title: A near-infrared FT-Raman (SERS) and electrochemical study of the synergistic effect of 1-[(1′,2′-dicarboxy)ethyl]-benzotriazole and KI on the dissolution of copper in aerated sulfuric acid

abstract: The adsorption on copper in aerated 0.5 m sulphuric acid (30°C) of 1-[(1′,2′-dicarboxy)ethyl]-benzotriazole (BTM) has been investigated by means of surface enhanced Raman scattering (SERS) techniques. The inhibition efficiency in aerated acid increased with increasing BTM concentration to a maximum (52%) at the 1 × 10–3 m level. Benzotriazole (BTAH) was found to be about 20% more efficient as an inhibitor for copper corrosion under the same conditions. BTM (1 × 10–3 m) showed increased inhibition with increasing pH reaching a maximum value of 78.3% at pH 8. Polarization studies showed that BTM suppressed both the cathodic and anodic corrosion reactions. The SERS studies suggest that, like BTAH, BTM inhibits copper corrosion by adsorption through the azole nitrogen. This study also showed that the performance of BTM can be significantly improved by adding KI. SERS indicates that the iodide ions displace the protonated BTM on the copper surface and this is followed by an overlayer of protonated BTM molecules.


title: Spike Timing–Dependent Plasticity: A Hebbian Learning Rule

abstract: Spike timing-dependent plasticity (STDP) as a Hebbian synaptic learning rule has been demonstrated in various neural circuits over a wide spectrum of species, from insects to humans. The dependence of synaptic modification on the order of pre- and postsynaptic spiking within a critical window of tens of milliseconds has profound functional implications. Over the past decade, significant progress has been made in understanding the cellular mechanisms of STDP at both excitatory and inhibitory synapses and of the associated changes in neuronal excitability and synaptic integration. Beyond the basic asymmetric window, recent studies have also revealed several layers of complexity in STDP, including its dependence on dendritic location, the nonlinear integration of synaptic modification induced by complex spike trains, and the modulation of STDP by inhibitory and neuromodulatory inputs. Finally, the functional consequences of STDP have been examined directly in an increasing number of neural circuits in vivo.


title: Research on immunity-based intrusion detection technology for the Internet of Things

abstract: To detect the security threat in the Internet of Things (IoT), the mechanisms of artificial immune system are applied to the IoT environment. The application method of immune theory to the IoT environment is constructed. A method of intrusion detection for the IoT is proposed. Antigen, self and non-self in the IoT are simulated. Immature detector, mature detector and memory detector are defined to detect attacks in the IoT. To adapt the complicated and changeful environment of the IoT, detectors evolve dynamically to detect mutated, even new IoT attacks. The attack information library is defined. Attacks detected by detectors in the IoT are combined with the attack information library to alarm the manager of the IoT.


title: Breast ultrasound image segmentation: an optimization approach based on super-pixels and high-level descriptors

abstract: Breast cancer is the second most common cancer and the leading cause of cancer death among women. Medical imaging has become an indispensable tool for its diagnosis and follow up. During the last decade, the medical community has promoted to incorporate Ultra-Sound (US) screening as part of the standard routine. The main reason for using US imaging is its capability to differentiate benign from malignant masses, when compared to other imaging techniques. The increasing usage of US imaging encourages the development of Computer Aided Diagnosis (CAD) systems applied to Breast Ultra-Sound (BUS) images. However accurate delineations of the lesions and structures of the breast are essential for CAD systems in order to extract information needed to perform diagnosis. This article proposes a highly modular and flexible framework for segmenting lesions and tissues present in BUS images. The proposal takes advantage of optimization strategies using super-pixels and high-level de-scriptors, which are analogous to the visual cues used by radiologists. Qualitative and quantitative results are provided stating a performance within the range of the state-of-the-art.


title: A Survey on Clustering Algorithms for Wireless Sensor Networks

abstract: A wireless sensor network (WSN) consisting of a large number of tiny sensors can be an effective tool for gathering data in diverse kinds of environments. The data collected by each sensor is communicated to the base station, which forwards the data to the end user. Clustering is introduced to WSNs because it has proven to be an effective approach to provide better data aggregation and scalability for large WSNs. Clustering also conserves the limited energy resources of the sensors. This paper synthesises existing clustering algorithms in WSNs and highlights the challenges in clustering.


title: Sub-threshold startup charge pump using depletion MOSFET for a low-voltage harvesting application

abstract: This paper presents a fully integrated CMOS start-up circuit for a low voltage battery-less harvesting application. The proposed topology is based on a step-up charge pump using depletion transistors instead of enhancement transistors. With this architecture, we can obtain a self-starting voltage below the enhancement transistor's threshold due to its normally-on operation. The key advantages are the CMOS compatibility, inductor-less solution and no extra post-fabrication processing. The topology has been simulated in 0.18μm technology using a transistor-level model and has been compared to the traditional charge pump structure. The depletion-based voltage doubler charge pump enables operation from an input voltage as low as 250mV compared to 400mV in an enhancement-based one. The proposed topology can also achieve other conversion ratios such as 1:-1 inverter or 1:N step-up.


title: The Pen Is Mightier Than the Keyboard Advantages of Longhand Over Laptop Note Taking

abstract: Taking notes on laptops rather than in longhand is increasingly common. Many researchers have suggested that laptop note taking is less effective than longhand note taking for learning. Prior studies have primarily focused on students' capacity for multitasking and distraction when using laptops. The present research suggests that even when laptops are used solely to take notes, they may still be impairing learning because their use results in shallower processing. In three studies, we found that students who took notes on laptops performed worse on conceptual questions than students who took notes longhand. We show that whereas taking more notes can be beneficial, laptop note takers' tendency to transcribe lectures verbatim rather than processing information and reframing it in their own words is detrimental to learning.


title: Financial Risk and Returns Prediction with Modular Networked Learning.

abstract: An artificial agent for financial risk and returns' prediction is built with a modular cognitive system comprised of interconnected recurrent neural networks, such that the agent learns to predict the financial returns, and learns to predict the squared deviation around these predicted returns. These two expectations are used to build a volatility-sensitive interval prediction for financial returns, which is evaluated on three major financial indices and shown to be able to predict financial returns with higher than 80% success rate in interval prediction in both training and testing, raising into question the Efficient Market Hypothesis. The agent is introduced as an example of a class of artificial intelligent systems that are equipped with a Modular Networked Learning cognitive system, defined as an integrated networked system of machine learning modules, where each module constitutes a functional unit that is trained for a given specific task that solves a subproblem of a complex main problem expressed as a network of linked subproblems. In the case of neural networks, these systems function as a form of an "artificial brain", where each module is like a specialized brain region comprised of a neural network with a specific architecture.


title: A novel single-phase transformerless grid-connected inverter

abstract: Nowadays, the transformerless inverters have become a widespread trend in the single-phase grid-connected photovoltaic (PV) systems because of the low cost and high efficiency concerns. Unfortunately, due to the non-galvanic isolation configuration, the ground leakage current would appear through the PV parasitic capacitance into the ground, which induces the physical danger and serious EMI problems. A novel transformerless single-phase inverter with two unipolar SPWM control strategies is proposed in this paper. The inverter can guarantee no ground leakage current and high reliability by applying either of the SPWM strategies. Meanwhile, the low total harmonic distortion (THD) of the grid-connected current is achieved thanks to the alleviation of the dead time effect. Besides, the required input DC voltage is the same low as that of the full-bridge inverter. Furthermore, the output filter inductance is reduced greatly due to the three-level output voltage, which leads to the high power density and high efficiency. At last, a 1kW prototype has been built and tested to verify the theoretical analysis of the paper.


title: Facial Skin Surface Temperature Changes During a “Concealed Information” Test

abstract: When individuals who commit a crime are questioned, they often show involuntary physiological responses to remembered details of that crime. This phenomenon is the basis for the concealed information test, in which rarely occurring crime-related details are embedded in a series of more frequently occurring crime-irrelevant items while respiratory, cardiovascular, and electrodermal responses are recorded. Two experiments were completed to investigate the feasibility of using facial skin surface temperature (SST) measures recorded using high definition thermographic images as the physiological measure during a concealed information test. Participants were randomly assigned to nondeceptive or deceptive groups. Deceptive participants completed a mock-crime paradigm. A focal plane array thermal imaging radiometer was used to monitor SST while crime-relevant and crime-irrelevant items were verbally presented to each participant. During both experiments, there were significant facial SST differences between deceptive and nondeceptive participants early in the analysis interval. In the second experiment, hemifacial (i.e., "half-face" divided along the longitudinal axis) effects were combined with the bilateral responses to correctly classify 91.7% of participants. These results suggest that thermal image analysis can be effective in discriminating deceptive and nondeceptive individuals during a concealed information test.


