title: Regions and borders: Controversies in northern Ireland about the European union

abstract: Abstract This article is about the simultaneous effects on Northern Ireland of European integration; both ameliorating constitutional controversies and becoming part of the fundamental conflict. There is common ground about NI's material interests and their political representation in Brussels through Westminster. But policy which requires cross‐border co‐operation can be seen by unionists as ‘creeping’ Irish unification. NI is not unique in containing both regional advocates of better policy‐making within existing states and those who see the EU as an opportunity for a different political status. Concessions to some cross‐border co‐operation have been made in recent initiatives from various political quarters. Thus, the conclusion is that there is scope for participants of different persuasions to concentrate upon improving defects internal to the UK and upon the appropriate composition and functions of ad hoc cross‐border authorities.


title: Analysis and design of high frequency asymmetrical half bridge flyback converter

abstract: The asymmetrical half bridge flyback converter is gaining popularity because it can achieve ZVS operation. However, the relationship between voltage transfer ratio and duty cycle is nonlinear in the condition of high switching frequency and small blocking capacitor. It is improper to use the linear model to guide the design of asymmetrical half bridge flyback converter. This paper deduces a steady-state voltage transfer function of the converter at high frequency, and the effect of load is also taken into consideration. According to the steady-state voltage transfer function, the characteristics of voltage transfer ratio and maximum duty cycle can be determined. Finally, a 19 V/6 A, 250 kHz prototype is designed and built to verify the analytical results. The experimental results indicate that, compared to the linear model, the asymmetrical half bridge flyback converter designed according to the voltage transfer function can be controlled more accurately and has significant merits at high frequency.


title: GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation

abstract: In this paper, we propose Geometric Neural Network (GeoNet) to jointly predict depth and surface normal maps from a single image. Building on top of two-stream CNNs, our GeoNet incorporates geometric relation between depth and surface normal via the new depth-to-normal and normal-to-depth networks. Depth-to-normal network exploits the least square solution of surface normal from depth and improves its quality with a residual module. Normal-to-depth network, contrarily, refines the depth map based on the constraints from the surface normal through a kernel regression module, which has no parameter to learn. These two networks enforce the underlying model to efficiently predict depth and surface normal for high consistency and corresponding accuracy. Our experiments on NYU v2 dataset verify that our GeoNet is able to predict geometrically consistent depth and normal maps. It achieves top performance on surface normal estimation and is on par with state-of-the-art depth estimation methods.


Using Data Mining to Predict Automobile Insurance Fraud


title: Rethinking Database Algorithms for Phase Change Memory

abstract: Phase change memory (PCM) is an emerging memory technology with many attractive features: it is non-volatile, byte-addressable, 2–4X denser than DRAM, and orders of magnitude better than NAND Flash in read latency, write latency, and write endurance. In the near future, PCM is expected to become a common component of the memory/storage hierarchy for a wide range of computer systems. In this paper, we describe the unique characteristics of PCM, and their potential impact on database system design. In particular, we present analytic metrics for PCM endurance, energy, and latency, and illustrate that current approaches for common database algorithms such as B + -trees and Hash Joins are suboptimal for PCM. We present improved algorithms that reduce both execution time and energy on PCM while increasing write endurance.


title: Compact MIMO Antenna for Portable UWB Applications With Band-Notched Characteristic

abstract: A multiple-input-multiple-output (MIMO) band-notched antenna with a compact size of only $22 \times 36\;\mathrm{mm}^2 $ is proposed for portable ultrawideband (UWB) applications. The antenna consists of two square monopole-antenna elements, a T-shaped ground stub, a vertical slot cut on the T-shaped ground stub to reduce mutual coupling, and two strips on the ground plane to create a notched frequency band. Simulation and measurement are used to study the antenna performance in terms of impedance matching, isolation between the two input ports, radiation pattern, efficiency, peak gain, and envelope correlation coefficient. Results show that the antenna can operate from 3.1 to more than 11 GHz with a notched band in 5.15–5.85 GHz. In the center notched frequency, the efficiency drops to 7%, indicating a good interference suppression performance. The mutual coupling is less than $- 15\;\mathrm{dB}$ and the envelope correlation coefficient is less than 0.1. The performances of the MIMO antenna when installed on a PCB with a standard size, with an USB connector and device housing, are also studied. Results show that the proposed MIMO antenna is a good candidate for portable UWB applications.


title: Estimation of Illuminant Direction and Intensity of Multiple Light Sources

abstract: This paper presents a novel scheme for locating multiple light sources and estimating their intensities from a pair of stereo images of a sphere. No prior knowledge of the location and radius of the sphere is necessary. The sphere surface is not assumed to be a pure Lambertian surface, instead, it has both Lambertian and specular properties. The light source locating algorithm is based on the fact that the Lambertian intensity is not dependent on the direction of viewp oint, while the specular intensity is highly dependent on the direction of the view point. From this fact, we can use a pair of stereo images whose view point changes can be utilized to separate the image of the sphere into two images, one with Lambertian intensities, and the other with specular intensities. The specular image is used to find the directions of the light sources, and then Lambertian image model is used to find the intensities of the light sources. Experiments on both synthetic and real images show that the scheme is successful and robust in finding the directions of the light sources accurately with accurate intensity estimation.


title: Nonlinear control design of anti-lock braking systems combined with active suspensions

abstract: This paper develops the anti-lock braking control system integrated with active suspensions applied to a quartercar model by employing the nonlinear backstepping design schemes. In emergency, although the braking distance can be reduced by the control torque from disk/drum brakes, the braking time and distance can be further improved if the normal force generated from active suspension systems is considered simultaneously. Individual controller is designed for each subsystem and an integrated algorithm is constructed to coordinate these two subsystems. As a result, the integration of anti-lock braking and active suspension systems indeed enhances the system performance because of reduction of braking time and distance.


title: Physical Experience Enhances Science Learning

abstract: Three laboratory experiments involving students’ behavior and brain imaging and one randomized field experiment in a college physics class explored the importance of physical experience in science learning. We reasoned that students’ understanding of science concepts such as torque and angular momentum is aided by activation of sensorimotor brain systems that add kinetic detail and meaning to students’ thinking. We tested whether physical experience with angular momentum increases involvement of sensorimotor brain systems during students’ subsequent reasoning and whether this involvement aids their understanding. The physical experience, a brief exposure to forces associated with angular momentum, significantly improved quiz scores. Moreover, improved performance was explained by activation of sensorimotor brain regions when students later reasoned about angular momentum. This finding specifies a mechanism underlying the value of physical experience in science education and leads the way for classroom pra...


title: Non-Intrusive Energy Disaggregation Using Non-negative Matrix Factorization with Sum-to-k Constraint

abstract: Energy disaggregation or Non-Intrusive Load Monitoring (NILM) addresses the issue of extracting device-level energy consumption information by monitoring the aggregated signal at one single measurement point without installing meters on each individual device. Energy disaggregation can be formulated as a source separation problem where the aggregated signal is expressed as linear combination of basis vectors in a matrix factorization framework. In this paper, an approach based on Sum-to-k constrained Non-negative Matrix Factorization (S2K-NMF) is proposed. By imposing the sum-to-k constraint and the non-negative constraint, S2K-NMF is able to effectively extract perceptually meaningful sources from complex mixtures. The strength of the proposed algorithm is demonstrated through two sets of experiments: Energy disaggregation in a residential smart home, and HVAC components energy monitoring in an industrial building testbed maintained at the Oak Ridge National Laboratory (ORNL). Extensive experimental results demonstrate the superior performance of S2K-NMF as compared to state-of-the-art decomposition-based disaggregation algorithms.


title: Low Cost Dual Polarized Base Station Element for Long Term Evolution

abstract: A broadband low profile dual polarized patch antenna for fourth generation long term evolution (4G LTE) is presented in this communication. By employing two wideband feeding mechanisms for the two input ports, a dual polarized patch antenna element with the characteristics of high input port isolation and wide impedance bandwidth is successfully designed. A wide meandering probe (M-probe) and a pair of twin-L-probes are proposed to feed a patch for dual polarization. The proposed design with a wideband balun has impedance bandwidths of over 47% at the two input ports as well as over 37 dB isolation within the entire operating band. Over 8 dBi gain can be achieved at the two ports.


