Reviewerly/exHarmony huggingface dataset:
Multimodal computation or interpretation? Automatic vs. critical understanding of text-image relations in racist memes in English

Authors:
raw_author_name: Maria Grazia Sindoni
raw_affiliation_string: University of Messina (Italy), Department of Ancient and Modern Civilizations, Via Palatucci, Polo Annunziata, 98168 Messina, Italy

Abstract: This paper discusses the epistemological differences between the label 'multimodal' in computational and sociosemiotic terms by addressing the challenges of automatic detection of hate speech in racist memes, considered as germane families of multimodal artifacts. Assuming that text-image interplays, such is the case of memes, may be extremely complex to disentangle by AI-driven models, the paper adopts a sociosemiotic multimodal critical approach to discuss the challenges of automatic detection of hateful memes on the Internet. As a case study, we select two different English-language datasets, 1) the Hateful Memes Challenge (HMC) Dataset, which was built by the Facebook AI Research group in 2020, and 2) the Text-Image Cluster (TIC) Dataset, including manually collected user-generated (UG) hateful memes. By discussing different combinations of non-hateful/hateful texts and non-hateful/hateful images, we will show how humour, intertextuality, and anomalous juxtapositions of texts and images, as well as contextual cultural knowledge, may make AI-based automatic interpretation incorrect, biased or misleading. In our conclusions, we will argue the case for the development of computational models that incorporate insights from sociosemiotics and multimodal critical discourse analysis.
Developing a cognitive model of solid geometry based on Interpretive Structural Modeling method

Authors:
raw_author_name: Xiaopeng Wu
raw_affiliation_string: Faculty of Education, Northeast Normal University, Changchun, China; Faculty of Education, The University of Hong Kong, Hong kong, China
raw_author_name: Mingyue Ju
raw_affiliation_string: Faculty of Education, Northeast Normal University, Changchun, China

Abstract: With the advancement of science and artificial intelligence, education is experiencing significant innovation. The adaptive learning system is emerging as a promising approach to achieving personalized learning. The cognitive model plays a crucial role as the fundamental rationale behind the adaptive learning system. Currently, there is no uniform and highly operational method for constructing cognitive models. This study adopts Interpretive Structural Modeling (ISM) as the foundational approach for constructing a cognitive model of solid geometry. Based on literature and expert opinions, 17 cognitive attributes of high school solid geometry were identified. Subsequently, a questionnaire survey involving 40 experts was conducted to establish the contextual relationships among these attributes. Applying the ISM method resulted in a seven-level model. This model was then revised based on expert opinions to create the final cognitive model, revealing three primary paths within the domain of high school solid geometry. This paper contends that the use of the ISM method for constructing cognitive models is effective and objective. The resulting cognitive model unveils the content structure of high school solid geometry, and provides an innovative perspective on the construction of cognitive models.
Cryptanalysis and improvement of “group public key encryption scheme supporting equality test without bilinear pairings”

Authors:
raw_author_name: Qijia Zhang
raw_affiliation_string: State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China
raw_author_name: Youliang Tian
raw_affiliation_string: State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China

Abstract: Public key encryption with equality test (PKEET) is a novel primitive which supports equality comparisons on two encrypted messages. Currently, most of the existing PKEET schemes are based on bilinear pairing and require heavy computational overheads. To address this issue, Shen et al. recently proposed an efficient group public key encryption supporting equality test without bilinear pairings scheme. Compared with other schemes, their scheme reduces the usage of expensive bilinear pairing operations and enjoys higher computation efficiency. They claimed that their scheme achieved one-wayness security in the random oracle model and resisted offline message recovery attack. In this letter, we analyze Shen et al.'s scheme through two concrete attacks and demonstrate that their scheme can not support the above two security requirements. An improved scheme is provided to overcome the security vulnerabilities in their scheme. Performance analysis shows that our improved scheme has certain advantages in both computation overhead and storage overhead.
ASSL-HGAT: Active semi-supervised learning empowered heterogeneous graph attention network

Authors:
raw_author_name: Zhenyan Ji
raw_affiliation_string: School of Software Engineering, Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, 100044, China
raw_author_name: Deyan Kong
raw_affiliation_string: School of Software Engineering, Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, 100044, China
raw_author_name: Yanyan Yang
raw_affiliation_string: School of Software Engineering, Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, 100044, China
raw_author_name: Jiqiang Liu
raw_affiliation_string: School of Software Engineering, Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, 100044, China
raw_author_name: Zhao Li
raw_affiliation_string: Zhejiang University, No. 38, Yuquan Campus, Wuhan 430079, China

