title: Multi-agent based IoT smart waste monitoring and collection architecture

abstract: Solid waste management is one of the existing challenges in urban areas and it is becoming a critical issue due to rapid increase in population. Appropriate solid waste management systems are important for improving the environment and the well being of residents. In this paper, an Internet of Things (IoT) architecture for real time waste monitoring and collection has been proposed; able to improve and optimize solid waste collection in a city. Netlogo Multiagent platform has been used to simulate real time monitoring and smart decisions on waste management. Waste filling level in bins and truck collection process are abstracted to a multiagent model and citizen are involved by paying the price for waste collection services. Furthermore, waste level data are updated and recorded continuously and are provided to decision algorithms to determine the vehicle optimal route for waste collection to the distributed bins in the city. Several simulation cases executed and results validated. The presented solution gives substantial benefits to all waste stakeholders by enabling the waste collection process to be more efficient.


title: Graph-Based Segmentation for RGB-D Data Using 3-D Geometry Enhanced Superpixels

abstract: With the advances of depth sensing technologies, color image plus depth information (referred to as RGB-D data hereafter) is more and more popular for comprehensive description of 3-D scenes. This paper proposes a two-stage segmentation method for RGB-D data: 1) oversegmentation by 3-D geometry enhanced superpixels and 2) graph-based merging with label cost from superpixels. In the oversegmentation stage, 3-D geometrical information is reconstructed from the depth map. Then, a K-means-like clustering method is applied to the RGB-D data for oversegmentation using an 8-D distance metric constructed from both color and 3-D geometrical information. In the merging stage, treating each superpixel as a node, a graph-based model is set up to relabel the superpixels into semantically-coherent segments. In the graph-based model, RGB-D proximity, texture similarity, and boundary continuity are incorporated into the smoothness term to exploit the correlations of neighboring superpixels. To obtain a compact labeling, the label term is designed to penalize labels linking to similar superpixels that likely belong to the same object. Both the proposed 3-D geometry enhanced superpixel clustering method and the graph-based merging method from superpixels are evaluated by qualitative and quantitative results. By the fusion of color and depth information, the proposed method achieves superior segmentation performance over several state-of-the-art algorithms.


title: © 2002 Blackwell Science Ltd Intangible benefits valuation in ERP projects

abstract: . ::: The development, implementation and ownership of information systems, especially large-scale systems such as enterprise resource planning (ERP), has become progressively longer in duration and more cost intensive. As a result, IS managers are being required to justify projects financially based on their return. Historically, information systems have been difficult to quantify in monetary terms because of the intangible nature of many of the derived benefits, e.g. improved customer service. Using the case study methodology, this paper examines an attempt by a large computer manufacturer to incorporate intangibles into traditional cost–benefit analysis in an ERP project. The paper reviews the importance of intangibles, lists intangible benefits that are important in ERP projects and demonstrates the use of a scheme through which they can be incorporated into traditional evaluation techniques.


title: 3D Shape Segmentation via Shape Fully Convolutional Networks

abstract: Abstract We propose a novel fully convolutional network architecture for shapes, denoted by Shape Fully Convolutional Networks (SFCN). 3D shapes are represented as graph structures in the SFCN architecture, based on novel graph convolution and pooling operations, which are similar to convolution and pooling operations used on images. Meanwhile, to build our SFCN architecture in the original image segmentation fully convolutional network (FCN) architecture, we also design and implement a generating operation with bridging function. This ensures that the convolution and pooling operation we have designed can be successfully applied in the original FCN architecture. In this paper, we also present a new shape segmentation approach based on SFCN. Furthermore, we allow more general and challenging input, such as mixed datasets of different categories of shapes which can prove the ability of our generalisation. In our approach, SFCNs are trained triangles-to-triangles by using three low-level geometric features as input. Finally, the feature voting-based multi-label graph cuts is adopted to optimise the segmentation results obtained by SFCN prediction. The experiment results show that our method can effectively learn and predict mixed shape datasets of either similar or different characteristics, and achieve excellent segmentation results.


SINGLE TREE DETECTION IN FOREST AREAS WITH HIGH-DENSITY LIDAR DATA


title: 12E-2 X-Band Filters Utilizing AlN Thin Film Bulk Acoustic Resonators

abstract: A filter with aluminum nitride thin-film bulk acoustic resonators (FBAR) for the X-band was developed. The FBAR has an air gap beneath the resonator to isolate acoustically from a substrate, and is extremely thin to operate in the X-band. The FBAR has two structural features - a sacrificial layer to make the air gap was very thin in order to prevent the resonator from cracking on the edge of the air gap, and a resonator was deformed to dome shape by stresses of the films to keep the air gap. The fabricated FBAR operated successfully with a keff 2 of 6.40%, a resonance Q of 246, and anti-resonance Q of 462. The fabricated filter had a center frequency of 9.07 GHz, a fractional bandwidth of 3.1%, a minimum insertion loss of -1.7 dB, and an out-of-band rejection of -21 dB.


Entropy-based subspace clustering for mining numerical data


Characterizing and Detecting Skype-Relayed Traffic


title: The Influence of Locative Media on Social Information Sharing

abstract: Mobile phones and social media enable people to share information with others whenever they want, wherever they want. More recent developments allow people to augment their sharing experience by geo-tagging their information through GPS enabled phones. These 'locative media' can be used to facilitate navigation, way finding or to obtain information about a physical place or object. Other services let the users tag, rate public spaces and recommend them to other people. Furthermore, people can tell their stories by attaching their own context to a physical location. This report is a literature review in the field of locative media. In order to get a complete overview of the research that is done on location-based social networking, the most recent developments in the field are investigated. We also look at research methods used to test the user experience of this type of social media systems. Furthermore, we investigate the influence of location based media on the sharing habits and social practices of the users of these systems. The conclusions of this exploration in the field of locative media research lead to design guidelines and research methods that will be used in a user evaluation of a system for location based smart photo sharing.


title: Home-based exercise therapy in patients awaiting liver transplantation: protocol for an observational feasibility trial

abstract: Introduction Liver disease is the third most common cause of premature mortality in the UK. Liver failure accelerates frailty, resulting in skeletal muscle atrophy, functional decline and an associated risk of liver transplant waiting list mortality. However, there is limited research investigating the impact of exercise on patient outcomes pre and post liver transplantation. The waitlist period for patients listed for liver transplantation provides a unique opportunity to provide and assess interventions such as prehabilitation. Methods and analysis This study is a phase I observational study evaluating the feasibility of conducting a randomised control trial (RCT) investigating the use of a home-based exercise programme (HBEP) in the management of patients awaiting liver transplantation. Twenty eligible patients will be randomly selected from the Queen Elizabeth University Hospital Birmingham liver transplant waiting list. Participants will be provided with an individually tailored 12-week HBEP, including step targets and resistance exercises. Activity trackers and patient diaries will be provided to support data collection. For the initial 6 weeks, telephone support will be given to discuss compliance with the study intervention, achievement of weekly targets, and to address any queries or concerns regarding the intervention. During weeks 6–12, participants will continue the intervention without telephone support to evaluate longer term adherence to the study intervention. On completing the intervention, all participants will be invited to engage in a focus group to discuss their experiences and the feasibility of an RCT. Ethics and dissemination The protocol is approved by the National Research Ethics Service Committee North West - Greater Manchester East and Health Research Authority (REC reference: 17/NW/0120). Recruitment into the study started in April 2017 and ended in July 2017. Follow-up of participants is ongoing and due to finish by the end of 2017. The findings of this study will be disseminated through peer-reviewed publications and international presentations. In addition, the protocol will be placed on the British Liver Trust website for public access. Trial registration number NCT02949505; Pre-results.


title: Design and performance analysis of radial flux C-core switched reluctance motor for in-wheel electrical vehicle application

abstract: A 12/15 pole dual air-gap single rotor configuration with C-core radial flux switched reluctance motor (RFSRM) is proposed as a potential candidate for in-wheel electrical vehicle application with removable wheel/rotor, without disturbing the stator or stator winding. The proposed topology offers effective stator and rotor contact area without increasing the diameter of electrical motor and has smaller flux path compared to the conventional RFSRM. This arrangement offers good torque with low excitation current. It is easy to wound the winding at stator core compared to toothed or segmented type axial field SRM. In this paper phase inductance and torque are calculated analytically and compared with finite element analysis (FEA) results.


