Reviewerly/exHarmony huggingface dataset:
A Robotic Path Planning by Using Crow Swarm Optimization Algorithm

One of the most common problem in the design of robotic technology is the path planning.The challenge is choosing the robotics' path from source to destination with minimum cost.Meta-heuristic algorithms are popular tools used in a search process to get optimal solution.In this paper, we used Crow Swarm Optimization (CSO) to overcome the problem of choosing the optimal path without collision.The results of CSO compared with two meta-heuristic algorithms: PSO and ACO in addition to a hybrid method between these algorithms.The comparison process illustrates that the CSO better than PSO and ACO in path planning, but compared to hybrid method CSO was better whenever the smallest population.Consequently, the importance of research lies in finding a new method to use a new metahumanistic algorithm to solve the problem of robotic path planning.
Managing the Pandemic: The Italian Strategy for Fighting COVID-19 and the Challenge of Sharing Administrative Powers

This article analyses the administrative measures and, more specifically, the administrative strategy implemented in the immediacy of the emergency by the Italian government in order to determine whether it was effective in managing the COVID-19 pandemic throughout the country. In analysing the administrative strategy, the article emphasises the role that the current system of constitutional separation of powers plays in emergency management and how this system can impact health risk assessment. An explanation of the risk management system in Italian and European Union (EU) law is provided and the following key legal issues are addressed: (1) the notion and features of emergency risk regulation from a pandemic perspective, distinguishing between risk and emergency; (2) the potential and limits of the precautionary principle in EU law; and (3) the Italian constitutional scenario with respect to the main provisions regulating central government, regional and local powers. Specifically, this article argues that the administrative strategy for effectively implementing emergency risk regulation based on an adequate and correct risk assessment requires “power sharing” across the different levels of government with the participation of all of the institutional actors involved in the decision-making process: Government, Regions and local authorities. “And the flames of the tripods expired. And Darkness and Decay and the Red Death held illimitable dominion over all”. Edgar Allan Poe, The Mask of the Red Death, Complete Tales and Poems (New York, Vintage Books 1975) p 273
Feature and Language Selection in Temporal Symbolic Regression for Interpretable Air Quality Modelling

Air quality modelling that relates meteorological, car traffic, and pollution data is a fundamental problem, approached in several different ways in the recent literature. In particular, a set of such data sampled at a specific location and during a specific period of time can be seen as a multivariate time series, and modelling the values of the pollutant concentrations can be seen as a multivariate temporal regression problem. In this paper, we propose a new method for symbolic multivariate temporal regression, and we apply it to several data sets that contain real air quality data from the city of Wrocław (Poland). Our experiments show that our approach is superior to classical, especially symbolic, ones, both in statistical performances and the interpretability of the results.
Cross-Lingual Voice Conversion With Controllable Speaker Individuality Using Variational Autoencoder and Star Generative Adversarial Network

This paper proposes a non-parallel cross-lingual voice conversion (CLVC) model that can mimic voice while continuously controlling speaker individuality on the basis of the variational autoencoder (VAE) and star generative adversarial network (StarGAN).Most studies on CLVC only focused on mimicking a particular speaker voice without being able to arbitrarily modify the speaker individuality.In practice, the ability to generate speaker individuality may be more useful than just mimicking voice.Therefore, the proposed model reliably extracts the speaker embedding from different languages using a VAE.An F0 injection method is also introduced into our model to enhance the F0 modeling in the crosslingual setting.To avoid the over-smoothing degradation problem of the conventional VAE, the adversarial training scheme of the StarGAN is adopted to improve the training-objective function of the VAE in a CLVC task.Objective and subjective measurements confirm the effectiveness of the proposed model and F0 injection method.Furthermore, speaker-similarity measurement on fictitious voices reveal a strong linear relationship between speaker individuality and interpolated speaker embedding, which indicates that speaker individuality can be controlled with our proposed model.
Different triplet sampling techniques for lossless triplet loss on metric similarity learning

Metric embedding learning is a special form of supervised learning: instead of regression or classification a similarity value is predicted based on embedded vector distance. To implement such a behavior, first the Siamese architecture was introduced, where training is based on two input samples, and the transformation model seeks to minimize distance between same-category samples, and increase distance between different samples. To deal with the problem of overtraining, the triplet loss was introduced in 2015, considering three input samples at a training step. Triplet networks also highlighted a novel problem: sample selection is important to eliminate those training triplets, where the measured distance based similarity results in zero loss. To deal with this phenomena, triplet mining techniques are analyzed, while other researchers discussed the possibility of different triplet-based loss functions. In this paper, the so-called lossless triplet loss function is compared with the original triplet loss method, while applying different negative sampling methods.
Blockchain based voting system for Jordan parliament elections

