Message franking is introduced by Facebook in end-to-end encrypted messaging services. It allows to produce verifiable reports of malicious messages by including cryptographic proofs, called reporting tags, generated by Facebook. Recently, Grubbs et al. (CRYPTO'17) proceeded with the formal study of message franking and introduced committing authenticated encryption with associated data (CAEAD) as a core primitive for obtaining message franking. In this work, we aim to enhance the security of message franking and introduce forward security and updates of reporting tags for message franking. Forward security guarantees the security associated with the past keys even if the current keys are exposed and updates of reporting tags allow for reporting malicious messages after keys are updated. To this end, we firstly propose the notion of key-evolving message franking with updatable reporting tags including additional key and reporting tag update algorithms. Then, we formalize five security requirements: confidentiality, ciphertext integrity, unforgeability, receiver binding, and sender binding. Finally, we show a construction of forward secure message franking with updatable reporting tags based on CAEAD, forward secure pseudorandom generator, and updatable message authentication code.

The design and adjustment of the divergence in audio applications using nonnegative matrix factorization (NMF) is still open problem. In this study, to deal with this problem, we explore a representation of the divergence using neural networks (NNs). Instead of the divergence, our approach extends the multiplicative update algorithm (MUA), which estimates the NMF parameters, using NNs. The design of the extended MUA incorporates NNs, and the new algorithm is referred to as the deep MUA (DeMUA) for NMF. While the DeMUA represents the algorithm for the NMF, interestingly, the divergence is obtained from the incorporated NN. In addition, we propose theoretical guides to design the incorporated NN such that it can be interpreted as a divergence. By appropriately designing the NN, MUAs based on existing divergences with a single hyper-parameter can be represented by the DeMUA. To train the DeMUA, we applied it to audio denoising and supervised signal separation. Our experimental results show that the proposed architecture can learn the MUA and the divergences in sparse denoising and speech separation tasks and that the MUA based on generalized divergences with multiple parameters shows favorable performances on these tasks.

Visual object tracking is always a challenging task in computer vision. During the tracking, the shape and appearance of the target may change greatly, and because of the lack of sufficient training samples, most of the online learning tracking algorithms will have performance bottlenecks. In this paper, an improved real-time algorithm based on deep learning features is proposed, which combines multi-feature fusion, multi-scale estimation, adaptive updating of target model and re-detection after target loss. The effectiveness and advantages of the proposed algorithm are proved by a large number of comparative experiments with other excellent algorithms on large benchmark datasets.

Recently, the performances of discriminative correlation filter (CF) trackers are getting better and better in visual tracking. In this paper, we propose spatial-temporal regularization with precise state estimation based on discriminative correlation filter (STPSE) in order to achieve more significant tracking performance. First, we consider the continuous change of the object state, using the information from the previous two filters for training the correlation filter model. Here, we train the correlation filter model with the hand-crafted features. Second, we introduce update control in which average peak-to-correlation energy (APCE) and the distance between the object locations obtained by HOG features and hand-crafted features are utilized to detect abnormality of the state around the object. APCE and the distance indicate the reliability of the filter response, thus if abnormality is detected, the proposed method does not update the scale and the object location estimated by the filter response. In the experiment, our tracker (STPSE) achieves significant and real-time performance with only CPU for the challenging benchmark sequence (OTB2013, OTB2015, and TC128).

Differential Evolution (DE) algorithm is simple and effective. Since DE has been proposed, it has been widely used to solve various complex optimization problems. To further exploit the advantages of DE, we propose a new variant of DE, termed as ranking-based differential evolution (RDE), by performing ranking on the population. Progressively better individuals in the population are used for mutation operation, thus improving the algorithm's exploitation and exploration capability. Experimental results on a number of benchmark optimization functions show that RDE significantly outperforms the original DE and performs competitively in comparison with other two state-of-the-art DE variants.

