori education is scarce and results are inconsistent. One possible reason for the inconsistency is variations in Montessori implementation fidelity. To test whether outcomes vary according to implementation fidelity, we examined preschool children enrolled in high fidelity classic Montessori programs, lower fidelity Montessori programs that supplemented the program with conventional school activities, and, for comparison, conventional programs. Children were tested at the start and end of the school year on a range of social and academic skills. Although they performed no better in the fall, children in Classic Montessori programs, as compared with children in Supplemented Montessori and Conventional programs, showed significantly greater school-year gains on outcome measures of executive function, reading, math, vocabulary, and social problem-solving, suggesting that high fidelity Montessori implementation is associated with better outcomes than lower fidelity Montessori programs or conventional programs.


title: Early Vision Impairs Tactile Perception in the Blind

abstract: Researchers have known for more than a century that crossing the hands can impair both tactile perception and the execution of appropriate finger movements. Sighted people find it more difficult to judge the temporal order when two tactile stimuli, one applied to either hand, are presented and their hands are crossed over the midline as compared to when they adopt a more typical uncrossed-hands posture. It has been argued that because of the dominant role of vision in motor planning and execution, tactile stimuli are remapped into externally defined coordinates (predominantly determined by visual inputs) that takes longer to achieve when external and body-centered codes (determined primarily by somatosensory/proprioceptive inputs) are in conflict and that involves both multisensory parietal and visual cortex. Here, we show that the performance of late, but not of congenitally, blind people was impaired by crossing the hands. Moreover, we provide the first empirical evidence for superior temporal order judgments (TOJs) for tactile stimuli in the congenitally blind. These findings suggest a critical role of childhood vision in modulating the perception of touch that may arise from the emergence of specific crossmodal links during development.


Two-Dimensional Imaging of Flame Temperature Using Laser-Induced Fluorescence


title: Efficient Transfer Learning Method for Automatic Hyperparameter Tuning

abstract: We propose a fast and effective algorithm for automatic hyperparameter tuning that can generalize across datasets. Our method is an instance of sequential model-based optimization (SMBO) that transfers information by constructing a common response surface for all datasets, similar to Bardenet et al. (2013). The time complexity of reconstructing the response surface at every SMBO iteration in our method is linear in the number of trials (significantly less than previous work with comparable performance), allowing the method to realistically scale to many more datasets. Specifically, we use deviations from the per-dataset mean as the response values. We empirically show the superiority of our method on a large number of synthetic and real-world datasets for tuning hyperparameters of logistic regression and ensembles of classifiers.


title: Benefits, Adoption Barriers and Myths of Open Data and Open Government

abstract: Although a significant number of public organizations have embraced the idea of open data, many are still reluctant to do this. One root cause is that the publicizing of data represents a shift from a closed to an open system of governance, which has a significant impact upon the relationships between public agencies and the users of open data. Yet no systematic research is available which compares the benefits of an open data with the barriers to its adoption. Based on interviews and a workshop, the benefits and adoption barriers for open data have been derived. The findings show that a gap exists between the promised benefits and barriers. They furthermore suggest that a conceptually simplistic view is often adopted with regard to open data, one which automatically correlates the publicizing of data with use and benefits. Five ‘myths’ are formulated promoting the use of open data and placing the expectations within a realistic perspective. Further, the recommendation is given to take a user’s view and to actively govern the relationship between government and its users.


title: Robustness analysis for PID controller optimized using PFPSO for underactuated gantry crane system

abstract: Gantry Crane System is widely used for material transportation. In this system, uncontrolled oscillation always be found during the moving process. This problem may extend the carrying time and cause safety problem. In this paper, it presents a method for controlling a Gantry Crane System based on Proportional-Integral-Derivative (PID) controller strategy. A combination of the Priority Fitness Scheme and Particle Swarm Optimization (PFPSO) is used to optimize the parameters of PID and PD controller (KP, KI, KD, KPS and KDS). A disturbance rejection control technique in which PID controller is employed to control accurately and robustly the trolley along a desired position with the minimize payload oscillation. Results from the simulation study clearly show that PID controller is a robust controller as a disturbance rejection and able to move the trolley to a desired position with minimize payload oscillation.