Abstract: Recently, heterogeneous graph attention network (HGAT) has been widely applied to various machine learning tasks and achieved remarkable results with sufficient labeled data. However, it is noteworthy that in many tasks, labeled data is scarce and the data labeling process is expensive. To that end, this paper presents a novel framework for learning from data with limited labels by organically integrating active learning (AL) and semi-supervised learning (SSL) into heterogeneous graph network. Our framework consists of three components: heterogeneous information network (HIN), HGAT and active semi-supervised learning strategy (ASSL). The adjustable HIN is first constructed by fusing multi-modal features. Then the HGAT is used to encode HIN and the double-layer attention mechanism can mitigate noises during information fusion. The ASSL is further designed to enrich the high-quality labeled training data for model train. Samples selected by uncertainty-aware AL and samples labeled by pseudo-label selection based SSL are finally mixed to iteratively train the ASSL-HGAT framework. Experimental results prove that ASSL-HGAT surpasses the compared state-of-the-art methods on five representative semi-supervised benchmark datasets under the same experimental settings.
Complementary Labels Learning with Augmented Classes

Authors:
raw_author_name: Zhongnian Li
raw_affiliation_string: MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, 210000, Jiangsu, China; School of Computer Science and Technology, China University of Ming and Technogy, Xuzhou, 221000, Jiangsu, China
raw_author_name: Mengting Xu
raw_affiliation_string: College of Computer Science and Technology, Zhejiang University, Hangzhou, 310000, Zhejiang, China
raw_author_name: Xinzheng Xu
raw_affiliation_string: School of Computer Science and Technology, China University of Ming and Technogy, Xuzhou, 221000, Jiangsu, China
raw_author_name: Daoqiang Zhang
raw_affiliation_string: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210000, Jiangsu, China; MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, 210000, Jiangsu, China

Abstract: Complementary Labels Learning (CLL) arises in many real-world tasks such as private questions classification and online learning, which aims to alleviate the annotation cost compared with standard supervised learning. Unfortunately, most previous CLL algorithms were in a stable environment rather than an open and dynamic scenarios, where data collected from unseen augmented classes in the training process might emerge in the testing phase. In this paper, we propose a novel problem setting called Complementary Labels Learning with Augmented Classes (CLLAC), which brings the challenge that classifiers trained by complementary labels should not only be able to classify the instances from observed classes accurately, but also recognize the instance from the Augmented Classes in the testing phase. Specifically, by using unlabeled data, we propose an unbiased estimator of classification risk for CLLAC, which is guaranteed to be provably consistent. Moreover, we provide generalization error bound for proposed method which shows that the optimal parametric convergence rate is achieved for estimation error. Finally, the experimental results on several benchmark datasets verify the effectiveness of the proposed method.
JAHAN: A framework for procedural generation of game maps from design specifications

Authors:
raw_author_name: Hassan Haghighi
raw_affiliation_string: Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
raw_author_name: Mojtaba Vahidi-Asl
raw_affiliation_string: Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran

Abstract: Procedural content generation is a field that focuses on creating game assets employing algorithmic procedures. A critical asset in many game genres is the map. Due to the sophistication of the game map development, procedural map generation has been the subject of many studies. Considering the fundamental complexity of the game maps, many of these studies try to engage human designers in the generation process to improve the output of algorithmic procedures. However, these studies have not paid adequate attention to important non-functional requirements such as generality, comprehensiveness, controllability, and adaptability to iterative development. This paper presents a procedural map generation framework that places map designers at the center of a pipeline architecture. This framework allows designers to configure different pipeline modules and specify the characteristics of their desired map. Moreover, this framework has assertion checking features that facilitate map evaluation. We demonstrate the capabilities of this framework through one running example, along with three case studies that generate maps for first-person shooter, real-time strategy, and dungeon crawler games. Furthermore, we present a user study to show the suitability of this framework in iterative development processes and demonstrate how it reduces the labor of human designers.
Engineering Qubit Coupling to Reservoir Mode: Optimizing Circuitry to Extend Coherence Time

Authors:
raw_author_name: Ahmad Salmanogli
raw_affiliation_string: Ankara Yildirim Beyazit University, Engineering Faculty, Electrical and Electronic Department, Ankara, Turkey

Abstract: The research investigates how reservoir modes influence the decay and dephasing rates of a qubit, utilizing a circuit model where the qubit interacts with N distinct oscillators. The Lagrangian and Hamiltonian of the system are deduced theoretically, and the analysis delves into how circuit components like the qubit and coupling capacitors affect the decay rate. Notably, the study reveals that the coupling capacitor exerts a more pronounced influence on the relaxation time compared to the qubit capacitor. Moreover, it quantifies the repercussions of bosonic modes affected by coupling to reservoir modes on the qubit coherence time. As a result, the primary objective of the investigation is to examine the impact of circuit elements on qubit behavior, offering valuable insights into the determinants of qubit decay and dephasing rates.
Quantum-enhanced chaotic image encryption: Strengthening digital data security with 1-D sine-based chaotic maps and quantum coding