Chapter 18 Building Intelligent Tutoring Systems : An Overview


title: Neural Architecture Search with Bayesian Optimisation and Optimal Transport

abstract: Bayesian Optimisation (BO) refers to a class of methods for global optimisation of a function $f$ which is only accessible via point evaluations. It is typically used in settings where $f$ is expensive to evaluate. A common use case for BO in machine learning is model selection, where it is not possible to analytically model the generalisation performance of a statistical model, and we resort to noisy and expensive training and validation procedures to choose the best model. Conventional BO methods have focused on Euclidean and categorical domains, which, in the context of model selection, only permits tuning scalar hyper-parameters of machine learning algorithms. However, with the surge of interest in deep learning, there is an increasing demand to tune neural network \emph{architectures}. In this work, we develop NASBOT, a Gaussian process based BO framework for neural architecture search. To accomplish this, we develop a distance metric in the space of neural network architectures which can be computed efficiently via an optimal transport program. This distance might be of independent interest to the deep learning community as it may find applications outside of BO. We demonstrate that NASBOT outperforms other alternatives for architecture search in several cross validation based model selection tasks on multi-layer perceptrons and convolutional neural networks.


title: EduCTX: A blockchain-based higher education credit platform

abstract: Blockchain technology enables the creation of a decentralized environment, where transactions and data are not under the control of any third party organization. Any transaction ever completed is recorded in a public ledger in a verifiable and permanent way. Based on the blockchain technology, we propose a global higher education credit platform, named EduCTX. This platform is based on the concept of the European Credit Transfer and Accumulation System (ECTS). It constitutes a globally trusted, decentralized higher education credit, and grading system that can offer a globally unified viewpoint for students and higher education institutions (HEIs), as well as for other potential stakeholders, such as companies, institutions, and organizations. As a proof of concept, we present a prototype implementation of the environment, based on the open-source Ark Blockchain Platform. Based on a globally distributed peer-to-peer network, EduCTX will process, manage, and control ECTX tokens, which represent credits that students gain for completed courses, such as ECTS. HEIs are the peers of the blockchain network. The platform is a first step toward a more transparent and technologically advanced form of higher education systems. The EduCTX platform represents the basis of the EduCTX initiative, which anticipates that various HEIs would join forces in order to create a globally efficient, simplified, and ubiquitous environment in order to avoid language and administrative barriers. Therefore, we invite and encourage HEIs to join the EduCTX initiative and the EduCTX blockchain network.


title: Experimental Demonstration on 2.5-m Spatial Resolution and 1 $^{\circ}$C Temperature Uncertainty Over Long-Distance BOTDA With Combined Raman Amplification and Optical Pulse Coding

abstract: The combination of Raman amplification and optical pulse coding (OPC) is proposed to reduce the frequency uncertainty in a long-distance Brillouin optical time-domain analyzer (BOTDA) under higher spatial resolution. The experimental characterization of temperature sensing with measurement accuracy ~ 1°C under ~ 2.5-m spatial resolution over a whole length of sensing fiber is demonstrated. The sensing fiber is composed of 49.6-km fiber at 10.8-GHz Brillouin frequency shift (BFS) and 25-km fiber at 11.005 GHz.


title: Breast ultrasound image segmentation: a survey

abstract: PURPOSE ::: Breast cancer is the most common form of cancer among women worldwide. Ultrasound imaging is one of the most frequently used diagnostic tools to detect and classify abnormalities of the breast. Recently, computer-aided diagnosis (CAD) systems using ultrasound images have been developed to help radiologists to increase diagnosis accuracy. However, accurate ultrasound image segmentation remains a challenging problem due to various ultrasound artifacts. In this paper, we investigate approaches developed for breast ultrasound (BUS) image segmentation. ::: ::: ::: METHODS ::: In this paper, we reviewed the literature on the segmentation of BUS images according to the techniques adopted, especially over the past 10 years. By dividing into seven classes (i.e., thresholding-based, clustering-based, watershed-based, graph-based, active contour model, Markov random field and neural network), we have introduced corresponding techniques and representative papers accordingly. ::: ::: ::: RESULTS ::: We have summarized and compared many techniques on BUS image segmentation and found that all these techniques have their own pros and cons. However, BUS image segmentation is still an open and challenging problem due to various ultrasound artifacts introduced in the process of imaging, including high speckle noise, low contrast, blurry boundaries, low signal-to-noise ratio and intensity inhomogeneity CONCLUSIONS: To the best of our knowledge, this is the first comprehensive review of the approaches developed for segmentation of BUS images. With most techniques involved, this paper will be useful and helpful for researchers working on segmentation of ultrasound images, and for BUS CAD system developers.


title: Unsupervised Domain Adaptation by Backpropagation

abstract: Top-performing deep architectures are trained on massive amounts of labeled data. In the absence of labeled data for a certain task, domain adaptation often provides an attractive option given that labeled data of similar nature but from a different domain (e.g. synthetic images) are available. Here, we propose a new approach to domain adaptation in deep architectures that can be trained on large amount of labeled data from the source domain and large amount of unlabeled data from the target domain (no labeled target-domain data is necessary). ::: As the training progresses, the approach promotes the emergence of "deep" features that are (i) discriminative for the main learning task on the source domain and (ii) invariant with respect to the shift between the domains. We show that this adaptation behaviour can be achieved in almost any feed-forward model by augmenting it with few standard layers and a simple new gradient reversal layer. The resulting augmented architecture can be trained using standard backpropagation. ::: Overall, the approach can be implemented with little effort using any of the deep-learning packages. The method performs very well in a series of image classification experiments, achieving adaptation effect in the presence of big domain shifts and outperforming previous state-of-the-art on Office datasets.


title: Broadband Sounding Rocket Antenna for Dual-Band Telemetric and Payload Data Transmission

abstract: This paper presents and investigates a broadband sounding rocket antenna developed for dual-band data transmission of telemetric and payload systems. This single-element antenna permits the use of both the well-established 869.4-869.65 MHz and 2.4-2.4835 GHz frequency bands available in Europe to implement a redundant transmission of telemetric and tracking data desired for reliability. Furthermore, the available spectrum in the upper band offers a high-speed wireless link for transmitting payload data. To preserve the mechanical integrity and aerodynamics of rocket, the antenna is integrated in its nose cone. The proposed low-cost and low-weight structure is based on PCB (Printed Circuit Board) pieces assembled in a LEGO-like manner to form a broadband 3-Dimentional (3-D) bi-conical antenna, which is fed by a diplexer integrated in its base. A prototype has been fabricated, and validated using a reduced-length fuselage in a near-field measurement system.


title: Adaptive real-time threat assessment under uncertainty and conflict

abstract: This paper describes an adaptive fusion human-machine system supporting an analyst in recognizing threat. The system is designed in the framework of Transferable Belief Model for processing complex unreliable and uncertain data streams coming from multiple sources to improve threat recognition and detect new “unknown” threat. The focus of the paper is on the latter: designing a method of detection of possible “unknown” threat. Such problem arises in the open world environment, for example, in multisensor automatic target recognition systems, or in the situation assessment problem requiring selection of, sometime unknown, hypotheses about the state of the environment. The method of detecting possible unknown threat is based on the notion of conflict, which happens in the uncertain environment when multiple sources of information disagree. The results of a case study designed for sequential decision making for threat recognition in the littoral environment are also presented.


title: Design and fabrication of a substrate integrated waveguide bandstop filter

abstract: This paper presents a novel design for SIW bandstop filters. The network comprises a substrate integrated waveguide, SIW, cavity resonator coupled to a strip line to give a bandstop response. This is the first time such a bandstop SIW filter has been demonstrated. All simulation was done in HFSS and ADS Momentum. The fabricated filter is small and compact, operating at a frequency of 9 GHz.


title: Accurate and Efficient Traffic Sign Detection Using Discriminative AdaBoost and Support Vector Regression

abstract: Real-time traffic sign detection and recognition has been receiving increasingly more attention in recent years due to the popularity of driver-assistance systems and autonomous vehicles. This paper proposes an accurate and efficient traffic sign detection technique by exploring AdaBoost and support vector regression (SVR) for discriminative detector learning. Different from the reported traffic sign detection techniques, a novel saliency estimation approach is first proposed, where a new saliency model is built based on the traffic sign-specific color, shape, and spatial information. By incorporating the saliency information, enhanced feature pyramids are built to learn an AdaBoost model that detects a set of traffic sign candidates from images. A novel iterative codeword selection algorithm is then designed to generate a discriminative codebook for the representation of sign candidates, as detected by the AdaBoost, and an SVR model is learned to identify the real traffic signs from the detected sign candidates. Experiments on three public data sets show that the proposed traffic sign detection technique is robust and obtains superior accuracy and efficiency.


title: Automatic Software Bug Triage System (BTS) Based on Latent Semantic Indexing and Support Vector Machine

abstract: A bug triage system is used for validation and allocation of bug reports to the most appropriate developers. An automatic bug triage system may reduce the software maintenance time and improve its quality by correct and timely assignment of new bug reports to the appropriate developers. In this paper, we present the techniques behind an automatic bug triage system, which is based on the categorization of bug reports. In order to obtain an automatic bug triage system we used these techniques and performed comparative experiments. We downloaded 1,983 resolved bug reports along with the developer activity data from the Mozilla open source project. We extracted the relevant features like report title, report summary etc., from each bug report, and extracted developer’s name who resolved the bug reports from the developers activity data. We processed the extracted textual data, and obtained the term-to-document matrix using parsing, filtering and term weighting methods. For term weighting methods we used simple term frequency and TF×IDF (term frequency inverse document frequency) methods. Furthermore, we reduced the dimensionality of the obtained term-to-document matrix by applying feature selection and latent semantic indexing methods. Finally we used seven different machine learning methods for the classification of bug reports. The best obtained bug triage system is based on latent semantic indexing and support vector machine having 44.4% classification accuracy. The average precision and recall values are 30% and 28%, respectively.


