Reviewerly/exHarmony huggingface dataset:
Effects of Cryptocurrencies on Global Economics: A Review Study

The features of a new global economy appear stronger and faster than the changes recorded by the economy throughout history, and the “Covid-19” pandemic has accelerated these transformations, achieving on the ground a global experience to deal with digital payment via credit cards and the use of online payment applications, and it marks the beginning of the end of the era of paper currencies. And the trend towards investing in crypto and digital currencies on the part of investors, in conjunction with the competition of countries around the world to win the issuance of the first digital currency issued by central banks. There are many questions about replacing the digital currency with gold or the dollar in a rapid transformation that changes the trends of the global economy, and the number of cryptocurrencies around the world has reached about 4337 currencies with a market value of $ 1.003 trillion, “Bitcoin” holds the largest share by 66% with a market value of $ 664 billion. Despite many official warnings against investing in these unregulated currencies, fintech startups are witnessing a surge in demand from consumers who are rushing to invest in cryptocurrencies amid great interest and momentum in Bitcoin. Like Revolut, eToro and Luno, cryptocurrency demand surged on their platforms amid the resurgence of Bitcoin prices.
DIALOG-22 RuATD Generated Text Detection

Text Generation Models (TGMs) succeed in creating text that matches human language style reasonably well. Detectors that can distinguish between TGM-generated text and human-written ones play an important role in preventing abuse of TGM. In this paper, we describe our pipeline for the two DIALOG-22 RuATD tasks: detecting generated text (binary task) and classification of which model was used to generate text (multiclass task) (Shamardina et al., 2022). We achieved 1st place on the binary classification task with an accuracy score of 0.82995 on the private test set and 4th place on the multiclass classification task with an accuracy score of 0.62856 on the private test set. We proposed an ensemble method of different pre-trained models based on the attention mechanism.
Streaming Multiscale Deep Equilibrium Models

We present StreamDEQ, a method that infers frame-wise representations on videos with minimal per-frame computation. In contrast to conventional methods where compute time grows at least linearly with the network depth, we aim to update the representations in a continuous manner. For this purpose, we leverage the recently emerging implicit layer models, which infer the representation of an image by solving a fixed-point problem. Our main insight is to leverage the slowly changing nature of videos and use the previous frame representation as an initial condition on each frame. This scheme effectively recycles the recent inference computations and greatly reduces the needed processing time. Through extensive experimental analysis, we show that StreamDEQ is able to recover near-optimal representations in a few frames time, and maintain an up-to-date representation throughout the video duration. Our experiments on video semantic segmentation and video object detection show that StreamDEQ achieves on par accuracy with the baseline (standard MDEQ) while being more than $$3\times $$ faster. Code and additional results are available at https://ufukertenli.github.io/streamdeq/ .
The Dilemma of Telecommunication Fraud Crime——An Analysis of China’s Governance Model as a Sample

In order to promote the international cooperative governance of telecommunication fraud crime and optimize China’s legal issues on this crime, this paper takes China’s governance model in telecommunication fraud crime as the analysis sample, and proposes the optimization path of governance of telecommunication fraud from both domestic and foreign aspects by analyzing the current shortcomings of China’s domestic legislation, law enforcement, and judicature, as well as the problems in international cooperation such as off-site evidence collection and criminal judicial assistance.
Sentiment Analysis from Indonesian Twitter Data Using Support Vector Machine And Query Expansion Ranking

Sentiment analysis is a computational study of a sentiment opinion and an overflow of feelings expressed in textual form. Twitter has become a popular social network among Indonesians. As a public figure running for president of Indonesia, public opinion is very important to see and consider the popularity of a presidential candidate. Media has become one of the important tools used to increase electability. However, it is not easy to analyze sentiments from tweets on Twitter apps, because it contains unstructured text, especially Indonesian text. The purpose of this research is to classify Indonesian twitter data into positive and negative sentiments polarity using Support Vector Machine and Query Expansion Ranking so that the information contained therein can be extracted and from the observed data can provide useful information for those in need. Several stages in the research include Crawling Data, Data Preprocessing, Term Frequency – Inverse Document Frequency (TF-IDF), Feature Selection Query Expansion Ranking, and data classification using the Support Vector Machine (SVM) method. To find out the performance of this classification process, it will be entered into a configuration matrix. By using a discussion matrix, the results show that calcification using the proposed reached accuracy and F-measure score in 77% and 68% respectively.
Multi-sensor track fusion algorithm research and simulation analysis

