Reviewerly/exHarmony huggingface dataset:
Multi-view clustering via efficient representation learning with anchors

Multi-view spectral clustering has gained considerable attention due to its potential to enhance clustering performance. Although many methods have shown promising results, they often suffer from high time complexity and are not suitable for large-scale datasets. On the other hand, anchor-based methods are well-known for their efficiency. These methods typically learn the similarity relationship between instances and anchors and then convert it into the similarity relationship between instances, involving a considerable number of calculations. To address this issue, we propose a novel method called Multi-view clustering via Efficient Representation LearnIng with aNchors (MERLIN) in this paper. Instead of learning the instance–instance relationship, MERLIN approaches the clustering problem from the perspective of representation learning. Specifically, MERLIN selects the same anchors for different views and utilizes these anchors to learn a consensus representation that integrates information from all views. Additionally, MERLIN adaptively learns weights for different views to fully exploit the complementary information among multiple views. In comparison with seven state-of-the-art baseline methods across five datasets, MERLIN demonstrates both efficiency and effectiveness in handling multi-view datasets and is suitable for handling large-scale datasets.
PlayNet: real-time handball play classification with Kalman embeddings and neural networks

Abstract Real-time play recognition and classification algorithms are crucial for automating video production and live broadcasts of sporting events. However, current methods relying on human pose estimation and deep neural networks introduce high latency on commodity hardware, limiting their usability in low-cost real-time applications. We present PlayNet, a novel approach to real-time handball play classification. Our method is based on Kalman embeddings, a new low-dimensional representation for game states that enables efficient operation on commodity hardware and customized camera layouts. Firstly, we leverage Kalman filtering to detect and track the main agents in the playing field, allowing us to represent them in a single normalized coordinate space. Secondly, we utilize a neural network trained in nonlinear dimensionality reduction through fuzzy topological data structure analysis. As a result, PlayNet achieves real-time play classification with under 55 ms of latency on commodity hardware, making it a promising addition to automated live broadcasting and game analysis pipelines.
Blockchain Based e-Voting System

Voting is one of the fundamental rights of any citizen and conducting a voting is a very tedious process as a lot of manpower is required, important arrangement is required and above all a belief that a person’s vote is safely casted. On a website there are two options: login and registration (for new users). At the time of registration user data is verified and stored in the database. Entire Database required for the voting system is managed by using the XAMPP server and data will be stored in the associated database. After successful login on the website the user will have to fill a KYC form which will store important information about the user. Ethereum is used for implementation of blockchain and for coding purposes solidity is used. Gas value is generated for each block (for each candidate), this assigned gas value is unique and equal for all the blocks initially. For each new vote some gas value gets spent and the counter gets incremented. These gas values can only be seen by the main developer.
A Controller for Si MZI-Based Spanke-Benes Optical Switch Fabric With Automatic Calibration Capability

We demonstrate an FPGA-based controller for Si MZI-based Spanke-Benes 4 × 4 optical switch fabric that can automatically calibrate each individual switching cell and select the desired routing configuration. In order to minimize the power consumption for optical switching operation, the optimal heater voltages for the cross and the bar state of each switching cell is determined during the calibration. The operation of our controller is successfully verified with a Si MZI-based Spanke-Benes 4 × 4 optical switch.
Medical text classification based on the discriminative pre-training model and prompt-tuning

Medical text classification, as a fundamental medical natural language processing task, aims to identify the categories to which a short medical text belongs. Current research has focused on performing the medical text classification task using a pre-training language model through fine-tuning. However, this paradigm introduces additional parameters when training extra classifiers. Recent studies have shown that the “prompt-tuning” paradigm induces better performance in many natural language processing tasks because it bridges the gap between pre-training goals and downstream tasks. The main idea of prompt-tuning is to transform binary or multi-classification tasks into mask prediction tasks by fully exploiting the features learned by pre-training language models. This study explores, for the first time, how to classify medical texts using a discriminative pre-training language model called ERNIE-Health through prompt-tuning. Specifically, we attempt to perform prompt-tuning based on the multi-token selection task, which is a pre-training task of ERNIE-Health. The raw text is wrapped into a new sequence with a template in which the category label is replaced by a [UNK] token. The model is then trained to calculate the probability distribution of the candidate categories. Our method is tested on the KUAKE-Question Intention Classification and CHiP-Clinical Trial Criterion datasets and obtains the accuracy values of 0.866 and 0.861. In addition, the loss values of our model decrease faster throughout the training period compared to the fine-tuning. The experimental results provide valuable insights to the community and suggest that prompt-tuning can be a promising approach to improve the performance of pre-training models in domain-specific tasks.
Website Performance Testing Using Speed Testing Model: A Case of Reputable Indonesian Journals

