Distributed computing is one of the powerful solutions for computational tasks that need the massive size of dataset. Lagrange coded computing (LCC), proposed by Yu et al. [15], realizes private and secure distributed computing under the existence of stragglers, malicious workers, and colluding workers by using an encoding polynomial. Since the encoding polynomial depends on a dataset, it must be updated every arrival of new dataset. Therefore, it is necessary to employ efficient algorithm to construct the encoding polynomial. In this paper, we propose Newton coded computing (NCC) which is based on Newton interpolation to construct the encoding polynomial. Let K, L, and T be the number of data, the length of each data, and the number of colluding workers, respectively. Then, the computational complexity for construction of an encoding polynomial is improved from O(L(K+T)log 2(K+T)log log (K+T)) for LCC to O(L(K+T)log (K+T)) for the proposed method. Furthermore, by applying the proposed method, the computational complexity for updating the encoding polynomial is improved from O(L(K+T)log 2(K+T)log log (K+T)) for LCC to O(L) for the proposed method.

Various types of indices for estimating functional connectivity have been developed over the years that have introduced effective approaches to discovering complex neural networks in the brain. Two significant examples are the phase lag index (PLI) and transfer entropy (TE). Both indices have specific benefits; PLI, defined using instantaneous phase dynamics, achieves high spatiotemporal resolution, whereas transfer entropy (TE), defined using information flow, reveals directed network characteristics. However, the relationship between these indices remains unclear. In this study, we hypothesize that there exists a complementary relationship between PLI and TE to discover new aspects of functional connectivity that cannot be detected using either PLI or TE. To validate this hypothesis, we evaluated the synchronization in a coupled Rössler model using PLI and TE. Consequently, we proved the existence of non-linear relationships between PLI and TE. Both indexes exhibit a specific trend that demonstrates a linear relationship in the region of small TE values. However, above a specific TE value, PLI converges to a constant irrespective of the TE value. In addition to this relational difference in synchronization, there is another characteristic difference between these indices. Moreover, by virtue of its finer temporal resolution, PLI can capture the temporal variability of the degree of synchronization, which is called dynamical functional connectivity. TE lacks this temporal characteristic because it requires a longer evaluation period in this estimation process. Therefore, combining the advantages of both indices might contribute to revealing complex spatiotemporal functional connectivity in brain activity.

Source retrieval is the primary task of plagiarism detection. It searches the documents that may be the sources of plagiarism to a suspicious document. The state-of-the-art approaches usually rely on the classical information retrieval models, such as the probability model or vector space model, to get the plagiarism sources. However, the goal of source retrieval is to obtain the source documents that contain the plagiarism parts of the suspicious document, rather than to rank the documents relevant to the whole suspicious document. To model the “partial matching” between documents, this paper proposes a Partial Matching Convolution Neural Network (PMCNN) for source retrieval. In detail, PMCNN exploits a sequential convolution neural network to extract the plagiarism patterns of contiguous text segments. The experimental results on PAN 2013 and PAN 2014 plagiarism source retrieval corpus show that PMCNN boosts the performance of source retrieval significantly, outperforming other state-of-the-art document models.

Logic encryption is an IC protection technique which inserts extra logic and key inputs to hide a circuit's functionality. An encrypted circuit needs to be activated with a secret key for being functional. SAT attack and Removal attack are two most advanced decryption methods that have shown their effectiveness to break most of the existing logic encryption methods within a few hours. In this paper, we propose SMARTLock, a SAT attack and reMoval Attack-Resistant Tree-based logic Locking method, for resisting them simultaneously. To encrypt a circuit, the method finds large AND and OR functions in it and encrypts them by inserting duplicate tree functions. There are two types of structurally identical tree encryptions that aim to resist SAT attack and Removal attack, respectively. The experimental results show that the proposed method is effective for encrypting a set of benchmarks from ISCAS'85, MCNC, and IWLS. 16 out of 40 benchmarks encrypted by the proposed method with the area overhead of no more than 5% are uncrackable by SAT attack within 5 hours. Additionally, compared to the state-of-the-art logic encryption methods, the proposed method provides better security for most benchmarks.

For capacity expansion of the Quasi-Zenith Satellite System (QZSS) safety confirmation system, frame slotted ALOHA with flag method has previously been proposed as an access control scheme. While it is always able to communicate in an optimum state, its maximum channel efficiency is only 36.8%. In this paper, we propose adding a reservation channel (R-Ch) to the frame slotted ALOHA with flag method to increase the upper limit of the channel efficiency. With an R-Ch, collision due to random channel selection is decreased by selecting channels in multiple steps, and the channel efficiency is improved up to 84.0%. The time required for accommodating 3 million mobile terminals, each sending one message, when using the flag method only and the flag method with an R-Ch are compared. It is shown that the accommodating time can be reduced to less than half by adding an R-Ch to the flag method.

