title: Fundamental limitations of conventional-FET biosensors: Quantum-mechanical-tunneling to the rescue

abstract: Electrical detection of biomolecules using Field-Effect-Transistors (FETs) [1–5] is very attractive, since it is label-free, inexpensive, allows scalability and on-chip integration of both sensor and measurement systems. Nanostructured FETs, especially nanowires have gained special importance due to their high electrostatic control and large surface-to-volume ratio. In order to configure the FET as a biosensor (Fig. 1(a)), the dielectric/oxide layer on the semiconductor is functionalized with specific receptors. These receptors capture the desired target biomolecules (a process called conjugation), which due to their charge produce gating effect on the semiconductor, thus changing its electrical properties such as current, conductance etc. Thus it is intuitive, that greater the response of the FET to the gating effect, higher will be its sensitivity where sensitivity can be defined as the ratio of change in current due to biomolecule conjugation to the initial current (before conjugation). While the highest response to gating effect can be obtained in the subthreshold region, the conventional FETs (CFET) suffer severely due to the theoretical limitation on the minimum achievable Subthreshold Swing (SS) of [K B T/q ln(10)] due to the Boltzmann tyranny (Fig. 1(b)) effect where K B is the Boltzmann constant and T is the temperature. This also poses fundamental limitations on the sensitivity and response time of CFET based biosensors [6]. In recent times, Tunnel- FETs have attracted a lot of attention for low power digital applications [7]–[17], due to their ability to overcome the fundamental limitation in SS (60 mV/decade) of CFETs. Recently, it has been shown that the superior subthreshold behavior of TFETs can be leveraged to achieve highly efficient biosensors [6]. This is possible, thanks to the fundamentally different current injection mechanism in TFETs in the form of band-to-band tunneling [17]. The working principle of TFET biosensors is illustrated in Fig. 1c.


title: Reconstruction of nasal tip and columella.

abstract: Reconstruction of nasal tip and columella defects is demanding area with a range of reconstructive options, varying in complexity depending on requirements from simple skin grafting to multiple stage reconstruction with regional flaps. A framework is suggested to aid the reader in choice of reconstruction by classifying the defect based on size and the requirements of one to three layer (full thickness) reconstruction.


title: An empirical study on predicting user acceptance of e-shopping on the Web

abstract: This study develops an extended model to predict consumer acceptance of electronic-shopping (e-shopping) based on the theory of reasoned action (TRA) and the technology acceptance model (TAM). Our model was tested using data collected from 212 questionnaires and analyzed using multiple regression. The results show that individual attitudes toward e-shopping are strongly and positively correlated with user acceptance. The empirical results confirmed that perceived ease of use of trading on-line (PEOUT) and perceived usefulness (PU) significantly determine individual attitudes toward e-shopping, as well as confirming the significant effect of perceived ease of use of the Web on PEOUT, which in turn affects PU. However, PU was not found to affect user acceptance significantly. Additionally, user satisfaction with the Internet/WWW and perceptions of information, system and service were shown to affect user acceptance significantly. Overall, the proposed model could be used to predict consumer willingness to shop on the Web.


title: Actor-Critic based Training Framework for Abstractive Summarization

abstract: We present a training framework for neural abstractive summarization based on actor-critic approaches from reinforcement learning. In the traditional neural network based methods, the objective is only to maximize the likelihood of the predicted summaries, no other assessment constraints are considered, which may generate low-quality summaries or even incorrect sentences. To alleviate this problem, we employ an actor-critic framework to enhance the training procedure. For the actor, we employ the typical attention based sequence-to-sequence (seq2seq) framework as the policy network for summary generation. For the critic, we combine the maximum likelihood estimator with a well designed global summary quality estimator which is a neural network based binary classifier aiming to make the generated summaries indistinguishable from the human-written ones. Policy gradient method is used to conduct the parameter learning. An alternating training strategy is proposed to conduct the joint training of the actor and critic models. Extensive experiments on some benchmark datasets in different languages show that our framework achieves improvements over the state-of-the-art methods.


title: Single-Layer Broadband Phase Shifter Using Multimode Resonator and Shunt $\lambda $ /4 Stubs

abstract: This paper presents a new method for single-layer broadband phase shifter without needing any coupling structure. The phase shifter consists of a multimode resonator and two shunt $\lambda $ /4 stubs. Meanwhile, the bandwidth of proposed phase shifters can be achieved more than 100%. For prescribed return loss, phase shift value, and phase error, this paper develops a synthesis approach to determine all parameters of designed phase shifter. In addition, both the return-loss bandwidth and phase-error bandwidths can be considered simultaneously so as to determine the return loss and phase error for a desired bandwidth. The 45°, 90°, 135°, and 180° phase shifter are theoretically designed and presented. To validate the design method, a broadband 90° phase shifter is designed, fabricated, and measured. The synthesized, simulated, and measured results are found in good agreement with each other, and the experimental results show that the phase shifter achieves 90° ± 5°, $\vert S_{11}\vert dB, and insertion loss < 0.7 dB over the bandwidth from 1.43 to 4.45 GHz (102.7%).


title: DiffSharp: Automatic Differentiation Library

abstract: In this paper we introduce DiffSharp, an automatic differentiation (AD) library designed with machine learning in mind. AD is a family of techniques that evaluate derivatives at machine precision with only a small constant factor of overhead, by systematically applying the chain rule of calculus at the elementary operator level. DiffSharp aims to make an extensive array of AD techniques available, in convenient form, to the machine learning community. These including arbitrary nesting of forward/reverse AD operations, AD with linear algebra primitives, and a functional API that emphasizes the use of higher-order functions and composition. The library exposes this functionality through an API that provides gradients, Hessians, Jacobians, directional derivatives, and matrix-free Hessian- and Jacobian-vector products. Bearing the performance requirements of the latest machine learning techniques in mind, the underlying computations are run through a high-performance BLAS/LAPACK backend, using OpenBLAS by default. GPU support is currently being implemented.


title: Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines

abstract: This paper describes our participation in Task 5 track 2 of SemEval 2017 to predict the sentiment of financial news headlines for a specific company on a continuous scale between -1 and 1. We tackled the problem using a number of approaches, utilising a Support Vector Regression (SVR) and a Bidirectional Long Short-Term Memory (BLSTM). We found an improvement of 4-6% using the LSTM model over the SVR and came fourth in the track. We report a number of different evaluations using a finance specific word embedding model and reflect on the effects of using different evaluation metrics.


title: Toward Combining Speed, Efficiency, Versatility, and Robustness in an Autonomous Quadruped

abstract: This paper provides an overview about Starl ETH : a compliant quadrupedal robot that is designed to study fast, efficient, versatile, and robust locomotion. The platform is driven by highly compliant series elastic actuation, which makes the system fully torque controllable, energetically efficient, and well suited for dynamic maneuvers. Using model-based control strategies, this medium dog-sized machine is capable of various gaits ranging from static walking to dynamic running over challenging terrain. Starl ETH is equipped with an onboard PC, batteries, and various sensor equipment that enables enduring autonomous operation. In this paper, we provide an overview about the underlying locomotion control algorithms, outline a real-time control and simulation environment, and conclude the work with a number of experiments to demonstrate the performance of the presented hardware and controllers.


Implicit Terrains: Meaning, Measurement, and Spatial Metaphors in Organizational Theory


title: Torque ripple reduction of switched reluctance motor drive with adaptive sliding mode control and Particle Swarm Optimization

abstract: In this paper, for speed control with torque ripple minimization of switched reluctance motor drives an optimal speed controller based on Elitist-Mutated Multi-Objective Particle Swarm Optimization (EM-MOPSO) with good accuracies, and performances is presented. The control mechanism is composed of an adaptive sliding mode speed controller to determine the appropriate current command and a hysteresis current controller. In the adaptive sliding mode controller, the heuristic parameters are usually determined by a tedious and repetitive trial-and-error process. By using EM-MOPSO, the trial-and error process is eliminated. The optimal parameters of adaptive sliding mode conrtoller and turn-on and turn-off angles are chosen by applying a multi-objective function, including both Integrals Squared Error (ISE) of speed and torque ripple. The performance of this EM-MOPSO based controller has been demonstrated by simulation in MATLAB/SIMULINK software for a four phases, 4-kw SRM.


title: Privacy, Trust and Identity in Pervasive Computing: A Review of Technical Challenges and Future Research Directions

abstract: Developments in pervasive computing introduced a new world of computing where networked processors embedded and distributed in everyday objects communicating with each other over wireless links. Computers in such environments work in the background while establishing connections among them dynamically and hence will be less visible and intrusive. Such a vision raises questions about how to manage issues like privacy, trust and identity in those environments. In this paper, we review the technical challenges that face pervasive computing environments in relation to each of these issues. We then present a number of security related considerations and use them as a basis for comparison between pervasive and traditional computing. We will argue that these considerations pose particular concerns and challenges to the design and implementation of pervasive environments which are different to those usually found in traditional computing environments. To address these concerns and challenges, further research is needed. We will present a number of directions and topics for possible future research with respect to each of the three issues.


