Reviewerly/exHarmony huggingface dataset:
Ground Target Tracking Using an Airborne Angle-Only Sensor with Terrain Uncertainty and Sensor Biases

Airborne angle-only sensors can be used to track stationary or mobile ground targets. In order to make the problem observable in 3-dimensions (3-D), the height of the target (i.e., the height of the terrain) from the sea-level is needed to be known. In most of the existing works, the terrain height is assumed to be known accurately. However, the terrain height is usually obtained from Digital Terrain Elevation Data (DTED), which has different resolution levels. Ignoring the terrain height uncertainty in a tracking algorithm will lead to a bias in the estimated states. In addition to the terrain uncertainty, another common source of uncertainty in angle-only sensors is the sensor biases. Both these uncertainties must be handled properly to obtain better tracking accuracy. In this paper, we propose algorithms to estimate the sensor biases with the target(s) of opportunity and algorithms to track targets with terrain and sensor bias uncertainties. Sensor bias uncertainties can be reduced by estimating the biases using the measurements from the target(s) of opportunity with known horizontal positions. This step can be an optional step in an angle-only tracking problem. In this work, we have proposed algorithms to pick optimal targets of opportunity to obtain better bias estimation and algorithms to estimate the biases with the selected target(s) of opportunity. Finally, we provide a filtering framework to track the targets with terrain and bias uncertainties. The Posterior Cramer-Rao Lower Bound (PCRLB), which provides the lower bound on achievable estimation error, is derived for the single target filtering with an angle-only sensor with terrain uncertainty and measurement biases. The effectiveness of the proposed algorithms is verified by Monte Carlo simulations. The simulation results show that sensor biases can be estimated accurately using the target(s) of opportunity and the tracking accuracies of the targets can be improved significantly using the proposed algorithms when the terrain and bias uncertainties are present.
Artificial neural networks for voice activity detection Technology

Currently, the direction of voice biometrics is actively developing, which includes two related tasks of recognizing the speaker by voice: the verification task, which consists in determining the speaker's personality, and the identification task, which is responsible for checking the belonging of the phonogram to a particular speaker. An open question remains related to improving the quality of the verification identification algorithms in real conditions and reducing the probability of error.&#x0D; In this work study Voice activity detection algorithm is proposed, which is a modification of the algorithm based on pitch statistics; VAD is investigated as a component of a speaker recognition system by voice, and therefore the main purpose of its work is to improve the quality of the system as a whole. On the example of the proposed modification of the VAD algorithm and the energy-based VAD algorithm, the analysis of the influence of the choice on the quality of the speaker recognition system is carried out.&#x0D;
Information policy: challenges for national libraries and associations in the context of the Covid-19 pandemic

The urgency of studying the first experience of activity of the library-information institutions of the world in the period of the Coronavirus pandemic COVID-19, which engulfed the world in spring of 2020, is substantiated the importance of consideration of conclusions and forecasts made by libraries, archives, and other cultural institutions in terms of internally systemic organizations. The purpose of the article is to investigate the response of national libraries and archives of the world, national and interstate professional associations to the challenges faced by the world’s libraries and information institutions in the context of coronavirus pandemic and analysis of forecasts of changes in the forms and working conditions of these institutions. The study uses the methods of analysis, synthesis, and synthesis of information from open sources, the method of forecasting and evaluation of expert opinions. The study, especially the formation of forecasted conclusions, is based on the COVID-19 Impact Survey conducted by the Conference of Directors of National Libraries of the World (CDNL), jointly with IFLA in April 2020. For the prediction of consequences for the library and information systems, it is necessary to monitor the state’s reaction, as the organization and support of the systems that provide the society with access to information is part of the state information policy. An important basis of such forecasting should be the analysis of activity and reaction of the most national institutions — libraries, archives, museums, and their professional associations. Collectively, the obtained information can provide a more or less complete picture of the views and crisis-release forecasts that are projected as a consequence of long-term quarantine, or more effective actions, in case the repetition of quarantine measures will take place yet.
Performance Evaluation of Camera-Based Time to Collision Calculation with Different Detectors&amp;Descriptors

Nowadays, the demand for producing and using autonomous vehicle is increasing.Due to the latest developments in technology, the capabilities of these vehicles in accident prevention are increasing.As a result of the accuracy of these capabilities, it is very important because it is human life.In today's technology, the collision time calculation called TTC (Time to Collision) can be done in two different ways.The first of these methods is lidar-based calculation.In this paper TTC will be calculated using the camera-based method with different combinations of detectors and descriptors.Pros and cons of these methods will be discussed.The aim of this paper is to expose an exacting performance for related methods, especially its diverse combinations are used matching.In these experiments images are used for 10 images taken from real time traffic scenario of preceding vehicle.This paper includes seven methods for detectors and 6 methods for descriptors.These detectors and descriptors are used in 42 different combinations.The analysis includes four parameters such as total keypoint detection, total matches, total time in ms and performance ratio which is total matches divided by total time.
We in hard science articles

