Reviewerly/exHarmony huggingface dataset:
An empirical investigation of task scheduling and VM consolidation schemes in cloud environment

Cloud computing has evolved as a new paradigm in Internet computing, offering services to the end-users and large-organizations, on-demand and pay-per-the-usage basis with high availability, elasticity, scalability and resiliency. In order to improve the performance of the Cloud system, handling multiple heterogeneous tasks concurrently, an appropriate task scheduler is required. To meet the user’s requirements in terms of Quality of Service (QoS) parameters, the task scheduling algorithm should identify the order in which tasks should be executed. Energy efficiency is the significant challenge in today’s task scheduling to meet the prerequisite for green computing. By increasing resource utilization at the data centers, virtual machine (VM) Consolidation is also recognized as the most widely used and promising approach in terms of energy consumption and system performance. However, excessive VM Consolidation could constitute a violation of the Service Level Agreement (SLA). The paper makes a contribution by outlining the numerous approaches that researchers have used thus far to achieve the goals of scheduling and VM Consolidation, assuring energy efficiency, and maintaining system performance. This would give readers a better understanding of the problems and the potential for improvement while assisting them in selecting the ideal scheduling algorithm with Consolidation technique. Additionally, the techniques are divided into three categories: those that primarily focus on task scheduling; those that target Consolidation; and complete computation, integrating task scheduling with VM Consolidation. Further classification for the scheduling algorithms include heuristic, meta-heuristic, greedy, and hybrid task scheduling algorithms. In addition to a summary of the benefits and drawbacks of the suggested algorithms, prospective research directions and recent developments in this area is also covered in this paper.
Federated Alternate Training (Fat): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging

Federated Learning (FL) aims to train a machine learning (ML) model in a distributed fashion to strengthen data privacy with limited data migration costs. It is a distributed learning framework naturally suitable for privacy-sensitive medical imaging datasets. However, most current FL-based medical imaging works assume silos have ground truth labels for training. In practice, label acquisition in the medical field is challenging as it often requires extensive labor and time costs. To address this challenge and leverage the unannotated data silos to improve modeling, we propose an alternate training-based framework, Federated Alternate Training (FAT), that alters training between annotated data silos and unannotated data silos. Annotated data silos exploit annotations to learn a reasonable global segmentation model. Meanwhile, unannotated data silos use the global segmentation model as a target model to generate pseudo labels for self-supervised learning. We evaluate the performance of the proposed framework on two naturally partitioned Federated datasets, KiTS19 and FeTS2021, and show its promising performance.
Weighted Load Balancing Method for Heterogeneous Clusters on Hybrid Clouds

In recent years, edge device and AI services have been expected to utilize scalable cloud computing to handle large amounts of processing. In the cloud, load-balancing techniques distribute the load evenly to many nodes to achieve high throughput. At the same time, the shift to hybrid cloud computing requires additional nodes with different generations or different types of computational resources to achieve high performance in an environment with heterogeneous computational performance. Heterogeneity raises concerns that the current uniform load-balancing will result in overloaded or underloaded nodes, which degrade responsiveness. Therefore, this study proposes a weighted load-balancing method to improve responsiveness in clusters with nonuniform computational performance. The proposed method is effective in improving the average response time by about 20%, the maximum response time by about 45%, and the response time variance by about 70% compared to load-balancing with a load balancer developed by Google.
Reinforcement Learning for Continuous Control: A Quantum Normalized Advantage Function Approach

In this study, we present a new approach to quantum reinforcement learning that can handle tasks with a range of continuous actions. Our method uses a quantum version of the classic normalized advantage function (QNAF), only needing the Q-value network created by a quantum neural network and avoiding any policy network. We implemented the method by TensorFlow framework. When tested against standard Gym benchmarks, QNAF outperforms classical NAF and prior quantum methods in terms of fewer adjustable parameters. Furthermore, it shows improved stability, reliably converging regardless of changes in initial random parameters.
Reducing Inference Latency with Concurrent Architectures for Image Recognition at Edge

Satisfying the high computation demand of modern deep learning architectures is challenging for achieving low inference latency. The current approaches in decreasing latency only increase parallelism within a layer. This is because architectures typically capture a single-chain dependency pattern that prevents efficient distribution with a higher concurrency (i.e., simultaneous execution of one inference among devices). Such single-chain dependencies are so widespread that even implicitly biases recent neural architecture search (NAS) studies. In this visionary paper, we draw attention to an entirely new space of NAS that relaxes the single-chain dependency to provide higher concurrency and distribution opportunities. To quantitatively compare these architectures, we propose a score that encapsulates crucial metrics such as communication, concurrency, and load balancing. Additionally, we propose a new generator and transformation block that consistently deliver superior architectures compared to current state-of-the-art methods. Finally, our preliminary results show that these new architectures reduce the inference latency and deserve more attention.
Blockchain perspectives, mining, and types: An introductory tutorial