Covid-19 pandemic has stressed more than any-time before the necessity for conducting election processes in an electronic manner, where voters can cast their votes remotely with complete security, privacy, and trust. The different voting schema in different countries makes it very difficult to utilize a one fits all system. This paper presents a blockchain based voting system (BBVS) applied to the Parliamentary elections system in the country of Jordan. The proposed system is a private and centralized blockchain implemented in a simulated environment. The proposed BBVS system implements a hierarchical voting process, where a voter casts votes at two levels, one for a group, and the second for distinct members within the group. This paper provides a novel blockchain based e-Voting system, which proves to be transparent and yet secure. This paper utilizes synthetic voter benchmarks to measure the performance, accuracy and integrity of the election process. This research introduced and implemented new algorithms and methods to maintain acceptable performance both at the time of creating the blockchain(s) for voters and candidates as well as at the time of casting votes by voters.
Robust speaker verification by combining MFCC and entrocy in noisy conditions

Automatic speaker recognition may achieve remarkable performance in matched training and test conditions. Conversely, results drop significantly in incompatible noisy conditions. Furthermore, feature extraction significantly affects performance. Mel-frequency cepstral coefficients MFCCs are most commonly used in this field of study. The literature has reported that the conditions for training and testing are highly correlated. Taken together, these facts support strong recommendations for using MFCC features in similar environmental conditions (train/test) for speaker recognition. However, with noise and reverberation present, MFCC performance is not reliable. To address this, we propose a new feature 'entrocy' for accurate and robust speaker recognition, which we mainly employ to support MFCC coefficients in noisy environments. Entrocy is the fourier transform of the entropy, a measure of the fluctuation of the information in sound segments over time. Entrocy features are combined with MFCCs to generate a composite feature set which is tested using the gaussian mixture model (GMM) speaker recognition method. The proposed method shows improved recognition accuracy over a range of signal-to-noise ratios.
Optical Machine Learning Using Time-Lens Deep Neural NetWorks

As a high-throughput data analysis technique, photon time stretching (PTS) is widely used in the monitoring of rare events such as cancer cells, rough waves, and the study of electronic and optical transient dynamics. The PTS technology relies on high-speed data collection, and the large amount of data generated poses a challenge to data storage and real-time processing. Therefore, how to use compatible optical methods to filter and process data in advance is particularly important. The time-lens proposed, based on the duality of time and space as an important data processing method derived from PTS, achieves imaging of time signals by controlling the phase information of the timing signals. In this paper, an optical neural network based on the time-lens (TL-ONN) is proposed, which applies the time-lens to the layer algorithm of the neural network to realize the forward transmission of one-dimensional data. The recognition function of this optical neural network for speech information is verified by simulation, and the test recognition accuracy reaches 95.35%. This architecture can be applied to feature extraction and classification, and is expected to be a breakthrough in detecting rare events such as cancer cell identification and screening.
The Review on Eye Tracking Technology Application in Digital Learning Environments

New technologies arrive in our daily lives that actualize an urgent issue of preparing of specialists in specific technological fields.Hence, the necessity in providing of relevant education and training opportunities, appropriate digital learning tools, learning management systems and new learning approaches become more pressing.An understanding of students learning needs, their learning experience and activity peculiarities can help a lot to ensure learning process more efficient.And eye tracking technology could be considered as a significant instrument to be used in such studies.This paper provides a brief review on eye tracking technology employed for the findings on an analysis of learning management system user experience, as well as discusses prospective model development directions in this field.
Semantic interoperability Based on the European Materials and Modelling Ontology and its Ontological Paradigm: Mereosemiotics

The European Materials and Modelling Ontology (EMMO) has recently been advanced in the computational molecular engineering and multiscale modelling communities as a top-level ontology, aiming to support semantic interoperability and data integration solutions, e.g., for research data infrastructures.The present work explores how top-level ontologies that are based on the same paradigmthe same set of fundamental postulates -as the EMMO can be applied to models of physical systems and their use in computational engineering practice.This paradigm, which combines mereology (in its extension as mereotopology) and semiotics (following Peirce's approach), is here referred to as mereosemiotics.Multiple conceivable ways of implementing mereosemiotics are compared, and the design space consisting of the possible types of top-level ontologies following this paradigm is characterized.
INTERNAL CONTROL SYSTEM, UTILIZATION OF ACCOUNTING INFORMATION TECHNOLOGY, ON VILLAGE FUND MANAGEMENT ACCOUNTABILITY

