Reviewerly/exHarmony huggingface dataset:
Separation of geochemical anomalies from the sample data of unknown distribution population using Gaussian mixture model

The separation of geochemical anomalies from the sample data of unknown distribution population is a great challenge, as it is difficult to determine the correct model for the unknown population distribution. Gaussian mixture model is a linear combination of several Gaussians. By using enough number of Gaussians and by adjusting parameters, the model can generate very complex probability density, which can approximate almost any continuous probability. Therefore, the Gaussian mixture model can fit the sample data of unknown distribution population, and those data points that do not conform to the model are considered as anomalies. The method was used to separate multivariate anomalies from the geochemical survey data of 1:200,000 scale collected from the Baishan district, Jilin Province, China, and compared with one-class support vector machine. The programs running the two models took 18.67 and 32.14 s, respectively; the receiver operating characteristic curves of the two models intersect each other in the ROC space; and area under the curves of the two models are 0.851 and 0.855 respectively. The “best” threshold determined by using the Youden index was used to separate geochemical anomalies. The anomalies separated from the modeling results of the two models occupy respectively 14.46% and 14.49% of the study area and contain respectively 83% and 70% of the known mineral deposits. Therefore, Gaussian mixture model is comparable to one-class support vector machine in geochemical anomaly detection. It can be used as a geochemical anomaly detector with high performance and data modeling efficiency.
Sentiment Lexicon Construction With Hierarchical Supervision Topic Model

In this paper, we propose a novel hierarchical supervision topic model to construct a topic-adaptive sentiment lexicon (TaSL) for higher-level classification tasks. It is widely recognized that sentiment lexicon as a useful prior knowledge is crucial in sentiment analysis or opinion mining. However, many existing sentiment lexicons are constructed ignoring the variability of the sentiment polarities of words in different topics or domains. For example, the word “amazing” can refer to causing great surprise or wonder but can also refer to very impressive and excellent. In TaSL, we solve this issue by jointly considering the topics and sentiments of words. Documents are represented by multiple pairs of topics and sentiments, where each pair is characterized by a multinomial distribution over words. Meanwhile, this generating process is supervised under hierarchical supervision information of documents and words. The main advantage of TaSL is that the sentiment polarity of each word in different topics can be sufficiently captured. This model is beneficial to construct a domain-specific sentiment lexicon and then effectively improve the performance of sentiment classification. Extensive experimental results on four publicly available datasets, MR, OMD, semEval13A, and semEval16B were presented to demonstrate the usefulness of the proposed approach. The results have shown that TaSL performs better than the existing manual sentiment lexicon (MPQA), the topic model based domain-specific lexicon (ssLDA), the expanded lexicons(Weka-ED, Weka-STS, NRC, Liu's), and deep neural network based lexicons (nnLexicon, HIT, HSSWE).
Quantum low probability of intercept

Conventional cryptography—such as the Rivest–Shamir–Adleman public-key infrastructure—may be rendered insecure by the ever-increasing capabilities of classical computers and the emergence of quantum computers. Quantum key distribution and post-quantum cryptography are presently being pursued as solutions to the quantum threat, but they offer no protection against an adversary who has obtained decryption keys by hacking the computer where they are stored, or by bribing a code clerk who has access to them. This paper introduces a protocol, which we call quantum low probability of intercept (QLPI), that has the potential to solve the key-disclosure problem. It transmits a ciphertext in such a way that laws of physics prevent an eavesdropper's obtaining anything but an error-ridden version of that ciphertext from an individual attack or a restricted class of collective attacks. Consequently, even were an adversary to possess the decryption key, the plain text could not be recovered from such attacks. Furthermore, QLPI is capable of gigabits per second communication rates on optical fiber over metropolitan-area distances without space-division or wavelength-division multiplexing and without the need for any new technology.
Short-term nonlinear autoregressive photovoltaic power forecasting using statistical learning approaches and in-situ observations

Due to the low total cost of production, Photovoltaic energy constitutes an important part of the renewable energy installed in the world. However, photovoltaic energy is volatile in nature because it depends on weather conditions, which makes the integration, control and exploitation of this type of energy difficult for grid operators. In the traditional grid architecture, system operators have accumulated enough experience that enables them to determine how much operating reserves are required to maintain system reliability based on statistical tools. Still, with the introduction of renewable energy (wind and photovoltaic), the grid structure has changed, and to maintain grid stability, it is becoming fundamental to know renewable energy state and production that can be combined with other less variable and more predictable sources to satisfy the energy demand. Therefore, renewable energy forecasting is a straightforward way to integrate safely this kind of energy into the current electric grid, especially photovoltaic power forecasting, which is still at a relative infancy stage compared to wind power forecasting, which has reached a relatively mature stage. The goal of this work is to present, first, a short-term offline forecasting model that uses only in-situ (local) collected data. Also, the performances of several pure non-linear auto-regressive models are investigated against those of non-linear auto-regressive models with exogenous inputs. For this purpose, two well-known statistical learning techniques, namely Feed Forward Neural Network and Least Square Support Vector Regression, have been used. To test the performance of the models, the results obtained are compared with those of a benchmark model. In this paper, we used the persistent model as well as a multivariate polynomial regression model as benchmark.
TQM, games design and the implications of integration in Industry 4.0 systems

