This paper proposes a natural facial and head behavior recognition method using hybrid dynamical systems. Most existing facial and head behavior recognition methods focus on analyzing deliberately displayed prototypical emotion patterns rather than complex and spontaneous facial and head behaviors in natural conversation environments. We first capture spatio-temporal features on important facial parts via dense feature extraction. Next, we cluster the spatio-temporal features using hybrid dynamical systems, and construct a dictionary of motion primitives to cover all possible elemental motion dynamics accounting for facial and head behaviors. With this dictionary, the facial and head behavior can be interpreted into a distribution of motion primitives. This interpretation is robust against different rhythms of dynamic patterns in complex and spontaneous facial and head behaviors. We evaluate the proposed approach under natural tele-communication scenarios, and achieve promising results. Furthermore, the proposed method also performs favorably against the state-of-the-art methods on three benchmark databases.

Fault localization is essential for solving the issue of software faults. Aiming at improving fault localization, this paper proposes a deep learning-based fault localization with contextual information. Specifically, our approach uses deep neural network to construct a suspiciousness evaluation model to evaluate the suspiciousness of a statement being faulty, and then leverages dynamic backward slicing to extract contextual information. The empirical results show that our approach significantly outperforms the state-of-the-art technique Dstar.

In this work, two new proposed NMF models are developed for facial expression recognition. They are called maximum volume constrained nonnegative matrix factorization (MV_NMF) and maximum volume constrained graph nonnegative matrix factorization (MV_GNMF). They achieve sparseness from a larger simplicial cone constraint and the extracted features preserve the topological structure of the original images.

This paper aims to improve the ability of low voltage ride through (LVRT) of doubly-fed induction generation (DFIG) under the asymmetric grid fault. The traditional rotor of the Crowbar device requires a large reactive support during the period of protection, which causes large fluctuations to the reactive power of the output grid while cut in and off for Crowbar. This case would influence the quality and efficiency of entire power system. In order to solve the fluctuation of reactive power and the stability of the wind power system, this paper proposes the coordinated control of the fuzzy-neural D-STATCOM and the rotor of the Crowbar. The simulation results show that the system has the performance of the rotor current with faster decay and faster dynamic response, high steady-state characteristic during the grid fault, which improve the ability of LVRT of DFIG.

High power dissipation can occur by high launch-induced switching activity when the response to a test pattern is captured by flip-flops (FFs) in at-speed scan testing, resulting in excessive IR drop. IR drop may cause significant capture-induced yield loss in the deep submicron era. It is known that test modification methods using X-identification and X-filling are effective to reduce power dissipation in the capture cycle. Conventional low power dissipation oriented X-filling methods consecutively select FFs and assign values to decrease the number of transitions on the FFs. In this paper, we propose a novel low power dissipation oriented X-filling method using SAT Solvers that conducts simultaneous X-filling for some FFs. We also proposed a selection order of FFs based on a correlation coefficient between transitions of FFs and power dissipation. Experimental results show that the proposed method was effective for ISCAS'89 and ITC'99 benchmark circuits compared with justification-probability-based fill.

Nonnegative Matrix Factorization (NMF) with sparseness and smoothness constraints has attracted increasing attention. When these properties are considered, NMF is usually formulated as an optimization problem in which a linear combination of an approximation error term and some regularization terms must be minimized under the constraint that the factor matrices are nonnegative. In this paper, we focus our attention on the error measure based on the Euclidean distance and propose a new iterative method for solving those optimization problems. The proposed method is based on the Hierarchical Alternating Least Squares (HALS) algorithm developed by Cichocki et al. We first present an example to show that the original HALS algorithm can increase the objective value. We then propose a new algorithm called the Gauss-Seidel HALS algorithm that decreases the objective value monotonically. We also prove that it has the global convergence property in the sense of Zangwill. We finally verify the effectiveness of the proposed algorithm through numerical experiments using synthetic and real data.

The purpose of this study is to investigate the effects of device size on non-visual icon search using a touch interface with voice output. We conducted an experiment in which twelve participants searched for the target icons with four different-sized touchscreen devices. We analyzed the search time, search strategies and subjective evaluations. As a result, mobile devices with a screen size of 4.7 inches had the shortest search time and obtained the highest subjective evaluation among the four devices.

This work presents a new approach which derives a learned data representation method through matrix factorization on the complex domain. In particular, we introduce an encoding matrix-a new representation of data-that satisfies the simplicial constraint of the projective basis matrix on the field of complex numbers. A complex optimization framework is provided. It employs the gradient descent method and computes the derivative of the cost function based on Wirtinger's calculus.

