Reviewerly/exHarmony huggingface dataset:
Developers’ Perception of GitHub Actions: A Survey Analysis

GitHub Actions is a powerful tool for automating workflows on GitHub repositories, with thousands of Actions currently available on the GitHub Marketplace. So far, the research community has conducted mining studies on Actions, with much of the focus on CI/CD. However, the motivation and best practices of developers for using, developing, and debugging Actions are unknown. To address this gap, we conducted a survey study with 90 Action users and developers. Our findings indicate that developers prefer Actions with verified creators and more stars when choosing between similar Actions, and often switch to alternative Actions when faced with bugs or a lack of documentation. We also found that developers find the composition of YAML files, which are essential for Action integration, challenging and error-prone. They primarily rely on Q&A forums to fix issues with these YAML files. Finally, we observed that developers would not likely adopt Actions when there are concerns around complexity and security risks. Our study summarizes developers’ perceptions, decision-making process, and challenges in using, developing, and debugging Actions. We provide recommendations for improving the visibility, re-usability, documentation, and support surrounding GitHub Actions.
Large Language Models in the Workplace: A Case Study on Prompt Engineering for Job Type Classification

This case study investigates the task of job classification in a real-world setting, where the goal is to determine whether an English-language is appropriate for a graduate or entry-level position. We explore multiple approaches to text classification, including supervised approaches such as traditional models like Support Vector Machines (SVMs) and state-of-the-art deep learning methods such as DeBERTa. We compare them with Large Language Models (LLMs) used in both few-shot and zero-shot classification settings. To accomplish this task, we employ prompt engineering, a technique that involves designing prompts to guide the LLMs towards the desired output. Specifically, we evaluate the performance of two commercially available state-of-the-art GPT-3.5-based language models, text-davinci-003 and gpt-3.5-turbo. We also conduct a detailed analysis of the impact of different aspects of prompt engineering on the model's performance. Our results show that, with a well-designed prompt, a zero-shot gpt-3.5-turboclassifier outperforms all other models, achieving a 6% increase in Precision@95% Recall compared to the best supervised approach. Furthermore, we observe that the wording of the prompt is a critical factor in eliciting the appropriate "reasoning" in the model, and that seemingly minor aspects of the prompt significantly affect the model's performance.
Self-Sovereign Identity Management for Hierarchical Federated Learning in Vehicular Networks

There has been a rapid increase in the number of connected vehicles with a huge amount of data exchange between these vehicles that needs to be communicated, processed and analyzed reliably and efficiently. For secure and decentralized authentication, self-sovereign identity (SSI) management in vehicular networks have attracted attention in recent years. Hierarchical deployment frameworks, on the other hand, can provide secure and efficient knowledge sharing for vehicular networks with heterogeneous and geographically distributed vehicles and infrastructure in 6G networks. In this paper, we explore the joint use of hierarchical federated learning, as a collaborative machine learning framework, and hierarchical SSI management in vehicular networks, highlighting its advantages, limitations. At the end of the paper, we also provide two illustrative use cases.
Artificial intelligence-assisted mindfulness in tourism, hospitality, and events

Purpose Following the increasing trend of artificial intelligence (AI) research in hospitality literature, this critical reflection paper aims to identify AI-assisted mindfulness as a critical yet under-investigated issue and to contribute feasible directions for future research. Design/methodology/approach The authors first conceptualize a framework explaining the effects of mindfulness design in AI interventions on improving human mindfulness. The authors then identify opportunities for interventions in AI-assisted mindfulness for the tourism, hospitality and events industries. Finally, the authors propose potential themes for AI-assisted mindfulness research. Findings This study contributes three major conceptual works. First, we conceptualize a framework of AI-assisted mindfulness, showcasing that the scope of AI-assisted mindfulness spans from AI interventions to state mindfulness and then to trait mindfulness. Second, the authors offer two approaches to strategic thinking, one from mindfulness (i.e. mindfulness-focused niche markets and activities) and one from AI applications (i.e. AI-facilitated devices and platforms), to identify opportunities for AI-assisted mindfulness interventions. Third, for both management- and marketing-oriented AI-assisted mindfulness research, the authors propose 18 themes. Research limitations/implications This critical reflection paper offers directions for future knowledge creation in AI-assisted mindfulness in the tourism, hospitality and events industries. Originality/value To the best of the authors’ knowledge, this critical reflection paper serves as the first in hospitality and tourism literature to systematically propose the research issue of AI-assisted mindfulness, offering directions and themes for future research.
Partial Decision Tree Forest: A Machine Learning Model for the Geosciences

