This paper shows a virus scanning engine using two-stage matching. In the first stage, a binary CAM emulator quickly detects a part of the virus pattern, while in the second stage, the MPU detects the full length of the virus pattern. The binary CAM emulator is realized by an index generation unit (IGU) based on row-shift decomposition. The proposed system uses two off-chip SRAMs and a small FPGA. Thus, the cost and the power consumption are lower than the TCAM-based system. The system loaded 1,290,617 ClamAV virus patterns. As for the area and throughput, this system outperforms existing two-stage matching systems using FPGAs.

This paper explores the problem of semantic enrichment of mathematical expressions. We formulate this task as the translation of mathematical expressions from presentation markup to content markup. We use MathML, an application of XML, to describe both the structure and content of mathematical notations. We apply a method based on statistical machine translation to extract translation rules automatically. This approach contrasts with previous research, which tends to rely on manually encoded rules. We also introduce segmentation rules used to segment mathematical expressions. Combining segmentation rules and translation rules strengthens the translation system and archives significant improvements over a prior rule-based system.

The mining problem over data streams has recently been attracting considerable attention thanks to the usefulness of data mining in various application fields of information science, and sequence data streams are so common in daily life. Therefore, a study on mining sequential patterns over sequence data streams can give valuable results for wide use in various application fields. This paper proposes a new framework for mining novel interesting sequential patterns over a sequence data stream and a mining method based on the framework. Assuming that a sequence with small time-intervals between its data elements is more valuable than others with large time-intervals, the novel interesting sequential pattern is defined and found by analyzing the time-intervals of data elements in a sequence as well as their orders. The proposed framework is capable of obtaining more interesting sequential patterns over sequence data streams whose data elements are highly correlated in terms of generation time.

Semantic concept in an utterance is obtained by a fuzzy matching methods to solve problems such as words' variation induced by automatic speech recognition (ASR), or missing field of key information by users in the process of spoken language understanding (SLU). A two-stage method is proposed: first, we adopt conditional random field (CRF) for building probabilistic models to segment and label entity names from an input sentence. Second, fuzzy matching based on similarity function is conducted between the named entities labeled by a CRF model and the reference characters of a dictionary. The experiments compare the performances in terms of accuracy and processing speed. Dice similarity and cosine similarity based on TF score can achieve better accuracy performance among four similarity measures, which equal to and greater than 93% in F1-measure. Especially the latter one improved by 8.8% and 9% respectively compared to q-gram and improved edit-distance, which are two conventional methods for string fuzzy matching.

Multi-label classification allows a sample to belong to multiple classes simultaneously, which is often the case in real-world applications such as text categorization and image annotation. In multi-label scenarios, taking into account correlations among multiple labels can boost the classification accuracy. However, this makes classifier training more challenging because handling multiple labels induces a high-dimensional optimization problem. In this paper, we propose a scalable multi-label method based on the least-squares probabilistic classifier. Through experiments, we show the usefulness of our proposed method.

Small delay defects can cause serious issues such as very short lifetime in the recent VLSI devices. Delay measurement is useful to detect small delay defects in manufacturing testing. This paper presents a design for delay measurement to detect small delay defects on global routing resources, such as double, hex and long lines, in a Xilinx Virtex 4 based FPGA. This paper also shows a measurement method using the proposed design. The proposed measurement method is based on an existing one for SoC using delay value measurement circuit (DVMC). The proposed measurement modifies the construction of configurable logic blocks (CLBs) and utilizes an on-chip DVMC newly added. The number of configurations required by the proposed measurement is 60, which is comparable to that required by stuck-at fault testing for global routing resources in FPGAs. The area overhead is low for general FPGAs, in which the area of routing resources is much larger than that of the other elements such as CLBs. The area of every modified CLB is 7% larger than an original CLB, and the area of the on-chip DVMC is 22% as large as that of an original CLB. For recent FPGAs, we can estimate that the area overhead is approximately 2% or less of the FPGAs.

A coarse-grained reconfigurable architecture (CGRA) is typically hybrid architecture, which is composed of a reconfigurable processing unit (RPU) and a host microprocessor. Many computation-intensive kernels (e.g., loop nests) are often mapped onto RPUs to speed up the execution of programs. Thus, mapping optimization of loop nests is very important to improve the performance of CGRA. Processing element (PE) utilization rate, communication volume and reconfiguration cost are three crucial factors for the performance of RPUs. Loop transformations can affect these three performance influencing factors greatly, and would be of much significance when mapping loops onto RPUs. In this paper, a joint loop transformation approach for RPUs is proposed, where the PE utilization rate, communication cost and reconfiguration cost are under a joint consideration. Our approach could be integrated into compilers for CGRAs to improve the operating performance. Compared with the communication-minimal approach, experimental results show that our scheme can improve 5.8% and 13.6% of execution time on motion estimation (ME) and partial differential equation (PDE) solvers kernels, respectively. Also, run-time complexity is acceptable for the practical cases.

