Reviewerly/exHarmony huggingface dataset:
Cryptanalysis of iterative encryption and image sharing scheme based on the VQ attack

Reversible data hiding in encrypted images (RDHEI) is widely employed to protect privacy in images stored on cloud storage. Recently, Yu et al. [22] proposed an effective image encryption scheme called iterative encryption and image sharing (IEIS), which is highly compatible with RDHEI. In this study, we analyze the characteristics of IEIS and present a cryptanalysis scheme based on vector quantization (VQ) attack. To capture the pixel-changing pattern of an image block, the concept of pixel difference matrix (PDM) is introduced. VQ attack is subsequently utilized to estimate the ciphertext block. Furthermore, we employ a well-trained convolutional neural network (CNN) based discriminator to identify the proper block permutation sequence within the secret key space. Experimental results demonstrate the efficacy of our proposed cryptanalysis scheme. Based on our study, we conclude that the IEIS scheme raises security concerns.
CDS-Net: Cooperative dual-stream network for image manipulation detection

To accurately locate manipulated regions, many existing approaches employ a dual-stream framework to extract a wide range of manipulation clues, including local noise, edge artifacts, and global inconsistency. However, these approaches treat each stream in isolation and fail to consider the complementary and mutual guidance ability between the streams. Moreover, we notice the use of vanilla vision transformers in previous approaches can result in disruptions of object semantics, causing incomplete predictions. To address these challenges, we introduce the cooperative dual-stream network (CDS-Net) comprising an RGB Stream and a Noise Stream. In the Noise Stream, we propose a K-means Transformer (KT) that encourages both inter-patch and intra-patch information transmission to mitigate the semantic fragmentation phenomenon caused by patch partitioning. Additionally, we introduce a novel Feature Interaction Block (FIB) that explicitly encourages cross-stream collaboration at each encoding stage. Comprehensive experiments on publicly available datasets demonstrate the effectiveness and robustness of CDS-Net.
What Do Infrastructure-as-Code Practitioners Discuss: An Empirical Study on Stack Overflow

Background. Infrastructure-as-Code (IaC) is an emerging practice to manage cloud infrastructure resources for software systems. Modern software development has evolved to embrace IaC as a best practice for consistently provisioning and managing infrastructure using various tools such as Terraform and Ansible. However, recent studies highlighted that developers still encounter various challenges with IaC tools. Aims. We aim in this paper to understand the different challenges that developers encounter with IaC and analyze the trend of seeking assistance on Q&A platforms in the context of IaC. To this end, we conduct a large-scale empirical study investigating developers' discussions in Stack Overflow. Method. We first collect IaC-relevant tags on Stack Overflow, constituting a dataset that comprises 52,692 questions and 64,078 answers. Then, we group questions into specific topics using the Latent Dirichlet Allocation (LDA) method, which we optimize using a Genetic Algorithm (GA) for parameter's fine-tuning. Finally, to gain better insights, we analyze the identified topics based on different criteria such as popularity and difficulty. Results. Our findings reveal an average yearly increase of 150% in terms of IaC-related questions and 135% in terms of users between 2011 and 2022. Furthermore, we observe that IaC questions revolve around seven main topics: server configuration, policy configuration, networking, deployment pipelines, variable management, templating, and file management. Notably, we found that server configuration and file management are the most popular topics, i.e., the most discussed among IaC developers, while the deployment pipelines and templating topics are the most difficult. Conclusions. Our results shed light on IaC challenges that are often encountered by developers on popular Q&A platforms. These findings reveal important implications for practitioners seeking better support for IaC tools in real-world settings and for researchers to better understand the IaC community needs and further investigate IaC in different aspects.
What are Pros and Cons? Stance Detection and Summarization on Feature Request

BACKGROUND: In an online issue tracking system, e.g., GitHub Issue Tracker, feature requests and the associated comment stream provide valuable crowd-generated knowledge for requirements elicitation. To decide whether a feature request should be accepted or not, stakeholders need to identify the comments for/against the feature and understand the two-sided opinions, which is time- and effort-consuming considering the abundant information embedded in lengthy comment stream per feature request. AIMS: This paper proposes VoteBot for automatically detecting stance (for/against) and summarizing the related opinions on a feature request, which can facilitate the decision making (i.e., voting) of feature requests. To our best knowledge, such an approach is previously unexplored for crowd-based requirements elicitation. METHOD: VoteBot is a relation-aware approach, which incorporates three types of relations among the comments or among the comment sentences to better understand the discussions about feature requests. Specifically, it extracts the reply-to relation among the comments, and incorporates it into a BERT-based classifier for stance detection. It also designs a graph-based ranking algorithm, and incorporates semantic relevance and argumentative relations for stance summarization. RESULTS: The automatic evaluation on 250 feature requests with 6,598 comments from five GitHub projects, and the evaluation with practitioners on five new projects, show the promising results. CONCLUSIONS: VoteBot is effective in stance detection and stance summarization, and potentially useful for understanding feature requests and associated discussions in real-world practice.
Blockchain Application in the Waste Trading