Today, the world of information systems and business processes is experiencing a shift in its status quo. Systems designed and operated in a centralized manner are now getting decentralized, either completely or partly, thanks to blockchain technology—specifically, distributed ledger technology (DLT), which is enabling this paradigm shift.
Human training as a holistic construction in the hybridization between mind and body

The present article is a bibliographic review on human formation, structured from the mind and body hybridity in the everyday relationship. The purpose of the text is to demonstrate that human formation goes beyond the purely cognitive question, building a relationship between the practical situations of the experience with the conceptual ones. For this purpose, authors from the field of philosophy, sociology, theology and education, such as Maurice Merleau-Ponty, James K. A. Smith and Martin Heidegger, were chosen to theoretically support human formation through mind and body hybridity. The research was developed theoretically, analyzing the writings of the authors mentioned above and others, based on the investigation of the hybridity of mind and body in human formation in the family, school and society. In this way, broadening the understanding of education, relating mind and body hybridity, enables a holistic human education, which contemplates the complexity of life, and contributes to greater assertiveness in relation to the preparation of students to interpret the context of today's society. With the present study, it was verified that the human formation, from the mind and body hybridism, is a construction that occurs in all spaces and throughout life, however, in the phase of childhood, adolescence and youth, the bases that guide choices in adult life, having a breadth that relates mind and body.
Analyzing Translation Errors in Indonesian-English Business Letters

This paper discussed the translation error from a business letter made based on American Translation Association (ATA's) made by EFL Learners'. In this research, the researchers used a descriptive qualitative method. The data source in this research is two EFL Learners' who are in translation class with better English skills than other data. Data collection in this study was carried out by taking two data from a total of 36 business letter translating task data given by translation class lecturers. The data is classified based on the type of error according to ATA's Framework for Standard Error Making Classification. The results of the analysis show that there are 18 translation errors in 11 types of 26 types of translation errors with style error as the most frequently found error. Style error is the error that has the highest nominal by appearing five times out of 18 the number of errors with a percentage 27,8%. Finding errors from the two data proves that EFL Learners' who are studying translation studies still have deficiencies. Educators and students must work together so that translation errors produced by EFL Learners' can be reduced.
EXPLORING FACTORS THAT CAUSE THE STUDENTS DEMOTIVATION IN LEARNING ENGLISH GRAMMAR AND ENGLISH WRITING STIBA IEC STUDENTS DURING COVID-19 PANDEMIC

This research aims to find out what are the demotivating factors experienced by STIBA IEC students while studying English grammar and English writing during a pandemic. In addition, researcher also finds out what are the main factors that make them demotivated and what suggestions do students want to convey so that online learning can take place well. A qualitative method is used in this research where the researcher interviewed 10 participants who came from semester 2 and semester 4 students from three different shift classes. As a result, there are 2 kinds of influencing factors, namely external factors and internal factors. External and internal factors are divided into several kinds. The implication of this is readers can find out what factors are experienced by students and can get solutions to existing problems.
ESP: THE DEVELOPMENT AND THE CHALLENGES

The term "English for Specific Purposes" (ESP) refers to a method of teaching English that is centered on the learner's objectives and needs. The ESP technique is a well-liked option in the context of learning English in Indonesia, both at higher schools or universities, especially for students outside of the English major. This method is also consistent with government policies in the area of education, which place an emphasis on the goals of learning English, namely to improve students' proficiency in using English, particularly for academic needs and professional careers with an emphasis on reading skills that enable students to understand authentic material topics relevant to their majors and effectively. This study uses library research, which compiles extensive documentation on a single or a number of topics. Both original sources and secondary sources are used in this investigation. This article presents theoretical research on the following development and challenges: The Fundamental Idea Regarding English for Specific Purposes (ESP); The Needs Analysis Function in ESP; ESP as a Method for Teaching Languages; Curriculum for ESP; ESP Curriculum Design and Goals for teaching ESP
SYNTACTICAL ERROR ANALYSIS OF ABSTRACT THESIS BY UNDER GRADUATE STUDENTS OF SEKOLAH TINGGI BAHASA ASING (STIBA)-IEC BEKASI

