Reviewerly/exHarmony huggingface dataset:
Enhancing Ductal Carcinoma Classification Using Transfer Learning with 3D U-Net Models in Breast Cancer Imaging

Breast cancer ranks among the leading causes of death for women globally, making it imperative to swiftly and precisely detect the condition to ensure timely treatment and enhanced chances of recovery. This study focuses on transfer learning with 3D U-Net models to classify ductal carcinoma, the most frequent subtype of breast cancer, in histopathology imaging. In this research work, a dataset of 162 microscopic images of breast cancer specimens is utilized for breast histopathology analysis. Preprocessing the original image data includes shrinking the images, standardizing the intensities, and extracting patches of size 50 × 50 pixels. The retrieved patches were employed to construct a basic 3D U-Net model and a refined 3D U-Net model that had been previously trained on an extensive medical image segmentation dataset. The findings revealed that the fine-tuned 3D U-Net model (97%) outperformed the simple 3D U-Net model (87%) in identifying ductal cancer in breast histopathology imaging. The fine-tuned model exhibited a smaller loss (0.003) on the testing data (0.041) in comparison to the simple model. The disparity in the training and testing accuracy reveals that the fine-tuned model may have overfitted to the training data indicating that there is room for improvement. To progress in computer-aided diagnosis, the research study also adopted various data augmentation methodologies. The experimental approach that was put forward achieved state-of-the-art performance, surpassing the benchmark techniques used in previous studies in the same field, and exhibiting greater accuracy. The presented scheme has promising potential for better cancer detection and diagnosis in practical applications of mammography.
A multi-view-CNN framework for deep representation learning in image classification

Deep representation learning in image classification is an area in computer vision where deep Convolutional Neural Networks (CNNs) have flourished. Nevertheless, developing an efficient image recognition model for real world applications is a challenging task, since image datasets are characterized by instances with a large amount of noise and redundant information. Thus, it is essential to incorporate an intelligent feature extraction and filtering method in order to create robust and efficient image representations. In this work, we propose a Multi-View-CNN framework which drastically boosts the performance of pre-trained CNN models, such as ResNet and VGG in image classification applications. In this approach different type of views of the same initial image are used in order to extract different types of features utilizing pre-trained CNN models. However, in order to reduce the huge dimensional size of the raw CNN’s output features and create a robust image representation, the Principal Component Analysis (PCA) dimension reduction method is applied. Then, all these extracted feature vectors are concatenated building a final composite feature representation of the initial image dataset. Finally, this augmented feature vector is used for training a linear model (Logistic Regression) in order to perform the final classification tasks. The main findings of this work are summarized as follows. First, the proposed Multi-View-CNN framework managed to drastically increase the performance results of pre-trained CNN models. Second, the incorporation of PCA as a final layer into the main CNN topology, instead of using the classical dimension reduction layer components such as Averaging and Max Pooling operations, managed to significantly improve the performance. The whole implementation code of this framework alongside with the datasets used in our experimental simulations was uploaded to our public GitHub repository to the following link: https://github.com/EmmanuelPintelas/A-Multi-View-CNN-Framework-for-Deep-Representation-Learning-in-Image-Classification.
SKIPP’D: A SKy Images and Photovoltaic Power Generation Dataset for short-term solar forecasting

Large-scale integration of photovoltaics (PV) into electricity grids is challenged by the intermittent nature of solar power. Sky-image-based solar forecasting using deep learning has been recognized as a promising approach to predicting the short-term fluctuations. However, there are few publicly available standardized benchmark datasets for image-based solar forecasting, which limits the comparison of different forecasting models and the exploration of forecasting methods. To fill these gaps, we introduce SKIPP'D—a SKy Images and Photovoltaic Power Generation Dataset. The dataset contains three years (2017–2019) of quality-controlled down-sampled sky images and PV power generation data that is ready-to-use for short-term solar forecasting using deep learning. In addition, to support the flexibility in research, we provide the high resolution, high frequency sky images and PV power generation data as well as the concurrent sky video footage. We also include a code base containing data processing scripts and baseline model implementations for researchers to reproduce our previous work and accelerate their research in solar forecasting.
IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation

