Reviewerly/exHarmony huggingface dataset:
Data-Driven Model for PV Power Generation Patterns Extraction via Unsupervised Machine Learning Methods

Photovoltaic solar power generation varies from its nominal value over a wide range due to weather parameters intermittency and Sun path during a day. This intermittent behavior poses complications in the grid management systems while the solar power penetration rate grows incessantly. This paper concentrates on extracting photovoltaic power generation patterns to obtain a complete picture of dominant generation patterns at a daily time scale. In the proposed data-driven model, the high dimensional temporal features of the daily solar power output samples are transformed to the lower feature space through singular value decomposition, and then the K-means algorithm, the unsupervised machine learning technique, is applied to extract the five distinct dominant patterns. The results show that each pattern corresponds to different weather types such as sunny, mostly sunny, partially sunny, partially cloudy, and cloudy.
Federated Feature Selection for Horizontal Federated Learning in IoT Networks

Under horizontal federated learning (HFL) in the Internet of Things (IoT) scenarios, different user data sets have significant similarities on the feature spaces, the final goal is to build a high-performance global model. However, not all features are great contributors when training the global HFL model, some features even impair the HFL. Besides, the curse of dimension will delay the training time and cause more energy consumption (EC). In this case, it is critical to remove irrelevant features from the local and select the useful overlapping features from a federated global perspective. In addition, the uncertainty of data being labeled and the nonindependent and identically distributed (non-IID) client data should also consider. This article introduces an unsupervised federated feature selection approach (named FSHFL) for HFL in IoT networks. First, a feature relevance outlier detection method is applied to the HFL participants to remove the useless features, which combines with the improved one-class support vector machine. Besides, a feature relevance hierarchical clustering (FRHC) algorithm is proposed for HFL overlapping feature selection. Experiment results on four IoT data sets show that the proposed methods can select better-federated feature sets among HFL participants, thus improving the performance of the HFL system. Specifically, the global model accuracy improves up to 1.68% since fewer irrelevant features. Moreover, FSHFL can lower the average training time as high as 6.9%. Finally, when the global model gets the same test accuracy, FSHFL can decrease the average EC of training the model by approximately 2.85% compared to federated average and roughly 68.39% compared to Fed-SGD.
Pseudo-Mono for Monocular 3D Object Detection in Autonomous Driving

Current monocular 3D object detection algorithms generally suffer from inaccurate depth estimation, which leads to reduction of detection accuracy. The depth error from image-to-image generation for the stereo view is insignificant compared with the gap in single-image generation. Therefore, a novel pseudo-monocular 3D object detection framework is proposed, which is called Pseudo-Mono. Particularly, stereo images are brought into monocular 3D detection. Firstly, stereo images are taken as input, then a lightweight depth predictor is used to generate the depth map of input images. Secondly, the left input images obtained from stereo camera are used as subjects, which generate enhanced visual feature and multi-scale depth feature by depth indexing and feature matching probabilities, respectively. Finally, sparse anchors set by the foreground probability maps and the multi-scale feature maps are used as reference points to find the suitable initialization approach of object query. The encoded visual feature is adopted to enhance object query for enabling deep interaction between visual feature and depth feature. Compared with popular monocular 3D object detection methods, Pseudo-Mono is able to achieve richer fine-grained information without additional data input. Extensive experimental results on the datasets of KITTI, NuScenes, and MS-COCO demonstrate the generalizability and portability of the proposed method. The effectiveness and efficiency of Pseudo-Mono have been demonstrated by extensive ablation experiments. Experiments on a real vehicle platform have shown that the proposed method maintains high performance in complex real-world environments.
Memory Pooling With CXL

Compute Express Link (CXL) has recently attracted great attention thanks to its excellent hardware heterogeneity management and resource disaggregation capabilities. Even though there is yet no commercially available product or platform integrating CXL into memory pooling, it is expected to make memory resources practically and efficiently disaggregated much better than ever before. In this article, we propose directly accessible memory disaggregation, DirectCXL that straight connects a host processor complex and remote memory resources over CXL’s memory protocol (CXL.mem). Our empirical evaluation shows that DirectCXL exhibits around 7× better performance than remote direct memory access (RDMA)-based memory pooling for diverse real-world workloads.
Optimized Blockchain Sharding Model Based on Node Trust and Allocation

