Reviewerly/exHarmony huggingface dataset:
Analysis of the Changes in 2019-nCov Before and After the Implementation of the "Required Swab PCR-Test for Entry into West Kalimantan via Air Transport" Policy

Implementing policies during the 2019-nCov pandemic are expected to reduce the number of cases added every day. West Kalimantan is one of the provinces that implements a policy of obliging to include negative results on the PCR-test swab every time they use air transportation to West Kalimantan. In this study, we wanted to know whether there were differences in data behavior before and after implementing the policy. These differences can be analyzed simply by looking at the descriptive statistics of the data. Furthermore, in this study, a time series analysis was also carried out, and the data patterns and the suitable models representing the data. Time series analysis is also needed to predict the next 5 days related to the addition of 2019-nCov cases in West Kalimantan. In modeling, modifications have been made by partitioning the data into two data, namely data before the policy is implemented and the rest is data after the policy is implemented. The result shows that the suitable model for before and after the policy is applied is ARIMA (1,0,0) and ARIMA (7,0,0)(1,0,0)7, respectively. This model shows a better performance in translating problems than using the entire data as input in modeling. The smaller MSE value indicates this than using the ARIMA model (1,0,0) for the entire data (without partition). Therefore, in the prediction stage, a model with partitioned data is used. The results showed that there was a decrease in daily cases in the next five days.
Supporting Product Management Lifecycle with Common Best Practices

Product Lifecycle Management is a process that helps projects to pass through various phases of software development and maintenance. Since phases are usually associated with entry- and exit-criteria that could be considered onerous or excessively effort-prone, implementation of PLM poses various risks. In this paper we show how Common Best Practices could support the software teams in meeting PLM requirements and facilitate smoother transition between phases.
Evaluating Usability and User Feedback in an Immersive Virtual Reality Environment for Computer Science Education

Virtual reality enables new approaches to teaching various learning content. However, the development of learning environments is complex and there are only a few guidelines that can be used in the development of VR learning environments. This paper addresses this gap and presents the evaluation of a computer science education simulation. The questionnaire of the UXIVE model, combined with feedback analysed with systematic, rule-guided qualitative text analysis, was used to analyse the influence of user experience components. The result is a concept to transfer usability outcomes in a structured way into best practices and conditions for success.
THE ROLE OF BLOCKCHAIN TECHNOLOGY IN THE UNIVERSAL METAVERSE FROM THE PERSPECTIVE OF COMPETITIVE RELATIONS IN BUSINESS. CHALLENGES AND UNCERTAINTIES

Blockchain technology is becoming one of the most prominent trends in finance and digital innovation since the creation of the Internet. Blockchain technology makes data private, permanent and verifiable. Data and transaction logging is public, but encryption protects them from prying eyes and tampering. The architecture of the blockchain database and the associated protocols provide a solid foundation for creating new classes of applications. This is just the theory. In practice, however, blockchain technology has proven to be somewhat less miraculous. It is powerful and effective in a wide range of areas, but in addition to the well-publicized benefits of blockchain, experts have discovered a set of disadvantages of blockchain technology. That's why the Bitcoin blockchain is often referred to as the "open register" of Bitcoin. Experts around the world, with the exception of a few skeptics, see blockchain as a high-impact technology that has the potential to revolutionize the world of finance, gaming, entertainment and more. Starting from the analysis carried out by specialists in the field on their website, and from information on this technology, the present study brings some arguments to draw the attention of the more or less informed reader to the risks and dangers to which they are exposed and to whom this technology is applied in sensitive areas regarding the life, health, freedom and privacy of the citizens of this planet.Even if the role of this technology in some fields is undeniable, it must still be treated with great care from the perspective of respecting the fundamental rights and freedoms of human beings, the universal values that mankind has gained in centuries of human effort and sacrifice for current and future generations to evolve without repeating the mistakes of history. History makes fun of those who do not know it, repeating itself, said Nicola Iorga.
Building and Validating a Clinical Ultrasound Image Reporting Model

