The recent cyber-attacks utilize various malware as a means of attacks for the attacker's malicious purposes. They are aimed to steal confidential information or seize control over major facilities after infiltrating the network of a target organization. Attackers generally create new malware or many different types of malware by using an automatic malware creation tool which enables remote control over a target system easily and disturbs trace-back of these attacks. The paper proposes a generation method of malware behavior patterns as well as the detection techniques in order to detect the known and even unknown malware efficiently. The behavior patterns of malware are generated with Multiple Sequence Alignment (MSA) of API call sequences of malware. Consequently, we defined these behavior patterns as a “feature-chain” of malware for the analytical purpose. The initial generation of the feature-chain consists of extracting API call sequences with API hooking library, classifying malware samples by the similar behavior, and making the representative sequences from the MSA results. The detection mechanism of numerous malware is performed by measuring similarity between API call sequence of a target process (suspicious executables) and feature-chain of malware. By comparing with other existing methods, we proved the effectiveness of our proposed method based on Longest Common Subsequence (LCS) algorithm. Also we evaluated that our method outperforms other antivirus systems with 2.55 times in detection rate and 1.33 times in accuracy rate for malware detection.

A low-cost prototypic Kinect-based rehabilitation system was developed for recovering balance capability of stroke patients. A total of 16 stroke patients were recruited to participate in the study. After excluding 3 patients who failed to finish all of the rehabilitation sessions, only the data of 13 patients were analyzed. The results exhibited a significant effect in recovering balance function of the patients after 3 weeks of balance training. Additionally, the questionnaire survey revealed that the designed system was perceived as effective and easy in operation.

In this paper, we present a new encoding/decoding method for dynamic multidimensional datasets and its implementation scheme. Our method encodes an n-dimensional tuple into a pair of scalar values even if n is sufficiently large. The method also encodes and decodes tuples using only shift and and/or register instructions. One of the most serious problems in multidimensional array based tuple encoding is that the size of an encoded result may often exceed the machine word size for large-scale tuple sets. This problem is efficiently resolved in our scheme. We confirmed the advantages of our scheme by analytical and experimental evaluations. The experimental evaluations were conducted to compare our constructed prototype system with other systems; (1) a system based on a similar encoding scheme called history-offset encoding, and (2) PostgreSQL RDBMS. In most cases, both the storage and retrieval costs of our system significantly outperformed those of the other systems.

Recently, a high dimensional classification framework has been proposed to introduce spatial and anatomical priors in classical single kernel support vector machine optimization scheme, wherein the sequential minimal optimization (SMO) training algorithm is adopted, for brain image analysis. However, to satisfy the optimization conditions required in the single kernel case, it is unreasonably assumed that the spatial regularization parameter is equal to the anatomical one. In this letter, this approach is improved by combining SMO algorithm with multiple kernel learning to avoid that assumption and optimally estimate two parameters. The improvement is comparably demonstrated by experimental results on classification of Alzheimer patients and elderly controls.

Research on emotion recognition using electroencephalogram (EEG) of subjects listening to music has become more active in the past decade. However, previous works did not consider emotional oscillations within a single musical piece. In this research, we propose a continuous music-emotion recognition approach based on brainwave signals. While considering the subject-dependent and changing-over-time characteristics of emotion, our experiment included self-reporting and continuous emotion annotation in the arousal-valence space. Fractal dimension (FD) and power spectral density (PSD) approaches were adopted to extract informative features from raw EEG signals and then we applied emotion classification algorithms to discriminate binary classes of emotion. According to our experimental results, FD slightly outperformed PSD approach both in arousal and valence classification, and FD was found to have the higher correlation with emotion reports than PSD. In addition, continuous emotion recognition during music listening based on EEG was found to be an effective method for tracking emotional reporting oscillations and provides an opportunity to better understand human emotional processes.

In this paper, we propose an undirected model of learning systems, named max-min-degree neural network, to realize centralized-decentralized collaborative computing. The basic idea of the proposal is a max-min-degree constraint which extends a k-degree constraint to improve the communication cost, where k is a user-defined degree of neurons. The max-min-degree constraint is defined such that the degree of each neuron lies between kmin and kmax. Accordingly, the Boltzmann machine is a special case of the proposal with kmin=kmax=n, where n is the full-connected degree of neurons. Evaluations show that the proposal is much better than a state-of-the-art model of deep learning systems with respect to the communication cost. The cost of the above improvement is slower convergent speed with respect to data size, but it does not matter in the case of big data processing.

