Reviewerly/exHarmony huggingface dataset:
Abnormal behavior detection in video using BiFPN-EfficientNet based on cosine distance optimization

With the explosive growth of the number of videos on surveillance and the Internet. How to intelligently detect the large amount of video data has become a core task in the current video security surveillance system and network security, which is of great significance to urban security control and network environment governance. This paper introduce the idea of metric learning and design a video abnormal behavior detection network EBML applied to small sample size to address the problem that specific abnormal behavior cannot be detected and abnormal behavior samples are scarce in most methods. Firstly, EfficientNet is used as the backbone network for feature extraction, then, bringing in an improved BiFPN to mitigate the feature loss caused by too many networks and information loss, and enhances the ability to fuse low-level semantic information. Finally, the scalable Cosine distance metric is introduced into the Softmax of BiFPN-EfficientNet, which makes the gap between similar features shrink continuously and the gap between dissimilar features increase continuously, thus improving the accuracy of video anomalous behavior detection. By testing on the VCAD dataset, the accuracy reaches 96.3% and the F1 value is 0.94, which has an increase of 7.8% and 0.09 respectively, compared with the benchmark model EfficientNet. Experiments show that the algorithm can better balance speed and accuracy in video anomaly detection and can meet the demand of video anomalous behavior detection under small samples.
PCFormer: an efficient and simple feature mixer for few-shot lesion segmentation

In recent years, Transformers have demonstrated significant potential in segmentation tasks. Self-attention are generally believed to be crucial for improving the performance. However, these excellent results are based on large-scale imaging datasets, and there is not suitable for low data regimes such as lesion segmentation task. In this paper, we proposes adopting a few-shot data-friendly pre-trained convolution kernel to replace self-attention as the feature mixer. Specifically, we have designed a new information transmission layer which extracts feature information through convolution with initialization information parameters, and performs feature mapping through MLP. To verify its effectiveness, we conducted experiments on two low data sets, CVC-ClinicDB and 2018 Data Science Bowl challenge dataset. Surprisingly, we proposed scheme achieves a Dice Similarity Coefficient (DSC) of 93.17%, which is 19.18% and 12.64% higher than the two baselines TransU-Net and SegFormer with self-attention, respectively. Therefore, PCFormer is encouraging and can be relied upon for few-shot lesion segmentation.
A generative model for Chinese character style fusion based on generative adversarial networks

Generative adversarial networks (GANs) have achieved remarkable success in image translation tasks. However, existing models and theories are still limited in their ability to generate new fonts that combine the styles of both font domains. Most current models only perform style conversion, which often results in generated fonts that are biased towards the target font domain, such as Chinese characters. Moreover, the long training time of these models makes them impractical for real-world applications. To address these issues, we propose a novel approach for font generation based on the pix2pix and AC-GAN frameworks. Our model can learn the mapping from the source font domain X to the target font domain Y without biasing the generated fonts towards either font domain. During training, the generator is optimized to produce high-quality new fonts that combine the styles of both font domains, while the discriminator is trained to distinguish between real and generated fonts. Experimental results show that our approach can generate 2555 new fonts with both font styles from a training set of 943 characters in a short time. The generated fonts are visually appealing and combine the styles of both font domains, achieving the goal of fusing styles and generating new fonts.
Design of XSRP-based automatic identification system for analog modulated signals

The automatic identification of the modulation mode of the communication signal is the main task of the intelligent receiver, which is now widely used in a variety of fields, including civil and military. In this paper, an XSRP-based analog modulated signal automatic identification system is designed, and the overall program block diagram is drawn using LabVIEW software. The design of the automatic identification system is divided into seven modules, focusing on the following two modules: the first is the extraction of each feature parameter when the modulated signals of AM, DSB, SSB and FM are subdivided layer by layer, and the second is the simulation of modulation mode prediction using the library function of MATLAB BP neural network algorithm. Simulations were carried out in conjunction with the XSRP software radio platform, with suitable parameters and sample sizes selected for validation, and improvements made to feature parameters to improve system performance. Computer simulations show that the XSRP-based analogue modulated signals are eventually identified correctly at no less than 95% and can be expected to be used for the detection and fast identification of signals in practical non-collaborative communication systems.
Metallogenic Model for Pb-Zn Deposits in Clastic Rocks of the Dahai Mining Area, Northeast Yunnan: Evidence from H-O-S-Sr-Pb Isotopes

