title: Personalizing LinkedIn Feed

abstract: LinkedIn dynamically delivers update activities from a user's interpersonal network to more than 300 million members in the personalized feed that ranks activities according their "relevance" to the user. This paper discloses the implementation details behind this personalized feed system at LinkedIn which can not be found from related work, and addresses the scalability and data sparsity challenges for deploying the system online. More specifically, we focus on the personalization models by generating three kinds of affinity scores: Viewer-ActivityType Affinity, Viewer-Actor Affinity, and Viewer-Actor-ActivityType Affinity. Extensive experiments based on online bucket tests (A/B experiments) and offline evaluation illustrate the effect of our personalization models in LinkedIn feed.


title: Scalable, adaptive and survivable trust management for community of interest based Internet of Things systems

abstract: An Internet of Things (IoT) system connects a large amount of tags, sensors, and mobile devices to facilitate information sharing, enabling a variety of attractive applications. It challenges the design and evaluation of IoT systems to meet the scalability, compatibility, extendibility, dynamic adaptability and resiliency requirements. In this paper, we design and evaluate a scalable, adaptive and survivable trust management protocol in dynamic IoT environments. Recognizing that entities in an IoT system are connected through social networks of entity owners, we consider a community of interest (CoI) based social IoT where nodes form into communities of interest. Given inter-CoI vs. intra-CoI social connections among entity owners as input, we identify best trust protocol settings for achieving convergence, accuracy, dynamic adaptability and resiliency properties in the presence of dynamically changing conditions and malicious nodes performing trust-related attacks. For scalability, we consider a design by which a node only keeps trust information of a subset of nodes meeting its interest and performs minimum computation to update trust. We validate our design by extensive simulation considering both limited and ideal (unlimited) storage space. The results demonstrate that our trust management protocol using limited storage space achieves a similar performance level compared with the one under ideal storage space, and a newly joining node can quickly build up trust towards other nodes with desirable accuracy and convergence behavior.


title: Generating Music from Literature

abstract: We present a system, TransProse, that automatically generates musical pieces from text. TransProse uses known relations between elements of music such as tempo and scale, and the emotions they evoke. Further, it uses a novel mechanism to determine sequences of notes that capture the emotional activity in text. The work has applications in information visualization, in creating audio-visual e-books, and in developing music apps.


title: Experimenting with ARM TrustZone -- Or: How I Met Friendly Piece of Trusted Hardware

abstract: ARM TrustZone is an emerging embedded security technology based on the split of a system into a secure and normal world. Currently a number of System-on-Chip platforms exist, which include support for ARM TrustZone. Unfortunately relatively little work has been done on open-source software for TrustZone systems. In our belief this is partly caused by the lack of cost-efficient development boards, which allow access to ARM TrustZone features. This paper discusses experiences made by the authors with a rather inexpensive development board and shows how system-level development on TrustZone-enabled hardware is possible in class-room settings.


title: Ankle-Foot-Orthosis Control in Inclinations and Stairs

abstract: A control procedure is proposed for an ankle-foot-orthosis (AFO) for different gait situations, such as inclinations and stairs. This paper presents a novel AFO control of the ankle angle. A magneto-rheological damper was used to achieve ankle damping during foot down and locking at swing, thereby avoiding foot slap as well as foot drop. The controller used feedback from the ankle angle only. Still it was capable of not only adjusting damping within a gait step but also changing control behavior depending on level walking, ascending and descending stairs. As a consequence, toe strike was possible in stair gait as opposed to heel strike in level walking. Tests verified the expected behavior in stair gait and in level walking where gait speed and ground inclinations varied. The self-adjusted AFO is believed to improve gait comfort in slopes and stairs.


title: AdaSum: an adaptive model for summarization

abstract: Topic representation mismatch is a key problem in topic-oriented summarization for the specified topic is usually too short to understand/interpret. This paper proposes a novel adaptive model for summarization, AdaSum, under the assumption that the summary and the topic representation can be mutually boosted. AdaSum aims to simultaneously optimize the topic representation and extract effective summaries. This model employs a mutual boosting process to minimize the topic representation mismatch for base summarizers. Furthermore, a linear combination of base summarizers is proposed to further reduce the topic representation mismatch from the diversity of base summarizers with a general learning framework. We prove that the training process of AdaSum can enhance the performance measure used. Experimental results on DUC 2007 dataset show that AdaSum significantly outperforms the baseline methods for summarization (e.g. MRP, LexRank, and GSPS).


title: Automatic lung field segmentation using novel feature extraction and unsupervised learning

abstract: Obtaining accurate and automated lung field segmentation is a challenging step in the development of Computer-Aided Diagnosis (CAD) system. In this paper, fully automatic lung field segmentation is proposed. Initially, novel features are extracted by considering spatial interaction of the neighbouring pixels. Then constrained non-negative matrix factorization (CNMF) factorized the data matrix obtained from the visual appearance model into basis and coefficient matrices. Initial lung segmentation is achieved by applying unsupervised learning on the coefficient matrix. 2-D region growing operation removes trachea and bronchi appearing in the initial lung segmentation. The experimental results on different database shows that the proposed method produces significant DSC 0.973 as compared to the existing lung segmentation algorithms.


title: Two-Phase Low-Energy N-Modular Redundancy for Hard Real-Time Multi-Core Systems

abstract: This paper proposes an N-modular redundancy (NMR) technique with low energy-overhead for hard real-time multi-core systems. NMR is well-suited for multi-core platforms as they provide multiple processing units and low-overhead communication for voting. However, it can impose considerable energy overhead and hence its energy overhead must be controlled, which is the primary consideration of this paper. For this purpose the system operation can be divided into two phases: indispensable phase and on-demand phase. In the indispensable phase only half-plus-one copies for each task are executed. When no fault occurs during this phase, the results must be identical and hence the remaining copies are not required. Otherwise, the remaining copies must be executed in the on-demand phase to perform a complete majority voting. In this paper, for such a two-phase NMR, an energy-management technique is developed where two new concepts have been considered: i ) Block-partitioned scheduling that enables parallel task execution during on-demand phase, thereby leaving more slack for energy saving, ii ) Pseudo-dynamic slack, that results when a task has no faulty execution during the indispensable phase and hence the time which is reserved for its copies in the on-demand phase is reclaimed for energy saving. The energy-management technique has an off-line part that manages static and pseudo-dynamic slacks at design time and an online part that mainly manages dynamic slacks at run-time. Experimental results show that the proposed NMR technique provides up to 29 percent energy saving and is 6 orders of magnitude higher reliable as compared to a recent previous work.


title: An Integrated Architecture for Demand Response Communications and Control

abstract: In the competitive electricity structure, demand response programs enable customers to react dynamically to changes in electricity prices. The implementation of such programs may reduce energy costs and increase reliability. To fully harness such benefits, existing load controllers and appliances need around-the-clock price information. Advances in the development and deployment of advanced meter infrastructures (AMIs), building automation systems (BASs), and various dedicated embedded control systems provide the capability to effectively address this requirement. In this paper we introduce a meter gateway architecture (MGA) to serve as a foundation for integrated control of loads by energy aggregators, facility hubs, and intelligent appliances. We discuss the requirements that motivate the architecture, describe its design, and illustrate its application to a small system with an intelligent appliance and a legacy appliance using a prototype implementation of an intelligent hub for the MGA and ZigBee wireless communications.


title: Sensor enabled wearable RFID technology for mitigating the risk of falls near beds

abstract: The increasing ageing population around the world and the increased risk of falling among this demographic, challenges society and technology to find better ways to mitigate the occurrence of such costly and detrimental events as falls. The most common activity associated with falls is bed transfers; therefore, the most significant high risk activity. Several technological solutions exist for bed exiting detection using a variety of sensors which are attached to the body, bed or floor. However, lack of real life performance studies, technical limitations and acceptability are still key issues. In this research, we present and evaluate a novel method for mitigating the high falls risk associated with bed exits based on using an inexpensive, privacy preserving and passive sensor enabled RFID device. Our approach is based on a classification system built upon conditional random fields that requires no preprocessing of sensorial and RF metrics data extracted from an RFID platform. We evaluated our classification algorithm and the wearability of our sensor using elderly volunteers (66-86 y.o.). The results demonstrate the validity of our approach and the performance is an improvement on previous bed exit classification studies. The participants of the study also overwhelmingly agreed that the sensor was indeed wearable and presented no problems.


title: RGB-D Scene Classification via Multi-modal Feature Learning

abstract: Most of the past deep learning methods which are proposed for RGB-D scene classification use global information and directly consider all pixels in the whole image for high-level tasks. Such methods cannot hold much information about local feature distributions, and simply concatenate RGB and depth features without exploring the correlation and complementarity between raw RGB and depth images. From the human vision perspective, we recognize the category of one unknown scene mainly relying on the object-level information in the scene which includes the appearance, texture, shape, and depth. The structural distribution of different objects is also taken into consideration. Based on this observation, constructing mid-level representations with discriminative object parts would generally be more attractive for scene analysis. In this paper, we propose a new Convolutional Neural Networks (CNNs)-based local multi-modal feature learning framework (LM-CNN) for RGB-D scene classification. This method can effectively capture much of the local structure from the RGB-D scene images and automatically learn a fusion strategy for the object-level recognition step instead of simply training a classifier on top of features extracted from both modalities. The experimental results on two popular datasets, i.e., NYU v1 depth dataset and SUN RGB-D dataset, show that our method with local multi-modal CNNs outperforms state-of-the-art methods.


