Reviewerly/exHarmony huggingface dataset:
Hybrid Cloud Energy Management for Edge Computing

Recently, many service providers or telecom operators are offering hybrid cloud services with edge computing to consumers, such as gaming, streaming, and manufacturing. However, the energy management of data centers is emphasized for enterprises in global ESG policy. Therefore, we propose the energy management architecture for hybrid cloud with edge computing which includes physical and virtual resources. In addition, we also propose the energy saving policy to improve resource utilization and cooling efficiency.
A Brief Survey on Removing Temporal Redundancy in Video Object Detection

There is a large amount of inter-frame local spatial-temporal redundancy in natural videos. This paper presents a survey on the current significant temporal redundancy removal technologies necessary for real-time AI video object detection especially for mobile platforms. Temporal redundancy removal has once made great contributions to video data compression. However, the methods used in video data compression cannot be applied to video object detection directly. The same basis is that each frame in videos are classified as key frames or non-key frames, and then different types of computations are applied for each frame category. Two major directions in using the temporal information in video object detection are developed, i.e., exploiting the data redundancy between consecutive frames to increase execution speed, and aggregating the temporal features to improve detection accuracy. This paper gives a quick but in-depth analytical overview for the state-of-the-arts in this field.
An Efficient Distributed K-SVD Algorithm for Image Reconstruction

K-SVD, as a sparse representation method of the over-complete dictionary, has been widely used in image processing. However, the K-SVD dictionary requires to be bigger, resulting in a large amount of computation and longtime consumption during the experiment. Therefore, an efficient distributed K-SVD approach (DK-SVD+) for image reconstruction is proposed. Firstly, the image is decomposed into small overlapping patches, which can speed up processing and increase the precision of images that are reconstructed. Secondly, an optimized iterative shrinkage method is introduced in the sparse coding stage, which can speed up the convergence of the model. The experimental results illustrate that our proposed model performs better than the existing K-SVD and deep K-SVD models. At the noise level of 25, its average value of PSNR is 1.79dB higher than K-SVD and 0.15dB higher than DK-SVD, and it consumes the shortest time.
Research on Interaction between Dancers’ Motion Capture and Robotic Arm

HCI (Human-Computer Interaction) is a trend in technological art, it can build an immersive experience in an interactive field. This paper focuses on intelligently and dynamically capturing dancers' motion, quantifying the emotional representation of dancers. We define the gesture's emotional type (positive and negative), and the gesture's definitions given are expansion and contraction of the limb. Finally, the interaction between the 3D coordinates and the movement of the robotic arm can be captured by the pose of the dancer. Based on our best results, the image resolution is 960*540, and we achieve a real-time effect of up to 30fps.
A Rational Graph Filtering Method for the Implementation of Heat Kernel Smoothing

In this paper, a decentralized implementation of heat kernel smoothing (HKS) is presented by using a rational graph filtering method. First, the basics of graph signal processing (GSP) are briefly described. Then, the centralized implementation of HKS using matrix exponential is studied. Next, the Taylor series expansion of exponential function and Prony method are applied to determine the filter coefficients of a rational graph filter. Because only linear equations need to be solved, the proposed design method is easy to be used. Finally, the performance of the proposed HKS method is evaluated by using the irregular data captured from the sensor network.
Architecture of Intelligent Interactive System Based on Artificial Intelligence

Networking and are the future development directions of interactive service systems. Internet popularization and rich social channels have realistic needs for intelligent interactive services. High-quality and low-cost intelligent interactive solutions have become the main issues that need to be addressed in various industries. Relying on artificial intelligence technology, intelligent means such as intent recognition, semantic understanding, dialogue and judgment were introduced, the integration of scene design and knowledge fragmentation processing technology has brought new means and opportunities for the realization of interactive service intelligence. The problems faced by the existing interactive system were analyzed. On this basis, the key functional elements and system architecture of the intelligent interactive system were designed.
Research on Force Atlas Dynamic Network Algorithm for Intelligent Speech Recognition Based on Machine Computers

This paper proposes a Force Atlas dynamic network algorithm based on machine-computer intelligent speech recognition. The algorithm uses the Force Atlas algorithm to construct a speech recognition network and combines machine learning technology to real-time adjust and optimize the network. Specifically, the algorithm connects different speech units into a dynamic speech recognition network through feature extraction and pattern recognition of speech signals. On this basis, the Force Atlas algorithm is used to lay out and adjust the network to achieve fast and accurate recognition of voice signals. Experimental results show that the algorithm has a high recognition rate and robustness, and can be effectively applied to the field of machine computer intelligent speech recognition.
Optimizing Patient Medical Records Grouping through Data Mining and K-Means Clustering Algorithm: A Case Study at RSUD Mohammad Natsir Solok

