Reviewerly/exHarmony huggingface dataset:
Leveraging Semantic Text Analysis to Improve the Performance of Transformer-Based Relation Extraction

Authors:
raw_author_name: Majid Latifi
raw_affiliation_string: Department of Computer Science, University of York, Deramore Lane, York YO10 5GH, UK
raw_author_name: Mominul Ahsan
raw_affiliation_string: Department of Computer Science, University of York, Deramore Lane, York YO10 5GH, UK

Abstract: Keyword extraction from Knowledge Bases underpins the definition of relevancy in Digital Library search systems. However, it is the pertinent task of Joint Relation Extraction, which populates the Knowledge Bases from which results are retrieved. Recent work focuses on fine-tuned, Pre-trained Transformers. Yet, F1 scores for scientific literature achieve just 53.2, versus 69 in the general domain. The research demonstrates the failure of existing work to evidence the rationale for optimisations to finetuned classifiers. In contrast, emerging research subjectively adopts the common belief that Natural Language Processing techniques fail to derive context and shared knowledge. In fact, global context and shared knowledge account for just 10.4% and 11.2% of total relation misclassifications, respectively. In this work, the novel employment of semantic text analysis presents objective challenges for the Transformer-based classification of Joint Relation Extraction. This is the first known work to quantify that pipelined error propagation accounts for 45.3% of total relation misclassifications, the most poignant challenge in this domain. More specifically, Part-of-Speech tagging highlights the misclassification of complex noun phrases, accounting for 25.47% of relation misclassifications. Furthermore, this study identifies two limitations in the purported bidirectionality of the Bidirectional Encoder Representations from Transformers (BERT) Pre-trained Language Model. Firstly, there is a notable imbalance in the misclassification of right-to-left relations, which occurs at a rate double that of left-to-right relations. Additionally, a failure to recognise local context through determiners and prepositions contributes to 16.04% of misclassifications. Furthermore, it is highlighted that the annotation scheme of the singular dataset utilised in existing research, Scientific Entities, Relations and Coreferences (SciERC), is marred by ambiguity. Notably, two asymmetric relations within this dataset achieve recall rates of only 10% and 29%.
Claude 2.0 Large Language Model: tackling a real-world classification problem with a new Iterative Prompt Engineering approach

Authors:
raw_author_name: Loredana Caruccio
raw_affiliation_string: Department of Computer Science, University of Salerno, Fisciano, Salerno, Italy
raw_author_name: Stefano Cirillo
raw_affiliation_string: Department of Computer Science, University of Salerno, Fisciano, Salerno, Italy
raw_author_name: Giuseppe Polese
raw_affiliation_string: Department of Computer Science, University of Salerno, Fisciano, Salerno, Italy
raw_author_name: Giandomenico Solimando
raw_affiliation_string: Department of Computer Science, University of Salerno, Fisciano, Salerno, Italy
raw_author_name: Genoveffa Tortora
raw_affiliation_string: Department of Computer Science, University of Salerno, Fisciano, Salerno, Italy

Abstract: In the last year, Large Language Models (LLMs) have transformed the way of tackling problems, opening up new perspectives in various works and research fields,due to their ability to generate and understand human languages. In this regard, the recent release of Claude 2.0 has contributed to the processing of more complex prompts. In this scenario, the goal of this paper is to evaluate the effectiveness of Claude 2.0 in a specific classification task. In particular, we considered the Forest cover-type problem, concerning the prediction of a cover-type value according to the geospatial characterization of target worldwide areas. To this end, we propose a novel iterative prompt template engineering approach, which integrates files by exploiting prompts and evaluates the quality of responses provided by the LLM. Moreover, we conducted several comparative analyses to evaluate the effectiveness of Claude 2.0 with respect to online and batch learning models. The results demonstrated that, although some online and batch models performed better than Claude 2.0, the new iterative prompt engineering approach improved the quality of responses, leading to better performance with increases ranging from 14% to 32% in terms of accuracy, precision, recall, and F1-score.
CoDC: Accurate Learning with Noisy Labels via Disagreement and Consistency

Authors:
raw_author_name: Yongfeng Dong
raw_affiliation_string: Hebei Engineering Research Center of Data-Driven Industrial Intelligent, Hebei University of Technology, Tianjin 300401, China; Hebei Province Key Laboratory of Big Data Computing, Hebei University of Technology, Tianjin 300401, China; School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
raw_author_name: Jiawei Li
raw_affiliation_string: Hebei Engineering Research Center of Data-Driven Industrial Intelligent, Hebei University of Technology, Tianjin 300401, China; Hebei Province Key Laboratory of Big Data Computing, Hebei University of Technology, Tianjin 300401, China; School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
raw_author_name: Zhen Wang
raw_affiliation_string: Hebei Engineering Research Center of Data-Driven Industrial Intelligent, Hebei University of Technology, Tianjin 300401, China; Hebei Province Key Laboratory of Big Data Computing, Hebei University of Technology, Tianjin 300401, China; School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China

