In current large-scale IT systems, troubleshooting has become more complicated due to the diversification in the causes of failures, which has increased operational costs. Thus, clarifying the troubleshooting process also becomes important, though it is also time-consuming. We propose a method of automatically extracting a workflow, a graph indicating a troubleshooting process, using multiple trouble tickets. Our method extracts an operator's actions from free-format texts and aligns relative sentences between multiple trouble tickets. Our method uses a stochastic model to detect a resolution, a frequent action pattern that helps us understand how to solve a problem. We validated our method using real trouble-ticket data captured from a real network operation and showed that it can extract a workflow to identify the cause of a failure.

With the recent explosion of geographic data generated by smartphones, sensors, and satellites, a data storage that can handle the massive volume of data and support high-computational spatial queries is becoming essential. Although key-value stores efficiently handle large-scale data, they are not equipped with effective functions for supporting geographic data. To solve this problem, in this paper, we present G-HBase, a high-performance geographical database based on HBase, a standard key-value store. To index geographic data, we first use Geohash as the rowkey in HBase. Then, we present a novel partitioning method, namely binary Geohash rectangle partitioning, to support spatial queries. Our extensive experiments on real datasets have demonstrated an improved performance with k nearest neighbors and range query in G-HBase when compared with SpatialHadoop, a state-of-the-art framework with native support for spatial data. We also observed that performance of spatial join in G-HBase is on par with SpatialHadoop and outperforms SJMR algorithm in HBase.

This paper addresses the issue of modeling the discourse nature of lyrics and presented the first study aiming at capturing the two common discourse-related notions: storylines and themes. We assume that a storyline is a chain of transitions over topics of segments and a song has at least one entire theme. We then hypothesize that transitions over topics of lyric segments can be captured by a probabilistic topic model which incorporates a distribution over transitions of latent topics and that such a distribution of topic transitions is affected by the theme of lyrics. Aiming to test those hypotheses, this study conducts experiments on the word prediction and segment order prediction tasks exploiting a large-scale corpus of popular music lyrics for both English and Japanese (around 100 thousand songs). The findings we gained from these experiments can be summarized into two respects. First, the models with topic transitions significantly outperformed the model without topic transitions in word prediction. This result indicates that typical storylines included in our lyrics datasets were effectively captured as a probabilistic distribution of transitions over latent topics of segments. Second, the model incorporating a latent theme variable on top of topic transitions outperformed the models without such variables in both word prediction and segment order prediction. From this result, we can conclude that considering the notion of theme does contribute to the modeling of storylines of lyrics.

We present an 82.5GS/s over-sampling based burst-mode clock and data recovery (BM-CDR) IC chip-set comprising an 82.5GS/s over-sampling IC using 8×10.3GHz multi-phase clocks and a dual-rate data selector logic IC to realize the 10.3Gb/s and 1.25Gb/s dual-rate burst-mode fast-lock operation required for 10-Gigabit based fiber-to-the-x (FTTx) services supported by 10-Gigabit Ethernet passive optical network (10G-EPON) systems. As the key issue for designing the proposed 82.5GS/s BM-CDR, a fresh study of the optimum number of multi-phase clocks, which is equivalent to the sampling resolution, is undertaken, and details of the 10.3Gb/s cum 1.25/Gb/s dual-rate optimum phase data selection logic based on a blind phase decision algorithm, which can realize a full single-platform dual-rate BM-CDR, ate also presented. By using the power of the proposed 82.5GS/s over-sampling BM-CDR in cooperation with our dual-rate burst-mode optical receiver, we further demonstrated that a short dual-rate and burst-mode preamble of 256ns supporting receiver settling and CDR recovery times was successfully achieved, while obtaining high receiver sensitivities of -31.6dBm at 10.3Gb/s and -34.6dBm at 1.25Gb/s and a high pulse-width distortion tolerance of +/-0.53UI, which are superior to the 10G-EPON standard.

In this paper, an optimal method is proposed to design sparse-coefficient notch filters with principal basic vectors in the column space of a matrix constituted with frequency samples. The proposed scheme can perform in two stages. At the first stage, the principal vectors can be determined in the least-squares sense. At the second stage, with some components of the principal vectors, the notch filter design is formulated as a linear optimization problem according to the desired specifications. Optimal results can form sparse coefficients of the notch filter by solving the linear optimization problem. The simulation results show that the proposed scheme can achieve better performance in designing a sparse-coefficient notch filter of small order compared with other methods such as the equiripple method, the orthogonal matching pursuit based scheme and the L1-norm based method.

