Reviewerly/exHarmony huggingface dataset:
Scaling Agile Adoption Motivators from Management Perspective: An Analytical Hierarchy Process Approach

Agile methodology has been noted as the mostly used method for software development over the past few years. This methodology guarantees the rapid delivery of software products with lower cost and greater customer contentment. The adoption of agile methods was initially applied in the software development industry for single, small project teams following the principles of Agile Manifesto. The intense need of the time is to identify from management perspective the motivators that can be focused on to adopt agile methodologies in large-scale projects. Therefore, the main purpose of the study is to develop from management perspective a priority-based taxonomy of motivators that helps in scaling agile methodologies in large-scale projects. The taxonomy is developed in two steps. In the first step, data are collected from a survey and open literature. In the second step, the analytical hierarchy process (AHP) is implemented on the collected data that produced the proposed priority-based taxonomy of the motivators. With the help of AHP model, the 21 motivators of successful project management are categorized into four main categories, i.e., “organization management,” “team,” “customer and technology,” and “process.” Furthermore, the AHP analysis is used to rank the motivators and their respective categories in order of their importance. The findings of this study are further reviewed and validated by the agile experts in other organizations. The proposed priority-based taxonomy of motivators can be used as a handbook that gives guidelines on scalability of agile methodologies to large projects, which helps in successful management of large-scale projects.
Screening of Evaluation Index and Construction of Evaluation Index System for Mine Ventilation System

The mine ventilation system is an indispensable component to improve coal mining efficiency and ensure the safety of production. Only by clearly grasping the comprehensive evaluation quality of the ventilation system can effective countermeasures be formulated. This paper establishes an evaluation index system for mine ventilation systems by combining a qualitative survey with quantitative research. Specifically, the primary indicators are screened through R-type clustering and the coefficient of variation method. The weight of each index is determined by the entropy weight method. Moreover, the technique for order preference by similarity to ideal solution (TOPSIS) method is used to evaluate the quality of the mine ventilation system. Finally, this evaluation system is used to evaluate the ventilation renovation project in the production mining area of the Sihe mine. The evaluation results verify the effectiveness of the establishment of the mine ventilation evaluation index system and of the evaluation methods.
Image Quality Evaluation of Sanda Sports Video Based on BP Neural Network Perception

In the special sports camera, there are subframes. A lens is composed of multiple frames. It will be unclear if a frame is cut out. The definition of video screenshots lies in the quality of video. To get clear screenshots, we need to find clear video. The purpose of this paper is to analyze and evaluate the quality of sports video images. Through the semantic analysis and program design of video using computer language, the video images are matched with the data model constructed by research, and the real-time analysis of sports video images is formed, so as to achieve the real-time analysis effect of sports techniques and tactics. In view of the defects of rough image segmentation and high spatial distortion rate in current sports video image evaluation methods, this paper proposes a sports video image evaluation method based on BP neural network perception. The results show that the optimized algorithm can overcome the slow convergence of weights of traditional algorithm and the oscillation in error convergence of variable step size algorithm. The optimized algorithm will significantly reduce the learning error of neural network and the overall error of network quality classification and greatly improve the accuracy of evaluation. Sanda motion video image quality evaluation method based on BP (back propagation) neural network perception has high spatial accuracy, good noise iteration performance, and low spatial distortion rate, so it can accurately evaluate Sanda motion video image quality.
Quantum Leap for Quantum Primacy

Two experimental quantum computers tackle the most complex problems yet, suggesting an end to the debate on whether quantum "primacy"—the point at which a quantum computer outperforms the best possible classical computer—can be reached.
Distance Education and Student Satisfaction Regarding the Pedagogical Support Services Provided in Virtual Teaching-learning Environments

The objective of this article is to know the satisfaction of students with online pedagogical support services, generated by the context of distance education due to the COVID-19 pandemic, which have been provided with various technologi-cal tools. During the development of the research, it was determined that there is a satisfaction with the pedagogical support of 61.2%, being the indicators of health services and welfare university, those that present the highest satisfaction both with 62.3%, on the other hand, are the indicators registrations and license plates, and psychopedagogical services those that present a greater dissatisfaction of 32.7 and 32.2, respectively. Although the pedagogical support provided by the university must continue to be improved, a slight improvement has been deter-mined with the previous semester in which the adaptation of online services be-gan, since dissatisfaction on the part of students decreased by 9.18%.
3D Correspondence Grouping with Compatibility Features

