Reviewerly/exHarmony huggingface dataset:
Video Coding Impairment Models

In this chapter, multiple video quality models that can be used to predict the video coding impairment factor of the proposed framework are described. In general, two approaches are taken into account; first a direct modeling approach, where the overall video coding impairment is directly predicted based on the developed model. The second approach uses the multidimensional approach where the sub-dimensions of video quality (cf. Sect. 2.5.5.2 ) are first predicted, then the overall video coding impairment is predicted based on the predicted sub-dimensions of video quality. The first figure in this chapter illustrates the two approaches as well as a short overview of where each proposed quality model is placed within the framework. The chapter is structured based on the type of video quality model according to the level of access to the video information (cf. Sect. 2.6 ), planning, bitstream-based, and signal-based video quality models.
AI in human behavior analysis

Various types of biosignals can be measured and utilized to monitor and predict user behavior. In particular, by using wearable sensors and imaging techniques, it has become possible to obtain related information while minimizing user behavior disruption. However, existing analytic models for user behavior prediction do not accurately grasp the feature of data and have a problem in that the accuracy of behavior prediction is poor when applied in the real world. Researchers have utilized artificial intelligence (AI) technology based on machine/deep learning to solve these problems. In this chapter, the authors reviewed how AI and deep learning techniques have contributed to analyzing human behavior. The authors proposed implications and future tasks for human behavior analysis using AI based on the review.
Community-Led Documentation of Nafsan (Erakor, Vanuatu)

We focus on a collaboration between community members and visiting linguists in Erakor, Vanuatu, aiming to build the capacity of community-based researchers to undertake and sustain documentation of Nafsan, the local indigenous language. We focus on the technical and procedural skills required to collect, manage, and work with audio and video data, and give an overview of the outcomes of a community-led documentation after initial training. We discuss the benefits and challenges of this type of project from the perspective of the community researchers and the external linguists. We show that community-led documentation such as this project in Erakor, in which data management and archiving are incorporated into the documentation process, has crucial benefits for both the community and the linguists. The two most salient benefits are: a) long-term documentation of linguistic and cultural practices calibrated towards community’s needs, and b) collection of larger quantities of data by community members, and often of better quality and scope than those collected by visiting linguists, which, besides being readily available for research, have a great potential for training and testing emerging language technologies for less-resourced languages, such as Automatic Speech Recognition (ASR).
Analyzing Mobile Banking Security using Biometric Authentication

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A Novel Blockchain-Based Scientific Publishing System

Scientific publication industry is dominated by a few publishers that use centralized systems which decrease the quality of studies as well as making the publication process longer. To eliminate the traditional publication system deficiencies, we proposed a decentralized blockchain based scientific publication platform. Our proposed system using Ethereum smart contracts to accelerate the publication process, decreased the biased evaluation process while reducing the publication cost. The proposed model also improves the quality of the scientific studies by adding new features to the publication process. The suggested system has been implemented by using Ethereum Virtual Machine (EVM) which consists of front-end, middleware, and back-end. When author submit an article, the system find most suitable editors, and reviewers for related fields automatically. After the publication process finishes, editors, reviewers, cited authors and other contributors get rewarded as a system token-based cryptocurrency.
Analysis of Machine Learning Algorithms for Retrieval of Ontological Knowledge from Unstructured Text

In this article, we analyzed various machine learning algorithms for extraction of knowledge from unstructured text data. In order to extract meaningful information from unstructured text, we should know ontology of the domain. Ontology can be represented in terms of information tables and ontology graphs. Ontology graph may have taxonomic and non-taxonomic relations. Retrieving taxonomic relations are easier as compare to non-taxonomic relations. Non-taxonomic relations are easier to interpret to non-expert users. Attribution and classification are vital phases in analysis of unstructured text data. To accurately identify non-taxonomic relations from domain, semantics of domain need to be explored. For this machine learning classifiers need to be investigated in depth for feature classification. We evaluated Artificial Neural Network (ANN), Support Vector Machine (SVM) and Bay’s classifiers for feature classification. Experimentation shows that SVM machine learning classifier is best suited for extraction of knowledge from unstructured text data.
Forecasting of photovoltaic output power using machine learning and deep learning algorithms

