title: On the design of an energy-harvesting protocol stack for Body Area Nano-NETworks

abstract: Abstract Body Area Nano-NETworks (BANNETs) consist of integrated nano-machines, diffused in the human body for collecting diagnostic information and tuning medical treatments. Endowed with communication capabilities, such nano-metric devices can interact with each other and the external micro/macro world, thus enabling advanced health-care services (e.g., therapeutic, monitoring, sensing, and telemedicine tasks). Due to limited computational and communication capabilities of nano-devices, as well as their scarce energy availability, the design of powerful BANNET systems represents a very challenging research activity for upcoming years. Starting from the most significant and recent findings of the research community, this work provides a further step ahead by proposing a hierarchical network architecture, which integrates a BANNET and a macro-scale health-care monitoring system and two different energy-harvesting protocol stacks that regulate the communication among nano-devices during the execution of advanced nano-medical applications. The effectiveness of devised solutions and the comparison with the common flooding-based communication technique have been evaluated through computer simulations. Results highlight pros and cons of considered approaches and pave the way for future activities in the Internet of Nano-Things and nano-medical research fields.


title: Teaching 3D geometry to deformable part models

abstract: Current object class recognition systems typically target 2D bounding box localization, encouraged by benchmark data sets, such as Pascal VOC. While this seems suitable for the detection of individual objects, higher-level applications such as 3D scene understanding or 3D object tracking would benefit from more fine-grained object hypotheses incorporating 3D geometric information, such as viewpoints or the locations of individual parts. In this paper, we help narrowing the representational gap between the ideal input of a scene understanding system and object class detector output, by designing a detector particularly tailored towards 3D geometric reasoning. In particular, we extend the successful discriminatively trained deformable part models to include both estimates of viewpoint and 3D parts that are consistent across viewpoints. We experimentally verify that adding 3D geometric information comes at minimal performance loss w.r.t. 2D bounding box localization, but outperforms prior work in 3D viewpoint estimation and ultra-wide baseline matching.


title: Switched-Mode Operational Amplifiers and Their Application to Continuous-Time Filters in Nanoscale CMOS

abstract: We introduce a new class of feedback amplifiers, called switched-mode operational amplifiers (SMOAs) that address voltage-swing limitations of classical feedback amplifiers in scaled CMOS technologies. By exploiting the increased timing resolution available in scaled CMOS, SMOAs encode analog signal information in the time domain and provide near-rail-to-rail output-signal swing, high output-stage efficiency and better linearity. A 4th-order, 70 MHz continuous-time active-RC Butterworth filter is presented in 65 nm CMOS to demonstrate the advantages of SMOAs. The filter consumes 25.4 mW from a 0.6 V supply and achieves 55.8 dB peak SNDR while operating at a full-scale of 873 mV ppd. Thanks to SMOAs, the full-scale (73% of the 0.6 V supply voltage) and the bandwidth are respectively, a 2.5 × and 6.2 × improvement over other state-of-the-art low-voltage filters.


title: Event perception: A mind/brain perspective

abstract: People perceive and conceive of activity in terms of discrete events. Here the authors propose a theory according to which the perception of boundaries between events arises from ongoing perceptual processing and regulates attention and memory. Perceptual systems continuously make predictions about what will happen next. When transient errors in predictions arise, an event boundary is perceived. According to the theory, the perception of events depends on both sensory cues and knowledge structures that represent previously learned information about event parts and inferences about actors’ goals and plans. Neurological and neurophysiological data suggest that representations of events may be implemented by structures in the lateral prefrontal cortex and that perceptual prediction error is calculated and evaluated by a processing pathway, including the anterior cingulate cortex and subcortical neuromodulatory systems.


title: Recognizing Abnormal Heart Sounds Using Deep Learning

abstract: The work presented here applies deep learning to the task of automated cardiac auscultation, i.e. recognizing abnormalities in heart sounds. We describe an automated heart sound classification algorithm that combines the use of time-frequency heat map representations with a deep convolutional neural network (CNN). Given the cost-sensitive nature of misclassification, our CNN architecture is trained using a modified loss function that directly optimizes the trade-off between sensitivity and specificity. We evaluated our algorithm at the 2016 PhysioNet Computing in Cardiology challenge where the objective was to accurately classify normal and abnormal heart sounds from single, short, potentially noisy recordings. Our entry to the challenge achieved a final specificity of 0.95, sensitivity of 0.73 and overall score of 0.84. We achieved the greatest specificity score out of all challenge entries and, using just a single CNN, our algorithm differed in overall score by only 0.02 compared to the top place finisher, which used an ensemble approach.


title: Graph-to-Sequence Learning using Gated Graph Neural Networks

abstract: Many NLP applications can be framed as a graph-to-sequence learning problem. Previous work proposing neural architectures on this setting obtained promising results compared to grammar-based approaches but still rely on linearisation heuristics and/or standard recurrent networks to achieve the best performance. In this work, we propose a new model that encodes the full structural information contained in the graph. Our architecture couples the recently proposed Gated Graph Neural Networks with an input transformation that allows nodes and edges to have their own hidden representations, while tackling the parameter explosion problem present in previous work. Experimental results show that our model outperforms strong baselines in generation from AMR graphs and syntax-based neural machine translation.


Evaluating Crowdsourcing through Amazon Mechanical Turk as a Technique for Conducting Music Perception Experiments


title: Ultra-low power time synchronization using passive radio receivers

abstract: Considering its central importance to sensor networks, time synchronization has received extensive attention by the research community. Nevertheless, we argue in this paper that existing approaches introduce undesirable trade-offs. For example, while GPS offers excellent accuracy for outdoor deployments, the high cost and power consumption of GPS receivers make them prohibitive to many applications. Message-passing protocols, such as FTSP, introduce different sets of compromises and constraints. In this paper, we present an inexpensive and ultra-low power (< 100 μA) mote peripheral, we term the Universal Time Signal Receiver, that leverages the availability of time signals transmitted by dedicated radio stations around the globe to provide access to UTC time with millisecond-level accuracy. We present experimental results measuring signal availability, quality of synchronization across motes, and power consumption. We show that the proposed universal time signal receiver achieves global time synchronization and for applications where millisecond-level precision is sufficient, it consumes up to 1,000 times less energy than GPS or FTSP.


title: A Block Gathering Based on Mobile Web Page Segmentation Algorithm

abstract: Compared to html page, the mobile web page has different features. The mobile web page is arranged through some important tags such as , >table>, which are called Gathering Node in this paper. Based on these Gathering Nodes, we proposed a Block Gathering Based Page Segmentation algorithm (BGBPS). The experiment shows that BGBPS algorithm has great effect on semantic segmentation of mobile web page.


title: Image super-resolution: The techniques, applications, and future

abstract: Super-resolution (SR) technique reconstructs a higher-resolution image or sequence from the observed LR images. As SR has been developed for more than three decades, both multi-frame and single-frame SR have significant applications in our daily life. This paper aims to provide a review of SR from the perspective of techniques and applications, and especially the main contributions in recent years. Regularized SR methods are most commonly employed in the last decade. Technical details are discussed in this article, including reconstruction models, parameter selection methods, optimization algorithms and acceleration strategies. Moreover, an exhaustive summary of the current applications using SR techniques has been presented. Lastly, the article discusses the current obstacles for future research.


title: Neurites grow faster towards the cathode than the anode in a steady field

abstract: We explanted fragments of embryonic chick dorsal root ganglia on to polylysine coated glass and cultured them in a medium containing one unit of nerve growth factor plus enough methylcellulose to give viscosities from 0.01-3,000 poise. We allowed them to grow out in the absence of a field, and then selected explants with halos of neurites which were relatively dense, relatively symmetrical, and practically free of glial cells. These selected explants were then exposed to electrical fields of up to 140 mV/mm for some hours. In media with viscosities of one poise or less, the field some times dragged the central cell mass of an explant towards the anode. However, in cases where the central cell mass did not move, fields of 70-140 mV/mm induced that sector of each neurite halo which faced the cathode to grow out several times faster than the one facing the anode.


title: An investigation of web crawler behavior: characterization and metrics Abstract

abstract: In this paper, we present a characterization study of search-engine crawlers. For the purposes of our work, we use Web-server access logs from five academic sites in three different countries. Based on these logs, we analyze the activity of different crawlers that belong to five search engines: Google, AltaVista, Inktomi, FastSearch and CiteSeer. We compare crawler behavior to the characteristics of the general World-Wide Web traffic and to general characterization studies. We analyze crawler requests to derive insights into the behavior and strategy of crawlers. We propose a set of simple metrics that describe qualitative characteristics of crawler behavior, vis-a-vis a crawler's preference on resources of a particular format, its frequency of visits on a Web site, and the pervasiveness of its visits to a particular site. To the best of our knowledge, this is the first extensive and in depth characterization of search-engine crawlers. Our results and observations provide useful insights into crawler behavior and serve as basis of our ongoing work on the automatic detection of Web crawlers.


