The arms race between offense and defense in the cloud impels the innovation of techniques for monitoring attacks and unauthorized activities. The promising technique of virtual machine introspection (VMI) becomes prevalent for its tamper-resistant capability. However, some elaborate exploitations are capable of invalidating VMI-based tools by breaking the assumption of a trusted guest kernel. To achieve a more reliable and robust introspection, we introduce a practical approach to monitor and detect attacks that attempt to subvert VMI in this paper. Our approach combines supervised machine learning and hardware architectural events to identify those malicious behaviors which are targeted at VMI techniques. To demonstrate the feasibility, we implement a prototype named HyperMon on the Xen hypervisor. The results of our evaluation show the effectiveness of HyperMon in detecting malicious behaviors with an average accuracy of 90.51% (AUC).

The rising systems programming language Rust is fast, efficient and memory safe. However, improperly dereferencing raw pointers in Rust causes new safety problems. In this paper, we present a detailed analysis into these problems and propose a practical hybrid approach to detecting unsafe raw pointer dereferencing behaviors. Our approach employs pattern matching to identify functions that can be used to generate illegal multiple mutable references (We define them as thief function) and instruments the dereferencing operation in order to perform dynamic checking at runtime. We implement a tool named UnsafeFencer and has successfully identified 52 thief functions in 28 real-world crates*, of which 13 public functions are verified to generate multiple mutable references.

Handoff plays an important role in vehicular networks due to high movement of vehicles. To provide seamless connectivity under Access Points (AP), this paper proposes an adaptive handoff triggering method to minimize communication time for a vehicle with an AP switch (i.e., whether and when to trigger a handoff process). In the proposed method, combined with an improved data transmission rate based trigger, handoff triggering decision is executed based on three different communication methods (called C-Dire, C-Relay and C-ALLRelay) to minimize the transmission delay when a vehicle moves from an AP to another. Transmission delay is derived through considering vehicle mobility and transmission rate diversity. The simulation results show that the proposed method is proven to be adaptive to vehicular networks.

In modern cryptographic systems, physical unclonable functions (PUFs) are efficient mechanisms for many security applications, which extract intrinsic random physical variations to generate secret keys. The classical PUFs mainly exhibit static challenge-response behaviors and generate static keys, while many practical cryptographic systems need reconfigurable PUFs which allow dynamic keys derived from the same circuit. In this paper, the concept of reconfigurable multi-port PUFs (RM-PUFs) is proposed. RM-PUFs not only allow updating the keys without physically replacement, but also generate multiple keys from different ports in one clock cycle. A practical RM-PUFs construction is designed based on asynchronous clock and fabricated in TSMC low-power 65 nm CMOS process. The area of test chip is 1.1 mm2, and the maximum clock frequency is 0.8 GHz at 1.2 V. The average power consumption is 27.6 mW at 27. Finally, test results show that the RM-PUFs generate four reconfigurable 128-bit secret keys, and the keys are secure and reliable over a range of environmental variations such as supply voltage and temperature.

This paper presents a novel model in the field of image co-saliency detection. Previous works simply design low level handcrafted features or extract deep features based on image patches for co-saliency calculation, which neglect the entire object perception properties. Besides, they also neglect the problem of visual similar region's mismatching when designing co-saliency calculation model. To solve these problems, we propose a novel strategy by considering both local prediction and global refinement (LPGR). In the local prediction stage, we train a deep convolutional saliency detection network in an end-to-end manner which only use the fully convolutional layers for saliency map prediction to capture the entire object perception properties and reduce feature redundancy. In the global refinement stage, we construct a unified co-saliency refinement model by integrating global appearance similarity into a co-saliency diffusion function, realizing the propagation and optimization of local saliency values in the context of entire image group. To overcome the adverse effects of visual similar regions' mismatching, we innovatively incorporates the inter-images saliency spread constraint (ISC) term into our co-saliency calculation function. Experimental results on public datasets demonstrate consistent performance gains of the proposed model over the state-of-the-art methods.

In general, semi-supervised clustering can outperform unsupervised clustering. Since 2001, pairwise constraints for semi-supervised clustering have been an important paradigm in this field. In this paper, we show that pairwise constraints (ECs) can affect the performance of clustering in certain situations and analyze the reasons for this in detail. To overcome these disadvantages, we first outline some exemplars constraints. Based on these constraints, we then describe a semi-supervised clustering framework, and design an exemplars constraints expectation-maximization algorithm. Finally, standard datasets are selected for experiments, and experimental results are presented, which show that the exemplars constraints outperform the corresponding unsupervised clustering and semi-supervised algorithms based on pairwise constraints.

In this paper, a novel consecutive-pilot design is proposed to suppress phase noise (PHN) in orthogonal frequency-division multiplexing (OFDM) system. The estimation of PHN is performed by a cross-correlation between the received and locally generated pilots in frequency-domain. Simulations show that the proposed scheme can effectively ameliorate the impairment due to PHN, at the cost of acceptable additional transmission bandwidth and low implementation complexity.

