Reviewerly/exHarmony huggingface dataset:
Long-term high frequency features for synthetic speech detection

Over the recent years, detection of spoofing attack has become pivotal for automatic speaker verification systems. The long-term information has been found to be useful for the detection of synthetic speech attacks. In this work, we focus on investigating high frequency information from the long-term constant-Q transform (CQT) based features. For this purpose, first we derive two novel feature, namely, inverted constant-Q coefficients (ICQC) and inverted constant-Q cepstral coefficients (ICQCC) using discrete cosine transform (DCT) on inverted octave power spectrum and inverted linear power spectrum, respectively. Further, we extend the work by use of overlapped block transform with DCT which can avoid the features extracted from full frequency-band are easily affected by local noise in some particular frequency bands. The two other novel features thus derived from inverted octave blocks and inverted linear blocks are referred to as constant-Q block coefficients (ICBC) and inverted constant-Q linear block coefficients (ICLBC), respectively. We study the proposed features on ASVspoof 2015, noisy ASVspoof 2015 and ASVspoof 2019 logical access corpora. For the proposed four features, our experimental results showed that ICQBC, ICBC, ICQC provide the best performance on ASVspoof 2015, noisy ASVspoof 2015 and ASVspoof 2019 logical access databases, respectively.
Fully automated scheme for computer‐aided detection and breast cancer diagnosis using digitised mammograms

Breast cancer becomes a significant public health problem in the world. During the early detection of breast cancer, it is a very challenging task to classify accurately the benign–malignant patterns in digital mammograms. This study proposes a new fully automated computer-aided diagnosis (CAD) system for breast cancer diagnosis with high-accuracy and low-computational requirements. The expectation–maximisation algorithm is investigated to extract automatically the region of interests (ROIs) within mammograms. The standard shape, statistical, and textural features of ROIs are extracted and combined with multi-resolution and multi-orientation features derived from a new feature extraction technique based on wavelet-based contourlet transform. A hybrid feature selection approach based on combining the support vector machine recursive feature elimination with correlation bias reduction algorithm is proposed. Also, the authors investigate a new similarity-based learning algorithm, called Q, for benign–malignant classification. The proposed CAD system is applied to real clinical mammograms, and the experimental results demonstrate the superior performance of the proposed CAD system over other existing CAD systems in terms of accuracy 98.16%, sensitivity 98.63%, specificity 97.80%, and computational time 2.2 s. This reveals the effectiveness of the proposed CAD system in improving the accuracy of breast cancer diagnosis in real-time systems.
Kaldi-based DNN Architectures for Speech Recognition in Romanian

Kaldi NNET3 is at the moment the leading speech recognition toolkit on many well-known tasks such as LibriSpeech, TED-LIUM or TIMIT. Several versions of the time-delay neural network (TDNN) architecture were recently proposed, implemented and evaluated for acoustic modeling with Kaldi: plain TDNN, convolutional TDNN (CNN-TDNN), long short-term memory TDNN (TDNN-LSTM) and TDNN-LSTM with attention. To the best of our knowledge, this is the first paper to describe in-depth these various flavors of TDNN, providing details regarding the speech features used at input, constituent layers and their dimensionality, regularization techniques etc. The various acoustic models were evaluated in conjunction with n-gram and recurrent language models in an automatic speech recognition (ASR) experiment for the Romanian language. We report significantly better results over the previous ASR systems for the same Romanian ASR tasks.
Anomaly Detection for Industrial Control Systems Using K-Means and Convolutional Autoencoder

With the rise of Internet of Things technology and smart manufacturing, the issues of cyber security in industrial control systems (ICS) have become of great concern. Critical infrastructures, such as power plants and water treatment facilities, which heavily rely on ICS, have increasingly come under cyberattack in recent years. These cyberattacks pose severe threats to public safety as well as national security. Therefore, protecting ICS from cyberattack is an important research issue in the era of information technology and operation technology convergence. In this study, we propose an anomaly detection method based on semi-supervised techniques for industrial control systems, using the k-means and convolutional autoencoder methods. Experimental results show that the proposed method outperforms the other methods in almost all performance metrics for gas pipeline and water storage tank datasets.
Competing Explanations of Competing Explanations: Accounting for Conflict Between Scientific and Folk Explanations

