Reviewerly/exHarmony huggingface dataset:
PELATIHAN PEMANFAATAN APLIKASI MENDELEY DESKTOP UNTUK MENGELOLA REFERENSI KARYA ILMIAH MAHASISWA

References are the most important part of writing good scientific work. There are not a few scientific works that are not supported by relevant references. This is due to the lack of author skills in utilizing good reference management applications. Coupled with the various styles of reference writing, the writer often makes mistakes in writing references according to the appropriate style of the context. This community service activity aims to improve students' skills in utilizing the Mendeley reference management application as a tool for managing references. Service activities are carried out through three stages, namely planning, implementation and evaluation. Mendeley training activities are carried out in a hybrid manner, online and offline. The training participants were final-year students totaling 48 students. The results of the training activities included, first, students could improve their skills in managing references with Mendeley which could assist them in writing their final assignment. Second, there are several obstacles in the implementation of this training such as technical constraints in the form of an internet network, lack of power plugs, incompatible laptops, and limited space. Third, the trainees felt that Mendeley's training would be beneficial for the completion of their final assignment.
Research on SPOC Based Blended Teaching of College English under the Epidemic

Due to the impact and impact of the epidemic, China's education and teaching model has undergone tremendous changes. China has actively improved its online teaching system, created a series of online education and teaching software, actively advocated online teaching, and actively carried out college English teaching activities using internet platforms. After the end of the epidemic, universities gradually resumed offline teaching. This article is based on the research on SPOC's blended teaching of college English under the epidemic situation. By analyzing the overview of SPOC's model, we understand the impact of SPOC's blended teaching of college English on teaching effectiveness, propose the problems and countermeasures of SPOC's blended teaching of college English, provide specific teaching implementation strategies, and promote educational reform and development, to provide reference value for relevant educators.
Introducing Arabic-SQuADv2.0 for Effective Arabic Machine Reading Comprehension

Machine Reading Comprehension (MRC), known as the ability of computers to read and understand unstructured text and then answer questions, is still an open research field. MRC is considered one of the most research-demanding sub-tasks in Natural Language Processing (NLP) and Natural Language Understanding (NLU). MRC introduces multiple research challenges. One of these challenges is that the models should be trained to answer all questions and abstain from answering when the answer is not covered in the given context. Another challenge lies in dataset availability. These challenges are amplified for non-Latin-based languages; Arabic as an example. Currently, available Arabic MCR datasets are either small-sized high-quality collections or large-sized low-quality datasets. Additionally, they do not include unanswerable questions. This lack of resources depicts the model as incapable of real-world deployments. To tackle these challenges, this paper proposes a novel large-size high-quality Arabic MRC dataset that includes unanswerable questions, named “Arabic-SQuAD v2.0'”. The dataset consists of 96051 triplets {question, context, answer} in an attempt to help enrich the field of Arabic-MRC. Furthermore, a Machine Learning (ML)-based model is introduced that is capable of effectively solving Arabic MRC-with-unanswerable questions. The results of the proposed model are satisfactory and comparable with Latin-based language models. Furthermore, the results show a significant improvement of the current state-of-the-art Arabic MRC. To be exact, the model scores 71.49 F1-score and 65.12 Exact Match (EM). This proposed dataset and implementation pave the way to further Arabic MRC; aiming to reach a state when MRC models could mimic human text reasoning.
Exploration on Quick Response (QR) Code Behaviour in Commerce based Platforms Using Machine Learning

The "rapid response" code, or QR code, is made to quickly decode vast amounts of data. Any managed device, such as a smartphone, is able to capture it, and it is simple to access simply scanning the 2D matrix code. The dataset is analyzed utilizing machine learning techniques, such as the confusion matrix score utilized for the multinomial naive Bayes algorithm's performance analysis. The QR code generation is limited to single product and is extended now to include all products. Due to its ability to provide clients with benefits including speedy, error-free access and the ability to store a lot of data. Generally, many people are using the online payment for any transaction for flexibility and one can do at any place at any time. For bulk or huge payment, cash is not a good option. Hence many retailers join in the e-wallet companies and make their payment so flexible and faster transaction. Because of these benefits, QR code has becoming widespread.
A Secure Network for Streamlined and HighPerformance Consensus Algorithm based on Blockchain Technology

