Reviewerly/exHarmony huggingface dataset:
Ciphertext-only attacks on the double random phase encryption based on redundancy vulnerability

The double random phase encoding techniques have received considerable attention from researchers in recent years because of their advantages of parallel and high-speed processing capability. Meanwhile, the security of the cryptosystem is also one of the major concerns. We experimentally showed the ciphertext redundancy vulnerability of the coherent double random phase encryption (DRPE) system. Based on the statistical ergodicity of speckles and the consistency of the energy spectral density (ESD), we have proved that the method can retrieve the most plaintext information from partial ciphertext alone. We performed the simulation and experimental results to verify whether the algorithm is effective. The ciphertext redundancy of the DRPE system is analyzed from the results of the ciphertext occlusion test. There is a risk of plaintext leakage in this scheme, as long as the average ESD can be estimated from the sub-images. The results will further our understanding of the limitation of current optical security techniques. The DRPE system has potential redundancy risk. This vulnerability allows a cryptanalyst to estimate the plaintext information with only a half or less ciphertext.
Computer Vision Controlling an Autonomous Unmanned Aerial Vehicle Flight over a Railway

In this work, an algorithm to control the autonomous quadcopter flight along the railway, without an operator and without the use of satellite navigation systems has been developed. The quadcopter camera image processing determines the rails position and direction and the software generates signals to control flight characteristics, such as pitch, yaw, and roll, to fly over the railway at a given height. The algorithm does not require significant computing resources, therefore its hardware does not affect the quadrocopter weight and size parameters and power consumption, and therefore does not affect the range and duration of its flight. Quadrocopter tests have confirmed the algorithm feasibility and reliability.
SUMSUM@FNS-2020 Shared Task

This paper describes the SUMSUM systems submitted to the Financial Narrative Summarization Shared Task (FNS-2020). We explore a section-based extractive summarization method tailored to the structure of financial reports: our best system parses the report Table of Contents (ToC), splits the report into narrative sections based on the ToC, and applies a BERT-based classifier to each section to determine whether it should be included in the summary. Our best system ranks 4th, 1st, 2nd and 17th on the Rouge-1, Rouge-2, Rouge-SU4, and Rouge-L official metrics, respectively. We also report results on the validation set using an alternative set of Rouge-based metrics that measure performance with respect to the best-matching of the available gold summaries.
A Review of Cross-Domain Text-to-SQL Models.

WikiSQL and Spider, the large-scale cross-domain text-to-SQL datasets, have attracted much attention from the research community. The leaderboards of WikiSQL and Spider show that many researchers propose their models trying to solve the text-to-SQL problem. This paper first divides the top models in these two leaderboards into two paradigms. We then present details not mentioned in their original paper by evaluating the key components, including schema linking, pretrained word embeddings, and reasoning assistance modules. Based on the analysis of these models, we want to promote understanding of the text-to-SQL field and find out some interesting future works, for example, it is worth studying the text-to-SQL problem in an environment where it is more challenging to build schema linking and also worth studying combing the advantage of each model toward text-to-SQL.
Subspace-Weighted Consensus Clustering for High-Dimensional Data

Consensus clustering aims to combine multiple base clusters into a probably better and more robust clustering result. Despite the significant progress in recent years, the existing consensus clustering approaches are mostly designed for general-purpose scenarios, yet often lack the ability to effectively and efficiently deal with high-dimensional data. To this end, this paper proposes a subspace-weighted consensus clustering approach, which is based on two key observations in high-dimensional data. First, the cluster structures often lie in different subspaces in high-dimensional feature space. Second, the features in high-dimensional data may be of different importance and should be treated differently. Specifically, we utilize the Laplacian score to estimate the importance of different features. Then the weighted random sampling is performed repeatedly to produce a set of diverse random subspaces, in which multiple base clusters can thereby be generated. Further, the reliability of each base clustering is evaluated and weighted by considering the reliability of the features in the corresponding subspace, after which a subspace-weighted bipartite graph can be constructed and efficiently partitioned to obtain the final consensus clustering result. Experimental results on ten real-world high-dimensional datasets demonstrate the effectiveness and efficiency of the proposed approach.
PSO Trajectory Planner Using Kinematic Controllers that Ensure Smooth Differential Robot Velocities

