Reviewerly/exHarmony huggingface dataset:
Reversible Data Hiding scheme for color images based on skewed histograms and cross-channel correlation

Reversible data hiding (RDH) is an undetectable communication technology that enables restoration of the original cover image after extracting the hidden secret data from the image. It's being widely used in sevaral fields such as military, medical, etc,. Till date, various RDH have been developed to increase the amount of hidden data while maintaining their quality. However, the problem of traditional trade-off between the embedding capacity (EC) and image quality still persists. For this, we propose a new RDH method for color images based on skewed histograms and cross-channel correlation. The skewed histogram is generated with the help of extreme predictors, aids in achieving less distortion by only incorporating pixels from the peak and short tail. Further, the proposed method splits the secret data (or payload) based on each channel's characteristics so that the most smooth channel can be exploited for embedding to further benefit in reducing the caused distortion and increasing the EC. Moreover, embedding in the complex regions of images is done with the help of more comprehensive predictor which also takes into account various possible edges. Thus, the proposed method achieves greater EC with better quality stego-images than the related and existing RDH methods for color images.
EPOS-DCAT-AP 2.0 – State of Play on the Application Profile for Metadata Exchange in the EPOS RI

Metadata application profiles are widely employed to enable the exchange of metadata between different systems or platforms. DCAT-AP is an application profile of the W3C Data Catalog Vocabulary (DCAT) used as a cross-domain and cross-platform metadata interchange format for data catalogues operated in the European Union. Several extensions of DCAT-AP have been created to address domain-specific requirements. Due to the inherent evolving nature of Research Infrastructures, maintenance and regular updates of such profiles are necessary activities in order to assess the matching with existing and new community requirements. In this paper, we give an overview of the new release of the EPOS-DCAT Application Profile based on DCAT-AP v2.1.0. EPOS-DCAT-AP has been developed, maintained, and adopted by the European Plate Observing System (EPOS) Research Infrastructure to capture the heterogeneity of the assets provided by diverse scientific communities and to increase findability, accessibility, and usability of multidisciplinary data. EPOS-DCAT-AP is the result of a collaborative ongoing effort of various expertise. This application profile addresses several requirements, and it is suitable for a wide range of applications therefore it can be adopted by other Research Infrastructures.
Towards Reducing Communications in Sparse Matrix Kernels

The significant presence that many-core devices like GPUs have these days, and their enormous computational power, motivates the study of sparse matrix operations in this hardware. The essential sparse kernels in scientific computing, such as the sparse matrix-vector multiplication (SpMV), usually have many different high-performance GPU implementations. Sparse matrix problems typically imply memory-bound operations, and this characteristic is particularly limiting in massively parallel processors. This work revisits the main ideas about reducing the volume of data required by sparse storage formats and advances in understanding some compression techniques. In particular, we study the use of index compression combined with sparse matrix reordering techniques. The systematic experimental evaluation on a large set of real-world matrices confirms that this approach is promising, achieving meaningful data storage reductions.
Evolution of Breast Cancer Recurrence Risk Prediction: A Systematic Review of Statistical and Machine Learning–Based Models

Selection of appropriate adjuvant therapy to ultimately reduce the risk of breast cancer (BC) recurrence is a challenge for medical oncologists. Several automated risk prediction models have been developed using retrospective clinical data and have evolved significantly over the years in terms of predictors of recurrence, data usage, and predictive techniques (statistical/machine learning [ML]).Following PRISMA guidelines, we performed a systematic literature review of the aforementioned statistical and ML models published between January 2008 and December 2022 through searching five digital databases-PubMed, ScienceDirect, Scopus, Cochrane, and Web of Science. The comprehensive search yielded a total of 163 papers and after a screening process focusing on papers that dealt exclusively with statistical/ML methods, only 23 papers were deemed appropriate for further analysis. We benchmarked the studies on the basis of development, evaluation metrics, and validation strategy with an added emphasis on racial diversity of patients included in the studies.In total, 30.4% of the included studies use statistical techniques, while 69.6% are ML-based. Among these, traditional ML models (support vector machines, decision tree, logistic regression, and naïve Bayes) are the most frequently used (26.1%) along with deep learning (26.1%). Deep learning and ensemble learning provide the most accurate predictions (AUC = 0.94 each).ML-based prediction models exhibit outstanding performance, yet their practical applicability might be hindered by limited interpretability and reduced generalization. Moreover, predictive models for BC recurrence often focus on limited variables related to tumor, treatment, molecular, and clinical features. Imbalanced classes and the lack of open-source data sets impede model development and validation. Furthermore, existing models predominantly overlook African and Middle Eastern populations, as they are trained and validated mainly on Caucasian and Asian patients.
RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search