This study is motivated by observation data, which shows that the majority of students believe writing is difficult. To address this issue, the author conducts research to determine the types of syntactical errors, the dominant type of syntactical errors, and the factors that influence the students' syntactical errors. The descriptive qualitative research method is used in this study. The data are drawn from abstract theses written by undergraduate students at Sekolah Tinggi Bahasa Asing (STIBA)-IEC Bekasi from 2017 to 2019. The research results have been categorized according to James' (1998) classification of syntactical errors proposed in Error in Language Learning and Use. The analysis reveals the existence of five distinct types of syntactical errors, namely omission, addition, misformation, misordering, and blend. Among these, misformation emerges as the predominant type of syntactical error committed by the students. Furthermore, the occurrence of these syntactical errors is found to be influenced by both inter-lingual and intra-lingual factors.
Real-Time Face Recognition System with Enhanced Security Features using Deep Learning

&#x0D; &#x0D; &#x0D; &#x0D; Abstract: Identification of people and mask detection has long been a captivating topic, in terms of research and business. This topic has received increasing attention in recent phases due to the speedy advancement of Artificial Intelligence (AI). Nowadays, a lot of applications, including phone unlocking systems, criminal identification systems, and even home security systems, use face recognition as a common technique. Due to the fact that this method only requires a facial image instead of other dependencies like a key or card, it is more secure. Face detection and face identification are often the first two elements of a human recognition system. Even during COVID-19, it is considered the best way to stop the spread of the COVID-19 virus is by wearing a face mask. The risk of contracting the virus can be reduced by almost 70% only by wearing a face mask. In order to promote community health. This Study aims to produce a highly precise and real-time method that can effectively recognize people and identify non-mask faces in public. When a person stands in front of the device, this application detects the human face automatically using detection, extraction, and recognition algorithms. The proposed work applies the Viola-Jones algorithm for face recognition and the YOLOv5 algorithm for mask detection and classification. When the proposed work is tested, this shows higher accuracy in mask detection which is 92.8%.&#x0D; &#x0D; &#x0D; &#x0D;
THE EFFECTIVENESS OF AUDIO-VISUAL MEDIA TO IMPROVE STUDENTS’ SPEAKING ABILITY IN MADRASAH TSANAWIYAH (MTs) MA’ARIF MIFTAHUL HUDA LEMPUYANG ANJATAN INDRAMAYU

This study is an attempt to determine the effect of using audio-visual media on improving student learning abilities at MTs Ma’arif Miftahul Huda Lempuyang Anjatan Indramayu. This research is to answer the questions: (1) How is the students’ speaking ability in MTs Ma’arif Miftahul Huda Lempuyang Anjatan Indramayu? (2) How is the fluency of students’ speaking ability in MTs Ma’arif Miftahul Huda Lempuyang Anjatan Indramayu? (3) Hoe effective is audio-visual media to improve students’ speaking ability in MTs Ma’arif Miftahul Huda Lempuyang Anjatan Indramayu? This study used quantitative method.The data collection method used the pre-test method, the treatment method and the post-test method. The population in this study was 251 and a sample of 71 students were taken from grade 8. The results of this study showed the average score of students was 55,5 after being given treatment, there was a significant change in the improvement, the average score of students was 71. Improved learning outcome after using audio-visual media by 15,5 %. From this research, it can be concluded that there is effectiveness in using audio-visual media to improve students’ speaking ability
Pengembangan Media Pembelajaran Berbasis Android pada Materi Mengenal Sejarah Agama Buddha SMP di Kecamatan Gladagsari

The advancement of science and technology has progressed rapidly, especially in the field of education. In this era, the world of education is evolving swiftly, marked by the development of learning media, including android-based learning tools. In the junior high schools of the Gladagsari district, there's still limited utilization or development of learning materials, particularly those based on android. The educational media currently in use consists of basic tools like PowerPoint, Google Classroom, and Google Sites.This situation has piqued the researcher's interest in conducting a study with the aim of developing android-based learning materials for junior high school students. This research employs the Research and Development methodology, following the ADDIE development model. Data is collected through interviews, questionnaires, and data checklists. The result of this research is an android-based learning media product focused on the history of the Buddhist religion for junior high schools in the Gladagsari district.This product has been evaluated by media experts, achieving an 80% approval rate, and subject matter experts, with a 97% approval rate. Furthermore, the feedback from students, with a 91% approval rate classified as "Excellent," indicates its suitability for use in education.
EA-Market: Empowering Real-Time Big Data Applications with Short-Term Edge SLA Leases

