Given an undirected graph G, an edge dominating set is a subset F of edges such that each edge not in F is adjacent to some edge in F, and computing the minimum size of an edge dominating set is known to be NP-hard. Since the size of any edge dominating set is at least half of the maximum size µ(G) of a matching in G, we study the problem of testing whether a given graph G has an edge dominating set of size ⌈µ(G)/2⌉ or not. In this paper, we prove that the problem is NP-complete, whereas we design an O^*(2.0801^µ(G)/2)-time and polynomial-space algorithm to the problem.

In this paper, we investigate a relationship between many-one-like autoreducibility and completeness for classes of functions computed by polynomial-time nondeterministic Turing transducers. We prove two results. One is that any many-one complete function for these classes is metric many-one autoreducible. The other is that any strict metric many-one complete function for these classes is strict metric many-one autoreducible.

In this paper we study a recently proposed variant of the facility location problem, called the r-gathering problem. Given an integer r, a set C of customers, a set F of facilities, and a connecting cost co(c, f) for each pair of c ∈ C and f ∈ F, an r-gathering of customers C to facilities F is an assignment A of C to open facilities F' ⊆ F such that at least r customers are assigned to each open facility. We give an algorithm to find an r-gathering with the minimum cost, where the cost is max_{c ∈ C}{co(c, A(c))}, when all C and F are on the real line.

Given a graph G=(V, E), where V and E are vertex and edge sets of G, and a subset V_NT of vertices called a non-terminal set, the minimum spanning tree with a non-terminal set V_NT, denoted by MSTNT, is a connected and acyclic spanning subgraph of G that contains all vertices of V with the minimum weight where each vertex in a non-terminal set is not a leaf. On general graphs, the problem of finding an MSTNT of G is NP-hard. We show that if G is an outerplanar graph then finding an MSTNT of G is linearly solvable with respect to the number of vertices.

A ladder lottery, known as “Amidakuji” in Japan, is one of the most popular lotteries. In this paper, we consider the problems of enumeration, counting, and random generation of the ladder lotteries. For given two positive integers n and b, we give algorithms of enumeration, counting, and random generation of ladder lotteries with n lines and b bars. The running time of the enumeration algorithm is O(n + b) time for each. The running time of the counting algorithm is O(nb³) time. The random generation algorithm takes O(nb³) time for preprocess, and then it generates a ladder lottery in O(n + b) for each uniformly at random.

We propose a cache-aware method to accelerate texture-based volume rendering on a graphics processing unit (GPU) that is compatible with the compute unified device architecture. The proposed method extends a previous method such that it can maximize the average rendering performance while rotating the viewing direction around a volume. To realize this, the proposed method performs in-place rotation of volume data, which rearranges the order of voxels to allow consecutive threads (warps) to refer to voxels with the minimum access strides. Experiments indicate that the proposed method replaces the worst texture cache (TC) hit rate of 42% with the best TC hit rate of 93% for a 1024³-voxel volume. Thus, the average frame rate increases by a factor of 1.6 in the proposed method compared with that in the previous method. Although the overhead of in-place rotation slightly decreases the frame rate from 2.0 frames per second (fps) to 1.9 fps, this slowdown occurs only with a few viewing directions.

To identify problems in a software development process, we have been developing an automated measurement tool called TaskPit, which monitors software development tasks such as programming, testing and documentation based on the execution history of software applications. This paper introduces the system requirements, design and implementation of TaskPit; then, presents two real-world case studies applying TaskPit to actual software development. In the first case study, we applied TaskPit to 12 software developers in a certain software development division. As a result, several concerns (to be improved) have been revealed such as (a) a project leader spent too much time on development tasks while he was supposed to be a manager rather than a developer, (b) several developers rarely used e-mails despite the company's instruction to use e-mail as much as possible to leave communication records during development, and (c) several developers wrote too long e-mails to their customers. In the second case study, we have recorded the planned, actual, and self reported time of development tasks. As a result, we found that (d) there were unplanned tasks in more than half of days, and (e) the declared time became closer day by day to the actual time measured by TaskPit. These findings suggest that TaskPit is useful not only for a project manager who is responsible for process monitoring and improvement but also for a developer who wants to improve by him/herself.

