Reviewerly/exHarmony huggingface dataset:
ChatGLM-6B Fine-Tuning for Cultural and Creative Products Advertising Words

ChatGLM-6B is a bilingual conversational language model based on the General Language Model (GLM) architecture. It efficiently blends natural language responses, drawn from Chinese Q&A feedback, with supervised learning and feedback reinforcement strategies guided by humans. These efforts produce responses aligned with human tastes and expectations. As we find ourselves in an era where traditional culture is being disseminated through the dynamic growth of the creative and cultural industries, this study zeroes in on ChatGLM-6B's feature allowing local deployment on standard consumer graphics cards. Utilising a dataset generated for this purpose, the ChatGLM-6B model undergoes fine-tuning via the P-Tuning v2 method at an int-4 quantisation level. Consequently, this endows the model with the capacity to generate lively and captivating advertising phrases for cultural and creative goods.
Transfer Learning for Tang Tri-Color Cultural Relic Image Recognition

Tang Tri-Color (TTC) cultural relic image recognition refers to the classification and recognition of TTC images according to their appearance. TTC is the popular pottery during the Tang Dynasty in China, and its production techniques are included in the list of China's intangible cultural heritage. Preserving and promoting TTC is of great importance. However, there are issues with incomplete cultural relic information and outdated design and production techniques in TTC. Therefore, this paper introduces transfer learning to recognize and classify TTC images. This can assist in organizing TTC cultural relic information, research for archaeologists, and the design work of TTC producers. This paper collects a dataset of TTC images, which is used to train the recognition model in a semi-automatic and semi-manual manner. The ResNet-152 network is used for transfer learning, resulting in a simple and efficient training of the recognition model. When using the recognition model, inputting a TTC image will provide its corresponding category. The goal of recognition and classification is achieved. Through evaluation on a test dataset, the accuracy of the model is determined to be 0.95, indicating its excellent recognition capabilities.
Operation Energy Consumption Calculation Model Based on Energy Conservation

Aiming at the shortcomings of the existing motion energy consumption calculation algorithm in accuracy, a motion energy consumption calculation model based on energy conservation is proposed. Taking the energy conservation theorem as an example, the energy conversion formula of the human body is established. The three-axis acceleration sensor is used to obtain the monitoring data, calculate the changes of human kinetic energy and potential energy, and calculate the energy consumption of walking, running and up and down stairs. The accuracy of the calculation model in energy consumption calculation is verified by comparative experiments. Compared with the calculation model of pedometer energy consumption, energy consumption calculation based on eigenvalue IAtot, energy consumption calculation based on eigenvalue Counts, calorie energy consumption calculation and energy balance energy consumption calculation, the calculation model of motion energy consumption simplifies the amount of data required for calculation, simplifies the calculation steps, and has higher accuracy.
Investigation on the Activation Display of Chinese Ancient Poetry with Metaverse Technology

The application of metaverse technology to the development of traditional culture has received a lot of attention. Immersive digital products can not only draw emphasis to the preservation and transmission of traditional culture, but also improve the efficiency and quality of traditional cultural content dissemination. This paper focuses on the application of metaverse technology to the digitization of ancient Chinese poetry. First, we show the main application scenarios of metaverse technology based on the investigation of its main architecture. Then, we analyze the challenges facing the digitization of ancient Chinese poetry and give a typical framework for the application of metaverse technology to the digitization of ancient poetry. Finally, we discuss the challenges and future directions. The integration of metaverse and ancient poetry studied in this paper will give new vitality to the development of ancient poetry and promote the continuous innovation and development of traditional culture.
DHAN-WGAN: Adversarial Image Colorization Using Deep Hybrid Attention Aggregation Network

While the gray-scale image colorization algorithms based on deep learning bring automatic and efficient coloring methods, there are still a series of problems such as inaccurate coloring details, great ill-posed coloring effects, and low saturation of the generated images. To address these issues, this paper proposes a Deep Hybrid Attention Aggregation Network based on Wasserstein Generative Adversarial Network: DHAN-WGAN. In the generator, we use dynamic convolution and attention mechanisms for finer coloring, and a global prior of image scene categories is incorporated to achieve better colorization. In view of the deficiencies of traditional Generative Adversarial Networks (GANs), this paper uses improved Wasserstein GAN with Gradient Penalty (WGAN-GP) for more stable training. Experiments prove that DHAN-WGAN can have a more accurate and more saturated colorization effect on gray-scale images.
A Graph Isomorphism Algorithm based on Continuous-Time Quantum Walks

