Cold boot attack is a side channel attack that recovers data from memory, which persists for a short period after power is lost. In the course of this attack, the memory gradually degrades over time and only a corrupted version of the data may be available to the attacker. Recently, great efforts have been made to reconstruct the original data from a corrupted version of AES key schedules, based on the assumption that all bits in the charged states tend to decay to the ground states while no bit in the ground state ever inverts. However, in practice, there is a small number of bits flipping in the opposite direction, called reverse flipping errors. In this paper, motivated by the latest work that formulates the relations of AES key bits as a Boolean Satisfiability problem, we move one step further by taking the reverse flipping errors into consideration and employing off-the-shelf SAT and MaxSAT solvers to accomplish the recovery of AES-128 key schedules from decayed memory images. Experimental results show that, in the presence of reverse flipping errors, the MaxSAT approach enables reliable recovery of key schedules with significantly less time, compared with the SAT approach that relies on brute force search to find out the target errors. Moreover, in order to further enhance the efficiency of key recovery, we simplify the original problem by removing variables and formulas that have relatively weak relations to the whole key schedule. Experimental results demonstrate that the improved MaxSAT approach reduces the scale of the problem and recover AES key schedules more efficiently when the decay factor is relatively large.

Aiming at the problem of spatial focus regions distribution dispersion and dislocation in feature pyramid networks and insufficient feature dependency acquisition in both spatial and channel dimensions, this paper proposes a spatial-temporal aggregated shuffle attention for video instance segmentation (STASA-VIS). First, an mixed subsampling (MS) module to embed activating features from the low-level target area of feature pyramid into the high-level is designed, so as to aggregate spatial information on target area. Taking advantage of the coherent information in video frames, STASA-VIS uses the first ones of every 5 video frames as the key-frames and then propagates the keyframe feature maps of the pyramid layers forward in the time domain, and fuses with the non-keyframe mixed subsampled features to achieve time-domain consistent feature aggregation. Finally, STASA-VIS embeds shuffle attention in the backbone to capture the pixel-level pairwise relationship and dimensional dependencies among the channels and reduce the computation. Experimental results show that the segmentation accuracy of STASA-VIS reaches 41.2%, and the test speed reaches 34FPS, which is better than the state-of-the-art one stage video instance segmentation (VIS) methods in accuracy and achieves real-time segmentation.

In this paper, a Many Integrated Core Architecture (MIC) accelerated parallel method of moment (MoM) algorithm is proposed to solve electromagnetic problems in practical applications, where MIC means a kind of coprocessor or accelerator in computer systems which is used to accelerate the computation performed by Central Processing Unit (CPU). Three critical points are introduced in this paper in detail. The first one is the design of the parallel framework, which ensures that the algorithm can run on distributed memory platform with multiple nodes. The hybrid Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) programming model is designed to achieve the purposes. The second one is the out-of-core algorithm, which greatly breaks the restriction of MIC memory. The third one is the pipeline algorithm which overlaps the data movement with MIC computation. The pipeline algorithm successfully hides the communication and thus greatly enhances the performance of hybrid MIC/CPU MoM. Numerical result indicates that the proposed algorithm has good parallel efficiency and scalability, and twice faster performance when compared with the corresponding CPU algorithm.

This paper presents a novel method for weakly supervised semantic segmentation of 3D point clouds using a novel graph and edge convolutional neural network (GECNN) towards 1% and 10% point cloud with labels. Our general framework facilitates semantic segmentation by encoding both global and local scale features via a parallel graph and edge aggregation scheme. More specifically, global scale graph structure cues of point clouds are captured by a graph convolutional neural network, which is propagated from pairwise affinity representation over the whole graph established in a d-dimensional feature embedding space. We integrate local scale features derived from a dynamic edge feature aggregation convolutional neural networks that allows us to fusion both global and local cues of 3D point clouds. The proposed GECNN model is trained by using a comprehensive objective which consists of incomplete, inexact, self-supervision and smoothness constraints based on partially labeled points. The proposed approach enforces global and local consistency constraints directly on the objective losses. It inherently handles the challenges of segmenting sparse 3D point clouds with limited annotations in a large scale point cloud space. Our experiments on the ShapeNet and S3DIS benchmarks demonstrate the effectiveness of the proposed approach for efficient (within 20 epochs) learning of large scale point cloud semantics despite very limited labels.

