Reviewerly/exHarmony huggingface dataset:
Even “lurkers” download: The behavior and illegal activities of members on a child sexual exploitation TOR hidden service

Knowledge about online child sexual exploitation (CSE) offenders mostly remains limited to offender populations known by the criminal justice system. Because of its hidden and anonymous nature, knowledge on those offenders active on the Darkweb is especially scarce. For the current study, researchers had access to a unique dataset of member communication on a Darkweb CSE website, as well as members' virtual movements (or clicks) across the side, regardless of being verbally active or not. This offered a unique opportunity to establish behavioral patterns of members who were unaware that they were being observed. This paper summarizes the results of a descriptive analysis of the growth of member count, the frequency with which members were online, and detailed member behavior such as their activity on certain sub-forums and their downloading activity. Main findings include that although only 3.4 % of the members were communicatively active, the vast majority of 93.6 % of members downloaded CSE material. Results indicate that regardless of being verbally active, most members do engage with the site's content, and thus, effectively impact the CSE community. This has important implications for law enforcement practice, which will be discussed. • This study had access to all clicks of members of a Darkweb child sexual exploitation website • A minority of forum members was communicatively active on the website • The majority of forum members (93.6 %) downloaded child sexual abuse material • On average, these forum members download 77 files in a two-week period • Non-communicating members still emotionally engage with the site's content
CSVO: Clustered Sparse Voxel Octrees—A Hierarchical Data Structure for Geometry Representation of Voxelized 3D Scenes

When representing the geometry of voxelized three-dimensional scenes (especially if they have been voxelized to high resolutions) in a naive—uncompressed—form, one may end up using vast amounts of data. These can easily attack the available memory capacity of the graphics card, the operating memory or even secondary storage of computer. A viable solution to this problem is to use domain-specific hierarchical data structures, based on octant trees or directed acyclic graphs, which, among other advantages, provide a compact binary representation that can thus be considered to be their compressed encoding. These data structures include—inter alia—sparse voxel octrees, sparse voxel directed acyclic graphs and symmetry-aware sparse voxel directed acyclic graphs. The paper deals with the proposal of a new domain-specific hierarchical data structure: the clustered sparse voxel octrees. It is designed to represent the geometry of voxelized three-dimensional scenes and can be constructed using the out-of-core algorithm proposed in the paper. The advantage of the presented data structure is in its compact binary representation, achieved by omitting a significant number of pointers to child nodes (82.55% in case of Angel Lucy model in 1283 voxels resolution) and by using a wider range of child node pointer lengths, including 8b, 16b and 32b. We achieved from 6.57 to 6.82 times more compact encoding, compared to sparse voxel octrees, whose all node components were 32b aligned, and from 4.11 to 4.27 times more compact encoding, when not all node components were 32b aligned.
Uncertainty-Aware Hierarchical Reinforcement Learning Robust to Noisy Observations

This work proposes UA-HRL, an uncertainty-aware hierarchical reinforcement learning framework for mitigating the problems caused by noisy sensor data. The system is composed of an ensemble of predictive models that learns the environment’s underlying dynamics and estimates the uncertainty through their prediction variances and a two-level Hierarchical Reinforcement Learning agent that integrates the uncertainty estimates into the decision-making process. It is also shown how frame-stacking can be combined with the uncertainty estimation for the agent to make better decisions despite the aleatoric noise present in the observations. In the end, results obtained in a simulation environment are presented and discussed.
Decreasing land use and increasing information infrastructure: Big data analytics driven integrated online learning framework in rural education

Nowadays, Big Data Analytics (BDA) attracted considerable interest due to its tremendous social and scientific value. The use of traditional rural education land is being replaced by the emerging network classroom. With the fast-growing data and information numbers at present, the IT world’s educational system are changing rapidly. The performance of rural school students is less satisfactory, and many rural schools have disadvantageous of having distanced from a nearby university. The schools in the countryside are located in the remote land. Rural schools are smaller and costlier to operate and likely to have a teacher shortage. These problems are all expected to be solved by the application of big data. In this paper, Big Data Analytics driven Integrated Online Learning Framework (BDAIOLF) has been proposed for rural education. The study showed that the conditions, social impact, performance, and effort expectations facilitating behavioral intentions have been essential determinants for the use of ICT for educational purposes. Data have been collected through a literature study, interviews between individuals and focus groups. Key findings indicated that rural school success is influenced by a number of factors relating to the core values of education, the teacher-learner relationship, the proficiency of English, and parental participation in teaching and learning. In rural schools, however, teaching and learning are influenced by factors such as topics, the work environment, genetic characteristics, and environmental conditions. The construction of information infrastructure is an important reason for the reduction of rural education land.
Snatch Theft Detection Using Deep Learning Models

