title: Security-aware mapping for CAN-based real-time distributed automotive systems

abstract: Cyber-security is a rising issue for automotive electronic systems, and it is critical to system safety and dependability. Current in-vehicles architectures, such as those based on the Controller Area Network (CAN), do not provide direct support for secure communications. When retrofitting these architectures with security mechanisms, a major challenge is to ensure that system safety will not be hindered, given the limited computation and communication resources. We apply Message Authentication Codes (MACs) to protect against masquerade and replay attacks on CAN networks, and propose an optimal Mixed Integer Linear Programming (MILP) formulation for solving the mapping problem from a functional model to the CAN-based platform while meeting both the security and the safety requirements. We also develop an efficient heuristic for the mapping problem under security and safety constraints. To the best of our knowledge, this is the first work to address security and safety in an integrated formulation in the design automation of automotive electronic systems. Experimental results of an industrial case study show the effectiveness of our approach.


title: Instantaneous centre of rotation estimation of an omnidirectional mobile robot

abstract: Redundantly actuated mobile robots using conventional wheels need a precise coordination of their actuators in order to guarantee a safe and precise motion without generating high internal forces and slippage. Using the instantaneous centre of rotation (ICR) of the chassis to describe this motion is a well established method. But the ICR is a mathematical concept which is hardly achieved on a real robot. This paper addresses the problem of ICR estimation of a non-holonomic omnidirectional mobile robot using conventional wheels. Instead of estimating the ICR in the working space, our approach estimates it in the actuators' space. The algorithm is presented in its general form and then adapted for a particular robot. The use of the algorithm with other omnidirectional robots is also discussed. Results from extensive testing done in simulation as well as with a real robot are presented, demonstrating the effectiveness of the proposed method.


title: Efficient pattern matching algorithms in IDS

abstract: An Intrusion Detection System (IDS) is responsible for handling the detection of malicious activities. Various intrusion detection technologies are widely used. In those technologies, the pattern matching based on knowledge is the most popular technology. It compares a series of attack characteristic with user's activity that has already been discovered, and then intrusion will be detected. Its match speed affects the rate of drop packet, and false alarm, the accuracy and the real-time performance of system in a direct way. However, the intrusion detection technology relies heavily on pattern matching algorithms, as the choice of pattern matching algorithms directly affect the detection rate, so it is more important. This study is about evolution of four pattern matching algorithms namely KMP Algorithm, BM Algorithm, Enhanced BM Algorithm, One Enhanced BM Algorithm and analyzing the efficiency of algorithms using various kinds of patterns.


title: Instance Selection Techniques for Memory-Based Collaborative Filtering

abstract: Collaborative filtering (CF) has become an important data mining technique to make personalized recommendations for books, web pages or movies, etc. One popular algorithm is the memory-based collaborative filtering, which predicts a user’s preference based on his or her similarity to other users (instances) in the database. However, the tremendous growth of users and the large number of products, memory-based CF algorithms results in the problem of deciding the right instances to use during prediction, in order to reduce executive cost and excessive storage, and possibly to improve the generalization accuracy by avoiding noise and overfitting. In this paper, we focus our work on a typical user preference database that contains many missing values, and propose four novel instance reduction techniques called TURF1-TURF4 as a preprocessing step to improve the efficiency and accuracy of the memory-based CF algorithm. The key idea is to generate prediction from a carefully selected set of relevant instances. We evaluate the techniques on the well-known EachMovie data set. Our experiments showed that the proposed algorithms not just dramatically speed up the prediction, but also improved the accuracy.


title: A taxonomy of ETL activities

abstract: Extract-Transform-Load (ETL) activities are software modules responsible for populating a data warehouse with operational data, which have undergone a series of transformations on their way to the warehouse. The whole process is very complex and of signifi-cant importance for the design and maintenance of the data ware-house. A plethora of commercial ETL tools are already available in the market. However, each one of them follows a different ap-proach for the modeling of ETL activities; i.e., of the building blocks of an ETL workflow. As a result, so far there is no standard or unified approach for describing such activities. In this paper, we are working towards the identification of generic properties that characterize ETL activities. In doing so, we follow a black-box approach and provide a taxonomy that characterizes ETL activities in terms of the relationship of their input to their output and provide a normal form that is based on interpreted semantics for the black box activities. Finally, we show how the proposed taxonomy can be used in the construction of larger modules, i.e., ETL archetype patterns, which can be used for the composition and optimization of ETL workflows.


title: Wavelet-based video coding using adaptive overlapped block motion compensation

abstract: In this paper, we propose a video coding scheme that combines adaptive overlapped block motion compensation (AOBMC) with wavelet-based zerotree coding techniques. AOBMC combines two different motion models: conventional block motion compensation (CBMC) and OBMC. Each model has the respective superiority in some case of motion compensation. This adaptive block-wise motion compensation between both models is used to get a smooth motion field. Smoothness properties of the motion field are employed to reduce temporal redundancies in the image sequence, offering substantial improvements in wavelet-based video coding quality over a variety of bit rates. For exploiting the spatial redundancies in the wavelet subband structure, we introduce a novel technique of zerotree quantization coding based on the human vision system. Our simulation results show that the proposed hybrid wavelet motion compensation video coding scheme provides a performance gain of 0.8-1.2 dB PSNR when compares to the standard H.263 test model TMN-5.


An ef fi cient and effective convolutional auto-encoder extreme learning machine network for 3 d feature learning


title: Hormonal Effect on Mandarin Orange (Citrus reticulata Blanco) Micro-propagation

abstract: Tissue culture is the best option to produce disease free seedling of the fruit crop rapidly. Micro-propagation and use of the in-vitro grafting (micro-grafting) is very helpful for production of virus free planting materials in mandarin. Different levels of the in-vitro hormone affect the success of callusing, shooting and plant regeneration in mandarin. Shoot bud, flower bud and in-vitro seedling epicotyl was used as explants to study the hormonal effect on mandarin micro-propagation. Similarly, 10 levels of BAP and IAA combination on MS media for mandarin tissue culture were used. Observation was done for 100 test tubes per treatment combination after 4, 8 and 12 weeks of culture. Data was arc sine transformed for analysis. Shooting from explants was significantly higher (71.72%) on medium level of the BAP (0.5 mg/L) and IAA (0.2 mg/L) using in-vitro seedling stem as explant, however, it was 27.91% for stem bud as explant. Stem bud showed higher level of callusing (6.15%, p<0.001) in mandarin orange. However, flower bud didn’t develop shoot in mandarin tissue culture. Increment of the in-vitro regeneration of the shooting and callusing was observed by the increment of the in-vitro incubation duration in mandarin orange tissue culture.


title: Orthographic Disambiguation Incorporating Transliterated Probability

abstract: Orthographic variance is a fundamental problem for many natural language processing applications. The Japanese language, in particular, contains many orthographic variants for two main reasons: (1) transliterated words allow many possible spelling variations, and (2) many characters in Japanese nouns can be omitted or substituted. Previous studies have mainly focused on the former problem; in contrast, this study has addressed both problems using the same framework. First, we automatically collected both positive examples (sets of equivalent term pairs) and negative examples (sets of inequivalent term pairs). Then, by using both sets of examples, a support vector machine based classifier determined whether two terms (t1 and t2) were equivalent. To boost accuracy, we added a transliterated probability P (t1|s)P (t2|s), which is the probability that both terms (t1 and t2) were transliterated from the same source term (s), to the machine learning features. Experimental results yielded high levels of accuracy, demonstrating the feasibility of the proposed approach.


title: An interleaved high step-up DC-DC converter with reduced voltage stress across semiconductors

abstract: The high voltage gain converter is widely employed in renewable energy conversions such as photovoltaic and fuel cell power generation systems. An interleaved high step-up DC-DC converter with coupled inductor and voltage multiplier cell is proposed in this paper. The voltage multiplier cell is composed of two diodes, two capacitors and two coupled inductors. During the switch-on interval in each phase, the corresponding capacitors are charged in parallel by the coupled inductors. Similarly, during the switch-off interval in the same phase, the corresponding capacitors are discharged in series to pump their energy to the load. In this way the proposed converter can achieve high voltage conversion ratio. Other features of the proposed converter are low voltage stress across the main switches due to clamp capacitors, lower voltage stress across diodes compared to the conventional converters, low input current ripple due to interleaved structure, and alleviation of the diodes reverse recovery problems. Switches with lower RDS-ON can be used that decreases conduction losses. The principle operation of the proposed converter is given by detailed mathematical analysis. Performance of the proposed converter is validated by simulation results in PSCAD-EMTDC.


title: Survey on Energy Consumption Entities on the Smartphone Platform

abstract: The full degree of freedom in mobile systems heavily depends on the energy provided by the mobile phone's batteries. Their capacity is in general limited and for sure not keeping pace as the mobile devices are crammed up with new functionalities. The discrepancy of Moore's law, offering twice the processing power at least each second year, and the development in batteries, which did not even double over the last decade, makes a shift in researchers' way of designing networks, protocols, and the mobile device itself. The bottleneck to take care of in the design process of mobile systems is not only the wireless data rate, but even more the energy limitation as the customers ask for new energy-hungry services, e.g., requiring faster connections or even multiple air interfaces, and longer standby or operational times of their mobile devices at the same time. In this survey, the energy consuming entities of a mobile device such as wireless air interfaces, display, mp3 player and others are measured and compared. The presented measurement results allow the reader to understand what the energy hungry parts of a mobile device are and use those findings for the design of future mobile protocols and applications. All results presented in this work and further results are made public on our web page [2].


