Reviewerly/exHarmony huggingface dataset:
Lightweight Blockchain Solutions: Taxonomy, Research Progress, and Comprehensive Review

The proliferation of resource-constrained devices has become prevalent across various digital applications, including smart homes, smart healthcare, and smart transportation, among others. However, the integration of these devices brings many security issues. To address these concerns, Blockchain technology has been widely adopted due to its robust security characteristics, including immutability, cryptography, and distributed consensus. However, implementing blockchain within these networks is highly challenging due to the limited resources of the employed devices and the resource-intensive requirements of the blockchain. To overcome these challenges, a multitude of researchers have proposed lightweight blockchain solutions specifically designed for resource-constrained networks. In this paper, we present a taxonomy of lightweight blockchain solutions proposed in the literature. More precisely, we identify five areas within the "lightweight" concept, namely, blockchain architecture, device authentication, cryptography model, consensus algorithm, and storage method. We discuss the various methods employed in each "lightweight" category, highlighting existing gaps and identifying areas for improvement. Our review highlights the missing points in existing systems and paves the way to building a complete lightweight blockchain solution for networks of resource-constrained devices.
A fine-grained comparison of pragmatic language understanding in humans and language models

Pragmatics and non-literal language understanding are essential to human communication, and present a long-standing challenge for artificial language models. We perform a fine-grained comparison of language models and humans on seven pragmatic phenomena, using zero-shot prompting on an expert-curated set of English materials. We ask whether models (1) select pragmatic interpretations of speaker utterances, (2) make similar error patterns as humans, and (3) use similar linguistic cues as humans to solve the tasks. We find that the largest models achieve high accuracy and match human error patterns: within incorrect responses, models favor literal interpretations over heuristic-based distractors. We also find preliminary evidence that models and humans are sensitive to similar linguistic cues. Our results suggest that pragmatic behaviors can emerge in models without explicitly constructed representations of mental states. However, models tend to struggle with phenomena relying on social expectation violations.
Depth perception between dots and the background face reduces trypophobic discomfort

Studies have shown that viewing a cluster of dots evokes feelings of discomfort in viewers and that the discomfort becomes especially strong when the dots are placed on background images of human skin. This phenomenon has been explained by the physical properties and spatial and semantic relationships between the dots and the background. However, it was not known whether the perceived, as well as the physical, spatial relationships contributes to the generation of discomfort.We evoked illusory depth perception between black dots and the background face by drawing shadow-like gray dots around the black dots, while keeping the same black dots at the same positions, and examined whether illusory depth perception could increase or decrease discomfort. In each trial, participants viewed one of the following types of facial images: (a) face-only (face without dots), (b) a cluster of black dots on the face, (c) a cluster of gray dots on the face, and (d) a cluster of black dots and shadow-like gray dots on the face. After seeing each picture, they evaluated how much discomfort they felt from viewing the picture using a Likert scale and reported whether they perceived depth between the dots and the face.Participants felt discomfort toward all three types of faces with dots, that is, faces with black dots, gray dots, and both. However, interestingly, participants felt less discomfort when both black and gray dots were presented on the face than when only black dots were presented. The participants perceived depth between the black dots and the face in 85% of the trials with black dots and shadow-like gray dots, and there was a significant correlation between discomfort and frequency of depth perception. However, in the trials with black dots only and gray dots only, they perceived depth in only 18% and 27% of the trials, respectively, and the correlations between the frequencies of depth perception and discomfort were not significant.Our results suggest that the perceived spatial relationship, such as attached vs. separate, as well as the physical spatial relationship, contribute to the generation of discomfort.
FFT-based Convolution Neural Network on Silicon Photonics Platform

We present our implementation of an on-chip FFT-based optical Convolution Neural Network. By exploiting the integration capabilities of Silicon Photonics, we can integrate high-speed optical FFT into a single Photonic Integrated Circuit (PIC), showing design, packaging, and initial results.
Full Contextual Attention for Multi-resolution Transformers in Semantic Segmentation

Transformers have proved to be very effective for visual recognition tasks. In particular, vision transformers construct compressed global representations through self-attention and learnable class tokens. Multi-resolution transformers have shown recent successes in semantic segmentation but can only capture local interactions in high-resolution feature maps. This paper extends the notion of global tokens to build GLobal Attention Multi-resolution (GLAM) transformers. GLAM is a generic module that can be integrated into most existing transformer backbones. GLAM includes learnable global tokens, which unlike previous methods can model interactions between all image regions, and extracts powerful representations during training. Extensive experiments show that GLAM-Swin or GLAM-Swin-Unet exhibit substantially better performances than their vanilla counterparts on ADE20K and Cityscapes. Moreover, GLAM can be used to segment large 3D medical images, and GLAM-nnFormer achieves new state-of-the-art performance on the BCV dataset.
Breast Tumor Tissue Image Classification Using DIU-Net

