To realize an information-centric networking, IPFS (InterPlanetary File System) generates a unique ContentID for each content by applying a cryptographic hash to the content itself. Although it could improve the security against attacks such as falsification, it makes difficult to realize a similarity search in the framework of IPFS, since the similarity of contents is not reflected in the proximity of ContentIDs. To overcome this issue, we propose a method to apply a locality sensitive hash (LSH) to feature vectors extracted from contents as the key of indexes stored in IPFS. By conducting experiments with 10,000 random points corresponding to stored contents, we found that more than half of randomly given queries return a non-empty result for the similarity search, and yield an accurate result which is outside the σ confidence interval of an ordinary flooding-based method. Note that such a collection of random points corresponds to the worst case scenario for the proposed scheme since the performance of similarity search could improve when points and queries follow an uneven distribution.

Many scientific and technological resources (STR) cannot meet the needs of real demand-based industrial services. To address this issue, the characteristics of scientific and technological resource services (STRS) are analyzed, and a method of the optimal combination of demand-based STR based on multi-community collaborative search is then put forward. An optimal combined evaluative system that includes various indexes, namely response time, innovation, composability, and correlation, is developed for multi-services of STR, and a hybrid optimal combined model for STR is constructed. An evaluative algorithm of multi-community collaborative search is used to study the interactions between general communities and model communities, thereby improving the adaptive ability of the algorithm to random dynamic resource services. The average convergence value CMCCSA=0.00274 is obtained by the convergence measurement function, which exceeds other comparison algorithms. The findings of this study indicate that the proposed methods can preferably reach the maximum efficiency of demand-based STR, and new ideas and methods for implementing demand-based real industrial services for STR are provided.

Cache prefetching technique brings huge benefits to performance improvement, but it comes at the cost of microarchitectural security in processors. In this letter, we deep dive into internal workings of a DCUIP prefetcher, which is one of prefetchers equipped in Intel processors. We discover that a DCUIP table is shared among different execution contexts in hyperthreading-enabled processors, which leads to another microarchitectural vulnerability. By exploiting the vulnerability, we propose a DCUIP poisoning attack. We demonstrate an AES encryption key can be extracted from an AES-NI implementation by mounting the proposed attack.

Convolutional neural networks (CNNs) have dominated a range of applications, from advanced manufacturing to autonomous cars. For energy cost-efficiency, developing low-power hardware for CNNs is a research trend. Due to the large input size, the first few convolutional layers generally consume most latency and hardware resources on hardware design. To address these challenges, this paper proposes an innovative architecture named SLIT to extract feature maps and reconstruct the first few layers on CNNs. In this reconstruction approach, total multiply-accumulate operations are eliminated on the first layers. We evaluate new topology with MNIST, CIFAR, SVHN, and ImageNet datasets on image classification application. Latency and hardware resources of the inference step are evaluated on the chip ZC7Z020-1CLG484C FPGA with Lenet-5 and VGG schemes. On the Lenet-5 scheme, our architecture reduces 39% of latency and 70% of hardware resources with a 0.456 W power consumption compared to previous works. Even though the VGG models perform with a 10% reduction in hardware resources and latency, we hope our overall results will potentially give a new impetus for future studies to reach a higher optimization on hardware design. Notably, the SLIT architecture efficiently merges with most popular CNNs at a slightly sacrificing accuracy of a factor of 0.27% on MNIST, ranging from 0.5% to 1.5% on CIFAR, approximately 2.2% on ImageNet, and remaining the same on SVHN databases.

Most gait recognition approaches rely on silhouette-based representations due to high recognition accuracy and computational efficiency. A fundamental problem for those approaches is how to extract individuality-preserved silhouettes from real scenes accurately. Foreground colors may be similar to background colors, and the background is cluttered. Therefore, we propose a method of individuality-preserving silhouette extraction for gait recognition using standard gait models (SGMs) composed of clean silhouette sequences of various training subjects as shape priors. The SGMs are smoothly introduced into a well-established graph-cut segmentation framework. Experiments showed that the proposed method achieved better silhouette extraction accuracy by more than 2.3% than representative methods and better identification rate of gait recognition (improved by more than 11.0% at rank 20). Besides, to reduce the computation cost, we introduced approximation in the calculation of dynamic programming. As a result, without reducing the segmentation accuracy, we reduced 85.0% of the computational cost.