Purpose Voluntary participation, feedback loops, rules and goals are key elements of total quality management (TQM). The purpose of this paper is to determine if these four elements which make TQM successful are the same elements that make computer games successful. If this is the case, what are the implications for developers of Human Computer Interfaces (HCI) in Industry 4.0. Design/methodology/approach This paper is a systematic literature review of recent literature on engagement in games and user experiences and HCI design for industry followed by interpretation of the literature. The findings from the literature review are analysed and compared to TQM. Findings Good game design and TQM share four key components: goals, rules, a feedback system (including rewards) and voluntary participation. There is an opportunity for HCI developers to use a user experience lens inherent in games evolution and to expand on the design and motivational elements that have made games and TQM successful at motivating and engaging. Kuutti’s (1995) proposal of activity theory puts forward a promising framework for making systems engaging. There are positive implications merging good games design and TQM in socio-technic systems which could improve engagement and quality in companies implementing in Industy 4.0. Research limitations/implications The implications of achieving increased engagement in HCI systems similar to those seen in companies that have successfully implemented TQM could lead to greater productivity in companies operating in the highly technical environments of Industry 4.0. Originality/value The originality of this paper is threefold: first, a description of the origins in industry of voluntary participation, feedback loops, rules and goals and their relationship to TQM; second, a systematic literature review of the same elements in computer games design; and third, the implications for developers of HCI systems in Industry 4.0.
Deep learning in material recovery: Development of method to create training database

Increasing the rate of material identification, separation and recovery is a priority in resource management and recovery, and rapid, low cost imaging and interpretation is key. This study uses different combinations of cameras, illuminations and data augmentation techniques to create databases of images to train deep neural networks for the recognition of fibre materials. Using a limited set of 24 material samples sized 1200 cm2, it compares the outcome of reducing them to 30 cm2. The best classification accuracies obtained range from 76.6% to 77.5% indicating it is possible to overcome problems such as limited available materials, time, or storage capabilities, by using a setup with 5 cameras, 5 lights and applying simple software image manipulation techniques. The same method can be used to create deep neural network training databases to recognise a wider range of materials typically found in solid waste streams, in real-time. Furthermore, it offers flexibility as the classification cameras could be deployed at different stages within solid waste processing plants, providing feedback for process control, with the potential of increasing plant efficiency and reducing costs.
Symbiotic organisms search algorithm for optimal evolutionary controller tuning of fractional fuzzy controllers

With the development of technology and the practical needs of complex engineering applications, fuzzy controllers have been widely applied. In contrast to a traditional integer-order fuzzy controller, a fractional fuzzy controller can extend the integral and differential order of a fuzzy controller to any real number, which describes the controlled object more accurately and enhances its control performance. However, a fractional fuzzy controller has a larger number of control parameters, which makes it difficult to calibrate. Because the parameter controller tuning values of the fuzzy controller clearly influence its control performance, this paper proposes to optimize the parameter controller tuning process using the symbiotic organisms search algorithm. A large number of simulation tests were carried out to compare the symbiotic organisms search-based parameter controller tuning method with parameter controller tuning based on five other representative swarm intelligence algorithms. The experimental results show that the symbiotic organisms search algorithm better optimizes the parameters of the fractional fuzzy controller.
Comparative Study on Local Binary Patterns for Mammographic Density and Risk Scoring

Breast density is considered to be one of the major risk factors in developing breast cancer. High breast density can also affect the accuracy of mammographic abnormality detection due to the breast tissue characteristics and patterns. We reviewed variants of local binary pattern descriptors to classify breast tissue which are widely used as texture descriptors for local feature extraction. In our study, we compared the classification results for the variants of local binary patterns such as classic LBP (Local Binary Pattern), ELBP (Elliptical Local Binary Pattern), Uniform ELBP, LDP (Local Directional Pattern) and M-ELBP (Mean-ELBP). A wider comparison with alternative texture analysis techniques was studied to investigate the potential of LBP variants in density classification. In addition, we investigated the effect on classification when using descriptors for the fibroglandular disk region and the whole breast region. We also studied the effect of the Region-of-Interest (ROI) size and location, the descriptor size, and the choice of classifier. The classification results were evaluated based on the MIAS database using a ten-run ten-fold cross validation approach. The experimental results showed that the Elliptical Local Binary Pattern descriptors and Local Directional Patterns extracted most relevant features for mammographic tissue classification indicating the relevance of directional filters. Similarly, the study showed that classification of features from ROIs of the fibroglandular disk region performed better than classification based on the whole breast region.
IL-GAN: Illumination-invariant representation learning for single sample face recognition

