Reviewerly/exHarmony huggingface dataset:
Coevolution of Generative Adversarial Networks

Generative adversarial networks (GAN) became a hot topic, presenting impressive results in the field of computer vision. However, there are still open problems with the GAN model, such as the training stability and the hand-design of architectures. Neuroevolution is a technique that can be used to provide the automatic design of network architectures even in large search spaces as in deep neural networks. Therefore, this project proposes COEGAN, a model that combines neuroevolution and coevolution in the coordination of the GAN training algorithm. The proposal uses the adversarial characteristic between the generator and discriminator components to design an algorithm using coevolution techniques. Our proposal was evaluated in the MNIST dataset. The results suggest the improvement of the training stability and the automatic discovery of efficient network architectures for GANs. Our model also partially solves the mode collapse problem.
FetchSGD: Communication-Efficient Federated Learning with Sketching.

Existing approaches to federated learning suffer from a communication bottleneck as well as convergence issues due to sparse client participation. In this paper we introduce a novel algorithm, called FetchSGD, to overcome these challenges. FetchSGD compresses model updates using a Count Sketch, and then takes advantage of the mergeability of sketches to combine model updates from many workers. A key insight in the design of FetchSGD is that, because the Count Sketch is linear, momentum and error accumulation can both be carried out within the sketch. This allows the algorithm to move momentum and error accumulation from clients to the central aggregator, overcoming the challenges of sparse client participation while still achieving high compression rates and good convergence. We prove that FetchSGD has favorable convergence guarantees, and we demonstrate its empirical effectiveness by training two residual networks and a transformer model.
ICT Skills of University Students from the Faculty of Sport and Physical Education during the COVID-19 Pandemic

Background: (1) The COVID-19 pandemic has generated significant changes in teaching methods around the world, and the ideal of online education has become a reality. (2) Methods: A questionnaire was modified for this study in order to determine the following levels of Information and Communication Technologies (ICT) skills: file creation, file management, the use of emails, the use of the internet, and online communication. In total, 360 students from the Faculty of Sports and Physical Education (FSPE), University of Novi Sad, participated in the study. (3) Results: The results show that there are differences between the estimated level of ICT skills and the ICT skills used in online education, such that students estimate their level of ICT skills as being higher than is necessary for online education (p &lt; 0.05). There is also a correlation between the satisfaction with online education and ICT skills, showing that students with higher ICT knowledge are more satisfied with online education (p &lt; 0.05). There is another correlation between the satisfaction with online education and the frequency of ICT use—the more ICTs are included, the more satisfied the students will be (p &lt; 0.05). (4) Conclusions: The results of this study can serve as a recommendation for the implementation of FSPE students’ training in ICT skills, as well as an important basis for the systematic creation, improvement, and sustainability of online education in universities.
Improving speech recognition models with small samples for air traffic control systems

In the domain of air traffic control (ATC) systems, efforts to train a practical automatic speech recognition (ASR) model always faces the problem of small training samples since the collection and annotation of speech samples are expert- and domain-dependent task. In this work, a novel training approach based on pretraining and transfer learning is proposed to address this issue, and an improved end-to-end deep learning model is developed to address the specific challenges of ASR in the ATC domain. An unsupervised pretraining strategy is first proposed to learn speech representations from unlabeled samples for a certain dataset. Specifically, a masking strategy is applied to improve the diversity of the sample without losing their general patterns. Subsequently, transfer learning is applied to fine-tune a pretrained or other optimized baseline models to finally achieves the supervised ASR task. By virtue of the common terminology used in the ATC domain, the transfer learning task can be regarded as a sub-domain adaption task, in which the transferred model is optimized using a joint corpus consisting of baseline samples and new transcribed samples from the target dataset. This joint corpus construction strategy enriches the size and diversity of the training samples, which is important for addressing the issue of the small transcribed corpus. In addition, speed perturbation is applied to augment the new transcribed samples to further improve the quality of the speech corpus. Three real ATC datasets are used to validate the proposed ASR model and training strategies. The experimental results demonstrate that the ASR performance is significantly improved on all three datasets, with an absolute character error rate only one-third of that achieved through the supervised training. The applicability of the proposed strategies to other ASR approaches is also validated.
Unsupervised outlier detection in multidimensional data

