Reviewerly/exHarmony huggingface dataset:
Research on Knowledge Representation Methods for Fire Incidents Based on Knowledge Graphs

Sudden fire incidents have become one of the most frequent and common major disasters that threaten public safety and social development. The data released by authorities is mostly represented in text form, which provides limited and unclear descriptions of emergency response procedures. The low level of data structuring results in some data being unable to be effectively transformed into valuable information for reuse, leading to the wastage of data resources and hindering further processing as well as subsequent applications of case event information. In order to structure and process fire incident information, and enhance the value of information utilization for assisting decision-making, we focus on the fire incident data recorded in a state in the United States as the case data. Considering the characteristics of fire incident data, knowledge representation learning methods based on knowledge graphs such the TransE model and the TransH model are applied. By adopting the idea of distributed representation, the data is embedded into low-dimensional dense vectors, which can significantly improve computational efficiency, alleviate data sparsity issues, and meet the requirements of downstream applications. Finally, the effectiveness of the proposed research method is validated by evaluating the embedding effects of the knowledge representation process using the evaluation metrics of link prediction tasks. Our experimental results demonstrate that TransH significantly outperforms TransE on link prediction tasks.
Multi-State Bayesian Network-Based Reliability Analysis of Hydraulic System

Targeting at multi-state and uncertain faults in daily using and maintaining a hydraulic system, in this paper a fault tree model is first constructed by using the fault tree analysis method and then transformed into a Bayesian network; then the calculation process of the Bayesian network is simplified by using the bucket exclusion method to get the top-event occurrence rate; and finally the bi-directional reasoning function of Bayesian network is employed to calculate the posterior probability of each component through backward reasoning when the top event fault occurs, and the weak links of the system are found out and rectifying measures are given, so in this way theoretical basis is provided for improving the reliability of the hydraulic system.
You Augment Me: Exploring ChatGPT-based Data Augmentation for Semantic Code Search

Code search plays a crucial role in software development, enabling developers to retrieve and reuse code using natural language queries. While the performance of code search models improves with an increase in high-quality data, obtaining such data can be challenging and expensive. Recently, large language models (LLMs) such as ChatGPT have made remarkable progress in both natural and programming language understanding and generation, offering user-friendly interaction via simple prompts. Inspired by these advancements, we propose a novel approach ChatDANCE, which utilizes high-quality and diverse augmented data generated by a large language model and leverages a filtering mechanism to eliminate low-quality augmentations. Specifically, we first propose a set of ChatGPT prompting rules that are specifically designed for source code and queries. Then, we leverage ChatGPT to rewrite code and queries based on the according prompts and then propose a filtering mechanism which trains a cross-encoder from the backbone model UniXcoder to filter out code and query pairs with low matching scores. Finally, we re-train the backbone model using the obtained high-quality augmented data. Experimental results show that ChatDANCE achieves state-of-the-art performance, improving the best baseline by 13.2% (R@1) and 7% (MRR). Surprisingly, we find that this augment-filter-retrain strategy enables the backbone model (UniXcoder) to self-grow. Moreover, extensive experiments show the effectiveness of each component and ChatDANCE has stable performance under different hyperparameter settings. In addition, we conduct qualitative and quantitative analyses to investigate why ChatDANCE works well and find that it learns a more uniform distribution of representations and effectively aligns the code and query spaces. We have made the code and data anonymously available at https://anonymous.4open.science/r/ChatDANCE.
A Framework for Automating the Measurement of DevOps Research and Assessment (DORA) Metrics

The DevOps Research and Assessment (DORA) metrics have been widely accepted by the software industry as a powerful method to quantify DevOps performance, leading to significant interest in their measurement. Existing proprietary solutions are highly customised, and require specific types of cloud infrastructure, limiting their suitability for projects such as libraries, frameworks, and open source projects. To address this gap, we present a framework which operationalizes the DORA metrics independently of a project’s software development life-cycle or type of deployment. We demonstrate the general applicability of this framework by using it to calculate the throughput and stability of 304 popular open source repositories. We find that the time-series data it produces provides meaningful insights into the trending direction of a project’s recent and retrospective throughput and stability performance, especially when significant changes in metrics are correlated with major events in the project’s history. We conclude with recommendations for augmenting our approach with additional information such as bug criticality when such information is available.
How do Developers Improve Code Readability? An Empirical Study of Pull Requests

