Reviewerly/exHarmony huggingface dataset:
Research on Power Transformer Fault Prediction Model Based on LSTM Neural Network

Power transformer is one of the core equipment of power grid system, and the related research on safe and stable operation of power transformer has always been the focus of attention in the power industry. In recent years, neural networks have developed rapidly and have been widely used in the power industry. Studies have shown that the neural network method has strong applicability to the prediction and diagnosis of the power transformer operating state. Aiming at the limitations of traditional neural networks that cannot use time series information and have long incubation periods and various types of power transformer faults, this paper establishes a power transformer dissolved characteristic gas time series data prediction model based on long and short-term memory neural networks.
Hybrid Local-Global Optimum Search Using Particle Swarm Gravitation Search Algorithm (HLGOS-PSGSA) for Waveguide Selection

Multiple beam combination in optical interferometry with concurrent measurement of intricate visibilities, around each possible baseline, is a trending research area. In this work, a hybrid method is proposed for three different waveguide arrays and several waveguides are excited simultaneously in each array. Each waveguide array acts as a beam combiner, the output of which determines the field intensity of each waveguide mode. The output intensity depends on the waveguide selected for excitation. Thus, waveguide selection is the major factor that can affect the output intensity. The main goal of this research is to provide an effective solution for the selection of waveguides, to provide high visibility and intensity at the output of the multi-beam combiner. In addition to this, the use of metaheuristic optimization algorithms to solve the problem of waveguide selection is proposed. To accomplish this, firstly, an analytical study has been conducted to analyze the performance of optimization algorithms, including PSO, FA and GSA, and then the results of these algorithms have been compared with the conventional approaches. And finally, a model of Hybrid Local-Global Optimum Search Algorithm using Particle Swarm Gravitation Search Algorithm (HLGOS-PSGSA) has been developed for waveguide selection. The performance of the proposed hybrid model is examined in MATLAB simulation software. The simulated outcomes are determined for PSO, FA, and GSA-based models, as well as, for the proposed hybrid model, in terms of normalized intensity, visibility, and min-max 1/SNR values. The results obtained from simulation show that the PSO and GSA-based models are giving better results, followed by FA and conventional approaches. This worked as a motivation behind using PSO and GSA together in the proposed system, resulting in higher intensity and visibility values. Thus, the proposed hybrid model is concluded to be more efficient and convenient, for selecting optimum waveguides from the array, to attain an optimum output.
Tools and Services of the Cloud-Based Systems of Open Science Formation in the Process of Teachers’ Training and Professional Development

The article defines the tools and services of cloud-based systems of open science formation in the process of teachers’ training and professional development. The model of the cloud-oriented methodical system of preparation of teachers of natural and mathematical subjects for work in scientific lyceum is substantiated and described. In particular, the use of the tools and services at each level of the methodological system is described. Preliminary stages of designing a cloud-oriented system of training teachers of natural sciences and mathematics to work in a scientific lyceum are considered. The analysis and assessment of the state of use of cloud-oriented systems are carried out. It was found that the classic stages of designing a cloud-oriented system of training teachers of natural sciences and mathematics to work in a scientific lyceum are: analysis of the research problem, formulation of the design goal (tasks, hypotheses, plan), construction of a prototype, test prototype systems), analysis of test results, adjustment of the components of the prototype depending on the analysis and widespread implementation of the designed system.
Microseismic Response Characteristics Induced by Mining Activities: A Case Study

For deep coal mines, geological disturbances or mining activities can cause damage to coal and rock masses, easily inducing dynamic disasters such as rock bursts or coal and gas outbursts, which seriously threaten the safety production of coal mine. In this paper, first, the Liyazhuang Coal Mine introduced the SOS MS (Sensor Observation Service Microseismic) monitoring system from the Polish Research Institute and verified the accuracy of its positioning. Then, to study the characteristics of MS signals induced by different mining activities, based on the field monitoring results, the waveform and frequency spectrum characteristics of MS signals at different energy levels induced by different mining activities are analyzed, and the energy levels of MS signals caused by different mining activities are classified. Studies have shown that there are large differences in the waveform and spectral characteristics of MS signals at different energy levels. High-level energy seismic signals are characterized by large amplitudes, low frequencies, relatively concentrated distribution, long vibration duration, and slow attenuation. In addition, the relationship between mining activities and MS events is also statistically analyzed based on the spatial and temporal distribution of MS events with the advancement of working face, and the results show that the increase of vibration frequency and energy can be precursor information for roof pressure. With the advance of the working face, the MS energy has the characteristic of periodic distribution, which is consistent with the periodic weighting revealed by the working resistance of the support. This study provides a reference for further revealing the energy release mechanism of rock burst.
Think-Aloud Surveys

