Reviewerly/exHarmony huggingface dataset:
Quantitative Geochemical Prediction from Spectral Measurements and Its Application to Spatially Dispersed Spectral Data

The efficacy of predicting geochemical parameters with a 2-chain workflow using spectral data as the initial input is evaluated. Spectral measurements spanning the approximate 400–25000 nm spectral range are used to train a workflow consisting of a non-negative matrix function (NMF) step, for data reduction, and a random forest regression (RFR) to predict eight geochemical parameters. Approximately 175,000 spectra with their corresponding chemical analysis were available for training, testing and validation purposes. The samples and their spectral and chemical parameters represent 9399 drillcore. Of those, approximately 20,000 spectra and their accompanying analysis were used for training and 5000 for model validation. The remaining pairwise data (150,000 samples) were used for testing of the method. The data are distributed over two large spatial extents (980 km2 and 3025 km2, respectively) and allowed the proposed method to be tested against samples that are spatially distant from the initial training points. Global R2 scores and wt.% RMSE on the 150,000 validation samples are Fe (0.95/3.01), SiO2 (0.96/3.77), Al2O3 (0.92/1.27), TiO (0.68/0.13), CaO (0.89/0.41), MgO (0.87/0.35), K2O (0.65/0.21) and LOI (0.90/1.14), given as Parameter (R2/RMSE), and demonstrate that the proposed method is capable of predicting the eight parameters and is stable enough, in the environment tested, to extend beyond the training sets initial spatial location.
Comprehensive methodology for geological risk and multy-risk assessment

This paper presents methodology for geological risk and multy-risk assessment. It is fully in accordance with IEC 31010:2019 and JRC recommendations and has been already applied in the practice. The methodology demonstrating flexibility, adaptability and sustainability within a set of all geological hazards. It is successfully applied to the complex risk assessment of Primorsko municipality.
Interdisciplinary teaching and learning activity: how the use of personal experiences, art, history, ICT and team work enhance motivation

In this work a teaching and learning activity was developed to first establish connections between art forms expressing students’ feelings related to COVID-19 pandemic. And secondly, to associate those art manifestations with the historical period and events that occurred when they were performed, by using time line ICT tools. This teaching and learning activity was done with Primary School Teacher trainees, within the framework of the Teaching and Learning of Social Sciences subject of the academic year 2020–2021 at the University of Alcala. Results showed a variety of art forms and styles chosen by the students and therefore historical related periods, from Rapa Nui sculptures to hyper-realism paintings. The use of chronological time lines helps the perception of the historical time, being a useful didactic resource. Including personal experiences boost learning motivation. This teaching and learning activity can be adapted and applied in social sciences subjects to the Primary School level.
Document Clustering Analysis Based on Hybrid Cuckoo Search and K-means Algorithm

The clustering is an interesting technique for unsupervised document organization in the World Wide Web (WWW). The most widely used partitioning clustering algorithm is K-means. However, it has an issue with random initialization, which might lead to local optimum situations. In fact, metaheuristics-based clustering has demonstrated their efficiency to reach a global solution instead of local one. The Cuckoo search (CS) has been widely used for the clustering problem. However, the number of iterations grows dramatically when the dataset is high dimensional like the documents. In this study, the hybridization cuckoo search and K-means algorithms for the document clustering are analyzed. So, three hybrid algorithms are investigated and compared. The performance and the efficiency of the proposed algorithms are evaluated using Reuters 21578 Text Categorization Benchmark Dataset. The obtained results show the capability of the new approaches to generate more compact clustering and enhancing purity and F -measure clustering qualities.
Fine-grained Traffic Sign Detection and Matching Algorithm

With the development of intelligent driving, the autonomous recognition of traffic signals plays an important role in providing traffic information for the autonomous driving systems. In this paper, we propose a mechanism to detect the traffic signs from an image of the traffic scene and to match the same sign from the pictures shot in different time and weather conditions. The dataset we use, provided by Baidu, contains 19 fine-grained objects belonging to 3 coarse categories, most of these objects are very small and different categories of traffic signs have various frequencies of appearance. To accurately accomplish the fine-grained detection task with such an imbalance dataset, we divide the process of traffic signs detection into two parts: detection and fine classification, and use different methods of data augmentation on different categories of data to alleviate the imbalance issue. Except for the sign detection, we also applied a matching mechanism to match the same target signs under different road conditions with metric learning algorithms. As a result, our model achieves results comparable to the top results of related contest.
Sparsity-constrained Graph Nonnegative Matrix Factorization for Clustering

