title: Selection of optimal credit card fraud detection models using a coefficient sum approach

abstract: During the past years various fraud detection techniques were proposed and used to reduce fraudulent activities. Selection an optimal fraud detection model becomes keen area of interest for researchers in the field of anomaly detection. Methods and tools for fraud detection model selection which were used in the literature used a limited no of model selection criteria for finding the detection of fraud in various areas. So for the first time we propose a approach based on a Coefficient sum method. This approach is used for ranking of Fraud detection models for finding the optimal model from various fraud detection models. For ranking the FDMs, various model selection criteria, with a set of FDMs are require Real data sets are used for illustration of the Coefficient sum method. The result of this paper gives a ranking method based on the Sum value of each criteria of FDM.


title: Mobile Human Airbag System for Fall Protection Using MEMS Sensors and Embedded SVM Classifier

abstract: This paper introduces a mobile human airbag system designed for fall protection for the elderly. A Micro Inertial Measurement Unit ( muIMU) of 56 mm times 23 mm times 15 mm in size is built. This unit consists of three dimensional MEMS accelerometers, gyroscopes, a Bluetooth module and a Micro Controller Unit (MCU). It records human motion information, and, through the analysis of falls using a high-speed camera, a lateral fall can be determined by gyro threshold. A human motion database that includes falls and other normal motions (walking, running, etc.) is set up. Using a support vector machine (SVM) training process, we can classify falls and other normal motions successfully with a SVM filter. Based on the SVM filter, an embedded digital signal processing (DSP) system is developed for real-time fall detection. In addition, a smart mechanical airbag deployment system is finalized. The response time for the mechanical trigger is 0.133 s, which allows enough time for compressed air to be released before a person falls to the ground. The integrated system is tested and the feasibility of the airbag system for real-time fall protection is demonstrated.


title: Performance of optical flow techniques

abstract: While different optical flow techniques continue to appear, there has been a lack of quantitative evaluation of existing methods. For a common set of real and synthetic image sequences, we report the results of a number of regularly cited optical flow techniques, including instances of differential, matching, energy-based, and phase-based methods. Our comparisons are primarily empirical, and concentrate on the accuracy, reliability, and density of the velocity measurements; they show that performance can differ significantly among the techniques we implemented.


title: DETECTING,TRACKING AND COUNTING FISH IN LOW QUALITY UNCONSTRAINED UNDERWATER VIDEOS

abstract: In this work a machine vision system capable of analysing underwater videos for detecting, tracking and counting fish is presented. The real-time videos, collected near the Ken-Ding sub-tropical coral reef waters are managed by EcoGrid, Taiwan and are barely analysed by marine biologists. The video processing system consists of three subsystems: the video texture analysis, fish detection and tracking modules. Fish detection is based on two algorithms computed independently, whose results are combined in order to obtain a more accurate outcome. The tracking was carried out by the application of the CamShift algorithm that enables the tracking of objects whose numbers may vary over time. Unlike existing fish-counting methods, our approach provides a reliable method in which the fish number is computed in unconstrained environments and under several scenarios (murky water, algae on camera lens, moving plants, low contrast, etc.). The proposed approach was tested with 20 underwater videos, achieving an overall accuracy as high as 85%.


The pharmacological properties of cannabis


title: Distortion-Aware Convolutional Filters for Dense Prediction in Panoramic Images

abstract: There is a high demand of 3D data for 360\(^\circ \) panoramic images and videos, pushed by the growing availability on the market of specialized hardware for both capturing (e.g., omni-directional cameras) as well as visualizing in 3D (e.g., head mounted displays) panoramic images and videos. At the same time, 3D sensors able to capture 3D panoramic data are expensive and/or hardly available. To fill this gap, we propose a learning approach for panoramic depth map estimation from a single image. Thanks to a specifically developed distortion-aware deformable convolution filter, our method can be trained by means of conventional perspective images, then used to regress depth for panoramic images, thus bypassing the effort needed to create annotated panoramic training dataset. We also demonstrate our approach for emerging tasks such as panoramic monocular SLAM, panoramic semantic segmentation and panoramic style transfer.


title: Value Prediction Network

abstract: This paper proposes a novel deep reinforcement learning (RL) architecture, called Value Prediction Network (VPN), which integrates model-free and model-based RL methods into a single neural network. In contrast to typical model-based RL methods, VPN learns a dynamics model whose abstract states are trained to make option-conditional predictions of future values (discounted sum of rewards) rather than of future observations. Our experimental results show that VPN has several advantages over both model-free and model-based baselines in a stochastic environment where careful planning is required but building an accurate observation-prediction model is difficult. Furthermore, VPN outperforms Deep Q-Network (DQN) on several Atari games even with short-lookahead planning, demonstrating its potential as a new way of learning a good state representation.


title: Illumination and motion-based video enhancement for night surveillance

abstract: This work presents a context enhancement method of low illumination video for night surveillance. A unique characteristic of the algorithm is its ability to extract and maintenance the meaningful information like highlight area or moving objects with low contrast in the enhanced image, meanwhile recover the surrounding scene information by fusing the daytime background image. A main challenge comes from the extraction of meaningful area in the night video sequence. To address this problem, a novel bidirectional extraction approach is presented. In evaluation experiments with real data, the notable information of the night video is extracted successfully and the background scene is fused smoothly with the night images to show enhanced surveillance video for observers.


title: Studies on Ontology-based irrigation management information systems modeling

abstract: Research and development irrigation management information systems are the important measures of making irrigation management more modernized and standardized. The difficulties of building information systems have been increased along with the continuous development of information technology and the complexity of information systems, information systems put forward higher request to “shared” and “reuse”. Ontology-based information systems modeling can eliminate semantic differences, and carry out knowledge sharing and interoperability of different systems. In this paper, we introduce several common models which used in information systems modeling briefly; and then we introduce ontology, summarize ontology-based information systems modeling process; finally, we discuss the applications of ontology-based information systems modeling in irrigation management information systems preliminary.


title: A COMPARISON OF ENTERPRISE ARCHITECTURE FRAMEWORKS

abstract: An Enterprise Architecture Framework (EAF) maps all of the software development processes within the enterprise and how they relate and interact to fulfill the enterprise’s mission. It provides organizations with the ability to understand and analyze weaknesses or inconsistencies to be identified and addressed. There are a number of already established EAF in use today; some of these frameworks were developed for very specific areas, whereas others have broader functionality. This study provides a comparison of several frameworks that can then be used for guidance in the selection of an EAF that meets the needed criteria.


title: An Investigation of Newton-Sketch and Subsampled Newton Methods

abstract: ABSTRACTSketching, a dimensionality reduction technique, has received much attention in the statistics community. In this paper, we study sketching in the context of Newton's method for solving fin...


title: Cross-layer cooperation to boost multipath TCP performance in cloud networks

abstract: Cloud networking imposes new requirements in terms of connection resiliency and throughput among virtual machines, hypervisors and users. A promising direction is to exploit multipath communications, yet existing protocols have a so limited scope that performance improvements are often unreachable. Generally, multipathing adds signaling overhead and in certain conditions may in fact decrease throughput due to packet arrival disorder. At the transport layer, the most promising protocol is Multipath TCP (MPTCP), a backward compatible TCP extension allowing to balance the load on several TCP subflows, ideally following different physical paths, to maximize connection throughput. Current implementations create a full mesh between hosts IPs, which can be suboptimal. For situation when at least one end-point network is multihomed, we propose to enhance its subflow creation mechanism so that MPTCP creates an adequate number of subflows considering the underlying path diversity offered by an IP-in-IP mapping protocol, the Location/Identifier Separation Protocol (LISP). We defined and implemented a cross-layer cooperation module between MPTCP and LISP, leading to an improved version of MPTCP we name Augmented MPTCP (A-MPTCP). We evaluated A-MPTCP for a realistic Cloud access use-case scenario involving one multi-homed data-center. Results from a large-scale test bed show us that A-MPTCP can halve the transfer times with the simple addition of one additional LIS-Penabled MPTCP subflow, hence showing promising performance for Cloud communications between multi-homed users and multihomed data-centers.


title: Discovery of potent IDO1 inhibitors derived from tryptophan using scaffold-hopping and structure-based design approaches

abstract: Indoleamine 2,3-dioxygenase 1 (IDO1) is frequently hijacked by tumors to escape the host immune response, and the enzyme is now firmly established as an attractive target for cancer immunotherapy. To identify novel IDO1 inhibitors suitable for drug development, a scaffold-hopping strategy combined with the average electrostatic potentials calculation was ultilized to design novel benzoxazolinone derivatives. Among these, compounds 7e, 7f and 9c exhibited the inhibitory potency in the low micromolar range and displayed negligible level of cytotoxicity against HeLa cells. Treatment with these three compounds promoted the proliferation of T lymphocyte and led to the dramatic decrease of regulatory T cells in the B16F1 cells and naive T cells co-culture system. Subsequent spectroscopic experiments suggested that these benzoxazolinones formed a coordinate bond with the heme iron to stabilize the complex. This study suggested that the benzoxazolinone was an interesting scaffold for discovering novel IDO1 inhibitors, and these compounds are attractive candidates for further development.


