Reviewerly/exHarmony huggingface dataset:
Analyzing Network Level Information

This chapter provides a brief description of the methods employed for collecting initial information about a given suspicious online communication message, including header and network information; and how to forensically analyze the dataset to attain the information that would be necessary to trace back to the source of the crime. The header content and network information are usually the immediate sources for collecting preliminary information about a given collection of suspicious online messages. The header analysis of an e-mail corpus identifying all the senders, the recipients associated with each sender, and the frequency of messages exchanged between users helps an investigator to understand the overall nature of e-mail communication. Electronic messages like e-mails or virtual network data present a potential dataset or a source of evidence containing personal communications, critical business communications, or agreements. When a crime is committed, it is always possible for the perpetrator to manipulate e-mails or any electronic evidence, forging the details to remove relevant evidence or tampering the data to mislead the investigator. Possible manipulation of such evidence may include backdating, executing time-stamp changes, altering the message sender, recipient, or message content, etc. However, such attempts of manipulation and misleading can be detected by examining the message header. By examining e-mail header and analyzing network information through forensic analysis, investigators can gain valuable insight into the source of a message that is otherwise not traceable through the message body. Investigators can utilize a range of existing algorithms and models and build on leveraging typical forensic planning. Such models focus on what type of information should be collected, ensuring the forensically sound collection and preservation of identified Electronically Stored Information (ESI). By applying these models, it is possible to achieve a full analysis and collect all the relevant information pertaining to the crime. The collected finding is then compiled to reconstruct the whole crime scene, deduct more accurate and logical conclusions [1].
Optimal Trajectory Generation for Discretized Terminal Conditions based on Reference Lines

In the paper, optimal trajectory generation method is proposed, which produces trajectories approaching the target location with smooth accelerations and continuous curvatures, fulfilling the vehicle kinematic restrictions. Firstly, a fixed number of alternative trajectories are generated in the Frenet-Serret Frame via initial and terminal states of motion based on a global path which is acquired by global path planning in a topological map. These meet the requirement of comfortability and feasibility for autonomous driving. And then the best trajectory is chosen from alternatives as optimal trajectory with a small cost in association with non-holonomic vehicle constraints and obstacle avoidance. The proposed method satisfies distance-keeping, velocity-keeping, collision-avoiding, etc. The performance of the approach is verified by practical experiments at last.
Graph-Based Multi-document Text Summarization Using NLP

Text summarization is the process of generating the condensed view of the text by selecting useful and relevant information from the original source documents. It is a sub-topic of natural language Processing. Text summarization is a technique for understanding the aim of any document, to visualize large text document within short duration. Summarization provides flexibility and convenience. Business leaders, analyst, and researchers must undergo huge number of documents on a day-to-day basis to stay ahead, and an oversized portion of their time is spent just deciding what document has relevancy and what is not. By finding out important and relevant sentences and creating summaries, it is possible to quickly check whether or not a document is worth reading. In this paper, we propose to design automatic text summarizer to summarize the multiple text documents. The input to the system is the multiple sources of news articles. Important sentences from the source document are selected and arranged in the destination documents or the summarized documents. This is called as the extraction technique in automatic text summarization. Here, one document visited is being processed, and its summary is generated using extraction technique through which a weighted graph is constructed using language processing.
중국어 표현어휘 교육을 위한 한․중 어휘대조: 『한국어 학습용 어휘 목록』과 ≪漢語水平詞彙與漢字等級大綱≫을 대상으로

Assuming that it is necessary to consider Korean words frequently used by Korean learners for the education of Chinese vocabulary expressions, this article serves as a preliminary study for designing the education of Chinese vocabulary expressions for Korean learners. The words in Level A of the Vocabulary List for Learning Korean are compared with the words in the Chinese Proficiency Outline for Chinese Vocabulary and Characters (漢語水平詞彙與漢字等級大綱). The result is that there is a significant difference between the two vocabulary lists. Therefore, it is suggested that a separate plan should be prepared for the education of Chinese vocabulary expressions for Korean learners. In addition, various issues have been noted in the process of comparison regarding designing of educational contents.
Chapter 3. Syntactic annotation of the Rhapsodie corpus