Graph isomorphism is an essential problem in graph theory and widely used in a variety of applications. With graph size increasing, graph isomorphism become difficult for classical computation since there is no polynomial algorithm for general graphs yet. Considering the advantages of quantum computation over classical computation in various tasks, quantum graph isomorphism algorithms have been studied to explore the potentials of quantum computation in graph isomorphism. In this work, we propose a graph isomorphism algorithm based on continuous-time quantum walks, where we construct the graph certificates from the output probabilities of continuous-time quantum walks on the tested graphs with added self-loops to graph vertices, and distinguish graphs by comparing the graph certificates. Numerous tests on diverse graph classes demonstrate the effectiveness of the proposed GI algorithm, which is not limited to a specific topological structure graph. Moreover, we test the proposed algorithm on classes of strongly regular graphs, and the results show that the distinguishing ability of the algorithm can be improved with the increasement of the added self-loops to distinct vertices.
A Kinship Authentication Algorithm Based on Deep Convolutional Neural Networks

The method based on deep learning can extract the nonlinear features of the face through the neural network and then make decisions at the classification layer. This is a typical end-to-end convolutional neural network model, which eliminates the complex image preprocessing stage. Then, pairs of tagged training data were input into the network for iteration to optimize the parameters of the training depth network model. The trained deep network model is used to classify the input test images, and the final accuracy rate is obtained by statistics. The experiment verifies that the improved network based on Siamese network can be effectively applied to the kinship identity authentication algorithm to carry out the task of face recognition comparison, with high accuracy, and effectively narrow the scope of kinship authentication.
A Dataset for Generating Chinese Landscape Painting

A dataset was constructed for intelligent generation of Chinese landscape paintings based on deep learning, which provides the scientific assistance for inheriting the traditional Chinese culture. This dataset is originated from the two of top ten paintings in ancient China, i.e., “Dwelling in the Fuchun Mountains” with ink wash style and “A Thousand Li of Rivers and Mountains” with style of blue and green. This dataset contains landscape paintings and their corresponding sketches, as well as photos of natural landscapes. The samples of landscape paintings are achieved with the size of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$256\times 256$</tex> due to the technique of image preprocessing, the samples of sketches are generated by the canny edge detector, and the samples of landscape photos are formed by image flipping. The above samples of landscape paintings, sketches, and photos make the dataset more flexible for the generation of landscape paintings. Based on this dataset, landscape paintings can be generated from sketches and photos respectively, and landscape paintings can be transferred from one style to another. The experimental results demonstrate the suitability of the dataset for the field of landscape painting generation. The constructed dataset will be publicly available to the community.
Latency Minimization for Federated Learning over Wireless Networks under Energy Constraint

In recent years, a distributed training frame has gradually replaced the traditional cloud-based centralized training, which is called Federated Learning (FL). It allows users of mobile devices to train their respective data locally and upload the upgraded models to cloud servers for aggregation, instead of uploading their data to the base station for centralized training, thus protecting their privacy. With one federated learning training session, the cloud server sends an initialized model to the devices, which use their respective local datasets to train the model, and then upload the upgraded models to the cloud server for aggregation. Then repeat the step until the model converges. However, this training frame still has some issues, such as high training latency and energy consumption. We formulate a nonlinear mixed-integer programming problem for minimizing training latency under energy constraint, and then we solve it by decoupling it into two subproblems. The subproblems are resource allocation and device scheduling problem, respectively. For the resource allocation subproblem, a method based on binary search is proposed to find a numerical solution for CPU frequency and transmit power allocated to the device. For the device scheduling subproblem, we propose a greedy policy to select the device that consumes the least time in model training in each iteration. The Simulation shows that the policy we proposed outperforms other policies and achieves a better model accuracy under the energy constraint.
Text To Video: Enhancing Video Generation Using Diffusion Models And Reconstruction Network

This paper proposes a method to improve the quality of generated videos in text to video generation techniques based on diffusion models, which suffer from low quality and poor continuity.The method involves dynamically adjusting the noise frame connections to enhance the video quality. A Reconstruction Net is introduced to automatically adjust the noise correlation among frames during the training process. Experimental results demonstrate that this method can enhance the quality of generated videos, improve video continuity, enhance the representation of image details, and strengthen the correspondence between generated and original videos. This research is of significant importance in advancing the development of text-based video generation techniques based on diffusion models.
StaticTracker: A Diff Tool for Static Code Warnings