When debugging bugs, programmers often prepare test cases to reproduce buggy behaviours. However, for concurrent programs, test cases alone are typically insufficient to reproduce buggy behaviours, due to the nondeterminism of multi-threaded executions. In literature, various approaches have been proposed to reproduce buggy behaviours for concurrency bugs deterministically, but to the best of our knowledge, they are still limited. In particular, we have recognized three debugging scenarios from programming practice, but existing approaches can handle only one of the scenarios. In this paper, we propose a novel approach, called SPDebugger, that provides finer-grained thread controlling over test cases, programs under test, and even third party library code, to reproduce the predesigned thread execution schedule. The evaluation shows that SPDebugger handles more debugging scenarios than the state-of-the-art tool, called IMUnit, with similar human effort.

This paper proposes secure regenerating codes that are composed of non-secure regenerating codes and a new all-or-nothing transform. Unlike the previous analysis of secure regenerating codes, the security of the proposed codes is analyzed in the sense of the indistinguishability. The advantage of the proposed codes is that the overhead caused by the security against eavesdropping is much less than that of previous secure regenerating codes. The security of the proposed codes against eavesdropping mainly depends on the new all-or-nothing transform.

Network servers and applications commonly use static IP addresses and communication ports, making themselves easy targets for network reconnaissances and attacks. Moving target defense (MTD) is an innovatory and promising proactive defense technique. In this paper, we develop a novel MTD mechanism, called Random Port and Address Hopping (RPAH). The goal of RPAH is to hide network servers and applications and resist network reconnaissances and attacks by constantly changing their IP addresses and ports. In order to enhance the unpredictability, RPAH integrates source identity, service identity and temporal parameter in the hopping to provide three hopping frequencies, i.e., source hopping, service hopping and temporal hopping. RPAH provides high unpredictability and the maximum hopping diversities by introducing port and address demultiplexing mechanism, and provides a convenient attack detection mechanism with which the messages from attackers using invalid or inactive addresses/ports will be conveniently detected and denied. Our experiments and evaluation on campus network and PlanetLab show that RPAH is effective in resisting various network reconnaissance and attack models such as network scanning and worm propagation, while introducing an acceptable operation overhead.

Wireless LAN (WLAN) roaming systems, such as eduroam, enable the mutual use of WLAN facilities among multiple organizations. As a consequence of the strong demand for WLAN roaming, it is utilized not only at universities and schools but also at the venues of large events such as concerts, conferences, and sports events. Moreover, it has also been reported that WLAN roaming is useful in areas afflicted by natural disasters. This paper presents a novel WLAN roaming system over Wireless Mesh Networks (WMNs) that is useful for the use cases shown above. The proposed system is based on two methods as follows: 1) Automatic authentication path generation method decreases the WLAN roaming system deployment costs including the wiring cost and configuration cost. Although the wiring cost can be reduced by using WMN technologies, some additional configurations are still required if we want to deploy a secure user authentication mechanism (e.g. IEEE 802.1X) on WLAN systems. In the proposed system, the Access Points (APs) can act as authenticators automatically using RadSec instead of RADIUS. Therefore, the network administrators can deploy 802.1X-based authentication systems over WMNs without additional configurations on-site. 2) Local authentication method makes the system deployable in times of natural disasters, in particular when the upper network is unavailable or some authentication servers or proxies are down. In the local authentication method, users and APs can be authenticated at the WMN by locally verifying the digital certificates as the authentication credentials.

