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The Biesituobie A-type batholith in West Junggar in NW China of the Central Asian Orogenic Belt contains metapelite xenoliths derived from the contact metamorphic aureole. These xenoliths could be divided into two types: biotite-muscovite-andalusite hornfels and cordierite-alkaline feldspar-sillimanite-corundum gneiss, indicating prograde metamorphism. The phase equilibrium modeling on the cordierite-alkaline feldspar-sillimanite-corundum gneiss sample yielded a peak condition of P = 3.0−4.0 kbar, T = 760−800 °C. Similarly, the Na-in-cordierite geothermometer yielded a peak condition of T = 771−780 °C. The temperature of the peak metamorphism could be considered as a lower limit of the temperature of the Biesituobie A-type batholith. On the contrary, the Ti-in-zircon geothermometer applied to the Biesituobie A-type batholith zircons yields a mean temperature of 672 ± 22 °C. It suggested that the application of Ti-in-zircon geothermometer on A-type magma may involve a >100 °C underestimation. Zircon U-Pb dating indicates that the age of the contact metamorphism is between 263 and 286 Ma, consistent with the age of the pluton at 274 Ma. The result of 760−800 °C calculated from the contact metamorphic aureole of the Biesituobie batholith put a lower limit on considering the temperature condition of the A-type granite series from a metamorphic constraint.

Translation, a key vehicle in spreading a country’s traditional culture, is an important skill for foreign language majors. For Chinese English majors, this means they should have the competence to translate traditional Chinese culture well in English. At present, there has been limited research on teaching translation of typical traditional Chinese culture, especially translating its food culture to the world. And recipe, a typical instructional text epitomizing features of its representative cuisine, plays an important role in this field. Given the situation, it is imperative to explore pedagogical possibilities in teaching translation of Chinese recipes to English. This paper details the implementation of Sydney School Genre Pedagogy (SSGP) in tandem with parallel texts. One translation lecturer implemented the pedagogy with one class consisting of 37 third-year English majors for 3 weeks. Translation pre-test and post-test were administered to the students before and after the intervention. Paired samples t-tests were applied to analyze the data generated from both tests. Comparison of students’ English translations of Chinese recipes before and after the intervention shows that students significantly improved their genre competence and produced better translations in terms of macro-structural and micro-lexical adjustments. These findings indicate the potential scaffolding effects of SSGP integrated with parallel texts in teaching translation, especially texts with distinctive generic conventions. The pedagogical efficacy observed may prove conducive to international communication of Chinese cuisine, and Chinese traditional culture at large.

Computer networks are facing the challenge of stealthy crossfire attacks that flood through persistent routes (PRs) towards their decoys at a low rate for disrupting end-to-end connectivity of the target. At first, the PRs can be stealthily probed at the initial stage of the attack. Later, some undefended and vulnerable PRs can be speculated at the renaissance stage of the attack, which yet remains unconcerned. To achieve an effective defense against the two-stage attacks, this paper investigates a new persistent route diversification defense (PRDD) mechanism to mitigate each identified PR under the attacks. The PRDD effectively stops the flooding on the PRs to mitigate their congestion. Meanwhile, it makes the adversary unable to probe or speculate the PRs under their corresponding attack stages. Thus, it disables every flooding choice for the adversary, avoiding the attacks. The PRDD is designed with scalable algorithmic complexity in computation and overhead. The PRDD is extensively assessed using NS-3 and Mininet, and the results show the following. (a) It is more effective in mitigating more attacked PRs compared with the existing solutions. (b) The defense performance of the PRDD remains highly scalable in computation, while maintaining an acceptable overhead.

