Reviewerly/exHarmony huggingface dataset:
Cybersecurity and privacy in smart bioprinting

Bioprinting is a versatile technology gaining rapid adoption in healthcare fields such as tissue engineering, regenerative medicine, drug delivery, and surgical planning. Although the current state of the technology is in its infancy, it is envisioned that its evolution will be enabled by the integration of the following technologies: Internet of Things (IoT), Cloud computing, Artificial Intelligence/Machine Learning (AI/ML), NextGen Networks, and Blockchain. The product of this integration will eventually be a smart bioprinting ecosystem. This paper presents the smart bioprinting ecosystem as a multilayered architecture and reviews the cyber security challenges, vulnerabilities, and threats in every layer. Furthermore, the paper presents privacy preservation solutions and provides a purview of the open research challenges in the smart bioprinting ecosystem.
Blockchain-Based Healthcare: Trend Mapping through Bibliometric Analysis

Blockchain technology has gained significant attention in the healthcare industry due to its potential to address critical challenges related to data security, interoperability, and patient privacy. Due to the growing research in this area, it has become essential to gain a comprehensive understanding of the evolving landscape and identify the current state of research and future directions in the field of blockchain technology in healthcare. This paper aims to achieve this by conducting a bibliometrics analysis to map the trends and research landscape of blockchain-based healthcare. The bibliometric analysis of scholarly articles and conference papers published in the Scopus database was used to identify the yearly trend of publications, influential authors, leading countries, prominent research institutions, and top journals in this field. Additionally, co-occurrence keywords were analysed to explore the key themes and identify emerging trends and future research directions in blockchain-based healthcare. This study provides valuable insights for researchers, practitioners, and policymakers interested in understanding the current state and future prospects of blockchain technology in the healthcare domain. It also serves as a primary reference source for further studies in this area.
Function as a Function

Function as a Service (FaaS) and the associated serverless computing paradigm alleviates users from resource management and allows cloud platforms to optimize system infrastructure under the hood. Despite significant advances, FaaS infrastructure still leaves much room to improve performance and resource efficiency. We argue that both higher performance and resource efficiency are possible --- while maintaining secure isolation --- if we are willing to revisit the FaaS programming model and system software design. We propose Dandelion, a clean-slate FaaS system that rethinks the programming model by treating serverless functions as pure functions, thereby explicitly separating computation and I/O. This new programming model enables a lightweight yet secure function execution system. It also makes functions more amenable to hardware acceleration and enables dataflow-aware function orchestration. Our initial prototype of Dandelion achieves 45× lower tail latency for cold starts compared to Firecracker. For 95% hot function invocations, Dandelion achieves 5× higher peak throughput.
From text to threats: A language model approach to software vulnerability detection

Abstract In the rapidly-evolving landscape of software development, the detection of vulnerabilities in source code has become of paramount importance. Our study introduces a novel knowledge distillation (KD) technique aimed at enhancing vulnerability detection in software codebases. Using benchmark datasets such as SARD, SeVC, Devign, and D2A, we assess the prowess of the KD method when applied to different classifiers, specifically GPT2, CodeBERT, and LSTM. The empirical results are revealed a marked improvement in the performance of these classifiers upon the implementation of the KD technique, particularly with the GPT-2 model demonstrating the most promising outcomes. This work underscores the potential of integrating transformer-based learning models, like GPT-2, with knowledge distillation for more efficient and accurate vulnerability detection.
Multi-Method Analysis of Histopathological Image for Early Diagnosis of Oral Squamous Cell Carcinoma Using Deep Learning and Hybrid Techniques

