Reviewerly/exHarmony huggingface dataset:
Application of Geoportal’s Web-Technologies in GIS, Case Study: Interactive Geology Atlas

Abstract: A unique geoinformation service “The Interactive Geology Atlas” has been created and launched based on the data obtained by the Authors during years of research and evaluation of integrated biostratigraphic, paleomagnetic and sedimentological data as well as published references materials. The Interactive Atlas includes geological sections comprehensively studied, described and mapped. Contrary to analogous services, the Atlas covers more territory of Paratethys and time laps and allows to add various content depending on the user requirements. All maps (basemaps) are available for study both individually and joint for comprehensive analysis through the compiling the unified legend to all layers and maps. The combination of different data allows to track how the paleogeographic conditions of the Paratethys territory have evolved and to highlight the main events associated with fluctuations in climate, salinity, etc. Besides being scientifically interesting, the Atlas can become useful in geologic exploration with a view to evaluate the potential areas of mineral resources. For instance, paleogeographic maps are crucial in this regard. FAIR principles have become the reference criteria for improving datasets: Findability, Accessibility, Interoperability, and Reusability.
Nonlinear optimization algorithm using monotonically increasing quantization resolution

Abstract We propose a quantized gradient search algorithm that can achieve global optimization by monotonically reducing the quantization step with respect to time when quantization is composed of integer or fixed‐point fractional values applied to an optimization algorithm. According to the white noise hypothesis states, a quantization step is sufficiently small and the quantization is well defined, the round‐off error caused by quantization can be regarded as a random variable with identically independent distribution. Thus, we rewrite the searching equation based on a gradient descent as a stochastic differential equation and obtain the monotonically decreasing rate of the quantization step, enabling the global optimization by stochastic analysis for deriving an objective function. Consequently, when the search equation is quantized by a monotonically decreasing quantization step, which suitably reduces the round‐off error, we can derive the searching algorithm evolving from an optimization algorithm. Numerical simulations indicate that due to the property of quantization‐based global optimization, the proposed algorithm shows better optimization performance on a search space to each iteration than the conventional algorithm with a higher success rate and fewer iterations.
Secure cloud‐based data storage scheme using postquantum integer lattices‐based signcryption for IoT applications

Abstract The primary objective of postquantum cryptography (also known as quantum‐resistant cryptography) is to develop the cryptographic systems that need to be robust against both quantum and classical computers, and can also interoperate with the existing communications protocols and networks. In an Internet of Things (IoT) environment, the communicated messages contain sensitive information that are transmitted over an open channel, where message integrity and data privacy become challenging tasks. Although several traditional cryptographic security protocols can be applied for IoT security and data privacy, such as authentication, access control, key agreement, and digital signature, but they are not resilient against quantum attacks. To overcome these issues, in this article, we first present an advanced and efficient construction of postquantum lattice‐based signcryption scheme, and then apply the constructed lattice‐based signcryption in IoT applications, where data sensed by the deployed IoT smart devices is securely stored at the cloud, via the gateway nodes (aggregators). The data stored at the cloud servers cannot be even modified by them due to the involved signatures generated by the aggregators. The formal security analysis shows the robustness of our designed lattice‐based signcryption scheme. Other detailed information security analysis and a performance analysis with the traditional number‐theoretical based public key cryptosystems show the efficacy, and significantly better security and functionality features of the proposed scheme under the lattice‐based postquantum context.
Blockchain for content protection in E-healthcare: A case study for COVID-19

In the present scenario of globalization, technology advancement has changed the concept of data sharing drastically giving it an altogether new dimension. Availability of data online is susceptible to misuse if not secured. Hence, content protection has become a major issue and is a challenging task for researcher to attain a balance between availability and protection. Keeping in view this challenge, the paper aims at providing an insight into the emerging blockchain technology and its application in various sectors such as finance, data management, commercial and particularly healthcare. This paper further highlights COVID-19 pandemic disease management using blockchain technology with a brief discussion of the solution to resist coronavirus infection and its major challenges with solutions in implementing inherent key features of the blockchain.
Semi-Supervised Learning for Forklift Activity Recognition from Controller Area Network (CAN) Signals

