Reviewerly/exHarmony huggingface dataset:
Model Chemlab and Phet Simulator: A Didactic Resource for Chemistry Learning in Undergraduate Students

The objective of the research was to determine the incidence of the application of the Model Chemlab and PhET simulator as a didactic resource to improve learning achievement in the Chemistry Laboratory course in undergraduate students during the 2022 academic year. The study used a quantitative methodology with a pre-experimental experimental design. The population consisted of 257 students of the Science, Technology and Environment Studies Program (CTA) of the Faculty of Education Sciences (FCEDUC) of the National University of the Altiplano (UNA) Puno, from which a non-probabilistic sample of 14 students of the IV cycle was selected, The Model Chemlab and PhET simulators were used in the learning sessions of the Chemistry Laboratory course with the purpose of improving the students' learning achievement. Before carrying out the experiment, a pre-test was administered, and after using the simulators, a post-test was conducted to determine the impact of the application of the simulators on the study variables. The results showed a significant improvement, with a t-value of 17.393 and a significance level of ?=0.000 these findings indicate that the students achieved an efficient use and management of the simulators, which favored their learning in the Chemistry Laboratory course.
A preliminary study on artificial intelligence oracles and smart contracts: A legal approach to the interaction of two novel technological breakthroughs

Artificial Intelligence and Smart Contracts are two cutting-edge technological achievements of the so-called 4th Industrial Revolution era. Both have already had a significant impact on various aspects of modern life, including transactions, and each one has already been under scientific investigation. Instead, their interaction has not become the subject of a debate, although it can further (positively) affect the transactions. This interconnection takes place through specific mechanisms, called Oracles, which can be, among others, highly sophisticated Artificial Intelligence systems (autonomous systems). The present article aims to present the role of the Artificial Intelligence Oracles throughout the ‘smart contractual procedure’, as well as to shed light on the potential (new) legal issues this interconnection may raise. The main result of this article is to indicate the appropriate legal directions in case of Artificial Intelligence Oracles’ failures, based on the most prevalent current approaches to AI's (the user's) contractual and/or non-contractual liability. The major research's conclusion is that the Artificial Intelligence Oracle's failures may result in one of the following situations: (a) breach of a (smart) contract, (b) unjust enrichment, (c) conclusion of a (voidable) smart contract that should not have been concluded, or (d) non-conclusion of a smart contract that should have been concluded. The responsibility of each person participating in the ‘smart contractual procedure’, i.e. the contractual parties, the blockchain platform and the Artificial Intelligence user/owner (or even the Artificial Intelligence system itself), as well as the AI provider or designer, is examined in each of the afore-mentioned situations separately. Given that legislative initiatives have already begun, the present article aspires to contribute to the consistent address of the newly raised legal issues.
Simulating Users in Interactive Web Table Retrieval

Considering the multimodal signals of search items is beneficial for retrieval effectiveness. Especially in web table retrieval (WTR) experiments, accounting for multimodal properties of tables boosts effectiveness. However, it still remains an open question how the single modalities affect user experience in particular. Previous work analyzed WTR performance in ad-hoc retrieval benchmarks, which neglects interactive search behavior and limits the conclusion about the implications for real-world user environments.
Automated breast tumor ultrasound image segmentation with hybrid UNet and classification using fine-tuned CNN model

IntroductionBreast cancer stands as the second most deadly form of cancer among women worldwide. Early diagnosis and treatment can significantly mitigate mortality rates.PurposeThe study aims to classify breast ultrasound images into benign and malignant tumors. This approach involves segmenting the breast's region of interest (ROI) employing an optimized UNet architecture and classifying the ROIs through an optimized shallow CNN model utilizing an ablation study.MethodSeveral image processing techniques are utilized to improve image quality by removing text, artifacts, and speckle noise, and statistical analysis is done to check the enhanced image quality is satisfactory. With the processed dataset, the segmentation of breast tumor ROI is carried out, optimizing the UNet model through an ablation study where the architectural configuration and hyperparameters are altered. After obtaining the tumor ROIs from the fine-tuned UNet model (RKO-UNet), an optimized CNN model is employed to classify the tumor into benign and malignant classes. To enhance the CNN model's performance, an ablation study is conducted, coupled with the integration of an attention unit. The model's performance is further assessed by classifying breast cancer with mammogram images.ResultThe proposed classification model (RKONet-13) results in an accuracy of 98.41 %. The performance of the proposed model is further compared with five transfer learning models for both pre-segmented and post-segmented datasets. K-fold cross-validation is done to assess the proposed RKONet-13 model's performance stability. Furthermore, the performance of the proposed model is compared with previous literature, where the proposed model outperforms existing methods, demonstrating its effectiveness in breast cancer diagnosis. Lastly, the model demonstrates its robustness for breast cancer classification, delivering an exceptional performance of 96.21 % on a mammogram dataset.ConclusionThe efficacy of this study relies on image pre-processing, segmentation with hybrid attention UNet, and classification with fine-tuned robust CNN model. This comprehensive approach aims to determine an effective technique for detecting breast cancer within ultrasound images.
Strategi dan Manfaat Layanan Open Access Perpustakaan dalam Peningkatan Budaya Literasi

