Reviewerly/exHarmony huggingface dataset:
Blockchain-based authenticable (k,n) multi-secret image sharing scheme

We aim to devise a technique to share k number of secret images to n number of participants with ( n ≥ k ) in such a way that only k or more participants can collaborate to get the original k secret images. The proposed approach relies on the development of a spanning tree from a complete graph G for the share image generation in which each of the k pixels retrieved from the k secret images is considered to be one of the k vertices of G. Each of the spanning trees of G is represented by a unique sequence of numbers known as Prufer sequences, and each participant is assigned a random Prufer sequence. The Prufer sequence of a participant is used to generate the share images for that participant. The share images are kept in a blockchain using interplanetary file system. This scheme is suitable for protecting sensitive images through distributed storage while eliminating the single points of failure. Because the blockchain is an immutable decentralized public record, anyone can verify their shares as well as the shares of other participants, thereby preventing cheating by any participant. The scheme presents a lossless reconstruction of k secret images, making it perfect for use in military applications, medical applications, and other applications in which lossless recovery is a crucial element. Extensive experimental analysis confirms that the proposed scheme is secure, cheat proof, and lossless.
A Two-Stage Preference Learning Method based on Graph Neural Networks for Preference Service

Preference learning refers to learning the preferences for a collection of alternatives based on observed or revealed preference information, which are usually represented in the form of an order relation. Some of the existing preference learning methods are parametric in nature, and such methods are faster to train but less expressive. To address this issue, we map the preference learning onto the graph structure, introduce the concept of graph neural networks (GNNs), and propose a two-stage preference learning method based on GNNs, which consists of preference relation prediction stage and object preference ranking stage. The first stage is used to judge the preferences between pairs of objects, the second stage is used to correct the inconsistent preference information of the first stage and rank all objects. In Stage 1, we turn the prediction problem into an edge classification problem on the graph, design a multilayer perceptron (MLP) model to extract edge features, and mine preference information with the help of GNNs. In Stage 2, we construct a comparator neural network structure that takes pairwise preference information as input and generates a score for each object as output. The ranking of the object scores determines the objects’ preference order. Experiments conducted on preference learning datasets have shown that our method achieves significant performance improvements over existing preference learning methods when evaluated in the context of preference service.
Demonstration of a quantum-classical coprocessing protocol for simulating nuclear reactions

Quantum computers hold great promise for exact simulations of nuclear dynamical processes (e.g., scattering and reactions), which are paramount to the study of nuclear matter at the limit of stability and in the formation of chemical elements in stars. However, quantum simulations of the unitary (real) time dynamics of fermionic many-body systems require a currently prohibitive number of reliable and long-lived qubits. We propose a co-processing algorithm for the simulation of real-time dynamics in which the time evolution of the spatial coordinates is carried out on a classical processor, while the evolution of the spin degrees of freedom is carried out on quantum hardware. We demonstrate this hybrid scheme with the simulation of two neutrons scattering at the Lawrence Berkeley National Laboratory's Advanced Quantum Testbed. After implementing error mitigation strategies to improve the accuracy of the algorithm in addition to a combination of circuit compression techniques and tomography as methods to elucidate the onset of decoherence, our results validate the principle of the proposed co-processing scheme. A generalization of this present scheme will open the way for (real-time) path integral simulations of nuclear scattering.
Blockchain-Based Smart Farm Security Framework for the Internet of Things

Smart farming, as a branch of the Internet of Things (IoT), combines the recognition of agricultural economic competencies and the progress of data and information collected from connected devices with statistical analysis to characterize the essentials of the assimilated information, allowing farmers to make intelligent conclusions that will maximize the harvest benefit. However, the integration of advanced technologies requires the adoption of high-tech security approaches. In this paper, we present a framework that promises to enhance the security and privacy of smart farms by leveraging the decentralized nature of blockchain technology. The framework stores and manages data acquired from IoT devices installed in smart farms using a distributed ledger architecture, which provides secure and tamper-proof data storage and ensures the integrity and validity of the data. The study uses the AWS cloud, ESP32, the smart farm security monitoring framework, and the Ethereum Rinkeby smart contract mechanism, which enables the automated execution of pre-defined rules and regulations. As a result of a proof-of-concept implementation, the system can detect and respond to security threats in real time, and the results illustrate its usefulness in improving the security of smart farms. The number of accepted blockchain transactions on smart farming requests fell from 189,000 to 109,450 after carrying out the first three tests while the next three testing phases showed a rise in the number of blockchain transactions accepted on smart farming requests from 176,000 to 290,786. We further observed that the lesser the time taken to induce the device alarm, the higher the number of blockchain transactions accepted on smart farming requests, which demonstrates the efficacy of blockchain-based poisoning attack mitigation in smart farming.
HAHNet: a convolutional neural network for HER2 status classification of breast cancer