Accurate CPU Power Modeling for Multicore Smartphones


title: Password hardening based on keystroke dynamics

abstract: We present a novel approach to improving the security of passwords. In our approach, the legitimate user's typing patterns (e.g., durations of keystrokes, and latencies between keystrokes) are combined with the user's password to generate a hardened password that is convincingly more secure than conventional passwords against both online and offline attackers. In addition, our scheme automatically adapts to gradual changes in a user's typing patterns while maintaining the same hardened password across multiple logins, for use in file encryption or other applications requiring a longterm secret key. Using empirical data and a prototype implementation of our scheme, we give evidence that our approach is viable in practice, in terms of ease of use, improved security, and performance


title: Texture Synthesis Using Shallow Convolutional Networks with Random Filters

abstract: Here we demonstrate that the feature space of random shallow convolutional neural networks (CNNs) can serve as a surprisingly good model of natural textures. Patches from the same texture are consistently classified as being more similar then patches from different textures. Samples synthesized from the model capture spatial correlations on scales much larger then the receptive field size, and sometimes even rival or surpass the perceptual quality of state of the art texture models (but show less variability). The current state of the art in parametric texture synthesis relies on the multi-layer feature space of deep CNNs that were trained on natural images. Our finding suggests that such optimized multi-layer feature spaces are not imperative for texture modeling. Instead, much simpler shallow and convolutional networks can serve as the basis for novel texture synthesis algorithms.


title: Modeling Syntactic Context Improves Morphological Segmentation

abstract: The connection between part-of-speech (POS) categories and morphological properties is well-documented in linguistics but underutilized in text processing systems. This paper proposes a novel model for morphological segmentation that is driven by this connection. Our model learns that words with common affixes are likely to be in the same syntactic category and uses learned syntactic categories to refine the segmentation boundaries of words. Our results demonstrate that incorporating POS categorization yields substantial performance gains on morphological segmentation of Arabic.


title: How useful is photo-realistic rendering for visual learning?

abstract: Data seems cheap to get, and in many ways it is, but the process of creating a high quality labeled dataset from a mass of data is time-consuming and expensive.


title: Effects of Visual Realism and Moving Detail on Cybersickness

abstract: In this study we compare two conditions of visual detail - modern graphics and detail reduction through cel-shading - in experiencing Cybersickness during virtual movement along a preprogrammed path within a scene depicting a real-world outdoor museum viewed with Oculus CV1. The Cybersickness experience was quantified with the Fast-Motion-Sickness (FMS) scale and the Simulator Sickness Questionnaire (SSQ). We found weak evidence for realistic graphics being more sickness-inducing. Also, FMS scores peaked whenever a participant was entering a building.


title: A compact 8-element antenna array for GPS digital beamforming

abstract: The low broadcast power of the GPS satellites makes signals particularly susceptible to interference and therefore GPS antennas with capability to mitigate interference are highly desirable [1]. One of the effective approaches against interference can be realised by the use of Controlled Radiation Pattern Antennas (CRPAs) [2]. Existing CRPA GPS arrays typically have a circular aperture with one reference element located at the centre and other auxiliary elements inscribe the circumference of the aperture. These auxiliary elements combine with the central element to form pattern nulls in the direction of interfering signals. These shaped patterns enhance the system robustness in the presence of strong radio frequency sources [3]. A key factor in the array performance is the number of auxiliary elements. More elements meant that a null can be steered in the direction of interference more precisely, thereby improving the overall signal-to-noise ratio (SNR). It also enables more flexibility in placing nulls on interfering sources [4]. For small platforms, the aperture available for the array is limited and therefore it is advantageous to design a compact array. In this paper, an 8-element antenna array for GPS digital beamforming has been proposed and developed.


title: 3D Deep Learning for Biological Function Prediction from Physical Fields

abstract: Predicting the biological function of molecules, be it proteins or drug-like compounds, from their atomic structure is an important and long-standing problem. Function is dictated by structure, since it is by spatial interactions that molecules interact with each other, both in terms of steric complementarity, as well as intermolecular forces. Thus, the electron density field and electrostatic potential field of a molecule contain the"raw fingerprint"of how this molecule can fit to binding partners. In this paper, we show that deep learning can predict biological function of molecules directly from their raw 3D approximated electron density and electrostatic potential fields. Protein function based on EC numbers is predicted from the approximated electron density field. In another experiment, the activity of small molecules is predicted with quality comparable to state-of-the-art descriptor-based methods. We propose several alternative computational models for the GPU with different memory and runtime requirements for different sizes of molecules and of databases. We also propose application-specific multi-channel data representations. With future improvements of training datasets and neural network settings in combination with complementary information sources (sequence, genomic context, expression level), deep learning can be expected to show its generalization power and revolutionize the field of molecular function prediction.


