Reviewerly/exHarmony huggingface dataset:
On the material of the cromlech-crepidoma stone blocks of the Novooleksandrivka kurgan

Purpose. Determination of the rocks used for the building of the 4th millennium BCE megalithic construction in Novooleksandrivka (Dnipropetrovsk Oblast, Ukraine) as well as establishing the places of their probable provenance. Methodology. The study was performed using the method of comparative mineralogical and petrographic analysis in thin sections of the building stone and the rocks from the outcrops in the valleys of the rivers Mokra Sura and Dnipro. The chemical composition of the petrographically identical rocks was also compared using X-ray fluorescence analysis. Findings. Nine specimens of rocks that represent all the species of granitoids used for the cromlech-crepidoma construction of the Novooleksandrivka kurgan Sura-Oba were analyzed. As a result of the conducted research, it was ascertained that they are represented by plagiogranites (trondhjemites), tonalites, granite gneisses, migmatites and pegmatites. Besides, some granitoids comprise small xenoliths of biotite gneiss. All the indicated rocks are characteristic for the occurrences of the Surskyi and Dnipropetrovskyi plagiogranitoid complexes, widespread in the valley of the river Morka Sura the right tributary of the river Dnipro, where the monument is directly located. Granitoids with the massive structure were most likely to be delivered from the occurrences located upstream of the river Mokra Sura in the village of Sursko-Lytovske. Migmatites and gneiss-like granites occur both downstream and upstream of the river from the village of Novooleksandrivka. Originality. For the first time, the building stone of the megalithic construction, discovered during the excavations of the Sura-Oba kurgan in the village of Novooleksandrivka (Dnipropetrovsk Oblast, Ukraine) has been studied using mineralogical and petrographic analysis; in addition, the probable places of its extraction were determined. Practical value. The results obtained can be used in conducting research on history and archaeology, as well as in popular science works and excursion activities.
Controlling The Fréchet Variance Improves Batch Normalization on the Symmetric Positive Definite Manifold

Symmetric positive definite (SPD) matrices, and in particular co-variance matrices as data descriptors find widespread application in various fields but also pure machine learning. SPD matrices form a Riemannian manifold, demanding machine learning methods that take this structure into account. In this work, we extend upon previous works and propose a batch normalization algorithm for the SPD manifold that can be readily combined with SPD neural networks and unlike previous works controls both the Fréchet mean and variance on the SPD manifold. The proposed method is validated in simulations and datasets with small sample sizes from three application domains: action recognition from human motion trajectories, image classification and mental imagery detection of electroencephalographic (EEG) signals. The combined results show a systematic performance increase upon previous works and tangent space approximations, as well as improved robustness to low signal-to-noise ratios and lack of data as often encountered in neuroimaging and brain-computer interfacing (BCI).
A multi-scale adaptive feature enhancement network for image denoising

Recently, deep learning has been widely used in image denoising. However, most of the existing deep learning-based methods are not adequate in blind denoising for additive white Gaussian noise (AWGN) images and real-world noisy images, which are still noisy or blurred. The difficulty is how to handle different noise levels and different types of noise with only one pre-trained model. In this paper, we propose a multi-scale adaptive feature enhancement network (MFENet) to improve the performance on blind image denoising. The MFENet is based on residual learning and batch normalization to speed up the network convergence. In the MFENet, dilated convolution and deformable convolution can expand the receptive field to obtain rich information from different scales. The deformable convolution is also able to adjust the sampling position to fit different shapes of objects. Spatial attention is used to enhance important features in the large amount of information. The experimental results show that the proposed method for blind denoising outperforms the state-of-the-art methods on both synthetic and real-world noisy images.
Referential genome sequence compression with low memory consumption

With the rapid development of genome sequencing technology, a large amount of genome data has been generated, it also brings the storage problem of this massive data. Therefore, the compression of genome data has become a research hotspot. We propose a new genome data compression algorithm called LCMRGC (low memory consumption referential genome compressor) for FASTA format sequences. The algorithm uses the suffix array data structure to support the search of matching strings, and uses the binary search method to accelerate accurate matching, so as to obtain better compression ratio. Experiment results on standard genome data show that the proposed algorithm significantly reduces the memory requirement for program operation, and is competitive in compression ratio and compression time.
Calculation of Position for Odd Numbers to Change Length in Valuated Binary Tree