title: Pleiotropic effects of the ea7 photoperiod response gene on the morphology and agronomic traits in barley

abstract: The photoperiod-insensitive barley mutant ‘Atsel’, carrying the recessive gene ea7, was studied together with the donor variety ‘Atlas’ (wild-type, Ea7) under different daylengths with the aim of analysing pleiotropic effects. Grown under long and short photoperiods ‘Atsel’ flowered about 10 days and 34 days, respectively, earlier than ‘Atlas’. The significantly shorter life-cycle of the photoperiod-insensitive mutant resulted in several changes of plant morphology. Tillering, plant height, number of leaves and number of internodes were reduced. A lower number of florets per main spike was observed for ‘Atsel’, but only in the long photoperiod experiment. Finally, photoperiod insensitivity combined with a lower grain yield per plant was most pronounced under long-day treatment. The data are comparable with results obtained from single chromosome recombinant lines of wheat that have differences in their photoperiod response caused by the genes Ppd1 or Ppd2.


title: Modular multilevel converter for large-scale multistring photovoltaic energy conversion system

abstract: This paper presents a new grid connected photovoltaic energy conversion system configuration for large scale power plants. The grid-tied converter is based on a modular multilevel converter using voltage source H-bridge cells. The proposed converter is capable of concentrating a multimegawatt PV plant with distributed string MPPT tracking capability, high power quality and increased efficiency compared to the classic two-level voltage source converters. The main challenge is to handle the inherent power unbalances which may occur, not only between the different cells of one phase of the converter, but also between the three phases. The control strategy to deal with these unbalances is analyzed in this paper. Simulation results for a downsized 7 level MMC composed of 18 H-bridge cells and PV strings are presented to validate the proposed topology and control method.


title: Low cross-polarization ultra-wideband arrays (student submission)

abstract: This work presents the development of a low cross-polarization ultra-wideband end-fire notch array element, termed the Sliced Notch Antenna (SNA). The proposed element topology solves the long-standing problem of polarization purity degradation in typical end-fire notch arrays (Vivaldi arrays) when scanned in the non-principal planes by re-configuring the high-profile continuous metal Vivaldi fin into disconnected and vertically stacked metal strips. Single-polarized PCB-based SNA arrays and their Vivaldi array counterparts are designed, fabricated, and measured to comparitively evaluate performance. Dual-polarized implementations of both arrays are alike in that they operate over a 10:1 (1.2–12 GHz) bandwidth with VSWR<2.2 out to θo = 60° scans as infinite arrays; however simulations suggest the proposed SNA array offers around 20 dB polarization isolation improvement near the highest frequency for D-plane scans out to θo = {45, 60}°. The technical approach will be expanded further in the presentation and complete measurement results of 19×19 array prototypes will be provided at the conference.


title: A public transportation journey planner enabled by IoT data analytics

abstract: Using the CityPulse framework we developed a proof of concept application for Brasov public transportation company. The application provides route recommendations and incident notifications for the citizens who travel by bus. This is achieved by processing in real-time the data streams about bus arrivals in stations and the incidents reported by citizens. The proof of concept application was presented to the representatives of Brasov municipality and public transport company.


title: Metaheuristics in combinatorial optimization: Overview and conceptual comparison

abstract: The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behavior of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.


title: Wireless Power transmission for solar input

abstract: Wireless Power transmission is the transmission of electrical power from the source to load without the use of wires. Wireless transfer of electrical energy eliminates the use of copper cables and conducting wires. Previously wireless transfer of power has been achieved for AC source or for charged batteries. In this project we have used output from the solar panel as the output. Since the output from the solar panel is very small it has been stepped up to appropriate values using the DC-DC step up converter. The stepped up DC is then converted into an oscillating signal. This signal is then transferred wirelessly using a transformer. The electrical energy is transferred using the magnetic coupling that exists between the transformer setup. The oscillating signal is the passed through a rectifier circuit and converted into DC before it is fed into the load. The proposed wireless transmission is verified using MATLAB/SIMULINK.


title: Linear predictive hidden Markov models and the speech signal

abstract: A method for modelling time series is presented and then applied to the analysis of the speech signal. A time series is represented as a sample sequence generated by a finite state hidden Markov model with output densities parameterized by linear prediction polynomials and error variances. These objects are defined and their properties developed. The theory culminates in a theorem that provides a computationally efficient iterative scheme to improve the model. The theorem has been used to create models from speech signals of considerable length. One such model is examined with emphasis on the relationship between states of the model and traditional classes of speech events. A use of the method is illustrated by an application to the talker verification problem.


title: Interpretation of Mammogram and Chest X-Ray Reports Using Deep Neural Networks - Preliminary Results

abstract: Radiology reports are an important means of communication between radiologists and other physicians. These reports express a radiologist's interpretation of a medical imaging examination and are critical in establishing a diagnosis and formulating a treatment plan. In this paper, we propose a Bi-directional convolutional neural network (Bi-CNN) model for the interpretation and classification of mammograms based on breast density and chest radiographic radiology reports based on the basis of chest pathology. The proposed approach helps to organize databases of radiology reports, retrieve them expeditiously, and evaluate the radiology report that could be used in an auditing system to decrease incorrect diagnoses. Our study revealed that the proposed Bi-CNN outperforms the random forest and the support vector machine methods.


title: PinMe: Tracking a Smartphone User around the World

abstract: With the pervasive use of smartphones that sense, collect, and process valuable information about the environment, ensuring location privacy has become one of the most important concerns in the modern age. A few recent research studies discuss the feasibility of processing sensory data gathered by a smartphone to locate the phone’s owner, even when the user does not intend to share his location information, e.g., when the user has turned off the Global Positioning System (GPS) on the device. Previous research efforts rely on at least one of the two following fundamental requirements, which impose significant limitations on the adversary: (i) the attacker must accurately know either the user’s initial location or the set of routes through which the user travels and/or (ii) the attacker must measure a set of features, e.g., device acceleration, for different potential routes in advance and construct a training dataset. In this paper, we demonstrate that neither of the above-mentioned requirements is essential for compromising the user’s location privacy. We describe PinMe, a novel user-location mechanism that exploits non-sensory/sensory data stored on the smartphone, e.g., the environment’s air pressure and device’s timezone, along with publicly-available auxiliary information, e.g., elevation maps, to estimate the user’s location when all location services, e.g., GPS, are turned off. Unlike previously-proposed attacks, PinMe neither requires any prior knowledge about the user nor a training dataset on specific routes. We demonstrate that PinMe can accurately estimate the user’s location during four activities (walking, traveling on a train, driving, and traveling on a plane). We also suggest several defenses against the proposed attack.


IEEE 802.15.4g Based Wi-SUN Communication Systems


title: Government innovation through social media

abstract: Abstract Social media in government is becoming one of the major trends in Electronic Government (e-government) research and practice worldwide. During the last few years, several authors have addressed the potential of social media for the innovation of public sector organizations. Most of these contributions have focused on the technical dimensions of social media, while other aspects have not attracted equal attention. In contrast, this introductory article interrogates the role of social media in the basic areas of e-government: government information flows and the availability of government information; the use of information technology to create and provide innovative government services; the impact of information technology on the relationships between the governed and those governing; and the increasing importance of information policies and information technologies for democratic practices. Accordingly, the next few pages propose and develop three dimensions of social media in government: tools, goals, and topics. We think that these dimensions could help to better understand the use of social media in government settings. Then, after a brief review of current trends in social media and government research, we present the articles included in this special issue. Finally, we present some practical lessons and suggest ideas for future research. This special issue could be seen as a starting point for the development of innovation through social media in public administrations around the world.


title: Random Forest Classification for Detecting Android Malware

abstract: Internet connected smartphone devices play a crucial role in the application domain of Internet of Things. These devices are being widely used for day-to-day activities such as remotely controlling lighting and heating at homes, paying for parking, and recently for paying for goods using saved credit card information using Near Field Communication (NFC). Android is the most popular smartphone platform today. It is also the choice of malware authors to obtain secure and private data. In this paper we exclusively apply the machine learning ensemble learning algorithm Random Forest supervised classifier on an Android feature dataset of 48919 points of 42 features each. Our goal was to measure the accuracy of Random Forest in classifying Android application behavior to classify applications as malicious or benign. Moreover, we wanted to focus on detection accuracy as the free parameters of the Random Forest algorithm such as the number of trees, depth of each tree and number of random features selected are varied. Our experimental results based on 5-fold cross validation of our dataset shows that Random Forest performs very well with an accuracy of over 99 percent in general, an optimal Out-Of-Bag (OOB) error rate [3] of 0.0002 for forests with 40 trees or more, and a root mean squared error of 0.0171 for 160 trees.


