Reviewerly/exHarmony huggingface dataset:
A Scheme for Robust Federated Learning with Privacy-preserving Based on Krum AGR

The sensitive information of participants would be leaked to an untrustworthy server through gradients in federated learning. Encrypted aggregation of uploaded parameters could resolve this issue. However, it brings challenges to the defense of model poisoning attacks in federated learning while solving the privacy problem. To address this issue, a robust federated learning scheme with privacy-preserving (RFLP) is proposed to eliminate the impact of model poisoning attacks while protecting the privacy of participants against untrusted servers. Specifically, an abnormal gradients detecting method is designed to achieve robust federated learning under encrypted aggregation using Pailliar homomorphic encryption. It is based on the concept of Krum aggregation algorithm (AGR), but utilizes privacy-preserving data features, thereby ensuring privacy. To reduce the rounds of communication in robust aggregation, a multidimensional homomorphic encryption approach is constructed. Besides, an aggregated signature authentication method is also constructed to ensure data integrity during transmission. The experiment results show that the training accuracy of RFLP with 10% malicious participants is 11.9% and 15.3% higher than that without robust aggregation.
Demo: A Talking-head Semantic Communication System for 6G

Semantic communication has received increasing attention due to its potential to improve the communication efficiency by transmitting the semantics. Take video conferencing as an example, although video conferencing services have grown exponentially, few reliable and stable communication methods have been developed for scenarios with low bandwidth conditions. Keypoint-based facial feature extraction and face video reconstruction have been developed in a relatively mature manner, thus providing technical support for the design of ultra-low bit rate video conferencing systems. In this paper, we propose a method to achieve ultra-low bit rate face video semantic communication by transmitting keypoints on faces and implementing video reconstruction at the receiver side via a generative approach. In addition, we devise an efficient keypoint compression method that significantly reduces the transmission bit rate without significantly degrading the video quality. Experiments show that our method achieves better video communication than H.264, achieving up to 10 times the code rate savings with comparable human perception quality.
A Personal Privacy Risk Assessment Framework Based on Disclosed PII

Protecting personal identifiable information (PII) is essential for privacy and data protection. The leakage of PII can lead to privacy and safety issues like personal embarrassment, workplace discrimination, and identity theft. Driven by privacy laws and regulations, business is becoming more diligent in privacy protection when handling PII. Individual users, on the other hand, are free to produce and share content online that might contain sensitive information. This paper proposed a personal privacy risk assessment framework from the user's perspective. The risk score would help PII owners assess their privacy risks so that they can more actively control their information release and protect their privacy.
Extraction and Association Co-Occurrence Analysis of Hotwords in Tibetan Web

Hotwords mining is an important part of network public opinion analysis research, and has been applied successfully to Chinese and English texts, but the research on minority languages is still in its infancy. This paper proposes a hotwords extraction algorithm for Tibetan web texts and mines the association among hotwords in order to provide a reference for the subsequent research on Tibetan opinion analysis. Firstly, a large number of Tibetan articles were collected from the Internet to construct the corpus. Then the corpus was divided into four parts according to the release time. Based on the word frequency, we introduced the position and the length weight to calculate the words weight, and extract the hotwords of the four time periods. Finally, the frequent itemset extraction method in association rule mining was used to obtain the frequent co-occurrence word sets from Tibetan text and it is analyzed to track relevant hot events. The analyses show that the hotwords extracted by the algorithm in this paper can effectively reflect the propaganda focus of the websites and the focus of the Tibetan people in different time periods.
Two Dimensional SOST: Extract Multi-Dimensional Leakage for Side-Channel Analysis on Cryptosystems

In 2021, Perin et al. proposed a horizontal attack framework against elliptic curve scalar multiplication (ECSM) operation based on the work of Nascimento et al. Their framework consists roughly of three steps. First, they apply k-means on the iteration traces from multiple ECSM executions, then, the results of clustering are used to make a leakage metric trace by using sum-of-squared t-values (SOST), based on the leakage metric trace, the points of interest (POI) are selected. Second, they apply k-means on those POIs to get initial labels for the scalar bits, the accuracy of initial labels is only 52%. Third, wrong bits are corrected by using an iterative deep learning framework. Our work focus on improving the horizontal attack framework by replacing SOST with our proposed two dimensional SOST (2D-SOST) to improve the efficiency of POI selection under unsupervised context. 2D-SOST can extract leakage information between dimensions while SOST can only extract information on one dimension which limits its performance. By replacing SOST with 2D-SOST, our method improves the accuracy of clustering algorithm from an average of 58% to an average of 74%. We also simplified the framework used in original paper and finally recover scalar bits successfully under the configuration where the original paper can not.
Research on Target Recognition Based on Improved YOLOv3 Algorithm

