Reviewerly/exHarmony huggingface dataset:
Identifying Factors Influencing on Agile Practices for Software Development

Agile practices are activities or actions that are utilized during software development to improve the quality and productivity of the resulting software. Those practices are influenced by a set of factors that affect the realization of their objectives. The purpose of this study is to determine the influence of organizational factors and the personnel working on the software development project to the agile practices. To this end, a model has been proposed that is composed of six critical success factors derived from the theories of administrative and human behavior and four groups of agile practices, and resulted in identifying 13 factor-practice relationships. The results from 146 questionnaires addressed to individuals involved in the software development process show that the factors of Confidence, Perception of Self-efficacy, Integrity and Availability of the Information and Experiences Learned, and Media Used all have a noteworthy positive influence on teamwork agile practices.
An Empirical Study on the Evolution of Design Smells

The evolution of software systems often leads to its architectural degradation due to the presence of design problems. In the literature, design smells have been defined as indicators of such problems. In particular, the presence of design smells could indicate the use of constructs that are harmful to system maintenance activities. In this work, an investigation on the nature and presence of design smells has been performed. An empirical study has been conducted considering the complete history of eight software systems, commit by commit. The detection of instances of multiple design smell types has been performed at each commit, and the analysis of the relationships between the detected smells and the maintenance activities, specifically due to refactoring activities, has been investigated. The proposed study evidenced that classes affected by design smells are more subject to change, especially when multiple smells are detected in the same classes. Moreover, it emerged that in some cases these smells are removed, and this occurs involving more smells at the same time. Finally, results indicate that smells removals are not correlated to the refactoring activities.
Joint Intelligence Ranking by Federated Multiplicative Update

The joint intelligence ranking of intelligent systems like autonomous driving is of great importance for building a more general, extensive, and universally accepted intelligence evaluation scheme. However, due to issues such as privacy security and industry or area competition, the integration of isolated test results may face large unimaginable difficulty in information security and encrypted model training. To address this, we derive the federated multiplicative update (FMU) algorithm with boundary constraints to solve the nonnegative matrix factorization based joint intelligence ranking. The encrypted learning process is developed to alternate original computation steps in multiplicative update algorithms. Owning feasible property for the fast convergence and secure exchange of variables, the proposed framework outperforms the previous work on both real and simulated data. Further experimental analysis reveals that the introduced federated mechanism does not harm the overall time efficiency.
Dual method cryptography image by two force secure and steganography secret message in IoT

With the go on the evolution of both computer and internet technology, videos, sounds, and scripts are used more and more often. It can be used in sundry techniques in ciphering and data concealing. The objective of this paper is leading to the suggestion of a new method of the combination between encryption and concealment of information so as to make it difficult to identify the transmitted datavia networks. This study has used two force secure (2FS) to encrypt the images, in other words, the SF is frequent twice on the image, to obtain powerful encryption then the concealing of the secret message is done inside the cryptography of the image has been performed using a secret key (cosine curve), and this stego-encryption image has been transformed forthe Internet of things storage in the database in IoT (data flow), when the user needs any information can be access inviaof internet of things (IoTs). The outcome of the proposed system is obtained tobe evaluated through different measures, such aspeak signal noise ratio (PSNR), mean square error (MSE), entropy,correlation coefficient, and histogram. The proposed system is good, efficient, fast, has high security, robustness, and transparency.
Pointly-supervised scene parsing with uncertainty mixture