title: Defense of DDoS attack for cloud computing

abstract: Though cloud computing is targeted to provide better utilization of resources using virtualization techniques and to take up much of the work load from the client, it is fraught with security risks. One of most serious threats is Distributed Denial of service attack (DDoS) which is easy to implement and affective. In this paper, we proposed an approach called SBTA which based on the use of SOA to traceback and find the source of DDoS attacks. We further propose a filter approach called Cloud-filter. Experiments show the defense system which is a combination of SBTA and Cloud-filter is effective in Cloud Computing. It was able to trace and identify the source most of these attacks messages of these attacks and reduce the packets that reconstruction path required. It also had a high detection rates with low false positives.


title: Autonomous Vehicles: Disengagements, Accidents and Reaction Times

abstract: Autonomous vehicles are being viewed with scepticism in their ability to improve safety and the driving experience. A critical issue with automated driving at this stage of its development is that it is not yet reliable and safe. When automated driving fails, or is limited, the autonomous mode disengages and the drivers are expected to resume manual driving. For this transition to occur safely, it is imperative that drivers react in an appropriate and timely manner. Recent data released from the California trials provide compelling insights into the current factors influencing disengagements of autonomous mode. Here we show that the number of accidents observed has a significantly high correlation with the autonomous miles travelled. The reaction times to take control of the vehicle in the event of a disengagement was found to have a stable distribution across different companies at 0.83 seconds on average. However, there were differences observed in reaction times based on the type of disengagements, type of roadway and autonomous miles travelled. Lack of trust caused by the exposure to automated disengagements was found to increase the likelihood to take control of the vehicle manually. Further, with increased vehicle miles travelled the reaction times were found to increase, which suggests an increased level of trust with more vehicle miles travelled. We believe that this research would provide insurers, planners, traffic management officials and engineers fundamental insights into trust and reaction times that would help them design and engineer their systems.


title: Learning to Synthesize Motion Blur

abstract: We present a technique for synthesizing a motion blurred image from a pair of unblurred images captured in succession. To build this system we motivate and design a differentiable “line prediction” layer to be used as part of a neural network architecture, with which we can learn a system to regress from image pairs to motion blurred images that span the capture time of the input image pair. Training this model requires an abundance of data, and so we design and execute a strategy for using frame interpolation techniques to generate a large-scale synthetic dataset of motion blurred images and their respective inputs. We additionally capture a high quality test set of real motion blurred images, synthesized from slow motion videos, with which we evaluate our model against several baseline techniques that can be used to synthesize motion blur. Our model produces higher accuracy output than our baselines, and is several orders of magnitude faster than baselines with competitive accuracy.


title: 4th generation multiresonator-based chipless RFID tag utilizing spiral EBGs

abstract: 4th generation chipless tag based on multiresonators is presented. The tag consist of a UWB monopole antenna loaded with multiple cascaded spiral EBGs which have a 1∶1 correspondence with data bits. The 4th generation tag is less sensitive to tag orientation and has smaller size than earlier generations of the tag. A proof-of-concept tag is developed and tested which has 6-bits and operates between 2 – 2.5 GHz. The chipless RFID tag is designed for ultra-low cost tagging of paper/plastic items on conveyor belt applications.


title: Probabilistic model checking of the next-generation airborne collision avoidance system

abstract: We present a probabilistic model checking approach for evaluating the safety and operational suitability of the Airborne Collision Avoidance System X (ACAS X). This system issues advisories to pilots when the risk of mid-air collision is imminent, and is expected to be equipped on all large, piloted aircraft in the future. We developed an approach to efficiently compute the probabilities of generically specified events and the most likely sequences of states leading to those events within a discrete-time Markov chain model of aircraft flight and ACAS X. The probabilities and sequences are computed for all states in the model. Events of interest include near mid-air collisions (NMACs) and undesirable sequences of advisories that affect operational suitability. We have validated numerous observations of the model with higher-fidelity simulations of the full system. This analysis has revealed several characteristics of ACAS X's behavior.


title: Feature Extraction and Pattern Recognition for Human Motion by a Deep Sparse Autoencoder

abstract: Human motion data is high-dimensional time-series data, and it usually contains measurement error and noise. Recognizing human motion on the basis of such high-dimensional measurement row data is often difficult and cannot be expected for high generalization performance. To increase generalization performance in a human motion pattern recognition task, we employ a deep sparse auto encoder to extract low-dimensional features, which can efficiently represent the characteristics of each motion, from the high-dimensional human motion data. After extracting low-dimensional features by using the deep sparse auto encoder, we employ random forests to classify low-dimensional features representing human motion. In experiments, we compared using the row data and three types of feature extraction methods - principal component analysis, a shallow sparse auto encoder, and a deep sparse auto encoder - for pattern recognition. The experimental results show that the deep sparse auto encoder outperformed the other methods with the highest average recognition accuracy, 75.1%, and the lowest standard deviation, ±3.30%. The proposed method, application of a deep sparse auto encoder, thus enabled higher recognition accuracy, better generalization and more stability than could be achieved with the other methods.


title: Big Data management in smart grid: concepts, requirements and implementation

abstract: A smart grid is an intelligent electricity grid that optimizes the generation, distribution and consumption of electricity through the introduction of Information and Communication Technologies on the electricity grid. In essence, smart grids bring profound changes in the information systems that drive them: new information flows coming from the electricity grid, new players such as decentralized producers of renewable energies, new uses such as electric vehicles and connected houses and new communicating equipments such as smart meters, sensors and remote control points. All this will cause a deluge of data that the energy companies will have to face. Big Data technologies offers suitable solutions for utilities, but the decision about which Big Data technology to use is critical. In this paper, we provide an overview of data management for smart grids, summarise the added value of Big Data technologies for this kind of data, and discuss the technical requirements, the tools and the main steps to implement Big Data solutions in the smart grid context.


title: Neural systems for recognition of familiar faces

abstract: Immediate access to information about people that we encounter is an essential requirement for effective social interactions. In this manuscript we briefly review our work and work of others on familiar face recognition and propose a modified version of our model of neural systems for face perception with a special emphasis on processes associated with recognition of familiar faces. We argue that visual appearance is only one component of successful recognition of familiar individuals. Other fundamental aspects include the retrieval of “person knowledge” – the representation of the personal traits, intentions, and outlook of someone we know – and the emotional response we experience when seeing a familiar individual. Specifically, we hypothesize that the “theory of mind” areas, that have been implicated in social and cognitive functions other than face perception, play an essential role in the spontaneous activation of person knowledge associated with the recognition of familiar individuals. The amygdala and the insula, structures that are involved in the representation of emotion, also are part of the distributed network of areas that are modulated by familiarity, reflecting the role of emotion in face recognition.


title: A Kullback-Leibler Divergence Based Kernel for SVM Classification in Multimedia Applications

abstract: Over the last years significant efforts have been made to develop kernels that can be applied to sequence data such as DNA, text, speech, video and images. The Fisher Kernel and similar variants have been suggested as good ways to combine an underlying generative model in the feature space and discriminant classifiers such as SVM's. In this paper we suggest an alternative procedure to the Fisher kernel for systematically finding kernel functions that naturally handle variable length sequence data in multimedia domains. In particular for domains such as speech and images we explore the use of kernel functions that take full advantage of well known probabilistic models such as Gaussian Mixtures and single full covariance Gaussian models. We derive a kernel distance based on the Kullback-Leibler (KL) divergence between generative models. In effect our approach combines the best of both generative and discriminative methods and replaces the standard SVM kernels. We perform experiments on speaker identification/verification and image classification tasks and show that these new kernels have the best performance in speaker verification and mostly outperform the Fisher kernel based SVM's and the generative classifiers in speaker identification and image classification.


Aspect Specific Sentiment Analysis using Hierarchical Deep Learning


title: Handwriting Recognition with Large Multidimensional Long Short-Term Memory Recurrent Neural Networks

abstract: Multidimensional long short-term memory recurrent neural networks achieve impressive results for handwriting recognition. However, with current CPU-based implementations, their training is very expensive and thus their capacity has so far been limited. We release an efficient GPU-based implementation which greatly reduces training times by processing the input in a diagonal-wise fashion. We use this implementation to explore deeper and wider architectures than previously used for handwriting recognition and show that especially the depth plays an important role. We outperform state of the art results on two databases with a deep multidimensional network.


title: Robotic Agriculture Machine

abstract: This paper presents a system with high speed of operation for an advanced agriculture process which includes cultivation based on robotic platform. The robotic system is an electromechanical (conveys a sense that it has agency of its own) and artificial agent which is steered by DC motor which has four wheels. The farm is cultivated by the machine, depending on the crop considering particular rows & specific columns. The infrared sensor detects the obstacles in the path and it also senses turning position of vehicle at end of land. The seed block can be detected and solved using water pressure. The machine can be controlled remotely and solar panel is used to charge DC battery. Assembly language is used in programming the microcontrollers. The microcontroller is used to control and monitor the process of system motion of vehicle with the help of DC motor. The result of implemented unit is also presented.


title: Design Rule Spaces: A New Model for Representing and Analyzing Software Architecture

abstract: In this paper, we propose an architecture model called Design Rule Space (DRSpace). We model the architecture of a software system as multiple overlapping DRSpaces, reflecting the fact that any complex software system must contain multiple aspects, features, patterns, etc. We show that this model provides new ways to analyze software quality. In particular, we introduce an Architecture Root detection algorithm that captures DRSpaces containing large numbers of a project's bug-prone files, which are called Architecture Roots (ArchRoots). After investigating ArchRoots calculated from 15 open source projects, the following observations become clear: from 35 to 91 percent of a project's most bug-prone files can be captured by just 5 ArchRoots, meaning that bug-prone files are likely to be architecturally connected. Furthermore, these ArchRoots tend to live in the system for significant periods of time, serving as the major source of bug-proneness and high maintainability costs. Moreover, each ArchRoot reveals multiple architectural flaws that propagate bugs among files and this will incur high maintenance costs over time. The implication of our study is that the quality, in terms of bug-proneness, of a large, complex software project cannot be fundamentally improved without first fixing its architectural flaws.


