Federated Learning Attacks and Defenses: A Survey

In terms of artificial intelligence, there are several security and privacy deficiencies in the traditional centralized training methods of machine learning models by a server. To address this limitation, federated learning (FL) has been proposed and is known for breaking down ``data silos" and protecting the privacy of users. However, FL has not yet gained popularity in the industry, mainly due to its security, privacy, and high cost of communication. For the purpose of advancing the research in this field, building a robust FL system, and realizing the wide application of FL, this paper sorts out the possible attacks and corresponding defenses of the current FL system systematically. Firstly, this paper briefly introduces the basic workflow of FL and related knowledge of attacks and defenses. It reviews a great deal of research about privacy theft and malicious attacks that have been studied in recent years. Most importantly, in view of the current three classification criteria, namely the three stages of machine learning, the three different roles in federated learning, and the CIA (Confidentiality, Integrity, and Availability) guidelines on privacy protection, we divide attack approaches into two categories according to the training stage and the prediction stage in machine learning. Furthermore, we also identify the CIA property violated for each attack method and potential attack role. Various defense mechanisms are then analyzed separately from the level of privacy and security. Finally, we summarize the possible challenges in the application of FL from the aspect of attacks and defenses and discuss the future development direction of FL systems. In this way, the designed FL system has the ability to resist different attacks and is more secure and stable.Comment: IEEE BigData. 10 pages, 2 figures, 2 table

Adult image detection combining bovw based on region of interest and color moments

Abstract. To prevent pornography from spreading on the Internet effectively, we propose a novel method of adult image detection which combines bag-ofvisual-words (BoVW) based on region of interest (ROI) and color moments (CM). The goal of BoVW is to automatically mine the local patterns of adult contents, called visual words. The usual BoVW method clusters visual words from the patches in the whole image and adopts the weighting schemes of hard assignment. However, there are many background noises in the whole image and soft-weighting scheme is better than hard assignment. Therefore, we propose the method of BoVW based on ROI, which includes two perspectives. Firstly, we propose to create visual words in ROI for adult image detection. The representative power of visual words can be improved because the patches in ROI are more indicative to adult contents than those in the whole image. Secondly, soft-weighting scheme is adopted to detect adult images. Moreover, CM is selected by evaluating some commonly-used global features to be combined with BoVW based on ROI. The experiments and the comparison with the state-of-the-art methods show that our method is able to remarkably improve the performance of adult image detection

Spatial genetic subdivision among populations of Pampus chinensis between China and Pakistan: testing the barrier effect of the Malay Peninsula

Tissue samples from 84 Pampus chinensis individuals were collected from four geographic regions within the Indo–Pacific Ocean and analyzed using mitochondrial and nuclear DNA markers. Distinct genetic heterogeneity was found for both types of markers between Chinese and Pakistani populations, while the diversity of this species was high in all populations. In combination with published information on marine species with similar distributions, these results suggested that the Malay Peninsula, or a less effective supplement, played a role in shaping the contemporary genetic structure. This population structure was presumably reflected in P. chinensis, whose populations were genetically isolated during Pleistocene glaciations and then did not experience secondary contact between previous refuge populations. However, P. chinensis showed genetic continuity in China or Pakistan, which indicated that the populations in different geographical regions constituted a single panmictic stock with high gene flow, respectively. The spatial genetic subdivision evident among populations indicates that P. chinensis in this Indo–Pacific region should be managed as different independent stocks to guide the sustainability of this fisheries resource

A Unified Framework for 3D Point Cloud Visual Grounding

Thanks to its precise spatial referencing, 3D point cloud visual grounding is essential for deep understanding and dynamic interaction in 3D environments, encompassing 3D Referring Expression Comprehension (3DREC) and Segmentation (3DRES). We argue that 3DREC and 3DRES should be unified in one framework, which is also a natural progression in the community. To explain, 3DREC help 3DRES locate the referent, while 3DRES also facilitate 3DREC via more fine-grained language-visual alignment. To achieve this, this paper takes the initiative step to integrate 3DREC and 3DRES into a unified framework, termed 3D Referring Transformer (3DRefTR). Its key idea is to build upon a mature 3DREC model and leverage ready query embeddings and visual tokens from the 3DREC model to construct a dedicated mask branch. Specially, we propose Superpoint Mask Branch, which serves a dual purpose: i) By harnessing on the inherent association between the superpoints and point cloud, it eliminates the heavy computational overhead on the high-resolution visual features for upsampling; ii) By leveraging the heterogeneous CPU-GPU parallelism, while the GPU is occupied generating visual and language tokens, the CPU concurrently produces superpoints, equivalently accomplishing the upsampling computation. This elaborate design enables 3DRefTR to achieve both well-performing 3DRES and 3DREC capacities with only a 6% additional latency compared to the original 3DREC model. Empirical evaluations affirm the superiority of 3DRefTR. Specifically, on the ScanRefer dataset, 3DRefTR surpasses the state-of-the-art 3DRES method by 12.43% in mIoU and improves upon the SOTA 3DREC method by 0.6% [email protected]. The codes and models will be released soon