With the rapid development of science and technology, intelligent technology has penetrated into all walks of life, and a full coverage monitoring system has been built to realize all-round video monitoring without dead ends. Video can be used for real-time monitoring, and once abnormal situations are found, they can be handled in time. At present, with the rapid development of artificial intelligence, machine vision has been widely used in all walks of life. In this paper, real-time detection is realized by obtaining monitoring video, which not only saves a lot of labor costs, but also can find out and take corresponding measures in time when there are abnormal behaviors such as falling or smoking in public places, thus reducing personal injuries and property losses in logistics warehousing. Based on the above situation, this paper takes the analysis of abnormal behavior in logistics warehouse as a foothold, selects YOLOv3 as the basic algorithm, and improves it, and puts forward YOLOv3-PS4. Because the detection of warehouse personnel in logistics warehouse is different from other multi-category detection, it is necessary to optimize the algorithm to achieve a refined effect, in order to improve the ability of feature extraction and positioning, and adapt to the detection task in logistics warehouse scene.
Estimating Location Distribution under Local Differential Privacy

Location data plays an important role in many applications, and often reflects sensitive information of users, which requires privacy protection. Generally, existing discrete frequency estimation methods based on local differential privacy convert location data into one-dimensional points and perturb them, which can protect location privacy and collect location frequencies. However, it often ignores the similarity of distributions between close locations, leading to a decrease in the accuracy of the frequency estimation. In this paper, to protect location privacy while preserving location distribution characteristics, we propose the Location Centric Mechanism (LCM) under local differential privacy to collect the frequencies of discrete locations. LCM can be applied to input domains of any shape by normalizing the original region into a square region. LCM outputs locations in a region centered on the real location with a high probability, thus preserving effective information of the location distributions. We formally prove that LCM satisfies local differential privacy, and the estimated results are unbiased. Experimental results show that LCM outperforms existing mechanisms.
An Improved Sheep Counting Detection Method Based on Fusion Allocation Strategy and Multi-Objective Loss Function

Chinais a big country in animal husbandry, and sheep breeding is an important part of animal husbandry. With the scale and modernization of the sheep farm, good management of the sheep shed is an essential factor to ensure the health of the sheep, including counting the sheep number in real time. This paper proposes an object detection method based on fusion allocation strategy and multi-objective loss function for sheep number counting. The fusion allocation strategy selects the pre-selection box based on the cross-grid strategy, and then calculates the minimum allocation loss function to distinguish the positive and negative samples, and the multi-objective loss function changes the confidence loss to VarifocalLoss, which strengthens the detection performance of the model for multi-objective situation. The experimental training was carried out on a single 1080Ti GPU, and the comparative analysis was based on the attention depthwise YOLO detection model. The result shows that the mAP value of the model reached 87.81%, which is superior to the previous model, and proves the feasibility of the model in actual application scenario.
A Denoising Method for Motor Vibration Signal Based on VMD and Wavelet Threshold

Industrial production relies heavily on electric motors, but the motor vibration signals collected are often noisy, making it difficult to analyze the motor's performance. As a noise reduction method, the wavelet threshold has been used. However, the conventional methods of processing signals using soft and hard thresholds have issues such as the pseudo-Gibbs phenomenon and deviation in signal reconstruction. This paper proposes a solution to tackle the problems by introducing an algorithm that combines the variational mode decomposition (VMD) with a modified wavelet threshold noise reduction method. The improved sparrow search algorithm (SSA) is first applied to determine the optimal values of the number of modal components K and the penalty factor <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$a$</tex> . Then VMD decomposes the acquired motor vibration signal to obtain K intrinsic mode functions (IMF). Finally, the IMFs containing noise is processed by an improved wavelet threshold noise reduction method, which is used to obtain the noise-reduced signal after reconstruction. The effectiveness and superiority of the method proposed in this paper is verified by comparing with other methods in the experiments. The results show that the proposed noise reduction method effectively reduces noise interference in the motor vibration signal and is practical for use in industrial applications.
ETIDCNet:Efficient Text Image Deblurring Convolution Neural Network

In order to restore blurred document images with unknown degradation, many blind deblurring algorithms for single document images have been proposed. However, the evaluation of these algorithms mainly used lower resolution images. Therefore, it is not clear how well these algorithms perform on real-world larger blurred text images and how we can measure progress in the field. In this paper, our goal is to advance the research and application of text image deblurring, we propose a new end-to-end network ETIDCNet (Efficient text image deblurring convolution neural network), which can remove the blur of high-resolution images. Specifically, we designed two modules: EMAM (Efficient mixed attention block) and 3DAM (Simplified Three-dimensional attention block) to enhance ETIDCNet's capabilities. Qualitative and quantitative experiments show that ETIDCNet can solve the text image blur task well. In addition, we construct a TIDD data set containing 1000 high-resolution text images from multiple fields such as information technology, social science, agricultural science, medicine and health, and engineering technology.
Enhancing Model Performance via Vertical Federated Learning for Non-Overlapping Data Utilization

