Reviewerly/exHarmony huggingface dataset:
Finding Regions of Counterfactual Explanations via Robust Optimization

Counterfactual explanations (CEs) play an important role in detecting bias and improving the explainability of data-driven classification models. A CE is a minimal perturbed data point for which the decision of the model changes. Most of the existing methods can only provide one CE, which may not be achievable for the user. In this work, we derive an iterative method to calculate robust CEs (i.e., CEs that remain valid even after the features are slightly perturbed). To this end, our method provides a whole region of CEs, allowing the user to choose a suitable recourse to obtain a desired outcome. We use algorithmic ideas from robust optimization and prove convergence results for the most common machine learning methods, including decision trees, tree ensembles, and neural networks. Our experiments show that our method can efficiently generate globally optimal robust CEs for a variety of common data sets and classification models. History: Accepted by Andrea Lodi, Area Editor for Design &amp; Analysis of Algorithms—Discrete. Funding: This work was supported by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek [Grant OCENW.GROOT.2019.015, Optimization for and with Machine Learning (OPTIMAL)]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2023.0153 .
The Temporal and Spatial Characteristics of Wind–Photovoltaic–Hydro Hybrid Power Output Based on a Cloud Model and Copula Function

In a high proportion of wind–photovoltaic–hydro hybrid power systems, fluctuation and dispersion make it difficult to accurately quantify the output characteristics. Therefore, in this study, a cloud model and copula correlation coefficient matrix were constructed for a hybrid power generation system based on the output data. Multiple backward cloud transformation based on the sampling-with-replacement method was proposed to calculate the improved entropy and hyperentropy to analyze the fluctuation range and dispersion degree quantitatively. A similarity index was proposed to evaluate the similarity between wind power, PV power, and hydropower. A suitable copula function was selected, and the Kendall and Spearman coefficients show the correlation relationships of the hybrid systems. The temporal and spatial characteristics of the hybrid systems were analyzed based on the two models. A typical example in Qinghai proved the effectiveness and applicability of the method. The results show that the correlation between photovoltaic power and hydropower is better and that, in summer, hydropower can be used to adjust the output of renewable energy.
Parameter instance learning with enhanced vision transformers for aerial person re‐identification

Summary In an agnostic space environment, aerial person re‐identification (Re‐ID) is a task that the query person may not occur in the gallery set, it is considered a subordinate task within the domain of open‐world person Re‐ID, and is a more challenging and practical application research. The aerial person images, captured by unmanned aerial vehicles, present more significant challenges such as weak appearance features, fewer individual person samples and occlusion due to variations in camera height and viewing angles compared to ground‐level images. Most state‐of‐the‐arts person Re‐ID methods developed for open‐world datasets rely heavily on local convolutional neural networks but exhibit suboptimal performance when directly applied to aerial person Re‐ID tasks. In this article, a parameter instance learning based on vision transformers (ViT) model is introduced for the design of aerial person Re‐ID. Initially, we employ a self‐supervised paradigm grounded in parameter instance discrimination, aiming to capture feature alignment and instance similarity. Subsequently, using labeled training data, we optimize the network model through the calculation of two types of loss functions. Finally, we employ a feature enhancement strategy utilizing zero‐padding and displacement techniques. This strategy effectively and directly enhances the robustness of the ViT model against issues such as occlusion and misalignment. We conducted experiments on a Re‐ID dataset to validate the effectiveness of the method. Our approach achieves a mean average precision of 57.31% and a Rank‐1 accuracy of 65.29% on the aerial person Re‐ID dataset PRAI‐1581.
Adaptive Dual Aggregation Network with Normalizing Flows for Low-Light Image Enhancement

Low-light image enhancement (LLIE) aims to improve the visual quality of images taken under complex low-light conditions. Recent works focus on carefully designing Retinex-based methods or end-to-end networks based on deep learning for LLIE. However, these works usually utilize pixel-level error functions to optimize models and have difficulty effectively modeling the real visual errors between the enhanced images and the normally exposed images. In this paper, we propose an adaptive dual aggregation network with normalizing flows (ADANF) for LLIE. First, an adaptive dual aggregation encoder is built to fully explore the global properties and local details of the low-light images for extracting illumination-robust features. Next, a reversible normalizing flow decoder is utilized to model real visual errors between enhanced and normally exposed images by mapping images into underlying data distributions. Finally, to further improve the quality of the enhanced images, a gated multi-scale information transmitting module is leveraged to introduce the multi-scale information from the adaptive dual aggregation encoder into the normalizing flow decoder. Extensive experiments on paired and unpaired datasets have verified the effectiveness of the proposed ADANF.
Knowledge distillation representation and <scp>DCNMIX</scp> quality prediction‐based Web service recommendation