Federated learning (FL) allows multiple medical institutions to collaboratively learn a global model without centralizing client data. It is difficult, if possible at all, for such a global model to commonly achieve optimal performance for each individual client, due to the heterogeneity of medical images from various scanners and patient demographics. This problem becomes even more significant when deploying the global model to unseen clients outside the FL with unseen distributions not presented during federated training. To optimize the prediction accuracy of each individual client for medical imaging tasks, we propose a novel unified framework for both Inside and Outside model Personalization in FL (IOP-FL). Our inside personalization uses a lightweight gradient-based approach that exploits the local adapted model for each client, by accumulating both the global gradients for common knowledge and the local gradients for client-specific optimization. Moreover, and importantly, the obtained local personalized models and the global model can form a diverse and informative routing space to personalize an adapted model for outside FL clients. Hence, we design a new test-time routing scheme using the consistency loss with a shape constraint to dynamically incorporate the models, given the distribution information conveyed by the test data. Our extensive experimental results on two medical image segmentation tasks present significant improvements over SOTA methods on both inside and outside personalization, demonstrating the potential of our IOP-FL scheme for clinical practice. Code is available at https://github.com/med-air/IOP-FL.
Geometry- and Accuracy-Preserving Random Forest Proximities

Random forests are considered one of the best out-of-the-box classification and regression algorithms due to their high level of predictive performance with relatively little tuning. Pairwise proximities can be computed from a trained random forest and measure the similarity between data points relative to the supervised task. Random forest proximities have been used in many applications including the identification of variable importance, data imputation, outlier detection, and data visualization. However, existing definitions of random forest proximities do not accurately reflect the data geometry learned by the random forest. In this paper, we introduce a novel definition of random forest proximities called Random Forest-Geometry- and Accuracy-Preserving proximities (RF-GAP). We prove that the proximity-weighted sum (regression) or majority vote (classification) using RF-GAP exactly matches the out-of-bag random forest prediction, thus capturing the data geometry learned by the random forest. We empirically show that this improved geometric representation outperforms traditional random forest proximities in tasks such as data imputation and provides outlier detection and visualization results consistent with the learned data geometry.
Ancilia: Scalable Intelligent Video Surveillance for the Artificial Intelligence of Things

With the advancement of vision-based artificial intelligence, the proliferation of the Internet of Things connected cameras, and the increasing societal need for rapid and equitable security, the demand for accurate real-time intelligent surveillance has never been higher. This article presents Ancilia, an end-to-end scalable, intelligent video surveillance system for the Artificial Intelligence of Things. Ancilia brings state-of-the-art artificial intelligence to real-world surveillance applications while respecting ethical concerns and performing high-level cognitive tasks in real time. Ancilia aims to revolutionize the surveillance landscape, to bring more effective, intelligent, and equitable security to the field, resulting in safer and more secure communities without requiring people to compromise their right to privacy.
A survey on the efficiency, reliability, and security of data query in blockchain systems

With the emergence of digital currencies, blockchain systems have been committed to the efficient and trusted storage of data, building decentralized, tamper-proof, persistent, and anonymous digital ledger. Researchers have devoted considerable attention to query methods on blockchain platforms, leading to numerous developments and advancements. There is an imperative need for a comprehensive investigation of all these efforts, as well as their most recent progress and results. This study strives to provide a comprehensive and thorough survey of notable works and recent progress related to query technologies and theories. In general, blockchain query methods rely on utilizing distributed databases, data indexing structures, and cryptographic algorithms to achieve efficient, verifiable, and secure queries. Beyond the above, we examine existing issues of blockchain query technologies and theories on query efficiency, reliability, and security. In the end, this work concludes with a summarization on typical scenarios of blockchain query schemes, as well as a discussion of future challenges to be addressed in future research.
Digital Workplaces and Information Security Behavior of Business Employees: An Empirical Study of Saudi Arabia

In the post pandemic era, the telecommuting of business employees has widely become acceptable in organizations, which demands extensive dependence on digital technologies. In addition, this poses additional security threats for business employees as well as organizations. In order to better respond to security threats, business employees must have a higher level of awareness of the potential threats that are relevant to digital infrastructure used within the workplace. In this paper, we present a quantitative study conducted in line with the theory of planned behavior to gain insight into employee behavior toward information security within different business sectors in Saudi Arabia. The key factors chosen for our model were password management, infrastructure security management, email management, organizational security policy, organizational support and training, and the perception of the level of security. We have applied structured equation modelling to identify most of the relevant factors based on the respondents’ feedback. The results based on the business employee behavior showed that they respondents did not perceive all of the constructs of our model as relevant security factors, which can potentially result in security lapses. This indicates that more security-related measures should be put in place and that business employees should be updated periodically about potential security threats. To this effect, we divided the studied security measures into those which should be implemented at organizational and individual levels. The results will potentially help business managers to design appropriate security trainings, guidelines, and policies for their employees to ensure more information security awareness and protect their technological infrastructure, especially within home office environments.
An integrative machine learning framework for classifying SEER breast cancer

