Reviewerly/exHarmony huggingface dataset:
RKHS based State Estimator for Radar Sensor in Indoor Application

For the estimation of targets&#x0027; states (location, velocity, and acceleration) from nonlinear radar measurements, usually, the improved version of well known Kalman filter: extended Kalman filter (EKF) and unscented Kalman filter (UKF) are used. However, EKF and UKF approximates the nonlinear measurement function either by Jacobian or using sigma points. Consequently, because of the approximation of the measurement function, the EKF and UKF cannot achieve high estimation accuracy. The potential solution is to replace the approximation of nonlinear measurement function with its estimate, obtained in high dimensional reproducing kernel Hilbert space (RKHS). An ample amount of research has been done in this direction, and the combined filter is termed RKHS based Kalman filter. However, there is a shortage of literature dealing with estimating the dynamic state of the target in an indoor environment using RKHS based Kalman filter. Therefore, in this paper, we propose the use of RKHS based Kalman filter for indoor application. Specifically, we validate the suitability of the RKHS based Kalman filtering approach using simulations performed over three different target motion models.
Efficient Optimized Spike Encoding of Multivariate Time-series

Spiking neural network (SNN) are emerging as a bio-plausible AI paradigm best suited for energy constrained edge use case. However the performance of SNNs largely depends upon the information content of the spike trains generated from real valued data by spike encoders - which are often found to be lossy. In this work, we have proposed a mutual information based optimisation technique of spike encoding to be used on multivariate time-series data. When tested using a spiking reservoir network, the technique is found to increase the network performance by upto 6% while performing classification task on different multivariate sensor data having variety of attributes.
The vision of self-evolving computing systems

Computing systems are omnipresent; their sustainability has become crucial for our society. A key aspect of this sustainability is the ability of computing systems to cope with the continuous change they face, ranging from dynamic operating conditions, to changing goals, and technological progress. While we are able to engineer smart computing systems that autonomously deal with various types of changes, handling unanticipated changes requires system evolution, which remains in essence a human-centered process. This will eventually become unmanageable. To break through the status quo, we put forward an arguable opinion for the vision of self-evolving computing systems that are equipped with an evolutionary engine enabling them to evolve autonomously. Specifically, when a self-evolving computing systems detects conditions outside its operational domain, such as an anomaly or a new goal, it activates an evolutionary engine that runs online experiments to determine how the system needs to evolve to deal with the changes, thereby evolving its architecture. During this process the engine can integrate new computing elements that are provided by computing warehouses. These computing elements provide specifications and procedures enabling their automatic integration. We motivate the need for self-evolving computing systems in light of the state of the art, outline a conceptual architecture of self-evolving computing systems, and illustrate the architecture for a future smart city mobility system that needs to evolve continuously with changing conditions. To conclude, we highlight key research challenges to realize the vision of self-evolving computing systems.
Tracking Control for Nonlinear Systems With Actuator Saturation via Interval Type-2 T–S Fuzzy Framework

In this work, the problem of tracking control for discrete-time nonlinear actuator-saturated systems via interval type-2 (IT2) T-S fuzzy framework is investigated. Improved on the (type-1) T-S fuzzy system, the IT2 T-S fuzzy system has a better capability for the expression of system uncertainty, and correspondingly, it will increase the difficulty of analysis, especially for the membership-functions-dependent (MFD) method. In addition, in this case, the control input nonlinearity caused by actuator saturation will complicate the stability analysis of the systems. We make an attempt to address the challenges that the information of membership functions (MFs) is underutilized or not utilized, by developing an MFD analysis approach, which allows the enhancement of design flexibility of IT2 fuzzy controller and effectiveness of lessening the conservativeness of the analysis result. The piecewise MFs which are formed by connecting the sample point on or close to the original IT2 MFs are utilized to approximate the original IT2 MFs, and the error between the piecewise MFs and the original upper and lower MFs is taken into account in the stability analysis. To acquire the linear matrix inequality-based (LMI-based) constraint, the actuator saturation is converted to a sector nonlinear issue. H∞ performance is considered to limit the difference between the reference system and the control saturated system. Examples are presented to illustrate the validity of the results.
Deep-attack over the deep reinforcement learning

