News articles usually represent a biased viewpoint on contentious issues, potentially causing social problems. To mitigate this media bias, we propose a novel framework for predicting orientation of a news article by analyzing social user behaviors in Twitter. Highly active users tend to have consistent behavior patterns in social network by retweeting behavior among users with the same viewpoints for contentious issues. The bias ratio of highly active users is measured to predict orientation of users. Then political orientation of a news article is predicted based on the bias ratio of users, mutual retweeting and opinion analysis of tweet documents. The analysis of user behavior shows that users with the value of 1 in bias ratio are 88.82%. It indicates that most of users have distinctive orientation. Our prediction method based on orientation of users achieved 88.6% performance in accuracy. Experimental results show significant improvements over the SVM classification. These results show that proposed detection method is effective in social network.

An LSI Chip-Package-Board integrated power noise simulation model and its validity is discussed in this paper. A unified power delivery network (PDN) of LSI chip, package, and printed circuit board (PCB) is connected with on-chip power supply current models with capacitor charging expression. The proposed modeling flow is demonstrated for the 32-bit microprocessor in a 1.0V 90nm CMOS technology. The PDN of the system that includes a chip, bonding wires and a printed circuit board is modeled in an equivalent circuit. The on-chip power supply noise monitoring technique and the magnetic probe method is applied for validating simulation results. Simulations and measurements explore power supply noise generation with the dependency on operating frequencies in the wide range from 10MHz to 300MHz, under the operation mode of dynamic frequency scaling, and in the long time operation with various operation codes. It is confirmed that the proposed power supply noise simulation model is helpful for the noise estimation throughout the design phase of the LSI system.

The objective of our research is to develop a support system for creating presentation speech, especially speech that explains relations between two slides (complementary speech). Complementary speech is required between slides whose relations are difficult to understand from their contents, such as texts, figures, and tables. If presenters could notice relations between created slides that are recognized by audiences, they would prepare appropriate complementary speech at the right places. To make presenters notice slides where complementary speech is needed, our system analyzes relations between slides based on their texts and visualizes them. Four slide relations are defined and the method for detecting these relations from the slide texts is proposed. Then, analyzed relations are arranged in two-dimensional spaces that represent sequential relation and inclusive relation of their topics. The experimental results showed that most detected slide relations were the same as what examinees understood, and visualization of slide relations was useful in creating complementary speech, especially for less-experienced presenter.

This paper describes an ultrasonic system that estimates the cell quantity of an artificial culture bone, which is effective for appropriate treat with a composite of this material and Bone Marrow Stromal Cells. For this system, we examine two approaches for analyzing the ultrasound waves transmitted through the cultured bone, including stem cells to estimate cell quantity: multiple regression and fuzzy inference. We employ two characteristics from the obtained wave for applying each method. These features are the amplitude and the frequency; the amplitude is measured from the obtained wave, and the frequency is calculated by the cross-spectrum method. The results confirmed that the fuzzy inference method yields the accurate estimates of cell quantity in artificial culture bone. Using this ultrasonic estimation system, the orthopaedic surgeons can choose the composites that contain favorable number of cells before the implantation.

This paper presents a resilient cache memory for dynamic variation tolerance in a 40-nm CMOS. The cache can perform sustained operations under a large-amplitude voltage droop. To realize sustained operation, the resilient cache exploits 7T/14T bit-enhancing SRAM and on-chip voltage/temperature monitoring circuit. 7T/14T bit-enhancing SRAM can reconfigure itself dynamically to a reliable bit-enhancing mode. The on-chip voltage/temperature monitoring circuit can sense a precise supply voltage level of a power rail of the cache. The proposed cache can dynamically change its operation mode using the voltage/temperature monitoring result and can operate reliably under a large-amplitude voltage droop. Experimental result shows that it does not fail with 25% and 30% droop of Vdd and it provides 91 times better failure rate with a 35% droop of Vdd compared with the conventional design.

In this paper, we focus on the multi-way relaying channel where K users wish to exchange information with each other within two phases. Precoding at each user and the relay is carefully constructed to ensure that the signals from the same user pair are grouped together and cross-pair interference can be cancelled. Reliable detection is challenging at the relay since the observation constellation is no longer one of the regular ones, due to the fact that a relay observation is the superposition of the messages from one of the $rac{1}{2}K(K-1)$ user pairs. When the trellis coded modulation is used at each node, a simple constellation mapping function and a reduced-states decoding scheme can be applied at the relay, which result in much lower complexity. Furthermore, a modified version of the decoding method is also developed which is called the re-encoding-avoidance scheme at the relay. Monte-Carlo simulation results are provided to demonstrate the performance of the proposed scheme.

In this letter, we propose a new secure and efficient certificateless aggregate signature scheme which has the advantages of both certificateless public key cryptosystem and aggregate signature. Based on the computational Diffie-Hellman problem, our scheme can be proven existentially unforgeable against adaptive chosen-message attacks. Most importantly, our scheme requires short group elements for aggregate signature and constant pairing computations for aggregate verification, which leads to high efficiency due to no relations with the number of signers.

