Reviewerly/exHarmony huggingface dataset:
Cost-Effective Distributed Booster Load Balancer in Amazon Cloud Environment

The amazon cloud provided an opportunity to build the infrastructure from anywhere in the world. Through the AWS cloud, multiple virtual machines can be created and integrated with a virtual network. The fixed size of virtual machines does not suit the time of a growing organization. The sales business trading will change time-to-time. While building the cloud setup, starting all the virtual machines at a time may lead to resource wastage. This issue could be eliminated by the Distributed Booster Load Balancer (DBLB) which can distribute the load equally among the machine during peak hours and launch the instance based on demand. In the case of non-peak hours whenever the threshold limit exceeds the number of virtual machines gets increased and the load is distributed equally to all machines. Whenever the load is up to the fixed threshold the newly created virtual machine gets stopped and resumes back based on the load. The proposed approach is demonstrated in the AWS cloud portal which provides good target response time, throughput, and load distribution.
Comparative Analysis of Different hand gestures Detection and Recognition Techniques

Hand Gesture is a wide technology aspect of computer Science. It provides a good technology tool for human computer interaction. With some mathematical algorithms, Humans can interact with computers without using any mechanical devices. Also for deaf and mute people, communication hindrance can be remediate. Hand gesture is the most expressive and frequent among any type of expression language. It can put up sign language recognition to a new level. This paper is oriented towards a comparative analysis of different types of hand gesture Detection and recognition methods. In addition, the result and problems with different approaches are tabulated. These have different gesture types and so different number of gesture classes. Most of these methods produce accuracy of 90-95%. The best accuracy achieved for static hand gestures is 95.7% which is based on the ML algorithm.
Olonite Questionnaire, Survey and Observation Response Extract

Many research have only focused on a single way of analysing the collected data when other available data are available. This study proposes a template for researchers who intend to use either the qualitative or quantitative means in extracting data for analysis. The template shows the unified Olonite Questionnaire, Survey and Observation Response Extract Template (UNOQESRET) in which the first segment of the template shows the Olonite Questionnaire Response Extract (OQRE) the second segment shows the Olonite Survey Response Extract (OSRE) while the third segment captured the Olonite Observation Extract (OOEX). The UNOQESRE entails the OQRE, OSRE and the OOEX for research that entails both qualitative and quantitative. This paper recommends the Olonite Questionnaire, Survey and Observation Response Extract Template (UNOQESRET) for researchers who probably might need to extract information on a question-answer format, surveys and observation in a single research for possible triangulation.
Part Ii: What Can Be Done about the Length of Proceedings at the ICC?

As internal and external perceptions of the ICC’s capabilities become more reasonable and grounded, twenty years after the adoption of the Rome Statute, it is important to learn from the cases heard so far. The length of those proceedings is just one aspect of the ICC’s work that requires careful attention.
INFLUENCE OF GOOGLE CLASSROOM-BASED LEARNING MEDIA AND INTERPERSONAL INTELLIGENCE ON SCIENCE LITERACY IN THE ERA OF THE COVID-19 PANDEMIC

This study aims to: (1) analyze the differences in students' science literacy taught with Google Classroom online learning media compared to students taught with Zoom meeting online learning media; (2) analyze the differences in science literacy of students who have high interpersonal intelligence compared to students who have low interpersonal intelligence; and (3) analyze the interaction between online learning media and interpersonal intelligence on students' science literacy. The sample in this study was 54 students of Class V of The State Elementary School 165735 Tebing Tinggi City. Data collection in this study through interpersonal intelligence questionnaires and student science literacy tests. Hypothesis testing was carried out with the Two Way Anova test. The results showed that: (1) The science literacy of students taught with Google Classroom online learning media was higher than the online learning media Zoom Meeting (Fcount = 5.654 and the sig. value of 0.021 &lt; 0.05); (2) The science literacy of students who have high interpersonal intelligence is higher than that of students who have low interpersonal intelligence (Fcount = 3,840 and sig. 0.046 &lt; 0.05); and (3) There is an interaction between learning media and interpersonal intelligence in influencing students' science literacy (Fcount = 6.217 and sig. value 0.016 &lt; 0.05).
Desain Sistem Informasi Tri Dharma Perguruan Tinggi Univesitas Hamzanwadi

