Reviewerly/exHarmony huggingface dataset:
Multi-extended Target Tracking Algorithm with Unknown Clutter Information

In this paper, the problem of unknown clutter prior information for multiple extended targets is considered by introducing the network flow theory. To achieve multiple extended target tracking using this technique, a hierarchical clustering algorithm is developed to segment the measurement set. In particular, we build a multiple extended target (MET) network flow model with a multi-constrained minimum cost optimization function, as well as use the A-star search algorithm to obtain optimal associated tracks. The proposed algorithm is compared with an extended target Gaussian mixture probability hypothesis density (ET-GM-PHD) filter, and the simulation results show that the proposed method has improved estimation and tracking performance.
Latent Class Analysis with Ordered Latent Classes

Assuming ordered latent classes in the population, latent class analysis is discussed. First, latent distance model, which is an improved version of the Guttman scaling model and may be called the latent Guttman scaling, is treated. An ML estimation procedure for the model parameters is constructed by the EM algorithm, and by using practical examples the method is demonstrated. A method for assessing the latent Guttman scaling is also considered. Second, an approach for dealing with ordered latent classes by the logistic model, which is referred to as latent ordered-class analysis in this book, is treated. The method is applied for analyzing practical data sets given in Chap. 2 . Third, a two-parameter latent trait model is treated and the ML estimation of the parameters is discussed through the EM algorithm. Finally, a summary of the present chapter and discussions leading to further studies are provided.
The Substructure for Estimation of Miscellaneous Data Failures Using Distributed Clustering Techniques

Miscellaneous data prediction provides an analytical mechanism for explaining the failure experience, which can include the method based on distributed clustering technique. The principle drawback of the fashions is that the error estimation is completely formulated through using a supervised control mechanism where the failure rate is estimated. Although, at the same time as stretching any advanced software, the past failure records or the previous failure statistics may not be available, and in such conditions, it is much tough to become aware of the failure modules with the aid of using the above-mentioned miscellaneous data process techniques. Hence, it is very important to increase the reliability that could take care of those situations efficiently. This failure statistics are used to examine the accuracy of Uniqueness, Sensitiveness, and Frequency rate. The unsupervised cluster and its type procedure uses the distributed clustering technique to become aware of the malfunctioning and flawless factors. Initially, it identified the average and the values much smaller than the average taken into consideration of flawless factors. Finally, they are considered to be flawed factors. Using this method, the whole dataset is converted to dual form and a distributed clustering technique is applied.
Unsupervised Modeling of Workloads as an Enabler for Supervised Ensemble-based Prediction of Resource Demands on a Cloud

Understanding the resource demands and managing the resources for future workloads is a challenging task in the cloud. Typically, applications receive dynamic and time-varying workloads. Forecasting future workload and subsequently inferring future resource requirements will aid in better capacity planning and resource utilization. In this paper, we propose an unsupervised approach to predict the number of utilization profiles required to model a workload. An ensemble-based workload forecasting is proposed to predict future workloads. For forecasting, three models, namely ARIMA, XGBoost, and LSTM, are ensembled. Both forecasting and classification are used to predict the number of resources required so that the problem of over-provisioning and under-provisioning of the resources can be addressed during capacity planning and provisioning. The error rate is decreased by 15% with ensemble model when compared with individual models for prediction.
Children Emotions Detection and Learning using a Fairy Tale Serious Game

Children learn emotional ways of expressing their feelings in several ways.They learn at home as well as in their interactions with their friends at school.The problem is that sometimes they have a difficulty in learning emotions.This study aims to help children learn about emotions and help other children learn about emotions as well.The scope is generalized to all children but will be initially applied to a specific age.The used approach is by using "Serious Games".A Serious Game was written using C# language, to be used by two groups of children: "Teachers" group and "students' group.This serious game describes a fairy tale with pictures, text, facts, and questions.It is customizable for any fairy tale that you may want.The initial work includes the "The Ugly Duckling" tale.In the future, the written game will be tested and the results will be analyzed.
A machine learning-based static analysis warning prioritization

Static analysis tools (SATs) can automatically detect software defects by analyzing the software source code. However, there are a large number of false positives in the warnings generated by SATs, which hinder the usability of SATs. To address this problem, this paper proposes an approach to constructing a machine learning-based warning prioritization model. Our approach explores the 91 warning features and obtains an optimal feature set with 25 features. Subsequently, the machine learning model is applied to learn a warning prioritization model based on the optimal feature set. To evaluate the warning prioritization model, we conduct the experimental evaluation on a real, open source, and large-scale warning dataset. The experimental results show that our approach is better than the random ranking algorithm. On average, in comparison with the random ranking algorithm, our approach reduces the number of false positives by 30.56% – 48.18% when developers check the warning list to find each true defect.
A Heuristic Approach for Analyzing Some Reading Behaviors of Online News Viewers Using RF and KNN

