title: The viability of web-derived polarity lexicons

abstract: We examine the viability of building large polarity lexicons semi-automatically from the web. We begin by describing a graph propagation framework inspired by previous work on constructing polarity lexicons from lexical graphs (Kim and Hovy, 2004; Hu and Liu, 2004; Esuli and Sabastiani, 2009; Blair-Goldensohn et al., 2008; Rao and Ravichandran, 2009). We then apply this technique to build an English lexicon that is significantly larger than those previously studied. Crucially, this web-derived lexicon does not require WordNet, part-of-speech taggers, or other language-dependent resources typical of sentiment analysis systems. As a result, the lexicon is not limited to specific word classes -- e.g., adjectives that occur in WordNet -- and in fact contains slang, misspellings, multiword expressions, etc. We evaluate a lexicon derived from English documents, both qualitatively and quantitatively, and show that it provides superior performance to previously studied lexicons, including one derived from WordNet.


title: Limits of location privacy under anonymization and obfuscation

abstract: The prevalence of mobile devices and location-based services (LBS) has generated great concerns regarding the LBS users' privacy, which can be compromised by statistical analysis of their movement patterns. A number of algorithms have been proposed to protect the privacy of users in such systems, but the fundamental underpinnings of such remain unexplored. Recently, the concept of perfect location privacy was introduced and its achievability was studied for anonymization-based LBS systems, where user identifiers are permuted at regular intervals to prevent identification based on statistical analysis of long time sequences. In this paper, we significantly extend that investigation by incorporating the other major tool commonly employed to obtain location privacy: obfuscation, where user locations are purposely obscured to protect their privacy. Since anonymization and obfuscation reduce user utility in LBS systems, we investigate how location privacy varies with the degree to which each of these two methods is employed. We provide: (1) achievability results for the case where the location of each user is governed by an i.i.d. process; (2) converse results for the i.i.d. case as well as the more general Markov Chain model. We show that, as the number of users in the network grows, the obfuscation-anonymization plane can be divided into two regions: in the first region, all users have perfect location privacy; and, in the second region, no user has location privacy.


title: 2.3 ppm/°c 40 nW MOSFET-only voltage reference

abstract: A MOSFET-only sub-bandgap voltage reference at less than 50 nW and with very low temperature coefficient is introduced. It consists of a threshold voltage extractor circuit and a proportional to absolute temperature voltage generator, using no resistors. The behavior of the circuit is analytically described, a design methodology is proposed and simulation results for a 0.13 µm CMOS process are presented. It allows a reference voltage below the bandgap, 625 mV in this design example, achieving a temperature coefficient of 2.3 ppm/°C for the -40 to 125 °C temperature range. The circuit consumes 40 nW at 27°C and under 1.2 V supply, being the implemented silicon area 0.0099 mm².


title: IEEE 802.11ah: sub-1-GHz license-exempt operation for the internet of things

abstract: IEEE 802.11ah Task Group has been developing an amendment to the 802.11 standard to define sub-1-GHz license-exempt operation to support sensors and Internet of Things applications. This article presents an overview of major physical layer and MAC layer features of 802.11ah.


title: A survey on virtual machine migration and server consolidation frameworks for cloud data centers

abstract: Modern Cloud Data Centers exploit virtualization for efficient resource management to reduce cloud computational cost and energy budget. Virtualization empowered by virtual machine (VM) migration meets the ever increasing demands of dynamic workload by relocating VMs within Cloud Data Centers. VM migration helps successfully achieve various resource management objectives such as load balancing, power management, fault tolerance, and system maintenance. However, being resource-intensive, the VM migration process rigorously affects application performance unless attended by smart optimization methods. Furthermore, a Cloud Data Centre exploits server consolidation and DVFS methods to optimize energy consumption. This paper reviews state-of-the-art bandwidth optimization schemes, server consolidation frameworks, DVFS-enabled power optimization, and storage optimization methods over WAN links. Through a meticulous literature review of state-of-the-art live VM migration schemes, thematic taxonomies are proposed to categorize the reported literature. The critical aspects of virtual machine migration schemes are investigated through a comprehensive analysis of the existing schemes. The commonalties and differences among existing VM migration schemes are highlighted through a set of parameters derived from the literature. Finally, open research issues and trends in the VM migration domain that necessitate further consideration to develop optimal VM migration schemes are highlighted.


title: Training Classifiers with Natural Language Explanations

abstract: Training accurate classifiers requires many labels, but each label provides only limited information (one bit for binary classification). In this work, we propose BabbleLabble, a framework for training classifiers in which an annotator provides a natural language explanation for each labeling decision. A semantic parser converts these explanations into programmatic labeling functions that generate noisy labels for an arbitrary amount of unlabeled data, which is used to train a classifier. On three relation extraction tasks, we find that users are able to train classifiers with comparable F1 scores from 5-100$\times$ faster by providing explanations instead of just labels. Furthermore, given the inherent imperfection of labeling functions, we find that a simple rule-based semantic parser suffices.


title: Analysis and Design of a Single-Stage Parallel AC-to-DC Converter

abstract: In this paper, a single-stage (S2) parallel ac-to-dc converter based on single-switch two-output boost-flyback converter is presented. The converter contains two semistages. One is the boost-flyback semistage, which transfers partial input power transferred to load directly through one power flow path and has excellent self-power factor correction property when operating in discontinuous conduction mode even though the boost output is close to the peak value of the line voltage. The other one is the flyback dc-to-dc (dc/dc) semistage that provides the output regulation on another parallel power flow path. With this design, the power conversion efficiency is improved and the current stress of control switch is reduced. Furthermore, the calculation process of power distribution and bulk capacitor voltage, design equations, and design procedure for key parameters are also presented. By following the procedure, an 80 W prototype converter has been built and tested. The experimental results show that the measured line harmonic current at the worst condition complies with the IEC61000-3-2 class D limits, the maximum bulk capacitor voltage is about 415.4 V, and the maximum efficiency is about 85.8%. Hence, the proposed S2 converter is suitable for universal input usage.


title: Designing a low-noise, high-resolution, and portable four channel acquisition system for recording surface electromyographic signal

abstract: In current years, the application of biopotential signals has received a lot of attention in literature. One of these signals is an electromyogram (EMG) generated by active muscles. Surface EMG (sEMG) signal is recorded over the skin, as the representative of the muscle activity. Since its amplitude can be as low as 50 µV, it is sensitive to undesirable noise signals such as power‑line interferences. This study aims at designing a battery‑powered portable four channel sEMG signal acquisition system. The performance of the proposed system was assessed in terms of the input voltage and current noise, noise distribution, and synchronization and input noise level among different channels. The results indicated that the designed system had several inbuilt operational merits such as low referred to input noise (lower than 0.56 µV between 8 Hz and 1000 Hz), considerable elimination of power‑line interference and satisfactory recorded signal quality in terms of signal‑to‑noise ratio. The muscle conduction velocity was also estimated using the proposed system on the brachial biceps muscle during isometric contraction. The estimated values were in then normal ranges. In addition, the system included a modular configuration to increase the number of recording channels up to 96.


title: Uranium(VI) transport through liquid membrane supported with trioctylphosphine oxide.

abstract: Carrier-mediated transport of uranium(VI) has been studied by means of a liquid membrane supported on a microporous polymer flat-sheet or a tube. Trioctylphosphine oxide (TOPO) was used as a mobile carrier in order to transport uranium from acidic media. Uranium was extracted in the liquid membrane as UO2(NO3)2• 2TOPO and stripped into the carbonate solutions as UO2(CO3)34- Except for very dilute carriers, the rate of transport seems to be controlled not by the chemical reactions but by the diffusional process in the membrane phase. In the case of the tubing membrane, considerable time lag was observed for the recovery of uranium in the stripping side owing to thick wall of the tubing support.


title: Automatic Music Genre Classification using Modulation Spectral Contrast Feature

abstract: In this paper, we proposed a novel feature, called octave-based modulation spectral contrast (OMSC), for music genre classification. OMSC is extracted from long-term modulation spectrum analysis to represent the time-varying behavior of music signals. Experimental results have shown that OMSC outperforms MFCC and OSC. If OMSC is integrated with MFCC and OSC, the classification accuracy is 84.03% for seven music genre classification.


Optimal Linear Combinations of Neural Networks


title: Design of a planar power transformer for high voltage, high frequency use

abstract: A high-voltage (15 kV) power transformer using multilayer printed circuit board (ML-PCB) technology is presented for applications in switched-mode power supplies, whose output power is 4.5 kW, and inverting frequency is 20 kHz. A new configuration for the magnetic core is proposed based on planar EI cores. The parallel connection of planar cores was adopted as a unit, and several units were cascaded to form the high voltage transformer. Manufacturing the PCB involves small tolerances, so the transformer exhibits parasitic effects which are highly reproducible from a production viewpoint. The mechanical configuration of a laboratory prototype is discussed, as well as the electrical, parasitic, and thermal behaviors. The transformer was tested and found to have an efficiency of better than 96%. Compared with traditional high voltage transformers, this transformer has good thermal behavior, good line insulation properties and a high power density.