title: Applications of Ontologies and Problem- Solving Methods

abstract: The Workshop on Applications of Ontologies and Problem-Solving Methods (PSMs), held in conjunction with the Thirteenth Biennial European Conference on Artificial Intelligence (ECAI-98), was held on 24 to 25 August 1998. Twenty-six people participated, and 16 papers were presented. Participants included scientists and practitioners from both the ontology and PSM communities. The first day was devoted to paper presentations and discussions. The second (half) day, a joint session was held with two other workshops: (1) Building, Maintaining, and Using Organizational Memories and (2) Intelligent Information Integration. The reason for the joint session was that in all three workshops, ontologies play a prominent role, and the goal was to bring together researchers working on related issues in different communities. The workshop ended with a discussion about the added value of a combined ontologies-PSM workshop compared to separate workshops.


title: Change detection from remotely sensed images: From pixel-based to object-based approaches

abstract: The appetite for up-to-date information about earth’s surface is ever increasing, as such information provides a base for a large number of applications, including local, regional and global resources monitoring, land-cover and land-use change monitoring, and environmental studies. The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. A large number of change detection methodologies and techniques, utilizing remotely sensed data, have been developed, and newer techniques are still emerging. This paper begins with a discussion of the traditionally pixel-based and (mostly) statistics-oriented change detection techniques which focus mainly on the spectral values and mostly ignore the spatial context. This is succeeded by a review of object-based change detection techniques. Finally there is a brief discussion of spatial data mining techniques in image processing and change detection from remote sensing data. The merits and issues of different techniques are compared. The importance of the exponential increase in the image data volume and multiple sensors and associated challenges on the development of change detection techniques are highlighted. With the wide use of very-high-resolution (VHR) remotely sensed images, object-based methods and data mining techniques may have more potential in change detection.


title: Real-time haptic manipulation and cutting of hybrid soft tissue models by extended position-based dynamics

abstract: This paper systematically describes an interactive dissection approach for hybrid soft tissue models governed by extended position-based dynamics. Our framework makes use of a hybrid geometric model comprising both surface and volumetric meshes. The fine surface triangular mesh with high-precision geometric structure and texture at the detailed level is employed to represent the exterior structure of soft tissue models. Meanwhile, the interior structure of soft tissues is constructed by coarser tetrahedral mesh, which is also employed as physical model participating in dynamic simulation. The less details of interior structure can effectively reduce the computational cost during simulation. For physical deformation, we design and implement an extended position-based dynamics approach that supports topology modification and material heterogeneities of soft tissue. Besides stretching and volume conservation constraints, it enforces the energy preserving constraints, which take the different spring stiffness of material into account and improve the visual performance of soft tissue deformation. Furthermore, we develop mechanical modeling of dissection behavior and analyze the system stability. The experimental results have shown that our approach affords real-time and robust cutting without sacrificing realistic visual performance. Our novel dissection technique has already been integrated into a virtual reality-based laparoscopic surgery simulator. Copyright © 2015 John Wiley & Sons, Ltd.


title: Adaptive Beamforming Based Inband Fronthaul for Cost-Effective Virtual Small Cell in 5G Networks

abstract: In order to exploit the potential capacity of 5G, the deployment of ultra-dense small cells is an approach that can dramatically increase the radio resource reuse factor and network capacity. However, network densification with a large number of small cells brings challenges due to increased network complexity, deployment cost and inter-cell interference. In this paper, a new 5G architecture with virtual small cells (VSCs), which are dynamically formed by grouping a number of user devices in close proximity and adapted according to traffic condition, is proposed to improve the cost and energy efficiency compared with the traditional fixed deployment of small cells. In each virtual small cell, one mobile device is selected as a cell head (CH) to aggregate intra- cell traffic using unlicensed band transmissions and then communicates with its macro-cell base station in a licensed band through beamformed transmission, which reduces the inter-cell interference and improves spectrum efficiency. In this paper, a highly directional beamforming technique is employed to enable a dedicated inband fronthaul link for VSC. Our work focuses on how to design adaptive beamforming to minimize the transmit power under throughput requirements and power constraints. Both the mathematical analysis and simulation results demonstrate that VSCs can increase power efficiency dramatically while providing flexibility and reduced cellular load, when compared with macrocell only deployment and traditional fixed small cells scenario.


title: Limited-memory reduced-Hessian methods for unconstrained optimization, Numerical Analysis

abstract: Limited-memory BFGS quasi-Newton methods approximate the Hessian matrix of second derivatives by the sum of a diagonal matrix and a fixed number of rank-one matrices. These methods are particularly effective for large problems in which the approximate Hessian cannot be stored explicitly. ::: It can be shown that the conventional BFGS method accumulates approximate curvature in a sequence of expanding subspaces. This allows an approximate Hessian to be represented using a smaller reduced matrix that increases in dimension at each iteration. When the number of variables is large, this feature may be used to define limited-memory reduced-Hessian methods in which the dimension of the reduced Hessian is limited to save storage. Limited-memory reduced-Hessian methods have the benefit of requiring half the storage of conventional limited-memory methods. ::: In this paper, we propose a particular reduced-Hessian method with substantial computational advantages compared to previous reduced-Hessian methods. Numerical results from a set of unconstrained problems in the CUTE test collection indicate that our implementation is competitive with the limited-memory codes L-BFGS and L-BFGS-B.


title: State-of-the-Art Predictive Maintenance Techniques*

abstract: Condition-based maintenance techniques for industrial equipment and processes are described in this paper together with examples of their use and discussion of their benefits. These techniques are divided here into three categories. The first category uses signals from existing process sensors, such as resistance temperature detectors (RTDs), thermocouples, or pressure transmitters, to help verify the performance of the sensors and process-to-sensor interfaces and also to identify problems in the process. The second category depends on signals from test sensors (e.g., accelerometers) that are installed on plant equipment (e.g., rotating machinery) in order to measure such parameters as vibration amplitude. The vibration amplitude is then trended to identify the onset of degradation or failure. This second category also includes the use of wireless sensors to provide additional points for collection of data or allow plants to measure multiple parameters to cover not only vibration amplitude but also ambient temperature, pressure, humidity, etc. With each additional parameter that can be measured and correlated with equipment condition, the diagnostic capabilities of the category can increase exponentially. The first and second categories just mentioned are passive, which means that they do not involve any perturbation of the equipment or the process being monitored. In contrast, the third category is active. That is, the third category involves injecting a test signal into the equipment (sensors, cables, etc.) to measure its response and thereby diagnose its performance. For example, the response time of temperature sensors (RTDs and thermocouples) can be measured by the application of the step current signal to the sensor and analysis of the sensor response to the application of the step current. Cable anomalies can be located by a similar procedure referred to as the time domain reflectometry (TDR). This test involves a signal that is sent through the cable to the end device. Its reflection is then recorded and compared to a baseline to identify impedance changes along the cable and thereby identify and locate anomalies. Combined with measurement of cable inductance (L), capacitance (C), and loop resistance (R), or LCR testing, the TDR method can identify and locate anomalies along a cable, identify moisture in a cable or end device, and even reveal gross problems in the cable insulation material. There are also frequency domain reflectometry (FDR) methods, reverse TDR, trending of insulation resistance (IR) measurement, and other techniques which can be used in addition to or instead of TDR and LCR to provide a wide spectrum of tools for cable condition monitoring. The three categories of techniques described in this paper are the subject of current research and development projects conducted by the author and his colleagues at the AMS Corporation with funding from the U.S. Department of Energy (DOE) under the Small Business Innovation Research (SBIR) program.


title: Synonym Extraction Using A Semantic Distance On A Dictionary

abstract: Synonyms extraction is a difficult task to achieve and evaluate. Some studies have tried to exploit general dictionaries for that purpose, seeing them as graphs where words are related by the definition they appear in, in a complex network of an arguably semantic nature. The advantage of using a general dictionary lies in the coverage, and the availability of such resources, in general and also in specialised domains. We present here a method exploiting such a graph structure to compute a distance between words. This distance is used to isolate candidate synonyms for a given word. We present an evaluation of the relevance of the candidates on a sample of the lexicon.


title: Reformulation of solar cell physics to facilitate experimental separation of recombination pathways

abstract: Experimentally identifying the spatial distribution of recombination in a solar cell is challenging, with only semi-quantitative information available from conventional characterization techniques. We develop a formulation of solar cell physics, based upon well-justified analytic approximations, to quantitatively extract information about recombination in different cell regions. We derive the dependence of VOC on light-intensity, temperature, and strength of recombination in the space-charge, quasi-neutral, and interface regions. Expanding the scope and utility of commonly used characterization techniques, we apply this formulation to evaluate the spatial distribution of recombination in exemplary crystalline silicon heterojunction and polycrystalline Cu(In,Ga)Se2 solar cells.


