Reviewerly/exHarmony huggingface dataset:
Online training: The application of the Society 5.0 concept

Abstract Society 5.0 has been introduced, and technology has played an essential role in achieving the aim of improving the prosperity of human beings. Businesses that often provide training to their employees must adapt to how the training course is conducted. While most training courses are conducted face-to-face, online training courses with technology utilization must be considered. Thus, this research intends to look at the effectiveness of online training in the context of Business English training and the perspectives of the subjects on online training methods. Mixed methods research was conducted with 30 subjects in total. A pre-test and a post-test were administered to investigate the effectiveness, and a questionnaire was distributed to explore the subjects’ perspectives. The results have shown that online training is effective as the test results show a dramatic increase. In addition, the subjects also state their approval of training via online methods.
Design And Implementation Of Gesture Based Human Computer Interface

Design and Implementation of gesture based human computer interface using microcontroller, image processing and standers computer system is presented. The purpose of design system is to solve real time problem of any disable persons using hand movement and performs basic task by recognition of static and dynamic gesture of hand. The propose system work with the classification of hand gesture with many sensors mounted at wearable gloves. The practical implementation is in the form of glove equipped with Accelerometer sensors for sensing hand movement and transmitter receiver module that assists the disable persons to communicate with world in natural way where user can transmit and receive the original data without need any other mode of communication. The main purpose of this paper is interaction of human with computer interface (HCI) that connects man and machine. The combined static and dynamic human gesture can be recognized with set of rules and regulation based on permutation and computation for an input signal of microcontrollers. All this instruction will be encrypted by the help of Advance Microcontroller. Basically this hand gesture recognition is based on three main phases-detection, tracking and recognition. This paper provides existing interaction related to gesture recognition system for human computer interaction and try to implement it in prototype.
Hardware and Software System for Collection, Storage and Visualization Meteorological Data from a Weather Stand

In the modern world, the use of measuring sensors and microcontrollers is growing every year. Their application ranges from household appliances to industrial automation. The article describes the process of developing and creating a software and hardware system designed for collection, storage and visualization meteorological data. The system includes a stand with weather sensors, a microcontroller and a software. It allows user to receive data from the stand with a microcontroller and to interact with the MongoDB database. As a result of the research and the work, a prototype of a hardware and software system was developed, which provides the tracking of atmospheric environmental indicators, their processing, storage and visualization. In addition, the possibilities for further project development are described.
Adaptive Transformed Unscented Simplex Cubature Kalman Filter for Target Tracking

This paper peruses the Doppler-bearing passive target tracking (DBT), a task that is subject to the nonlinear measurement equations. These measurement equations form the target tracking a nontrivial duty. In order to defeat these problems, many studies have reported various nonlinear estimation techniques, such as sigma-point Kalman filters. These types of filters have demonstrated superior performances for nonlinear system parameter estimation. Consequently, the main contribution of this paper is to improve the performance of the sigma-point Kalman filters. Thereafter, this paper introduces a transformed unscented simplex cubature Kalman filter, in which adaptation techniques are utilized to reduce the dependency of the proposed filter performance on the priori statistical information of the process and measurement noises. Moreover, the proposed filter is initialized by the range-parameterised Unscented Kalman filter. The Cramér–Rao lower bound (CRLB) determines the best accessible accuracy of estimators, which is utilized in this paper to accredit the performance of the proposed filter. The validity and feasibility of the proposed filter are confirmed by Monte Carlo simulations.
Towards a knowledge graph-based approach for context-aware points-of-interest recommendations

Context-aware Recommender Systems (CARS) are becoming an integral part of the everyday life by providing users the ability to retrieve relevant information based on their contextual situation. To increase the predictive power considering many parameters, such as mood, hunger level and user preferences, information from heterogeneous sources should be leveraged. However, these data sources are typically isolated and unexplored and the efforts for integrating them are exacerbated by variety of data structures used for their modelling and costly pre-processing operations. We propose a Knowledge Graph-based approach to allow integration of data according to abstract semantic models for Points-of-Interests (POI)s recommendation scenarios. By enriching data with information about attributes, relationships and their meaning, additional knowledge can be derived from what already exists. We demonstrate the applicability of the proposed approach with a concrete example showing benefits of the retrieving the dispersed data with a unified access mechanism.
Algorithm of Fuzzy Controller Membership Function Allocation at Fuzzification Stage

