Reviewerly/exHarmony huggingface dataset:
Empirical evaluation of the performance of data sampling and feature selection techniques for software fault prediction

The application of Software Fault Prediction (SFP) in the software development life cycle to predict the faulty class at the early stage has piqued the interest of various scholars. In the SFP domain, during research analysis, it got realized that there has been very little work instigated on addressing both class imbalance and feature redundancy problems jointly to enhance the performance and prediction accuracy of SFP models. It has been perceived in the literature survey the study of droughts with the comprehensive comparative analysis of different sampling and feature selection strategies together. This research builds an extensive assessment of distinct combinations of different feature selection and sampling approaches, to effectively overcome the problems of class overlap, class imbalance, and feature redundancy. The objective is to determine the best combination that will produce results with a higher degree of accuracy and an effective SFP model. Considering the above erudition, the study has applied 8 different sampling techniques along with 10 feature selection algorithms against 56 open-source projects. The comparative analysis is performed against 5346 variants of input datasets by applying 8 different classifiers to predict the faulty class. In addition, the research paper presents an intensive assessment and performance of these techniques individually against all the input projects. We have considered accuracy and Area Under the ROC (receiver operating characteristic curve) Curve (AUC) performance metrics to compare the performance of different models developed using the classification algorithm. For each project in the proposed work, we evaluated a total of 792 combinations that were produced using 10 feature selection methods, 1 all metrics dataset, 8 sampling methods, 1 original, unsampled dataset, and 8 classifiers. The empirical result indicates that, against 21 projects out of 54 projects, Synthetic Minority Over Sampling Technique Edited (SMOTEE) with correlation-based feature selection (FS2) combination outperformed with the highest AUC value which is 38.89 % of projects. Additionally, according to experimental results, the highest AUC values were attained by 24.07 % of projects using the SMOTEE, FS2, and RF combination. The results of the statical analysis test reveal that 93.42 % of the combinational pairs of different sampling and feature selection approaches demonstrated a significant variance in the performance of the distinct combinations of sampling and feature selection techniques. The empirical result indicates the performance of the SFP Model is adversely impacted by class imbalance and irrelevance. The outcome indicates for more than 75% of projects, the performance of trained models improved with an AUC value between a range of 0.805 to 0.99 post-application of sampling and feature selection strategies, in comparison without the use of feature selection and sampling techniques.
Deep learning for 6D pose estimation of objects — A case study for autonomous driving

Nowadays, the potential benefits and implementation of autonomous driving have attracted widespread attention from both industry and academia. This study will solve view-invariant object detection and semantic key-point pose assumptions from a single RGB image. A machine learning method for estimating the absolute pose of an on-road vehicle for autonomous driving from monocular vision alone without the help of additional sensors is a complex task. The main purpose of this work is to identify other vehicles on the road and estimate their exact angular position from a single image with improved accuracy. The focus of the study is to create a new algorithm by applying a potentially deep convoluted neural network followed by a repetitive neural structure for more accurate 6D pose inference. A 6D pose hypothesis is presented in this study, based on a deep hybrid architecture for individual vehicles of an end-to-end approach to a task consisting of a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN). In this work, we will use a large-scale dataset consistent with the understanding of a 3D car instance called ApolloCar3D. The data set contains 5,277 real-life street scenes with examples of about 60K cars. By comparison, the ApolloCar3D is twenty times larger than the PASCAL3D+ and KITTI datasets. Ultimately, the idea is to efficiently eliminate motionless cars and predict the next pose given in the speed context, allowing a comprehensive evaluation, and passing the output through LSTM (long short-term memory) with an additional filter layer. The new filter added to the LSTM will efficiently filter and isolate stationary or parking vehicles and focus on on-road vehicles. Since the LSTM has a non-linear high-dimensional hidden memory state, it can preserve the past continuity of each generation's data history and pay more attention to those road vehicles rather than parked or stationary vehicles to act accordingly. So for each new vehicle, the pose estimator classifier can use LSTM memory and compare the historical pose with the newly filtered data. The successful implementation of this innovative concept will lead to significant improvements in the real-life traffic situation in the field of computer vision and autonomous driving.
Performance Analysis of Long Short-Term Memory Predictive Neural Networks on Time Series Data