Target tracking is extremely important in the military field. In multi-sensor data fusion processing, track fusion techniques are required to make the target position information more accurate and reliable. In this paper, the target motion uses uniform and uniformly accelerated motion models, and the smoothing of the motion trajectory uses Kalman filtering. In this paper, Convex combination fusion algorithm, covariance weighted fusion algorithm and adaptive fusion algorithm are used to fuse the track data, and analyze the performance of these three algorithms with fusion trajectory, root mean square error of fusion comparison and algorithm time complexity comparison. The simulation results show that all three algorithms can extract useful information from the track data and improve the target tracking accuracy.
Image Verification and Emotion Detection using Effective Modelling Techniques

The feelings expressed on the face reflect the manner of thinking and provide useful insights of happenings inside the brain.Face Detection enables us to identify a face.Recognizing the facial expressions for different emotions is to familiarize the machine with human like capacity to perceive and identify human feelings, which involves classifying the given input images of the face into one of the seven classes which is achieved by building a multi class classifier.The proposed methodology is based on convolutional neural organizations and works on 48x48 pixel-based grayscale images.The proposed model is tested on various images and gives the best accuracy when compared with existing functionalities.It detects faces in images, recognizes them and identifies emotions and shows improved performance because of data augmentation.The model is experimented with varying depths and pooling layers.The best results are obtained sequential model of six layers of Convolutional Neural Network and softmax activation function applied to last layer.The approach works for real time data taken from videos or photos.
SPATIAL STUDY OF SOCIAL FACILITIES PLACEMENT IN DENSE SETTLEMENT USING SPACE SYNTAX ANALYSIS CASE STUDY: BANJAR BUALU, BENOA VILLAGE, BALI

Banjar Bualu area is one of the densely populated residential areas in Benoa Village, Bali. The high population must be balanced with the number of social facilities to meet the needs for social, cultural, and economic activities of the community. But in reality, there is still a lack of social facilities built to accommodate the social activities of the local community. Social facilities play a role in providing space for socializing and interaction, so their location must be easily accessible from residential areas. This study aims to provide recommendations for the location of social facilities in the Banjar Bualu residential area, Benoa Village. The method used is a qualitative method with an exploratory approach through space syntax analysis with DepthmapX software, to see integration, connectivity, and also the potentials of space that can be used as social facilities. The method of data collection was carried out by non-participant observation and secondary data related to maps of the road network and buildings from settlements in the Banjar Bualu area.
NFT — the latest digital copyright object or form of expression

Keywords: NFT, copyright, work, token, blockchain, digital environment, judicialexpertise&#x0D; The article is dedicated to current issues of NFT legal protection. There wereanalysed the legal nature of NFT as an object of copyright, the legal regime of NFT —the work, and some proposals for improving the legal regulation of these relations aresubstantiated. It has been proven that in a separate group of copyright objects it is expedientto allocate new digital objects that appear recently — NFT, multimediaworks, video games (computer games), Internet site, blockchain, and others. The legalnature of new digital copyright objects has been found to be controversial. It is establishedthat in the scientific literature NFT is interpreted as an independent object ofcopyright, as a way to use the object of copyright (for example, conversion of art into adigital token form), as an objective form of expression (digital token form), as securitydigital document, as a way of technical protection of the copyright object from encroachmenton it. It has been proven that converting a work into an NFT form is away to commercialize a copyright object. Furthermore, it is substantiated that NFT isa form of expression (existence) of the object of copyright, and NFT-work (photography,video, painting, drawing, music etc.) in the presence of originality (creative nature)is the object of copyright, which is fixed in specific digital tokenized form.It has been established that tokenization of works provides additional opportunitiesfor copyright protection of such objects due to blockchain — the technology onwhich NFT operates.It has been proven that NFT as a digital certificate certifies the uniqueness of thework, which allows increasing its value and avoid illegal distribution in the digital environmentin the future (which is typical of almost all digital and digitized copyrightobjects). Likewise, it has been proven that most NFT disputes should be resolvedbased on the findings of judicial intellectual property and computer science expertise.It has been established that copyright law is not adapted to regulate the use of worksin the digital environment, so NFT works have an undefined legal regime as objects ofcopyright. It has been proven that the current copyright law, which is imperfect in regulatingrelations in the digital environment in general, should be applied to the relationsrelated to the creation, use, exercise, and protection of NFT works.
PENILAIAN MEDIA PEMBELAJARAN E-MODUL MATERI KERAH KEMEJA

