Reviewerly/exHarmony huggingface dataset:
Parametric Software Effort Estimation Based on Optimizing Correction Factors and Multiple Linear Regression

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Context:</i> Effort estimation is one of the essential phases that must be accurately predicted in the early stage of software project development. Currently, solving problems that affect the estimation accuracy of Use Case Points-based methods is still a challenge to be addressed. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> This paper proposes a parametric software effort estimation model based on Optimizing Correction Factors and Multiple Regression Models to minimize the estimation error and the influence of unsystematic noise, which has not been considered in previous studies. The proposed method takes advantage of the Least Squared Regression models and Multiple Linear Regression models on the Use Case Points-based elements. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method:</i> We have conducted experimental research to evaluate the estimation accuracy of the proposed method and compare it with three previous related methods, i.e., 1) the baseline estimation method – Use Case Points, 2) Optimizing Correction Factors, and 3) Algorithmic Optimization Method. Experiments were performed on datasets (Dataset D1, Dataset D2, and Dataset D3). The estimation accuracy of the methods was analysed by applying various unbiased evaluation criteria and statistical tests. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> The results proved that the proposed method outperformed the other methods in improving estimation accuracy. Statistically, the results proved to be significantly superior to the three compared methods based on all tested datasets. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</i> Based on our obtained results, the proposed method has a high estimation capability and is considered a helpful method for project managers during the estimation phase. The correction factors are considered in the estimation process.
Contrastive Explanations for Model Interpretability

Contrastive explanations clarify why an event occurred in contrast to another.They are inherently intuitive to humans to both produce and comprehend.We propose a method to produce contrastive explanations in the latent space, via a projection of the input representation, such that only the features that differentiate two potential decisions are captured.Our modification allows model behavior to consider only contrastive reasoning, and uncover which aspects of the input are useful for and against particular decisions.Additionally, for a given input feature, our contrastive explanations can answer for which label, and against which alternative label, is the feature useful.We produce contrastive explanations via both highlevel abstract concept attribution and low-level input token/span attribution for two NLP classification benchmarks.Our findings demonstrate the ability of label-contrastive explanations to provide fine-grained interpretability of model decisions. 1
Automated Detection and Classification of Cervical Cancer Using Pap Smear Microscopic Images: A Comprehensive Review and Future Perspectives

Computer-aided categorization of smear images has been considered challenging in the past few decades.Cervical cancer is the main cause of mortality among women worldwide and is more prevalent in underdeveloped countries.This disease can be successfully treated, even fully cured, if detected in its early phase.Computerized image analysis methods are primarily of great interest as they provide significant benefits for clinicians with reliable and timely diagnosis of the samples.Dedicated image analysis algorithms provide a mathematical description of the region of interest which provide great support to pathologists for decision-making.In this review, we have outlined state-of-the-art techniques expressed in prominent publications on the computer-assisted diagnostic system for cancer detection.By utilizing the domain aspects of cervical cancer, suitable methods and techniques are explored and presented.This review also presents knowledge to assess the methodology used in the literature and emphasized some of the inadequacies and weaknesses in the reviewed methods.The study accentuates the future directions pertinent to the development of a cost-effective, automated disease classification system that should be a significant advantage for countries with limited resources and treatment services.
Exploiting Machine Learning to Detect Malicious Nodes in Intelligent Sensor-Based Systems Using Blockchain

In this paper, a blockchain-based secure routing model is proposed for the Internet of Sensor Things (IoST). The blockchain is used to register the nodes and store the data packets’ transactions. Moreover, the Proof of Authority (PoA) consensus mechanism is used in the model to avoid the extra overhead incurred due to the use of Proof of Work (PoW) consensus mechanism. Furthermore, during routing of data packets, malicious nodes can exist in the IoST network, which eavesdrop the communication. Therefore, the Genetic Algorithm-based Support Vector Machine (GA-SVM) and Genetic Algorithm-based Decision Tree (GA-DT) models are proposed for malicious node detection. After the malicious node detection, the Dijkstra algorithm is used to find the optimal routing path in the network. The simulation results show the effectiveness of the proposed model. PoA is compared with PoW in terms of the transaction cost in which PoA has consumed 30% less cost than PoW. Furthermore, without Man In The Middle (MITM) attack, GA-SVM consumes 10% less energy than with MITM attack. Moreover, without any attack, GA-SVM consumes 30% less than grayhole attack and 60% less energy than mistreatment. The results of Decision Tree (DT), Support Vector Machine (SVM), GA-DT, and GA-SVM are compared in terms of accuracy and precision. The accuracy of DT, SVM, GA-DT, and GA-SVM is 88%, 93%, 96%, and 98%, respectively. The precision of DT, SVM, GA-DT, and GA-SVM is 100%, 92%, 94%, and 96%, respectively. In addition, the Dijkstra algorithm is compared with Bellman Ford algorithm. The shortest distances calculated by Dijkstra and Bellman are 8 and 11 hops long, respectively. Also, security analysis is performed to check the smart contract’s effectiveness against attacks. Moreover, we induced three attacks: grayhole attack, mistreatment attack, and MITM attack to check the resilience of our proposed system model.
Cross-Domain Empirical Risk Minimization for Unbiased Long-Tailed Classification

