Reviewerly/exHarmony huggingface dataset:
Design of spike voltage generation system for tank gun ammunition setting test

Abstract In order to test the voltage spike interference of the tank gun ammunition setter, a spike voltage generation system is designed in this paper. The system adopts rectifier filter and series voltage stabilizing circuit, combined with single chip microcomputer control circuit, and converts 220V, 50HZ alternating current into spike voltage through post-stage RLC series circuit, and studies the influence of post-stage circuit inductance value on spike voltage rise time. Theoretical calculations and simulations verify that the system can generate spike voltages that comply with GJB298-87, which is of great significance to the test of voltage spike interference of tank artillery ammunition mounters and other military vehicle electronic equipment.
경계를 넘어서는 지역사 교육의 모색

It is natural that we should the concern for the education of the local history because school of living and studying is located within the local community. The study and education of local history was developed with abundance since 1990s.The education of the local history is emphasized in the context of criticism on the education of nationalistic history which was controled by the Ministry of Education for a long time, and would be more prompted if localized curriculum could be introduced instead of national curriculum.I emphasized not the immutable local identity but the opened local identity for constructing flexible identity. So recommended connecting local-country-legion-global in multiperspectivity, dealing not only his or her local but other local community, dealing with sensitive and local history and cooperating with teachers of other subjects.The widening horizon of the education of the local history needs the development of curriculum for the local history and the construct of internet platform for the study of the local history as well as cooperation with local researcher including NGO. The education of the local history is civic education in terms of the awareness and the practice about the problem of the local community.
Design and development of constructing the basic probability assignment function software in D-S evidence theory

Abstract The basic probability assignment function is the basis of D-S evidence theory and the premise of evidence combination. A kind of basic probability assignment function generation software is designed and developed in this paper. After the index values is input, the software can automatically generate the basic probability assignment values, which meet the requirement of the users. The main functions, key algorithms, program logic, development language and interface design of the software are introduced. The calculation case is given to describe the software.
A Model for Achieving Keyword Search on Dynamic Encrypted Cloud Data with Symmetric Key Based Verification

Verifiable Searchable Symmetric Encryption is one of the crucial cloud protection method, that widely enables subscribers to access the encrypted details in the cloud by using keywords. While making the best use of our knowledge, the current SSE systems are supporting various information that is mostly influenced by asymmetric key cryptography which in turn requires a lot of time-consuming activities. Thus, the method for attaining the key phrase hunt of powerful encrypted cloud information with effective verification is a very crucial problem that needs to be dealt with most prominent care. In order to bring a considerable solution for this upcoming issue, we can consider attaining a key phrase hunt of powerful encrypted cloud information with symmetric key based verification as well as suggest a useful pattern within this paper. To be able to allow for the effective verification of powerful details, we model a novel method called, AAT (Accumulation authentication tag) that completely depends on the symmetric key which is a part of cryptography. It’s ideal work is to create an authentication tag for every key phrase. So as to attain effective update, in this paper we have modeled a brand-new protected index composed by the search engines depending on the orthogonal checklist along with a verification checklist that contains AATs. The protection evaluation as well as the overall performance analysis benefits demonstrates that the suggested plan is efficient and secure.
An Unbalanced Security Protocol for IoT devices

Abstract Authenticated key exchange (AKE) protocol is a crucial process in Internet of Things. This paper aims to customize the Elliptic Curve Diffie-Hellman (ECDH)-based AKE according to the computational capability of devices. We propose a method to unbalance the computations in the ECDH key exchange scheme by configuring the more powerful device undertake one scalar multiplication on behalf of the computationally limited one. The analysis results show that the computationally unbalanced protocols are more friendly to computationally limited devices than the original one.
Unstructured with a Point: Validation and Robustness Evaluation of Point-Cloud Based Path Planning

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Robust autonomous navigation in unstructured environments is an unsolved problem and critical to the operation of autonomous military and rescue ground vehicles. Two-dimensional path planners operating on occupancy grids or costs maps can produce infeasible paths when the operational area includes complex terrain. Recently, sample-based path planners that plan on LiDAR-acquired point-cloud maps have been proposed. These approaches require no discretization of the operational area and provide direct pose estimation by modeling vehicle and terrain interaction. In this paper, we show that direct sample-based path planning on point clouds is effective and robust in unstructured environments. Robustness is demonstrated by completing a system parameter sensitivity analysis of the system in an Unreal simulation environment and partnered with field validation.&lt;/div&gt;&lt;/div&gt;
PREDIKSI JUMLAH PENJUALAN TERHADAP JUMLAH PERMINTAAN MENGGUNAKAN METODE REGRESI LINEAR BERGANDA