Inspired by the observation that pathologists pay more attention to the nuclei regions when analyzing pathological images, this study utilized soft segmentation to imitate the visual focus mechanism and proposed a new segmentation–classification joint model to achieve superior classification performance for breast cancer pathology images. Aiming at the characteristics of different sizes of nuclei in pathological images, this study developed a new segmentation network with excellent cross-scale description ability called DIU-Net. To enhance the generalization ability of the segmentation network, that is, to avoid the segmentation network from learning low-level features, we proposed the Complementary Color Conversion Scheme in the training phase. In addition, due to the disparity between the area of the nucleus and the background in the pathology image, there is an inherent data imbalance phenomenon, dice loss and focal loss were used to overcome this problem. In order to further strengthen the classification performance of the model, this study adopted a joint training scheme, so that the output of the classification network can not only be used to optimize the classification network itself, but also optimize the segmentation network. In addition, this model can also provide the pathologist model’s attention area, increasing the model’s interpretability. The classification performance verification of the proposed method was carried out with the BreaKHis dataset. Our method obtains binary/multi-class classification accuracy 97.24/93.75 and 98.19/94.43 for 200× and 400× images, outperforming existing methods.
Optimization of K-Nearest Neighbors Algorithm with Cross Validation Techniques for Diabetes Prediction with Streamlit

The problem that occurs in the application of K-Nearest Neighbors as a classification algorithm is the frequent occurrence of overfitting in data processing. This can be overcome by using cross-validation techniques in evaluating the algorithm model and minimizing overfitting. Then the performance of diabetes prediction accuracy is unknown using the K-Nearest Neighbors algorithm with cross-validation technique. The data used comes from the National Institute of Digestive and Kidney Diabetes in 2021. The case study in this study is to find out the initial screening for diabetes is supported by the results of algorithm accuracy and real time application of streamlit-based users. The purpose of this study was to optimize the accuracy results with a cross validation technique supported by the k-nearest neighbors algorithm in the study of diabetes data. The method used is the k-nearest neighbors algorithm which is supported by cross validation technique for optimal accuracy results. Then the application of a streamlit-based interactive web application for testing the accuracy results used by the user to see the probability that the user has diabetes. The results showed that the optimization of the Cross Validation technique supported by the KNearest Neighbors algorithm model worked well. The results of the confusion matrix using the cross validation technique are more accurate in terms of the advantages of using the cross-validation technique itself. So that the classification report which has a value of 95% is more accurate than the accuracy which is worth 92% because of the use of cross-validation techniques that can minimize overfitting in addition to considerations of the accuracy value and the implementation of streamlit-based interactive web applications for user testing is going well.
AdvCat: Domain-Agnostic Robustness Assessment for Cybersecurity-Critical Applications with Categorical Inputs

Machine Learning-as-a-Service systems (MLaaS) have been largely developed for cybersecurity-critical applications, such as detecting network intrusions and fake news campaigns. Despite effectiveness, their robustness against adversarial attacks is one of the key trust concerns for MLaaS deployment. We are thus motivated to assess the adversarial robustness of the Machine Learning models residing at the core of these securitycritical applications with categorical inputs. Previous research efforts on accessing model robustness against manipulation of categorical inputs are specific to use cases and heavily depend on domain knowledge, or require white-box access to the target ML model. Such limitations prevent the robustness assessment from being as a domain-agnostic service provided to various real-world applications. We propose a provably optimal yet computationally highly efficient adversarial robustness assessment protocol for a wide band of ML-driven cybersecurity-critical applications. We demonstrate the use of the domain-agnostic robustness assessment method with substantial experimental study on fake news detection and intrusion detection problems.
The Lookup Table Regression Model for Histogram-Valued Symbolic Data

