Reviewerly/exHarmony huggingface dataset:
Uncovering the dark side of AI-based decision-making: A case study in a B2B context

Over the last decade, many organizations worldwide have been assimilating Artificial Intelligence (AI) technologies to increase their productivity and attain a competitive advantage. As with any technology, intelligence systems come with potential downsides. Despite the efforts to mitigate any negative consequences of AI, businesses and employees continue to confront the dilemmas of adopting AI, so it is essential to explore in detail the rising concerns around such technologies. In this paper, we used a single case study to investigate the dark aspects of AI in a Norwegian energy trading firm. We gathered data through semi-structured interviews and secondary data. Specifically, we interviewed AI managers, traders and developers who have worked on deploying and using AI tools over the last three years. Our aim is to identify the dark side of AI use in trading, how AI trading bots affect the relationship between traders and AI developers and how the firm adjusts to this new reality. The findings indicate that negative or unintended consequences of AI can be grouped into three clusters related to (1) the nature of the work; (2) conflicts and effects; and (3) responsibility. The paper concludes with future research and practical implications that can help organiziations mitigate the negative aspects of AI use.
Random bit generation based on a self-chaotic microlaser with enhanced chaotic bandwidth

Abstract Chaotic semiconductor lasers have been widely investigated for high-speed random bit generation, which is applied for the generation of cryptographic keys for classical and quantum cryptography systems. Here, we propose and demonstrate a self-chaotic microlaser with enhanced chaotic bandwidth for high-speed random bit generation. By designing tri-mode interaction in a deformed square microcavity laser, we realize a self-chaotic laser caused by two-mode internal interaction, and achieve an enhanced chaotic standard bandwidth due to the photon–photon resonance effect by introducing the third mode. Moreover, 500 Gb/s random bit generation is realized and the randomness is verified by the NIST SP 800-22 statistics test. Our demonstration promises the applications of microlasers in secure communication, chaos radar, and optical reservoir computing, and also provides a platform for the investigations of multimode nonlinear laser dynamics.
Towards Robust Multimodal Sentiment Analysis Under Uncertain Signal Missing

Multimodal Sentiment Analysis (MSA) has attracted widespread research attention recently. Most MSA studies are based on the assumption of signal completeness. However, many inevitable factors in real applications lead to uncertain signal missing, causing significant degradation of model performance. To this end, we propose a Robust multimodal Missing Signal Framework (RMSF) to handle the problem of uncertain signal missing for MSA tasks and can be generalized to other multimodal patterns. Specifically, a hierarchical cross modal interaction module in RMSF exploits potential complementary semantics among modalities via coarse- and fine-grained cross modal attention. Furthermore, we design an adaptive feature refinement module to enhance the beneficial semantics of modalities and filter redundant features. Finally, we propose a knowledge integrated self-distillation module that enables dynamic knowledge integration and bidirectional knowledge transfer within a single network to precisely reconstruct missing semantics. Comprehensive experiments are conducted on two datasets, indicating that RMSF significantly improves MSA performance under both uncertain missing-signal and complete-signal cases.
Understanding the brain with attention: A survey of transformers in brain sciences

Abstract Owing to their superior capabilities and advanced achievements, Transformers have gradually attracted attention with regard to understanding complex brain processing mechanisms. This study aims to comprehensively review and discuss the applications of Transformers in brain sciences. First, we present a brief introduction of the critical architecture of Transformers. Then, we overview and analyze their most relevant applications in brain sciences, including brain disease diagnosis, brain age prediction, brain anomaly detection, semantic segmentation, multi‐modal registration, functional Magnetic Resonance Imaging (fMRI) modeling, Electroencephalogram (EEG) processing, and multi‐task collaboration. We organize the model details and open sources for reference and replication. In addition, we discuss the quantitative assessments, model complexity, and optimization of Transformers, which are topics of great concern in the field. Finally, we explore possible future challenges and opportunities, exploiting some concrete and recent cases to provoke discussion and innovation. We hope that this review will stimulate interest in further research on Transformers in the context of brain sciences.
Enhancing model performance in detecting lodging areas in wheat fields using UAV RGB Imagery: Considering spatial and temporal variations