Static bug detectors help improve software quality by detecting code issues (e.g., code smells or bugs). However, static bug detectors are underutilized in practice due to various reasons. One primary reason is that static bug detectors often report an overwhelming number of static warnings for one software revision. To facilitate better adoption of static bug detectors in software development, we propose a tool, namely StaticTracker, that specializes in tracking the evolution of static code warnings.StaticTracker analyzes each commit and produces the changes of static code warnings caused by the commit, i.e., a diff for static code warnings. We integrate StaticTracker in continuous integration through Git Hooks. Whenever developers push code to a git repository, StaticTracker is automatically activated to identify disappeared and newly-introduced warnings by the commit. We implement StaticTracker for two static bug detectors (Spotbugs and PMD) and evaluate StaticTracker on the recent commits of two open-source projects (Druid and Jedis). Our evaluation shows that StaticTracker is effective in reducing the overwhelming static code warnings that developers need to investigate and achieves an accuracy of 89.8%, which outperforms the state-of-the-art tracking approach with an accuracy of 68.5%.We open source the tool at https://github.com/ljj430/tracking-static-warnings_tool_demo, and the demo video is at https://www.youtube.com/watch?v=2WMOjoq1Nbs.
Software Testing and Code Refactoring: A Survey with Practitioners

Nowadays, software testing professionals are commonly required to develop coding skills to work on test automation. One essential skill required from those who code is the ability to implement code refactoring, a valued quality aspect of software development; however, software developers usually encounter obstacles in successfully applying this practice. In this scenario, the present study aims to explore how software testing professionals (e.g., software testers, test engineers, test analysts, and software QAs) deal with code refactoring to understand the benefits and limitations of this practice in the context of software testing. We followed the guidelines to conduct surveys in software engineering and applied three sampling techniques, namely convenience sampling, purposive sampling, and snowballing sampling, to collect data from testing professionals. We received answers from 80 individuals reporting their experience refactoring the code of automated tests. We concluded that in the context of software testing, refactoring offers several benefits, such as supporting the maintenance of automated tests and improving the performance of the testing team. However, practitioners might encounter barriers in effectively implementing this practice, in particular, the lack of interest from managers and leaders. Our study raises discussions on the importance of having testing professionals implement refactoring in the code of automated tests, allowing them to improve their coding abilities.
Understanding the Motivations, Challenges, and Practices of Software Rejuvenation

The continuous evolution of programming languages has brought benefits and new challenges for software developers. In recent years, we have witnessed a rapid release of new versions of mainstream programming languages like Java. While these advancements promise better security, enhanced performance, and increased developers’ productivity, the constant release of new language versions has posed a particular challenge for practitioners: how to keep their systems up-to-date with new language releases. This thesis aims to understand the pains, motivations, and practices developers follow during rejuvenating efforts—a particular kind of software maintenance whose goal is to avoid obsolesce due to the evolution of programming languages. To this end, we are building and validating a theory using a mixed methods study. In the first study, we interviewed 23 software developers and used the Constructivist Grounded Theory Method to identify recurrent challenges and practices used in rejuvenation efforts. In the second study, we mined the software repositories of open-source projects written in C++ and JavaScript to identify the adoption of new language features and whether or not software developers conduct large rejuvenation efforts. The first study highlights the benefits of new feature adoption and rejuvenation, revealing developer methods and challenges. The second study emphasizes open-source adoption trends and patterns for modern features. In the third and final study, our goal is to share our theory on software rejuvenation with practitioners through the Focus Group method with industrial patterns.
Highway Visibility Estimation in Foggy Weather via Multi-Scale Fusion Network

Poor visibility has a significant impact on road safety and can even lead to traffic accidents. The traditional means of visibility monitoring no longer meet the current needs in terms of temporal and spatial accuracy. In this work, we propose a novel deep network architecture for estimating the visibility directly from highway surveillance images. Specifically, we employ several image feature extraction methods to extract detailed structural, spectral, and scene depth features from the images. Next, we design a multi-scale fusion network to adaptively extract and fuse vital features for the purpose of estimating visibility. Furthermore, we create a real-scene dataset for model learning and performance evaluation. Our experiments demonstrate the superiority of our proposed method to the existing methods.
Benchmarking Causal Study to Interpret Large Language Models for Source Code