Recent adversarial attack developments have made reinforcement learning more vulnerable, and different approaches exist to deploy attacks against it, where the key is how to choose the right timing of the attack. Some work tries to design an attack evaluation function to select critical points that will be attacked if the value is greater than a certain threshold. This approach makes it difficult to find the right place to deploy an attack without considering the long-term impact. In addition, there is a lack of appropriate indicators of assessment during attacks. To make the attacks more intelligent as well as to remedy the existing problems, we propose the reinforcement learning-based attacking framework by considering the effectiveness and stealthy spontaneously, while we also propose a new metric to evaluate the performance of the attack model in these two aspects. Experimental results show the effectiveness of our proposed model and the goodness of our proposed evaluation metric. Furthermore, we validate the transferability of the model, and also its robustness under the adversarial training.
Performance evaluation of wireless compressed‐image transmission over discrete Fourier transform‐based orthogonal frequency division multiple access system

Orthogonal frequency division multiple access (OFDMA) has been attracting much attention in the recent generation of wireless communications to meet the required demands arising from the explosive growth of Internet, multimedia and broadband services. Currently, images transmission is one of the main parts of wireless communication. However, an image file transmission requires sending a large amount of data which consumes a huge bandwidth so that the image must be compressed to reduce the amount of information to be transmitted. The aim of this paper is to evaluate the wireless transmission of the compressed image over discrete Fourier transform (DFT)-based OFDMA DFT-OFDMA system for different compression methods, different modulation schemes and different subcarriers mapping schemes over three wireless channels including SUI3, vehicular A and uniform. The required minimum signal-to-noise ratio (SNR) to recover the transmitted compressed image is determined to evaluate the system performance. For the sake of diversity, nine compression techniques are used in this work. The MATLAB simulator is used to determine the required minimum SNR to recover the transmitted compressed image for each compression method. The results show that the performance of the set partitioning in hierarchical trees three-dimensional for true colour images (SPIHT-3D) method is nearly better than that of other compression methods, especially when the QPSK and interleaved system are considered.
Exploring the Spread of Zika

Vector-borne diseases pose a major public health threat. Combined, these diseases contribute significantly to illness and mortality worldwide and have an adverse impact on development and economic growth of nations. Public health stakeholders seeking to control and prevent these diseases are confronted with a myriad of challenges. Some of these difficulties are related to the nature of the data, the uncertainty of disease dynamics, and volatility of human-environment interactions. Visualization tools are capable of ameliorating some of these challenges. In this paper, the authors demonstrate how interactive visualizations can support stakeholders' decision-making tasks. In particular, they present a visualization tool they created that can support control efforts related to the recent Zika outbreak in Brazil.
George Michael memorial fellowship recipients announced

The 2018 ACM-IEEE CS George Michael Memorial HPC Fellowship recipients are Linda Gesenhues of the Federal University of Rio de Janeiro and Markus Höhnerbach of RWTH Aachen University. The Fellowships are jointly presented by ACM and the IEEE Computer Society.
Learning Language Through Facebook

This article seeks to account for the experiences of tertiary students of German when a Facebook group and associated tasks is introduced as an assessed element of their language course. Ethnographic methods were applied, such as pre-, post questionnaires, interviews and fieldnotes. The data was collected by the instructor/researcher and subjected to thematic analysis. The students (n = 23) enjoyed Facebook but mostly used the site passively and only actively posted when required. This led to a flurry of posts at deadlines and less engagement between deadlines. Students wanted more feedback than the instructor was giving them, in an effort to make the interaction feel more authentic and spontaneous, as it is in non-educational, social Facebook.
A New View of Privacy in Social Networks

Many smartphone-based applications need microdata, but publishing a microdata table may leak respondents' privacy. Conventional researches on privacy-preserving data publishing focus on providing identical privacy protection to all data requesters. Considering that, instead of trapping in a small coterie, information usually propagates from friend to friend. The authors study the privacy-preserving data publishing problem on a mobile social network. Along a propagation path, a series of tables will be locally created at each participant, and the tables' privacy-levels should be gradually enhanced. However, the tradeoff between these tables' overall utility and their individual privacy requirements are not trivial: any inappropriate sanitization operation under a lower privacy requirement may cause dramatic utility loss on the subsequent tables. For solving the problem, the authors propose an approximation algorithm by previewing the future privacy requirements. Extensive results show that this approach successfully increases the overall data utility, and meet the strengthening privacy requirements.
Digital Storage Method of Enterprise Financial Files Based on Artificial Intelligence

The development of computers, networks and communications has greatly strengthened the ability to produce, process, and disseminate digital information, and the storage, transmission and processing of digital information is more convenient than other forms of storage. This article mainly studies the digital storage method of enterprise financial files based on artificial intelligence. Analyze the functions and characteristics of artificial intelligence, list the artificial intelligence technologies that need to be used in the financial risk prevention research mechanism, and affirm the feasibility of the basic education financial risk prevention mechanism from the technical level. Through standardized interfaces and standardized data conversion formats, the financial accounting system and other business systems are seamlessly connected to exchange and circulate data, realize the circulation of internal data, and realize the electronicization of internal original vouchers. After the realization of electronic accounting files, it is estimated that 900,000 sheets of paper can be saved each year, paper costs can be saved 450,000 yuan, IT consumables costs can be saved about 800,000 yuan, mailing costs, personnel costs and warehouse costs are reduced by 500,000, and a total cost savings of 235 thousand yuan. By implementing the electronic management of accounting files, enterprises have supported the electronic accounting of the entire business process, and promoted the automation of financial processing and the speed of business operations.
Discussion

