Most existing methods on research collaborator recommendation focus on promoting collaboration within a specific discipline and exploit a network structure derived from co-authorship or co-citation information. To find collaboration opportunities outside researchers' own fields of expertise and beyond their social network, we present an interdisciplinary collaborator recommendation method based on research content similarity. In the proposed method, we calculate textual features that reflect a researcher's interests using a research grant database. To find the most relevant researchers who work in other fields, we compare constructing a pairwise similarity matrix in a feature space and exploiting existing social networks with content-based similarity. We present a case study at the Graduate University for Advanced Studies in Japan in which actual collaborations across departments are used as ground truth. The results indicate that our content-based approach can accurately predict interdisciplinary collaboration compared with the conventional collaboration network-based approaches.

The Infrastructure-as-a-Service (IaaS) cloud is attracting applications due to the scalability, dynamic resource provision, and pay-as-you-go cost model. Scheduling scientific workflow in the IaaS cloud is faced with uncertainties like resource performance variations and unknown failures. A schedule is said to be robust if it is able to absorb some degree of the uncertainties during the workflow execution. In this paper, we propose a novel workflow scheduling algorithm called Dynamic Earliest-Finish-Time (DEFT) in the IaaS cloud improving both makespan and robustness. DEFT is a dynamic scheduling containing a set of list scheduling loops invoked when some tasks complete successfully and release resources. In each loop, unscheduled tasks are ranked, a best virtual machine (VM) with minimum estimated earliest finish time for each task is selected. A task is scheduled only when all its parents complete, and the selected best VM is ready. Intermediate data is sent from the finished task to each of its child and the selected best VM before the child is scheduled. Experiments show that DEFT can produce shorter makespans with larger robustness than existing typical list and dynamic scheduling algorithms in the IaaS cloud.

This letter proposes a Non-uniform Cell-based Index (NCI) to enable clients to quickly process window queries in the wireless spatial data broadcast environment. To improve the access time, NCI reduces the probe wait time by equalized spacing between indexes, using non-uniformly partitioned cells of data space. Through the performance evaluation, we show the proposed NCI outperforms the existing index schemes for window queries to spatial data in respect of access time.

Iterative channel estimation and decoding receivers have evolved over the years, most especially with Turbo and LPDC codes. Nevertheless, few works have determined the performance of symbol level Reed-Solomon (RS) codes in iterative receiver structures. The iterative channel estimation and symbol level RS decoding receiver structure found in literature concentrate on M-QAM systems over flat Rayleigh fading channels. In this paper, attention is focused on the performance of RS codes in iterative channel estimation and decoding receiver structures for Orthogonal Frequency Division Multiplexing (OFDM) systems on frequency-selective Rayleigh fading channels. Firstly, the paper extends the Koetter and Vardy (KV) RS iterative receiver structure over flat Rayleigh fading channels to frequency-selective Rayleigh fading channels. In addition, the paper develops a symbol level RS iterative receiver structure for OFDM systems on frequency-selective Rayleigh fading channels based on the Parity-check matrix Transformation Algorithm (PTA). The performance of the RS-KV and RS-PTA iterative receiver structures for OFDM systems are documented through computer simulation. The simulation results verify that both iterative receiver structures are suitable for real time RS OFDM wireless applications. The results also show that the developed RS-PTA iterative receiver structure is a low complexity and high performance alternative to the RS-KV iterative receiver structure.

This paper proposes a method for evaluating a physical gait motion based on a 3D human skeleton measured by a depth sensor. While similar methods measure and evaluate the motion of only a part of interest (e.g., knee), the proposed method comprehensively evaluates the motion of the full body. The gait motions with a variety of physical disabilities due to lesioned body parts are recorded and modeled in advance for gait anomaly detection. This detection is achieved by finding lesioned parts a set of pose features extracted from gait sequences. In experiments, the proposed features extracted from the full body allowed us to identify where a subject was injured with 83.1% accuracy by using the model optimized for the individual. The superiority of the full-body features was validated in in contrast to local features extracted from only a body part of interest (77.1% by lower-body features and 65% by upper-body features). Furthermore, the effectiveness of the proposed full-body features was also validated with single universal model used for all subjects; 55.2%, 44.7%, and 35.5% by the full-body, lower-body, and upper-body features, respectively.

