title: Virtualization on Internet of Things Edge Devices With Container Technologies: A Performance Evaluation

abstract: Lightweight virtualization technologies have revolutionized the world of software development by introducing flexibility and innovation to this domain. Although the benefits introduced by these emerging solutions have been widely acknowledged in cloud computing, recent advances have led to the spread of such technologies in different contexts. As an example, the Internet of Things (IoT) and mobile edge computing benefit from container virtualization by exploiting the possibility of using these technologies not only in data centers but also on devices, which are characterized by fewer computational resources, such as single-board computers. This has led to a growing trend to more efficiently redesign the critical components of IoT/edge scenarios (e.g., gateways) to enable the concept of device virtualization. The possibility for efficiently deploying virtualized instances on single-board computers has already been addressed in recent studies; however, these studies considered only a limited number of devices and omitted important performance metrics from their empirical assessments. This paper seeks to fill this gap and to provide insights for future deployments through a comprehensive performance evaluation that aims to show the strengths and weaknesses of several low-power devices when handling container-virtualized instances.


title: Alpha and Beta brainwave characteristics to binaural beat treatment

abstract: This paper presents the affect of binaural beats tone of 10Hz (Deep Meditation) to Alpha and Beta brain wave using EEG. Thirty three (33) right handed students (22 females and 11 males) were randomly selected from Universiti Teknologi MARA (UiTM) Shah Alam. They were willing to be involved after their examination period to investigate this research work. Nowadays, people easily get stressed due to several external factors like family problems, money problem, life satisfaction and other similar factors. Hence, some people meditate by listening to the sound related to relax mode like binaural beats in order to release stress. In this paper, EPOC EMOTIV electroencephalography (EEG) instrumentation with 14 electrodes is used. During data collection, all subjects must close their eyes. The EEG data recorded is processed offline using a program which is developed in MATLAB and the data is further analyzed using Microsoft Excel. The increase on Alpha brainwave and decrease on Beta brainwave after listening to binaural beat shows that subjects are affected by stimuli given. The majority of the subjects are affected by the binaural beat.


title: Massive MIMO as enabler for communications with drone swarms

abstract: Massive multiple-input multiple-output (MIMO) is an emerging technology for mobile communications, where a large number of antennas are employed at the base station to simultaneously serve multiple single-antenna terminals with very high capacity. In this paper, we study the potentials and challenges of utilizing massive MIMO for unmanned aerial vehicles (UAVs) communication. We consider a scenario where multiple single-antenna UAVs simultaneously communicate with a ground station (GS) equipped with a large number of antennas. Specifically, we discuss the achievable uplink (UAV to GS) capacity performance in the case of line-of-sight (LoS) conditions. We also study the type of antenna polarization that should be used in order to maintain a reliable communication link between the GS and the UAVs. The results obtained using a realistic geometric model show that massive MIMO is a potential enabler for high-capacity UAV networks.


title: Survey, comparison and evaluation of cross platform mobile application development tools

abstract: Mobile application development is becoming more challenging with diverse platforms and their software development kits. In order to reduce the cost of development and reach out to maximum users across several platforms, developers are migrating to cross platform application development tools. In this paper, we provide several decision criteria beyond the portability concerns for choosing suitable cross platform tool for application development. The desirable requirements in a cross platform framework are identified. A general architecture for cross platform application development is discussed. Then a survey of several write once run anywhere tools (PhoneGap, Titanium, Sencha Touch) are provided along with a classification and comparison among the tools. To examine the performance in terms of CPU, memory usage, power consumption, Android test applications are developed using such tools. It is found that PhoneGap consumes less memory, CPU and power since it does not included dedicated UI components. Finally the paper summarizes the contributions and concludes with some future directions.


title: Recurrent Instance Segmentation

abstract: Instance segmentation is the problem of detecting and delineating each distinct object of interest appearing in an image. Current instance segmentation approaches consist of ensembles of modules that are trained independently of each other, thus missing opportunities for joint learning. Here we propose a new instance segmentation paradigm consisting in an end-to-end method that learns how to segment instances sequentially. The model is based on a recurrent neural network that sequentially finds objects and their segmentations one at a time. This net is provided with a spatial memory that keeps track of what pixels have been explained and allows occlusion handling. In order to train the model we designed a principled loss function that accurately represents the properties of the instance segmentation problem. In the experiments carried out, we found that our method outperforms recent approaches on multiple person segmentation, and all state of the art approaches on the Plant Phenotyping dataset for leaf counting.


title: The Nihongo Tutorial System An Intelligent Tutoring System for Technical Japanese Language Instruction

abstract: The Nihongo Tutorial System is an intelligent tutoring system designed to assist English-speaking scientists and engineers in acquiring reading proficiency in Japanese technical literature. The system provides individualized lessons by matching a student's technical area of interest and Japanese language ability with the available instructional materials which are derived from actual technical articles. This approach is designed to maximize comprehension through context by assuring an appropriate amount of new material and by drawing on the student's expertise in the subject matter. The instructional texts are encoded in a data structure that maintains syntactic, semantic, phonetic, and morphological information that can be delivered to the student upon request. The system also provides on-line supplementary materials including a character dictionary, a general dictionary, a grammar dictionary, and several technical dictionaries for additional information. Thus the Nihongo Tutorial System provides an efficient aid for acquiring the vocabulary, syntax, and style of Japanese texts from a specific technical discipline.


title: Predicting Vulnerable Software Components via Text Mining

abstract: This paper presents an approach based on machine learning to predict which components of a software application contain security vulnerabilities. The approach is based on text mining the source code of the components. Namely, each component is characterized as a series of terms contained in its source code, with the associated frequencies. These features are used to forecast whether each component is likely to contain vulnerabilities. In an exploratory validation with 20 Android applications, we discovered that a dependable prediction model can be built. Such model could be useful to prioritize the validation activities, e.g., to identify the components needing special scrutiny.


title: Finite Automata, Digraph Connectivity, and Regular Expression Size

abstract: Recently lower bounds on the minimum required size for the conversion of deterministic finite automata into regular expressions and on the required size of regular expressions resulting from applying some basic language operations on them, were given by Gelade and Neven [8]. We strengthen and extend these results, obtaining lower bounds that are in part optimal, and, notably, the presented examples are over a binary alphabet, which is best possible. To this end, we develop a different, more versatile lower bound technique that is based on the star height of regular languages. It is known that for a restricted class of regular languages, the star height can be determined from the digraph underlying the transition structure of the minimal finite automaton accepting that language. In this way, star height is tied to cycle rank, a structural complexity measure for digraphs proposed by Eggan and Buchi, which measures the degree of connectivity of directed graphs.


title: Hippocampal Remapping after Partial Inactivation of the Medial Entorhinal Cortex

abstract: Summary Hippocampal place cells undergo remapping when the environment is changed. The mechanism of hippocampal remapping remains elusive but spatially modulated cells in the medial entorhinal cortex (MEC) have been identified as a possible contributor. Using pharmacogenetic and optogenetic approaches, we tested the role of MEC cells by examining in mice whether partial inactivation in MEC shifts hippocampal activity to a different subset of place cells with different receptive fields. The pharmacologically selective designer Gi-protein-coupled muscarinic receptor hM4D or the light-responsive microbial proton pump archaerhodopsin (ArchT) was expressed in MEC, and place cells were recorded after application of the inert ligand clozapine-N-oxide (CNO) or light at appropriate wavelengths. CNO or light caused partial inactivation of the MEC. The inactivation was followed by substantial remapping in the hippocampus, without disruption of the spatial firing properties of individual neurons. The results point to MEC input as an element of the mechanism for remapping in place cells.


title: Structure-aware transfer of facial blendshapes

abstract: This paper presents a novel mesh deformation method for transferring reference facial blendshapes to a source face model. The presented method uses information provided by a source and a reference face model to analyze the mesh similarities and, consequently, to transfer the deformation. This is achieved by considering the distribution of vertices between the two face models in conjunction with the gradients of the meshes. Both of these components are assigned to an optimization function that is solved in two steps: (i) distribution transfer and (ii) gradient maintenance optimization. Finally, in order to analyze the efficiency of such a method, the presented method was evaluated against previous solutions in terms of deformation transfer error, and computational time.


title: Online, interactive learning of gestures for human/robot interfaces

abstract: We have developed a gesture recognition system, based on hidden Markov models, which can interactively recognize gestures and perform online learning of new gestures. In addition, it is able to update its model of a gesture iteratively with each example it recognizes. This system has demonstrated reliable recognition of 14 different gestures after only one or two examples of each. The system is currently interfaced to a Cyberglove for use in recognition of gestures from the sign language alphabet. The system is being implemented as part of an interactive interface for robot teleoperation and programming by example.


