Reviewerly/exHarmony huggingface dataset:
Construction of a numerical model and algorithm for solving two-dimensional problems of filtration of multicomponent liquids, taking into account the moving “oil-water” interface

The paper considers a two-dimensional mathematical model of the filtration of a viscous, incompressible fluid in a deformable porous medium. The article describes a mathematical model of the problem at the “oil-water” interface with a system of parabolic differential equations. The problem posed is solved numerically using the differential-difference method based on the longitudinal-transverse scheme and the differential sweep method to determine the unknown boundary. To obtain a differential-difference problem, an algorithmic representation of a hidden scheme of alternating directions (longitudinal-transverse scheme) is used. The resulting system of differential-difference equations with initial conditions is known for each time layer along straight lines along the x axis, and then along each y axis it is solved by a differential sweep along a straight line, where the values just found correspond to time, i.e., the layer is taken as initial conditions. By approximating the differential equations in time, the position of the interface is determined for each time layer.
A Novel Data Extraction Framework Using Natural Language Processing (DEFNLP) Techniques

Evidence through data is critical if government has to address threats faced by the nation, such as pandemics or climate change. Yet several facts about data necessary to inform evidence and science is locked inside publications. We introduced the new dataset, Coleridge Initiative - Show US the Data, to discover how the data can be used for the public good. In this research, we demonstrate a general Data Extraction Framework Using Natural Language Processing (DEFNLP) Techniques which challenges data scientists to show how publicly funded data has been used to serve science and society using NLP techniques and models. The proposed framework uses NLP libraries and techniques like SpaCy and NER respectively and different huggingface Question Answering (QA) models to predict the datasets used in publications. DEFNLP findings can assist the government in making more wiser decisions, transparent public investments and quickly find the information they need. Until now such issue having large dataset which belongs to numerous research areas has not been addressed. This approach is domain independent and therefore can be applied to all kind of case studies and scenarios which require data extraction. Our methodology sets the state-of-the-art on Coleridge Initiative dataset, reaching the highest score of 0.497 using salti bert QA model than other QA models (e.g., Longformer or BigBird) which impressively raised the leaderboard score with an outcome of 0.654. Its computation time to answer each query on CPU is 0.0696s and has suitable hyperparameters for our dataset as maximum answer length is 64, greater batch size as well as learning rate. In terms of timing and performance, each epoch took around 5 minutes on average on a computer with output size of 3.27kB which is again far better than other frameworks.
Generating discrete uniform distribution from a biased coin using number-theoretic method

In this article, a new algorithm generating discrete uniform distribution on n elements from a binary random source is proposed by introducing the notion of coprimity and modulo function in number theory. The algorithm is a generalization of Von Neumann’s algorithm for n=2 [4] and Dijkstra’s algorithm for prime n [1]. The proof for the validity of the algorithm introduces the notion of random variable in residual classes, and combines the ideas and techniques from both probability theory and number theory.
Metaverse and Its Communication. The Future is Here. True or False?

Abstract This text analyses the metaverse: its definitions and concepts, its meaning and impact; digital art, fashion and their communication; its performance issues, its failures, and criticism. It compares existing metaverse definitions and concepts, evaluates of their advantages and disadvantages, their chance of coming true, their present degree of evolution. Closer looks are dedicated to the influence of a virtual space on art and fashion, their communication and dissemination and to the metaverse failures, problems, and criticism. Not only the metaverse technology, but also its intended innovation of communication, work, education, healthcare, business, products, and social interactions, suffer from performance, quality and security issues. Today, irreconcilable visions of the metaverse coexist: a valuable, socially useful metaverse and a consumer and entertainment metaverse. It is impossible to predict the future, but any virtual world concept will have to solve these critical issues. Not the metaverse, but the greatest crisis of our times (with climate change, pandemic, war, economic crisis) is altering the products and their communication, undermining the luxury, fashion and art sector. It is changing the value of identity and producing a paradigm shift. Something must end, to allow the growth of something new: because change is life.
Communicating Digitalised Supply Chain Transparency: Towards a Guide for Fashion SMEs