Hierarchical multi-tenancy, which enables tenants to be divided into subtenants, is a flexible and scalable architecture for representing subsets of users and application resources in the real world. However, the resource isolation and sharing relations for tenants with hierarchies are more complicated than those between tenants in the flat Multi-Tenancy Architecture. In this paper, a hierarchical tenant-based access control model based on Administrative Role-Based Access Control in Software-as-a-Service is proposed. Autonomous Areas and AA-tree are used to describe the autonomy and hierarchy of tenants, including their isolation and sharing relationships. AA is also used as an autonomous unit to create and deploy the access permissions for tenants. Autonomous decentralized authorization and authentication schemes for hierarchical multi-tenancy are given out to help different level tenants to customize efficient authority and authorization in large-scale SaaS systems.

Recently, the Services Oriented Architectures (SOA) have been recognized as the key to the integration and interoperability of different applications and systems that coexist in an organization. However, even though the use of SOA has increased, some applications are unable to use it. That is the case of mission critical information applications, whose requirements such as high reliability, non-stop operation, high flexibility and high performance are not satisfied by conventional SOA infrastructures. In this article we present a novel approach of combining SOA with Autonomous Decentralized Systems (ADS) in order to provide an infrastructure that can satisfy those requirements. We have named this infrastructure Autonomous Decentralized Service Oriented Architecture (ADSOA). We present the concept and architecture of ADSOA, as well as the Loosely Couple Delivery Transaction and Synchronization Technology for assuring the data consistency and high reliability of the application. Moreover, a real implementation and evaluation of the proposal in a mission critical information system, the Uniqueness Verifying Public Key Infrastructure (UV-PKI), is shown in order to prove its effectiveness.

This paper presents a varactor-based all-digital phase-locked loop (ADPLL) with a multi-phase digitally controlled oscillator (DCO) for near-threshold voltage operation. In addition, a new all-digital reference spur suppression (RSS) circuit with multiple phases random-sampling techniques to effectively spread the reference clock frequency is proposed to randomize the synchronized DCO register behavior and reduce the reference spur. Because the equivalent reference clock frequency is reserved, the loop behavior is maintained. The area of the proposed spur suppression circuit is only 4.9% of the ADPLL (0.038 mm2). To work reliably at the near-threshold region, a multi-phase DCO with NMOS varactors is presented to acquire precise frequency resolution and high linearity. In the near-threshold region (VDD =0.52 V), the ADPLL only dissipates 269.9 μW at 100 MHz output frequency. It has a reference spur of -52.2 dBc at 100 MHz output clock frequency when the spur suppression circuit is deactivated. When the spur suppression circuit is activated, the ADPLL shows a reference spur of -57.3 dBc with the period jitter of 0.217% UI.

Drive-by download attacks force users to automatically download and install malware by redirecting them to malicious URLs that exploit vulnerabilities of the user's web browser. In addition, several evasion techniques, such as code obfuscation and environment-dependent redirection, are used in combination with drive-by download attacks to prevent detection. In environment-dependent redirection, attackers profile the information on the user's environment, such as the name and version of the browser and browser plugins, and launch a drive-by download attack on only certain targets by changing the destination URL. When malicious content detection and collection techniques, such as honeyclients, are used that do not match the specific environment of the attack target, they cannot detect the attack because they are not redirected. Therefore, it is necessary to improve analysis coverage while countering these adversarial evasion techniques. We propose a method for exhaustively analyzing JavaScript code relevant to redirections and extracting the destination URLs in the code. Our method facilitates the detection of attacks by extracting a large number of URLs while controlling the analysis overhead by excluding code not relevant to redirections. We implemented our method in a browser emulator called MINESPIDER that automatically extracts potential URLs from websites. We validated it by using communication data with malicious websites captured during a three-year period. The experimental results demonstrated that MINESPIDER extracted 30,000 new URLs from malicious websites in a few seconds that conventional methods missed.