In Internet applications, when users search for information, the search engines invariably return some invalid webpages that do not contain valid information. These invalid webpages interfere with the users' access to useful information, affect the efficiency of users' information query and occupy Internet resources. Accurate and fast filtering of invalid webpages can purify the Internet environment and provide convenience for netizens. This paper proposes an invalid webpage filtering model (HAIF) based on deep learning and hierarchical attention mechanism. HAIF improves the semantic and sequence information representation of webpage text by concatenating lexical-level embeddings and paragraph-level embeddings. HAIF introduces hierarchical attention mechanism to optimize the extraction of text sequence features and webpage tag features. Among them, the local-level attention layer optimizes the local information in the plain text. By concatenating the input embeddings and the feature matrix after local-level attention calculation, it enriches the representation of information. The tag-level attention layer introduces webpage structural feature information on the attention calculation of different HTML tags, so that HAIF is better applicable to the Internet resource field. In order to evaluate the effectiveness of HAIF in filtering invalid pages, we conducted various experiments. Experimental results demonstrate that, compared with other baseline models, HAIF has improved to various degrees on various evaluation criteria.

Scalable networking for scientific research data transfer is a vital factor in the progress of data-intensive research, such as collaborative research on observation of black hole. In this paper, investigations of the nature of practical research traffic allow us to introduce optical flow switching (OFS) and contents delivery network (CDN) technologies into a wide area network (WAN) to realize highly scalable networking. To measure the scalability of networks, energy consumption in the WAN is evaluated by considering the practical networking equipment as well as reasonable assumptions on scientific research data transfer networks. In this study, we explore the energy consumption performance of diverse Japan and US topologies and reveal that the energy consumption of a routing and wavelength assignment algorithm in an OFS scheduler becomes the major hurdle when the number of nodes is high, for example, as high as that of the United States of America layer 1 topology. To provide computational scalability of a network dimensioning algorithm for the CDN based WAN, a simple heuristic algorithm for a surrogate location problem is proposed and compared with an optimal algorithm. This paper provides intuitions and design rules for highly scalable research data transfer networks, and thus, it can accelerate technology advancements against the encountering big-science problems.

In this study, we propose a secure data-providing system by using a verifiable attribute-based keyword search (VABKS), which also has the functions of privacy preservation and feedback to providers with IP anonymous server. We give both theoretic and experimental result, which show that our proposed system is a secure system with real-time property. One potential application of the system is to Integrated Broadcast-Broadband (IBB) services, which acquire information related to broadcast programs via broadband networks. One such service is a recommendation service that delivers recommendations matching user preferences (such as to TV programs) determined from the user's viewing history. We have developed a real-time system outsourcing data to the cloud and performing keyword searches on it by dividing the search process into two stages and performing heavy processing on the cloud side.

The performance of packet processing applications is dependent on the memory access speed of network systems. Table lookup requires fast memory access and is one of the most common processes in various packet processing applications, which can be a dominant performance bottleneck. Therefore, in Network Function Virtualization (NFV)-aware environments, on-chip fast cache memories of a CPU of general-purpose hardware become critical to achieve high performance packet processing speeds of over tens of Gbps. Also, multiple types of applications and complex applications are executed in the same system simultaneously in carrier network systems, which require adequate cache memory capacities as well. In this paper, we propose a packet processing architecture that utilizes interleaved 3 Dimensional (3D)-stacked Dynamic Random Access Memory (DRAM) devices as off-chip Last Level Cache (LLC) in addition to several levels of dedicated cache memories of each CPU core. Entries of a lookup table are distributed in every bank and vault to utilize both bank interleaving and vault-level memory parallelism. Frequently accessed entries in 3D-stacked DRAM are also cached in on-chip dedicated cache memories of each CPU core. The evaluation results show that the proposed architecture reduces the memory access latency by 57%, and increases the throughput by 100% while reducing the blocking probability but about 10% compared to the architecture with shared on-chip LLC. These results indicate that 3D-stacked DRAM can be practical as off-chip LLC in parallel packet processing systems.

