Reviewerly/exHarmony huggingface dataset:
Antitrust &amp;amp; Regulatory Compliance in The 21st Century: New Challenges, New Opportunities and the Role of AI

We have previously made the case that the high penalties for cartels, the expansion of leniency programs, the new incentive provided by the U.S. Department of Justice, and the increased use of screening methods by competition authorities and private litigants would motivate corporations to enhance their antitrust compliance programs. In particular, that corporations would increasingly incorporate screens as part of such programs. We have, indeed, observed a greater focus on antitrust compliance over the last few years, even more so with new regulations on competition in the tech space.Antitrust compliance was, for a long time, part of a process that had to exist because it was simply required, and it was not characterized by many as a specially exciting and dynamic area. But a combination of recent new demands and challenges from an ever more complex and intense regulatory environment, as well as new powerful technologies and tools, are transforming this area of work into a highly creative and value-added activity which, of course, extends much beyond antitrust compliance to several of the functions of the corporation as a whole.Just as we have, for some time, been advocating for the use of screens in antitrust compliance, in this article we will advocate for the use of AI in that same context. Both detection and deterrence of anticompetitive conduct could get a major boost with the assistance of these tools, allowing corporations to stay ahead of the game in a cost-efficient way, potentially significantly minimizing the risk of non-compliance and regulatory violations, at a time when the stakes for non-compliance are becoming very high.
Advancing National Security: The Multi National Intelligence Security System (MNSI-System)

The Multi-Level National Security Intelligence System (MNSI-System) is founded on the effectiveness of multi-level mega-data analysis frameworks, coupled with the utilization of multidimensional graphs as a potent analytical tool within artificial intelligence. This approach offers a holistic perspective in comprehending and addressing complex military and national security challenges. The primary impetus behind the development of the MNSI-System is to provide a novel artificial intelligence tool capable of efficiently assessing dynamic and intricate scenarios, including potential wars, conflicts, and border issues. This tool aims to evaluate diverse strategies in a comprehensive manner. Consequently, the MNSI-System endeavors to furnish armed forces, intelligence agencies, and governments with an alternative artificial intelligence approach.
Toward high imperceptibility deep JPEG steganography based on sparse adversarial attack

JPEG steganography is an important branch of information hiding. However, with the development of deep learning-based steganalysis models, steganography is facing great challenges. To better resist these steganalysis models, this paper proposes a deep JPEG steganography framework based on sparse adversarial attacks. According to the vulnerability of deep steganalysis models to adversarial attacks, sparse adversarial attacks are introduced into the deep JPEG steganographic structure to improve the security of stego images. In addition, to resist steganalysis from JPEG and spatial domains, both JPEG and spatial domain steganalysis models are involved in adversarial training. Finally, to further enhance the imperceptibility of adversarial stego images, the visual perception loss is designed from the perspective of human eyes. Experimental results indicate that compared with the existing methods, the proposed method has higher imperceptibility and security and can resist modern deep steganalysis models in both JPEG and spatial domains to a certain extent. The source code is available at https://github.com/imagecbj/SAE-JS.
Validating the credibility of solar simulation tools using a real-world case study

Solar energy is being recognized around the world for generating clean power using photovoltaic (PV) panels. Inadequate planning and sizing of PV panel systems leads to increased costs, emphasising the significance of reviewing solar energy simulation tools for accuracy and credibility. Even with the current literature, it could be challenging to select the software tools that are appropriate for the research. This paper identified, reviewed and validated four system-based (System Advisor Model (SAM), PVsyst, PV*Sol and Solarius PV) and three online software tools (Helioscope, PV-Watts and PV-GIS). It provides necessary information on the software tools which compiled in a features matrix under seven categories. The validation process involves using real-world data from an 84.5KW solar plant installed on a building in Nagpur, India, to assess the accuracy and performance of the software tools. The statistical metrics used for validation are Absolute Deviation (AD), Root Mean Square Error (RMSE) accompanied by Mean Bias Error (MBE) and Nash-Sutcliffe Efficiency (NSE) for a comprehensive understanding of the accuracy and reliability. The findings show a discrepancy in the simulated output data and the recorded data of the tools with PV*Sol and Solarius PV showcasing the least error. In conclusion, the findings of this research provide valuable insights and guidance for users seeking to make informed decisions in selecting the most appropriate Solar PV simulation tool tailored to their specific needs and requirements.
Object drift determination network based on dual-template joint decision-making in long-term visual tracking

