Reviewerly/exHarmony huggingface dataset:
Multimodal Sentiment Analysis Based on Attentional Temporal Convolutional Network and Multi-Layer Feature Fusion

Multimodal sentiment analysis aims to extract and integrate information from different modalities to accurately identify the sentiment expressed in multimodal data. How to effectively capture the relevant information within a specific modality and how to fully exploit the complementary information among multiple modalities are two major challenges in multimodal sentiment analysis. Traditional approaches fail to obtain the global contextual information of long time-series data when extracting unimodal temporal features, and they usually fuse the features from multiple modalities with the same method and ignore the correlation between different modalities when modeling inter-modal interactions. In this paper, we first propose an Attentional Temporal Convolutional Network (ATCN) to extract unimodal temporal features for enhancing the feature representation ability, then introduce a Multi-layer Feature Fusion (MFF) model to improve the effectiveness of multimodal fusion, which fuses the different-level features by different methods according to the correlation coefficient between the features, and cross-modal multi-head attention is used to fully explore the potential relationship between the low-level features. The experimental results on SIMS and CMU-MOSI datasets show that the proposed model achieves superior performance on sentiment analysis tasks compared to state-of-the-art baselines.
Multimodal negative sentiment recognition of online public opinion on public health emergencies based on graph convolutional networks and ensemble learning

To improve the effect of multimodal negative sentiment recognition of online public opinion on public health emergencies, we constructed a novel multimodal fine-grained negative sentiment recognition model based on graph convolutional networks (GCN) and ensemble learning. This model comprises BERT and ViT-based multimodal feature representation, GCN-based feature fusion, multiple classifiers, and ensemble learning-based decision fusion. Firstly, the image-text data about COVID-19 is collected from Sina Weibo, and the text and image features are extracted through BERT and ViT, respectively. Secondly, the image-text fused features are generated through GCN in the constructed microblog graph. Finally, AdaBoost is trained to decide the final sentiments recognized by the best classifiers in image, text, and image-text fused features. The results show that the F1-score of this model is 84.13% in sentiment polarity recognition and 82.06% in fine-grained negative sentiment recognition, improved by 4.13% and 7.55% compared to the optimal recognition effect of image-text feature fusion, respectively.
‘Versailles literature’ on WeChat Moments – humblebragging with digital technologies

Versailles literature is a newly emerged phenomenon on Chinese social networking sites. It is in essence an indirect form of self-praise. Despite its popularity on social media, there is little research on this phenomenon. This study investigates the pragmatic strategies for performing VL on WeChat Moments. The results showed that Versailles literature often includes strategies such as implicit self-praise, modified explicit self-praise, and self-praise through comments and replies, among which the tactics that have not been observed before, such as bragging in a foreign language, creating false ‘question–answer’ or ‘compliment–response’ interactions and commenting to the adverts. These strategies are implemented in various ways by virtue of the technological affordance of WeChat Moments, in order to balance the disclosure and the dissimulation of the object of praise. The study also discusses the distinctive features of VL and explains the absence of some frequent strategies of self-praising in Versailles literature.
Automated counting of white blood cells in thin blood smear images

Blood cell counting plays a crucial role in clinical diagnosis to evaluate the overall health condition of an individual. Traditionally, blood cells are manually counted using a hemocytometer; however, this task has been found to be time-consuming and error-prone. Recently, machine learning-based approaches have been employed to effectively automate counting tasks. In this work, the fifth version of the 'you only look once' (YOLOv5) object detection method was adopted to automatically detect and count white blood cells (WBCs) in porcine blood smear images. YOLOv5 was chosen because of its speed and accuracy. The dataset used in this study was collected specifically for this WBC counting task. Our experimental results exhibit the high speed and efficiency of YOLOv5 in detecting and counting WBCs, having obtained an accuracy of 89.25% and a mean average precision at 0.5 intersection over union threshold (mAP 0.5) of 99%.
Causal inference in cognitive neuroscience

Abstract Causal inference is a key step in many research endeavors in cognitive science and neuroscience, and particularly cognitive neuroscience. Statistical knowledge is sufficient for prediction and diagnosis, but causal knowledge is required for action and intervention. Most statistics courses and textbooks emphasize the difficulty of causal inference, focusing on the maxim that “correlation does not mean causation”: there can be multiple causal possibilities, often many of them, consistent with given observed statistics. This paper focuses instead on the conceptual issues and assumptions that confront causal and other kinds of inference, primarily focusing on cognitive neuroscience. We connect inference methods with goals and challenges, and provide concrete guidance about how to select appropriate tools for the scientific task. This article is categorized under: Psychology &gt; Theory and Methods Philosophy &gt; Foundations of Cognitive Science
Associations between Social Competence, Perceived Parents’ Prosocial Educational Goals and Adolescents’ Hate Speech Perpetration in School

