Reviewerly/exHarmony huggingface dataset:
Design of Multi-platforms Sensor Management System for UAV Autonomous Detection and Perception

Aiming at the requirements of multiple UAVs self-organizing management and cooperative detection in confrontation environment, an open multi-platforms sensor management system is proposed. The implementation structure of the system is described in detail, and the design of functional modules, management strategies and implementation methods are given. The scheme can respond to various types and levels of avionics tasks, and can comprehensively consider and balance the system's many task requirements and various service requests, so that the overall performance of the system can be optimized. It is proved that the self-organizing management and intelligent task assignment of multiple UAVs can be realized and the autonomous detection ability and task completion rate can be effectively improved in this scheme.
Using Research to Ensure Equity in a Cybersecurity Education Pathway

This poster describes how we are using research to inform the development of a cybersecurity education pathway to attract and retain students from groups that are underrepresented in computing fields. The partners include a non-profit research organization, a community-based tech workforce center, a community college, and a K-12 school district that serves predominantly Latinx students. The poster describes our goals, activities, the data we have collected, and how they are being used to create a sustainable pathway from high school to college that attracts a diversity of students. We describe our stages of research utilization, as well as the challenges that we are facing related to using research to ensure equity in the cybersecurity education pathway.
Advanced Biometric Voting machine linked with Aadhaar and PAN card

"Vote" means to choose a candidate from a list. By casting the votes, the Citizens choose a candidate from a list. Eliminating the bogus vote by linking the voting machine with Aadhaar card as well as Pan Card is the goal of the project. In this system, voting is based on Aadhaar and PAN databases. Since all the data is stored in the database of a particular place of a booth, such voting system is more efficient.
A Proactive University Library Book Recommender System

Too many options on internet are the reason for information overload problem to obtain relevant information. Recommender system is a technique that filters information from large sets of data and recommends the most relevant ones based on people’s preference. Collaborative and content-based techniques are the core techniques used to implement recommender system. A combined use of both collaborative and content-based techniques called hybrid technique provides relatively good recommendations by avoiding common problems from each techniques. In this research, a pro-active University library book recommender system has been proposed in which Hybrid filtering is used for enhanced and more accurate recommendations. The prototype designed was able to recommend highest 10 books for each user. We evaluated the accuracy of the results using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). A measure value of 0.84904 MAE and 0.9579 RMSE found by our system shows that the combined use of both techniques can give an improved prediction accuracy of university library book recommender system.
Variational Inference of Infinite Generalized Gaussian Mixture Models with Feature Selection

This paper presents a variational learning framework for the infinite generalized Gaussian mixture (IGGM) model. The generalized Gaussian distribution (GGD) has a proven capability in modeling complex multidimensional data due to the flexibility of its shape parameter. Infinite model addresses the model selection problem; i.e., determination of the number of clusters without recourse to the classical selection criteria such that the number of mixture components increases automatically to best model available data accordingly. We also incorporate feature selection to consider the features that are most appropriate in constructing an approximate model in terms of clustering accuracy. Experimental results on a medical application and image categorization show the effectiveness of the proposed algorithm.
A New Method of Assigning Trust to User Addresses in Bitcoin

Trust of user address (i.e., wallet address) in Bitcoin is dependent on not who user is but the amount of Bitcoin currency in user's wallet address. So we cannot necessarily trust a Bitcoin user with wallet address. Actually, there is no method for assigning trust to user addresses in Bitcoin. Although we can provide trust to user address by inserting certificate into Bitcoin transaction using OP_RETURN, it still has some problems about wastefulness. In this paper, we propose a new method that efficiently assigns trust to user address using Bitcoin transfer only, which does not need to use OP_RETURN. Our proposed method can realize a trust providing mechanism for “Blockchain 1.0” without adding any new function. Main idea of our method is that “token coin” (Bitcoin transfer itself) can be used as a trust providing mechanism. For feasibility study, we experimentally show the trust assigning in the Bitcoin testnet. Furthermore, our method can be easily adopted into other cryptocurrencies.
The Security Policies Optimization Problem for Composite Information Systems

The problem of optimizing the security policy for the composite information system is formulated. Subject-object model for information system is used. Combining different types of security policies is formalized. The target function for the optimization task is recorded. The optimization problem for combining two discretionary security policies is solved. The case of combining two mandatory security policies is studied. The main problems of optimization the composite security policy are formulated. +50 CHMBOJIOB!!!
Weighted automata sequence kernel: Unification and generalization