Abstract The digitalisation of fashion supply chain transparency has gained increased attention in recent years. Technology solutions that have arisen based on advanced technologies and Web3 include smart tags, forensic tracers and blockchain platformisation. Whereby current reports discuss supply chain transparency from the perspective of the data, technical solutions and policy [1, 2], little attention is given to the fashion firms that are to adopt these technologies. Finding themselves in the midst of the supply chain transparency polemic, small to medium brands are still at a loss as to transformation and communication strategies [3]. This paper examines the standpoint of the small-scale brand, its challenges and needs in the face of digital transformation and lays the groundwork for the development of Web3 technology adoption guidelines–that can ultimately form, not only part of their implementation but also their communication strategy. Applying the theoretical framework of organisational theory, it provides evidence of successful practice through case study methodology. The study contributes to knowledge of organisational theory in the context of adjusting to rapid and complex change triggered by both external and internal demands for adopting advanced technology.
Identification of Changing Personnel with Double-Layer Network Fusion and Bi-Level Monitoring Mechanism

Person re-identification (Re-ID) is an essential part of visual surveillance that aims to identify and locate persons from multiple network cameras without conflicting viewpoints. Although significant advances have been made in recent years with the use of deep learning, there are still many challenges in vision such as occlusion, exposure, background clutter, misalignment, scale, perspective, low resolution and illumination, and cross-camera methods. Dressing redefinition is a hot topic in education right now. Most existing methods assume that people's clothes do not change in a short time, but they do not apply when people change clothes. Accordingly, this article introduces a double-layer garment changer re-identification network that integrates the secondary care process through clustering and fine-grained knowledge in space and training the garment classification branch to increase the sensitivity of the network to garment characteristics. In this method, auxiliary equipment such as human bone is not used and the complexity of the model is greatly reduced compared to other methods. This article runs experiments on the famous redefined PRCC data and large-scale long-term dataset (LaST). Experimental results show that the method in this article is superior to existing methods.
A Smart Neuro-Centric Approach to Predict Heart Attacks for Child Using IOT

Cardiovascular disease leads to heart attack disease even child children developing Asian countries. This Cardiovascular disease may affect the heart for various reasons for the child. The main objective of this research work is used to track and monitor the child from heart attack and also to protect the child from theft using GPS location with a wearable sensor. The Sensor embedded in the chain will monitor and track the child’s neuron activities based on the heartbeat, temperature, and GPS location of the Child. This Research work is classified into two sections. The first Section is used to track the neuro-centric activities of the child in terms of temperature, and heartbeat. If the Heartbeat is low or high and similarly if the child accidentally or incidentally body temperature is high. The information will be passed to their respective parents. In the Second Section, the child can be protected from theft using GPS Location. Initially, the parents had to set their border location, if the child cross the border, the alert information will be passed to the parents. This research work will be effective and efficient with Sensors using IoT to protect children physically and location-based.
Empowering Future Scholars: Coaching Workshop for Junior Researchers

In recent years, there has been a growing focus on HCI research in non-western contexts, attracting junior researchers from diverse backgrounds to actively engage in this global community. However, there are limited avenues for junior researchers to learn the unwritten practices and nuances of the research process. Our workshop addresses this need through a 3-hour coaching session where junior researchers are paired with experienced senior researchers to work on a concrete research task of the junior researcher’s choice, providing valuable mentorship and guidance.
Adapting Forests to an Uncertain Climate

Forests across the world play a crucial role in the fight against the climate catastrophe as well as mass extinction that characterise the Anthropocene. However, they are also increasingly threatened by destructive human practices such as agriculture and mining, but also climate change itself. This article focuses on forests in Germany, which have been devastated in recent years by heat, drought and bark beetles. Hence, forests and associated forestry practices are in urgent need of adaptation to a different climate. Several digital applications have been developed to assist with this effort. Adaptation is complicated by the epistemological challenge of climate change, that the uncertainty of how exactly climate change will affect specific local sites, as well as future markets for forest products, poses. In this short paper we review how two applications address this uncertainty in their approach to supporting the climate adaptation of forests and draw out preliminary lessons for HCI research and design.
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Let R2=Fpm+uFpm, where u2=0. In this paper, we determine the algebraic structures of all skew constacyclic codes of lengths ps and 2ps over R2 for special automorphisms. We also characterize the structure of (Euclidean) dual of some special skew constacyclic codes of length 2ps and calculate the number of codewords of them. As a special case, if we restrict our attention to the polynomial ring R2[x], we obtain most of results for constacyclic codes of lengths ps and 2ps over R2.
Community Networks powered by Community Currencies

In this demo, we will show how a mutual credit-based community voucher or currency integrated with a community-owned network and a local content server could incentivise users to become better custodians of commons infrastructure. This could lead to the generation of more locally relevant digital content and the expansion, use, and stability of a community voucher to support wider local markets that embrace local digital and physical goods and services.
Effective Working Environment and Factor for A Software Engineer in Companies That Are Not ICT Based

