Reviewerly/exHarmony huggingface dataset:
Kesulitan Membaca Permulaan Siswa Kelas I SDN 105 Pekanbaru

This study aims to determine the difficulty of reading the beginning of grade I students, the factors that influence the difficulty of reading the beginning of the students of class I, and how to overcome the difficulty of reading the beginning of the students of class I SDN 105 Pekanbaru. The type of research used is a case study with a qualitative approach. The subjects in this study were class ID students and class ID teachers at SDN 105 Pekanbaru. Data collection techniques were carried out by tests, observations, interviews, and documentation. The data collection instruments in this study were test sheets, observation sheets, interview guidelines, and document review. The results showed that 3 students had difficulty reading the beginning. The types of difficulties experienced by students are difficulties in recognizing and distinguishing letters, difficulties in spelling words that contain diphthongs, consonants, and affixes, reading haltingly, and pronouncing wrong words. Factors that influence students' initial reading difficulties are psychological factors and environmental factors. Psychological factors occur due to lack of motivation, namely students lack of motivation and parental attention and environmental factors occur because of the background of students at home, namely students do not take Kindergarten (TK) education and students do not repeat learning at home. The way or solution in overcoming students' initial reading difficulties is by using learning media, giving rewards (gifts), providing support and motivation, and including reading lessons.
Rapid Petroleum Engineering Technique Check and Applications

Petroleum engineering has the characteristics of multi-specialty, multi-discipline and multi-process. Field engineers need to carry out lots of technical inquiry, analysis, check and verification to ensure the effectiveness of petroleum engineering implementation. In recent years, the rapid development of mobile internet technology has promoted the upgrading of petroleum engineering digitization and intellectualization, but still focus on the digital processing of basic well engineering information, remote management of engineering operational process and intelligent analysis & decision-making in petroleum engineering. Work still needs to be carried out in the field of Petroleum Engineering Technique Check (PETC) onsite. This paper reviews technical status of PETC, study the advantages and disadvantages of different implementation modes for PETC’s three categories which are conventional engineering technique inquiry & check, engineering technique analysis & check, and complex engineering experience knowledge sharing & check, analyzes the development trends of rapid PETC, puts forward a new PETC android mobile application which is Engineering Technology Check elitebook (ETC) to achieve the aim of rapid PETC. ETC application includes three core functions: technical query & check, technical analysis & check, and knowledge sharing & check. It can meet the needs of rapid check of PETC onsite and promote the progress of petroleum engineering technology.
Supervised Contrastive Multi-tasking Learning Based Hierarchical Yoga Pose Classification Using CNNs

In this paper, we propose a technique for hierarchical yoga pose classification in a multi-tasking framework. Novelty lies in the proposed supervised contrastive combined loss function. We propose the usage of linear combination of three loss functions: cross entropy, self-supervised contrastive loss and supervised contrastive loss in a multi-tasking manner. We introduce radial and cosine margin into the formulation of self-supervised and supervised contrastive loss to pull feature embeddings of same classes closer together compared to feature embeddings of different classes. We use a two stage transfer learning based end to end training methodology trained over novel supervised contrastive multi-tasking combined loss function in the first stage and later fine tune over cross entropy multi-tasking loss in the second stage. We apply our methodology on the publicly available Yoga-82 large-scale dataset. We report peak Top-1 yoga pose classification accuracy of 94.86% over 6 pose classes (Yoga-6), 91.9% over 20 pose classes (Yoga-20) and 87.17% over 82 pose classes (Yoga-82). Our proposed method achieves 5% improvement over Top-1 classification accuracy in Yoga-6, 7.3% improvement in Yoga-20 and 8.1% improvement in Yoga-82 in comparison with state of the art (SOTA) methodology. We achieve SOTA accuracies in all three hierarchies.
Clustering into Three Groups on a Quantum Processor of Five Spins S = 1, Controlled by Pulses of Resonant RF Fields

We consider a quantum processor based on five qutrits represented by spins S = 1, which is driven by radio frequency (RF) pulses selective in transitions between adjacent levels. Numerical simulation of the implementation of the quantum-adiabatic clustering algorithm was performed on the example of partitioning a set of six points into three groups. We find the amplitudes and durations of rectangular RF pulses, as well as the durations of free evolution intervals in the control pulse sequence, which made it possible to engineer a time-dependent effective Hamiltonian in the discrete-time approximation. Also we studied the dependence of the implementation fidelity on the parameters. We took quadrupole nuclei as qutrits, but the results obtained will be useful for controlling quantum processors based on qutrits represented by other systems.
Subsumptions of Algebraic Rewrite Rules

