Reviewerly/exHarmony huggingface dataset:
The Research of the Relationship Between Artificial Intelligence and Human Brain

Artificial intelligence is a technology and method that utilizes computers and algorithms to simulate and implement human intelligence. It can learn and optimize through a large number of data and algorithms to achieve various intelligent tasks and functions, such as natural language processing, image recognition, intelligent decision-making, etc. Artificial intelligence technology has been widely applied in fields such as healthcare, finance, transportation, and manufacturing, becoming an important force driving economic and social development. Artificial intelligence is closely related to the human brain. This article analyzes the relationship between artificial intelligence and the human brain, including their connections and differences, imitation mechanisms, neural networks, and deep learning.
Combining common heuristic sets for a new exhaustive usability evaluation of e-learning interface design

Abstract With global school shut-downs due to the pandemic, in 2020 many educational institutions and universities were making use of e-learning like never before, but designing lesson plans for a digital space came with its own set of challenges. In most modern universities, education is still based on traditional learning methods and the teacher-centred physical classrooms. However, with nationwide school closures, teachers had no choice but to turn to e-learning for solutions. Usability evaluation of this kind of platform, therefore, has become essential, and a priority in the application development process. A set of usability evaluation methods have been developed to allow specialists to systematically determine whether graphical interfaces are usable. The usability assessment questionnaire is one of the most used methods and is intended to be answered by end-users of the application. This method is preferred due to its simplicity and low demand for resources. In this paper, a usability evaluation which combines existing work is presented. Based on existing heuristics, we propose a new questionnaire for evaluating e-learning interface design. In addition, we illustrate two different methodologies for evaluating e-learning interface design, and provide a quantitative rate, Usability Percentage (UP) , which complements the usual qualitative data provided by this kind of evaluation.
Research on Segmentation of Intestinal Polyps Based on Improved Transformer and FCN

The rising prevalence of colon cancer necessitates early detection through colonoscopy. Deep learning segmentation technology has emerged as a reliable tool for detecting lesions, with convolutional neural networks (CNNs) leading advancements in medical image processing. However, manual polyp segmentation during colonoscopic examinations is time-consuming, highlighting the need for automated approaches. This study introduces a novel parallel branching structure to address limitations and improve contextual information and low-level detail handling. The structure extracts complementary features, enriching image analysis. Additionally, a quadratic complete volume integral branch enhances segmentation performance. We present our innovative model and methodology for polyp segmentation, advancing automated detection in colonoscopy. Experimental results demonstrate the effectiveness and robustness of our approach. By integrating deep learning techniques and leveraging our parallel branching structure, our method achieves superior segmentation accuracy for efficient and accurate polyp detection.
Presence and Flow as Moderators in XR-Based Sustainability Education

Virtual reality (VR) and augmented reality (AR) are emerging technologies with a variety of potential benefits for sustainability education. Here, learning processes such as flow and presence seem to determine the learning experience. Therefore, this paper presents the results of a mixed-methods study investigating a VR- and AR-based learning application on biodiversity developed by greenpeace. A total of 156 students tested the application addressing the Amazon rainforest and rated its efficacy in terms of effects on knowledge, interest, and attitude. Pre- and post-questionnaires as well as focus groups were used to uncover within-subject effects. The study results revealed that flow and presence had a moderating effect on knowledge and that this effect is strongest in learners with little prior knowledge. Presence also showed a moderating effect on one of three attitude measures. In general, the learning application was able to increase knowledge and improve attitude in this sample. The focus groups also revealed that the students engaged with environmental topics even after the experience. They also formed ideas for more environmentally friendly behavioral change. Moreover, the students described the application as impressive, captivating, and realistic. It can be concluded that presence and flow are crucial processes for learning with VR and AR technologies.
Search for the Substantiation of Reasonable Native Elemental Background Values and Reference Variables in Topsoil on Glaciogenic and Postglacial Deposits in a Vilnius Peri-Urban Area