Fast and accurate assessment of wheat lodging holds significant importance for disaster prevention and agricultural insurance claim settlement. However, lodging detection is often influenced by the spatial and temporal heterogeneity of fields, leading to inconsistent lodging features in images captured at different times and locations. This inconsistency poses challenges for evaluating model performance and obtaining consistent results. In this study, we proposed a comprehensive lodging area detection model performance evaluation method for multi-year and multi-phenological periods. Three evaluation indicators—accuracy, potential, and stability—were introduced to assess model performance from different perspectives. Additionally, a weighted method is employed to combine the results of multiple experiments. To evaluate the performance of lodging area segmentation in wheat fields, we selected three feature adaptive models (CBAM-unet, SE-unet, and Swin-transformer) along with the Unet model. The experimental results are as follows: (1) Swin-transformer achieved the highest weighted average accuracy (Acccp) of 83.53% among the four models evaluated. (2) Swin-transformer exhibited the highest upper limit of segmentation accuracy, reaching 97.98%. However, its average accuracy in the prediction experiment was 85% of the upper limit, suggesting potential for optimization. (3) CBAM-unet demonstrated the lowest overall weighted segmentation accuracy variance (35.92) compared to the other three models, indicating higher stability. Based on the experimental results it can be seen that our proposed method and model can address the challenge of evaluating lodging area detection models under spatial and temporal heterogeneity.
Enhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning

Accurate diagnosis of disease often depends on the exhaustive examination of Whole Slide Images (WSI) at microscopic resolution. Efficient handling of these data-intensive images requires lossy compression techniques. This paper investigates the limitations of the widely-used JPEG algorithm, the current clinical standard, and reveals severe image artifacts impacting diagnostic fidelity. To overcome these challenges, we introduce a novel deep-learning (DL)-based compression method tailored for pathology images. By enforcing feature similarity of deep features between the original and compressed images, our approach achieves superior Peak Signal-to-Noise Ratio (PSNR), Multi-Scale Structural Similarity Index (MS-SSIM), and Learned Perceptual Image Patch Similarity (LPIPS) scores compared to JPEG-XL, Webp, and other DL compression methods. Our method increases the PSNR value from 39 (JPEG80) to 41, indicating improved image fidelity and diagnostic accuracy. Our approach can help to drastically reduce storage costs while maintaining large levels of image quality. Our method is online available.
Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Model

It can be challenging to identify brain MRI anomalies using supervised deep-learning techniques due to anatomical heterogeneity and the requirement for pixel-level labeling. Unsupervised anomaly detection approaches provide an alternative solution by relying only on sample-level labels of healthy brains to generate a desired representation to identify abnormalities at the pixel level. Although, generative models are crucial for generating such anatomically consistent representations of healthy brains, accurately generating the intricate anatomy of the human brain remains a challenge. In this study, we present a method called the masked-denoising diffusion probabilistic model (mDDPM), which introduces masking-based regularization to reframe the generation task of diffusion models. Specifically, we introduce Masked Image Modeling (MIM) and Masked Frequency Modeling (MFM) in our self-supervised approach that enables models to learn visual representations from unlabeled data. To the best of our knowledge, this is the first attempt to apply MFM in denoising diffusion probabilistic models (DDPMs) for medical applications. We evaluate our approach on datasets containing tumors and numerous sclerosis lesions and exhibit the superior performance of our unsupervised method as compared to the existing fully/weakly supervised baselines. Project website: https://mddpm.github.io/ .
Energy, exergy, economical and environmental analysis of photovoltaic solar panel for fixed, single and dual axis tracking systems: An experimental and theoretical study

This investigation focuses on energetic, exegetic, economical and environmental analysis of PV solar system using fixed, single- and dual-axes tracking systems under climatic weather of Zakho city/north of Iraq. Experiments are carried out on 5th September 2022. The energy and exergy analyses are used to predict the performance of three solar panels. The theoretical work includes technical, economical and environmental analysis of proposed 1MW PV solar power plant are presented using similar characteristics of the experimental data of hourly meteorological climatic conditions during 2022. The findings display that the tracking systems have significant influences on 4–E performances. The experimental results displayed that electrical output power gain and thermal exergy output are increased when using tracking systems, where the exergy losses for single- and dual-axes tracking systems are decreased as compared to fixed solar plane. The maximum improvements in the electrical output power gain of PV solar panels using dual– and single–axes tracking systems are nearly reached to 40 % at 8 AM, 13 % (for single) and 20 %(for dual) at 12 PM and 30 % at 17PM as compared to fixed solar panel. The theoretical results display that the yielded produced energy for single- and dual-axes are increased by 16.5 % and 25.5 %, respectively, as compared to fixed panel. The economic findings display that the cost of energy for single-axes, dual-axes and fixed tracking systems are 4.89, 4.41 and 8.26, respectively. Finally, the use of tracking systems reduces the CO2 emission about 4000–4500 tCO2 annually.
On the interpretability of Fuzzy Cognitive Maps