The Tri Dharma is three obligations that exist in higher education. The three obligations include education or teaching, research, and community service. Each component of higher education, especially the academic community, is responsible for realizing and implementing the Tri Dharma of Higher Education. Each component of the obligation in the Tri Dharma of higher education must be equipped with various physical evidence, such as documents, learning media, etc. Every University in Indonesia must have a system that can record all evidence of the implementation of the Tri Dharma. Therefore, this study aims to develop software that can help Hamzanwadi University record every progress of the three Tri Dharma components of higher education that the academic community has carried out at Hamzanwadi University, especially Lecturers. The development model used in this study is the Waterfall Development model (Requirements Analysis, Design, Development, Testing, Maintenance), but this study only describes the Design stage. The results obtained are in the form of system requirements, client needs, system architecture, flowcharts, and DFD of the designed system
Cross-Lingual Information Retrieval Random Shuffling Methodology

In this project, we evaluate the effectiveness of random shuffling in the cross-lingual information retrieval (CLIR) process. We extended the monolingual Word2Vec model to a multilingual one via the random shuffling process. We then evaluate the cross-lingual word embeddings (CLEs) in terms of retrieving parallel sentences, whereby the query sentence is in a source language and the parallel sentence is in some targeted language. Our experiments on three language pairs showed that models trained on a randomly shuffled dataset outperforms randomly initialized word embeddings substantially, despite its simplicity. We also explored smart shuffling, a more sophisticated CLIR technique which makes use of word alignment and bilingual dictionaries to guide the shuffling process. Due to the complexity of the implementation and unavailability of open source codes, we defer experimental comparisons to future work.
Analysing Supercomputer Nodes Behaviour with the Latent Representation of Deep Learning Models

Anomaly detection systems are vital in ensuring the availability of modern High-Performance Computing (HPC) systems, where many components can fail or behave wrongly. Building a data-driven representation of the computing nodes can help with predictive maintenance and facility management. Luckily, most of the current supercomputers are endowed with monitoring frameworks that can build such representations in conjunction with Deep Learning (DL) models. In this work, we propose a novel semi-supervised DL approach based on autoencoder networks and clustering algorithms (applied to the latent representation) to build a digital twin of the computing nodes of the system. The DL model projects the node features into a lower-dimensional space. Then, clustering is applied to capture and reveal underlying, non-trivial correlations between the features. The extracted information provides valuable insights for system administrators and managers, such as anomaly detection and node classification based on their behaviour and operative conditions. We validated the approach on 240 nodes from the Marconi 100 system, a Tier-0 supercomputer located in CINECA (Italy), considering a 10-month period.
GRADUAL INFORMATION COGNITION THROUGH THE EXPERIENCE USING MR

The paper aims to clarify the value of MR as a tool for presenting information and promoting dialogue in the design process. First, we constructed an MR-based information presentation tool, which was exhibited in the event space. After that, we conducted user observation and analyzed the scenes. The effectiveness of MR was recognized as a tool to induce users to view information in a way that the information presenters intended through experiential sequences, and to make it easier for users of the tool to clarify the main and sub-information through looping in the experience.
A Study on the Relational Communication Curriculum for Conflict Management in the Church

The church couldn’t be free from the inner conflict in church history. That is why the heterogeneity of church members from various backgrounds has revealed differences in major areas related to theology, ministry, mission, education and so on. Conflict within the church is not just a matter for the church. That's because church members are connected to society. Conflicts in the church may be overcome or resolved by the church, but in some cases, they may be known outside the church. If conflicts in the church are known outside the church, it is highly likely that the reason, content, and degree of the conflict are not common or difficult to understand from a social perspective. In this case, society may look at the church negatively while questioning the nature of the church or may be skeptical about the church's social role and responsibility. These results can cause the church to eventually shrink the inner or outer functions of the church. The primary purpose of this study is to provide a curriculum for conflict management that can be performed in the church. So, when church members who have learned this curriculum based on relational communication face various conflicts in the church, they have the ability to ease and resolve the conflicts. Furthermore, through this, the ultimate purpose is to faithfully perform not only the inner function of the church but also the social responsibility. To this end, I will explore interdisciplinary capabilities by analyzing various types of conflicts within the church, their causes, and attempts to resolve them through domestic and foreign literature. During the COVID-19 pandemic, the church's relationship between church members was estranged as non-face-to-face worship was established by quarantine guidelines such as prohibition of gatherings and social distancing. After the COVID-19 pandemic, the church will face various demands. For example, it may be a re-joining of church members who have left due to prohibition of gathering and social distancing, restoring spirituality, re-operating various church programs, restoring passive and disconnected relationships, and opportunities for lost communication. These demands are likely to demand a more strengthened communication environment as a reward for the situation that has been suppressed. The relational communication curriculum provided in this study will contribute to managing the conflict that the demands for more strengthened communication can cause.
Optimization of Entrepreneurship Education for College Students Based on Improved Random Forest Algorithm