Linked data endpoints are online query gateways to semantically annotated linked data sources. In order to query these data sources, SPARQL query language is used as a standard. Although a linked data endpoint (i.e. SPARQL endpoint) is a basic Web service, it provides a platform for federated online querying and data linking methods. For linked data consumers, SPARQL endpoint availability and discovery are crucial for live querying and semantic information retrieval. Current studies show that availability of linked datasets is very low, while the locations of linked data endpoints change frequently. There are linked data respsitories that collect and list the available linked data endpoints or resources. It is observed that around half of the endpoints listed in existing repositories are not accessible (temporarily or permanently offline). These endpoint URLs are shared through repository websites, such as Datahub.io, however, they are weakly maintained and revised only by their publishers. In this study, a novel metacrawling method is proposed for discovering and monitoring linked data sources on the Web. We implemented the method in a prototype system, named SPARQL Endpoints Discovery (SpEnD). SpEnD starts with a “search keyword” discovery process for finding relevant keywords for the linked data domain and specifically SPARQL endpoints. Then, the collected search keywords are utilized to find linked data sources via popular search engines (Google, Bing, Yahoo, Yandex). By using this method, most of the currently listed SPARQL endpoints in existing endpoint repositories, as well as a significant number of new SPARQL endpoints, have been discovered. We analyze our findings in comparison to Datahub collection in detail.

This paper proposes a new approach for generating pseudo random multi-valued (including binary-valued) sequences. The approach uses a primitive polynomial over an odd characteristic prime field ${p}$, where p is an odd prime number. Then, for the maximum length sequence of vectors generated by the primitive polynomial, the trace function is used for mapping these vectors to scalars as elements in the prime field. Power residue symbol (Legendre symbol in binary case) is applied to translate the scalars to k-value scalars, where k is a prime factor of p-1. Finally, a pseudo random k-value sequence is obtained. Some important properties of the resulting multi-valued sequences are shown, such as their period, autocorrelation, and linear complexity together with their proofs and small examples.

The present paper introduces the construction of quadriphase sequences having a zero-correlation zone. For a zero-correlation zone sequence set of N sequences, each of length l, the cross-correlation function and the side lobe of the autocorrelation function of the proposed sequence set are zero for the phase shifts τ within the zero-correlation zone z, such that |τ|≤z (τ ≠ 0 for the autocorrelation function). The ratio $rac{N(z+1)}{ell}$ is theoretically limited to one. When l=N(z+1), the sequence set is called an optimal zero-correlation sequence set. The proposed zero-correlation zone sequence set can be generated from an arbitrary Hadamard matrix of order n. The length of the proposed sequence set can be extended by sequence interleaving, where m times interleaving can generate 4n sequences, each of length 2m+3n. The proposed sequence set is optimal for m=0,1 and almost optimal for m>1.

In this letter, efficient closed-form formulas for the exact and asymptotic average bit error probability (ABEP) of space shift keying (SSK) systems are derived over Rayleigh fading channels with imperfect channel state information (CSI). Specifically, for a generic 2×NR multiple-input multiple-output (MIMO) system with the maximum likelihood (ML) detection, the impact of imperfect CSI is taken into consideration in terms of two types of channel estimation errors with the fixed variance and the variance as a function of the number of pilot symbols and signal-to-noise ratio (SNR). Then, the explicit evaluations of the bit error floor (BEF) and asymptotic SNR loss are carried out based on the derived asymptotic ABEP formula, which accounts for the impact of imperfect CSI on the SSK system. The numerical results are presented to validate the exactness of our theoretical analysis.