MLPDA (Maximum Likelihood Probabilistic Data Association) has attracted a great deal of attention as an effective target track extraction method in high false density environments. However, to extract an accelerated target track on a 2-dimensional plane, the computational load of the conventional MLPDA is extremely high, since it needs to search for the most-likely position, velocity and acceleration of the target in 6-dimensional space. In this paper, we propose VG-MLPDA (Variable Gating MLPDA), which consists of the following two steps. The first step is to search the target's position and velocity among candidates with the assumed acceleration by using variable gates, which take into account both the observation noise and the difference between assumed and true acceleration. The second step is to search the most-likely position, velocity and acceleration using a maximization algorithm while reducing the gate volume. Simulation results show the validity of our method.

Spectral subtraction (SS) is an additive noise removal method which is derived in an extensive framework. In spectral subtraction, it is assumed that speech and noise spectra follow Gaussian distributions and are independent with each other. Hence, noisy speech also follows a Gaussian distribution. Spectral subtraction formula is obtained by maximizing the likelihood of noisy speech distribution with respect to its variance. However, it is well known that noisy speech observed in real situations often follows a heavy-tailed distribution, not a Gaussian distribution. In this paper, we introduce a q-Gaussian distribution in the non-extensive statistics to represent the distribution of noisy speech and derive a new spectral subtraction method based on it. We found that the q-Gaussian distribution fits the noisy speech distribution better than the Gaussian distribution does. Our speech recognition experiments using the Aurora-2 database showed that the proposed method, q-spectral subtraction (q-SS), outperformed the conventional SS method.

We propose a non-photorealistic rendering method for generating moire-picture-like color images from color photographs. The proposed method is performed in two steps. First, images with a staircasing effect are generated by a bilateral filter. Second, moire patterns are generated with an improved bilateral filter called an anti-bilateral filter. The characteristic of the anti-bilateral filter is to emphasize gradual boundaries.

This paper presents a fine-grain bit-serial reconfigurable VLSI architecture using multiple-valued switch blocks and binary logic modules. Multiple-valued signaling is utilized to implement a compact switch block. A binary-controlled current-steering technique is introduced, utilizing a programmable three-level differential-pair circuit to implement a high-performance low-power arbitrary two-variable binary function, and increase the noise margins in comparison with the quaternary-controlled differential-pair circuit. A current-source sharing technique between a series-gating differential-pair circuit and a current-mode D-latch is proposed to reduce the current source count and improve the speed. It is demonstrated that the power consumption and the delay of the proposed multiple-valued cell based on the binary-controlled current-steering technique and the current-source-sharing technique are reduced to 63% and 72%, respectively, in comparison with those of a previous multiple-valued cell.

Face retrieval in news video has been identified as a challenging task due to the huge variations in the visual appearance of the human face. Although several approaches have been proposed to deal with this problem, their extremely high computational cost limits their scalability to large-scale video datasets that may contain millions of faces of hundreds of characters. In this paper, we introduce approaches for face retrieval that are scalable to such datasets while maintaining competitive performances with state-of-the-art approaches. To utilize the variability of face appearances in video, we use a set of face images called face-track to represent the appearance of a character in a video shot. Our first proposal is an approach for extracting face-tracks. We use a point tracker to explore the connections between detected faces belonging to the same character and then group them into one face-track. We present techniques to make the approach robust against common problems caused by flash lights, partial occlusions, and scattered appearances of characters in news videos. In the second proposal, we introduce an efficient approach to match face-tracks for retrieval. Instead of using all the faces in the face-tracks to compute their similarity, our approach obtains a representative face for each face-track. The representative face is computed from faces that are sampled from the original face-track. As a result, we significantly reduce the computational cost of face-track matching while taking into account the variability of faces in face-tracks to achieve high matching accuracy. Experiments are conducted on two face-track datasets extracted from real-world news videos, of such scales that have never been considered in the literature. One dataset contains 1,497 face-tracks of 41 characters extracted from 370 hours of TRECVID videos. The other dataset provides 5,567 face-tracks of 111 characters observed from a television news program (NHK News 7) over 11 years. We make both datasets publically accessible by the research community. The experimental results show that our proposed approaches achieved a remarkable balance between accuracy and efficiency.

Anomaly-based worm detection is a complement to existing signature-based worm detectors. It detects unknown worms and fills the gap between when a worm is propagated and when a signature is generated and downloaded to a signature-based worm detector. A major obstacle for its deployment to personal computers (PCs) is its high false positive alarms since a typical PC user lacks the skill to handle exceptions flagged by a detector without much knowledge of computers. In this paper, we exploit the feature of personal computers in which the user interacts with many running programs and the features combining various network characteristics. The model of a program's network behaviors is conditioned on the human interactions with the program. Our scheme automates detection of unknown worms with dramatically reduced false positive alarms while not compromising low false negatives, as proved by our experimental results from an implementation on Windows-based PCs to detect real world worms.

