Reviewerly/exHarmony huggingface dataset:
Integrating Interactive Detection of Code Smells into Scrum: Feasibility, Benefits, and Challenges

(Context) Code smells indicate poor coding practices or design flaws, suggesting deeper software quality issues. While addressing code smells promptly improves software quality, traditional detection techniques often fail in continuous detection during software development. (Problem Statement) More recently, Interactive Detection (ID) technique has been proposed, enabling the detection of code smells continuously. Although the use of this technique by developers and organizations is promising, there are no practical recommendations for its use in the context of software development. (Goal) The objective of this study was to propose and evaluate the integration of ID into the widely adopted Scrum framework for agile software development. (Method) To achieve this objective, we utilized a mixed-method approach that combined a comprehensive literature review and expert knowledge to propose the integration. Furthermore, we conducted a focus group and a controlled experiment involving software development activities to evaluate this integration. (Results) The findings revealed that this integration significantly benefitted software development, such as early detection of code smells, increased effectiveness in code smell detection, and improved code quality. These findings shed light on the potential benefits of adopting this integration, offering valuable insights for developers and researchers. (Conclusions) This research emphasized the importance of continuous code smell detection as an integral part of agile development and opened avenues for further research in code quality management within agile methodologies.
Modified Architecture of Tomasulo’s Algorithm using Adaptive Branch Predictor

This paper proposes modified architecture to implement the Tomasulo’s Algorithm using Adaptive branch prediction scheme, which evaluate the branch outcome by taking both local and global history dynamically. The choice of local vs. global branch prediction is made dynamically on a path based predictor that decides which branch must be taken on past correctness of choice.
Emotional Intelligence of a Teacher as a Necessary Condition for the Education of Emotional Responsiveness in Younger Schoolchildren

The article is devoted to one of the most important aspects of psychological and pedagogical support of the process of educating younger schoolchildren, namely, the formation of an emotional component in the structure of a developing personality. Emotional responsiveness of children, its content and features of manifestation depend on many factors and conditions, among which a special place is occupied by the emotional intelligence of the teacher as the central subject of educational activity, managing and controlling the entire course of pedagogical communication, which, in turn, affects the mental development, behavior and further socialization of students. The authors of the article, based on the results of an empirical study, analyze the psychological and pedagogical resources of the emotional intelligence of a teacher, whose creative and cognitive components directly and indirectly determine the ability of a younger student to empathy, empathy, responsiveness, empathy.&#x0D; In the proposed material, the emphasis is placed on the fact that the emotional intelligence of a teacher is an active motivating condition for the personal development of schoolchildren, and therefore requires a deeper study in the context of education in the educational environment of the school.
A Novel Method of Teaching Quality Monitoring and Evaluation from the Perspective of Educational Balance

Exploring the problem of teaching quality monitoring and evaluation from the perspective of educational balance can help educators and policy makers discover educational inequality problems, thereby promoting fair allocation of educational resources and opportunities. In light of this, this study attempts to research teaching quality monitoring and evaluation from the perspective of educational balance. At first, the mechanism of promoting educational balance by information technology was expounded, and the principle and necessity of a comprehensive assessment based on the evaluation results of different evaluators were explained. Then, this study innovatively introduced the whitening weight function (WWF)-grey evaluation model into the conventional grey clustering evaluation, calculated the unit comprehensive clustering coefficients of the indexes of teaching quality evaluation from the perspective of educational balance, and gave fair, accurate, and effective evaluations. After that, the grey GM (1,1) model was applied to the prediction of teaching quality improvement effect, the future trend of teaching quality changes was predicted based on history data, and the results provided references for teaching reform and educational decision-making. At last, experimental results verified the validity of the proposed method.
G*: A New Approach to Bounding Curvature Constrained Shortest Paths through Dubins Gates

We consider a Curvature-constrained Shortest Path (CSP) problem on a 2D plane for a robot with minimum turning radius constraints in the presence of obstacles.We introduce a new bounding technique called Gate* (G*) that provides optimality guarantees to the CSP.Our approach relies on relaxing the obstacle avoidance constraints but allows a path to travel through some restricted sets of configurations called gates which are informed by the obstacles.We also let the path to be discontinuous when it reaches a gate.This approach allows us to pose the bounding problem as a least-cost problem in a graph where the cost of traveling an edge requires us to solve a new motion planning problem called the Dubins gate problem.In addition to the theoretical results, our numerical tests show that G* can significantly improve the lower bounds with respect to the baseline approaches, and by more than 60% in some instances.
Biomedical document relation extraction with prompt learning and KNN