title: Applying real-time scheduling theory to the synchronous data flow model of computation

abstract: Schedulability analysis techniques that are well understood within the real-time scheduling community are applied to the analysis of recurrent real-time workloads that are modeled using the synchronous data-flow graph (SDFG) model. An enhancement to the standard SDFG model is proposed, that permits the specification of a real-time latency constraint between a specified input and a specified output of an SDFG. A technique is derived for transforming such an enhanced SDFG to a collection of traditional 3-parameter sporadic tasks, thereby allowing for the analysis of systems of SDFG tasks using the methods and algorithms that have previously been developed within the real-time scheduling community for the analysis of systems of such sporadic tasks. The applicability of this approach is illustrated by applying prior results from real-time scheduling theory to construct an exact preemptive uniprocessor schedulability test for collections of recurrent processes that are each represented using the enhanced SDFG model.


title: Fast Online Clustering with Randomized Skeleton Sets

abstract: We present a new fast online clustering algorithm that reliably recovers arbitrary-shaped data clusters in high throughout data streams. Unlike the existing state-of-the-art online clustering methods based on k-means or k-medoid, it does not make any restrictive generative assumptions. In addition, in contrast to existing nonparametric clustering techniques such as DBScan or DenStream, it gives provable theoretical guarantees. To achieve fast clustering, we propose to represent each cluster by a skeleton set which is updated continuously as new data is seen. A skeleton set consists of weighted samples from the data where weights encode local densities. The size of each skeleton set is adapted according to the cluster geometry. The proposed technique automatically detects the number of clusters and is robust to outliers. The algorithm works for the infinite data stream where more than one pass over the data is not feasible. We provide theoretical guarantees on the quality of the clustering and also demonstrate its advantage over the existing state-of-the-art on several datasets.


title: Through Glass Via (TGV) disc loaded monopole antennas for millimeter-wave wireless interposer communication

abstract: A monopole antenna loaded with a circular disc is integrated on a glass interposer layer for millimeter-wave wireless communication applications. A Through Glass Via (TGV) is used as a main radiator and the circular disc is for a impedance matching. An omnidirectional radiation pattern formed by the monopole antenna allows in-plane wireless communication whose distance is much larger than 1cm. This can solve problems such as cross talk and time delay caused by the conventional wire bonding approach. A 77 GHz (W-band) antenna is designed and fabricated as a prototype on a glass substrate. The simulated peak gain of the antenna is 1.23 dBi. Also, as this antenna concept is scalable, the same architecture has been exercised for a 5.8 GHz industrial, scientific and medical, ISM band application. The design, fabrication, and characterization are detailed. The measured results of the return loss and radiation pattern agree well with the simulation results.


Supply Chain Management based on Blockchain: A Systematic Mapping Study


title: Inertial Odometry on Handheld Smartphones

abstract: Building a complete inertial navigation system using the limited quality data provided by current smartphones has been regarded challenging, if not impossible. This paper shows that by careful crafting and accounting for the weak information in the sensor samples, smartphones are capable of pure inertial navigation. We present a probabilistic approach for orientation and use-case free inertial odometry, which is based on double-integrating rotated accelerations. The strength of the model is in learning additive and multiplicative IMU biases online. We are able to track the phone position, velocity, and pose in real-time and in a computationally lightweight fashion by solving the inference with an extended Kalman filter. The information fusion is completed with zero-velocity updates (if the phone remains stationary), altitude correction from barometric pressure readings (if available), and pseudo-updates constraining the momentary speed. We demonstrate our approach using an iPad and iPhone in several indoor dead-reckoning applications and in a measurement tool setup.


title: An Extension of Particle Swarm Optimization (E-PSO) Algorithm for Solving Economic Dispatch Problem

abstract: Producing the energy power that meets the load demands at a minimum cost while satisfying the constraints is known as economic dispatch. Economic dispatch becomes one of the most complex problems in the planning and operation of a power system that aims to determine the optimal generation scheduling at minimum cost. For that reason many optimization researches on finding an optimal solution regarding the total cost of generation have been carried out. This paper presents the implementation of the extension of the PSO (E-PSO) system in solving the continuous nonlinear function of the cost curves of the generator. In this paper, a 6-unit generation system has been applied to show the effectiveness of the E-PSO compared to the standard PSO. The results show that E-PSO is capable in solving the economic dispatch problem in term of minimizing the total cost of generation while considering the generator limits and transmission losses.


title: Success with Style: Using Writing Style to Predict the Success of Novels

abstract: Predicting the success of literary works is a curious question among publishers and aspiring writers alike. We examine the quantitative connection, if any, between writing style and successful literature. Based on novels over several different genres, we probe the predictive power of statistical stylometry in discriminating successful literary works, and identify characteristic stylistic elements that are more prominent in successful writings. Our study reports for the first time that statistical stylometry can be surprisingly effective in discriminating highly successful literature from less successful counterpart, achieving accuracy up to 84%. Closer analyses lead to several new insights into characteristics of the writing style in successful literature, including findings that are contrary to the conventional wisdom with respect to good writing style and readability.


title: Millimeter-Wave Propagation Channel Characterization for Short-Range Wireless Communications

abstract: This paper presents and analyzes the results of millimeter-wave 60-GHz frequency range propagation channel measurements that are performed in various indoor environments for continuous-route and direction-of-arrival (DOA) measurement campaigns. The statistical parameters of the propagation channel, such as the number of paths, the RMS delay spread, the path loss, and the shadowing, are inspected. Moreover, the interdependencies of different characteristics of the multipath channel are also investigated. A linear relationship between the number of paths and the delay spread is found, negative cross correlation between the shadow fading and the delay spread can be established, and an upper bound exponential model of the delay spread and the path loss is developed to estimate the worst case of the RMS delay spread at given path loss. Based on the DOA measurements that are carried out in a room [line of sight (LOS)] and in a corridor with both LOS and nonline-of-sight (NLOS) scenarios, radio-wave propagation mechanisms are studied. It is found that considering the direct wave and the first-order reflected waves from smooth surfaces is sufficient in the LOS cases. Transmission loss is very high; however, diffraction is found to be a significant propagation mechanism in NLOS propagation environments. The results can be used for the design of 60-GHz radio systems in short-range wireless applications.


title: A theoretical model of intentional social action in online social networks

abstract: Online social networks (Facebook, MySpace, LinkedIn and the like) have become truly significant new phenomena in human communication and interaction patterns and may have a profound impact in the way people communicate and connect with each other. In this study, the decision to use an online social network is conceptualized as intentional social action and the relative impact of the three modes of social influence processes (compliance, internalization, and identification) on intentional social action to use (collective intention) is examined. An empirical study of Facebook users (n=389) found that collective intention to use a social networking site is determined by both subjective norm and social identity. Further, social identity is found to be a second-order latent construct comprised of cognitive, evaluative, and affective (first-order) components. Implications for research and practice are discussed.


title: Control of Upper-Limb Power-Assist Exoskeleton Using a Human-Robot Interface Based on Motion Intention Recognition

abstract: Recognition of the wearer’s motion intention plays an important role in the study of power-assist robots. In this paper, an intention-guided control strategy is proposed and applied to an upper-limb power-assist exoskeleton. Meanwhile, a human-robot interface comprised of force-sensing resistors (FSRs) is designed to estimate the motion intention of the wearer's upper limb in real time. Moreover, a new concept called the “intentional reaching direction (IRD)” is proposed to quantitatively describe this intention. Both the state model and the observation model of IRD are obtained by studying the upper limb behavior modes and analyzing the relationship between the measured force signals and the motion intention. Based on these two models, the IRD can be inferred online using an adapted filtering technique. Guided by the inferred IRD, an admittance control strategy is deployed to control the motions of three DC motors placed at the corresponding joints of the robotic arm. The effectiveness of the proposed approaches is finally confirmed by experiments on a 3 degree-of-freedom (DOF) upper-limb robotic exoskeleton.


title: Focused Natural Deduction

abstract: Natural deduction for intuitionistic linear logic is known to be full of non-deterministic choices. In order to control these choices, we combine ideas from intercalation and focusing to arrive at the calculus of focused natural deduction. The calculus is shown to be sound and complete with respect to first-order intuitionistic linear natural deduction and the backward linear focusing calculus.


title: Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work

abstract: Deep networks thrive when trained on large scale data collections. This has given ImageNet a central role in the development of deep architectures for visual object classification. However, ImageNet was created during a specific period in time, and as such it is prone to aging, as well as dataset bias issues. Moving beyond fixed training datasets will lead to more robust visual systems, especially when deployed on robots in new environments which must train on the objects they encounter there. To make this possible, it is important to break free from the need for manual annotators. Recent work has begun to investigate how to use the massive amount of images available on the Web in place of manual image annotations. We contribute to this research thread with two findings: (1) a study correlating a given level of noisily labels to the expected drop in accuracy, for two deep architectures, on two different types of noise, that clearly identifies GoogLeNet as a suitable architecture for learning from Web data; (2) a recipe for the creation of Web datasets with minimal noise and maximum visual variability, based on a visual and natural language processing concept expansion strategy. By combining these two results, we obtain a method for learning powerful deep object models automatically from the Web. We confirm the effectiveness of our approach through object categorization experiments using our Web-derived version of ImageNet on a popular robot vision benchmark database, and on a lifelong object discovery task on a mobile robot.


