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The growing demand for cyber security professionals with practical knowledge is boosting the development and conduct of cyber security exercises around the world. Scenarios stand a central position of the exercise, which sets the stage for later action by providing contextual information that the participants will need during the exercise. To manage the increasing numbers of scenario creation in the different contexts, we propose an ontology to model scenarios for cyber security exercises. This ontology identifies aspects of scenario modeling relevant to cyber security that can be used as a means to achieve a defined taxonomy of knowledge items and a standard vocabulary for cyber scenarios. With the semantic framework based on RDF/OWL, this ontology provides a common structure at a semantic level that allows scenarios to be shared and reused across applications and community boundaries. In this paper, we present the design, implementation, and evaluation of the proposed ontology.

We present the full-resolution correspondence learning for cross-domain images, which aids image translation. We adopt a hierarchical strategy that uses the correspondence from coarse level to guide the fine levels. At each hierarchy, the correspondence can be efficiently computed via PatchMatch that iteratively leverages the matchings from the neighborhood. Within each PatchMatch iteration, the ConvGRU module is employed to refine the current correspondence considering not only the matchings of larger context but also the historic estimates. The proposed CoCosNet v2, a GRU-assisted PatchMatch approach, is fully differentiable and highly efficient. When jointly trained with image translation, full-resolution semantic correspondence can be established in an unsupervised manner, which in turn facilitates the exemplar-based image translation. Experiments on diverse translation tasks show that CoCosNet v2 performs considerably better than state-of-the-art literature on producing high-resolution images., CVPR 2021 oral presentation

With the continuous development of computer technology and information network technology, as well as the continuous improvement of the current research level of various disciplines, the connotation of the disciplines is becoming more affluent and more prosperous, and the barriers between the disciplines are gradually penetrating. In this context, the integration between disciplines has become an inevitable trend in the development of the times, and the resulting cross direction has essential research value for improving the theories and methods of disciplines. Therefore, it is crucial to grasp the connections between disciplines to promote the development of the discipline. Based on the literature data of the CSSCI citation database, this paper analyzes the literature index data of service design discipline in the last six years (2014–2019) with the computer intelligent measurement algorithms. With the help of the computer statistical analysis software, this paper clarifies the development status and academic influence of service design discipline in China from the perspective of literature research, reveals the citation patterns of the discipline of service design and its deep connections with related disciplines, and explores the development prospects of the discipline in the current development trend. As a consequence, this work can deepen the related researchers' understanding to service design discipline, and furthermore assist them to construct this emerging discipline better.

In this work, we study various existing benchmarks for deepfake detection researches. In particular, we examine a novel two-stage face swapping algorithm, called FaceShifter, for high fidelity and occlusion aware face swapping. Unlike many existing face swapping works that leverage only limited information from the target image when synthesizing the swapped face, FaceShifter generates the swapped face with high-fidelity by exploiting and integrating the target attributes thoroughly and adaptively. FaceShifter can handle facial occlusions with a second synthesis stage consisting of a Heuristic Error Acknowledging Refinement Network (HEAR-Net), which is trained to recover anomaly regions in a self-supervised way without any manual annotations. Experiments show that existing deepfake detection algorithm performs poorly with FaceShifter, since it achieves advantageous quality over all existing benchmarks. However, our newly developed Face X-Ray method can reliably detect forged images created by FaceShifter.

Live working is an important method to improve the reliability of the power distribution network. In order to protect the safety of worker, the insulating protective tool, such as insulating blanket, is used to wrap the high voltage conductor. The temperature rise characteristics of the insulating blanket have been researched in this paper. The thermal-fluid coupled calculation is used to simulate the temperature rise and heat dissipation process of the insulating blanket in the live working of 10 kV distribution line (type is JL/G1A-70/10). The simplify method of the steel core aluminum stranded conductor is put forward. The effect of length and thinness of the insulating blanket, wind speed, and sunshine intensity on the temperature distribution of the insulating blanket is analyzed. Results show that the temperature distribution is basically the same when the stranded conductor is simplified to a single conductor. The maximum temperature difference between inside and outside of the insulating blanket is about 22 K under the natural convection environment. The effect of thickness on temperature rise is greater than that of length for this insulating blanket. The wind speed and sunshine have a large influence on the temperature distribution of the insulating blanket.

