Reviewerly/exHarmony huggingface dataset:
Analysis and Application of the Certainty Factor Method in the Diagnostic Expert System for Broiler Chicken Diseases

An expert system is a system that uses human knowledge recorded in a computer to solve problems that normally require human expertise. Certainty Factor is a clinical parameter value given by MYCIN to indicate the level of trust. The certainty factor is also a way of combining belief and disbelief in a single number. Certainty factor introduces the concepts of belief/belief and disbelief/unbelief. In the system analysis stage, the author analyzes to find weaknesses in the system that is currently running in an organization or company with the aim that the system can be proposed for improvement. Implementing the program that has been designed requires a tool in the form of a computer or laptop, which requires three supporting components to operate the computer or laptop itself, such as hardware, software, and brainware. The expert system application has been running with the Certainty Factor method and to make a diagnosis on chickens the user can choose a value according to the Certainty Factor Interpretation that has been given and processed with the CF expert. Based on the tests carried out, the value of system accuracy with experts is almost the same . And the Inference engine works well, according to the pre-programmed rules. After diagnosing a chicken disease, the system will display how to treat and handle sick broiler chickens
Blockchain based secure energy trading in V2G infrastructure

With the quick expansion of renewable energy sources, the distribution of energy trade has changed from a centralized to a decentralized model. Developing new energy trading systems has widely embraced blockchain as a distributed public ledger technology. Blockchain-based energy trade does, however, face several difficult problems, such as low efficiency, high transaction costs, and security and privacy concerns. Many approaches have been put forth to address these problems. This study looks closely at the blockchain-based energy trade in the electrical power grid. The difficulties with energy trade on the blockchain are listed and briefly discussed. Then, the existing energy trading systems are examined based on their primary goals. Theoretical energy trading strategies are also mentioned. Our proposed model and mathematics to build the system are explained with the mitigation of security attacks. New blockchain designs, ideas, and products are constantly emerging to overcome the limits of old solutions, creating a positive feedback loop. Blockchain-based energy trading has been a prominent research topic. The blockchain-based energy trading system is improved by combining various blockchain types internally and with other technologies better to meet the operational needs of contemporary power networks.
Towards a DevSecOps-Enabled Framework for Risk Management of Critical Infrastructures

Risk Management is a cornerstone of daily business operations that ensures their sustained viability over the long term. It is a critical function for any business as it helps identify potential threats and opportunities and enables informed decision-making. When placed within the context of critical infrastructures, a risk management profile attains a heightened dimension. The integration of security practices within the software development pipeline, commonly known as DevSecOps, is a novel approach to enhance software application security. This approach has been touted as a transformative solution that not only promotes the adoption of security practices but also provides financial benefits by mitigating risk levels and ensuring uninterrupted business operations. The objective of this paper is to present a conceptual framework to manage risk inside critical infrastructures in the DevSecOps context. This framework is built upon three pillars: action, state, and contrivance. It aims to: (i) facilitate the comprehension of risk, (ii) provide an incentive mechanism towards enhancing risk management, (iii) contribute to both human and machine contrivance, and (iv) ensure the quality of the information retrieved by involving all teams.
Block chain integration with cloud/edge/fog IoT system for security issues

Over the last several years, the IoT has made significant strides. In conventional IoT ecosystems, sensors feed data onto fog devices, which then upload the data to the Cloud. IoT is susceptible to threats to privacy and security because it lacks inherent security safeguards. With its “security by design,” Blockchain (BC) may assist in meeting the important security needs in IoT. Blockchain solutions may assist address most IoT architectural flaws by avoiding trusted third parties, protecting against a single point of failure, and avoiding trusted third parties altogether. To protect against possible attacks and lower transaction costs, distributed IoT systems may be built using the blockchain&apos;s capabilities, such as decentralization, consensus mechanisms, data encryption, and smart contracts. The performance of IoT security might be improved by using blockchain, a decentralized and open database platform. Researchers have been motivated by this to look at blockchain adoption in the IoT ecosystem. This study presents a survey on the integration of blockchain technology for security concerns with cloud, edge, and fog IoT systems. The goal of this study is to examine current research trends on the use of BC-related methodologies and tools in an IoT environment.
New applications of morphological methodology for the early detection of cancer

Morphological structure defines the basic internal structure of any texture or pattern. The morphological analysis gives the idea of hidden features whether they are present within the target structure or not. For diagnosis and treatment of cancer diseases it is necessary to identify and analyze properly the hidden structure of the affected cell at the beginning stage of metastasis. Here we aim to assess qualitatively the status of carcinoma cells and their progress towards metastasis phase using morphological methodologies such as erosion, dilation, skeletonization, branchpoints, top to hat transformation and end point transformation followed by histogram feature extraction technique. The histogram distribution of the transformed images was found to be an effective tool to indicate metastasis.
Layer-adaptive Gradient Clipping in Quantization-Aware Training