Wireless sensor networks are being used in many disaster-related applications. Certain types of disasters are studied and modeled with different and dynamic risk estimations in different areas, hence requiring different levels of monitoring. Such nonuniform and dynamic coverage requirements pose a challenge to a sensor coverage problem. This work proposes the Mobile sensor Relocation using Delaunay triangulation And Shifting on Hill climbing (MR-DASH) approach, which calculates an appropriate location for each mobile sensor as an attempt to maximize coverage ratio. Based on a probabilistic sensing model, it constructs a Delaunay triangulation from static sensors' locations and vertices of interesting regions. The resulting triangles are then prioritized based on their sizes and corresponding levels of requirement so that mobile sensors can be relocated accordingly. The proposed method was both compared with an existing previous work and demonstrated with real-world disaster scenarios by simulation. The result showed that MR-DASH gives appropriate target locations that significantly improve the coverage ratio with relatively low total sensors' moving distance, while properly adapting to variations in coverage requirements.

A floating-point multiplier with concurrent error detection capability by partial duplication is proposed. It uses a truncated multiplier for checking of the significand (mantissa) multiplication instead of full duplication. The proposed multiplier can detect any erroneous output with error larger than one unit in the last place (1 ulp) of the significand, which may be overlooked by residue checking. Its circuit area is smaller than that of a fully duplicated one. Area overhead of a single-precision multiplier is about 78% and that of a double-precision one is about 65%.

Social Media has already become a new arena of our lives and involved different aspects of our social presence. Users' personal information and activities on social media presumably reveal their personal interests, which offer great opportunities for many e-commerce applications. In this paper, we propose a principled latent variable model to infer user consumption preferences at the category level (e.g. inferring what categories of products a user would like to buy). Our model naturally links users' published content and following relations on microblogs with their consumption behaviors on e-commerce websites. Experimental results show our model outperforms the state-of-the-art methods significantly in inferring a new user's consumption preference. Our model can also learn meaningful consumption-specific topics automatically.

Narrow swipe sensor based systems have drawn more and more attention in recent years. However, the size of captured image is significantly smaller than that obtained from the traditional area fingerprint sensor. Under this condition the available minutiae number is also limited. Therefore, only employing minutiae with the standard associated feature can hardly achieve high verification accuracy. To solve this problem, we present a novel Hybrid Minutiae Descriptor (HMD) which consists of two modules. The first one: Minutiae Ridge-Valley Orientation Descriptor captures the orientation information around minutia and also the trace points located at associated ridge and valley. The second one: Gabor Binary Code extracts and codes the image patch around minutiae. The proposed HMD enhances the representation capability of minutiae feature, and can be matched very efficiently. Experiments conducted over public databases and the database captured by the narrow swipe sensor show that this innovative method gives rise to significant improvements in reducing FRR (False Reject Rate) and EER (Equal Error Rate).

Traditional speech enhancement (SE) algorithms usually have fluctuant performance when they deal with different types of noisy speech signals. In this paper, we propose multi-task Bayesian compressive sensing based speech enhancement (MT-BCS-SE) algorithm to achieve not only comparable performance to but also more stable performance than traditional SE algorithms. MT-BCS-SE algorithm utilizes the dependence information among compressive sensing (CS) measurements and the sparsity of speech signals to perform SE. To obtain sufficient sparsity of speech signals, we adopt overcomplete dictionary to transform speech signals into sparse representations. K-SVD algorithm is employed to learn various overcomplete dictionaries. The influence of the overcomplete dictionary on MT-BCS-SE algorithm is evaluated through large numbers of experiments, so that the most suitable dictionary could be adopted by MT-BCS-SE algorithm for obtaining the best performance. Experiments were conducted on well-known NOIZEUS corpus to evaluate the performance of the proposed algorithm. In these cases of NOIZEUS corpus, MT-BCS-SE is shown that to be competitive or even superior to traditional SE algorithms, such as optimally-modified log-spectral amplitude (OMLSA), multi-band spectral subtraction (SSMul), and minimum mean square error (MMSE), in terms of signal-noise ratio (SNR), speech enhancement gain (SEG) and perceptual evaluation of speech quality (PESQ) and to have better stability than traditional SE algorithms.