A joint continuous phase frequency shift keying (CPFSK) modulation and physical-layer network coding (PNC), i.e., CPFSK-PNC, is proposed for two-way relay channels (TWRCs). This letter discusses the signal detection of the CPFSK-PNC scheme with emphasis on the maximum-likelihood sequence detection (MLSD) algorithm for the relay receiver. The end-to-end error performance of the proposed CPFSK-PNC scheme is evaluated through simulations.

Proof-of-concept cyclic analog-to-digital converters (ADCs) have been designed and fabricated in 90-nm CMOS technology. The measurement results of an experimental prototype demonstrate the effectiveness of the proposed switched-capacitor (SC) architecture to realize a non-binary ADC based on β expansion. Different from the conventional binary ADC, a simple 1-bit/step structure for an SC multiplying digital-to-analog converter (MDAC) is proposed to present residue amplification by β (1 < β < 2). The redundancy of non-binary ADCs with radix β tolerates the non-linear conversion errors caused by the offsets of comparators, the mismatches of capacitors, and the finite DC gains of amplifiers, which are used in the MDAC. We also employed a radix value estimation algorithm to obtain an effective value of β for non-binary encoding; it can be realized by merely adding a simple conversion sequence and digital circuits. As a result, the power penalty of a high-gain wideband amplifier and the required accuracy of the circuit elements for a high-resolution ADC were largely relaxed so that the circuit design was greatly simplified. The implemented ADC achieves a measured peak signal-to-noise-and-distortion-ratio (SNDR) of 60.44dB, even with an op-amp with a poor DC gain (< 50dB) while dissipating 780µW in analog circuits at 1.4V and occupying an active area of 0.25 × 0.26mm2.

This paper describes a new method for lossy audio signal compression via compressive sensing (CS). In this method, a structured shrinkage operator is employed to decompose the audio signal into three layers, with two sparse layers, tonal and transient, and additive noise, and then, both the tonal and transient layers are compressed using CS. Since the shrinkage operator is able to take into account the structure information of the coefficients in the transform domain, it is able to achieve a better sparse approximation of the audio signal than traditional methods do. In addition, we propose a sparsity allocation algorithm, which adjusts the sparsity between the two layers, thus improving the performance of CS. Experimental results demonstrated that the new method provided a better compression performance than conventional methods did.

A new type of the affine projection (AP) algorithms which incorporates the sparsity condition of a system is presented. To exploit the sparsity of the system, a weighted l1-norm regularization is imposed on the cost function of the AP algorithm. Minimizing the cost function with a subgradient calculus and choosing two distinct weightings for l1-norm, two stochastic gradient based sparsity regularized AP (SR-AP) algorithms are developed. Experimental results show that the SR-AP algorithms outperform the typical AP counterparts for identifying sparse systems.

The auto-correlation function (ACF) of Binary Offset Carrier (BOC) modulated signals has multiple peaks which raise the problem of ambiguity in acquisition and tracking. In this paper, the ACF is split into several sub-correlation functions (SCFs) through dividing the integration period of ACF into several partials. Then a pseudo correlation function (PCF) is constructed from the SCFs through a combination function to eliminate all side-peaks. The unambiguous tracking method based on the PCF achieves better code phase tracking accuracy than the conventional methods in AWGN environment. It only requires half computation cost of Bump-Jumping (BJ) and nearly quarter of Double-Estimator, although offers slightly less accurate tracking than BJ and Double-Estimator in multi-path environment. Moreover, this method suits all kinds of BOC signals without any auxiliary correlators.

A 7bit 1GS/s flash ADC using two bit active interpolation and background offset calibration is proposed and tested. It achieves background calibration using 36 pre-amplifiers with 139 comparators. To cancel the offset, two pre-amplifiers and 12 comparators are set to offline in turn while the others are operating. A two bit active interpolation design and an offset cancellation scheme are implemented in the latch stage. The interpolation and background calibration significantly reduce analog input signal as well as reference voltage load. Fabricated with the 90nm CMOS process, the proposed ADC consumes 95mW under a 1.2V power supply.

As the recent growth of online social network services such as Facebook and Twitter, people are able to easily share information with each other by writing posts or commenting for another's posts. In this paper, we firstly suggest a method of discovering information flows of posts on Facebook and their underlying contexts by incorporating process mining and text mining techniques. Based on comments collected from Facebook, the experiment results illustrate how the proposed method can be applied to analyze information flows and contexts of posts on social network services.

k-NN classification has been applied to classify normal tissues in MR images. However, the intensity inhomogeneity of MR images forces conventional k-NN classification into significant misclassification errors. This letter proposes a new interleaved method, which combines k-NN classification and bias field estimation in an energy minimization framework, to simultaneously overcome the limitation of misclassifications in conventional k-NN classification and correct the bias field of observed images. Experiments demonstrate the effectiveness and advantages of the proposed algorithm.