The Dahai Pb-Zn mining area is located in the northwestern Pb-Zn district in northeastern Yunnan Province in the Sichuan-Yunnan-Guizhou Pb-Zn metallogenic triangle (SYGT), east of the Xiaojiang fault. Numerous Pb-Zn deposits (spots) occur in clastic rocks in this area. In this study, the Maliping, Laoyingqing, and Jinniuchang Pb-Zn deposits, representative clastic rocks in the Dahai mining area, were selected as research objects. The results of H-O-S-Sr-Pb isotope analyses show that the three deposits mainly formed through the mixing of a basinal brine with a hydrothermal fluid derived from deep within the underlying (deformed) basement, and brines leached organic matter from wall rocks. The δ34S values range from −2.62–30.30‰. The S isotope results show two different sources of reduced S: one in the Laoyingqing deposit derived from the S reduction generated by the pyrolysis of sulfur-bearing organic matter in the carbonaceous slate of the Kunyang Group, and the second in the Maliping and Jinniuchang deposits derived from the S reduction generated by the thermochemical sulfur reduction (TSR) of seawater sulfate in the Lower Cambrian Yuhucun Formation and Sinian Dengying Formation. The Pb isotope results show that the Pb sources of the three deposits are derived from basement rocks (Kunyang Group) with a small portion derived from Devonian–Permian carbonate rocks and Dengying Formation dolomite, both of which have undergone homogenization during mineralization. The Sr content varied from 0.09629 to 0.2523 × 10−6, and the study shows that the main source of Sr is a mixture of ore-forming fluid flowing through basement rocks (Kunyang Group) and through sedimentary cover. However, most of the Sr in the Maliping deposit is derived from marine carbonate, and in the Laoyingqing deposit, it is provided by basement rocks (Kunyang Group). Based on a comparative study of the deposits, the Pb-Zn deposits in the clastic rocks of the Dahai mining area and the SYGT belong to the same metallogenic system and were formed under the same metallogenic geological background. Finally, a unified metallogenic model of the two types of fluid migration and mixed mineralization of the Pb-Zn deposit in clastic rocks of the Dahai mining area is proposed. The metallogenic model provides a basis for the study of the Pb-Zn metallogenic system and guidance for deep and peripheral prospecting in this area.
Implementation of campus supervision system based on Yolov5 and SlowFast optimization model

With the end of the COVID-19 epidemic in China, colleges and universities across the country have restored the original order and implemented offline teaching. However, the turnover rate of campus personnel is also gradually increasing, and the traditional campus supervision system has pain points such as old and complex system and low efficiency. In order to solve the above problems and better assist the monitoring and management of the campus, we propose and make a campus monitoring system based on computer vision technology. We use the Yolov5 model for object detection, the optimized deepsort algorithm for object tracking, and the SlowFast network model open sourced by the Facebook team for behavior prediction. Through verification and actual test, the model has a range accuracy of 0.5 meters to 30 meters, and has strong anti-physical interference. Therefore, we conclude that this model has better efficiency and a wide range of application scenarios.
L-PRNU: Low-Complexity Privacy-Preserving PRNU-Based Camera Attribution Scheme

A personal camera fingerprint can be created from images in social media by using Photo Response Non-Uniformity (PRNU) noise, which is used to identify whether an unknown picture belongs to them. Social media has become ubiquitous in recent years and many of us regularly share photos of our daily lives online. However, due to the ease of creating a PRNU-based camera fingerprint, the privacy leakage problem is taken more seriously. To address this issue, a security scheme based on Boneh–Goh–Nissim (BGN) encryption was proposed in 2021. While effective, the BGN encryption incurs a high run-time computational overhead due to its power computation. Therefore, we devised a new scheme to address this issue, employing polynomial encryption and pixel confusion methods, resulting in a computation time that is over ten times faster than BGN encryption. This eliminates the need to only send critical pixels to a Third-Party Expert in the previous method. Furthermore, our scheme does not require decryption, as polynomial encryption and pixel confusion do not alter the correlation value. Consequently, the scheme we presented surpasses previous methods in both theoretical analysis and experimental performance, being faster and more capable.
A Conceptual Design of an AI-Enabled Decision Support System for Analysing Donor Behaviour in Nonprofit Organisations

Analysing and understanding donor behaviour in nonprofit organisations (NPOs) is challenging due to the lack of human and technical resources. Machine learning (ML) techniques can analyse and understand donor behaviour at a certain level; however, it remains to be seen how to build and design an artificial-intelligence-enabled decision-support system (AI-enabled DSS) to analyse donor behaviour. Thus, this paper proposes an AI-enabled DSS conceptual design to analyse donor behaviour in NPOs. A conceptual design is created following a design science research approach to evaluate an AI-enabled DSS’s initial DPs and features to analyse donor behaviour in NPOs. The evaluation process of the conceptual design applied formative assessment by conducting interviews with stakeholders from NPOs. The interviews were conducted using the Appreciative Inquiry framework to facilitate the process of interviews. The evaluation of the conceptual design results led to the recommendation for efficiency, effectiveness, flexibility, and usability in the requirements of the AI-enabled DSS. This research contributes to the design knowledge base of AI-enabled DSSs for analysing donor behaviour in NPOs. Future research will combine theoretical components to introduce a practical AI-enabled DSS for analysing donor behaviour in NPOs. This research is limited to such an analysis of donors who donate money or volunteer time for NPOs.
Consideration of FedProx in Privacy Protection