Attacking Fieldbus Communications in ICS: Applications to the SWaT Testbed


title: Query optimization techniques for partitioned tables

abstract: Table partitioning splits a table into smaller parts that can be accessed, stored, and maintained independent of one another. From their traditional use in improving query performance, partitioning strategies have evolved into a powerful mechanism to improve the overall manageability of database systems. Table partitioning simplifies administrative tasks like data loading, removal, backup, statistics maintenance, and storage provisioning. Query language extensions now enable applications and user queries to specify how their results should be partitioned for further use. However, query optimization techniques have not kept pace with the rapid advances in usage and user control of table partitioning. We address this gap by developing new techniques to generate efficient plans for SQL queries involving multiway joins over partitioned tables. Our techniques are designed for easy incorporation into bottom-up query optimizers that are in wide use today. We have prototyped these techniques in the PostgreSQL optimizer. An extensive evaluation shows that our partition-aware optimization techniques, with low optimization overhead, generate plans that can be an order of magnitude better than plans produced by current optimizers.


title: Extending Summation Precision for Network Reduction Operations

abstract: Double precision summation is at the core of numerous important algorithms such as Newton–Krylov methods and other operations involving inner products, such as matrix multiplication and dot products. However, the effectiveness of summation is limited by the accumulation of rounding errors due to compressed representations, which are an increasing problem with the scaling of modern HPC systems and data sets that can easily perform summations with millions or billions of operands. To reduce the impact of precision loss, researchers have proposed increased- and arbitrary-precision libraries that provide reproducible error or even bounded error accumulation for large sums. However, such libraries increase computation and communication time significantly, and do not always guarantee an exact result. In this article, we propose fixed-point representations of double precision variables that enable arbitrarily large summations without error and provide exact and reproducible results. We call this format big integer (BigInt). Even though such formats have been studied for local processor computations, we make the case that using fixed-point representation for distributed computation over a system-wide network is feasible with performance comparable to that of double-precision floating point summation. This is possible by the inclusion of simple and inexpensive logic into modern NICs, or by using the programmable logic found in many modern NICs, in order to accelerate performance on large-scale systems in order to avoid waking up processors.


title: Improving multi-layer spiking neural networks by incorporating brain-inspired rules

abstract: This paper introduces seven brain-inspired rules that are deeply rooted in the understanding of the brain to improve multi-layer spiking neural networks (SNNs). The dynamics of neurons, synapses, and plasticity models are considered to be major characteristics of information processing in brain neural networks. Hence, incorporating these models and rules to traditional SNNs is expected to improve their efficiency. The proposed SNN model can mainly be divided into three parts: the spike generation layer, the hidden layers, and the output layer. In the spike generation layer, non-temporary signals such as static images are converted into spikes by both local and global feature-converting methods. In the hidden layers, the rules of dynamic neurons, synapses, the proportion of different kinds of neurons, and various spike timing dependent plasticity (STDP) models are incorporated. In the output layer, the function of classification for excitatory neurons and winner take all (WTA) for inhibitory neurons are realized. MNIST dataset is used to validate the classification accuracy of the proposed neural network model. Experimental results show that higher accuracy will be achieved when more brain-inspired rules (with careful selection) are integrated into the learning procedure.


title: Search-and-Compute on Encrypted Data

abstract: Private query processing on encrypted databases allows users to obtain data from encrypted databases in such a way that the user’s sensitive data will be protected from exposure. Given an encrypted database, the users typically submit queries similar to the following examples:


title: Human-in-the-Loop Challenges for Entity Matching: A Midterm Report

abstract: Entity matching (EM) has been a long-standing challenge in data management. In the past few years we have started two major projects on EM (Magellan and Corleone/Falcon). These projects have raised many human-in-the-loop (HIL) challenges. In this paper we discuss these challenges. In particular, we show how these challenges forced us to revise our solution architecture, from a typical RDBMS-style architecture to a very human-centric one, in which human users are first-class objects driving the EM process, using tools at pain-point places. We discuss how such solution architectures can be viewed as combining "tools in the loop" with "human in the loop". Finally, we discuss lessons learned which can potentially be applied to other problem settings. We also hope that more researchers will investigate EM, as it can be a rich "playground" for HIL research.


title: Enabling Localized Peer-to-Peer Electricity Trading Among Plug-in Hybrid Electric Vehicles Using Consortium Blockchains

abstract: We propose a localized peer-to-peer (P2P) electricity trading model for locally buying and selling electricity among plug-in hybrid electric vehicles (PHEVs) in smart grids. Unlike traditional schemes, which transport electricity over long distances and through complex electricity transportation meshes, our proposed model achieves demand response by providing incentives to discharging PHEVs to balance local electricity demand out of their own self-interests. However, since transaction security and privacy protection issues present serious challenges, we explore a promising consortium blockchain technology to improve transaction security without reliance on a trusted third party. A localized P 2P E lectricity T rading system with CO nsortium blockchai N (PETCON) method is proposed to illustrate detailed operations of localized P2P electricity trading. Moreover, the electricity pricing and the amount of traded electricity among PHEVs are solved by an iterative double auction mechanism to maximize social welfare in this electricity trading. Security analysis shows that our proposed PETCON improves transaction security and privacy protection. Numerical results based on a real map of Texas indicate that the double auction mechanism can achieve social welfare maximization while protecting privacy of the PHEVs.


title: Active music therapy in Parkinson's disease: an integrative method for motor and emotional rehabilitation.

abstract: ADL 5 activities of daily living; ANOVA 5 analysis of variance; HM 5 Happiness Measure; MS 5 motor subscale; MT 5 music therapy; PD 5 Parkinson’s disease; PDQL 5 Parkinson’s Disease Quality of Life Questionnaire; PT 5 physical therapy; UPDRS 5 Unified Parkinson’s Disease Rating Scale.


title: Tweeting for learning: A critical analysis of research on microblogging in education published in 2008–2011

abstract: This study critically analyzed the current body of published research on microblogging in education ( MIE) to build a deep and comprehensive understanding of this increasingly popular phenomenon. Twenty-one studies on MIE in 2008-2011 were selected based on the selection criteria and analyzed to answer the following questions: What types of research have been published on MIE? How was microblogging used for teaching and learning in these studies? What educational benefits did microblogging have on teaching and learning? What suggestions and implications did the current research have for future MIE research and practices? The analysis suggests that microblogging has a potential to encourage participation, engagement, reflective thinking as well as collaborative learning under different learning settings. The quality of research, however, varies greatly, suggesting a need for rigorous research on MIE. The analysis has implications for MIE practices as well as research and development efforts. Practitioner Notes What is already known about this topic Microblogging has a potential to facilitate learning., Research on microblogging has been conducted under different educational settings., What this paper adds What are the characteristics of the current research on microblogging in education., How educators and researchers integrated microblogging to achieve different educational goals as identified in these studies., What are the identified educational effects of using microblogging for teaching and learning., What are the challenges and suggestions of using microblogging in teaching and learning., Implications for practice and/or policy Future research on microblogging in education should go beyond formal higher education settings by considering learning occurring in other settings., Future research needs to observe and analyze how learners participate and learn in microblogging-based environments over time., Innovative data collection and analysis methods are needed to understand the interaction and learning that occur in such environments., Future research is needed to identify effective approaches of microblogging integration. [ABSTRACT FROM AUTHOR]


title: A Parallel Algorithm for POMDP Solution

abstract: Most exact algorithms for solving partially observable Markov decision processes (POMDPs) are based on a form of dynamic programming in which a piecewise-linear and convex representation of the value function is updated at every iteration to more accurately approximate the true value function. However, the process is computationally expensive, thus limiting the practical application of POMDPs in planning. To address this current limitation, we present a parallel distributed algorithm based on the Restricted Region method proposed by Cassandra, Littman and Zhang [1]. We compare performance of the parallel algorithm against a serial implementation Restricted Region.


title: Two is More Valid Than One: Examining the Factor Structure of the Self-Compassion Scale (SCS)

abstract: The Self-Compassion Scale (SCS; Neff, 2003a) is the most widely used measure of self-compassion. Self-compassion, as measured by the SCS, is robustly linked to psychological health (Macbeth & Gumley, 2012; Zessin, Dickhaüser, & Garbade, 2015). The SCS is currently understood as exhibiting a higher-order structure comprised of 6 first-order factors and 1 second-order general self-compassion factor. Recently, some researchers have questioned the internal validity of this 1-factor conceptualization, and posit that the SCS may instead be comprised of 2 general factors-self-compassion and self-coldness. The current paper provides an in-depth examination of the internal structure of the SCS using oblique, higher-order, and bifactor structural models in a sample of 1,115 college students. The bifactor model comprised of 2 general factors-self-compassion and self-coldness-and 6 specific factors demonstrated the best fit to the data. Results also indicated the Self-Coldness factor accounted for unique variance in depression, anxiety, and stress, whereas the Self-Compassion factor only accounted for unique variance in its association with depression, providing further evidence for the presence of 2 distinct factors. Results did not provide support for the 1-factor composition of self-compassion currently used in research. Implications for using, scoring, and interpreting the SCS are discussed. (PsycINFO Database Record


title: Technology probes: inspiring design for and with families

abstract: We describe a new method for use in the process of co-designing technologies with users called technology probes. Technology probes are simple, flexible, adaptable technologies with three interdisciplinary goals: the social science goal of understanding the needs and desires of users in a real-world setting, the engineering goal of field-testing the technology, and the design goal of inspiring users and researchers to think about new technologies. We present the results of designing and deploying two technology probes, the messageProbe and the videoProbe, with diverse families in France, Sweden, and the U.S. We conclude with our plans for creating new technologies for and with families based on our experiences.