Hossain, Shahadat Hasan, Md. Manzurul Barid, MimunThis paper focuses on the factors that motivate readers to get their news online by analyzing readers’ reading behaviors. It sheds light on the variety of electronic press media, especially on various online news hubs. We have gathered several opinions regarding reading news online. We use the K-nearest neighbor (KNN) algorithm and the random forest (RF) algorithm for the analysis [1, 13]. In addition, a heuristic approach is hereby unveiled to analyze the activities of online news viewers. As a result, we find some factors that influenced the readers most to read news online, which assist the news agencies in understanding how newsreaders are behaving on online news platforms. Furthermore, we have results on the relative contributions, which show that these models are more accurate in predicting the factors inspiring the readers to surf news online than that of no existing one to the best of our knowledge. Additionally, as far as observed in previous literature, our paper have directed a new path on the subject matter besides some mentionable results.
Study of hourly site-pair correlations of the clear-sky index and its predictor variables for long-term resource planning of solar cities

‘Solar City’ plans are being promulgated to reduce cities’ dependence on conventional fossil fuel-based power. Variability of solar radiation is a critical issue for these solar city plans. This paper aims to study this crucial issue by studying hourly site-pair correlations of clear-sky index variation and their underlying factors within a city. The analysis has been conducted for eight Indian cities, which have been proposed as prospective solar cities by the Government of India. The underlying factors taken into consideration are distance, relative humidity, wind speed, and temperature. Principal component analysis and regression analysis have been conducted to find the relationship of the site-pair correlations of clear-sky index with the identified predictor variables. The results show that though the underlying model remains predominantly similar across cities and zones, the predictor variables and the degree of fit change depending upon the local geographical and climatic peculiarities of various cities and regions.
The Latent Markov Chain Model

The latent Markov chain model is discussed, and the relationship between the model and the latent class model is considered. An ML estimation procedure for the latent Markov chain model is constructed via the EM algorithm, and a property of the procedure, which is preferable for the parameter estimation, is derived. The latent Markov chain model with missing manifest observations is also treated. Numerical examples are given to demonstrate the ML estimation procedure. Moreover, this chapter considers a continuous-time version of the latent Markov chain model as well.
A Novel Method That Identifies The Hidden Properties Of A Person’s Name In Kanji Or Hanzi

Most Japanese persons’ Kanji names have similar characters with ’Hanzi’. For example, ’吉田悠人’ and ’欧阳沛南’ are two persons’ names. ’吉田悠人’ is a Kanji name, ’欧阳沛南’ is a Hanzi name. It is a challenge for a computer to identify the name origins between ’Hanzi’ and ’Kanji’ of a person’s name. In addition, we found that there are limited existing methods to identify more than one hidden property of people’s names at one time. In this paper, we aim to build a method that can identify the hidden properties of Hanzi names and Kanji names. Our novel method can point out the origin, the surname and the given name of a person’s name. The evaluation result shows that our method is better than an existing system ’TextBlob’ on predicting the origins of people’s names. The accuracy of our proposed method is 86.87%.
Smart Glasses and Augmented Reality for Maintenance 4.0

Maintenance 4.0 is the current trend in automation and data networking in industrial technology. This includes the internet of things (IoT), wireless sensors, cloud computing, artificial intelligence (AI), machine learning, big data, and augmented reality (Tanuska and al. 2021) . Augmented reality designates a virtual interface, in 2D or 3D (Regenbrecht, Wagner and Baratoff 2002) , which enriches reality by superimposing additional information (Daponte and al. 2014) ; although it is relatively new in the maintenance domain. The application of augmented reality technology has the potential to increase productivity, significantly reduce costs, and majorly improve product quality. It also helps to reduce (if not eliminate) downtime and alert maintenance technicians when it is time to perform a check-up, informing them about potential problems along with a repair guide. Using this innovative technology, a maintenance technician could be directed by a smartphone handset or ‘smart glasses’ to the exact location of a piece of equipment in need of a maintenance check. In-situ maintenance history of that specific equipment would appear on the screen or glass lens and the wireless sensors can be initiated. In this project, we have created smart glasses enhanced by augmented reality that aims to allow a maintainer accurate and detailed hands-free access to information through recognition of the equipment in front of him or her (e.g., information in a technical file, the history of interventions, or prevention and repair actions) while permitting clear scope of vision. We have also created an Android app that can connect to the glasses to perform historical updates, add new data, or display PDF files for further information on the equipment (detailed photos, diagrams). This application also includes Google Sheets, which displays a database for the lenses that allows a service manager to monitor the tasks that employees perform.
Potential New Evidence of Non-Utilitarian Symbols in Epigravettian Industry (Czech Republic)?

