Edge nodes provide service cooperatively in edge computing, where third-party nodes are common. However, they cannot be fully trusted and can intentionally alter service results (e.g., edge tampering). Although some mechanisms can help detect edge tampering, they come with additional detection overhead. It is essential to note that most edge nodes are willing to serve honestly. Therefore, it is reasonable to decrease the detection frequency for those nodes, which helps reduce the overhead. Reputation is the common way to evaluate trustworthiness. In this article, we propose a reputation incentive scheme called RIETD, which evaluates the reputations of edge nodes using their detection results. Moreover, RIETD is loosely coupled with detection mechanisms as an external service. Reputation is the fundamental parameter in RIETD, as it determines an acrlong EN’s appraisal weight on other nodes, personalized detection strategy, and node’s obtained revenues in one service. We demonstrate that RIETD does not significantly reduce the overall detection capability while the overhead is reduced effectively. For instance, when the tampering rate of edge nodes is 10%, and the target detection rate is 90% of a specific detection mechanism, RIETD reduces overhead by approximately 60%. If a full-reputation node is detected to have tampered with the results, its reputation recovery time is similar to the time required for reputation to improve from 0 to 1. Moreover, a node’s expected revenue is lower than that of an honest node, emphasizing the importance of serving honestly and continuously for edge nodes to earn higher revenue.

Network-based Intrusion Detection(NID) is an effective means to deal with network attacks. NID is able to detect different types of network attacks by analyzing network traffic. However, in the real world, network traffic contains majority and minority class attacks as well as a large number of normal traffic samples. The imbalance in the number of training samples of various types of network traffic makes network intrusion detection very poor. Due to the lack of training samples, traditional NID can’t learn the characteristics of minority class attacks, which leads to the failure of NID to detect minority class attacks. Therefore, in order to solve the problem brought by imbalanced data, we propose a network intrusion detection algorithm based on the sampling and improved One-vs-All(OVA) technique. The dataset is balanced by downsampling the majority class data based on K-means clustering and oversampling the minority class data based on Auxiliary Classifier Generative Adversarial Network(ACGAN), improve classification accuracy through OVA-based model training and testing. We conduct validation experiments on the NSL-KDD dataset, and the experimental results show that the proposed method achieves excellent results in terms of Accuracy, Precision, Recall and F1-score. Compared with existing state-of-the-art methods, the proposed method not only achieves excellent detection performance with low false positive rate, but also addresses the learning problem of imbalanced data more effectively.

Enterprise relation extraction aims to detect pairs of enterprise entities and identify the business relations between them from unstructured or semi-structured text data, and it is crucial for several real-world applications such as risk analysis, rating research and supply chain security. However, previous work mainly focuses on getting attribute information about enterprises like personnel and corporate business, and pays little attention to enterprise relation extraction. To encourage further progress in the research, we introduce the CEntRE, a new dataset constructed from publicly available business news data with careful human annotation and intelligent data processing. Moreover, we propose a joint entity and relation extraction network, which is capable of discovering enterprise entities and extracting business relations between them accurately. The network firstly encodes input sequences with strong semantic augmentation to learn contextual representation for each token, then a conditional random field (CRF) module is used for entity extraction. Subsequently, entity pairs are built and a new encoder based on the entity pairs is applied to get global information for relation extraction. Finally, a biaffine classifier is deployed to classify the relations. Extensive experiments on CEntRE demonstrate the effectiveness of our proposed method compared with other six excellent models, and thus our model can be considered as one strong baseline. The data and code are available at: https://github.com/LiuPeiP-CStMining_Entity_Relations_Among_Enterprises

With more and more mobile devices (such as smart phones and tablets) supporting augmented reality (AR), using these devices to sketch in mid-air has become a popular application direction. However, due to the small display size of these devices, the user's field of view is limited and cannot see the context of graphics, which leads to the drawn graphics deviating from their expectations. In this article, we applied sonification technology to mid-air sketching with mobile AR device, and proposed a new method to address this problem. In our first experiment, we verified the feasibility of our method. Our experimental results showed that sonification can effectively reduce the deviation caused by a narrow field of view. In our second experiment, we further explored the application ability of this method in a wider range of sketching. Our experimental results showed that sonification can improve the aspect ratio of the drawn graphics. In addition, the results of the NASA-TLX questionnaire showed that the participants' mental demand and effort decreased significantly, and their subjective performance increased significantly. We proposed a new method, which can effectively improve the accuracy of mid-air sketching with a mobile AR device.