Breast cancer is a significant health issue for women, and human epidermal growth factor receptor-2 (HER2) plays a crucial role as a vital prognostic and predictive factor. The HER2 status is essential for formulating effective treatment plans for breast cancer. However, the assessment of HER2 status using immunohistochemistry (IHC) is time-consuming and costly. Existing computational methods for evaluating HER2 status have limitations and lack sufficient accuracy. Therefore, there is an urgent need for an improved computational method to better assess HER2 status, which holds significant importance in saving lives and alleviating the burden on pathologists.This paper analyzes the characteristics of histological images of breast cancer and proposes a neural network model named HAHNet that combines multi-scale features with attention mechanisms for HER2 status classification. HAHNet directly classifies the HER2 status from hematoxylin and eosin (H&E) stained histological images, reducing additional costs. It achieves superior performance compared to other computational methods.According to our experimental results, the proposed HAHNet achieved high performance in classifying the HER2 status of breast cancer using only H&E stained samples. It can be applied in case classification, benefiting the work of pathologists and potentially helping more breast cancer patients.
A Novel Efficient Dynamic Throttling Strategy for Blockchain-Based Intrusion Detection Systems in 6G-Enabled VSNs

Vehicular Social Networks (VSNs) have emerged as a new social interaction paradigm, where vehicles can form social networks on the roads to improve the convenience/safety of passengers. VSNs are part of Vehicle to Everything (V2X) services, which is one of the industrial verticals in the coming sixth generation (6G) networks. The lower latency, higher connection density, and near-100% coverage envisaged in 6G will enable more efficient implementation of VSNs applications. The purpose of this study is to address the problem of lateral movements of attackers who could compromise one device in a VSN, given the large number of connected devices and services in VSNs and attack other devices and vehicles. This challenge is addressed via our proposed Blockchain-based Collaborative Distributed Intrusion Detection (BCDID) system with a novel Dynamic Throttling Strategy (DTS) to detect and prevent attackers’ lateral movements in VSNs. Our experiments showed how the proposed DTS improve the effectiveness of the BCDID system in terms of detection capabilities and handling queries three times faster than the default strategy with 350k queries tested. We concluded that our DTS strategy can increase transaction processing capacity in the BCDID system and improve its performance while maintaining the integrity of data on-chain.
The third-person perspective full-body illusion induced by visual-tactile stimulation in virtual reality for stroke patients

This paper attempts to induce the third-person perspective full body illusion (3PP-FBI) with virtual reality (VR) in stroke patients. Nineteen individuals with stroke were recruited. The 3PP-FBI induction method, which was well-established in healthy individuals, using synchronous visual-tactile stimulation on one body part was used. Questionnaire scores and proprioceptive drift values were collected under different conditions for characterizing the induced 3PP-FBI. Results showed that synchronous visual-tactile stimulation of a single body part (back or upper limb) was sufficient to elicit 3PP-FBI in stroke patients, forming a sense of ownership (SOO) over the entire virtual body. Moreover, the intensity of 3PP-FBI was stronger when the back was stimulated, compared to stimulating the impaired upper limb. This study demonstrated the viability of visual-guided rehabilitation training while having a SOO to a virtual body from the third-person perspective, in anticipation of achieving better rehabilitation outcome for movements beyond the first-person perspective.
Adaptive reference image formation for onboard correlation-extreme system for moving objects support

To increase the efficiency of the correlation-extremal tracking system for moving objects, it is proposed to use Interactive Multiple Model (IMM), which includes Kalman filters of the 0th and 1st order and a Singer filter of the 0th order. The structure of the meter was obtained as a result of statistical synthesis according to the criterion of minimum a posteriori risk with a quadratic loss function. The statistical synthesis is based on the semi-Markov brightness model, which takes into account the random nature of the transition time from one state to another.
Utilizing augmented reality for the assembly and disassembly of panelized construction

