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Uncertainty measures are important tools for analyzing various data. However, there are relatively few studies on the uncertainty measures for covering rough set models. In this paper, from the viewpoint of the lower and upper approximations, we propose new uncertainty measures, the lower rough entropy and the upper rough entropy, for covering rough set models. Then, we define the concepts of the joint entropy and the conditional entropy in the covering rough set models. Some important properties of these measures are obtained, and their relationships are investigated. Furthermore, we provide a certain characterization of reducible element of a covering by means of the proposed measures, and apply the proposed rough entropy to evaluate the significance of covering granules of a covering. Finally, we apply these rough entropies to measure a dual degree between covering lower and upper approximations. The theoretical analysis and examples show that the proposed uncertainty measures for covering rough set models are reasonable and useful.

The purpose of this study is to propose novel and efficient competitiveness indexes to measure the level of competition among container shipping operators based on a specific region. These indexes should require only basic data, which is full container throughput on the basis of terminal/ hinterland and ship operator. This study takes advantages of two methods to propose novel indexes as alternatives to Herfindahl Hirschman Index (HHI), which is very popular to measure level of competition. Originally named Competition-based Overall Similarity Measurement Index (COSMI) and Entropy Competitiveness Index (ECI) utilize overall similarity measure from clustering analysis and entropy methodologies, respectively. Both indexes have been proposed with two variants for each. COSMI200+ ignores the throughput of each SO having an amount less than 200 Twentyfoot Equivalent Units (TEUs), but COSMITOP5 takes into account only the top 5 SOs in terms of local throughput in a hinterland. ECI-JOINT includes a joint entropy coefficient which is constant for each hinterland, but ECI-VAR takes into account a variable entropy coefficient defined by the number of ship operators in each hinterland. Analyzing a dataset for the terminals located in Turkey, the Entropy Competitiveness Index (by means of ECI-JOINT variant) has been exhibited as a good alternative to HHI with a great correlation coefficient with it: 0.97. Theoretically, Competitionbased Overall Similarity Measurement Index (by means of COSMITOP5 variant) seems a promising method, but it is highly affected by outliers and inconstant numbers of ship operators per route, indicating a moderate correlation coefficient with HHI: 0.45., Bu çalışmanın amacı, belli bir bölge bazlı olarak konteyner gemi operatörleri arasındaki rekabet seviyesini ölçmeye yarayan yeni ve etkin rekabet indeksleri oluşturmaktır. Bu indeksler terminal/bölge ve gemi operatörü bazlı olarak, sadece başlıca verilerden olan dolu konteyner sevkiyat verilerine ihtiyaç duymalıdır. Bu çalışma, rekabet seviyesi ölçümünde popüler olan Herfindahl Hirschman İndeksine (HHI) alternatif yeni indeksler oluşturabilmek için iki farklı yöntemden yararlanmaktadır. Özgün olarak Rekabet Bazlı Toplam Benzerlik Ölçüsü İndeksi (COSMI) ve Entropi Rekabet İndeksi (ECI) olarak adlandırılan bu indeksler, sırasıyla kümeleme analizi toplam benzerlik ölçüsü ve entropi yöntemlerinden yararlanmaktadırlar. Çalışmada her iki indeksin ikişer uyarlaması incelenmiştir. COSMI200+, bir lokal bölgedeki 200 TEU’dan daha az yükleme-tahliye performansı gösteren gemi operatörlerini gözardı ederken, COSMITOP5 sadece en yüksek performansa sahip 5 gemi operatörünü dikkate almaktadır. ECI-JOINT uyarlamasında sabit olan ortak bir entropi katsayısı kullanılırken, ECI-VAR uyarlamasında her bir lokal bölgedeki gemi operatörü sayısına göre değişen entropi katsayısı kullanılmaktadır. Türkiye’de mukim terminallere ait verilerin analizi, Entropi Rekabet İndeksi’nin (ECI-JOINT uyarlaması ile) HHI ile 0,97 değerinde korelasyon katsayısına sahip olduğunu ve bu indekse iyi bir alternatif olduğunu göstermektedir. Teorik olarak Rekabet Bazlı Toplam Rekabet Ölçüsü İndeksi (COSMITOP5 uyarlaması ile) umut veren bir yöntemi barındırsa da, dışadüşenler ve rota bazlı değişken gemi operatörü sayısı nedeniyle HHI ile 0,45 değerinde orta dereceli bir korelasyon katsayısına sahiptir.