Information Extraction for Ontology Learning


Building the Moroccan Darija WordNet (MDW) using Bilingual Resources


title: BlinDar: An invisible eye for the blind people making life easy for the blind with Internet of Things (IoT)

abstract: Blindness is a condition in which an individual loses the ocular perception. Mobility and self-reliability for the visually impaired and blind people has always been a problem. In this paper a smart Electronic Traveling Aid (ETA) called BlinDar has been proposed. This smart guiding ETA ameliorates the life of blind as it is well equipped with Internet of Things (IoT) and is meant to aid the visually impaired and blind to walk without constraint in close as well as open environments. BlinDar is a highly efficient, reliable, fast responding, light weight, low power consuming and cost effective device for the blind. Ultrasonic sensors have been used to detect the obstacle and potholes within a range of 2m. GPS and ESP8266 Wi-Fi module has been used for sharing the location with the cloud. MQ2 gas sensor is used for detecting fire in path and a RF Tx/Rx module for finding the stick when it is misplaced. Arduino Mega2560 is the microcontroller used, which has 54 digital I/O pins which makes the interfacing of components easy.


title: 15.3 A 2.4GHz ADPLL with digital-regulated supply-noise-insensitive and temperature-self-compensated ring DCO

abstract: Due to the high supply sensitivity of ring voltage-controlled oscillators (RVCOs) ([oscillation frequency change %] / [VDD change %] typically lies in the range from 1 to 2 [1]), an LDO has to provide over 40dB power-supply-rejection ratio (PSRR) to maintain VCO phase noise. However, the voltage dropout of an LDO consumes extra power and voltage headroom, which is unacceptable in low-voltage design. Moreover, the device noise from the LDO degrades the phase-noise performance. Recently published works [1-5] employ analog compensation techniques to lower supply sensitivity, and [2] incorporates a hybrid background calibration scheme for robustness. However, the additional current sources and active devices embedded in the oscillator [1-5] increase power and noise. In this work, a DCO with passive devices and all-digital calibration mitigates supply sensitivity under PVT variation, while maintaining phase noise and power consumption. The digital background-calibration logic regulates the oscillator supply to an optimally insensitive point by monitoring a digital loop filter (DLF) code, leveraging an advantage of an ADPLL [6].


title: Control of Complex Maneuvers for a Quadrotor UAV using Geometric Methods on SE(3)

abstract: This paper provides new results for control of complex flight maneuvers for a quadrotor unmanned aerial vehicle (UAV). The flight maneuvers are defined by a concatenation of flight modes or primitives, each of which is achieved by a nonlinear controller that solves an output tracking problem. A mathematical model of the quadrotor UAV rigid body dynamics, defined on the configuration space $\SE$, is introduced as a basis for the analysis. The quadrotor UAV has four input degrees of freedom, namely the magnitudes of the four rotor thrusts; each flight mode is defined by solving an asymptotic optimal tracking problem. Although many flight modes can be studied, we focus on three output tracking problems, namely (1) outputs given by the vehicle attitude, (2) outputs given by the three position variables for the vehicle center of mass, and (3) output given by the three velocity variables for the vehicle center of mass. A nonlinear tracking controller is developed on the special Euclidean group $\SE$ for each flight mode, and the closed loop is shown to have desirable closed loop properties that are almost global in each case. Several numerical examples, including one example in which the quadrotor recovers from being initially upside down and another example that includes switching and transitions between different flight modes, illustrate the versatility and generality of the proposed approach.


title: Deep Metric Learning for Practical Person Re-Identification

abstract: Various hand-crafted features and metric learning methods prevail in the field of person re-identification. Compared to these methods, this paper proposes a more general way that can learn a similarity metric from image pixels directly. By using a"siamese"deep neural network, the proposed method can jointly learn the color feature, texture feature and metric in a unified framework. The network has a symmetry structure with two sub-networks which are connected by Cosine function. To deal with the big variations of person images, binomial deviance is used to evaluate the cost between similarities and labels, which is proved to be robust to outliers. Compared to existing researches, a more practical setting is studied in the experiments that is training and test on different datasets (cross dataset person re-identification). Both in"intra dataset"and"cross dataset"settings, the superiorities of the proposed method are illustrated on VIPeR and PRID.


title: Antenna Concepts for Millimeter-Wave Automotive Radar Sensors

abstract: Automotive radars are on the market since 1999, both in the frequency range around 24 GHz as well as 76.5 GHz, with a new frequency band ranging from 77 to 81 GHz intended for medium and short-range sensors. The choice and design of the respective sensor antennas are determined by the requirement for high gain and low loss combined with small size and depth for vehicle integration, the challenges by the millimeter-wave frequency range, and a great cost pressure for this commercial application. Consequently, planar antennas are dominating in the lower frequency range, while lens and reflector antennas had been the first choice at 76.5 GHz, partly in folded configurations. With increasing requirements towards a much more detailed observation of the scenery in front or around the vehicle, multibeam antennas or scanning antennas have been designed, and solutions based on (digital) beamforming with a number of integrated antennas are in use or under development. General antenna concepts, partly including some system aspects, as well as three realized antenna configurations will be described in detail in this contribution.


title: Understanding the Formation of Trust in IT Artifacts

abstract: IT artifacts often take the role of a trustee in a trust relationship between users and IT artifacts. The goal of this paper is to increase the understanding of the formation of trust in such trust relationships. Instead of using the predominant theoretical foundation of interpersonal trust, we use the theoretical foundation of trust in automation from the HCI discipline for studying the formation of trust. Since we aim at creating insights on the formation of trust and its dimensions, we develop a formative first-order, formative second-order measurement model for trust. To evaluate the impact of the single indicators and dimensions on trust, we conduct a laboratory experiment. Our results show that the dimensions performance, process and purpose have a comparable impact on trust, and that indicators related to user data are especially important. The results complement existing insights, deepening the understanding of the formation of trust in IT artifacts.


title: Emotion Detection from text: A Survey

abstract: This survey describes recent works in the field of Emotion Detection from text, being a part of the broader area of Affective Computing. This survey has been inspired on the well-known fact that, despite there is a lot of work on emotional detection systems, a lot of work is expected to be done yet. The increment of these systems is due to the large amount of emotional data available in Social Web. Detecting emotions from text have attracted the attention of many researchers in computational linguistics because it has a wide range of applications, such as suicide prevention or measuring well-being of a community. This paper mainly collects works based on lexical and machine learning approaches and these works are classificated in accordance with the emotional model and the approach used.


title: A General View for Network Embedding as Matrix Factorization

abstract: We propose a general view that demonstrates the relationship between network embedding approaches and matrix factorization. Unlike previous works that present the equivalence for the approaches from a skip-gram model perspective, we provide a more fundamental connection from an optimization (objective function) perspective. We demonstrate that matrix factorization is equivalent to optimizing two objectives: one is for bringing together the embeddings of similar nodes; the other is for separating the embeddings of distant nodes. The matrix to be factorized has a general form: S-β. The elements of $\mathbfS $ indicate pairwise node similarities. They can be based on any user-defined similarity/distance measure or learned from random walks on networks. The shift number β is related to a parameter that balances the two objectives. More importantly, the resulting embeddings are sensitive to β and we can improve the embeddings by tuning β. Experiments show that matrix factorization based on a new proposed similarity measure and β-tuning strategy significantly outperforms existing matrix factorization approaches on a range of benchmark networks.


title: Achieving air-ground communications in 802.11 networks with three-dimensional aerial mobility

abstract: Increasing availability of autonomous small-size aerial vehicles leads to a variety of applications for aerial exploration and surveillance, transport, and other domains. Many of these applications rely on networks between aerial nodes, that will have high mobility dynamics with vehicles moving in all directions in 3D space and positioning in different orientations, leading to restrictions on network connectivity. In this paper, we propose a simple antenna extension to 802.11 devices to be used on aerial nodes. Path loss and small-scale fading characteristics of air-to-ground links are analyzed using signal strength samples obtained via real-world measurements at 5 GHz. Finally, network performance in terms of throughput and number of retransmissions are presented. Results show that a throughput of 12Mbps can be achieved at distances in the order of 300m.


title: Boosting energy efficiency with mirrored data block replication policy and energy scheduler

abstract: Energy efficiency is one of the major challenges in big datacenters. To facilitate processing of large data sets in a distributed fashion, the MapReduce programming model is employed in these datacenters. Hadoop is an open-source implementation of MapReduce which contains a distributed file system. Hadoop Distributed File System provides a data block replication scheme to preserve reliability and data availability. The distribution of the data block replicas over the nodes is performed randomly by meeting some constraints (e.g., preventing storage of two replicas of a data block on a single node). This study makes use of flexibility in the data block placement policy to increase energy efficiency in datacenters. Furthermore, inspired by Zaharia et al.'s delay scheduling algorithm, a scheduling algorithm is introduced, which takes into account energy efficiency in addition to fairness and data locality properties. Computer simulations of the proposed method suggest its superiority over Hadoop's standard settings.


title: A Taxonomy and Survey of Intrusion Detection System Design Techniques, Network Threats and Datasets

abstract: With the world moving towards being increasingly dependent on computers and automation, one of the main challenges in the current decade has been to build secure applications, systems and networks. Alongside these challenges, the number of threats is rising exponentially due to the attack surface increasing through numerous interfaces offered for each service. To alleviate the impact of these threats, researchers have proposed numerous solutions; however, current tools often fail to adapt to ever-changing architectures, associated threats and 0-days. This manuscript aims to provide researchers with a taxonomy and survey of current dataset composition and current Intrusion Detection Systems (IDS) capabilities and assets. These taxonomies and surveys aim to improve both the efficiency of IDS and the creation of datasets to build the next generation IDS as well as to reflect networks threats more accurately in future datasets. To this end, this manuscript also provides a taxonomy and survey or network threats and associated tools. The manuscript highlights that current IDS only cover 25% of our threat taxonomy, while current datasets demonstrate clear lack of real-network threats and attack representation, but rather include a large number of deprecated threats, hence limiting the accuracy of current machine learning IDS. Moreover, the taxonomies are open-sourced to allow public contributions through a Github repository.


