Recent years have witnessed a rapid increase in cyber-attacks through unauthorized accesses and DDoS attacks. Since packet classification is a fundamental technique to prevent such illegal communications, it has gained considerable attention. Packet classification is achieved with a linear search on a classification rule list that represents the packet classification policy. As such, a large number of rules can result in serious communication latency. To decrease this latency, the problem is formalized as optimal rule ordering (ORO). In most cases, this problem aims to find the order of rules that minimizes latency while satisfying the dependency relation of the rules, where rules ri and rj are dependent if there is a packet that matches both ri and rj and their actions applied to packets are different. However, there is a case in which although the ordering violates the dependency relation, the ordering satisfies the packet classification policy. Since such an ordering can decrease the latency compared to an ordering under the constraint of the dependency relation, we have introduced a new model, called relaxed optimal rule ordering (RORO). In general, it is difficult to determine whether an ordering satisfies the classification policy, even when it violates the dependency relation, because this problem contains unsatisfiability. However, using a zero-suppressed binary decision diagram (ZDD), we can determine it in a reasonable amount of time. In this paper, we present a simulated annealing method for RORO which interchanges rules by determining whether rules ri and rj can be interchanged in terms of policy violation using the ZDD. The experimental results show that our method decreases latency more than other heuristics.

A paradigm shift toward edge computing infrastructures that prioritize small footprint and scalable/easy-to-estimate performance is increasing. In this paper, we propose the following to improve the footprint and the scalability of systolic arrays: (1) column multithreading for reducing the number of physical units and maintaining the performance even for back-to-back floating-point accumulations; (2) a cascaded peer-to-peer AXI bus for a scalable multichip structure and an intra-chip parallel local memory bus for low latency; (3) multilevel loop control in any unit for reducing the startup overhead and adaptive operation shifting for efficient reuse of local memories. We designed a systolic array with a single column × 64 row configuration with Verilog HDL, evaluated the frequency and the performance on an FPGA attached to a ZYNQ system as an AXI slave device, and evaluated the area with a TSMC 28nm library and memory generator and identified the following: (1) the execution speed of a matrix multiplication/a convolution operation/a light-field depth extraction, whose size larger than the capacity of the local memory, is 6.3× / 9.2× / 6.6× compared with a similar systolic array (EMAX); (2) the estimated speed with a 4-chip configuration is 19.6× / 16.0× / 8.5×; (3) the size of a single-chip is 8.4 mm2 (0.31× of EMAX) and the basic performance per area is 2.4×.

This paper proposes a generative moment matching network (GMMN)-based post-filtering method for providing inter-utterance pitch variation to singing voices and discusses its application to our developed mixing method called neural double-tracking (NDT). When a human singer sings and records the same song twice, there is a difference between the two recordings. The difference, which is called inter-utterance variation, enriches the performer's musical expression and the audience's experience. For example, it makes every concert special because it never recurs in exactly the same manner. Inter-utterance variation enables a mixing method called double-tracking (DT). With DT, the same phrase is recorded twice, then the two recordings are mixed to give richness to singing voices. However, in synthesized singing voices, which are commonly used to create music, there is no inter-utterance variation because the synthesis process is deterministic. There is also no inter-utterance variation when only one voice is recorded. Although there is a signal processing-based method called artificial DT (ADT) to layer singing voices, the signal processing results in unnatural sound artifacts. To solve these problems, we propose a post-filtering method for randomly modulating synthesized or natural singing voices as if the singer sang again. The post-filter built with our method models the inter-utterance pitch variation of human singing voices using a conditional GMMN. Evaluation results indicate that 1) the proposed method provides perceptible and natural inter-utterance variation to synthesized singing voices and that 2) our NDT exhibits higher double-trackedness than ADT when applied to both synthesized and natural singing voices.

A convolutional neural network (CNN) for broadband direction of arrival (DOA) estimation of far-field electromagnetic signals is presented. The proposed algorithm performs a nonlinear inverse mapping from received signal to angle of arrival. The signal model used for algorithm is based on the circular antenna array geometry, and the phase component extracted from the spatial covariance matrix is used as the input of the CNN network. A CNN model including three convolutional layers is then established to approximate the nonlinear mapping. The performance of the CNN model is evaluated in a noisy environment for various values of signal-to-noise ratio (SNR). The results demonstrate that the proposed CNN model with the phase component of the spatial covariance matrix as the input is able to achieve fast and accurate broadband DOA estimation and attains perfect performance at lower SNR values.

