Reviewerly/exHarmony huggingface dataset:
Funder: Future-Proof Unbiased Decentralized Randomness

A trustworthy source of randomness is a crucial component of many decentralized and crypto-based application systems, especially blockchain consensus. A decentralized random beacon (DRB) periodically outputs a new source of randomness generated using a distributed technique, such as publicly verifiable secret sharing (PVSS) or distributed verifiable random functions (VRF). These protocols offer a variety of efficiency versus randomness quality tradeoffs, but guarantee security under a variety of configurations, assumptions, and adversarial models. This paper aims to provide a future-proof unbiased decentralized randomness (abbreviated as Funder) via a post-quantum threshold VRF for sustainable proof-of-stake blockchain. We also provide a generic compiler for achieving post-quantum VRF from a classical VRF solution, but our approach makes use of symmetric-key primitivesOur novel compiler is validated and evaluated using the ZKBoo and ZKB++ quantumsecure zero-knowledge systems, respectively. The implementation of the proof-of-concept demonstrates that the overheads introduced by our solution are acceptable for real-world deployments even in the present day. In addition, we demonstrate the protocol’s possible application in lottery-based proof-of-stake consensus protocols.
MSA-GCN: A Multi-information Selection Aggregation Graph Convolutional Network for Breast Tumor Grading

Physicians typically combine multi-modal data to make a graded diagnosis of breast tumors. However, most existing breast tumor grading methods rely solely on image information, resulting in limited accuracy in grading. This paper proposes a Multi-information Selection Aggregation Graph Convolutional Networks (MSA-GCN) for breast tumor grading. Firstly, to fully utilize phenotypic data reflecting the clinical and pathological characteristics of tumors, an automatic combination screening and weight encoder is proposed for phenotypic data, which can construct a population graph with improved structural information. Then, a graph structure is designed through similarity learning to reflect the correlation between patient image features. Finally, a multi-information selection aggregation mechanism is employed in the graph convolution model to extract the effective features of multi-modal data and enhance the classification performance of the model. The proposed method is evaluated on different clinical datasets from the Digital Database for Screening Mammography (DDSM) and INbreast. The average classification accuracies are 90.74% and 85.35%, respectively, surpassing the performance of existing methods. In conclusion, our method effectively fuses image and non-image information, leading to a significant improvement in the accuracy of breast tumor grading.
Batch-Mode Active Learning of Gaussian Process Regression With Maximum Model Change

This article proposes a batch-mode active learning (AL) method of Gaussian process regression (GPR), which is based on the expected model change maximization. Unlike existing strategies for measuring the model change, weight’s information gain (WIG) caused by model training is introduced to measure the model change in this article, which has a lower computational cost. First, two methods for calculating the WIG of the GPR are given from the perspective of accuracy and easy realization, respectively. Then, two AL algorithms are designed by using enumeration and greedy sampling. Finally, experiments on three datasets from various domains have verified the effectiveness of the proposed AL algorithms.
Hierarchical Graph Pattern Understanding for Zero-Shot Video Object Segmentation

The optical flow guidance strategy is ideal for obtaining motion information of objects in the video. It is widely utilized in video segmentation tasks. However, existing optical flow-based methods have a significant dependency on optical flow, which results in poor performance when the optical flow estimation fails for a particular scene. The temporal consistency provided by the optical flow could be effectively supplemented by modeling in a structural form. This paper proposes a new hierarchical graph neural network (GNN) architecture, dubbed hierarchical graph pattern understanding (HGPU), for zero-shot video object segmentation (ZS-VOS). Inspired by the strong ability of GNNs in capturing structural relations, HGPU innovatively leverages motion cues (i.e., optical flow) to enhance the high-order representations from the neighbors of target frames. Specifically, a hierarchical graph pattern encoder with message aggregation is introduced to acquire different levels of motion and appearance features in a sequential manner. Furthermore, a decoder is designed for hierarchically parsing and understanding the transformed multi-modal contexts to achieve more accurate and robust results. HGPU achieves state-of-the-art performance on four publicly available benchmarks (DAVIS-16, YouTube-Objects, Long-Videos and DAVIS-17). Code and pre-trained model can be found at https://github.com/NUST-Machine-Intelligence-Laboratory/HGPU.
Mosaic: An Architecture for Scalable &amp; Interoperable Data Views