Image boundary detection or image segmentation is an important step in image analysis. However, choosing appropriate parameters for boundary detection algorithms is necessary to achieve good boundary detection results. Image boundary detection fusion with unsupervised parameters can output a final consensus boundary, which is generally better than using unsupervised or supervised image boundary detection algorithms. In this study, we theoretically examine why image boundary detection fusion can work well and we propose a mixture model for image boundary detection fusion (MMIBDF) to achieve good consensus segmentation in an unsupervised manner. All of the segmentation algorithms are treated as new features and the segmentation results obtained by the algorithms are the values of the new features. The MMIBDF is designed to sample the boundary according to a discrete distribution. We present an inference method for MMIBDF and describe the corresponding algorithm in detail. Extensive empirical results demonstrate that MMIBDF significantly outperforms other image boundary detection fusion algorithms and the base image boundary detection algorithms according to most performance indices.

We consider some attacks on multi-prime RSA (MPRSA) with a modulus N = p1p2 . . . pr (r ≥ 3). It is believed that the small private exponent attack on the MPRSA is less effective than that on RSA (see Hinek et al.'s work at SAC 2003), which means smaller private exponents can be used in the MPRSA to speed up the decryption process. Our work shows that even if a private exponent is significantly beyond Hinek et al.'s bound, it still may be insecure if the prime difference Δ (Δ = pr - p1 = Nγ, supposing p1 < p2 < … < pr) is small, i.e. 0 < γ < 1/r. Specifically, by taking full advantage of prime properties, our small private exponent attack reveals that the MPRSA is insecure when $delta<1-sqrt{1+2gamma-3/r}$ (if $gammagerac{3}{2r}-rac{1+delta}{4}$) or $deltale rac{3}{r}-rac{1}{4}-2gamma$ (if $gamma < rac{3}{2r}-rac{1+delta}{4}$), where δ is the exponential of the private exponent d with base N, i.e., d = Nδ. In addition, we present a Fermat-like factoring attack which factors N efficiently when Δ < N1/r2. These proposed attacks surpass previous works (e.g. Bahig et al.'s at ICICS 2012), and are proved effective in practice.

This paper proposes an efficient video object segmentation approach that is tolerant to complex scene dynamics. Unlike existing approaches that rely on estimating object-like proposals on an intra-frame basis, the proposed approach employs temporally consistent foreground hypothesis using nonlinear regression of saliency guided proposals across a video sequence. For this purpose, we first generate salient foreground proposals at superpixel level by leveraging a saliency signature in the discrete cosine transform domain. We propose to use a random forest based nonlinear regression scheme to learn both appearance and shape features from salient foreground regions in all frames of a sequence. Availability of such features can help rank every foreground proposals of a sequence, and we show that the regions with high ranking scores are well correlated with semantic foreground objects in dynamic scenes. Subsequently, we utilize a Markov Random Field to integrate both appearance and motion coherence of the top-ranked object proposals. A temporal nonlinear regressor for generating salient object support regions significantly improves the segmentation performance compared to using only per-frame objectness cues. Extensive experiments on challenging real-world video sequences are performed to validate the feasibility and superiority of the proposed approach for addressing dynamic scene segmentation.

This work presents an approximate global optimization method for image halftone by fusing multi-scale information of the tree model. We employ Gaussian mixture model and hidden Markov tree to characterized the intra-scale clustering and inter-scale persistence properties of the detailed coefficients, respectively. The model of multiscale perceived error metric and the theory of scale-related perceived error metric are used to fuse the statistical distribution of the error metric of the scale of clustering and cross-scale persistence. An Energy function is then generated. Through energy minimization via graph cuts, we gain the halftone image. In the related experiment, we demonstrate the superior performance of this new algorithm when compared with several algorithms and quantitative evaluation.

Base station coordination is considered as a promising technique to mitigate inter-cell interference and improve the cell-edge performance in cellular orthogonal frequency division multiple-access (OFDMA) networks. The problem to design an efficient radio resource allocation scheme for coordinated cellular OFDMA networks incorporating base station coordination has been only partially investigated. In this contribution, a novel radio resource allocation algorithm with universal frequency reuse is proposed to support base station coordinated transmission. Firstly, with the assumption of global coordination between all base station sectors in the network, a coordinated subchannel assignment algorithm is proposed. Then, by dividing the entire network into a number of disjoint coordinated clusters of base station sectors, a reduced-feedback algorithm for subchannel assignment is proposed for practical use. The utility function based on the user average throughput is used to balance the efficiency and fairness of wireless resource allocation. System level simulation results demonstrate that the reduced-feedback subchannel assignment algorithm significantly improves the cell-edge average throughput and the fairness index of users in the network, with acceptable degradation of cell-average performance.