title: Varactor-Tuned Dual-Mode Bandpass Filters

abstract: This paper presents a new type of varactor-tuned dual-mode bandpass filter. Since the two operating modes (i.e., the odd and even modes) in a dual-mode microstrip open-loop resonator do not couple to each other, the tuning of the passband frequency becomes simple with a single dc-bias circuit while keeping nearly constant absolute bandwidth. Design equations and procedures are derived, and two two-pole tunable bandpass filters of this type are demonstrated experimentally.


title: Distributed SDN Control: Survey, Taxonomy, and Challenges

abstract: As opposed to the decentralized control logic underpinning the devising of the Internet as a complex bundle of box-centric protocols and vertically integrated solutions, the software-defined networking (SDN) paradigm advocates the separation of the control logic from hardware and its centralization in software-based controllers. These key tenets offer new opportunities to introduce innovative applications and incorporate automatic and adaptive control aspects, thereby, easing network management and guaranteeing the user’s quality of experience. Despite the excitement, SDN adoption raises many challenges including the scalability and reliability issues of centralized designs that can be addressed with the physical decentralization of the control plane. However, such physically distributed, but logically centralized systems bring an additional set of challenges. This paper presents a survey on SDN with a special focus on the distributed SDN control. Besides reviewing the SDN concept and studying the SDN architecture as compared to the classical one, the main contribution of this survey is a detailed analysis of state-of-the-art distributed SDN controller platforms which assesses their advantages and drawbacks and classifies them in novel ways (physical and logical classifications) in order to provide useful guidelines for SDN research and deployment initiatives. A thorough discussion on the major challenges of distributed SDN control is also provided along with some insights into emerging and future trends in that area.


title: Furosemide action on collecting ducts: effect of prostaglandin synthesis inhibition.

abstract: The effect of furosemide on inner medullary collecting duct chloride reabsorption has not been determined, and the blunting of furosemide action by drugs that inhibit prostaglandin synthesis, while known to occur, has not been examined in detail. The effect of indomethacin and meclofenamate on furosemide diuresis was studied in the rat using clearance and collecting duct microcatheterization methods. Furosemide-treated control animals showed complete inhibition of chloride, sodium, and water reabsorption in the inner medullary collecting duct. Rats given indomethacin or meclofenamate before and during furosemide administration showed marked reduction of the chloriuresis, natriuresis, and diuresis. Reduced delivery of sodium and chloride to the beginning of the inner medullary collecting duct, associated with a decrease in glomerular filtration rate and increased reabsorption in more proximal nephron segments, was largely responsible for the reduced natriuresis and chloriuresis during inhibition of prostaglandin synthesis. In addition, indomethacin increased collecting duct NaCl reabsorption toward normal, but meclofenamate showed no such effect. The results indicate that furosemide inhibits medullary collecting duct reabsorption of chloride, sodium, and water in the rat. The blunting of diuretic action seen with inhibition of prostaglandin synthesis is largely, although not entirely, due to effects of indomethacin and meclofenamate on furosemide action at nephron sites proximal to the collecting duct.


Challenges with Industrialization of Atomic Layer Deposition of Silicon Nitride


title: Learning Multi-view Deep Features for Small Object Retrieval in Surveillance Scenarios

abstract: With the explosive growth of surveillance videos, object retrieval has become a significant task for security monitoring. However, visual objects in surveillance videos are usually of small size with complex light conditions, view changes and partial occlusions, which increases the difficulty level of efficiently retrieving objects of interest in a large-scale dataset. Although deep features have achieved promising results on object classification and retrieval and have been verified to contain rich semantic structure property, they lack of adequate color information, which is as crucial as structure information for effective object representation. In this paper, we propose to leverage discriminative Convolutional Neural Network (CNN) to learn deep structure and color feature to form an efficient multi-view object representation. Specifically, we utilize CNN trained on ImageNet to abstract rich semantic structure information. Meanwhile, we propose a CNN model supervised by 11 color names to extract deep color features. Compared with traditional color descriptors, deep color features can capture the common color property across different illumination conditions. Then, the complementary multi-view deep features are encoded into short binary codes by Locality-Sensitive Hash (LSH) and fused to retrieve objects. Retrieval experiments are performed on a dataset of 100k objects extracted from multi-camera surveillance videos. Comparison results with several popular visual descriptors show the effectiveness of the proposed approach.


title: A Review on Internet of Things (IoT), Internet of Everything (IoE) and Internet of Nano Things (IoNT)

abstract: The current prominence and future promises of the Internet of Things (IoT), Internet of Everything (IoE) and Internet of Nano Things (IoNT) are extensively reviewed and a summary survey report is presented. The analysis clearly distinguishes between IoT and IoE which are wrongly considered to be the same by many people. Upon examining the current advancement in the fields of IoT, IoE and IoNT, the paper presents scenarios for the possible future expansion of their applications.


title: Velocity and acceleration cones for kinematic and dynamic constraints on omni-directional mobile robots

abstract: We consider the problems of kinematic and dynamic constraints, with actuator saturation and wheel slippage avoidance, for motion planning of a holonomic three-wheeled omni-directional robot. That is, the motion planner must not demand more velocity and acceleration at each time instant than the robot can provide. A new coupled non-linear dynamics model is derived. The novel concepts of Velocity and Acceleration Cones are proposed for determining the kinematic and dynamic constraints. The Velocity Cone is based on kinematics; we propose two Acceleration Cones, one for avoiding actuator saturation and the other for avoiding wheel slippage. The wheel slippage Acceleration Cone was found to dominate. In practical motion, all commanded velocities and accelerations from the motion planner must lie within these cones for successful motion. Case studies, simulations, and experimental, validations are presented for our dynamic model and controller, plus the Velocity and Acceleration Cones.


title: The bionic hand movement using myo sensor and neural networks

abstract: One part of human body in which very useful in this life is a hand. This life will be disrupted if one or both of these hands do not work, due to disability or amputation. One of the contributions that help this condition is the bionic hand. This study have developed a bionic hand that consist of six servo motors to drive the fingers movement. Myo sensor is used in this bionic hand, it is located in the forearm. It has been used to record the muscle signals. Feature extraction of the muscle signal was formulated by using root mean square (rms). Kind of the bionic hand movement was found by identifying the movement signal pattern using back propagation through the artificial neural network. The output data obtained from back propagation was processed by microcontroller and transmitted to the servo motors for getting a movement of the bionic hand. Based on experimental data, five kinds of movement were tested in this system, average of recognition for each movement: handful accuracy movement 65%; pointing-fingers 75%; wave in 100%; wave out 75%; and spread 100%. Testing for the whole movements (five kinds) were performed with the average error 17%.


title: FAST VEHICLE DETECTION AND TRACKING IN AERIAL IMAGE BURSTS

abstract: Caused by the rising interest in traffic surveillance for simulations and decision management many publications concentrate on automatic vehicle detection or tracking. Quantities and velocities of different car classes form the data basis for almost every traffic model. Especially during mass events or disasters a wide-area traffic monitoring on demand is needed which can only be provided by airborne systems. This means a massive amount of image information to be handled. In this paper we present a combination of vehicle detection and tracking which is adapted to the special restrictions given on image size and flow but nevertheless yields reliable information about the traffic situation. Combining a set of modified edge filters it is possible to detect cars of different sizes and orientations with minimum computing effort, if some a priori information about the street network is used. The found vehicles are tracked between two consecutive images by an algorithm using Singular Value Decomposition. Concerning their distance and correlation the features are assigned pairwise with respect to their global positioning among each other. Choosing only the best correlating assignments it is possible to compute reliable values for the average velocities.


title: WiFi-Assisted 60 GHz Wireless Networks

abstract: Despite years of innovative research and development, gigabit-speed 60 GHz wireless networks are still not mainstream. The main concern for network operators and vendors is the unfavorable propagation characteristics due to short wavelength and high directionality, which renders the 60 GHz links highly vulnerable to blockage and mobility. However, the advent of multi-band chipsets opens the possibility of leveraging the more robust WiFi technology to assist 60 GHz in order to provide seamless, Gbps connectivity. In this paper, we design and implement MUST, an IEEE 802.11-compliant system that provides seamless, high-speed connectivity over multi-band 60 GHz and WiFi devices. MUST has two key design components: (1) a WiFi-assisted 60 GHz link adaptation algorithm, which can instantaneously predict the best beam and PHY rate setting, with zero probing overhead; and (2) a proactive blockage detection and switching algorithm which can re-direct ongoing user traffic to the robust interface within sub-10 ms latency. Our experiments with off-the-shelf 802.11 hardware show that MUST can achieve 25-60% throughput gain over state-of-the-art solutions, while bringing almost 2 orders of magnitude cross-band switching latency improvement.


title: A Neural Approach to Blind Motion Deblurring

abstract: We present a new method for blind motion deblurring that uses a neural network trained to compute estimates of sharp image patches from observations that are blurred by an unknown motion kernel. Instead of regressing directly to patch intensities, this network learns to predict the complex Fourier coefficients of a deconvolution filter to be applied to the input patch for restoration. For inference, we apply the network independently to all overlapping patches in the observed image, and average its outputs to form an initial estimate of the sharp image. We then explicitly estimate a single global blur kernel by relating this estimate to the observed image, and finally perform non-blind deconvolution with this kernel. Our method exhibits accuracy and robustness close to state-of-the-art iterative methods, while being much faster when parallelized on GPU hardware.


