Reviewerly/exHarmony huggingface dataset:
Estimation of uranium concentration in ore samples with machine learning methods on HPGe gamma-ray spectra

Within the scope of determining the concentration of uranium in ore samples by gamma-ray spectrometry, we tested a series of machine-learning (ML) algorithms with a database including 1288 HPGe gamma spectra measured by Orano Mining. Instead of detecting and identifying peaks, a global interpretation of the spectra is carried out. Two different approaches were used. First, we reduced the size and the dimension of the dataset by selecting 728 spectra acquired with a same experimental setup and by resampling their 8192 channels into 168 energy bands according to the important peaks due to the natural uranium, thorium and potassium activity. Classical ML algorithms have been trained on this reduced dataset and the best uranium concentration predictions show Symmetric Mean Absolute Percentage Error lower than 6%. In a second step, the complete dataset with 1288 gamma spectra including six different measurement setups was used to train deep neural network with a re-sampling of the spectra into 2048 channels. Despite the small dataset, a Convolutional Neural Network (CNN) model provides satisfactory results with mean errors lower than 15% on this broader and more complex dataset in terms of uranium concentrations and experimental setups. These outcomes shows that ML methods can predict uranium concentration with similar uncertainties as classical gamma-ray spectroscopy (10% to 20%), but without requiring an expert knowledge to interpret the spectra.
From Truth Degree Comparison Games to Sequents-of-Relations Calculi for Gödel Logic

Abstract We introduce a game for (extended) Gödel logic where the players’ interaction stepwise reduces claims about the relative order of truth degrees of complex formulas to atomic truth comparison claims. Using the concept of disjunctive game states this semantic game is lifted to a provability game, where winning strategies correspond to proofs in a sequents-of-relations calculus.
Investigating Cognitive Load in Energy Network Control Rooms: Recommendations for Future Designs

This study analyzed and explored the cognitive load of Australian energy market operators managing one of the longest inter-connected electrical networks in the world. Each operator uses a workstation with seven screens in an active control room environment, with a large coordination screen to show information and enable collaboration between different control centers. Cognitive load was assessed during both training scenarios and regular control room operations via the integration of subjective and physiological measures. Eye-tracking glasses were also used to analyze the operators gaze behavior. Our results indicate that different events (normal or unexpected), different participants for the same session, and different periods of one session all have varying degrees of cognitive load. The system design was observed to be inefficient in some situations and to have an adverse affect on cognitive load. In critical situations for instance, operator collaboration was high and the coordination screen was used heavily when collaborating between two control centers, yet integration with the system could be improved. Eye tracking data analysis showed that the layout of applications across the seven screens was not optimal for many tasks. Improved layout strategies, potential combination of applications, redesigning of certain applications, and linked views are all recommended for further exploration in addition to improved integration of procedures and linking alarms to visual cues.
A live digital forensics approach for quantum mechanical computers

In this paper, an exploration of reversing quantum computer gates is addressed as an avenue for collecting forensic evidence from a quantum computer. To date, little forensic research exists on quantum computing systems in general, and practically no experiments exist in the live recovery context. This work discusses the means for live forensics of quantum computers via both a look at current research on the matter, and through a demonstration of live data collection. The results are a combination of analysis conducted on real quantum systems to produce a quantum forensic methodology. Furthermore, this work will highlight the viability of live forensics, and largely refute Overill's assertion that it is not possible to perform live forensics on quantum systems. We believe that this work represents a very strong step towards revolutionizing the entire field of quantum forensics.
TDOPS: Time series based deduplication and optimal data placement strategy for IIoT in cloud environment

As a result of the advancements in Industry 4.0, the amount of data collected within industries are continuously expanding to achieve an innovative environment within the industry by maximizing asset usage. Meanwhile, the redundancy rate is increasing in cloud storage, which has an impact on data storage and analysis. To lower the rate of redundancy, the proposed system comprises a Time series-based deduplication technique. In the Time series-based deduplication technique, the Adaptive Multi-Pattern Boyer Moore Horspool (AM-BMH) algorithm, and Merkle tree were used to produce time-series data. Another significant challenge is that the geographically distributed cloud system has encountered that the data placement methodology with high-priced transportation costs for data transmission. To overcome this issue, an optimal data placement strategy using Modified Distribution is proposed. Thus the proposed Time Series-based Deduplication and Optimal Data Placement Strategy (TDOPS) is found to be effective when compared with the existing system. The various parameters like space reduction, efficient retrieval, data transportation costs, and data transmission time are taken into the account in the cloud environment for an evaluation. The proposed scheme saves 98 percent of storage space, 55 percent computation overhead, and improves 60% of cloud storage efficacy.
Multi-view clustering by virtually passing mutually supervised smooth messages