Dimensionality reduction on statistical manifolds


title: A hybrid genetic approach for stereo matching

abstract: In this paper we present a genetic algorithm (GA)-based approach for the stereo matching problem. More precisely, the approach presented is a combination of a simple dynamic programming algorithm, commonly used for stereo matching, with a practical GA-based optimization scheme. The performance of our scheme was evaluated on standard test data of the Middlebury benchmark. Specifically, the number of incorrect disparities on these data decreases by approximately 20% in comparison to the original approach (without the use of a GA).


title: Design of a solar powered smart irrigation system (SPSIS) using WSN as an IoT device

abstract: The Internet has always been a means of communication between people, but with the technological development and changing requirements and lifestyle, this network has become a tool of communication between things of all types and sizes, and is known as Internet of things (IoT) for this reason. One of the most promising applications of IoT technology is the automated irrigation systems. The aim of this paper is to propose a methodology of the implementation of wireless sensor networks as an IoT device to develop a smart irrigation management system powered by solar energy.


title: The Use of Wearable Inertial Motion Sensors in Human Lower Limb Biomechanics Studies: A Systematic Review

abstract: Wearable motion sensors consisting of accelerometers, gyroscopes and magnetic sensors are readily available nowadays. The small size and low production costs of motion sensors make them a very good tool for human motions analysis. However, data processing and accuracy of the collected data are important issues for research purposes. In this paper, we aim to review the literature related to usage of inertial sensors in human lower limb biomechanics studies. A systematic search was done in the following search engines: ISI Web of Knowledge, Medline, SportDiscus and IEEE Xplore. Thirty nine full papers and conference abstracts with related topics were included in this review. The type of sensor involved, data collection methods, study design, validation methods and its applications were reviewed.


title: Effects of Pinatubo aerosol on stratospheric ozone at mid‐latitudes

abstract: Mid-latitude ozone data from ER-2 aircraft measurements in 1989, 1991 and 1992 were examined to determine how sulfate aerosols from the eruption of Mt. Pinatubo had affected ozone at about 18 km. N2O was used as a tracer to help distinguish between chemical and dynamical aerosol effects. At 20–45°N in February 1992, ozone was about 10–20% lower than February 1989 and 1991, with respect to N2O. Data from Aug. 1991-Mar. 1992 showed changes in ozone with respect to N2O, but the magnitude of those changes was not correlated with the magnitude of the changes in aerosol surface area density.


title: Robust task-space control of hydraulic robots

abstract: This paper presents a model-based robust controller for hydraulically driven robot manipulators. The approach guarantees that the output error of the plant remains within a prescribed bound despite of model uncertainties. Manipulator dynamics and actuator dynamics including the dynamics of the valves are taken into account. The stability proof is based on the Lyapunov method and passivity arguments. It is shown that the stability proof is not restricted to the proposed control law of the hydraulic actuators but also holds for arbitrary hydraulic force control laws as long as certain requirements are fulfilled.


title: Problems in the Adoption of Agile-Scrum Methodologies: A Systematic Literature Review

abstract: Agile methodologies are focused on the people and functional product delivery in short periods of time. There are methodologies that change considerably the work habits of software developers. Scrum is an agile methodology that involves an iterative, incremental, and empiric process. Besides it is designed to add value, focus, clarity and transparency to the activities and products of a project. Nowadays, most companies are interested in the adoption of agile methodologies. Although Scrum is a light process and easy to understand, its adoption sometimes is difficult. Agile methodologies are not obvious by themselves, so they are difficult to introduce in the culture of a company. In order to identify the problems presented during the adoption, a Systematic Literature Review is performed focusing in Scrum. We found several problems, these are categorized in four groups: people, process, project, and company (organization). The results represent a basis to propose a framework to support the agile adoption.


title: Mmdenselstm: An Efficient Combination of Convolutional and Recurrent Neural Networks for Audio Source Separation

abstract: Deep neural networks have become an indispensable technique for audio source separation (SS). It was recently reported that a variant of CNN architecture called MM-DenseNet was successfully employed to solve the SS problem of estimating source amplitudes, and state-of-the-art results were obtained for DSD 100 dataset. To further enhance MMDenseNet, here we propose a novel architecture that integrates long short-term memory (LSTM) in multiple scales with skip connections to efficiently model long-term structures within an audio context. The experimental results show that the proposed method outperforms MMDenseNet, LSTM and a blend of the two networks. The number of parameters and processing time of the proposed model are significantly less than those for simple blending. Furthermore, the proposed method yields better results than those obtained using ideal binary masks for a singing voice separation task.


title: Elastic urban video surveillance system using edge computing

abstract: During the past decade, the concepts and applications of Internet of Things (IoT) are pervasively propagated to the academia and industries. The widely distributed IoT devices contribute to building an effective smart urban surveillance system, which manages the regular operations and handles emergencies. The real time monitoring uploads massive amounts of data to the backbone network and requires prompt feedbacks. The recent rapid development of "Edge Computing" (also called "Fog Computing" or Mobile Edge Computing in different literature) aims at pushing the computation and storage resources from the remote data center to the edge of network for reducing the burden of backbone and the computing latency In this paper, we design a three-tier edge computing system architecture to elastically adjust computing capacity and dynamically route data to proper edge servers for the real-time surveillance applications. A system prototype integrating Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) is implemented in an OpenStack based virtualization environment. Moreover, we introduce schemes of resource reallocation and workload balance in urgent situations. Experimental results of the prototype show the great potentials of using edge computing for future large-scale and distributed smart urban surveillance applications.


