Reviewerly/exHarmony huggingface dataset:
GCK-Maps: A Scene Unbiased Representation for Efficient Human Action Recognition

Human action recognition from visual data is a popular topic in Computer Vision, applied in a wide range of domains. State-of-the-art solutions often include deep-learning approaches based on RGB videos and pre-computed optical flow maps. Recently, 3D Gray-Code Kernels projections have been assessed as an alternative way of representing motion, being able to efficiently capture space-time structures. In this work, we investigate the use of GCK pooling maps, which we called GCK-Maps, as input for addressing Human Action Recognition with CNNs. We provide an experimental comparison with RGB and optical flow in terms of accuracy, efficiency, and scene-bias dependency. Our results show that GCK-Maps generally represent a valuable alternative to optical flow and RGB frames, with a significant reduction of the computational burden.
Continual Source-Free Unsupervised Domain Adaptation

Source-free Unsupervised Domain Adaptation (SUDA) approaches inherently exhibit catastrophic forgetting. Typically, models trained on a labeled source domain and adapted to unlabeled target data improve performance on the target while dropping performance on the source, which is not available during adaptation. In this study, our goal is to cope with the challenging problem of SUDA in a continual learning setting, i.e., adapting to the target(s) with varying distributional shifts while maintaining performance on the source. The proposed framework consists of two main stages: i) a SUDA model yielding cleaner target labels—favoring good performance on target, and ii) a novel method for synthesizing class-conditioned source-style images by leveraging only the source model and pseudo-labeled target data as a prior. An extensive pool of experiments on major benchmarks, e.g., PACS, Visda-C, and DomainNet demonstrates that the proposed Continual SUDA (C-SUDA) framework enables preserving satisfactory performance on the source domain without exploiting the source data at all.
Using file and folder naming and structuring to improve automated detection of child sexual abuse images on the Dark Web

Increasing dissemination of child sexual abuse material (CSAM), especially on the Dark Web, has necessitated greater reliance on automated detection tools. These tools typically match images and videos to known CSAM databases, which is an ineffective method for identifying unknown CSAM. To identify potential complimentary methods, we analysed 162 unique known images, displayed 7289 times on 988 Dark Web websites, to determine if patterns in file/folder naming and structuring tendencies existed on websites. Overall, websites prioritised organisation (ease of access) over obfuscation (security) and hosted almost all images they displayed. File/folder names were commonly alphanumeric, however, there was evidence of sequence file naming patterns. Webpages displaying CSAM were explicitly named, often using underage and/or incest-related keywords. Structuring patterns revealed presence of website copies (mirrors) which can impede effective CSAM removal. Recommendations for supplementing automated detection techniques are discussed.
Solving combined economic and emission dispatch problems using reinforcement learning-based adaptive differential evolution algorithm

Nowadays, economic and environmental concerns in production have become increasingly significant. To address these issues, the Combined Economic and Emission Dispatch (CEED) problem has been introduced to optimize the power generation process by considering fuel cost and emitted substances. However, due to the nonlinearity and nonconvexity of the objective function, the optimization of CEED remains a challenge. In this paper, we develop a Reinforcement Learning-based Adaptive Differential Evolution (RLADE) algorithm to enhance the optimization performance. The mutation strategy and crossover probability of RLADE are optimized using Reinforcement Learning (RL) to respectively ensure better convergence speed and searchability. Additionally, two modifications of RL, namely the adaptive population size-based state division and fitness-ranking-based reward mechanism, are proposed to improve the accuracy of state division and reward calculation in RL. The experiments conducted in this paper consider two objective formulation methods of CEED problems, namely the quadratic and cubic criterion functions. The mean values and standard deviations of the obtained solutions were utilized to assess the performance of RLADE, as well as other comparative algorithms, namely DE algorithm and two RL-based DE variants. The results clearly demonstrate that RLADE surpasses its counterparts with proportion of 100%, 85.7%, and 100% for the 6-unit and 11-unit quadratic CEED problems, as well as cubic criterion functions, in terms of both search accuracy and convergence ability. Furthermore, the significance of RLADE's superiority is confirmed through the Wilcoxon's signed rank test.
Lyceum coworking as a motivator for students to work together

