Reviewerly/exHarmony huggingface dataset:
Trio-based collaborative multi-view graph clustering with multiple constraints

Multi-view graph clustering is an attentional research topic in recent years due to its wide applications. According to recent surveys, most existing works focus on incorporating comprehensive information among multiple views to achieve the clustering task. However, these studies pay less attention to explore the collaborative relationship between fusion-view features and independence-view features. To make full use of view relationships and enhance the complementary benefits of different views in graphs, we propose a trio-based collaborative learning framework for multi-view graph representation clustering (TCMGC) that drives the multiple auto-clustering constraints. We utilize the triplet operations (trio-based) to guarantee the independence and complementarity between each view and complete clustering tasks collaboratively. Meanwhile, we propose a joint optimization objective to improve the overall performance of representation learning and clustering. Experimental results on four real-world benchmark datasets show that the proposed TCMGC has promising performance compared with state-of-the-art baseline methods.
Phase Compensation of the Non-Uniformity of the Liquid Crystal on Silicon Spatial Light Modulator at Pixel Level

Phase compensation is a critical step for the optical measuring system using spatial light modulator (SLM). The wavefront distortion from SLM is mainly caused by the phase modulation non-linearity and non-uniformity of SLM’s physical structure and environmental conditions. A phase modulation characteristic calibration and compensation method for liquid crystal on silicon spatial light modulator (LCoS-SLM) with a Twyman-Green interferometer is illustrated in this study. A method using two sequences of phase maps is proposed to calibrate the non-uniformity character over the whole aperture of LCoS-SLM at pixel level. A phase compensation matrix is calculated to correct the actual phase modulation of the LCoS-SLM and ensure that the designed wavefront could be achieved. Compared with previously known compensation methods, the proposed method could obtain the phase modulation characteristic curve of each pixel on the LCoS-SLM, rather than a mono look-up table (LUT) curve or multi-LUT curves corresponding to an array of blocks over the whole aperture of the LCoS-SLM. The experiment results show that the phase compensation precision could reach a peak-valley value of 0.061λ in wavefront and this method can be applied in generating freeform wave front for precise optical performance.
Simplifying Reinforced Feature Selection via Restructured Choice Strategy of Single Agent

Feature selection aims to select a subset of features to optimize the performances of downstream predictive tasks. Recently, multi-agent reinforced feature selection (MARFS) has been introduced to automate feature selection, by creating agents for each feature to select or deselect corresponding features. Although MARFS enjoys the automation of the selection process, MARFS suffers from not just the data complexity in terms of contents and dimensionality, but also the exponentially-increasing computational costs with regard to the number of agents. The raised concern leads to a new research question: Can we simplify the selection process of agents under reinforcement learning context so as to improve the efficiency and costs of feature selection? To address the question, we develop a single-agent reinforced feature selection approach integrated with restructured choice strategy. Specifically, the restructured choice strategy includes: 1) we exploit only one single agent to handle the selection task of multiple features, instead of using multiple agents. 2) we develop a scanning method to empower the single agent to make multiple selection/deselection decisions in each round of scanning. 3) we exploit the relevance to predictive labels of features to prioritize the scanning orders of the agent for multiple features. 4) we propose a convolutional auto-encoder algorithm, integrated with the encoded index information of features, to improve state representation. 5) we design a reward scheme that take into account both prediction accuracy and feature redundancy to facilitate the exploration process. Finally, we present extensive experimental results to demonstrate the efficiency and effectiveness of the proposed method.
Geology and geochemistry-based metallogenic exploration model for the eastern Tethys Himalayan metallogenic belt, Tibet

