Reviewerly/exHarmony huggingface dataset:
ETHOS.PASSION: An open-source workflow for rooftop photovoltaic potential assessments from satellite imagery

The assessment of rooftop photovoltaic potential has become increasingly accurate due to the expanding availability of satellite imagery and improvements in computer vision methods. However, the analysis of satellite imagery is impeded by a lack of transparency, reproducibility, and standardized description of the methods employed. Studies are heterogeneous, target different types of potential with redundant efforts, and are mostly not open source or use private datasets for training. With respect to the estimation of photovoltaic potential, this study proposes a conceptual frame of reference for clearly identifying tasks, their relationships, and their data. Additionally, the open-source workflow ETHOS.PASSION is introduced, which integrates the assessment of geographical, technical and economic potentials of regions under consideration along with the calculation of surface areas, orientations and slopes of individual rooftop sections. ETHOS.PASSION also includes the detection of superstructures, i.e., obstacles such as windows or existing photovoltaic installations. The novel two-look approach combines two deep learning models identifying rooftops and sections, and an additional model for the identification of superstructures. The three models show a mean Intersection Over Union between classes of 0.8478, 0.7531 and 0.4927 respectively, and more importantly display consistent results amongst randomly sampled real world images. The final results are evaluated for multiple datasets and compared against other studies, with a case study in the Aachen region of Germany being presented.
Breast Cancer Prediction Framework Based on Iterative Optimization with Bayesian Hyperparameter Tuning

Breast cancer (BC) is a major health concern affecting women worldwide, and early detection is crucial for effective treatment and improved survival rates. In this study, we propose a novel BC prediction framework based on iterative optimization with Bayesian hyperparameter tuning applied to the Wisconsin Diagnostic Breast Cancer (WDBC) and Surveillance, Epidemiology, and End Results (SEER) datasets. Our approach employs various Machine Learning (ML) algorithms, including tree-based, support vector machine (SVM)-based, K-nearest neighbor (KNN)-based, tree-based ensemble, and artificial neural network (ANN)-based ML models. The results demonstrated that the optimized models generally outperformed their non-optimized counterparts. Notably, the optimized AdaBoost model achieved a remarkable performance with 100% accuracy, precision, recall, and F1-score on the WDBC dataset. The optimized GentleBoost model exhibited a high performance of 95.3% accuracy, 97.4% precision, 93.1% recall, 95.2% F1-score, and 0.99 area under the curve (AUC) on the SEER dataset. These findings highlight the potential of our proposed framework for enhancing BC prediction accuracy and robustness, paving the way for future research and clinical application.
An open-source dataset for arabic fine-grained emotion recognition of online content amid COVID-19 pandemic

Emotion recognition is a crucial task in Natural Language Processing (NLP) that enables machines to comprehend the feelings conveyed in the text. The task involves detecting and recognizing various human emotions like anger, fear, joy, and sadness. The applications of emotion recognition are diverse, including mental health diagnosis, student support, and the detection of online suspicious behavior. Despite the substantial amount of literature available on emotion recognition in various languages, Arabic emotion recognition has received relatively little attention, leading to a scarcity of emotion-annotated corpora. This article presents the ArPanEmo dataset, a novel dataset for fine-grained emotion recognition of online posts in Arabic. The dataset comprises 11,128 online posts manually labeled for ten emotion categories or neutral, with Fleiss' kappa of 0.71. It is unique in that it focuses on the Saudi dialect and addresses topics related to the COVID-19 pandemic, making it the first and largest of its kind. Python's packages were utilized to collect online posts related to the COVID-19 pandemic from three sources: Twitter, YouTube, and online newspaper comments between March 2020 and March 2022. Upon collection of the online posts, each one underwent a semi-automatic classification process using a lexicon of emotion-related terms to determine whether it belonged to the neutral or emotion category. Subsequently, manual labeling was conducted to further categorize the emotional data into fine-grained emotion categories. We anticipate that the ArPanEmo dataset will enrich Arabic NLP resources and help in the development of machine learning and deep learning tools to identify emotions in a given text. It will also contribute to developing systems that monitor online suspicious behaviors or mental health disorders. The final dataset is formatted in CSV, consisting of three columns: the number of the post, the post's text, and the corresponding emotion label. This format facilitates incorporating and utilizing the dataset in any machine learning research.
A dependency-based hybrid deep learning framework for target-dependent sentiment classification