As federated learning continues to increase in scale, the impact caused by device and data heterogeneity is becoming more severe. FedProx, as a comparison algorithm, is widely used as a solution to deal with system heterogeneity and statistical heterogeneity in several scenarios. However, there is no work that comprehensively investigates the enhancements that FedProx can bring to current secure federation algorithms in terms of privacy protection. In this paper, we combine differential privacy and personalized differential privacy with FedProx, propose the DP-Prox and PDP-Prox algorithms under different privacy budget settings and simulate the algorithms on multiple datasets. The experiments show that the proposed algorithms not only significantly improve the convergence of the privacy algorithms under different heterogeneity conditions, but also achieve similar or even better accuracy than the baseline algorithm.
DALib: A Curated Repository of Libraries for Data Augmentation in Computer Vision

Data augmentation is a fundamental technique in machine learning that plays a crucial role in expanding the size of training datasets. By applying various transformations or modifications to existing data, data augmentation enhances the generalization and robustness of machine learning models. In recent years, the development of several libraries has simplified the utilization of diverse data augmentation strategies across different tasks. This paper focuses on the exploration of the most widely adopted libraries specifically designed for data augmentation in computer vision tasks. Here, we aim to provide a comprehensive survey of publicly available data augmentation libraries, facilitating practitioners to navigate these resources effectively. Through a curated taxonomy, we present an organized classification of the different approaches employed by these libraries, along with accompanying application examples. By examining the techniques of each library, practitioners can make informed decisions in selecting the most suitable augmentation techniques for their computer vision projects. To ensure the accessibility of this valuable information, a dedicated public website named DALib has been created. This website serves as a centralized repository where the taxonomy, methods, and examples associated with the surveyed data augmentation libraries can be explored. By offering this comprehensive resource, we aim to empower practitioners and contribute to the advancement of computer vision research and applications through effective utilization of data augmentation techniques.
A Graph DB-Based Solution for Semantic Technologies in the Future Internet

With the progressive improvements in the power, effectiveness, and reliability of AI solutions, more and more critical human problems are being handled by automated AI-based tools and systems. For more complex or particularly critical applications, the level of knowledge, not just information, must be handled by systems where explicit relationships among objects are represented and processed. For this purpose, the knowledge representation branch of AI proposes Knowledge Graphs, widely used in the Semantic Web, where different online applications may interact by understanding the meaning of the data they process and exchange. This paper describes a framework and online platform for the Internet-based knowledge graph definition, population, and exploitation based on the LPG graph model. Its main advantages are its efficiency and representational power and the wide range of functions that it provides to its users beyond traditional Semantic Web reasoning: network analysis, data mining, multistrategy reasoning, and knowledge browsing. Still, it can also be mapped onto the SW.
Global Market Perceptions of Cryptocurrency and the Use of Cryptocurrency by Consumers: A Pilot Study

Cryptocurrencies, like Bitcoin and Ethereum, have garnered global attention in recent years as digital alternatives to traditional fiat currencies. This paper explores the complex landscape of cryptocurrency adoption, consumer behavior, and perceptions. Beginning with the origin of cryptocurrencies and the dominance of Bitcoin with its USD 1.23 trillion market capitalization, the paper highlights popular online platforms facilitating Bitcoin trading. It also examines the varying legal statuses and regulations across different countries, with a notable divide between Eastern and Western nations, attributed to factors like wealth, risk tolerance, and government restrictions. The role of blockchain technology as the foundation of cryptocurrencies is explained, emphasizing its role in ensuring secure and transparent transactions. The paper delves into the processes involved in handling cryptocurrencies, including the blockchain, exchanges, wallets, and mining. Consumer behavior and the factors influencing cryptocurrency usage are analyzed, with a focus on speculation, algorithm trust, spending power, and demographics. Survey findings and case studies from diverse geographical areas reveal patterns of adoption and local consumer perceptions. The paper concludes by discussing the cryptocurrency market’s inherent volatility and sensitivity to regulatory changes, as well as the different types of cryptocurrencies and online exchanges shaping this evolving financial landscape. Overall, it offers insights into the complex dynamics surrounding cryptocurrency adoption and its potential impact on global finance.
Context-based argumentation frameworks and multi-agent consensus building