Long short-term memory neural networks have been proposed as a means of creating accurate models from large time series data originating from various fields. These models can further be utilized for prediction, control, or anomaly-detection algorithms. However, finding the optimal hyperparameters to maximize different performance criteria remains a challenge for both novice and experienced users. Hyperparameter optimization algorithms can often be a resource-intensive and time-consuming task, particularly when the impact of the hyperparameters on the performance of the neural network is not comprehended or known. Teacher forcing denotes a procedure that involves feeding the ground truth output from the previous time-step as input to the current time-step during training, while during testing feeding back the predicted values. This paper presents a comprehensive examination of the impact of hyperparameters on long short-term neural networks, with and without teacher forcing, on prediction performance. The study includes testing long short-term memory neural networks, with two variations of teacher forcing, in two prediction modes, using two configurations (i.e., multi-input single-output and multi-input multi-output) on a well-known chemical process simulation dataset. Furthermore, this paper demonstrates the applicability of a long short-term memory neural network with a modified teacher forcing approach in a process state monitoring system. Over 100,000 experiments were conducted with varying hyperparameters and in multiple neural network operation modes, revealing the direct impact of each tested hyperparameter on the training and testing procedures.
Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning

While preserving the privacy of federated learning (FL), differential privacy (DP) inevitably degrades the utility (i.e., accuracy) of FL due to model perturbations caused by DP noise added to model updates. Existing studies have considered exclusively noise with persistent root-mean-square amplitude and overlooked an opportunity of adjusting the amplitudes to alleviate the adverse effects of the noise. This paper presents a new DP perturbation mechanism with a time-varying noise amplitude to protect the privacy of FL and retain the capability of adjusting the learning performance. Specifically, we propose a geometric series form for the noise amplitude and reveal analytically the dependence of the series on the number of global aggregations and the (ϵ,δ)-DP requirement. We derive an online refinement of the series to prevent FL from premature convergence resulting from excessive perturbation noise. Another important aspect is an upper bound developed for the loss function of a multi-layer perceptron (MLP) trained by FL running the new DP mechanism. Accordingly, the optimal number of global aggregations is obtained, balancing the learning and privacy. Extensive experiments are conducted using MLP, supporting vector machine, and convolutional neural network models on four public datasets. The contribution of the new DP mechanism to the convergence and accuracy of privacy-preserving FL is corroborated, compared to the state-of-the-art Gaussian noise mechanism with a persistent noise amplitude.
Fast and Accurate Fault Detection and Classification in Transmission Lines using Extreme Learning Machine

To provide stability and a continuous supply of power, the detection and classification of faults in the transmission lines (TLs) are crucial in this modern age. It is required to remove a faulty section from a healthy section to provide safety and to minimize power loss due to the fault. In the contemporary world, machine learning (ML) is extensively used in every aspect of life. In this study, a spontaneous fault detection (FD) and fault classification (FC) system based on ML has been proposed. MATLAB Simulink was employed to simulate two different TLs and to generate normal and fault data (Per unit voltage and current) of ten different types. TL-1 consisted of a single generator and a single load whereas TL-2 consisted of two generators and three loads. Upon normalizing the data, an extreme learning machine (ELM) algorithm was used as the classifier. Two different ELM models were developed for FD and FC purposes through training. The method achieved fault classification accuracies of 99.18% and 99.09% for the TL-1 and TL-2 respectively. On the other hand, fault detection accuracies of 99.53% and 99.60% were achieved for the TL-1 and TL-2. The proposed ELM model compared to a traditional artificial neural network (ANN) model demonstrated relatively a shorter processing time and reduced computational complexity. In addition, the proposed method outperformed the existing state-of-the-art methods.
An astonishing regularity in student learning rate

Leveraging a scientific infrastructure for exploring how students learn, we have developed cognitive and statistical models of skill acquisition and used them to understand fundamental similarities and differences across learners. Our primary question was why do some students learn faster than others? Or, do they? We model data from student performance on groups of tasks that assess the same skill component and that provide follow-up instruction on student errors. Our models estimate, for both students and skills, initial correctness and learning rate, that is, the increase in correctness after each practice opportunity. We applied our models to 1.3 million observations across 27 datasets of student interactions with online practice systems in the context of elementary to college courses in math, science, and language. Despite the availability of up-front verbal instruction, like lectures and readings, students demonstrate modest initial prepractice performance, at about 65% accuracy. Despite being in the same course, students' initial performance varies substantially from about 55% correct for those in the lower half to 75% for those in the upper half. In contrast, and much to our surprise, we found students to be astonishingly similar in estimated learning rate, typically increasing by about 0.1 log odds or 2.5% in accuracy per opportunity. These findings pose a challenge for theories of learning to explain the odd combination of large variation in student initial performance and striking regularity in student learning rate.
Experimental evaluation of digitally verifiable photonic computing for blockchain and cryptocurrency