Summary Web service recommendation as an emerging topic attracts increasing attention due to its important practical significance. As the number of available Web services continues to grow, users face the challenge of searching the most suitable services that meet their specific needs. Quality of service (QoS)‐based service recommendation becomes a popular approach to address this issue. However, existing QoS‐based service recommendation methods are inability to effectively capture valuable content and structural information from services. These methods often rely solely on low‐order explicit feature intersections in QoS information, do not fully utilize the high‐order implicit feature intersections, and ignore the rich semantic information existing in service descriptions and user preferences. To address this problem, this paper proposes a Web service recommendation method via combining knowledge distillation representation and DCNMIX quality prediction. This method combines content‐based and structure‐based service classification and service prediction based on multi‐dimensional service quality information. First, it builds a service relationship network using semantic features extracted from service descriptions. Second, it designs a graph neural network knowledge distillation framework. The teacher model extracts the knowledge of the graph neural network model, and the student model learns the structure‐based and feature‐based prior knowledge of the service relationship network. Then the student model is used to learn the knowledge of the teacher model, classify Web services, and obtain service representations. Finally, based on service representations and multi‐dimensional QoS information, it exploits the DCNMIX model to learn the explicit and implicit features intersections of Web services and obtain the prediction score and ranking of Web services. The experimental results on the ProgrammableWeb dataset show that the proposed method outperforms the state‐of‐the‐art baselines in terms of Recall, F1, Logloss, and AUC_ROC.
Analogue Quantum Simulation with Fixed-Frequency Transmon Qubits

We experimentally assess the suitability of transmon qubits with fixed frequencies and fixed interactions for the realization of analogue quantum simulations of spin systems. We test a set of necessary criteria for this goal on a commercial quantum processor using full quantum process tomography and more efficient Hamiltonian tomography. Significant single qubit errors at low amplitudes are identified as a limiting factor preventing the realization of analogue simulations on currently available devices. We additionally find spurious dynamics in the absence of drive pulses, which we identify with coherent coupling between the qubit and a low dimensional environment. With moderate improvements, analogue simulation of a rich family of time-dependent many-body spin Hamiltonians may be possible.
New independent daily global solar radiation estimation models based on the day number of the year

Solar radiation data are necessary information for the analysis and prediction of certain environmental processes and the planning of related energy projects. Many models have been proposed in the literature for their estimation due to their unavailability in some locations, especially in developing countries. However, peoples are currently finding more accurate and simple models. One type of these models is the day of the year-based (DYB) model, which has as merit over others, the fact that it does not depend on any environmental parameter. In this work, nine existing DYB models are calibrated with Levenberg–Marquardt nonlinear curve fitting technique for the estimation of solar radiation in the regional cities of Cameroon. In addition, two new DYB models are proposed. The results show that one of the two proposed models (M11) best estimates the solar radiation in nine sites and is ranked second in one site with the root mean square error in the range [0.2365; 0.3039], the mean absolute percentage error in the range [0.1873; 0.2348], and the correlation coefficient in the range [0.8332; 0.9164]. The new model M11 is therefore recommended for estimating solar radiation of some sites of Cameroon and in regions with similar climate.
A Machine Learning-Based Pipeline for the Extraction of Insights from Customer Reviews

The efficiency of natural language processing has improved dramatically with the advent of machine learning models, particularly neural network-based solutions. However, some tasks are still challenging, especially when considering specific domains. This paper presents a model that can extract insights from customer reviews using machine learning methods integrated into a pipeline. For topic modeling, our composite model uses transformer-based neural networks designed for natural language processing, vector-embedding-based keyword extraction, and clustering. The elements of our model have been integrated and tailored to better meet the requirements of efficient information extraction and topic modeling of the extracted information for opinion mining. Our approach was validated and compared with other state-of-the-art methods using publicly available benchmark datasets. The results show that our system performs better than existing topic modeling and keyword extraction methods in this task.
AGCN-Domain: Detecting Malicious Domains with Graph Convolutional Network and Attention Mechanism