Kernel density estimation, which is a non-parametric method about estimating probability density distribution of random variables, has been used in feature selection. However, existing feature selection methods based on kernel density estimation seldom consider interval-valued data. Actually, interval-valued data exist widely. In this paper, a feature selection method based on kernel density estimation for interval-valued data is proposed. Firstly, the kernel function in kernel density estimation is defined for interval-valued data. Secondly, the interval-valued kernel density estimation probability structure is constructed by the defined kernel function, including kernel density estimation conditional probability, kernel density estimation joint probability and kernel density estimation posterior probability. Thirdly, kernel density estimation entropies for interval-valued data are proposed by the constructed probability structure, including information entropy, conditional entropy and joint entropy of kernel density estimation. Fourthly, we propose a feature selection approach based on kernel density estimation entropy. Moreover, we improve the proposed feature selection algorithm and propose a fast feature selection algorithm based on kernel density estimation entropy. Finally, comparative experiments are conducted from three perspectives of computing time, intuitive identifiability and classification performance to show the feasibility and the effectiveness of the proposed method.

In this paper, some new results and inequalities of dynamic failure entropy (DFE) defined by Abbasnejad (2011) will be obtained. Then weighted versions of failure and survival entropies of order β introduced and some properties and inequalities based on these uncertainty measures found. Then weighted DFE will be introduced and some properties investigated. Finally, empirical cumulative entropy is applied to estimate the new information measures.

© 2013 IEEE. Distributed Denial of Service (DDoS) attacks are increasingly harmful to the cyberspace nowadays. The attackers can now easily launch a bigger and more challenging DDoS attack both towards and with Internet-of-Things (IoT) devices, due to the fast popularization of them. Because of the characteristic of fast overwhelming, it is important to make fast as well as accurate response to DDoS attacks, and the real-time performance can be even more important to prevent and legitimate the attacks. Among the methods proposed by researchers, the entropy-based detection method provides a sensitive and reliable performance. However, the balance between computational complexity and recognition accuracy remains a challenge. In this paper, we propose a detection method that consists of 3 main parts in different aspects: a sliding time window to fasten the entropy calculation, a single-directional filter to realize early detection during the DDoS progress but not after the crash, and a quintile deviation check algorithm to optimize the detection result. These will eventually lead to a real-time and high-efficient performance to recognize IoT DDoS attacks as soon as possible.

In this paper a novel descriptor is proposed for anomaly detection in crowd videos at a global level. Traditional approaches for anomaly detection in crowd videos face the dilemma of trade-off between high accuracy and real time performance. In order to resolve this issue, we propose an efficient descriptor composed of three different features extracted from the optical flow (OF) of a video sequence. The first feature is the sum of the optical flow field magnitude computed after applying a threshold. The second feature is the joint entropy of the OF magnitude of two consecutive frames used to measure the dissimilarity. The third feature is the variance computed from a space–time cuboid constructed using history of the OF field magnitude. Performance of the proposed descriptor is evaluated on the widely used UMN dataset in terms of accuracy and processing time. For UMN dataset, the proposed descriptor provides the highest area under the curve (AUC) compared to the approaches already published in literature.