We address the overlooked unbiasedness in existing long-tailed classification methods: we find that their overall improvement is mostly attributed to the biased preference of "tail" over "head", as the test distribution is assumed to be balanced; however, when the test is as imbalanced as the long-tailed training data---let the test respect Zipf's law of nature---the "tail" bias is no longer beneficial overall because it hurts the "head" majorities. In this paper, we propose Cross-Domain Empirical Risk Minimization (xERM) for training an unbiased test-agnostic model to achieve strong performances on both test distributions, which empirically demonstrates that xERM fundamentally improves the classification by learning better feature representation rather than the "head vs. tail" game. Based on causality, we further theoretically explain why xERM achieves unbiasedness: the bias caused by the domain selection is removed by adjusting the empirical risks on the imbalanced domain and the balanced but unseen domain.
Satellite Image Compression and Denoising With Neural Networks

Earth observation through satellite images is crucial to help economic activities as well as to monitor the impact of human activities on ecosystems. Current satellite systems are subjected to strong computational complexity constraints. Thus, image compression is performed onboard with specifically tailored algorithms while image denoising is performed on the ground. In this letter, we intend to address satellite image compression and denoising with neural networks. The first proposed approach uses a single neural architecture for joint onboard compression and denoising. The second proposed approach sequentially uses a first neural architecture for onboard compression and a second one for on ground denoising. For both approaches, the onboard architectures are lightened as much as possible, following the procedure proposed by Alves de Oliveira <i>et al.</i> (2021). The two approaches are shown to outperform the current satellite imaging system and their respective pros and cons are discussed.
Learning Optical Flow, Depth, and Scene Flow Without Real-World Labels

Self-supervised monocular depth estimation enables robots to learn 3D perception from raw video streams. This scalable approach leverages projective geometry and ego-motion to learn via view synthesis, assuming the world is mostly static. Dynamic scenes, which are common in autonomous driving and human-robot interaction, violate this assumption. Therefore, they require modeling dynamic objects explicitly, for instance via estimating pixel-wise 3D motion, i.e. scene flow. However, the simultaneous self-supervised learning of depth and scene flow is ill-posed, as there are infinitely many combinations that result in the same 3D point. In this letter we propose DRAFT, a new method capable of jointly learning depth, optical flow, and scene flow by combining synthetic data with geometric self-supervision. Building upon the RAFT architecture, we learn optical flow as an intermediate task to bootstrap depth and scene flow learning via triangulation. Our algorithm also leverages temporal and geometric consistency losses across tasks to improve multi-task learning. Our DRAFT architecture simultaneously establishes a new state of the art in all three tasks in the self-supervised monocular setting on the standard KITTI benchmark.
An Enhanced Grey Wolf Optimizer with a Velocity-Aided Global Search Mechanism

This paper proposes a novel variant of the Grey Wolf Optimization (GWO) algorithm, named Velocity-Aided Grey Wolf Optimizer (VAGWO). The original GWO lacks a velocity term in its position-updating procedure, and this is the main factor weakening the exploration capability of this algorithm. In VAGWO, this term is carefully set and incorporated into the updating formula of the GWO. Furthermore, both the exploration and exploitation capabilities of the GWO are enhanced in VAGWO via stressing the enlargement of steps that each leading wolf takes towards the others in the early iterations while stressing the reduction in these steps when approaching the later iterations. The VAGWO is compared with a set of popular and newly proposed meta-heuristic optimization algorithms through its implementation on a set of 13 high-dimensional shifted standard benchmark functions as well as 10 complex composition functions derived from the CEC2017 test suite and three engineering problems. The complexity of the proposed algorithm is also evaluated against the original GWO. The results indicate that the VAGWO is a computationally efficient algorithm, generating highly accurate results when employed to optimize high-dimensional and complex problems.
Specific characteristics of digital violence and digital crime