The paper first investigates the node in tree on how the length of digits changes when the node is in decimal expression and presents a method to calculate the position where the number of digits is changed. Several formulas are proved with detailed mathematical reasoning. The paper then demonstrates application of the method in analyzing big integers such as the RSA numbers. An example to analyze RSA-155 is exhibited to make readers have a more concrete understanding of the method. Experiments show the method is helpful to reduce searching time when searching for a data set of numbers with equal length.
RLWE and PLWE over cyclotomic fields are not equivalent

Abstract We prove that the Ring Learning With Errors (RLWE) and the Polynomial Learning With Errors (PLWE) problems over the cyclotomic field $${\mathbb {Q}}(\zeta _n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Q</mml:mi> <mml:mo>(</mml:mo> <mml:msub> <mml:mi>ζ</mml:mi> <mml:mi>n</mml:mi> </mml:msub> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> are not equivalent. Precisely, we show that reducing one problem to the other increases the noise by a factor that is more than polynomial in n . We do so by providing a lower bound, holding for infinitely many positive integers n , for the condition number of the Vandermonde matrix of the n th cyclotomic polynomial.
Context-Aware Mask Prediction Network for End-to-End Text-Based Speech Editing

The text-based speech editor allows the editing of speech through intuitive cutting, copying, and pasting operations to speed up the process of editing speech. However, the major drawback of current systems is that edited speech often sounds unnatural and it is not obvious how to synthesize records according to a new word not appearing in the transcript. This paper proposes a novel end-to-end text-based speech editing method called context-aware mask prediction network (CampNet), which avoids the unnatural phenomenon caused by cut-copy-paste operation in the traditional method and can synthesize a new word not appearing in the transcript. Besides, three text-based speech editing operations based on CampNet are designed: deletion, replacement, and insertion. These operations can comprehensively cover different kinds of situations that text-based speech editing can face. The subjective and objective experiments on VCTK and LibriTTS data sets show that the speech editing results based on CampNet are better than TTS technology, manual editing, and VoCo method (the combination of speech synthesis and speech conversion). We also conducted detailed ablation experiments to explore the effect of the CampNet structure on its performance. Examples of generated speech can be found at https://hairuo55.github.io/CampNet-demo.
DCNGAN: A Deformable Convolution-Based GAN with QP Adaptation for Perceptual Quality Enhancement of Compressed Video

In this paper, we propose a deformable convolution-based generative adversarial network (DCNGAN) for perceptual quality enhancement of compressed videos. DCNGAN is also adaptive to the quantization parameters (QPs). Compared with optical flows, deformable convolutions are more effective and efficient to align frames. Deformable convolutions can operate on multiple frames, thus leveraging more temporal information, which is beneficial for enhancing the perceptual quality of compressed videos. Instead of aligning frames in a pairwise manner, the deformable convolution can process multiple frames simultaneously, which leads to lower computational complexity. Experimental results demonstrate that the proposed DCNGAN outperforms other state-of-the-art compressed video quality enhancement algorithms.
EPPSA: Efficient Privacy-Preserving Statistical Aggregation Scheme for Edge Computing-Enhanced Wireless Sensor Networks

In edge computing-enhanced wireless sensor networks (WSNs), multidimensional data aggregation can optimize the utilization of computation resources for data collection. How to improve the efficiency of data aggregation has gained considerable attention in both academic and industrial fields. This article proposes a new efficient privacy-preserving statistical aggregation scheme (EPPSA) for WSNs, in which statistical data can be calculated without exposing the total number of sensor devices to control center. The EPPSA scheme supports multiple statistical aggregation functions, including arithmetic mean, quadratic mean, weighted mean, and variance. Furthermore, the EPPSA scheme adopts the modified Montgomery exponentiation algorithms to improve the aggregation efficiency in the edge aggregator. The performance evaluation shows that the EPPSA scheme gets higher aggregation efficiency and lower communication load than the existing statistical aggregation schemes.
Underwater Stereo Matching Via Unsupervised Appearance And Feature Adaptation Networks

Stereo matching has been widely used to estimate depth maps in terrestrial environments. However, it is difficult to achieve appealing performance in underwater environments, since adequate underwater stereo data with groundtruth depth information is not easily available for training an underwater depth estimation model. In addition, the domain gap also leads to the failure of directly applying existing models of terrestrial scenes to underwater scenes. Therefore, this paper proposes a novel underwater depth estimation network which can infer depth maps from real underwater stereo images in an unsupervised adaptation manner. The proposed learning pipeline contains style adaptation (SA) in appearance space and feature adaptation (FA) in semantic space to progressively adapt the depth estimation models to underwater domain. Experimental results show that by integrating the proposed adaptation modules into the off-the-shelf stereo matching backbones, our method achieves a superior performance of underwater depth estimation compared to other state-of-the-art methods.
Ganet: Unary Attention Reaches Pairwise Attention Via Implicit Group Clustering in Light-Weight CNNs

