Reviewerly/exHarmony huggingface dataset:
Unsupervised Image Segmentation by Mutual Information Maximization and Adversarial Regularization

Semantic segmentation is one of the basic, yet essential scene understanding tasks for an autonomous agent. The recent developments in supervised machine learning and neural networks have enjoyed great success in enhancing the performance of the state-of-the-art techniques for this task. However, their superior performance is highly reliant on the availability of a large-scale annotated dataset. In this letter, we propose a novel fully unsupervised semantic segmentation method, the so-called Information Maximization and Adversarial Regularization Segmentation (InMARS). Inspired by human perception which parses a scene into perceptual groups, rather than analyzing each pixel individually, our proposed approach first partitions an input image into meaningful regions (also known as superpixels). Next, it utilizes Mutual-Information-Maximization followed by an adversarial training strategy to cluster these regions into semantically meaningful classes. To customize an adversarial training scheme for the problem, we incorporate adversarial pixel noise along with spatial perturbations to impose photometrical and geometrical invariance on the deep neural network. Our experiments demonstrate that our method achieves the state-of-the-art performance on two commonly used unsupervised semantic segmentation datasets, COCO-Stuff, and Potsdam.
A Privacy Protection Scheme for IoT Big Data Based on Time and Frequency Limitation

Various applications of the Internet of Things assisted by deep learning such as autonomous driving and smart furniture have gradually penetrated people’s social life. These applications not only provide people with great convenience but also promote the progress and development of society. However, how to ensure that the important personal privacy information in the big data of the Internet of Things will not be leaked when it is stored and shared on the cloud is a challenging issue. The main challenges include (1) the changes in access rights caused by the flow of manufacturers or company personnel while sharing and (2) the lack of limitation on time and frequency. We propose a data privacy protection scheme based on time and decryption frequency limitation that can be applied in the Internet of Things. Legitimate users can obtain the original data, while users without a homomorphic encryption key can perform operation training on the homomorphic ciphertext. On the one hand, this scheme does not affect the training of the neural network model, on the other hand, it improves the confidentiality of data. Besides that, this scheme introduces a secure two-party agreement to improve security while generating keys. While revoking, each attribute is specified for the validity period in advance. Once the validity period expires, the attribute will be revoked. By using storage lists and setting tokens to limit the number of user accesses, it effectively solves the problem of data leakage that may be caused by multiple accesses in a long time. The theoretical analysis demonstrates that the proposed scheme can not only ensure safety but also improve efficiency.
Revitalizing blockchain technology potentials for smooth academic records management and verification in low-income countries

Higher education systems in low-income countries are usually fragile and sensitive to the surrounding political and economic environment. This environment poses various problems for students, graduates, and universities with regard to issuance and verification of academic records management. The process of verification and accreditation of certificates is long and costly for both students and universities. This study endeavors to address these problems by introducing an innovative solution based on blockchain technology. We propose establishing a national hybrid blockchain-based platform that brings together all possible stakeholders in the field of higher education, including students, universities, government agencies, policymakers, and companies from the business sector. This study evaluates the costs and benefits from establishing this platform and investigates the possible commercialization of its contracts. Furthermore, we test the feasibility of this platform in two of the most instable low-income countries: Syria and Sudan. We find that this platform is feasible and can serve as a marketplace for all stakeholders in the higher education system and would increase the internationalization of higher education. The establishment of this platform will ensure inclusive and equitable education and promotes lifelong learning opportunities for students from low-income countries and thus meets the fourth goal of Sustainable Development.
Self-Supervised Wasserstein Pseudo-Labeling for Semi-Supervised Image Classification

The goal is to use Wasserstein metric to provide pseudo labels for the unlabeled images to train a Convolutional Neural Networks (CNN) in a Semi-Supervised Learning (SSL) manner for the classification task. The basic premise in our method is that the discrepancy between two discrete empirical measures (e.g., clusters) which come from the same or similar distribution is expected to be less than the case where these measures come from completely two different distributions. In our proposed method, we first pre-train our CNN using a self-supervised learning method to make a cluster assumption on the unlabeled images. Next, inspired by the Wasserstein metric which considers the geometry of the metric space to provide a natural notion of similarity between discrete empirical measures, we leverage it to cluster the unlabeled images and then match the clusters to their similar class of labeled images to provide a pseudo label for the data within each cluster. We have evaluated and compared our method with state-of-the-art SSL methods on the standard datasets to demonstrate its effectiveness.
Classical Machine Learning Versus Deep Learning for the Older Adults Free-Living Activity Classification