RSUD Mohammad Natsir Solok, located in Solok City, provides comprehensive individual health services within its premises, offering both inpatient and outpatient care with 24-hour service availability. Inpatient services encompass emergency care and basic health services. A crucial component of healthcare operations is medical records, which consist of documented information pertaining to patient identity, examinations, treatments, procedures, and other services rendered. Medical records are essential and should be meticulously created in written or electronic form to ensure completeness and clarity. One common challenge encountered in maintaining medical records is the presence of overlapping data. To tackle this issue, data mining techniques are employed, with clustering being the primary method of choice. The K-Means algorithm is specifically utilized for clusterization purposes. By applying this data mining process and grouping patient medical records, valuable insights into the patterns of disease spread across different villages can be obtained. After applying K-Means clustering method, four distinct clusters were identified. The first cluster comprises 562 items, the second has 406 items, and the third and fourth have 791 and 279 items, respectively. These findings can serve as a reference for the local government, particularly the Solok City Health Office, to facilitate disease prevention initiatives and awareness campaigns. Decision-making related to disease sources, diagnosis, age, and gender of the affected patient can be informed by this data analysis.
MMM: Authenticated Encryption with Minimum Secret State for Masking

We propose a new authenticated encryption (AE) mode MMM that achieves the minimum memory size with masking. Minimizing the secret state is the crucial challenge in the low-memory AE suitable for masking. Here, the minimum secret state is s + b bits, composed of s bits for a secret key and b bits for a plaintext block. HOMA appeared in CRYPTO 2022 achieved this goal with b = 64, but choosing a smaller b was difficult because b = s/2 is bound to the block size of the underlying primitive, meaning that a block cipher with an unrealistically small block size (e.g., 8 bits) is necessary for further improvement. MMM addresses the issue by making b independent of the underlying primitive while achieving the minimum (s + b)-bit secret state. Moreover, MMM provides additional advantages over HOMA, including (i) a better rate, (ii) the security under the multi-user model, (iii) and a smaller transmission cost. We instantiate two variants, MMM-8 (with b = 8) and MMM-64 (with b = 64), using the standard tweakable block cipher SKINNY-64/192. With a (d + 1)-masking scheme, MMM-8 (resp. MMM-64) is smaller by 56d + 184 (resp. 128) bits compared with HOMA. As a result of hardware performance evaluation, MMM-8 and MMM-64 achieved smaller circuit areas than HOMA with all the examined protection orders d ∈ [0, 5]. MMM-8’s circuit area is only 81% of HOMA with d = 5, and MMM-64 achieves more than x3 speed-up with a smaller circuit area.
Research on the Reform of Translation Teaching for English Majors by TBLT under the Background of AI

With the continuous development of society and the changes of the times, English has gradually become one of a fundamental requirement for talents. As the cradle of cultivating talents, colleges and universities play a pivotal role in educational reform. In the new era, the development of modern information technology demands English teaching to maintain innovation and keep pace with the times. The task-based language teaching approach is used in the translation teaching for English majors in colleges and universities to explore a new mode of English teaching. In teaching, by allowing students to complete the corresponding target tasks, develop students&amp;#39; ability to comprehensively use information, and promote the effective cultivation of students&amp;#39; English translation ability, thereby improving students&amp;#39; English cross-cultural level and translation ability. College English teaching should prioritize the cultivation of students&amp;#39; practical translation ability. Therefore, based on the characteristics of task-based language teaching, artificial intelligence technology is introduced into English translation teaching for English majors to assist in completing tasks using intelligent translation, so as to realizing a new model of student-centered English teaching. Taking English translation teaching as an example, this paper discusses how to reform the translation teaching for English majors in colleges and universities under the new situation of artificial intelligence application in the field of translation.
Automated Pentesting and Threat Simulation

The first significant factor leading to a data breach is the intentional theft or sabotage by outside attackers, which costs $3.33 million on average. To protect systems and applications from such catastrophes, it is essential to find security flaws in advance. Employing ethically qualified hackers to simulate threat actors for penetration tests is a frequent practice. This paper introduces a solution that combines the capabilities of different other tools to automate the complete pentesting process on a single platform due to the difficult nature of a pentest and the scarcity of trained professionals. Cybersecurity experts and other IT employees may do simple, automated, and hassle-free testing thanks to a user-friendly GUI. The web application interface of the tool, which is housed in the cloud, enables users to automate all kinds of scans, evaluations, and exploitations. It connects to the user's network via a node in the same network. In terms of automating the aforementioned pentesting process-venator, the suggested approach is novel.
The Effects of Research Data Management Services: Associating the Data Curation Lifecycle with Open Research Output