Working as Software Engineer in the healthcare industry for four years. There were rules guiding the work environment of healthcare professionals but none for Software engineers, who tend to overwork. Such resulted in health issues. Not ICT-based organizations need an understanding of the working factors of the Software Engineer to make them effectively produce. The panelists will discuss this and give their recommendations.
“Do you collect data to give to the university or do you do the work to benefit people?”: Indigenous Data Sovereignty in Environmental Contexts

This paper analyzes the current practices of Indigenous data sovereignty in environmental research and activism in the United States, as known by the settler government. The CARE principles are a widely adopted set of guidelines for Indigenous data sovereignty with guidelines being collective benefit, authority to control, respect and ethics, yet there exists little detail on current practices of operationalization and implementation of the CARE principles. This research specifically identified opportunities to further clarify how environmental data can be managed in accordance with the CARE principles. Using current literature, we examine how sustainability and Human-Computer Interaction (HCI) research could better incorporate Indigenous data sovereignty and governance. Through three interviews with Indigenous environmental practitioners, we use inductive and deductive analysis to understand current thoughts and practices. In a forestry analysis case study with the Penobscot Nation, we examine specifically how the CARE principles could be implemented into a research project. The interviews and case study reveal design considerations such as emphasizing roles in responsibility and ethics to be taken into future HCI research involving Indigenous data sovereignty in environmental contexts.
Removing Image Artifacts From Scratched Lens Protectors

A protector is placed in front of the camera lens for mobile devices to avoid damage, while the protector itself can be easily scratched accidentally, especially for plastic ones. The artifacts appear in a wide variety of patterns, making it difficult to see through them clearly. Removing image artifacts from the scratched lens protector is inherently challenging due to the occasional flare artifacts and the co-occurring interference within mixed artifacts. Though different methods have been proposed for some specific distortions, they seldom consider such inherent challenges. In our work, we consider the inherent challenges in a unified framework with two cooperative modules, which facilitate the performance boost of each other. We also collect a new dataset from the real world to facilitate training and evaluation purposes. The experimental results demonstrate that our method outperforms the baselines qualitatively and quantitatively. The code and datasets will be released at https://github.com/wyf0912/flare-removal
Optimization of Outlier Nodes of DBSCAN using PSO in Zone-Based Wireless Sensor Networks

In real world applications, large amount of raw sensed data is collected by deploying wireless sensor networks. Amidst a lot of challenges faced from WSNs, the key challenge is to extract high level of intelligent knowledge from the sensed data. Outlier nodes play a very important role in finding rare patterns in domains such as web purchase analysis, intrusion detection and fraud analysis. The clusters in zone - based Wireless Sensor Networks, is challenging with the limitation of the area. The clusters are of different shapes based on the densities of the nodes. DBSCAN (Density Based Spatial Clustering of Applications with Noise) can detect the clusters of arbitrary shapes and identifies the outlier nodes effectively. The shape of Clusters is not limited with the density of the clusters. Reorganizing the spatial structures of the clusters to maintain the shape of clusters for efficiency is still challenging with the nodes of low power and low computation capabilities. In this paper, DBSCAN in ZSEP-E is improved with Particle Swarm Optimization by reorganizing the outlier nodes in the optimized clusters. Simulations conducted with this proposed model, provided better network performance in the zones with more energy utilization and network lifetime. Simulation results reveal that the proposed approach yields increased network lifetime, throughput and energy utilization than the existing protocol.
The Online Course “The Organization of the Students Project Activities of Computer Studies in the Context of the Educational Technopark”: Ideas, Approaches and Development

The article deals with the issues of the organization of the project activities of Computer Studies on the basis of the educational technopark. The authors describe the experience of the development of the online course “The organization of the students project activities of Computer Studies in the context of the educational technopark” with the technical assistance of the technopark in Volgograd State Socio-Pedagogical University.
Identification of Malaria Disease Using Machine Learning Models

Malaria, caused by Plasmodium parasites in the bloodstream spread by infected mosquitoes, is a highly severe and sometimes deadly disease. Image analysis and machine learning can enhance diagnosis by quantifying parasitemia on blood slides. The building of an autonomous, accurate, and effective model can significantly reduce the need for trained laborers. This article discusses computer-assisted approaches for finding malaria parasites in blood smear images. These procedures consist of obtaining the dataset, preprocessing the images, segmenting the red blood cells, extracting and choosing features, and classifying the images. The approach is based on well-known Convolutional neural network (CNN) models of Plasmodium parasites and erythrocytes. The trained CNN and VGG-19 are given images of infected and uninfected erythrocytes from the same dataset. VGG 19 gives 96% detection accuracy where CNN achieves 94%.
Spectral-DP: Differentially Private Deep Learning through Spectral Perturbation and Filtering