기존의 양자화는 배치 정규화 레이어를 폴드하므로 정규화 효과를 사용하지 못하여 정확도 감소가 발생한다. 본 논문에서는 이 정확도 저하를 해결하기 위해 양자화 후 학습에 그래디언트 클리핑을 적용하여 안정적인 성능을 이끌어냈으며 각 레이어에 적응적으로 그래디언트 클리핑을 위한 입력값으로 랜덤 클리핑 벡터를 취하는 MLP를 추가한 신경망을 제안한다. Quantization in neural network folds the batch normalization layer, so the normalization effect cannot be used and accuracy can be degraded. To avoid the accuracy degradation, this paper achieves stabilized training by applying Adaptive Gradient Clipping (AGC) to Quantization-Aware Training (QAT). Also, we propose a neural network with MLP that takes random clipping vector as inputs for gradient clipping adaptively for each layer.
Protect medical records by using blockchain technology

&lt;span&gt;Recently, medical records have been stored and shared locally, which creates a risk of data loss or corruption; these records may be stored and shared through cloud-based central data centers; nonetheless, this strategy has disadvantages, such as the need for significant storage space and the privacy concerns associated with network-wide data sharing. Medical records require increased security and confidentiality, furthermore, these records must be protected when transmitted to and shared with doctors of the same specialization, to solve this problem, blockchain technology was used as a decentralized technology that offers a secure and immutable way to store and protect information. In this paper, a website is designed that uses blockchain technology to save medical records and employs smart contract technology in the ethereum blockchain to govern the creation and display of files and sharing. An interplanetary file system was used to offer a mechanism for the decentralized storage of medical images and reports while maintaining their accessibility on a global scale, it is available only for doctors who have the authority to access them, and the proposed system proved efficient in saving and sharing medical records with high security and less cost.&lt;/span&gt;
Template-Based Conjecturing for Automated Induction in Isabelle/HOL

Proof by induction plays a central role in formal verification. However, its automation remains as a formidable challenge in Computer Science. To solve inductive problems, human engineers often have to provide auxiliary lemmas manually. We automate this laborious process with template-based conjecturing, a novel approach to generate auxiliary lemmas and use them to prove final goals. Our evaluation shows that our working prototype, TBC, achieved 50% point improvement of success rates for problems at intermediate difficulty level.
Efficient background subtraction method based on fast independent component analysis in video-surveillance

Modern video surveillance has now become an active area of research with a large set of requirements and various applications. In order to detect moving objects in video surveillance scenes, background subtraction techniques are the most used. In this paper, we developed and tested an efficient background subtraction technique in video surveillance based on the fast-independent component analysis (fast-ICA) method. The proposed technique initiated, first, on the use of a developed fast-ICA algorithm in order to estimate the de-mixing matrix and the denoising matrix parameters. Second, the estimated foreground can simply model by multiplying the data matrix with the de-mixing matrix. After that, the data matrix is multiplied by the denoising matrix for removing the noise. In addition, we propose a pre-processing and post-processing operations to effectively segment the true foreground objects and improve our results. The proposed method is evaluated on the publicly available change detection datasets CDnet 2012 and CDnet 2014 using performance parameters such as recall, precision and experimental results show that our algorithm can detect effectively and accurately the moving objects in several background and foreground conditions compared to other methods in literature with real-time frame rate.
Advising OpenMP Parallelization via A Graph-Based Approach with Transformers

There is an ever-present need for shared memory parallelization schemes to exploit the full potential of multi-core architectures. The most common parallelization API addressing this need today is OpenMP. Nevertheless, writing parallel code manually is complex and effort-intensive. Thus, many deterministic source-to-source (S2S) compilers have emerged, intending to automate the process of translating serial to parallel code. However, recent studies have shown that these compilers are impractical in many scenarios. In this work, we combine the latest advancements in the field of AI and natural language processing (NLP) with the vast amount of open-source code to address the problem of automatic parallelization. Specifically, we propose a novel approach, called OMPify, to detect and predict the OpenMP pragmas and shared-memory attributes in parallel code, given its serial version. OMPify is based on a Transformer-based model that leverages a graph-based representation of source code that exploits the inherent structure of code. We evaluated our tool by predicting the parallelization pragmas and attributes of a large corpus of (over 54,000) snippets of serial code written in C and C++ languages (Open-OMP-Plus). Our results demonstrate that OMPify outperforms existing approaches — the general-purposed and popular ChatGPT and targeted PragFormer models — in terms of F1 score and accuracy. Specifically, OMPify achieves up to 90% accuracy on commonly-used OpenMP benchmark tests such as NAS, SPEC, and PolyBench. Additionally, we performed an ablation study to assess the impact of different model components and present interesting insights derived from the study. Lastly, we also explored the potential of using data augmentation and curriculum learning techniques to improve the model’s robustness and generalization capabilities. The dataset and source code necessary for reproducing our results are available at https://github.com/Scientific-Computing-Lab-NRCN/OMPify .
PENATALAKSANAAN FISIOTERAPI PADA GANGGUAN GERAK DAN FUNGSI EKSTREMITAS BAWAH PADA POST OP BONE GRAFT PADA DISTAL FEMUR SINISTRA E.C ANEURYSMAL BONE CYST : CASE REPORT