In a user experiment, we tried out a novel data collection approach consisting of combining surveys with the think aloud method. We coin the phrase “think-aloud survey method”, where participants think-aloud while completing a questionnaire. We analyzed the transcripts and found that the think aloud survey provides deeper insights into the reasoning behind the participants’ Likert scale choices and responses to open-ended questions, along with an enhanced understanding of the tasks and prompting the participants to explicate their choices. All resulting in more insights for design. The think- aloud survey method further offers other insights into usability issues and encourages the participants to provide system improvement proposals. The think-aloud survey method is useful for researchers and practitioners applying a large number of usability evaluations and wanting to dig deeper into the motivations for choice.
Nearest Neighbors-Based Forecasting for Electricity Demand Time Series in Streaming

This paper presents a new forecasting algorithm for time series in streaming named StreamWNN. The methodology has two well-differentiated stages: the algorithm searches for the nearest neighbors to generate an initial prediction model in the batch phase. Then, an online phase is carried out when the time series arrives in streaming. In particular, the nearest neighbor of the streaming data from the training set is computed and the nearest neighbors, previously computed in the batch phase, of this nearest neighbor are used to obtain the predictions. Results using the electricity consumption time series are reported, showing a remarkable performance of the proposed algorithm in terms of forecasting errors when compared to a nearest neighbors-based benchmark algorithm. The running times for the predictions are also remarkable.
ARTIFICIAL INTELLIGENCE IN COMPUTER VISION

Computer vision is an area of research concerned with assisting computers in seeing. Computer vision issues aim to infer something about the world from observed picture data at the most abstract level. It is a multidisciplinary subject that may be loosely classified as a branch of artificial intelligence and machine learning, both of which may include using specific techniques and using general-purpose learning methods. As an interdisciplinary field of research, it may seem disorganized, with methods taken and reused from various engineering and computer science disciplines. While one specific vision issue may be readily solved with a hand-crafted statistical technique, another may need a vast and sophisticated ensemble of generic machine learning algorithms. Computer vision as a discipline is at the cutting edge of science. As with any frontier, it is thrilling and chaotic, with often no trustworthy authority to turn to. Numerous beneficial concepts lack a theoretical foundation, and some theories are rendered ineffective in reality; developed regions are widely dispersed, and often one seems totally unreachable from the other.
Generalization in Text-based Games via Hierarchical Reinforcement Learning

Deep reinforcement learning provides a promising approach for text-based games in studying natural language communication between humans and artificial agents.However, the generalization still remains a big challenge as the agents depend critically on the complexity and variety of training tasks.In this paper, we address this problem by introducing a hierarchical framework built upon the knowledge graph-based RL agent.In the high level, a meta-policy is executed to decompose the whole game into a set of subtasks specified by textual goals, and select one of them based on the KG.Then a subpolicy in the low level is executed to conduct goal-conditioned reinforcement learning.We carry out experiments on games with various difficulty levels and show that the proposed method enjoys favorable generalizability.
Augmented Reality Experience for Real-World Objects, Monuments, and Cities

Augmented reality experience enables us to view real-world objects in 3D by overlapping real-world objects with digital 3d objects to provide a much-enhanced user experience. This paper explains and presents the ways to construct a 3D (3 Dimension) asset of the real world like cities, monuments, and other objects using blender and then the3D digital asset will be incorporated into our application. So that whenever our marker scans the object i.e., the 3D asset gets overlayed. The main idea of this concept is to experience real-world objects in the absence of real-world objects. Let us say that, a person wants to see the Eiffel tower and he/she searches it on Google. Now, the person could only see the images of Eiffel tower. To experience it in 3D he/she can use Google earth but it does not provide an original 3D experience. So, this is the point where augmented reality enters into the scene. The proposed research work has created a 3D asset by using a blender tool. Now, that asset will be imported and applied it to a marker. Whenever, this marker is scanned by using our application, the 3D effect of Eiffel tower will be overlayed on the screen. Augmented Reality is overlapping real-world objects with 3d objects. The main objective of augmented reality is that users cannot notice the discrepancy between augmented objects and real-world objects. AR is a wholly distinct technology, which helps our daily living and many other experiences so improved. It uses our most common hardware such as mobiles, cameras, etc. This makes this technology very beneficial and effortless to use. It is also a lot more different from VR in terms of hardware. But most of the purpose is the same.
Further results on permutation polynomials and complete permutation polynomials over finite fields

