Reviewerly/exHarmony huggingface dataset:
Introduction: Azure Chatbots

In this introductory chapter, we take a simple initial and down-to-earth look at artificial intelligence (AI) and chatbots. We examine Azure and Azure chatbots, and last, study how to build chatbots using Azure chatbots. Readers with technical backgrounds and nontechnical backgrounds can gain an understanding of chatbots. In general, if you are fond of technology, passionate about digital transformation, and have a desire to bring about change management, generate user engagement, and create high impact in terms of customer satisfaction, then this book is for you.
Acute Myeloid Leukemia Diagnosis Using Deep Learning

The diagnosis of AML based on blood and bone marrow morphological examination has been used for long time. It is often time consuming and come out with less accurate results. Utilizing deep learning for diagnosis of diseases is gaining remarkable attention in the biomedical field. Deep learning is used for recognition and classification of AML by automatically extracting features from the smearless blood images using supervised learning thus can improve the accuracy of AML diagnosis.
Iterative Optical Flow Refinement for High Resolution Images

These days convolutional neural networks (CNNs) are one of the most popular techniques for optical flow estimation yielding exceptional results on many benchmarks. However, the size of their perceptive fields are fixed and displacements not present in the training set cannot be estimated reliably. Additionally, these CNNs need to learn numerous parameters which increases memory consumption. Consequently, their application to high resolution images is impractical, as displacement and memory consumption increase prohibitively. To overcome these limitations, we present an iterative flow refinement approach that can adapt any flow estimation CNN to arbitrary resolution images. In a pyramidal approach, we perform flow estimation for image patches at multiple increasing image resolutions while matching the patches based on the flow of previous iterations. We evaluate our approach using different baselines, displacements and resolutions. The results show that flow estimators can be adapted to high resolution and even panorama images while preserving fine details and reliably handling large displacements without retraining.
A Critical Appraisal of Crime Over Social Networking Sites in the Context of India

An inescapable part of our everyday lives, visual communication is a key driver of engagement on social media. These are redesigned their news feed to allow greater emphasis on visual content, resulting in greater interaction. This chapter discusses the current scenario of cyber and social media crime in India and how the government has incorporated the necessities to fight against it. It will also include the types of social media crime enumerating the provisions of Information Technology Act, Indian Penal Code. Through this chapter, the author discusses the various types of cybercrimes, which are cyber defamation, cyber pornography, cyber stalking, fraudulent transaction and misrepresentation, hacking. The author laid emphasis on what legislations are in action to deal with such crimes and how strictly the offenders are punished. The author also discusses the competency of the present legislation and how the loopholes, if any, can be filled to make the virtual world a better place for everyone.
Introducing the MySQL Shell

Oracle has continued to live up to its commitment to making MySQL better. This has empowered the MySQL engineering division to reach higher and further with each new release. The newest version, MySQL 8, contains more new features and enhancements than any other release. As a result, MySQL continues to be the world’s most popular open source database system.
The {DUSTER} Attack: Tor Onion Service Attribution Based on Flow Watermarking with Track Hiding

Tor is a distributed network composed of volunteer relayswhich are designed to preserve the sender-receiver anonymityof communications on the Internet. Despite the use of theonion routing paradigm, Tor is vulnerable to traffic analysisattacks. In this paper, we present DUSTER, an active trafficanalysis attack based on flow watermarking that exploits avulnerability in Tor’s congestion control mechanism in order to link a Tor onion service with its real IP address. Theproposed watermarking system embeds a watermark at thedestination of a Tor circuit which is propagated throughoutthe Tor network and can be detected by our modified Torrelays in the proximity of the onion service. Furthermore,upon detection, the watermark is cancelled so that the targetonion service remains unaware of its presence. We performeda set of experiments over the real Tor network in order toevaluate the feasibility of this attack. Our results show thattrue positive rates above 94% and false-positive rates below0.05% can be easily obtained. Finally, we discuss a solution tomitigate this and other traffic analysis attacks which exploitTor’s congestion control.
Matrix Factorization on Complex Domain for Face Recognition

Matrix factorization on complex domain is a natural extension of nonnegative matrix factorization, but it is still a very new trend in face recognition. In this chapter, we present two complex matrix factorization-based models for face recognition, in which the objective functions are the real-valued functions of complex variables. Our first model aims to build a learned base, which is embedded within original space. The second model finds the base whose volume is maximized. Experimental results on datasets with and without outliers show that our proposed algorithms are more effective than competitive algorithms.
Cooperation and Coordination Regimes by Deep Q-Learning in Multi-agent Task Executions

