Reviewerly/exHarmony huggingface dataset:
Research on Methods and Applications Related to Question-and-Answer Dialogue Systems

In the face of more and more network data information, search engines have gradually become the main retrieval method to obtain relevant information knowledge. However, in today's increasingly explosive development of information on the Internet, by contrast, traditional search engines have problems such as semantic understanding and complicated answers. Therefore, question answering systems are more important. The automatic question answering system generally adopts natural language processing related technologies. When users ask questions, the system automatically judges and gives answers. It involves computer linguistics, machine learning, artificial intelligence and other popular technology research. According to different classification criteria, the automatic question answering system is roughly divided into open field automatic question answering system and stereotyped automatic question answering system.. This thesis investigates methods and applications related to question-and-answer dialogue systems. On the methodological side, we introduce commonly used datasets and the principles and techniques of text, speech and visual question and answer systems, and analyse in detail the excellent example ChatGPT. In terms of applications, we present the application of Q&amp;A dialogue systems in search engines, smart campuses. There is some reference value.
Human Trespass Detection Based on Lightweight YOLO-v5 and RNN in Restricted Area

Trespassing endangers the security of individuals and property, disrupts social order, undermines social trust and increases the number of social groups used to maintain social order. In this paper, a new contribution as a method to combat trespassing which involves the monitoring of human behavior for prediction is presented. This method includes two parts: image and text description. In this work we investigate lightweight human behavior detection models based on YOLO-v5 and RNN. We use the same dataset for different models and study various model metrics (e.g., model accuracy and running speed) to compare the performance of different models. For image and video, we used pruning algorithm to lightweight the YOLO-v5 model while ensuring accuracy. For text description, we used different Image-Caption (RNN and CLIP) models to describe human behavior. Finally, corresponding validation experiments were implemented to validate the method proposed in this paper.
The Application of VR Technology in Film Production

In recent years, with the continuous maturity of virtual reality technology, the scope of application in various fields has been expanding. VR technology has gradually moved to the film industry. Since 2014, VR technology has become a new round of discussion and practice in the film industry. This paper will conduct an in-depth discussion on the production status and influence of VR films from three aspects: the actual production of VR films, the challenges VR technology brings to the current film industry, and the prospect of VR technology application in the film industry. This paper also researches the practical application and influence of VR technology in film production. The future development prospect of VR in film is discussed and analyzed, and the relevant conclusion and summary are made at the end of the article.
Area Division Using Affinity Propagation for Multi-Robot Coverage Path Planning

In the wake of advancing technology, autonomous vehicles and robotic systems have burgeoned in popularity across a spectrum of applications ranging from mapping and agriculture to reconnaissance missions. These practical implementations have brought to light an array of scientific challenges, a crucial one among them being Coverage Path Planning (CPP). CPP, the strategic planning of a path that ensures comprehensive coverage of a defined area, while being widely examined in the context of a single-robot system, has found its complexity magnified in the multi-robot scenario. A prime hurdle in multi-robot CPP is the division and allocation of the operation area among the robots. Traditional methods, largely reliant on the number of robots and their initial positions to segment the space, often culminate in suboptimal area division. This deficiency can occasionally render the problem unsolvable due to the sensitivity of most area division algorithms to the robots’ starting points. Addressing this predicament, our research introduced an innovative methodology that employs Affinity Propagation (AP) for area allocation in multi-robot CPP. In our approach, the area is partitioned into ‘n’ clusters through AP, with each cluster subsequently assigned to a robot. Although the model operates under the assumption of an unlimited robot count, it offers flexibility during execution, allowing the user to modify the AP algorithm’s similarity function factor to regulate the number of generated clusters. Serving as a significant progression in multi-robot CPP, the proposed model provides an innovative approach to area division and path optimization, thereby setting a strong foundation for future exploration and practical enhancements in this field.
A Method for Measuring Parameters of Defective Ellipse Based on Vision

Ellipse detection has a very wide range of applications in the field of object detection, especially in the geometric size detection of inclined microporous parts. However, due to the processing methods applied to the parts, there are certain defects in the features. The existing ellipse detection methods do not meet the needs of rapid detection due to the problems of false detection and time consumption. This article proposes a method of quickly obtaining defective ellipse parameters based on vision. It mainly uses the approximation principle of circles to repair defective circles, then combines this with morphological processing to obtain effective edge points, and finally uses the least squares method to obtain elliptical parameters. By simulating the computer-generated images, the results demonstrate that the center fitting error of the simulated defect ellipses with major and minor axes of 600 and 400 pixels is less than 1 pixel, the major and minor axis fitting error is less than 3 pixels, and the tilt angle fitting error is less than 0.1°. Further, experimental verification was conducted on the engine injection hole. The measurement results show that the surface size deviation was less than 0.01 mm and the angle error was less than 0.15°, which means the parameters of defective ellipses can obtained quickly and effectively. It is thus suitable for engineering applications, and can provide visual guidance for the precise measurement of fiber probes.
Hidden Semi-Markov Models-Based Visual Perceptual State Recognition for Pilots