&lt;abstract&gt;&lt;p&gt;In this paper, by employing the AGW criterion and determining the number of solutions to some equations over finite fields, we further investigate nine classes of permutation polynomials over $ \mathbb{F}_{p^n} $ with the form $ (x^{p^m}-x+\delta)^{s_1}+(x^{p^m}-x+\delta)^{s_2}+x $ and propose five classes of complete permutation polynomials over $ \mathbb{F}_{p^{2m}} $ with the form $ ax^{p^m}+bx+h(x^{p^m}-x) $.&lt;/p&gt;&lt;/abstract&gt;
Unsupervised Image Decomposition with Phase-Correlation Networks

The ability to decompose scenes into their object components is a desired property for autonomous agents, allowing them to reason and act in their surroundings. Recently, different methods have been proposed to learn object-centric representations from data in an unsupervised manner. These methods often rely on latent representations learned by deep neural networks, hence requiring high computational costs and large amounts of curated data. Such models are also difficult to interpret. To address these challenges, we propose the Phase-Correlation Decomposition Network (PCDNet), a novel model that decomposes a scene into its object components, which are represented as transformed versions of a set of learned object prototypes. The core building block in PCDNet is the Phase-Correlation Cell (PC Cell), which exploits the frequency-domain representation of the images in order to estimate the transformation between an object prototype and its transformed version in the image. In our experiments, we show how PCDNet outperforms state-of-the-art methods for unsupervised object discovery and segmentation on simple benchmark datasets and on more challenging data, while using a small number of learnable parameters and being fully interpretable.
Scale Invariant Domain Generalization Image Recapture Detection

Recapturing and rebroadcasting of images are common attack methods in insurance frauds and face identification spoofing, and an increasing number of detection techniques were introduced to handle this problem. However, most of them ignored the domain generalization scenario and scale variances, with an inferior performance on domain shift situations, and normally were exacerbated by intra-domain and inter-domain scale variances. In this paper, we propose a scale alignment domain generalization framework (SADG) to address these challenges. First, an adversarial domain discriminator is exploited to minimize the discrepancies of image representation distributions among different domains. Meanwhile, we exploit triplet loss as a local constraint to achieve a clearer decision boundary. Moreover, a scale alignment loss is introduced as a global relationship regularization to force the image representations of the same class across different scales to be undistinguishable. Experimental results on four databases and comparison with state-of-the-art approaches show that better performance can be achieved using our framework.
Monocular, One-Stage, Regression of Multiple 3D People

This paper focuses on the regression of multiple 3D people from a single RGB image. Existing approaches predominantly follow a multi-stage pipeline that first detects people in bounding boxes and then independently regresses their 3D body meshes. In contrast, we propose to Regress all meshes in a One-stage fashion for Multiple 3D People (termed ROMP). The approach is conceptually simple, bounding box-free, and able to learn a per-pixel representation in an end-to-end manner. Our method simultaneously predicts a Body Center heatmap and a Mesh Parameter map, which can jointly describe the 3D body mesh on the pixel level. Through a body-center-guided sampling process, the body mesh parameters of all people in the image are easily extracted from the Mesh Parameter map. Equipped with such a fine-grained representation, our one-stage framework is free of the complex multi-stage process and more robust to occlusion. Compared with state-of-the-art methods, ROMP achieves superior performance on the challenging multi-person benchmarks, including 3DPW and CMU Panoptic. Experiments on crowded/occluded datasets demonstrate the robustness under various types of occlusion. The released code is the first real-time implementation of monocular multi-person 3D mesh regression.
Towards Robust and Transferable IIoT Sensor based Anomaly Classification using Artificial Intelligence

The increasing deployment of low-cost industrial IoT (IIoT) sensor platforms on industrial assets enables great opportunities for anomaly classification in industrial plants. The performance of such a classification model depends highly on the available training data. Models perform well when the training data comes from the same machine. However, as soon as the machine is changed, repaired, or put into operation in a different environment, the prediction often fails. For this reason, we investigate whether it is feasible to have a robust and transferable method for AI based anomaly classification using different models and pre-processing steps on centrifugal pumps which are dismantled and put back into operation in the same as well as in different environments. Further, we investigate the model performance on different pumps from the same type compared to those from the training data.
Visiting the Invisible: Layer-by-Layer Completed Scene Decomposition

