Reviewerly/exHarmony huggingface dataset:
A Novel Framework for Distributed and Collaborative Federated Learning based on Blockchain and Smart Contracts

Blockchain and federated learning, as two key technologies for trusted and privacy-preserving collaboration in distributed environments, have been intensively studied in recent years. Federated learning aims to train a centralized global model from decentralized datasets without leaking user privacy, while blockchain helps establish mutual trusts among multiple clients with technical features such as tamper-proof, anonymity, security and traceability, among others. Specially, blockchain-based smart contracts can perform complex logics and behaviors in an efficient and accurate fashion to schedule collaborations. Therefore, leveraging these advantages, this paper proposed a novel framework for enabling distributed and collaborative federated learning based on blockchain and smart contracts, and discussed its major components, i.e., the blockchain-based distributed architecture, the smart-contract-based scheduling, as well as the incentive mechanism design for federated learning. We also discussed the potential application scenarios of our framework, which can be expected to help establish safer, fairer, smarter, and more efficient collaborations for distributed federated learning.
SensorDAO: A New Framework of Sensor Governance for Internet of Vehicles

High-performance sensors play a crucial role in IoV (Internet of Vehicles) to achieve advanced autonomous driving. Typically, IoV involves extensive data sharing and centralizes data storage, posing significant security challenges for sensors. Although blockchain offers a decentralized platform for data exchange, it can’t guarantee the reliability and trustworthiness of each sensor in IoV. Besides, blockchain primarily focuses on technical aspects and falls short of establishing a comprehensive organizational and operational framework for sensors. To address these challenges, we introduce the concept of DAO (Decentralized Autonomous Organization and Operation) into sensors and propose a new framework for reliable sensor operations in IoV, namely SensorDAO. It utilizes the collaboration of on-chain and off-chain layers to support various scenarios at the application layer. We also provide key technical elements to construct SensorDAO, including distributed storage, intelligent algorithms, governance, and incentive mechanisms.
Fusion Orchestration Guidelines (FOG) for Collaborative Computing and Network Data Fusion

In recent years, there has been the opportunity to execute data fusion methods onboard a multi-sensor system. Typically, the motivation was to process the sensor data at the collection location, but the processing power was limited. Hence, for many decades, the limitation of edge-based (or far edge) was only available to systems with small-data message processing. Currently, processing larger-size data at the device edge is possible and has been coordinated with text, imagery, seismic, acoustic, and radar sensing. Hence, future edge-based heterogeneous data fusion methods would be facilitated from various architectures developed from big data, cyber-physical sensing, machine learning, and software advancements. This paper compares cloud-fog-edge developments to support data fusion multi-domain architecture considerations. A feasibility study shows the benefits of fog computing using the analytical hierarchy processing (AHP) metrics of timeliness, accuracy, confidence/credibility, throughput, and security.
A Design Model And Comparison of Fixed And Tracking Photovoltaic Systems for A Single-family House in Erbil, Iraq

Solar power through the use of photovoltaic (PV) system is the most advanced and profitable renewable energy application; however, there are still a number of obstacles facing this technology, including technical, financial, and political barriers. According to meteorological data of Iraq, the annual average solar radiation is about 1800-2390 kWh/m2/year, which is a considerable amount of energy if properly utilized. This study proposes a design model of PV installation system and the model is applied to three different PV systems for installation in a residential household in Erbil, Iraq. The study examines the feasibility of the plants proposed and compares the efficiency of both fixed and tracking PV systems for an off-grid PV system. The PVsyst software is used for household electricity load estimation and solar energy requirements, such as the appropriate number of panels, maximizing AC power generation, the storage capacity of the battery, and charge controller size to fulfill the required load. To calculate the annual energy generation, the design data was used in the simulations. Three scenarios were simulated; fixed panels, East-West single tracking, and dual-axis tracking systems based on the altitude and azimuth tracking angles. The study compared both the photovoltaic properties and the amount of energy generated by the installed systems. The results showed that the dual-axis tracking system is 30% and single-axis tracking system is 21% more efficient than the fixed PV system, although the latter offers an economically better alternative.
Ways and Benefits of Integration of Social Media in Primary School English Learning in Malaysia: A Systematic Literature Review