We present a simple yet effective method for 3D correspondence grouping. The objective is to accurately classify initial correspondences obtained by matching local geometric descriptors into inliers and outliers. Although the spatial distribution of correspondences is irregular, inliers are expected to be geometrically compatible with each other. Based on such observation, we propose a novel feature representation for 3D correspondences, dubbed compatibility feature (CF), to describe the consistencies within inliers and inconsistencies within outliers. CF consists of top-ranked compatibility scores of a candidate to other correspondences, which purely relies on robust and rotation-invariant geometric constraints. We then formulate the grouping problem as a classification problem for CF features, which is accomplished via a simple multilayer perceptron (MLP) network. Comparisons with nine state-of-the-art methods on four benchmarks demonstrate that: 1) CF is distinctive, robust, and rotation-invariant; 2) our CF-based method achieves the best overall performance and holds good generalization ability.
A Noble Classification Framework for Data Glove Classification of a Large Number of Hand Movements

The recognition of hand movements is an important method for human-computer interaction (HCI) technology, and it is widely used in virtual reality and other HCI areas. While many valuable efforts have been made, efficient ways to capture over 20 types of hand movements with high accuracy by one data glove are still lacking. This paper addresses a new classification framework for 52 hand movements. This classification framework includes the following two parts: the movement detection algorithm and the movement classification algorithm. The fine K-nearest neighbor (Fine KNN) is the core of the movement detection algorithm. The movement classification algorithm is composed of downsampling in data preparation and a new deep learning network named the DBDF network. Bidirectional Long Short-Term Memory (BiLSTM) is the main part of the DBDF network. The results of experiments using the Ninapro DB1 dataset demonstrate that our work can classify more types of hand movements than related algorithms with a precision of 93.15%.
Service-oriented tools for automating digital twin development

The paper represents a prototype of service-oriented tools for developing digital twins. These tools automate most of the stages in preparing and carrying out a computational experiment reducing the possibility of human error. Computational experiments based on simulation modeling are performed using the proposed tools. As an example in applying the represented tools, a digital twin of a heat pump used as environmentally-friendly equipment of an infrastructure object on the Baikal natural territory is considered. Owing to the growing anthropogenic load, special attention is paid to the objects located at the coast of Lake Baikal. During the simulation modeling, both the heat pump performance characteristics and retrospective meteorological data are used to predict climatic conditions and select optimal control parameters. In addition, the computational experiment has obviously shown that heat pump use significantly reduces the harmful effect on the environment.
Enabling data to develop an AI-based application for detecting malaria and dengue

Data, as we know, is no more an option for medical care reforms. It is the central theme over which the entire process of health reforms rests. Any AI application, and its benefits in health care, takes advantage of data. The data which has been acquired and stored over the span of several years is at various levels, such as patient admission, diagnostics, treatment summary, lab reports, scans and x-rays and patient discharge summary. Though it is easy to state “we need data,” in reality, we do have data stored in multiple repositories, such as hospital medical records, doctors’ office, clinical and insurance records, among others. However, the challenge remains in enabling this data, which is mostly handwritten or scanned image of handwritten records, into a format that is computable. This is the foremost challenge in developing AI-based solutions. Indian health data is stored in multiple health repositories of our country. The second important challenge is the need to integrate the data that is available in different repositories. This case study discusses a pioneering work undertaken in a 1,500 bed hospital in the coastal district of Karnataka in developing an AI-based application based on the hospital data, for detecting malaria and dengue. This case study lists the various challenges faced at every phase of developing this healthcare application.
LightOn Optical Processing Unit : Scaling-up AI and HPC with a Non von Neumann co-processor

Beyond pure Von Neumann processing Scalability of AI / HPC models is limited by the Von Neumann bottleneck for accessing massive amounts of memory, driving up power consumption.
Multi object tracking using gradient-based learning model in video-surveillance

