Reviewerly/exHarmony huggingface dataset:
Exploration of themes based twitter data in fraud-forensic accounting studies

The rapid development of information technology is also followed by developing databases and information based on social media. Social media Twitter is one area for disclosing accounting information in tweets, retweets, and other posts. This study aims to explore fraud-forensic accounting disclosures from the viewpoint of the four largest public accounting firms in the world (The Big 4) on the Twitter database. The approach used in this research is exploratory qualitative, with the content analysis method using NVivo software R1. The data collected a target of around 1000+ tweets. Researchers logged onto Twitter (as @agunggdeagung) and searched for all tweets containing the hashtag #fraud #forensic #accounting # big4. This research produces the theme of business, knowledge, time, and reports, and the themes of accounting, fraud, and forensics, which have already been defined as keywords in disclosure on social media Twitter. This research implies that in carrying out a qualitative approach with the twitter database, elements of the domain, community (user), information posts, and powerful data-based analysis software must be fulfilled.
Chaos follow the leader algorithm: Application to data classification

Classification is the process of systematically arranging the data into multiple groups, which gives a better understanding of large datasets. Considerable research has been carried out on data classification to develop a predictive model with high accuracy. This paper introduces chaos in the Follow the leader (FTL) optimization algorithm to achieve global optima for imbalanced data classification problems. In the FTL algorithm, the random parameter ω may not give enough exploration to the particles, which may lead to local stagnation problems. One of the solutions to this problem can be the replacement of the random parameter ω by a chaotic variable. For a detailed study on the chaotic behavior of ω in FTL, the algorithm is implemented with ten different chaotic maps and tested on twenty-four standard benchmark functions. Thereafter, the best chaos FTL (cFTL) algorithm is tested on six different complex real engineering problems and compared with ten meta-heuristic algorithms. Moreover, to further validate the performance of cFT, it has been applied to ten standard classification datasets collected from UCI and Kaggle data repositories. The obtained results show that cFTL outperforms FTL and other meta-heuristic algorithms.
Efficient and privacy-preserving decentralized energy trading scheme in a blockchain environment

Blockchain technology provides the possibility for the realization of decentralized energy trading. However, the blockchain confronts privacy risks and efficiency issues, which hinder its application in energy trading. In this paper, to solve this problem, we propose an efficient and privacy-preserving decentralized energy trading scheme in a blockchain environment. We propose an account mapping algorithm to solve the privacy attack problem. We propose a new transaction processing method, which can protect private data and improve execution efficiency by two-layer transaction processing. Finally, we verify the effectiveness of the proposed scheme for privacy protection and trading efficiency of decentralized energy transactions through experiments.
Learn one size to infer all: Exploiting translational symmetries in delay-dynamical and spatiotemporal systems using scalable neural networks

We design scalable neural networks adapted to translational symmetries in dynamical systems, capable of inferring untrained high-dimensional dynamics for different system sizes. We train these networks to predict the dynamics of delay-dynamical and spatiotemporal systems for a single size. Then, we drive the networks by their own predictions. We demonstrate that by scaling the size of the trained network, we can predict the complex dynamics for larger or smaller system sizes. Thus, the network learns from a single example and by exploiting symmetry properties infers entire bifurcation diagrams.
BPRT: A blockchain‐based privacy‐preserving transaction scheme based on an efficient broadcast encryption with personalized messages

Abstract As blockchain has drawn more and more attention, much research has developed different transaction schemes with privacy preserving specialized in various fields. The nature of blockchain makes the transaction information public, which presents much risk about the leakage of user privacy. It is quite necessary to design a flexible scheme to provide privacy preserving of transaction contents with reliable audit in blockchain that is lacked in the previous works. In this paper, we propose a blockchain‐based privacy‐preserving transaction scheme based on an efficient broadcast encryption with personalized messages called BPRT. First, we propose a fully anonymous broadcast encryption with personalized messages (BEPM). Compared with existing works, it is fully anonymous, and it has lower computation cost without pairing operations. It also avoids the key escrow problem existing in other BEPM schemes. Then, we construct the BPRT based on our BEPM. Our BPRT ensures the privacy preserving of transaction contents in group transmissions, and the encrypted transaction contents avoid exposing user identity information at the same time. It enables auditor to moderately and reliably make audit of transaction contents. Furthermore, we present formal security analysis of the proposed scheme. It has been proved that our scheme satisfies transaction confidentiality, receiver anonymity and audit reliability. Subsequently, experimental results indicate that the proposed BEPM enables fast encryption and decryption, and the computation of our BPRT is in milliseconds.
Efficient Top-K Identical Frequent Itemsets Mining without Support Threshold Parameter from Transactional Datasets Produced by IoT-Based Smart Shopping Carts