A Trusted Safety Verifier for Process Controller Code


A Mixed Fragmentation Methodology for Initial Distributed Database Design


title: A Hybrid Approach for Enhancing Data Security by Combining Encryption and Steganography

abstract: In this era of fast growing technologies more attention needs to be paid for Security of multimedia data transferred over internet .Now a day's every one depends on the internet for data, so confidential data needs protection from third party. This can be achieved by using Cryptography, Compression and Steganography, all three together. These methods individually provide part of security so, when they are combined together, multi-level security can be provided. In the existing system, DCT is used for compression purpose which provides lossy compression and block cipher methods are used for encryption of secret data. Although these approaches are relatively secure, but high processing is required, it involves computational overheads and processing speed is less. Hence there are various techniques proposed by authors to provide security of the data, In our proposed system a hybrid approach of Compression, Double-Encryption and Steganography is employed to increase encryption speed, reduce processing time and also provides more security, authentication, authorization, Integration of data and also maintains confidentiality.


title: Temporal Abstraction in Reinforcement Learning

abstract: Decision making usually involves choosing among different courses of action over a broad range of time scales. For instance, a person planning a trip to a distant location makes high-level decisions regarding what means of transportation to use, but also chooses low-level actions, such as the movements for getting into a car. The problem of picking an appropriate time scale for reasoning and learning has been explored in artificial intelligence, control theory and robotics. In this dissertation we develop a framework that allows novel solutions to this problem, in the context of Markov Decision Processes (MDPs) and reinforcement learning. ::: In this dissertation, we present a general framework for prediction, control and learning at multiple temporal scales. In this framework, temporally extended actions are represented by a way of behaving (a policy) together with a termination condition. An action represented in this way is called an option. Options can be easily incorporated in MDPs, allowing an agent to use existing controllers, heuristics for picking actions, or learned courses of action. ::: The effects of behaving according to an option can be predicted using multi-time models, learned by interacting with the environment. In this dissertation we develop multi-time models, and we illustrate the way in which they can be used to produce plans of behavior very quickly, using classical dynamic programming or reinforcement learning techniques. ::: The most interesting feature of our framework is that it allows an agent to work simultaneously with high-level and low-level temporal representations. The interplay of these levels can be exploited in order to learn and plan more efficiently and more accurately. We develop new algorithms that take advantage of this structure to improve the quality of plans, and to learn in parallel about the effects of many different options.


Overcoming Innovation Resistance beyond Status Quo Bias - A Decision Support System Approach (Research-in-Progress)


title: Consumer credit risk: Individual probability estimates using machine learning

abstract: Consumer credit scoring is often considered a classification task where clients receive either a good or a bad credit status. Default probabilities provide more detailed information about the creditworthiness of consumers, and they are usually estimated by logistic regression. Here, we present a general framework for estimating individual consumer credit risks by use of machine learning methods. Since a probability is an expected value, all nonparametric regression approaches which are consistent for the mean are consistent for the probability estimation problem. Among others, random forests (RF), k-nearest neighbors (kNN), and bagged k-nearest neighbors (bNN) belong to this class of consistent nonparametric regression approaches. We apply the machine learning methods and an optimized logistic regression to a large dataset of complete payment histories of short-termed installment credits. We demonstrate probability estimation in Random Jungle, an RF package written in C++ with a generalized framework for fast tree growing, probability estimation, and classification. We also describe an algorithm for tuning the terminal node size for probability estimation. We demonstrate that regression RF outperforms the optimized logistic regression model, kNN, and bNN on the test data of the short-term installment credits.


title: A vision radar system for car safety driving applications

abstract: This paper presents a vision radar system (VRS) integrating Lane Departure Warning System (LDWS) and Forward Collision Warning System (FCWS) for car safety driving applications. A dynamic threshold, which improves the accuracy of lane and vehicle detection in various weather conditions, and multiple frame approval, which conquers the effect of some tough scenes, are proposed. The designed radar-map facilitates warning message issuing of safety distance for forward and potential cut-in vehicles. The proposed VRS can achieve 22 fps at 720×480 (D1) resolution on Freescale i.MX6 embedded platform.


title: Performance of a mobile tracking algorithm for fading mobile channels

abstract: This paper presents tracking performance of a simple algorithm for adaptation of complex base band weights of the transmit antenna in a fading channel. The transmit antenna at the base station updates its weights on the basis of feedback from the mobile to converge to its optimal weights. The first feedback consists of a bit that indicates which of the two normalized perturbed weights sent along with the pilot signal from the base station delivered more power to the mobile. The second is sent when the power difference between the powers delivered by the two normalized perturbed weights decreases as compared to the previous minimum. It makes a coarse stochastic gradient algorithm and converges to those weights that maximize the power delivered to the receiver. Simulations in slow and fast fading channels of this algorithm show improved performance as compared to gradient sign feedback algorithm.


title: Pattern-Based Contextual Anomaly Detection in HVAC Systems

abstract: This paper presents detailed anomaly detection evaluation on operational time-series data of Internet of Things (IoT) based household devices in general and Heating, Ventilation and Air Conditioning (HVAC) systems in specific. Due to the number of issues observed during evaluation of widely used distance-based, statistical-based, and cluster-based anomaly detection techniques, we also present a pattern-based approach for anomaly detection in HVAC time-series data. The usage and number of IoT based HVAC systems are enormously increasing and will have a major share in IoT based household devices in the near future. The operational and usage log of these devices contains different sensor values logged with time, containing normal data points, and long-term anomalies. The state-of-the-art methods for anomaly detection are unable to detect these long-term anomalies, which reflect the deteriorating effect of a sensor. The presented approach overcomes this problem by building a knowledge base of long/short -term patterns based on normal data points which keep growing over the time. In addition to the detected anomalies and in contrast to the existing methods, the presented method gives meaningful anomaly score for a number of HVAC systems. We evaluate the presented approach on real operational data, collected over the period of 2.5 years. Evaluation results show that the presented approach outperforms the state-of-the-art methods for anomaly detection with the area under the curve (AUC) value of 99.4%. Discord detection results of the proposed technique on another dataset from a different domain show the generic and adaptive nature of our technique.


title: Development of a University Lecture Timetable using Modified Genetic Algorithms Approach

abstract: Lecture timetabling is a very important process in any educational institutions. It is an open ended problem in which courses must be arranged around a set of timeslots and venues so that some constraints are satisfied. It constitutes a class of difficult-to-solve combinatorial optimization problems that lacks analytical solution methods. The main objective of the research is to solve this problem by using modified Genetic Algorithms approach The implementation of the timetabling problem was characterized by crossover, mutation and selection scheme modifications with the introduction of replacement, test and repair strategy. In particular, single crossover, uniform crossover and rank based selection schemes were employed. The timetabling system was subjected to five different mutation rates: 0.02, 0.04, 0.10, 0.25 and 0.50. The results of the experiment revealed that best and feasible timetable was generated at the least considered mutation rate of 0.02 and fitness function of 0.95. None of the hard constraints were violated, so the timetable generated was feasible. Also, it was discovered mutation rate has greater effect on feasible timetable generation; no conflict of courses was experience when mutation rate was reduced to 0.02.


title: Effect of Different Crosslinking Technologies on Hyaluronic Acid Behavior: A Visual and Microscopic Study of Seven Hyaluronic Acid Gels.

abstract: Abstract The mechanical, rheological, and pharmacological properties of hyaluronic acid (HA) gels differ by their proprietary crosslinking technologies. To examine the different properties of a range of HA gels using simple and easily reproducible laboratory tests to better understand their suitability for particular indications. Hyaluronic acid gels produced by one of 7 different crosslinking technologies were subjected to tests for cohesivity, resistance to stretch, and microscopic examination. These 7 gels were: non-animal stabilized HA (NASHA® [Restylane®]), 3D Matrix (Surgiderm® 24 XP), cohesive polydensified matrix (CPM® [Belotero® Balance]), interpenetrating network-like (IPN-like [Stylage® M]), Vycross® (Juvederm Volbella®), optimal balance technology (OBT® [Emervel Classic]), and resilient HA (RHA® [Teosyal Global Action]). Cohesivity varied for the 7 gels, with NASHA being the least cohesive and CPM the most cohesive. The remaining gels could be described as partially cohesive. The resistance to stretch test confirmed the cohesivity findings, with CPM having the greatest resistance. Light microscopy of the 7 gels revealed HA particles of varying size and distribution. CPM was the only gel to have no particles visible at a microscopic level. Hyaluronic acid gels are produced with a range of different crosslinking technologies. Simple laboratory tests show how these can influence a gel's behavior, and can help physicians select the optimal product for a specific treatment indication. Versions of this paper have been previously published in French and in Dutch in the Belgian journal Dermatologie Actualite. Micheels P, Sarazin D, Tran C, Salomon D. Un gel d'acide hyaluronique est-il semblable a son concurrent? Derm-Actu. 2015;14:38-43. J Drugs Dermatol. 2016;15(5):600-606..


title: A 2.4-GHz CMOS Class-E Synchronous Rectifier

abstract: A class-E synchronous rectifier has been designed and implemented using 0.13- $\mu {\text {m}}$ CMOS technology. A design methodology based on the theory of time-reversal duality has been used where a class-E amplifier circuit is transformed into a class-E rectifier circuit. The methodology is distinctly different from other CMOS RF rectifier designs which use voltage multiplier techniques. Power losses in the rectifier are analyzed including saturation resistance in the switch, inductor losses, and current/voltage overlap losses. The rectifier circuit includes a 50- $\Omega $ single-ended RF input port with on-chip matching. The circuit is self-biased and completely powered from the RF input signal. Experimental results for the rectifier show a peak RF-to-dc conversion efficiency of 30% measured at a frequency of 2.4 GHz.