title: Sparse Coding on Symmetric Positive Definite Manifolds Using Bregman Divergences

abstract: This paper introduces sparse coding and dictionary learning for symmetric positive definite (SPD) matrices, which are often used in machine learning, computer vision, and related areas. Unlike traditional sparse coding schemes that work in vector spaces, in this paper, we discuss how SPD matrices can be described by sparse combination of dictionary atoms, where the atoms are also SPD matrices. We propose to seek sparse coding by embedding the space of SPD matrices into the Hilbert spaces through two types of the Bregman matrix divergences. This not only leads to an efficient way of performing sparse coding but also an online and iterative scheme for dictionary learning. We apply the proposed methods to several computer vision tasks where images are represented by region covariance matrices. Our proposed algorithms outperform state-of-the-art methods on a wide range of classification tasks, including face recognition, action recognition, material classification, and texture categorization.


title: Electronic health records implementation: An evaluation of information system impact and contingency factors

abstract: Abstract Objective This paper provides a review of EHR (electronic health record) implementations around the world and reports on findings including benefits and issues associated with EHR implementation. Materials and methods A systematic literature review was conducted from peer-reviewed scholarly journal publications from the last 10 years (2001–2011). The search was conducted using various publication collections including: Scopus, Embase, Informit, Medline, Proquest Health and Medical Complete. This paper reports on our analysis of previous empirical studies of EHR implementations. We analysed data based on an extension of DeLone and McLean's information system (IS) evaluation framework. The extended framework integrates DeLone and McLean's dimensions, including information quality, system quality, service quality, intention of use and usage, user satisfaction and net benefits, together with contingent dimensions, including systems development, implementation attributes and organisational aspects, as identified by Van der Meijden and colleagues. Results A mix of evidence-based positive and negative impacts of EHR was found across different evaluation dimensions. In addition, a number of contingent factors were found to contribute to successful implementation of EHR. Limitations This review does not include white papers or industry surveys, non-English papers, or those published outside the review time period. Conclusion This review confirms the potential of this technology to aid patient care and clinical documentation; for example, in improved documentation quality, increased administration efficiency, as well as better quality, safety and coordination of care. Common negative impacts include changes to workflow and work disruption. Mixed observations were found on EHR quality, adoption and satisfaction. The review warns future implementers of EHR to carefully undertake the technology implementation exercise. The review also informs healthcare providers of contingent factors that potentially affect EHR development and implementation in an organisational setting. Our findings suggest a lack of socio-technical connectives between the clinician, the patient and the technology in developing and implementing EHR and future developments in patient-accessible EHR. In addition, a synthesis of DeLone and McLean's framework and Van der Meijden and colleagues' contingent factors has been found useful in comprehensively understanding and evaluating EHR implementations.


An Artificial Bee Colony Optimization based Channel Estimation Design for MIMO-OFDM Systems


title: Unsupervised cross-lingual knowledge transfer in DNN-based LVCSR

abstract: We investigate the use of cross-lingual acoustic data to initialise deep neural network (DNN) acoustic models by means of unsupervised restricted Boltzmann machine (RBM) pre-training. DNNs for German are pretrained using one or all of German, Portuguese, Spanish and Swedish. The DNNs are used in a tandem configuration, where the network outputs are used as features for a hidden Markov model (HMM) whose emission densities are modeled by Gaussian mixture models (GMMs), as well as in a hybrid configuration, where the network outputs are used as the HMM state likelihoods. The experiments show that unsupervised pretraining is more crucial for the hybrid setups, particularly with limited amounts of transcribed training data. More importantly, unsupervised pretraining is shown to be language-independent.


title: GenProg: A Generic Method for Automatic Software Repair

abstract: This paper describes GenProg, an automated method for repairing defects in off-the-shelf, legacy programs without formal specifications, program annotations, or special coding practices. GenProg uses an extended form of genetic programming to evolve a program variant that retains required functionality but is not susceptible to a given defect, using existing test suites to encode both the defect and required functionality. Structural differencing algorithms and delta debugging reduce the difference between this variant and the original program to a minimal repair. We describe the algorithm and report experimental results of its success on 16 programs totaling 1.25 M lines of C code and 120K lines of module code, spanning eight classes of defects, in 357 seconds, on average. We analyze the generated repairs qualitatively and quantitatively to demonstrate that the process efficiently produces evolved programs that repair the defect, are not fragile input memorizations, and do not lead to serious degradation in functionality.


Data rate User Data rate Spectrum Efficiency Connection Density Latency Mobility Power Efficiency Traffic Density Reliability Machine-to-Machine Critical


title: A Survey of Model Comparison Approaches and Applications

abstract: This survey paper presents the current state of model comparison as it applies to Model-Driven Engineering. We look specifically at how model matching is accomplished, the application of the approaches, and the types of models that approaches are intended to work with. Our paper also indicates future trends and directions. We find that many of the latest model comparison techniques are geared towards facilitating arbitrary meta models and use similarity-based matching. Thus far, model versioning is the most prevalent application of model comparison. Recently, however, work on comparison for versioning has begun to stagnate, giving way to other applications. Lastly, there is wide variance among the tools in the amount of user effort required to perform model comparison, as some require more effort to facilitate more generality and expressive power.


title: Synthesis and release behavior of bactericides intercalated Mg-Al layered double hydroxides

abstract: In this paper, two charge-neutral and poorly water-soluble bactericides (BC), hexaconazole and triadimenol, were first encapsulated in micelles derived from anionic surfactant, calcium dodecylbenzenesulfonate (DBS), and then were successfully intercalated into the gallery of Mg–Al layered double hydroxides (LDHs) by using ion exchange, coprecipitation and reconstruction methods, respectively, to obtain BC–LDHs nanohybrids. The loading amounts of hexaconazole-LDHs nanohybrids are obviously higher than those of triadimenol-LDHs nanohybrids. The release kinetics of bactericides from the nanohybrids was investigated. It was found that the bactericide release kinetic processes of the nanohybrids can be described with pseudo-second-order model. The initial release rates and equilibrium percent releases of the nanohybrids are obviously dependent of synthesis methods. The nanohybrids can well control the release of bactericides, showing they are a potential pesticide controlled-release formulation.


title: Learning 6-DOF Grasping Interaction via Deep Geometry-Aware 3D Representations

abstract: This paper focuses on the problem of learning 6- DOF grasping with a parallel jaw gripper in simulation. Our key idea is constraining and regularizing grasping interaction learning through 3D geometry prediction. We introduce a deep geometry-aware grasping network (DGGN) that decomposes the learning into two steps. First, we learn to build mental geometry-aware representation by reconstructing the scene (i.e., 3D occupancy grid) from RGBD input via generative 3D shape modeling. Second, we learn to predict grasping outcome with its internal geometry-aware representation. The learned outcome prediction model is used to sequentially propose grasping solutions via analysis-by-synthesis optimization. Our contributions are fourfold: (1) To best of our knowledge, we are presenting for the first time a method to learn a 6-DOF grasping net from RGBD input; (2) We build a grasping dataset from demonstrations in virtual reality with rich sensory and interaction annotations. This dataset includes 101 everyday objects spread across 7 categories, additionally, we propose a data augmentation strategy for effective learning; (3) We demonstrate that the learned geometry-aware representation leads to about 10% relative performance improvement over the baseline CNN on grasping objects from our dataset. (4) We further demonstrate that the model generalizes to novel viewpoints and object instances.


title: Review: The integration of mobile (tele) robotics and wireless sensor networks: A survey

abstract: Researchers have been extensively, but often independently, investigating Wireless Sensor Networks (WSNs), Mobile Robotics, and Teleoperation. Lately, due to the complementary nature of these areas, there is a growing trend in integrating them to support various applications that can go beyond passive monitoring and allow us to actually interact with the environment through autonomous and teleoperated robots. Such applications can extensively be used in medicine, science, military, industry, nuclear power stations, underwater, and space explorations. In this paper, we start with a review of the history of WSNs, robotics and mobility in WSNs, and teleoperation. We then introduce a system model integrating these areas as Wireless Sensor and Robot Networks with Teleoperation capabilities (WSRNT). Using this model, we define the problems associated with WSRNT. Finally, we discuss the recent research efforts and open research challenges in merging these fields with respect to major WSRNT tasks and communication requirements.


title: Hierarchical Bayesian Data Fusion for Robotic Platform Navigation

abstract: Data fusion has become an active research topic in recent years. Growing computational performance has allowed the use of redundant sensors to measure a single phenomenon. While Bayesian fusion approaches are common in general applications, the computer vision field has largely relegated this approach. Most object following algorithms have gone towards pure machine learning fusion techniques that tend to lack flexibility. Consequently, a more general data fusion scheme is needed. Within this work, a hierarchical Bayesian fusion approach is proposed, which outperforms individual trackers by using redundant measurements. The adaptive framework is achieved by relying on each measurement's local statistics and a global softened majority voting. The proposed approach was validated in a simulated application and two robotic platforms.


title: Winding Machine for Automated Production of an Innovative Air-Gap Winding for Lightweight Electric Machines

abstract: This paper presents a newly developed winding machine, which enables an automated production of stator-mounted air-gap windings with meander structure. This structure has very high accuracy requirements. Therefore, automation is realized by the interaction of 15 actuators and a compound construction with 13 degrees of freedom. The programming works with discrete open-loop motion control to generate the kinematics. Above all, a flexible prototype of the winding machine is developed, manufactured, and tested for a motor with external rotor. Finally, experimental results of the developed automation for air-gap windings with meander structure are presented.