Oral cancer is a fatal disease and ranks seventh among the most common cancers throughout the whole globe. Oral cancer is a type of cancer that usually affects the head and neck. The current gold standard for diagnosis is histopathological investigation, however, the conventional approach is time-consuming and requires professional interpretation. Therefore, early diagnosis of Oral Squamous Cell Carcinoma (OSCC) is crucial for successful therapy, reducing the risk of mortality and morbidity, while improving the patient's chances of survival. Thus, we employed several artificial intelligence techniques to aid clinicians or physicians, thereby significantly reducing the workload of pathologists. This study aimed to develop hybrid methodologies based on fused features to generate better results for early diagnosis of OSCC. This study employed three different strategies, each using five distinct models. The first strategy is transfer learning using the Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201 models. The second strategy involves using a pre-trained art of CNN for feature extraction coupled with a Support Vector Machine (SVM) for classification. In particular, features were extracted using various pre-trained models, namely Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201, and were subsequently applied to the SVM algorithm to evaluate the classification accuracy. The final strategy employs a cutting-edge hybrid feature fusion technique, utilizing an art-of-CNN model to extract the deep features of the aforementioned models. These deep features underwent dimensionality reduction through principal component analysis (PCA). Subsequently, low-dimensionality features are combined with shape, color, and texture features extracted using a gray-level co-occurrence matrix (GLCM), Histogram of Oriented Gradient (HOG), and Local Binary Pattern (LBP) methods. Hybrid feature fusion was incorporated into the SVM to enhance the classification performance. The proposed system achieved promising results for rapid diagnosis of OSCC using histological images. The accuracy, precision, sensitivity, specificity, F-1 score, and area under the curve (AUC) of the support vector machine (SVM) algorithm based on the hybrid feature fusion of DenseNet201 with GLCM, HOG, and LBP features were 97.00%, 96.77%, 90.90%, 98.92%, 93.74%, and 96.80%, respectively.
Predicting and explaining with machine learning models: Social science as a touchstone

Machine learning (ML) models recently led to major breakthroughs in predictive tasks in the natural sciences. Yet their benefits for the social sciences are less evident, as even high-profile studies on the prediction of life trajectories have shown to be largely unsuccessful – at least when measured in traditional criteria of scientific success. This paper tries to shed light on this remarkable performance gap. Comparing two social science case studies to a paradigm example from the natural sciences, we argue that, in addition to explanation, prediction is an important goal of social science – and we identify constraints that impede pure ML prediction from being successful in that field. As a remedy, we outline elements of an integrative modelling approach that combines explanatory models and predictive ML models.
A direct approach for L1-norm minimisation

A straightforward algorithm is proposed for L1-norm minimisation. The proposed algorithm is based on grey wolf optimisation which is a meta-heuristic method. Although L1-norm is an efficient tool for robust estimation and outlier detection, the complexity of its implementation made it less useful than L2-norm since after formulation of the L1-norm minimisation for a certain problem one must solve a linear programming problem by a search method while here we only need to set the corresponding L1-norm target function. Two geodetic examples approve the efficiency of the proposed approach.
Implementation and Evaluation of a Federated Learning Framework on Raspberry PI Platforms for IoT 6G Applications

With the advent of 6G technology, the proliferation of interconnected devices necessitates a robust, fully connected intelligence network. Federated Learning (FL) stands as a key distributed learning technique, showing promise in recent advancements. However, the integration of novel Internet of Things (IoT) applications and virtualization technologies has introduced diverse and heterogeneous devices into wireless networks. This diversity encompasses variations in computation, communication, storage resources, training data, and communication modes among connected nodes. In this context, our study presents a pivotal contribution by analyzing and implementing FL processes tailored for 6G standards. Our work defines a practical FL platform, employing Raspberry Pi devices and virtual machines as client nodes, with a Windows PC serving as a parameter server. We tackle the image classification challenge, implementing the FL model via PyTorch, augmented by the specialized FL library, Flower. Notably, our analysis delves into the impact of computational resources, data availability, and heating issues across heterogeneous device sets. Additionally, we address knowledge transfer and employ pre-trained networks in our FL performance evaluation. This research underscores the indispensable role of artificial intelligence in IoT scenarios within the 6G landscape, providing a comprehensive framework for FL implementation across diverse and heterogeneous devices.
An Efficient Detector with Auxiliary Network for Remote Sensing Object Detection

Over the past few decades, advances in satellite and aerial imaging technology have made it possible to acquire high-quality remote sensing images. As one of the most popular research directions of computer vision, remote sensing object detection is widely researched due to the wide application in military and civil fields. The algorithms based on convolutional neural network have made great achievements in the field of object detection. However, plenty of small and densely distributed remote sensing objects against complex background pose some challenges to object detection. In this work, an efficient anchor-free based remote sensing object detector based on YOLO (You Only Look Once) is constructed. Firstly, the backbone network is simplified for the high efficiency of detection. In order to extract the features of densely distributed objects effectively, the detection scales are adjusted based on the backbone network. Secondly, aiming at the shortcomings of CBAM, the improved CJAM (Coordinate Joint Attention Mechanism) is proposed to deal with object detection under complex background. In addition, feature enhancement modules DPFE (Dual Path Feature Enhancement) and IRFE (Inception-ResNet-Feature Enhancement) as well as PRes2Net (Parallel Res2Net) are proposed. We combine CJAM with the above modules to create DC-CSP_n, CSP-CJAM-IRFE, and CJAM-PRes2Net for better feature extraction. Thirdly, a lightweight auxiliary network is constructed to integrate the low-level and intermediate information extracted from remote sensing images into the high-level semantic information of the backbone network. The auxiliary network allows the detector to locate the target efficiently. Fourthly, Swin Transformer is introduced into the ‘Neck’ part of the network so that the network can effectively grasp the global information. The mAP on DOTA1.5 and VEDAI datasets, which both contain a large number of small objects, reached 77.07% and 63.83%, respectively. Compared with advanced algorithms such as YOLO V4, YOLO V5s, YOLO V5l, and YOLO V7, our approach achieves the highest mAP.
Starting November, all Rogue Scholar blog posts will be archived by the Internet Archive