Mosaic is an architecture for greater scalability, extensibility, and interoperability of interactive data views. Mosaic decouples data processing from specification logic: clients publish their data needs as declarative queries that are then managed and automatically optimized by a coordinator that proxies access to a scalable data store. Mosaic generalizes Vega-Lite's selection abstraction to enable rich integration and linking across visualizations and components such as menus, text search, and tables. We demonstrate Mosaic's expressiveness, extensibility, and interoperability through examples that compose diverse visualization, interaction, and optimization techniques—many constructed using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vgplot</i> , a grammar of interactive graphics in which graphical marks act as Mosaic clients. To evaluate scalability, we present benchmark studies with order-of-magnitude performance improvements over existing web-based visualization systems—enabling flexible, real-time visual exploration of billion+ record datasets. We conclude by discussing Mosaic's potential as an open platform that bridges visualization languages, scalable visualization, and interactive data systems more broadly
ResTran: Long Distance Relationship on Image Forgery Detection

Image tampering detection is becoming more and more important in image forensics, especially in today’s society where advanced image editing tools are becoming more and more popular. At the same time, with the rapid growth of digital image technology and multimedia data, it is very important to ensure the authenticity and integrity of visual information. Therefore, to address this issue, we propose a novel image tampering detection method using a deep convolutional network (ResNet) combined with a Transformer decoding layer. While traditional algorithms only solve a single tamper detection problem, our method can detect multiple tampering means. We use ResNet as the backbone of feature extraction to obtain rich and hierarchical features from input images. We then employ Transformer’s decoding layer to process these features, enabling the model to capture long-distance dependencies and complex patterns, which we believe can further improve the accuracy of image tampering detection. In the experiments, we conduct experiments on three datasets (CASIA, NIST and IMD2020) to verify the performance of the model. According to the experimental results, our model performed well on the CASIA and IMD2020 datasets, and also achieved good results in the NIST dataset. Furthermore, we also test two main types of image tampering: Copy-move and Splicing. Our model performs very well in detecting the Splicing type of image tampering. The experimental results show that this study fully demonstrates the potential and effectiveness of deep convolutional networks (such as ResNet) and Transformer decoding layers in image tampering detection, and also verifies the high performance and excellent generalization ability of the model.
Road Scene Multi-Object Detection Algorithm Based on CMS-YOLO

To address issues such as low detection accuracy and limited real-time performance in road scene detection, a novel road scene detection algorithm based on CMS-YOLO is proposed in this paper. In this algorithm, an efficient backbone called the cross-stage partial DWNeck is devised. By using large-scale depthwise separable convolutions and residual structures, it enables the acquisition of more comprehensive feature information, thereby increasing both the receptive field and the richness of extracted features. Meanwhile, a feature pyramid called the multi-scale fusion feature pyramid network is designed to strengthen the fusion of shallow and deep-level information, effectively preventing the loss of feature information in the transmission process. Besides, a new decoupled head structure called the special decoupled head is introduced, which effectively addresses the conflict between classification and regression tasks through a three-layer joint output structure. Finally, experiments were conducted on two publicly available datasets, namely Udacity Self-Driving and BDD100K. Experimental results indicate that the CMS-YOLO algorithm achieved an impressive detection accuracy of 90.3% and 59.1% in mAP@0.5, demonstrating a remarkable improvement of 4.2% and 7.2% over YOLOv5 respectively. Moreover, in real-world scenarios, the algorithm achieves an impressive real-time detection speed of 34.5 frames per second. These results demonstrate that CMS-YOLO not only meets but also surpasses the requirements for detection accuracy and real-time performance for object detection in autonomous driving scenarios.
Multi-Type Feature Extraction and Early Fusion Framework for SMS Spam Detection

SMS spam is a pervasive issue that affects millions worldwide, leading to significant inconvenience, time wastage, and potential financial scams. Given the prevalence and potential harm, accurate and real-time detection of SMS spam is crucial. This paper proposes a novel approach to SMS spam detection involving five steps: preprocessing, feature extraction, feature fusion, feature selection, and classification. Our model is designed to simultaneously capture local, temporal, and global text message features using a hybrid deep learning model to enhance feature representation. We evaluated our model using the UCI dataset, comparing it with traditional and deep learning algorithms such as RF and BERT using cross-validation to ensure the robustness of our results. Our proposed method exhibited superior performance, achieving a good accuracy of 99.56%, surpassing other methods. The effectiveness of this method in SMS spam detection proved its potential for real-world implementation, where it could substantially mitigate the prevalence and impact of SMS spam.
INFLUENCING FACTORS ON CREATIVITY IN DISTRIBUTED TEAMS — SYSTEMATIC LITERATURE REVIEW

Creativity has been identified as a critical activity in distributed product development. Various methods and tools support distributed product development and distributed teams in general, but none with a focus on creative problem solving. To tackle this challenge, a systematic literature review has been conducted to gather the influencing factors on distributed creativity to be used as a starting point for supporting creativity in distributed product development teams. Within this submission, an impact model is developed, focusing on the success factors of creativity. Furthermore, the interconnection between the factors is modelled. Barriers to creative problem solving are included as well but will be the focus of a following publication.
FOAM DRESSING ON WOUND HEALING PROSES IN POST FRACTURE OPERATION PATIENTS