Abstract: Inspired by the biological nervous system, deep neural networks (DNNs) are able to achieve remarkable performance in various tasks. However, they struggle to handle label noise, which can poison the memorization effects of DNNs. Co-teaching-based methods are popular in learning with noisy labels. These methods cross-train two DNNs based on the small-loss criterion and employ a strategy using either "disagreement" or "consistency" to obtain the divergence of the two networks. However, these methods are sample-inefficient for generalization in noisy scenarios. In this paper, we propose CoDC, a novel Co-teaching-basedmethod for accurate learning with label noise via both Disagreement and Consistency strategies. Specifically, CoDC maintains disagreement at the feature level and consistency at the prediction level using a balanced loss function. Additionally, a weighted cross-entropy loss is proposed based on information derived from the historical training process. Moreover, the valuable knowledge involved in "large-loss" samples is further developed and utilized by assigning pseudo-labels. Comprehensive experiments were conducted on both synthetic and real-world noise and under various noise types. CoDC achieved 72.81% accuracy on the Clothing1M dataset and 76.96% (Top1) accuracy on the WebVision1.0 dataset. These superior results demonstrate the effectiveness and robustness of learning with noisy labels.
RII-GAN: Multi-scaled Aligning-Based Reversed Image Interaction Network for Text-to-Image Synthesis

Authors:
raw_author_name: Haofei Yuan
raw_affiliation_string: School of Information Science and Engineering, East China University of Science and Technology, No.130 Meilong Road, Shanghai, 200237, China
raw_author_name: Hongqing Zhu
raw_affiliation_string: School of Information Science and Engineering, East China University of Science and Technology, No.130 Meilong Road, Shanghai, 200237, China
raw_author_name: Suyi Yang
raw_affiliation_string: Department of Mathematics, Natural, Mathematical and Engineering Sciences, King’s College London, Strand, London, WC2R 2LS, England, UK
raw_author_name: Ziying Wang
raw_affiliation_string: School of Information Science and Engineering, East China University of Science and Technology, No.130 Meilong Road, Shanghai, 200237, China
raw_author_name: Nan Wang
raw_affiliation_string: School of Information Science and Engineering, East China University of Science and Technology, No.130 Meilong Road, Shanghai, 200237, China

Abstract: Abstract The text-to-image (T2I) model based on a single-stage generative adversarial network (GAN) has significantly succeeded in recent years. However, the generation model based on GAN has two disadvantages: the generator does not introduce any image feature manifold structure, which makes it challenging to align the image and text features. Another is the image’s diversity; the text’s abstraction will prevent the model from learning the actual image distribution. This paper proposes a reversed image interaction generative adversarial network (RII-GAN), which consists of four components: text encoder, reversed image interaction network (RIIN), adaptive affine-based generator, and dual-channel feature alignment discriminator (DFAD). RIIN indirectly introduces the actual image distribution into the generation network, thus overcoming the problem that the network lacks the learning of the actual image feature manifold structure and generating the distribution of text-matching images. Each adaptive affine block (AAB) in the proposed affine-based generator can adaptively enhance text information, establishing an updated relation between original independent fusion blocks and the image feature. Moreover, this study designs a DFAD to capture important feature information of images and text in two channels. Such a dual-channel backbone improves semantic consistency by utilizing a particular synchronized bi-modal information extraction structure. We have performed experiments on publicly available datasets to prove the effectiveness of our model.
Computer intelligent simulation based on wireless sensor networks application in big data financial management

Authors:
raw_author_name: Ruipeng Gu
raw_affiliation_string: Business Department, Suzhou Vocational University, Suzhou, 215000, Jiangsu, China

Abstract: The advent of big data has revolutionized financial management by providing a more comprehensive and accurate data analysis platform. This transformation necessitates innovation and reform in traditional financial management practices to effectively address the challenges posed by big data. In this study, a simulation model is proposed that combines high-resolution imaging algorithms and computer technology to analyze the intricacies of big data financial management. By collecting and processing vast amounts of financial data, the simulation model employs high-resolution imaging algorithms for data analysis and pattern recognition. This enables the refinement and control of financial management through precise analysis and prediction. The results demonstrate that the simulation model, based on high-resolution imaging algorithms, yields more accurate financial data analysis and prediction, empowering companies to make well-informed decisions. The utilization of this method significantly enhances the efficiency and accuracy of financial management, reducing manual errors and decision deviations. Compared to traditional financial management methods, this approach promotes the refinement and control of financial management, thereby propelling the development of modern company management.
Visual search patterns during exploration of naturalistic scenes are driven by saliency cues in individuals with cerebral visual impairment