title: P&O MPPT implementation using MATLAB/Simulink

abstract: In this paper, a mathematical analysis of a photovoltaic cell is presented for the single diode and double diode cell configurations. The model of the double diode representation was implemented using a proprietary algorithm and MATLAB/Simulink. To track the point of maximum power, the perturb and observe (P&O) algorithm method was implemented. It was shown that the point of maximum power is always located at the expected position, is readily tracked, and maintained by minute increases and decreases in voltage. These encouraging results indicate opportunities for the utilization of this algorithm and its hardware implementation.


title: Multinomial logistic regression modelling of obesity and overweight among primary school students in a rural area of Negeri Sembilan

abstract: Multinomial logistic regression is widely used to model the outcomes of a polytomous response variable, a categorical dependent variable with more than two categories. The model assumes that the conditional mean of the dependent categorical variables is the logistic function of an affine combination of predictor variables. Its procedure gives a number of logistic regression models that make specific comparisons of the response categories. When there are q categories of the response variable, the model consists of q-1 logit equations which are fitted simultaneously. The model is validated by variable selection procedures, tests of regression coefficients, a significant test of the overall model, goodness-of-fit measures, and validation of predicted probabilities using odds ratio. This study used the multinomial logistic regression model to investigate obesity and overweight among primary school students in a rural area on the basis of their demographic profiles, lifestyles and on the diet and food intake. ...


Cyber attack prediction using social data analysis


Accurate and efficient stereo processing by semi-global matching and mutual information


An Approach To Mode and Anomaly Detection with Spacecraft Telemetry Data


title: Battery Charger for Electric Vehicle Traction Battery Switch Station

abstract: This paper presents the functionality of a commercialized fast charger for a lithium-ion electric vehicle propulsion battery. The device is intended to operate in a battery switch station, allowing an up-to 1-h recharge of a 25-kWh depleted battery, removed from a vehicle. The charger is designed as a dual-stage-controlled ac/dc converter. The input stage consists of a three-phase full-bridge diode rectifier combined with a reduced rating shunt active power filter. The input stage creates an uncontrolled pulsating dc bus while complying with the grid codes by regulating the total harmonic distortion and power factor according to the predetermined permissible limits. The output stage is formed by six interleaved groups of two parallel dc-dc converters, fed by the uncontrolled dc bus and performing the battery charging process. The charger is capable of operating in any of the three typical charging modes: constant current, constant voltage, and constant power. Extended simulation and experimental results are shown to demonstrate the functionality of the device.


title: Mop: an efficient and generic runtime verification framework

abstract: Monitoring-Oriented Programming (MOP1) [21, 18, 22, 19] is a formal framework for software development and analysis, in which the developer specifies desired properties using definable specification formalisms, along with code to execute when properties are violated or validated. The MOP framework automatically generates monitors from the specified properties and then integrates them together with the user-defined code into the original system. The previous design of MOP only allowed specifications without parameters, so it could not be used to state and monitor safety properties referring to two or more related objects. In this paper we propose a parametric specification formalism-independent extension of MOP, together with an implementation of JavaMOP that supports parameters. In our current implementation, parametric specifications are translated into AspectJ code and then weaved into the application using off-the-shelf AspectJ compilers; hence, MOP specifications can be seen as formal or logical aspects. Our JavaMOP implementation was extensively evaluated on two benchmarks, Dacapo [14] and Tracematches [8], showing that runtime verification in general and MOP in particular are feasible. In some of the examples, millions of monitor instances are generated, each observing a set of related objects. To keep the runtime overhead of monitoring and event observation low, we devised and implemented a decentralized indexing optimization. Less than 8% of the experiments showed more than 10% runtime overhead; in most cases our tool generates monitoring code as efficient as the hand-optimized code. Despite its genericity, JavaMOP is empirically shown to be more efficient than runtime verification systems specialized and optimized for particular specification formalisms. Many property violations were detected during our experiments; some of them are benign, others indicate defects in programs. Many of these are subtle and hard to find by ordinary testing.


title: Controlling Social Dynamics with a Parametrized Model of Floor Regulation

abstract: Turn-taking is ubiquitous in human communication, yet turn-taking between humans and robots continues to be stilted and awkward for human users. The goal of our work is to build autonomous robot controllers for successfully engaging in human-like turn-taking interactions. Towards this end, we present CADENCE, a novel computational model and architecture that explicitly reasons about the four components of floor regulation: seizing the floor, yielding the floor, holding the floor, and auditing the owner of the floor. The model is parametrized to enable the robot to achieve a range of social dynamics for the human-robot dyad. In a between-groups experiment with 30 participants, our humanoid robot uses this turn-taking system at two contrasting parametrizations to engage users in autonomous object play interactions. Our results from the study show that: (1) manipulating these turn-taking parameters results in significantly different robot behavior; (2) people perceive the robot's behavioral differences and consequently attribute different personalities to the robot; and (3) changing the robot's personality results in different behavior from the human, manipulating the social dynamics of the dyad. We discuss the implications of this work for various contextual applications as well as the key limitations of the system to be addressed in future work.


title: Nonradioactive, digoxigenin-labelled DNA probes for the detection of five RNA species present in beet necrotic yellow vein virus.

abstract: Complementary DNA (cDNA) clones corresponding to each of five distinct RNA species of beet necrotic yellow vein (BNYVV) were synthesized and identified. The sizes of each cDNA clone for RNAs 1,2,3,4 and 5 molecules were 3.0, 1.7, 1.8, 1.5 and 1.4 kbp, respectively. cDNA inserts to RNA 2 were covered at a part of the 3'regions, and those of RNAs 3,4 and 5 were almost full-length. The plasmids containing each of cDNA inserts were labeled with digoxigenin by the random priming method. Northern blot hybridization tests showed that individual probes hybridized specially to each of the five RNAs. Good results were obtained with 1 to 10 ng of RNA as a mixture of five RANs, but the probe to RNA 3, RNA 4 or RNA 5 gave a weak signal with hererologous RNAs when more than 10 ng RNA was used. In dot blot hybridization, the limit of detection was about 10 pg RNA, but if a higher content of RNA was spotted, cross reaction occurred using heterologous RNAs. For laboratory and field isolates of BNYVV, each of RNAs 3,4 and 5 was easily detected by Northern blot hybridization in total nucleic acids extracted from Tetragonia expansa leaves inoculated mechanically, but not from roots of sugar-beet plants inoculated by the fungus Polymyxa betae. However, satisfactory results were obtained with partially purified or concentrated preparations from roots. These findings indicate that the digoxigenin-labeled probes are useful for the identification and detection of RNAs contained in field and laboratory isolates of BNYVV.


title: Standalone Hybrid Wind-Solar Power Generation System Applying Dump Power Control Without Dump Load

abstract: This paper proposes a unique standalone hybrid power generation system, applying advanced power control techniques, fed by four power sources: wind power, solar power, storage battery, and diesel engine generator, and which is not connected to a commercial power system. Considerable effort was put into the development of active-reactive power and dump power controls. The result of laboratory experiments revealed that amplitudes and phases of ac output voltage were well regulated in the proposed hybrid system. Different power sources can be interconnected anywhere on the same power line, leading to flexible system expansion. It is anticipated that this hybrid power generation system, into which natural energy is incorporated, will contribute to global environmental protection on isolated islands and in rural locations without any dependence on commercial power systems.


Sapiens Digital : From Digital Immigrants and Digital Natives to Digital Wisdom


title: Stress and Memory: Behavioral Effects and Neurobiological Mechanisms

abstract: Stress is a potent modulator of learning and memory processes. Although there have been a few attempts in the literature to explain the diversity of effects (including facilitating, impairing, and lack of effects) described for the impact of stress on memory function according to single classification criterion, they have proved insufficient to explain the whole complexity of effects. Here, we review the literature in the field of stress and memory interactions according to five selected classifying factors (source of stress, stressor duration, stressor intensity, stressor timing with regard to memory phase, and learning type) in an attempt to develop an integrative model to understand how stress affects memory function. Summarizing on those conditions in which there was enough information, we conclude that high stress levels, whether intrinsic (triggered by the cognitive challenge) or extrinsic (induced by conditions completely unrelated to the cognitive task), tend to facilitate Pavlovian conditioning (in a linear-asymptotic manner), while being deleterious for spatial/explicit information processing (which with regard to intrinsic stress levels follows an inverted U-shape effect). Moreover, after reviewing the literature, we conclude that all selected factors are essential to develop an integrative model that defines the outcome of stress effects in memory processes. In parallel, we provide a brief review of the main neurobiological mechanisms proposed to account for the different effects of stress in memory function. Glucocorticoids were found as a common mediating mechanism for both the facilitating and impairing actions of stress in different memory processes and phases. Among the brain regions implicated, the hippocampus, amygdala, and prefrontal cortex were highlighted as critical for the mediation of stress effects.


