Reviewerly/exHarmony huggingface dataset:
Univariate and multivariate short-term solar power forecasting of 25MWac Pasir Gudang utility-scale photovoltaic system using LSTM approach

The generation of solar photovoltaic (PV) systems in Malaysia has great potential due to the abundance of sunlight and high irradiation level. Malaysia’s unique location near the equator makes solar energy the most attractive option for future energy sources. However, the erratic weather leads to variability of power generation, especially during high feed-in of solar energy that will eventually affect grid system stability. Hereof, solar power forecasting is critical, especially in operating utility-scale photovoltaic systems or large-scale solar (LSS) plants. This paper presents an approach to forecasting solar power generation for 10-min to 180-min ahead based on univariate and multivariate using Long -Short-Term Memory (LSTM) technique. The model performances are evaluated based on a real dataset from the 25MWac Pasir Gudang LSS plant from July 2021 to May 2022. The result shows that LSTM with a univariate model outperformed the multivariate model for the short-term forecasting (10-min to 50-min ahead) by 2.09% of RMSE. However, multivariate outperformed univariate for the longer forecasting horizon at 180-min ahead by 34.87% of RMSE. The forecasting output for the multivariate model that only uses historical meteorological data is less reliable than the multivariate model that uses historical meteorological and AC power output data. This research finding is envisaged to provide benefits to the grid system operation, planning, maintenance, and scheduling, thereby improving the reliability of the LSS plant.
Knowledge-based recommendation with contrastive learning

Knowledge Graphs(KGs) have been incorporated as external information into recommendation systems to ensure the high-confidence system. Recently, Contrastive Learning(CL) framework has been widely used in knowledge-based recommendation, owing to the ability to mitigate data sparsity and it considers the expandable computing of the system. However, existing CL-based methods still have the following shortcomings in dealing with the introduced knowledge: (1) For the knowledge view generation, they only perform simple data augmentation operations on KGs, resulting in the introduction of noise and irrelevant information, and the loss of essential information. (2)For the knowledge view encoder, they simply add the edge information into some GNN models, without considering the relations between edges and entities. Therefore, this paper proposes a Knowledge-based Recommendation with Contrastive Learning(KRCL) framework, which generates dual views from user-item interaction graph and KG. Specifically, through data enhancement technology, KRCL introduces historical interaction information, background knowledge and item-item semantic information. Then, a novel relation-aware GNN model is proposed to encode the knowledge view. Finally, through the designed contrastive loss, the representations of the same item in different views are closer to each other. Compared with various recommendation methods on benchmark datasets , KRCL has shown significant improvement in different scenarios.
CIE: A Cloud-Based Information Extraction System for Named Entity Recognition in AWS, Azure, and Medical Domain

This research introduces an extended Information Extraction system for Named Entity Recognition (NER) that allows machine learning (ML) practitioners and medical domain experts to customize and develop their own models using transformers and a range of Cloud resources. Our system provides support for the entire process of managing Cloud resources, including hardware, computing, storage, and training services for NER models. The paper discusses the design and development of two prototypes that target the AWS and Azure Cloud, which were evaluated by experts using the cognitive walkthrough methodology. Additionally, the paper presents quantitative evaluation results that showcase the promising performance of our NER model training approach in the medical domain, outperforming existing approaches.
Securing an Authenticated Privacy Preserving Protocol in a Group Signature Scheme Based on a Group Ring

Group signatures are a leading competing signature technique with a substantial amount of research. With group settings, group signatures provide user anonymity. Any group member with access to the group can generate a signature while remaining anonymous. The group manager, however, has the authority to expose and identify the signer if required. Since the privacy of the sender should be preserved, this is a conflict between privacy and accountability. Concerning high performance on security, we propose a novel, well-balanced security and privacy group signature scheme based on a general linear group over group ring. To the best of our knowledge, our work represents the first comprehensive framework for a group signature scheme that utilizes generic linear groups over group rings. We demonstrate that the competing security goals of message trustworthiness, privacy, and accountability are effectively resolved by our protocol. The results of the performance evaluation and simulation demonstrate that our protocol achieves strong security, system robustness, and high-performance efficiency, making it suitable for practical applications.
Combining imitation and deep reinforcement learning to human-level performance on a virtual foraging task

