Reviewerly/exHarmony huggingface dataset:
The Concept of Wealth (<i>māl</i>) in the Sharīʿah and Its Relation to Digital Assets

The study aims to address the concept of wealth from the perspective of the Sharīʿah and its relationship to contemporary digital assets such as cryptocurrency (Bitcoin). The qualitative research approach is adopted for this research. The data is collected from several resources such as books, journal articles, reports, conferences papers, and websites. The data is analyzed through an explanatory research approach by providing scholars’ opinions and their arguments on the subject matter. The findings of this study show that Sharīʿah provides certain conditions to consider something as asset or currency from the Sharīʿah perspective. In regard to cryptocurrency (Bitcoin), the Sharīʿah rules vary based on the types of cryptocurrencies and whether cryptocurrency meets the conditions to be considered as currency or assets. Moreover, either cryptocurrency is an asset or currency, it should not contravene any Sharīʿah laws, such as ribā (interest) and gharar (uncertainty). Since Sharīʿah does not limit the currency to dirhams and dīnārs, Bitcoin can be considered as an alternative to the current currencies in a digital form. This would enable financial markets to move faster and secure transaction methods in the future. The policymakers might find this study beneficial to issue new guidelines and policy documents. The findings of the study might be interesting for cryptocurrency enthusiasts and investors to have a deeper understanding of cryptocurrency from the Sharīʿah viewpoint.
An extendable data structure for incremental stable perfect hashing

We consider the problem of dynamically assigning n elements unique indices, known as hashcodes, in the range [(1+o(1))n]. This problem is known as perfect hashing and is considered a fundamental building block in the design of more involved data structures. The challenge we address is that of designing a data structure that meets several, seemingly opposing, requirements: (1) the range and the space of the data structure must be, at all times, proportional to the current cardinality nt of the input set, and (2) the hashcodes it assigns must be stable in that the hashcode of an element must not change while the element is continuously in the set. A simple argument shows that these two desiderata are impossible to achieve when arbitrary deletions and insertions are allowed.
Forecast Method of Distributed Photovoltaic Power Generation Based on EM-WS-CNN Neural Networks

In order to cope with the challenges of dispatching of power grids brought by large-scale distributed photovoltaic power generation related to production and consumers, a maximum expected sample weighted convolutional neural network (EM-WS-CNN) is proposed to forecast the distributed photovoltaic output. First, the distance correlation coefficient and the principal component analysis method are used to extract the comprehensive meteorological factors from the original meteorological data, and then the 6 statistical indexes of the comprehensive meteorological factors and historical power data are used as the clustering characteristics. The historical data are divided into different weather types by using the maximum expectation clustering, and the training samples are weighted based on the membership matrix. Finally, the weighted training data are used to construct the EM-WS-CNN model. In the experimental analysis, the above-mentioned method is compared with the CNN model, and the results show that the proposed method has higher accuracy and robustness.
Research on Action Recognition Based on Deep Learning with Long Short-Term Memory Network and Attention Mechanism

Action recognition is a basic and challenging task in the field of computer vision. In this paper, a deep learning action recognition method based on attention mechanism is proposed and successfully applied to several public data sets, with outstanding performance. Firstly, the video frames are sampled based on the improved sampling algorithm, and the video data enhancement algorithm is proposed to preprocess the original data, which will reduce the overfitting probability of the recognition model and reduce the white noise of the data. Then, feature selection is carried out through attention-based residual network. Finally, we completed the action classification by LSTM model and softmax. In addition, a series of ablation experiments were designed to verify the validity of the proposed model. The results indicate that compared with the traditional action recognition model, the proposed method can effectively extract key features, reduce the overfitting caused by a small number of samples, reduce the interference of redundant information through the screening of low-information video frames, and complete the action recognition accurately, quickly, and efficiently.
Co-optimizing for task performance and energy efficiency in evolvable robots

Evolutionary robotics is concerned with optimizing autonomous robots for one or more specific tasks. Remarkably, the energy needed to operate autonomously is hardly ever considered. This is quite striking because energy consumption is a crucial factor in real-world applications and ignoring this aspect can increase the reality gap. In this paper, we aim to mitigate this problem by extending our robot simulator framework with a model of a battery module and studying its effect on robot evolution. The key idea is to include energy efficiency in the definition of fitness. The robots will need to evolve to achieve high gait speed and low energy consumption. Since our system evolves the robots’ morphologies as well as their controllers, we investigate the effect of the energy extension on the morphologies and on the behavior of the evolved robots. The results show that by including the energy consumption, the evolution is not only able to achieve higher task performance (robot speed), but it reaches good performance faster. Inspecting the evolved robots and their behaviors discloses that these improvements are not only caused by better morphologies, but also by better settings of the robots’ controller parameters.
The Rasmussen Cognition Model for Cognitive Radar