As we all know, the random forest algorithm has the advantages of high classification intensity and wide application range. Nevertheless, it still has a lot of room for improvement. This paper introduces the basic idea and working principle of classification algorithm and random forest algorithm, so this paper proposes some improved algorithms on the basis of all current research studies. College students, as the most innovative and adaptable group, have gradually become a development direction of China’s education reform to cultivate their innovative employment ability and develop innovative employment education. By constructing a model of the relationship between innovation and entrepreneurship education and college students’ employability, this paper examines the impact of innovation and entrepreneurship education mode on college students’ employment and entrepreneurship. At the same time, the model is tested by improving the random forest algorithm. The process of talent quality evaluation is to select talents with the best comprehensive quality based on various indicators of students’ performance in school, which can be regarded as a classification problem of unbalanced data sets. The improved random forest algorithm proposed in this paper has little difference in precision and recall rate when it is used for talent training evaluation but has a certain degree of improvement in accuracy rate, which meets the design requirements.
Proposal of an Efficient Encryption Algorithm for Securing Communicated Information

Cryptography protects data in storage, transmission, and process. The means of communication are susceptible to security threats and may be compromised by attackers who can breach the privacy of confidential data. Most of the traditional used symmetric algorithm uses a single key for encrypting the data. If this single key is compromised, the data is no longer secure. This paper will propose a new symmetric block cipher encryption algorithm that uses multiple keys instead of a single one. Therefore, even if one or more keys are compromised, a complete set of keys is required for a successful attack. The strength of the proposed algorithm relies on massive built-in database sets that are represented as keys. The program will pick from these keys based on random measures. The algorithm is programmed in Python, and a performance comparison with another symmetric algorithm Advanced Encryption Algorithm (AES) is provided, which shows promising results.
UCSP: A Framework to Tackle the Challenge of Dependency Chain in Cloud Forensics

Coined around 2006, the term ‘cloud computing’ has risen since and has revolutionized how we work. Parallelly, the traditional forensics practices have been modified to include cloud computing services as a part of the investigation. However, the journey has not been easy. With the enormous advantages that cloud computing entails, it has been quite a challenge to conduct digital forensics over the cloud. It is essential to address the challenges as well as to innovate the technological field. This paper tries to put forth several challenges faced by cloud forensic investigators and summarize them. One of them is the increasing dependency of cloud providers on other cloud providers, and how it challenges forensic practices. Many vendors collaborate and provide services in conjunction with other vendors, and this teaming results in many hindrances while going for a forensic investigation. This paper will try to see the challenges from the confidentiality, integrity, and availability (CIA’s) lens. Moreover, a logical model, explained with an example, is proposed to help cope with this challenge.
Small object segmentation in medical image

Now the mortality of cancer is high, which seriously threatens the safety of human life. It is very difficult to accurately segment small objects such as cancer lesions or cells. In order to improve the accuracy of small object segmentation, this paper proposes to use RPN module to frame and select small objects, then use FSRCNN to enlarge the low resolution picture, and finally use a simple U-shaped segmentation model to segment small objects. The model has been evaluated on the cell data set and achieved good results.
Registration and authentication of teachers and students in the virtual classroom based on 3D shape and deep learning

Virtual classroom is a challenging research topic which is often discussed. It may be one part of metaverse. In this paper, we introduce 3D shape estimation for the registration and authentication of teachers and students in a virtual classroom, a new deep learning method, called geometric generative model, and 2D to 3D mapping algorithm DensePose are used. In the Login of the virtual classroom, the 3D human body reconstructed by the 2D image input of the teacher or the student is used as the message base of the identification and the authentication. Experiments of the first step of the virtual classroom, that is login and authentication based on 3D Shape model of the teacher and student is going to be done.
An Incorporated Solution to Support Elder People in Staying in Their Familiar Surroundings

The SAVE system is an incorporated solution whose main purpose is to support elder people in staying in their familiar surroundings for as long as possible, whilst still be safe and optimally cared for. From an architectural point of view, the SAVE system is designed on the model of microservices-based architecture, which exposes a Representational State Transfer (REST) communication interface, making use of the HTTP/S protocol. The user interfaces (the frontend) of the web applications are developed using the Angular framework and the data is retrieved using the REST APIs of the backend (built on the Spring Boot framework). The messages they exchange with each other are formalized using the JavaScript Object Notation (JSON) format. To ensure data persistence, a relational database management system (Oracle’s MySQL) was employed, providing a more efficient solution for organizing information in the system. We created a web application—SAVE Admin Centre—to manage data efficiently and securely within the SAVE system, which consists of several modules: Dashboard, RO Data, Data Kit, Kits, Devices, Device Types and Users. The data collection sub-system was design to be able to connect to different data sources through data adapters. The security of the SAVE system comes from the use of public key infrastructure (PKI), through HTTPS, whilst using standard protocols to ensure maximum interoperability.
Global Ethics