Sequence kernels have been widely used for learning from sequential data. In recent years, a significant effort has been devoted to sequence kernels focusing on individual problems, and so devising several approaches. As a contribution in developing a unified theory of machine learning, in this paper, we complement our previous general framework that deals with sequence kernels, termed weighted automata sequence kernel. We provide a more formal presentation of the framework fully supported with proofs. In fact, the mapping of a string s to a high dimensional feature space can be modeled by a formal power series that can be realized by a weighted automaton (WA) As representing all subsequences of the string s. The computation of the kernel K(s,t) between two strings is the behavior of the intersection weighted automaton As,t=As∩At. Regarding kernel computation efficiency, we propose a forward lookup automaton intersection technique to prevent unsuccessful ε-paths while evaluating the WA computations. The experiments use the Reuters-21578 collection. The results reveal that the kernel evaluation using our proposed technique is faster than the one that uses standard intersection. In order To highlight the unification aspect of our model, we study the relationship between our general framework and a variety of common sequence kernels. Finally, we prove that the proposed formalization can be generalized to sequence multiset kernels showing the robustness of our approach. In our case, the new defined sequence multiset kernel can also be seen as a tree kernel.
Diagnosis of Breast Cancer Based on Support Vector Machine and Random Forest Methods

A surge of breast cancer in health issues has set new challenges to clinical routine. With the help of data and image processing, artificial intelligence is possible to assist or automate doctors in diagnosis. On the basis of Wisconsin Breast Cancer Diagnosis (WDBC) database, this paper concentrated on Support Vector Machine (SVM) classifier and Random Forest (RF) to evaluate the features extracted from the tumor nucleus. Then the conclusion was made that SVM classifier has better prediction effect, with 97% AUC and accuracy. And the worst of the perimeter and concave points are two most important features among all the 30 characters. Hence only a model with 2 features can realize the idea results when the efficiency rate is high and data collection is simple. The outcomes showed that the promising factors to be applied to clinical diagnosis so as to suggest possible results, help laymen interpret pathological examination results, enhance the work efficiency of medical staff and mitigate their pressure.
Multilevel term analysis for adaptive document filtering1

Humans tend to organize information in documents in a logical and intentional way. This organization, which we call textual structure, is commonly in terms of sections, chapters, paragraphs, or sentences. This structure facilitates the understanding of the content that we want to transmit to the readers. However, such structure, in which we usually encode the semantic content of information, is not usually exploited by the filtering methods for the construction of a user profile. In this work, we propose the use of term relations considering different context levels for enhancing document filtering. We propose methods for obtaining the representation, considering the existence of imbalance between the documents that satisfy the information needs of users, as well as the Cold Start problem (having scarce information) during the initial construction of the user profile. The experiments carried out allowed to assess the impact, in terms of T11SU measure, on the filtering task of the proposed representation.
Deep Neural Network Acoustic Model Baseline for Character-Level Transcription of Naturally Spoken Czech Language

Heavy portion of previous work in automated speech recognition (ASR) conducts their research and provides results exclusively for English spoken language, for which there are many ready-to-use datasets publicly available. ASR research for other less widely utilized languages suffers from the lack of high-quality speech datasets and insufficient experimental results to compare with new research. In this article, we aim to remedy this problem for the Czech language by proposing Deep Neural Network-based Acoustic Model (AM) architecture as well as providing experimental results for character-level transcription of naturally spoken utterances. The AM architecture was developed by utilizing working solutions from modern English language ASR research and further tailored for better performance on Czech language datasets by conducting a comparative experimental study for several versions of the AM. The models were trained on up to 331 h of naturally spoken Czech language utterances from the PDTSC 1.0 and ORAL2013 datasets and validated on a 5.5-hour excerpt from the PDTSC 1.0 dataset. The results show that our final AM architecture can reach an average of $$ 26.6 \% $$ transcript character error rate (CER) on the validation set when trained with all of the available training data. We believe that the final AM architecture presented in this paper and the experimental results can serve as a baseline for further Czech language ASR research.
An Automatic Database Generation Algorithm for Local Optimization of CNN Object Detector for Edge Devices