The library acts as a container for various sources of information. However, as technology advances, libraries must be able to offer open services so that the community and students can make the most of existing resources that can awaken a culture of literacy. This study uses a qualitative approach with a literature study paradigm. The findings show that the concept of an open access-based digital library makes it easier for users to increase literacy culture. Open access digital libraries have the potential to help increase access to and use of information and literature, promote literacy, and help expand knowledge and innovation. The strategies that can be implemented in library open access services to improve literacy culture are: 1) facilitating open access through an easy-to-use online access system. 2) guidance and training for students and the community, 3) Collaboration with schools and universities, 4) Marketing and promotion, 5) Provision of quality resources, 6) Literacy activity programs, such as joint reading events, literacy workshops, and writing competitions.
CFANet: Context Feature Fusion and Attention Mechanism Based Network for Small Target Segmentation in Medical Images

Medical image segmentation plays a crucial role in clinical diagnosis, treatment planning, and disease monitoring. The automatic segmentation method based on deep learning has developed rapidly, with segmentation results comparable to clinical experts for large objects, but the segmentation accuracy for small objects is still unsatisfactory. Current segmentation methods based on deep learning find it difficult to extract multiple scale features of medical images, leading to an insufficient detection capability for smaller objects. In this paper, we propose a context feature fusion and attention mechanism based network for small target segmentation in medical images called CFANet. CFANet is based on U-Net structure, including the encoder and the decoder, and incorporates two key modules, context feature fusion (CFF) and effective channel spatial attention (ECSA), in order to improve segmentation performance. The CFF module utilizes contextual information from different scales to enhance the representation of small targets. By fusing multi-scale features, the network captures local and global contextual cues, which are critical for accurate segmentation. The ECSA module further enhances the network’s ability to capture long-range dependencies by incorporating attention mechanisms at the spatial and channel levels, which allows the network to focus on information-rich regions while suppressing irrelevant or noisy features. Extensive experiments are conducted on four challenging medical image datasets, namely ADAM, LUNA16, Thoracic OAR, and WORD. Experimental results show that CFANet outperforms state-of-the-art methods in terms of segmentation accuracy and robustness. The proposed method achieves excellent performance in segmenting small targets in medical images, demonstrating its potential in various clinical applications.
Convolutional neural network model by deep learning and teaching robot in keyboard musical instrument teaching

Keyboard instruments play a significant role in the music teaching process, providing students with an enjoyable musical experience while enhancing their music literacy. This study aims to investigate the current state of keyboard instrument teaching in preschool education, identify existing challenges, and propose potential solutions using the literature review method. In response to identified shortcomings, this paper proposes integrating intelligent technology and subject teaching through the application of teaching robots in keyboard instrument education. Specifically, a Convolutional Neural Network model of Deep Learning is employed for system debugging, enabling the teaching robot to analyze students' images and movements during musical instrument play and deliver targeted teaching. Feedback from students who participated in keyboard instrument teaching with the robot indicates high satisfaction levels. This paper aims to diversify keyboard instruments' teaching mode, introduce the practical application of robots in classroom teaching, and facilitate personalized teaching catering to individual students' aptitudes.
DAWN: Direction-aware Attention Wavelet Network for Image Deraining

Single image deraining aims to remove rain perturbation while restoring the clean background scene from a rain image. However, existing methods tend to produce blurry and over-smooth outputs, lacking some textural details. Wavelet transform can depict the contextual and textural information of an image at different levels, showing impressive capability of learning structural information in the images to avoid artifacts, and thus has been recently explored to consider the inherent overlap of background and rain perturbation in both the pixel domain and the frequency embedding space. However, the existing wavelet-based methods ignore the heterogeneous degradation for different coefficients due to the inherent directional characteristics of rain streaks, leading to inter-frequency conflicts and compromised deraining results. To address this issue, we propose a novel Direction-aware Attention Wavelet Network (DAWN) for rain streaks removal. DAWN has several key distinctions from existing wavelet transform-based methods: 1) introducing the vector decomposition to parameterize the learning procedure, where the rain streaks are derived into the vertical (V) and horizontal (H) components to learn the specific representation; 2) a novel direction-aware attention module (DAM) to fit the projection and transformation parameters to characterize the direction-specific rain components, which helps accurate texture restoration; 3) exploring practical composite constraints on the structure, details, and chrominance aspects for high-quality background restoration. Our proposed DAWN delivers significant performance gains on nine datasets across image deraining and object detection tasks, exceeding the state-of-the-art method MPRNet by 0.88 dB in PSNR on the Test1200 dataset with only 35.5% computation cost.
On-off switch and sign change for a nonlocal Josephson diode in spin-valve Andreev molecules