WISDOM X, DISAANA and D-SUMM: Large-scale NLP Systems for Analyzing Textual Big Data


title: Control of myoelectric prosthetic hand based on surface EMG

abstract: In this paper, a robot hand with two fingers, thumb and index finger, aiming at application to a myoelectric prosthetic hand was built to implement fundamental motions, such as grasping and holding, required in daily life. In order to control each finger of the developed robot hand independently, an identifier which recognizes the finger motions based on the surface EMG using the neural networks was constructed, and recognition rate of the finger motions was examined. Experiment for online finger operation of the built robot hand was carried out using the identifier, and fingers of the robot hand were able to be operated as intended.


title: Using social network analysis to prevent money laundering

abstract: We analyzed over 33,000 financial operations involving an Italian factoring company.We present network construction techniques based on different risk factors.Social network metrics are an important addition to anti-money laundering.Cluster analysis on tacit link networks can help identify suspicious actors.Our models and metrics are easy to replicate and to integrate into existing systems. This research explores the opportunities for the application of network analytic techniques to prevent money laundering. We worked on real world data by analyzing the central database of a factoring company, mainly operating in Italy, over a period of 19 months. This database contained the financial operations linked to the factoring business, together with other useful information about the company clients.We propose a new approach to sort and map relational data and present predictive models - based on network metrics - to assess risk profiles of clients involved in the factoring business. We find that risk profiles can be predicted by using social network metrics. In our dataset, the most dangerous social actors deal with bigger or more frequent financial operations; they are more peripheral in the transactions network; they mediate transactions across different economic sectors and operate in riskier countries or Italian regions. Finally, to spot potential clusters of criminals, we propose a visual analysis of the tacit links existing among different companies who share the same owner or representative. Our findings show the importance of using a network-based approach when looking for suspicious financial operations and potential criminals.


title: Assessing antidepressant activity in rodents: recent developments and future needs.

abstract: Animal models are indispensable tools in the search to identify new antidepressant drugs and to provide insights into the neuropathology that underlies the idiopathic disease state of depression. As new targets are developed, both serendipitously and through hypothesis-driven research, existing animal paradigms are being modified and new tests are being developed to detect antidepressant actions of compounds acting on a broad range of neural and genetic targets. This review focuses on recent findings regarding some of the most widely employed animal models used currently to predict antidepressant potential. Emphasis is placed on recent modifications to such paradigms that have increased their utility and reliability. Furthermore, some key issues that need to be addressed for future discovery of novel antidepressant agents are examined, and the available data on genetically altered mice that might lead to the discovery of novel targets for antidepressant action are collated.


title: DDoS Attack Mitigation in Internet of Things Using Software Defined Networking

abstract: Securing Internet of Things (IoT) systems is a challenge because of its multiple points of vulnerability. A spate of recent hacks and security breaches has unveiled glaring vulnerabilities in the IoT. Due to the computational and memory requirement constraints associated with anomaly detection algorithms in core networks, commercial in-line (part of the direct line of communication) Anomaly Detection Systems (ADSs) rely on sampling-based anomaly detection approaches to achieve line rates and truly-inline anomaly detection accuracy in real-time. However, packet sampling is inherently a lossy process which might provide an incomplete and biased approximation of the underlying traffic patterns. Moreover, commercial routers uses proprietary software making them closed to be manipulated from the outside. As a result, detecting malicious packets on the given network path is one of the most challenging problems in the field of network security. We argue that the advent of Software Defined Networking (SDN) provides a unique opportunity to effectively detect and mitigate DDoS attacks. Unlike sampling-based approaches for anomaly detection and limitation of proprietary software at routers, we use the SDN infrastructure to relax the sampling-based ADS constraints and collect traffic flow statistics which are maintained at each SDN-enabled switch to achieve high detection accuracy. In order to implement our idea, we discuss how to mitigate DDoS attacks using the features of SDN infrastructure.


title: Voltage Control Techniques in Uninterruptible Power Supply Inverters: a Review

abstract: Uninterruptible power supply (UPS) systems are required for supplying sinusoidal output voltage for linear and nonlinear loads. They must be highly reliable and fast in dynamic response. Many control strategies have been applied to UPS inverters. The basic objectives of UPS control systems are tracking ability and robustness. Generally, the tasks of output voltage control for UPS inverters are providing fast dynamic responses and maintaining a perfect sinuso- idal voltage waveform even with nonlinear or changing loads. To achieve these aims, many controllers have been proposed in the literature. In this paper a comprehensive review of the control techniques of UPS systems with advantages and disadvantages of each is carried out. Copyright © 2011 Praise Worthy Prize S.r.l. - All rights reserved.


title: Monitoring children’s developmental progress using augmented toys and activity recognition

abstract: Previous research has established the connection between the way in which children interact with objects and the potential early identification of children with autism. Those findings motivate our own work to develop "smart toys," objects embedded with wireless sensors that are safe and enjoyable for very small children, that allow detailed interaction data to be easily recorded. These sensor-enabled toys provide opportunities for autism research by reducing the effort required to collect and analyze a child's interactions with objects. In the future, such toys may be a useful part of clinical and in-home assessment tools. In this paper, we discuss the design of a collection of smart toys that can be used to automatically characterize the way in which a child is playing. We use statistical models to provide objective, quantitative measures of object play interactions. We also developed a tool to view rich forms of annotated play data for later analysis. We report the results of recognition experiments on more than fifty play sessions conducted with adults and children as well as discuss the opportunities for using this approach to support video annotation and other applications.


title: Motion guidance using Haptic Feedback based on vibrotactile illusions

abstract: In this paper we present a wearable Haptic Feedback Device to convey intuitive motion direction to the user through haptic feedback based on vibrotactile illusions. Vibrotactile illusions occur on the skin when two or more vibrotactile actuators in proximity are actuated in coordinated sequence, causing the user to feel combined sensations, instead of separate ones. By combining these illusions we can produce various sensation patterns that are discernible by the user, thus allowing to convey different information with each pattern. A method to provide information about direction through vibrotactile illusions is introduced on this paper. This method uses a grid of vibrotactile actuators around the arm actuated in coordination. The sensation felt on the skin is consistent with the desired direction of motion, so the desired motion can be intuitively understood. We show that the users can recognize the conveyed direction, and implemented a proof of concept of the proposed method to guide users' elbow flexion/extension motion.


title: Microtask programming: building software with a crowd

abstract: Microtask crowdsourcing organizes complex work into workflows, decomposing large tasks into small, relatively independent microtasks. Applied to software development, this model might increase participation in open source software development by lowering the barriers to contribu-tion and dramatically decrease time to market by increasing the parallelism in development work. To explore this idea, we have developed an approach to decomposing programming work into microtasks. Work is coordinated through tracking changes to a graph of artifacts, generating appropriate microtasks and propagating change notifications to artifacts with dependencies. We have implemented our approach in CrowdCode, a cloud IDE for crowd development. To evaluate the feasibility of microtask programming, we performed a small study and found that a small crowd of 12 workers was able to successfully write 480 lines of code and 61 unit tests in 14.25 person-hours of time.


title: Design of Integrated Light Guide Plate With Functional Structure of Enhanced Diffusion Length for Ultra-slim LED Backlight Unit

abstract: An integrated light guide plate (LGP) with a functional structure for a longer diffusion length to decrease the thickness was proposed and designed for the large area direct-type LED backlight unit (BLU). The integrated LGP is composed of a concave cone structure on the top surface and a rugged bottom surface. The light emitted from the LED sources can be reflected on the top surface and diffuse effectively inside the LGP and in the bottom air-gap, which is placed below the LGP to realize sufficient diffusion length even in the ultra-slim structure. After comparing the optical simulation results with the measured data from the fabricated hexagonal unit cell of an LGP, we verified our design concept and realized the integrated LGP. The final LGP for a 55-inch LED display shows a brightness uniformity of 90.5% with a reduced number of 81 LEDs and a thickness of 6 mm, a remarkably reduced value compared with previous direct-type large area LED backlight units.


title: Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework

abstract: Intrusion detection systems (IDSs) play an important role in identifying malicious attacks and threats in networking systems. As fundamental tools of IDSs, learning based classification methods have been widely employed. When it comes to detecting network intrusions in small sample sizes (e.g., emerging intrusions), the limited number and imbalanced proportion of training samples usually cause significant challenges in training supervised and semi-supervised classifiers. In this paper, we propose a general network intrusion detection framework to address the challenges of both \emph{data scarcity} and \emph{data imbalance}. The novelty of the proposed framework focuses on incorporating deep adversarial learning with statistical learning and exploiting learning based data augmentation. Given a small set of network intrusion samples, it first derives a Poisson-Gamma joint probabilistic generative model to generate synthesised intrusion data using Monte Carlo methods. Those synthesised data are then augmented by deep generative neural networks through adversarial learning. Finally, it adopts the augmented intrusion data to train supervised models for detecting network intrusions. Comprehensive experimental validations on KDD Cup 99 dataset show that the proposed framework outperforms the existing learning based IDSs in terms of improved accuracy, precision, recall, and F1-score.


title: Pixels to Voxels: Modeling Visual Representation in the Human Brain

abstract: The human brain is adept at solving difficult high-level visual processing problems such as image interpretation and object recognition in natural scenes. Over the past few years neuroscientists have made remarkable progress in understanding how the human brain represents categories of objects and actions in natural scenes. However, all current models of high-level human vision operate on hand annotated images in which the objects and actions have been assigned semantic tags by a human operator. No current models can account for high-level visual function directly in terms of low-level visual input (i.e., pixels). To overcome this fundamental limitation we sought to develop a new class of models that can predict human brain activity directly from low-level visual input (i.e., pixels). We explored two classes of models drawn from computer vision and machine learning. The first class of models was based on Fisher Vectors (FV) and the second was based on Convolutional Neural Networks (ConvNets). We find that both classes of models accurately predict brain activity in high-level visual areas, directly from pixels and without the need for any semantic tags or hand annotation of images. This is the first time that such a mapping has been obtained. The fit models provide a new platform for exploring the functional principles of human vision, and they show that modern methods of computer vision and machine learning provide important tools for characterizing brain function.