What does it mean for an algebraic rewrite rule to subsume another rule (that may then be called a subrule)? We view subsumptions as rule morphisms such that the simultaneous application of a rule and a subrule (i.e. the application of a subsumption morphism) yields the same result as a single application of the subsuming rule. Simultaneous applications of categories of rules are obtained by Global Coherent Transformations and illustrated on graphs in the DPO approach. Other approaches are possible since these transformations are formulated in an abstract Rewriting Environment, and such environments exist for various approaches to Algebraic Rewriting, including DPO, SqPO and PBPO.
OFFENSE FOR DEFENSE: THE ART AND SCIENCE OF CYBERSECURITY RED TEAMING

AbstractAbstractThe article delves into the methodologies, tools, techniques, and strategies employed in Red Teaming, as well as the planning practices that underpin successful engagements. The success of Red Teaming engagements depends on the expertise of the Red Teamers, who possess a comprehensive understanding of cybersecurity principles, technologies, and best practices. Furthermore, the article highlights the strategic application of cyber deception techniques, such as honeypots, honeynets, and decoy systems, to enhance an organization’s ability to identify and respond to emerging threats. The article also emphasizes the importance of the continuous improvement and adaptation of strategies and techniques in response to evolving threats and emerging technologies. In addition, the article underscores the collaborative and iterative approach of Red Teaming engagements, which ensures that organizations can effectively adapt to and mitigate the risks posed by an ever-evolving threat landscape. By meticulously documenting and analyzing instances where the Blue Team successfully intercepts the Red Team’s efforts, organizations can develop a comprehensive understanding of their security posture and make informed decisions to enhance their defenses. With the constant evolution of cyber threats, Red Teaming is becoming increasingly important, and organizations that embrace it will be better equipped to protect their critical assets and defend against the relentless onslaught of cyber threats. DISCLOSURE STATEMENTNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsKevin Lynn McLaughlinKevin Lynn McLaughlin, PhD, CISO, CISM, CISSP, PMP, ITIL Master, LSSBB, GIAC-GSLC, CRISC, is a highly accomplished cybersecurity expert with a diverse background in law enforcement, corporate security, and cybersecurity. He proudly served in the U.S. Army and was a U.S. Special Agent before making a significant impact in the world of corporate security. With over 39 years of experience in the field, Dr. McLaughlin has demonstrated his expertise in creating and leading three Global Cybersecurity Programs for Fortune 300 companies, establishing Global Security Operations Centers, and designing and implementing a Global Cybersecurity Architecture. He is a veteran in global cyber investigations, having led over 800 investigations, and is a skilled executive manager who has led Global Cyber and Corporate Security teams. Kevin is a highly sought after speaker, having spoken at RSA, and has advised Board of Directors on various cybersecurity topics. He is also an expert in executive protection and securing critical manufacturing, manufacturing, consumer goods, and healthcare environments.
Bi-response truncated spline nonparametric regression with optimal knot point selection using generalized cross-validation in diabetes mellitus patient's blood sugar levels

This article discusses statistical modeling implemented in the health sector. This study used a bi-response nonparametric regression method with truncated spline estimation that used two response variables. The nonparametric regression method is used when the regression curve is not known for its shape and pattern. This study aims to model the blood sugar levels of people with diabetes mellitus. The data used are blood sugar levels of people with diabetes mellitus before fasting, blood sugar levels of people with diabetes mellitus two hours after fasting, cholesterol levels, and triglyceride levels. Determination of the optimal knot point using Generalized Cross-Validation. The parameter estimation method used is Weighted Least-Squares. The best model was obtained from the study results, namely the bi-response truncated spline regression model with three-knot points where the minimum GCV value is 8.573 and has an R2 value of 99.62%.
Sistem Informasi Pajak Reklame Menerapkan Metode Prototype

The Advertising Tax Information System is a system developed to assist the Palembang City Regional Tax Management Agency in managing advertisement tax data and information. This study aims to overcome the problems that exist in the current advertisement tax management system, where the advertisement tax payment process is still manual and inefficient, and difficulties in tracking advertisement tax payments and ensuring that all payments are made on time, therefore it takes a system that can control it all using information technology. Through this system, billboard tax payments can be made online and tax data can be managed more efficiently and accurately. This research uses a system development method using a prototype and is complemented by system analysis and design. The result of this research is a billboard tax information system that is able to simplify the payment process and tax data management, and has succeeded in increasing the accuracy of billboard tax data by 25%, from 80% to 95%. This system is expected to provide benefits for the Palembang City Regional Tax Management Agency and taxpayers in terms of time and cost efficiency which can make it easier for taxpayers, admins, and leaders to properly carry out registration, reporting, extension, and closing advertisement taxes
Research Hotspots and Frontiers of Blockchain Technology Based on Citespace

