Reviewerly/exHarmony huggingface dataset:
Multimodal learning with only image data: A deep unsupervised model for street view image retrieval by fusing visual and scene text features of images

Abstract As one of the classic tasks in information retrieval, the core of image retrieval is to identify the images sharing similar features with a query image, aiming to enable users to find the required information from a large number of images conveniently. Street view image retrieval, in particular, finds extensive applications in many fields, such as improvements to navigation and mapping services, formulation of urban development planning scheme, and analysis of historical evolution of buildings. However, the intricate foreground and background details in street view images, coupled with a lack of attribute annotations, render it among the most challenging issues in practical applications. Current image retrieval research mainly uses the visual model that is completely dependent on the image visual features, and the multimodal learning model that necessitates additional data sources (e.g., annotated text). Yet, creating annotated datasets is expensive, and street view images, which contain a large amount of scene texts themselves, are often unannotated. Therefore, this paper proposes a deep unsupervised learning algorithm that combines visual and text features from image data for improving the accuracy of street view image retrieval. Specifically, we employ text detection algorithms to identify scene text, utilize the Pyramidal Histogram of Characters encoding predictor model to extract text information from images, deploy deep convolutional neural networks for visual feature extraction, and incorporate a contrastive learning module for image retrieval. Upon testing across three street view image datasets, the results demonstrate that our model holds certain advantages over the state‐of‐the‐art multimodal models pre‐trained on extensive datasets, characterized by fewer parameters and lower floating point operations. Code and data are available at https://github.com/nwuSY/svtRetrieval .
Harnessing generative artificial intelligence to support nature‐based solutions

Abstract The ongoing biodiversity and climate change crises require society to adopt nature‐based solutions that integrate and enhance ecosystems. To achieve successful implementation of nature‐based solutions, it is vital to communicate scientific information about their benefits and suitability. This article explores the potential of generative artificial intelligence (GenAI) as a tool for automating and scaling up science communication, outreach, and extension for nature‐based solutions. To illustrate the potential of GenAI, we present three case study examples; (1) reporting scientific information on ecosystem services, future land use options, and nature‐based solutions for farms (2) interactively providing guidance in response to homeowner questions about biodiversity‐friendly garden design and (3) visualising potential future scenarios of landscape change that incorporate diverse nature based and technological solutions. These examples demonstrate potential applications which may be relevant to other systems and types of nature‐based solutions. While GenAI for nature‐based solutions offers significant opportunities, this new technology brings risks of bias, false information, data privacy, mistrust, and high energy usage. Alongside technological development, we require integrated social research into ethics, public acceptability, and user experience, to maximise the benefits of GenAI while limiting these risks. GenAI offers an opportunity to accelerate the dissemination of nature‐based design strategies and reach a broader audience, by synthesising information and producing tailored content for specific users and locations. By harnessing the power of GenAI alongside human expertise, we can support nature‐based solutions to tackle the complex challenges of future sustainability. Read the free Plain Language Summary for this article on the Journal blog.
Multimodal computation or interpretation? Automatic vs. critical understanding of text-image relations in racist memes in English

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

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”

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

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

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

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

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

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

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.
Open hardware solutions in quantum technology

Quantum technologies, such as communication, computing, and sensing, offer vast opportunities for advanced research and development. While an open-source ethos currently exists within some quantum technologies, especially in quantum computer programming, we argue that there are additional advantages in developing open quantum hardware (OQH). Open quantum hardware encompasses open-source software for the control of quantum devices in labs, blueprints, and open-source toolkits for chip design and other hardware components, as well as openly accessible testbeds and facilities that allow cloud-access to a wider scientific community. We provide an overview of current projects in the OQH ecosystem, identify gaps, and make recommendations on how to close them at present. More open quantum hardware would accelerate technology transfer to and growth of the quantum industry and increase accessibility in science.
An Empirical Investigation on Awareness Regarding Cryptocurrencies Among Investors Under Different Market Regimes

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

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.
AYURVEDIC INTERVENTION IN THE MANAGEMENT OF PANDU ROGA (DIMORPHIC ANAEMIA)

Pandu Roga is an Ayurvedic ailment clinically comparable to many types of Anaemia. This study was done to establish the effect of Ayurvedic management on the patient of Pandu Roga (Dimorphic Anaemia). In the IPD, Arogyashala Rugnalaya, Nashik, from 08/01/2022 to 17/01/2022, A 69-year Male patient, suffering from Pandu Roga (Dimorphic Anaemia) was treated with Ayurvedic management. Shamana Chikitsa (ayurvedic medicinal treatment) And Virechana Karma (Therapeutic Purgation) were administered. The patient felt a significant increase in Hb g% and other hematological investigations. Symptoms were assessed as per the criteria of assessment, and improvement was seen in symptoms. Panduta (Pallor) 75%, Daurbalya (General weakness) 75%, Ayasaja Shwasa (Dyspnoea on exertion) 66.66%, Pindikodveshtana (Leg cramps) 50%, Nidraluta (Sleepiness) 50%, Shiroruk (Headache)100%, Bhrama (Giddiness)0%, Sadana (Fatigue) 66.66%, Gatra Shoola (Body aches) 66.66%, Agnimandya (Impaired appetite) 66.66%, Aruchi (Anorexia) 66.66%. This case study demonstrates that Ayurvedic management was significant in Pandu Roga (Dimorphic Anaemia).
A Distributed System Fault Diagnosis System based on Machine Learning

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.