This article presents a novel and remarkably efficient method of computing the statistical G-test made possible by exploiting a connection with the fundamental elements of information theory: by writing the G statistic as a sum of joint entropy terms, its computation is decomposed into easily reusable partial results with no change in the resulting value. This method greatly improves the efficiency of applications that perform a series of G-tests on permutations of the same features, such as feature selection and causal inference applications because this decomposition allows for an intensive reuse of these partial results. The efficiency of this method is demonstrated by implementing it as part of an experiment involving IPC–MB, an efficient Markov blanket discovery algorithm, applicable both as a feature selection algorithm and as a causal inference method. The results show outstanding efficiency gains for IPC–MB when the G-test is computed with the proposed method, compared to the unoptimized G-test, but also when compared to IPC–MB++, a variant of IPC–MB which is enhanced with an AD–tree, both static and dynamic. Even if this proposed method of computing the G-test is presented here in the context of IPC–MB, it is in fact bound neither to IPC–MB in particular, nor to feature selection or causal inference applications in general, because this method targets the information-theoretic concept that underlies the G-test, namely conditional mutual information. This aspect grants it wide applicability in data sciences.

In robotics perception, numerous tasks rely on point cloud registration. However, currently there is no method that can automatically detect misaligned point clouds reliably and without environment-specific parameters. We propose "CorAl", an alignment quality measure and alignment classifier for point cloud pairs, which facilitates the ability to introspectively assess the performance of registration. CorAl compares the joint and the separate entropy of the two point clouds. The separate entropy provides a measure of the entropy that can be expected to be inherent to the environment. The joint entropy should therefore not be substantially higher if the point clouds are properly aligned. Computing the expected entropy makes the method sensitive also to small alignment errors, which are particularly hard to detect, and applicable in a range of different environments. We found that CorAl is able to detect small alignment errors in previously unseen environments with an accuracy of 95% and achieve a substantial improvement to previous methods., Accepted and presented at 10th European Conference on Mobile Robots (ECMR 2021)

Cross-frequency phase–amplitude coupling (PAC) plays an important role in neuronal oscillations network, reflecting the interaction between the phase of low-frequency oscillation (LFO) and amplitude of the high-frequency oscillations (HFO). Thus, we applied four methods based on permutation analysis to measure PAC, including multiscale permutation mutual information (MPMI), permutation conditional mutual information (PCMI), symbolic joint entropy (SJE), and weighted-permutation mutual information (WPMI). To verify the ability of these four algorithms, a performance test including the effects of coupling strength, signal-to-noise ratios (SNRs), and data length was evaluated by using simulation data. It was shown that the performance of SJE was similar to that of other approaches when measuring PAC strength, but the computational efficiency of SJE was the highest among all these four methods. Moreover, SJE can also accurately identify the PAC frequency range under the interference of spike noise. All in all, the results demonstrate that SJE is better for evaluating PAC between neural oscillations.

The fault diagnosis of gearbox and bearing in wind turbine is crucial to improve service life and reduce maintenance cost. This paper proposes a novel fault diagnosis method based on refined generalized composite multi-scale state joint entropy (RGCMSJE), robust spectral learning framework for unsupervised feature selection (RSFS) and extreme learning machine (ELM) to identify the different health conditions of gearboxes, including feature extraction, feature reduction and pattern recognition. In this method, MAED is firstly adopted to assist RGCMSJE in parameter selection. Second, RGCMSJE is utilized to extract the multi-scale features of gearbox vibration signal and construct high-dimension feature set. Thirdly, RSFS method is used to reduce the dimension of high-dimensional RGCMSJE feature set. In the end, the obtained low-dimensional features are input to the ELM classifier to realize fault pattern recognition. Through two gearbox fault diagnosis experiments, the effectiveness of the fault diagnosis method is verified. The analysis results show that this method can effectively and accurately identify different fault types of wind turbine gearbox.

Train wheel flats are formed when wheels slip on rails. Crucial for passenger comfort and the safe operation of train systems, early detection and quantification of wheel-flat severity without interrupting railway operations is a desirable and challenging goal. Our method involves identifying the wheel-flat size by using a model updating strategy based on dynamic measurements. Although measurement and modelling uncertainties influence the identification results, they are rarely taken into account in most wheel-flat detection methods. Another challenge is the interpretation of time series data from multiple sensors. In this article, the size of the wheel flat is identified using a model-falsification approach that explicitly includes uncertainties in both measurement and modelling. A two-step important point selection method is proposed to interpret high-dimensional time series in the context of inverse identification. Perceptually important points, which are consistent with the human visual identification process, are extracted and further selected using joint entropy as an information gain metric. The proposed model-based methodology is applied to a field train track test in Singapore. The results show that the wheel-flat size identified using the proposed methodology is within the range of true observations. In addition, it is also shown that the inclusion of measurement and modelling uncertainties is essential to accurately evaluate the wheel-flat size because identification without uncertainties may lead to an underestimation of the wheel-flat size.

