Reviewerly/exHarmony huggingface dataset:
An Improved YOLOX Algorithm based on Structural Re-parameterized CBAM for Wild Animals Detection

We use the structural re-parameterization method to optimize the generation process of the attention map. CBAM has long been among the most widely used attention techniques because of its simplicity and superior performance. The success of RepVgg shows great application potential of the structural re-parameterization methods, which we notice that could be used to further strengthen the ability of the CBAM. Based on YOLOX, we evaluate our method on a self-built wild animals' dataset. The result shows better performance compared to the original CBAM. Notably, the gain is FREE OF CHARGE. Compared to the baseline model, no extra parameters or FLOPs are introduced during the inference stage. Experiments prove that it is feasible to apply the idea of structural re-parameterization in the generation process of attention mechanism. The proposed method shows great application potential on wild animal detection task.
A Novel Method of Deep Neural Network Model Protection with Key and Backdoor Watermark

Deep neural network (DNN) has achieved great success in many fields. However, as training a good model often needs massive high-quality data and computation resources, it is very important to protect the intellectual property (IP) of neural networks. We find that the relevance of preventing the stealing of the learned DNN is the same as verifying the suspicious model. In this research, we proposed a novel method to watermark Deep Neural Networks and authorize specified users. Specifically, the input must go through a watermark network with our designed backdoor and key network before it can be put into the target network. Experiment results demonstrated that our method is effective in extracting watermark and preventing infringement without affecting the performance of the target network. Our proposed protection network has achieved excellent results in the ability to identify the triggered set and the non-trigger set, the ability to identify the key, and the quality of the output of watermark.
Design of Gesture Recognition System Based on OpenCV

As a new type of human-computer interaction, gesture recognition has gradually become an important research direction in the field of smart city construction. The traditional gesture recognition system relies on various types of sensors, which has high requirements for hardware and application environment. The recognition accuracy will be affected by the accuracy and service life of electronic components. Therefore, there is an urgent need for natural and easy-to-understand, people-oriented new recognition technology based on computer platform. The OpenCV used in this paper is an open source computer vision library, which contains a wealth of image processing algorithms, which can be easily and quickly applied to gesture recognition systems. This paper makes a complete exploration of gesture recognition system based on OpenCV. Firstly, this paper studies the advantages and disadvantages of different skin color models for skin color detection, and selects a skin color model that is robust in both normal indoor environment and strong light conditions. Secondly, the gray image segmented by skin color is morphologically processed, and the contour frame of gesture information is taken by Fourier descriptor algorithm to obtain the position and boundary information of hand skin color region. Then, this paper extracts and calculates the characteristics of the collected data, and uses the support vector machine algorithm to analyze the gesture recognition of the image captured by the camera. Finally, the above research modules are effectively collected, and the graphical interface is written by PyQT5 for human-computer interaction. The system design framework and the specific process of implementing the system are given. Then a relatively complete experiment was carried out on the system to verify the accuracy of the recognition.
Analysis of DF-GAN-based Text-to-Image

Adversarial generation network is widely used in the field of text generation image. Complex, detailed image generation can be a good way to improve the work efficiency of relevant industry practitioners. In the aspects of poster and art design, generating images according to the given text and language description can greatly improve the work efficiency of the design industry staff, and replace them to carry out ordinary and general work, so that the labor force can be liberated from the boring work and carry out more creation. Compared with the previous network models, the generator structure of the latest DF-GAN model is more simplified, which can generate high-resolution images, and the text and image match well. Therefore, in this paper, DF-GAN, a new added-generation network model proposed recently, is firstly selected to reproduce the model, and the test results of the model are evaluated by FID and generated images, and the optimization effects of DFBlock generator, MA-GP target discriminator and other components on the model are analyzed. Secondly, we tried to optimize the model results from the aspects of increasing training times and modifying data sets. Finally, the advantages of the model in semantic consistency are verified by comparing the results of text word order switching. According to the final results, DF-GAN does show a more accurate and clear effect in the generation of coarse-grained bird images, and the semantic consistency is well verified.
The Progress of Face Recognition on Artificial Intelligence

Face recognition is one of the most active research areas in computer vision and pattern recognition. It has many practical and commercial applications, including recognition, access control, forensics, and human-computer interaction. Therefore, it has received extensive attention from academia and industry. Face recognition technology includes feature extraction, feature selection, and classifier design. On this basis, this paper expounds the implementation methods and research progress of face recognition technology, analyzes the current mature face recognition algorithms, proposes distributed face recognition for the demand of large-scale real-time face recognition, and forecasts the development direction of face recognition.
Karasu: A Collaborative Approach to Efficient Cluster Configuration for Big Data Analytics