Authors:
raw_author_name: Kerri Walter
raw_affiliation_string: Translational Vision Lab, Department of Psychology, Northeastern University, Boston, MA, USA
raw_author_name: Claire E. Manley
raw_affiliation_string: The Laboratory for Visual Neuroplasticity, Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, 20 Staniford Street, Boston, MA, 02114, USA
raw_author_name: Peter J. Bex
raw_affiliation_string: Translational Vision Lab, Department of Psychology, Northeastern University, Boston, MA, USA
raw_author_name: Lotfi B. Merabet
raw_affiliation_string: The Laboratory for Visual Neuroplasticity, Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, 20 Staniford Street, Boston, MA, 02114, USA

Abstract: Abstract We investigated the relative influence of image salience and image semantics during the visual search of naturalistic scenes, comparing performance in individuals with cerebral visual impairment (CVI) and controls with neurotypical development. Participants searched for a prompted target presented as either an image or text cue. Success rate and reaction time were collected, and gaze behavior was recorded with an eye tracker. A receiver operating characteristic (ROC) analysis compared the distribution of individual gaze landings based on predictions of image salience (using Graph-Based Visual Saliency) and image semantics (using Global Vectors for Word Representations combined with Linguistic Analysis of Semantic Salience) models. CVI participants were less likely and were slower in finding the target. Their visual search behavior was also associated with a larger visual search area and greater number of fixations. ROC scores were also lower in CVI compared to controls for both model predictions. Furthermore, search strategies in the CVI group were not affected by cue type, although search times and accuracy showed a significant correlation with verbal IQ scores for text-cued searches. These results suggest that visual search patterns in CVI are driven mainly by image salience and provide further characterization of higher-order processing deficits observed in this population.
Online neuro-fuzzy model learning of dynamic systems with measurement noise

Authors:
raw_author_name: Wen Gu
raw_affiliation_string: Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough, LE11 3TU, UK
raw_author_name: Jianglin Lan
raw_affiliation_string: James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK
raw_author_name: Byron Mason
raw_affiliation_string: Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough, LE11 3TU, UK

Abstract: Abstract Model identification of nonlinear time varying dynamic systems is challenging because the system behaviours may vary significantly in different operational conditions. If the changes are insufficiently captured by training data, the trained model is unable to capture the system response well when the operational condition changes. The model performance may also be deteriorated in real-time implementation due to the noise in sensors or the environment. This paper presents a self-adaptive Neuro-Fuzzy (NF) modelling framework to address these challenges. The NF model, trained offline based on experimental data, combines the Auto-Regressive with eXogenous (ARX) models and Gaussian activation functions to capture the nonlinear system behaviours. During online implementation, the ARX model parameters are updated using new data through a recursive generalised least squares method, which embeds a noise model to eliminate effects of the noise. The online updating algorithm has a provable convergence guarantee and enables the proposed NF model to adapt to changes in system behaviours automatically. Efficacy of the algorithm is verified through two numerical examples and an experiment on a commercial automotive engine.
Zk-SNARKs-Based Anonymous Payment Channel in Blockchain

Authors:
raw_author_name: Yunwei Guo
raw_affiliation_string: School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
raw_author_name: Haochen Liang
raw_affiliation_string: School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China
raw_author_name: Liehuang Zhu
raw_affiliation_string: School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China
raw_author_name: Keke Gai
raw_affiliation_string: School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China

Abstract: Payment channels serve as an effective solution to the scalability problem of cryptocurrencies, which significantly increase transaction rates by allowing users to conduct large-scale offline transactions off-chain without posting everything to the blockchain. However, the existing payment channels lack privacy protection for the transaction amount and the linking relationship between the two parties to the transaction. Therefore, in order to address the scalability and privacy issues of cryptocurrencies such as Bitcoin, this paper proposes a zk-SNARKs-based anonymous payment channel (zk-APC), which supports an unlimited number of off-chain payments between the payer and the payee and protects the privacy of the participants. Specifically, the proposed scheme achieves relational anonymity and amount privacy for both on-chain and off-chain transactions in the payment channel through utilizing zero-knowledge proof (zk-SNARKs) and commitment schemes. This paper proves that the proposed method is more effective than similar schemes through a performance evaluation.
Learning and Repair of Deep Reinforcement Learning Policies from Fuzz-Testing Data

Authors:
raw_author_name: Martin Tappler
raw_affiliation_string: Institute of Software Technology, Graz University of Technology, Graz, Austria TU Graz - SAL, DES Lab, Silicon Austria Labs, Graz, Austria
raw_author_name: Andrea Pferscher
raw_affiliation_string: Institute of Software Technology, Graz University of Technology, Graz, Austria
raw_author_name: Bernhard K. Aichernig
raw_affiliation_string: Institute of Software Technology, Graz University of Technology, Graz, Austria
raw_author_name: Bettina Könighofer
raw_affiliation_string: Institute of Applied Information Processing and Communications, Graz University of Technology, Graz, Austria