Graph nonnegative matrix factorization (GNMF) is superior for mining the intrinsic geometric structure embedded in high-dimensional data. As the sparsity of the factorized matrices is crucial for clustering, the l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> norm is commonly used in the formulated optimization problem to enforce the sparseness which makes the problem NP-hard and discontinuous. In this paper, the sparse graph nonnegative matrix factorization (SGNMF) is formulated as a global optimization problem by using the sum of inverted Gaussian functions to approximate the l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> norm, the multiplicative update rules are developed to solve the problem with guaranteed convergence. The superior performance of the proposed approach is substantiated by clustering tests on four public datasets.
Continuous ZND (Zhang Neural Dynamics) Model for Generalized Sinkhorn Scaling of Time-Varying Matrix

In this paper, we first propose a Zhang neural dynamics (ZND) model for the generalized Sinkhorn scaling of time-varying matrix. Specifically, by using the dimensional reduction technique, a continuous-time ZND model of time-varying matrix scaling is proposed and analyzed. In addition, the corresponding theoretical proofs are given, which prove the theoretical validity of the proposed ZND model. Moreover, two numerical experiments containing a square case and a rectangle case are also conducted. Numerical experiments and results substantiate the effectiveness and accuracy of the proposed ZND model.
A Survey of Machine Learning-based IoT Intrusion Detection Techniques

Research on data anomaly intrusion detection in the Internet of Things (IoT) environment is still insufficient, and there are still many practical problems that need to be solved urgently. At present, the deep integration of Artificial Intelligence (AI) technology and IoT, and intelligent technologies such as machine learning have gradually been applied to the field of intrusion detection of the Internet of Things. Therefore, this paper conducts the latest and in-depth research on the efficient and accurate intrusion detection technology used in computers, analyzes the security threats facing the Internet of Things, and summarizes the latest technology and evaluation indicators of intrusion detection. This paper can promote the improvement of large-scale industrial network security detection performance under the new situation to a certain extent and has important theoretical significance and practical application value for ensuring the rapid and healthy development of today's industrial IoT security environment.
Optimizing Multibit Spread Spectrum Audio Watermarking for Internet of Things

The development of Internet has lead the technology to Internet of Things (IoT). As the Internet of Things is used for sending many types of data through many interconnected devices, the risk of stealing the data owner's right has been increased. But, even though this risk comes up, the high demand for sending data through IoT is still high. Thus, watermarking schemes is used for preventing this problem. Watermarking can be used for protecting the owner's rights in the form of a digital image, and this will be useful to be implemented in IoT. In this paper, an enhancement of Spread Spectrum (SS) based audio watermarking system against MP3 Compression attacks using Genetic Algorithm process is proposed. The main idea of the system will be using Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) as the pre-processing method and then using Multibit Spread Spectrum to embed the watermark. The simulation results show that the Genetic Algorithm could optimize between imperceptibility and robustness resulting a good result against the MP3 Compression attack.
Visual Information Oriented Knowledge Graph

Knowledge graphs (KGs) have been widely applied in many IoT application fields. However, existing knowledge graphs primarily focus on simple structured data, ignoring complex data types that are increasingly popular in artificial intelligence domain, such as image, video or text. In this paper, we propose a new pipeline for visual knowledge graph(VKG), in which visual relation detection and knowledge graph construction are integrated into an end-to-end network structure. The proposed network includes three parts 1) object localization, 2) visual relation detection, 3) the construction of visual knowledge graph by relation triples. In particular, we use information processed by object detection to predict the relation between entities on visual relation detection and obtain the knowledge triples. According to the visual knowledge triples, we visualize the constructed visual knowledge graph. The superiority of visual relation detection are shown in the experiments over competitive baselines on the common datasets. Moreover, we also show the performance of visual knowledge graphs based on the result of relation detection.
Evaluating the Impact of the Number of Secret Sharing Participants on Stego Image

Transmitting secret information that is constantly carried out on the internet can increase security risks. In this research, we implement the histogram-based method and prediction-error expansion scheme to work on this issue. Here, the stego image produced by these methods is processed with Shamir's secret sharing to protect further the data hidden inside it. To reconstruct the stego image, at least some participants must combine the share images. Then, the data hidden in the stego image can be retrieved. Based on the results, the lower the number of participants, the better the PSNR value of the shared images. Although a more significant number of participants can make it harder to obtain the stego image, it lowers the imperceptibility of the shared images. From the experiment, we find that all shared images have PSNR values lower than 30 dB, and further research should focus on improving the quality of the share images.
Quaternion-Based Non-Linear Motion Estimation Method

