Reviewerly/exHarmony huggingface dataset:
Second All-Union Programming Conference

The paper presents the history of organization and holding of the Second All-Union Programming Conference (VKP-2) - a notable event in the life of the programming society in the period of establishing of computer science and one of the most popular professions of the future. The work uses on materials from the Digital Archive of the Academician A.P. Ershov, one of the founders of the national programming.
Towards zero-shot cross-lingual named entity disambiguation

In cross-Lingual Named Entity Disambiguation (XNED) the task is to link Named Entity mentions in text in some native language to English entities in a knowledge graph.XNED systems usually require training data for each native language, limiting their application for low resource languages with small amounts of training data.Prior work have proposed so-called zero-shot transfer systems which are only trained in English training data, but required native prior probabilities of entities with respect to mentions, which had to be estimated from native training examples, limiting their practical interest.In this work we present a zero-shot XNED architecture where, instead of a single disambiguation model, we have a model for each possible mention string, thus eliminating the need for native prior probabilities.Our system improves over prior work in XNED datasets in Spanish and Chinese by 32 and 27 points, and matches the systems which do require native prior information.We experiment with different multilingual transfer strategies, showing that better results are obtained with a purpose-built multilingual pre-training method compared to state-of-the-art generic multilingual models such as XLM-R.We also discovered, surprisingly, that English is not necessarily the most effective zero-shot training language for XNED into English.For instance, Spanish is more effective when training a zero-shot XNED system that disambiguates Basque mentions with respect to an English knowledge graph.
Evolved response thresholds generalize across problem instances for a deterministic-response multiagent system

In this work, we use a multiobjective genetic algorithm to evolve agent response thresholds for a decentralized swarm and demonstrate that swarms with evolved thresholds outperform swarms with thresholds set using other methods. In addition, we provide evidence that the effectiveness of evolved thresholds is due in part to the evolutionary process being able to find, not just good distributions of thresholds for a given task across all agents, but also good combinations of thresholds over all tasks for individual agents. Finally, we show that thresholds evolved for some problem instances can effectively generalize to other problem instances with very different task demands.
A Robust Digital Watermarking for Medical Images Based on PHTs-DCT

In recent years, digital watermarking technology has been widely used to solve the protection of privacy issues in medical images. However, the existing algorithms are failed to solve the protection problems against geometric attacks. To solve this problem, a robust digital watermarking algorithm for medical images based on PHTs-DCT is proposed in this paper. For embedding watermark, DCT is applied to reduce the redundant data of the image, then the coefficients are calculated via PHTs and the DCT to construct a binary feature sequence. Moreover, the watermark information is encrypted by Logistic mapping, and the zero watermark is constructed and stored in a third party medical image protection database. Extensive experiments have shown that the proposed algorithm has good robustness to geometric attacks and common attacks, especially anti-translation, rotation, and compression attacks.
Ground Target Motion Estimation based on visual measurements from a UAV platform

This paper presents an on-board target motion estimation solution to the problem of standoff tracking of a moving ground target using unmanned aerial vehicles (UAV). As target motion estimation error is sensitive to choice of the UAV flight path, the estimation problem is solved in conjunction with a UAV guidance law. A tactical fixed-wing UAVequipped with a gimbaled camera is considered. The onboard estimator is designed such that the UAV can track a moving ground target and provide the target's motion estimation solution in real time. An estimator solution based on Kalman Filtering is proposed with a guidance law based on inverse kinematics. Additional results based on other estimators proposed in the literature will be shown for comparison purposes Numerical simulations are performed to verify the feasibility and benefit of the approach. The results show the filters exhibit stable convergence periods for both position and velocity estimates in the presence of tracking loss events. These filters show similar performances in relatively fairy conditions. The Kalman filter approach, integrating a dynamic model and measurements, is preferred since it may provide an early stability in the estimation of the target's state and it is capable to cover tracking loss events.
Controlling Fake Reviews

In this study, I theoretically analyze fake reviews on a platform market using models where a seller creates fake reviews through incentivized transactions, and its sales depend on its rating based on a review history. The platform can control the incentive for fake reviews by changing the parameters of the rating system, such as its filtering policy and weights, for past reviews. At equilibrium, the number of fake reviews increases as quality increases but decreases as reputation improves. Since fake reviews have a positive relationship with a product’s underlying quality, under some parameters, rational consumers find a rating more informative when fake reviews exist, while credulous consumers suffer from a bias caused by boosted reputation. A stringent filtering policy can decrease the expected amount of fake reviews and the bias of credulous consumers, but at the same time, it can decrease the informativeness of a rating system for rational consumers. In terms of the weight placed on the review history, rational consumers benefit from higher weights on past reviews than from optimal weights without fake reviews.
Leadership in Professional Vocabulary Acquisition at ESL classes in Small Groups of Students Majoring in Management