Different types of non-utilitarian symbols are common in the Palaeolithic. A huge number of these pieces are especially connected with the Upper Palaeolithic and Mesolithic. Pieces are frequent during the typical Gravettian (Pavlovian) phase, as well as during the late phase of the Gravettian (called Willendorf – Kostienki ca 29.5 – 24 ky). After the Late phase of the Gravettian, before the Magdalenian culture, non-utilitarian symbols in central Europe are rare. We notice only sporadic pieces at randomly found sites from the period of the Epiaurignacian and Epigravettian. The different kinds of scratches on the cortex of the lithic chipped pieces can be understood as one example of possible non-utilitarian symbols. We present analyses of such scratches found in the well-dated stratigraphic context. According to the character of the items, we consider these scratches to be a result of the knapping processes.
Deep Transfer Learning Techniques for Automated Classification of Histological Childhood Medulloblastoma Images

The automated classification invaded the world of health, but some diseases are still diagnosed manually as is the case of medulloblastoma (MB) cancer, which is a high-risk malignant tumor in the Central Nervous System (CNS). This tumor has many types and is diagnosed by biopsy when examining the histological images, which takes a lot of effort and time. In this paper, we propose to perform an automated Childhood Medulloblastoma classification based on Convolutional Neural Networks (CNN) by exploiting the knowledge learned through different datasets (medical PatchCamelyon (PCam) and ImageNet) with transfer learning. A comparative study is carried out by following two strategies: deep feature extractor and partial fine-tuning, we applied the most popular architectures namely VGG-16, VGG-19, ResNet-50, Inception-V3. Experiments prove that there is an important role of similarity when dealing with deep features extractor or partial fine-tuning. Indeed, when there is a high similarity between the scope application, the deep features extractor is the best, otherwise, partial fine-tuning is more suitable. In our case application, results demonstrate that the process of partial fine-tuning using Inception-V3 pre-trained on ImageNet achieved the best result with an accuracy of 99.16 %.
Commentary on “Visualization in Operations Management Research”

This commentary paper highlights the merits of appropriate use of visualization tools in the original paper and summarizes potential research topics of visualization for operations management problems.
A Language Model Based Analysis of Pupils’ Practical Work with IR Cameras

In this chapter we will provide examples of a possible way of using a language based model in relation to Field, Tenor and Mode to analyse science teaching and learning in a classroom context with primary pupils. We want to examine how teachers and pupils coordinate a range of semiotic resources in their moment-to-moment interaction when they interact with an IR-camera. In the result we find that the visual graphical mode of the IR-camera image function, together with teacher’s statements, questions and gestures, structure the dialgoue in a step by step kind of scaffolding way for the pupils. The results of this study indicate that the IR-camera have the potential of beeing a true pedagogical resource for pupils meaning making of thermal phenomena.
A Novel Topic Extraction Model for Science and Technology Demand Data

There are few studies focus on enterprise science and technology demand data, which is very important for enterprise development and innovation. These data are scattered on several websites and contain a lot of noise, which make it difficult to accurately analyze their topic. In this paper, the topic extraction algorithm based on deep learning is proposed to obtain the topic of demand in various industries. We adopt topic features clustering method to refine the classification of science and technology demand data. Keyword extraction method is proposed to filter the extracted theme words. The extracted topics are combined with time series to analyze the evolution of the topics and show the applicability of the extracted results of the science and technology demand data. A lot of experiments are conducted to verify the effectiveness of our algorithm. The optimal parameters and the number of topics are also analyzed in the experiments.
Shopping Training System for Autistic Children Based on Virtual Reality

Shopping is a necessary skill in human social life, but autistic children may have difficulties and problems in every aspect of the shopping process even to adulthood. The shopping training system for autistic children, combined with ABA intervention therapy, adopts the principle of behavior shaping to design and develop autistic children's problems such as difficulty in concentration, rigid behavior, incorrect communication and expression, and ignorance of rules. Based on virtual reality technology, the autistic children's shopping training system uses simulated real shopping environment to gradually guide autistic children to interact with virtual environment and acquire shopping rules and key skills. Through the test of 5 3-5-year-old high functioning autistic children and 5 older autistic children, 90% of them can quickly adapt to the virtual environment, and 70% of them have significantly improved their shopping skills. 90% completed primary tasks and 30% completed some advanced tasks.
Speed and Quality of Optimization Algorithms