title: High speed obstacle avoidance using monocular vision and reinforcement learning

abstract: We consider the task of driving a remote control car at high speeds through unstructured outdoor environments. We present an approach in which supervised learning is first used to estimate depths from single monocular images. The learning algorithm can be trained either on real camera images labeled with ground-truth distances to the closest obstacles, or on a training set consisting of synthetic graphics images. The resulting algorithm is able to learn monocular vision cues that accurately estimate the relative depths of obstacles in a scene. Reinforcement learning/policy search is then applied within a simulator that renders synthetic scenes. This learns a control policy that selects a steering direction as a function of the vision system's output. We present results evaluating the predictive ability of the algorithm both on held out test data, and in actual autonomous driving experiments.


title: Developing a Multi-Device Immersive TV-Experience: User Expectations and Practices

abstract: This paper discusses the results of a closed lab pilot test featuring a documentary on a football school in Porto. The documentary was displayed as regular directed TV-content on an HD TV-screen and participants could use a Samsung Gear VR HMD and a tablet to watch it in 360°. 27 participants participated in the test and testing took place with individual viewers and in teams of 2. The aim of the lab test was to investigate how people would make use of this multi-device set-up (which devices do they use, length of the interaction, do they stand or sit down) and whether they consider this a social experience. Also, the influence of the 360° experience on the storyline was questioned. Each session was observed and afterwards an interview took place to discuss the experience. Respondents were rather enthusiastic about this experience. Even though the HMD tends to isolate people, it was still considered a social experience and people talked about the content and the experience. A main limitation of the set-up was the difference in audiovisual quality between the content on TV and the content on the VR and the tablet, which was not in HD.


title: Identifying and Utilising a Farmer Typology for Targeted Practice Change Programs: A Case Study of Changing Water Supply in the Wimmera Mallee

abstract: Farmer typology research has become a popular tool for designing farm extension programs and targeting key messages to particular farmer groups or segments. This approach was used to assist in a practice change program to encourage farmers to capture the benefits and new enterprise opportunities arising from conversion to the more efficient piped water supply delivered by the Wimmera Mallee pipeline in Victoria. In particular, the paper outlines how this typology research refocused the activities undertaken to achieve adoption of new farming enterprises and changes in water use on farms in the region. The paper concludes that farmer typologies can provide valuable information for focusing practice change programs to cater for different farming styles with different motivations for modifying farming practice.


title: Comparative study of MPPT algorithms under variable resistive load

abstract: This paper proposed a fuzzy logic control (FLC) based maximum power point tracking (MPPT) of a photovoltaic (PV) system under standard temperature condition (STC) and variable connected load. The PV system including a PV module BP SX150S, buck-boost DC-DC converter, MPPT, and resistive load is modeled and simulated using Matlab/Simulink package. In order to compare the performance of the conventional perturb and observe (P&O) and FLC based MPPT methods at STC (G=1000 W/m2 and T=25 °C) and rapid variation at connected load, two times, rising time (tr) and recovering time, are calculated. From the simulation results we can observe that the FLC based MPPT method can track the MPP of the PV module faster and with less tr and recovering time at transient state and effectively eliminates the power oscillation around the MPP of the PV module at steady state, as compared with the perturb and observe (P&O) method, using the duty cycle (D) of the DC-DC converter as a control variable. Hence, the PV system with the proposed tracking method has better transient and steady performances.


title: The Golden Beauty: Brain Response to Classical and Renaissance Sculptures

abstract: Is there an objective, biological basis for the experience of beauty in art? Or is aesthetic experience entirely subjective? Using fMRI technique, we addressed this question by presenting viewers, naive to art criticism, with images of masterpieces of Classical and Renaissance sculpture. Employing proportion as the independent variable, we produced two sets of stimuli: one composed of images of original sculptures; the other of a modified version of the same images. The stimuli were presented in three conditions: observation, aesthetic judgment, and proportion judgment. In the observation condition, the viewers were required to observe the images with the same mind-set as if they were in a museum. In the other two conditions they were required to give an aesthetic or proportion judgment on the same images. Two types of analyses were carried out: one which contrasted brain response to the canonical and the modified sculptures, and one which contrasted beautiful vs. ugly sculptures as judged by each volunteer. The most striking result was that the observation of original sculptures, relative to the modified ones, produced activation of the right insula as well as of some lateral and medial cortical areas (lateral occipital gyrus, precuneus and prefrontal areas). The activation of the insula was particularly strong during the observation condition. Most interestingly, when volunteers were required to give an overt aesthetic judgment, the images judged as beautiful selectively activated the right amygdala, relative to those judged as ugly. We conclude that, in observers naive to art criticism, the sense of beauty is mediated by two non-mutually exclusive processes: one based on a joint activation of sets of cortical neurons, triggered by parameters intrinsic to the stimuli, and the insula (objective beauty); the other based on the activation of the amygdala, driven by one's own emotional experiences (subjective beauty).


title: Photovoltaic cell modeling for solar energy powered sensor networks

abstract: Photovoltaic scavenging circuits have been presented to reduce installation and maintenance costs of wireless sensor networks. When small-size photovoltaic modules are adopted, optimizing the efficiency of the harvesting process and tracking the maximum power point (MPP) becomes very difficult, and the development of a photovoltaic harvester has to be preceded by extensive simulations. The paper focuses on the definition of the model for a small PV cell allowing the simulation of harvester systems. The model is validated on a case study of MPPT circuit for sensor networks.


title: Stereo Vision in Structured Environments by Consistent Semi-Global Matching

abstract: This paper considers the use of stereo vision in structured environments. Sharp discontinuities and large untextured areas must be anticipated, but complex or natural shapes of objects and fine structures should be handled as well. Additionally, radiometric differences of input images often occur in practice. Finally, computation time is an issue for handling large or many images in acceptable time. The Semi-Global Matching method is chosen as it fulfills already many of the requirements. Remaining problems in structured environments are carefully analyzed and two novel extensions suggested. Firstly, intensity consistent disparity selection is proposed for handling untextured areas. Secondly, discontinuity preserving interpolation is suggested for filling holes in the disparity images that are caused by some filters. It is shown that the performance of the new method on test images with ground truth is comparable to the currently best stereo methods, but the complexity and runtime is much lower.


title: Look Deeper into Depth: Monocular Depth Estimation with Semantic Booster and Attention-Driven Loss

abstract: Monocular depth estimation benefits greatly from learning based techniques. By studying the training data, we observe that the per-pixel depth values in existing datasets typically exhibit a long-tailed distribution. However, most previous approaches treat all the regions in the training data equally regardless of the imbalanced depth distribution, which restricts the model performance particularly on distant depth regions. In this paper, we investigate the long tail property and delve deeper into the distant depth regions (i.e. the tail part) to propose an attention-driven loss for the network supervision. In addition, to better leverage the semantic information for monocular depth estimation, we propose a synergy network to automatically learn the information sharing strategies between the two tasks. With the proposed attention-driven loss and synergy network, the depth estimation and semantic labeling tasks can be mutually improved. Experiments on the challenging indoor dataset show that the proposed approach achieves state-of-the-art performance on both monocular depth estimation and semantic labeling tasks.


How emotional are you? Neural Architectures for Emotion Intensity Prediction in Microblogs


title: © 2002 Monetary Authority of Macau Financial Development and Economic Growth in Macau

abstract: A video system receives an instruction to perform a function such as a display or record operation. Computer-executable instructions are for determining the status of a toggle setting (e.g., ON/OFF or TV/VIDEO status) of a video recorder (e.g., a VCR), and for ascertaining whether the toggle setting of the video recorder needs to be toggled to perform the function. The instructions are also for transmitting a toggle instruction to the video recorder if needed to perform, for example, the display or record operation. Thus, the setting of the video recorder automatically occurs without requiring complex user interaction.


title: A Multiagent Approach to $Q$-Learning for Daily Stock Trading

abstract: The portfolio management for trading in the stock market poses a challenging stochastic control problem of significant commercial interests to finance industry. To date, many researchers have proposed various methods to build an intelligent portfolio management system that can recommend financial decisions for daily stock trading. Many promising results have been reported from the supervised learning community on the possibility of building a profitable trading system. More recently, several studies have shown that even the problem of integrating stock price prediction results with trading strategies can be successfully addressed by applying reinforcement learning algorithms. Motivated by this, we present a new stock trading framework that attempts to further enhance the performance of reinforcement learning-based systems. The proposed approach incorporates multiple Q-learning agents, allowing them to effectively divide and conquer the stock trading problem by defining necessary roles for cooperatively carrying out stock pricing and selection decisions. Furthermore, in an attempt to address the complexity issue when considering a large amount of data to obtain long-term dependence among the stock prices, we present a representation scheme that can succinctly summarize the history of price changes. Experimental results on a Korean stock market show that the proposed trading framework outperforms those trained by other alternative approaches both in terms of profit and risk management.