The widespread adoption of the psychology of sustainable development shows that it encourages people to take sustainable actions in their daily lives, as well as how these actions affect their well-being and connection with the environment. This study dedicated to on the importance of a sustainable lifestyle, as well as the thought process underlying our decisions, and how this affects the quality training of future teachers who can be able be responsible not only for their work, but also for the amount of work performed by the whole team. All this is possible only if there are innovative laboratories to stimulate cognitive activity and support the development of the innovative potential of the educational organization itself. Despite the growing attention to the role of coworking in business, there are currently no publications about its organization and functioning within an educational organization. There is no comprehensive understanding of its importance as innovative spaces for the development and maintenance of the innovative space of organizations with the possibility of developing the younger generation. For this reason, the innovative laboratory on the basis of the lyceum (lyceum coworking) is a tool for the formation of motivation for teaching physics, which allows solving problems together in project activities. In particular, the study actualizes the concept of "joint activity" as a necessary component of the functioning of innovation laboratories.
Stakeholders’ opinions support the people-process-technology framework for implementing digital transformation in higher education

ABSTRACTABSTRACTThis article discusses the digital transformation of higher education in Egypt as distance education was abruptly implemented in early 2020. For eight months after that, 38,784 media items were crowdsourced reflecting spontaneous opinions, expectations, sentiments and experiences with the new digital learning. This random sample was drawn from digitised and online news articles, TV and radio programmes, and social media posts by students, parents, academics, education experts and media professionals who represented the media and public opinion. This article recommends the people-process-technology framework for the digital transformation of higher education based on previous research and digital archival research findings. It highlights its components and integrated approach, and discusses its implementation in Egypt and developing countries. Moreover, the framework will be the foundation for further research in higher education's digital transformation, operations, human resources and information technology.KEYWORDS: Distance educationdigital transformationEgypthigher educationcrowdsourced AcknowledgmentsAcumen Media Intelligence contributed access to their archives and AI capabilities in addition to the time and dedication of their expert staff to support this research. Without their support, this research would not have been possible.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsAhmed TaherAhmed Taher has taught marketing at The University of Georgia, Offenburg University in Germany, and The American University in Cairo since 1991. He holds a BSc in Civil Engineering, an MBA from the Ohio State University and a PhD from the University of Georgia. He won the Excellence in Teaching Award from the University of Georgia in 1995 and the AUC in 2005–06. Dr Taher has published about 25 articles and five books.
The cloud sovereignty nexus: How the European Union seeks to reverse strategic dependencies in its digital ecosystem

Abstract How does the European Union balance the need to migrate data to the cloud with the imperative of reducing its dependence on foreign cloud providers? Cloud computing is a critical technology for the competitiveness of the European Union (EU) in the digital economy. This paper argues that the EU is adopting a host of regulatory requirements and industrial policy tools—which fall under the umbrella term of ‘data sovereignty’—not only to protect the confidentiality of European data, but also to counter the dominance of US vendors in the European cloud market. To demonstrate how data sovereignty principles are woven into current EU policy initiatives, the paper presents two case studies: Gaia‐X and the European Alliance for Industrial Data, Edge, and Cloud. Linking data sovereignty to the lack of a competitive European cloud ecosystem sheds light on the strategic dimension of cloud computing in a way that treating them separately would not do.
OAMSFNet: Orientation-Aware and Multi-Scale Feature Fusion Network for shadow detection in remote sensing images via pseudo shadow

ABSTRACTRemote sensing images (RSI) of urban regions often exhibit shadows cast by buildings and other objects, which may result in imprecise analysis and interpretation. Therefore, shadow detection plays a significant role in RSI scene understanding. Current approaches have paid little attention to the presence of pseudo shadows and their confounding effects on detection results. We tackle these questions in the spatial context of orientation-awareness, effectively capturing the intricate relationships between shadows and ground objects. Here, we introduce a novel deep learning network named the Orientation-Aware and Multi-Scale Feature Fusion Network (OAMSFNet) due to noise reduction considerations. The proposed OAMSFNet comprises Shadow Aware Feature Encoder (SFE), Orientation-Aware Context Module (OCM), and Multi-Scale Feature Pyramid (MFP). Furthermore, a multi-scale feature fusion algorithm has been devised to enhance the detection and segmentation capabilities of the model in shadow regions. Finally, a comparative study was conducted on the Aerial Imagery dataset for Shadow Detection (AISD) in both quantitative and qualitative aspects. The experimental results show that our method outperforms state-of-the-art methods while maintaining a lightweight model design, indicating that our approach exhibits remarkable accuracy and stability in excellent agreement with predictions. OAMSFNet achieved an average F-score of 89.85%, surpassing the straightforward semantic segmentation model SegNet by 9.94%, and demonstrated remarkable efficiency enhancements, with FLOPs reduced to as low as 11.48 G, resulting in efficiency improvements of 66.7%-96.5% compared to other shadow detection models.KEYWORDS: Shadow detectionpseudo shadowdeep learningremote sensing imagesspatial context Disclosure statementThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.Data availability statementThe Aerial Imagery dataset for Shadow Detection used for training and testing is available at https://github.com/RSrscoder/AISD.Additional informationFundingThis work was supported by the National Natural Science Foundation of China (Grant number 41971365), the Major Science and Technology Project of the Ministry of Water Resources (Grant number SKR-2022037), the Chongqing Higher Education Teaching Reform Research Project (Grant number 201019S), and the Chongqing Graduate Research Innovation Project (Grant number CYS22448)
Iterative threshold-based Naïve bayes classifier

