Reviewerly/exHarmony huggingface dataset:
An Adaptive Ellipse Distance Density Peak Fuzzy Clustering Algorithm Based on the Multi-target Traffic Radar

In the multi-target traffic radar scene, the clustering accuracy between vehicles with close driving distance is relatively low. In response to this problem, this paper proposes a new clustering algorithm, namely an adaptive ellipse distance density peak fuzzy (AEDDPF) clustering algorithm. Firstly, the Euclidean distance is replaced by adaptive ellipse distance, which can more accurately describe the structure of data obtained by radar measurement vehicles. Secondly, the adaptive exponential function curve is introduced in the decision graph of the fast density peak search algorithm to accurately select the density peak point, and the initialization of the AEDDPF algorithm is completed. Finally, the membership matrix and the clustering center are calculated through successive iterations to obtain the clustering result.The time complexity of the AEDDPF algorithm is analyzed. Compared with the density-based spatial clustering of applications with noise (DBSCAN), k-means, fuzzy c-means (FCM), Gustafson-Kessel (GK), and adaptive Euclidean distance density peak fuzzy (Euclid-ADDPF) algorithms, the AEDDPF algorithm has higher clustering accuracy for real measurement data sets in certain scenarios. The experimental results also prove that the proposed algorithm has a better clustering effect in some close-range vehicle scene applications. The generalization ability of the proposed AEDDPF algorithm applied to other types of data is also analyzed.
New correlation relations in classical and quantum systems with different numbers of subsystems1

Abstract We present a review of the general approach to the problem of correlations in classical statistics and quantum statistics of systems with different numbers of subsystems and demonstrate the information-entropic relations for systems without subsystems recently obtained for Shannon entropies. We present the example of a single-qudit state corresponding to the N -level atom, consider explicitly the qutrit state, and show that qutrit can be interpreted as a set of several qubits. For each of these qubits, there exist corresponding von Neumann entropies, and constraints for these entropies determine the hidden correlations between the qubits in spite of the fact that the qutrit does not contain any subsystem. These constraints are expressed in terms of nonnegativity of the mutual information introduced, which usually exists only for the states of systems with subsystems. The value of information parameterizes the hidden correlations of artificial qubits in the system. We discuss examples of some qudits.
Leyzer: A Dataset for Multilingual Virtual Assistants

In this article we present the Leyzer dataset, a multilingual text corpus designed to study multilingual and cross-lingual natural language understanding (NLU) models and the strategies of localization of virtual assistants. The proposed corpus consists of 20 domains across three languages: English, Spanish and Polish, with 186 intents and a wide range of samples, ranging from 1 to 672 sentences per intent. We describe the data generation process, including creation of grammars and forced parallelization. We present a detailed analysis of the created corpus. Finally, we report the results for two localization strategies: train-on-target and zero-shot learning using multilingual BERT models.
Review on Generative Adversarial Networks

Lately, supervised learning is hugely adopted in computer vision. But unsupervised learning has earned less consideration. A branch of CNNs classified as generative adversarial networks (GANs) is made acquainted, it has some architectural restraints, and exhibit that they are a tough contender for unsupervised learning. Training on different datasets of images, it displays conclusive proof that the adversarial pair learns a hierarchy of portrayal from parts to scenes in both the discriminator and generator. Also, the learned features can be used for variety of innovative tasks, indicating their appropriateness as general image representation.
Forecasting for a Fractured Land: A Case Study of the Communication and Use of Aftershock Forecasts from the 2016 Mw 7.8 Kaikōura Earthquake in Aotearoa New Zealand

Abstract Operational earthquake forecasts (OEFs) are represented as time-dependent probabilities of future earthquake hazard and risk. These probabilities can be presented in a variety of formats, including tables, maps, and text-based scenarios. In countries such as Aotearoa New Zealand, the U.S., and Japan, OEFs have been released by scientific organizations to agencies and the public, with the intent of providing information about future earthquake hazard and risk, so that people can use this information to inform their decisions and activities. Despite questions being raised about the utility of OEF for decision-making, past earthquake events have shown that agencies and the public have indeed made use of such forecasts. Responses have included making decisions about safe access into buildings, cordoning, demolition safety, timing of infrastructure repair and rebuild, insurance, postearthquake building standards, postevent land-use planning, and public communication about aftershocks. To add to this body of knowledge, we undertook a survey to investigate how agencies and GNS Science staff used OEFs that were communicated following the Mw 7.8 2016 Kaikōura earthquake in Aotearoa New Zealand. We found that agencies utilized OEFs in many of the ways listed previously, and we document individual employee’s actions taken in their home-life context. Challenges remain, however, regarding the interpretation of probabilistic information and applying this to practical decision-making. We suggest that science agencies cannot expect nontechnical users to understand and utilize forecasts without additional support. This might include developing a diversity of audience-relevant OEF information for communication purposes, alongside advice on how such information could be utilized.
Enriching feature engineering for short text samples by language time series analysis