One of the main challenges in target-dependent sentiment classification (TDSC) is dealing with sentences that contain multiple targets with varying polarities. Traditional sentiment analysis has shown the effectiveness of language characteristics. Therefore, we propose a method to extract target semantic-related tokens from sentences in order to simplify the sentiment classification task. To achieve this, we establish six grammatical principles that utilize grammatical knowledge to filter the relevant descriptions of targets. Since a target is typically a noun and acts as a subject, we summarize the six rules to extract the contexts contained in the objects and subordinate clauses. We use dependency parsing to analyze the grammatical relations between the target and its context. We design a data pre-processing method called Text Filtering (TF) to automate this procedure. After executing the TF algorithm, we pass the target-related words to a simple classifier to predict their sentiment polarities. Rather than feeding these features directly to a network and letting it learn features on its own, our approach employs dependency relations to extract context linked to the target. This provides the network with meaningful and representative features, resulting in superior results. We conduct ablation studies to investigate the effectiveness of the proposed TF algorithm. In the restaurant hard dataset, our approach improves accuracy by 13.76% and macro-F1 by 14.65% compared to a CNN-based method where TF is not implemented.
An innovative analysis of Leveraging Transfer Learning for Accurate Image Recognition

Transfer learning is a powerful technique used to leverage knowledge learned from one domain and transfer it to another domain, reducing the time needed to train a machine learning model. This paper presents a novel approach to apply transfer learning to improve accuracy in image recognition tasks. The authors first use classical transfer learning algorithms to analyze a publicly available image dataset and extract features relevant to the recognition problem. Then, they combine these extracted features with a convolutional neural network (CNN) to improve the classification accuracy of the CNN. The authors evaluate the performance of their approach using multiple datasets, such as MNIST and SVHN, and demonstrate the superiority in terms of high recognition accuracy, low memory requirements and fewer training iterations. The results show that the proposed approach outperforms the baseline CNNs, with an improvement of up to 10%. This approach could be used by research groups and computer vision systems that are concerned with accurate image recognition tasks.
Application of Internet of Things and Machine learning in improving supply chain financial risk management System

The incorporation of the Internet of Things (IoT) and machine learning (ML) methods has attracted significant interest in a variety of sectors in previous years. This article investigates the use of IoT and ML to improve supply chain financial (SCF) threat management. The supply chain is a complicated network with many key players, and financial threat management is important to its sustainability and success. IoT and ML possess inherent advantages in SCF due to their technology properties. They also have significant opportunities to build trust to solve big challenges in SCF, which aids financial development in the Tonkin Gulf area. This paper focuses on introducing the study on using Machine learning innovation in SCF in the Tonkin Gulf area and aims to offer suggestions on how supply chain finance could evolve there using Machine learning. This paper suggests supply chain finance game applications for pertinent investigations as well as blockchain innovation, supply chain banking threats assessment on the IoT and machine learning, and supply chain finance implementation study methodologies in the Tonkin Gulf region.
An intelligent optimization framework to predict the vulnerable range of tumor cells using Internet of things

The intelligent optimization framework for predicting the vulnerable range of tumor cells using Internet of Things (IoT) provides a robust and efficient solution for monitoring, assessing, and managing cancer treatment. This framework is based on a combination of deep learning algorithms, big data analytics, cloud computing, and IoT technology. The main purpose of this framework is to develop an optimal solution for detecting the vulnerable range of tumor cells using the latest advances in image processing, machine learning and natural language processing. This framework collects data from various medical or health-related sources such as patient records, imaging results, and medical reports and stores them on the cloud. It filters real-time data at micro-levels to generate a more accurate analysis of the vulnerable range of tumor cells in order to make better proactive healthcare decisions. In this framework, the deep learning algorithms are used to develop predictive models that can accurately detect the vulnerable range of tumor cells. The big data analytics platform is used to analyze the collected data patterns for more refined predictions. Finally, the cloud computing technology provides a distributed computing platform to take advantage of the data from different sources. By integrating the latest advances in machine learning, natural language processing, and cloud computing, the intelligent optimization framework for predicting the vulnerable range of tumor cells using IoT provides an efficient and accurate solution for optimizing the diagnosis and treatment of cancer, enabling doctors to provide better proactive care to their patients.
Leveraging regression models for rule based complex event processing