How to Group Crashes Effectively : Comparing Manually and Automatically Grouped Crash


title: Performance Enhancement for Network I/O Virtualization with Efficient Interrupt Coalescing and Virtual Receive-Side Scaling

abstract: Virtualization is a key technology in cloud computing; it can accommodate numerous guest VMs to provide transparent services, such as live migration, high availability, and rapid checkpointing. Cloud computing using virtualization allows workloads to be deployed and scaled quickly through the rapid provisioning of virtual machines on physical machines. However, I/O virtualization, particularly for networking, suffers from significant performance degradation in the presence of high-speed networking connections. In this paper, we first analyze performance challenges in network I/O virtualization and identify two problems—conventional network I/O virtualization suffers from excessive virtual interrupts to guest VMs, and the back-end driver does not efficiently use the computing resources of underlying multicore processors. To address these challenges, we propose optimization methods for enhancing the networking performance: 1) Efficient interrupt coalescing for network I/O virtualization and 2) virtual receive-side scaling to effectively leverage multicore processors. These methods are implemented and evaluated with extensive performance tests on a Xen virtualization platform. Our experimental results confirm that the proposed optimizations can significantly improve network I/O virtualization performance and effectively solve the performance challenges.


title: Are Web Users Really Markovian? ABSTRACT

abstract: User modeling on the Web has rested on the fundamental assumption of Markovian behavior --- a user's next action depends only on her current state, and not the history leading up to the current state. This forms the underpinning of PageRank web ranking, as well as a number of techniques for targeting advertising to users. In this work we examine the validity of this assumption, using data from a number of Web settings. Our main result invokes statistical order estimation tests for Markov chains to establish that Web users are not, in fact, Markovian. We study the extent to which the Markovian assumption is invalid, and derive a number of avenues for further research.


A Survey of Mobile Computing Technologies and Applications


title: Beyond the Kalman Filter: Particle Filters for Tracking Applications

abstract: Part I Theoretical concepts: introduction suboptimal nonlinear filters a tutorial on particle filters Cramer-Rao bounds for nonlinear filtering. Part II Tracking applications: tracking a ballistic object bearings-only tracking range-only tracking bistatic radar tracking tracking targets through blind Doppler terrain aided tracking detection and tracking of stealthy targets group and extended object tracking.


title: Game-Theoretic Approach to Feedback-Driven Multi-stage Moving Target Defense

abstract: The static nature of computer networks allows malicious attackers to easily gather useful information about the network using network scanning and packet sniffing. The employment of secure perimeter firewalls and intrusion detection systems cannot fully protect the network from sophisticated attacks. As an alternative to the expensive and imperfect detection of attacks, it is possible to improve network security by manipulating the attack surface of the network in order to create a moving target defense. In this paper, we introduce a proactive defense scheme that dynamically alters the attack surface of the network to make it difficult for attackers to gather system information by increasing complexity and reducing its signatures. We use concepts from systems and control literature to design an optimal and efficient multi-stage defense mechanism based on a feedback information structure. The change of attack surface involves a reconfiguration cost and a utility gain resulting from risk reduction. We use information- and control-theoretic tools to provide closed-form optimal randomization strategies. The results are corroborated by a case study and several numerical examples.


title: Synchronous Generator Brushless Field Excitation and Voltage Regulation via Capacitive Coupling Through Journal Bearings

abstract: Wound field synchronous generators (WFSG) are the standard electromechanical converter for back-up and utility scale power generation. Maintenance costs may be minimized by adopting noncontact or “brushless” technologies to replace sliding slip ring connections for rotor field excitation. This paper presents a brushless excitation approach using ceramic insulated sleeve (journal) bearings with oil lubrication to form capacitively coupled slip rings, in contrast to more traditional inductive brushless exciters and rotary transformers. This capacitive power transfer (CPT) approach exhibits advantages including low weight, low volume, and has a relatively simple construction using off-the-shelf components. Analysis, design, and prototype construction of the CPT system are presented. Experimental results demonstrate that 1.7 nF of capacitive coupling transfers 340 W to the rotor field winding of a 10 kW 208 V WFSG. Voltage regulation of a WFSG is demonstrated during steady state and 1 per unit load step changes yielding a NEMA-MG1 class G2 rating.


title: Zero-shot Sequence Labeling: Transferring Knowledge from Sentences to Tokens

abstract: Can attention- or gradient-based visualization techniques be used to infer token-level labels for binary sequence tagging problems, using networks trained only on sentence-level labels? We construct a neural network architecture based on soft attention, train it as a binary sentence classifier and evaluate against token-level annotation on four different datasets. Inferring token labels from a network provides a method for quantitatively evaluating what the model is learning, along with generating useful feedback in assistance systems. Our results indicate that attention-based methods are able to predict token-level labels more accurately, compared to gradient-based methods, sometimes even rivaling the supervised oracle network.


title: A planar orthomode transducer

abstract: The planar orthomode transducer (OMT) described in this paper couples two microstrip inputs to two orthogonal modes in a square waveguide. One microstripline feeds a 180/spl deg/ hybrid that in turn excites two waveguide probes. A second microstrip directly feeds a third waveguide probe. Mode symmetry provides isolation between the two feeds. All circuitry and probes are fabricated on a single substrate to minimize fabrication cost. An experimental prototype has 30 to 40 dB isolation at millimeter wave frequencies. The low cost and small size of this structure make it appropriate for use in commercial communications applications.


title: Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy

abstract: When AI systems interact with humans in the loop, they are often called on to provide explanations for their plans and behavior. Past work on plan explanations primarily involved the AI system explaining the correctness of its plan and the rationale for its decision in terms of its own model. Such soliloquy is wholly inadequate in most realistic scenarios where the humans have domain and task models that differ significantly from that used by the AI system. We posit that the explanations are best studied in light of these differing models. In particular, we show how explanation can be seen as a "model reconciliation problem" (MRP), where the AI system in effect suggests changes to the human's model, so as to make its plan be optimal with respect to that changed human model. We will study the properties of such explanations, present algorithms for automatically computing them, and evaluate the performance of the algorithms.


title: A Deep Bag-of-Features Model for Music Auto-Tagging

abstract: Feature learning and deep learning have drawn great attention in recent years as a way of transforming input data into more effective representations using learning algorithms. Such interest has grown in the area of music information retrieval (MIR) as well, particularly in music audio classification tasks such as auto-tagging. In this paper, we present a two-stage learning model to effectively predict multiple labels from music audio. The first stage learns to project local spectral patterns of an audio track onto a high-dimensional sparse space in an unsupervised manner and summarizes the audio track as a bag-of-features. The second stage successively performs the unsupervised learning on the bag-of-features in a layer-by-layer manner to initialize a deep neural network and finally fine-tunes it with the tag labels. Through the experiment, we rigorously examine training choices and tuning parameters, and show that the model achieves high performance on Magnatagatune, a popularly used dataset in music auto-tagging.


title: Occupational Therapy Interventions for Adolescents With Autism Spectrum Disorder

abstract: Evidence Connection articles provide a clinical application of systematic reviews developed in conjunction with the American Occupational Therapy Association's (AOTA's) Evidence-Based Practice Project. In this Evidence Connection article, we describe a case report of an adolescent with autism spectrum disorder. The occupational therapy assessment and treatment processes for school, home, community, and transition settings are described. Findings from the systematic reviews on this topic were published in the September/October 2015 issue of the American Journal of Occupational Therapy and in AOTA's Occupational Therapy Practice Guidelines for Individuals With Autism Spectrum Disorder. Each article in this series summarizes the evidence from the published reviews on a given topic and presents an application of the evidence to a related clinical case. Evidence Connection articles illustrate how the research evidence from the reviews can be used to inform and guide clinical decision making.


title: Special-purpose optics to reduce power dissipation in computational sensing and imaging systems

abstract: The central insight in computational sensing and imaging is that the joint design of optics and digital signal processing can lead to electrooptical imaging systems that are superior to those created through sequential design (optics then signal processing). Here “superior” means requiring fewer optical elements, enabling a wider range of form factors, supporting improved performance for a given optics budget, or enabling new imaging functionalities. Merit functions governing joint design depend solely on the digital output; thus the intermediate optical image need not “look good” but merely contain the information such that the final digital output can be computed. This approach underlies a range of new sensing technologies, including lensless imaging based on diffractive optics.[1], [2]


title: Blockchain world - Feeding the blockchain beast if bitcoin ever does go mainstream, the electricity needed to sustain it will be enormous

abstract: Bitcoin "miners" are electromagnetic alchemists, effectively turning megawatt-hours of electricity into the world's fastest-growing currency. Their intensive computational activity cryptographically secures the virtual currency, approves transactions, and, in the process, creates new bitcoins for the miners, as payment. And it does another thing, too: It uses an absolutely stunning amount of power. The ever-expanding racks of processors used by miners already consume as much electricity as a small city. It's a problem that experts say is bad and getting worse. "The concern that people continue to debate is, where does this end?" says Michael Reed, head of blockchain technology for Intel. The Bitcoin leech sucking on the world's power grids has been held in check, so far, by rapid gains in the energy efficiency of mining hardware. But energy and blockchain analysts are worried about the possibility of a perfect storm: Those efficiency gains are slowing while bitcoin value is rising fast-and its potential transaction growth is immense.


title: The Influence of the Psychology of Programming on a Language Design: Project Status Report

abstract: Research in Psychology of Programming (PoP) and related fields over the past thirty years has identified many important usability issues for programming languages and tools. However, when new programming languages are designed these findings do not seem to have much impact, so popular modern languages continue to exhibit many of the same old problems. This paper reviews the progress of an ongoing project to elevate the influence of PoP on the design of a new programming language. In the context of designing a new programming language for children, we cataloged and interpreted the prior work, performed new studies where questions remained unanswered, and have focused on usability throughout the design. In addition to producing a system that is easier to learn and use than existing systems, we hope to exemplify a process that could be adopted by other language designers to improve the usability of their systems.


