title: Roles of Moringa oleifera Leaf Extract in Improving the Impact of High Dietary Intake of Monosodium Glutamate-Induced Liver Toxicity, Oxidative Stress, Genotoxicity, DNA Damage, and PCNA Alterations in Male Rats

abstract: It is common for food to be made more palatable through the use of the flavour enhancer monosodium glutamate, also known as vetsin powder. The purpose of the study described in this paper was to explore how vetsin-induced hepatic toxicity, DNA fragmentation, damage, and oxidative stress modifications could be mitigated with moringa leaf extract (MLE). To that end, 40 male rats were separated into four groups: normal control, positive control or MLE, vetsin, and vetsin combined with MLE. Results indicated that, compared to the control group, the levels of serum alanine aminotransferase (ALT), aminotransferase (AST), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), liver malondialdehyde (MDA), DNA damage, injury, PCNA, and P53 expressions were significantly enhanced by the administration of vetsin (). However, the vetsin group had significantly reduced levels of albumin, globulin, total protein, liver glutathione (GSH), superoxide dismutase enzyme (SOD), catalase, and glutathione S-transferase (GST) enzyme activities () by comparison to control. Meanwhile, modifications in liver functions, oxidative stress, DNA damage, liver injury, and PCNA expression were alleviated when vetsin was administered alongside MLE. The authors conclude that vetsin may have many side effects and that MLE can ameliorate biochemical changes, oxidative stress, hepatic injury, PCNA, and P53 alterations induced by vetsin administration.


title: A minimalist approach to Remote Attestation

abstract: Embedded computing devices increasingly permeate many aspects of modern life: from medical to automotive, from building and factory automation to weapons, from critical infrastructures to home entertainment. Despite their specialized nature as well as limited resources and connectivity, these devices are now becoming an increasingly popular and attractive target for attacks, especially, malware infections. A number of approaches have been suggested to detect and/or mitigate such attacks. They vary greatly in terms of application generality and underlying assumptions. However, one common theme is the need for Remote Attestation, a distinct security service that allows a trusted party (verifier) to check the internal state of a remote untrusted embedded device (prover). Many prior methods assume some form of trusted hardware on the prover, which is not a good option for small and low-end embedded devices. To this end, we investigate the feasibility of Remote Attestation without trusted hardware. This paper provides a systematic treatment of Remote Attestation, starting with a precise definition of the desired service and proceeding to its systematic deconstruction into necessary and sufficient properties. Next, these are mapped into a minimal collection of hardware and software components that result in secure Remote Attestation. One distinguishing feature of this line of research is the need to prove (or, at least argue for) architectural minimality - an aspect rarely encountered in security research. This work also provides a promising platform for attaining more advanced security services and guarantees.


title: Online and Linear-Time Attention by Enforcing Monotonic Alignments

abstract: Recurrent neural network models with an attention mechanism have proven to be extremely effective on a wide variety of sequence-to-sequence problems. However, the fact that soft attention mechanisms perform a pass over the entire input sequence when producing each element in the output sequence precludes their use in online settings and results in a quadratic time complexity. Based on the insight that the alignment between input and output sequence elements is monotonic in many problems of interest, we propose an end-to-end differentiable method for learning monotonic alignments which, at test time, enables computing attention online and in linear time. We validate our approach on sentence summarization, machine translation, and online speech recognition problems and achieve results competitive with existing sequence-to-sequence models.


title: A new ART-counterpropagation neural network for solving a forecasting problem

abstract: This study presents a novel Adaptive resonance theory-Counterpropagation neural network (ART-CPN) for solving forecasting problems. The network is based on the ART concept and the CPN learning algorithm for constructing the neural network. The vigilance parameter is used to automatically generate the nodes of the cluster layer for the CPN learning process. This process improves the initial weight problem and the adaptive nodes of the cluster layer (Kohonen layer). ART-CPN involves real-time learning and is capable of developing a more stable and plastic prediction model of input patterns by self-organization. The advantages of ART-CPN include the ability to cluster, learn and construct the network model for forecasting problems. The network was applied to solve the real forecasting problems. The learning algorithm revealed better learning efficiency and good prediction performance. q 2004 Elsevier Ltd. All rights reserved.


title: Analytics using R for predicting credit defaulters

abstract: Nowadays there are many risks related to bank loans, especially for the banks so as to reduce their capital loss. The analysis of risks and assessment of default becomes crucial thereafter. Banks hold huge volumes of customer behaviour related data from which they are unable to arrive at a judgement if an applicant can be defaulter or not. Data Mining is a promising area of data analysis which aims to extract useful knowledge from tremendous amount of complex data sets. This work aims to develop a model and construct a prototype for the same using a data set available in the UCI repository. The model is a decision tree based classification model that uses the functions available in the R Package. Prior to building the model, the dataset is pre-processed, reduced and made ready to provide efficient predictions. The final model is used for prediction with the test dataset and the experimental results prove the efficiency of the built model.


title: Ephemeral Content Popularity at the Edge and Implications for On-Demand Caching

abstract: The ephemeral content popularity seen with many content delivery applications can make indiscriminate on-demand caching in edge networks highly inefficient, since many of the content items that are added to the cache will not be requested again from that network. In this paper, we address the problem of designing and evaluating more selective edge-network caching policies. The need for such policies is demonstrated through an analysis of a dataset recording YouTube video requests from users on an edge network over a 20-month period. We then develop a novel workload modelling approach for such applications and apply it to study the performance of alternative edge caching policies, including indiscriminate caching and cache on $k$ th request for different $k$ . The latter policies are found able to greatly reduce the fraction of the requested items that are inserted into the cache, at the cost of only modest increases in cache miss rate. Finally, we quantify and explore the potential room for improvement from use of other possible predictors of further requests. We find that although room for substantial improvement exists when comparing performance to that of a perfect “oracle” policy, such improvements are unlikely to be achievable in practice.


title: Actions as space-time shapes

abstract: Human action in video sequences can be seen as silhouettes of a moving torso and protruding limbs undergoing articulated motion. We regard human actions as three-dimensional shapes induced by the silhouettes in the space-time volume. We adopt a recent approach by Gorelick et al. (2004) for analyzing 2D shapes and generalize it to deal with volumetric space-time action shapes. Our method utilizes properties of the solution to the Poisson equation to extract space-time features such as local space-time saliency, action dynamics, shape structure and orientation. We show that these features are useful for action recognition, detection and clustering. The method is fast, does not require video alignment and is applicable in (but not limited to) many scenarios where the background is known. Moreover, we demonstrate the robustness of our method to partial occlusions, non-rigid deformations, significant changes in scale and viewpoint, high irregularities in the performance of an action and low quality video


title: Fuel consumption prediction of fleet vehicles using Machine Learning: A comparative study

abstract: Ability to model and predict the fuel consumption is vital in enhancing fuel economy of vehicles and preventing fraudulent activities in fleet management. Fuel consumption of a vehicle depends on several internal factors such as distance, load, vehicle characteristics, and driver behavior, as well as external factors such as road conditions, traffic, and weather. However, not all these factors may be measured or available for the fuel consumption analysis. We consider a case where only a subset of the aforementioned factors is available as a multi-variate time series from a long distance, public bus. Hence, the challenge is to model and/or predict the fuel consumption only with the available data, while still indirectly capturing as much as influences from other internal and external factors. Machine Learning (ML) is suitable in such analysis, as the model can be developed by learning the patterns in data. In this paper, we compare the predictive ability of three ML techniques in predicting the fuel consumption of the bus, given all available parameters as a time series. Based on the analysis, it can be concluded that the random forest technique produces a more accurate prediction compared to both the gradient boosting and neural networks.


title: Application of Non-Orthogonal Multiple Access in LTE and 5G Networks

abstract: As the latest member of the multiple access family, non-orthogonal multiple access (NOMA) has been recently proposed for 3GPP LTE and is envisioned to be an essential component of 5G mobile networks. The key feature of NOMA is to serve multiple users at the same time/frequency/ code, but with different power levels, which yields a significant spectral efficiency gain over conventional orthogonal MA. The article provides a systematic treatment of this newly emerging technology, from its combination with MIMO technologies to cooperative NOMA, as well as the interplay between NOMA and cognitive radio. This article also reviews the state of the art in the standardization activities concerning the implementation of NOMA in LTE and 5G networks.


title: Highly sensitive sensor for detection of initial slip and its application in a multi-fingered robot hand

abstract: Tactile sensors for slip detection are essential for implementing human-like gripping in a robot hand. In previous studies, we proposed flexible, thin and lightweight slip detection sensors utilizing the characteristics of pressure-sensitive conductive rubber. This was achieved by using the high-frequency vibration component generated in the process of slipping of the gripped object in order to distinguish between slipping of the object and changes in the normal force. In this paper, we design a slip detection sensor for a multi-fingered robot hand and examine the influence of noise caused by the operation of such a hand. Finally, we describe an experiment focusing on the adjustment of the gripping force of a multi-fingered robot hand equipped with the developed sensors


title: Applying Data Mining Techniques to e-Learning Problems

abstract: This chapter aims to provide an up-to-date snapshot of the current state of research and applications of Data Mining methods in e-learning. The cross-fertilization of both areas is still in its infancy, and even academic references are scarce on the ground, although some leading education-related publications are already beginning to pay attention to this new field. In order to offer a reasonable organization of the available bibliographic information according to different criteria, firstly, and from the Data Mining practitioner point of view, references are organized according to the type of modeling techniques used, which include: Neural Networks, Genetic Algorithms, Clustering and Visualization Methods, Fuzzy Logic, Intelligent agents, and Inductive Reasoning, amongst others. From the same point of view, the information is organized according to the type of Data Mining problem dealt with: clustering, classification, prediction, etc.


