Reviewerly/exHarmony huggingface dataset:
Enhancing Once-For-All: A Study on Parallel Blocks, Skip Connections and Early Exits

The use of Neural Architecture Search (NAS) techniques to automate the design of neural networks has become increasingly popular in recent years. The proliferation of devices with different hardware characteristics using such neural networks, as well as the need to reduce the power consumption for their search, has led to the realisation of Once-For-All (OFA), an eco-friendly algorithm characterised by the ability to generate easily adaptable models through a single learning process. In order to improve this paradigm and develop high-performance yet eco-friendly NAS techniques, this paper presents OFAv2, the extension of OFA aimed at improving its performance while maintaining the same ecological advantage. The algorithm is improved from an architectural point of view by including early exits, parallel blocks and dense skip connections. The training process is extended by two new phases called Elastic Level and Elastic Height. A new Knowledge Distillation technique is presented to handle multi-output networks, and finally a new strategy for dynamic teacher network selection is proposed. These modifications allow OFAv2 to improve its accuracy performance on the Tiny ImageNet dataset by up to 12.07% compared to the original version of OFA, while maintaining the algorithm flexibility and advantages.
A synthetic dataset of liver disorder patients

The data in this article include 10,000 synthetic patients with liver disorders, characterized by 70 different variables, including clinical features, and patient outcomes, such as hospital admission or surgery. Patient data are generated, simulating as close as possible real patient data, using a publicly available Bayesian network describing a casual model for liver disorders. By varying the network parameters, we also generated an additional set of 500 patients with characteristics that deviated from the initial patient population. We provide an overview of the synthetic data generation process and the associated scripts for generating the cohorts. This dataset can be useful for the machine learning models training and validation, especially under the effect of dataset shift between training and testing sets.
Uji Akurasi Arah Kiblat Menggunakan Fitur Kompas Kiblat Pada Aplikasi Quran Kemenag Versi 2.1.4

Quran Kemenag is an Android-based application or software that is easy to download on the Google PlayStore which has one interesting feature, namely the Qibla compass. The Qibla compass feature is interesting because it utilizes the device's compass sensor that users can immediately find out the Qibla direction indicated by the feature and according to the circumstances of the surrounding environment, starting with calibration first. With the Qibla compass feature in the Quran Kemenag application, it can make it easier for people to determine the Qibla direction and can use it whenever and wherever they are. The purpose of this study is to explain in more detail the method of measuring the Qibla direction using Qibla compass on Quran kemenag application and to explain in detail the accuracy of the Qibla direction using the Qibla compass on Quran Kemenag application. The research method is included in the type of qualitative research that is descriptive (descriptive research) and classified as field research (Field Research), namely direct observation of objects studied in the field. The primary data sources for this research are observation data and measurement results data from features in the Quran Kemenag application Version 2.1.4. The secondary data is reports of collaborative community service activities for lecturers and students. This research found the results that Qibla compass on Quran Kemenag application is very easy to use, namely by opening the application then clicking the compass feature and placing the cellphone on a calibrated flat plane, the feature shows the Qibla direction. The level of accuracy of the Qibla direction from the compass feature is different from the theodolite results, namely by a difference of 1'-2'. So that the differences make the Qibla compass feature on Quran Kemenag application is not suitable to be used as the main reference in determining the Qibla direction, for example for the construction of mosques and meunasah. But this application can be used in an emergency.
METHODOLOGICAL REFLECTION OF THE CONCEPT OF "PROFESSIONAL AND ROLE COMMUNICATION" IN TERMS OF ACTIVITY OF MODERN PRIMARY SCHOOL TEACHER

From the unknown to the understandable, from the achieved to the standardized, from the socialized to the valuable, from the meaningful to the spiritual against the background of the found meaning of life" (Anatolii Furman, 2009, [11, p. 1])Abstract.According to methodological reflection, the author's ideogenesis of the concept of "professional and role communication" in terms of activity of modern primary school teacher was substantiated.The status of the above concept was proved based on methodological principles and method of analytical philosophy (according to Ya.V. Shramko).Methodological principles of personality formation were analyzed (according to G. Allport), theoretical and methodological provisions of the theory of role self-realization of personality (P.P. Hornostai) and principles of formation of professional roles of primary school teacher according to the theory "role structure of personality" (Z.M. Miroshnyk) were determined, the concept of "role communication" in newly created groups in the educational process was analyzed (L.O.Korobanova).For the first time, the signs of the concept of "professional and role communication" were formulated according to three interrelated components.The first component is a value and motivational component, which is formed on the semantic basis of spiritual and aesthetic values and taking into account the determinants of professional and role communication -performance of professional duties with higher quality, promotion of the development of semantic value in the
Hybrid Models for Knowledge Tracing: a Systematic Literature Review

