Reviewerly/exHarmony huggingface dataset:
Voting System based on BlockChain and using Iris Recognition

The Voting System in India is often corrupted and altered. The idea is to create an unbiased, secure, fast and efficient Voting System. This project is based on the domain of Block Chain wherein the voting database will be highly secure and processed quickly. The Iris Recognition is another aspect of this system which makes the process flawless. The usage of Blockchain based Database to store the data will make the voting information immutable, reliable and can be quickly accessed. The authorized results tracking makes the voting process more secure, quick and efficient since it is semi-automated. The Iris Recognition identifies the unique patterns of Irises using infrared light and generates an encrypted bit pattern which should match at the time of voting, in order to confirm the individual&#x0027;s identity is true. The process of Iris Scanning is backed up with fingerprint recognition exclusively for blind voting candidates since their Iris pattern may not be measured precisely by the Iris Recognition System.
Explainable Artificial Intelligence (XAI) for Knowledge Management (KM)

Due to access to large datasets and advances in computing power, machine learning models for prediction and analysis problems have gained significant importance since the last decade. The methods of ML are widely used to develop decision systems for disease detection and drug prescription. We know that AI is very powerful and provides enormously accurate results. Yet it is extremely opaque, and accuracy should not be the only measure of trust. We can also say that it is like a black box, where the outside observer does not know what is happening inside the box. It becomes highly risky and dubious when we entrust such important decisions to a system that cannot tell us how it came to that conclusion. The difficulty for the system in not being able to explain its answers is also called the black box problem. Science and society are far from being concerned only with performance. The problem widens as concerns about ethical AI increase. It is difficult to explain the details of such black box models to a person with a different background.
An Elastica Geodesic Approach with Convexity Shape Prior

The minimal geodesic models based on the Eikonal equations are capable of finding suitable solutions in various image segmentation scenarios. Existing geodesic-based segmentation approaches usually exploit the image features in conjunction with geometric regularization terms (such as curve length or elastica length) for computing geodesic paths. In this paper, we consider a more complicated problem: finding simple and closed geodesic curves which are imposed a convexity shape prior. The proposed approach relies on an orientation-lifting strategy, by which a planar curve can be mapped to an high-dimensional orientation space. The convexity shape prior serves as a constraint for the construction of local metrics. The geodesic curves in the lifted space then can be efficiently computed through the fast marching method. In addition, we introduce a way to incorporate region-based homogeneity features into the proposed geodesic model so as to solve the region-based segmentation issues with shape prior constraints.
Digital Speech Files Encryption based on Hénon and Gingerbread Chaotic Maps

Speech encryption approaches are used to prevent eavesdropping, tracking, and other security concerns in speech communication. In this paper, a new cryptography algorithm is proposed to encrypt digital speech files. Initially, the digital speech files are rearranged as a cubic model with six sides to scatter speech data. Furthermore, each side is encrypted by random keys that are created by using two chaotic maps (Hénon and Gingerbread chaotic maps). Encryption for each side of the cube is achieved, using the based map vector that is generated randomly by using a simple random function. Map vector that consists of six bits, each bit refers to one of the specific chaotic maps that generate a random key to encrypt each face of the cube. Results show that the pseudo-random keys created by using chaotic maps for cryptographic speech file have an acceptable characteristic concerning randomness tests, which is confirmed in this paper by using five statistical tests. The final evaluation of the speech encryption algorithm is measured by using different quality metrics, and the results show that the algorithm can achieve resist encryption.
Local Object Tracking Using Infrared Array for Bed-exit Behavior Recognition

Bed-exit behavior recognition can be the first line of defense to prevent a subsequent fall and injuries, especially for patients with a high fall risk.The techniques adopted to recognize bedexit behavior include sensor-and vision-based processing.Generally, vision-based techniques can obtain a wide range of activity information to ensure a good recognition performance.Privacy concerns, however, impede the potential use of vision-based techniques and require the monitoring of activities in only a limited region.This paper focuses on behavior analysis using sensor-based techniques to deal with privacy concerns and other practical issues such as environmental cleaning and behavior differentiation between patients and caregivers.A local object tracking (LOT) technique based on an array of multiple reflective infrared (IR) sensors is developed to monitor user activities in a limited region.The proposed IR-based LOT technique utilizes a finite state machine (FSM) to differentiate the bed-exit activities from a caregiver and in-bed user activities.Furthermore, this bed-exit recognition system is realized as a product prototype to examine its performance in a real ward environment.The experimental results show a correct recognition rate of 99% for 26 bedside activities, four of which are caregiver activities, 16 of which are the everyday activities of the in-bed patient, and six of which are bedexit activities.In a satisfaction survey conducted at a medical institution, 89% of participants (33 caregivers and 22 patients) considered the system to be effective and 90% of them were satisfied with the quality of the bed-exit recognition prototype.
Metaheuristics for empirical software measurements