We propose a Multiple Zones-based (M-Zone) routing protocol to discover node-disjoint multiplath routing efficiently and effectively in large-scale MANETs. Compared with single path routing, multipath routing can improve robustness, load balancing and throughput of a network. However, it is very difficult to achieve node-disjoint multipath routing in large-scale MANETs. To ensure finding node-disjoint multiple paths, the M-Zone protocol divides the region between a source and a destination into multiple zones based on geographical location and each path is mapped to a distinct zone. Performance analysis shows that M-Zone has good stability, and the control complexity and storage complexity of M-Zone are lower than those of the well-known AODVM protocol. Simulation studies show that the average end-to-end delay of M-Zone is lower than that of AODVM and the routing overhead of M-Zone is less than that of AODVM.

This letter presents a 0.5 V low-voltage op-amp in a standard 0.18 µm CMOS process for switched-capacitor circuits. Unlike other two-stage 0.5 V op-amp architectures, this op-amp consists of CMOS inverters that utilize floating voltage sources and forward body bias for obtaining high-speed operation. And two improved common-mode rejection circuits are well combined to achieve low power and chip area reduction. Simulation results indicate that the op-amp has an open-loop gain of 62 dB, and a high unity gain bandwidth of 56 MHz. The power consumption is only 350 µW.

A new hybrid brain-computer interface (BCI), which is based on sequential controls by eye tracking and steady-state visual evoked potentials (SSVEPs), has been proposed for high-speed spelling in virtual reality (VR) with a 40-target virtual keyboard. During target selection, gaze point was first detected by an eye-tracking accessory. A 4-target block was then selected for further target selection by a 4-class SSVEP BCI. The system can type at a speed of 1.25 character/sec in a cue-guided target selection task. Online experiments on three subjects achieved an averaged information transfer rate (ITR) of 360.7 bits/min.

A quantized index for evaluating the pattern similarity of two different datasets is designed by calculating the number of correlated dictionary atoms. Guided by this theory, task-specific biometric recognition model transferred from state-of-the-art DNN models is realized for both face and vein recognition.

This paper proposes a novel system called the cyclic prefix reconstructable time domain synchronous orthogonal frequency division multiplexing ( CPR-TDS-OFDM ) system, which uses a new frame structure and restores the cyclicity of the received OFDM block with low complexity. Simulation results show that the CPR-TDS-OFDM system outperforms the conventional TDS-OFDM system in high-speed fading channels.

Feature representation is a key component of most visual tracking algorithms. It is difficult to deal with complex appearance changes with low-level hand-crafted features due to weak representation capacities of such features. In this paper, we propose a novel tracking algorithm through combining a joint dictionary pair learning with convolutional neural networks (CNN). We utilize CNN model that is trained on ImageNet-Vid to extract target features. The CNN includes three convolutional layers and two fully connected layers. A dictionary pair learning follows the second fully connected layer. The joint dictionary pair is learned upon extracted deep features by the trained CNN model. The temporal variations of target appearances are learned in the dictionary learning. We use the learned dictionaries to encode target candidates. A linear combination of atoms in the learned dictionary is used to represent target candidates. Extensive experimental evaluations on OTB2015 demonstrate the superior performances against SOTA trackers.

In this paper, estimation accuracy of channel frequency response (CFR) according to least squared (LS) criterion with two transmit antennas for the time domain synchronous-orthogonal frequency division multiplexing (TDS-OFDM) system is investigated. To minimize the estimation variance, the conditions to guide the pseudo-noise (PN) sequence design are discussed and three training sequence design schemes are proposed accordingly. Simulations show that the proposed PN sequence design scheme is effective, while the implementation complexity for the channel estimation is low.

An improved spectrum sensing method based on PN autocorrelation (PNAC) for Digital Terrestrial Television Multimedia Broadcasting (DTMB) system is proposed in this paper. The low bound of miss-detection probability and the decision threshold for a given false alarm probability are studied. The performances of proposed method and existing methods are compared through computer simulations under both non-time dispersive channel and time dispersive channel. Simulation results show that the proposed method has better performance than the original PNAC-based method, and is more robust to both carrier frequency offset (CFO) and time dispersion of the channel than the existing method based on PN cross-correlation (PNCC).

Both gender and identity recognition task with hand vein information is solved based on the proposed cross-selected-domain transfer learning model. State-of-the-art recognition results demonstrate the effectiveness of the proposed model for pattern recognition task, and the capability to avoid over-fitting of fine-tuning DCNN with small-scaled database.

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.

This letter provides an efficient massive multiple-input multiple-output (MIMO) detector based on quasi-newton methods to speed up the convergence performance under realistic scenarios, such as high user load and spatially correlated channels. The proposed method leverages the information of the Hessian matrix by merging Barzilai-Borwein method and Limited Memory-BFGS method. In addition, an efficient initial solution based on constellation mapping is proposed. The simulation results demonstrate that the proposed method diminishes performance loss to 0.7dB at the bit-error-rate of 10-2 at 128×32 antenna configuration with low complexity, which surpasses the state-of-the-art (SOTA) algorithms.