Reviewerly/exHarmony huggingface dataset:
Wavelet-based information theory in quantitative assessment of AFM images’ quality

The quantitative assessment of the image quality produced by atomic force microscopy (AFM) is an ongoing and challenging task. In our study, we demonstrate Shannon's application of information theory for measuring image quality. Specifically, we propose quantifying the loss of image information due to the various distortion processes by exploring the relationship between image information based on the information channel capacity (ICC), spectral image representation, and visual quality. Since the ideal image is unavailable, the power and noise spectrum, the critical input information for the image quality evaluation, must be robustly estimated in the proposed method. The classical, most popular Welch method for spectral estimation uses an average of several windowed periodograms and can produce biased spectrum estimates. Therefore, in our work, we discuss an alternative technique based on the wavelet transform that can be applied to solve this challenging problem, specifically in the case of noisy, uncertain AFM measurements. Finally, we validate the performance of the enhanced ICC-wavelet-based algorithm with noisy measurement AFM data.
WaterPairs: a paired dataset for underwater image enhancement and underwater object detection

Abstract Due to its importance in marine engineering and aquatic robotics, underwater image enhancement works as a preprocessing step to improve the performance of high-level vision tasks such as underwater object detection and recognition. Although several studies exhibit that underwater image enhancement algorithms can boost the detection accuracy of detectors, no work has focused on studying the relationship between these two tasks. This is mainly because current underwater datasets lack either bounding box annotations or high-quality reference images, based on which detection accuracy or image quality assessment metrics are calculated. To examine how underwater image enhancement methods affect underwater object detection tasks, we provide a large-scale underwater object detection dataset with both bounding box annotations and high-quality reference images, namely, the WaterPairs dataset. The WaterPairs dataset offers a platform for researchers to comprehensively study the influence of underwater image enhancement algorithms on underwater object detection tasks. We will release our dataset at https://github.com/IanDragon/WaterPairs once this paper is accepted.
Multimodal abstractive summarization using bidirectional encoder representations from transformer with attention mechanism

In recent decades, abstractive text summarization using multimodal input has attracted many researchers due to the capability of gathering information from various sources to create a concise summary. However, the existing methodologies based on multimodal summarization provide only a summary for the short videos and poor results for the lengthy videos. To address the aforementioned issues, this research presented the Multimodal Abstractive Summarization using Bidirectional Encoder Representations from Transformers (MAS-BERT) with an attention mechanism. The purpose of the video summarization is to increase the speed of searching for a large collection of videos so that the users can quickly decide whether the video is relevant or not by reading the summary. Initially, the data is obtained from the publicly available How2 dataset and is encoded using the Bidirectional Gated Recurrent Unit (Bi-GRU) encoder and the Long Short Term Memory (LSTM) encoder. The textual data which is embedded in the embedding layer is encoded using a bidirectional GRU encoder and the features with audio and video data are encoded with LSTM encoder. After this, BERT based attention mechanism is used to combine the modalities and finally, the BI-GRU based decoder is used for summarizing the multimodalities. The results obtained through the experiments that show the proposed MAS-BERT has achieved a better result of 60.2 for Rouge-1 whereas, the existing Decoder-only Multimodal Transformer (D-MmT) and the Factorized Multimodal Transformer based Decoder Only Language model (FLORAL) has achieved 49.58 and 56.89 respectively. Our work facilitates users by providing better contextual information and user experience and would help video-sharing platforms for customer retention by allowing users to search for relevant videos by looking at its summary.
Redox conditions of Datangpo-type manganese ores constrained by statistical analysis of pyrite framboids and iron isotopes