Internet of Things (IoT)-backed smart shopping carts are generating an extensive amount of data in shopping markets around the world. This data can be cleaned and utilized for setting business goals and strategies. Artificial intelligence (AI) methods are used to efficiently extract meaningful patterns or insights from such huge amounts of data or big data. One such technique is Association Rule Mining (ARM) which is used to extract strategic information from the data. The crucial step in ARM is Frequent Itemsets Mining (FIM) followed by association rule generation. The FIM process starts by tuning the support threshold parameter from the user to produce the number of required frequent patterns. To perform the FIM process, the user applies hit and trial methods to rerun the aforesaid routine in order to receive the required number of patterns. The research community has shifted its focus towards the development of top-K most frequent patterns not using the support threshold parameter tuned by the user. Top-K most frequent patterns mining is considered a harder task than user-tuned support-threshold-based FIM. One of the reasons why top-K most frequent patterns mining techniques are computationally intensive is the fact that they produce a large number of candidate itemsets. These methods also do not use any explicit pruning mechanism apart from the internally auto-maintained support threshold parameter. Therefore, we propose an efficient TKIFIs Miner algorithm that uses depth-first search strategy for top-K identical frequent patterns mining. The TKIFIs Miner uses specialized one- and two-itemsets-based pruning techniques for topmost patterns mining. Comparative analysis is performed on special benchmark datasets, for example, Retail with 16,469 items, T40I10D100K and T10I4D100K with 1000 items each, etc. The evaluation results have proven that the TKIFIs Miner is at the top of the line, compared to recently available topmost patterns mining methods not using the support threshold parameter.
A Study on the Perception of Aesthetics before Confucius in China

The purpose of this study is to explore whether the conditions revealed through the analysis of the perception of beauty can be used in modern times. Before Confucius, many politicians and thinkers discussed issues related to beauty and art. The materials are completely preserved in ancient times such as『Zuǒchuán』,『Guóyǔ』,『Shàngshū』,『Zhōuyì』,『Zhōulǐ』. The entire process from the end of the Xuzhou to Confucius has basically developed from the beauty of sensual pleasure given by the "five tastes", "five colors", and "five voices" to the recognition that beauty is the harmony of opposing elements. The reference to beauty before Confucius, in terms of the substance of thought, has already dealt with some important issues about the essence of beauty in depth, and has a somewhat philosophical content. And it has already begun to examine aesthetics in the physiological and psychological aspects, fundamentally becoming the first cornerstone of the theory of beauty in later Chinese aesthetics. In the development of human aesthetic consciousness, the sense of taste plays a major role. This is because the sense of taste most clearly expresses some of the major characteristics of the sense of taste (directness, superutility, differences in personal taste).
Debiased Representation Learning in Recommendation via Information Bottleneck

How to effectively mitigate the bias of feedback in recommender systems is an important research topic. In this article, we first describe the generation process of the biased and unbiased feedback in recommender systems via two respective causal diagrams, where the difference between them can be regarded as the source of system-induced biases. We then define this difference as a confounding bias and propose a new perspective on debiased representation learning to alleviate it. Specifically, for the case with biased feedback alone, we derive the conditions that need to be satisfied to obtain a debiased representation from the causal diagrams. Then, we propose a novel framework called debiased information bottleneck (DIB) to optimize these conditions and then find a tractable solution for it. The proposed framework constrains the model to learn a biased embedding vector with independent biased and unbiased components in the training phase, and uses only the unbiased component in the test phase to deliver more accurate recommendations. We further propose a variant of DIB by relaxing the independence between the biased and unbiased components. Finally, we conduct extensive experiments on a public dataset and a real product dataset to verify the effectiveness of the proposed framework.
Steganalysis of Context-Aware Image Steganography Techniques Using Convolutional Neural Network