Adaptive governance is increasingly considered a feasible approach to address the uncertainties and complexities of social-ecological system (SES), whereas its role on SES coupling has not been sufficiently testified. Empirical evidence is provided in this paper with the case of northwestern China, a region struggling with economic backwardness and ecological vulnerability. Given the ambiguities in scholarship on the causal relationship between adaptive governance and SES coupling, we develop a theoretical framework to outline the driving mechanism of adaptive governance by focusing on its impact on ecosystem service (ES) delivery. Within the framework, ecosystem governance and social system governance are identified as pathways of adaptive governance, which are estimated on their effects on SES coupling by FGLS. The results show that (1) only the synergy of them can positively promote SES coupling rather than isolated one of them, and (2) only social system governance presents a lustrous role in restraining the effect of resource-dependence on SES coupling as opposed to ecosystem governance. The practice of northwestern China again evidences its key leader’s role in seizing the opportunity window and social innovation. The results further uncover the necessity of synthesizing the social and ecological dimensions for shaping adaptive governance and the direction of targeted reforms for catalyzing the transition to adaptive governance.

Hyperspectral image (HSI) has become one of the most important remote sensing techniques for object interpretation by its abundant band information. As the data dimension increases, band se...

Network function virtualization (NFV), has been widely adopted in existing networks since it brings lower capital expenditure and operating expense, as well as flexible and elastic deployment. Based on NFV, dynamic Service function chain (SFC) provides more comprehensive and thorough traffic steering over a series of middleboxes, e.g., DPI, and IDS. Nevertheless, dynamic SFC involves internal state migration in middleboxes, making it hard to allocate SFC requests. We propose a reconfigurable SFC scheduling approach with consideration on resource constraints and race of the state migration to collaborate the execution of the SFC reconfiguration, in the reasonable fashion based on a heuristic algorithm. The evaluation is conducted through discrete event simulation to validate the efficiency of our reconfigurable SFC scheduling, and the results demonstrate that the proposed method outperforms First Come First Serve scheduling and random scheduling.

While the celebrated graph neural networks yield effective representations for individual nodes of a graph, there has been relatively less success in extending to the task of graph similarity learning. Recent work on graph similarity learning has considered either global-level graph-graph interactions or low-level node-node interactions, however ignoring the rich cross-level interactions (e.g., between each node of one graph and the other whole graph). In this paper, we propose a multi-level graph matching network (MGMN) framework for computing the graph similarity between any pair of graph-structured objects in an end-to-end fashion. In particular, the proposed MGMN consists of a node-graph matching network for effectively learning cross-level interactions between each node of one graph and the other whole graph, and a siamese graph neural network to learn global-level interactions between two input graphs. Furthermore, to compensate for the lack of standard benchmark datasets, we have created and collected a set of datasets for both the graph-graph classification and graph-graph regression tasks with different sizes in order to evaluate the effectiveness and robustness of our models. Comprehensive experiments demonstrate that MGMN consistently outperforms state-of-the-art baseline models on both the graph-graph classification and graph-graph regression tasks. Compared with previous work, MGMN also exhibits stronger robustness as the sizes of the two input graphs increase., Comment: Accepted by IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS)

The reliability of the insulator has directly affected the stable operation of electric power system. The detection of defective insulators has always been an important issue in smart grid systems. However, the traditional transmission line detection method has low accuracy and poor real-time performance. We present an insulator defect detection method based on CenterNet. In order to improve detection efficiency, we simplified the backbone network. In addition, an attention mechanism is utilized to suppress useless information and improve the accuracy of network detection. In image preprocessing, the blurring of some detected images results in the samples being discarded, so we use super-resolution reconstruction algorithm to reconstruct the blurred images to enhance the dataset. The results show that the AP of the proposed method reaches 96.16% and the reasoning speed reaches 30FPS under the test condition of NVIDIA GTX 1080 test conditions. Compared with Faster R-CNN, YOLOV3, RetinaNet and FSAF, the detection accuracy of proposed method is greatly improved, which fully proves the effectiveness of the proposed method.