We investigate the coordination structures generated by deep Q-network (DQN) with various types of input by using a distributed task execution game. Although cooperation and coordination are mandatory for efficiency in multi-agent systems (MAS), they require sophisticated structures or regimes for effective behaviors. Recently, deep Q-learning has been applied to multi-agent systems to facilitate their coordinated behavior. However, the characteristics of the learned results have not yet been fully clarified. We investigate how information input to DQNs affect the resultant coordination and cooperation structures. We examine the inputs generated from local observations with and without the estimated location in the environment. Experimental results show that they form two types of coordination structures—the division of labor and the targeting of near tasks while avoiding conflicts—and that the latter is more efficient in our game. We clarify the mechanism behind and the characteristics of the generated coordination behaviors.
Secure Online-Efficient Interval Test based on Empty-Set Check

In the age of information and communications technology (ICT), not only collecting data but also using such data is provided in various services. It is necessary to ensure data privacy in such services while providing efficient computation and communication complexity. In this paper, we propose the first interval test designed according to the notion of online and offline phases by executing our new empty-set check. Our protocol is proved to ensure both server and client privacy. Furthermore, neither the computational complexity of a client in the online phase nor the communicational complexity from a server to a client depends on the size of the set. As a result, even in a practical situation in which one server receives requests from numerous clients, the waiting time for a client to obtain the result of an interval test can be minimized.
Phonetic lessons from automatic phonemic transcription: preliminary reflections on Na (Sino-Tibetan) and Tsuut’ina (Dene) data

Automatic phonemic transcription tools now reach high levels of accuracy on a single speaker with relatively small amounts of training data: on the order of 100 to 250 minutes of transcribed speech. Beyond its practical usefulness for language documentation, use of automatic transcription also yields some insights for phoneticians. The present report illustrates this by going into qualitative error analysis on two test cases, Yongning Na (Sino-Tibetan) and Tsuut’ina (Dene). Among other benefits, error analysis allows for a renewed exploration of phonetic detail: examining the output of phonemic transcription software compared with spectrographic and aural evidence. From a methodological point of view, the present report is intended as a case study in Computational Language Documentation: an interdisciplinary approach that associates fieldworkers (“diversity linguists”) and computer scientists with phoneticians/phonologists.
The technique to evaluate pupils’ intellectual and personal important qualities for ICT competences

The paper presents the ICT technique for assessment of schoolchildren abilities, intellectual and personal important qualities for ICT competences formation, as well as research in this domain. The results of comparative analysis of abilities of pupils with mathematical and IT abilities in non-profiled schools in relation to “average” abilities are presented after results of pilot study. Examples of methodical developments are given. Some expected and unexpected results of the experimental research are discussed.
On Learning Nominal Automata with Binders

We investigate a learning algorithm in the context of nominal automata, an extension of classical automata to alphabets featuring names.This class of automata captures nominal regular languages; analogously to the classical language theory, nominal automata have been shown to characterise nominal regular expressions with binders.These formalisms are amenable to abstract modelling resource-aware computations.We propose a learning algorithm on nominal regular languages with binders.Our algorithm generalises Angluin's L algorithm with respect to nominal regular languages with binders.We show the correctness and study the theoretical complexity of our algorithm.
Securing the Data Deduplication to Improve the Performance of Systems in the Cloud Infrastructure

Data duplication is a data quality problem which may exist in database system where the same record is stored multiple times in the same or different database systems. Data duplication issue may lead to issues like data redundancy, wasted cost, lost income, negative impact on response rate, ROI, and brand reputation, poor customer service, inefficiency and lack of productivity, decreased user adoption, inaccurate reporting, less informed decisions, and poor business process. The solution to the problem of data duplication may be countered with data deduplication which is often termed as intelligent compression or single instance storage. Data deduplication eradicates duplicate copies of information resulting in the reduction of storage overheads and in enhancement of various performance parameters. The recent study on data deduplication has shown that there exists modern data redundancy in primary storage in the cloud infrastructure. Data redundancy can be reduced in primary storage system of cloud architecture using data deduplication. The research work carried out highlights the identified and established methods of data deduplication based on capacity and performance parameters. In the research work, the authors have proposed a performance-oriented data (POD) deduplication scheme which improves performance and primary storage system in the cloud. In addition to this, security analysis using encryption technique has also been performed and demonstrated to protect the sensitive data after the completion of deduplication process.
Modelling and querying lists in RDF: A pragmatic study