title: Vitamin D : A Millenium Perspective

abstract: Vitamin D is one of the oldest hormones that have been made in the earliest life forms for over 750 million years. Phytoplankton, zooplankton, and most plants and animals that are exposed to sunlight have the capacity to make vitamin D. Vitamin D is critically important for the development, growth, and maintenance of a healthy skeleton from birth until death. The major function of vitamin D is to maintain calcium homeostasis. It accomplishes this by increasing the efficiency of the intestine to absorb dietary calcium. When there is inadequate calcium in the diet to satisfy the body's calcium requirement, vitamin D communicates to the osteoblasts that signal osteoclast precursors to mature and dissolve the calcium stored in the bone. Vitamin D is metabolized in the liver and then in the kidney to 1,25-dihydroxyvitamin D [1,25(OH)(2)D]. 1,25(OH)(2)D receptors (VDR) are present not only in the intestine and bone, but in a wide variety of other tissues, including the brain, heart, stomach, pancreas, activated T and B lymphocytes, skin, gonads, etc. 1,25(OH)(2)D is one of the most potent substances to inhibit proliferation of both normal and hyperproliferative cells and induce them to mature. It is also recognized that a wide variety of tissues, including colon, prostate, breast, and skin have the enzymatic machinery to produce 1,25(OH)(2)D. 1,25(OH)(2)D and its analogs have been developed for treating the hyperproliferative disease psoriasis. Vitamin D deficiency is a major unrecognized health problem. Not only does it cause rickets in children, osteomalacia and osteoporosis in adults, but may have long lasting effects. Chronic vitamin D deficiency may have serious adverse consequences, including increased risk of hypertension, multiple sclerosis, cancers of the colon, prostate, breast, and ovary, and type 1 diabetes. There needs to be a better appreciation of the importance of vitamin D for overall health and well being.


Interaction Design for Recommender Systems


title: Finite-Element Analysis of Short-Circuit Electromagnetic Force in Power Transformer

abstract: Transient electromagnetic forces in radial and axial directions induce critical mechanical stress on windings and transformers. In this paper, short-circuit electromagnetic forces that are exerted on transformer windings are investigated. A 3-D transformer model is considered to calculate the transient electromagnetic forces. The magnetic vector potential, magnetic flux density, and electromagnetic forces due to the short-circuit transient currents applied to the power transformer are analyzed by a coupled electromechanical finite-element method. The results obtained are compared with the analytical results and show good agreement. The numerical modeling technique dealt with in this paper is expected to be useful in the design of power transformers.


title: RECOVERING INTRINSIC SCENE CHARACTERISTICS FROM IMAGES

abstract: We suggest that an appropriate role of early visual processing is to describe a scene in terms of intrinsic (vertical) characteristics -- such as range, orientation, reflectance, and incident illumination -- of the surface element visible at each point in the image. Support for this idea comes from three sources: the obvious utility of intrinsic characteristics for higher-level scene analysis; the apparent ability of humans to determine these characteristics, regardless of viewing conditions or familiarity with the scene; and a theoretical argument that such a description is obtainable, by a noncognitive and nonpurposive process, at least, for simple scene domains. The central problem in recovering intrinsic scene characteristics is that the information is confounded in the original light-intensity image: a single intensity value encodes all the characteristics of the corresponding scene point. Recovery depends on exploiting constraints, derived from assumptions about the nature of the scene and the physics of the imaging process.


THE BLACK-SCHOLES MODEL AND EXTENSIONS


title: Re-examining price as a predictor of answer quality in an online q&a site

abstract: Online question-answering services provide mechanisms for knowledge exchange by allowing users to ask and answer questions on a wide range of topics. A key question for designing such services is whether charging a price has an effect on answer quality. Two field experiments using one such service, Google Answers, offer conflicting answers to this question. To resolve this inconsistency, we re-analyze data from Harper et al. [5] and Chen et al. [2] to study the price effect in greater depth. Decomposing the price effect into two different levels yields results that reconcile those of the two field experiments. Specifically, we find that: (1) a higher price significantly increases the likelihood that a question receives an answer and (2) for questions that receive an answer, there is no significant price effect on answer quality. Additionally, we find that the rater background makes a difference in evaluating answer quality.


title: Exploring continued online service usage behavior: The roles of self-image congruity and regret

abstract: The expectation-confirmation model (ECM) of continued information systems (IS) use has proven to be successful across online service contexts. Previous studies based on ECM have focused on a referent (i.e., comparison standard) that is centered on the target IS (i.e., target online service). The effect of this referent, captured through confirmation, has been strongly demonstrated. Yet, few studies have explored the saliency of two additional reference effects, captured through self-image congruity and regret, in online service continuance. To fill this knowledge gap, this paper attempts to develop a research model that extends the ECM perspective in view of the additional contributions of regret and self-image congruity on two post-adoption beliefs (perceived usefulness and perceived enjoyment) and continuance intention. For this extension, we synthesized the extant literature on continued IS use, self-image congruity, and regret. The model was empirically tested within the context of a social network service. Our analysis result shows that self-image congruity plays a key role in forming the two post-adoption beliefs. It is also found that the absolute effect of regret on continuance intention is larger than the effects of other antecedents identified in IS. Overall, this study preliminarily confirms the salience of self-image congruity and regret in online service continuance.


title: Natural Hand Gestures Recognition System for Intelligent HCI: A Survey

abstract: Gesture recognition is to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head, and/or body. Hand Gestures have greater importance in designing an intelligent and efficient human–computer interface. The applications of gesture recognition are manifold, ranging from sign language through medical rehabilitation to virtual reality. In this paper a survey on various recent gesture recognition approaches is provided with particular emphasis on hand gestures. A review of static hand posture methods are explained with different tools and algorithms applied on gesture recognition system, including connectionist models, hidden Markov model, and fuzzy clustering. Challenges and future research directions are also highlighted.


title: A tutorial on blockchain and applications to secure network control-planes

abstract: Recent interest about the blockchain technology brought questions about its application to other systems than the cryptocurrency one. In this paper we present blockchain and discuss key applications to network systems in the literature.


title: Design of HERIC configuration based grid connected single phase transformer less photovoltaic inverter

abstract: In a grid connected photovoltaic system, the main aim is to design an efficient solar inverter with higher efficiency and which also controls the power that the inverter injects into the grid. The effectiveness of the general PV system anticipate on the productivity by which the direct current of the solar module is changed over into alternating current. The fundamental requirement to interface the solar module to the grid with increased productivity includes: Low THD of current injected to the grid, maximum power point, and high power factor. In this paper, a two stage topology without galvanic isolation is been carried out for a single phase grid connected photovoltaic inverter. The output from the PV panel is given to the DC/DC boost converter, maximum power point tracking (MPPT) control technique is being used to control the gate pulse of the IGBT of boost converter. The boosted output is fed to the highly efficient and reliable inverter concept (HERIC) inverter in order to convert DC into AC with higher efficiency.


title: Dual Iterative Hard Thresholding: From Non-convex Sparse Minimization to Non-smooth Concave Maximization

abstract: Iterative Hard Thresholding (IHT) is a class of projected gradient descent methods for optimizing sparsity-constrained minimization models, with the best known efficiency and scalability in practice. As far as we know, the existing IHT-style methods are designed for sparse minimization in primal form. It remains open to explore duality theory and algorithms in such a non-convex and NP-hard setting. In this article, we bridge the gap by establishing a duality theory for sparsity-constrained minimization with $\ell_2$-regularized objective and proposing an IHT-style algorithm for dual maximization. Our sparse duality theory provides a set of sufficient and necessary conditions under which the original NP-hard/non-convex problem can be equivalently solved in a dual space. The proposed dual IHT algorithm is a super-gradient method for maximizing the non-smooth dual objective. An interesting finding is that the sparse recovery performance of dual IHT is invariant to the Restricted Isometry Property (RIP), which is required by all the existing primal IHT without sparsity relaxation. Moreover, a stochastic variant of dual IHT is proposed for large-scale stochastic optimization. Numerical results demonstrate that dual IHT algorithms can achieve more accurate model estimation given small number of training data and have higher computational efficiency than the state-of-the-art primal IHT-style algorithms.


title: The Quest to Replace Passwords: A Framework for Comparative Evaluation of Web Authentication Schemes

abstract: We evaluate two decades of proposals to replace text passwords for general-purpose user authentication on the web using a broad set of twenty-five usability, deployability and security benefits that an ideal scheme might provide. The scope of proposals we survey is also extensive, including password management software, federated login protocols, graphical password schemes, cognitive authentication schemes, one-time passwords, hardware tokens, phone-aided schemes and biometrics. Our comprehensive approach leads to key insights about the difficulty of replacing passwords. Not only does no known scheme come close to providing all desired benefits: none even retains the full set of benefits that legacy passwords already provide. In particular, there is a wide range from schemes offering minor security benefits beyond legacy passwords, to those offering significant security benefits in return for being more costly to deploy or more difficult to use. We conclude that many academic proposals have failed to gain traction because researchers rarely consider a sufficiently wide range of real-world constraints. Beyond our analysis of current schemes, our framework provides an evaluation methodology and benchmark for future web authentication proposals.


title: Power system reliability evaluation using a state space classification technique and particle swarm optimisation search method

abstract: It is well-known that the reliability evaluation of composite power systems is computationally demanding. This work introduces a state space classification (SSC) technique that classifies a system’s state space into failure, success, and unclassified subspaces without performing power flow analysis. The SSC technique was developed based on calculating the maximum capacity flow of the transmission lines and the available generation. An algorithm, which is developed based on a directed binary particle swarm optimisation, was developed to search for failure states in the unclassified subspaces. The key element in controlling the particle swarm optimisation (PSO) search method to search for failure states in the unclassified subspaces is the selection of the weighting factors of the velocity update rule. The work presented in this study proposes an intelligent PSO based search method to adjust these weighting factors in a dynamic fashion. The effectiveness of the proposed method was demonstrated on three test systems, the Institute of Electrical and Electronics Engineers reliability test system (IEEE RTS), the modified IEEE RTS and the Saskatchewan Power Corporation in Canada. The results have shown that the reliability indices obtained using the proposed method correspond closely with those obtained using Monte Carlo simulation with less computation burden.