Skip Graph is a promising distributed data structure for large scale systems and known for its capability of range queries. Although several methods of routing range queries in Skip Graph have been proposed, they have inefficiencies such as a long path length or a large number of messages. In this paper, we propose a novel routing method for range queries named Split-Forward Broadcasting (SFB). SFB introduces a divide-and-conquer approach, enabling nodes to make full use of their routing tables to forward a range query. It brings about a shorter average path length than existing methods, as well as a smaller number of messages by avoiding duplicate transmission. We clarify the characteristics and effectiveness of SFB through both analytical and experimental comparisons. The results show that SFB can reduce the average path length roughly 30% or more compared with a state-of-the-art method.

In this paper, we describe a novel low-delay 4K 120-fps real-time HEVC decoder with a parallel processing architecture that conforms to the HEVC main 4:2:2 10 profile. It supports the hierarchical temporal scalable streams required for Ultra High Definition high-frame-rate broadcasting and also supports low-delay and high-bitrate decoding for video transmission uses. To achieve this support, the decoding processes are parallelized and pipelined at the frame level, slice level, and coding tree unit row level. The proposed decoder was implemented on three FPGAs operated at 133 and 150 MHz, and it achieved 300-Mbps stream decoding and 37-msec end-to-end delay with our concurrently developed 4K 120-fps encoder.

A context-aware system always needs to adapt its behaviors according to context changes; therefore, modeling context-aware requirements is a complex task. With the increasing use of mobile computing, research on methods of modeling context-aware requirements have become increasingly important, and a large number of relevant studies have been conducted. However, no comprehensive analysis of the challenges and achievements has been performed. The methodology of systematic literature review was used in this survey, in which 68 reports were selected as primary studies. The challenges and methods to confront these challenges in context-aware requirement modeling are summarized. The main contributions of this work are: (1) four challenges and nine sub-challenges are identified; (2) eight kinds of methods in three categories are identified to address these challenges; (3) the extent to which these challenges have been solved is evaluated; and (4) directions for future research are elaborated.

The objective of this letter is to present a family of q-ary codes with parameters $[rac{q^m-1}{q-1},rac{q^m-1}{q-1}-2m,d]$, where m is a positive integer, q is a power of an odd prime and 4≤d≤5. The parameters are proved to be optimal or almost optimal with respect to an upper bound on linear codes.

Utilization of local memory from real-time embedded systems to high performance systems with multi-core processors has become an important factor for satisfying hard deadline constraints. However, challenges lie in the area of efficiently managing the memory hierarchy, such as decomposing large data into small blocks to fit onto local memory and transferring blocks for reuse and replacement. To address this issue, this paper presents a compiler optimization method that automatically manage local memory of multi-core processors. The method selects and maps multi-dimensional data onto software specified memory blocks called Adjustable Blocks. These blocks are hierarchically divisible with varying sizes defined by the features of the input application. Moreover, the method introduces mapping structures called Template Arrays to maintain the indices of the decomposed multi-dimensional data. The proposed work is implemented on the OSCAR automatic parallelizing compiler and evaluations were performed on the Renesas RP2 8-core processor. Experimental results from NAS Parallel Benchmark, SPEC benchmark, and multimedia applications show the effectiveness of the method, obtaining maximum speed-ups of 20.44 with 8 cores utilizing local memory from single core sequential versions that use off-chip memory.

The equivalent transmission-path model is a propagation-oriented channel model for predicting the bit error rate due to intersymbol interference in single-input single-output systems. We extend this model to develop a new calculation scheme for maximal-ratio combining diversity reception in single-input multiple-output configurations. A key part of the study is to derive a general formula expressing the joint probability density function of the amplitude ratio and phase difference of the two-path model. In this derivation, we mainly take a theoretical approach with the aid of Monte Carlo simulation. Then, very high-accuracy estimation of the average bit error rate due to intersymbol interference (ISI) for CQPSK calculated based on the model is confirmed by computer simulation. Finally, we propose a very simple calculation formula for the prediction of the BER due to ISI that is commonly applicable to various modulation/demodulation schemes, such as CQPSK, DQPSK, 16QAM, and CBPSK in maximal-ratio combining diversity reception.

This letter proposes a new superpixel segmentation algorithm based on global similarity and contour region transformation. The basic idea is that pixels surrounded by the same contour are more likely to belong to the same object region, which could be easily clustered into the same superpixel. To this end, we use contour scanning to estimate the global similarity between pixels and corresponded centers. In addition, we introduce pixel's gradient information of contour transform map to enhance the pixel's global similarity to overcome the missing contours in blurred region. Benefited from our global similarity, the proposed method could adherent with blurred and low contrast boundaries. A large number of experiments on BSDS500 and VOC2012 datasets show that the proposed algorithm performs better than traditional SLIC.