The Tethyan Himalaya is the fourth metallogenic belt discovered to date within the Tibetan Plateau and contains chromite, gold and Sb–polymetallic mineralization. The belt records a series of complex tectonic–magmatic events that include the break-up, subduction, collision, detachment, and obduction of the Eastern Gondwana continent, all of which generated ideal conditions for ore formation. However, this area remains under-explored, with only limited knowledge of the deposit types present in this area and their relationship to tectonic and magmatic events. This has hampered mineral exploration in the region. This study uses previous research and mineral exploration in this region, combined with knowledge of mineralization in adjacent regions, to suggest the presence of three main deposit types (ophiolitic chromite, orogenic Au, and hydrothermal vein Sb–polymetallic deposits), and highlights the potential for this area to host as yet undiscovered island arc-type porphyry Cu–Au and leucogranite-related Sn–(Be) mineralization. This study summaries the relationship between the tectonic evolution and magmatism within the region and the mineralization, establishing a temporal and spatial framework for mineralization within the eastern Tethys Himalayan metallogenic belt. Improved knowledge of the geochemical signatures of mineralization, obtained using compositional data analysis (CoDA) combined with geological information, enabled the identification of nine compositional balances, including the presence of sedimentary units, accretionary wedge structures, leucogranites, mafic–ultramafic rocks, chromite mineralization, Au mineralization, and Sb–polymetallic mineralization. This study establishes a predictive mineral exploration model for the eastern Tethys Himalayan metallogenic belt and combines this model with geological and geochemical data to identify areas for future exploration. These targets include a 50-km-long ophiolitic belt in the Nedong area that is highly prospective for chromite mineralization; a positive Au–Cu–Mo geochemical anomaly in the Qonggyai–Qusum area that is prospective for orogenic Au mineralization; and positive Au–Cu–Mo geochemical anomalies in the Nianzha, Sangri, and Gyaca areas that are prospective for exploration for Xiongcun-type island arc-associated porphyry Cu–Au mineralization, as well as deep-seated extensions to known Sb–polymetallic mineralization and the inner and outer contacts of the Lhozhag and Cuonadong leucogranite, all of which are prospective for exploration for Sn–(Be) mineralization.
Ensemble-Based Logistic Model Trees for Website Phishing Detection

The adverse effects of website phishing attacks are often damaging and dangerous as the information gathered from unsuspecting users are used inappropriately and recklessly. Several solutions have been proposed to curb website phishing attacks and to mitigate its impact. However, most of these solutions are rather ineffective due to the evolving and dynamic processes used for phishing attacks. Recently, machine learning (ML)-based solutions are deployed in addressing the phishing attacks due to its ability to deal with the dynamic nature of phishing attacks. Nonetheless, ML solutions suffer drawbacks in the case of high false alarm rates and the need to further improve the detection accuracies of existing ML solutions as proposed in the literature. Considering the dynamism of phishing attacks, there is a continuous need for novel and effective ML-based methods for detecting phishing websites. This study proposed an ensemble-based Logistic Model Trees (LMT) for website phishing attack detection. LMT is the combination of logistic regression and tree induction methods into a single model tree. Experimental results showed that the proposed methods (ABLMT: AdaBoostLMT and BGLMT: BaGgingLMT) are highly effective for website phishing attack detection with the least accuracy of 97.18% and 0.996 AUC values. Besides, the proposed methods outperform some ML-based phishing attack models from recent existing studies. Hence, the proposed methods are recommended for addressing website phishing attacks with dynamic properties.
Classifier Selection and Ensemble Model for Multi-class Imbalance Learning in Education Grants Prediction

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Meaningful Answer Generation of E-Commerce Question-Answering

In e-commerce portals, generating answers for product-related questions has become a crucial task. In this article, we focus on the task of product-aware answer generation , which learns to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes. However, safe answer problems (i.e., neural models tend to generate meaningless and universal answers) pose significant challenges to text generation tasks, and e-commerce question-answering task is no exception. To generate more meaningful answers, in this article, we propose a novel generative neural model, called the Meaningful Product Answer Generator ( MPAG ), which alleviates the safe answer problem by taking product reviews, product attributes, and a prototype answer into consideration. Product reviews and product attributes are used to provide meaningful content, while the prototype answer can yield a more diverse answer pattern. To this end, we propose a novel answer generator with a review reasoning module and a prototype answer reader. Our key idea is to obtain the correct question-aware information from a large-scale collection of reviews and learn how to write a coherent and meaningful answer from an existing prototype answer. To be more specific, we propose a read-and-write memory consisting of selective writing units to conduct reasoning among these reviews . We then employ a prototype reader consisting of comprehensive matching to extract the answer skeleton from the prototype answer. Finally, we propose an answer editor to generate the final answer by taking the question and the above parts as input. Conducted on a real-world dataset collected from an e-commerce platform, extensive experimental results show that our model achieves state-of-the-art performance in terms of both automatic metrics and human evaluations. Human evaluation also demonstrates that our model can consistently generate specific and proper answers.
Mineral prospectivity modelling using singularity mapping and multifractal analysis of stream sediment geochemical data from the auriferous Hutti-Maski schist belt, S. India