This paper proposes a post-hoc explanation method for computing concept attribution in Fuzzy Cognitive Map (FCM) models used for scenario analysis, based on SHapley Additive exPlanations (SHAP) values. The proposal is inspired by the lack of approaches to exploit the often-claimed intrinsic interpretability of FCM models while considering their dynamic properties. Our method uses the initial activation values of concepts as input features, while the outputs are considered as the hidden states produced by the FCM model during the recurrent reasoning process. Hence, the relevance of neural concepts is computed taking into account the model's dynamic properties and hidden states, which result from the interaction among the initial conditions, the weight matrix, the activation function, and the selected reasoning rule. The proposed post-hoc method can handle situations where the FCM model might not converge or converge to a unique fixed-point attractor where the final activation values of neural concepts are invariant. The effectiveness of the proposed approach is demonstrated through experiments conducted on real-world case studies.
An Ensemble Data Security on Cloud Healthcare Systems

The widespread use of cloud computing in healthcare systems has resulted in numerous advantages, such as increased scalability, cost efficiency, and access to medical data. The sensitive nature of healthcare data, on the other hand, poses significant challenges to its security and privacy in the cloud environment. In this study, an Advanced Integrated Data Security (AIDS) approach for cloud-based healthcare systems aims to address the vulnerabilities caused by data breaches and unauthorized access. The proposed AIDS is the data security framework that employs several security mechanisms to improve the overall security of healthcare data stored and processed in the cloud. It combines complementary techniques such as encryption, access control, anomaly detection, and intrusion prevention to create a comprehensive security infrastructure. Encryption algorithms transform healthcare data into an unreadable format that can only be accessed with appropriate decryption keys, ensuring the confidentiality and integrity of the data. An advanced role-based access control system is developed to manage user permissions and prevent unauthorized access to sensitive information. Anomaly detection techniques also detect abnormal patterns and behaviors in the cloud environment. Potential security breaches can be seen in real-time by continuously monitoring system logs and network traffic, allowing for prompt response and mitigation measures. Intrusion prevention systems actively prevent and block unauthorized access attempts, network attacks, and malicious activities to improve overall security. These systems analyze incoming and outgoing traffic using various detection algorithms to identify and thwart potential threats. The ensemble data security strategy capitalizes on the synergistic effects of combining multiple security mechanisms. The framework improves the resilience of cloud-based healthcare systems against data breaches and cyber threats by integrating encryption, access control, anomaly detection, and intrusion prevention. This work lays the groundwork for future research and development on data security in cloud-based healthcare systems. The proposed ensemble approach provides a robust and comprehensive solution for protecting sensitive healthcare data while enabling secure and trusted cloud-based healthcare services.
Formalizing ϕ-Calculus: A Purely Object-Oriented Calculus of Decorated Objects

Many calculi exist for modeling various features of object-oriented languages. Many of them are based on λ -calculus and focus either on statically typed class-based languages or dynamic prototype-based languages. We formalize the untyped calculus of decorated objects, informally presented by Bugayenko, which is defined in terms of objects and relies on decoration as a primary mechanism of object extension. It is not based on λ -calculus, yet with only four basic syntactic constructions is just as complete (in particular, it is Turing complete and possesses the Church-Rosser property). We also provide a sound translation to Wand's λ -calculus with records and concatenation, and discuss the key differences of these calculi.
Exploring User Experience and Usability in a Metaverse Learning Environment for Students: A Usability Study of the Artificial Intelligence, Innovation, and Society (AIIS)

