Reviewerly/exHarmony huggingface dataset:
Long Document Summarization with Prioritized Input

Document summarization is an important sub-field of natural language processing, with the potential to help people save time and make decisions on what content to read in depth. Much of the success in summarization has been applied to short documents, however the summarization of longer documents has the potential to be far more beneficial. In this paper we explore multiple techniques of prioritizing the content of long documents before using a transformer model to generate summaries. We report positive results for both rouge-l and rouge 2 scores using prioritized text to fine-tune a transformer model and generate summaries compared to using standard input text.
Diverse Image Colorization Based on Diffusion Model

Image colorization refers to the process of coloring a grayscale image. In the field of computer vision, image colorization is a very crucial task, because it can transform grayscale images into real and natural color images, which can provide better visualization effects and more visual information. This paper proposes a method based on the pre-trained diffusion model and control network, so that the model can obtain coloring results with bright colors, natural textures, and diverse results, and can handle a variety of control input conditions. Furthermore, we innovatively proposes a spatial attention module to process reference map control inputs and improve coloring results. Through the test results of a large number of various types of data, such as validation datasets with GT images and old photos without GT images, we show that the model in this paper has excellent performance in grayscale image colorization and strong versatility, which can adapt to different scenarios and different types of test datasets, demonstrating that our algorithm has practical application value.
Extracting Textual Information from Website Using Mixed Rules

Textual information extraction refers to extracting meaningful information from text documents. Text is one of the essential carriers of information. Mining textual information can help people obtain knowledge and make better decisions. In today’s world, web pages contain more textural information than other sources and are more easily processed by computers. At the same time, the structure of web pages is becoming increasingly complex, making it more challenging to extract meaningful textual information from web pages than before. In this paper, we first reviewed the existing methods for extracting textual information from web pages, then discussed an assumption about the classification of web page layouts, and then proposed a mixed extraction system which aims at extracting textual information from a randomly given website by first classifying pages and then adopting a different extraction strategy for each type of page. Finally, we test this extraction system on web page from different sources. The result shows that the system has a certain level of accuracy for page classification tasks. It also has a certain level of extraction effectiveness for pages that are correctly classified.
An Exploratory Study on the Occurrence of Self-Admitted Technical Debt in Android Apps

Technical debt describes situations where developers write less-than-optimal code to meet project milestones. However, this debt accumulation often results in future developer effort to live with or fix these quality issues. To better manage this debt, developers may document their sub-optimal code as comments in the code (i.e., self-admitted technical debt or SATD). While prior research has investigated the occurrence and characteristics of SATD, this research has primarily focused on non-mobile systems. With millions of mobile applications (apps) in multiple genres available for end-users, there is a lack of research on sub-optimal code developers intentionally implement in mobile apps.In this study, we examine the occurrence and characteristics of SATD in 15,614 open-source Android apps. Our findings show that even though such apps contain occurrences of SATD, the volume per app (a median of 4) is lower than in non-mobile systems, with most debt categorized as Code Debt. Additionally, we identify typical elements in an app that are prone to intentional sub-optimal implementations. We envision our findings supporting researchers and tool vendors with building tools and techniques to support app developers with app maintenance.
Automatically Identifying Relations Between Self-Admitted Technical Debt Across Different Sources

Self-Admitted Technical Debt or SATD can be found in various sources, such as source code comments, commit messages, issue tracking systems, and pull requests. Previous research has established the existence of relations between SATD items in different sources; such relations can be useful for investigating and improving SATD management. However, there is currently a lack of approaches for automatically detecting these SATD relations. To address this, we proposed and evaluated approaches for automatically identifying SATD relations across different sources. Our findings show that our approach outperforms baseline approaches by a large margin, achieving an average F1-score of 0.829 in identifying relations between SATD items. Moreover, we explored the characteristics of SATD relations in 103 open-source projects and describe nine major cases in which related SATD is documented in a second source, and give a quantitative overview of 26 kinds of relations.
The Type to Take Out a Loan? A Study of Developer Personality and Technical Debt