title: Why do people play games? A meta-analysis

abstract: During the last decade games have arguably become the largest form of leisure information systems (IS). However, today games are also increasingly being employed for a variety of instrumental purposes. Although games have garnered a substantial amount of research attention during the last decade, research literature is scattered and there is still a lack of a clear and reliable understanding of why games are being used, and how they are placed in the established utilitarian-hedonic continuum of information systems. To address this gap, we conducted a meta-analysis of the quantitative body of literature that has examined the reasons for using games (48 studies). Additionally, we compared the findings across games that are intended for either leisure or instrumental use. Even though games are generally regarded as a pinnacle form of hedonically-oriented ISs, our results show that enjoyment and usefulness are equally important determinants for using them (though their definitive role varies between game types). Therefore, it can be posited that games are multi-purpose ISs which nevertheless rely on hedonic factors, even in the pursuit of instrumental outcomes. The present study contributes to and advances our theoretical and empirical understanding of multi-purpose ISs and the ways in which they are used.


title: Can we analyze big data inside a DBMS?

abstract: Relational DBMSs remain the main data management technology, despite the big data analytics and no-SQL waves. On the other hand, for data analytics in a broad sense, there are plenty of non-DBMS tools including statistical languages, matrix packages, generic data mining programs and large-scale parallel systems, being the main technology for big data analytics. Such large-scale systems are mostly based on the Hadoop distributed file system and MapReduce. Thus it would seem a DBMS is not a good technology to analyze big data, going beyond SQL queries, acting just as a reliable and fast data repository. In this survey, we argue that is not the case, explaining important research that has enabled analytics on large databases inside a DBMS. However, we also argue DBMSs cannot compete with parallel systems like MapReduce to analyze web-scale text data. Therefore, each technology will keep influencing each other. We conclude with a proposal of long-term research issues, considering the "big data analytics" trend.


title: Present and aspects for cadaver surgical training in Japan

abstract: In overseas countries the cadaver surgical training (CST) has been very common as evaluation systems for new operative method and medical devices. However, in Japan basic autopsy training is indispensable for nurturing doctors at schools of medicine, while the clinical doctors have been using cadavers for the progress and technique, or the like, of a surgery with the scope of clinical autopsy. In the consequence, in 2013 the guideline "Autopsy for clinical medicine training and research" was put in effect over a series of discussions for the purpose of society acknowledgement of CST. The subsidized project "Training for the development of practical technique of a surgery" by the Ministry of Health and Labor has been offered publicly for private organizations and expected a nationwide expansion. I review the current situation of CST in Japan and introduce CST at Ehime University School of Medicine. This editorial is based on the background of CST in Japan and referred to the outlook for the future.


title: A Comparison of Online and Offline Consumer Brand Loyalty

abstract: In this study we compare consumer brand loyalty in online and traditional shopping environments for over 100 brands in 19 grocery product categories. The online purchase data come from a large traditional grocery retailer that also operates an online store for its products. The offline data corresponds to the exact same brands and categories bought in traditional stores by a panel of homes operated by ACNielsen for purchases made in the same city and over the same time period. We compare the observed loyalty with a baseline model, a new segmented Dirichlet model, which has latent classes for brand choice and provides a very accurate model for purchase behavior. The results show that observed brand loyalty for high market share brands bought online is significantly greater than expected, with the reverse result for small share brands. In contrast, in the traditional shopping environment, the difference between observed and predicted brand loyalty is not related to brand share.


title: Effect of a relaxation time spectrum on mechanics of polymer melt spinning

abstract: Abstract The mechanics of isothermal melt spinning are determined for a viscoelastic liquid with constant physical properties but with a spectrum of relaxation times. The relaxation time distribution leads to higher forces and more linear velocity profiles than would be obtained for a Maxwellian material of the same mean properties in shear. This result explains the observation of several earlier studies that relaxation times inferred with a Maxwell-type model from spinning experiments are larger than those measured rheogoniometrically.


title: Image-based indoor positioning system: fast image matching using omnidirectional panoramic images

abstract: In this paper, we developed an image-based indoor localization system using omnidirectional panoramic images to which location information is added. By the combination of the robust image matching by PCA-SIFT and fast nearest neighbor search algorithm based on Locality Sensitive Hashing (LSH), our system can estimate users' positions with high accuracy and in a short time. To improve the precision, we introduced the "confidence" of the image matching results. We conducted experiments at the Railway Museum and we obtained 426 omnidirectional panoramic reference images and 1067 supplemental images for image matching. Experimental results using 126 test images demonstrated that the location detection accuracy is above 90% with about 2.2s of processing time.