title: A framework to support the evaluation, adoption and improvement of agile methods in practice

abstract: Agile methods are often seen as providing ways to avoid overheads typically perceived as being imposed by traditional software development environments. However, few organizations are psychologically or technically able to take on an agile approach rapidly and effectively. Here, we describe a number of approaches to assist in such a transition. The Agile Software Solution Framework (ASSF) provides an overall context for the exploration of agile methods, knowledge and governance and contains an Agile Toolkit for quantifying part of the agile process. These link to the business aspects of software development so that the business value and agile process are well aligned. Finally, we describe how these theories are applied in practice with two industry case studies using the Agile Adoption and Improvement Model (AAIM).


title: Entity Linking: An Issue to Extract Corresponding Entity With Knowledge Base

abstract: Entity linking is a task to extract query mentions in documents, and then link them to their corresponding entities in a knowledge base. It can improve the performances of knowledge network construction, knowledge fusion, information retrieval, natural language processing, and knowledge base population. In this paper, we introduce the difficulties and applications of entity linking and focus on the main methods to address this issue. At last, we list the knowledge bases, data sets, and the evaluation criterion and some challenges of entity linking.


title: Vocabulometer: A Web Platform for Document and Reader Mutual Analysis

abstract: We present the Vocabulometer, a reading assistant system designed to record the reading activity of a user with an eye tracker and to extract mutual information about the users and the read documents. The Vocabulometer stands as a web platform and can be used for analyzing the comprehension of the user, the comprehensibility of the document, predicting the difficult words, recommending document according to the reader's in order to increase his skills, etc. Since the last years, with the development of low-cost eye trackers, the technology is now accessible for many people, which will allow using data mining and machine learning algorithms for the mutual analysis of documents and readers.


14 Requirements Engineering for Agile Methods


title: Towards the web of things: Web mashups for embedded devices

abstract: In the \Internet of Things" vision, the physical world becomes integrable with computer networks. Embedded computers or visual markers on everyday objects allow things and information about them to be accessible in the digital world. However, this integration is based on competing standards and requires custom solutions, thus requires extensive time and technical expertise. Based on the success of Web 2.0 mashup applications, we propose a similar approach for integrating real-world devices to the Web, allowing for them to be easily combined with other virtual and physical resources. In this paper we discuss possible integration method, in particular how the REST principles can be applied to embedded devices. Then we illustrate these principles with two concrete implementations: on the Sun SPOT platform and on the Ploggs wireless energy monitors. Finally, we show how RESTful interactions can be leveraged to quickly create new prototypes and mashups that combine the physical and virtual world.


title: High-Performance Design of HBase with RDMA over InfiniBand

abstract: HBase is an open source distributed Key/Value store based on the idea of Big Table. It is being used in many data-center applications (e.g. Face book, Twitter, etc.) because of its portability and massive scalability. For this kind of system, low latency and high throughput is expected when supporting services for large scale concurrent accesses. However, the existing HBase implementation is built upon Java Sockets Interface that provides sub-optimal performance due to the overhead to provide cross-platform portability. The byte-stream oriented Java sockets semantics confine the possibility to leverage new generations of network technologies. This makes it hard to provide high performance services for data-intensive applications. High Performance Computing (HPC) domain has exploited high performance and low latency networks such as Infini Band for many years. These interconnects provide advanced network features, such as Remote Direct Memory Access (RDMA), to achieve high throughput and low latency along with low CPU utilization. RDMA follows memory-block semantics, which can be adopted efficiently to satisfy the object transmission primitives used in HBase. In this paper, we present a novel design of HBase for RDMA capable networks via Java Native Interface (JNI). Our design extends the existing open-source HBase software and makes it RDMA capable. Our performance evaluation reveals that latency of HBase Get operations of 1KB message size can be reduced to 43.7µs with the new design on QDR platform (32 Gbps). This is about a factor of 3.5 improvement over 10 Gigabit Ethernet (10 GigE) network with TCP Offload. Throughput evaluations using four HBase region servers and 64 clients indicate that the new design boosts up throughput by 3 X times over 1 GigE and 10 GigE networks. To the best of our knowledge, this is first HBase design utilizing high performance RDMA capable interconnects.


The Effects of the Social Structure of Digital Networks on Viral Marketing Performance


title: A Vision-Based Parking Lot Management System

abstract: Goals of parking lot management system include counting the number of parked vehicles, monitoring the changes of the parked vehicles over the time, and identifying the stalls available. To decrease the cost of the production, an integrated vision-based system is a good choice. In this paper, we propose a vision-based parking management system to manage an outdoor parking lot by four cameras set up at loft of buildings around it, sending information, including real-time display, to database of ITS center via internet. This system enables drivers to find parking spaces available or monitoring the parking lot where they parked their cars easily by wireless communication device. To increase accuracy, in the beginning, color manage is done to all input images, maintaining color consistency. Then, an adaptive parking lot background model is generated. The adequate color of each parking space is found out using statistical method in color image sequences captured by a camera, and foreground is extracted based on color information. The result will be further modified by shadow detection based on luminance analysis. Vision-based parking management system can manage large area by just several cameras. Adjusting position of the camera can easily make this system suitable for most cases. Besides, this system is endurable and is easy-installed because of its simple equipment.


A Google-Proof Collection of French Winograd Schemas


title: Computerised approaches for the detection of diabetic retinopathy using retinal fundus images: a survey

abstract: Eye-related disease such as diabetic retinopathy (DR) is a medical ailment in which the retina of the human eye is smashed because of damage to the tiny retinal blood vessels in the retina. Ophthalmologists identify DR based on various features such as the blood vessels, textures and pathologies. With the rapid development of methods of analysis of biomedical images and advanced computing techniques, image processing-based software for the detection of eye disease has been widely used as an important tool by ophthalmologists. In particular, computer vision-based methods are growing rapidly in the field of medical images analysis and are appropriate to advance ophthalmology. These tools depend entirely on visual analysis to identify abnormalities in Retinal Fundus images. During the past two decades, exciting improvement in the development of DR detection computerised systems has been observed. This paper reviews the development of analysing retinal images for the detection of DR in three aspects: automatic algorithms (classification or pixel to pixel methods), detection methods of pathologies from retinal fundus images, and extraction of blood vessels of retinal fundus image algorithms for the detection of DR. The paper presents a detailed explanation of each problem with respect to retinal images. The current techniques that are used to analyse retinal images and DR detection issues are also discussed in detail and recommendations are made for some future directions.


title: Resilience Training Program Reduces Physiological and Psychological Stress in Police Officers

abstract: Research suggests that police work is among the most stressful occupations in the world and officers typically suffer a variety of physiological, psychological, and behavioral effects and symptoms. Officers operating under severe or chronic stress are likely to be at greater risk of error, accidents, and overreactions that can compromise their performance, jeopardize public safety, and pose significant liability costs to the organization. Therefore, this study explored the nature and degree of physiological activation typically experienced of officers on the job and the impact of the Coherence Advantage resilience and performance enhancement training on a group of police officers from Santa Clara County, California. Areas assessed included vitality, emotional well-being, stress coping and interpersonal skills, work performance, workplace effectiveness and climate, family relationships, and physiological recalibration following acute stressors. Physiological measurements were obtained to determine the real...


title: Secure extensions to the OLSR protocol

abstract: Phosphine or phosphite gold complexes of thioethanol and derivatives thereof, such as ether or ester derivatives, having anti-arthritic activity are prepared by reaction of an appropriate phosphine or phosphite gold halide with thioethanol or its corresponding derivative.


title: Explaining variation in the economic value of irrigation water using psychological capital: a case study from Ndumo B and Makhathini, KwaZulu-Natal, South Africa

abstract: This study investigates the economic value of irrigation water using data collected from 200 smallholder producers in Makhathini and Ndumo areas, KwaZulu-Natal.The study accounts for psychological capital (individual mind-set and attitude affecting motivation to take initiatives) as an important aspect of farm management. This concept focuses more on the ‘soft’ aspects of farm management, which have not been adequately studied in the context of smallholder farming. A sustainable livelihoods framework is employed as a conceptual framework and the role of psychological capital is integrated to explain the variation in water values. The study employs the residual valuation method to estimate water values, principal component analysis to generate an index for psychological capital, and the general linear model to explain variation in water values. The findings suggest that variation in water values was mainly influenced by the location of the farmer, farmer type, physical capital, social capital, land size, farming experience, crop type and psychological capital. The results reinforce the importance of institutional arrangements and collective bargaining as an important element of managing a smallholder farm to increase the economic value of water. To build and develop positive psychological capital for smallholders, it is recommended that government should re-visit the usual model of ‘hand-outs’ (input, finance). It is time to re-consider direct farmer support that entails being heavily involved in their day-to-day activities (i.e., purchasing inputs and running the irrigation schemes on their behalf). Going forward, the focus should rather be on enabling them to change their behaviour to be self-reliant and own their own destiny through on-farm and off-farm economic activities. Moreover, government and other development partners have to understand the long-term behavioural impact (on farmers) of what they do, for instance, entrenching expectations. Keywords: economic value of irrigation water, psychological capital, residual valuation method, general linear model


title: Comparative study of background subtraction algorithms

abstract: In this paper, we present a comparative study of several state of the art background subtraction methods. Approaches ranging from simple background subtraction with global thresholding to more sophisticated statistical methods have been implemented and tested on different videos with ground truth. The goal of this study is to provide a solid analytic ground to underscore the strengths and weaknesses of the most widely implemented motion detection methods. The methods are compared based on their robustness to different types of video, their memory requirement, and the computational effort they require. The impact of a Markovian prior as well as some post-processing operators are also evaluated. Most of the videos used in this study come from state-of-the-art benchmark databases and represent different challenges such as poor signal-to-noise ratio, multimodal background motion and camera jitter. Overall, this study not only helps better understand to which type of videos each method suits best but also estimate how better sophisticated methods are, compared to basic background subtraction methods.