The attention mechanism has been widely explored to construct a long-range connection which is beyond the realm of convolutions. The two groups of attention, unary and pair-wise attention, seem like being incompatible as fire and water due to the completely different operations. In this paper, we propose a Group Attention (GA) block to bridge the gap between these two attentions and merely leverage unary attention to lightweightly reach the effect of pairwise attention, based on the implicit group clustering of light-weight CNNs. Compared with the conventional pairwise attention, i.e, Non-Local networks, our method artfully bypasses the burdensome pixel-pair calculation to save a huge computational cost, that is a big advantage of our work. Experiments on the task of image classification demontrate the effectiveness and efficiency of our GA block to enhance the light-weight models. Code will be released at https://github.com/ChiSuWq/GANet.
Classification Algorithms and Dataflow Implementation

The implementation of data mining methods on dataflow computers enables an easy use of parallelism, but it also faces numerous obstacles. The problem underlying the impossibility of using currently developed algorithms in their existing form is their adaptation to von Neumann computer model, which assumes sequential calculations and intensive use of memory. This is one of the reasons why there are no fully developed classification algorithms on dataflow computer models in the open literature at the moment when this text is written. This chapter summarizes the characteristics that can be used as directions in the future construction of algorithms and outlines drafts for two implementations of the K-nearest neighbor algorithm.
User-Centered Virtual Reality Environments for Elementary School Children in Times of COVID-19

Derived from the current situation, in which physical contact and closeness between people must be taken care of due to COVID-19, emerging technologies such as virtual reality have taken a fundamental role in continuing with activities in various areas of society. This chapter proposes the use of virtual reality environments as an alternative for the acquisition of learning skills for children in an immersive and interactive way with user-centered content. The design elements of the software, the user-centered aspects, and the involved actors considered for the production of the proposed interactive virtual reality environments are presented. As a result, the evaluation of the user experience resulting from the use of the proposed interactive environments with elementary school children is presented. Virtual reality is a technology that nowadays can be an effective support for the acquisition of learning skills through the execution of various tasks designed by a multidisciplinary group and above all encourages the user to continue acquiring learning skills in a friendly and fun way.
Russian State Library as the Center for Coordination and Regulatory and Methodological Support of Bibliographic Activities in the Country

An important direction in the implementation of the functional activities of libraries at various levels is the coordination of bibliographic work and mandatory methodological support. The issues of coordination interactions are poorly covered in the professional press. For library science, information about the activities of domestic libraries in this area remains not only a source of information, but also an object of scientific study. The purpose of the article is to study and analyze the activities of the Russian State Library as a coordination center at the national (interlibrary) level. It is established that the 1960s–1980s were the period of the highest level of development of national coordination. The analysis of existing methodological and instructional documents made it possible to determine that the leading role in the complex of such products belongs to methodological recommendations. The study of published documents for the intended and reader purpose was carried out. The practical application of the recommendations by libraries made it possible to improve the processes of bibliographic service, optimize reference and bibliographic funds, develop common principles and approaches to the scientific processing of catalogs and card files. The influence of the Russian State Library on the interrelationships of libraries in the humanitarian branch allowed us to establish a stable stage of development of coordination based on the distribution of forces in optimal conditions for scientific activity. Basing on modern realities, it is necessary to study the existing experience and resume coordination cooperation at the Russian level.
Framing the Design Space of Multimodal Mid-Air Gesture and Speech-Based Interaction With Mobile Devices for Older People

Multimodal human–computer interaction has been sought to provide not only more compelling interactive experiences, but also more accessible interfaces to mobile devices. With the advance in mobile technology and in affordable sensors, multimodal research that leverages and combines multiple interaction modalities (such as speech, touch, vision, and gesture) has become more and more prominent. This article provides a framework for the key aspects in mid-air gesture and speech-based interaction for older adults. It explores the literature on multimodal interaction and older adults as technology users and summarises the main findings for this type of users. Building on these findings, a number of crucial factors to take into consideration when designing multimodal mobile technology for older adults are described. The aim of this work is to promote the usefulness and potential of multimodal technologies based on mid-air gestures and voice input for making older adults' interaction with mobile devices more accessible and inclusive.
IMPQ: Reduced Complexity Neural Networks Via Granular Precision Assignment