Recently, the demand for the use of deep learning algorithm in edge devices is increasing. Deep learning algorithm needs high computation power and large memory resources. However, edge devices require high accuracy and real-time performance with limited resources. In order to overcome this problem, the lightweight shallow networks have been proposed, but their accuracy is much lower than the existing dense networks. We observed that edge devices such as surveillance and security CCTVs are located at the fixed area. We focused these environments where local optimization, which retrains the object detector using a new local database, is very effective to improve the detection accuracy. Local optimization needs additional annotation work for local training database, which is tiresome and time-consuming. We proposed an automatic database generation algorithm for local optimization, which uses a pre-trained object detector and a background model. The proposed algorithm generates the training images by overlaying the extracted objects from the object detector on the background image from background modelling.
Aspectual Analysis of Digital Transformation and New Academic Professionals

Higher education institutions (HEIs) are currently developing a significant research interest in transferring from traditional to novel practices in teaching and learning through the use of modern technological tools and platforms. The integration of digital technologies in higher education has tended to focus on improving academic professionals in developing countries like Saudi Arabia. This chapter was driven by a desire to understand ICT implementation in higher education institutions (HEIs) by professionals using digital transformation in Saudi Arabia. This chapter discusses the implementation of digital transformation in teaching and learning at HEIs in Saudi Arabia. This aim is achieved throughout several objectives, beginning by reviewing the related literature and presenting theoretical frameworks. The literature review will provide the possibility of identifying the focal trends related to the topic.
Intelligent University Identity Identification System Based on FaceNet and FSRNet

University identification is one of the important ways to ensure the security management of today’s open campus. It can effectively detect and identify the suspicious person’s identity and ensure the security of the university campus. However, traditional university identity recognition systems have slow detection speeds, relatively low recognition rates, and inability to process low-resolution images. In view of the above problems, this paper proposes a university identity recognition system based on FaceNet and FSRNet. Combining virtual sample generation technology, face rectification algorithm, and improved MTCNN face detection. It effectively improves performance indicators such as detection speed and recognition rate, and provides solutions for low-resolution face images. The experimental results show that compared with the traditional university identity recognition system, the implementation method proposed in this paper improves the recognition rate of unconstrained natural environment by 1–2%, reduces the system face detection time, and effectively resolves low-resolution Facial face image for information restoration.
COMPOSITIONAL STATISTICAL MODELS UNDER A BAYESIAN APPROACH: AN APPLICATION TO TRAFFIC ACCIDENT DATA IN FEDERAL HIGHWAYS IN BRAZIL

This study considers the use of a composicional statistical model under a Bayesian approach using Markov Chain Monte Carlo simulation methods applied for road traffic victims ocurring in federal roads of Brazil in a specified period of time.The main motivation of the present study is based on a database with information on the injury severity of each person involved in an accident occurred in federal highways in Brazil during a time period ranging from January, 2018 to April, 2019 reported by the federal highway police office of Brazil.Four types of events associated with each injured person (uninjured, minor injury, serious injury and death) are grouped for each state of Brazil in each month characterizing compositional multivariate data.Such kind of data requires specific modeling and inference approaches that differ from the traditional use of multivariate models assuming multivariate normal distributions.The proportion events associated to the accidents (uninjured, minor injuries, serious injuries and deaths) are considered as a sample of vectors of proportions adding to a value one together with some covariates such as pavement conditions in each province, regions of Brazil, months and years that may affect the severity of the injury of each person involved in an accident.From the obtained results, it is observed that the proportions of serious accidents and deaths are affected by some covariates as the different regions of Brazil and years.
READING INTEREST, READING SELF-EFFICACY, AND READING COMPETENCY IN THE JUNIOR HIGH SCHOOL

The research aimed at describing and correlating the students’ reading interest, reading self-efficacy, and their reading competency in Sekolah Menengah Pertama Negeri (SMPN) 3 Singaraja. This research used a quantitative research design. The sample consisted of 102 students in three classes of the eighth-grade students that were chosen by using cluster quota sampling. Three instruments to collect the data such as a reading interest scale, a reading self-efficacy scale, and an objective reading competency test. The data were analyzed descriptively and inferentially. The result revealed that the students’ reading interest, reading self-efficacy, and reading competency on high-order-items are classified into moderate levels. The pair-wise correlation between the students’ reading interest and reading self-efficacy, the students reading interest and reading competency, the students’ reading self-efficacy and reading competency on high-order-items are correlated positively and significantly, as well as the multiple relationships between the three variables.
The Effectiveness of Using an E-Book in ELT: Worldwide Cases