Background: Technical debt (TD) has been widely discussed in software engineering research, and there is an emerging literature linking it to developer characteristics. However, developer personality has not yet been studied in this context. Aims and Method: We explore the relationship between various personality traits (Five Factor Model, regulatory focus, and narcissism) of developers and the introduction and removal of TD. To this end, we complement an existing TD dataset with novel self-report personality data gathered by surveying developers, and analyze 2,145 commits from 19 developers. Results: We find that conscientiousness, emotional stability, openness to experience, and prevention focus are negatively associated with TD. There were no significant results for extraversion, agreeableness, promotion focus, or narcissism. Conclusions: We take our results as first evidence that developer personality has a systematic influence on the introduction and removal of TD. This has implications not only for future research, which could, for example, study the effects of personality on downstream consequences of TD like defects, but also for software engineering practitioners who may, for example, consider developer personality in staffing decisions.
Technical Debt Classification in Issue Trackers using Natural Language Processing based on Transformers

Background: Technical Debt (TD) needs to be controlled and tracked during software development. Support to automatically track TD in issue trackers is limited. Aim: We explore the usage of a large dataset of developer-labeled TD issues in combination with cutting-edge Natural Language Processing (NLP) approaches to automatically classify TD in issue trackers. Method: We mine and analyze more than 160GB of textual data from GitHub projects, collecting over 55,600 TD issues and consolidating them into a large dataset (GTD dataset). We use such datasets to train and test Transformer ML models. Then we test the model’s generalization ability by testing them on six unseen projects. Finally, we re-train the models including part of the TD issues from the target project to test their adaptability. Results and conclusion: (i) We create and release the GTD dataset, a comprehensive dataset including TD issues from 6,401 public repositories with various contexts; (ii) By training Transformers using the GTD dataset, we achieve performance metrics that are promising; (iii) Our results are a significant step forward towards supporting the automatic classification of TD in issue trackers, especially when the models are adapted to the context of unseen projects after fine-tuning.
Identifying Code Changes for Architecture Decay via a Metric Forest Structure

During long-term software evolution, it is inevitable that an accumulation of changes leads to architectural erosion and debt. Diverse metric-based methods have been developed to identify architectural problems that violate design principles and degrade software maintainability. However, there still exists a gap between the implementation-level metrics and architecture-level metrics. Consequently, it hinders the comprehensibility, interpretability, and indicative(-bility) of the measurement results. To fill this gap, we propose the dbMIT to identify potential code changes that make architecture decay. Our dbMIT first integrates popular metrics such as the CK suite. Then dbMIT constructs a forest structure of metrics, serving as a knowledge base to relate the metrics across levels together. The forest aims to link the architecture-level metrics and implementation-level metrics. Via pre-defined rules using the forest structure, our dbMIT identifies code changes that potentially cause the architecture decay. The usage of forest structure of metrics makes it easy for developers to understand detection results, explain why the detected code changes are potential contributors to the decay, and indicate the code scope for resolution. We also contribute a web-based tool to integrate our dbMIT. Our experiments on the collected projects demonstrate the effectiveness of dbMIT against a history-based ground-truth.
Robust Programmable Photonic Circuits Based on a Floquet-Lieb Topological Waveguide Lattice

We propose a robust programmable photonic integrated circuit platform based on a 2D Floquet-Lieb topological lattice. Reconfigurable topological photonic lattices typically require creating topologically distinct domains to guide interface modes, which requires a large number of coupling elements to be tuned and severely constraints the realizable circuit configurations. Here by taking advantage of the natural hopping sequence of light in the Floquet-Lieb microring lattice, we show that line defect modes and flat-band resonance modes can be excited by tuning only selective coupling elements, thus enabling efficient light steering and localization in the lattice interior. We show how basic circuit elements such as waveguide bends, splitters, combiners, and resonators, can be formed in the lattice, which can be used to construct general photonic integrated circuits. Compared to conventional topologically-trivial waveguide meshes, our topological photonic lattice requires fewer tuning elements while offering better tolerance to defects and random variations due to topological protection. The proposed topological lattice can thus provide an efficient and robust platform for implementing classical and quantum integrated photonic circuits.
Simulating Our Way to Safer Software: A Tale of Integrating Microarchitecture Simulation and Leakage Estimation Modeling

An important step to protect software against sidechannel vulnerability is to rigorously evaluate it on the target hardware using standard leakage tests. Recently, leakage estimation tools have received a lot of attention to improve this timeconsuming process. Despite their advancements, existing tools often neglect the impact of microarchitecture and its underlying events in their leakage model which leads to inaccuracies. This paper takes the first step in addressing these issues by integrating a physical side-channel leakage estimation tool into a microarchitectural simulator. To achieve this, we first systematically explore the impact of various architecture and microarchitecture activities and their underlying interactions on the produced physical side-channel signals and integrate that into the microarchitecture model. Second, to create a comprehensive leakage estimation report, we leverage taint tracking and symbolic execution to accurately analyze different paths and inputs. The final outcome of this work is a tool that takes a binary and generates a leakage report that covers architecture and microarchitecture-related leakages for both data-dependent and path-dependent information leakage scenarios.
Improving Cluster Utilization Through Adaptive Resource Management for Deep Neural Network and CPU Jobs Colocation