title: An actuated physical puppet as an input device for controlling a digital manikin

abstract: We present an actuated handheld puppet system for controlling the posture of a virtual character. Physical puppet devices have been used in the past to intuitively control character posture. In our research, an actuator is added to each joint of such an input device to provide physical feedback to the user. This enhancement offers many benefits. First, the user can upload pre-defined postures to the device to save time. Second, the system is capable of dynamically adjusting joint stiffness to counteract gravity, while allowing control to be maintained with relatively little force. Third, the system supports natural human body behaviors, such as whole-body reaching and joint coupling. This paper describes the user interface and implementation of the proposed technique and reports the results of expert evaluation. We also conducted two user studies to evaluate the effectiveness of our method.


title: A 10-bit CMOS DAC With Current Interpolated Gamma Correction for LCD Source Drivers

abstract: This paper presents a compact 10-bit digital-to-analog converter (DAC) for LCD source drivers. The cyclic DAC architecture is used to reduce the area of LCD column drivers when compared to the use of conventional resistor-string DACs. The current interpolation technique is proposed to perform gamma correction after D/A conversion. The gamma correction circuit is shared by four DAC channels using the interleave technique. A prototype 10-bit DAC with gamma correction function is implemented in 0.35 μm CMOS technology and its average die size per channel is 0.053 mm2, which is smaller than those of the R-DACs with gamma correction function. The settling time of the 10-bit DAC is 1 μs, and the maximum INL and DNL are 2.13 least significant bit (LSB) and 1.30 LSB, respectively.


title: Copy-move forgery detection in digital images

abstract: The ready availability of image-editing software makes it important to ensure the authenticity of images. This thesis concerns the detection and localization of cloning, or Copy-Move Forgery (CMF), which is the most common type of image tampering, in which part(s) of the image are copied and pasted back somewhere else in the same image. Post-processing can be used to produce more realistic doctored images and thus can increase the difficulty of detecting forgery. ::: This thesis presents three novel methods for CMF detection, using feature extraction, surface fitting and segmentation. The Dense Scale Invariant Feature Transform (DSIFT) has been improved by using a different method to estimate the canonical orientation of each circular block. The Fitting Function Rotation Invariant Descriptor (FFRID) has been developed by using the least squares method to fit the parameters of a quadratic function on each block curvatures. In the segmentation approach, three different methods were tested: the SLIC superpixels, the Bag of Words Image and the Rolling Guidance filter with the multi-thresholding method. We also developed the Segment Gradient Orientation Histogram (SGOH) to describe the gradient of irregularly shaped blocks (segments). ::: The experimental results illustrate that our proposed algorithms can detect forgery in images containing copy-move objects with different types of transformation (translation, rotation, scaling, distortion and combined transformation). Moreover, the proposed methods are robust to post-processing (i.e. blurring, brightness change, colour reduction, JPEG compression, variations in contrast and added noise) and can detect multiple duplicated objects. In addition, we developed a new method to estimate the similarity threshold for each image by optimizing a cost function based probability distribution. This method can detect CMF better than using a fixed threshold for all the test images, because our proposed method reduces the false positive and the time required to estimate one threshold for different images in the dataset. Finally, we used the hysteresis to decrease the number of false matches and produce the best possible result.


title: Multiple multidimensional knapsack problem and its applications in cognitive radio networks

abstract: In this paper, a new variant of the standard knapsack problem is investigated and applied in cognitive radio networks. More specifically, the centralized spectrum allocation in cognitive radio networks is formulated as a multiple multidimensional knapsack problem. We propose an exact solution and a heuristic algorithm with guaranteed performance. The performance of the proposed algorithms are compared numerically.


title: Parallel convolutional-linear neural network for motor imagery classification

abstract: Deep learning, recently, has been successfully applied to image classification, object recognition and speech recognition. However, the benefits of deep learning and accompanying architectures have been largely unknown for BCI applications. In motor imagery-based BCI, an energy-based feature, typically after spatial filtering, is commonly used for classification. Although this feature corresponds to the estimate of event-related synchronization/desynchronization in the brain, it neglects energy dynamics which may contain valuable discriminative information. Because traditional classiication methods, such as SVM, cannot handle this dynamical property, we proposed an architecture that inputs a dynamic energy representation of EEG data and utilizes convolutional neural networks for classification. By combining this network with a static energy network, we saw a significant increase in performance. We evaluated the proposed method and compared with SVM on a multi-class motor imagery dataset (BCI competition dataset IV-2a). Our method outperforms SVM with static energy features significantly (p < 0.01).


Can Rumour Stance Alone Predict Veracity


title: LSTM-Based Goal Recognition in Latent Space

abstract: Approaches to goal recognition have progressively relaxed the requirements about the amount of domain knowledge and available observations, yielding accurate and efficient algorithms capable of recognizing goals. However, to recognize goals in raw data, recent approaches require either human engineered domain knowledge, or samples of behavior that account for almost all actions being observed to infer possible goals. This is clearly too strong a requirement for real-world applications of goal recognition, and we develop an approach that leverages advances in recurrent neural networks to perform goal recognition as a classification task, using encoded plan traces for training. We empirically evaluate our approach against the state-of-the-art in goal recognition with image-based domains, and discuss under which conditions our approach is superior to previous ones.


title: A TOTP-Based Two Factor Authentication Scheme for Hyperledger Fabric Blockchain

abstract: In this paper, we propose a new authentication method to prevent authentication vulnerability of Claim Token method of Membership Service provide in Private BlockChain. We chose Hyperledger Fabric v1.0 using JWT authentication method of membership service. TOTP, which generate OTP tokens and user authentication codes that generate additional time-based password on existing authentication servers, has been applied to enforce security and two-factor authentication method to provide more secure services.


title: Real-Time Bidding by Reinforcement Learning in Display Advertising

abstract: The majority of online display ads are served through real-time bidding (RTB) --- each ad display impression is auctioned off in real-time when it is just being generated from a user visit. To place an ad automatically and optimally, it is critical for advertisers to devise a learning algorithm to cleverly bid an ad impression in real-time. Most previous works consider the bid decision as a static optimization problem of either treating the value of each impression independently or setting a bid price to each segment of ad volume. However, the bidding for a given ad campaign would repeatedly happen during its life span before the budget runs out. As such, each bid is strategically correlated by the constrained budget and the overall effectiveness of the campaign (e.g., the rewards from generated clicks), which is only observed after the campaign has completed. Thus, it is of great interest to devise an optimal bidding strategy sequentially so that the campaign budget can be dynamically allocated across all the available impressions on the basis of both the immediate and future rewards. In this paper, we formulate the bid decision process as a reinforcement learning problem, where the state space is represented by the auction information and the campaign's real-time parameters, while an action is the bid price to set. By modeling the state transition via auction competition, we build a Markov Decision Process framework for learning the optimal bidding policy to optimize the advertising performance in the dynamic real-time bidding environment. Furthermore, the scalability problem from the large real-world auction volume and campaign budget is well handled by state value approximation using neural networks. The empirical study on two large-scale real-world datasets and the live A/B testing on a commercial platform have demonstrated the superior performance and high efficiency compared to state-of-the-art methods.


title: Psychophysiological stress and emg activity of the trapezius muscle

abstract: Although it is generally assumed that mental stress induces muscular tension, the experimental data have, so far, been inconclusive. Likely explanations for these inconsistent findings are (a) too small subject samples in some experiments, (b) the use of only one type of stress stimulation, and (c) the lack of objective (physiological) measurements documenting the stress-inducing properties of the experimental treatment. Furthermore, the effect of mental stress and physical load separately, versus the combined influence of physical and mental load on muscular tension, has not been investigated earlier. Therefore. the aim of the present experiment was to examine the effects of mental stress as well as of physical load, separately and in combination, on perceived stress, physiological stress responses, and on muscular tension as reflected in electromyographical (EMG) activity of the trapezius muscle. Sixty two female subjects were individually exposed to mental arithmetic, the Stroop color word test (CWT), the cold pressor test, standardized test contractions (TCs), and the CWT combined with a TC. Compared to baseline, the stress session induced significant increases in systolic and diastolic blood pressure, heart rate, urinary catecholamines, salivary Cortisol, and self-reported stress. Each of the two mental stress tests induced a significant increase in EMG activity, The CWT caused a rise in EMG activity also during the TC, which was significantly more pronounced than the increase induced by the CWT alone. Blood pressure responses and self-reported stress followed the same pattern as the EMG activity. The results are consistent with the assumption that psychological stress plays a role in musculoskeletal disorders by increasing muscular tension both in low-load work situations and in the absence of physical load. It is also indicated that the stress-induced increase in muscular tension is accentuated on top of a physical load.