Waste is a complex and severe problem with detrimental environmental impacts if not appropriately managed. The process of buying and selling waste is essential in waste management. With its smart contracts, blockchain allows transactions to be carried out automatically without involving third parties, thereby speeding up the process and increasing efficiency and security. This research applies blockchain to record waste buying and selling transactions. We selected Binance Smart Chain (BSC) as a blockchain platform and Solidity 0.8.0 to build smart contracts containing structures, events, and functions that manage transactions. Autotask connected to the relayer records transactions on the blockchain network without requiring direct intervention from the user. We set up credentials from apiKey and apiSecret to connect to OpenZeppelin Defender, then enter the contract address deployed to the BSC network. After that, we create a variable to accommodate the deployed smart contract's Application Binary Interface (ABI). System testing uses the black box and System Usability Scale (SUS). Of the ten test scenarios in the black box, everything ran smoothly. System testing using SUS resulted in a score of 76 and a Net Promoter Score (NPS) value of 70%. This score indicates that the system is deemed acceptable and included in the good category. This system can guarantee transparency in the waste buying and selling system, hoping to increase accountability and trust in the community.
Synthesizing Procedural Landscape for Volcanic Eruption Simulation in Virtual Reality

Virtual reality (VR) has emerged as a powerful tool for simulating and visualizing various natural phenomena, offering immersive experiences for education, research, and entertainment. In this paper, we present a novel approach for synthesizing procedural landscapes to simulate volcanic eruptions in virtual reality. We combine geological principles, procedural generation techniques, and immersive VR technologies to create a realistic and interactive volcanic eruption simulation. According to a series of numerical experiments and preliminary user studies, we show the correctness of our proposed technical approach. Our approach has the potential to enhance understanding of volcanic processes and contribute to the development of virtual geoscience environments. We describe the underlying methodology, system architecture, user experience, and discuss the potential applications and future directions in geoscience edutainment.
Introducing test theory to deep neural network testing: reliability, validity, difficulty and discrimination

Researchers have developed various test methods and tools to ensure the performance and security of deep neural network (DNN) applications and detect potential defects as much as possible. However, there is still a lack of a mature and comprehensive DNN test theory, leading to the uneven quality of test tools and inconsistent evaluation methods, which pose challenges for the selection and application of test tools. Test Theory (TT) has developed a series of mature theoretical approaches and has validated its usability over a long history and in a wide range of application scenarios. This paper analyses the DNN test process, defines the concept of DNN test tools, and surveys the current research status and limitations of the DNN test field. Inspired by TT, this paper systematically introduces TT into DNN testing for the first time building the theoretical model of reliability, validity, difficulty, and discrimination that conform to the characteristics of DNN, which provides a new research perspective for DNN testing and is a preliminary study for establishing DNN testing theory.
Application of multimodal speech recognition based on deep neural networks in interpretation teaching

In recent years, although speech recognition technology has been widely used, it also faces some problems. This paper studies multimodal speech recognition in interpreting based on deep neural network. Firstly, the deep learning method and its related theoretical basis are introduced. Then, the advantages of speech corpus denoising based on acoustic expert feature extraction and training algorithm, convolution decomposition method and interpretation element analysis are described. Finally, through the experimental verification, it is proved that the recognition system can effectively improve students’ interpretation efficiency and accuracy, and the accuracy rate is more than 93%.
Research on the construction method of knowledge graph for programming language

Knowledge graph can be used to clarify the curriculum knowledge system and describe the knowledge point and its structural relationship. In order to improve the learning efficiency and level of learners in the process of programming language learning, the typical examples related to knowledge can be incorporated into the knowledge graph. In this study, the knowledge graph of C# programming language is constructed as an example: first, the data is obtained; Second, the ontology is constructed and the data is processed. Then use the Neo4j graph database to store knowledge; Finally, knowledge retrieval and visualization are realized, and knowledge graph can be used to assist teaching and personalized learning.
Comparative studies of artificial neuron network in medicine