title: Deep CNN with color lines model for unmarked road segmentation

abstract: Road detection from a monocular camera is an important perception module in any advanced driver assistance or autonomous driving system. Traditional techniques [1, 2, 3, 4, 5, 6] work reasonably well for this problem, when the roads are well maintained and the boundaries are clearly marked. However, in many developing countries or even for the rural areas in the developed countries, the assumption does not hold which leads to failure of such techniques. In this paper we propose a novel technique based on the combination of deep convolutional neural networks (CNNs), along with color lines model [7] based prior in a conditional random field (CRF) framework. While the CNN learns the road texture, the color lines model allows to adapt to varying illumination conditions. We show that our technique outperforms the state of the art segmentation techniques on the unmarked road segmentation problem. Though, not a focus of this paper, we show that even on the standard benchmark datasets like KITTI [8] and CamVid [9], where the road boundaries are well marked, the proposed technique performs competitively to the contemporary techniques.


title: Personalization method for tourist point of interest (POI) recommendation

abstract: Today, travelers are provided large amount information which includes Web sites and tourist magazines about introduction of tourist spot. However, it is not easy for users to process the information in a short time. Therefore travelers prefer to receive pertinent information easier and have that information presented in a clear and concise manner. This paper proposes a personalization method for tourist Point of Interest (POI) Recommendation.


title: AMR-to-text Generation with Synchronous Node Replacement Grammar

abstract: This paper addresses the task of AMR-to-text generation by leveraging synchronous node replacement grammar. During training, graph-to-string rules are learned using a heuristic extraction algorithm. At test time, a graph transducer is applied to collapse input AMRs and generate output sentences. Evaluated on SemEval-2016 Task 8, our method gives a BLEU score of 25.62, which is the best reported so far.


title: BlueSentinel: a first approach using iBeacon for an energy efficient occupancy detection system

abstract: In the last years, the concept of smart buildings has been proposed and proved to be an effective solution to tackle the problem of reducing the power consumption of complex (both residential and commercial) buildings, while providing the users with a very high level of comfort. In this context, knowing the exact position of users inside the buildings has been identified as a needed feature to optimize the behavior of the building itself. Recently, using the occupants mobile devices as sensors has been validated as an effective solution to have accurate occupancy detection systems, even if no energy efficient solution in therm of battery consumption has been found so far. On the contrary, with this work, we present BLUE-SENTINEL, an accurate and power efficient method to identify the occupants of each room of a smart building using mobile devices as source of information. The proposed approach faces the occupancy detection problem with a good accuracy by exploiting iBeacon, a very recent low-power technology proposed by Apple. In particular, since the iBeacon protocol is built upon Bluetooth Low Energy (BLE), it represents a very highly power-efficient solution. In addition to this, the iBeacon technology is characterized by a good level of compatibility and portability, supporting both iOS- and Android-based devices. The proposed approach has been validated in a real environment with a prototype system released as open source showing how this technology is suitable for the occupancy detection in a smart building.


title: An Evaluation of Space Time Cube Representation of Spatiotemporal Patterns

abstract: Space time cube representation is an information visualization technique where spatiotemporal data points are mapped into a cube. Information visualization researchers have previously argued that space time cube representation is beneficial in revealing complex spatiotemporal patterns in a data set to users. The argument is based on the fact that both time and spatial information are displayed simultaneously to users, an effect difficult to achieve in other representations. However, to our knowledge the actual usefulness of space time cube representation in conveying complex spatiotemporal patterns to users has not been empirically validated. To fill this gap, we report on a between-subjects experiment comparing novice users' error rates and response times when answering a set of questions using either space time cube or a baseline 2D representation. For some simple questions, the error rates were lower when using the baseline representation. For complex questions where the participants needed an overall understanding of the spatiotemporal structure of the data set, the space time cube representation resulted in on average twice as fast response times with no difference in error rates compared to the baseline. These results provide an empirical foundation for the hypothesis that space time cube representation benefits users analyzing complex spatiotemporal patterns.


title: On brewing fresh espresso: LinkedIn's distributed data serving platform

abstract: Espresso is a document-oriented distributed data serving platform that has been built to address LinkedIn's requirements for a scalable, performant, source-of-truth primary store. It provides a hierarchical document model, transactional support for modifications to related documents, real-time secondary indexing, on-the-fly schema evolution and provides a timeline consistent change capture stream. This paper describes the motivation and design principles involved in building Espresso, the data model and capabilities exposed to clients, details of the replication and secondary indexing implementation and presents a set of experimental results that characterize the performance of the system along various dimensions. When we set out to build Espresso, we chose to apply best practices in industry, already published works in research and our own internal experience with different consistency models. Along the way, we built a novel generic distributed cluster management framework, a partition-aware change- capture pipeline and a high-performance inverted index implementation.


title: Optimized Cartesian K-Means

abstract: Product quantization-based approaches are effective to encode high-dimensional data points for approximate nearest neighbor search. The space is decomposed into a Cartesian product of low-dimensional subspaces, each of which generates a sub codebook. Data points are encoded as compact binary codes using these sub codebooks, and the distance between two data points can be approximated efficiently from their codes by the precomputed lookup tables. Traditionally, to encode a subvector of a data point in a subspace, only one sub codeword in the corresponding sub codebook is selected, which may impose strict restrictions on the search accuracy. In this paper, we propose a novel approach, named optimized cartesian K-means (ock-means), to better encode the data points for more accurate approximate nearest neighbor search. In ock-means, multiple sub codewords are used to encode the subvector of a data point in a subspace. Each sub codeword stems from different sub codebooks in each subspace, which are optimally generated with regards to the minimization of the distortion errors. The high-dimensional data point is then encoded as the concatenation of the indices of multiple sub codewords from all the subspaces. This can provide more flexibility and lower distortion errors than traditional methods. Experimental results on the standard real-life data sets demonstrate the superiority over state-of-the-art approaches for approximate nearest neighbor search.


title: Interior Permanent-Magnet Synchronous Motors for Adjustable-Speed Drives

abstract: Interior permanent-magnet (IPM)synchronous motors possess special features foradjustable-speed operation whichdistinguish themfromother classes ofacmachines. Theyarerobust highpower- density machines capable ofoperating athighmotorandinverter efficiencies overwidespeedranges, including considerable ranges of constant-power operation. Themagnetcostisminimized bythelow magnetweight requirements oftheIPMdesign. Theimpact oftheburied- magnetconfiguration onthemotor's electromagnetic characteristics is discussed. Therotor magnetic circuit saliency preferentially increases the quadrature-axis inductance andintroduces areluctance torque terminto theIPMmotor's torque equation. Theelectrical excitation requirements fortheIPMsynchronous motorarealsodiscussed. Thecontrol ofthe sinusoidal phasecurrents inmagnitude andphaseangle withrespect to therotor orientation provides ameansforachieving smoothresponsive torque control. A basic feedforward algorithm forexecuting this typeof current vector torque control isdiscussed, including theimplications of current regulator saturation athighspeeds. Thekeyresults areillustrated using acombination ofsimulation andprototype IPMdrive measure- ments. I.INTRODUCTION


Business Modelling and Convergence 2


A Logical Basis for Measuring Critical Thinking Skills.


title: Gaussian Processes for Signal Strength-Based Location Estimation

abstract: Estimating the location of a mobile device or a robot from wireless signal strength has become an area of highly active research. The key problem in this context stems from the complexity of how signals propagate through space, especially in the presence of obstacles such as buildings, walls or people. In this paper we show how Gaussian processes can be used to generate a likelihood model for signal strength measurements. We also show how parameters of the model, such as signal noise and spatial correlation between measurements, can be learned from data via hyperparameter estimation. Experiments using WiFi indoor data and GSM cellphone connectivity demonstrate the superior performance of our approach.


title: Single-chip 5.8GHz DSRC transceiver with dual-mode of ASK and Pi/4-QPSK

abstract: We have developed a fully integrated 0.25 mum SiGe-BiCMOS transceiver with a power amplifier (PA), transmit / receive switch (T/R SW), orthogonal modulator / demodulator, received signal strength indicator (RSSI), and phase locked loop (PLL) for Dedicated Short Range Communications (DSRC) mobile terminals with Amplifier Shift Keying (ASK) and Pi/4- shifted Quadrature Phase Shift Keying (Pi/4-QPSK) dual-mode modulation. Transmitter architecture is based on the sliding-IF topology using 4.6 GHz VCO and a 1/4 divider. Receiver architecture is based on the fixed-IF super-heterodyne. For image spurious rejection in the transmitter, a passive band elimination filter (BEF) comprised of a differential inductor is implemented. The 3.1mm x 2.97 mm die is packaged in 5.8 mm x 5.8 mm 48 pin- VQFN.


title: A New Adaptive Weighted Mean Filter for Removing Salt-and-Pepper Noise

abstract: In this letter, we propose a new adaptive weighted mean filter (AWMF) for detecting and removing high level of salt-and-pepper noise. For each pixel, we firstly determine the adaptive window size by continuously enlarging the window size until the maximum and minimum values of two successive windows are equal respectively. Then the current pixel is regarded as noise candidate if it is equal to the maximum or minimum values, otherwise, it is regarded as noise-free pixel. Finally, the noise candidate is replaced by the weighted mean of the current window, while the noise-free pixel is left unchanged. Experiments and comparisons demonstrate that our proposed filter has very low detection error rate and high restoration quality especially for high-level noise.


title: Self-control as limited resource: regulatory depletion patterns.

abstract: If self-regulation conforms to an energy or strength model, then self-control should be impaired by prior exertion. In Study 1, trying to regulate one's emotional response to an upsetting movie was followed by a decrease in physical stamina. In Study 2, suppressing forbidden thoughts led to a subsequent tendency to give up quickly on unsolvable anagrams. In Study 3, suppressing thoughts impaired subsequent efforts to control the expression of amusement and enjoyment. In Study 4, autobiographical accounts of successful versus failed emotional control linked prior regulatory demands and fatigue to self-regulatory failure. A strength model of self-regulation fits the data better than activation, priming, skill, or constant capacity models of self-regulation.


title: Arduino-based smart garbage monitoring system: Analysis requirement and implementation

abstract: With the growth of cities, the waste produced also increases. Many of the world's developing cities are still lacking in the area of waste management, in particular, the collection of garbage within the cities. Because of this, garbage tends to pile up in certain areas. This not only poses a health risk to the surrounding communities, but also creates unpleasant environments for the residents. To provide a solution to this problem, a smart garbage monitoring system is therefore proposed to tackle the issues faced. This system will allow the city authorities to better manage their resources in the collection of garbage and provide a platform that will allow for an efficient garbage collection system.