Combinatorial interaction testing has been well studied in recent years, and has been widely applied in practice. It generally aims at generating an effective test suite (an interaction test suite) in order to identify faults that are caused by parameter interactions. Due to some constraints in practical applications (e.g. limited testing resources), for example in combinatorial interaction regression testing, prioritized interaction test suites (called interaction test sequences) are often employed. Consequently, many strategies have been proposed to guide the interaction test suite prioritization. It is, therefore, important to be able to evaluate the different interaction test sequences that have been created by different strategies. A well-known metric is the Average Percentage of Combinatorial Coverage (shortly APCCλ), which assesses the rate of interaction coverage of a strength λ (level of interaction among parameters) covered by a given interaction test sequence S. However, APCCλ has two drawbacks: firstly, it has two requirements (that all test cases in S be executed, and that all possible λ-wise parameter value combinations be covered by S); and secondly, it can only use a single strength λ (rather than multiple strengths) to evaluate the interaction test sequence - which means that it is not a comprehensive evaluation. To overcome the first drawback, we propose an enhanced metric Normalized APCCλ (NAPCC) to replace the APCCλ Additionally, to overcome the second drawback, we propose three new metrics: the Average Percentage of Strengths Satisfied (APSS); the Average Percentage of Weighted Multiple Interaction Coverage (APWMIC); and the Normalized APWMIC (NAPWMIC). These metrics comprehensively assess a given interaction test sequence by considering different interaction coverage at different strengths. Empirical studies show that the proposed metrics can be used to distinguish different interaction test sequences, and hence can be used to compare different test prioritization strategies.

This paper presents a method for automatic rectification of design bugs in processors. Given a golden sequential instruction-set architecture model of a processor and its erroneous detailed cycle-accurate model at the micro-architecture level, we perform symbolic simulation and property checking combined with concrete simulation iteratively to detect the buggy location and its corresponding fix. We have used the truth-table model of the function that is required for correction, which is a very general model. Moreover, we do not represent the truth-table explicitly in the design. We use, instead, only the required minterms, which are obtained from the output of our backend formal engine. This way, we avoid adding any new variable for representing the truth-table. Therefore, our correction model is scalable to the number of inputs of the truth-table that could grow exponentially. We have shown the effectiveness of our method on a complex out-of-order superscalar processor supporting atomic execution of instructions. Our method reduces the model size for correction by 6.0x and total correction time by 12.6x, on average, compared to our previous work.

D2D (Device-to-Device) communication has received considerable attention in recent years as one of the key technologies for future communication systems. Among the typical D2D communication systems, FlashLinQ (FLQ) adopted single-tone OFDM (Orthogonal Frequency Division Multiplexing) transmission which enables wide-sense discovery and distributed channel-aware link scheduling. Although synchronization based on a CES (Common External Source) is basically assumed in FLQ, a means to support devices when they are unable to use a CES is still necessary. In most OFDM systems, CFO (Carrier Frequency Offset) induces ICI (Inter Channel Interference) which degrades overall system performance drastically. Especially in D2D systems, ICI can be amplified due to different path losses between link and a precise estimation and correction of CFO is very important. Many CFO estimation algorithms based on DA (Data Aided) and NDA (None Data Aided) were proposed for OFDM systems, but there are several constraint conditions on frequency synchronization in D2D systems. Therefore, in this paper, we propose a new NDA-CFO estimation method for OFDM based D2D systems. The proposed method is based on the characteristics of single-tone OFDM signal, and is composed of two estimation stages: initial estimation and feed-back estimation. In initial estimation, the estimation of CFO is obtained by using two correlation results in a symbol. Also, estimation range can be adaptively defined as the distance between the two windows. In feed-back estimation, the distance between the two correlation results is gradually increased by re-using the estimated CFO and the correlation results. Therefore, more precise CFO estimation can be obtained. A numerical analysis and performance evaluation verify that the proposed method has a large estimation range and achieves precise estimation performance compared to the conventional methods.

The numbers of files in file systems have increased dramatically in recent years. Office workers spend much time and effort searching for the documents required for their jobs. To reduce these costs, we propose a new method for recommending files and operations on them. Existing technologies for recommendation, such as collaborative filtering, suffer from two problems. First, they can only work with documents that have been accessed in the past, so that they cannot recommend when only newly generated documents are inputted. Second, they cannot easily handle sequences involving similar or differently ordered elements because of the strict matching used in the access sequences. To solve these problems, such minor variations should be ignored. In our proposed method, we introduce the concepts of abstract files as groups of similar files used for a similar purpose, abstract tasks as groups of similar tasks, and frequent abstract workflows grouped from similar workflows, which are sequences of abstract tasks. In experiments using real file-access logs, we confirmed that our proposed method could extract workflow patterns with longer sequences and higher support-count values, which are more suitable as recommendations. In addition, the F-measure for the recommendation results was improved significantly, from 0.301 to 0.598, compared with a method that did not use the concepts of abstract tasks and abstract workflows.

Graph pattern mining has played important roles in network analysis and information retrieval. However, temporal characteristics of networks have not been estimated sufficiently. We propose time graph pattern mining as a new concept of graph mining reflecting the temporal information of a network. We conduct two case studies of time graph pattern mining: extensively discussed topics on blog sites and a book recommendation network. Through examination of case studies, we ascertain that time graph pattern mining has numerous possibilities as a novel means for information retrieval and network analysis reflecting both structural and temporal characteristics.