The long-term goal of artificial intelligence (AI) is to provide machines the capabilities to learn, think and reason like humans. To achieve these long-term goals, it is necessary to introduce human cognitive-like abilities into AI systems to create truly self-adaptive artificially intelligent systems. This marriage of human cognitive skills with “machines” creates hybrid systems that have characteristics of both. The question becomes, which human cognitive model is appropriate for hybrid artificial intelligent systems. The purpose of this paper is to discuss the development of cognitive models to be infused into a modern radar system to create a Cognitive Radar System (CRS). The notion of a hybrid artificially intelligent system can be divided into two main categories: (a) human-in-the-loop systems with hybrid augmented intelligence requiring human-AI communication/ collaboration, and (b) a cognitive computing-based AI in which a fully cognitive model is infused into the machine to allow fully autonomous operation. Here, we discuss the first type, human-in-the-loop cognitive radar systems that provide intelligent decision support and analysis for radar systems. The design of hybrid artificial intelligence methods and algorithms are presented with applications to improvement to modern radar systems, utilizing a Rasmussen Cognition Model (RCM), which we feel is appropriate for a hybrid cognitive system utilized to create a Cognitive Radar System (CRS).
Target Detection and Adaptive Tracking Based on Multisensor Data Fusion in a Smoke Environment

Robust and accurate target detection and tracking in complex environments such as smoke are prerequisites for intelligent vehicle safe trajectory planning and navigation. Existing target detection and tracking methods have disadvantages, such as a high miss detection rate and apparent tracking deviation (position drift and size deviation of the tracking box) in a smoke environment. In response to these problems, we propose utilizing multisensor data fusion technology to improve the target detection recall and propose a new adaptive KCF tracking algorithm to improve tracking accuracy. We collected data from the environment of smoke to train the object detection network and designed a radar noise filter based on an empirical model to improve the accuracy and credibility of target detection. We then use a decision-level fusion strategy to fuse the image detection results and the radar filtering results. Finally, we utilize the fusion data to correct the tracking results online. The proposed method is evaluated on a self-developed intelligent vehicle platform, and the experimental results prove the algorithm's feasibility. This work provides a feasible solution to the problem of target detection and tracking in smoke environments.
Indirect Website Evaluation: Currently Available Tools

For most institutions, the World Wide Web has been the main source of information in recent years. Evaluation of a website reveals the overall and actual performance of that website. Identifying a website’s strengths and weaknesses is significantly important to any institution. Therefore, in this study, we seek the answer to the question “When evaluating a website, what kind of questionnaires can be used?” The purpose of this article is to give an overview of different questionnaires for the evaluation of web pages. In addition, it explains how these questionnaires can be used. To do this, we used a simple review of the internet to find questionnaires that evaluate the quality of websites.
Twin relaxed least squares regression with classwise mean constraint for image classification

This paper presents a twin relaxed least squares regression (TRLSR) framework with classwise mean constraint for image classification. The primary objective of TRLSR is to learn discriminative projections with enhanced interclass margins while preserving the intrinsic structure of the data. To this end, we introduce a relaxed regression target matrix together with a twin matrix to allow greater flexibility in learning the projections compared to using the conventional strict binary label matrix. In addition, a classwise mean constraint is introduced to retain the intraclass similarity of the data, which is beneficial in learning more discriminative projections. An ℓ2,1-norm based regularization on the optimized projections is incorporated to extract more significant features while limiting the impact of noise and overfitting. The performance of the proposed technique on several public data sets for face recognition, object classification, action recognition and scene classification applications is demonstrated. The proposed method is shown to outperform the state-of-the-art approaches.
Impact of Data Quality on Question Answering System Performances