The present research featured how learning a foreign language in small groups under the guidance of a student with leadership qualities improves professional vocabulary. Such ESL classes proved useful for students majoring in management. Their work was organized in such a way that students' roles were close to their personal characteristics, i.e. action-, people-, and thinking-oriented. A diagnostic test revealed the students' level of proficiency in English. A psychological training made it possible to identify potential leaders. The students in the experimental group were divided into small subgroups and had to master professional vocabulary under the guidance of the leader. In the control group, the subgroups consisted of either leaders or subordinates. Vocabulary acquisition in the experimental subgroups appeared to be 25 % higher than in the control subgroups. Therefore, teamwork under the guidance of a leader proved quite effective.
Treatment of Real Wastewater from Organic Contaminants by Using Biochar

In a number of water use schemes (water and wastewater treatment), physicochemical treatment methods, in particular adsorption, have been successfully used. However, widespread adoption of these methods is hindered by the high cost of sorbents. One of the promising solutions to these problem of sorption treatment is the use of biochar. Due to its high specific surface area, well-developed porous structure, and surface functionality, biochar has been used as inexpensive adsorbent. The purpose of the laboratory experiments is to determine the sorption properties of biochar obtained from pine – sorption capacity, sorption rate, and purification effect – while removing organic contaminants from real paper mill wastewater. The sorption properties of biochar were studied under static and dynamic conditions, varying the size of the fractions, and processing time. The sorption characteristics of biochar of all the studied fractions were established and it was shown that the most effective sorbent in removing organic contaminants is the 1-biochar’s fraction of (sorption capacity of 11.9 mg of COD per of sorbent). The data obtained enable theoretically substantiate the presence of certain functional groups on the surface of the researched biochar, as well as to develop technological regimes for the treatment of natural and waste waters of a certain composition with its help.
The Impact of AR Technology on the Knowledge Learning Experience of Science and Technology Museums

With the advent of the new era of knowledge economy, the development of Science and Technology Museums in China has entered a period of rapid development. However, the one-way and outdated display methods of the existing Science and Technology Museums are difficult to give young people a good tour experience and learning experience. And many intelligent methods are changing and leading the future development of Science and Technology Museums, such as AR technology. This article draws the conclusion that AR technology has a positive impact on the knowledge learning experience of Science and Technology Museums through the method of comparative experiments. At the same time, it provides constructive suggestions for young people in the knowledge learning experience of Science and Technology Museums and the development direction of future Science and Technology Museums.
Simulation and Detection of Small Drones/Suspicious UAVs in Drone Grid

Today’s technology is evolving towards autonomous systems and the demand in autonomous drones, cars, robots, etc. has increased drastically in the past years. This project presents a solution for autonomous real-time visual detection and tracking of hostile drones by moving cameras equipped on surveillance drones. The algorithm developed in this project, based on state-of-art machine learning and computer vision methods, succeeds at autonomously detecting and tracking a single drone by moving a camera and can run at real-time. The project can be divided into two main parts: the detection and the tracking. The detection is based on the YOLOv3 (You Only Look Once v3) algorithm and a sliding window method. The tracking is based on the GOTURN (Generic Object Tracking Using Regression Networks) algorithm, which allows the tracking of generic objects at high speed. In order to allow autonomous tracking and enhance the accuracy, a combination of GOTURN and tracking by detection using YOLOv3 was developed.
Developing and testing a tool to classify sentiment analysis

The era has faced with explosive growth in data generation.Data generation has undergone a renaissance change.This availability of data has led a paradigm shift in the E-commerce sector; data is no longer a by-product of business activities, but are the asset to a company it helps in providing insights which are required in satisfying customers' needs.This paper provides an overview of sentiment analysis of product reviews based on different algorithms and its efficiency in determining positive from negative reviews based on N-gram, Bigram with the application of Count-Vectorizer and (Term Frequency-Inverse Document Frequency) (TFIDF) Matrix.Different classification models have been employed to check the prediction accuracy of the unlabeled text.Based on the above classification and tool has been developed which predicts the incoming reviews and classify its sentiment polarity.
Implementation of Grover's algorithm on the IBMQ: constraints and error assessment