This study aims to create e-module in men's shirt collar material and to conduct the feasibility evaluation of e-module learning media in men's shirt collar material. The research evaluation was carried out by a panel of material and media experts. The research methodology used pre-experimental (one-shot case study). Research created e-module learning media that combines component such as layout, texts, audio, images, and videos using the Flip PDF Professional application assisted by CorelDRAW and Adobe Premiere Pro. E-module learning media in this men's shirt collar material has been declared very feasible based on aspects of content feasibility, linguistic aspects, presentation aspects, graphic aspects, software engineering aspects, visual communication aspects, stand alone aspects, and adaptive aspects. The results obtained for the assessment of learning media for material aspects are 88.04%, and media aspects are 82.4%, so in general the e-module learning media for men’s shirt collar material has been declared very feasible. So that e-module learning media that the researchers arranged can be used as a learning media and students can study independently.
An Efficient Sample Steering Strategy for Correlation Filter Tracking

Nowadays, many researchers utilize the correlation filter based trackers to address the severe appearance and motion changes in video sequences. A correlation filter (CF) can be evaluated based on the relationship between the object ground truth region (local) and the steering samples region (semi-local), with spatial and visual selective attention in parallel to human visual perception. In the proposed work, initially, an efficient correlation filter is estimated with the circulant sample matrices and the steering strategy. To effectively remove the boundary effect of the CF, the Histogram of Gradients (HoGs) are extracted for positive and negative samples which in turn referred the semi-local domains. Finally, the linear combination of the two domain models is used to determine the location of the object. The objective of the proposed visual tracking method (OT-Steer) is to apparently minimise the distractions and to effectively remove the boundary using the combination of object tracking and steered approach. The proposed OT-Steer model has been evaluated with various tracking benchmark datasets and results are compared. The results depict that the OT-Steer plays a vital role in effectively dealing with distractions during the tracking process with the low computational cost for efficient visual tracking.
A Proposed Secured Health Monitoring System for the Elderly using Blockchain Technology in Nigeria

A large number of connected smart objects and sensors, as well as the establishment of seamless data exchange between them, have been made possible by the Internet of Things (IoT) technology's recent rapid development. As a result, there is a high demand for platforms for data analysis and data storage, such as cloud computing and fog computing. IoT makes it possible to customize apps for older persons as well as for rapidly expanding markets that must modify their products to match the preferences of their customers. This study suggests a framework for a decision-support system and a protected health monitoring system using IoT data collected from senior residents' homes. The study intends to provide security to the users’ data from the point of acquiring of the data to the relaying of the data to cloud and to the alert generation using blockchain technology. Further research should focus on key management and security, as well as the capability to easily replace lost or compromised keys.
$\lambda$-CONSTACYCLIC CODES OVER FINITE KRASNER HYPERFIELDS

The class of constacyclic codes plays an important role in the theory or error-correcting codes. They are considered as a remarkable generalization of cyclic codes. In this paper, we study constacyclic codes over finite Krasner hyperfields in which we characterize them by their generating polynomial. Moreover, we study the dual of these codes by finding their parity check polynomial.
Development History and Concept Analysis of Tangible Interaction Design

Objectives: In view of the chaotic and fuzzy phenomenon of tangible interaction design and related concepts, this study sorts out and summarizes the development history of tangible interaction design, discusses and defines the concept of tangible interaction design, and analyzes the concept development relationship of tangible interaction design. Methodology: Through literature research, this paper sorts out the development history of tangible interaction design, and defines its concept and relationship. With Hornecker’s framework of tangible interaction as the prototype, this study establishes a model for interaction element analysis, and conducts a comparative analysis of the proportions of elements in the concept of tangible interaction design, thus identifying the development relationship of tangible interaction design and interaction elements. Conclusion: The development of tangible interaction design is roughly divided into three stages, namely the germination of physical input, the development of physical-digital coupling, and the transformation of experience orientation. Such concepts as Graspable Interface, Tangible Interface, and Radical Atoms all fall into the category of Tangible Interaction Design. The development of tangible interaction design and interaction elements enhance each other, from physical elements as the core to the tangible interactive interface enhancing expressiveness through technology, to the physical and expressive-based tangible interaction system with space and embodiment, reflecting the development history of experience-oriented tangible interaction design.
Hydroclimatic Data Prediction using a New Ensemble Group Method of Data Handling Coupled with Artificial Bee Colony Algorithm