On accomplishing an efficacious object tracking, the activity of an object concerned becomes notified in a forthright manner. An accurate form of object tracking task necessitates a robust object tracking procedures irrespective of hardware assistance. Such approaches inferred a vast computational complexity to track an object with high accuracy in a stipulated amount of processing time. On the other hand, the tracking gets affected owing to the existence of varied quality diminishing factors such as occlusion, illumination changes, shadows etc., In order to rectify all these inadequacies in tracking an object, a novel background normalization procedure articulated on the basis of a textural pattern is proposed in this paper. After preprocessing an acquired image, employment of an Environmental Succession Prediction algorithm for discriminating disparate background environment by background clustering approach have been accomplished. Afterward, abstract textural characterizations through utilization of a Probability based Gradient Pattern (PGP) approach for recognizing the similarity between patterns obtained so far. Comparison between standardized frame obtained in prior and those processed patterns detects the motion exposed by an object and the object concerned gets identified within a blob. Hence, the system is resistant towards illumination variations. These illumination variation was interpreted in object tracking residing within a dynamic background. Devised approach certainly outperforms other object tracking methodologies like Group Target Tracking (GTT), ViPER-GT, grabcut, snakes in terms of accuracy and average time. Proposed PGP-based pattern texture analysis is compared with Gamifying Video Object (GVO) approach and hence, it evidently outperforms in terms of precision, recall and F1 measure.
A SURVEY ON TRAFFIC SIGN RECOGNITION AND DETECTION

Classifying traffic signs is an indispensable task for autonomous driving systems. Depending on the country, traffic signs possess a wide variability in their visual appearance making it harder for classification systems to succeed. Either the classifier should be fine-tuned or a bigger collection of images should be used. Nowadays, Intelligent Autonomous Vehicles together with Advanced Driver Assistance Systems (ADAS) deal with the problem of traffic sign recognition. It is a challenging real world computer vision problem due to the differentand complex scenarios they are placed into.After images are classified they are made into the groups like highway signs, speed signs, danger signs etc.
Pengaruh Algoritma ADASYN dan SMOTE terhadap Performa Support Vector Machine pada Ketidakseimbangan Dataset Airbnb

Traveling activities are increasingly being carried out by people in the world. Some tourist attractions are difficult to reach hotels because some tourist attractions are far from the city center, Airbnb is a platform that provides home or apartment-based rentals. In lodging offers, there are two types of hosts, namely non-super host and super host. The super-host badge is obtained if the innkeeper has a good reputation and meets the requirements. There are advantages to being a super host such as having more visibility, increased earning potential and exclusive rewards. Support Vector Machine (SVM) algorithm classification process by these criteria data. Data set is unbalanced. The super host population is smaller than the non-super host. Overcoming the imbalance, this over sampling technique is carried out using ADASYN and SMOTE. Research goal was to decide the performance of ADASYN and sampling technique, SVM algorithm. Data analyses used over sampling which aims to handle unbalanced data sets, and confusion matrix used for testing Precision, Recall, and F1-SCORE, and Accuracy. Research shows that SMOTE SVM increases the accuracy rate by 1 percent from 80% to 81%, which is influenced by the increase in the True (minority) label test results and a decrease in the False label test results (majority), the SMOTE SVM is better than ADASYN SVM, and SVM without over sampling.
Understanding copyright issues entailing deepfakes in India

Abstract With increase in the scope, scale and sophistication of digitisation, the line between fake and real is blurred. In the recent years, digital disinformation in the form of fake news or morphed pictures/videos has become a new normal on internet. Adding to this list are the artificial intelligence generated-Deepfakes. The wide use of deepfakes in cinematography, fake pornography, political campaigns etc. have fascinated the legal fraternity across the globe. Deepfakes raises many legal alarms including threat to privacy, cyber security, right to publicity, forgery, defamation, criminal intimidation, sedition, copyright infringement, copyright protection, authorship and the list continues. The concern of this article is to understand the copyright issues embroiled in deepfakes in Indian context. The first point of discussion is the sustainability of the claim of copyright infringement or right of publicity arising out of the modification and republication of original work to create deepfakes. Secondly, the creative use of deepfakes like in advanced cinematography would require copyright protection to the author. However, the issue is identifying the author of the deepfake – is it artificial intelligence or the human causing its creation.
Speech Encryption Scheme Based on Ciphertext Policy Hierarchical Attribute in Cloud Storage

In order to ensure the confidentiality and secure sharing of speech data, and to solve the problems of slow deployment of attribute encryption systems and fine-grained access control in cloud storage, a speech encryption scheme based on ciphertext policy hierarchical attributes was proposed. First, perform hierarchical processing of the attributes of the speech data to reflect the hierarchical structure and integrate the hierarchical access structure into a single-access structure. Second, use the attribute fast encryption framework to construct the attribute encryption scheme of the speech data, and use the integrated access to the speech data; thus, the structure is encrypted and uploaded to the cloud for storage and sharing. Finally, use the hardness of decisional bilinear Diffie–Hellman (DBDH) assumption to prove that the proposed scheme is secure in the random oracle model. The theoretical security analysis and experimental results show that the proposed scheme can achieve efficient and fine-grained access control and is secure and extensible.
Model-Independent Lens Distortion Correction Based on Sub-Pixel Phase Encoding