The recent development of neural architecture search (NAS) has enabled us to automatically discover architectures of neural networks with high performance within a few days. Convolutional neural networks extract fruitful features by repeatedly applying standard operations (convolutions and poolings). However, these operations also extract useless or even disturbing features. Attention mechanisms enable neural networks to discard information of no interest, having achieved the state-of-the-art performance. While a variety of attentions for CNNs have been proposed, current NAS methods have paid a little attention to them. In this study, we propose a novel NAS method that searches attentions as well as operations. We examined several patterns to arrange attentions and operations, and found that attentions work better when they have their own search space and follow operations. We demonstrate the superior performance of our method in experiments on CIFAR-10, CIFAR-100, and ImageNet datasets. The found architecture achieved lower classification error rates and required fewer parameters compared to those found by current NAS methods.

Image deformations caused by different steganographic methods are typically extremely small and highly similar, which makes their detection and identification to be a difficult task. Although recent steganalytic methods using deep learning have achieved high accuracy, they have been made to detect stego images to which specific steganographic methods have been applied. In this letter, a staganalytic method is proposed that uses hierarchical residual neural networks (ResNet), allowing detection (i.e. classification between stego and cover images) and identification of four spatial steganographic methods (i.e. LSB, PVD, WOW and S-UNIWARD). Experimental results show that using hierarchical ResNets achieves a classification rate of 79.71% in quinary classification, which is approximately 23% higher compared to using a plain convolutional neural network (CNN).

Maximum inner product search (MIPS) problem has gained much attention in a wide range of applications. In order to overcome the curse of dimensionality in high-dimensional spaces, most of existing methods first transform the MIPS problem into another approximate nearest neighbor search (ANNS) problem and then solve it by Locality Sensitive Hashing (LSH). However, due to the error incurred by the transmission and incomprehensive search strategies, these methods suffer from low precision and have loose probability guarantees. In this paper, we propose a novel search method named Adaptive-LSH (AdaLSH) to solve MIPS problem more efficiently and more precisely. AdaLSH examines objects in the descending order of both norms and (the probably correctly estimated) cosine angles with a query object in support of LSH with extendable windows. Such extendable windows bring not only efficiency in searching but also the probability guarantee of finding exact or approximate MIP objects. AdaLSH gives a better probability guarantee of success than those in conventional algorithms, bringing less running times on various datasets compared with them. In addition, AdaLSH can even support exact MIPS with probability guarantee.

A method is presented for increasing wireless LAN (WLAN) capacity in high-density environments with IEEE 802.11ax systems. We propose using coordinated scheduling of trigger frames based on our mobile cooperative control concept. High-density WLAN systems are managed by a management server, which gathers wireless environmental information from user equipment through cellular access. Hierarchical clustering of basic service sets is used to form synchronized clusters to reduce interference and increase throughput of high-density WLAN systems based on mobile cooperative control. This method increases uplink capacity by up to 19.4% and by up to 11.3% in total when WLAN access points are deployed close together. This control method is potentially effective for IEEE 802.11ax WLAN systems utilized as 5G mobile network components.

Training deep learning (DL) is a computationally intensive process; as a result, training time can become so long that it impedes the development of DL. High performance computing clusters, especially supercomputers, are equipped with a large amount of computing resources, storage resources, and efficient interconnection ability, which can train DL networks better and faster. In this paper, we propose a method to train DL networks distributed with high efficiency. First, we propose a hierarchical synchronous Stochastic Gradient Descent (SGD) strategy, which can make full use of hardware resources and greatly increase computational efficiency. Second, we present a two-level parameter synchronization scheme which can reduce communication overhead by transmitting parameters of the first layer models in shared memory. Third, we optimize the parallel I/O by making each reader read data as continuously as possible to avoid the high overhead of discontinuous data reading. At last, we integrate the LARS algorithm into our system. The experimental results demonstrate that our approach has tremendous performance advantages relative to unoptimized methods. Compared with the native distributed strategy, our hierarchical synchronous SGD strategy (HSGD) can increase computing efficiency by about 20 times.