The Neural Theory of Metaphor


title: UWB antipodal vivaldi antennas with protruded dielectric rods for higher gain, symmetric patterns and minimal phase center variations

abstract: In this paper, the authors proposed a technique to improve the gain, narrow the H-plane beamwidth, and minimize the phase center variations with frequency by utilizing Vivaldi antenna with a protruded dielectric rod. A sample antenna was fabricated and measured, and preliminary measured results are very promising, and in good agreement with the simulation results.


title: Securing Input Data of Deep Learning Inference Systems via Partitioned Enclave Execution

abstract: Deep learning systems have been widely deployed as backend engines of artificial intelligence (AI) services for their approaching-human performance in cognitive tasks. However, end users always have some concerns about the confidentiality of their provisioned input data, even for those reputable AI service providers. Accidental disclosures of sensitive user data might unexpectedly happen due to security breaches, exploited vulnerabilities, neglect, or insiders. In this paper, we systematically investigate the potential information exposure in deep learning based AI inference systems. Based on our observation, we develop DeepEnclave, a privacy-enhancing system to mitigate sensitive information disclosure in deep learning inference pipelines. The key innovation is to partition deep learning models and leverage secure enclave techniques on cloud infrastructures to cryptographically protect the confidentiality and integrity of user inputs. We formulate the information exposure problem as a reconstruction privacy attack and quantify the adversary's capabilities with different attack strategies. Our comprehensive security analysis and performance measurement can act as a guideline for end users to determine their principle of partitioning deep neural networks, thus to achieve maximum privacy guarantee with acceptable performance overhead.


title: 3D object classification using salient point patterns with application to craniofacial research

abstract: This paper presents a new 3D shape representation and classification methodology developed for use in craniofacial dysmorphology studies. The methodology computes low-level features at each point of a 3D mesh representation, aggregates the features into histograms over mesh neighborhoods, learns the characteristics of salient point histograms for each particular application, and represents the points in a 2D spatial map based on a longitude-latitude transformation. Experimental results on the medical classification tasks show that our methodology achieves higher classification accuracy compared to medical experts and existing state-of-the-art 3D descriptors. Additional experimental results highlight the strength and advantage of the flexible framework that allows the methodology to generalize from specific medical classification tasks to general 3D object classification tasks.


title: The SP theory of intelligence: distinctive features and advantages

abstract: This paper aims to highlight distinctive features of the "SP theory of intelligence" and its apparent advantages compared with some AI-related alternatives. The theory is outlined in an appendix. The distinctive features and advantages are summarised: simplification and integration of observations and concepts, and of structures and processes in computing systems; the SP theory is itself a theory of computing; a central role for information compression via the matching and unification of patterns, and for multiple alignment; transparency in the representation and processing of knowledge; the discovery of 'natural' structures via information compression (DONSVIC); interpretation of aspects of mathematics; interpretation of phenomena in human perception and cognition; realisation of abstract concepts in terms of neurons and their inter-connections ("SP-neural"). Distinctive features and advantages of the SP system compared with alternatives in: minimum length encoding and related concepts; deep learning in neural networks; universal search; Bayesian networks and other models for AI; analysis and production of natural language; learning natural language; exact and inexact reasoning; representation and processing of diverse forms of knowledge; IBM's Watson; problems associated with big data, and in the development of intelligence in autonomous robots. In conclusion, a major strength of SP system is that it can provide a firm foundation for the long-term development of AI and, at the same time, it may deliver some benefits and applications on relatively short timescales. It is envisaged that a high-parallel, open-source version of the SP machine will be created, hosted on an existing high-performance computer and derived from the existing SP computer model. This will be a means for researchers everywhere to explore what can be done with the system, and to create new versions of it.


Analyzing Bicycle Sharing System User Destination Choice Preferences : An Investigation of Chicago ’ s Divvy System


title: Web-based GIS application for real-time interaction of underground infrastructure through virtual reality

abstract: Real-time visualization in web-based system remains challenging due to the amount of information associated to a 3D urban models. However, these 3D models are not able to provide advanced management of urban infrastructures, such as underground facilities. Nowadays, 3D GIS is considered the appropriate tool to provide accurate analysis and decision support based on spatial data. This paper presents a web-GIS application for 3D visualization, navigation, interaction and analysis of underground infrastructures through virtual reality. The growth of underground cities is a complex problem without easy solutions. In general, these infrastructures cannot be directly visualized. Thus, subsoil mapping can help us to develop a clearer representation of underground's pipes, cables or water mains. In addition, the approach of virtual reality provides an immersive experience and novelty interaction to acquire a complete knowledge about underground city structures. Experimental results show an integral application for the efficient management of underground infrastructure in real-time.


title: Generating chinese classical poems with statistical machine translation models

abstract: This paper describes a statistical approach to generation of Chinese classical poetry and proposes a novel method to automatically evaluate poems. The system accepts a set of keywords representing the writing intents from a writer and generates sentences one by one to form a completed poem. A statistical machine translation (SMT) system is applied to generate new sentences, given the sentences generated previously. For each line of sentence a specific model specially trained for that line is used, as opposed to using a single model for all sentences. To enhance the coherence of sentences on every line, a coherence model using mutual information is applied to select candidates with better consistency with previous sentences. In addition, we demonstrate the effectiveness of the BLEU metric for evaluation with a novel method of generating diverse references.


title: G.fast: evolving the copper access network

abstract: Continuous improvements in communication technologies over copper pair access networks have enabled hybrid fiber/copper access networks that provide ever increasing broadband data rates. Most recently, field trials have demonstrated that crosstalk cancellation, also referred to as vectoring, and bonding two pairs to push data rates of very high speed DSL above 100 Mb/s on a single copper pair and above 200 Mb/s on two copper pairs. Looking ahead, the G.fast project group of the ITU-T is currently defining a new copper access technology to provide aggregate (upstream and downstream) data rates of up to 1 Gb/s over distances up to 250 m. This article provides an overview of some key challenges that need to be addressed for G.fast.


title: Neural Networks for Fingerprinting-Based Indoor Localization Using Ultra-Wideband

abstract: This paper discusses the use of neural networks in an underground radio-localization system. In a highly aggressive environment such as mines, reliability and robustness are essential to any operational system. Using UWB as the physical wireless propagation medium and combined with fingerprinting-geolocation and neural networks, this work tends to overcome many of the problems encountered in indoor environments. Full description of the system and the adopted approach will help accentuate the role of neural networks in improving the overall performance. Moreover a comparison between MLP and RBF performance is presented, providing a clear evidence of the role and importance of the neural networks in offering good accuracy and precision to the final system.


title: Selfishness, Altruism and Message Spreading in Mobile Social Networks

abstract: Many kinds of communication networks, in particular social and opportunistic networks, rely at least partly on on humans to help move data across the network. Human altruistic behavior is an important factor determining the feasibility of such a system. In this paper, we study the impact of different distributions of altruism on the throughput and delay of mobile social communication system. We evaluate the system performance using four experimental human mobility traces with uniform and community-biased traffic patterns. We found that mobile social networks are very robust to the distributions of altruism due to the nature of multiple paths. We further confirm the results by simulations on two popular social network models. To the best of our knowledge, this is the first complete study of the impact of altruism on mobile social networks, including the impact of topologies and traffic patterns.


title: An Augmented Reality Virtual Glasses Try-On System

abstract: This paper presents a virtual try-on system to correctly visualize 3D objects (e.g., glasses) in the face of a given user. By capturing the image and depth information of a user through a low-cost RGB-D camera, we apply a face tracking technique to detect specific landmarks in the facial image. These landmarks and the point cloud reconstructed from the depth information are combined to optimize a 3D facial morphable model that fits as good as possible to the user's head and face. At the end, we deform the chosen 3D objects from its rest shape to a deformed shape matching the specific facial shape of the user. The last step projects and renders the 3D object into the original image, with enhanced precision and in proper scale, showing the selected object in the user's face. We validate the performance of our system on eight different subjects (four male and four female) and show results numerically and visually. Our results demonstrate that, by fitting a facial model to the user's face, the rendered virtual 3D objects look more realistic.


