Reviewerly/exHarmony huggingface dataset:
An improved physical ZKP for nonogram and nonogram color

Nonogram is a pencil puzzle consisting of a rectangular white grid where the player has to paint some cells black according to given constraints. Chien and Hon (Proceedings of the 5th international conference on fun with algorithms (FUN), 2010) constructed a physical card-based zero-knowledge proof protocol for Nonogram, which enables a prover to physically show that he/she knows a solution of the puzzle without revealing it. However, their protocol requires special tools such as scratch-off cards and a sealing machine, making it impractical to implement in real world. The protocol also has a nonzero soundness error. In this paper, we develop a more practical card-based protocol for Nonogram with perfect soundness that uses only regular paper cards. We also show how to modify our protocol to make it support Nonogram Color, a generalization of Nonogram where the player has to paint the cells with multiple colors.
A Breast Cancer Diagnosis Framework Based on Machine Learning

Breast cancer is becoming the leading cause of mortality among women. One of the most prevalent diseases in women, breast cancer is brought on by a variety of clinical, lifestyle, social, and economic variables. Predictive approaches based on machine learning offer methods for diagnosing breast cancer sooner. It may be found using a variety of analytical methods, including Breast MRI, X-ray, thermography, mammograms, ultrasound, etc. The most prevalent technique for performance evaluation uses accuracy measures, and the Convolutional Neural Network (CNN) is the most accurate and widely used model for breast cancer diagnosis. The Wisconsin Breast Cancer Datasets (WBCD) were used to evaluate the suggested method. Out of a total of 569 samples, 273 samples were chosen for this experiment as the test data, while the other samples were utilized for training and validation. The review's findings showed that the Convolutional Neural Network (CNN) is the most effective and widely used model for finding breast cancer, and that the most often used technique for judging performance is accuracy metrics. The application of deep learning to such a wide range of real-world issues is astounding.
Shared-Specific Feature Learning With Bottleneck Fusion Transformer for Multi-Modal Whole Slide Image Analysis

The fusion of multi-modal medical data is essential to assist medical experts to make treatment decisions for precision medicine. For example, combining the whole slide histopathological images (WSIs) and tabular clinical data can more accurately predict the lymph node metastasis (LNM) of papillary thyroid carcinoma before surgery to avoid unnecessary lymph node resection. However, the huge-sized WSI provides much more high-dimensional information than low-dimensional tabular clinical data, making the information alignment challenging in the multi-modal WSI analysis tasks. This paper presents a novel transformer-guided multi-modal multi-instance learning framework to predict lymph node metastasis from both WSIs and tabular clinical data. We first propose an effective multi-instance grouping scheme, named siamese attention-based feature grouping (SAG), to group high-dimensional WSIs into representative low-dimensional feature embeddings for fusion. We then design a novel bottleneck shared-specific feature transfer module (BSFT) to explore the shared and specific features between different modalities, where a few learnable bottleneck tokens are utilized for knowledge transfer between modalities. Moreover, a modal adaptation and orthogonal projection scheme were incorporated to further encourage BSFT to learn shared and specific features from multi-modal data. Finally, the shared and specific features are dynamically aggregated via an attention mechanism for slide-level prediction. Experimental results on our collected lymph node metastasis dataset demonstrate the efficiency of our proposed components and our framework achieves the best performance with AUC (area under the curve) of 97.34%, outperforming the state-of-the-art methods by over 1.27%.
Improving the Performance of Quantum Cryptography by Using the Encryption of the Error Correction Data

Security of quantum key distribution (QKD) protocols rely solely on quantum physics laws, namely, on the impossibility to distinguish between non-orthogonal quantum states with absolute certainty. Due to this, a potential eavesdropper cannot extract full information from the states stored in their quantum memory after an attack despite knowing all the information disclosed during classical post-processing stages of QKD. Here, we introduce the idea of encrypting classical communication related to error-correction in order to decrease the amount of information available to the eavesdropper and hence improve the performance of quantum key distribution protocols. We analyze the applicability of the method in the context of additional assumptions concerning the eavesdropper's quantum memory coherence time and discuss the similarity of our proposition and the quantum data locking (QDL) technique.
Multi-modal deep learning for automated assembly of periapical radiographs