title: Regional inequality in China : trends, explanations and policy responses

abstract: 1. Regional Inequality in China: An Overview Shenggen Fan, Ravi Kanbur and Xiaobo Zhang Part I Trends in Regional Inequality 2. Which Regional Inequality: Rural-Urban or Coast-Inland? An Application to China Ravi Kanbur and Xiaobo Zhang 3. What Difference Do Polarisation Measures Make? An Application to China Xiaobo Zhang and Ravi Kanbur 4. Fifty Years of Regional Inequality in China: A Journey Through Central Planning, Reform and Openness Ravi Kanbur and Xiaobo Zhang 5. The Emerging Urban Poverty and Inequality in China: Evidence from Household Survey Fang Cheng, Xiaobo Zhang and Shenggen Fan 6. Spatial Inequality in Education and Health Care in China Ravi Kanbur and Xiaobo Zhang Part II Explanations and Policy Responses 7. Resource Abundance and Regional Development in China Xiaobo Zhang, Li Xing, Shenggen Fan, and Xiaopeng Luo 8. How Does Globalization Affect Regional Inequality within a Developing Country? Evidence from China Xiaobo Zhang and Kevin H. Zhang 9. China's WTO Accession: Impacts on Regional Agricultural Income--A Multi-regional General equilibrium Analysis Xinshen Diao, Shenggen Fan and Xiaobo Zhang 10. Infrastructure and Regional Economic Development in Rural China Shenggen Fan and Xiaobo Zhang 11. Public Investment and Regional Inequality in Rural China Xiaobo Zhang and Shenggen Fan 12. Reforms, Investment and Poverty in Rural China Shenggen Fan, Linxiu Zhang, and Xiaobo Zhang 13. Fiscal Decentralization and Political Centralization in China: Implications for Growth and Inequality Xiaobo Zhang 14. Social Entitlement Exchange and Balanced Economic Growth Xiaopeng Luo and Xiaobo Zhang


title: Interference in body area networks: Distance does not dominate

abstract: Inter-network interference is a significant source of difficulty for wireless body area networks. Movement, proximity and the lack of central coordination all contribute to this problem. We compare the interference power of multiple Body Area Network (BAN) devices when a group of people move randomly within an office area. We find that the path loss trend is dominated by local variations in the signal, and not free-space path loss exponent.


title: Overview of data backup and disaster recovery in cloud

abstract: Today, in every organization are generated in large volume of data in electronic format that required the safety storage services. Data backup and Disaster Recovery / Business Continuity issues are becoming fundamental in networks since the importance and societal value of digital data is continuously increasing. Every organization requires a business continuity plan (BCP) or disaster recovery plan (DRP) and data backup which falls within the cost constraints while achieving the target recovery requirements in terms of recovery time objective (RTO) and recovery point objective (RPO). The organizations must identify the probable consequences that can cause disasters and evaluate their impact. There is an obvious need of supporting data for resilience against major failures; in many situations the process of storing backup data is also enforced by the law. The aim of this paper is to overview of various techniques in data backup and disaster recovery systems in the cloud environment.


title: Usability metric for mobile application: a goal question metric (GQM) approach

abstract: Many usability guidelines have been created in numerous areas and mobile devices application is included as well. However, there is not much published works in relation to the usability guidelines that comes up together with metric. Although a number of measurement models (e.g. The Metric for Usability Standard in Computing [MUSiC]) have been produced for evaluating usability, they are not focusing on mobile application. This paper will make an attempt to review the existing measurement models and will further explain the development of usability metric using GQM approach. Further research will firstly develop a set of usability guidelines for mobile application which will be used to develop a metric for usability measurement.


Preservation of the activity of NADPH oxidase in human monocyte/macrophages.


title: Disintermediation of Inter-Blockchain Transactions

abstract: Different versions of peer-to-peer electronic cash exist as data represented by separate blockchains. Payments between such systems cannot be sent directly from one party to another without going through a financial institution. Bitcoin provided part of the solution but its utility is limited to intra-blockchain transactions. The benefits are lost if a trusted third party is required to execute inter-blockchain transactions. We propose a solution to the inter-blockchain transaction problem using the same fundamental principles of Bitcoin. The protocol is described by the Uberledger framework, a hierarchical meta-blockchain layer that encapsulates information regarding the fidelity of peer-to-peer transaction facilitators.


title: An integrated framework for project portfolio selection

abstract: Abstract The task of selecting project portfolios is an important and recurring activity in many organizations. There are many techniques available to assist in this process, but no integrated framework for carrying it out. This paper simplifies the project portfolio selection process by developing a framework which separates the work into distinct stages. Each stage accomplishes a particular objective and creates inputs to the next stage. At the same time, users are free to choose the techniques they find the most suitable for each stage, or in some cases to omit or modify a stage if this will simplify and expedite the process. The framework may be implemented in the form of a decision support system, and a prototype system is described which supports many of the related decision making activities.


title: Multi-Agent Real Time Scheduling System for Taxi Companies

abstract: ABSTRACT The paper gives an overview of a multi-agent real-time scheduling solution for taxi companies. It covers problem definition, describes a multi-agent approach to real-time scheduling, outlines the system architecture and gives performance matrices. Key design decisions are also discussed. The most important achievements are the ability of the system to re-schedule taxi service before confirming order acceptance to the client and the system ability to update schedules in intervals between two events, in real time. The multi-agent approach described in this paper is applicable to real-time scheduling and optimization of a wide range of business and social system. Categories and Subject Descriptors I.2.11 [ Computing Methodologies ] : Artificial Intelligence – distributed artificial intelligence General Terms As a result, transportation companies either deal wAlgorithms, Management, Performance, Design, Experimentation. Keywords Multi-agent systems, taxi, dynamic scheduling, events, real time


title: Popular agile approaches in software development: Review and analysis

abstract: The selection of a software development methodology is vital activity in any software project. It has a great impact on customer satisfaction and business welfare. Normally the selection is based on experience or in some cases a rational. In the third world countries and especially in Sudan, where this research is conducted, the choice of a software methodology is always a struggle in software companies. Probably is because most of these companies whether are startup or have humble resources such as small to medium enterprises (SMEs). Currently the government of Sudan (the main buyer of local software products) is pushing the software industry to produce quality products and to reduce risks of buying software products from foreign companies, therefore quality products is real concern for these companies. Usually, start-up or SMEs software companies seeking for rapid and agile way of development accompanied with plug & play engineering practices. Agile software development methodologies become lately the way forward quality assurance. Since the last decade, agile methods have increased popularity and usage among software industries. These methods came to tackle requirement changes quickly, satisfy customers, support interaction, communication and produce high quality products. There are a lot of debates in the literature about agile methods especially on the most newest and used ones. However, academic research on how these SMEs can select the appropriate method still limited. The aim of this paper is to filling this gap by systematically reviewing the existing literature on the most popular agile software development methodologies. This paper has three purposes: first, it proposes a definition and discussion of the most popular agile software development methods. Second, it explores the software SMEs challenges and formulates it into criteria for comparison purposes. Third, it compares these methods and shows their similarities and differences against the defined criteria. Based on this analysis, future research needs are identified and discussed.


title: An Optimized Cost-Sensitive SVM for Imbalanced Data Learning

abstract: Class imbalance is one of the challenging problems for machine learning in many real-world applications. Cost-sensitive learning has attracted significant attention in recent years to solve the problem, but it is difficult to determine the precise misclassification costs in practice. There are also other factors that influence the performance of the classification including the input feature subset and the intrinsic parameters of the classifier. This paper presents an effective wrapper framework incorporating the evaluation measure (AUC and G-mean) into the objective function of cost sensitive SVM directly to improve the performance of classification by simultaneously optimizing the best pair of feature subset, intrinsic parameters and misclassification cost parameters. Experimental results on various standard benchmark datasets and real-world data with different ratios of imbalance show that the proposed method is effective in comparison with commonly used sampling techniques.


title: Semi-automatic generation of as-built BIM façade geometry from laser and image data

abstract: This article outlines a new semi-automatic approach for generating accurate BIM facade models for existing buildings from laser and image data. Two new developments for as-built BIM modelling are presented in this article. The first is a new library of reusable parametric objects designed for modelling classical architectural elements from survey data. These library objects are dynamic and have parameters that can instantly alter the shape, size and other properties of objects. The parametric architectural objects have been designed from historic manuscripts and architectural pattern books. These parametric objects were built using an embedded programming language within the ArchiCAD BIM software called Geometric Description Language (GDL). The second development which is described in more detail in this article is a complete parametric building facade. This parametric building facade can be used as a template for fast and efficient generation of BIM facade geometry. The design of this parametric facade incorporates concepts from procedural modelling which is an automated approach to generating 3D geometries based on rules and algorithms. Parametric architectural objects are automatically combined with rules to generate many different facade arrangements which are controlled by user parameters. When automatically generating a facade, the initial position and size of elements are estimated using classical architectural proportions. Object can then be graphically edited individually or in groups to match the computer generated geometry to survey data. The parametric facade template has also been implemented with the Geometric Description Language for ArchiCAD BIM software. This enables the tools developed to utilise the full benefits of BIM software which includes automated construction or conservation documents, semantic object oriented objects based on IFC semantic classes, automatic lists of objects and material and the ability to add and link additional information to the model. Initial user tests have indicated that the parametric facade is more efficient than previous methods for creating accurate facade models from survey data. The facade template also provides an easier solution for generating facade models when compared to existing methods. Non-specialist users with little experience in 3D modelling can easily generate and modify the facade template by altering parameters graphically or from a dialogue box.


title: Representing Sentence Structure in Hidden Markov Models for Information Extraction

abstract: We study the application of Hidden Markov Models (HMMs) to learning information extractors for -ary relations from free text. We propose an approach to representing the grammatical structure of sentences in the states of the model. We also investigate using an objective function during HMM training which maximizes the ability of the learned models to identify the phrases of interest. We evaluate our methods by deriving extractors for two binary relations in biomedical domains. Our experiments indicate that our approach learns more accurate models than several baseline approaches.


