An increasing number of psychological studies have demonstrated that the six basic expressions of emotions are not culturally universal. However, automatic facial expression recognition (FER) systems disregard these findings and assume that facial expressions are universally expressed and recognized across different cultures. Therefore, this paper presents an analysis of Western-Caucasian and East-Asian facial expressions of emotions based on visual representations and cross-cultural FER. The visual analysis builds on the Eigenfaces method, and the cross-cultural FER combines appearance and geometric features by extracting Local Fourier Coefficients (LFC) and Facial Fourier Descriptors (FFD) respectively. Furthermore, two possible solutions for FER under multicultural environments are proposed. These are based on an early race detection, and independent models for culture-specific facial expressions found by the analysis evaluation. HSV color quantization combined with LFC and FFD compose the feature extraction for race detection, whereas culture-independent models of anger, disgust and fear are analyzed for the second solution. All tests were performed using Support Vector Machines (SVM) for classification and evaluated using five standard databases. Experimental results show that both solutions overcome the accuracy of FER systems under multicultural environments. However, the approach which individually considers the culture-specific facial expressions achieved the highest recognition rate.

The elastic optical network (EON) is a promising new optical technology that uses spectrum resources much more efficiently than does traditional wavelength division multiplexing (WDM). This paper focuses on the routing, modulation level, spectrum and transceiver allocation (RMSTA) problems of the EON. In contrast to previous works that consider only the routing and spectrum allocation (RSA) or routing, modulation level and spectrum allocation (RMSA) problems, we additionally consider the transceiver allocation problem. Because transceivers can be used to regenerate signals (by connecting two transceivers back-to-back) along a transmission path, different regeneration sites on a transmission path result in different spectrum and transceiver usage. Thus, the RMSTA problem is both more complex and more challenging than are the RSA and RMSA problems. To address this problem, we first propose an integer linear programming (ILP) model whose objective is to optimize the balance between spectrum usage and transceiver usage by tuning a weighting coefficient to minimize the cost of network operations. Then, we propose a novel virtual network-based heuristic algorithm to solve the problem and present the results of experiments on representative network topologies. The results verify that, compared to previous works, the proposed algorithm can significantly reduce both resource consumption and time complexity.

CMOS microprocessors are limited in their capacity for clock speed improvement because of increasing computing power, i.e., they face a power-wall problem. Single-flux-quantum (SFQ) circuits offer a solution with their ultra-fast-speed and ultra-low-power natures. This paper introduces our contributions towards ultra-high-speed cryogenic SFQ computing. The first step is to design SFQ microprocessors. From qualitatively and quantitatively evaluating past-designed SFQ microprocessors, we have found that revisiting the architecture of SFQ microprocessors and on-chip caches is the first critical challenge. On the basis of cross-layer discussions and analysis, we came to the conclusion that a bit-parallel gate-level pipeline architecture is the best solution for SFQ designs. This paper summarizes our current research results targeting SFQ microprocessors and on-chip cache architectures.

We report room-temperature atomic layer deposition (ALD) of SnO2 using tetramethyltin (TMT) as a precursor and plasma-excited humidified argon as an oxidizing gas and investigate the saturation behaviors of these gases on SnO2-covered Si prisms by IR absorption spectroscopy to determine optimal precursor/oxidizer injection conditions. TMT is demonstrated to adsorb on the SnO2 surface by reacting with surface OH groups, which are regenerated by oxidizing the TMT-saturated surface by plasma-excited humidified argon. We provide a detailed discussion of the growth mechanism. We also report the RT ALD application to the RT TFT fabrication.

Based on imperfect channel state information (CSI), the energy efficiency (EE) of downlink distributed antenna systems (DASs) with multiple receive antennas is investigated assuming composite Rayleigh fading channels. A new EE is introduced which is defined as the ratio of the average transmission rate to the total consumed power. According to this definition, an optimal power allocation (PA) scheme is developed for maximizing EE in a DAS subject to the maximum transmit power constraint. It is shown that a PA solution for the constrained EE optimization does exist and is unique. A Newton method based practical iterative algorithm is presented to solve PA. To avoid the iterative calculation, a suboptimal PA scheme is derived by means of the Lambert function, which yields a closed-form PA. The developed schemes include the ones under perfect CSI as special cases, and only need the statistical CSI. Thus, they have low overhead and good robustness. Moreover, the theoretical EE under imperfect CSI is derived for performance evaluation, and the resulting closed-form EE expression is obtained. Simulation results indicate that the theoretical EE can match the corresponding simulated value well, and the developed suboptimal scheme has performance close to optimal one, but with lower complexity.

This paper describes why we require access system virtualization. The purpose of access system virtualization is different from that of core network virtualization. Therefore, a specific approach should be considered such as the separation of software and hardware, interface standardization, or deep softwarization.