While instrumental ensemble playing can benefit children's music education and collaboration skill development, it requires extensive training on music and instruments, which many school children lack. To help children with limited music training experience instrumental ensemble playing, we created EnseWing, an interactive system that offers such an experience. In this paper, we report the design of the EnseWing experience and a two-month field study. Our results show that EnseWing preserves the music and ensemble skills from traditional instrumental ensemble and provides more collaboration opportunities for children.

Body-based motion gestures have been gaining popularity in designing interactive systems. However, theories and design guidelines on the use of body movement in design have not been fully evaluated. This article investigates human ability to perform discrete target selection tasks using stretching action through two controlled experiments. The experimental results indicate that: (1) the range of the discrete levels of depth that users can easily discriminate with arm stretching is up to 16 with full visual feedback, but is down to 4 without the feedback; (2) dwelling, a gesture with keeping a hand motionless and the cursor within a target area for a certain amount of time, may be the best gesture for confirmation command; (3) full visual feedback can improve the user performance; and (4) arm stretching action can be modeled using Fitts’ law. We also discuss the design potentials for Stretch Widgets based on these results.

In China, hundreds of millions of migrant workers have moved to cities or coastal regions for more or better-paid jobs and have left their children behind at their rural homes. Separated by thousands of kilometers, these "left-behind" children and their migrant parents use mobile phones as their primary - and often only - method of maintaining family connections. To better understand the use of technology in this long-distance communication, we conducted a multi-phased study using interviews and surveys in three different Chinese rural areas. In this paper, we report our findings on how these children communicate with their migrant parents and what information they exchange. We also discuss design implications derived from these findings that may improve communication between left-behind children and their parents.

With the fast economic growth and urbanization of many developing countries come concerns that their children now have fewer opportunities to express creativity and develop collaboration skills, or to experience their local cultural heritage. We propose to address these concerns by creating technologies inspired by traditional arts, and allowing children to create and collaborate through playing with them. ShadowStory is our first attempt in this direction, a digital storytelling system inspired by traditional Chinese shadow puppetry. We present the design and implementation of ShadowStory and a 7-day field trial in a primary school. Findings illustrated that ShadowStory promoted creativity, collaboration, and intimacy with traditional culture among children, as well as interleaved children's digital and physical playing experience.

Recently, pen tilting has been explored in pen-based user interfaces and has shown potential to improve user interaction in various tasks (e.g., menu selection, modeless object manipulation). However, some basic questions concerning pen tilting behaviors, such as the ideal range, azimuth size, and direction of pen tilting, have not been thoroughly investigated. In this paper, we report our empirical studies on user performances in basic pen tilting tasks. First, we conducted a baseline study, which helps us to determine tilting directions, tilting ranges, and the thresholds that separate incidental pen tilting actions from intentional actions used for interaction. Based on the results from the baseline study, we designed an experiment to investigate user performances in goal tilting in different tilting ranges, azimuth sizes, and directions. Drawing on the results of our data analyses on task completion time, error rate, and pen tip movements, we discussed values of tilting parameters like titling range, minimal azimuth size, and tilting direction.

In many developing countries such as India and China, low educational levels often hinder economic empowerment. In this paper, we argue that mobile learning games can play an important role in the Chinese literacy acquisition process. We report on the unique challenges in the learning Chinese language, especially its logographic writing system. Based on an analysis of 25 traditional Chinese games currently played by children in China, we present the design and implementation of two culturally inspired mobile group learning games, Multimedia Word and Drumming Strokes. These two mobile games are designed to match Chinese children's understanding of everyday games. An informal evaluation reveals that these two games have the potential to enhance the intuitiveness and engagement of traditional games, and children may improve their knowledge of Chinese characters through group learning activities such as controversy, judgments and self-correction during the game play.