Knowledge management is important for government agencies in improving service delivery to their customers and data inter-operation within and across organizations. Building organizational knowledge repository for government agency has unique challenges. In this paper, we propose that enterprise ontology can provide support for government agencies in capturing organizational taxonomy, best practices and global data schema. A case study of a large-scale adoption for the Thailand's Excise Department is elaborated. A modular design approach of the enterprise ontology for the excise tax domain is discussed. Two forms of organizational knowledge: global schema and standard practices were captured in form of ontology and rule-based knowledge. The organizational knowledge was deployed to support two KM systems: excise recommender service and linked open data. Finally, we discuss some lessons learned in adopting the framework in the government agency.

Capital expenditure (CAPEX) reduction and efficient wavelength allocation are critical for the future access networks. Elastic lambda aggregation network (EλAN) based on WDM and OFDM technologies is expected to realize efficient wavelength allocation. In this paper, we propose adaptive bandwidth allocation (ABA) algorithm for EλAN under the conditions of crowded networks, in which modulation format, symbol rate and the number of sub-carriers are adaptively decided based on the distance of PON-section, QoS and bandwidth demand of each ONU. Network simulation results show that the proposed algorithm can effectively reduce the total bandwidth and achieve steady high spectrum efficiency and contribute to the further reduction of CAPEX of future optical access networks.

The widespread application of mobile electronic devices has triggered a boom in energy consumption, especially in user equipment (UE). In this paper, we investigate the energy-efficiency (EE) of a UE experiencing the worst channel conditions, which is termed worst-EE. Due to the limited battery of the mobile equipment, worst-EE is a suitable metric for EE fairness optimization in the uplink transmissions of orthogonal frequency division multiple access (OFDMA) networks. More specifically, we determine the optimal power and sub-carrier allocation to maximize the worst-EE with respect to UEs' transmit power, sub-carriers and statistical quality-of-service (QoS). In order to maximize the worst-EE, we formulate a max-min power and sub-carrier allocation problem, which involves nonconvex fractional mixed integer nonlinear programming, i.e., NP-hard to solve. To solve the problem, we first relax the allocation of sub-carriers, formulate the upper bound problem on the original one and prove the quasi-concave property of objective function. With the aid of the Powell-Hestenes-Rockfellar (PHR) approach, we propose a fairness EE sub-carrier and power allocation algorithm. Finally, simulation results demonstrate the advantages of the proposed algorithm.

This paper proposes a transmission control protocol (TCP) for long distance high-speed wireless communications, including free-space optical communications (FSOC). Extreme high frequency of wireless communications enables high-speed bit rate, but frequent signal error, including burst error, can be a quite severe problem for ordinary high-speed TCPs. To achieve 10Gbps or higher data transfer throughput on FSOC, the proposed TCP (designated “TCP-FSO”) has improved and new features including multi-layer congestion control, retransmission control with packet loss point estimation, delay-based ACK congestion control, and ACK retransmission control. We evaluated data transfer throughput of TCP-FSO and the other TCPs, by throughput model analysis and experiment on real implementation. Obtained results show that TCP-FSO achieves far higher data transfer throughput than other high-speed TCPs. For example, it achieved a thousand times higher throughput than the other high-speed TCPs in a real FSOC environment.

We propose a radiation-hardened Flip-Flop (FF) with stacked transistors based on the Adaptive Coupling Flip-Flop (ACFF) with low power consumption in a 65 nm FDSOI process. The slave latch in ACFF is much weaker against soft errors than the master latch. We design several FFs with stacked transistors in the master or slave latches to mitigate soft errors. We investigate radiation hardness of the proposed FFs by α particle and neutron irradiation tests. The proposed FFs have higher radiation hardness than a conventional DFF and ACFF. Neutron irradiation and α particle tests revealed no error in the proposed AC Slave-Stacked FF (AC_SS FF) which has stacked transistors only in the slave latch. We also investigate radiation hardness of the proposed FFs by heavy ion irradiation. The proposed FFs maintain higher radiation hardness up to 40 MeV-cm2/mg than the conventional DFF. Stacked inverters become more sensitive to soft errors by increasing tilt angles. AC_SS FF achieves higher radiation hardness than ACFF with the performance equivalent to that of ACFF.