While deep neural network (DNN) models are mainly trained using GPUs, many companies and research institutions build shared GPU clusters. These clusters host DNN training jobs, DNN inference jobs, and CPU jobs (jobs in traditional areas). DNN training jobs require GPU for main computation and CPU for auxiliary computation. Some DNN inference jobs could rely solely on CPU, while others must utilize both CPU and GPU. Our investigation demonstrates that the number of cores allocated to a training job significantly impacts its performance, and that DNN inference jobs can make use of the limited CPU cores on the GPU nodes. To accomplish this, we characterize representative deep learning models in terms of their CPU core requirements for their training jobs and inference jobs, and investigate their sensitivity to other CPU-side resource contention. Based on the characterization, we propose SODA, a scheduling system comprised of an adaptive CPU allocator, a multi-array job scheduler, a hardware-aware inference job placer, and a real-time contention eliminator. The experimental results indicate that SODA increases GPU utilization by an average of 19.9%, while maintaining the quality of service target for all DNN inference jobs and the queuing performance of CPU jobs.
A Linearly Convergent Optimization Framework for Learning Graphs From Smooth Signals

Learning graph structures from a collection of smooth graph signals is a fundamental problem in data analysis and has attracted much interest in recent years. Although various optimization formulations of the problem have been proposed in the literature, existing methods for solving them either are not practically efficient or lack strong convergence guarantees. In this article, we consider a unified graph learning formulation that captures a wide range of static and time-varying graph learning models and develop a first-order method for solving it. By showing that the set of Karush-Kuhn-Tucker points of the formulation possesses a so-called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">error bound property</i> , we establish the linear convergence of our proposed method. Moreover, through extensive numerical experiments on both synthetic and real data, we show that our method exhibits sharp linear convergence and can be substantially faster than a host of other existing methods. To the best of our knowledge, our work is the first to develop a first-order method that not only is practically efficient but also enjoys a linear convergence guarantee when applied to a large class of graph learning models.
Counterfactual Mix-Up for Visual Question Answering

Counterfactuals have been shown to be a powerful method in Visual Question Answering in the alleviation of Visual Question Answering’s unimodal bias. However, existing counterfactual methods tend to generate samples that are not diverse or require auxiliary models to synthesize additional data. In this regard, we propose a more diverse and simple counterfactual sample synthesis method called Counterfactual Mix-Up (CoMiU), which generates counterfactual image features and questions through batch-wise swapping in local object- and word-level. This method efficiently facilitates the generation of more abundant and diverse counterfactual samples, which help improve the robustness of Visual Question Answering models. Moreover, with the creation of diverse counterfactual samples, we introduce two more robust and stable contrastive loss functions, namely Batch-Contrastive loss and Answer-Contrastive loss. We test our method on various challenging Visual Question Answering robustness testing setups to show the advantages of the proposed method compared with the current state-of-the-art methods.
Revisiting AUC-Oriented Adversarial Training With Loss-Agnostic Perturbations

The Area Under the ROC curve (AUC) is a popular metric for long-tail classification. Many efforts have been devoted to AUC optimization methods in the past decades. However, little exploration has been done to make them survive adversarial attacks. Among the few exceptions, AdAUC presents an early trial for AUC-oriented adversarial training with a convergence guarantee. This algorithm generates the adversarial perturbations globally for all the training examples. However, it implicitly assumes that the attackers must know in advance that the victim is using an AUC-based loss function and training technique, which is too strong to be met in real-world scenarios. Moreover, whether a straightforward generalization bound for AdAUC exists is unclear due to the technical difficulties in decomposing each adversarial example. By carefully revisiting the AUC-orient adversarial training problem, we present three reformulations of the original objective function and propose an inducing algorithm. On top of this, we can show that: 1) Under mild conditions, AdAUC can be optimized equivalently with score-based or instance-wise-loss-based perturbations, which is compatible with most of the popular adversarial example generation methods. 2) AUC-oriented AT does have an explicit error bound to ensure its generalization ability. 3) One can construct a fast SVRG-based gradient descent-ascent algorithm to accelerate the AdAUC method. Finally, the extensive experimental results show the performance and robustness of our algorithm in five long-tail datasets.
Multiview Graph Contrastive Learning for Multivariate Time-Series Anomaly Detection in IoT