Comparison of Profiling Power Analysis Attacks Using Templates and Multi-Layer Perceptron Network


title: A visualization framework for team sports captured using multiple static cameras

abstract: We present a novel approach for robust localization of multiple people observed using a set of static cameras. We use this location information to generate a visualization of the virtual offside line in soccer games. To compute the position of the offside line, we need to localize players' positions, and identify their team roles. We solve the problem of fusing corresponding players' positional information by finding minimum weight K-length cycles in a complete K-partite graph. Each partite of the graph corresponds to one of the K cameras, whereas each node of a partite encodes the position and appearance of a player observed from a particular camera. To find the minimum weight cycles in this graph, we use a dynamic programming based approach that varies over a continuum from maximally to minimally greedy in terms of the number of graph-paths explored at each iteration. We present proofs for the efficiency and performance bounds of our algorithms. Finally, we demonstrate the robustness of our framework by testing it on 82,000 frames of soccer footage captured over eight different illumination conditions, play types, and team attire. Our framework runs in near-real time, and processes video from 3 full HD cameras in about 0.4s for each set of corresponding 3 frames.


title: The clustering of massive galaxies at z~0.5 from the first semester of BOSS data

abstract: We calculate the real- and redshift-space clustering of massive galaxies at z~0.5 using the first semester of data by the Baryon Oscillation Spectroscopic Survey (BOSS). We study the correlation functions of a sample of 44,000 massive galaxies in the redshift range 0.4<z<0.7. We present a halo-occupation distribution modeling of the clustering results and discuss the implications for the manner in which massive galaxies at z~0.5 occupy dark matter halos. The majority of our galaxies are central galaxies living in halos of mass 10^{13}Msun/h, but 10% are satellites living in halos 10 times more massive. These results are broadly in agreement with earlier investigations of massive galaxies at z~0.5. The inferred large-scale bias (b~2) and relatively high number density (nbar=3e-4 h^3 Mpc^{-3}) imply that BOSS galaxies are excellent tracers of large-scale structure, suggesting BOSS will enable a wide range of investigations on the distance scale, the growth of large-scale structure, massive galaxy evolution and other topics.


title: Learning to suggest: a machine learning framework for ranking query suggestions

abstract: We consider the task of suggesting related queries to users after they issue their initial query to a web search engine. We propose a machine learning approach to learn the probability that a user may find a follow-up query both useful and relevant, given his initial query. Our approach is based on a machine learning model which enables us to generalize to queries that have never occurred in the logs as well. The model is trained on co-occurrences mined from the search logs, with novel utility and relevance models, and the machine learning step is done without any labeled data by human judges. The learning step allows us to generalize from the past observations and generate query suggestions that are beyond the past co-occurred queries. This brings significant gains in coverage while yielding modest gains in relevance. Both offline (based on human judges) and online (based on millions of user interactions) evaluations demonstrate that our approach significantly outperforms strong baselines.


title: Induction of the SOS gene (umuC) by 4-quinolone antibacterial drugs.

abstract: Induction by the 4-quinolone group of antibacterial drugs of the umuC gene, the SOS function most involved in error-prone DNA repair (together with umuD), was assessed in a strain of Escherichia coli harbouring a umuC::lacZ gene fusion. All 4-quinolones tested induced this umuC::lacZ fusion, with maximum induction at 4-quinolone concentrations close to their minimum inhibitory concentrations for this strain, and the SOS Inducing Potential (SOSIP) was closely related to antibacterial activity. Mitomycin C, a known mutagen, was a slightly better inducer (in terms of SOSIP) than any of the quinolones. In contrast, induction by 4-quinolones of the sfiA (sulA) gene, an SOS function involved in cell division inhibition, was better than induction by mitomycin C in an E. coli strain harbouring an sfiA::lacZ gene fusion. The umuC gene fusion was induced at lower concentrations of 4-quinolone than was the sfiA gene fusion.


title: AOSE Methodologies and Comparison of Object Oriented and Agent Oriented Software Testing

abstract: In today's world, the complexities in developing software is growing faster. So, the software is failed to produce flexibility, robustness, efficiency and reliability. To satisfy these demands, agent oriented technique is evolved in the software engineering field. The agent oriented model is an alternative for object oriented model because the object orientation is not capable of solving these problems. The agent oriented technology is adapted because it solves the problems related to the social and organization which is not possible by the traditional model. It also provides solutions for real world problems. This is successful by introducing a new paradigm called agent. An agent is autonomous, proactive, responsiveness and sociality. So, it can act individually or it can interact with other agents in MAS (Multi-Agent System) to solve the problems. It can handle complex tasks and also has the ability to perform flexible action in an unpredictable environment. Hence, we need a methodology to implement the agent oriented technology for developing a system. Therefore, the Agent Oriented Software Engineering (AOSE) methodologies are evolved. Even though the AOSE methodologies has many advantages, they are not complete. The Software Development Life Cycle (SDLC) includes phases such as analysis, design, implementation and testing. A methodology is said to be complete only when it covers all the phases of the SDLC. The AOSE methodology does not include the testing phase and so it is not complete. Testing is defined as the process of detecting the errors. As testing is essential, it should be included in the AOSE methodologies. The Object Oriented Software Testing (OOST) is used in the object oriented methodologies, but it cannot be incorporated in the agent oriented methodologies. This is because the object and the agent differs in some categories like agent properties which cannot be adapted by an object. Hence, a new testing technique was introduced to test the agent oriented systems is called as Agent Oriented Software Testing (AOST). It is the extension of OOST in which the objects are replaced by the agents. The individual agent, the communication between the agents and the communication between agent and environment are tested using AOST technique.


title: Hybrid MPPT controller for accurate and quick tracking

abstract: The PV output is fluctuating with respect to solar insolation and temperature, and the output power is maximum if it operate at MPP. So, tracking with accuracy and faster convergence is very much important. There are so many techniques for tracking MPP. In this work, controller with incremental conductance and fractional short circuit current is explained, which will have the advantages of individual controllers. Initially, fractional short circuit method drives the PV to nearby MPP condition because of its higher tracking speed. Then incremental conductance will take care for higher accuracy. As insolation is continuously varying, fractional method is better due to its quick tracking. So, accuracy of incremental conductance and faster convergence of fractional short circuit controller are added together in this controller. MATLAB/Simulink simulation results are included in this paper to demonstrate the effectiveness of proposed control technique.


title: Development of a UAV-LiDAR System with Application to Forest Inventory

abstract: We present the development of a low-cost Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) system and an accompanying workflow to produce 3D point clouds. UAV systems provide an unrivalled combination of high temporal and spatial resolution datasets. The TerraLuma UAV-LiDAR system has been developed to take advantage of these properties and in doing so overcome some of the current limitations of the use of this technology within the forestry industry. A modified processing workflow including a novel trajectory determination algorithm fusing observations from a GPS receiver, an Inertial Measurement Unit (IMU) and a High Definition (HD) video camera is presented. The advantages of this workflow are demonstrated using a rigorous assessment of the spatial accuracy of the final point clouds. It is shown that due to the inclusion of video the horizontal accuracy of the final point cloud improves from 0.61 m to 0.34 m (RMS error assessed against ground control). The effect of the very high density point clouds (up to 62 points per m2) produced by the UAV-LiDAR system on the measurement of tree location, height and crown width are also assessed by performing repeat surveys over individual isolated trees. The standard deviation of tree height is shown to reduce from 0.26 m, when using data with a density of 8 points perm2, to 0.15mwhen the higher density data was used. Improvements in the uncertainty of the measurement of tree location, 0.80 m to 0.53 m, and crown width, 0.69 m to 0.61 m are also shown.


title: Phantom movements and pain An fMRI study in upper limb amputees

abstract: Using functional MRI, we investigated 14 upper limb amputees and seven healthy controls during the execution of hand and lip movements and imagined movements of the phantom limb or left hand. Only patients with phantom limb pain showed a shift of the lip representation into the deafferented primary motor and somatosensory hand areas during lip movements. Displacement of the lip representation in the primary motor and somatosensory cortex was positively correlated to the amount of phantom limb pain. Thalamic activation was only present during executed movements in the healthy controls. The cerebellum showed no evidence of reorganizational changes. In amputees, movement of the intact hand showed a level of activation similar to movement of the right dominant hand in the healthy controls. During imagination of moving the phantom hand, all patients showed significantly higher activation in the contralateral primary motor and somatosensory cortices compared with imagination of hand movements in the controls. In the patients with phantom limb pain but not the pain-free amputees, imagined movement of the phantom hand activated the neighbouring face area. These data suggest selective coactivation of the cortical hand and mouth areas in patients with phantom limb pain. This reorganizational change may be the neural correlate of phantom limb pain.


title: Jointly learning to align and convert graphemes to phonemes with neural attention models

abstract: We propose an attention-enabled encoder-decoder model for the problem of grapheme-to-phoneme conversion. Most previous work has tackled the problem via joint sequence models that require explicit alignments for training. In contrast, the attention-enabled encoder-decoder model allows for jointly learning to align and convert characters to phonemes. We explore different types of attention models, including global and local attention, and our best models achieve state-of-the-art results on three standard data sets (CMUDict, Pronlex, and NetTalk).