Physical activity has a strong influence on mental and physical health and is essential in healthy ageing and wellbeing for the ever-growing elderly population. Wearable sensors can provide a reliable and economical measure of activities of daily living (ADLs) by capturing movements through, e.g., accelerometers and gyroscopes. This study explores the potential of using classical machine learning and deep learning approaches to classify the most common ADLs: walking, sitting, standing, and lying. We validate the results on the ADAPT dataset, the most detailed dataset to date of inertial sensor data, synchronised with high frame-rate video labelled data recorded in a free-living environment from older adults living independently. The findings suggest that both approaches can accurately classify ADLs, showing high potential in profiling ADL patterns of the elderly population in free-living conditions. In particular, both long short-term memory (LSTM) networks and Support Vector Machines combined with ReliefF feature selection performed equally well, achieving around 97% F-score in profiling ADLs.
Conditions Affecting the Release of Heavy and Rare Earth Metals from the Mine Tailings Kola Subarctic

In the Kola Subarctic, a mining industry has developed, which is a source of environmental pollution with heavy metals. The objects of study were the tailings of three large mining enterprises in the region: apatite-nepheline, complex and loparite ores. The geotechnical characteristics were studied, and the granulometric composition of the samples was established. The main minerals that make up the material of ore dressing tailings have been determined. Using inductively coupled plasma mass spectrometry, the content of trace elements, in particular heavy metals and rare earth elements, has been established. The enrichment factor, the geoaccumulation indexes, the potential ecological risk index factor and the potential environmental hazard index have been calculated. Priority pollutants characteristics for specific objects have been identified. It is noted that the finely dispersed material of the tailings of loparite and complex ores is 1.5–3 times enriched in heavy and rare earth metals in comparison with the total material of the tailings. In laboratory conditions, experiments were carried out to simulate the process of interaction of dust particles with soil solutions containing different amounts of dissolved organic matter and at average seasonal temperatures. It was found that a decrease in the pH of the solution and an increase in the amount of organic carbon and temperature lead to the mobilization of heavy and rare earth metals from the tailings.
Review and classification of content recommenders in E-learning environment

E-learning recommender systems are becoming more popular due to the massive learning materials available online and the changing pedagogy. A content recommender system in the e-learning domain helps the learners by suggesting appropriate learning resources based on their preferences and learning goals. This paper presents a literature review on the recent studies conducted on content recommenders in the e-learning domain. The articles chosen for the review are mainly studies on personalized and adaptive learning systems. For that, we have collected and analyzed a set of journal papers published in this field during the period 2015–2020. Based on the analysis, we have categorized the different recommendation techniques, data inputs, algorithms, similarity measures, and evaluation metrics used in these studies. The paper also highlights the current trends in the recommendation process and the merits and demerits of the selected studies. Thus it provides an insight into the current state-of-the-art.
Hyperspectral remote sensing for detecting geotechnical problems at Ray mine

While many or most geotechnical problems at open-pit mines are related to geological structures or discontinuities, highwall movement and failure can also occur as a consequence of nonstructural geological factors. Nonstructural causes of movement are not amenable to detection by conventional geotechnical sensing techniques such as LiDAR. In this case study, we applied hyperspectral remote sensing for large-scale mapping and detection of minerals at a non-structure-related ground instability in the highwalls of the Ray mine near Tucson, Arizona. The spectral images, obtained and integrated with radar images and the geological map, show that the dominant spectrally active mineral underlying the unstable area is the swelling clay montmorillonite, whereas kaolinite and white mica are more common in more stable parts of the highwall. The montmorillonite is concentrated in an outcropping altered diabase and conglomerate that underlie more competent rocks, providing a potential lift and slip surface. This study shows that hyperspectral remote sensing can aid in geotechnical slope characterization, particularly for nonstructural causes of failure. We provide a brief overview of best practices for hyperspectral remote sensing in geotechnical applications (combining drone- and tripod-mounted sensors, integrating hyperspectral with LiDAR and radar data, and using an iteratively refined spectral library based on site-specific sampling supported by ground truth).
Heterogeneous Domain Adaptation by Information Capturing and Distribution Matching

