Reviewerly/exHarmony huggingface dataset:
Reconfigurable Constraint-Based Reactive Framework for Assistive Robotics With Adaptable Levels of Autonomy

In this work, we propose a constraint-based reactive framework that allows an easy customization of assistive robotic systems to comply with the particular needs of users with different levels of mobility. This framework enables the specification of modular assistive reactive behaviors which can be easily composed to allocate autonomy over robot-related or task-related variables. Such assistive behaviors can be systematically modulated online, which results in adaptable levels of autonomy. Modulation can be performed directly by the user through suitable human-machine interfaces, or can be performed automatically by the computer. Although our framework also shows potential for handling other types of tasks, we focus on assisting the user with reaching tasks. We experimentally validated the flexibility and applicability of our framework by presenting three assistive use cases, each targeting users with different level of mobility. Each of those cases was easily reconfigured to include various HMIs (e.g. SpaceMouse, eye tracker, etc), exploiting different assistive strategies. During the experiments, the level of autonomy could be automatically or manually adapted.
Touchless Information Provision and Facial Expression Training Using Kinect

In this report, we present a touchless information provision system and facial expression training using Kinect that can be operated intuitively. This system is intended to allow the user to easily acquire various information by hand gestures. Further, the application has a facial expression training application that brings the advantages of a gaming user interface to the field of healthcare. This system was evaluated and its basic design was verified through actual experiments.
Studying the Role of Social Bots During Cyber Flash Mobs

A Cyber Flash Mob (CFM) is an event that is organized via social media, email, SMS, or other forms of digital communication technologies in which a group of people (who might have an agenda) get together online or offline to collectively conduct an act and then quickly disperse. In addition to the humans participating in these events, non-humans, i.e., artificial agents or social bots - which are computer software programmed to accomplish some tasks on your behalf such as tweeting, retweeting, and liking a tweet - also participate in a CFM. In this research, we study the shared orientations of the CFMs' participants and try to understand the role of social bots in disseminating CFMs' agendas by examining the communication network of these accounts, the toxicity of their posts, and the artifacts, e.g., the URLs they share. The goal is to understand how social bots help CFM organizers advertise, recruit, and share their products (e.g., videos, pictures) on various social media platforms.
Alternative Inverse Perspective Mapping Homography Matrix Computation for ADAS Systems Using Camera Intrinsic and Extrinsic Calibration Parameters

This paper presents a simple novel IPM matrix computation method, which does not require any additional solutions to calculate homography matrices. This method only requires determining the distance from camera to projecting surface. It’s simplicity allows execution even on very small energy-efficient embedded device processors without use of complex libraries such as OpenCV, that cannot be deployed there. Furthermore, it has strictly analytical solution, which allows avoidance of false optimums that are probable in heuristic methods for optimization. We provide formulas, that are easy transferable to any device and can be calculated using standard math libraries.
Implementation of the «Cloud Technologies in Education» Supplementary Vocational Education Program

The need of the state in personnel ready to use digital technologies in their activities has been established. The activities aimed at developing digital competences of people have been considered. The structure, content, and instructional implications of implementation of the “Cloud Technologies in Education” supplementary vocational education program have been presented.
Aspect-Based Sentiment Analysis for Posts on Friday Prayer During MCO in Malaysia

Analysis of sentiment (or opinion mining) is a technique used to determine whether a polarity of data has become positive, negative, or neutral. It studies the opinions, feelings, emotions, and stances of people using an algorithmic process that understands the opinions of a particular topic based on the methodology of Natural Language Processing (NLP). It has gained popularity in recent years and it has played a vital role in a variety of fields, such as online product reviews and social media analysis (Twitter, Facebook, etc.). This paper presents the findings of a research conducted to investigate people’s sentiment toward a government decision that temporarily suspending Friday prayers in all the mosques, as a response to the pandemic of COVID-19 in the country, due to The Malaysia Movement Control Order (MCO) 1.0 as a precautionary measure. A collection of tweets were crawled based on the #solatjumaat hashtag, then it was grouped into one corpus as a new dataset for further text preprocessing and sentiment analysis process. It applies a Python language with an adaption of Malaya, a Natural-Language-Toolkit library created especially for text in Malay Language verse for the treatment techniques. A visualization of the outcome will illustrate the finding of people's feelings for this study.
A novel architecture for enhancing Electronic Health Record interoperability: A Blockchain-based approach