Using denoising autoencoder for emotion recognition.


title: The relationship between technology adoption and strategy in business-to-business markets The case of e-commerce $

abstract: Abstract Firms have been traditionally advised to adopt information and communication technologies (ICT) to support the achievement of existing business objectives. However, entry into business-to-business e-commerce may require the concurrent adoption of new business strategies. This paper argues, therefore, that ICT analysis needs to be considered at the same time and at the same strategic level, as internal, competitor and market analysis. This is because the source of the competitive advantage brought by ICT has changed, to be largely obtained through increase in customers' perceived value. The literature presents a confused picture of the likely consequences to a firm of its adoption of e-commerce. This paper concludes that any such adoption must be evaluated in the environment of the individual firm. A multidimensional analytical framework, taking a combined informational and marketing perspective, is presented to assist with such evaluations.


title: Neurostream: Scalable and Energy Efficient Deep Learning with Smart Memory Cubes

abstract: High-performance computing systems are moving towards 2.5D and 3D memory hierarchies, based on High Bandwidth Memory (HBM) and Hybrid Memory Cube (HMC) to mitigate the main memory bottlenecks. This trend is also creating new opportunities to revisit near-memory computation. In this paper, we propose a flexible processor-in-memory (PIM) solution for scalable and energy-efficient execution of deep convolutional networks (ConvNets), one of the fastest-growing workloads for servers and high-end embedded systems. Our co-design approach consists of a network of Smart Memory Cubes (modular extensions to the standard HMC) each augmented with a many-core PIM platform called NeuroCluster. NeuroClusters have a modular design based on NeuroStream coprocessors (for Convolution-intensive computations) and general-purpose RISC-V cores. In addition, a DRAM-friendly tiling mechanism and a scalable computation paradigm are presented to efficiently harness this computational capability with a very low programming effort. NeuroCluster occupies only 8 percent of the total logic-base (LoB) die area in a standard HMC and achieves an average performance of 240 GFLOPS for complete execution of full-featured state-of-the-art (SoA) ConvNets within a power budget of 2.5 W. Overall 11 W is consumed in a single SMC device, with 22.5 GFLOPS/W energy-efficiency which is 3.5X better than the best GPU implementations in similar technologies. The minor increase in system-level power and the negligible area increase make our PIM system a cost-effective and energy efficient solution, easily scalable to 955 GFLOPS with a small network of just four SMCs.


title: High-order CFD methods: current status and perspective

abstract: After several years of planning, the 1st International Workshop on High-Order CFD Methods was successfully held in Nashville, Tennessee, on January 7-8, 2012, just before the 50th Aerospace Sciences Meeting. The American Institute of Aeronautics and Astronautics, the Air Force Office of Scientific Research, and the German Aerospace Center provided much needed support, financial and moral. Over 70 participants from all over the world across the research spectrum of academia, government labs, and private industry attended the workshop. Many exciting results were presented. In this review article, the main motivation and major findings from the workshop are described. Pacing items requiring further effort are presented.


title: B-Fetch: Branch Prediction Directed Prefetching for Chip-Multiprocessors

abstract: For decades, the primary tools in alleviating the "Memory Wall" have been large cache hierarchies and data prefetchers. Both approaches, become more challenging in modern, Chip-multiprocessor (CMP) design. Increasing the last-level cache (LLC) size yields diminishing returns in terms of performance per Watt; given VLSI power scaling trends, this approach becomes hard to justify. These trends also impact hardware budgets for prefetchers. Moreover, in the context of CMPs running multiple concurrent processes, prefetching accuracy is critical to prevent cache pollution effects. These concerns point to the need for a light-weight prefetcher with high accuracy. Existing data prefetchers may generally be classified as low-overhead and low accuracy (Next-n, Stride, etc.) or high-overhead and high accuracy (STeMS, ISB). We propose B-Fetch: a data prefetcher driven by branch prediction and effective address value speculation. B-Fetch leverages control flow prediction to generate an expected future path of the executing application. It then speculatively computes the effective address of the load instructions along that path based upon a history of past register transformations. Detailed simulation using a cycle accurate simulator shows a geometric mean speedup of 23.4% for single-threaded workloads, improving to 28.6% for multi-application workloads over a baseline system without prefetching. We find that B-Fetch outperforms an existing "best-of-class" light-weight prefetcher under single-threaded and multi programmed workloads by 9% on average, with 65% less storage overhead.


Under consideration forpublication in Knowledge and Information Systems Compressed Representations for Web and Social Graphs 1


title: Effects of caffeine on human behavior

abstract: The literature suggests that the following effects on behavior of adult humans may occur when individuals consume moderate amounts of caffeine. (1) Caffeine increases alertness and reduces fatigue. This may be especially important in low arousal situations (e.g. working at night). (2) Caffeine improves performance on vigilance tasks and simple tasks that require sustained response. Again, these effects are often clearest when alertness is reduced, although there is evidence that benefits may still occur when the person is unimpaired. (3) Effects on more complex tasks are difficult to assess and probably involve interactions between the caffeine and other variables which increase alertness (e.g. personality and time of day). (4) In contrast to the effects of caffeine consumption, withdrawal of caffeine has few effects on performance. There is often an increase in negative mood following withdrawal of caffeine, but such effects may largely reflect the expectancies of the volunteers and the failure to conduct "blind" studies. (5) Regular caffeine usage appears to be beneficial, with higher users having better mental functioning. (6) Most people are very good at controlling their caffeine consumption to maximise the above positive effects. For example, the pattern of consumption over the day shows that caffeine is often consumed to increase alertness. Indeed, many people do not consume much caffeine later in the day since it is important not to be alert when one goes to sleep. In contrast to effects found from normal caffeine intake, there are reports that have demonstrated negative effects when very large amounts are given or sensitive groups (e.g. patients with anxiety disorders) were studied. In this context caffeine has been shown to increase anxiety and impair sleep. There is also some evidence that fine motor control may be impaired as a function of the increase in anxiety. Overall, the global picture that emerges depends on whether one focuses on effects that are likely to be present when caffeine is consumed in moderation by the majority of the population or on the effects found in extreme conditions. The evidence clearly shows that levels of caffeine consumed by most people have largely positive effects on behavior. Excessive consumption can lead to problems, especially in sensitive individuals.


title: to the journal pertain. Use of the Separation Property to Derive Liquid State Machines with Enhanced Classification Performance

abstract: Liquid State Machines constitute a powerful computational tool for carrying out complex real time computations on continuous input streams. Their performance is based on two properties, approximation and separation. While the former depends on the selection of class functions for the readout maps, the latter needs to be evaluated for a particular liquid architecture. In the current paper we show how the Fisher's Discriminant Ratio can be used to effectively measure the separation of a Liquid State Machine. This measure is then used as a fitness function in an evolutionary framework that searches for suitable liquid properties and architectures in order to optimize the performance of the trained readouts. Evaluation results demonstrate the effectiveness of the proposed approach.


title: BOLT: Energy Disaggregation by Online Binary Matrix Factorization of Current Waveforms

abstract: In this paper we introduce BOLT, a novel approach for the problem of energy disaggregation that performs online binary matrix factorization on a sequence of high frequency current cycles collected in a building to infer additive subcomponents of the current signal. The system learns these constituent current waveforms in an unsupervised fashion and, in a subsequent step, seeks to find combinations of these subcomponents that constitute appliances. By doing so, points in time when appliances are active and, to some degree, their power consumption can be estimated by BOLT. Our system treats energy disaggregation as a binary matrix factorization problem and uses a neural network, with binary activations in the one but last layer and a linear output layer, to solve it. The algorithmic performance of the proposed method is evaluated on a publicly available dataset. Furthermore, we show that, once the model is trained, the algorithm can perform inference in real-time on inexpensive off-the-shelf and general purpose hardware which allows leveraging high-frequency information without having to explicitly transmit and store large amounts of data to a centralized repository.


title: Partitioning Graphs into Balanced Components

abstract: We consider the k-balanced partitioning problem, where the goal is to partition the vertices of an input graph G into k equally sized components, while minimizing the total weight of the edges connecting different components. We allow k to be part of the input and denote the cardinality of the vertex set by n. This problem is a natural and important generalization of well-known graph partitioning problems, including minimum bisection and minimum balanced cut. ::: ::: We present a (bi-criteria) approximation algorithm achieving an approximation of O(√log n log k), which matches or improves over previous algorithms for all relevant values of k. Our algorithm uses a semidefinite relaxation which combines l22 metrics with spreading metrics. Surprisingly, we show that the integrality gap of the semidefinite relaxation is Ω(log k) even for large values of k (e.g., k = nΩ(1), implying that the dependence on k of the approximation factor is necessary. This is in contrast to previous approximation algorithms for k-balanced partitioning, which are based on linear programming relaxations and their approximation factor is independent of k.


title: Markov chain fingerprinting to classify encrypted traffic

abstract: In this paper, we propose stochastic fingerprints for application traffic flows conveyed in Secure Socket Layer/Transport Layer Security (SSL/TLS) sessions. The fin- gerprints are based on first-order homogeneous Markov chains for which we identify the parameters from observed training application traces. As the fingerprint parameters of chosen applications considerably differ, the method results in a very good accuracy of application discrimination and provides a possibility of detecting abnormal SSL/TLS sessions. Our analysis of the results reveals that obtaining application discrimination mainly comes from incorrect implementation practice, the misuse of the SSL/TLS protocol, various server configurations, and the application nature. fingerprints of sessions to classify application traffic. We call a fingerprint any distinctive feature allowing identification of a given traffic class. In this work, a fingerprint corresponds to a first-order homogeneous Markov chain reflecting the dynamics of an SSL/TLS session. The Markov chain states model a sequence of SSL/TLS message types appearing in a single direction flow of a given application from a server to a client. We have studied the Markov chain fingerprints for twelve representative applications that make use of SSL/TLS: PayPal (an electronic service allowing online payments and money transfers), Twitter (an online social networking and micro- blogging service), Dropbox (a file hosting service), Gadu- Gadu (a popular Polish instant messenger), Mozilla (a part of Mozilla add-ons service responsible for verification of the software version), MBank and PKO (two popular European online banking services), Dziekanat (student online service), Poczta (student online mail service), Amazon S3 (a Simple Storage Service) and EC2 (an Elastic Compute Cloud), and Skype (a VoIP service). The resulting models exhibit a specific structure allowing to classify encrypted application flows by comparing its message sequences with fingerprints. They can also serve to reveal intrusions trying to exploit the SSL/TLS protocol by establishing abnormal communications with a server.