Despite their attractive properties, networked virtual environments (net-VEs) are notoriously difficult to design, implement and test due to the concurrency, real-time and networking features in these systems. The current practice for net-VE design is basically trial and error, empirical, and totally lacks formal methods. This paper proposes to apply a Petri net formal modeling technique to a net-VE: NICE (Narrative Immersive Constructionist/Collaborative Environment), predict the net-VE performance based on simulation, and improve the net-VE performance. NICE is essentially a network of collaborative virtual reality systems called CAVE-(CAVE Automatic Virtual Environment). First, we present extended fuzzy-timing Petri net models of both CAVE and NICE. Then, by using these models and Design/CPN as the simulation tool, we have conducted various simulations to study real-time behavior, network effects and performance (latencies and jitters) of NICE. Our simulation results are consistent with experimental data.

This paper describes a new method for lossy audio signal compression via compressive sensing (CS). In this method, a structured shrinkage operator is employed to decompose the audio signal into three layers, with two sparse layers, tonal and transient, and additive noise, and then, both the tonal and transient layers are compressed using CS. Since the shrinkage operator is able to take into account the structure information of the coefficients in the transform domain, it is able to achieve a better sparse approximation of the audio signal than traditional methods do. In addition, we propose a sparsity allocation algorithm, which adjusts the sparsity between the two layers, thus improving the performance of CS. Experimental results demonstrated that the new method provided a better compression performance than conventional methods did.

Most unsupervised video segmentation algorithms are difficult to handle object extraction in dynamic real-world scenes with large displacements, as foreground hypothesis is often initialized with no explicit mutual constraint on top-down spatio-temporal coherency despite that it may be imposed to the segmentation objective. To handle such situations, we propose a multiscale saliency flow (MSF) model that jointly learns both foreground and background features of multiscale salient evidences, hence allowing temporally coherent top-down information in one frame to be propagated throughout the remaining frames. In particular, the top-down evidences are detected by combining saliency signature within a certain range of higher scales of approximation coefficients in wavelet domain. Saliency flow is then estimated by Gaussian kernel correlation of non-maximal suppressed multiscale evidences, which are characterized by HOG descriptors in a high-dimensional feature space. We build the proposed MSF model in accordance with the primary object hypothesis that jointly integrates temporal consistent constraints of saliency map estimated at multiple scales into the objective. We demonstrate the effectiveness of the proposed multiscale saliency flow for segmenting dynamic real-world scenes with large displacements caused by uniform sampling of video sequences.

We report the growth of carbon nanofibers (CNFs) from carbon particles by chemical vapor deposition (CVD) with ultrasonic neblizer using ethanol as carbon source. Dense CNFs having diameters of several tens of nanometers have been successfully synthesized by the CVD without using any metal catalysts. The carbon particles formed from decompostion of fullerene were found to be suitable for the synthesis of CNFs. Details of the optimum conditions for producing CNFs and the expected growth mechanism are also described.

As a with-carry analog (based on modular arithmetic) of the usual Walsh-Hadamard transform (WHT), arithmetic Walsh transform (AWT) has been used to obtain analogs of some properties of Boolean functions which are important in the design and analysis of cryptosystems. The existence of nonzero linear structure of Boolean functions is an important criterion to measure the weakness of these functions in their cryptographic applications. In this paper, we find more analogs of linear structures of Boolean functions from AWT. For some classes of n-variable Boolean functions f, we find necessary and sufficient conditions for the existence of an invariant linear structure and a complementary linear structure 1n of f. We abstract out a sectionally linear relationship between AWT and WHT of n-variable balanced Boolean functions f with linear structure 1n. This result show that AWT can characterize cryptographic properties of these functions as long as WHT can. In addition, for a diagonal Boolean function f, a recent result by Carlet and Klapper says that the AWT of f can be expressed in terms of the AWT of a diagonal Boolean function of algebraic degree at most 3 in a larger number of variables. We provide for the result a complete and more modular proof which works for both even and odd weights (of the parameter c in the Corollary 19 by Carlet and Klapper (DCC 73(2): 299-318, 2014).