Periapical radiographs are oftentimes taken in series to display all teeth present in the oral cavity. Our aim was to automatically assemble such a series of periapical radiographs into an anatomically correct status using a multi-modal deep learning model. 4,707 periapical images from 387 patients (on average, 12 images per patient) were used. Radiographs were labeled according to their field of view and the dataset split into a training, validation, and test set, stratified by patient. In addition to the radiograph the timestamp of image generation was extracted and abstracted as follows: A matrix, containing the normalized timestamps of all images of a patient was constructed, representing the order in which images were taken, providing temporal context information to the deep learning model. Using the image data together with the time sequence data a multi-modal deep learning model consisting of two residual convolutional neural networks (ResNet-152 for image data, ResNet-50 for time data) was trained. Additionally, two uni-modal models were trained on image data and time data, respectively. A custom scoring technique was used to measure model performance. Multi-modal deep learning outperformed both uni-modal image-based learning (p<0.001) and time-based learning (p<0.05). The multi-modal deep learning model predicted tooth labels with an F1-score, sensitivity and precision of 0.79, respectively, and an accuracy of 0.99. 37 out of 77 patient datasets were fully correctly assembled by multi-modal learning; in the remaining ones, usually only one image was incorrectly labeled. Multi-modal modeling allowed automated assembly of periapical radiographs and outperformed both uni-modal models. Dental machine learning models can benefit from additional data modalities. Like humans, deep learning models may profit from multiple data sources for decision-making. We demonstrate how multi-modal learning can assist assembling periapical radiographs into an anatomically correct status. Multi-modal learning should be considered for more complex tasks, as clinically a wealth of data is usually available and could be leveraged.
Performance comparison between multi-center histopathology datasets of a weakly-supervised deep learning model for pancreatic ductal adenocarcinoma detection

Abstract Background Pancreatic ductal carcinoma patients have a really poor prognosis given its difficult early detection and the lack of early symptoms. Digital pathology is routinely used by pathologists to diagnose the disease. However, visually inspecting the tissue is a time-consuming task, which slows down the diagnostic procedure. With the advances occurred in the area of artificial intelligence, specifically with deep learning models, and the growing availability of public histology data, clinical decision support systems are being created. However, the generalization capabilities of these systems are not always tested, nor the integration of publicly available datasets for pancreatic ductal carcinoma detection (PDAC). Methods In this work, we explored the performace of two weakly-supervised deep learning models using the two more widely available datasets with pancreatic ductal carcinoma histology images, The Cancer Genome Atlas Project (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). In order to have sufficient training data, the TCGA dataset was integrated with the Genotype-Tissue Expression (GTEx) project dataset, which contains healthy pancreatic samples. Results We showed how the model trained on CPTAC generalizes better than the one trained on the integrated dataset, obtaining an inter-dataset accuracy of 90 . 62% ± 2 . 32 and an outer-dataset accuracy of 92 . 17% when evaluated on TCGA + GTEx. Furthermore, we tested the performance on another dataset formed by tissue micro-arrays, obtaining an accuracy of 98 . 59%. We showed how the features learned in an integrated dataset do not differentiate between the classes, but between the datasets, noticing that a stronger normalization might be needed when creating clinical decision support systems with datasets obtained from different sources. To mitigate this effect, we proposed to train on the three available datasets, improving the detection performance and generalization capabilities of a model trained only on TCGA + GTEx and achieving a similar performance to the model trained only on CPTAC. Conclusions The integration of datasets where both classes are present can mitigate the batch effect present when integrating datasets, improving the classification performance, and accurately detecting PDAC across different datasets.
Plexcitonics: plasmon–exciton coupling for enhancing spectroscopy, optical chirality, and nonlinearity

Plexcitonics is a rapidly developing interdisciplinary field that holds immense potential for the creation of innovative optical technologies and devices.
Stimulating HRM practices through innovative culture: an empirical study on the UAE’s government agencies

Purpose There is an assumption that innovative culture is considered to contribute to improving human resource management (HRM) practices. However, very little is known about this topic as well as innovations within the context of the government sector. Based on that, this study aims to investigate, theoretically and empirically, the impact of innovative culture on HRM practices with the moderating role of knowledge sharing in this association. Design/methodology/approach The data was obtained from government agencies in the United Arab Emirates (UAE). This study used structural equation modelling to test the proposed hypotheses and SPSS to perform a preliminary analysis. Findings The findings show that innovative culture significantly and positively affects HRM practices. Moreover, knowledge sharing plays an important moderating role between innovative culture and HRM. Research limitations/implications The findings have several theoretical and practical implications. The study provides useful insights into the significance of innovative culture and knowledge sharing in stimulating HRM. The findings will help government agencies improve their employees’ capabilities by customising knowledge sharing and creating a culture that supports innovative activities within their agencies. Originality/value Private-sector organisations are concerned about factors that enhance innovative activities as one of the factors of development, improvement and enhancement of competitive advantage. However, in government, especially in the UAE, research into the part played by innovation remains uncommon, particularly into the role of knowledge sharing and innovative culture in enhancing HRM practices. To the best of the researchers’ knowledge, this is one of the pioneer studies to examine the impact of both innovative culture and knowledge sharing on HRM in a government environment context.
Experiences of Technology-Facilitated Abuse Among Sexual and Gender Minorities