Pilots' loss of situational awareness is one of the human factors affecting aviation safety. Numerous studies have shown that pilot perception errors are one of the main reasons for a lack of situational awareness without a proper system to detect these errors. The main objective of this study is to examine the changes in pilots' eye movements during various flight tasks from the perspective of visual awareness. The pilot's gaze rule scanning strategy is mined through cSPADE, while a hidden semi-Markov model-based model is used to detect the pilot's visuoperceptual state, linking the correlation between the hidden state and time. The performance of the proposed algorithm is then compared with that of the hidden Markov model (HMM), and the more flexible hidden semi-Markov model (HSMM) is shown to have an accuracy of 93.55%.
Multiuser Incomplete Preference K-Nearest Neighbor Query Method Based on Differential Privacy in Road Network

In view of the existing research in the field of k-nearest neighbor query in the road network, the incompleteness of the query user’s preference for data objects and the privacy protection of the query results are not considered, this paper proposes a multiuser incomplete preference k-nearest neighbor query algorithm based on differential privacy in the road network. The algorithm is divided into four parts; the first part proposes a multiuser incomplete preference completion algorithm based on association rules. The algorithm firstly uses the frequent pattern tree proposed in this paper to mine frequent item sets, then uses frequent item sets to mine strong correlation rules, and finally completes multiuser incomplete preference based on strong correlation rules. The second part proposes attribute preference weight coefficient based on multiuser’ s different preferences and clusters users accordingly. The third part compares the dominance of the query object, filters the data with low dominance, and performs a k-neighbor query. The fourth part proposes a privacy budget allocation method based on differential privacy technology. The method uses the Laplace mechanism to add noise to the result release and balance the privacy and availability of data. Theoretical research and experimental analysis show that the proposed method can better deal with the multiuser incomplete preference k-nearest neighbor query and privacy protection problems in the road network.
D-UAP: Initially Diversified Universal Adversarial Patch Generation Method

With the rapid development of adversarial example technologies, the concept of adversarial patches has been proposed, which can successfully transfer adversarial attacks to the real world and fool intelligent object detection systems. However, the real-world environment is complex and changeable, and the adversarial patch attack technology is susceptible to real-world factors, resulting in a decrease in the success rate of attack. Existing adversarial-patch-generation algorithms have a single direction of patch initialization and do not fully consider the impact of initial diversification on its upper limit of adversarial patch attack. Therefore, this paper proposes an initial diversified adversarial patch generation technology to improve the effect of adversarial patch attacks on the underlying algorithms in the real world. The method uses YOLOv4 as the attack model, and the experimental results show that the attack effect of the adversarial-patch-attack method proposed in this paper is higher than the baseline 8.46%, and it also has a stronger attack effect and fewer training rounds.
Judders, Jabs, and Slips: Automation, Anomalies, Movements

In May 2019, the “Automation and Me” event invited digital artists from around the world to come together in an unused shopping center unit in the heart of Leeds, United Kingdom, to critically respond to and explore the themes of automation, embodiment, and identity. In this article, we draw on Tobias Matzner’s argument that what algorithms are and do “emerges in a complex interplay of social practices, material properties, discourses, mathematical abstractions, code” to think through the event. In so doing, we explore a number of complex interplays as they were felt and lived by all of us who participated in it. We reflect on the experiences during the workshops through the particular framework of automation and feminist new materialism to explore how AI technologies and the complex surveillance of the smart environment replicated, on the one hand, existing and oppressive prejudice and, on the other hand, worked to constantly recalibrate and manipulate the context that humans and nonhumans navigate.
Global-Local Dynamic Adversarial Learning for Cross-Domain Sentiment Analysis