The Effect of Digitizing and Gamifying Quizzing in Classrooms


title: Design a single band microstrip patch antenna at 60 GHz millimeter wave for 5G application

abstract: This proposed paper, a single band microstrip patch antenna for 5G wireless application is presented. This proposed antenna is suitable for the millimeter wave frequency. The single band antenna consist of new H slot and E slot loaded on the radiating patch with the 50 ohms microstrip line feeding used. This single band antenna is simulated on a Rogers RT5880 dielectric substrate have relative permittivity 2.2, loss tangent 0.0009, and height 1.6mm. The antenna is simulated by Electromagnetic simulation, computer software technology Microwave studio. The proposed single band antenna and simulated result on return loss, VSWR, surface current and 3D radiation pattern is presented. The simulated antenna shows the return loss −40.99dB at 60 GHz millimeter wave 5G wireless application presented.


title: Dictionary transfer for image denoising via domain adaptation

abstract: The idea of using overcomplete dictionaries with prototype signal atoms for sparse representation has found many applications, among which image denoising is considered as an active research topic. However, the standard process to train a new dictionary for image denoising requires the whole image (or most parts) as input, which is costly; training the dictionary on just a few patches would result in overfitting. We instead propose a dictionary learning approach for image denoising via transfer learning. We transfer the source domain dictionary to a target domain for image denoising via a dictionary-regularization term in the energy function. Thus, we have a new dictionary that is trained from only a few patches of the target noisy image. We measure the performance on various corrupted images, and show that our method is fast and comparable to the state of the art. We also demonstrate cross-domain transfer (photo to medical image).


title: Numerical analysis of damage progression and strength of countersunk composite joints

abstract: Abstract A numerical investigation is conducted into the damage progression and strength of bolted joints between fibre-reinforced composite laminates using countersunk fasteners. Experimental tests were previously conducted on a bearing test specimen and countersunk fastener single-lap joints. In this work, computational models are developed for Abaqus/Explicit, with continuum shells employed to model in-plane ply failure. The bolt–nut assembly is modelled with rigid elements, and the models account for bolt torque and frictional contact. The material properties required in the computational model are determined from standard tests, with the compression fracture toughness of composite plies calibrated against experimental data from the bearing test. The analysis approach captures the load-carrying capability of all configurations, and provides reasonable accuracy in predicting damage patterns. The effects of bolt torque, clearance and countersink height ratio are investigated, and the analysis results compare well with experimental findings. Furthermore, the analysis provides rich insight into the damage progression and joint behaviour at the ply level, with the in-plane and through-thickness damage patterns mapped for increasing applied load. Delamination is incorporated using a cohesive element layer at the start of the countersunk region, though has minimal influence on damage progression and load-carrying capability, which agrees with the experimental results.


title: A System for Real-time Twitter Sentiment Analysis of 2012 U.S. Presidential Election Cycle

abstract: This paper describes a system for real-time analysis of public sentiment toward presidential candidates in the 2012 U.S. election as expressed on Twitter, a micro-blogging service. Twitter has become a central site where people express their opinions and views on political parties and candidates. Emerging events or news are often followed almost instantly by a burst in Twitter volume, providing a unique opportunity to gauge the relation between expressed public sentiment and electoral events. In addition, sentiment analysis can help explore how these events affect public opinion. While traditional content analysis takes days or weeks to complete, the system demonstrated here analyzes sentiment in the entire Twitter traffic about the election, delivering results instantly and continuously. It offers the public, the media, politicians and scholars a new and timely perspective on the dynamics of the electoral process and public opinion.


title: Innovation, openness & platform control

abstract: We examine how exercising control over a technology platform can increase profits and innovation. Benefits depend on using a platform as a governance mechanism to influence ecosystem parters. Results can inform innovation strategy, antitrust and intellectual property law, and management of competition.


title: A Digitally Controlled Oscillator in a 65-nm CMOS process for SoC clock generation

abstract: This paper presents a CMOS 1.1–2.8 GHz 10 bits digitally controlled oscillator (DCO) for high speed clocking of SoCs. The DCO includes only 269 tuning cells, which is possible thanks to an original algorithm based on weighted combined thermometer code, used for the DCO frequency control. The control circuit of the DCO includes only binary-to-thermometer decoders: that was possible with the proposed technique of virtual extension of number of the DCO ring. It was implemented in 65-nm CMOS technology, with semi-custom layout design allowed to optimize the area on silicon. The design was validated by transistor-level ELDO extracted schematic simulation. Oscillator shows a good linearity in the frequency tunning range, with average power consumption 6mW/GHz with 1.1V supply voltage. Typical phase noise with 1MHz offset and 2GHz carrying frequency is −86.12dBc/Hz.


title: Traffic Light Control System for Emergency Vehicles Using Radio Frequency

abstract: Traffic congestion problem is a phenomena which contributed huge impact to the transportation system in country. This causes many problems especially when there are emergency cases at traffic light intersections which are always busy with many vehicles. A traffic light controller system is designed in order to solve these problems. This system was designed to be operated when it received signal from emergency vehicles based on radio frequency (RF) transmission and used the Programmable Integrated Circuit (PIC) 16F877A microcontroller to change the sequence back to the normal sequence before the emergency mode was triggered. This system will reduce accidents which often happen at the traffic light intersections because of other vehicle had to huddle for given a special route to emergency vehicle. As the result, this project successful analyzing and implementing the wireless communication; the radio frequency (RF) transmission in the traffic light control system for emergency vehicles. The prototype of this project is using the frequency of 434 MHz and function with the sequence mode of traffic light when emergency vehicles passing by an intersection and changing the sequence back to the normal sequence before the emergency mode was triggered. In future, this prototype system can be improved by controlling the real traffic situation, in fact improving present traffic light system technology.


title: A research on calibration of low-precision MEMS inertial sensors

abstract: Micro-electromechanical systems (MEMS) are an emerging technology that has the potential for a multitude of uses. MEMS sensors are cost-effective units with small dimensions, but suffer from large random errors compared with their mechanical predecessors. It is hard to have an accurate description of sensor errors because the characteristics of errors are quite different among the MEMS inertial sensors with the same specifications. Installation errors result from the imperfection in welding the MEMS sensor which consists of a triaxial accelerometer and a triaxial gyroscope on a single-chip. Low-precision MEMS inertial sensors exhibit biases, scale factors, and large installation errors. According to the error characteristics of MEMS inertial sensors, a mathematical model was analyzed. The validity of the six-position test was verified by using the correction between the triaxial accelerometer and the triaxial gyroscope on one single-chip. One method to identify the intersection angle among the sensitive axes and the measured vector was analyzed. The test results show that the calibration can effectively correct the bias, scale factor and installation error of MEMS inertial sensors.


title: Affective computation on EEG correlates of emotion from musical and vocal stimuli

abstract: Affective interface that acquires and detects the emotion of the user can potentially enhance the human-computer interface experience. In this paper, an affective brain-computer interface (ABCI) is proposed to perform affective computation on electroencephalogram (EEG) correlates of emotion. The proposed ABCI extracts EEG features from subjects while exposed to 6 emotionally-related musical and vocal stimuli using kernel smoothing density estimation (KSDE) and Gaussian mixture model probability estimation (GMM). A classification algorithm is subsequently used to learn and classify the extracted EEG features. An inter-subject validation study is performed on healthy subjects to assess the performance of ABCI using a selection of classification algorithms. The results show that ABCI that employed the Bayesian network and the One-Rule classifier yielded a promising inter-subject validation accuracy of 90%.


title: Clever support: efficient support structure generation for digital fabrication

abstract: We introduce an optimization framework for the reduction of support structures required by 3D printers based on Fused Deposition Modeling (FDM) technology. The printers need to connect overhangs with the lower parts of the object or the ground in order to print them. Since the support material needs to be printed first and discarded later, optimizing its volume can lead to material and printing time savings. We present a novel, geometry-based approach that minimizes the support material while providing sufficient support. Using our approach, the input 3D model is first oriented into a position with minimal area that requires support. Then the points in this area that require support are detected. For these points the supporting structure is progressively built while attempting to minimize the overall length of the support structure. The resulting structure has a tree-like shape that effectively supports the overhangs. We have tested our algorithm on the MakerBot® Replicator™ 2 printer and we compared our solution to the embedded software solution in this printer and to Autodesk® Meshmixer™ software. Our solution reduced printing time by an average of 29.4% (ranging from 13.9% to 49.5%) and the amount of material by 40.5% (ranging from 24.5% to 68.1%).