This chapter presents the principles underlying the syntactic annotation and in particular the reasons for separating this annotation into two levels, micro and macrosyntax, presented in Chapters 4 and 6. The particular focus on paradigmatic piles (coordination, lists, reformulation, disfluencies, etc., see Chapter 5) is justified, as well as the integration of hard-to-describe elements such as disfluencies or discourse markers at various level of the syntactic structure. Our decision to discard the notion of sentence and replace it by the notions of Illocutionary Units [IUs] and Government Units [GUs] is discussed.
Segmentation of biomedical images with joint unsupervised learning

The convolutional neural networks with supervised training solve problems of semantic segmentation in a wide area of biomedical applications. However, this approach requires a significant size of the training dataset. Unsupervised learning can provide segmentation of some images without training dataset. Joint unsupervised learning is an approach that combines neural networks with another algorithm of data clusterization. In this preliminary study, we test the joint unsupervised learning approach with minor modifications for the problem of semantic segmentation at several datasets of biomedical images.
Visualization of Explainable Artificial Intelligence Techniques Using Variable Importance with Its Applications to Health Information Data

Objectives: Deep learning techniques have been actively used in the medical field where precise diagnosis and results are very important.Deep neural network-based models utilizing big data from medical records are supporting medical opinions and are revolutionizing the medical industry.In addition, the convolutional neural network model shows excellent performance in analyzing image data and are used for image classification and X-ray/CT image reconstruction.Methods: In this paper, we conducted a visualization study using structured and unstructured data in the medical field.Results: In order to determine input variables affecting mortality and to evaluate their importance, a total of five techniques, namely, the augmented neural network model with multi-task learning, random forest, extra tree, gradient boosting and xgboost are applied to the intensive care unit data.Variable importance is calculated for each technique, and these indicators are all converted to ratios in consideration of the differences considering the patient group as a stratification variable.The converted values are shown in three graphs, a lollipop graph, a bubble chart graph, and a heat map graph.Through the visualization, it was easy to see which variables were relatively important for each technique and to what extent.InceptionResnetV2 was used as a classification model for skin cancer image data, and LIME and Grad-CAM were applied to the model to easily identify the characteristics of each cancer.Conclusions: Through this study, we apply several explainable artificial intelligence techniques to medical data to enhance understanding of the results of analysis and to help identify and visualize important input variables and features.
Seleksi Fitur Klasifikasi Penyakit Diabetes Menggunakan Particle Swarm Optimization (PSO) Pada Algoritma Naive Bayes

Diabetes is a serious chronic disease that occurs because the pancreas does not produce enough insulin (a hormone that regulates blood sugar or glucose), or when the body cannot effectively use the insulin it produces. WHO data shows that the incidence of non-communicable diseases in 2004 reached 48 , 30% is slightly higher than the incidence rate of infectious diseases, namely 47.50% [1]. According to the Ministry of Health in 2012 diabetes caused 1.5 million deaths. Some Indonesian people, this disease is better known as diabetes or blood sugar. This research was developed through secondary data processing from the Pima Indians Diabetes Dataset health database which was taken from the Kaggle dataset and can be accessed through https://www.kaggle.com/uciml/pima-indians-diabetes-database. Where the data itself consists of 768 records with several medical predictor variables (Pregnancies, Glucose, Blood Pressure, Skin Thickness, Insulin, BMI, Diabetes Pedigree Function, Age and Outcome). Then the data will be processed using the Particle Swarm Optimization (PSO) feature selection to increase the accuracy value and the Naive Bayes algorithm to determine the accuracy results of the diagnosis of diabetes. From the results of research that has been done for the accuracy of the classification algorithm Naive Bayes is 74.61%, while the accuracy of the classification algorithm with Particle Swarm Optimization is 77.34% with an accuracy difference of 2.73%. So it can be concluded that the application of the Particle Swarm Optimization technique is able to select attributes in the Naive Bayes Algorithm, and can produce a better level of diabetes diagnosis accuracy than using only the individual method, namely the Naive Bayes algorithm.Keywords: Diabetes, Particle Swarm Optimization, Naive Bayes Algorithm
Fast Fitting Method of Complex Network Based on Deep Learning

In the process of complex network modeling training, when the uncertainty relation is used to judge whether the overfitting phenomenon occurs, the overfitting parameters in the discriminant are too large to be determined, which greatly limits the role of the discriminant in guiding the modeling process of complex network. The purpose of this paper is to provide technical advice for rapid network fitting through the research on the method of rapid network fitting based on deep learning (DL). This paper proposes a fast network fitting method based on DL. Simulation results show that the speed of this method is 21.67% higher than that of the traditional algorithm, which proves that this method is feasible.
Gender, Sexuality, and the Author