Image steganography is applied to hide some secret information. Occasionally, steganography is used for malicious purposes to hide inappropriate information. In this paper, a new deep neural network was proposed to detect context-aware steganography techniques. In the proposed scheme, a high-boost filter was applied to alleviate the high-frequency while retaining the low-frequency details. The high-boost image was processed by thirty SRM high-pass filters to obtain thirty high-boost SRM filtered images. In the proposed CNN, two skip connections were used to collect information from multiple connections simultaneously. A clipped ReLU layer was considered in spite of the general ReLU layer. In constructing the CNN, a bottleneck approach was followed for an effective convolution. Only a single global average pooling layer was used to retain the complete flow of information. SVM was utilized instead of the softmax classifier to improve the detection accuracy. In the experimental results, the proposed technique was better than the existing techniques in terms of the detection accuracy and computational cost. The proposed scheme was verified on BOWS2 and BOSSBase datasets for the HILL, S-UNIWARD, and WOW context-aware steganography algorithms.
Algorithmic transparency and interpretability measures improve radiologists’ performance in BI-RADS 4 classification

To evaluate the perception of different types of AI-based assistance and the interaction of radiologists with the algorithm's predictions and certainty measures.In this retrospective observer study, four radiologists were asked to classify Breast Imaging-Reporting and Data System 4 (BI-RADS4) lesions (n = 101 benign, n = 99 malignant). The effect of different types of AI-based assistance (occlusion-based interpretability map, classification, and certainty) on the radiologists' performance (sensitivity, specificity, questionnaire) were measured. The influence of the Big Five personality traits was analyzed using the Pearson correlation.Diagnostic accuracy was significantly improved by AI-based assistance (an increase of 2.8% ± 2.3%, 95 %-CI 1.5 to 4.0 %, p = 0.045) and trust in the algorithm was generated primarily by the certainty of the prediction (100% of participants). Different human-AI interactions were observed ranging from nearly no interaction to humanization of the algorithm. High scores in neuroticism were correlated with higher persuasibility (Pearson's r = 0.98, p = 0.02), while higher consciousness and change of accuracy showed an inverse correlation (Pearson's r = -0.96, p = 0.04).Trust in the algorithm's performance was mostly dependent on the certainty of the predictions in combination with a plausible heatmap. Human-AI interaction varied widely and was influenced by personality traits.• AI-based assistance significantly improved the diagnostic accuracy of radiologists in classifying BI-RADS 4 mammography lesions. • Trust in the algorithm's performance was mostly dependent on the certainty of the prediction in combination with a reasonable heatmap. • Personality traits seem to influence human-AI collaboration. Radiologists with specific personality traits were more likely to change their classification according to the algorithm's prediction than others.
Intelligent Adaptive Optimisation Method for Enhancement of Information Security in IoT-Enabled Environments

The usage of the Internet increased dramatically during the start of the twenty-first century, entangling the system with a variety of services, including social media and e-commerce. These systems begin producing a large volume of data that has to be secured and safeguarded from unauthorised users and devices. In order to safeguard the information of the cyber world, this research suggests an expanded form of differential evolution (DE) employing an intelligent mutation operator with an optimisation-based design. It combines a novel mutation technique with DE to increase the diversity of potential solutions. The new intelligent mutation operator improves the security, privacy, integrity, and authenticity of the information system by identifying harmful requests and responses and helping to defend the system against assault. When implemented on an e-commerce application, the performance of the suggested technique is assessed in terms of confidentiality, integrity, authentication, and availability. The experimental findings show that the suggested strategy outperforms the most recent evolutionary algorithm (EA).
A Transformer-Based Model for Super-Resolution of Anime Image

Image super-resolution (ISR) technology aims to enhance resolution and improve image quality. It is widely applied to various real-world applications related to image processing, especially in medical images, while relatively little appliedto anime image production. Furthermore, contemporary ISR tools are often based on convolutional neural networks (CNNs), while few methods attempt to use transformers that perform well in other advanced vision tasks. We propose a so-called anime image super-resolution (AISR) method based on the Swin Transformer in this work. The work was carried out in several stages. First, a shallow feature extraction approach was employed to facilitate the features map of the input image's low-frequency information, which mainly approximates the distribution of detailed information in a spatial structure (shallow feature). Next, we applied deep feature extraction to extract the image semantic information (deep feature). Finally, the image reconstruction method combines shallow and deep features to upsample the feature size and performs sub-pixel convolution to obtain many feature map channels. The novelty of the proposal is the enhancement of the low-frequency information using a Gaussian filter and the introduction of different window sizes to replace the patch merging operations in the Swin Transformer. A high-quality anime dataset was constructed to curb the effects of the model robustness on the online regime. We trained our model on this dataset and tested the model quality. We implement anime image super-resolution tasks at different magnifications (2×, 4×, 8×). The results were compared numerically and graphically with those delivered by conventional convolutional neural network-based and transformer-based methods. We demonstrate the experiments numerically using standard peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), respectively. The series of experiments and ablation study showcase that our proposal outperforms others.
APR-QKDN: A Quantum Key Distribution Network Routing Scheme Based on Application Priority Ranking