Heterogeneous domain adaptation (HDA) is a challenging problem because of the different feature representations in the source and target domains. Most HDA methods search for mapping matrices from the source and target domains to discover latent features for learning. However, these methods barely consider the reconstruction error to measure the information loss during the mapping procedure. In this paper, we propose to jointly capture the information and match the source and target domain distributions in the latent feature space. In the learning model, we propose to minimize the reconstruction loss between the original and reconstructed representations to preserve information during transformation and reduce the Maximum Mean Discrepancy between the source and target domains to align their distributions. The resulting minimization problem involves two projection variables with orthogonal constraints that can be solved by the generalized gradient flow method, which can preserve orthogonal constraints in the computational procedure. We conduct extensive experiments on several image classification datasets to demonstrate that the effectiveness and efficiency of the proposed method are better than those of state-of-the-art HDA methods.
Container lifecycle‐aware scheduling for serverless computing

Abstract Elastic scaling in response to changes on demand is a main benefit of serverless computing. When bursty workloads arrive, a serverless platform launches many new containers and initializes function environments (known as cold starts), which incurs significant startup latency. To reduce cold starts, platforms usually pause a container after it serves a request, and reuse this container for subsequent requests. However, this reuse strategy cannot efficiently reduce cold starts because the schedulers are agnostic of container lifecycle. For example, it may ignore soon available containers or evict soon needed containers. We propose a container lifecycle‐aware scheduling strategy for serverless computing, CAS. The key idea is to control distribution of requests and determine creation or eviction of containers according to different lifecycle phases of containers. We implement a prototype of CAS on OpenWhisk. Our evaluation shows that CAS reduces 81% cold starts and therefore brings a 63% reduction at 95th percentile latency compared with native scheduling strategy in OpenWhisk when there is worker contention between workloads, and does not add significant performance overhead.
FESTA: Flow Estimation via Spatial-Temporal Attention for Scene Point Clouds

Scene flow depicts the dynamics of a 3D scene, which is critical for various applications such as autonomous driving, robot navigation, AR/VR, etc. Conventionally, scene flow is estimated from dense/regular RGB video frames. With the development of depth-sensing technologies, precise 3D measurements are available via point clouds which have sparked new research in 3D scene flow. Nevertheless, it remains challenging to extract scene flow from point clouds due to the sparsity and irregularity in typical point cloud sampling patterns. One major issue related to irregular sampling is identified as the randomness during point set abstraction/feature extraction—an elementary process in many flow estimation scenarios. A novel Spatial Abstraction with Attention (SA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) layer is accordingly proposed to alleviate the unstable abstraction problem. Moreover, a Temporal Abstraction with Attention (TA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) layer is proposed to rectify attention in temporal domain, leading to benefits with motions scaled in a larger range. Extensive analysis and experiments verified the motivation and significant performance gains of our method, dubbed as Flow Estimation via Spatial-Temporal Attention (FESTA), when compared to several state-of-the-art benchmarks of scene flow estimation.
An optimal model identification for solid oxide fuel cell based on extreme learning machines optimized by improved Red Fox Optimization algorithm

The present study proposes an efficient method for optimal model parameters estimation of the Solid Oxide Fuel Cell by considering its nonlinear dynamic behavior. The approach is relied on using a novel optimal model of Extreme Learning Machines (ELM) network based on metaheuristics. The main purpose is to minimize the Mean Squared Error (MSE) between the empirical output voltage data and the output voltage of the model by the suggested optimized ELM network. The proposed ELM network is optimized by an enhanced design of the Red Fox Optimizer (IRFO) Algorithm to provide optimal results. The suggested ELM-IRFO method is then testified on a Solid Oxide Fuel Cell case study and its results are compared with the GWO-RHNN method from the literature and ELM-RFO method to show its effectiveness. The final results showed that the proposed ELM-IRFO has the minimum value of the Mean Squared Error (MSE) against the other comparative methods.
An efficient image encryption scheme based on fractal Tromino and Chebyshev polynomial

Abstract The security of digital content during transmission and storage through insecure communication links and databases is a challenging issue in today's world. In this article, an encryption scheme based on fractal Tromino and Chebyshev polynomial-based generated chaotic matrix is presented. The scheme fulfills the most fundamental aspect of encryption that is diffusion and confusion. For confusion highly non-linear, pre-defined S-boxes are used. The proposed scheme has been tested using state-of-the-art key performance indicators including differential analysis, statistical analysis. Information entropy analysis, mean square error, and NIST-based randomness analysis. The encrypted images have the highest practically achievable entropy of 7.999 and the time analysis shows that the proposed system is suitable for real-time implementation. The rest of the results indicates that the proposed cryptosystem possesses high immunity toward various attacks. The security analysis compared with the existing scheme shows the strength of the suggested scheme.
Deep Texture Recognition via Exploiting Cross-Layer Statistical Self-Similarity