Many Linked Data datasets model elements in their domains in the form of lists: a countable number of ordered resources. When pub- lishing these lists in RDF, an important concern is making them easy to consume. Therefore, a well-known recommendation is to find an existing list modelling solution, and reuse it. However, a specific domain model can be implemented in different ways and vocabularies may provide al- ternative solutions. In this paper, we argue that a wrong decision could have a significant impact in terms of performance and, ultimately, the availability of the data. We take the case of RDF Lists and make the hy- pothesis that the efficiency of retrieving sequential linked data depends primarily on how they are modelled (triple-store invariance hypothe- sis). To demonstrate this, we survey different solutions for modelling sequences in RDF, and propose a pragmatic approach for assessing their impact on data availability. Finally, we derive good (and bad) practices on how to publish lists as linked open data. By doing this, we sketch the foundations of an empirical, task-oriented methodology for benchmark- ing linked data modelling solutions.
Natural Language Question/Answering with User Interaction over a Knowledge Base

In the demo, we present RecipeFinder, a system for searching the information from knowledge graphs with natural language. The sys-tem has following characteristics: (1) It supports human-computer interaction, to resolve question ambiguity; (2) It provides graphical interface to help users refine questions
MODERNIZATION OF PROFESSIONAL AND TECHNOLOGY TRAINING FOR INTENDING TEACHERS IN THE CONTEXT OF STEM-EDUCATION DEVELOPMENT

The supply of highly qualified scientists, technologists, engineers and mathematicians is perceived as being vital in the development of the national economy. The development of STEM education is one of the most promising ways to solve this problem, an integral part of achieving that is the proper training of STEM-discipline teachers.Modernization of the teacher training system for labor education and technology, the definition of the role and place of technological education is essential for the effective implementation of STEM-training, since without building the appropriate level of technological culture of all participants in the educational process, a full-fledged implementation of the STEM-oriented approach to learning is impossible.The article is devoted to the relevant problem of modern pedagogical education – the professional training of intending teachers of labor education and technologies to the introduction of STEM-education, innovative learning technology.The purpose of the paper is to determine the role and place of the technology teacher and the subject of “Technology” in the STEM system. The tasks of the paper are to carry out a systematic analysis of modern trends in the development of technological education in Ukraine and abroad, and to find ways to improve the professional training of teachers.Modernization of technological education system is considered as one of the factors for successful implementing the tasks of developing STEM-education. The authors of the study widely use the experience and materials of scientists from the United States and Great Britain.Findings from this study demonstrate teacher’s recognition and implementation of STEM education, their knowledge and understanding of that knowledge, which is actually linked to the effectiveness of STEM delivery with their own learning practice.The perspectives of technological education development are determined, the necessity of further researches of theoretical and methodical bases for introduction of STEM-learning in the educational branch of “Technologies” is substantiated.
Copy–Move Image Forgery Detection Using Gray-Tones with Texture Description

Copy–move forgeryDixit, Anuja is a well-known image forgerySoumen, Bag technique. In this image manipulation method, a certain area of the image is replicated and affixed over the same image on different locations. Most of the times replicated segments suffer from multiple post-processing and geometrical attacks to hide sign of tampering. We have used block-based method for forgery detection. In block-based proficiencies, image is parted into partially overlapping blocks. Features are extracted corresponding to blocks. In the proposed scheme, we have computed Gray-Level Co-occurrence Matrix (GLCM) for blocks. Singular Value Decomposition (SVD) is applied over GLCM to find singular values. We have calculated Local Binary Pattern (LBP) for all blocks. The singular values and LBP features combinedly construct feature vector corresponding to blocks. These feature vectors are sorted lexicographically. Further, similar blocks discovered to identify replicated section of image. To ensure endurance of the proposed methods, Detection Accuracy (DA), False Positive Rate (FPR), and F-Measure are calculated and compared with existing methods. Experimental results establish the validity of proposed scheme for precise detection, even when meddled region of image sustain distortion due to brightness change, blurring, color reduction, and contrast adjustment.
An image computable model of visual shape similarity

Shape is one of the most important sources of visual information about objects. When presented with sets of unfamiliar objects, we can usually judge how similar or different they are from one another. Yet there are many possible features that could provide the basis for such judgments. How does the visual system compute similarity relationships between shapes? Here, we developed and tested a model of human visual shape similarity (‘Shape-Comp’), based on 94 image-computable shape features (e.g., area, compactness, shape context, Fourier descriptors). To test the model, we trained Generalized Adversarial Networks (GANs) on thousands of silhouettes of animals. Drawing samples from the latent space learned by the network allows us to synthesize novel 2D shapes that are related to one another in systematic ways. We created 8 sets of 24 shapes of novel naturalistic shapes. Using a multi-arrangement method (Kreigeskorte and Mur, 2012), observers dragged shapes into spatial configurations in which distances represent perceived shape similarity within each set. Representational Similarity Analysis revealed that human shape similarity was highly correlated with feature distance in the GAN’s latent space. We then compared human shape similarity judgements to the predictions of ShapeComp, where the weights of the model’s features were established by fitting on a training set of human judgements. In untrained test sets, the results reveal that ShapeComp accounts for most of the variance in human shape similarity. Using carefully selected stimulus sets, we can tease apart the relative importance of different features in the model, and evaluate the successes and failures of ShapeComp in predicting a host of phenomena related to shape, materials and visual appearance more generally. Together, these findings show that ShapeComp is a powerful tool for investigating the representation of shape and object categories in the human visual system.
Text Segmentation Based on Word Embedding on Indonesian Quran Translation by Greedy with Window Approaching