Abstract Argumentation must be conducted within specific contexts that involve particular social norms and values. For decision-making, the divergence of opinions among participants does not lie solely in disagreements of common sense and beliefs but mainly stems from differences in the priority orderings over values. In this paper, we discuss how to build consensus among participants holding different value orderings based on an extended structured argumentation framework that takes contextual factors into account. Compared with other formal systems for multi-agent reasoning based on argumentation, the context-based argumentation system especially emphasizes the dynamic nature of contexts. In addition, we refer to a pragmatic perspective to discuss how people manage to achieve the consensus they expect by changing contexts during an argument.
Comparative Analysis of the Prox Penalty and Bregman Algorithms for Image Denoising

Image restoration is an interesting ill-posed problem. It plays a critical role in the concept of image processing. We are looking for an image that is as near to the original as possible among images that have been skewed by Gaussian and additive noise. Image deconstruction is a technique for restoring a noisy image after it has been captured. The numerical results achieved by the prox-penalty method and the split Bregman algorithm for anisotropic and isotropic TV denoising problems in terms of image quality, convergence, and signal noise rate (SNR) are compared in this paper. It should be mentioned that isotropic TV denoising is faster than anisotropic. Experimental results indicate that the prox algorithm produces the best high-quality output (clean, not smooth, and textures are preserved). In particular, we obtained (21.4, 21) the SNR of the denoising image by the prox for sigma 0.08 and 0.501, such as we obtained (10.0884, 10.1155) the SNR of the denoising image by the anisotropic TV and the isotropic TV for sigma 0.08 and (-1.4635, -1.4733) for sigma 0.501.
Enhancing framelet GCNs with generalized p-Laplacian regularization

Abstract Graph neural networks (GNNs) have achieved remarkable results for various graph learning tasks. However, one of the recent challenges for GNNs is to adapt to different types of graph inputs, such as heterophilic graph datasets in which linked nodes are more likely to contain a different class of labels and features. Accordingly, an ideal GNN model should adaptively accommodate all types of graph datasets with different labeling distributions. In this paper, we tackle this challenge by proposing a regularization framework on graph framelet with the regularizer induced from graph p -Laplacian. By adjusting the value of p , the p -Laplacian based regularizer restricts the solution space of graph framelet into the desirable region based on the graph homophilic features. We propose an algorithm to effectively solve a more generalized regularization problem and prove that the algorithm imposes a ( p -Laplacian based) spectral convolution and diagonal scaling operation to the framelet filtered node features. Furthermore, we analyze the denoising power of the proposed model and compare it with the predefined framelet denoising regularizer. Finally, we conduct empirical studies to show the prediction power of the proposed model in both homophily undirect and heterophily direct graphs with and without noises. Our proposed model shows significant improvements compared to multiple baselines, and this suggests the effectiveness of combining graph framelet and p -Laplacian.
Application of 3D Image Technology in Rural Planning

The well-being of villages and villagers is directly related to the development of urban-rural relations. Rural development is an important part of poverty alleviation, as well as the main goal and means of rural rejuvenation, Because rural planning affects rural economic development and rural revitalization. Electronic imaging can improve the speed of rural area planning, and can also model the planned scheme. However, the current rural planning still lacks top-level design, which cannot improve the overall structural design of rural planning, and the data resources of rural planning are not perfect. Therefore, this paper studied the direction of rural planning and design by analyzing the focus, problems and external environment characteristics of rural planning, and then analyzed the application characteristics in rural planning according to the process of 3D image technology. By reducing the complex design links in rural planning, the office efficiency of planning and the 3D visualization of planning model can be promoted, so as to improve the effect of rural planning and the construction service of rural planning. The simulated annealing algorithm showed that the planning efficiency and average planning speed of 3D image application in rural planning were gradually increasing; the average planning efficiency was about 1.80, and the average planning speed was about 1.05. The planning efficiency increased by 1.20 in the whole process, while the average planning speed increased by 0.33 in the whole process. Through comparison, it can be seen that the planning rationality of rural planning under 3D image was 12.16% higher than the original one, while the measurement error rate of some teachers was 47.28% lower. In a word, 3D imaging and electronic imaging can improve the architectural design and layout planning of rural planning.
Model-based Gradient Search for Permutation Problems

Global random search algorithms are characterized by using probability distributions to optimize problems. Among them, generative methods iteratively update the distributions by using the observations sampled. For instance, this is the case of the well-known Estimation of Distribution Algorithms. Although successful, this family of algorithms iteratively adopts numerical methods for estimating the parameters of a model or drawing observations from it. This is often a very time-consuming task, especially in permutation-based combinatorial optimization problems. In this work, we propose using a generative method, under the model-based gradient search framework, to optimize permutation-coded problems and address the mentioned computational overheads. To that end, the Plackett–Luce model is used to define the probability distribution on the search space of permutations. Not limited to that, a parameter-free variant of the algorithm is investigated. Conducted experiments, directed to validate the work, reveal that the gradient search scheme produces better results than other analogous competitors, reducing the computational cost and showing better scalability.