This paper presents a strategy, Weighted Voting of Discriminative Regions (WVDR), to improve the face recognition performance, especially in Small Sample Size (SSS) and occlusion situations. In WVDR, we extract the discriminative regions according to facial key points and abandon the rest parts. Considering different regions of face make different contributions to recognition, we assign weights to regions for weighted voting. We construct a decision dictionary according to the recognition results of selected regions in the training phase, and this dictionary is used in a self-defined loss function to obtain weights. The final identity of test sample is the weighted voting of selected regions. In this paper, we combine the WVDR strategy with CRC and SRC separately, and extensive experiments show that our method outperforms the baseline and some representative algorithms.

Coaxial connectors are extensively used in electrical systems and the degradation of the connector can alter the signal that is being transmitted and leads to faults, which is one of the major causes of low communication quality. In this work, the failure features caused by the degraded connector contact surface were studied. The relationship between the DC resistance and decreased real contact areas was given. Considering the inductance properties and capacitive coupling at high frequencies, the impedance characteristics of the degraded connector were discussed. Based on the transmission line theory and experimental measurement, an equivalent lump circuit of the coaxial connector was developed. For the degraded contact surface, the capacitance was analyzed, and the frequency effect was investigated. According to the high frequency characteristics of the degraded connector, a fault detection and location method for coaxial connectors in RF system was developed using a neural network method. For connectors suffering from different levels of pollution, their impedance modulus varies continuously. Considering the range of the connector's impedance parameters, the fault modes were determined. Based on the scattering parameter simulation of a RF receiver front-end circuit, the S11 and S21 parameters were obtained as feature parameters and Monte Carlo simulations were conducted to generate training and testing samples. Based on the BP neural network algorithm, the fault modes were classified and the results show the diagnosis accuracy was 97.33%.

In this letter, we investigate the price-based power allocation with rate proportional fairness constraint in downlink non-orthogonal multiple access (NOMA) systems. The Stackelberg game is utilized to model the interaction between the base station (BS) and users. The revenue maximization problem of the BS is first converted to rate allocation problem, then the optimal rate allocation for each user is obtained by variable substitution. Finally, a price-based power allocation with rate proportional fairness (PAPF) algorithm is proposed based on the relationship between rate and transmit power. Simulation results show that the proposed PAPF algorithm is superior to the previous price-based power allocation algorithm in terms of fairness index and minimum normalized user (MNU) rate.

We have proposed a fish farm monitoring system for the efficient farming of tuna. In our system, energy efficient and adaptive control of sensor node is highly important. In addition, since a sensor node is attached to the fish, the transmission range of sensor node is not omni-directional. In this paper, we propose a data gathering mechanism for fish farm monitoring by extending a traditional desyncronization mechanism. In our proposed mechanism, by utilizing acknowledgment packets from the sink node, distributed and adaptive timing control of packet transmission is accomplished. In addition, we apply a reassignment mechanism and a sleep mechanism for improving the performance of our proposed mechanism. Through simulation experiments, we show that the performance of our proposed mechanism is higher than that of comparative mechanisms.

Community detection is an important task in the social network analysis field. Many detection methods have been developed; however, they provide little semantic interpretation for the discovered communities. We develop a framework based on joint matrix factorization to integrate network topology and node content information, such that the communities and their semantic labels are derived simultaneously. Moreover, to improve the detection accuracy, we attempt to make the community relationships derived from two types of information consistent. Experimental results on real-world networks show the superior performance of the proposed method and demonstrate its ability to semantically annotate communities.

With the successful development and rapid advancement of social networking technology, people tend to exchange and share information via online social networks, such as Facebook and LINE.Massive amounts of information are aggregated promptly and circulated quickly among people. However, with the enormous volume of human-interactions, various types of swindles via online social networks have been launched in recent years. Effectively detecting fraudulent activities on social networks has taken on increased importance, and is a topic of ongoing interest. In this paper, we develop a fraud analysis and detection system based on real-time messaging communications, which constitute one of the most common human-interacted services of online social networks. An integrated platform consisting of various text-mining techniques, such as natural language processing, matrix processing and content analysis via a latent semantic model, is proposed. In the system implementation, we first collect a series of fraud events, all of which happened in Taiwan, to construct analysis modules for detecting such fraud events. An Android-based application is then built for alert notification when dubious logs and fraud events happen.