Internet of Things (IoT) systems typically generate large amounts of sensory signals that get involved to represent the states of the systems. Most existing methods focus on learning the temporal patterns of the signals to detect anomalies. However, the performance is limited due to two critical problems. First, the relationships between different signals are rarely considered, resulting in missing important information for representation. Second, the high sensitivity of time series makes it difficult to use conventional methods for data augmentation, which limits the improvement of representation and generalization capabilities. In this work, we propose a novel reconstruction-based framework with contrastive learning from multiple views to address these two issues. Specifically, intrasignal and intersignal graph structures are formed and learned in parallel to model the temporal context and capture the dependency relationships between signals, respectively. Multiview graph contrastive learning strategy is designed to improve the graph representation. We also provide an adaptive data augmentation method to generate graph views for contrastive learning, which helps to accurately capture valuable intrinsic patterns from two different perspectives. Finally, the contrastive learning task and the reconstruction task are jointly trained. Extensive experiments on four real-world data sets demonstrate that our method outperforms the existing state-of-the-art baselines.
Night-Time Measurement and Skeleton Recognition Using Unmanned Aerial Vehicles Equipped With LiDAR Sensors Based on Deep-Learning Algorithms

Robots, unmanned vehicles, and unmanned aerial vehicles (UAVs) are widely used as mobile platforms to facilitate the scanning range and coordinate positioning of vision and recognition for analyzing surrounding environmental information. However, during the night, image recognition is not efficient. In this study, a UAV equipped with a LiDAR sensor was used to detect night-time environmental information and recognize human characteristics. The point data of the foreground and background were separated using the background difference method and density-based spatial clustering of applications with noise (DBSCAN) to automatically distinguish and classify objects by the distance difference between adjacent points. Root mean square error (RMSE) was calculated by surface fitting, and the surface state of the object was summarized as a condition to determine whether the object is human. Image processing was performed on point cloud data, and skeleton recognition was performed using the surface image of the object and artificial intelligence (AI) algorithm. Thus, an automatic recognition system was established. Hyperparameters were adjusted for optimal modeling. Finally, VGG16 was used for feature extraction to obtain a mean average precision (mAP) of 95.3%. Human body recognition using skeleton recognition based on RMSE surface analysis approached 93.8% during the night. The website information of recognition results was established through the server terminal computer to present the current measured environmental state and recognize a human body in the night-time environment. This technology can be used to search for victims during disaster relief scenarios and reduce the difficulty and time required for night-time search and rescue operations.
Blockchain Enabled Smart Healthcare System Using Jellyfish Search Optimization With Dual-Pathway Deep Convolutional Neural Network

Blockchain (BC) and Artificial intelligence (AI) based technologies have earned a better reputation amongst the research community, especially in the medical field. BC technology has emerged as a promising solution to revolutionize the medical field by addressing challenges related to efficiency, data security, and interoperability. A BC-aided smart healthcare system leverages the immutable and decentralized nature of BC to construct a secured and transparent ecosystem to manage processes and healthcare data. It leverages the secure and decentralized nature of BC to optimize the processes, security, interoperability, and efficiency of medical data. The existing system is exposed to security attacks on healthcare data. It can be necessary to construct a real-time detection device utilizing a cyber-physical system (CPS) with BC technology in a significant way. This article designs a novel Blockchain-Enabled Smart Healthcare System using Jellyfish Search Optimization with Dual-Pathway Deep Convolutional Neural Network (JSO-DPCNN) technique. The presented JSO-DPDCNN technique exploits the concept of BC-enabled secure data transmission and DL-based diagnosis model for moneypox disease on smart healthcare monitoring. To accomplish this, the JSO-DPCNN technique uses Ethereum-based public BC to secure the privacy of healthcare images. In addition, the JSO-DPCNN technique applies a feature extraction module using DPCNN, which extracts the suitable set of features in the input images. Moreover, the multiplicative long short-term memory (MLSTM) approach was used for the disease detection process. Lastly, the JSO system can be employed for the parameter tuning of the MLSTM model. The simulation result of the JSO-DPCNN system was executed on a benchmark medical dataset. The comprehensive outcomes highlighted the significant outcome of the JSO-DPCNN approach in terms of different measures.