As one of the most widely used applications in domain adaption (DA), Cross-domain sentiment analysis (CDSA) aims to tackle the barrier of lacking in sentiment labeled data. Applying an adversarial network to DA to reduce the distribution discrepancy between source and target domains is a significant advance in CDSA. This adversarial DA paradigm utilizes a single global domain discriminator or a series of local domain discriminators to reduce marginal or conditional probability distribution discrepancies. In general, each discrepancy has a different effect on domain adaption. However, the existing CDSA algorithms ignore this point. Therefore, in this paper, we propose an effective, novel and unsupervised adversarial DA paradigm, Global-Local Dynamic Adversarial Learning (GLDAL). This paradigm is able to quantitively evaluate the weights of global distribution and every local distribution. We also study how to apply GLDAL to CDSA. As GLDAL can effectively reduce the distribution discrepancy between domains, it performs well in a series of CDSA experiments and achieves improvements in classification accuracy compared to similar methods. The effectiveness of each component is demonstrated through ablation experiments on different parts and a quantitative analysis of the dynamic factor. Overall, this approach achieves the desired DA effect with domain shifts.
Medical image segmentation method based on multi‐scale feature and U‐net network

Abstract In medical image segmentation (MIS), better segmentation results can be obtained by training the deeper neural network. However, directly building too deep network will cause problems such as gradient disappearance, which will affect the segmentation effect. Therefore, a dilated inception U‐Net (DIU)‐net network is constructed by combining the multi‐scale feature fusion (MSFF) method and the concept of Inception in Google net based on U‐net, and its effectiveness is verified by experiments. The DIU‐net network's training accuracy has been improved in the lung computed tomography (CT) and fundus vascular CT image data sets. And the attenuation of the loss function is relatively stable, with the highest accuracy of 99.6%. In comparison of evaluation indicators, the values of different indicators of DIU‐net in the two data sets are higher than those of the comparison network. The DICE coefficient of DIU‐net in the lung CT image in the experiment is 0.986 on average, which is 0.2% higher than that of ResU‐net. SE value is 0.985, which is 1.9% higher than SegNet. Specificity value is slightly higher than the second segmentation effect. F1 score is 0.985, 0.6% higher than ResU‐net, area under curve value is 0.99, 0.7% higher than FCN‐8 s. In general, the DIU‐net network proposed in the study will not cause gradient disappearance and other problems in the experiment. At the same time, this method also shows high efficiency and has strong feasibility for the actual MIS.
On the Efficiency of a Lightweight Authentication and Privacy Preservation Scheme for MQTT

The Internet of Things (IoT) deployment in emerging markets has increased dramatically, making security a prominent issue in IoT communication. Several protocols are available for IoT communication; among them, Message Queuing Telemetry Transport (MQTT) is pervasive in intelligent applications. However, MQTT is designed for resource-constrained IoT devices and, by default, does not have a security scheme, necessitating an additional security scheme to overcome its weaknesses. The security vulnerabilities in MQTT inherently lead to overhead and poor communication performance. Adding a lightweight security framework for MQTT is essential to overcome these problems in a resource-constrained environment. The conventional MQTT security schemes present a single trusted scheme and perform attribute verification and key generation, which tend to be a bottleneck at the server and pave the way for various security attacks. In addition to that, using the same secret key for an extended period and a flawed key revocation system can affect the security of MQTT. To address these issues, we propose an Improved Ciphertext Policy-Attribute-Based Encryption (ICP-ABE) integrated with a lightweight symmetric encryption scheme, PRESENT, to improve the security of MQTT. In this work, the PRESENT algorithm enables the secure sharing of blind keys among clients. We evaluated a previously proposed ICP-ABE scheme from the perspective of energy consumption and communication overhead. Furthermore, we evaluated the efficiency of the scheme using provable security and formal methods. The simulation results showed that the proposed scheme consumes less energy in standard and attack scenarios than the simple PRESENT, Key Schedule Algorithm (KSA)-PRESENT Secure Message Queue Telemetry Transport (SMQTT), and ECC-RSA frameworks, with a topology of 30 nodes. In general, the proposed lightweight security framework for MQTT addresses the vulnerabilities of MQTT and ensures secure communication in a resource-constrained environment, making it a promising solution for IoT applications in emerging markets.
WHORU: Improving Abstractive Dialogue Summarization with Personal Pronoun Resolution

With the abundance of conversations happening everywhere, dialogue summarization plays an increasingly important role in the real world. However, dialogues inevitably involve many personal pronouns, which hinder the performance of existing dialogue summarization models. This work proposes a framework named WHORU to inject external personal pronoun resolution (PPR) information into abstractive dialogue summarization models. A simple and effective PPR method for the dialogue domain is further proposed to reduce time and space consumption. Experiments demonstrated the superiority of the proposed methods. More importantly, WHORU achieves new SOTA results on SAMSum and AMI datasets.
GenCo: A Generative Learning Model for Heterogeneous Text Classification Based on Collaborative Partial Classifications

