Reviewerly/exHarmony huggingface dataset:
New sediment provenance approach based on orthonormal log ratio transformation of geochemical and heavy mineral data: Sources of eolian sands from the southeastern Adriatic archipelago

To date, much effort has been made to overcome obstacles involved in inferring the provenance of sediments based on heavy mineral assemblages and geochemical fingerprints of minerals. These efforts have been concentrated on two main avenues. First, efforts have focused on disentangling physical and chemical properties of minerals and their behavior during transport. Second, efforts have centered on processing the obtained data in a way that takes into account the properties of heavy minerals and geochemical data sets. More specifically, to consider the limitations that arise from the nature of point count and compositional data. In this manuscript, a novel workflow for provenance is proposed based on the fact that point count data, although not compositional, carry compositional information. A case study is presented to infer the provenance of late Quaternary eolian sands from the southeastern Adriatic archipelago for which two different sources have been previously proposed. Heavy mineral data were represented in orthonormal log ratio coordinates. In this way, variables which compensate for hydraulic sorting and/or selective dissolution were obtained, with the major advantage that there are no restrictions for their use with statistical methods originally developed for real Euclidian space. To support conclusions based on heavy mineral data, we analyzed the geochemical composition of pyroxenes, which were also discriminated using a novel approach. As a result, the Albania-Montenegro shelf is determined to be a sediment source for eolian sands from Korčula, Lastovo and Mljet Islands. Hvar Island has a Neretva River signature, and Vis Island has a dominantly Neretva River signature with a small portion of sediments from the Albania-Montenegro shelf.
RSPCN: Super-Resolution of Digital Elevation Model Based on Recursive Sub-Pixel Convolutional Neural Networks

The digital elevation model (DEM) is known as one kind of the most significant fundamental geographical data models. The theory, method and application of DEM are hot research issues in geography, especially in geomorphology, hydrology, soil and other related fields. In this paper, we improve the efficient sub-pixel convolutional neural networks (ESPCN) and propose recursive sub-pixel convolutional neural networks (RSPCN) to generate higher-resolution DEMs (HRDEMs) from low-resolution DEMs (LRDEMs). Firstly, the structure of RSPCN is described in detail based on recursion theory. This paper explores the effects of different training datasets, with the self-adaptive learning rate Adam algorithm optimizing the model. Furthermore, the adding-“zero” boundary method is introduced into the RSPCN algorithm as a data preprocessing method, which improves the RSPCN method’s accuracy and convergence. Extensive experiments are conducted to train the method till optimality. Finally, comparisons are made with other traditional interpolation methods, such as bicubic, nearest-neighbor and bilinear methods. The results show that our method has obvious improvements in both accuracy and robustness and further illustrate the feasibility of deep learning methods in the DEM data processing area.
EAN: Event Adaptive Network for Enhanced Action Recognition

Efficiently modeling spatial–temporal information in videos is crucial for action recognition. To achieve this goal, state-of-the-art methods typically employ the convolution operator and the dense interaction modules such as non-local blocks. However, these methods cannot accurately fit the diverse events in videos. On the one hand, the adopted convolutions are with fixed scales, thus struggling with events of various scales. On the other hand, the dense interaction modeling paradigm only achieves sub-optimal performance as action-irrelevant parts bring additional noises for the final prediction. In this paper, we propose a unified action recognition framework to investigate the dynamic nature of video content by introducing the following designs. First, when extracting local cues, we generate the spatial–temporal kernels of dynamic-scale to adaptively fit the diverse events. Second, to accurately aggregate these cues into a global video representation, we propose to mine the interactions only among a few selected foreground objects by a Transformer, which yields a sparse paradigm. We call the proposed framework as Event Adaptive Network because both key designs are adaptive to the input video content. To exploit the short-term motions within local segments, we propose a novel and efficient Latent Motion Code module, further improving the performance of the framework. Extensive experiments on several large-scale video datasets, e.g., Something-to-Something V1 &V2, Kinetics, and Diving48, verify that our models achieve state-of-the-art or competitive performances at low FLOPs. Codes are available at: https://github.com/tianyuan168326/EAN-Pytorch .
Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks

As neural networks gain widespread adoption in embedded devices, there is a growing need for model compression techniques to facilitate seamless deployment in resource-constrained environments. Quantization is one of the go-to methods yielding state-of-the-art model compression. Most quantization approaches take a fully trained model, then apply different heuristics to determine the optimal bit-precision for different layers of the network, and finally retrain the network to regain any drop in accuracy. Based on Activation Density-the proportion of non-zero activations in a layer-we propose a novel in-training quantization method. Our method calculates optimal bit-width/precision for each layer during training yielding an energy-efficient mixed precision model with competitive accuracy. Since we train lower precision models progressively during training, our approach yields the final quantized model at lower training complexity and also eliminates the need for re-training. We run experiments on benchmark datasets like CIFAR-10, CIFAR-100, TinyImagenet on VGG19/ResNet18 architectures and report the accuracy and energy estimates for the same. We achieve up to 4.5× benefit in terms of estimated multiply-and-accumulate (MAC) reduction while reducing the training complexity by 50% in our experiments. To further evaluate the energy benefits of our proposed method, we develop a mixed-precision scalable Process In Memory (PIM) hardware accelerator platform. The hardware platform incorporates shift-add functionality for handling multibit precision neural network models. Evaluating the quantized models obtained with our proposed method on the PIM platform yields about 5× energy reduction compared to baseline 16-bit models. Additionally, we find that integrating activation density based quantization with activation density based pruning (both conducted during training) yields up to ~ 198× and ~44× energy reductions for VGG19 and ResNet18 architectures respectively on PIM platform compared to baseline 16-bit precision, unpruned models.
Process of Materials Picking Using Augmented Reality

The thesis describes the research conducted on the effectiveness of purposed Augmented Reality solutions for material identification in the production system. The research was carried out at the Smart Factory laboratory of the Poznan University of Technology and was divided into two stages. In the first stage, the research has been conducted to identify both of the best variants of application and the AR smart glasses which were used in second stage. The time of identification and completion of production parts obtained using the AR technique and the traditional technique were compared during the second stage of the research. The results of the second experiment show that the AR method is efficacious for identifying materials in a production system; however, the traditional method time is shorter. This proves that the traditional method is more effective and therefore AR solutions need to be improved.
Not Enough LESS: An Improved Algorithm for Solving Code Equivalence Problems over $$\mathbb {F}_q$$

Recently, a new code based signature scheme, called LESS, was proposed with three concrete instantiations, each aiming to provide 128 bits of classical security [3]. Two instantiations (LESS-I and LESS-II) are based on the conjectured hardness of the linear code equivalence problem, while a third instantiation, LESS-III, is based on the conjectured hardness of the permutation code equivalence problem for weakly self-dual codes. We give an improved algorithm for solving both these problems over sufficiently large finite fields. Our implementation breaks LESS-I and LESS-III in approximately 25 s and 2 s respectively on an Intel i5-8400H CPU. Since the field size for LESS-II is relatively small $$(\mathbb {F}_7)$$ our algorithm does not improve on existing methods. Nonetheless, we estimate that LESS-II can be broken with approximately $$2^{44}$$ row operations.
KFC: An Efficient Framework for Semi-Supervised Temporal Action Localization

In temporal action localization (TAL), semi-supervised learning is a promising technique to mitigate the cost of precise boundary annotations. Semi-supervised approaches employing consistency regularization (CR), encouraging models to be robust to the perturbed inputs, have achieved great success in image classification problems. The success of CR is largely depended on the perturbations, where instances are perturbed to train a robust model without altering their semantic information. However, the perturbations for image or video classification tasks are not fit to apply to TAL. Since videos in TAL are too long to train the model with raw videos in an end-to-end manner. In this paper, we devise a method named K-farthest crossover to construct perturbations based on video features and apply it to TAL. Motivated by the observation that features in the same action instance become more and more similar during the training process while those in different action instances or backgrounds become more and more divergent, we add perturbations to each feature along temporal axis and adopt CR to encourage the model to retain this observation. Specifically, for a feature, we first find the top-k dissimilar features and average them to form a perturbation. Then, similar to chromosomal crossover, we select a large part of the feature and a small part of the perturbation to recombine a perturbed feature, which preserves the feature semantics yet enough discrepancy.
A Deep Learning Approach for Active S-Box Prediction of Lightweight Generalized Feistel Block Ciphers