Single sample per person face recognition influenced by varying illumination is a tricky issue. Conventional techniques for illumination-invariant face recognition either realize illumination normalization on the whole face, or learn the illumination-invariant representation from the face image. This paper holds the opinion that deep learning method, which is more similar to the behavior of primate brain, can leverage the advantages of both the conventional techniques. Motivated by the success of generative adversarial network in image representation, this paper proposes IL-GAN model based on the basic structures of variational auto-encoder and generative adversarial network, generating the Controlled Illumination-level Face Image while preserves identity character as well performing a powerful latent representation from the face image, which encodes illumination-invariant signatures. Moreover, this model can be adopted in single sample per person face recognition. Meanwhile, this research proposes an novel illumination level estimation method based on singular value decomposition to generate the Controlled Illumination-level Face Image optionally. Finally, the performances of the proposed method and other state-of-the-art techniques are verified on the Extended Yale B, CMU PIE, IJB-A and our Self-built Driver Face databases. The experimental results indicate that the IL-GAN model outperforms previous approaches for single sample per person face recognition under varying illumination.
Nonlinear system identification using a simplified Fuzzy Broad Learning System: Stability analysis and a comparative study

The Fuzzy Broad Learning System (Fuzzy BLS) is established by replacing the feature nodes of a Broad Learning System with the Takagi–Sugeno–Kang (TSK) fuzzy sub-systems. K-means algorithm is employed to cluster the input data so as to reduce computation complexity. And the parameters of a Fuzzy BLS are computed analytically by pseudoinverse. We investigate the learning algorithms of Fuzzy BLS comprehensively and apply them to nonlinear system identification in this paper: First of all, we develop an iterative learning algorithm for updating the weights in top layer and the weights connecting the fuzzy sub-systems to the enhancement nodes by gradient descent. Secondly, we analyze and prove the Lyapunov stability of Fuzzy BLS with this iterative algorithm. Then, we consider the fuzzy c-means for clustering input data in the part of fuzzy sub-systems, as well as randomly generated centers for Gaussian membership functions. There are several different learning algorithms due to the choice of clustering methods and calculating parameters by pseudoinverse or gradient descent iteratively, which are compared with each other in detail by system identification problems. It is concluded that the learning algorithms which calculate weights by pseudoinverse always outperform the ones that update them iteratively, no matter which clustering method is chosen. The fuzzy c-means, c-means and random centers each has its own merits in our experiments. In addition, Fuzzy BLS trained by the proposed algorithms demonstrates its superiority over the state-of-the-art neuro-fuzzy models in identifying nonlinear systems.
Delegatable Anonymous Credentials from Mercurial Signatures

In a delegatable anonymous credential system, participants may use their credentials anonymously as well as anonymously delegate them to other participants. Such systems are more usable than traditional anonymous credential systems because a popular credential issuer can delegate some of its responsibilities without compromising users’ privacy. They also provide stronger privacy guarantees than traditional anonymous credential systems because the identities of credential issuers are hidden. The identity of a credential issuer may convey information about a user’s identity even when all other information about the user is concealed. The only previously known constructions of delegatable anonymous credentials were prohibitively inefficient. They were based on non-interactive zero-knowledge (NIZK) proofs. In this paper, we provide a simple construction of delegatable anonymous credentials and prove its security in the generic group model. Our construction is direct, not based on NIZK proofs, and is therefore considerably more efficient. In fact, in our construction, only five group elements are needed per link to represent an anonymous credential chain. Our main building block is a new type of signature scheme, a mercurial signature, which allows a signature $$\sigma $$ on a message M under public key $${\mathsf {pk}}$$ to be transformed into a signature $$\sigma '$$ on an equivalent but unlinkable message $$M'$$ under an equivalent but unlinkable public key $${\mathsf {pk}}'$$ .
Variational Bayesian adaptive Kalman filter for asynchronous multirate multi-sensor integrated navigation system