This paper uses Citespace bibliometric tool to visually analyze the core literature in the field of blockchain technology, and sort out the hot spots and frontiers of the research in the field of blockchain technology. 3522 core journal papers retrieved from CNKI database. Through author cooccurrence analysis, institution co-occurrence analysis, keyword clustering analysis and hotspot word emergence analysis of the above literature, it is concluded that the research hotspots of blockchain technology include consensus mechanism, smart contract and other technologies, as well as privacy protection, supply chain, copyright protection and other applications. The study found that future research should focus on the combination of blockchain with other fields or other hot technologies. The above research provides relevant reference for researchers in the field of blockchain.
The Influence of English Learning Apps on College Students’ English Learning

Under the mode of “dual-line” education, various English learning apps emerge. These apps have become a tool for students in colleges and universities to assist their English learning. This paper, based on the information collected from the undergraduate students in the NingboTech University, investigates the aim, habit, and preference of their using English learning apps. It also reveals the influence of English learning apps on those students, which leads to some proposals for the English teaching and learning as well as the design of the English learning apps.
Use Cases of Attack Graph in Threat Analysis And Risk Assessment for The Automotive Domain

Attack graph is a commonly used methodology in IT security to represent all paths in the system that can lead to a successful attack. It is used to visually represent and automatically analyze security vulnerabilities in the system, as well as to analyze potential defense strategies. Nevertheless, attack graphs are not widely used in the automotive domain. Cognitive mobility systems rely on autonomous decision making by its participants. In order to enable trust in vehicles to make correct decisions, we must ensure that they are immune to failure and malicious manipulation. This trust requires a high level of safety and security. For a smart car, with multiple external connections, the complexity of the attack surface increases dramatically. Ensuring the security of the vehicle depends on a large number of interrelated factors. Thus we need methods, tools and know-how for evaluating the security postures of vehicles. The new ISO/SAE 21434 standard specifies the technical requirements for cyber-security management of road vehicles. It defines the Threat Analysis and Risk Assessment (TARA) for the analysis and assessment of cyber-security risks to a given automotive system. We propose a generic model to automate the attack path generation and analysis in the TARA. We also discuss several use cases for this model, including the enumeration of possible attack paths, the automatic evaluation of the feasibility and risk of each path, and the construction of a defence graph to ensure the security of the system.
Secure Electronic Health System Using Elliptic Curve Cryptography in Cloud

Current advances in Information and Communication Technology (ICT) have a better contribution to all sectors. In particular, Information Technology (IT) resulted in the most important innovation within the health sector known as electronic health (e-Health). A cloud-based background is essential for the e-Health Management. However, with the benefits of e-Health in cloud computing, the benefits remain to be hampered by security and privacy challenges. A comprehensive analysis of modern and existing literature on the processes and methods used to address issues associated to safety and privacy at e-Health is focused in this paper. The strength and weakness of some of these processes are discussed. The cloud-based models are differentiated with an effort. The Security and privacy necessities had been discussed and provided. Guidelines were also provided on how to test and conclude guidelines for e-Health on a computer cloud have been provided. The Attribute-based Encryption (ABE) is considered to be good for assuring privacy in e-Health, however limited calculation whilst real-time recording is close and impacts its overall performance due to the two matching functions. Also to view that finding a way to do this bi-linear performance will improve ABE efficiency. Seeking a solution is considering a good e-Health research site. Finally, a secure and reliable digital compliance framework is proposed using Attribute-based Encryption (ABE) which can ensure capability, consistency, and controlled access to the health records framework. The facilities will ensure complete safety and confidentiality between health care provider and patients
Plasma etching for fabrication of complex nanophotonic lasers from bonded InP semiconductor layers

Integrating optically active III-V materials on silicon/insulator platforms is one potential path towards improving the energy efficiency and performance of modern computing. Here we demonstrate the applicability of direct wafer bonding combined with plasma etching to the fabrication of complex nanophotonic systems out of InP layers. We explore and optimise the plasma etching of InP, validating existing processes and developing improved ones. We explore the use of microdisk lasing as a way to evaluate fabrication fidelity, and demonstrate that we can create complex lasing systems of interest to us: coupled disk cavities and random network lasers.
Spider Web-based Dynamic Key for Secured Transmission and Data-Aware Blockchain Encryption for the Internet of Things

In the developed field of information and communication systems, the utilization of the Internet of Things (IoT) devices is greatly increasing. In their applications, data gathering with limited energy consumption and good security is essentially needed nowadays. The network is energy limited as the sensor nodes are operated using battery power. The nodes in the network are vulnerable to several attacks and the proposed data transmission scheme should provide network security. Security is a main concern in data aggregation. In this work, a secured data aggregation scheme with compression technique and blockchain-based encryption added routing is presented. The data transmitted by the device are added with a hash key generated using a spider web-based dynamic key (SWDK) generation process. The SWDK-added compressed data are transferred in the process of blockchain encryption routing. Then compression-based data aggregation is utilized to reduce the data size and sequentially the transmission cost. The devices in the network transmit data to the base station/server in the network through gateways. The simulation results prove that the proposed work gives reduced transmission cost and energy consumption compared with the existing works. The network throughput increases due to the sharing of security keys, as well as network latency and packet dropping, which are reduced in the proposed work.
From Virtual World Back to Real Classrooms?

