Reviewerly/exHarmony huggingface dataset:
Complexity Classification of Object-Oriented Projects Based on Class Model Information Using Quasi-Opposition Rao Algorithm-Based Neural Networks

On the basis of the surveys conducted over a period of time, it is realized that the top four reasons for failure of software projects are functionality issues, failure to meet deadline, quality issues, and budget overruns. Customer goodwill, which is extremely important for continued business, is greatly influenced by timely delivery of quality software products. A rational project schedule with allocation of appropriate resources is essential for achieving these goals. This requires a complexity assessment of the projects by the organization so that a project team of suitable size and skill set can be decided at a higher level. This work proposed a classification approach of object-oriented projects using the information present in unified modeling language (UML) class models. The classification approach combined the best attributes of quasi-opposition-based Rao algorithms (QORA) and artificial neural networks (ANNs) is named as QORA-ANN. The classification of a project into three classes, namely large scale, medium scale, and small scale, has been done using the training data consisting of a number of contemporary object-oriented projects executed in various IT firms. We conduct an extensive evaluation of QORA-ANN, radial basis function network (RBFN), and logistic regression on the project dataset and make a comparative study. It is found that the result obtained by QORA-ANN approach is promising than RBFN and logistic regression.
On the Automatisation of the Realisation of a Banduke Table

Banduke tables are a traditional Japanese mean of communicating rankings and other organisation charts. They are well-known in Japan especially thanks to their continued usage to announce details of sumo wrestling competitions. Such tables were also used in earlier centuries for instance to publish the names of the wealthiest individuals in the country. Traditional banduke tables feature a characteristic, visually impacting design which is difficult to reproduce on computer systems. And, the automatisation of the generation of such a chart is even more challenging. In this paper, in order to keep this cultural heritage alive, we investigate this automatisation issue and propose a system which generates banduke tables. The obtained results are very promising in that the charts that can be produced by our proposal look very realistic. Besides, we have also quantitatively evaluated the generated tables by comparing them to original documents, some of which were obtained with the help of museums of Japanese history.
Deep Knnor for Imbalanced Regression

Predictive performance of Machine Learning (ML) models deteriorate heavily if the training data is not balanced. In case of classification, several methods have been propose to bring balance to these datasets, however the regression problem has a limited number of solutions. The reason being that predicting the dependent variable adds a layer of complexity on top of creating synthetic features. Motivated by AutoEncoders, this paper proposes an AutoInflater neural network with an exponential loss function to inflate features and predict the dependent variable, specifically for low dimension data. The K-Nearest Neighbor OveRsampling (KNNOR) technique for unbalanced classification is further extended and adapted in this paper into KNNOR-Regression for general datasets with continuous target variables. The proposed approaches have been applied on imbalanced data of both low and high dimensions. Classifiers trained on the datasets augmented by the proposed method generated a lower error rate in comparison to the state-of-art regression augmentation algorithms. The proposed augmentation method is easy to use and has been made open source for reference.
A Framework for Masked-Image Recognition System in COVID-19 Era

Face Recognition techniques have been widely developed and used for many years. Several approaches and models are adopted and successfully used to perform face recognition in airports, supermarkets, banks, etc. However, with the emergence of the COVID-19 pandemic, the whole world came across the requirement to use face masks. The mask’s partial covering of the face makes some well-known face recognition algorithms perform poorly or even fail. This paper has developed a real-time framework to detect, recognize, and identify people to authenticate them before accessing an app, device, or location. The newly created framework offers a unique set of capabilities, including the ability for users to select from various authentication methods based on their preferences or circumstances. The application’s face recognition section uses cutting-edge AI and computer vision algorithms to offer the user accurate face detection and recognition, even when the face is partially hidden behind a mask.
On the Impossibility of Discursive-Material Closures a Case of Banned TV Channels in Ukraine

Drawing on the ideas of Laclau and Mouffe (1985) and Carpentier (2017), this paper shows how, on the one hand, discursive-material assemblages within a network environment prevent the possibility of hermetic discursive closures, while, on the other hand, they may weaken discourses, preventing them from serving as a mobilizing force for social change. To illustrate this, the paper discusses the case of Volodymyr Zelensky, the president of Ukraine, who banned oppositional television channels in a bid to shut down oppositional discourse by both discursive (synecdochic reduction of oppositional arguments) and material means (the physical closure of the studios). Analysis presented in the paper draws on the discourse theory of Ernesto Laclau and Chantal Mouffe and Nico Carpentier’s Discursive-Material Knot.
Smart Cybercrime Classification for Digital Forensics with Small Datasets

