Reviewerly/exHarmony huggingface dataset:
Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case

Recently there has been a series of studies in knowledge graph embedding (KGE), which attempts to learn the embeddings of the entities and relations as numerical vectors and mathematical mappings via machine learning (ML). However, there has been limited research that applies KGE for industrial problems in manufacturing. This paper investigates whether and to what extent KGE can be used for an important problem: quality monitoring for welding in manufacturing industry, which is an impactful process accounting for production of millions of cars annually. The work is in line with Bosch research of data-driven solutions that intends to replace the traditional way of destroying cars, which is extremely costly and produces waste. The paper tackles two very challenging questions simultaneously: how large the welding spot diameter is; and to which car body the welded spot belongs to. The problem setting is difficult for traditional ML because there exist a high number of car bodies that should be assigned as class labels. We formulate the problem as link prediction, and experimented popular KGE methods on real industry data, with consideration of literals. Our results reveal both limitations and promising aspects of adapted KGE methods.
BVI-VFI: A Video Quality Database for Video Frame Interpolation

Video frame interpolation (VFI) is a fundamental research topic in video processing, which is currently attracting increased attention across the research community. While the development of more advanced VFI algorithms has been extensively researched, there remains little understanding of how humans perceive the quality of interpolated content and how well existing objective quality assessment methods perform when measuring the perceived quality. In order to narrow this research gap, we have developed a new video quality database named BVI-VFI, which contains 540 distorted sequences generated by applying five commonly used VFI algorithms to 36 diverse source videos with various spatial resolutions and frame rates. We collected more than 10,800 quality ratings for these videos through a large scale subjective study involving 189 human subjects. Based on the collected subjective scores, we further analysed the influence of VFI algorithms and frame rates on the perceptual quality of interpolated videos. Moreover, we benchmarked the performance of 33 classic and state-of-the-art objective image/video quality metrics on the new database, and demonstrated the urgent requirement for more accurate bespoke quality assessment methods for VFI. To facilitate further research in this area, we have made BVI-VFI publicly available at https://github.com/danier97/BVI-VFI-database.
LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning

The automation of code review activities, a long-standing pursuit in software engineering, has been primarily addressed by numerous domain-specific pre-trained models. Despite their success, these models frequently demand extensive resources for pre-training from scratch. In contrast, Large Language Models (LLMs) provide an intriguing alternative, given their remarkable capabilities when supplemented with domain-specific knowledge. However, their potential for automating code review tasks remains largely unexplored.In response to this research gap, we present LLaMA-Reviewer, an innovative framework that leverages the capabilities of LLaMA, a popular LLM, in the realm of code review. Mindful of resource constraints, this framework employs parameter-efficient fine-tuning (PEFT) methods, delivering high performance while using less than 1% of trainable parameters.An extensive evaluation of LLaMA-Reviewer is conducted on two diverse, publicly available datasets. Notably, even with the smallest LLaMA base model consisting of 6.7B parameters and a limited number of tuning epochs, LLaMA-Reviewer equals the performance of existing code-review-focused models.The ablation experiments provide insights into the influence of various fine-tuning process components, including input representation, instruction tuning, and different PEFT methods. To foster continuous progress in this field, the code and all PEFT-weight plugins have been made open-source.
ConvMHSA-SCVD: Enhancing Smart Contract Vulnerability Detection through a Knowledge-Driven and Data-Driven Framework

Smart contracts are essential for executing computing logic on blockchain networks. However, they are also susceptible to various vulnerabilities. In recent years, the detection of smart contract vulnerabilities has become a significant concern due to the substantial losses caused by hacker attacks. Traditional vulnerability detection approaches rely on expert rules, which often suffer from limitations in accuracy and completeness. Deep learning-based methods offer better coverage of vulnerabilities but may overlook certain vulnerability characteristics and suffer from overfitting during training. In this paper, we propose a novel approach called ConvMHSA-SCVD, which combines knowledge-driven and data-driven algorithms together to detect smart contract vulnerabilities. By incorporating feature selection, data balancing, and a combination of multi-channel convolution and multi-head self-attention neural networks, our ConvMHSA-SCVD achieves effective vulnerability detection in smart contracts. Extensive experiments demonstrate that our approach outperforms the state-of-the-art method in accuracy and F1 score, with improvements ranging from 0.4% to 3.84% and 1.28% to 1.90%, respectively.
Weakly guided attention model with hierarchical interaction for brain CT report generation

