Reviewerly/exHarmony huggingface dataset:
KYC Optimization using Blockchain Smart Contract Technology

In the present scenario, it is vital for any organization, especially the financial organizations, to understand customers and their financial dealings better.KYC is a process to verify identity and related details of corresponding customers.The current KYC mechanism has a severe concern in financial institutions as it requires separate ledger for the separate financial organizations.Every institution has its KYC process, which sometimes may include third-party, which may cause increased maintenance cost, time and redundancy.There is considerable wastage of costs in the form of opportunity cost, maintenance cost, customer verification cost and many more of around $27 million according to an economic survey.The current KYC process is very time-consuming, and it decreases the user experience.We have proposed an enhanced KYC system using blockchain technology to improve the existing KYC system.An inherent feature of the DLT is used to remove the third-party involvement, and smart contracts are used to build our logic in the mobility of the data.Blockchain technology has various types of cryptographic security which provide a safer place to transact over an unsecured channel.Using the facility of DLT, cryptography and consensus mechanism of blockchain, the proposed model of KYC process can optimize storing, updating, sharing of data and accessing operations along with enhanced security, transparency and privacy.It also enhances customer ownership and improves customer experience.It not only reduces the time duration and document update problem but also saves opportunity cost, aggregation, cost, maintenance cost and many more costs, which can affect the performance of any organization.
SIGNATURE IDENTIFICATION AND VERIFICATION SYSTEMS: A COMPARATIVE STUDY ON THE ONLINE AND OFFLINE TECHNIQUES

Handwritten signature identification and verification has become an active area of research in recent years. Handwritten signature identification systems are used for identifying the user among all users enrolled in the system while handwritten signature verification systems are used for authenticating a user by comparing a specific signature with his signature that is stored in the system. This paper presents a review for commonly used methods for pre-processing, feature extraction and classification techniques in signature identification and verification systems, in addition to a comparison between the systems implemented in the literature for identification techniques and verification techniques in online and offline systems with taking into consideration the datasets used and results for each system
Structured Co-reference Graph Attention for Video-grounded Dialogue

A video-grounded dialogue system referred to as the Structured Co-reference Graph Attention (SCGA) is presented for decoding the answer sequence to a question regarding a given video while keeping track of the dialogue context. Although recent efforts have made great strides in improving the quality of the response, performance is still far from satisfactory. The two main challenging issues are as follows: (1) how to deduce co-reference among multiple modalities and (2) how to reason on the rich underlying semantic structure of video with complex spatial and temporal dynamics. To this end, SCGA is based on (1) Structured Co-reference Resolver that performs dereferencing via building a structured graph over multiple modalities, (2) Spatio-temporal Video Reasoner that captures local-to-global dynamics of video via gradually neighboring graph attention. SCGA makes use of pointer network to dynamically replicate parts of the question for decoding the answer sequence. The validity of the proposed SCGA is demonstrated on AVSD@DSTC7 and AVSD@DSTC8 datasets, a challenging video-grounded dialogue benchmarks, and TVQA dataset, a large-scale videoQA benchmark. Our empirical results show that SCGA outperforms other state-of-the-art dialogue systems on both benchmarks, while extensive ablation study and qualitative analysis reveal performance gain and improved interpretability.
Dynamic based trajectory estimation and tracking in an uncertain environment

This paper develops smoothing data association based on integrated probabilistic data association (FLIPDA-S) tracker to identify a multicopter UAV (MUAV) and estimate its trajectory in an uncertain and cluttered environment. Recently, a number of unidentified MUAVs have been flown in prohibited airspace and a radar system has been utilized to identify the MUAV and track the trajectory. However, target tracking methods could have difficulties to track the target MUAV through randomly distributed and moving objects. The vehicle state estimation (VSE) algorithm, which utilize FLIPDA-S in this paper, adopts UAV kinematics and dynamics for tracking a UAV in clutter and false targets with significant UAV identification performance. The performance of the method has been validated with and without an initial position of the target MUAV. Both numerical simulation and experiments are demonstrated to verify the effectiveness and accuracy of FLIPDA-S with VSE algorithm to track UAVs in an uncertain and clutter environment.
Evaluation of Robot Professor Technology in Teaching and Business