Andreev molecules consist of two coherently coupled Josephson junctions and permit nonlocal control over supercurrents. By making the barriers magnetic and thus creating a spin valve, we predict that a nonlocal Josephson diode effect occurs that is switchable via the magnetic configuration of the barriers. The diode effect is turned on, off, or changes its sign depending on whether the spin valve is in a parallel, normal, or antiparallel configuration. These results offer a way to exert complete control over a nonlocal Josephson diode effect via the spin degree of freedom.
Research Trends in Cybercrime and Cybersecurity: A Review Based on Web of Science Core Collection Database

Studies on cybercrime and cybersecurity have expanded in both scope and breadth in recent years.This study offers a bibliometric review of research trends in cybercrime and cybersecurity over the past 26 years based on Web of Science core collection database.Specifically, we examine the growth of scholarship and the expanded scope of subject categories and relevant journals.We also analyze the research collaboration network based on authors' affiliated institutions and countries.Finally, we identify major topics within the fields, how each topic relates to -and diverges from -one another, and their evolution over time.Overall, we illustrate the scientific landscape of cybercrime and cybersecurity scholarship by quantitatively synthesizing major components of existing literature.This study offers actionable insights to help researchers identify key research resources, establish/expand collaboration networks, and investigate emerging research topics in this increasingly important domain in criminology and criminal justice.
Exploring the management of multi-sectoral cybersecurity information-sharing networks

This research explores why and how members of the Information Sharing and Analysis Center (ISAC) share cybersecurity information to prevent cyber threats in Taiwan, and factors that encourage or discourage this behavior. The literature on information sharing has traditionally emphasized the motives for doing so and/or the structure of the sharing platform/network, leaving a gap in our understanding on how its formal and informal network rules shape, influence, and collide. By applying Ostrom's (2007) institutional analysis and development framework to Taiwanese Regional- and Sectoral-ISACs to qualitative data from 40 in-depth interviews across central/local governments, private companies, state-owned enterprises, and non-governmental organizations, this paper analyzes the institutional rules-in-use at the operational, collective, and constitutional levels. Our qualitative empirical study aims to induct the various institutional rules-in-use embedded in the ISAC networks, and its findings regarding inter-organizational crisis-management information sharing may have implications for cross-boundary participation in other nations.
Combination of Local Features and Deep Learning to Historical Manuscripts Dating

While paleographers face various challenges in the dating process of historical manuscripts, computer scientists encounter multiple obstacles in automating them. To address this problem, machine learning and deep learning techniques which have proven effective in other domains have been used. This study introduces a system that integrates two primary methods of feature extraction - deep learning and hand-crafted features. The Harris-detector was utilized to extract key-points from the manuscripts, and the K-means algorithm was applied to cluster them. From these clusters, patches of size nxn were extracted. Then, a densnet model was trained on these patches using transfer learning, finally we used majority voting on document patches to determine the date based on document level. The effectiveness of this approach was evaluated on two datasets, MPS and CLaMM, and achieved a CS of 96% and 65% and an MAE of 4.01 years and 20.1 years respectively for both datasets.
Learning Specialized Activation Functions for Physics-Informed Neural Networks

Physics-informed neural networks (PINNs) are known to suffer from optimization difficulty.In this work, we reveal the connection between the optimization difficulty of PINNs and activation functions.Specifically, we show that PINNs exhibit high sensitivity to activation functions when solving PDEs with distinct properties.Existing works usually choose activation functions by inefficient trial-and-error.To avoid the inefficient manual selection and to alleviate the optimization difficulty of PINNs, we introduce adaptive activation functions to search for the optimal function when solving different problems.We compare different adaptive activation functions and discuss their limitations in the context of PINNs.Furthermore, we propose to tailor the idea of learning combinations of candidate activation functions to the PINNs optimization, which has a higher requirement for the smoothness and diversity on learned functions.This is achieved by removing activation functions which cannot provide higher-order derivatives from the candidate set and incorporating elementary functions with different properties according to our prior knowledge about the PDE at hand.We further enhance the search space with adaptive slopes.The proposed adaptive activation function can be used to solve different PDE systems in an interpretable way.Its effectiveness is demonstrated on a series of benchmarks.Code is available at https://github.com/LeapLabTHU/AdaAFforPINNs.
Quantum Communications Feasibility Tests over a UK-Ireland 224 km Undersea Link