Efficiency of WhatsApp as a Means of Disseminating Educational Information


A Hybrid Ant Colony Optimization Algorithm for Path Planning of Robot in Dynamic Environment


title: Controlling Expressivity using Input Codes in Neural Network based TTS

abstract: This paper presents a study on the use of input codes in the neural network acoustic modeling for expressive TTS. Specifically, we use different kinds of input codes, augmented with the linguistic features, as the input of a BLSTM-based acoustic model, to control the expressivity of the synthesized speech. The input codes, in one-hot representation, include dialogue code, sentiment code and sentence position code. The dialogue code indicates whether the text is a dialogue or narration in an audiobook story. The sentiment code is obtained from a sentiment analysis tool, which labels each sentence as positive, negative and neutral. The sentence position code indicates the position of the sentence in the paragraph. We believe these codes are highly related to the expressiveness of the audiobook speech. Experiments on the data from the Blizzard Challenge 2017 demonstrate the effectiveness of the use of input codes in the neural network approach for expressive TTS.


title: Detection of Stress using Wearable Sensors in IoT Platform

abstract: Stress is a natural reaction to various stress-inducing factors which can lead to physiological and behavioral changes. If persists for a longer period, stress can cause harmful effects on our body. The body sensors along with the concept of the Internet of Things can provide rich information about one's mental and physical health. The proposed work concentrates on developing an IoT system which can efficiently detect the stress level of a person and provide a feedback which can assist the person to cope with the stressors. The system consists of a smart band module and a chest strap module which can be worn around wrist and chest respectively. The system monitors the parameters such as Electro dermal activity and Heart rate in real time and sends the data to a cloud-based ThingSpeak server serving as an online IoT platform. The computation of the data is performed using a ‘MATLAB Visualization’ application and the stress report is displayed. The authorized person can log in, view the report and take actions such as consulting a medical person, perform some meditation or yoga exercises to cope with the condition.


title: A Hybrid Approach for Movie Recommendation System Using Feature Engineering

abstract: Recommender system is used to recommend items and services to the users and provide recommendations based on prediction. The prediction performance plays vital role in the quality of recommendation. To improve the prediction performance, this paper proposed a new hybrid method based on naive Bayesian classifier with Gaussian correction and feature engineering. The proposed method is experimented on the well known movie lens 100k data set. The results show better results when compared with existing methods.


title: Effective Drusen Segmentation from Fundus Images for Age-Related Macular Degeneration Screening

abstract: Automatic screening of Age-related Macular Degeneration (AMD) is important for both patients and ophthalmologists. The major sign of contracting AMD at the early stage is the appearance of drusen, which are the accumulation of extracellular material and appear as yellow-white spots on the retina. In this paper, we propose an effective approach for drusen segmentation towards AMD screening. The major novelty of the proposed approach is that it employs an effective way to train a drusen classifier from a weakly labeled dataset, meaning only the existence of drusen is known but not the exact locations or boundaries. We achieve this by employing Multiple Instance Learning (MIL). Moreover, our proposed approach also tracks the drusen boundaries by using Growcut, with the output of MIL as initial seeds. Experiments on 350 fundus images with 96 of them with AMD demonstrates that our approach outperforms the state-of-the-art methods on the task of early AMD detection and achieves satisfying performance on the task of drusen segmentation.


title: Deconstructing the touch experience

abstract: In this paper, we evaluate the performance and experience differences between direct touch and mouse input on horizontal and vertical surfaces using a simple application and several validated scales. We find that, not only are both speed and accuracy improved when using the multi-touch display over a mouse, but that participants were happier and more engaged. They also felt more competent, in control, related to other people, and immersed. Surprisingly, these results cannot be explained by the intuitiveness of the controller, and the benefits of touch did not come at the expense of perceived workload. Our work shows the added value of considering experience in addition to traditional measures of performance, and demonstrates an effective and efficient method for gathering experience during inter-action with surface applications. We conclude by discussing how an understanding of this experience can help in designing touch applications.


How to Attack Biometric Systems in Your Spare Time


title: Multi-objective particle swarm optimization for ontology alignment

abstract: In computer science design and implementation of high-tech areas in the modern society is accompanied by increasing the role of ontological knowledge base. Accumulation of shared ontologies is seen as a mechanism of unlimited knowledge acquisition about the world. However, the problem of integration, matching and alignment of ontologies is not solved yet. The problem of ontology alignment is to find such a structure and permissible parameters that provide the optimal values for one or more quality criteria. It should be noted that today there are many methods to compute the similarity between two discrete elements of different ontologies. Integration of up-to-date similarity computation techniques allows obtaining a versatile and accurate result. One of approach is based on the weights. Typically, the weights are assigned manually or by specific approaches. The main shortcoming of existing approaches is the lack of optimality. This article proposes a new combined approach for ontology alignment based on Latent Semantic Indexing and multi-objective particle swarm optimization method. For objective functions two criteria were chosen: the accuracy and recall. To obtain an optimal population the method of local search was employed to replace the worst of the population in the new generation. Experimental research of the suggested approach confirms the effectiveness of it.


title: Inclusion of e-commerce workflow with NoSQL DBMS: MongoDB document store

abstract: In today's open market, e-commerce has developed its strategy to process various business applications. There applications are developed with the platforms like Magento, Zen Cart, Prestashop, Spree etc., to run a successful online store. These are also used for customer e-commerce apps and require a scalable database for storing the data. The NoSQL database provides an efficient storage access and processing environment with horizontally scalable and replicable strategy over RDBMS. Because of its schema less structure, NoSQL databases provides access to the users to dynamically change the data model applications. Due to this characteristics, NoSQL databases are being used in real time analysis. In order to build a successful online and scalable store, in this study, we choose the database as Document-Oriented NoSQL database for the inclusion of e-commerce workflow. As a new finding, in this study, we will ascertain design and working strategy of e-commerce based on MongoDB, which is a widely used Document-Oriented NoSQL database to process large scale business applications.


title: Global Positioning System: A Case Study Focused on Systems Engineering

abstract: A case study was conducted which describes the application of systems engineering during the concept validation, system design and development, and production phases of the GPS program. The case examines the applied systems engineering processes, as well as the interactions of the GPS Joint Program Office, the prime contractors, and the multitude of government agencies that were associated with the program's development and fielding. The systems engineering process is traced from the initiation of studies and the development of key technologies that established the vision of a satellite navigation system in the 1960s, through the multi-phase joint program that resulted in a full operational capability release in 1995. Numerous interviews were conducted with individuals who personally directed, managed and engineered the program, from which the systems engineering story, and the top four learning principles, emerged.


title: MMIC-Based Quadrature Hybrid Quasi-Circulators for Simultaneous Transmit and Receive

abstract: This paper presents a new type of monolithic microwave integrated circuit (MMIC)-based active quasi-circulator using phase cancellation and combination techniques for simultaneous transmit and receive (STAR) phased-array applications. The device consists of a passive core of three quadrature hybrids and active components to provide active quasi-circulation operation. The core of three quadrature hybrids can be implemented using Lange couplers. The device is capable of high isolation performance, high-frequency operation, broadband performance, and improvement of the noise figure (NF) at the receive port by suppressing transmit noise. For passive quasi-circulation operation, the device can achieve 35-dB isolation between the transmit and receive port with 2.6-GHz bandwidth (BW) and insertion loss of 4.5 dB at X-band. For active quasi-operation, the device is shown to have 2.3-GHz BW of 30-dB isolation with 1.5-dB transmit-to-antenna gain and 4.7-dB antenna-to-receive insertion loss, while the NF at the receive port is approximately 5.5 dB. The device is capable of a power stress test up to 34 dBm at the output ports at 10.5 GHz. For operation with typical 25-dB isolation, the device is capable of operation up to 5.6-GHz BW at X-band. The device is also shown to be operable up to W -band by simulation with ~15-GHz BW of 20-dB isolation. The proposed architecture is suitable for MMIC integration and system-on-chip applications.


title: On the Control and Properties of Supercoiled Polymer Artificial Muscles

abstract: Robotics have long sought an actuation technology comparable to or as capable as biological muscle tissue. Natural muscles exhibit a high power-to-weight ratio, inherent compliance and damping, fast action, and a high dynamic range. They also produce joint displacements and forces without the need for gearing or additional hardware. Recently, supercoiled commercially available polymer threads (sewing thread or nylon fishing lines) have been used to create significant mechanical power in a muscle-like form factor. Heating and cooling the polymer threads causes contraction and expansion, which can be utilized for actuation. In this paper, we describe the working principle of supercoiled polymer (SCP) actuation and explore the controllability and properties of these threads. We show that under appropriate environmental conditions, the threads are suitable as a building block for a controllable artificial muscle. We leverage off-the-shelf silver-coated threads to enable rapid electrical heating while the low thermal mass allows for rapid cooling. We utilize both thermal and thermomechanical models for feed-forward and feedback control. The resulting SCP actuator regulates to desired force levels in as little as 28 ms. Together with its inherent stiffness and damping, this is sufficient for a position controller to execute large step movements in under 100 ms. This controllability, high performance, the mechanical properties, and the extremely low material cost are indicative of a viable artificial muscle.