Monitoring partial discharge (PD) activities inside electrical substation using few ultra high frequency (UHF) sensors mounted around the periphery of the substation could be a useful and economical tool. In this work, an experimental study has been conducted in the laboratory and in substations to investigate the localisation ability of the UHF PD detection system. For localisation, one needs to calculate time difference of arrivals (TDOAs) between the UHF waveforms obtained from the multiple sensors mounted around the substation. After that these TDOAs are used for localisation of the PD sources. In this paper a novel joint entropy (JE)-based TDOAs calculation method is proposed and also compared with the existing techniques. For localisation of PD sources, a modified direction of arrival (DOA)-based method for the near field is proposed, as found to be suitable when the sensors are mounted around the substation. Obtained results in TDOA estimation using the proposed JE method is in line with other technique; however, it was found to provide consistent results and is useful in an extreme situation. Modified DOA-based localisation algorithm found to provide improved performance due to the elimination of approximation as required in previously reported far-field-based localisation system for the air-insulated substations.

In this paper, we propose the selection of most suitable mother wavelet function for Turkish phonemes using discrete wavelet transform. The determination of most similar mother wavelet function to the signal has been a challenge in speech processing. The optimum mother wavelet function for Turkish phonemes have been determined by using quantitative measures which are energy and Shannon entropy, information theoretic measures which are joint entropy, conditional entropy, mutual information, and relative entropy from wavelet coefficients of the phonemes. In this study, 101 potential functions were investigated to determine the most appropriate mother wavelet. Experimental results show that the most appropriate wavelet functions for /ç/ and /ş/ phonemes which are unvoiced fricatives have been found as Bi-orthogonal 3.9 and Bi-orthogonal 5.5, respectively. By considering all the results, it is seen that the Bi-orthogonal 3.1 and Discrete Meyer wavelet functions are the most suitable mother wavelets for all other phonemes.

Inspired on the classical Bayesian method also called mutual information, by generalizing the classical case to quantum approach to study the multipartite correlation in quantum image by using the flexible representation of quantum images (FRQI) method. Some important entanglement measures are calculated including von Neumann entropy, conditional entropy, joint entropy, discord and mutual information. We take two typical images of size 2 × 2 pixels and 8 × 8 pixels and study their entanglement measures from different classical and quantum point of view. The quantum image of size 2 × 2 is studied under the change of color for one pixel, but the quantum image of 8 × 8 is treated under translation transform. We find that the classical joint entropy remains invariant under the change of color for one pixel but the quantum entropy is sensitive to such change. It is shown that the total correlation IT could arrive to the double amount of the classical joint entropy. The discord in the classical case is zero, but it varies much in the quantum case.

This article considers the problem of estimation of the parameter of DUS-exponential distribution based on upper records through classical as well as Bayesian procedures. Maximum likelihood estimator is calculated under the classical scheme and Bayes estimator is obtained under the squared error loss function by using MCMC technique. The performances of both the estimators are compared on the basis of their estimated mean squared errors. The asymptotic and HPD intervals for the unknown parameter is also calculated. In addition to that, the entropy of ith upper record and joint entropy based on m upper records are obtained for considered distribution. A real data set is considered to illustrate the suitability of the proposed methodology.

