Using local ecological knowledge to assess the status of the Critically Endangered Chinese giant salamander Andrias davidianus in Guizhou Province, China

The Critically Endangered Chinese giant salamander Andrias davidianus, the world's largest amphibian, is severely threatened by unsustainable exploitation of wild individuals. However, field data with which to assess the salamander's status, population trends, or exploitation across its geographical range are limited, and recent field surveys using standard ecological field techniques have typically failed to detect wild individuals. We conducted community-based fieldwork in three national nature reserves (Fanjingshan, Leigongshan and Mayanghe) in Guizhou Province, China, to assess whether local ecological knowledge constitutes a useful tool for salamander conservation. We collected a sample of dated salamander sighting records and associated data from these reserves for comparative assessment of the relative status of salamander populations across the region. Although Fanjingshan and Leigongshan are still priority sites for salamander conservation, few recent sightings were recorded in either reserve, and respondents considered that salamanders had declined locally at both reserves. The species may already be functionally extinct at Mayanghe. Although respondent data on threats to salamanders in Guizhou are more difficult to interpret, overharvesting was the most commonly suggested explanation for salamander declines, and it is likely that the growing salamander farming industry is the primary driver of salamander extraction from Guizhou's reserves. Questionnaire-based surveys can collect novel quantitative data that provide unique insights into the local status of salamander populations, and we advocate wide-scale incorporation of this research approach into future salamander field programmes

GraphMoco:a Graph Momentum Contrast Model that Using Multimodel Structure Information for Large-scale Binary Function Representation Learning

In the field of cybersecurity, the ability to compute similarity scores at the function level is import. Considering that a single binary file may contain an extensive amount of functions, an effective learning framework must exhibit both high accuracy and efficiency when handling substantial volumes of data. Nonetheless, conventional methods encounter several limitations. Firstly, accurately annotating different pairs of functions with appropriate labels poses a significant challenge, thereby making it difficult to employ supervised learning methods without risk of overtraining on erroneous labels. Secondly, while SOTA models often rely on pre-trained encoders or fine-grained graph comparison techniques, these approaches suffer from drawbacks related to time and memory consumption. Thirdly, the momentum update algorithm utilized in graph-based contrastive learning models can result in information leakage. Surprisingly, none of the existing articles address this issue. This research focuses on addressing the challenges associated with large-scale BCSD. To overcome the aforementioned problems, we propose GraphMoco: a graph momentum contrast model that leverages multimodal structural information for efficient binary function representation learning on a large scale. Our approach employs a CNN-based model and departs from the usage of memory-intensive pre-trained models. We adopt an unsupervised learning strategy that effectively use the intrinsic structural information present in the binary code. Our approach eliminates the need for manual labeling of similar or dissimilar information.Importantly, GraphMoco demonstrates exceptional performance in terms of both efficiency and accuracy when operating on extensive datasets. Our experimental results indicate that our method surpasses the current SOTA approaches in terms of accuracy.Comment: 22 pages,7 figure

A DNS Tunnel Sliding Window Differential Detection Method Based on Normal Distribution Reasonable Range Filtering

A covert attack method often used by APT organizations is the DNS tunnel, which is used to pass information by constructing C2 networks. And they often use the method of frequently changing domain names and server IP addresses to evade monitoring, which makes it extremely difficult to detect them. However, they carry DNS tunnel information traffic in normal DNS communication, which inevitably brings anomalies in some statistical characteristics of DNS traffic, so that it would provide security personnel with the opportunity to find them. Based on the above considerations, this paper studies the statistical discovery methodology of typical DNS tunnel high-frequency query behavior. Firstly, we analyze the distribution of the DNS domain name length and times and finds that the DNS domain name length and times follow the normal distribution law. Secondly, based on this distribution law, we propose a method for detecting and discovering high-frequency DNS query behaviors of non-single domain names based on the statistical rules of domain name length and frequency and we also give three theorems as theoretical support. Thirdly, we design a sliding window difference scheme based on the above method. Experimental results show that our method has a higher detection rate. At the same time, since our method does not need to construct a data set, it has better practicability in detecting unknown DNS tunnels. This also shows that our detection method based on mathematical models can effectively avoid the dilemma for machine learning methods that must have useful training data sets, and has strong practical significance

SDF-Pack: Towards Compact Bin Packing with Signed-Distance-Field Minimization

Robotic bin packing is very challenging, especially when considering practical needs such as object variety and packing compactness. This paper presents SDF-Pack, a new approach based on signed distance field (SDF) to model the geometric condition of objects in a container and compute the object placement locations and packing orders for achieving a more compact bin packing. Our method adopts a truncated SDF representation to localize the computation, and based on it, we formulate the SDF minimization heuristic to find optimized placements to compactly pack objects with the existing ones. To further improve space utilization, if the packing sequence is controllable, our method can suggest which object to be packed next. Experimental results on a large variety of everyday objects show that our method can consistently achieve higher packing compactness over 1,000 packing cases, enabling us to pack more objects into the container, compared with the existing heuristics under various packing settings

SLAM: A Malware Detection Method Based on Sliding Local Attention Mechanism

Since the number of malware is increasing rapidly, it continuously poses a risk to the field of network security. Attention mechanism has made great progress in the field of natural language processing. At the same time, there are many research studies based on malicious code API, which is also like semantic information. It is a worthy study to apply attention mechanism to API semantics. In this paper, we firstly study the characters of the API execution sequence and classify them into 17 categories. Secondly, we propose a novel feature extraction method based on API execution sequence according to its semantics and structure information. Thirdly, based on the API data characteristics and attention mechanism features, we construct a detection framework SLAM based on local attention mechanism and sliding window method. Experiments show that our model achieves a better performance, which is a higher accuracy of 0.9723

Systematic analysis of histone acetylation regulators across human cancers

Abstract Background Histone acetylation (HA) is an important and common epigenetic pathway, which could be hijacked by tumor cells during carcinogenesis and cancer progression. However, the important role of HA across human cancers remains elusive. Methods In this study, we performed a comprehensive analysis at multiple levels, aiming to systematically describe the molecular characteristics and clinical relevance of HA regulators in more than 10000 tumor samples representing 33 cancer types. Results We found a highly heterogeneous genetic alteration landscape of HA regulators across different human cancer types. CNV alteration may be one of the major mechanisms leading to the expression perturbations in HA regulators. Furthermore, expression perturbations of HA regulators correlated with the activity of multiple hallmark oncogenic pathways. HA regulators were found to be potentially useful for the prognostic stratification of kidney renal clear cell carcinoma (KIRC). Additionally, we identified HDAC3 as a potential oncogene in lung adenocarcinoma (LUAD). Conclusion Overall, our results highlights the importance of HA regulators in cancer development, which may contribute to the development of clinical strategies for cancer treatment

Additional file 5 of Comprehensive analysis reveals the potential value of inflammatory response genes in the prognosis, immunity, and drug sensitivity of lung adenocarcinoma

Additional file 5. Table S4. Multivariate Cox analysis (stepwise regression models) for the construction of an IRGS