English is one of the essential subjects for today’s learning. Learning that integrates elements to help students in understanding the basic rules or grammar of English language is advisable to be applied, including by integrating the use of social media. Many extensive researches on various aspects of online English language learning have been conducted in the past studies. However, there has been limited focus on integrating social media in primary school English language learning. Therefore, this study aims to examine the methods and advantages gained from the integration of social media in primary school English language learning. This systematic literature review incorporates five selected articles that underwent a selection and exclusion process based on the guidelines provided by PRISMA 2020. The study results indicate that informal methods are more commonly used for integrating social media into English language learning. Additionally, the study highlights the advantages gained by teachers and students through the integration of social media in English language learning.
The WuC-Adam algorithm based on joint improvement of Warmup and cosine annealing algorithms

&lt;abstract&gt; &lt;p&gt;The Adam algorithm is a common choice for optimizing neural network models. However, its application often brings challenges, such as susceptibility to local optima, overfitting and convergence problems caused by unstable learning rate behavior. In this article, we introduce an enhanced Adam optimization algorithm that integrates Warmup and cosine annealing techniques to alleviate these challenges. By integrating preheating technology into traditional Adam algorithms, we systematically improved the learning rate during the initial training phase, effectively avoiding instability issues. In addition, we adopt a dynamic cosine annealing strategy to adaptively adjust the learning rate, improve local optimization problems and enhance the model's generalization ability. To validate the effectiveness of our proposed method, extensive experiments were conducted on various standard datasets and compared with traditional Adam and other optimization methods. Multiple comparative experiments were conducted using multiple optimization algorithms and the improved algorithm proposed in this paper on multiple datasets. On the MNIST, CIFAR10 and CIFAR100 datasets, the improved algorithm proposed in this paper achieved accuracies of 98.87%, 87.67% and 58.88%, respectively, with significant improvements compared to other algorithms. The experimental results clearly indicate that our joint enhancement of the Adam algorithm has resulted in significant improvements in model convergence speed and generalization performance. These promising results emphasize the potential of our enhanced Adam algorithm in a wide range of deep learning tasks.&lt;/p&gt; &lt;/abstract&gt;
Filter design for continuous-discrete Takagi-Sugeno fuzzy system with finite frequency specifications

&lt;abstract&gt;&lt;p&gt;This paper was concerned with the problem of filter design for the continuous-discrete system in the Takagi-Sugeno (T-S) fuzzy model. In a known finite frequency (FF) domain, an FF $ H_\infty $ performance was defined for the nonlinear continuous-discrete system. With the designed filter, sufficient conditions were then established for the filtering error system to be asymptotically stable and having a prescribed FF $ H_\infty $ performance. After that, a systematic method for the filter design was proposed. Finally, an example was provided to check effectiveness of the derived results.&lt;/p&gt;&lt;/abstract&gt;
An Innovative Analysis of Time Series-Based Detection Models for Improved Cancer Detection in Modern Healthcare Environments

Early detection of cancer is important for successful treatment and improved survival of many cancer types. Technological advances have enabled researchers to develop more precise and reliable methods of cancer detection that go beyond traditional methods, such as biopsy and imaging. Through methods such as blood tests, MRI scans, and gene expression profiling, it is now possible to quickly and accurately diagnose many types of cancer. Early detection of cancer can lead to improved outcomes for patients and can even help save lives. Time series analysis is a data mining technique used to identify and analyze the temporal patterns in datasets. The proposed model reached 91.30% accuracy, 90.11% precision, 92.46% recall, and a 90.12% F1-score. This enhanced version of time series analysis incorporates multiple layers of data sources and uses advanced machine learning algorithms to identify patterns that could signal the presence of a tumor. Innovations in time series analysis for cancer detection can have a significant impact on modern healthcare. Time series analysis is a mathematical method used to analyze trends in data over multiple periods. It can be used to identify patterns that may indicate early signs of cancer.
Quafu-Qcover: Explore Combinatorial Optimization Problems on Cloud-based Quantum Computers

Abstract We introduce Quafu-Qcover, an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends. Quafu-Qcover provides a standardized and comprehensive workflow that utilizes the Quantum Approximate Optimization Algorithm (QAOA). It facilitates the automatic conversion of the original problem into a quadratic unconstrained binary optimization (QUBO) model and its corresponding Ising model, which can be subsequently transformed into a weight graph. The core of Qcover relies on a graph decomposition-based classical algorithm, which efficiently derives the optimal parameters for the shallow QAOA circuit. Quafu-Qcover incorporates a dedicated compiler capable of translating QAOA circuits into physical quantum circuits that can be executed on Quafu cloud quantum computers. Compared to a general-purpose compiler, our compiler demonstrates the ability to generate shorter circuit depths, while also exhibiting superior speed performance. Additionally, the Qcover compiler has the capability to dynamically create a library of qubits coupling substructures in real-time, utilizing the most recent calibration data from the superconducting quantum devices. This ensures that computational tasks can be assigned to connected physical qubits with the highest fidelity. The Quafu-Qcover allows us to retrieve quantum computing sampling results using a task ID at any time, enabling asynchronous processing. Moreover, it incorporates modules for results preprocessing and visualization, facilitating an intuitive display of solutions for combinatorial optimization problems. We hope that Quafu-Qcover can serve as an instructive illustration for how to explore application problems on the Quafu cloud quantum computers.
Test Case Prioritization for Software Product Line: A Systematic Mapping Study