title: Exact matrix completion via convex optimization

abstract: We consider a problem of considerable practical interest: the recovery of a data matrix from a sampling of its entries. Suppose that we observe m entries selected uniformly at random from a matrix M. Can we complete the matrix and recover the entries that we have not seen? ::: We show that one can perfectly recover most low-rank matrices from what appears to be an incomplete set of entries. We prove that if the number m of sampled entries obeys m >= C n^{1.2} r log n for some positive numerical constant C, then with very high probability, most n by n matrices of rank r can be perfectly recovered by solving a simple convex optimization program. This program finds the matrix with minimum nuclear norm that fits the data. The condition above assumes that the rank is not too large. However, if one replaces the 1.2 exponent with 1.25, then the result holds for all values of the rank. Similar results hold for arbitrary rectangular matrices as well. Our results are connected with the recent literature on compressed sensing, and show that objects other than signals and images can be perfectly reconstructed from very limited information.


title: Investigation of 3-pass laser grooving process development for low-k devices

abstract: The low-k dielectric materials in wafer fabrication are widely used. The current advanced mechanical dicing is not able to meet the requirement of low-k technology, since the low-k materials are harder and brittle. Laser dicing is developed for low-k wafer dicing. There are various laser dicing techniques in the market. Depending on the wafer thickness, laser dicing can either be a though cut or a partial cut process, which is commonly known as laser grooving. In this paper, the study work mainly focus on laser grooving and mechanical dicing process for Low-k wafer. The laser grooving used in this study is a 3-pass laser beam, and the laser grooving process setup is discussed. High power optical microscope, SEM, FIB and IR were the inspection tools used in this study. After laser grooving, the characterize of laser grooving, including the die edge quality, grooving geometry, die strength, mechanical dicing and reliability results are all investigated. The die was packaged into a 19×19mm 529IO MAPBGA package. Post assembly, CSAM and electrical test were performed on the assembled parts at T0, post MSL3/260degree C, post 264h UHST (110°C/85%RH), and post TC500cycles (−55°C to 125°C). With optimized parameter, a good dicing quality was get, without metal burr, metal remaining, micro cracks on side wall etc. The packaged sample had passed all stress. No laser grooving and mechanical dicing defect related fail. It is concluded that the 3-pass laser grooving is a good solution for low-k wafer dicing.


title: ClassSheets: automatic generation of spreadsheet applications from object-oriented specifications

abstract: Spreadsheets are widely used in all kinds of business applications. Numerous studies have shown that they contain many errors that sometimes have dramatic impacts. One reason for this situation is the low-level, cell-oriented development process of spreadsheets.We improve this process by introducing and formalizing a higher-level object-oriented model termed ClassSheet. While still following the tabular look-and feel of spreadsheets, ClassSheets allow the developer to express explicitly business object structures within a spreadsheet, which is achieved by integrating concepts from the UML (Unified Modeling Language). A stepwise automatic transformation process generates a spreadsheet application that is consistent with the ClassSheet model. Thus, by deploying the formal underpinning of ClassSheets, a large variety of errors can be prevented that occur in many existing spreadsheet applications today.The presented ClassSheet approach links spreadsheet applications to the object-oriented modeling world and advocates an automatic model-driven development process for spreadsheet applications of high quality.


title: DOI: 10.1142/S1793206810000839 MEASURING SELF-REGULATED LEARNING SKILLS THROUGH SOCIAL INTERACTIONS IN A TEACHABLE AGENT ENVIRONMENT

abstract: We have developed a learning environment where students teach a computer agent using visual representations, and can monitor the agent's learning progress by asking her questions and having her take quizzes. The system provides self-regulated learning and metacognitive support via dialog-embedded prompts from Betty, the teachable agent, and Mr. Davis, the mentor agent. Our primary goals have been to support learning of complex science topics in middle school classrooms and facilitate development of metacognitive skills to support future learning. In this paper, we discuss methods that we have employed for detecting and characterizing students' behavior patterns from their activity sequences on the system. In particular, we discuss a method for learning hidden Markov models (HMM) from the activity logs. We demonstrate that the HMM structure corresponds to students' aggregated behavior patterns in the learning environment. Overall, the HMM technique allows us to go beyond simple frequency and sequence analyses, such as individual activity and pre-defined pattern counts, instead using exploratory methods to examine how these activities cohere in larger patterns over time. The paper outlines a study conducted in a 5th grade science classroom, presents the models derived from the students' activity sequences, interprets the model structure as aggregate patterns of their learning behaviors, and links these patterns to students' use of self-regulated learning strategies. The results illustrate that those who teach an agent demonstrate better learning performance and better use of metacognitive monitoring behaviors than students who only learn for themselves. We also observed more advanced and focused monitoring behaviors in the students who received metacognitive strategy feedback from the mentor agent while they taught the teachable agent.


Transfer of Glass Manufacturing Technology in the Sixteenth and Seventeenth Centuries from Southern to Northern Europe: Using Trace Element Patterns to Reveal the Spread from Venice via Antwerp to London


title: Open source cloud computing management platforms: Introduction, comparison, and recommendations for implementation

abstract: An increase in the development and use of Open Source (OS) has been observed in recent years, in particular in the fields of programming, operating systems, and application software. According to studies carried out by Gartner and Forrester however, OS is not yet used intensively for structuring and managing cloud computing platforms. This is all the more remarkable in that it is in this field in particular that it would be possible to structure cloud management platforms efficiently and cost-effectively using the OS systems available today. They are available as alternatives to commercial suppliers and enable private, hybrid and community cloud solutions to be implemented. However, many commercial enterprises, public administrations and other institutions do not take advantage of them, partly due to uncertainty and a lack of information about such possibilities. This article therefore examines this subject, compares existing Open Source cloud computing management platforms and provides specific recommendations for use.


title: How smartphones are changing the face of mobile and participatory healthcare: an overview, with example from eCAALYX

abstract: The latest generation of smartphones are increasingly viewed as handheld computers rather than as phones, due to their powerful on-board computing capability, capacious memories, large screens and open operating systems that encourage application development. This paper provides a brief state-of-the-art overview of health and healthcare smartphone apps (applications) on the market today, including emerging trends and market uptake. Platforms available today include Android, Apple iOS, RIM BlackBerry, Symbian, and Windows (Windows Mobile 6.x and the emerging Windows Phone 7 platform). The paper covers apps targeting both laypersons/patients and healthcare professionals in various scenarios, e.g., health, fitness and lifestyle education and management apps; ambient assisted living apps; continuing professional education tools; and apps for public health surveillance. Among the surveyed apps are those assisting in chronic disease management, whether as standalone apps or part of a BAN (Body Area Network) and remote server configuration. We describe in detail the development of a smartphone app within eCAALYX (Enhanced Complete Ambient Assisted Living Experiment, 2009-2012), an EU-funded project for older people with multiple chronic conditions. The eCAALYX Android smartphone app receives input from a BAN (a patient-wearable smart garment with wireless health sensors) and the GPS (Global Positioning System) location sensor in the smartphone, and communicates over the Internet with a remote server accessible by healthcare professionals who are in charge of the remote monitoring and management of the older patient with multiple chronic conditions. Finally, we briefly discuss barriers to adoption of health and healthcare smartphone apps (e.g., cost, network bandwidth and battery power efficiency, usability, privacy issues, etc.), as well as some workarounds to mitigate those barriers.


title: Energy harvester for rotating environments using offset pendulum and nonlinear dynamics

abstract: We present an energy harvester for environments that rotate through the Earth’s gravitational field. Example applications include shafts connected to motors, axles, propellers, fans, and wheels or tires. Our approach uses the unique dynamics of an offset pendulum along with a nonlinear bistable restoring spring to improve the operational bandwidth of the system. Depending on the speed of the rotating environment, the system can act as a bistable oscillator, monostable stiffening oscillator, or linear oscillator. We apply our approach to a tire pressure monitoring system mounted on a car rim. Simulation and experimental test results show that the prototype generator is capable of directly powering an RF transmission every 60 s or less over a speed range of 10 to 155 kph.


