Reviewerly/exHarmony huggingface dataset:
Establishment of exhibition and excursion activities in the Imperial Public Library

The way of development in the field of library and exhibition activities of the Imperial Public Library (hereinafter – IPB) in the period from 1850 to 1917 is presented. The role of IPB employees M. A. Korf, V. V. Stasov, V. I. Sobolshchikov in the development of the first methods for organizing exhibitions in the Library is characterized. The topic of creating the first Library showcases designed by librarian and architect IPB V. I. Sobolshchikov is highlighted. IPB’s participation in Russian and foreign exhibitions is reflected. The history of the first sightseeing tours of the library halls with visits to temporary and permanent exhibitions is highlighted. The «Guide to the Imperial Public Library» created by the staff of the IPB, containing a brief history of the creation of the Library, information about exhibitions and excursions, as well as rules for the use of IPB funds, etc., is considered.
Editorial for the Special Issue “Advances in Machine Learning and Mathematical Modeling for Optimization Problems”

Machine learning and deep learning have made tremendous progress over the last decade and have become the de facto standard across a wide range of image, video, text, and sound processing domains, from object recognition to image generation [...]
Fourier Analysis Meets Runtime Analysis: Precise Runtimes on Plateaus

We propose a new method based on discrete Fourier analysis to analyze the time evolutionary algorithms spend on plateaus. This immediately gives a concise proof of the classic estimate of the expected runtime of the (1 + 1) evolutionary algorithm on the Needle problem due to Garnier, Kallel, and Schoenauer (1999).
VisQualdex: a Comprehensive Guide to Good Data Visualization

The rapid influx of low-quality data visualisations is one of the main challenges in today's communication. Misleading, unreadable, or confusing visualisations spread misinformation, failing to fulfill their purpose. The lack of proper tooling further heightens the problem of the quality assessment process. Therefore, we propose VisQualdex, a systematic set of guidelines isnpired by the Grammar of Graphics for evaluating the quality of data visualisations. To increase the practical impact of VisQualdex, we make these guidelines available in the form of the web server, visqual.info.
A split-federated learning and edge-cloud based efficient and privacy-preserving large-scale item recommendation model

Abstract The combination of federated learning and recommender system aims to solve the privacy problems of recommendation through keeping user data locally at the client device during the model training session. However, most existing approaches rely on user devices to fully compute the deep model designed for the large-scale item recommendation; therefore, imposing high calculation and communication overheads on resource-constrained user devices. Consequently, achieving efficient federated recommendations across ubiquitous mobile devices remains an open research problem. To this end, in this paper we propose an efficient and privacy-preserving federated learning framework which is based on the cloud-edge collaboration for large-scale item recommendation called SpFedRec. In our method, to reduce the computation and communication cost of the federated two-tower model, a split learning approach is applied to migrate the item model from participants’ edge devices to the computationally powerful cloud side and compress item data while transmitting. Meanwhile, to enhance the feature representation, the Squeeze-and-Excitation network mechanism is used on the backbone model to optimize the perception of dominant features. Moreover, because the gradients transmitted contain private information about the user; therefore, we propose a multi-party circular secret-sharing chain based on secret sharing for better privacy protection. Extensive experiments using plausible assumptions on two real-world datasets demonstrate that our proposed method improves the average computation time and communication cost by 23% and 49%, respectively. Furthermore, the proposed model accomplishes comparable performance with other state-of-art federated recommendation models.
PointCLIMB: An Exemplar-Free Point Cloud Class Incremental Benchmark

Point clouds offer comprehensive and precise data regarding the contour and configuration of objects. Employing such geometric and topological 3D information of objects in class incremental learning can aid endless application in 3D-computer vision. Well known 3D-point cloud class incremental learning methods for addressing catastrophic forgetting generally entail the usage of previously encountered data, which can present difficulties in situations where there are restrictions on memory or when there are concerns about the legality of the data. Towards this we pioneer to leverage exemplar free class incremental learning on Point Clouds. In this paper we propose PointCLIMB: An exemplar Free Class Incremental Learning Benchmark. We focus on a pragmatic perspective to consider novel classes for class incremental learning on 3D point clouds. We setup a benchmark for 3D Exemplar free class incremental learning. We investigate performance of various backbones on 3D-Exemplar Free Class Incremental Learning framework. We demonstrate our results on ModelNet40 dataset.
RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment

Generative foundation models are susceptible to implicit biases that can arise from extensive unsupervised training data. Such biases can produce suboptimal samples, skewed outcomes, and unfairness, with potentially serious consequences. Consequently, aligning these models with human ethics and preferences is an essential step toward ensuring their responsible and effective deployment in real-world applications. Prior research has primarily employed Reinforcement Learning from Human Feedback (RLHF) to address this problem, where generative models are fine-tuned with RL algorithms guided by a human-feedback-informed reward model. However, the inefficiencies and instabilities associated with RL algorithms frequently present substantial obstacles to the successful alignment, necessitating the development of a more robust and streamlined approach. To this end, we introduce a new framework, Reward rAnked FineTuning (RAFT), designed to align generative models effectively. Utilizing a reward model and a sufficient number of samples, our approach selects the high-quality samples, discarding those that exhibit undesired behavior, and subsequently enhancing the model by fine-tuning on these filtered samples. Our studies show that RAFT can effectively improve the model performance in both reward learning and other automated metrics in both large language models and diffusion models.
Codes over the non-unital non-commutative ring $E$ using simplicial complexes