Pointly-supervised learning is an important topic for scene parsing, as dense annotation is extremely expensive and hard to scale. The state-of-the-art method harvests pseudo labels by applying thresholds upon softmax outputs (logits). There are two issues with this practice: (1) Softmax output does not necessarily reflect the confidence of the network output. (2) There is no principled way to decide on the optimal threshold. Tuning thresholds can be time-consuming for deep neural networks. Our method, by contrast, builds upon uncertainty measures instead of logits and is free of threshold tuning. We motivate the method with a large-scale analysis of the distribution of uncertainty measures, using strong models and challenging databases. This analysis leads to the discovery of a statistical phenomenon called uncertainty mixture. Specifically speaking, for each independent category, the distribution of uncertainty measures for unlabeled points is a mixture of two components (certain v.s. uncertain samples). The phenomenon of uncertainty mixture is surprisingly ubiquitous in real-world datasets like PascalContext and ADE20k. Inspired by this discovery, we propose to decompose the distribution of uncertainty measures with a Gamma mixture model, leading to a principled method to harvest reliable pseudo labels. Beyond that, we assume the uncertainty measures for labeled points are always drawn from the certain component. This amounts to a regularized Gamma mixture model. We provide a thorough theoretical analysis of this model, showing that it can be solved with an EM-style algorithm with convergence guarantee. Our method is also empirically successful. On PascalContext and ADE20k, we achieve clear margins over the baseline, notably with no threshold tuning in the pseudo label generation procedure. On the absolute scale, since our method collaborates well with strong baselines, we reach new state-of-the-art performance on both datasets.
Biologically Inspired Visual System Architecture for Object Recognition in Autonomous Systems

Computer vision is currently one of the most exciting and rapidly evolving fields of science, which affects numerous industries. Research and development breakthroughs, mainly in the field of convolutional neural networks (CNNs), opened the way to unprecedented sensitivity and precision in object detection and recognition tasks. Nevertheless, the findings in recent years on the sensitivity of neural networks to additive noise, light conditions, and to the wholeness of the training dataset, indicate that this technology still lacks the robustness needed for the autonomous robotic industry. In an attempt to bring computer vision algorithms closer to the capabilities of a human operator, the mechanisms of the human visual system was analyzed in this work. Recent studies show that the mechanisms behind the recognition process in the human brain include continuous generation of predictions based on prior knowledge of the world. These predictions enable rapid generation of contextual hypotheses that bias the outcome of the recognition process. This mechanism is especially advantageous in situations of uncertainty, when visual input is ambiguous. In addition, the human visual system continuously updates its knowledge about the world based on the gaps between its prediction and the visual feedback. CNNs are feed forward in nature and lack such top-down contextual attenuation mechanisms. As a result, although they process massive amounts of visual information during their operation, the information is not transformed into knowledge that can be used to generate contextual predictions and improve their performance. In this work, an architecture was designed that aims to integrate the concepts behind the top-down prediction and learning processes of the human visual system with the state-of-the-art bottom-up object recognition models, e.g., deep CNNs. The work focuses on two mechanisms of the human visual system: anticipation-driven perception and reinforcement-driven learning. Imitating these top-down mechanisms, together with the state-of-the-art bottom-up feed-forward algorithms, resulted in an accurate, robust, and continuously improving target recognition model.
Generating Natural Counterfactual Visual Explanations

Counterfactual explanations help users to understand the behaviors of machine learning models by changing the inputs for the existing outputs. For an image classification task, an example counterfactual visual explanation explains: "for an example that belongs to class A, what changes do we need to make to the input so that the output is more inclined to class B." Our research considers changing the attribute description text of class A on the basis of the attributes of class B and generating counterfactual images on the basis of the modified text. We can use the prediction results of the model on counterfactual images to find the attributes that have the greatest effect when the model is predicting classes A and B. We applied our method to a fine-grained image classification dataset and used the generative adversarial network to generate natural counterfactual visual explanations. To evaluate these explanations, we used them to assist crowdsourcing workers in an image classification task. We found that, within a specific range, they improved classification accuracy.
HHA: An Attentive Prediction Model for Academic Abnormality

Warning students with poor performance in advance based on historical academic data, namely, the academic abnormality prediction is important task in education. The majority of existing methods focus on digging out abnormal complex clues from historical data, while ignoring two basic considerations:(1)these works fail to handle unrecorded/missing data when this part is sparse; (2)these works ignore the complex relationship between courses. The different courses are used as the attention weight vector for abnormality prediction, but they do not notice the mutual influence between courses. To this end, we contribute a Hybrid Neural Network Model based on High-Order Attention Mechanism, called HHA, to address the academic abnormality prediction problem. Specifically, we first exploit Generative Adversarial Network(GAN) to mine hidden factors in the unrecorded/missing data reasonably by simulating student behavior. Thereafter, a high-order attention mechanism is proposed to measure the importance of course and course combination. Lastly, a multi-layer projection abstracts feature and classifies whether the student is abnormal. By experimenting on real-world dataset, we demonstrate the effectiveness and rationality of our proposed model.
A Hybrid Deep Learning Approach for Sentiment Analysis using CNN and Improved SVM with Multi Objective Swarm Optimization for Domain Independent Datasets

