Reviewerly/exHarmony huggingface dataset:
Contrastive Coincidental Correctness Representation Learning

A test suite is indispensable for fault localization by providing useful execution information of its test cases for locating suspicious statements of being faulty. There exists a type of test cases known as coincidental correctness (CC) test cases, which executes the faulty statement whereas produces the anticipated output. The existing studies have shown CC test cases harmfully impact fault localization effectiveness. Therefore, it is crucial to detect CC test cases to mitigate the adverse impact of CC test cases on fault localization.To address this issue, we propose ContraCC: a CC test cases detection method using contrastive learning. The insight of ContraCC is that the internal structural information of source test case execution data should be beneficial for CC detection whereas there is a lack of suitable representation methods. Inspired by the insight, ContraCC uses contrastive learning to learn new differentiated representations as test case vectors, which differentiate between similar and dissimilar pairs of test cases by maximizing their similarity within the same class and minimizing it between different classes. Based on the contrastive learning representations (i.e., test case vectors), ContraCC adopts multi-layer perceptron for binary classification to detect CC in downstream tasks. To evaluate the effectiveness of ContraCC, we conduct large-scale experiments on widely-used benchmarks by comparing ContraCC with five state-of-the-art CC test cases detection methods and applying ContraCC for fault localization. The experimental results show that ContraCC outperforms four state-of-the-art methods (e.g., from 10% to 84% improvement in Top-N on the best-performing baseline NeuralCCD) and significantly improves fault localization effectiveness (e.g., 24% improvement on the best-performing baseline Dstar).
GraBit: A Sequential Model-Based Framework for Smart Contract Vulnerability Detection

The security of smart contracts has garnered considerable attention given the potential for substantial financial losses and erosion of trust in blockchain platforms. Numerous methods have been proposed to detect vulnerabilities in smart contracts. Notably, as the number of smart contracts continues to proliferate, automated techniques based on deep learning (DL) are making remarkable progress. However, a significant challenge persists in acquiring an efficient embedding representation that is compatible with DL models with input length restrictions. In this paper, we propose a novel detection method named GraBit for identifying reentrancy vulnerability-one of the most critical vulnerabilities in smart contracts. GraBit leverages the pre-trained model GraphCodeBERT to embed both the source code and concise key data flow graphs extracted from the code. Additionally, we customize a sequential model based on Bi-directional Long Short-Term Memory and attention mechanism to effectively capture contextual semantic information. To evaluate the performance of GraBit, we conduct extensive experiments on a public large-scale dataset. Our experimental results reveal that GraBit achieves a remarkable F1-score of 94.44% in detecting reentrancy vulnerability, outperforming state-of-the-art methods.
Docker-Based Assignment Evaluations in E-Learning

This scholarly discourse presents a novel architecture for automating student assignment evaluations leveraging the Docker environment. The innovative design promotes individualized technology selection by students for problem-solving in academic assignments. The assessment of these assignments relies on the utilization of RESTful APIs encapsulated in the student-submitted containers. A cornerstone of the design is its emphasis on system security and scalability. To achieve this, the evaluation process employs sandboxing techniques such as containerization and temporary user creation. Scalability is addressed through an asynchronous task queue for testing, fostering adaptability to varied workload intensities. The proposed system was employed as a demonstrative case study for the database technologies course at the Faculty of Informatics and Information Technologies, Slovak University of Technology in Bratislava. Practical implementation of this model revealed enhanced efficiency in assignment evaluation, promoting the integration of technology and pedagogy. The system is made publicly available as an open-source project, allowing for further collaborative development and adaptations to diverse educational contexts. This research advances the current understanding of how Docker-based environments can significantly augment e-learning systems, thereby proposing a promising avenue for future scholarly exploration.
Self-supervised enhanced thyroid nodule detection in ultrasound examination video sequences with multi-perspective evaluation