The use of generative learning models in natural language processing (NLP) has significantly contributed to the advancement of natural language applications, such as sentimental analysis, topic modeling, text classification, chatbots, and spam filtering. With a large amount of text generated each day from different sources, such as web-pages, blogs, emails, social media, and articles, one of the most common tasks in NLP is the classification of a text corpus. This is important in many institutions for planning, decision-making, and creating archives of their projects. Many algorithms exist to automate text classification tasks but the most intriguing of them is that which also learns these tasks automatically. In this study, we present a new model to infer and learn from data using probabilistic logic and apply it to text classification. This model, called GenCo, is a multi-input single-output (MISO) learning model that uses a collaboration of partial classifications to generate the desired output. It provides a heterogeneity measure to explain its classification results and enables a reduction in the curse of dimensionality in text classification. Experiments with the model were carried out on the Twitter US Airline dataset, the Conference Paper dataset, and the SMS Spam dataset, outperforming baseline models with 98.40%, 89.90%, and 99.26% accuracy, respectively.
Vulnerability diffusions in software product networks

Abstract During software product development, the combination of digital resources (such as application programming interfaces and software development kits) establishes loose and tight edges between nodes, which form a software product network (SPN). These edges serve as observable conduits that may help practitioners and researchers better understand how vulnerabilities diffuse through SPNs. We apply network theory to analyze data from over 12 years of records extracted from the National Vulnerability Database. We contribute novel measures established using machine learning to gauge the properties influencing vulnerability diffusion within an SPN. We observed an SPN having a discernable shape that changed over time via network updates. We propose hypotheses and find empirical evidence that vulnerability diffusion is influenced by edge dynamics, developer responses, and their interaction. Implications for practice are that increased developer responses reduce software vulnerability diffusion attributed to edge dynamics.
A Study of Training Approaches of a Hybrid Summarisation Model Applied to Patent Dataset

Patents are recognised as an important source of scientific knowledge. The automatic summarisation process of patents can assist in the organisation, and, consequently, the access to the contents of patent databases. The main contribution of this work is to carry out a study of training approaches of a hybrid summarisation model to create concise, single sentence summaries for patent documents. The experiments were executed using a dataset containing more than 80,000 patents, made available by the United States Patent and Trademark Office. Comparative experiments between the selected model and seven state-of-the-art models in extractive, abstractive and hybrid text summarisation (HTS) were performed. The results obtained showed that the selected approach produces better results than extractive and HTS models, and yields good prospects in extremely concise summaries. It is concluded that the study of different training approaches, coupled with the analysis of the attention words weights in the final results, is an important step in this process, impacting directly the choice of the final summarisation model. Besides this, the results of the experiments suggest that the removal of stop words from the input text did not generate better results, although the attention words extracted with the model without stop words were, in general, better.
Analysis of Regional Differences, Dynamic Evolution and Convergence Characteristics of The Level of Rural Revitalization in China

To help rural revitalization, it is extremely important to understand the inter-provincial characteristics. This paper uses the entropy value method to measure the comprehensive index of rural revitalization level, and reveals the regional differences and dynamic evolution trend of rural revitalization with the help of Dagum Gini coefficient and kernel density method, and finally uses the convergence model to test the spatial and temporal convergence. The study finds that the level of rural revitalization is low but tends to increase year by year. Regional differences are the main reason for the differences in rural revitalization, but they are decreasing. There is a polarization phenomenon of inter-provincial rural revitalization level, while there is a tendency for low-level provinces to catch up with high-level provinces. Finally, it is suggested that all kinds of factors should be fully activated to actively promote the coordinated development of regional rural revitalization level.
TFPA: A traceable federated privacy aggregation protocol

Federated learning is gaining significant interests as it enables model training over a large volume of data that is distributedly stored over many users. However, Malicious or dishonest aggregator still possible to infer sensitive information and even restore private data from local model updates even destroy the process of training. To solve the problem, researchers have proposed many excellent methods based on privacy protection technologies, such as secure multiparty computation (MPC), homomorphic encryption (HE), and differential privacy. But these methods don’t only ignore users’ address and identity privacy, but also include nothing about a feasible scheme to trace malicious users and malicious gradients. In this paper, we propose a general decentralized byzantine-fault-tolerant federated learning protocol, named TFPA, which can integrate multiple learning algorithms. This protocol can not only ensure the accuracy of aggregation under the adversary setting of $$4f+1$$ , but also provide user address privacy and identity privacy assurance. In addition, we also provide a heuristic malicious gradient discovery and tracking scheme to help participants better resist malicious gradients and ensure the fairness of aggregation to a certain extent. We evaluate our framework on Linear Regression, Logistic Regression, SVM, MLP and RNN, and attain good results both in accuracy and performance. Last but not least, we also simply prove the correction and security of TFPA.