The main job of a radiologist is to understand the essential information hidden in medical images and write diagnostic reports, which are very helpful for subsequent clinical treatment. However, due to the difficulty of interpreting medical images, it requires long-term training. If the training time is short and the experience is insufficient, it will lead to errors in subsequent clinical diagnosis. In addition, the aging population has increased the workload for radiologists, especially with more elderly patients. Therefore, in the case of insufficient workforce and time costs, this study established an Encoder-Decoder architecture for ultrasound image report generation. The Encoder used Faster RCNN to extract lesion-related features from the image, while the Decoder used LSTM to describe the lesion features in words. This approach can effectively assist radiologists in writing diagnostic reports. Faster RCNN and LSTM have shown superior performance in computer vision and natural language processing, but their performance may not be as expected when the dataset is insufficient. Especially, the collection of medical images and reports is difficult, which may result in generated reports that cannot be used. Therefore, this study introduces the idea of prior knowledge, which integrates the lesion organs classified by Faster RCNN and the lesion image features into LSTM to improve the accuracy of describing the lesion organ names and reduce the model’s description errors of organs in small samples, thus increasing the trust of physicians in the report. Finally, in the experimental results, introducing prior knowledge of lesion organ names has a better effect, and the generated reports all contain organ names related to ultrasound images.
Assessing One-Class and Binary Classification Approaches for Identifying Medicare Fraud

Machine learning research on Medicare fraud detection is of national importance, primarily due to the extensive financial losses caused by this deceptive practice. Our big data study focuses on the Medicare Part D dataset, which we utilize to detect healthcare fraud perpetrated by physicians. In this paper, we compare and contrast One-Class Classification (OCC) and binary classification by examining eight different classifiers. The metrics applied in this analysis are Area Under the Receiver Operating Characteristic Curve (AUC) and Area Under the Precision-Recall Curve (AUPRC). Our findings indicate that binary classification outperforms OCC in Medicare fraud detection. Furthermore, we establish that the Decision Tree-based classifiers employed in the research are the most effective, with CatBoost delivering the best performance.
Retracted: Study on the Construction and Development Path of Gymnasiums considering Ecological Environment Restrictions

[This retracts the article DOI: 10.1155/2022/2700957.].
Enhancing Noisy Binary Search Efficiency through Deep Reinforcement Learning

Noisy binary search (NBS) aims to find the closest element to a target value within a sorted array through erroneous queries. In an ideal NBS environment where the error rate remains constant, and the costs of all queries are the same, the maximum likelihood estimation (MLE) procedure has been proven to be the optimal decision strategy. However, in some non-ideal NBS problems, both the error rates and the costs are dependent on the queries, and in some cases, finding the optimal decision strategies can be intractable. We propose to use deep reinforcement learning to approximate the optimal decision strategy in the NBS problem, in which an intelligent agent is used to interact with the NBS environment. A dueling double deep Q-network guides the agent to take action at each step, either to generate a query or to stop the search and predict the target value. An optimized policy will be derived by training the network in the NBS environment until convergence. By evaluating our proposed algorithm on a non-ideal NBS environment, visual field test, we show that the performance of our proposed algorithm surpasses baseline visual field testing algorithms by a large margin.
Korus—Transpiling Speech Into Code Using NLP

The aim of the project is to develop a solution to enable voice python coding using natural language models. The hugging face dataset contains necessary data sets for image classification, voice classification etc. These datasets are optimized and can be used with the transformer model available in PyTorch, and Tensor Flow. Along with directly using the datasets, we can create our own datasets using the hugging face dataset library which does all the necessary work to perform data cleaning and preparation, and also splits the dataset into training data and testing data. These models are open source and free of the cost allowing them to be cost-effective. Further improvements can be added on top of the pre-trained models for further performance improvements. Along with this, there will be also a code snippet fetching mechanism which can automatically lookup up the code and pass it to the main area of automation i.e. end user screen. Currently, we will be focusing on catching syntax which can later turn to be more generic as we keep on improving the model for understanding generic statements. The goal is to assist the developer in times to reduce the overall stress of typing code.
PENGARUH MEDIA AUDIO VISUAL TERHADAP KEMAMPUAN MENULIS TEKS EKSPLANASI SISWA SMK

The audio-visual learning media is a learning media with moving pictures and sound, which can attract learners to learning. This study aims to describe the effect of using audio-visual media on the explanatory text writing skills of vocational high school students. The population of this research was all students XI SMK Ar-Rahman Kediri with a total of 106 students. The research method used is a quasi-experimental research method (Quasi Experiment) with a pretest-posttest nonequivalent-group design. Samples were taken to represent the population using a purposive sampling technique. The data analysis technique used is the t-test technique (t-test). The purpose of using the t-test is to find out the difference between the two values. Based on the inferential statistical data analysis, namely the t-test post-test of the control and experimental groups, the t value bigger than t table, so that Ho is rejected and Ha is accepted. Thus it can be concluded that there is a significant level of effect (5%) of the use of audio-visual media on the explanatory text writing skills of class XI students.
Analysis of e-learning readiness level implementation in islamic senior high school south jakarta, indonesia: Review in biological learning

