Reviewerly/exHarmony huggingface dataset:
Issue Link Label Recovery and Prediction for Open Source Software

Modern open source software development heavily relies on the issue tracking systems to manage their feature requests, bug reports, tasks, and other similar artifacts. Together, those “issues” form a complex network with links to each other. The heterogeneous character of issues inherently results in varied link types and therefore poses a great challenge for users to create and maintain the label of the link manually. The goal of most existing automated issue link construction techniques ceases with only examining the existence of links between issues. In this work, we focus on the next important question of whether we can assess the type of issue link automatically through a data-driven method. We analyze the links between issues and their labels used the issue tracking system for 66 open source projects. Using three projects, we demonstrate promising results when using supervised machine learning classification for the task of link label recovery with careful model selection and tuning, achieving F1 scores of between 0.56-0.70 for the three studied projects. Further, the performance of our method for future link label prediction is convincing when there is sufficient historical data. Our work signifies the first step in systematically manage and maintain issue links faced in practice.
Practical Post-quantum Few-Time Verifiable Random Function with Applications to Algorand

In this work, we introduce the first practical post-quantum verifiable random function (VRF) that relies on well-known (module) lattice problems, namely Module-SIS and Module-LWE. Our construction, named LB-VRF, results in a VRF value of only 84 bytes and a proof of around only 5 KB (in comparison to several MBs in earlier works), and runs in about 3 ms for evaluation and about 1 ms for verification. In order to design a practical scheme, we need to restrict the number of VRF outputs per key pair, which makes our construction few-time. Despite this restriction, we show how our few-time LB-VRF can be used in practice and, in particular, we estimate the performance of Algorand using LB-VRF. We find that, due to the significant increase in the communication size in comparison to classical constructions, which is inherent in all existing lattice-based schemes, the throughput in LB-VRF-based consensus protocol is reduced, but remains practical. In particular, in a medium-sized network with 100 nodes, our platform records a 1.14 $$\times $$ to 3.4 $$\times $$ reduction in throughput, depending on the accompanying signature used. In the case of a large network with 500 nodes, we can still maintain at least 24 transactions per second. This is still much better than Bitcoin, which processes only about 5 transactions per second.
Performance Evaluation of Offline Speech Recognition on Edge Devices

Deep learning–based speech recognition applications have made great strides in the past decade. Deep learning–based systems have evolved to achieve higher accuracy while using simpler end-to-end architectures, compared to their predecessor hybrid architectures. Most of these state-of-the-art systems run on backend servers with large amounts of memory and CPU/GPU resources. The major disadvantage of server-based speech recognition is the lack of privacy and security for user speech data. Additionally, because of network dependency, this server-based architecture cannot always be reliable, performant and available. Nevertheless, offline speech recognition on client devices overcomes these issues. However, resource constraints on smaller edge devices may pose challenges for achieving state-of-the-art speech recognition results. In this paper, we evaluate the performance and efficiency of transformer-based speech recognition systems on edge devices. We evaluate inference performance on two popular edge devices, Raspberry Pi and Nvidia Jetson Nano, running on CPU and GPU, respectively. We conclude that with PyTorch mobile optimization and quantization, the models can achieve real-time inference on the Raspberry Pi CPU with a small degradation to word error rate. On the Jetson Nano GPU, the inference latency is three to five times better, compared to Raspberry Pi. The word error rate on the edge is still higher, but it is not too far behind, compared to that on the server inference.
Secure KNN Classification Scheme Based on Homomorphic Encryption for Cyberspace

With the advent of the intelligent era, more and more artificial intelligence algorithms are widely used and a large number of user data are collected in the cloud server for sharing and analysis, but the security risks of private data breaches are also increasing in the meantime. CKKS homomorphic encryption has become a research focal point in the cryptography field because of its ability of homomorphic encryption for floating-point numbers and comparable computational efficiency. Based on the CKKS homomorphic encryption, this paper implements a secure KNN classification scheme in cloud servers for Cyberspace (CKKSKNNC) and supports batch calculation. This paper uses the CKKS homomorphic encryption scheme to encrypt user data samples and then uses Euclidean distance, Pearson similarity, and cosine similarity to compute the similarity between ciphertext data samples. Finally, the security classification of the samples is realized by voting rules. This paper selects IRIS data set for experimental, which is the classification data set commonly used in machine learning. The experimental results show that the accuracy of the other three similarity algorithms of the IRIS data is around 97% except for the Pearson correlation coefficient, which is almost the same as that in plaintext, which proves the effectiveness of this scheme. Through comparative experiments, the efficiency of this scheme is proved.
Usability of WebXR Visualizations in Urban Planning