Text-based person search aims to retrieve the specified person images given a textual description. The key to tackling such a challenging task is to learn powerful multi-modal representations. Towards this, we propose a Relation and Sensitivity aware representation learning method (RaSa), including two novel tasks: Relation-Aware learning (RA) and Sensitivity-Aware learning (SA). For one thing, existing methods cluster representations of all positive pairs without distinction and overlook the noise problem caused by the weak positive pairs where the text and the paired image have noise correspondences, thus leading to overfitting learning. RA offsets the overfitting risk by introducing a novel positive relation detection task (i.e., learning to distinguish strong and weak positive pairs). For another thing, learning invariant representation under data augmentation (i.e., being insensitive to some transformations) is a general practice for improving representation's robustness in existing methods. Beyond that, we encourage the representation to perceive the sensitive transformation by SA (i.e., learning to detect the replaced words), thus promoting the representation's robustness. Experiments demonstrate that RaSa outperforms existing state-of-the-art methods by 6.94%, 4.45% and 15.35% in terms of Rank@1 on CUHK-PEDES, ICFG-PEDES and RSTPReid datasets, respectively. Code is available at: https://github.com/Flame-Chasers/RaSa.
HDFormer: High-order Directed Transformer for 3D Human Pose Estimation

Human pose estimation is a challenging task due to its structured data sequence nature. Existing methods primarily focus on pair-wise interaction of body joints, which is insufficient for scenarios involving overlapping joints and rapidly changing poses. To overcome these issues, we introduce a novel approach, the High-order Directed Transformer (HDFormer), which leverages high-order bone and joint relationships for improved pose estimation. Specifically, HDFormer incorporates both self-attention and high-order attention to formulate a multi-order attention module. This module facilitates first-order "joint-joint", second-order "bone-joint", and high-order "hyperbone-joint" interactions, effectively addressing issues in complex and occlusion-heavy situations. In addition, modern CNN techniques are integrated into the transformer-based architecture, balancing the trade-off between performance and efficiency. HDFormer significantly outperforms state-of-the-art (SOTA) models on Human3.6M and MPI-INF-3DHP datasets, requiring only 1/10 of the parameters and significantly lower computational costs. Moreover, HDFormer demonstrates broad real-world applicability, enabling real-time, accurate 3D pose estimation. The source code is in https://github.com/hyer/HDFormer.
The Shapley Value in Database Management

Attribution scores can be applied in data management to quantify the contribution of individual items to conclusions from the data, as part of the explanation of what led to these conclusions. In Artificial Intelligence, Machine Learning, and Data Management, some of the common scores are deployments of the Shapley value, a formula for profit sharing in cooperative game theory. Since its invention in the 1950s, the Shapley value has been used for contribution measurement in many fields, from economics to law, with its latest researched applications in modern machine learning. Recent studies investigated the application of the Shapley value to database management. This article gives an overview of recent results on the computational complexity of the Shapley value for measuring the contribution of tuples to query answers and to the extent of inconsistency with respect to integrity constraints. More specifically, the article highlights lower and upper bounds on the complexity of calculating the Shapley value, either exactly or approximately, as well as solutions for realizing the calculation in practice.
Lightweight Multiscale Spatiotemporal Locally Connected Graph Convolutional Networks for Single Human Motion Forecasting

Human motion forecasting is an important and challenging task in many computer vision application domains. Recent work concentrates on utilizing the timing processing ability of recurrent neural networks (RNNs) to achieve smooth and reliable results in short-term prediction. However, as evidenced by previous works, RNNs suffer from error accumulation, leading to unreliable results. In this paper, we propose a simple feed-forward deep neural network for motion prediction, which takes into account temporal smoothness between frames and spatial dependencies between human body joints. We design Lightweight Multiscale Spatiotemporal Locally Connected Graph Convolutional Networks (MST-LCGCN) for Single Human Motion Forecasting to implicitly establish the spatiotemporal dependence in the process of human movement, where different scales fuse dynamically during training. The entire model is action-agnostic and follows a framework of encoder-decoder. The encoder consists of temporal GCNs (TGCNs) to capture motion features between frames and locally connected spatial GCNs (SGCNs) to extract spatial structure among joints. The decoder uses temporal convolution networks (TCNs) to maintain its extensibility for long-term prediction. Considerable experiments show that our approach outperforms previous methods on the Human3.6M and CMU Mocap datasets while only requiring much fewer parameters. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Accuracy and real-time performance are the two most significant evaluation factors for the challenge of human motion forecasting. Existing methods tend to use models with a huge amount of parameters, sacrificing operation speed to obtain a small increase in accuracy. However, in practical scenarios, the slowdown in speed makes predictions meaningless. Therefore, we propose a lightweight MST-LCGCN network to learn human action patterns over time. To obtain higher accuracy, we extract features from the spatial and temporal dimensions to contain more information; to obtain faster operation speed, we design our network while reducing unnecessary depth as much as possible. We demonstrate the advantages of our model in terms of efficiency and accuracy through extensive quantitative and qualitative experiments on two datasets. Our network will be helpful for robots to avoid obstacles in advance and compensate for network delays, and we will apply them to real life in the future.
“Rumyantsev Readings — 2023”: Diversity of Research Formats and Practices