Random graphs and complex networks


title: A survey on cloud forensics challenges and solutions

abstract: In recent years, cloud computing has gained popularity, and it is now used to support various areas of human life. Cloud forensics has been introduced to help forensic investigators find potential evidence against cloud criminal activities and maintain the security and integrity of the information stored in the cloud. While great research in the area has been carried out concerning challenges and solutions, the research on methodologies and frameworks is still in its infancy. This article focuses on the methodological aspects of cloud forensics. It critically reviews cloud forensics' existing challenges and solutions, and it explores, based on a detailed review of the area, all the work that has been carried out both in digital and cloud forensic methodologies mainly for supporting the investigation of security incidents in cloud. Furthermore, the detailed comparison reveals similarities and drawbacks of the existing methodologies providing some novel future research directions. Finally, the specific paper can be considered as a starting point for researchers wishing to design cloud-forensicable services over the cloud. Copyright © 2016 John Wiley & Sons, Ltd.


title: Energy bounds for codes and designs in Hamming spaces

abstract: We obtain universal bounds on the energy of codes and for designs in Hamming spaces. Our bounds hold for a large class of potential functions, allow unified treatment, and can be viewed as a generalization of the Levenshtein bounds for maximal codes.


title: Persistent self-supervised learning: From stereo to monocular vision for obstacle avoidance

abstract: Self-Supervised Learning (SSL) is a reliable learning mechanism in which a robot uses an original, trusted sensor cue for training to recognize an additional, complementary sensor cue. We study for the first time in SSL how a robot's learning behavior should be organized, so that the robot can keep performing its task in the case that the original cue becomes unavailable. We study this persistent form of SSL in the context of a flying robot that has to avoid obstacles based on distance estimates from the visual cue of stereo vision. Over time it will learn to also estimate distances based on monocular appearance cues. A strategy is introduced that has the robot switch from stereo vision based flight to monocular flight, with stereo vision purely used as 'training wheels' to avoid imminent collisions. This strategy is shown to be an effective approach to the 'feedback-induced data bias' problem as also experienced in learning from demonstration. Both simulations and real-world experiments with a stereo vision equipped AR drone 2.0 show the feasibility of this approach, with the robot successfully using monocular vision to avoid obstacles in a 5 x 5 room. The experiments show the potential of persistent SSL as a robust learning approach to enhance the capabilities of robots. Moreover, the abundant training data coming from the own sensors allows to gather large data sets necessary for deep learning approaches.


A SCADA test bed For Cyber Security


Detecting opinion spam and fake news using n-gram analysis and semantic similarity


title: Content-based Identification of Audio Material Using MPEG-7 Low Level Description

abstract: Along with investigating similarity metrics between audio material, the topic of robust matching of pairs of audio content has gained wide interest recently. In particular, if this matching process is carried out using a compact representation of the audio content ("audio fingerprint"), it is possible to identify unknown audio material by means of matching it to a database with the fingerprints of registered works. This paper presents a system for reliable, fast and robust identification of audio material which can be run on the resources provided by today's standard computing platforms. The system is based on a general pattern recognition paradigm and exploits low level signal features standardized within the MPEG-7 framework, thus enabling interoperability on a world-wide scale. Compared to similar systems, particular attention is given to issues of robustness with respect to common signal distortions, i.e. recognition performance for processed/modified audio signals. The system's current performance figures are benchmarked for a range of real-world signal distortions, including low bitrate coding and transmission over an acoustic channel. A number of interesting applications are discussed.


title: Joint 3-D vessel segmentation and centerline extraction using oblique Hough forests with steerable filters

abstract: CONTRIBUTIONS ::: We propose a novel framework for joint 3-D vessel segmentation and centerline extraction. The approach is based on multivariate Hough voting and oblique random forests (RFs) that we learn from noisy annotations. It relies on steerable filters for the efficient computation of local image features at different scales and orientations. ::: ::: ::: EXPERIMENTS ::: We validate both the segmentation performance and the centerline accuracy of our approach both on synthetic vascular data and four 3-D imaging datasets of the rat visual cortex at 700 nm resolution. First, we evaluate the most important structural components of our approach: (1) Orthogonal subspace filtering in comparison to steerable filters that show, qualitatively, similarities to the eigenspace filters learned from local image patches. (2) Standard RF against oblique RF. Second, we compare the overall approach to different state-of-the-art methods for (1) vessel segmentation based on optimally oriented flux (OOF) and the eigenstructure of the Hessian, and (2) centerline extraction based on homotopic skeletonization and geodesic path tracing. ::: ::: ::: RESULTS ::: Our experiments reveal the benefit of steerable over eigenspace filters as well as the advantage of oblique split directions over univariate orthogonal splits. We further show that the learning-based approach outperforms different state-of-the-art methods and proves highly accurate and robust with regard to both vessel segmentation and centerline extraction in spite of the high level of label noise in the training data.


title: SIFT implementation and optimization for general-purpose gpu

abstract: With the addition of free programmable components to modern graphics hardware, graphics processing units (GPUs) become increasingly interesting for general purpose computations, especially due to utilizing parallel buffer processing. In this paper we present methods and techniques that take advantage of modern graphics hardware for real-time tracking and recognition of feature-points. The focus lies on the generation of feature vectors from input images in the various stages. For the generation of feature-vectors the Scale Invariant Feature Transform (SIFT) method [Low04a] is used due to its high stability against rotation, scale and lighting condition changes of the processed images. We present results of the various stages for feature vector generation of our GPU implementation and compare it to the CPU version of the SIFT algorithm. The approach works well on Geforce6 series graphics board and above and takes advantage of new hardware features, e.g. dynamic branching and multiple render targets (MRT) in the fragment processor [KF05]. With the presented methods feature-tracking with real time frame rates can be achieved on the GPU and meanwhile the CPU can be used for other tasks.


title: Multi-modal Auto-Encoders as Joint Estimators for Robotics Scene Understanding

abstract: We explore the capabilities of Auto-Encoders to fuse the information available from cameras and depth sensors, and to reconstruct missing data, for scene understanding tasks. In particular we consider three input modalities: RGB images; depth images; and semantic label information. We seek to generate complete scene segmentations and depth maps, given images and partial and/or noisy depth and semantic data. We formulate this objective of reconstructing one or more types of scene data using a Multi-modal stacked Auto-Encoder. We show that suitably designed Multi-modal Auto-Encoders can solve the depth estimation and the semantic segmentation problems simultaneously, in the partial or even complete absence of some of the input modalities. We demonstrate our method using the outdoor dataset KITTI that includes LIDAR and stereo cameras. Our results show that as a means to estimate depth from a single image, our method is comparable to the state-of-the-art, and can run in real time (i.e., less than 40ms per frame). But we also show that our method has a significant advantage over other methods in that it can seamlessly use additional data that may be available, such as a sparse point-cloud and/or incomplete coarse semantic labels.


title: Assessing and Mapping of Road Surface Roughness based on GPS and Accelerometer Sensors on Bicycle-Mounted Smartphones

abstract: The surface roughness of roads is an essential road characteristic. Due to the employed carrying platforms (which are often cars), existing measuring methods can only be used for motorable roads. Until now, there has been no effective method for measuring the surface roughness of un-motorable roads, such as pedestrian and bicycle lanes. This hinders many applications related to pedestrians, cyclists and wheelchair users. In recognizing these research gaps, this paper proposes a method for measuring the surface roughness of pedestrian and bicycle lanes based on Global Positioning System (GPS) and accelerometer sensors on bicycle-mounted smartphones. We focus on the International Roughness Index (IRI), as it is the most widely used index for measuring road surface roughness. Specifically, we analyzed a computing model of road surface roughness, derived its parameters with GPS and accelerometers on bicycle-mounted smartphones, and proposed an algorithm to recognize potholes/humps on roads. As a proof of concept, we implemented the proposed method in a mobile application. Three experiments were designed to evaluate the proposed method. The results of the experiments show that the IRI values measured by the proposed method were strongly and positively correlated with those measured by professional instruments. Meanwhile, the proposed algorithm was able to recognize the potholes/humps that the bicycle passed. The proposed method is useful for measuring the surface roughness of roads that are not accessible for professional instruments, such as pedestrian and cycle lanes. This work enables us to further study the feasibility of crowdsourcing road surface roughness with bicycle-mounted smartphones.


title: A deep multi-task learning approach for ECG data analysis

abstract: Deep learning is an advanced representation learning method and can automatically discover hidden features from raw data. Researchers have attempted to adopt it for ECG data analysis in the past few years. However, traditional deep learning algorithms usually require great efforts and experience to fine-tune the neural networks during their training processes. Moreover, these algorithms may suffer from a sharply declined accuracy when a well-trained model is directly applied to analyze the data from another group of patients. To address these issues, we propose a deep multi-task learning scheme for ECG data analysis which only requires limited efforts to fine-tune the network and can enable the trained model to be well applied to other datasets. Specifically, we first convert the ECG data analysis problem into a multi-task learning problem by dividing the ECG data analysis into multiple tasks. We then construct the multiple datasets for each task. Finally, we design a deep parameter-sharing network which inserts parameter-sharing neural layers in traditional neural networks. We conduct experiments by using the MIT-BIH database to validate the performance of our proposed scheme. Results illustrate that our proposed scheme can improve the accuracy of ECG data analysis by up to about 5.1%.


title: Quantitative Study of Anomeric Forms of Glucose Produced by α-Glucosidases and Glucoamylases

abstract: A gas-liquid chromatographic method was applied to the determination of anomeric forms of sugar produced by carbohydrases. Anomeric forms of glucose released from maltotriose, phenyl amaltoside and phenyl a-glucoside were determined quantitatively. Thirteen a-glucosidases tested, including a-glucosidase from honey bee and acid α-glucosidase from pig's liver, were found to produce a-glucose exclusively, and two kinds of glucoamylases, only β-glucose. This method proved very useful for the determination of the anomeric forms of sugar produced. It was confirmed that mammalian acid a-glucosidase does not belong to the category of exo-1, 4-α-glucosidase (EC 3.2.1.3).