The utilization of E-Learning as one of the innovations in learning provides a richer and more flexible digital learning experience, where learning can be done anytime and anywhere without being limited by space or time. Its use requires good physical and mental readiness so that e-learning can run optimally. This study aims to determine the level of readiness for the implementation of e-learning in Islamic Senior High School South Jakarta, Indonesia. A survey method was used in this study, survey data were collected through a questionnaire adapted from the Aydin &amp; Tasci ELR model which uses four factors of readiness namely human, self-development, technology, and innovation. The four factors are constructed from resources, skills, and attitudes. The sample in this research is Islamic Senior High School in South Jakarta, Indonesia. The results showed that Islamic Senior High School in South Jakarta was categorized as ready and could be continued in implementing e-learning (Mtotal=4.24 &gt; 4.21). There are two factors that are categorized as ready and can be continued, namely technology and innovation factors, the other two factors, namely human beings and self-development are categorized as ready but need a little improvement. The resource aspect is the main focus in human factors, self-development, and innovation that requires a little improvement. These factors can be the focus of attention. They can be used as a reference for Islamic Senior High School A in South Jakarta in making improvements and improvements in the implementation and utilization of e-learning.
Object Detection for Traffic Scenarios Based on Scale-Aware Label Assignment and Dynamic Class Suppression Loss

For object detection in traffic scenarios, small-sized and long-tail distributed objects are major challenges. However, most previous studies consider these two important problems as irrelevant issues. In addition, these works achieve improvements at the cost of significant computation increase. In this article, we unveil that both object size and frequency should be taken into consideration in a unified manner. We first propose a scale-aware label assignment strategy to focus on relatively small objects; and it intuitively adjusts sampling areas according to the target size. Based on the assignment results, a dynamic class suppression loss is designed to boost the performance on tail categories from a statistic-free perspective. We conduct extensive experiments on typical benchmarks; results show that the proposed method achieves more than 6.5% improvements over the baseline. Without aggravating the computation burden, our method effectively enhances object detection performance in traffic scenarios.
ANALISIS DAYA TAMPUNG AIR PADA SALURAN DRAINASE DI LINGKUNGAN ART CENTRE KOTA DENPASAR

Flood is a natural event that can have a negative impact, for example, causing health problems and the safety of living things. The drainage channel studied in this study is the Tukad Kelandis River which stretches from Jl. Katrangan to Jl. Nusa Indah which crosses the Denpasar City Art Center. In 2021 there was a flood at the Art Center, the impact was flooding under the Ksiranawa Building where the art exhibition was held. For this problem, it is necessary to conduct a study (research) on the profile of the flood front as a flood prevention measure. Before modeling the water level profile with HEC-RAS, when estimating the capacity of the drainage channel, a quantitative method must be calculated, namely by measuring the condition of the canal in the case study. From the results of flood simulations with return times of 2, 5, 10, 20 and 25 years. The results of the evaluation of the current channel discharge at all points of the channel experienced flooding with return periods of 2, 5, 10, 20 - 25 years because the results of the current channel discharge and water level exceeded. The profile is shown in the results at all channel points where the sum of the actual water level (E.G., level) and water level feet (W.S. level) is greater than the capacity of the water discharge in the channel. In addition, at the water level (Crit. W.S.) in the Sta. 2 is 5.05 m, which is less than (W.S. level) which is 6.69 m, so the flow is supercritical.
Highlighting Named Entities in Input for Auto-formulation of Optimization Problems

Operations research deals with modeling and solving real-world problems as mathematical optimization problems. While solving mathematical systems is accomplished by analytical software, formulating a problem as a set of mathematical operations has been typically done manually by domain experts. Recent machine learning methods have shown promise in converting textual problem descriptions to corresponding mathematical formulations. This paper presents an approach that converts linear programming word problems into mathematical formulations. We leverage the named entities in a problem description and augment the input to highlight these entities. Our approach achieves the highest accuracy among all submissions to the NL4Opt competition, securing first place in the generation track.
Pembelajaran Menyenangkan sebagai Upaya Memberdayakan Potensi dan Menumbuhkan Minat Belajar Siswa SDIT Quratu A’yun Kota Gorontalo