Extended reality (XR) technology is increasingly often considered in practical applications related to urban planning and smart city management. It offers many advantages as a new visualization technique that gives its users access to places that are not available in material space and a unique perspective on existing objects. It can provide immersive multi-sensory experience that can induce emotional response in participatory planning. However, standard mode of implementation that relies on mobile phone applications and VR headsets has a disadvantage when it comes to availability and accessibility. Here we test the WebXR solution that can mitigate those problems. We have created six AR and VR environments that resembled common urban planning scenarios and conducted usability tests with people having planning and GIS background. Results indicate that WebXR can provide useful solution in urban planning when the interface and environment resemble common practices and situations encountered in real life. Environments that have introduced new digital affordances like AR measurements or semi-transparent walkable scale models were rated lower. Users evaluated presented environment as having high usability and expressed their positive attitude toward using XR in their professional practice mainly as a participatory and visualization tool.
Generating natural adversarial examples with universal perturbations for text classification

Recent works have demonstrated the vulnerability of text classifiers to universal adversarial attacks, which are splicing carefully designed word sequences into the original text. These word sequences are natural, and adversarial examples generated by splicing them with the original text are unnatural. In this paper, we propose a framework for generating natural adversarial examples with an adversarially regularized autoencoder (ARAE) model and an inverter model. The framework maps discrete text into the continuous space, get the conversion of adversarial examples by adding universal adversarial perturbations in the continuous space, then generates natural adversarial examples. In order to achieve universal adversarial attacks, we design a universal adversarial perturbations search (UAPS) algorithm with the gradient of the loss function of the target classifier. Perturbations found by the UAPS algorithm can be directly added to the conversion of the original text in the continuous space. On two textual entailment datasets, we evaluate the fooling rate of generated adversarial examples on two RNN-based architectures and one Transformer-based architecture. The results show that all architectures are vulnerable to the adversarial examples. For example, on the SNLI dataset, the accuracy of the ESIM model for the “entailment” category drops from 88.35% to 2.26%. While achieving a high fooling rate, generated adversarial examples have good performance in naturalness. By further analysis, adversarial examples generated in this paper have transferability in neural networks.
Real-Time Pedestrians Detection by YOLOv5

Real-time detection of objects is receiving growing attention. The pedestrian is the most critical object that needs to be detecting and tracking by autonomous vehicles. The major challenges to this mission are caused by the difference in objects like pedestrians in age, gender, clothing, lighting, backgrounds, and occlusion. This paper starts with a brief introduction of problem-related to pedestrians, objects detection framework and Neural Networks Algorithms, and Real-Time Systems. And we focus on pedestrians as moving objects that need to detect, track, and solve problems related to computer vision. And based on our study we present a suggested solution for solving problems related to Pedestrians Detection in real-time particular. These techniques aim to be used in many applications such as Autonomous Vehicles, and Advanced Driver Assistance Systems (ADAS).
Rectifying Pseudo Labels

Graph Convolutional Networks (GCNs) are powerful representation learning methods for non-Euclidean data. Compared with the Euclidean data, labeling the non-Euclidean data is more expensive. Meanwhile, most existing GCNs only utilize few labeled data but ignore most of the unlabeled data. To address this issue, we design a novel end-to-end Iterative Feature Clustering Graph Convolutional Networks (IFC-GCN) that enhances the standard GCN with an Iterative Feature Clustering (IFC) module. The proposed IFC module constrains node features iteratively based on the predicted pseudo labels and feature clustering. Further, we design an EM-like framework for IFC-GCN training, which improves the network performance by rectifying the pseudo labels and the node features alternately. Theoretical analysis and experimental results show that our proposed IFC module can effectively modify the node features. Experimental results on public datasets demonstrate that IFC-GCN outperforms state-of-the-art methods on the semi-supervised node classification task.
Human Eye Vision Algorithm (HEVA): A Novel Approach for the Optimization of Combinatorial Problems

In today's optimization paradigm, reducing time to obtain the optimal solution for a combinatorial optimization problem is a significant challenge. The proposed algorithm is based upon the power of human eye vision for minimizing the time needed to achieve the best solution to a combinatorial optimization problem. To improve the attribute position in a particular domain to reach their target in a foggy climate, researchers used the population's visualization ability to see a way to traverse and gain adequate heuristic information. The suggested algorithm has been empirically shown to work on Howard H. Rosen's hypothesis, which was formulated in 1960. When added to the fitness function of Rosen rock using MATLAB 8.5-R2015a, this new optimization algorithm minimizes the cost from 100 to 54.6342.
BlockChain Technology based Smart Contract Agreement on REMIX IDE