Readability models and tools have been proposed to measure the effort to read code. However, these models are not completely able to capture the quality improvements in code as perceived by developers. To investigate possible features for new readability models and production-ready tools, we aim to better understand the types of readability improvements performed by developers when actually improving code readability, and identify discrepancies between suggestions of automatic static tools and the actual improvements performed by developers. We collected 370 code readability improvements from 284 Merged Pull Requests (PRs) under 109 GitHub repositories and produce a catalog with 26 different types of code readability improvements, where in most of the scenarios, the developers improved the code readability to be more intuitive, modular, and less verbose. Surprisingly, SonarQube only detected 26 out of the 370 code readability improvements. This suggests that some of the catalog produced has not yet been addressed by SonarQube rules, highlighting the potential for improvement in Automatic static analysis tools (ASAT) code readability rules as they are perceived by developers.
Mining and Fusing Productivity Metrics with Code Quality Information at Scale

Productivity in software development is a complex, multi-faceted concept expressed as a combination of effectiveness and efficiency. From a quantitative lens, productivity is often interpreted from a collection of activities and metrics such as the number of commits, lines of code added and removed, and the number of issues closed. Software development team managers often seek to track developers’ activity and productivity for short-term planning and medium-term team performance measurement. Existing tools and platforms analyze and visualize individual aspects of developers’ activity, productivity, or quality. However, a tool that fuses multiple information streams representing productivity and quality aspects is missing. The proposed tool QConnect fills the gap by mining, analyzing, and fusing information from software development-relevant streams. QConnect, on the one hand, mines the repository and issue tracking metadata from GitHub and Jira issue tracking system; on the other hand, it gathers information related to code quality using external tools Designite and RefactoringMiner. By tying-in productivity measures with code quality information, stakeholders can assess not only how fast but also how well the project is progressing.Demo: tool website and demo video.
Real-time Traffic Classification for 5G NSA Encrypted Data Flows With Physical Channel Records

The classification of fifth-generation New-Radio (5G-NR) mobile network traffic is an emerging topic in the field of telecommunications. It can be utilized for quality of service (QoS) management and dynamic resource allocation. However, traditional approaches such as Deep Packet Inspection (DPI) can not be directly applied to encrypted data flows. Therefore, new real-time encrypted traffic classification algorithms need to be investigated to handle dynamic transmission. In this study, we examine the real-time encrypted 5G Non-Standalone (NSA) application-level traffic classification using physical channel records. We generate a noise-limited 5G NSA trace dataset with traffic from multiple applications. We develop a new pipeline to convert sequences of physical channel records into numerical vectors. We propose our solution based on Light Gradient Boosting Machine (LGBM) due to its advantages in fast parallel training and low computational burden in practical scenarios. Our experiments demonstrate that our algorithm can achieve 95% accuracy on the classification task with a state-of-the-art response time as quick as 10ms.
A Two-Dimensional Deep Network for RF-based Drone Detection and Identification Towards Secure Coverage Extension

As drones become increasingly prevalent in human life, they also raise security concerns such as unauthorized access and control, as well as collisions and interference with manned aircraft. Therefore, ensuring the ability to accurately detect and identify between different drones holds significant implications for coverage extension. Assisted by machine learning, radio frequency (RF) detection can recognize the type and flight mode of drones based on the sampled drone signals. In this paper, we first utilize Short-Time Fourier Transform (STFT) to extract two-dimensional features from the raw signals, which contain both time-domain and frequency-domain information. Then, we employ a Convolutional Neural Network (CNN) built with ResNet structure to achieve multi-class classifications. Our experimental results show that the proposed ResNet-STFT can achieve higher accuracy and faster convergence on the extended dataset. Additionally, it exhibits balanced performance compared to other baselines on the raw dataset.
Integrating Datasets with Discrete and Natural Language Annotations for Person Retrieval

Person retrieval video using natural language description (NLD) is an emerging research area and depends largely on dataset diversity. Unifying datasets increases overall quality; therefore, the paper presents a case study on merging two different style data sets; one has NLD with images (CUHK-PEDES), and the other has discrete annotations with videos (AVSS). The unifying framework brings out the practical challenges and their solution. Explicit discussions on data set merging frameworks are missing in the literature, and our work will facilitate the researchers’ requirements.
MoRFF: Multi-View Object Detection for Connected Autonomous Driving under Communication and Localization Limitations

