A Cost-effective Shuffling Method against DDoS Attacks using Moving Target Defense

Moving Target Defense (MTD) has emerged as a newcomer into the asymmetric field of attack and defense, and shuffling-based MTD has been regarded as one of the most effective ways to mitigate DDoS attacks. However, previous work does not acknowledge that frequent shuffles would significantly intensify the overhead. MTD requires a quantitative measure to compare the cost and effectiveness of available adaptations and explore the best trade-off between them. In this paper, therefore, we propose a new cost-effective shuffling method against DDoS attacks using MTD. By exploiting Multi-Objective Markov Decision Processes to model the interaction between the attacker and the defender, and designing a cost-effective shuffling algorithm, we study the best trade-off between the effectiveness and cost of shuffling in a given shuffling scenario. Finally, simulation and experimentation on an experimental software defined network (SDN) indicate that our approach imposes an acceptable shuffling overload and is effective in mitigating DDoS attacks

Research on the Evaluation of Green Logistics Based on Cloud Model

Businesses According to the theory of sustainable development, combining with the current development status of the social logistics industry and the characteristics of green logistics, constructing a green logistics evaluation index system. Using cloud model and Delphi method to calculate the cloud weight of green logistics evaluation index, qualitative and quantitative conversion of evaluation index is realized by cloud generator. Take Jiangsu Province as an example to do empirical research, using the cloud model and its algorithm to get the evaluation cloud of green logistics, observing the evaluation result directly and discovering problem easy by comparing the evaluation cloud chart with ruler cloud chart. The evaluation results show that the cloud model is more reasonable, and the credibility of the evaluation results is improved

Can the physiological tolerance hypothesis explain herb richness patterns along an elevational gradient? A trait-based analysis

Many taxa show their highest species richness at intermediate elevations, but the underlying reasons for this remain unclear. Here, we suggest that the physiological tolerance hypothesis can explain species richness patterns along elevational gradients, and we used functional diversity to test this hypothesis. We analyzed herb species richness, functional diversity, and environmental conditions along a 1300 m elevational gradient in a temperate forest, Beijing, China. We found that herb richness exhibited a “hump-shaped” relationship with elevation, with peak richness at approximately 1800 m. Functional diversity showed a significant unimodal relationship with elevation. The duration of high temperatures (≥ 300C: DHT) was the best predictor for herb richness and functional diversity along the gradient from 1020 to 1800 m, which suggest richness is limited by high temperature at low elevations. While along the gradient from 1800 to 2300 m, the duration of low temperatures (≤ 0°C: DLT) was the most powerful explanatory variable, which indicated at high elevations, richness reduced due to low temperature. Our analyses showed that the functional diversity of traits related to drought-tolerance (leaf mass per area, leaf area, and leaf hardiness) exhibited negative relationships with DHT, while functional diversity of traits related to freezing-tolerance (leaf thickness and hair density) exhibited negative relationships with DLT. Taken together, our results demonstrated that the richness-elevation relationship is consistent with the physiological tolerance hypothesis: at low elevations, richness is limited by high temperatures, and at high elevations, richness is reduced due to low temperatures. We concluded that our results provide trait-based support for the physiological tolerance hypothesis, suggesting that mid-elevations offer the most suitable environmental conditions, thus higher numbers of species are able to persist

Multi-Attribute Decision Making Method Based on Aggregated Neutrosophic Set

Multi-attribute decision-making refers to the decision-making problem of selecting the optimal alternative or sorting the scheme when considering multiple attributes, which is widely used in engineering design, economy, management and military, etc. But in real application, the attribute information of many objects is often inaccurate or uncertain, so it is very important for us to find a useful and efficient method to solve the problem

KERM: Knowledge Enhanced Reasoning for Vision-and-Language Navigation

Vision-and-language navigation (VLN) is the task to enable an embodied agent to navigate to a remote location following the natural language instruction in real scenes. Most of the previous approaches utilize the entire features or object-centric features to represent navigable candidates. However, these representations are not efficient enough for an agent to perform actions to arrive the target location. As knowledge provides crucial information which is complementary to visible content, in this paper, we propose a Knowledge Enhanced Reasoning Model (KERM) to leverage knowledge to improve agent navigation ability. Specifically, we first retrieve facts (i.e., knowledge described by language descriptions) for the navigation views based on local regions from the constructed knowledge base. The retrieved facts range from properties of a single object (e.g., color, shape) to relationships between objects (e.g., action, spatial position), providing crucial information for VLN. We further present the KERM which contains the purification, fact-aware interaction, and instruction-guided aggregation modules to integrate visual, history, instruction, and fact features. The proposed KERM can automatically select and gather crucial and relevant cues, obtaining more accurate action prediction. Experimental results on the REVERIE, R2R, and SOON datasets demonstrate the effectiveness of the proposed method.Comment: Accepted by CVPR 2023. The code is available at https://github.com/XiangyangLi20/KER

MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme

Causal inference permits us to discover covert relationships of various variables in time series. However, in most existing works, the variables mentioned above are the dimensions. The causality between dimensions could be cursory, which hinders the comprehension of the internal relationship and the benefit of the causal graph to the neural networks (NNs). In this paper, we find that causality exists not only outside but also inside the time series because it reflects a succession of events in the real world. It inspires us to seek the relationship between internal subsequences. However, the challenges are the hardship of discovering causality from subsequences and utilizing the causal natural structures to improve NNs. To address these challenges, we propose a novel framework called Mining Causal Natural Structure (MCNS), which is automatic and domain-agnostic and helps to find the causal natural structures inside time series via the internal causality scheme. We evaluate the MCNS framework and impregnation NN with MCNS on time series classification tasks. Experimental results illustrate that our impregnation, by refining attention, shape selection classification, and pruning datasets, drives NN, even the data itself preferable accuracy and interpretability. Besides, MCNS provides an in-depth, solid summary of the time series and datasets.Comment: 9 pages, 6 figure

GridMM: Grid Memory Map for Vision-and-Language Navigation

Vision-and-language navigation (VLN) enables the agent to navigate to a remote location following the natural language instruction in 3D environments. To represent the previously visited environment, most approaches for VLN implement memory using recurrent states, topological maps, or top-down semantic maps. In contrast to these approaches, we build the top-down egocentric and dynamically growing Grid Memory Map (i.e., GridMM) to structure the visited environment. From a global perspective, historical observations are projected into a unified grid map in a top-down view, which can better represent the spatial relations of the environment. From a local perspective, we further propose an instruction relevance aggregation method to capture fine-grained visual clues in each grid region. Extensive experiments are conducted on both the REVERIE, R2R, SOON datasets in the discrete environments, and the R2R-CE dataset in the continuous environments, showing the superiority of our proposed method

Damage Effect of Terrorist Attack Explosion-induced Shock Wave in a Double-deck Island Platform Metro Station

The objective of this research was to reasonably assess the damage to people and station structures caused by terrorist attack explosion at metro stations, taking the Liyuan station of Wuhan metro which adopts double-deck island platform as an typical example. The TNT explosion process inside the metro station was calculated and analyzed using the dynamic finite element numerical simulation software LS-DYNA. First, the peak overpressure curve and the positive pressure time curve of the shock wave of explosive under the condition of confined space in the metro station were obtained. Then, through the comparison and analysis of the theoretical formulas of explosive shock wave propagation characteristics, the accuracy and reliability of numerical calculation methods and model parameters were verified. At last, combining with the overpressure criterion of shock wave in explosive air, the distribution characteristics of the casualties in the metro station under the explosion shock wave are analyzed, and the dynamic response and damage effect of the pillar structure of the metro station under the explosion shock wave are studied

LibriSQA: Advancing Free-form and Open-ended Spoken Question Answering with a Novel Dataset and Framework

While Large Language Models (LLMs) have demonstrated commendable performance across a myriad of domains and tasks, existing LLMs still exhibit a palpable deficit in handling multimodal functionalities, especially for the Spoken Question Answering (SQA) task which necessitates precise alignment and deep interaction between speech and text features. To address the SQA challenge on LLMs, we initially curated the free-form and open-ended LibriSQA dataset from Librispeech, comprising Part I with natural conversational formats and Part II encompassing multiple-choice questions followed by answers and analytical segments. Both parts collectively include 107k SQA pairs that cover various topics. Given the evident paucity of existing speech-text LLMs, we propose a lightweight, end-to-end framework to execute the SQA task on the LibriSQA, witnessing significant results. By reforming ASR into the SQA format, we further substantiate our framework's capability in handling ASR tasks. Our empirical findings bolster the LLMs' aptitude for aligning and comprehending multimodal information, paving the way for the development of universal multimodal LLMs. The dataset and demo can be found at https://github.com/ZihanZhaoSJTU/LibriSQA