Semantic search engine : A survey


Data Mining : A Book Recommender System Using Frequent Pattern Algorithm


title: Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation

abstract: We propose a unified approach for bottom-up hierarchical image segmentation and object proposal generation for recognition, called Multiscale Combinatorial Grouping (MCG). For this purpose, we first develop a fast normalized cuts algorithm. We then propose a high-performance hierarchical segmenter that makes effective use of multiscale information. Finally, we propose a grouping strategy that combines our multiscale regions into highly-accurate object proposals by exploring efficiently their combinatorial space. We also present Single-scale Combinatorial Grouping (SCG), a faster version of MCG that produces competitive proposals in under five second per image. We conduct an extensive and comprehensive empirical validation on the BSDS500, SegVOC12, SBD, and COCO datasets, showing that MCG produces state-of-the-art contours, hierarchical regions, and object proposals.


title: Reinforcement Learning based Control of Traffic Lights in Non-stationary Environments: A Case Study in a Microscopic Simulator

abstract: Coping with dynamic changes in traffic volume has been the object of recent publications. Recently, a method was proposed, which is capable of learning in non-stationary scenarios via an approach to detect context changes. For particular scenarios such as the traffic control one, the performance of that method is better than a greedy strategy, as well as other reinforcement learning approaches, such as Q-learning and Prioritized Sweeping. The goal of the present paper is to assess the feasibility of applying the above mentioned approach in a more realistic scenario, implemented by means of a microscopic traffic simulator. We intend to show that to use of context detection is suitable to deal with noisy scenarios where non-stationarity occurs not only due to the changing volume of vehicles, but also because of the random behavior of drivers in what regards the operational task of driving (e.g. deceleration probability). The results confirm the tendencies already detected in the previous paper, although here the increase in noise makes the learning task much more difficult, and the correct separation of contexts harder.


A Sequence Model Approach to Relation Extraction in Portuguese.


title: Read + Verify: Machine Reading Comprehension with Unanswerable Questions

abstract: Machine reading comprehension with unanswerable questions aims to abstain from answering when no answer can be inferred. In addition to extract answers, previous works usually predict an additional “no-answer” probability to detect unanswerable cases. However, they fail to validate the answerability of the question by verifying the legitimacy of the predicted answer. To address this problem, we propose a novel read-then-verify system, which not only utilizes a neural reader to extract candidate answers and produce no-answer probabilities, but also leverages an answer verifier to decide whether the predicted answer is entailed by the input snippets. Moreover, we introduce two auxiliary losses to help the reader better handle answer extraction as well as no-answer detection, and investigate three different architectures for the answer verifier. Our experiments on the SQuAD 2.0 dataset show that our system obtains a score of 74.2 F1 on test set, achieving state-of-the-art results at the time of submission (Aug. 28th, 2018).


title: Problematic Gaming and Subjective Well-Being: How Does Mindfulness Play a Role?

abstract: Video game use becomes problematic when it disrupts the ability to fulfill social and personal responsibilities. It has also been associated with poorer subjective well-being (i.e., life satisfaction, positive and negative affectivity). Research suggests that dispositional (or trait) mindfulness (e.g., general tendency to have focused awareness of the present moment, with acceptance) is associated with greater subjective well-being. Building on research on dispositional mindfulness’ protective role against other problematic behaviours (e.g., pathological gambling), this study assessed its influence on the relationship between problematic gaming and subjective well-being. A sample of 514 frequent gamers (37.9% female; M age = 22.56 years, SD = 4.3) completed an online survey. Structural equation modelling revealed that dispositional mindfulness partially negatively mediated the relationship between problematic gaming and both life satisfaction and negative affectivity while fully positively mediating that between problematic gaming and positive affectivity, suggesting dispositional mindfulness may be clinically useful in problematic gaming prevention and interventions.


title: ATLAS: a probabilistic algorithm for high dimensional similarity search

abstract: Given a set of high dimensional binary vectors and a similarity function (such as Jaccard and Cosine), we study the problem of finding all pairs of vectors whose similarity exceeds a given threshold. The solution to this problem is a key component in many applications with feature-rich objects, such as text, images, music, videos, or social networks. In particular, there are many important emerging applications that require the use of relatively low similarity thresholds. We propose ATLAS, a probabilistic similarity search algorithm that in expectation finds a 1 - δ fraction of all similar vector pairs. ATLAS uses truly random permutations both to filter candidate pairs of vectors and to estimate the similarity between vectors. At a 97.5% recall rate, ATLAS consistently outperforms all state-of-the-art approaches and achieves a speed-up of up to two orders of magnitude over both exact and approximate algorithms.


Yelp Food Recommendation System


title: Exome RNA sequencing reveals rare and novel alternative transcripts

abstract: RNA sequencing has become an important method to perform hypothesis-free characterization of global gene expression. One of the limitations of RNA sequencing is that most sequence reads represent highly expressed transcripts, whereas low level transcripts are challenging to detect. To combine the benefits of traditional expression arrays with the advantages of RNA sequencing, we have used whole exome enrichment prior to sequencing of total RNA. We show that whole exome capture can be successfully applied to cDNA to study the transcriptional landscape in human tissues. By introducing the exome enrichment step, we are able to identify transcripts present at very low levels, which are below the level of detection in conventional RNA sequencing. Although the enrichment increases the ability to detect presence of transcripts, it also lowers the accuracy of quantification of expression levels. Our results yield a large number of novel exons and splice isoforms, suggesting that conventional RNA sequencing methods only detect a small fraction of the full transcript diversity. We propose that whole exome enrichment of RNA is a suitable strategy for genome-wide discovery of novel transcripts, alternative splice variants and fusion genes.


title: Graph Data Anonymization, De-Anonymization Attacks, and De-Anonymizability Quantification: A Survey

abstract: Nowadays, many computer and communication systems generate graph data. Graph data span many different domains, ranging from online social network data from networks like Facebook to epidemiological data used to study the spread of infectious diseases. Graph data are shared regularly for many purposes including academic research and for business collaborations. Since graph data may be sensitive, data owners often use various anonymization techniques that often compromise the resulting utility of the anonymized data. To make matters worse, there are several state-of-the-art graph data de-anonymization attacks that have proven successful in recent years. In this paper, we survey the graph data anonymization, de-anonymization, and de-anonymizability quantification techniques in the past decade. Specifically, we systematically classify, summarize, and analyze state-of-the-art graph data anonymization, de-anonymization, and de-anonymizability quantification techniques. For existing graph data anonymization techniques, we classify them into six categories and analyze their utility performance with respect to 15 fundamental graph utility metrics and seven high-level application utility metrics. For existing de-anonymization attacks, we classify them into two categories and examine their performance with respect to scalability, practicability, robustness, etc. We also analyze the resistance of existing graph anonymization techniques against existing graph de-anonymizaiton attacks. For existing de-anonymizability quantifications, we classify them according to whether they consider seed information or not, and analyze them in terms of their soundness. Our analysis demonstrates that: 1) most anonymization schemes can partially or conditionally preserve most graph utility while losing some application utility and 2) state-of-the-art anonymization schemes are vulnerable to several or all of the emerging structure-based de-anonymization attacks. The actual vulnerability of each anonymization algorithm depends on how much and which data utility it preserves. Based on our summarization and analysis, we discuss the research evolution, future directions, and challenges in the research area of graph data anonymization, de-anonymization, and de-anonymizability quantification.


title: A Hidden Markov Model for Vehicle Detection and Counting

abstract: To reduce roadway congestion and improve traffic safety, accurate traffic metrics, such as number of vehicles travelling through lane-ways, are required. Unfortunately most existing infrastructure, such as loop-detectors and many video detectors, do not feasibly provide accurate vehicle counts. Consequently, a novel method is proposed which models vehicle motion using hidden Markov models (HMM). The proposed method represents a specified small region of the roadway as 'empty', 'vehicle entering', 'vehicle inside', and 'vehicle exiting', and then applies a modified Viterbi algorithm to the HMM sequential estimation framework to initialize and track vehicles. Vehicle observations are obtained using an Adaboost trained Haar-like feature detector. When tested on 88 hours of video, from three distinct locations, the proposed method proved to be robust to changes in lighting conditions, moving shadows, and camera motion, and consistently out-performed Multiple Target Tracking (MTT) and Virtual Detection Line(VDL) implementations. The median vehicle count error of the proposed method is lower than MTT and VDL by 28%, and 70% respectively. As future work, this algorithm will be implemented to provide the traffic industry with improved automated vehicle counting, with the intent to eventually provide real-time counts.