It is vital to combat crimes by predicting and detecting the occurrence of crime, especially in urban cities. Hence this study proposed investigating the capability of six deep learning models, namely the AlexNet, GoogleNet, ResNet18, ResNet50, ResNet101 and InceptionV3, in determining the most optimum model for snatch theft detection. Two categories of databases comprising 13000 images of snatch theft and non-snatch activities were generated from 120 videos obtained from the Google and YouTube platforms. These images are further used for training and testing these six DL models, along with data augmentation implemented during training to avoid overfitting. However, it was found that overfitting occurred based on training and testing accuracy plots, and hence, it was decided to re-train the model using an early stopping method. Thus, upon completion of re-training all six models, it was found that all six models showed a good-fit condition, with ResNet 50 attaining the highest testing accuracy of 98.9% and 100% sensitivity. As for specificity, ResNet 101 showed the highest value, precisely 97.7%.
To the Question of the Practical Implementation of “Digital Immortality” Technologies: New Approaches to the Creation of AI

On the basis of the principle of dialectical symmetry put forward within the framework of the philosophy of dialectical positivism, it is shown that the scheme of personality structure according to Jung should be clarified. It should include an element that makes this scheme symmetrical - the collective conscious (the term is formed by analogy with the term collective unconscious). This approach allows us to distinguish between the concepts of intellect, mind and consciousness. In particular, the intellect is interpreted as a structural component of the personality, most closely adjacent to the collective conscious. It is shown that it is this structural component of personality that can be converted into a digital format already at this stage of research by using methods for decoding algorithms for the operation of convolutional and similar neural networks, which we previously proposed based on new methods of digital signal processing based on the use of non-binary Galois fields. It is shown that the digital reconstruction of a separate component of the personality - intelligence - can be considered as the first step towards the implementation of digital immortality technologies.
RF2Net: salient object detection using level set loss and reverse attention fusion feed network

Salient object detection is a fundamental problem in the research of image and vision. However, traditional models have low confidence and low recall. Although deep learning methods can better locate objects, the boundaries are often not detailed enough. To address these issues, we propose a salient object detection model (RF2Net) that combines traditional level set methods with deep learning. RF2Net incorporates the idea of level set structured loss and reverse attention mechanism on the basis of F3Net. First, RF2Net uses a new loss function that combines BCE(Binary Cross Entropy) loss, weight level set loss and weight MAE(Mean Absolute Error) loss with multi-indicator joint supervision. Through the role of the level set loss operator, it is possible to better focus on the whole of the image instead of pixel-by-pixel supervision like the BCE loss. The introduction of the reverse attention mechanism can effectively reduce the noise during feature fusion between layers and achieve the purpose of improving accuracy. The experiments are compared with 12 state-of-the-art methods on 4 datasets, and MAE, maxF and avgF are all higher than other algorithms in HKU-IS dataset. At the same time, we also conduct ablation experiments on the DUTS dataset and the ECSSD dataset to verify the effectiveness of the algorithm. The ablation experimental results show that the proposed algorithm can effectively improve the effect of salient object detection.
Video stabilization based on GMS and warping transform

Video stabilization is a video enhancement technology that improves the original video quality by eliminating unnecessary camera motion. In the last decade of research, video stabilization has changed from a simple solution aimed at computational simplicity to a complex solution aimed at stabilization effects. We propose a novel method based on Grid-based Motion Statistics(GMS) and warping transformation, stabilizing video with less cropping. Specifically, feature points are firstly matched by GMS, and RANSAC is applied within each frame to estimate the motion vectors accurately. Furthermore, we incorporate predicted adaptive path smoothing to produce stable trajectories and generate stable video with warping transformation. Moreover, to the best of our knowledge, the proposed algorithm has less cropping and better stability than previous work. The experimental results demonstrate the performance of our method on a large variety of consumer videos.
Edge-Aware Graph Neural Network for Multi-Hop Path Reasoning over Knowledge Base