In this paper we propose a method for estimating time-to-contact in scattering media. Images taken in scattering media are often unclear and blurry, making it difficult to detect appropriate geometric information from these images for computing the 3 dimensional properties of the scene. Therefore, instead of searching for geometric information, we attempt to use photometric information instead. In our approach, we use the observed image intensity. The method proposed in this paper is able to utilize the effect of scattering media on the resultant image and estimate the time-to-contact toward objects without any prior knowledge of the scene, cameras, and the scattering media. This method is then evaluated using simulated and real images.

The quality assessment of screen content images (SCIs) has been attractive recently. Different from natural images, SCI is usually a mixture of picture and text. Traditional quality metrics are mainly designed for natural images, which do not fit well into the SCIs. Motivated by this, this letter presents a simple and effective method to naturalize SCIs, so that the traditional quality models can be applied for SCI quality prediction. Specifically, bicubic interpolation-based up-sampling is proposed to achieve this goal. Extensive experiments and comparisons demonstrate the effectiveness of the proposed method.

The Android pattern unlock is a widely adopted graphical password system that requires a user to draw a secret pattern connecting points arranged in a grid. The theoretical security of pattern unlock can be defined by the number of possible patterns. However, only upper bounds of the number of patterns have been known except for 3×3 and 4×4 grids for which the exact number of patterns was found by brute-force enumeration. In this letter, we present the first lower bound by computing the minimum number of visible points from each point in various subgrids.

This paper presents a hybrid push/pull streaming scheme to take advantage of both the interval caching-based push method and the mesh-based pull method. When a new peer joins, a mesh-based pull method is adopted to avoid the overhead to reorganize the structure only if all of its potential preceding peers are likely to leave before the end of its playback. Otherwise, an interval caching-based push method is adopted so that the better performance of the push method can be maintained until it completes the playback. We demonstrate that our proposed scheme outperforms compared with when either the interval caching-based push method or mesh-based pull method is employed alone.

A text-dependent i-vector extraction scheme and a lexicon-based binary vector (L-vector) representation are proposed to improve the performance of text-dependent speaker verification. I-vector and L-vector are used to represent the utterances for enrollment and test. An improved cosine distance kernel is constructed by combining i-vector and L-vector together and is used to distinguish both speaker identity and lexical (or text) diversity with back-end support vector machine (SVM). Experiments are conducted on RSR 2015 Corpus part 1 and part 2, the results indicate that at most 30% improvement can be obtained compared with traditional i-vector baseline.

The compressive sensing (CS) theory has been widely used in synthetic aperture radar (SAR) imaging for its ability to reconstruct image from an extremely small set of measurements than what is generally considered necessary. Because block-based CS approaches in SAR imaging always cause block boundaries between two adjacent blocks, resulting in namely the block artefacts. In this paper, we propose a weighted overlapped block-based compressive sensing (WOBCS) method to reduce the block artefacts and accomplish SAR imaging. It has two main characteristics: 1) the strategy of sensing small and recovering big and 2) adaptive weighting technique among overlapped blocks. This proposed method is implemented by the well-known CS recovery schemes like orthogonal matching pursuit (OMP) and BCS-SPL. Promising results are demonstrated through several experiments.

Feature extractor plays an important role in visual tracking, but most state-of-the-art methods employ the same feature representation in all scenes. Taking into account the diverseness, a tracker should choose different features according to the videos. In this work, we propose a novel feature adaptive correlation tracker, which decomposes the tracking task into translation and scale estimation. According to the luminance of the target, our approach automatically selects either hierarchical convolutional features or histogram of oriented gradient features in translation for varied scenarios. Furthermore, we employ a discriminative correlation filter to handle scale variations. Extensive experiments are performed on a large-scale benchmark challenging dataset. And the results show that the proposed algorithm outperforms state-of-the-art trackers in accuracy and robustness.