Using Block-Level Features for Genre Classification , Tag Classification and Music Similarity Estimation,” in Submission to Audio Music Similarity and Retrieval Task of MIREX


title: Aura Minora: A user centric IOT architecture for Smart City

abstract: For a typical smart city many IoT (Internet of Things) architectures have been proposed to integrate the various ICT Solutions. Since there are many perspectives, the emphasis and the design of each architecture vary. So at the moment there is no standard architecture. The best approach is to adapt architecture for its intended use. Most of the current IOT architectures are service centered. After surveying literature, there exists a gap in a user centered approach with intelligent edge devices. We propose Aura Minora: a novel architecture for IOT based on a bottom-up approach. Our approach in this paper is to see, how this user centric architecture would help end users as well as the smart city governing body to use existing Internet infrastructure to provide services. We also focus on user privacy and security aspect within this architecture. We further discuss the various views like informational, functional and deployment view of our architecture.


title: Millimeter-wave radar for vital signs monitoring

abstract: We present a new type of sensor for vital signs monitoring. The primary advantages of the millimeter-wave radar are ability to operate remotely, without electrodes or wires and through any clothing or fur. Since radar carrier wavelength is about 3 mm, we achieve significant noise immunity, sensitivity and accuracy. Thus it is not only detector, but a powerful tool for exploring heart and respiration mechanics. Series of lab tests with animals (rat, rabbit) and human were carried out. Experimental human heartbeat measurement data for monitoring distance of 2 meters is shown and compared to data from a commercial portable ECG device.


title: Architectural requirements prioritization and analysis applied to software technology evaluation

abstract: In this short paper, we summarize an industrial project in which we developed and applied the Attribute Hierarchy-based Evaluation of Architectural Designs (AHEAD) method for selecting a software technology to form the basis for the next-generation architecture of a complex commercial software application. AHEAD leverages the Software Engineering Institute's Attribute-Driven Design (ADD) method and the Analytic Hierarchy Process (AHP) for evaluating software technologies that have important architectural impact. The core activities of AHEAD include elicitation, prioritization, and analysis of architectural requirements. The goal of these requirements activities was to establish and apply objective criteria for selecting, prototyping, and evaluating software technology alternatives. We found that using AHEAD brought greater objectivity to prioritization of architectural requirements and to the technical judgments of the software technology options.


title: Structure-Based Sybil Detection in Social Networks via Local Rule-Based Propagation

abstract: Social networks are known to be vulnerable to the so-called Sybil attack, in which an attacker maintains massive Sybils and uses them to perform various malicious activities. Therefore, Sybil detection in social networks is a basic security research problem. Structure-based methods have been shown to be promising at detecting Sybils. Existing structure-based methods can be classified into two categories: Random Walk (RW)-based methods and Loop Belief Propagation (LBP)-based methods. RW-based methods cannot leverage labeled Sybils and labeled benign users simultaneously, which limits their detection accuracy, and/or they are not robust to noisy labels. LBP-based methods are not scalable, and they cannot guarantee convergence. In this work, we propose SybilSCAR, a novel structure-based method to detect Sybils in social networks. SybilSCAR is Scalable, Convergent, Accurate, and Robust to label noise. We first propose a framework to unify RW-based and LBP-based methods. Under our framework, these methods can be viewed as iteratively applying a (different) local rule to every user, which propagates label information among a social graph. Second, we design a new local rule, which SybilSCAR iteratively applies to every user to detect Sybils. We compare SybilSCAR with state-of-the-art RW-based methods and LBP-based methods both theoretically and empirically. Theoretically, we show that, with proper parameter settings, SybilSCAR has a tighter asymptotical bound on the number of Sybils that are falsely accepted into a social network than existing structure-based methods. Empirically, we perform evaluation using both social networks with synthesized Sybils and a large-scale Twitter dataset (41.7M nodes and 1.2B edges) with real Sybils, and our results show that 1) SybilSCAR is substantially more accurate and more robust to label noise than state-of-the-art RW-based methods; and 2) SybilSCAR is more accurate and one order of magnitude more scalable than state-of-the-art LBP-based methods.


A Review of Knowledge Management in the Public Sector: A Taxonomy


title: A cache architecture for counting bloom filters: theory and application

abstract: Within packet processing systems, lengthy memory accesses greatly reduce performance. To overcome this limitation, network processors utilize many different techniques, for example, utilizing multilevel memory hierarchies, special hardware architectures, and hardware threading. In this paper, we introduce a multilevel memory architecture for counting Bloom filters. Based on the probabilities of incrementing of the counters in the counting Bloom filter, a multi-level cache architecture called the cached counting Bloom filter (CCBF) is presented, where each cache level stores the items with the same counters. To test the CCBF architecture, we implement a software packet classifier that utilizes basic tuple space search using a 3-level CCBF. The results of mathematical analysis and implementation of the CCBF for packet classification show that the proposed cache architecture decreases the number of memory accesses when compared to a standard Bloom filter. Based on the mathematical analysis of CCBF, the number of accesses is decreased by at least 53%. The implementation results of the software packet classifier are at most 7.8% (3.5% in average) less than corresponding mathematical analysis results. This difference is due to some parameters in the packet classification application such as number of tuples, distribution of rules through the tuples, and utilized hashing functions.


title: Power Management Strategies for a Microgrid With Multiple Distributed Generation Units

abstract: This paper addresses real and reactive power management strategies of electronically interfaced distributed generation (DG) units in the context of a multiple-DG microgrid system. The emphasis is primarily on electronically interfaced DG (EI-DG) units. DG controls and power management strategies are based on locally measured signals without communications. Based on the reactive power controls adopted, three power management strategies are identified and investigated. These strategies are based on 1) voltage-droop characteristic, 2) voltage regulation, and 3) load reactive power compensation. The real power of each DG unit is controlled based on a frequency-droop characteristic and a complimentary frequency restoration strategy. A systematic approach to develop a small-signal dynamic model of a multiple-DG microgrid, including real and reactive power management strategies, is also presented. The microgrid eigen structure, based on the developed model, is used to 1) investigate the microgrid dynamic behavior, 2) select control parameters of DG units, and 3) incorporate power management strategies in the DG controllers. The model is also used to investigate sensitivity of the design to changes of parameters and operating point and to optimize performance of the microgrid system. The results are used to discuss applications of the proposed power management strategies under various microgrid operating conditions


title: Dual-coded compressive hyperspectral imaging

abstract: This Letter presents a new snapshot approach to hyperspectral imaging via dual-optical coding and compressive computational reconstruction. We demonstrate that two high-speed spatial light modulators, located conjugate to the image and spectral plane, respectively, can code the hyperspectral datacube into a single sensor image such that the high-resolution signal can be recovered in postprocessing. We show various applications by designing different optical modulation functions, including programmable spatially varying color filtering, multiplexed hyperspectral imaging, and high-resolution compressive hyperspectral imaging.


title: Mixing realities in Shared Space: an augmented reality interface for collaborative computing

abstract: In the Shared Space project, we explore, innovate, design and evaluate future computing environments that will radically enhance interaction between human and computers as well as interaction between humans mediated by computers. In particular, we investigate how augmented reality enhanced by physical and spatial 3D user interfaces can be used to develop effective face-to-face collaborative computing environments. How will we interact in such collaborative spaces? How will we interact with each other? What new applications can be developed using this technology? These are the questions that we are trying to answer in research on Shared Space. The paper provides a short overview of Shared Space, its directions, technologies and applications.


title: Modeling of multi-layer beam with interlayer slips

abstract: In the early 90th the IBOIS-EPFL developed a new kind of shell structure. The ribs were made with simple planks which are waved together to build a spacial ribbed shell. The first application was the Polydome in 1993, and the most fascinating has been the Expodach in Hannover in year 2000 [1]. However, the calculation and the realization of such structures requires particular knowledge and experience. That is’s why the construction of such structures is something exceptional. In addition to the anisotropy of material wood, the spatial structure of laminated and screwed beams has a structural anisotropy. They constitute highly unspecified static systems. Currently the engineer does not have any effective method to calculate these kinds of spatial structures, made out of curved screwed lamellate boards. The existing approximations for complex curved structures are not satisfying. The main differences are noticed especially upon the analysis of the stability of structures subject to horizontal loads. The following article will compare a 6-layered beam with inter-layer slips in different load cases and situations. The beam is composed of 6 planks with a section of 140/27mm. The connections between the layers are screws. The studied parameters are the distance between the connector, the length of the beam and 3 different load cases. A total of 24 elements have been tested and compared between the laboratory test and different theories. A very important parameter is the stiffness of the connector. Thus additional tests have been made to simulate the stiffness of the screws used. A bi-exponential law has been generated to be used in the theoretical evaluation of the tests. The actual theories which have been used to simulate the behavior of the sample are gamma- Method of Mohler-Schelling, Appendix F of the Germand standard E- DIN 1052 of Prof. Kreuzinger, framework systems developed by Kneidl and Hartmann and finally a multiy-layer finite element developed at the LSC- EPFL by Prof. Frei and Dr. Krawczyk.


title: A Multivariate Analysis of Youth Violence and Aggression: The Influence of Family, Peers, Depression, and Media Violence

abstract: Objective To examine the multivariate nature of risk factors for youth violence including delinquent peer associations, exposure to domestic violence in the home, family conflict, neighborhood stress, antisocial personality traits, depression level, and exposure to television and video game violence. Study design A population of 603 predominantly Hispanic children (ages 10-14 years) and their parents or guardians responded to multiple behavioral measures. Outcomes included aggression and rule-breaking behavior on the Child Behavior Checklist (CBCL), as well as violent and nonviolent criminal activity and bullying behavior. Results Delinquent peer influences, antisocial personality traits, depression, and parents/guardians who use psychological abuse in intimate relationships were consistent risk factors for youth violence and aggression. Neighborhood quality, parental use of domestic violence in intimate relationships, and exposure to violent television or video games were not predictive of youth violence and aggression. Conclusion Childhood depression, delinquent peer association, and parental use of psychological abuse may be particularly fruitful avenues for future prevention or intervention efforts.


