Reviewerly/exHarmony huggingface dataset:
A Training Method Using DNN-guided Layerwise Pretraining for Deep Gaussian Processes

This paper proposes a novel framework of training method of deep Gaussian processes (DGPs). DGPs are deep architecture models based on stacked multiple GPs, which can overcome the limitation of single-layer GPs. Although a stochastic variational inference (SVI)- based method has been proposed for DGP training with an arbitrary amount of training data, it does not always update model parameters appropriately due to repeated Monte Carlo sampling of multiple GPs. To resolve this problem, we propose a pretraining method to determine initial parameters of DGPs. In the proposed method, layer-wise training of single-layer GPs is performed using the hidden-layer values obtained by a deep neural network (DNN) that has an analogous structure to a target DGP model. The proposed method utilizes the characteristics of single-layer GPs and deep neural network (DNN) whose training is easier than DGP's. Experimental results using two speech synthesis databases with approximately 600 K and 1.4 M training data points, respectively, which were composed of hundreds of input and output features, gave the effectiveness of the proposed method.
Boosting Memory-Based Collaborative Filtering Using Content-Metadata

Recommendation systems are widely used in conjunction with many popular personalized services, which enables people to find not only content items they are currently interested in, but also those in which they might become interested. Many recommendation systems employ the memory-based collaborative filtering (CF) method, which has been generally accepted as one of consensus approaches. Despite the usefulness of the CF method for successful recommendation, several limitations remain, such as sparsity and cold-start problems that degrade the performance of CF systems in practice. To overcome these limitations, we propose a content-metadata-based approach that uses content-metadata in an effective way. By complementarily combining content-metadata with conventional user-content ratings and trust network information, our proposed approach remarkably increases the amount of suggested content and accurately recommends a large number of additional content items. Experimental results show a significant enhancement of performance, especially under a sparse rating environment.
Privacy-aware Feature Extraction for Gender Discrimination versus Speaker Identification

This paper introduces a deep neural network based feature extraction scheme that aims to improve the trade-off between utility and privacy in speaker classification tasks. In the proposed scenario we develop a feature representation that helps to maximize the performance of a gender classifier while minimizing additional speaker identity information. Our approach is to use variational information feature extraction that allows for gender discrimination (utility) but minimizes the information level of the features, thus discouraging speaker identification adversarial attacks (privacy). We analyze the model's loss function and the budget scaling factor used to control the balance of utility vs. privacy. It is experimentally shown that the proposed method reduces privacy risks without significantly deprecating utility and that it also generalizes well to new speaker contexts.
Investigation of Trace and Critical Elements (Including Actinides) in Flotation Sulphide Concentrates of Kassandra Mines (Chalkidiki, Greece)

Pyrite/arsenopyrite (Py-AsPy), galena (PbS), and sphalerite (ZnS) concentrates from the flotation plants of Olympias and Stratoni (Kassandra mines, Chalkidiki, N. Greece) were investigated for their major, trace, minor, and critical element contents, including actinides associated to natural radioactivity. It is revealed that in addition to the Pb, Zn, Ag, and Au being exploited by Hellas Gold S.A., there are also significant concentrations of Sb and Ga (Sb: &gt;0.2 wt.% in PbS concentrate; Ga:25 ppm in ZnS concentrate), but no considerable contents of Bi, Co, V, or REE. Concerning other elements, As was found in elevated concentrations (&gt;1 wt.% in Py-(As)Py-AsPy Olympias concentrate and almost 1 wt.% in Stratoni PbS and ZnS concentrates) together with Cd (specifically in ZnS concentrate). However, actinides occurred in very low concentrations (U &lt; 2 ppm and Th &lt; 0.5 ppm in all examined concentrates), limiting the possibility of natural radioactivity in the Hellas Gold S.A. products. The concentrations of the natural radionuclides (238U, 232Th, and 40K) are much lower than those of commercial granitic rocks, and thus the associated radioactive dose is insignificant.
Sets, Graphs, and Things We Can See: A Formal Combinatorial Ontology for Empirical Intra-Site Analysis