This paper proposes a novel file transfer scheme named "Jam-packing file transfer" that consists of a call admission control and a packet scheduling mechanism. This combination can efficiently multiplex the traffic of file transfer and provides a guaranteed delivery time. Simulation results show the highness of extreme the multiplexing efficiency as the improvement in call blocking probabilities compared with the conventional rate-based reservation schemes. Furthermore, simulations of the packet scheduling indicate that file deliveries are done at the predicted delivery time.

In this letter, the interference cancellation technique is introduced to single carrier (SC) block transmission systems in sparse Rician frequency selective fading channels, and an effective equalizer is presented. Hard decision on the transmitted signal is made by commonly used SC equalizers, and every multipath signal can be constructed by the initial solution and channel state information. Then, final demodulation result is obtained by the line-of-sight component in the received signal which can be achieved by cancelling the other multipath signals in the received signal. The solution can be further used to construct the multipath signals allowing a multistage detector with higher performance to be realized. It is shown by Monte Carlo simulations in an SUI-5 channel that the new scheme offers dramatically higher performance than traditional equalization schemes.

Orthogonal frequency division multiplexing (OFDM) is a critical technology in 3G evolution systems, which can effectively avoid intra-cell interference, but may bring with serious inter-cell interference. Inter-cell interference cancellation is one of effective schemes taken in mitigating inter-cell interference, but for many existing schemes in inter-cell interference cancellation, various generalized spatial diversities are taken, which always bring with extra interference and blind spots, or even need to acquire extra information on source and channel. In this paper, a novel inter-cell interference mitigation method is proposed for 3G evolution systems. This method is based on independent component analysis in blind source separation, and the input signal to interference plus noise ratio (SINR) is set as objective function. By generalized eigenvalue decomposition and algorithm iterations, maximum signal noise ratio (SNR) can be obtained in output. On the other hand, this method can be worked with no precise knowledge of source signal and channel information. Performance evaluation shows that such method can mitigate inter-cell interference in a semi-blind state, and effectively improve output SNR with the condition that lower input SINR, higher input SNR and longer lengths of the processing frame.

The Navigational Programming (NavP) methodology is based on the principle of self-migrating computations. It is a truly incremental methodology for developing parallel programs: each step represents a functioning program, and each intermediate program is an improvement over its predecessor. The transformations are mechanical and straightforward to apply. We illustrate our methodology in the context of matrix multiplication, showing how the transformations lead from a sequential program to a fully parallel program. The NavP methodology is conducive to new ways of thinking that lead to ease of programming and high performance. Even though our parallel algorithm was derived using a sequence of mechanical transformations, it displays certain performance advantages over the classical handcrafted Gentleman's Algorithm.

Multi-objective evolutionary algorithms are widely used in many engineering optimization problems and artificial intelligence applications. Ant lion optimizer is an outstanding evolutionary method, but two issues need to be solved to extend it to the multi-objective optimization field, one is how to update the Pareto archive, and the other is how to choose elite and ant lions from archive. We develop a novel multi-objective variant of ant lion optimizer in this paper. A new measure combining Pareto dominance relation and distance information of individuals is put forward and used to tackle the first issue. The concept of time weight is developed to handle the second problem. Besides, mutation operation is adopted on solutions in middle part of archive to further improve its performance. Eleven functions, other four algorithms and four indicators are taken to evaluate the new method. The results show that proposed algorithm has better performance and lower time complexity.

Segmenting foreground objects from highly dynamic scenes with missing data is very challenging. We present a novel unsupervised segmentation approach that can cope with extensive scene dynamic as well as a substantial amount of missing data that present in dynamic scene. To make this possible, we exploit convex optimization of total variation beforehand for images with missing data in which depletion mask is available. Inpainting depleted images using total variation facilitates detecting ambiguous objects from highly dynamic images, because it is more likely to yield areas of object instances with improved grayscale contrast. We use a conditional random field that adapts to integrate both appearance and motion knowledge of the foreground objects. Our approach segments foreground object instances while inpainting the highly dynamic scene with a variety amount of missing data in a coupled way. We demonstrate this on a very challenging dataset from the UCSD Highly Dynamic Scene Benchmarks (HDSB) and compare our method with two state-of-the-art unsupervised image sequence segmentation algorithms and provide quantitative and qualitative performance comparisons.