High frame rate (HFR) video is attracting strong interest since it is considered as a next step toward providing Ultra-High Definition video service. For instance, the Association of Radio Industries and Businesses (ARIB) standard, the latest broadcasting standard in Japan, defines a 120 fps broadcasting format. The standard stipulates temporally scalable coding and hierarchical transmission by MPEG Media Transport (MMT), in which the base layer and the enhancement layer are transmitted over different paths for flexible distribution. We have developed the first ever MMT transmitter/receiver module for 4K/120fps temporally scalable video. The module is equipped with a newly proposed encapsulation method of temporally scalable bitstreams with correct boundaries. It is also designed to be tolerant to severe network constraints, including packet loss, arrival timing offset, and delay jitter. We conducted a hierarchical transmission experiment for 4K/120fps temporally scalable video. The experiment demonstrated that the MMT module was successfully fabricated and capable of dealing with severe network constraints. Consequently, the module has excellent potential as a means to support HFR video distribution in various network situations.

In this paper, experimental data of non-arcing circuit breaking phenomena in electrical contacts are presented. A dark bridge that is a non-luminous bridge between electrical contacts is an effective factor for the non-arcing circuit break. A facility of a cantilever system was established to precisely control a position of an electrode. By using this facility, dark bridges between contacts were made and the dark bridges were observed by a microscopic camera system.

The carrier-phase multipath effect can seriously affect the accuracy of GPS-based positioning in static short baseline applications. Although several kinds of methods based on time domain and spatial domain techniques have been proposed to mitigate this error, they are still limited by the accuracy of the multipath model and the effectiveness of the correction strategy. After analyzing the existing methods, a new method based on adaptive thresholding wavelet packet transform (AW) and time domain bootstrap spatial domain search strategy (TB) is presented (AWTB). Taking advantage of adaptive thresholding wavelet packet transform, we enhance the precision of the correction model and the efficiency of the extraction method. In addition, by adopting the proposed time domain bootstrap spatial domain strategy, the accuracy and efficiency of subsequent multipath correction are improved significantly. Specifically, after applying the adaptive thresholding wavelet packet method, the mean improvement rate in the RMS values of the single-difference L1 residuals is about 27.93% compared with the original results. Furthermore, after applying the proposed AWTB method, experiments show that the 3D positioning precision is improved by about 38.51% compared with the original results. Even compared with the method based on stationary wavelet transform (SWT), and the method based on wavelet packets denoising (WPD), the 3D precision is improved by about 26.94% over the SWT method and about 22.96% over the WPD method, respectively. It is worth noting that, although the mean time consumption of the proposed algorithm is larger than the original method, the increased time consumption is not a serious burden for overall performance.

Side-channel Attack, such as simple power analysis and differential power analysis (DPA), is an efficient method to gather the key, which challenges the security of crypto chips. Side-channel Attack logs the power trace of the crypto chip and speculates the key by statistical analysis. To reduce the threat of power analysis attack, an innovative method based on random execution and register randomization is proposed in this paper. In order to enhance ability against DPA, the method disorders the correspondence between power trace and operands by scrambling the data execution sequence randomly and dynamically and randomize the data operation path to randomize the registers that store intermediate data. Experiments and verification are done on the Sakura-G FPGA platform. The results show that the key is not revealed after even 2 million power traces by adopting the proposed method and only 7.23% slices overhead and 3.4% throughput rate cost is introduced. Compared to unprotected chip, it increases more than 4000× measure to disclosure.

This paper presents a Siamese architecture model with two identical Convolutional Neural Networks (CNNs) to identify code clones; two code fragments are represented as Abstract Syntax Trees (ASTs), CNN-based subnetworks extract feature vectors from the ASTs of pairwise code fragments, and the output layer produces how similar or dissimilar they are. Experimental results demonstrate that CNN-based feature extraction is effective in detecting code clones at source code or bytecode levels.

The conventional linear or tiled address maps can degrade performance and memory utilization when traffic patterns are not matched with an underlying address map. The address map is usually fixed at design time. Accordingly, it is difficult to adapt to given applications. Modern embedded system usually accommodates memory management units (MMUs). As a result, depending on virtual address patterns, the system can suffer from performance overheads due to page table walks. To alleviate this performance overhead, we propose to cluster and rearrange tiles to construct an MMU-aware configurable address map. To construct the clustered tiled map, the generic tile number remapping algorithm is presented. In the presented scheme, an address map is configured based on the adaptive dimensioning algorithm. Considering image processing applications, a design, an analysis, an implementation, and simulations are conducted. The results indicate the proposed method can improve the performance and the memory utilization with moderate hardware costs.