A critical aspect of analysing an archaeological site is identifying the network of relationships between the things we find and the locations where we find them. These associations are typically determined by a combination of quantitative analyses and the professional knowledge and intuition of the archaeologist, but where exactly is the boundary between what is truly empirical field data and what is inferred through our prior knowledge and field methods? How can we best support those inferences? This paper is a critical evaluation of that boundary to firmly ground, as much as possible, a quantitative analysis on only that which we can directly observe – the thing and its location – and derive associations from that basis alone. To do so, the approach described here relies on a combination of set and graph theories rather than statistical or spatial methods. This revised ontology allows a formalization, in combinatorial terms, for describing an underlying structure to contexts and assemblages that suggests a clear association between archaeological site analysis and a well-studied class of set and graph covering problems. This, in turn, points towards potential algorithmic solutions for a more holistic parsing of the total relationships between sites, contexts, assemblages, proveniences, and artefacts.
Root Identification Tool for Arabic Verbs

Numerous Arabic morphology systems have been devoted toward morphed requirements of words that are required by other text analyzers.Term rooting is an essential requirement in those systems, yet rooting module in the state-of-the-art morphology systems insufficiently meet that requirement, especially verb term.Consequently, due to termination in stemming term rather than a rooting term.Since the stem of the verb is not the root of the verb, it is not feasible to generate or inference verb's derivations and whole it's surface forms (patterns) such tense, number, mood, person, aspect, and others of verb irregular patterns.Therefore, we propose a new model for identifying the verb's root produced in a tool (RootIT) in order to overcome verb root extraction without disambiguation out of traditional methods, applied in current morphology systems.A major design goal of this system is that it can be used as a standalone tool and can be integrated, in a good manner, with other linguistic analyzers.The adopted approach is a mapping surface verb with full-scale derivative verbs discharged previously in the relational database.Moreover, the proposed system is tested on the adopted dataset from PATB verbs extracted from CoreNLP system.The extracted dataset, containing more than (7950) distinguishes verbs belonging to (1938) different roots.The results obtained outstrip the best-compared system by (2.74%) of high accuracy.
A Fuzzy Directional Median Filter for Fixed-value Impulse Noise Removal

In this paper, we designed a new fuzzy directional weighted median filter to remove fixed-value impulse noise. Our approach discriminates smooth regions from edges, and apply the median filter on edges along the direction of minimum directional standard deviation. Impulse detection is carried out using a set of fuzzy rules based on directional differences and standard deviation. Objective and subjective evaluations demonstrated that our method is successful in impulse noise removal compared to classical median, and classical directional weighted median filter.
Supporting Medical Decisions for Treating Rare Diseases Through Genetic Programming

Casa dos Marcos is the largest specialized medical and residential center for rare diseases in the Iberian Peninsula. The large number of patients and the uniqueness of their diseases demand a considerable amount of diverse and highly personalized therapies, that are nowadays largely managed manually. This paper aims at catering for the emergent need of efficient and effective artificial intelligence systems for the support of the everyday activities of centers like Casa dos Marcos. We present six predictive data models developed with a genetic programming based system which, integrated into a web-application, enabled data-driven support for the therapists in Casa dos Marcos. The presented results clearly indicate the usefulness of the system in assisting complex therapeutic procedures for children suffering from rare diseases.
Feature-based Facebook reviews process model for e-management using data mining

The data generated from online communication acts as potential gold mines for discovering knowledge for researchers. A large amount of data is also generated in the form of web documents, emails, blogs, and feedback, etc. Text analytics is being significantly employed to mine important information. Opinion mining is the process of extracting human thoughts and perceptions from unstructured texts. The showstopper for designing an opinion mining system for analyzing reviews arise from the fact that customer reviews are often noisy. These reviews are informally written. In addition, they are subjected to spelling mistakes, grammatical errors, improper punctuation and irrational capitalization. This paper focuses on analyzing the different classification and clustering algorithms aimed at extracting and consolidating opinions of customers from social media sites like Facebook, Twitter and through surveys, at multiple levels of granularity to monitor and measure customer satisfaction. Ours is an automated approach, in which the system aids in the process of knowledge assimilation for knowledge-based building and also performs the analytics. Domain experts ratify the knowledge base and also provide training datasets for the system to intuitively gather more instances for ratification. The system identifies opinion expressions as phrases containing opinion words, opinionated features and also opinion modifiers. These expressions are categorized as positive, negative or neutral. Opinion expressions are identified and categorized using localized linguistic techniques. Opinions can be congregated at any desired level of specificity i.e. feature level or product level, user level or service level, etc. We have developed a system based on this approach, which provides the user with a platform to analyze opinion expressions crawled from a set of pre-defined datasets.
Combining a robust PCA of logratio transformed data and geostatistical sequential Gaussian simulation approach for geochemical characterization of orogenic gold deposits: a case study from the Alut area, NW of Iran