The aim of this chapter is to highlight the way in which gender and sexuality have inflected conceptions of authorship. Several categories such as that of author, language, and text (with a special emphasis on the latter) come to bear on the criticism of literature produced by individuals reflecting the spectrum of gender diversity. I here examine a wide array of English-language fictional texts, mostly from England and the US, but also autobiographies or other forms of life-writing from the English Renaissance to the beginning of the twenty-first century, with one incursion, at the end, into a non-Western context. I distinguish between five phases of authorship: (1) Fathering the text; (2) (M)Othering the text; (3) En-gendering the Text; (4) Queering the text and the author; and (5) Transgendering the text.
Research and Development of the Control System for Leather Cutting Machine

Aiming at the low level of automation of leather cutting equipment, the control system of leather cutting machine based on industrial personal computer and MC1004 motion controller was developed. Firstly, according to the characteristics and functional requirements of leather cutting control, a dual CPU hardware platform based on industrial personal computer and MC1004 motion controller was built. Then, using the industrial personal computer as the host computer, based on the QT5.9 software, the host computer software for the leather cutting control system was developed by using modular design method, which realized functions such as DXF file parsing, graphic display and interaction, processing program generation, and processing simulation. Aiming at the problem that the cutting path has too much idle travel, an improved ant colony algorithm was proposed to optimize the cutting path. The M1004 motion controller was used as the slave computer. Based on the Autothink software, the MC1004 motion controller was redeveloped, and the processing code parsing, tangential tracking, motion control and other functions were realized. Finally, the control system of the leather cutting machine was tested and leather cutting experiments were carried out. The experimental results show that the control system can run stably, the processing speed is fast, and it can meet the processing requirements.
Polynomial Approximation of Inhomogeneous Differential Equations for Modeling of Electronic Device

One of forms of description transient processes there are inhomogeneous differential equations in electronic devices. Such equations also are widely used in the theory of control for description of processes in a temporary realm and determination of transmission functions, which allows conducting researches in a frequency area. Therefore development of simple, rapid, reliable and effective methods of their decision remains an actual problem. The method of decision of inhomogeneous differential equations of n-order is described with the use of polynomial approximation. Dignity of method consists in that a method is analogical to the analytical methods of decision of equations, because approximation can be executed on great enough temporal intervals and a temporal step can be set to great enough. Demonstration over of method is brought on the example of decision of inhomogeneous differential equation of the third order. The offered method differs in simplicity, evidentness and comfort for realization on computer.
Privacy Preserving CTL Model Checking through Oblivious Graph Algorithms

Model checking is the problem of verifying whether an abstract model $\mathcalM of a computational system meets a specification of behavior φ. We apply the cryptographic theory of secure multiparty computation (MPC) to model checking. With our construction, adversarial parties D and A holding $\mathcalM and φ respectively may check satisfaction --- notationally, whether $\mathcalM |= φ --- while maintaining privacy of all other meaningful information. Our protocol adopts oblivious graph algorithms to provide for secure computation of global explicit state model checking with specifications in Computation Tree Logic (CTL), and its design ameliorates the asymptotic overhead required by generic MPC schemes. We therefore introduce the problem of privacy preserving model checking (PPMC) and provide an initial step towards applicable and efficient constructions.
Real-time Normalization and Gaussian Convolution Module on FPGA

Several neural network technologies are currently being studied, but their training and execution speeds are still an issue. This paper presents a method for implementing a normalization and Gaussian convolution module on FPGA to accelerate the deep learning process in real-time.
The Summarize of Body Behaviour Semantic Computing Key Technology in Video Scene

The moving body behavior semantic computing in video scene is a multi-disciplinary research subject and has extensive application prospect. The key technology of it includes: the extracting semantic of background and the classifying of scene, the nature language description and reasoning of background spatial relationship and human motion's behavior. The main research achievements in recent 10 years of the three key techniques are separately introduced in this paper. Based on the summary advantages and disadvantages of these methods, the developing trends of the three key techniques is forecasted at last.
Amplifying Quantum States with a Superconducting Microstrip Kinetic Inductance Travelling Wave Amplifier

Superconducting quantum circuits, typically operated below -100 dBm , require low-noise amplifiers with large dynamic range and wide bandwidth. Our microstrip kinetic inductance travelling wave parametric amplifier answers these requirements with up to 20 dB amplification.
Profile Information Analysis of Twitter Social Network