Abstract In this case study, we are extending feature engineering approaches for short text samples by integrating techniques which have been introduced in the context of time series classification and signal processing. The general idea of the presented feature engineering approach is to tokenize the text samples under consideration and map each token to a number, which measures a specific property of the token. Consequently, each text sample becomes a language time series, which is generated from consecutively emitted tokens, and time is represented by the position of the respective token within the text sample. The resulting language time series can be characterised by collections of established time series feature extraction algorithms from time series analysis and signal processing. This approach maps each text sample (irrespective of its original length) to 3970 stylometric features, which can be analysed with standard statistical learning methodologies. The proposed feature engineering technique for short text data is applied to two different corpora: the Federalist Papers data set and the Spooky Books data set. We demonstrate that the extracted language time series features can be successfully combined with standard machine learning approaches for natural language processing and have the potential to improve the classification performance. Furthermore, the suggested feature engineering approach can be used for visualizing differences and commonalities of stylometric features. The presented framework models the systematic feature engineering based on approaches from time series classification and develops a statistical testing methodology for multi-classification problems.
Cooperative Co-Evolutionary Genetic Programming for High Dimensional Problems

We propose a framework for Cooperative Co-Evolutionary Genetic Programming (CCGP) that considers co-evolution at three different abstraction levels: genotype, feature and output level. A thorough empirical evaluation is carried out on a real-world high dimensional ML problem (image denoising). Results indicate that GP’s performance is enhanced only when cooperation happens at an output level (ensemble-alike). The proposed co-evolutionary ensemble approach is compared against a canonical GP implementation and a GP customized for image processing tasks. Preliminary results show that the proposed framework obtains superior average performance in comparison to the other GP models. Our most relevant finding is the empirical evidence showing that the proposed CCGP model is a promising alternative to specialized GP implementations that require knowledge of the problem’s domain.
A dual-imaging framework for multi-scale measurements of fatigue crack evolution in metallic materials

This paper presents a dual-imaging methodology to investigate fatigue crack initiation & propagation along the lateral and transverse directions in Al7075 flat specimens having a one-sided V-notch. The framework consists of a confocal and a digital microscope. The confocal microscope can detect the short crack initiation at a length-scale of 3 µm while the digital microscope has the capability of reliably measuring cracks at a scale of 0.5 mm. The apparatus provides a unique advantage of characterizing the multi-scale fatigue crack evolution without any significant restrictions on maximum specimen size and maximum loading or frequency.
Integrating Multi-Scalar Sampling Strategies for Archaeological Sediment Chemistry

Archaeological sediment chemistry is a method for using the distribution of chemical elements across a site or landscape to elucidate site boundaries, site structures, and use of space. Archaeologists have sought to implement it at a number of scales, from the analysis of single excavated features to site prospection in regional survey. This article presents a model for an ongoing, multi-scalar collection strategy that builds on previous global work in sediment chemistry. Analyzed data derive from the Bronze Age (2nd millennium b.c.) citadel of Kaymakçı, Turkey. The article presents the results of laboratory x-ray fluorescence (XRF) analysis of both surface and sub-surface samples associated with one excavation area to test the effectiveness of different sample collection strategies by analyzing relationships between sample chemical signatures. The results suggest that a multi-scalar geochemical dataset aids in intra-site feature prospection, site stratigraphy, and nuanced interpretations of the use of space.
The Classifier Algorithm for Recognition of Basic Driving Scenarios

Doniec, Rafał Sieciński, Szymon Piaseczna, Natalia Mocny-Pachońska, Katarzyna Lang, Marta Szymczyk, JacekThis paper concerns three common but not recognizable driving practices based on sequences of signals from accelerometers, gyroscopes and electrooculograms obtained using state-of-the-art smart eyeglasses. As a result we obtained recordings from twenty volunteers with a set of flagged data assigned to specific actions taken while driving a car during driving lessons. Proposed method achieved a high level of accuracy based on data from smart eyeglasses, predicting appropriate activity classes in approximately 85% of cases without prior knowledge.
Bayesian tests for circular uniformity