The blockchain technology has been widely adopted for various applications due to its decentralization, transparency, and security features. Consensus algorithms, such as Proof of Work (PoW) and Proof of Stake (PoS), are fundamental components of blockchain technology, ensuring the integrity and validity of the blockchain network. However, the current consensus algorithms face challenges such as scalability, energy consumption, and security threats. To address these challenges, a new secure network for streamlined and high-performance consensus algorithm based on blockchain technology has been proposed. This new network incorporates the advantages of PoW and PoS, resulting in a hybrid consensus algorithm that is more efficient and secure than the existing algorithms. Additionally, the new network utilizes a dynamic sharding mechanism to improve scalability, reducing the overall processing time of transactions. The simulation results help identify potential vulnerabilities and inefficiencies in the consensus algorithm. Optimal combinations of block interval and propagation delay are determined based on specific use cases, balancing high throughput with security and consensus stability. The study also validates the security of Proof-of-Work (PoW) by comparing the fraction of generated blocks with the expected blocks based on miners' hashing power. This study establishes a foundation for future improvements in consensus algorithms, contributing to their evolution and facilitating the implementation of blockchain applications in various domains such as finance, healthcare, supply chain management, and more. The proposed solution aims to provide a more robust and efficient blockchain platform that can handle a higher volume of transactions while maintaining its security features.
Decipher Clinical and Genetic Underpins of Breast Cancer Survival with Machine Learning Methods

Breast cancer is one of the most common cancers among women in the world, with more than two million new cases of breast cancer every year. This disease is associated with numerous clinical and genetic characteristics. In recent years, machine learning technology has been increasingly applied to the medical field, including predicting the risk of malignant tumors such as breast cancer. Based on clinical and targeted sequencing data of 1980 primary breast cancer samples, this article aimed to analyze these data and predict living conditions after breast cancer. After data engineering, feature selection, and comparison of machine learning methods, the light gradient boosting machine model was found the best with hyperparameter tuning (precision = 0.818, recall = 0.816, f1 score = 0.817, roc-auc = 0.867). And the top 5 determinants were clinical features age at diagnosis, Nottingham Prognostic Index, cohort and genetic features rheb, nr3c1. The study shed light on rational allocation of medical resources and provided insights to early prevention, diagnosis and treatment of breast cancer with the identified risk clinical and genetic factors.
Central Bank Digital Currency and Corporate Cash Holdings: Evidence from China’s e-CNY pilot

This study investigates the impact of central bank digital currency (CBDC) adoption on corporate cash holdings, exploiting China’s e-CNY staggered pilot as an exogenous shock. Our results, based on a difference-in-differences design, show that firms in pilot areas significantly reduce their cash holdings after e-CNY’s introduction. We argue that this is because e-CNY’s lower transaction costs, increased transparency, and enhanced traceability reduce firms’ operational, precautionary, and agency motives to hoard excess cash. Additionally, we find that firms without financing privilege (private firms and those lacking political connections), firms more vulnerable to expropriation, firms with less liquid stocks, and firms experiencing volatile stock returns benefit more from e-CNY. Finally, our dynamic model suggests that e-CNY facilitates quicker cash holding adjustments for firms toward their target cash holding levels. This study pioneers the investigation of CBDC’s economic implications for the corporate sector, highlighting its role in lowering transaction costs, enhancing traceability, and improving financial inclusivity.
Enhancing Emotion Detection with Adversarial Transfer Learning in Text Classification

Emotion detection in text-based content, such as opinions, comments, and textual interactions, holds pivotal significance in enabling computers to comprehend human emotions. This symbiotic understanding between machines and human languages, powered by technological advancements like Natural Language Processing and artificial intelligence, has revolutionized the dynamics of human-computer interaction. The complexity of emotion detection, although challenging, has surged in importance across diverse domains, encompassing customer service, healthcare, and surveillance of social media interactions. Within the realm of text analysis, the quest for accurate emotion detection necessitates a profound exploration of cutting-edge methodologies. This pursuit is further intensified by the imperative to fortify models against adversarial attacks, a pressing concern in deep learning-based approaches. To address this critical challenge, this paper introduces a pioneering technique—adversarial transfer learning—specifically tailored for emotion classification in text analysis. By infusing adversarial training into the model architecture, the proposed approach emerges a solution that not only mitigates the vulnerabilities of existing methods but also fortifies the model against adversarial intrusions. In realizing the potential of the proposed approach, a diverse array of datasets is harnessed for comprehensive training. The empirical results vividly demonstrate the efficacy of this approach, showcasing its superior performance when compared to state-of-the-art methodologies. Notably, the suggested approach yields in advancements in classification accuracy. In particular, the deployment of the Adversarial transfer learning methodology has increased in accuracy of 17.35%. This study, therefore, encapsulates a dual achievement: the introduction of an innovative approach that leverages adversarial transfer learning for emotion classification, and the subsequent empirical validation of its unparalleled efficiency. The implications reverberate across multiple sectors, extending the horizons of accurate emotion detection and laying a foundation for the next stride in human-computer interaction and emotion analysis.
Résumés en Français