title: Application of decision-making techniques in supplier selection: A systematic review of literature

abstract: Despite the importance of decision-making (DM) techniques for construction of effective decision models for supplier selection, there is a lack of a systematic literature review for it. This paper provides a systematic literature review on articles published from 2008 to 2012 on the application of DM techniques for supplier selection. By using a methodological decision analysis in four aspects including decision problems, decision makers, decision environments, and decision approaches, we finally selected and reviewed 123 journal articles. To examine the research trend on uncertain supplier selection, these articles are roughly classified into seven categories according to different uncertainties. Under such classification framework, 26 DM techniques are identified from three perspectives: (1) Multicriteria decision making (MCDM) techniques, (2) Mathematical programming (MP) techniques, and (3) Artificial intelligence (AI) techniques. We reviewed each of the 26 techniques and analyzed the means of integrating these techniques for supplier selection. Our survey provides the recommendation for future research and facilitates knowledge accumulation and creation concerning the application of DM techniques in supplier selection.


title: Processing tough supramolecular hydrogels with tunable strength of polyion complex

abstract: Abstract The rapid developments of tough hydrogels have promoted the applications of this kind of soft materials in loading bearing systems. However, tough gels usually have poor processibility, which is crucial for the construction of complex structures with gels. In this paper, we demonstrate the processing of tough polyion complex (PIC) hydrogels by simple compression moulding or extruding, which relies on the distinct strength of ionic bonds during and after the processing. The PIC precipitates formed by mixing the solutions of polycation and polyanion, poly(3-(methacryloylamino)propyl-trimethylammonium chloride) and poly(sodium p-styrenesulfonate), are plasticized by saline solution with selected concentration. These raw materials, initially weak and viscoelastic, are easily processed into different shapes. After swelling the as-prepared gels in water to dialyze out the salt and counterions of PIC, tough equilibrated gels are obtained with the imposed shapes during processing. These gels with 50–70 wt% water show good mechanical properties, with tensile fracture stress, fracture strain and tearing fracture energy being 3.7 MPa, 700% and 8 × 10 3 J/m 2 , respectively. These mechanical properties can be effectively tailored by tuning the charge ratio of PIC. However, the concentration of saline water has negligible influence on the mechanical properties of final equilibrated gels. The resultant PIC gels also exhibit stimuli-triggered healing properties owing to the dynamic and reversible nature of the ionic bonds. This study should promote the applications of tough PIC gels in structural elements of soft actuators and synthetic tissues.


title: Rule-Based Problem Classification in IT Service Management

abstract: Problem management is a critical and expensive element for delivering IT service management and touches various levels of managed IT infrastructure. While problem management has been mostly reactive, recent work is studying how to leverage large problem ticket information from similar IT infrastructures to probatively predict the onset of problems. Because of the sheer size and complexity of problem tickets, supervised learning algorithms have been the method of choice for problem ticket classification, relying on labeled (or pre-classified) tickets from one managed infrastructure to automatically create signatures for similar infrastructures. However, where there are insufficient preclassified data, leveraging human expertise to develop classification rules can be more efficient. In this paper, we describe a rule-based crowdsourcing approach, where experts can author classification rules and a social networkingbased platform (called xPad) is used to socialize and execute these rules by large practitioner communities. Using real data sets from several large IT delivery centers, we demonstrate that this approach balances between two key criteria: accuracy and cost effectiveness.


title: IRIS: Tapping wearable sensing to capture in-store retail insights on shoppers

abstract: We investigate the possibility of using a combination of a smartphone and a smartwatch, carried by a shopper, to get insights into the shopper's behavior inside a retail store. The proposed IRIS framework uses standard locomotive and gestural micro-activities as building blocks to define novel composite features that help classify different facets of a shopper's interaction/experience with individual items, as well as attributes of the overall shopping episode or the store. Besides defining such novel features, IRIS builds a novel segmentation algorithm, which partitions the duration of an entire shopping episode into atomic item-level interactions, by using a combination of feature-based landmarking, change point detection and variable-order HMM-based sequence prediction. Experiments with 50 real-life grocery shopping episodes, collected from 25 shoppers, we show that IRIS can demarcate item-level interactions with an accuracy of approx. 91%, and subsequently characterize item-and-episode level shopper behavior with accuracies of over 90%.


How Large U.S. Companies Can Use Twitter and Other Social Media to Gain Business Value


title: New Conceptualizations of Practice: Common Principles in Three Paradigms Suggest New Concepts for Training:

abstract: We argue herein that typical training procedures are far from optimal. The goat of training in real-world settings is, or should be, to support two aspects of posttraining performance: (a) the level of performance in the long term and (b) the capability to transfer that training to related tasks and altered contexts. The implicit or explicit assumption of those persons responsible for training is that the procedures that enhance performance and speed improvement during training will necessarily achieve these two goals. However, a variety of experiments on motor and verbal learning indicate that this assumption is often incorrect. Manipulations that maximize performance during training can be detrimental in the long term; conversely, manipulations that degrade the speed of acquisition can support the long-term goals of training. The fact that there are parallel findings in the motor and verbal domains suggests that principles of considerable generality can be deduced to upgrade training procedures.


title: Analysis of E-commerce security protocols SSL and SET

abstract: Today is the era of information technology. E-commerce is the major achievement of this era. In E-commerce, the transaction takes place over the network. During various phases of an electronic transaction, the information such as product specification, order details, payment, and delivery information travels over the comparison of the two most popular E-commerce transactions secure protocols SSL and SET.  Things involved such as tangible and intangible good and services. Also, it enhances the processes of the business and changes the way it delivers services to clients. In contrast, Ecommerce comes also with some problems such as authentication and identification.


title: Experimental study of heat transfer enhancement in a liquid piston compressor/expander using porous media inserts

abstract: The efficiency and power density of gas compression and expansion are strongly dependent on heat transfer during the process. Since porous media inserts can significantly increase heat transfer surface area, their addition to a liquid piston compressor/expander has been hypothesized to reduce the time to complete the compression or expansion process and hence the power density for a given thermodynamic efficiency; or to increase the thermodynamic efficiency at a fixed power density. This paper presents an experimental investigation on heat transfer with porous inserts during compression for a pressure ratio of 10 and during expansion for a pressure ratio of 6. A baseline case without inserts and five cases with different porous inserts are tested in a compression experiment: 3 interrupted ABS inserts with plate spacing of 2.5, 5, and 10mm and 2 aluminum foam inserts sized with 10 and 40 pores per inch. The 2.5mm and 5mm interrupted plate inserts were also tested in expansion experiments. Porous inserts are found, in compression, to increase power-density by 39-fold at 95% efficiency and to increase efficiency by 18% at 100kW/m3 power density; in expansion, power density is increased three fold at 89% efficiency, and efficiency is increased by 7% at 150kW/m3. Surface area increase is found to be the predominant cause in the improvement in performance. Thus, a liquid piston compressor/expander together with a porous medium may be used in applications requiring high compression ratios, high efficiencies, and high power density such as in an open-accumulator compressed air energy storage (CAES) system or a compressor for compressed natural gas (CNG).


title: Political Polarization on Twitter

abstract: In this study we investigate how social media shape the networked public sphere and facilitate communication between communities with different political orientations. We examine two networks of political communication on Twitter, comprised of more than 250,000 tweets from the six weeks leading up to the 2010 U.S. congressional midterm elections. Using a combination of network clustering algorithms and manually-annotated data we demonstrate that the network of political retweets exhibits a highly segregated partisan structure, with extremely limited connectivity between left- and right-leaning users. Surprisingly this is not the case for the user-to-user mention network, which is dominated by a single politically heterogeneous cluster of users in which ideologically-opposed individuals interact at a much higher rate compared to the network of retweets. To explain the distinct topologies of the retweet and mention networks we conjecture that politically motivated individuals provoke interaction by injecting partisan content into information streams whose primary audience consists of ideologically-opposed users. We conclude with statistical evidence in support of this hypothesis.


title: Inverted E-Shaped Wearable Textile Antenna for Medical Applications

abstract: A miniaturized textile antenna is designed and demonstrated for ISM band applications at 2.4 GHz. The proposed antenna uses appropriate loading of a rectangular slot/notch with a strip line inserted to form an inverted E-shaped antenna. The structure is simple, compact, and easy to manufacture using only fabric material. The antenna size is 75% smaller than a conventional antenna. When the antenna is subjected to bending, its performance proves sustainable under deformation. Each slot/notch and strip line is translated to its equivalent circuit and then integrated to form the whole equivalent circuit of the proposed antenna. The results from the equivalent circuits and simulation show acceptable agreement. The antenna shows an improvement of size miniaturization of $30 \times 20 \times 0.7$ mm3 and demonstrates an impedance bandwidth of 15% and efficiency of 79%, showing that it is a promising candidate for incorporation into wearable systems.


title: Light-Weight Security and Data Provenance for Multi-Hop Internet of Things

abstract: Due to the limited resources and scalability, the security protocols for the Internet of Things (IoT) need to be light-weighted. The cryptographic solutions are not feasible to apply on small and low-energy devices of IoT because of their energy and space limitations. In this paper, a light-weight protocol to secure the data and achieving data provenance is presented for the multi-hop IoT network. The Received Signal Strength Indicator (RSSI) of communicating IoT nodes are used to generate the link fingerprints. The link fingerprints are matched at the server to compute the correlation coefficient. Higher the value of correlation coefficient, higher the percentage of the secured data transfers. Lower value gives the detection of adversarial node in between a specific link. Data provenance has also been achieved by comparison of packet header with all the available link fingerprints at the server. The time complexity is computed at the node and server level, which is $O(1)$ . The energy dissipation is calculated for the IoT nodes and overall network. The results show that the energy consumption of the system presented in this paper is 52–53 mJ for each IoT node and 313.626 mJ for the entire network. The RSSI values are taken in real time from MICAz motes and simulations are performed on MATLAB for adversarial node detection, data provenance, and time-complexity. Experimental results show that up to 97% correlation is achieved when no adversarial node is present in the IoT network.


