Reviewerly/exHarmony huggingface dataset:
Learning to rank software modules for effort-aware defect prediction

Effort-Aware Software Defect Prediction (EADP) ranks software modules according to the defect density of software modules, which allows testers to find more defects while reviewing a certain amount of code, and allocates testing resources more effectively. However, the recently proposed CBS+ and EASC methods tend to rank the software modules with more LOC (Lines of Code) first. Therefore, there are less inspected modules when inspecting the top 20% LOC via CBS+ and EASC. Although the two methods achieve the high Precision@20% value, the Recall@20% and PofB@20% (Proportion of the found Bugs when inspecting the top 20% LOC) values of the two methods are low. Therefore, we propose a method called EALTR to construct the EADP model by directly maximizing the found bugs when inspecting the top 20% LOC. EALTR uses the linear model to build the EADP model, and then employs the composite differential evolution algorithm to generate a set of coefficient vectors for the linear model. Finally, EALTR selects the coefficient vector that achieves the highest PofB@20% value on the training dataset to construct the EADP model. Our experimental results on eleven project datasets with 41 releases show that the EALTR method performs better than CBS+ and EASC in terms of Recall@20% and PofB@20%.
An Analysis and Research on Sentiments of Twitter Data

Nowadays social networking or we can say that social media becomes the main source of gathering information all around the world. We are able to know opinions of public and from that we can generate sentiments of that topics. So in this regard we can say that sentiment analysis is a technology and a method to analyze the sentiments of a topic and which predicts that a piece of writing indicates a positive, negative and neutral sentiment and nature on the topic. In this project or paper, we can talk about twitter which is the most famous platform where people can add their tweets and opinions and share their ideas as by posting tweets. The study mainly have a look on sentiment or centers around assessments of information of twitter data that’s why this project is all about twitter sentiment analysis. The main work of project or this research paper is all about making a suitable program that knows and analyze the sentiments of tweets. Here the process of analyzing sentiments becomes the most important part for main applications and the people who trying to find out response in field of market, political elections. The aim is just to develop a function which tells automatic sentiment classification of daily tweets.
A blockchain approach on security of health records for children suffering from dyslexia during pandemic COVID-19

The Electronic Health Record (EHR) systems provide health information about patients. Data security, integrity, and management of EHR are crucial problems. Records can be modified and altered by different stockholders as the different users may be using them in more than one form. We provide a new approach, methodology, and system for calculating dyslexia symptoms in this research with a machine learning algorithm and secure dyslexia data storage using blockchain technology. The major role of our paper is to test a primary-age group student against dyslexia, a student detected in such early years of his life for such a disability then he or she can be easily cured for the disabilities and can spend the rest of his life normally. For this, we will be using various machine learning algorithms. Dyslexic patterns and a large amount of data can be shared for future clinical research, statistical analysis, and quality assurance because the framework is language-independent and built on Blockchain and a decentralized big data repository. This paper presents the design, execution, and test results, demonstrating the dyslexia health management system's high potential for worldwide deployment using blockchain technology.
Review on Methods to Predict Metastasis of Breast Cancer Using Artificial Intelligence

Breast cancer is the most prevalent invasive malignancy in women worldwide. Around 70% of breast cancer patients will have cancer recurrence, which can emerge as a local, regional, or distant tumor recurrence. Breast cancer recurrence is normally observed after chemotherapy. Breast cancer metastasis is primarily observed in bone, liver, lung, and brain. PET scans are available to identify metastasis, but for saving lives, this research study is focusing on prediction approaches. If metastasis is anticipated before tumor development in a secondary part of the body, the patient's life expectancy can be extended. This paper explores the reasons for metastasis and methods to predict the occurrence of metastasis before the formation of tumor in other parts of the human body. Two vital prediction methods viz., lymph node analysis and detection of circulating tumor cells (CTCs) using deep learning techniques like convolution neural networks (CNNs), cycle-consistent generative adversarial community (CycleGAN), lymph node assistant (LYNA) based on inception V3 deep learning structure are illustrated in this paper.
Deep Learning-Based Cryptanalysis of a Simplified AES Cipher

Recently, Deep Neural Networks have shown great deal of reliability and applicability as its applications spread in different areas. This paper proposes a cryptanalysis model based on Deep Neural Network, the neural network takes in plaintexts and their corresponding ciphertexts to predict the secret key of the cipher. We proposes two different approaches, in the first we use multi-layer perceptron (MLP). While in the second, the cryptanalysis problem is modeled as a multi-label classification problem, we introduce appropriate Deep Neural Network based methods for tackling such problem. We illustrate the effectiveness of the approach of the DNN-based cryptanalysis by attacking on Simplified AES block cipher. Therefore, specific metrics are readapted to the cryptanalysis context and used to evaluate the proposed schemes. The results indicate that treating cryptanalysis problem as multi-label classification is more suitable and can be a useful and promising tool for the cryptanalysis task.
PV power prediction based on Artificial Neural Network optimized by Genetic Algorithm