title: populationsindividual assignment to worldwide Ancestry informative markers for fine-scale

abstract: Background and aims The analysis of large-scale genetic data from thousands of individuals has revealed the fact that subtle population genetic structure can be detected at levels that were previously unimaginable. Using the Human Genome Diversity Panel as reference (51 populations - 650,000 SNPs), this works describes a systematic evaluation of the resolution that can be achieved for the inference of genetic ancestry, even when small panels of genetic markers are used. Methods and results A comprehensive investigation of human population structure around the world is undertaken by leveraging the power of Principal Components Analysis (PCA). The problem is dissected into hierarchical steps and a decision tree for the prediction of individual ancestry is proposed. A complete leave-one-out validation experiment demonstrates that, using all available SNPs, assignment of individuals to their self-reported populations of origin is essentially perfect. Ancestry informative genetic markers are selected using two different metrics (In and correlation with PCA scores). A thorough cross-validation experiment indicates that, in most cases here, the number of SNPs needed for ancestry inference can be successfully reduced to less than 0.1% of the original 650,000 while retaining close to 100% accuracy. This reduction can be achieved using a novel clustering-based redundancy removal algorithm that is also introduced here. Finally, the applicability of our suggested SNP panels is tested on HapMap Phase 3 populations. Conclusion The proposed methods and ancestry informative marker panels, in combination with the increasingly more comprehensive databases of human genetic variation, open new horizons in a variety of fields, ranging from the study of human evolution and population history, to medical genetics and forensics.


title: Auto-tuning of PID parameters with programmable logic controller

abstract: In this study, PID control algorithm for a level control system is designed and applied to a programmable logic controller in order to meet the requirements of engineering students' hands on training on PID control of processes. The auto-tuning mode of operation based on PID analog measurements in a closed-loop control system is preferred to present how stable, less time consuming solution it is. Trial and error method is also used for determining PID parameters in order to compare the results with the auto-tuning method.


title: Visual media retargeting

abstract: The increasing variety of commonly used display devices, especially mobile devices, requires adapting visual media to different resolutions and aspect ratios - a process called "retargeting." The media retargeting problem is further accentuated by the explosion of image and video content on the web. This course presents a comparative overview of the latest research in visual-media retargeting. It focuses on content-aware approaches, which, contrary to traditional scaling and cropping, adapt to the salient information within the image or video and rescale the content while preserving visually important information. Topics include: •Algorithmic details and practical considerations of the retargeting pipeline, including its two main parts (saliency estimation and resizing operators). •Recent trends in retargeting operators, namely discrete graph-based approaches, also known as seam carving. •Continuous methods that operate by image and video warping. •Temporally coherent video retargeting and multi-operator frameworks. The course illuminates the theoretical foundations and practical issues involved in media retargeting, and provides attendees a comprehensive understanding of the state of the art. It includes many live demos of the various resizing techniques.


title: A survey of mobile malware in the wild

abstract: Mobile malware is rapidly becoming a serious threat. In this paper, we survey the current state of mobile malware in the wild. We analyze the incentives behind 46 pieces of iOS, Android, and Symbian malware that spread in the wild from 2009 to 2011. We also use this data set to evaluate the effectiveness of techniques for preventing and identifying mobile malware. After observing that 4 pieces of malware use root exploits to mount sophisticated attacks on Android phones, we also examine the incentives that cause non-malicious smartphone tinkerers to publish root exploits and survey the availability of root exploits.


title: Facial expression recognition using Stochastic Neighbor Embedding and SVMs

abstract: Facial expression recognition (FER) has become a hot topic in the computer vision and pattern recognition communities because of its great potential applications in many areas such as human computer nature interaction, animation etc‥ In this paper, we present a new approach to facial expression recognition, which uses Stochastic Neighbor Embedding (SNE) for reducing the high dimensional data of facial expression images into a relatively low dimension data and then uses support vector machine (SVM) as the classifier for the expression classification afterwards. The proposed new algorithm is applied to facial expression recognition on Japanese Female Facial Expression (JAFFE) database, better performance is gained compared with those traditional algorithms, such as PCA and LDA etc‥ The results have further proved the effectiveness of our proposed algorithm.


title: Open data kit sensors: a sensor integration framework for android at the application-level

abstract: Smartphones can now connect to a variety of external sensors over wired and wireless channels. However, ensuring proper device interaction can be burdensome, especially when a single application needs to integrate with a number of sensors using different communication channels and data formats. This paper presents a framework to simplify the interface between a variety of external sensors and consumer Android devices. The framework simplifies both application and driver development with abstractions that separate responsibilities between the user application, sensor framework, and device driver. These abstractions facilitate a componentized framework that allows developers to focus on writing minimal pieces of sensor-specific code enabling an ecosystem of reusable sensor drivers. The paper explores three alternative architectures for application-level drivers to understand trade-offs in performance, device portability, simplicity, and deployment ease. We explore these tradeoffs in the context of four sensing applications designed to support our work in the developing world. They highlight a range of sensor usage models for our application-level driver framework that vary data types, configuration methods, communication channels, and sampling rates to demonstrate the framework's effectiveness.


title: 2D-3D face recognition via Restricted Boltzmann Machines

abstract: This paper proposes a new scheme for the 2D-3D face recognition problem. Our proposed framework mainly consists of Restricted Boltzmann Machines (RBMs) and a correlation learning model. In the framework, a single-layer network based on RBMs is adopted to extract latent features over two different modalities. Furthermore, the latent hidden layer features of different models are projected to formulate a shared space based on correlation learning. Then several different correlation learning schemes are evaluated against the proposed scheme. We evaluate the advocated approach on a popular face dataset-FRGCV2.0. Experimental results demonstrate that the latent features extracted using RBMs are effective in improving the performance of correlation mapping for 2D-3D face recognition.


title: An enhanced MPPT technique for high gain DC-DC converter for photovoltaic applications

abstract: This paper presents an improved Maximum Power Point Tracking(MPPT)of Photovoltaic (PV) systems for switched coupled inductor step up DC-DC converter. PV array can feed power to the load through DC-DC converter boosting the output voltage. Due to the stochastic behaviour of solar energy, MPPT control technique of PV arrays is required to operate at maximum power point. The main contribution of this paper is enhancement of Perturb and Observe (P&O) method and fuzzy controller for varying solar radiation. The characteristics corresponding to output power, output voltage and efficiency for P&O and fuzzy controller are obtained and their performance is compared. The comparison of gain values for conventional boost converter, high step up non isolated DC-DC converter and positive output superlift Luo converter has also been done in this work. The advantages of high voltage gain of switched coupled inductor converter has been integrated with the search for maximum power point. The closed loop control using PI controller facilitates the provision of maximum power to the system at all varying insolations. The closed loop PI controller with P&O tracking for PV system results inconstant output voltage of 396V for varying insolation.


Activity Classification using Smartphone Accelerometer Data


title: DEEP-SEE: Joint Object Detection, Tracking and Recognition with Application to Visually Impaired Navigational Assistance

abstract: In this paper, we introduce the so-called DEEP-SEE framework that jointly exploits computer vision algorithms and deep convolutional neural networks (CNNs) to detect, track and recognize in real time objects encountered during navigation in the outdoor environment. A first feature concerns an object detection technique designed to localize both static and dynamic objects without any a priori knowledge about their position, type or shape. The methodological core of the proposed approach relies on a novel object tracking method based on two convolutional neural networks trained offline. The key principle consists of alternating between tracking using motion information and predicting the object location in time based on visual similarity. The validation of the tracking technique is performed on standard benchmark VOT datasets, and shows that the proposed approach returns state-of-the-art results while minimizing the computational complexity. Then, the DEEP-SEE framework is integrated into a novel assistive device, designed to improve cognition of VI people and to increase their safety when navigating in crowded urban scenes. The validation of our assistive device is performed on a video dataset with 30 elements acquired with the help of VI users. The proposed system shows high accuracy (>90%) and robustness (>90%) scores regardless on the scene dynamics.


SUPERVISED AND UNSUPERVISED MACHINE-LEARNING APPROACHES TO MINERAL SEGMENTATION


ON LASER SPOT DETECTION SYSTEM BASED ON IMAGE PROCESSING TECHNIQUES


title: Peer-to-Peer Crowdfunding: Information and the Potential for Disruption in Consumer Lending

abstract: Can peer-to-peer lending (P2P) disintermediate and mitigate information frictions in lending so that choices and outcomes for at least some borrowers and investors are improved? I offer a framing of issues and survey the nascent literature on P2P. On the investor side, P2P disintermediates an asset class of consumer loans, and investors may be able to capture rents associated with the removal of a layer of financial intermediation. Risk and portfolio choice questions linger prior to any inference. On the borrower side, evidence suggests that proximate knowledge (direct or inferred) unearths soft information. Thus, P2P may be able to offer pricing and/or access benefits to potential borrowers. Early research suggests that the future of consumer lending will involve more big data and reintermediation of underwriting by all types of financial institutions. I ask many more questions than current research can answer, hoping to motivate future research.


title: iParaphrasing: Extracting Visually Grounded Paraphrases via an Image

abstract: A paraphrase is a restatement of the meaning of a text in other words. Paraphrases have been studied to enhance the performance of many natural language processing tasks. In this paper, we propose a novel task iParaphrasing to extract visually grounded paraphrases (VGPs), which are different phrasal expressions describing the same visual concept in an image. These extracted VGPs have the potential to improve language and image multimodal tasks such as visual question answering and image captioning. How to model the similarity between VGPs is the key of iParaphrasing. We apply various existing methods as well as propose a novel neural network-based method with image attention, and report the results of the first attempt toward iParaphrasing.