title: Extreme learning machine: RBF network case

abstract: A new learning algorithm called extreme learning machine (ELM) has recently been proposed for single-hidden layer feedforward neural networks (SLFNs) to easily achieve good generalization performance at extremely fast learning speed. ELM randomly chooses the input weights and analytically determines the output weights of SLFNs. This paper shows that ELM can be extended to radial basis function (RBF) network case, which allows the centers and impact widths of RBF kernels to be randomly generated and the output weights to be simply analytically calculated instead of iteratively tuned. Interestingly, the experimental results show that the ELM algorithm for RBF networks can complete learning at extremely fast speed and produce generalization performance very close to that of SVM in many artificial and real benchmarking function approximation and classification problems. Since ELM does not require validation and human-intervened parameters for given network architectures, ELM can be easily used.


title: Sub-Story Detection in Twitter with Hierarchical Dirichlet Processes

abstract: Social media has now become the de facto information source on real world events. The challenge, however, due to the high volume and velocity nature of social media streams, is in how to follow all posts pertaining to a given event over time, a task referred to as story detection. Moreover, there are often several different stories pertaining to a given event, which we refer to as sub-stories and the corresponding task of their automatic detection as sub-story detection. This paper proposes hierarchical Dirichlet processes (HDP), a probabilistic topic model, as an effective method for automatic sub-story detection. HDP can learn sub-topics associated with sub-stories which enables it to handle subtle variations in sub-stories. It is compared with state- of-the-art story detection approaches based on locality sensitive hashing and spectral clustering. We demonstrate the superior performance of HDP for sub-story detection on real world Twitter data sets using various evaluation measures. The ability of HDP to learn sub-topics helps it to recall the sub- stories with high precision. Another contribution of this paper is in demonstrating that the conversational structures within the Twitter stream can be used to improve sub-story detection performance significantly.


Selecting Domain-Specific Concepts for Question Generation With Lightly-Supervised Methods


title: Energy scavenging for mobile and wireless electronics

abstract: Energy harvesting has grown from long-established concepts into devices for powering ubiquitously deployed sensor networks and mobile electronics. Systems can scavenge power from human activity or derive limited energy from ambient heat, light, radio, or vibrations. Ongoing power management developments enable battery-powered electronics to live longer. Such advances include dynamic optimization of voltage and clock rate, hybrid analog-digital designs, and clever wake-up procedures that keep the electronics mostly inactive. Exploiting renewable energy resources in the device's environment, however, offers a power source limited by the device's physical survival rather than an adjunct energy store. Energy harvesting's true legacy dates to the water wheel and windmill, and credible approaches that scavenge energy from waste heat or vibration have been around for many decades. Nonetheless, the field has encountered renewed interest as low-power electronics, wireless standards, and miniaturization conspire to populate the world with sensor networks and mobile devices. This article presents a whirlwind survey through energy harvesting, spanning historic and current developments.


title: Bayesian Neural Word Embedding

abstract: Recently, several works in the domain of natural language processing presented successful methods for word embedding. Among them, the Skip-Gram (SG) with negative sampling, known also as word2vec, advanced the state-of-the-art of various linguistics tasks. In this paper, we propose a scalable Bayesian neural word embedding algorithm that can be beneficial to general item similarity tasks as well. The algorithm relies on a Variational Bayes solution for the SG objective and a detailed step by step description of the algorithm is provided. We present experimental results that demonstrate the performance of the proposed algorithm and show it is competitive with the original SG method.


title: Planar Magnetic Components in More Electric Aircraft: Review of Technology and Key Parameters for DC–DC Power Electronic Converter

abstract: The more electric aircraft (MEA) has motivated aircraft manufacturers since few decades. Indeed, their investigations lead to the increase of electric power in airplanes. The challenge is to decrease the weight of embedded systems and therefore, the fuel consumption. This is possible thanks to new efficient power electronic converters made of new components. As magnetic components represent a great proportion of their weight, planar components are an interesting solution to increase the power density of some switching mode power supplies. This paper presents the benefits and drawbacks of high-frequency planar transformers in dc/dc converters, different models developed for their design and different issues in MEA context related to planar’s specific geometry and technology.


title: Targeting Infeasibility Questions on Obfuscated Codes

abstract: Software deobfuscation is a crucial activity in security analysis and especially, in malware analysis. While standard static and dynamic approaches suffer from well-known shortcomings, Dynamic Symbolic Execution (DSE) has recently been proposed has an interesting alternative, more robust than static analysis and more complete than dynamic analysis. Yet, DSE addresses certain kinds of questions encountered by a reverser namely feasibility questions. Many issues arising during reverse, e.g. detecting protection schemes such as opaque predicates fall into the category of infeasibility questions. In this article, we present the Backward-Bounded DSE, a generic, precise, efficient and robust method for solving infeasibility questions. We demonstrate the benefit of the method for opaque predicates and call stack tampering, and give some insight for its usage for some other protection schemes. Especially, the technique has successfully been used on state-of-the-art packers as well as on the government-grade X-Tunnel malware -- allowing its entire deobfuscation. Backward-Bounded DSE does not supersede existing DSE approaches, but rather complements them by addressing infeasibility questions in a scalable and precise manner. Following this line, we propose sparse disassembly, a combination of Backward-Bounded DSE and static disassembly able to enlarge dynamic disassembly in a guaranteed way, hence getting the best of dynamic and static disassembly. This work paves the way for robust, efficient and precise disassembly tools for heavily-obfuscated binaries.


title: Text Classification with Topic-based Word Embedding and Convolutional Neural Networks

abstract: Recently, distributed word embeddings trained by neural language models are commonly used for text classification with Convolutional Neural Networks (CNNs). In this paper, we propose a novel neural language model, Topic-based Skip-gram, to learn topic-based word embeddings for biomedical literature indexing with CNNs. Topic-based Skip-gram leverages textual content with topic models, e.g., Latent Dirichlet Allocation (LDA), to capture precise topic-based word relationship and then integrate it into distributed word embedding learning. We then describe two multimodal CNN architectures, which are able to employ different kinds of word embeddings at the same time for text classification. Through extensive experiments conducted on several real-world datasets, we demonstrate that combination of our Topic-based Skip-gram and multimodal CNN architectures outperforms state-of-the-art methods in biomedical literature indexing, clinical note annotation and general textual benchmark dataset classification.


title: Hybrid Recommendation System for Tourism

abstract: This paper adopts item-based collaborative filtering to predict the interests of an active tourist by collecting preferences or taste information from a number of other tourists. Our proposed mechanism is able to predict a set recommended tourism places of elicited rating places (e.g., ratings of 1 through 5 stars) for the active tourist pre-traveling places. Furthermore, giving restriction of traveling factors, such as budge and time, the recommendation system will refine the exact set of tourism places by applying genetic algorithm mechanism. Finally, the system is based on minimum cost to schedule traveling path from a set of selected places by the using genetic algorithm approach. Our proposed hybrid recommendation algorithm focuses on the refining efficiency and provides multi-functional tourism information.


title: Text2Scene: Generating Abstract Scenes from Textual Descriptions

abstract: In this paper, we propose an end-to-end model that learns to interpret natural language describing a scene to generate an abstract pictorial representation. The pictorial representations generated by our model comprise the spatial distribution and attributes of the objects in the described scene. Our model uses a sequence-to-sequence network with a double attentive mechanism and introduces a regularization strategy. These scene representations can be sampled from our model similarly as in language-generation models. We show that the proposed model, initially designed to handle the generation of cartoon-like pictorial representations in the Abstract Scenes Dataset, can also handle, under minimal modifications, the generation of semantic layouts corresponding to real images in the COCO dataset. Human evaluations using a visual entailment task show that pictorial representations generated with our full model can entail at least one out of three input visual descriptions 94% of the times, and at least two out of three 62% of the times for each image.


title: Facebook Photo Activity Associated with Body Image Disturbance in Adolescent Girls

abstract: Abstract The present study examined the relationship between body image and adolescent girls' activity on the social networking site (SNS) Facebook (FB). Research has shown that elevated Internet “appearance exposure” is positively correlated with increased body image disturbance among adolescent girls, and there is a particularly strong association with FB use. The present study sought to replicate and extend upon these findings by identifying the specific FB features that correlate with body image disturbance in adolescent girls. A total of 103 middle and high school females completed questionnaire measures of total FB use, specific FB feature use, weight dissatisfaction, drive for thinness, thin ideal internalization, appearance comparison, and self-objectification. An appearance exposure score was calculated based on subjects' use of FB photo applications relative to total FB use. Elevated appearance exposure, but not overall FB usage, was significantly correlated with weight dissatisfaction, drive fo...


title: Budgeted Bandit Problems with Continuous Random Costs

abstract: We study the budgeted bandit problem, where each arm is associated with both a reward and a cost. In a budgeted bandit problem, the objective is to design an arm pulling algorithm in order to maximize the total reward before the budget runs out. In this work, we study both multi-armed bandits and linear bandits, and focus on the setting with continuous random costs. We propose an upper condence bound based algorithm for multi-armed bandits and a condence ball based algorithm for linear bandits, and prove logarithmic regret bounds for both algorithms. We conduct simulations on the proposed algorithms, which verify the eectiveness of our proposed algorithms.