The development of information technology makes the computer not only as a data processing tool, but as a supporting means to complete all work both at work and at home. The use of computers has also penetrated the world of economy and trade which acts as a tool in maximizing business, Gemilang Plastik is a manufacturing company of Plastic Pellets. This business produces a lot of Plastic Seed production for sale so this business has many advantages from the sale. Because the number of requests varies, it is difficult to determine the exact number of sales each day to maximize profits. To anticipate this, it is necessary to make sales predictions, an alternative use of predictions that aims to predict sales levels. In this study the author uses the waterfall method for developing his system, and uses PHP as the programming language, and Framework. The method used is the method of Multiple Linear Regression, the analysis of the design of this system uses the Unified Modeling Language (UML). By using this method, the sale of plastic pellets can be predicted how many sales of plastic pellets will come.
Blockchain Technology: Architecture, Challenges, and Applications

In the present day, blockchain technology has been established as a wide-reaching phenomenon recognized by an astounding number of people. Nonetheless, past all the clamor and publications, a large number of individuals have an extremely constricted understanding of it. This paper blends and abridges the attributes that institute it as a prominent architecture. This paper also covers the shortcomings and challenges of this radical technology. Furthermore, it brings into focus, Stablecoin, the new kid on the block(chain). Closing with the explanation of a number of pioneering applications.
Active Methodologies for Physics Teaching Through Virtual Learning Environments

Education currently undergoes great transformations thanks to the rise of new technologies, which has allowed the emergence of new educational paradigms that show new ways of teaching and learning, undoubtedly these elements make us think of new scenarios for education such as virtual learning environments, where teaching-learning process follows a different script from the traditional one. Therefore, the present research goal is to design and implement a virtual learning environment (VLE) for physics teaching. For this proposal, a quasi-experimental study was carried out consisting of two groups: experimental and control. At the beginning, a pre-test was applied which showed minimal differences in both groups and after the use of VLE, a post-test was applied in order to measure the impact of the resource used in the experimental group. The study population consisted of 27 students; at the end, a student’s test of independent samples was applied, which evidenced a significant difference regarding academic performance between the control and experimental groups. The mentioned elements allow to make effective the positive impact of the design and implementation of VLE elements in physics teaching.
DVRCNN: Dark Video Post-processing Method for VVC

Low-light videos are usually accompanied with acquisition noise, motion blur and some other specific distortions, which makes it hard to compress by the video coding technologies and generates less satisfying compressed videos. In order to enhance both the subjective and objective quality of compressed dark videos, we propose a novel learning based post-processing scheme for the most recent VVC. Specifically, we adopt a multi-scale residue learning structure, named Dark Video Restoration Convolutional Neural Network (DVRCNN), as an additional out-loop post-processing method. To avoid the over-smooth effect by MSE metric, SSIM and texture loss are also added to the final loss function. Luma and chroma components are decomposed then fed to two corresponding models separately. Compared with VVC baseline on the six sequences given in ICME 2020 Grand Challenge, our approach significantly reduces the BD-rate by 36.08% and achieves a fair promotion on both objective and subjective quality, especially for the low bit-rate compressed sequence with severe distortion. Validation results show that the proposed model generates well to continuous scenes and variable bitrates.
Absolute Transformation and CLAHE E-Voting System Using Blockchain Technology

Nowadays the growth of digital technology has increased since it is more helpful for people. The main threats in the general ballot system are security and transparency. The current voting system is a centralized system based on a single organization. Main issue in the ordinary method is that only one organization maintains the entire database since it is centralized. The problem is that the organization can make unauthorized access to the database and alter the data or remove the data from the database which leads to loss/change of data. The better solution for this problem is making the existing election system decentralized. For making the database decentralized we use block chain technology. By using this technology we can make the datas more secure and not accessed by anyone. So there is no loss/change of data. Implementing blockchain in e-voting systems can reduce the unauthorized database manipulations. AES algorithm can be used to encrypt the data from the fingerprint sensor. This paper also presents various studies on the existing e-voting systems and shows how our voting system is better and different from the former.
Modeling Analysis of Carrier-based Aircraft’s Scheduling on Flight Deck

