Background: Sleep is recognized as an all-important physiological process, which also contributes to maintaining several bodily functions and systems. According to the Pittsburgh Sleep Quality Index (PSQI), also known as the most widely used tool in the field of subjective assessment of self-perceived sleep quality, a combination of acupoints could be more effective than single acupoint treatment in improving sleep quality. Methods: The present study was based on the extracted eligible studies rooted in a previous meta-analysis that worked on the basis of association rule mining and examined the potential kernel acupoint combinations for improving sleep quality. Results: Depending on the Apriori algorithm, we summarized 26 acupoints as binary data from the 32 eligible studies based on a previous meta-analysis and analyzed them. The top 10 most frequently selected acupoints were HT7, SP6, PC6, KI1, GV20, EM5, EX-HN3, EX-HN16, KI3, and MA-TF1. Furthermore, as deduced from 21 association rules, the primary relevant rules in the combination of acupoints are {EX-HN3, EX-HN16}=>{GV20} and {HT7, KI1}=>{PC6}. Conclusions: In order to use acupuncture to improve sleep quality, integrating {EX-HN3, EX-HN16, GV20} with {HT7, KI1, PC6} acupoints could be deemed as the kernel acupoint combination.

While more and more digital application services move to the cloud virtualization environment, the network security challenges are equally striking. In general, these network attacks can be detected by deploying network intrusion detection systems (NIDSs) to the cloud platform. As clients in the cloud can create many virtual machines (VMs) to run their services privately, all detection rules are usually loaded into NIDSs to avoid any oversight, and cause damage to the performance of NIDS. This work presents a new architecture for building an efficient NIDS to the cloud virtualization environment. By resolving the virtual system information from operating systems' kernel map in hypervisor layer, the services in the cloud can be identified exactly and the required detection rules can be adopted dynamically. The experiment results show that the proposed NIDS is efficient and effective.

A high-accuracy and low-complexity TOA-based algorithm is proposed to estimate mobile station (MS) location. First, a factor-graph-based distributive approach is used, which can work in a multi-state system as well. By effectively exchanging the available soft-information or stochastic property of the variables, this distributive approach can perform almost as well as conventional algorithm while requiring much less computational workload. Second, data screening method was applied to reduce complexity and data discrimination to improve accuracy. Simulation results demonstrate that this approach can maintain good accuracy while achieving low complexity. Since this approach is accurate and easy to implement, it will better satisfy the demands of real applications.

This paper presents a low-power, variable block-size and irregular LDPC decoding. Our proposed LDPC decoder uses nanometer technology running the well-known TDMP and SMSA decoding algorithm. We further improved the design with pipeline structure, parallel computation and without any memory unit. Therefore, we can utilize only one routing network to route three different block-size data. The prototype architecture is being implemented on 90 nm VLSI technology. Because this VLSI technology has multi-Vth layers, we can make the design more effective. Compared to recent state-of-the-art architectures, the proposed variable block-size LDPC decoder has 450 MHz clock frequency, 349.48 K gate counts, 168 mW power dissipation, and 1.215 Gbps throughput.

Implementation of iterative QR decomposition (QRD) architecture based on the modified Gram-Schmidt (MGS) algorithm is proposed in this paper. In order to achieve computational efficiency with robust numerical stability, a triangular systolic array (TSA) for QRD of large size matrices is presented. Therefore, the TSA architecture can be modified into iterative architecture for reducing hardware cost that is called iterative QRD (IQRD). The IQRD hardware is constructed by the diagonal process (DP) and the triangular process (TP) with fewer gate counts and lower power consumption than TSAQRD. For a 4times4 matrix, the hardware area of the proposed IQRD can reduce about 76% of the gate counts in TSAQRD. For a generic square matrix of order n IQRD, the latency required is 2n-1 time units, which is based on the MGS algorithm. Thus, the total clock latency is only n(2n+3) cycles.

Behavior-based detection methods have the ability to detect unknown malicious software (malware). The success of behavior-based detection methods must depend on sufficient number of abnormal behavior models. Insufficient number of abnormal behavior models can lead to high false positive and/or false negative rates. The majority of abnormal behavior models can only be derived by observing application behavior at lower level. However the traditional approaches are not very efficient in this type of analysis. In this paper, we present Holography,a virtual hardware-level tool to capture actions of malware programs. Holography does not rely on any driver that is installed on an operating system to log the execution profile of malware programs. Instead, Holography relies on only hardware level information to capture actions of malware programs. As a result, Holography is invisible to malware programs and therefore cannot be disabled or bypassed by malware programs.

In this paper, we propose a Semantic Graphs for Image Search (SGIS) system, which provides a novel way for image search by utilizing collaborative knowledge in Wikipedia and network analysis to form semantic graphs for search-term suggestion. The collaborative article editing process of Wikipediapsilas contributors is formalized as bipartite graphs that are folded into networks between terms. When user types in a search term, SGIS automatically retrieves an interactive semantic graph of related terms that allow users easily find related images not limited to a specific search term. Interactive semantic graph then serves as an interface to retrieve images through existing commercial search engines. This method significantly saves userspsila time by avoiding multiple search keywords that are usually required in generic search engines. It benefits both naive user who does not possess a large vocabulary (e.g., students) and professionals who look for images on a regular basis. In our experiments, 85% of the participants favored SGIS system than commercial search engines.

Covert channels are secret communication paths, which existance is not expected in the original system design. Covert channels can be used as legimate tools of censorship resistance, anonimity and privacy preservation to address issues with "national" firewalls, citizen profiling and other "unethical" uses of information technology. Current steganographic methods that implement covert channels within network traffic, are highly dependent on particular media data or network protocol to hide data. In this paper we investigate the methods and an algorithm for implementing adaptive covert communication system that works on real-world Internet, capable of using multiple application-level protocols as its communication media and can be implemented as network application, therefore requires no system modifications of communicating nodes. The key difference from previous solutions is the use of adaptive redundant mechanism, which allows real-time underlying protocol switching and adaptation to the dynamic network configuration changes.

In this paper, we propose a Collaborative Knowledge Semantic Graphs Image Search (CKSGIS) system. It provides a novel way to conduct image search by utilizing the collaborative nature in Wikipedia and by performing network analysis to form semantic graphs for search-term expansion. The collaborative article editing process used by Wikipedia's contributors is formalized as bipartite graphs that are folded into networks between terms. When a user types in a search term, CKSGIS automatically retrieves an interactive semantic graph of related terms that allow users to easily find related images not limited to a specific search term. Interactive semantic graph then serve as an interface to retrieve images through existing commercial search engines. This method significantly saves users' time by avoiding multiple search keywords that are usually required in generic search engines. It benefits both naïve users who do not possess a large vocabulary and professionals who look for images on a regular basis. In our experiments, 85% of the participants favored CKSGIS system rather than commercial search engines.

The look-up-table based digital adaptive predistortion (DAPD-LUT) is a low-cost and popular power amplifier (PA) linearization scheme. Most existing DAPD-LUT schemes adopt uniform spacing in indexing its LUT entries, completely regardless of the system state information (SSI): 1) the PA characteristics and 2) the input signal statistics, and are therefore sub-optimum. Some other existing DAPD-LUT schemes assume either one or both the SSI are perfectly known, and make use of this informations to design a fixed LUT index spacing. Without prior knowledge of the SSI, we propose a low-complexity iterative procedure to update LUT spacing for a DAPD-LUT scheme, which means the proposed scheme is capable of adapting itself to optimize the DAPD-LUT scheme for PA with all kinds of characteristic processing signals.