Empirical software measurement is a dynamic research domain within software engineering. Measuring the software attributes during software development is crucial to ensure the successful delivery of a desired software product. It involves the measurement of multiple dynamic metrics based on software process and software product. The traditional approaches to measure the current software practices are not so appropriate due to the changing development process and increasing complexity. The changing environment requires techniques to make measurements that are adaptable to the changes. Machine learning (ML)-based techniques are prominently being deployed for measuring the software empirically. Past three decades witness the successful application of ML-based techniques for empirical measurements of software. ML is powerful enough to accurately measure the software attributes. But the synergy of ML algorithms and metaheuristic algorithm is apparently more promising in the domain of software measurement. Since 2001, metaheuristics are being fused with ML-based techniques to optimize the accuracy of the predictor based on ML. The estimation of software cost (or effort), estimation of development time (or schedule), prediction of software quality, and early detection of software anomalies are key tasks in the most popular research sector of entire field of software measurement. The vital role of metaheuristics along with ML in empirical software measurements is as follows. First, metaheuristics are used to optimize the hyperparameters of ML algorithms; second, to mitigate the problem of the curse of dimensionality of data; and third, to select the best ML model for the candidate problem. This study proposes a full-fledged description to the application of metaheuristic algorithms to empirical software measurements. The chapter begins with the introduction to the metaheuristic approach and its application to measure software empirically in conjunction with ML algorithms. Then, the state of the art of deployment of metaheuristics in the domain of software measurements is elaborated. Later, the study discusses the three major application areas of metaheuristics: hyperparameter tuning, dimensionality reduction, and model selection in detail. For each of these application areas, case studies are quoted for clearer insight into the techniques and their applications. The author proposes a novel metaheuristic technique in this chapter for feature selection. It brings more clarity for devising new methods. Then the chapter is concluded with special remarks on future scope and references made.
Design of robust cubature fission particle filter algorithm in multi-source cooperative navigation

Abstract As a part of the multi-source cooperative navigation scheme, data fusion has a significant impact on the quality of state estimation. Particle filtering has gradually become the focus of many fusion methods due to its unique theoretical advantages in nonlinear non-Gaussian systems. However, the particle degradation and the resulting sample impoverishment restrict its application in complex engineering scenarios. In this paper, a robust cubature fission particle filter (RCFPF) is proposed to deal with these problems. First, in the framework of cubature rule, Huber function is used to combine the L2 norm and L1 norm to improve the importance density function(IDF), suppress the observation noise. Meanwhile, the proposed distribution(PD) is further optimized by combining the Gaussian distribution with Laplace distribution to alleviate particle degradation. Second, the particle swarm is fissioned before resampling, and the particle weight is reconstructed by fission of high weight particles and covering low weight particles to inhibit sample impoverishment. The vehicle experiments of multi-source cooperative navigation show that the proposed algorithm achieves better test results in accuracy and robustness than extended Kalman filter (EKF), strong tracking particle filter (STPF), and cubature particle filter (CPF).
Research on Emotional Analysis Platform of College Students' Public Opinion Information Based on Big Data

Starting from the current emotional analysis of college students' public opinion information by using big data at home and abroad, this paper introduces the theoretical basis of emotional analysis model of college students in detail. On the basis of this theory, the emotional analysis model of college students is established, and the experimental design of emotional analysis platform is conducted with this model. The research results can provide reference for grading monitoring and crisis warning of public opinion information in big data network, and at the same time give more accurate decision support for schools and society.
Polarization-Entangled Two-Photon Absorption in Inhomogeneously Broadened Ensembles

Entangled photons are promising candidates for a variety of novel spectroscopic applications. In this paper, we simulate two-photon absorption (TPA) of entangled photons in a molecular ensemble with inhomogeneous broadening. We compare our results with a homogeneously broadened case and comment on the consequences for the possible quantum enhancement of TPA cross sections. We find that, while there are differences in the TPA cross section, this difference always remains small and of the order unity. We further consider the impact of the polarization degrees of freedom and carry out the orientational average of a model system Hamiltonian. We find that certain molecular geometries can give rise to a substantial polarization dependence of the entangled TPA rate. This effect can increase the TPA cross section by up to a factor of five.
Crowd Anomaly Detection via Spatial Constraints and Meaningful Perturbation