A single-dimensional interface which enables users to obtain diverse localizations of audio sources is proposed. In many conventional interfaces for arranging audio sources, there are multiple arrangement parameters, some of which allow users to control positions of audio sources. However, it is difficult for users who are unfamiliar with these systems to optimize the arrangement parameters since the number of possible settings is huge. We propose a simple, single-dimensional interface for adjusting arrangement parameters, allowing users to sample several diverse audio source arrangements and easily find their preferred auditory localizations. To select subsets of arrangement parameters from all of the possible choices, auditory-localization space vectors (ASVs) are defined to represent the auditory localization of each arrangement parameter. By selecting subsets of ASVs which are approximately orthogonal, we can choose arrangement parameters which will produce diverse auditory localizations. Experimental evaluations were conducted using music composed of three audio sources. Subjective evaluations confirmed that novice users can obtain diverse localizations using the proposed interface.

Bloom Filter is a bit array (a one-dimensional storage structure) that provides a compact representation for a set of data, which can be used to answer the membership query in an efficient manner with a small number of false positives. It has a lot of applications in many areas. In this paper, we extend the design of Bloom Filter by using a multi-dimensional matrix to replace the one-dimensional structure with three different implementations, namely OFFF, WOFF, FFF. We refer the extended Bloom Filter as Feng Filter. We show the false positive rates of our method. We compare the false positive rate of OFFF with that of the traditional one-dimensional Bloom Filter and show that under certain condition, OFFF has a lower false positive rate. Traditional Bloom Filter can be regarded as a special case of our Feng Filter.

Wireless Ad hoc networks have been rapidly developed in recent years since they promise a wide range of applications. However, their structures, which are based on the IEEE 802.11 standard, cause a severe unfairness problem in bandwidth sharing among different users. This is an extreme drawback because in wireless ad hoc networks, all users need to be treated fairly regardless of their geographical positions. In this paper, we propose a method to improve the fairness among flows by sensing channel access of other nodes based on the information obtained at the link layer and then, controlling the packet sending rate from the link layer to the MAC layer and the dequeue rate from the queue. Simulation results show that the proposed method achieves a better fairness with a good total throughput compared to conventional methods.

Network equipment, such as routers, switches, and RADIUS servers, generate various log messages induced by network events such as hardware failures and protocol flaps. In large production networks, analyzing the log messages is crucial for diagnosing network anomalies; however, it has become challenging due to the following two reasons. First, the log messages are composed of unstructured text messages generated in accordance with vendor-specific rules. Second, network events that induce the log messages span several geographical locations, network layers, protocols, and services. We developed a method to tackle these obstacles consisting of two techniques: statistical template extraction (STE) and log tensor factorization (LTF). The former leverages a statistical clustering technique to automatically extract primary templates from unstructured log messages. The latter builds a statistical model that collects spatial-temporal patterns of log messages. Such spatial-temporal patterns provide useful insights into understanding the impact and patterns of hidden network events. We evaluate our techniques using a massive amount of network log messages collected from a large operating network and confirm that our model fits the data well. We also investigate several case studies that validate the usefulness of our method.

People's body movements in daily face-to-face communication influence each other. For instance, during a heated debate, the participants use more gestures and other body movements, while in a calm discussion they use fewer gestures. This “coevolution” of interpersonal body movements occurs on multiple time scales, like minutes or hours. However, the multi-time-scale coevolution in daily communication is not clear yet. In this paper, we explore the minute-to-minute coevolution of interpersonal body movements in daily communication and investigate the characteristics of this coevolution. We present quantitative data on upper-body movements from thousand test subjects from seven organizations gathered over several months via wearable sensors. The device we employed measured upper-body movements with an accelerometer and the duration of face-to-face communication with an infrared ray sensor on a minute-by-minute basis. We defined a coevolution measure between two people as the number of per-minute changes of their body movement and compared the indices for face-to-face and non-face-to-face situations. We found that on average, the amount of people's body movements changed correspondingly for face-to-face communication and that the average rate of coevolution in the case of face-to-face communication was 3-4% higher than in the case of non-face-to-face situation. These results reveal minute-to-minute coevolution of upper-body movements between people in daily communication. The finding suggests that the coevolution of body movement arises in multiple time scales.

This paper aims to propose a Fallback Control System isolated from cyber-attacks against networked control systems. The fallback control system implements maintaining functionality after an incident. Since cyber-attacks tamper with the communication contents of the networked control systems, the fallback control system is installed in a control target side. The fallback control system detects the incident without the communication contents on field network. This system detects an incident based on a bilinear observer and a switched Lyapunov function. When an incident is detected, normal operation is switched to fallback operation automatically. In this paper, a practical experiment with Ball-Sorter simulating a simple defective discriminator as a part of Factory Automation systems is shown. Assumed cyber-attacks against Ball-Sorter are Man In The Middle attack and Denial of Service attack.