This paper presents the Lookup Table Regression Model (LTRM) for histogram-valued symbolic data. We first transform the given symbolic data to a numerical data table by the quantile method. Then, under the selected response variable, we apply the Monotone Blocks Segmentation (MBS) to the obtained numerical data table. If the selected response variable and some remained explanatory variable(s) organize a monotone structure, the MBS generates a Lookup Table composed of interval values. For a given object, we search the nearest value of an explanatory variable, then the corresponding value of the response variable becomes the estimated value. If the response variable and the explanatory variable(s) are covariate but they follow to a non-monotonic structure, we need to divide the given data into several monotone substructures. For this purpose, we apply the hierarchical conceptual clustering to the given data, and we obtain Multiple Lookup Tables by applying the MBS to each of substructures. We show the usefulness of the proposed method by using an artificial data set and real data sets.
Cyclic Federated Learning Method Based on Distribution Information Sharing and Knowledge Distillation for Medical Data

Federated learning has been attracting increasing amounts of attention for its potential applications in disease diagnosis within the medical field due to privacy preservation and its ability to solve data silo problems. However, the inconsistent distributions of client-side data significantly degrade the performance of traditional federated learning. To eliminate the adverse effects of non-IID problems on federated learning performance on multiple medical institution datasets, this paper proposes a cyclic federated learning method based on distribution information sharing and knowledge distillation for medical data (CFL_DS_KD). The method is divided into two main phases. The first stage is an offline preparation process in which all clients train a generator model on local datasets and pass the generator to neighbouring clients to generate virtual shared data. The second stage is an online process that can also be mainly divided into two steps. The first step is a knowledge distillation learning process in which all clients first initialise the task model on the local datasets and share it with neighbouring clients. The clients then use the shared task model to guide the updating of their local task models on the virtual shared data. The second step simply re-updates the task model on the local datasets again and shares it with neighbouring clients. Our experiments on non-IID datasets demonstrated the superior performance of our proposed method compared to existing federated learning algorithms.
Modern Methods and Methodology of Historical and Biographical Research

The development of science in the Soviet autonomous republics was the result of a consistent policy of the government, especially when the institutions have opened up. The Institute of History, Language and Literature (IHLL) occupies a worthy place among the scientific collectives of UFRC of RAS. It is one of the oldest research institutions of Bashkortostan (BASSR). Human factor, a specific personality, has always been playing an important role in the history of many crucial events of research institutions of the 20th century. IHLL started its activity by solving urgent issues of Bashkir writing, national literature, the accumulation of folklore, archaeological and ethnographic material, etc. Therefore, a lot depended on the personality of the leader. Being opened as The Society for the Study of Everyday life, Culture and History of Bashkiria with Nuriagzam Tagirov at its head in 1922, it was renamed The Bashkir Research Institute of National Culture, where Mikhail Arefyevich Solyanov became the first director. The development of Russian historical personalism in the twentieth century allowed us to restore many biographies of the Institute employees, also of the eminent personalities of our republic and Russia. It is significant that on this particular 100th anniversary of the institution these methods and methodology made possible to clarify many aspects of our first director workdays, to link the IHLL through his personality with such renowned organizations as The Russian State Library named after V. I. Lenin and MSLI. The role of M. A. Solyanov was bright but short-lived. After all historical personalism has its scientific and practical significance. The work will find its reader among historians, philosophers, teachers of the humanities.
Commentary: Multidimensional discrete chaotic maps

GENERAL COMMENTARY article Front. Phys., 06 December 2022Sec. Interdisciplinary Physics Volume 10 - 2022 | https://doi.org/10.3389/fphy.2022.1094240
Improved Remora Optimization Algorithm with Mutualistic Strategy for Solving Constrained Engineering Optimization Problems

Recently, a new swarm intelligence optimization algorithm called the remora optimization algorithm (ROA) was proposed. ROA simulates the remora’s behavior of the adsorption host and uses some formulas of the sailfish optimization (SFO) algorithm and whale optimization algorithm (WOA) to update the solutions. However, the performance of ROA is still unsatisfactory. When solving complex problems, ROA’s convergence ability requires further improvement. Moreover, it is easy to fall into local optimization. Since the remora depends on the host to obtain food and optimize ROA performance, this paper introduces the mutualistic strategy to strengthen the symbiotic relationship between the remora and the host. Meanwhile, chaotic tent mapping and roulette wheel selection are added to further improve the algorithm’s performance. By incorporating the above improvements, this paper proposes an improved remora optimization algorithm with a mutualistic strategy (IROA) and uses 23 benchmark functions in different dimensions and CEC2020 functions to validate the performance of the proposed IROA. Experimental studies on six classical engineering problems demonstrate that the proposed IROA has excellent advantages in solving practical optimization problems.
Machine-learning cosmology from void properties