Domain Name System (DNS) plays an infrastructure role in providing the directory service for mapping domains to IPs on the Internet. Considering the foundation and openness of DNS, it is not surprising that adversaries register massive domains to enable multiple malicious activities, such as spam, command and control (C&amp;C), malware distribution, click fraud, etc. Therefore, detecting malicious domains is a significant topic in security research. Although a substantial quantity of research has been conducted, previous work has failed to fuse multiple relationship features to uncover the deep underlying relationships between domains, thus largely limiting their level of performance. In this paper, we proposed AGCN-Domain to detect malicious domains by combining various relations. The core concept behind our work is to analyze relations between domains according to their behaviors in multiple perspectives and fuse them intelligently. The AGCN-Domain model utilizes three relationships (client relation, resolution relation, and cname relation) to construct three relationship feature graphs to extract features and intelligently fuse the features extracted from the graphs through an attention mechanism. After the relationship features are extracted from the domain names, they are put into the trained classifier to be processed. Through our experiments, we have demonstrated the performance of our proposed AGCN-Domain model. With 10% initialized labels in the dataset, our AGCN-Domain model achieved an accuracy of 94.27% and the F1 score of 87.93%, significantly outperforming other methods in the comparative experiments.
Cyberbullying Text Identification based on Deep Learning and Transformer-based Language Models

In the contemporary digital age, social media platforms like Facebook, Twitter, and YouTube serve as vital channels for individuals to express ideas and connect with others. Despite fostering increased connectivity, these platforms have inadvertently given rise to negative behaviors, particularly cyberbullying. While extensive research has been conducted on high-resource languages such as English, there is a notable scarcity of resources for low-resource languages like Bengali, Arabic, Tamil, etc., particularly in terms of language modeling. This study addresses this gap by developing a cyberbullying text identification system called BullyFilterNeT tailored for social media texts, considering Bengali as a test case. The intelligent BullyFilterNeT system devised overcomes Out-of-Vocabulary (OOV) challenges associated with non-contextual embeddings and addresses the limitations of context-aware feature representations. To facilitate a comprehensive understanding, three non-contextual embedding models GloVe, FastText, and Word2Vec are developed for feature extraction in Bengali. These embedding models are utilized in the classification models, employing three statistical models (SVM, SGD, Libsvm), and four deep learning models (CNN, VDCNN, LSTM, GRU). Additionally, the study employs six transformer-based language models: mBERT, bELECTRA, IndicBERT, XML-RoBERTa, DistilBERT, and BanglaBERT, respectively to overcome the limitations of earlier models. Remarkably, BanglaBERT-based BullyFilterNeT achieves the highest accuracy of 88.04% in our test set, underscoring its effectiveness in cyberbullying text identification in the Bengali language.
Customer engagement and intention to purchase attitudes of generation Z consumers toward emojis in digital marketing communications

Purpose Emojis are quickly becoming a popular new language in social media and marketing. The capability to express emotions and make message understanding easier is one of the primary reasons for using emojis. The aim of this research was to determine the influence of perceived usefulness, perceived ease of use, trust, and involvement on customer engagement due to emojis used in digital marketing communications among Generation Z (Gen Z) in South Africa. Design/methodology/approach Following the descriptive research approach, quantitative research was used in this study. A questionnaire (self-administered) was utilized to test the effectiveness of using emojis among 1,000 young consumers. Structural equation modeling was used to test the hypotheses. Findings The findings of the study yielded positive relationships between the variables, namely between trust and involvement; involvement and the perceived ease of use; involvement and perceived usefulness; perceived ease of use and perceived usefulness; trust and customer engagement; perceived usefulness and customer engagement; involvement and customer engagement; customer engagement and intention to purchase; trust and intention to purchase; and perceived usefulness and intention to purchase. Practical implications This study can help organizations in emerging markets use emojis in their digital marketing communications to engage customers and stimulate intention to purchase among young people, especially the Gen Z cohort, who seek organizations and brands that understand and connect with them. Originality/value By investigating the effects of emojis in digital marketing communications, this study contributes to the customer-centric process and the literature on emoji usage while also involving a credible digital language when communicating with members of Gen Z. By extending TAM, the findings of this study contribute to the TAM literature by demonstrating that emoji usage in digital marketing communications positively influences various attitudinal associations among Gen Z consumers.
Automatic pipeline fault detection using one-dimensional convolutional bidirectional long short-term memory networks with wide first-layer kernels

