Reviewerly/exHarmony huggingface dataset:
GPC: Generative and General Pathology Image Classifier

Deep learning has been increasingly incorporated into various computational pathology applications to improve its efficiency, accuracy, and robustness. Although successful, most previous approaches for image classification have crucial drawbacks. There exist numerous tasks in pathology, but one needs to build a model per task, i.e., a task-specific model, thereby increasing the number of models, training resources, and cost. Moreover, transferring arbitrary task-specific model to another task is still a challenging problem. Herein, we propose a task-agnostic generative and general pathology image classifier, so called GPC, that aims at learning from diverse kinds of pathology images and conducting numerous classification tasks in a unified model. GPC, equipped with a convolutional neural network and a Transformer-based language model, maps pathology images into a high-dimensional feature space and generates pertinent class labels as texts via the image-to-text classification mechanism. We evaluate GPC on six datasets for four different pathology image classification tasks. Experimental results show that GPC holds considerable potential for developing an effective and efficient universal model for pathology image analysis.
Federated Model Aggregation via Self-supervised Priors for Highly Imbalanced Medical Image Classification

In the medical field, federated learning commonly deals with highly imbalanced datasets, including skin lesions and gastrointestinal images. Existing federated methods under highly imbalanced datasets primarily focus on optimizing a global model without incorporating the intra-class variations that can arise in medical imaging due to different populations, findings, and scanners. In this paper, we study the inter-client intra-class variations with publicly available self-supervised auxiliary networks. Specifically, we find that employing a shared auxiliary pre-trained model, like MoCo-V2, locally on every client yields consistent divergence measurements. Based on these findings, we derive a dynamic balanced model aggregation via self-supervised priors (MAS) to guide the global model optimization. Fed-MAS can be utilized with different local learning methods for effective model aggregation toward a highly robust and unbiased global model. Our code is available at https://github.com/xmed-lab/Fed-MAS .
Valori modali dell’imperfetto in italiano e in bulgaro. Una rassegna contrastiva

Besides its temporal semantics, the imperfect tense can express a wide range of modal meanings, which occur most typically in spoken varieties of language. The paper contrastively explores the modal uses of the imperfect tense in Italian and Bulgarian, a Romance and Slavic language respectively, and shows to what extent they are similar or diverge in the two languages. Although the basic notional configuration of the imperfect tense in Italian and Bulgarian seems to be exactly the same, occurrences of modal meanings are less frequent in Bulgarian than in Italian. Possible explanatory hypotheses suggest that the peculiarities of the tense system (dedicated future-in-the-past form; specific narrative mood), as well as the different syntactic rules governing the sequence of tenses in related sentences, put major constraints on the development of modal meanings of the imperfect tense in Bulgarian.
Methodology for promoting online educational services using Internet marketing tools

The article is devoted to the research and development of methods for promoting online educational services using Internet marketing tools. The modern features of the promotion of online educational services are clarified (using the example of a driving school). The comparison of various Internet marketing tools for the promotion of online educational services of a commercial online driving school is carried out, which indicates the high efficiency of such tools as a website, mobile application, social networks, SEO and SERM optimization, contextual advertising, webinars and email newsletters. The conceptual algorithm and structure of the stages of promotion of online educational services of a driving school are substantiated. Based on the results of the study, it is concluded that the presented methodology allows taking into account multiple factors–features when preparing a strategy for promoting online educational services using Internet marketing tools. In the activities of a commercial driving school, this technique can be reproduced with reference to the previously presented Internet marketing tools and indicators of their application, as well as specific services. Focusing on the use of a bunch of tools allows you to achieve a synergistic effect and get a high result.
Artificial Intelligence and the Future for Charities

This article delves into the transformative potential of Artificial Intelligence (AI) in reshaping the landscape of charitable organizations. As technological advancements continue to accelerate, charities are presented with novel opportunities to enhance their operations, outreach, and impact. The paper explores how AI-driven solutions can streamline administrative tasks, optimize resource allocation, and personalize donor engagement, thereby allowing charities to operate more efficiently and effectively. By examining the intersection of AI and charitable work, this paper sheds light on the prospects and responsibilities tied to leveraging AI for a brighter future for charities and the causes they champion.
Feedback as a key factor in business development

