Reviewerly/exHarmony huggingface dataset:
I can feel AI failure: the impact of service failure type and failure assessment on customer recovery expectation

Purpose Based on appraisal theory and social response theory, this study aims to explore the mechanism of AI failure types on consumer recovery expectation from the perspective of service failure assessment and validate the moderate role of anthropomorphism level. Design/methodology/approach Three scenario-based experiments were conducted to validate the research model. First, to test the effect of robot service failure types on customer recovery expectation; second, to further test the mediating role of perceived controllability, perceived stability and perceived severity; finally, to verify the moderating effect of anthropomorphic level. Findings Non-functional failures reduce consumer recovery expectation compared to functional failures; perceived controllability and perceived severity play a mediating role in the impact of service failure types on recovery expectation; the influence of service failure types on perceived controllability and perceived severity is moderated by the anthropomorphism level. Originality/value The findings enrich the influence mechanism and boundary conditions of service failure types, and have implications for online enterprise follow-up service recovery and improvement of anthropomorphic design.
Semantics-preserved Graph Siamese Representation Learning

Currently, the expressive power of momentum-driven graph Siamese representation learning is mainly constrained by flawed positive selection strategies and data augmentation strategies. To solve these issues, we propose a Semantics-preserved Graph Siamese Representation Learning method named SGSRL. Firstly, we propose a local structure-aware positive selection strategy to reduce the generation of false positive samples. Specifically, the local structural similarity of nodes is obtained through the comparison between corresponding sampled subgraphs, where the anchor node’s subgraph is pooled to simplify the calculation. In addition, we propose a class information-aware feature augmentation method. Compared with directly modifying the inputs of the original graph, augmentation in the feature space is more controllable and intuitive. Specifically, we design an auxiliary contrastive learning task to train a class vector generator, and then overlay the class-oriented augmentation vectors generated by this generator onto the randomly augmented feature vectors. This joint feature augmentation strategy increases the variance between positive samples without changing the semanticity of nodes. To validate the superiority of our method, we conduct experiments on five real graph datasets for both node classification and node clustering tasks. The results demonstrate that our proposed SGSRL always outperforms state-of-art baselines.
A secure recharge scheme for blockchain payment channels

The payment channel is a prominent solution to scale the throughput of decentralized blockchain ledgers. It reduces the load on-chain by enabling off-chain micropayments without exhausting blockchain resources. However, the balance of a channel could become depleted due to payments going in one direction, making subsequent payments in that direction impossible. Several solutions have been proposed in recent years to address this issue. Nevertheless, ensuring both privacy and efficiency while maintaining applicability to edge nodes remains a challenging task. In this paper, we present PCRECHARGE, a solution to revive a depleted payment channel. Its key idea is to recharge the channel by conducting an on-chain payment and a reverse off-chain payment. The main challenge of this solution is to ensure that both payments must be performed atomically. To address this challenge, we conceive a pay-or-refund mechanism and integrate it into PCRECHARGE. It introduces another two transactions pay and refund, enabling an honest party to publish one of them to get his coins back. The most prominent feature of this mechanism lies in its independence from specific scripting and its avoidance of costly cryptographic tools, making it suitable for wide deployment. We provide comprehensive analyses and experimental evaluations to demonstrate the security and high efficiency of PCRECHARGE. Compared to reconstructing a channel, our approach reduces the transaction size by 66% in the honest case and addresses the limitations of current solutions, particularly their poor application to edge nodes.
Integrating platforms through content-based graph representation learning

The advancements in education technology have encouraged businesses to offer a wide range of educational resources aimed at teaching labor market-related skills. However, the ever-changing abundance of information in both course and job platforms makes it difficult for job seekers to identify the right learning resources for a particular job opportunity. As a result, it is important to integrate and analyze information from both platforms to recommend suitable courses and jobs to them. Meanwhile, integrating information from these distinct platforms for analysis purposes poses a challenge due to the presentation of data in free text. Previous studies have mainly focused on downstream tasks in one area and few have integrated both of them. Additionally, linking skills from these platforms through methods such as crowdsourcing or annotation mechanisms may not be reliable due to potential biases or inconsistencies. This study proposes a novel approach to integrate data from course and job advertising platforms using graph representation learning and embedding algorithms. The proposed approach predicts the relationship between the two platforms and has been tested in the form of link prediction tasks using several graph learning methods with RMSE scores below 0.1 and AUC scores between 70-75%. These results imply that our approach can be further refined and has the potential to be used in building recommendation systems that help address the problem of information overload among students and job seekers.
Weight prediction and recognition of latent subject terms based on the fusion of explicit &amp; implicit information about keyword