Existing scene understanding systems mainly focus on recognizing the visible parts of a scene, ignoring the intact appearance of physical objects in the real-world. Concurrently, image completion has aimed to create plausible appearance for the invisible regions, but requires a manual mask as input. In this work, we propose a higher-level scene understanding system to tackle both visible and invisible parts of objects and backgrounds in a given scene. Particularly, we built a system to decompose a scene into individual objects, infer their underlying occlusion relationships, and even automatically learn which parts of the objects are occluded that need to be completed. In order to disentangle the occluded relationships of all objects in a complex scene, we use the fact that the front object without being occluded is easy to be identified, detected, and segmented. Our system interleaves the two tasks of instance segmentation and scene completion through multiple iterations, solving for objects layer-by-layer. We first provide a thorough experiment using a new realistically rendered dataset with ground-truths for all invisible regions. To bridge the domain gap to real imagery where ground-truths are unavailable, we then train another model with the pseudo-ground-truths generated from our trained synthesis model. We demonstrate results on a wide variety of datasets and show significant improvement over the state-of-the-art.
Research and Application of Intersection Similarity Algorithm Based on KNN Classification Model

The study on the characteristics of traffic intersections can solve the traffic jam, route design and other important traffic problems. This paper classifies the intersection features based on machine learning algorithm. The KNN classification model is trained to classify the traffic import data collected, and the similarity of the intersection is calculated and matched according to the feature extraction and weighting technology. Thus, a new algorithm of intersection similarity matching is established, and it is studied and applied. The algorithm can match similar intersections, and provide model and theoretical support for traffic department and related technicians.
Tailored Immersion: Implementing Personalized Components Into Virtual Reality for Veterans With Post-Traumatic Stress Disorder

With the application of virtual reality (VR), tailored interventions can be created that mirror the traumatic experiences of veterans with post-traumatic stress disorder (PTSD). Visual elements can be mimicked, and auditory and other senses stimulated. In doing so, the degree of immersion can be adjusted to optimize the therapeutic process. Objectively measuring the sensory immersion is key to keep subjects within their personal window of tolerance. Based on this information the therapist can decide manipulate the sensory stimulation embedded in the treatment. The objectives of this article are to explore the different immersive design aspects of VRET that can be modified to influence the experienced presence in veterans with PTSD, and to discuss possible methods of measuring the emotional response facilitated by immersive design aspects and experienced presence. Four design aspects are discussed: system, sensory cues, narrative and challenge. We also report on a user experiment in three veterans that informed on quality and depth of immersion. Believability of the neutral virtual environment was important for maintaining the veterans’ presence within the VR experience. The immersive design aspects that were personalized and supportive in the narrative of the veteran such as music and self-selected images appeared to have a strong influence on recall and reliving of the traumatic events. Finally, in order to increase the therapeutic effect in veterans with PTSD, the highlighted design aspects should be recognized and tailored to maximize immersion in virtual reality exposure therapy.
Video Lessons and E-learning Can Overcome Ban of Face-to-face Lessons in Teaching Mathematics

A pandemic situation connected with a spread of COVID-19 has significantly changed methods of teaching at universities. Previously unimaginable ban on face-to-face teaching has become a reality overnight. At best, a number of students present in a class was significantly limited, at worst, face-to-face lessons were completely banned. Naturally, there was a danger of significant decrease of student’s knowledge. The paper deals with efficient utilization of e-learning in teaching Combinatorics and Data Processing at the Facuity of Education, Tmava University. We compare two different methods of learning: blended learning and e-learning. The results of the experiment prove that the ban of face-to-face lessons does not automatically mean the decrease of student’s knowledge. Thus, e-learning has potential to overcome problems associated with COVID-19 pandemic situation at universities.
Formalized Soundness and Completeness of Epistemic Logic

We strengthen the foundations of epistemic logic by formalizing the family of normal modal logics in the proof assistant Isabelle/HOL. We define an abstract canonical model and prove a truth lemma for any logic in the family. We then instantiate it with logics based on various epistemic principles to obtain completeness results for systems from K to S5. Our work gives a disciplined treatment of completeness-via-canonicity arguments and demonstrates their compositionality.
Minimal Multi-Layer Modifications of Deep Neural Networks

Deep neural networks (DNNs) have become increasingly popular in recent years. However, despite their many successes, DNNs may also err and produce incorrect and potentially fatal outputs in safety-critical settings, such as autonomous driving, medical diagnosis, and airborne collision avoidance systems. Much work has been put into detecting such erroneous behavior in DNNs, e.g., via testing or verification, but removing these errors after their detection has received lesser attention. We present here a new tool, called 3M-DNN, for repairing a given DNN, which is known to err on some set of inputs. The novel repair procedure implemented in 3M-DNN computes a modification to the network’s weights that corrects its behavior, and attempts to minimize this change via a sequence of calls to a backend, black-box DNN verification engine. To the best of our knowledge, our method is the first one that allows repairing the network by simultaneously modifying multiple layers. This is achieved by splitting the network into sub-networks, and applying a single-layer repairing technique to each component. We evaluated 3M-DNN tool on an extensive set of benchmarks, obtaining promising results.