A multi-agent differential robot system requires a definite algorithm to behave as a swarm with goal searching capabilities. The classic Particle Swarm Optimization (PSO) algorithm is designed for particles with no mass or physical dimensions unlike differential robots. Therefore, it cannot be directly used to search for a goal using a robotic swarm. We propose the use of the PSO as a trajectory planner to enable the agents to collectively find the optimal path to a goal. The proposed PSO trajectory planner uses multiple known parameters such as inertia weight, a constriction parameter, two scaling factors and two random weighing factors (for the cognitive and social components of the PSO) optimized for differential robots. The planner takes into account the restrictions derived from the kinematic equations of the robotic agents and the finite dimensions of the search space. For that purpose, it is coupled with the necessary controllers to map particle velocities into smooth and continuous differential robot velocities. Four different controllers were tested, including the Transformed Unicycle Controller (TUC), Transformed Unicycle with LQR (TUC-LQR), Transformed Unicycle with LQI (TUC-QI), and a Lyapunov-stable Pose Controller (LSPC). The TUC-LQI controller outperformed the others in terms of achieving smooth continuous differential robot velocities, while following the paths generated by the PSO trajectory planner.
A Context-Aware Computing Method of Sentence Similarity Based on Frame Semantics

Sentence similarity computing is a typical technology used in natural language processing, which aims at finding valuable information from documents. By adapting advanced technologies, such as machine learning and deep learning, current sentence similarity computing methods mainly deal with key words and structures of sentences. The main drawback of current methods is taking no consideration of the influence of sentences context. In this paper, we propose a frame semantics theory based computing method that is built upon FrameNet. By quantitatively analyzing the semantic relations among frames, sentences similarity can be calculated based on the frames that are evoked by the sentences. We also carry out experiments to evaluate the performance of this method with the help of a prototype software tool we developed.
Applying Machine Learning to Anomaly Detection in Car Insurance Sales

Financial revenue, in the insurance sector, is systematically rising. This growth is, primarily, related to an increasing number of sold policies. While there exists a substantial body of work focused on discovering insurance fraud, e.g. related to car accidents, an open question remains, is it possible to capture incorrect data in the sales systems. Such erroneous data can result in financial losses. It may be caused by mistakes made by the sales person(s), but may be also a result of a fraud. In this work, research is focused on detecting anomalies in car insurance contracts. It is based on a dataset obtained from an actual insurance company, based in Poland. This dataset is thoroughly analysed, including preprocessing and feature selection. Next, a number of anomaly detection algorithms are applied to it, and their performance is compared. Specifically, clustering algorithms, dynamic classifier selection, and gradient boosted decision trees, are experimented with. Furthermore, the scenario where the size of the dataset is increasing is considered. It is shown that use of, broadly understood, machine learning has a realistic potential to facilitate anomaly detection, during insurance policy sales.
Depth Estimation Using Blob Detection for Stereo Vision Images

Object detection is one of the most important research areas in image processing. Here, we propose to employ dual camera system to precisely find the distance, the height, the width of object that are applicable in areas like autonomous system navigation, vehicle navigation, etc. One of the most popular approach is the passive approach; however, its performance is usually limited by complicated scenes and texture-fewer areas. Therefore, actual and resourceful estimation algorithms of stereo depth have been a long-time problem. To solve those problems, an enhanced methodology that embraces three steps are proposed. Foremost, the images are segmented using thresholding technique, and a blob detection technique is used to find the objects in the scene. Then, the center of gravity is found for the objects. It helps to avoid the rectification process. Finally, the distance of the objects is found using the disparity values of the objects. Experiment results are promising.
A secure and efficient multi cloud-based data storage and retrieval using hash-based verifiable secret sharing scheme

As the availability of many smart devices rises, fast and easy access to data as well as sharing more information is felt. Cloud computing is a computational approach that shares configurable resources such as network, servers, storage space, applications and services on the internet. The confidentiality, integrity, and availability of the data, reducing computational cost and communication channel between the data owner and cloud service providers are essential parts of cloud computing. In the paper, we propose a new scheme to construct a secure cloud data storage based on the verifiable secret sharing scheme with public verifiability to protect data integrity. And the verification phase does not depend on any computational assumptions. The proposed scheme cannot only detect the cheating but also identify who are the cheaters. It is worth noting that the proposed scheme is more efficient compared with the other secret sharing-based cloud data storage since heavy and complex computation is not required.
The Structural Relationship between College Student Satisfaction and Career Attitude Maturity and Job Preparation Behavior in Physical Education Department

The purpose of this research is to establish the relationship between the academic satisfaction and career attitude maturity of university students in the Department of Physical Education, the determination, purpose, certainty, independence, readiness, and job preparation behavior. To this end, 221 valid samples were used to produce the following results: First, the academic satisfaction level of university students in the Department of Physical Education had a static effect on job preparation behavior. Second, the academic satisfaction level of the Department of Physical Education and College Students had a static effect on the determination, certainty, independence and readiness. Third, the career attitude of the department of physical education and college students had a static effect on job preparation behavior with determination, purpose and readiness. On the other hand, the certainty of career attitude has had a negative impact on job-seeking behavior.
Universal Dependencies for Manx Gaelic