As a result of the continuous growth in the amount of geological data, machine learning (ML) offers an opportunity to contribute to solving problems in geosciences. However, digital geology applications introduce new challenges for machine learning due to the unique geoscience properties encountered in each problem, requiring novel research in ML. This paper proposes a novel machine learning method, entitled “Partial Decision Tree Forest (PART Forest)”, to overcome these challenges introduced by geoscience problems and offers potential advancements in both machine learning and geoscience disciplines. The effectiveness of the proposed PART Forest method was illustrated in mineral classification. This study aims to build an intelligent ML model that automatically classifies the minerals in terms of their crystal structures (triclinic, monoclinic, orthorhombic, tetragonal, hexagonal, and trigonal) by taking into account their chemical compositions and their physical and optical properties. In the experiments, the proposed PART Forest method demonstrated its superiority over one of the well-known ensemble learning methods, random forest, in terms of accuracy, precision, recall, f-score, and AUC (area under the curve) metrics.
Improved Differential Cryptanalysis on SPECK Using Plaintext Structures

Plaintext structures are a commonly-used technique for improving differential cryptanalysis. Generally, there are two types of plaintext structures: multiple-differential structures and truncated-differential structures. Both types have been widely used in cryptanalysis of S-box-based ciphers while for SPECK, an Addition-Rotation-XOR (ARX) cipher, the truncated-differential structure has not been used so far. In this paper, we investigate the properties of modular addition and propose a method to construct truncated-differential structures for SPECK. Moreover, we show that a combination of both types of structures is also possible for SPECK. For recovering the key of SPECK, we propose dedicated algorithms and apply them to various differential distinguishers, which helps to obtain a series of improved attacks on all variants of SPECK. The results show that the combination of both structures helps to improve the data and time complexity at the same time, as in the cryptanalysis of S-box-based ciphers.
Gains from the transition from face-to-face to digital/online modality to improve education in emergency

Education during emergencies demands attention from both institutions and governments, as the incidence of disasters and crises continues to increase worldwide due to various challenges that threaten humanity, including climate change, terrorism, food and water scarcity, energy crises, poverty, and inequality. Emergency remote teaching (ERT) is a critical factor in providing academic opportunities, building resilience, and promoting recovery for affected individuals. This research examines the challenges associated with transitioning from face-to-face to online/digital lessons, according to the perceptions of 105 professors in a Mexican private university, 2 months after mandatory online/digital teaching was implemented. By analyzing data collected through a survey using statistical and data mining techniques, we identified factors that should be considered when designing future education in emergency situations. The study found that while most professors positively evaluated their performance in ERT, they still preferred face-to-face teaching due to the lack of personal interaction and distractions. Professors adapted to ERT with technological training provided by the institution, but reported an increase in workload and the importance of the balance between job responsibilities and family issues. The study also revealed that professors’ performance in ERT depended on the conditions of their teaching space at home. Furthermore, the study suggested the need for the development of an improved assessment framework for online evaluation methods, and integration of the most effective online and face-to-face teaching strategies for everyday academic contexts. The sudden change in education modality represented a fantastic opportunity to introduce teaching-learning-evaluating strategies and technologies while forcing institutions to evolve into the digital environment. Therefore, every education stakeholder should identify and implement best practices of the online and face-to-face modalities to improve future education in emergencies.
Policy-Based Spam Detection of Tweets Dataset

Spam communications from spam ads and social media platforms such as Facebook, Twitter, and Instagram are increasing, making spam detection more popular. Many languages are used for spam review identification, including Chinese, Urdu, Roman Urdu, English, Turkish, etc.; however, there are fewer high-quality datasets available for Urdu. This is mainly because Urdu is less extensively used on social media networks such as Twitter, making it harder to collect huge volumes of relevant data. This paper investigates policy-based Urdu tweet spam detection. This study aims to collect over 1,100,000 real-time tweets from multiple users. The dataset is carefully filtered to comply with Twitter’s 100-tweet-per-hour limit. For data collection, the snscrape library is utilized, which is equipped with an API for accessing various attributes such as username, URL, and tweet content. Then, a machine learning pipeline consisting of TF-IDF, Count Vectorizer, and the following machine learning classifiers: multinomial naïve Bayes, support vector classifier RBF, logical regression, and BERT, are developed. Based on Twitter policy standards, feature extraction is performed, and the dataset is separated into training and testing sets for spam analysis. Experimental results show that the logistic regression classifier has achieved the highest accuracy, with an F1-score of 0.70 and an accuracy of 99.55%. The findings of the study show the effectiveness of policy-based spam detection in Urdu tweets using machine learning and BERT layer models and contribute to the development of a robust Urdu language social media spam detection method.
A mixed-categorical correlation kernel for Gaussian process