ABSTRACTABSTRACTHate speech is a global challenge, requiring research on adolescents' hate speech perpetration in schools. We examined whether perceived parents' prosocial educational goals moderate the association between social competencies (social perspective-taking, openness to diversity) and hate speech perpetration in schools. In total, 1,719 adolescents (aged 11–18 years) from 22 Swiss schools completed selfreport questionnaires (December 2020 to April 2021). Social perspective-taking and hate speech perpetration correlated negatively, alongside openness to diversity and hate speech perpetration. Perceived parents' prosocial educational goals strengthened the negative association between openness to diversity and hate speech perpetration in schools but did not moderate the association with social perspective-taking.KEYWORDS: Hate speechprosocial educational goalssocial perspective-takingschoolopenness to diversityadolescents Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis research was funded by the German Research Foundation (DFG), grant number WA 4275/2-1, BI 1046/9-1, SCHU 1370/4-1 and the University of Teacher Education Bern, grant number 19s 0008 01. Ethical clearance was obtained from the Commission for Research and Development of the University of Teacher Education Bern in Switzerland (approval number 19s 0008 01).
Encryption of Color Images with an Evolutionary Framework Controlled by Chaotic Systems

In the past decade, a large amount of important digital data has been created and stored in the form of color images; the protection of such data from undesirable accesses has become an important problem in information security. In this paper, a new approach based on an evolutionary framework is proposed for the secure encryption of color images. The image contents in a color image are first fully scrambled with a sequence of bit-level operations determined by a number of integer keys. A scrambled image is then encrypted with keys generated from an evolutionary process controlled by a set of chaotic systems. Analysis and experiments show that the proposed approach can generate encrypted color images with high security. In addition, the performance of the proposed approach is compared with that of a few state-of-the-art approaches for color image encryption. The results of the comparison suggest that the proposed approach outperforms the other approaches in the overall security of encrypted images. The proposed approach is thus potentially useful for applications that require color image encryption.
Multi-source term estimation based on parallel particle filtering and dynamic state space in unknown radiation environments

When hazardous sources threaten the environment, source term estimation is a crucial concern. Robotics provides a secure solution to the issue but still encounters challenges in uncertain environments. Rapidly inferring source parameters is a prerequisite for unmanned source search. However, when the number of sources exceeds one, the sensor can only measure the intensity of the coupled field. We propose a novel online multi-radiation source term estimation framework for autonomous source-searching tasks. The method incrementally constructs the state space of Bayesian estimation to adapt to diverse scenarios. Inspired by multi-sensor fusion and particle filtering, this framework employs multi-layer particles and updates them simultaneously to enhance the efficiency of fitting observations. Subsequently, the approach corrects (eliminate and randomly generate) the particles based on Poisson kriging interpolation to prevent particles in redundant layers from interfering with estimation. Furthermore, we compare the performances of our proposed algorithm with those in simulated and real experiments. Various metrics demonstrate that the suggested framework is accurate and robust.
QuestVille: Procedural Quest Generation Using NLP Models

Developers face a time-consuming task when creating quests in video games. To ease this burden, Procedural Content Generation (PCG) techniques can be used to automatically generate quests. While PCG has been applied to various areas of game development, it can be difficult to create meaningful narratives for quests. This paper presents a new method for generating engaging quests by combining PCG with Natural Language Processing (NLP) using the models BERT and GPT-2 in a case study game called QuestVille. The paper details the implementation of these models and the challenges encountered. The results suggest that the use of BERT and GPT-2 has potential for creating compelling narrative content. Advancements in AI research may improve on the limitations discussed.
Adaptive Local Cross-Channel Vector Pooling Attention Module for Semantic Segmentation of Remote Sensing Imagery

Adding an attention module to the deep convolution semantic segmentation network has significantly enhanced the network performance. However, the existing channel attention module focusing on the channel dimension neglects the spatial relationship, causing location noise to transmit to the decoder. In addition, the spatial attention module exemplified by self-attention has a high training cost and challenges in execution efficiency, making it unsuitable to handle large-scale remote sensing data. We propose an efficient vector pooling attention (VPA) module for building the channel and spatial location relationship. The module can locate spatial information better by performing a unique vector average pooling in the vertical and horizontal dimensions of the feature maps. Furthermore, it can also learn the weights directly by using the adaptive local cross-channel interaction. Multiple weight learning ablation studies and comparison experiments with the classical attention modules were conducted by connecting the VPA module to a modified DeepLabV3 network using ResNet50 as the encoder. The results show that the mIoU of our network with the addition of an adaptive local cross-channel interaction VPA module increases by 3% compared to the standard network on the MO-CSSSD. The VPA-based semantic segmentation network can significantly improve precision efficiency compared with other conventional attention networks. Furthermore, the results on the WHU Building dataset present an improvement in IoU and F1-score by 1.69% and 0.97%, respectively. Our network raises the mIoU by 1.24% on the ISPRS Vaihingen dataset. The VPA module can also significantly improve the network’s performance on small target segmentation.
A Deep Learning Algorithm for Evaluating the Quality of English Teaching