In this paper the Robot technology development will be evaluated in teaching and business. Impacts hypothesis of robot’s applications are discussed in addition to the types of the robots with time line. The results of this study show the advantages and disadvantages of using the Robot teacher in classroom and business and it shows that the Robot teacher performance is not like the human teacher due to the senses that owned by human being. Robot impacts minimizes the employment worldwide and it is predicted that around 800 million of global workers will lose their jobs in 2030. The results show that impacts hypothesis is minimized through improving the performance of robot technology by using AI Artificial Intelligence, also the Robot applications in the global health system have contributed effectively and successfully to prevention of the spread of the coronavirus pandemic. The Sophia Robot with AI improvements, behaves with good feelings, senses and debating friendly and it is approved by UNDP on 2018. In this study, it is concluded that the robot is not like human being, although developing human-like robot is still out of reach. Also, AI is the best tool for enhancing the Robot technology and increase the employment involvement rate in near future, which is one of the future challenges and studies proposed in this research, industry 4.
From cloud computing security towards homomorphic encryption: A comprehensive review

“Cloud computing” is a new technology that revolutionized the world of communications and information technologies. It collects a large number of possibilities, facilities, and developments, and uses the combining of various earlier inventions into something new and compelling. Despite all features of cloud computing, it faces big challenges in preserving data confidentiality and privacy. It has been subjected to numerous attacks and security breaches that have prompted people to hesitate to adopt it. This article provided comprehensive literature on the cloud computing concepts with a primary focus on the cloud computing security field, its top threats, and the protection against each one of them. Data security/privacy in the cloud environment is also discussed and homomorphic encryption (HE) was highlighted as a popular technique used to preserve the privacy of sensitive data in many applications of cloud computing. The article aimed to provide an adequate overview of both researchers and practitioners already working in the field of cloud computing security, and for those new in the field who are not yet fully equipped to understand the detailed and complex technical aspects of cloud computing.
Bayesian non-parametric detection heterogeneity in ecological models

Detection heterogeneity is inherent to ecological data, arising from factors such as varied terrain or weather conditions, inconsistent sampling effort, or heterogeneity of individuals themselves. Incorporating additional covariates into a statistical model is one approach for addressing heterogeneity, but there is no guarantee that any set of measurable covariates will adequately address the heterogeneity, and the presence of unmodelled heterogeneity has been shown to produce biases in the resulting inferences. Other approaches for addressing heterogeneity include the use of random effects, or finite mixtures of homogeneous subgroups. Here, we present a non-parametric approach for modeling detection heterogeneity for use in a Bayesian hierarchical framework. We employ a Dirichlet process mixture which allows a flexible number of population subgroups without the need to pre-specify this number of subgroups as in a finite mixture. We describe this non-parametric approach, then consider its use for modeling detection heterogeneity in two common ecological motifs: capture–recapture and occupancy modeling. For each, we consider a homogeneous model, finite mixture models, and the non-parametric approach. We compare these approaches using simulation, and observe the non-parametric approach as the most reliable method for addressing varying degrees of heterogeneity. We also present two real-data examples, and compare the inferences resulting from each modeling approach. Analyses are carried out using the nimble package for R, which provides facilities for Bayesian non-parametric models.
How recommender systems can transform airline offer construction and retailing

Recommender systems have already been introduced in several industries such as retailing and entertainment, with great success. However, their application in the airline industry remains in its infancy. We discuss why this has been the case and why this situation is about to change in light of IATA's New Distribution Capability standard. We argue that recommender systems, as a component of the Offer Management System, hold the key to providing customer centricity with their ability to understand and respond to the needs of the customers through all touchpoints during the traveler journey. We present six recommender system use cases that cover the entire traveler journey and we discuss the particular mind-set and needs of the customer for each of these use cases. Recent advancements in Artificial Intelligence have enabled the development of a new generation of recommender systems to provide more accurate, contextualized and personalized offers to customers. This paper contains a systematic review of the different families of recommender system algorithms and discusses how the use cases can be implemented in practice by matching them with a recommender system algorithm.
An Intelligent Task Scheduling Mechanism for Autonomous Vehicles via Deep Learning