The Russian State Library hosted the annual International Scientific and Practical Conference “Rumyantsev Readings” on 18—20 April 2023. The event was organized by the Ministry of Culture of the Russian Federation, the Russian State Library and the Library Assembly of Eurasia. This conference has been held for more than 40 years, and it covers the wide range of topics which invariably attracts a large number of specialists from different regions of Russia and other countries. In 2023 it was attended by representatives of Russia, Armenia, Azerbaijan, Belarus, Kazakhstan, Moldova, Tajikistan, Uzbekistan. 282 people were present in person; 2,102 connections were recorded during the broadcasting of the sessions. The conference topics covered a wide range of issues on the theory and practice of library science, bibliography science and book science. Among its main directions: the history of libraries, as well as pages of life and activities of people associated with them, disclosure of book depository funds, research of collections and individual copies of manuscript and printed monuments, multifaceted activities of modern libraries. One of the main goals of the conference is to expand intercultural interaction and co-operation between cultural, educational and scientific institutions. Within the framework of the Rumyantsev Readings, the work of the sections, which have been traditional for many years, as well as timed to the dates and events of 2023, was organized. This year, special emphasis was placed on the topics “Enlightening and Educational Mission of Libraries (for the Year of Teacher and Mentor in Russia)” and “The Role of Libraries in Preserving and Developing Intercultural Communication (for the Year of Russian as a Language of Interethnic Communication in the Commonwealth of Independent States)”. In addition to the plenary session, the conference program included 12 sections, round tables and special events. By the beginning of the conference, a collection of its materials was published in three parts, including 160 articles by 174 authors. The Rumyantsev Readings continue to be a popular scientific and practical event among library professionals and representatives of other educational, scientific and cultural institutions, contributing to the development of scientific research, as well as to the development of the library’s research in the field of librarianship.
Nonlinear neurons with human-like apical dendrite activations

In order to classify linearly non-separable data, neurons are typically organized into multi-layer neural networks that are equipped with at least one hidden layer. Inspired by some recent discoveries in neuroscience, we propose a new model of artificial neuron along with a novel activation function enabling the learning of nonlinear decision boundaries using a single neuron. We show that a standard neuron followed by our novel apical dendrite activation (ADA) can learn the XOR logical function with 100% accuracy. Furthermore, we conduct experiments on six benchmark data sets from computer vision, signal processing and natural language processing, i.e. MOROCO, UTKFace, CREMA-D, Fashion-MNIST, Tiny ImageNet and ImageNet, showing that the ADA and the leaky ADA functions provide superior results to Rectified Linear Units (ReLU), leaky ReLU, RBF and Swish, for various neural network architectures, e.g. one-hidden-layer or two-hidden-layer multi-layer perceptrons (MLPs) and convolutional neural networks (CNNs) such as LeNet, VGG, ResNet and Character-level CNN. We obtain further performance improvements when we change the standard model of the neuron with our pyramidal neuron with apical dendrite activations (PyNADA). Our code is available at: https://github.com/raduionescu/pynada .
A Vision Towards the Future of Cryptocurrencies Rooting, its Financial Significance, and Legal Challenges in its Use

Encrypted currencies have become an undeniable reality among investors and speculators in various countries around the world, which necessitates standing on the most prominent issues raised by these currencies, so the researchers decided to discuss in detail the theoretical side of encrypted currencies from various perspectives such as the concept, history, reality, future, and the Islamic perspective on it.In this article, we discuss the notion of financial technology, how it began, and where it has reached, coinciding with the business world seeing a new revolution symbolized by the birth of cryptocurrencies and how the world is rapidly approaching it.Furthermore, we discuss the types of currencies and the most famous cryptocurrencies with their advantages and disadvantages in dealing with their applications, their legal regulation, and the extent of legal liability arising from their misuse, followed by a detailed presentation about the blockchain, we also talk about the relationship between cryptocurrencies, their requirements, risks, and struggles, and finally we will talk about cryptocurrencies from the point of view of investors.
The relationship between the degree of violation of Bell-CHSH inequality and measurement uncertainty in two classes of spin squeezing mechanisms