The purpose of this community service activity is to empower students’ potential in English through fun learning activities, as well as to foster students’ interest in learning English. This activity is conducted by implementing several games and songs in learning processes. The 5th grade students of SDIT Qurratu A’yun, which was located in Kota Tengah Districst, Gorontalo City, with a total number of 20 students were taken as the sample of this activity. This community service activity was carried out for eight meetings using various methods and materials that were considered fun for the students. The result showed that the students were interested and motivated in learning English that led to their vocabulary improvement.
Explicit Refinement Types

We present λ ert , a type theory supporting refinement types with &lt;em&gt;explicit proofs&lt;/em&gt;. Instead of solving refinement constraints with an SMT solver like DML and Liquid Haskell, our system requires and permits programmers to embed proofs of properties within the program text, letting us support a rich logic of properties including quantifiers and induction. We show that the type system is sound by showing that every refined program erases to a simply-typed program, and by means of a denotational semantics, we show that every erased program has all of the properties demanded by its refined type. All of our proofs are formalised in Lean 4.
LKD-STNN: A Lightweight Malicious Traffic Detection Method for Internet of Things Based on Knowledge Distillation

The purpose of malicious traffic detection and identification in the Internet of Things is to detect the intrusion of malicious traffic within the Internet of Things network into Internet of Things devices. Detection and identification play a key role in ensuring the security of the Internet of Things. At this time, great success has been achieved with deep learning in the field of malicious traffic detection and identification. However, due to resource limitations such as computation weaknesses and low edge network node storage capacity in the Internet of Things, a high-complexity model based on deep learning cannot be deployed and applied. In this paper, we propose a lightweight malicious traffic detection and recognition model named Lightweight Knowledge Distillation-Space Time Neural Network (LKD-STNN) based on knowledge distillation deep learning for the Internet of Things. We use knowledge distillation to build a lightweight student model by depth-wise separable convolution and BiLSTM to realize a lightweight student model and obtain multidimensional characteristic information. According to the characteristics of knowledge distillation, we propose an adaptive temperature function that can adaptively and dynamically change the temperature during the process of knowledge transfer so that different softening characteristics can be obtained during the training process. Then, the weight is updated by combining loss functions to improve the performance of the student model. The experimental results show that with the publicly available malicious traffic datasets for the Internet of Things, the ToN-IoT and IoT-23, our model not only reduces the complexity of the model and the number of model parameters to less than 1% of the teacher model but also reaches an accuracy of more than 98%, indicating that our model can be applied to the multiclassification identification of malicious traffic in the Internet of Things.
Using Machine Learning for Efficient Smishing Detection

The prevalence of mobile phones and messaging apps has made smishing attacks a growing concern. Attackers use smishing to deceive individuals into revealing sensitive information, downloading malicious software, or performing other harmful actions. These disregarded responses cause significant financial loss to individuals or organizations. We proposed using a machine learning model to analyze message contents and detect phishing URLs. Our experiments collected and manually annotated text messages from mobile phones, and both natural language processing techniques and machine-learning algorithms were applied to the dataset simultaneously. The results show that our proposed method achieved an F1 score of 94%, showing high effectiveness and efficiency in accurately detecting Smishing attacks.
Training students to choose their agile practices and tools

We present our experiences in training computer science students in agile software development over two academic years. The product to build was a specialized Twitter client, with instructors refining its requirements throughout the course. We observed over a hundred students divided into teams of approximately five members each. To support agile collaboration and self-tracking, we provided students with a comprehensive software development environment consisting solely of opensource tools. Before commencing their cooperation, we encouraged students to engage in team-building activities to foster better mutual understanding. We adopted the Essence approach to instill an agile mindset and facilitated retrospectives tailored to the students’ needs. Key findings include the effective use of the adaptable Scrum framework with support from the Essence approach to agile teamwork. Although the constraint of using exclusively on-premises open-source software tools posed some challenges for the students, all produced data and documents were accessible for inspection by the instructors. Additionally, the utilization of a product quality model and team maturity model proved valuable for evaluating and comparing the teams. Notably, all teams successfully completed their tasks within the designated timeframe.
Research on a Predictive Model Based on Machine Learning DART-XGBOOST Integration

This article aims to propose a prediction model based on the DART XGBOOST algorithm. Firstly, the DART (with Dropout decision tree) is used for ensemble learning, and finally the XGBOOST algorithm is combined through clustering algorithm. Based on this, MSE and R2 are set as test indicators, and 25 sets of data are used as empirical objects. The results show that: (1) DART XGBOOST has high robustness and generalization performance, Can perform model prediction well (2) When the number of DARTs is 500, the model accuracy converges and reaches the optimal level. This algorithm provides a certain reference for proposing prediction models.