Abstract The iterative Threshold-based Naïve Bayes (iTb-NB) classifier is introduced as a (simple) improved version of the previously introduced non-iterative Threshold-based Naïve Bayes (Tb-NB) classifier. iTb-NB starts from a Natural Language text-corpus and allows the user to quantify with a numeric value a sentiment (positive or negative) from a specific test. Differently from Tb-NB, iTb-NB is an algorithm aimed at estimating multiple threshold values that concur to refine Tb-NB’s decision rules when classifying a text into positive (negative) based on its content. Observations with sentiment scores close to the threshold are marked to be reclassified, hence a new decision rule is defined for them. Such “iterative” process improves the quality of predictions w.r.t. Tb-NB but keeping the possibility to utilize its results as the input of useful post-hoc analyses. The effectiveness of iTb-NB is evaluated analyzing hotel guests’ reviews from all hotels located in the Sardinia region and available on Booking.com . Furthermore, iTb-NB is compared with Tb-NB in terms of model accuracy, resistance to noise, and computational efficiency.
Enhancing Email Accessibility for Visually Impaired Individuals

This paper presents an innovative, voice-based email system designed to improve email accessibility for visually impaired individuals. The proposed system leverages Artificial Intelligence and speech recognition technologies to convert speech to text and text to speech, enabling visually impaired individuals to send and receive emails using voice commands. The system offers an intuitive user interface, secure authentication measures, and robust database architecture to ensure seamless and secure user experience. Comparative analysis with traditional email systems reveals the superior functionality and inclusivity of the proposed system. Despite certain limitations, future enhancements promise to further refine the system, paving the way for a more inclusive digital communication environment.
Can the principle of the ‘right to be forgotten’ be applied to academic publishing? Probe from the perspective of personal rights, archival science, open science and post‐publication peer review

Abstract In this paper, we reflect on how the principle of the ‘right to be forgotten’ (RTBF), specifically the right to erasure as enshrined in Article 17 (and to some extent Art. 19 and Art. 21) of the General Data Protection Regulation (GDPR; Regulation (EU) 2016/679), or the right to delete undesirable, unflattering or reputation‐damaging archived records of oneself from search engines or databases, might apply to academic publishing. In particular, we focus on archival (library and information) science, post‐publication peer review, and reflect on whether RTBF is compatible with open science principles. Even though RTBF became enshrined in EU law in 2018, its trans‐Atlantic export to the United States faces resistance because it is seen as being incompatible with First Amendment rights. We ponder the pertinence of the debate regarding local versus global applicability of RTBF when considering the transnational nature of some collaborative research. Although RTBF applies broadly to search engines such as Google, we question whether authors have this right and also whether publishers are subjected to this law with respect to science databases, or even ‘local’ (i.e., publisher‐controlled) archives, especially in the light of retractions or withdrawals, in which data and files are removed from a preprint or journal's website or, in extreme cases, where all or most bibliometric information is scrubbed clean, as in the case of ‘silent retractions’. We extend our reflections further to appreciate whether authors or editors are entitled to RTBF in extreme instances of misconduct or fraud. The fundamental right to privacy and personal choice, as is suggested by (or enshrined in) RTBF, is not—in our view—compatible with several principles related to the integrity of data and information, or even their preservation, and may be diametrically opposed, depending on the situation. We encourage wider debate on this budding pertinent issue as a fundamental aspect of academic rights and freedoms.
Appraisal of Architectural Ambiances in a Future District

Auditing future public places that have not yet been constructed can be a laborious, time-consuming, and expensive task. However, the human factor plays a crucial role in successful infrastructure design. By involving users early in the design process, valuable insights can be gained prior to the physical construction, resulting in more appealing spaces for users. In this research, we explore the potential of non-immersive virtual reality to perceive atmospheres in architectural projects. We investigate suitable methodologies for studying this subject and examine its educational implications in architecture. The study focuses on a large-scale neighborhood currently undergoing complete reconstruction. We extract and model four environments in 3D, offering a virtual tour of these spaces and their infrastructures to both expert and non-expert participants. Through a questionnaire, we collect their responses to evaluate the architectural atmospheres of these distinct areas. This article analyzes the expressed feelings and provides projections on the anticipated sensations once the real estate project is completed. Additionally, it compares the expectations of professionals with the impressions of the public based on virtual visits. By emphasizing outdoor architecture and employing a larger city 3D model in the experimentation, this study contributes to the existing literature on participatory studies. The empirical research findings underscore the advantages of involving users early in the design process for buildings and streetscapes, leading to an enhanced user experience before implementing infrastructure renovations.
Anomaly Detection Approach for Sensor Networks in Coal Mine Solid Backfilling Working Faces Based on Transformer

