Reviewerly/exHarmony huggingface dataset:
Q-Learning Based Adaptive Flow Control

With the rapid development of the internet and the expansion of its application scale, the volatility and complexity of network flow in highly concurrent environments have become increasingly prominent. Traditional static control strategies have been difficult to meet the actual needs. Traditional flow control requires manual parameter setting based on thresholds, which not only requires a large number of parameter settings but also affects system availability. This paper proposes an adaptive flow control algorithm based on q-learning, designs and implements a q-learning model for adaptive flow control, and considers the three elements of state, action and reward, where the state includes CPU utilization, average response time, requests per second, and concurrent threads;actions include flow control decisions, that is pass or reject; rewards represent the feedback results of each decision. The algorithm can use the trained q-learning model to select the best flow control decision based on the current state, so as to realize the adaptive adjustment and control of network flow. In the case of high network load, the algorithm will intelligently adjust the flow control strategy to reduce network congestion and delay, improve network performance and reliability, and solve the problem of system instability caused by high throughput under high concurrency.
Critical Analysis of Blockchain-based Security for The Internet of Things in Enhancing the Future Technology

The application of “Internet of Things” is a new phenomenon which has supported organisation, individuals and others to transform the manner in which the business operations and other aspects are being connected. The IoT based future technology in an effective manner. The implementation of Blockchain tend to contain more strong protections against any form of data leakage, tampering of the information Hence, it has been stated by many researchers that the application of Blockchain based security support in enhancing the future technology through IoT. This research focuses in understanding the opportunities and address key aspects related to security aspects through IoT in creating better future technology. The researchers tend to use both primary data and secondary data for performing the study, the researchers collate the data from the respondents through survey method, the analysis are then made using AMOS statistical software and meaningful interpretation were presented based on the analysis.
Control of Inverted Pendulum System with Single Input Interval Type-2 Fuzzy PD Controllers

Single input type-2 fuzzy controllers gain increasing popularity for different control problems. In this study, a single input type-2 (SIT2) fuzzy control system is introduced for the control of an inverted pendulum system. The control system consists of two SIT2 fuzzy PD (SIT2-FPD) controllers for the control of the cart position and the pendulum angle. The SIT2-FPD controller is constructed by cascading a single input fuzzy controller with a classical PD controller. In the design of SIT2-FPD controllers, three triangular interval type-2 membership functions are assigned for the input variable error, and three singleton membership functions are used to define the output variable. In order to show the control and robustness performance of the SIT2-FPD control system, simulation studies are performed for different operating conditions and the performance results are compared with the performance of a classical PD control system. The performance comparison results show that the SIT2 fuzzy control system exhibits better control and robustness performance than the classical PD control system.
Digital Forensics for Ancient Manuscripts Using Local Thickness and Independent Component Analysis

Degeneration and destruction of the ancient manuscripts have sustained through time presenting serious problems for their preservation and interpretation. Nevertheless, it contains important to be restored, authenticated, and studied using digital image forensics, a specialist area of image analysis. This study aims to identify to explore the possibility of patterns or detailed information that cannot be observed by the naked eye in these ancient manuscripts. In this study, it applicates ancient Indonesian manuscripts from different periods. To analyse it, we apply an analytical method with Local Thickness and Independent Component Analysis (ICA) approaches. In the ICA experiment, no significant details or information were found. However, in the local thickness analysis, there were differences in local thickness between two manuscripts from different periods. The value of this work is to enhance knowledge about digital imaging in ancient manuscripts.
Design of a Skin Cancer Detection Classification with Python GUI and Tensorflow

Skin cancer, which is generally classified into Melanoma Skin Cancer (MSC) and Non-Melanoma Skin Cancer (NMSC) namely Squamous Cell Carcinoma (SCC) and Basal Cell Carcinoma (BCC), is one of the most common types of cancer in humans. In this research, an application based on Python GUI and TensorFlow, known as "Early Detection Skin Cancer", is designed to detect and classify skin cancer. This application uses the Convolutional Neural Network (CNN) classification method to analyse and identify skin lesions that have the potential to become cancerous. CNN is then used to identify whether the lesion is a type of SCC, BCC, or MSC. The test was carried out using validation image data from skin cancer patients at Hasan Sadikin Hospital, Bandung, with a total of 26 image data samples. The test results show that this application is capable of classifying skin cancer with 100% accuracy for MEL, 55.56% for BCC, and 42.86% for SCC. However, the quality of the images of the lesions taken can affect the classification results.
Slim and robust eye tracker on eyeglass temples with NIR patterned mirrors