The problem with distributed representations generated by neural networks is that the meaning of the features is difficult to understand. We propose a new method that gives a specific meaning to each node of a hidden layer by introducing a manually created word semantic vector dictionary into the initial weights and by using paragraph vector models. We conducted experiments to test the hypotheses using a single domain benchmark for Japanese Twitter sentiment analysis and then evaluated the expandability of the method using a diverse and large-scale benchmark. Moreover, we tested the domain-independence of the method using a Wikipedia corpus. Our experimental results demonstrated that the learned vector is better than the performance of the existing paragraph vector in the evaluation of the Twitter sentiment analysis task using the single domain benchmark. Also, we determined the readability of document embeddings, which means distributed representations of documents, in a user test. The definition of readability in this paper is that people can understand the meaning of large weighted features of distributed representations. A total of 52.4% of the top five weighted hidden nodes were related to tweets where one of the paragraph vector models learned the document embeddings. For the expandability evaluation of the method, we improved the dictionary based on the results of the hypothesis test and examined the relationship of the readability of learned word vectors and the task accuracy of Twitter sentiment analysis using the diverse and large-scale benchmark. We also conducted a word similarity task using the Wikipedia corpus to test the domain-independence of the method. We found the expandability results of the method are better than or comparable to the performance of the paragraph vector. Also, the objective and subjective evaluation support each hidden node maintaining a specific meaning. Thus, the proposed method succeeded in improving readability.

In this paper, a new generalized cyclotomy over Zpq is presented based on cyclotomy and Chinese remainder theorem, where p and q are different odd primes. Several new construction methods for binary sequence pairs of period pq with ideal two-level correlation are given by utilizing these generalized cyclotomic classes. All the binary sequence pairs from our constructions have both ideal out-of-phase correlation values -1 and optimum balance property.

In this paper, we propose a quality-level control method based on quality of experience (QoE) characteristics for HTTP adaptive streaming (HAS). The proposed method works as an adaptive bitrate controller on the HAS client. The proposed method consists of two operations: buffer-aware control and QoE-aware control. We implement the proposed method on an actual dynamic adaptive streaming over HTTP (DASH) program and evaluate the QoE performance of the proposed method via both objective and subjective evaluations. The results show that the proposed method effectively improves both objective and subjective QoE performances by preventing stalling events and quality-level switchings that have a negative influence on subjective QoE performance.

Traffic signal phase and timing (TSPaT) information is valuable for various applications, such as velocity advisory systems, navigation systems, collision warning systems, and so forth. In this paper, we focus on learning baseline timing cycle lengths for fixed-time traffic signals. The cycle length is the most important parameter among all timing parameters, such as green lengths. We formulate the cycle length learning problem as a period estimation problem using a sparse set of noisy observations, and propose the most frequent approximate greatest common divisor (MFAGCD) algorithms to solve the problem. The accuracy performance of our proposed algorithms is experimentally evaluated on both simulation data and the real taxi GPS trajectory data collected in Shanghai, China. Experimental results show that the MFAGCD algorithms have better sparsity and outliers tolerant capabilities than existing cycle length estimation algorithms.

To prevent constraints or defects of a single sensor from malfunctions, this paper proposes a fire detection system based on the Dempster-Shafer theory with multi-sensor technology. The proposed system operates in three stages: measurement, data reception and alarm activation, where an Arduino is tasked with measuring and interpreting the readings from three types of sensors. Sensors under consideration involve smoke, light and temperature detection. All the measured data are wirelessly transmitted to the backend Raspberry Pi for subsequent processing. Within the system, the Raspberry Pi is used to determine the probability of fire events using the Dempster-Shafer theory. We investigate moderate settings of the conflict coefficient and how it plays an essential role in ensuring the plausibility of the system's deduced results. Furthermore, a MySQL database with a web server is deployed on the Raspberry Pi for backlog and data analysis purposes. In addition, the system provides three notification services, including web browsing, smartphone APP, and short message service. For validation, we collected the statistics from field tests conducted in a controllable and safe environment by emulating fire events happening during both daytime and nighttime. Each experiment undergoes the No-fire, On-fire and Post-fire phases. Experimental results show an accuracy of up to 98% in both the No-fire and On-fire phases during the daytime and an accuracy of 97% during the nighttime under reasonable conditions. When we take the three phases into account, the accuracy in the daytime and nighttime increase to 97% and 89%, respectively. Field tests validate the efficiency and accuracy of the proposed system.