In recent times, IoT devices have surged enormously, which creates a lot of raw data that is dynamic in nature, and processing it in real time to find useful information is challenging. Complex event processing involves the analysis of large volumes of real-time data to identify patterns and events of interest. These events are formed based on a predefined set of rules, and since rules are created by domain experts and for dynamic data, there is a requirement for a robust model that can eliminate manual intervention for rule generation. In this paper, to help the domain experts, a regression-based model is proposed so that more accurate decision-making can be performed by finding more robust event patterns. For regression-based rule implementation, three models are compared: logistic regression, ridge regression, and support vector machine. The models are trained using an IoT temperature dataset and tested using a synthetically generated dataset with the same set of parameters.The ridge regression performed best among all the models, with an accuracy, precision, recall, and f1 score of 99% 97% 96% 95% among all. The entire experiment was carried out with the apache flink ecosystem and pattern API.
SCALE-IT: Distributed and Realistic Simulation Frameworks for Testing Cloud-Based Software

Cloud-based software testing presents unique challenges due to the distributed nature of cloud applications and the need for realistic environment simulation. This paper addresses these challenges by proposing two novel solutions. The first is a distributed testing framework specifically designed for cloud-based software. This framework leverages service virtualization and automated orchestration to efficiently test software components across multiple servers, reducing testing time and resource consumption while improving coverage. The second solution is a realistic environment simulation framework for cloud-based software testing. This framework employs emulation and simulation techniques to replicate diverse deployment scenarios, network conditions, and user interactions, enhancing the accuracy and effectiveness of performance, scalability, and security testing. Experimental results demonstrate significant improvements in testing efficiency, effectiveness, and accuracy compared to traditional methods. For instance, the distributed testing framework reduced testing time by 18.75% and lowered CPU and memory utilization. The realistic environment simulation framework improved response times under various load scenarios and increased throughput during scalability testing. These proposed solutions pave the way for more robust and reliable cloud-based software, ensuring a seamless user experience and driving the continued advancement of cloud technologies.
Explicit Congestion Notification-Based Congestion Control Algorithm for High-Performing Data Centers

A high-performing data center is vital for the smooth operation of organizations and institutions. However, the widespread utilization of data centers has resulted in heavy traffic and various impairments such as TCP-Incast, buffer overflow, and long queues. The reliability of the Transmission Control Protocol (TCP) has been called into question due to its unsatisfactory performance in the data center environment in maintaining high throughput and meeting deadlines. Several approaches have been implemented to address these impairments, including TCP modifications, increased buffer sizes, and improved congestion algorithms. This paper presents an enhanced algorithm that employs the Explicit Congestion Notification (ECN) mechanism to inform network devices of the extent and location of congestion, with the aim of minimizing congestion and improving flow completion time. The simulation results demonstrate that the proposed algorithm is capable of meeting flow deadlines, with the average Flow Completion Time significantly better than other state-of-the-art solutions.
Metaverse Adoption in UAE Higher Education: A Hybrid SEM-ANN Approach

The concept of the Metaverse is gaining popularity in higher education due to its potential applications in addressing engagement and interaction challenges, particularly in the context of distance learning. However, the readiness of higher education institutions to adopt Metaverse technologies and their impact on learning competencies and academic performance remains uncertain. This paper presents an empirical study conducted in UAE higher education institutions to investigate the determinants of Metaverse adoption and its influence on academic performance. The research framework integrates the resource-based view (RBV) theory, dynamic capabilities theory, and the ADKAR model. A questionnaire was developed to assess organizational and technological factors related to Metaverse adoption. The collected data was analyzed using Structural Equation Modeling (SEM) and Artificial Neural Network (ANN) hybrid approach. The findings provide insights into the readiness of UAE universities to adopt and benefit from Metaverse deployment in their programs, offering practical implications for improving learning outcomes.
Global Differential Privacy for Distributed Metaverse Healthcare Systems

Metaverse-enabled digital healthcare systems are expected to exploit an unprecedented amount of personal health data, while ensuring that sensitive or private information of individuals are not disclosed. Machine learning and artificial intelligence (ML/AI) techniques can be widely utilized in metaverse healthcare systems, such as virtual clinics and intelligent consultations. In such scenarios, the key challenge is that data privacy laws might not allow virtual clinics to share their medical data with other parties. Moreover, clinical AI/ML models themselves carry extensive information about the medical datasets, such that private attributes can be easily inferred by malicious actors in the metaverse (if not rigorously privatized). In this paper, inspired by the idea of “incognito mode”, which has recently been developed as a promising solution to safeguard metaverse users' privacy, we propose global differential privacy for the distributed metaverse healthcare systems. In our scheme, a randomized mechanism in the format of artificial “mix-up” noise is applied to the federated clinical ML/AI models before sharing with other peers. This way, we provide an adjustable level of distributed privacy against both the malicious actors and honest-but-curious metaverse servers. Our evaluations on breast cancer Wisconsin dataset (BCWD) highlight the privacy-utility trade-off (PUT) in terms of diagnosis accuracy and loss function for different levels of privacy. We also compare our private scheme with the non-private centralized setup in terms of diagnosis accuracy.
AI Trust: Can Explainable AI Enhance Warranted Trust?