Geochemical indices used to identify the emerging anomalies of potentially harmful elements in topsoil depend on background values (BVs). For urban sites, it is reasonable to estimate native BVs through the targeted selection of peri-urban sampling sites or by distinguishing a useful background subset (BS) within the peri-urban dataset. Here, the goals were to examine the influence of Quaternary deposits on various types of topsoil variables, identify the variables most helpful for cluster analysis intended for the choice of background subset (BS), and compare background values (BVs) based on different background subsets. Composite topsoil samples from a peri-urban area were used for the determination of the following variables: contents of 26 elements and components of the bulk mineralogical composition, as well as the sand, silt, and clay fractions and loss-on-ignition (LOI) at 550 °C and at 950 °C. Although Quaternary lithology influences topsoil elemental contents or granulometric fractions, percentages of illite, kaolinite, orthoclase, quartz, albite, dolomite, and LOI at 550 °C, the choice of BS, according to it, is not recommended, as BVs based on topsoil texture are superior. However, cluster analysis using topsoil fractions &lt; 2, &lt;63, and &gt;63 μm or the contents of Al, Fe, K, Ti, Ga, Nb, Rb, and Si are preferable. It is recommended to use these reference variables for the selection of BS.
The Operational Mechanism of Agricultural Products Supply Chain Finance Based on the Mode-Capability-Customer Matching Approach

The key to successful agricultural product supply chain finance of commercial banks lies in coordinating the relationship among the “supply chain finance model”, “banking serviceability”, and “user satisfaction”. Previous research on agricultural products supply chain finance rarely incorporated the operational level of commercial banks. To fill this gap, this paper transforms the operational problem of the agricultural product supply chain finance of commercial banks into a matching problem between the agricultural product supply chain finance model and customers under the constraint of serviceability. First, a matching model of “mode-capability-customer” of agricultural products supply chain finance is constructed based on the theory of bilateral matching; second, a measurement index system of matching degree and satisfaction is constructed, where matching degree and satisfaction are calculated; finally, the optimal matching model is studied. This paper proposes a model that matches agricultural supply chain finance with customer satisfaction, considering bank serviceability constraints. It improves the implementation of agricultural product supply chain finance by commercial banks. The study uses surveys and evaluations to assess stakeholder satisfaction and preferences. An operational research model optimizes the matching process between supply chain finance models and recipients, ensuring suitability. The paper provides a theoretical basis for rural financial services and practical recommendations for commercial bank supply chain finance, with valuable real-world applications.
Does Attitude Mediate the Relationship Between Perceived Ease of Use, Perceived Usefulness, and Student Intention to Adopt Online Teaching and Learning?

The purpose of the study was to determine if students' attitude towards online teaching and learning (T&amp;L) mediate the relationship between perceived usefulness, perceived ease of use, and intention to adopt only T&amp;L in South Africa. The institutions of higher learning were forced to adopt the online teaching model in 2020 due to the coronavirus pandemic regulations that inhibited people from moving around, including visitation to their workplace. Students were also not allowed to visit their campuses, but had to stay and learn online, at home. To achieve the objectives of the study, a quantitative survey was adopted, and data was collected from 112 students registered with a distance learning institution in South Africa. The results of the study show students' attitude mediates the relationship between perceived ease of use, perceived usefulness, and their intention to adopt online T&amp;L.
Development of Smart Hospitals Based on the Internet of Things and Cloud Computing

With the expansion of the internet of things (IoT) and its impact on today's life, a significant improvement can be seen in hospital services. The IoT allows patients to take control of their condition. This method can reduce the anxiety of patients to some extent. On the other hand, by collecting the patient's data during the hospital stay, doctors gain clearer views of the patient's body physiology and can provide more specialized and special care plans. With the method provided by the IoT, the interaction between the doctor and the patient increases, and it also helps by monitoring the patient's health remotely and reducing the time spent in the hospital. Storing information from the IoT locally is not only expensive but also brings certain complications for medical centers. For this reason, hospitals are turning to cloud storage of patient information. In this research, in addition to describing the basic features of smart hospitals based on the IoT and cloud computing, a hospital chart based on these technologies is also presented.
Pattern unlocking guided multi‐modal continuous authentication for smartphone with multi‐branch context‐aware representation learning and auto encoder

Abstract Widely accepted explicit authentication protocols are vulnerable to a series of attacks, for example, shoulder surfing and smudge attacks, leaving users with the constant burden of periodic password changes. As such, we propose a novel framework for continuous authentication on smartphones. This approach is guided by pattern unlocking, which is widely used and will not cause learning cost. After collecting multi‐modal data that describe both behavioral and contextual information, we employ a multi‐branch context‐aware attention network as the representation learner to perform feature extraction, then an auto encoder is then used for authentication. To overcome challenges, including cold‐start and few‐shot training, which is less discussed in other works, we incorporate transfer learning with a coarse‐to‐fine pre‐training workflow. Additionally, we deploy a hierarchical approach to offload model tuning overhead from smartphones. Extensive experiments on more than 68 000 real‐world recordings validate the effectiveness of the proposed method, with an EER (equal error rate) of 2.472% under mixed contexts, which consistently outperforms state‐of‐the‐art approaches under both static and mixed contexts.
Smart Contracts as a Third Party Coordinator