The metaverse, a rapidly evolving research area, is a virtual, interconnected universe that seamlessly integrates digital and physical realms, facilitating immersive experiences, social interaction, and economic activities across various fields, including computer science, business, and education. Despite its potential to transform current pedagogies and learning experiences through engaging, interactive, and fascinating educational techniques, research is limited in exploring its application, usability, and user experience, particularly for Thai undergraduate students in learning computer engineering courses. In response, we designed and developed an innovative metaverse-based learning system called the AIIS collaborative learning interface, tailored to European undergraduate students. While the original intent of AIIS was to cater to medical students, in this study, our objective was to assess its usability and applicability for computer science and engineering students, specifically focusing on the Asian demographic. After an initial pilot with European students in their local context, the research was extended to Thailand, where 21 Thai undergraduate students evaluated the platform’s usability. The observations from this study indicate that AIIS provides a dynamic, user-friendly learning environment. Nevertheless, the evaluation process unveiled some usability flaws that offer informative directions for future enhancements. Drawing from our observations, we formulated usability guidelines to guide designers and developers toward crafting more efficient metaverse systems, particularly those intended for educational use. Our results also underline the considerable promise that metaverse technologies hold for advancing higher education globally.
A bibliometric and multi-disciplinary quasi-systematic analysis of social robots: Past, future, and insights of human-robot interaction

Social robots and their novel applications are slowly emerging to become an indispensable part of society and businesses. While the field of social robotics has been very multidisciplinary since its inception, currently, there is a scarcity of reviews that takes stock of all the past and present literature to critically, comprehensively, and holistically understand how business and management and social science scholars can develop scholarship and contribute to social robotics research. To fill this gap in the literature, we conducted a bibliometric analysis of 4211 articles gathered from Web of Science and SCOPUS databases from all disciplines contributing to understanding social robots area to describe the intellectual and conceptual structures of research in social robots. This is followed by a quasi-systematic review of themes identified in bibliometric analysis to critically analyze the knowledge gaps in existing research and develop research questions that will help in the diffusion and adoption of this technology within the consumer and business space, irrespective of the application sector. Our findings show that better conceptualization of social robots (what they are), acceptance studies in the approximation phase of adoption, and developing measures for cyber resilience and human-centric design will shape societal and organizational perception.
Multi-task Pre-training Language Model for Semantic Network Completion

Semantic networks, exemplified by the knowledge graph, serve as a means to represent knowledge by leveraging the structure of a graph. While the knowledge graph exhibits promising potential in the field of natural language processing, it suffers from incompleteness. This article focuses on the task of completing knowledge graphs by predicting linkages between entities, which is fundamental yet critical. Traditional methods based on translational distance struggle when dealing with unseen entities. In contrast, semantic matching presents itself as a potential solution due to its ability to handle such cases. However, semantic matching-based approaches necessitate large-scale datasets for effective training, which are typically unavailable in practical scenarios, hindering their competitive performance. To address this challenge, we propose a novel architecture for knowledge graphs known as LP-BERT, which incorporates a language model. LP-BERT consists of two primary stages: multi-task pre-training and knowledge graph fine-tuning. During the pre-training phase, the model acquires relationship information from triples by predicting either entities or relations through three distinct tasks. In the fine-tuning phase, we introduce a batch-based triple-style negative sampling technique inspired by contrastive learning. This method significantly increases the proportion of negative sampling while maintaining a nearly unchanged training time. Furthermore, we propose a novel data augmentation approach that leverages the inverse relationship of triples to enhance both the performance and robustness of the model. To demonstrate the effectiveness of our proposed framework, we conduct extensive experiments on three widely used knowledge graph datasets: WN18RR, FB15k-237, and UMLS. The experimental results showcase the superiority of our methods, with LP-BERT achieving state-of-the-art performance on the WN18RR and FB15k-237 datasets.
Microseismic event waveform classification using CNN-based transfer learning models

The efficient processing of large amounts of data collected by the microseismic monitoring system (MMS), especially the rapid identification of microseismic events in explosions and noise, is essential for mine disaster prevention. Currently, this work is primarily performed by skilled technicians, which results in severe workloads and inefficiency. In this paper, CNN-based transfer learning combined with computer vision technology was used to achieve automatic recognition and classification of multi-channel microseismic signal waveforms. First, data collected by MMS was generated into 6-channel original waveforms based on events. After that, sample data sets of microseismic events, blasts, drillings, and noises were established through manual identification. These datasets were split into training sets and test sets according to a certain proportion, and transfer learning was performed on AlexNet, GoogLeNet, and ResNet50 pre-training network models, respectively. After training and tuning, optimal models were retained and compared with support vector machine classification. Results show that transfer learning models perform well on different test sets. Overall, GoogLeNet performed best, with a recognition accuracy of 99.8%. Finally, the possible effects of the number of training sets and the imbalance of different types of sample data on the accuracy and effectiveness of classification models were discussed.