Object drift determination is a crucial issue in long-term tracking. Most existing object drift determination criteria require manually selecting different thresholds on different datasets to determine whether the object is lost. In this case, choosing the appropriate threshold is a complex problem. An object drift determination network based on dual-template joint decision-making is proposed to address this issue. The proposed object drift determination network not only does not require selection of thresholds on different datasets, and can be used as a plug-and-play module of short-term visual tracking algorithm to achieve long-term visual tracking tasks with good generalization ability. The proposed object drift determination network is applied to four short-term baseline trackers and constructs four long-term visual tracking algorithms. Experimental results verify that all four improved algorithms significantly improve long-term visual tracking performance compared to the original algorithms. In addition, the determined speed of the object drift determination network proposed in this paper reaches 111 FPS, which has little effect on the long-term visual tracking speed.
Classification of Malignant or Benign Cancer using Neural Networks

Cancer detection and predetection are being highly researched and studied recently due to the high density spread of this disease. It occurs when abnormal cells divide continuously without limitation where it can spread randomly to different parts of the body. Different types of cancer are recorded targeting the blood, lungs, bones, liver, and many other organs. People with cancer have similar symptoms such as lumps, abnormal bleeding, eating problems, fatigue, weight loss, and some more severe issues. Breast Cancer, which will be addressed in this work, is a type of cancer that hits women in general. It is dangerous especially when cells around the breast area overgrow and cause lumps on the breasts. In order to pre-detect and try to avoid breast cancer., it's important to perform annual mammography check. This work is aimed to create a classification system that uses neural network algorithms to conclude whether a certain tumor is a malignant or a benign tumor. Back Propagation algorithm as well as supervised learning were used and tested with highly satisfying successful classification.
Machine Failure Prediction: : A Comparative Anomaly Detection

Anomaly detection techniques seek to uncover unusual changes in the expected behavior of target indicators and, when used for intrusion detection, suspect assaults whenever the mentioned deviations are found. This technique is crucial in identifying and flagging abnormal instances in various domains. Several anomaly detection algorithms have been suggested, tested experimentally, and assessed in qualitative and quantitative surveys in the literature. However, there is a scarcity of comparative research, and methodological shortcomings are observed in existing studies. This paper investigates the performance of ten popular anomaly detection models for feature correlation analysis for predictive maintenance to detect machine failure with the most known approaches. The models considered are Local Outlier Factor (LOF), K-Nearest Neighbors (KNN), Support Vector Machines, Elliptic Envelope, Isolation Forest, Decision Tree, Extra Trees, Random Forest, AdaBoost, and Gradient Boosting. We evaluate the models using two scenarios: one with two correlated features and another with all features focused on correlated features. The evaluation metrics used for comparison are assessed by GridSearchCV and RandomizedSearchCV and compared to the cross-validation methods.
Enhancing Classification Performance in Knee Magnetic Resonance Imaging Using Adversarial Data Augmentation

The utilization of adversarial data augmentation has demonstrated the potential capability to enhance the classification performance in training deep neural networks to perform computer vision tasks. In this paper, we investigate the effectiveness of this approach as a strategy for enlarging a knee Magnetic Resonance Imaging (MRI) dataset by adding adversarial perturbation. Specifically, we use the Fast Gradient Sign Method (FGSM) to perturb a subset of the training dataset as extra training images to re-train a baseline model that was trained under the same configuration as the top-ranked model on the MRNet leaderboard. Particularly, unlike most of the current work, we investigate the impact of two hyperparameters (attack magnitude and the proportion of data to be re-trained) on the performance of area under the ROC curve (AUC), accuracy, sensitivity, and specificity. Additionally, our results show that adversarial data augmentation can further improve the well-trained baseline model's AUC by 0.26%, as well as provide a slight improvement in specificity at the same classification threshold. These findings underscore the potential advantages of adversarial data augmentation as a technique for optimizing the decision boundaries of deep learning models. The code of this work will be available on GitHub after the paper is published.
Perceived Trust of Stakeholders: Predicting the Use of COBIT 2019 to Reduce Information Asymmetry

The objective of this study is to ascertain the impact of perceived trust and COBIT on information asymmetry. However, the problem statement of this study could be framed as how the trust of stakeholders was influenced by the adoption of COBIT 2019 to mitigate IA within the context of Iraq. Survey data were collected from stakeholders of the Commercial Bank of Iraq (CBIQ) who possessed knowledge about COBIT 2019. A total of 122 valid questionnaires were completed and utilized for data collection. To establish both reliability and validity, as well as to assess the proposed hypotheses, a combined measurement and structural analysis were conducted using the smart-PLS technique on the valid dataset. The findings reveal that the adoption of COBIT 2019 to mitigate information asymmetry (IA) exerts a positive and significant influence on perceived trust (PT). Trust did not significantly affect IA, whereas IA was negatively affected by the implementation of COBIT. In addition to providing bank stakeholders with a comprehensive understanding of the factors influencing IA, this research can aid them in making informed judgments regarding the assessment of accounting information quality.
Empirical Investigation of the Diverse Refactoring Effects on Software Quality: The Role of Refactoring Tools and Software Size