In this paper, we propose a comprehensive anomaly detection model for the perception system of backfilling working faces. The model consists of three main components: the position encoding module, the time series prediction module, and the anomaly detection module. The position encoding module extracts global temporal information, including year, month, and day, based on the timestamp information of the data. The time series prediction module utilizes the Temporal Fusion Transformers model, which considers the latent correlations among different variables for accurate predictions. The anomaly detection module assesses the data's abnormality by comparing the predicted values with the actual values. We validate the performance of the model on a real-world dataset, demonstrating its high accuracy and strong generalization capabilities for various types of sensor data in backfilling working faces. This research provides a viable solution for anomaly monitoring and management in backfilling operations, with significant practical implications.
Polarizing white light interferometry for phase measurements using two simultaneous interferograms

Our research introduces a design for a polarization phase-shifting white light interferometric system (PPS-WLIS) that operates in a transmissive mode for measuring the slope phase of transparent objects. It comprises a cyclic path interferometer (lateral shear interferometer) coupled with a multiplexing Michelson interferometer. The system uses polarization to produce two parallel interferograms with polarization modulated with relative shifts simultaneously. To determine the optical phase, we used a two-step algorithm for phase demodulation that does not necessitate precise phase shifts, making the system more straightforward to operate. As a result, we could observe variations in the object associated with optical phase changes. Furthermore, our method simplifies the phase-shift interferometry process by requiring only one capture, making it an effective way to examine objects at dynamic events. As an illustration, we demonstrated the temperature measurement generated by a section of a candle flame.
MIMO Signals Classification with Cauchy Noise in Space-air-ground Integrated Networks

An average likelihood ratio test (ALRT)-based methodology is proposed for automatic modulation classification (AMC) of orthogonal space-time block code (STBC) based multiple-input multiple-output (MIMO) systems. Most existing works for modulation classification of MIMO systems assume the addictive noise is Gaussian. However, the actual noise in practical systems often exhibits non-Gaussian characteristics. To address this, we propose a practical MIMO system model that assumes the additive noise is Cauchy. The proposed approach analyzes the ALRT functions corresponding to different modulation types and employs zero-forcing (ZF) equalization algorithm to reduce the computational complexity of the likelihood functions. Performance analysis is carried out for two scenarios: Alamouti-STBC systems (Al-STBC) with a 2 × 2 transmit and receive antenna configuration and space-time transmit diversity (STTD) with a 4×4. Simulation results indicate that the correct recognition rate can exceed 90% when the generalized signal-to-noise ratio (GSNR) is 10dB under the STTD model.
Improved YOLOv8 Insulator Fault Detection Algorithm Based on BiFormer

To address the problem that insulator small faults in transmission lines are difficult to identify and combine the features of fault images, this paper proposes a fault detection algorithm based on improved YOLOv8 for insulator small targets. Firstly, we use the C2f-DCN module in the backbone extraction network to solve the problem that it is difficult to obtain the features of small targets due to the image deformation caused by compressed pixels. Secondly, we add the BiFormer attention module at the bottom of the backbone network, which can focus on the small fault features of insulators in the complex background and improve the feature representation capability of the network. According to the experiments, the detection accuracy of the improved model in this paper is 93.2%, the recall rate is 84.3%, the mAP is 91.8%, the number of parameters is 34.2×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> , and the number of floating point operations (FLOPs) is 17.9G; compared with YOLOv8, the detection accuracy, recall rate, and mAP are improved by 4.1%, 3.2%, and 3.8%, respectively, and the results show that the algorithm is the complex environment has significantly improved the detection accuracy of small targets.
Funny but not Credible? Why Using (Many) Emojis Decreases Message Credibility and Source Trustworthiness

Emojis are paralinguistic elements that are used in computer-mediated communication to express emotions, convey tone, or simply depict objects or statements. This study examines how the (excessive) use of emojis affects knowledge, message credibility, and source trustworthiness. To this end, we conducted three online experiments using social media posts, in which we systematically varied the number of emojis and the source of the message. The results show that displaying many emojis decreases message credibility and source trustworthiness. As a mechanism explaining this effect, we also demonstrate that displaying many emojis is perceived as a persuasive attempt, triggering affective and cognitive reactance, and ultimately reducing message credibility. Moreover, the study shows that using (a large amount of) emojis in a message negatively affects factual knowledge.