Combinatorial explosion remains a common issue in testing. Due to the vast number of product variants, the number of test cases required for comprehensive coverage has significantly increased. One of the techniques to efficiently tackle this problem is prioritizing the test suites using a regression testing method. However, there is a lack of comprehensive reviews focusing on test case prioritization in SPLs. To address this research gap, this paper proposed a systematic mapping study to observe the extent of test case prioritization usage in Software Product Line Testing. The study aims to classify various aspects of SPL-TCP (Software Product Line â€“ Test Case Prioritization), including methods, criteria, measurements, constraints, empirical studies, and domains. Over the last ten years, a thorough investigation uncovered twenty-four primary studies, consisting of 12 journal articles and 12 conference papers, all related to Test Case Prioritization for SPLs. This systematic mapping study presents a comprehensive classification of the different approaches to test case prioritization for Software Product Lines. This classification can be valuable in identifying the most suitable strategies to address specific challenges and serves as a guide for future research works. In conclusion, this mapping study systematically classifies different approaches to test case prioritization in Software Product Lines. The results of this study can serve as a valuable resource for addressing challenges in SPL testing and provide insights for future research.
TA-Sum: The Extractive Summarization Research Based on Topic Information

&lt;p&gt;Text summarization is divided into extractive summarization and abstractive summarization. The extractive summarization technology aims to extract some main phrases and sentences from the original text to form a short summary for people to read quickly. However, extractive summarization is faced with problems such as poor sentence coherence and incomplete information, which makes it difficult to screen out important sentences from the source text. DNN (Deep Neural Network) is widely used for text summarization task. This paper proposes a TA-Sum model based on the neural topic model. Introducing the topic information can help people understand the relevant main content of source text quickly. We obtain the topic information using the neural topic model and implement the attention mechanism to fuse the topic information with the text representation, which improves the semantic integrity and completeness of the summary. The experimental results on the large-scale English data sets CNN/Daily mail are improved by 0.37%, 0.11%, and 0.17% respectively compared with BertSum, which demonstrates the effectiveness of our method.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
The Impact of Online Reviews Manipulation on Consumer Purchase Decision Based on The Perspective of Consumers’ Perception

&lt;p&gt;Consumers depend on online reviews to influences their purchase decisions. On account of that,many vendors and retailers try to manipulate online reviews to mislead potential consumers to take risky purchase decisions. Many scholars have conducted a lot of research on the impact of online product reviews on consumer behavior and sales. However, the existing work are mainly based on the premise of real product reviews, but few attentions have been paid of fake ones. Based on the recognition results of deceptive reviews, this article explores whether consumers be aware or perceive it when deceptive reviews are flooding the online review system, and further analyze what influence will be imposed on final purchase decision with different perception. The empirical analysis of the questionnaire survey show that in the context of two different perceptions of consumers, deceptive reviews have significant differences in the results of purchase decisions. In addition, research also shows that consumers&amp;rsquo; persuasive knowledge plays a moderating role between perceived deception and purchase decision.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Martial Art Learning Optimization: A Novel Metaheuristic Algorithm for Night Image Enhancement

&lt;p&gt;This paper proposes a human behavior-based optimization algorithm, Martial Arts Learning Optimization (MALO), for optimization problems in continuous spaces. The algorithm simulates the process of characters in martial arts learning so as to apply it to optimization problems. Characters in martial arts stories usually go through multiple stages of learning martial arts, such as self-study and leader teaching. Multiple learning stages of characters are modeled in this paper, utilizing the wisdom of the characters learning martial arts in the novel, enabling the optimization process. To verify and analyze the performance of the proposed algorithm, the algorithm is numerically tested on 30 benchmark functions, and it is found that its performance was better than the state-of-the-art nine algorithms. In addition, the algorithm is also used to solve the problem of nighttime image brightness enhancement. Compared with other image enhancement methods, the proposed MALO algorithm has superior results in both visual effects and quantitative image quality assessments.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
In-Air Hand Gesture Signature Recognition Using Multi-Scale Convolutional Neural Networks