Collaborative training of machine learning models is essential in the era of big data. Federated learning ensures secure data sharing among multiple parties without compromising privacy. It includes various approaches like horizontal federated learning, vertical federated learning, and federated transfer learning. Vertical federated learning enables participants to train on different feature spaces while sharing sample labels. However, existing vertical federated learning schemes rely on participants having sufficient overlapping samples, limiting their effectiveness in scenarios with limited overlapping data. This poses challenges, particularly in domains like the medical industry where collecting enough overlapping samples is difficult. Traditional approaches fail to utilize the non-overlapping portion of the sample data, resulting in suboptimal model performance due to insufficient training data. To address this issue, we propose a novel scheme for training neural network models within the vertical federated learning framework using non-overlapping samples. Our scheme leverages fuzzy prediction to handle non-overlapping samples, improving data utilization and enhancing model performance. Crucially, our approach ensures participants' data privacy by not requiring the sharing of original data or model parameters. Experimental results validate the efficacy and efficiency of our proposed scheme.
Authenticated Identity-Based Encryption Scheme with Equality Test for Cloud-Based Social Network

Enabling registered users to match friends with the same interest, location, etc. is the most fundamental service provided by social network. With expanding openness of social network, the amount of data is growing exponentially. Cloud computing is then introduced into social network to mitigate the issue of storing and analyzing a substantial amount of data. How to realize user matching while protecting users' privacy remains a key challenge in cloud-based social network. As a cryptography tool, identity-based encryption with equality test (IBEET) can be used to match users with the same interest, location, etc. without decrypting the corresponding ciphertexts, therefore, it can well meet the key challenge in cloud-based social network. In this article, we propose an authenticated identity-based encryption with equality test (A-IBEET) scheme based on the observation that the cloud server may recover users' private information from the ciphertexts through offline message recovery attack (OMRA). Our scheme provides stronger security guarantee for social network users by resisting against OMRA in single-server setting. It could better protect users' privacy without sacrificing efficiency compared with related works.
Separable high-capacity reversible data hiding in encrypted images based on multiple predictive compression coding

Reversible data hiding in encrypted images (RDHEI) is a well-established technique in which additional information is embedded to ensure data imperceptibility and full reversibility. For RDHEI, how to utilize more redundant space to improve the embedding capacity (EC) under the premise of true reversibility is still a challenging task. We propose a separable RDHEI scheme based on multiple predictive compression coding. The image is classified by texture complexity based on gray level co-occurrence matrix. For different blocks, adaptive weight prediction (AWP) and global optimization adaptive weight prediction (GOAWP) are proposed to improve prediction accuracy. More importantly, GOAWP solves the problem of inaccurate prediction of texture block. Furthermore, methods based on pixel difference compression, pixel bit-based difference compression, and block plane-based difference compression are proposed to vacate more embedding space. The proposed scheme is separable. For the content owner, above two predictors and three compression methods are adopted to vacate space for hiding. For the data hider, the secret bits are embedded into the encrypted image by directly replacing the spare bits without obtaining any information of the original image. For the receiver, the desired information can be retrieved according to the key in his/her possession. By taking full advantage of the pixel correlation and multiple predictive compression coding, the proposed method successfully achieves a significant improvement in capacity. Experimental results show that the highest payload of our scheme reaches 3.6185 bpp, which exhibits appreciated EC in comparison with state-of-the-art methods. Security and compression performance is emphasized, where the key space of our scheme is as high as 2 × 1015 × ∞ × 1015, the average information entropy of the embedded compressed image is 7.8891, and the correlation coefficients between original images and the compressed and embedded compressed images are close to 0, indicating a weak correlation, thereby greatly improving security.
State-space modeling approach for fringe pattern demodulation

A spatial carrier fringe demodulation technique is proposed based on a state-space modeling approach for phase estimation. The fringe background intensity, carrier frequency, and phase quadrature components are considered to be the elements of the state vector, which are estimated simultaneously. The state estimation is performed using the extended Kalman filter. The simulation and experimental results are provided to demonstrate the performance comparison of the proposed method with popular and state-of-the-art methods in terms of noise robustness and phase estimation accuracy.
Identifying Karst Aquifer Recharge Area Using Environmental Stable Isotopes and Hydrochemical Data: A Case Study in Nusa Penida Island