Myanmar language also known as Burmese language is a free order of word language and syntactic patterns of one word can vary based on the position and the structure in the sentence. There are many ambiguous part of speech (POS) tags on one word in the sentence of Myanmar text. This research work presents disambiguation for the POS of the words in written Myanmar text. We aim at removing this ambiguity on Myanmar word and assigning single POS to each word of sentence. This is demonstrated on the following ideas: (i) input the sentence and segmented into words using syllable segmentation rules and forward maximum matching approach with monolingual Myanmar dictionary and (ii) apply the Joint Entropy (JE) for POS ambiguous for each word in the sentence with monolingual Myanmar tagged corpus. Joint probability value could be given the useful and accurate disambiguation of POS for free order and structure of words in Myanmar text. The monolingual Myanmar tagged corpus and tagged dictionary are created including 620 sentences and 15,000 words, respectively. This study attempts practical word segmentation and POS tagging system which can really overcome bottleneck of the machine translation system for Myanmar to other languages and research activities related to natural language processing (NLP).

The registration of Infrared (IR) and visible images is an important prerequisite for image processing tasks such as image fusion, target detection and tracking, and remote sensing. The registration task of the IR and visible images usually involves two problems: (1) extracting consistent features from multi-sensor images is difficult and (2) similarity measurement methods such as normalized mutual information (NMI) algorithms are prone to falling into local extremities. To solve these complications, this study proposes a grayscale weight with window algorithm (GWW) to extract common strong edge features from IR and visible images, reduce the joint entropy values and local extreme values of NMI, and improve the performance of NMI to calculate IR and visible images for registration. Qualitative and quantitative experiments demonstrate that the GWW can effectively extract the common features of IR and visible image pairs, improve the performance of the surface peak, increase the ratio of primary and secondary peaks, and effectively reduce the local extremum. The performance of NMI combined with the GWW algorithm is better than the traditional MI, NMI, and ECC, and has better matching accuracy and higher matching probability. The registration of IR and visible images can be fully realized by NMI combined with the GWW algorithm.

Blind image quality assessment (BIQA) aims to evaluate the quality of an image without pristine image objectively, which is highly desired in many perception-oriented image processing systems. Distortions degrade the visual contents and cause image quality degradation. Moreover, the visual contents of an image suffer from individual degradations by different types and different levels of distortions, which makes us difficult to analyze the quality degradation. From the perspective of information theory, there is a decrease in the amount of the visual contents when images are distorted. Therefore, in this paper, the image quality is assessed through its visual entropy degradation. Researches on the neuroscience indicate that the simple cells in the local receptive field can be characterized as being spatially localized and oriented, then the local intensity, gradient, and orientation features are extracted to represent the visual contents of an image. By deducing the joint entropy equation, the joint entropy is related to the statistical distributions. Next, in order to measure the visual entropy, the joint statistical distributions of those features are calculated. Finally, by measuring the degradations on these distributions of distorted images, a novel BIQA method is proposed. The experimental results on the databases of LIVE, CSIQ, and TID2013 show that the proposed method has superior performance than other state-of-the-art BIQA methods.

Multi-label feature selection that identifies important features from the original feature set of multi-labeled datasets has been attracting considerable attention owing to its generality compared to conventional single-label feature selection. The unimportant features are filtered by scoring the dependency of features to labels. In conventional multi-label feature filter studies, the score function is obtained by approximating a dependency measure such as joint entropy because direct calculation is often impractical due to the presence of multiple labels with limited training patterns. Although the efficacy of approximation can differ depending on the characteristics of the multi-label dataset, conventional methods presume a certain approximation method, leading to a degenerated feature subset if the presumed approximation is inappropriate for the given dataset. In this study, we propose a strategy for selecting an approximation among a series of approximations depending on the number of involved variables and consequently instantiate a score function based on the chosen approximation. The experimental results demonstrate that the proposed strategy and score function outperform conventional multi-label feature selection methods.

Meditation, a self-regulation practice, attracts attention on its physiological and psychological effects, and its effects on nonlinear complexity of heartbeats are analyzed in our contribution. Heartbeats of Chinese Chi, Kundalini Yoga practitioners and non-meditation metronomic breathing subjects, all from physionet, contribute to our research. We introduce the meditative heart rates in time and frequency domains, and extract their nonlinear dynamics by local dynamic and global static symbolic time-series analysis. Shannon entropy for each individual symbolization and joint entropy for the two kinds of symbolic transformations are applied to detect heartbeats’ nonlinear complexity. Test results prove that symbolic entropy is an exquisite quantitative analysis to characterize and distinguish nonlinear complexity of the physiological signals. Heart rates, during meditation, show highest regularities and lowest nonlinear complexities, and meditators, even in pre-meditation status, have lower heartbeats nonlinear complexity than the non-meditation volunteers, which suggest that meditation enhances cardiac self-regulation and reduces the nonlinear dynamic complexity of heart rates.