The speed of a web journal to load pages reflects the website performance. It has influenced user satisfaction. This study aims to study and analyze the performance of journal websites in Indonesia that are accredited by SINTA 1 by the Ministry of Research and Technology of the Republic of Indonesia. This test uses parameters owned by GTmetrix tools and calculated using descriptive statistical methods. This research hypothesis regarding the reliability of website speed testing has been tested and confirmed based on experiments. The research involved 61 journal websites, which were taken from December 2019 to February 2020. 366 individual website speed tests from 6 test server region have done, and recommendations have been given on test results. The test result showed that parameter based on PageSpeed score provided the value of 7.9% (grades A to B) and 92.1% (grade C to F) and YSlow score provided a value of 2.5% (grade B) and 97.5% (C to F). The experimental results prove that the performance of the journal website is still a lot in the low category. With the recommendation, it was expected that the speed and responsiveness of the journal website would increase
Measurement-induced quantum walks on an IBM quantum computer

We study a quantum walk of a single particle that is subject to stroboscopic projective measurements on a graph with two sites. This two-level system is the minimal model of a measurement-induced quantum walk. The mean first detected transition and return time are computed on an IBM quantum computer as a function of the hopping matrix element between the sites and the on-site potential. The experimentally monitored quantum walk reveals the theoretically predicted behavior, such as the quantization of the first detected return time and the strong increase of the mean first detected transition time near degenerate points, with high accuracy.
Explainable contextual anomaly detection using quantile regression forests

Abstract Traditional anomaly detection methods aim to identify objects that deviate from most other objects by treating all features equally. In contrast, contextual anomaly detection methods aim to detect objects that deviate from other objects within a context of similar objects by dividing the features into contextual features and behavioral features. In this paper, we develop connections between dependency-based traditional anomaly detection methods and contextual anomaly detection methods. Based on resulting insights, we propose a novel approach to inherently interpretable contextual anomaly detection that uses Quantile Regression Forests to model dependencies between features. Extensive experiments on various synthetic and real-world datasets demonstrate that our method outperforms state-of-the-art anomaly detection methods in identifying contextual anomalies in terms of accuracy and interpretability.
Utility theory and mathematical analysis of risk attitude

In a world full of uncertainty, individuals have to make different choices. Based on their risk preferences, decision-making processes differ. Risk pre­ference encounters an individual’s willingness to bear the risk. Usually, economists dis­ti­ng­u­ish three types. Particularly they mention risk-averse, risk-seeking, and risk-neutral individuals. Risk-averse is the person who prefers a sure amount of cash over gambling on cash, while risk-seeking is the person who favors gambling on cash over a sure amount of cash, and risk-neutral is the person who is indifferent between sure amount and gambling. Even though it is not directly possible to observe an individual's risk preferences, the utility theory can be used to depict a choice for the risk level. The following paper aims to determine the individual’s risk preference from utility functions and provide mathematical analysis.
Offline Reinforcement Learning with Diffusion-Based Behavior Cloning Term

To address the distributional shift problem in offline reinforcement learning, policy constraint methods aim to minimize the divergence between the current policy and the behavior policy. One type of policy constraint method is the regularization constraint method, which adds regularization terms to online reinforcement learning algorithms. However, some of these regularization terms may be too restrictive and limited by the expressive power of the generative model. To relax the strict distribution matching constraint, this paper proposes TD3+diffusion-based BC algorithm, which contains a behavior cloning term incorporating the diffusion model as a regularization constraint term. The diffusion model has a strong expressive power and can achieve support set matching, which means it can learn actions with a high probability in a given state and avoid actions outside of the distribution. Our algorithm matches or surpasses state-of-the-art algorithms on most tasks in the D4RL benchmark, as shown by the experimental results.
Pengembangan Media Pembelajaran Local History dalam Meningkatkan Kesadaran Sejarah Siswa SMA

The development of teaching materials in technology-based history learning aims to improve the quality of education through the use of technology and increase historical awareness. The use of Local History learning media has an appeal for use in learning local history. Innovation in the use of the Local History application elaborates on the use of technology in learning local history in increasing students' historical awareness. The aim of this research is to develop teaching materials based on local content to increase students' historical awareness through the use of technological development. This study u tilized research and development methods . By using a 4D model development research approach (Define, Design, Develop, Disseminate). The results showed that the use of Local History teaching materials using the Likert scale validation test could increase high school students' historical awareness. This is based on the results of the validation test which consists of the Local History application design validation test which shows a result of 100. Then the application teaching material test shows a result of 100. And finally the test of the Local History application on the effectiveness level of application development which shows a result of 100.
Amazon products reviews classification based on machine learning, deep learning methods and BERT