This paper describes an investigation into the validity of an automatic assessment method of the learner-build concept map by comparing it with two well-known manual methods. We have previously proposed the Kit-Build (KB) concept map framework where a learner builds a concept map by using only a provided set of components, known as the set “kit”. In this framework, instant and automatic assessment of a learner-build concept map has been realized. We call this assessment method the “Kit-Build method” (KB method). The framework and assessment method have already been practically used in classrooms in various schools. As an investigation of the validity of this method, we have conducted an experiment as a case study to compare the assessment results of the method with the assessment results of two other manual assessment methods. In this experiment, 22 university students attended as subjects and four as raters. It was found that the scores of the KB method had a very strong correlation with the scores of the other manual methods. The results of this experiment are one of evidence to show the automatic assessment of the Kit-Build concept map can attain almost the same level of validity as well-known manual assessment methods.

We develop a technique for representing variable names and name binding which is a mechanism of associating a name with an entity in many formal systems including logic, programming languages and mathematics. The idea is to use a general form of graph links (or edges) called hyperlinks to represent variables, graph nodes as constructors of the formal systems, and a graph type called hlground to define substitutions. Our technique is based on simple notions of graph theory in which graph types ensure correct substitutions and keep bound variables distinct. We encode strong reduction of the untyped λ-calculus to introduce our technique. Then we encode a more complex formal system called System F<:, a polymorphic λ-calculus with subtyping that has been one of important theoretical foundations of functional programming languages. The advantage of our technique is that the representation of terms, definition of substitutions, and implementation of formal systems are all straightforward. We formalized the graph type hlground, proved that it ensures correct substitutions in the λ-calculus, and implemented hlground in HyperLMNtal, a modeling language based on hypergraph rewriting. Experiments were conducted to test this technique. By this technique, one can implement formal systems simply by following the steps of their definitions as described in papers.

Mobile cloud computing (MCC) has been proposed as a new approach to enhance mobile device performance via computation offloading. The growth in cloud computing energy consumption is placing pressure on both the environment and cloud operators. In this paper, we focus on energy-efficient resource management in MCC and aim to reduce cloud operators' energy consumption through resource management. We establish a deterministic resource management model by solving a combinatorial optimization problem with constraints. To obtain the resource management strategy in deterministic scenarios, we propose a deterministic strategy algorithm based on the adaptive group genetic algorithm (AGGA). Wireless networks are used to connect to the cloud in MCC, which causes uncertainty in resource management in MCC. Based on the deterministic model, we establish a stochastic model that involves a stochastic optimization problem with chance constraints. To solve this problem, we propose a stochastic strategy algorithm based on Monte Carlo simulation and AGGA. Experiments show that our deterministic strategy algorithm obtains approximate optimal solutions with low algorithmic complexity with respect to the problem size, and our stochastic strategy algorithm saves more energy than other algorithms while satisfying the chance constraints.

In this paper, we propose a new enhancement method for color images. In color image processing, hue preserving is required. The proposed method is performed into HSI color space whose gamut is same as RGB color space. The differential gray-level histogram equalization (DHE) is effective for gray scale images. The proposed method is an extension version of the DHE for color images, and furthermore, the enhancement degree is variable by introducing two parameters. Since our processing method is applied to not only intensity but also saturation, the contrast and the colorfulness of the output image can be varied. It is an important issue how to determine the two parameters. Thus, we give the guideline for how to decide the two parameters. By using the guideline, users can easily obtain their own enhancement images.

The spread of optical access broadband networks using Fiber to the Home (FTTH) has not reached the rural areas of developing countries. The current state of global deployment of ICT indicates that it is difficult to sell network systems as stand-alone products due to prohibitive costs, and the demand is for total services that include construction, maintenance, and operation. Moreover, there is a need to offer proposals that include various solutions utilizing broadband networks, as well as for a business model that takes the sustainability of those solutions into consideration. In this paper, we discuss the issues in constructing broadband networks, introduce case studies of solutions using broadband networks for solving social issues in rural areas of developing countries, and discuss the challenges in the deployment of the solutions.

