Previously, it is not obvious to what extent was accepted for the assessors when we see the 3D image (including multi-view 3D) which the luminance change may affect the stereoscopic effect and assessment generally. We think that we can conduct a general evaluation, along with a subjective evaluation, of the luminance component using both the S-CIELAB color space and CIEDE2000. In this study, first, we performed three types of subjective evaluation experiments for contrast enhancement in an image by using the eight viewpoints parallax barrier method. Next, we analyzed the results statistically by using a support vector machine (SVM). Further, we objectively evaluated the luminance value measurement by using CIEDE2000 in the S-CIELAB color space. Then, we checked whether the objective evaluation value was related to the subjective evaluation value. From results, we were able to see the characteristic relationship between subjective assessment and objective assessment.

Due to the increase of outsourcing by IC vendors, we face a serious risk that malicious third-party vendors insert hardware Trojans very easily into their IC products. However, detecting hardware Trojans is very difficult because today's ICs are huge and complex. In this paper, we propose a hardware-Trojan classification method for gate-level netlists to identify hardware-Trojan infected nets (or Trojan nets) using a support vector machine (SVM) or a neural network (NN). At first, we extract the five hardware-Trojan features from each net in a netlist. These feature values are complicated so that we cannot give the simple and fixed threshold values to them. Hence we secondly represent them to be a five-dimensional vector and learn them by using SVM or NN. Finally, we can successfully classify all the nets in an unknown netlist into Trojan ones and normal ones based on the learned classifiers. We have applied our machine-learning-based hardware-Trojan classification method to Trust-HUB benchmarks. The results demonstrate that our method increases the true positive rate compared to the existing state-of-the-art results in most of the cases. In some cases, our method can achieve the true positive rate of 100%, which shows that all the Trojan nets in an unknown netlist are completely detected by our method.

Based on the reconstruction of the augmented interference-plus-noise (IPN) covariance matrix (CM) and the estimation of the desired signal's extended steering vector (SV), we propose a novel robust widely linear (WL) beamforming algorithm. Firstly, an extension of the iterative adaptive approach (IAA) algorithm is employed to acquire the spatial spectrum. Secondly, the IAA spatial spectrum is adopted to reconstruct the augmented signal-plus-noise (SPN) CM and the augmented IPNCM. Thirdly, the extended SV of the desired signal is estimated by using the iterative robust Capon beamformer with adaptive uncertainty level (AU-IRCB). Compared with several representative robust WL beamforming algorithms, simulation results are provided to confirm that the proposed method can achieve a better performance and has a much lower complexity.

Image sensor communication (ISC), a type of visible light communication, is an emerging wireless communication technology that uses LEDs to transmit a signal and uses an image sensor in a camera to receive the signal. This paper discusses the present status of and future trends in ISC by describing the essential characteristics and features of ISC. Moreover, we overview the products and expected future applications of ISC.

Appearance-based generic object recognition is a challenging problem because all possible appearances of objects cannot be registered, especially as new objects are produced every day. Function of objects, however, has a comparatively small number of prototypes. Therefore, function-based classification of new objects could be a valuable tool for generic object recognition. Object functions are closely related to hand-object interactions during handling of a functional object; i.e., how the hand approaches the object, which parts of the object and contact the hand, and the shape of the hand during interaction. Hand-object interactions are helpful for modeling object functions. However, it is difficult to assign discrete labels to interactions because an object shape and grasping hand-postures intrinsically have continuous variations. To describe these interactions, we propose the interaction descriptor space which is acquired from unlabeled appearances of human hand-object interactions. By using interaction descriptors, we can numerically describe the relation between an object's appearance and its possible interaction with the hand. The model infers the quantitative state of the interaction from the object image alone. It also identifies the parts of objects designed for hand interactions such as grips and handles. We demonstrate that the proposed method can unsupervisedly generate interaction descriptors that make clusters corresponding to interaction types. And also we demonstrate that the model can infer possible hand-object interactions.

Transfer boosting, a branch of instance-based transfer learning, is a commonly adopted transfer learning method. However, currently popular transfer boosting methods focus on binary classification problems even though there are many multi-classification tasks in practice. In this paper, we developed a new algorithm called MultiTransferBoost on the basis of TransferBoost for multi-classification. MultiTransferBoost firstly separated the multi-classification problem into several orthogonal binary classification problems. During each iteration, MultiTransferBoost boosted weighted instances from different source domains while each instance's weight was assigned and updated by evaluating the difficulty of the instance being correctly classified and the “transferability” of the instance's corresponding source domain to the target. The updating process repeated until it reached the predefined training error or iteration number. The weight update factors, which were analyzed and adjusted to minimize the Hamming loss of the output coding, strengthened the connections among the sub binary problems during each iteration. Experimental results demonstrated that MultiTransferBoost had better classification performance and less computational burden than existing instance-based algorithms using the One-Against-One (OAO) strategy.