title: The Contemporary Theory of Metaphor

abstract: The Contemporary Theory of Metaphor George Lakoff Do not go gentle into that good night. -Dylan Thomas Death is the mother of beauty . . . -Wallace Stevens, “Sunday Morningq Introduction These famous lines by Thomas and Stevens are examples of what classical theorists, at least since Aristotle, have referred to as metaphor: instances of novel poetic language in which words like “mother,q “go,q and “nightq are not used in their normal everyday senses. In classical theories of language, metaphor was seen as a matter of language not thought. Metaphorical expressions were assumed to be mutually exclusive with the realm of ordinary everyday language: everyday language had no metaphor, and metaphor used mechanisms outside the realm of everyday conventional language. The classical theory was taken so much for granted over the centuries that many people didn't realize that it was just a theory. The theory was not merely taken to be true, but came to be taken as definitional. The word “metaphorq was defined as a novel or poetic linguistic expression where one or more words for a concept are used outside of its normal conventional meaning to express a “similarq concept. But such issues are not matters for definitions; they are empirical questions. As a cognitive scientist and a linguist, one asks: What are the generalizations governing the linguistic expressions referred to classically as “poetic metaphors?q When this question is answered rigorously, the classical theory turns out to be false. The generalizations governing poetic metaphorical expressions are not in language, but in thought: They are general mappings across conceptual domains. Moreover, these general principles which take the form of conceptual mappings, apply not just to novel poetic expressions, but to much of ordinary everyday language. In short, the locus of metaphor is not in language at all, but in the way we conceptualize one mental domain in terms of another. The general theory of metaphor is given by characterizing such cross-domain mappings. And in the process, everyday abstract concepts like time, states, change, causation, and purpose also turn out to be metaphorical. The result is that metaphor (that is, cross-domain mapping) is absolutely central to ordinary natural language semantics, and that the study of literary metaphor is an extension of the study of everyday metaphor. Everyday metaphor is characterized by a huge system of thousands of cross-domain mappings, and this system is made use of in novel metaphor.


title: The changing context of regional governance of FDI in England

abstract: This paper analyses relations between sub-national institutional actors responsible for the attraction and retention of foreign direct investment, other �governance� actors in regional business systems � local and sub-regional government, cluster/sectoral bodies, RDA and LEP executives, and those involved in the coordination of skills provision � and subsidiaries of foreign-owned multinational corporations. It is based on qualitative research in two regions of England conducted between 2008 and 2011. Within a context of international competition for investment within global production networks, it explores recent politically driven changes in sub-national governance, including the abolition of Regional Development Agencies, alongside the more long-standing instability of economic development and skills coordination in England. The analysis is centred on an argument that a more adequate understanding of sub-national economic governance requires the active integration of perspectives on political systems of governance, and embedded patterns of economic coordination, as analysed in the varieties of capitalism literature.


Formative Research : A Methodology for Creating and Improving Design Theories


Artificial Intelligence in Education


Serve as Role Models ? The Impact of Instructor Gender on Female Students By


title: It is not for fun: An examination of social network site usage

abstract: Social networking sites (SNS) have become a significant component of people's daily lives and have revolutionized the ways that business is conducted, from product development and marketing to operation and human resource management. However, there have been few systematic studies that ask why people use such systems. To try to determine why, we proposed a model based on uses and gratifications theory. Hypotheses were tested using PLS on data collected from 148 SNS users. We found that user utilitarian (rational and goal-oriented) gratifications of immediate access and coordination, hedonic (pleasure-oriented) gratifications of affection and leisure, and website social presence were positive predictors of SNS usage. While prior research focused on the hedonic use of SNS, we explored the predictive value of utilitarian factors in SNS. Based on these findings, we suggest a need to focus on the SNS functionalities to provide users with both utilitarian and hedonic gratifications, and suggest incorporating appropriate website features to help users evoke a sense of human contact in the SNS context.


title: A Novel Framework for Robustness Analysis of Visual QA Models

abstract: Deep neural networks have been playing an essential role in many computer vision tasks including Visual Question Answering (VQA). Until recently, the study of their accuracy was the main focus of research but now there is a trend toward assessing the robustness of these models against adversarial attacks by evaluating their tolerance to varying noise levels. In VQA, adversarial attacks can target the image and/or the proposed main question and yet there is a lack of proper analysis of the later. In this work, we propose a flexible framework that focuses on the language part of VQA that uses semantically relevant questions, dubbed basic questions, acting as controllable noise to evaluate the robustness of VQA models. We hypothesize that the level of noise is negatively correlated to the similarity of a basic question to the main question. Hence, to apply noise on any given main question, we rank a pool of basic questions based on their similarity by casting this ranking task as a LASSO optimization problem. Then, we propose a novel robustness measure Rscore and two largescale basic question datasets (BQDs) in order to standardize robustness analysis for VQA models.