Using technology in English language teaching has been demands in Covid-19 pandemic situation. Students all over the world have studied from home, so have the teachers. Therefore, using technology is necessary as a medium in teaching the English language. It is necessary to use a technology that has interactive functions to ensure the quality of teaching and learning, as well as to keep students’ motivation high. This study is a library study which aimed to analyze the effectiveness of using E-book in ELT from previous related studies worldwide from 2015-2019. Further implications on the effectiveness of using e-book and suggestions to the future studies were discussed.
Research on Parallel System for Motion States Monitoring of the Planetary Rover

In the course of patrol and exploration on extraterrestrial celestial bodies, it is hard to monitor the state of the planetary rover in real time due to the large communication delay and tight link between the target planet and the earth. In order to solve this problem, a parallel system is developed which simulates the state of the environment variable and the planetary rover simultaneously to realize the real-time monitoring of the rover. The test and analysis of the system is also introduced in this paper. Based on this system, the level of telemetry data interpretation of planetary rover can be effectively improved to ensure the safety of planetary rover in the process of surface detection of extraterrestrial bodies.
The circulant hash revisited

Abstract At ProvSec 2013, Minematsu presented the circulant hash, an almost-xor universal hash using only the xor and rotation operations. The circulant hash is a variant of Carter and Wegman’s H 3 hash as well as Krawczyk’s Toeplitz hash, both of which are hashes based on matrix-vector multiplication over 𝔽 2 . In this paper we revisit the circulant hash and reinterpret it as a multiplication in the polynomial ring 𝔽 2 [ x ]/( x n + 1). This leads to simpler proofs, faster implementations in modern computer chips, and newer variants with practical implementation advantages.
Constant-Time Updates Using Token Mechanics

Smart contracts are code fragments that automatically execute on distributed ledgers or blockchains. Smart contracts are typically executed by all block producers on a proof-of-work blockchain. As a result, smart contracts have their computational resources limited in order to prevent denial of service attacks on the blockchain, which would decrease its responsiveness, i.e. block creation throughput. Subsequently, updating values for a large number of records becomes infeasible on the scale of existing centralized services. We present a solution to this problem, when all numerical values of records need to be updated, i.e. in/de-creased proportionally. Our solution uses a mechanism similar to cryptocurrency tokens to track record values and enables constant-time updates for these values.
A Study of Direct Speech Complementation with Embedding Verbs: Collostructional Analysis

Non-relational verbs, as opposed to relational ones, cannot replace their complement clause with a complex nominal, meaning that they do not denote a proposition, as the Relational Analysis states. However, direct speech seems to be a proper replacement for the complement clause in the non-relational verb cases. This paper deals with the analysis of some of the most representative taxonomies of embedding verbs using the British National Corpus, to check whether they can occur with direct speech complements; the collostructional analysis, which is a technique of statistical significance; and the programming language R to do it in a computational and automatic way. Thus, the collostructional method will measure the strength between the embedding verbs and their corresponding complement clauses in the direct speech form.&#x0D;
Authorship Analysis Approaches

This chapter presents an overview of authorship analysis from multiple standpoints. It includes historical perspective, description of stylometric features, and authorship analysis techniques and their limitations.
Study on an Adaptive Learning Support System Design based on Model-based Development

This research proposes an adaptive smart learning support system, whose target is a typing support system, based on the Model-Based Development (MBD) approach. The proposed typing support system adaptively adjusts the level of work so that the typing skill of a learner is smoothly grown. In this paper, the goal of an adaptive smart learning support system is briefly explained, and a concrete design scheme of a part of the support system based on the MBD approach is presented. This paper also mentions the work in progress of developing an actual typing support system.
A multi-agent system for optimizing physiological collection based on adaptive strategies

Wearable devices have emerged from the evolution of communication and information technology, along with the miniaturization of electronic components. These devices monitor the user's physiology on a periodic basis and generally have low battery autonomy. This article proposes the Odin system, a multi-agent system that performs real-time analysis of physiological data in order to adapt the collection according to the user's health condition. Two types of adaptation were proposed in terms of adapting the collection. The first, wait-time adaptation (WTA), consists of collecting physiological data through a sensor defined as the main sensor, where the frequency of the collection is based on the user's condition. Meanwhile, the second, paused sensors adaptation (PSA), manages the activation status of secondary sensors when the main sensor suggests a possible risk to the user's health. An evaluation was implemented through two steps. First, a simulation was performed with requests control to optimize the parameters of the data collection. These results showed an increase of 214% in battery life in the adaptive scenario compared to one with no adaptivity. Second, Odin's adaptivity was evaluated in terms of a real dataset, which allowed for the reduction of the number of requests. This reduction optimized the battery consumption, with a 66% improvement over data collection with no adaptivity.