While the existing multi-view affinity propagation (AP)-based clustering method inevitably works with more than one random initialization and parameter, a novel algorithm called MVCPMM is proposed from a new perspective to achieve more consistent multi-view clustering results with only one random initialization and one parameter. The proposed virtual function nodes added between the variable nodes of the subgraphs of the AP factor graphs (i.e., individual views), enable the core of MVCPMM to pass mutually supervised smooth messages across different views and subsequently exchange the messages within individual views in a mutually supervised manner to encourage the clustering quality of individual views. In addition to maintaining the cluster diversity of individual views, MVCPMM penalizes the changes in the cluster structures of different views by using mutually supervised smooth messages as bidirectional cross-view messages, which can effectively improve the consensus of exemplars across different views. Experimental results on both synthetic and benchmark multi-view datasets demonstrate the superiority of MVCPMM in contrast to several state-of-the-art multi-view clustering methods in terms of both clustering performance and clustering consistency across different views.
Underwater image enhancement using the revised haze-lines method

Visible light is servely absorbed and scattered by the water when it is propagated underwater. The attenuiation coefficient, including the absorbtion and scattering, is related to the light wavelength closely. As a result, underwater image suffers from low contrast, low visibility and color distortion, leading to the difficulty increasement of visible light detection for underwater objects. In order to improve the quality of the underwater image, a revised veiling light estimation and image recovery approach is proposed based on the haze-lines method. Image region is splited into strikes to find the haze-lines. The consumption of veiling light estimation is reduced. Underwater image recovery results show that image with different color objects is enhanced obviously with the revised hazelines method. Except for the improvement of contrast and visibility, color fidelity is corrected as well, which is benefit for object detection.
Community networks as models to address connectivity gaps in underserved communities

The mounting evidence that current efforts to meet international goals for increasing Internet connectivity are likely to fall short has heightened interest in alternative connectivity models such as community networks. Already, there are many such initiatives globally taking differentiated approaches towards their setup and operation. This results in fragmented knowledge on the initiatives and systematic analysis of these models is limited. This study applies the People, Technology, Organization and Environment (TOE) framework to examine 21 community networks across countries in different continents to produce insights into their structure and components. The aim is to identify ways in which initiatives become sustainable. Data for the study were collected in two steps. First, qualitative data were gathered using in-depth interviews to produce a database of case studies. Second, the qualitative data were quantized, which enables the researchers to produce descriptive data. Overall, the study findings demonstrate the existence of several technology options, but the main technology used for community level connectivity is Wi-Fi. Initiatives also adopt different organizational approaches, but strategic partnerships are critical for the success of the initiatives. While strategic partners provide support for the initial set up lack of business models affect the sustainability of initiatives. Also, while environment-related issues (regulation, infrastructure) play a significant role, they are malleable as shown by changes in regulatory regimes. This study findings are useful to practitioners implementing community networks, researchers looking to gain more insights on these models and policy makers responsible for the telecommunication, and digital policies.
Performance Evaluation using Online Machine Learning Packages for Streaming Data

Online machine learning concept concentrates on real-time and dynamic data. This dynamic data is in the form of data streams which is a continuous flow of data of infinite length. Recently many online machine learning tools were made available for evaluating performance metrics. Most of the approaches use online machine learning packages such as MLlib, spark streaming, scikit-multiflow, Creme and River. In this paper, performance evaluation metrics are evaluated for streaming data for three popular classification models: Logistic Regression (LR), K-Nearest Neighbors (KNN) and Gaussian Naive Bayes. For the evaluations, Creme and River online machine learning packages have been used. In the implementation phase, metrics are evaluated for four popular datasets: Elec2, Phishing, Bananas, and CreditCard. The accuracy, ROC-AUC, precision, recall and F1-score performance metrics are used for the comparison of the online classifiers. After analyzing the results of the four datasets, it is observed that the performance evaluation using the River online machine learning package is more accurate and reliable.
High temporal variability of surface solar irradiance due to cloud enhancement effect over the Western Ghat mountains in peninsular India