Eye trackers play a crucial role in the development of future display systems, such as head-mounted displays and augmented reality glasses. However, ensuring robustness and accuracy in gaze estimation poses challenges, particularly with limited space available for the transmitter and receiver components within these devices. To address the issues, we propose what we believe is a novel eye tracker design mounted on foldable temples, which not only supports accurate gaze estimation but also provides slim form-factor and unobstructed vision. Our temple-mounted eye tracker utilizes a near-infrared imaging system and incorporates a patterned near-infrared mirror for calibration markers. We present wearable prototypes of the eye tracker and introduce a unique calibration and gaze extraction algorithm by considering the mirror's spatial reflectance distribution. The accuracy of gaze extraction is evaluated through tests involving multiple users with realistic scenarios. We conclude with an evaluation of the results and a comprehensive discussion on the applicability of the temple-mounted eye tracker.
Active relation discovery: Towards general and label-aware open relation extraction

Open Relation Extraction (OpenRE) aims to discover novel relations from open domains. Previous OpenRE methods mainly suffer from two problems: (1) Insufficient capacity to discriminate between known and novel relations. When extending conventional test settings to a more general setting where test data might also come from seen classes, existing approaches have a significant performance decline. (2) Secondary labeling must be performed before practical application. Existing methods cannot label human-readable and meaningful types for novel relations, which is urgently required by the downstream tasks. To address these issues, we propose the Active Relation Discovery (ARD) framework, which utilizes relational outlier detection for discriminating known and novel relations and involves active learning for labeling novel relations. Extensive experiments on three real-world datasets show that ARD significantly outperforms previous state-of-the-art methods on both conventional and our proposed general OpenRE settings. The source code and datasets will be available for reproducibility.
The wrapped XLindley distribution

Abstract In the study of many environmental phenomena, circular statistical analysis and applications to directional data are crucial. In this paper, we introduce the wrapped XLindley distribution, a novel circular distribution with a single parameter. We derive expressions for the distribution’s characteristic function, trigonometric moments, and other associated descriptive measures. The unknown distributional parameter is estimated using the maximum likelihood, least squares, and weighted least squares approach, and the accuracy of these estimates is tested using a simulation study. Finally, to clarify the suggested distribution’s modeling potential, we fit the proposed model to two circular real-world data sets and evaluate the goodness of fit of this distribution in comparison to the wrapped Lindley, wrapped modified Lindley, Von Mises, Jones–Pewsey, and Kato–Jones distributions.
Enhanced artificial intelligence workflow for predicting production within the Bakken formation

In this chapter, a relationship is established between completion parameters and production patterns in the field given a dataset from the Bakken Formation in northwest North Dakota. A random forest model is employed alongside a Bayesian neural network (BNN) model to predict production given a set of predictive features found through a series of feature selection methods. The BNN model utilizes Markov chain Monte Carlo via Langevin dynamics to sample from the probability distribution and estimate uncertainty. Various training and testing dataset scenarios are created through random sampling and clustering to reduce the sampling bias within the training and testing datasets. This ensures that the machine learning models are being trained and tested on data coming from similar geological regions with similar production rate values. With the integration of these techniques, a better understanding of the parameters useful for optimizing oil production is possible.
Improvement of data imbalance for digital soil class mapping in Eastern China

A fine-resolution digital soil class map is needed. However, the problem of imbalanced data leads to an inaccurate spatial distribution of the digital soil class map, and the spatial resolution of digital soil class maps at a large scale is low in existing studies. Based on these points, an algorithm of over-sampling and under-sampling was introduced to solve the problem of imbalanced data, and to improve the performance of soil classification model. 316 topsoil samples with eight main soil classes at the great group level were collected in Eastern China. Eight out of twelve prediction variables were determined after the importance evaluation by “Mean Decrease Accuracy” in the random forest (RF) model, including digital elevation model (DEM), enhanced vegetation index (EVI), land surface wetness index (LSWI), land surface temperature (LST), normalized differenced vegetation index (NDVI), and soil texture components. RF model was also applied to complete digital soil class mapping, and the results of treated (over-sampling and under-sampling by randomly increasing or decreasing the number of samples) and untreated data were compared and discussed. Research results indicated that modeling by imbalanced data resulted in uncertain soil classes mapping, with minority classes were lost and with lower accuracies than those of balanced data (overall accuracy = 83.83 %, kappa coefficient = 0.79). After over-sampling and under-sampling treatments, these problems were well solved with an overall accuracy of 96.72 % and a kappa coefficient of 0.93. The accuracy of soil class prediction for minority classes were improved by 12.5 %–54.5 %. Compared to the existing conventional soil map, the new map with a fine resolution of 30 × 30 m is time-effective and more detailed. Validation (point-validation and map-to-map comparison) of the predicted map showed that the output is reliable and can provide a reference for other soil and environmental studies without major difficulties.
An Introduction to ALISA and Its Usage for an Industrial Railway System Case Study