Healthcare has been improved by information and communication technologies. Electronic health records are one of the critical drivers of this transition. Moreover, the number of records on personal health grows exponentially with the rise of mobile and omnipresent computing. This trend is characterized by wearable computing technology and various kinds of sensors related to health domain. Results in need of an automated data storage method that healthcare professionals and patients can use. There is currently no trusted medical data sharing and storage system in the chain of health value. Therefore, no platform is used to track the traceability of patients across the entire healthcare chain. Our goal is to create an integrated view based on interoperability principles that will collect such data from patients' medical records, based on the use of sensors from different wearable devices and other sources. The data collected is consolidated into a single electronic health records system, where the patient is the owner of data by interconnecting multiple healthcare providers. This strategy offers improved privacy advantages while ensuring safe access to medical records for patients. The original paper contributes to the description of uniting the entire medical value chain, the design of the system architectural model, the creation of system components, and the validation through a proof of concept (PoC). The PoC can show the feasibility of data exchange between health care professionals and incorporating the health records of heterogeneous sources like health care systems and sensors.
SCKHA: A New Stream Cipher Algorithm Based on Key Hashing and Splitting Technique

Cryptographic algorithms are playing an important role in the information security field. Strong and unbreakable algorithms provide high security and good throughput. The strength of any encryption algorithm is basically based on the degree of difficulty to obtain the encryption key by such cyber-attacks as brute. It is supposed that the bigger the key size, the more difficult it is to compute the key. But increasing the key size will increase both the computational complexity and the processing time of algorithms. In this paper, we proposed a reliable, effective, and more secure symmetric stream cipher algorithm for encryption and decryption called Symmetric Cipher based on Key Hashing Algorithm (SCKHA). The idea of this algorithm is based on hashing and splitting the encryption symmetric key. Hashing the key will hide the encrypted key to prevent any intruder from forging the hash code, and, thus, it satisfies the purpose of security, authentication, and integrity for a message on the network. In addition, the algorithm is secure against a brute-force attack by increasing the resources it takes for testing each possible key. Splitting the hashed value of the encryption key will divide the hashed key into two key chunks. The encryption process performed using such one chunk based on some calculations on the plaintext. This algorithm has three advantages that are represented in computational simplicity, security and efficiency. Our algorithm is characterized by its ability to search on the encrypted data where the plaintext character is represented by two ciphertext characters (symbols).
Regularization by Inexact Krylov Methods with Applications to Blind Deblurring

This paper is concerned with the regularization of large-scale discrete inverse problems by means of inexact Krylov methods.Specifically, we derive two new inexact Krylov methods that can be efficiently applied to unregularized or Tikhonov-regularized least squares problems, and we study their theoretical properties, including links with their exact counterparts and strategies to monitor the amount of inexactness.We then apply the new methods to separable nonlinear inverse problems arising in blind deblurring, where both the sharp image and the parameters defining the blur are unkown.When employing a variable projection method jointly with the new inexact solvers in this setting, the latter can naturally handle varying inexact blurring parameters while solving the linear deblurring subproblems, allowing for a much reduced number of total iterations and substantial computational savings with respect to their exact counterparts.
The Optimal Distributed Kalman Filtering Fusion With Linear Equality Constraint

In this paper, the optimal distributed Kalman filtering fusion with linear equality constraint (LEC) is proposed. When the Kalman filter subject to LEC is applied in state estimation of distributed linear dynamic system, all local error covariance matrices are singular, which is a challenge for most of the existing distributed fusion algorithms. However, the new fusion algorithm is rigorously derived with matrix analysis technique on the Moore-Penrose inverse. Updating state estimate and error covariance matrix, the new fusion algorithm only uses local estimates and their corresponding error covariances. We have proved theoretically that the distributed Kalman filtering with LEC (DKF-LEC) just using all local measurements is equivalent to the centralized Kalman filtering with LEC (CKF-LEC), which means it is optimal. Furthermore, we also prove the optimality of the proposed fusion formula for Kalman filtering with LEC and feedback. In addition, the simulation results show that the proposed fusion formula for Kalman filtering with LEC have the same performance of the centralized Kalman filtering with LEC whether considering feedback or not. It is consistent with the theoretical result that the DKF-LEC is equivalent to CKF-LEC as the estimate of CKF-LEC is disassembled into local estimates in distributed system. And the performance of the new fusion formula for Kalman filtering with LEC and feedback is obviously better than those without feedback.
Spectrum Monitoring of Radio Digital Video Broadcasting Based on an Improved Generative Adversarial Network