title: Analysis and Design of an Ultralow-Power CMOS Relaxation Oscillator

abstract: This paper presents the design of a low-voltage ultralow-power relaxation oscillator without external components. The application field for this oscillator is the clock generation of low-power wake-up functions for battery-operated systems. A detailed analysis of the oscillator, including the temperature performance, is derived and verified with experimental results. The oscillator operates at a typical frequency of 3.3 kHz and consumes 11 nW from a 1-V supply at room temperature, and a temperature drift of less than 500 ppm/°C is achieved over the temperature range of -20°C to 80°C. An efficient design implementation has resulted in a cell area of 0.1 mm2 in a standard 0.35- μm digital CMOS technology.


title: Digitally-controlled RF self-interference canceller for full-duplex radios

abstract: This paper addresses the self-interference (SI) cancellation in a full-duplex radio transceiver. In particular, we focus on shared-antenna based full-duplex transceivers where the self-interference coupling channel is always frequency-selective and can also be strongly time-varying depending on the antenna matching characteristics and reflections from the surroundings. A novel digitally-controlled RF self-interference canceller structure is described, being able to process the signals in a frequency-selective manner as well as track adaptively the time-varying SI features, stemming from the fast digital control loop. A complete demonstrator board is developed, reported and measured, incorporating both the RF processing and the digital control processing. Comprehensive RF measurements are then also carried out and reported at 2.4GHz ISM band, evidencing more than 40dBs of active RF cancellation gain up to 80MHz instantaneous waveform bandwidths. Furthermore, real-time self-adaptive tracking features are successfully demonstrated.


title: Semi-supervised adaptation of RNNLMs by fine-tuning with domain-specific auxiliary features

abstract: Recurrent neural network language models (RNNLMs) can be augmented with auxiliary features, which can provide an extra modality on top of the words. It has been found that RNNLMs perform best when trained on a large corpus of generic text and then fine-tuned on text corresponding to the sub-domain for which it is to be applied. However, in many cases the auxiliary features are available for the sub-domain text but not for the generic text. In such cases, semi-supervised techniques can be used to infer such features for the generic text data such that the RNNLM can be trained and then fine-tuned on the available in-domain data with corresponding auxiliary features. ::: ::: In this paper, several novel approaches are investigated for dealing with the semi-supervised adaptation of RNNLMs with auxiliary features as input. These approaches include: using zero features during training to mask the weights of the feature sub-network; adding the feature sub-network only at the time of fine-tuning; deriving the features using a parametric model and; back-propagating to infer the features on the generic text. These approaches are investigated and results are reported both in terms of PPL and WER on a multi-genre broadcast ASR task.


title: All-digital TX frequency synthesizer and discrete-time receiver for Bluetooth radio in 130-nm CMOS

abstract: We present a single-chip fully compliant Bluetooth radio fabricated in a digital 130-nm CMOS process. The transceiver is architectured from the ground up to be compatible with digital deep-submicron CMOS processes and be readily integrated with a digital baseband and application processor. The conventional RF frequency synthesizer architecture, based on the voltage-controlled oscillator and the phase/frequency detector and charge-pump combination, has been replaced with a digitally controlled oscillator and a time-to-digital converter, respectively. The transmitter architecture takes advantage of the wideband frequency modulation capability of the all-digital phase-locked loop with built-in automatic compensation to ensure modulation accuracy. The receiver employs a discrete-time architecture in which the RF signal is directly sampled and processed using analog and digital signal processing techniques. The complete chip also integrates power management functions and a digital baseband processor. Application of the presented ideas has resulted in significant area and power savings while producing structures that are amenable to migration to more advanced deep-submicron processes, as they become available. The entire IC occupies 10 mm/sup 2/ and consumes 28 mA during transmit and 41 mA during receive at 1.5-V supply.


Bilateral mammary carcinoma; immunologic implications.


title: A Feature Study for Classification-Based Speech Separation at Low Signal-to-Noise Ratios

abstract: Speech separation can be formulated as a classification problem. In classification-based speech separation, supervised learning is employed to classify time-frequency units as either speech-dominant or noise-dominant. In very low signal-to-noise ratio (SNR) conditions, acoustic features extracted from a mixture are crucial for correct classification. In this study, we systematically evaluate a range of promising features for classification-based separation using six nonstationary noises at the low SNR level of -5 dB, which is chosen with the goal of improving human speech intelligibility in mind. In addition, we propose a new feature called multi-resolution cochleagram (MRCG). The new feature is constructed by combining four cochleagrams at different spectrotemporal resolutions in order to capture both the local and contextual information. Experimental results show that MRCG gives the best classification results among all evaluated features. In addition, our results indicate that auto-regressive moving average (ARMA) filtering, a post-processing technique for improving automatic speech recognition features, also improves many acoustic features for speech separation.


Optimized advertising content delivery in affiliate networks


title: Mobile Apps in Cardiology : Review

abstract: BACKGROUND ::: Cardiovascular diseases are the deadliest diseases worldwide, with 17.3 million deaths in 2008 alone. Among them, heart-related deaths are of the utmost relevance; a fact easily proven by the 7.25 million deaths caused by ischemic heart disease alone in that year. The latest advances in smartphones and mHealth have been used in the creation of thousands of medical apps related to cardiology, which can help to reduce these mortality rates. ::: ::: ::: OBJECTIVE ::: The aim of this paper is to study the literature on mobile systems and applications currently available, as well as the existing apps related to cardiology from the leading app stores and to then classify the results to see what is available and what is missing, focusing particularly on commercial apps. ::: ::: ::: METHODS ::: Two reviews have been developed. One is a literature review of mobile systems and applications, retrieved from several databases and systems such as Scopus, PubMed, IEEE Xplore, and Web of Knowledge. The other is a review of mobile apps in the leading app stores, Google play for Android and Apple's App Store for iOS. ::: ::: ::: RESULTS ::: Search queries up to May 2013 located 406 papers and 710 apps related to cardiology and heart disease. The most researched section in the literature associated with cardiology is related to mobile heart (and vital signs) monitoring systems and the methods involved in the classification of heart signs in order to detect abnormal functions. Other systems with a significant number of papers are mobile cardiac rehabilitation systems, blood pressure measurement, and systems for the detection of heart failure. The majority of apps for cardiology are heart monitors and medical calculators. Other categories with a high number of apps are those for ECG education and interpretation, cardiology news and journals, blood pressure tracking, heart rate monitoring using an external device, and CPR instruction. There are very few guides on cardiac rehabilitation and apps for the management of the cardiac condition, and there were no apps that assist people who have undergone a heart transplant. ::: ::: ::: CONCLUSIONS ::: The distribution of work in the field of cardiology apps is considerably disproportionate. Whereas some systems have significant research and apps are available, other important systems lack such research and lack apps, even though the contribution they could provide is significant.


title: Information hiding using steganography

abstract: Due to advances in ICT, most information is kept electronically. Consequently, the security of information has become a fundamental issue. Besides cryptography, steganography can be employed to secure information. Steganography is a technique of hiding information in digital media. In contrast to cryptography, the message or encrypted message is embedded in a digital host before passing it through the network, thus the existence of the message is unknown. Besides hiding data for confidentiality, this approach of information hiding can be extended to copyright protection for digital media: audio, video, and images.


Dimensionality Reduction


Detecting Novel Attacks by Identifying


Adaptive Image Segmentation


title: SPEAKER IDENTIFICATION USING MEL FREQUENCY CEPSTRAL COEFFICIENTS

abstract: This paper presents a security system based on speaker identification. Mel frequency Cepstral Coefficients{MFCCs} have been used for feature extraction and vector quantization technique is used to minimize the amount of data to be handled .


title: White Blood Cell Segmentation by Color-Space-Based K-Means Clustering

abstract: White blood cell (WBC) segmentation, which is important for cytometry, is a challenging issue because of the morphological diversity of WBCs and the complex and uncertain background of blood smear images. This paper proposes a novel method for the nucleus and cytoplasm segmentation of WBCs for cytometry. A color adjustment step was also introduced before segmentation. Color space decomposition and k-means clustering were combined for segmentation. A database including 300 microscopic blood smear images were used to evaluate the performance of our method. The proposed segmentation method achieves 95.7% and 91.3% overall accuracy for nucleus segmentation and cytoplasm segmentation, respectively. Experimental results demonstrate that the proposed method can segment WBCs effectively with high accuracy.


title: Use of Ultrasound-Activated Resorbable Poly-D-L-Lactide Pins (SonicPins) and Foil Panels (Resorb-X) for Horizontal Bone Augmentation of the Maxillary and Mandibular Alveolar Ridges

abstract: Horizontal bone augmentation of the maxillary and mandibular alveolar ridges has been conventionally performed using mini titanium alloy screws. The titanium alloy screws are used to fixate corticocancellous block grafts to the recipient site or for tenting the mucoperiosteum to retain particulate bone grafts. Nonresorbable guided tissue regenerative membranes reinforced with titanium have also been developed to use with particulate bone grafts to augment alveolar ridge defects. This report demonstrates the use of resorbable ultrasound-activated pins and resorbable foil panels developed by KLS Martin for augmenting the alveolar ridges with particulate bone grafts.