title: Cognitive Compositional Semantics using Continuation Dependencies

abstract: This paper describes a graphical semantic representation based on bottom-up ‘continuation’ dependencies which has the important property that its vertices define a usable set of discourse referents in working memory even in contexts involving conjunction in the scope of quantifiers. An evaluation on an existing quantifier scope disambiguation task shows that non-local continuation dependencies can be as reliably learned from annotated data as representations used in a state-of-the-art quantifier scope resolver, suggesting that continuation dependencies may provide a natural representation for scope information.


title: Rule-Based Control of Battery Energy Storage for Dispatching Intermittent Renewable Sources

abstract: Integrating a battery energy storage system (BESS) with a solar photovoltaic (PV) system or a wind farm can make these intermittent renewable energy sources more dispatchable. This paper focuses on the development of a control strategy for optimal use of the BESS for this purpose. The paper considers a rule-based control scheme, which is the solution of the optimal control problem defined, to incorporate the operating constraints of the BESS, such as state of charge limits, charge/discharge current limits, and lifetime. The goal of the control is to have the BESS provide as much smoothing as possible so that the renewable resource can be dispatched on an hourly basis based on the forecasted solar/wind conditions. The effectiveness of this control strategy has been tested by using an actual PV system and wind farm data and it is shown that the BESS can indeed help to cope with variability in wind's and solar's generation.


title: Adoption of Internet Banking in Sri Lanka: An Extension to Technology Acceptance Model

abstract: This study develops an extended model to predict customer adoption of Internet banking based on the Technology Acceptance Model (TAM) integrating with perceived risks, perceived web site features. In particular, drawing from the perceived risk construct, six specific risk facets; security, privacy, social, time, performance and financial risk synthesized with the construct perceived web site features which has two variables; perceived system quality and perceived information quality which are integrated with the technology acceptance model (TAM) variables; perceived usefulness and perceived ease of use to propose a theoretical model to predict customers’ adoption of Internet banking. An online questionnaire was designed and sent out to Internet banking users in the selected three local commercial banks in Sri Lanka. Respondents participated through extensive personalized e-mail invitations through the selected commercial banks. The extended model is then tested using the data collected and analyzed using multiple-regression. The results indicated that the adoption of Internet banking is positively affected by perceived usefulness, perceived security, perceived social facet, and perceived system quality and those variables were found to be the most influential factors explaining the adoption of Internet banking services. In addition, analysis has revealed that there is a moderated impact on the relationship between the independent variables and dependent variable through respondents’ age, income levels and working hours.


title: Gender differences in anxiety disorders: prevalence, course of illness, comorbidity and burden of illness.

abstract: Women have consistently higher prevalence rates of anxiety disorders, but less is known about how gender affects age of onset, chronicity, comorbidity, and burden of illness. Gender differences in DSM-IV anxiety disorders were examined in a large sample of adults (N ¼20,013) in the United States using data from the Collaborative Psychiatric Epidemiology Studies (CPES). The lifetime and 12-month male:female prevalence ratios of any anxiety disorder were 1:1.7 and 1:1.79, respectively. Women had higher rates of lifetime diagnosis for each of the anxiety disorders examined, except for social anxiety disorder which showed no gender difference in prevalence. No gender differences were observed in the age of onset and chronicity of the illness. However, women with a lifetime diagnosis of an anxiety disorder were more likely than men to also be diagnosed with another anxiety disorder, bulimia nervosa, and major depressive disorder. Furthermore, anxiety disorders were associated with a greater illness burden in women than in men, particularly among European American women and to some extend also among Hispanic women. These results suggest that anxiety disorders are not only more prevalent but also more disabling in women than in men.


Security and Privacy in Device-to-Device ( D 2 D ) Communication : A Review


title: 11-(3-Chloro-2-hydroxypropoxy)-2,3,9-trimethoxychromeno[3,4-b]chromen-12(6H)-one

abstract: In the title compound, C(22)H(21)ClO(8), the rotenoid core is nearly planar (r.m.s. deviation 0.114 Å), with the largest deviations from the least-squares plane being 0.286 (3) and 0.274 (2) Å. An inter-molecular O-H⋯O hydrogen bond links two mol-ecules into a centrosymmetric dimer having an R(2) (2)(18) ring motif.


title: Surface EMG classification using moving approximate entropy

abstract: Moving approximate entropy has been proposed as a new method to extract information from the surface electromyographic signal. Twenty subjects performed wrist flexion/extension, isometric contraction and co-contraction while electromyographic signals were recorded with surface electrodes. A moving data window of 200 values was applied to the data (moving approximate entropy). The results show that there is regularity in an EMG signal at the beginning and end of a muscle contraction with low regularity during the middle part.


title: Process‐centric business intelligence

abstract: Purpose – The purpose of this paper is to deliver an insight into the interaction effects of process‐oriented management and business intelligence (BI).Design/methodology/approach – The paper takes up publications from the fields of BI and business process management and analyzes the state‐of‐the‐art of process‐centric business intelligence (PCBI). To highlight the potentials and limitations of the concept, two exemplary use cases are presented and discussed in depth. Furthermore, a vision for the technical implementation is sketched.Findings – PCBI is found to play an important role in an organization's strive for competitiveness. The concept's potential benefits are significant. However, the overall levels of adoption and maturity of the concept within real‐world organizations appear to be rather low at the moment.Research limitations/implications – The paper discusses solely two exemplary use cases – the most that could be done within the scope of a journal publication. Therefore, the explanatory power...


title: Quorum Sensing Circuit and Reactive Oxygen Species Resistance in Deinococcus sp.

abstract: Genus Deinococcus is characterized by an increased resistance toward reactive oxygen species (ROS). The chromosome of five strains belonging to this genus has been sequenced and the presence of a luxS-like gene was deduced from their genome sequences. The aim of this study was to assess if a complete QS circuit is present in Deinococcus sp. and if this QS is associated with ROS. Primers for searching luxS-like gene and the putative receptor gene, namely ai2R, were designed. AI-2 signal production was evaluated by luminescence analysis using Vibrio harveyi BB170 as reporter strain. AI-2 signal was also evaluated by competitive assays using cinnamaldehyde, ascorbic acid, and 3-mercaptopropionic acid as interfering molecules. Potassium tellurite and metronidazole were used as oxidative stressors. A luxS-like gene as well as an ai2R gene was detected in strain UDEC-P1 by PCR. Cell-free supernatant of strain UDEC-P1 culture induced luminescence in V. harveyi BB170, and this property was inhibited with the three interfering molecules. The oxidative stressors metronidazole and potassium tellurite decreased Deinococcus sp. viability, but increased luminescence of the reporter strain. The results demonstrate that both a functional luxS-like gene and a putative receptor for AI-2 signal are present in Deinococcus sp. strain UDEC-P1. This finding also suggests that a complete QS circuit is present in this genus, which could be related to oxidative stress.


title: Electromagnetic Tracking in Medicine—A Review of Technology, Validation, and Applications

abstract: Object tracking is a key enabling technology in the context of computer-assisted medical interventions. Allowing the continuous localization of medical instruments and patient anatomy, it is a prerequisite for providing instrument guidance to subsurface anatomical structures. The only widely used technique that enables real-time tracking of small objects without line-of-sight restrictions is electromagnetic (EM) tracking. While EM tracking has been the subject of many research efforts, clinical applications have been slow to emerge. The aim of this review paper is therefore to provide insight into the future potential and limitations of EM tracking for medical use. We describe the basic working principles of EM tracking systems, list the main sources of error, and summarize the published studies on tracking accuracy, precision and robustness along with the corresponding validation protocols proposed. State-of-the-art approaches to error compensation are also reviewed in depth. Finally, an overview of the clinical applications addressed with EM tracking is given. Throughout the paper, we report not only on scientific progress, but also provide a review on commercial systems. Given the continuous debate on the applicability of EM tracking in medicine, this paper provides a timely overview of the state-of-the-art in the field.


title: Development of a poultice for electrochemical desalination of porous building materials: desalination effect and pH changes

abstract: Salt induced deterioration of structures and stones are generally recognized as a frequent and serious problem. The deterioration is especially undesired in relation to cultural heritage as it is impossible to recreate original material (e.g. original murals). By presence of salts in decorated vaults two different techniques are applied: poultices or establishment of climate chambers. Both techniques can result in ion transport away from the valuable surfaces with murals, but satisfying desalination has not been obtained according to conservators from the Danish National Museums mural preservation section in consistence with the present available literature. In the present paper the possibility for salt removal by utilizing a well known and accepted transport process, electromigration, is investigated, i.e. movement of ions in a solution in an applied electric DC field. An experimental laboratory setup was designed to approximate real conditions in vaults and with ion contents corresponding to normal heavily polluted church vaults (1.0 wt% Chloride, added as NaCl). During the electromigration process acid and base is produced at the electrodes due to electrode reactions and in a clarifying experiment with a traditional poultice significant pH changes and an absence of satisfying high desalination effect was measured. The new idea in the present paper was to introduce a calculated amount of buffer components corresponding to the productions during the electrode processes to a poultice (a solid) to minimize the adverse effects and to optimize on the effects. The results showed good ability to retain neutral pH values in the substrate which is of major importance when the method should be applied on existing structures. Also the desalination process continued until a very low and harmless salt content was reached after introduction of the buffer components.