One of the main security requirements for symmetric-key block ciphers is resistance against differential cryptanalysis.This is commonly assessed by counting the number of active substitution boxes (S-boxes) using search algorithms or mathematical solvers that incur high computational costs.These costs increase exponentially with respect to block cipher size and rounds, quickly becoming inhibitive.Conventional S-box enumeration methods also require niche cryptographic knowledge to perform.In this paper, we overcome these problems by proposing a data-driven approach using deep neural networks to predict the number of active S-boxes.Our approach trades off exactness for real-time efficiency as the bulk of computational work is brought over to pre-processing (training).Active S-box prediction is framed as a regression task whereby neural networks are trained using features such as input and output differences, number of rounds, and permutation pattern.We first investigate the feasibility of the proposed approach by applying it on a reduced (4-branch) generalized Feistel structure (GFS) cipher.Apart from optimizing a neural network architecture for the task, we also explore the impact of each feature and its representation on prediction error.We then extend the idea to 64-bit GFS ciphers by first training neural networks using data from five different ciphers before using them to predict the number of active S-boxes for TWINE, a lightweight block cipher.The best performing model achieved the lowest root mean square error of 1.62 and R 2 of 0.87, depicting the feasibility of the proposed approach.
Buddy biking: a user study on social collaboration in a virtual reality exergame for rehabilitation

Abstract Virtual reality (VR)-based rehabilitation is a growing technological field, which gradually becomes integrated into existing programs. However, technology has to support human behavior and -needs, including social relatedness, to achieve health-related outcomes. Elderly people have high risk of loneliness, and VR has technological affinity for natural social interaction. Previous studies have relied on competitiveness rather than collaborative elements, but research shows that competitiveness can lead to (feelings of) stress and aggressive behavior in some individuals. This article presents a mixed methods study to gather end-user feedback on a social VR scenario that encourages inter-player collaboration on a virtual tandem bike. Outpatients ( $$n=11$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>11</mml:mn> </mml:mrow> </mml:math> , 64% males, $$60\pm 11$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>60</mml:mn> <mml:mo>±</mml:mo> <mml:mn>11</mml:mn> </mml:mrow> </mml:math> years) were invited to participate with a co-player (friend or family). Participants biked on average 10.7 (± 3) minutes with a mean speed of 14.8 kmph (± 5.8). The results indicate potential and feasibility for the collaborative social biking application. Participants reported excellent usability-scores (85 ± 5), high intrinsic motivation in all categories: enjoyment (6.5 ± 0.5), effort/importance (6.4 ± 0.3), relatedness (6.3 ± 0.7) and minimal increase in symptoms of nausea, oculomotor and disorientation. Furthermore, participants found the social aspect enjoyable, agreed that collaboration eased tasks and that they lost track of exercise duration. Interpersonal interaction between participants varied, but was mostly positively rated valence, even if the sense of copresence was limited by physical constraints and avatar representation. Most participants expressed that they would use the program again, but future studies should explore how to improve location and appearance of the virtual coactor, as well as implement additional tasks.
SurfaceNet: Adversarial SVBRDF Estimation from a Single Image

In this paper we present SurfaceNet, an approach for estimating spatially-varying bidirectional reflectance distribution function (SVBRDF) material properties from a single image. We pose the problem as an image translation task and propose a novel patch-based generative adversarial network (GAN) that is able to produce high-quality, high-resolution surface reflectance maps. The employment of the GAN paradigm has a twofold objective: 1) allowing the model to recover finer details than standard translation models; 2) reducing the domain shift between synthetic and real data distributions in an unsupervised way.An extensive evaluation, carried out on a public benchmark of synthetic and real images under different illumination conditions, shows that SurfaceNet largely outperforms existing SVBRDF reconstruction methods, both quantitatively and qualitatively. Furthermore, SurfaceNet exhibits a remarkable ability in generating high-quality maps from real samples without any supervision at training time.Source code available at https://github.com/perceivelab/surfacenet.
Learning active quasistatic physics-based models from data

Humans and animals can control their bodies to generate a wide range of motions via low-dimensional action signals representing high-level goals. As such, human bodies and faces are prime examples of active objects, which can affect their shape via an internal actuation mechanism. This paper explores the following proposition: given a training set of example poses of an active deformable object, can we learn a low-dimensional control space that could reproduce the training set and generalize to new poses? In contrast to popular machine learning methods for dimensionality reduction such as auto-encoders, we model our active objects in a physics-based way. We utilize a differentiable, quasistatic, physics-based simulation layer and combine it with a decoder-type neural network. Our differentiable physics layer naturally fits into deep learning frameworks and allows the decoder network to learn actuations that reach the desired poses after physics-based simulation. In contrast to modeling approaches where users build anatomical models from first principles, medical literature or medical imaging, we do not presume knowledge of the underlying musculature, but learn the structure and control of the actuation mechanism directly from the input data. We present a training paradigm and several scalability-oriented enhancements that allow us to train effectively while accommodating high-resolution volumetric models, with as many as a quarter million simulation elements. The prime demonstration of the efficacy of our example-driven modeling framework targets facial animation, where we train on a collection of input expressions while generalizing to unseen poses, drive detailed facial animation from sparse motion capture input, and facilitate expression sculpting via direct manipulation.
A Survey of Image Encryption Algorithms