In recent years FPGA has become popular in CNN acceleration, and many CNN-to-FPGA toolchains are proposed to fast deploy CNN on FPGA. However, for these toolchains, updating CNN network means regeneration of RTL code and re-implementation which is time-consuming and may suffer timing-closure problems. So, we propose HBDCA: a toolchain and corresponding accelerator. The CNN on HBDCA is defined by the content of BRAM. The toolchain integrates UpdateMEM utility of Xilinx, which updates content of BRAM without re-synthesis and re-implementation process. The toolchain also integrates TensorFlow Lite which provides high-accuracy quantization. HBDCA supports 8-bits per-channel quantization of weights and 8-bits per-layer quantization of activations. Upgrading CNN on accelerator means the kernel size of CNN may change. Flexible structure of HBDCA supports kernel-level parallelism with three different sizes (3×3, 5×5, 7×7). HBDCA implements four types of parallelism in convolution layer and two types of parallelism in fully-connected layer. In order to reduce access number to memory, both spatial and temporal data-reuse techniques were applied on convolution layer and fully-connect layer. Especially, temporal reuse is adopted at both row and column level of an Input Feature Map of convolution layer. Data can be just read once from BRAM and reused for the following clock. Experiments show by updating BRAM content with single UpdateMEM command, three CNNs with different kernel size (3×3, 5×5, 7×7) are implemented on HBDCA. Compared with traditional design flow, UpdateMEM reduces development time by 7.6X-9.1X for different synthesis or implementation strategy. For similar CNN which is created by toolchain, HBDCA has smaller latency (9.97µs-50.73µs), and eliminates re-implementation when update CNN. For similar CNN which is created by dedicated design, HBDCA also has the smallest latency 9.97µs, the highest accuracy 99.14% and the lowest power 1.391W. For different CNN which is created by similar toolchain which eliminate re-implementation process, HBDCA achieves higher speedup 120.28X.

IoT devices operate with a battery and have embedded firmware in flash memory. If the embedded firmware is not kept up to date, there is a possibility of problems that cannot be linked with other IoT networks, so it is necessary to maintain the latest firmware with frequent updates. However, because firmware updates require developers and equipment, they consume manpower and time. Additionally, because the device must be active during the update, a low-power operation is not possible due to frequent flash memory access. In addition, if an unexpected interruption occurs during an update, the device is unavailable and requires a reliable update. Therefore, this paper aims to improve the reliability of updates and low-power operation by proposing a technique of performing firmware updates at high speed. In this paper, we propose a technique to update only a part of the firmware stored in nonvolatile flash memory without pre-processing to generate delta files. The firmware is divided into function blocks, and their addresses are collectively managed in a separate area called a function map. When updating the firmware, only the new function block to be updated is transmitted from the host downloader, and the bootloader proceeds with the update using the function block stored in the flash memory. Instead of transmitting the entire new firmware and writing it in the memory, using only function block reduces the amount of resources required for updating. Function-blocks can be called indirectly through a function map, so that the update can be completed by modifying only the function map regardless of the physical location. Our evaluation results show that the proposed technique effectively reduces the time cost, energy consumption, and additional memory usage overhead that can occur when updating firmware.

In 2019, a completely new algorithm, spherical evolution (SE), was proposed. The brand new search style in SE has been proved to have a strong search capability. In order to take advantage of SE, we propose a novel method called the ladder descent (LD) method to improve the SE' population update strategy and thereafter propose a ladder spherical evolution search (LSE) algorithm. With the number of iterations increasing, the range of parent individuals eligible to produce offspring gradually changes from the entire population to the current optimal individual, thereby enhancing the convergence ability of the algorithm. Experiment results on IEEE CEC2017 benchmark functions indicate the effectiveness of LSE.

This paper proposes a temporal domain difference based secondary background modeling algorithm for surveillance video coding. The proposed algorithm has three key technical contributions as following. Firstly, the LDBCBR (Long Distance Block Composed Background Reference) algorithm is proposed, which exploits IBBS (interval of background blocks searching) to weaken the temporal correlation of the foreground. Secondly, both BCBR (Block Composed Background Reference) and LDBCBR are exploited at the same time to generate the temporary background reference frame. The secondary modeling algorithm utilizes the temporary background blocks generated by BCBR and LDBCBR to get the final background frame. Thirdly, monitor the background reference frame after it is generated is also important. We would update the background blocks immediately when it has a big change, shorten the modeling period of the areas where foreground moves frequently and check the stable background regularly. The proposed algorithm is implemented in the platform of IEEE1857 and the experimental results demonstrate that it has significant improvement in coding efficiency. In surveillance test sequences recommended by the China AVS (Advanced Audio Video Standard) working group, our method achieve BD-Rate gain by 6.81% and 27.30% comparing with BCBR and the baseline profile.