title: Integrated LiFi(Light Fidelity) for smart communication through illumination

abstract: Wireless technology has bloomed to a great extent that it require wireless technology to transmit a lot of data every day. Nowadays, wireless communications has become important in communication process. The main way to transmit wireless data is by using electromagnetic waves i.e. radio waves. However, radio waves can support less bandwidth because of compact spectrum availability and intrusion. Solution to this is data transmission using Visible Light Communication (VLC). Wi-Fi deals with wireless coverage within premises, whereas Li-Fi is perfect for high compactness wireless data coverage in defined area and for mitigating radio interference issues. In Li-Fi basically we focus to transmitting multimedia data between two terminals using LED's. Li-Fi is a transmission of data through illumination, in which data can be sent through a LED light bulb that varies in intensity faster than human eye can follow. The light which we are using in our daily life is not only used for providing light but also for communication by illumination. Transmission of image through LiFi technology is done. Using visible light for data transmission including many advantages and eliminates the disadvantages of transmission of data through electromagnetic waves.


title: On Traffic-Aware Partition and Aggregation in MapReduce for Big Data Applications

abstract: The MapReduce programming model simplifies large-scale data processing on commodity cluster by exploiting parallel map tasks and reduce tasks. Although many efforts have been made to improve the performance of MapReduce jobs, they ignore the network traffic generated in the shuffle phase, which plays a critical role in performance enhancement. Traditionally, a hash function is used to partition intermediate data among reduce tasks, which, however, is not traffic-efficient because network topology and data size associated with each key are not taken into consideration. In this paper, we study to reduce network traffic cost for a MapReduce job by designing a novel intermediate data partition scheme. Furthermore, we jointly consider the aggregator placement problem, where each aggregator can reduce merged traffic from multiple map tasks. A decomposition-based distributed algorithm is proposed to deal with the large-scale optimization problem for big data application and an online algorithm is also designed to adjust data partition and aggregation in a dynamic manner. Finally, extensive simulation results demonstrate that our proposals can significantly reduce network traffic cost under both offline and online cases.