Data and image security is one of the most important aspects of information storage as well as the massive and still expanding internet. The frequent use of images of various types, especially images of documents, requires efficient encryption algorithms to ensure the security and stability of image storage and transmission. Among the various image encryption strategies proposed in the literature, this paper selected several algorithms with different characteristics to highlight the state-of-the-art attempts to meet the everincreasing demand for image protection. This paper briefly explains the cryptography algorithms and compares them in terms of usage, quality standards, security, and other metrics.
Evolutionary Multi-Objective Model Compression for Deep Neural Networks

While deep neural networks (DNNs) deliver state-of-the-art accuracy on various applications from face recognition to language translation, it comes at the cost of high computational and space complexity, hindering their deployment on edge devices. To enable efficient processing of DNNs in inference, a novel approach, called Evolutionary Multi-Objective Model Compression (EMOMC), is proposed to optimize energy efficiency (or model size) and accuracy simultaneously. Specifically, the network pruning and quantization space are explored and exploited by using architecture population evolution. Furthermore, by taking advantage of the orthogonality between pruning and quantization, a two-stage pruning and quantization co-optimization strategy is developed, which considerably reduces time cost of the architecture search. Lastly, different dataflow designs and parameter coding schemes are considered in the optimization process since they have a significant impact on energy consumption and the model size. Owing to the cooperation of the evolution between different architectures in the population, a set of compact DNNs that offer trade-offs on different objectives (e.g., accuracy, energy efficiency and model size) can be obtained in a single run. Unlike most existing approaches designed to reduce the size of weight parameters with no significant loss of accuracy, the proposed method aims to achieve a trade-off between desirable objectives, for meeting different requirements of various edge devices. Experimental results demonstrate that the proposed approach can obtain a diverse population of compact DNNs that are suitable for a broad range of different memory usage and energy consumption requirements. Under negligible accuracy loss, EMOMC improves the energy efficiency and model compression rate of VGG-16 on CIFAR-10 by a factor of more than 8 9. X and 2.4 X, respectively.
Unpaired font family synthesis using conditional generative adversarial networks

Automatic font image synthesis has been an extremely active topic in recent years. Various deep learning-based approaches have been proposed to tackle this font synthesis task by considering it as an image-to-image translation problem in a supervised setting. However, all such approaches mainly focus on one-to-one font mapping, i.e., synthesizing a single font style, making it difficult to handle more practical problems such as the font family synthesis, which is a one-to-many mapping problem. Moreover, this font family synthesis is more challenging because it is an unsupervised image-to-image translation problem, i.e., no paired dataset is available during training. To address this font family synthesis problem, we propose a method that utilizes a single generator to conditionally produce various font family styles to form a font family. To the best of our knowledge, our proposed method is the first to synthesize a font family (multiple font styles belonging to a font), instead of synthesizing a single font style. More specifically, our method is trained to learn a font family by conditioning on various styles, e.g., normal, bold, italic, bold-italic, etc. After training, given an unobserved single font style (normal style font as an input), our method can successfully synthesize the remaining styles (e.g., bold, italic, bold-italic, etc.) to complete the font family. Qualitative and quantitative experiments were conducted to demonstrate the effectiveness of our proposed method.
The evolution of a toolkit for smart-thing design with children through action research

Several workshops use toolkits to engage children in the design of smart things, that is, everyday things like toys enhanced with computing devices and capabilities. In general, the toolkits focus on one design stage or another, e.g., ideation or programming. Few toolkits are created to guide children through an entire design process. This paper presents a toolkit for smart-thing design with children. It revolves around SNaP, a card-based board game for children. The toolkit serves to frame the entire design process and guide them through their exploration, ideation, programming and prototyping of their own smart things. By embracing action research, the toolkit was adopted in actions with children, namely, design workshops. Results of actions were reflected over by considering children’s benefits, and they were used to make the toolkit evolve across cycles of action, reflection and development. The paper reports on the latest evolution cycles, ending with the 2020 cycle for continuing smart-thing design during COVID-19 times. The paper concludes with general reflections concerning action research and design with children, toolkits for framing smart-thing design with children, on-going and future work.