title: Heptaband Inverted-F Antenna for Metal-Rimmed Mobile Phone Applications

abstract: An inverted-F antenna for heptaband WWAN/LTE operations is proposed and applied for the mobile phone surrounded by a metal rim with two slits. The proposed strategy provides a simple and effective solution for designing the multiband antenna for metal-rimmed mobile phone applications. In order to obtain remarkable radiation performance of the antenna, the metal rim with a height of 5 mm is cut into three parts. By taking advantage of this three-part frame, the proposed antenna is capable of covering GSM850/900/DCS/PCS/UMTS 2100/LTE 2300/2500 operating bands. The operation mechanisms of the proposed antenna have been analyzed carefully. To demonstrate the above method, the proposed antenna is fabricated, and the characteristics such as reflection coefficients, radiation efficiency, and radiation patterns have been measured. Reasonable results show that the proposed antenna is a candidate for WWAN/LTE mobile phone applications.


title: Simultaneous Localization And Mapping: Present, Future, and the Robust-Perception Age

abstract: Simultaneous Localization and Mapping (SLAM) consists in the concurrent construction of a representation of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. The paper serves as a tutorial for the non-expert reader. It is also a position paper: by looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: do robots need SLAM? Is SLAM solved?


title: MOMCC: Market-Oriented Architecture for Mobile Cloud Computing Based on Service Oriented Architecture

abstract: The vision of augmenting computing capabilities of mobile devices, especially smartphones with least cost is likely transforming to reality leveraging cloud computing. Cloud exploitation by mobile devices breeds a new research domain called Mobile Cloud Computing (MCC). However, issues like portability and interoperability should be addressed for mobile augmentation which is a non-trivial task using component-based approaches. Service Oriented Architecture (SOA) is a promising design philosophy embraced by mobile computing and cloud computing communities to stimulate portable, complex application using prefabricated building blocks called Services. Utilizing distant cloud resources to host and run Services is hampered by long WAN latency. Exploiting mobile devices in vicinity alleviates long WAN latency, while creates new set of issues like Service publishing and discovery as well as clientserver security, reliability, and Service availability. In this paper, we propose a market-oriented architecture based on SOA to stimulate publishing, discovering, and hosting Services on nearby mobiles, which reduces long WAN latency and creates a business opportunity that encourages mobile owners to embrace Service hosting. Group of mobile phones simulate a nearby cloud computing platform. We create new role of Service host by enabling unskilled mobile owners/users to host Services developed by skilled developers. Evidently, Service availability, reliability, and Service-oriented mobile application portability will increase towards green ubiquitous computing in our mobile cloud infrastructure.


title: Active learning: theory and applications to automatic speech recognition

abstract: We are interested in the problem of adaptive learning in the context of automatic speech recognition (ASR). In this paper, we propose an active learning algorithm for ASR. Automatic speech recognition systems are trained using human supervision to provide transcriptions of speech utterances. The goal of Active Learning is to minimize the human supervision for training acoustic and language models and to maximize the performance given the transcribed and untranscribed data. Active learning aims at reducing the number of training examples to be labeled by automatically processing the unlabeled examples, and then selecting the most informative ones with respect to a given cost function for a human to label. In this paper we describe how to estimate the confidence score for each utterance through an on-line algorithm using the lattice output of a speech recognizer. The utterance scores are filtered through the informativeness function and an optimal subset of training samples is selected. The active learning algorithm has been applied to both batch and on-line learning scheme and we have experimented with different selective sampling algorithms. Our experiments show that by using active learning the amount of labeled data needed for a given word accuracy can be reduced by more than 60% with respect to random sampling.


title: Learning, Reward, and Decision Making

abstract: In this review, we summarize findings supporting the existence of multiple behavioral strategies for controlling reward-related behavior, including a dichotomy between the goal-directed or model-based system and the habitual or model-free system in the domain of instrumental conditioning and a similar dichotomy in the realm of Pavlovian conditioning. We evaluate evidence from neuroscience supporting the existence of at least partly distinct neuronal substrates contributing to the key computations necessary for the function of these different control systems. We consider the nature of the interactions between these systems and show how these interactions can lead to either adaptive or maladaptive behavioral outcomes. We then review evidence that an additional system guides inference concerning the hidden states of other agents, such as their beliefs, preferences, and intentions, in a social context. We also describe emerging evidence for an arbitration mechanism between model-based and model-free reinforce...


title: DESIGNING ANMPI-BASED PARALLEL AND DISTRIBUTED MACHINE LEARNING PLATFORM ON LARGE-SCALE HPC CLUSTERS

abstract: This paper presents the design of an MPI-based parallel and distributed machine learning platform on large-scale HPC clusters. Researchers and practitioners can implement easily a class of parallelizable machine learning algorithms on the platform, or port quickly an existing non-parallel implementation of a parallelizable algorithm to the platform with only minor modifications. Complicated functions in parallel programming such as scheduling, caching and load balancing are handled automatically by the platform. The platform performance was evaluated in a series of stress tests by using a k-means clustering task on 7,500 hours of speech data (about 2.7 billion 52dimensional feature vectors). Good scalability is demonstrated on an HPC cluster with thousands of CPU cores.


title: Lexicon-Based Sentiment Analysis in the Social Web

abstract: Sentiment analysis is a compelling issue for both information producers and consumers. We are living in the “age of customer”, where customer knowledge and perception is a key for running successful business. The goal of sentiment analysis is to recognize and express emotions digitally. This paper presents the lexicon-based framework for sentiment classification, which classifies tweets as a positive, negative, or neutral. The proposed framework also detects and scores the slangs used in the tweets. The comparative results show that the proposed system outperforms the existing systems. It achieves 92% accuracy in binary classification and 87% in multi-class classification.


title: Systematic review of the use of prophylactic dressings in the prevention of pressure ulcers

abstract: This systematic review considers the evidence supporting the use of prophylactic dressings for the prevention of pressure ulcer. Electronic database searches were conducted on 25 July 2013. The searches found 3026 titles and after removal of duplicate records 2819 titles were scanned against the inclusion and exclusion criteria. Of these, 2777 were excluded based on their title and abstract primarily because they discussed pressure ulcer healing, the prevention and treatment of other chronic and acute wounds or where the intervention was not a prophylactic dressing (e.g. underpads, heel protectors and cushions). Finally, the full text of 42 papers were retrieved. When these 42 papers were reviewed, 21 were excluded and 21 were included in the review. The single high-quality randomised controlled trial (RCT) and the growing number of cohort, weak RCT and case series all suggest that the


title: Psychometric Properties of the Family Support Scale Adapted for African American Women with Type 2 Diabetes Mellitus.

abstract: Objective : The purpose of our study was twofold: 1) adapt and test a social support measure specific to the experiences of African American women with type 2 diabetes mellitus (T2DM); 2) examine its relationship to psychosocial measures. Research Design and Methods : 200 rural African American women with uncontrolled T2DM participating in a randomized controlled trial completed surveys at baseline on their social support, empowerment, self-care, self-efficacy, depression and diabetes distress. Exploratory factor analysis and correlation analysis were conducted to test the psychometric properties of the Dunst Family Support Scale adapted for AA women with T2DM (FSS-AA T2DM) and its relationship with other psychosocial measures. Results : The 16 items of the FSS-AA T2DM loaded onto three distinct factors: parent and spouse/partner support, community and medical support, and extended family and friends support. Reliability for the entire scale was good (Cronbach’s a5.90) and was acceptable to high across these three factors (Cronbach’s a of .86, .83, and .83 respectively). All three factors were significantly correlated with self-reported empowerment, self-care, self-efficacy, depression and diabetes distress, although the pattern was different for each factor. FSS-AA-T2DM showed good concurrent validity when compared with similar items on the Diabetes Distress Scale. Conclusion : The FSS-AA T2DM, a 16-item scale measuring social support among rural African American women with T2DM, is internally consistent and reliable. Findings support the utility of this screening tool in this population, although additional testing is needed with other groups in additional settings. ( Ethn Dis . 2015;25[2]:193–199)


title: Fault tree analysis method and its application based on fuzzy reasoning

abstract: A new kind of fault tree analysis based on fuzzy reasoning was proposed in this paper. On the basis of some former researches, fuzzy numbers was adopted to indicate the probabilities of bottom events to deal with the uncertainties existing in estimation of the probabilities. At the same time, uncertainties existing in the relation between causal events and resultant events were taken into account, and a fuzzy reasoning system was applied to fault tree analysis .According to different logic gate in fault tree analysis, the fuzzy reasoning system adopted different reasoning methods. As a fuzzy number, the result of the fuzzy reasoning is a normal fuzzy set. The fault tree analysis based on fuzzy reasoning proved feasible by an example of fault analysis of ship equipment. In conclusion, the research supplies a new method for the reliability analysis of the system..


title: Robust unit commitment problem with demand response and wind energy

abstract: Currently, both demand response (DR) strategy and renewable energy have been adopted to improve power generation efficiency and reduce greenhouse gas emission. However, the uncertainty and intermittent generation pattern in wind farms and the complexity of demand side management pose huge challenges. In this paper, we analytically investigate how to integrate DR and wind energy with fossil fuel generators to (i) minimize power generation cost; and (2) fully take advantage of the wind energy with the managed demand to reduce greenhouse emission. We first build a two-stage robust unit commitment (UC) model to obtain day-ahead generator schedules where wind uncertainty is captured by a polytopic uncertainty set. Then, we extend our model to include DR strategy such that both price levels and generator schedules will be derived for the next day. For these two challenging models, we derive their mathematical properties and develop a novel solution method. Our computational study on an IEEE 118-bus system with 36 units shows that robust UC models can fully make use of wind generation with less generation cost. Also, the developed algorithm is computationally superior to classical Benders decomposition method.