With the rapid development of the Internet of Things (IoT) and artificial intelligence, autonomous vehicles have received much attention in recent years. Safe driving is one of the essential concerns of self-driving cars. The main problem in providing better safe driving requires an efficient inference system for real-time task management and autonomous control. Due to limited battery life and computing power, reducing execution time and resource consumption can be a daunting process. This paper addressed these challenges and developed an intelligent task management system for IoT-based autonomous vehicles. For each task processing, a supervised resource predictor is invoked for optimal hardware cluster selection. Tasks are executed based on the earliest hyper period first (EHF) scheduler to achieve optimal task error rate and schedule length performance. The single-layer feedforward neural network (SLFN) and lightweight learning approaches are designed to distribute each task to the appropriate processor based on their emergency and CPU utilization. We developed this intelligent task management module in python and experimentally tested it on multicore SoCs (Odroid Xu4 and NVIDIA Jetson embedded platforms). Connected Autonomous Vehicles (CAV) and Internet of Medical Things (IoMT) benchmarks are used for training and testing purposes. The proposed modules are validated by observing the task miss rate, resource utilization, and energy consumption metrics compared with state-of-art heuristics. SLFN-EHF task scheduler achieved better results in an average of 98% accuracy, and in an average of 20–27% reduced in execution time and 32–45% in task miss rate metric than conventional methods.
Gutzwiller wave function on a digital quantum computer

The determination of the ground state of quantum many-body systems via digital quantum computers rests upon the initialization of a sufficiently educated guess. This requirement becomes more stringent the greater the system. Preparing physically motivated Ans\"atze on quantum hardware is therefore important to achieve a quantum advantage in the simulation of correlated electrons. In this spirit, we introduce the Gutzwiller wave function (GWF) within the context of the digital quantum simulation of the Fermi-Hubbard model. We present a quantum routine to initialize the GWF that comprises two parts. In the first, the noninteracting state associated with the $U=0$ limit of the model is prepared. In the second, the nonunitary Gutzwiller operator that selectively removes states with doubly occupied sites from the wave function is performed by adding to every lattice site an ancilla qubit, the measurement of which in the $|0\ensuremath{\rangle}$ state confirms the operator was applied. Due to its nondeterministic nature, we estimate the success rate of the algorithm in generating the GWF as a function of the lattice size and the interaction strength $U/t$. The scaling of the quantum circuit metrics and its integration in general quantum simulation algorithms are also discussed.
Multi-level Protection (Mlp) Policy Implementation using Graph Database

Retracted: After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IJACSA`s Publication Principles. We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
Stochastic-Biomechanic Modeling and Recognition of Human Movement Primitives, in Industry, Using Wearables

In industry, ergonomists apply heuristic methods to determine workers’ exposure to ergonomic risks; however, current methods are limited to evaluating postures or measuring the duration and frequency of professional tasks. The work described here aims to deepen ergonomic analysis by using joint angles computed from inertial sensors to model the dynamics of professional movements and the collaboration between joints. This work is based on the hypothesis that with these models, it is possible to forecast workers’ posture and identify the joints contributing to the motion, which can later be used for ergonomic risk prevention. The modeling was based on the Gesture Operational Model, which uses autoregressive models to learn the dynamics of the joints by assuming associations between them. Euler angles were used for training to avoid forecasting errors such as bone stretching and invalid skeleton configurations, which commonly occur with models trained with joint positions. The statistical significance of the assumptions of each model was computed to determine the joints most involved in the movements. The forecasting performance of the models was evaluated, and the selection of joints was validated, by achieving a high gesture recognition performance. Finally, a sensitivity analysis was conducted to investigate the response of the system to disturbances and their effect on the posture.
Correlating Crime and Social Media: Using Semantic Sentiment Analysis

Crimes occur all over the world and with regularly changing criminal strategies, law enforcement agencies need to manage them adequately and productively.If these agencies have prior data on the crime or an early indication of the eventual felonious activity, it would encourage them to have some strategic preferences so that they can deploy their restricted and elite assets at the spot of a suspected crime or even better explore it to the point of anticipation.So, integration of social media content can act as a catalyst in bridging the gap between these challenges as we are aware of the fact that almost all our population uses social media and their life, thoughts, and, mindset are available digitally through their social media profiles.In this paper, an attempt has been made to predict crime pattern using geo-tagged tweets from five regions of India.We hypothesized that publicly available data from Twitter may include features that can portray a correlation between Tweets and the Crime pattern using Data Mining.We have further applied Semantic Sentiment Analysis using Bi-directional Long Short memory (BiLSTM) and feed forward neural network to the tweets to determine the crime intensity across a region.The performance of our prosed approach is 84.74 for each class of sentiment.The results showed a correlation between crime pattern predicted from Tweets and actual crime incidents reported.
Teaching and learning experience with a virtual classroom in the field of pedagogical support due to Covid-19