The demand for the deployment of deep neural networks (DNN) on resource-constrained Edge platforms is ever increasing. Today’s DNN accelerators support mixed-precision computations to enable reduction of computational and storage costs but require networks with precision at variable granularity, i.e., network, layer or kernel level. However, the problem of granular precision assignment is challenging due to an exponentially large search space and efficient methods for such precision assignment are lacking. To address this problem, we introduce the iterative mixed-precision quantization (IMPQ) framework to allocate precision at variable granularity. IMPQ employs a sensitivity metric to order the weight/activation groups in terms of the likelihood of misclassifying input samples due to its quantization noise. It iteratively reduces the precision of the weights and activations of a pretrained full-precision network starting with the least sensitive group. Compared to state-of-the-art methods, IMPQ reduces computational costs by 2× -to-2.5× for compact networks such as MobileNet-V1 on ImageNet with no accuracy loss. Our experiments reveal that kernel-wise granular precision assignment provides 1.7× higher compression than layer-wise assignment.
Graph-Based Point Cloud Denoising Using Shape-Aware Consistency For Free-Viewpoint Video

We propose a novel graph-based denoising method to correct the quantization error (step noise) arising in the process of generating the visual hull, a commonly used technique to synthesize free-viewpoint video. To reduce this step noise effectively, we propose two new notions of consistency, pixel value consistency and normal vector consistency. The resulting denoising method involves a first step of graph construction using the proposed consistency metrics, followed by graph filtering of the 3D point cloud coordinates. Our experiments show that our approach provides visually and quantitatively better performance than state-of-the-art methods.
Grassmannian Dimensionality Reduction Using Triplet Margin Loss for Ume Classification of 3d Point Clouds

We consider the problem of classifying 3-D objects undergoing rigid transformations. It has been shown that the rigid transformation universal manifold embedding (RTUME) provides a mapping from the orbit of observations on some object to a single low-dimensional linear subspace of Euclidean space. This linear subspace is invariant to the geometric transformations. In the classification problem the RTUME subspace extracted from an experimental observation is tested against a set of subspaces representing the different object manifolds, in search for the nearest class. We elaborate on the design problem of the RTUME operator in the case where the point cloud sampled from the object is sparse, noisy, and non-uniformly sampled. By introducing metric learning and negative-mining techniques into the framework of Grassmannian dimensionality reduction for universal manifold embedding, we improve classification performance for these challenging sampling conditions.
Multi‐label hybrid radar signals recognition and visualization based on interpretable convolutional classification layer and discriminative region suppression

As the electromagnetic environment becomes increasingly complex, the radar receivers may receive multiple radar signals simultaneously. However, the current radar signal recognition algorithm based on deep learning can only predict a single class. The interpretable multi-label hybrid radar signal recognition framework based on the interpretable convolutional classification layer and discriminative region suppression is proposed. The proposed interpretable convolutional classification layer is a weakly supervised localization method that can directly localize the discriminative regions of the corresponding class from the feature map of the model without additional computation. The proposed method can recognize, localize and separate hybrid radar signals in time-frequency images, which improves the interpretability and transparency of the model. In addition, the feature pyramid network is adopted to improve the spatial resolution of the interpretable feature maps. Experiments with eight different modulation types of mixed radar signals show that the recognition accuracy for hybrid radar signals achieves 97.4% at 0 dB.
Adaptive Actor-Critic Bilateral Filter

Recent research on edge-preserving image smoothing has suggested that bilateral filtering is vulnerable to maliciously perturbed filtering input. However, while most prior works analyze the adaptation of the range kernel in one-step manner, in this paper we take a more constructive view towards multi-step framework with the goal of unveiling the vulnerability of bilateral filtering. To this end, we adaptively model the width setting of range kernel as a multi-agent reinforcement learning problem and learn an adaptive actor-critic bilateral filter from local image context during successive bilateral filtering operations. By evaluating on eight benchmark datasets, we show that the performance of our filter outperforms that of state-of-the-art bilateral-filtering methods in terms of both salient structures preservation and insignificant textures and perturbation elimination.
Preserving Trajectory Privacy in Driving Data Release

Real-time data transmissions from a vehicle enhance road safety and traffic efficiency by aggregating data in a central server for data analytics. When drivers share their instantaneous vehicular information for a service provider to perform a legitimate task, a curious service provider may also infer private information it has not been authorized for. In this paper, we propose a privacy preservation framework based on the Hilbert Schmidt Independence Criterion (HSIC) to sanitize driving data to protect the vehicle’s trajectory from adversarial inference while ensuring the data is still useful for driver behavior detection. We develop a deep learning model to learn the HSIC sanitizer and demonstrate through two datasets that our approach achieves better utility-privacy trade-offs when compared to three other benchmarks.