Reviewerly/exHarmony huggingface dataset:
Exclusive Association Sampling to Improve Bayesian Multi-Target Tracking

Multi-target tracking has been studied for many years, yet it remains a challenging problem, particularly in terms of implementing data association when tracking targets over several time steps. To achieve robustness, probabilistic approaches have been proposed, including Bayesian multi-target tracking methods. However, these approaches involve high calculation costs, which are incompatible with real-time applications. We propose exclusive association sampling (EAS) to improve the efficiency of Bayesian multi-target tracking methods. Although EAS is a simple procedure composed of random sorting and sampling based on the observation probability, it can be employed to increase the efficiency of association candidate generation and the calculation of statistical values. In this study, we proposed two Bayesian multi-target tracking methods based on EAS: stochastic joint probabilistic data association (SJPDA) and Rao-Blackwellized particle filter with EAS (RBPF with EAS). Evaluation of these methods with simulated data shows that integrating EAS into these methods can enhance their speed and accuracy. Moreover, evaluation on open datasets used for pedestrian tracking on camera sequences shows that the proposed methods achieve significantly better performance on some important metrics compared with representative methods.
Target Tracking of Radar Seeker Based on Converted Measurement Kalman Filter

For the requirement of sea-surface target tracking, based on CA motion model, the statistical distribution of the measurement deviation of radar seeker resulting from coordinate transformation and the prediction deviation caused by SIMU error was deduced in this paper. By removing such deviations in the measurement equation and the prediction equation in Kalman filter, the filter is able to converge correctly. Simulation results show the stable target tracking is achieved after a brief convergence by this method.
Methodology to organize independent work of students in mathematics lessons in primary school

It is independent work that is the most important condition for self-regulation of the personality, its creative capabilities. But why are there so many people who are dependent, unable to make the necessary decisions in time, without looking back at others? This question comes up again and again in society, the long-standing dispute between teachers from different countries does not cease. The student's independent work is the main way of fostering independence. The school's many years of experience is the best proof of this. The article discusses the correctness of the organization of independent work, since I believe that independent work serves as an effective means of forming a personality, encourages mental independence in children.
Algorithmic Competition, Collusion, and Price Discrimination

Rapid, recent technological change has brought forward a new form of “algorithmic competition.” Firms can and do draw on supercharged connectivity, mass data collection, algorithmic processing, and automated pricing to engage in what can be called “robo-selling.” But algorithmic competition can also produce results that harm consumers. Notably, robo-selling may make anticompetitive collusion more likely, all things being equal. Additionally, the possibility of new forms of algorithmic price discrimination may also cause consumers to suffer. There are no easy solutions, particularly because algorithmic competition also promises significant benefits to consumers. As a result, this chapter sets forth some approaches to each of these issues, necessarily tentative, to address the changes that algorithmic competition is likely to bring.
Comparative Study of Open-Source NOSQL Document-Based Databases

In today’s world, we have large volume of data, so handling and analysing of such large data is also important. This data comprises of both structured as well as unstructured data. In the traditional databases, we cannot store the unstructured data and cannot handle storage of such large amount of data. This paper presents the comparative study of open-source NOSQL databases. It performs the comparison between the Arango DB, Couch DB, Mongo DB, Orient DB, and Raven DB. By using this comparison, we analyse the performance of technologies that are present in the NOSQL databases and the advantages of the NOSQL databases. In this paper, the final outcome is to find which database is better among all the document-based NOSQL databases and also know the performance of all the document-based NOSQL databases.
Prediction of global solar radiation in India: analysing the influence of variable input parameter (VIP) on the model’s output performance

Monthly average daily global solar radiation is estimated using several mathematical models available in the literature. Various input parameters and their different combinations are explored to formulate the empirical models. The formulation of the global solar radiation model is carried out for 22 locations across India using eight input parameters. Global solar radiation models are divided into eight categories starting from one variable input parameter to eight variable input parameters. 255 global solar radiation models are developed and analysed using statistical error indicators. Initially, increase in the input parameter increases the performance of global solar radiation drastically, but expanding the input parameter beyond five does not improve the performance appreciably. Thus, five variable input parameters are enough to predict global solar radiation with less mathematical complexity and computational time. Out of the eight-input parameters considered, the climatic conditions outperform the geographical conditions with sunshine duration as the most promising input.
Geography and Stock Data Types