Nonlinear Wavelet Methods for Recovery of Signals, Densities, and Spectra from Indirect and Noisy Data


title: Raloxifene and hormone replacement therapy increase arachidonic acid and docosahexaenoic acid levels in postmenopausal women

abstract: Estrogens may affect the essential n-6 and n-3 fatty acids arachidonic acid (AA; C20:4n-6) and docosahexaenoic acid (DHA; C22:6n-3). Therefore, we investigated the long-term effects of hormone replacement therapy and raloxifene, a selective estrogen-receptor modulator, in two randomized, double-blind, placebo-controlled studies. In study I, 95 healthy, non-hysterectomized, early postmenopausal women (age range 47-59 years) received one of the following treatments: daily raloxifene 60 mg (n=24), daily raloxifene 150 mg (n=23), 0.625 mg conjugated equine estrogens (CEE) plus 2.5 mg medroxyprogesterone acetate (MPA; n=24), or placebo (n=24). In study II, 30 men (age range 60-69 years) received daily 120 mg raloxifene (n=15) or placebo (n=15). In study I, plasma cholesteryl ester fatty acids were measured at baseline and after 6, 12, and 24 months in 83 (drop out rate 13%), 73 (23%), and 70 (25%) women respectively. In study II, fatty acids were measured at baseline and after 3 months in 29 men (drop out rate 3%). In postmenopausal women, administration of 150 mg raloxifene increased AA by a mean of +6.1% (P=0.055, not significant). Administration of CEE plus MPA increased AA by +14.1% (P<0.0005). Mean changes in DHA were +22.1% (P=0.003) and +14.9% (P=0.047) respectively, as compared with placebo. In men, 120 mg raloxifene for 3 months did not significantly affect AA (-5.2%; P=0.342) or DHA (+4.0%; P=0.755), but it increased testosterone levels by +19.8% (P=0.006). Administration of raloxifene 150 mg/day as well as CEE plus MPA to postmenopausal women increases the proportion of AA and DHA in plasma cholesteryl esters during a follow-up of 2 years. Short term administration of raloxifene in elderly men did not affect AA or DHA. The synthesis of AA and DHA from precursors may be enhanced through an estrogen receptor-dependent pathway.


title: A theoretical framework of collaborative knowledge building with wikis: a systemic and cognitive perspective

abstract: Wikis provide new possibilities for collaborative knowledge building with artifacts. This paper presents a theoretical framework for describing and understanding the processes which lead to collaborative knowledge building. The model borrows from the systemic approach of Luhmann as well as from Piaget's theory of equilibration. It describes people's learning activities as processes of externalization and internalization. Individual learning happens through internal processes of assimilation and accommodation, whereas changes in a wiki's information space are due to activities of external assimilation and external accommodation. All these equilibration activities are caused by subjectively perceived differences between an individuals' knowledge space and the wiki's information space. Differences of medium level are considered to cause cognitive conflicts which activate the described processes of equilibration.


title: Supply management orientation and supplier/buyer performance

abstract: Abstract Academics and practitioners agree that excellence in supply management results in better quality, customer service, and channel performance. Yet, most of these studies are either conceptual in nature or actual case studies. The primary objective of this research is to test the impact of a supply management orientation (SMO) on the suppliers' operational performance and buyers' competitive priorities (cost, quality, delivery, flexibility). Three major research hypotheses associated with SMO, Supplier Performance (SP), and Buyer Performance (BP) are tested using a confirmatory structural equation modeling approach. The results of this research support the conclusion that an improvement (increase) in the SMO improves both the suppliers' and buyers' performance (i.e., a win–win situation for the supply chain). In addition, the influence of SMO on delivery- and quality-related performance is more statistically significant than on cost or flexibility performance. In fact, when volume and process flexibility are top competitive priorities, a supply chain management orientation may not be an effective way to achieve the desired flexibility. The article ends by discussing other conclusions and suggests directions for future research.


title: Challenges and opportunities in remote monitoring of perishable products

abstract: Abstract Temperature deviations during transport and storage still cause a significant amount of food loss. A large portion of this loss could be avoided if information regarding deviating transport conditions and resulting changes in remaining shelf life of packed food would be available in real-time. In this study, we detail a prototype of such an intelligent container. The technical system, and results from tests in trans-ocean transportation of bananas are presented. The system is also able to predict hot-spots, which were identified as the most crucial risk for product loss. Although several technical solutions for remote container monitoring (RCM) and wireless temperature data logging are available on the market, a wide application of the concept of real-time shelf life monitoring is still lacking. Most problems result from the splitting of the cold chain into multiple actors, and different manufacturers and target customer groups for RMC and for wireless sensor systems.


title: Spam filtering using hybrid local-global Naive Bayes classifier

abstract: This paper propose a novel learning framework for classification of messages into spam and legit. We introduce a classification method based on feature space segmentation. Naive Bayes (NB) model is a statistical filtering process which uses previously gathered knowledge. Instead of using a single classifier, we propose the use of local and global classifier, based on Bayesian hierarchal framework. This helps in achieving multi-task learning, as simultaneous extraction of knowledge can be achieved while achieving classification accuracy. Knowledge among different task can be shared while learning for task specific.


title: The art of UHF RFID antenna design: impedance-matching and size-reduction techniques

abstract: Radio-frequency identification technology, based on the reader/tag paradigm, is quickly permeating several aspects of everyday life. The electromagnetic research mainly concerns the design of tag antennas having high efficiency and small size, and suited to complex impedance matching to the embedded electronics. Starting from the available but fragmented open literature, this paper presents a homogeneous survey of relevant methodologies for the design of UHF passive tag antennas. Particular care is taken to illustrate, within a common framework, the basic concepts of the most-used design layouts. The design techniques are illustrated by means of many noncommercial examples.


Towards a Simple but Useful Ontology Design Pattern Representation Language


title: Configuring value for competitive advantage: on chains, shops, and networks

abstract: Building on Thompson’s (1967) typology of long-linked, intensive, and mediating technologies, this paper explores the idea that the value chain, the value shop, and the value network are three distinct generic value configuration models required to understand and analyze firm-level value creation logic across a broad range of industries and firms. While the long-linked technology delivers value by transforming inputs into products, the intensive technology delivers value by resolving unique customer problems, and the mediating technology delivers value by enabling direct and indirect exchanges between customers. With the identification of alternative value creation technologies, value chain analysis is both sharpened and generalized into what we propose as a value configuration analysis approach to the diagnosis of competitive advantage. With the long-linked technology and the corresponding value chain configuration model as benchmark, the paper reviews the distinctive logic and develops models of the value shop and the value network in terms of primary activity categories, drivers of cost and value, and strategic positioning options. © 1998 John Wiley & Sons, Ltd.


title: Building optimal information systems automatically: configuration space exploration for biomedical information systems

abstract: Software frameworks which support integration and scaling of text analysis algorithms make it possible to build complex, high performance information systems for information extraction, information retrieval, and question answering; IBM's Watson is a prominent example. As the complexity and scaling of information systems become ever greater, it is much more challenging to effectively and efficiently determine which toolkits, algorithms, knowledge bases or other resources should be integrated into an information system in order to achieve a desired or optimal level of performance on a given task. This paper presents a formal representation of the space of possible system configurations, given a set of information processing components and their parameters (configuration space) and discusses algorithmic approaches to determine the optimal configuration within a given configuration space (configuration space exploration or CSE). We introduce the CSE framework, an extension to the UIMA framework which provides a general distributed solution for building and exploring configuration spaces for information systems. The CSE framework was used to implement biomedical information systems in case studies involving over a trillion different configuration combinations of components and parameter values operating on question answering tasks from the TREC Genomics. The framework automatically and efficiently evaluated different system configurations, and identified configurations that achieved better results than prior published results.


title: Particle swarm optimization method in multiobjective problems

abstract: This paper constitutes a first study of the Particle Swarm Optimization (PSO) method in Multiobjective Optimization (MO) problems. The ability of PSO to detect Pareto Optimal points and capture the shape of the Pareto Front is studied through experiments on well-known non-trivial test functions. The Weighted Aggregation technique with fixed or adaptive weights is considered. Furthermore, critical aspects of the VEGA approach for Multiobjective Optimization using Genetic Algorithms are adapted to the PSO framework in order to develop a multi-swarm PSO that can cope effectively with MO problems. Conclusions are derived and ideas for further research are proposed.


title: Imago: presence and emotion in virtual reality

abstract: As virtual reality becomes available to a wider audience, filmmakers are being challenged to create ever more immersive and creative narratives for this quickly evolving medium. While many of the early attempts at virtual reality filmmaking have been devoted to the creation of quick paced and aw-inspiring experiences, filmmakers such as Chris Milk and Oculus Story Studio have been more focused on more story driven experiences, using the medium to tell narratives in a new and exciting way. Project Hypnos was created at Carnegie Mellon University to explore immersive narrative filmmaking techniques for virtual reality. The resulting proof-of-concept film, Imago, was created using the techniques developed during this research. Imago plays with the concept of a viewer's presence in a film in an attempt to elicit both negative and positive emotions. The result is a story-driven experience that comes to life through live-action drama, dance, and abstract CG imagery.