Lens distortion can introduce deviations in visual measurement and positioning. The distortion can be minimized by optimizing the lens and selecting high-quality optical glass, but it cannot be completely eliminated. Most existing correction methods are based on accurate distortion models and stable image characteristics. However, the distortion is usually a mixture of the radial distortion and the tangential distortion of the lens group, which makes it difficult for the mathematical model to accurately fit the non-uniform distortion. This paper proposes a new model-independent lens complex distortion correction method. Taking the horizontal and vertical stripe pattern as the calibration target, the sub-pixel value distribution visualizes the image distortion, and the correction parameters are directly obtained from the pixel distribution. A quantitative evaluation method suitable for model-independent methods is proposed. The method only calculates the error based on the characteristic points of the corrected picture itself. Experiments show that this method can accurately correct distortion with only 8 pictures, with an error of 0.39 pixels, which provides a simple method for complex lens distortion correction.
Redesign of E-Commerce Mobile Application with Design Thinking Method: A Case Study of RP2, Online Household Retailer

RP2 is one of the growing e-commerce sites in Indonesia offering products for home and living. Recently, RP2 launches a mobile application to reach users by using mobile devices to facilitate purchases on RP2. However, the development of this mobile application has not been through a good application development process, so several weaknesses are found. The aim of the research was to redesign the RP2 mobile application using the design thinking method. The method had five stages: empathize, define, ideate, prototype, and test. However, the research only focused on the first four stages. Moreover, a survey using a questionnaire and interviews were done with 34 users of the RP2 mobile application. The research results are a prototype of the RP2 mobile application, which has been tailored to users’ needs and the project owner’s input. Changes have been made to the RP2 mobile application using the design thinking method. Changes are applied in several views, such as home, navigation and notifications, search bar, Product Detail Page (PDP), cart and payment, and profiles. With these changes, it is expected that users of the RP2 mobile application will increase, so the number of transactions through the RP2 mobile application will increase too.
A Trainable Monogenic ConvNet Layer Robust in Front of Large Contrast Changes in Image Classification

Convolutional Neural Networks (ConvNets) at present achieve remarkable performance in image classification tasks. However, current ConvNets cannot guarantee the capabilities of the mammalian visual systems such as invariance to contrast and illumination changes. Some ideas to overcome the illumination and contrast variations usually have to be tuned manually and tend to fail when tested with other types of data degradation. In this context, we present a new bio-inspired entry layer, M6, which detects low-level geometric features (lines, edges, and orientations) which are similar to patterns detected by the V1 visual cortex. This new trainable layer is capable of coping with image classification even with large contrast variations. The explanation for this behavior is the monogenic signal geometry, which represents each pixel value in a 3D space using quaternions, a fact that confers a degree of explainability to the networks. We compare M6 with a conventional convolutional layer (C) and a deterministic quaternion local phase layer (Q9). The experimental setup is designed to evaluate the robustness of our M6 enriched ConvNet model and includes three architectures, four datasets, three types of contrast degradation (including non-uniform haze degradations). The numerical results reveal that the models with M6 are the most robust in front of any kind of contrast variations. This amounts to a significant enhancement of the C models, which usually have reasonably good performance only when the same training and test degradation are used, except for the case of maximum degradation. Moreover, the Structural Similarity Index Measure (SSIM) is used to analyze and explain the robustness effect of the M6 feature maps under any kind of contrast degradations.
Research on Action Recognition Method of Dance Video Image Based on Human-Computer Interaction

Human-computer interaction technology simplifies the complicated procedures, which aims at solving the problems of inadequate description and low recognition rate of dance action, studying the action recognition method of dance video image based on human-computer interaction. This method constructs the recognition process based on human-computer interaction technology, constructs the human skeleton model according to the spatial position of skeleton, motion characteristics of skeleton, and change angles of skeleton, describes the dance posture features by generating skeleton node graph, and extracts the key frames of dance video image by using the clustering algorithm to recognize the dance action. The experimental results show that the recognition rate of this method under different entropy values is not less than 88%. Under the test conditions of complex, dark, bright, and multiuser interference, this method can make the model to describe the dance posture accurately. Furthermore, the average recognition rates are 93.43%, 91.27%, 97.15%, and 89.99%, respectively. It is suitable for action recognition of most dance video images.