High frame rate and ultra-low delay matching system plays an important role in various human-machine interactive applications, which demands better performance in matching deformable and out-of-plane rotating objects. Although many algorithms have been proposed for deformation tracking and matching, few of them are suitable for hardware implementation due to complicated operations and large time consumption. This paper proposes a hardware-oriented template update and recovery method for high frame rate and ultra-low delay deformation matching system. In the proposed method, the new template is generated in real time by partially updating the template descriptor and adding new keypoints simultaneously with the matching process in pixels (proposal #1), which avoids the large inter-frame delay. The size and shape of region of interest (ROI) are made flexible and the Hamming threshold used for brute-force matching is adjusted according to pixel position and the flexible ROI (proposal #2), which solves the problem of template drift. The template is recovered by the previous one with a relative center-shifting vector when it is judged as lost via region-wise difference check (proposal #3). Evaluation results indicate that the proposed method successfully achieves the real-time processing of 784fps at the resolution of 640×480 on field-programmable gate array (FPGA), with a delay of 0.808ms/frame, as well as achieves satisfactory deformation matching results in comparison with other general methods.

Although correlation filter-based trackers have demonstrated excellent performance for visual object tracking, there remain several challenges to be addressed. In this work, we propose a novel tracker based on the correlation filter framework. Traditional trackers face difficulty in accurately adapting to changes in the scale of the target when the target moves quickly. To address this, we suggest a scale adaptive scheme based on prediction scales. We also incorporate a speed-based adaptive model update method to further improve overall tracking performance. Experiments with samples from the OTB100 and KITTI datasets demonstrate that our method outperforms existing state-of-the-art tracking algorithms in fast motion scenes.

As IT systems, including network systems using SDN/NFV technologies, become large-scaled and complicated, the cost of system management also increases rapidly. Network operators have to maintain their workflow in constructing and consistently updating such complex systems, and thus these management tasks in generating system update plan are desired to be automated. Declarative system update with state space search is a promising approach to enable this automation, however, the current methods is not enough scalable to practical systems. In this paper, we propose a novel heuristic approach to greatly reduce computation time to solve system update procedure for practical systems. Our heuristics accounts for structural bottleneck of the system update and advance search to resolve bottlenecks of current system states. This paper includes the following contributions: (1) formal definition of a novel heuristic function specialized to system update for A* search algorithm, (2) proofs that our heuristic function is consistent, i.e., A* algorithm with our heuristics returns a correct optimal solution and can omit repeatedly expansion of nodes in search spaces, and (3) results of performance evaluation of our heuristics. We evaluate the proposed algorithm in two cases; upgrading running hypervisor and rolling update of running VMs. The results show that computation time to solve system update plan for a system with 100 VMs does not exceed several minutes, whereas the conventional algorithm is only applicable for a very small system.

Kernel updates are a part of daily life in contemporary computer systems. They usually require an OS reboot that involves restarting not only the kernel but also all of the running applications, causing downtime that can disrupt software services. This downtime issue has been tackled by numerous approaches. Although dynamic translation of the running kernel image, which is a representative approach, can conduct kernel updates at runtime, its applicability is inherently limited. This paper describes Dwarf, which shortens downtime during kernel updates and covers more types of updates. Dwarf launches the newer kernel in the background on the same physical machine and forces the kernel to inherit the running states of the older kernel. We implemented a prototype of Dwarf on Xen 4.5.2, Linux 2.6.39, Linux 3.18.35, and Linux 4.1.6. Also, we conducted experiments using six applications, such as Apache, MySQL, and memcached, and the results demonstrate that Dwarf's downtime is 1.8 seconds in the shortest case and up to 10× shorter than that of the normal OS reboot.