In forensic investigations, automation support for cybercrime classification is crucial for efficient and effective resolution. Although numerous technologies have been developed to assist digital forensic investigators and incident responders in data analysis, investigation still requires a significant amount of manual effort, which is time-consuming and costly. Machine learning has been proven to be a promising strategy for automated data classification in recent studies. However, in order to train the model, most, if not all, machine learning algorithms rely substantially on a large number of annotated datasets. In this paper, based on a few historical case records, we propose an effective and efficient approach to classifying cybercrime, starting with small datasets. Our proposed method uses a Siamese Network Architecture with two identical Convolutional Neural subnetworks, as well as a Deep Learning Model. To classify novel graph data, a similarity metric forecasting method is applied, which permits the categorization of new data from a limited number of cybercrime records. In addition, to improve the accuracy of our Deep Learning Model, we apply forensic knowledge graph technology. This technique assembles pieces of information gathered from security logs into a vast, connected graph to expose a case's contextual background. Even with a small number of labeled samples trained in the Deep Learning model, our proposed approach is capable of identifying cybercrime data reliably and automatically from our experimental assessments. As a result, our approach demonstrates that automated cybercrime investigations can be both successful and feasible.
Set-Based Counterfactuals in Partial Classification

Given a class label y assigned by a classifier to a point x in feature space, the counterfactual generation task, in its simplest form, consists of finding the minimal edit that moves the feature vector to a new point $$x'$$ , which the classifier maps to a pre-specified target class $$y'\ne y$$ . Counterfactuals provide a local explanation to a classifier model, by answering the questions “Why did the model choose y instead of $$y'$$ : what changes to x would make the difference?". An important aspect in classification is ambiguity: typically, the description of an instance is compatible with more than one class. When ambiguity is too high, a suitably designed classifier can map an instance x to a class set Y of alternatives, rather than to a single class, so as to reduce the likelihood of wrong decisions. In this context, known as set-based classification, one can discuss set-based counterfactuals. In this work, we extend the counterfactual generation problem – normally expressed as a constrained optimization problem – to set-based counterfactuals. Using non-singleton counterfactuals, rather than singletons, makes the problem richer under several aspects, related to the fact that non-singleton sets allow for a wider spectrum of relationships among them: (1) the specification of the target set-based class $$Y'$$ is more varied (2) the target solution $$x'$$ that ought to be mapped to $$Y'$$ is not granted to exist, and, in that case, (3) since one might end up with the availability of a number of feasible alternatives to $$Y'$$ , one has to include the degree of partial fulfillment of the solution into the loss function of the optimization problem.
Video Question Answering

The video question answering task, which was first introduced in 2014, is a more complex task than the classical visual (static image) question answering task. For video question answering tasks, both datasets and models are essential for research. Therefore, in this chapter, we first illustrate the most popular datasets for video question answering, ranging from datasets containing physical objects to those characterizing the real world, and subsequently introduce several models based on the encoder-decoder framework.
Empirical Study on Development of User Experience Questionnaire for Sanitation Robots

Although sanitation robots are widely used in various scenes, their user experience (UX) is criticized by end users. The purpose of this paper is to develop the UX questionnaire of sanitation robot through the design of sanitation robot. Firstly, using the AEIOU framework to study the user's work process and Analytic hierarchy process(AHP) to classify and rank the needs of the tools they are equipped; secondly, we conduct literature research and expert interviews to establish indicators of UX (IoUX), and using card sorting and K-means clustering to determine typical samples of the collected samples; thirdly quantification theory I (QTI) was adopted to construct a mathematical model of IoUX and product elements; fourth, according to the result of QTI, We designed a sanitation robot and used IoUX to measure the UX; finally, we used the measured data to test the reliability and analysis of variance of the scale, and determined 8 IoUX affecting sanitation robots. This positivist method of constructing questionnaires can be reproduced to obtain more valuable UX metrics for other products.
Efficient Generation of Program Execution Hash