title: Acoustic-prosodic entrainment in Slovak, Spanish, English and Chinese: A cross-linguistic comparison

abstract: It is well established that speakers of Standard American English entrain, or become more similar to each other as they speak, in acoustic-prosodic features of their speech as well as other behaviors. Entrainment in other languages is less well understood. This work uses a variety of metrics to measure acoustic-prosodic entrainment in four comparable corpora of task-oriented conversational speech in Slovak, Spanish, English and Chinese. We report the results of these experiments and describe trends and patterns that can be observed from comparing acoustic-prosodic entrainment in these four languages. We find evidence of a variety of forms of entrainment across all the languages studied, with some evidence of individual differences as well within the languages.


title: Analysis and Practical Considerations in Implementing Multiple Transmitters for Wireless Power Transfer via Coupled Magnetic Resonance

abstract: Multiple transmitters can be used to simultaneously transmit power wirelessly to a single receiver via strongly coupled magnetic resonance. A simple circuit model is used to help explain the multiple-transmitter wireless power transfer system. Through this particular scheme, there is an increase in gain and “diversity” of the transmitted power according to the number of transmit coils. The effect of transmitter resonant coil coupling is also shown. Resonant frequency detuning due to nearby metallic objects is observed, and the extent of how much tuning can be done is demonstrated. A practical power line synchronization technique is proposed to synchronize all transmit coils, which reduces additional dedicated synchronization wiring or the addition of an RF front-end module to send the reference driving signal.


title: What Drives Consumers to Click on Social Media Ads? The Roles of Content, Media, and Individual Factors

abstract: Social media has become an ever-expanding realm as more and more consumers are spending tremendous amount of time on it. Businesses are taking advantage of this channel to promote their products and services through social media advertising. In particular, display ads have a prominent presence accompanying social media feeds. This study aims to develop an understanding of the multi-faceted factors that drive consumers to respond to social media advertising. The roles of content, media, and individual factors are examined. A research model is developed and tested using data collected from an online-survey of 613 social media users. Our results show compelling evidence that the effectiveness of display ads on social media is driven by ad content, ad-media congruity, and consumers' individual factors. Practical and theoretical implications are discussed.


Effects of peening on fatigue crack propagation


title: Local Directional Pattern (LDP) – A Robust Image Descriptor for Object Recognition

abstract: This paper presents a novel local feature descriptor, theLocal Directional Pattern (LDP), for describing localimage feature. A LDP feature is obtained by computing theedge response values in all eight directions at each pixelposition and generating a code from the relative strengthmagnitude. Each bit of code sequence is determined byconsidering a local neighborhood hence becomes robust innoisy situation. A rotation invariant LDP code is alsointroduced which uses the direction of the most prominentedge response. Finally an image descriptor is formed todescribe the image (or image region) by accumulating theoccurrence of LDP feature over the whole input image (orimage region). Experimental results on the Brodatz texturedatabase show that LDP impressively outperforms theother commonly used dense descriptors (e.g.,Gabor-wavelet and LBP).


title: Structured labeling for facilitating concept evolution in machine learning

abstract: Labeling data is a seemingly simple task required for training many machine learning systems, but is actually fraught with problems. This paper introduces the notion of concept evolution, the changing nature of a person's underlying concept (the abstract notion of the target class a person is labeling for, e.g., spam email, travel related web pages) which can result in inconsistent labels and thus be detrimental to machine learning. We introduce two structured labeling solutions, a novel technique we propose for helping people define and refine their concept in a consistent manner as they label. Through a series of five experiments, including a controlled lab study, we illustrate the impact and dynamics of concept evolution in practice and show that structured labeling helps people label more consistently in the presence of concept evolution than traditional labeling.


title: Solutionism, the Game: Design Fictions for Positive Aging

abstract: This paper reports a qualitative study of thriving older people and illustrates the findings with design fiction. Design research has been criticized as "solutionist" i.e. solving problems that don't exist or providing "quick fixes" for complex social, political and environmental problems. We respond to this critique by presenting a "solutionist" board game used to generate design concepts. Players are given data cards and technology dice, they move around the board by pitching concepts that would support positive aging. We argue that framing concept design as a solutionist game explicitly foregrounds play, irony and the limitations of technological intervention. Three of the game concepts are presented as design fictions in the form of advertisements for products and services that do not exist. The paper argues that design fiction can help create a space for design beyond solutionism.


title: Look-up table (LUT) method for inverse halftoning

abstract: In this paper we propose look-up table (LUT) based methods for inverse halftoning of images. The LUT for inverse halftoning is obtained from the histogram gathered from a few sample halftone images and corresponding original images. The method is extremely fast (no filtering is required) and the PSNR and visual image quality achieved is comparable to the best methods known for inverse halftoning. The LUT inverse halftoning method does not depend on the specific properties of the halftoning method, and can be applied to any halftoning method. Then, an algorithm for template selection for LUT inverse halftoning is introduced. We demonstrate the performance of the LUT inverse halftoning algorithm on error diffused images and ordered dithered images. We also extend LUT inverse halftoning to color halftones.


Design of Efficient 16-Bit Parallel Prefix Ladner-Fischer Adder 1


title: “Meaningful” social inferences: Effects of implicit theories on inferential processes

abstract: Abstract Perceivers’ shared theories about the social world have long featured prominently in social inference research. Here, we investigate how fundamental differences in such theories influence basic inferential processes. Past work has typically shown that integrating multiple interpretations of behavior during social inference requires cognitive resources. However, three studies that measured or manipulated people’s beliefs about the stable versus dynamic nature of human attributes (i.e., their entity vs. incremental theory, respectively) qualify these past findings. Results revealed that, when interpreting others’ actions, perceivers’ theories selectively facilitate the consideration of interpretations that are especially theory-relevant. While experiencing cognitive load, entity theorists continued to incorporate information about stable dispositions (but not about dynamic social situations) in their social inferences, whereas incremental theorists continued to incorporate information about dynamic social situations (but not about stable traits). Implications of these results for how perceivers find meaning in behavior are discussed.


title: Sulfolobus acidocaldarius Transports Pentoses via a Carbohydrate Uptake Transporter 2 (CUT2)-Type ABC Transporter and Metabolizes Them through the Aldolase-Independent Weimberg Pathway

abstract: ABSTRACT Sulfolobus spp. possess a great metabolic versatility and grow heterotrophically on various carbon sources, such as different sugars and peptides. Known sugar transporters in Archaea predominantly belong to ABC transport systems. Although several ABC transporters for sugar uptake have been characterized in the crenarchaeon Sulfolobus solfataricus, only one homologue of these transporters, the maltose/maltooligomer transporter, could be identified in the closely related Sulfolobus acidocaldarius. Comparison of the transcriptome of S. acidocaldarius MW001 grown on peptides alone and peptides in the presence of d-xylose allowed for the identification of the ABC transporter for d-xylose and l-arabinose transport and the gaining of deeper insights into pentose catabolism under the respective growth conditions. The d-xylose/l-arabinose substrate binding protein (SBP) (Saci_2122) of the ABC transporter is unique in Archaea and shares more similarity to bacterial SBPs of the carbohydrate uptake transporter-2 (CUT2) family than to any characterized archaeal one. The identified pentose transporter is the first CUT2 family ABC transporter analyzed in the domain of Archaea. Single-gene deletion mutants of the ABC transporter subunits exemplified the importance of the transport system for d-xylose and l-arabinose uptake. Next to the transporter operon, enzymes of the aldolase-independent pentose catabolism branch were found to be upregulated in N-Z-Amine and d-xylose medium. The α-ketoglutarate semialdehyde dehydrogenase (KGSADH; Saci_1938) seemed not to be essential for growth on pentoses. However, the deletion mutant of the 2-keto-3-deoxyarabinoate/xylonate dehydratase (KDXD [also known as KDAD]; Saci_1939) was no longer able to catabolize d-xylose or l-arabinose, suggesting the absence of the aldolase-dependent branch in S. acidocaldarius. IMPORTANCE Thermoacidophilic microorganisms are emerging model organisms for biotechnological applications, as their optimal growth conditions resemble conditions used in certain biotechnologies such as industrial plant waste degradation. Because of its high genome stability, Sulfolobus acidocaldarius is especially suited as a platform organism for such applications. For use in (ligno)cellulose degradation, it was important to understand pentose uptake and metabolism in S. acidocaldarius. This study revealed that only the aldolase-independent Weimberg pathway is required for growth of S. acidocaldarius MW001 on d-xylose and l-arabinose. Moreover, S. acidocaldarius employs a CUT2 ABC transporter for pentose uptake, which is more similar to bacterial than to archaeal ABC transporters. The identification of pentose-inducible promoters will expedite the metabolic engineering of S. acidocaldarius for its development into a platform organism for (ligno)cellulose degradation.


title: Shy of the Character Limit: "Twitter Mood Predicts the Stock Market" Revisited

abstract: In the 2011 article “Twitter Mood Predicts the Stock Market” by Johan Bollen, Huina Mao, and Xiaojun Zeng, published in Journal of Computational Science, the authors estimated a proprietary measure of Twitter ‘calm’-ness and found that this measure Granger-caused increases in the Dow Jones Industrial Average from February 28, 2008 to November 3, 2008. The paper and related work has attracted attention by journalists, finance practitioners, and academics. In just six years the paper accumulated 2,514 Google Scholar citations and 678 Web of Science citations (as of April 24, 2017). We subject the paper to thoroughgoing scrutiny, including an attempt to replicate the findings in-sample. Using the authors’ subsampling technique, we replicate their sample window (less the since-deleted and hence unavailable tweets) and extend it backwards to include 2007. Constructing multiple measures of Twitter mood using word-count methods and standard sentiment analysis tools, we are unable to reproduce the p-value pattern that they found. We find evidence of a statistically significant Twitter mood effect in their subsample, but not in the backwards extended sample, a result consistent with data snooping. We find no evidence that our measures of Twitter mood aid in predicting the stock market out of sample. Congruously, the hedge fund set up in late 2010 to implement the Twitter mood strategy, Derwent Capital Markets, failed and closed in early 2012.