We develop a framework to learn bio-inspired foraging policies using human data. We conduct an experiment where humans are virtually immersed in an open field foraging environment and are trained to collect the highest amount of rewards. A Markov Decision Process (MDP) framework is introduced to model the human decision dynamics. Then, Imitation Learning (IL) based on maximum likelihood estimation is used to train Neural Networks (NN) that map human decisions to observed states. The results show that passive imitation substantially underperforms humans. We further refine the human-inspired policies via Reinforcement Learning (RL) using the on-policy Proximal Policy Optimization (PPO) algorithm which shows better stability than other algorithms and can steadily improve the policies pre-trained with IL. We show that the combination of IL and RL match human performance and that the artificial agents trained with our approach can quickly adapt to reward distribution shift. We finally show that good performance and robustness to reward distribution shift strongly depend on combining allocentric information with an egocentric representation of the environment.
Efficient Complex Aggregate Queries with Accuracy Guarantee Based on Execution Cost Model over Knowledge Graphs

Knowledge graphs (KGs) have gained prominence for representing real-world facts, with queries of KGs being crucial for their application. Aggregate queries, as one of the most important parts of KG queries (e.g., “ What is the average price of cars produced in Germany?”), can provide users with valuable statistical insights. An efficient solution for KG aggregate queries is approximate aggregate queries with semantic-aware sampling (AQS). This balances the query time and result accuracy by estimating an approximate aggregate result based on random samples collected from a KG, ensuring that the relative error of the approximate aggregate result is bounded by a predefined error. However, AQS is tailored for simple aggregate queries and exhibits varying performance for complex aggregate queries. This is because a complex aggregate query usually consists of multiple simple aggregate queries, and each sub-query influences the overall processing time and result quality. Setting a large error bound for each sub-query yields quick results but with a lower quality, while aiming for high-quality results demands a smaller predefined error bound for each sub-query, leading to a longer processing time. Hence, devising efficient and effective methods for executing complex aggregate queries has emerged as a significant research challenge within contemporary KG querying. To tackle this challenge, we first introduced an execution cost model tailored for original AQS (i.e., supporting simple queries) and founded on Taylor’s theorem. This model aids in identifying the initial parameters that play a pivotal role in the efficiency and efficacy of AQS. Subsequently, we conducted an in-depth exploration of the intrinsic relationship of the error bounds between a complex aggregate query and its constituent simple queries (i.e., sub-queries), and then we formalized an execution cost model for complex aggregate queries, given the accuracy constraints on the error bounds of all sub-queries. Harnessing the multi-objective optimization genetic algorithm, we refined the error bounds of all sub-queries with moderate values, to achieve a balance of query time and result accuracy for the complex aggregate query. An extensive experimental study on real-world datasets demonstrated our solution’s superiority in effectiveness and efficiency.
Levy Flight and Chaos Theory-Based Gravitational Search Algorithm for Image Segmentation

Image segmentation is one of the pivotal steps in image processing due to its enormous application potential in medical image analysis, data mining, and pattern recognition. In fact, image segmentation is the process of splitting an image into multiple parts in order to provide detailed information on different aspects of the image. Traditional image segmentation techniques suffer from local minima and premature convergence issues when exploring complex search spaces. Additionally, these techniques also take considerable runtime to find the optimal pixels as the threshold levels are increased. Therefore, in order to overcome the computational overhead and convergence problems of the multilevel thresholding process, a robust optimizer, namely the Levy flight and Chaos theory-based Gravitational Search Algorithm (LCGSA), is employed to perform the segmentation of the COVID-19 chest CT scan images. In LCGSA, exploration is carried out by Levy flight, while chaotic maps guarantee the exploitation of the search space. Meanwhile, Kapur’s entropy method is utilized for segmenting the image into various regions based on the pixel intensity values. To investigate the segmentation performance of ten chaotic versions of LCGSA, firstly, several benchmark images from the USC-SIPI database are considered for the numerical analysis. Secondly, the applicability of LCGSA for solving real-world image processing problems is examined by using various COVID-19 chest CT scan imaging datasets from the Kaggle database. Further, an ablation study is carried out on different chest CT scan images by considering ground truth images. Moreover, various qualitative and quantitative metrics are used for the performance evaluation. The overall analysis of the experimental results indicated the efficient performance of LCGSA over other peer algorithms in terms of taking less computational time and providing optimal values for image quality metrics.
Self-Tuning Process Noise in Variational Bayesian Adaptive Kalman Filter for Target Tracking

Many practical systems, such as target tracking, navigation systems, autonomous vehicles, and other applications, are usually applied in dynamic conditions. Thus, the actual noise statistics characteristics of these systems are generally time varying and unknown, which will deteriorate the state estimation accuracy of the Kalman filter (KF) and even cause filter diverging. To address this issue, this paper proposes an adaptive process noise covariance (Qk)-based variational Bayesian adaptive Kalman filter (AQ-VBAKF) algorithm. Firstly, the adaptive factor is introduced to self-tune the process noise covariance; the adaptive factor is obtained based on the innovation sequences, which can adapt to the input measurement values. Then, the VB solution is applied to approximate the time variant and unknown measurement noise covariance. Therefore, this proposed algorithm can adjust the process noise covariance and the measurement noise covariance simultaneously based on the variable input signals, which can improve the self-adaptive ability of the state estimation filter in dynamic conditions. According to the dynamic target tracking test results, the proposed AQ-VBAKF outperforms several other existing filtering methods in estimation accuracy, robustness, and computational efficiency.
Multimodal analysis and the oncology patient: Creating a hospital system for integrated diagnostics and discovery