Distributed computing systems often require verifiable computing techniques in case their node is untrusted. To verify a node&#x2019;s computation result, proof-of-work (PoW) is often utilized as a basis of verifiable computing method; however, this mechanism is only valid for computations producing results based on specific algorithm (e.g., AES decryption). To date, there is no efficient PoW mechanism applicable to arbitrary algorithm or a computation that does not produce any tangible output (e.g., void function). This paper proposes <i>execution hash</i> to serve as a proof for a program&#x2019;s idempotent computation result without relying on its algorithm. Two versions of execution hash generation methods were designed and implemented and the efficacy was evaluated in terms of performance and reliability. Implementation was based on LLVM/Clang 6.0 and evaluation was based on open-source software, including GNU binutils/coreutils and Google&#x2019;s OSSFuzz projects.
Natural Language Processing of Text-Based Metrics for Image Captioning

A rapidly increasing interest has been focused on natural language processing and computer vision study by automatically producing descriptive sentences for images in present trends. Image processing is a success factor, especially involving somaticized image knowledge and creating reliable, appropriately organized explanation phrases. In this paper, a dynamic program that uses the VCG16 platform to develop image descriptions and a long short-term memory (LSTM) to contain appropriate words with generated text keywords successfully is proposed. This paper determines the NLP method’s usefulness using Flickr8 K-statistical models and demonstrates that their model delivers accurate performance compared to the Bleu metric. The Bleu metric is an automated system for measuring a machine translation’s performance by monitoring the effectiveness of text transformed from natural to other languages. The results have been justified with images that satisfy the NLP algorithm.
Multi-Branch Lstm Encoded Features for Forecasting Earthquakes

The forecasting of earthquakes is a crucial issue in seismology, in order to save as many lives as possible. To better catalyze and interpret seismic data, an accurate and precise algorithm is needed to detect and locate earthquakes about to befall in the near future. Earthquakes are spatially and temporally correlated because of crust movement. Herein, the idea of mapping the earthquake features into low-dimensional space to obtain the latent features is presented. This mapping is achieved by a multi-branch child-parent long short term memory (LSTM) network and accelerated with a graphics processing unit (GPU). Compared with other state-of-the-art schemes, results show that the proposed GPU Multi-branch LSTM encoded features performed better for forecasting earthquakes with a fused deep learning predictor (GMLEF-DL predictor).
Problem Structuring Combined with Sentiment Analysis to Product-Service System Performance Management

The success of any business model depends on how well it is capable of satisfying customers’ requirements. Therefore, organizations must be able to understand their customer’s desires and evaluate how it is performing. This is even more true in innovative contexts such as the implementation of circular economy (CE) concepts (e.g., value optimization, systems thinking, collaboration) by businesses, which defines the so-called circular business models (CBM). Considering the growing potential of text analytics techniques to extract and provide useful information from customers’ reviews or comments in social media, this article proposes a framework to integrate aspect-based sentiment analysis (ABSA) and a problem structuring method (PSM) to support the investigation of a product-service system (PSS) performance in CE context. The proposed framework aims to aid an organization to incorporate stakeholders’ perspectives to support a systemic analysis of a CBM’s current performance, which may support better-informed decisions of performance management. The proposed approach, integrating ABSA and PSM, presented in this paper was grounded by two systematic literature reviews, which showed its potential contribution and novelty.
Feature Engineering

As the amount of data generated and collected grows, analyzing and modeling so many input variables get more difficult. So, it is important to reduce model complexity and establish simple, accurate and robust models. Feature engineering is the process of using domain knowledge to extract input variables from raw data, prioritize them and select the best ones so that machine learning algorithms work well and model performance is improved.
Glowworm Optimization with Deep Learning Enabled Cybersecurity in Social Networks

Recently, the exponential utilization of Internet has posed several cybersecurity issues in social networks. Particularly, cyberbulling becomes a common threat to users in real time environment. Automated detection and classification of cyberbullying in social networks become an essential task, which can be derived by the use of machine learning (ML) and deep learning (DL) approaches. Since the hyperparameters of the DL model are important for optimal outcomes, appropriate tuning strategy becomes important by the use of metaheuristic optimization algorithms. In this study, an effective glowworm swarm optimization (GSO) with deep neural network (DNN) model named EGSO-DNN is derived for cybersecurity in social networks. The proposed EGSO-DNN technique is mainly intended to identify the presence of cyberbullying on social networking sites. Besides, the EGSO-DNN technique involves different levels of pre-processing to transform the raw data into useful format. In addition, word2vec based feature extraction technique is applied to generate a set of feature vectors. Finally, the DNN model is used for the detection and classification of the DNN model where the hyperparameters of the DNN model are adjusted proficiently by the use of GSO algorithm. In order to ensure the supremacy of the EGSO-DNN technique, a series of simulations were carried out and the results are tested using benchmark datasets. The comparative analysis reported the improvements of the EGSO-DNN technique over the recent approaches maximum prec_n, reca_l, and F1_score of 0.9974, 0.9959, and 0.9966.
Generative Deep Belief Model for Improved Medical Image Segmentation