title: Constructing a Natural Language Inference Dataset using Generative Neural Networks

abstract: Natural Language Inference is an important task for Natural Language Understanding. It is concerned with classifying the logical relation between two sentences. In this paper, we propose several text generative neural networks for constructing Natural Language Inference datasets suitable for training classifiers. To evaluate the models, we propose a new metric - the accuracy of the classifier trained on the generated dataset. The accuracy obtained with our best generative model is only 2.7% lower than the accuracy of the classifier trained on the original, manually constructed dataset. The model learns a mapping embedding for each training example. By comparing various metrics we show that datasets that obtain higher ROUGE or METEOR scores do not necessarily yield higher classification accuracies. We also provide analysis of what are the characteristics of a good dataset including the distinguishability of the generated datasets from the original one.


title: The Digital Self-Tuning Control of Step a Down DC-DC Converter

abstract: A digital self-tuning control technique of DC-DC Buck converter is considered and thoroughly analyzed in this paper. The development of the small-signal model of the converter, which is the key of the control design presented in this work, is based on the state-space averaged (SSA) technique. Adaptive control has become a widely-used term in DC-DC conversion in recent years. A digital self-tuning Dahlin PID and a /Keviczky PID controller based on recursive least- squares estimation are developed and designed to be applied to the voltage mode control (VMC) approach operating in a continuous conduction mode (CCM). A comparative study of these two digital self-tuning controllers for step change in input voltage magnitude or output load is also carried out. The simulation results obtained using a Matlab SimPowerSystems toolbox to validate the effectiveness of the proposed strategies are also given and discussed extensively.


title: Feature Extraction and Automatic Recognition of Plant Leaf Using Artificial Neural Network

abstract: In this paper we propose an automatic recognition system for wild flowers. Two photos, one of the flower and one of the leaves taken from directly above or at a close angle of a single wild flower, were used as a single set. The objective (flower, leaf) is extracted from each image using a clustering method, then recognition is performed using a piecewise linear discriminant function after finding 10 features in the picture of a flower and 11 features in the picture of a leaf. We performed experiments on 20 sets of 34 species of wild flowers that grow around our university campus in the spring and early summer. The results of the experiment showed a recognition rate of 96.0% when all of the features (21) were used. Next we performed an experiment to select the features particularly useful for recognition. These results showed that six features for the pictures of flowers and two features for the pictures of leaves were the most effective, and with them a recognition rate of 96.8% could be reached. © 2003 Wiley Periodicals, Inc. Syst Comp Jpn, 34(10): 90–101, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10099


title: Disease detection on the leaves of the tomato plants by using deep learning

abstract: The aim of this work is to detect diseases that occur on plants in tomato fields or in their greenhouses. For this purpose, deep learning was used to detect the various diseases on the leaves of tomato plants. In the study, it was aimed that the deep learning algorithm should be run in real time on the robot. So the robot will be able to detect the diseases of the plants while wandering manually or autonomously on the field or in the greenhouse. Likewise, diseases can also be detected from close-up photographs taken from plants by sensors built in fabricated greenhouses. The examined diseases in this study cause physical changes in the leaves of the tomato plant. These changes on the leaves can be seen with RGB cameras. In the previous studies, standard feature extraction methods on plant leaf images to detect diseases have been used. In this study, deep learning methods were used to detect diseases. Deep learning architecture selection was the key issue for the implementation. So that, two different deep learning network architectures were tested first AlexNet and then SqueezeNet. For both of these deep learning networks training and validation were done on the Nvidia Jetson TX1. Tomato leaf images from the PlantVillage dataset has been used for the training. Ten different classes including healthy images are used. Trained networks are also tested on the images from the internet.


title: A Joint Speaker-Listener-Reinforcer Model for Referring Expressions

abstract: Referring expressions are natural language constructions used to identify particular objects within a scene. In this paper, we propose a unified framework for the tasks of referring expression comprehension and generation. Our model is composed of three modules: speaker, listener, and reinforcer. The speaker generates referring expressions, the listener comprehends referring expressions, and the reinforcer introduces a reward function to guide sampling of more discriminative expressions. The listener-speaker modules are trained jointly in an end-to-end learning framework, allowing the modules to be aware of one another during learning while also benefiting from the discriminative reinforcer's feedback. We demonstrate that this unified framework and training achieves state-of-the-art results for both comprehension and generation on three referring expression datasets. Project and demo page: https://vision.cs.unc.edu/refer


title: Statistical Investigation of the User Effects on Mobile Terminal Antennas for 5G Applications

abstract: In this paper, the user effects on mobile terminal antennas at 28 GHz are statistically investigated with the parameters of body loss, coverage efficiency, and power in the shadow. The data are obtained from the measurements of 12 users in data and talk modes, with the antenna placed on the top and bottom of the chassis. In the measurements, the users hold the phone naturally. The radiation patterns and shadowing regions are also studied. It is found that a significant amount of power can propagate into the shadow of the user by creeping waves and diffractions. A new metric is defined to characterize this phenomenon. A mean body loss of 3.2–4 dB is expected in talk mode, which is also similar to the data mode with the bottom antenna. A body loss of 1 dB is expected in data mode with the top antenna location. The variation of the body loss between the users at 28 GHz is less than 2 dB, which is much smaller than that of the conventional cellular bands below 3 GHz. The coverage efficiency is significantly reduced in talk mode, but only slightly affected in data mode.


title: A deep ensemble learning method for monaural speech separation

abstract: Monaural speech separation is a fundamental problem in robust speech processing. Recently, deep neural network (DNN)-based speech separation methods, which predict either clean speech or an ideal time-frequency mask, have demonstrated remarkable performance improvement. However, a single DNN with a given window length does not leverage contextual information sufficiently, and the differences between the two optimization objectives are not well understood. In this paper, we propose a deep ensemble method, named multicontext networks, to address monaural speech separation. The first multicontext network averages the outputs of multiple DNNs whose inputs employ different window lengths. The second multicontext network is a stack of multiple DNNs. Each DNN in a module of the stack takes the concatenation of original acoustic features and expansion of the soft output of the lower module as its input, and predicts the ratio mask of the target speaker; the DNNs in the same module employ different contexts. We have conducted extensive experiments with three speech corpora. The results demonstrate the effectiveness of the proposed method. We have also compared the two optimization objectives systematically and found that predicting the ideal time-frequency mask is more efficient in utilizing clean training speech, while predicting clean speech is less sensitive to SNR variations.


title: Circular range search on encrypted spatial data

abstract: Searchable encryption is a promising technique enabling meaningful search operations to be performed on encrypted databases while protecting user privacy from untrusted third-party service providers. However, while most of the existing works focus on common SQL queries, geometric queries on encrypted spatial data have not been well studied. Especially, circular range search is an important type of geometric query on spatial data which has wide applications, such as proximity testing in Location-Based Services and Delaunay triangulation in computational geometry. In this poster, we propose two novel symmetric-key searchable encryption schemes supporting circular range search. Informally, both of our schemes can correctly verify whether a point is inside a circle on encrypted spatial data without revealing data privacy or query privacy to a semi-honest cloud server. We formally define the security of our proposed schemes, prove that they are secure under Selective Chosen-Plaintext Attacks, and evaluate their performance through experiments in a real-world cloud platform (Amazon EC2). To the best of our knowledge, this work represents the first study in secure circular range search on encrypted spatial data.


title: Verifying Distributed Directory-based Cache Coherence Protocols: S3.mp, a Case Study

abstract: This paper presents the results for the verification of the S3.mp cache coherence protocol. The S3.mp protocol uses a distributed directory with limited number of pointers and hardware supported overflow handling that keeps processing nodes sharing a data block in a singly linked list. The complexity of the protocol is high and its validation is challenging because of the distributed algorithm used to maintain the linked lists and the non-FIFO network. We found several design errors, including an error which only appears in verification models of more than three processing nodes, which is very unlikely to be detected by intensive simulations. We believe that methods described in this paper are applicable to the verification of other linked list based protocols such as the IEEE Scalable Coherent Interface.


title: Exploring evolution and emerging trends in business model study: a co-citation analysis

abstract: This paper is an early attempt of using co-citation analysis to sort out and to analyze the development and evolution of a latest hot area, business model. A dataset of 1498 records published between 1995 and 2015 is collected from Web of Science database. The empirical results show the latest hot topics in the business model study focus on business model innovation and value creation. In addition, technology oriented articles and strategy oriented articles provide some of the main perspectives of business model study. The conclusions and implications of business model in this paper will be particularly illuminating for both academic research and enterprises' practice application.


title: Memory augmented neural network for source separation

abstract: Recurrent neural network (RNN) based on long short-term memory (LSTM) has been successfully developed for single-channel source separation. Temporal information is learned by using dynamic states which are evolved through time and stored as an internal memory. The performance of source separation is constrained due to the limitation of internal memory which could not sufficiently preserve long-term characteristics from different sources. This study deals with this limitation by incorporating an external memory in RNN and accordingly presents a memory augmented neural network for source separation. In particular, we carry out a neural Turing machine to learn a separation model for sequential signals of speech and noise in presence of different speakers and noise types. Experiments show that speech enhancement based on memory augmented neural network consistently outperforms that using deep neural network and LSTM in terms of short-term objective intelligibility measure.