Many countries have deregulated their electricity retail markets to offer lower electricity charges to consumers. However, many consumers have not switched their suppliers after the deregulation, and electricity suppliers do not tend to reduce their charges intensely. This paper proposes an electricity market model and evolutionary game to analyze the behavior of consumers in electricity retail markets. Our model focuses on switching costs such as an effort at switching, costs in searching for other alternatives, and so on. The evolutionary game examines whether consumers choose a strategy involving exploration of new alternatives with the searching costs as “cooperators” or not. Simulation results demonstrate that the share of cooperators was not improved by simply giving rewards for cooperators as compensation for searching costs. Furthermore, the results also suggest that the degree of cooperators in a network among consumers has a vital role in increasing the share of cooperators and switching rate.

In this letter, the performance of a state-of-the-art deep learning (DL) algorithm in [5] is analyzed and evaluated for orthogonal frequency-division multiplexing (OFDM) receivers, in the presence of harmonic spur interference. Moreover, a novel spur cancellation receiver structure and algorithm are proposed to enhance the traditional OFDM receivers, and serve as a performance benchmark for the DL algorithm. It is found that the DL algorithm outperforms the traditional algorithm and is much more robust to spur carrier frequency offset.

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.

In this paper, we propose a distributed subgradient-based method over quantized and event-triggered communication networks for constrained convex optimization. In the proposed method, each agent sends the quantized state to the neighbor agents only at its trigger times through the dynamic encoding and decoding scheme. After the quantized and event-triggered information exchanges, each agent locally updates its state by a consensus-based subgradient algorithm. We show a sufficient condition for convergence under summability conditions of a diminishing step-size.

Near real-time periodic tasks, which are popular in multimedia streaming applications, have deadline periods that are longer than the input intervals thanks to buffering. For such applications, the conventional frame-based schedulings cannot realize the optimal scheduling due to their shortsighted deadline assumptions. To realize globally energy-efficient executions of these applications, we propose a novel task scheduling algorithm, which takes advantage of the long deadline period. We confirm our approach can take advantage of the longer deadline period and reduce the average power consumption by up to 18%.

This paper proposes a simple source data exchange method for channel switching in space-time block code. If one transmits source data on another antenna, then the receiver should change combining method in order to adapt it. No one except knowing the channel switching sequence can decode the received data correctly. In case of exchanging data for channel switching, four orthogonal frequency division multiplexing symbols are exchanged according to a format of space-time block code. In this paper, I proposes two simple sign exchanges without exchanging four orthogonal-frequency division multiplexing symbols which occurs a different combining and channel switching method in the receiver.

Time-sequence data is high dimensional and contains a lot of information, which can be utilized in various fields, such as insurance, finance, and advertising. Personal data including time-sequence data is converted to anonymized datasets, which need to strike a balance between both privacy and utility. In this paper, we consider low-rank matrix factorization as one of anonymization methods and evaluate its efficiency. We convert time-sequence datasets to matrices and evaluate both privacy and utility. The record IDs in time-sequence data are changed at regular intervals to reduce re-identification risk. However, since individuals tend to behave in a similar fashion over periods of time, there remains a risk of record linkage even if record IDs are different. Hence, we evaluate the re-identification and linkage risks as privacy risks of time-sequence data. Our experimental results show that matrix factorization is a viable anonymization method and it can achieve better utility than existing anonymization methods.

A key driver of software business growth in developing countries is the survival of software small and medium-sized enterprises (SMEs). Quality of products is a critical factor that can indicate the future of the business by building customer confidence. Software development agencies need to be aware of meeting international standards in software development process. In practice, consultants and assessors are usually employed as the primary solution, which can impact the budget in case of small businesses. Self-assessment tools for software development process can potentially reduce time and cost of formal assessment for software SMEs. However, the existing support methods and tools are largely insufficient in terms of process coverage and semi-automated evaluation. This paper proposes to apply a knowledge-based approach in development of a self-assessment and gap analysis support system for the ISO/IEC 29110 standard. The approach has an advantage that insights from domain experts and the standard are captured in the knowledge base in form of decision tables that can be flexibly managed. Our knowledge base is unique in that task lists and work products defined in the standard are broken down into task and work product characteristics, respectively. Their relation provides the links between Task List and Work Product which make users more understand and influence self-assessment. A prototype support system was developed to assess the level of software development capability of the agencies based on the ISO/IEC 29110 standard. A preliminary evaluation study showed that the system can improve performance of users who are inexperienced in applying ISO/IEC 29110 standard in terms of task coverage and user's time and effort compared to the traditional self-assessment method.

This work develops a system called CLAP that detects and classifies “potentially unwanted applications” (PUAs) such as adware or remote monitoring tools. Our approach leverages DNS queries made by apps. Using a large sample of Android apps from third-party marketplaces, we first reveal that DNS queries can provide useful information for detection and classification of PUAs. We then show that existing DNS blacklists are limited when performing these tasks. Finally, we demonstrate that the CLAP system performs with high accuracy.