Artificial Neuron Network is a powerful tool help us solve various problems through its great computing capacity, which prompts its application in all fields. As a result of certain treatments of most of diseases, artificial neuron network could assist experts in analyzing state of an illness. A good prediction depends on difficulty of object and reasonable methods, and it is evaluated by appropriate metrics. this paper did a classification of the application of artificial neuron network in medicine based on Shan Yang’s work and its reference. Eventually, this paper presents some suggestions about the better directions to study in future.
Contextual copy-paste data augmentation for object detection

From the perspective of increasing the number of objects in the object detection dataset and ensuring their quality, this paper proposes contextual copy-paste data augmentation for object detection, which generates new images that match the objective situation by copying and pasting objects in the dataset. We train a object background classifier based on the background features of the same class objects. Use the trained classifier to classify the image background and paste the object that matches the background based on the classification results. The experimental results on PASCAL VOC 2012 showed that the algorithm we proposed effectively increased the number of objects in the dataset. The mean average precision of the four selected object detectors on the PASCAL VOC 2012 test set increased by about 0.9%, proving the effectiveness of our proposed data augmentation algorithm for object detection tasks.
A Structural Model of the Relationship between Teacher’s Job Motivation and Socio-Emotional Skills and Social Capital Mediated by Self-Efficacy

A Structural Model of the Relationship between Teacher’s Job Motivation and Socio-Emotional Skills and Social Capital Mediated by Self-Efficacy
Text2SQL model oriented to power grid information retrieval

The massive data in the power grid system is stored in various databases. However, when users want to query the contents of the databases, they need to write structured query language (SQL), which is difficult for non-professionals. This paper focuses on the research of Text2SQL technology in the power domain based on the Seq2Seq model. On one hand, the BERT model is introduced to classify the queries of power equipment defect texts, solving the problem of inaccurate prediction of aggregate functions in SQL statements. On the other hand, the decoding strategy with fused rule-constrained attention mechanism is employed to mine the data features of the input text, addressing the issue of information loss in input sequences. Finally, the improved Seq2Seq model is proposed by integrating the above strategies. Experimental results show that compared to the Seq2Seq model, the proposed algorithm achieves a 24.88% improvement in accuracy.
Aviation emergency rescue virtual training system based on virtual reality

[Objectives] In order to improve the emergency response speed and emergency response capability of aviation emergency rescue, and solve the pain points of long emergency preparation time, single emergency training scene and high cost of actual combat training, a virtual training system for aviation emergency rescue is created by using virtual simulation engine. [Process and Method] The three-dimensional modeling technology was used to truly restore the disaster accident scene and rescue disposal environment. The A* algorithm was improved by the Bessel curve principle to carry out the path planning simulation of the rescue equipment handling process. Based on the integrity of the virtual operation, the correctness of the operation steps, and the rationality of the operation time, a scoring evaluation system was established. Users can choose the learning mode or training mode independently according to the knowledge, select the airborne rescue equipment and complete the simulation of the assembly process and assembly mode of the rescue equipment. [Results] Through the operational test and functional test, the results show that the system can effectively simulate the installation method and emergency treatment method of airborne rescue equipment, and improve the response speed of emergency rescue.
An improved person re-identification method

In this paper, an attention method for person re-identification is proposed. We focus on different scales of features and strive to extract corresponding features at different fine-grained levels. By stacking attention modules with different finegrainedness to guide fine-grained and coarse-grained attention learning. The effectiveness of the proposed method is verified by experiments.
High capacity secret embedding strategy for QR codes based on EMD

QR codes have become the most prevalent means of information transmission in today's world, finding widespread usage across various domains. Nonetheless, being a publicly available technology, QR codes entail inherent risks of exposing sensitive information during the transmission process. To address this vulnerability, this paper introduces a QR code secret embedding scheme based on an enhanced EMD method, aiming to safeguard sensitive information within QR codes. In this scheme, secret messages are transformed into binary data streams, and an improved EMD strategy is employed to precisely adjust the pixel values of the cover QR code, facilitating the embedding of confidential data. Despite the modifications, the resulting marked QR code remains legible and meaningful, thereby diminishing the curiosity of potential attackers. With the aid of a customized scanner, the secret information can be swiftly extracted. Compared to existing approaches, this scheme boasts a higher capacity for secret payload and superior embedding efficiency.
Experiment design for skeleton-based pedestrian abnormal-behavior recognition