Objective.Ultrasound is the most commonly used examination for the detection and identification of thyroid nodules. Since manual detection is time-consuming and subjective, attempts to introduce machine learning into this process are ongoing. However, the performance of these methods is limited by the low signal-to-noise ratio and tissue contrast of ultrasound images. To address these challenges, we extend thyroid nodule detection from image-based to video-based using the temporal context information in ultrasound videos.Approach.We propose a video-based deep learning model with adjacent frame perception (AFP) for accurate and real-time thyroid nodule detection. Compared to image-based methods, AFP can aggregate semantically similar contextual features in the video. Furthermore, considering the cost of medical image annotation for video-based models, a patch scale self-supervised model (PASS) is proposed. PASS is trained on unlabeled datasets to improve the performance of the AFP model without additional labelling costs.Main results.The PASS model is trained by 92 videos containing 23 773 frames, of which 60 annotated videos containing 16 694 frames were used to train and evaluate the AFP model. The evaluation is performed from the video, frame, nodule, and localization perspectives. In the evaluation of the localization perspective, we used the average precision metric with the intersection-over-union threshold set to 50% (AP@50), which is the area under the smoothed Precision-Recall curve. Our proposed AFP improved AP@50 from 0.256 to 0.390, while the PASS-enhanced AFP further improved the AP@50 to 0.425. AFP and PASS also improve the performance in the valuations of other perspectives based on the localization results.Significance.Our video-based model can mitigate the effects of low signal-to-noise ratio and tissue contrast in ultrasound images and enable the accurate detection of thyroid nodules in real-time. The evaluation from multiple perspectives of the ablation experiments demonstrates the effectiveness of our proposed AFP and PASS models.
A Joint Entity-Relation Detection and Generalization Method Based on Syntax and semantic for Chinese Intangible Cultural Heritage Texts

[Purpose/Significance] The annotation of natural language corpus not only facilitates researchers to extract knowledge from it, but also helps to achieve deeper mining of the corpus. But the annotated corpus in the humanities knowledge domain is less. And the semantic annotation of humanities texts is difficult, because it requires a high domain background for researchers, even requires the participation of domain experts. Based on this, this study proposes a method for detecting entities and relations in domain which is lack of annotated corpus, and provides a referenceable idea for constructing conceptual models based on textual instances. [Method/Process] Based on syntactic and semantic features, this study proposes SPO triple recognition rules from the perspective of giving priority to predicates and generalization rules from the perspective of triple's content and the meaning of its predicate. The recognition rules are used to extract text-descriptive SPO triples centered on predicates. After clustering and adjusting triples, use the generalization rules proposed in this study to obtain coarse-grained entities and relations, and then form a conceptual model. [Results/Conclusions] This study recognizes SPO triples with high precision and summarization from descriptive texts, generalizes them and then forms a domain conceptual model. The method proposed in this paper provides a research idea for entity-relation detection in a domain with missing annotated corpus, and the formed domain conceptual model provides a reference for building a domain Linked Data Graph. The feasibility of the method is verified through practice on texts related to the four traditional Chinese festivals.
Histopathological breast cancer classification using CNN

In the year 2021, there were 2.26 million new diagnoses of breast cancer cases among women globally. Over the years, various imaging methods such as mammography, Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), X-ray, and Computer Tomography (CT) have been used as helpful diagnostic tools for cancer prognosis. However, these methods expose the patient to rays that have the potential to be cancerous. Added to the cancerous risk, due to the fatigue experienced by histopathologists, many errors can be introduced in such diagnosis. Recently, Computer Aided Diagnosis (CAD) via Convolutional Neural Network (CNN) has been employed to overcome the fatigue and decrease in concentration experienced by histopathologists and clinicians during a cancer diagnosis. Though CNN has shown better results than conventional cancer diagnosis methods, using a single dataset for CNN training has been an accuracy challenge in diagnosing breast cancer. This study proposed a classification of histopathological images of breast cancer using CNN. A hybrid dataset was curated by preprocessing the histopathological dataset from two popular datasets, namely, BreakHis and Histo to the best of our knowledge this combination of is new. The cancer tissue images were classified as either Malignant or Benign. Instead of using a model with random weights from scratch, this study uses transfer learning to build four pre-trained models, namely DenseNet201, ResNet50, ResNet101 and MobileNet-v2. Five protocol setups for the dataset were set up and divided into 70% and 30% for training and testing, respectively. Of the four pre-trained models, DensNet201 achieved an accuracy of 91.37% and a sensitivity level of 100% at a 200x magnification factor. Our best-performed model outperforms state-of-the-art models by achieving an accuracy of 91.37%.
Triple Dual Learning for Opinion-based Explainable Recommendation

Recently, with the aim of enhancing the trustworthiness of recommender systems, explainable recommendation has attracted much attention from the research community. Intuitively, users’ opinions toward different aspects of an item determine their ratings (i.e., users’ preferences) for the item. Therefore, rating prediction from the perspective of opinions can realize personalized explanations at the level of item aspects and user preferences. However, there are several challenges in developing an opinion-based explainable recommendation: (1) The complicated relationship between users’ opinions and ratings. (2) The difficulty of predicting the potential (i.e., unseen) user-item opinions because of the sparsity of opinion information. To tackle these challenges, we propose an overall preference-aware opinion-based explainable rating prediction model by jointly modeling the multiple observations of user-item interaction (i.e., review, opinion, rating). To alleviate the sparsity problem and raise the effectiveness of opinion prediction, we further propose a triple dual learning-based framework with a novelly designed triple dual constraint . Finally, experiments on three popular datasets show the effectiveness and great explanation performance of our framework.
Factorized Recurrent Neural Network with Attention for Language Identification and Content Detection