Migration of many aspects of human life and work into the online sphere has become an integral part of everyday life and it is difficult to imagine the functioning of any aspect of life without the Internet and the space in which most human interactions take place. One such large and significant new form has created a large number of smaller ones, and conditioned the transformation of things and phenomena from the physical world into completely new digital forms. The same happened with violence, as a phenomenon, a pattern of behavior and a part of human nature from the very beginnings of civilization, which took its new form being called a digital violence. It does not necessarily have to be online, but it is necessarily related to digital devices. To the problem of digital crime there should be added a digital violence as a contemporary problem, especially when we talk about legal regulatory mechanisms, although these two phenomena, with frequent overlaps, do not necessarily have to be contained in each other, but they are equally important. Online and offline digital spaces represent just a new field in which violence and crimes are committed, but the virtual characteristic of these spaces is only an additional specific feature, because their consequences are felt in the physical, real world, and in that sense, the border between virtual and real is invisible. This paper aims to explore the ontological determinants of violence and digital violence, to identify specific forms of digital violence and digital crime, as well as to analyze existing regulations aimed at combating digital violence and digital crime.
Research on Event Logic Knowledge Graph Construction Method of Robot Transmission System Fault Diagnosis

Knowledge graph technology has important guiding significance for efficient and orderly fault diagnosis of robot transmission system. Taking the historical robot maintenance logs of robot transmission system as the research object, a top-down fault diagnosis event logic knowledge graph construction method is proposed. Firstly, we define event arguments of fault phenomenon and fault cause events, define event argument classes and relation between classes, and construct an event logic knowledge ontology model. According to the event logic knowledge ontology, the fault diagnosis event argument entity and relation in the corpus are labeled, and an event logic knowledge extraction dataset is formed. Secondly, an event argument entity and relation joint extraction model is proposed. Using stacked bidirectional long short-term memory(BiLSTM) to obtain deep context features of text. As a supplement to stacked BiLSTM, self-attention mechanism extracts character dependency features from multiple subspaces, and uses conditional random field(CRF) to realize entity recognition. The character dependency features are mapped to the entity label weight embedding, and spliced with deep context features to extract relations. Bidirectional graph convolutional network(BiGCN) is introduced for relation inference, graph convolution features are used to update deep context features to perform joint extraction in the second phase. Experimental results show that this method can improve the effect of event argument entity and relation joint extraction and is better than other methods. Finally, an event logic knowledge graph of robot transmission system fault diagnosis is constructed, which provides decision support for autonomous fault diagnosis of robot transmission system.
Intelligent and Real-Time Detection and Classification Algorithm for Recycled Materials Using Convolutional Neural Networks

In recent years, the production of municipal solid waste has constantly been increasing. Recycling is becoming more and more important, as it is the only way that we can have a clean and sustainable environment. Recycling, however, is a process that is not fully automated; large volumes of waste materials need to be processed manually. New and novel techniques have to be implemented in order to manage the increased volume of waste materials at recycling factories. In this paper, we propose a novel methodology that can identify common waste materials as they are being processed on a moving belt in waste collection facilities. An efficient waste material detection and classification system is proposed, which can be used in real integrated solid waste management systems. This system is based on a convolutional neural network and is trained using a custom dataset of images, taken on site from actual moving belts in waste collection facilities. The experimental results indicate that the proposed system can outperform existing algorithms found in the literature in real-world conditions, with 92.43% accuracy.
Semantic and Sentiment Analysis of Selected Bhagavad Gita Translations Using BERT-Based Language Framework

It is well known that translations of songs and poems not only break rhythm and rhyming patterns, but can also result in loss of semantic information.The Bhagavad Gita is an ancient Hindu philosophical text originally written in Sanskrit that features a conversation between Lord Krishna and Arjuna prior to the Mahabharata war.The Bhagavad Gita is also one of the key sacred texts in Hinduism and is known as the forefront of the Vedic corpus of Hinduism.In the last two centuries, there has been a lot of interest in Hindu philosophy from western scholars; hence, the Bhagavad Gita has been translated in a number of languages.However, there is not much work that validates the quality of the English translations.Recent progress of language models powered by deep learning has enabled not only translations but a better understanding of language and texts with semantic and sentiment analysis.Our work is motivated by the recent progress of language models powered by deep learning methods.In this paper, we present a framework that compares selected translations (from Sanskrit to English) of the Bhagavad Gita using semantic and sentiment analyses.We use hand-labelled sentiment dataset for tuning state-of-art deep learning-based language model known as bidirectional encoder representations from transformers (BERT).We provide sentiment and semantic analysis for selected chapters and verses across translations.Our results show that although the style and vocabulary in the respective translations vary widely, the sentiment analysis and semantic similarity shows that the message conveyed are mostly similar.
Video Watermarking System