title: SoundWrite: Text Input on Surfaces through Mobile Acoustic Sensing

abstract: Interacting with explosively growing mobile devices is becoming imperative. This paper presents SoundWrite, a mobile acoustic sensing system that enables text input into commercial off-the-shelf devices without any accessories. SoundWrite leverages the embedded microphone to capture subtle audio signals emitted from writing text on common found surfaces (eg., a wood table). It then extracts distinguishable features from both time and frequency information of received signals to recognize the text. We prototype SoundWrite on Smartphones as an Android application, and perform in-depth evaluation. The evaluation results validate the effectiveness and robustness of SoundWrite, and demonstrate that it could achieve an average recognition accuracy of above 90%.


title: A survey of security techniques for the border gateway protocol (BGP)

abstract: Web surfing is an example (and popular) Internet application where users desire services provided by servers that exist somewhere in the Internet. To provide the service, data must be routed between the user's system and the server. Local network routing (relative to the user) can not provide a complete route for the data. In the core Internet, a portion of the network controlled by a single administrative authority, called an autonomous system (AS), provides local network support and also exchanges routing information with other ASes using the border gateway protocol (BGP). Through the BGP route exchange, a complete route for the data is created. Security at this level in the Internet is challenging due to the lack of a single administration point and because there are numerous ASes which interact with one another using complex peering policies. This work reviews recent techniques to secure BGP. These security techniques are categorized as follows: 1) cryptographic/attestation, 2) database, 3) overlay/group protocols, 4) penalty, and 5) data-plane testing. The techniques are reviewed at a high level in a tutorial format, and shortcomings of the techniques are summarized as well. The depth of coverage for particular published works is intentionally kept minimal, so that the reader can quickly grasp the techniques. This survey provides a basis for evaluation of the techniques to understand coverage of published works as well as to determine the best avenues for future research.


title: Large scale image annotation: learning to rank with joint word-image embeddings

abstract: Image annotation datasets are becoming larger and larger, with tens of millions of images and tens of thousands of possible annotations. We propose a strongly performing method that scales to such datasets by simultaneously learning to optimize precision at k of the ranked list of annotations for a given image and learning a low-dimensional joint embedding space for both images and annotations. Our method both outperforms several baseline methods and, in comparison to them, is faster and consumes less memory. We also demonstrate how our method learns an interpretable model, where annotations with alternate spellings or even languages are close in the embedding space. Hence, even when our model does not predict the exact annotation given by a human labeler, it often predicts similar annotations, a fact that we try to quantify by measuring the newly introduced "sibling" precision metric, where our method also obtains excellent results.


title: Applications of RFID in Pharmaceutical Industry

abstract: Security and safety are two important features desired in pharmaceutical supply chain and achieving the same is a challenging task. The need to secure and authenticate pharmaceutical products has increased tremendously with the emerging counterfeit product market. The motivation to introduce counterfeit pharmaceutical products in the supply chain could be to gain rapid economic benefits or affecting the reputation of strong brand in the pharmaceutical industry. RFID technology can be used to deter counterfeiting attempts. It can also be used in various other domains in the pharmaceutical industry. The main aim of this paper is to outline all the applications of RFID in the pharmaceutical industry. After explaining the main applications, we discuss how information hiding techniques could be used with RFID to offer efficient expiry date management, pharmaceutical tamper detection, and fraud detection and prevention.


title: Coexistence of the α and δ phases in an as-cast uranium-rich U–Zr alloy

abstract: Abstract Uranium–zirconium alloys are being investigated for use in ultrahigh burn-up, metallic inert matrix nuclear fuels. Characterization of these alloys in the transmission electron microscope with spatial resolutions that are inaccessible by other techniques shows that the orthorhombic α and hexagonal, intermetallic δ phases coexist in an as-cast uranium-rich U-10 wt.% Zr alloy. Analyses reveal the chemistries of and crystallographic relationship between the two phases.


Thermal Feasibility of Die-Stacked Processing in Memory


title: Using a fuzzy approach to support financial analysis in the corporate acquisition process

abstract: Abstract In the global market place, many companies have had to adapt their strategies to meet significant challenges. A strategy adopted by some companies has been international expansion via acquisitions. The need for expert knowledge to determine an appropriate company to acquire has been complicated by the sheer size of the global market place. The costs associated with this in relation to time and personnel have created the need for a computerised expert system to be developed. This paper endeavours to show how a proposed fuzzy based system can assist in the identification of a company for acquisition. The authors discuss the manipulation of the magnitude of fuzzy membership functions to communicate priorities within the system. The fuzzy system is designed to assist financial experts in identifying a suitable company for acquisition in the corporate acquisition process. This includes the deliberate weighting of certain inputs and results above others in the decision-making process. The system attempts to learn and simulate the human precedence given to particular financial statistics in company analysis. The system uses the magnitude of the fuzzy membership functions to reflect the human precedence given to each financial ratio. This enables a particular company's strengths and weakness to be considered while concurrently considering their significance and relevance to the acquiring organisation. The system will enable a larger number of companies to be analysed in a more time and cost-effective manner. The development of this system is intended to illustrate that a fuzzy system can aid the financial experts of an acquiring organisation in the global acquisition process.


title: Vision system for empty PET-bottle classification inside the box

abstract: With this paper we would like to present a vision system to classify empty PET-bottles inside the box. With this classification it becomes possible to convey only these boxes to the production, which contain a greater portion of usable bottles and therefore the following linear sorting becomes more efficient. The camera-system in the developed control-unit takes images from the top of the boxes and a special flashlight illuminates the passing boxes. The system- computer is a PC 486/33 including a frame grabber for the image acquisition. A light barrier synchronizes the image-processing-hardware, the camera system and the flashlight. The processing software extracts features of the different forms of bottles to classify the bottles. The complete image processing is done by the system-computer with a process speed of around 3600 boxes/h. The detection of the very small differences between the bottles is the main problem of the feature extraction inside the box. Beyond it, leftovers are possible in every bottle. Even these bottles are classified correctly, too. The integrated SPC (stored program control) controls the box-selector to convey only these boxes to the refilling-process containing a greater portion of usable bottles. A prototype of the control-unit is at work since February 1994.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.


title: Features of similarity.

abstract: The metric and dimensional assumptions that underlie the geometric representation of similarity are questioned on both theoretical and empirical grounds. A new set-theoretical approach to similarity is developed in which objects are represented as collections of features, and similarity is described as a feature-matching process. Specifically, a set of qualitative assumptions is shown to imply the contrast model, which expresses the similarity between objects as a linear combination of the measures of their common and distinctive features. Several predictions of the contrast model are tested in studies of similarity with both semantic and perceptual stimuli. The model is used to uncover, analyze, and explain a variety of empirical phenomena such as the role of common and distinctive features, the relations between judgments of similarity and difference, the presence of asymmetric similarities, and the effects of context on judgments of similarity. The contrast model generalizes standard representations of similarity data in terms of clusters and trees. It is also used to analyze the relations of prototypicality and family resemblance


title: Iterative Multi-View Plane Fitting

abstract: Machine system for continuous formation of molecularly oriented plastic bottles by blowing heat-conditioned parisons comprising a modular unit including a parison forming section for simultaneously forming a plurality, preferably four, of parisons in injection molds, each mold having a neck ring assembly; a blowing or bottle forming section for simultaneously blowing the same number of bottles in blow molds as there are parisons in the parison forming section, and an invert arm assembly having a plurality of arms constructed and arranged between said parison forming section and bottle forming section so that the extreme end of each invert arm will retain the neck ring assembly of the injection mold, and in a first position the arms and neck rings are in operable association with the parison injection molds and in a second position are in operable association with the blow molds. In the method of operation, the arms of the invert arm assembly retain the neck rings of the parison forming molds, the parisons are simultaneously molded, the invert arm and neck ring assembly inverted, i.e., rotated 180 degrees, depositing the formed parisons and neck rings in operable association with an open blow mold. After blow mold closing, the neck rings are opened and the invert arms carrying the neck rings are again inverted, i.e., rotated 180 degrees in the opposite direction, to the first invert movement, and the cycle begins again. The machine system provides for rapid manufacture of bottles, is compact, and makes efficient use of machine components.


title: A Viewpoint Invariant Approach for Crowd Counting

abstract: This paper describes a viewpoint invariant learning-based method for counting people in crowds from a single camera. Our method takes into account feature normalization to deal with perspective projection and different camera orientation. The training features include edge orientation and blob size histograms resulted from edge detection and background subtraction. A density map that measures the relative size of individuals and a global scale measuring camera orientation are estimated and used for feature normalization. The relationship between the feature histograms and the number of pedestrians in the crowds is learned from labeled training data. Experimental results from different sites with different camera orientation demonstrate the performance and the potential of our method


title: Agent-based software architecture for distributed measurement systems and cyber-physical systems design

abstract: This paper reports about a research project aimed to the development of an agent-based software architecture for a Distributed Measurement System (DMS) as a Cyber Physical Systems (CPS) part. Agents are used because they naturally support the modeling of the interaction between the measurement nodes and provide the concept of action, useful to implement measurement procedures. A support to model continuity is novel in the approach, that is, the possibility of using the same model for property analysis using simulation, down to final implementation and real-time execution. A case study is presented concerning DMS devoted to real-time monitoring and control of an electric power system.