Edge computing promises to bring low-latency and high-throughput computing, but the limited edge resources may cause frequent congestion and lead to unstable and unpredictable performance. To ensure performance guarantee, application owners can establish Service-Level Agreements (SLAs) with the edge provider for resource reservation or priority usage. But it is cost-inefficient for application owners to lease long-term SLAs based on peak demands, as demands can fluctuate, and the leased resources may be idle or underutilized at most times. This paper studies market mechanism design for short-term edge SLA leases, focusing on real-time big data applications with throughput and latency goals. Applications submit short-term SLA requests to serve users with guaranteed performance during peak hours. As SLA requests arrive over time, the edge provider dynamically provisions edge resources to fulfill the requests, while charging application owners based on the current demands. We design EA-Market, an online combinatorial auction mechanism that achieves a competitive social welfare, while guaranteeing truthfulness, budget balance, individual rationality, and computational efficiency. Notably, our mechanism enables each application owner to bid without knowledge of the edge infrastructure, and gives edge provider full control over resource provisioning to fulfill the requests. We perform theoretical analysis and simulations to evaluate the efficacy of our mechanism.
<i>IEEE Potentials</i> 40th anniversary [Editorial]

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE Potentials</i> , the bimonthly magazine published by IEEE for undergraduate and graduate students interested in the fields of engineering, technology, and science, has a rich history spanning over 40 years. In this column, we will explore the evolution of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE Potentials</i> , its significant milestones, and its impact on the field of engineering and technology education.
Profil Klinikopatologi Meningioma di Instalasi Patologi Anatomi RSUD Dr. Saiful Anwar Malang Periode Tahun 2016-2020

Background. The most common intracranial neoplasms worldwide is meningioma. It has been classified into three grades by World Health Organization (WHO) in which each grade has several histological variants. The prognosis of meningioma is affected by many factors including histopathological type of tumor, age, and gender.&#x0D; Objective. The aim of this study is to determine the frequency of meningioma based on histopathological type, age, and gender from 2016 to 2020 in Saiful Anwar General Hospital Malang.&#x0D; Methods. This study was a quantitative descriptive study and data was collected from medical records from 2016 to 2020 in Department of Anatomic Pathology, Medical Faculty, Brawijaya University, Saiful Anwar General Hospital Malang. Histopathological type of meningioma, age, and gender were acquired.&#x0D; Results and Discussion. From 2016 to 2020, there were 96 eligible cases of meningioma. Ratio of men to women was 1:12.7. Meningioma was most commonly found in age group 41­50 years old (43.75%). The most common histopathological type of meningioma was meningotheliomatous meningioma (37.5%). The average of meningioma cases was 19 per year. In this study, there was an increase in incidence rate of meningioma in 2017, and decreased in 2020. It may happen because The government has a better referral system to provide health care, thus meningioma can be detected earlier.&#x0D; Conclusion. Frequency of meningioma is higher in women than men, and increases with age until 50 years, which then decreases. Meningotheliomatous meningioma is the most common histopathological type in this study.&#x0D; &#x0D;
Developing Reading Access

The awakening of women has been felt through movements and forms of struggle for hundreds of years, and this awakening has given birth to many influential women in the world. In Indonesia, many women's movements face various dynamic and persistent struggles to acquire an education. One association addressing these struggles is Aisyiyah, headquartered in East Java. Aisyiyah is a grassroots organization that aims to lessen the inequality experienced by women, especially in the areas of education and literacy. This study used a qualitative research method with a descriptive approach to focus on how Aisyiyah women navigate their educational journeys to build and provide access to reading for women in East Java so that Aisyiyah women are fortified to participate in modern life. The location of this research was in East Java, Indonesia, namely the areas of Sidoarjo Regency, Malang City, and Banyuwangi Regency, with a total of 30 respondents consisting of Aisyiyah leaders and members. The results represent the collaborative efforts of Aisyiyah and the larger Indonesian community in increasing public awareness of the importance of women’s education and literacy.
Semi-BGSegNet: A Semi-Supervised Boundary-Guided Breast Tumor Segmentation Network

Segmentation allows localization and visualization of breast lesions of interest, thus essential for precise diagnosis, prognosis and treatment. However, accurate segmentation from magnetic resonance imaging (MRI) faces following challenges: 1) multiscale breast tumors with a large size range; 2) image quality degradation caused by motion artifacts; 3) blurred tumor boundaries especially malignant tumors; 4) limited labeled MR images. To address these challenges, we propose a semi-supervised boundary-guided segmentation network (Semi-BGSegNet) for breast tumors. Specifically, we first construct a boundary-guided baseline segmentation network (BGSegNet) that integrated multi-scale and attention modules to the encoder-decoder architecture. A dynamic boundary loss is combined with the cross-entropy loss to guide the update of BGSegNet. Then, we develop a lightweight pixel-level discriminator to discriminate predictions from labels. The discriminator can also provide pseudo labels for unlabeled data for semi-supervised training of BGSegNet. Experiments demonstrated the effectiveness of our model compared to other supervised and semi-supervised methods.