The hand signature is a unique handwritten name or symbol that serves as a proof of identity. Due to its practicality and widespread use, hand signature is still used by financial institutions as a means of verifying and validating the identity of their customers. The emergence of the COVID-19 global pandemic has raised hygiene concerns regarding the conventional touch-based hand signature recognition system, which often requires sharing the acquisition devices among the public. This paper presents in-air hand gesture signature recognition using convolutional neural networks to address this concern. We designed a shallow multi-scale convolutional neural network using 3x3 and 5x5 kernel filter sizes to extract features on different scales. The feature maps from these two filters are then concatenated to provide more robust features, which improve the modelâ€™s performance.Â The experiment results show that the proposed architecture outperforms other architectures, which obtained the highest accuracy of 93.00%. On the other hand, our architecture consumed significantly fewer computational resources, requiring only an average of 3 minutes and 33 seconds to train. Additionally, the performance of the proposed architecture could be further enhanced by integrating it with recurrent neural networks (RNN). This integrated architecture of convolutional recurrent neural networks (C-RNN) can capture spatio-temporal features simultaneously.
In what way does blockchain technology tie with the efficacy of accounting practice in Nigerian accounting firms?

The digital transformation sweeping across industries in recent years has had a significant influence on the field of accounting. This study specifically aimed to assess the impact of blockchain technology on the effectiveness of accounting practices in Nigeria. This study used a qualitative approach and the study's primary data came from the distribution of a structured questionnaire to the targeted respondents, which comprised accountants, finance analysts, and blockchain experts. The total population was 178 registered accounting firms in Nigeria as of 31st December 2022. A purposive sampling technique was used to select 123 firms from which 3 questionnaires were distributed to each of the firms selected, totaling 369, which served as the sample size. Data were analyzed through descriptive statistics and inferential statistics. The overall results found that blockchain technology had a significant positive effect on the efficacy of accounting practice in Nigeria. Thus, this study recommends that accounting firms in Nigeria should consider investing in blockchain technology to improve data security, transparency, and efficiency.
Anti-Compression Contrastive Facial Forgery Detection

Forgery of facial images and videos has increased the concern about digital security. It has led to the significant development of detecting forgery data recently. However, the data, especially the videos published on the Internet, are usually compressed with lossy compression algorithms such as H.264. The compressed data could significantly degrade the performance of recent detection algorithms. The existing anti-compression algorithms focus on enhancing the performance in detecting heavily compressed data but less consider the compression adaption to the data from various compression levels. We believe creating a forgery detection capable of handling data compressed with unknown levels is important. To enhance the performance of such models, we consider the weak compressed and strong compressed data as two views of the original data and they should have similar representation and relationships with other samples. We propose a novel anti-compression forgery detection framework by maintaining closer relations within data under different compression levels. Specifically, our algorithm measures the pair-wise similarity within data as the relations, ensuring that relationships between weakly and strongly compressed data remain consistent. This enhances the discriminative power for detecting highly compressed data. To achieve a better strong compressed data relation guided by the less compressed one, we apply video-level contrastive learning for weak compressed data, which forces the model to produce similar representations within the same video and far from the negative samples. The experiment results show that the proposed algorithm could boost performance for strong compressed data while improving the accuracy rate when detecting clean data.
LOIS: Looking Out of Instance Semantics for Visual Question Answering

Visual question answering (VQA) has been intensively studied as a multimodal task, requiring efforts to bridge vision and language for correct answer inference. Recent attempts have developed various attention-based modules for solving VQA tasks. However, the performance of model inference is largely bottlenecked by visual semantic comprehension. Most existing detection methods rely on bounding boxes, remaining a serious challenge for VQA models to comprehend and correctly infer the causal nexus of contextual object semantics in images. To this end, we propose a finer model framework without bounding boxes in this work, termed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Looking Out of Instance Semantics (LOIS)</i> to address this crucial issue. LOIS can achieve more fine-grained feature descriptions to generate visual facts. Furthermore, to overcome the label ambiguity caused by instance masks, two types of relation attention modules: 1) intra-modality and 2) inter-modality, are devised to infer the correct answers from different visual features. Specifically, we implement a mutual relation attention module to model sophisticated and deeper visual semantic relations between instance objects and background information. In addition, our proposed attention model can further analyze salient image regions by focusing on important word-related questions. Experimental results on four benchmark VQA datasets prove that our proposed method has favorable performance in improving visual reasoning capability. Our code is available on GitHub ( <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ArcherCYM/LOIS</uri> ).