Abstract Due to the inherent broadcast nature of wireless communication systems, instances of radio jamming are common, such as natural interference and man‐made interference, resulting in increasing demands for radio monitoring. A spectrum monitoring method based on a generative adversarial network model that is one of the most promising approaches of learning any kind of data distribution using unsupervised learning was proposed in this paper for the detection of anomaly spectrum with impulse noise. To validate the performance of the proposed model, both the simulated data set and the measured data set of radio digital video broadcasting were used to train and test the model. Experiments on the two data sets reached a consistent conclusion: as long as the energy of the interference is greater than a certain threshold, the detection accuracy increases with the increase of the interference power and pulse width. Compared with the existing anomaly detection models, our model was faster and more stable.
A General Model for Automated Algorithm Design

This chapter presents a newly defined novel combinatorial optimisation problem, namely, the General Combinatorial Optimisation Problem (GCOP), whose decision variables are a set of elementary algorithm components. The combinations of these algorithm components, i.e. solutions of GCOP, thus represent different search algorithms. The objective of GCOP is to find the optimal combinations of algorithm components for solving optimisation problems. Solving the GCOP is thus equivalent to automatically designing the best search algorithms for optimisation problems. The definition of the GCOP is presented with a new taxonomy which categorises relevant literature on automated algorithm design into three lines of research, namely, automated algorithm configuration, selection and composition. Based on the decision space under consideration, the algorithm design itself is defined as an optimisation problem. Relevant literature is briefly reviewed, motivating a new line of exciting and challenging directions on the emerging research of automated algorithm design.
ANS-based compression and encryption with 128-bit security

Abstract The bulk of Internet interactions is highly redundant and also security sensitive. To reduce communication bandwidth and provide a desired level of security, a data stream is first compressed to squeeze out redundant bits and then encrypted using authenticated encryption. This generic solution is very flexible and works well for any pair of (compression, encryption) algorithms. Its downside, however, is the fact that the two algorithms are designed independently. One would expect that designing a single algorithm that compresses and encrypts (called compcrypt) should produce benefits in terms of efficiency and security. The work investigates how to design a compcrypt algorithm using the ANS entropy coding. First, we examine basic properties of ANS and show that a plain ANS with a hidden encoding table can be broken by statistical attacks. Next, we study ANS behavior when its states are chosen at random. Our compcrypt algorithm is built using ANS with randomized state jumps and a sponge MonkeyDuplex encryption. Its security and efficiency are discussed. The design provides 128-bit security for both confidentiality and integrity/authentication. Our implementation experiments show that our compcrypt algorithm processes symbols with a rate up to 269 MB/s (with a slight loss of compression rate) 178 MB/s.
Exploring Reasoning Schemes: A Dataset for Syllogism Figure Identification

Argument mining aims at extracting structured arguments from texts. Both argument schemes and their structures are studied. A syllogism is a traditional argument scheme and its figures are important in classical logic. Existing research has not yet investigated syllogism figures. Here, we fill this gap by presenting a study on automatic identification of syllogism figures. We prepared a novel dataset of 8.6k syllogisms. We annotated their figures and carried out identification tasks using both supervised and weakly-supervised approaches. Experimental results show that both approaches are adequate.
Backing the Right Horse: Gamification and Mixed Realities in Higher Education

The present paper focuses on studying which are the key elements that need to be taken into consideration when designing online tools that can improve a learners’ knowledge-acquisition process. The implementation of gamification and augmented reality in specific adult learning environments have not yet been largely implemented, and thus require greater understanding. Through a previous study in the framework of the Erasmus+ Project number 2019-1-ES01-KA203-065558, AR was deemed a great success in helping to increase students’ motivation. Following on those results and considering the elements needed to fully develop knowledge, a comprehensive detail of the key elements for online platform design are outlined. Through a greater advancement of these type of digitalization processes, the industry of education can provide better environments to consolidate and enlarge learning acquisition practices.
A Machine Learning-based Approach for Advanced Monitoring of Automated Equipment for the Entertainment Industry

Machine Learning-based approaches are revolutionizing the way in which complex systems and machines are monitored and controlled. In this work, we present a smart monitoring system that combines a Big Data architecture with an unsupervised anomaly detection technique, targeting the automated equipment in the entertainment industry. Anomaly detection uses state-of-the-art univariate and multivariate algorithms, as well as recently proposed techniques in the field of explainable artificial intelligence, to achieve enhanced monitoring capabilities and optimize service operations. The monitoring system is here presented and tested on a real world case study, i.e., an amusement park ride.
A Visual Analytics Approach for Structural Differences Among Graphs via Deep Learning