In solid-state systems, spin squeezing states have acquired much interest in quantum information processing since they are not only essential from a basic physics standpoint but also have many applications, notably reducing noise in interferometry. This paper addresses the study of quantum nonlocality as examined by the violation of the Bell and Clauser–Horne–Shimony–Holt (CHSH) inequality in two classes of spin squeezing states and how this relates to the quantum-memory-assisted entropic uncertainty relation (QMA–EUR). The spin squeezing states in the two classes are systematically generated by two twisting nonlinear interactions. These separately describe two spin-[Formula: see text] interaction mechanisms that undergo one-axis twisting (OAT) and two-axis countertwisting (TAC). We investigate each spin squeezing mechanism in a thermal bath ([Formula: see text]) with a transverse magnetic field. Our main results indicate that the degree of violation of the Bell-CHSH inequality and the entropic uncertainty relation (EUR) is typically anticorrelated in both mechanisms. We demonstrate that the spin squeezing states in the OAT mechanism violate the Bell-CHSH inequality and thus exhibit Bell nonlocality only at low temperatures ([Formula: see text]), whereas in the TAC mechanism, increasing the strength of the spin squeezing interaction leads to a violation of the Bell-CHSH inequality even at finite temperatures. We have also shown that there is always an expansion of the EUR and a decrease in the degree of violation of the Bell-CHSH inequality in the high-temperature regime. In addition, the transverse magnetic field exhibits different behavior under the considered temperature regimes in the two classes of spin squeezing states. Other results will also be discussed.
Tunable nonreciprocal photon correlations induced by directional quantum squeezing

We investigate nonreciprocal photon correlations in coupled microring resonators with directional quantum squeezing (quantum parametric amplification). We show that the degeneracy of two whispering-gallery modes (WGMs) in a microring resonator are broken by applying parametric amplification to one of the WGMs, which makes tunable chiral photon-qubit coupling and chiral photon hopping between two resonators feasible. Selecting optimal quantum squeezing strength, we predict the appearance of nonreciprocal unconventional photon blockade resulting from the interferences among different photon transition-dissipative paths both analytically and numerically. Moreover, flexible conversion between photon bunching and antibunching can be easily realized in a wide range of parameter regimes by modulating the quantum squeezing field. This work provides an alternative way for tunable nonreciprocal photon coherence manipulations in chiral quantum science and technologies.
Malicious Traffic Identification with Self-Supervised Contrastive Learning

As the demand for Internet access increases, malicious traffic on the Internet has soared also. In view of the fact that the existing malicious-traffic-identification methods suffer from low accuracy, this paper proposes a malicious-traffic-identification method based on contrastive learning. The proposed method is able to overcome the shortcomings of traditional methods that rely on labeled samples and is able to learn data feature representations carrying semantic information from unlabeled data, thus improving the model accuracy. In this paper, a new malicious traffic feature extraction model based on a Transformer is proposed. Employing a self-attention mechanism, the proposed feature extraction model can extract the bytes features of malicious traffic by performing calculations on the malicious traffic, thereby realizing the efficient identification of malicious traffic. In addition, a bidirectional GLSTM is introduced to extract the timing features of malicious traffic. The experimental results show that the proposed method is superior to the latest published methods in terms of accuracy and F1 score.
Consistent-Teacher: Towards Reducing Inconsistent Pseudo-Targets in Semi-Supervised Object Detection

In this study, we dive deep into the inconsistency of pseudo targets in semi-supervised object detection (SSOD). Our core observation is that the oscillating pseudo-targets undermine the training of an accurate detector. It injects noise into the student's training, leading to severe overfitting problems. Therefore, we propose a systematic solution, termed Consistent-Teacher, to reduce the inconsistency. First, adaptive anchor assignment (ASA) substitutes the static IoU-based strategy, which enables the student network to be resistant to noisy pseudo-bounding boxes. Then we calibrate the subtask predictions by designing a 3D feature alignment module (FAM-3D). It allows each classification feature to adaptively query the optimal feature vector for the regression task at arbitrary scales and locations. Lastly, a Gaussian Mixture Model (GMM) dynamically revises the score threshold of pseudo-bboxes, which stabilizes the number of ground truths at an early stage and remedies the unreliable supervision signal during training. Consistent-Teacher provides strong results on a large range of SSOD evaluations. It achieves 40.0 mAP with ResNet-50 backbone given only 10% of annotated MS-COCO data, which surpasses previous base-lines using pseudo labels by around 3 mAP. When trained on fully annotated MS-COCO with additional unlabeled data, the performance further increases to 47.7 mAP. Our code is available at https://github.com/Adamdad/ConsistentTeacher.