We use the IBMQ machine to implement a quantum database circuit. The database is made of a single multi- column table with multiple superposed records using Toffoli, CNOT and single quantum qubit gates. We present an optimized quantum circuit which allows us to select an item from the table, according to a specific oracle. Our circuit is based only on a single auxiliary qubit to perform the Selective Inversion (SI) and the Inversion Around the Average (IAA), which constitute the Grover operator. We analyze the difference between the generated transpired circuits while executing the original circuit on different IBMQ machines, namely the Melbourne and Athens quantum machines. We discuss the physical constraints causing the difference between the simulation on the composer and the executions of the circuits on the real IBMQ devices.
Artificial Intelligence: Creating More Possibilities for Programmatic Advertising

Artificial intelligence is creating more possibilities for programmatic advertising. Based on the practice of programmatic advertising in China, this article makes the following contributions: (1) points out the current challenges the programmatic advertising needs to cope with; (2) introduces both how AI drives the evolution of the whole chain of programmatic advertising and how the technology works in the specific links of programmatic advertising; (3) shows the latest programmatic marketing solution for full stack AI decision-making platform through two actual cases in China; and (4) reflects on the use of AI in programmatic advertising.
Factors Influencing the Acceptance of Blockchain Technology: Real Estate Industry

Blockchain is an innovative distributed ledger technology enabling the exchange of value online without the need of a central authority. Blockchain provides a decentralized and trustless network, secured by cryptography and consensus mechanisms. The acceptance of blockchain technology greatly impact the real estate owners, financiers, builders and more. Hence, this study attempts to find out how buyers and sellers of real estates are accepting blockchain technology by integrating and analysing a proposed hybrid model of UTAUT (Unified Theory of Acceptance and Use of Technology) combined with TRI (Technology Readiness Index). This study utilizes a quantitative research method and aims to identify the key influencing factors in the acceptance of blockchain technology. It is crucial to fully comprehend the potential of blockchain technology as it is a new revolutionary technology. This paper proposed a conceptual model that tests the performance expectancy, effort, social influence, facilitating condition, innovativeness, optimism, discomfort and insecurity in influencing the user intention behaviour in accepting blockchain technology. A pilot-test was conducted to test the research model. The results showed that all composite reliability values except for ‘Social Influence’ were found to be above the recommended value of 0.70, suggesting strong indicator reliability of the constructs.
Android-Based Classroom Monitoring System for Teacher Using QR Code Technology

This study is to develop an android-based classroom monitoring system for teacher using QR code technology for classroom management of the institutions administration and easy access of information for report generation regularly. The study is simply development of the system and tested through developmental process which is tested and implemented successfully. The major output is to (1) develop a module in using QR code in smartphone; (2) to develop module in saving information into the device locally; and (3) to develop a module that retrieves into the local server. The result of the project management where very successful using the JavaScript Object Notation (JSON) which serves as the imported library in the development. The study implemented successfully during the class scheduled last term of school year 2019-2020.
Adversarial Attacks in Banknote Recognition Systems

Numerous computer vision algorithms have been designed to help detect the values of the paper banknotes for blind and visually impaired individuals. Previous algorithms relied mostly on traditional methods that are less efficient, accurate, and transferable than a convolutional neural network. So in our study, we used the convolutional neural networks to perform banknote image classification. We trained our network with 1,000 Thai banknote images. Our network could classify real-life banknotes used in our study with an accuracy of 100%; however, we have also found out that our convolutional neural network model is vulnerable to adversarial attacks. We then retrained the network using adversarial training which reduced the attack success rate by approximately 50%. Overall, because we did our experiments using MobileNet, which is a relatively small convolutional neural network that can be used in a mobile phone, we have trained an accurate and robust banknote recognition convolutional neural network that could be integrated into a mobile app or wearable device for the visually impaired.
Survey on Classification and Summarization of Documents

Text classification and summarization has become one of the important concepts in analyzing and understanding various text documents related different fields. It is used in various different fields like spam email detection, twitter sentiment analysis, summarizing textual documents, etc. There have been few works in dataset formation and analysis for different complex documents like research papers, survey papers etc. This survey concentrates on reviewing and comparing various approaches used for analysis of such complex documents.
Best Practices IT security - What the customer wanted and what the market offers