Malware has been one of the most damaging threats to computers that span across multiple operating systems and various file formats. To defend against ever-increasing and ever-evolving malware, tremendous efforts have been made to propose a variety of malware detection that attempt to effectively and efficiently detect malware so as to mitigate possible damages as early as possible. Recent studies have shown that, on the one hand, existing ML and DL techniques enable superior solutions in detecting newly emerging and previously unseen malware. However, on the other hand, ML and DL models are inherently vulnerable to adversarial attacks in the form of adversarial examples. In this paper, we focus on malware with the file format of portable executable (PE) in the family of Windows operating systems, namely Windows PE malware, as a representative case to study the adversarial attack methods in such adversarial settings. To be specific, we start by first outlining the general learning framework of Windows PE malware detection based on ML/DL and subsequently highlighting three unique challenges of performing adversarial attacks in the context of Windows PE malware. Then, we conduct a comprehensive and systematic review to categorize the state-of-the-art adversarial attacks against PE malware detection, as well as corresponding defenses to increase the robustness of Windows PE malware detection. Finally, we conclude the paper by first presenting other related attacks against Windows PE malware detection beyond the adversarial attacks and then shedding light on future research directions and opportunities. In addition, a curated resource list of adversarial attacks and defenses for Windows PE malware detection is also available at https://github.com/ryderling/adversarial-attacks-and-defenses-for-windows-pe-malware-detection., Comment: Accepted by ELSEVIER Computers & Security (COSE)

In the macroscopic situation of ethno-history in the East Asia, the mainstream of ethnic relationships in diverse regions has generally come along with the expansion of the Huaxia and Han nationality, as well as its interaction, conflicts, and assimilation with the neighboring cultures in “Four Directions”. The process of the so-called “Huaxianization” (华夏化) and “sinicization” (汉化) pushed forward step by step from the “Central Plains” and “Central Nation” in the middle and lower reaches of the Yellow River, outward to the peripheries of its “Four Directions”, and from the mainland to the oceanic areas. In this process, the main pattern of ethnic interaction presented in a differentiated concentric geopolitical order of the “Central Nation (中国)”- peripheral “Four Directions” (四方) with “Nine States” (九州) and “Various States” (万国)—“Four Seas” as the “Gullied Boundary of China Nation” (四海为壑), finally resulting in the unity of China Nation of “Assimilation and Integration of Pluralistic Cultures” (多元一体) with the Han ethnicity as its core.

The Han people of southern China living in the coastal regions lying to the south of the lower reaches of the Yangtze River show a series of distinctive essences of physique, language or dialect, lifestyle and economic pattern, religion and worship of supernatural spirits, cultural orientations to the oceans and maritime character and so on. The maritime orientation of “living on boats as home, depending on sea as lifestyle, trading with Maritime Fan (诸番) ” was the most prominent characters of the Han people in south of China, especially in the southeast coast in Zhejiang, Fujian, and Guangdong. The accumulation and assimilation of the prehistoric and early indigenous Bai Yue (百越), Island Yi (岛夷) and proto-Austronesian of the Maritime Region of Southeastern Asia, as well as the immigration and acculturation of the overseas ethnic groups of foreign Maritime Fan since the medieval ages, were the two main sources of the formation and development of the unique maritime cultural character of the Han people in southeast coast of China.

The complex and variant patterns of costume were important parts of the human cultures in the history. Generally, the early dress of human beings started from the non-woven fabrics such as grass leaves, barks, beast coats and fur, and other natural resources, and developed into woven cloth products of fiber thread with warp and weft structure.The clothing variants are the cultural representation of ethnic groups, showing the different costume traditions and cultural features. In the ethnic cultural system of Asia–Pacific region, the ancient clothing made of cotton, hemp, silk, and wool fibers in the inland region of East Asia centered on the Central Plains has a long tradition since prehistory, represented by the so-called “Clothing and Dressing Civilization” of Huaxia and Han nationality in its sinocentrism, while on the coast of southern China, Southeast Asia, and the Pacific Islands, the non-woven bark cloth of the indigenous Bai Yue system and Austronesian presents another distinctive costume culture.