In this chapter, numerous examples of the application of machine learning and deep learning algorithms for short-term photovoltaic output power forecasting are presented and discussed. The first application is about forecasting one-step ahead photovoltaic output power, using machine learning algorithms; in this application, only historical data are used. The second application focuses on the use of artificial neural networks to forecast photovoltaic output power using measured photovoltaic power and other meteorological parameters. The third application concerns the investigation of ensemble learning methods for one-step ahead forecasting. The fourth application deals with the utilization of deep learning natural networks for one-step and multistep ahead forecasting of output photovoltaic power. The investigated algorithms include deep neural networks like LSTM, bidirectional LSTM, GRU, CNN, and hybrid configurations. The fifth application shows how to qualify variation in the predicted results by calculating the confidence interval and the prediction interval, which is very important for analyzing the uncertainties of the forecasters.
Model-Based Approaches to Multi-attribute Diverse Matching

Bipartite b-matching is a classical model that is used for utility maximization in various applications such as marketing, healthcare, education, and general resource allocation. Multi-attribute diverse weighted bipartite b-matching (MDWBM) balances the quality of the matching with its diversity. The recent paper by Ahmadi et al. (2020) introduced the MDWBM but presented an incorrect mixed integer quadra-tic program (MIQP) and a flawed local exchange algorithm. In this work, we develop two constraint programming (CP) models, a binary quadratic programming (BQP) model, and a quadratic unconstrained binary optimization (QUBO) model for both the unconstrained and constrained MDWBM. A thorough empirical evaluation using commercial solvers and specialized QUBO hardware shows that the hardware-based QUBO approach dominates, finding best-known solutions on all tested instances up to an order of magnitude faster than the other approaches. CP is able to achieve better solutions than BQP on unconstrained problems but under-performs on constrained problems.
Using Augmented Cognition to Examine Differences in Online Handwriting Recognition for Native and Non-native Writers

The handwritten recognition (HWR) is a complex task with variety of challenges associated with natural language, variety in the styles of writing, variety and nuances of alphabets etc. The core research in handwritten recognition focuses around Latin alphabet and corresponding languages. However, differences between the languages using Latin as their main script are still major: from changed letter frequencies to additional letters. Additionally, handwriting practices and styles are not developed consistently within the same language; for example - cursive vs print calligraphy. As a result of globalization estimated 50% of world’s population speaks second language [1]. Researching characteristics of non-native handwriting has been done by various educational and second language research purposes but remains largely unaddressed in the context of augmented cognition using online handwritten recognition. We researched differences and similarities of online handwriting between native and non-native speakers of English, Georgian, Chinese and Korean speakers. We have also examined related research for Arabic, Italian and Malay handwritings. As a result, we have identified key characteristics of non-native speakers’ distinguishing from the native ones. In addition, we have identified differences based on writers’ individual maturity with the second language.
Intelligent Rock Recognition Based on Deep Learning

At present, the application of deep learning to image recognition has become one of the research hotspots. It can use the least program code and spend the least time to build a deep learning model to train, evaluate accuracy and make predictions. Based on this, after preprocessing the acquired magmatic rock data set such as classification and data enhancement, Keras is used to construct pre-training models such as VGGNet, Xception, and convolutional neural network training models. In the training process, we can get satisfactory accuracy and small loss by adjusting the hyper-parameters of the model and comparing different batch sizes. After the accuracy of the model is stable, the pre-training model and the convolutional neural network model are integrated using the voting method. Compared with a single model, the fusion model has a certain improvement in model accuracy.
Secured Medical Data Transfer Using Reverse Data Hiding System Through Steganography

Reversible Data Hiding (RDH) is the process of transferring secret data hidden inside cover media to the recipient so the recipient can securely retrieve both the secret data and cover media.The RDH approach is applied in this study in the field of telemedicine, and medical-secret data is conveyed privately via medical cover video.Morse code-based data encryption technique tends to encrypt the medical-secret data by compression using the Arithmetic coding technique.Discrete Shearlet transform (DST) compresses the selected frame from the medical cover video and the compressed secret data is embedded into the compressed frame using logical operations.On a receiver side, the reversal process is carried over and the original cover frame will be retrieved from the stego frame after secret medical data is retrieved.Password is included on the receiver side for authentication purposes.Parameters such as encryption time, compression ratio, Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) are calculated and at the maximum iteration of the proposed algorithm, the highest PSNR value is achieved.In terms of Structural Similarity Index Measure (SSIM), a linear increase from 0.91 to 0.99 is evident for iterations.In comparison with other techniques, the proposed algorithm reaches a higher SSIM of 0.999 after the maximum number of iterations.Thus, the proposed method provides more security and authentication to the recipient.
Planar Motion Estimation for Multi-camera System