In contrast with the research of new models, little attention has been paid to the impact of low or high-quality data feeding a dialogue system.The present paper makes the first attempt to fill this gap by extending our previous work on question-answering (QA) systems by investigating the effect of misspelling on QA agents and how context changes can enhance the responses.Instead of using large language models trained on huge datasets, we propose a method that enhances the model's score by modifying only the quality and structure of the data feed to the model.It is important to identify the features that modify the agent performance because a high rate of wrong answers can make the students lose their interest in using the QA agent as an additional tool for distant learning.The results demonstrate the accuracy of the proposed context simplification exceeds 85%.These findings shed light on the importance of question data quality and context complexity construct as key dimensions of the QA system.In conclusion, the experimental results on questions and contexts showed that controlling and improving the various aspects of data quality around the QA system can significantly enhance his robustness and performance.
Using the case survey methodology for finding high-quality grey literature in software engineering

Background: Mining online accounts of practitioner experience can provide new evidence for software engineering researchers. However, we need methods for assessing quality at vast scale. Objectives: We investigate the challenge of finding high-quality grey literature, defining high-quality in terms of a document’s relevance to the consumer and its credibility. Method: Building on previous research, we use a version of the case survey methodology for automating the identification and assessment of high-quality grey literature. Results: We develop a model of credibility assessment within software engineering research and demonstrate our case survey methodology and credibility assessment model in practice. We use it to conduct a grey literature review of High Performing Teams (HPT). Conclusions: The paper provides a foundation for future research on automated quality and credibility assessment. Adoption of the tools and methodology presented can help researchers effectively search for and select higher-quality blog-like content.
Complexity of symbolic representation in working memory of Transformer correlates with the complexity of a task

Even though Transformers are extensively used for Natural Language Processing tasks, especially for machine translation, they lack an explicit memory to store key concepts of processed texts. This paper explores the properties of the content of symbolic working memory added to the Transformer model decoder. Such working memory enhances the quality of model predictions in machine translation task and works as a neural-symbolic representation of information that is important for the model to make correct translations. The study of memory content revealed that translated text keywords are stored in the working memory, pointing to the relevance of memory content to the processed text. Also, the diversity of tokens and parts of speech stored in memory correlates with the complexity of the corpora for machine translation task.
A Comparative Study of Text Genres in English-Chinese Translation Effects Based on Deep Learning LSTM

In recent years, neural network-based English-Chinese translation models have gradually supplanted traditional translation methods. The neural translation model primarily models the entire translation process using the "encoder-attention-decoder" structure. Simultaneously, grammar knowledge is essential for translation, as it aids in the grammatical representation of word sequences and reduces grammatical errors. The focus of this article is on two major studies on attention mechanisms and grammatical knowledge, which will be used to carry out the following two studies. Firstly, in view of the existing neural network structure to build translation model caused by long distance dependent on long-distance information lost in the delivery, leading to problems in terms of the translation effect which is not ideal, put forward a kind of embedded attention long short-term memory (LSTM) network translation model. Secondly, in view of the lack of grammatical prior knowledge in translation models, a method is proposed to integrate grammatical information into translation models as prior knowledge. Finally, the proposed model is simulated on the IWSLT2019 dataset. The results show that the proposed model has a better representation of source language context information than the existing translation model based on the standard LSTM model.
Dynamics of the social construction of knowledge: an empirical study of Zhihu in China

This study examined how the knowledge market promotes knowledge construction on question-and-answer (Q&A) websites. Data were collected from Zhihu, one of the largest Q&A sites in China. Hierarchical linear modeling was employed to estimate the dynamics of information accumulation, that is, the provision of informative content as factual construction. By employing information accumulation as the objective measure of knowledge construction, we determined that online knowledge construction was facilitated by a competitive marketplace of ideas. In addition, participation, temporal, and discourse features affected the dynamics of information accumulation. In specific, active users contributed significantly less to information accumulation than did ordinary users. Information accumulation shows a naturally decaying process represented as a function of answer order. The time interval between answers at the two preceding time points reduced the informativeness of answers at the subsequent time point. Answers with a higher readability score reduced the informativeness of subsequent answers. The results indicate that knowledge construction on Q&A sites unfolds as a process of mass collaboration among users.The online version contains supplementary material available at 10.1140/epjds/s13688-022-00346-6.
Error-disturbance uncertainty relations in Faraday measurements