Datangpo-type manganese ores which originated in the Cryogenian Datangpo Formation, are overlaid and underlaid by Nantuo diamicite and Tiesi'ao silistones, respectively. A thorough understanding of the manganese mineralization process is currently limited by the continued uncertainty regarding the metallogenic redox condition of the Mn-bearing rock series. This study employed statistical analysis of pyrite framboid sizes and iron isotope (δ56Fe) to determine the redox conditions and constrain the mineralization process of Datangpo-type manganese ores in South China, considering the Gaolou in Chongqing, Yanglizhang in Guizhou, and Minle and Zhenxing in Hunan as examples. The results showed that Tiesi'ao siltstones mainly developed relatively larger diameter (7.29–7.68 μm) framboidal pyrites, whereas mudstones in the Datangpo Formation developed relatively smaller diameter (2.63–5.56 μm) framboidal pyrites. Furthermore, manganese ores produce non-framboidal pyrites. In three profiles, framboidal pyrite concentrations were found to be negatively correlated with Mn contents. These characteristics suggested that siltstones, mudstones, and manganese ores were deposited in oxic-anoxic, anoxic-euxinic, and oxic-anoxic conditions, respectively. The δ56Fe of whole rocks (δ56FeWR) exhibited a range from −0.73 ‰ to +0.48 ‰ (average of −0.32 ‰), whereas pyrite (δ56FePy) varied from +0.03 ‰ to +0.83 ‰ (average of +0.36 ‰). Furthermore, δ56FePy in manganese ore (average = +0.27 ‰) were lighter than those in mudstone (average = +0.49 ‰). These results of this study demonstrated that oxidation deposition played a role in pyrite formation, with manganese ores exhibiting more oxic conditions compared to mudstones. The findings of this study suggested that idiomorphic pyrite formation was similar to that of manganese ore, in which Mn precipitated in the form of manganese (hydro)oxides under oxic seawater, and then converted into rhodochrosite under anoxic sulfidic diagenetic conditions, with the involvement of microbes. This research has the potential to enhance comprehension regarding redox conditions and the regulation of sedimentary manganese ores via redox transformation.
Blockchain in asset management: An extensive review of opportunities and challenges

Blockchain technology has emerged as a transformative force within the financial sector, particularly in asset management. This paper provides a comprehensive review of the opportunities and challenges associated with integrating blockchain in asset management practices. The opportunities presented by blockchain in asset management are multifaceted. The decentralized and immutable nature of blockchain enables increased transparency and security in the tracking and transfer of assets. Smart contracts, a key feature of blockchain, automate and streamline various processes, reducing operational costs and minimizing the risk of errors. This technology also facilitates real-time settlement and enhances liquidity, contributing to increased efficiency in asset management workflows. However, along with these opportunities come substantial challenges. Regulatory uncertainties, interoperability issues, and the need for industry-wide standards are significant hurdles that the asset management sector must overcome to fully harness the potential of blockchain. Security concerns related to the custody and protection of digital assets, as well as the scalability of blockchain networks, also demand careful consideration. Moreover, the integration of blockchain in asset management necessitates a paradigm shift in organizational structures and industry practices. Stakeholders must adapt to new ways of managing and transferring assets, embracing the cultural and technological changes inherent in blockchain adoption. Education and collaboration among industry participants are crucial for navigating the complexities of this transformative technology. In conclusion, this extensive review highlights the immense potential of blockchain in revolutionizing asset management while shedding light on the formidable challenges that need to be addressed for successful implementation. As the industry continues to explore and adopt blockchain solutions, collaboration and proactive measures will be essential in maximizing the benefits and mitigating the risks associated with this groundbreaking technology.
A survey on multi-agent reinforcement learning and its application

Multi-agent reinforcement learning (MARL) has been a rapidly evolving field. This paper presents a comprehensive survey of MARL and its applications. We trace the historical evolution of MARL, highlight its progress, and discuss related survey works. Then, we review the existing works addressing inherent challenges and those focusing on diverse applications. Some representative stochastic games, MARL means, spatial forms of MARL, and task classification are revisited. We then conduct an in-depth exploration of a variety of challenges encountered in MARL applications. We also address critical operational aspects, such as hyperparameter tuning and computational complexity, which are pivotal in practical implementations of MARL. Afterward, we make a thorough overview of the applications of MARL to intelligent machines and devices, chemical engineering, biotechnology, healthcare, and societal issues, which highlights the extensive potential and relevance of MARL within both current and future technological contexts. Our survey also encompasses a detailed examination of benchmark environments used in MARL research, which are instrumental in evaluating MARL algorithms and demonstrate the adaptability of MARL to diverse application scenarios. In the end, we give our prospect for MARL and discuss their related techniques and potential future applications.
The Effect of Storytelling Learning Using Role Playing Model Assisted by Audiovisual Media on Students Written and Storytelling Skills of Students Grade V Elementary School in Pringsurat Sub-District Temanggung District