People who hold scientific explanations for natural phenomena also hold folk explanations, and the two types of explanations compete under some circumstances. Here, we explore the question of why folk explanations persist in the face of a well-understood scientific alternative, a phenomenon known as explanatory coexistence. We consider two accounts: an associative account, where coexistence is driven by low-level associations between co-occurring ideas in experience or discourse, and a theory-based account, where coexistence reflects high-level competition between distinct sets of causal expectations. We present data that assess the relative contributions of these two accounts to the cognitive conflict elicited by counterintuitive scientific ideas. Participants (134 college undergraduates) verified scientific statements like "air has weight" and "bacteria have DNA" as quickly as possible, and we examined the speed and accuracy of their verifications in relation to measures of associative information (lexical co-occurrence of the statements' subjects and predicates) and theory-based expectations (ratings of whether the statements' subjects possess theory-relevant attributes). Both measures explained a significant amount of variance in participants' responses, but the theory-based measures explained three to five times more. These data suggest that the cognitive conflict elicited by counterintuitive scientific ideas typically arises from competing theories and that such ideas might be made more intuitive by strengthening scientific theories or weakening folk theories.
Imbalanced Twitter Sentiment Analysis using Minority Oversampling

Micro-Blogging platforms have become one of the popular medium which reflects opinion/sentiment of social events and entities. Machine learning based sentiment analyses have been proven to be successful in finding people's opinion using redundantly available data. However, current study has pointed out that the data being used to train such machine learning models could be highly imbalanced. In the current study live tweets from Twitter have been used to systematically study the effect of class imbalance problem in sentiment analysis. Minority oversampling method is employed here to manage the imbalanced class problem. Two well-known classifiers Support Vector Machine and Multinomial Naïve Bayes have been used for classifying tweets into positive or negative sentiment classes. Results have revealed that minority oversampling based methods can overcome the imbalanced class problem to a greater extent.
Transferred Multi-Perception Attention Networks for Remote Sensing Image Super-Resolution

Image super-resolution (SR) reconstruction plays a key role in coping with the increasing demand on remote sensing imaging applications with high spatial resolution requirements. Though many SR methods have been proposed over the last few years, further research is needed to improve SR processes with regard to the complex spatial distribution of the remote sensing images and the diverse spatial scales of ground objects. In this paper, a novel multi-perception attention network (MPSR) is developed with performance exceeding those of many existing state-of-the-art models. By incorporating the proposed enhanced residual block (ERB) and residual channel attention group (RCAG), MPSR can super-resolve low-resolution remote sensing images via multi-perception learning and multi-level information adaptive weighted fusion. Moreover, a pre-train and transfer learning strategy is introduced, which improved the SR performance and stabilized the training procedure. Experimental comparisons are conducted using 13 state-of-the-art methods over a remote sensing dataset and benchmark natural image sets. The proposed model proved its excellence in both objective criterion and subjective perspective.
Analysis and Comparison of Robotics 3D Simulators

Validating robotics projects is a hard task. It demands more than intellectual effort, but also specific hardware usage, turning the designed project expensive and because of that, unviable to be executed. This process can be improved by the use of 3D simulators, which have been developed directed to scenarios such as architecture, games and virtual reality experiences, and can also be applied to robotics. There are available many 3D simulator options in the market, paid or free. Considering the 3D simulator options, a robotics project designer may have questions on how to choose the best suitable one. From that, this paper intends to analyze these software focusing on robotics simulation, proposing to clarify some questions and guide the simulator choice. After a literature review made in two stages (a more general search regarding 3D simulators and then the final choice considering abstract and conclusion), it was possible to choose the three most used ones by the robotic community: V-Rep, Gazebo, and Unity. We made a more in-depth study about them in order to evaluate and describe important features related to robotics. Later, we also did experiments using each tool intending to analyze terms relative to usability. The experiment consisted of replicating the same standard 3D scene in the three software. Based on the literature review and the experiments, it became possible to punctuate some observations. In terms of usability, V-Rep and Unity presented a significant degree of freedom considering scene editing, besides both having a friendly interface, while Gazebo showed to be more limited and it has a poor user interface. In the three simulators it is possible to perform a connection with ROS, but with different complexities, being Gazebo and V-Rep the easiest ones. They both have more features and facilities when considering developing a robotics project, leaving Unity behind.
Revisiting Spatio-Angular Trade-off in Light Field Cameras and Extended Applications in Super-Resolution

Light field cameras (LFCs) have received increasing attention due to their wide-spread applications. However, current LFCs suffer from the well-known spatio-angular trade-off, which is considered an inherent and fundamental limit for LFC designs. In this article, by doing a detailed optical analysis of the sampling process in an LFC, we show that the effective resolution is generally higher than the number of micro-lenses. This contribution makes it theoretically possible to super-resolve a light field. Further optical analysis proves the “2D predictable series” nature of the 4D light field, which provides new insights for analyzing light field using series processing techniques. To model this nature, a specifically designed epipolar plane image (EPI) based CNN-LSTM network is proposed to super-resolve a light field in the spatial and angular dimensions simultaneously. Rather than leveraging semantic information, our network focuses on extracting geometric continuity in the EPI domain. This gives our method an improved generalization ability and makes it applicable to a wide range of previously unseen scenes. Experiments on both synthetic and real light fields demonstrate the improvements over state-of-the-arts, especially in large disparity areas.
Remote Generation of Wigner Negativity through Einstein-Podolsky-Rosen Steering