Design of Endoscope Holder for Otology Surgery


title: Microstrip Array Antenna With 2-D Steerable Focus in Near-Field Region

abstract: A microstrip planar array antenna is presented in this paper, which is designed to have near-field focused beams, and it is capable of steering its focus on the designed focal plane. The array is composed of eight series-fed linear arrays, which are able to scan the focus by frequency around 10 GHz in the H-plane, whereas, E-plane focus scan can be achieved by feeding the linear arrays with different phases. The proposed array can be used in near-field scan systems, e.g., microwave imaging system, with a faster scan rate than mechanically scanned antennas and a lower cost than array antennas with full phase control elements. Theoretical analysis is presented in this paper, and is verified by simulations and experiments.


title: Fast Automatic Heart Chamber Segmentation from 3D CT Data Using Marginal Space Learning and Steerable Features

abstract: Multi-chamber heart segmentation is a prerequisite for global quantification of the cardiac function. The complexity of cardiac anatomy, poor contrast, noise or motion artifacts makes this segmentation problem a challenging task. In this paper, we present an efficient, robust, and fully automatic segmentation method for 3D cardiac computed tomography (CT) volumes. Our approach is based on recent advances in learning discriminative object models and we exploit a large database of annotated CT volumes. We formulate the segmentation as a two step learning problem: anatomical structure localization and boundary delineation. A novel algorithm, marginal space learning (MSL), is introduced to solve the 9-dimensional similarity search problem for localizing the heart chambers. MSL reduces the number of testing hypotheses by about six orders of magnitude. We also propose to use steerable image features, which incorporate the orientation and scale information into the distribution of sampling points, thus avoiding the time-consuming volume data rotation operations. After determining the similarity transformation of the heart chambers, we estimate the 3D shape through learning-based boundary delineation. Extensive experiments on multi-chamber heart segmentation demonstrate the efficiency and robustness of the proposed approach, comparing favorably to the state-of-the-art. This is the first study reporting stable results on a large cardiac CT dataset with 323 volumes. In addition, we achieve a speed of less than eight seconds for automatic segmentation of all four chambers.


title: Epidemiology of Alzheimer Disease

abstract: The global prevalence of dementia has been estimated to be as high as 24 million, and is predicted to double every 20 years until at least 2040. As the population worldwide continues to age, the number of individuals at risk will also increase, particularly among the very old. Alzheimer disease is the leading cause of dementia beginning with impaired memory. The neuropathological hallmarks of Alzheimer disease include diffuse and neuritic extracellular amyloid plaques in brain that are frequently surrounded by dystrophic neurites and intraneuronal neurofibrillary tangles. The etiology of Alzheimer disease remains unclear, but it is likely to be the result of both genetic and environmental factors. In this review we discuss the prevalence and incidence rates, the established environmental risk factors, and the protective factors, and briefly review genetic variants predisposing to disease.


Detecting opinion spams and fake news using text classification


title: Vertical GAAFETs for the Ultimate CMOS Scaling

abstract: In this paper, we compare the performances of FinFETs, lateral gate-all-around (GAA) FETs, and vertical GAAFETs (VFETs) at 7-nm node dimensions and beyond. Comparison is done at ring oscillator level accounting not only for front-end of line devices but also for interconnects. It is demonstrated that FinFETs fail to maintain the performance at scaled dimensions, while VFETs demonstrate good scalability and eventually outperform lateral devices both in speed and power consumption. Lateral GAAFETs show better scalability with respect to FinFETs but still consume 35% more energy per switch than VFETs if made under 5-nm node design rules.


title: Disfluencies and human language comprehension

abstract: Spoken language contains disfluencies, which include editing terms such as uh and um as well as repeats and corrections. In less than ten years the question of how disfluencies are handled by the human sentence comprehension system has gone from virtually ignored to a topic of major interest in computational linguistics and psycholinguistics. We discuss relevant empirical findings and describe a computational model that captures how disfluencies influence parsing and comprehension. The research reviewed shows that the parser, which presumably evolved to handle conversations, deals with disfluencies in a way that is efficient and linguistically principled. The success of this research program reinforces the current trend in cognitive science to view cognitive mechanisms as adaptations to real-world constraints and challenges.


title: Depth map creation and image-based rendering for advanced 3DTV services providing interoperability and scalability

abstract: Due to enormous progress in the areas of auto-stereoscopic 3D displays, digital video broadcast and computer vision algorithms, 3D television (3DTV) has reached a high technical maturity and many people now believe in its readiness for marketing. Experimental prototypes of entire 3DTV processing chains have been demonstrated successfully during the last few years, and the motion picture experts group (MPEG) of ISO/IEC has launched related ad hoc groups and standardization efforts envisaging the emerging market segment of 3DTV. In this context the paper discusses an advanced approach for a 3DTV service, which is based on the concept of video-plus-depth data representations. It particularly considers aspects of interoperability and multi-view adaptation for the case that different multi-baseline geometries are used for multi-view capturing and 3D display. Furthermore it presents algorithmic solutions for the creation of depth maps and depth image-based rendering related to this framework of multi-view adaptation. In contrast to other proposals, which are more focused on specialized configurations, the underlying approach provides a modular and flexible system architecture supporting a wide range of multi-view structures.


title: Dermoscopy: basic concepts

abstract: Dermoscopy is a very useful technique for the analysis of pigmented skin lesions. It represents a link between clinical and histological views, permitting an earlier diagnosis of skin melanoma. It also helps in the diagnosis of many other pigmented skin lesions, such as seborrheic keratosis, pigmented basal cell carcinoma, hemangioma, blue nevus, atypical nevus, and mole, which can often clinically simulate melanoma. In this article, dermoscopy is reviewed from its history to the basic concepts of the interpretation of dermoscopic images. The goal is to introduce this subject to those not yet familiar with it, in order to instigate and encourage the training and practice of this technique of growing importance for everyday usage.


title: Analysis of Load Balancing Techniques in Cloud Computing

abstract: Cloud Computing is an emerging computing paradigm. It aims to share data, calculations, and service transparently over a scalable network of nodes. Since Cloud computing stores the data and disseminated resources in the open environment. So, the amount of data storage increases quickly. In the cloud storage, load balancing is a key issue. It would consume a lot of cost to maintain load information, since the system is too huge to timely disperse load. Load balancing is one of the main challenges in cloud computing which is required to distribute the dynamic workload across multiple nodes to ensure that no single node is overwhelmed. It helps in optimal utilization of resources and hence in enhancing the performance of the system. A few existing scheduling algorithms can maintain load balancing and provide better strategies through efficient job scheduling and resource allocation techniques as well. In order to gain maximum profits with optimized load balancing algorithms, it is necessary to utilize resources efficiently. This paper discusses some of the existing load balancing algorithms in cloud computing and also their challenges.


title: A Comparison of Parametric and Nonparametric Approaches to ROC Analysis of Quantitative Diagnostic Tests

abstract: Receiver operating characteristic (ROC) analysis, which yields indices of accuracy such as the area under the curve (AUC), is increasingly being used to evaluate the performances of diagnostic tests that produce results on continuous scales. Both par ametric and nonparametric ROC approaches are available to assess the discriminant capacity of such tests, but there are no clear guidelines as to the merits of each, particularly with non-binormal data. Investigators may worry that when data are non- Gaussian, estimates of diagnostic accuracy based on a binormal model may be dis torted. The authors conducted a Monte Carlo simulation study to compare the bias and sampling variability in the estimates of the AUCs derived from parametric and nonparametric procedures. Each approach was assessed in data sets generated from various configurations of pairs of overlapping distributions; these included the binormal model and non-binormal pairs of distributions where one or both pair members were mixtures of Gaussian (...


title: Review of background subtraction methods using Gaussian mixture model for video surveillance systems

abstract: Foreground detection or moving object detection is a fundamental and critical task in video surveillance systems. Background subtraction using Gaussian Mixture Model (GMM) is a widely used approach for foreground detection. Many improvements have been proposed over the original GMM developed by Stauffer and Grimson (IEEE Computer Society conference on computer vision and pattern recognition, vol 2, Los Alamitos, pp 246---252, 1999. doi:10.1109/CVPR.1999.784637) to accommodate various challenges experienced in video surveillance systems. This paper presents a review of various background subtraction algorithms based on GMM and compares them on the basis of quantitative evaluation metrics. Their performance analysis is also presented to determine the most appropriate background subtraction algorithm for the specific application or scenario of video surveillance systems.


title: VSRank: A Novel Framework for Ranking-Based Collaborative Filtering

abstract: Collaborative filtering (CF) is an effective technique addressing the information overload problem. CF approaches generally fall into two categories: rating based and ranking based. The former makes recommendations based on historical rating scores of items and the latter based on their rankings. Ranking-based CF has demonstrated advantages in recommendation accuracy, being able to capture the preference similarity between users even if their rating scores differ significantly. In this study, we propose VSRank, a novel framework that seeks accuracy improvement of ranking-based CF through adaptation of the vector space model. In VSRank, we consider each user as a document and his or her pairwise relative preferences as terms. We then use a novel degree-specialty weighting scheme resembling TF-IDF to weight the terms. Extensive experiments on benchmarks in comparison with the state-of-the-art approaches demonstrate the promise of our approach.