Weight prediction and recognition of subject term in documents is widely used in application fields such as literature recommendation and keyword retrieval. Unlike traditional methods which only focus on searching for subject terms from existing keywords in documents, this paper proposes a recognition method of latent subject term outside documents based on a one-class collaborative filtering algorithm that fuses explicit and implicit information. A matrix factorization model based on the analysis of document activity and subject term popularity is constructed to measure the correlation probability between documents and subject terms that do not appear in the current document. After these subject terms being divided into Latent Subject Terms (LST) and Irrelevant Subject Terms (IST), two methods for predicting the weight of these types of subject terms is introduced, which include Hybrid Filling with Preference Coefficients (HFPC), and Zero Filling. In order to verify the effectiveness of subject term recognition, many collaborative filtering recommendation algorithms are conducted with the filled document keyword matrix. On the basis of not changing these algorithms, MAE and FCP can be improved by 28.01% and 22.79% at most, while P@N and NDCG@N can be improved by 22.37% and 27.06%, respectively.
Inductive reasoning in minds and machines.

Induction-the ability to generalize from existing knowledge-is the cornerstone of intelligence. Cognitive models of human induction are largely limited to toy problems and cannot make quantitative predictions for the thousands of different induction arguments that have been studied by researchers, or to the countless induction arguments that could be encountered in everyday life. Leading large language models (LLMs) go beyond toy problems but fail to mimic observed patterns of human induction. In this article, we combine rich knowledge representations obtained from LLMs with theories of human inductive reasoning developed by cognitive psychologists. We show that this integrative approach can capture several benchmark empirical findings on human induction and generate human-like responses to natural language arguments with thousands of common categories and properties. These findings shed light on the cognitive mechanisms at play in human induction and show how existing theories in psychology and cognitive science can be integrated with new methods in artificial intelligence, to successfully model high-level human cognition. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Real-Time Dynamic Data Driven Deformable Registration for Image-Guided Neurosurgery: Computational Aspects.

Current neurosurgical procedures utilize medical images of various modalities to enable the precise location of tumors and critical brain structures to plan accurate brain tumor resection. The difficulty of using preoperative images during the surgery is caused by the intra-operative deformation of the brain tissue (brain shift), which introduces discrepancies concerning the preoperative configuration. Intra-operative imaging allows tracking such deformations but cannot fully substitute for the quality of the pre-operative data. Dynamic Data Driven Deformable Non-Rigid Registration (D4NRR) is a complex and time-consuming image processing operation that allows the dynamic adjustment of the pre-operative image data to account for intra-operative brain shift during the surgery. This paper summarizes the computational aspects of a specific adaptive numerical approximation method and its variations for registering brain MRIs. It outlines its evolution over the last 15 years and identifies new directions for the computational aspects of the technique.
Plant Image Classification with Nonlinear Motion Deblurring Based on Deep Learning

Despite the significant number of classification studies conducted using plant images, studies on nonlinear motion blur are limited. In general, motion blur results from movements of the hands of a person holding a camera for capturing plant images, or when the plant moves owing to wind while the camera is stationary. When these two cases occur simultaneously, nonlinear motion blur is highly probable. Therefore, a novel deep learning-based classification method applied on plant images with various nonlinear motion blurs is proposed. In addition, this study proposes a generative adversarial network-based method to reduce nonlinear motion blur; accordingly, the method is explored for improving classification performance. Herein, experiments are conducted using a self-collected visible light images dataset. Evidently, nonlinear motion deblurring results in a structural similarity index measure (SSIM) of 73.1 and a peak signal-to-noise ratio (PSNR) of 21.55, whereas plant classification results in a top-1 accuracy of 90.09% and F1-score of 84.84%. In addition, the experiment conducted using two types of open datasets resulted in PSNRs of 20.84 and 21.02 and SSIMs of 72.96 and 72.86, respectively. The proposed method of plant classification results in top-1 accuracies of 89.79% and 82.21% and F1-scores of 84% and 76.52%, respectively. Thus, the proposed network produces higher accuracies than the existing state-of-the-art methods.
Towards Privacy-Preserving Federated Neuromorphic Learning via Spiking Neuron Models

Federated learning (FL) has been broadly adopted in both academia and industry in recent years. As a bridge to connect the so-called “data islands”, FL has contributed greatly to promoting data utilization. In particular, FL enables disjoint entities to cooperatively train a shared model, while protecting each participant’s data privacy. However, current FL frameworks cannot offer privacy protection and reduce the computation overhead at the same time. Therefore, its implementation in practical scenarios, such as edge computing, is limited. In this paper, we propose a novel FL framework with spiking neuron models and differential privacy, which simultaneously provides theoretically guaranteed privacy protection and achieves low energy consumption. We model the local forward propagation process in a discrete way similar to nerve signal travel in the human brain. Since neurons only fire when the accumulated membrane potential exceeds a threshold, spiking neuron models require significantly lower energy compared to traditional neural networks. In addition, to protect sensitive information in model gradients, we add differently private noise in both the local training phase and server aggregation phase. Empirical evaluation results show that our proposal can effectively reduce the accuracy of membership inference attacks and property inference attacks, while maintaining a relatively low energy cost. blueFor example, the attack accuracy of a membership inference attack drops to 43% in some scenarios. As a result, our proposed FL framework can work well in large-scale cross-device learning scenarios.
A Study on Webtoon Generation Using CLIP and Diffusion Models