One of the important issues in the field of contract implementation is the security and speed of contract implementation in a way that can gain the trust of the contracting parties. In response to this challenge, one of these tools are smart contracts, which compared to other types of electronic contracts have features such as high speed and security and low cost in forming the contract. Smart contracts are currently used in various fields such as finance, law, banking, stock exchange, blockchain, government, industry, charity, etc. These contracts have many advantages, such as reducing costs, increasing speed, improving security, eliminating intermediaries, and preventing fraud. In simpler terms, smart contracts have taken over the task of making transactions with tools, the most important of which is the blockchain platform. Hence, in this research, the framework of using smart contracts in the field of e-commerce is discussed and the advantages, disadvantages, how to apply, successful experiences, and perspectives for the future of these contracts are presented.
Implementation of Load Balancing Using Per Connection Classifier Method Using Mikrotik RB1100

The Implementation of Load Balancing Using Per Connection Classifier Method Using Mikrotik RB1100&#x0D; &#x0D; By :&#x0D; Pedro Maldini Siagian 1)&#x0D; Hari Aspriyono 2)&#x0D; Eko Prasetiyo Rohmawan 2)&#x0D; &#x0D; Poltekkes Kemenkes Bengkulu as a university which has 2.000 students and 250 employees, has internet access through 2 providers, namely Icon Dedicated and Icon Broadband. Even though it has 2 internet lines, the distribution of internet line that is evenly distributed still cannot be implemented. Load Balancing carried out on Mikrotik RB1100 at Poltekkes Kemenkes Bengkulu can help distribute internet traffic evenly, and can overcome lost connections due to backing up broken connections with available connections. Load Balancing used Per Connection Classifier is a method that will send a set of packets over several existing links, taking into original account of packets. In this method, packets coming from one communication session will be routed through only one link. Through QoS (Quality of Service) testing that the result was that load balancing carried out was running well and as expected Poltekkes Kemenkes Bengkulu which can evenly distribute the connection traffic used.&#x0D; &#x0D; Keywords : Load Balancing, Per Connection Classifier, Mikrotik RB1100, Poltekkes Kemenkes Bengkulu&#x0D; &#x0D; Infomation:&#x0D; 1) Student&#x0D; 2) Supervisors
A GCN- and Deep Biaffine Attention-Based Classification Model for Course Review Sentiment

In recent years, the increasing use of online surveys for course evaluation in schools has led to an outpouring of evaluation texts. These texts, with their emotional polarity, can give schools the most direct feedback. Emotional analysis on course evaluation, therefore, has great implications. However, the not-so-rigid text grammar and rich text content pose a challenge for sentiment analysis in Chinese course evaluation. To solve this problem, this paper proposes a sentiment classification model BiLSTM-GCN-Att (BGAN). Here, BiLSTM is used to extract the features of the text and output the hidden state vector. Then, the deep biaffine attention mechanism is used to analyze the dependence of the text and generate a dependency matrix. Next, input the hidden state vector to the GCN. Finally, the softmax function is used as the output layer of the model to perform sentiment classification. The model proves effective and experimental results, showing that the BGAN achieved a maximum improvement of 11.02% and 14.47% in precision and F1-score respectively compared with the classical models.
Radio Telescope Surface Measurement via Deep Learning

Abstract This paper proposes a new method for accurately measuring the surface deformation of radio telescope antennas based on deep learning. A deep convolutional neural network is used to predict surface deformations by mapping the near-field intensity of the antenna, instead of relying entirely on a physical model. The proposed method could offer precise measurement of surface deformations in real time with only a single image of near-field intensity pattern. To optimize the deep learning model, a preliminary U-net based deep convolutional neural network (DCNN) model was developed based on a large data set generated by an approximate physical model, a partial differential equation (PDE). The network parameters were then fine-tuned using transfer learning with a small data set obtained by high precision numerical simulation. During this process, the fine-tuning layers that achieved optimal performance for the U-net network was studied. The final results show that the proposed method significantly improves the accuracy of antenna surface deformation recovery. Additionally, singular value decomposition (SVD) technology is employed to denoise the intensity image, which facilitates the application of the proposed method to actual deformation measurement.
Chaotic Neural Network with Nonlinear Delayed Self-feedback and Applications