title: Thematic Identification of 'Little Science': Trends in Portuguese IS&LS Literature by Controlled Vocabulary and Co-Word Analysis

abstract: This study presents an overview of IS&LS thematic trends in Portugal between 2001 and 2012. The results were obtained by means of an analysis, using expeditious qualitative and quantitative techniques, of the bibliographic records of proceedings papers identified during this period. These records were processed using two techniques: a manual subject classification and an automated co-word analysis of the Author-Assigned Keywords. From this we designed cluster and co-occurrence maps, using the VOSviewer and the Pajek software packages. The results indicated an accentuated dynamism in the thematic evolution of this documental corpus, apart from revealing a significant difference among the themes transmitted in nationally and internationally visible production.


title: Mobile App Classification with Enriched Contextual Information

abstract: The study of the use of mobile Apps plays an important role in understanding the user preferences, and thus provides the opportunities for intelligent personalized context-based services. A key step for the mobile App usage analysis is to classify Apps into some predefined categories. However, it is a nontrivial task to effectively classify mobile Apps due to the limited contextual information available for the analysis. For instance, there is limited contextual information about mobile Apps in their names. However, this contextual information is usually incomplete and ambiguous. To this end, in this paper, we propose an approach for first enriching the contextual information of mobile Apps by exploiting the additional Web knowledge from the Web search engine. Then, inspired by the observation that different types of mobile Apps may be relevant to different real-world contexts , we also extract some contextual features for mobile Apps from the context-rich device logs of mobile users. Finally, we combine all the enriched contextual information into the Maximum Entropy model for training a mobile App classifier. To validate the proposed method, we conduct extensive experiments on 443 mobile users’ device logs to show both the effectiveness and efficiency of the proposed approach. The experimental results clearly show that our approach outperforms two state-of-the-art benchmark methods with a significant margin.


title: End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners

abstract: For human drivers, having rear and side-view mirrors is vital for safe driving. They deliver a more complete view of what is happening around the car. Human drivers also heavily exploit their mental map for navigation. Nonetheless, several methods have been published that learn driving models with only a front-facing camera and without a route planner. This lack of information renders the self-driving task quite intractable. We investigate the problem in a more realistic setting, which consists of a surround-view camera system with eight cameras, a route planner, and a CAN bus reader. In particular, we develop a sensor setup that provides data for a 360-degree view of the area surrounding the vehicle, the driving route to the destination, and low-level driving maneuvers (e.g. steering angle and speed) by human drivers. With such a sensor setup we collect a new driving dataset, covering diverse driving scenarios and varying weather/illumination conditions. Finally, we learn a novel driving model by integrating information from the surround-view cameras and the route planner. Two route planners are exploited: 1) by representing the planned routes on OpenStreetMap as a stack of GPS coordinates, and 2) by rendering the planned routes on TomTom Go Mobile and recording the progression into a video. Our experiments show that: 1) 360-degree surround-view cameras help avoid failures made with a single front-view camera, in particular for city driving and intersection scenarios; and 2) route planners help the driving task significantly, especially for steering angle prediction.


title: OCR For Printed Urdu Script Using Feed Forward Neural Network

abstract: This paper deals with an Optical Character Recognition system for printed Urdu, a popular Pakistani/Indian script and is the third largest understandable language in the world, especially in the subcontinent but fewer efforts are made to make it understandable to computers. Lot of work has been done in the field of literature and Islamic studies in Urdu, which has to be computerized. In the proposed system individual characters are recognized using our own proposed method/ algorithms. The feature detection methods are simple and robust. Supervised learning is used to train the feed forward neural network. A prototype of the system has been tested on printed Urdu characters and currently achieves 98.3% character level accuracy on average .Although the system is script/ language independent but we have designed it for Urdu characters only.


title: A new control method for permanent magnet synchronous machines with observer

abstract: The controllability and observability of permanent magnet synchronous machines (PMSM) is analyzed by using mathematic model of PMSM. Based upon which, a full order state observer is established for stator flux of PMSM. This observer is employed to PMSM direct torque control (DTC) system to substitute traditional pure integrator stator flux observer. Simulation results show that the full order observer achieves satisfied estimation of stator flux, especially near zero speed, and offers great robustness to parameter variation.


title: Maintaining connectivity in UAV swarm sensing

abstract: In many applications, Unmanned Aerial Vehicles (UAVs) provide an indispensable platform for gathering information about the situation on the ground. However, to maximise information gained about the environment, such platforms require increased autonomy to coordinate the actions of multiple UAVs. This has led to the development of flight planning and coordination algorithms designed to maximise information gain during sensing missions. However, these have so far neglected the need to maintain wireless network connectivity. In this paper, we address this limitation by enhancing an existing multi-UAV planning algorithm with two new features that together make a significant contribution to the state-of-the-art: (1) we incorporate an on-line learning procedure that enables UAVs to adapt to the radio propagation characteristics of their environment, and (2) we integrate flight path and network routing decisions, so that modelling uncertainty and the affect of UAV position on network performance is taken into account.


title: The genus Leptoplana (Leptoplanidae, Polycladida) in the Mediterranean basin. Redescription of the species Leptoplana mediterranea (Bock, 1913) comb. nov.

abstract: A revision of the genus Leptoplana Ehrenberg, 1831 in the Mediterranean basin is undertaken. This revision deals with the distribution and validity of the species of Leptoplana known for the area. The Mediterranean sub-species polyclad, Leptoplana tremellaris forma mediterranea Bock, 1913 is elevated to the specific level. Leptoplana mediterranea comb. nov. is redescribed from the Lake of Tunis, Tunisia. This flatworm is distinguished from Leptoplana tremellaris mainly by having a prostatic vesicle provided with a long diverticulum attached ventrally to the seminal vesicle, a genital pit closer to the male pore than to the female one and a twelve-eyed hatching juvenile instead of the four-eyed juvenile of L. tremellaris. The direct development in L. mediterranea is described at 15 °C.


The "Art" Of Log Correlation - Tools And Techniques For Correlating Events And Log Files


title: An ANN-Based Synthesis Model for the Single-Feed Circularly-Polarized Square Microstrip Antenna With Truncated Corners

abstract: An artificial neural network-based synthesis model is proposed for the design of single-feed circularly-polarized square microstrip antenna (CPSMA) with truncated corners. To obtain the training data sets, the resonant frequency and Q-factor of square microstrip antennas are calculated by empirical formulae. Then the size of the truncated corners and the operation frequency with the best axial ratio are obtained. Using the Levenberg-Marquardt (LM) algorithm, a three hidden layered network is trained to achieve an accurate synthesis model. At last, the model is validated by comparing its results with the electromagnetic simulation and measurement. It is extremely useful to antenna engineers for directly obtaining patch physical dimensions of the single-feed CPSMA with truncated corners.


title: Improving learning of computational thinking using computational creativity exercises in a college CSI computer science course for engineers

abstract: Promoting computational thinking is a priority in CS education and other STEM and non-STEM disciplines. Our innovative, NSF-funded IC2Think project blends computational and creative thinking. In Spring 2013, we deployed Computational Creativity Exercises (CCE) designed to engage creative competencies (Surrounding, Capturing, Challenging and Broadening) in an introductory CSI course for engineering students. We compared this CCE implementation semester (80 students, 95% completing 3 or 4 CCEs) to the Fall 2013 semester of the same course (55 students) without CCEs. CCE implementation students had significantly higher scores on a CS concepts and skills knowledge test (F(1, 132) = 7.72, p < 01, partial Eta2 = .055; M=7.47 to M=6.13; 13 items) and significantly higher self-efficacy for applying CS knowledge in their field (F(1, 153) = 12.22, p < .01, partial Eta2 = .074; M=70.64 to M=61.47; 100-point scale). CCE implementation students had significantly higher study time (t(1, 136) = 2.08, p = .04; M=3.88 to M=3.29; 7-point scale) and significantly lower lack of regulation, which measures difficulties with studying (t(1, 136) = 2.82, p = .006; M=2.80 to M=3.21; 5-point scale). The addition of computational creativity exercises to CS courses may improve computational thinking and learning of CS knowledge and skills.


title: Oscillometric measurement of systolic and diastolic blood pressures validated in a physiologic mathematical model

abstract: The oscillometric method of measuring blood pressure with an automated cuff yields valid estimates of mean pressure but questionable estimates of systolic and diastolic pressures. Existing algorithms are sensitive to differences in pulse pressure and artery stiffness. Some are closely guarded trade secrets. Accurate extraction of systolic and diastolic pressures from the envelope of cuff pressure oscillations remains an open problem in biomedical engineering. A new analysis of relevant anatomy, physiology and physics reveals the mechanisms underlying the production of cuff pressure oscillations as well as a way to extract systolic and diastolic pressures from the envelope of oscillations in any individual subject. Stiffness characteristics of the compressed artery segment can be extracted from the envelope shape to create an individualized mathematical model. The model is tested with a matrix of possible systolic and diastolic pressure values, and the minimum least squares difference between observed and predicted envelope functions indicates the best fit choices of systolic and diastolic pressure within the test matrix. The model reproduces realistic cuff pressure oscillations. The regression procedure extracts systolic and diastolic pressures accurately in the face of varying pulse pressure and arterial stiffness. The root mean squared error in extracted systolic and diastolic pressures over a range of challenging test scenarios is 0.3 mmHg. A new algorithm based on physics and physiology allows accurate extraction of systolic and diastolic pressures from cuff pressure oscillations in a way that can be validated, criticized, and updated in the public domain.