General Epidemiological Parameters of Viral Hepatitis A, B, C, and E in Six Regions of China: A Cross-Sectional Study in 2007

BACKGROUND: Viral hepatitis is a serious health burden worldwide. To date, few reports have addressed the prevalence of hepatitis A, B, C, and E in China. Therefore, the general epidemiological parameters of viral hepatitis remain unknown. PRINCIPAL FINDINGS: In this cross-sectional study, we performed a serological prevalence analysis of viral hepatitis A, B, C, and E in 8,762 randomly selected Chinese subjects, which represented six areas of China. The overall prevalence of anti-Hepatitis C virus antibody (anti-HCV) was 0.58%, which was much lower than was estimated by WHO. The prevalences of Hepatitis B virus surface antigen (HBsAg), anti-Hepatitis B virus surface protein antibody (HBsAb), and anti-Hepatitis B virus core protein antibody (HBcAb) were 5.84%, 41.31%, and 35.92%, respectively, whereas in the group of subjects less than 5 years old, these prevalences were 1.16%, 46.77%, and 8.69% respectively, which suggests that the Hepatitis B virus (HBV)-carrier population is decreasing, and the nationwide HBV vaccine program has contributed to the lowered HBV prevalence in the younger generation in China. Meanwhile, a large deficit remains in coverage provided by the national HBV immune program. In addition, our data suggested the possibility that HBsAb may not last long enough to protect people from HBV infection throughout life. The overall prevalence of anti-Hepatitis A virus antibody (anti-HAV) and anti-Hepatitis E virus antibody (anti-HEV) were as high as 72.87% and 17.66%, respectively. The indices increased with age, which suggests that a large proportion of Chinese adults are protected by latent infection. Furthermore, the pattern of HEV infection was significantly different among ethnic groups in China. CONCLUSIONS: Our study provided much important information concerning hepatitis A, B, C, and E prevalence in China and will contribute to worldwide oversight of viral hepatitis

Organic iron at ultralow doses catalyzes hydrogen peroxide to eliminate cyanobacterial blooms: a study on algicidal effects and mechanisms under natural conditions

Hydrogen peroxide (H2O2) is gaining recognition as an eco-friendly and highly effective algicide for combating cyanobacterial blooms. This study investigates the algicidal potential of H2O2 catalyzed by both inorganic and organic iron. Our findings indicate that inorganic iron (FeSO4) shows minimal catalytic activity on H2O2 under varying light conditions. In contrast, organic iron, specifically the combination of H2O2, EDTANaFe, and light irradiation, demonstrates significant algicidal effects. The optimal dosages were identified as 10 mg/L for H2O2 and 3 mg/L for Fe3+.The limited efficacy of inorganic iron stems from the transformation of Fe2+ to Fe3+ ions via the Fenton reaction. Under neutral conditions, Fe3+ ions precipitate as large-sized goethite, which adheres to the extracellular polymeric substances (EPS) of cyanobacterial cells, thereby hindering H2O2 catalysis and hydroxyl radical (·OH) formation in natural waters. Conversely, the combination of light radiation and organic iron salts greatly enhances the algicidal efficiency of H2O2. This synergy accelerates H2O2 decomposition and facilitates the production of a substantial amount of OH radicals by altering the Gibbs free energy. Thus, bright and sunny conditions, particularly in the afternoon, are crucial for effectively combating cyanobacterial blooms using Fenton-like reagents. The methodology presented in this study offers a viable solution to global cyanobacteria bloom issues and elucidates the mechanisms driving its efficacy

A Coordinated Battery Swapping Service Management Scheme Based on Battery Heterogeneity

The service management based on battery heterogeneity has become an increasingly important research problem in battery swapping technology. In this paper, with the method of bipartite matching, we first theoretically analyse the offline optimization problem of battery swapping service under battery heterogeneity. Nevertheless, the information of global view used in offline optimization solution cannot be known in advance during real-time operation. To address the disadvantage, an online framework comprising several sub-procedures is proposed for heterogeneous battery implementation. Firstly, by incorporating battery swapping station (BSS) local status such as charging and waiting queue of heterogeneous batteries, a charging slot allocation mechanism is designed. Utilizing the proposed allocation method, the charging priority is determined by the proportion of heterogeneous batteries demand, so as to guarantee charging fairness. Secondly, with the help of reservation information, the proposed allocation method can further be improved by predicting the future arrival distribution of heterogeneous types of electric vehicles. Thirdly, according to the service demand prediction based on long short-term memory neural network, joint optimization of BSS-selection and charging cost can be achieved by charging power adjustment. Simulation results indicate the desirable performance of proposed scheme in balancing the demands of multi-party participators

Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis

In the quest for artificial general intelligence, Multi-modal Large Language Models (MLLMs) have emerged as a focal point in recent advancements. However, the predominant focus remains on developing their capabilities in static image understanding. The potential of MLLMs in processing sequential visual data is still insufficiently explored, highlighting the absence of a comprehensive, high-quality assessment of their performance. In this paper, we introduce Video-MME, the first-ever full-spectrum, Multi-Modal Evaluation benchmark of MLLMs in Video analysis. Our work distinguishes from existing benchmarks through four key features: 1) Diversity in video types, spanning 6 primary visual domains with 30 subfields to ensure broad scenario generalizability; 2) Duration in temporal dimension, encompassing both short-, medium-, and long-term videos, ranging from 11 seconds to 1 hour, for robust contextual dynamics; 3) Breadth in data modalities, integrating multi-modal inputs besides video frames, including subtitles and audios, to unveil the all-round capabilities of MLLMs; 4) Quality in annotations, utilizing rigorous manual labeling by expert annotators to facilitate precise and reliable model assessment. 900 videos with a total of 254 hours are manually selected and annotated by repeatedly viewing all the video content, resulting in 2,700 question-answer pairs. With Video-MME, we extensively evaluate various state-of-the-art MLLMs, including GPT-4 series and Gemini 1.5 Pro, as well as open-source image models like InternVL-Chat-V1.5 and video models like LLaVA-NeXT-Video. Our experiments reveal that Gemini 1.5 Pro is the best-performing commercial model, significantly outperforming the open-source models. Our dataset along with these findings underscores the need for further improvements in handling longer sequences and multi-modal data. Project Page: https://video-mme.github.ioComment: Project Page: https://video-mme.github.i

A review of genetic resources and trends of omics applications in donkey research: focus on China

Omics methodologies, such as genomics, transcriptomics, proteomics, metabolomics, lipidomics and microbiomics, have revolutionized biological research by allowing comprehensive molecular analysis in livestock animals. However, despite being widely used in various animal species, research on donkeys has been notably scarce. China, renowned for its rich history in donkey husbandry, plays a pivotal role in their conservation and utilization. China boasts 24 distinct donkey breeds, necessitating conservation efforts, especially for smaller breeds facing extinction threats. So far, omics approaches have been employed in studies of donkey milk and meat, shedding light on their composition and quality. Similarly, omics methods have been utilized to explore the molecular basis associated with donkey growth, meat production, and quality traits. Omics analysis has also unraveled the critical role of donkey microbiota in health and nutrition, with gut microbiome studies revealing associations with factors such as pregnancy, age, transportation stress, and altitude. Furthermore, omics applications have addressed donkey health issues, including infectious diseases and reproductive problems. In addition, these applications have also provided insights into the improvement of donkey reproductive efficiency research. In conclusion, omics methodologies are essential for advancing knowledge about donkeys, their genetic diversity, and their applications across various domains. However, omics research in donkeys is still in its infancy, and there is a need for continued research to enhance donkey breeding, production, and welfare in China and beyond

Genome and pan-genome assembly of asparagus bean (Vigna unguiculata ssp. sesquipedialis) reveal the genetic basis of cold adaptation

Asparagus bean (Vigna unguiculata ssp. sesquipedialis) is an important cowpea subspecies. We assembled the genomes of Ningjiang 3 (NJ, 550.31 Mb) and Dubai bean (DB, 564.12 Mb) for comparative genomics analysis. The whole-genome duplication events of DB and NJ occurred at 64.55 and 64.81 Mya, respectively, while the divergence between soybean and Vigna occurred in the Paleogene period. NJ genes underwent positive selection and amplification in response to temperature and abiotic stress. In species-specific gene families, NJ is mainly enriched in response to abiotic stress, while DB is primarily enriched in respiration and photosynthesis. We established the pan-genomes of four accessions (NJ, DB, IT97K-499-35 and Xiabao II) and identified 20,336 (70.5%) core genes present in all the accessions, 6,507 (55.56%) variable genes in two individuals, and 2,004 (6.95%) unique genes. The final pan genome is 616.35 Mb, and the core genome is 399.78 Mb. The variable genes are manifested mainly in stress response functions, ABC transporters, seed storage, and dormancy control. In the pan-genome sequence variation analysis, genes affected by presence/absence variants were enriched in biological processes associated with defense responses, immune system processes, signal transduction, and agronomic traits. The results of the present study provide genetic data that could facilitate efficient asparagus bean genetic improvement, especially in producing cold-adapted asparagus bean