Western Ghat (WG) mountains along the west coast of peninsular India play an important role in the distribution of monsoon rainfall on its windward and leeward sides. The study unravels the increase in cloudiness and also increase in the Cloud Enhancement of Solar irradiance (CES) during the dry and wet seasons in the windward (high altitude) and the leeward (low altitude) sides of WG. From May 2018 to May 2021, the global horizontal irradiance (GHI) reveals wide temporal and spatial variability with a high frequency of CES events, even exceeding the extraterrestrial value around the noontime during the wet season (May to October). The observed excess irradiance due to partial/transparent clouds compared to clear sky maxima is more than 400 W/m2 on the windward and 350 W/m2 on the leeward. With the increase in cloudy days, diffused irradiance also increased with greater values during CES events. CES induced positive shortwave cloud radiative forcing (CRF) varied up to +400 W/m2 (warming effect), while negative CRF varied up to −1000 W/m2 (cooling effect). During monsoon, the warming due to frequent CES events could locally offset a large cooling to some extent. Clouds resulting in huge irradiance variability in a day could also induce large fluctuations in renewable solar energy. Such short-term sporadic local events are averaged out in satellite data.
End‐to‐end Tangut character database building and recognition method

Character recognition is an important research topic nowadays, and a large amount of excellent work has appeared. In contrast, research related to the recognition of Tangut characters is still in the initial stage. Creating databases and effective recognition methods that can support the recognition of Tangut characters remain a great challenge. In this paper, a labeling method based on Multi-Model and Multi-Prediction (MMMP) is proposed, which built a Tangut character database (TCD) and an enhanced database (called “TCD-E”) covering 6077 classes, and five test sets were also built for specific tasks. To recognize Tangut characters effectively and quickly, a 5-layer end-to-end Tangut Characters Recognition Network (TCRNet) based on CNN using shallow neural networks is designed. Its recognition accuracy on TCD-E reaches 97.96 % $\%$ . Based on TCRNet, an end-to-end Similar Tangut Characters Recognition Network (STCRNet) is further proposed by improving the loss function by combining the softmax loss function with the central loss function, and its test accuracy on similar Tangut characters test set (called “TCD-E-S”) is 0.70 % $\%$ higher than TCRNet. Experiments show that TCD and TCD-E can provide data support for Tangut character recognition. The recognition accuracy of TCRNet and STCRNet surpasses the previous best results.
MVVA-Net: a Video Aesthetic Quality Assessment Network with Cognitive Fusion of Multi-type Feature–Based Strong Generalization

With the increasing popularity of short videos on various social media platforms, there is a great challenge for evaluating the aesthetic quality of these videos. In this paper, we first construct a large-scale and properly annotated short video aesthetics (SVA) dataset. We further propose a cognitive multi-type feature fusion network (MVVA-Net) for video aesthetic quality assessment. MVVA-Net consists of two branches: intra-frame aesthetics branch and inter-frame aesthetics branch. These two branches take different types of video frames as input. The inter-frame aesthetic branch extracts the inter-frame aesthetic features based on the sequential frames extracted at fixed intervals, and the intra-frame aesthetic branch extracts the intra-frame aesthetic features based on the key frames extracted by the inter-frame difference method. Through the adaptive fusion of inter-frame aesthetic features and intra-frame aesthetic features, the video aesthetic quality can be effectively evaluated. At the same time, MVVA-Net has no fixed number of input frames, which greatly enhances the generalization ability of the model. We performed quantitative comparison and ablation studies. The experimental results show that the two branches of MVVA-Net can effectively extract the intra-frame aesthetic features and inter-frame aesthetic features of different videos. Through the adaptive fusion of intra-frame aesthetic features and inter-frame aesthetic features for video aesthetic quality assessment, MVVA-Net achieves better classification performance and stronger generalization ability than other methods. In this paper, we construct a dataset of 6900 video shots and propose a video aesthetic quality assessment method based on non-fixed model input strategy and multi-type features. Experimental results show that the model has a strong generalization ability and achieved a good performance on different datasets.
Mining Order-preserving Submatrices under Data Uncertainty: A Possible-world Approach and Efficient Approximation Methods