In 2020 spring semester due to Covid-19, we had to quickly transform classical university courses to online ones. It was very hard, because we and the students either did not have too many experiences for using distance teaching methods and tools. During emergency situation, we got to know a lot of different IT tools and teaching practice to enhance our education quality and students used to these comfortable and modern possibilities. Now, the emergency situation is over, and we face a new problem. Is it possible to go back and continue education the same way as we did before Covid-19—which was proven to be good and effective? We made surveys just after the starting of distance education and after the first semester back to classical face-to-face teaching to collect the opinions of students. In this paper, we should like to present the experienced change in students’ attitude and their need which we should follow.
Exploring the Types of Relationship Perceptions between ‘Ecological Education’ and ‘Environmental Education’ of Ecology-Environment Education Instructors: Focusing on Language Game and Life Forms

Objectives The purpose of this study is to explore how ecology-environment education instructors perceive the relationship between ‘ecological education’ and ‘environmental education’ based on Wittgenstein's concept of the language game and whether there are differences in relationship perception types depending on their background variables.&#x0D; Methods 57 ecology-environment education instructors participated in the study, and a questionnaire was organized to select a Van diagram that matches one's thoughts among the types of relationships between ecological education and environmental education and explain why.&#x0D; Results As for the types of perception, “ecological=environmental”, “ecological⊃environmental”, “ecological⊂environmental”, and “ecological∩environmental” appeared in the order of frequency. However, even with the same type of response, there were various reasons for choosing. Through this, it was confirmed that while recognizing a common area in values and content knowledge between ecological education and environmental education, it has various perceptions in everyday language according to individual experiences and backgrounds. There was no difference in perception of relationship types according to gender, career period, age group, and final academic level.&#x0D; Conclusions It was confirmed that there is a family resemblance between ecological education and environmental education by exploring various types recognized by ecology-environment education instructors as language game concepts. In addition, it suggested the need to continuously pursue diversity and expand education.
A Study on Prediction of Solar Power Generation based on Synoptic Meteorological Observation Data

최근 태양광 발전 예측에 대한 연구는 대부분 신재생에너지의 불확실한 발전 출력 특성을 극복하기 위해 기계학습방법이나 기상 데이터들을 활용하는 방식으로 발전량을 예측하는 연구들이 진행되고 있다. 그러나 기계학습모델은 구축 및 운용을 위해서 상대적으로 더 많은 연산량이 요구되며 이에 따라 과적합 현상이 발생하여 실제 데이터에 대한 오차가 증가 할 가능성이 높다. 따라서 기계학습보다 상대적으로 더 작은 연상량으로 구축 및 운영할 수 있는 다중선형회귀분석을 활용하여 태양광 발전량 예측 모델을 구축할 수 있는 연구가 필요하다. 본 논문에서는 다중선형회귀분석과 발전과 밀접한 기상정보를 활용한 태양광 발전량 예측 모델을 제안한다. 제안된 예측 모델은 149시간 동안 수집된 태양광 발전량 데이터와 기상 데이터를 수집하여 각 기상변수들과 발전량과의 상관관계를 분석하였다. 이후 각 독립변수에 대한 다중선형회귀분석을 수행하였고, 통계적으로 유의한 변수들만을 선별하여 발전량예측을 위한 다중선형회귀모델을 구축하였다. 구현된 다중선형회귀모델의 성능을 평가하기 위해 결정계수, RMSE, MAE를 각각 계산하고 기존의 기계학습 방법을 사용한 태양광 발전량 예측 모델들과 성능을 비교하였다. Recently, most of the studies on prediction of solar power generation are conducted to predict the amount of power generation by using machine learning methods or meteorological data to overcome the uncertain characteristics of power generation output of new and renewable energy. However, machine learning models require a relatively large amount of computation for construction and operation, and as a result, there is a high possibility that errors for actual data will increase due to overfitting. In this paper, we propose a solar power generation prediction model using multiple linear regression analysis and meteorological information closely related to power generation. The proposed prediction model collected solar power generation data and meteorological data collected for 149 hours and analyzed the correlation between each meteorological variable and power generation. Afterwards, multiple linear regression analysis was performed on each independent variable, and a multiple linear regression model was constructed to predict power generation by selecting only statistically significant variables. To evaluate the performance of the implemented multiple linear regression model, the coefficient of determination, RMSE, and MAE were calculated, respectively, and the performance was compared with solar power generation prediction models using existing machine learning methods.