The Role Playing learning approach aims to make learning more meaningful for students. The Role Playing learning approach, combined with multimedia resources, stands out as a chosen and viable strategy for overcoming emerging challenges. In this study, experimental research with a quantitative approach was applied to assess the impact of using storytelling learning models paired with various media on storytelling skills and writing stories in a fifth-grade elementary school class. Based on the assessment process that has been carried out, the results in this study are an assessment of typical differences in comprehension tests between experimental and control groups supporting the acceptance of H1 versus H0. With a bidirectional significance probability value of 0.005, H1 is accepted, and H0 is rejected because 0.000 is less than 0.05. Therefore, this study ensures that the Role Playing learning model has a significant influence on students' storytelling skills. Similarly, the evaluation of typical differences in comprehension tests between the experimental group and the control group also favored the acceptance of H1 over H0. With a bidirectional significance probability value of 0.005, H1 is accepted, and H0 is rejected because 0.000 is less than 0.05. In summary, role playing has a significant influence on students' storytelling skills. Assessment of typical differences in comprehension tests between the experimental group and the control group led to acceptance of H1 versus H0. With a bidirectional significance probability value of 0.005, H1 is accepted, and H0 is rejected because 0.000 is less than 0.05. In conclusion, Role Playing significantly affects students' ability to tell stories and write stories. Keywords: role playing, storytelling skills, story writing skills, audio-visual media.
Experimental Integration of Quantum Key Distribution and Post‐Quantum Cryptography in a Hybrid Quantum‐Safe Cryptosystem

Abstract Quantum key distribution (QKD) and post‐quantum cryptography (PQC) are the two counter measures against cryptographic attacks via quantum computing. While QKD offers information theoretic security but limited authentication scalability, PQC facilitates scalable authentication in high density networks but is not information theoretic secure. Therefore, an ideal quantum‐safe framework should efficiently leverage the complementarity of both techniques. However, despite growing efforts in integrating both, current realizations have focused on channel authentication, and a complete cryptosystem addressing both hybrid authentication and hybrid key exchange is yet to be demonstrated. Here, an authenticated hybrid key exchange protocol is introduced that incorporates PQC and QKD in a modular and information‐theoretic secure architecture. The quantum‐safe protocol is inherently resilient to catastrophic cryptographic failures and provides both forward and post‐compromise security. As proof‐of‐concept implementation, the cryptosystem on a QKD hardware prototype is integrated, with the QKD processing, PQC key exchange and secret state masking via physical unclonable functions (PUFs) all running on a single field programmable gate array (FPGA). This work paves the way for the deployment of versatile and modular quantum‐safe networks that exploit the complementarity of PQC and QKD.
Multiple Dimensions of Machismo: Linguistic Considerations for Latino Sexual Minority Men in the United States

The literature differentiates between two domains of machismo: traditional machismo and caballerismo. Research has largely focused on measuring machismo among English speakers. We evaluated whether Estrada’s (2011) 2-factor model of machismo was invariant across language (English vs. a direct Spanish translation). A series of multigroup confirmatory factor analyses were conducted between respondents who completed the survey in English ( n = 428) and Spanish ( n = 102). Analyses suggested the hypothesized 2-factor model did not fit across language groups. While the traditional 2-factor structure emerged in the English language data, exploratory factor analysis indicated a 3-factor structure of machismo among Spanish-speaking respondents. One of the new factors (inherent machismo), among Spanish-speaking respondents, was associated with internalized heterosexism, suggesting that the new factor structure may capture the belief that masculine men are superior. These findings suggest there is a need for the development of culturally appropriate Spanish language assessment.
KolamNetV2: efficient attention-based deep learning network for tamil heritage art-kolam classification

Abstract In India, kolam, commonly referred to as rangoli, is a traditional style of art. It involves using rice flour, chalk, or coloured powders to create elaborate patterns and motifs on the ground. Kolam is a common daily ritual in many regions of India, especially in South India, where it is seen as a significant cultural tradition and a means to greet visitors. Unfortunately, as a result of people’s hectic lives nowadays, the habit of drawing kolam on a regular basis is dwindling. The art of making kolam patterns is in danger of disappearing as so many individuals no longer have the time or space to do it on a regular basis. Therefore, it is imperative that ancient art be conserved and digitally documented in order to enlighten our next generation about kolam and its classifications. Deep learning has become a powerful technique because of its ability to learn from raw image data without the aid of a feature engineering process. In this article, we attempted to understand the types of Kolam images using the proposed deep architecture called KolamNetV2. KolamNetV2 comprises EfficientNet and attention layers, ensuring high accuracy with minimal training data and parameters. We evaluated KolamNetV2 to reveal its ability to learn the various types in our challenging Kolam dataset. The experimental findings show that the proposed network achieves fine enhancement in performance metrics viz, precision-0.7954, recall-0.7846, F1score-0.7854 and accuracy-81%. We compared our results with state-of-the-art deep learning methodologies, proving the astounding capability. Graphical Abstract
Information Tools for Organizing Distance Education Within the Framework of Digitalization of the Educational Process