Using local time frames to treat non-stationary real world signals as stationary yields Quasi-Stationary Signals (QSS). In this paper, direction of arrival (DOA) estimation of uncorrelated non-circular QSS is analyzed by applying a novel technique to achieve larger consecutive lags using coprime array. A scheme of virtual extension of coprime array is proposed that exploits the difference and sum co-array which can increase consecutive co-array lags in remarkable number by using less number of sensors. In the proposed method, cross lags as well as self lags are exploited for virtual extension of co-arrays both for differences and sums. The method offers higher degrees of freedom (DOF) with a larger number of non-negative consecutive lags equal to MN+2M+1 by using only M+N-1 number of sensors where M and N are coprime with congenial interelement spacings. A larger covariance matrix can be achieved by performing covariance like computations with the Khatri-Rao (KR) subspace based approach which can operate in undetermined cases and even can deal with unknown noise covariances. This paper concentrates on only non-negative consecutive lags and subspace based method like Multiple Signal Classification (MUSIC) based approach has been executed for DOA estimation. Hence, the proposed method, named Virtual Extension of Coprime Array imbibing Difference and Sum (VECADS), in this work is promising to create larger covariance matrix with higher DOF for high resolution DOA estimation. The coprime distribution yielded by the proposed approach can yield higher resolution DOA estimation while avoiding the mutual coupling effect. Simulation results demonstrate its effectiveness in terms of the accuracy of DOA estimation even with tightly aligned sources using fewer sensors compared with other techniques like prototype coprime, conventional coprime, Coprime Array with Displaced Subarrays (CADiS), CADiS after Coprime Array with Compressed Inter-element Spacing (CACIS) and nested array seizing only difference co-array.

This paper addresses the issue of text matching for plagiarism detection. This task aims at identifying the matching plagiarism segments in a pair of suspicious document and its plagiarism source document. All the time, heuristic-based methods are mainly utilized to resolve this problem. But the heuristics rely on the experts' experiences and fail to integrate more features to detect the high obfuscation plagiarism matches. In this paper, a statistical machine learning approach, named the Ranking-based Text Matching Approach for Plagiarism Detection, is proposed to deal with the issues of high obfuscation plagiarism detection. The plagiarism text matching is formalized as a ranking problem, and a pairwise learning to rank algorithm is exploited to identify the most probable plagiarism matches for a given suspicious segment. Especially, the Meteor evaluation metrics of machine translation are subsumed by the proposed method to capture the lexical and semantic text similarity. The proposed method is evaluated on PAN12 and PAN13 text alignment corpus of plagiarism detection and compared to the methods achieved the best performance in PAN12, PAN13 and PAN14. Experimental results demonstrate that the proposed method achieves statistically significantly better performance than the baseline methods in all twelve document collections belonging to five different plagiarism categories. Especially at the PAN12 Artificial-high Obfuscation sub-corpus and PAN13 Summary Obfuscation plagiarism sub-corpus, the main evaluation metrics PlagDet of the proposed method are even 22% and 43% relative improvements than the baselines. Moreover, the efficiency of the proposed method is also better than that of baseline methods.

Performance based on multi-party discussion has been reported to be superior to that based on individuals. However, it is impossible that all participants simultaneously express opinions due to the time and space limitations in a large-scale discussion. In particular, only a few representative discussants and audiences can speak in conventional unidirectional discussions (e.g., panel discussion), although many participants gather for the discussion. To solve these problems, in this study, we proposed a cyber-physical discussion using “COLLAGREE,” which we developed for building consensus of large-scale online discussions. COLLAGREE is equipped with functions such as a facilitator, point ranking system, and display of discussion in tree structure. We focused on the relationship between satisfaction with the discussion and participants' desire to express opinions. We conducted the experiment in the panel discussion of an actual international conference. Participants who were audiences in the floor used COLLAGREE during the panel discussion. They responded to questionnaires after the experiment. The main findings are as follows: (1) Participation in online discussion was associated with the satisfaction of the participants; (2) Participants who desired to positively express opinions joined the cyber-space discussion; and (3) The satisfaction of participants who expressed opinions in the cyber-space discussion was higher than those of participants who expressed opinions in the real-space discussion and those who did not express opinions in both the cyber- and real-space discussions. Overall, active behaviors in the cyber-space discussion were associated with participants' satisfaction with the entire discussion, suggesting that cyberspace provided useful alternative opportunities to express opinions for audiences who used to listen to conventional unidirectional discussions passively. In addition, a complementary relationship exists between participation in the cyber-space and real-space discussions. These findings can serve to create a user-friendly discussion environment.