This study adopted the cross-modal priming paradigm to explore the effects of the emotional face priming on the processing of phonetic emotion under two language conditions (L1: Chinese, L2: English), The results show that: (1) there is no difference in response time and accuracy rate between Chinese and English when phonetic emotions are activated; (2) under Chinese or English condition, the positive emotional face processing may have an effect on the processing of phonetic emotion and thus causing a consistent emotional priming effect, while the negative facial emotional processing shows no any effects on the processing of phonetic emotion. This study indicates that there appears to be no linguistic difference in the classification of phonetic emotional words when emotional faces are used as priming target. In addition, under both language conditions, there exists only positive emotional priming effect.

The influence of bilingual education and bilingual experience on personal information processing ability is a hot topic in international research in recent years. The refresh function operation under the emotional background is a more complex cognitive operation compared with the single working memory refresh processing, which is significantly related to the individual's emotional regulation and social adaptation. This study attempts to explore whether bilingual experience has an impact on the refresh function of working memory in the context of emotions, and further investigate the role of emotional stimulation types and cognitive load in this process. The experiment was carried out using the emotional n-back paradigm. The results showed that bilingual experience promoted the refresh function of working memory under the emotional background, and the higher the cognitive load intensity was, the more obvious the promoting effect was. In addition, negative background emotional information can promote the refresh function.

Working memory is the core system for processing and manufacturing information in the process of people’ cognitive activity. The precious researchers have found that bilingual learning (bilingual experience) is beneficial to working memory development. Working memory operation under emotional context is a more complex and higher cognitive activity, compared with the non-emotional context, it decides the ability of individual's emotional regulation and social adaptation. This research focuses on the working memory differences in storage function and the function of cognitive load, among the students with different bilingual experiences in emotional context from the aspect of recognition and information processing. The results show that the proficient bilingual had the advantages of working memory no matter in emotional context or in unemotional context, especially with obvious advantages in complex tasks. This study point out that bilingual experience is beneficial to promote of individual's information processing ability, and the bilingual education should be implemented actively.

Based on the cross-channel priming paradigm, this paper investigates the influence of phonetic emotional words on emotional face classification in two language environments (L1: Chinese, L2: English). With phonetic emotional words as priming target, the results show that: (1) emotional face classification in Chinese is faster than that of in English. and the accuracy rate of emotional face classification in Chinese is higher than that of in English. (2) participants' classification of positive emotional face is significantly faster than that of negative emotional face. (3) in both Ll and L2, participants' responses are significantly faster when the valence of positive phonetic emotional words is consistent with emotional faces, while their responses are relatively slower when the valence of negative phonetic emotional words is in accord with emotional faces.

The deployment of large-scale high-performance computer systems requires a thorough acceptance process to validate systems’ specifications, including performance projections made using a predefined set of benchmarks at the time the system was proposed. In this paper, we describe such a process for the recently deployed Summit and Sierra supercomputers, starting from benchmarks projections made four years in advance to system availability, up to measurements prior to the system acceptance. This is an industry experience paper, reflecting on the “art of benchmarking” throughout the entire development of these supercomputers, highlighting the codesign process and the success in the benchmarks projections.

Detectors based on deep learning tend to detect multi-scale faces on a single input image for efficiency. Recent works, such as FPN and SSD, generally use feature maps from multiple layers with different spatial resolutions to detect objects at different scales, e.g., high-resolution feature maps for small objects. However, we find that such multi-layer prediction is not necessary. Faces at all scales can be well detected with features from a single layer of the network. In this paper, we carefully examine the factors affecting face detection across a large range of scales, and conclude that the balance of training samples, including both positive and negative ones, at different scales is the key. We propose a group sampling method which divides the anchors into several groups according to the scale, and ensure that the number of samples for each group is the same during training. Our approach using only the last layer of FPN as features is able to advance the state-of-the-arts. Comprehensive analysis and extensive experiments have been conducted to show the effectiveness of the proposed method. Our approach, evaluated on face detection benchmarks including FDDB and WIDER FACE datasets, achieves state-of-the-art results without bells and whistles.