This study focuses on harnessing deep-learning-based text-to-image transformation techniques to help webtoon creators’ creative outputs. We converted publicly available datasets (e.g., MSCOCO) into a multimodal webtoon dataset using CartoonGAN. First, the dataset was leveraged for training contrastive language image pre-training (CLIP), a model composed of multi-lingual BERT and a Vision Transformer that learnt to associate text with images. Second, a pre-trained diffusion model was employed to generate webtoons through text and text-similar image input. The webtoon dataset comprised treatments (i.e., textual descriptions) paired with their corresponding webtoon illustrations. CLIP (operating through contrastive learning) extracted features from different data modalities and aligned similar data more closely within the same feature space while pushing dissimilar data apart. This model learnt the relationships between various modalities in multimodal data. To generate webtoons using the diffusion model, the process involved providing the CLIP features of the desired webtoon’s text with those of the most text-similar image to a pre-trained diffusion model. Experiments were conducted using both single- and continuous-text inputs to generate webtoons. In the experiments, both single-text and continuous-text inputs were used to generate webtoons, and the results showed an inception score of 7.14 when using continuous-text inputs. The text-to-image technology developed here could streamline the webtoon creation process for artists by enabling the efficient generation of webtoons based on the provided text. However, the current inability to generate webtoons from multiple sentences or images while maintaining a consistent artistic style was noted. Therefore, further research is imperative to develop a text-to-image model capable of handling multi-sentence and -lingual input while ensuring coherence in the artistic style across the generated webtoon images.
ANALYSIS SENTIMENTS IN FACEBOOK DOWN CASE USING VADER AND NAIVE BAYES CLASSIFICATION METHOD

Abstrak Facebook adalah media sosial terbesar di dunia. Semua aplikasi media sosial besutan Facebook tidak bisa diakses secara bersamaan dalam waktu kurang lebih 6 jam. Hal ini tidak hanya terjadi di Indonesia, tetapi di seluruh negara di dunia, pada tanggal 5-6 Oktober waktu Indonesia. Dengan adanya kasus ini, berbagai komentar dan opini dari masyarakat di Twitter terkait kasus Facebook pun turun. Komentar positif atau negatif bermunculan di twitter. Analisis sentimen digunakan untuk mengidentifikasi komentar positif dan negatif. Pada penelitian ini, komentar positif dan negatif akan diklasifikasikan menggunakan klasifikasi Vader dan nave bayes. Data yang terkumpul sebanyak 500 data dari twitter terkait down case facebook. Dari hasil perhitungan diperoleh sentimen positif sebanyak 33,92% dan sentimen negatif dengan hasil 66,08%. Berdasarkan hasil visualisasi dengan wordcloud, kata yang paling banyak muncul adalah kata facebook down untuk sentimen positif dan negatif. Hasil yang didapatkan dari tabel Confusion Matrix dari model klasifikasi menggunakan data sharing, 80% data training dan 20% data testing, dengan metode klasifikasi menggunakan Naive Bayes dengan pembobotan kata TF-IDF, nilai akurasinya sebesar 73,69% dan untuk Count Vektorizer adalah 70,18%. AbstractFacebook is the largest social media in the world. All social media applications made by Facebook cannot be accessed simultaneously in approximately 6 hours. This happens not only in Indonesia, but in all countries in the world, on October 5-6, Indonesian time. With this case, various comments and opinios from people on Twitter related to the Facebook case were down. Positive or negative comments popping up on twitter. Sentiment analysis is used to identify positive and negative comments. In this study, positive and negative comments will be classified using Vader and nave Bayes classification. The data collected was 500 data from twitter related to the Facebook down case. From the calculation results, positive sentiment was obtained as much as 33.92% and negative sentiment with 66.08% results. Based on the results of the visualization with wordcloud, the words that appear the most are the word facebook down for positive and negative sentiments. The results obtained from the confusion matrix table from the classification model using data sharing, 80% training data and 20% testing data, with the classification method using Naive Bayes with TF-IDF word weighting, the accuracy value is 73.69% and for the Count Vectorizer is 70.18%.
A Study on the Changes in Policy after the Focus Event: Corona 19