Based on the analysis of 4137 authentic instances in hard science articles this paper investigates the first-person pronoun we with regard to its semantic references co-selection patterns and discourse functions. Findings suggest that overall we occurs quite frequently in hard science articles but within the hard sciences the occurrence of we varies greatly from one discipline to another. Semantic references of we are divided into four groups: self-reference we author-reader we discipline we and general we. The self-reference we accounts for the vast majority of all the occurrences of we which implies that in most cases the use of we in hard science articles may appear to be “reader-oriented” but may actually express the writers’ own point of view. This paper also generalizes the primary discourse functions under each type of semantic reference the frequent co-selection patterns of we to perform each function and the collocating verbs in each pattern. It is found that we with a specific reference tends to collocate with certain verbs and to occur in certain patterns in order to realize particular functions whilst a specific function also promotes and restricts the use of we in certain patterns and with certain references. In this sense the use of we is the co-selection result of patterns semantic references and discourse functions.
3D lithostratigraphic model of the Palaeogene in the Avren Plateau

On the basis of primary lithological data derived from 53 boreholes’ sections and these after the lithostratigraphic interpretation of 5 seismic profiles, a 3D lithostratigraphic model of the Palaeogene rocks in the Avren Plateau was created in purpose to elucidate the spatial distribution and relationships of the established lithostratigraphic units. It includes six formal lithostratigraphical units: the Komarevo, Beloslav, Dikilitash, Aladan, Avren, and Ruslar Formations.
Multi-attention and Multi-layer Hashing for Cross-Modal Retrieval

The research on cross-modal retrieval has broadened the access to various forms of data resources, among which cross-modal hashing methods have gained widespread attention owing to their excellent effects. However, existing hashing-based methods cannot to establish a deep inter-model correlation and fully employ the semantic information at the same time, and single-layer hashing may cause the hash representations not robust enough. To deal with above issues, we come up with a new method with multi-attention and multi-layer hashing to achieve the goal of mutual retrieval between different forms of data. Firstly, we apply the modal attention of multi-attention to capture the bit-level dependencies between cross-modal features to build a deeper inter-modal correlation. Meanwhile, the part of semantic attention is applied to retain common semantic information, which will improve the accuracy of retrieval tasks. Secondly, to learn a more robust hash representations, multi-layer hashing is used to jointly complete the learning task of hash representations and avoid the shortcomings of single-layer hashing. Through a series of experiments on two cross-modal datasets, it is indicated that the model we presented is better than the general approach in most evaluation metrics.
Information Recommendation Method of Network Blended Skill Learning Based on Personalized Adaptive of Electric Power Staff

In question of low satisfaction and accuracy of traditional staff skill information recommendation learning course, this paper comprehensively uses the relevant raw data generated by various skills training. Modularize the sub-factor sequence using the modular matrix, and calculate the mean value of the correlation coefficient corresponding to each sub-factor sequence. This paper uses the coupling degree function to study the coupling relationship between the network hybrid information, and completes the modular processing of the network hybrid information. This article judges whether the weight value of the network hybrid information is higher than the threshold set in advance, and calculates the weight of the network hybrid information. Define the similarity between the network hybrid information, and obtain the similarity distribution of the network hybrid information in the information concentration. Personalized adaptive learning improves the learning demand orientation of network hybrid information. The purpose of calculating the skill similarity of electric power employees is to design a network hybrid information recommendation algorithm to realize the comprehensive analysis and processing of the skill status of electric power employees. Training managers and trainers can optimize training programs based on the feedback analysis results, and accurately train, consolidate and improve the technical capabilities of employees. Experimental results show that the recommendation algorithm has greatly improved the convergence speed and accuracy.
An Improved Kidney Tumor Prediction Using Deep Convolutional Neural Network-Restricted Boltzmann Machine Technique in Medical Image Segmentation