Explainable artificial intelligence (XAI), known to produce explanations so that predictions from AI models can be understood, is commonly used to mitigate possible AI mistrust. The underlying premise is that the explanations of the XAI models enhance AI trust. However, such an increase may depend on many factors. This article examined how trust in an AI recommendation system is affected by the presence of explanations, the performance of the system, and the level of risk. Our experimental study, conducted with 215 participants, has shown that the presence of explanations increases AI trust, but only in certain conditions. AI trust was higher when explanations with feature importance were provided than with counterfactual explanations. Moreover, when the system performance is not guaranteed, the use of explanations seems to lead to an overreliance on the system. Lastly, system performance had a stronger impact on trust, compared to the effects of other factors (explanation and risk).
On the Use of Power Amplifier Nonlinearity Quotient to Improve Radio Frequency Fingerprint Identification in Time-Varying Channels

Radio frequency fingerprint identification (RFFI) is a lightweight device authentication technique particularly desirable for power-constrained devices, e.g., the Internet of things (IoT) devices. Similar to biometric fingerprinting, RFFI exploits the intrinsic and unique hardware impairments resulting from manufacturing, such as power amplifier (PA) nonlinearity, to develop methods for device detection and classification. Due to the nature of wireless transmission, received signals are volatile when communication environments change. The resulting radio frequency fingerprints (RFFs) are distorted, leading to low device detection and classification accuracy. We propose a PA nonlinearity quotient and transfer learning classifier to design the environment-robust RFFI method. Firstly, we formalized and demonstrated that the PA nonlinearity quotient is independent of environmental changes. Secondly, we implemented transfer learning on a base classifier generated by data collected in an anechoic chamber, further improving device authentication and reducing disk and memory storage requirements. Extensive experiments, including indoor and outdoor settings, were carried out using LoRa devices. It is corroborated that the proposed PA nonlinearity quotient and transfer learning classifier significantly improved device detection and device classification accuracy. For example, the classification accuracy was improved by 33.3% and 34.5% under indoor and outdoor settings, respectively, compared to conventional deep learning and spectrogram-based classifiers.
FedECS: Client Selection for Optimizing Computing Energy in Federated Learning

With the rapid development of artificial intelligence, computing energy consumption has become a research focus. Due to privacy protection, it is difficult to exchange data between participants, which leads to repeated training of the same model for different clients. This paper aims to reduce the computing energy consumption caused by repeated training of the same model. This paper first proves the effect of Federated Learning (FL) on lowering computing energy consumption. Then considering the client computing architecture, this paper proposes an FL algorithm for the energy-efficiency-ratio-based client selection (FedECS). The proposed algorithm reduces the total energy consumption of FL through the trade-off of iteration rounds and energy consumption per round. In addition, this paper performs extensive simulations, including different data partitions and computing architecture distributions. Experiments show that compared with the FedAvg algorithm, the proposed algorithm can reduce energy consumption by 17.2% ∼ 48.2%.
Defending Federated Learning from Backdoor Attacks: Anomaly-Aware FedAVG with Layer-Based Aggregation

Federated Learning (FL) is susceptible to backdoor adversarial attacks during the training process, which poses a significant threat to the model's performance. Existing adversarial mitigation solutions mainly rely on the neural network (NN) model statistics and discard an entire client model if attacked. This approach is not feasible as it results in suboptimal performance. Hence, it is crucial to develop lightweight backdoor attack mitigation solutions that efficiently utilize clients' model statistics. To address this issue, we propose (Layer Based Anomaly Aware) LBAA-FedAVG, a modified version of the common aggregation mechanism FedAVG. Our proposed framework employs a clustering-based technique and addresses each NN layer individually. Depending on the type of adversarial attack, this method selectively eliminates one or multiple layers of the NN during the aggregation process. Furthermore, we focused on the model inversion attack and varied the percentage of compromised clients from 10% to 50%. Our experimental findings demonstrate that LBAA-FedAVG outperforms Federated Averaging (FedAVG) in reducing the negative effects of backdoor adversarial attacks. The complexity analysis suggests that the extra training time is the only additional resource limitation in LBAA-FedAVG, which is 19% greater than that of FedAVG. Additionally, we conducted experiments on short-term load forecasting using grid-level datasets to show the effectiveness of LBAA-FedAVG in lightweight backdoor attack mitigation in FL settings, offering a trade-off between time efficiency and enhanced defense.