As the manual creation of domain models and also of linked data is very costly, the extraction of knowledge from structured and unstructured data has been one of the central research areas in the Semantic Web field in the last two decades. Here, we look specifically at the extraction of formalized knowledge from natural language text, which is the most abundant source of human knowledge available. There are many tools on hand for information and knowledge extraction for English natural language, for written Thai language the situation is different. The goal of this work is to assess the state-of-the-art of research on formal knowledge extraction specifically from Thai language text, and then give suggestions and practical research ideas on how to improve the state-of-the-art. To address the goal, first we distinguish nine knowledge extraction for the Semantic Web tasks defined in literature on knowledge extraction from English text, for example taxonomy extraction, relation extraction, or named entity recognition. For each of the nine tasks, we analyze the publications and tools available for Thai text in the form of a comprehensive literature survey. Additionally to our assessment, we measure the self-assessment by the Thai research community with the help of a questionnaire-based survey on each of the tasks. Furthermore, the structure and size of the Thai community is analyzed using complex literature database queries. Combining all the collected information we finally identify research gaps in knowledge extraction from Thai language. An extensive list of practical research ideas is presented, focusing on concrete suggestions for every knowledge extraction task - which can be implemented and evaluated with reasonable effort. Besides the task-specific hints for improvements of the state-of-the-art, we also include general recommendations on how to raise the efficiency of the respective research community.

Many life-log analysis applications, which transfer data from cameras and sensors to a Cloud and analyze them in the Cloud, have been developed as the use of various sensors and Cloud computing technologies has spread. However, difficulties arise because of the limited network bandwidth between such sensors and the Cloud. In addition, sending raw sensor data to a Cloud may introduce privacy issues. Therefore, we propose a pipelined method for distributed deep learning processing between sensors and the Cloud to reduce the amount of data sent to the Cloud and protect the privacy of users. In this study, we measured the processing times and evaluated the performance of our method using two different datasets. In addition, we performed experiments using three types of machines with different performance characteristics on the client side and compared the processing times. The experimental results show that the accuracy of deep learning with coarse-grained data is comparable to that achieved with the default parameter settings, and the proposed distributed processing method has performance advantages in cases of insufficient network bandwidth between realistic sensors and a Cloud environment. In addition, it is confirmed that the process that most affects the overall processing time varies depending on the machine performance on the client side, and the most efficient distribution method similarly differs.

Facial expressions are generated by the actions of the facial muscles located at different facial regions. The spatial dependencies of different spatial facial regions are worth exploring and can improve the performance of facial expression recognition. In this letter we propose a joint convolutional bidirectional long short-term memory (JCBLSTM) framework to model the discriminative facial textures and spatial relations between different regions jointly. We treat each row or column of feature maps output from CNN as individual ordered sequence and employ LSTM to model the spatial dependencies within it. Moreover, a shortcut connection for convolutional feature maps is introduced for joint feature representation. We conduct experiments on two databases to evaluate the proposed JCBLSTM method. The experimental results demonstrate that the JCBLSTM method achieves state-of-the-art performance on Multi-PIE and very competitive result on FER-2013.

In mobile and wireless networks, controlling data delivery latency is one of open problems due to the stochastic nature of wireless channels, which are inherently unreliable. This paper explores how the current best-effort throughput-oriented wireless services might evolve into latency-sensitive enablers of new mobile applications such as remote three-dimensional (3D) graphical rendering for interactive virtual/augmented-reality overlay. Assuming that the signal propagation delay and achievable throughput meet the standard latency requirements of the user application, we examine the idea of trading excess/federated bandwidth for the elimination of non-negligible delay of data re-ordering, caused by temporal transmission failures and buffer overflows. The general system design is based on (i) spatially diverse data delivery over multiple paths with uncorrelated outage likelihoods; and (ii) forward packet-loss protection (FPP), creating encoding redundancy for proactive recovery of intolerably delayed data without end-to-end retransmissions. Analysis and evaluation are based on traces of real life traffic, which is measured in live carrier-grade long term evolution (LTE) networks and campus WiFi networks, due to no such system/environment yet to verify the importance of spatial diversity and encoding redundancy. Analysis and evaluation reveal the seriousness of the latency problem and that the proposed FPP with spatial diversity and encoding redundancy can minimize the delay of re-ordering. Moreover, a novel FPP effectiveness coefficient is proposed to explicitly represent the effectiveness of EPP implementation.

This paper proposes a triangular active charge injection method to reduce resonant power supply noise by injecting the adequate amount of charge into the supply line of the LSI in response to the current consumption of the core circuit. The proposed circuit is composed of three key components, a voltage drop detector, an injection controller circuit and a canceling capacitor circuit. In addition to the theoretical analysis of the proposed method, the measurement results indicate that our proposed method with active capacitor can realize about 14% noise reduction compared with the original noise amplitude. The proposed circuit consumes 25.2 mW in steady state and occupies 0.182 mm2.