This paper proposes an approach to improve segmentation based on word embedding. The Greedy splitting is a major of the element in text segmentation. The previous weakness of this method, we need to define how many segments should be provided(K). The nature of segmentation does not provide the number of segments (K). In this research, greedy splitting was improved by applying window approaching, to minimize the greedy process by defining the number of sentences or words that would be examined. We assumed there only two segments on the local block or one splitting by getting the lowest score. We conducted the experiments by Quran as dataset and compared with other segmentation algorithms. The results showed that the proposed method was average at 0.68 (WindofDiff). Whereas TextTiling was reached 0.74, C99 at 0.743 and Original Greedy at 0.83 also the number of K segments could be searched by itself. Moreover, we found phenomena in the uselessness of stopword removal had an effect was indicated by decreased WindowDiff.
Entropy Measures for Data Analysis II: Theory, Algorithms and Applications

Entropies and entropy-like quantities are playing an increasing role in modern non-linear data analysis [...]
Knowledge, Attitude and Practices (KAP) related to Pandu Roga (Iron Deficiency Anaemia) among Adolescent Girls attending Ayurveda Tertiary Care Hospital and Nearby Schools - A Survey

Introduction: According to Ayurveda, depletion of Rasa and Rakta Dhatu results in yellowish-white discoloration of body known as Pandu Roga. Its features resemble with IDA (Iron Deficiency Anaemia). It is very common among female adolescents. UNICEF observed that more than half of population of adolescent girls in India are anemic. Lack of knowledge is one of the most significant reason for nutritional problems like anaemia. Aim: To evaluate the level of knowledge, attitude and practices regarding Pandu Roga (Iron Deficiency Anaemia) among female adolescents. Material and Methods: In this study 200 adolescent girls aged between 10 to 16 years visiting Kaumarabhritya OPD, A.I.I.A hospital and nearby government schools were assessed on the basis of a ‘Knowledge, Attitude and Practices (KAP) Questionnaire’ regarding Pandu Roga (IDA). Result: It was found that most of the screened adolescents had poor knowledge of Pandu Roga (IDA), its causes, prevention and management. Discussion: Pandu Roga (IDA) is a dangerous health problem globally, if not attended in time may result into grave outcomes, hence emphasis should be made on its prevention in addition to the cure. Conclusion: There is a need to focus on various health education programs for prevention of nutritional problems like IDA.
Open+ Technology with Remote Staff Support Improves Branch Access and Enhances Community Engagement

Hamilton Public Library’s northernmost rural branch in Ontario’s Greater Toronto and Hamilton Area (GTHA) leveraged technology to offer customers Extended Access to the library, a valued community resource. Using a unique model of virtual assistance to support customers remotely, customers connect with information staff at the system’s Central Library, when needed. Library hours increased from 17 to 60 hours per week: customer visits and programming participation subsequently increased. This successful pilot encouraged a second Extended Access Branch, with early data suggesting similar positive results. Now, with careful planning and attention to the pilot principles, additional locations are planned.
Practice Teaching Reform of Discrete Mathematics Model based on D3.js

From the counting principle of discrete mathematics, this paper puts forward a new teaching model, to guide students through the use of D3.js visual graphics library, to realize the dynamic displays of Fibonacci sequence, Yang hui triangle, Pigeonhole principle and principle of Permutation and Combination and realize the visualization platform of counting principle. This teaching model combines the theoretical learning of discrete mathematics with the D3.js experiment. In addition, we have designed the teaching schedule and specific teaching implementation program, so that our teaching model is feasible. At the same time, we also put forward the corresponding teaching evaluation model to realize the evaluation of students' comprehensive performance. This model can not only stimulate students' interest in theoretical learning and improve their practical innovation ability, but also help to further improve the practical teaching system and improve the level and quality of practical teaching. Finally, we hope that the teaching and learning of the counting principle of discrete mathematics will become a good opportunity to experience the beauty of computer programming and mathematics, and thus, more people will have a strong interest in these two aspects, so that they can contribute their own strength to society.