Introduction : Aneurysmal bone cyst (ABC) is a benign bone lesion that can cause localized pain, swelling, and pathological fractures. Treatment of aneurysmal bone cysts (ABC) can be done with traditional measures in the form of intralesional curettage with or without bone grafting. After surgery, postoperative side effects will appear such as incision pain, swelling, decreased muscle strength and limited range of motion (ROM).&#x0D; Purpose : to determine the effect of exercise on reduce pain, increase muscle strength and increase the range of motion of joints&#x0D; Methods : the research method used is a case report. The interventions provided included Isometric Exercise, Stretching, NMES on Quadset, AROM &amp; PROM Exercise, and static loading exercises on the left leg.&#x0D; Results : evaluation of pain scale with NPRS, ROM with goniometer, muscle strength with MMT, range of motion with Goniometer.&#x0D; Conclusions : there is an effect of giving exercise on reducing pain, increasing muscle strength and increasing ROM
Methods of developing management competencies of education workers in postgraduate education

The study aimed to identify methods for enhancing management quality in educational institutions and the professional competencies of education workers. Methods. The study involved the method of expert evaluations and the questionnaire survey. The obtained data were processed using the methods of mathematical statistics. Results. A total of 43% of respondents said they were satisfied with training programmes, while 45% replied affirmatively to online training courses and coaching. However, when asked whether the method helped develop management competencies, online training courses ranked second with 55% “yes” answers, behind coaching with 76%. Training programmes were the least effective method, according to the questionnaire, with only 23% affirmative answers. Conclusions. The study found that education workers' management competencies can be improved through methods like training programs, online courses, and coaching. The analysis of the results of the questionnaire survey showed that in general all three methods can be effective for the development of management competencies of education workers. Prospects. Further research could be improved by extending a sample size and using a variety of methods to evaluate the effectiveness of training programmes.
On-chip quantum secure communications

We review our efforts in integrating optical hardware for quantum key distribution onto photonic chips and in engineering the first standalone photonic integrated QKD system. Our approach tackles various system integration challenges related to packaging, optoelectronic design and power consumption. The quantum hardware is assembled in pluggable interconnects that guarantee efficient thermal management and forward compatibility of a same host electronics with successive generations of chips. Autonomous operation and long-term stability are demonstrated in realistic operation conditions. Our work offers new pathways for practical implementations of QKD and its viable deployment at large scales.
TFD-MelGAN: personalized voiceprint feature speech synthesis based on multi-domain signal processing

In recent years, with the popularity of deep learning, speech synthesis technology has developed rapidly and achieved many good achievements. Among them, the technology of speech synthesis for personalized voiceprint features has also become a research focus. In the existing work, the model for personalized voiceprint feature speech synthesis based on GANs has achieved certain results. The model successfully synthesized speech with personalized voiceprint features in a non-autoregressive way, but the audio quality of the synthesized speech and efficiency was low, and the model training time was long. In this paper, we improve the model through techniques such as multi-domain signal processing. Specifically, we reduce a lot of training time by optimizing several parameters of the model. In addition, the architecture of the model has been improved to a certain extent, which effectively improves the MOS score of synthesized speech.
Abnormal Behavior Recognition for Human Motion Based on Improved Deep Reinforcement Learning

Recognizing abnormal behavior recognition (ABR) is an important part of social security work. To ensure social harmony and stability, it is of great significance to study the identification methods of abnormal human motion behavior. Aiming at the low accuracy of human motion ABR method, ABR method for human motion based on improved deep reinforcement learning (DRL) is proposed. First, the background image is processed in combination with the Gaussian model; second, the background features and human motion trajectory features are extracted, respectively; finally, the improved DRL model is constructed, and the feature information is input into the improvement model to further extract the abnormal behavior features, and the ABR of human motion is realized through the interaction between the agent and the environment. The different methods were examined based on UCF101 data set and HiEve data set. The results show that the accuracy of human motion key point acquisition and posture estimation accuracy is high, the proposed method sensitivity is good, and the recognition accuracy of human motion abnormal behavior is as high as 95.5%. It can realize the ABR for human motion and lay a foundation for the further development of follow-up social security management.
한일양국조약관계 : 과거, 현재 그리고 미래