This article explores how to effectively foster a feedback culture within an organization. It outlines key principles for providing constructive, motivating, and impactful feedback, including timeliness, an unbiased approach, the use of «I–messages», a positive orientation, openness, and dialogue. Additionally, the article delves into methods of integrating feedback into the organization’s culture, such as one–on–one meetings, feedback cycles, performance evaluations, and cultivating a growth mindset. Furthermore, ethical principles such as confidentiality, objectivity, honesty, and an individualized approach are discussed. By adhering to these approaches and principles and implementing a feedback system within the organization’s culture, companies can create an atmosphere of continuous improvement, trust, and collaboration. A feedback culture rooted in these principles contributes to both the personal growth of employees and motivates them to align their actions with the organization’s goals, ultimately enhancing overall business effectiveness.
Enhancing robot evolution through Lamarckian principles

Evolutionary robot systems offer two principal advantages: an advanced way of developing robots through evolutionary optimization and a special research platform to conduct what-if experiments regarding questions about evolution. Our study sits at the intersection of these. We investigate the question "What if the 18th-century biologist Lamarck was not completely wrong and individual traits learned during a lifetime could be passed on to offspring through inheritance?" We research this issue through simulations with an evolutionary robot framework where morphologies (bodies) and controllers (brains) of robots are evolvable and robots also can improve their controllers through learning during their lifetime. Within this framework, we compare a Lamarckian system, where learned bits of the brain are inheritable, with a Darwinian system, where they are not. Analyzing simulations based on these systems, we obtain new insights about Lamarckian evolution dynamics and the interaction between evolution and learning. Specifically, we show that Lamarckism amplifies the emergence of 'morphological intelligence', the ability of a given robot body to acquire a good brain by learning, and identify the source of this success: newborn robots have a higher fitness because their inherited brains match their bodies better than those in a Darwinian system.
An Approach for CT Image Conversion Using Filtering Based on Quaternion Mathematics

Computed tomography (CT) images are widely used in medical examination applications. They are constructed from the raw data using different kernels. However, due to the technology used, the software finds problems with image segmentation in the presence of noise or blurred edges. In this paper, we use quaternion mathematics for image de-noising by applying the bilateral filtering algorithm to reconstruct a pure soft image from the images reconstructed with sharp kernels, without using the raw data. Our output is compared to reconstructed images with different denoising techniques, such as non-local means and wavelet denoising. The reconstructed images using other techniques are assessed using structural index similarity (SSIM) and peak signal-to-noise ratio (PSNR). Results show high similarity by comparing the outputs of sharp kernels to that of the B30f. Furthermore, the results show that the proposed work can efficiently increase the similarity between the images reconstructed with hard and soft kernels.
Image Analysis System for Augmented Reality Games

This article presents the idea of a vision system allowing the analysis of images from multiple cameras for the needs of augmented reality systems. The presented solution allows for the effective determination of the positions and movement paths of people moving in urban space and their activities. The article presents the key modules of multi-view image analysis, including the gamer detection and tracking (MOT) subsystem and the user activity detection subsystem. The discussed subsystems have been tested in order to determine the effectiveness of the proposed algorithms for determining the movement path and recognizing activity.
A Non-deep Approach to Classifying Movie Genres Based on Multimodal Data

Multimodal data processing has recently become popular due to technological advances and easier access to real video, audio, images, or text data. This data type is often processed using deep neural networks associated with high time and computational complexity. The present work addresses the problem of classifying a multimodal MM-IMDb dataset, representing the problem of recognizing a movie genre based on a poster and a brief description of the plot. For experiments, 20 binary subsets were separated, from which features were then extracted. Features from the text were obtained using the tf-idf method, while the posters were reduced to a single color. Computer experiments on the resulting tabular data were conducted separately on both modalities, as in the concatenated feature space. The results confirmed that classical approaches to feature extraction could allow satisfactory quality classification of multimodal data to be obtained even when using relatively simple pattern recognition algorithms.
A New Assessment of Convolutional Neural Networks for Texture Directionality Detection

Image texture analysis is ubiquitous as it finds applications in many scientific fields of interest, including biomedical and material science. The detection of meaningful texture properties such as directionality remains a challenging task, due to the complexity of texture. We build upon our past work on the design of convolutional neural networks (CNNs) for texture directionality detection. The CNNs are trained on a library of synthetic textures with known directionality and varying perturbation levels. The present effort focuses on enhancing the training data through a new perturbation procedure and a more diverse set of synthetic textures. We study the performance of new CNN architectures, such as grouped CNNs, on the enhanced synthetic texture library. The results yield novel insight into CNN-based texture directionality detection. Shallow and grouped CNNs show better performance than deep CNNs, unlike previously. We discuss this performance shift and its implications, and suggest possible future work directions.
Using Instagram in Teaching English to the Students of SMAN 6 Pinrang