Given two data sets of user preferences and product attributes in addition to a set of query products, the aggregate reverse rank (ARR) query returns top-k users who regard the given query products as the highest aggregate rank than other users. ARR queries are designed to focus on product bundling in marketing. Manufacturers are mostly willing to bundle several products together for the purpose of maximizing benefits or inventory liquidation. This naturally leads to an increase in data on users and products. Thus, the problem of efficiently processing ARR queries become a big issue. In this paper, we reveal two limitations of the state-of-the-art solution to ARR query; that is, (a) It has poor efficiency when the distribution of the query set is dispersive. (b) It has to process a large portion user data. To address these limitations, we develop a cluster-and-process method and a sophisticated indexing strategy. From the theoretical analysis of the results and experimental comparisons, we conclude that our proposals have superior performance.

Many mobile systems need to achieve both high performance and low memory usage, and the total performance of such the systems can be largely affected by the effectiveness of GC. Hence, the recent popularization of mobile devices makes the GC performance play one of the important roles on the wide range of platforms. The response performance degradation caused by suspending all processes for GC has been a well-known potential problem. Therefore, GC algorithms have been actively studied and improved, but they still have not reached any fundamental solution. In this paper, we focus on the point that the same objects are redundantly marked during the GC procedure implemented on DalvikVM, which is one of the famous runtime environments for the mobile devices. Then we propose a hardware support technique for improving marking routine of GC. We installed a set of tables to a processor for managing marked objects, and redundant marking for marked objects can be omitted by referring these tables. The result of the simulation experiment shows that the percentage of redundant marking is reduced by more than 50%.

Many of affine motion compensation techniques proposed thus far employ least-square-based techniques in estimating affine parameters, which requires a hardware structure different from conventional block-matching-based one. This paper proposes a new affine motion estimation/compensation framework friendly to block-matching-based parameter estimation, and applies it to an HEVC encoder to demonstrate its coding efficiency and computation cost. To avoid a nest of search loops, a new affine motion model is first introduced by decomposing the conventional 4-parameter affine model into two 3-parameter ones. Then, a block-matching-based fast parameter estimation technique is proposed for the models. The experimental results given in this paper show that our approach is advantageous over conventional techniques.

Malware phylogeny refers to inferring evolutionary relationships between instances of families. It has gained a lot of attention over the past several years, due to its efficiency in accelerating reverse engineering of new variants within families. Previous researches mainly focused on tree-based models. However, those approaches merely demonstrate lineage of families using dendrograms or directed trees with rough evolution information. In this paper, we propose a novel malware phylogeny construction method taking advantage of persistent phylogeny tree model, whose nodes correspond to input instances and edges represent the gain or lost of functional characters. It can not only depict directed ancestor-descendant relationships between malware instances, but also show concrete function inheritance and variation between ancestor and descendant, which is significant in variants defense. We evaluate our algorithm on three malware families and one benign family whose ground truth are known, and compare with competing algorithms. Experiments demonstrate that our method achieves a higher mean accuracy of 61.4%.

This paper proposes a deep learning-based non-intrusive objective speech intelligibility estimation method based on recurrent neural network (RNN) with long short-term memory (LSTM) structure. Conventional non-intrusive estimation methods such as standard P.563 have poor estimation performance and lack of consistency, especially, in various noise and reverberation environments. The proposed method trains the LSTM RNN model parameters by utilizing the STOI that is the standard intrusive intelligibility estimation method with reference speech signal. The input and output of the LSTM RNN are the MFCC vector and the frame-wise STOI value, respectively. Experimental results show that the proposed objective intelligibility estimation method outperforms the conventional standard P.563 in various noisy and reverberant environments.

In this letter, we propose a sequential convolutional residual network, where we first analyze a tangled network architecture using simplified equations and determine the critical point to untangle the complex network architecture. Although the residual network shows good performance, the learning efficiency is not better than expected at deeper layers because the network is excessively intertwined. To solve this problem, we propose a network in which the information is transmitted sequentially. In this network architecture, the neighboring layer output adds the input of the current layer and iteratively passes its result to the next sequential layer. Thus, the proposed network can improve the learning efficiency and performance by successfully mitigating the complexity in deep networks. We show that the proposed network performs well on the Cifar-10 and Cifar-100 datasets. In particular, we prove that the proposed method is superior to the baseline method as the depth increases.

This paper presents a novel TV event detection method for automatically generating TV program digests by using Twitter data. Previous studies of TV program digest generation based on Twitter data have developed TV event detection methods that analyze the frequency time series of tweets that users made while watching a given TV program; however, in most of the previous studies, differences in how Twitter is used, e.g., sharing information versus conversing, have not been taken into consideration. Since these different types of Twitter data are lumped together into one category, it is difficult to detect highlight scenes of TV programs and correctly extract their content from the Twitter data. Therefore, this paper presents a highlight scene detection method to automatically generate TV program digests for TV programs based on Twitter data classified by Twitter user behavior. To confirm the effectiveness of the proposed method, experiments using 49 soccer game TV programs were conducted.