title: Type Analysis for JavaScript

abstract: JavaScript is the main scripting language for Web browsers, and it is essential to modern Web applications. Programmers have started using it for writing complex applications, but there is still little tool support available during development. ::: ::: We present a static program analysis infrastructure that can infer detailed and sound type information for JavaScript programs using abstract interpretation. The analysis is designed to support the full language as defined in the ECMAScript standard, including its peculiar object model and all built-in functions. The analysis results can be used to detect common programming errors --- or rather, prove their absence, and for producing type information for program comprehension. ::: ::: Preliminary experiments conducted on real-life JavaScript code indicate that the approach is promising regarding analysis precision on small and medium size programs, which constitute the majority of JavaScript applications. With potential for further improvement, we propose the analysis as a foundation for building tools that can aid JavaScript programmers.


title: INFTY: an integrated OCR system for mathematical documents

abstract: An integrated OCR system for mathematical documents, called INFTY, is presented. INFTY consists of four procedures, i.e., layout analysis, character recognition, structure analysis of mathematical expressions, and manual error correction. In those procedures, several novel techniques are utilized for better recognition performance. Experimental results on about 500 pages of mathematical documents showed high character recognition rates on both mathematical expressions and ordinary texts, and sufficient performance on the structure analysis of the mathematical expressions.


title: Watermarking cryptographic capabilities

abstract: A watermarking scheme for programs embeds some information called a mark into a program while preserving its functionality. No adversary can remove the mark without damaging the functionality of the program. In this work, we study the problem of watermarking various cryptographic programs such as pseudorandom function (PRF) evaluation, decryption, and signing. For example, given a PRF key K, we create a marked program C that evaluates the PRF F(K,). An adversary that gets C cannot come up with any program C* in which the mark is removed but which still evaluates the PRF correctly on even a small fraction of the inputs. The work of Barak, Goldreich, Impagliazzo, Rudich, Sahai, Vadhan, and Yang (CRYPTO'01 and Journal of ACM 59(2)) shows that, assuming indistinguishability obfuscation (iO), such watermarking is impossible if the marked program C evaluates the original program with perfect correctness. In this work we show that, assuming iO, such watermarking is possible if the marked program C is allowed to err with even a negligible probability, which would be undetectable to the user. Our watermarking schemes are public key, namely we use a secret marking key to embed marks in programs, and a public detection key that allows anyone to detect marks in programs. Our schemes are secure against chosen program attacks, that is even if the adversary is given oracle access to the marking functionality. We emphasize that our security notion of watermark non-removability considers arbitrary adversarial strategies to modify the marked program, in contrast to the prior works (Nishimaki, EUROCRYPT '13).


title: Elastic Stream Processing for the Internet of Things

abstract: Emerging trends like Big Data and the Internet of Things pose new challenges to established data stream processing engines. Especially, with the advent of the Internet of Things, the data that has to be processed can become very large. Since companies usually aim for cost efficiency, engines need to support resource elasticity to minimize the operational cost while maintaining real-time processing capabilities. In the work at hand, we propose and realize the distributed Platform for Elastic Stream Processing (PESP). An extensive evaluation demonstrates the practical feasibility and efficiency of the system design. The evaluation shows that PESP is able to reduce cost by 20% with minimal effects on the Quality of Service in comparison to an over-provisioning baseline. Compared to an under-provisioning baseline, PESP allows a Quality of Service improvement of 72%.


title: Geodesic Matting: A Framework for Fast Interactive Image and Video Segmentation and Matting

abstract: An interactive framework for soft segmentation and matting of natural images and videos is presented in this paper. The proposed technique is based on the optimal, linear time, computation of weighted geodesic distances to user-provided scribbles, from which the whole data is automatically segmented. The weights are based on spatial and/or temporal gradients, considering the statistics of the pixels scribbled by the user, without explicit optical flow or any advanced and often computationally expensive feature detectors. These could be naturally added to the proposed framework as well if desired, in the form of weights in the geodesic distances. An automatic localized refinement step follows this fast segmentation in order to further improve the results and accurately compute the corresponding matte function. Additional constraints into the distance definition permit to efficiently handle occlusions such as people or objects crossing each other in a video sequence. The presentation of the framework is complemented with numerous and diverse examples, including extraction of moving foreground from dynamic background in video, natural and 3D medical images, and comparisons with the recent literature.


title: The use of prolonged peripheral neural blockade after lower extremity amputation: the effect on symptoms associated with phantom limb syndrome.

abstract: BACKGROUND ::: Phantom limb syndrome (PLS) is common after limb amputations, involving up to 90% of amputees. Although many different therapies have been evaluated, none has been found to be highly effective. Therefore, we evaluated the efficacy of a prolonged perineural infusion of a high concentration of local anesthetic solution in preventing PLS. ::: ::: ::: METHODS ::: A perineural catheter was placed immediately before or during surgery in 71 patients undergoing lower extremity amputation. A continuous infusion of 0.5% ropivacaine was started intraoperatively at 5 mL/h using an elastomeric (nonelectronic) pump, and continued for 4 to 83 days after surgery. PLS was evaluated on the first postoperative day and then 1, 2, 3, and 4 weeks, and 3, 6, 9, and 12 months after surgery. To evaluate the presence and severity of PLS while the patient was receiving the ropivacaine infusion, it was discontinued for 6 to 12 hours before each assessment period (i.e., until the sensation in the extremity returned). The severity of phantom limb and stump pain was assessed using a 5-point verbal rating scale (VRS), with 0 = no pain to 4 = intolerable pain, and "phantom" sensations were recorded as present or absent. If the VRS score was >1 or significant phantom sensations were present, the ropivacaine infusion was immediately restarted at 5 mL/h. If the VRS score remained at 0 to 1 and the patient had not experienced phantom sensations for 48 hours, the infusion was permanently discontinued and the catheter was removed. ::: ::: ::: RESULTS ::: Median duration of the local anesthetic infusion was 30 days (95% confidence interval, 25-30 days). On postoperative day 1, 73% of the patients complained of severe-to-intolerable pain (visual analog scale >2). However, the incidence of severe-to-intolerable phantom limb pain was only 3% at the end of the 12-month evaluation period. At the end of the 12-month period, the percentage of patients with VRS pain scores were 0 = 84%, 1 = 10%, 2 = 3%, 3 = 3%, and 4 = none. However, phantom limb sensations were present in 39% of patients at the end of the 12-month evaluation period. All patients were able to manage the elastomeric catheter infusion system at home. ::: ::: ::: CONCLUSION ::: Use of a prolonged postoperative perineural infusion of ropivacaine 0.5% seems to be an effective therapy for the treatment of phantom limb pain and sensations after lower extremity amputation.


title: Torque ripple reduction of a synchronous reluctance motor for electric vehicle applications

abstract: Synchronous reluctance motor (SynRM) is a potential candidate for high starting torque requirements of traction drives. Any demagnetization risk is prevented since there is not any permanent magnet on the rotor or stator structure. On the other hand, the high rotor starting current problem, that is common in induction machines is ignored since there is not any winding on the rotor. Indeed, absence of permanent magnet in motor structure and its simplicity leads to lower finished cost in comparison with other competitors. Also high average torque and low ripple content is important in electrical drives employed in electric vehicle applications. High amount of torque ripple is one of the problems of SynRM, which is considered in many researches. In this paper, a new design of the SynRM is proposed in order to reduce the torque ripple while maintaining the average torque. For this purpose, auxiliary flux barriers in the rotor structure are employed that reduce the torque ripple significantly. Proposed design electromagnetic performance is simulated by finite element analysis. It is shown that the proposed design reduces torque ripple significantly without any reduction in average torque.


A Named Entity Recognizer for Filipino Texts


title: Detecting near-duplicates for web crawling

abstract: Near-duplicate web documents are abundant. Two such documents differ from each other in a very small portion that displays advertisements, for example. Such differences are irrelevant for web search. So the quality of a web crawler increases if it can assess whether a newly crawled web page is a near-duplicate of a previously crawled web page or not. In the course of developing a near-duplicate detection system for a multi-billion page repository, we make two research contributions. First, we demonstrate that Charikar's fingerprinting technique is appropriate for this goal. Second, we present an algorithmic technique for identifying existing f-bit fingerprints that differ from a given fingerprint in at most k bit-positions, for small k. Our technique is useful for both online queries (single fingerprints) and all batch queries (multiple fingerprints). Experimental evaluation over real data confirms the practicality of our design.


title: QRS Duration or QRS Morphology: What Really Matters in Cardiac Resynchronization Therapy?

abstract: The beneficial effects of cardiac resynchronization therapy (CRT) have been well established in large, randomized trials. Despite the documented success of this treatment strategy, a significant proportion of patients with heart failure do not achieve the desired response. The aim of this review was to delineate factors contributing to a successful CRT response, emphasizing the interrelated roles of QRS morphology and QRS interval duration. More data are available on QRS duration, as this factor has been used as an enrollment criterion in clinical trials. Response to CRT seems to increase as the QRS duration becomes longer, with greatest benefit in QRS duration ≥150 ms. Recent data have placed more emphasis on QRS morphology, demonstrating variability in clinical response between patients with left bundle branch block, non-left bundle branch block, and right bundle branch block morphology. Notably, myocardial scarring and cardiac dimensions, among other variables, may alter heterogeneity in ventricular activation. Understanding the electrophysiological underpinnings of the QRS complex has become important not only to predict response but also to facilitate the patient-specific delivery of resynchronization therapy.