Today I am happy to announce an important milestone for the Rogue Scholar science blog archive. Starting November 1st, all blog posts from participating blogs will automatically be archived by the Internet Archive. Front Matter has signed a contract with Internet Archive to use their Archive-It service, and archiving will start in November.
Grasping AI: experiential exercises for designers

Abstract Artificial intelligence (AI) and machine learning (ML) are increasingly integrated into the functioning of physical and digital products, creating unprecedented opportunities for interaction and functionality. However, there is a challenge for designers to ideate within this creative landscape, balancing the possibilities of technology with human interactional concerns. We investigate techniques for exploring and reflecting on the interactional affordances, the unique relational possibilities, and the wider social implications of AI systems. We introduced into an interaction design course ( n = 100) nine ‘AI exercises’ that draw on more than human design, responsible AI, and speculative enactment to create experiential engagements around AI interaction design. We find that exercises around metaphors and enactments make questions of training and learning, privacy and consent, autonomy and agency more tangible, and thereby help students be more reflective and responsible on how to design with AI and its complex properties in both their design process and outcomes.
Effectiveness of neural networks and transfer learning to forecast photovoltaic power production

Artificial Neural Networks (ANNs) can successfully be integrated into smart models for energy prediction, but require large datasets for training. This investigation presents an innovative methodology for photovoltaic power generation forecasting with ANNs, when only a limited amount of real data is available, and has been tested and validated on a real-life photovoltaic installation. Feature selection identifies which meteorological features most impact photovoltaic power generation. A simulator, which accurately models a real photovoltaic installation, is used to create an artificial, but accurate and realistic, dataset of power generation large enough to effectively train and test different ANNs. These are then exploited on a portion of real, but limited, dataset of power generated by the real photovoltaic installation on which the simulator is modeled. Finally, different transfer learning techniques are used to tune the ANN models with the remaining portion of the real, but limited, dataset of photovoltaic power generation.
Integrating explainable artificial intelligence and blockchain to smart agriculture: Research prospects for decision making and improved security

Food safety hazards can be discovered and avoided using XAI and blockchain technology. The immutable and transparent ledger of blockchain technology can be used to maintain track of perishable food items, allowing for more rapid and precise detection of contamination and immediate removal from shelves. Using blockchain technology, smart agriculture can streamline the supply chain by connecting farmers directly with their customers. As a result, community members may be more confident in meeting their own dietary needs. Combining XAI, blockchain, and smart agriculture has far-reaching societal and economic implications. More efficiency, openness, and sustainability in the food chain might benefit farmers, consumers, and the world. This study provides a detailed bibliometric overview and visualization of integrating two prominent and promising technologies, explainable AI and Blockchain, into Smart Agriculture. In this study, the author implemented analysis in four phases, and for each phase, the author chose different strings, which provided different analysis results. 2479 articles are taken for “smart agriculture”, 103 articles for “Smart agriculture and blockchain”, 37 articles for “blockchain and explainable,” and finally, seven articles for “smart agriculture and explainable AI”. In this study, the mapping program VOSviewer is used for Network analysis. This study uses the co-occurrence, co-citation, and bibliographic coupling employed in VOSviewer to uncover significant focus areas and prominent authors and publications. By using a variety of publications, research was conducted on vital topics for this integration; as a consequence, influence and collaborations began to take place, ultimately leading to development.
Self-supervised learning of a tailored Convolutional Auto Encoder for histopathological prostate grading