SILK: sketching interfaces like krazy


Continuous Relaxation Training of Discrete Latent Variable Image Models


title: A fully general, exact algorithm for nesting irregular shapes

abstract: This paper introduces a fully general, exact algorithm for nesting irregular shapes. Both the shapes and material resource can be arbitrary nonconvex polygons. Moreover, the shapes can have holes and the material can have defective areas. Finally, the shapes can be arranged using both translations and arbitrary rotations (as opposed to a finite set of rotation angles, such as 0 $$^\circ $$ ? and 180 $$^\circ $$ ? ). The insight that has made all this possible is a novel way to relax the constraint that the shapes not overlap. The key idea is to inscribe a few circles in each irregular shape and then relax the non-overlap constraints for the shapes by replacing them with non-overlap constraints for the inscribed circles. These relaxed problems have the form of quadratic programming problems (QPs) and can be solved to optimality to provide valid lower bounds. Valid upper bounds can be found via local search with strict non-overlap constraints. If the shapes overlap in the solution to the relaxed problem, new circles are inscribed in the shapes to prevent this overlapping configuration from recurring, and the QP is then resolved to obtain improved lower bounds. Convergence to any fixed tolerance is guaranteed in a finite number of iterations. A specialized branch-and-bound algorithm, together with some heuristics, are introduced to find the initial inscribed circles that approximate the shapes. The new approach, called "QP-Nest," is applied to three problems as proof of concept. The most complicated of these is a problem due to Milenkovic that has four nonconvex polygons with 94, 72, 84, and 74 vertices, respectively. QP-Nest is able prove global optimality when nesting the first two or the first three of these shapes. When all four shapes are considered, QP-Nest finds the best known solution, but cannot prove optimality.


title: Failure data analytics to build failure prediction mechanisms

abstract: With ever-growing complexity of computer systems, proactive failure management is turning out to be an effective and essential approach for enhancing availability. Several techniques have been proposed to develop failure prediction models [3]. In this paper we have concentrated on the process to build up a failure prediction model based on the failure reports (service ticket logs) from hardware storage devices.


title: Quantitative Gait Measurement With Pulse-Doppler Radar for Passive In-Home Gait Assessment

abstract: In this paper, we propose a Pulse-Doppler radar system for in-home gait assessment of older adults. A methodology has been developed to extract gait parameters including walking speed and step time using Doppler radar. The gait parameters have been validated with a Vicon motion capture system in the lab with 13 participants and 158 test runs. The study revealed that for an optimal step recognition and walking speed estimation, a dual radar set up with one radar placed at foot level and the other at torso level is necessary. An excellent absolute agreement with intra-class correlation coefficients of 0.97 was found for step time estimation with the foot level radar. For walking speed, although both radars show excellent consistency they all have a system offset compared to the ground truth due to walking direction with respect to the radar beam. The torso level radar has a better performance (9% offset on average) in the speed estimation compared to the foot level radar (13-18% offset). Quantitative analysis has been performed to compute the angles causing the systematic error. These lab results demonstrate the capability of the system to be used as a daily gait assessment tool in home environments, useful for fall risk assessment and other health care applications. The system is currently being tested in an unstructured home environment. Language: en


title: Remote Data Auditing in Cloud Computing Environments: A Survey, Taxonomy, and Open Issues

abstract: Cloud computing has emerged as a long-dreamt vision of the utility computing paradigm that provides reliable and resilient infrastructure for users to remotely store data and use on-demand applications and services. Currently, many individuals and organizations mitigate the burden of local data storage and reduce the maintenance cost by outsourcing data to the cloud. However, the outsourced data is not always trustworthy due to the loss of physical control and possession over the data. As a result, many scholars have concentrated on relieving the security threats of the outsourced data by designing the Remote Data Auditing (RDA) technique as a new concept to enable public auditability for the stored data in the cloud. The RDA is a useful technique to check the reliability and integrity of data outsourced to a single or distributed servers. This is because all of the RDA techniques for single cloud servers are unable to support data recovery; such techniques are complemented with redundant storage mechanisms. The article also reviews techniques of remote data auditing more comprehensively in the domain of the distributed clouds in conjunction with the presentation of classifying ongoing developments within this specified area. The thematic taxonomy of the distributed storage auditing is presented based on significant parameters, such as scheme nature, security pattern, objective functions, auditing mode, update mode, cryptography model, and dynamic data structure. The more recent remote auditing approaches, which have not gained considerable attention in distributed cloud environments, are also critically analyzed and further categorized into three different classes, namely, replication based, erasure coding based, and network coding based, to present a taxonomy. This survey also aims to investigate similarities and differences of such a framework on the basis of the thematic taxonomy to diagnose significant and explore major outstanding issues.


title: Accelerated Machine Learning Using TensorFlow and SYCL on OpenCL Devices

abstract: Machine learning is being used in more and more artificial intelligence applications. While existing machine learning frameworks mostly support NVIDIA CUDA GPUs, there has been little research dedicated to targeting other devices through open standards such as OpenCL. In this paper, we explain how machine learning applications can harness the power of OpenCL using open standards and how, by using SYCL, TensorFlow can be extended to include customized operations running on OpenCL devices.


title: Search and Rescue Robotics

abstract: Hydrogen, embrittlement in metals, alloys or welded portions thereof, which causes cracks, breakage and the like, is prevented by emitting an ionizing radiation to a metal, alloy or a welded portion thereof to diffuse and exhale hydrogen charged in said metal.


title: Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis

abstract: Dimensionality Reduction (DR) is a core building block in visualizing multidimensional data. For DR techniques to be useful in exploratory data analysis, they need to be adapted to human needs and domain-specific problems, ideally, interactively, and on-the-fly. Many visual analytics systems have already demonstrated the benefits of tightly integrating DR with interactive visualizations. Nevertheless, a general, structured understanding of this integration is missing. To address this, we systematically studied the visual analytics and visualization literature to investigate how analysts interact with automatic DR techniques. The results reveal seven common interaction scenarios that are amenable to interactive control such as specifying algorithmic constraints, selecting relevant features, or choosing among several DR algorithms. We investigate specific implementations of visual analysis systems integrating DR, and analyze ways that other machine learning methods have been combined with DR. Summarizing the results in a “human in the loop” process model provides a general lens for the evaluation of visual interactive DR systems. We apply the proposed model to study and classify several systems previously described in the literature, and to derive future research opportunities.


title: Forensic detection of median filtering in digital images

abstract: In digital image forensics, prior works are prone to the detection of malicious tampering. However, there is also a need for developing techniques to identify general content-preserved manipulations, which are employed to conceal tampering trails frequently. In this paper, we propose a blind forensic algorithm to detect median filtering (MF), which is applied extensively for signal denoising and digital image enhancement. The probability of zero values on the first order difference map in texture regions can serve as MF statistical fingerprint, which distinguishes MF from other operations. Since anti-forensic techniques enjoy utilizing MF to attack the linearity assumption of existing forensics algorithms, blind detection of the non-linear MF becomes especially significant. Both theoretically reasoning and experimental results verify the effectiveness of our proposed MF forensics scheme.


title: Integrating formal verification in an online judge for e-Learning logic circuit design

abstract: This paper investigates the use of formal verification techniques to create online judges that can assist in teaching logic circuit design. Formal verification not only contributes to give an exact assessment about correctness, but also saves the instructor the tedious task of designing test cases. The paper explains how formal verification has been integrated in an online judge. It also describes the courseware created for a course on logic circuits and the successful experience of using it in a one-week summer course with students from secondary and high school.


title: ARmatika: 3D game for arithmetic learning with Augmented Reality technology

abstract: Learning mathematics is one of the most important aspects that determine the future of learners. However, mathematics as one of the subjects is often perceived as being complicated and not liked by the learners. Therefore, we need an application with the use of appropriate technology to create visualization effects which can attract more attention from learners. The application of Augmented Reality technology in digital game is a series of efforts made to create a better visualization effect. In addition, the system is also connected to a leaderboard web service in order to improve the learning motivation through competitive process. Implementation of Augmented Reality is proven to improve student's learning motivation moreover implementation of Augmented Reality in this game is highly preferred by students.


title: Dissociating cognitive from affective theory of mind: A TMS study

abstract: Investigated the role of the right dorsolateral prefrontal cortex (DLPFC) in cognitive theory-of-mind (ToM) as opposed to affective ToM processes. Subjects included 28 males with a mean age of 24 years who were screened for cognitive dysfunction using a reasoning subtest of the German intelligence test battery Leistungspruefungssystem (LPS), and the Trail Making Test A and B (TMT). The site used for repeated transcranial magnetic stimulation (rTMS) was located in subjects using MRI. 1 Hz rTMS was used to interfere offline with cortical function of the right DLPFC. Immediately following stimulation, subjects were tested with the German version of the Yoni ToM task in which ability to judge mental states via the analysis of verbal cues, eye gaze, and facial expression was assessed. Items on the task were categorized as either cognitive ToM items, affective ToM items, or control physical condition. Results showed a selective effect of rTMS over the right DLPFC on performance in cognitive ToM items but not in affective ToM items. Furthermore, rTMS over the right DLPFC was associated with an acceleration of reaction times in cognitive ToM items. In addition, the reaction times in affective ToM items were consistently faster than reaction times in cognitive ToM items in both conditions. It is concluded that the exact role of the right DLPFC in networks mediating ToM was unidentifiable, and possible explanations of the results are discussed. Cognitive ToM functions are assumed to be independent from affective ToM, and the important role of the right DLPFC in ToM should be further explored.


Prediction of mandibular growth rotation


Cultivate Self-Efficacy for Personal and Organizational Effectiveness


title: Pedestrian navigation aids: information requirements and design implications

abstract: Recent years have seen an increased interest in navigational services for pedestrians. To ensure that these services are successful, it is necessary to understand the information requirements of pedestrians when navigating, and in particular, what information they need and how it is used. A requirements study was undertaken to identify these information requirements within an urban navigation context. Results show that landmarks were by far the most predominant navigation cue, that distance information and street names were infrequently used, and that information is used to enable navigation decisions, but also to enhance the pedestrian’s confidence and trust. The implications for the design of pedestrian navigation aids are highlighted.


title: Forecasting of Multivariate Time Series via Complex Fuzzy Logic

abstract: Multivariate time series consist of sequential vector-valued observations of some phenomenon over time. Time series forecasting (for both univariate and multivariate case) is a well-known, high-value machine learning problem, in which the goal is to predict future observations of the time series based on prior ones. Several learning algorithms based on complex fuzzy logic have recently been shown to be very accurate and compact forecasting models. However, these models have only been tested on univariate and bivariate datasets. There has as yet been no investigation of more general multivariate datasets. We report on the extension of the adaptive neuro-complex-fuzzy inferential system learning architecture to the multivariate case. We investigate single-input-single-output, multiple-input-single-output, and multiple-input-multiple-output variations of the architecture, exploring their performance on four multivariate time series. We also explore modifications to the forward- and backward-pass computations in the architecture. We find that our best designs are superior to the published results on these datasets, and at least as accurate as kernel-based prediction algorithms.