Selecting the right resources for big data analytics jobs is hard because of the wide variety of configuration options like machine type and cluster size.As poor choices can have a significant impact on resource efficiency, cost, and energy usage, automated approaches are gaining popularity.Most existing methods rely on profiling recurring workloads to find nearoptimal solutions over time.Due to the cold-start problem, this often leads to lengthy and costly profiling phases.However, big data analytics jobs across users can share many common properties: they often operate on similar infrastructure, using similar algorithms implemented in similar frameworks.The potential in sharing aggregated profiling runs to collaboratively address the cold start problem is largely unexplored.We present Karasu, an approach to more efficient resource configuration profiling that promotes data sharing among users working with similar infrastructures, frameworks, algorithms, or datasets.Karasu trains lightweight performance models using aggregated runtime information of collaborators and combines them into an ensemble method to exploit inherent knowledge of the configuration search space.Moreover, Karasu allows the optimization of multiple objectives simultaneously.Our evaluation is based on performance data from diverse workload executions in a public cloud environment.We show that Karasu is able to significantly boost existing methods in terms of performance, search time, and cost, even when few comparable profiling runs are available that share only partial common characteristics with the target job.
What Caused the EHR to Crash? Lessons Learned

After a power disruption in a medical office building led to a catastrophic shutdown of the IT servers and ultimately to a loss of the use of EHR and administrative IT systems, a root cause analysis was applied to identify the security threats to the IT systems of a multispecialty group and ways to mitigate this threat in the future.
CVSS Base Score Prediction Using an Optimized Machine Learning Scheme

The Common Vulnerability Scoring System (CVSS) is commonly used to measure the severity of software vulner-abilities. It consists of a CVSS Score Vector (i.e., a vector of metrics) and a CVSS Base Score calculated based on the vector. The Base Score is widely used by electric utilities to measure the risk levels of vulnerabilities and prioritize remediation actions. However, the process of determining the CVSS metric values is currently very time-consuming since it is manually done by human experts based on text descriptions of vulnerabilities, which increases the delays of remediating vulnerabilities and hence increases security risks at electric utilities. In this paper, we develop an efficient and effective solution to automatically predict the CVSS Base Score of vulnerabilities primarily based on their text descriptions, leveraging Natural Language Processing and machine learning techniques. Text descriptions for tens of thousands of vulnerabilities are comprehensively interpreted and vectorized using Doc2Vec, fed to a neural network with a condensed regression structure, which is then fine-tuned using Bayesian Optimization. By exploring and selecting the most efficient option at each stage of development, we create an optimized scheme that predicts CVSS Base Scores with very low error. Our work shows that it is possible to effectively predict CVSS Base Scores using simple but optimized neural networks. It makes crucial progress toward addressing the inefficiencies of the current CVSS severity assessment process through automation.
LSBlocFL: A secure federated learning model combining blockchain and lightweight cryptographic solutions

In the area of privacy protection, federated learning has received a lot of attention as an emerging distributed network training model that can effectively protect the privacy of users' local data while completing the training task.However, existing federated learning (FL) models still face many security threats: (1) attackers can steal private data through gradients; (2) imposition of security measures reduces model accuracy; and (3) node crashes lead to failure of model training.To address these problems, this paper proposes a lightweight and secure blockchain federated learning(LSBlocFL) model with the following main ideas: (1) optimize the gradient encryption computation based on differential privacy (DP) and fully homomorphic encryption (FHE) scheme to ensure model accuracy while protecting privacy security; (2) build a decentralized FL training model by combining blockchain network to improve the security of model training environment; and (3) design user failure response mechanism. The model can guarantee accuracy under privacy protection, optimize encryption computation overhead, and enhance the decentralized secure network environment. Finally, experimental validation results on the CIFAR-10 dataset show that LSBlocFL has practically good performance.
Predicting rock hardness using Gaussian weighted moving average filter on borehole data and machine learning