&lt;p&gt;Knowledge Tracing is a well-known problem often tackled through purely data-driven approaches. In recent years, many advances have been made in research with various machine learning and deep learning techniques. Despite their satisfactory performance, they have some pitfalls, e.g. modeling one skill at a time, ignoring the relationships between different skills, or inconsistency for the predictions, i.e. sudden spikes and falls across time steps. For this reason, hybrid machine-learning techniques have also been explored. With this systematic literature review, we aim to illustrate this field's state of the art. Specifically, we want to identify the potential and the frontiers in integrating prior knowledge sources in the traditional machine learning pipeline as a supplement to the normally considered data. We applied a qualitative analysis to distill a taxonomy with three dimensions: knowledge source, knowledge representation, and knowledge integration. Exploiting this taxonomy, we also conducted a quantitative analysis to detect the most common approaches.&lt;/p&gt;
Evaluasi Penggunaan E-Module di Kalangan Mahasiswa: Studi Pengembangan di UIN Suska Riau

E-Modules are modifications of conventional modules by combining the use of information technology, so that existing modules can be more interesting and interactive. Because with e-Modules we can add multimedia facilities (images, animations, audio and video) in it. This study aims to evaluate the use of E-Modules based on a Contextual Approach among students as an educational arena. The type of research used in this research is R&amp;D (Research and Development) with the Dick &amp; Carey development model put forward by Walter Dick, Lou Carey, and James O. The research was conducted involving 58 respondents, including 3 lecturers, 20 teachers and 35 students. The data collection tool used was an online questionnaire consisting of 5 constructs with 27 questions with a cronbach alpha of 0.966. The data that has been collected is then analyzed using the SPSS version 23.00 for Windows. The results of the study show that students' assessment of the use of the E-Module in the five constructs includes: 1). E-Module suitability with the learning model used (4.07) in the "good" category 2). The use of E-Module and learning methods according to (4.09) in the "good" category 3). Completeness and arrangement of E-Module descriptions (4.11) in the "good" category 4). Use of language, spelling of words and sentences (4.17) in the "good" category 5). The graphic component (4.10) is in the "good" category and has an overall average of 4.10 in the "good" category. Thus, educators and prospective educators are expected to be able to produce interesting and effective learning media.
STUDENTS’ DIFFICULTIES IN ENGLISH SPEAKING LESSON AT THE ELEVENTH GRADE OF STATE SENIOR HIGH SCHOOL 04 MUKO-MUKO

This research aimed to describe the students’ difficulties encountered by the eleventh-grade students of State Senior High School 04 Muko-Muko in English Speaking Lesson and to describe the factors causing the difficulties in English Speaking Lesson at the eleventh grade of State Senior High School 04 Muko-Muko. This research used mix method research design. The subject of this research was eleventh grade students at State Senior High School 04 Muko-Muko. The sample of the research was 31 students for questionnaires and 10 students for interview. The instruments of this research were questionnaire adapted from Rahayu (2020) and interview. The researcher analyzed the data by by finding the percentage of each question item in questionnaire while interview data was analyzed by data reduction, data display, and drawing conclusion. This study found two findings. First, the result showed that there were five psychological difficulties encountered by the eleventh grade students of State Senior High School 04 Muko-Muko in English Speaking Lesson, namely lack of motivation, anxiety, shyness, fear of mistakes, and lack of confidence. Second, based on five psychological difficulties, there are several factors causing namely no awareness to learn english, no interest of English, no knowledge of the importance of English, students’ low English skill in vocabulary and pronunciation, friends’ negative responses, teacher’s bad responses, and students’ terrible belief in their English skill. To sum up, the next study is recommended to find the strategies to overcome the students’ difficulties in English speaking lesson.
Internet of Things and Big Data for a Sustainable Smart University

This study aims to analyze existing research regarding sustainable smart universities via internet of things and big data. Document analysis was conducted using 1,737 research documents relevant to the domain of smart university, smart campus, sustainable university, internet of things, and big data from international databases including Scopus, IEEE, Web of Science and ERIC. The matrix analysis method was used to identify the relevant publications produced 158 items and six cluster themes and proposed conceptual frameworks illustrating elements necessary for a sustainable smart university via internet of things and big data. The illustrated networks demonstrated that a smart campus involves a technology of internet of things and big data. The targets for a smart campus/smart university includes resource management, security management and the provision of a digital service to students and staff. By linking a smart campus with sustainability, the network models show that some research focus on applying smart technologies to improve the sustainability of a campus by concentrating on natural resource management and environmental care.
Comprehensive Review on Human Behavior Detection Using Robotics