Recently, there has been a growing interest for mixed-categorical meta-models based on Gaussian process (GP) surrogates. In this setting, several existing approaches use different strategies either by using continuous kernels (e.g., continuous relaxation and Gower distance based GP) or by using a direct estimation of the correlation matrix. In this paper, we present a kernel-based approach that extends continuous exponential kernels to handle mixed-categorical variables. The proposed kernel leads to a new GP surrogate that generalizes both the continuous relaxation and the Gower distance based GP models. We demonstrate, on both analytical and engineering problems, that our proposed GP model gives a higher likelihood and a smaller residual error than the other kernel-based state-of-the-art models. Our method is available in the open-source software SMT.
Multi-view intuitionistic fuzzy support vector machines with insensitive pinball loss for classification of noisy data

Multi-view support vector machines (MvSVMs) have been widely used to solve multi-view classification problems. However, the conventional MvSVMs often overlook the presence of noise and outliers that commonly exist in the original data. In this paper, we propose two novel multi-view intuitionistic fuzzy support vector machines with insensitive pinball loss that can not only handle the general multi-view classification problems but also be robust to noisy data. In the proposed convex optimization models, the pinball loss is incorporated into the multi-view learning, which enables the maximization of the quantization distance. Moreover, to utilize multi-view information more effectively, the intuitionistic fuzzy score is introduced to assign a weight to each multi-view sample. The intuitionistic fuzzy score combines the membership and non-membership functions, which provides an efficient mechanism to assign weights to the multi-view samples. Further, we provide a discussion of the proposed models with the state-of-the-art technologies. Experiments are conducted on a series of datasets, and the results show that the proposed convex models outperform several state-of-the-art models. The code is available at 1.
Semantic-guided graph neural network for heterogeneous graph embedding

Heterogeneous Graph Neural Network (HGNN) has shown a great promising in embedding complex structural and semantic information of heterogeneous graph. However, many popular HGNNs fail to capture the meaningful characteristics to distinguish heterogeneous nodes, known as the semantic confusion problem. In this paper, we hold that semantic confusion problem can be addressed by jumping knowledge toward semantics enrichment, and propose a general framework for heterogeneous graph embedding named Semantic-guided Graph Neural Network (SGNN). Here, we develop novel two-level fusion mechanisms in both node and semantic aggregation. In the node-level, we aggregate the local neighbors with jumping knowledge to learn an enhanced local representation. In the semantic-level, we maximize the common representation to extract the jumping knowledge from multiple semantics in latent space. The common representation is injected into semantic-level aggregation as jumping knowledge to guide the model to pay more attention on target semantics. In the end, the semantic confusion problem is shown to be alleviated in the two-level semantic-guided aggregation framework of SGNN through both theory and experiments. Experimental results demonstrate that SGNN gains highest results in real-world tasks. We also perform validation experiments to evaluate the effectiveness of aggregation mechanism towards semantic confusion in each level, and the results show superiority of SGNN.
A novel content-based image retrieval system with feature descriptor integration and accuracy noise reduction

Efficient algorithms, intelligent approaches, and intensive use of computers are crucial to extracting reliable information from large-scale data sets. It is the case, for example, in retrieving information from multimedia data. As different systems integrate individual electronic devices such as smartphones and cameras into storage, sharing, and social media platforms, the amount of multimedia data has increased dramatically in recent years. Therefore, content-based image retrieval (CBIR) is quite challenging. In this paper, we introduce a new image descriptors integration to represent the visual attributes of images. Furthermore, we noticed a pattern between different CBIR systems in which the first images retrieved are more likely to be assertive than those in the middle and final positions. Then, we investigated the interactions between the first images retrieved from CBIR systems to present a novel method for reducing accuracy noise. The performance of the proposed method is evaluated in different data sets (Corel-1K, Corel-5K, Corel-10K, GHIM-10K) and compared to related works. The experimental results demonstrate that the proposal consistently retrieves images with similar content. The code prepared by the authors is publicly available.
Enhancing the security of image steganography via multiple adversarial networks and channel attention modules