This study introduces large-scale field experiments of VoiceTra, which is the world's first speech-to-speech multilingual translation application for smart phones. In the study, approximately 10 million input utterances were collected since the experiments commenced. The usage of collected data was analyzed and discussed. The study has several important contributions. First, it explains system configuration, communication protocol between clients and servers, and details of multilingual automatic speech recognition, multilingual machine translation, and multilingual speech synthesis subsystems. Second, it demonstrates the effects of mid-term system updates using collected data to improve an acoustic model, a language model, and a dictionary. Third, it analyzes system usage.

Using a matrix approach based on a Markov process, we investigate the reliability of a circular connected-(1,2)-or-(2,1)-out-of-(m,n):F lattice system for the i.i.d. case. We develop a modified linear lattice system that is equivalent to this circular system, and propose a methodology that allows the systematic calculation of the reliability. It is based on ideas presented by Fu and Hu [6]. A partial transition probability matrix is used to reduce the computational complexity of the calculations, and closed formulas are derived for special cases.

Load size for a service on the Internet changes remarkably every hour. Thus, it is expected for service system scales to change dynamically according to load size. KVS (key-value store) is a scalable DBMS (database management system) widely used in largescale Internet services. In this paper, we focus on Cassandra, a popular open-source KVS implementation, and discuss methods for improving dynamic scaling performance. First, we evaluate node joining time, which is the time to complete adding a node to a running KVS system, and show that its bottleneck process is disk I/O. Second, we analyze disk accesses in the nodes and indicate that some heavily accessed files cause a large number of disk accesses. Third, we propose two methods for improving elasticity, which means decreasing node adding and removing time, of Cassandra. One method reduces disk accesses significantly by keeping the heavily accessed file in the page cache. The other method optimizes I/O scheduler behavior. Lastly, we evaluate elasticity of our methods. Our experimental results demonstrate that the methods can improve the scaling-up and scaling-down performance of Cassandra.

This paper presents a hybrid push/pull streaming scheme to take advantage of both the interval caching-based push method and the mesh-based pull method. When a new peer joins, a mesh-based pull method is adopted to avoid the overhead to reorganize the structure only if all of its potential preceding peers are likely to leave before the end of its playback. Otherwise, an interval caching-based push method is adopted so that the better performance of the push method can be maintained until it completes the playback. We demonstrate that our proposed scheme outperforms compared with when either the interval caching-based push method or mesh-based pull method is employed alone.

We propose a cache-aware method to accelerate texture-based volume rendering on a graphics processing unit (GPU) that is compatible with the compute unified device architecture. The proposed method extends a previous method such that it can maximize the average rendering performance while rotating the viewing direction around a volume. To realize this, the proposed method performs in-place rotation of volume data, which rearranges the order of voxels to allow consecutive threads (warps) to refer to voxels with the minimum access strides. Experiments indicate that the proposed method replaces the worst texture cache (TC) hit rate of 42% with the best TC hit rate of 93% for a 10243-voxel volume. Thus, the average frame rate increases by a factor of 1.6 in the proposed method compared with that in the previous method. Although the overhead of in-place rotation slightly decreases the frame rate from 2.0 frames per second (fps) to 1.9 fps, this slowdown occurs only with a few viewing directions.

In this paper, we propose to use a strategy for the two-user Gaussian X channel with limited receiver cooperation in the general case consisting of two parts: 1) the transmission scheme where the superposition coding is used and 2) the cooperative protocol where the two-round strategy based on quantize-map-and-forward (QMF) is employed. We image that a Gaussian X channel can be considered as a superposition of two Gaussian interference channels based on grouping of the sent messages from each transmitter to the corresponding receivers. Finally, we give an achievable rate region for the general case of this channel.

An 8-issue superscalar core generally requires a 24-port RAM for the register file. The area and energy consumption of a multiported RAM increase in proportional to the square of the number of ports. A register cache can reduce the area and energy consumption of the register file. However, earlier register cache systems suffer from lower IPC caused by register cache misses. Thus, we proposed the Non-Latency-Oriented Register Cache System (NORCS) to solve the IPC problem with a modified pipeline. We evaluated NORCS mainly from the viewpoint of microarchitecture in the original article, and showed that NORCS maintains almost the same IPC as conventional register files. Researchers in NVIDIA adopted the same idea for their GPUs. However, the evaluation was not sufficient from the viewpoint of LSI design. In the original article, we used CACTI to evaluate the area and energy consumption. CACTI is a design space exploration tool for cache design, and adopts some rough approximations. Therefore, this paper shows design of NORCS with FreePDK45, an open source process design kit for 45nm technology. We performed manual layout of the memory cells and arrays of NORCS, and executed SPICE simulation with RC parasitics extracted from the layout. The results show that, from a full-port register file, an 8-entry NORCS achieves a 75.2% and 48.2% reduction in area and energy consumption, respectively. The results also include the latency which we did not present in our original article. The latencies of critical path is 307ps and 318ps for an 8-entry NORCS and a conventional multiported register file, respectively, when the same two cycles are allocated to register file read.