This study is devoted to the camera motion estimation from a video sequence obtained with that camera. The most common approach to solution of this problem is to find an essential matrix that contains mixed information about rotation and translation of camera movement. Moreover, this technique gives incorrect solution if the world points are coplanar. We propose a method for finding motion parameters in quaternion terms using an initial approximation based on linear methods. The proposed representation allows us to independently determine rotation and translation parameters.
Safety Supervision Method of Power Work Site Based on Computer Machine Learning and Image Recognition

Abstract China’s traditional power system has been unable to meet the needs of society and the development of The Times. Under the background of intelligence, it is necessary to reform the power industry and increase the application of mobile application technology in the power system, so as to realize the precise management of the power system. The application of mobile application technology in the field operation of electric power construction, based on computer machine learning and image recognition, is helpful to realize the sustainable development of electric power enterprises, improve the service level of electric power enterprises and promote the on-site safety supervision.
Exploring Set-Inspired Similarity Measures for Collaborative Filtering Recommendation

The similarity measure is an important component used in collaborative filtering recommender systems (CFRSs) to determine the set of users having the same behavior with regard to the selected items. The measure is typically defined on sets of real-valued or discrete-valued vectors. For discrete-valued vectors, similarity measures are inspired by the comparison of sets and the cardinality of sets. In this paper, we aim to explore set-inspired similarity measures for CFRSs, including Fuzzy sets index, Jaccard index, Sorensen coefficient, and Symmetric difference, with four collaborative filtering methods: (i) user-based, (ii) item-based, (iii) user clustering-based, and (iv) item clustering-based methods. We conduct extensive experiments to evaluate the effect of different measures on the benchmark datasets. An important result is that all four of these measures outperform the Pearson coefficient and Cosine measures in both recommendation effectiveness and computation time. Empirical evidence also shows that the Symmetric difference measure provides better results than all remaining measures.
Balancing Imbalanced Datasets Using Generative Adversarial Neural Networks

In this paper, the problem of balancing machine learning datasets and its potential solution using generative adversarial networks is presented. Several training methods and models in generating minority class samples were examined using two well-known datasets: MNIST and CIFAR10. In order to achieve data imbalance, a percentage of samples was artificially removed from one class in both datasets. Generative adversarial network is initialized with the parameters of the autoencoder trained over the same dataset. Generated noise is limited based on the distribution of the latent space of the autoencoder. Three methods of generative adversarial network training which provide class label context to the network were examined. Two methods gave satisfactory results for the problem of generating samples from the MNIST dataset, while the method of duplicating labels proved unsatisfactory.
Detecting Integer Overflow Errors in Java Source Code via Machine Learning

Integer overflow is a common cause of software failure and security vulnerability. Existing approaches to detecting integer overflow errors rely on traditional static code analysis and dynamic testing. This paper presents a novel machine learning-based approach that predicts integer overflow errors by treating source code as text. It exploits text classifiers to determine whether each method in a given Java program contains an integer overflow error. As the training data is essential, we have constructed a comprehensive dataset to accounts for (a) integer overflow errors of all integer types and operations in Java (i.e., positive samples); (b) various programming techniques for preventing integer overflow errors (i.e., negative samples); and (c) malicious scenarios that may mislead text classifiers (i.e., adversarial samples). We have trained three classifiers, BERT, fastText, and NBSVM, that represent different text embedding techniques. BERT, as a representative deep-learning transformer, has achieved the highest performance scores and remained robust even when tested with the adversarial samples.
Deep Learning For Noisy Communication System

In modern communication systems, channels vary with time due to the mobility of the transmitter (Tx) and the receiver (Rx). To achieve error-free communication in changing environments, Tx and Rx must effectively adapt to different noise levels. However, exiting frameworks face challenges during adaptation, leading to poor communication. In this paper, an independent pre-training collaborative learning framework is designed for tuning Tx and the Rx. The proposed framework incorporates more realistic challenges encountered in communication systems such as lack of feedback channels and the lack of updates at the Rx side. The experimental results show that the proposed approach can reduce noise up to 50% more than existing approaches and within a short training time.
A Method for Accurate Driver Status Monitoring using Domain Adaptation after Deployment