title: A Shape Ontology Framework for Bird Classification

abstract: Current research on shape based classification has been generally aimed at utilising various visual features. Previous research has shown that the existing knowledge in a specific domain can assist in understanding the image content. Ontologies are currently being used for explicit representation of the domain knowledge. In this paper, two contributions are presented: 1) a shape ontology framework which constitutes both domain and shape ontologies and in which domain and shape ontologies are mapped to each other. 2) A new approach for automatic construction of shape ontology. The experimental results are promising. Future work will focus on validating the framework and automatic method of the shape ontology construction for a much larger dataset.


title: Collaborative future event recommendation

abstract: We demonstrate a method for collaborative ranking of future events. Previous work on recommender systems typically relies on feedback on a particular item, such as a movie, and generalizes this to other items or other people. In contrast, we examine a setting where no feedback exists on the particular item. Because direct feedback does not exist for events that have not taken place, we recommend them based on individuals' preferences for past events, combined collaboratively with other peoples' likes and dislikes. We examine the topic of unseen item recommendation through a user study of academic (scientific) talk recommendation, where we aim to correctly estimate a ranking function for each user, predicting which talks would be of most interest to them. Then by decomposing user parameters into shared and individual dimensions, we induce a similarity metric between users based on the degree to which they share these dimensions. We show that the collaborative ranking predictions of future events are more effective than pure content-based recommendation. Finally, to further reduce the need for explicit user feedback, we suggest an active learning approach for eliciting feedback and a method for incorporating available implicit user cues.


title: Interactive furniture layout using interior design guidelines

abstract: We present an interactive furniture layout system that assists users by suggesting furniture arrangements that are based on interior design guidelines. Our system incorporates the layout guidelines as terms in a density function and generates layout suggestions by rapidly sampling the density function using a hardware-accelerated Monte Carlo sampler. Our results demonstrate that the suggestion generation functionality measurably increases the quality of furniture arrangements produced by participants with no prior training in interior design.


title: A Framework for Evaluating Mobile App Repackaging Detection Algorithms

abstract: Because it is not hard to reverse engineer the Dalvik bytecode used in the Dalvik virtual machine, Android application repackaging has become a serious problem. With repackaging, a plagiarist can simply steal others’ code violating the intellectual property of the developers. More seriously, after repackaging, popular apps can become the carriers of malware, adware or spy-ware for wide spreading. To maintain a healthy app market, several detection algorithms have been proposed recently, which can catch some types of repackaged apps in various markets efficiently. However, they are generally lack of valid analysis on their effectiveness. After analyzing these approaches, we find simple obfuscation techniques can potentially cause false negatives, because they change the main characteristics or features of the apps that are used for similarity detections. In practice, more sophisticated obfuscation techniques can be adopted (or have already been performed) in the context of mobile apps. We envision this obfuscation based repackaging will become a phenomenon due to the arms race between repackaging and its detection. To this end, we propose a framework to evaluate the obfuscation resilience of repackaging detection algorithms comprehensively. Our evaluation framework is able to perform a set of obfuscation algorithms in various forms on the Dalvik bytecode. Our results provide insights to help gauge both broadness and depth of algorithms’ obfuscation resilience. We applied our framework to conduct a comprehensive case study on AndroGuard, an Android repackaging detector proposed in Black-hat 2011. Our experimental results have demonstrated the effectiveness and stability of our framework.


Addicted to cellphones: exploring the psychometric properties between the nomophobia questionnaire and obsessiveness in college students


title: Identifying Aedes aegypti Mosquitoes by Sensors and One-Class Classifiers

abstract: Yellow fever, zika, and dengue are some examples of arboviruses transmitted to the humans by the Aedes aegypti mosquitoes. The efforts to curb the transmission of these viral diseases are focused on the vector control. However, without the knowledge of the exact location of the insects with a reduced time delay, the use of techniques as chemical control becomes costly and inefficient. Recently, an optical sensor was proposed to gather real-time information about the spatio-temporal distributions of insects, supporting different vector control techniques. In field conditions, the assumption of knowledge of all classes of the problem, it is hard to be fulfilled. For this reason, we address the problem of insect classification by one-class classifiers, where the learning is performed only with positive examples (target class). In our experiments, we identify Aedes aegypti mosquitos with an AUC = 0.87.


title: Actor-Action Semantic Segmentation with Region Masks

abstract: In this paper, we study the actor-action semantic segmentation problem, which requires joint labeling of both actor and action categories in video frames. One major challenge for this task is that when an actor performs an action, different body parts of the actor provide different types of cues for the action category and may receive inconsistent action labeling when they are labeled independently. To address this issue, we propose an end-to-end region-based actor-action segmentation approach which relies on region masks from an instance segmentation algorithm. Our main novelty is to avoid labeling pixels in a region mask independently - instead we assign a single action label to these pixels to achieve consistent action labeling. When a pixel belongs to multiple region masks, max pooling is applied to resolve labeling conflicts. Our approach uses a two-stream network as the front-end (which learns features capturing both appearance and motion information), and uses two region-based segmentation networks as the back-end (which takes the fused features from the two-stream network as the input and predicts actor-action labeling). Experiments on the A2D dataset demonstrate that both the region-based segmentation strategy and the fused features from the two-stream network contribute to the performance improvements. The proposed approach outperforms the state-of-the-art results by more than 8% in mean class accuracy, and more than 5% in mean class IOU, which validates its effectiveness.


title: Relationship Between Peer Victimization, Cyberbullying, and Suicide in Children and Adolescents

abstract: IMPORTANCE Peer victimization is related to an increased chance of suicidal ideation and suicide attempts among children and adolescents. OBJECTIVE To examine the relationship between peer victimization and suicidal ideation or suicide attempts using meta-analysis. DATA SOURCES Ovid MEDLINE, PsycINFO, and Web of Science were searched for articles from 1910 to 2013. The search terms were bully*, teas*, victim*, mobbing, ragging, and harassment in combination with the term suic*. Of the 491 studies identified, 34 reported on the relationship between peer victimization and suicidal ideation, with a total of 284 375 participants. Nine studies reported on the relationship between peer victimization and suicide attempts, with a total of 70 102 participants. STUDY SELECTION Studies were eligible for inclusion if they reported an effect size on the relationship between peer victimization and suicidal ideation or suicide attempt in children or adolescents. DATA EXTRACTION AND SYNTHESIS Two observers independently coded the effect sizes from the articles. Data were pooled using a random effects model. MAIN OUTCOMES AND MEASURES This study focused on suicidal ideation and suicide attempts. Peer victimization was hypothesized to be related to suicidal ideation and suicide attempts. RESULTS Peer victimization was found to be related to both suicidal ideation (odds ratio, 2.23 [95% CI, 2.10-2.37]) and suicide attempts (2.55 [1.95 -3.34]) among children and adolescents. Analyses indicated that these results were not attributable to publication bias. Results were not moderated by sex, age, or study quality. Cyberbullying was more strongly related to suicidal ideation compared with traditional bullying. CONCLUSIONS AND RELEVANCE Peer victimization is a risk factor for child and adolescent suicidal ideation and attempts. Schools should use evidence-based practices to reduce bullying. Language: en


title: Optimized graph extraction and locomotion prediction for redirected walking

abstract: Redirected walking with advanced planners such as MPCRed or FORCE requires both knowledge about the virtual environment - mostly in the form of a skeleton graph representing the virtual environment - and a robust prediction of the user's actions. This paper presents methods for both parts and evaluates them with a number of test cases.


title: Evolutionary game design

abstract: It is easy to create new combinatorial games but more difficult to predict those that will interest human players. We examine the concept of game quality, its automated measurement through self-play simulations, and its use in the evolutionary search for new high-quality games. A general game system called Ludi is described and experiments conducted to test its ability to synthesize and evaluate new games. Results demonstrate the validity of the approach through the automated creation of novel, interesting, and publishable games.


title: Cognitive Status and Form of Reference in Multimodal Human-Computer Interaction

abstract: We analyze a corpus of referring expressions collected from user interactions with a multimodal travel guide application. The analysis suggests that, in dramatic contrast to normal modes of human-human interaction, the interpretation of referring expressions can be computed with very high accuracy using a model which pairs an impoverished notion of discourse state with a simple set of rules that are insensitive to the type of referring expression used. We attribute this result to the implicit manner in which the interface conveys the system’s beliefs about the operative discourse state, to which users tailor their choice of referring expressions. This result offers new insight into the way computer interfaces can shape a user’s language behavior, insights which can be exploited to bring otherwise difficult interpretation problems into the realm of tractability.


title: Efficient symbolic execution for analysing cryptographic protocol implementations

abstract: The analysis of code that uses cryptographic primitives is unfeasible with current state-of-the-art symbolic execution tools. We develop an extension that overcomes this limitation by treating certain concrete functions, like cryptographic primitives, as symbolic functions whose execution analysis is entirely avoided; their behaviour is in turn modelled formally via rewriting rules. We define concrete and symbolic semantics within a (subset) of the low-level virtual machine LLVM. We then show our approach sound by proving operational correspondence between the two semantics. We present a prototype to illustrate our approach and discuss next milestones towards the symbolic analysis of fully concurrent cryptographic protocol implementations.