Differential privacy is a widely accepted measure of privacy in the context of deep learning algorithms, and achieving it relies on a noisy training approach known as differentially private stochastic gradient descent (DP-SGD). DP-SGD requires direct noise addition to every gradient in a dense neural network, the privacy is achieved at a significant utility cost. In this work, we present Spectral-DP, a new differentially private learning approach which combines gradient perturbation in the spectral domain with spectral filtering to achieve a desired privacy guarantee with a lower noise scale and thus better utility. We develop differentially private deep learning methods based on Spectral-DP for architectures that contain both convolution and fully connected layers. In particular, for fully connected layers, we combine a block-circulant based spatial restructuring with Spectral-DP to achieve better utility. Through comprehensive experiments, we study and provide guidelines to implement Spectral-DP deep learning on benchmark datasets. In comparison with state-of-the-art DP-SGD based approaches, Spectral-DP is shown to have uniformly better utility performance in both training from scratch and transfer learning settings.
Extreme Learning Machine: A Comprehensive Survey of Theories &amp; Algorithms

The Extreme Learning Machine (ELM), a quick and effective approach for training single-hidden-layer feedforward neural networks, is thoroughly reviewed in this paper. The objective is to provide a systematic review and analysis of the various theories, algorithms, and applications of ELM, which have not been adequately covered in the literature. The study examines the theoretical foundations of ELM, provides an overview of various ELM algorithms, analyzes the applications of ELM in different domains, and identifies future research directions and challenges in the field of ELM. The findings suggest that ELM is a promising and effective learning algorithm with many benefits as improved learning speed, good generalisation performance, and low complexity. The implications of this research for practice and research are significant. However, more empirical studies are necessary to evaluate its performance in real-world applications, and further research is required to assess its effectiveness in different domains. Future work should focus on exploring the potential of ELM in deep learning, enhancing the interpretability of ELM models, and developing new algorithms that can improve the learning efficiency of ELM. Overall, this survey provides a valuable reference for researchers and practitioners to better understand the potential and limitations of the ELM algorithm.
BLEDiff: Scalable and Property-Agnostic Noncompliance Checking for BLE Implementations

In this work, we develop an automated, scalable, property-agnostic, and black-box protocol noncompliance checking framework called BLEDiff that can analyze and uncover noncompliant behavior in the Bluetooth Low Energy (BLE) protocol implementations. To overcome the enormous manual effort of extracting BLE protocol reference behavioral abstraction and security properties from a large and complex BLE specification, BLEDiff takes advantage of having access to multiple BLE devices and leverages the concept of differential testing to automatically identify deviant noncompliant behavior. In this regard, BLEDiff first automatically extracts the protocol FSM of a BLE implementation using the active automata learning approach. To improve the scalability of active automata learning for the large and complex BLE protocol, BLEDiff explores the idea of using a divide and conquer approach. BLEDiff essentially divides the BLE protocol into multiple sub-protocols, identifies their dependencies and extracts the FSM of each sub-protocol separately, and finally composes them to create the large protocol FSM. These FSMs are then pair-wise tested to automatically identify diverse deviations. We evaluate BLEDiff with 25 different commercial devices and demonstrate it can uncover 13 different deviant behaviors with 10 exploitable attacks.
Locally Differentially Private Frequency Estimation Based on Convolution Framework

Local differential privacy (LDP) collects user data while protecting user privacy and eliminating the need for a trusted data collector. Several LDP protocols have been proposed and deployed in real-world applications. Frequency estimation is a fundamental task in the LDP protocols, which enables more advanced tasks in data analytics. However, the existing LDP protocols amplify the added noise in estimating the frequencies and therefore do not achieve optimal performance in accuracy. This paper introduces a convolution framework to analyze and optimize the estimated frequencies of LDP protocols. The convolution framework can equivalently transform the original frequency estimation problem into a deconvolution problem with noise. We thus add the Wiener filter-based deconvolution algorithms to LDP protocols to estimate the frequency while suppressing the added noise. Experimental results on different real-world datasets demonstrate that our proposed algorithms can lead to significantly better accuracy for state-of-the-art LDP protocols by orders of magnitude for the smooth dataset. And these algorithms also work on non-smooth datasets, but only to a limited extent. Our code is available at https://github.com/SEUNICK/LDP.