Document-level relation extraction is designed to recognize connections between entities a cross sentences or between sentences. The current mainstream document relation extraction model is mainly based on the graph method or combined with the pre-trained language model, which leads to the relatively complex process of the whole workflow. In this work, we propose biomedical relation extraction based on prompt learning to avoid complex relation extraction processes and obtain decent performance. Particularity, we present a model that combines prompt learning with T5 for document relation extraction, by integrating a mask template mechanism into the model. In addition, this work also proposes a few-shot relation extraction method based on the K-nearest neighbor (KNN) algorithm with prompt learning. We select similar semantic labels through KNN, and subsequently conduct the relation extraction. The results acquired from two biomedical document benchmarks indicate that our model can improve the learning of document semantic information, achieving improvements in the relation F1 score of 3.1% on CDR.
PENGEMBANGAN MEDIA PEMBELAJARAN ULAR TANGGA PADA MATA PELAJARAN IPS TERPADU

Learning media can help teachers by offering explanations for the lessons that need to be taught. The purpose of this project is to create instructional materials for Snakes and Ladders in social studies for class VIII at SMP Panca Setya 2 Sintang in the academic year 2022–2023. Research and development research utilizing the ADDIE model is the research methodology used. Studying through experiments design for a single subject experiment. Both qualitative and quantitative data are present in the research data. For the school year 2022–2023, 25 eighth-grade SMP Panca Setya 2 Sintang students served as the research subjects. using surveys, observation, and documentation as data collecting methods. Product practicality and feasibility assessments are part of data analysis. The findings of this study are presented in the form of educational media materials for the social studies game of snakes and ladders for class VIII junior high school pupils. Based on the media's classification as very good, the feasibility evaluation was made. The material's viability is rated as being very good. Evaluation of usefulness using very doable standards. According to the questionnaire, the SMP Panca Setya 2 Sintang IPS students' and teachers' responses in terms of materials and media are rated as meeting very good requirements
The Impact of AI Chatbot-Based Learning on Students’ Motivation in English Writing Classroom

ChatGPT, a revolutionary online application, has caused significant concern in the education sector. Being among the most heavily dependent on writing tests, foreign language teachers were among the most concerned, and their anxiety was impacted by media attention to the chatbot’s much-hyped functionality. Learning using chatbots is a significant technological invention that bridges the gap between technology and education. The purpose of this research was to investigate how AI chatbots improved students’ motivation to learn English writing. The quasi-experiment was conducted on 73 college students. The results show that AI chatbot-based learning enhances the development of students’ motivation for learning English writing. AI chatbots can provide more detailed and comprehensive feedback to students than a teacher might be able to provide on their own. This can help students better understand their strengths and weaknesses and make more targeted improvements. The implications of the findings can help instructors to incorporate chatbot-based learning in the classroom.
Evaluation and Analysis of the Status Quo and Impact Degree of Mine Geological Environment in Heilongjiang Province

To identify the geological and environmental problems and hazards of significant mines in Heilongjiang Province and put forward specific countermeasures for mine geological environmental protection and treatment and restoration, the issue of the unclear geological environment due to mine mining was solved. Through data collection, remote sensing interpretation, on-site investigation, and other working methods, the causes and impacts of mine geological and environmental problems were analyzed, and the results were achieved: ① the production status of critical mines in the province was investigated; ② Find out the impact of mine production on the geological environment;③ The degree of the geological, environmental impact of the mine was analyzed, and specific prevention and control measures were proposed.
Facial Image Manipulation Detection Using Cellular Automata and Transfer Learning

In digital media forensics, detecting manipulated facial images is a crucial topic. With the advancement of synthetic face generation technologies, different types of face morphing have raised serious concerns in social media implications. Hence, we have worked on detecting the manipulated face image thoroughly. Despite just using a multi-task learning approach, we propose a facial image manipulation detection scheme using cellular automata (CA) and transfer learning (TL) as feature extractors to enhance the classifier model’s feature maps. The extracted features point out the informative regions to refine the binary classification power. We have collected datasets that contain diverse facial forgeries. We have also built our dataset, keeping every possible morphing way in mind. We have analyzed the datasets thoroughly and demonstrated that using a combination of CA and TL improves manipulated fake detection on different morphing techniques.
Security Analysis of WG-7 Lightweight Stream Cipher Against Cube Attack