This study aims to find out the effectiveness of using Instagram as an English teaching medium. This type of research is experimental research. The design of this study is a pre-experimental design, which uses a one-group pretest-posttest design by only using the pretest and posttest as sources of data without a control group. Between the pretest and posttest, the researcher gave a treatment. The sample in this study was X2 class students, totaling 32 people. In collecting data, the researcher used pretest-posttest, questionnaire, and observation. The results of this research show that the use of Instagram as an English learning medium is effectively able to improve students' abilities in narrative text writing. This is based on the results of data analysis the mean value of students' pretest results is 53.72 and the standard deviation value is 5.572, while on the posttest, students' scores have increased, where the mean is 80.94 and the standard deviation value is 8.140. As for the paired T-test results obtained, the value of Sig. (2-tailed) is less than 0.05 (p =.000).
Hollow n-grams Vectorizer for Natural Language Processing Problems

Words don’t come easy, which fosters the use of generative artificial intelligence models in ongoing popularity of widely available applications such as ChatGPT. The result is an even greater flood of online content that takes time to process. It is where Natural Language Processing tools for classification come in handy. Distinguishing fake news, event types, and other tasks can help process everyday information. In practice, such systems must work on data streams where fast prediction is needed. To achieve it, methods not based on neural networks can be used. Instead, they require feature extractions from the text to convert it to the model input. The primary methods used for this purpose are bag-of-words and n-grams, which allow converting the corpus of texts into a numerical format. This paper proposes a new strategy for creating n-grams – Hollow n-grams – which can be used to create classifiers ensembles with a higher generalization ability than models based on regular n-grams only.
TO ASSESS THE LEVEL OF SATISFACTION OF BUYERS IN THE REAL ESTATE SECTOR

The interpretation of customer satisfaction is a complex task due to its subjective nature, which is contingent upon the individual preferences and experiences of each customer. The Real Estate industry presents a heightened level of complexity as there exist numerous factors that contribute to the determination of customer satisfaction. Customer satisfaction is closely linked to the perception of the customer, whereby meeting the customer’s perceived expectations results in their satisfaction. This research is on the Indian real estate business. This study was primarily conducted to assess the level of satisfaction of buyers in the real estate sector. The principal goal of this study was to examine the satisfaction level of buyers while buying a residential property. The paper is helpful for real estate agents. It helps real estate agents understand what buyers want and how they think.
ANALYZING SURVEY DATA - USING CLASSIFICATION RESULTS FOR BETTER DATA UNDERSTANDING

Appears in: ICERI2023 Proceedings Publication year: 2023Pages: 9145-9148ISBN: 978-84-09-55942-8ISSN: 2340-1095doi: 10.21125/iceri.2023.2346Conference name: 16th annual International Conference of Education, Research and InnovationDates: 13-15 November, 2023Location: Seville, Spain
THE FUTURE OF EDUCATION: A QUANTITATIVE EXPERIMENT TO EXPLORE THE POTENTIAL OF AN AI-BASED LEARNING ASSISTANT (AIEDN) WITHIN GRAMMAR AND SECONDARY SCHOOLS IN GERMANY

THE FUTURE OF EDUCATION: A QUANTITATIVE EXPERIMENT TO EXPLORE THE POTENTIAL OF AN AI-BASED LEARNING ASSISTANT (AIEDN) WITHIN GRAMMAR AND SECONDARY SCHOOLS IN GERMANY
Online confidence interval estimation for federated heterogeneous optimization

From a statistical viewpoint, it is essential to perform statistical inference in federated learning to understand the underlying data distribution. Due to the heterogeneity in the number of local iterations and in local datasets, traditional statistical inference methods are not competent in federated learning. This paper studies how to construct confidence intervals for federated heterogeneous optimization problems. We introduce the rescaled federated averaging estimate and prove the consistency of the estimate. Focusing on confidence interval estimation, we establish the asymptotic normality of the parameter estimate produced by our algorithm and show that the asymptotic covariance is inversely proportional to the client participation rate. We propose an online confidence interval estimation method called separated plug-in via rescaled federated averaging. This method can construct valid confidence intervals online when the number of local iterations is different across clients. Since there are variations in clients and local datasets, the heterogeneity in the number of local iterations is common. Consequently, confidence interval estimation for federated heterogeneous optimization problems is important.
THE LEARNING OF SOFT SKILLS IN HIGHER EDUCATION - A STUDENT PERSPECTIVE ON SKILLS’ LEARNING AS A PART OF THE VAKEN PROCESS