We examine error-disturbance relations in the quantum measurement of spin systems using an atom-light interface scheme. We model a single spin-1/2 system that interacts with a polarized light meter via a Faraday interaction. We formulate the error and disturbance of the model and examine the uncertainty relations. We find that, for the coherent light meter in pure polarization, both the error and disturbance behave like the cyclic oscillations due to the Faraday rotation in both the light and spin polarizations. We also examine a class of polarization-squeezed light meter, where we apply the phase-space approximation and characterize the role of squeezing. We derive the error-disturbance relations for these cases and find that the Heisenberg-Arthurs-Kelly uncertainty is violated while the tight Branciard-Ozawa uncertainty always holds. We note that, in the limit of weak interaction strength, the error and disturbance come to obey the unbiasedness condition and hence the Heisenberg-Arthurs-Kelly relation holds. The work contributes to our understanding of the quantum measurement of spin systems under the atom-light interface framework and may hold potential applications in quantum metrology, quantum state estimation, and control.
Applicability of Visual Cryptography and MAC Address in Online Voting System Security

Since everyone’s day-to-day schedule has become incredibly hectic, many people have stopped voting in person. One solution to this issue is online voting. However, there are several drawbacks to voting online as, there are questions about protection and privacy. Phishing is a very popular attack tactic used by attackers to seduce the electorate in order to steal and abuse the credentials of a legitimate user. The main goal of this paper is to find the safest way to hold an election that ensures secrecy, fairness, and privacy by implementing adequate security measures that enable voters to vote using Visual Cryptography (VC) and MAC address reading. At the time of the creation of the account, two similar shares are produced, one is shared with the voter, and the other share is available on the system’s server for user login at the time of election. When a user opens the file that contains the share, the MAC address of the user’s machine is stored, and the count variable will be modified from 0 to 1. The count variable is used to ensure that no third person accesses the file that contains the share. These two shares will be matched by using a VC scheme after the MAC address and value of the count variable are verified. Before the referendum, users/citizens should be educated on how to vote online.
Uncertainty estimation based adversarial attack in multi-class classification

Abstract Model uncertainty has gained popularity in machine learning due to the overconfident predictions derived from standard neural networks which are not trustworthy. Recently, Monte-Carlo based adversarial attack (MC-AA) has been proposed as a simple uncertainty estimation method which is powerful in capturing data points that lie in the overlapping distribution of the decision boundary. MC-AA produces uncertainties by performing back-and-forth perturbations of a given data point towards the decision boundary using the idea of adversarial attacks. Despite its efficacy against other uncertainty estimation methods, this method has been only examined on binary classification problems. Thus, we present and examine MC-AA with multi-class classification tasks. We point out the limitation of this method with multiple classes which we tackle by converting multiclass problem into ‘one-versus-all’ classification. We compare MC-AA against other recent model uncertainty methods on Cora – a graph structured dataset – and MNIST – an image dataset. Thus, the conducted experiments are performed using a variety of deep learning algorithms to perform the classification. Consequently, we discuss the best results of model uncertainty with Cora data using LEConv model of AUC-score 0.889 and MNIST data using CNN of AUC-score 0.98 against other uncertainty estimation methods.
Enhanced IoT- Assisted Wearable Data Analytics for Patient Monitoring and Healthcare Systems

Recent technological advancements have opened the path for smart gadgets, including wearable devices. Wearable sensors can predict human body motions and track a variety of medical symptoms associated with chronic diseases. The proposed study utilizes IoT sensors to monitor human bodily states, with the obtained data being saved in a cloud context. The data saved in the cloud environment is analyzed further using data analysis algorithms to enable clinicians’ efficient monitoring and prompt response. Moreover, data fluctuation may assist physicians in notifying them of emergencies, preserving a precious human life. The proposal might be implemented efficiently in emergency medical settings when patients need continual monitoring and the greatest attention from medical professionals. The suggested idea might be used to deploy several life-threatening conditions such as spinal muscular atrophy, hypertrophic cardiomyopathy, and Covid patient monitoring.
Stealth in quantum illumination with a probabilistic mixed strategy

Quantum illumination and quantum entanglement provide an efficient method for detecting low-reflectivity targets in bright thermal-noise environments. Until now, almost all studies on quantum illumination assume that a target has only two mutually exclusive choices of being present and absent. In this study, we investigate the performance of quantum illumination when a target adopts a probabilistic mixed strategy. Remarkably, we show that the probabilistic mixed strategy can help the target hide its presence. This conclusion holds for asymptotic quantum illumination with joint measurement and practical quantum illumination with mode-by-mode local measurement. We also verify our result for Gaussian quantum illumination with a two-mode squeezed vacuum state and non-Gaussian quantum illumination with the NOON state. The mixed strategy is successful because of the large amount of resources consumed in target detection. This work is the next step toward better understanding of the stealth technique in future quantum illumination.