Abstract The scheduling efficiency of carrier-based aircrafts on flight deck is an important factor on improving the sortie rates of aircrafts. Based on the key process of the sortie of deck-based aircrafts, the main research directions in the field of the scheduling of carrier-based aircrafts are summarized, which are the path planning and collision detection of carrier-based aircrafts, the integrated scheduling method of carrier-based aircraft fleets, and the coordinated scheduling strategies of carrier-based aircrafts. Then, the researches of carrier-based aircrafts dispatch system are introduced. The progress and shortcomings of the existing research results were pointed out, also, the feasible development directions in the future are proposed, which provides a reference for the research on the scheduling of aircrafts on flight deck.
Semantic and Formal Representation of Cognitive Models for the Metacognitive Architecture CARINA

A cognitive model is a computational model of internal information processing mechanisms of the brain for the purposes of comprehension and prediction. CARINA metacognitive architecture runs cognitive models. However, CARINA does not currently have mechanisms to store and learn from cognitive models executed in the past. Semantic knowledge representation is a field of study which concentrates on using formal symbols to a collection of propositions, objects, object properties, and relations among objects. In CARINA Beliefs are a form of represent the semantic knowledge. The aim of this chapter is to formally describe a CARINA-based cognitive model through of denotational mathematics and to represent these models using a technique of semantic knowledge representation called beliefs. All the knowledge received by CARINA is stored in the semantic memory in the form of beliefs. Thus, a cognitive model represented through beliefs will be ready to be stored in semantic memory of the metacognitive architecture CARINA. Finally, an illustrative example is presented.
Color and Texture Feature Based Scene Classification

Right now, overall trademark based multiscale multiresolution multistructure (M3LBP) neighborhood parallel example and nearby characteristic based totally min blend feature extraction is proposed for scene category. To extract international functions, characterize the leading spatial features in a couple of scale, a couple of choice, more than one structure way. The micro/macro shape facts and rotation invariance are guaranteed inside the worldwide function extraction approach. Neighborhood function extraction, coloration histogram characteristic (CHF) can thoroughly explain the spatial coloration statistics of an image. It also describes the image brightness, color statistics of a photo, which encompass the picture coloration distribution, photo assessment. The CHF can be computed from the min max shade quantizes. Ultimately Fused feature instance amongst nearby and international capabilities because the scene descriptor to prepare a portion based absolutely extreme finding a workable pace for scene style is outfitted. The proposed strategy is radically assessed on benchmark scene datasets (the 21 magnificence land use scene), and the trial results show that the proposed procedure prompts predominant kind standard execution as contrasted and the realm of-work of art style systems.
Semantic-Based Vectorization Technique for Hindi Language

Word embeddings capture contextual information about individual words in a vector form. These representations of words help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words. Earlier vectorization techniques have mainly focused onto words only as they do not consider contextual information of word. Recently popular known representation learning known as word2vec word embedding has shown a significant improvement in state of art. As the technique is language dependent, most of the work done has been focused on the English language only. Research in non-Latin languages such as Hindi has been of limited because the unavailability of proper corpus and hence not so efficient word embeddings. We aim to embed words from a Hindi corpus scrapped from various newspapers and other literary sources. Using bidirectional LSTM, we train a neural model that embeds entire sentential contexts and target words in the same low-dimensional space, which is optimized to reflect inter-dependencies between targets and their entire sentential context as a whole. To test the model, we will try to predict the missing word in a sentence by passing the preceding and succeeding parts of sentences to the model.
Significance, Relevance and Explainability in the Machine Learning Age: An Econometrics and Financial Data Science Perspective

Although machine learning is frequently associated with neural networks, it also comprises econometric regression approaches and other statistical technique whose accuracy enhances with increasing observation. What constitutes high quality machine learning is yet unclear though. Proponents of deep learning (i.e. neural networks) value computation efficiency over human interpretability and tolerate the “black box” appeal of their algorithms, whereas proponents of explainable artificial intelligence (xai) employ traceable “white box” methods (e.g. regressions) to enhance explainability to human decision makers. We extend Brooks et al.’s (2019) work on significance and relevance as assessment critieria in econometrics and financial data science to contribute to this debate. Specifically, we identify explainability as the Achilles heel of classic machine learning approaches such as neural networks, which are not fully replicable, lack transparency and traceability and therefore do not permit any attempts to establish causal inference. We conclude suggesting routes for future research to advance the design and efficiency of “white box” algorithms.
Automatic Pose Quality Assessment for Adaptive Human Pose Refinement

