Abstract:The cuckoo filter is an efficient probabilistic data structure that can quickly determine whether an element exists in a given set. The cuckoo filter is widely used in computer networks, IoT applications, and database systems. These systems usually involve the handling of massive amounts of data and numerous concurrent requests in practice. A cuckoo filter that supports high concurrency can significantly improve system throughput and data processing capabilities, which is crucial to system performance enhancement. Therefore, a cuckoo filter that supports lock-free concurrency is designed. The filter achieves high-performance lookup, insertion, and deletion through the two-stage query, separation of path exploration and element migration, and atomic migration based on multi-word compare-and-swap. Theoretical analysis and experimental results indicate that the lock-free concurrent cuckoo filter significantly improves the concurrent performance of the most cutting-edge algorithms in current times. The lookup throughput of a lock-free concurrent cuckoo filter is on average 1.94 times that of a cuckoo filter using fine-grained locks.

Abstract:The transport layer is a key component in the network protocol stack, which is responsible for providing end-to-end services for applications between different hosts. Existing transport layer protocols such as TCP provide users with some basic security protection mechanisms, e.g., error controls and acknowledgments, which ensures the consistency of datagrams sent and received by applications between different hosts to a certain extent. However, these security protection mechanisms of the transport layer have serious flaws. For example, the sequence number of TCP datagrams is easy to be guessed and inferred, and the calculation of the datagram’s checksum depends on the vulnerable sum of the complement algorithm. As a result, the existing transport layer security mechanisms cannot guarantee the integrity and security of the datagram, which allows a remote attacker to craft a fake datagram and inject it into the target network stream, thus poisoning the target network stream. The attack against the transport layer occurs at the basic layers of the network protocol stack, which can bypass the security protection mechanisms enforced at the upper application layer and thus cause serious damage to the network infrastructure. After investigating various attacks over network protocols and the related security vulnerabilities in recent years, this study proposes a method for enhancing the security of the transport layer? based on lightweight chain verification, namely LightCTL. Based on the hash verification, LightCTL enables both sides of a TCP connection to create a mutually verifiable consensus on transport layer datagrams, so as to prevent attackers or middlemen from stealing and forging sensitive information. As a result, LightCTL can successfully foil various attacks against the network protocol stack, including TCP connection reset attacks based on sequence number inferring, TCP hijacking attacks, SYN flooding attacks, man-in-the-middle attacks, and datagram replay attacks. Besides, LightCTL does not need to modify the protocol stack of intermediate network devices such as routers. It only needs to modify the checksum and the related parts of the end protocol stack. Therefore, LightCTL can be easily deployed and significantly improves the security of network systems.

Abstract:The electroencephalograph (EEG) driven brain-computer interaction can promote daily life and rehabilitation training for physically disabled people, however, EEG has several problems such as low signal-noise ratio, significant individual difference, and these problems result in the low accuracy and efficiency for EEG feature extraction and classification. In the context of reducing numbers of electrodes and increasing identified classes, this study proposed an approach to classify motor imagery (MI) EEG signal based on convolutional neural network (CNN). Firstly, based on existed approaches, experiments were conducted and the CNN was constructed with three convolution layers, three pooling layers, and two full-connection layers. Secondly, MI experiment was conducted with the imagination of left hand movement, right hand movement, foot movement, and resting state, and the MI EEG data were collected at the same time. Thirdly, the MI EEG data set were used to build the classification model based on CNN, and the experiment results indicate that the average accuracy of classification is 82.81%, which is higher than the related classification algorithms. Finally, the classification model was applied in the online classification of MI EEG, and a BCI prototype system was designed and implemented to drive the real-time human-robot interaction. The prototype system can help users to control motion states of the humanoid robot, such as raising hands, moving forward. Furthermore, the experimental results show that the average accuracy of robot controlling reaches to 80.31%, and it verifies the proposed approach not only can classify MI EEG data with high accuracy in real time, but also promote applications of human-robot interaction with BCI.