Technology-facilitated abuse (TFA) against sexual and gender minority (SGM) adults is a serious though understudied issue. Few studies have examined the types, extent, and perpetrators of TFA against SGMs, and those that have analyzed any of these phenomena have mostly done so with samples of youths. This article offers results of a nationally representative survey on experiences of TFA among a sample of 2,752 U.S. adults aged 18 to 35 years, including 504 SGMs. The prevalence and types of TFA against SGMs were examined using a 27-item inventory capturing six general types of TFA, including surveillance, cyber-interference/communications, reputational harm, monitoring/tracking, fraud, and controlling/limiting access. Respondents could also indicate their relationship to the perpetrator. Results revealed significant differences in the prevalence, types, and perpetrators of TFA against SMGs (compared to non-SGMs), such that SGMs experienced greater levels of TFA victimization, were more likely to be victimized by perpetrators who were not intimate or ex-intimate partners, and were more likely to experience all forms of TFA, except for monitoring/tracking. No significant differences were found for general experiences of TFA victimization between cisgender and non-cisgender individuals or between sexual minority males and sexual minority females. Results thus show that although SGMs and non-SGMs experience similar types of TFA, SGMs experience TFA at higher rates. These findings provide a vital foundation for future work on TFA victimization among SGMs and offer insights for policy and practice, particularly for practitioners and clinicians working with SGMs. Findings indicate a potential need for greater access to health care, victim services, technological support, and legal aid for SGMs due to their increased risk of TFA victimization.
Popularisation of Scientific Knowledge: Event Formats in Libraries of the Novosibirsk Region

The resourcefulness of the library event is examined within the framework of libraries’ capacity to participate in science-society communication. The library event is most often studied by researchers in the context of leisure activities. The concept of “leisure” is multifaceted, it includes such elements as play, recreation, entertainment, self-education, self-development, daily life etc. Leisure activities can be associated, both for the organizers and the visitors, with different motives and goals. The professional press often highlights the stimulation of reading activity, but also the desire to disseminate knowledge in a non-trivial way, the self-presentation of the library, the demonstration of its openness to the public, etc. In this context, traditional and new formats of work for libraries, new trends (e. g. comprehensiveness, eventfulness of events, etc.) are discussed. It is obvious that library event is an actively developing phenomenon, which requires theoretical reflection. The purpose of this article is to analyze the communicative resource of library events on the example of popular science events in the libraries of the Novosibirsk Region. The library event is considered as a level of direct communication, and its purpose is to provide patrons with leisure and intellectual communication. The research is based on the information materials presented on the official websites of public libraries. As a result of the study the following conclusions are obtained: the nature of popular science events is mostly event-based, but only large libraries are involved in wide-scale complex activities. Both science communicators and librarians are involved in the preparation of the events, using different formats. All popular science events held in libraries are in line with the deficit model of communication between science and society.
Markerless human pose estimation for biomedical applications: a survey

Markerless Human Pose Estimation (HPE) proved its potential to support decision making and assessment in many fields of application. HPE is often preferred to traditional marker-based Motion Capture systems due to the ease of setup, portability, and affordable cost of the technology. However, the exploitation of HPE in biomedical applications is still under investigation. This review aims to provide an overview of current biomedical applications of HPE. In this paper, we examine the main features of HPE approaches and discuss whether or not those features are of interest to biomedical applications. We also identify those areas where HPE is already in use and present peculiarities and trends followed by researchers and practitioners. We include here 25 approaches to HPE and more than 40 studies of HPE applied to motor development assessment, neuromuscolar rehabilitation, and gait &amp;amp; posture analysis. We conclude that markerless HPE offers great potential for extending diagnosis and rehabilitation outside hospitals and clinics, toward the paradigm of remote medical care .
Smooth path planning method of agricultural vehicles based on improved Hybrid A*

Smooth path planning is a significant part of agricultural vehicles, and it is the key to ensuring the safe operation of agricultural vehicles, shortening the mileage, and improving operating efficiency. However, subject to the dynamic constraints of the Ackermann structure, the path planning system of agricultural vehicles faces the complex situation of irregular boundaries and obstacles in the field, these all increase the difficulty of path planning research. Aiming at the above problems, a smooth path planning algorithm based on Hybrid A* and Reeds-Shepp curves is proposed. The generated path not only satisfies the incomplete constraints of agricultural vehicles but also can generate optimal smooth paths for different operation scenarios, effectively improving the efficiency of agricultural vehicle operations. Reduced unnecessary mileage. Through the simulation verification of the ROS platform, the algorithm can obtain a smoother and easier-to-follow obstacle avoidance path, which verifies the practicability, reliability, and flexibility of the algorithm.
Measurement of Attitude in Language Learning with AI (MALL:AI)