Identifying the recharge area of karst aquifers is scientifically challenging due to the complexity of karst groundwater flow characteristics. It is essential to identify the recharge zones to conserve the groundwater resources contained within these aquifers. This paper proposes a combined methods for identifying recharge area of karst aquifers on Nusa Penida Island using stable isotopes (18O and 2H) and a hydrogeochemical approach. Based on the analysis of δ18O and δ2H values and their spatial distribution, it is possible to retrace karst aquifer recharge areas using average isotope elevations. In the meantime, groundwater facies will be determined in the karst region of Nusa Penida using the hydrogeochemical approach with the Piper diagram. According to the calculation of water stable isotope results, the average elevation of the groundwater recharge area at the study site is between 62 - 450 meters. If applied to the location of the study, the recharge area is almost entirely distributed across Nusa Penida. In addition, based on the hydrogeochemical analysis, the groundwater type at the study site can be divided into three categories: Na-Cl, mixed, and Mg-HCO3. The water types indicate that the groundwater in Nusa Penida comes from a combination of old water stored from its internal geological formations and mix with modern rainwater.&#x0D;
Scene sketch semantic segmentation with hierarchical Transformer

Convolutional Neural Networks (CNNs) have always been the dominant method for scene sketch semantic segmentation, but their performance seems to have plateaued due to the limitation of local receptive fields. To address this problem, we propose SketchSeger, a hierarchical Transformer-based model for scene sketch semantic segmentation. Accurate scene sketch segmentation relies on both high-level semantics and low-level details. To obtain better segmentation performance, we designed an MLP-based feature fusion module for the model decoder to merge feature maps captured at different scales efficiently. Compared to CNN-based models, SketchSeger exhibits a stronger ability in contextual modeling and can obtain global receptive fields even in its shallow layers. Besides the model architecture, the absence of large-scale pre-training datasets also presents a significant challenge for advancing scene sketch semantic segmentation. To promote further research, we propose a novel hand-drawn style scene sketch synthesis method and use it to synthesize a dataset containing 300,000 annotated scene sketches. We conduct extensive experiments and visual analysis to validate the efficacy of our proposed SketchSeger model and dataset synthesis approach. The results show that SketchSeger significantly outperforms state-of-the-art models on three benchmark datasets (SketchyScene, SKY-Scene, and TUB-Scene) with similar parameter scales. Codes and datasets are available at https://github.com/jayangcs/SketchSeger.
Arendt in the Metaverse: Four properties of eXtended Reality that imperil <i>factual truth</i> and democracy

This article explores the democratic implications of the wide adoption of XR (eXtended Reality) technologies, drawing on Hannah Arendt’s political theory and her concept of common reality. Arendt argues that the intersubjective construction of a common reality is what enables the establishment of factual truth and that both are prerequisites for a functional democracy. Without them, ideology can be established as truth by anti-democratic forces with power over media outlets, paving a path toward totalitarianism. This article argues that XR technologies can be used to inhibit the construction of a common reality through the same individualization of media experiences that has been shown to impede democratic processes in the social media context. The companies generating increased revenue through individualized micro-segmentation are now also vying for dominance in the XR media arena. It is argued in this article that such individualization can impede the co-construction of a common reality and a factual truth because XR media are hyper-persuasive and capable of altering an individual’s overall perception of reality. The viability of such platform-controlled individualization in XR is demonstrated through an Arendt-based critique of four properties related to XR technologies: Hypertargeted personalization, false memory creation, reality indistinguishability, and predictive processing theory. Based on these analyses, the article concludes that XR policy and regulation must consider how XR gives platforms unprecedented persuasive powers as they become capable of altering a user’s reality perception remotely and how, per Arendt, this may threaten democracy.
Digital natives and their learning habits in acquiring knowledge

The knowledge provided to students should be presented in an engaging way and using the latest technologies. The world in which society, and especially young people, functions is described as a nanosecond culture, i.e. one in which everything is expected to be available immediately. The recent years have shown that teachers are becoming aware that they need to adjust the educational materials presented and the way they conduct exercises and lectures by expanding their pedagogical and technological skills to accommodate new teaching requirements.The aim of the article is to define what educational needs are desirable and necessary in the process of acquiring knowledge by students and to determine what learning habits young people have. The first part of the article attempts to present some theoretical aspects of learning in the era of digitization. Then, a quantitative study was performed using the Cawi method. The subjects of the study were students of a technical university, both part-time and full-time. A survey was carried out with the participation of 203 student representatives. The study was carried out in the period September - November 2022.