title: Changing concepts of working memory

abstract: Working memory is thought to be limited in capacity, holding a fixed, small number of items, but it has recently been proposed that working memory might be conceptualized as a limited resource that is distributed flexibly between all items to be maintained in memory. In this review, the authors consider emerging evidence for this proposal.


title: Effects of mobile phone use on academic performance of college going young adults in India

abstract: Mobile phones have become the most popular way to communicate with other individuals. While cell phones have become less of a status symbol and more of a fashion statement, they have created an unspoken social dependency. Adolescents and young adults are more likely to engage in SMS messing, making phone calls, accessing the internet from their phone or playing a mobile driven game. Once pervaded by boredom, teenagers resort to instant connection, to someone, somewhere. Sensation seeking behavior has also linked adolescents and young adults to have the desire to take risks with relationships, rules and roles. Individuals seek out entertainment and avoid boredom at all times be it appropriate or inappropriate. Cell phones are used for entertainment, information and social connectivity. It has been demonstrated that individuals with low self – esteem use cell phones to form and maintain social relationships. They form an attachment with cell phone which molded their mind that they cannot function without their cell phone on a day-to-day basis. In this context, the study attempts to examine the extent of use of mobile phone and its influence on the academic performance of the students. A face to face survey using structured questionnaire was the method used to elicit the opinions of students between the age group of 18-25 years in three cities covering all the three regions the State of Andhra Pradesh in India. The survey was administered among 1200 young adults through two stage random sampling to select the colleges and respondents from the selected colleges, with 400 from each city. In Hyderabad, 201 males and 199 females participated in the survey. In Visakhapatnam, 192 males and 208 females participated. In Tirupati, 220 males and 180 females completed the survey. Two criteria were taken into consideration while choosing the participants for the survey. The participants are college-going and were mobile phone users. Each of the survey responses was entered and analyzed using SPSS software. The Statistical Package for Social Sciences (SPSS - 16) had been used to work out the distribution of samples in terms of percentages for each specified parameter.


Deep Neural Networks with Massive Learned Knowledge


title: Bimanual gesture keyboard

abstract: Gesture keyboards represent an increasingly popular way to input text on mobile devices today. However, current gesture keyboards are exclusively unimanual. To take advantage of the capability of modern multi-touch screens, we created a novel bimanual gesture text entry system, extending the gesture keyboard paradigm from one finger to multiple fingers. To address the complexity of recognizing bimanual gesture, we designed and implemented two related interaction methods, finger-release and space-required, both based on a new multi-stroke gesture recognition algorithm. A formal experiment showed that bimanual gesture behaviors were easy to learn. They improved comfort and reduced the physical demand relative to unimanual gestures on tablets. The results indicated that these new gesture keyboards were valuable complements to unimanual gesture and regular typing keyboards.


title: A self-adaptive reconfiguration scheme for throughput maximization in municipal WMNs

abstract: Multi-hop wireless mesh network (WMN) is considered to be an economical technique for last-mile broadband Internet access (Tang et?al., 2006). Fueled by the rapid development of the Internet of Things, it is increasing important to provide robust and high-quality urban wireless mesh networks (WMNs) for public service. However, WMNs encounter frequent link failures during their lifetime in practical use as a consequence of large-scale wireless interference, block buildings, etc., which deeply influences the performance of the network. These failures should be recovered in time or prevented with foresight. Tremendous labor and management cost should also be avoided. In this paper, we focus on an interesting phenomenon called "loop effect" in urban WMNs which may cause frequent link failures. A scheme named anti-loop network reconfiguration system (ALRS) for link recovery in municipal WMNs with QoS requirement is proposed and analyzed. ALRS takes the historical information into consideration to achieve maximum throughput, interference avoidance and self-reconfiguration with no requirement on mesh node antennas. Our evaluation results show that ALRS outperforms all the existing techniques in improving network throughput and reducing the occurrence probability of continuous link failure. We propose a self-adaptive reconfiguration scheme for link recovery in municipal WMN.Our scheme achieves maximum throughput.The possibility of link failure occurrence drops in municipal WMN.


title: Financial News Classification using SVM

abstract: Stock market prediction is an attractive research problem to be investigated. News contents are one of the most important factors that have influence on market. Considering the news impact in analyzing the stock market behavior, leads to more precise predictions and as a result more profitable trades. So far various prototypes have been developed which consider the impact of news in stock market prediction. In this paper, the main components of such forecasting systems have been introduced.


title: Credit Card Fraud Detection with Artificial Immune System

abstract: We apply Artificial Immune Systems(AIS) [4] for credit card fraud detection and we compare it to other methods such as Neural Nets(NN) [8] and Bayesian Nets(BN) [2], Naive Bayes(NB) and Decision Trees(DT) [13]. Exhaustive search and Genetic Algorithm(GA) [7] are used to select optimized parameters sets, which minimizes the fraud cost for a credit card database provided by a Brazilian card issuer. The specifics of the fraud database are taken into account, such as skewness of data and different costs associated with false positives and negatives. Tests are done with holdout sample sets, and all executions are run using Weka [18], a publicly available software. Our results are consistent with the early result of Maes in [12] which concludes that BN is better than NN, and this occurred in all our evaluated tests. Although NN is widely used in the market today, the evaluated implementation of NN is among the worse methods for our database. In spite of a poor behavior if used with the default parameters set, AIS has the best performance when parameters optimized by GA are used.


Analyzing the language evolution of a science classroom via a topic model


title: Poisoning behavioral malware clustering

abstract: Clustering algorithms have become a popular tool in computer security to analyze the behavior of malware variants, identify novel malware families, and generate signatures for antivirus systems. However, the suitability of clustering algorithms for security-sensitive settings has been recently questioned by showing that they can be significantly compromised if an attacker can exercise some control over the input data. In this paper, we revisit this problem by focusing on behavioral malware clustering approaches, and investigate whether and to what extent an attacker may be able to subvert these approaches through a careful injection of samples with poisoning behavior. To this end, we present a case study on Malheur, an open-source tool for behavioral malware clustering. Our experiments not only demonstrate that this tool is vulnerable to poisoning attacks, but also that it can be significantly compromised even if the attacker can only inject a very small percentage of attacks into the input data. As a remedy, we discuss possible countermeasures and highlight the need for more secure clustering algorithms.


title: Intriguingly Simple and Fast Transit Routing

abstract: This paper studies the problem of computing optimal journeys in dynamic public transit networks. We introduce a novel algorithmic framework, called Connection Scan Algorithm (CSA), to compute journeys. It organizes data as a single array of connections, which it scans once per query. Despite its simplicity, our algorithm is very versatile. We use it to solve earliest arrival and multi-criteria profile queries. Moreover, we extend it to handle the minimum expected arrival time (MEAT) problem, which incorporates stochastic delays on the vehicles and asks for a set of (alternative) journeys that in its entirety minimizes the user’s expected arrival time at the destination. Our experiments on the dense metropolitan network of London show that CSA computes MEAT queries, our most complex scenario, in 272 ms on average.


title: Asynchronous stochastic gradient descent for DNN training

abstract: It is well known that state-of-the-art speech recognition systems using deep neural network (DNN) can greatly improve the system performance compared with conventional GMM-HMM. However, what we have to pay correspondingly is the immense training cost due to the enormous parameters of DNN. Unfortunately, it is difficult to achieve parallelization of the minibatch-based back-propagation (BP) algorithm used in DNN training because of the frequent model updates. In this paper we describe an effective approach to achieve an approximation of BP - asynchronous stochastic gradient descent (ASGD), which is used to parallelize computing on multi-GPU. This approach manages multiple GPUs to work asynchronously to calculate gradients and update the global model parameters. Experimental results show that it achieves a 3.2 times speed-up on 4 GPUs than the single one, without any recognition performance loss.


title: A New Pan-Sharpening Method With Deep Neural Networks

abstract: A deep neural network (DNN)-based new pansharpening method for the remote sensing image fusion problem is proposed in this letter. Research on representation learning suggests that the DNN can effectively model complex relationships between variables via the composition of several levels of nonlinearity. Inspired by this observation, a modified sparse denoising autoencoder (MSDA) algorithm is proposed to train the relationship between high-resolution (HR) and low-resolution (LR) image patches, which can be represented by the DNN. The HR/LR image patches only sample from the HR/LR panchromatic (PAN) images at hand, respectively, without requiring other training images. By connecting a series of MSDAs, we obtain a stacked MSDA (S-MSDA), which can effectively pretrain the DNN. Moreover, in order to better train the DNN, the entire DNN is again trained by a back-propagation algorithm after pretraining. Finally, assuming that the relationship between HR/LR multispectral (MS) image patches is the same as that between HR/LR PAN image patches, the HR MS image will be reconstructed from the observed LR MS image using the trained DNN. Comparative experimental results with several quality assessment indexes show that the proposed method outperforms other pan-sharpening methods in terms of visual perception and numerical measures.