Representing and analyzing structural differences among graphs help gain insight into the difference related patterns such as dynamic evolutions of graphs. Conventional solutions leverage representation learning techniques to encode structural information, but lack an intuitive way of studying structural semantics of graphs. In this article, we propose a representation-and-analysis scheme for structural differences among graphs. We propose a deep-learning-based embedding technique to encode multiple graphs while preserving semantics of structural differences. We design and implement a web-based visual analytics system to support comparative study of features learned from the embeddings. One distinctive feature of our approach is that it supports semantics-aware construction, quantification, and investigation of latent relations encoded in graphs. We validate the usability and effectiveness of our approach through case studies with three datasets.
An Improved Multi-target Tracking Algorithm for Automotive Radar

Abstract An improved multi-target tracking algorithm for automotive radar is proposed in this paper. In the application of automotive radar, in addition to the motion generated by the target maneuver, the maneuver of the radar-equipped vehicle will also cause the target to move relative to the radar. A single motion model for the target cannot accurately describe the motion state of the target relative to the radar. To solve the above problems, the nonlinear state equations of various motion models and their process noise covariance matrix are derived, and the Interacting Multiple Model (IMM) algorithm, the Joint Probabilistic Data Association (JPDA) algorithm and the Unscented Kalman Filter with Doppler measurement (DUKF) are combined. At the same time, the correlation coefficient of range measurement error and velocity measurement error of Frequency Modulated Continuous Wave (FMCW) radar is derived, and the relationship between it and FMCW radar parameters is studied. By simulation, its influence on target tracking is assessed, final results indicate the reference value for the application of automotive radar in intelligent driving.
Synchronous Stream Encryption Using an Additional Channel to Set the Key

The article describes the possibility to refuse the direct transfer of the key from the sender to the recipient in the synchronous stream encryption scheme. We suggest creating a table of correspondence between the key information K, and the time of sending an email or phone call. The sender will have one instance of such a table, and the recipient will have another. The format of the key information in the simplest case includes the PRNG key itself, which determines the logic of operation of the PRNG nonlinear function, the code of the type of the used PRNG nonlinear function, and the PRNG design code. We offer 2 ways of interaction between the sender and the recipient of messages. Consider two possible options for encryption schemes: when the superposition of a sequence from the output of the PRNG on the input sequence is performed by the XOR operation, and when the encryption process is performed with a stochastic transformation of R, and the operation decryption operation backward stochastic transformation R-1. We present the scheme of one of the simplest possible variants for constructing the block R of the stochastic transformation.
3D Face Point Cloud Super-Resolution Network

With the development of consumer-level depth sensors, 3D face point cloud data can be easily captured now. However, such data are often accompanied by low resolution, noise, and holes. At the same time, high-precision 3D scanners are bulky and can not be widely used in daily applications due to costs and inconvenience. To fill the gap between low and high resolution 3D faces, we propose a two-stage framework named the face point cloud super-resolution network (FPSRN) to recover high-resolution 3D face data from the low-resolution counterparts. As the human faces can be aligned into a unified coordinate system, we formulate point cloud super-resolution as a z-coordinate prediction problem. Cascaded auto-encoders are employed to retain both global structure and boundary information of different face regions during super-resolution. Compared with state- of-the-art point cloud completion methods and depth estimation methods, our method improves the Earth-Mover’s Distance (EMD) and the Root Mean Square Error (RMSE) metrics by 43% and 25%, respectively.
A low complexity bit parallel polynomial basis systolic multiplier for general irreducible polynomials and trinomials

The bit parallel multiplication scheme is characterized by an important feature called concurrency, which makes its execution faster. The output through this scheme is generated in every clock cycle, after taking ‘m’ clock cycles in the beginning. In this paper, a polynomial basis systolic multiplier over irreducible polynomials ( x m + t m − 1 x m − 1 + . . .. t 2 x 2 + t 1 x 1 + t 0 ) in GF(2 m ) is designed specifically for odd ‘m’, which takes ‘m’ clock cycles and this is further, used to design the low complexity bit-parallel architecture for general irreducible polynomials and trinomials ( x m + x k + 1 ). The area complexity of the proposed multiplier for general irreducible polynomials matches with the best existing multiplier with a 17% reduction in time complexity due to a considerable decrease in critical path delay. To the best of our knowledge, the area-delay product of the proposed multipliers is the lowest achieved when compared with the best multipliers available in the literature. FPGA implementation results also show that the proposed multiplier has 59% less space complexity and 47% less time complexity than the best-reported multiplier for m = 233.