Sharding technology is a promising solution for improving the scalability of blockchain systems. However, it faces the problem of allocating suitable trusted nodes into separate shards to satisfy security and efficiency requirements. Existing blockchain sharding methods fail to consider shard trust difference, communication latency difference, and node count difference among shards. This tends to increase the risk of a blockchain failure. This work proposes a novel blockchain sharding model for node allocation by considering shard trust difference. Its key idea is to allocate nodes of different trust levels to suitable shards to make shards have almost the same trust, such that shards’ reliability increases and blockchain failure probability decreases. To reduce the communication delay among shards, this work considers the communication latency difference and node count difference among shards. It proposes a sharding algorithm to iteratively adjust node allocations such that an optimal or near-optimal node allocation set is obtained. Simulation results show that the proposed method can effectively improve shard security and the performance of blockchain sharding compared with two state-of-the-art methods, i.e., Monoxide and Rapidchain, in terms of throughput, latency, and blockchain failure probability.
Deep Multi-Magnification Similarity Learning for Histopathological Image Classification

Precise classification of histopathological images is crucial to computer-aided diagnosis in clinical practice. Magnification-based learning networks have attracted considerable attention for their ability to improve performance in histopathological classification. However, the fusion of pyramids of histopathological images at different magnifications is an under-explored area. In this paper, we proposed a novel deep multi-magnification similarity learning (DSML) approach that can be useful for the interpretation of multi-magnification learning framework and easy to visualize feature representation from low-dimension (e.g., cell-level) to high-dimension (e.g., tissue-level), which has overcome the difficulty of understanding cross-magnification information propagation. It uses a similarity cross entropy loss function designation to simultaneously learn the similarity of the information among cross-magnifications. In order to verify the effectiveness of DMSL, experiments with different network backbones and different magnification combinations were designed, and its ability to interpret was also investigated through visualization. Our experiments were performed on two different histopathological datasets: a clinical nasopharyngeal carcinoma and a public breast cancer BCSS2021 dataset. The results show that our method achieved outstanding performance in classification with a higher value of area under curve, accuracy, and F-score than other comparable methods. Moreover, the reasons behind multi-magnification effectiveness were discussed.
A machine learning model for improving virtual machine migration in cloud computing

Cloud Computing is a paradigm allowing access to physical and application resources online via the Internet. These resources are virtualized using virtualization software to make them available to users as a service. Virtual machines (VMs) migration technique provided by virtualization technology impacts the performance of the cloud. It is a significant concern in this environment. When allocating resources, the distribution of VMs is unbalanced, and their movement from one server to another can increase energy consumption and network overhead, necessitating an improvement in VM migrations. This paper addresses the VMs migration issue by applying a machine learning model to reduce the VMs migration number and energy consumption. The proposed algorithm (named VMLM) is based on improving VM's migration process and selection. It has been benchmarked with JVCMMD and EVSP solutions. The simulation results demonstrate the efficiency of our proposal, which includes two phases the machine learning preparing stage and the VMs migration stage.
Comparison of Wavelet Artificial Neural Network, Wavelet Support Vector Machine, and Adaptive Neuro-Fuzzy Inference System Methods in Estimating Total Solar Radiation in Iraq

Estimating the amount of solar radiation is very important in evaluating the amount of energy that can be received from the sun for the construction of solar power plants. Using machine learning tools to estimate solar energy can be a helpful method. With a high number of sunny days, Iraq has a high potential for using solar energy. This study used the Wavelet Artificial Neural Network (WANN), Wavelet Support Vector Machine (WSVM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) techniques to estimate solar energy at Wasit and Dhi Qar stations in Iraq. RMSE, EMA, R2, and IA criteria were used to evaluate the performance of the techniques and compare the results with the actual measured value. The results showed that the WANN and WSVM methods had similar results in solar energy modeling. However, the results of the WANN technique were slightly better than the WSVM technique. In Wasit and Dhi Qar stations, the value of R2 for the WANN and WSVM methods was 0.89 and 0.86, respectively. The value of R2 in the WANN and WSVM methods in Wasit and Dhi Qar stations was 0.88 and 0.87, respectively. The ANFIS technique also obtained acceptable results. However, compared to the other two techniques, the ANFIS results were lower, and the R2 value was 0.84 and 0.83 in Wasit and Dhi Qar stations, respectively.
Emoji Alter the Perception of Emotion in Affectively Neutral Text messages