title: Rolling circle amplification shows a sinusoidal template length-dependent amplification bias

abstract: Biophysical properties of DNA such as its longitudinal and torsional persistence length govern many processes and phenomena in biology, DNA nanotechnology and biotechnology. It has, for example, long been known that the circularization efficiency of short DNA fragments shows a periodic pattern where fragments with integer helical turns circularize much more efficiently than those with odd helical half turns due to stronger stacking of duplex ends. Small DNA circles can serve as templates for rolling circle amplification (RCA), which is a common and extremely robust amplification mechanism for nucleic acids. We discovered a strong template length-dependent amplification efficiency bias of RCA with the same periodicity as B-DNA. However, stacking cannot explain the mechanism behind this bias as the presence of the polymerase in the bifurcation fork inhibits base stacking of ends. Instead, coarse-grained molecular dynamics simulations imply that different amplification efficiencies come from a varying fraying probability of the last two downstream base pairs. We conclude that an increased strain-promoted fraying probability can increase the polymerization rate compared to a relaxed template.


title: A traffic sign detection algorithm based on deep convolutional neural network

abstract: Traffic sign detection plays an important role in driving assistance systems and traffic safety. But the existing detection methods are usually limited to a predefined set of traffic signs. Therefore we propose a traffic sign detection algorithm based on deep Convolutional Neural Network (CNN) using Region Proposal Network(RPN) to detect all Chinese traffic sign. Firstly, a Chinese traffic sign dataset is obtained by collecting seven main categories of traffic signs and their subclasses. Then a traffic sign detection CNN model is trained and evaluated by fine-tuning technology using the collected dataset. Finally, the model is tested by 33 video sequences with the size of 640×480. The result shows that the proposed method has towards real-time detection speed and above 99% detection precision. The trained model can be used to capture the traffic sign from videos by on-board camera or driving recorder and construct a complete traffic sign dataset.


title: High Performance Current Source Inverter Fed Induction Motor Drive with Minimal Harmonic Distortion

abstract: This paper discusses the minimization of the line- and motor-side harmonics in a high power current source drive system. The proposed control achieves speed regulation over the entire speed range with enhanced transient performance and minimal harmonic distortion of the line and motor currents while limiting the switching frequency of current source converters to a maximum of 540 Hz. To minimize the motor current harmonic distortion, space vector modulation (SVM) and selective harmonic elimination (SHE) schemes are optimally implemented according to different drive operating conditions. In order to suppress line- side resonant harmonics, an active damping method using a combination of a virtual harmonic resistor and a three-step modulation signal regulator is employed. The performance of the proposed current source drive is verified by simulation for a 1 MVA system and experiments on a 10 kVA gate-commutated thyristor (GCT) based laboratory drive system.


title: Use of RFID Based Real Time Location Tracking System to Curb Diversion of Transit Goods in East Africa

abstract: In this paper, RFID based Real Time Location Tracking System (RTLS) for tracking transit goods is discussed. The system was designed to address the problem of diversions of transit wet cargo destined for hinterland countries into the local markets, so as to evade payment of duties and taxes. Transit goods are currently tracked manually and by use of manual physical escorts. Reliance on manual methods has rendered the system open to abuse by unscrupulous traders and clearing agents; at times in collusion with government officers. To be effective in tracking wet cargo, the required solution was established to be one that not only provides information in real time but also with features for intervention when cargo is perceived to be at risk. This paper discusses the design and implementation of RTLS for tracking wet cargo, and points out the strenghts, benefits, weaknesses and limitations of the solution. The effectiveness of the solution was demonstrated in addressing the weaknesses of the current manual tracking system in curbing diversion for wet cargo. In addition, the use of RTLS led to a reduction in turn around times by between thirty to forty percent compared to those which are manualy tracked.


title: Modified post-filter to recover modulation spectrum for HMM-based speech synthesis

abstract: This paper proposes a modified post-filter to recover a Modulation Spectrum (MS) in HMM-based speech synthesis. To alleviate the over-smoothing effect which is one of the major problems in HMM-based speech synthesis, the MS-based post-filter has been proposed. It recovers the utterance-level MS of the generated speech trajectory, and we have reported its benefit to the quality improvement. However, this post-filter is not applicable to various lengths of speech parameter trajectories, such as phrases or segments, which are shorter than an utterance. To address this problem, we propose two modified post-filters, (1) the time-invariant filter with a simplified conversion form and (2) the segment-level post-filter which applicable to a short-term parameter sequence. Furthermore, we also propose (3) the post-filter to recover the phoneme-level MS of HMM-state duration. Experimental results show that the modified post-filters also yield significant quality improvements in synthetic speech as yielded by the conventional post-filter.


title: Simple empirical software effort estimation model

abstract: Context: An effort estimation model with more than 20 parameters is not very useful at early conceptual phase if you don't have a logical approach for specifying the input values. Goal: This paper presents a simple approach for predicting software development effort. Method: The regression model uses product size and application types to predict effort. Product size is measured in terms of the equivalent source lines of code. The analysis is based on empirical data collected from 317 very recent projects implemented within the United States Department of Defense over the course of nine years beginning in 2004. Result: Statistical results showed that source lines of code and application type are significant contributors to development effort. Conclusion: The equation is simpler and more viable to use for early estimates than traditional parametric cost models. The effect of product size on software effort shall be interpreted along with application domain.


title: Self-disclosure on Facebook among female users and its relationship to feelings of loneliness

abstract: More lonely people disclosed private information such as Address than the connected.Less lonely people did not disclose any private information compared to the connected.Less lonely people disclosed their Views and their Wall than those connected people.More lonely people did not disclose Views and their Wall compared to connected people.No other significant associations between loneliness and other variables were found. The purpose of this study is to determine if there is a relationship between loneliness and self-disclosure in social networking sites (i.e., Facebook). The study collected data from six hundred and sixteen (616) female Facebook users whose profiles were publicly available online. Out of these 616 Facebook users, half (308) were categorised as 'connected' and the remaining 308 users were categorised as 'lonely'; based on clearly stating this feeling in their latest wall posting. Data for each attribute in the user's Facebook profile was recorded as a binomial outcome (Present (1)/Absent (0)). Attributes were grouped together and the binomial responses totalled. The results of this study have shown that more 'lonely' people disclosed their Personal Information, Relationship Information, and Address than 'connected' people and more 'connected' people disclosed their Views and their Wall than 'lonely' people. The study has found no other significant associations between loneliness and the other variables. In addition, in the discussion section the article also highlights the implications of self-disclosure on SNSs users' wellbeing.


title: Dynamic interference and timeout-based CAC scheme for multimedia cellular networks

abstract: In this paper, an adaptive QoS oriented CAC function is proposed to meet the rapidly increasing demand for providing multimedia services with diversified quality requirements. It decides whether the connection request is to be admitted into the system based on the interference level and the waiting time before access. This CAC scheme, based on service class differentiation aims at maximizing the use of available radio resource and meeting the QoS requirement of higher priority users as much as possible while maintaining the minimum requirements of lower priority users, especially when the system suffers from congestion. The new CAC policy achieves a good performance and capacity gain.


title: A methodology for applying k-nearest neighbor to time series forecasting

abstract: In this paper a methodology for applying k-nearest neighbor regression on a time series forecasting context is developed. The goal is to devise an automatic tool, i.e., a tool that can work without human intervention; furthermore, the methodology should be effective and efficient, so that it can be applied to accurately forecast a great number of time series. In order to be incorporated into our methodology, several modeling and preprocessing techniques are analyzed and assessed using the N3 competition data set. One interesting feature of the proposed methodology is that it resolves the selection of important modeling parameters, such as k or the input variables, combining several models with different parameters. In spite of the simplicity of k-NN regression, our methodology seems to be quite effective.


title: Inductive Categorization: A Methodology to Examine the Basis for Categorization and Induction in Infancy

abstract: ABSTRACT Three experiments with a methodology called inductive categorization examined 12- 16-, and 20-month-olds' categorization of animals and vehicles with and without functional parts as well as their inductive inferences about the motion properties of the objects in these classes. The experiments showed that infants at 12 months use object parts to categorize after a brief learning period, infants at 16 months attend spontaneously to object parts to categorize, and infants at 20 months use object parts and other features to categorize. The experiments also revealed that 12-montholds have little knowledge about the motion properties of objects, 16-month-olds have associated specific object parts with those properties, and 20-month-olds have generalized from object parts to other features. Taken together, these experiments provide support for the domain-general approach to early concept development, and they are the first to show a relationship between inductive inference and categorization in infancy. KEYWORDS: infancy, induction, categorization, object features. INTRODUCTION One of the key functions of concepts - mental representations of the object, entities, and events in the worlds-is to provide a basis for categorization and generalization. Only a fraction of the entities, objects, features, and events in the world can be experienced directly; therefore, we must rely on categorization and inductive inference to determine which things in the world belong together or are alike in some way and how to generalize a specific observation to other instances. For example, on encountering a novel mammal that barks and has fur one would conclude that it belongs to the category of "dog", that it is selfpropelled, moves towards goals, and chases squirrels. As adults, we seamlessly and effortlessly accomplish these tasks-that is, we have carved nature accurately at its joints - yet how we reach this representational end-state currently remains unknown. The experiments in this paper were designed to address key, as yet unanswered, questions relating to the development of object concepts; namely, (1) what is the effect of high and low within-category part similarity on infants' categorization and induction?; and (2) what is the relationship between categorization and induction in infancy and is it informative about the content of infants' developing representations? The last 20 years has witnessed a proliferation of research on the early development of categorization. One of the most fruitful avenues of this research has relied on the sequential touching or object manipulation procedure, in which the systematicity of infants' spontaneous successive touches to objects are interpreted as indicative of categorization. Using this paradigm, it was found that infants at 18 and 20 months categorize animals and vehicles as different (e.g., animals vs. vehicles) but not basic-level contrasts within these domains (e.g., dogs and horses) (Mandler & Bauer, 1988; Mandler, Bauer, & McDonough, 1991). According to Mandler and colleagues, these and similar data support the view that infants' categorization and induction are not based on surface features (see also Mandler & McDonough, 1996, 1998). They argued that exemplars within a superordinate domain (e.g., animals) share few surface properties and therefore categorization of such perceptually diverse stimuli must be based on conceptual categorization (e.g., animacy, movement abilities, or class relations), with properties perceptible in the input playing only a secondary role. A corollary of this view is that infants possess specialized processes, innate modules, or skeletal principles that facilitate rapid conceptual understanding and allow infants within the first year of life to learn about objects' surface properties as well as those that are sporadically available in the perceptual input (e.g. movement) (Gelman, 1990; Leslie, 1995; Mandler, 1992). …