The vertiginous development of technology and knowledge globalization has generated a high interest on social networks within organizations, where its presence has multiplied exponentially in recent years. That is why, it is proposed in this paper to analyze data extracted from social networks, specifically from Twitter, aiming to obtain different data elements that allow management and analysis of the opinions provided by users on different. This information is very useful for client management and acknowledging preferences of brands and organizations. In particular, this work responds to the following research questions: (1) How does the Twitter user behave in different brands? and (2) How do opinions on the network affect the company’s Twitter profile? In this manuscript, we present the tweet user profile for information analysis via a practical software architecture proposal, which is composed by four layers (extraction of the data source, ETL processes (extraction, transformation, and loading), selection of database, and visualization of the results). The implementation and dashboards of this architecture come from the study case of different types of organizations: banking, telephony, shopping, and supermarkets. The processing of the data corresponds to the extraction of tweets generated by the Twitter users of the organizations. Then, the ETL process is obtained via the useful Spoon from Pentaho Data Integration. The processed data is employed to build the final database, and finally, the generated information is visualized by utilizing dashboards from Qlik Sense Desktop. The results of this study evidence that it is possible to implement a practical architecture to analyze the model information of the Twitter user profile through dashboard; consequently, the organizations can opportunely realize better decisions.
Hands-on Undergraduate Labs on Anonymity Cryptographic Algorithms

This is an innovative practice full paper. In past projects, we have successfully used a private TOR (anonymity network) platform that enabled our students to explore the end-to-end inner workings of the TOR anonymity network through a number of controlled hands-on lab assignments. These have saisfied the needs of curriculum focusing on networking functions and algorithms. To be able to extend the use and application of the private TOR platform into cryptography courses, there is a desperate need to enhance the platform to allow the development of hands-on lab assignments on the cryptographic algorithms and methods utilized in the creation of TOR secure connections and end-to-end circuits for anonymity. In tackling this challenge, and since TOR is open source software, we identify the cryptographic functions called by the TOR algorithms in the process of establishing TLS connections and creating end-to-end TOR circuits as well tearing them down. We instrumented these functions with the appropriate code to log the cryptographic keys dynamically created at all nodes involved in the creation of the end to end circuit between the Client and the exit relay (connected to the target server).We implemented a set of pedagogical lab assignments on a private TOR platform and present them in this paper. Using these assignments, students are able to investigate and validate the cryptographic procedures applied in the establishment of the initial TLS connection, the creation of the first leg of a TOR circuit, as well as extending the circuit through additional relays (at least two relays). More advanced assignments are created to challenge the students to unwrap the traffic sent from the Client to the exit relay at all onion skin layers and compare it with the actual traffic delivered to the target server.
Authorship in Ancient Egypt

Around 1200 BCE, at the height of Egyptian literary culture, a scribe named Mersekhmet wrote a school text in which he praised his own profession as the best one to reach immortality
Optimality of Network Structure Identification

In this chapter, we discuss optimality of network structure identification algorithms. We introduce a concept of optimality in the sense of minimization of the risk function. We investigate optimality of identification procedures for two problems: concentration graph identification and threshold graph identification. We restrict our study to the risk functions associated with additive losses. We prove optimality of simultaneous inference for concentration graph identification in Gaussian partial correlation network in the class of unbiased procedures. For threshold graph identification, we prove optimality of simultaneous inference in Gaussian Pearson correlation network in the class of statistical procedures invariant under scale/shift transformations. Finally, we prove optimality of simultaneous inference for threshold graph identification in sign similarity network.
Combined Method Based on Source Text and Representation for Text Enhancement

Text classification is a basic and important work in natural language processing (NLP). The existing text classification models are powerful. However, training such a model requires a large number of labeled training sets, but in the actual scene, insufficient data is often faced with. The lack of data is mainly divided into two categories: cold start and low resources. To solve this problem, text enhancement methods are usually used. In this paper, the source text enhancement and representation enhancement are combined to improve the enhancement effect. Five sets of experiments are designed to verify that our method is effective on different data sets and different classifiers. The simulation results show that the accuracy is improved and the generalization ability of the classifier is enhanced to some extent. We also find that the enhancement factor and the size of the training data set are not positively related to the enhancement effect. Therefore, the enhancement factor needs to be selected according to the characteristics of the data.