Linear regression is widely used in flood quantile study that consists of meteorological and physiographical variables. However, linear regression does not capture the complex nonlinear relationship between predictor and target variables. It is rare to find a hydrological application using the group method of data handling (GMDH) model, artificial bee colony (ABC) algorithm, and ensemble technique, precisely predicting ungauged sites. GMDH model is known to be an effective model in complying with a nonlinear relationship. Therefore, in this paper, we enhance the GMDH model by implementing the ABC algorithm to optimize the parameter of partial description GMDH model with some transfer functions, namely polynomial, radial basis, sigmoid and hyperbolic tangent function. Then, ensemble averaging combines the output from those various transfer functions and becomes the new ensemble GMDH model coupled with the ABC algorithm (EGMDH-ABC) model. The results show that this method significantly improves the prediction performance of the GMDH model. The EGMDH-ABC model satisfies the nonlinearity in data to produce a better estimation. Also, it provides more robust, accurate, and efficient results.
IMPLEMENTASI METODE DATA MINING K-MEANS CLUSTERING TERHADAP DATA PEMBAYARAN TRANSAKSI MENGGUNAKAN BAHASA PEMROGRAMAN PYTHON PADA CV DIGITAL DIMENSI

CV Digital Dimensi is a company engaged in the printing sector, which is a subsidiary of the XG Group located in Jakarta. In order to be able to compete with other companies, the company does not only focus on products and services but also efforts to build personal relationships with customers. Existing payment transaction data so far have not been utilized as much as possible in determining the company's business strategy, therefore the implementation of data mining is necessary to analyze and explore the available data to find new information that is more valuable and useful for the company. The K-Means Clustering method is a data mining technique to obtain data groups by maximizing the similarity of characteristics within the cluster and maximizing the differences between clusters. The purpose of this study is to apply the K-Means Clustering method to group sales transaction data on CV Digital Dimensi and display the results in the form of visual graphics using the Python programming language and Scikit-Learn library. The results of this study succeeded in classifying sales transaction data into five clusters and can be used as a reference in determining the company's business strategy.
Reading in Digital Era: A Study on Enhancing Reading Skills

One of the basic skills of language learning is reading skill. It is integral to develop comprehension, pronunciation and intonation skills. It also helps to improve vocabulary and communication skills, broadens the horizons and enhances the knowledge of the students. Hence, it is very essential to inculcate the habit of reading among the students from a very young age. With the surge of digital tools, several modern methods can be adopted to make reading more interesting and engaging for the students. Availability of online books, graphic literature, use of digital art in literature, interactive novels and write-ups - all of it have indeed made reading easy and enjoyable to the students. However, there are several challenges involved in it as the majority of the students are not very actively involved in any reading process. The present paper aims to discuss the scope of reading, its types, methods and tools that can be used in the classroom to build reading skills, active utilisation of digital tools, and the difficulties involved in it. The paper also attempts to analyse the impact of digital media on the reading habits of the students and how it can be utilised for the benefit of the students.
An Improved particle swarm optimization based on lévy flight and simulated annealing for high dimensional optimization problem

Particle swarm optimization (PSO) is a simple metaheuristic method to implement with robust performance. PSO is regarded as one of the numerous researchers' most well-studied algorithms. However, two of its most fundamental problems remain unresolved. PSO converges onto the local optimum for high-dimensional optimization problems, and it has slow convergence speeds. This paper introduces a new variant of a particle swarm optimization algorithm utilizing Lévy flight-McCulloch, and fast simulated annealing (PSOLFS). The proposed algorithm uses two strategies to address high-dimensional problems: hybrid PSO to define the global search area and fast simulated annealing to refine the visited search region. In this paper, PSOLFS is designed based on a balance between exploration and exploitation. We evaluated the algorithm on 16 benchmark functions for 500 and 1,000 dimension experiments. On 500 dimensions, the algorithm obtains the optimal value on 14 out of 16 functions. On 1,000 dimensions, the algorithm obtains the optimal value on eight benchmark functions and is close to optimal on four others. We also compared PSOLFS with another five PSO variants regarding convergence accuracy and speed. The results demonstrated higher accuracy and faster convergence speed than other PSO variants. Moreover, the results of the Wilcoxon test show a significant difference between PSOLFS and the other PSO variants. Our experiments' findings show that the proposed method enhances the standard PSO by avoiding the local optimum and improving the convergence speed.