The regional stream sediment geochemical survey has become an all the more important exploration technique for delineating potential mineral zones to target exploration. However, the effective appraisal of pathfinder elements has remained a fundamental challenge in exploration geochemistry. In this regard, the singularity mapping technique based on modern nonlinear theory and fractal-multifractal geometry is a new efficient method to distinguish thresholds of anomalies based on stream sediment geochemical data. The auriferous Hutti-Maski schist belt in Eastern Dharwar Craton (EDC), S. India has been taken up for delineation future targets using local singularity analysis (LSA) of stream sediment geochemical data. Ordinary Singularity Mapping (OSM) and Spatially Weighted Singularity Mapping (SWSM) are employed for mapping singularity of gold pathfinder elements (Au, As, Ag, Hg, Sb, Se, Cu, and Zn). In SWSM, a distance-based fuzzy weighting method is used to estimate the spatial weighting factor with respect to ore controlling shear zones. The local isotropic singularity index and the local anisotropic singularity index are estimated for each of the eight elements. The Student's t-statistics based on Weight of Evidence (WOE) model is used to determine the threshold singularity index value to separate geochemical anomaly from background. Principal Component Analysis (PCA) is applied to singularity maps of multiple elements derived using OSM and SWSM techniques. The first principal component (PC-1) scores estimated by the OSM-PCA and SWSM-PCA lead to comprehensive geochemical anomaly maps. Results of two different singularity mapping techniques are compared using Geochemical Mineralization Probability Index (GMPI), Concentration-Area (C-A) fractal analysis and Prediction-Area (P-A) plots. It is observed that the SWSM method results in a smaller target with higher predictive ability compared to OSM method that delineates a larger target area.
Trustworthiness for Transportation Ecosystems: The Blockchain Vehicle Information System

Modern transportation systems, such as computer networks, have become increasingly faster, aiming to “shorten” distances and travel time. This trend allows thinking about new services and induces to reconsider existing ones starting from new technologies, as for Intelligent Transportation Systems. Thereby, an all-encompassing scenario laying at the intersection of several domains, including manufacturing, logistics, traveling, insurance, maintenance, and trading, with the transportation one, can be envisioned. The building block for the resulting transportation ecosystem is an information system that is able to gather and connect all involved stakeholders and domains around the concept of mobility and vehicle to address complex multifaceted problems in an efficient and trustworthy way. This paper proposes the adoption of distributed ledgers to implement such a vehicle-centric information system, distributing data across the network while ensuring trustworthiness. Starting from the vehicle lifecycle immutable and certified information, new services for cross-cuttingly addressing diversity and complexity in the transportation ecosystem can be implemented. The proposed solution merges Multichain and MongoDB technologies to achieve a trade-off between trustworthiness and performance, storing only the metadata in the Multichain network. Its effectiveness is demonstrated by an example on a vehicle trading service showing an overhead for the proposed solution below 18% against a pure MongoDB one on I/O operations.
Achieving Ultimate Noise Tolerance in Quantum Communication

Quantum communication is the most advanced cryptographic method to distribute secret keys securely through public channels, but actual performance is still limited, due to noise. This study explores the ultimate noise tolerance of quantum communication by considering a method based on spectral, temporal, and spatial mode filtering of single photons with high efficiency. The authors also show that by actively filtering photons to a nearly single mode, quantum communication can occur in environments that are noisier by three orders of magnitude than what traditional methods tolerate. This approach could bring quantum communication under realistic conditions a step closer to reality.
Performance Evaluation of Deep Learning-Based Prostate Cancer Screening Methods in Histopathological Images: Measuring the Impact of the Model’s Complexity on Its Processing Speed

Prostate cancer (PCa) is the second most frequently diagnosed cancer among men worldwide, with almost 1.3 million new cases and 360,000 deaths in 2018. As it has been estimated, its mortality will double by 2040, mostly in countries with limited resources. These numbers suggest that recent trends in deep learning-based computer-aided diagnosis could play an important role, serving as screening methods for PCa detection. These algorithms have already been used with histopathological images in many works, in which authors tend to focus on achieving high accuracy results for classifying between malignant and normal cases. These results are commonly obtained by training very deep and complex convolutional neural networks, which require high computing power and resources not only in this process, but also in the inference step. As the number of cases rises in regions with limited resources, reducing prediction time becomes more important. In this work, we measured the performance of current state-of-the-art models for PCa detection with a novel benchmark and compared the results with PROMETEO, a custom architecture that we proposed. The results of the comprehensive comparison show that using dedicated models for specific applications could be of great importance in the future.
Self-Paced Feature Attention Fusion Network for Concealed Object Detection in Millimeter-Wave Image