title: A wavelet-based image . . .

abstract: Abstract The objective of image fusion is to combine information from multiple images of the same scene. The result of image fusion is a new image which is more suitable for human and machine perception or further image-processing tasks such as segmentation, feature extraction and object recognition. Different fusion methods have been proposed in literature, including multiresolution analysis. This paper is an image fusion tutorial based on wavelet decomposition, i.e. a multiresolution image fusion approach. We can fuse images with the same or different resolution level, i.e. range sensing, visual CCD, infrared, thermal or medical. The tutorial performs a synthesis between the multiscale-decomposition-based image approach (Proc. IEEE 87 (8) (1999) 1315), the ARSIS concept (Photogramm. Eng. Remote Sensing 66 (1) (2000) 49) and a multisensor scheme (Graphical Models Image Process. 57 (3) (1995) 235). Some image fusion examples illustrate the proposed fusion approach. A comparative analysis is carried out against classical existing strategies, including those of multiresolution.


title: Interpretation of Artificial Neural Networks: Mapping Knowledge-Based Neural Networks into Rules

abstract: We propose and empirically evaluate a method for the extraction of expert-comprehensible rules from trained neural networks. Our method operates in the context of a three-step process for learning that uses rule-based domain knowledge in combination with neural networks. Empirical tests using real-worlds problems from molecular biology show that the rules our method extracts from trained neural networks: closely reproduce the accuracy of the network from which they came, are superior to the rules derived by a learning system that directly refines symbolic rules, and are expert-comprehensible.


title: Security and Privacy Challenges in the Smart Grid

abstract: Global electrical grids are verging on the largest technological transformation since the introduction of electricity into the home. The antiquated infrastructure that delivers power to our homes and businesses is being replaced with a collection of digital systems called the smart grid. This grid is the modernization of the existing electrical system that enhances customers' and utilities' ability to monitor, control, and predict energy use.


title: Dataflow and Type-based Formulations for Reference Immutability

abstract: Reference immutability enforces the property that a reference cannot be used to mutate the referenced object. There are several type-based formulations for reference immutability in the literature. However, we are not aware of a dataflow formulation. In this paper, we present a dataflow formulation for reference immutability using CFL-reachability, as well as a type-based formulation using viewpoint adaptation, a key concept in ownership types. We observe analogies between the dataflow formulation and the type-based formulation.


title: E-government success factors: Mapping practical tools to theoretical foundations

abstract: Strategies are systematic and long-term approaches to problems. Federal, state, and local governments are investing in the development of strategies to further their e-government goals. These strategies are based on their knowledge of the field and the relevant resources available to them. Governments are communicating these strategies to practitioners through the use of practical guides. The guides provide direction to practitioners as they consider, make a case for, and implement IT initiatives. This article presents an analysis of a selected set of resources government practitioners use to guide their e-government efforts. A selected review of current literature on the challenges to information technology initiatives is used to create a framework for the analysis. A gap analysis examines the extent to which IT-related research is reflected in the practical guides. The resulting analysis is used to identify a set of commonalities across the practical guides and a set of recommendations for future development of practitioner guides and future research into e-government initiatives.


1 The Political Economy of New Media Revisited : Platformisation , Mediatisation , and the Politics of Algorithms


title: Adaptive decision support system for estimating financial risks

abstract: A computer based decision support system is proposed the basic tasks of which are adaptive model constructing and forecasting of financial risks. The DSS development is ::: based on the system analysis principles, i.e. the possibility for taking into consideration of some stochastic and information uncertainties, forming alternatives for models and ::: forecasts, and tracking of the computing procedures correctness during all stages of data processing. A modular architecture is implemented that provides a possibility for the further enhancement and modification of the system functional possibilities with new ::: forecasting and parameter estimation techniques. A high quality of final result is achieved thanks to appropriate tracking of the computing procedures at all stages of data processing during computational experiments: preliminary data processing, model constructing, and forecasts estimation. The tracking is performed with appropriate set of statistical quality parameters. Examples are given for estimation of financial credit. The examples solved show that the system developed has good perspectives for the practical use. It is supposed that the system will find its applications as an extra tool for decision making when developing the strategies for financial companies and enterprises of various types.


title: CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM

abstract: The representation of geometry in real-time 3D perception systems continues to be a critical research issue. Dense maps capture complete surface shape and can be augmented with semantic labels, but their high dimensionality makes them computationally costly to store and process, and unsuitable for rigorous probabilistic inference. Sparse feature-based representations avoid these problems, but capture only partial scene information and are mainly useful for localisation only. We present a new compact but dense representation of scene geometry which is conditioned on the intensity data from a single image and generated from a code consisting of a small number of parameters. We are inspired by work both on learned depth from images, and auto-encoders. Our approach is suitable for use in a keyframe-based monocular dense SLAM system: While each keyframe with a code can produce a depth map, the code can be optimised efficiently jointly with pose variables and together with the codes of overlapping keyframes to attain global consistency. Conditioning the depth map on the image allows the code to only represent aspects of the local geometry which cannot directly be predicted from the image. We explain how to learn our code representation, and demonstrate its advantageous properties in monocular SLAM.


title: LSTM-Based System-Call Language Modeling and Robust Ensemble Method for Designing Host-Based Intrusion Detection Systems

abstract: In computer security, designing a robust intrusion detection system is one of the most fundamental and important problems. In this paper, we propose a system-call language-modeling approach for designing anomaly-based host intrusion detection systems. To remedy the issue of high false-alarm rates commonly arising in conventional methods, we employ a novel ensemble method that blends multiple thresholding classifiers into a single one, making it possible to accumulate 'highly normal' sequences. The proposed system-call language model has various advantages leveraged by the fact that it can learn the semantic meaning and interactions of each system call that existing methods cannot effectively consider. Through diverse experiments on public benchmark datasets, we demonstrate the validity and effectiveness of the proposed method. Moreover, we show that our model possesses high portability, which is one of the key aspects of realizing successful intrusion detection systems.


title: The Internet technology for defect detection system with deep learning method in smart factory

abstract: The most obvious difference between the recent smart factory and the traditional automation factory is that the techniques about internet of thing (IoT) are introduced. The smart factory that employs IoT techniques intelligently manages the automated manufacturing equipment and automated defect detection equipment to improve production efficiency and quality significantly. The equipments used in the smart factory are manufacturing equipments, functional testing equipment and defect detection equipment. Defect detection equipment, mainly in the entire product manufacturing and packaging and functional testing process, the establishment of checkpoints, phased detection of semi-finished products to identify defective and defective products selected. As a result, the defective product no longer goes through all of the following processes, thus reducing costs and improving the yield of the final product shipped. In the development of classification algorithm, a huge breakthrough has been made in the deep learning algorithm in recent years. Therefore, in recent years, many studies have tried to apply deep learning algorithm to various fields. However, most of the current studies focus on the performance of the deep learning algorithm in their applications. Fewer studies research the environmental design for the system employing deep learning algorithm. For example, the network architecture for the system employing deep learning algorithm is less discussed by studies. Therefore, this study presents a architecture of smart factory which employing deep learning algorithm in defect detection system. Thereafter, this study presents the network architecture for the proposed smart factory. Finally, the internet technology for defect detection system with deep learning method in smart factory is presented.


title: Where Do Creative Interactions Come From? The Role of Tie Content and Social Networks

abstract: Understanding the determinants of creativity at the individual and organizational level has been the focus of a long history of research in various disciplines from the social sciences, but little attention has been devoted to studying creativity at the dyadic level. Why are some dyadic interactions more likely than others to trigger the generation of novel and useful ideas in organizations? As dyads conduit both knowledge and social forces, they offer an ideal setting to disentangle the effects of knowledge diversity, tie strength, and network structure on the generation of creative thoughts. This paper not only challenges the current belief that sporadic and distant dyadic relationships (weak ties) foster individual creativity but also argues that diverse and strong ties facilitate the generation of creative ideas. From a knowledge viewpoint, our results suggest that ties that transmit a wide (rather than narrow) set of knowledge domains (within the same tie) favor creative idea generation if exchanges occur with sufficient frequency. From a social perspective, we find that strong ties serve as effective catalysts for the generation of creative ideas when they link actors who are intrinsically motivated to work closely together. Finally, this paper also shows that dyadic network cohesion (i.e., the connections from the focal dyad to common contacts) does not always hinder the generation of creative ideas. Our empirical evidence suggests that when cohesion exceeds its average levels, it becomes detrimental to creative idea generation. Hypotheses are tested in a sociometric study conducted within the development department of a software firm.