Purposes. The modern automatic control systems represent the complexes of interacting technical devices and elements which operate based on different physical principles. Resolution of many challenging scientific and technical tasks can be simplified by using the multi-cascade fuzzy systems. Synthesis of individual modules of these complex control systems will be associated with a range of related time-consuming tasks. The approach for tuning the fuzzification blocks of fuzzy multi-cascade controllers proposed will make it possible to simplify a process of modeling this class of systems targeted at resolution of multi-objective control tasks.Methods and approaches. Synthesis and implementation of the intelligent control system is based on integral and differential calculus elements, basic provisions of automatic control theory and standard control procedures implemented using fuzzy logic unit.Findings: involves study and implementation of complex control laws for automation systems, which will essentially extend the intellectual opportunities while resolution of multi-objective and multifactor problems, as well as improve universality of these systems for the entire class of complex control objects. The approach for tuning the parameters of fuzzy multi-cascade systems proposed will, at the same time, reduce the efforts while the synthesis of control actions, as well as reduce the structural and algorithmic complexity of these modules with increasing the intellectual properties of automation systems as a whole.Originality/Value: involves formulation of mathematical description of a complex multi-cascade control system considering internal and external factors, and study of different models both complex and simple multi-cascade automatic control systems, as well as implementation of multi-cascade system modeling methods taking into account all the special features of functioning of a process object.
Unsupervised Exemplar-Domain Aware Image-to-Image Translation

Image-to-image translation is used to convert an image of a certain style to another of the target style with the original content preserved. A desired translator should be capable of generating diverse results in a controllable many-to-many fashion. To this end, we design a novel deep translator, namely exemplar-domain aware image-to-image translator (EDIT for short). From a logical perspective, the translator needs to perform two main functions, i.e., feature extraction and style transfer. With consideration of logical network partition, the generator of our EDIT comprises of a part of blocks configured by shared parameters, and the rest by varied parameters exported by an exemplar-domain aware parameter network, for explicitly imitating the functionalities of extraction and mapping. The principle behind this is that, for images from multiple domains, the content features can be obtained by an extractor, while (re-)stylization is achieved by mapping the extracted features specifically to different purposes (domains and exemplars). In addition, a discriminator is equipped during the training phase to guarantee the output satisfying the distribution of the target domain. Our EDIT can flexibly and effectively work on multiple domains and arbitrary exemplars in a unified neat model. We conduct experiments to show the efficacy of our design, and reveal its advances over other state-of-the-art methods both quantitatively and qualitatively.
Information algebras in the theory of imprecise probabilities

In this paper we create a bridge between desirability and information algebras: we show how coherent sets of gambles, as well as coherent lower previsions, induce such structures. This allows us to enforce the view of such imprecise-probability objects as algebraic and logical structures; moreover, it enforces the interpretation of probability as information, and gives tools to manipulate them as such.
Multimodal Fusion for Image and Text Classification with Feature Selection and Dimension Reduction

Abstract Internet has become an important information platform, and it is very important to accurately understand the multimedia information of the Internet. In this paper, our main task is to do classification based on pictures and texts collected from the Internet, which is a classification problem of multimodal fusion in practice. However, when multimodal data is put together, there may occur the dimension disaster problem. We apply feature selection (FS) and dimension reduction (DR) in feature levels both in later fusion and early fusion to solve this problem. The classification accuracies in different models obtain improvements in different levels respectively. We also discuss the relation between single modals and multimodal in later fusion. In our experiments, images and text can be classified by multimodal models under FS/DR, and of which with the help the multimedia information from the Internet can be analysed better to help enterprises provide better services and products, and then carry out better network marketing and promotion.
Learning BiLSTM-based Embeddings for Relation Prediction in Temporal Knowledge Graph

Abstract At present, as the knowledge graph continues to increase, problems such as incomplete information in the graph also emerge, mainly reflected in the lack of links between entities [1], and the current commonly solution is to learn their representations that can represent more semantic information for the entities and relations in the graph. This article focuses on the temporal knowledge graph (TKG), consistent with the goal in the static knowledge graph, this paper tries to propose a better representation learning method to solve the link prediction problem. According to the time information in the temporal knowledge graph, the paper constructs a bag of words about time, uses the bag of words model to decompose the time in the temporal knowledge graph into sequences, and learns its semantic information together with relations and entities. The embeddings obtained in this way can learn more semantic information. In addition, proving the effectiveness of the model through experiments on the currently commonly used temporal knowledge graphs.
Defense for adversarial videos by self-adaptive JPEG compression and optical texture