In the process of logistics task allocation, the number of logistics truck is limited. How to make use of the limited delivery truck to deliver all the goods in the region to the destination quickly and reduce the transportation cost as far as possible is one of the objectives of logistics enterprise development. This paper presents a modified ant colony optimization for this problem. The algorithm combines ant colony optimization and k-opt algorithm to solve the instability of the optimization result of ant colony optimization. Through combinatorial optimization algorithm, the logistics task is rationalized and distributed scientifically, so as to improve the efficiency of truck use, thus improving the core competitiveness of logistics enterprises.

We propose a framework based on Generative Adversarial Networks to disentangle the identity and attributes of faces, such that we can conveniently recombine different identities and attributes for identity preserving face synthesis in open domains. Previous identity preserving face synthesis processes are largely confined to synthesizing faces with known identities that are already in the training dataset. To synthesize a face with identity outside the training dataset, our framework requires one input image of that subject to produce an identity vector, and any other input face image to extract an attribute vector capturing, e.g., pose, emotion, illumination, and even the background. We then recombine the identity vector and the attribute vector to synthesize a new face of the subject with the extracted attribute. Our proposed framework does not need to annotate the attributes of faces in any way. It is trained with an asymmetric loss function to better preserve the identity and stabilize the training process. It can also effectively leverage large amounts of unlabeled training face images to further improve the fidelity of the synthesized faces for subjects that are not presented in the labeled training face dataset. Our experiments demonstrate the efficacy of the proposed framework. We also present its usage in a much broader set of applications including face frontalization, face attribute morphing, and face adversarial example detection.

Despite the security community's emphasis on the importance of building secure open source software (OSS), the number of new vulnerabilities found in OSS is increasing. In addition, software security is about the people that develop and use those applications and how their vulnerable behaviors can lead to exploitation. This leads to a need for reiteration of software security studies for OSS developments to understand the existing security practices and the security weakness among them. In this paper, a systematic review method with a sociotechnical analysis approach is applied to identify, extract and analyze the security studies conducted in the context of open source development. The findings include: (1) System verification is the most cited security area in OSS research; (2) The socio-technical perspective has not gained much attention in this research area; and (3) No research has been conducted focusing on the aspects of security knowledge management in OSS development.

This paper presents a Neural Aggregation Network (NAN) for video face recognition. The network takes a face video or face image set of a person with a variable number of face images as its input, and produces a compact, fixed-dimension feature representation for recognition. The whole network is composed of two modules. The feature embedding module is a deep Convolutional Neural Network (CNN) which maps each face image to a feature vector. The aggregation module consists of two attention blocks which adaptively aggregate the feature vectors to form a single feature inside the convex hull spanned by them. Due to the attention mechanism, the aggregation is invariant to the image order. Our NAN is trained with a standard classification or verification loss without any extra supervision signal, and we found that it automatically learns to advocate high-quality face images while repelling low-quality ones such as blurred, occluded and improperly exposed faces. The experiments on IJB-A, YouTube Face, Celebrity-1000 video face recognition benchmarks show that it consistently outperforms naive aggregation methods and achieves the state-of-the-art accuracy., Comment: Post CVPR2017 version with minor typo fix

The understanding of application characteristics such as hardware resource requirements and communication patterns is key in building highly utilized high performance computing systems for target workloads at a reasonable cost and with available technology. The characterization drives the design decision of both hardware and software. Memory access pattern is a key factor as data movement is a major challenge in today's system design. In this paper, we describe how memory characterization unveils key differences among applications and helps to shape the system design space. With examples, it is shown how memory characterization can be used in the hardware architecture design and application enablement to achieve better performance and system utilization.