In the world of information security, BlockChain Technology has gained a lot of importance in the recent past as it uses hashing algorithms like MD 5 and SHA thereby enabling that the contents of the block are secure and immutable. In the current research paper effort has been made to exploit the Smart contract feature of BlockChain technology. A new state of the art Smart Contract System between the Customer, Car Dealer and Car Manufacturer has been designed, developed and deployed. This would ensure that seamless financial transactions can be executed amongst all the three parties as on the receipt of timely payment from the customer, the Car manufacturer can deliver the vehicle in a hassle free manner.
GOMP-FIT: Grasp-Optimized Motion Planning for Fast Inertial Transport

High-speed motions in pick-and-place operations are critical to making robots cost-effective in many automation scenarios, from warehouses and manufacturing to hospitals and homes. However, motions can be too fast-such as when the object being transported has an open-top, is fragile, or both. One way to avoid spills or damage, is to move the arm slowly. We propose an alternative: Grasp-Optimized Motion Planning for Fast Inertial Transport (GOMP-FIT), a time-optimizing motion planner based on our prior work, that includes con-straints based on accelerations at the robot end-effector. With GOMP-FIT, a robot can perform high-speed motions that avoid obstacles and use inertial forces to its advantage. In experiments transporting open-top containers with varying tilt tolerances, whereas GOMP computes sub-second motions that spill up to 90 % of the contents during transport, GOMP-FIT generates motions that spill 0 % of contents while being slowed by as little as 0 % when there are few obstacles, 30 % when there are high obstacles and 45-degree tolerances, and 50 % when there 15-degree tolerances and few obstacles. Videos and more at: https://berkeleyautomation.github.io/gomp-fit/.
Predictive Monitoring of Traffic Rules

Predicting the trajectories of other road users relies to a large extent on the assumption that they adhere to the legally binding traffic rules. Hence, when this assumption does not hold anymore, the prediction becomes invalid, putting autonomous vehicles relying on such predictions in a critical situation. We propose a solution to this problem by predicting traffic rule violations. All traffic rules are modeled by temporal logic, and we provide real-valued generalizations of required logical predicates to obtain features for prediction with neural networks. The usefulness of our approach is demonstrated by predicting rule violations on a dataset recorded from a highway. Our results show that directly learning traffic rule violations using the features from temporal logic formulas often performs better compared to separately predicting and monitoring trajectories.
An Immersive Open Source Environment Using Godot

We present a sample implementation of a Virtual and Augmented Reality immersive environment based on Free and Libre Open Source Hardware and Software and the HTC Vive system, used to enhance the immersive experience of the user and to track her/his movements. The sense of immersion has increased and stimulated using a footplate and a Tibetan bridge, connected to the virtual world as Augmented Reality applications and implemented through an Arduino board, thereby adopting a low cost, open source hardware and software approach. The proposed architecture is relatively affordable from the cost point of view, easy to implement, configure and adapt to different contexts. It can be of great help for organizing laboratory classes for young students to afford the implementation of virtual worlds and Augmented Reality applications.
Cache-based GNN System for Dynamic Graphs

Graph Neural Networks (GNNs) have achieved great success in downstream applications due to their ability to learn node representations. However, in many applications, graphs are not static. They often evolve with changes, such as the adjustment of node attributes or graph structures. These changes require node representations to be updated accordingly. It is non-trivial to apply current GNNs to update node representations in a scalable manner. Recent research proposes two types of solutions. The first solution, sampling neighbors for the influenced nodes, requires expensive processing for each node. The second solution, reducing the repeated computations by merging the shared neighbors, cannot speed up the updating process if the influenced nodes do not share neighbors. Most importantly, the above solutions ignore the hidden representations obtained in the previous times that can be reused to accelerate the representation updating. In this paper, we propose a general cache-based GNN system to accelerate the representation updating. Specifically, we cache a set of hidden representations obtained in the previous times, and then reuse them in the next time. To identify valuable hidden representations, we first estimate the number of hidden representations and their combinations that can be reused. Secondly, we formulate the k-assembler problem that selects k representations to maximize the saved time for the next updating process. Experiments on three real-world graphs show that the cache-based GNN system can significantly speed up the representation updating for various GNNs.
Citadel

Many organizations own data but have limited machine learning expertise (data owners). On the other hand, organizations that have expertise need data from diverse sources to train truly generalizable models (model owners). With the advancement of machine learning (ML) and its growing awareness, the data owners would like to pool their data and collaborate with model owners, such that both entities can benefit from the obtained models. In such a collaboration, the data owners want to protect the privacy of its training data, while the model owners desire the confidentiality of the model and the training method that may contain intellectual properties. Existing private ML solutions, such as federated learning and split learning, cannot simultaneously meet the privacy requirements of both data and model owners.
Design and optimization of photovoltaic system with a week ahead power forecast using autoregressive artificial neural networks