The archaeological cultures from Neolithic Age to early Iron Age in the Southeast of China including south of Jiangsu (江苏) and Anhui (安徽), Zhejiang (浙江), Jiangxi (江西), southeast of Hunan (湖南), Fujian (福建), Guangdong (广东), Guangxi (广西), Hainan (海南), Taiwan (台湾) and the adjacent coast of Vietnam, compose one of the special segment in the unity of “Assimilation and Integration of Pluralistic Cultures” in prehistoric and early history of China. These regional cultures with the continuingly temporal sequence have developed for thousands of years from early Neolithic Age to early Iron Age, “Relying on Huaxia Nationality of Central Nation and Facing Maritime Barbarians of Austronesian”, are just the material cultural heritages of the indigenous Miao, Man, Bai Yue and their ancestors in the “Southeastern Direction” of the ancient Chinese history.

Despite being a coastal country located to the west of the Pacific, ancient China essentially had a continental cultural pattern, with its vision turned toward the mainland, and a geopolitical order of land-sea interactions of ancient civilization centered on the Central Plains (Zhongyuan, 中原) around the middle and lower reaches of the Yellow River and surrounded by “Peripheral Barbarians in Four Directions” (四方蛮夷) within “Four Seas” (四海). Nevertheless, these peripheral maritime “barbarian” Yi (夷) and Yue (越) and the oversea maritime Fan (番) had been active and developed along the southeast coast of China at the edge of these “Four Directions”. Here they had objectively played an important and indispensable role in the ancient history of Chinese civilization, from the native seafaring tradition of “being good at using boats” in the prehistoric and early historical period to the medieval and late historical “Maritime Silk Road” from Han (汉) to Tang (唐) dynasties.

Ancient “Bai Yue” (百越) and “Austronesian” are indigenous peoples with very close relationship, distributing from south China, Southeast Asia, and the Pacific Islands. The relationship between Bai Yue and Proto-Austronesian has long been studied in both Chinese and Euro-American academies. During most of the twentieth century, Chinese historians and archaeologists mainly discussed the origins of Malay ethnics as one branch of Austronesian within the academic framework of the ethno-history of Bai Yue centering on the southeast coast of China, while western academic peers mainly based on the linguistic investigation of modern Austronesian and carried out multi-disciplines’ research on the origin of Proto-Austronesian.

Astronomical navigation was a kind of sea route orientating and steering practice the ancient seamen used to judge the direction, relative position, and track of ships in the blue water of deep sea by observing the stars, other celestial bodies, and their regular occurrence in the sky, as one of maritime piloting methods employed by ancient Chinese navigators. The comparative study of ethno-archaeology revealed that the representative astronomical navigation practices recorded in the ancient Chinese literatures are highly consistent with the “star observation method” and “star measuring method” used by both the local seamen in south China and the Austronesian navigators in Pacific, showing the close cultural connection between them.

The “Maritime Region of Southeastern Asia” between the south coast of China and Southeast Asia was once an important cross-border community in the multicultural lineages of human history. During the Mesolithic age around ten thousand years ago and the era of synchronically global and tremendous cultural change in human prehistory, the indigenous cultural connotation in this region and its unique model of cultural evolution along with both inheritance, continuation, and innovation between the Paleolithic and Neolithic age, are of great significance in the cultural history of humankind and Asia–Pacific ethno-history.

The historical documents and archaeological discoveries inform that sophisticated maritime cultures had been developed thousands of years ago along southeast coast of China and adjacent Southeast Asia. The indigenous Bai Yue (百越) ethnicities carried out early navigation between the coastal region East and Southeast Asia since Neolithic age, that is earlier before than the migration of Han people from North to South 2000 years ago (Chang, K.C. 1989; Rolett, B.V. 2007; Wu, C.M. 2019). These Neolithic seafaring groups have also been taken as the origin of the Pacific Austronesians (Chang, K.C. et al. 1964; Chang, K.C. 1987a; Rolett, B.V. et al. 2002; Wu, C.M. 2012a). By what kind of craft did they take on the great sea thousands of years ago? Archaeologists, historians, ethno-historians, and maritime culture researchers argued with different viewpoints.