A microsleep is a brief and an involuntary sleep-related loss of consciousness of up to 15 s. We investigated the performances of seven pairwise inter-channel relationships–covariance, Pearson’s correlation coefficient, wavelet cross-spectral power, wavelet coherence, joint entropy, mutual information, and phase synchronization index–in continuous prediction of microsleep states from EEG. These relationships were used as the feature sets of a linear discriminant analysis (LDA) and a linear support vector machine classifiers. Priors for both classifiers were incorporated to address the class imbalance in the training data sets. Each feature set was extracted from a 5-s window of EEG with the step of 0.25 s and was demeaned with respect to the mean of first 2 min. The sequential forward selection (SFS) method, based on a serial combination of the correlation coefficient, Fisher score-based filter, and an LDA-based wrapper, was used to select features from each training set. The comparison was based on 16-channel EEG data from eight subjects who had performed a 1-D visuomotor task for two 1-h sessions. The prediction performances were evaluated using leave-one-subject-out cross-validation. For both classifiers, non-normalized feature sets were found to perform better than normalized feature sets. Furthermore, demeaning the non-normalized features considerably improved the prediction performance. Overall, the LDA classifier with joint entropy features resulted in the best average prediction performances (phi, AUCPR, and AUCROC) of (0.47, 0.50, and 0.95). Joint entropy between O1 and O2 from theta frequency band was the most informative feature.

The estimation of relative entropy measures such as mutual information, conditional and joint entropy, or transfer entropy requires the estimation of conditional and joint densities. When the data are continuous, a multi-variate kernel density estimation or a discretization scheme is usually applied. The problem with this discretization approach is that for mutual information as well as transfer entropy the resulting measure does not converge monotonically to the true value when the number of discrete bins is increased. In the absence of a distribution theory, hypothesis testing is only possible by means of bootstrapping. We propose to estimate the necessary joint and conditional frequencies by means of quantile regression. This allows us to avoid arbitrary binning and all associated problems. Moreover, due to the semi-parametric nature of this approach, the computational burden is decisively reduced compared to multi-variate kernel density estimation. Instead, we show that we can flexibly use quantile regressions to estimate densities in order to calculate joint, conditional and transfer entropy as well as mutual information. Furthermore, by casting our into a Generalized Method of Moments framework, we develop the asymptotic theory to conduct inference on relative entropy measures for multiple variables.

Number: 11 Publisher: Multidisciplinary Digital Publishing Institute; Information theory is a unifying mathematical theory to measure information content, which is key for research in cryptography, statistical physics, and quantum computing [...]

The social network plays a significant role in the exchange of information in the system. Information sharing includes the exchange of ideas, thoughts, opinions about some product, politics, etc. It is essential to find the amount of information shared between the nodes in the network. Therefore, we introduced a quantum mechanical model for information sharing in the social network. The model considers different states of network components (nodes) and encodes the state of mind of individuals in the network. The node’s importance in the information flow is evaluated through the quantum walk approach. Besides, we also consider some classical mechanics parameters like different centrality measures to find mutual information shared between a node with its neighbor through the concept of marginal entropy and joint entropy. In the end, the results of the classical approach and quantum mechanical approach are compared and analyzed for a network constructed using different network models and validated through some real-world datasets.