title: The Identification of Noteworthy Hotel Reviews for Hotel Management

abstract: User-generated content (UGC) plays an increasingly important role for knowledge sharing among Internet users in recent years. Many travel sites encourage users to share their experiences and express their opinions on attractions, accommodations, restaurants, etc. The UGC about travel provide precious information to the staff in travel industry as well as the end users. Most of previous works in travel recommendation focus on identifying attractions/hotels or reviews that are useful to the customers by analyzing UGC. In this paper, we argue that reviews that are noteworthy for hotel management is critical to the success of hotels in the competitive travel industry. We employ two hotel managers to conduct an examination on Taiwan’s hotel reviews in Tripadvisor.com and they report that noteworthy reviews can be characterized by their content features, sentiments, and writing qualities. We propose three methods for representing content features. Through the experiments using tripadvisor.com data, we find that, in terms of content feature representation, LDA method achieves comparable performance to TF-IDF method with higher recall and much fewer features. In addition, all the three types of features are important in identifying noteworthy hotel reviews. Specifically, content features are shown to have the most impact on precisions, whereas sentiments and review qualities impact more on recalls.


title: Cost comparison of running web applications in the cloud using monolithic, microservice, and AWS Lambda architectures

abstract: Large Internet companies like Amazon, Netflix, and LinkedIn are using the microservice architecture pattern to deploy large applications in the cloud as a set of small services that can be independently developed, tested, deployed, scaled, operated, and upgraded. However, aside from gaining agility, independent development, and scalability, how microservices affect the infrastructure costs is a major evaluation topic for companies adopting this pattern. This paper presents a cost comparison of a web application developed and deployed using the same scalable scenarios with three different approaches: 1) a monolithic architecture, 2) a microservice architecture operated by the cloud customer, and 3) a microservice architecture operated by the cloud provider. Test results show that microservices can help reduce infrastructure costs in comparison with standard monolithic architectures. Moreover, the use of services specifically designed to deploy and scale microservices, such as AWS Lambda, reduces infrastructure costs by 70% or more, and unlike microservices operated by cloud customers, these specialized services help to guarantee the same performance and response times as the number of users increases. Lastly, we also describe the challenges we faced while implementing and deploying microservice applications, and include a discussion on how to replicate the results on other cloud providers.


title: Cross-modal Hallucination for Few-shot Fine-grained Recognition

abstract: State-of-the-art deep learning algorithms generally require large amounts of data for model training. Lack thereof can severely deteriorate the performance, particularly in scenarios with fine-grained boundaries between categories. To this end, we propose a multimodal approach that facilitates bridging the information gap by means of meaningful joint embeddings. Specifically, we present a benchmark that is multimodal during training (i.e. images and texts) and single-modal in testing time (i.e. images), with the associated task to utilize multimodal data in base classes (with many samples), to learn explicit visual classifiers for novel classes (with few samples). Next, we propose a framework built upon the idea of cross-modal data hallucination. In this regard, we introduce a discriminative text-conditional GAN for sample generation with a simple self-paced strategy for sample selection. We show the results of our proposed discriminative hallucinated method for 1-, 2-, and 5- shot learning on the CUB dataset, where the accuracy is improved by employing multimodal data.


title: Global geometric similarity scheme for feature selection in fault diagnosis

abstract: This work presents a global geometric similarity scheme (GGSS) for feature selection in fault diagnosis, which is composed of global geometric model and similarity metric. The global geometric model is formed to construct connections between disjoint clusters in fault diagnosis. The similarity metric of the global geometric model is applied to filter feature subsets. To evaluate the performance of GGSS, fault data from wind turbine test rig is collected, and condition classification is carried out with classifiers established by Support Vector Machine (SVM) and General Regression Neural Network (GRNN). The classification results are compared with feature ranking methods and feature wrapper approaches. GGSS achieves higher classification accuracy than the feature ranking methods, and better time efficiency than the feature wrapper approaches. The hybrid scheme, GGSS with wrapper, obtains optimal classification accuracy and time efficiency. The proposed scheme can be applied in feature selection to get better accuracy and efficiency in condition classification of fault diagnosis.


title: Health-aware Food Recommender System

abstract: With the rapid changes in the food variety and lifestyles, many people are facing the problem of making healthier food decisions to reduce the risk of chronic diseases such as obesity and diabetes. To this end, our recommender system not only offers recipe recommendations that suit the user's preference but is also able to take the user's health into account. It is developed on a mobile platform by considering that our application may be directly used in the kitchen. This demo paper summarizes the complete human-computer interaction design, the implemented health-aware recommendation algorithm and preliminary user feedback.


title: Double hand-gesture interaction for walk-through in VR environment

abstract: In this paper, we present a double hand-gesture interaction (DHGI) method for walk-through in VR environment with an Oculus Rift headset and Leap Motion function. The user can control the avatar (first-person view) to move (walk/run) forward or backward by turning the user's left palm upward or downward, and by turning the avatar to the left or right with the right thumb pointing toward either direction. Compared with the results of the joystick input device and portal method using Oculus Rift Touches, the objective and subjective findings of this study indicate that DHGI is intuitive, easy to learn, easy to use, and causes low fatigue. Moreover, the user feedback shows that DHGI significantly improves immersion and reduces the sense of motion sickness in VR.


title: Application of IEC 60079-10-1 Edition 2.0 for Hazardous Area Classification

abstract: This document provides guidance on the application of International Electrotechnical Commission (IEC) 60079-10-1 Edition 2.0: Explosive Atmospheres—Part 10-1: Classification of Areas—Explosive Gas Atmospheres. The IEC 60079-10-1 Ed. 2.0 document incorporates significant revisions from previous editions in both technical content and design approach to classifying hazardous locations where flammable gas or vapors may be present. The design concepts incorporated into the document are introduced with application guidance provided in the context of real-world examples.


title: Towards webcam-based tracking for interventional navigation

abstract: PURPOSE: Optical tracking is a commonly used tool in computer assisted surgery and surgical training; however, many current generation commercially available tracking systems are prohibitively large and expensive for certain applications. We developed an open source optical tracking system using the Intel RealSense SR300 webcam with integrated depth sensor. In this paper, we assess the accuracy of this tracking system. METHODS: The PLUS toolkit was extended to incorporate the ArUco marker detection and tracking library. The depth data obtained from the infrared sensor of the Intel RealSense SR300 was used to improve accuracy. We assessed the accuracy of the system by comparing this tracker to a high accuracy commercial optical tracker. RESULTS: The ArUco based optical tracking algorithm had median errors of 20.0mm and 4.1 degrees in a 200x200x200mm tracking volume. Our algorithm processing the depth data had a positional error of 17.3mm, and an orientation error of 7.1 degrees in the same tracking volume. In the direction perpendicular to the sensor, the optical only tracking had positional errors between 11% and 15%, compared to errors in depth of 1% or less. In tracking one marker relative to another, a fused transform from optical and depth data produced the best result of 1.39% error. CONCLUSION: The webcam based system does not yet have satisfactory accuracy for use in computer assisted surgery or surgical training.


title: The Impact of Organizational Culture on Job Satisfaction, Employess Commitment and Turn over Intention

abstract: The purpose of the present research was to explore the impact of organizational culture on the job satisfaction, employees' commitment and the retention of the employees in the organization. The research was based on primary data, and this research was conducted on the employee's working in different organizations within territory of Multan region, Punjab, Pakistan. Data was collected through questionnaire consisting of 24 questions; the sample consisted of 235 employees of different Organizations (MCB Layyah, U micro Finance Bank Layyah, Layyah Sugar Mill, Thermal Power Plant Muzaffar Garh, and NRSP DG Khan). The Correlation analysis test was applied through SPSS to find out the results of research. Results indicated that nature of organization significantly effects on Job Satisfaction and turnover intentions. So the findings of research proved that organizational culture is important element which highly influences the employee commitment, job satisfaction and retention.


Understanding Trending Topics in Twitter.


title: Opinion mining in social media: Modeling, simulating, and forecasting political opinions in the web

abstract: Abstract Affordable and ubiquitous online communications (social media) provide the means for flows of ideas and opinions and play an increasing role for the transformation and cohesion of society – yet little is understood about how online opinions emerge, diffuse, and gain momentum. To address this problem, an opinion formation framework based on content analysis of social media and sociophysical system modeling is proposed. Based on prior research and own projects, three building blocks of online opinion tracking and simulation are described: (1) automated topic, emotion and opinion detection in real-time, (2) information flow modeling and agent-based simulation, and (3) modeling of opinion networks, including special social and psychological circumstances, such as the influence of emotions, media and leaders, changing social networks etc. Finally, three application scenarios are presented to illustrate the framework and motivate further research.


title: Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome

abstract: This paper describes a new real-time urban monitoring system. The system uses the Localizing and Handling Network Event Systems (LocHNESs) platform developed by Telecom Italia for the real-time evaluation of urban dynamics based on the anonymous monitoring of mobile cellular networks. In addition, data are supplemented based on the instantaneous positioning of buses and taxis to provide information about urban mobility in real time, ranging from traffic conditions to the movements of pedestrians throughout the city. This system was exhibited at the Tenth International Architecture Exhibition of the Venice Biennale. It marks the unprecedented monitoring of a large urban area, which covered most of the city of Rome, in real time using a variety of sensing systems and will hopefully open the way to a new paradigm of understanding and optimizing urban dynamics.