Universities play a huge role in the cultivation of talents. Especially in the context of internationalization, the teaching of English as a common language is becoming more and more important. This paper introduced the traditional methods for evaluating the quality of English teaching, established a deep learning algorithm for evaluating the quality of English teaching with the evaluation indicators of the traditional methods combined with the convolutional neural network (CNN) algorithm, conducted simulation experiments on the CNN algorithm, and compared it with the support vector machine (SVM) algorithm. The results showed that the scores obtained by the CNN algorithm had some errors with the actual scores but were much lower than the scores obtained by the SVM algorithm, and the CNN algorithm consumed a shorter time in computing. This paper used the CNN algorithm combined with evaluation indexes constructed by the analytic hierarchy process (AHP) method to evaluate the quality of English teaching and verified the effectiveness of the CNN algorithm through a comparison with the SVM algorithm, which provides an effective reference for intelligent evaluation of English teaching quality.
ERGM: A multi-stage joint entity and relation extraction with global entity match

Joint entity and relation extraction is a fundamental and important task in the process of building knowledge graphs. At present, many researchers have proposed their own methods to solve this task, but these studies often have some limitations, such as irrelevant relation prediction, and lack of information interaction between the relation and entity. The complex model structure leads to inefficiency and does not make good use of the associations between the various subtasks. We propose a novel lightweight joint extraction model based on a global entity matching strategy. Specifically, the proposed framework contains three components: Relation Extraction Module, Relation Attention Based Entity Recognition Module and Global Entity Pairing Module. The Relation Extraction Module extracts candidate relations in the sentence, and the Relation Attention Based Entity Recognition Module introduces a relation attention mechanism based on the obtained candidate relations to fuse the information of the relations so as to better identify entities in the sentence. Then use entity vector representations to construct an affine transformation-based global entity matching matrix under a specific relation for triple extraction. Our model decomposes entity and relation extraction into three sub-tasks, which greatly simplifies the model structure, and the tasks are interrelated, making full use of the relevant information. In addition, we introduce a negative sampling strategy to alleviate the exposure bias problem of the model. We validate Our model on public dataset, it not only can effectively solve the triple overlap problem but also achieved a significant time performance speedup and effectively reduce memory occupation.
An Extensive Review on Different Strategies of Multimedia Data Mining

Multimedia data mining is the process of extracting the media data from the data store. In today's time, there is a huge amount of multimedia data generated from different data sources every single second. The multimedia data is generally used for sharing the information or conveying the messages through different media such as text, audio, images, video, animation and graphics. As nowadays, we are using huge amount of digital sources for communication so it is also important to manage such a huge data in a proper order. Managing the data in proper order also help us to filter or mine the data from the data stores. The data should be organized in an appropriate manner and should be categorized properly so that the required data should be retrieved from the data store within less amount of time. Multimedia data mining is used for the purpose of extracting the data from the data store.
Fraud Detection in Banking Transactions Using Machine Learning

Vulnerability in banking systems has exposed us to fraudulent acts, which cause severe damage to both customers and the bank in terms of loss of money and reputation. Financial fraud in banks is estimated to result in a significant amount of financial loss annually. Early detection of this helps to mitigate the fraud, by developing a counter strategy and recovering from such losses. A machine learning-based approach is proposed in this paper to contribute to fraud detection successfully. The artificial intelligence (AI) based model will speed up the check verification to counteract the counterfeits and lower the damage. In this paper, we analyzed numerous intelligent algorithms trained on a public dataset to find the correlation of certain factors with fraudulence. The dataset utilized for this research is resampled to minimize the high class of imbalance in it and analyzed the data using the proposed algorithm for better accuracy.
Wavelet-Based Self-Attention GAN With Collaborative Feature Fusion for Image Inpainting