Crowd anomaly detection is a practical and challenging problem to computer vision and VideoGIS due to abnormal events’ rare and diverse nature. Consequently, traditional methods rely on low-level reconstruction in a single image space, easily affected by unimportant pixels or sudden variations. In addition, real-time detection for crowd anomaly detection is challenging, and localization of anomalies requires other supervision. We present a new detection approach to learn spatiotemporal features with the spatial constraints of a still dynamic image. First, a lightweight spatiotemporal autoencoder has been proposed, capable of real-time image reconstruction. Second, we offer a dynamic network to obtain a compact representation of video frames in motion, reducing false-positive anomaly alerts by spatial constraints. In addition, we adopt the perturbation visual interpretation method for anomaly visualization and localization to improve the credibility of the results. In experiments, our results provide competitive performance across various scenarios. Besides, our approach can process 52.9–63.4 fps in anomaly detection, making it practical for crowd anomaly detection in video surveillance.
Responsible handling of ethics in data publication

A global working group has developed recommendations for the responsible handling of the growing range of ethics cases related to data publication, but further community work is needed toward a responsible and cohesive data publishing ecosystem.
Enhancement of online education system by using a multi-agent approach

Multi-Agent System (MAS) is popular in the fields where cooperative effort is required to fulfil the purpose of the end product. This study creates a Multi-Agent System as a potential solution for the implementation of an online education system in order to bring about swift and accurate responses from the system and thus a meaningful conversation between the learners and the system. The existing work on online education system employing MAS does not depict the means of flow of information from the browser to the internal MAS and also lacks the adaptivity element with respect to the changing demands of the learners. The study attempts to bridge this gap by employing a system with Event-Condition-Action model and intelligent agents which include a message passing agent to depict the means of message flow from the browser to the MAS and an adaptive course organiser agent which organises adaptive educational content with respect to the changing needs of the learner. The outcome of the study is the design and simulation of the said system as a standalone entity that can be attached to a virtual learning environment (VLE), with the addition of pedagogical agents performing different functionalities to form an adaptive system. The system is evaluated by validating the results generated for various case studies using a validation tool. The expected behaviour is that the resulting course agenda agrees with the learning mode preference of the user provided initially and the results are expected to change according to the changing user preferences through a self-assessment questionnaire.
What Is the Relationship between the Sense of Presence and Learning in Virtual Reality? A 24-Year Systematic Literature Review

Abstract The sense of presence is an important aspect of experiences in Virtual Reality (VR), an emerging technology in education, leading this construct to be increasingly researched in parallel to learning purposes. However, there is not a consensus in the literature on the outcomes of this association. Aiming to outline a panorama in this regard, a systematic literature review was conducted, with a comprehensive analysis of 140 primary studies recovered from five worldwide databases. The analysis shows an overview of 24 years of areas, factors, and methodological approaches that seem to be more inclined to benefit from the sense of presence toward learning purposes. We contribute to the advancement of state of the art by providing an understanding of the relationship among these variables, identifying potential ways to benefit from the sense of presence to further leverage the use of VR for learning purposes.
Decision Support System for Economic Management of Large Enterprises Based on Artificial Intelligence

In order to improve the economic management effect of large enterprises, a decision support system for economic management of large enterprises based on artificial intelligence is designed. The system hardware and software are designed, respectively. The system hardware consists of basic information module, business management module, personnel management module, salary and welfare management module, system management module, and database module. With the support of artificial intelligence technology, build a BP neural network model, and the model was trained, through continuous learning rate adjustment; in the process of training error lower sales forecast results, according to the result of prediction in enterprise comprehensive benefit maximization as the goal, set up large enterprises economic management decision-making model, large enterprise economic management decision-making algorithm design. The test results show that the system has good fault tolerance, reliability, robustness, and high efficiency, the system response time is short, the decision accuracy is high, and the practical application effect is good.
Blockchains: Practical Approaches