title: Events versus States: Empirical Correlates of Lexical Classes

abstract: Events Versus States: Empirical Correlates of Lexical Classes Silvia Gennari (sgen@wam.umd.edu) Cognitive Neuroscience of Language Laboratory 1401 Marie Mount Hall. University of Maryland, College Park, MD 20742 David Poeppel (dpoeppel@deans.umd.edu) Cognitive Neuroscience of Language Laboratory 1401 Marie Mount Hall. University of Maryland, College Park, MD 20742 Abstract Philosophers and linguists have claimed that verb meanings are divided into semantic types or superordinate categories that differ in internal conceptual structure. In particular, eventive verbs, which have internal causal structure are distinguished from stative verbs, which have no internal causal structure. In this paper, we explore the processing consequences of assuming that the lexical representations of verb meanings differ in the complexity of their internal representations. We conducted two experiments, a lexical decision task and a self-paced reading study, that investigated how verb types of different complexity are processed. We predicted that the conceptually more complex eventive verbs would take longer to process than stative verbs. In both experiments, this prediction was confirmed. This lends support to theories of verb concepts that propose classifications based on internal representations and shows that there are discrete and abstract conceptual categories in the domain of events. Introduction An important question in cognitive science concerns how word meanings (or lexical concepts) are internally represented. Although considerable progress has been made in the domain of nominal concepts since Rosch’s studies, the nature and organization of verb concepts is less well understood. Early studies on verb meanings investigated whether verbs had internal semantic structure, as proposed in linguistic theories, but failed to find evidence supporting such a view (e.g., Fodor, Garrett, Walker, & Parkes 1980, Kintsch, 1974, Rayner & Duffy, 1986). For example, Rayner and Duffy (1986) measured the eye-fixation time on verbs during reading that were assumed to differ in internal complexity. They found no reading time differences corresponding to the semantic complexity of the verbs. This sort of finding, together with Fodor and colleagues’ theoretical arguments (Fodor, 1975, Fodor, Fodor, & Garrett, 1975, Fodor & Lepore, 1998), was taken to support the view that verb meanings are atomic and lack internal structure. However, recent psycholinguistic studies challenge this view. Several sentence processing experiments have shown that lexical semantic properties such as selectional restrictions and verb-specific thematic roles (agent vs. patient) are quickly accessed by the processor when parsing syntactic ambiguities (e.g., Trueswell, Tanenhaus & Kello, 1993, Trueswell, Tanenhaus & Garnsey, 1994). More relevant to verb concepts per se, McRae, Ferretti & Amyote (1997) have shown that thematic roles have internal conceptual structure (as object categories do) and that their feature structure is quickly accessed by the parser when resolving syntactic ambiguities. Moreover, Ferretti, McRae & Hatherell (2001) have shown that verbs prime their typical agents, patients and instruments (e.g., praying primes nun). They argue that verbs activate event schemas or generalized situation based knowledge that facilitate accessing the meaning of their typical participants. Finally, McKoon & Macfarland (2000) have found processing correlates of two types of verb meanings, those that are conceptualized as either externally caused events (e.g., break) or internally caused ones (e.g., grow). These verb types are assumed to differ in internal lexical complexity, particularly in their causal components (see also Gentner 1981). Taken together, these findings suggest that there is some internal structure in verb meanings: thematic structure and event types. The work presented here further investigates verb concepts, i.e., how verbs, which refer to events, are processed and internally represented. In particular, we ask whether there are verb-general concepts and structures beyond and above the idiosyncratic meanings of individual verbs. We follow numerous linguistic and philosophical studies (as in McKoon & Macfarland, 2000) in assuming common structural and causal properties across classes of verbs that define superordinate concepts. Thus, beyond the existence of typical agent-verb-relations (that between nuns and praying), there may be more abstract structural or conceptual properties that organize our knowledge of events stored in the lexicon. The classification of verbs and their semantic properties has been the topic of numerous philosophical and linguistic studies (Vendler, 1967). Following traditional Aristotelian classes, these studies have argued that there is a typology of events underlying verb uses. Verb types appear to be universal (Smith, 1991) and are supposed to reflect the way speakers conceptualize the domain of events, i.e., the semantic/conceptual properties they assign to a particular actual occurrence. One general distinction typically made between verb meanings is, among others, that between states and events (Vendler 1967, Dowty, 1979, Taylor 1977, Bach, 1986, Verkuyl, 1993, Jackendoff, 1990, Rappaport-Hovav & Levin 1998). The distinction seems cognitively basic because it is grounded in causal properties: eventive verbs typically denote a cause and a change from an initial state to a resulting one (e.g. write, destroy), while stative verbs simply denote properties or stable relations between participants (e.g. love, belong, contain) (Dowty, 1979, Parsons 1990). The


title: Urban Pulse: Capturing the Rhythm of Cities

abstract: Cities are inherently dynamic. Interesting patterns of behavior typically manifest at several key areas of a city over multiple temporal resolutions. Studying these patterns can greatly help a variety of experts ranging from city planners and architects to human behavioral experts. Recent technological innovations have enabled the collection of enormous amounts of data that can help in these studies. However, techniques using these data sets typically focus on understanding the data in the context of the city, thus failing to capture the dynamic aspects of the city. The goal of this work is to instead understand the city in the context of multiple urban data sets. To do so, we define the concept of an “urban pulse” which captures the spatio-temporal activity in a city across multiple temporal resolutions. The prominent pulses in a city are obtained using the topology of the data sets, and are characterized as a set of beats. The beats are then used to analyze and compare different pulses. We also design a visual exploration framework that allows users to explore the pulses within and across multiple cities under different conditions. Finally, we present three case studies carried out by experts from two different domains that demonstrate the utility of our framework.


title: Speech recognition by machine: A review

abstract: This paper provides a review of recent developments in speech recognition research. The concept of sources of knowledge is introduced and the use of knowledge to generate and verify hypotheses is discussed. The difficulties that arise in the construction of different types of speech recognition systems are discussed and the structure and performance of several such systems is presented. Aspects of component subsystems at the acoustic, phonetic, syntactic, and semantic levels are presented. System organizations that are required for effective interaction and use of various component subsystems in the presence of error and ambiguity are discussed.


Image Quality Assessment by Comparing CNN Features between Images


title: Exploiting addition schemes for the improvement of optimized radix-2 and radix-4 fft butterflies

abstract: In FFT computation, the butterflies play a central role, since they allow the calculation of complex terms. Therefore, the optimization of the butterfly can contribute for the power reduction in FFT architectures. In this paper we exploit different addition schemes in order to improve the efficiency of 16 bit-width radix-2 and radix-4 FFT butterflies. Combinations of simultaneous addition of three and seven operands are inserted in the structures of the butterflies in order to produce power-efficient structures. The used additions schemes include Carry Save Adder (CSA), and adder compressors. The radix-2 and radix-4 butterflies were implemented in hardware description language and synthesized to 45nm Nangate Open Cell Library using Cadence RTL Compiler. The main results show that both radix-2 and radix-4 butterflies, with CSA, are more efficient when compared with the same structures with other adder circuits.