Abstract Detection and removal of outliers in a dataset is a fundamental preprocessing task without which the analysis of the data can be misleading. Furthermore, the existence of anomalies in the data can heavily degrade the performance of machine learning algorithms. In order to detect the anomalies in a dataset in an unsupervised manner, some novel statistical techniques are proposed in this paper. The proposed techniques are based on statistical methods considering data compactness and other properties. The newly proposed ideas are found efficient in terms of performance, ease of implementation, and computational complexity. Furthermore, two proposed techniques presented in this paper use transformation of data to a unidimensional distance space to detect the outliers, so irrespective of the data’s high dimensions, the techniques remain computationally inexpensive and feasible. Comprehensive performance analysis of the proposed anomaly detection schemes is presented in the paper, and the newly proposed schemes are found better than the state-of-the-art methods when tested on several benchmark datasets.
“Feeling the Sensor Feeling you”: A Soma Design Exploration on Sensing Non-habitual Breathing

Though seemingly straightforward and habitual, breathing is a complex bodily function. Problematising the space of designing for breathing as a non-habitual act pertaining to different bodies or situations, we conducted a soma design exploration together with a classical singer. Reflecting on how sensors could capture the impact and somatic experience of being sensed led us to develop a new sensing mechanism using shape-change technologies integrated in the Breathing Shell: a wearable that evokes a reciprocal experience of “feeling the sensor feeling you” when breathing. We contribute with two design implications: 1) Enabling reflections of the somatic impact of being sensed in tandem with the type of data captured, 2) creating a tactile impact of the sensor data on the body. Both implications aim to deepen one’s understanding of how the whole soma relates to or with biosensors and ultimately leading to designing for symbiotic experiences between biosensors and bodies.
A Critical Assessment of the Use of SSQ as a Measure of General Discomfort in VR Head-Mounted Displays

Based on a systematic literature review of more than 300 papers published over the last 10 years, we provide indicators that the simulator sickness questionnaire (SSQ) is extensively used and widely accepted as a general discomfort measure in virtual reality (VR) research – although it actually only accounts for one category of symptoms. This results in important other categories (digital eye strain (DES) and ergonomics) being largely neglected. To contribute to a more comprehensive picture of discomfort in VR head-mounted displays, we further conducted an online study (N=352) on the severity and relevance of all three symptom categories. Most importantly, our results reveal that symptoms of simulator sickness are significantly less severe and of lower prevalence than those of DES and ergonomics. In light of these findings, we critically discuss the current use of SSQ as the only discomfort measure and propose a more comprehensive factor model that also includes DES and ergonomics.
Image encryption scheme based on alternate quantum walks and discrete cosine transform

As an important information medium, the digital image exists widely on the Internet. Quantum walks have the property of encrypting information. For the eneryption problem of optical digital images, an encryption scheme based on discrete cosine transform (DCT) and alternate quantum walks (AQW) is proposed in this paper. First, we use AQW and XOR operation to preprocess images in the spatial domain. Then, AQW are used to generate two random phase masks which can operate the preprocessed image and the DCT image, respectively. Finally, the encrypted image is obtained by using discrete cosine inverse exchange. The control parameters of AQW can replace the random phase mask as the key in the encryption and decryption process, so it is convenient for key management and transmission. The experimental simulation carried out the analysis of the image pixel histogram, the correlation of adjacent pixels, the robustness against noise and the sensitivity of secret keys, the results show that the image encryption method has strong security.
Pixel-level and bit-level image encryption method based on Logistic-Chebyshev dynamic coupled map lattices

This paper proposes two new chaotic systems. One is one-dimensional Logistic-Chebyshev map (1DLCM) and the other is Logistic-Chebyshev dynamic coupled map lattices (LCDCML). LCDCML uses 1DLCM as the dynamic coupling coefficient, which meet the cryptographic characteristics and are suitable for studying image encryption. Based on these two systems, a pixel-level and bit-level image encryption algorithm is proposed. The specific encryption algorithm first generates the chaotic sequence by the LCDCML system. The scrambling part adopts pixel-level and bit-level scrambling combined with chaotic sequences. The pixel-level scrambling uses dynamic row and column index scrambling. The bit-level scrambling uses dynamic circular traversal and dynamic cyclic shifts. Then, the image is diffused by a special formula combined with chaotic sequences. Finally, this encryption scheme can also be extended to color images. Experimental simulation and performance analysis show that the encryption algorithm has higher security.
Hidden Markov models: Pitfalls and opportunities in ecology

