Reviewerly/exHarmony huggingface dataset:
Dual-axis solar tracker with satellite compass and inclinometer for automatic positioning and tracking

For increasing the solar energy harvesting performance, any solar tracking system must track the sun at the right angle during periods of daytime. We proposed an enhanced solar tracking instrument to solve the issues of low sunlight incident angles and manual orientation/elevation calibrations. This novel tracker used two Global Positioning System (GPS) receivers with Real-Time Clock (RTC) forming a satellite compass and combined with an inclinometer to follow the sun trajectory correctly without manual aligning the heading of system to orientation of North-South. Such a dual-axis instrument was controlled by a microcontroller unit (MCU) integrated with servo-motors, satellite compass and inclinometer. The embedded Solar Position Algorithm (SPA) can determine the angles of the sun vector at the given date, time and geographical information from the satellite compass. The power generated by the proposed tracking instrument, compared with a fixed-tilted photovoltaic (PV) tracker, was proven in scenarios of fixing, rotating and moving. The proposed solar tracker with satellite compass after field measurement extracted more energy than the fixed-tilted solar tracker under fixed location with mostly clear day 35.91%, in different locations with clear day 38.72% and 38.40%; in heading variation and moving conditions were 60.00% and 113.70%. Therefore, this solar tracker is not only suitable for PV grid but also well suitable for mobile vehicles with keeping the panel aligned with the sun automatically, whether geographical position in northern or southern Hemisphere independently and without solar panel tilted orientation adjustment to achieve the robust, convenient and reliable requirements. • An enhanced open-loop tracking system with GPS for auto-positioning • Satellite compass consisted of GPS and RTC calibrates solar position in real time. • Tracking operation controlled by MCU with embedded SPA and satellite compass
Dense Attention-Guided Cascaded Network for Salient Object Detection of Strip Steel Surface Defects

Recently, more and more researchers have paid attention to the surface defect detection of strip steel. However, the performance of existing methods usually fails to detect the defect regions from some complex scenes, especially with the noise disturbance and diverse defect types. Therefore, this article proposes an end-to-end dense attention-guided cascaded network (DACNet) to detect salient objects (i.e., defects) on the strip steel surface, where the proposed DACNet is a U-shape network including an encoder and a decoder. The encoder first deploys multiresolution convolutional branches (i.e., high/medium/low) in a cascaded way. Concretely, the cascaded feature integration (CFI) unit fuses the deep features from the last convolutional blocks of multiresolution branches, yielding the enhanced high-level deep semantic feature. Subsequently, coupled with the multilevel deep features from high-resolution branch, the new multiscale deep features are capable of characterizing various defects. Then, driven by the dense attention mechanism which enables the deeper attention cues flow into decoding stages, the decoder progressively integrates the multiscale deep features into the final saliency map, where the dense attention is designed to steer deep features pay more concerns to the defect regions. Comprehensive experiments are conducted on the public strip steel datasets, and the experimental results demonstrate that our model consistently outperforms the state-of-the-art models in all evaluation metrics.
Improving Medical Images Classification With Label Noise Using Dual-Uncertainty Estimation

Deep neural networks are known to be data-driven and label noise can have a marked impact on model performance. Recent studies have shown great robustness to classic image recognition even under a high noisy rate. In medical applications, learning from datasets with label noise is more challenging since medical imaging datasets tend to have instance-dependent noise (IDN) and suffer from high observer variability. In this paper, we systematically discuss the two common types of label noise in medical images - disagreement label noise from inconsistency expert opinions and single-target label noise from biased aggregation of individual annotations. We then propose an uncertainty estimation-based framework to handle these two label noise amid the medical image classification task. We design a dual-uncertainty estimation approach to measure the disagreement label noise and single-target label noise via improved Direct Uncertainty Prediction and Monte-Carlo-Dropout. A boosting-based curriculum training procedure is later introduced for robust learning. We demonstrate the effectiveness of our method by conducting extensive experiments on three different diseases with synthesized and real-world label noise: skin lesions, prostate cancer, and retinal diseases. We also release a large re-engineered database that consists of annotations from more than ten ophthalmologists with an unbiased golden standard dataset for evaluation and benchmarking. The dataset is available at https://mmai.group/peoples/julie/.
Federated Learning with Class Imbalance Reduction

Federated learning (FL) is a promising technique that enables a large amount of edge computing devices to collaboratively train a global learning model. Due to the communication limitation, only a subset of devices can be engaged to train and transmit the trained model to centralized server for aggregation. Since the local data distribution varies among all devices, class imbalance problem arises along with the unfavorable client selection, resulting in a slow converge rate of the global model. In this paper, we design an estimation scheme to reveal the class distribution without the awareness of raw data. According to the estimation scheme, we propose a multi-arm bandit based algorithm that can select the client set with minimal class imbalance. The proposed algorithm can significantly improve the convergence performance of the global model. Simulation results demonstrate the effectiveness of the proposed algorithm.
Finding A Needle in a Haystack: Automated Mining of Silent Vulnerability Fixes