The detection and recognition of pedestrian abnormal behavior have been currently attractive research hotspots in surveillance video content-analysis. However, due to the complexity and diversity of pedestrian abnormal behavior, the accurate and efficient behavior-recognition remains a challenging issue in the field of pedestrian safety supported by computer vision technologies. Previous studies mostly focused on detecting and locating abnormal behavior from given videos rather than recognizing pedestrian behavior, which may be incompetent in a few practical scenarios since the threat of each anomaly is needed to be evaluated. As individual pedestrian behaviors are closely related to human postures, in this study, we propose a novel experiment design approach for pedestrian abnormal-behavior recognition based on pose estimation and behavior recognition. We first classify and annotate the common abnormal behaviors in public crowds’ abnormal-behavior datasets, such as ShanghaiTech and CUHK Avenue. Then, the pose estimation toolbox, Openpose, is employed to extract skeleton sequences of behaviors annotated. Finally, the skeleton sequences are classified by the spatial-temporal graph convolutional network (ST-GCN) to implement behavior recognition. This proposed experiment design-approach can provide a technical support to validate the mathematic models or algorithms about pedestrian abnormal behavior recognition.
Application of Multimedia Data Feature Extraction Technology in Teaching Classical Oil Painting

The cross-modal oil painting image generated by traditional methods makes it easy to miss the important information of the target part, and the generated image lacks realism. This paper combines the feature extraction technology of multimedia data with the generation confrontation network in deep learning, puts forward a generation model of classic oil painting, and applies it to university teaching. Firstly, the key frame extraction algorithm is used to extract the key frames in the video, and the channel attention network is introduced into the pre-trained ResNet-50 network to extract the static features of 2D images in short oil painting videos. Then, the depth feature mapping is carried out in the time dimension by using the double-stream I3D network, and the feature representation is enhanced by combining static and dynamic features. Finally, the high-dimensional features in the depth space are mapped to the two-dimensional space by using the opposition generation network to generate classic oil painting pictures.
Dense mapping of large outdoor scenes based on Pseudo-LiDAR

In order to solve the limitations of SLAM system when using RGB-D camera to achieve dense mapping of large outdoor scenes, this paper proposes a visual SLAM dense mapping system based on Pseudo-LiDAR. First, this method does not require a depth sensor and generates Pseudo-LiDAR by a separate module to provide geometric information to the system. The input stereo images are used to generate depth map by employing depth estimation network PSMNet, and the coordinates of each pixel point in the 3D world are calculated by combining the a priori camera parameters, which is Pseudo-LiDAR. Next, the Pseudo-LiDAR and stereo images are input to SLAM for pose estimation using the surfel representation to further achieve dense mapping. The ideas in this paper are experimentally validated on a large outdoor KITTI dataset and demonstrate a more robust dense mapping effect.
E-books in Spanish university libraries: An analysis of online social sciences degrees

This paper analyzes the availability, via both library lending licenses and open access, of digital versions of the monographs that professors recommend in online bachelor's degrees courses in social sciences at Spanish public universities. Monographs are the type of document most recommended by professors when it comes to teaching and learning in higher education. Digital format is most suitable for online degrees, considering the added effort entailed for libraries to get these documents owing to the geographic dispersion of the students. For this purpose, all the resources in the recommended reading section of the syllabi from a sample of online degree programs in social sciences for the 2022–2023 academic year were extracted. Then, the catalogs of the corresponding university libraries were consulted to check the availability of the monographs on the reading lists. In addition, the catalogs of the usual suppliers to Spanish university libraries were consulted to check the availability of lending licenses for libraries. Finally, types of digital lending platforms are analyzed. Monographs account for 85.6 % of the documents referenced in courses' recommended reading lists. Only 21.0 % of monographs are available in digital format, either through library lending (17.4 %) or open access (4.9 %). On the contrary, practically the entirety of reading lists is available for lending through university libraries in printed format. University libraries could make the effort to acquire even more digital lending licenses for a portion of the books recommended by professors, but in this way they could only obtain 40.9 % of the monographs in digital format. Therefore, the publishing industry is not currently meeting the needs of online university education in the social sciences, owing to a large part of its catalog – especially older works – not being offered in digital format as well as the lack of library lending licenses.
Tight and attainable quantum speed limit for open systems

We develop an intuitive geometric picture of quantum states, define a particular state distance, and derive a quantum speed limit (QSL) for open systems. Our QSL is attainable because any initial state can be driven to a final state by the particular dynamics along the geodesic. We present the general condition for dynamics along the geodesic for our QSL. As evidence, we consider the generalized amplitude damping dynamics and the dephasing dynamics to demonstrate the attainability. In addition, we also compare our QSL with others by strict analytic processes as well as numerical illustrations, and show our QSL is tight in many cases. It indicates that our paper is significant in tightening the bound of evolution time.