As the number of cyber-attacks is increasing, cybersecurity is evolving to a key concern for any business. Artificial Intelligence (AI) and Machine Learning (ML) (in particular Deep Learning - DL) can be leveraged as key enabling technologies for cyber-defense, since they can contribute in threat detection and can even provide recommended actions to cyber analysts. A partnership of industry, academia, and government on a global scale is necessary in order to advance the adoption of AI/ML to cybersecurity and create efficient cyber defense systems. In this paper, we are concerned with the investigation of the various deep learning techniques employed for network intrusion detection and we introduce a DL framework for cybersecurity applications., IEEE Latin-American Conference on Communications (LATINCOM) 2020

Code retrieval is to find the code snippet from a large corpus of source code repositories that highly matches the query of natural language description. Recent work mainly uses natural language processing techniques to process both query texts (i.e., human natural language) and code snippets (i.e., machine programming language), however neglecting the deep structured features of query texts and source codes, both of which contain rich semantic information. In this paper, we propose an end-to-end deep graph matching and searching (DGMS) model based on graph neural networks for the task of semantic code retrieval. To this end, we first represent both natural language query texts and programming language code snippets with the unified graph-structured data, and then use the proposed graph matching and searching model to retrieve the best matching code snippet. In particular, DGMS not only captures more structural information for individual query texts or code snippets but also learns the fine-grained similarity between them by cross-attention based semantic matching operations. We evaluate the proposed DGMS model on two public code retrieval datasets with two representative programming languages (i.e., Java and Python). Experiment results demonstrate that DGMS significantly outperforms state-of-the-art baseline models by a large margin on both datasets. Moreover, our extensive ablation studies systematically investigate and illustrate the impact of each part of DGMS., Accepted by ACM Transactions on Knowledge Discovery from Data (ACM TKDD)

To solve the problem that the details of fusion images are not retained well and the information of feature targets is incomplete, we proposed a new fusion method of infrared (IR) and visible (VI) image—IR and VI image fusion method of dual non-subsampled contourlet transform (NSCT) and pulse-coupled neural network (PCNN). The method makes full use of the flexible multi-resolution and multi-directional of NSCT, and the global coupling and pulse synchronization excitation characteristics of PCNN, effectively combining the features of IR image with the texture details of VI image. Experimental results show that the algorithm can combine IR and VI image features well. At the same time, the obtained fusion image can better display the texture information of image. The fusion performance in contrast, detail information and other aspects is better than the classical fusion algorithm, which has better visual effect and evaluation index.

Network function virtualization allows network functions to be implemented and managed flexibly as a service function chain (SFC) in the data plane to process flows. However, software-based SFCs lead to poor performance compared to proprietary middleboxes. Moreover, existing solutions tackling performance issues suffer from the development complexity incurred by hardware details. To address these problems, we leverage both the high performance of P4-capable devices and the high flexibility of P4 language. In this paper, we present a P4 Service Chaining framework (P4SC), which tackles multiple challenges for P4 to support the SFC implementation. P4SC provides a suite of primitives allowing efficient SFC expression, and a converter and a generator converting input SFC requests to the corresponding P4 program. Here, an algorithm based on longest common subsequence (LCS) is used to allow simultaneously implementing multiple SFCs. Moreover, P4SC offers a runtime manager for flexible SFC management at runtime. It also provides an automatic integration mechanism to integrate P4-based NFs into P4SC. We implement a P4SC prototype, which supports three types of P4-capable devices. The experimental results show that P4SC outperforms state of the arts with orders-of-magnitude SFC performance improvement while maintains high flexibility.