THE LEARNING OF SOFT SKILLS IN HIGHER EDUCATION - A STUDENT PERSPECTIVE ON SKILLS’ LEARNING AS A PART OF THE VAKEN PROCESS
Signature for Objects: Formalizing How to Authenticate Physical Data and More

While the integrity of digital data can be ensured via digital signatures, ensuring the integrity of physical data, i.e., objects, is a more challenging task. For example, constructing a digital signature on data extracted from an object does not necessarily guarantee that an adversary has not tampered with the object or replaced this with a cleverly constructed counterfeit. This paper proposes a new concept called signatures for objects to guarantee the integrity of objects cryptographically. We first need to consider a mechanism that allows us to mathematically treat objects which exist in the physical world. Thus, we define a model called an object setting in which we define physical actions, such as a way to extract data from objects and test whether two objects are identical. Modeling these physical actions via oracle access enables us to naturally enhance probabilistic polynomial-time algorithms to algorithms having access to objects—we denote these physically enhanced algorithms (PEAs). Based on the above formalization, we introduce two security definitions for adversaries modeled as PEAs. The first is unforgeability, which is the natural extension of EUF-CMA security, meaning that any adversary cannot forge a signature for objects. The second is confidentiality, which is a privacy notion, meaning that signatures do not leak any information about signed objects. With these definitions in hand, we show two generic constructions: one satisfies unforgeability by signing extracted data from objects; the other satisfies unforgeability and confidentiality by combining a digital signature with obfuscation.
Witness-Authenticated Key Exchange, Revisited: Extensions to Groups, Improved Models, Simpler Constructions

We study witness-authenticated key exchange (WAKE), in which parties authenticate through knowledge of a witness to any NP statement. WAKE achieves generic authenticated key exchange in the absence of trusted parties; WAKE is most suitable when a certificate authority is either unavailable or undesirable, as in highly decentralized networks. In practice WAKE approximates witness encryption, its elusive non-interactive analogue, at the cost of minimal interaction. This work is the first to propose, model and build witness-authenticated key exchange amongst groups of more than two parties, as well as the first to provide practical and provably secure constructions in the two-party case for general NP statements. Specifically our contributions are: Additionally, we study the specialized two-party case and provide a critique of prior work on this topic (Ngo et al., Financial Crypto ’21) by pinpointing nontrivial weaknesses in the model, constructions and security proofs seen therein. We rectify those limitations with this work, significantly diverging in our techniques, design and approach.
Player-Replaceability and Forensic Support Are Two Sides of the Same (Crypto) Coin

Player-replaceability is a property of a blockchain protocol that ensures every step of the protocol is executed by an unpredictably random (small) set of players; this guarantees security against a fully adaptive adversary and is a crucial property in building permissionless blockchains. Forensic Support is a property of a blockchain protocol that provides the ability, with cryptographic integrity, to identify malicious parties when there is a safety violation; this provides the ability to enforce punishments for adversarial behavior and is a crucial component of incentive mechanism designs for blockchains. Player-replaceability and strong forensic support are both desirable properties, yet, none of the existing blockchain protocols have both properties. Our main result is to construct a new BFT protocol that is player-replaceable and has maximum forensic support. The key invention is the notion of a “transition certificate”, without which we show that natural adaptations of extant BFT and longest chain protocols do not lead to the desired goal of simultaneous player-replaceability and forensic support. (The full version of paper is available in https://eprint.iacr.org/2022/1513 .)
Control strategy for a dual-axis sun tracker based on a radiometric cube to maximize the power output of the PV system

The use of solar energy as an energy source is essential to reduce greenhouse gas emissions. In a photovoltaic system, the use of a sun tracker can produce up to 40% more energy than a fixed system. However, it is important, especially in temperate climates, to have an effective control strategy that can adapt the movement of the solar tracker for all weather conditions. This work presents a new control strategy for a dual-axis sun tracker based on a radiometric cube with four photodiode sensors on its East, West, South and Zenith faces. The optimal direction that maximises the irradiance received by the sun tracker can be determined through the mathematical expression of the irradiances on the four faces of the cube, considering an isotropic model for the diffuse luminance of the sky. This new control strategy adjusts the movement of the solar tracker to an optimal position in all weather conditions. The proposed control strategy provides a 40.5% gain in energy output on a cloudy day compared to the standard chronometric sun tracking strategy. For periods of clear and highly variable sky, the difference between the two strategies is only 0.15% and 1%.