A comprehensive understanding of the hardness of ore being handled and processed in a mining operation can significantly improve operational efficiencies. This is feasible by providing valuable data to support decision-making through the mining value chain (drilling, blasting, loading, comminution). This study presents the results of a machine learning (ML) approach for rock hardness prediction using rock’s geophysical and geochemical features. Core samples from several mine sites were logged using a multi-sensor core logging (MSCL) system. Measurements include ultrasonic P- and S-wave velocity, elemental concentration via portable X-Ray fluorescence analyzers (pXRF), and Leeb rebound hardness, measured every 30 cm along 564 m of core samples. K-Means and PCA were used for better interpretation of the data. Supervised ML models (XGBoost and Random Forest) were utilized to predict rock hardness using the elemental concentrations and ultrasonic velocities as predictors. Since the data was collected automatically with predefined intervals, some of the measurement points were near fractures or veins. The Gaussian weighted moving average (WMA) was used to smooth out variations in geochemistry or hardness caused by local features that do not reflect the overall rock characteristics. This approach is effective for building ML models to become less susceptible to local rock features. It was concluded that the rock hardness could be effectively predicted using only geochemistry, and the process of collecting P- and S-wave velocity for hardness prediction can be skipped.
Steered quantum coherence and quantum Fisher information in spin-chain system

In this paper, we investigate steered quantum coherence, i.e., the l1 norm of steered coherence and the relative entropy of steered coherence, and the quantum Fisher information in the Gibbs state of two-qubit XXZ systems. Their variations with respect to the temperature, external magnetic field, and interaction intensities are analyzed both analytically and numerically in detail. The similar behaviors among these three quantum measures in the XXZ model are presented.
A cost-sensitive ensemble deep forest approach for extremely imbalanced credit fraud detection

AbstractCredit fraud detection modeling helps prevent default risks and reduce economic losses, and increasingly sophisticated methods have been designed for predicting the default probability of clients. In such problems, the fact that the class of fraud clients is much smaller than the class of good clients makes it a challenge to detect the fraud class. To minimize the financial losses in extremely imbalanced datasets, this paper delivers a novel cost-sensitive ensemble model under the framework of deep forest. The model first introduces a cost-sensitive strategy to assign a higher cost to the fraud class, thereby improving the attention of the model to the fraud samples. As everyone knows, for the basic classifiers of ensemble learning, the greater their differences, the better the performance after ensemble. So the model adds superior cost-sensitive base classifiers into the cascade structure to improve the overall performance. The model also introduces Type II error as the convergence index to automatically adjust the depth of the cascade structure. The experiments conducted on the European credit dataset and a private electronic transaction dataset are presented to demonstrate the performance of the proposed method. The results indicate that the proposed model outperforms most benchmarks in detecting fraud samples.Keywords: Credit fraud detectionExtremely imbalanced modelingCost-sensitive strategyType II errorJEL Classification: C53C61 Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Natural Science Foundation of China [grant numbers 71971051, 92046023, 71971207, and 71731003]; the ‘Three Three Three Talent Project’ funded project in Hebei Province [grant number A202001067]; Postgraduate Innovation Ability Training Funding Project of Hebei Province [project number CXZZBS2023166]; MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at the University of Chinese Academy of Sciences, and Fundamental Research Funds for the Central Universities.
Personalized Diversification for Neural Re-ranking in Recommendation

Re-ranking, as the final stage of the multi-stage recommender systems (MRS), aims at modeling the listwise context and the cross-item interactions between the candidate items. The objective is usually the overall utility (e.g., total clicks or revenue) of the re-ranked list, which is determined not only by the relevance, but also by the diversity of the list. However, existing methods equally promote diversity for all users and often compromise the relevance ranking. In reality, users have different diversity preferences and we should diversify the list tailored to individual users’ interests and needs. Users’ behavior history contains rich information which may be used for inferring their diversity preferences, but has rarely been explored in existing work. In this work, we propose a novel neural re-ranking with personalized diversification method (dubbed RAPID) to address the above challenge. RAPID explicitly models each user’s preference distribution over different topics by exploiting the intra- and inter-topic interactions from the user’s behavior history. The personalized diversity gain brought by each candidate item is then measured by the item’s marginal diversity and the learned personalized preference. The relevance and the personalized diversity are jointly optimized in an end-to-end manner to automatically manage the relevance-diversity tradeoff. Experimental results on two public datasets and a proprietary dataset show that RAPID outperforms the state-of-the-art with the highest utility and the best relevance-diversity tradeoff. We further prove that RAPID has a regret bound of $\tilde O(\sqrt n )$ on utility, which provides theoretical guarantee that its performance is near-optimal.
Geographically distributed data management to support large-scale data analysis