Machine Activity Recognition (MAR) can be used to monitor manufacturing processes and find bottlenecks and potential for improvement in production. Several interesting results on MAR techniques have been produced in the last decade, but mostly on construction equipment. Forklift trucks, which are ubiquitous and highly important industrial machines, have been missing from the MAR research. This paper presents a data-driven method for forklift activity recognition that uses Controller Area Network (CAN) signals and semi-supervised learning (SSL). The SSL enables the utilization of large quantities of unlabeled operation data to build better classifiers; after a two-step post-processing, the recognition results achieve balanced accuracy of 88% for driving activities and 95% for load-handling activities on a hold-out data set. In terms of the Matthews correlation coefficient for five activity classes, the final score is 0.82, which is equal to the recognition results of two non-domain experts who use videos of the activities. A particular success is that context can be used to capture the transport of small weight loads that are not detected by the forklift’s built-in weight sensor.
Ad astra or astray: Exploring linguistic knowledge of multilingual BERT through NLI task

Abstract Recent research has reported that standard fine-tuning approaches can be unstable due to being prone to various sources of randomness, including but not limited to weight initialization, training data order, and hardware. Such brittleness can lead to different evaluation results, prediction confidences, and generalization inconsistency of the same models independently fine-tuned under the same experimental setup. Our paper explores this problem in natural language inference, a common task in benchmarking practices, and extends the ongoing research to the multilingual setting. We propose six novel textual entailment and broad-coverage diagnostic datasets for French, German, and Swedish. Our key findings are that the mBERT model demonstrates fine-tuning instability for categories that involve lexical semantics, logic, and predicate-argument structure and struggles to learn monotonicity, negation, numeracy, and symmetry. We also observe that using extra training data only in English can enhance the generalization performance and fine-tuning stability, which we attribute to the cross-lingual transfer capabilities. However, the ratio of particular features in the additional training data might rather hurt the performance for model instances. We are publicly releasing the datasets, hoping to foster the diagnostic investigation of language models (LMs) in a cross-lingual scenario, particularly in terms of benchmarking, which might promote a more holistic understanding of multilingualism in LMs and cross-lingual knowledge transfer.
Flexible and accurate phase-to-coordinate calibration method

This paper presents a method that can accurately determine the mapping between the phase and three-dimensional (3D) coordinates for digital fringe projection systems. This method first extracts the rotation and translation of each calibration target pose, calculates 3D coordinates for each pixel, and then establishes pixel-wise relationship between each coordinate and phase. Experimental results demonstrate that the proposed method achieves higher calibration accuracy comparing with the traditional structured light system calibration method.
The use of antithesis and other contrastive relations in argumentation

This paper presents a study of use of the rhetorical figure of antithesis and other contrastive relations in several modern-day environmental science policy journal articles on issues of food security, climate change, and water resource management. The articles present the conflicting perspectives of environmentalists and engineers, i.e., the view that nature should be preserved and protected versus the view that it should be engineered to solve human problems. The main contribution of this paper is a taxonomy characterizing argumentative uses of contrastive relations in these articles based on our semantic/pragmatic interpretation of text including, in some cases, discourse coherence relations. The number of examples and the breadth of the taxonomy is indicative of the key role of contrastive relations in argumentation in this genre. This investigation is a necessary step towards comprehensive computational approaches to detecting antithesis and other contrastive relations and to identifying their argumentative roles.
Review of FPGA-Based Accelerators of Deep Convolutional Neural Networks

Recent research has shown that Deep Convolutional Neural Networks (CNNs) are extremely accurate in a big range of cognitive tasks. This has piqued the interest of researchers. FPGAs are regarded as an attractive platform for hardware acceleration of CNNs due to their performance, flexibility, and energy efficiency. To improve the performance of CNNs, numerous accelerators have been employed, including ASICs, FPGAs, and graphics processors (GPUs). The use of FPGAs to accelerate deep learning networks is primarily driven by their high power efficiency and their ability to maximize parallelism. To provide comparisons and differences regarding the various techniques used to implement and optimize CNN algorithms on FPGA, the works were categorized into categories. Recent advances in deep learning networks using FPGA-based accelerators are described in this paper. This review should guide future advances in hardware acceleration and make deep learning research more efficient.
Anticipating and eliminating redundant computations in accelerated sparse training

Deep Neural Networks (DNNs) are the state of art in image, speech, and text processing. To address long training times and high energy consumption, custom accelerators can exploit sparsity, that is zero-valued weights, activations, and gradients. Proposed sparse Convolution Neural Network (CNN) accelerators support training with no more than one dynamic sparse convolution input. Among existing accelerator classes, the only ones supporting two-sided dynamic sparsity are outer-product-based accelerators. However, when mapping a convolution onto an outer product, multiplications occur that do not correspond to any valid output. These Redundant Cartesian Products (RCPs) decrease energy efficiency and performance. We observe that in sparse training, up to 90% of computations are RCPs resulting from the convolution of large matrices for weight updates during the backward pass of CNN training.
A Movie Recommendation System Design Using Association Rules Mining and Classification Techniques