Abstract Previous studies of emoji effects on text sentiment demonstrate mixed findings. Further, these studies are limited by confounds, e.g., underlying text sentiment, lack of ecological validity. We considered emoji effects on the emotional valence of affectively neutral English-language text messages. We additionally considered differences across US-American, British, and Danish participants. 217 participants considered screenshots of question-and-response text message exchanges with/without emoji, in a 4 (emoji type: no emoji, negative, neutral, positive) × 3 (nationality: American, British, Danish) mixed-factors design. Cumulative link mixed-effects models demonstrated that messages + negative emoji were rated more-negatively than any other emoji conditions. Responses + positive emoji were rated more-positively than any other emoji condition. Responses + neutral emoji and responses without emoji were perceived as equally emotive. There was no emoji type × nationality interaction, suggesting that emoji effects were consistent across participants. Findings are considered viz linguistic processing, social interactions, education, marketing, and public health interventions.
A comprehensive and systematic review of the banking systems based on pay-as-you-go payment fashion and cloud computing in the pandemic era

The early identification of COVID-19 is critical to prevent the disease from spreading at the community level. Cloud computing allows healthcare providers to enhance patient care, exchange information more quickly, increase operational efficiency, and save expenses. Thus, this research investigates cloud computing applications in the context of an outbreak. We gathered, summarized, and evaluated scientific papers on cloud computing throughout an outbreak that were released between 2020 and April 2022 for this Systematic Literature Review (SLR). We employed a pre-defined review methodology to examine commonly known electronic datasets. Keywords were used to look for all publications connected to the subject. 18 papers were chosen for this SLR after thoroughly following the research selection method. This review paper offers current state-of-the-art outcomes and methodologies on cloud computing throughout an epidemic, outlining research gaps and providing directions for future research. The results showed that cloud computing plays a crucial role in addressing and relieving side effects in vital situations such as the coronavirus epidemic. We tried to provide a comprehensive study, but non-English articles were not included in this study, so future studies can provide a more comprehensive study by considering all articles in Chinese, Japanese, etc.
Performance and Next-Generation Development of the Finite-Fault Rupture Detector (FinDer) within the United States West Coast ShakeAlert Warning System

ABSTRACT Rapid fault rupture information is important to estimate seismic ground motions and damage in large earthquakes, and is, therefore, of great value for earthquake early warning (EEW) and rapid response. The Finite-Fault Rupture Detector (FinDer) algorithm computes earthquake line-source models by comparing spatial distributions of high-frequency seismic amplitudes with precomputed template maps. FinDer is one of two seismic EEW algorithms currently adopted by the United States West Coast ShakeAlert EEW system. Between March 2018 and October 2022, FinDer detected 1048 earthquakes (2.3 ≤ M ≤ 7.1) inside the FinDer-reporting region in California, Oregon, and Washington with a median detection time of 8.5 s (75th and 95th percentile: 11.5 s, 38.9 s) after event origin and median errors (first report) of 6.7 km (75th and 95th percentile: 10.5 km, 25.5 km) in location, −0.45 s (mean ± st. dev.: 0.1 ± 5.9 s) in origin time, and 0.33 units (mean ± st. dev.: 0.33 ± 0.31 m.u.) in magnitude. Ground motions estimated using FinDer source parameters are in excellent agreement with observed peak ground accelerations, and residuals are, on average, 30% smaller than if predicted from catalog source parameters. This suggests that FinDer’s simple source parameter terms are accounting for more complex high-frequency source characteristics. This article summarizes the performance of FinDer in ShakeAlert and describes the recent improvements to the algorithm addressing issues encountered during real-time operation. This includes the handling of latent seismic data, robust event detection in regions with sparse instrumentation, enabling faster magnitude convergence in large earthquakes, use of fault- and scenario-specific earthquakes (e.g., along the Cascadia subduction zone or San Andreas fault), as well as increased robustness of FinDer in complex earthquake sequences. We demonstrate the performance of the new FinDer version 3 algorithm using waveform playbacks of selected events along the U.S. West Coast, Japan, and China, including both historic and synthetic earthquakes.
Mitigating bureaucratic inefficiencies through blockchain technology in Africa