Modern wound dressing is a technique of wound care that focuses on moist principles so that the use of wound dressing plays an important role in accelerating the wound healing process. This study aims to determine the Effect of Modern Wound Care with Foam Dressing on the Wound Healing Process of Post Patient's Femur Fracture Surgery. The study used a pre-experiment one group pretest posttest design. The sampling used was accident sampling. The independent variable of the study was modern wound care with foam dressing, and the dependent variable of the study was the wound healing process, assessed by Bates-Jensen Wound Assessment tools. The results of the study before the intervention, the mean Bates-Jensen Wound Assessment score was 23.50 with a standard deviation of 1.080, a minimum value of 23.00 and a maximum value of 26.00. The results of the study after the intervention, the mean score was 15.50 with a standard deviation of 0.850, a minimum value of 15.50 and a maximum value of 17.00. Post operative wound care with foam dressings is expected to be applied and routinely carried out according to the wound care schedule of postoperative patients in order to improve the quality of health services.
Intelligent Sign Multi-Language Real-Time Prediction System with Effective Data Preprocessing

A multidisciplinary approach for developing an intelligent sign multi-language recognition system to greatly enhance deaf-mute communication will be discussed and implemented. This involves designing a low-cost glove-based sensing system, collecting large and diverse datasets, preprocessing the data, and using efficient machine learning models. Furthermore, the glove is integrated with a user-friendly mobile application called “Life-sign” for this system. The main goal of this work is to minimize the processing time of machine learning classifiers while maintaining higher accuracy performance. This is achieved by using effective preprocessing algorithms to handle noisy and inconsistent data. Testing and iterating approaches have been applied to various classifiers to refine and improve their accuracy in the recognition process. Additionally, the Extra Trees (ET) classifier has been identified as the best algorithm, with results proving successful gesture prediction at an average accuracy of about 99.54%. A smart optimization feature has been implemented to control the size of data transferred via Bluetooth, allowing for fast recognition of consecutive gestures. Real-time performance has been measured through extensive experimental testing on various consecutive gestures, specifically referring to Arabic Sign Language (ArSL). The results have demonstrated that the system guarantees consecutive gesture recognition with a lower delay of 50 milliseconds.
A Grammar-based multi-objective neuroevolutionary algorithm to generate fully convolutional networks with novel topologies

The design of complex and deep neural networks is often performed by identifying and combining building blocks and progressively selecting the most promising combination. Neuroevolution automates this process by employing evolutionary algorithms to guide the search. Within this field, grammar-based evolutionary algorithms have been demonstrated to be powerful tools to describe and thus encode complex neural architectures effectively. Following this trend, the present work proposes a novel grammar-based multi-objective neuroevolutionary for generating Fully Convolutional Networks. The proposed method, named Multi-Objective gRammatical Evolution for FUlly convolutional Networks (MOREFUN), includes a new efficient way to encode skip connections, facilitating the description of complex search spaces and the injection of domain knowledge in the search procedure, generation of fully convolutional networks, upsampling of lower-resolution inputs in multi-input layers, usage of multi-objective fitness, and inclusion of data augmentation and optimiser settings to the grammar. Our best networks outperformed previous grammar evolution algorithms, achieving 90.5% accuracy on CIFAR-10 without using transfer learning, ensembles, or test-time data augmentation. Our best models had 13.39±5.25 trainable parameters and the evolutionary process required 90 min per generation.
Heterogeneity effects in micro-beam XRF scanning spectroscopy of binary powdered mixtures and lake sediments

Micro-beam X-ray fluorescence (XRF) scanning spectroscopy is very useful for analyzing heterogeneous samples from earth and environmental sciences with high spatial resolution. However, XRF intensity depends on grain size and mineralogical composition, known as the heterogeneous effect, although little quantitative evaluation has been conducted. In this study, the binary powdered mixtures of Fe2O3 in the CaCO3 or SiO2 matrix with nine grain size fractions (0.3–605 μm), four Fe2O3 concentrations (5–20 wt%), and fine-grained sedimentary cores from Lake Baikal were used for analysis, using a scanning X-ray analytical microscope (SXAM), and then evaluated by making a simple modification of the Berry–Furuta–Rhodes model. The results of binary powdered samples show that the Fe intensity decreases as the grain size increases, but if the grain size is constant, its intensity has a linear relationship with the composition of Fe2O3. The experimental data are in good agreement with theoretical curves. The theory predicts that if a phase that contains fluorescent elements has narrow ranges of concentration and grain size, the micro-beam XRF spectroscopy enables highly precise calibration from the XRF intensity to element concentration. The theoretical curves of the Lake Baikal sediment core suggest that the Fe intensity has about a 30% maximum difference in the median grain size range of 3.9–28.2 μm. This variation appears in a scatter of regression between Fe intensity and concentration, but it scarcely affected the XRF intensity variability of sediment composition.
Renyi entropy analysis of a deep convolutional representation for texture recognition