Radars require sophisticated networks and IT components, so IT security is essential. However, radar systems are located in harsh environments and therefore have high demands regarding functional and non-functional requirements. A selection of suitable products is a challenge that cannot be solved alone.
Explanation Mining

Explanations are used to provide an understanding of a concept, procedure, or reasoning to others. Although explanations are present online ubiquitously within textbooks, discussion forums, and many more, there is no way to mine them automatically to assist learners in seeking an explanation. To address this problem, we propose the task of Explanation Mining. To mine explanations of educational concepts, we propose a baseline approach based on the Language Modeling approach of information retrieval. Preliminary results suggest that incorporating knowledge from a model trained on the ELI5 (Explain Like I'm Five) dataset in the form of a document prior helps increase the performance of a standard retrieval model. This is encouraging because our method requires minimal in-domain supervision, as a result, it can be deployed for multiple online courses. We also suggest some interesting future work in the computational analysis of explanations.
An improved predictor of daily stock index based on a genetic filter

In this study, a genetic algorithm-based filter feature selection was applied to the data on the rates of change in various economic indices used worldwide to predict the rates of change of the Korea Composite Stock Price Index (KOSPI). The fitness function is composed of a combination of the results of information gains, F-test, and correlation coefficients. Data for the past 12 years (from 2007 to 2018) were divided into sections according to the yearly time intervals, and feature selection was applied to the data in each section. It was found that the amount of calculation time required for a genetic filter was approximately 75% lower compared to that required for a previous genetic wrapper. It was also found that the mean absolute error of the genetic filter was approximately 26% lower compared to that of the genetic wrapper. The analytic results verified that the genetic filter exhibited better calculation performance and required less time to calculate the optimal solution. Furthermore, a new combination of features related to the KOSPI was obtained.
Minimum Vector Variance Estimator in Outlier labeling of Multivariate Data: Application to HIV patient in Indonesia

ABSTRACTAn outlier is an observation whose pattern does not follow the majority of the data. Outliers in this study were characterized by extreme distance values, both very small and very large, exceeding the predetermined value. The method used in this research is Minimum Vector Variance (MVV) method because it has good computational efficiency and is robust against outliers. Based on the MVV algorithm applied to data on HIV patients in Indonesia in 2016-2018. The results showed that the MVV method produced more extreme distances than the Mahalanobis distance in labeling outliers. In the research data, it is found that there are 16 regions including outliers of the 34 observation used.
Blockchains – achieving consensus in oil and gas business processes

As the oil and gas industry comes under increasing pressure from government, activist groups and society on carbon offsetting, a trend that is building is the disclosure of consistent, comparable and reliable data. As companies develop their abatement plans, it is critical that the accounting and reporting of activities are made transparent or companies risk not achieving the social license to operate they are aiming to build. This proposal will discuss the opportunity for operators to use blockchain to digitise the information and processes that operate between unincorporated joint ventures, companies and third parties they deal with – customers, suppliers, government and society. At its heart, blockchain is a distributed ledger that offers the potential to make processes between companies more efficient through standardised processes and trusted data; in the same way companies have implemented internal Enterprise Resource Planning systems to achieve this goal within their own entities. The current landscape of systems and processes has been set up to optimise internally, but when processes cross company boundaries, they execute via email and analogue processes. Gartner predicts that by 2030 $3.1 trillion of goods and services will be tracked by blockchain technology. This is a technology that will revolutionise supply chains and company-to-company interactions. The energy sector has embraced contractors and suppliers who support engineering, operations, maintenance, services and supply of parts. Blockchain is therefore a technology that offers great opportunity in optimising in-bound supply chain and increasingly will be required to communicate on their outbound product with more transparent and trusted data enabled by blockchain technology.
Minimum Time Search Methods for Unmanned Aerial Vehicles

Darsini, R. Ganvkar, Nikita Gurunathan, Karthik Dash, Ranjita KumariThis paper analyzes and compares methods used to evaluate and obtain the best trajectories of multiple Unmanned Aerial Vehicles (UAV) that have to find a lost target as soon as possible. The analysis shows how the high-complexity of this problem, named Minimum Time Search (MTS), makes it a good candidate to solve it with different bio-inspired and probabilistic-based approaches and also elaborates some of the advantages and drawbacks of these approaches. The paper also compares and relates two different mathematical formulations that can be used to evaluate the UAV trajectories based on the mean-time to detect the targets with the sensors on-board the UAVs.