title: Immune evasion in human papillomavirus-associated cervical cancer

abstract: Tumour-associated viruses produce antigens that, on the face of it, are ideal targets for immunotherapy. Unfortunately, these viruses are experts at avoiding or subverting the host immune response. Cervical-cancer-associated human papillomavirus (HPV) has a battery of immune-evasion mechanisms at its disposal that could confound attempts at HPV-directed immunotherapy. Other virally associated human cancers might prove similarly refractive to immuno-intervention unless we learn how to circumvent their strategies for immune evasion.


title: Modulation of light radiation during input into waveguide by resonance excitation of surface plasmons

abstract: The process of transfer of energy of a surface plasmon polariton between modes located on two surfaces of a metal layer is studied. It is shown that this process strongly depends on the refractive indices of dielectrics surrounding the metal layer. The possibilities of high-speed optical signal modulation, based on this phenomenon, are discussed.


title: A Curved-Drilling Approach in Core Decompression of the Femoral Head Osteonecrosis Using a Continuum Manipulator

abstract: In conventional core decompression of osteonecrosis, surgeons cannot successfully reach the whole area of the femoral head due to rigidity of the instruments currently used. To address this issue, we present design and fabrication of a novel steerable drill using a continuum dexterous manipulator (CDM) and two different flexible cutting tools passing through the lumen of the CDM. A set of experiments investigated functionality and efficiency of the curved-drilling approach and the flexible tools on simulated cancellous bone. Geometry of the cutter head, rotational and feed velocity of the tool, and pulling tension of the CDM cables have been identified as the effective curved-drilling parameters. Considering these parameters, we investigated drilling trajectory, contact force, and mass removal for various combinations of feed-velocities (0.05, 0.10, and 0.15 mm/s) and cable tensions (6, 10, 15, and 25 N) with constant rotational speed of 2250 r/min. Results show that: first, pulling tension of the cable is the most dominant parameter affecting the curved-drilling trajectory; and second, the proposed steerable drill is able to achieve 40° bend without buckling. Based on these results we developed a method for planning drill trajectories and successfully verified abilities for S-shape and multiple-branch drilling. The verification experiments were performed on both simulated and human cadaveric bones.


title: Automatic billing system using Li-Fi module

abstract: Large super markets have a great variety of goods and different supermarkets may have different distribution of commodity. Most of the customers find it difficult to stand in long queue for billing the purchased products. This causes waste of time and wrong billing for wrong customers. This project provides a great solution to all these problems. Most recently LIFI is new emerging technology in the trend. In this project data transfer is processed between products and the mobile phone. Each and every product is having LIFI transmitter and it store the encoded data similar to the product id, cost of product and quantity. Here the mobile is integrated with LIFI receiver via OTG communication in the shopping cart. It can read the commodities' information when the LIFI transmitter holding goods are chosen by the customers, each information of the goods can be entered by using the mobile LIFI and when the product is kept into the trolley, which also contains the LIFI module, double check the product identity. After completing the purchase, the payment is processed in mobile itself via mobile banking system. Finally the cart section will verify the payment and purchase of product which will again cross check the products by the trolley module when we come out of the exit section of the shopping center. If the product is mismatched at this stage it immediately alerts the owner. This technology is used in this project for finding out the information of the commodities.


title: Inertially stabilized platform technology Concepts and principles

abstract: We introduced some of the basic principles, techniques, and key design issues common to ISPs used in many diverse scientific, military, and commercial applications, and we touched on some of the less intuitive effects that must be dealt with. Many of these effects can influence the initial configuration of the system as well as design details later in the design process. The successful design of an ISP usually requires a multidisciplinary design team. The design of an ISP must often be closely coordinated with that of other major subsystems such as the primary sensor and the optics. The role of the systems engineer in the design process is perhaps the most critical because other members of the team may not be aware of the consequences of many of the effects discussed above. Inertially stabilized platforms (ISPs) are used to stabilize and point a broad array of sensors, cameras, telescopes, and weapon systems.


title: Battery storage systems in electric power systems

abstract: Energy storage has been the most challenging and complex issue of the industry whether it is the electric utilities or for industrial applications. The constant need for efficient energy storage has seen the emerging new technologies which promise reliability, productivity and the use of renewables. Energy storage can balance the fluctuations in supply and meet the ever growing demand of electricity. For short duration requirements battery storage can bring about frequency control and stability and for longer duration requirements they can bring about energy management or reserves. Storage can also be used to complement primary generation as they can be used to produce energy during off peak periods and this energy produced can be stored as reserve power as presented in the paper. Storage can play a multi-function role in the electric supply network to manage the resources effectively.


title: Low cost 24GHz patch array antenna for high sensitivity EM sensor

abstract: A low cost patch array antenna for high sensitivity electromagnetic (EM) sensor is presented. The operating frequency band of the antenna is 24.05∼24.25 GHz. Array structure is the symmetrical pattern by Chebyshev polynomial and the feed point is located in the middle of the array. Also, the gain of the array antenna can be increased by controlling the inclined angle of the side wings which are connected with the ground plane. It is proved through simulation and the measurement results that the operating frequency and the side-lobe level (SLL) are rarely changed when the inclined angle of the side wings is varied.


title: Holistic 3D Scene Parsing and Reconstruction from a Single RGB Image

abstract: We propose a computational framework to jointly parse a single RGB image and reconstruct a holistic 3D configuration composed by a set of CAD models using a stochastic grammar model. Specifically, we introduce a Holistic Scene Grammar (HSG) to represent the 3D scene structure, which characterizes a joint distribution over the functional and geometric space of indoor scenes. The proposed HSG captures three essential and often latent dimensions of the indoor scenes: i) latent human context, describing the affordance and the functionality of a room arrangement, ii) geometric constraints over the scene configurations, and iii) physical constraints that guarantee physically plausible parsing and reconstruction. We solve this joint parsing and reconstruction problem in an analysis-by-synthesis fashion, seeking to minimize the differences between the input image and the rendered images generated by our 3D representation, over the space of depth, surface normal, and object segmentation map. The optimal configuration, represented by a parse graph, is inferred using Markov chain Monte Carlo (MCMC), which efficiently traverses through the non-differentiable solution space, jointly optimizing object localization, 3D layout, and hidden human context. Experimental results demonstrate that the proposed algorithm improves the generalization ability and significantly outperforms prior methods on 3D layout estimation, 3D object detection, and holistic scene understanding.


title: Paying Attention to Attention: Highlighting Influential Samples in Sequential Analysis

abstract: In (Yang et al. 2016), a hierarchical attention network (HAN) is created for document classification. The attention layer can be used to visualize text influential in classifying the document, thereby explaining the model's prediction. We successfully applied HAN to a sequential analysis task in the form of real-time monitoring of turn taking in conversations. However, we discovered instances where the attention weights were uniform at the stopping point (indicating all turns were equivalently influential to the classifier), preventing meaningful visualization for real-time human review or classifier improvement. We observed that attention weights for turns fluctuated as the conversations progressed, indicating turns had varying influence based on conversation state. Leveraging this observation, we develop a method to create more informative real-time visuals (as confirmed by human reviewers) in cases of uniform attention weights using the changes in turn importance as a conversation progresses over time.


title: The use of telephone befriending in low level support for socially isolated older people - an evaluation

abstract: There is increasing policy recognition that the alleviation of social isolation and loneliness in older people should be prioritised. Recently, technology, such as telephone networks and the Internet, has received attention in supporting isolated and lonely older people. Despite lack of evidence, telephone befriending has been considered an effective low-level method to decrease loneliness among older people. This study evaluated the impact of a national befriending scheme for isolated and/or lonely older people, involving eight project sites across the UK 2007–2008. The purpose was to assess the impact of different models of telephone-based befriending services on older people’s health and well-being. A mixed methods approach was used. This paper reports on the findings from 40 in-depth interviews with older service recipients. The most important finding was that the service helped older people to gain confidence, re-engage with the community and become socially active again. Three topics were identified: why older people valued the service, what impact it had made on their health and well-being and what they wanted from the service. In addition, nine subthemes emerged: life is worth living, gaining a sense of belonging, knowing they had a friend, a healthy mind is a healthy body, the alleviation of loneliness and anxiety, increased self-confidence, ordinary conversation, a trusted and reliable service, the future – giving something back. In conclusion, the findings present in-depth qualitative evidence of the impact of telephone befriending on older people’s well-being. Befriending schemes provide low-cost means for socially isolated older people to become more confident and independent and develop a sense of self-respect potentially leading to increased participation and meaningful relationships.


title: Taxonomy-Based Discovery and Annotation of Functional Areas in the City

abstract: Mapping the functional use of city areas (e.g., mapping clusters of hotels or of electronic shops) enables a variety of applications (e.g., innovative way-finding tools). To do that mapping, researchers have recently processed geo-referenced data with spatial clustering algorithms. These algorithms usually perform two consecutive steps: they cluster nearby points on the map, and then assign labels (e.g., 'electronics') to the resulting clusters. When applied in the city context, these algorithms do not fully work, not least because they consider the two steps of clustering and labeling as separate. Since there is no reason to keep those two steps separate, we propose a framework that clusters points based not only on their density but also on their semantic relatedness. We evaluate this framework upon Foursquare data in the cities of Barcelona, Milan, and London. We find that it is more effective than the baseline method of DBSCAN in discovering functional areas. We complement that quantitative evaluation with a user study involving 111 participants in the three cities. Finally, to illustrate the generalizability of our framework, we process temporal data with it and successfully discover seasonal uses of the city.