Welch–Gong (WG) is a hardware-oriented LFSR-based stream cipher. WG-7 is a version of the eStream submission Welch–Gong, used for RFID encryption and authentication purposes. It offers 80-bit cryptographic security. In modern days, almost all ciphers achieve security by exploiting the nonlinear feedback structure. In this paper, we investigate the security of the nonlinear feedback-based initialization phase of the WG-7 stream cipher using the conventional bit-based division property of cube attack, by considering the cipher in a non-blackbox polynomial setting. In our work, we mount the cube attack using mixed-integer-linear-programming (MILP) models. The results of our attack enable us to recover the secret key of WG-7 after 20 rounds of initialization utilizing $$2^{10}$$ keystream bits in $$2^{73}$$ time. We show that our proposed attack takes significantly lower data complexity. To the best of our knowledge, our attack is the first one that investigates the security of the nonlinear feedback-based initialization phase of WG-7 cipher.
Efficient complexity based adaptive system for cloud resources

Cloud computing is a new IT concept, that is no longer solely applicable to the economic system however as nicely very beneficial in science. The issue of asset distribution and income expansion is likewise similarly significant, particularly about cloud security. This achieves the need of various displaying strategies including however not restricted, to security danger, asset assignment, and income boost models. These offerings are billed on a utilization basis. The cloud services are provided by the CSPs to the end users in an optimized way by using our mathematical proposed algorithm. This proposed algorithm is simulated in the cloud simulator. It prompts financially savvy arrangements by lessening the execution season of enormous application testing. As a piece of framework assets, cloud testing can accomplish its productivity by dealing with the boundaries like organization traffic, Circle Stockpiling, and Smash speed. In this paper, we propose another fluffy numerical model to accomplish a superior degree for the above boundaries. The results of the outcomes informs that the proposed algorithm CROS (Cloud Resource Optimized System) performances are better.
A Blockchain-Based Biometric Protection and Authentication Mechanism

The probability of biometric information leakage, the dependability on authentication modules, and the lack of transparency in the management of biometric information are pertinent security flaws in biometric authentication systems. This paper presents a biometric authentication system utilizing blockchain, which provides a distributed mechanism for storing biometric templates and conducting biometric authentication. Data hashing scheme (dHash) is employed for generating biometric image hashes before storing the biometric templates to the blockchain, thereby securing biometric templates and minimizing the risk of leakage. We perform experimental evaluations utilizing the FVC2004 DB1, DB2, DB3, and DB4 data sets on the Hyperledger Fabric platform, and performance is assessed using the Caliper benchmark tool. Evaluation reflects reliable and safe authentication, measuring the average latency and throughput for the creation and authentication of biometric templates. The proposed blockchain-based authentication system will offer high throughput and low latency over a variety of applications.
Rancang Bangun Aplikasi Point Of Sale Berbasis Web Studi Kasus Pada Toko Toys Amanah

The development of information technology is growing so fast which has resulted in the emergence of increasingly fierce business competition. The application of information technology in the retail business can certainly help in simplifying and accelerating business processes. Toys Amanah is a family shop located on Jl. Kemandoran Pekayon RT 04/RW 023 Pekayon Jaya Village, South Bekasi District, Bekasi City, West Java Province. This shop sells a variety of toys and office stationery supplies. In its business process, this store still carries out sales transaction activities manually using receipts and notebooks for recording sales data and collecting data on goods. Therefore, in the transaction process, errors often occur in calculating the amount of money that must be paid by customers and it takes a lot of time to record reports and frequent inventory data errors that result in discrepancies between the inventory data and the physical goods. The next problem is that the store wants to give discounts to customers who often come in order to increase customer loyalty, but there is no system that can record loyal customers. In defining requirements and analyzing and designing systems for companies, data collection methods are needed by means of literature study, observation, interviews, design, implementation, and testing. For the analysis method and the concept of this information system, the unified modeling language (UML) model is used. The results of this study will be the construction of a web-based point of sale information system which is expected to be able to deal with the problems that exist in the store. This information system is made using the PHP programming language, MySQL database. With this point of sale information system, it can handle complaints experienced by the store in the process of the system running. Such as an error in the sales transaction process and data collection of goods and the length of time required to process sales reports.
Validating Translation

Given a representation of (meta)data that dcterms:conformsTo some data profile, you may wish to translate it to another data profile. If a resource is accesible from an HTTP server, then as a client you may negotiate the content representation in a standard way.
Defending Against Data Poisoning Attack in Federated Learning With Non-IID Data