International Statistical ReviewVolume 88, Issue 2 p. 324-326 Discussions on Horseshoe Regularisation for Machine Learning in Complex and Deep Models Discussion Soumya Ghosh, Corresponding Author Soumya Ghosh ghoshso@us.ibm.com MIT-IBM Watson AI Lab and Center for Computational Health, IBM Research, Cambridge, MA, USA E-mail: ghoshso@us.ibm.comSearch for more papers by this authorFinale Doshi-Velez, Finale Doshi-Velez School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USASearch for more papers by this author Soumya Ghosh, Corresponding Author Soumya Ghosh ghoshso@us.ibm.com MIT-IBM Watson AI Lab and Center for Computational Health, IBM Research, Cambridge, MA, USA E-mail: ghoshso@us.ibm.comSearch for more papers by this authorFinale Doshi-Velez, Finale Doshi-Velez School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USASearch for more papers by this author First published: 20 April 2020 https://doi.org/10.1111/insr.12377 Linked article: Bhadra et al. (2020) Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume88, Issue2August 2020Pages 324-326 RelatedInformation
Ontology-Based Human-Computer Cloud for Decision Support

A variety of information processing and decision support tasks (especially in the context of smart city or smart tourist destination) rely both on the automated and human-based procedures. The article proposes a multi-layer cloud environment that, first, unifies various kinds of resources used by these information processing and decision-support scenarios (hardware, software, and human), and second, implements an ontology-based automatic service composition procedures that can be used to build ad hoc decision-support services for problems unknown in advance. The service composition is based on uniform description of all parts of the environment with a help of ontologies. The article describes the architecture and models of the novel human-computer cloud environment. It also describes several scenarios of decision support in tourism leveraging the proposed human-computer cloud concept.
Deep Hashing Based Model for Image Similarity Retrieval in DNA Storage

With the rapid development of information technology, the total amount of data generated by human society is growing at an incredible rate, and will soon exceed the storage capacity of existing storage media. DNA molecules, as a natural information storage media, have a broad application prospect because of its high theoretical storage density. With the increase of DNA storage capacity, image similarity retrieval is another important image retrieval method besides random access. DNA hybridization is sequence pairing reaction based on DNA sequence similarity, enabling similarity retrieval in DNA storage. In the context of image similarity retrieval in DNA storage, the key problem is how to map images to DNA sequences while maintaining similarity. In this paper, we model this problem as a variant of deep hashing and then propose a novel DNA-encoded image deep hashing model. We test our model on CIFAR-10 dataset and the results show that our performance is comparable to the state-of-the-art binary-encoded image hashing model, proving that DNA-encoded image similarity retrieval is sufficiently efficient. Our work also provides a novel modeling idea and appropriate evaluation metrics for future research.
Introduction

In this chapter, the objective of this work, which is to introduce many-valuedness to meta-logical notions like consequence, consistency/inconsistency, tautologihood, etc. involved in a logical discourse, is stated. To arrive at this end the issues that have been sailed through are (i) three levels inherent in a logic discourse, (ii) from many-valued logics, fuzzy logics to graded consequence: a brief overview, (iii) a general discussion on uncertainty and vagueness, (iv) notion of consequence in classical logic and (v) finally some motivations for lifting many-valuedness to the meta-level.
Simulation of Lidar-Based Robot Detection Task using ROS and Gazebo