In this letter, a novel perceptually motivated single channel speech enhancement approach based on Deep Neural Network (DNN) is presented. Taking into account the good masking properties of the human auditory system, a new DNN architecture is proposed to reduce the perceptual effect of the residual noise. This new DNN architecture is directly trained to learn a gain function which is used to estimate the power spectrum of clean speech and shape the spectrum of the residual noise at the same time. Experimental results demonstrate that the proposed perceptually motivated speech enhancement approach could achieve better objective speech quality when tested with TIMIT sentences corrupted by various types of noise, no matter whether the noise conditions are included in the training set or not.

To improve the performance of capsule endoscope, it is important to add location information to the image data obtained by the capsule endoscope. There is a disadvantage that a lot of existing localization techniques require to measure channel model parameters in advance. To avoid such a troublesome pre-measurement, this paper pays attention to capsule endoscope localization based on an electromagnetic imaging technology which can estimate not only the location but also the internal structure of a human body. However, the electromagnetic imaging with high resolution has huge computational complexity, which should prevent us from carrying out real-time localization. To ensure the accurate real-time localization system without pre-measured model parameters, we apply genetic algorithm (GA) into the electromagnetic imaging-based localization method. Furthermore, we evaluate the proposed GA-based method in terms of the simulation time and the location estimation accuracy compared to the conventional methods. In addition, we show that the proposed GA-based method can perform more accurately than the other conventional methods, and also, much less computational complexity of the proposed method can be accomplished than a greedy algorithm-based method.

Many selected mapping (SLM) schemes have been proposed to reduce the peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signal sequences. In this paper, an efficient selection (ES) method of the OFDM signal sequence with minimum PAPR among many alternative OFDM signal sequences is proposed; it supports various SLM schemes. Utilizing the fact that OFDM signal components can be sequentially generated in many SLM schemes, the generation and PAPR observation of the OFDM signal sequence are processed concurrently. While the u-th alternative OFDM signal components are being generated, by applying the proposed ES method, the generation of that alternative OFDM signal components can be interrupted (or stopped) according to the selection criteria of the best OFDM signal sequence in the considered SLM scheme. Such interruption substantially reduces the average computational complexity of SLM schemes without degradation of PAPR reduction performance, which is confirmed by analytical and numerical results. Note that the proposed method is not an isolated SLM scheme but a subsidiary method which can be easily adopted in many SLM schemes in order to further reduce the computational complexity of considered SLM schemes.

Fan-in/fan-out devices are necessary for the construction of multi-core fiber communication systems. A fan-out device using a capillary is proposed and made by connecting a tapered fiber bundle and a multi-core fiber. The tapered fiber bundle is elongated so that the core arrangement and the mode field diameter (MFD) of single-core fibers agree with those of the multi-core fiber. Suppressing the MFD change is necessary to reduce the coupling loss of the fan-out device. While elongating the fiber bundle, the MFD decreases at the beginning until the core reaches a certain core diameter, and then it begins to increase. We suppress the MFD change of the fan-out device by using this phenomenon. The average insertion loss at both ends of a multi-core fiber was approximately 1.6dB when the fabricated fan-in/fan-out devices were connected to the multi-core fiber.

The growth of the size of the routing tables limits the scalability of the conventional IP routing. As scalable routing schemes for large-scale networks are highly demanded, this paper proposes and evaluates an efficient geometric routing scheme and related low-cost node design applicable to large-scale networks. The approach guarantees that greedy forwarding on derived coordinates will result in successful packet delivery to every destination in the network by relying on coordinates deduced from a spanning tree of the network. The efficiency of the proposed scheme is measured in terms of routing quality (stretch) and size of the coordinates. The cost of the proposed router is quantified in terms of area complexity of the hardware design and all the evaluations involve comparison with a state-of-the-art approach with virtual coordinates in the hyperbolic plane. Extensive simulations assess the proposal in large topologies consisting of up to 100K nodes. Experiments show that the scheme has stretch properties comparable to geometric routing in the hyperbolic plane, while enabling a more efficient hardware design, and scaling considerably better in terms of storage requirements for coordinate representation. These attractive properties make the scheme promising for routing in large networks.

Wheeze is a general sign for obstructive airway diseases whose clinical diagnosis mainly depends on auscultating or X-ray imaging with subjectivity or harm. Therefore, this paper introduces an automatic, noninvasive method to detect wheeze which consists of STFT decomposition, preprocessing of the spectrogram, correlation-coefficients calculating and duration determining. In particular, duration determining takes the Haas effect into account, which facilitates us to achieve a better determination. Simulation result shows that the sensibility (SE), the specificity (SP) and the accuracy (AC) are 88.57%, 97.78% and 93.75%, respectively, which indicates that this method could be an efficient way to detect wheeze.