Abstract: Advanced video watermarking may be a procedure for inserting extra information beside video flag. Inserted information is utilized for copyright proprietor recognizable proof. A number of video watermarking techniques are proposed. These methods misuse distinctive ways in arrange to implant a strong watermark and to preserve the first video flag constancy. In this paper, Computerized video watermarking strategies and assaults on watermarks are displayed.
Patchless Multi-Stage Transfer Learning for Improved Mammographic Breast Mass Classification

Despite great achievements in classifying mammographic breast-mass images via deep-learning (DL), obtaining large amounts of training data and ensuring generalizations across different datasets with robust and well-optimized algorithms remain a challenge. ImageNet-based transfer learning (TL) and patch classifiers have been utilized to address these challenges. However, researchers have been unable to achieve the desired performance for DL to be used as a standalone tool. In this study, we propose a novel multi-stage TL from ImageNet and cancer cell line image pre-trained models to classify mammographic breast masses as either benign or malignant. We trained our model on three public datasets: Digital Database for Screening Mammography (DDSM), INbreast, and Mammographic Image Analysis Society (MIAS). In addition, a mixed dataset of the images from these three datasets was used to train the model. We obtained an average five-fold cross validation AUC of 1, 0.9994, 0.9993, and 0.9998 for DDSM, INbreast, MIAS, and mixed datasets, respectively. Moreover, the observed performance improvement using our method against the patch-based method was statistically significant, with a p-value of 0.0029. Furthermore, our patchless approach performed better than patch- and whole image-based methods, improving test accuracy by 8% (91.41% vs. 99.34%), tested on the INbreast dataset. The proposed method is of significant importance in solving the need for a large training dataset as well as reducing the computational burden in training and implementing the mammography-based deep-learning models for early diagnosis of breast cancer.
Strong quantum nonlocality in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> -partite systems

A set of multipartite orthogonal quantum states is strongly nonlocal if it is locally irreducible for every bipartition of the subsystems [Phys. Rev. Lett. 122, 040403 (2019)]. Although this property has been shown in three-, four- and five-partite systems, the existence of strongly nonlocal sets in $N$-partite systems remains unknown when $N\geq 6$. In this paper, we successfully show that a strongly nonlocal set of orthogonal entangled states exists in $(\mathbb{C}^d)^{\otimes N}$ for all $N\geq 3$ and $d\geq 2$, which for the first time reveals the strong quantum nonlocality in general $N$-partite systems. For $N=3$ or $4$ and $d\geq 3$, we present a strongly nonlocal set consisting of genuinely entangled states, which has a smaller size than any known strongly nonlocal orthogonal product set. Finally, we connect strong quantum nonlocality with local hiding of information as an application.
Medical image diagnosis of prostate tumor based on PSP-Net+VGG16 deep learning network

Prostate cancer is the most common cancer of the male reproductive system. With the development of medical imaging technology, magnetic resonance images (MRI) have been used in the diagnosis and treatment of prostate cancer because of its clarity and non-invasiveness. Prostate MRI segmentation and diagnosis experience problems such as low tissue boundary contrast. The traditional segmentation method of manually drawing the contour boundary of the tissue cannot meet the clinical real-time requirements. How to quickly and accurately segment the prostate tumor has become an important research topic.This paper proposes a prostate tumor diagnosis based on the deep learning network PSP-Net+VGG16. The deep convolutional neural network segmentation method based on the PSP-Net constructs a atrous convolution residual structure model extraction network. First, the three-dimensional prostate MRI is converted to two-dimensional image slices, and then the slice input of the two-dimensional image is trained based on the PSP-Net neural network; and the VGG16 network is used to analyze the region of interest and classify prostate cancer and normal prostate.According to the experimental results, the segmentation method based on the deep learning network PSP-Net is used to identify the data set samples. The segmentation accuracy is close to the Dice similarity coefficient and Hausdorff distance, and even exceeds the traditional prostate image segmentation method. The Dice index reached 91.3%, and the technique is superior in speed of processing. The predicted tumor markers are very close to the actual markers manually by clinicians; the classification accuracy and recognition rates of prostate MRI based on VGG16 are as high as 87.95% and 87.33%, and the accuracy rate and recall rate of the network model are relatively balanced. The area under curve index is also higher than other models, with good generalization ability.Experiments show that prostate cancer diagnosis based on the deep learning network PSP-Net+VGG16 is superior in accuracy and processing time compared to other algorithms, and can be well applied to clinical prostate tumor diagnosis.