Hourly solar photovoltaic (PV) power measurements in a specific period and site are very important for designing and supervising PV systems. However, in most cases these measures are absent. The focus of this research study is to offer for PV systems engineers an efficient model based on Artificial Neural Network (ANN). To predict the output power of a PV system installed in Agadir city (320°25’39"N 9°35’53"W), Morocco. The architecture has a great impact on the convergence of the Neural Network. This architecture is optimized using Genetic Algorithm (GA). The aim of this approach is to find the optimal number of hidden layers, the activation function and the initial vector of weights. The attained results confirm the accuracy of the proposed model to predict the hourly PV power output in case of missing real values. Consequently, electrical power engineers can adopt this prediction model to design and supervise the PV systems and integrated it into the power grid.
Spectrum Analysis for Fully Connected Neural Networks

This article studies the meaning of parameters of fully connected neural networks with single hidden layer from the perspective of spectrum. Under the constraints of numerical range, the corresponding relationship between parameters and the spectrum of network function can be established by the Fourier series coefficients of the activation function, which is truncated and periodically extended. This work is substantiated on the Mixed National Institute of Standards and Technology (MNIST) handwritten dataset and two illustrative examples with certain spectra. The simulations complete the conversion between spectrum and parameters with high precision and give the significance of hidden nodes to the spectrum of network function. Some algorithms derived from these properties, such as the parameter initialization method using spectrum and the pruning method by sorting amplification weights, are also presented to introduce how spectrum analysis affects neural network decision-making. Thus, spectrum analysis has great potential in network interpretation.
Privacy Issues in mHealth Systems Using Blockchain

Mobile health (mHealth) systems are gaining ground supplementing traditional in-person interactions with caregivers using mobile services. Although mHealth systems have been accepted by almost all parties in the medical field as an option in many areas, both security and privacy issues remain, and these are still major concerns for both sides -- stakeholders and mHealth users. During the COVID-19 pandemic, the need for improving mHealth applications became more obvious and important as health-related offerings are more personalized, would be available anywhere anytime. Blockchains, used in cryptocurrencies, have provided cybersecurity on a mobile device yet privacy remains a concern. This paper explains our motivation, and investigates proof of work, and proof of stakes concepts used by blockchains for validity and immutability. We explain the Chameleon hash function (CHF) as solution that could be used to ensure Privacy rights for the users in mHealth system using security of blockchain. Future directions are identified to develop and design more secure Chameleon hash function that is adaptable to the mHealth system and ensure Privacy of Electronic Health Records (EHR) using blockchain network system .
Synthesis and Online Re-planning Framework for Time-Constrained Behavior Tree

Currently, there are some works on automatically generating behavior trees, but they rarely consider task completion efficiency. In this paper, we first propose a new domain knowledge definition method. In addition to making use of the advantages of behavior trees, it also considers the time efficiency of the tasks. Furthermore, this paper proposes a novel algorithm of automatically generating behavior trees with time constraints including construction, simulation, and online re-planning. Our method can find the behavior tree with the shortest time to complete the tasks. Additionally, it also maintains a good reactivity to environmental changes. To illustrate the effectiveness of our method, we provide experimental validation in a simulated scenario.
Privacy-preserving Hamming distance Protocol and Its Applications

Hamming distance is one of the core problems in similarity calculation. It is a new problem in cryptography to calculate Hamming distance between two objects on the premise of privacy protection. It has important theoretical significance and application value in graph recognition, machine learning, artificial intelligence, bioinformatics and other aspects which are related to data privacy protection. In this paper, a new coding method is designed, which encodes the information of both sides to be calculated, transforms the problem into the one of finding the number of the same numbers between the two sets. Combined with the idea of XOR, we also solves the privacy protection problem of Hamming distance calculation with the help of homomorphic encryption algorithm. The solution in this paper is universal. The simulation results show that the protocol in this paper is secure. The analysis shows that the protocol can calculate the Hamming distance between two objects efficiently and safely.
With the Mediation of Internal Audit, Can Artificial Intelligence Eliminate and Mitigate Fraud?