With rapid advances in AI technology, autonomous driving is close to becoming a reality. Nevertheless, most car accidents are still caused by the driver’s forward-looking negligence, and driver’s intervention is still required. Therefore, monitoring the driver status has become an essential task for preventing car accidents. Many studies have attempted to solve the concern by applying a pre-trained neural network. However, the performance of a pre-trained neural network is deteriorated due to the distributional shift between training data and field data. In this paper, we propose a method to retain the performance of the pre-trained neural network by mitigating the detrimental effect of the distributional shift. In addition, we will show that the proposed method can be implemented on an embedded platform where memory size and computing power are limited.
Comparative analysis on Deep Convolution Neural Network models using Pytorch and OpenCV DNN frameworks for identifying optimum fruit detection solution on RISC-V architecture

Making computer detect desired object have always been an area of interest for humans. Object detection can be implemented using following stages: feature extraction, object localization followed by identifying object in input image. Most of the present-day object detection work is focused around x86 and ARM architectures. Researchers constantly strive to either identify better object detection architectures, updated models, improved model accuracies or reduce prediction time. In this paper, multiple pre-trained Deep Neural Network (DNN) models such as Region Based Convolutional Neural Network (RCNN), Fast RCNN, Faster RCNN. You Only Look Once (YOLO) V3 and Single Shot Multibox Detector (SSD) are used to identify fruits in given input image on RISC- V architecture. In order to bring uniformity across all DNN models, all these models are pre-trained on COCO datasets. Experimental results have shown that out of various DNN models tested for object recognition, YOLO and SSD-MobileNet gives optimum performance in terms of accuracy and inference time on RISC- V architecture.
Characteristics of Potential Tsunami Evacuee and Evacuation Infrastucture in Pangandaran Beach, Indonesia

Abstract Pangandaran Regency is a strategic area that supports various economic and social activities. However, the existence of this strategic area is threatened by the potential of tsunami disaster, especially from the megathrust along the South Coast of Java Island. One of the efforts to enhance tsunami preparedness is to conduct evacuation plan and drill. Based on previous tsunami occurrence, it is known that success of evacuation during disaster is still limited by lack of community knowledge, preparedness, and limited infrastructure such as informative evacuation route maps, evacuation signs and proper evacuation sites. Therefore, the purpose of this study is to identify people's behavior in carrying out evacuations and actual condition of the current evacuation infrastructure which is later be used to obtain comprehensive information as the basis for evacuation plans preparation. This study aims to disseminate questionnaires to 174 respondents in Pangandaran Tourism Area and map tsunami evacuation infrastructure. This study shows a high awareness of tsunami hazards, in which 82% of respondents choose to evacuate after experiencing an earthquake or getting an early tsunami warning. In addition to the result, some respondents still do not know the route or evacuation site. Uninformed shelter destination and route choice result in potential congestion on the main roads, which slows evacuation by compromising survival rates. Three Pangandaran official TES can accommodate approximately 11,900 people, while the capacity of alternative TES if utilized 100% can accommodate 21,291 people. This indicates that the official TES and the new Alternative TEST can accommodate the villagers and some of tourists if the number of tourists reaches the maximum. Therefore, the existing building can be utilized as Temporary Evacuation Sites. Furthermore, there is a call to evacuation signs maintenance, in which some signs are currently in inappropriate condition such as damaged or show misleading information.
Determination of measurement fidelity for a superconducting photon-number resolving detector with micron-wide strips

Abstract We report a study of multiphoton detection fidelity (or accuracy) for sequential photon-number resolving detectors based on micron-wide superconducting strips. It was found that an increase in the width of the superconducting strips by a factor of 5 leads to an improvement in the measurement accuracy by more than a factor of 10.
Fuzzy Inference of the Effectiveness Factors of The Computational Model for the Diagnosis of Breast Cancer

This work is devoted to building the foundations of a new method for modifying machine learning algorithms. At the first stage, a factorial model of highly informative breast cancer diagnostic features was built. About factor loads, Mamdani's fuzzy conclusion was applied to establish the level of features' influence on the classification result. Changing the function of calculating the error of the method by this new knowledge will make it possible to more accurately and adequately set the machine learning model.
Sequential Recommender System of Educational Contents with End-to-End Title Feature Extraction for Reducing Utility Gap

Conventional sequential recommender system uses content features extracted by a hand-craft method, and a pre-trained deep learning method. This causes the utility gap between content features and the purpose of sequential recommendations. To reduce the utility gap, we propose a sequential recommender system with end-to-end title feature extraction. Specifically, we develop a convolutional text feature extraction method for title features. Because we can optimize text features for the sequential recommendation in the end-to-end manner, we can reduce the utility gap. Experimental results for real educational contents and teachers’ operating histories on Forestanet show the effectiveness of our proposed method.