Distributed compressed video sensing (DCVS), combining advantages of compressed sensing and distributed video coding, is developed as a novel and powerful system to get an encoder with low complexity. Nevertheless, it is still unclear how to explore the method to achieve an effective video recovery through utilizing realistic signal characteristics as much as possible. Based on this, we present a novel spatiotemporal dictionary learning (DL) based reconstruction method for DCVS, where both the DL model and the l1-analysis based recovery with correlation constraints are included in the minimization problem to achieve the joint optimization of sparse representation and signal reconstruction. Besides, an alternating direction method with multipliers (ADMM) based numerical algorithm is outlined for solving the underlying optimization problem. Simulation results demonstrate that the proposed method outperforms other methods, with 0.03-4.14 dB increases in PSNR and a 0.13-15.31 dB gain for non-key frames.

The recent cyber-attacks utilize various malware as a means of attacks for the attacker's malicious purposes. They are aimed to steal confidential information or seize control over major facilities after infiltrating the network of a target organization. Attackers generally create new malware or many different types of malware by using an automatic malware creation tool which enables remote control over a target system easily and disturbs trace-back of these attacks. The paper proposes a generation method of malware behavior patterns as well as the detection techniques in order to detect the known and even unknown malware efficiently. The behavior patterns of malware are generated with Multiple Sequence Alignment (MSA) of API call sequences of malware. Consequently, we defined these behavior patterns as a “feature-chain” of malware for the analytical purpose. The initial generation of the feature-chain consists of extracting API call sequences with API hooking library, classifying malware samples by the similar behavior, and making the representative sequences from the MSA results. The detection mechanism of numerous malware is performed by measuring similarity between API call sequence of a target process (suspicious executables) and feature-chain of malware. By comparing with other existing methods, we proved the effectiveness of our proposed method based on Longest Common Subsequence (LCS) algorithm. Also we evaluated that our method outperforms other antivirus systems with 2.55 times in detection rate and 1.33 times in accuracy rate for malware detection.

The recent popularization of social network services (SNSs), such as YouTube, Dailymotion, and Facebook, enables people to easily publish their personal videos taken with mobile cameras. However, at the same time, such popularity has raised a new problem: video privacy. In such social videos, the privacy of people, i.e., their appearances, must be protected, but naively obscuring all people might spoil the video content. To address this problem, we focus on videographers' capture intentions. In a social video, some persons are usually essential for the video content. They are intentionally captured by the videographers, called intentionally captured persons (ICPs), and the others are accidentally framed-in (non-ICPs). Videos containing the appearances of the non-ICPs might violate their privacy. In this paper, we developed a system called BEPS, which adopts a novel conditional random field (CRF)-based method for ICP detection, as well as a novel approach to obscure non-ICPs and preserve ICPs using background estimation. BEPS reduces the burden of manually obscuring the appearances of the non-ICPs before uploading the video to SNSs. Compared with conventional systems, the following are the main advantages of BEPS: (i) it maintains the video content, and (ii) it is immune to the failure of person detection; false positives in person detection do not violate privacy. Our experimental results successfully validated these two advantages.

Storage-Class Memory (SCM) and NAND flash hybrid Solid-State Drive (SSD) has advantages of high performance and low power consumption compared with NAND flash only SSD. In this paper, first, three SSD configurations are investigated. Three different SCMs are used with 0.1 µs, 1 µs and 10 µs read/write latencies, respectively, and the required SCM/NAND flash capacity ratios are analyzed to maintain the same SSD performance. Next, by using the three SSD configurations, the variation of SSD reliability, performance and cost are analyzed by changing error correction strengths. The SSD reliability of acceptable SCM and NAND flash Bit Error Rates (BERs) is limited by achieving specified SSD performance with error correction, and/or limited by SCM and NAND flash parity size and SSD cost. Lastly, the SSD replacement cost is also analyzed by considering the limitation of NAND flash write/erase cycles. The purpose of this paper is to provide a design guideline for obtaining high performance, highly reliable and cost-effective SCM/NAND hybrid structure SSD with ECC.

A novel activation method for a B dopant implanted in a Si substrate using a soft X-ray undulator was examined. As the photon energy of the irradiated soft X-ray approached the energy of the core level of Si 2p, the activation ratio increased. The effect of soft X-ray irradiation on B activation was remarkable at temperatures lower than 400°C. The activation energy of B activation by soft X-ray irradiation (0.06 eV) was lower than that of B activation by furnace annealing (0.18 eV). The activation of the B dopant by soft X-ray irradiation occurs at low temperature, although the activation ratio shows small values of 6.2×10-3 at 110°C. The activation by soft X-ray is caused not only by thermal effects, but also electron excitation and atomic movement.