title: Multi-player alpha-beta pruning

abstract: Korf, R.E., Multi-player alpha-beta pruning (Research Note), Artificial Intelligence 48 (1991) 99-111. We consider the generalization of minimax search with alpha-beta pruning to non-cooperative, perfect-information games with more than two players. The minimax algorithm was generalized in [2] to the maxn algorithm applied to vectors of n-tuples representing the evaluations for each of the players. If we assume an upper bound on the sum of the evaluations for each player, and a lower bound on each individual evaluation, then shallow alpha-beta pruning is possible, but not deep pruning. In the best case, the asymptotic branching factor is reduced to (1 + 4bv'~b-"s~-3)/2. In the average case, however, pruning does not reduce the asymptotic branching factor. Thus, alpha-beta pruning is found to be effective only in the special case of two-player games. In addition, we show that it is an optimal directional algorithm for two players.


title: Typical DoS/DDoS Threats under IPv6

abstract: The DoS/DDoS attacks are always the leading threats to the Internet. With the development of Internet, IPv6 is inevitably taking the place of IPv4 as the main protocol of Internet. So the security issues of IPv6 become the focus of the present research. In this paper we mainly focus on the typical DoS/DDoS attacks under IPv6, which including the DoS attacks pertinent to IPv6 Neighbor Discovery protocol and DDoS attacks based on the four representative attack modes, they are respectively TCP-Flood, UDP-Flood, ICMP-Flood and Smurf. We do these attack experiments under IPv6 with and without IPSec configuration respectively. The experiments without IPSec validate the effectiveness of the typical DoS/DDoS attacks under IPv6, and those with IPSec show the effectiveness of IPSec against these attacks whose source addresses are spoofed.


title: Ontology for E-Business Models

abstract: Business models have become more complex with the emergence of new and affordable information and communication technologies (ICTs). Companies increasingly act in networks and offer complex value proposition through a multitude of distribution channels. For managers it is ever harder to keep track of how their companies really work and how and where exactly the money is made. Of course every manager and entrepreneur does have an intuitive understanding of how his business works and how value is created. In other words he does have an intuitive understanding of the company’s business model, but even though this business model influences all important decisions, in many cases she or he is rarely able to communicate it in a clear and simple way (Linder and Cantrell 2000). And how can one decide on a particular business issue or change it, if it is not clearly understood by the parties involved? In this chapter we will try to tackle the business model issue and construct and outline the sketch of an ontology (rigorous framework) for e-business models based on an extensive literature review. We aim at showing how the fusion of the ideas in business model literature and the ideas of enterprise ontologies creates an appropriate basis for the development of a range of new management tools in the e-business domain. By merging the conceptually rich business model approach with the more rigorous ontological approach and by applying them to e-business, we achieve an appropriate foundation for tools that would allow the understanding, sharing and communication, change, measuring and simulation of e-business models. In the next section we give an overview of related work. As shown by Linder (Linder and Cantrell 2000), most people speak about business models when they really only mean parts of a business model. We think that the existing business model literature essentially attacks one, two or rarely all of the following three elements, which make up a business model: revenue and product aspects, business actor and network aspects and finally, marketing specific aspects. We propose an e-business model ontology that highlights the relevant e-business issues and elements that firms have to think of, in order to operate successfully in the Internet era. An ontology is nothing else than a rigorously defined framework that provides a shared and common understanding of a domain that can be communicated between people and heterogeneous and widely spread application systems (Fensel 2001). We suggest adopting a framework which emphasizes on the following issues that a business model has to address: - [Product innovation] What business the company is in, the product innovation and the value proposition offered to the market? - [Customer relationship] Who the company's target customers are, how it delivers them the products and services, and how it builds a strong relationships with them? - [Infrastructure management] How the company efficiently performs infrastructure or logistics issues, with whom, and as what kind of virtual enterprise? and finally,


title: Symbolic execution for software testing: three decades later

abstract: The challenges---and great promise---of modern symbolic execution techniques, and the tools to help implement them.


title: Electrical Impedance Spectro-Tomography Based on Dielectric Relaxation Model

abstract: An electrical impedance spectro-tomography consists of a quasi-adjacent sensing method and a frequency-time difference imaging for relatively complex dielectric object monitoring has been proposed. A quasi-adjacent sensing method is designed to measure an electrical impedance spectroscopy based on four-wire measurement technique at the multi-electrodes electrical impedance tomography sensor. A frequency-time difference imaging for spatial distribution monitoring is needed, as the electrochemical reaction incur during the short time meanwhile the spatial geometry tends to be constant. An artificial set of phantoms were made and the proposed technique was capable to show the Cole–Cole parameters as a dielectric relaxation model on each measurement pair. Compare with the adjacent method, using the quasi-adjacent method show Re(Z) is two times higher, Imag(Z) is ten times higher, and a higher and homogeneous sensitivity map area are 60% wider. A negative resistance at high-frequency measurement also can be eliminated successfully by using the proposed method.


Digital Supply Chain Transformation toward Blockchain Integration


title: Give it time: Neural evidence for distorted time perception and enhanced memory encoding in emotional situations

abstract: Abstract Time perception is compromised in emotional situations, yet our ability to remember these events is enhanced. Here we suggest how the two phenomena might be functionally linked and describe the neural networks that underlie this association. We found that participants perceived an emotionally aversive stimulus longer than it was, compared to an immediately following neutral stimulus. These time estimation errors were in the same trials associated with better recognition memory for the emotionally aversive stimuli and poorer memory for the neutral stimuli. Functional imaging revealed that the superior frontal gyrus was activated during time perception with aversive stimuli, and the amygdala, putamen and insula showed activations that are specific to time estimation errors in this aversive context. We further found that activity in the insula and putamen was correlated with memory performance but only during over-estimation of time with the aversive stimuli. We suggest that processing is accelerated during the experience of emotionally aversive events, presumably in the service of memory-related operations, resulting in better encoding but at the expense of time perception accuracy.


title: When Does Cotraining Work in Real Data?

abstract: Cotraining, a paradigm of semisupervised learning, is promised to alleviate effectively the shortage of labeled examples in supervised learning. The standard two-view cotraining requires the data set to be described by two views of features, and previous studies have shown that cotraining works well if the two views satisfy the sufficiency and independence assumptions. In practice, however, these two assumptions are often not known or ensured (even when the two views are given). More commonly, most supervised data sets are described by one set of attributes (one view). Thus, they need be split into two views in order to apply the standard two-view cotraining. In this paper, we first propose a novel approach to empirically verify the two assumptions of cotraining given two views. Then, we design several methods to split single view data sets into two views, in order to make cotraining work reliably well. Our empirical results show that, given a whole or a large labeled training set, our view verification and splitting methods are quite effective. Unfortunately, cotraining is called for precisely when the labeled training set is small. However, given small labeled training sets, we show that the two cotraining assumptions are difficult to verify, and view splitting is unreliable. Our conclusions for cotraining's effectiveness are mixed. If two views are given, and known to satisfy the two assumptions, cotraining works well. Otherwise, based on small labeled training sets, verifying the assumptions or splitting single view into two views are unreliable; thus, it is uncertain whether the standard cotraining would work or not.


title: A practical model for subsurface light transport

abstract: This paper introduces a simple model for subsurface light transport in translucent materials. The model enables efficient simulation of effects that BRDF models cannot capture, such as color bleeding within materials and diffusion of light across shadow boundaries. The technique is efficient even for anisotropic, highly scattering media that are expensive to simulate using existing methods. The model combines an exact solution for single scattering with a dipole point source diffusion approximation for multiple scattering. We also have designed a new, rapid image-based measurement technique for determining the optical properties of translucent materials. We validate the model by comparing predicted and measured values and show how the technique can be used to recover the optical properties of a variety of materials, including milk, marble, and skin. Finally, we describe sampling techniques that allow the model to be used within a conventional ray tracer.


title: Variational Capsules for Image Analysis and Synthesis

abstract: A capsule is a group of neurons whose activity vector models different properties of the same entity. This paper extends the capsule to a generative version, named variational capsules (VCs). Each VC produces a latent variable for a specific entity, making it possible to integrate image analysis and image synthesis into a unified framework. Variational capsules model an image as a composition of entities in a probabilistic model. Different capsules' divergence with a specific prior distribution represents the presence of different entities, which can be applied in image analysis tasks such as classification. In addition, variational capsules encode multiple entities in a semantically-disentangling way. Diverse instantiations of capsules are related to various properties of the same entity, making it easy to generate diverse samples with fine-grained semantic attributes. Extensive experiments demonstrate that deep networks designed with variational capsules can not only achieve promising performance on image analysis tasks (including image classification and attribute prediction) but can also improve the diversity and controllability of image synthesis.


title: An LSTM Based System for Prediction of Human Activities with Durations

abstract: Human activity prediction is an interesting problem with a wide variety of applications like intelligent virtual assistants, contextual marketing, etc. One formulation of this problem is jointly predicting human activities (viz. eating, commuting, etc.) with associated durations. Herein a deep learning system is proposed for this problem. Given a sequence of past activities and durations, the system estimates the probabilities for future activities and their durations. Two distinct Long-Short Term Memory (LSTM) networks are developed that cater to different assumptions about the data and achieve different modeling complexities and prediction accuracies. The networks are trained and tested with two real-world datasets, one being publicly available while the other collected from a field experiment. Modeling on the segment level public dataset mitigates the cold-start problem. Experiments indicate that compared to traditional approaches based on sequence mining or hidden Markov modeling, LSTM networks perform significantly better. The ability of LSTM networks to detect long term correlations in activity data is also demonstrated. The trained models are each less than 500KB in size and can be deployed to run in real-time on a mobile device without any dependencies on the cloud. This can help applications like mobile personal assistants by providing predictive context.


title: End-to-end, sequence-to-sequence probabilistic visual odometry through deep neural networks

abstract: This paper studies visual odometry (VO) from the perspective of deep learning. After tremendous efforts in the robotics and computer vision communities over the past few decades, state-of-the-art V...