title: FEDD: Feature Extraction for Explicit Concept Drift Detection in time series

abstract: A time series is a sequence of observations collected over fixed sampling intervals. Several real-world dynamic processes can be modeled as a time series, such as stock price movements, exchange rates, temperatures, among others. As a special kind of data stream, a time series may present concept drift, which affects negatively time series analysis and forecasting. Explicit drift detection methods based on monitoring the time series features may provide a better understanding of how concepts evolve over time than methods based on monitoring the forecasting error of a base predictor. In this paper, we propose an online explicit drift detection method that identifies concept drifts in time series by monitoring time series features, called Feature Extraction for Explicit Concept Drift Detection (FEDD). Computational experiments showed that FEDD performed better than error-based approaches in several linear and nonlinear artificial time series with abrupt and gradual concept drifts.


title: Real-time challenge balance in an RTS game using rtNEAT

abstract: This paper explores using the NEAT and rtNEAT neuro-evolution methodologies to generate intelligent opponents in real-time strategy (RTS) games. The main objective is to adapt the challenge generated by the game opponents to match the skill of a player in real-time, ultimately leading to a higher entertainment value perceived by a human player of the game. Results indicate the effectiveness of NEAT and rtNEAT but demonstrate their limitations for use in real-time strategy games.


title: Cowden syndrome.

abstract: BACKGROUND ::: Cowden syndrome is a rare genodermatosis charactarized by presence of multiple hamartomas. The aim of the study was to specify the clinical, therapeutic and prognostic aspects of Cowden syndrome. ::: ::: ::: CASES REPORT ::: Our study included 4 patients with Cowden syndrome, 2 males and 2 females between 14 and 46 years old. Clinical examination of the skin revealed facials papules (4 cases), acral keratosis (1 case), translucent keratotic papules (2 cases). Oral examination revealed papules (4 cases), papillomatosis (4 cases), gingival hypertrophy (4 cases) and scrotal tongue (2 cases). Investigations revealed thyroid lesions (2 cases), fibrocystic disease and lipoma of the breast in 1 case, "glycogenic acanthosis" (1 case), macrocephaly (2 cases), dysmorphic face (1 case) and lichen nitidus (1 case). Oral etretinate and acitretine were temporary efficient in 2 patients. Topical treatment with tretinoin lotion resulted in some improvement in cutaneous, but not mucosal lesions in one patient. No cancer was revealed. ::: ::: ::: CONCLUSION ::: The pathognomonic mucocutaneous lesions were found in all patients. However, no degenerative lesions have been revealed. A new association of Cowden syndrome with lichen nitidus was found. Treatment with oral retinoids was efficient on cutaneous lesions.


title: Exploration of underwater life with an acoustically controlled soft robotic fish

abstract: Closeup exploration of underwater life requires new forms of interaction, using biomimetic creatures that are capable of agile swimming maneuvers, equipped with cameras, and supported by remote human operation. Current robotic prototypes do not provide adequate platforms for studying marine life in their natural habitats. This work presents the design, fabrication, control, and oceanic testing of a soft robotic fish that can swim in three dimensions to continuously record the aquatic life it is following or engaging. Using a miniaturized acoustic communication module, a diver can direct the fish by sending commands such as speed, turning angle, and dynamic vertical diving. This work builds on previous generations of robotic fish that were restricted to one plane in shallow water and lacked remote control. Experimental results gathered from tests along coral reefs in the Pacific Ocean show that the robotic fish can successfully navigate around aquatic life at depths ranging from 0 to 18 meters. Furthermore, our robotic fish exhibits a lifelike undulating tail motion enabled by a soft robotic actuator design that can potentially facilitate a more natural integration into the ocean environment. We believe that our study advances beyond what is currently achievable using traditional thruster-based and tethered autonomous underwater vehicles, demonstrating methods that can be used in the future for studying the interactions of aquatic life and ocean dynamics.


title: A Review of Exploratory Factor Analysis Decisions and Overview of Current Practices: What We Are Doing and How Can We Improve?

abstract: Authors within the fields of cyberpsychology and human−computer interaction have demonstrated a particular interest in measurement and scale creation, and exploratory factor analysis (EFA) is an extremely important statistical method for these areas of research. Unfortunately, EFA requires several statistical and methodological decisions to which the best choices are often unclear. The current article reviews five primary decisions and provides direct suggestions for best practices. These decisions are (a) the data inspection techniques, (b) the factor analytic method, (c) the factor retention method, (d) the factor rotation method, and (e) the factor loading cutoff. Then the article reviews authors’ choices for these five EFA decisions in every relevant article within seven cyberpsychology and/or human–computer interaction journals. The results demonstrate that authors do not employ the recommended best practices for most decisions. Particularly, most authors do not inspect their data for violations of a...


title: Combining two-view constraints for motion estimation

abstract: In this paper we describe two methods for estimating the motion parameters of an image sequence. For a sequence of N images, the global motion can be described by N-1 independent motion models. On the other hand, in a sequence there exist as many as /sub 2///sup N(N-1)/ pairwise relative motion constraints that can be solve for efficiently. In this paper we show how to linearly solve for consistent global motion models using this highly redundant set of constraints. In the first case, our method involves estimating all available pairwise relative motions and linearly fining a global motion model to these estimates. In the second instance, we exploit the fact that algebraic (i.e. epipolar) constraints between various image pairs are all related to each other by the global motion model. This results in an estimation method that directly computes the motion of the sequence by using all possible algebraic constraints. Unlike using reprojection error, our optimisation method does not solve for the structure of points resulting in a reduction of the dimensionality of the search space. Our algorithms are used for both 3D camera motion estimation and camera calibration. We provide real examples of both applications.


title: Meaningful change in cancer-specific quality of life scores: Differences between improvement and worsening

abstract: Introduction: There has been increased recent attention to the clinical meaningfulness of group change scores on health-related quality of life (HRQL) questionnaires. It has been assumed that improvements and declines of comparable magnitude have the same meaning or value. Method: We assessed 308 cancer patients with the Functional Assessment of Cancer Therapy (FACT) and a Global Rating of Change. Patients were classified into five levels of change in HRQL and its dimensions based upon their responses to retrospective ratings of change after 2 months: sizably worse, minimally worse, no change, minimally better, and sizably better. Raw score and standardized score changes on the FACT-G subscales and total score were then compared across different categories of patient-rated change. Results: The relationship between actual FACT change scores and retrospective ratings of change was modest but usually statistically significant (r: 0.07 to 0.35). Change scores associated with each retrospective rating category were evaluated to determine estimates of meaningful difference. Patients who reported global worsening of HRQL dimensions had considerably larger change scores than those reporting comparable global improvements. Although related to a ceiling effect, this remained true even after removing cases that began near the ceiling of the questionnaire. Discussion: Relatively small gains in HRQL have significant value. Comparable declines may be less meaningful, perhaps due to patients' tendency to minimize personal negative evaluations about one's condition. This has important implications for the interpretation of the meaningfulness of change scores in HRQL questionnaires. Factors such as adaptation to disease, response shift, dispositional optimism and the need for signs of clinical improvement may be contributing to the results and should be investigated in future studies.


title: Socio-demographic correlates influencing the trend of intestinal parasitic infestation in a rural community of West Bengal, India

abstract: Aim ::: The aim of this study was to estimate the change in magnitude of intestinal parasitic infestation, distribution of various socio-demographic factors and their possible influence on the parasitic infestation in the community over a period of 4 years.


title: A classification of glycosyl hydrolases based on amino acid sequence similarities.

abstract: The amino acid sequences of 301 glycosyl hydrolases and related enzymes have been compared. A total of 291 sequences corresponding to 39 EC entries could be classified into 35 families. Only ten sequences (less than 5% of the sample) could not be assigned to any family. With the sequences available for this analysis, 18 families were found to be monospecific (containing only one EC number) and 17 were found to be polyspecific (containing at least two EC numbers). Implications on the folding characteristics and mechanism of action of these enzymes and on the evolution of carbohydrate metabolism are discussed. With the steady increase in sequence and structural data, it is suggested that the enzyme classification system should perhaps be revised.


title: Universal soft pneumatic robotic gripper with variable effective length

abstract: In this study, we built a four-fingered soft robotic gripper with tunable effective finger lengths. This robotic model is purely made of soft materials and allows two working modes that require very simple control: 1) deflate the soft fingers for bending to one direction therefore to open the gripper “claw” and 2) inflate the fingers with compressed air for bending to the reverse direction, therefore to grip objects reversibly. Systematic tests of the gripping performance of the soft robotic model were conducted for 5 effective finger lengths ranging from 30 mm to 100 mm. Under each effective length, we measured the pull-off force of 8 sphere-shaped objects with diameters from 20 to 90mm, and five typical geometric shaped objects including sphere, cubic and cylinder etc. We also measured the pull-off force of gripping objects with different stiffness. Notably, we found that each object with different size prefer a “sweet” effective finger length for generating maximum pull-off force. We show that tunable effective finger length for the soft robot can significantly improve the performance when gripping multiple objects. Current soft robotic prototype exhibits a simple-control, low-cost approach of grasping objects with different size, weight, and shape as well as material stiffness, and may open up new avenues for future industrial gripping.