A Semantic Role-based Approach to Open-Domain Automatic Question Generation


title: Two-Dimensional Sensor System for Automotive Crash Prediction

abstract: This paper focuses on the use of magnetoresistive and sonar sensors for imminent collision detection in cars. The magnetoresistive sensors are used to measure the magnetic field from another vehicle in close proximity, to estimate relative position, velocity, and orientation of the vehicle from the measurements. First, an analytical formulation is developed for the planar variation of the magnetic field from a car as a function of 2-D position and orientation. While this relationship can be used to estimate position and orientation, a challenge is posed by the fact that the parameters in the analytical function vary with the type and model of the encountered car. Since the type of vehicle encountered is not known a priori, the parameters in the magnetic field function are unknown. The use of both sonar and magnetoresistive sensors and an adaptive estimator is shown to address this problem. While the sonar sensors do not work at very small intervehicle distance and have low refresh rates, their use during a short initial time duration leads to a reliable estimator. Experimental results are presented for both a laboratory wheeled car door and for a full-scale passenger sedan. The results show that planar position and orientation can be accurately estimated for a range of relative motions at different oblique angles.


title: Gaussian Copula Variational Autoencoders for Mixed Data

abstract: The variational autoencoder (VAE) is a generative model with continuous latent variables where a pair of probabilistic encoder (bottom-up) and decoder (top-down) is jointly learned by stochastic gradient variational Bayes. We first elaborate Gaussian VAE, approximating the local covariance matrix of the decoder as an outer product of the principal direction at a position determined by a sample drawn from Gaussian distribution. We show that this model, referred to as VAE-ROC, better captures the data manifold, compared to the standard Gaussian VAE where independent multivariate Gaussian was used to model the decoder. Then we extend the VAE-ROC to handle mixed categorical and continuous data. To this end, we employ Gaussian copula to model the local dependency in mixed categorical and continuous data, leading to {\em Gaussian copula variational autoencoder} (GCVAE). As in VAE-ROC, we use the rank-one approximation for the covariance in the Gaussian copula, to capture the local dependency structure in the mixed data. Experiments on various datasets demonstrate the useful behaviour of VAE-ROC and GCVAE, compared to the standard VAE.


title: Consistency of Online Random Forests

abstract: As a testament to their success, the theory of random forests has long been outpaced by their application in practice. In this paper, we take a step towards narrowing this gap by providing a consistency result for online random forests.


title: A Printed Xi-Shaped Left-Handed Metamaterial on Low-Cost Flexible Photo Paper

abstract: A Xi-shaped meta structure, has been introduced in this paper. A modified split-ring resonator (MSRR) and a capacitive loaded strip (CLS) were used to achieve the left-handed property of the metamaterial. The structure was printed using silver metallic nanoparticle ink, using a very low-cost photo paper as a substrate material. Resonators were inkjet-printed using silver nanoparticle metallic ink on paper to make this metamaterial flexible. It is also free from any kind of chemical waste, which makes it eco-friendly. A double negative region from 8.72 GHz to 10.91 GHz (bandwidth of 2.19 GHz) in the X-band microwave spectra was been found. Figure of merit was also obtained to measure any loss in the double negative region. The simulated result was verified by the performance of the fabricated prototype. The total dimensions of the proposed structure were 0.29 λ × 0.29 λ × 0.007 λ. It is a promising unit cell because of its simplicity, cost-effectiveness, and easy fabrication process.


title: Hardware Enforcement of Application Security Policies Using Tagged Memory

abstract: Computers are notoriously insecure, in part because application security policies do not map well onto traditional protection mechanisms such as Unix user accounts or hardware page tables. Recent work has shown that application policies can be expressed in terms of information flow restrictions and enforced in an OS kernel, providing a strong assurance of security. This paper shows that enforcement of these policies can be pushed largely into the processor itself, by using tagged memory support, which can provide stronger security guarantees by enforcing application security even if the OS kernel is compromised. ::: ::: We present the Loki tagged memory architecture, along with a novel operating system structure that takes advantage of tagged memory to enforce application security policies in hardware. We built a full-system prototype of Loki by modifying a synthesizable SPARC core, mapping it to an FPGA board, and porting HiStar, a Unix-like operating system, to run on it. One result is that Loki allows HiStar, an OS already designed to have a small trusted kernel, to further reduce the amount of trusted code by a factor of two, and to enforce security despite kernel compromises. Using various workloads, we also demonstrate that HiStar running on Loki incurs a low performance overhead.


title: Delving into egocentric actions

abstract: We address the challenging problem of recognizing the camera wearer's actions from videos captured by an egocentric camera. Egocentric videos encode a rich set of signals regarding the camera wearer, including head movement, hand pose and gaze information. We propose to utilize these mid-level egocentric cues for egocentric action recognition. We present a novel set of egocentric features and show how they can be combined with motion and object features. The result is a compact representation with superior performance. In addition, we provide the first systematic evaluation of motion, object and egocentric cues in egocentric action recognition. Our benchmark leads to several surprising findings. These findings uncover the best practices for egocentric actions, with a significant performance boost over all previous state-of-the-art methods on three publicly available datasets.


PITCH DETECTION ALGORITHM : AUTOCORRELATION METHOD AND AMDF


title: Automated Wireless Meter Reading System for Monitoring and Controlling Power Consumption

abstract: The use of wireless automation in almost all the fields of power, gas and water generation, distribution and billing has come of age. Here with the inclusion of wireless communication with the automation may lead to paradigm change in the current trend. The design presents a new methodology for avoiding the high construction and maintenance costs in the existing meter reading technology. Apart the use of wireless meter reading with network technologies has become need of the day. The designed system avoids the human intervention in Power Management. The Consumer has to pay the bill in time, if couldn't, the power connection may be disconnected automatically from the remote server. It displays the corresponding billing information on LCD and data is sent to the server through the GSM Module. The ARM7 based hardware system consists of a processor core board and the peripheral board. The entire programming for microcontroller operation is based on Embedded C Language. This system provides efficient meter reading, avoiding the billing error and reduces the maintenance cost. This paper also addresses advantages of implementing the GSM communication module and design detail and discusses the advanced security of the data communications. consumer to reduce or stop the meter reading/power supply The human error can open an opportunity for corruption done by the human meter reader. So the problem which arises in the billing system can become inaccurate and inefficient. The availability of wireless communication media has made the exchange of information fast, secured and accurate. The digital implementation caused the rapid utilization of devices such as computers and telecommunication devices. Communication media like the internet, GSM networks, etc exists everywhere. Wireless meter reading puts more control into the hands of both utilities and consumers by giving them more detailed information about power consumption (1). This allows utilities to better regulate supply. So, remote wireless meter reading system and management kinds of network technologies has become a trend now. In the work presented here, a technique has been developed to read electricity meter readings from a remote server automatically using the existing GSM networks (3) for cellular phones. This technique can be applied for gas or water meters as well. The meters send the meter readings like kilo-watt-hour (kWh), voltage, current, bill, etc. by SMS to a central server. The central server then stores the information in database for analysis and sends the bill to the customer mobile phone. The SMS based data collection can be done very quickly and efficiently. Data can be collected after any desired time interval such as hourly, daily, weekly, or monthly basis. As there is no human intervention in the entire process, there is no chance of human error and corruption. In the extremely bad weather conditions like heavy snow, rain, storm, etc the system will not hamper on collecting data as long as GSM networks are stable. The development cost of the SMS based remote meter will be higher than conventional meter but the electric supplier revenue will increase in the successive months because it will eliminate the possibility of corruption done by the customer or as of a reader. Remote meter can be used in residential apartments and especially in industrial consumers where bulk energy is consumed.


title: CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE Genetic Influence on Human Psychological Traits A

abstract: There is now a large body of evidence that supports the conclusion that individual differences in most, if not all, reliably measured psychological traits, normal and abnormal, are substantively influenced by genetic factors. This fact has important implications for research and theory building in psychology, as evidence of genetic influence unleashes a cascade of questions regarding the sources of variance in such traits. A brief list of those questions is provided, and representative findings regarding genetic and environmental influences are presented for the domains of personality, intelligence, psycho- logical interests, psychiatric illnesses, and social attitudes. These findings are consistent with those reported for the traits of other species and for many human physical traits, suggesting that they may represent a general biological phenomenon. KEYWORDS—behavior genetics; heritability; individual differ- ences


title: A Distributed Neural Network Architecture for Hexapod Robot Locomotion

abstract: We present fully distributed neural network architecture for controlling the locomotion of a hexapod robot. The design of this network is directly based on work on the neuroethology of insect locomotion. Previously, we demonstrated in simulation that this controller could generate a continuous range of statically stable insect-like gaits as the activity of a single command neuron was varied and that it was robust to a variety of lesions. We now report that the controller can be utilized to direct the locomotion of an actual six-legged robot, and that it exhibits a range of gaits and degree of robustness in the real world that is quite similar to that observed in simulation.