Circular data are data measured in angles or directions, which occur in a wide variety of scientific fields. An often investigated hypothesis is that of circular uniformity, or isotropy. Frequentist methods for assessing the circular uniformity null hypothesis exist, but do not allow the user faced with an insignificant result to distinguish lack of power from support for the null hypothesis. Bayesian hypothesis tests, which solve this issue and several others, are developed here. They are easy to compute and perform well, which is shown in a simulation. Two alternative hypotheses are considered. One is based on the von Mises distribution and performs well against unimodal alternatives. Another is based on a kernel density, which acts as an omnibus test against all other scenarios. Assessing the performance of the tests using different priors, it is shown that they are powerful and allow more elaborate conclusions than classical tests of circular uniformity.
CASINet: Content-Adaptive Scale Interaction Networks for scene parsing

Objects at different spatial positions in an image exhibit different scales. Adaptive receptive fields are expected to capture suitable ranges of context for accurate pixel level semantic prediction. Recently, atrous convolution with different dilation rates has been used to generate features of multi-scales through several branches which are then fused for prediction. However, there is a lack of explicit interaction among the branches of different scales to adaptively make full use of the contexts. In this paper, we propose a Content-Adaptive Scale Interaction Network (CASINet) to exploit the multi-scale features for scene parsing. We build CASINet based on the classic Atrous Spatial Pyramid Pooling (ASPP) module, followed by a proposed contextual scale interaction (CSI) module, and a scale adaptation (SA) module. Specifically, in the CSI module, for each spatial position of some scale, instead of being limited by a fixed set of convolutional filters that are shared across different spatial positions for feature learning, we promote the adaptivity of the convolutional filters to spatial positions. We achieve this by the context interaction among the features of different scales. The SA module explicitly and softly selects the suitable scale for each spatial position and each channel. Ablation studies demonstrate the effectiveness of the proposed modules. We achieve state-of-the-art performance on three scene parsing benchmarks Cityscapes, ADE20K and LIP.
Joint multi-scale discrimination and region segmentation for person re-ID

Most existing person re-identification methods are mainly based on human part partition with horizontal stripes or human body semantic segmentation. In this paper, we propose a method called MDRS (Multi-scale Discriminative network with Region Segmentation) to integrate multi-scale discriminative feature learning, horizontal stripe partition and semantic segmentation in a single framework, in which multi-scale horizontal stripe partition and usage of both global and local features make the framework be robust to human pose variation, occlusion and background clutter, and semantic segmentation boosts the performance of person identification via shared multi-scale feature extraction. MDRS is trained end-to-end with a multi-task learning strategy that considers three tasks simultaneously: person identification, triplet prediction and pixel-wise semantic segmentation. Comprehensive experiments confirm that our approach exceeds many methods and robustly achieves excellent performances on mainstream evaluation datasets including Market-1501, DukeMTMC-reid and CUHK03.
Privacy Preserving Deep Learning using Secure Multiparty Computation

Many different types of problems have been solved using deep learning in recent pasts. Deep learning techniques are useful for finding solutions to different types of data type's right from structures to semi structures or unstructured. Problems that are based on clustering, classification regression are effectively implemented using deep learning techniques. This utility of machine and deep learning techniques calls for keeping these services on cloud. Providing machine learning as a service to cloud opens problems of security concern of the data involved in training that belongs to different parties involved in training and also the security concerns arises for the data model being trained. This paper has implemented a privacy preserving technique based on secure multi-party computation that creates secret shared to solve the privacy issues for the data involved in training. Our experimental analysis is carried out using MNIST dataset for hand written character recognition as data for learning problem. Experimental analysis indicated that MNIST dataset can be trained to better accuracy using secure multiparty computation and keep the data secured on the network. The PyTorch and PySyft libraries are used for experimentation.
A Fuzzy Drift Correlation Matrix for Multiple Data Stream Regression

How to handle concept drift problem is a big challenge for algorithms designed for the data streams.Currently, techniques related to the concept drift problem focus on single data stream.However, it normally needs to handle multiple relevant data streams in the real-world application.Current concept drift methods can not be directly used in the multistream setting.They can only be limitedly applied on each stream separately, which omits the drift correlation between streams.In the multi-stream scenario, when drift occurs in a stream, other streams may face or have faced a similar drift problem as well.This pattern of simultaneous or delayed occurrence of drift is critical to analyze and predict multiple streams as a whole dynamic system.To fill the gap in the multi-stream scenario, this paper proposes a fuzzy drift variance (FDV) to measure the correlated drift patterns among streams.FDA is able to present how the pattern of drift occurrence for any two streams correlates and how delayed this correlation is.Seven synthetic streams are designed to validate FDA.The experimental results show a good presentation ability of FDA for drift-correlated multiple streams.
Privacy-preserving facial recognition based on temporal features