title: Smart farm computing systems for animal welfare monitoring

abstract: Smart sensing and computing have become important concepts in the last few years, creating opportunities in the new sector of smart agriculture. A few commercial smart agriculture systems have been introduced to this end, and albeit closed for experimentation, are paving the way for high-tech innovations for crop and livestock agriculture. In this paper, we focus on open and low-cost concepts for smart fog (edge) computing systems to create a smart farm animal welfare monitoring system. We develop an open source system that enables networking and computing of edge devices but also processing of data in a server - all being a connected system that we refer to as smart farm computing system. We propose to use Raspberry Pis as edge devices to monitor the animals and the farm environment, and we let the edge devices communicate with a local farm controller. The proposed farm computing system conceptually creates a fog computing layer and is further connected with cloud computing systems and a mobile application. We demonstrate that a low-cost and open computing and sensing system can effectively monitor multiple parameters related to animal welfare.


title: Drawing-Based Procedural Modeling of Chinese Architectures

abstract: This paper presents a novel modeling framework to build 3D models of Chinese architectures from elevation drawing. Our algorithm integrates the capability of automatic drawing recognition with powerful procedural modeling to extract production rules from elevation drawing. First, different from the previous symbol-based floor plan recognition, based on the novel concept of repetitive pattern trees, small horizontal repetitive regions of the elevation drawing are clustered in a bottom-up manner to form architectural components with maximum repetition, which collectively serve as building blocks for 3D model generation. Second, to discover the global architectural structure and its components' interdependencies, the components are structured into a shape tree in a top-down subdivision manner and recognized hierarchically at each level of the shape tree based on Markov Random Fields (MRFs). Third, shape grammar rules can be derived to construct 3D semantic model and its possible variations with the help of a 3D component repository. The salient contribution lies in the novel integration of procedural modeling with elevation drawing, with a unique application to Chinese architectures.


title: Data Mining for the Internet of Things: Literature Review and Challenges

abstract: The massive data generated by the Internet of Things (IoT) are considered of high business value, and data mining algorithms can be applied to IoT to extract hidden information from data. In this paper, we give a systematic way to review data mining in knowledge view, technique view, and application view, including classification, clustering, association analysis, time series analysis and outlier analysis. And the latest application cases are also surveyed. As more and more devices connected to IoT, large volume of data should be analyzed, the latest algorithms should be modified to apply to big data. We reviewed these algorithms and discussed challenges and open research issues. At last a suggested big data mining system is proposed.


title: A probabilistic graphical model for learning as search

abstract: This work describes ongoing research on web information retrieval using queries which require prolonged browser interactions in exploring, revising, resubmitting, and studying results produced by the search engine. Over the last two decades or so, web search has improved significantly in a lot of areas, but has not made much progress at the difficult end of the online search spectrum — one that is precisely characterized by queries with complex information needs. Such queries, we call learning queries, require extended browser interaction by the user for satisfactory results. The main goal of the research is to address the difficulty of learning queries by lessening browser interactions through the implementation of a probabilistic graphical model. The expected outcome is an improvement in relevance, inspired by automatic discovery of collaborative search structures in conjunction with leveraging current search engine capabilities. To attain the project goal, we design and implement a browser extension that collects post-query browsing details based on browser interactions by users. The post-query browsing details are used to create a database of query-indexed trees for validating the model proposed. The work presented here describes the design and implementation of browser extensions and the project methodology.


title: A Robust Environmental Sound Recognition System using Frequency Domain Features

abstract: In ubiquitous environments, analysis and classification of sound plays a critical role in various acoustic-based recognition systems. This work aims to contribute towards building an automatic sound recognition system that can understand the surrounding environment by the audio information. In this paper, an acoustic signal based context awareness system is proposed for detecting sound events in five different real-world environment.This approach is based on Back Propagation Neural Network (BPNN) classifier using a new feature set from frequency-domain features. The experiments on various categories illustrate that the results of recognition are significant and effective.


title: Time sequence summarization to scale up chronology-dependent applications

abstract: In this paper, we present the concept of Time Sequence Summarization to support chronology-dependent applications on massive data sources. Time sequence summarization takes as input a time sequence of events that are chronologically ordered. Each event is described by a set of descriptors. Time sequence summarization produces a concise time sequence that can be substituted for the original time sequence in chronology-dependent applications. We propose an algorithm that achieves time sequence summarization based on a generalization, grouping and concept formation process. Generalization expresses event descriptors at higher levels of abstraction using taxonomies while grouping gathers similar events. Concept formation is responsible for reducing the size of the input time sequence of events by representing each group created by one concept. The process is performed in a way such that the overall chronology of events is preserved. The algorithm computes the summary incrementally and has reduced algorithmic complexity. The resulting output is a concise representation, yet, informative enough to directly support chronology-dependent applications. We validate our approach by summarizing one year of financial news provided by Reuters.


title: Grounding New Words on the Physical World in Multi-Domain Human-Robot Dialogues

abstract: This paper summarizes our ongoing project on developing an architecture for a robot that can acquire new words and their meanings while engaging in multidomain dialogues. These two functions are crucial in making conversational service robots work in real tasks in the real world. Household robots and office robots need to be able to work in multiple task domains and they also need to engage in dialogues in multiple domains corresponding to those task domains. Lexical acquisition is necessary because speech understanding cannot be done without enough knowledge on words that are possibly spoken in the task domain. Our architecture is based on a multi-expert model in which multiple domain experts are employed and one of them is selected based on the user utterance and the situation to engage in the control of the dialogue and physical behaviors. We incorporate experts that have an ability to acquire new lexical entries and their meanings grounded on the physical world through spoken interactions. By appropriately selecting those experts, lexical acquisition in multi-domain dialogues becomes possible. An example robotic system based on this architecture that can acquire object names and location names demonstrates the viability of the architecture.


title: Theory-oriented software engineering

abstract: Abstract There has been a growing interest in the role of theory within Software Engineering (SE) research. For several decades, researchers within the SE research community have argued that, to become a ‘real’ engineering science, SE needs to develop stronger theoretical foundations. However, so far, the role of theory is neither fully appreciated nor well understood in SE research. Without a good common understanding of what theory is, what it constitutes in SE research, and the various roles it can play in SE research, it is difficult to appreciate how theory building can help to strengthen SE research. In this paper we discuss the importance of theory and conceptualization, and review the key components that comprise a theory. We then present the Research Path Schema (RPS), which is an adaptation of an analytical framework from the social sciences. The RPS defines a research study as consisting of three components: some phenomenon, system or substance that a researcher is interested in; some technique or method to study that substance; and some form of conceptualization or theory that provides an explanation for, or abstraction of the observations made in a study. Different research studies have a different archetypical ‘architecture,’ depending on the selection of these three components. Consequently, the role of the conceptualization or theory will be different for each archetypical study design, or selected research path. We conclude this paper by outlining a number of implications for future SE research, and argue for a Theory-Oriented Software Engineering research perspective, which can complement the recent focus on Evidence Based Software Engineering.


title: Functional imaging of ‘theory of mind

abstract: Our ability to explain and predict other people’s behaviour by attributing to them independent mental states, such as beliefs and desires, is known as having a ‘theory of mind’. Interest in this very human ability has engendered a growing body of evidence concerning its evolution and development and the biological basis of the mechanisms underpinning it. Functional imaging has played a key role in seeking to isolate brain regions specific to this ability. Three areas are consistently activated in association with theory of mind. These are the anterior paracingulate cortex, the superior temporal sulci and the temporal poles bilaterally. This review discusses the functional significance of each of these areas within a social cognitive network. One aspect of social cognition sets us apart from other primates. It underpins our ability to deceive, cooperate and empathize, and to read others’ body language. It also enables us to accurately anticipate other people’s behaviour, almost as if we had read their minds. This exceptional capacity is known as having a ‘theory of mind’, or mentalizing. It underlies our ability to explain and predict the behaviour of ourselves and others by attributing to them independent mental states, such as beliefs, desires, emotions or intentions. Theory of mind is an automatic, highlevel and – almost without exception – human function. In these circumstances we can have no data from animal studies directly relevant to the brain systems underlying this ability. Furthermore, neuropsychology data, although identifying involvement of brain regions such as the frontal lobes and amygdala, are limited by their ability to pinpoint functions to specific structures. Thus, in recent years, functional imaging has become one of the more powerful methods for studying the neural correlates of this important human ability. Theory of mind mechanisms


title: Fatal Cutaneous γ/δ T-Cell Lymphoma with Central Nerve System Metastasis

abstract: Cutaneous γ/δ T-cell lymphoma is an extremely rare and highly aggressive tumor that is often resistant to treatment, such as polychemotherapy and radiotherapy. Due to the complexity of clinical, pathologic, and immunohistochemical features of this disease entity, the physician should perform a careful evaluation; however, treatment should be rapid and aggressive. We present a case of fatal cutaneous γ/δ T-cell lymphoma of a 55-year old woman who died after recurrence with central nerve system metastasis.


title: Design and evaluation of a parallel-series elastic actuator for lower limb exoskeletons

abstract: This paper presented a novel compliant actuator used for lower limb exoskeletons. The compliant joint consists of a series elastic actuator (SEA) and parallel elastic (PE) unit. SEA has various advantages as the actuator of assistive exoskeletons, such as low output impedance, impact absorption, precise force control and high stability. We designed and fabricated a novel SEA as the primary joint actuator which is compact, adjustable and low-cost. Meanwhile an additional elastic unit is installed in parallel with the SEA to improve energy utilization by storing and releasing energy during motion cycles. An adaptive stable controller is designed to realize the joint following motion to a virtual limb. The algorithm can identify and compensate the undetermined contact stiffness between the joint output and the virtual limb. Finally, the performance of the actuator is evaluated through motion tracking and energy-conservation experiments. Preliminary results indicate the validity of the design and imply its potential usage in lower limb exoskeletons.