Multi-hop path reasoning over knowledge base aims at finding answer entities for an input question by walking along a path of triples from graph structure data, which is a crucial branch in the knowledge base question answering (KBQA) research field. Previous studies rely on deep neural networks to simulate the way humans solve multi-hop questions, which do not consider the latent relation information contained in connected edges, and lack of measuring the correlation between specific relations and the input question. To address these challenges, we propose an edge-aware graph neural network for multi-hop path reasoning task. First, a query node is directly added to the candidate subgraph retrieved from the knowledge base, which constructs what we term a query graph. This graph construction strategy makes it possible to enhance the information flow between the question and the nodes for the subsequent message passing steps. Second, question-related information contained in the relations is added to the entity node representations during graph updating; meanwhile, the relation representations are updated. Finally, the attention mechanism is used to weight the contribution from neighbor nodes so that only the information of neighbor nodes related to the query can be injected into new node representations. Experimental results on MetaQA and PathQuestion-Large (PQL) benchmarks demonstrate that the proposed model achieves higher Hit@1 and F1 scores than the baseline methods by a large margin. Moreover, ablation studies show that both the graph construction and the graph update algorithm contribute to performance improvement.
Qualitative assessment of uranium ore concentrates and related materials using scanning electron microscopy

Several studies have evaluated the morphology of uranium compounds produced under controlled conditions at the laboratory scale, but it is unclear whether the morphological characteristics of these materials persist in commercially produced uranium ore concentrates (UOCs). To assess the morphology in "real-world" UOCs, we qualitatively evaluated the morphological profile of secondary electron images from over 100 commercial UOCs using a previously published lexicon. We observe differences between samples with differing chemical composition and samples with similar chemical composition and differing provenance. This work contextualizes morphology for commercially produced UOCs and will provide a basis for future machine learning efforts.
Super-resolution for semantic segmentation

Image segmentation is a classical problem in the field of computer vision. With the extensive development of deep learning, it has achieved much progress in semantic segmentation. However, the mainstream networks used in deep learning such as Fast-SCNN, U-Net, which still face challenges in image segmentation. A common problem is that linear interpolation is used in the up-sampling stage of these networks to obtain high-resolution images. Due to the lack of sufficient feature information, the contours of the objects in the image are blurred and grided. For this reason, we propose a new super-resolution (SR) method to replace the up-sampling with linear interpolation in the network model. Five representative networks integrated with our proposed SR module are used for verification on the CamVid data set. The experimental results show that our method has a 2%~4% improvement in mIoU (the mean value of Intersection over Union) and a 2%~3% improvement in pixel accuracy, which demonstrates its generalization and effectiveness of our method.
Path Planning and Landing for Unmanned Aerial Vehicles Using AI

Latest trends, societal needs and technological advances have led to an unparalleled expansion in the use of Unmanned Aerial Vehicles (UAV) for military and civilian applications. Such systems are becoming increasingly popular in many operations, since they reduce costs, facilitate activities and can increase the granularity of surveillance or delivery. Beyond the Visual Line of Sight (BVLOS) capabilities are becoming recently a pivotal aspect for the UAV industry, and raise the demand for extended levels of autonomy in order to increase the efficiency of flight operations. The present study examines two main aspects of BVLOS operations, namely trajectory planning and self-landing, and demonstrates how well-established path planning techniques, such as the A* and Dijkstra algorithms, can be used to ensure the shortest trajectory length from point A to point B for a UAV under multiple obstacles and constraints and the least number of error corrections. Extensive simulation results showcase the effectiveness of the proposed method. It also provides evidence of the use of computer vision algorithms for detecting the landing site and assisting the UAV to safely land.
Equivalence Between Classical Epidemic Model and Quantum Tight-Binding Model

The equivalence between classical epidemic model and non-dissipative and dissipative quantum tight-binding model is derived. Classical epidemic model can reproduce the quantum entanglement emerging in the case of electrostatically coupled qubits described by von-Neumann entropy both in non-dissipative and dissipative case. The obtained results shows that quantum mechanical phenomena might be almost entirely simulated by classical statistical model. It includes the quantum like entanglement and superposition of states. Therefore coupled epidemic models expressed by classical systems in terms of classical physics can be the base for possible incorporation of quantum technologies and in particular for quantum like computation and quantum like communication. The classical density matrix is derived and described by the equation of motion in terms of anticommutator. Existence of Rabi like oscillations is pointed in classical epidemic model. Furthermore the existence of Aharonov-Bohm effect in quantum systems can also be reproduced by the classical epidemic model. Every quantum system made from quantum dots and described by simplistic tight-binding model by use of position-based qubits can be effectively described by classical model with very specific structure of S matrix that has twice bigger size as it is the case of quantum matrix Hamiltonian. Obtained results partly question fundamental and unique character of quantum mechanics and are placing ontology of quantum mechanics much in the framework of classical statistical physics what can bring motivation for emergence of other fundamental theories bringing suggestion that quantum mechanical is only effective and phenomenological but not fundamental picture of reality.
Graph Emotion Distribution Learning Using EmotionGCN