Robust principal component analysis (PCA) has been proved to be an efficient multivariate statistical method for extraction of a multivariate structure of geochemical data containing outlier values. Furthermore, because of the compositional characteristics of geochemical data, logratio transformation approaches also have to be implemented to transform the data from the simplex space into real space. In order to model the extracted principal component of transformed data, we used sequential Gaussian simulation (SGS) to overcome the drawbacks associated with the traditional kriging method, e.g. bias conditionally relevant to underestimation and overestimation, the smoothing effect and so on. The results of implementing robust PCA on both untransformed and logratio transformed data, which have been acquired from an orogenic gold deposit, indicate that robust PCA analysis of untransformed data is not capable of certifying element association of gold occurrences. However, in robust PCA analysis of transformed data, both varieties of gold mineralization, associated with pyrite mineral and quartz veins, have been revealed. The first variety of gold occurrence is detected in an integrated PC obtained from PC1, PC2 and PC3, which are associated with Pb, As, Hg, Mo, S and Sb elements in quartz–sulphide veins. The second one, PC4, could be correlated with quartz veins. Furthermore, the results of the geostatistical SGS approach are compatible with the detected anomaly in the Alut gold deposit.
Recognition of Online Handwriting with Variability on Smart Devices

Online handwriting recognition enables a convenient user interface for smart devices. However, readability of the handwriting varies based on the device user and the writing surface. State of art handwriting recognition systems are not yet robust with respect to different variability in writing text which results from user movement, writing speed, available space to write on, sloppiness etc. In this work we employ hybrid deep neural network architectures based on Bidirectional Long Short Term Memory (BLSTM) and Connectionist Temporal Classification (CTC) networks to recognize user independent dynamic handwriting with variability. Evaluated on IAMOnDB database, we show that our proposed model achieves state of the art accuracy with minimal data preprocessing and recognizes text with variability. We further compress the model using knowledge distillation to deploy on resource constrained smart devices. Our novel strategy of training student model for CTC networks enjoys significant model size reduction with moderate performance degradation.
Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning

Abstract In many application areas, prediction rules trained based on high-dimensional data are subsequently applied to make predictions for observations from other sources, but they do not always perform well in this setting. This is because data sets from different sources can feature (slightly) differing distributions, even if they come from similar populations. In the context of high-dimensional data and beyond, most prediction methods involve one or several tuning parameters. Their values are commonly chosen by maximizing the cross-validated prediction performance on the training data. This procedure, however, implicitly presumes that the data to which the prediction rule will be ultimately applied, follow the same distribution as the training data. If this is not the case, less complex prediction rules that slightly underfit the training data may be preferable. Indeed, a tuning parameter does not only control the degree of adjustment of a prediction rule to the training data, but also, more generally, the degree of adjustment to the distribution of the training data. On the basis of this idea, in this paper we compare various approaches including new procedures for choosing tuning parameter values that lead to better generalizing prediction rules than those obtained based on cross-validation. Most of these approaches use an external validation data set. In our extensive comparison study based on a large collection of 15 transcriptomic data sets, tuning on external data and robust tuning with a tuned robustness parameter are the two approaches leading to better generalizing prediction rules.
Prediction of Preferred Personality for Friend Recommendation in Social Networks using Artificial Neural Network

Social Networking sites have nowadays become the most common way to communicate over online for people around the world. For making friends in social network, there remains an underlying friend recommendation framework which suggests friends to the users. However, most of the existing friend recommendation frameworks consider only the number of mutual friends, geo-location, mutual interests etc. to recommend one person as a friend to another. But, in real life, people, who have similar personalities, tend to become friends to each other, application of which is completely missing in the modern friend recommendation frameworks. Hence, we have proposed a personality based friend recommendation framework in this paper, which consists of a 3-Layered Artificial Neural Network for friend preference classification and a distance-based sorted subset selection procedure for friend recommendation. Our model tends to achieve a fairly high precision, recall, fl-measure and accuracy of around 85 %,85%,82% and 83% respectively in the friend choice classification task.
Using 3D Residual Network for Spatio-temporal Analysis of Remote Sensing Data

In this paper, we propose an approach to recognize spatio-temporal changes from remote sensing data. Instead of performing independent analysis on each instance of satellite imagery, we proposed a 3D Convolutional Neural Network (CNN) based on the ResNet architecture. Our approach takes as input a 3D spatio-temporal block comprising of spatial as well as temporal data from multiple years. We predict four key transition classes namely construction, destruction, cultivation and decultivation. In our proposed architecture, we introduced Leaky ReLU instead of ReLU which improves the overall performance as it solves the dying ReLU problem. We also provided dataset and annotations <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> for these four classes and have evaluated the efficacy of our approach on data from three different cities.
Transferring Piano Performance Control across Environments