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn Email Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Résumés en Français. Am J Intellect Dev Disabil 1 November 2023; 128 (6): 494–495. doi: https://doi.org/10.1352/1944-7558-128.6.494 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest Search
Face Recognition Using Modified Histogram of Oriented Gradients and Convolutional Neural Networks

We are aiming in this work to develop an improved face recognition system for person-dependent and person-independent variants. To extract relevant facial features, we are using the convolutional neural network. These features allow comparing faces of different subjects in an optimized manner. The system training module firstly recognizes different subjects of dataset, in another approach, the module processes a different set of new images. Use of CNN alone for face recognition has achieved promising recognition rate, however many other works have showed declined in recognition rate for many complex datasets. Further, use of CNN alone exhibits reduced recognition rate for large scale databases. To overcome the above problem, we are proposing a modified spatial texture pattern extraction technique namely modified Histogram oriented gradient (m-HOG) for extracting facial image features along three gradient directions along with CNN algorithm to classify the face image based on the features. In the preprocessing stage, the face region is captured by removing the background from the input face images and is resized to 100×100. The m-HOG features are retrieved using histogram channels evenly distributed between 0 and 180 degrees. The obtained features are resized as a matrix having dimension 66×198 and which are passed to the CNN to extract robust and discriminative features and are classified using softmax classification layer. The recognition rates obtained for L-Spacek, NIR, JAFFE and YALE database are 99.80%, 91.43%, 95.00% and 93.33% respectively and are found to be better when compared to the existing methods.
Topology identification of low-voltage station based on robust total least squares method

The low-voltage station area has a long-standing problem of line topology confusion, which leads to difficulties in locating faults in the distribution network and making maintenance difficult. In order to accurately identify the topological relationships of low-voltage stations, we proposes a low-voltage station topology identification algorithm based on the robust total least squares. We use the least squares method as the theoretical basis. Anti-variance estimation is added to the algorithm to further improve its ability to suppress coarse variances and thus improve recognition accuracy. The algorithm collects the active current values of customer meters in the station area at multiple time points. These data are used as the basis for establishing matrix equations to identify the topological relationships in the station area. Finally, the algorithm is compared with the least squares and weighted least squares to verify the accuracy of the algorithm. The results show that the Robust Total Least Squares has high accuracy for the topological identification of low-voltage station and is suitable for the application requirements of low-voltage station area.
Analyzing the development trend of renewable energy based on knowledge graph and patent data

The development and utilization of renewable energy gradually becomes an important part of national strategy. First, in order to find the research hotspots and the key technologies in the field of renewable energy, the experiments used Cite Space to analyze the Knowledge Graph of relevant research literature. Then, the domestic and foreign related patent data in the field of renewable energy were retrieved through the Wanfang database. Finally, so as to analyze the current development stage and predict the future development trend of China's renewable energy technology, Logistic model was used to make a comparative analysis of the life cycle of relevant technologies in this field.
Modern information technology to enable intelligent monitoring of water projects

Safety monitoring is an important means to ensure the safety of water engineering. Modern information technologies such as cloud computing, the Internet of Things, big data, artificial intelligence, and mobile internet can provide a comprehensive and collaborative intelligent monitoring solution for water engineering, which includes "eyes, brains, and hands". This article focuses on the research results of three-dimensional perception and intelligent management in water engineering safety monitoring. Among them, three-dimensional perception technology includes GNSS application in canyon areas, UAV+, real-time adjustment of measurement robots, intelligent inspection, intelligent image analysis of water engineering safety evaluation elements, and smart sensors; Intelligent management technology includes GIS+BIM intelligent matching and embedding, integrated data management, finite element online analysis and intelligent triggering warning. These achievements provide technical support for the construction of a security monitoring technology system with "three-dimensional perception, full coverage, visual management, and intelligent application throughout the entire process", and have good research potential and application prospects.
Understanding the Blockchain adoption in the energy trading system for COVID-19 and beyond

Blockchain technology (BCT) is altering the Energy trade and leading to an upsurge in the contemporary Energy trading system. Like in developed states, BCT is essential to improve the traditional energy trading systems in developing states for COVID-19 and beyond. This study aim is to examine the significant constructs that impact a user’s intention to adopt BCT for Energy firms. The proposed model is based on the integration of TAM constructs with Information System Success Theory (ISS) and Theory of Planned Behavior (TPB), using structural equation modeling. Based on the study findings, all constructs except (service and system quality on perceived usefulness and attitude on intention to use) were found to have a significant influence during the distributed ledger technology (DLT) adoption for COVID-19 and beyond. Consequently, the subsequent implications are assessed for post pandemic, although with obvious restrictions.
Customers Adaptation of E-banking services; extending TAM through Anthpmorphism in Saudi Arabia