The importance of recommendation systems is increasing day by day due to the massive number of data and information-overloaded arising from the internet. This data can be collected in predictive datasets; these datasets can be processed and analysed via data mining methods. In this paper, an efficient hybrid movie recommender system has been designed using the association rules mining technique and K-nearest neighbours (KNN) algorithm as a classification method. The K-nearest neighbours (KNN) algorithm subsystem was used to create the first candidate list through a practical MovieLens dataset, which was retrieved from the source of the NetFlix network. Beside, the Apriori algorithm subsystem is used to analyse the same dataset and create the second list. Finally, the proposed system creates a final recommended list by matching the two lists. The results of the proposed system provide better performance than the existing systems in terms of the important degree. The important degree gives a better accuracy rate than the existing techniques used.
Triplet-based Domain Adaptation (Triple-DARE) for Lab-to-field Human Context Recognition

Human Context Recognition (HCR) from smart-phone sensor data is an essential task in Context-Aware (CA) systems including those targeting healthcare and security. Two types of smartphone HCR studies (and datasets) have become popular for training HCR models: a) scripted and b) Unscripted/In-the-wild. Supervised machine learning HCR models can achieve good performance on scripted datasets due to their high quality labels but such models generalize poorly to in-the-wild datasets which are more representative of real-world scenarios. In-the-wild datasets are often imbalanced, have missing or wrong labels, with a diversity of phone placements and smartphone models. Lab-to-field approaches try to train HCR models to learn a robust data representation from a high-fidelity, scripted dataset that is used to improve performance on noisy in-the-wild datasets that have similar labels without having to incur the high expense of gathering high-quality labeled dataset. In this paper, leveraging coincident datasets with the same HCR labels collected in separate scripted and unscripted studies, we propose Triplet-based Domain Adaptation for context REcognition (Triple-DARE), a novel lab-to-field neural networks method with three key components: 1) a domain alignment loss to learn domain-invariant embeddings, 2) a classification loss to maintain task-discriminative features, 3) a joint fusion triplet loss designed to increase intra-class compactness and inter-class separation in the embedding space of multi-labeled datasets. In rigorous evaluation, Triple-DARE improved on the F1-score and classification accuracy of state-of-the-art HCR baselines by 6.3% and 4.5%, respectively, and on HCR models with no adaptation by 44.6% and 10.7%, respectively.
Blind quality assessment of tone-mapped images using multi-exposure sequences

The tone mapping operator (TMO) enables high dynamic range (HDR) images to be presented on low dynamic range (LDR) consumer electronic devices. However, the results obtained by this method are not always ideal due to the reduced number of bits. In comparison, the multi-exposure image fusion (MEF) bypasses the intermediate HDR image composition and directly produces an image presented on standard devices. Inspired by this, this paper proposes a quality assessment method for tone-mapped image (TMI) based on generating multi-exposure sequences. Specifically, the method uses a generative adversarial network (GAN) to generate a set of sequences with different exposure levels based on the TMIs. Then a two-branch convolutional neural network (CNN) is used to extract features from the tone-mapped images and the multi-exposure reference sequences, respectively. Finally, the transformer is used to mine the intrinsic connections between TMIs and multi-exposure sequences and learn the mapping relationships from feature space to quality space. We conducted extensive experiments on the ESPL-LIVE HDR database. The applicability and effectiveness of the proposed method are verified by comparing and analyzing relevant features and model configurations with existing mainstream evaluation algorithms. • The exposure GAN module generates multiple SDR images with different exposures to provide auxiliary information. • A two-branch CNN is used to extract main features and auxiliary features. • Transformer excavates the internal connection of image and processes the feature sequence related to image quality.
An Optimized Sanitization Approach for Minable Data Publication

Minable data publication is ubiquitous since it is beneficial to sharing/trading data among commercial companies and further facilitates the development of data-driven tasks. Unfortunately, the minable data publication is often implemented by publishers with limited privacy concerns such that the published dataset is minable by malicious entities. It prohibits minable data publication since the published data may contain sensitive information. Thus, it is urgently demanded to present some approaches and technologies for reducing the privacy leakage risks. To this end, in this paper, we propose an optimized sanitization approach for minable data publication (named as SA-MDP). SA-MDP supports association rules mining function while providing privacy protection for specific rules. In SA-MDP, we consider the trade-off between the data utility and the data privacy in the minable data publication problem. To address this problem, SA-MDP designs a customized particle swarm optimization (PSO) algorithm, where the optimization objective is determined by both the data utility and the data privacy. Specifically, we take advantage of PSO to produce new particles, which is achieved by random mutation or learning from the best particle. Hence, SA-MDP can avoid the solutions being trapped into local optima. Besides, we design a proper fitness function to guide the particles to run towards the optimal solution. Additionally, we present a preprocessing method before the evolution process of the customized PSO algorithm to improve the convergence rate. Finally, the proposed SA-MDP approach is performed and verified over several datasets. The experimental results have demonstrated the effectiveness and efficiency of SA-MDP.
Solar Position Algorithm Based on the Kepler Equation