With the Fourth Industrial Revolution (4IR) wave engulfing African governments, the need to do, and use something new has already infiltrated many public sector organizations. While modern technologies are being embraced in the private sector, African governments are emulating new technologies and other Information Communication Technologies (ICTs) to advance their economies while managing the risk that these sophisticated technologies can trigger. Blockchain technology is one of the emerging 4IR technology that is believed to have the capacity to mitigate bureaucratic inefficiencies, although scholars argue implementing such comes at a higher price. To understand how blockchain can help reduce inefficiencies in African bureaucracy, the researchers employed the systematic literature review analysis where documents from various databases such as Scopus, Web of Science, and Google Scholar were systematically sampled depending on how they offer meaningful data concerning blockchain implementation. The analyses of these secondary sources revealed multiple challenges and opportunities associated with blockchain technology in the African government. The challenges include poor project management, weak institutions that do not uphold accountability and transparency in data entry using blockchain, unavailability of blockchain infrastructure, risk-averse attitude, and absence of institutional readiness. By implementing enabling technology policies in government, the study revealed that blockchain could help improve taxation in African bureaucracies and mitigate data altering and errors while maximizing efficiency. Further merits in public healthcare and education can be realized by using blockchain technology. The conclusions drawn from this study have shown that for African bureaucracy to thrive using blockchain technology, there is a need to prepare public sector institutions to embrace blockchain technology. At the same time, investment in soft and technical skills remains fundamental to mitigate inefficiencies in public service provision. Institutional readiness is another deterrent to blockchain technology as public administration regard this technology as demanding since it may require change, and management where institutions and structures are reshuffled to respond to the demands of blockchain technology in the delivery of public goods.
Insights into Regional Metallogeny from Detailed Compositional Studies of Alluvial Gold: An Example from the Loch Tay Area, Central Scotland

Compositional features of a total of 1887 gold alluvial particles from six localities to the south of Loch Tay in central Scotland were interpreted to establish different types of source mineralization. Populations of gold particles from each locality were grouped according to alloy and inclusion signatures. Inclusion suites provided the primary discriminant with gold from Group 1 localities showing a narrow range of simple sulphide and sulphoarsenide inclusion species, whereas a wide range of minerals including molybdenite, bornite and various Bi and Te- bearing species were identified in gold from Group 2 localities. Whilst the range of Ag in alloys in all populations was similar, higher incidences of measurable Hg and Cu were detected in Group 1 and Group 2 gold samples respectively. The application of compositional templates of gold from other localities worldwide indicated that Group 1 gold is orogenic and Group 2 gold is a mixture of porphyry and epithermal origin; a result that is sympathetic to the spatial relationships of sample localities with local lithologies. This approach both provides an enhanced level of understanding of regional gold metallogeny where in situ sources remain undiscovered, and permits clearer targeting of deposit types during future exploration.
Underwater self-supervised monocular depth estimation and its application in image enhancement

Accurate depth estimation is necessary for imaging model-based underwater image enhancement. However, limited by the length of baseline, traditional binocular-based methods are not easy to deploy in underwater scenarios, while monocular methods are more appropriate due to their availability and cost. Therefore, we propose a self-supervised model (called UWdepth) to estimate the depth of underwater scenes with monocular sequences. Addressing the challenges posed by the scale of data and the complexity of motion in underwater scenes, we design an iterative pose network and further introduce a depth consistency loss to achieve more accurate inter-frame motion prediction and depth estimation. The predicted depth can be used to precisely enhance the image based on the underwater imaging model. Experiments show that UWdepth outperforms existing depth estimation models in terms of multiple evaluation indexes. Furthermore, by applying UWdepth, we can enhance underwater images with Akkaynak–Treibitz imaging model, achieving better indexes and visual perception quality than existing traditional and deep-learning based underwater image enhancement algorithms.
Partial Unbalanced Feature Transport for Cross-Modality Cardiac Image Segmentation

Deep learning based approaches have achieved great success on the automatic cardiac image segmentation task. However, the achieved segmentation performance remains limited due to the significant difference across image domains, which is referred to as domain shift. Unsupervised domain adaptation (UDA), as a promising method to mitigate this effect, trains a model to reduce the domain discrepancy between the source (with labels) and the target (without labels) domains in a common latent feature space. In this work, we propose a novel framework, named Partial Unbalanced Feature Transport (PUFT), for cross-modality cardiac image segmentation. Our model facilities UDA leveraging two Continuous Normalizing Flow-based Variational Auto-Encoders (CNF-VAE) and a Partial Unbalanced Optimal Transport (PUOT) strategy. Instead of directly using VAE for UDA in previous works where the latent features from both domains are approximated by a parameterized variational form, we introduce continuous normalizing flows (CNF) into the extended VAE to estimate the probabilistic posterior and alleviate the inference bias. To remove the remaining domain shift, PUOT exploits the label information in the source domain to constrain the OT plan and extracts structural information of both domains, which are often neglected in classical OT for UDA. We evaluate our proposed model on two cardiac datasets and an abdominal dataset. The experimental results demonstrate that PUFT achieves superior performance compared with state-of-the-art segmentation methods for most structural segmentation.