title: Research on the Application of SIFT Algorithm in UAV Remote Sensing Image Feature Extraction

abstract: UAV remote sensing is characterized by fast speed and flexibility and applied widely. The extraction and matching for feature point of UAV image is a foundational work in the application of UAV remote sensing. SIFT is a currently widely used feature point extraction algorithm that keeps invariant as the scale changes. It is also invariant as image changes in other aspects. Meanwhile it maintains certain stability to noise and light changes. SIFT is a kind of compute-intensive mission and usually takes much time. The author made parallel processing for SIFT algorithm and good accelerator ratio has been achieved on the premise of ensuring good result of feature point extraction and matching. Subject Categories and Descriptors:


Tangible User Interfaces


title: On ISP-friendly rate allocation for peer-assisted VoD

abstract: Peer-to-peer (P2P) content distribution is able to greatly reduce dependence on infrastructure servers and scale up to the demand of the Internet video era. However, the rapid growth of P2P applications has also created immense burden on service providers by generating significant ISP-unfriendly traffic, such as cross-ISP and inter-POP traffic. In this work, we consider the unique properties of peer-assisted Video-on-Demand (VoD) and design a distributed rate allocation algorithm, which can significantly cut down on ISP-unfriendly traffic without much impact on server load. Through extensive packet-level simulation with both synthetic and real-world traces, we show that the rate allocation algorithm can achieve substantial additional gain, on top of previously proposed schemes advocating ISP-friendly topologies.


title: Culture, threat, and mental illness stigma: identifying culture-specific threat among Chinese-American groups.

abstract: We incorporate anthropological insights into a stigma framework to elucidate the role of culture in threat perception and stigma among Chinese groups. Prior work suggests that genetic contamination that jeopardizes the extension of one's family lineage may comprise a culture-specific threat among Chinese groups. In Study 1, a national survey conducted from 2002 to 2003 assessed cultural differences in mental illness stigma and perceptions of threat in 56 Chinese-Americans and 589 European-Americans. Study 2 sought to empirically test this culture-specific threat of genetic contamination to lineage via a memory paradigm. Conducted from June to August 2010, 48 Chinese-American and 37 European-American university students in New York City read vignettes containing content referring to lineage or non-lineage concerns. Half the participants in each ethnic group were assigned to a condition in which the illness was likely to be inherited (genetic condition) and the rest read that the illness was unlikely to be inherited (non-genetic condition). Findings from Study 1 and 2 were convergent. In Study 1, culture-specific threat to lineage predicted cultural variation in stigma independently and after accounting for other forms of threat. In Study 2, Chinese-Americans in the genetic condition were more likely to accurately recall and recognize lineage content than the Chinese-Americans in the non-genetic condition, but that memorial pattern was not found for non-lineage content. The identification of this culture-specific threat among Chinese groups has direct implications for culturally-tailored anti-stigma interventions. Further, this framework might be implemented across other conditions and cultural groups to reduce stigma across cultures.


title: Differences in e-commerce behavior between neighboring countries: the case of France and Belgium

abstract: It is often (implicitly) assumed that e-commerce behavior is similar in countries with a similar culture in terms of Hofstede's dimensions. Through a study of e-commerce behavior of 969 private consumers and company representatives on 50 products, this paper proves there are actually significant differences in e-commerce behavior (information finding, suppler selection, and purchase) between people from two neighboring, culturally similar countries: Belgium and France. Furthermore, contrary to what was found in various US studies, very few differences in e-commerce behavior were detected between men and women in these two countries. Furthermore, clear differences were detected between age groups for product information finding and supplier selection but not for product purchase. Finally, to a big extent differences in e-commerce behavior between private consumers of two countries also exist between company representatives of the two countries.


title: A practical approach to dialogue response generation in closed domains

abstract: We describe a prototype dialogue response generation model for the customer service domain at Amazon. The model, which is trained in a weakly supervised fashion, measures the similarity between customer questions and agent answers using a dual encoder network, a Siamese-like neural network architecture. Answer templates are extracted from embeddings derived from past agent answers, without turn-by-turn annotations. Responses to customer inquiries are generated by selecting the best template from the final set of templates. We show that, in a closed domain like customer service, the selected templates cover $>$70\% of past customer inquiries. Furthermore, the relevance of the model-selected templates is significantly higher than templates selected by a standard tf-idf baseline.


title: A Visual Analytics Approach for Understanding Reasons behind Snowballing and Comeback in MOBA Games

abstract: To design a successful Multiplayer Online Battle Arena (MOBA) game, the ratio of snowballing and comeback occurrences to all matches played must be maintained at a certain level to ensure its fairness and engagement. Although it is easy to identify these two types of occurrences, game developers often find it difficult to determine their causes and triggers with so many game design choices and game parameters involved. In addition, the huge amounts of MOBA game data are often heterogeneous, multi-dimensional and highly dynamic in terms of space and time, which poses special challenges for analysts. In this paper, we present a visual analytics system to help game designers find key events and game parameters resulting in snowballing or comeback occurrences in MOBA game data. We follow a user-centered design process developing the system with game analysts and testing with real data of a trial version MOBA game from NetEase Inc. We apply novel visualization techniques in conjunction with well-established ones to depict the evolution of players' positions, status and the occurrences of events. Our system can reveal players' strategies and performance throughout a single match and suggest patterns, e.g., specific player' actions and game events, that have led to the final occurrences. We further demonstrate a workflow of leveraging human analyzed patterns to improve the scalability and generality of match data analysis. Finally, we validate the usability of our system by proving the identified patterns are representative in snowballing or comeback matches in a one-month-long MOBA tournament dataset.


title: The Heisenberg representation of quantum computers, talk at

abstract: Since Shor`s discovery of an algorithm to factor numbers on a quantum computer in polynomial time, quantum computation has become a subject of immense interest. Unfortunately, one of the key features of quantum computers--the difficulty of describing them on classical computers--also makes it difficult to describe and understand precisely what can be done with them. A formalism describing the evolution of operators rather than states has proven extremely fruitful in understanding an important class of quantum operations. States used in error correction and certain communication protocols can be described by their stabilizer, a group of tensor products of Pauli matrices. Even this simple group structure is sufficient to allow a rich range of quantum effects, although it falls short of the full power of quantum computation.


title: Cloud-based secure health monitoring: Optimizing fully-homomorphic encryption for streaming algorithms

abstract: There are many incentives for healthcare providers to shift their datacenters to the cloud. However, privacy of patient health information is a major concern when processing medical data off-site. One possible solution is the use of Fully Homomorphic Encryption (FHE), but this solution is too slow for most applications. We present a technique that increases efficiency and parallelism for certain algorithms under FHE. Through simulations, we demonstrate that our method yields about 20x speedup in a sample application. This is a significant step towards practical FHE-based medical remote monitoring.


title: How the 'uses and gratifications' theory explains the usage of professional networking sites: comparison between individual members and companies members

abstract: Social media continues to gain enormous popularity. Therefore, it is not surprising that this attracts the interest of researchers to study this cyber-social phenomenon. Previous contributions focused hugely on Online Social Networks (ONS), but this paper uses the ‘Uses & Gratifications’ Theory to explain the member behaviour in the field of Professional Networking Sites (PNS) by considering the motivations usage for both individuals and companies. For this purpose, a qualitative research was conducted using the ‘Uses & Gratifications’ Theory to explain the user behaviour in the field of professional networking sites. To collect data, we conducted 8 face-to-face interviews with companies and 5 focus groups with individuals. The results permit to identify two types of motivations: contextual motivations and generic motivations in which a comparison with those previously found in the field of OSNs may be done. A discussion of the results is presented in the light of the rewards and gratifications gained from using professional networking sites.


abstract: Deploying state-of-the-art technologies is vital and inevitable in assistive healthcare to cope with emerging services such as remote monitoring, collaborative consultation, and electronic health record. Grid computing has succeeded somewhat in enabling the sharing of resources across organizations but has not been deployed widely due to its complex implementation and interface. Cloud computing overcomes this aspect by allowing simple and easy user access, coping with users’ dynamic and elastic demands, providing metered usage for its resources and hence is increasingly being adopted by individual users as well as enterprise users. The Cloud may just be the right technology for healthcare infrastructure. However, several serious issues concerning security, data protection and ownership, quality of services, and mobility need to be resolved before Cloud computing can be widely adopted.. This paper proposes Mobile Cloud for Assistive Healthcare (MoCAsH) as an infrastructure for assistive healthcare. Besides inheriting the advantages of Cloud computing, MoCAsH embraces important concepts of mobile sensing, active sensor records, and collaborative planning by deploying intelligent mobile agents, context-aware middleware, and collaborative protocol for efficient resource sharing and planning. MoCAsH addresses security and privacy issues by deploying selective and federated P2P Cloud to protect data, preserve data ownership and strengthen aspects of security. It also addresses various quality-of-service issues concerning critical responses and energy consumption.


title: Metaface learning for sparse representation based face recognition

abstract: Face recognition (FR) is an active yet challenging topic in computer vision applications. As a powerful tool to represent high dimensional data, recently sparse representation based classification (SRC) has been successfully used for FR. This paper discusses the metaface learning (MFL) of face images under the framework of SRC. Although directly using the training samples as dictionary bases can achieve good FR performance, a well learned dictionary matrix can lead to higher FR rate with less dictionary atoms. An SRC oriented unsupervised MFL algorithm is proposed in this paper and the experimental results on benchmark face databases demonstrated the improvements brought by the proposed MFL algorithm over original SRC.