Following the coordinated vulnerability disclosure model, a vulnerability in open source software (OSS) is sug-gested to be fixed "silently", without disclosing the fix until the vulnerability is disclosed. Yet, it is crucial for OSS users to be aware of vulnerability fixes as early as possible, as once a vulnerability fix is pushed to the source code repository, a malicious party could probe for the corresponding vulnerability to exploit it. In practice, OSS users often rely on the vulnerability disclosure information from security advisories (e.g., National Vulnerability Database) to sense vulnerability fixes. However, the time between the availability of a vulnerability fix and its disclosure can vary from days to months, and in some cases, even years. Due to manpower constraints and the lack of expert knowledge, it is infeasible for OSS users to manually analyze all code changes for vulnerability fix detection. Therefore, it is essential to identify vulnerability fixes automatically and promptly. In a first-of-its-kind study, we propose VulFixMiner, a Transformer-based approach, capable of automatically extracting semantic meaning from commit-level code changes to identify silent vulnerability fixes. We construct our model using sampled commits from 204 projects, and evaluate using the full set of commits from 52 additional projects. The evaluation results show that VulFixMiner outperforms various state-of-the-art baselines in terms of AUC (i.e., 0.81 and 0.73 on Java and Python dataset, respectively) and two effort-aware performance metrics (i.e., EffortCost, P <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</inf> ). Especially, with an effort of inspecting 5% of total LOC, VulFixMiner can identify 49% of total vulnerability fixes. Additionally, with manual verification of sampled commits that were identified as vulnerability fixes, but not marked as such in our dataset, we observe that 35% (29 out of 82) of the commits are for fixing vulnerabilities, indicating VulFixMiner is also capable of identifying unreported vulnerability fixes.
Breast lesions classifications of mammographic images using a deep convolutional neural network-based approach

Breast cancer is one of the worst illnesses, with a higher fatality rate among women globally. Breast cancer detection needs accurate mammography interpretation and analysis, which is challenging for radiologists owing to the intricate anatomy of the breast and low image quality. Advances in deep learning-based models have significantly improved breast lesions' detection, localization, risk assessment, and categorization. This study proposes a novel deep learning-based convolutional neural network (ConvNet) that significantly reduces human error in diagnosing breast malignancy tissues. Our methodology is most effective in eliciting task-specific features, as feature learning is coupled with classification tasks to achieve higher performance in automatically classifying the suspicious regions in mammograms as benign and malignant. To evaluate the model's validity, 322 raw mammogram images from Mammographic Image Analysis Society (MIAS) and 580 from Private datasets were obtained to extract in-depth features, the intensity of information, and the high likelihood of malignancy. Both datasets are magnificently improved through preprocessing, synthetic data augmentation, and transfer learning techniques to attain the distinctive combination of breast tumors. The experimental findings indicate that the proposed approach achieved remarkable training accuracy of 0.98, test accuracy of 0.97, high sensitivity of 0.99, and an AUC of 0.99 in classifying breast masses on mammograms. The developed model achieved promising performance that helps the clinician in the speedy computation of mammography, breast masses diagnosis, treatment planning, and follow-up of disease progression. Moreover, it has the immense potential over retrospective approaches in consistency feature extraction and precise lesions classification.
Evaluation of machine learning models for predicting daily global and diffuse solar radiation under different weather/pollution conditions

Due to the rapid development of solar energy and photovoltaic industries in China, it is crucial to provide the reliable and accurate solar radiation predictions. In this work, three commonly used machine learning models for predicting global and diffuse solar radiation were assessed in eight Chinese cities, representing different geoclimatic and pollutant conditions. According to the results; regarding the nRMSE, nMAE, nMBE and R values, coastal locations (such as Shanghai, Guangzhou, etc.) obtained higher values than inland locations (such as Lanzhou and Wuhan). Moreover, the SVM (support vector machine) highly outperformed the other models in all locations, regardless of whether the study area was arid, semiarid, semihumid or humid, followed by GLMNET (generalized linear modeling) and RF (random forest). In addition, when assessing the SVM in different locations under different climatic and pollution conditions, it was indicated that the accuracy of solar radiation prediction was closely related to the weather and pollution condition levels. In general, the global solar radiation prediction error was in line with the weather condition levels. The prediction error increased as the weather level increased. However, the relationship between the pollution condition levels and the global solar radiation prediction showed a non-linear relationship. Moreover, for the prediction results of diffuse solar radiation, its variation law with different weather and pollution condition levels was almost different from that of global solar radiation. The maximum high error occurrence probability of global solar radiation and diffuse solar radiation appeared at pollution levels 5 and 1, respectively. Overall, the SVM model demonstrated its reliability in radiation prediction under slight pollution and stable weather conditions. This is crucial in locations with scarce meteorological data and can be used to optimize the selection of geographical locations for photovoltaic power station construction.
Federated Learning based Audio Semantic Communication over Wireless Networks