In this paper, robust stability of nonlinear feedback systems with unknown disturbance is considered by using the operator-based right coprime factorization method. For dealing with the unknown disturbance, a new design scheme and a nonlinear controller are given. That is, robust stability of the nonlinear systems with unknown disturbance is guaranteed by combining right coprime factorization with the proposed controller. Simultaneously, adverse effects resulting from the disturbance are removed by using the proposed nonlinear operator controller. Finally, a simulation example is given to show the effectiveness of the proposed design scheme of this paper.

This paper proposes a new antenna array design of Massive MIMO for capacity enhancement in line of sight (LOS) environments. Massive MIMO has two key problems: the heavy overhead of feeding back the channel state information (CSI) for very large number of transmission and reception antenna element pairs and the huge computation complexity imposed by the very large scale matrixes. We have already proposed a practical application of Massive MIMO, that is, Massive Antenna Systems for Wireless Entrance links (MAS-WE), which can clearly solve the two key problems of Massive MIMO. However, the conventional antenna array arrangements; e.g. uniform planar array (UPA) or uniform circular array (UCA) degrade the system capacity of MAS-WE due to the channel spatial correlation created by the inter-element spacing. When the LOS component dominates the propagation channel, the antenna array can be designed to minimize the inter-user channel correlation. We propose an antenna array arrangement to control the grating-lobe positions and achieve very low channel spatial correlation. Simulation results show that the proposed arrangement can reduce the spatial correlation at CDF=50% value by 80% compared to UCA and 75% compared to UPA.

When performing measurements in an outdoor field environment, various interference factors occur. So, many studies have been performed to increase the accuracy of the localization. This paper presents a novel probability-based approach to estimating position based on Apollonius circles. The proposed algorithm is a modified method of existing trilateration techniques. This method does not need to know the exact transmission power of the source and does not require a calibration procedure. The proposed algorithm is verified in several typical environments, and simulation results show that the proposed method outperforms existing algorithms.

Improvements in information technology have made it easier for industry to communicate with their customers, raising hopes for a scheme that can estimate when customers will want to make purchases. Although a number of models have been developed to estimate the time-varying purchase probability, they are based on very restrictive assumptions such as preceding purchase-event dependence and discrete-time effect of covariates. Our preliminary analysis of real-world data finds that these assumptions are invalid: self-exciting behavior, as well as marketing stimulus and preceding purchase dependence, should be examined as possible factors influencing purchase probability. In this paper, by employing the novel idea of hierarchical time rescaling, we propose a tractable but highly flexible model that can meld various types of intrinsic history dependency and marketing stimuli in a continuous-time setting. By employing the proposed model, which incorporates the three factors, we analyze actual data, and show that our model has the ability to precisely track the temporal dynamics of purchase probability at the level of individuals. It enables us to take effective marketing actions such as advertising and recommendations on timely and individual bases, leading to the construction of a profitable relationship with each customer.

There are increasing demands for improved analysis of multimodal data that consist of multiple representations, such as multilingual documents and text-annotated images. One promising approach for analyzing such multimodal data is latent topic models. In this paper, we propose conditionally independent generalized relational topic models (CI-gRTM) for predicting unknown relations across different multiple representations of multimodal data. We developed CI-gRTM as a multimodal extension of discriminative relational topic models called generalized relational topic models (gRTM). We demonstrated through experiments with multilingual documents that CI-gRTM can more effectively predict both multilingual representations and relations between two different language representations compared with several state-of-the-art baseline models that enable to predict either multilingual representations or unimodal relations.

In Twitter-like services, countless messages are being posted in real-time every second all around the world. Timely knowledge about what kinds of information are diffusing in social media is quite important. For example, in emergency situations such as earthquakes, users provide instant information on their situation through social media. The collective intelligence of social media is useful as a means of information detection complementary to conventional observation. We have developed a system for monitoring and analyzing information diffusion data in real-time by tracking retweeted tweets. A tweet retweeted by many users indicates that they find the content interesting and impactful. Analysts who use this system can find tweets retweeted by many users and identify the key people who are retweeted frequently by many users or who have retweeted tweets about particular topics. However, bursting situations occur when thousands of social media messages are suddenly posted simultaneously, and the lack of machine resources to handle such situations lowers the system's query performance. Since our system is designed to be used interactively in real-time by many analysts, waiting more than one second for a query results is simply not acceptable. To maintain an acceptable query performance, we propose a capacity control method for filtering incoming tweets using extra attribute information from tweets themselves. Conventionally, there is a trade-off between the query performance and the accuracy of the analysis results. We show that the query performance is improved by our proposed method and that our method is better than the existing methods in terms of maintaining query accuracy.