title: Actions of quinidine and apamin on after-hyperpolarization of the spike in circular smooth muscle cells of the guinea-pig ileum

abstract: The effects of quinine and quinidine on membrane potential and action potential were investigated in circular smooth muscle of the guinea-pig ileum and the findings compared with the actions of apamin. In addition to results obtained from microelectrode experiments, the actions of quinidine and apamin on membrane currents were assessed using the single cell voltage clamp method. Quinine (above 0.2 mmol/l) and quinidine (above 0.08 mmol/l) depolarized the membrane, increased the membrane resistance and blocked generation of the after-hyperpolarization of the spike. Higher concentrations of both agents reduced the amplitude of the action potential and further depolarized the membrane. Quinidine and quinine possessed much the same action, with the former being more potent than the latter. Apamin, an inhibitor of the Ca-dependent K current, did not inhibit the after-hyperpolarization of the spike and had no effect on the membrane potential. In voltage clamp experiments, a depolarizing pulse (above -30 mV from -60 mV; 200 ms duration) elicited an inward current, followed by an outward current. With application of 2.5 mmol/l Mn instead of Ca, the outward current was subclassified into the Mn sensitive (Ca-dependent) and Mn resistant (voltage-dependent) K currents. Apamin (0.1 mumol/l) did not modify membrane currents evoked in the circular muscle cell, while, 0.1 mmol/l quinidine inhibited both the Ca- and voltage-dependent K outward currents, and Ca inward current. Our observation suggest that apamin-insensitive Ca-dependent K channels are present in the smooth muscle membrane and that they probably participate in the falling phase and after-hyperpolarization of the action potential.


title: Predicting Dropout Student: An Application of Data Mining Methods in an Online Education Program

abstract: This study examined the prediction of dropouts through data mining approaches in an online program. The subject of the study was selected from a total of 189 students who registered to the online Information Technologies Certificate Program in 2007-2009. The data was collected through online questionnaires (Demographic Survey, Online Technologies Self-Efficacy Scale, Readiness for Online Learning Questionnaire, Locus of Control Scale, and Prior Knowledge Questionnaire). The collected data included 10 variables, which were gender, age, educational level, previous online experience, occupation, self efficacy, readiness, prior knowledge, locus of control, and the dropout status as the class label (dropout/not). In order to classify dropout students, four data mining approaches were applied based on k-Nearest Neighbour (k-NN), Decision Tree (DT), Naive Bayes (NB) and Neural Network (NN). These methods were trained and tested using 10-fold cross validation. The detection sensitivities of 3-NN, DT, NN and NB classifiers were 87%, 79.7%, 76.8% and 73.9% respectively. Also, using Genetic Algorithm (GA) based feature selection method, online technologies self-efficacy, online learning readiness, and previous online experience were found as the most important factors in predicting the dropouts.


title: Manufacturing Strategy, Capabilities and Performance

abstract: This dissertation addresses the topic of manufacturing strategy, especially the manufacturing capabilities and operational performance of manufacturing plants. Manufacturing strategy research aims ...


title: Ringing suppressing method in 13.56MHz resonant inverter for wireless power transfer systems

abstract: This paper presents the ringing suppressing method in class D resonant inverter operating at 13.56MHz for wireless power transfer systems. The ringing loop in half-bridge topology inverter at 13.56MHz strongly effect to the stable, performance and efficiency of the inverter. Typically, this ringing can be damped by using a snubber circuit which is placed parallel to the MOSFETs or using a damping circuit which is place inside the ringing loop. But in the resonant inverter with high power and high frequency, the snubber circuit or general damping circuit may reduce performance and efficiency of inverter. A new damping circuit combining with drive circuit design solution is proposed in this paper. The simulation and experiment results showed that the proposed design significantly suppresses the ringing current and ringing voltage in the circuit. The power loss on the MOSFETs is reduced while the efficiency of inverter increases 2% to obtain 93.1% at 1.2kW output power. The inverter becomes more stable and compact.


title: A Bayesian Exploration-Exploitation Approach for Optimal Online Sensing and Planning with a Visually Guided Mobile Robot

abstract: We address the problem of online path planning for optimal sensing with a mobile robot. The objective of the robot is to learn the most about its pose and the environment given time constraints. We use a POMDP with a utility function that depends on the belief state to model the finite horizon planning problem. We replan as the robot progresses throughout the environment. The POMDP is high-dimensional, continuous, non-differentiable, nonlinear, non-Gaussian and must be solved in real-time. Most existing techniques for stochastic planning and reinforcement learning are therefore inapplicable. To solve this extremely complex problem, we propose a Bayesian optimization method that dynamically trades off exploration (minimizing uncertainty in unknown parts of the policy space) and exploitation (capitalizing on the current best solution). We demonstrate our approach with a visually-guide mobile robot. The solution proposed here is also applicable to other closely-related domains, including active vision, sequential experimental design, dynamic sensing and calibration with mobile sensors.


title: Laser sensor based localization of mobile robot using Unscented Kalman Filter

abstract: The objective is to determine mobile robots position and orientation by integrating information received from laser distance sensor and encoders. The robot is maneuvered in a known environment, and the laser ranging finder can get information of geometrical primitives like lines and polygons to extract landmarks of the environment. With the off-line map, the position and orientation of the robot can be estimated. To improve the precision of our localization system, we present a sensor-data-fusion method using Unscented Kalman Filter (UKF).


title: De-virtualizing social events: understanding the gap between online and offline participation for event invitations

abstract: One growing use of computer-based communication media is for gathering people to initiate or sustain social events. Although the use of computer-mediated communication and social network sites such as Facebook for event promotion is becoming popular, online participation in an event does not always translate to offline attendance. In this paper, we report on an interview study of 31 participants that examines how people handle online event invitations and what influences their online and offline participation. The results show that people's event participation is shaped by their social perceptions of the event's nature (e.g., public or private), their relationships to others (e.g., the strength of their connections to other invitees), and the medium used to communicate event information (e.g., targeted invitation via email or spam communication via Facebook event page). By exploring how people decide whether to participate online or offline, the results illuminate the sophisticated nature of the mechanisms that affect participation and have design implications that can bridge virtual and real attendance.


title: Memory Models for Intelligent Social Companions

abstract: Memory gives us identity, shapes our personality and drives our reactions to different situations in life. We actively create expectations, track the fulfilment of these expectations and dynamically modify our memory when new experiences demand it. Yet up to date, many important social aspects of human memory to artificial intelligent social agents have not been given much attention. Thus, this article is going to review on the current state-of-the-art of memory models for social companions, particularly on memory aspects that have been explored and proved useful and other potential memory aspects that would be beneficial if included.


title: Improvement of Grayscale Image Segmentation Based on PSO Algorithm

abstract: Image segmentation is the base of image 3D reconstruction. It is a critical step in image processing. Threshold segmentation is a simple and important method in grayscale image segmentation. Maximum Entropy method is a common threshold segmentation method. This method only utilizes gray information. In order to adequately utilize spatial information of greyscale image, an improved 2D entropy segmentation method is proposed. This new method is called PSO-SDAIVE algorithm. In this new method, the computation of 2D entropy is improved. Otherwise, Particle Swarm Optimization(PSO) algorithm is used to solve maximum of improved entropy. Maximum takes as the optimal image segmentation threshold. In this paper, two head CT images are segmented in experiment. Compare with other segmentation method. Experimental results show that this new method can quickly and accurately obtain segmentation threshold. Otherwise, this method has strong anti-noise capability and saves computation time.


title: Data profiling for data quality improvement with OpenRefine

abstract: Data profiling is an information analysis technique on data stored inside database. Data profiling purpose is to ensure data quality by detecting whether the data in the data source compiles with the established business rules. Profiling could be performed using multiple analysis techniques depending on the data element to be analyzed. The analysis process also influenced by the data profiling tool being used. This paper describes tehniques of profiling analysis using open-source tool OpenRefine. The method used in this paper is case study method, using data retrieved from BPOM Agency website for checking commodity traditional medicine permits. Data attributes that became the main concern of this paper is Nomor Ijin Edar (NIE / distribution permit number) and registrar company name. The result of this research were suggestions to improve data quality on NIE and company name, which consists of data cleansing and improvement to business process and applications.


title: Exploiting effects of parts in fine-grained categorization of vehicles

abstract: Fine-grained categorization has become a hot topic in computer vision. Based on the theory that part information is crucial for fine-grained categorization, we proposed a part-based categorization method for vehicles, consisting vehicle parts localization, part-based vehicle representation and classification. There were three contributions we made in this work: 1) we analyzed discriminative powers of parts for fine-grained categorization; 2) we proposed a frame of how to integrate discriminative powers of parts into categorization, and proved that it can achieve better performance than treating every part equally; 3) we provided an annotated dataset with parts for vehicle categorization.


title: A review on hadoop — HDFS infrastructure extensions

abstract: Apache's Hadoop1 as of now is pretty good but there are scopes of extensions and enhancements. A large number of improvements are proposed to Hadoop which is an open source implementation of Google's Map/Reduce framework. It enables distributed, data intensive and parallel applications by decomposing a massive job into smaller tasks and a massive data set into smaller partitions such that each task processes a different partition in parallel. Hadoop uses Hadoop distributed File System (HDFS) which is an open source implementation of the Google File System (GFS) for storing data. Map/Reduce application mainly uses HDFS for storing data. HDFS is a very large distributed file system that uses commodity hardware and provides high throughput as well as fault tolerance. Many big enterprises believe that within a few years more than half of the world's data will be stored in Hadoop. HDFS stores files as a series of blocks and are replicated for fault tolerance. Strategic data partitioning, processing, layouts, replication and placement of data blocks will increase the performance of Hadoop and a lot of research is going on in this area. This paper reviews some of the major enhancements suggested to Hadoop especially in data storage, processing and placement.