Workflow nets (WF-nets for short) are a standard way to automate business processes. Well-Structured WF-nets (WS WF-nets for short) are an important subclass of WF-nets because they have a well-balanced capability to expression power and analysis power. In this paper, we revealed structural and behavioral properties of WS WF-nets. Our results on structural properties are: (i) There is no EFC but non-FC WF-net in WS WF-nets; (ii) A WS WF-net is sound iff it is a van Hee et al.'s ST-net. Our results on behavioral properties are: (i) Any WS WF-net is safe; (ii) Any WS WF-net is separable; (iii) A necessary and sufficient condition on reachability of sound WS WF-net (N,[pIk]). Finally we illustrated the usefulness of the proposed properties with an application example of analyzing workflow evolution.

With the rapid development of information and Web technologies, people are facing ‘information overload’ in their daily lives. The personalized recommendation system (PRS) is an effective tool to assist users extract meaningful information from the big data. Collaborative filtering (CF) is one of the most widely used personalized recommendation techniques to recommend the personalized products for users. However, the conventional CF technique has some limitations, such as the low accuracy of of similarity calculation, cold start problem, etc. In this paper, a PRS model based on the Support Vector Machine (SVM) is proposed. The proposed model not only considers the items' content information, but also the users' demographic and behavior information to fully capture the users' interests and preferences. An improved Particle Swarm Optimization (PSO) algorithm is also proposed to improve the performance of the model. The efficiency of the proposed method is verified by multiple benchmark datasets.

Research on intrusion-tolerant systems (ITSs) is being conducted to protect critical systems which provide useful information services. To provide services reliably, these critical systems must not have even a single point of failure (SPOF). Therefore, most ITSs employ redundant components to eliminate the SPOF problem and improve system reliability. However, systems that include identical components have common vulnerabilities that can be exploited to attack the servers. Attackers prefer to exploit these common vulnerabilities rather than general vulnerabilities because the former might provide an opportunity to compromise several servers. In this study, we analyze software vulnerability data from the National Vulnerability Database (NVD). Based on the analysis results, we present a scheme that finds software combinations that minimize the risk of common vulnerabilities. We implement this scheme with CSIM20, and simulation results prove that the proposed scheme is appropriate for a recovery-based intrusion tolerant architecture.

Speaker verification is the task of determining whether two utterances represent the same person. After representing the utterances in the i-vector space, the crucial problem is only how to compute the similarity of two i-vectors. Metric learning has provided a viable solution to this problem. Until now, many metric learning algorithms have been proposed, but they are usually limited to learning a linear transformation. In this paper, we propose a nonlinear metric learning method, which learns an explicit mapping from the original space to an optimal subspace using deep Restricted Boltzmann Machine network. The proposed method is evaluated on the NIST SRE 2008 dataset. Since the proposed method has a deep learning architecture, the evaluation results show superior performance than some state-of-the-art methods.

Malware proliferation has become a serious threat to the Internet in recent years. Most current malware are subspecies of existing malware that have been automatically generated by illegal tools. To conduct an efficient analysis of malware, estimating their functions in advance is effective when we give priority to analyze malware. However, estimating the malware functions has been difficult due to the increasing sophistication of malware. Actually, the previous researches do not estimate the functions of malware sufficiently. In this paper, we propose a new method which estimates the functions of unknown malware from APIs or categories observed by dynamic analysis on a host. We examine whether the proposed method can correctly estimate the malware functions by the supervised machine learning techniques. The results show that our new method can estimate the malware functions with the average accuracy of 83.4% using API information.

Video data mining based on topic models as an emerging technique recently has become a very popular research topic. In this paper, we present a novel topic model named sequential correspondence hierarchical Dirichlet processes (Seq-cHDP) to learn the hidden structure within video data. The Seq-cHDP model can be deemed as an extended hierarchical Dirichlet processes (HDP) model containing two important features: one is the time-dependency mechanism that connects neighboring video frames on the basis of a time dependent Markovian assumption, and the other is the correspondence mechanism that provides a solution for dealing with the multimodal data such as the mixture of visual words and speech words extracted from video files. A cascaded Gibbs sampling method is applied for implementing the inference task of Seq-cHDP. We present a comprehensive evaluation for Seq-cHDP through experimentation and finally demonstrate that Seq-cHDP outperforms other baseline models.

Wireless caching is one of the promising technologies to mitigate the traffic burden of cellular networks and the large cost of deploying a higher volume of wired backhaul by introducing caching storage. In the manner of “cutting” wired equipments, all types of vehicles can be readily leveraged as serving access points with caching storage, where their moving nature should be taken into account to improve latency and data throughput. In this paper, we consider a mobility-aware vehicular caching which has a role in offloading delay-tolerable contents from cellular networks. We first clarify the influence of mobility in cellular caching networks, then set the mobility-aware optimization problem of vehicular caching to carry on delay-tolerable contents. Trace-driven numerical results based on rural and urban topographies show that, in presence of individual demand for delay-tolerable contents, the proposed vehicular caching scheme enhances the quality-of-service (QoS) (maximally twofold) relying on the contents delivery being centrally or distributedly controlled.