Breast cancer is the commonest type of cancer in women worldwide and the leading cause of mortality for females. The aim of this research is to classify the alive and death status of breast cancer patients using the Surveillance, Epidemiology, and End Results dataset. Due to its capacity to handle enormous data sets systematically, machine learning and deep learning has been widely employed in biomedical research to answer diverse classification difficulties. Pre-processing the data enables its visualization and analysis for use in making important decisions. This research presents a feasible machine learning-based approach for categorizing SEER breast cancer dataset. Moreover, a two-step feature selection method based on Variance Threshold and Principal Component Analysis was employed to select the features from the SEER breast cancer dataset. After selecting the features, the classification of the breast cancer dataset is carried out using Supervised and Ensemble learning techniques such as Ada Boosting, XG Boosting, Gradient Boosting, Naive Bayes and Decision Tree. Utilizing the train-test split and k-fold cross-validation approaches, the performance of various machine learning algorithms is examined. The accuracy of Decision Tree for both train-test split and cross validation achieved as 98%. In this study, it is observed that the Decision Tree algorithm outperforms other supervised and ensemble learning approaches for the SEER Breast Cancer dataset.
A meta‐analysis of the effects of augmented reality technologies in interactive learning environments (2012–2022)

Abstract This study conducts meta‐analytical estimations based on 70 empirical studies regarding inconsistent effect sizes of augmented reality in interactive learning environments. It finds that compared with traditional tools, augmented reality in interactive learning environments significantly enhances overall learning effectiveness ( g = 0.717, 95% confidence interval [CI] = 0.606–0.827) at the 0.05 level. The moderating analysis finds that augmented reality in interactive learning environments significantly enhances (1) students' acceptance of technological systems and attitudes toward their courses, (2) comfort, engagement, and self‐efficacy, (3) learning motivations (measured by attention, perceived relevance to learning objectives, confidence, satisfaction, and interest), (4) critical thinking and practical skills, and (5) knowledge acquisition outcomes (including memorization, retention, and application). Interactive augmented reality has insignificant influences on students' flow experience, collaboration, and communication at the 0.05 level, while significantly reducing cognitive load at the 0.05 level. The findings in this study may enlighten further studies on educational technologies and extend applications of augmented reality in education.
Cryptographic Data Security for Reliable Wireless Sensor Network

Wireless Sensor Networks (WSNs) are widely utilized in a variety of applications. Attackers might interfere with equipment, and eavesdrop on conversations. Attackers can also modify the data which has been communicated or connect unauthorized devices to the network if such devices are deployed in the physical environment. To ensure WSN security, messages exchanged between communicating nodes must be encrypted, and for encryption and decryption, the network must store a key. Consequently, Key management is of significant importance in achieving security in WSN. Obtaining such a key arrangement in a resource-constrained environment is required for secure and energy-efficient data transmission in WSN. Current algorithms have certain security issues, such as vulnerability to plaintext attacks, brute force attacks, side-channel attacks, and computational complexity. Cryptography along with clustering has been found to be a practical and efficient technique to improve the secure and energy-efficient data transmission in WSNs. In this article we have used an asymmetric Elliptic Curve Cryptography (ECC) method for key generation whereas encryption and decryption of data, has been done by a hybrid of Advanced Encryption Standard (AES) and ECC cryptography A novel algorithm has been developed that incorporates both techniques (Advanced Encryption Standard (AES) and ECC cryptography) along with clustering through standard LEACH protocol for improving energy efficiency, data security, and network lifetime. The proposed algorithm has been able to overcome the issue of key exchange that plagues AES, simpler than ECC and more reliable than AES. More significantly, it is able to encounter a variety of security threats including side-channel attacks. The proposed algorithm is better and has been compared to current techniques with respect to time complexity, encryption time and decryption time to demonstrate its efficacy.
POSEIDON: A Data Augmentation Tool for Small Object Detection Datasets in Maritime Environments