This study aims to investigate influence of perceived anthropomorphism, perceived ease of use, perceived usefulness, privacy concerns, as well as attitude on intention to adopt AI banking services. The research follows a positivistic and deductive reasoning approach, utilizing experimental techniques in a cross-sectional design. Data of 210 responses collected through a questionnaire distributed via Google Docs were analyzed using Smart PLS3. The results indicate that intention to adopt AI banking services is influenced by perceived anthropomorphism, perceived ease of use, perceived usefulness, and privacy concerns through attitude. Strong correlations among all variables were observed, highlighting the significant and positive impact of artificial intelligence on encouraging acceptance of advanced technology in banking sector in Kingdom of Saudi Arabia. Future research is recommended to test various other variables using the same research model in different countries. Practical implications include the need for senior managers and policymakers in financial institutions to formulate relevant policies and marketing strategies aligned with customer needs. This research study's primary objective is to prospect and examine factors impelling consumer adoption intentions of artificial intelligence in banking sector in Kingdom of Saudi Arabia.
Influence of Strategic Implementations on Performance of Organizations in Rwanda: A Case Study of Water and Sanitation Corporation (WASAC)

The general objective of this study was to assess the influence of strategic implementation on the organizational performance in Rwanda. The specific objectives of the study were to determine the influence of the strategy implementation on the timely goal’s achievement of WASAC, assess the effect of the strategy implementation on employees’ commitment in WASAC and analyze the contribution of the strategy implementation on internal process performance in WASAC. &nbsp;The study was anchored on resource-based theory, the agency theory and the balanced scorecard model. The study adopted the descriptive research design. The study population comprised of 135 employees of WASAC/Head Office. Using stratified sampling technique, a sample size of 101 respondents was established through Slovin formula, and a 100% response rate was obtained.&nbsp; The researcher used primary data in this study. A closed end questionnaire was utilized. The study used a descriptive research design and correlation analysis to understand general trends, and multiple regression for hypothesis testing, all analyzed through SPSS 26 software. The results of correlation analysis showed that there is a positive and significant relationship between the strategy implementation and Timely goals achievement (r=0.721 and sig=0.00&lt;0.01), Employees’ commitment (r=0.758 and sig=0.00&lt;0.01), and internal process efficiency (r= (0.701 and sig=0.00&lt;0.01). Multiple regression analysis revealed that strategy implementation variables contribute 60.84% of the timely goals achievement, 53.3% of the employee commitment, and 58% of the Internal process efficiency in WASAC Ltd. Based on the hypotheses testing within ANOVA, all null hypotheses were rejected at 5% level of significance due to the fact that the results indicated that there is positive and significant effect of strategy implementation on performance of WASAC. The study recommended that WASAC implement mechanism which should ensure that it adapts quickly its budget on evolving changes occurring while implementing its strategy, and that it enhances more its strategic implementation so that they ensure it reaches its goals within specified time frames. The study also recommends that WASAC Ltd prioritize the three identified independent variables: resource allocation, strategic leadership, and strategy evaluation systems, in their strategy implementation process. The strategy evaluation systems should also be continually updated and refined. Keywords: Strategic Implementations, Performance of Organizations, Water and Sanitation Corporation (WASAC), Rwanda
Inverses of r-primitive k-normal elements over finite fields

This article studies the existence of elements $$\alpha $$ in finite fields $$\mathbb {F}_{q^n}$$ such that both $$\alpha $$ and its inverse $$\alpha ^{-1}$$ are r-primitive and k-normal over $$\mathbb {F}_q$$ . We define a characteristic function for the set of k-normal elements and use it to establish a sufficient condition for the existence of the desired pair $$(\alpha ,\alpha ^{-1})$$ . Moreover, we find that for $$n\ge 7$$ , there always exists a pair $$(\alpha ,\alpha ^{-1})$$ of 1-primitive and 1-normal elements in $$\mathbb {F}_{q^n}$$ over $$\mathbb {F}_q$$ . Additionally, we obtain that for $$n=5,6$$ , if $$\textrm{gcd}(q,n)=1$$ , there always exists such a pair in $$\mathbb {F}_{q^n}$$ , except for the field $$\mathbb {F}_{4^5}$$ .