This study seeks to understand the relationship between research data management (RDM) services framed in the data curation life cycle and the production of open data. An electronic questionnaire was distributed to US researchers and RDM specialists, and the results were analyzed using Chi-Square tests for association. The data curation life cycle does associate with the production of open data and shareable research, but tasks like data management plans have stronger associations with the production of open data. The findings analyze the intersection of these concepts and provide insight into RDM services that facilitate the production of open data and shareable research.
A Novel Method of Automatic Modulation Classification with an Optimised 1D DBSCAN

Automatic modulation classification is an important goal in the creation of a system which can adapt to and demodulate a range of wireless modulation schemes. In this paper, a new method of automatic modulation classification using clustering is proposed. Constellation points are decomposed into arguments and absolute values. Then this data is clustered to find characteristic features about incoming data which is then used to determine the modulation scheme with a machine learning based classifier. The algorithm shows a comparable classification accuracy with the state-of-the-art technologies at 20dB SNR with less computing complexity and comparable execution speed. The computing complexity is further improved with an optimisation for unidimensional clustering which achieves a minimum speed-up of $$10^2$$ by reducing computational complexity from $$O(n^2)$$ to O(nlog(n)).
Findings of the TSAR-2022 Shared Task on Multilingual Lexical Simplification

Horacio Saggion, Sanja Štajner, Daniel Ferrés, Kim Cheng Sheang, Matthew Shardlow, Kai North, Marcos Zampieri. Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022). 2022.
GMU-WLV at TSAR-2022 Shared Task: Evaluating Lexical Simplification Models

This paper describes team GMU-WLV submission to the TSAR shared-task on multilingual lexical simplification. The goal of the task is to automatically provide a set of candidate substitutions for complex words in context. The organizers provided participants with ALEXSIS a manually annotated dataset with instances split between a small trial set with a dozen instances in each of the three languages of the competition (English, Portuguese, Spanish) and a test set with over 300 instances in the three aforementioned languages. To cope with the lack of training data, participants had to either use alternative data sources or pre-trained language models. We experimented with monolingual models: BERTimbau, ELECTRA, and RoBERTA-largeBNE. Our best system achieved 1st place out of sixteen systems for Portuguese, 8th out of thirty-three systems for English, and 6th out of twelve systems for Spanish.
THE EFFECT OF YOUTUBE ON SPEAKING ABILITY OF SENIOR HIGH STUDENTS

YouTube is a rich source for teaching speaking in EFL contexts. You Tube also enables students to learn language of the world. The present study aimed to know whether secondary students in Indonesia who frequently watched You Tube in English speak better that than those who seldom. This was an ex-post-facto study. A questionnaire and speaking test were designed to students speaking ability. T-test and ANOVA were used to measure the different effects of You Tube. The present study revealed that the speaking ability of the subjects who watched You Tube exceeded the speaking ability of those who seldom watched You Tube. Based on the T-test analysis, YouTube gave good effect on students’ speaking ability, however based on the ANOVA analysis, You Tube gave the highest effect on the vocabulary aspect of their speaking ability.
The Effects of Circuit Training with Outdoor Exercise Equipment on the Health-Related Physical Fitness and Physical Self-Efficacy of Students in a Low Fitness Level

The purposes of this study are to plan a circuit training exercise program with outdoor exercise equipment on physical fitness and physical self-efficacy. among the 1st grade male students of T High School in Gimpo City, 40 students lowphysical strength were the subjects of the study. The findings were as follows: First, the students engaged in self-directed physical activities by devising a specific instructional plan for circuit training exercise and concrete circuit practice methods and participating in them for themselves to promote their health-related physical fitness; secondly, the circuit training exercise program stimulated the students' internal participation motivation and encouraged them to improve their health-related physical fitness in muscular strength, power, flexibility, and cardiopulmonary endurance; and finally, the circuit training exercise program helped the students shake off the stereotype that exercise was tiresome and hard, change their perception of exercise as an easy and fun activity, and improve their physical self-efficacy.
ANALYSIS OF THE MAIN CHARACTERS IN STORYLINE THE FILM “NANTI KITA CERITA TENTANG HARI INI”