In the last few decades, the robotics world has seen great progress at all levels, from personal assistant robots to multi-robotic and intelligent swarm systems. Simulation platforms play a critical role in this improvement due to efficiency, flexibility, and fault tolerance they provide during the cycles of developing and testing new strategies and algorithms. In this paper, we model a new mobile robot equipped with a 2D laser range finder using Robot Operating System (ROS) and use this robot model to develop a robot detection method in Gazebo simulation environment. Detecting surrounding objects and distinguishing robots from these objects (kin detection) are essential in multi-robot and swarm robotic applications. In this paper, we use Lidar to handle this task by applying the following steps: (1) acquisition of laser data and pre-processing, (2) segmentation of data using the point-distance-based segmentation method, (3) eliminating segments that don’t fit a certain size (Lidar size) using features for each segment, and (4) checking remaining segments to test if they fit the Lidar's shape (which is a circle with known radius) or not by using the circle fitting method. Two different scenarios are discussed in the experiments section. In the first scenario, many cylindrical objects with radius different from the robot’s Lidar were used in addition to a robot, and thus objects are distinguished from the robot in the third step without using the fourth step which may be a complex operation. In the second scenario, various objects with a similar radius were used, and due to the similarity in the radius between the Lidar and the other objects, it was necessary to apply all the method’s steps to detect the kin robots. It was noticed from experiments that the accuracy of the results depends on two main factors: the distance between the observer robot and other objects or robots and the amount of noise in the Lidar measurements.
Run-Time Compositional Software Platform for Autonomous NXT Robots

Autonomous Robots normally perform tasks in unstructured environments, with little or no continuous human guidance. This calls for context-aware, self-adaptive software systems. This paper aims at providing a flexible adaptive middleware platform to seamlessly integrate multiple adaptation logics during the run-time. To support such an approach, a reconfigurable middleware system “ACCADA” was designed to provide compositional adaptation. During the run-time, context knowledge is used to select the most appropriate adaptation modules so as to compose an adaptive system best-matching the current exogenous and endogenous conditions. Together with a structure modeler, this allows robotic applications' structure to be autonomously (re)-constructed and (re)-configured. This paper applies this model on a Lego NXT robot system. A remote NXT model is designed to wrap and expose native NXT devices into service components that can be managed during the run-time. A dynamic UI is implemented which can be changed and customized according to system conditions. Results show that the framework changes robot adaptation behavior during the run-time.
Mobile Cloud Resource Management

Providing mobile cloud services requires seamless integration between various platforms to offer mobile users optimum performance. To achieve this, many fundamental problems such as bandwidth availability and reliability, resource scarceness, and finite energy must be addressed before rolling out such services. This chapter aims to explore technological challenges for mobile cloud computing in the area of resource management focusing on both parts of the infrastructure: mobile devices and cloud networks. Starting with introducing mobile cloud computing, it then stresses the importance of resource management in the operation of mobile cloud services presenting various types of resources available for cloud computing. Furthermore, it examines the various types of resource management techniques available for mobile clouds. Finally, future directions in the field of resource management for mobile cloud computing environment are presented.
Preventive Effectiveness of Distant-Image Screen Display Toward Accommodative Lag and Nearwork-Induced Transient Myopia: A Self-Controlled Study

Background: Nearwork was the primary cause of myopia and it was difficult to be reduced due to the need of teaching. We develpment the distant-image screen (DIS) which can simulate looking far away on a child’s study table. This research aimed to assess its effectiveness in preventing nearwork-induced transient myopia and accommodative lag.Methods: In this self-controlled study, subjects were randomly divided into two groups by a central computer system. The first group watched a 10 minutes video on the tablet and then watched the same video for 10 minutes on the DIS after a 30 min break. The second group watched the video in reverse order. The diopters before video viewing, the accommodative lag at the start of viewing and 5 min of viewing, the nearwork-induced transient myopia at the end of viewing were measured by a open phoropter.Findings: Between Jun 6, 2020, and Jun 30, 2021, 62 volunteers (mean age: 28 years, 95% CI 18 to 38) were recruited. The diopters before viewing tablet and DIS were −0.31D (-1.28 to 0.65) and −0.28D (-1.32 to 0.76), respectively, with no significant difference (p=0.735). At the start of video viewing, the accommodative lag of the wathching tablet was 0.59D ( -1.28 to 2.45) and that of DIS was 0.01D (-1.15 to 1.17), with a significant difference (p<0.001). After 5 min of video viewing, the accommodative lag of wathching tablet was 0.67D (-0.92 to 2.26) and that of DIS was 0.06D (-1.12 to 1.24), with a significant difference (p< 0.001). After video viewing was completed, the nearwork-induced transient myopia of wathching tablet and DIS were 0.19D (-0.32 to 0.70) and 0.03D (-0.36 to 0.42), respectively, with a significant difference (p<0.001).Interpretation: The DIS can significantly prevent nearwork-induced accommodative lag and transient myopia by simulating the view distance from near to far, indicating the potential of this device to prevent the occurrence and development of myopia.Funding: Beijing Municipal Science and Technology Project (Z201100005520042)Declaration of Interest: No conflicting relationship exists for any authorEthical Approval: The research was approved by the Human Studies Committee at Beijing Tongren Hospital (Beijing, China) in accordance with the Declaration of Helsinki and the Code of Ethics of the World Medical Association