Despite demonstrated outstanding effectiveness in various computer vision tasks, Deep Neural Networks (DNNs) are known to be vulnerable to adversarial examples. Nowadays, adversarial attacks as well as their defenses w.r.t. DNNs in image domain have been intensively studied, and there are some recent works starting to explore adversarial attacks w.r.t. DNNs in video domain. However, the corresponding defense is rarely studied. In this paper, we propose a new two-stage framework for defending video adversarial attack. It contains two main components, namely self-adaptive Joint Photographic Experts Group (JPEG) compression defense and optical texture based defense (OTD). In self-adaptive JPEG compression defense, we propose to adaptively choose an appropriate JPEG quality based on an estimation of moving foreground object, such that the JPEG compression could depress most impact of adversarial noise without losing too much video quality. In OTD, we generate "optical texture" containing high-frequency information based on the optical flow map, and use it to edit Y channel (in YCrCb color space) of input frames, thus further reducing the influence of adversarial perturbation. Experimental results on a benchmark dataset demonstrate the effectiveness of our framework in recovering the classification performance on perturbed videos.
Toward Text-independent Cross-lingual Speaker Recognition Using English-Mandarin-Taiwanese Dataset

Over 40% of the world's population is bilingual. Existing speaker identification/verification systems, however, assume the same language type for both enrollment and recognition stages. In this work, we investigate the feasibility of employing multilingual speech for biometric applications. We establish a dataset containing audio recorded in English, Mandarin and Taiwanese. Three acoustic features, namely, i-vector, d-vector and x-vector have been evaluated for both speaker verification (SV) and identification (SI) tasks. Preliminary experimental results indicate that x-vector achieves the best overall performance. Additionally, the model trained with hybrid data demonstrates the highest accuracy, at the cost of extra data collection efforts. In SI tasks, we obtained over 91 % cross-lingual accuracy in all models using 3-second audio. In SV tasks, the EER among cross-lingual test is at most 6.52 %, which is observed on the model trained by English corpus. The outcome suggests the feasibility of adopting cross-lingual speech in building text-independent speaker recognition systems.
A bat optimization algorithm with moderate orientation and perturbation of trend

In order to improve the convergence speed and optimization accuracy of the bat algorithm, a bat optimization algorithm with moderate optimal orientation and random perturbation of trend is proposed. The algorithm introduces the nonlinear variation factor into the velocity update formula of the global search stage to maintain a high diversity of bat populations, thereby enhanced the global exploration ability of the algorithm. At the same time, in the local search stage, the position update equation is changed, and a strategy that towards optimal value modestly is used to improve the ability of the algorithm to local search for deep mining. Finally, the adaptive decreasing random perturbation is performed on each bat individual that have been updated in position at each generation, which can improve the ability of the algorithm to jump out of the local extremum, and to balance the early global search extensiveness and the later local search accuracy. The simulating results show that the improved algorithm has a faster optimization speed and higher optimization accuracy.
Creating the Virtual: The Role of 3D Models in the Product Development Process for Physical and Virtual Consumer Goods

The role of 3D models has substantially changed for companies that focus on the creation of consumer goods. For manufacturing and retail firms, virtual objects are today the predominant medium for product development and customization while virtual world and game developers not only build their entire products based on 3D models but found that selling virtual goods in games and virtual worlds can be more lucrative than selling the actual virtual environment. The objective of this study is to emphasize the role of 3D models in the product development processes and to identify similarities and differences between both domains based on a literature review. The results imply that 3D models are today prevalent in the entire value chain of both domains, while non-functional attributes of 3D models are of increasing value. A commonality is the growing importance of the user as source of knowledge for and creator of 3D models.
Cross-spectrum Face Recognition Using Subspace Projection Hashing

Cross-spectrum face recognition, e.g. visible to thermal matching, remains a challenging task due to the large variation originated from different domains. This paper proposed a subspace projection hashing (SPH) to enable the cross-spectrum face recognition task. The intrinsic idea behind SPH is to project the features from different domains onto a common subspace, where matching the faces from different domains can be accomplished. Notably, we proposed a new loss function that can (i) preserve both inter-domain and intra-domain similarity; (ii) regularize a scaled-up pairwise distance between hashed codes, to optimize projection matrix. Three datasets, Wiki, EURECOM VIS-TH paired face and TDFace are adopted to evaluate the proposed SPH. The experimental results indicate that the proposed SPH outperforms the original linear subspace ranking hashing (LSRH) in the benchmark dataset (Wiki) and demonstrates a reasonably good performance for visible-thermal, visible-near-infrared face recognition, therefore suggests the feasibility and effectiveness of the proposed SPH.
Healthcare and data privacy requirements for e-health cloud: A qualitative analysis of clinician perspectives