title: Studying Complex Adaptive Systems

abstract: Complex adaptive systems (cas) – systems that involve many components that adapt or learn as they interact – are at the heart of important contemporary problems. The study of cas poses unique challenges: Some of our most powerful mathematical tools, particularly methods involving fixed points, attractors, and the like, are of limited help in understanding the development of cas. This paper suggests ways to modify research methods and tools, with an emphasis on the role of computer-based models, to increase our understanding of cas.


title: A quantitative study of virtual machine live migration

abstract: Virtual machine (VM) live migration is a critical feature for managing virtualized environments, enabling dynamic load balancing, consolidation for power management, preparation for planned maintenance, and other management features. However, not all virtual machine live migration is created equal. Variants include memory migration, which relies on shared backend storage between the source and destination of the migration, and storage migration, which migrates storage state as well as memory state. We have developed an automated testing framework that measures important performance characteristics of live migration, including total migration time, the time a VM is unresponsive during migration, and the amount of data transferred over the network during migration. We apply this testing framework and present the results of studying live migration, both memory migration and storage migration, in various virtualization systems including KVM, XenServer, VMware, and Hyper-V. The results provide important data to guide the migration decisions of both system administrators and autonomic cloud management systems.


title: Real-time scheduling in LTE for smart grids

abstract: The latest wireless network, 3GPP Long Term Evolution (LTE), is considered to be a promising solution for smart grids because it provides both low latency and large bandwidth. However, LTE was not originally intended for smart grids applications, where data generated by the grid have specific delay requirements that are different from traditional data or voice communications. In this paper, the specific requirements imposed by a smart grids on the LTE communication infrastructure is first determined. The latency offered by the LTE network to smart grids components is investigated and an empirical mathematical model of the distribution of the latency is established. It is shown by experimental results that with the current LTE up-link scheduler, smart grid latency requirements are not always satisfied and that only a limited number of components can be accommodated. To overcome such a deficiency, a new scheduler of the LTE medium access control is proposed for smart grids. The scheduler is based on a mathematical linear optimization problem that considers simultaneously both the smart grid components and common user equipments. An algorithm for the solution to such a problem is derived based on a theoretical analysis. Simulation results based on this new scheduler illustrate the analysis. It is concluded that LTE can be effectively used in smart grids if new schedulers are employed for improving latency.


title: Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation

abstract: Credit-risk evaluation is a very challenging and important management science problem in the domain of financial analysis. Many classification methods have been suggested in the literature to tackle this problem. Neural networks, especially, have received a lot of attention because of their universal approximation property. However, a major drawback associated with the use of neural networks for decision making is their lack of explanation capability. While they can achieve a high predictive accuracy rate, the reasoning behind how they reach their decisions is not readily available. In this paper, we present the results from analysing three real-life credit-risk data sets using neural network rule extraction techniques. Clarifying the neural network decisions by explanatory rules that capture the learned knowledge embedded in the networks can help the credit-risk manager in explaining why a particular applicant is classified as either bad or good. Furthermore, we also discuss how these rules can be visualized as a decision table in a compact and intuitive graphical format that facilitates easy consultation. It is concluded that neural network rule extraction and decision tables are powerful management tools that allow us to build advanced and userfriendly decision-support systems for credit-risk evaluation.


title: MedleyDB: A Multitrack Dataset for Annotation-Intensive MIR Research

abstract: We introduceMedleyDB: a dataset of annotated, royaltyfree multitrack recordings. The dataset was primarily developed to support research on melody extraction, addressing important shortcomings of existing collections. For each song we provide melody f0 annotations as well as instrument activations for evaluating automatic instrument recognition. The dataset is also useful for research on tasks that require access to the individual tracks of a song such as source separation and automatic mixing. In this paper we provide a detailed description of MedleyDB, including curation, annotation, and musical content. To gain insight into the new challenges presented by the dataset, we run a set of experiments using a state-of-the-art melody extraction algorithm and discuss the results. The dataset is shown to be considerably more challenging than the current test sets used in the MIREX evaluation campaign, thus opening new research avenues in melody extraction research.


title: Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples

abstract: Image compression-based approaches for defending against the adversarial-example attacks, which threaten the safety use of deep neural networks (DNN), have been investigated recently. However, prior works mainly rely on directly tuning parameters like compression rate, to blindly reduce image features, thereby lacking guarantee on both defense efficiency (i.e. accuracy of polluted images) and classification accuracy of benign images, after applying defense methods. To overcome these limitations, we propose a JPEG-based defensive compression framework, namely “feature distillation”, to effectively rectify adversarial examples without impacting classification accuracy on benign data. Our framework significantly escalates the defense efficiency with marginal accuracy reduction using a twostep method: First, we maximize malicious features filtering of adversarial input perturbations by developing defensive quantization in frequency domain of JPEG compression or decompression, guided by a semi-analytical method; Second, we suppress the distortions of benign features to restore classification accuracy through a DNN-oriented quantization refine process. Our experimental results show that proposed “feature distillation” can significantly surpass the latest input-transformation based mitigations such as Quilting and TV Minimization in three aspects, including defense efficiency (improve classification accuracy from ∼ 20% to ∼ 90% on adversarial examples), accuracy of benign images after defense (≤ 1% accuracy degradation), and processing time per image (∼ 259× Speedup). Moreover, our solution also can provide the best defense efficiency (∼ 60% accuracy) against the latest BPDA attack with least accuracy reduction (∼ 1%) on benign images among all other input-transformation based defense methods.


title: Semantic Adversarial Deep Learning

abstract: Fueled by massive amounts of data, models produced by machine-learning (ML) algorithms, especially deep neural networks, are being used in diverse domains where trustworthiness is a concern, including automotive systems, finance, health care, natural language processing, and malware detection. Of particular concern is the use of ML algorithms in cyber-physical systems (CPS), such as self-driving cars and aviation, where an adversary can cause serious consequences. However, existing approaches to generating adversarial examples and devising robust ML algorithms mostly ignore the semantics and context of the overall system containing the ML component. For example, in an autonomous vehicle using deep learning for perception, not every adversarial example for the neural network might lead to a harmful consequence. Moreover, one may want to prioritize the search for adversarial examples towards those that significantly modify the desired semantics of the overall system. Along the same lines, existing algorithms for constructing robust ML algorithms ignore the specification of the overall system. In this paper, we argue that the semantics and specification of the overall system has a crucial role to play in this line of research. We present preliminary research results that support this claim.


title: The Semantics of Prosody: Acoustic and Perceptual Evidence of Prosodic Correlates to Word Meaning

abstract: This investigation examined whether speakers produce reliable prosodic correlates to meaning across semantic domains and whether listeners use these cues to derive word meaning from novel words. Speakers were asked to produce phrases in infant-directed speech in which novel words were used to convey one of two meanings from a set of antonym pairs (e.g., big/small). Acoustic analyses revealed that some acoustic features were correlated with overall valence of the meaning. However, each word meaning also displayed a unique acoustic signature, and semantically related meanings elicited similar acoustic profiles. In two perceptual tests, listeners either attempted to identify the novel words with a matching meaning dimension (picture pair) or with mismatched meaning dimensions. Listeners inferred the meaning of the novel words significantly more often when prosody matched the word meaning choices than when prosody mismatched. These findings suggest that speech contains reliable prosodic markers to word meaning and that listeners use these prosodic cues to differentiate meanings. That prosody is semantic suggests a reconceptualization of traditional distinctions between linguistic and nonlinguistic properties of spoken language.


title: A toolkit for ring-LWE cryptography

abstract: Recent advances in lattice cryptography, mainly stemming from the development of ring-based primitives such as ring-LWE, have made it possible to design cryptographic schemes whose efficiency is competitive with that of more traditional number-theoretic ones, along with entirely new applications like fully homomorphic encryption. Unfortunately, realizing the full potential of ring-based cryptography has so far been hindered by a lack of practical algorithms and analytical tools for working in this context. As a result, most previous works have focused on very special classes of rings such as power-of-two cyclotomics, which significantly restricts the possible applications.


title: Modeling Insurance Fraud Detection Using Imbalanced Data Classification

abstract: This paper proposes an innovative insurance fraud detection method to deal with the imbalanced data distribution. The idea is based on building insurance fraud detection models using Decision tree (DT), Support vector machine (SVM) and Artificial Neural Network (ANN), on data partitions derived from under-sampling (with-replacement and without-replacement) of the majority class and merging it with the minority class. Throughout the paper, ten-fold cross validation method of testing is used. Its originality lies in the use of several partitioning under-sampling approaches and choosing the best. Results from a publicly available automobile insurance fraud detection data set demonstrate that DT performs slightly better than other algorithms, so DT model was used to compare between different partitioning-under-sampling approaches. Empirical results illustrate that the proposed model gave better results.