This paper proposes a novel approach for privacy-preserving facial recognition based on the new feature computation technique: Local Binary Pattern from Temporal Planes (LBP-TP) that extracts information from only the XT or YT planes of a video sequence; in contrast to previous work that depend significantly on spatial information within the video frames. To our knowledge, this is the first known facial recognition work that does not rely on the spatial plane, nor that requires processing a facial input. The removal of this spatial reliance therefore withholds the facial appearance information from public view, where only one-dimensional spatial information that varies across time are extracted for recognition. Privacy is thus assured, yet without impeding the facial recognition task which is vital for many security applications such as street surveillance and perimeter access control. Experimental results indicate that the proposed method achieves accuracy of 99.56%, 98.19% and 100% for the recent CASME II, CAS(ME)2 and Honda/UCSD databases respectively. In addition, a 66% reduction in the number of bytes required for storage and recognition was also observed from these experiments. The outcomes of this research demonstrate that privacy in face recognition can be preserved, without compromising its security (i.e., recognition accuracy) and efficiency.
On the development of a coupled nonlinear telegraph-diffusion model for image restoration

In this work, we propose a telegraph coupled partial differential equation (TCPDE) based model for image restoration. The new framework interpolates between a couple of nonlinear telegraph equation and a parabolic equation. This proposed strategy can be applied to significantly preserve the oscillatory and texture pattern in an image, even in low signal-to-noise ratio (SNR). First, we prove that the present model has a unique global weak solution using Banach's fixed point theorem. Then we apply our model over a set of gray level images to illustrate the image denoising ability of the proposed model. The experimental results of the proposed model are reported, which found better in terms of noise elimination and edge preservation, with respect to the recently developed hyperbolic–parabolic PDE based models as well as coupled diffusion-based model.
Zero-Shot Classification of Biomedical Articles with Emerging MeSH Descriptors

Although numerous applications that have been developed during the last years produce vast amounts of data, the inability to obtain their ground truth target values has triggered the appearance of several new machine learning (ML) variants that tackle such phenomena. The main reasons why this happens are the evolutionary nature that characterizes the majority of real-world problems, highly hindering the conventional approaches to be applied because of incompatibility, as well as the noisy sources of data or even the shortage of available training data to produce robust predictive models. The objective of this work is to provide a new ML approach in the field of zero-shot classification, focused on classifying abstracts that come from PubMed, a well-known resource of publications from the biomedical field. The proposed approach differs in that it uses bioBERT embeddings for transforming the textual data into a new semantic space exploiting them on sentence-level, instead of adopting the usual n-grams solution. Moreover, its asset of constructing a learning model without demanding any collected training data leads to an instance-based approach, while at the same time, it can be used as an internal mechanism for assigning labels to collected unlabeled training data, creating appropriate weakly supervised learning batch-based variants. Our evaluations over 3 different MeSH terms highlights the usefulness of these approaches against a state-of-the-art approach and a well-defined baseline, respectively.
Personalization of E-Learning Process Using AI-Powered Chatbot Integration

Personalized learning path is considered as one of the most effective way to archive the best outcomes from the eLearning process. The introduction of artificial intelligence (AI) and machine learning (ML) into the educational process is one of the most important trends of modern eLearning since it can provide flexibility of learning and can performing adaptation of the learning process based on the personal needs of each user. AI technologies are positioned as the best solution for the analysis of each learner’s personal requirements and for generating a corresponding learning path with personalized educational content. One of the possible outcomes of introducing AI and ML into the eLearning process is the usage of AI-powered chatbots. Chatbots could significantly simplify the learning process by providing learners with the resources they are looking for which have previously been tailored for their learning personal characteristics. The integration of chatbots provides answers corresponding to the personal requirements of each learner into the eLearning system and could cover the gap in real-time consulting, which is not available with offline courses. Development of a chatbot is a trivial task for modern software engineering. However the determination of a chatbot behavior and its source dictionary is a complex task that requires development and implementation of additional analytic algorithms and models. This paper describes an approach for the design and development of a chatbot for an eLearning system with the personalization of consulting materials in-line with the learner’s personal needs.