Abstract: Reinforcement learning from demonstrations (RLfD) is a promising approach to improve the exploration efficiency of reinforcement learning (RL) by learning from expert demonstrations in addition to interactions with the environment. In this paper, we propose a framework that combines techniques from search-based testing with RLfD with the goal to raise the level of dependability of RL policies and to reduce human engineering effort. Within our framework, we provide methods for efficiently training, evaluating, and repairing RL policies. Instead of relying on the costly collection of demonstrations from (human) experts, we automatically compute a diverse set of demonstrations via search-based fuzzing methods and use the fuzz demonstrations for RLfD. To evaluate the safety and robustness of the trained RL agent, we search for safety-critical scenarios in the black-box environment. Finally, when unsafe behavior is detected, we compute demonstrations through fuzz testing that represent safe behavior and use them to repair the policy. Our experiments show that our framework is able to efficiently learn high-performing and safe policies without requiring any expert knowledge.
Toward Automatically Completing GitHub Workflows

Authors:
raw_author_name: Antonio Mastropaolo
raw_affiliation_string: Software Institute, Universita della Svizzera italiana, Lugano, Switzerland
raw_author_name: Fiorella Zampetti
raw_affiliation_string: University of Sannio, Benevento, Italy
raw_author_name: Gabriele Bavota
raw_affiliation_string: Software Institute, Universita della Svizzera Italiana, Lugano, Switzerland
raw_author_name: Massimiliano Di Penta
raw_affiliation_string: University of Sannio, Benevento, Italy

Abstract: Continuous integration and delivery (CI/CD) are nowadays at the core of software development. Their benefits come at the cost of setting up and maintaining the CI/CD pipeline, which requires knowledge and skills often orthogonal to those entailed in other software-related tasks. While several recommender systems have been proposed to support developers across a variety of tasks, little automated support is available when it comes to setting up and maintaining CI/CD pipelines. We present GH-WCOM (GitHub Workflow COMpletion), a Transformer-based approach supporting developers in writing a specific type of CI/CD pipelines, namely GitHub workflows. To deal with such a task, we designed an abstraction process to help the learning of the transformer while still making GH-WCOM able to recommend very peculiar workflow elements such as tool options and scripting elements. Our empirical study shows that GH-WCOM provides up to 34.23% correct predictions, and the model's confidence is a reliable proxy for the recommendations' correctness likelihood.
Fine-SE: Integrating Semantic Features and Expert Features for Software Effort Estimation

Authors:
raw_author_name: Yue Li
raw_affiliation_string: State Key Laboratory of Novel Software Technology, Software Institute, Nanjing University, Nanjing, Jiangsu, China
raw_author_name: Liming Dong
raw_affiliation_string: State Key Laboratory of Novel Software Technology, Software Institute, Nanjing University, Nanjing, Jiangsu, China
raw_author_name: He Zhang
raw_affiliation_string: State Key Laboratory of Novel Software Technology, Software Institute, Nanjing University, Nanjing, Jiangsu, China

Abstract: Reliable effort estimation is of paramount importance to software planning and management, especially in industry that requires effective and on-time delivery. Although various estimation approaches have been proposed (e.g., planning poker and analogy), they may be manual and/or subjective, which are difficult to apply to other projects. In recent years, deep learning approaches for effort estimation that rely on learning expert features or semantic features respectively have been extensively studied and have been found to be promising. Semantic features and expert features describe software tasks from different perspectives, however, in the literature, the best combination of these two features has not been explored to enhance effort estimation. Additionally, there are a few studies that discuss which expert features are useful for estimating effort in the industry. To this end, we investigate the potential 13 expert features that can be used to estimate effort by interviewing 26 enterprise employees. Based on that, we propose a novel model, called Fine-SE, that leverages semantic features and expert features for effort estimation. To validate our model, a series of evaluations are conducted on more than 30,000 software tasks from 17 industrial projects of a global ICT enterprise and four open-source software (OSS) projects. The evaluation results indicate that Fine-SE provides higher performance than the baselines on evaluation measures (i.e., mean absolute error, mean magnitude of relative error, and performance indicator), particularly in industrial projects with large amounts of software tasks, which implies a significant improvement in effort estimation. In comparison with expert estimation, Fine-SE improves the performance of evaluation measures by 32.0%-45.2% in within-project estimation. In comparison with the state-of-the-art models, Deep-SE and GPT2SP, it also achieves an improvement of 8.9%-91.4% in industrial projects. The experimental results reveal the value of integrating expert features with semantic features in effort estimation.