In this paper, we propose efficient solutions to relative pose estimation with a multi-camera system. We focus on the case where the system navigates under planar motion, and propose two new algorithms: the non-minimal linear 6-point algorithm and the minimal 3-point algorithm. For the 6-point algorithm, we use a simple and easy-to-implement way to avoid the SVD (singular value decomposition) induced degenerate configuration, which happens in the multi-camera system based relative pose estimation. The minimal 3-point algorithm results in a system of polynomials with respect to three unknowns, and we show that it can be converted to solve a univariate polynomial in degree 4. The proposed algorithms are compared with the state-of-the-art methods on both synthetic data and public real-world images. Experimental results show very promising performance in terms of accuracy, robustness and efficiency.
Chatbot Using Deep Learning

Chatbots are pieces of computer software that use Natural Language Processing (NLP) to reach out to humans. The development of conversation is a crucial component of any Chatbot. The implementation of a good Chatbot model remains a significant challenge, despite recent advances in NLP and Artificial Intelligence (AI). It can be used for a variety of tasks. Generally, it should understand what the user is trying to accomplish and respond accordingly. Until now, a plethora of features have been introduced that have significantly improved the conversational capabilities of chatbots. This paper proposes a method for developing a chatbot based on deep neural network. The data is learned and processed using a neural network layered with multiple layers. The novelty of the proposed model is that, the bot can be trained on any input data based on the userâ€™s needs and requirements, meaning that it was a generalized one. Text to speech conversion is added to make it more user friendly.
P-Sphere Hull: An Explicit Domain Model for Trusted AI

AI has an extrapolation problem: standard pipelines do not recognize when a new input is out of domain (OOD) with respect to the training data. In principle, a prediction can be trusted when the input is in a well-sampled region of feature space. However, high-dimensional datasets can have complex topologies and low contrast between dense and sparse regions, making this judgment difficult. This paper proposes a framework to explicitly model the boundary of a high-dimensional training domain in feature space, so that we can test each new input for membership before making a prediction. In this manner, we can build AI applications that are trustworthy by design, because they respond appropriately when presented with an input that does not look like their training data. The domain model, called a P-Sphere Hull, is a data description and validation framework that can be learned independently of the predictive model and used with any AI technique. It is implemented as an open-source python package, available on the author's public github [11].
AGELESS - Applications for Geriatric Exercises: Learning of the Elderly by Sensors and Software

This paper proposes the study of a system intended to improve the quality of life and the autonomy of the elderly population through physical exercise controlled by several applications. The goal is to evaluate the evolution of three physical components in two distinct groups of elderly in different institutions. There is a control group and a study group per institution. The experimental protocol is based on distinct scenarios. In one scenario, the idea is to assess the impact of exercise preview on emotional (Neurofeedback) and motor (Biofeedback) levels. For that, users will have access to an application to preview on a monitor the sequence of exercises to be performed later in the classroom. During the preview, a BCI device will be used to evaluate parameters of the EEG signal (stress, immersion and anxiety). In another scenario, the evaluation of the impact and dynamism of using an application of augmented reality at the motor level considering the three components under analysis (balance, strength and flexibility) is done while users, in a classroom environment, will follow the proposed tasks. A monitoring application with several modules, for user's data, for professional configuration modes and for data visualization, is also included. All the applications use a range of gamification techniques, appropriate to the users’ physical condition, to motivate them. There is also the application of an automatic taxonomy to facilitate the selection of an appropriate exercise to be proposed to a particular individual at a specific moment.
Internal Similarity Network for Rejoining Oracle Bone Fragment Image

Aiming at a low accuracy of the existing convolution network technology in judging whether texture of oracle bone fragment image pair can be put back together, in this paper, the internal similarity network (ISN) is given to rejoin oracle bone fragment image automatically. Firstly, an edge equidistant matching algorithm is proposed to search local matching edge coordinate sequence of oracle bone fragment image; Then target mask based method is designed to put two images into a whole and cut local region image by the local matching edge; At last, we calculate a convolution feature gradient map of the local region image texture, and internal similarity pooling layer is proposed to compute the similarity of the convolution feature gradient map, at last the internal similarity network is contributed to evaluate the similarity score of local region image texture and determined whether the oracle bone fragment is a coherent whole. Experiments show that the method achieved state-of-the-arts performance in rejoining oracle bone fragment images and has stronger adaptability.
Convolutional Neural Network (CNN) Synthesis for Resource-Constrained Platforms