Prefabricated construction allows for efficient resource usage while creating higher-quality products that can be assembled on-site within a short time. While this translates to significant benefits for the overall construction, challenges arise from an increased demand for trained prefabrication assembly workers. As prefabrication calls for skills differing from traditional construction, the local labor force can be negatively affected to impede the successful uptake of prefabricated construction. Upskilling the local workforce to take on prefabrication assembly and potential disassembly can solve this problem. This is more relevant to remote construction projects as they stand to gain more from prefabricated construction. This study presents two workflows for creating Augmented Reality (AR) solutions. The AR solutions are aimed to help workers transition between traditional and prefabrication assembly in a panelized construction project. They are: (1) using QR codes to identify a panel’s intended location and construction sequence and (2) using predefined markers to show required equipment and on-site assembly procedures. The solutions are delivered through smartphones, which are readily available and provide a cost-effective medium. Furthermore, developed workflows present an opportunity to implement Design for Disassembly (DfD) concepts in a project. The proposed workflows show the potential to substantially help communicate to the workers the instructions on both the panel assembly and disassembly activities and upskill the local workforce to support the transition to prefabrication assembly in construction projects.
Dual-Guided Brain Diffusion Model: Natural Image Reconstruction from Human Visual Stimulus fMRI

The reconstruction of visual stimuli from fMRI signals, which record brain activity, is a challenging task with crucial research value in the fields of neuroscience and machine learning. Previous studies tend to emphasize reconstructing pixel-level features (contours, colors, etc.) or semantic features (object category) of the stimulus image, but typically, these properties are not reconstructed together. In this context, we introduce a novel three-stage visual reconstruction approach called the Dual-guided Brain Diffusion Model (DBDM). Initially, we employ the Very Deep Variational Autoencoder (VDVAE) to reconstruct a coarse image from fMRI data, capturing the underlying details of the original image. Subsequently, the Bootstrapping Language-Image Pre-training (BLIP) model is utilized to provide a semantic annotation for each image. Finally, the image-to-image generation pipeline of the Versatile Diffusion (VD) model is utilized to recover natural images from the fMRI patterns guided by both visual and semantic information. The experimental results demonstrate that DBDM surpasses previous approaches in both qualitative and quantitative comparisons. In particular, the best performance is achieved by DBDM in reconstructing the semantic details of the original image; the Inception, CLIP and SwAV distances are 0.611, 0.225 and 0.405, respectively. This confirms the efficacy of our model and its potential to advance visual decoding research.
Stream learning under concept and feature drift: A literature survey

&lt;p class="Abstract"&gt;Stream data learning is an emerging machine learning topic, and it has many challenges. One of its challenges is the dynamic behavior or changes in the environment which leads to drifts. Two types of drift occur, namely, concept drift and feature drift. This article provides a survey on stream data learning with focusing on the issues of feature drift and the methods developed for handling it. After presenting the fundamental concepts and definition in this field, it provides an overview of the various models and methods developed for detecting feature drift and maintaining the validity of the machine learning models when the drift occurs. Furthermore, the article provides the generators used for creating dataset with feature drift to provide benchmarking for approaches of detecting or handling feature drift. The article provides also taxonomy of feature selection methods in both static and dynamic environment. It concludes that reinforcement-based models are promising for this task, and it lists various open challenges and future works in this area.&lt;/p&gt;
Examinee Authentication Using Multiple Types of Touch Gestures for Proxy Detection in Online Examinations

This study proposes a new method for detecting and preventing proxy test-taking in online examinations using a tablet device that authenticates the identity of examinees based on multiple types of touch gestures. We proved that the support vector machine has the best performance among classifiers for authentication when various touch gestures are used and various positions on the screen are touched. The results also showed that if support vector machine is used as the classifier, a handshape image taken from above or from the side can be used for identity authentication. In the future, we will improve the experimental environment to make it more similar to an actual online examination and investigate the possibility of performing authentication using touch gestures at arbitrary points on the touch screen of a tablet device.
China’s Nationwide Undergraduate CS Education Reforming Initiatives: the “101 plan”

The “101 plan” is launched by the China Ministry of Education at the end of 2021. This plan is an undergraduate CS education to better teach students the necessary knowledge and skills. This plan is expected to build famous courses, teachers, supporting textbooks, and platforms in the computer field in two stages. At present, the “101 plan” has achieved progress in curriculum system construction, white paper and textbook publishing, and classroom education improvement. Compared with domestic and worldwide education plans, the “101 plan” is a top-bottom education plan concerning how teachers teach and how well students learn.
Establishment of data mining-based public education administrative work automation system and student activity analysis

&lt;p class="abstract"&gt;There are several important factors in public education in Korea. Among them, it is very important to manage time to improve teachers’ educational capabilities and students’ grades. However, in Korea’s public education, the existing school administrative work system has to deal with miscellaneous procedures, hindering teachers from guiding students. As a result, students also give low trust in public education. This study introduces procedures for an integrated public education data management system to automate administrative tasks of teachers and increase students’ educational capabilities. By applying the Data Mining Problem Solving Methodology (ICAIS), we identified five stages in which data is processed. In addition, the activities required for students to go to college were processed with text mining techniques (from a simple word cloud to the construction of a neural network algorithm classification model), allowing students to check their grades themselves. Through this study, it reduces teachers’ chores, concentrates student education, and provides students with the educational purpose of a self-directed method that determines their career path.&lt;/p&gt;
An improved QFT-based quantum comparator and extended modular arithmetic using one ancilla qubit