Clinical Applications of the Miniscrew Anchorage System


title: Improving electrochemical performance of lanthanum strontium ferrite by decorating instead of doping cobaltite

abstract: Cobalt oxide is usually used as a dopant to improve the catalytic activity of Mn and Fe based perovskites such as lanthanum strontium ferrite (LSF) for solid oxide fuel cells. This work presents the catalytic improvement by decorating LSF surface with cobalt oxide, Co3O4, using infiltration technique. X-ray diffraction and high resolution electron microscopy analysis indicate that Co3O4 is chemically compatible with LSF at the intermediate-temperature range for solid oxide fuel cells. Electrochemical impedance spectrum demonstrates substantial reduction in interfacial polarization resistance, from 0.22 to 0.083 Ωcm2 at 700 °C when 5.84 wt. % Co3O4 is infiltrated into the bare LSF electrodes. Further analysing the impedance spectrum with distribution of relaxation time calculation suggests that the performance improvement is associated with the charge-transfer processes of the surface reaction. Meanwhile, the electrochemical conductivity relaxation measurement shows that Co3O4 particles can improve the surface reaction kinetics, increasing the oxygen surface exchange coefficient by a factor of about 5 at 700 °C. In addition, Co3O4 particles can increase the peak power density of the single cells from 0.865 Wcm−2 to 1.3 Wcm−2 at 800 °C with LSF based cathodes. The results clearly demonstrate an alternative mean to use cobalt oxide in improving the catalytic performance of Mn and Fe based perovskites.


On Immutability of Blockchains


title: Carbon nanomaterials for non-volatile memories

abstract: Carbon nanomaterials have greatly advanced non-volatile memory technology. In this Review, applications of various carbon nanomaterials as memory electrodes, interfacial engineering layers, memory selectors and resistive-switching media are discussed in the context of emerging non-volatile memory devices.


Generation using Generative Adversarial Training


title: Grasp analysis using deformable fingers

abstract: The human hand is unrivaled in its ability to grasp and manipulate objects, but we still do not understand all of its complexities. One benefit it has over traditional robot hands is the fact that our fingers conform to a grasped object's shape, giving rise to larger contact areas and the ability to apply larger frictional forces. In this paper, we demonstrate how we have extended our simulation and analysis system with finite element modeling to allow us to evaluate these complex contact types. We also propose a new contact model that better accounts for the deformations and show how grasp quality is affected. This work is part of a larger project to understand the benefits the human hand has in grasping.


title: The Impact of the new European General Data Protection Regulation (GDPR) on the Information Governance Toolkit in Health and Social Care with Special Reference to Primary Care in England

abstract: The desire for eHealth systems (technology) is ever growing as public institutions (governments), healthcare providers, and its users (patients) see the gains that could possibly arise from having systems like databases of patient health information in a single place which will facilitate the way healthcare can be access by patients and their caregivers. The aim of this paper is to provide a supportive environment for the health and social care workplace with special reference in the Primary Care sector in England on the impact and changes to the information governance toolkit (IGTK) as a result of the new European General Data Protection Regulation (GDPR) which will be implemented in full from May 2018 as agreed by the UK Government thereby replacing the UK Data Protection Act of 1998. These challenges will also include the implementation of the National Data Guardian (NDG) review of data security and opt-outs amongst others.


title: Compact High-Isolation Base-Station Duplexer Using Triple-Mode Ceramic Cavities

abstract: A microwave duplexer with high isolation is presented in this paper. The device is based on triple-mode filters that are built using silver-plated ceramic cuboids. To create a six-pole, six-transmission-zero filter in the DCS-1800 band, which is utilized in mobile communications, two cuboids are cascaded. To shift spurious harmonics, low dielectric caps are placed on the cuboid faces. These caps push the first cuboid spurious up in frequency by around 340 MHz compared to the uncapped cuboid, allowing a 700-MHz spurious free window. To verify the design, a DCS-1800 duplexer with 75-MHz widebands is built. It achieves around 1 dB of insertion loss for both the receive and transmit ports with around 70 dB of mutual isolation within only 20-MHz band separation, using a volume of only 30 cm3 .


title: How Many Samples are Needed to Learn a Convolutional Neural Network

abstract: A widespread folklore for explaining the success of Convolutional Neural Networks (CNNs) is that CNNs use a more compact representation than the Fully-connected Neural Network (FNN) and thus require fewer training samples to accurately estimate their parameters. We initiate the study of rigorously characterizing the sample complexity of estimating CNNs. We show that for an $m$-dimensional convolutional filter with linear activation acting on a $d$-dimensional input, the sample complexity of achieving population prediction error of $\epsilon$ is $\widetilde{O}(m/\epsilon^2)$ whereas the sample-complexity for its FNN counterpart is lower bounded by $\Omega(d/\epsilon^2)$ samples. Since, in typical settings $m \ll d$, this result demonstrates the advantage of using a CNN. We further consider the sample complexity of estimating a one-hidden-layer CNN with linear activation where both the $m$-dimensional convolutional filter and the $r$-dimensional output weights are unknown. For this model, we show that the sample complexity is $\widetilde{O}\left((m+r)/\epsilon^2\right)$ when the ratio between the stride size and the filter size is a constant. For both models, we also present lower bounds showing our sample complexities are tight up to logarithmic factors. Our main tools for deriving these results are a localized empirical process analysis and a new lemma characterizing the convolutional structure. We believe that these tools may inspire further developments in understanding CNNs.


title: The nuclear pore complex - structure and function at a glance

abstract: Nuclear pore complexes (NPCs) are indispensable for cell function and are at the center of several human diseases. NPCs provide access to the nucleus and regulate the transport of proteins and RNA across the nuclear envelope. They are aqueous channels generated from a complex network of evolutionarily conserved proteins known as nucleporins. In this Cell Science at a Glance article and the accompanying poster, we discuss how transport between the nucleoplasm and the cytoplasm is regulated, what we currently know about the structure of individual nucleoporins and the assembled NPC, and how the cell regulates assembly and disassembly of such a massive structure. Our aim is to provide a general overview on what we currently know about the nuclear pore and point out directions of research this area is heading to.


title: Multi-feature based benchmark for cervical dysplasia classification evaluation

abstract: Cervical cancer is one of the most common types of cancer in women worldwide. Most deaths due to the disease occur in less developed areas of the world. In this work, we introduce a new image dataset along with expert annotated diagnoses for evaluating image-based cervical disease classification algorithms. A large number of Cervigram images are selected from a database provided by the US National Cancer Institute. For each image, we extract three complementary pyramid features: Pyramid histogram in L*A*B* color space (PLAB), Pyramid Histogram of Oriented Gradients (PHOG), and Pyramid histogram of Local Binary Patterns (PLBP). Other than hand-crafted pyramid features, we investigate the performance of convolutional neural network (CNN) features for cervical disease classification. Our experimental results demonstrate the effectiveness of both our hand-crafted and our deep features. We intend to release this multi-feature dataset and our extensive evaluations using seven classic classifiers can serve as the baseline. HighlightsA new image dataset is introduced for evaluating cervical disease classification.The pyramid feature descriptor encodes complementary hand-crafted features.The deep convolutional neural network automatically learns features from images.Results using seven classic classifiers serve as the baseline for future research.


title: Identify influential nodes in complex networks based on modified TOPSIS

abstract: In complex networks, identifying influential nodes is the very important issue, which has a wide range of applications. In this paper, factor analysis method is used to mine the relationships of multiple metric measurements, and then a new evaluation method of node importance in complex networks based on modified technique for order performance by similarity to ideal solution (TOPSIS) approach is proposed. TOPSIS is utilized to aggregate the multi-attribute to obtain the evaluation of node importance of each node. The more centrality measures considered, the more results more exact and effective. Then, we use the SIS model to evaluate the performance. The experiment result is showed that out method have more the efficiency and practicability than the proposed method.


title: SCARE: Side-Channel Analysis Based Reverse Engineering for Post-Silicon Validation

abstract: Reverse Engineering (RE) has been historically considered as a powerful approach to understand electronic hardware in order to gain competitive intelligence or accomplish piracy. In recent years, it has also been looked at as a way to authenticate hardware intellectual properties in the court of law. In this paper, we propose a beneficial role of RE in post-silicon validation of integrated circuits (IC) with respect to IC functionality, reliability and integrity. Unlike traditional destructive RE approaches, we propose a fast non-destructive side-channel analysis approach that can hierarchically extract structural information from an IC through its transient current signature. Such a top-down side-channel analysis approach is capable of reliably identifying pipeline stages and functional blocks. It is also suitable to distinguish sequential elements from combinational gates. For extraction of random logic structures (e.g. control blocks and finite state machines) we combine side-channel analysis with logic testing based Boolean function extraction. The proposed approach is amenable to automation, scalable, and can be applied as part of post-silicon validation process to verify that each IC implements exclusively the functionality described in the specification and is free from malicious modification or Trojan attacks. Simulation results on a pipelined DLX processor demonstrate the effectiveness of the proposed approach.