Player pianos driven by computers are able to record and reproduce various performance control parameters, including pitch, timing, velocity and pedaling. However, the resulting sound of performance is not 100% reproducible in a new environment due to the difference in room acoustics and physical properties of the piano. Inspired by the Psychoacoustic studies which showed that human pianists adjust their controls in new environments for better performances, we have developed a system that automatically transfers performance control across environments in order to make the reproduced sound as similar as the original one. In specific, our work includes (1) a systematic measurement of the control-sound relationship of player pianos under different environments, and (2) a novel algorithm to adjust the control parameters through interpolating the measured control-sound functions. We evaluated the effectiveness of our method by conducting a listening test. Experimental results show that our algorithm outperforms the baseline significantly.
Addressing feature drift in data streams using iterative subset selection

Data streams are prone to various forms of concept drift over time including, for instance, changes to the relevance of features. This specific kind of drift is known as feature drift and requires techniques tailored not only to determine which features are the most important but also to take advantage of them. Feature selection has been studied and shown to improve classifier performance in standard batch data mining, yet it is mostly unexplored in data stream mining. This paper presents a novel method of feature subset selection specialized for dealing with the occurrence of feature drifts called Iterative Subset Selection (ISS), which splits the feature selection process into two stages by first ranking the features using some scoring function, and then iteratively selecting feature subsets using this ranking. This work further extends upon our prior work by exploring feeding information from the subset selection stage back into the ranking process. Applying our method to the Naïve Bayes and k-Nearest Neighbour classifier, we obtain compelling accuracy improvements when compared to existing works.
A Learning Approach for Wavelet Design

Wavelet analysis and perfect reconstruction filterbanks (PRFBs) are closely related. Desired properties on the wavelet could be translated to equivalent properties on a PRFB. We propose a new learning-based approach towards designing compactly supported orthonormal wavelets with a specified number of vanishing moments. We view PRFBs as a special class of convolutional autoencoders, which places the problem of wavelet/PRFB design within a learning framework. One could then deploy several state-of-the-art deep learning tools to solve the design problem. The PRFBs are learned by minimizing a squared-error loss function using gradient-descent optimization. The model is trained using a dataset containing random samples drawn from the standard normal distribution. We demonstrate that imposing orthonormality and vanishing moment constraints in the learning framework gives rise to filters that generate an orthonormal wavelet basis. We present results for learning PRFBs with filter lengths 2 and 8. As an illustration, we show that the proposed framework is able to learn the Daubechies wavelet with four vanishing moments, as well as wavelets with an arbitrary number of vanishing moments. For all our results, the signal-to-reconstruction error ratio is greater than 200 dB, implying that perfect reconstruction is indeed achieved accurately up to machine precision.
Implementation of Case-Based Reasoning and Nearest Neighbor Similarity for Peanut Disease Diagnosis

This research discusses the development of an expert system to diagnose peanut disease using Case-Based Reasoning (CBR) and Nearest Neighbor Similarity. CBR is a computer reasoning system using old knowledge to overcome new problems. It provides solutions by looking at the closest old case to new case. The diagnosis process is carried out by entering a new case containing the symptoms to be diagnosed into the system, then calculating similarity values between new cases on a case base using the nearest neighbor method. The average test results of the system to make an initial diagnosis of peanut disease indicate that the system is able to correctly recognize 100% peanut disease. Accuracy calculation uses the nearest neighbor similarity method with a threshold of 0.5, 0.6 and 0.7 respectively 97.22, 88.89%, and 80.55%.
Learning Efficient Sparse Structures in Speech Recognition

Recurrent neural networks (RNNs), especially long short-term memories (LSTMs) have been widely used in speech recognition and natural language processing. As the sizes of RNN models grow for better performance, the computation cost and therefore the required hardware resource increase rapidly. We propose an efficient structural sparsity (ESS) learning method for acoustic modeling in speech recognition. ESS aims to generate a model that offers higher execution efficiency while maintaining the accuracy. A three-step training pipeline is developed in our work. First, we apply the group Lasso regularization method during training process and learn a structural sparse model from scratch. Then the learned sparse structures will be fixed and cannot be changed. Finally, we retrain the model and update the nonzero parameters in the model. We applied our ESS method on classic HMM+LSTM model on Kaldi toolkit. The experimental results show that ESS can remove 72.5% weight groups in the weight matrices when slightly increasing the word error rate (WER) 1.1%.