Most state-of-the-art discriminative tracking approaches are based on either template appearance models or statistical appearance models. Despite template appearance models have shown excellent performance, they perform poorly when the target appearance changes rapidly. In contrast, statistic appearance models are insensitive to fast target state changes, but they yield inferior tracking results in challenging scenarios such as illumination variations and background clutters. In this paper, we propose an adaptive object tracking approach with complementary models based on template and statistical appearance models. Both of these models are unified via our novel combination strategy. In addition, we introduce an efficient update scheme to improve the performance of our approach. Experimental results demonstrate that our approach achieves superior performance at speeds that far exceed the frame-rate requirement on recent tracking benchmarks.

Sparse representation has been widely applied to visual tracking for several years. In the sparse representation framework, tracking problem is transferred into solving an L1 minimization issue. However, during the tracking procedure, the appearance of target was affected by external environment. Therefore, we proposed a robust tracking algorithm based on the traditional sparse representation jointly particle filter framework. First, we obtained the observation image set from particle filter. Furthermore, we introduced a 2D transformation on the observation image set, which enables the tracking target candidates set more robust to handle misalignment problem in complex scene. Moreover, we adopt the occlusion detection mechanism before template updating, reducing the drift problem effectively. Experimental evaluations on five public challenging sequences, which exhibit occlusions, illuminating variations, scale changes, motion blur, and our tracker demonstrate accuracy and robustness in comparisons with the state-of-the-arts.

One of the most promising compression methods for XML documents is the one that translates a given document to a tree grammar that generates it. A feature of this compression is that the internal structures are kept in production rules of the grammar. This enables us to directly manipulate the tree structure without decompression. However, previous studies assume that a given XML document does not have data values because they focus on direct retrieval and manipulation of the tree structure. This paper proposes a direct update method for XML documents with data values and shows the effectiveness of the proposed method based on experiments conducted on our implemented tool.

We present the concept of key-rotatable and security-updatable homomorphic encryption (KR-SU-HE) scheme, which is defined as a class of public-key homomorphic encryption in which the keys and the security of any ciphertext can be rotated and updated while still keeping the underlying plaintext intact and unrevealed. After formalising the syntax and security notions for KR-SU-HE schemes, we build a concrete scheme based on the Learning With Errors assumption. We then perform several careful implementations and optimizations to show that our proposed scheme is efficiently practical.

This paper presents a method to realize index generation functions using multiple Index Generation Units (IGUs). The architecture implements index generation functions more efficiently than a single IGU when the number of registered vectors is very large. This paper proves that independent linear transformations are necessary in IGUs for efficient realization. Experimental results confirm this statement. Finally, it shows a fast update method to IGUs.

Revocation is one of the major problems for access control systems. Especially, the revocation cost for the data outsourced in the third party environment such as cloud storage systems. The revocation in the cloud-based access control typically deals with the cryptographic operations that introduce costly overheads for key re-generation, file re-encryption, and key re-distribution. Also, the communication for retrieving files for re-encryption and loading them back to the cloud is another non-trivial cost for data owners. In this paper, we propose a Very Lightweight Proxy Re-Encryption (VL-PRE) scheme to efficiently support attribute-based revocation and policy update in the collaborative data sharing in cloud computing environment. To this end, we propose three-phase VL-PRE protocol including re-encryption key generation, re-encryption key update, and re-encryption key renewal for supporting the optimized attribute revocation and policy update. Finally, we conduct the experiments to evaluate the performance of our VL-PRE and show that it exhibits less computation cost with higher scalability in comparison with existing PRE schemes.

In this letter, we propose a garbage collection technique for non-volatile memory systems, called Migration Cost Sensitive Garbage Collection (MCSGC). Considering the migration overhead from selecting victim blocks, MCSGC increases the lifetime of memory systems and improves response time in garbage collection. Additionally, the proposed algorithm also improves the efficiency of garbage collection by separating cold data from hot data in valid pages. In the experimental evaluation, we show that MCSGC yields up to a 82% improvement in lifetime prolongation, compared with existing garbage collection, and it also reduces erase and migration operations by up to 30% and 29%, respectively.