This paper introduces low-power and small area injection-locking clock and data recovery circuit (CDR) for the wireline and wireless proximity link. By using signal conversion from differential input to common-mode output, the newly proposed edge detector can eliminate the usually used delay line and XOR-based edge detector, and provided low power operation and a small circuit area. The CDR test chip fabricated in a 65-nm CMOS process consumes 30mW from a 1.2- V supply at 12.5Gbps. The fabricated CDR achieved a BER lower than 10-12 and the recovered clock had an rms jitter of 0.87ps. The CDR area is 0.165mm2.

This paper realized the automatic recognition of Mycobacterium tuberculosis in Ziehl-Neelsen stained images by the conventional light microscopy, which can be used in the computer-aided diagnosis of the tuberculosis. We proposed a novel recognition method based on active shape model. First, the candidate bacillus objects are segmented by a method of marker-based watershed transform. Next, a point distribution model of the object shape is proposed to label the landmarks on the object automatically. Then the active shape model is performed after aligning the training set with a weight matrix. The deformation regulation of the object shape is discovered and successfully applied in recognition without using geometric and other commonly used features. During this process, a width consistency constraint is combined with the shape parameter to improve the accuracy of the recognition. Experimental results demonstrate that the proposed method yields high accuracy in the images with different background colors. The recognition accuracy in object level and image level are 92.37% and 97.91% respectively.

Network virtualization environments (NVEs) are emerging to meet the increasing diversity of demands by Internet users where a virtual network (VN) can be constructed to accommodate each specific application service. In the future Internet, diverse service providers (SPs) will provide application services on their own VNs running across diverse infrastructure providers (InPs) that provide physical resources in an NVE. To realize both efficient resource utilization and good QoS of each individual service in such environments, SPs should perform adaptive control on network and computational resources in dynamic and competitive resource sharing, instead of explicit and sufficient reservation of physical resources for their VNs. On the other hand, two novel concepts, software-defined networking (SDN) and network function virtualization (NFV), have emerged to facilitate the efficient use of network and computational resources, flexible provisioning, network programmability, unified management, etc., which enable us to implement adaptive resource control. In this paper, therefore, we propose an architectural design of network orchestration for enabling SPs to maintain QoS of their applications aggressively by means of resource control on their VNs efficiently, by introducing virtual network provider (VNP) between InPs and SPs as 3-tier model, and by integrating SDN and NFV functionalities into NVE framework. We define new north-bound interfaces (NBIs) for resource requests, resource upgrades, resource programming, and alert notifications while using the standard OpenFlow interfaces for resource control on users' traffic flows. The feasibility of the proposed architecture is demonstrated through network experiments using a prototype implementation and a sample application service on nation-wide testbed networks, the JGN-X and RISE.

This paper proposes a fast mode decision technique for intra prediction of High Efficiency Video Coding (HEVC) based on a reliability metric for motion vectors (RMMV). Since such a decision problem can be regarded as a kind of pattern classification, an efficient classifier is required for the reduction of computation complexity. This paper employs the RMMV as a classifier because the RMMV can efficiently categorize image blocks into flat(uniform), active, and edge blocks, and can estimate the direction of an edge block as well. A local search for angular modes is introduced to further speed up the decision process. An experiment shows the advantage of our technique over other techniques.

Drive-by download attacks force users to automatically download and install malware by redirecting them to malicious URLs that exploit vulnerabilities of the user's web browser. In addition, several evasion techniques, such as code obfuscation and environment-dependent redirection, are used in combination with drive-by download attacks to prevent detection. In environment-dependent redirection, attackers profile the information on the user's environment, such as the name and version of the browser and browser plugins, and launch a drive-by download attack on only certain targets by changing the destination URL. When malicious content detection and collection techniques, such as honeyclients, are used that do not match the specific environment of the attack target, they cannot detect the attack because they are not redirected. Therefore, it is necessary to improve analysis coverage while countering these adversarial evasion techniques. We propose a method for exhaustively analyzing JavaScript code relevant to redirections and extracting the destination URLs in the code. Our method facilitates the detection of attacks by extracting a large number of URLs while controlling the analysis overhead by excluding code not relevant to redirections. We implemented our method in a browser emulator called MINESPIDER that automatically extracts potential URLs from websites. We validated it by using communication data with malicious websites captured during a three-year period. The experimental results demonstrated that MINESPIDER extracted 30,000 new URLs from malicious websites in a few seconds that conventional methods missed.

Adding versatile interactions to imperative programming - C, Java and Android - is an essential task. Unfortunately, existing languages provide only limited constructs for user interaction. These constructs are usually in the form of unbounded quantification. For example, existing languages can take the keyboard input from the user only via the read(x)/scan(x) statement. Note that the value of x is unbounded in the sense that x can have any value. This statement is thus not useful for applications with bounded inputs. To support bounded choices, we propose new bounded-choice statements for user interation. Each input device (keyboard, mouse, touchpad, ...) naturally requires a new bounded-choice statement. To make things simple, however, we focus on a bounded-choice statement for keyboard - kchoose - to allow for more controlled and more guided participation from the user. We illustrate our idea via CBI, an extension of the core C with a new bounded-choice statement for the keyboard.