In the Big Data world, there is an exponential increase in the volume of numerous data, such like text, image, audio and video, as text is the largest among them.Sentiment analysis is a trendy application in text mining, where text data concerning the feelings or attitude of the consumer is collected using different methods or techniques.Sentiment detection of online product reviews is helpful to figure out emotions and viewpoints of customers.Many researchers have just developed the Deep Learning model for obtaining tremendous performance in NLP.This paper suggested novel deep-learning hybrid architecture that is highly effective for analyzing sentiments on domain independent datasets.We blend deep Convolutional Neural Networks (CNN) and support vector machines (SVM) for a better overall classification.The reason for using CNN is not only to extract local features but also the framework for predicting sentiments and combining CNN output into SVM progress the classification.In addition, we have adopted MSPSO (Multi-swarm Particle Swarm Optimization) system to obtain optimized feature selection and to train the SVM for further improved in sentiment classification.To demonstrate that our proposed method is of a generic nature, we have evaluated on datasets of online product reviews from various domain.Evaluation exposes that the implementation of the proposed approach is reliable among all the datasets and often outshine the state-of-art systems.
Performance evaluation of blockchains in the internet of things

The idea of blockchains technology (BT) in the Internet of Things (IoT) is to allow the physical things to trust in the transactions held within the IoT network. The BT is a distributed, decentralized, publicly shared its digital ledger and secured technology to eternally record the transactions across the shared database. The BT in IoT can be called Trust Machine to eliminate the intermediates and enables the physical things to trust with each other. This research evaluates the performance of BT in IoT. The simulated results are tested and can be used in the sustainable development of the integration of BT and IoT.
PECULIARITIES OF INNOVATIVE LEARNING IN A MODERN EDUCATIONAL ENVIRONMENT

The article highlights the main tendencies and trends of innovative learning in the modern educational environment, which include: the activity of innovative educational institutions; strengthening of integration processes; creation and dissemination of innovations based on information and communication technologies; the use of innovative teaching methods and forms.The theoretical overview of innovative pedagogical activity in educational environment has been reflected.The works of national and foreign authors, who studied the issues of innovative learning in the modern educational environment, the provision of educational informatization, the disclosure of organizational aspects of new innovative technologies introduction have been considered.The article states that one of the conditions for innovations introducing into education is acquaintance and training of teachers for the work with innovations, it indicates that the means of the training is the development of methodological materials (programs, textbooks, tools for creating interactive tutorials, video lessons, presentations, lesson development), the creation of elearning support, selection of links to specialized sites.Emphasis is placed on the use of innovative methods of pedagogical activity, which is a necessity for diversification of classes; it motivates the student's cognitive activity, forms the experience of solving non-standard tasks and contributes to extensive learning and sustainable mastering of technology of practical activity.Open form of learning, based on the ideas of independent, personally oriented learning, creativity in the application of knowledge and skills in practice, has been generalized.The analysis of US sources on the problem of studies differentiation has been conducted, it shows that most researchers point to different principles of support on a practical level of differentiated learning in the classroom, described in the article.It is noted that innovative pedagogical technologies in education focus teachers' attention on innovations related to the implementation of information and communication technologies and research methods and technologies.
A novel edge detection method based on adaptive threshold

Edge detection is a critical component of many vision systems, including object detectors and image segmentation algorithms. Threshold setting is crucial for edge detection. Traditional threshold setting method did not use the spatial information of edge, and the detected edges are either missing or have a large number of false edges. Based on the analysis of the change trend of edge connectivity and the number of edge segment as the threshold decrease, we firstly propose an adaptive threshold setting method according to the image self-information. Then, using the adaptive threshold, the gradient map created by first order differential operator and non-max suppression is binarized into edge map. Compared with other traditional single-threshold based edge detection methods, the proposed method can detect edges that are more complete and fewer fake edges, and this method is more stable and reliable.
Sana: A Gamified Rehabilitation Management System for Anterior Cruciate Ligament Reconstruction Recovery