Certain fields present significant challenges when attempting to train complex Deep Learning architectures, particularly when the available datasets are limited and imbalanced. Real-time object detection in maritime environments using aerial images is a notable example. Although SeaDronesSee is the most extensive and complete dataset for this task, it suffers from significant class imbalance. To address this issue, we present POSEIDON, a data augmentation tool specifically designed for object detection datasets. Our approach generates new training samples by combining objects and samples from the original training set while utilizing the image metadata to make informed decisions. We evaluate our method using YOLOv5 and YOLOv8 and demonstrate its superiority over other balancing techniques, such as error weighting, by an overall improvement of 2.33% and 4.6%, respectively.
Path Planning for Obstacle Avoidance of Robot Arm Based on Improved Potential Field Method

In medical and surgical scenarios, the trajectory planning of a collaborative robot arm is a difficult problem. The artificial potential field (APF) algorithm is a classic method for robot trajectory planning, which has the characteristics of good real-time performance and low computing consumption. There are many variants of the APF algorithm, among which the most widely used variants is the velocity potential field (VPF) algorithm. However, the traditional VPF algorithm has inherent defects and problems, such as easily falling into local minimum, being unable to reach the target, poor dynamic obstacle avoidance ability, and safety and efficiency problems. Therefore, this work presents the improved velocity potential field (IVPF) algorithm, which considers direction factors, obstacle velocity factor, and tangential velocity. When encountering dynamic obstacles, the IVPF algorithm can avoid obstacles better to ensure the safety of both the human and robot arm. The IVPF algorithm also does not easily fall into a local problem when encountering different obstacles. The experiments informed the RRT* algorithm, VPF algorithm, and IVPF algorithm for comparison. Compared with the informed RRT* and VPF algorithm, the result of experiments indicate that the performances of the IVPF algorithm have significant improvements when dealing with different obstacles. The main aim of this paper is to provide a safe and efficient path planning algorithm for the robot arm in the medical field. The proposed algorithm can ensure the safety of both the human and the robot arm when the medical and surgical robot arm is working, and enables the robot arm to cope with emergencies and perform tasks better. The application of the proposed algorithm could make the collaborative robots work in a flexible and safe condition, which could open up new opportunities for the future development of medical and surgical scenarios.
A survey on smart contract vulnerabilities: Data sources, detection and repair

Smart contracts contain many built-in security features, such as non-immutability once being deployed and non-involvement of third parties for contract execution. These features reduce security risks and enhance users’ trust towards smart contracts. However, smart contract security issues still persist, resulting in huge financial losses. Contract publishers cannot fully cover contract vulnerabilities through contract version updating. These security issues affect further development of blockchain technologies. So far, there are many related studies focusing on smart contract security issues and tend to discuss from a particular perspective (e.g., development cycle, vulnerability attack methods, security detection tools, etc.). However, smart contract security is a complicated issue that needs to be explored from a multi-dimensional perspective. In this paper, we explore smart contract security from the perspectives of vulnerability data sources, vulnerability detection, and vulnerability defense. We first analyze the existing security issues and challenges of smart contracts, investigate the existing vulnerability classification frameworks and common security vulnerabilities, followed by reviewing the existing contract vulnerability injection, detection, and repair methods. We then analyze the performance of existing security methods. Next, we summarize the current status of smart contract security-related research. Finally, we summarize the state of the art and future trends of smart contract security-related research. This paper aims to provide systematic knowledge and references to this research field.
Covert channels in blockchain and blockchain based covert communication: Overview, state-of-the-art, and future directions

Traditional network covert channels have become insecure due to the continuous improvement of traffic analysis techniques. As an emerging technology combined with cryptographic techniques, consensus algorithms, P2P network, blockchain has features like decentralization, traceability, immutability, anonymity, transparency, and security, which makes blockchain an ideal platform for covert channel and covert communication. Benefits of blockchain for covert communication include wide access, high capacity covert channels, identity anonymity and information concealment, and robust communication channel. In the paper, we conduct a systematic analysis on covert channels in blockchain from the layer architecture of blockchain. Covert channels are present in data layer, network layer, incentive layer and contract layer, as block structure, transaction structure, cryptographic schemes, P2P network, transaction fee, and smart contract. There are also various covert channels in different layers of blockchain applications. We make a literature review on covert channels in blockchain applications and blockchain based covert communication schemes. Current researches on blockchain based covert communication mainly focus on blockchain based cryptocurrencies, including Bitcoin, Ethereum, Zcash and Monero. There are also some explorations which combine blockchain with images to achieve higher channel capacity for covert communication. Finally, open challenges and future directions on blockchain based covert communication are discussed.