Excel has added two new data types, Geography and Stocks. The way they work is similar. The Geography Data Type has field data pertaining to countries, provinces, regions, states, counties, cities, and towns. The Stock Data Type has field data pertaining to Stocks, Bonds, and Mutual Funds. You can display just the data you want from a list of options, or the data can be used in formulas. From the Geographic data type you can get data such as population, Median gross rent, persons per household, housing units, etc. The data you can get depends upon the type of location. The Stock Data Type provides data such as price, previous close, 52 week high and low, change, change %, etc.
Neural (Knowledge Graph) Question Answering Using Synthetic Training Data

Deep learning requires volume, quality, and variety of training data. In neural question answering, a trade-off between quality and volume comes from the need to either manually curate or construct realistic question answering data, which is costly, or else augmenting, weakly labeling or generating training data from smaller datasets, leading to low variety and sometimes low quality. What can be done to make the best of this necessary trade-off? What can be understood from the endeavor to seek such solutions?
Distributed Services Architecture

This article consists only of a collection of slides from the author's conference presentation.
Compound Words’ Classification - A Cognitive Linguistic Based Study

This study attempts to classify compound words on the basis of Cognitive Linguistics and compares their usage trends using Computational Linguistics. In order to study compound words, it is very important to study the structure of a sentence because compound word in essence, is a condensed form of a sentence. After the Chomskyan Revolution, the concept of Cognitive linguistics in the structure of a sentence came into limelight. He explains about d-structure (deep structure), which determines the logic or meaning and s-structure (surface structure) that is the phonetic part. Lees, working with Transformational Generative Grammar (TGG), treated compound words, not as separate units but as a kind of embedded sentences and hinted for possible presence of d-structure and s-structure in compound words, which this study tries to investigate. Then on the basis of the Idealized Cognitive Model proposed by Lakoff and Fauconnier, compound words have been classified into transparent, opaque and counterintuitive compound words. Using Google Books Corpus, this study also compares their usage trends. This is done using usage frequency, defined in this work, which is analogous to productivity for affixed words calculated by G. E. Booij. Each class of compound word formed on the basis of ICM is found to have different usage frequency and the possible reasons for this are discussed.
Enhancing EFL students’ reading comprehension through a metacognitive strategy in Education 4.0

Reading comprehension plays an important role in students’ performance and needs to be enhanced in the Industry 4.0 era. Reading without good comprehension is meaningless because comprehension is an absolute fundamental for reading activity. With respect to this fact, teachers as facilitators in Education 4.0 need to facilitate students to enhance their reading comprehension using learning strategies. One of the solutions to solve poor reading comprehension of EFL students is a metacognitive strategy. This study aims to discuss the use of metacognitive strategies to enhance the reading comprehension of EFL students in Education 4.0.
High Performance, Low Power Architecture of 5-stage FIR Filter using Modified Montgomery Multiplier

In the field of VLSI, enhancement is prominent. Arithmetic circuits are one of the influential sectors in today's end products of electronics, where multipliers are one of the deciding factors of efficiency. Multiplier plays an important role in different applications such as digital signal processing in which it acts as a key hardware block. As time rolls down, the technology exposed the ways for the initiation of many hardware and software implementations of the faster multipliers. One among them is the Montgomery multiplier. The fundamental operation in the Montgomery multiplier is the modular multiplication. It is mainly used in FIR filters, which in-turn has numerous applications such as speech analysis, multi-rate signal processing, adaptive filters, and averaging filters. With the usage of proposed compressor in the conventional design of the multiplier, the number of transistor count has been declined by a significant amount and made the design into an optimal area design. This paper presents a modified Montgomery multiplier design and its implementation in the 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> order FIR filter. The entire design simulation is carried out using CMOS and PTL logic in 45 nm technology. There is an escalation in the result outcomes, and the multiplier has an area efficiency of 65% and a power reduction of about 68% in comparison with conventional design.
Enhancing Adversarial Examples with Flip-Invariance and Brightness-Invariance

Despite of achieving remarkable success in computer vision tasks, convolutional neural networks still face the threat of adversarial examples, crafted by adding small human-invisible perturbations on clean inputs. Usually, most of existing black-box adversarial attacks show extremely low transferability while encountering powerful defense models. In this paper, based on the observed invariant property of convolutional neural networks (i.e., the models could maintain accuracy to transformed images), we propose two new methods to improve the transferability which are called as the flip-invariant attack method (FIM) and the brightness-invariant attack method (BIM), respectively. Both the novel approaches derive multiple different logit outputs by inputting the transformed copies of the original image into the white-box model. Simultaneously, the ensemble of these outputs is attacked to avoid overfitting the white-box model and generating more transferable adversarial examples. Moreover, the newly-proposed FIM and BIM methods can be naturally combined with other gradient-based methods. Extensive experiments on the ImageNet dataset prove that our methods achieve higher attack success rate and higher transferability than previous gradient-based attack methods.
Practical Applications of Investigating the Investment Behaviors in Cryptocurrency