Brain Computed Tomography (CT) report generation, which aims to assist radiologists in diagnosing cerebrovascular diseases efficiently, is challenging in feature representation for dozens of images and language descriptions with several sentences. Existing report generation methods have achieved significant achievement based on the encoder-decoder framework and attention mechanism. However, current research has limitations in solving the many-to-many alignment between the multi-images of Brain CT imaging and the multi-sentences of Brain CT report, and fails to attend to critical images and lesion areas, resulting in inaccurate descriptions. In this paper, we propose a novel Weakly Guided Attention Model with Hierarchical Interaction, named WGAM-HI, to improve Brain CT report generation. Specifically, WGAM-HI conducts many-to-many matching for multiple visual images and semantic sentences via a hierarchical interaction framework with a two-layer attention model and a two-layer report generator. In addition, two weakly guided mechanisms are proposed to facilitate the attention model to focus more on important images and lesion areas under the guidance of pathological events and Gradient-weighted Class Activation Mapping (Grad-CAM) respectively. The pathological event acts as a bridge between the essential serial images and the corresponding sentence, and the Grad-CAM bridges the lesion areas and pathology words. Therefore, under the hierarchical interaction with the weakly guided attention model, the report generator generates more accurate words and sentences. Experiments on the Brain CT dataset demonstrate the effectiveness of WGAM-HI in attending to important images and lesion areas gradually, and generating more accurate reports.
Scalable Optical Convolutional Neural Networks Based on Free-Space Optics Using Lens Arrays and a Spatial Light Modulator

A scalable optical convolutional neural network (SOCNN) based on free-space optics and Koehler illumination was proposed to address the limitations of the previous 4f correlator system. Unlike Abbe illumination, Koehler illumination provides more uniform illumination and reduces crosstalk. The SOCNN allows for scaling of the input array and the use of incoherent light sources. Hence, the problems associated with 4f correlator systems can be avoided. We analyzed the limitations in scaling the kernel size and parallel throughput and found that the SOCNN can offer a multilayer convolutional neural network with massive optical parallelism.
THE IMPACT OF ORGANIZATIONAL TRUST ON EMPLOYEE RETENTION INTENTION: THE MEDIATING EFFECTS OF MANAGEMENT INNOVATION AND JOB SATISFACTION

This study focuses on exploring the crucial impact of organizational trust on employees' intention to remain within a complex and dynamic organizational environment. It thoroughly investigates the mediating roles of management innovation and job satisfaction within this relationship. Employing a quantitative research design, large-scale empirical data were collected from employees in Chinese enterprises to validate hypotheses and correlation patterns. The findings reveal significant positive associations between organizational trust, management innovation, job satisfaction, and employees' intention to remain. Specifically, both management innovation and job satisfaction mediate the relationship between organizational trust and employees' intention to remain, thereby strengthening this connection. This research provides substantial insights for human resource management, aiding in the formulation of effective talent retention strategies that enhance employee loyalty, intention to remain, and ultimately foster the stability and growth of enterprises. In the post-pandemic era, this study offers practical guidance for human resource management in Chinese enterprises and propels innovative practices in organizational development. By in-depth analysis of influencing factors, this research furnishes theoretical support and empirical groundwork for business decision-makers and the academic community alike.
Homogeneous and Heterogeneous Optimization for Unsupervised Cross-Modality Person Re-Identification in Visual Internet of Things

Cross-modality visible-infrared person re-identification (VI-ReID) has attracted widespread concern due to its scalability in 24-h video surveillance of the Visual Internet of Things (VIoT). Driven by enough annotated training data, supervised VI-ReID has achieved superior performance. However, annotating a large amount of cross-modality data is extremely time-consuming, which limits its employment in real-world scenarios. Existing several works neglect the image-level discrepancy and could not obtain reliable feature-level heterogeneous correlation. In the paper, we propose a novel Homogeneous and Heterogeneous Optimization with Modality Style Adaptation (HHO) mechanism to eliminate intra-modality and inter-modality discrepancies without any label information for unsupervised VI-ReID. Specifically, we present the modality style adaptation strategy to transfer unlabeled cross-modality pedestrian styles, which not only increases the image diversity but also bridges the inter-modality gap. Meanwhile, we employ the clustering algorithm to generate pseudo labels for each modality. The homogeneous feature optimization is developed to extract intra-modality pedestrian features. Furthermore, we propose heterogeneous feature optimization to eliminate the inter-modality discrepancy. To this end, a heterogeneous feature search (HFS) module is designed to mine reliable cross-modality signals for each identity. These reliable heterogeneous features are constrained to generate the compact feature distribution, while different identities are forced to be separated. The homogeneous and heterogeneous optimization are seamlessly integrated to learn cross-modality robust features. Abundant experiments prove the superiority of HHO, which gains superior performance.
Graph computing and graph database applications in power system online dispatch