As blockchain technology and cryptocurrency become increasingly mainstream, photonic computing has emerged as an efficient hardware platform that reduces ever-increasing energy costs required to verify transactions in decentralized cryptonetworks. To reduce sensitivity of these verifications to photonic hardware error, we propose and experimentally demonstrate a cryptographic scheme, LightHash, that implements robust, low-bit precision matrix multiplication in programmable silicon photonic networks. We demonstrate an error mitigation scheme to reduce error by averaging computation across circuits, and simulate energy-efficiency-error trade-offs for large circuit sizes. We conclude that our error-resistant and efficient hardware solution can potentially generate a new market for decentralized photonic blockchain.
An Efficient and Secure Model Using Adaptive Optimal Deep Learning for Task Scheduling in Cloud Computing

Recently, cloud computing resources have become one of the trending technologies that permit the user to manage diverse resources and a huge amount of data in the cloud. Task scheduling is considered one of the most significant challenges and ineffective management results in performance degradation. It is necessary to schedule the task effectively with maximum resource utilization and minimum execution time. Therefore, this paper proposes a novel technique for effective task scheduling with enhanced security in the cloud computing environment. A novel convolutional neural network optimized modified butterfly optimization (CNN-MBO) algorithm is proposed for scheduling the tasks, thereby maximizing the throughput and minimizing the makespan. Secondly, a modified RSA algorithm is employed to encrypt the data, thereby providing secure data transmission. Finally, our proposed approach is simulated under a cloudlet simulator and the evaluation results are analyzed to determine its performance. In addition to this, the proposed approach is compared with various other task scheduling-based approaches for various performance metrics, namely, resource utilization, response time, as well as energy consumption. The experimental results revealed that the proposed approach achieved minimum energy consumption of 180 kWh, a minimum response time of the 20 s, a minimum execution time of 0.43 s, and maximum utilization of 98% for task size 100.
Adaptive Memorization With Group Labels for Unsupervised Person Re-Identification

Re-identification (re-ID) aims to identify a person’s images across different cameras. However, the domain differences between different datasets make it a challenge for re-ID models trained on one dataset to be adapted to another. A variety of unsupervised domain adaptation methods tend to transfer learned knowledge from one domain to another by optimizing with pseudo-labels. Though impressive performances have been achieved, there are still some limitations. To be specific, these methods always generate one pseudo label for each unlabeled sample, which is hard to describe a person accurately and introduces a large number of noisy labels by one-shot clustering, thus hindering the retraining process and limiting generalization. To build more comprehensive descriptions of samples and mitigate the effects of noisy pseudo labels, this paper proposes an Adaptive Memorization with Group labels (AdaMG) framework for unsupervised person re-ID, which resists noisy labels and exploits the diversity of samples by developing a multi-branch structure with the adaptive memorization. In particular, group labels are generated for one sample in the unseen domain to learn more complementary and diverse features through clustering. Meanwhile, to better optimize the neural networks with noisy data, multiple memory structures are designed in AdaMG, which are updated adaptively according to the confidence of samples. Comprehensive experimental results have demonstrated that our proposed method can achieve excellent performances on benchmark datasets.
Assessing the Impact of Contextual Information in Hate Speech Detection

In recent years, hate speech has gained relevance in social networks and other virtual media because of its intensity and its relationship with violent acts against members of protected groups. Due to the huge amounts of user-generated contents, a great effort has been made to develop automatic tools to aid the analysis and moderation of this speech, at least in its most threatening forms. One of the limitations of current approaches to automatic hate speech detection is the lack of context; most studies and resources focus on isolated messages, without considering any type of conversational context or even the topic being discussed. This severely restricts the available information for determining whether a post on a social network should be tagged as hateful or not. In this work, we assess the impact of adding contextual information to the hate speech detection task. In particular, we study a Twitter subdomain consisting of replies to posts by news outlets, which provides a natural environment for contextualized hate speech detection.We collected a novel corpus in the Rioplatense dialectal variety of Spanish focusing on hate speech associated with the COVID-19 pandemic, and manually annotated it using carefully designed guidelines. Our classification experiments using state-of-the-art transformer-based machine learning techniques show evidence that adding contextual information improves the performance of hate speech detection for two proposed tasks (binary and multi-label prediction), increasing their Macro F1 by 4.2 and 5.5 points, respectively. These results highlight the importance of exploiting contextual information for the task of hate speech detection. We make our code, models, and corpus available for further research.
Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learning