title: Improving news articles recommendations via user clustering

abstract: Although commonly only item clustering is suggested by Web mining techniques for news articles recommendation systems, one of the various tasks of personalized recommendation is categorization of Web users. With the rapid explosion of online news articles, predicting user-browsing behavior using collaborative filtering (CF) techniques has gained much attention in the web personalization area. However common CF techniques suffer from problems like low accuracy and performance. This research proposes a new personalized recommendation approach that integrates both user and text clustering based on our developed algorithm, W-kmeans, with other information retrieval (IR) techniques, like text categorization and summarization in order to provide users with the articles that match their profiles. Our system can easily adapt over time to divertive user preferences. Furthermore, experimental results show that by aggregating item and user clustering with multiple IR techniques like categorization and summarization, our recommender generates results that outperform the cases where each or both of them are used, but clustering is not applied.


title: Does it matter that we do not Agree on the definition of poverty? a comparison of four approaches

abstract: While there is world-wide agreement on poverty reduction as an overriding goal of development policy, there is little agreement on the definition of poverty. Four approaches to the definition and measurement of poverty are reviewed in this paper: the monetary, capability, social exclusion and participatory approaches. The theoretical underpinnings of the various measures and problems of operationalizing them are pointed out. It is argued that each is a construction of reality, involving numerous judgements, which are often not transparent. The different methods have different implications for policy, and also, to the extent that they point to different people as being poor, for targeting. Empirical work in Peru and India shows that there is significant lack of overlap between the methods with, for example, nearly half the population identified as in poverty according to monetary poverty but not in capability poverty, and conversely. This confirms similar findings elsewhere. Hence, the definition of poverty does matter for poverty eradication strategies.


title: Knowledge-aware Multimodal Dialogue Systems

abstract: By offering a natural way for information seeking, multimodal dialogue systems are attracting increasing attention in several domains such as retail, travel etc. However, most existing dialogue systems are limited to textual modality, which cannot be easily extended to capture the rich semantics in visual modality such as product images. For example, in fashion domain, the visual appearance of clothes and matching styles play a crucial role in understanding the user's intention. Without considering these, the dialogue agent may fail to generate desirable responses for users. In this paper, we present a Knowledge-aware Multimodal Dialogue (KMD) model to address the limitation of text-based dialogue systems. It gives special consideration to the semantics and domain knowledge revealed in visual content, and is featured with three key components. First, we build a taxonomy-based learning module to capture the fine-grained semantics in images the category and attributes of a product). Second, we propose an end-to-end neural conversational model to generate responses based on the conversation history, visual semantics, and domain knowledge. Lastly, to avoid inconsistent dialogues, we adopt a deep reinforcement learning method which accounts for future rewards to optimize the neural conversational model. We perform extensive evaluation on a multi-turn task-oriented dialogue dataset in fashion domain. Experiment results show that our method significantly outperforms state-of-the-art methods, demonstrating the efficacy of modeling visual modality and domain knowledge for dialogue systems.


title: Lesions to the Motor System Affect Action Perception

abstract: Several studies have shown that the motor system is involved in action perception, suggesting that action concepts are represented through sensory-motor processes. Such conclusions imply that motor system impairments should diminish action perception. To test this hypothesis, a group of 10 brain-damaged patients with hemiplegia (specifically, a lesion at the motor system that affected the contralesional arm) viewed point-light displays of arm gestures and attempted to name each gesture. To create the dynamic stimuli, patients individually performed simple gestures with their unaffected arm while being videotaped. The videotapes were converted into point-light animations. Each action was presented as it had been performed, that is, as having been produced by the observer's unaffected arm, and in its mirror reversed orientation, that is, as having been produced by the observer's hemiplegic arm. Action recognition accuracy by patients with hemiplegia was compared with that by 8 brain-damaged patients without any motor deficit and by 10 healthy controls. Overall, performance was better in control observers than in patients. Most importantly, performance by hemiplegic patients, but not by nonhemiplegic patients and controls, varied systematically as a function of the observed limb. Action recognition was best when hemiplegic patients viewed actions that appeared to have been performed by their unaffected arm. Action recognition performance dropped significantly when hemiplegic patients viewed actions that appeared to have been produced with their hemiplegic arm or the corresponding arm of another person. The results of a control study involving the recognition of point-light defined animals in motion indicate that a generic deficit to visual and cognitive functions cannot account for this laterality-specific deficit in action recognition. Taken together, these results suggest that motor cortex impairment decreases visual sensitivity to human action. Specifically, when a cortical lesion renders an observer incapable of performing an observed action, action perception is compromised, possibly by a failure to map the observed action onto the observer's contralesional hemisoma.


title: RadSS: A radiolarian classifier using support vector machines

abstract: Radiolarian assemblages have played a significant role as a biostratigraphic and paleoenvironmental tool used in the geological settings. These species can be used in studying sediments lacking calcareous fossils. Easy identification of these species would allow micropaleontologists to proceed further into studying the structure and way of living of these Radiolarians. RaDSS is a decision support system that could help researchers in classifying microphotographs of Radiolarian species through image processing and machine learning algorithms such as SVM.


title: A 10Gb/s 4.1mW 2-IIR + 1-discrete-tap DFE in 28nm-LP CMOS

abstract: Abstract—A half-rate decision feedback equalizer (DFE) with two infinite impulse response (IIR) filters and one discrete-time tap is presented. The two IIR filters have different time constants to cancel the long tail of the pulse response. The discrete-tap cancels the first post-cursor inter-symbol interference term. The system can operate with a low transmit swing of 150mVpp-diff and 24 dB channel loss at the Nyquist frequency while consuming 4.1mW at 10 Gb/s. The receiver, including the DFE, clock buffers and clock phase adjustment, occupies an area of 8,760 μm and was fabricated in an ST 28nm LP CMOS process.


title: The value of spreader grafts in rhinoplasty: a critical review

abstract: The value of spreader grafts in rhinoplasty cannot be underestimated. Various studies have demonstrated that they play a valuable role in the restoration of nasal dorsum aesthetics, provide support for the nasal valve and maintain the straightened position of the corrected deviated cartilaginous septal dorsum. However, there is still controversy on the extent of its value in nasal patency. This study reviews the literature and describes the values and limitations of spreader grafts in rhinoplasty and the alternatives to classic spreader grafts.


title: Methods of recommender system: A review

abstract: There are number of users and items in any type of recommender system. There are numerous information on internet and so many visitors on websites which add some challenges for generating recommender system. A recommender system extracts the user preferences or interests from the related data sets so there is low information overload. Therefore, new recommendation system is required which will provide more quality recommendations for huge data sets. So, for these types of issues we have discovered several techniques of recommendation techniques which are three types such as: Content-based filtering, Collaborative filtering and Hybrid filtering. This paper also analyzes different algorithms in each type of recommender system.


title: A Container-Based Edge Cloud PaaS Architecture Based on Raspberry Pi Clusters

abstract: Cloud technology is moving towards multi-cloud environments with the inclusion of various devices. Cloud and IoT integration resulting in so-called edge cloud and fog computing has started. This requires the combination of data centre technologies with much more constrained devices, but still using virtualised solutions to deal with scalability, flexibility and multi-tenancy concerns. Lightweight virtualisation solutions do exist for this architectural setting with smaller, but still virtualised devices to provide application and platform technology as services. Containerisation is a solution component for lightweight virtualisation solution. Containers are furthermore relevant for cloud platform concerns dealt with by Platform-as-a-Service (PaaS) clouds like application packaging and orchestration. We demonstrate an architecture for edge cloud PaaS. For edge clouds, application and service orchestration can help to manage and orchestrate applications through containers. In this way, computation can be brought to the edge of the cloud, rather than data from the Internet-of-Things (IoT) to the cloud. We show that edge cloud requirements such as cost-efficiency, low power consumption, and robustness can be met by implementing container and cluster technology on small single-board devices like Raspberry Pis. This architecture can facilitate applications through distributed multi-cloud platforms built from a range of nodes from data centres to small devices, which we refer to as edge cloud. We illustrate key concepts of an edge cloud PaaS and refer to experimental and conceptual work to make that case.


title: A Roofline Model of Energy

abstract: We describe an energy-based analogue of the time-based roofline model. We create this model from the perspective of algorithm designers and performance tuners, with the intent not of making exact predictions, but rather, developing highlevel analytic insights into the possible relationships among the time, energy, and power costs of an algorithm. The model expresses algorithms in terms of operations, concurrency, and memory traffic; and characterizes the machine based on a small number of simple cost parameters, namely, the time and energy costs per operation or per word of communication. We confirm the basic form of the model experimentally. From this model, we suggest under what conditions we ought to expect an algorithmic time-energy trade-off, and show how algorithm properties may help inform power management.


title: Making "Safe": Community-Centered Practices in a Virtual World Dedicated to Children with Autism

abstract: The use of online games and virtual worlds is becoming increasingly prominent, particularly in children and young adults. Parents have concerns about risks their children might encounter in these online spaces. Parents dynamically manage the boundaries between safe and unsafe spaces online through both explicit and implicit means. In this work, we use empirical data gathered from a digital ethnog-raphy of a Minecraft server, Autcraft, to explore how par-ents of children with autism continually create a "safe" virtual world through both implicit and explicit means. In par-ticular, we demonstrate how their actions in these spaces define and produce "safety," shedding light on our theoreti-cal understanding of child safety in online spaces.