Proposal for development in the mathematics division of an Automatic Computing Engine (ACE) Universal Turing Machine


title: Automatic Railway Traffic Object Detection System Using Feature Fusion Refine Neural Network under Shunting Mode

abstract: Many accidents happen under shunting mode when the speed of a train is below 45 km/h. In this mode, train attendants observe the railway condition ahead using the traditional manual method and tell the observation results to the driver in order to avoid danger. To address this problem, an automatic object detection system based on convolutional neural network (CNN) is proposed to detect objects ahead in shunting mode, which is called Feature Fusion Refine neural network (FR-Net). It consists of three connected modules, i.e., the depthwise-pointwise convolution, the coarse detection module, and the object detection module. Depth-wise-pointwise convolutions are used to improve the detection in real time. The coarse detection module coarsely refine the locations and sizes of prior anchors to provide better initialization for the subsequent module and also reduces search space for the classification, whereas the object detection module aims to regress accurate object locations and predict the class labels for the prior anchors. The experimental results on the railway traffic dataset show that FR-Net achieves 0.8953 mAP with 72.3 FPS performance on a machine with a GeForce GTX1080Ti with the input size of 320 × 320 pixels. The results imply that FR-Net takes a good tradeoff both on effectiveness and real time performance. The proposed method can meet the needs of practical application in shunting mode.


title: Multi-task Deep Reinforcement Learning with PopArt

abstract: The reinforcement learning (RL) community has made great strides in designing algorithms capable of exceeding human performance on specific tasks. These algorithms are mostly trained one task at the time, each new task requiring to train a brand new agent instance. This means the learning algorithm is general, but each solution is not; each agent can only solve the one task it was trained on. In this work, we study the problem of learning to master not one but multiple sequentialdecision tasks at once. A general issue in multi-task learning is that a balance must be found between the needs of multiple tasks competing for the limited resources of a single learning system. Many learning algorithms can get distracted by certain tasks in the set of tasks to solve. Such tasks appear more salient to the learning process, for instance because of the density or magnitude of the in-task rewards. This causes the algorithm to focus on those salient tasks at the expense of generality. We propose to automatically adapt the contribution of each task to the agent’s updates, so that all tasks have a similar impact on the learning dynamics. This resulted in state of the art performance on learning to play all games in a set of 57 diverse Atari games. Excitingly, our method learned a single trained policy - with a single set of weights - that exceeds median human performance. To our knowledge, this was the first time a single agent surpassed human-level performance on this multi-task domain. The same approach also demonstrated state of the art performance on a set of 30 tasks in the 3D reinforcement learning platform DeepMind Lab.


title: Learning regression and verification networks for long-term visual tracking

abstract: Compared with short-term tracking, the long-term tracking task requires determining the tracked object is present or absent, and then estimating the accurate bounding box if present or conducting image-wide re-detection if absent. Until now, few attempts have been done although this task is much closer to designing practical tracking systems. In this work, we propose a novel long-term tracking framework based on deep regression and verification networks. The offline-trained regression model is designed using the object-aware feature fusion and region proposal networks to generate a series of candidates and estimate their similarity scores effectively. The verification network evaluates these candidates to output the optimal one as the tracked object with its classification score, which is online updated to adapt to the appearance variations based on newly reliable observations. The similarity and classification scores are combined to obtain a final confidence value, based on which our tracker can determine the absence of the target accurately and conduct image-wide re-detection to capture the target successfully when it reappears. Extensive experiments show that our tracker achieves the best performance on the VOT2018 long-term challenge and state-of-the-art results on the OxUvA long-term dataset.


title: TRANSIT— ultrafast shortest-path queries with linear-time preprocessing

abstract: We introduce the concept of transit nodes, as a means for preprocessing a road network, with given coordinates for each node and a travel time for each edge, such that point-to-point shortest-path queries can be answered extremely fast. The transit nodes are a set of nodes as small as possible with the property that every shortest path that is non-local in the sense that it covers a certain not too small euclidean distance passes through at least on of these nodes. With such a set and precomputed distances from each node in the graph to its few, closest transit nodes, every non-local shortest path query becomes a simple matter of combining information from a few table lookups. For the US road network, which has about 24 million nodes and 58 million edges, we achieve a worst-case query processing time of about 10 microseconds (not milliseconds) for 99% of all queries. This improves over the best previously reported times by two orders of magnitude.


title: Argument Mining: A Machine Learning Perspective

abstract: Argument mining has recently become a hot topic, attracting the interests of several and diverse research communities, ranging from artificial intelligence, to computational linguistics, natural language processing, social and philosophical sciences. In this paper, we attempt to describe the problems and challenges of argument mining from a machine learning angle. In particular, we advocate that machine learning techniques so far have been under-exploited, and that a more proper standardization of the problem, also with regards to the underlying argument model, could provide a crucial element to develop better systems.


title: Music Recommendation Using Content and Context Information Mining

abstract: Mobile devices such as smart phones are becoming popular, and realtime access to multimedia data in different environments is getting easier. With properly equipped communication services, users can easily obtain the widely distributed videos, music, and documents they want. Because of its usability and capacity requirements, music is more popular than other types of multimedia data. Documents and videos are difficult to view on mobile phones' small screens, and videos' large data size results in high overhead for retrieval. But advanced compression techniques for music reduce the required storage space significantly and make the circulation of music data easier. This means that users can capture their favorite music directly from the Web without going to music stores. Accordingly, helping users find music they like in a large archive has become an attractive but challenging issue over the past few years.


title: Validation of Measured Damping Trends for Flight-Like Vehicle Panel/Equipment Including a Range of Cable Harness Assemblies

abstract: This validation study examines the e ect on vibroacoustic response resulting from the installation of cable bundles on a curved orthogrid panel. Of interest is the level of damping provided by the installation of the cable bundles and whether this damping could be potentially leveraged in launch vehicle design. The results of this test are compared with baseline acoustic response tests without cables. Damping estimates from the measured response data are made using a new software tool that leverages a nite element model of the panel in conjunction with advanced optimization techniques. While the full test series is not yet complete, the rst con guration of cable bundles that was assessed e ectively increased the viscous critical damping fraction of the system by as much as 0.02 in certain frequency ranges.


title: A computational model of empathy for interactive agents

abstract: Abstract Empathy has been defined in the scientific literature as the capacity to relate another’s emotional state and assigned to a broad spectrum of cognitive and behavioral abilities. Advances in neuroscience, psychology and ethology made it possible to refine the defined functions of empathy to reach a working definition and a model of empathy. Recently, cognitive science and artificial intelligence communities made attempts to model empathy in artificial agents, which can provide means to test these models and hypotheses. A computational model of empathy not only would help to advance the technological artifacts to be more socially compatible, but also understand the empathy mechanisms, test theories, and address the ethics and morality problems the Artificial Intelligence (AI) community is facing today. In this paper, we will review the empathy research from various fields, gather the requirements for empathic capacity and construct a model of empathy that is suitable for interactive conversational agents.


title: Finding Structure in Unstructured Processes: The Case for Process Mining

abstract: Today there are many process mining techniques that allow for the automatic construction of process models based on event logs. Unlike synthesis techniques (e.g., based on regions), process mining aims at the discovery of models (e.g., Petri nets) from incomplete information (i.e., only example behavior is given). The more mature process mining techniques perform well on structured processes. However, most of the existing techniques fail miserably when confronted with unstructured processes. This paper attempts to "bring structure to the unstructured" by using an integrated combination of abstraction and clustering techniques. The ultimate goal is to present process models that are understandable by analysts and that lead to improved system/process redesigns.


title: Discovering Cyber Terrorism Using Trace Pattern

abstract: Nowadays, as the Internet user increased, the number of cyber threats is also increased. Internet has provided a medium for criminal to do the crime and become the target for cyber terrorist to spread their negative propaganda, and promote extreme activities. One of the crimes is cyber terrorism. Cyber terrorism became more sophisticated and it difficult to discover its activities. Hence, this paper proposes tracing technique for discovering cyber terrorism based on trace pattern. Trace pattern will represent the behavior and activities of cyber terrorism. Cyber terrorist's website is used as the datasets. Using tracing technique, cyber terrorist's activities are identified by extraction and classifying the traces to the keyword that is usually used by the terrorist. Then, the traces will be linked with the cyber terrorism components in order to identify the relationship between them. Using trace pattern, the verification process will be conducted to verify the traces in order to identify the cyber terrorism activities and potential terrorist. This trace pattern can be used in facilitating the forensic investigation process in discovering cyber terrorism activities.


title: Optimation of Xen scheduler for multitasking

abstract: The rise of cloud computing brings virtualization technology continues to heat up. Based on Xen's I/O virtualization subsystem, under the virtual machine environment which has multi-type tasks, the existing schedulers can't achieve response with I/O-bound tasks in time. This paper presents ECredit scheduler combined complexity evaluation of I/O task in Xen's I/O virtualization subsystem. It prioritizes the I/O-bound task realizing fair scheduling. The experiments show that the optimized scheduling algorithm can reduce the response time of I/O-bound task and improve the performance of the virtual system.