In the new types of power systems, characterized by the dominance of renewable energies, uncertain resources such as wind and solar power will play a significant role in power generation. However, the inherent unpredictability and fluctuation of these renewable sources make the operating conditions complex and variable. To address this, intelligent online dispatch applications that integrate optimization and artificial intelligence techniques need to be implemented. In terms of online dispatch, estimating system states and optimizing resources require computing a large number of variables while adhering to operating constraints. Graph computing and graph database technologies have shown promising results in power system online analysis. This paper analyzes the applications of these graph computing techniques to enhance online dispatch and provides a graph computing based-grid online analysis architecture. This architecture offers a range of online situational awareness applications and decision-making support.
Blockchain-Based Federated Learning: Incentivizing Data Sharing and Penalizing Dishonest Behavior

With the increasing importance of data sharing for collaboration and innovation, it is becoming more important to ensure that data is managed and shared in a secure and trustworthy manner. Data governance is a common approach to managing data, but it faces many challenges such as data silos, data consistency, privacy, security, and access control. To address these challenges, this paper proposes a comprehensive framework that integrates data trust in federated learning with InterPlanetary File System, blockchain, and smart contracts to facilitate secure and mutually beneficial data sharing while providing incentives, access control mechanisms, and penalizing any dishonest behavior. The experimental results demonstrate that the proposed model is effective in improving the accuracy of federated learning models while ensuring the security and fairness of the data-sharing process. The research paper also presents a decentralized federated learning platform that successfully trained a CNN model on the MNIST dataset using blockchain technology. The platform enables multiple workers to train the model simultaneously while maintaining data privacy and security. The decentralized architecture and use of blockchain technology allow for efficient communication and coordination between workers. This platform has the potential to facilitate decentralized machine learning and support privacy-preserving collaboration in various domains.
CerviCell-detector: An object detection approach for identifying the cancerous cells in pap smear images of cervical cancer

<h2>Abstract</h2> Cervical cancer is the second most commonly seen cancer in women. It affects the cervix portion of the vagina. The most preferred diagnostic test required for screening for cervical cancer is the pap smear test. Pap smear is a time-consuming test as it requires detailed analysis by expert cytologists. Cytologists can screen around 100 to 1000 slides depending upon the availability of advanced equipment. It requires substantial time and effort to carefully examine each slide, identify and classify cells, and make accurate diagnoses. Prolonged periods of visual inspection can increase the likelihood of human errors, such as overlooking abnormalities or misclassifying cells. The sheer volume of slides to be screened can exacerbate fatigue and impact diagnostic accuracy. Due to this reason Artificial intelligence (AI) based computer-aided diagnosis system for the classification and detection of pap smear images is needed. There are some AI-based solutions proposed in the literature, still, an effective and accurate system is under research. In this paper, we implement a state-of-the-art object detection model with a newly available CRIC dataset which follows the Bethesda system for nomenclature. Object detection models implemented are YOLOv5 which uses the CSPNet backbone, Faster R–CNN which has Region Proposal Network (RPN) and Detectron2 framework created by Facebook AI Research (FAIR) Group. ResNext model is implemented among the available models from Detectron2. The CRIC dataset is preprocessed and augmented using Roboflow tool. The performance measures of Average Precision and mean Average precision over the Intersection over Union (IoU) are used to evaluate the effectiveness of the models. The models performed better for two classes namely Normal and Abnormal compared to six classes from the Bethesda system. The highest mean Average Precision (mAP) is observed on the augmented dataset for YOLOv5 models for binary classification with 83 % mAP with IoU in the range of 0.50–0.95.
Artificial Intelligence: Exploring utility in detection and typing of fungus with futuristic application in fungal cytology

Abstract Artificial Intelligence (AI) is an emerging, transforming and revolutionary technology that has captured attention worldwide. It is translating research into precision oncology treatments. AI can analyse large or big data sets requiring high‐speed specialized computing solutions. The data are big in terms of volume and multimodal with the amalgamation of images, text and structure. Machine learning has identified antifungal drug targets, and taxonomic and phylogenetic classification of fungi based on sequence analysis is now available. Real‐time identification tools and user‐friendly mobile applications for identifying fungi have been discovered. Akin to histopathology, AI can be applied to fungal cytology. AI has been fruitful in cytopathology of the thyroid gland, breast, urine and uterine cervical lesions. AI has a huge scope in fungal cytology and would certainly bear fruit with its accuracy, reproducibility and capacity for handling big data. The purpose of this systematic review was to highlight the AI's utility in detecting fungus and its typing with a special focus on future application in fungal cytology. We also touch upon the basics of AI in brief.
REFRAME–A software framework for managing reactive dependencies in object-oriented applications