The rapid development of research in music artificial intelligence (AI) and industrial practice across Asia presents an opportunity to rethink and potentially reorient discourses of technological ethics, which have historically been driven by Western thought. In a previously published theoretical survey, we presented possible non-Western interpretations of three core ethical principles proposed for the ‘ethically aligned design’ of autonomous and intelligent systems – namely, ‘human rights,’ ‘well-being,’ and ‘awareness of misuse’ – as they pertain to music AI. Drawing on a series of interviews, this chapter complements our earlier discussions as it centres on actual practice by various stakeholders across Asia, including music AI researchers, developers, and practitioners. How do researchers in Asia perceive the technologies they develop, and the ways they work, in relation to these guidelines for ethical engineering? How do they perceive their relation to or responsibility for the music ecosystem, both local and global? How can the developers and users of music AI minimise harm, maximise benefit, and constructively participate in a global creative research environment? By highlighting these stakeholders’ explicit and implicit considerations of the above principles in specific cultural, musical, and technological contexts, this chapter contributes an empirical perspective to our call for a pluralistic, cross-cultural perspective on ethics of music AI.
State of the art in telepresence

course Share on State of the art in telepresence Authors: Jason Lawrence Google Research Google ResearchView Profile , Ye Pan Shanghai Jiao Tong University Shanghai Jiao Tong UniversityView Profile , Dan B. Goldman Google Research Google ResearchView Profile , Rachel McDonnell Trinity College Dublin Trinity College DublinView Profile , Julie Robillard UBC UBCView Profile , Carol O'Sullivan Trinity College Dublin Trinity College DublinView Profile , Yaser Sheikh Reality Labs Research Reality Labs ResearchView Profile , Michael Zollhoefer Reality Labs Research Reality Labs ResearchView Profile , Jason Saragih Reality Labs Research Reality Labs ResearchView Profile Authors Info & Claims SIGGRAPH '22: ACM SIGGRAPH 2022 CoursesAugust 2022 Article No.: 14Pages 1–159https://doi.org/10.1145/3532720.3535673Published:02 August 2022Publication History 0citation450DownloadsMetricsTotal Citations0Total Downloads450Last 12 Months450Last 6 weeks12 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Start-Ups

This chapter explores the historical reaction of the global music industry to technological developments and reflects how the innovation journey from phonograph to the AI music has frequently been met with industrial and societal resistance. Kiely’s unique professional experience is candidly expressed in this chapter, providing a personal insight and understanding about the drivers and motivations within the IT industries. Common anxieties about AI are addressed to encourage musicians to embrace the coming changes to the music ecosystem and adapt to the new state of the art. This chapter addresses the underlying need of the oversight over the copyright issues connected to AI-generated content. Mick Kiely emphasises the impact of the COVID-19 pandemic and discusses the transition to the online platforms that are based on state-of-the-art technology. He illustrates the impact TikTok has on the music industry. This chapter looks into the necessity for AI regulations and reflects upon personal experience in this field.
Sentiment Analysis Destinasi Wisata Berdasarkan Opini Masyarakat Menggunakan Naive Bayes

The topic used in this research is to discuss the problem of public opinion on social media related to tourist destinations in Bekasi Regency by implementing the Naive Bayes algorithm to conduct sentiment analysis on existing opinions. This study aims to analyze public opinion on social media towards tourist destinations in Bekasi Regency using the Naive Bayes algorithm. The data used in this study are posts or comments from the public on social media facebook as much as 1000 data. The method of data collection is done manually. The data analysis technique in this study are changing non-standard words, labelling, text preprocessing and naive bayes analysis methods. The results of this study indicate that positive opinion dominates compared to negative and neutral opinions with the results obtained at F1 positive score 83.5%, F1 negative score 68.2% and F1 neutral score 59.5% with positive recall 81%, negative 82% and neutral 55% precision positive 85%, negative 58% and neutral 64% with an accuracy rate of 76%.
Development of an M-Learning application for early childhood education in emotional consciousness

Mobile Learning (M-Learning) is an educational innovation strategy with the potential to contribute to the development of emotional awareness competencies in early childhood. For this reason, an application prototype was built with gamification elements aimed at preschool students, in four vulnerable educational contexts in the city of Popayan (Colombia). The design methodology followed an iterative process in four phases – Analysis, Design, Testing, and Delivery – and resulted in the validation of the visual design of the application by the end users, as well as a registration of the software. Additionally, the validation tests allowed us to conclude that the application in development facilitates the reflection, recognition, and management of their own emotions for infants.