Practical Applications In Investigating the Investment Behaviors in Cryptocurrency from the Fall 2020 issue of The Journal of Alternative Investments, authors Dingli Xi, Timothy Ian O’Brie, and Elnaz Irannezhad (all of the University of Queensland in St. Lucia, Australia) investigate who is likely to invest in cryptocurrency initial coin offerings (ICOs). ICOs constitute an innovative way to raise capital for business startups and other projects—but are also very high-risk propositions for investors. Most ICOs are scams, with only 4% successfully raising money for projects. This massive fraud and the regulatory crackdowns in response to it have made ICO fundraising much more difficult. Those who wish to raise funds via ICOs therefore need better knowledge of their target investor market. The authors surveyed ICO investors to assess who invests in ICOs and what factors encourage or discourage ICO investing. They found ICO investors tend to be male, affluent, better educated, and working in banking or IT—while women, business owners, educators, and stock market investors are less likely to invest in ICOs. Financial professionals can use these and the authors’ other findings to help advise businesses and organizations about how to market their ICOs. TOPICS:Currency, emerging, in markets
Cooperative Navigation with the Vector Delay/Frequency Lock Loop

This paper aims to improve GNSS signal processing for collaborating receivers through the development of a Cooperative Vector Delay/Frequency Lock Loop (CVDFLL), a variant of the conventional Vector Delay/Frequency Lock Loop (VDFLL) utilized in standalone receivers. The VDFLL enhances signal processing by taking advantage of spatial correlation between navigation satellites. The CVDFLL expands this advantage to spatial correlation between receivers of similar user dynamics. The algorithm relies on the pseudorange-state formulation of the VDFLL for collaborative processing as a means to keep the cooperative network federated. Additionally, this formulation is expected to be less sensitive to dissimilar dynamics between receivers. Monte-Carlo simulation and experimental results show that cooperative vector tracking can be very robust when employing several receivers. However, the analysis also suggests that the CVDFLL should be studied with a position-state formulation of the VDFLL due to its robust nature as well as the generally poor performance of the pseudorange-state filter.
Research on the Application of Computer-aided Translation in the Field of Professional English

Abstract With the economic globalization and the increasingly frequent economic and cultural cooperation between China and foreign countries, higher and faster requirements have been put forward for the translation work. The traditional translation mode has been unable to meet the current translation work. Then computer-aided translation has become the mainstream of today’s translation work. This paper aims to study the application and research of computer translation in the field of professional English.
Research and Application of eID Digital Identity

At present, Internet applications generally use "name and citizen ID number" to identify the online users. Although it plays a unique role in distinguishing subjects, it also causes large-scale personal information disclosure, which frequently leads to identity theft, account theft, telecommunication and network fraud. To deal with these problems, the article proposes to use eID digital identity to build a national unified digital identity system, and elaborates the design principle and application of eID digital identity.
Building the Human Firewall: Combating Phishing through Collective Action of Individuals Using Leaderboards

Phishing is an increasing organizational threat that causes billions in losses and damage to productivity, trade secrets, and reputation each year. This work explores how organizations can use gamification techniques to improve phishing detection efforts by individuals to create a human firewall. We build on cognitive evaluation theory to begin a new area of inquiry in gamification of IT security. With three experiments in a mock work setting, we test leaderboard components of validation, attribution, incentives, and public presentation for improvements in experiential (e.g., motivation) and instrumental outcomes (e.g., hits and false positives) in phishing reporting. Our findings suggest public attribution with rewards and punishments best balance the competing necessities of accuracy with widespread reporting. Further, our results demonstrate leaderboardsâ€™ unique benefits to phishing reporting over and above other phishing mitigation techniques (training and warnings). However, we noted that unintended consequences in false alarms may arise from shifts in motivation resulting from public display of incentives.
Digital Watermarking for Enriched Video Streams in Edge Computing Architectures Using Chaotic Mixtures and Physical Unclonable Functions