Image inpainting is a significant task in the applications of computer vision, that aims to fill in damaged regions with visually realistic contents. With the development of deep learning, generative adversarial network (GAN)-based image inpainting approaches have achieved remarkable progress. However, these methods only utilize one-sided structure information to assist inpainting, which can not achieve satisfactory results, especially when synthesizing large-area missing complex images. In order to tackle this problem, a wavelet-based self-attention GAN (WSA-GAN) with collaborative feature fusion is proposed, which is embedded with a wavelet-based self-attention (WSA) and a collaborative feature fusion (CFF). The WSA is designed to conduct long-range dependence among multi-scale frequency information to highlight significant structure details for better generating texture boundaries. The CFF is presented to couple channel-guided space and space-affected channel streams to facilitate the interaction of spatial and channel features, which can effectively avoid potential domain conflicts. Besides, a novel wavelet consistency loss and a hierarchical pyramid feature matching (PFM) discriminator are introduced to stabilize the model training. Extensive experiments on three public datasets, including Paris StreetView, CelebA-HQ and Places, demonstrate that the proposed method outperforms the state-of-the-art methods both quantitatively and qualitatively.
The Weaknesses of Adversarial Camouflage in Overhead Imagery

Machine learning is increasingly critical for analysis of the ever-growing corpora of overhead imagery. Advanced computer vision object detection techniques have demonstrated great success in identifying objects of interest such as ships, automobiles, and aircraft from satellite and drone imagery. Yet relying on computer vision opens up significant vulnerabilities, namely, the susceptibility of object detection algorithms to adversarial attacks. In this paper we explore the efficacy and drawbacks of adversarial camouflage in an overhead imagery context. While a number of recent papers have demonstrated the ability to reliably fool deep learning classifiers and object detectors with adversarial patches, most of this work has been performed on relatively uniform datasets and only a single class of objects. In this work we utilize the VisDrone dataset, which has a large range of perspectives and object sizes. We explore four different object classes: bus, car, truck, van. We build a library of 24 adversarial patches to disguise these objects, and introduce a patch translucency variable to our patches. The translucency (or alpha value) of the patches is highly correlated to their efficacy. Further, we show that while adversarial patches may fool object detectors, the presence of such patches is often easily uncovered, with patches on average 24% more detectable than the objects the patches were meant to hide. This raises the question of whether such patches truly constitute camouflage. Source code is available at https://github.com/IQTLabs/camolo.
Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools Segmentation

A common problem with segmentation of medical images using neural networks is the difficulty to obtain a significant number of pixel-level annotated data for training. To address this issue, we proposed a semi-supervised segmentation network based on contrastive learning. In contrast to the previous state-of-the-art, we introduce Min-Max Similarity (MMS), a contrastive learning form of dual-view training by employing classifiers and projectors to build all-negative, and positive and negative feature pairs, respectively, to formulate the learning as solving a MMS problem. The all-negative pairs are used to supervise the networks learning from different views and to capture general features, and the consistency of unlabeled predictions is measured by pixel-wise contrastive loss between positive and negative pairs. To quantitatively and qualitatively evaluate our proposed method, we test it on four public endoscopy surgical tool segmentation datasets and one cochlear implant surgery dataset, which we manually annotated. Results indicate that our proposed method consistently outperforms state-of-the-art semi-supervised and fully supervised segmentation algorithms. And our semi-supervised segmentation algorithm can successfully recognize unknown surgical tools and provide good predictions. Also, our MMS approach could achieve inference speeds of about 40 frames per second (fps) and is suitable to deal with the real-time video segmentation.
BSDCE-IoV: Blockchain-Based Secure Data Collection and Exchange Scheme for IoV in 5G Environment

The Internet of Vehicles (IoV) is a network that connects vehicles and their environment: in-built devices, pedestrians, and infrastructure through the Internet using heterogeneous access technologies. During communication between vehicles, roadside units, and control rooms, data confidentiality and privacy are critical issues that require effective measures. Several works have been proposed for securing IoV environments based on vehicles-to-infrastructure authentication; However, some schemes have security vulnerabilities, while others have shown efficiency issues. Due to its decentralization, stability, and transaction tracking capabilities, Blockchain as an emerging technology presents a potential solution for IoV security. This article provides an in-depth examination of the benefits of blockchain for a 5G-based IoV environment. In particular, we propose and evaluate a novel blockchain-based secure data exchange (BSDCE-IoV) scheme based on Elliptic Curve Cryptography algorithm. Our solution is designed to eliminate several potential attacks that pose a threat to the IoV environment. Deep examination using the Real-or-Random oracle model and Scyther tool, in addition to the informal security analysis, validates the scheme regarding security and privacy. The Multi-precision Integer and Rational Arithmetic Cryptographic Library (MIRACL) assesses the computational and communication overhead. Computational and communicative overheads were also evaluated using the Multi-precision Integer and Rational Arithmetic Cryptographic Library (MIRACL). BSDCE-IoV shows higher performance in terms of security, functionality, and time delay than a number of recent selective work in IoV security.