Recently emerged federated learning (FL) is an attractive distributed learning framework in which numerous wireless end-user devices can train a global model with the data remained autochthonous. Compared with the traditional machine learning framework that collects user data for centralized storage, which brings huge communication burden and concerns about data privacy, this approach can not only save the network bandwidth but also protect the data privacy. Despite the promising prospect, Byzantine attack, an intractable threat in conventional distributed network, is discovered to be rather efficacious against FL as well. In this paper, we conduct a comprehensive investigation of the state-of-the-art strategies for defending against Byzantine attacks in FL. We first provide a taxonomy for the existing defense solutions according to the techniques they used, followed by an across-the-board comparison and discussion. Then we propose a new Byzantine attack method called weight attack to defeat those defense schemes, and conduct experiments to demonstrate its threat. The results show that existing defense solutions, although abundant, are still far from fully protecting FL. Finally, we indicate possible countermeasures for weight attack, and highlight several challenges and future research directions for mitigating Byzantine attacks in FL.
Field-matching attention network for object detection

Feature pyramid network (FPN) is widely used in object detection in order to divide and conquer objects of different scales and to fuse high and low-level features, and it has achieved encouraging achievements in multi-scale object processing. However, due to the mismatch between receptive fields at different stages, the direct fusion of the two features from different receptive fields may be unable to achieve satisfactory results. Moreover, simple lateral connections in FPN may lead to loss of spatial relationships and details. To alleviate these problems, in this paper we propose a field-matching attention network (FMANet) for object detection. Particularly, we first propose a receptive field dilated module (RFDM), which is used to normalize receptive fields between features at different stages to the same scale. Furthermore, to capture the spatial informations and details, we build a dual attention module (DAM) by employing the spatial attention and channel attention. Utilizing both spatial and channel attention mechanisms simultaneously improves performance while maintaining speed. Finally, experimental results reveal that our proposed FMANet with DSPDarkNet-53 as backbone achieves a competitive detection performance.
Isolation Forest Based Anomaly Detection and Fault Localization for Solar PV System

The decrease in fossil fuel reserves has prompted a global move toward distributed energy resources. For this reason, solar PV power generation has recently gained much attention as a feasible renewable energy source. However, large-scale generation is challenging if there are anomalies in individual solar PV panels. This will reduce the efficiency of the PV system and create a potential fire hazard. In this perspective, the anomaly detection technique discloses system anomalies accurately and effectively. Identified anomalies will localize the event for an improved generation. This paper addresses the performance analysis of using the isolation forest technique to identify anomalies in the PV system and the rule-based fault localization technique to identify defective panel events. In the developed model, the isolation forest technique found around 453 anomalies in 45,740 observations, and approximately six panels indicated a fault in the system. The accuracy score is found to be approximately 0.9886. The proposed fault detection method will help detect the faults in solar power systems.
A self-competitive mutation strategy for Differential Evolution algorithms with applications to Proportional–Integral–Derivative controllers and Automatic Voltage Regulator systems

The Differential Evolution (DE) algorithm is a powerful and simple optimizer for solving various optimization problems. Based on the literature, DE has shown suitable performance in exploring search spaces and locating global optimums. However, it is typically slow in extracting the problem solution. In this paper, the exploration ability of the DE algorithm is augmented with a competitive control parameter ω based on the value of the objective function of the mutating members. A new mutation strategy is introduced, subtracting weaker members from superior weaker ones. The proposed DE algorithm, which is referred to as the self-competitive DE, has been employed for solving real-world optimization problems. Several DE algorithms are enhanced with the proposed parameter ω, and the efficiencies of the resulting enhanced algorithms are tested. Furthermore, the optimal Proportional–Integral–Derivative (PID) controller tuning for an Automatic Voltage Regulator (AVR) system is used to investigate the effectiveness of the proposed strategy in solving real-world optimization problems. Simulation results demonstrate a good performance of the proposed parameter ω over several other well-known DE algorithms.