Language identification and content detection are essential for ensuring effective digital communication, and content moderation. While extensive research has primarily focused on well-known and widely spoken languages, challenges persist when dealing with indigenous and resource-limited languages, especially between closely similar languages such as Ethiopian languages. This article aims to simultaneously identify the language of a given text and detect its content, and to achieve this, we propose a novel attention-based recurrent neural network framework. The proposed method has an attention-embedded Bidirectional-LSTM architecture with two classifiers that identify the language of a given text and content within the text. The two classifiers share a common feature space before they branched at their task-specific layers where both layers are assisted by attention mechanism. We use five different topics in Six Ethiopian Languages the dataset consists of nearly 22,624 sentences. We compared our result with the classical NLP techniques, the proposed method shortened the data prepossessing steps. We evaluated the model performance using the accuracy metric, achieving results of 98.88% for language identification and 96.5% for text content detection. The dataset, source code, and pretrained model are available at https://github.com/bdu-birhanu/LID_TCD .
rHDP: An Aspect Sharing-Enhanced Hierarchical Topic Model for Multi-Domain Corpus

Learning topic hierarchies from a multi-domain corpus is crucial in topic modeling as it reveals valuable structural information embedded within documents. Despite the extensive literature on hierarchical topic models, effectively discovering inter-topic correlations and differences among subtopics at the same level in the topic hierarchy, obtained from multiple domains, remains an unresolved challenge. This article proposes an enhanced nested Chinese restaurant process (nCRP), nCRP+, by introducing an additional mechanism based on Chinese restaurant franchise (CRF) for aspect-sharing pattern extraction in the original nCRP. Subsequently, by employing the distribution extracted from nCRP+ as the prior distribution for topic hierarchy in the hierarchical Dirichlet processes (HDP), we develop a hierarchical topic model for multi-domain corpus, named rHDP. We describe the model with the analogy of Chinese restaurant franchise based on the central kitchen and propose a hierarchical Gibbs sampling scheme to infer the model. Our method effectively constructs well-established topic hierarchies, accurately reflecting diverse parent-child topic relationships, explicit topic aspect sharing correlations for inter-topics, and differences between these shared topics. To validate the efficacy of our approach, we conduct experiments using a renowned public dataset and an online collection of Chinese financial documents. The experimental results confirm the superiority of our method over the state-of-the-art techniques in identifying multi-domain topic hierarchies, according to multiple evaluation metrics.
A novel Hybrid Exhaustive Search and data preparation technique with multi-objective Discrete Hopfield Neural Network

The primary objective in building predictive analytics models is to achieve optimal accuracy with real datasets. The limitations of existing models lie in their storage capacity, which hinders the progress of generating high accuracy. When a model storage capacity is limited, it may struggle to process large datasets and encounter underfitting issues, preventing it from capturing the complexities of the data. Hence, this paper addresses these challenges by introducing a novel approach to predictive analytics, focusing on expanding the storage capacity of the Discrete Hopfield Neural Network (DHNN). First, this paper employs satisfiability logic to represent the attributes of a dataset in the DHNN. This logic representation enables the model to establish a connection between neurons and attributes, enabling efficient information processing. Second, to introduce a multi–objective DHNN, a key innovation that enhances the model’s storage capacity. In this context, a novel training algorithm named Hybrid Exhaustive Search is developed to optimize the DHNN’s training phase. Third, this paper introduces new data preparation techniques, including feature selection and a method for identifying the best–induced logic. This best–induced logic explains and extracts dataset relationships. Finally, the proposed model is evaluated based on four reputable metrics and a variety of datasets primarily collected from the UCI Repository. The performance of the proposed model is compared with three existing models. Through extensive experiments and rigorous evaluation, the proposed model can outperform existing models in all metrics demonstrating the effectiveness of expanded storage capacity and novel data preparation techniques employed.
Selling mode choice and blockchain adoption in an e-commerce platform with information disclosure