title: Axel: a heterogeneous cluster with FPGAs and GPUs

abstract: This paper describes a heterogeneous computer cluster called Axel. Axel contains a collection of nodes; each node can include multiple types of accelerators such as FPGAs (Field Programmable Gate Arrays) and GPUs (Graphics Processing Units). A Map-Reduce framework for the Axel cluster is presented which exploits spatial and temporal locality through different types of processing elements and communication channels. The Axel system enables the first demonstration of FPGAs, GPUs and CPUs running collaboratively for N-body simulation. Performance improvement from 4.4 times to 22.7 times has been achieved using our approach, which shows that the Axel system can combine the benefits of the specialization of FPGA, the parallelism of GPU, and the scalability of computer clusters.


title: A Line Tracking Algorithm Based on Image Processing

abstract: Line follower robots can be used for many important robotic and industrial tasks. One of the most important factors in these methods is the computational requirement of the algorithm. The paper presents a novel method for the line following problem using image processing technique. Our main goal is to find an efficient way with real-time computing to track a line using a camera-equipped robot. To measure the effectiveness of the presented method, we compare two line following robots with the line tracking method being the only difference between them. The first robot uses the proposed camera-based method and the other robot has reflective optical sensors in order to determine the position of the line. These real life experiments demonstrate the efficiency of the presented method.


title: Solution of 16-Queens Problem Using 3-variable Affine Boolean Functions (2^{n+1})

abstract: The present paper focuses on solution to 16-Queens problem highlights the process of placing the 16-affine functions of 3-variable Boolean function as Queen. The classification procedure reported in [1] describes a method to find a solution for the 16-Queens Problem. A complete method has been devised for finding some possible solutions of 16-Queen problems using iterative method instead of backtracking.


title: Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approach

abstract: Sepsis is a leading cause of mortality in intensive care units (ICUs) and costs hospitals billions annually. Treating a septic patient is highly challenging, because individual patients respond very differently to medical interventions and there is no universally agreed-upon treatment for sepsis. Understanding more about a patient's physiological state at a given time could hold the key to effective treatment policies. In this work, we propose a new approach to deduce optimal treatment policies for septic patients by using continuous state-space models and deep reinforcement learning. Learning treatment policies over continuous spaces is important, because we retain more of the patient's physiological information. Our model is able to learn clinically interpretable treatment policies, similar in important aspects to the treatment policies of physicians. Evaluating our algorithm on past ICU patient data, we find that our model could reduce patient mortality in the hospital by up to 3.6% over observed clinical policies, from a baseline mortality of 13.7%. The learned treatment policies could be used to aid intensive care clinicians in medical decision making and improve the likelihood of patient survival.


title: A Data Mining Approach for Prediction of Heart Disease Using Neural Networks

abstract: Heart disease diagnosis is a complex task which requires much experience and knowledge. Traditional way of predicting Heart disease is doctor’s examination or number of medical tests such as ECG, Stress Test, and Heart MRI etc. Nowadays, Health care industry contains huge amount of heath care data, which contains hidden information. This hidden information is useful for making effective decisions. Computer based information along with advanced Data mining techniques are used for appropriate results. Neural network is widely used tool for predicting Heart disease diagnosis. In this research paper, a Heart Disease Prediction system (HDPS) is developed using Neural network. The HDPS system predicts the likelihood of patient getting a Heart disease. For prediction, the system uses sex, blood pressure, cholesterol like 13 medical parameters. Here two more parameters are added i.e. obesity and smoking for better accuracy. From the results, it has been seen that neural network predict heart disease with nearly 100% accuracy.


title: Retrieval from software libraries for bug localization: a comparative study of generic and composite text models

abstract: From the standpoint of retrieval from large software libraries for the purpose of bug localization, we compare five generic text models and certain composite variations thereof. The generic models are: the Unigram Model (UM), the Vector Space Model (VSM), the Latent Semantic Analysis Model (LSA), the Latent Dirichlet Allocation Model (LDA), and the Cluster Based Document Model (CBDM). The task is to locate the files that are relevant to a bug reported in the form of a textual description by a software developer. We use for our study iBUGS, a benchmarked bug localization dataset with 75 KLOC and a large number of bugs (291). A major conclusion of our comparative study is that simple text models such as UM and VSM are more effective at correctly retrieving the relevant files from a library as compared to the more sophisticated models such as LDA. The retrieval effectiveness for the various models was measured using the following two metrics: (1) Mean Average Precision; and (2) Rank-based metrics. Using the SCORE metric, we also compare the retrieval effectiveness of the models in our study with some other bug localization tools.


title: Learning Criteria and Evaluation Metrics for Textual Transfer between Non-Parallel Corpora

abstract: We consider the problem of automatically generating textual paraphrases with modified attributes or stylistic properties, focusing on the setting without parallel data (Hu et al., 2017; Shen et al., 2017). This setting poses challenges for learning and evaluation. We show that the metric of post-transfer classification accuracy is insufficient on its own, and propose additional metrics based on semantic content preservation and fluency. For reliable evaluation, all three metric categories must be taken into account. We contribute new loss functions and training strategies to address the new metrics. Semantic preservation is addressed by adding a cyclic consistency loss and a loss based on paraphrase pairs, while fluency is improved by integrating losses based on style-specific language models. Automatic and manual evaluation show large improvements over the baseline method of Shen et al. (2017). Our hope is that these losses and metrics can be general and useful tools for a range of textual transfer settings without parallel corpora.


title: Word embedding with hellinger PCA to detect the sentiment of bengali text

abstract: The sentiment of a sentence or a comment can be detected more accurately by applying Word Embeddings. This article presents the idea of word co-occurrence matrix and Skip-Gram to determine the actual contexts of the words, Hellinger PCA to determine the most similar words and generate a sliding window of most probable context words around each word. It is shown that, by applying Word Embeddings to classify the sentiment of a comment achieves higher accuracy with larger corpus. For our corpus of 2500 comments, the accuracy achieved is 70%, which is rapidly increasing with the size of the corpus.


title: Immunofluorescent antibodies in patients with bird-fancier's lung.

abstract: To assess its value as a screen for avian antibodies, indirect immunofluorescence against avian intestinal tissue has been to test sera from thirty-nine patients with documented bird-fancier's lung disease, thirty-eight asymptomatic bird-fanciers and 257 controls without known avian contact. Immunofluorescent antibodies occurred more frequently than precipitins among patients with BFL and asymptomatic bird-fanicers. Globular fluorescence within the mucus occurred only in patients with avian contact, although other fluorescent antibodies were seen with control patients. No particular pattern was confined to patients with the lung disease. When included in an autoantibody profile, indirect immunofluorescence provides a sensitive and convenient alternative to precipitin methods in screening for avian antibodies.


title: A Bag-of-Features Approach for the Classification of Melanomas in Dermoscopy Images: The Role of Color and Texture Descriptors

abstract: The identification of melanomas in dermoscopy images is still an up to date challenge. Several Computer Aided-Diagnosis Systems for the early diagnosis of melanomas have been proposed in the last two decades. This chapter presents an approach to diagnose melanomas using Bag-of-features, a classification method based on a local description of the image in small patches. Moreover, a comparison between color and texture descriptors is performed in order to assess their discriminative power. The presented results show that local descriptors allow an accurate representation of dermoscopy images and achieve good classification scores: Sensitivity \(=\) 93 % and Specificity \(=\) 88 %. Furthermore it shows that color descriptors perform better than texture ones in the detection of melanomas.


title: Cross Device Matching for Online Advertising with Neural Feature Ensembles : First Place Solution at CIKM Cup 2016

abstract: We describe the 1st place winning approach for the CIKM Cup 2016 Challenge. In this paper, we provide an approach to reasonably identify same users across multiple devices based on browsing logs. Our approach regards a candidate ranking problem as pairwise classification and utilizes an unsupervised neural feature ensemble approach to learn latent features of users. Combined with traditional hand crafted features, each user pair feature is fed into a supervised classifier in order to perform pairwise classification. Lastly, we propose supervised and unsupervised inference techniques.


title: Fast Binary Counters Based on Symmetric Stacking

abstract: In this brief, a new binary counter design is proposed. It uses 3-bit stacking circuits, which group all of the “1” bits together, followed by a novel symmetric method to combine pairs of 3-bit stacks into 6-bit stacks. The bit stacks are then converted to binary counts, producing 6:3 counter circuits with no xor gates on the critical path. This avoidance of xor gates results in faster designs with efficient power and area utilization. In VLSI simulations, the proposed counters are 30% faster than existing parallel counters and also consume less power than other higher order counters. Additionally, using the proposed counters in existing counter-based Wallace tree multiplier architectures reduces latency and power consumption for 64 and 128-bit multipliers.


title: A Null-Space Method for the Phase Unwrapping of Multitemporal SAR Interferometric Stacks

abstract: Multitemporal differential interferometric synthetic aperture radar analysis is of fundamental importance in the monitoring of Earth surface displacements. In this context, a key role for the reconstruction of the deformation maps and time series is played by the phase unwrapping (PhU) that reconstructs the unrestricted phase signals starting from the measured wrapped versions, i.e., the interferograms. PhU is typically carried out independently for each interferogram in the 2-D azimuth-range domain via the efficient minimum cost flow (MCF) optimization technique. Recently, it has been proposed a two-step (TS) strategy that exploits both the temporal and the spatial structures of the available interferograms. The MCF algorithm is applied in this case also in the temporal/spatial baseline domain, and this step is combined with the classical 2-D space unwrapping. However, the restriction on the use of the MCF algorithm in the baseline domain poses limitations on the interferogram generation scheme. We present a formulation which makes use of the overdetermined nature of the operator that relates the phase differences to the absolute phase values: the problem is addressed in a more general framework that can cope with the 3-D (2-D space and time) nature of the data. This formulation is derived with reference to the sequential (TS) approach to overcome its restrictions on the interferogram generation. The new algorithm is validated on both simulated and real data. Moreover, the use of this new formulation for a full 3-D unwrapping is also addressed.