Negativity of the Wigner function is seen as a crucial resource for reaching a quantum computational advantage with continuous variable systems. However, these systems, while they allow for the deterministic generation of large entangled states, require an extra element such as photon subtraction to obtain such negativity. Photon subtraction is known to affect modes beyond the one where the photon is subtracted, an effect which is governed by the correlations of the state. In this Letter, we build upon this effect to remotely prepare states with Wigner negativity. More specifically, we show that photon subtraction can induce Wigner negativity in a correlated mode if and only if that correlated mode can perform Einstein-Podolsky-Rosen steering in the mode of subtraction.
Learning to synthesise the ageing brain without longitudinal data

How will my face look when I get older? Or, for a more challenging question: How will my brain look when I get older? To answer this question one must devise (and learn from data) a multivariate auto-regressive function which given an image and a desired target age generates an output image. While collecting data for faces may be easier, collecting longitudinal brain data is not trivial. We propose a deep learning-based method that learns to simulate subject-specific brain ageing trajectories without relying on longitudinal data. Our method synthesises images conditioned on two factors: age (a continuous variable), and status of Alzheimer's Disease (AD, an ordinal variable). With an adversarial formulation we learn the joint distribution of brain appearance, age and AD status, and define reconstruction losses to address the challenging problem of preserving subject identity. We compare with several benchmarks using two widely used datasets. We evaluate the quality and realism of synthesised images using ground-truth longitudinal data and a pre-trained age predictor. We show that, despite the use of cross-sectional data, our model learns patterns of gray matter atrophy in the middle temporal gyrus in patients with AD. To demonstrate generalisation ability, we train on one dataset and evaluate predictions on the other. In conclusion, our model shows an ability to separate age, disease influence and anatomy using only 2D cross-sectional data that should be useful in large studies into neurodegenerative disease, that aim to combine several data sources. To facilitate such future studies by the community at large our code is made available at https://github.com/xiat0616/BrainAgeing.
Computational Commensality: From Theories to Computational Models for Social Food Preparation and Consumption in HCI

Food and eating are inherently social activities taking place, for example, around the dining table at home, in restaurants, or in public spaces. Enjoying eating with others, often referred to as "commensality", positively affects mealtime in terms of, among other factors, food intake, food choice, and food satisfaction. In this paper we discuss the concept of "Computational Commensality", that is, technology which computationally addresses various social aspects of food and eating. In the past few years, Human-Computer Interaction started to address how interactive technologies can improve mealtimes. However, the main focus has been made so far on improving the individual's experience, rather than considering the inherently social nature of food consumption. In this survey, we first present research from the field of social psychology on the social relevance of Food- and Eating-related Activities (F&EA). Then, we review existing computational models and technologies that can contribute, in the near future, to achieve Computational Commensality. We also discuss the related research challenges and indicate future applications of such new technology that can potentially improve F&EA from the commensality perspective.
Detection and identification of significant events in historical aircraft trajectory data

A large amount of data is produced every day by stakeholders of the Air Traffic Management (ATM) system, in particular airline operators, airports, and air navigation service providers (ANSP). Most data is kept private for many reasons, including commercial and security concerns. More than data, shared information is precious, as it leverages intelligent decision-making support tools designed to smoothen daily operations. We present a framework to detect, identify and characterise anomalies in past aircraft trajectory data. It is based on an open source of ADS-B based aircraft trajectories, and extracted information can benefit a wide range of stakeholders: Air Traffic Control (ATC) training centres could play more realistic simulations; ANSP may improve capacity indicators; academics improve safety models and risk estimations; and commercial stakeholders, like airlines and airports, may use such information to improve short-term predictions and optimise their operations. The technique is based on autoencoding artificial neural networks applied on flows of trajectories, which provide a useful reading grid associating cluster analysis with quantified level of abnormality. In particular, we find that the highest anomaly scores correspond to poor weather conditions, whereas anomalies with a lower score relate to ATC tactical actions.
Augmented Reality Application for Preschool Children with Unity 3D Platform