Medical image assessment is based on segmentation at its fundamental stage. Deep neural networks have been more popular for segmentation work in recent years. However, the quality of labels has an impact on the training performance of these algorithms, particularly in the medical image domain, where both the interpretation cost and inter-observer variation are considerable. For this reason, a novel optimized deep learning approach is proposed for medical image segmentation. Optimization plays an important role in terms of resources used, accuracy, and the time taken. The noise in the raw medical image are processed using Quasi-Continuous Wavelet Transform (QCWT). Then, feature extraction and selection are done after the pre-processing of the image. The features are optimally selected by the Golden Eagle Optimization (GEO) method. Specifically, the processed image is segmented accurately using the proposed Generative Heap Belief Network (GHBN) technique. The execution of this research is done on MATLAB software. According to the results of the experiments, the proposed framework is superior to current techniques in terms of segmentation performance with a valid accuracy of 99%, which is comparable to the other methods.
Overview of Application of Blockchain Technology in e-Government

Initially created as a technology to support peer-to-peer electronic cash systems, in recent years blockchain technology has been moving away from finance technology into other areas of application that could benefit from characteristics such as decentralization, transparency, and immutability. Significant researches are being done in fields of public services. In this paper, some of the prominent real-life applications of blockchain technology in e-government systems are presented. General benefits that could come to e-government systems from using this technology are also presented. Some open questions that exist in compatibility between e-government and blockchain technology are also being discussed, together with obstacles in the process of full integration of e-government and blockchain technology.
Research on Network Security Situation Assessment Model Based on Double AHP

Network security situation assessment is an important part of the situational awareness research process and the most important thing. Situation assessment refers to the assessment of the current security status of the system through real-time analysis of network security situation awareness data and the use of appropriate models and methods. The current network boundaries are gradually disintegrating, and the power Internet is moving toward a trend of complex architecture, increased exposure of attack surfaces, and wider business scope. It is necessary to research mobile security monitoring under the zero-trust architecture to improve business security. Business hazard detection and risk prevention capabilities, and network security situation assessment are the focus of mobile security monitoring research. Aiming at the possible risks in network security, this paper proposes an improved AHP (Analytic Hierarchy Process) to comprehensively evaluate situational awareness information. First, build a single-node hierarchical analysis model based on the analytic hierarchy process inside the node, and calculate the evaluation result of the single-node equipment. Subsequently, the single-node equipment is used as a hierarchical analysis factor, and the hierarchical analysis model has been constructed again, and the situation assessment results of the distributed system composed of multiple nodes are comprehensively calculated. This network security situation assessment model based on double AHP provides a concrete and feasible scheme for the distributed system from single-point situation assessment to multi-point integration situation assessment.
Comparative Study of Face Detection Using Deep Learning Techniques: HOG and CNN

Face identification is a challenging task for machines, yet it comes naturally to people. In social interactions, the face is significant because it communicates a pers/on's identity and feelings. Humans do not have the same ability as machines to recognize diverse faces. With the rapid expansion of video and image databases, intelligent systems have a massive demand for automatic information understanding and analysis. Face detection is indeed crucial in face recognition. Face detection is the technique for identifying human faces in visual images automatically. Face detection, on the other hand, is challenging because of the large number of variations in image appearance, such as difference in stance, occlusion, image orientation, illumination, and facial expression. This paper demonstrates how to use OpenCV in Python to build and generate a face identification system using deep learning. Deep learning is a method for detecting faces that appears to be an adequate method due to its high accuracy. The Histogram of Oriented Gradients (HOG) and Convolutional Neural Network (CNN) are two common forms of face detection image recognition libraries that have been compared (CNN) in the study. Face detection applications such as blurring, expressions, age, and gender detection are also described in this research work.