Despite the recent success of convolutional neural networks in computer vision in general, texture images still pose an important challenge to those models, especially when dealing with textures “in the wild”. In this context, deep learning models can benefit from the introduction of features long known to be useful for texture modeling. And that is the case of entropy, a measure of texture regularity that has played an important role in classical computer vision. Based on this observation, here we propose an alternative analysis over deep convolutional neural features based on entropy for texture representation and, particularly, texture classification. More precisely, we couple a module to the convolutional backbone that locally computes the Renyi entropy of the latent representation at multiple levels. The rationale for using Renyi entropy is essentially two-fold: (1) It connects the concept of entropy with multifractal theory, another well explored measure especially in domains of application where we seek some physical interpretation of the descriptors, e.g., in medicine, biology, and others; (2) It has an extra degree of freedom (α parameter) that can be fine-tuned. The main contribution of this study is the development of a strategy that can improve the performance of convolutional neural networks in texture recognition tasks, adding low computational cost. The effectiveness of our method is verified in texture classification of benchmark datasets, as well as in a practical task of plant species identification. Our method achieves a competitive accuracy of 84.5% in the classification of KTH-TIPS-2b and 80.9% in FMD, which are two challenging benchmark databases. Besides, an accuracy of 91.6% is obtained in the plant identification application, to our knowledge outperforming all the results previously published on this task. In both scenarios, the proposed descriptors outperform several approaches from the state-of-the-art, confirming the method potential as a rich and robust solution for texture analysis in general. The results also suggest that information-theoretical measures like entropy can be a reliable source of information to compose a precise and robust latent representation of texture images.
Fermions and Bosons

Featuring new coverage of quantum engineering and quantum information processing, the third edition of this bestselling textbook continues to provide a uniquely practical introduction to the fundamentals of quantum mechanics. It features straightforward explanations of quantum effects, suitable for readers from all backgrounds; real-world engineering problems showcasing the practical application of theory to practice, providing a relevant and accessible introduction to cutting-edge quantum applications; over 60 accessible worked examples using MATLAB, allowing deepened understanding through computational exploration and visualization; and a new chapter on quantum engineering, introducing state-of-the-art concepts in quantum information processing and quantum device design. Updated throughout and supported online by downloadable MATLAB code, exam questions, and solutions to over 150 homework problems for instructors, this is the ideal textbook for senior undergraduate and graduate students in applied science, applied physics, engineering, and materials science studying a first course in quantum mechanics.
Toward Quantum Engineering

Featuring new coverage of quantum engineering and quantum information processing, the third edition of this bestselling textbook continues to provide a uniquely practical introduction to the fundamentals of quantum mechanics. It features straightforward explanations of quantum effects, suitable for readers from all backgrounds; real-world engineering problems showcasing the practical application of theory to practice, providing a relevant and accessible introduction to cutting-edge quantum applications; over 60 accessible worked examples using MATLAB, allowing deepened understanding through computational exploration and visualization; and a new chapter on quantum engineering, introducing state-of-the-art concepts in quantum information processing and quantum device design. Updated throughout and supported online by downloadable MATLAB code, exam questions, and solutions to over 150 homework problems for instructors, this is the ideal textbook for senior undergraduate and graduate students in applied science, applied physics, engineering, and materials science studying a first course in quantum mechanics.
Toward Quantum Mechanics

Featuring new coverage of quantum engineering and quantum information processing, the third edition of this bestselling textbook continues to provide a uniquely practical introduction to the fundamentals of quantum mechanics. It features straightforward explanations of quantum effects, suitable for readers from all backgrounds; real-world engineering problems showcasing the practical application of theory to practice, providing a relevant and accessible introduction to cutting-edge quantum applications; over 60 accessible worked examples using MATLAB, allowing deepened understanding through computational exploration and visualization; and a new chapter on quantum engineering, introducing state-of-the-art concepts in quantum information processing and quantum device design. Updated throughout and supported online by downloadable MATLAB code, exam questions, and solutions to over 150 homework problems for instructors, this is the ideal textbook for senior undergraduate and graduate students in applied science, applied physics, engineering, and materials science studying a first course in quantum mechanics.