Low-temperature bonding methods of optoelectronic chips, such as laser diodes (LD) and photodiode (PD) chips, have been the focus of much interest to develop highly functional and compact optoelectronic devices, such as microsensors and communication modules. In this paper, room-temperature bonding of the optoelectronic chips with Au thin film to coined Au stud bumps with smooth surfaces (Ra: 1.3nm) using argon and hydrogen gas mixture atmospheric-pressure plasma was demonstrated in ambient air. The die-shear strength was high enough to exceed the strength requirement of MIL-STD-883F, method 2019 (×2). The measured results of the light-current-voltage characteristics of the LD chips and the dark current-voltage characteristics of the PD chips indicated no degradation after bonding.

We propose a cooperative spectrum sensing scheme based on sub-Nyquist sampling in cognitive radios. Our main purpose is to understand the uncertainty caused by sub-Nyquist sampling and to present a sensing scheme that operates at low sampling rates. In order to alleviate the aliasing effect of sub-Nyquist sampling, we utilize cooperation among secondary users and the sparsity order of channel occupancy. The simulation results show that the proposed scheme can achieve reasonable sensing performance even at low sampling rates.

In tree-based wireless sensor networks (WSNs), multihop sensor nodes require a longer time frame to send sensed data to a sink node as the number of hops increases. The time taken for delivery of sensed data becomes a critical issue when a large WSN is deployed. This paper proposes a new data collection scheme with rapid data delivery that utilizes the so-called mobile agent technique. The proposed scheme achieves high data collection efficiency while not relying on route optimization unlike conventional data collection techniques. Simulation results show that the larger the size or the maximum hops of the network, the more effective the proposed scheme becomes. Effectiveness of the proposed scheme is also confirmed through field experiments with actual sensor devices.

Negative electron affinity (NEA) surfaces can be formed by alternating supply of alkali metals (e.g. Cs, Rb, K) and oxygen on semiconductor surfaces. We have studied adsorption structures of Cs on an As-terminated (2×4) (001) GaAs surface using scanning tunneling microscopy (STM). We found that the initial adsorption of Cs atoms occurs around the step sites in the form of Cs clusters and that the size of clusters is reduced by successive exposure to O2, indicating that As-terminated (2×4) surfaces are relatively stable compared to Ga-terminated surfaces and are not broken by the Cs clusters adsorption.

We design a new hash table for high-speed networking that reduces main memory accesses even when the ratio of inserted items to the table size is high, at which point previous schemes no longer work. This improvement comes from a new design of a summary, called expanded keys, exploiting recent multiple hash functions and Bloom filter theories.

A projective Reed-Muller (PRM) code, obtained by modifying a Reed-Muller code with respect to a projective space, is a doubly extended Reed-Solomon code when the dimension of the related projective space is equal to 1. The minimum distance and the dual code of a PRM code are known, and some decoding examples have been presented for low-dimensional projective spaces. In this study, we construct a decoding algorithm for all PRM codes by dividing a projective space into a union of affine spaces. In addition, we determine the computational complexity and the number of correctable errors of our algorithm. Finally, we compare the codeword error rate of our algorithm with that of the minimum distance decoding.

This paper proposes an algorithm that solves the node-to-set disjoint paths problem in an n-Möbius cube in polynomial-order time of n. It also gives a proof of correctness of the algorithm as well as estimating the time complexity, O(n4), and the maximum path length, 2n-1. A computer experiment is conducted for n=1,2,...,31 to measure the average performance of the algorithm. The results show that the average time complexity is gradually approaching to O(n3) and that the maximum path lengths cannot be attained easily over the range of n in the experiment.

Zero-shot learning refers to the object classification problem where no training samples are available for testing classes. For zero-shot learning, attribute transfer plays an important role in recognizing testing classes. One popular method is the indirect attribute prediction (IAP) model, which assumes that all attributes are independent and equally important for learning the zero-shot image classifier. However, a more practical assumption is that different attributes contribute unequally to the classifier learning. We therefore propose assigning different weights for the attributes based on the relevance probabilities between the attributes and the classes. We incorporate such weighed attributes to IAP and propose a relevance probability-based indirect attribute weighted prediction (RP-IAWP) model. Experiments on four popular attributed-based learning datasets show that, when compared with IAP and RFUA, the proposed RP-IAWP yields more accurate attribute prediction and zero-shot image classification.

Pseudorandom binary sequences balanced and with optimal autocorrelation have many applications in the stream cipher, communication, coding theory, etc. Constructing a binary sequences with three-level autocorrelation is equivalent to finding the corresponding characteristic set of the sequences that should be an almost difference set. In the work of T.W. Cusick, C. Ding, and A. Renvall in 1998, the authors gave the necessary and sufficient conditions by which a set of octic residues modulo an odd prime forms an almost difference set. In this paper we show that no integers verify those conditions by the theory of generalized Pell equations. In addition, by relaxing the definition of almost difference set given by the same authors, we could construct two classes of modified almost difference sets and two ones of difference sets from the set of octic residues.