The relationship between the Republic of Korea and Japan is intertwined with history, politics, culture, and social context that the relationship between the two is perceived as complex. Moreover, it is inevitable that disagreements continue to exist between the two regarding Japan’s colonization of Korea in 1910. However, the relationship between the two States can also be perceived as cooperative. The cooperative relationship between the two States can be seen through their bilateral treaty relations. This article aims to analyze the bilateral treaties between ROK and Japan and interpret them with the rules of interpretation under the law of treaties. This article will demonstrate that despite the historical and political disputes between the two States, the States also maintain a cooperative relationship.
An Investigation on the Effects of Exemplars Selection on Convergence and Diversity in Large-Scale Particle Swarm Optimizer

The selection of candidate exemplars significantly influences the performance of particle swarm optimizer (PSO) variants. Motivated by this phenomenon, this paper makes an investigation on how the selection of candidate exemplars affects convergence acceleration and diversity preservation. In this proposed investigation, two aspects are taken into consideration, including the selection model and the number of candidate exemplars. On the one hand, three different selection models are utilized so that the general quality of selected exemplars can be adjusted. On the other hand, the number of candidate exemplars changes from small to large so as to improve the exemplars’ dominance among the swarm. In order to respectively and independently analyze the effects of the two aspects, comprehensive experiments are conducted to test the roles of exemplars numbers and exemplars selection model to optimization performance. The conclusions exhibit a guiding and instructive significance for exemplars selection in particle swarm optimizers.
Distribution and Evaluation of Lead and Cadmium in Some Soils Surrounding East Baghdad Oil Field

Soil acts as a last sink for elements that people release into the environment through a range of activities due to its physiochemical characteristics. These substances, whether are organic or mineral pollutants, accumulate in the soil and constitute a significant risk to the ecosystem in general because they mess with the chemical and physical equilibrium of the soil, get into the food chain, and eventually get to people. When pollutant concentrations during the bioaccumulated process exceed the global standards for what is regarded as a contaminant in water, air, and soil. Nine soil samples were collected from different sites and two samples from each site at two depths (0-20 and 20-40 cm) to determine if there were any differences in element concentrations at each depth near the East Baghdad oil field in Iraq in February 2022 and analyzed to determine the impacts of industrialization and pollutants related to urbanization. When the grain sizes of the soil samples were analysed, the predominated size fractions were Loam + Clay Loam. The trace elements analysis using atomic absorption spectrometry for the examined soil samples showed levels of Pb and Cd within the global average. The physicochemical characteristic of soil samples was examined. The pH of soil samples from the study area ranged from 7.24 to 7.87, with a mean value of 7.631, indicating that the soil is slightly alkaline.
Bitcoin Price Short-term Forecast Using Twitter Sentiment Analysis

The goal of the article is to develop an innovative forecasting approach based on the Random Forest and fuzzy logic models for predicting crypto-asset prices (IFSs, PFSs, q-ROFSs). The baseline forecast horizon is 90 days (additional horizons are 30, 60, 120 and 150 days), which allows to estimate the significance of the chosen features and the impact of time on the forecast accuracy. The paper proposes an optimal data selection approach for the Random Forest and fuzzy logic models to improve the prediction of the daily closing price of Bitcoin, using online social network activity, trading parameters, technical indicators, and data on other cryptocurrencies. This paper utilizes a tree-based machine learning prediction and a fuzzy logic model for Bitcoin. The article attempts to prove that automated Bitcoin forecasting using machine learning algorithms is very effective for the cryptocurrency market. Nevertheless, the latter is characterized by high volatility, significant rate hikes of the most liquid cryptocurrencies (mainly Bitcoin). Therefore, investments in cryptocurrencies, especially long-term ones, involve significant risks. This defines the paper’s significance for investors and regulators. As shown by simulation studies of data selection approaches generalizing the accuracy performance of the Random Forest and fuzzy logic models to real preferences of forecasting, even under significant noise measurements, the proposed selection approach leads to fast convergence of estimates. The accuracy of the model’s results exceed 85.21 on a 90-day time horizon.
Making A Collection Count: A Holistic Approach to Library Collection Management.

"Making A Collection Count: A Holistic Approach to Library Collection Management.." Journal of Electronic Resources Librarianship, 35(3), pp. 244–245