Optimization problems are crucial in artificial intelligence. Optimization algorithms are generally used to adjust the performance of artificial intelligence models to minimize the error of mapping inputs to outputs. Current evaluation methods on optimization algorithms generally consider the performance in terms of quality. However, not all optimization algorithms for all test cases are evaluated equal from quality, the computation speed should be also considered for optimization tasks. In this paper, we investigate the quality and speed of optimization algorithms in optimization problems, instead of the one-for-all evaluation of quality. We select the well-known optimization algorithms (Bayesian optimization and evolutionary algorithms) and evaluate them on the benchmark test functions in terms of quality and speed. The results show that BO is suitable to be applied in the optimization tasks that are needed to obtain desired quality in the limited function evaluations, and the EAs are suitable to search the optimal of the tasks that are allowed to find the optimal solution with enough function evaluations. This paper provides the recommendation to select suitable optimization algorithms for optimization problems with different numbers of function evaluations, which contributes to the efficiency that obtains the desired quality with faster speed for optimization problems.
One Bad Apple Spoils the Bunch: Transaction DoS in MimbleWimble Blockchains

As adoption of blockchain-based systems grows, more attention is being given to privacy of these systems. Early systems like BitCoin provided few privacy features. As a result, systems with strong privacy guarantees, including Monero, Zcash, and MimbleWimble have been developed. Compared to BitCoin, these cryptocurrencies are much less understood. In this paper, we focus on MimbleWimble, which uses the Dandelion++ protocol for private transaction relay and transaction aggregation to provide transaction content privacy. We find that in combination these two features make MimbleWimble susceptible to a new type of denial-of-service attacks. We design, prototype, and evaluate this attack on the Beam network using a private test network and a network simulator. We find that by controlling only 10% of the network nodes, the adversary can prevent over 45% of all transactions from ending up in the blockchain. We also discuss several potential approaches for mitigating this attack.
Traffic Generation for Data Centre Networks

Benchmarking is commonly used in research fields, such as computer architecture design and machine learning, as a powerful paradigm for rigorously assessing, comparing, and developing novel technologies. However, the data centre network (DCN) community lacks a standard open-access and reproducible traffic generation framework for benchmark workload generation. Driving factors behind this include the proprietary nature of traffic traces, the limited detail and quantity of open-access network-level data sets, the high cost of real world experimentation, and the poor reproducibility and fidelity of synthetically generated traffic. This is curtailing the community's understanding of existing systems and hindering the ability with which novel technologies, such as optical DCNs, can be developed, compared, and tested. We present TrafPy; an open-access framework for generating both realistic and custom DCN traffic traces. TrafPy is compatible with any simulation, emulation, or experimentation environment, and can be used for standardised benchmarking and for investigating the properties and limitations of network systems such as schedulers, switches, routers, and resource managers. We give an overview of the TrafPy traffic generation framework, and provide a brief demonstration of its efficacy through an investigation into the sensitivity of some canonical scheduling algorithms to varying traffic trace characteristics in the context of optical DCNs. TrafPy is open-sourced via GitHub [1] and all data associated with this manuscript via RDR [2].
Specific compactness as a criterion for classifying the text by functional style

Ongoing growth of the information in the text form requires, in particular, new methods in the information technologies for natural language processing. For various reasons automatic classification of texts is one of the most important tasks in preparation of the text corps for computer linguistics and Big Data analysis. In this paper, we propose a new approach to the automatic classification of texts by the functional styles. The so-called specific compactness, which is the development of the idea of the fractal-driven irregularity, acts as a quantitative criterion for the classification. As an example, for eight texts in Russian, the specific compactness is calculated. It is shown that when using this approach (for at least two significantly different fonts) the functional styles of the texts are clearly distinguished.
A Study of Dynamic Convolutional Neural Network Technique for SCOTUS Legal Opinions Data Classification

The quantity of legal information that is being produced on a daily basis in courts is growing enormously. The processing of such data has been gaining considerable attention thanks to their availability in an electronic form and the advancement made in Artificial Intelligence applications. Indeed, deep learning has offered promising results when used in the field of natural language processing (NLP). Neural Networks such as recurrent neural network and convolutional neural networks have been used for different NLP tasks like information retrieval, document classification and sentiment analysis. In this paper, we present a Neural Network based model with a dynamic input length for classifying legal opinions from cases seen by the Supreme Court of the United States (SCOTUS) ( https://www.kaggle.com/gqfiddler/scotus-opinions ). The proposed model, tested over a real-world legal opinions dataset, by the way, proved better performance than the other baseline methods.