Authors:
raw_author_name: Mujeeb Ur Rehman
raw_affiliation_string: Cyber Technology Institute, School of Computer Science and Informatics, De Montfort University, UK

Abstract: The proposed work delves into the utilization of chaotic maps along with the quantum mechanisms to strengthen the security of the digital images along with addressing the limitations posed by conventional 1-D chaotic systems characterized by their pseudorandom and periodic attributes. Leveraging the inherent uncertainty in quantum theory, the proposed research introduces a new image encryption scheme. This method is built upon the concepts of two-dimensional quantum coding and the 1-D sine-based chaotic map (1-D SBCM). In the proposed encryption scheme, initially, a random sequence is created using 1-D SBCM by varying the seed parameters. This sequence is subsequently utilized for the purpose of scrambling. After that, a pseudorandom number generator (PRNG) is meticulously devised, drawing inspiration from the concepts of quantum coherence. This PRNG generates a confidential code stream that defies predictability and remains incongruent with the plaintext image, thereby contributing to heightened security levels. Subsequently, the research employs the novel enhanced quantum representation (NEQR) model, capitalizing on its advanced capabilities. Within this framework, the study introduces a quantum right cyclic shift operator, as well as a quantum XOR operator. These operators play a vital role in the formation of highly robust encrypted images. The application of these quantum operators not only enhances the security measures but also increases the complexity of the encryption procedure Statistical evidence derived from comprehensive experimentation corroborates the efficacy of the proposed image encryption scheme. Through rigorous analysis, it becomes evident that the system exhibits a robust performance in terms of strong security. The integration of quantum principles and quantum coding demonstrates that the proposed encryption scheme is resilient against potential threats.
An approach for handwritten Chinese text recognition unifying character segmentation and recognition

Authors:
raw_author_name: Ming-Ming Yu
raw_affiliation_string: School of Artificial Intelligence, University of Chinese Academy of Sciences, 100049, Beijing, China; State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institution of Automation, Chinese Academy of Sciences, Beijing, 100190, China
raw_author_name: Fei Yin
raw_affiliation_string: State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institution of Automation, Chinese Academy of Sciences, Beijing, 100190, China
raw_author_name: Cheng-Lin Liu
raw_affiliation_string: School of Artificial Intelligence, University of Chinese Academy of Sciences, 100049, Beijing, China; State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institution of Automation, Chinese Academy of Sciences, Beijing, 100190, China

Abstract: Text line recognition methods can be categorized into explicit segmentation based and implicit segmentation based ones. Explicit segmentation based methods require character-level annotation during training, while implicit segmentation based methods, trained on line-level annotated data, face alignment drift challenges. Though some methods have been proposed to address these challenges using weakly supervised object detection, they often rely on cumbersome pseudo-box generation processes and complex decoding. In this paper, we propose a unified framework to overcome these challenges, achieving high accuracy in text recognition and character segmentation. To eliminate the need of character-level annotated real text line data in training, we introduce a novel training paradigm that utilizes character-level annotated synthetic data and line-level annotated real data jointly. For synthetic data, candidate characters are explicitly aligned with labeled characters to generate hard labels for supervising model training. For real data, implicit alignment is produced by Connectionist Temporal Classification (CTC) mapping to provide soft labels for weakly-supervised model training. And for inference, we propose two decoding strategies leveraging the advantages of Non-Maximum Suppression (NMS) and CTC decoding. Extensive experiments on benchmark datasets demonstrate the superior performance of our method in text recognition and character localization, even with minimal amounts of character-level annotated line data.
An Empirical Investigation on Awareness Regarding Cryptocurrencies Among Investors Under Different Market Regimes

Authors:
raw_author_name: Ángel Acevedo Duque
raw_affiliation_string: Grupo de Investigación de Estudios Organizacionales (GIOS), Universidad Áutonoma de Chile, Santiago, Chile