Recurrent Neural Networks and Pitch Representations for Music Tasks


title: Automatically Predicting Peer-Review Helpfulness

abstract: Identifying peer-review helpfulness is an important task for improving the quality of feedback that students receive from their peers. As a first step towards enhancing existing peer-review systems with new functionality based on helpfulness detection, we examine whether standard product review analysis techniques also apply to our new context of peer reviews. In addition, we investigate the utility of incorporating additional specialized features tailored to peer review. Our preliminary results show that the structural features, review uni-grams and meta-data combined are useful in modeling the helpfulness of both peer reviews and product reviews, while peer-review specific auxiliary features can further improve helpfulness prediction.


title: Modeling IoT Applications with SysML4IoT

abstract: The Internet of Things (IoT) is a paradigm thatrefers to the ubiquitous presence around us of physical objectsequipped with sensing, networking and processing capabilitiesthat allow them to cooperate with each other and with theirenvironment to reach common goals. General objectives of IoTare improving the quality of human life and optimizing industrialprocesses, by automating tasks that humans must perform. Suchbenefits are obtained by developing IoT applications. Theheterogeneity and interdisciplinary present in the development ofany IoT application classifies such a task as a SystemsEngineering problem. However, to the best of our knowledge, noSystems Engineering approach to develop IoT applications hasbeen proposed so far. The objective of this paper is to introduce aModel-Based Systems Engineering methodology for IoTapplication development, focusing on the design phase, whichaims at helping organizations to develop IoT applications to fullyachieve the benefits of this new paradigm. The approach iscomprised of a method and a tool, which are aligned with provenconcepts from Systems Engineering and Software Architecture literature.


title: Orientation Modulation for Data Hiding in Clustered-Dot Halftone Prints

abstract: We present a new framework for data hiding in images printed with clustered dot halftones. Our application scenario, like other hardcopy embedding methods, encounters fundamental challenges due to extreme bilevel quantization inherent in halftoning, the stringent requirements of image fidelity, and other unavoidable printing and scanning distortions. To overcome these challenges, while still allowing for automated extraction of the embedded data and a high embedding capacity, we propose a number of innovations. First, we perform the embedding jointly with the halftoning by employing an analytical halftone threshold function that allows steering of the halftone spot orientation within each halftone cell based upon embedded data. In this process, image fidelity is emphasized and, if necessary, the capability to recover individual data values is sacrificed resulting in unavoidable erasures and errors. To overcome these and other sources of errors, we propose a suitable data detection and error control methodology based upon a statistical representation for the print-scan channel that effectively models the channel dependence upon the cover image gray-level. To combat the geometric distortion inherent in the print-scan process, we exploit the periodic halftone structure to recover from global scaling and rotation and propose a novel decision directed synchronization technique that counters locally varying printing distortion. Experimental results demonstrate the power of the proposed framework: we achieve high operational rates while preserving halftone image quality.


title: Making Sense out of a Jungle of JavaScript Frameworks: towards a Practitioner-friendly Comparative Analysis

abstract: The field of Web development is entering the HTML5 and CSS3 era and JavaScript is becoming increasingly influential. A large number of JavaScript frameworks have been recently promoted. Practitioners applying the latest technologies need to choose a suitable JavaScript framework (JSF) in order to abstract the frustrating and complicated coding steps and to provide a cross-browser compatibility. Apart from benchmark suites and recommendation from experts, there is little research helping practitioners to select the most suitable JSF to a given situation. The few proposals employ software metrics on the JSF, but practitioners are driven by different concerns when choosing a JSF. As an answer to the critical needs, this paper is a call for action. It proposes a research design towards a comparative analysis framework of JSF, which merges researcher needs and practitioner needs.


title: Materials Design for New Superconductors

abstract: Since the announcement in 2011 of the Materials Genome Initiative by the Obama administration, much attention has been given to the subject of materials design to accelerate the discovery of new materials that could have technological implications. Although having its biggest impact for more applied materials like batteries, there is increasing interest in applying these ideas to predict new superconductors. This is obviously a challenge, given that superconductivity is a many body phenomenon, with whole classes of known superconductors lacking a quantitative theory. Given this caveat, various efforts to formulate materials design principles for superconductors are reviewed here, with a focus on surveying the periodic table in an attempt to identify cuprate analogues.


title: A collaborative approach for improving the security of vehicular scenarios: The case of platooning

abstract: Abstract Autonomous vehicles platooning has received considerable attention in recent years, due to its potential to significantly benefit road transportation, improving traffic efficiency, enhancing road safety and reducing fuel consumption. The Vehicular ad hoc Networks and the de facto vehicular networking standard IEEE 802.11p communication protocol are key tools for the deployment of platooning applications, since the cooperation among vehicles is based on a reliable communication structure. However, vehicular networks can suffer different security threats. Indeed, in collaborative driving applications, the sudden appearance of a malicious attack can mainly compromise: (i) the correctness of data traffic flow on the vehicular network by sending malicious messages that alter the platoon formation and its coordinated motion; (ii) the safety of platooning application by altering vehicular network communication capability. In view of the fact that cyber attacks can lead to dangerous implications for the security of autonomous driving systems, it is fundamental to consider their effects on the behavior of the interconnected vehicles, and to try to limit them from the control design stage. To this aim, in this work we focus on some relevant types of malicious threats that affect the platoon safety, i.e. application layer attacks (Spoofing and Message Falsification) and network layer attacks (Denial of Service and Burst Transmission), and we propose a novel collaborative control strategy for enhancing the protection level of autonomous platoons. The control protocol is designed and validated in both analytically and experimental way, for the appraised malicious attack scenarios and for different communication topology structures. The effectiveness of the proposed strategy is shown by using PLEXE, a state of the art inter-vehicular communications and mobility simulator that includes basic building blocks for platooning. A detailed experimental analysis discloses the robustness of the proposed approach and its capabilities in reacting to the malicious attack effects.


Khnum-Data Management for a Dynamically Scalable Computing Utility


title: Intelligent Transportation System For Vehicular Ad-Hoc Networks

abstract: Vehicular ad hoc networks (VANETs) are a one form of wireless networks. It is used for communication between vehicles on roads. The conventional routing protocols are suitable for mobile ad hoc networks (MANETs). But it"s poorly in VANETs. As communication links break often happen in VANETs compare than in MANETs, the reliable routing is more difficult in the VANET. Research work has been done to the routing reliability of VANETs on highways. In this paper, we use the intelligent transportation system for VANETs. The intelligent transportation system helps to capture the future positions of the vehicles and determines the reliable routes preemptively. This paper is the first to propose an intelligent transportation system gives reliable routing process. A new mechanism is developed to find the vehicles information from the source vehicle to the destination vehicle. Through the simulation results, that the proposed scheme significantly give good result compare than other literature survey.


An Application of Category Theory to the Study of Complex Networks


title: 3D Garment Digitisation for Virtual Wardrobe Using a Commodity Depth Sensor

abstract: A practical garment digitisation should be efficient and robust to minimise the cost of processing a large volume of garments manufactured in every season. In addition, the quality of a texture map needs to be high to deliver a better user experience of VR/AR applications using garment models such as digital wardrobe or virtual fitting room. To address this, we propose a novel pipeline for fast, low-cost, and robust 3D garment digitisation with minimal human involvement. The proposed system is simply configured with a commodity RGB-D sensor (e.g. Kinect) and a rotating platform where a mannequin is placed to put on a target garment. Since a conventional reconstruction pipeline such as Kinect Fusion (KF) tends to fail to track the correct camera pose under fast rotation, we modelled the camera motion and fed this as a guidance of the ICP process in KF. The proposed method is also designed to produce a high-quality texture map by stitching the best views from a single rotation, and a modified shape from silhouettes algorithm has been developed to extract a garment model from a mannequin.


title: Road scene segmentation via fusing camera and lidar data

abstract: This paper presents an approach for pixel-wise object segmentation for road scenes based on the integration of a color image and an aligned 3D point cloud. In light of the advantage of range information in object discovery, we first produce initial object hypotheses by clustering the sparse 3D point cloud. The image pixels registered to the clustered 3D points are taken as samples to learn each object's prior knowledge. The priors are represented by Gaussian Mixture Models (GMMs) of color and 3D location information only, requiring no high-level features. We further formulate the segmentation problem within a Conditional Random Field (CRF) framework, which incorporates the learned prior models, together with hard constraints placed on the registered pixels and pairwise spatial constraints to achieve final results. Our algorithm is validated on the challenging KITTI dataset which contains diverse complicated road scenarios. Both qualitative and quantitative evaluation results show the superiority of our algorithm.