This work describes a pedagogical proposal for the dictation of a degree course, which originally had a face-to-face modality, sustained with the e-learning modality in the 2020 school year due to the social health situation caused by Covid-19. An analytical framework based on the pedagogical model of dimensions was applied, adapted for the virtual design of the Medical Informatics subject, of the Medicine career of the National University of Santiago del Estero, Argentina. The proposed dimensioning in the field of the pedagogical accompaniment process projected by the authorities of the faculty, allowed to program the active participation of the students and organize the monitoring of learning, through the axes proposed by the chair: individual work, group work, interactions and interventions. Regarding the results obtained on educational mediation in the virtual classroom, it was possible to identify and propose the guidelines and resources in a new pedagogical dimension on didactic strategies; thus, this proposed dimension is considered a highly usable component for teaching work in search of an autonomous student, in terms of achieving goals in their teaching and learning process, as shown by the presented experience.
Obstacle Distance Measurement Under Varying Illumination Conditions Based on Monocular Vision Using a Cable Inspection Robot

Obstacle distance measurement is one of the key technologies for autonomous navigation of high-voltage transmission line inspection robots. To address the robustness of obstacle distance measurement under varying illumination conditions, this article develops a research method that fuses image enhancement with robot monocular vision so that the robot can adapt to various levels of illumination running along the transmission line. During the inspection of high-voltage transmission lines in such an overexposed (excessively bright) environment, a specular highlight suppression method is proposed to suppress the specular reflections in an image; when scene illumination is insufficient, a robust low-light image enhancement method based on a tone mapping algorithm with weighted guided filtering is presented. Based on the monocular vision measurement principle, the error generation mechanism is analyzed through experiments, and we introduce the parameter modification mechanism. The two proposed image enhancement methods outperform other state-of-the-art enhancement algorithms in qualitative and quantitative analyses. The experimental results show that the measurement error is less than 3% for static distance measurements and less than 5% for dynamic distance measurements within 6 m. The proposed method can meet the requirements of high-accuracy positioning, real-time performance and strong robustness. This method greatly contributes to the sustainable development of inspection robots in the power industry.
The association between interpersonal conflict, turnover intention and knowledge hiding: The mediating role of employee cynicism and moderating role of emotional intelligence

Academia is prone to incivility and interpersonal conflict like any other workplace environment, although incivility in academia is manifested in behaviors such as undermining colleagues’ professional standing, intelligence and authority; reprobating other’s accomplishments; and hiding knowledge from other faculty members. The autonomy, independence, academic freedom, and tenure in academia lead to a working environment (culture) with different “rules of engagement”, governed by the faculty members themselves. This study examines the impact of employee cynicism on faculty’s interpersonal conflict as a source of stress, which leads to undesirable organizational behaviors, namely higher turnover intention and knowledge hiding behavior; furthermore, the role of faculty’s emotional intelligence as a moderator on the relationship between interpersonal conflict among faculty members and turnover intention has been investigated as a second objective of this study. The study uses a quantitative method of research and analysis, by collecting data from 200 faculty members in private higher education institutions. The study’s hypotheses were tested by Smart PLS3 (SEM) to conclude that: 1) interpersonal conflict directly influences turnover intention and knowledge hiding behavior; 2) employee cynicism has no mediating effect in the relationship between interpersonal conflict, and turnover intention or knowledge hiding behavior; 3) Faculty’s emotional intelligence moderates the relationship between interpersonal conflict and turnover intention.
Data processing method of noise logging based on cubic spline interpolation

Abstract Noise logging is a method to determine the natural noise in a well. In the actual production logging process, it is a common situation that the noise data is not continuous with depth, which can be solved by the cubic spline interpolation method. This method is a piecewise interpolation method, which fits with a three-time polynomial in each segment interval, and adds up the polynomials on all intervals to get the interpolation formula. The interpolated data points are a smooth transition with good stability. In this article, the original noise data is interpolated to continuous noise data by cubic spline interpolation method, and then evaluated by noise imaging logging, which presents good results. The cubic-spline interpolation method ensures the internal connection of noise data, and also realizes the fine description and recognition of the characteristics of the output layer. This method will provide a low-cost and effective logging evaluation method for production logging, which has a wide range of applications.