In recent years, Augmented Reality (AR) applications have been introduced especially in a number of different areas by being used more advanced lenses in the cameras in addition to the processor and image processing capacities of smart phones. Nowadays, from Mobile AR games to entertainment applications, from online shopping to Industry 4.0 solutions, Augmented Reality has become one of the most used technologies. Within the scope of this study, the principle of the fun learning of children in preschool age in Turkey has been discussed. An AR application was developed for preschool children to make their learning more fun. In this AR application, Unity 3D platform and Vuforia SDK Augmented Reality Library were utilized.
Deep Contextualized Self-training for Low Resource Dependency Parsing

Neural dependency parsing has proven very effective, achieving state-of-the-art results on numerous domains and languages. Unfortunately, it requires large amounts of labeled data, which is costly and laborious to create. In this paper we propose a self-training algorithm that alleviates this annotation bottleneck by training a parser on its own output. Our Deep Contextualized Self-training (DCST) algorithm utilizes representation models trained on sequence labeling tasks that are derived from the parser’s output when applied to unlabeled data, and integrates these models with the base parser through a gating mechanism. We conduct experiments across multiple languages, both in low resource in-domain and in cross-domain setups, and demonstrate that DCST substantially outperforms traditional self-training as well as recent semi-supervised training methods. 1
Digital Versus Paper Reading Processes and Links to Comprehension for Middle School Students

This study explores digital and paper reading processes and outcomes for 371 fifth to eighth graders completing a reading task similar to standardized testing. Results showed students highlighted and annotated more when reading the paper versus digital text. Also, reading on paper versus digitally was slightly supportive of reading comprehension for the longer section of text. For behaviors, digital highlighting and looking back at the paper text were supportive of reading comprehension, whereas paper highlighting was negatively related, likely because paper highlighting tended to occur often outside of important areas of the text. Paper and digital annotating, online dictionary use, and digital look-back did not link to comprehension, when controlling for other covariates. Links to theory, research, and practice are discussed.
A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

This article presents the Optimised Stream clustering algorithm (OpStream), a novel approach to cluster dynamic data streams. The proposed system displays desirable features, such as a low number of parameters and good scalability capabilities to both high-dimensional data and numbers of clusters in the dataset, and it is based on a hybrid structure using deterministic clustering methods and stochastic optimisation approaches to optimally centre the clusters. Similar to other state-of-the-art methods available in the literature, it uses “microclusters” and other established techniques, such as density based clustering. Unlike other methods, it makes use of metaheuristic optimisation to maximise performances during the initialisation phase, which precedes the classic online phase. Experimental results show that OpStream outperforms the state-of-the-art methods in several cases, and it is always competitive against other comparison algorithms regardless of the chosen optimisation method. Three variants of OpStream, each coming with a different optimisation algorithm, are presented in this study. A thorough sensitive analysis is performed by using the best variant to point out OpStream’s robustness to noise and resiliency to parameter changes.
A New Loss Function for CNN Classifier Based on Predefined Evenly-Distributed Class Centroids

With the development of convolutional neural networks (CNNs) in recent years, the network structure has become more and more complex and varied, and has achieved very good results in pattern recognition, image classification, object detection and tracking.For CNNs used for image classification, in addition to the network structure, more and more researches focus on the improvement of the loss function, so as to enlarge the inter-class feature differences, and reduce the intra-class feature variations as soon as possible.Besides the traditional Softmax, typical loss functions include L-Softmax, AM-Softmax, ArcFace, and Center loss, etc.Based on the concept of predefined evenly-distributed class centroids (PEDCC) in CSAE network, this paper proposes a PEDCC-based loss function called PEDCC-Loss, which can make the inter-class distance maximal and intra-class distance small enough in latent feature space.Multiple experiments on image classification and face recognition have proved that our method achieve the best recognition accuracy, and network training is stable and easy to converge.Code is available in https://github.com/ZLeopard/PEDCC-Loss
TeleGam: Combining Visualization and Verbalization for Interpretable Machine Learning

While machine learning (ML) continues to find success in solving previously-thought hard problems, interpreting and exploring ML models remains challenging. Recent work has shown that visualizations are a powerful tool to aid debugging, analyzing, and interpreting ML models. However, depending on the complexity of the model (e.g., number of features), interpreting these visualizations can be difficult and may require additional expertise. Alternatively, textual descriptions, or verbalizations, can be a simple, yet effective way to communicate or summarize key aspects about a model, such as the overall trend in a model's predictions or comparisons between pairs of data instances. With the potential benefits of visualizations and verbalizations in mind, we explore how the two can be combined to aid ML interpretability. Specifically, we present a prototype system, TeleGam, that demonstrates how visualizations and verbalizations can collectively support interactive exploration of ML models, for example, generalized additive models (GAMs). We describe TELEGAM's interface and underlying heuristics to generate the verbalizations. We conclude by discussing how TeleGam can serve as a platform to conduct future studies for understanding user expectations and designing novel interfaces for interpretable ML.