This study considers an asynchronous multirate data integration problem in the linear state space model with unknown and time-varying statistical parameters of the measurement noises. To improve performance of the multirate adaptive Kalman filter algorithm, a multi-sensor adaptive Kalman filtering algorithm based on variational Bayesian approximations has been developed in an asynchronous multirate multi-sensor integrated navigation system. The proposed filtering algorithm estimates measurement noise variances of the sensors adaptively and also it is robust to anomalous measurements of sensors and however, multirate adaptive Kalman filter is required to use an appropriate algorithm for outlier rejection to achieve a reliable and optimal estimation of position, velocity, and orientation. A navigation system composed of a strapdown inertial navigation system along with Doppler velocity log, inclinometer and depth meter with different sampling rates is designed to evaluate performance of multirate error state Kalman filter (MESKF) and multirate adaptive error state Kalman filter (MAESKF) algorithms and the proposed algorithm. Results of two experimental tests show that the average relative root mean square error (RMSE) of the position estimated by the proposed filtering algorithm can be decreased approximately 57% and 36% when compared to that of MESKF and MAESKF algorithms, respectively.
Single Image Dehazing using CNN

Haze is a natural phenomenon in which the dust, smoke and other particles alter the vision of the sky to reduce the visibility. Hazy images cause various visibility problems for traffic user, tourists everywhere, especially in hilly areas where haze and fog are very common. In this paper, a method for single image dehazing using convolutional neural network is proposed. Outdoor images have been used on which particular filters are applied to find the haze in image. Hazy images contain small value in only one-color alpha channel from Red, Blue, green RGB channel. The intensity of these pixels is mainly bestowed by air light depth map. Estimating these low value points of haze transmission map are useful to obtain a high quality dehazed image. An end-to-end encoder-decoder training model is utilized to achieve a high quality dehazed image. The approach is validated on datasets which consists of around 1500 outdoor images. The method also gives transmission map of the hazy image which can further be used to enhance visibility of the scene.
Deep Multi-Level Semantic Hashing for Cross-Modal Retrieval

With the rapid growth of multimodal data, the cross-modal search has widely attracted research interests. Due to its efficiency on storage and computing, hashing-based methods are broadly used for large scale cross-modal retrieval. Most existing hashing methods are designed based on binary supervision, which transforms complex relationships of multi-label data into simple similar or dissimilar. However, few methods have explored the rich semantic information implicit in multi-label data to improve the accuracy of searching results. In this paper, the multi-level semantic supervision generating approach is proposed by exploring the label relevance. And a deep hashing framework is designed for multi-label image-text cross retrieval tasks. It can simultaneously capture the binary similarity and the complex multi-level semantic structure of data in different forms. Moreover, the effects of three different convolutional neural networks, CNN-F, VGG-16, and ResNet-50, on the retrieval results are compared. The experimental results on an open source cross-modal dataset show that our approach outperforms several state-of-the-art hashing methods, and the retrieval result on the CNN-F network is better than VGG-16 and ResNet-50.
Event-Triggered Risk-Sensitive State Estimation for Hidden Markov Models

An event-triggered risk-sensitive state estimation problem for hidden Markov models is investigated in this work. The event-triggered scheme considered is fairly general, which covers most existing event-triggered conditions. By utilizing the reference probability measure approach, this estimation problem is reformulated as an equivalent one and solved. We show that the event-triggered risk-sensitive maximum a posteriori probability estimates can be obtained based on a newly defined unnormalized information state, which has a linear recursive form. Furthermore, the explicit solutions for two major classes of event-triggered conditions are derived if the measurement noise is Gaussian. A numerical comparison is provided to illustrate the effectiveness of the proposed results.
Extended Object Tracking via Positive and Negative Information Fusion

In this paper, a new information fusion and state estimation algorithm is proposed for extended object tracking. An extended object has a spatial extent that is relatively larger than a sensor's resolution, and thus, multiple measurements are obtained from the object at each sensor scan. For example, in lidar-based tracking applications, a car can be considered as an extended object where a lidar sensor measures multiple laser lights reflected from various points on the car's surface. The extended object tracking is a challenging task since multiple measurements from the object need to be systematically assimilated to estimate the shape as well as the kinematic states (e.g., position and velocity) of the object. In particular, the multiple measurements along with sensor-to-object geometry provide positive (where the object exists) and negative (where the object should not exist) information, and these two types of information need to be incorporated in the estimation process. In the proposed algorithm, novel measurement models are developed for each type of information based on Gaussian process regression and virtual measurement generation. Based on those models, the extended object tracking problem is formulated as a Bayesian inference problem for which the unscented Kalman filter is used for its robustness and efficiency. The effectiveness of the proposed algorithm is demonstrated with illustrative car tracking examples.