Active metric learning is the problem of incrementally selecting high-utility batches of training data (typically, ordered triplets) to annotate, in order to progressively improve a learned model of a metric over some input domain as rapidly as possible. Standard approaches, which independently assess the informativeness of each triplet in a batch, are susceptible to highly correlated batches with many redundant triplets and hence low overall utility. While a recent work [20] proposes batch-decorrelation strategies for metric learning, they rely on ad hoc heuristics to estimate the correlation between two triplets at a time. We present a novel batch active metric learning method that leverages the Maximum Entropy Principle to learn the least biased estimate of triplet distribution for a given set of prior constraints. To avoid redundancy between triplets, our method collectively selects batches with maximum joint entropy, which simultaneously captures both informativeness and diversity. We take advantage of the submodularity of the joint entropy function to construct a tractable solution using an efficient greedy algorithm based on Gram-Schmidt orthogonalization that is provably \(\left( 1 - \frac{1}{e} \right) \)-optimal. Our approach is the first batch active metric learning method to define a unified score that balances informativeness and diversity for an entire batch of triplets. Experiments with several real-world datasets demonstrate that our algorithm is robust, generalizes well to different applications and input modalities, and consistently outperforms the state-of-the-art.

In medical imaging, the construction of a reference shape from a set of segmentation results from different algorithms or image modalities is an important issue when dealing with the evaluation of segmentation without knowing the gold standard or when an evaluation of the inter or intra expert variability is needed. It is also interesting to build this consensus shape to merge the results obtained for the same target object from automatic or semi-automatic segmentation methods. In this paper, to deal with both segmentation fusion and evaluation, we propose to define such a “mutual shape” as the optimum of a criterion using both the mutual information and the joint entropy of the segmentation methods. This energy criterion is justified using the similarities between quantities of information theory and area measures and is presented in a continuous variational framework. We investigate the applicability of our framework for the fusion and evaluation of segmentation methods in multimodal MR images of diffuse intrinsic pontine glioma (DIPG).

The definition and effect of translation literality (word-for-word translation, as opposed to sense-for-sense translation) have been a topic in translation studies for very many years. Schaeffer and Carl (2014) introduced measures to quantify the literality of observed translations based on their distance to a hypothetical absolutely literal translation assuming monotonicity (identical word order), compositionality (one-to-one translation), and entrenchment (lack of translation variation). This chapter introduces measures to assess the rendered translation literality, which reformulates the three literality criteria in an information-theoretic framework. We introduce rendered literality measures on the word and segment levels as the joint self-information and the joint entropy of the three literality criteria. These literality measures respect the additional requirement for translations to be grammatical according to target language (TL) constraints (i.e., rendered) and rank sets of alternative translations according to their rendered literality. We evaluate the predictive potential of rendered translation literality on translation duration within the multiLing dataset for different languages and different translation production modes. Our data show that the most probable translations respecting TL constraints are also most monotone, compositional, and entrenched translations. We conclude that the information-theoretic literality measures are powerful new predictors for behavioral measures, such as word and segment translation duration.

In this research paper, it is proved that an approximation to the Gibbs-Shannon entropy measure naturally leads to the Tsallis entropy for the real parameter q = 2. Several interesting measures based on the input as well as the output of a discrete memoryless channel are provided and some of the properties of those measures are discussed. It is expected that these results will be of utility in Information Theoretic research.

We propose a joint entropy loading and simplified Volterra filter (VF) to experimentally demonstrate a 200-Gb/s DMT signal transmission over 10-km SSMF using a 32-GHz EML. The BER is below 20% SD-FEC threshold.

Cyber Physical Systems (CPS) represent a technology that connects the physical world and the virtual world to intelligently enhance the operational environment they are operating in. CPS represent complex systems that potentially offer a variety of features like improving the reliability, cost efficiency or maintainability of physical systems. Nevertheless, these improvements are still accompanied by unprecedented complexity, high uncertainty, and overall vulnerability of the CPS. This paper provides an analysis of CPS as sources of dynamic complexity in Cyber-Physical-System of Systems (CPSS) via networks of information transferring, storing, and generating agents. Dynamic system complexity and the impact on the CPS and CPSS is explored via Shannon Entropy and joint entropy. As a final step, a trade-off relationship between the benefits and drawbacks of increased dynamic complexity in CPS and CPSS is presented and leads to the conclusion that both are effective tools for value generation in which dynamic complexity leads to decreased system efficiency.