Automatic medical image segmentation has become increasingly important as contemporary medical imaging has become more widely available and used. Existing image segmentation solutions however lack the necessary functionality for simple medical image segmentation pipeline design. Pipelines that have already been deployed are frequently standalone software that has been optimised for a certain public data collection. As a result, the open-source python module deep-Convolutional neural network-Restricted Boltzmann Machine (deep CNNRBM) was introduced in this research work. The goal of Deep CNN-purpose RBMs is to have an easy-touse API that allows for the rapid creation of medical image segmentation transmission lines that include data augmentation, metrics, data I/O pre-processing, patch wise analysis, a library of pre-built deep neural networks, and fully automated assessment. Similarly, comprehensive pipeline customisation is possible because of strong configurability and many open interfaces. The dataset of Kidney tumor Segmentation challenge 2019 (KiTS19) acquired a strong predictor with respect to the standard 3D U-net model after cross-validation using deep CNNRBM. To that purpose, deep CNN-RBM, an expressive deep learning medical image segmentation architecture is introduced. The CNN sub-model captures frame-level spatial features automatically while the RBM submodel fuses spatial data over time to learn higher-level semantics in kidney tumor prediction. A neural network recognises medical picture segmentation, which is initiated using RBM to second-order collected data and then fine-tuned using back propagation to be more differential. According to the simulation outcome, the proposed deep CNN-RBM produced good classification results on the kidney tumour segmentation dataset.
Metadata based multi-class text classification in engineering project platform

Enterprises usually establish digital platforms to store and share projects as well as business data. Those files in digital platforms usually have metadata that gives information about the life of a document. Metadata is useful to retrieve useful information about the document. They can be generated fully automatically or given manually by users. Sometimes those manually given metadata can be left out, insufficient, or even wrong. Those metadata stay unaware until the files are being searched for a purpose later on. If the number of files with insufficient metadata is enormous, the filtering of files from platform according to specific metadata becomes difficult. Manually correction can be tedious and enormously time intensive. This paper introduces a method for correcting faulty metadata based on other correct metadata with text classification, natural language processing technique, and linguistic synonymy information in the engineering project platform. The proposed method is evaluated on a real-world dataset. Even though the available information from metadata is limited, the method still gives a promising result and has the advantage of its high computing speed.
Search systems as a tool of socio-communicative structures of society

The article analyses search engines as tools of social communication and searches for scientific information, presents a list of the newest national and world search engines, databases, repositories. An assessment of the newest field of social communication, such as digital humanities. The purpose of the article is to evaluate modern search engines as a means of obtaining scientific information, social communication of scientists, to review and evaluate the latest means of search engine optimization. The object of the study is Internet communication. The subject of research is search engines and databases of scientific information. Nowadays search engines, as a means of finding information, have now become an important element of social communication for scientists in the world. Thematic search engines, databases of scientific articles, and thematic repositories play an important role in storing information and providing information on both scientific articles and scientists in general. Global developments are currently not far behind and domestic developments are among them. The following search engines are considered: Google Scholar, Directory of Open Access Journals (DOAJ), PubMed, JSTOR, Web of Science, Science. gov, CiteSeerx, Scopus, Prometheus, Open Ukrainian Citation Index (OUCI), Philosophy Documentation Center, Publons. The Ukrainian public project of mass open online courses Prometheus and the National Repository of Academic Texts deserve special attention. In addition, the relevance of search results remains an important issue for the search for information and social communication for scientists. The most important algorithms used by search engines today, as well as promising directions for the future are considered. In this aspect, digital humanities, which integrate the methodologies of traditional humanities (history, philosophy, linguistics, literature, art, archeology, music, etc.) with computer sciences, are beginning to play an important role today.
Harold Pinter’in "Gitgel Dolap" Eserine Yapısalcı Bir Yaklaşım

Structuralism is an approach that seeks to decode the encoded whole consisting of a system to explore a textual work’s deep structure from the surface structure. This study analyses Harold Pinter’s “The Dumb Waiter” (1960) according to the structuralist approach. The harmony of the play’s deep structure performs on binary opposition between Ben and Gus who repeat mechanical behaviours and conversations since they are imprisoned for a while in a narrow space. Concrete mechanical elements such as the lavatory and the dumb waiter also repeat the same activities. Although the play is over, the melody continues since Ben and Gus are given no role other than being hitmen and victims. As for the deep meaning, individuals who appear as a functional part of the mechanism show the possibility that the oppressive mechanism may become inoperable as a result of questioning the mechanism.
Energy Trading System and Strategy Based on Blockchain and Deep Learning