title: Bayesian Compressive Sensing

abstract: The data of interest are assumed to be represented as N-dimensional real vectors, and these vectors are compressible in some linear basis B, implying that the signal can be reconstructed accurately using only a small number M Lt N of basis-function coefficients associated with B. Compressive sensing is a framework whereby one does not measure one of the aforementioned N-dimensional signals directly, but rather a set of related measurements, with the new measurements a linear combination of the original underlying N-dimensional signal. The number of required compressive-sensing measurements is typically much smaller than N, offering the potential to simplify the sensing system. Let f denote the unknown underlying N-dimensional signal, and g a vector of compressive-sensing measurements, then one may approximate f accurately by utilizing knowledge of the (under-determined) linear relationship between f and g, in addition to knowledge of the fact that f is compressible in B. In this paper we employ a Bayesian formalism for estimating the underlying signal f based on compressive-sensing measurements g. The proposed framework has the following properties: i) in addition to estimating the underlying signal f, "error bars" are also estimated, these giving a measure of confidence in the inverted signal; ii) using knowledge of the error bars, a principled means is provided for determining when a sufficient number of compressive-sensing measurements have been performed; iii) this setting lends itself naturally to a framework whereby the compressive sensing measurements are optimized adaptively and hence not determined randomly; and iv) the framework accounts for additive noise in the compressive-sensing measurements and provides an estimate of the noise variance. In this paper we present the underlying theory, an associated algorithm, example results, and provide comparisons to other compressive-sensing inversion algorithms in the literature.


title: Exploring the comparative salience of restaurant attributes: A conjoint analysis approach

abstract: The comparative salience of restaurant attributes is explored by employing a conjoint analysis.The importance of each restaurant attribute varies in diverse contexts of restaurant types and customers origin.Four major and fourteen minor propositions are suggested for future empirical research.This study helps restaurant managers better prepare for customers needs and expectations. This study explores how travelers select a restaurant for dining out, given that restaurant customers consider diverse attributes in making a selection. By applying a conjoint analysis, an exploratory multiple-case study is conducted for three restaurants in New York City. Findings from Study 1 (an overall travelers group) and Study 2 (two different country-of-residence groups: foreign and domestic travelers) show that food, value, atmosphere, and service are considered as substantially important criteria in selecting restaurants, in that order. However, results from Study 3 examining different restaurant types (low-priced food stand, low-priced indoor, and high-priced indoor) reveal that the food attribute is the most important factor, regardless of restaurant types, whereas the other attributes rankings vary. Results from Study 4 dividing the sample by both traveler origin and restaurant type demonstrate a total disparity in the importance ranking for all attributes. This study suggests that a conjoint analysis is an appropriate method for restaurant sector research in predicting the most important determinants consumers perceive among restaurant attributes. The findings may help restaurant managers develop specific strategies that fit the needs and expectations of different customer groups in terms of their type of restaurant.


title: Measuring social spam and the effect of bots on information diffusion in social media

abstract: Bots have been playing a crucial role in online platform ecosystems, as efficient and automatic tools to generate content and diffuse information to the social media human population. In this chapter, we will discuss the role of social bots in content spreading dynamics in social media. In particular, we will first investigate some differences between diffusion dynamics of content generated by bots, as opposed to humans, in the context of political communication, then study the characteristics of bots behind the diffusion dynamics of social media spam campaigns.


title: Genetic inactivation of D-amino acid oxidase enhances extinction and reversal learning in mice

abstract: Activation of the N-methyl-D-aspartate receptor (NMDAR) glycine site has been shown to accelerate adaptive forms of learning that may benefit psychopathologies involving cognitive and perseverative disturbances. In this study, the effects of increasing the brain levels of the endogenous NMDAR glycine site agonist D-serine, through the genetic inactivation of its catabolic enzyme D-amino acid oxidase (DAO), were examined in behavioral tests of learning and memory. In the Morris water maze task (MWM), mice carrying the hypofunctional Dao1 G181R mutation demonstrated normal acquisition of a single platform location but had substantially improved memory for a new target location in the subsequent reversal phase. Furthermore, Dao1 G181R mutant animals exhibited an increased rate of extinction in the MWM that was similarly observed following pharmacological administration of D-serine (600 mg/kg) in wild-type C57BL/6J mice. In contextual and cued fear conditioning, no alterations were found in initial associative memory recall; however, extinction of the contextual fear memory was facilitated in mutant animals. Thus, an augmented level of D-serine resulting from reduced DAO activity promotes adaptive learning in response to changing conditions. The NMDAR glycine site and DAO may be promising therapeutic targets to improve cognitive flexibility and inhibitory learning in psychiatric disorders such as schizophrenia and anxiety syndromes.


title: Ensemble of Deep Neural Networks with Probability-Based Fusion for Facial Expression Recognition

abstract: Convolutional neural network (CNN) is a very effective method to recognize facial emotions. However, the preprocessing and selection of parameters of these methods heavily depend on the human experience and require a large amount of trial-and-errors. This paper presents an ensemble of convolutional neural networks method with probability-based fusion for facial expression recognition, where the architecture of CNN was adapted by using the convolutional rectified linear layer as the first layer and multiple hidden maxout layers. It was constructed by randomly varying parameters and architecture around the optimal values for CNN, where each CNN as the base classifier was trained to output a probability for each class. These probabilities were then fused through the probability-based fusion method. The conducted experiments on benchmark data sets validated our method, which had better accuracy than the compared methods. The proposed method was novel and efficient for facial expression recognition.


title: Refactoring programs to secure information flows

abstract: Adding a sound information flow security policy to an existing program is a difficult task that requires major analysis of andchanges to the program. In this paper we show how refactoring programs into distinct components of high and low security is a useful methodology to aid in the production of programs with sound information flow policies. Our methodology proceeds as follows. Given a program with no information flow controls, a program sliceris used to identify code that depends on high security inputs. High security code so identified is then refactored into a separate component, which may be accessed by the low security component via public method calls. A security policy that labels input data and checks the output points can then enforce the desired end-to-end security property. Controlled information releases can occur at explicit declassification points if deemed safe. The result is a well-engineered program with explicit interfaces between components of different security levels.


title: Recommending ticket resolution using feature adaptation

abstract: In recent years, IT Service Providers have been rapidly introducing automation to their service delivery model. Driven by market pressure to reduce cost and maintain quality of services, they are looking for technologies that will allow rapid progress towards attainment of truly automated service delivery. Software monitoring systems are designed to actively collect and signal event occurrences and, when necessary, automatically generate incident tickets. Repeating events generate similar tickets, which in turn have a vast number of repeated problem resolutions likely to be found in earlier tickets. In our work, we develop techniques to recommend an appropriate resolution for incoming events by making use of similarities between the events and historical resolutions of similar events. The traditional KNN (K Nearest Neighbor) algorithm has been first applied to recommend resolutions for incoming tickets. Massive heterogeneous applications as well as various monitoring software are running on clients' servers to accomplish required tasks and to monitor system health via different metrics. It leads to generation of correlated tickets that have different symptom descriptions but similar resolutions. Furthermore, change of servers' environments can also induce similar situations in which ticket descriptions differ before and after change but could have similar resolutions. These correlated tickets cause performance degradation in ticket resolution recommendation. Therefore, we propose using SCL (structural corresponding learning) based feature adaptation to uncover feature mapping in different time intervals. Moreover, to put more insights into the periodic regularities existing in our ticket datasets, we apply our algorithm on tickets grouped by different time interval granularities. Extensive empirical evaluations on real-world ticket data sets demonstrate the effectiveness and efficiency of our proposed methods.


title: n-gram geo-trace modeling

abstract: As location-sensing smart phones and location-based services gain mainstream popularity, there is increased interest in developing techniques that can detect anomalous activities. Anomaly detection capabilities can be used in theft detection, remote elder-care monitoring systems, and many other applications. In this paper we present an n-gram based model for modeling a user's mobility patterns. Under the Markovian assumption that a user's location at time t depends only on the last n - 1 locations until t - 1, we can model a user's idiosyncratic location patterns through a collection of n-gram geo-labels, each with estimated probabilities. We present extensive evaluations of the n-gram model conducted on real-world data, compare it with the previous approaches of using T-Patterns and Markovian models, and show that for anomaly detection the n-gram model outperforms existing work by approximately 10%. We also show that the model can use a hierarchical location partitioning system that is able to obscure a user's exact location, to protect privacy, while still allowing applications to utilize the obscured location data for modeling anomalies effectively.


title: A krill herd algorithm for efficient text documents clustering

abstract: Recently, due to the huge growth of web pages, social media and modern applications, text clustering technique has emerged as a significant task to deal with a huge amount of text documents. Some web pages are easily browsed and tidily presented via applying the clustering technique in order to partition the documents into a subset of homogeneous clusters. In this paper, two novel text clustering algorithms based on krill herd (KH) algorithm are proposed to improve the web text documents clustering. In the first method, the basic KH algorithm with all its operators is utilized while in the second method, the genetic operators in the basic KH algorithm are neglected. The performance of the proposed KH algorithms is analyzed and compared with the k-mean algorithm. The experiments were conducted using four standard benchmark text datasets. The results showed that the proposed KH algorithms outperformed the k-mean algorithm in term of clusters quality that is evaluated using two common clustering measures, namely, Purity and Entropy.