title: Global prevalence of sleep deprivation in students and heavy media use

abstract: The latest two international educational assessments found global prevalence of sleep deprivation in students, consistent with what has been reported in sleep research. However, despite the fundamental role of adequate sleep in cognitive and social functioning, this important issue has been largely overlooked by educational researchers. Drawing upon evidence from sleep research, literature on the heavy media use by children and adolescents, and data from web analytics on youth-oriented game sites and mobile analytics on youth-oriented game apps, we argue that heavy media use, particularly digital game play, may be an important contributor to sleep deprivation in students. Therefore, educational researchers, policy makers, teachers, and parents should pay greater attention to student sleep and develop programs and interventions to improve both quality and quantity of student sleep.


title: Identifying Emotional and Informational Support in Online Health Communities

abstract: A large number of online health communities exist today, helping millions of people with social support during difficult phases of their lives when they suffer from serious diseases. Interactions between members in these communities contain discussions on practical problems faced by people during their illness such as depression, side-effects of medications, etc and answers to those problems provided by other members. Analyzing these interactions can be helpful in getting crucial information about the community such as dominant health issues, identifying sentimental effects of interactions on individual members and identifying influential members. In this paper, we analyze user messages of an online cancer support community, Cancer Survivors Network (CSN), to identity the two types of social support present in them: emotional support and informational support. We model the task as a binary classification problem. We use several generic and novel domain-specific features. Experimental results show that we achieve high classification performance. We, then, use the classifier to predict the type of support in CSN messages and analyze the posting behaviors of regular members and influential members in CSN in terms of the type of support they provide in their messages. We find that influential members generally provide more emotional support as compared to regular members in CSN.


title: Multivariate pattern analysis for MEG: A comparison of dissimilarity measures

abstract: Abstract Multivariate pattern analysis (MVPA) methods such as decoding and representational similarity analysis (RSA) are growing rapidly in popularity for the analysis of magnetoencephalography (MEG) data. However, little is known about the relative performance and characteristics of the specific dissimilarity measures used to describe differences between evoked activation patterns. Here we used a multisession MEG data set to qualitatively characterize a range of dissimilarity measures and to quantitatively compare them with respect to decoding accuracy (for decoding) and between-session reliability of representational dissimilarity matrices (for RSA). We tested dissimilarity measures from a range of classifiers (Linear Discriminant Analysis – LDA, Support Vector Machine – SVM, Weighted Robust Distance – WeiRD, Gaussian Naive Bayes – GNB) and distances (Euclidean distance, Pearson correlation). In addition, we evaluated three key processing choices: 1) preprocessing (noise normalisation, removal of the pattern mean), 2) weighting decoding accuracies by decision values, and 3) computing distances in three different partitioning schemes (non-cross-validated, cross-validated, within-class-corrected). Four main conclusions emerged from our results. First, appropriate multivariate noise normalization substantially improved decoding accuracies and the reliability of dissimilarity measures. Second, LDA, SVM and WeiRD yielded high peak decoding accuracies and nearly identical time courses. Third, while using decoding accuracies for RSA was markedly less reliable than continuous distances, this disadvantage was ameliorated by decision-value-weighting of decoding accuracies. Fourth, the cross-validated Euclidean distance provided unbiased distance estimates and highly replicable representational dissimilarity matrices. Overall, we strongly advise the use of multivariate noise normalisation as a general preprocessing step, recommend LDA, SVM and WeiRD as classifiers for decoding and highlight the cross-validated Euclidean distance as a reliable and unbiased default choice for RSA.


title: An Image-based Deep Spectrum Feature Representation for the Recognition of Emotional Speech

abstract: The outputs of the higher layers of deep pre-trained convolutional neural networks (CNNs) have consistently been shown to provide a rich representation of an image for use in recognition tasks. This study explores the suitability of such an approach for speech-based emotion recognition tasks. First, we detail a new acoustic feature representation, denoted as deep spectrum features, derived from feeding spectrograms through a very deep image classification CNN and forming a feature vector from the activations of the last fully connected layer. We then compare the performance of our novel features with standardised brute-force and bag-of-audio-words (BoAW) acoustic feature representations for 2- and 5-class speech-based emotion recognition in clean, noisy and denoised conditions. The presented results show that image-based approaches are a promising avenue of research for speech-based recognition tasks. Key results indicate that deep-spectrum features are comparable in performance with the other tested acoustic feature representations in matched for noise type train-test conditions; however, the BoAW paradigm is better suited to cross-noise-type train-test conditions.


title: High PSR Low Drop-Out Regulator With Feed-Forward Ripple Cancellation Technique

abstract: A low drop-out (LDO) regulator with a feed-forward ripple cancellation (FFRC) technique is proposed in this paper. The FFRC-LDO achieves a high power-supply rejection (PSR) over a wide frequency range. Complete analysis and design steps of the FFRC-LDO are presented in this paper. Kelvin connection is also used to increase the gain-bandwidth of the LDO allowing for faster transient performance. The LDO is implemented in 0.13 ¿m CMOS technology and achieves a PSR better than - 56 dB up to 10 MHz for load currents up to 25 mA. Load regulation of 1.2 mV for a 25 mA step is measured, and the whole LDO consumes a quiescent current of 50 ¿A with a bandgap reference circuit included. To our knowledge, this is the first LDO that achieves such a high PSR up to 10 MHz.


title: The Normalization of Occurrence and Co-occurrence Matrices in Bibliometrics using Cosine Similarities and Ochiai Coefficients

abstract: We prove that Ochiai similarity of the co-occurrence matrix is equal to cosine similarity in the underlying occurrence matrix. Neither the cosine nor the Pearson correlation should be used for the normalization of co-occurrence matrices because the similarity is then normalized twice, and therefore over-estimated; the Ochiai coefficient can be used instead. Results are shown using a small matrix (5 cases, 4 variables) for didactic reasons, and also Ahlgren et al.'s (2003) co-occurrence matrix of 24 authors in library and information sciences. The over-estimation is shown numerically and will be illustrated using multidimensional scaling and cluster dendograms. If the occurrence matrix is not available (such as in internet research or author co-citation analysis) using Ochiai for the normalization is preferable to using the cosine.


title: Synaptic Vesicular Localization and Exocytosis of L-Aspartate in Excitatory Nerve Terminals: A Quantitative Immunogold Analysis in Rat Hippocampus

abstract: To elucidate the role of aspartate as a signal molecule in the brain, its localization and those of related amino acids were examined by light and electron microscopic quantitative immunocytochemistry using antibodies specifically recognizing the aldehyde-fixed amino acids. Rat hippocampal slices were incubated at physiological and depolarizing [K+] before glutaraldehyde fixation. At normal [K+], aspartate-like and glutamate-like immunoreactivities were colocalized in nerve terminals forming asymmetrical synapses on spines in stratum radiatum of CA1 and the inner molecular layer of fascia dentata (i.e., excitatory afferents from CA3 and hilus, respectively). During K+ depolarization there was a loss of aspartate and glutamate from these terminals. Simultaneously the immunoreactivities strongly increased in glial cells. These changes were Ca2+-dependent and tetanus toxin-sensitive and did not comprise taurine-like immunoreactivity. Adding glutamine at CSF concentration prevented the loss of aspartate and glutamate and revealed an enhancement of aspartate in the terminals at moderate depolarization. In hippocampi from animals perfused with glutaraldehyde during insulin-induced hypoglycemia (to combine a strong aspartate signal with good ultrastructure) aspartate was colocalized with glutamate in excitatory terminals in stratum radiatum of CA1. The synaptic vesicle-to-cytoplasmic matrix ratios of immunogold particle density were similar for aspartate and glutamate, significantly higher than those observed for glutamine or taurine. Similar results were obtained in normoglycemic animals, although the nerve terminal contents of aspartate were lower. The results indicate that aspartate can be concentrated in synaptic vesicles and subject to sustained exocytotic release from the same nerve endings that contain and release glutamate.


title: Information Technology and Its Role in India’s Economic Development: A Review

abstract: Information technology (IT) is an example of a general purpose technology that has the potential to play an important role in economic growth, as well as other dimensions of economic and social development. This paper reviews several interrelated aspects of the role of information technology in the evolution of India’s economy. It considers the unexpected success of India’s software export sector and the spillovers of this success into various IT enabled services, attempts to make IT and its benefits available to India’s rural masses, e-commerce for the country’s growing middle class, the use and impacts of IT in India’s manufacturing sector, and various forms of e-governance, including internal systems as well as citizen interfaces. The paper concludes with an overall assessment of these different facets of IT in the context of the Indian economy.


title: Benefits of multisensory learning

abstract: Studies of learning, and in particular perceptual learning, have focused on learning of stimuli consisting of a single sensory modality. However, our experience in the world involves constant multisensory stimulation. For instance, visual and auditory information are integrated in performing many tasks that involve localizing and tracking moving objects. Therefore, it is likely that the human brain has evolved to develop, learn and operate optimally in multisensory environments. We suggest that training protocols that employ unisensory stimulus regimes do not engage multisensory learning mechanisms and, therefore, might not be optimal for learning. However, multisensory-training protocols can better approximate natural settings and are more effective for learning.


title: Reducing systematic review workload through certainty-based screening

abstract: Display Omitted Active learning is promising in the areas with complex topics in systematic reviews.Certainty criteria is promising to accelerate screening regardless of the topic.Certainty criteria performs as well as uncertainty criteria in classification.Weighting positive instances is promising to overcome the data imbalance.Unsupervised methods enhance the classification performance. In systematic reviews, the growing number of published studies imposes a significant screening workload on reviewers. Active learning is a promising approach to reduce the workload by automating some of the screening decisions, but it has been evaluated for a limited number of disciplines. The suitability of applying active learning to complex topics in disciplines such as social science has not been studied, and the selection of useful criteria and enhancements to address the data imbalance problem in systematic reviews remains an open problem. We applied active learning with two criteria (certainty and uncertainty) and several enhancements in both clinical medicine and social science (specifically, public health) areas, and compared the results in both. The results show that the certainty criterion is useful for finding relevant documents, and weighting positive instances is promising to overcome the data imbalance problem in both data sets. Latent dirichlet allocation (LDA) is also shown to be promising when little manually-assigned information is available. Active learning is effective in complex topics, although its efficiency is limited due to the difficulties in text classification. The most promising criterion and weighting method are the same regardless of the review topic, and unsupervised techniques like LDA have a possibility to boost the performance of active learning without manual annotation.