This study analyzes policy changes in response to COVID-19. Following the discovery of the first domestic case on January 20, 2020, the infectious disease crisis alert was raised from “watch” to “caution” to “warning”. This study classifies COVID-19 as a focal event and uses Birkland's ERPL model to analyze the process of policy agendization and policy change after the event. The results of the analysis show that, first, the focal event significantly increased the number of media reports at each stage of the epidemic crisis warning. Second, collective mobilization took place through the formation of the National Assembly Special Committee on COVID-19, the Health and Welfare Committee, and government expert group meetings. Third, the Special Committee as well as the standing committee discussed some proposals. Finally, some amendments were passed by the National Assembly. In conclusion, this study found that media coverage of the focal event of COVID-19 had a significant impact on policy changes.
A Fusion Encoder with Multi-Task Guidance for Cross-Modal Text–Image Retrieval in Remote Sensing

In recent years, there has been a growing interest in remote sensing image–text cross-modal retrieval due to the rapid development of space information technology and the significant increase in the volume of remote sensing image data. Remote sensing images have unique characteristics that make the cross-modal retrieval task challenging. Firstly, the semantics of remote sensing images are fine-grained, meaning they can be divided into multiple basic units of semantic expression. Different combinations of basic units of semantic expression can generate diverse text descriptions. Additionally, these images exhibit variations in resolution, color, and perspective. To address these challenges, this paper proposes a multi-task guided fusion encoder (MTGFE) based on the multimodal fusion encoding method, the progressiveness of which has been proved in the cross-modal retrieval of natural images. By jointly training the model with three tasks: image–text matching (ITM), masked language modeling (MLM), and the newly introduced multi-view joint representations contrast (MVJRC), we enhance its capability to capture fine-grained correlations between remote sensing images and texts. Specifically, the MVJRC task is designed to improve the model’s consistency in joint representation expression and fine-grained correlation, particularly for remote sensing images with significant differences in resolution, color, and angle. Furthermore, to address the computational complexity associated with large-scale fusion models and improve retrieval efficiency, this paper proposes a retrieval filtering method, which achieves higher retrieval efficiency while minimizing accuracy loss. Extensive experiments were conducted on four public datasets to evaluate the proposed method, and the results validate its effectiveness.
UniRender: Reconstructing 3D Surfaces from Aerial Images with a Unified Rendering Scheme

While recent advances in the field of neural rendering have shown impressive 3D reconstruction performance, it is still a challenge to accurately capture the appearance and geometry of a scene by using neural rendering, especially for remote sensing scenes. This is because both rendering methods, i.e., surface rendering and volume rendering, have their own limitations. Furthermore, when neural rendering is applied to remote sensing scenes, the view sparsity and content complexity that characterize these scenes will severely hinder its performance. In this work, we aim to address these challenges and to make neural rendering techniques available for 3D reconstruction in remote sensing environments. To achieve this, we propose a novel 3D surface reconstruction method called UniRender. UniRender offers three improvements in locating an accurate 3D surface by using neural rendering: (1) unifying surface and volume rendering by employing their strengths while discarding their weaknesses, which enables accurate 3D surface position localization in a coarse-to-fine manner; (2) incorporating photometric consistency constraints during rendering, and utilizing the points reconstructed by structure from motion (SFM) or multi-view stereo (MVS), to constrain reconstructed surfaces, which significantly improves the accuracy of 3D reconstruction; (3) improving the sampling strategy by locating sampling points in the foreground regions where the surface needs to be reconstructed, thus obtaining better detail in the reconstruction results. Extensive experiments demonstrate that UniRender can reconstruct high-quality 3D surfaces in various remote sensing scenes.
Comparative Analysis of Reading Text Readability in Chinese Junior High School English Teaching Textbook Based on Corpus

Readability is one of the important factors for students to consider when choosing a text to read. Based on a self-built corpus of Junior High School textbooks in China, this paper mainly investigates the differences in readability between reading texts in junior high school English textbooks. From vocabulary, sentence, and comprehensive indicators, this study makes quantitative statistics on the factors that affect the readability of English textbook texts to understand the readability of different textbook texts and to explore whether there are differences in the readability of eight textbooks. Finally, the results show significant differences in the readability of the eight editions of English readers. The average word length and new words rate are the highest in Popular Science Press, which reach 4.43 words and 8.40%, respectively. Among the average sentence length and the frequency of compound sentences, the Shandong Education Press has the highest, going 11.36 words and 11.03%, and confirmed that the Shandong Education Press has the highest difficulty. The textbook arranger can take the difficulty of the reading text into account once they have determined the readability of the text. They can then arrange or select a textbook appropriate for students, enabling them to comprehend and master the text more effectively.
Some statistical challenges in automated driving systems

Data sharing is not applicable to this article as no new data were created or analyzed in this study.