Abstract:With the development of digital image processing technology and computer supported cooperative work, eye tracking has been applied in the process of multiuser collaborative interaction. However, existed eye tracking technique can only track single users gaze, and the computing framework for multiple users gaze data tracking is not mature; besides, the calibration process is much complex, and the eye tracking data recording, transition, and visualization mechanisms need to be further explored. Hence, this study proposed a new collaborative calibration method based on gradient optimization algorithms, so as to simplify the calibration process; and then in order to optimize the eye tracking data transition and management, the computing framework oriented to multiple users eye tracking is proposed. Furthermore, to explore the influence of visual attention caused by visualization of eye tracking data sharing among multiple users, visualizations such as dots, clusters and trajectories are designed, and it is validated that the dots could improve the efficiency for collaborative visual search tasks. Finally, the code collaborative review systems are designed and built based on eye tracking, and this system could record, deliver, and visualize the eye tracking data in the forms of dots, code borders, code background, lines connected codes, among the code reviewing process. The user experiment result shows that, compared to the no eye tracking data sharing condition, sharing eye tracking data among multiple users can reduce the bug searching time with 20.1%, significantly improves the efficiency of collaborative work, and it validates the effectiveness of the proposed approach.

Abstract:In recent years, deep learning in the computer vision has made great progress, showing good application prospects in medical image reading. In this paper, a model with construction of two-level deep convolution neural network is designed to achieve feature extraction, feature blend, and classification of the fundus photo. By comparing with doctors diagnosis, it is shown that the output of the model is highly consistent with the doctors diagnosis. In addition, an improved method of fine-grained image classification using weak supervised learning is proposed. Finally, future research direction is discussed.

Abstract:Based on advances in computing technology and information technology, social networks have emerged as a new tool for people to exchange information and build interaction networks, and have become a key topic for social software studies in social computing. Social network forensics seeks to acquire, organize, analyze and visualize user information as direct, objective and fair evidence from a third-party perspective. Along with the rapid development of the Internet, social network forensics faces new challenges in dealing with user information being diverse, real-time and dynamic, huge in volume, and interactive, and also photo trustworthiness. It therefore has become a hot issue for opinion analysis, affective computing, content analysis in social networking relations, as well as individual, group and social behaviors in social networks and social computing. This paper designs a forensic model for social network forensics, and implements it on Sina microblogging. This model provides user information analysis, facial image recognition, and location presentation for trustworthiness analysis of digital evidence, and applies visualization to help reduce the difficulty of analysis and forensics on massive data from social networks.

Abstract:In P2P (peer-to-peer) streaming systems, server bandwidth consumption can be reduced by enhancing utilization ratio of nodes’ (users’) output (uplink) bandwidth capacity. With the ability of achieving maximum throughput of multicast, network coding has the potential to contribute to the enhancement. This article applies random linear network coding (RLNC) to P2P streaming system, and modeled transmission of P2P streaming. Greedy, rarest-first and random streaming algorithms are studied comparatively through streaming algorithm optimizations based on the framework of transmission model. Optimization results indicate that the random streaming algorithm that fetches data packets evenly and equally can utilize nodes’ output bandwidth more efficiently, which can reduce operating costs of service provider. Finally, by analyzing solutions of optimization model, guidelines are proposed as principles of streaming algorithm design for real P2P streaming systems.

Abstract:Based on IP path information, by analyzing a general model of AS border, the concept of AS border sequence is introduced, and a series of AS border judging rules that discover the AS border division law hidden behind IP path information are proposed. Therefore, a method on judging AS borders named JBR (judging border by rules) is put forward. The experiment results show that JBR’s judging time is shorter than the method named JBA (judging border by alias), which is based on alias resolution, and it has advantage on judging both border addresses and border links.

Abstract:This paper surveys the mathematics behind Bloom filters, some important variations and network-related applications of Bloom filters. The current researches show that although Bloom filters start drawing significant attention from the academic community and there has been considerable progress, there are still many unknown dimensions to be explorered. The research trends of Bloom filter algorithm are foreseen in the end.

Abstract:Based on the analysis of 13 well-known available bandwidth systems and the experience in designing, building, and deploying the BNeck system, the fundamental problems with available bandwidth measurement systems are analyzed comprehensively.