According to GLOBOCAN 2020, prostate cancer is the second most common cancer in men worldwide and the fourth most prevalent cancer overall. For pathologists, grading prostate cancer is challenging, especially when discriminating between Grade 3 (G3) and Grade 4 (G4). This paper proposes a Self-Supervised Learning (SSL) framework to classify prostate histopathological images when labeled images are scarce. In particular, a tailored Convolutional Auto Encoder (CAE) is trained to reconstruct <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$128\times 128\times 3$</tex> patches of prostate cancer Whole Slide Images (WSIs) as a pretext task. The downstream task of the proposed SSL paradigm is the automatic grading of histopathological patches of prostate cancer. The presented framework reports promising results on the validation set, obtaining an overall accuracy of 83% and on the test set, achieving an overall accuracy value of 76% with F1-score of 77% in G4.
A spatial perspective on renewable energy optimization: Case study of southern Tunisia Using GIS and multicriteria decision making

Renewable energy systems have emerged as a viable option to mitigate the environmental impacts of traditional fossil fuels. However, the intermittent nature of these renewables, such as solar and wind, makes it challenging to ensure a stable energy supply using only one type. Therefore, combining more than a single technology offers significant advantages in addressing the limitations associated with each individual system. Nevertheless, developing these systems requires substantial financial investments, making it crucial to identify the most suitable locations prior to installing them. In this article, the prime objective was to propose a preliminary evaluation of land suitability for constructing solar and wind hybrid facilities (PV–wind, PV–CSP, and CS–wind) in Tataouine, southern Tunisia. To this end, a GIS-based MCDA methodology was developed based on an extensive literature review and experts’ feedback while considering climate, topography, accessibility, and environmental factors. The results obtained revealed that the optimal area for a CSP–PV hybrid system is about 793 km 2 , indicating that this combination has the highest potential in terms of available resources and compatibility. On the other hand, well-suited locations for hosting CSP–wind and PV–wind systems covered areas of 412 and 333 km 2 , respectively. Such specific locations are capable of generating an annual technical potential of 316.169, 91.252, and 62.970 TWh for CSP–PV, CSP–wind, and PV–wind, respectively. Interestingly, comprising almost all of the most appropriate sites, Remada and Dhiba stand as the ideal locations for accommodating such hybrid systems. Considering this outcome, Tataouine can position itself as a model for renewable energy adoption in Tunisia. Therefore, it is imperative for policymakers, investors, and local communities to collaborate and embrace these hybrid systems to capitalize on this immense potential and pave the way for a greener and more prosperous future.
Beyond the Eye: Multisensory Contributions to the Sensation of Illusory Self-Motion (Vection)

Vection is typically defined as the embodied illusion of self-motion in the absence of real physical movement through space. Vection can occur in real-life situations (e.g., 'train illusion') and in virtual environments and simulators. The vast majority of vection research focuses on vection caused by visual stimulation. Even though visually induced vection is arguably the most compelling type of vection, the role of nonvisual sensory inputs, such as auditory, biomechanical, tactile, and vestibular cues, have recently gained more attention. Non-visual cues can play an important role in inducing vection in two ways. First, nonvisual cues can affect the occurrence and strength of vection when added to corresponding visual information. Second, nonvisual cues can also elicit vection in the absence of visual information, for instance when observers are blindfolded or tested in darkness. The present paper provides a narrative review of the literature on multimodal contributions to vection. We will discuss both the theoretical and applied relevance of multisensory processing as related to the experience of vection and provide design considerations on how to enhance vection in various contexts.
Bitcoin in Conventional Markets: A Study on Blockchain-Induced Reliability, Investment Slopes, Financial and Accounting Aspects

Based on traditional market theory, this study aims to investigate whether conventional market investment slopes affect the unconventional Bitcoin market, considering both normal conditions and crises. This study examines three main characteristics of the economy-intensive blockchain system, namely reliability, investment slopes, financial and accounting aspects that ultimately determine the confidence in the choice to invest in cryptocurrency. The analysis focuses on the study of the Bitcoin (BTC) investment slopes during January 2014–April 2023, considering the specifics of blockchain technology and the inferences of ethics, reliability and real-world data on investment Tassets in the context of conventional regulated markets. Using an econometric model that incorporates reliability analysis techniques, factorial comparisons and multinomial regression using economic crisis periods as a dummy variable, this study reveals important findings for practical and academic purposes. The results of this study show that the investment slopes of Bitcoin (BTC) are mostly predictable for downward trends, when statistically significant correlations with the investment slopes of conventional stock markets are observable. The moderate or high increase in performance slopes pose several challenges for predictive analysis, as they are influenced by other factors than conventional regulated market performance inferences. The results of this study are of intense interest to researchers and investors alike, as they demonstrate that investment slopes analysis sheds light on the intricacies of investment decisions, allowing a comprehensive assessment of both conventional markets and Bitcoin transactions.