The Life of Human has always been complex in certain situations irrespective of whoever it maybe. One of the most essential fact of Human Life is to make comfortable, warm interaction and skill supportive between humans to understand each other. Now a days a Robots has became a very effective and challenging things in every field. Right from starting age of life toward the daily life work environment such as manufacturing, transport, space articulation, healthcare, the mass production of consumer and industrial goods. So, the robots can also be useful for detection of Human Behavior. Rather than people trusting on the psychological theories for judging people.Therefore, it is necessary to take seriously to being communicated between a human and robot. In this work we propose a methodology in form of flow-chart. The related technique, required material for detection of human behaviour is also presented there.
Message From the New Editor-in-Chief

As the new Editor-in-Chief (EiC), it is my great honor and pleasure to present you the first 2023 issue of IEEE Transactions on Cognitive Communications and Networking (TCCN), which includes 18 interesting papers reporting the latest advances in spectrum sharing and artificial intelligence (AI) empowered communications and networking. My work just started in January, following Dr. Ying-Chang Liang who was one of the co-founders of IEEE TCCN and served as EiC from 2019 to 2022.
Analysis of the development path of short video news communication based on the 5G era

Short video emerged as a new form of communication and was applied to news communication, resulting in short video news. Video news has changed the mode of news communication centered on mainstream media and enriched the form of news communication. In the coming era of 5G network, the development of short video news will receive stronger technical support and a new development path will emerge. Short video news to ultra high-definition communication advanced development; Add new concept science and technology elements, short video news presentation forms diversified development; The content classification is refined to open the iterative development of short video news; Algorithm technology enables short video news to develop into intelligent scene.
Invisible Geolocation Signature Extraction From a Single Image

&lt;p&gt;Published in IEEE Transactions on Information Forensics and Security (T-IFS) on 23 June 2022. This version on TechRxiv contains supplemental material.&lt;/p&gt;
Cyclic Hypersequent System for Transitive Closure Logic

Abstract We propose a cut-free cyclic system for Transitive Closure Logic (TCL) based on a form of hypersequents , suitable for automated reasoning via proof search. We show that previously proposed sequent systems are cut-free incomplete for basic validities from Kleene Algebra (KA) and Propositional Dynamic Logic (PDL), over standard translations. On the other hand, our system faithfully simulates known cyclic systems for KA and PDL, thereby inheriting their completeness results. A peculiarity of our system is its richer correctness criterion, exhibiting ‘alternating traces’ and necessitating a more intricate soundness argument than for traditional cyclic proofs.
Discriminative feature learning through feature distance loss

Abstract Ensembles of convolutional neural networks have shown remarkable results in learning discriminative semantic features for image classification tasks. However, the models in the ensemble often concentrate on similar regions in images. This work proposes a novel method that forces a set of base models to learn different features for a classification task. These models are combined in an ensemble to make a collective classification. The key finding is that by forcing the models to concentrate on different features, the classification accuracy is increased. To learn different feature concepts, a so-called feature distance loss is implemented on the feature maps. The experiments on benchmark convolutional neural networks (VGG16, ResNet, AlexNet), popular datasets (Cifar10, Cifar100, miniImageNet, NEU, BSD, TEX), and different training samples (3, 5, 10, 20, 50, 100 per class) show the effectiveness of the proposed feature loss. The proposed method outperforms classical ensemble versions of the base models. The Class Activation Maps explicitly prove the ability to learn different feature concepts. The code is available at: https://github.com/2Obe/Feature-Distance-Loss.git .
Risk-Return Analysis of Select Crypto Currencies: During 2018-2022

At present, everyone is crazy about 'Crypto-Asset' or 'Crypto-currency' (also popularly known as Bit-coin). An investor who acquires intangible digital assets through "cryptocurrencies" should consider that they may or may not make a get returns. Sometimes there is a possibility of a loss of the entire real asset of the investor. Every investor faced the unexpected market conditions during the period i.e. 2020-21. Hence, this paper made an attempt to focus risk and return on investments in select crypto currencies during the period i.e. 2018-19 and 2021-22. This paper focus on whether Cryptocurrency is an intangible digital asset or a way of vanishing fiat currency?
Amplitude-based implementation of the unit step function on a quantum computer

Modeling nonlinear activation functions on quantum computers is vital for quantum neurons employed in fully quantum neural networks, however, remains a challenging task. We introduce an amplitude-based implementation for approximating nonlinearity in the form of the unit step function on a quantum computer. Our approach expands upon repeat-until-success protocols, suggesting a modification that requires a single measurement only. We describe two distinct circuit types which receive their input either directly from a classical computer or as a quantum state when embedded in a more advanced quantum algorithm. All quantum circuits are theoretically evaluated using numerical simulation and executed on Noisy Intermediate-Scale Quantum hardware. We demonstrate that reliable data with high precision can be obtained from our quantum circuits involving up to eight qubits and up to 25 CX-gate applications, enabled by state-of-the-art hardware-optimization techniques and measurement error mitigation.
A Machine Learning-based Optimization Approach for Pre-copy Live Virtual Machine Migration