Electrospinning of Nanofibers for Energy Applications


title: An Estimation of Rotation and Translation in Image Separation Problem

abstract: The image separation problem is considered, where observed images are weighted superpositions of translations and rotations of original images. An algorithm to estimate the number of original images, relative rotation angles, and relative translation parameters for two observed images is proposed. Numerical experiments demonstrate the usefulness of the proposed algorithm.


title: An Analysis of Model-Based Interval Estimation for Markov Decision Processes

abstract: Several algorithms for learning near-optimal policies in Markov Decision Processes have been analyzed and proven efficient. Empirical results have suggested that Model-based Interval Estimation (MBIE) learns efficiently in practice, effectively balancing exploration and exploitation. This paper presents a theoretical analysis of MBIE and a new variation called MBIE-EB, proving their efficiency even under worst-case conditions. The paper also introduces a new performance metric, average loss, and relates it to its less ''online'' cousins from the literature.


title: Development of an Active dv/dt Control Algorithm for Reducing Inverter Conducted EMI with Minimal Impact on Switching Losses

abstract: This work investigates the use of an active gate control circuit to reduce the EMI produced by switching power converters. The active gate drive circuit makes it possible to adjust the voltage transition rate of a MOS-gated semiconductor switch on a pulse-by-pulse basis during PWM operation. This paper shows that tailored application of this circuit in a hard- switched inverter can be used to reduce the conducted common- mode EMI generated by the inverter while minimizing the incremental increase of the switching losses produced as a result of this control. An active dv/dt control algorithm that has been developed for a three-phase inverter to achieve this EMI reduction has been implemented for testing. Experimental results show that EMI can be reduced using this active dv/dt control algorithm with lower total switching losses than a scheme that achieves a similar EMI reduction using adjustment of the gate drive resistors alone.


title: Preparation of Fluorine Doped Tin Oxide Film by Ultrasonic Spray Pyrolysis

abstract: Fluorine doped tin oxide (FTO) film is one of the most promising transparent conducting materials. It can be used for Low-E glasses, thin film solar cells, displays, etc. FTO film can be fabricated by various techniques. The technique of sol-gel combined with ultrasonic spray pyrolysis gives the possibility to produce high-quality large-scale FTO films. In this paper, the FTO sol is successfully prepared using pentahydrate stannic chloride (SnCl4•5H2O), hydrogen fluoride (HF) and methanol. Using the FTO sol, FTO films are prepared by ultrasonic spray pyrolysis technique. X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and atomic force microscopy (AFM) are used to characterize the FTO films coated on glass substrates. Results indicate that the as-deposited films are polycrystalline SnO2 phase with tetragonal crystal structure, and that the average grain size for the samples is 160nm. The optical and electrical properties of the FTO film are also analyzed.


title: Myocardial infarction detection and classification — A new multi-scale deep feature learning approach

abstract: This paper presents an efficient detection and classification algorithm of multiple-class myocardial infarction (MI) (i.e., prior and acute), which is one of mortality diseases for humans. However, feature extraction is one of the challenges in MI classification as the extracted features may not be optimized for class separation. To this end, we propose a new deep feature learning based MI detection and classification approach. It seeks to learn a representation of the extracted features that optimize the classification performance. Moreover, to further enhance the feature learning process, we incorporate multi-scale discrete wavelet transformation into the feature learning process to facilitate the extraction of MI features at specific frequency resolutions/scales. Finally, softmax regression is employed to build a multi-class classifier based on the learned optimal representation of the features. Experimental results using public ECG datasets obtained from the PTB diagnostic database show that the proposed approach can achieve better performance than other state-of-the-art approaches in terms of sensitivity and specificity. The effectiveness and good performance of the proposed approach may serve as an attractive alternative to MI classification or other related applications.


title: Energy harvesting for IoT sensors utilizing MEMS technology

abstract: The growing need for alternative sources to power sensors and portable devices has led to many power-scavenging solutions from ambient energy sources. As the technology scales down and the supply voltage drops, the complexity of power scavenging circuits increases. This paper presents a new transducer circuit utilizing MEMS technology together with a super-diode to convert ambient vibration energy to usable electrical energy efficiently. An integrated MEMS actuator containing a switch is used to eliminate the loss of efficiency due to the threshold voltage of diodes commonly used to implement electrostatic transducers. A super-diode has also been used to eliminate the need for synchronization while increasing the overall efficiency. Simulation results using Cadence design tools indicate an increased efficiency by more than 27% compared to the conventional electrostatic transducer.


title: Multi-tenancy performance benchmark for web application platforms

abstract: Cloud environments reduce data center operating costs through resource sharing and economies of scale. Infrastructure-as-a-Service is one example that leverages virtualization to share infrastructure resources. However, virtualization is often insufficient to provide Software-as-a-Service applications due to the need to replicate the operating system, middleware and application components for each customer. To overcome this problem, multi-tenancy has emerged as an architectural style that allows to share a single Web application instance among multiple independent customers, thereby significantly improving the efficiency of Software-as-a-Service offerings. A number of platforms are available today that support the development and hosting of multi-tenant applications by encapsulating multi-tenancy specific functionality. Although a lack of performance guarantees is one of the major obstacles to the adoption of cloud computing, in general, and multi-tenant applications, in particular, these kinds of applications and platforms have so far not been in the focus of the performance and benchmarking community. In this paper, we present an extended version of an existing and widely accepted application benchmark adding support for multi-tenant platform features. The benchmark is focused on evaluating the maximum throughput and the amount of tenants that can be served by a platform. We present a case study comparing virtualization and multi-tenancy. The results demonstrate the practical usability of the proposed benchmark in evaluating multi-tenant platforms and gives insights that help to decide for one sharing approach.


Learning Word Vectors in Deep Walk using Convolution.


title: Parts supply management system for automobile parts manufacturing companies

abstract: This paper proposes an efficient and effective BOM management scheme for small and medium manufacturing (SME) companies that produce automobile parts. Automobile part’s manufacturers produce very various products due to various automobile characteristics such as types, colors, and options of cars. These products are classified into product groups with common pars and like this product groups with commonality are regarded as product family. This paper proposes a BOM data model for product family structures and implements the practical EXCEL‐based BOM management system for real small and medium manufacturing (SME) companies that produces automobile parts.


title: Cooperative load balancing for hierarchical SDN controllers

abstract: Software-defined networking (SDN) has been proposed as a new networking paradigm which separates the control and data forwarding planes. The controllers deployed as centralized network control entities are crucial for meeting the performance and efficiency requirements of these systems. The placement of these network entities, switch assignment and their load-aware operation are challenging research questions. Although various load-balancing schemes are proposed in the literature, this research topic is yet to be explored adequately. In this work, we discuss load-balancing issues in SDN and propose a load-balancing scheme, namely Cooperative Load Balancing Scheme For Hierarchical SDN Controllers (COLBAS), for hierarchical controller configurations. Moreover, we evaluate the performance of our scheme and investigate important performance factors.


title: Predicting Students ’ Performance using Modified ID3 Algorithm

abstract: The ability to predict performance of students is very crucial in our present education system. We can use data mining concepts for this purpose. ID3 algorithm is one of the famous algorithms present today to generate decision trees. But this algorithm has a shortcoming that it is inclined to attributes with many values. So , this research aims to overcome this shortcoming of the algorithm by using gain ratio(instead of information gain) as well as by giving weights to each attribute at every decision making point. Several other algorithms like J48 and Naive Bayes classification algorithm are also applied on the dataset. The WEKA tool was used for the analysis of J48 and Naive Bayes algorithms. The results are compared and presented. The dataset used in our study is taken from the School of Computing Sciences and Engineering (SCSE), VIT University. Keyword- data mining, educational data mining (EDM), decision tree, gain ratio, weighted ID3 I. INTRODUCTION Educational Data Mining (EDM) is attracting a lot of researchers for developing methods from educational institutions' data that can be used in the improvement of quality of higher education. EDM uses the large amounts of information present in the educational institutes' databases about teaching-learning practices for the development of models which are beneficial for all the participants in the educational process. The prediction of students' performance in any higher institute has become one of the most important needs of that institute in order to improve the quality of the teaching process of that institution. Through this process we get to know the needs of the students and hence we can fulfil those needs to get better results. Students who need special attention from the teachers can also be identified through this process. A number of algorithms are available for predicting the performance of students. Some of them being Artificial Neural Network (ANN), Decision Trees, Clustering, Naive Bayes algorithm, Decision Trees being most commonly used. II. RELATED WORK


title: Exploring VM Introspection: Techniques and Trade-offs

abstract: While there are a variety of existing virtual machine introspection (VMI) techniques, their latency, overhead, complexity and consistency trade-offs are not clear. In this work, we address this gap by first organizing the various existing VMI techniques into a taxonomy based upon their operational principles, so that they can be put into context. Next we perform a thorough exploration of their trade-offs both qualitatively and quantitatively. We present a comprehensive set of observations and best practices for efficient, accurate and consistent VMI operation based on our experiences with these techniques. Our results show the stunning range of variations in performance, complexity and overhead with different VMI techniques.We further present a deep dive on VMI consistency aspects to understand the sources of inconsistency in observed VM state and show that, contrary to common expectation, pause-and-introspect based VMI techniques achieve very little to improve consistency despite their substantial performance impact.