In this paper, an optimized design procedure for designing Photovoltaic system to generate power outputs is proposed. The power outputs generated using different design procedures depicted the variation in power produced which will work as a yardstick in deciding the choice of design procedure by the electric utilities. In addition, an Autoregressive Neural Network model to provide a week ahead forecast of PV Power output is also proposed. The proposed model is trained using Levenberg-Marquardt optimizer and the performance is validated using Mean absolute percentage error (MAPE). The forecast power output will be used by the energy operators in the correct dispatch of energy. The MAPE of the proposed model was found around 5.44. Further, the proposed model was compared with various time-series models developed using Waikato Environment for Knowledge Analysis (WEKA) which depicted the best performance by our method.
Multi-phase trajectory optimization of aerospace vehicle using sequential penalized convex relaxation

This paper presents a novel methodology for solving the multi-phase trajectories of aerospace vehicles in the framework of convex optimization. As a result of the inherent non-smooth, non-linear, and staged features of the problem, the application of convex optimization is confronted by three categories of tough non-convex terms, non-convex functions, phase linkage constraints, and free endpoints. To overcome these difficulties, a combination of a time-projection approach, and a sequential relaxation and penalization method is developed in this paper. Firstly, free singularity points from linkage constraints and endpoints are eliminated by projecting the time history onto a normalized time interval. Subsequently, the resulting fixed-time problem is equivalently converted in to a pre-semi-definite form and sequentially relaxed as an approximated convex optimization problem. Furthermore, to increase the convergence of the proposed method, a penalization term is included to control the search direction toward the true solution. Analyses are performed to ensure that the convergence and robustness of the proposed algorithm is well guaranteed. A numerical comparison of the results with those from the state-of-art pseudo-spectral nonlinear programming solver GPOPS suggests that the algorithm outputs similar trajectories, but in only one tenth of the computation time.
A comparative analysis of CGAN‐based oversampling for anomaly detection

In this work, the problem of anomaly detection in imbalanced datasets, framed in the context of network intrusion detection is studied. A novel anomaly detection solution that takes both data-level and algorithm-level approaches into account to cope with the class-imbalance problem is proposed. This solution integrates the auto-learning ability of Reinforcement Learning with the oversampling ability of a Conditional Generative Adversarial Network (CGAN). To further investigate the potential of a CGAN, in imbalanced classification tasks, the effect of CGAN-based oversampling on the following classifiers is examined: Naïve Bayes, Multilayer Perceptron, Random Forest and Logistic Regression. Through the experimental results, the authors demonstrate improved performance from the proposed approach, and from CGAN-based oversampling in general, over other oversampling techniques such as Synthetic Minority Oversampling Technique and Adaptive Synthetic.
What drives the accuracy of PV output forecasts?

In this paper, 180 papers on photovoltaic (PV) output forecasting were reviewed and a database of forecast errors was extracted for statistical analysis. The paper shows that among the forecast models, hybrid models are most likely to become the primary form of PV output forecasting in the future. The use of data processing techniques is positively correlated with the forecast quality, while the lengths of the forecast horizons and out-of-sample test sets have negative effects on the forecast accuracy. The paper also found that the use of data normalization, the wavelet transform, and the inclusion of clear sky index and numerical weather prediction variables are the most effective data processing techniques. Furthermore, the paper found some evidence of “cherry picking” in the reporting of errors and we recommend that the test sets be at least one year long to avoid any distortion in the performance of the models.
DiCE4EL: Interpreting Process Predictions using a Milestone-Aware Counterfactual Approach

Predictive process analytics often apply machine learning to predict the future states of a running business process. However, the internal mechanisms of many existing predictive algorithms are opaque and a human decision-maker is unable to understand why a certain activity was predicted. Recently, counterfactuals have been proposed in the literature to derive human-understandable explanations from predictive models. Current counterfactual approaches consist of finding the minimum feature change that can make a certain prediction flip its outcome. Although many algorithms have been proposed, their application to multi-dimensional sequence data like event logs has not been explored in the literature.In this paper, we explore the use of a recent, popular model-agnostic counterfactual algorithm, DiCE, in the context of predictive process analytics. The analysis reveals that DiCE is unable to derive explanations for process predictions, due to (1) process domain knowledge not being taken into account, (2) long traces of process execution that often tend to be less understandable, and (3) difficulties in optimising the counterfactual search with categorical variables. We design an extension of DiCE, namely DiCE4EL (DiCE for Event Logs), that can generate counterfactual explanations for process prediction, and propose an approach that supports deriving milestone-aware counterfactual explanations at key intermediate stages along process execution to promote interpretability. We apply our approach to a publicly available real-life event log and the analysis results demonstrate the effectiveness of the proposed approach.