title: Online Multiperson Tracking-by-Detection from a Single, Uncalibrated Camera

abstract: In this paper, we address the problem of automatically detecting and tracking a variable number of persons in complex scenes using a monocular, potentially moving, uncalibrated camera. We propose a novel approach for multiperson tracking-by-detection in a particle filtering framework. In addition to final high-confidence detections, our algorithm uses the continuous confidence of pedestrian detectors and online-trained, instance-specific classifiers as a graded observation model. Thus, generic object category knowledge is complemented by instance-specific information. The main contribution of this paper is to explore how these unreliable information sources can be used for robust multiperson tracking. The algorithm detects and tracks a large number of dynamically moving people in complex scenes with occlusions, does not rely on background modeling, requires no camera or ground plane calibration, and only makes use of information from the past. Hence, it imposes very few restrictions and is suitable for online applications. Our experiments show that the method yields good tracking performance in a large variety of highly dynamic scenarios, such as typical surveillance videos, webcam footage, or sports sequences. We demonstrate that our algorithm outperforms other methods that rely on additional information. Furthermore, we analyze the influence of different algorithm components on the robustness.


title: Direct least squares fitting of ellipses

abstract: This paper presents a new efficient method for fitting ellipses to scattered data. Previous algorithms either fitted general conics or were computationally expensive. By minimizing the algebraic distance subject to the constraint 4ac-b/sup 2/=1 the new method incorporates the ellipticity constraint into the normalization factor. The new method combines several advantages: 1) it is ellipse-specific so that even bad data will always return an ellipse; 2) it can be solved naturally by a generalized eigensystem, and 3) it is extremely robust, efficient and easy to implement. We compare the proposed method to other approaches and show its robustness on several examples in which other nonellipse-specific approaches would fail or require computationally expensive iterative refinements.


title: Compiling probabilistic graphical models using sentential decision diagrams

abstract: Knowledge compilation is a powerful approach to exact inference in probabilistic graphical models, which is able to effectively exploit determinism and context-specific independence, allowing it to scale to highly connected models that are otherwise infeasible using more traditional methods (based on treewidth alone). Previous approaches were based on performing two steps: encode a model into CNF, then compile the CNF into an equivalent but more tractable representation (d-DNNF), where exact inference reduces to weighted model counting. In this paper, we investigate a bottom-up approach, that is enabled by a recently proposed representation, the Sentential Decision Diagram (SDD). We describe a novel and efficient way to encode the factors of a given model directly to SDDs, bypassing the CNF representation. To compile a given model, it now suffices to conjoin the SDD representations of its factors, using an apply operator, which d-DNNFs lack. Empirically, we find that our simpler approach to knowledge compilation is as effective as those based on d-DNNFs, and at times, orders-of-magnitude faster.


title: A computer model of context-dependent perception in a very simple world

abstract: AbstractWe propose the foundations of a computer model of scientific discovery that takes into account certain psychological aspects of human observation of the world. To this end, we simulate two main components of such a system. The first is a dynamic microworld in which physical events take place, and the second is an observer that visually perceives entities and events in the microworld. For reason of space, this paper focuses only on the starting phase of discovery, which is the relatively simple visual inputs of objects and collisions.


title: Efficient musical noise suppression for speech enhancement system

abstract: Noise reduction techniques that are relying on spectral weighting rules often generate annoying musical noise artifacts in the processed signal. In this paper, we present a postfilter (PF) for the spectral weighting gains that is capable of reducing musical noise in a simple but efficient way. It includes a robust detector for speech pauses and low SNR conditions and adaptively smoothes the weighting gains over frequency based on soft-decisions. Objective and subjective measurements show consistent improvements if the postfilter is applied to conventional noise reduction techniques.


title: Crossing the bridge over Norman's Gulf of Execution: revealing feedforward's true identity

abstract: Feedback and affordances are two of the most well-known principles in interaction design. Unfortunately, the related and equally important notion of feedforward has not been given as much consideration. Nevertheless, feedforward is a powerful design principle for bridging Norman's Gulf of Execution. We reframe feedforward by disambiguating it from related design principles such as feedback and perceived affordances, and identify new classes of feedforward. In addition, we present a reference framework that provides a means for designers to explore and recognize different opportunities for feedforward.


title: Dermatoscopy of flat pigmented facial lesions: diagnostic challenge between pigmented actinic keratosis and lentigo maligna.

abstract: BACKGROUND ::: The similarity between clinical pictures of pigmented actinic keratosis (PAK) and lentigo maligna (LM) is well known. ::: ::: ::: OBJECTIVES ::: To investigate the frequency of dermatoscopic findings suggestive of LM/lentigo maligna melanoma (LMM) in the other facial pigmented skin lesions (FPSL) and to assess the distinguishing dermoscopic criteria of PAK and LM. ::: ::: ::: METHODS ::: Eighty-nine FPSL were evaluated with conventional dermatoscopy. The lesions showing one or more dermatoscopic features considered as specific patterns for the diagnosis of LM/LMM, mainly slate-grey to black dots and globules, slate-grey areas, annular-granular pattern, asymmetrical pigmented follicular openings, black blotches, rhomboidal structures, hyperpigmented rim of follicular openings, slate-grey streaks and dark streaks, were included in the study selectively. ::: ::: ::: RESULTS ::: PAK was diagnosed in 67, LM or LMM in 20 and lichen planus-like keratosis in two lesions, histopathologically. Eleven essential dermatoscopic features were observed in facial PAK: slate-grey dots (70%); annular-granular pattern (39%); rhomboidal structures (36%); pseudonetwork (36%); black globules (34%); slate-grey globules (33%); black dots (30%); asymmetrical pigmented follicular openings (25%); hyperpigmented rim of follicular openings (21%); slate-grey areas (18%); and streaks (3%). ::: ::: ::: CONCLUSIONS ::: PAK has a striking similarity to LM/LMM in clinical and dermatoscopic features, thus representing a diagnostic challange. All dermatoscopic findings except black blotches were observed in PAK. As dermatoscopic diagnosis of a pigmented skin lesion cannot be based on the presence of a single criterion, we may conclude that histopathology still remains the gold standard for correct diagnosis.


title: Aligning Where to See and What to Tell: Image Captioning with Region-Based Attention and Scene-Specific Contexts

abstract: Recent progress on automatic generation of image captions has shown that it is possible to describe the most salient information conveyed by images with accurate and meaningful sentences. In this paper, we propose an image captioning system that exploits the parallel structures between images and sentences. In our model, the process of generating the next word, given the previously generated ones, is aligned with the visual perception experience where the attention shifts among the visual regions—such transitions impose a thread of ordering in visual perception. This alignment characterizes the flow of latent meaning, which encodes what is semantically shared by both the visual scene and the text description. Our system also makes another novel modeling contribution by introducing scene-specific contexts that capture higher-level semantic information encoded in an image. The contexts adapt language models for word generation to specific scene types. We benchmark our system and contrast to published results on several popular datasets, using both automatic evaluation metrics and human evaluation. We show that either region-based attention or scene-specific contexts improves systems without those components. Furthermore, combining these two modeling ingredients attains the state-of-the-art performance.


title: Towards understanding the effects of individual gamification elements on intrinsic motivation and performance

abstract: Research on the effectiveness of gamification has proliferated over the last few years, but the underlying motivational mechanisms have only recently become object of empirical research. It has been suggested that when perceived as informational, gamification elements, such as points, levels and leaderboards, may afford feelings of competence and hence enhance intrinsic motivation and promote performance gains. We conducted a 24 online experiment that systematically examined how points, leaderboards and levels, as well as participants' goal causality orientation influence intrinsic motivation, competence and performance (tag quantity and quality) in an image annotation task. Compared to a control condition, game elements did not significantly affect competence or intrinsic motivation, irrespective of participants' causality orientation. However, participants' performance did not mirror their intrinsic motivation, as points, and especially levels and leaderboard led to a significantly higher amount of tags generated compared to the control group. These findings suggest that in this particular study context, points, levels and leaderboards functioned as extrinsic incentives, effective only for promoting performance quantity. We experimentally studied the effects of individual game elements on motivation and performance.Gamification increased the number of tags in an image annotation task.Gamification did not affect intrinsic motivation or competence need satisfaction.Lack of motivational effects likely due to the way gamification was implemented.Results suggest that in the given context game elements acted as extrinsic incentives.


title: An efficient cloud storage model for cloud computing environment

abstract: Cloud computing is a new trend of information technology and computing system. In the traditional computing infrastructure, operating system software, applications and data are typically stored and managed on an individual user's computer. Cloud-computing has a different form from traditional way. All kinds of service and software are stored, accessed, and used via third party servers connected to the internet. If cloud computing was used, data and information can be easily shared and managed by user and service provider. Furthermore the user can do their works without application or software. When the user wants work using the software or application, the user just access cloud computing system via the internet. On the other hand, cloud computing has large data and information. Therefore the efficient method for large data/information management for cloud computing is necessary. In this paper, we proposed a cloud storage model for cloud computing environment. The model is to consider the relation between the cloud structures; service provider, application, and user.


title: Total phallic reconstruction after penile amputation for carcinoma

abstract: OBJECTIVE ::: To report our experience of total phallic reconstruction (TPR) with the use of the radial-artery free flap (RAFF) in patients who have had a subtotal penectomy for penile or urethral cancer, as the goal of TPR is the creation of a sensate and cosmetically acceptable phallus with an incorporated neourethra that allows the patient to void while standing, and with the insertion of a penile implant to resume sexual activities, improving quality of life. ::: ::: ::: PATIENTS AND METHODS ::: The notes of the 15 patients who had undergone RAFF TPR between January 1998 and May 2008 were reviewed retrospectively. The surgical outcome, cosmesis of the phallus, complications, eventual need for revision surgery and patient satisfaction were recorded during the follow-up. ::: ::: ::: RESULTS ::: This technique allowed the reconstruction of a cosmetically acceptable phallus in all patients. After a median (range) follow-up of 20 (1-68) months all patients were satisfied with the cosmesis and size of the phallus, and 14 were able to void while standing. Among the seven patients who have subsequently had insertion of a penile prosthesis, five can now engage in sexual intercourse. Urethral strictures and fistulae were the commonest complications in, respectively, three and four cases, and one patient had his penile prosthesis explanted because of infection. ::: ::: ::: CONCLUSIONS ::: RAFF phalloplasty is an excellent technique for TPR, giving excellent cosmetic and functional results.