As the foundation of quantum secure communication, the quantum key distribution (QKD) network is impossible to construct by using the operation mechanism of traditional networks. In the meantime, most of the existing QKD network routing schemes do not fit some specific quantum key practicality scenarios. Aiming at the special scenario of high concurrency and large differences in application requirements, we propose a new quantum key distribution network routing scheme based on application priority ranking (APR-QKDN). Firstly, the proposed APR-QKDN scheme comprehensively uses the application's priority, the total amount of key requirements, and the key update rate for prioritizing a large number of concurrent requests. The resource utilization and service efficiency of the network are improved by adjusting the processing order of requests. Secondly, the queuing strategy of the request comprehensively considers the current network resource situation. This means the same key request may adopt different evaluation strategies based on different network resource environments. Finally, the performance of the APR-QKDN routing scheme is compared with the existing schemes through simulation experiments. The results show that the success rate of application key requests of the APR-QKDN routing scheme is improved by at least 5% in the scenario of high concurrency.
Assisting in requirements goal modeling

Goal modeling plays an imperative role in early requirements engineering, which has been investigated for decades. There have been many studies that show the usefulness of requirements goal models. However, the establishment of goal models is typically done manually, which is time-consuming and has a steep learning curve. In this paper, we propose a semi-automatic framework for constructing iStar models, which is a well-known goal modeling language. Specifically, we first investigate the practical needs of iStar modelers on the automation of iStar modeling by holding interviews, based on which we propose an interactive and iterative modeling process. Our proposal takes advantage of human decisions and artificial intelligence algorithms, respectively, aiming at achieving low modeling costs while maintaining the quality of models. We then propose a hybrid approach for automatically extracting goal model snippets from requirements text, which implements the automatic tasks of our proposed process. The proposed method combines logical reasoning with deep learning techniques so as to unleash the power of domain knowledge to assist with automation tasks. We have performed a series of experiments for evaluation. The experimental results show that our method achieves the F1-measure of 90.34% for actor entity extraction, 93.14% for intention entity extraction, and 83.18% for actor relation extraction, which can efficiently establish high-quality goal models. The artifacts are available at Zenodo1.
A Novel Data Augmentation Method for Improving the Accuracy of Insulator Health Diagnosis

Performing ultrasonic nondestructive testing experiments on insulators and then using machine learning algorithms to classify and identify the signals is an important way to achieve an intelligent diagnosis of insulators. However, in most cases, we can obtain only a limited number of data from the experiments, which is insufficient to meet the requirements for training an effective classification and recognition model. In this paper, we start with an existing data augmentation method called DBA (for dynamic time warping barycenter averaging) and propose a new data enhancement method called AWDBA (adaptive weighting DBA). We first validated the proposed method by synthesizing new data from insulator sample datasets. The results show that the AWDBA proposed in this study has significant advantages relative to DBA in terms of data enhancement. Then, we used AWDBA and two other data augmentation methods to synthetically generate new data on the original dataset of insulators. Moreover, we compared the performance of different machine learning algorithms for insulator health diagnosis on the dataset with and without data augmentation. In the SVM algorithm especially, we propose a new parameter optimization method based on GA (genetic algorithm). The final results show that the use of the data augmentation method can significantly improve the accuracy of insulator defect identification.
Bug localization in game software engineering

Video games have characteristics that differentiate their development and maintenance from classic software development and maintenance. These differences have led to the coining of the term Game Software Engineering to name the emerging subfield that intersects Software Engineering and video games. One of these differences is that video game developers perceive more difficulties than other non-game developers when it comes to locating bugs. Our work proposes a novel way to locate bugs in video games by means of evolving simulations. As the baseline, we have chosen BLiMEA, which targets classic software engineering and uses bug reports and the defect localization principle to locate bugs. We also include Random Search as a sanity check in the evaluation. We evaluate the approaches in a commercial video game (Kromaia). The results for F-measure range from 46.80%. to 70.28% for five types of bugs. Our approach improved the results of the baseline by 20.29% in F-measure. To the best of our knowledge, this is the first approach that is designed specifically for bug localization in video games. A focus group with professional video game developers has confirmed the acceptance of our approach. Our approach opens a new research direction for bug localization for both game software engineering and possibly classic software engineering.