Manx Gaelic is one of the three Q-Celtic languages, along with Irish and Scottish Gaelic. We present a new dependency treebank for Manx consisting of 291 sentences and about 6000 tokens, annotated according to the Universal Dependency (UD) guidelines. To the best of our knowledge, this is the first annotated corpus of any kind for Manx. Our annotations generally follow the conventions established by the existing UD treebanks for Irish and Scottish Gaelic, although we highlight some areas where the grammar of Manx diverges, requiring new analyses. We use 10-fold cross validation to evaluate the accuracy of dependency parsers trained on the corpus, and compare these results with delexicalised models transferred from Irish and Scottish Gaelic.
Fine-grained linguistic evaluation for state-of-the-art Machine Translation

This paper describes a test suite submission providing detailed statistics of linguistic performance for the state-of-the-art German-English systems of the Fifth Conference of Machine Translation (WMT20). The analysis covers 107 phenomena organized in 14 categories based on about 5,500 test items, including a manual annotation effort of 45 person hours. Two systems (Tohoku and Huoshan) appear to have significantly better test suite accuracy than the others, although the best system of WMT20 is not significantly better than the one from WMT19 in a macro-average. Additionally, we identify some linguistic phenomena where all systems suffer (such as idioms, resultative predicates and pluperfect), but we are also able to identify particular weaknesses for individual systems (such as quotation marks, lexical ambiguity and sluicing). Most of the systems of WMT19 which submitted new versions this year show improvements.
Archives in a Graph. The Records in Contexts Ontology within the framework of standards and practices of Archival Description

Description is one of the key activities of archival profession, a guarantee for correct management, access and conservation of archives. The cornerstones of the tradition, formalized in the last decade of the twentieth century in the standards of the International Council on Archives, are the respect of fonds and their contexts. The ICA standardization renewal process started in 2012 has produced two versions, both in draft, of the Reference Model of the brand new standard RiC-Records in Contexts, on which the very recent RiC-Ontology has been based. The descriptive hierarchy and the concept of the finding aid – even electronic – as a document isolated from the Linked Open Data available in the infosphere are overcome. This contribution aims to present RiC-CM and RiC-O, their criticalities and perspectives, contextualizing them within the debate and practices of archival description.
Towards Safe and Socially Compliant Map-Less Navigation by Leveraging Prior Demonstrations

This paper presents a learning-based approach for safe and socially compliant map-less navigation in dynamic environments. Our approach maps directly 2D-laser range findings and other measurements to motion commands, and a combination of imitation learning and reinforcement learning is deployed. We show that, by leveraging prior demonstrations, the training time for RL can be reduced by 60% and its performance is greatly improved. We use Constrained Policy Optimization (CPO) and specially designed rewards so that a safe and socially compliant behavior is achieved. Experiment results prove that the obtained navigation policy is capable of generalizing to unseen dynamic scenarios.
THE NEW GENERATION NATIONAL RESEARCH COMPUTER NETWORK AS AN INFRASTRUCTURE AND SERVICE PLATFORM FOR A UNIFIED DIGITAL SPACE OF SCIENTIFIC KNOWLEDGE

The paper provides general information on the architecture of the new generation National Research Computer Network (NIKS), operated and evolved by JSCC RAS. The opportunities provided by NIKS for improvement of a unified digital space of scientific knowledge, increasing the level of accessibility, information security and network protection of its resources and services are denoted. The specialized scientific services and services for collective use including those built on the technologies of federated authentication and authorization and developed on the basis of the NIKS infrastructure are presented.
Learning the Concept Embeddings of Ontology

The semantic similarities among concepts play an important role in many tasks. Ontology represents the semantic relationship among concepts. Traditional methods use the path-length between concepts in the ontology to calculate their semantic similarity. However, this simple method cannot present semantic relationship among concepts well. This study seeks to learn the concept embeddings in ontology, and then use the cosine similarity of two embeddings to inform their sematic similarity. We developed a framework, called concept2vec, to perform the task. The experimental results demonstrate that our work is effective on learning representation of concepts in ontology.
Real-Time Texture Extraction Based on the Improved Median Robust Extended Local Binary Pattern

Local binary patterns (LBP) are considered as the most computation efficient and high-performance texture features. Among all variants of the LBPs, Median Robust Extended Local Binary Pattern (MRELBP) [1] is considered as one of the highest-performance one. Based on the MRELBP, this paper proposes an improved real-time texture extraction architecture implemented on a field-programmable gate array (FPGA) device. The fixed-point fraction is used to replace the double float-point fraction, only results in less than 1% difference to the accuracy. The proposed system is implemented on Xilinx Zynq-7045, and the extraction time is linear with the size of the input image. The extraction time of the 128x128 image in the dataset Outex-TC is merely 85.64μs, which is more than 4800 times faster than Liu et al. implemented on MATLAB [2].
Perancangan Sistem Keamanan Ganda Brangkas Berbasis Telegram Menggunakan Mikrokontroler ESP32-CAM