Originally proposed for Bitcoin, Ethereum and other digital currencies, blockchain technology is now the most innovative framework for decentralized data management.This technology has not yet revealed its full potential to transform all economic and social processes.Its characteristics, interpreted and implemented in practice, are about to have their revolutionary impact on the possibilities for safe, fair and objective digitalization of the physical environment.The purpose of this paper is to present some general and specific aspects in research on applications of blockchain technology in intelligent agriculture.The main elements of the technology, popular definitions, focus areas in risk management concepts and the implications for internal audit and control procedures in companies that adopting blockchain in intelligent agriculture are briefly discussed.In summary, an algorithm for selecting of blockchain platforms and an algorithm for text mining in the domain of plant genetic resources are presented.A blockchain-based approach to modelling a supply chain in smart crop production is proposed, which is illustrated with a conceptual example of blockchain architecture and smart contract functions design.The future development of this study will focus on detailed analysis of existing blockchain standards, improving descriptions of functional levels in the supply chain network model and blockchain architecture design.
On Explainable AI and Abductive Inference

Modern explainable AI (XAI) methods remain far from providing human-like answers to ‘why’ questions, let alone those that satisfactorily agree with human-level understanding. Instead, the results that such methods provide boil down to sets of causal attributions. Currently, the choice of accepted attributions rests largely, if not solely, on the explainee’s understanding of the quality of explanations. The paper argues that such decisions may be transferred from a human to an XAI agent, provided that its machine-learning (ML) algorithms perform genuinely abductive inferences. The paper outlines the key predicament in the current inductive paradigm of ML and the associated XAI techniques, and sketches the desiderata for a truly participatory, second-generation XAI, which is endowed with abduction.
Analysis of Data Inflation Energy and Gasoline Price by Vector Autoregressive Model

The study of multivariate time series data analysis has become many topics of research in the fields of economics and business. In the present study, we will analyze data energy inflation and gasoline prices of Indonesia over the years from 2014 to 2020. The purpose of this study is to obtain the best model of the dynamic relationship between inflation and gasoline prices. The dynamic modeling that will be used in this research is modeling using the Vector Autoregressive (VAR) model. From the analysis results, the best model is the VAR model with order 3 (p=3), VAR(3). Based on the best model, VAR(3), further studies will be discussed with regard to Granger causality analysis, Impulse Response Function, and Forecasting.
On the antiderivatives of xp/(1 − x) with an application to optimize loss functions for classification with neural networks

Abstract Supervised learning in neural nets means optimizing synaptic weights W such that outputs y ( x ; W ) for inputs x match as closely as possible the corresponding targets t from the training data set. This optimization means minimizing a loss function ${\mathscr{L}}(\mathbf {W})$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>L</mml:mi> <mml:mo>(</mml:mo> <mml:mi>W</mml:mi> <mml:mo>)</mml:mo> </mml:math> that usually motivates from maximum-likelihood principles, silently making some prior assumptions on the distribution of output errors y − t . While classical crossentropy loss assumes triangular error distributions, it has recently been shown that generalized power error loss functions can be adapted to more realistic error distributions by fitting the exponent q of a power function used for initializing the backpropagation learning algorithm. This approach can significantly improve performance, but computing the loss function requires the antiderivative of the function f ( y ) := y q − 1 /(1 − y ) that has previously been determined only for natural $q\in \mathbb {N}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>q</mml:mi> <mml:mo>∈</mml:mo> <mml:mi>ℕ</mml:mi> </mml:math> . In this work I extend this approach for rational q = n /2 m where the denominator is a power of 2. I give closed-form expressions for the antiderivative ${\int \limits } f(y) dy$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>∫</mml:mo> <mml:mi>f</mml:mi> <mml:mo>(</mml:mo> <mml:mi>y</mml:mi> <mml:mo>)</mml:mo> <mml:mi>d</mml:mi> <mml:mi>y</mml:mi> </mml:math> and the corresponding loss function. The benefits of such an approach are demonstrated by experiments showing that optimal exponents q are often non-natural, and that error exponents q best fitting output error distributions vary continuously during learning, typically decreasing from large q &gt; 1 to small q &lt; 1 during convergence of learning. These results suggest new adaptive learning methods where loss functions could be continuously adapted to output error distributions during learning.
Neural Network Field Transformation and Its Application in HMC

We propose a generic construction of Lie group agnostic and gauge covariant neural networks, and introduce constraints to make the neural networks continuous differentiable and invertible.We combine such neural networks and build gauge field transformations that is suitable for Hybrid Monte Carlo (HMC).We use HMC to sample lattice gauge configurations in the transformed space by the neural network parameterized gauge field transformations.Tested with 2D U(1) pure gauge systems at a range of couplings and lattice sizes, compared with direct HMC sampling, the neural network transformed HMC (NTHMC) generates Markov chains of gauge configurations with improved tunneling of topological charges, while allowing less force calculations as the lattice coupling increases.