The article is devoted to the study of information means of an educational institution for the organization of distance education within the framework of digitalization of the educational process, which sets out the principles and means of effective functioning of distance education. Specialized automated information systems developed with the active use of computer and telecommunications tools make it possible to globally increase the efficiency of distance learning. The purpose of this work is to draw conclusions based on the results of a study of information means for organizing distance education using automated information systems. The results of the study allow us to conclude that the effective functioning of the mechanism of continuous education using information systems contributes to the implementation of educational activities of students, ensuring an improvement in the quality of professional competencies applicable in the professional practice of a future specialist. The results of the study allow us to conclude that the effective functioning of the mechanism of continuous education using information systems contributes to the implementation of educational activities of students, ensuring an improvement in the quality of professional competencies applicable in the professional practice of a future specialist.
Transductive meta-learning with enhanced feature ensemble for few-shot semantic segmentation

This paper addresses few-shot semantic segmentation and proposes a novel transductive end-to-end method that overcomes three key problems affecting performance. First, we present a novel ensemble of visual features learned from pretrained classification and semantic segmentation networks with the same architecture. Our approach leverages the varying discriminative power of these networks, resulting in rich and diverse visual features that are more informative than a pretrained classification backbone that is not optimized for dense pixel-wise classification tasks used in most state-of-the-art methods. Secondly, the pretrained semantic segmentation network serves as a base class extractor, which effectively mitigates false positives that occur during inference time and are caused by base objects other than the object of interest. Thirdly, a two-step segmentation approach using transductive meta-learning is presented to address the episodes with poor similarity between the support and query images. The proposed transductive meta-learning method addresses the prediction by first learning the relationship between labeled and unlabeled data points with matching support foreground to query features (intra-class similarity) and then applying this knowledge to predict on the unlabeled query image (intra-object similarity), which simultaneously learns propagation and false positive suppression. To evaluate our method, we performed experiments on benchmark datasets, and the results demonstrate significant improvement with minimal trainable parameters of 2.98M. Specifically, using Resnet-101, we achieve state-of-the-art performance for both 1-shot and 5-shot Pascal-[Formula: see text], as well as for 1-shot and 5-shot COCO-[Formula: see text].
Parallel GEMM-based convolution for deep learning on multicore RISC-V processors

Abstract We address the efficient implementation of the convolution operator on the GAP8 parallel ultra-low power platform (PULP), a heterogeneous multi-core processor equipped with a fabric controller (FC); a cluster of eight compute cores; and a four-level memory hierarchy with scratchpads instead of conventional, hardware-assisted cache memories. Our solution for this platform transforms the convolution into a general matrix–matrix multiplication ( gemm ) via the lowering approach, demonstrating that it is possible to attain reasonable performance on the GAP8 by carefully adapting techniques such as tiling and loop parallelism, which are mainstream in the multi-threaded, cache-aware realization of gemm .
Gender-transformative action, social norms and economic empowerment

Feminist advocacy for ‘gender transformative’ approaches to development, public policy and humanitarian action that account for social norms has surged in recent years. This article intervenes in the debate around norms and implications for transformative approaches. We draw on a unique set of quantitative, global ‘gender data’ collected in 2020 and 2021 and examine how social norms inform women’s experiences of economic empowerment, as well as how these relationships map onto the current debates around interventions to address social norms and the form these interventions ought to take. Our data show that social norms matter for access to and control over resources; in addition, they illustrate that an individual belief in gender equality is fairly common around the world but that such individual beliefs frequently do not coincide with what people think their neighbours believe. These findings suggest a need for consideration of factors beyond individual attitudes towards and beliefs in gender-transformative interventions for women’s economic empowerment.
@llegra: a chatbot for Vallader

Abstract Extinct and endangered languages have been preserved primarily through audio conservation and the collection and digitization of scripts and have been promoted through targeted language acquisition efforts. Another possibility would be to build conversational agents like chatbots or voice assistants that can master these languages. This would provide an artificial, active conversational partner which has knowledge of the vocabulary and grammar and allows one to learn with it in a different way. The chatbot, @llegra, with which one can communicate in the Rhaeto-Romanic idiom Vallader was developed in 2023 based on GPT-4. It can process and output text and has voice output. It was additionally equipped with a manually created knowledge base. After laying the conceptual groundwork, this paper presents the preparation and implementation of the project. In addition, it summarizes the tests that native speakers conducted with the chatbot. A critical discussion elaborates advantages and disadvantages. @llegra could be a new tool for teaching and learning Vallader in a memorable and entertaining way through dialog. It not only masters the idiom, but also has extensive knowledge about the Lower Engadine, that is, the area where Vallader is spoken. In conclusion, it is argued that conversational agents are an innovative approach to promoting and preserving languages.