Recent research has shown that various refactoring methods have very diverse, often opposing, and contradicting impacts on software quality characteristics. While using refactoring methods to enhance software quality, developers confront difficulties in choosing effective refactoring methods. To date, there is a lack of research that has studied factors that might explain conflicting or divergent findings about the influence of refactoring methods on software quality. As a result, two factors were identified, explored, and fully evaluated in this study: refactoring tools and software size. Four of the most regularly used refactoring methods (Add Parameter, Remove Parameter, Extract Class, and Extract Superclass) in practice were selected and independently implemented in five case studies of varied sizes (small, medium, and large). The results give solid evidence that these factors have a substantial role in determining the diverse impacts of refactoring methods on quality characteristics. These insights may help software engineers understand how to employ refactoring methods to enhance software quality while taking these factors into account. Moreover, the results may provide guidelines for software engineers to apply refactoring methods to enhance the quality of software systems, depending on the optimal situation for employing the refactoring methods.
Sentence Detailing and Its Applications

The purpose of this study is to observe whether there is a performance improvement when we train the multilingual Text-to-Text Transfer Transformer (mT5), which is a transformer model in Natural Language Processing (NLP), with a dataset having sentences constructed using a set of given words as a pre-process before we train the model with a text-from-title dataset directly. Given words were considered as concept-set and the model was expected to learn the concept like commonsense knowledge from news to generate appropriate sentences after being trained with title-to-text dataset as well. We named this method “Sentence Detailing” due to its feature of generating sentences by adding details to a set of words. In addition to the text generation from title, we also examined this method under the topic of data augmentation.
Feature Analysis for Sequential Recommender Systems Using Transformer-Based Architectures

Recommender systems assist users by suggesting relevant items among a large collection of items. Sequential and session-based recommender systems make recommendations based on the order of items users interact with. These systems take into account varying tastes of the users and can work well for short interaction sequences. In this paper, we make a comprehensive feature analysis in session-based recommendation. We divide the features into different groups and analyze the effects of both the feature groups and the individual features on the performance of the recommender system. We employ the Transformers4Rec framework and three datasets. The results show that time-based features contain the most salient information and using short sequences of past data yields better recommendations.
Instance Weighting in Neural Networks for Click-Through Rate Prediction

The instances on which a learning algorithm is most undecided can be weighted more during training to guide the learning model towards spending more effort on the difficult instances. We introduce three instance weighting algorithms to weigh the loss obtained. All of these instance weighting methods improve loss, AUC, and F1 results. We demonstrate the improvements on four different classifiers and on two different datasets. The improvements in loss reach 2.8% and in AUC reach 0.73% for Masknet on the Avazu dataset.
Unsupervised Stereoscopic Video Style Transfer

The creative style transfer across photos and videos involves transferring one style to another while maintaining the recipient image/video content. However, it is a challenging task, particularly for stereoscopic video, to preserve stereoscopic properties. This paper proposes a stereoscopic video style transfer method that maintains the temporal, depth, and stylization features. We suggest loosening the objective function to resolve the conflict between style transfer and temporal consistency for each left and right view to make the stylization loss term more motion-resistant. We use a zero-shot video style transfer framework for each left and right video frame. To make the stylized features consistent against the stereo features and consider the cross-view information in the stylized stereo video, we extend the parallax attention mechanism (PAM) as ePAM and use it to combine the left and right information. We compare quantitative and qualitatively with other image and video style transfer methods. Experimental results demonstrate the competitive performance of our method over the state-of-the-art.
SawtiEshara: An Avatar-Based Translator from Saudi Speech to Saudi SignLanguage

The primary objective of this study is to develop innovative mobile applications that can convert spoken words into Saudi sign language with the aid of advanced machine learning and multimedia technologies. To achieve this, the research leveraged 3D avatar techniques, finger movement tracking, and animation techniques, without the need for traditional video imaging. This study stands out as the first of its kind, catering specifically to the needs of the Saudi sign language community. To this end, the researchers incorporated a digital copy of the Saudi sign language dictionary, as well as an Avatar-based translator. The study has the potential to significantly improve the quality of life for individuals who rely on sign language for communication by providing a more accessible and efficient means of communication.
Assistant Solutions in Software Engineering: A Systematic Literature Review