Authentication in advanced video applications is a pending challenge, especially in those scenarios where video streams are enriched with additional information from sensors and other similar devices. Traditional solutions require remote devices (such as cameras) to store private keys, a situation that has been proved to be very risky. On the other hand, standard authentication methods, such as digital signatures or secure sessions, prevent systems to operate at real-time, as they are very computationally costly operations which, besides, are designed to work with information blocks, not with streams. Other solutions, furthermore, require the integration of gateways or aggregation points in video infrastructures, which creates bottlenecks and difficulties the dynamic adaptation of systems to the environmental conditions and devices’ lifecycle. Therefore, in this paper, we address this problem by proposing an authentication procedure based on digital watermarking. In our proposal, video infrastructures are organized as edge computing architectures, where enriched video streams are protected by watermarks and devices may delegate functionalities dynamically. This new watermarking technology is based on chaotic mixtures and secret keys provided by Physical Unclonable Functions. In order to evaluate the performance of the proposed solution an experimental validation is also carried out.
Design and application of support capability evaluation system for complex system

Abstract Aiming at the problem of low accuracy and effectiveness of complex system support capability assessment, an index system was constructed on the basis of studying the characteristics of support capability generation elements, in which the systematicness, comprehensiveness and the importance of key links of complex system support were considered, a complex system supportability evaluation model was proposed based on weight scores and key levels, and the score and level of the complex system supportability evaluation were obtained through comprehensive evaluation. A corresponding evaluation system was designed which provided the means and support for the informatization of support capability evaluation of complex systems.
IMPLEMENTATION OF C4.5 ALGORITHM TO ASSIST IN THE SELECTION OF FLOOR CONSTRUCTION PROJECTS

The country of Indonesia is a developing country. This is indicated by the improvement of the development process and business. So, many development projects will appear. With the changing world in the industry with e-Government governance, project data is no longer in paper form. With the emergence of data - a lot of companies need to do project management activities in determining the project strategy to be taken so as not to affect the final results in determining the taking of the project. Then do a lot of research on the project data that appears in the construction world. The method used for this research is the C4.5 Algorithm which is one of the modern algorithms for data mining processes. C4.5 algorithm is also called a decision tree (decision tree) which is one of the classification methods with a tree structure representation. The concept is to collect data and be made into a decision tree based on the rules needed to get an outcome. The pattern and results obtained will be used for recommendations in determining which projects the company will take. The values generated using Rapidminer are Accuracy 97.18%, precision 100%, and recall 94.31%. With the result, 1205 recommended floor construction projects were taken and 784 projects were recommended not to be taken.
Influence Factors of Using Modern Teaching Technology in the Classroom of Junior Middle School Teachers Under the Background of Artificial Intelligence—Analysis Based on HLM

The application of AI in classroom teaching not only creates a vivid simulation situation, but also improves students’ direct “on-site” experience through human-computer interaction, which is helpful for students’ thinking development and deep understanding. But at present, the ability of teachers to use information technology is not satisfactory. Based on the data of CEPS survey, we make a study that uses an analysis of the HLM in two levels to explore factors which influence junior teachers’ use of modern education technology. The research based on the result of a questionnaire of 797 teachers. The result firstly shows that there is a strong relationship between a week’s workload, the pressure of school management, job title, class performance and teachers’ use of modern education technology. Secondly, we find at the school level the better school hardware condition is, the more likely they are to use modern education technology. There is no significant relationship between learning atmosphere and teachers’ use of modern education technology. Thirdly, at the perspective of the regulation of cross level, school hardware condition promotes the relationship between the teachers’ a week’s workload and teachers’ use of modern education technology.
Reinforcement Learning Enhanced Heterogeneous Graph Neural Network.

Heterogeneous Information Networks (HINs), involving a diversity of node types and relation types, are pervasive in many real-world applications. Recently, increasing attention has been paid to heterogeneous graph representation learning (HGRL) which aims to embed rich structural and semantics information in HIN into low-dimensional node representations. To date, most HGRL models rely on manual customisation of meta paths to capture the semantics underlying the given HIN. However, the dependency on the handcrafted meta-paths requires rich domain knowledge which is extremely difficult to obtain for complex and semantic rich HINs. Moreover, strictly defined meta-paths will limit the HGRL's access to more comprehensive information in HINs. To fully unleash the power of HGRL, we present a Reinforcement Learning enhanced Heterogeneous Graph Neural Network (RL-HGNN), to design different meta-paths for the nodes in a HIN. Specifically, RL-HGNN models the meta-path design process as a Markov Decision Process and uses a policy network to adaptively design a meta-path for each node to learn its effective representations. The policy network is trained with deep reinforcement learning by exploiting the performance of the model on a downstream task. We further propose an extension, RL-HGNN++, to ameliorate the meta-path design procedure and accelerate the training process. Experimental results demonstrate the effectiveness of RL-HGNN, and reveals that it can identify meaningful meta-paths that would have been ignored by human knowledge.