Bit Error Rate Performance in OFDM System Using MMSE & MLSE Equalizer Over Rayleigh Fading Channel Through The BPSK , QPSK , 4 QAM & 16 QAM Modulation Technique


title: MiPPS : A Generative Model for Multi-Manifold Clustering

abstract: We propose a generative model for high dimensional data consisting of intrinsically low dimensional clusters that are noisily sampled. The proposed model is a mixture of probabilistic principal surfaces (MiPPS) optimized using expectation maximization. We use a Bayesian prior on the model parameters to maximize the corresponding marginal likelihood. We also show empirically that this optimization can be biased towards a good local optimum by using our prior intuition to guide the initialization phase The proposed unsupervised algorithm naturally handles cases where the data lies on multiple connected components of a single manifold and where the component manifolds intersect. In addition to clustering, we learn a functional model for the underlying structure of each component cluster as a parameterized hyper-surface in ambient noise.This model is used to learn a global embedding that we use for visualization of the entire dataset. We demonstrate the performance of MiPPS in separating and visualizing land cover types in a hyperspectral dataset.


title: UI On The Fly: Generating A Multimodal User Interface

abstract: UI on the Fly is a system that dynamically presents coordinated multimodal content through natural language and a small-screen graphical user interface. It adapts to the user's preferences and situation. Multimodal Functional Unification Grammar (MUG) is a unification-based formalism that uses rules to generate content that is coordinated across several communication modes. Faithful variants are scored with a heuristic function.


title: Evolutionary attribute ordering in Bayesian networks for predicting the metabolic syndrome

abstract: The metabolic syndrome is a set of risk factors that include abdominal obesity, insulin resistance, dyslipidemia and hypertension. It has affected around 25% of adults in the US and become a serious problem in Asian countries recently due to the change in dietary habit and life style. On the other hand, Bayesian networks that are the models to solve the problems of uncertainty provide a robust and transparent formalism for probabilistic modeling, so they have been used as a method for diagnostic or prognostic model in medical domain. Since the K2 algorithm, a well-known algorithm for Bayesian networks structure learning, is influenced by an input order of the attributes, an optimization of BN attribute ordering has been studied as a research issue. This paper proposes a novel ordering optimization method using a genetic algorithm based on medical expert knowledge in order to solve this problem. For experiments, we use the dataset examined twice in 1993 and 1995 in Yonchon County of Korea. It has 18 attributes of 1193 subjects participated in both surveys. Using this dataset, we make the prognostic model of the metabolic syndrome using Bayesian networks with an optimized ordering by evolutionary approach. Through an ordering optimization, the prognostic model of higher performance is constructed, and the optimized Bayesian network model by the proposed method outperforms the conventional BN model as well as neural networks and k-nearest neighbors. Finally, we present the application program using the prognostic model of the metabolic syndrome in order to show the usefulness of the proposed method.


title: Factors Underlying the Increase in Carotid Intima-Media Thickness in Borderline Hypertensives

abstract: Abstract —To define the role played by various risk and behavioral factors in the increase of carotid intima-media thickness (IMT) observed in borderline hypertensives. Using B-mode ultrasonography, we compared 97 borderline hypertensives enrolled in the HARVEST study to 27 normotensive controls. Intima-media thickness was measured in the right and left common carotid artery, bulb, and internal carotid artery. Mean IMT (m-IMT), maximum IMT (M-IMT), the mean of M-IMT (M-MAX), and the prevalence of raised lesions (IMT>1 mm) were established. Compared to the controls, higher systolic BP, diastolic BP, mean arterial blood pressure levels and body mass index (BMI) were present in the borderline hypertensives, whereas age, smoking, physical activity, serum cholesterol, and triglycerides were similar. After adjusting for age, sex, heart rate, BMI, smoking, serum cholesterol, triglycerides, and physical activity, higher values of m-IMT and M-IMT were present in most carotid segments of borderline hypertensives compared with controls. After further adjustment for systolic BP and diastolic BP, differences were no longer significant. The adjusted M-MAX was 0.59±0.12 in borderline hypertensives compared with 0.50±0.10 in controls ( P <0.001). After adjustment for systolic BP and diastolic BP it was 0.58±0.11 in borderline hypertensives compared with 0.50±0.12 in controls ( P <0.005). In the various carotid segments, the prevalence of raised lesions was 1.2% in borderline hypertensives compared with 0.3% in controls ( P <0.001). In the multivariate analysis m-IMT, M-IMT, and M-MAX were related to ambulatory mean arterial pressure, systolic BP and diastolic BP, serum cholesterol and triglycerides, BMI, age, and physical activity. Higher IMT values were found in subjects who were physically active than in those who were sedentary. In borderline hypertensives, an increase in IMT takes place not only in the common carotid artery but also in the bulb and the internal carotid segment. Blood pressure levels are a main determinant of m-IMT while the interaction of BP with other risk factors such as age and plasma lipids is more relevant for advanced intima-media thickening such as M-MAX.


title: CanTree: a canonical-order tree for incremental frequent-pattern mining

abstract: Since its introduction, frequent-pattern mining has been the subject of numerous studies, including incremental updating. Many existing incremental mining algorithms are Apriori-based, which are not easily adoptable to FP-tree-based frequent-pattern mining. In this paper, we propose a novel tree structure, called CanTree (canonical-order tree), that captures the content of the transaction database and orders tree nodes according to some canonical order. By exploiting its nice properties, the CanTree can be easily maintained when database transactions are inserted, deleted, and/or modified. For example, the CanTree does not require adjustment, merging, and/or splitting of tree nodes during maintenance. No rescan of the entire updated database or reconstruction of a new tree is needed for incremental updating. Experimental results show the effectiveness of our CanTree in the incremental mining of frequent patterns. Moreover, the applicability of CanTrees is not confined to incremental mining; CanTrees can also be applicable to other frequent-pattern mining tasks including constrained mining and interactive mining.


title: Cybersecurity: A Stochastic Predictive Model to Determine Overall Network Security Risk Using Markovian Process

abstract: There ::: are several security metrics developed to protect the computer networks. In ::: general, common security metrics focus on qualitative and subjective aspects of ::: networks lacking formal statistical models. In the present study, we propose a ::: stochastic model to quantify the risk associated with the overall network using ::: Markovian process in conjunction with Common Vulnerability Scoring System (CVSS) ::: framework. The model we developed uses host access graph to represent the ::: network environment. Utilizing the developed model, one can filter the large ::: amount of information available by making a priority list of vulnerable nodes existing in the network. Once a priority list is prepared, network administrators ::: can make software patch decisions. Gaining in depth understanding of the risk ::: and priority level of each host helps individuals to implement decisions like ::: deployment of security products and to design network topologies.


title: Enterprise Architecture Management Patterns for Enterprise Architecture Visioning

abstract: Enterprise architecture (EA) management is one of the major challenges of modern enter-prises. It aims at aligning business and IT in order to optimize their interaction. The generalmake-up of the enterprise is reected in the EA, which comprises both business and IT as-pects { ranging from visions (business, as well as IT visions are of interest), via businessprocesses, and business applications, to infrastructure elements, like e.g. application serversor hardware.Documenting and managing the EA is an advanced topic, as the application landscape, whichis part of the EA often includes a few hundreds up to a few thousand business applicationsand their interconnections in a medium-sized or large company. Thereby, managing the EAis a task, that has to be executed as the need for a exible IT is an integral concern of mostcompanies. Nevertheless, other reasons for maintaining an EA documentation exist, such ascompliance requirements or economic causes, i.e. the cost reduction of the IT function.This article includes patterns on EA Visioning, which are part of the EAM Pattern Cata-log, a pattern language for enterprise architecture management [BEL


title: Distortion estimation techniques in solving visual CAPTCHAs

abstract: This paper describes two distortion estimation techniques for object recognition that solve EZ-Gimpy and Gimpy-r, two of the visual CAPTCHAs ("completely automated public turing test to tell computers and humans apart") with high degrees of success. A CAPTCHA is a program that generates and grades tests that most humans can pass but current computer programs cannot pass. We have developed a correlation algorithm that correctly identifies the word in an EZ-Gimpy challenge image 99% of the time and a direct distortion estimation algorithm that correctly identifies the four letters in a Gimpy-r challenge image 78% of the time.


title: Estimating and Optimizing the Impact of Inventory on Consumer Choices in a Fashion Retail Setting

abstract: Problem definition: In fashion retailing, the display of product inventory at the store is important to capture consumers’ attention. Higher inventory levels might allow more attractive displays an...


title: 1.5 billion words Arabic Corpus

abstract: This study is an attempt to build a contemporary linguistic corpus for Arabic language. The corpus produced, is a text corpus includes more than five million newspaper articles. It contains over a billion and a half words in total, out of which, there is about three million unique words. The data were collected from newspaper articles in ten major news sources from eight Arabic countries, over a period of fourteen years. The corpus was encoded with two types of encoding, namely: UTF-8, and Windows CP-1256. Also it was marked with two mark-up languages, namely: SGML, and XML.