In recent years, convolutional neural networks (CNNs) have become a prominent tool for texture recognition. The key of existing CNN-based approaches is aggregating the convolutional features into a robust yet discriminative description. This paper presents a novel feature aggregation module called CLASS (Cross-Layer Aggregation of Statistical Self-similarity) for texture recognition. We model the CNN feature maps across different layers, as a dynamic process which carries the statistical self-similarity (SSS), one well-known property of texture, from input image along the network depth dimension. The CLASS module characterizes the cross-layer SSS using a soft histogram of local differential box-counting dimensions of cross-layer features. The resulting descriptor encodes both cross-layer dynamics and local SSS of input image, providing additional discrimination over the often-used global average pooling. Integrating CLASS into a ResNet backbone, we develop CLASSNet, an effective deep model for texture recognition, which shows state-of-the-art performance in the experiments.
UrbanVR: An immersive analytics system for context-aware urban design

Urban design is a highly visual discipline that requires visualization for informed decision making. However, traditional urban design tools are mostly limited to representations on 2D displays that lack intuitive awareness. The popularity of head-mounted displays (HMDs) promotes a promising alternative with consumer-grade 3D displays. We introduce UrbanVR, an immersive analytics system with effective visualization and interaction techniques, to enable architects to assess designs in a virtual reality (VR) environment. Specifically, UrbanVR incorporates 1) a customized parallel coordinates plot (PCP) design to facilitate quantitative assessment of high-dimensional design metrics, 2) a series of egocentric interactions, including gesture interactions and handle-bar metaphors, to facilitate user interactions, and 3) a viewpoint optimization algorithm to help users explore both the PCP for quantitative analysis, and objects of interest for context awareness. Effectiveness and feasibility of the system are validated through quantitative user studies and qualitative expert feedbacks.
Towards Analysis-Friendly Face Representation With Scalable Feature and Texture Compression

Compactly representing visual information plays a fundamental role in optimizing the ultimate utility of myriad visual data-centered applications. Numerous approaches have been proposed to efficiently compress the texture and visual features for human visual perception and machine intelligence, respectively; however, much less work has been dedicated to studying the interactions between them. Here, we investigate the integration of feature and texture compression and show that a universal and collaborative visual information representation can be achieved in a hierarchical way. In particular, we study feature and texture compression in a scalable coding framework, where the base layer serves as the deep learning feature and the enhancement layer targets to perfectly reconstruct the texture. Based on the strong generative capability of deep neural networks, the gap between the base feature layer and enhancement layer is further filled with feature-level texture reconstruction, with the goal of further constructing texture representations from features. As such, the residuals between the original and reconstructed texture could be further conveyed in the enhancement layer. To improve the efficiency of the proposed framework, the base layer neural network is trained in a multitask manner such that the learned features enjoy both high-quality reconstruction and high-accuracy analysis. The framework and optimization strategies are further applied in face image compression, and promising coding performance has been achieved in terms of both rate-fidelity and rate-accuracy evaluations.
FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian Noise

We consider the challenging blind denoising problem for Poisson-Gaussian noise, in which no additional information about clean images or noise level parameters is available. Particularly, when only "single" noisy images are available for training a denoiser, the denoising performance of existing methods was not satisfactory. Recently, the blind pixelwise affine image denoiser (BP-AIDE) was proposed and significantly improved the performance in the above setting, to the extent that it is competitive with denoisers which utilized additional information. However, BP-AIDE seriously suffered from slow inference time due to the inefficiency of noise level estimation procedure and that of the blind-spot network (BSN) architecture it used. To that end, we propose Fast Blind Image Denoiser (FBI-Denoiser) for Poisson-Gaussian noise, which consists of two neural network models; 1) PGE-Net that estimates Poisson-Gaussian noise parameters 2000 times faster than the conventional methods and 2) FBI-Net that realizes a much more efficient BSN for pixelwise affine denoiser in terms of the number of parameters and inference speed. Consequently, we show that our FBI-Denoiser blindly trained solely based on single noisy images can achieve the state-of-the-art performance on several real-world noisy image benchmark datasets with much faster inference time (× 10), compared to BP-AIDE.