title: Vaginal erbium laser: the second-generation thermotherapy for the genitourinary syndrome of menopause

abstract: s / Maturitas 81 (2015) 126–143 133 results have been obtained from in vitro and in vivo studies concerning the role of estrogens and, especially progestins, in breast epithelial cell proliferation and in breast carcinogenesis. MPA has been found to trigger massive RANKL expression in mouse mammary gland predisposing the tissue to malignant changes and even onset of development of breast cancer. So far, there is little data available on the RANKL expression in human postmenopausal breast tissues (HBT). Aim: We studied the effects of E2, MPA, and some SERMs on explant cultures of normal, peritumoral and tumoral HBTs to evaluate their regulation of the progesterone/RANKL pathway. Methods: The non-malignant ormalignant HBTswere obtained from postmenopausal women undergoing breast surgeries. HBTs were cultured with or without E2, MPA and SERMs (ospemifene, raloxifene and tamoxifene) for 7 and 14 days. Further, cultured explants were studied for morphology, expression of markers for proliferation and apoptosis, expression of steroid hormone receptors and RANKL by immunohistochemistry, Western blots and qRT-PCR. Results: (1) E2, MPA and E2+MPA induce cell proliferation and RANKL induction in post-menopausal HBTs (2) RANKL induction is increased in tumoral HBTs (3) tamoxifen inhibited proliferation and RANKL expression. Conclusion: The results suggest that progesterone/RANKL axis has an important role in the response of HBTs to the steroids or drugs used in menopause. http://dx.doi.org/10.1016/j.maturitas.2015.02.104


title: From One Extreme to the Other: Negative Evaluation Anxiety and Disordered Eating as Candidates for the Extreme Female Brain:

abstract: Simon Baron-Cohen pioneered the idea that different brain types evolved to process information in gender specific ways. Here we expand this approach to looking at eating disorders as a byproduct of the extreme female brain. The incidence of eating disorders is higher among females, and recent findings show that hormones may play a role in eating disorders. We present new evidence from four studies that both an empathizing bias and hyper-mentalizing (as measures of the extreme female brain; EFB) are related to disordered eating and negative evaluation anxiety in women. We also advance the novel hypothesis that concerns about animal welfare (a unique expression of the EFB) may account for the relationship between vegetarianism and eating disorders.


title: A PatchMatch-Based Dense-Field Algorithm for Video Copy–Move Detection and Localization

abstract: We propose a new algorithm for the reliable detection and localization of video copy–move forgeries. Discovering well-crafted video copy–moves may be very difficult, especially when some uniform background is copied to occlude foreground objects. To reliably detect both additive and occlusive copy–moves, we use a dense-field approach, with invariant features that guarantee robustness to several postprocessing operations. To limit complexity, a suitable video-oriented version of PatchMatch is used, with a multiresolution search strategy, and a focus on volumes of interest. Performance assessment relies on a new dataset, designed ad hoc , with realistic copy–moves and a wide variety of challenging situations. Experimental results show the proposed method to detect and localize video copy–moves with good accuracy even in adverse conditions.


title: Determining the Veracity of Rumours on Twitter

abstract: While social networks can provide an ideal platform for up-to-date information from individuals across the world, it has also proved to be a place where rumours fester and accidental or deliberate misinformation often emerges. In this article, we aim to support the task of making sense from social media data, and specifically, seek to build an autonomous message-classifier that filters relevant and trustworthy information from Twitter. For our work, we collected about 100 million public tweets, including users’ past tweets, from which we identified 72 rumours (41 true, 31 false). We considered over 80 trustworthiness measures including the authors’ profile and past behaviour, the social network connections (graphs), and the content of tweets themselves. We ran modern machine-learning classifiers over those measures to produce trustworthiness scores at various time windows from the outbreak of the rumour. Such time-windows were key as they allowed useful insight into the progression of the rumours. From our findings, we identified that our model was significantly more accurate than similar studies in the literature. We also identified critical attributes of the data that give rise to the trustworthiness scores assigned. Finally we developed a software demonstration that provides a visual user interface to allow the user to examine the analysis.


title: Using Blockchain to push Software-Defined IoT Components onto Edge Hosts

abstract: Moving IoT components from the cloud onto edge hosts helps in reducing overall network traffic and thus minimizes latency. However, provisioning IoT services on the IoT edge devices presents new challenges regarding system design and maintenance. One possible approach is the use of software-defined IoT components in the form of virtual IoT resources. This, in turn, allows exposing the thing/device layer and the core IoT service layer as collections of micro services that can be distributed to a broad range of hosts. This paper presents the idea and evaluation of using virtual resources in combination with a permission-based blockchain for provisioning IoT services on edge hosts.