Stock Selection and Trading Based on Cluster Analysis of Trend and Momentum Indicators


title: Themes of Research on eGovernment in Developing Countries: Current Map and Future Roadmap

abstract: This paper reports a literature review of eGovernment research in developing countries published between 2005 and 2010. From a review of 108 papers, the present study found that the papers could be mapped into five main research themes: design/implementation, adoption, impact, evaluation, and context. For each main theme, several sub-themes were identified. The research approaches used to investigate each theme were described. In addition to presenting the current landscape of eGovernment research, this paper also provides future research directions related to the empirical, theoretical, and methodological domains.


title: Guidelines for including the grey literature and conducting multivocal literature reviews in software engineering

abstract: Abstract Context A Multivocal Literature Review (MLR) is a form of a Systematic Literature Review (SLR) which includes the grey literature (e.g., blog posts, videos and white papers) in addition to the published (formal) literature (e.g., journal and conference papers). MLRs are useful for both researchers and practitioners since they provide summaries both the state-of-the art and –practice in a given area. MLRs are popular in other fields and have recently started to appear in software engineering (SE). As more MLR studies are conducted and reported, it is important to have a set of guidelines to ensure high quality of MLR processes and their results. Objective There are several guidelines to conduct SLR studies in SE. However, several phases of MLRs differ from those of traditional SLRs, for instance with respect to the search process and source quality assessment. Therefore, SLR guidelines are only partially useful for conducting MLR studies. Our goal in this paper is to present guidelines on how to conduct MLR studies in SE. Method To develop the MLR guidelines, we benefit from several inputs: (1) existing SLR guidelines in SE, (2), a literature survey of MLR guidelines and experience papers in other fields, and (3) our own experiences in conducting several MLRs in SE. We took the popular SLR guidelines of Kitchenham and Charters as the baseline and extended/adopted them to conduct MLR studies in SE. All derived guidelines are discussed in the context of an already-published MLR in SE as the running example. Results The resulting guidelines cover all phases of conducting and reporting MLRs in SE from the planning phase, over conducting the review to the final reporting of the review. In particular, we believe that incorporating and adopting a vast set of experience-based recommendations from MLR guidelines and experience papers in other fields have enabled us to propose a set of guidelines with solid foundations. Conclusion Having been developed on the basis of several types of experience and evidence, the provided MLR guidelines will support researchers to effectively and efficiently conduct new MLRs in any area of SE. The authors recommend the researchers to utilize these guidelines in their MLR studies and then share their lessons learned and experiences.


title: The Construction of Moral Agency in Mother-Child Conversations about Helping and Hurting across Childhood and Adolescence.

abstract: This study examined mother-child conversations about children's and adolescents' past harmful and helpful actions. The sample included 100 mothers and their 7-, 11-, or 16-year-old children; each dyad discussed events when the child (a) helped a friend and (b) hurt a friend. Analyses suggested that conversations about help may serve to facilitate children's sense of themselves as prosocial moral agents; mothers focused on children's feelings of pride, positive judgments of the child's behavior, and positive insights about the child's characteristics that could be drawn from the event. In turn, conversations about harm were more elaborated and contentious than those about help, and reflected more complex maternal goals; although mothers highlighted children's wrongdoing (e.g., by noting negative consequences of the child's actions for others), they also engaged in a variety of strategies that may support children's ability to reconcile their harmful actions with a positive self-view (e.g., by noting what children did do well or their capacity for repair). With respect to age effects, results revealed that older children played an increasingly active and spontaneous role in discussions. Furthermore, as compared with 7-year-olds, conversations with 11- and 16-year-olds focused more on psychological insights that could be drawn from experiences and less on children's concrete harmful and helpful actions. Overall, results illuminate the processes whereby conversations with mothers may further children's developing understandings of their own and others' moral agency, and how discussions of prosocial and transgressive moral experiences may provide distinct but complementary opportunities for moral socialization.


title: Teleoperation of the SCHUNK S5FH under-actuated anthropomorphic hand using human hand motion tracking

abstract: Abstract This paper describes the development of a remote handling control of an anthropomorphic robotic hand, the SCHUNK S5FH, using the human hand as master by measuring its motion with OptiTrack Technology. The goal of this work is to enhance manipulation studies on the human hand and to instantly transfer those studies on robotic hands. A preliminary study on methods and devices used for fingers tracking led to the choice of a simplified kinematic model of the human hand on the basis of the available motion tracking system. Using the same criteria, the analysis of protocols for markers allocation led to define the number and a method for their arrangement on the fingers and palm. In order to overcome the limitation of the Motion Capture System, a method for identification and labeling has been developed according to their anatomical arrangement. Afterwards, the tracking is performed using the constraints between marker positions on the kinematic chain of the hand and a dynamic labeling algorithm robust with respect to noise, outliers and loss of markers. The validation is performed using the right hand of different subjects and considering different tasks involving flexion/extension and abduction/adduction of fingers and thumb opposition. For testing and validation, preliminary studies on synergies for manipulation tasks such as screwing a cup, has been conducted on the human hand and transferred on the robotic hand.


title: DROPS: Division and Replication of Data in Cloud for Optimal Performance and Security

abstract: Outsourcing data to a third-party administrative control, as is done in cloud computing, gives rise to security concerns. The data compromise may occur due to attacks by other users and nodes within the cloud. Therefore, high security measures are required to protect data within the cloud. However, the employed security strategy must also take into account the optimization of the data retrieval time. In this paper, we propose division and replication of data in the cloud for optimal performance and security (DROPS) that collectively approaches the security and performance issues. In the DROPS methodology, we divide a file into fragments, and replicate the fragmented data over the cloud nodes. Each of the nodes stores only a single fragment of a particular data file that ensures that even in case of a successful attack, no meaningful information is revealed to the attacker. Moreover, the nodes storing the fragments, are separated with certain distance by means of graph T-coloring to prohibit an attacker of guessing the locations of the fragments. Furthermore, the DROPS methodology does not rely on the traditional cryptographic techniques for the data security; thereby relieving the system of computationally expensive methodologies. We show that the probability to locate and compromise all of the nodes storing the fragments of a single file is extremely low. We also compare the performance of the DROPS methodology with 10 other schemes. The higher level of security with slight performance overhead was observed.


title: Eye Fixations and Cognitive Processes

abstract: Abstract This paper presents a theoretical account of the sequence and duration of eye fixation during a number of simple cognitive tasks, such as mental rotation, sentence verification, and quantitative comparison. In each case, the eye fixation behavior is linked to a processing model for the task by assuming that the eye fixates the referent of the symbol being operated on.


title: STONE: a stream-based DDoS defense framework

abstract: An effective Distributed Denial of Service (DDoS) defense mechanism must guarantee legitimate users access to an Internet service masking the effects of possible attacks. That is, it must be able to detect threats and discard malicious packets in a online fashion. Given that emerging data streaming technology can enable such mitigation in an effective manner, in this paper we present STONE, a stream-based DDoS defense framework, which integrates anomaly-based DDoS detection and mitigation with scalable data streaming technology. With STONE, the traffic of potential targets is analyzed via continuous data streaming queries maintaining information used for both attack detection and mitigation. STONE provides minimal degradation of legitimate users traffic during DDoS attacks and it also faces effectively flash crowds. Our preliminary evaluation based on an implemented prototype and conducted with real legitimate and malicious traffic traces shows that STONE is able to provide fast detection and precise mitigation of DDoS attacks leveraging scalable data streaming technology.


title: Towards Haskell in the cloud

abstract: We present Cloud Haskell, a domain-specific language for developing programs for a distributed computing environment. Implemented as a shallow embedding in Haskell, it provides a message-passing communication model, inspired by Erlang, without introducing incompatibility with Haskell's established shared-memory concurrency. A key contribution is a method for serializing function closures for transmission across the network. Cloud Haskell has been implemented; we present example code and some preliminary performance measurements.


title: Boundary Detection in Music Structure Analysis using Convolutional Neural Networks

abstract: The recognition of boundaries, e.g., between chorus and verse, is an important task in music structure analysis. The goal is to automatically detect such boundaries in audio signals so that the results are close to human annotation. In this work, we apply Convolutional Neural Networks to the task, trained directly on mel-scaled magnitude spectrograms. On a representative subset of the SALAMI structural annotation dataset, our method outperforms current techniques in terms of boundary retrievalF -measure at different temporal tolerances: We advance the state-of-the-art from 0:33 to 0:46 for tolerances of0:5 seconds, and from 0:52 to 0:62 for tolerances of3 seconds. As the algorithm is trained on annotated audio data without the need of expert knowledge, we expect it to be easily adaptable to changed annotation guidelines and also to related tasks such as the detection of song transitions.


title: Fast pedestrian detection based on object proposals and HOG

abstract: Research on pedestrian detection still presents a lot of space for improvements, both on speed and detection accuracy. State-of-the-art object proposals approach has shown the very effective computational efficiency in object detection. In this paper, we present a framework for pedestrian detection based on the object proposals. Instead of scaling the test image to different sizes, we generate a pyramid of HOG templates by simply scaling the primary HOG descriptor. For the test image, Histogram of Oriented Gradient feature vector is calculated only in the region of object proposals without using sliding window, and the corresponding HOG template is selected according to the size of proposal region. Experiment results show that our proposed methods can achieve the equal detection rate while using only 20% time.


title: Mine Detection Robot and Related Technologies for Humanitarian Demining

abstract: Currently, more than 100 million anti-personnel mines are under the ground all over the world. These mines not only disturb the economic development of mine-buried nations, but also injure or kill more than 2000 people a month. As a result, the removal of landmines has become a global emergency. The current method of removing mines manually is costly and dangerous. Moreover, removal of all mines by this method would require several hundred years (it would takes one thousand according to a CMAC report based on Cambodian Mine Action Center Current Activities 1998), during which time, more mines might be buried in war zones. There are three kinds of demining strategies. The first is human deminier based demining. The second is mechanical equipment based demining like Fig.1(Geneva International Center for Humanitarian Demining,2002). The third is advanced robot based demining. Currently, the most common demining approach is the first type. The second type is applied in some limited mine field area. The third type is partially tried as mine detection or brush cutting with the exception of research oriented robots and some of them will be expected in future demining approaches. Figure 2 shows the robotic brush cutter which was developed in the Demining Technology Workshop in Cambodia. This robot can cut grass and bush by teleoperation. Figure 3 shows the four legged mine detection robot which was studied by CSIC-IAI, Spain in 1999(Armada et al.2005). Under this extreme environment, a walking robot may be an effective and efficient means of detecting and removing mines while ensuring the safety of local residents and people engaged in the removal work. The six-legged and crawler type hybrid robot COMET-III(Nonami et al.2003) shown in Fig.4 with two manipulators based on the added stability, mobility, and functionality that this platform offers. This latest robot COMET-III, which is fully autonomous, has been developed by one of this paper’s authors. Source: Humanitarian Demining: Innovative Solutions and the Challenges of Technology, Book edited by: Maki K. Habib, ISBN 978-3-902613-11-0, pp. 392, February 2008, I-Tech Education and Publishing, Vienna, Austria O pe n A cc es s D at ab as e w w w .in te hw eb .c om


title: Identifying references to datasets in publications

abstract: Research data and publications are usually stored in separate and structurally distinct information systems. Often, links between these resources are not explicitly available which complicates the search for previous research. In this paper, we propose a pattern induction method for the detection of study references in full texts. Since these references are not specified in a standardized way and may occur inside a variety of different contexts --- i.e., captions, footnotes, or continuous text --- our algorithm is required to induce very flexible patterns. To overcome the sparse distribution of training instances, we induce patterns iteratively using a bootstrapping approach. We show that our method achieves promising results for the automatic identification of data references and is a first step towards building an integrated information system.