Comparable Corpora are valuable resources for many NLP applications, and extensive research has been done on information mining based on comparable corpora in recent years. While there are not enough large-scale available public comparable corpora at present, this paper presents a bi-directional CLIR-based method for creating comparable corpora from two independent news collections in different languages. The original Chinese document collections and English documents collections are crawled from XinHuaNet respectively and formatted in a consistent manner. For each document from the two collections, the best query keywords are extracted to represent the essential content of the document, and then the keywords are translated into the language of the other collection. The translated queries are run against the collection in the same language to pick up the candidate documents in the other language and candidates are aligned based on their publication dates and the similarity scores. Results show that our approach significantly outperforms previous approaches to the construction of Chinese-English comparable corpora.

Ternary content addressable memory (TCAM) is popular LSI for use in high-throughput forwarding engines on routers. However, the unique structure applied in TCAM consume huge amounts of power, therefore it restricts the ability to handle large lookup table capacity in IP routers. In this paper, we propose a commodity-memory based hardware architecture for the forwarding information base (FIB) application that solves the substantial problems of power and density. The proposed architecture is examined by a fabricated test chip with 40 nm embedded DRAM (eDRAM) technology, and the effect of power reduction verified is greatly lower than conventional TCAM based and the energy metric achieve 0.01 fJ/bit/search. The power consumption is almost 0.5 W at 250 Msps and 8M entries.

In this paper, we propose a new class of two dimensional (2D) M-channel (M-ch) non-separable filter banks (FBs) based on cosine modulated filter banks (CMFBs) via a new diagonally modulation scheme. Until now, many researchers have proposed 2D non-separable CMFBs. Nevertheless, efficient direction-selective CMFBs have not been yet. Thanks to our new modulations with diagonal shifts, proposed CMFBs have several frequency supports including direction-selective ones which cannot be realized by conventional ones. In a simulation, we show design examples of proposed CMFBs and their various directional frequency supports.

In this paper, a new finger vein recognition method based on Gabor wavelet and Local Binary Pattern (GLBP) is proposed. In the new scheme, Gabor wavelet magnitude and Local Binary Pattern operator are combined, so the new feature vector has excellent stability. We introduce Block-based Linear Discriminant Analysis (BLDA) to reduce the dimensionality of the GLBP feature vector and enhance its discriminability at the same time. The results of an experiment show that the proposed approach has excellent performance compared to other competitive approaches in current literatures.

This study investigated the test-retest reliability and the criterion-related validity of the Implicit Association Test (IAT [1]) that was developed for measuring shyness among Japanese people. The IAT has been used to measure implicit stereotypes, as well as self-concepts, such as implicit shyness and implicit self-esteem. We administered the shyness IAT and the self-esteem IAT to participants (N = 59) on two occasions over a one-week interval (Time 1 and Time 2) and examined the test-retest reliability by correlating shyness IATs between the two time points. We also assessed the criterion-related validity by calculating the correlation between implicit shyness and implicit self-esteem. The results indicated a sufficient positive correlation coefficient between the scores of implicit shyness over the one-week interval (r = .67, p < .01). Moreover, a strong negative correlation coefficient was indicated between implicit shyness and implicit self-esteem (r = -.72, p < .01). These results confirmed the test-retest reliability and the criterion-related validity of the Japanese version of the shyness IAT, which is indicative of the validity of the test for assessing implicit shyness.

In this paper, we propose a new online authoring tool for e-Learning system to meet the social demands for internationalized higher education. The tool includes two functions – an authoring function for creating video-based content by the instructor, and a viewing function for self-learning by students. In the authoring function, an instructor creates key markings onto the raw video stream to produce virtual video clips related to each slide. With key markings, some parts of the raw video stream can be easily skipped. The virtual video clips form an aggregated video stream that is used to synchronize with the slide presentation to create learning content. The synchronized content can be previewed immediately at the client computer prior to saving at the server. The aggregated video becomes the baseline for the viewing function. Based on aggregated video stream methodology, content editing requires only the changing of key markings without editing the raw video file. Furthermore, video and pointer synchronization is also proposed for enhancing the students' learning efficiency. In viewing function, video quality control and an adaptive video buffering method are implemented to support usage in various network environments. The total system is optimized to support cross-platform and cloud computing to break the limitation of various usages. The proposed method can provide simple authoring processes with clear user interface design for instructors, and help students utilize learning contents effectively and efficiently. In the user acceptance evaluation, most respondents agree with the usefulness, ease-of-use, and user satisfaction of the proposed system. The overall results show that the proposed authoring and viewing tools have higher user acceptance as a tool for e-Learning.

Data encryption and decryption are common operations in network-based application programs that must offer security. In order to keep pace with the high data input rate of network-based applications such as the multimedia data streaming, real-time processing of the data encryption/decryption is crucial. In this paper, we propose a new parallelization approach to improve the throughput performance for the de-facto standard data encryption and decryption algorithm, AES-CTR (Counter mode of AES). The new approach extends the size of the block encrypted at one time across the unit block boundaries, thus effectively encrypting multiple unit blocks at the same time. This reduces the associated parallelization overheads such as the number of procedure calls, the scheduling and the synchronizations compared with previous approaches. Therefore, this leads to significant throughput performance improvements on a computing platform with a general-purpose multi-core processor and a Graphic Processing Unit (GPU).