The purpose of this study is to determine artificial intelligence (AI)-supported Omani publicly listed organisations' attitudes towards fraud elimination and mitigation (FEM) concerns from the lens of agency theory with internal audit (IAF) serving as the mediator. A descriptive cross-sectional survey was conducted by involving Omani publicly listed companies as the unit of analysis. Data were gathered using an internet-based tool and analysed via PLS-SEM and SPSS. For results, AI displayed an insignificant but direct impact on FEM, whereas IAF exhibited a significant and direct effect on FEM. Apparently, AI exerted a significant and direct impact on IAF, while IAF mediated the correlation between AI and FEM. This study serves as a guidance to regulators, professional bodies, and organisations in amending their codes of corporate governance and organisational policies by initiating AI as a governance agent working for the principal, as well as operating towards eradicating fraud and fraudulent practices.
Learning to Learn With Variational Inference for Cross-Domain Image Classification

Learning models that can generalize to previously unseen domains to which we have no access is a fundamental yet challenging problem in machine learning. In this paper, we propose meta variational inference (MetaVI), a variational Bayesian framework of meta-learning for cross domain image classification. Within the meta learning setting, MetaVI is derived to learn a probabilistic latent variable model by maximizing a meta evidence lower bound (Meta ELBO) for knowledge transfer across domains. To enhance the discriminative ability of the model, we further introduce a Wasserstein distance based constraint to the variational objective, leading to the Wasserstein MetaVI, which largely improves classification performance. By casting into a probabilistic inference problem, MetaVI offers the first, principled variational meta-learning framework for cross domain learning. In addition, we collect a new visual recognition dataset to contribute a more challenging benchmark for cross domain learning, which will be released to the public. Extensive experimental evaluation and ablation studies on four benchmarks show that our Wasserstein MetaVI achieves new state-of-the-art performance and surpasses previous methods, demonstrating its great effectiveness.
A Neighbour-Similarity Based Community Discovery Algorithm

Discovery of communities in a network is a non-trivial task. Recently, many similarity functions have been introduced to cluster nodes of the same type, but tightness relationship between the nodes inside the clusters always remained as an issue. Another important issue is that for some difficult cases like selecting a community for a node that equally belongs to more than one community which may create an ambiguity in determining the node's best-fit community remain unaddressed in many community discovery algorithms. A single wrong prediction about a node's community may lead to an incorrect prediction for other nodes, resulting in a poor community structure. To address such issues, a Neighbour Based Community Detection(NBCD) algorithm based on two novel similarity measures using a similarity parameter $\alpha$ and a set of ground rules are proposed in this work. The similarity parameter $\alpha$ provides a choice for the user to select the tightness of the nodes within the communities. The NBCD algorithm is a two-step process: one is community detection and the next is node shifting. Extensive experiments are carried out on real-world benchmark data sets and artificial networks to assess the performance of the proposed algorithm. NBCD algorithm is compared with several recent and state-of-the-art community discovery algorithms. Normalized Mutual Information(NMI), Adjusted Mutual Information(AMI), Adjusted Rand Index(ARI), F-measure and Modularity are used as the performance measures. Experimental results prove that the proposed algorithm, NBCD, performs well with respect to all the performance measures and performs better than the other community detection algorithms considered.
Euclidean Distance Approximations From Replacement Product Graphs

We introduce a new chamfering paradigm, locally connecting pixels to produce path distances that approximate Euclidean space by building a small network (a replacement product) inside each pixel. These " RE -grid graphs" maintain near-Euclidean polygonal distance contours even in noisy data sets, making them useful tools for approximation when exact numerical solutions are unobtainable or impractical. The RE -grid graph creates a modular global architecture with lower pixel-to-pixel valency and simplified topology at the cost of increased computational complexity due to its internal structure. We present an introduction to chamfering replacement products with a number of case study examples to demonstrate the potential of these graphs for path-finding in high frequency and low resolution image spaces which motivate further study. Possible future applications include morphology, watershed segmentation, halftoning, neural network design, anisotropic image processing, image skeletonization, dendritic shaping, and cellular automata.
Analysis Model of Epidemic Speech based on BiLSTM and MCNN Structure

An analysis model of epidemic speech based on BiLSTM and MCNN structure is proposed in order to know the news and information about COVID-19 in time and the views and focus of citizens on the situation. BERT pre-training model is used to extract word vectors, and then the information of bidirectional long-term and short-term memory network and convolution neural network models at different levels is fused. Finally, the speech is classified into two categories, and whether its emotion is positive or negative is calculated. Experimental results show that this model can better classify the polarity of speech emotion than the previous word vector model and the traditional neural network model.
An Efficient Network Traffic Classification Method based on Combined Feature Dimensionality Reduction