title: Islanding detection in a distributed generation based hybrid system using intelligent pattern recognition techniques

abstract: In this paper, islanding in a distributed generation (DG) based hybrid system is presented based on extracted features of wavelet transform (WT), S-transform (ST), Hyperbolic S-transform (HST) and TT-Transform. The hybrid system comprises of DG resources like photovoltaic (PV), fuel cell (FC) and wind energy systems (WES) connected to the grid. The negative sequence component of the voltage signal extracted at the point of common coupling (PCC) is considered for detection of islanding of these resources from the grid. The study for different scenarios of DG system is presented in the form of time-frequency analysis. The energy content and standard deviation (STD) of HST contour is also reported in order to validate the graphical results. The results demonstrate the advantages of HS-transform, TT-Transform over wavelet transform and S-transform in detection of islanding events under noise-free as well as noisy scenarios.


title: Towards a Seamless Integration of Word Senses into Downstream NLP Applications

abstract: Lexical ambiguity can impede NLP systems from accurate understanding of semantics. Despite its potential benefits, the integration of sense-level information into NLP systems has remained understudied. By incorporating a novel disambiguation algorithm into a state-of-the-art classification model, we create a pipeline to integrate sense-level information into downstream NLP applications. We show that a simple disambiguation of the input text can lead to consistent performance improvement on multiple topic categorization and polarity detection datasets, particularly when the fine granularity of the underlying sense inventory is reduced and the document is sufficiently large. Our results also point to the need for sense representation research to focus more on in vivo evaluations which target the performance in downstream NLP applications rather than artificial benchmarks.


title: Design, fabrication, and analysis of a 3DOF, 3cm flapping-wing MAV

abstract: Significant advances in meso-scale prototyping are enabling rigid, articulated, and actuated microrobotic structures. Here, an elegant manufacturing paradigm is employed for the creation of a biologically- inspired flapping-wing micro air vehicle with similar dimensions to Dipteran insects. A novel wing transmission system is presented which contains one actuated and two passive degrees of freedom. The design and fabrication are detailed and the performance of the resulting structure is elucidated highlighting two key metrics: the wing trajectory and the thrust generated.


title: Fitting B-splines to scattered data new and old parameterization

abstract: Parametric representation for curves is important in computer-aided geometric design, medical imaging, computer vision, computer graphics, shape matching, and face/object recognition. They are far better alternatives to free form representation, which are plagued with unboundedness and stability problems. This paper deals with the problem of fitting approximating B-spline curves with high compression ratios (control to data point ratios) to scattered data, where the data might be noisy or locally deformed, and where the curve sample points might be non-uniformly sampled across the curve. The approximating B-spline is robust to noise and local deformation. B-splines are bounded, continuous, invariant to affine and perspective transformations, and have local shape controllability. A parameterization based on curvature is proposed as a meaningful choice for the topological parameter of the B-spline that leads to a superior capturing of the curve shape and of preserving corner points. We compare several parameterization methods both theoretically and experimentally, and show that the curvature based parameterization is superior for noise free data, noisy data, local deformation, and non-uniform sampling. The fitting method is fast, and non-iterative. The fitting errors are, by and large, sub-resolution, i.e., they are below or at the resolution of the scattered data (defined here as the average distance between curve points). We also show the extension of the method to 3D surface fitting.


title: T.: Adapting visual category models to new domains. In: ECCV

abstract: Domain adaptation is an important emerging topic in computer vision. In this paper, we present one of the first studies of domain shift in the context of object recognition. We introduce a method that adapts object models acquired in a particular visual domain to new imaging conditions by learning a transformation that minimizes the effect of domain-induced changes in the feature distribution. The transformation is learned in a supervised manner and can be applied to categories for which there are no labeled examples in the new domain. While we focus our evaluation on object recognition tasks, the transform-based adaptation technique we develop is general and could be applied to nonimage data. Another contribution is a new multi-domain object database, freely available for download. We experimentally demonstrate the ability of our method to improve recognition on categories with few or no target domain labels and moderate to large changes in the imaging conditions.


IMPLEMENTATION OF IOT IN SMART IRRIGATION SYSTEM USING ARDUINO PROCESSOR


title: Q-MUSE: A QUANTUM COMPUTER MUSIC SYSTEM DESIGNED FOR A PERFORMANCE FOR ORCHESTRA, ELECTRONICS AND LIVE INTERNET-CONNECTED PHOTONIC QUANTUM COMPUTER

abstract: Quantum computing is a form of unconventional computation utilizing quantum effects as a fundamental part of its calculations. It has already been used in practical signal encryption in the 2010 Soccer World Cup, and there is competition amongst many governments to build more powerful and practical quantum computers. Although quantum computing is the most widespread and invested in form of unconventional computation, there have been no implementations of artistic systems with live hardware quantum computers. Furthermore there is a vast gap between public understanding of classical digital computing and of quantum computing. Q-Muse is a quantum computer music system design for a specific performance. The Entangled Orchestra is a performance for Orchestra, Electronics and Live Internet-Connected Photonic Quantum Computer. There are many types of quantum computation hardware implementations including Nuclear Magnetic Resonance, Trapped Ions, and Optical Computing. Q-Muse incorporates the third of these - a system that utilizes wave guides, phase-shifters and beam splitters to compute with entangled photons. The processor is located at University of Bristol in the UK is accessed over the cloud. It can implement a Controlled NOT gate (CNOT) - an essential component in the construction of quantum processors. The CNOT gate is part of a two gate set that can be used to build any type of quantum computing process. The resulting musical performance will provide not only a representation for the quantum processes in the chip, but a proof-of-concept for using hardware quantum computing processors in the computer-aided arts.


title: Methodology for a cost evaluation of migration toward NGOA networks

abstract: Evolution of optical access networks promises to bring higher bandwidth to more customers. However, this evolution toward so-called next generation optical access (NGOA) networks also introduces additional challenges that operators and/or vendors have to address: how to properly estimate and compare different NGOA architectures and their evolutionary paths in terms of their economics. Calculating the total cost of ownership (TCO) for NGOA networks is a very complex target as it needs to involve good knowledge of the technology, the existing network infrastructure, and any migration-related processes. In this paper a complete methodology is presented for evaluating the TCO of the migration toward a NGOA network. It contains a detailed description of which key aspects have to be considered, which processes they affect, and how they are translated into costs in a logical manner. Finally, it also shows how this methodology has been applied to particular selected cases and how it gives a detailed view of all costs involved in migration. This approach opens up opportunities to cooperate in techno-economic research using it as a base. Both operators and vendors can also utilize this approach to get a useful economic background of their future investments and potential sales.


title: Amanuensis: The Programmer's Apprentice

abstract: This document provides an overview of the material covered in a course taught at Stanford in the spring quarter of 2018. The course draws upon insight from cognitive and systems neuroscience to implement hybrid connectionist and symbolic reasoning systems that leverage and extend the state of the art in machine learning by integrating human and machine intelligence. As a concrete example we focus on digital assistants that learn from continuous dialog with an expert software engineer while providing initial value as powerful analytical, computational and mathematical savants. Over time these savants learn cognitive strategies (domain-relevant problem solving skills) and develop intuitions (heuristics and the experience necessary for applying them) by learning from their expert associates. By doing so these savants elevate their innate analytical skills allowing them to partner on an equal footing as versatile collaborators - effectively serving as cognitive extensions and digital prostheses, thereby amplifying and emulating their human partner's conceptually-flexible thinking patterns and enabling improved access to and control over powerful computing resources.


title: Transgender face recognition with off-the-shelf pre-trained CNNs: A comprehensive study

abstract: Face recognition has become a ubiquitous way of establishing identity in many applications. Gender transformation therapy induces changes to face on both for structural and textural features. A challenge for face recognition system is, therefore, to reliably identify the subjects after they undergo gender change while the enrolment images correspond to pre-change. In this work, we propose a new framework based on augmenting and fine-tuning deep Residual Network-50 (ResNet-50). We employ YouTube database with 37 subjects whose images are self-captured to evaluate the performance of state-of-the-schemes. Obtained results demonstrate the superiority of the proposed scheme over twelve different state-of-the-art schemes with an improved Rank — 1 recognition rate.