The government prioritizes Villages in development, providing funds for Villages which are taken directly from the State Revenue and Expenditure Budget to be managed by the Village independently. The research objective was to determine the effect of the influence of the internal control system, the use of accounting information technology, and the competence of the Nagari apparatus on the accountability of village fund management. This type of quantitative research, a sample of 20 Nagari in Agam District, as many as 80 respondents, using convenience sampling techniques, the data in this study were collected using a questionnaire and analyzed using multiple regression analysis. The results of the research F test results F value 31.750&gt; F table 2.74, 95% confidence level and significant 0.05, Fcount &gt; F table conclusion that there is an effect of independent variables (X1, X2 and X3) on the dependent variable (Y) simultaneously. A separate t-test test found that the internal control system variable (X1) did not have a significant effect on the accountability of Village Fund management, the variable use of accounting information technology and the competence of Nagari officials (X2 and X3) had a significant effect on the accountability of Village Fund management.&#x0D; &#x0D; Keywords: Internal Control System, Utilization of Accounting Information Technology, Nagari Apparatus Competence, Accountability, Village Fund.
Comparison of Numerical Simulation of Epidemiological Model between Euler Method with 4th Order Runge Kutta Method

Coronavirus Disease 2019 has become global pandemic in the world. Since its appearance, many researchers in world try to understand the disease, including mathematics researchers. In mathematics, many approaches are developed to study the disease. One of them is to understand the spreading of the disease by constructing an epidemiology model. In this approach, a system of differential equations is formed to understand the spread of the disease from a population. This is achieved by using the SIR model to solve the system, two numerical methods are used, namely Euler Method and 4th order Runge-Kutta. In this paper, we study the performance and comparison of both methods in solving the model. The result in this paper that in the running process of solving it turns out that using the euler method is faster than using the 4th order Runge-Kutta method and the differences of solutions between the two methods are large.
Temporal Fusion Approach for Video Classification with Convolutional and LSTM Neural Networks Applied to Violence Detection

In modern smart cities, there is a quest for the highest level of integration and automation service. In the surveillance sector, one of the main challenges is to automate the analysis of videos in real-time to identify critical situations. This paper presents intelligent models based on Convolutional Neural Networks (in which the MobileNet, InceptionV3 and VGG16 networks had used), LSTM networks and feedforward networks for the task of classifying videos under the classes "Violence" and "Non-Violence", using for this the RLVS database. Different data representations held used according to the Temporal Fusion techniques. The best outcome achieved was Accuracy and F1-Score of 0.91, a higher result compared to those found in similar researches for works conducted on the same database.
Video-based Person Re-identification Based on Distributed Cloud Computing

Person re-identification(Re-ID) has been a hot research issues in the field of computer vision. In recent years, with the maturity of the theory, a large number of excellent methods have been proposed. However,Large scale data sets and huge networks make training a time-consuming process. At the same time, the parameters and their values generated during the training process also take up a lot of computer resources.Therefore, we apply distributed cloud computing method to perform person re-identification task. Using distributed data storage method, pedestrian data sets and parameters are stored in cloud nodes.In order to speed up operational efficiency and increase fault tolerance, we add data redundancy mechanism to copy and store data blocks to different nodes,and proposed a hash loop optimization algorithm to optimize the data distribution process. Moreover,we assign different layers of the Re-ID network to different nodes to complete the training in the way of model parallelism. By comparing and analyzing the accuracy and operation speed of the distributed model on the video-based data set MARS, the results show that our distributed model has a faster training speed.
Serverless streaming for emerging media: towards 5G network-driven cost optimization

Immersive 3D media is an emerging type of media that captures, encodes and reconstructs the 3D appearance of people and objects, with applications in tele-presence, teleconference, entertainment, gaming and other fields. In this paper, we discuss a novel concept of live 3D immersive media streaming in a serverless setting. In particular, we present a novel network-centric adaptive streaming framework which deviates from the traditional client-based adaptive streaming used in 2D video. In our framework the decisions for the production of the transcoding profiles are taken in a centralized manner, by considering consumer metrics vs provisioning costs and inferring the expected consumer quality of experience and behavior based on them. In addition, we demonstrate that a naive application of the serverless paradigm might be sub-optimal under some common immersive 3D media scenarios.
MR On-SeT: A Mixed Reality Occupational Health and Safety Training for World-Wide Distribution