Multi-person pose estimation from a 2D image is an essential technique for many computer vision tasks. Although the development of deep convolutional neural networks has brought large improvement to human pose estimation, some complex cases are still challenging to even state-of-the-art approaches. The forms of people in the images are diverse. The quality of estimated poses is difficult to guarantee. Estimated poses usually cannot be directly used in practical application scenarios. In this paper, we propose a pose quality assessment model and an adaptive human pose refinement method. The pose quality assessment model can measure per-joint pose quality with a quality score and select qualified estimated poses. The adaptive pose refinement method can handle each estimated pose respectively, until reaching a certain standard. Our experiments show the effectiveness of the pose quality assessment model and confirm that adaptive pose refinement method performs better than generally refining all poses once. Our adaptive pose refinement method reaches state-of-the-art performance.
ContrastInfoFace: Deep Contrastive Mutual Information Maximization for Face Recognition

Face recognition has attracted wide attention in recent years. The major issue in face recognition is to improve the robustness under various modalities, such as different illuminations, poses, or backgrounds. Due to the rapid development of deep learning, tremendous progress has been achieved. The mainstream to address the problem in face recognition is based on an intuitive objective: maximizing the inner-class similarity as well as the intra-class discrepancy. In this paper, inspired by the philosophy behind contrastive learning, we modify the InfoNCE loss which is usually used in unsupervised representation learning to achieve the objective while theoretically, a lower bound of the mutual information between the input faces of a certain identity and their corresponding representation is maximized. Besides, via our modification, there is a natural alignment of the representations under different modalities of a certain identity. Moreover, the experiment results based on the benchmark face recognition dataset empirically demonstrate the superiority of our method.
Effects of Social Media on the Migrant Image Formation

Today mass media are necessary for building public opinion. Social media are dominating while traditional ones are taking second place. Immigration and migrants are one of the topical issues and have been making international news for over a decade. Their image is shaped both in traditional and social media. This study analyzes the representation of migrants in English media texts and its semantic orientation. The analysis is based on iWeb corpus and the data includes 7 400 passages (199 190 words) of English Internet corpus. Automatic text mining software was used to define lexical associations and sentiment analysis was carried out with the help of computerized text analysis program. The results suggest that the emotional tone of the analysed media texts is mostly negative with economic migrants being the most negative image. The findings also indicate that migrants are represented as foreign invaders coming in great numbers and posing threat to the hosting community.
Set-theoretical approach to finding the range of possible values of the size of a group of objects in a situation of multiple indistinguishability

Abstract In situations where it is not possible to timely use new methods for assessing hazardous objects, traditional methods of risk assessment can be supplemented with new methods based on possible assessments of objective primary information. Evaluation of the destructive capacity and consequences of the use of high-risk systems becomes increasingly important as their structural and functional complexity grows. Despite the existence of the considered works on certain aspects, the known approaches to the consideration of the problem of indistinguishability of influencing factors are characterized by limited functionality. These capabilities do not fully provide the required level of reliability when making management decisions. In view of this, the problem of predicting equipment failure caused by an unfavourable coincidence of circumstances due to the indistinguishability of the initial data is one of the urgent tasks of risk management. Its solution is of significant theoretical and practical interest for many complex heterogeneous dynamical systems. The paper deals with a problem from the field of probabilistic modeling. The relevance of the performed research lies in the fact that the existing methods for predicting equipment failure in systems of potentially hazardous objects do not take into account the causes of accidents that occurred due to unfavourable circumstances. The aim of the work is to develop a method for the set-theoretic modeling of the occurrence of problems with equipment due to an unfavourable coincidence of circumstances, which provides a range of possible values for the size of a group with a given composite very dangerous sign. To achieve this goal, the following tasks are solved: – finding the range of possible values of the number of groups of objects modeled by the intersection of indistinguishable sets and finding the range of possible values of the number of groups of objects modeled by the union and the difference of indistinguishable sets; – finding possible values of the number of groups of objects modeled by an arbitrary composite property and generalization of the problem, features of the set-theoretic apparatus of its solution. In the course of the study, it was found that a set of objects with a compound very dangerous feature of an arbitrary type can be associated with a set representing the union of intersections (intersection of unions) of sets of objects with simple features and their complements. Moreover, the operations of intersection, union, and complement constitute a complete set of operations for the most common version of Boolean algebra of sets.