Fault tolerance is an important feature for the system LSIs used in reliability-critical systems. Although redundancy techniques are generally used to provide fault tolerance, these techniques have significantly hardware costs. However, FPGAs can easily provide high reliability due to their reconfiguration ability. Even if faults occur, the implemented circuit can perform correctly by reconfiguring to a fault-free region of the FPGA. In this paper, we examine an FPGA-IP core loaded in SoC and introduce a fault-tolerant technology based on fault detection and recovery as a CAD-level approach. To detect fault position, we add a route to the manufacturing test method proposed in earlier research and identify fault areas. Furthermore, we perform fault recovery at the logic tile and multiplexer levels using reconfiguration. The evaluation results for the FPGA-IP core loaded in the system LSI demonstrate that it was able to completely identify and avoid fault areas relative to the faults in the routing area.

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.

Microblog, especially twitter, has become an integral part of our daily life for searching latest news and events information. Due to the short length characteristics of tweets and frequent use of unconventional abbreviations, content-relevance based search cannot satisfy user's information need. Recent research has shown that considering temporal and contextual aspects in this regard has improved the retrieval performance significantly. In this paper, we focus on microblog retrieval, emphasizing the alleviation of the vocabulary mismatch, and the leverage of the temporal (e.g., recency and burst nature) and contextual characteristics of tweets. To address the temporal and contextual aspect of tweets, we propose new features based on query-tweet time, word embedding, and query-tweet sentiment correlation. We also introduce some popularity features to estimate the importance of a tweet. A three-stage query expansion technique is applied to improve the relevancy of tweets. Moreover, to determine the temporal and sentiment sensitivity of a query, we introduce query type determination techniques. After supervised feature selection, we apply random forest as a feature ranking method to estimate the importance of selected features. Then, we make use of ensemble of learning to rank (L2R) framework to estimate the relevance of query-tweet pair. We conducted experiments on TREC Microblog 2011 and 2012 test collections over the TREC Tweets2011 corpus. Experimental results demonstrate the effectiveness of our method over the baseline and known related works in terms of precision at 30 (P@30), mean average precision (MAP), normalized discounted cumulative gain at 30 (NDCG@30), and R-precision (R-Prec) metrics.

Recognition of second language (L2) speech is a challenging task even for state-of-the-art automatic speech recognition (ASR) systems, partly because pronunciation by L2 speakers is usually significantly influenced by the mother tongue of the speakers. Considering that the expressions of non-native speakers are usually simpler than those of native ones, and that second language speech usually includes mispronunciation and less fluent pronunciation, we propose a novel method that maximizes unified acoustic and linguistic objective function to derive a phoneme set for second language speech recognition. The authors verify the efficacy of the proposed method using second language speech collected with a translation game type dialogue-based computer assisted language learning (CALL) system. In this paper, the authors examine the performance based on acoustic likelihood, linguistic discrimination ability and integrated objective function for second language speech. Experiments demonstrate the validity of the phoneme set derived by the proposed method.

Recurrent neural networks (RNNs) are a powerful model for sequential data. RNNs that use long short-term memory (LSTM) cells have proven effective in handwriting recognition, language modeling, speech recognition, and language comprehension tasks. In this study, we propose LSTM conditional random fields (LSTM-CRF); it is an LSTM-based RNN model that uses output-label dependencies with transition features and a CRF-like sequence-level objective function. We also propose variations to the LSTM-CRF model using a gate recurrent unit (GRU) and structurally constrained recurrent network (SCRN). Empirical results reveal that our proposed models attain state-of-the-art performance for named entity recognition.