In this paper, the problem of audio based semantic communication is investigated over wireless networks. In the considered model, wireless edge devices must transmit large-sized audio data to a server using semantic communication techniques. The techniques enable the transmission of audio semantic information which captures the contextual features of audio signals. To extract the semantic information from audio signals, a wave to vector (wav2vec) architecture based autoencoder that consists of convolutional neural networks (CNNs) is proposed. The proposed autoencoder enables high-accuracy audio transmission with small amounts of data. To further improve the accuracy of semantic information extraction, federated learning (FL) is implemented over multiple devices and a server. Simulation results show that the proposed algorithm can converge effectively and can reduce the mean square error (MSE) between the recovered audio signals and the source audio signals by nearly 100 times, compared to a traditional coding scheme.
An Empirical Study of the Impact of Hyperparameter Tuning and Model Optimization on the Performance Properties of Deep Neural Networks

Deep neural network (DNN) models typically have many hyperparameters that can be configured to achieve optimal performance on a particular dataset. Practitioners usually tune the hyperparameters of their DNN models by training a number of trial models with different configurations of the hyperparameters, to find the optimal hyperparameter configuration that maximizes the training accuracy or minimizes the training loss. As such hyperparameter tuning usually focuses on the model accuracy or the loss function, it is not clear and remains under-explored how the process impacts other performance properties of DNN models, such as inference latency and model size. On the other hand, standard DNN models are often large in size and computing-intensive, prohibiting them from being directly deployed in resource-bounded environments such as mobile devices and Internet of Things (IoT) devices. To tackle this problem, various model optimization techniques (e.g., pruning or quantization) are proposed to make DNN models smaller and less computing-intensive so that they are better suited for resource-bounded environments. However, it is neither clear how the model optimization techniques impact other performance properties of DNN models such as inference latency and battery consumption, nor how the model optimization techniques impact the effect of hyperparameter tuning (i.e., the compounding effect). Therefore, in this paper, we perform a comprehensive study on four representative and widely-adopted DNN models, i.e., CNN image classification , Resnet-50 , CNN text classification , and LSTM sentiment classification , to investigate how different DNN model hyperparameters affect the standard DNN models, as well as how the hyperparameter tuning combined with model optimization affect the optimized DNN models, in terms of various performance properties (e.g., inference latency or battery consumption). Our empirical results indicate that tuning specific hyperparameters has heterogeneous impact on the performance of DNN models across different models and different performance properties. In particular, although the top tuned DNN models usually have very similar accuracy, they may have significantly different performance in terms of other aspects (e.g., inference latency). We also observe that model optimization has a confounding effect on the impact of hyperparameters on DNN model performance. For example, two sets of hyperparameters may result in standard models with similar performance but their performance may become significantly different after they are optimized and deployed on the mobile device. Our findings highlight that practitioners can benefit from paying attention to a variety of performance properties and the confounding effect of model optimization when tuning and optimizing their DNN models.
A New Hybrid Based on Long Short-Term Memory Network with Spotted Hyena Optimization Algorithm for Multi-Label Text Classification

An essential work in natural language processing is the Multi-Label Text Classification (MLTC). The purpose of the MLTC is to assign multiple labels to each document. Traditional text classification methods, such as machine learning usually involve data scattering and failure to discover relationships between data. With the development of deep learning algorithms, many authors have used deep learning in MLTC. In this paper, a novel model called Spotted Hyena Optimizer (SHO)-Long Short-Term Memory (SHO-LSTM) for MLTC based on LSTM network and SHO algorithm is proposed. In the LSTM network, the Skip-gram method is used to embed words into the vector space. The new model uses the SHO algorithm to optimize the initial weight of the LSTM network. Adjusting the weight matrix in LSTM is a major challenge. If the weight of the neurons to be accurate, then the accuracy of the output will be higher. The SHO algorithm is a population-based meta-heuristic algorithm that works based on the mass hunting behavior of spotted hyenas. In this algorithm, each solution of the problem is coded as a hyena. Then the hyenas are approached to the optimal answer by following the hyena of the leader. Four datasets are used (RCV1-v2, EUR-Lex, Reuters-21578, and Bookmarks) to evaluate the proposed model. The assessments demonstrate that the proposed model has a higher accuracy rate than LSTM, Genetic Algorithm-LSTM (GA-LSTM), Particle Swarm Optimization-LSTM (PSO-LSTM), Artificial Bee Colony-LSTM (ABC-LSTM), Harmony Algorithm Search-LSTM (HAS-LSTM), and Differential Evolution-LSTM (DE-LSTM). The improvement of SHO-LSTM model accuracy for four datasets compared to LSTM is 7.52%, 7.12%, 1.92%, and 4.90%, respectively.
Harnessing digital technologies for poverty reduction. Evidence for low-income and lower-middle income countries