In the novel context of human and artificial intelligence (AI) connection, people seem to be getting more affiliated to AI nowadays in everyday life, which is also valid for education and language learning (LL) processes. Language learners’ attitudes towards AI mostly play a crucial role in their acceptance of AI initially as they embrace new technologic advances with a positive attitude. The goal of this study was to develop an instrument to measure the attitudes toward AI in LL process which is MALL:AI (attitude scale in LL with AI) of language learners. The participants were 174 university students from different regions of Türkiye as they are dominant using the new generation technologic tools such as digital educational tools or mobile applications based on AI. The MALL:AI scale was found to be valid and reliable with three factors such as communicative, behavioural, and cognitive skills, as a result of the data analysing. Three sub-factors captured different aspects of the items in line with their valence. Few existing scales for measurement the attitudes toward AI in education are different from the current one as the items were, specifically, grounded according to the LL process. The study suggested that language learners were highly satisfied and preferred to use AI in their LL process.
Total life insurance: Logics of anticipatory control and actuarial governance in insurance technology

Calling attention to the growing intersection between the insurance and technology sectors—or ‘insurtech’—this article is intended as a bat signal for the interdisciplinary fields that have spent recent decades studying the explosion of digitization, datafication, smartification, automation, and so on. Many of the dynamics that attract people to researching technology are exemplified, often in exaggerated ways, by emerging applications in insurance, an industry that has broad material effects. Based on in-depth mixed-methods research into insurance technology, I have identified a set of interlocking logics that underly this regime of actuarial governance in society: ubiquitous intermediation, continuous interaction, total integration, hyper-personalization, actuarial discrimination, and dynamic reaction. Together these logics describe how enduring ambitions and existing capabilities are motivating the future of how insurers engage with customers, data, time, and value. This article surveys each logic, laying out a techno-political framework for how to orient critical analysis of developments in insurtech and where to direct future research on this growing industry. Ultimately, my goal is to advance our understanding how insurance—a powerful institution that is fundamental to the operations of modern society—continues to change, and what dynamics and imperatives, whose desires and interests are steering that change. The stuff of insurance is far too important to be left to the insurance industry.
El impacto de la inteligencia artificial en la educación: transformación de la forma de enseñar y de aprender

Artificial intelligence (AI) has demonstrated its potential to transform numerous fields and education is no exception. In today’s digital age, AI has emerged as a powerful tool that is revolutionizing teaching and learning. In this article, we will explore the impact of artificial intelligence in education and how it is transforming the educational landscape at all levels. From personalizing learning to improving feedback and developing interactive resources, we will examine how AI is changing how we acquire knowledge and skills.
Realizing depth measurement and edge detection based on a single metasurface

Abstract How to simultaneously obtain the depth, edge, and other light information of the scene to accurately perceive the physical world is an important issue for imaging systems. However, such tasks usually require bulky optical components and active illumination methods. Here, we design and experimentally validate a single geometric metasurface that can achieve depth measurement or edge detection under incoherent or coherent light respectively. Double helix point source function is utilized, and three verification experiments are carried out, including double-helix beam calibration, 2D object and 3D object detection, respectively. Additionally, two-dimensional edge detection can also be achieved. This compact imaging system can enable new applications in various fields, from machine vision to microscopy.
Advanced Communication in Cyber Physical System Infrastructure, Protocols, and Challenges

In contemporary developments like Cyber Physical System (CPS) and the Internet of Things (IoT), computerized integrated connection is seen as a trend. Some of the unique research issues in CPS include security, confidentiality, business intelligence, participatory monitoring, and smart decision making. A secure infrastructure, reliable protocols, and high-quality solutions are just a few of the other Wireless Sensor Networks (WSN) obstacles that must be overcome. This paper provides a critical survey of advanced communications and sensing in CPS infrastructures, protocols as well as challenges. A CPS framework, including its protocols and implementations, are described in this work. Mobile Sensor Information Agent (MSIA) signifies a software-centric mobile sensor system. It operates as a plug-in with the CPS framework and scalability mobile apps. With the MSIA, data from numerous external sensors may be collected and sent to the cloud as needed. Integration with the real environment is essential to CPS's stability and security as well as its dependability and resilience. Emerging Software-Defined Networking (SDN) technologies may be coupled with CPS architecture as a communications network to build this system. Thereby, it is feasible to enhance the performance of the system utilizing an SDN-based CPS architecture