The active millimeter-wave (AMMW) scanner has been widely used for inspecting human security in public places in recent years owing to its ability to detect all kinds of objects under the clothes and be harmless to the body. However, it is really challenging to detect all concealed objects automatically and accurately due to inherent imaging noise, unknown object kind, and uncertain position. Recently, many existing methods, especially deep learning-based, have achieved good performances on concealed object detection. These methods work well for detecting a few kinds of large objects, but fail to perform on dim and incomplete hard objects. To address this task, a concealed object detection model with self-paced feature attention fusion network (SPFAFN) is proposed in this article. To be specific, the features with different scales are fused in a top-down manner to integrate details and global semantics to better detect small objects. During fusing multi-scale features, a hierarchical pyramid attention mechanism composed of channel and spatial attention is developed to perceive the object. Moreover, boosting self-paced learning is exploited to guide the model to learn hard samples that are difficultly detected. The proposed method is validated on two real-world datasets: an AMMW dataset and a publicly available passive millimeter-wave (PMMW) dataset. Experimental results demonstrate that the proposed approach is superior to the state-of-the-art methods, and achieves better performances on the two datasets with Average Precision (AP).
Validation and Performance of Satellite Meteorological Dataset MERRA-2 for Solar and Wind Applications

Fast-growing energy demand of the world makes the researchers focus on finding new energy sources or optimizing already-developed approaches. For an efficient use of solar and wind energy in an energy system, correct design and sizing of a power system is of high importance and improving or optimizing the process of data obtaining for this purpose leads to higher performance and lower cost per unit of energy. It is essential to have the most precise possible estimation of solar and wind energy potential and other local weather parameters in order to fully feed the demand and avoid extra costs. There are various methods for obtaining local data, such as local measurements, official organizational data, satellite obtained, and reanalysis data. In this paper, the Modern-Era Retrospective analysis for Research and Applications dataset version 2 (MERRA-2) dataset provided by NASA is introduced and its performance is evaluated by comparison to various locally measured datasets offered by meteorological institutions such as Meteonorm and Deutscher Wetterdienst (DWD, or Germany’s National Meteorological Service) around the world. After comparison, correlation coefficients from 0.95 to 0.99 are observed for monthly global horizontal irradiance values. In the case of air temperature, correlation coefficients of 0.99 and for wind speed from 0.81 to 0.99 are observed. High correlation with ground measurements and relatively low errors are confirmed, especially for irradiance and temperature values, that makes MERRA-2 a valuable dataset, considering its world coverage and availability.
Semantic Segmentation for Remote Sensing Images Based on Adaptive Feature Selection Network

Semantic segmentation plays a vital role in the segmentation of remote sensing field for its wide range of applications. The major current method for segmentation of remotely sensed imagery is using multiple scales strategy to improve the performance of segmentation networks. However, the ground object with uncertain scale in high-resolution aerial imagery is difficult to be segmented with conventional models. To address this problem, an adaptive feature selection module is designed, in which attention module learns weight contributions of each feature blocks in different scales. We employ the pyramid scene parsing network (PSPNet), DeepLabV3, and U-Net with the proposed module to conduct experiments on two benchmarks (the Vaihingen set and the WHU Building data set). The experimental results and comprehensive analysis validate the efficiency and practicability of the proposed method in semantic segmentation of remote sensing images.
UDC 2020 Challenge on Image Restoration of Under-Display Camera: Methods and Results

This paper is the report of the first Under-Display Camera (UDC) image restoration challenge in conjunction with the RLQ workshop at ECCV 2020. The challenge is based on a newly-collected database of Under-Display Camera. The challenge tracks correspond to two types of display: a 4k Transparent OLED (T-OLED) and a phone Pentile OLED (P-OLED). Along with about 150 teams registered the challenge, eight and nine teams submitted the results during the testing phase for each track. The results in the paper are state-of-the-art restoration performance of Under-Display Camera Restoration. Datasets and paper are available at https://yzhouas.github.io/projects/UDC/udc.html .
Credit Card Fraud Detection using Machine Learning and Deep Learning Techniques

In general, fraudulent activities are always intended to cause financial detriment to the second party. With the aggrandizement of digital money in various countries, the fraudulent activities will be even more increased. Credit card companies and Banks lose billions to such fraudulent activities every year, where it accounts to a huge part of their revenue and affects the jobs of various employees. The proposed research work discusses more about the different fraudulent activities associated with credit cards. While all of them cannot be dealt simultaneously, this research work discusses how Machine Learning and Neural Networks can be used to determine the potential fraudsters by referring to their previous mistakes and details of previous fraudsters. Machine Learning algorithms such as Multinomial Naive Bayes, Random Forest Regression, Logistic Regression, Support Vector Machine and a basic Neural Network are also used.
Coherence of operations and interferometry

Quantum coherence is one of the key features that fuels applications for which quantum mechanics exceeds the power of classical physics. This explains the considerable efforts that were undertaken to quantify coherence via quantum resource theories. An application of the resulting framework to concrete technological tasks is however largely missing. Here, we address this problem and connect the ability of an operation to detect or create coherence to the performance of interferometric experiments.