Investigative reports plagiarized from the web should be eliminated because such reports result in ineffective knowledge construction. In this study, we developed an investigative report writing support system for effective knowledge construction from the web. The proposed system attempts to prevent plagiarism by restricting copying and pasting information from web pages. With this system, students can verify information through web browsing, externalize their constructed knowledge as notes for report materials, write reports using these notes, and remove inadequacies in the report by reflection. A comparative experiment showed that the proposed system can potentially prevent web page plagiarism and make knowledge construction from the web more effective compared to a conventional report writing environment.

In this paper, we present a novel regression-based robust locality preserving projections (RRLPP) method to effectively deal with the issue of noise and occlusion in facial expression recognition. Similar to robust principal component analysis (RPCA) and robust regression (RR) approach, the basic idea of the presented RRLPP approach is also to lead in the low-rank term and the sparse term of facial expression image sample matrix to simultaneously overcome the shortcoming of the locality preserving projections (LPP) method and enhance the robustness of facial expression recognition. However, RRLPP is a nonlinear robust subspace method which can effectively describe the local structure of facial expression images. The test results on the Multi-PIE facial expression database indicate that the RRLPP method can effectively eliminate the noise and the occlusion problem of facial expression images, and it also can achieve better or comparative facial expression recognition rate compared to the non-robust and robust subspace methods meantime.

In this paper, the Voronoi region of the transmitted codeword is employed to improve the sphere bound on the maximum-likelihood decoding (MLD) performance of binary linear block codes over additive white Gaussian noise (AWGN) channels. We obtain the improved sphere bounds both on the frame-error probability and the bit-error probability. With the framework of the sphere bound proposed by Kasami et al., we derive the conditional decoding error probability on the spheres by defining a subset of the Voronoi region of the transmitted codeword, since the Voronoi regions of a binary linear block code govern the decoding error probability analysis over AWGN channels. The proposed bound improves the sphere bound by Kasami et al. and the sphere bound by Herzberg and Poltyrev. The computational complexity of the proposed bound is similar to that of the sphere bound by Kasami et al.

To improve the quality of waveforms and achieve a high input power factor (IPF) for matrix rectifier, a novel quasi sliding mode control (SMC) with adaptive compensation is proposed in this letter. Applying quasi-SMC can effective obviate the disturbances of time delay and spatial lag, and SMC based on continuous function is better than discontinuous function to eliminate the chattering. Furthermore, compared with conventional compensation, an adaptive quasi-SMC compensation without any accurate detection for internal parameters is easier to be implementated, which has shown a superior advance. Theoretical analysis and experiments are carried out to validate the correctness of the novel control scheme.

This paper addresses the issue of source retrieval in plagiarism detection. The task of source retrieval is retrieving all plagiarized sources of a suspicious document from a source document corpus whilst minimizing retrieval costs. The classification-based methods achieved the best performance in the current researches of source retrieval. This paper points out that it is more important to cast the problem as ranking and employ learning to rank methods to perform source retrieval. Specially, it employs RankBoost and Ranking SVM to obtain the candidate plagiarism source documents. Experimental results on the dataset of PAN@CLEF 2013 Source Retrieval show that the ranking based methods significantly outperforms the baseline methods based on classification. We argue that considering the source retrieval as a ranking problem is better than a classification problem.

Ring aggregation networks are widely employed for metro access networks. A layer-2 ring with Ethernet Ring Protection is a popular topology for carrier services. Since frames are forwarded along ring nodes, a fairness scheme is required to achieve throughput fairness. Although per-node fairness algorithms have been developed for the Resilient Packet Ring, the per-node fairness is insufficient if there is bias in a flow distribution. To achieve per-flow fairness, N Rate N+1 Color Marking (NRN+1CM) was proposed. However, NRN+1CM can achieve fairness in case there are sufficient numbers of available bits on a frame header. It cannot be employed if the frame header cannot be overwritten. Therefore, the application range of NRN+1CM is limited. This paper proposes a Signaling based Discard with Flags (SDF) scheme for per-flow fairness. The objective of SDF is to eliminate the drawback of NRN+1CM. The key idea is to attach a flag to frames according to the input rate and to discard them selectively based on the flags and a dropping threshold. The flag is removed before the frame is transmitted to another node. The dropping threshold is cyclically updated by signaling between ring nodes and a master node. The SDF performance was confirmed by employing a theoretical analysis and computer simulations. The performance of SDF was comparable to that of NRN+1CM. It was verified that SDF can achieve per-flow throughput fairness without using a frame header in ring aggregation networks.

A closed form frequency estimator is derived for estimating the frequency of a complex exponential signal, embedded in white Gaussian noise. The new estimator consists of the fast Fourier transform (FFT) as the coarse estimation and the phase of autocorrelation lags as the fine-frequency estimator. In the fine-frequency estimation, autocorrelations are calculated from the power-spectral density of the signal, based on the Wiener-Khinchin theorem. For simplicity and suppressing the effect of noise, only the spectrum lines around the actual tone are used. Simulation results show that, the performance of the proposed estimator is approaching the Cramer-Rao Bound (CRB), and has a lower SNR threshold compared with other existing estimators.