title: Probabilistic Soft Logic for Semantic Textual Similarity

abstract: Probabilistic Soft Logic (PSL) is a recently developed framework for probabilistic logic. We use PSL to combine logical and distributional representations of natural-language meaning, where distributional information is represented in the form of weighted inference rules. We apply this framework to the task of Semantic Textual Similarity (STS) (i.e. judging the semantic similarity of naturallanguage sentences), and show that PSL gives improved results compared to a previous approach based on Markov Logic Networks (MLNs) and a purely distributional approach.


title: Physiological changes in neurodegeneration — mechanistic insights and clinical utility

abstract: In this Review, Ahmed et al. look at the changes in physiological function that occur in neurodegenerative syndromes, consider the similarities and differences in these changes across conditions, and discuss the clinical utility of detecting these changes.


title: Applying ontological modeling on quranic "nature" domain

abstract: The holy Quran is the holy book of the Muslims. It contains information about many domains. Often people search for particular concepts of holy Quran based on the relations among concepts. An ontological modeling of holy Quran can be useful in such a scenario. In this paper, we have modeled nature related concepts of holy Quran using OWL (Web Ontology Language) / RDF (Resource Description Framework). Our methodology involves identifying nature related concepts mentioned in holy Quran and identifying relations among those concepts. These concepts and relations are represented as classes/instances and properties of an OWL ontology. Later, in the result section it is shown that, using the Ontological model, SPARQL queries can retrieve verses and concepts of interest. Thus, this modeling helps semantic search and query on the holy Quran. In this work, we have used English translation of the holy Quran by Sahih International, Protege OWL Editor and for querying we have used SPARQL.


title: Cylindrical elastic crawler mechanism for pipe inspection inspired by amoeba locomotion

abstract: The diverse pipe installations used today include pipelines in chemical plants, water pipes, and gas pipes. Severe accidents at such pipe installations must be prevented by regular pipe inspection and repair. As described herein, a novel tracked crawler mechanism is proposed for pipe inspection. This simple and compact cylindrical elastic tracked crawler has multiple crawler belts in axial symmetry to a cylindrical frame, driven solely by a single motor via a single worm. It is suitable for propulsion through a narrow pipe. It can propel itself upward in a pipe using elastic force generated by deforming the crawler belt passively. Moreover, the proposed tracked crawler can cross over a level difference and pass an elbow by deforming the crawler belt passively along the pipe shape. For a prototype tracked crawler, running performance experiments conducted in various pipe conditions yielded good results. This study clarified the relation between belt rigidity and traction force in theory and experiment respectively.


title: Atrial fibrillation detection using feature based algorithm and deep convolutional neural network

abstract: Aims: Electrocardiographic waveforms (ECG) are recognized as the most reliable method to detect abnormal heart rhythms such as atrial fibrillation. This task is challenging when the signals are distorted by noise. This paper presents an automatic classification algorithm to classify short lead ECGs in terms of abnormality of heart rhythm (AF or alternative rhythms) and quality (noisy recordings). Methods: To meet this end, at first baseline wander removal and Butterworth filter for each signal are applied as a preprocessing stage. Due to the existence of noise in recordings, high quality beats are selected for any further analysis using cycle quality assessment. Then, three sets of features defined as correlation coefficient, fractal dimension and variance of R peaks are extracted to predict noisy recordings. Two separate approaches are employed to classify other three classes. The first approach is the feature based methodology and the second one is the applying deep neural networks. In the first approach, features from different domains are extracted. The method for AF detection utilizes and characterizes variability in RR-intervals which are extracted by applying classic Pan-Tompkins algorithm. To improve the accuracy of the AF-detection, atrial activity is analyzed by understanding whether the P-wave is present in signal. This is done by investigating the morphology of P-waves. Heart rate abnormality and the existence of premature beats in a signal are regarded as two characteristics to distinguish non-AF rhythms. The whole sets of features are fed into a neural network classifier. Another approach uses the segments with 600 samples as the input of a 1 dimensional convolutional neural network. The output obtained from both approaches are combined using a decision table and finally the recordings are classified into three classes. Results: The proposed method is evaluated using scoring function from 2017 PhysioNet/CinC Challenge and achieved an overall score of 80% and 71% on the training dataset and hidden test dataset, respectively.


title: An incremental approach to scheduling during overloads in real-time systems

abstract: Proposes a novel scheduling framework for a real-time environment that experiences dynamic changes. This framework is capable of adjusting the system workload in incremental steps under overloaded conditions such that the most critical tasks in the system are always scheduled and the total value of the system is maximized. Each task has an assigned criticality value and consists of two parts: a mandatory part and an optional part. A timely answer is available after the mandatory part completes execution and its value may be improved by executing the entire optional part. Optional parts can be discarded in overloaded conditions. The process of selecting optional parts to discard while maximizing the value of the system requires the exploration of a potentially large number of combinations. Since this process is too time-consuming to be computed online, an approximate algorithm is executed incrementally whenever the processor would otherwise be idle, progressively refining the quality of the solution. This criterion allows the scheduler to handle overloads with low cost while maximizing the use of the available resources and without jeopardizing the temporal constraints of the most critical tasks in the system. Simulation results show that few stages of the algorithm need to be executed to achieve a performance with near-optimal results.


title: Logo recognition using Context Dependent criteria

abstract: In our day today life if we want to buy any product then we first see the brand or logo of that product whether that brand or logo is original or not. Depending upon this we conclude that product is original product otherwise fake product. So in this project we detect first the original logo which is present in the image by comparing original logo image with the test image by using two algorithms first CDS-SIFT and second CDS RANSAC. And then conclude that the proposed method is more efficient than the existing method in terms of Execution time, Accuracy, FRR(False Rejection Rate), FAR (False Acceptance Rate). The proposed method of logo detection is based on Context Dependent Similarity (CDS) kernel. CDS kernel's function is dependent upon three terms first energy function, second context criterion and third entropy term. Energy function balances the fidelity term. The analysis of proposed method is done using MATLAB and comparative analysis of proposed method against the nearest neighbor of existing SIFT method is done and claim that proposed method is best method. Secondly we will evaluate the performance parameters.


title: Graph Stream Summarization: From Big Bang to Big Crunch

abstract: A graph stream, which refers to the graph with edges being updated sequentially in a form of a stream, has important applications in cyber security and social networks. Due to the sheer volume and highly dynamic nature of graph streams, the practical way of handling them is by summarization. Given a graph stream G, directed or undirected, the problem of graph stream summarization is to summarize G as SG with a much smaller (sublinear) space, linear construction time and constant maintenance cost for each edge update, such that SG allows many queries over G to be approximately conducted efficiently. The widely used practice of summarizing data streams is to treat each stream element independently by e.g., hash- or sample-based methods, without maintaining the connections (or relationships) between elements. Hence, existing methods can only solve ad-hoc problems, without supporting diversified and complicated analytics over graph streams. We present TCM, a novel generalized graph stream summary. Given an incoming edge, it summarizes both node and edge information in constant time. Consequently, the summary forms a graphical sketch where edges capture the connections inside elements, and nodes maintain relationships across elements. We discuss a wide range of supported queries and establish some error bounds. In addition, we experimentally show that TCM can effectively and efficiently support analytics over graph streams, which demonstrates its potential to start a new line of research and applications in graph stream management.


Organizing for high reliability: Processes of collective mindfulness.


title: Compact Optical Payload for Daily Survey of Vegetation from Small Satellites

abstract: Recent advancement of optics fabrication, metrology and detectors are the basis for the development of a new compact instrument designed to provide daily revisit for the analysis of vegetation. The instrument has been optimized to improve multispectral imaging capabilities with respect to Spot-Vegetation, while minimizing mass and power to be accommodated on a small satellite. The new technologies used for this instrument allow shrinking the mass and reducing the power consumption of a factor 5 with respect to Spot-Vegetation. The new instrument is designed to fly on Proba-V, a small satellite developed to ensure continuation of the Spot-Vegetation products. The paper gives an overview of the payload, presents its performance and explains which innovations allow a very compact design. In particular, the paper presents the technology used for the fabrication of mirrors, the approach used for their alignment, and the tests results obtained so far on the first prototype of the telescope. A section of the paper describes the InGaAs detector developed for the SWIR channel, a long linear array able to operate uncooled. The paper concludes with a description of the mission, of the payload accommodation on the small satellite, and of the data produced.


title: Cyber physical systems in the context of Industry 4.0

abstract: We are currently experiencing the fourth Industrial Revolution in terms of cyber physical systems. These systems are industrial automation systems that enable many innovative functionalities through their networking and their access to the cyber world, thus changing our everyday lives significantly. In this context, new business models, work processes and development methods that are currently unimaginable will arise. These changes will also strongly influence the society and people. Family life, globalization, markets, etc. will have to be redefined. However, the Industry 4.0 simultaneously shows characteristics that represent the challenges regarding the development of cyber-physical systems, reliability, security and data protection. Following a brief introduction to Industry 4.0, this paper presents a prototypical application that demonstrates the essential aspects.