One of the most common solutions adopted by software researchers to address code generation is by training Large Language Models (LLMs) on massive amounts of source code. LLMs are rooted in the concept of emergent capabilities in which machines statistically learn complex patterns from code data. Although a number of studies have shown that LLMs have been effectively evaluated on popular accuracy metrics (e.g., BLEU, CodeBleu), previous research has largely overlooked the role of Causal Inference as a fundamental component of the interpretability of LLMs’ performance. Existing benchmarks and datasets are meant to highlight the difference between the expected and the generated outcome, but do not take into account confounding variables (e.g., lines of code, number of tokens, prompt size) that equally influence the accuracy metrics. The fact remains that, when dealing with generative software tasks by LLMs, no benchmark is available to tell researchers how to quantify neither the causal effect of SE-based treatments nor the correlation of confounders to the model’s performance. In an effort to bring statistical rigor to the evaluation of LLMs, this paper introduces a benchmarking strategy named Galeras comprised of curated testbeds for three SE tasks (i.e., code completion, code summarization, and commit generation) to help aid the interpretation of LLMs’ performance.We illustrate the insights of our benchmarking strategy by conducting a case study on the performance of ChatGPT under distinct prompt engineering methods. The results of the case study demonstrate the positive causal influence of prompt semantics on ChatGPT’s generative performance by an average treatment effect of ≈ 3%. Moreover, it was found that confounders such as prompt size are highly correlated with accuracy metrics (≈ 0.412). The end result of our case study is to showcase causal inference evaluations, in practice, to reduce confounding bias. By reducing the bias, we offer an interpretable solution for the accuracy metric under analysis.
DebtViz: A Tool for Identifying, Measuring, Visualizing, and Monitoring Self-Admitted Technical Debt

Technical debt, specifically Self-Admitted Technical Debt (SATD), remains a significant challenge for software developers and managers due to its potential to adversely affect long-term software maintainability. Although various approaches exist to identify SATD, tools for its comprehensive management are notably lacking. This paper presents DebtViz, an innovative SATD tool designed to automatically detect, classify, visualize and monitor various types of SATD in source code comments and issue tracking systems. DebtViz employs a Convolutional Neural Network-based approach for detection and a deconvolution technique for keyword extraction. The tool is structured into a back-end service for data collection and pre-processing, a SATD classifier for data categorization, and a front-end module for user interaction. DebtViz not only makes the management of SATD more efficient but also provides in-depth insights into the state of SATD within software systems, fostering informed decision-making on managing it. The scalability and deployability of DebtViz also make it a practical tool for both developers and managers in diverse software development environments. The source code of DebtViz is available at https://github.com/yikun-li/visdom-satd-management-system and the demo of DebtViz is at https://youtu.be/QXH6Bj0HQew.
An Empirical Study on Bugs Inside PyTorch: A Replication Study

Software systems are increasingly relying on deep learning components, due to their remarkable capability of identifying complex data patterns and powering intelligent behaviour. A core enabler of this change in software development is the availability of easy-to-use deep learning libraries. Libraries like PyTorch and TensorFlow empower a large variety of intelligent systems, offering a multitude of algorithms and configuration options, applicable to numerous domains of systems. However, bugs in those popular deep learning libraries also may have dire consequences for the quality of systems they enable; thus, it is important to understand how bugs are identified and fixed in those libraries.Inspired by a study of Jia et al., which investigates the bug identification and fixing process at TensorFlow, we characterize bugs in the PyTorch library, a very popular deep learning framework. We investigate the causes and symptoms of bugs identified during PyTorch’s development, and assess their locality within the project, and extract patterns of bug fixes. Our results highlight that PyTorch bugs are more like traditional software projects bugs, than related to deep learning characteristics. Finally, we also compare our results with the study on TensorFlow, highlighting similarities and differences across the bug identification and fixing process.
Deterministic Automatic Refactoring at Scale

In the context of continually growing large code repositories where code refactoring is an ongoing requirement, we highlight the effectiveness of OpenRewrite as a tool for conducting large-scale code refactoring. OpenRewrite leverages Lossless Semantic Trees (LST) to represent code and applies recipes to search and implement changes. These recipes are openly available and can be executed locally or accessed through the Moderne platform for public repositories. We provide a concise overview of the underlying technology, instructions for utilizing the tool, and we compare its performance against a manual approach and two prominent large language models (LLM): ChatGPT and StarChat-β. Our comparison is based on the execution time of the tool and the accuracy of the implemented changes. Additionally, we present three distinct use cases that demonstrate the versatile applications of the tool. A demonstration of OpenRewrite’s recipe which detects vulnerabilities and automatically fixes them is available at the following link: https://www.youtube.com/watch?v=L1-_cQUX-JA.