CS 224 W Project Final Report : Using community detection and link prediction to improve Amazon recommendations


title: Unsupervised Feature Selection Using Clustering Ensembles and Population Based Incremental Learning Algorithm

abstract: This paper describes a novel feature selection algorithm for unsupervised clustering, that combines the clustering ensembles method and the population based incremental learning algorithm. The main idea of the proposed unsupervised feature selection algorithm is to search for a subset of all features such that the clustering algorithm trained on this feature subset can achieve the most similar clustering solution to the one obtained by an ensemble learning algorithm. In particular, a clustering solution is firstly achieved by a clustering ensembles method, then the population based incremental learning algorithm is adopted to find the feature subset that best fits the obtained clustering solution. One advantage of the proposed unsupervised feature selection algorithm is that it is dimensionality-unbiased. In addition, the proposed unsupervised feature selection algorithm leverages the consensus across multiple clustering solutions. Experimental results on several real data sets demonstrate that the proposed unsupervised feature selection algorithm is often able to obtain a better feature subset when compared with other existing unsupervised feature selection algorithms.


title: Visual Experiences in the Blind induced by an Auditory Sensory Substitution Device

abstract: In this report, the phenomenology of two blind users of a sensory substitution device - "The vOICe" - that converts visual images to auditory signals is described. The users both report detailed visual phenomenology that developed within months of immersive use and has continued to evolve over a period of years. This visual phenomenology, although triggered through use of The vOICe, is likely to depend not only on online visualization of the auditory signal but also on the users' previous (albeit distant) experience of veridical vision (e.g. knowledge of shapes and visual perspective). Once established, the sensory substitution mapping between the auditory and visual domains is not confined to when the device is worn and, thus, may constitute an example of acquired synaesthesia.


title: Combi-Tor: Track-to-Track Association Framework for Automotive Sensor Fusion

abstract: The data association algorithm plays the vital role of forming an appropriate and valid set of tracks from the available tracks at the fusion center, which are delivered by different sensor's local tracking systems. The architecture of the data association module has to be designed taking into account the fusion strategy of the sensor fusion system, the granularity and the quality of the data provided by the sensors. The current generation environment perception sensors used for automotive sensor fusion are capable of providing estimated kinematic and as well as non-kinematic information on the observed targets. This paper focuses on integrating the kinematic and non-kinematic information in a track-to-track association (T2TA) procedure. A scalable framework called Combi-Tor is introduced here that is designed to calculate the association decision using likelihood ratio tests based on the available kinematic and non-kinematic information on the targets, which are tracked and classified by different sensors. The calculation of the association decision includes the uncertainty in the sensor's local tracking and classification modules. The required sufficient statistical derivations are discussed. The performance of this T2TA framework and the traditional T2TA scheme considering only the kinematic information are evaluated using Monte-Carlo simulation. The initial results obtained using the real world sensor data is presented.


title: Untangling Blockchain: A Data Processing View of Blockchain Systems

abstract: Blockchain technologies are gaining massive momentum in the last few years. Blockchains are distributed ledgers that enable parties who do not fully trust each other to maintain a set of global states. The parties agree on the existence, values and histories of the states. As the technology landscape is expanding rapidly, it is both important and challenging to have a firm grasp of what the core technologies have to offer, especially with respect to their data processing capabilities. In this paper, we first survey the state of the art, focusing on private blockchains (in which parties are authenticated). We analyze both in-production and research systems in four dimensions: distributed ledger, cryptography, consensus protocol and smart contract. We then present BLOCKBENCH, a benchmarking framework for understanding performance of private blockchains against data processing workloads. We conduct a comprehensive evaluation of three major blockchain systems based on BLOCKBENCH, namely Ethereum, Parity and Hyperledger Fabric. The results demonstrate several trade-offs in the design space, as well as big performance gaps between blockchain and database systems. Drawing from design principles of database systems, we discuss several research directions for bringing blockchain performance closer to the realm of databases.


SentiMozart: Music Generation based on Emotions


title: Speeding up Convolutional Neural Networks By Exploiting the Sparsity of Rectifier Units

abstract: Rectifier neuron units (ReLUs) have been widely used in deep convolutional networks. An ReLU converts negative values to zeros, and does not change positive values, which leads to a high sparsity of neurons. In this work, we first examine the sparsity of the outputs of ReLUs in some popular deep convolutional architectures. And then we use the sparsity property of ReLUs to accelerate the calculation of convolution by skipping calculations of zero-valued neurons. The proposed sparse convolution algorithm achieves some speedup improvements on CPUs compared to the traditional matrix-matrix multiplication algorithm for convolution when the sparsity is not less than 0.9.


title: Portfolio Choice and the Bayesian Kelly Criterion

abstract: We derive optimal gambling and investment policies for cases in which the underlying stochastic process has parameter values that are unobserved random variables. For the objective of maximizing logarithmic utility when the underlying stochastic process is a simple random walk in a random environment, we show that a state-dependent control is optimal, which is a generalization of the celebrated Kelly strategy: the optimal strategy is to bet a fraction of current wealth equal to a linear function of the posterior mean increment. To approximate more general stochastic processes, we consider a continuous-time analog involving Brownian motion. To analyze the continuous-time problem, we study the diffusion limit of random walks in a random environment. We prove that they converge weakly to a Kiefer process, or tied-down Brownian sheet. We then find conditions under which the discrete-time process converges to a diffusion, and analyze the resulting process. We analyze in detail the case of the natural conjugate prior, where the success probability has a beta distribution, and show that the resulting limit diffusion can be viewed as a rescaled Brownian motion. These results allow explicit computation of the optimal control policies for the continuous-time gambling and investment problems without resorting to continuous-time stochastic-control procedures. Moreover they also allow an explicit quantitative evaluation of the financial value of randomness, the financial gain of perfect information and the financial cost of learning in the Bayesian problem.


title: A tail inequality for quadratic forms of subgaussian random vectors

abstract: We prove an exponential probability tail inequality for positive semidefinite quadratic forms in a subgaussian random vector. The bound is analogous to one that holds when the vector has independent Gaussian entries.


title: DroidWard: An Effective Dynamic Analysis Method for Vetting Android Applications

abstract: As the number of Android malicious applications has explosively increased, effectively vetting Android applications (apps) has become an emerging issue. Traditional static analysis is ineffective for vetting apps whose code have been obfuscated or encrypted. Dynamic analysis is suitable to deal with the obfuscation and encryption of codes. However, existing dynamic analysis methods cannot effectively vet the applications, as a limited number of dynamic features have been explored from apps that have become increasingly sophisticated. In this work, we propose an effective dynamic analysis method called DroidWard in the aim to extract most relevant and effective features to characterize malicious behavior and to improve the detection accuracy of malicious apps. In addition to using the existing 9 features, DroidWard extracts 6 novel types of effective features from apps through dynamic analysis. DroidWard runs apps, extracts features and identifies benign and malicious apps with Support Vector Machine (SVM), Decision Tree (DTree) and Random Forest. 666 Android apps are used in the experiments and the evaluation results show that DroidWard correctly classifies 98.54% of malicious apps with 1.55% of false positives. Compared to existing work, DroidWard improves the TPR with 16.07% and suppresses the FPR with 1.31% with SVM, indicating that it is more effective than existing methods.


Energy management of virtual power plant to participate in the electricity market using robust optimization


title: Deep Clustering with Convolutional Autoencoders

abstract: Deep clustering utilizes deep neural networks to learn feature representation that is suitable for clustering tasks. Though demonstrating promising performance in various applications, we observe that existing deep clustering algorithms either do not well take advantage of convolutional neural networks or do not considerably preserve the local structure of data generating distribution in the learned feature space. To address this issue, we propose a deep convolutional embedded clustering algorithm in this paper. Specifically, we develop a convolutional autoencoders structure to learn embedded features in an end-to-end way. Then, a clustering oriented loss is directly built on embedded features to jointly perform feature refinement and cluster assignment. To avoid feature space being distorted by the clustering loss, we keep the decoder remained which can preserve local structure of data in feature space. In sum, we simultaneously minimize the reconstruction loss of convolutional autoencoders and the clustering loss. The resultant optimization problem can be effectively solved by mini-batch stochastic gradient descent and back-propagation. Experiments on benchmark datasets empirically validate the power of convolutional autoencoders for feature learning and the effectiveness of local structure preservation.