title: A machine learning approach for authorship attribution for Bengali blogs

abstract: In this paper we described an authorship attribution system for Bengali blog texts. We have presented a new Bengali blog corpus of 3000 passages written by three authors. Our study proposes a text classification system, based on lexical features such as character bigrams and trigrams, word n-grams (n = 1, 2, 3) and stop words, using four classifiers. We achieve best results (more than 99%) on the held-out dataset using Multi layered Perceptrons (MLP) amongst the four classifiers, which indicates MLP can produce very good results for big data sets and lexical n-gram based features can be the best features for any authorship attribution system.


title: Inverse Kinematics solution of PUMA 560 robot arm using ANFIS

abstract: One of the problems in robotics field is, finding the solution of Inverse Kinematics (IK) problem. Traditional methods such as iterative, geometric and algebraic are inadequate if the joint structure of the robot is more complex. As the complexity of manipulator increases, solution of IK is difficult and computationally expensive. In this paper, using the ability of Adaptive Neuro-Fuzzy Inference System (ANFIS) to learn from training data, it is possible to create ANFIS with limited mathematical representation of the system. In the proposed method, forward kinematics relations of robot arm are used to obtain the data for training of ANFIS. Simulation is carried out on a PUMA 560 robot arm to show the effectiveness of the approach.


title: A User's Guide to Compressed Sensing for Communications Systems

abstract: SUMMARY This survey provides a brief introduction to compressed sensing as well as several major algorithms to solve it and its various applications to communications systems. We firstly review linear simultaneous equations as ill-posed inverse problems, since the idea of compressed sensing could be best understood in the context of the linear equations. Then, we consider the problem of compressed sensing as an underdetermined linear system with a prior information that the true solution is sparse, and explain the sparse signal recovery based on � 1 optimization, which plays the central role in compressed sensing, with some intuitive explanations on the optimization problem. Moreover, we introduce some important properties of the sensing matrix in order to establish the guarantee of the exact recovery of sparse signals from the underdetermined system. After summarizing several major algorithms to obtain a sparse solution focusing on the � 1 optimization and the greedy approaches, we introduce applications of compressed sensing to communications systems, such as wireless channel estimation, wireless sensor network, network tomography, cognitive radio,


title: Leak detection of pipeline : An integrated approach of rough set theory and artificial bee colony trained SVM

abstract: The generation of leak along the pipeline carrying crude oils and liquid fuels results enormous financial loss to the industry and also affects the public health. Hence, the leak detection and localization problem has always been a major concern for the companies. In spite of the various techniques developed, accuracy and time involved in the prediction is still a matter of concern. In this paper, a novel leak detection scheme based on rough set theory and support vector machine (SVM) is proposed to overcome the problem of false leak detection. In this approach, 'rough set theory' is explored to reduce the length of experimental data as well as generate rules. It is embedded to enhance the decision making process. Further, SVM classifier is employed to inspect the cases that could not be detected by applied rules. For the computational training of SVM, this paper uses swarm intelligence technique: artificial bee colony (ABC) algorithm, which imitates intelligent food searching behavior of honey bees. The results of proposed leak detection scheme with ABC are compared with those obtained by using particle swarm optimization (PSO) and one of its variants, so-called enhanced particle swarm optimization (EPSO). The experimental results advocate the use of propounded method for detecting leaks with maximum accuracy.


title: Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems

abstract: Supporting high mobility in millimeter wave (mmWave) systems enables a wide range of important applications such as vehicular communications and wireless virtual/augmented reality. Realizing this in practice, though, requires overcoming several challenges. First, the use of narrow beams and the sensitivity of mmWave signals to blockage greatly impact the coverage and reliability of highly-mobile links. Second, highly-mobile users in dense mmWave deployments need to frequently hand-off between base stations (BSs), which is associated with critical control and latency overhead. Further, identifying the optimal beamforming vectors in large antenna array mmWave systems requires considerable training overhead, which significantly affects the efficiency of these mobile systems. In this paper, a novel integrated machine learning and coordinated beamforming solution is developed to overcome these challenges and enable highly-mobile mmWave applications. In the proposed solution, a number of distributed yet coordinating BSs simultaneously serve a mobile user. This user ideally needs to transmit only one uplink training pilot sequence that will be jointly received at the coordinating BSs using omni or quasi-omni beam patterns. These received signals draw a defining signature not only for the user location, but also for its interaction with the surrounding environment. The developed solution then leverages a deep learning model that learns how to use these signatures to predict the beamforming vectors at the BSs. This renders a comprehensive solution that supports highly-mobile mmWave applications with reliable coverage, low latency, and negligible training overhead. Simulation results show that the proposed deep-learning coordinated beamforming strategy approaches the achievable rate of the genie-aided solution that knows the optimal beamforming vectors with no training overhead.


title: A comparison of half-bridge resonant converter topologies

abstract: The half-bridge series-resonant, parallel-resonant, and combination series-parallel resonant converters are compared for use in low-output-voltage power supply applications. It is shown that the combination series-parallel converter, which takes on the desirable characteristics of the pure series and the pure parallel converter, avoids the main disadvantages of each of them. Analyses and breadboard results show that the combination converter can run over a large input voltage range and a large load range (no load to full load) while maintaining excellent efficiency. A useful analysis technique based on classical AC complex analysis is introduced. >


title: ENDA: embracing network inconsistency for dynamic application offloading in mobile cloud computing

abstract: Mobile Cloud Computing (MCC) enables smartphones to offload compute-intensive codes and data to clouds or cloudlets for energy conservation. Thus, MCC liberates smartphones from battery shortage and embraces more versatile mobile applications. Most pioneering MCC research work requires a consistent network performance for offloading. However, such consistency is challenged by frequent mobile user movements and unstable network quality, thereby resulting in a suboptimal offloading decision. To embrace network inconsistency, we propose ENDA, a three-tier architecture that leverages user track prediction, realtime network performance and server loads to optimize offloading decisions. On cloud tier, we first design a greedy searching algorithm to predict user track using historical user traces stored in database servers. We then design a cloud-enabled Wi-Fi access point (AP) selection scheme to find the most energy efficient AP for smartphone offloading. We evaluate the performance of ENDA through simulations under a real-world scenario. The results demonstrate that ENDA can generate offloading decisions with optimized energy efficiency, desirable response time, and potential adaptability to a variety of scenarios. ENDA outperforms existing offloading techniques that do not consider user mobility and server workload balance management.


title: WiG: WiFi-Based Gesture Recognition System

abstract: Most recently, gesture recognition has increasingly attracted intense academic and industrial interest due to its various applications in daily life, such as home automation, mobile games. Present approaches for gesture recognition, mainly including vision-based, sensor-based and RF-based, all have certain limitations which hinder their practical use in some scenarios. For example, the vision-based approaches fail to work well in poor light conditions and the sensor-based ones require users to wear devices. To address these, we propose WiG in this paper, a device-free gesture recognition system based solely on Commercial Off-The-Shelf (COTS) WiFi infrastructures and devices. Compared with existing Radio Frequency (RF)-based systems, WiG stands out for its systematic simplicity, extremely low cost and high practicability. We implemented WiG in indoor environment and conducted experiments to evaluate its performance in two typical scenarios. The results demonstrate that WiG can achieve an average recognition accuracy of 92% in line-of-sight scenario and average accuracy of 88% in the none-line-of sight scenario.


title: A Novel Buck–Boost Converter Combining KY and Buck Converters

abstract: In this letter, a buck-boost converter, i.e., 2D converter with a positive output voltage, is presented, which combines the KY converter and the traditional synchronously rectified (SR) buck converter. By doing so, the problem in voltage bucking of the KY converter can be solved, thereby increasing the application capability of the KY converter. Since such a converter operates in continuous conduction mode inherently, it possesses the nonpulsating output current, thereby not only decreasing the current stress on the output capacitor but also reducing the output voltage ripple. Above all, both the KY converter and the SR buck converter, combined into a buck-boost converter with no right-half plane zero, use the same power switches, thereby causing the required circuit to be compact and the corresponding cost to be down. Furthermore, during the magnetization period, the input voltage of the KY converter comes from the input voltage source, whereas during the demagnetization period, the input voltage of the KY converter comes from the output voltage of the SR buck converter.


title: Faecalibacterium prausnitzii and human intestinal health

abstract: Faecalibacterium prausnitzii is the most abundant bacterium in the human intestinal microbiota of healthy adults, representing more than 5% of the total bacterial population. Over the past five years, an increasing number of studies have clearly described the importance of this highly metabolically active commensal bacterium as a component of the healthy human microbiota. Changes in the abundance of F. prausnitzii have been linked to dysbiosis in several human disorders. Administration of F. prausnitzii strain A2-165 and its culture supernatant have been shown to protect against 2,4,6-trinitrobenzenesulfonic acid (TNBS)-induced colitis in mice. Here, we discuss the role of F. prausnitzii in balancing immunity in the intestine and the mechanisms involved.