The future quantum internet will leverage existing communication infrastructures, including deployed optical fibre networks, to enable novel applications that outperform current information technology. In this scenario, we perform a feasibility study of quantum communications over an industrial 224 km submarine optical fibre link deployed between Southport in the United Kingdom (UK) and Portrane in the Republic of Ireland (IE). With a characterisation of phase drift, polarisation stability and the arrival time of entangled photons, we demonstrate the suitability of the link to enable international UK-IE quantum communications for the first time.
Backpropagation-free training of deep physical neural networks

Recent successes in deep learning for vision and natural language processing are attributed to larger models but come with energy consumption and scalability issues. Current training of digital deep-learning models primarily relies on backpropagation that is unsuitable for physical implementation. In this work, we propose a simple deep neural network architecture augmented by a physical local learning (PhyLL) algorithm, which enables supervised and unsupervised training of deep physical neural networks without detailed knowledge of the nonlinear physical layer's properties. We trained diverse wave-based physical neural networks in vowel and image classification experiments, showcasing the universality of our approach. Our method shows advantages over other hardware-aware training schemes by improving training speed, enhancing robustness, and reducing power consumption by eliminating the need for system modeling and thus decreasing digital computation.
A3Track: Achieving Precise Target Tracking in Aerial Images With Receptive Field Alignment

Tracking arbitrary objects in aerial images presents formidable challenges to existing trackers. Among these challenges, the large scale variation and arbitrary geometry shape of visual targets are pronounced, resulting in two-fold mismatch issues between the feature receptive field and the tracking target. For one, there is a mismatch between the prior receptive field center and arbitrary-shaped targets. For another, the single receptive field mismatches the significantly scale-varied targets in the aerial imagery. To handle these challenges, we propose to Achieve precise Aerial tracking with receptive field Alignment, dubbed A3Track. The proposed A3Track is comprised of two modules: a Receptive Field Alignment (RFA) module and a Pyramid Receptive Field (PRF) module. First of all, we transform and update the receptive field center progressively, which drives the feature sampling location onto the targets’ main body, thus gradually yielding precise feature representation for arbitrary-shaped targets. We term this progressively updating process as the Receptive Field Alignment. Moreover, the PRF module constructs a set of pyramid features for the target, providing a multi-scale receptive field to handle the large scale variation of tracking objects. On four benchmarks, the new tracker A3Track achieves leading performance compared with existing methods and shows consistent improvements over baselines. The project is available at: https://chnleixu.github.io/A3Track-web/.
Retracted: Object-Based Image Retrieval Using the U-Net-Based Neural Network

[This retracts the article DOI: 10.1155/2021/4395646.].
Navigating the Void: Uncovering Research Gaps in the Detection of Data Poisoning Attacks in Federated Learning-Based Big Data Processing: A Systematic Literature Review

This systematic literature review scrutinizes the landscape of research at the intersection of federated learning, big data processing, and data poisoning attacks. Employing a meticulous search strategy across multiple databases, the study unveils a surge in annual scientific production, emphasizing a growing interest in federated learning and related fields. However, a critical research gap becomes evident during the investigation of data poisoning attacks specifically in the context of federated learning when processing big data. The most relevant keywords and a visually compelling word cloud further illuminate the prevailing themes and emphases within the literature, emphasizing the lack of explicit focus on detecting data poisoning attacks. This identified gap presents a significant avenue for future research, offering opportunities to enhance the security and robustness of federated learning systems against adversarial threats in large-scale data scenarios.
Have LLMs Advanced Enough? A Challenging Problem Solving Benchmark For Large Language Models

The performance of large language models (LLMs) on existing reasoning benchmarks has significantly improved over the past years. In response, we present JEEBench, a considerably more challenging benchmark dataset for evaluating the problem solving abilities of LLMs. We curate 515 challenging pre-engineering mathematics, physics and chemistry problems from the highly competitive IIT JEE-Advanced exam. Long-horizon reasoning on top of deep in-domain knowledge is essential for solving problems in this benchmark. Our evaluation on various open-source and proprietary models reveals that the highest performance, even after using techniques like self-consistency, self-refinement and chain-of-thought prompting, is less than 40%. The typical failure modes of GPT-4, the best model, are errors in algebraic manipulation, difficulty in grounding abstract concepts into mathematical equations accurately and failure in retrieving relevant domain-specific concepts. We also observe that by mere prompting, GPT-4 is unable to assess risk introduced by negative marking for incorrect answers. For this, we develop a post-hoc confidence-thresholding method over self-consistency, which enables effective response selection. We hope that our challenging benchmark will guide future re-search in problem-solving using LLMs.