Given a data matrix \( D \) , a submatrix \( S \) of \( D \) is an order-preserving submatrix (OPSM) if there is a permutation of the columns of \( S \) , under which the entry values of each row in \( S \) are strictly increasing. OPSM mining is widely used in real-life applications such as identifying coexpressed genes and finding customers with similar preference. However, noise is ubiquitous in real data matrices due to variable experimental conditions and measurement errors, which makes conventional OPSM mining algorithms inapplicable. No previous work on OPSM has ever considered uncertain value intervals using the well-established possible world semantics. We establish two different definitions of significant OPSMs based on the possible world semantics : (1) expected support-based and (2) probabilistic frequentness-based. An optimized dynamic programming approach is proposed to compute the probability that a row supports a particular column permutation, with a closed-form formula derived to efficiently handle the special case of uniform value distribution and an accurate cubic spline approximation approach that works well with any uncertain value distributions. To efficiently check the probabilistic frequentness, several effective pruning rules are designed to efficiently prune insignificant OPSMs; two approximation techniques based on the Poisson and Gaussian distributions, respectively, are proposed for further speedup. These techniques are integrated into our two OPSM mining algorithms, based on prefix-projection and Apriori, respectively. We further parallelize our prefix-projection-based mining algorithm using PrefixFPM, a recently proposed framework for parallel frequent pattern mining, and we achieve a good speedup with the number of CPU cores. Extensive experiments on real microarray data demonstrate that the OPSMs found by our algorithms have a much higher quality than those found by existing approaches.
Interactive Intelligent Tools for Creative Processes using Multimodal Information

The individual differences in human behavior, combined with the ubiquity and constant evolution of computing devices, led to different forms of human-computer interaction. Keyboard, touch, voice, or gesture, among others, are used to provide input and receive feedback. However, applications that provide users with multimodal interaction for creative processes are usually agnostic about media content and user profile, unable to highlight significant elements that may be useful to foster creativity. This work's primary goal is to design models and provide techniques to create intelligent interactive tools that use rich multimodal information to support people in creative processes.
Access Control of Object Detection Models Using Encrypted Feature Maps

In this paper, we propose an access control method for object detection models. The use of encrypted images or encrypted feature maps has been demonstrated to be effective in access control of models from unauthorized access. However, the effectiveness of the approach has been confirmed in only image classification models and semantic segmentation models, but not in object detection models. In this paper, the use of encrypted feature maps is shown to be effective in access control of object detection models for the first time.
A Linearly Convergent Douglas-Rachford Splitting Solver for Markovian Information-Theoretic Optimization Problems

In this work, we propose solving the Information Bottleneck (IB) and Privacy Funnel (PF) problems with Douglas-Rachford Splitting methods (DRS). We study a general Markovian information-theoretic Lagrangian that includes IB and PF into a unified framework. We prove the linear convergence of the proposed solvers using the Kurdyka- ojasiewicz inequality. Moreover, our analysis is beyond IB and PF and applies to any convex-weakly convex pair objectives. Based on the results, we develop two types of linearly convergent IB solvers, with one improves the performance of convergence over existing solvers while the other can be independent to the relevance-compression trade-off. Moreover, our results apply to PF, yielding a new class of linearly convergent PF solvers. Empirically, the proposed IB solvers IB obtain solutions that are comparable to the Blahut-Arimoto-based benchmark and is convergent for a wider range of the penalty coefficients than existing solvers. For PF, our non-greedy solvers can characterize the privacy-utility trade-off better than the clustering-based greedy solvers.
A Machine-learning-Aided Visual Analysis Workflow for Investigating Air Pollution Data

Analyzing air pollution data is challenging as there are various analysis focuses from different aspects: feature (what), space (where), and time (when). As in most geospatial analysis problems, besides high-dimensional features, the temporal and spatial dependencies of air pollution induce the complexity of performing analysis. Machine learning methods, such as dimensionality reduction, can extract and summarize important information of the data to lift the burden of understanding such a complicated environment. In this paper, we present a methodology that utilizes multiple machine learning methods to uniformly explore these aspects. With this methodology, we develop a visual analytic system that supports a flexible analysis workflow, allowing domain experts to freely explore different aspects based on their analysis needs. We demonstrate the capability of our system and analysis workflow supporting a variety of analysis tasks with multiple use cases.
A quantum searching model finding one of the edges of a subgraph in a complete graph

Some of the quantum searching models have been given by perturbed quantum walks. Driving some perturbed quantum walks, we may quickly find one of the targets with high probability. In this paper, we address a discrete-time quantum walk. We construct a quantum searching model finding one of the edges of a given subgraph in a complete graph. How to construct our model is that we label the arcs by $$+1$$ or $$-1$$ , and define a perturbed quantum walk by the sign function on the set of arcs. After that, we detect one of the edges labeled $$-1$$ by the induced sign function as fast as possible. This idea was firstly proposed by Segawa (Quantum Inf Process 20:182, 2021). They only addressed the case where the subgraph forms a matching, and obtained by a combinatorial argument that the time of finding one of the edges of the subgraph is quadratically faster than a classical searching model. In this paper, we show that the model is valid for any subgraph, i.e., we obtain by spectral analysis a quadratic speed-up for finding one of the edges of the subgraph in a complete graph.