In object-oriented (OO) applications, objects collaborate by invoking each other's behavior and data, thus forming dependencies. Sometimes, we need these dependencies to be reactive, so that, for example, an object automatically reacts to events and changes in other objects. The need for such dependencies often arises in event-driven systems, graphical user interfaces (GUI), animation, spreadsheet systems, etc. While in some cases reactive dependencies form fairly simple structures, in cases of implementing complex calculation models reactive dependencies can be especially numerous and can form complex dependency graphs. Understanding such graphs and manually handling reactive dependencies is extremely challenging and error-prone. Paradigms such as Reactive Programming (RP) make this task easier by providing programming languages with built-in abstractions and mechanisms for handling reactive dependencies. However, in the OO context, developers have no available solutions capable of handling complex dependency graphs. Instead, they often resort to implementing their own solutions rooted in a well-known, but limited Observer design pattern. While improved design patterns have emerged over time, they present smaller-scale solutions, oriented only on design reuse. Developers still need to invest significant effort to implement and test the proposed design. This indicates that there is a need for a larger-scale and implementation-level solution that developers could (re)use out-of-the-box. Therefore, this paper presents a software framework named REFRAME which provides built-in abstractions, mechanisms and tools for handling reactive dependencies in OO context.
PDKT: Introducing numbers 1-10 for kindergarten students using card media, does It improve?

Students who enter the school at the kindergarten level, commonly referred to as kindergarten, are at the stage where, at their current age, they can already use their ability to distinguish and remember in a tangible form. However, the problem is the need for language skills seen in some children who need help solving number problems. Teachers must choose a strategy for learning, especially mathematics. This study aims to improve the ability to introduce numbers through PDKT assisted by number card games in the children's group at TK 'Aisyiyah Bustanul Athfal 14 Samarinda. The type of research used was carried out collaboratively between researchers and classroom teachers and used qualitative and quantitative analysis. The method used is Classroom Action Research (CAR) with an observation approach. The research subjects were children aged 4-5 years consisting of 9 boys and six girls with six of 15 children. The results showed a significant change from cycle 1 to cycle two after improving learning by adding exciting media and fun and creative teacher strategies.
Quillbot As An AI-powered English Writing Assistant: An Alternative For Students to Write English

Quillbot is one of the modern era technologies that many students use in learning, it is also one of the online applications to improve writing. Quillbot can help students find writing errors such as punctuation and grammar and others. The purpose of this article is to find out students' perceptions of the use of quillbot as an alternative for students in writing English. This study uses a quantitative method, to find how the use of quillbot in helping English Foreign Language (EFL) to write English. data was collected through questionnaires and interviews that have been calculated and analyzed. The results show that students have a lot of positive perceptions of the use of quillbot in improving writing, as well as found advantages in quillbot that are very helpful to students. With this paper, students are expected to be able to apply it in learning activities related to English writing thus that the resulting writing becomes better.&#x0D; &#x0D;
XF2T: Cross-lingual Fact-to-Text Generation for Low-Resource Languages

Multiple business scenarios require an automated generation of descriptive human-readable text from structured input data. This has resulted into substantial work on fact-to-text generation systems recently. Unfortunately, previous work on fact-to-text (F2T) generation has focused primarily on English mainly due to the high availability of relevant datasets. Only recently, the problem of cross-lingual fact-to-text (XF2T) was proposed for generation across multiple languages alongwith a dataset, XAlign for eight languages. However, there has been no rigorous work on the actual XF2T generation problem. We extend XAlign dataset with annotated data for four more languages: Punjabi, Malayalam, Assamese and Oriya. We conduct an extensive study using popular Transformer-based text generation models on our extended multi-lingual dataset, which we call XAlignV2. Further, we investigate the performance of different text generation strategies: multiple variations of pretraining, fact-aware embeddings and structure-aware input encoding. Our extensive experiments show that a multi-lingual mT5 model which uses fact-aware embeddings with structure-aware input encoding leads to best results (30.90 BLEU, 55.12 METEOR and 59.17 chrF++) across the twelve languages. We make our code, dataset and model publicly available, and hope that this will help advance further research in this critical area.