Cloud computing has many benefits relevant to the healthcare industry. Although the adoption of cloud services for healthcare systems is increasing, employment of cloud services raises many security and privacy concerns for patients and healthcare providers. We still lack a clear set of requirements consented by the different stakeholders; here in particular IT and healthcare professionals. In this study, we examine whether user perspectives on requirements for e-health on the cloud are consistent with best practice guidelines and regulatory requirements. This work contributes to the requirements engineering phase for a secure e-health cloud framework developed in a European project (ASCLEPIOS, https://www.asclepios-project.eu/). We used qualitative analysis, based on in-depth interviews, to describe and characterize clinicians' perspectives on the requirements of cloud services for healthcare data security and privacy. We examined whether these user perspectives were in harmony with the regulatory framework of the General Data Protection Regulation (GDPR), and best practice guidelines of a relevant standard, ISO 18308:2011. Ten clinicians were identified and interviewed at six healthcare organizations in Norway, the Netherlands and Germany. While user perspectives were largely consistent with both GDPR and ISO, some concerning differences in access control were noted between large and small healthcare institutions.
Blockchain Applications in various sectors beyond:Bitcoin

A block chain is a collection or book of record to which everybody can use but does not has a centralized authority for controlling. It is a technology which helps the individuals as well as corporate to build a trustworthy and transparent relationship. Block chains is the technology behind Bitcoin, there are other industrial sectors in which block chain can be implemented to have a competitive advantage. Block chain technology is said to be the most important factor or force which will prove a revolution in the field of IT. Blockchain has various implications in healthcare, and has improvised mobile health applications, observing devices, arranging and storing of electronic medical records, clinical experimental data, and insurance information storage. Many block chain technology applications are available widely today and its implementation will provide strength for various application domain.This research paper provides all factors and resources with research and practical experience on Block chain technology and implication in practice, as well as subjective exploration of possible business and I.T cases.
A Toolbox for Verifiable Tally-Hiding E-Voting Systems

In most verifiable electronic voting schemes, one key step is the tally phase, where the election result is computed from the encrypted ballots. A generic technique consists in first applying (verifiable) mixnets to the ballots and then revealing all the votes in the clear. This however discloses much more information than the result of the election itself (that is, the winners, plus possibly some information required by law) and may offer the possibility to coerce voters. In this paper, we present a collection of building blocks for designing tally-hiding schemes based on multi-party computations. From these building blocks, we design a fully tally-hiding scheme for Condorcet elections. Our implementation shows that the approach is practical, at least for medium-size elections. Similarly, we provide the first tally-hiding schemes with no leakage for three important counting functions: D’Hondt, STV, and Majority Judgment. We prove that they can be used to design a private and verifiable voting scheme. We also unveil unknown flaws or leakage in some previously proposed tally-hiding schemes.
Comparison of Repeated Viewing in Traditional 2D and VR Movies: A Study on Brain Waves

This paper explores the effects of video repetitive stimulation on viewers in both 2D and Virtual Reality (VR) modes. Forty subjects were recruited to compare the electroencephalogram (EEG) activity under VR and traditional 2D videos repetitive stimulation. The results showed that the intensity of alpha wave activity was significantly reduced by repeated viewing of 2D movies, especially in central region, parietal region, frontal region, and temporal region (p<;0.05). This phenomenon was not found in the VR movie stimuli. In addition, VR movies resulted in stronger alpha and beta wave activity and higher levels of immersion and energy arousal.
Live Demonstration: Energy-Efficient Data Symbol Detection Via Boosted Learning for Multi-Actuator Data Storage Systems

The equipment includes a laptop, Xilinx ZCU102 Dev Board, and a hard-disk drive (HDD) interface module. Fig. 1 illustrates the demonstration setup. All of the devices will be powered by their own power adaptors. The HDD interface module, which is provided by the Data Storage Systems Center (DSSC) at Carnegie Mellon University, can be controlled by the laptop to generate the raw readback signals for the machine- learning (ML) module implemented on the Xilinx ZCU102 to perform data symbol detection. The classified outputs are sent back to the laptop for result analysis and demonstration with a graphical user interface (GUI).