Cosmic voids are the largest and most underdense structures in the Universe. Their properties have been shown to encode precious information about the laws and constituents of the Universe. We show that machine learning techniques can unlock the information in void features for cosmological parameter inference. We rely on thousands of void catalogs from the GIGANTES dataset, where every catalog contains an average of 11,000 voids from a volume of $1~(h^{-1}{\rm Gpc})^3$. We focus on three properties of cosmic voids: ellipticity, density contrast, and radius. We train 1) fully connected neural networks on histograms from individual void properties and 2) deep sets from void catalogs, to perform likelihood-free inference on the value of cosmological parameters. We find that our best models are able to constrain the value of $\Omega_{\rm m}$, $\sigma_8$, and $n_s$ with mean relative errors of $10\%$, $4\%$, and $3\%$, respectively, without using any spatial information from the void catalogs. Our results provide an illustration for the use of machine learning to constrain cosmology with voids.
The case method as an innovative method of education

In modern conditions, universities need to learn to respond flexibly to external challenges and provide the economy with effective personnel with a wide range of competencies. To achieve the corresponding results, it is necessary to introduce innovative educational technologies into the educational processes, which can help to increase the interest of students in the subject of study, the acquisition and development of flexible skills and competencies.&#x0D; This article analyzes the main advantages of innovative educational technology - the case method, considers the advantages of its use in the context of gaining competitive advantages by students, including in further employment.
The Latest Pedagogical Ideas for Development of Creative Abilities in Preschool Children

The article attempts an author's technology to develop the creative abilities of preschool children in drawing classes. The authors have reviewed the latest neurophysiological aspects of creativity and have taken them into account in modeling the framework model of the development of creative abilities of preschool children in drawing classes. The aim of the article is to pursue two successive theoretical and methodological steps: a natural neurophysiological justification of the educational law of forming the visual development of elementary school children valid for teachers' understanding and use and a framework morphed structuring of reflexive, cognitive, and reflective activity of schoolchildren. It is possible with synergetic and, in fact, parallel realization of sensitivity, figurative assimilation of signals of the surrounding world, acquisition of experience and (as a cumulative result) - spontaneous, but framed by facilitative intervention, creative realization of the child's deep needs. The main result of the article was that we realized: a training module can be represented as a synergy of educational-neurophysiological correlations. That is, training modules consist, on the one hand, of target, content, technological and productive settings and expectations, and on the other hand, these settings are inseparable from associative, sensual-aesthetic, activity and motivational (cognition - satisfaction) manifestations of children's psyche. The international significance of the article lies in the reception of the latest pedagogical and neurophysiological ideas for development of creative abilities in preschool children for development of the author’s technology. This can be used by practicing teachers as a model for implementation of pedagogical style in the cultural, social and educational conditions of the Eastern European region.
Information theoretical limits for quantum optimal control solutions: error scaling of noisy control channels

Accurate manipulations of an open quantum system require a deep knowledge of its controllability properties and the information content of the implemented control fields. By using tools of information and quantum optimal control theory, we provide analytical bounds (information-time bounds) to characterize our capability to control the system when subject to arbitrary sources of noise. Moreover, since the presence of an external noise field induces open quantum system dynamics, we also show that the results provided by the information-time bounds are in very good agreement with the Kofman-Kurizki universal formula describing decoherence processes. Finally, we numerically test the scaling of the control accuracy as a function of the noise parameters, by means of the dressed chopped random basis (dCRAB) algorithm for quantum optimal control.
Suntex

The key objective of this research project is to “create a new architectural textile, Suntex, by interweaving thin film solar cells and electrically conductive yarn into a structural technical textile, so it can generate energy while it is providing shade, structure or an aesthetic update to a building.”&#x0D; Textile has strong potential as a sustainable building material because it can be lightweight, material efficiency and low carbon. Moreover, its flexibility provides great design freedom and its transparency makes it very suitable for façade applications, maintaining views to the outside while providing solar shading. Suntex is a solar textile, currently in development, intended for textile architecture applications like textile façades. By combining three qualities, namely providing the building with energy generation, solar shading and a unique aesthetic appearance, which also promotes the acceptance of solar technology, it offers a positive climate impact.&#x0D; Suntex can be considered as a new type of membrane material for Building Integrated Photovoltaics (BIPV). With this innovative, constructive fabric, enormous surfaces that are still unused can be outfitted with energy-generating potential.&#x0D; This paper presents a design case to analyse the potential impact of Suntex as a textile façade. Based on insights into the development process and experiment results so far, it evaluates the feasibility and impact from a technical and design perspective.