Vocal Fold Disorder Detection by applying LBP Operator on Dysphonic Speech Signal


title: Mobile computing devices in higher education: Student perspectives on learning with cellphones, smartphones & social media

abstract: Abstract The purpose of this research was to explore teaching and learning when mobile computing devices, such as cellphones and smartphones, were implemented in higher education. This paper presents a portion of the findings on students' perceptions of learning with mobile computing devices and the roles social media played. This qualitative research study focused on students from three universities across the US. The students' teachers had been integrating mobile computing devices, such as cellphones and smartphones, into their courses for at least two semesters. Data were collected through student focus group interviews. Two specific themes emerged from the interview data: (a) advantages of mobile computing devices for student learning and (b) frustrations from learning with mobile computing devices. Mobile computing devices and the use of social media created opportunities for interaction, provided opportunities for collaboration, as well as allowed students to engage in content creation and communication using social media and Web 2.0 tools with the assistance of constant connectivity.


title: Human Detection and Character Recognition in TV-Style Movies

abstract: The objective of this master thesis is the recognition of human characters in TV-style video sequences. In order to recognize the same person at different time instances in a video sequence, the outward appearance of the person has to be described and learned with an appropriate model. The diversity in which humans can appear makes the task of human detection and character recognition to a particularly challenging problem. TVstyle movies provide an uncontrolled, realistic working environment for human detection and character recognition. Possible applications range from surveillance (e.g., intrusion detection) and security applications (e.g., person identification) to image retrieval or semiautomatic image annotation (e.g., automatic labeling of faces in personal photo albums).


title: Reinforcement Learning forTrueAdaptive Traffic Signal Control

abstract: The ability to exert real-time, adaptive control of transportation processes is the core of many intelligent transportation systems decision support tools. Reinforcement learning, an artificial intelligence approach undergoing development in the machine- learning community, offers key advantages in this regard. The ability of a control agent to learn relationships between control actions and their effect on the environment while pursuing a goal is a distinct improvement over prespecified models of the environment. Prespecified models are a prerequisite of conventional control methods and their accuracy limits the performance of control agents. This paper contains an introduction to Q-learning, a simple yet powerful reinforcement learning algorithm, and presents a case study involving application to traffic signal control. Encouraging results of the application to an isolated traffic signal, particularly under variable traffic conditions, are presented. A broader research effort is outlined, including extension to linear and networked signal systems and integration with dynamic route guidance. The research objective involves optimal control of heavily congested traffic across a two-dimensional road network—a challenging task for conventional traffic signal control methodologies.


title: A Fast Image Super-Resolution Algorithm Using an Adaptive Wiener Filter

abstract: A computationally simple super-resolution algorithm using a type of adaptive Wiener filter is proposed. The algorithm produces an improved resolution image from a sequence of low-resolution (LR) video frames with overlapping field of view. The algorithm uses subpixel registration to position each LR pixel value on a common spatial grid that is referenced to the average position of the input frames. The positions of the LR pixels are not quantized to a finite grid as with some previous techniques. The output high-resolution (HR) pixels are obtained using a weighted sum of LR pixels in a local moving window. Using a statistical model, the weights for each HR pixel are designed to minimize the mean squared error and they depend on the relative positions of the surrounding LR pixels. Thus, these weights adapt spatially and temporally to changing distributions of LR pixels due to varying motion. Both a global and spatially varying statistical model are considered here. Since the weights adapt with distribution of LR pixels, it is quite robust and will not become unstable when an unfavorable distribution of LR pixels is observed. For translational motion, the algorithm has a low computational complexity and may be readily suitable for real-time and/or near real-time processing applications. With other motion models, the computational complexity goes up significantly. However, regardless of the motion model, the algorithm lends itself to parallel implementation. The efficacy of the proposed algorithm is demonstrated here in a number of experimental results using simulated and real video sequences. A computational analysis is also presented.


title: Steady-State Mean-Square Error Analysis for Adaptive Filtering under the Maximum Correntropy Criterion

abstract: The steady-state excess mean square error (EMSE) of the adaptive filtering under the maximum correntropy criterion (MCC) has been studied. For Gaussian noise case, we establish a fixed-point equation to solve the exact value of the steady-state EMSE, while for non-Gaussian noise case, we derive an approximate analytical expression for the steady-state EMSE, based on a Taylor expansion approach. Simulation results agree with the theoretical calculations quite well.


title: Artist-directed dynamics for 2D animation

abstract: Animation artists enjoy the benefits of simulation but do not want to be held back by its constraints. Artist-directed dynamics seeks to resolve this need with a unified method that combines simulation with classical keyframing techniques. The combination of these approaches improves upon both extremes: simulation becomes more customizable and keyframing becomes more automatic. Examining our system in the context of the twelve fundamental animation principles reveals that it stands out for its treatment of exaggeration and appeal. Our system accommodates abrupt jumps, large plastic deformations, and makes it easy to reuse carefully crafted animations.


title: Exploring consumers acceptance of mobile payments – an empirical study

abstract: With the growing impetus of wireless revolution and rapid increase of mobile devices, it is evident that mobile commerce and payment are becoming a critical component of the new digital economy. Mobile devices are being transformed from simple communication devices to payments platforms. This paper looks into the status of mobile payment in Saudi Arabia in term of consumers’ acceptance and concerns towards mobile payments. This study showed that there is a bright future for m-payment in Saudi Arabia as majority of respondents showed their willingness to participate in such an activity. Security of mobile payment transactions and the unauthorised use of mobile phones to make a payment are great concerns to the mobile phone users. Spinning out from the survey results, a list of recommendations is provided that would be of importance to the mobile industry stakeholders in Saudi Arabia and other countries with similar market and consumer characteristics.


Deep&Dense Convolutional Neural Network for Hyperspectral Image Classification


title: How to Work a Crowd: Developing Crowd Capital Through Crowdsourcing

abstract: Traditionally, the term ‘crowd’ was used almost exclusively in the context of people who self-organized around a common purpose, emotion, or experience. Today, however, firms often refer to crowds in discussions of how collections of individuals can be engaged for organizational purposes. Crowdsourcing—defined here as the use of information technologies to outsource business responsibilities to crowds—can now significantly influence a firm's ability to leverage previously unattainable resources to build competitive advantage. Nonetheless, many managers are hesitant to consider crowdsourcing because they do not understand how its various types can add value to the firm. In response, we explain what crowdsourcing is, the advantages it offers, and how firms can pursue crowdsourcing. We begin by formulating a crowdsourcing typology and show how its four categories—crowd voting, micro-task, idea, and solution crowdsourcing—can help firms develop ‘crowd capital,’ an organizational-level resource harnessed from the crowd. We then present a three-step process model for generating crowd capital. Step one includes important considerations that shape how a crowd is to be constructed. Step two outlines the capabilities firms need to develop to acquire and assimilate resources (e.g., knowledge, labor, funds) from the crowd. Step three outlines key decision areas that executives need to address to effectively engage crowds.


title: Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke.

abstract: OBJECTIVE ::: To compare the effects of robot-assisted movement training with conventional techniques for the rehabilitation of upper-limb motor function after stroke. ::: ::: ::: DESIGN ::: Randomized controlled trial, 6-month follow-up. ::: ::: ::: SETTING ::: A Department of Veterans Affairs rehabilitation research and development center. ::: ::: ::: PARTICIPANTS ::: Consecutive sample of 27 subjects with chronic hemiparesis (>6mo after cerebrovascular accident) randomly allocated to group. ::: ::: ::: INTERVENTIONS ::: All subjects received twenty-four 1-hour sessions over 2 months. Subjects in the robot group practiced shoulder and elbow movements while assisted by a robot manipulator. Subjects in the control group received neurodevelopmental therapy (targeting proximal upper limb function) and 5 minutes of exposure to the robot in each session. ::: ::: ::: MAIN OUTCOME MEASURES ::: Fugl-Meyer assessment of motor impairment, FIMtrade mark instrument, and biomechanic measures of strength and reaching kinematics. Clinical evaluations were performed by a therapist blinded to group assignments. ::: ::: ::: RESULTS ::: Compared with the control group, the robot group had larger improvements in the proximal movement portion of the Fugl-Meyer test after 1 month of treatment (P<.05) and also after 2 months of treatment (P<.05). The robot group had larger gains in strength (P<.02) and larger increases in reach extent (P<.01) after 2 months of treatment. At the 6-month follow-up, the groups no longer differed in terms of the Fugl-Meyer test (P>.30); however, the robot group had larger improvements in the FIM (P<.04). ::: ::: ::: CONCLUSIONS ::: Compared with conventional treatment, robot-assisted movements had advantages in terms of clinical and biomechanical measures. Further research into the use of robotic manipulation for motor rehabilitation is justified.