Abstract Organizations widely use cloud computing to outsource their computing needs. One crucial issue of cloud computing is that services must be available to clients at all times. However, the cloud services may be temporarily unavailable due to maintenance of the cloud infrastructure, load balancing of services, defense against cyber attacks, power management, proactive fault tolerance, or resource usage. The unavailability of cloud services impacts negatively on the business model of cloud providers. One solution to tackle the service unavailability is Live Virtual Machine Migration (LVM), that is, moving virtual machines (VMs) from the source host machine to the destination host without disrupting the running application. Pre-copy memory migration is a common LVM approach used in most networked systems such as the cloud. The main difficulty with this approach is the high rate of frequently updating memory pages, referred to as "dirty pages. Transferring these updated or dirty pages during the pre-copy migration approach prolongs the total migration time. After a predefined iteration, the pre-copy approach enters the stop-and-copy phase and transfers the remaining memory pages. If the remaining pages are huge, the downtime or service unavailability will be very high -resulting in a negative impact on the availability of the running services. To minimize such service downtime, it is critical to find an optimal time to migrate a virtual machine in the pre-copy approach. To address the issue, this paper proposes a machine learning-based method to optimize pre-copy migration. It has mainly three stages (i) Feature selection (ii) Model generation and (iii) Application of the proposed model in pre-copy migration. The experiment results show that our proposed model outperforms other machine learning models in terms of prediction accuracy and it significantly reduces downtime or service unavailability during the migration process.
VIRTUAL LEADERSHIP OF ELEMENTARY AND MIDDLE SCHOOL PRINCIPALS DURING PANDEMIC ERA IN MAKASSAR CITY

In this situation of pandemic, schools have to adjust to the new trends of education wherein most things have to be done online. Different platforms are used to be able to mobilize teachers, parents, students and other stakeholders of the schools in Makassar City. Applications like Zoom, Google-meet, and WhatsApp are just some of the media that so far effective in communicating with those stakeholders. In this research, the researcher aims to determine ways and means on how these principals are able to mobilize and influence all the stakeholders so that goals and objectives of the school will be achieved. Based on the findings, principals have to use various platforms and need to conduct regular meetings to tall these stakeholders to ensure that education is well delivered. This paper applies the descriptive qualitative method wherein data is mainly collected by interviews and observations based on the objective of the research. These data are then reduced and analyzed to come up with a conclusion that is based on the main objective of the researcher
Secure Data Transactions based on Hash Coded Starvation Blockchain Security using Padded Ring Signature-ECC for Network of Things

Blockchain is one of the decentralized processes in a worldview that works with parallel and distributed ledger technology, the application process, and service-oriented design. To propose a Secure data Transaction based on Hash coded Starvation Blockchain security using Padded Ring signature-ECC for Network of Things. Initially, the crypto policy is authenticated based on the user-owner shared resource policy and access rights. This creates a Public blockchain environment with a P2P Blockchain network. The owner encrypts the data using optimized ECC through Hash-coded Starvation Blockchain security (HCSBS). This makes the encrypted block's provable partition chain Link (P2CL). The encrypted blocks are transmitted into the network of nodes monitored by NoT. During the data transmission, the Network of Things monitors the transaction flow to verify the authenticity over the network of nodes. The monitored data be securely stored in transaction Block storage with the hash-indexed block with chain ring policy (HICLP). This creates controller node aggregation over the transaction environment to securely transfer the data to the peer end. The User gets the access Key to decrypt the data with policy aggregated shared resource policy to access the data. The proposed system produces high security as well compared to the previous design.
Penerapan Algoritma K-Means Clustering dalam Penentuan Nilai Huruf pada Permainan Susun Kata Bahasa Bali

Word stacking is a very popular game in the world. In general word stacking games are called Scrabble games. Word stacking games are educational games that prioritize vocabulary. There are several important aspects in word stacking games, including the configuration of the reference word and the value of letters. However, until now, there are no word stacking games with game configurations that use Balinese as a language reference. The purpose of this study is to design a word stacking game in Balinese as a language reference and determine the value of each letter used in the word. In its design, the word used is sourced from the Second Edition of the Balinese Dictionary which consists of 10441 words. All the words will be grouped by the value using the K-Means Clustering method. Before all the words grouped with K-Means Clustering the words will be determined the frequency of occurrence of each letter in the word, appearance at the beginning and end of the word, and the frequency of the use of letters as a prefix and suffix. The results obtained from grouping with K-Means Clustering are letters A having the smallest value, namely 1 point and letters C, J, W, and Y have the largest value, 10 points.