Many e-commerce platforms, such as Amazon and JD.com, consider product information disclosure as a crucial retail strategy. However, due to potential consumer distrust in the disclosed information, these platforms may employ blockchain technology to validate the information and alleviate consumers’ doubts. This study presents a game-theoretical model to investigate the interaction among the manufacturer’s choice of selling mode, the platform’s decision regarding blockchain adoption, and information disclosure strategies in the presence of information asymmetry. Optimal pricing and information disclosure strategies are derived, and the impact of various parameters on the equilibrium results is analyzed. We find that the consumers’ privacy concerns and blockchain implementation costs both have negative effects on the equilibrium retail price, disclosed information amount and profits of the manufacturer and platform. Moreover, we also find that the introduction of blockchain has different effects on a platform’s pricing and information disclosure strategies depending on the market size. In addition, conditions are identified under which the manufacturer selects the optimal selling mode and the platform makes an optimal decision on blockchain adoption. The findings demonstrate that the cost of consumers’ privacy concerns and the commission rate play significant roles in determining the equilibrium selling mode selection and blockchain adoption strategies.
SQAB: Specific query anchor boxes for human–object interaction detection

Human-Object Interaction (HOI) detection is the cornerstone of advanced visual understanding, aiming to identify relationships and interactions among different objects in images. Transformer-based previous methods commonly utilize traditional query embeddings to predict HOI, but this approach suffers from slow training convergence. Although recent research defines HOI queries as reference points, their semantic information remains ambiguous, ignoring object scale differences. To address these issues, we propose to use anchor boxes as queries for HOI detection for the first time, which can significantly accelerate the convergence speed. Furthermore, in order to enable anchor boxes to focus on HOI features efficiently, we designed an end-to-end Specific Query Anchor Boxes (SQAB) network. Our method includes a Hierarchical Detection Branch (HDB) and an Interaction Refinement Branch (IRB). Firstly, HDB uses specific query anchor boxes for prediction on multi-scale feature maps and uses relation content queries to associate contextual information. In addition, IRB utilizes multi-scale body part masks to guide the model to focus on key interaction regions effectively between humans and objects, improving the performance of interaction categories. Experimental results show that SQAB performs superior to the baseline, only requiring 25 epochs of the training cycles on the widely used HOI benchmark datasets (V-COCO, HICO-DET, and HOI-A). On the HICO-DET and HOI-A datasets, mean average precision(mAP) increased by approximately 5.99 % and 3.02%, respectively. On the V-COCO dataset, SQAB increases mAP by up to 10.57%.
A fusion of VGG-16 and ViT models for improving bone tumor classification in computed tomography

Bone tumors present significant challenges in orthopedic medicine due to variations in clinical treatment approaches for different tumor types, which includes benign, malignant, and intermediate cases. Convolutional Neural Networks (CNNs) have emerged as prominent models for tumor classification. However, their limited perception ability hinders the acquisition of global structural information, potentially affecting classification accuracy. To address this limitation, we propose an optimized deep learning algorithm for precise classification of diverse bone tumors.Our dataset comprises 786 computed tomography (CT) images of bone tumors, featuring sections from two distinct bone species, namely the tibia and femur. Sourced from The Second Affiliated Hospital of Fujian Medical University, the dataset was meticulously preprocessed with noise reduction techniques. We introduce a novel fusion model, VGG16-ViT, leveraging the advantages of the VGG-16 network and the Vision Transformer (ViT) model. Specifically, we select 27 features from the third layer of VGG-16 and input them into the Vision Transformer encoder for comprehensive training. Furthermore, we evaluate the impact of secondary migration using CT images from Xiangya Hospital for validation.The proposed fusion model demonstrates notable improvements in classification performance. It effectively reduces the training time while achieving an impressive classification accuracy rate of 97.6%, marking a significant enhancement of 8% in sensitivity and specificity optimization. Furthermore, the investigation into secondary migration's effects on experimental outcomes across the three models reveals its potential to enhance system performance.Our novel VGG-16 and Vision Transformer joint network exhibits robust classification performance on bone tumor datasets. The integration of these models enables precise and efficient classification, accommodating the diverse characteristics of different bone tumor types. This advancement holds great significance for the early detection and prognosis of bone tumor patients in the future.
Evaluation of Perceptron Learning Algorithm for Day-Ahead Photovoltaic Power Forecasting

The output power of a photovoltaic system depends on weather conditions. It’s well established that the power output efficiencies from solar photovoltaic (PV) systems is very much dependent on weather parameters and deposited solid particles on the panel surface. This paper aims to address the demand for a more accurate prediction of their operational performance in order to maximise their contribution to the overall energy mix. This research applies Perceptron Learning Algorithm to forecast the day-head PV generation capacity whilst considering future and historical meteorological parameters and system data. Model evaluation results, for an ensuing 24-hour ahead period of interest, achieved forecast accuracy in excess of 90% compared to the recorded power from an installed campus-based micro-grid array.