We propose that an information technology and computational framework that would unify access to hospital digital information silos, and enable integration of this information using machine learning methods, would bring a new paradigm to patient management and research. This is the core principle of Integrated Diagnostics (ID): the amalgamation of multiple analytical modalities, with evolved information technology, applied to a defined patient cohort, and resulting in a synergistic effect in the clinical value of the individual diagnostic tools. This has the potential to transform the practice of personalized oncology at a time at which it is very much needed. In this article we present different models from the literature that contribute to the vision of ID and we provide published exemplars of ID tools. We briefly describe ongoing efforts within a universal healthcare system to create national clinical datasets. Following this, we argue the case to create "hospital units" to leverage this multi-modal analysis, data integration and holistic clinical decision-making. Finally, we describe the joint model created in our institutions.
The Impact of Artificial Intelligence Disclosure on Financial Performance

This study determines to what extent Jordanian banks refer to and use artificial intelligence (AI) technologies in their operation process and examines the impact of AI-related terms disclosure on financial performance. Content analysis is used to analyze the spread of AI and related information in the annual report textual data. Based on content analysis and regression analysis of data from 115 annual reports for 15 Jordanian banks listed in the Amman Stock Exchange for the period 2014 to 2021, the study reveals a consistent increase in the mention of AI-related terms disclosure since 2014. However, the level of AI-related disclosure remains weak for some banks, suggesting that Jordanian banks are still in the early stages of adopting and implementing AI technologies. The results indicate that AI-related keywords disclosure has an influence on banks’ financial performance. AI has a positive effect on accounting performance in terms of ROA and ROE and a negative impact on total expenses, which supports the dominant view that AI improves revenue and reduces cost and is also consistent with past literature findings. This study contributes to the growing body of AI literature, specifically the literature on AI voluntary disclosure, in several aspects. First, it provides an objective measure of the uses of AI by formulating an AI disclosure index that captures the status of AI adoption in practice. Second, it provides insights into the relationship between AI disclosure and financial performance. Third, it supports policymakers’, international authorities’, and supervisory organizations’ efforts to address AI disclosure issues and highlights the need for disclosure guidance requirements. Finally, it provides a contribution to banking sector practitioners who are transforming their operations using AI mechanisms and supports the need for more AI disclosure and informed decision making in a manner that aligns with the objectives of financial institutions.
Extracting Evaluation Factors of Social Resilience in Water Resource Protection Areas Using the Fuzzy Delphi Method

Development in water resource protection areas frequently grapples with balancing environmental sustainability and local economic growth. Consequently, a nuanced assessment of social resilience becomes imperative. This research presented a case study of the Water Resource Protection Area in Taipei, gauging its social resilience across five dimensions: “social support function”, “inclusive governance”, “economic allocation”, “built environment”, and “resources for sustainability”. From these, 49 influential factors that could impact the social resilience of the water resource protection area were discerned. Through the engagement of 21 experts via questionnaires and subsequent analysis using the Fuzzy Delphi method, this study identified 23 core factors. Notably, influential factors pertaining to the “social support function” significantly impacted the water resource protection area. The findings aim to streamline the evaluation of social resilience in water resource protection areas, potentially guiding future research utilizing the multi-criteria decision-making (MCDM) method and steering industrial developments in these areas.
End-to-End AUV Local Motion Planning Method Based on Deep Reinforcement Learning

This study aims to solve the problems of sparse reward, single policy, and poor environmental adaptability in the local motion planning task of autonomous underwater vehicles (AUVs). We propose a two-layer deep deterministic policy gradient algorithm-based end-to-end perception–planning–execution method to overcome the challenges associated with training and learning in end-to-end approaches that directly output control forces. In this approach, the state set is established based on the environment information, the action set is established based on the motion characteristics of the AUV, and the control execution force set is established based on the control constraints. The mapping relations between each set are trained using deep reinforcement learning, enabling the AUV to perform the corresponding action in the current state, thereby accomplishing tasks in an end-to-end manner. Furthermore, we introduce the hindsight experience replay (HER) method in the perception planning mapping process to enhance stability and sample efficiency during training. Finally, we conduct simulation experiments encompassing planning, execution, and end-to-end performance evaluation. Simulation training demonstrates that our proposed method exhibits improved decision-making capabilities and real-time obstacle avoidance during planning. Compared to global planning, the end-to-end algorithm comprehensively considers constraints in the AUV planning process, resulting in more realistic AUV actions that are gentler and more stable, leading to controlled tracking errors.
Three-Dimensional Path Planning of Deep-Sea Mining Vehicle Based on Improved Particle Swarm Optimization