This paper presents an overview of ALISA (Architecture- Led Incremental System Assurance) and its evaluation for a case study of the railway domain as presented during the ADEPT workshop collocated with the 26th Ada-Europe International Conference on Reliable Software Technologies.
Dynamic Fair Federated Learning Based on Reinforcement Learning

Federated learning enables a collaborative training and optimization of global models among a group of devices without sharing local data samples. However, the heterogeneity of data in federated learning can lead to unfair representation of the global model across different devices. To address the fairness issue in federated learning, we propose a dynamic q fairness federated learning algorithm with reinforcement learning, called DQFFL. DQFFL aims to mitigate the discrepancies in device aggregation and enhance the fairness of treatment for all groups involved in federated learning. To quantify fairness, DQFFL leverages the performance of the global federated model on each device and incorporates <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha-\text{fairness}$</tex> to transform the preservation of fairness during federated aggregation into the distribution of client weights in the aggregation process. Considering the sensitivity of parameters in measuring fairness, we propose to utilize reinforcement learning for dynamic parameters during aggregation. Experimental results demonstrate that our DQFFL outperforms the state-of-the-art methods in terms of overall performance, fairness and convergence speed.
Critical Success Factors and Challenges in Adopting Digital Transformation in the Saudi Ministry of Education

Many countries are using digital transformation to increase their productivity and organizational performance. In Saudi Arabia, digital transformation is a crucial part of their Saudi Vision 2030 plan, but it is still in its early stages. To understand the factors that affect the adoption of digital transformation. The study used a qualitative interview to identify the critical success factors and challenges in adopting digital transformation at the Ministry of Education of Saudi Arabia. The main results of the study show, first, the seven main success factors include technology, employee engagement, vendor partnerships, budget, top management support, culture, and strategy. Second, the main seven challenges include organizational and strategic stakes, resistance to change, governance, data, cost, and IT infrastructure. The study developed a framework that shows the main success factors and challenges that affect adopting digital transformation in the Ministry of Education. These findings can benefit many individuals and groups, such as academics, business people, and the public, and can apply this research in other contexts. This research aimed to determine the primary factors contributing to the success of digital transformation in the Ministry of Education and the challenges that arise when implementing it, specifically within the Saudi Arabian Ministry of Education.
Risk Analysis for Cryptocurrency: Challenges and Future Scope

The emergence of cryptocurrencies has significantly impacted the global economy and disrupted the established banking structures. However, the decentralised structure of cryptocurrencies and the lack of governmental control have brought about several hazards that require careful analysis and efficient management. This paper presents a comprehensive risk analysis of cryptocurrencies with a focus on the difficulties encountered and potential future options for risk mitigation. This study examines the hazards associated with cryptocurrency, considering elements such as techniques, tools, advantages, and disadvantages of available publications. It assesses how these risks affect a variety of stakeholders, such as investors and companies. This paper also explores the concept and adopts currently used by financial institutions and industry individuals in risk management. It assesses how well these techniques work to reduce the identified risks while highlighting their inherent drawbacks. It emphasizes the value of encouraging interdisciplinary collaboration among academia, industry specialists, and regulatory organizations to address the changing issues and create a future landscape of Bitcoin risk management.
Acoustic correlates of perceived personality from Korean utterances in a formal communicative setting