State-of-the-art Convolutional Neural Networks are designed to identify numerous object classes. Inference using such complex networks is fairly resource intensive, prohibiting their deployment on resource-constrained edge devices. In this context, the ability to classify an exhaustive list of categories is excessive for the demands of most IoT applications. Furthermore, designing a new custom-designed CNN for each new IoT application is inefficient. In this chapter we discuss two methodologies for automated synthesis of existing optimized CNN models to automatically construct a competitive CNN for a given IoT application whose objects of interest are a fraction of categories that the original CNN was designed to classify, such that the model’s inference resource requirement is proportionally scaled down.
Security- and privacy-preserving ML/DL paradigms for 5G communication technology in smart healthcare

With ever-evolving social, economic, and technological factors, there has been an increased demand for progressive healthcare systems. Recent years have seen extensive adoption and integration of machine learning (ML) and deep learning (DL) paradigms with edge computing, further evolving into an Internet of Things framework based on a distributed computing setup, especially in the time of the COVID-19 pandemic. Although constant research to fight the coronavirus is ongoing, many ML/DL techniques can facilitate smart health applications such as prediction of cardiac arrest, cataract detection, bacterial sepsis diagnoses, and Alzheimer’s disease. To accommodate the exponentially increasing healthcare data, an essential assistant and a prime infrastructure provider in the information and communication technology sector are the upcoming federated learning and decentralized computing. These can help solve several challenges in various types of learning that occur in wireless communication systems, such as security and privacy. Our findings discuss the present scenario of COVID-19 as a case study and review the different approaches of ML and DL in 5G and wireless communication for healthcare. It also gives an overview of the currently used privacy-preserving methods and techniques in healthcare for medical tabular data and imaging.
Stratified Transfer Learning of Touchscreen Behavior on Cross-Device for User Identification

Recently, most continuous authentication study focuses on a single device, such as a smart phone or smart watch. However, as the cost of mobile smart devices decreases and it becomes more common for users to have more than one mobile device, authentication systems must adapt to an environment where users are constantly switching between multiple devices. And applying different authentication solutions to different devices can present usability challenges, where using the same authentication technology affects the security of other devices. Therefore, we propose Stratified Transfer Learning (STL) for cross-device user identification by using user-multi-device behavioral biometry and finding that the same user has the same number of dimensions and the same markers on smartphones and tablets, but follows different feature distributions. First, we collect the touch screen behaviors of users when interacting with multiple devices (tablets, phones) and extract features for keystroke and swipe, and then use STL to learn the correlation of features and transform the multi-device feature domain into a new shared feature space for user recognition in a cross-device environment. Finally, we perform validation on the SU-AIS BB-MAS dataset, which has same-user-multi-device behavioral data. The experimental results show that applying cross-device generation with similar interaction patterns enables cross-device user identification.
Criminalistic Aspects of Production of Visual-Figurative Methods of Evidence Recording

The author analyses the process of proving as gathering, checking, evaluating and using the evidence in the purpose of determining the circumstances that have to be proven. It means that the author views the process of proving somewhat broader than it is cited in the Article 85 of the Criminal Law Code of the Russian Federation. The recording is the second element of the evidence gathering after they have been searched for and found. The author analyses the classification of the evidence recording. It can be of two kinds: obligatory or prescribed by the Criminal Procedure Law and optional or recommended by law (with regard to Criminal Procedure Law); the classification also divide them on subjective or depending on the qualities of the personality of the subject who record the evident. In this case his or her knowledge, skills and professional experience is taken into account. Objective, on the other hand, are based on physical, chemical or other processes (these processes are the basis of the recording). The article pays close attention to the means of the recording of evidence, especially to photo and video recording as these are the graphic ways to record evidence that are frequently used in practice. The author specifies that the content of the systems of methods of forensic and investigative photography implies including into it panoramic shooting, measuring, reproduction, identification photography and macro photography. Modern technical equipment and software for video recording allow to perform high-speed and slow-motion video shooting that can be viewed as special technical means that can be used while conducting examinations and some investigative actions.
Blockchain Redacting Algorithm with Storing Votes Inside the Chain

Today, there are two main approaches to editing the blockchain: additional chain usage and chameleon hash function usage. Editing the blockchain when using the additional chain looks like this: $${Block}_{N}^{Redaction}$$ "virtually" replaces $${Block}_{K}^{Main}$$ . Such a replacement is carried out by including information about $${Block}_{K}^{Main}$$ in $${Block}_{N}^{Redaction}$$ , as well as about $${Block}_{K-1}^{Main}$$ and $${Block}_{K+1}^{Main}$$ . However, chameleon hash function usage changes the situation radically. The chameleon hash function has a number of properties that must be taken into account when designing a redactable blockchain. The paper discusses the issues of editing a private blockchain based on a chameleon hash function.