Increasing technological advancements in developing assistants have gained attention because of its wide use in a variety of fields. In the realm of Software Engineering, developers are including these solutions in diverse software tasks. This research aims to identify and analyze the role and diversity of these solutions, including forms of interaction, functionalities, and technologies used for their implementation, evaluation, drawbacks, and current challenges. This research involves a systematic literature review of different approaches of assistant solutions in order to support software practitioners in developing their tasks. The solutions identified are oriented to recommendation systems, chatbots, and virtual assistants, covering a wide range of software processes such as requirements, design, construction, maintenance and, additionally management and process learning.
Research and Application of Key Technologies on Scientific Marine Data Integration, Submission and Credible Sharing

Most of the traditional scientific data integration, submission and sharing systems adopt the “centralized” storage management model, which uses both databases and local storage devices to store data and information. Although this approach can achieve effective management, it poses problems in data right confirmation, data ownership, data tampering, and so on. Based on the blockchain, this paper studies technologies on scientific marine data integration, submission and sharing, applies the technologies to the data integration and submission business of projects of marine science and technology programs of National Marine Data Center, builds a “decentralized” storage management model, and achieves traceability of the entire integration and submission process, and unforgeability of the integrated and submitted data.
Research on Abdominal Ultrasound Image Segmentation Method Based on Deep Learning

In the medical field, abdominal ultrasound examination can observe abdominal organs and lesions in real time and non-invasively, precisely dividing ultrasonic images is challenging due to low contrast and sound interference. This paper proposes a new network that combines convolution and self-attention mechanisms based on deep learning. It learns global dependencies by introducing a transformer, and uses a cross-channel attention (multi-head cross-attention) mechanism for multi-scale feature fusion. By introducing the transformer, the network can effectively learn the global features of the image and establish global dependencies. Multi-head cross-attention can make full use of the semantic information of the encoder and the high-resolution features of the decoder to achieve more accurate feature fusion and positioning. The method was verified on the public abdominal ultrasound data set. Compared with the U-Net network, the IoU and Dice coefficients were improved, showing a more accurate segmentation effect. This work shows that this method can significantly improve the performance of ultrasound image segmentation and is valuable for ultrasound medical image analysis.
Design, Development, and Evaluation of a Mobile Learning App for Basic Simple Emergency Management Education

The Indonesian Basic Health Research (Riskesdas) reported an increase in the injuries experienced by the Indonesian population of 11.9% in 2022. This fact indicates the urgent need to acquire the knowledge and skills to provide first aid and emergency management. In this modern digital era, app use is increasing rapidly, and the apps have been used as a portable and easy-to-operate learning medium. Therefore, this project mainly aimed to design, develop, and evaluate an app for first aid and simple emergency management education. This study is an experimental, descriptive study of the app's design and development process, which was carried out using the multimedia development life cycle model. A total of two tests, i.e., functional suitability and usability tests, were carried out to determine the app's feasibility. This study's findings indicate that the average functional suitability of the app was above 76%, indicating that all the app's features worked properly. The average usability was also above 76%; the criteria of usefulness ranged from 81 to 92% (excellent), the ease-of-use criteria ranged from 80–91% (excellent), the ease of learning criteria ranged from 85-89% (excellent), and the satisfaction criteria ranged from 83-90% (excellent). This indicates that the app was helpful for users, was easy to use and operate, and exceeded users' expectations. Therefore, it is assumed that student health volunteers or laypersons can utilize the app as a valid and reliable learning source.
Advanced Control of Positive Output Self-Lift Luo Converter Using Type-2 Fuzzy Neural Network via Elliptic Membership Function

In this paper, it is aimed to obtain an effective output voltage regulation performance from a positive output self-lift luo converter. For this purpose, firstly, a positive output self-lift luo converter was designed and an advanced control-based type-2 fuzzy neural network structure with elliptic type-2 fuzzy membership function was proposed to obtain an efficient output voltage regulation from the converter. The voltage regulation performance of the proposed voltage regulator was tested via comparative simulation studies using type-l fuzzy neural network in MATLAB/Simulink environment in terms of voltage regulation response time such as settling and recovery times under three different operational conditions. The results of the comparative simulation studies demonstrated that the proposed voltage regulator displayed a faster voltage regulation response than type-1 fuzzy neural network voltage regulator.