Abstract We propose a novel transient chaotic neural network model with a nonlinear delayed self-feedback term, aiming to leverage its complex dynamical behavior for algorithm optimization. To avoid the influence of chance, we conduct experiments using randomly generated data and analyze the model’s chaotic dynamical behavior by visualizing the stability of the neurons through inverse bifurcation diagrams and maximum Lyapunov exponent diagrams. The simulation results demonstrate the remarkable effectiveness of this new model in network optimization, with a success rate even reaching 100%, surpassing previous chaotic neural network models. Notably, in the following sections, all nonlinear terms are represented using trigonometric functions.
Automatic Matching of Optical and SAR Images Based on Attention Structure Features

Abstract Due to the disparity in imaging techniques, significant radiometric and geometric variances exist among optical and Synthetic Aperture Radar (SAR) images, making it a challenging task for achieving automatic and accurate matching in contemporary international academic research. Handcrafted structural features have shown some success in heterogeneous image matching in recent years. However, improving its matching performance manually proves to be difficult. As a result, this work presents a matching strategy based on attention-enhanced structural feature representation to improve optical and SAR images matching accuracy. In this research, a novel multi-branch global attention module is built using handmade structural feature extraction. This module can focus on the common information of structural feature descriptors in space and channel, extracting finer and more robust image features. Then, the proposed method utilizes the sum of squared difference (SSD) learning metric, which is based on the fast Fourier transform, to develop a loss function. This loss function is then used to train positive and negative samples in order to enhance the discriminative ability of the model. Experimental results obtained from training and testing on numerous optical and SAR datasets demonstrate that the proposed method significantly improves the accuracy of matching optical and SAR images compared to both current structural feature matching methods and advanced deep learning matching models.
Problems of Optimal Altitude Determination of Drones in Vertical Scanning Mode

Abstract This paper takes the UAV’s search for ship targets in the sea area as the background and uses the relationship between the probability of finding the target in the UAV’s radar imagery, the distance between the UAV and the target, and the UAV’s parameters, and according to the probability of the UAV finding a target based on multiple images. Taking the product of the discovery probability and the swept area as the index of search efficiency, the relationship between the probability of finding the target and the area of the swept sea area is balanced. The optimization model of the scanning sea area is established in the case of circular and rectangular to determine the optimal altitude of the UAV flights.
Working with troubles and failures in conversation between humans and robots: workshop report

This paper summarizes the structure and findings from the first Workshop on Troubles and Failures in Conversations between Humans and Robots. The workshop was organized to bring together a small, interdisciplinary group of researchers working on miscommunication from two complementary perspectives. One group of technology-oriented researchers was made up of roboticists, Human-Robot Interaction (HRI) researchers and dialogue system experts. The second group involved experts from conversation analysis, cognitive science, and linguistics. Uniting both groups of researchers is the belief that communication failures between humans and machines need to be taken seriously and that a systematic analysis of such failures may open fruitful avenues in research beyond current practices to improve such systems, including both speech-centric and multimodal interfaces. This workshop represents a starting point for this endeavour. The aim of the workshop was threefold: Firstly, to establish an interdisciplinary network of researchers that share a common interest in investigating communicative failures with a particular view towards robotic speech interfaces; secondly, to gain a partial overview of the "failure landscape" as experienced by roboticists and HRI researchers; and thirdly, to determine the potential for creating a robotic benchmark scenario for testing future speech interfaces with respect to the identified failures. The present article summarizes both the "failure landscape" surveyed during the workshop as well as the outcomes of the attempt to define a benchmark scenario.
An enhanced visualization image acquisition method for samples with poor conductivity under a conventional scanning electron microscope

The low-vacuum and low-accelerating-voltage modes are the most simple and practical ways to directly analyze poorly conductive samples in conventional scanning electron microscopy (SEM). However, structural feature information may disappear or be obscured in these imaging modes, making it challenging to identify and analyze some local microstructures of poorly conductive samples. To overcome this challenge, an enhanced visualization image acquisition method for samples with poor conductivity is proposed based on the image registration and multi-sensor fusion technology. Experiments demonstrate that the proposed method can effectively obtain enhanced visualization images containing clearer terrain information than the SEM source images, thereby providing new references for measuring and analyzing microstructures.