title: Person Re-identification by Attributes

abstract: Visually identifying a target individual reliably in a crowded environment observed by a distributed camera network is critical to a variety of tasks in managing business information, border control, and crime prevention. Automatic re-identification of a human candidate from public space CCTV video is challenging due to spatiotemporal visual feature variations and strong visual similarity between different people, compounded by low-resolution and poor quality video data. In this work, we propose a novel method for re-identification that learns a selection and weighting of mid-level semantic attributes to describe people. Specifically, the model learns an attribute-centric, parts-based feature representation. This differs from and complements existing low-level features for re-identification that rely purely on bottom-up statistics for feature selection, which are limited in discriminating and identifying reliably visual appearances of target people appearing in different camera views under certain degrees of occlusion due to crowdedness. Our experiments demonstrate the effectiveness of our approach compared to existing feature representations when applied to benchmarking datasets.


title: Comparative Study on Data Mining Classification Algorithms for Predicting Road Traffic Accident Severity

abstract: According to World Health Organization report the number of deaths by road traffic accident is more than 1.25 million people and every year with non-fatal accidents affecting more than 20–50 million people. Several factors are contributed on the occurrence of road traffic accident. In this study, data mining classification techniques applied to establish models (classifiers) to identify accident factors and to predict traffic accident severity using previously recorded traffic data. Using WEKA (Waikato Environment for Knowledge Analysis) data mining decision tree (J48, ID3 and CART) and Naive Bayes classifiers are built to model the severity of injury. The classification performance of all these algorithms is compared based on their results. The experimental result shows that the accuracy of J48 classifier is higher than others.


title: SAGE: whitebox fuzzing for security testing

abstract: SAGE has had a remarkable impact at Microsoft.


title: An implementation of optimized framework for action classification using multilayers neural network on selected fused features

abstract: In video sequences, human action recognition is a challenging problem due to motion variation, in frame person difference, and setting of video recording in the field of computer vision. Since last few years, applications of human activity recognition have increased significantly. In the literature, many techniques are implemented for human action recognition, but still they face problem in contrast of foreground region, segmentation, feature extraction, and feature selection. This article contributes a novel human action recognition method by embedding the proposed frames fusion working on the principle of pixels similarity. An improved hybrid feature extraction increases the recognition rate and allows efficient classification in the complex environment. The design consists of four phases, (a) enhancement of video frames (b) threshold-based background subtraction and construction of saliency map (c) feature extraction and selection (d) neural network (NN) for human action classification. Results have been tested using five benchmark datasets including Weizmann, KTH, UIUC, Muhavi, and WVU and obtaining recognition rate 97.2, 99.8, 99.4, 99.9, and 99.9%, respectively. Contingency table and graphical curves support our claims. Comparison with existent techniques identifies the recognition rate and trueness of our proposed method.


title: GPU acceleration of regular expression matching for large datasets: exploring the implementation space

abstract: Regular expression matching is a central task in several networking (and search) applications and has been accelerated on a variety of parallel architectures, including general purpose multi-core processors, network processors, field programmable gate arrays, and ASIC- and TCAM-based systems. All of these solutions are based on finite automata (either in deterministic or non-deterministic form) and mostly focus on effective memory representations for such automata. More recently, a handful of proposals have exploited the parallelism intrinsic in regular expression matching (i.e., coarse-grained packet-level parallelism and fine-grained data structure parallelism) to propose efficient regex-matching designs for GPUs. However, most GPU solutions aim at achieving good performance on small datasets, which are far less complex and problematic than those used in real-world applications. In this work, we provide a more comprehensive study of regular expression matching on GPUs. To this end, we consider datasets of practical size and complexity and explore advantages and limitations of different automata representations and of various GPU implementation techniques. Our goal is not to show optimal speedup on specific datasets, but to highlight advantages and disadvantages of the GPU hardware in supporting state-of-the-art automata representations and encoding schemes, approaches that have been broadly adopted on other parallel memory-based platforms.


title: Detecting and Recognizing Human-Object Interactions

abstract: To understand the visual world, a machine must not only recognize individual object instances but also how they interact. Humans are often at the center of such interactions and detecting human-object interactions is an important practical and scientific problem. In this paper, we address the task of detecting triplets in challenging everyday photos. We propose a novel model that is driven by a human-centric approach. Our hypothesis is that the appearance of a person -- their pose, clothing, action -- is a powerful cue for localizing the objects they are interacting with. To exploit this cue, our model learns to predict an action-specific density over target object locations based on the appearance of a detected person. Our model also jointly learns to detect people and objects, and by fusing these predictions it efficiently infers interaction triplets in a clean, jointly trained end-to-end system we call InteractNet. We validate our approach on the recently introduced Verbs in COCO (V-COCO) and HICO-DET datasets, where we show quantitatively compelling results.


Pneumatic Musculoskeletal Infant Robots


title: Relational Long Short-Term Memory for Video Action Recognition

abstract: Spatial and temporal relationships, both short-range and long-range, between objects in videos are key cues for recognizing actions. It is a challenging problem to model them jointly. In this paper, we first present a new variant of Long Short-Term Memory, namely Relational LSTM to address the challenge for relation reasoning across space and time between objects. In our Relational LSTM module, we utilize a non-local operation similar in spirit to the recently proposed non-local network to substitute the fully connected operation in the vanilla LSTM. By doing this, our Relational LSTM is capable of capturing long and short-range spatio-temporal relations between objects in videos in a principled way. Then, we propose a two-branch neural architecture consisting of the Relational LSTM module as the non-local branch and a spatio-temporal pooling based local branch. The local branch is introduced for capturing local spatial appearance and/or short-term motion features. The two-branch modules are concatenated to learn video-level features from snippet-level ones end-to-end. Experimental results on UCF-101 and HMDB-51 datasets show that our model achieves state-of-the-art results among LSTM-based methods, while obtaining comparable performance with other state-of-the-art methods (which use not directly comparable schema). Our code will be released.


title: Multilevel Diode-Clamped Converter for Photovoltaic Generators With Independent Voltage Control of Each Solar Array

abstract: In photovoltaic (PV) power systems where a set of series-connected PV arrays (PVAs) is connected to a conventional two-level inverter, the occurrence of partial shades and/or the mismatching of PVAs leads to a reduction of the power generated from its potential maximum. To overcome these problems, the connection of the PVAs to a multilevel diode-clamped converter is considered in this paper. A control and pulsewidth-modulation scheme is proposed, capable of independently controlling the operating voltage of each PVA. Compared to a conventional two-level inverter system, the proposed system configuration allows one to extract maximum power, to reduce the devices voltage rating (with the subsequent benefits in device-performance characteristics), to reduce the output-voltage distortion, and to increase the system efficiency. Simulation and experimental tests have been conducted with three PVAs connected to a four-level three-phase diode-clamped converter to verify the good performance of the proposed system configuration and control strategy.


title: iCanTrace: Avatar Personalization Through Selfie Sketches

abstract: This chapter introduces a novel system that allows users to generate customized cartoon avatars through a sketching interface. The rise of social media and personalized gaming has given a need for personalized virtual appearances. Avatars, self-curated and customized images to represent oneself, have become a common means of expressing oneself in these new media. Avatar creation platforms face the challenges of granting user significant control over the avatar creation and of encumbering the user with too many choices in their avatar customization. This chapter demonstrates a sketch-guided avatar customization system and its potential to simplify the avatar creation process.


title: A performance comparison of linux containers and virtual machines using Docker and KVM

abstract: Virtualization is a foundational element of cloud computing. Since cloud computing is slower than a native system, this study analyzes ways to improve performance. We compared the performance of Docker and Kernel-based virtual machine (KVM). KVM uses full virtualization, including \(\times \)86 hardware virtualization extensions. Docker is a solution provided by isolation in userspace instead of creating a virtual machine. The performance of KVM and Docker was compared in three ways. These comparisons show that Docker is faster than KVM.


title: NONZERO-SUM STOCHASTIC GAMES

abstract: Abstract : The paper extends the basic work that has been done on zero-sum stochastic games to those that are nonzero-sum. Appropriately defined equilibrium points are shown to exist for both the case where the players seek to maximize the total value of their discounted period rewards and the case where they wish to maximize their average reward per period. For the latter case, conditions required on the structure of the Markov chains are less stringent than those imposed in previous work on zero-sum stochastic games, extensions to n-person games and underlying semi-Markov processes are discussed, and finding an equilibrium point is shown to be equivalent to solving a certain nonlinear programming problem.


title: Improving recommendation accuracy using networks of substitutable and complementary products

abstract: Recommender systems are ubiquitous in applications ranging from e-commerce to social media, helping users to navigate a huge selection of items and to meet a variety of special needs and user tastes. Incorporating contextual knowledge into such systems — such as relational information — has proven to be an effective way to improve recommendation accuracy. A popular line of research aims to model relationships between users, through their connections in a social network. In contrast, we aim to model complex relationships between products, using data based on co-purchase and co-browsing behavior. Modeling such networks presents a variety of challenges, in particular because the features that make two items complementary (or likely to be co-purchased) are far more complex than mere similarity. To model these complex relationships we develop a method based on pairwise ranking and embedding learning to build representations of items based on their co-purchasing and co-browsing statistics. We conduct experiments on Amazon product data to demonstrate that modeling such relationships significantly improves accuracy compared to competitive baselines.


title: A Parallel Shortest Path Algorithm Based on Graph-Partitioning and Iterative Correcting

abstract: In this paper, we focus on satisfying the actual demands of quickly finding the shortest paths over real-road networks in an intelligent transportation system. A parallel shortest path algorithm based on graph partitioning and iterative correcting is proposed. After evaluating the algorithm using three real road networks, we conclude that our graph-partitioning and iterative correcting based parallel algorithm has good performance. In addition, it achieves more than a 15-fold speedup on 16 processors in an IBM cluster over these real road networks.