Federated learning (FL) is an emerging paradigm that allows participants to collaboratively train deep learning tasks while protecting the privacy of their local data. However, the absence of central server control in distributed environments exposes a vulnerability to data poisoning attacks, where adversaries manipulate the behavior of compromised clients by poisoning local data. In particular, data poisoning attacks against FL can have a drastic impact when the participant’s local data is non-independent and identically distributed (non-IID). Most existing defense strategies have demonstrated promising results in mitigating FL poisoning attacks, however, fail to maintain their effectiveness with non-IID data. In this work, we propose an effective defense framework, FL data augmentation (FLDA), which defends against data poisoning attacks through local data mixup on the clients. In addition, to mitigate the non-IID effect by exploiting the limited local data, we propose a gradient detection strategy to reduce the proportion of malicious clients and raise benign clients. Experimental results on datasets show that FLDA can effectively reduce the poisoning success rate and improve the global model training accuracy under poisoning attacks for non-IID data. Furthermore, FLDA can increase the FL accuracy by more than 12% after detecting malicious clients.
Integrated Multi-Elemental Soil Geochemical Survey as a Tool in Geological Mapping in the Tropics: A Case Study of Songbe Area of Osun State, West Africa

Soil geochemical survey of Songbe area, a typical tropical environment of Nigeria was carried out to determine the concentrations and distribution patterns of trace elements in order to decipher the underlying geology in the area, as a complimentary tool to geological mapping, especially in areas with little or no rock exposure.The soil samples were collected by driving a 1-meter auger down to a depth of 50 -80 cm into the ground after removing the top soil, to ensure adequate penetration into the "B-horizon".The samples were air-dried at room temperature for four weeks.Each sample was disaggregated gently with porcelain mortar and pestle.The pestle and mortar were washed and dried after each sample is processed to avoid inter-sample contamination.The samples were subjected to standard preparation and treatment prior to geochemical analyses determined by X-Ray Fluorescence (XRF) method on a PANalytical Axios Wavelength Dispersive spectrometer and LA-ICP-MS (Laser Ablaser Inductively coupled Plasma Mass Spectrometry) method at the Central Analytical Facility (CAF) of Stellenbosch University, South Africa.The point location maps were plotted using ArcView-Gis.The location point symbol map of each of the elements analysed revealed diverse patterns of distribution and showed considerable variations from one area to the other.Some of these distribution patterns did not perfectly correspond with the inferred geological boundaries, especially in areas with thick overburden.The point symbol maps of magnesium, iron, nickel and chromium (Mg, Fe, Ni and Cr) revealed anomalous and relatively high values in some areas underlain by quartzite, whereas anomalous and relatively high values of these ferromagnesian minerals are expected dominantly in areas underlain by mafic or ultramafic rocks.The symbol maps of tantalum and thorium showed dominantly low values for areas deduced to be underlain by charnockite with relatively high concentrations for pegmatitic bodies.The integration of the interpreted soil data with the point symbol maps of element concentrations superimposed on the produced geological map showed that most of the inferred rock boundaries were overestimated or underestimated and some mineral occurrences were yet to be accounted for.Hence, it was concluded that multielemental chemical analyses using soil as sampling media is a vital tool to delineate underlying geology in the tropics and is also an aid to geological mapping
Research on Application of character recognition technology in production and management of electric power enterprises

In the steady development of social economy, the electric power enterprise of our country faced with higher and higher energy consumption requirements, obtained excellent results in the technology theory innovation, gradually transformed the traditional management mode restriction, began to discuss how to quickly, promote the electric power enterprise to realize efficient management. As an important field of pattern recognition application, character recognition technology is applied in the management of electric power enterprises, which can dig more valuable content in depth on the basis of scientific processing of data information. Therefore, on the basis of understanding the current situation of production and operation of electric power enterprises, according to the basic concept and application field of word recognition technology, this paper studies how to apply word recognition technology in the field of production and operation of electric power enterprises, and puts forward corresponding management measures of electric power enterprises in the new era.
DCP-YOLOv7: dark channel prior image-dehazing YOLOv7 for vehicle detection in foggy scene

Aiming at the problem that the vehicle detection process in the foggy scene in automatic driving is vulnerable to the influence of fog, haze and other weather conditions, which causes a decrease in the quality of images captured by sensors, resulting in low accuracy of vehicle detection and positioning. This paper proposed a joint optimization scheme by combining DCP and YOLOV7 backbone detection networks called DCP-YOLOv7. This network consists of two modules: the image dehazing module DCP and the YOLOv7 detection module. Specifically, the DCP module performs image dehazing and YOLOv7 module performs vehicle detection. Experiments were conducted on the synthetic foggy VisDrone dataset to test the detection performance of DCP-YOLOv7. The results show that the mean Average Prediction (IoU=0.5) is 36.6% and 16.0% higher than that of SSD and Faster RCNN algorithm, reaching 86.7%, which can effectively detect vehicle object in foggy scenes.