The purpose of this study is to Analyze of The Main characters in Storyline The Film "Nanti Kita Cerita Tentang Hari Ini" directed by Angga Dwimas Sasongko. Before collecting data, first watched movies and read novels. This research method is descriptive qualitative. This film was adapted from novel by Marcella Febritrisia Putri. The first Indonesian to reach 2 million viewers in 2020. Film has won various awards. Storyline of this film tells the story of a family trying to maintain understanding and harmony. Conflicts in family are caused by in the past secrets that have not been revealed. This 121-minute film tells the story of a family of five, parents and three children. In my opinion interesting characters in this film are Narendra as father and Awan as youngest child. Narendra's character has endured traumatic feelings for years, making him seem overly possessive and indulgent to Awan, but on the other hand, Awan's character is actually disrespectful to her parents. Therefore, I conducted an analysis of two main characters. The approach is a literary study that has a big role for readers in accepting, experiencing and understanding literary works.
Job Tailored Resume Content Generation

Generally candidates apply to multiple jobs with a single resume and do not tend to customize their resume to match the job description. This hampers their chances of getting a resume shortlisted for the job. The project aims to help such candidates build job tailored resumes that help them create a customized and targeted resume for a specific job. The tool specifically targets candidates’ employment work history for resume content generation. We create a synthetic dataset built from candidates’ employment history and online job descriptions. We use natural language processing (NLP) techniques to extract and organize the dataset, experiment with multiple dataset variations and cite ways to effectively build the dataset for the proposed task. We then use natural language generation by fine tuning GPT-2 for the task of resume content generation. Finally we evaluate the fine tuned model on various metrics and report our findings.
Academic Institutional Repositories for Studies, Research and Preservation of Scientific Heritage in Latvia

Institutional repositories enable the storage and sharing of scholarly information and are an essential element of open science. The study assesses the current status of institutional repositories in Latvian higher education institutions and provides an overview of the development, design and management of repositories, resource provision and access.
The Role of Language Technologies in Digital Humanities (The Case of Parliamentary Debates)

The paper focuses on the use case of parliamentary debates as part of Digital Humanities. First, the ParlaMint project is outlined as a flagship initiative of CLARIN ERIC infrastructure. The project makes content from the national and regional parliaments visible, comparable and accessible for policy making and research. Then, the approaches are considered that have been applied in the creation of 31 corpora from national and regional parliaments. Last but not least, the utility of the multilingual resource is discussed.
Anonymity in decentralized apps: Study of implications for cybercrime investigations

In the digital age, cybercrime facilitated by anonymous communication apps raises significant concerns. Criminals exploit the anonymity provided by these apps, creating challenges for law enforcement and cybersecurity professionals when investigating and combating cybercrime. The complexity of decentralized applications (DApps) without centralized servers further complicates evidence certification. Although anonymity features to protect privacy, they impede the establishment of connections between digital accounts and real-world identities. In centralized server environments, data access for investigations is relatively straightforward. However, this study reveals that DApps present challenges due to decentralized control, anonymity, encrypted communication, and jurisdictional issues. DApps designed for anonymous communication allow users to interact without revealing their identities, making it challenging to trace criminals. While cybercrime investigations in centralized environments involve systematic evidence collection, correlation, analyzing communication patterns, collaboration with agencies, tracking IP addresses, legal authorization, and forensic analysis of digital devices, DApps-based investigations require vital intelligence gathering through open-source techniques (OSINT). This includes retrieving digital footprints, analyzing social media profiles, and tracing ownership information. Moreover, investigators may exploit human vulnerabilities, engage in deceptive communication, or use social engineering techniques to gather information while carefully considering the balance between user privacy and investigative requirements. In this study, we explore the many facets of anonymity in DApps and what challenges they impose for the investigation of cybercrime. The anonymity of users and their transactions in the context of new blockchain and decentralized technology presents difficulties for law enforcement. In the end, our research helps shed light on the complex relationship between anonymity in decentralized systems and the need for fairness online.
ENGLISH LEARNING USING NATURE-BASED LEARNING METHODS TO INCREASE STUDENT MOTIVATION

The purpose of this research is to study English learning using nature-based learning methods to increase students' motivation in learning. In the minds of some students there is still a paradigm that assumes that success in learning English is due to talent. This assumption encourages students to be lazy to learn English. They tend to be more desperate and give up when they encounter difficulties in learning. However, learning English can be done anywhere and anytime using any method. The goal is that students can be motivated to develop learning that is more interesting and has a positive impact. The learning environment makes students broader in thinking and expression. The benefits of environment-based media allow students to better understand natural phenomena that occur in everyday life. Utilization of the environment makes the learning process more meaningful with the outdoor model. Students can explore the natural environment and learn practical skills to understand and appreciate aspects of environmental life. Learning activities will be more interesting for students. In addition, the use of the environment encourages an increase in student learning activities using nature-based learning methods