In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Eco-geological environment quality assessment in a mining city: a case study of Jiangxia District, Wuhan City

This study investigates the eco-geological environmental challenges faced by Jiangxia District, a mining region in Wuhan City. By combining field surveys, literature analysis, and multi-source data such as remote sensing images and on-site monitoring data, an eco-geological environment quality (EGEQ) assessment system was developed. The system consists of six indicators representing the biological abundance, vegetation cover, land erosion, water abundance, surface deformation, and socio-economic characteristics of the assessed area. The grey relational analysis (GRA) method was utilized to calculate the weights of each indicator, and the eco-geological environment quality index (EGQI) value was obtained through the linear weighted method. The assessment results indicated a moderate grade of overall eco-geological condition in Jiangxia District between 2017 and 2021, with an 8.56% increase in the EGQI during that period. This suggests a stable and favorable eco-geo logical environment situation in the district during the specified timeframe.
Tackling Trust and Scalability of the Blockchain-Based Shared Manufacturing Concept

This paper explores the potential of blockchain technology to revolutionize the manufacturing industry through the concept of shared manufacturing. The Blockchain-based Shared Manufacturing concept envisions a decentralized platform that facilitates trustless and efficient collaborations between manufacturers and consumers. While this approach addresses several challenges associated with traditional manufacturing and supply chain management, it also faces issues related to establishing trust among participants and addressing challenges with blockchain scalability, including transaction throughput, privacy, and interoperability. This paper presents an analysis of these challenges and proposes potential solutions, including horizontal scaling via layer 2 solutions and parallel chains, and the use of social graphs, NFTs, and Self-Sovereign Identity for trust and reputation management. By addressing these challenges, the shared manufacturing concept can transform the manufacturing industry, paving the way for a more efficient, transparent, and secure ecosystem.
One class of continuous locally complicated functions related to infinite-symbol $\Phi$-representation of numbers

In the paper, we introduce and study a massive class of continuous functions defined on the interval $(0;1)$ using a special encoding (representation) of the argument with an alphabet $ \mathbb{Z}=\{0,\pm 1, \pm 2,...\}$ and base $\tau=\frac{\sqrt{5}-1}{2}$: $\displaystyle x=b_{\alpha_1}+\sum\limits_{k=2}^{m}(b_{\alpha_k}\prod\limits_{i=1}^{k-1}\Theta_{\alpha_i})\equiv\Delta^{\Phi}_{\alpha_1\alpha_2...\alpha_m(\emptyset)},\quadx=b_{\alpha_1}+\sum\limits_{k=2}^{\infty}(b_{\alpha_k}\prod\limits_{i=1}^{k-1}\Theta_{\alpha_i})\equiv\Delta^{\Phi}_{\alpha_1\alpha_2...\alpha_n...},$&#x0D; where $\alpha_n\in \mathbb{Z}$, $\Theta_n=\Theta_{-n}=\tau^{3+|n|}$,$b_n=\sum\limits_{i=-\infty}^{n-1}\Theta_i=\begin{cases}\tau^{2-n}, &amp; \mbox{if } n\leq0, \\1-\tau^{n+1}, &amp; \mbox{if } n\geq 0.\end{cases}$&#x0D; The function $f$, which is the main object of the study, is defined by equalities$\displaystyle\begin{cases}f(x=\Delta^{\Phi}_{i_1...i_k...})=\sigma_{i_11}+\sum\limits_{k=2}^{\infty}\sigma_{i_kk}\prod\limits_{j=1}^{k-1}p_{i_jj}\equiv\Delta_{i_1...i_k...},\\f(x=\Delta^{\Phi}_{i_1...i_m(\emptyset)})=\sigma_{i_11}+\sum\limits_{k=2}^{m}\sigma_{i_kk}\prod\limits_{j=1}^{k-1}p_{i_jj}\equiv\Delta_{i_1...i_m(\emptyset)},\end{cases}$ where an infinite matrix $||p_{ik}||$ ($i\in \mathbb{Z}$, $k\in \mathbb N$) satisfies the conditions&#x0D; 1) $|p_{ik}|&lt;1$ $\forall i\in \mathbb{Z}$, $\forall k\in \mathbb N;\quad$2) $\sum\limits_{i\in \mathbb{Z}}p_{ik}=1$ $\forall k\in\mathbb N$;&#x0D; 3) $0&lt;\sum\limits_{k=2}^{\infty}\prod\limits_{j=1}^{k-1}p_{i_jj}&lt;\infty~~\forall (i_j)\in L;\quad$4) $0&lt;\sigma_{ik}\equiv\sum\limits_{j=-\infty}^{i-1}p_{jk}&lt;1$ $\forall i\in \mathbb Z, \forall k\in \mathbb N.$&#x0D; This class of functions contains monotonic, non-monotonic, nowhere monotonic functions and functionswithout monotonicity intervals except for constancy intervals, Cantor-type andquasi-Cantor-type functions as well as functions of bounded and unbounded variation. The criteria for the function $f$ to be monotonic and to be a function of the Cantor type as well as the criterion of nowhere monotonicity are proved. Expressions for the Lebesgue measure of the set of non-constancy of the function and for the variation of the function are found. Necessary and sufficient conditions for thefunction to be of unbounded variation are established.
Transforming Judicial Systems with Blockchain: A Court Case Governance System for Tamper-Proof and Transparent Legal Processes