Abstract: Cryptocurrency is one of the most notable financial innovations in recent years. The present study provides a novel methodology to understand the level of awareness regarding cryptocurrencies among investors under different market regimes. The study used a survey questionnaire for a sample of 352 investors in sample Asian countries (India, Japan, South Korea, Singapore, Saudi Arabia, Bahrain, Oman, and Dubai). The study has developed eight hypotheses that have been tested considering independent (Statement of Responses About cryptocurrency-SORAC) and dependent variables (Market Factors-MF, Social Sentiment Factors-SF, Technological Factors-TF, Stocks-ST, Bank Deposits-BD) by incorporating t-test, regression, and correlation. The results revealed that cryptocurrency has a significant and positive influence on dependent variables among investors under different market regimes. The results from the analytical findings pave the way for the policymakers of these countries to help design robust mechanisms to regulate their cryptocurrencies under different market regimes.
Assembling a multi-platform ensemble social bot detector with applications to US 2020 elections

Authors:
raw_author_name: Lynnette Hui Xian Ng
raw_author_name: Kathleen M. Carley

Abstract: Abstract Bots have been in the spotlight for many social media studies, for they have been observed to be participating in the manipulation of information and opinions on social media. These studies analyzed the activity and influence of bots in a variety of contexts: elections, protests, health communication and so forth. Prior to this analyzes is the identification of bot accounts to segregate the class of social media users. In this work, we propose an ensemble method for bot detection, designing a multi-platform bot detection architecture to handle several problems along the bot detection pipeline: incomplete data input, minimal feature engineering, optimized classifiers for each data field, and also eliminate the need for a threshold value for classification determination. With these design decisions, we generalize our bot detection framework across Twitter, Reddit and Instagram. We also perform feature importance analysis, observing that the entropy of names and number of interactions (retweets/shares) are important factors in bot determination. Finally, we apply our multi-platform bot detector to the US 2020 presidential elections to identify and analyze bot activity across multiple social media platforms, showcasing the difference in online discourse of bots from different platforms.
A Distributed System Fault Diagnosis System based on Machine Learning

Authors:
raw_author_name: Yixiao Wang

Abstract: Now days distributed system becomes the mainstream system of information storage and processing. Compared with traditional systems, distributed systems are larger and more complex.However, the average probability of failure is higher and the difficulty, complexity of operation and maintenance are greatly increased. Therefore, it is necessary to use efficient methods to diagnose the system. Our aim is to use the trained model to diagnose the fault data of the distributed system, so we can obtain as high diagnostic accuracy as possible, and create a web side for users to use. The technique we proposed uses the integrated learning approach of Stacking to model the superposition of the raw data. To realize this, we trained with a dataset of 10,000 pieces of data and assessed accuracy every once in a while. Our best training results are about 80.69% accurate and can be used on the web side. By training data sets and analyzing distributed system faults with Stacking technology, a model with a test accuracy of 80.69% was obtained. Through this model and the web platform we built, the fault of distributed system can be diagnosed, and the diagnosis results are better than other models.
Research on Speech Communication Enhancement of English Web-based Learning Platform based on Human-computer Intelligent Interaction

Authors:
raw_author_name: Yufang Gu

Abstract: This study presents a novel web-based learning platform that leverages human-computer intelligent interaction to enhance English communication skills. The platform integrates cutting-edge technologies to create an immersive learning experience, combining natural language processing, speech recognition, and interactive exercises. Learners engage in real-time conversations with virtual tutors, receive personalized feedback, and access a vast repository of educational resources. The platform not only facilitates language acquisition but also encourages self-paced learning, making it a valuable tool for both educators and students. By harnessing the power of artificial intelligence, this web-based platform represents a significant advancement in the realm of English language education. To overcome these issues this paper proposed SVM with an improved satin Bower bird optimization algorithm (SVM-ISBBO). SVM-ISBBO uses fog computing services that minimize the latency and speeds up the process, effectively handling huge wearable devices. In this proposed work SVM-ISBBO monitors the students communication, vocal parameters, blood pressure, etc, and these values are obtained from wearable sensor devices and their notifications are sent back to teachers. Teachers diagnosed the student information and sent back the alert notifications to the students for taking proper medications. All this information is stored in fog-based cloud storage in a secure manner. The accuracy rate of KNN got 78.56%, NB got 81.74%, SVM got 85.15% and the proposed work of SVM-ISBBO got 92.34%.
Artificial Intelligence in “Ivory Tower”

Authors:
raw_author_name: D. V. Petrosyants
raw_affiliation_string: Financial University

Abstract: This article is devoted to the problem of adapting the higher education system to the new realities of introducing artificial intelligence into everyday life. The author proposes new constructs in communication between teachers and students, based on the use of modern research and development tools (neural networks, big data, artificial intelligence, etc.), which make it possible to improve the mastery of the material with minimal losses due to obsolescence and a decrease in the relevance of the proposed studying courses. Thus it maximizes the competitive advantages of university graduates who acquire competencies that are in high demand in the modern world.