To determine the thickness from MR images, segmentation, that is, boundary detection, of the two adjacent thin structures (e.g., femoral cartilage and acetabular cartilage in the hip joint) is needed before thickness determination. Traditional techniques such as zero-crossings of the second derivatives are not suitable for the detection of these boundaries. A theoretical simulation analysis reveals that the zero-crossing method yields considerable biases in boundary detection and thickness measurement of the two adjacent thin structures from MR images. This paper studies the accurate detection of hip cartilage boundaries in the image plane, and a new method based on a model of the MR imaging process is proposed for this application. Based on the newly developed model, a hip cartilage boundary detection algorithm is developed. The in-plane thickness is computed based on the boundaries detected using the proposed algorithm. In order to correct the image plane thickness for overestimation due to oblique slicing, a three-dimensional (3-D) thickness computation approach is introduced. Experimental results show that the thickness measurement obtained by the new thickness computation approach is more accurate than that obtained by the existing thickness computation approaches.

Although multi-user multiple-input multiple-output (MI-MO) systems provide high data rate transmission, they may suffer from interference. Block diagonalization and eigenbeam-space division multiplexing (E-SDM) can suppress interference. The transmitter needs to determine beamforming weights from channel state information (CSI) to use these techniques. However, MIMO channels change in time-varying environments during the time intervals between when transmission parameters are determined and actual MIMO transmission occurs. The outdated CSI causes interference and seriously degrades the quality of transmission. Channel prediction schemes have been developed to mitigate the effects of outdated CSI. We evaluated the accuracy of prediction of autoregressive (AR)-model-based prediction and Lagrange extrapolation in the presence of channel estimation error. We found that Lagrange extrapolation was easy to implement and that it provided performance comparable to that obtained with the AR-model-based technique.

Chronic liver disease is a major worldwide health problem. Diagnosis and staging of chronic liver diseases is an important issue. In this paper, we propose a quantitative method of analyzing local morphological changes for accurate and practical computer-aided diagnosis of cirrhosis. Our method is based on sparse and low-rank matrix decomposition, since the matrix of the liver shapes can be decomposed into two parts: a low-rank matrix, which can be considered similar to that of a normal liver, and a sparse error term that represents the local deformation. Compared with the previous global morphological analysis strategy based on the statistical shape model (SSM), our proposed method improves the accuracy of both normal and abnormal classifications. We also propose using the norm of the sparse error term as a simple measure for classification as normal or abnormal. The experimental results of the proposed method are better than those of the state-of-the-art SSM-based methods.

Wireless Local Area Networks (WLANs) are widely deployed for internet access. Multiple interfering Access Points (APs) lead to a significant increase in collisions, that reduces throughput and affects media traffic. Thus, interference mitigation among different APs becomes a crucial issue in Multi-Cell WLAN systems. One solution to this issue is to assign a different frequency channel to each AP so as to prevent neighboring cells from operating on the same channel. However, most of the existing WLANs today operate in the unlicensed 2.4GHz Industrial, Scientific and Medical (ISM) band, which suffers from lack of the available channels. Therefore, effective channel assignment to minimize the interference in Multi-Cell WLANs is necessary. In this article, we formulate the channel assignment problem as a mixed integer linear programming (MILP) problem that minimizes the worst case total interference power. The main advantage of this algorithm is that it provides a global solution and at the same time guarantees a non-overlapping channel assignment. We also propose a Lagrangian relaxation approach to transform the MILP into a low complexity linear program which can be solved efficiently in real time, even for large sized networks. Simulation results reveal that both the MILP algorithm and the Lagrangian relaxation approach provide a total interference reduction below the default setting of having all APs assigned the same channel. In addition, simulation results on cumulative density function (CDF) of the SINR at the user level prove the validity of the proposed algorithms.

This study analyzes emotions established between people while interacting in face-to-face conversation. By focusing on empathy and antipathy, especially the process by which they are perceived by external observers, this paper aims to elucidate the tendency of their perception and from it develop a computational model that realizes the automatic inference of perceived empathy/antipathy. This paper makes two main contributions. First, an experiment demonstrates that an observer's perception of an interacting pair is affected by the time lags found in their actions and reactions in facial expressions and by whether their expressions are congruent or not. For example, a congruent but delayed reaction is unlikely to be perceived as empathy. Based on our findings, we propose a probabilistic model that relates the perceived empathy/antipathy of external observers to the actions and reactions of conversation participants. An experiment is conducted on ten conversations performed by 16 women in which the perceptions of nine external observers are gathered. The results demonstrate that timing cues are useful in improving the inference performance, especially for perceived antipathy.