Network traffic classification is the key process of malicious traffic identification. It determines the type of network traffic from a large amount of network traffic data according to the corresponding features of various traffic so that it can identify the vulnerability attacks. Amone them, the machine learning-based network traffic classification method is widely used because of its high precision and easy deployment. However, when the dimensionality of network traffic data is high, the effectiveness of this method is greatly affected, and it is very time-consuming. Feature dimensionality reduction is a common method to solve this problem, but the data processed by the existing feature dimensionality reduction methods still have many irrelevant features and high-dimensional problems are not addressed. This paper proposes an efficient network traffic classification method based on combined feature dimensionality reduction. It first combines the ReliefF algorithm and the Principal Component Analysis (PCA) feature extraction algorithm to determine the key features and then apply it to the BanlaceBaggingRF classification model to classify and identify network traffic. A large amount of experimental results show that the combined feature dimensionality reduction method can effectively remove the redundant features in network traffic data and address high-dimensional space problems, and the accuracy of classification and recognition of the BanlanceBaggingRF model is also improved about 2% to 10% compared with the latest-of-the-art.
Predictive Performance Analysis of Ensemble Learners on BCD Dataset

Breast cancer disease (BCD) has now become a worldwide disease in human beings. The breast cancer cases are progressively rising especially in females. Initial detection of this disease can save a patient’s life and cost of treatment. There are many clinical diagnosis methods which are used for the detection of breast cancer. Among them supervised machine learning is a very powerful mechanism that helps in predicting the presence or absence of disease based on the laboratory test data of the patient. Presently, numerous machine learning algorithms are available for classifying the disease in patients, but the best amongst all is yet to be explored. Therefore, this research work contributes to find the outperforming model from six non-ensemble methods and four ensemble methods for breast cancer detection. This study claims AdaBoost the most stable and suitable model for BCD prediction with accuracy 97.1%, precision, recall, F1-measure and AUC 95.91 %.
Real Time Liveness Detection and Face Recognition with OpenCV and Deep Learning

Face has been used to identify people since the beginning of time. Face recognition and liveness detection in real time is a rapidly growing research area. In this paper, a real-time GUI (Graphical User Interface) based system with the combination of facial liveness detection and face recognition has been implemented. This proposed system is capable of detecting spoofing or fakeness. After detecting a real person, the system can determine whether or not the individual is known. This paper covers building a camera-based system using open-source software which accuracy was found 99.33 percentages. This proposed system can also rename or delete a known person without requiring retraining.
A 49.5 mW Multi-Scale Linear Quantized Online Learning Processor for Real-Time Adaptive Object Detection

Online training is essential to maintain a high object detection (OD) in various environments. However, additional computation workload, EMA, and high bit precision is the problem of conventional online learning scheme on mobile devices. Therefore, a low power real-time online learning OD processor is proposed with three key features. First, multi-scale linear quantization (MSLQ) and MSLQ-aware PE structure are proposed for low-bit computation. Second, channel-wise gradient skipping is proposed to reduce computation and EMA based on temporal correlation. These schemes reduce &#x007E;56&#x0025; of computation burden and &#x007E;30&#x0025; of EMA, and also improve detection accuracy. Lastly, gradient norm clipping with norm estimation achieves 3.8 mAP improvement at YouTube-Objects dataset by fast adaptation with under 1&#x0025; of the additional computation. Finally, the proposed online learning OD processor is implemented in 28 nm CMOS technology and occupies 1.2 mm<sup>2</sup>. The proposed processor achieves 78 mAP of detection accuracy at the YouTube-Objects dataset. Compared to the previous OD processor, this brief shows state-of-the-art performance by achieving 49.5 mW power consumption and 34.4 frame-per-second real-time online learning OD on mobile devices.
Employing Gated Mechanism to Incorporate Symbolic Features into Chinese Event Coreference Resolution

Event mentions are sparsely distributed in unstructured text where single event mention without coreferential relationship account for a large proportion. And most of the current studies focus on English events, whereas Chinese has the characteristics of pro-drop and zero entity coreference, making it more challenging to solve Chinese event coreference. In order to solve within-document Chinese event coreference, a new Gated Mechanism Neural Network (GMNN) based on the basic characteristics of events is proposed. First, the pre-trained language model BERT and a feedforward neural network are introduced to represent a small number of necessary basic features(event triggers and event arguments), and the vector representation of symbolic features (event subtypes and event basic attributes) is obtained by trainable embedding matrices; Then, the gated mechanism is used to filter the noise from the symbolic features, extract the useful information in the specific context, and form the event mention-pair representation together with the basic features; Last, a feedforward neural network is applied to calculate the coreference score of the mention-pair representation and output the coreference event cluster. The experiments on ACE2005 Chinese dataset show that the performance of GMNN is improved by 8.2% compared with the baseline.