title: Hypergraph Models and Algorithms for Data-Pattern-Based Clustering

abstract: In traditional approaches for clustering market basket type data, relations among transactions are modeled according to the items occurring in these transactions. However, an individual item might induce different relations in different contexts. Since such contexts might be captured by interesting patterns in the overall data, we represent each transaction as a set of patterns through modifying the conventional pattern semantics. By clustering the patterns in the dataset, we infer a clustering of the transactions represented this way. For this, we propose a novel hypergraph model to represent the relations among the patterns. Instead of a local measure that depends only on common items among patterns, we propose a global measure that is based on the cooccurences of these patterns in the overall data. The success of existing hypergraph partitioning based algorithms in other domains depends on sparsity of the hypergraph and explicit objective metrics. For this, we propose a two-phase clustering approach for the above hypergraph, which is expected to be dense. In the first phase, the vertices of the hypergraph are merged in a multilevel algorithm to obtain large number of high quality clusters. Here, we propose new quality metrics for merging decisions in hypergraph clustering specifically for this domain. In order to enable the use of existing metrics in the second phase, we introduce a vertex-to-cluster affinity concept to devise a method for constructing a sparse hypergraph based on the obtained clustering. The experiments we have performed show the effectiveness of the proposed framework.


title: Optimal Sink Deployment for Distributed Sensing of Spatially Nonstationary Phenomena

abstract: The optimal deployment of sinks in a sensor region for power efficient data gathering of a physical phenomenon is investigated in this work. In the system of consideration, nodes perform lossless distributed coding of the sensed data and the spatial statistics of the monitored phenomenon are possibly nonstationary due to heterogeneity in the sensing environment (e.g. variations in the altimetric profile). Non-stationary spatial statistics lead to uneven spatial profiles of the bit rates, unlike stationary statistics. The properties of rate profiles and the consequent optimal sink locations for a broad class of spatially non-stationary covariance models are studied by mathematical analysis and numerical examples.


title: Towards consistent visual-inertial navigation

abstract: United States. Office of Naval Research (N00014-12-1- 0093, N00014-10-1-0936, N00014-11-1-0688, and N00014-13-1-0588)


title: Deep IP flow inspection to detect beyond network anomalies

abstract: Abstract Taking into account the accelerated rate of network growth, the occurrence of anomalies becomes inevitable. A single anomaly can affect the network performance so it is crucial to detect its origin. However, when different kinds of anomalies are present at the same time, it becomes more complicated to detect their root causes. In addition, the network administrator has to deal with questions related to network health, such as bandwidth bottlenecks, and network misuse. Detecting these problems quickly is essential to take appropriate countermeasures. Although many solutions have been proposed to detect anomalies, they do not address other important questions related to network health. In this paper, a system capable of detecting and classifying the anomalies, and extracting detailed information from the network usage, is presented. A graph representation is used, allowing a deep inspection of the IP flows exchanged between the active devices in the network. The Tsallis entropy is applied to detect anomalies. Furthermore, the proposed system allows the network administrator to create metrics to monitor and acquire detailed information about the network equipment, services, and users. Tests using real and artificial datasets demonstrate the effectiveness of the proposed system to detect simultaneous anomalies, and to provide useful information for network-management tasks.


Development of Scientific Software and Practices for Software Development: A Systematic Literature Review.


title: Visual Relationship Detection Using Joint Visual-Semantic Embedding

abstract: Visual relationship detection can serve as the intermediate building block for higher level tasks such as image captioning, visual question answering, image-text matching. Due to the long tail of relationship distribution in real world images, zero-shot predication of relationships that it has never seen before can alleviate stress of collecting every possible relationship. Following zero-shot learning (ZSL) strategies, we propose a joint visual-semantic embedding model for visual relationship detection. In our model, the visual vector and semantic vector are projected to a shared latent space to learn the similarity between the two branches. In the semantic embedding, sequential features in terms of are learned to provide the context information and then concatenated with corresponding component vector of the relationship triplet. Experiments show that the proposed model achieves superior performance in zero-shot visual relationship detection and comparable results in non-zero-shot scenario.


title: Denoising autoencoder with modulated lateral connections learns invariant representations of natural images

abstract: Suitable lateral connections between encoder and decoder are shown to allow higher layers of a denoising autoencoder (dAE) to focus on invariant representations. In regular autoencoders, detailed information needs to be carried through the highest layers but lateral connections from encoder to decoder relieve this pressure. It is shown that abstract invariant features can be translated to detailed reconstructions when invariant features are allowed to modulate the strength of the lateral connection. Three dAE structures with modulated and additive lateral connections, and without lateral connections were compared in experiments using real-world images. The experiments verify that adding modulated lateral connections to the model 1) improves the accuracy of the probability model for inputs, as measured by denoising performance; 2) results in representations whose degree of invariance grows faster towards the higher layers; and 3) supports the formation of diverse invariant poolings.


title: Multi-Modal Clique-Graph Matching for View-Based 3D Model Retrieval

abstract: Multi-view matching is an important but a challenging task in view-based 3D model retrieval. To address this challenge, we propose an original multi-modal clique graph (MCG) matching method in this paper. We systematically present a method for MCG generation that is composed of cliques, which consist of neighbor nodes in multi-modal feature space and hyper-edges that link pairwise cliques. Moreover, we propose an image set-based clique/edgewise similarity measure to address the issue of the set-to-set distance measure, which is the core problem in MCG matching. The proposed MCG provides the following benefits: 1) preserves the local and global attributes of a graph with the designed structure; 2) eliminates redundant and noisy information by strengthening inliers while suppressing outliers; and 3) avoids the difficulty of defining high-order attributes and solving hyper-graph matching. We validate the MCG-based 3D model retrieval using three popular single-modal data sets and one novel multi-modal data set. Extensive experiments show the superiority of the proposed method through comparisons. Moreover, we contribute a novel real-world 3D object data set, the multi-view RGB-D object data set. To the best of our knowledge, it is the largest real-world 3D object data set containing multi-modal and multi-view information.


title: Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity

abstract: We consider the problem of extracting smooth, low-dimensional neural trajectories that summarize the activity recorded simultaneously from tens to hundreds of neurons on individual experimental trials. Current methods for extracting neural trajectories involve a two-stage process: the data are first "denoised" by smoothing over time, then a static dimensionality reduction technique is applied. We first describe extensions of the two-stage methods that allow the degree of smoothing to be chosen in a principled way, and account for spiking variability that may vary both across neurons and across time. We then present a novel method for extracting neural trajectories, Gaussian-process factor analysis (GPFA), which unifies the smoothing and dimensionality reduction operations in a common probabilistic framework. We applied these methods to the activity of 61 neurons recorded simultaneously in macaque premotor and motor cortices during reach planning and execution. By adopting a goodness-of-fit metric that measures how well the activity of each neuron can be predicted by all other recorded neurons, we found that GPFA provided a better characterization of the population activity than the two-stage methods.


title: Vehicle Collision Recognition and Monitoring System Based on AVR Platform

abstract: Security in travelling is primary concern for everyone. Demand for automobile has increased traffic and thereby causing more accidents on the road. People lose their lives because of poor emergency facilities. Preemption of the accidents taking place on the roads is not possible but at least after effects can be reduced. Proposed system ensures, making emergency facilities available to accident victims as early as possible by letting people or hospital or rescue team or police station know the accident spot with the help of accelerometer embedded in the vehicle. As vehicle rolls over on the road thereby changing position of the accelerometer, message is sent to the first aid center, rescue team or police station using GSM. With the help of space navigation system GPS locates the position of the vehicle where accident has occurred. So, this paper deals with the GSM-GPS module and accelerometer and ATMEGA 16 microcontroller mounted vehicle, which even meet with the accident, let people know about the accident location and provides situational awareness. This project is about making cars more intelligent, they provide basic information covering geographical co- ordinates to the police station.


title: IT-ENABLED BUSINESS INNOVATION: DOES CIO CAPABILITY MATTER? A PERSPECTIVE FROM INSTITUTIONAL ENTREPRENEURSHIP THEORY

abstract: Today IT has evolved from a mere efficiency tool to enabling business innovation and providing strategic value. As the highest level IT leader in organizations, CIO should be largely responsible for the success of IT-enabled business innovation. CIO must possess necessary skills, knowledge, and abilities to lead IT staff, business partners, and even high-level executives in IT-enabled business innovation. However, as IT innovation researchers have just begun to look at business transformation and innovation resulted from the application of new IT, insights on how CIO can leverage IT to enable business innovation are still scant. We aim to address the question of whether and how CIO capability impact on the success of IT-enabled business innovation. Anchoring on the theory of institutional entrepreneurship, we propose a conceptual model describing that CIO’s political savvy, communicative ability, strategic IT and business knowledge have positive impact on the success of ITenabled business innovation, through the mediating role of innovation legitimacy. The findings are expected to provide several theoretical implications for the areas of IT innovation and CIO effectiveness.


title: STI stress aware placement optimization based on geometric programming

abstract: Shallow trench isolation(STI) is the mainstream CMOS isolation technology for advanced integrated circuits. While STI process gives the isolation benefits due to its scalable characteristics, exploiting the compressive stress exerted by STI wells on device active regions to improve performance of devices has been one of the major industry focuses. However, in the present research of VLSI physical design, there has no yet a global optimization methodology on the whole chip layout to control the size of the STI wells, which affects the stress magnitude along with the size of active region of transistors. In this paper, we present a novel methodology that is capable of determining globally the optimal STI well width following the chip placement stage. The methodology is based on the observation that both of the terms in charge of chip width minimization and transistor channel mobility optimization in the objective function can be modeled as posynomials of the design variables, that is, the width of STI wells. Then, this stress aware placement optimization problem could be solved efficiently as a convex geometric programming (GP) problem. Finally, by a MOSEK GP problem solver, we do our STI width aware placement optimization on the given placements of some GSRC and IBM-PLACE benchmarks. Experiment results demonstrated that our methodology can obtain decent results with an acceptable runtime when satisfy the necessary location constraints from DRC specifications.