In this paper, a novel approach of a human motion classification system in wireless body area network (WBAN) using received radio signal strength was developed. This method enables us to classify human motions in WBAN using only the radio signal strength during communication without additional tools such as an accelerometer. The proposed human motion classification system has a potential to be used for improving communication quality in WBAN as well as recording daily-life activities for self-awareness tool. To construct the classification system, a numerical simulation was used to generate WBAN propagation channel in various motions at frequency band of 403.5MHz and 2.45GHz. In the classification system, a feature vector representing a characteristic of human motions was computed from time-series received signal levels. The proposed human motion classification using the radio signal strength based on WBAN simulation can classify 3-5 human motions with the accuracy rate of 63.8-95.7 percent, and it can classify the human motions regardless of frequency band. In order to confirm that the human motion classification using radio signal strength can be used in practice, the applicability of the classification system was evaluated by WBAN measurement data.

In this study, we propose a method for measuring a photoplethysmograph using a complementary metal-oxide-semiconductor image sensor (CMOS) or smartphone camera for the adaptation of a mobile health (m-health) services. The proposed algorithm consists of six procedures. Before measuring the photoplethysmograph, the human fingertip must make contact with the smartphone camera lens and turn on the camera light. The first procedure converts the red-green-blue (RGB) to a gray image from a camera image, Then, region of interest (ROI) must be detected from the obtained image. The third procedure calculates the baseline level to reduce direct current (DC) offset effect, before extracting the photoplethysmograph from the camera image. The baseline is filtered, and the last step oversamples the resulting baseline filtered data using cubic spline interpolation. The proposed algorithm has been tested on six people using CMOS image sensors of several smartphones, which can effectively acquire a PPG signal in any situation. We believe that the proposed algorithm could easily be adapted into any m-health system that used a CMOS image sensor.

Ultra-wideband (UWB) communications is used for medical information communication technology (MICT) as a dependable and safe communication technology in recent years. On the other hand, there are existing various UWB systems that are not used for MICT. Generally, these UWB systems use almost the same frequency band. Therefore, they interfere to each other in general transmission channel environment. In our previous work, a novel UWB pulse shape modulation using modified Hermite pulse is proposed as a multiple user access scheme. In this paper, we propose a mitigation method for inter-user interference and inter-system interference using combination of orthogonal pulse shape modulation and orthogonal matched filter (OMF) detector. The purposes of our system are to detect all signals of users in the same UWB system and to reduce the unknown interference from other UWB systems at the same time. This paper provides performance evaluation results based on both of analytical and numerical evaluation. Simulation results show that the proposed system can detect the signals that were transmitted from the same UWB system using orthogonal pulse set, while the proposed system can reduce the interference from unknown UWB systems at the same time. The theoretical analysis is expected that noise tolerance of our proposal will be deteriorated in the additive Gaussian noise channel in comparison with the conventional matched filter. It is confirmed that the numerical evaluation illustrates such noise tolerance equivalent to the theoretical analysis result.

Web Service Business Process Execution Language (BPEL) has become the de facto standard for developing instant service-oriented workflow applications in open environment. The correctness and reliability of BPEL processes have gained increasing concerns. However, the unique features (e.g., dead path elimination (DPE) semantics, parallelism, etc.) of BPEL language have raised enormous problems to it, especially in path feasibility analysis of BPEL processes. Path feasibility analysis of BPEL processes is the basis of BPEL testing, for it relates to the test case generation. Since BPEL processes support both parallelism and DPE semantics, existing techniques can't be directly applied to its path feasibility analysis. To address this problem, we present a novel technique to analyze the path feasibility for BPEL processes. First, to tackle unique features mentioned above, we transform a BPEL process into an intermediary model — BPEL control flow graph, which is proposed to abstract the execution flow of BPEL processes. Second, based on this abstraction, we symbolically encode every path of BPEL processes as some Satisfiability formulas. Finally, we solve these formulas with the help of Satisfiability Modulo Theory (SMT) solvers and the feasible paths of BPEL processes are obtained. We illustrate the applicability and feasibility of our technique through a case study.