When calculating the position of the sun, earth’s motion can be assumed to be an ellipse if the accuracy of calculation is required to be 0.01 degrees. Then, Kepler’s equation can be applied from the mean anomaly of the sun at a specific time to calculate the true anatomy of the sun at the time, and the sun’s position can be calculated. The average absolute error of calculating the sun’s altitude and azimuth is only 0.04 and 0.06 mrad, respectively, which can meet the requirements of a concentrated solar tracking system. This method only needs to correct the length of the regression year and the near point year for every 100 years, so it can be used for a long time.
Real-Time FPGA Implementation of Digital Video Watermarking Techniques using Co-Design Approach: Comparative Study

This paper presents a Genesys-2 FPGA implementation of three video watermarking techniques in both spatial and frequency domains followed by a comparative analysis. The video acquisition is realized using OV7670 camera used for real-time watermarking with each technique both in visible and invisible schemes with Video Graphics Adapter (VGA) display validations. The techniques implemented are based on Least Significant bit (LSB), additive spatial and additive frequency watermarking. All implementations have been realized in Software/Hardware (SW/HW) co-design using Vivado-HLS tool achieving low area, high speed of up to 130 MHz and low power consumption that did not exceed 827 mw all along with good watermarking performances of imperceptibility with fairly good robustness for most of the geometric and image processing attacks.
Facial recognition technology for policing and surveillance in the Global South: a call for bans

The use of facial recognition technology (FRT) for policing and surveillance is spreading across Asia, Africa and Latin America. Advocates are saying this technology can solve crimes, locate missing people and prevent terrorist attacks. Yet, as this article argues, deploying FRT for policing and surveillance poses a grave threat to civil society, especially systems to identify or track people without any criminal history. In every political system, this has the potential to deepen discriminatory policing, have a chilling effect on activism and turn everyone into a suspect. The dangers rise exponentially, moreover, in places with inconsistent rule of law, poor human rights records, weak privacy and data laws and authoritarian rulers – traits common across scores of countries now installing FRT. Regulating use is unlikely to prevent these harms, the article contends, given the powerful political and corporate forces in play, given the ways firms push legal limits, exploit loopholes and lobby legislators, and given the tendency over time of surveillance technology to creep across state agencies and into new forms of social control. Calls to ban FRT are growing louder by the day. This article makes the case for why bans are especially necessary in the Global South.
Corpus similarity measures remain robust across diverse languages

This paper experiments with frequency-based corpus similarity measures across 39 languages using a register prediction task. The goal is to quantify (i) the distance between different corpora from the same language and (ii) the homogeneity of individual corpora. Both of these goals are essential for measuring how well corpus-based linguistic analysis generalizes from one dataset to another. The problem is that previous work has focused on Indo-European languages, raising the question of whether these measures are able to provide robust generalizations across diverse languages. This paper uses a register prediction task to evaluate competing measures across 39 languages: how well are they able to distinguish between corpora representing different contexts of production? Each experiment compares three corpora from a single language, with the same three digital registers shared across all languages: social media, web pages, and Wikipedia. Results show that measures of corpus similarity retain their validity across different language families, writing systems, and types of morphology. Further, the measures remain robust when evaluated on out-of-domain corpora, when applied to low-resource languages, and when applied to different sets of registers. These findings are significant given our need to make generalizations across the rapidly increasing number of corpora available for analysis.
Knowledge Graph Construction Method for Power Grid Infrastructure

With the increasing of power consumption, the scale of power grid infrastructure construction is developing rapidly. A large amount of information will be generated in the power grid infrastructure construction, and traditional information management methods cannot make efficient use of this information. Knowledge graph is introduced to solve the mentioned issue. This paper focus on the concrete management in power grid infrastructure construction, introduces a power grid infrastructure knowledge graph constructing method. This method includes the following steps. Firstly, the original data of documents, tables, and news is obtained through a data collection module. Secondly, structured/unstructured data are preprocessed. Thirdly, the knowledge extraction is performed to obtain entities and relations. For unstructured data, BiLSTM-CRF is used for named entity recognition and then the BERT model is used for entity relationship extraction. Then, the technology of knowledge fusion is used to obtain the triple as entity-relation-entity. At last, knowledge is represented by storing the graph in Neo4j.