The aim of the present study was to find acoustic correlates of perceived personality from the speech produced in a formal communicative setting-that of Korean customer service employees in particular. This work extended previous research on voice personality impressions to a different sociocultural and linguistic context in which speakers are expected to speak politely in a formal register. To use naturally produced speech rather than read speech, we devised a new method that successfully elicited spontaneous speech from speakers who were role-playing as customer service employees, while controlling for the words and sentence structures they used. We then examined a wide range of acoustic properties in the utterances, including voice quality and global acoustic and segmental properties using Principal Component Analysis. Subjects of the personality rating task listened to the utterances and rated perceived personality in terms of the Big-Five personality traits. While replicating some previous findings, we discovered several acoustic variables that exclusively accounted for the personality judgments of female speakers; a more modal voice quality increased perceived conscientiousness and neuroticism, and less dispersed formants reflecting a larger body size increased the perceived levels of extraversion and openness. These biases in personality perception likely reflect gender and occupation-related stereotypes that exist in South Korea. Our findings can also serve as a basis for developing and evaluating synthetic speech for Voice Assistant applications in future studies.
Electromyography (EMG) Signal based Knee Abnormality Prediction using XGBoost Machine Learning Algorithm

When diagnosing illness, assessing sports injuries, and rehabilitating patients, Surface Electromyography (sEMG) signals play a crucial role. In this study, we use the machine learning technique extreme gradient boosting (XGBoost) to detect knee abnormalities. Four primary sequential steps involved in predicting knee abnormality for a publicly available dataset with 22 subject’s sEMG signals were recorded from three different lower-limb muscles while they are walking, sitting, and standing activities where 11 appeared healthy and 11 had knee abnormalities based on clinical assessment. First, we apply wavelet denoising the sEMG signals to eliminate noise and apply segmentation with a window size of 256 ms, overlapped it by 25%. In the third stage, feature extraction is done using Time Domain(TD), later feature reduction method using principal component analysis (PCA). Finally, a machine learning algorithm is used to predict lower limb knee abnormalities. In this research, various Machine Learning models are implemented for prediction, like SVM, Naive Bayes, XGBoost, and KNN, where the XGBoost classifier gives a good accuracy of 96% compared to another classifier.
Committed to Ethics: How Ethical Leadership and Ethical Climate Foster Knowledge Sharing in Private Higher Education Institutions

Private higher education in Lebanon is facing mounting challenges brought about by the COVID-19 pandemic and the unprecedented economic crisis that has left the once-distinguished sector struggling for survival. These crises, whose effects will last long after they are over, require private universities to bolster their education and reputation in order to remain afloat amid the drain of competent labour as well as widespread corruption that permeates all levels of society. By attracting and retaining ethical leaders, and fostering an ethical climate characterized by open communication, accountability, and trust, universities in Lebanon can stand out as resilient knowledge- intensive organizations and beacons of hope in a surrounding marked by despair. Drawing on social learning and social exchange theories, this quantitative study empirically examines the direct and indirect relationships among ethical leadership, ethical climate, and the bidirectional process of knowledge sharing (knowledge collecting and knowledge donating). In total, 585 responses from academic and non-academic staff employed in nine private higher education institutions in Lebanon were collected via a web-based self-administered questionnaire using the Qualtrics platform. Hierarchical regression analysis and Hayes’ PROCESS macro for SPSS were used to test the hypotheses. Findings reveal that ethical leadership positively influences employee knowledge sharing behaviour. Specifically, the study presents evidence that perceptions of the ethical climate serve as a mechanism through which ethical leadership affects knowledge collecting and knowledge donating in varying strengths. The findings encourage deeper consideration of ethics in higher education leadership and demonstrate the role universities must play in creating the conditions that facilitate knowledge sharing.
Design and Research of a Dual and Bidirectional Certificateless Identity Authentication Scheme Based on Hybrid SM Series Algorithms

Aiming at security issues such as user password leakage, password cracking, spoofing, replay attack, man-in-the-middle attack, and server-side attack that may occur during the identity authentication process of instant messaging system users, this paper uses a hybrid algorithms of SM2 and SM3 to design a set of a dual and bidirectional certificateless authentication scheme based on SM2, SM3 and random salt value. This solution includes a two-way password authentication security model based on SM2 and SM3+random salt value, and an intelligent secret medium challenge-response two-way authentication security model based on SM2 and SM3 and random salt value. The dual bidirectional security model implemented by using the hybrid SM series algorithms can achieve the full process of ciphertext transmission, storage, and verification during the authentication process, which can effectively prevent the leakage of personal information in the authentication process, and can effectively prevent fraud, man-in-the-middle attacks, password cracking, and account theft during the authentication process.
Address Clustering Heuristics for Account-Based Blockchain Networks: An Analysis Based on a Decentraland User Set

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