title: Review article Current status and future potential of computer-aided diagnosis in medical imaging

abstract: Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. The basic concept of CAD is to provide a computer output as a second opinion to assist radiologists' image interpretation by improving the accuracy and consistency of radiological diagnosis and also by reducing the image reading time. In this article, a number of CAD schemes are presented, with emphasis on potential clinical applications. These schemes include: (1) detection and classification of lung nodules on digital chest radiographs; (2) detection of nodules in low dose CT; (3) distinction between benign and malignant nodules on high resolution CT; (4) usefulness of similar images for distinction between benign and malignant lesions; (5) quantitative analysis of diffuse lung diseases on high resolution CT; and (6) detection of intracranial aneurysms in magnetic resonance angiography. Because CAD can be applied to all imaging modalities, all body parts and all kinds of examinations, it is likely that CAD will have a major impact on medical imaging and diagnostic radiology in the 21st century.


title: J-Force: Forced Execution on JavaScript

abstract: Web-based malware equipped with stealthy cloaking and obfuscation techniques is becoming more sophisticated nowadays. In this paper, we propose J-FORCE, a crash-free forced JavaScript execution engine to systematically explore possible execution paths and reveal malicious behaviors in such malware. In particular, J-FORCE records branch outcomes and mutates them for further explorations. J-FORCE inspects function parameter values that may reveal malicious intentions and expose suspicious DOM injections. We addressed a number of technical challenges encountered. For instance, we keep track of missing objects and DOM elements, and create them on demand. To verify the efficacy of our techniques, we apply J-FORCE to detect Exploit Kit (EK) attacks and malicious Chrome extensions. We observe that J-FORCE is more effective compared to the existing tools.


title: Quality of life, functioning and cognition in bipolar disorder and major depression: A latent profile analysis

abstract: This study aimed to identify profiles of functioning and quality of life (QOL) in depression (MDD), bipolar disorder (BD) and healthy adults, as well as the clinical, demographic and cognitive variables associated with each of these profiles. Participants completed the WHODAS 2.0 and WHOQOL-BREF, which were submitted to latent profile analysis. The four cluster solution provided the best fit for our data. Cluster 1 consisted mostly of healthy adults, and had the highest functioning and QOL. Clusters 2 contained older patients with subclinical depressive symptoms and psychiatric comorbidities, whose impairments in QOL and functioning were associated with mood symptoms and several cognitive abilities. Patients with MDD, BDI or BDII with mild to moderate depression, such as those in cluster 3, may benefit more significantly from interventions in cognitive flexibility, inhibition, planning, and sustained attention. Lastly, patients with mood disorders and clinically significant levels of depression, as well as a history of suicide attempts, like those in cluster 4, may benefit from interventions aimed at working memory, inhibitory control, and cognitive flexibility; that is, the three core executive functions. These findings should be further investigated, and used to guide treatments for patients with mood disorders and different patterns of functional impairment.


title: The ‘Good Enough’ Approach to Language Comprehension

abstract: Ferreira and colleagues argued that the language comprehension system creates syntactic and semantic representations that are merely ‘good enough’ (GE) given the task that the comprehender needs to perform. GE representations contrast with ones that are detailed, complete, and accurate with respect to the input. In this article, we review the original argument for GE processing, and we present new evidence that supports the concept: first, local interpretations are computed, which can interfere with global ones; second, new findings based on the recording of event-related potentials show the use of simple heuristics rather than compositional algorithms for constructing sentence meaning; and recent studies show that the comprehension system has mechanisms for handling disfluencies, but they work imperfectly. We argue that the GE approach to language comprehension is similar to the use of fast and frugal heuristics for decision-making, and that future research should explore this connection more thoroughly.


title: Sense-Amplifier-Based Flip-Flop With Transition Completion Detection for Low-Voltage Operation

abstract: A novel high-speed and highly reliable sense-amplifier-based flip-flop with transition completion detection (SAFF-TCD) is proposed for low supply voltage ( $V_{\mathrm {DD}}$ ) operation. The SAFF-TCD adopts the internally generated detection signal to indicate the completion of sense-amplifier stage transition. The detection signal gates the pull-down path of the sense-amplifier stage and the slave latch, thus overcoming the operational yield degradation, current contention, and glitches of previous SAFFs. The operational yield, speed, hold time, energy consumption, and area of the proposed and previous FFs are quantitatively compared for a wide range of $V_{\mathrm {DD}}$ with 22-nm FinFET technology. It is shown that the minimum $V_{\mathrm {DD}}$ of the SAFF-TCD is 573 mV lower than that of previous SAFFs, which means the SAFF-TCD can operate even when $V_{\mathrm {DD}}$ is in the near-threshold or subthreshold region. At 0.3–0.4 V, the SAFF-TCD operates twice as fast as the master–slave-based FF (MSFF) with a practical hold time. Even with these benefits, the energy consumption overhead is limited to less than 20% compared with that of MSFF, and the area is similar to that of previous SAFFs.


title: Group Normalization

abstract: Batch Normalization (BN) is a milestone technique in the development of deep learning, enabling various networks to train. However, normalizing along the batch dimension introduces problems --- BN's error increases rapidly when the batch size becomes smaller, caused by inaccurate batch statistics estimation. This limits BN's usage for training larger models and transferring features to computer vision tasks including detection, segmentation, and video, which require small batches constrained by memory consumption. In this paper, we present Group Normalization (GN) as a simple alternative to BN. GN divides the channels into groups and computes within each group the mean and variance for normalization. GN's computation is independent of batch sizes, and its accuracy is stable in a wide range of batch sizes. On ResNet-50 trained in ImageNet, GN has 10.6% lower error than its BN counterpart when using a batch size of 2; when using typical batch sizes, GN is comparably good with BN and outperforms other normalization variants. Moreover, GN can be naturally transferred from pre-training to fine-tuning. GN can outperform its BN-based counterparts for object detection and segmentation in COCO, and for video classification in Kinetics, showing that GN can effectively replace the powerful BN in a variety of tasks. GN can be easily implemented by a few lines of code in modern libraries.


title: A 90-nm CMOS Doherty power amplifier with minimum AM-PM distortion

abstract: A linear Doherty amplifier is presented. The design reduces AM-PM distortion by optimizing the device-size ratio of the carrier and peak amplifiers to cancel each other's phase variation. Consequently, this design achieves both good linearity and high backed-off efficiency associated with the Doherty technique, making it suitable for systems with large peak-to-average power ratio (WLAN, WiMAX, etc.). The fully integrated design has on-chip quadrature hybrid coupler, impedance transformer, and output matching networks. The experimental 90-nm CMOS prototype operating at 3.65 GHz achieves 12.5% power-added efficiency (PAE) at 6 dB back-off, while exceeding IEEE 802.11a -25 dB error vector magnitude (EVM) linearity requirement (using 1.55-V supply). A 28.9 dBm maximum Psat is achieved with 39% PAE (using 1.85-V supply). The active die area is 1.2 mm/sup 2/.


title: Exact Tensor Completion Using t-SVD

abstract: In this paper we focus on the problem of completion of multidimensional arrays (also referred to as tensors) from limited sampling. Our approach is based on a recently proposed tensor-Singular Value Decomposition (t-SVD) [1]. Using this factorization one can derive notion of tensor rank, referred to as the tensor tubal rank, which has optimality properties similar to that of matrix rank derived from SVD. As shown in [2] some multidimensional data, such as panning video sequences exhibit low tensor tubal rank and we look at the problem of completing such data under random sampling of the data cube. We show that by solving a convex optimization problem, which minimizes the tensor nuclear norm obtained as the convex relaxation of tensor tubal rank, one can guarantee recovery with overwhelming probability as long as samples in proportion to the degrees of freedom in t-SVD are observed. In this sense our results are order-wise optimal. The conditions under which this result holds are very similar to the incoherency conditions for the matrix completion, albeit we define incoherency under the algebraic set-up of t-SVD. We show the performance of the algorithm on some real data sets and compare it with other existing approaches based on tensor flattening and Tucker decomposition.


title: Inverse and direct dynamic modeling of Gough-Stewart robots

abstract: This paper presents closed-form solutions for the inverse and direct dynamic models of the Gough-Stewart parallel robot. The models are obtained in terms of the Cartesian dynamic model elements of the legs and of the Newton-Euler equation of the platform. The final form has an interesting and intuitive physical interpretation. The base inertial parameters of the robot, which constitute the minimum number of inertial parameters, are explicitly determined. The number of operations to compute the inverse and direct dynamic models are given.


title: Cluster synchronization for nonlinearly time-varying delayed coupling complex networks with stochastic perturbation via periodically intermittent pinning control

abstract: This paper investigate the mean square exponential cluster synchronization of a class complex networks with nonlinear hybrid time-varying delays and stochastic noises via periodically intermittent pinning control. To be more practical, the network topology can be directed and the time delay is assumed to be time-varying. Based on the Lyapunov stability theory, stochastic analysis theory and linear matrix inequalities(LMI), the sufficient conditions are derived to guarantee the mean square exponential cluster synchronization. Furthermore, our results can cover and extend the related ones in existing literatures. Some numerical simulations are provided to show the effectiveness of our theoretical results.