Pipeline networks are crucial components of modern infrastructure, and ensuring their reliable operation is essential for sustainable development. The percussion-based methods are considered promising for detecting pipeline faults due to their avoidance of constant-contact sensors and ease of implementation. However, the majority of existing percussion-based methods suffer from limitations such as the requirement for manual feature extraction, as well as subpar noise resilience and adaptability. This paper introduces a one-dimensional convolutional bidirectional long short-term memory network with wide first-layer kernels for the classification of percussion-induced acoustic signals, thus achieving automatic identification of pipeline leakage and water deposit conditions. This approach directly extracts features from audio signals using wide first-layer convolutional kernels, eliminating the need for manual feature extraction. Additionally, it employs bidirectional long short-term memory to effectively capture long-term signal dependencies from both past and future contexts. To validate the effectiveness of the method, two case studies were conducted on three groups of pipes. The results show that the proposed method demonstrates superior noise resistance and adaptability compared to other methods, and it also exhibits strong applicability to other percussion signal datasets. Additionally, the impact of different first convolutional kernel sizes on the noise resistance and adaptive performance of the model was investigated, which provides robust guidance for the effective processing of percussion-induced acoustic signals.
Sensorimotor adaptation in virtual reality: Do instructions and body representation influence aftereffects?

Abstract Perturbations in virtual reality (VR) lead to sensorimotor adaptation during exposure, but also to aftereffects once the perturbation is no longer present. An experiment was conducted to investigate the impact of different task instructions and body representation on the magnitude and the persistence of these aftereffects. Participants completed the paradigm of sensorimotor adaptation in VR. They were assigned to one of three groups: control group, misinformation group or arrow group. The misinformation group and the arrow group were each compared to the control group to examine the effects of instruction and body representation. The misinformation group was given the incorrect instruction that in addition to the perturbation, a random error component was also built into the movement. The arrow group was presented a virtual arrow instead of a virtual hand. It was hypothesised that both would lead to a lower magnitude and persistence of the aftereffect because the object identity between hand and virtual representation would be reduced, and errors would be more strongly attributed to external causes. Misinformation led to lower persistence, while the arrow group showed no significant differences compared to the control group. The results suggest that information about the accuracy of the VR system can influence the aftereffects, which should be considered when developing VR instructions. No effects of body representation were found. One possible explanation is that the manipulated difference between abstract and realistic body representation was too small in terms of object identity.
AI-Powered Innovation in Digital Transformation: Key Pillars and Industry Impact

Digital transformation systems generate a substantial volume of data, creating opportunities for potential innovation, particularly those driven by artificial intelligence. This study focuses on the intricate relationship between artificial intelligence and innovation as foundational elements in the digital transformation framework for sustained growth and operational excellence. This study provides a holistic perspective on the cultivation and pillars of AI-powered innovation, highlighting their pivotal role in revolutionizing industries, including healthcare, education, finance, manufacturing, transportation, and agriculture. The work emphasizes the key pillars essential for fostering AI-powered innovation, including monitoring performance measurement to use the power of the present, continuous learning and innovation, data analytics and insights, predictive analytics, and innovative product development. This study investigates how these pillars serve as the foundation for groundbreaking advancements, driving efficiency, enhancing decision-making processes, and fostering creativity within organizations. This study explores the significance of continuous learning, interdisciplinary collaboration, and industry partnerships in nurturing a thriving AI-powered innovation ecosystem. By understanding and harnessing these fundamental elements, businesses can navigate the complexities of the digital age, fostering innovation that not only optimizes processes but also enhances the overall human experience, ushering in a new era of technological excellence and societal progress.
Structure of Professional Competence Development of Future Fine Art Teachers: Integration of Technology, Method and Competence

The article gives the data about the structure of proficient competence of advancement: integration of innovation, strategy and competence. It was noted that within the development of professional competence, via the proficient characteristics, educational support of the instructive prepare based on prerequisites of qualification, and the determination of personal approaches to the execution of the educational process are vital. This research investigates the structure of professional competence development in future fine art teachers with a focus on the integration of technology, teaching methods, and subject competence. Qualitative research methods were employed to gain an in-depth understanding of how these factors interact to shape professional competence. Data were collected through classroom observations, interviews with faculty and students, and document analysis. Findings indicate that the integration of technology not only enhances the effectiveness of learning but also enriches students' artistic experiences. Innovative teaching methods, such as project-based learning and student collaboration, also play a crucial role in developing pedagogical and artistic skills. Furthermore, a strong subject competence in fine arts was identified as a key factor in shaping quality art teachers. Implications for curriculum development and art teacher training are discussed. This study provides valuable insights into integrating technology, methods, and subject competence in the formation of professional competence in future fine art teachers.