The anterior cruciate ligament (ACL) provides stabilization support for the back and forth motion of the knee joint. ACL ruptures account for 50% of all sports-related knee injuries with approximately 76.6% of them requiring reconstructive surgery, necessitating long-term patient rehabilitation. Compliance with rehabilitation management programs, following ACL reconstruction, is fundamental for the successful restoration of the knee’s kinematics and reducing the risk of secondary osteoarthritis. Existing recovery programs are often paper-based and require patients to perform exercises at home, unsupervised, resulting in a low level of self-efficacy; by promoting self-efficacy in home-based settings, rehabilitation outcomes can improve. This paper reports the design development of the Sana system, a mobile and wearable application that adopts behavioral design principles and gamification theory to improve long-term post-operative outcomes for ACL reconstruction recovery. A feasibility study was conducted from 15 October 2019–13 May 2020, employing the double diamond framework and a human-centered design approach (BS EN ISO 9241-210: 2019). Eighteen participants were recruited, including eight domain experts (in fields such as user experience design, human factors, and physiotherapy), and ten representative users who had undergone long-term rehabilitation for musculoskeletal injuries.
A Cross-Media Deep Relationship Classification Method Using Discrimination Information

Relation classification is one of the most fundamental tasks in the area of cross-media, which is essential for many practical applications such as information extraction, question&answer system, and knowledge base construction. In the cross-media semantic retrieval task, in order to meet the needs of cross-media uniform representation and semantic analysis, it is necessary to analyze the semantic potential relationship and construct semantic-related cross-media knowledge graph. The relationship classification technology is an important part of solving semantic correlation classification. Most of existing methods regard relation classification as a multi-classification task, without considering the correlation between different relationships. However, two relationships in the opposite directions are usually not independent of each other. Hence, this kind of relationships are easily confused in the traditional way. In order to solve the problem of confusing the relationships of the same semantic with different entity directions, this paper proposes a neural network fusing discrimination information for relation classification. In the proposed model, discrimination information is used to distinguish the relationship of the same semantic with different entity directions, the direction of entity in space is transformed into the direction of vector in mathematics by the method of entity vector subtraction, and the result of entity vector subtraction is used as discrimination information. The model consists of three modules: sentence representation module, relation discrimination module and discrimination fusion module. Moreover, two fusion methods are used for feature fusion. One is a Cascade-based feature fusion method, and another is a feature fusion method based on convolution neural network. In addition, this paper uses the new function added by cross-entropy function and deformed Max-Margin function as the loss function of the model. The experimental results show that the proposed discriminant feature is effective in distinguishing confusing relationships, and the proposed loss function can improve the performance of the model to a certain extent. Finally, the proposed model achieves 84.8% of the F1 value without any additional features or NLP analysis tools. Hence, the proposed method has a promising prospect of being incorporated in various cross-media systems.
Data mining in web personalization using the blended deep learning model

In general, multidimensional data (mobile application for example) contain a large number of unnecessary information. Web app users find it difficult to get the information needed quickly and effectively due to the sheer volume of data (big data produced per second). In this paper, we tend to study the data mining in web personalization using blended deep learning model. So, one of the effective solutions to this problem is web personalization. As well as, explore how this model helps to analyze and estimate the huge amounts of operations. Providing personalized recommendations to improve reliability depends on the web application using useful information in the web application. The results of this research are important for the training and testing of large data sets for a map of deep mixed learning based on the model of back-spread neural network. The HADOOP framework was used to perform a number of experiments in a different environment with a learning rate between -1 and +1. Also, using the number of techniques to evaluate the number of parameters, true positive cases are represent and fall into positive cases in this example to evaluate the proposed model.
Impact of COVID-19 on the education of medical students in Peru

The pandemic caused by Severe Acute Respiratory Syndrome 2 (SARS-CoV-2) caused by the Coronavirus disease 2019 (COVID-19) is a highly contagious disease that represents an emergency worldwide. On May 22, 2020 we counted 4,993,470 confirmed cases and 327,738 deaths among 188 countries/regions, at the same time there already existed 111,698 positive cases and 3,244 deaths in Peru already existed; data that will continue to expand and that places thousands of lives in danger, affecting states, municipalities and public and private institutions. In that regard, stricter measures are being taken with the obligatory quarantine, isolation and social distancing management, which directly affects all activities that involve in person gatherings