Vehicle-to-Everything network enabled connected autonomous driving has been regarded as a promising solution to realize advanced autonomous driving. However, non-ideal factors in wireless communication and localization severely limit the development. In this work, we propose MoRFF, a Mobility-robust Regional Features Fusion framework for multi-terminal multi-view object detection to realize wireless cooperative perception. To conquer the limited communication bandwidth, stochastic latency, and inaccurate positioning caused by wireless links and mobility, our method features a universal two-stage detection paradigm with deep metric learning, matching the same object from different viewpoints directly on the regional feature maps, and thus helps to greatly reduce the data size to transmit. Our proposed architecture only requires image data without any additional information such as geo-positions, sensor poses, or point clouds from LiDAR, and thus conducive to the promotion of connected autonomous driving. Experimental evaluations show that the proposed algorithm successfully benefits from other viewpoints, increases the detection precision of barely visible objects by 13.42%, and achieves tenfold promotion in communication bandwidth requirements. Furthermore, the proposed algorithm is robust under various communication delays.
Digital Filter Design via Recurrent Cartesian Genetic Programming

We introduce a method for the automatic program induction of Single-Input/Single-Output (SISO) Infinite Impulse Response (IIR) filters for Digital Signal Processing (DSP) applications. Recurrent Cartesian Genetic Programming (RCGP) evolves a population of DSP programs, represented as directed cyclic/acyclic graphs, to generate a filter whose magnitude response approximates that of a target filter. The Log-Spectral Distance (LSD) is used as a fitness measure to minimize the differences between the magnitude responses of these filters, and the filter with the smallest distance is output. We evaluated our method by generating a number of filters, and found that the accuracy of the generated filters depends on both the order of the target filter and the parameter values set for the RCGP algorithm.
VGG16-based Feature Fusion For Image Kyepoint Description

The detection and description of feature points or keypoints is a crucial stage in many complex computer vision tasks. Deep learning based methods are pushing the envelope in a variety of fields. Deep learning based approaches for feature point description can provide high-level abstract keypoint descriptors. The use of pre-trained networks for purposes other than those for which they were developed is an interesting area of research. To efficiently extract descriptors from the input image, we employed a pre-trained network particularly built for classification tasks. The proposed method is divided into two stages: keypoint detection and description. A traditional detector is used in keypoint detection stage to locate the image keypoints. A downscaled version of the input image is fed to the VGG 16 network in keypoint description stage. The first five convolutional layers of VGG 16 are used for keypoint description. By sampling the extracted features at the keypoint's locations and concatenating the extracted features, a new descriptor is generated. The size of the introduced descriptor is decreased by using a sparse random projection. The evaluation results suggest that the proposed descriptor is highly competitive with traditional descriptors and it outperforms some of them. The extracted descriptors can be used for different domains of images, by using task-specific pre-trained networks.
Connective Reconstruction-Based Novelty Detection

Detection of out-of-distribution samples is one of the critical tasks for real-world applications of computer vision. The advancement of deep learning has enabled us to analyze real-world data which contain unexplained samples, accentuating the need to detect out-of-distribution instances more than before. GAN-based approaches have been widely used to address this problem due to their ability to perform distribution fitting; however, they are accompanied by training instability and mode collapse. We propose a simple yet efficient reconstruction-based method that avoids adding complexities to compensate for the limitations of GAN models while outperforming them. Unlike previous reconstruction-based works that only utilize reconstruction error or generated samples, our proposed method simultaneously incorporates both of them in the detection task. Our model, which we call “Connective Novelty Detection” has two subnetworks, an autoencoder, and a binary classifier. The autoencoder learns the representation of the positive class by reconstructing them. Then, the model creates negative and connected positive examples using real and generated samples. Negative instances are generated by manipulating the real data, so their distribution is close to the positive class to achieve a more accurate boundary for the classifier. To boost the robustness of the detection of reconstruction error, connected positive samples are created by combining the real and generated samples. Finally, the binary classifier is trained using connected positive and negative examples. We demonstrate a considerable improvement in novelty detection over state-of-the-art methods on MNIST and Caltech-256 datasets.
Revisiting Classical Controller Design and Tuning with Genetic Programming

This paper introduces the application of a genetic programming (GP)-based method for the automated design and tuning of process controllers, representing a noteworthy advancement in artificial intelligence (AI) within the realm of control engineering. In contrast to already existing work, our GP-based approach operates exclusively in the time domain, incorporating differential operations such as derivatives and integrals without necessitating intermediate inverse Laplace transformations. This unique feature not only simplifies the design process but also ensures the practical implementability of the generated controllers within physical systems. Notably, the GP's functional set extends beyond basic arithmetic operators to include a rich repertoire of mathematical operations, encompassing trigonometric, exponential, and logarithmic functions. This broad set of operations enhances the flexibility and adaptability of the GP-based approach in controller design. To rigorously assess the efficacy of our GP-based approach, we conducted an extensive series of tests to determine its limits and capabilities. In summary, our research establishes the GP-based approach as a promising solution for automating the controller design process, offering a transformative tool to address a spectrum of control problems across various engineering applications.
Single- and Multi-Hop BERT Question Classifier for Open-Domain Question Answering (SiMQC)