title: Circularly Polarized Beam-Steering Antenna Array With Butler Matrix Network

abstract: A circularly polarized switched-beam patch antenna array with butler matrix is designed and experimentally demonstrated in this letter. With suitable rotation technique, circular polarized radiation can be improved for beam steering. Also, this technique enhances the axial-ratio (AR) bandwidth within its scanning angle. Measured results showed this array has an impedance and AR bandwidth over a frequency range of 5.75-6.2 GHz when the main beam of array is scanned to different directions. Good agreement is observed between simulation and measurement.


title: Speech Perception in Older Hearing Impaired Listeners: Benefits of Perceptual Training

abstract: Hearing aids (HAs) only partially restore the ability of older hearing impaired (OHI) listeners to understand speech in noise, due in large part to persistent deficits in consonant identification. Here, we investigated whether adaptive perceptual training would improve consonant-identification in noise in sixteen aided OHI listeners who underwent 40 hours of computer-based training in their homes. Listeners identified 20 onset and 20 coda consonants in 9,600 consonant-vowel-consonant (CVC) syllables containing different vowels (/ɑ/, /i/, or /u/) and spoken by four different talkers. Consonants were presented at three consonant-specific signal-to-noise ratios (SNRs) spanning a 12 dB range. Noise levels were adjusted over training sessions based on d’ measures. Listeners were tested before and after training to measure (1) changes in consonant-identification thresholds using syllables spoken by familiar and unfamiliar talkers, and (2) sentence reception thresholds (SeRTs) using two different sentence tests. Consonant-identification thresholds improved gradually during training. Laboratory tests of d’ thresholds showed an average improvement of 9.1 dB, with 94% of listeners showing statistically significant training benefit. Training normalized consonant confusions and improved the thresholds of some consonants into the normal range. Benefits were equivalent for onset and coda consonants, syllables containing different vowels, and syllables presented at different SNRs. Greater training benefits were found for hard-to-identify consonants and for consonants spoken by familiar than unfamiliar talkers. SeRTs, tested with simple sentences, showed less elevation than consonant-identification thresholds prior to training and failed to show significant training benefit, although SeRT improvements did correlate with improvements in consonant thresholds. We argue that the lack of SeRT improvement reflects the dominant role of top-down semantic processing in processing simple sentences and that greater transfer of benefit would be evident in the comprehension of more unpredictable speech material.


title: Start Later, Sleep Later: School Start Times and Adolescent Sleep in Homeschool Versus Public/Private School Students

abstract: Homeschooled students provide a naturalistic comparison group for later/flexible school start times. This study compared sleep patterns and sleep hygiene for homeschooled students and public/private school students (grades 6–12). Public/private school students (n = 245) and homeschooled students (n = 162) completed a survey about sleep patterns and sleep hygiene. Significant school group differences were found for weekday bedtime, wake time, and total sleep time, with homeschooled students waking later and obtaining more sleep. Homeschooled students had later school start times, waking at the same time that public/private school students were starting school. Public/private school students had poorer sleep hygiene practices, reporting more homework and use of technology in the hour before bed. Regardless of school type, technology in the bedroom was associated with shorter sleep duration. Later school start times may be a potential countermeasure for insufficient sleep in adolescents. Future studies shoul...


title: NLP on Spoken Documents Without ASR

abstract: There is considerable interest in interdisciplinary combinations of automatic speech recognition (ASR), machine learning, natural language processing, text classification and information retrieval. Many of these boxes, especially ASR, are often based on considerable linguistic resources. We would like to be able to process spoken documents with few (if any) resources. Moreover, connecting black boxes in series tends to multiply errors, especially when the key terms are out-of-vocabulary (OOV). The proposed alternative applies text processing directly to the speech without a dependency on ASR. The method finds long (~ 1 sec) repetitions in speech, and clusters them into pseudo-terms (roughly phrases). Document clustering and classification work surprisingly well on pseudo-terms; performance on a Switchboard task approaches a baseline using gold standard manual transcriptions.


title: Using Social Media for Actionable Disease Surveillance and Outbreak Management: A Systematic Literature Review

abstract: Objective ::: Research studies show that social media may be valuable tools in the disease surveillance toolkit used for improving public health professionals’ ability to detect disease outbreaks faster than traditional methods and to enhance outbreak response. A social media work group, consisting of surveillance practitioners, academic researchers, and other subject matter experts convened by the International Society for Disease Surveillance, conducted a systematic primary literature review using the PRISMA framework to identify research, published through February 2013, answering either of the following questions: ::: ::: Can social media be integrated into disease surveillance practice and outbreak management to support and improve public health? ::: Can social media be used to effectively target populations, specifically vulnerable populations, to test an intervention and interact with a community to improve health outcomes? ::: ::: ::: Examples of social media included are Facebook, MySpace, microblogs (e.g., Twitter), blogs, and discussion forums. For Question 1, 33 manuscripts were identified, starting in 2009 with topics on Influenza-like Illnesses (n = 15), Infectious Diseases (n = 6), Non-infectious Diseases (n = 4), Medication and Vaccines (n = 3), and Other (n = 5). For Question 2, 32 manuscripts were identified, the first in 2000 with topics on Health Risk Behaviors (n = 10), Infectious Diseases (n = 3), Non-infectious Diseases (n = 9), and Other (n = 10). ::: ::: ::: Conclusions ::: The literature on the use of social media to support public health practice has identified many gaps and biases in current knowledge. Despite the potential for success identified in exploratory studies, there are limited studies on interventions and little use of social media in practice. However, information gleaned from the articles demonstrates the effectiveness of social media in supporting and improving public health and in identifying target populations for intervention. A primary recommendation resulting from the review is to identify opportunities that enable public health professionals to integrate social media analytics into disease surveillance and outbreak management practice.


INNOVATIONS ITERATIVE MODEL RECONSTRUCTION : SIMULTANEOUSLY LOWERED COMPUTED TOMOGRAPHY RADIATION DOSE AND IMPROVED IMAGE QUALITY


title: When helping helps: Autonomous motivation for prosocial behavior and its influence on well-being for the helper and recipient.

abstract: Self-determination theory posits that the degree to which a prosocial act is volitional or autonomous predicts its effect on well-being and that psychological need satisfaction mediates this relation. Four studies tested the impact of autonomous and controlled motivation for helping others on well-being and explored effects on other outcomes of helping for both helpers and recipients. Study 1 used a diary method to assess daily relations between prosocial behaviors and helper well-being and tested mediating effects of basic psychological need satisfaction. Study 2 examined the effect of choice on motivation and consequences of autonomous versus controlled helping using an experimental design. Study 3 examined the consequences of autonomous versus controlled helping for both helpers and recipients in a dyadic task. Finally, Study 4 manipulated motivation to predict helper and recipient outcomes. Findings support the idea that autonomous motivation for helping yields benefits for both helper and recipient through greater need satisfaction. Limitations and implications are discussed.


title: Classification of learning outcomes: evidence from the computer games literature

abstract: Following up on an earlier issue of The Curriculum Journal (Vol. 16, No. 1), this article focuses on learning outcomes in the context of video games. Learning outcomes are viewed from two theoretical frameworks: Kirkpatrick's levels of evaluation and the CRESST model of learning. These are used to analyse the outcomes claimed in journal articles that report empirical work, indicating the usefulness of the frameworks, and the necessity to consider the role of affective learning. The article ends with some comments on the relationship of instructional design to effective games and learning outcomes.


title: Low Rank Representation on Grassmann Manifolds

abstract: Low-rank representation (LRR) has recently attracted great interest due to its pleasing efficacy in exploring low-dimensional subspace structures embedded in data. One of its successful applications is subspace clustering which means data are clustered according to the subspaces they belong to. In this paper, at a higher level, we intend to cluster subspaces into classes of subspaces. This is naturally described as a clustering problem on Grassmann manifold. The novelty of this paper is to generalize LRR on Euclidean space into the LRR model on Grassmann manifold. The new method has many applications in computer vision tasks. The paper conducts the experiments over two real world examples, clustering handwritten digits and clustering dynamic textures. The experiments show the proposed method outperforms a number of existing methods.