A court case governance system is a decentralised judicial system that uses blockchain technology to create a tamperproof, transparent, and secure form of record-keeping for legal processes. A distributed ledger powered by a network of computers is known as blockchain technology. Every transaction is digitally entered into the ledger, encrypted, and verified by the network of users. A court case governance system using blockchain technology can produce smart contracts, which are self-executing contracts with the contents of the agreement between the buyer and seller being directly placed into lines of code. Decentralising the process of automating contracts and arbitrating legal issues is possible with the help of these smart contracts. Users are given the power to draught and execute intelligent contracts, and disputes are resolved through decentralised arbitration. This strategy enables a method to incentivize jurors to evaluate cases fairly and accurately since judgements are upheld by smart contracts.
Decoding Electroencephalography Signal Response by Stacking Ensemble Learning and Adaptive Differential Evolution

Electroencephalography (EEG) is an exam widely adopted to monitor cerebral activities regarding external stimuli, and its signals compose a nonlinear dynamical system. There are many difficulties associated with EEG analysis. For example, noise can originate from different disorders, such as muscle or physiological activity. There are also artifacts that are related to undesirable signals during EEG recordings, and finally, nonlinearities can occur due to brain activity and its relationship with different brain regions. All these characteristics make data modeling a difficult task. Therefore, using a combined approach can be the best solution to obtain an efficient model for identifying neural data and developing reliable predictions. This paper proposes a new hybrid framework combining stacked generalization (STACK) ensemble learning and a differential-evolution-based algorithm called Adaptive Differential Evolution with an Optional External Archive (JADE) to perform nonlinear system identification. In the proposed framework, five base learners, namely, eXtreme Gradient Boosting, a Gaussian Process, Least Absolute Shrinkage and Selection Operator, a Multilayer Perceptron Neural Network, and Support Vector Regression with a radial basis function kernel, are trained. The predictions from all these base learners compose STACK’s layer-0 and are adopted as inputs of the Cubist model, whose hyperparameters were obtained by JADE. The model was evaluated for decoding the electroencephalography signal response to wrist joint perturbations. The variance accounted for (VAF), root-mean-squared error (RMSE), and Friedman statistical test were used to validate the performance of the proposed model and compare its results with other methods in the literature, including the base learners. The JADE-STACK model outperforms the other models in terms of accuracy, being able to explain around, as an average of all participants, 94.50% and 67.50% (standard deviations of 1.53 and 7.44, respectively) of the data variability for one step ahead and three steps ahead, which makes it a suitable approach to dealing with nonlinear system identification. Also, the improvement over state-of-the-art methods ranges from 0.6% to 161% and 43.34% for one step ahead and three steps ahead, respectively. Therefore, the developed model can be viewed as an alternative and additional approach to well-established techniques for nonlinear system identification once it can achieve satisfactory results regarding the data variability explanation.
Entropy suppression through quantum interference in electric pulses

The Schwinger process in strong electric fields creates particles and antiparticles that are entangled. The entropy of entanglement between particles and antiparticles has been found to be equal to the statistical Gibbs entropy of the produced system. Here we study the effect of quantum interference in sequences of electric pulses, and show that quantum interference suppresses the entanglement entropy of the created quantum state. This is potentially relevant to quantum-enhanced classical communications. Our results can be extended to a wide variety of two-level quantum systems.
A user-centered explainable artificial intelligence approach for financial fraud detection

This paper aims to produce user-centered explanations for financial fraud detection models based on Explainable artificial intelligence (XAI) methods. By combining an ensemble predictive model with an explainable framework based on Shapley values, we develop a financial fraud detection approach that is accurate and explainable at the same time. Our results show that the explainable framework can meet the requirements of different external stakeholders by producing local and global explanations. Local explanations can help understand why a specific prediction is identified as fraud, and global explanations reveal the overall logic of the whole ensemble model.