The current generation of dedicated Mixed Reality (MR) devices can be considered as the first generation, which is truly mobile while also being capable of sufficient tracking and rendering. These improvements offer new opportunities for the on-set use of MR devices enabling new ways of using MR. However, these new use cases raise challenges for the design and orchestration of MR applications as well as how these new technologies influence their field of application. In this paper, we present MR On-SeT, a MR occupational health and safety training application, which is based on the experiences of an operational division of a world-wide operating German company. The intended purpose of MR On-SeT is to increase employees’ awareness of potential hazards at industrial workplaces by using it in occupational health and safety training sessions. Since the application is used at various locations throughout the company’s world-wide subsidiaries, we were able to evaluate it through an expert survey with the occupational health and safety managers of seven plants in France, Germany, Japan, and Romania. They reported the condensed experience of around 540 training sessions collected within three months. The purpose of the evaluation was twofold: 1. to understand their perceived attitudes towards the application-in-use, and 2. to collect feedback they received from respondents in training sessions. The results suggest that MR On-SeT can be used to extend current, predominantly theoretical, methods of teaching occupational health and safety at work, which also motivates existing employees to actively engage in the training sessions. Based on the findings, several further design implications are proposed.
The Impact of Fix Up Strategy on Indonesian EFL Learners’ Reading Comprehension

This study investigated the impact of using Fix Up Strategy as a specific treatment to deal with the learners' problem in reading that cannot be overcome merely by general reading comprehension course.This was a kind of quasi-experimental research involving 69 students of Madrasah Tsanawiyah (MTs) Al-Hikmah Bandar Lampung, Indonesia.They were divided into two groups: 36 students of experimental group and 33 students of control group.Learners in experimental group were taught with Fix Up Strategy (FUS) and those in the control group were taught with Reading Aloud (RA), a conventional reading activity.The instrument consisted of 25 multiple choice questions that had been previously validated in the procedures of data collection.Then the instrument was given in pre-test and posttest.In addition, observations were also carried out in the process of teaching reading by using FUS.The data were analysed using t-test and the result showed that 1) the students in the treatment with FUS demonstrated a significant improvement in EFL reading comprehension, and 2) FUS contributed to the students' autonomy in learning reading, enhancement of students' awareness in reading, students' active involvement in reading practice.The finding claims that the students who practiced reading with FUS had a better comprehension than those with RA.Certain pedagogical implications related to the finding were also discussed.
Particle Swarm Optimization for Punjabi Text Summarization

Particle swarm optimization (PSO) algorithm is proposed to deal with text summarization for the Punjabi language. PSO is based on intelligence that predicts among a given set of solutions which is the best solution. The search is carried out by extremely high-speed particles. It updates particle position and velocity at the end of iteration so that during the development of generations, the personal best solution and global best solution are updated. Calculation within PSO is performed using fitness function which looks into various statistical and linguistic features of the Punjabi datasets. Two Punjabi datasets—monolingual Punjabi corpus from Indian Languages Corpora Initiative Phase-II and Punjabi-Hindi parallel corpus—are considered. The parallel corpus comprises 1,000 Punjabi sentences from the tourism domain while monolingual corpus contains 30,000 Punjabi sentences of the general domain. ROUGE measures evaluate summary where the highest measure, ROUGE-1, is achieved for parallel corpus with precision, recall, and F-measure as 0.7836, 0.7957, and 0.7896, respectively.
Design of Network Cascade Structure for Image Super-Resolution

Image super resolution is an important field of computer research.The current mainstream image super-resolution technology is to use deep learning to mine the deeper features of the image, and then use it for image restoration.However, most of these models mentioned above only trained the images in a specific scale and do not consider the relationships between different scales of images.In order to utilize the information of images at different scales, we design a cascade network structure and cascaded super-resolution convolutional neural networks.This network contains three cascaded FSRCNNs.Due to each sub FSRCNN can process a specific scale image, our network can simultaneously exploit three scale images, and can also use the information of three different scales of images.Experiments on multiple datasets confirmed that the proposed network can achieve better performance for image SR.
Underwater Communication Signal Recognition Using Sequence Convolutional Network

Automatic modulation recognition (AMR) is one of the essential parts in the intelligent communication system. In the underwater acoustic communication, it is a challenging work that promptly and easily recognizes the signal modulation schemes by conventional methods. The deep neural network method is a good solution to the problem, which creates a better recognition effect. The packets of data that are fed to the familiar neural network is constant. However, the packets of signal data on the communication course consistently change, which seriously reflects on the signal recognition veracity. A novel deep learning network with the sequence convolutional network in this paper is proposed, which is composed of one-dimensional sequence convolution of residual network modules and the variable convolution kernel range. By extracting the time-domain signal characteristics, the affection of various signal packets can be mitigated. In experiments, the employed network not only has more concentrated on the modulation recognition veracity, but also owns a lower parameter quantity and a shorter training time, which indicates ideal recognition results in the underwater communication environment. Moreover, it is more valuable to the real underwater communication system.