title: A Unified Learning Framework of Skip-Grams and Global Vectors

abstract: Log-bilinear language models such as SkipGram and GloVe have been proven to capture high quality syntactic and semantic relationships between words in a vector space. We revisit the relationship between SkipGram and GloVe models from a machine learning viewpoint, and show that these two methods are easily merged into a unified form. Then, by using the unified form, we extract the factors of the configurations that they use differently. We also empirically investigate which factor is responsible for the performance difference often observed in widely examined word similarity and analogy tasks.


title: Efficient distribution of requests in federated cloud computing environments utilizing statistical multiplexing

abstract: Abstract One of the main questions in cloud computing environments is how to efficiently distribute user requests or Virtual Machines (VMs) based on their resource needs over time. This question is also an important one when dealing with a cloud federation environment where rational cloud service providers are collaborating together by sharing customer requests. By considering intrinsic aspects of the cloud computing model one can propose request distribution methods that play on the strengths of this computing paradigm. In this paper we look at statistical multiplexing and server consolidation as such a strength and examine the use of the coefficient of variation and other related statistical metrics as objective functions which can be used in deciding on the request distribution mechanism. The complexity of using these objective functions is analyzed and heuristic methods which enable efficient request partitioning in a feasible time are presented & compared.


title: Video Game Addiction: Past, Present and Future

abstract: Gaming addiction has become a topic of increasing research interest. The last decade has witnessed a significant increase in the number of empirical studies examining various aspects of problematic video game play and video game addiction. This paper begins with a brief past history of how research into video game addiction has changed over the last three decades (i.e., the 1980s, 1990s and 2000s). It then examines more thoroughly the contemporary research literature by analyzing the (i) prevalence of problematic video game use and video game addiction, (ii) negative consequences of excessive video game use, (iii) factors associated with problematic video game use and video game addiction, and (iv) the treatment of problematic video game use and video game addiction. The paper concludes by looking at the trends in the field and a somewhat theoretical examination of what the future of video game addiction might be.


title: Behaviour Analysis of Multilayer Perceptrons with Multiple Hidden Neurons and Hidden Layers

abstract: The terms "Neural Network" (NN) and "Artificial Neural Network" (ANN) usually refer to a Multilayer Perceptron Network. It process the records one at a time, and "learn" by comparing their prediction of the record with the known actual record. The problem of model selection is considerably important for acquiring higher levels of generalization capability in supervised learning. This paper discussed behavioral analysis of different number of hidden layers and different number of hidden neurons. It's very difficult to select number of hidden layers and hidden neurons. There are different methods like Akaike's Information Criterion, Inverse test method and some traditional methods are used to find Neural Network architecture. What to do while neural network is not getting train or errors are not getting reduced. To reduce Neural Network errors, what we have to do with Neural Network architecture. These types of techniques are discussed and also discussed experiment and result. To solve different problems a neural network should be trained to perform correct classification..


title: Unsupervised Domain Adaptation with Adversarial Residual Transform Networks

abstract: Domain adaptation (DA) is widely used in learning problems lacking labels. Recent studies show that deep adversarial DA models can make markable improvements in performance, which include symmetric and asymmetric architectures. However, the former has poor generalization ability, whereas the latter is very hard to train. In this article, we propose a novel adversarial DA method named adversarial residual transform networks (ARTNs) to improve the generalization ability, which directly transforms the source features into the space of target features. In this model, residual connections are used to share features and adversarial loss is reconstructed, thus making the model more generalized and easier to train. Moreover, a special regularization term is added to the loss function to alleviate a vanishing gradient problem, which enables its training process stable. A series of experiments based on Amazon review data set, digits data sets, and Office-31 image data sets are conducted to show that the proposed ARTN can be comparable with the methods of the state of the art.


MATLAB based code for 3 D joint inversion of Magnetotelluric and Direct Current resistivity imaging data


title: GEM-NI: A System for Creating and Managing Alternatives In Generative Design

abstract: We present GEM-NI -- a graph-based generative-design tool that supports parallel exploration of alternative designs. Producing alternatives is a key feature of creative work, yet it is not strongly supported in most extant tools. GEM-NI enables various forms of exploration with alternatives such as parallel editing, recalling history, branching, merging, comparing, and Cartesian products of and for alternatives. Further, GEM-NI provides a modal graphical user interface and a design gallery, which both allow designers to control and manage their design exploration. We conducted an exploratory user study followed by in-depth one-on-one interviews with moderately and highly skills participants and obtained positive feedback for the system features, showing that GEM-NI supports creative design work well.


title: The privacy paradox Investigating discrepancies between expressed privacy concerns and actual online behavior A systematic literature review

abstract: This paper presents a systematic literature review discussing the privacy paradox.Users are concerned about their privacy but undertake little to protect their data.Risk-benefit evaluation or little risk assessment drives information disclosure.Design solutions of mobile apps should be adapted to different cognitive styles.Support users through automated user-centered systems and user-friendly interface. Also known as the privacy paradox, recent research on online behavior has revealed discrepancies between user attitude and their actual behavior. More specifically: While users claim to be very concerned about their privacy, they nevertheless undertake very little to protect their personal data. This systematic literature review explores the different theories on the phenomenon known as the privacy paradox.Drawing on a sample of 32 full papers that explore 35 theories in total, we determined that a users decision-making process as it pertains to the willingness to divulge privacy information is generally driven by two considerations: (1) risk-benefit evaluation and (2) risk assessment deemed be none or negligible. By classifying in accordance with these two considerations, we have compiled a comprehensive model using all the variables mentioned in the discussed papers. The overall findings of the systematic literature review will investigate the nature of decision-making (rational vs. irrational) and the context in which the privacy paradox takes place, with a special focus on mobile computing. Furthermore, possible solutions and research limitation issues will be discussed.


title: Current and Future Issues in BPM Research: A European Perspective from the ERCIS Meeting 2010

abstract: Business process management (BPM) is a still-emerging field in the academic discipline of Information Systems (IS). This article reflects on a workshop on current and future issues in BPM research that was conducted by seventeen IS researchers from eight European countries as part of the 2010 annual meeting of the European Research Center for Information Systems (ERCIS). The results of this workshop suggest that BPM research can meaningfully contribute to investigating a broad variety of phenomena that are of interest to IS scholars, ranging from rather technical (e.g., the implementation of software architectures) to managerial (e.g., the impact of organizational culture on process performance). It further becomes noticeable that BPM researchers can make use of several research strategies, including qualitative, quantitative, and design-oriented approaches. The article offers the participants' outlook on the future of BPM research and combines their opinions with research results from the academic literature on BPM, with the goal of contributing to establishing BPM as a distinct field of research in the IS discipline.


title: Training Probabilistic Spiking Neural Networks with First- To-Spike Decoding

abstract: Third-generation neural networks, or Spiking Neural Networks (SNNs), aim at harnessing the energy efficiency of spike-domain processing by building on computing elements that operate on, and exchange, spikes. In this paper, the problem of training a two-layer SNNs is studied for the purpose of classification, under a Generalized Linear Model (GLM) probabilistic neural model that was previously considered within the computational neuroscience literature. Conventional classification rules for SNNs operate offline based on the number of output spikes at each output neuron. In contrast, a novel training method is proposed here for a first-to-spike decoding rule, whereby the SNN can perform an early classification decision once spike firing is detected at an output neuron. Numerical results bring insights into the optimal parameter selection for the GLM neuron and on the accuracy-complexity trade-off performance of conventional and first-to-spike decoding.


title: Cellular Automata designs for out of plane Nanomagnet Logic

abstract: This paper demonstrates Cellular Automata (CA) designs from out of plane Nanomagnetic Logic (NML). We show that the regular, low-interconnection structure of CAs enables magnetologic designs, where the beneficial characteristics of NML devices (compact, low power operation) can be fully exploited. We also demonstrate how CA rules can be modified to result in fabrication-friendly NML layouts.


title: A simple linear time (1 + /spl epsiv/)-approximation algorithm for k-means clustering in any dimensions

abstract: We present the first linear time (1 + /spl epsiv/)-approximation algorithm for the k-means problem for fixed k and /spl epsiv/. Our algorithm runs in O(nd) time, which is linear in the size of the input. Another feature of our algorithm is its simplicity - the only technique involved is random sampling.


title: Evolview v2: an online visualization and management tool for customized and annotated phylogenetic trees

abstract: Evolview is an online visualization and management tool for customized and annotated phylogenetic trees. It allows users to visualize phylogenetic trees in various formats, customize the trees through built-in functions and user-supplied datasets and export the customization results to publication-ready figures. Its ‘dataset system’ contains not only the data to be visualized on the tree, but also ‘modifiers’ that control various aspects of the graphical annotation. Evolview is a single-page application (like Gmail); its carefully designed interface allows users to upload, visualize, manipulate and manage trees and datasets all in a single webpage. Developments since the last public release include a modern dataset editor with keyword highlighting functionality, seven newly added types of annotation datasets, collaboration support that allows users to share their trees and datasets and various improvements of the web interface and performance. In addition, we included eleven new ‘Demo’ trees to demonstrate the basic functionalities of Evolview, and five new ‘Showcase’ trees inspired by publications to showcase the power of Evolview in producing publication-ready figures. Evolview is freely available at: http://www.evolgenius.info/evolview/.


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