Abstract Hidden Markov models (HMMs) and their extensions are attractive methods for analysing ecological data where noisy, multivariate measurements are made of a hidden, ecological process, and where this hidden process is represented by a sequence of discrete states. Yet, as these models become more complex and challenging to understand, it is important to consider what pitfalls these methods have and what opportunities there are for future research to address these pitfalls. In this paper, we review five lesser known pitfalls one can encounter when using HMMs or their extensions to solve ecological problems: (a) violation of the snapshot property in continuous‐time HMMs; (b) biased inference from hierarchical HMMs when applied to temporally misaligned processes; (c) sensitive inference from using random effects to partially pool across heterogeneous individuals; (d) computational burden when using HMMs to approximate models with continuous state spaces; and (e) difficulty linking the hidden process to space or environment. This review is for ecologists and ecological statisticians familiar with HMMs, but who may be less aware of the problems that arise in more specialised applications. We demonstrate how each pitfall arises, by simulation or example, and discuss why this pitfall is important to consider. Along with identifying the problems, we highlight potential research opportunities and offer ideas that may help alleviate these pitfalls. Each of the methods we review are solutions to current ecological research problems. We intend for this paper to heighten awareness of the pitfalls ecologists may encounter when applying these more advanced methods, but we also hope that by highlighting future research opportunities, we can inspire ecological statisticians to weaken these pitfalls and provide improved methods.
DCMS: Cyber Security Breaches Survey 2019

A third (32%) of UK business and nearly a quarter (22%) of charities found themselves under cyber attack or suffered data breaches in the past year, according to the latest report from the Government's Department for Digital, Culture, Media &amp; Sport (DCMS). And these figures climb substantially for larger organisations. However, this is actually a fall compared to previous years.
Meta-ADD: A meta-learning based pre-trained model for concept drift active detection

Concept drift is a phenomenon that commonly happened in data streams and need to be detected, because it means the statistical properties of a target variable, which the model is trying to predict, change over time in an unseen way. Most current detection methods are based on a hypothesis test framework. As a result, in these detection methods, a hypothesis test is need to be set, and more importantly, cannot obtain the type of drift. The setting of a hypothesis test requires an understanding of data streams, and cannot obtain the type of concept drift results in the loss of drift information. Hence, in this paper, to get rid of the setting of hypothesis test, and obtain the type of concept drift, we propose Active Drift Detection based on Meta learning (Meta-ADD), a novel framework that learns to classify concept drift by offline pre-training a model on data stream with known drifts, then online fine-tuning model to improve detection accuracy. Specifically, in the pre-trained phase, we extract meta-features based on the error rates of various concept drift, after which a pre-trained model called meta-detector is developed via a prototypical neural network by representing various concept drift classes as corresponding prototypes. In the detection phase, the meta-detector is fine-tuned to adapt to the real data stream via a simple stream-based active learning. Hence, Meta-ADD does not need a hypothesis test to detect concept drifts and identify their types automatically, which can directly support drift understand. The experiment results verify the effectiveness of Meta-ADD.
CoNeRF: Controllable Neural Radiance Fields

We extend neural 3D representations to allow for intu-itive and interpretable user control beyond novel view ren-dering (i. e. camera control). We allow the user to annotate which part of the scene one wishes to control with just a small number of mask annotations in the training images. Our key idea is to treat the attributes as latent variables that are regressed by the neural network given the scene en-coding. This leads to afew-shot learning framework, where attributes are discovered automatically by the framework, when annotations are not provided. We apply our method to various scenes with different types of controllable attributes (e.g. expression control on human faces, or state control in movement of inanimate objects). Overall, we demonstrate, to the best of our knowledge, for the first time novel view and novel attribute re-rendering of scenes from a single video.
3D Gaussian Splatting for Real-Time Radiance Field Rendering

Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates. We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (≥ 30 fps) novel-view synthesis at 1080p resolution. First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space; Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene; Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering. We demonstrate state-of-the-art visual quality and real-time rendering on several established datasets.
Finite-Horizon Distributed State Estimation Under Randomly Switching Topologies and Redundant Channels

The distributed state estimation problem is examined for a kind of nonlinear time-varying stochastic systems through sensor networks (SNs) with randomly switching topologies as well as redundant channels. The random switches of the topologies for SNs are governed by Markovian jumping parameters and the redundant channels are introduced to help improve the capability of the network communication. We are interested in designing distributed state estimators so that the estimation error dynamics is confirmed to reach a prescribed level of average H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance in terms of a finite horizon. Through intensive stochastic analysis, we acquire some sufficient conditions that guarantee the existence of the expected state estimators whose gain parameters are obtained recursively by means of the solution to a series of matrix inequalities. A numerical simulation is carried out to illustrate the validity of the developed state estimation algorithm.
Regularized Gaussian Mixture Model for High-Dimensional Clustering

Finding low-dimensional representation of high-dimensional data sets is an important task in various applications. The fact that data sets often contain clusters embedded in different subspaces poses barrier to this task. Driven by the need in methods that enable clustering and finding each cluster's intrinsic subspace simultaneously, in this paper, we propose a regularized Gaussian mixture model (GMM) for clustering. Despite the advantages of GMM, such as its probabilistic interpretation and robustness against observation noise, traditional maximum-likelihood estimation for GMMs shows disappointing performance in high-dimensional setting. The proposed regularization method finds low-dimensional representations of the component covariance matrices, resulting in better estimation of local feature correlations. The regularization problem can be incorporated in the expectation maximization algorithm for maximizing the likelihood function of a GMM, with the M-step modified to incorporate the regularization. The M-step involves a determinant maximization problem, which can be solved efficiently. The performance of the proposed method is demonstrated using several simulated data sets. We also illustrate the potential value of the proposed method in applications using four real data sets.
Group theory-based optimization algorithm for solving knapsack problems

This paper proposes a group theory-based optimization algorithm (GTOA) for knapsack problems, which draws algebraic group operations into the evolution process. The key parts of GTOA are that the feasible solution of the knapsack problem is considered as an element of the direct product of groups and that the evolution process is implemented by multiplication and inverse operations of the direct product of groups. Based on the algorithms for handling infeasible solutions, GTOA is used to solve knapsack problems such as the Set-union knapsack problem, the Discounted {0-1} knapsack problem, and the Bounded knapsack problem. GTOA is validated to be an efficient algorithm for solving knapsack problems. A comparison between GTOA and existing evolutionary algorithms such as genetic algorithm, binary particle swarm optimization, binary artificial bee colony, and their improved variations is conducted and the comparative results show that GTOA has a better performance than other algorithms. In addition, GTOA is not only an efficient algorithm for solving knapsack problems but is also the first paradigm that applies group theory to directly design an evolutionary algorithm.
Multi-Person Pose Estimation via Multi-Layer Fractal Network and Joints Kinship Pattern

We propose an effective method to boost the accuracy of multi-person pose estimation in images. Initially, the three-layer fractal network was constructed to regress multi-person joints location heatmap that can help to enhance an image region with receptive field and capture more joints local-contextual feature information, thereby producing keypoints heatmap intermediate prediction to optimize human body joints regression results. Subsequently, the hierarchical bi-directional inference algorithm was proposed to calculate the degree of relatedness (call it Kinship) for adjacent joints, and it combines the Kinship between adjacent joints with the spatial constraints, which we refer to as joints kinship pattern matching mechanism, to determine the best matched joints pair. We iterate the above-mentioned joints matching process layer by layer until all joints are assigned to a corresponding individual. Comprehensive experiments demonstrate that the proposed approach outperforms the state-of-the-art schemes and achieves about 1% and 0.6% increase in mAP on MPII multi-person subset and MSCOCO 2016 keypoints challenge.