This paper contributes to understanding the relationship between ICT deployment and poverty alleviation in developing countries. It assess the digital technologies contribution to poverty reduction, through different channels of impact, like education, labor market, income and ICT-trade related activities. Using the sample of 40 developing countries between 1990 and 2019, it relies on macro data extracted from the World Bank Development Indicators (2021) and the World Telecommunication/ICT Indicators Database (2020). Methodological framework combines time trend analysis and locally weighted polynomial smoother, logistic growth model, and panel regression modelling techniques. Our major findings suggest growing ICT deployment, school enrolments, and increases in material wealth are significant drivers of poverty eradication in developing economies. However, the impact of digitalization on poverty is neither direct nor immediate. Therefore, we claim that national and local authorities, together with civil society must consider ICT as a key element of their broad development strategies.
A Survey on Cross‐Virtuality Analytics

Abstract Cross‐virtuality analytics (XVA) is a novel field of research within immersive analytics and visual analytics. A broad range of heterogeneous devices across the reality–virtuality continuum, along with respective visual metaphors and analysis techniques, are currently becoming available. The goal of XVA is to enable visual analytics that use transitional and collaborative interfaces to seamlessly integrate different devices and support multiple users. In this work, we take a closer look at XVA and analyse the existing body of work for an overview of its current state. We classify the related literature regarding ways of establishing cross‐virtuality by interconnecting different stages in the reality–virtuality continuum, as well as techniques for transitioning and collaborating between the different stages. We provide insights into visualization and interaction techniques employed in current XVA systems. We report on ways of evaluating such systems, and analyse the domains where such systems are becoming available. Finally, we discuss open challenges in XVA, giving directions for future research.
Trustworthy IoT Data Streaming Using Blockchain and IPFS

Today’s resource-constrained IoT streaming devices generate large amounts of data which is stored, processed, analyzed for value creation, and accessed using centralized systems, technologies, platforms, and services. Most existing systems leveraged for storing and accessing IoT streaming data fall short in providing transparency, traceability, reliability, trustworthiness, and security features. Also, they are vulnerable to the single point of failure problem due to centralization. In this paper, we propose a blockchain-based solution for resource-constrained IoT streaming devices that allows data chunks to be transferred in a decentralized, transparent, traceable, reliable, secure, and trustful manner. We preserve the privacy and confidentiality of the IoT streamed data through a proxy re-encryption network. We use the decentralized storage of the Interplanetary File System (IPFS) to store and share the IoT streaming data, thereby dealing with the large-size data storage problem. We present system diagrams and eleven algorithms along with their full implementation details. We perform security analysis to show our smart contract code is secure enough against well-known security threats and vulnerabilities. We compare our proposed approach with the existing solutions to show its novelty and effectiveness. We make our smart contract code publicly available on the GitHub repository.
Image-Text Embedding Learning via Visual and Textual Semantic Reasoning

As a bridge between language and vision domains, cross-modal retrieval between images and texts is a hot research topic in recent years. It remains challenging because the current image representations usually lack semantic concepts in the corresponding sentence captions. To address this issue, we introduce an intuitive and interpretable model to learn a common embedding space for alignments between images and text descriptions. Specifically, our model first incorporates the semantic relationship information into visual and textual features by performing region or word relationship reasoning. Then it utilizes the gate and memory mechanism to perform global semantic reasoning on these relationship-enhanced features, select the discriminative information and gradually grow representations for the whole scene. Through the alignment learning, the learned visual representations capture key objects and semantic concepts of a scene as in the corresponding text caption. Experiments on MS-COCO [1] and Flickr30K [2] datasets validate that our method surpasses many recent state-of-the-arts with a clear margin. In addition to the effectiveness, our methods are also very efficient at the inference stage. Thanks to the effective overall representation learning with visual semantic reasoning, our methods can already achieve very strong performance by only relying on the simple inner-product to obtain similarity scores between images and captions. Experiments validate the proposed methods are more than 30-75 times faster than many recent methods with code public available. Instead of following the recent trend of using complex local matching strategies [3], [4], [5], [6] to pursue good performance while sacrificing efficiency, we show that the simple global matching strategy can still be very effective, efficient and achieve even better performance based on our framework.