Three-dimensional path planning is instrumental in path decision making and obstacle avoidance for deep-sea mining vehicles (DSMV). However, conventional particle swarm algorithms have been prone to trapping in local optima and have slow convergence rates when applied to underwater robot path planning. In order to secure a safe and economical three-dimensional path for the DSMV from the mining area to the storage base in connection with innovative mining system, this paper proposes a multi-objective optimization algorithm based on improved particle swarm optimization (IPSO) path planning. Firstly, we construct an unstructured seabed mining area terrain model with hazardous obstacles. Consequently, by considering optimization objectives such as the path length, terrain undulation, comprehensive energy consumption, and crawler slippage rate, we convert the path planning problem into a multi-objective optimization problem, constructing a multi-objective optimization mathematical model. Following that, we propose an IPSO algorithm to tackle the multi-objective non-linear optimization problem, which enables global optimization for DSMV path planning. Finally, we conduct a comprehensive set of experiments using the MATLAB simulation platform and compare the proposed method with existing advanced methods. Experimental results indicate that the path planned by the IPSO exhibits superior performance in terms of path length, terrain undulation, energy consumption, and safety.
Nonsmooth Optimization-Based Hyperparameter-Free Neural Networks for Large-Scale Regression

In this paper, a new nonsmooth optimization-based algorithm for solving large-scale regression problems is introduced. The regression problem is modeled as fully-connected feedforward neural networks with one hidden layer, piecewise linear activation, and the L1-loss functions. A modified version of the limited memory bundle method is applied to minimize this nonsmooth objective. In addition, a novel constructive approach for automated determination of the proper number of hidden nodes is developed. Finally, large real-world data sets are used to evaluate the proposed algorithm and to compare it with some state-of-the-art neural network algorithms for regression. The results demonstrate the superiority of the proposed algorithm as a predictive tool in most data sets used in numerical experiments.
SANA: Sensitivity-Aware Neural Architecture Adaptation for Uniform Quantization

Uniform quantization is widely taken as an efficient compression method in practical applications. Despite its merit of having a low computational overhead, uniform quantization fails to preserve sensitive components in neural networks when applied with ultra-low bit precision, which could lead to a non-trivial accuracy degradation. Previous works have applied mixed-precision quantization to address this problem. However, finding the correct bit settings for different layers always demands significant time and resource consumption. Moreover, mixed-precision quantization is not well supported on current general-purpose machines such as GPUs and CPUs and, thus, will cause intolerable overheads in deployment. To leverage the efficiency of uniform quantization while maintaining accuracy, in this paper, we propose sensitivity-aware network adaptation (SANA), which automatically modifies the model architecture based on sensitivity analysis to make it more compatible with uniform quantization. Furthermore, we formulated four different channel initialization strategies to accelerate the quantization-aware fine-tuning process of SANA. Our experimental results showed that SANA can outperform standard uniform quantization and other state-of-the-art quantization methods in terms of accuracy, with comparable or even smaller memory consumption. Notably, ResNet-50-SANA (24.4 MB) with W4A8 quantization achieved 77.8% top-one accuracy on ImageNet, which even surpassed the 77.6% of the full-precision ResNet-50 (97.8 MB) baseline.
A Convolutional Neural Network of Low Complexity for Tumor Anomaly Detection

The automated detection of cancerous tumors has attracted interest during the last decade, due to the necessity of early and efficient diagnosis that will lead to the most effective possible treatment of the impending risk. Several machine learning and artificial intelligence methodologies have been employed aiming to provide trustworthy helping tools that will contribute efficiently to this attempt. In this article, we present a low-complexity convolutional neural network architecture for tumor classification enhanced by a robust image augmentation methodology. The effectiveness of the presented deep learning model has been investigated based on three datasets containing brain, kidney, and lung images, showing remarkable diagnostic efficiency with classification accuracies of 99.33%, 100%, and 99.7% for the three datasets, respectively. The impact of the augmentation preprocessing step has also been extensively examined using four evaluation measures. The proposed low-complexity scheme, in contrast to other models in the literature, renders our model quite robust to cases of overfitting that typically accompany small datasets frequently encountered in medical classification challenges. Finally, the model can be easily re-trained in case additional tumor images are included, as its simplistic architecture does not impose a significant computational burden.