Emotion of a person can be identified by the patterns in their ability to think, respond, communicate, or behave in a social environment. It is highly influenced by the emotion, making decisions, and exhibiting behaviors with the surroundings and other well-being. The emotion identification of person is very much useful in the case medical domain. The problem in detecting the emotions from these data is a single image can exhibit different emotions for different persons in their own perspective. With the advancement in the field of computer vision, the adoption of deep convolutional network paved a way to creating a convolutional neural network model which can learn these emotions from the input given and a graph convolutional network to estimate the probability distribution of whole data of emotions as well as the form the data of each emotion separately. The distribution of each emotion can be converted to a graph-based data so that it can be stored and used to train new models without the need for long training time. These graph data are more compatible and paves the way to perform further psychological analysis to extract patterns in them.
Noise Presence Detection in QR Code Images

A quick response (QR) code is symbols used to encode information such as key identifiers (website addresses, product, etc.) that can be printed and scanned electronically using image-based technology. However, it may include noise at the time of printing or scanning due to some environmental or mechanical factors. Therefore, the study analyzes various machine learning models to detect noise presence in QR code. For this, we first generated own dataset by creating 14,000 images of QR code, and then enhanced the dataset by adding several noises to the original QR code images. Later, it exploits several machine learning, deep learning and pre-trained models to segregate noisy images from original images. Experimental results show that ResNet101 and Xception models outperformed others by attaining 100% accuracy, recall, f1-score, and precision, each. Besides these, support vector machine (SVM) also performed better by accomplishing 99.6% accuracy on test set when trained over 70% of dataset.
Analysing Movement Patterns from Smartphones for the Training of Exoskeletons

In this paper, an approach to motion analysis that combines two different machine learning techniques, using a single smartphone inside a trouser pocket to record data, is proposed. The resulting program shall be used for data analysis during the use of an exoskeleton and for tracking movements of the lower extremities. The project focuses on the question of whether acceleration data from a smartphone are sufficient to make accurate predictions of movements. A Convolutional Neural Network is used for motion recognition and an agglomerative clustering algorithm is used for a further, more precise analysis of individual movement types. An overall concept and preliminary results of this project are discussed in this work.
Numerical Model Analysis of Atypical Gas Filtration in a Porous Medium

A new numerical model of gas filtration in a porous medium is proposed and investigated. Gas filtration is modeled with a nonlinear differential equation in partial derivatives. Methods of solving of the equation are discussed. Computer experiment basing on real-experiment-extracted input physical and geometrical parameters is performed and its results are analyzed. The received results agree well with the corresponding experimental data.
New Two-Parametric Mutation Operator for Inductive Modelling Using Combinatorial-Genetic Algorithm

Usually genetic algorithm (GA) uses crossover and mutation operator for solving optimization problems. But recent studies show that sometimes GA can be just as efficient using only one of these operators. In this paper, two-parametric mutation operator is proposed to generate models in combinatorial-genetic algorithm (COMBI-GA) when solving inductive modelling tasks. The algorithm efficiency is compared when using a pair of a crossover and a(0→1)-bit mutation or only two-parametrical (0↔1)-bit mutation when solving a real-world problem.
Transformation of Accounting Methods with the Use of Robotic Equipment with Artificial Intelligence

The current stage of development of production technologies is associated with the active use of robotic equipment with artificial intelligence, which can be used for collecting data which have information value for accounting and management. The necessity of improving the methods of accounting in the conditions of using artificial intelligence technologies to ensure the efficiency of projects on robotization of production and information processes has been proved.The purpose of the study lies in improvement of accounting techniques in terms of transformation of documentation, distribution of accounting data and calculation of the cost of products manufactured with the use robotic equipment with artificial intelligence.The directions of improvement of accounting methods under the influence of robotic equipment with artificial intelligence are determined through the following: the latest positioning of documentation in terms of accounting as a process of recombination of different volumes of data sets into visual electronic forms for demonstration not at the stage of robotic data collection, but at the request of stakeholders; calculation of the cost of products manufactured by robotic equipment, which provides accounting information on: material costs, wages and social security payments, depreciation of non-current assets, other operating expenses; automatic distribution of all accounting information between internal and external stakeholders according to their information requests and access rights to trade secrets.