title: On the approximation by single hidden layer feedforward neural networks with fixed weights

abstract: Single hidden layer feedforward neural networks (SLFNs) with fixed weights possess the universal approximation property provided that approximated functions are univariate. But this phenomenon does not lay any restrictions on the number of neurons in the hidden layer. The more this number, the more the probability of the considered network to give precise results. In this note, we constructively prove that SLFNs with the fixed weight 1 and two neurons in the hidden layer can approximate any continuous function on a compact subset of the real line. The proof is implemented by a step by step construction of a universal sigmoidal activation function. This function has nice properties such as computability, smoothness and weak monotonicity. The applicability of the obtained result is demonstrated in various numerical examples. Finally, we show that SLFNs with fixed weights cannot approximate all continuous multivariate functions.


title: In-Hand Manipulation in Young Children: Rotation of an Object in the Fingers

abstract: OBJECTIVE ::: The purpose of this study was to look at the development of two in-hand skills of rotation. Three questions were asked: (a) Do boys' and girls' performance differ significantly? (b) Does performance improve with age? and (c) Can periods of rapid improvement with age be seen on the tasks presented? ::: ::: ::: METHOD ::: The in-hand manipulation skill of rotation was measured in 154 right-handed children between the ages of 3-0 years and 6-11 years as well as in 13 adults. The participants were videotaped as they either turned over small pegs in a pegboard or rotated a peg in their fingertips. The number of pegs dropped when turning and placing them back in the board, the number of times a peg could be rotated in the fingertips, the time it took to complete each task, and the methods participate used to accomplish the two tasks were recorded from the videotapes. ::: ::: ::: RESULTS ::: The results indicated no differences between the performance of boys and girls on any of the variables studied, but performance did change with age, and periods of rapid change were identified. Further, when compared on methods used, the children did not achieve the same speed or consistency as the adult participants. ::: ::: ::: CONCLUSION ::: The development of in-hand manipulation skills of rotation involves improvement in the dimension of speed, method, and consistency. Observation of these skills in a child can add to a therapist's understanding of the child's fine motor abilities. The variability in children's performance needs to be considered in both evaluation and treatment planning.


title: Design and Layout Techniques for the Optimization of nMOS SPDT Series-Shunt Switches in a 130nm SiGe BiCMOS Technology

abstract: This work investigates various design and layout optimization approaches for MOSFET-based series-shunt, single-pole double-throw (SPDT) switches in a commercially-available 130 nm silicon-germanium (SiGe) BiCMOS technology. The incorporation of deep-trench isolation, additional substrate contacts, and additional gate resistance for the series nMOS device are examined, and the impact of these design and layout optimizations on the switches insertion loss, bandwidth, isolation and linearity performance have been quantified across frequency. This experiment has yielded a SPDT switch with an insertion loss of - 1.4 dB, -1.5 dB, and -2.0 dB, at 5.8 GHz, 10 GHz, and 20 GHz, respectively, and an input-referred third-order intercept point (II P3) of 21 dBiu at 9.5 GHz, without the use of any process adders or additional supply voltages.


title: Business Process Re-Engineering in Public Administration of Kingdom of Saudi Arabia

abstract: The government of Saudi Arabia is in the phase of transformation. Business process reengineering (BPR) can play a vital role in assessing this conversion. BPR methodologies provide ways to optimize the use of resources while maintaining high-quality services. The aim of this paper is to investigate the introduction of BPR in Saudi Arabia public sector. A framework is proposed to transform change using a knowledge based. The proposed solution is validated through survey. The results of the survey show that Saudi Governmental Agencies acquire the power to implement the BPR successfully especially if it is implemented with knowledge management and the BPR movement started at small scale.


title: AC^0 o MOD_2 Lower Bounds for the Boolean Inner Product

abstract: AC^0 o MOD_2 circuits are AC^0 circuits augmented with a layer of parity gates just above the input layer. We study AC^0 o MOD2 circuit lower bounds for computing the Boolean Inner Product functions. Recent works by Servedio and Viola (ECCC TR12-144) and Akavia et al. (ITCS 2014) have highlighted this problem as a frontier problem in circuit complexity that arose both as a first step towards solving natural special cases of the matrix rigidity problem and as a candidate for constructing pseudorandom generators of minimal complexity. We give the first superlinear lower bound for the Boolean Inner Product function against AC^0 o MOD2 of depth four or greater. Specifically, we prove a superlinear lower bound for circuits of arbitrary constant depth, and an ~Omega(n^2) lower bound for the special case of depth-4 AC^0 o MOD_2. Our proof of the depth-4 lower bound employs a new "moment-matching" inequality for bounded, nonnegative integer-valued random variables that may be of independent interest: we prove an optimal bound on the maximum difference between two discrete distributions’ values at 0, given that their first d moments match.


title: A survey on Datamining in Cyber Bullying

abstract: CRIME has always the very first thing that people want to avoid and police officer want to stop. A major challenge facing all law- enforcement and intelligence -gathering organizations is accurately and efficiently analyzing the growing volumes of crime data. Crime activity reports available from victims, governmental organizations, news press, and social networks play a significant role in public safety, including crime prevention, suppression and investigation, uniformed patrol and response. Cyber Crime is an area which covers crimes committed in the internet. Opportunities for connecting with classmates, friends and people with shared interests abound. Email, online chat, and social networking sites allow us to interact with people in the same town and people on the other side of the world. Unfortunately the opportunity for misuse comes with any new technology. There were sexual predators and bullies long before the advent of the internet and chat rooms. Cyber bullying and internet predation threaten minors, particular teens and teens who do not have adequate supervision when they use the computer. As the amount of criminal records growing every day, it is impossible to perform manual analysis on the dataset and extract useful information. Data mining has been studying for decades trying to get useful information out of large amount of data. Many efforts have used automated techniques to analyze different types of crimes, but without a unifying framework describing how to apply them. In particular, understanding the relationship between analysis capability and crime type characteristics can help investigators more effectively use those techniques to identify trends and patterns, address problem areas and even predict crimes.


title: Midfrontal theta and pupil dilation parametrically track subjective conflict (but also surprise) during intertemporal choice

abstract: Abstract Many everyday choices are based on personal, subjective preferences. When choosing between two options, we often feel conflicted, especially when trading off costs and benefits occurring at different times (e.g., saving for later versus spending now). Although previous work has investigated the neurophysiological basis of conflict during inhibitory control tasks, less is known about subjective conflict resulting from competing subjective preferences. In this pre-registered study, we investigated subjective conflict during intertemporal choice, whereby participants chose between smaller immediate versus larger delayed rewards (e.g., $15 today vs. $22 in 30 days). We used economic modeling to parametrically vary eleven different levels of conflict, and recorded EEG data and pupil dilation. Midfrontal theta power, derived from EEG, correlated with pupil responses, and our results suggest that these signals track different gradations of subjective conflict. Unexpectedly, both signals were also maximally enhanced when decisions were surprisingly easy. Therefore, these signals may track events requiring increased attention and adaptive shifts in behavioral responses, with subjective conflict being only one type of such event. Our results suggest that the neural systems underlying midfrontal theta and pupil responses interact when weighing costs and benefits during intertemporal choice. Thus, understanding these interactions might elucidate how individuals resolve self-control conflicts.


title: Parallel computations in graph theory

abstract: In parallel computation two approaches are common; namely unbounded parallelism and bounded parallelism. In this paper both approaches will be considered. The problem of unbounded parallelism is studied in section II and some lower and upper bounds on different connectivity problems for directed and undirected graphs are presented. In section III we mention bounded parallelism and three different k-parallel graph search techniques, namely k-depth search, breadth depth search, and breadth-first search. Each algorithm is analyzed with respect to the optimal serial algorithm. It is shown that for sufficiently dense graphs the parallel breadth first search technique is very close to the optimal bound. Techniques for searching sparse graphs are also discussed.


title: Principles of Motor Learning in Treatment of Motor Speech Disorders

abstract: Purpose There has been renewed interest on the part of speech-language pathologists to understand how the motor system learns and determine whether principles of motor learning, derived from studie...


title: Combining Active Learning and Reactive Control for Robot Grasping

abstract: Grasping an object is a task that inherently needs to be treated in a hybrid fashion. The system must decide both where and how to grasp the object. While selecting where to grasp requires learning about the object as a whole, the execution only needs to reactively adapt to the context close to the grasp's location. We propose a hierarchical controller that reflects the structure of these two sub-problems, and attempts to learn solutions that work for both. A hybrid architecture is employed by the controller to make use of various machine learning methods that can cope with the large amount of uncertainty inherent to the task. The controller's upper level selects where to grasp the object using a reinforcement learner, while the lower level comprises an imitation learner and a vision-based reactive controller to determine appropriate grasping motions. The resulting system is able to quickly learn good grasps of a novel object in an unstructured environment, by executing smooth reaching motions and preshaping the hand depending on the object's geometry. The system was evaluated both in simulation and on a real robot.


title: Defining the fascial system.

abstract: Fascia is a widely used yet indistinctly defined anatomical term that is concurrently applied to the description of soft collagenous connective tissue, distinct sections of membranous tissue, and a body pervading soft connective tissue system. Inconsistent use of this term is causing concern due to its potential to confuse technical communication about fascia in global, multiple discipline- and multiple profession-spanning discourse environments. The Fascia Research Society acted to address this issue by establishing a Fascia Nomenclature Committee (FNC) whose purpose was to clarify the terminology relating to fascia. This committee has since developed and defined the terms a fascia, and, more recently, the fascial system. This article reports on the FNC's proposed definition of the fascial system.