AbstractThe development of science and technology, especially in the field of Electronics and Information Technology, is expected to improve the technology of room safety systems. Room security system technology usually consists of valuable objects stored in a safe that has a lock system with a number of combination numbers. This safe is placed in a room with many security systems, such as security from security personnel, using laser signal detector technology or with CCTV cameras. However, along with technological developments, this room security system still has weaknesses such as laser light can be seen when it hits particles of dust and cigarette smoke and CCTV cameras are also easily known. Based on this, one of the efforts to secure the safe is to develop technological innovations in the form of remote system notifications and alerts via the telegram application. Based on this, the researcher proposes a safe space security system using a motion detection camera based on esp32-cam with google drive and telegram automatic storage. The result of the research is telegram notification to the user for early warning, which is 5 seconds. In addition, the system is able to receive warning system signals from motion detection results depending on the strength of the internet network..
Automatically Resolve Trouble Tickets with Hybrid NLP

Application service providers manage huge and complicated infrastructures. Like any complicated systems, things could go wrong due to various reasons (e.g. network connection response problems, infrastructure resource limitations, software malfunctioning issues, etc.) from time to time. How to quickly resolve issues when they happen becomes critical to help improve customer satisfaction and retention. Recently, fast advancement of natural language processing (NLP) algorithms have helped solving many practical problems by analyzing text information. Powerful algorithms have been developed to interpret human language and derive predictions. Ensemble models are also well suited to further improve performances as they can further explore the latent space to take advantage of features/weights discovered from a group of trained models. In this paper, we will introduce efficient machine learning algorithms to help application service providers manage and automatically resolve trouble tickets to significantly improve user experience and operational efficiency. We propose a hybrid approach to use both unsupervised clustering approach and supervised deep learning embedding to maximize the feature exploration and learning efficacy. Then we ensemble multiple optimized models to build the recommendation engine. This approach helps to incorporate the most relevant information, explore the corpus better for a given problem, managed to produce more consistent and robust predictions, and obtained better accuracy than any single model.
DDAT Target Tracking Algorithm Based on Occlusion Detection Mechanism

Aimed at the occlusion problem of target tracking in machine vision,an occlusion detection mechanism is introduced based on the original Distractor-Aware Tracking(DAT)algorithm framework,and a Detection-DAT(DDAT)algorithm is proposed.First,this mechanism extracts color characteristics of the target,calculates similarities between the target frames through color characteristics,and uses the similarity trends and the threshold values of the differences between the frames to determine whether the target has been occluded during tracking.Second,Naive Bayes and nearest neighbor classifiers are adopted to obtain the target frame in subsequent frames.Finally,similarity is applied to detect whether the target frame obtained by the two classifiers is the correct target frame.To verify the effectiveness of the algorithm,qualitative and quantitative comparisons with the DAT algorithm and other tracking algorithms were performed on the standard data set video sequence with occlusion properties.
Electronic voting research papers in web of science: A bibliometric analysis

Purpose - The main aim of the paper is to investigate and recognize trends in Electronic Voting research at aninternational level Design/methodology/approach - Retrievalof the data was from the database of Web of Science on the topic Electronic Votingthat covered the period from 1988- 2020 to identify useful contributions that have been producedand published in the respective field A total of 994 data records were retrieved from the database Several trends predominatingin Digital Voting research including well known contributing countries, adopted patterns of the authorship, the degree of co-authorship, cross-country co-authorship, prominent sources for research publication, recognition of research in courseof citation trends like average citation per year, top-cited papers, citations received/citations per paper, etc were discovered by analysis of the data Findings–There have been constructive growth in the literature since 1988 as revealed by the analysis As evident from the analysis, half of the research output was contributed by the five countries – USA, England, China, Spain, and Germany with a total of 491 journals published When it comes to the effectiveness of the papers, Denmark leads with a PEI of 4 87 It is followed by Canada with PEI of 4 60 and Scotland with PEI of 2 80 Most of the journals belong to Computers Science and Political Science categories of Web of Science Also, the number of times these papers are cited per year is increasing rapidly for the given duration This shows there is an increase in research in the field of Digital Voting COVID-19 pandemic can be made accountable for the irregularity in the trend in 2020 that has halted the research in the field On performing linear regression, it was observed that we can expect positive growth in the number of citations in the coming years © 2020 Ubiquity Press All rights reserved