Multi-hop Question Answering has recently received particular attention in research and practice, especially in the context of conversational systems and answering complex questions. Various architectures have been proposed to answer these complex multi-hop questions. However, in real-world scenarios, a conversational system should answer both simple (single-hop) and complex (multi-hop) questions. In this work, we propose an efficient BERT question classifier that supports retrievers in the question-answering systems to process single- and multi-hop questions. We also released a mixed dataset consisting of both single- and multi-hop questions. We show that utilizing our classifier inside the QA system can improve these systems' accuracy and enable them to answer both kinds of questions considering their complexity.
Estimation of Cholesterol for Multiple Food Dish on One Plate Using Computer Vision

High cholesterol is a dangerous condition that can lead to heart attacks, strokes, and even death. One of the causes is consuming foods that are high in cholesterol. Therefore, it is essential for us to know the cholesterol content of every food we consume daily. In an effort to help consumers manage their food intake, we conducted research to create an Android-based application that utilizes computer vision technology. In this research, we employed a deep learning approach to estimate the cholesterol content of various types of food served on a plate. We utilized the Single Shot Multibox Detector (SSD) method with the Mobilenet v2 architecture to detect food objects in images. Subsequently, the detected images were labeled with the respective cholesterol amounts for each food object, presented as bounding boxes. To implement this application in real-time on mobile devices, we used the Tensorflow object detection framework during the extraction process. Through our object detection testing, we achieved an accuracy rate of 93%. This result demonstrates that the Computer Vision approach using the SSD Mobilenet V2 architecture is capable of effectively detecting food objects.
Secure Control System Using Iterative Secret Sharing

Using the public network for distributed computations compromises the immunity of control loops to cyberattacks such as data manipulation and eavesdropping. These risks have forced control system engineers to find solutions to protect control systems against these dangers. Encrypted control studies these dangers and tries to propose new modified control systems to increase the immunity of the control systems. Secret sharing is one of the tools used in the encrypted control field. In this paper, we modify this tool to propose an algorithm to design a secure control system. This scheme while guarantees the security of the control systems, can attenuate the measurement noise as well. To achieve resilience against measurement noise, we can iteratively use secret sharing while adding white noise with suitable variance to the shares of our data. Increased security, secure distributed computation, and measurement noise attenuation are some of the results of using this control system scheme compared to the conventional control systems.
Deep Reinforcement Learning for Image-Based Multi-Agent Coverage Path Planning

Image-based Multi-Agent Coverage Path Planning (MACPP) utilizes images as input to control multiple agents touring all nodes in a map, minimizing task duration and node revisiting. State-of-the-art (SOTA) studies have applied Multi-Agent Deep Reinforcement Learning (MADRL) to automate MACPP, primarily focusing on minimizing task duration. However, these approaches overlook the issue of repeated node visits, resulting in longer task durations and limited real-world applicability. To tackle this challenge, we develop a novel MADRL solution, referred to as MADRL with Mask Soft Attention, to minimize task duration and node re-visiting simultaneously. Our method uses mask soft attention to extract key features from raw image observations while masking task-independent features, reducing computational complexity and improving sample efficiency. We also cascade a multi-actor-critic architecture to accommodate even more agents with ease. Each agent is equipped with an actor to learn an action policy, and a shared critic evaluates a state value. To validate our approach, we implement seven SOTA MADRL methods in the MACPP area as baselines. Simulation results show that our method significantly outperforms the baselines regarding task duration and the number of times the node is repeatedly visited.
Microcontroller-Based Multi-Objective Genetic Algorithm for Mobile Robots Path Planning

Path planning is a fundamental task for a mobile robot. Path planning is an attempt to find the most effective route from the starting point of the mobile robot's position to a certain destination. Some of the commonly used path planning methods are the A * algorithm, Djikstras algorithm, D* algorithm, and others. Apart from these methods, there are metaheuristic methods such as multi-objective genetic algorithms. This method adopts the principles of natural selection and Darwin's theory of evolution. However, this method has the disadvantage that it requires a large enough memory and a fast processor. Mobile robots usually use microcontrollers with limited memory and low-end processors. This study proposes a micro controller-based multi-objective genetic algorithm for path planning on mobile robots. The multi-objective genetic algorithm will be applied to the Atmega32Sp microcontroller to find the shortest path length and the least number of turns.