Nowadays, several companies prefer storing their data on multiple data centers with replication for many reasons. The data that spans various data centers ensures the fastest possible response time for customers and workforces who are geographically separated. It also provides protecting the information from the loss in case a single data center experiences a disaster. However, the amount of data is increasing at a rapid pace, which leads to challenges in storage, analysis, and various processing tasks. In this paper, we propose and design a geographically distributed data management framework to manage the massive data stored and distributed among geo-distributed data centers. The goal of the proposed framework is to enable efficient use of the distributed data blocks for various data analysis tasks. The architecture of the proposed framework is composed of a grid of geo-distributed data centers connected to a data controller (DCtrl). The DCtrl is responsible for organizing and managing the block replicas across the geo-distributed data centers. We use the BDMS system as the installed system on the distributed data centers. BDMS stores the big data file as a set of random sample data blocks, each being a random sample of the whole data file. Then, DCtrl distributes these data blocks into multiple data centers with replication. In analyzing a big data file distributed based on the proposed framework, we randomly select a sample of data blocks replicated from other data centers on any data center. We use simulation results to demonstrate the performance of the proposed framework in big data analysis across geo-distributed data centers.
A Bayesian approach for mixed effects state‐space models under skewness and heavy tails

Abstract Human immunodeficiency virus (HIV) dynamics have been the focus of epidemiological and biostatistical research during the past decades to understand the progression of acquired immunodeficiency syndrome (AIDS) in the population. Although there are several approaches for modeling HIV dynamics, one of the most popular is based on Gaussian mixed‐effects models because of its simplicity from the implementation and interpretation viewpoints. However, in some situations, Gaussian mixed‐effects models cannot (a) capture serial correlation existing in longitudinal data, (b) deal with missing observations properly, and (c) accommodate skewness and heavy tails frequently presented in patients' profiles. For those cases, mixed‐effects state‐space models (MESSM) become a powerful tool for modeling correlated observations, including HIV dynamics, because of their flexibility in modeling the unobserved states and the observations in a simple way. Consequently, our proposal considers an MESSM where the observations' error distribution is a skew‐ t . This new approach is more flexible and can accommodate data sets exhibiting skewness and heavy tails. Under the Bayesian paradigm, an efficient Markov chain Monte Carlo algorithm is implemented. To evaluate the properties of the proposed models, we carried out some exciting simulation studies, including missing data in the generated data sets. Finally, we illustrate our approach with an application in the AIDS Clinical Trial Group Study 315 (ACTG‐315) clinical trial data set.
DiffuCom: A novel diffusion model for comment generation

Given that the diffusion model has good advantages in diversity, an increasing number of researchers have recently begun exploring natural language generation. However, despite significant progress in modeling based on text diffusion, there are still several challenges to be overcome in comment tasks. On the one hand, the source sentences in the comment task are long. If each timestep in the diffusion carries and computes the source sentence, it will significantly increase the computational overhead. On the other hand, the diffusion model is essentially an iterative non-autoregressive model, which lacks target decomposition in the decoding. This makes vanilla cross-attention prone to overlook local perceptual content, leading to the problem of poor correlation. To solve the aforementioned problem, we propose a novel diffusion model for comment generation (named DiffuCom). Specifically, DiffuCom employs the transformer-based encoder–decoder structure, encoding only once during the reverse diffusion process, effectively reducing computational overhead. Additionally, we design a context-aware cross-attention module (CACAM) that integrates global and local cross-attention information through a gating mechanism, which more accurately captures relevant fragment information in the source sentence and improves the correlation. Second, self-conditioning technology was introduced in the training process, which can effectively improve the quality of the samples. Furthermore, to enhance the controllability of DiffuCom, we also propose a blended control. It combines class-conditional and classifier guidance by utilizing the difference between training and inference. Extensive experiments on four comment datasets demonstrate that the proposed DiffuCom model can generate comments that are both diverse and relevant.
Investigating the Effect of Interface Type on Embodied Interactions in 3D Gestural Human-Computer Interaction

Through emerging technologies, it is possible to use hand gestures to interact with computing systems in the form of embodied human-computer interaction (eHCI). There is much research done on improving gesture recognition accuracy and on understanding the factors that influence intuitive gesture choice; however, there is a lack of work investigating how to design the interface for 3D gestural interactions. Therefore, a between subjects experimental study was done to study the effect of interface design (e.g, 3D vs. 2D) on intuitive gesture choice and cognitive load for performing an embodied interaction. Two out of ten functions had the same intuitive gesture function mapping for 2D and 3D conditions. However, many of the functions had different mappings between the two different display types. The results illustrate the differences in embodied interactions between 2D and 3D interfaces, and future work should investigate the interface design comprehensively.