In order to better activate the vitality of the energy market and promote the marketization reform of the energy trading, this paper proposes an architecture of the energy trading system based on the blockchain and a consensus mechanism based on the transaction participation and random lottery election. On the basis of the system architecture, a pre-sale matchmaking transaction method is proposed to buy or sell energy in advance according to the forecast before energy production and consumption, and use the smart contract of blockchain to realize. Finally, the CNN-LSTM hybrid prediction model is designed, and the historical data recorded on the blockchain is used for energy load prediction to improve the accuracy of prediction. Experimental simulation shows that the proposed energy trading system and method based on blockchain can effectively reduce the number of transactions, improve the utilization rate of fragmented energy and the robustness of the trading system, and verify its effectiveness and feasibility. At the same time, experimental simulation proves that the CNN-LSTM model has higher prediction accuracy than the traditional single prediction model.
Preserving gauge invariance in neural networks

In these proceedings we present lattice gauge equivariant convolutional neural networks (L-CNNs) which are able to process data from lattice gauge theory simulations while exactly preserving gauge symmetry. We review aspects of the architecture and show how L-CNNs can represent a large class of gauge invariant and equivariant functions on the lattice. We compare the performance of L-CNNs and non-equivariant networks using a non-linear regression problem and demonstrate how gauge invariance is broken for non-equivariant models.
A survey on issues in cloud forensics with an experiment on time consumption

Forensic investigations on cloud platforms are an oft-discussed topic in current digital forensics. Significant growth in cloud platforms is expected in the coming decade. With such growth, cloud forensic investigations may require substantial changes in their approach. The paper surveys the most mentioned issues in cloud forensic literature. It is followed by a description of some of our current work aimed at solving those issues. The first issue that we tried to analyze was the issue of the trustworthiness of the evidence. We identified that the trustworthiness of the Cloud Service Providers is hardly discussed in the literature. Based on previous publications on similar issues on standalone computers, we provided an algorithm as an initial answer to the issue. The algorithm checks for the integrity of the evidence which will be affected in a tampering attempt. The next issue that we considered was time-taken for analysis (time complexity of forensic tools). While the issue has been indicated many times in the literature, we did not find many detailed experiments conducted with tools to observe the processing time over data source size. Therefore, the paper includes the results of an experiment that was performed using an Autopsy forensic tool to measure the time complexity of its operation with a number of source files with increasing sizes. Results indicated that the analyzing times usually increased with the size of the source file and that it might become unmanageable with increasing sizes.
Support for the provision of “digital immortality” based on physical informatics methods

The improvement of modern computer technology to achieve fundamentally new indicators can be carried out on the basis of various approaches. One of such approaches is the application of methods of physical informatics. Therefore, this paper considers the support of ensuring “digital immortality” based on physical computer science methods. On the basis of Wiener random processes, measures are proposed to ensure the security of computer nodes to ensure “digital immortality”. The calculated ratios from physical informatics adapted to determine the requirements for the design of information systems to ensure the digitalization of the brain are given.
Taming Congestion and Latency in Low-Diameter High-Performance Datacenters

High-performance computing (HPC) and data centers are showing a trend of merging into high-performance data centers (HPDC). HPDC is committed to providing extremely low latency for HPC or data center workloads. In addition to adopting a low-diameter network topology, HPDC also requires a more advanced congestion control mechanism. This paper implements the state-of-the-art congestion control method in the data centers on the Dragonfly topology and proposes Bowshot, a fast and accurate congestion control method for low-latency HPDC. Bowshot uses fine-grained feedback to accurately describe the network state. It uses switch feedback, ACK-padding, and ACK-first to reduce feedback delay. Bowshot uses switch calculation to reduce the overhead of congestion control. As the large-scale evaluation shows, Bowshot reduced the average flow completion time (FCT) by 33% and the 99th percentile FCT by 45% compared to the state-of-the-art work. Bowshot reduces the feedback delay by 89%. In addition, Bowshot maintains higher throughput and a near-zero queue length.
From Human Oral Instructions to General Representations of Knowledge: A New Paradigm for Industrial Robots Skill Teaching

Converting human oral instructions into general representations of knowledge which robots can understand, can realize more advanced behaviors for unmanned driving, drones, robots and other fields. This paper presents a novel paradigm based on two-stage structure that transforms human oral instructions into general representations of operating knowledge. Firstly, a Speech-to-Text module is used to realize automatic speech recognition, which results in a text expression for the input oral instruction. In the second stage, a Text-to-Knowledge module is used to realize natural language understanding, which introduces and visualizes task-related knowledge graphs converted from the text expression. To validate this paradigm, the task of computer motherboard assembly was chosen as an example, and a low-cost monophonic speech corpus named PC-CORPUS was built. This PC-CORPUS, is 3 h 44 min long and has 2278 wave audios. 14 speakers from different accent areas in China were invited in the recording. Then experiments were designed and carried out on this paradigm with PC-CORPUS. The average edit distance of this paradigm is 10.87%, comparing the triples of final visual knowledge representations with the ones were labeled by the experts.