Existing steganographic methods based on adversarial images can only design adversarial images for a single steganalyzer and cannot resist detection from the latest steganalyzers using convolutional neural networks such as SRNet and Zhu-Net. To address this issue, this paper proposes a novel method for enhancing the security of image steganography using multiple adversarial networks and channel attention modules. The proposed method employs generative adversarial networks based on the U-Net structure to generate high-quality adversarial images and uses the self-learning properties of the adversarial networks to iteratively optimize the parameters of multiple adversarial steganographic networks. This process generates high-quality adversarial images capable of misleading multiple steganalyzers. Additionally, the proposed scheme adaptively adjusts the distribution of adversarial noise in the original image using multiple lightweight channel attention modules in the generator, thus enhancing the anti-steganalysis ability of adversarial images. Furthermore, the proposed method utilizes multiple discrimination losses and MSE loss, dynamically combined to improve the quality of adversarial images and facilitate the network's rapid and stable convergence. Extensive experimental results demonstrate that the proposed algorithm can generate adversarial images with an average PSNR of 40.3 dB, and the success rate of misleading the advanced steganalyzers is over 93%. The security and generalization of the algorithm we propose exceed those of the compared steganographic methods.
Collaborative recommendation model based on multi-modal multi-view attention network: Movie and literature cases

The existing collaborative recommendation models that use multi-modal information emphasize the representation of users' preferences but easily ignore the representation of users' dislikes. Nevertheless, modelling users' dislikes facilitates comprehensively characterizing user profiles. Thus, the representation of users' dislikes should be integrated into the user modelling when we construct a collaborative recommendation model. In this paper, we propose a novel Collaborative Recommendation Model based on Multi-modal multi-view Attention Network (CRMMAN), in which the users are represented from both preference and dislike views. Specifically, the users' historical interactions are divided into positive and negative interactions, used to model the user's preference and dislike views, respectively. Furthermore, the semantic and structural information extracted from the scene is employed to enrich the item representation. We validate CRMMAN by designing contrast experiments based on two benchmark MovieLens-1M and Book-Crossing datasets. Movielens-1 m has about a million ratings, and Book-Crossing has about 300,000 ratings. Compared with the state-of-the-art knowledge-graph-based and multi-modal recommendation methods, the AUC, NDCG@5 and NDCG@10 are improved by 2.08%, 2.20% and 2.26% on average of two datasets. We also conduct controlled experiments to explore the effects of multi-modal information and multi-view mechanism. The experimental results show that both of them enhance the model's performance.
OpenMetaRec: Open-metapath heterogeneous dual attention network for paper recommendation

Paper recommendation based on author preferences has gained widespread attention. Current methods focus on the heterogeneous information network (HIN) to infer correlation clues based on the metapath with single-type association (closed-metapath). However, the closed-metapath approach only considers sparse correlations between a few type nodes, which brings great challenges for preference learning. To address this issue, we propose a novel metapath with multiple-type correlations (open-metapath) approach for paper recommendation to capture rich correlations between various type nodes in HIN. First, the academic heterogeneous information network is transformed into open-metapaths to acquire whole semantic correlations among varying type nodes, enriching sparse associations. Then, neighbor attention is designed to explore highly relevant structural and semantic information, which initializes author preferences and paper features. Next, metapath attention is constructed to measure the impact intensity of open-metapaths and distinguish their representations. Finally, static and interactive features are fused to learn vectors of authors and papers, providing desired papers. Substantial experiments demonstrate that the proposed model outperforms other existing models significantly.
A hybrid biogeography-based optimization algorithm to solve high-dimensional optimization problems and real-world engineering problems

According to our extensive investigation, Biogeography-based optimization (BBO) and its variants have not been applied to solve high-dimensional optimization problems. To make a breakthrough in this field, a new BBO variant with hybrid migration operator and feedback differential evolution mechanism, HFBBO, is proposed. Firstly, the example learning method is used to ensure the inferior solutions cannot destroy the superior solutions. Secondly, the hybrid migration operator is presented to balance the exploration and exploitation. It enables the algorithm to switch freely between local search and global search. Finally, the feedback differential evolution mechanism is designed to replace the random mutation operator. HFBBO can select the mutation mode intelligently by this mechanism to avoid getting stuck in local optima. Meanwhile, the Markov model is established to prove the convergence of HFBBO, and the complexity is also discussed. A series experiments are carried out on 24 benchmark functions, CEC2017 test suite and 12 real-world engineering problems. The results of the Wilcoxon's rank-sum test and Friedman's test show that HFBBO has better competitiveness and stability than the 27 compared algorithms. Furtherly, the performance of HFBBO is compared on 1000, 2000, 5000 and 10000 dimensions, respectively. Experimental results show that this method can effectively solve high-dimensional optimization problems.