Abstract Quantum comparators and modular arithmetic are fundamental in many quantum algorithms. Current research mainly focuses on operations between two quantum states. However, various applications, such as integer factorization, optimization, and financial risk analysis, commonly require one of the inputs to be classical. It requires many ancillary qubits, especially when subsequent computations are involved. In this paper, we propose a quantum–classical comparator based on the quantum Fourier transform. Then we extend it to compare two quantum integers and modular arithmetic. Proposed operators only require up to one ancilla qubit, which is optimal for qubit resources. We analyze limitations in the current modular addition circuit and develop it to process arbitrary quantum states in the entire n -qubit space. The proposed algorithms reduce computing resources and make them valuable for noisy intermediate-scale quantum computers.
Extended Kalman Filter Based Observer Design for Semilinear Infinite-Dimensional Systems

In many physical applications, the system's state varies with spatial variables as well as time. The state of such systems is modelled by partial differential equations and evolves on an infinite-dimensional space. Systems modelled by delay-differential equations are also infinite-dimensional systems. The full state of these systems cannot be measured. Observer design is an important tool for estimating the state from available measurements. For linear systems, both finite- and infinite-dimensional, the Kalman filter provides an estimate with minimum-variance on the error, if certain assumptions on the noise are satisfied. The extended Kalman filter (EKF) is one type of extension to nonlinear finite-dimensional systems. In this paper we provide an extension of the EKF to semilinear infinite-dimensional systems. Under mild assumptions we prove the well-posedness of equations defining the EKF. Local exponential stability of the error dynamics is shown. Only detectability is assumed, not observability, so this result is new even for finite-dimensional systems. The results are illustrated with implementation of finite-dimensional approximations of the infinite-dimensional EKF on an example.
Consumer Product Recommendation System using Adapted PSO with Federated Learning Method

In this paper, we proposed adapted particle swarm optimization integrated federated learning-based sentiment analysis integrated deep learning (aPSO-FLSADL) model for personalized recommendations of consumer electronics that leverage SentiWordNet and BERT for word embedding, CNN-BiLSTM based Federated learning model to train a global sentiment analysis model and mutation operator based modified particle swarm optimization for learning parameter optimization in federated learning environment. SentiWordNet is a sentiment lexicon that provides sentiment scores for words, while BERT is a powerful pre-trained deep learning model for natural language processing. Our approach involves pre-processing the text data, calculating sentiment scores using SentiWordNet, converting text data into word embedding using BERT, and assigning weights to words based on a defined weighting scheme. We evaluate the performance of our approach on a separate evaluation dataset including Amazon review dataset and CNET dataset. Based on the various evaluation metrics including accuracy, loss, hit ratio, we demonstrated the effectiveness of proposed aPSO-FLSADL in generating accurate and personalized recommendations. The depicted result shows that proposed aPSO-FLSADL achieved highest training and testing accuracy for both datasets and outperform over baseline models with maximum hit ratio for consumer electronics product recommendation.
Mapping Homophobia and Transphobia on Social Media

Abstract Introduction One of the consequences of the increase in the number of social network users has been the inappropriate use of social networks by some of these users. Hate speeches are frequently identified on social media, and these promote certain homophobic and transphobic attitudes, causing psychological consequences on users belonging to minority gender groups. With this work, it is intended to know the current state of the problem raised, to facilitate the activity of new researchers in an emerging field. Methodology Bibliographic analysis of 203 papers from the Scopus databases for the period between 1997 and 2022 using the VOSViewer software. The search for publications was carried out in February 2023. Results There is a positive trend in the number of relevant publications since 2017, mainly in 2021 and 2022. The research on homophobia and transphobia on social media in USA is prominent, with a high number of published articles, productive organizations, and influential authors. Twitter is shown to be the social network most widely used to spread homotransphobic hate speech. Environments conducive to the development of homotransphobic attitudes are identified as collective sports, mainly football and its supporters, as well as peer groups. Conclusions It is a growing problem that requires intervention at the societal level, requiring the development of legislation that moves away from heteronormativity, the development of mechanisms for automatic detection of homotransphobic discourse on social networks, and a multidisciplinary analysis and approach to control the problem as well as provide adequate social support to affected groups.