There are exactly two non-commutative rings of size $4$, namely, $E = \langle a, b ~\vert ~ 2a = 2b = 0, a^2 = a, b^2 = b, ab= a, ba = b\rangle$ and its opposite ring $F$. These rings are non-unital. A subset $D$ of $E^m$ is defined with the help of simplicial complexes, and utilized to construct linear left-$E$-codes $C^L_D=\{(v\cdot d)_{d\in D} : v\in E^m\}$ and right-$E$-codes $C^R_D=\{(d\cdot v)_{d\in D} : v\in E^m\}$. We study their corresponding binary codes obtained via a Gray map. The weight distributions of all these codes are computed. We achieve a couple of infinite families of optimal codes with respect to the Griesmer bound. Ashikhmin-Barg's condition for minimality of a linear code is satisfied by most of the binary codes we constructed here. All the binary codes in this article are few-weight codes, and self-orthogonal codes under certain mild conditions. This is the first attempt to study the structure of linear codes over non-unital non-commutative rings using simplicial complexes.
Active Cost-aware Labeling of Streaming Data

We study actively labeling streaming data, where an active learner is faced with a stream of data points and must carefully choose which of these points to label via an expensive experiment. Such problems frequently arise in applications such as healthcare and astronomy. We first study a setting when the data's inputs belong to one of $K$ discrete distributions and formalize this problem via a loss that captures the labeling cost and the prediction error. When the labeling cost is $B$, our algorithm, which chooses to label a point if the uncertainty is larger than a time and cost dependent threshold, achieves a worst-case upper bound of $\widetilde{O}(B^{\frac{1}{3}} K^{\frac{1}{3}} T^{\frac{2}{3}})$ on the loss after $T$ rounds. We also provide a more nuanced upper bound which demonstrates that the algorithm can adapt to the arrival pattern, and achieves better performance when the arrival pattern is more favorable. We complement both upper bounds with matching lower bounds. We next study this problem when the inputs belong to a continuous domain and the output of the experiment is a smooth function with bounded RKHS norm. After $T$ rounds in $d$ dimensions, we show that the loss is bounded by $\widetilde{O}(B^{\frac{1}{d+3}} T^{\frac{d+2}{d+3}})$ in an RKHS with a squared exponential kernel and by $\widetilde{O}(B^{\frac{1}{2d+3}} T^{\frac{2d+2}{2d+3}})$ in an RKHS with a Mat\'ern kernel. Our empirical evaluation demonstrates that our method outperforms other baselines in several synthetic experiments and two real experiments in medicine and astronomy.
Interactive Segmentation with Incremental Watershed Cuts

In this article, we propose an incremental method for computing seeded watershed cuts for interactive image segmentation. We propose an algorithm based on the hierarchical image representation called the binary partition tree to compute a seeded watershed cut. We show that this algorithm fits perfectly in an interactive segmentation process by handling user interactions, seed addition or removal, in time linear with respect to the number of affected pixels. Run time comparisons with several state-of-the-art interactive and non-interactive watershed methods show that the proposed method can handle user interactions much faster than previous methods achieving significant speedup from 15 to 90, thus improving the user experience on large images.
Wasserstein PAC-Bayes Learning: Exploiting Optimisation Guarantees to Explain Generalisation

PAC-Bayes learning is an established framework to both assess the generalisation ability of learning algorithms, and design new learning algorithm by exploiting generalisation bounds as training objectives. Most of the exisiting bounds involve a \emph{Kullback-Leibler} (KL) divergence, which fails to capture the geometric properties of the loss function which are often useful in optimisation. We address this by extending the emerging \emph{Wasserstein PAC-Bayes} theory. We develop new PAC-Bayes bounds with Wasserstein distances replacing the usual KL, and demonstrate that sound optimisation guarantees translate to good generalisation abilities. In particular we provide generalisation bounds for the \emph{Bures-Wasserstein SGD} by exploiting its optimisation properties.
Prompt Engineering and Calibration for Zero-Shot Commonsense Reasoning

Prompt engineering and calibration make large language models excel at reasoning tasks, including multiple choice commonsense reasoning. From a practical perspective, we investigate and evaluate these strategies on smaller language models. Through experiments on five commonsense reasoning benchmarks, we find that each strategy favors certain models, but their joint effects are mostly negative.
Spectral Transfer Guided Active Domain Adaptation For Thermal Imagery

The exploitation of visible spectrum datasets has led deep networks to show remarkable success. However, real-world tasks include low-lighting conditions which arise performance bottlenecks for models trained on large-scale RGB image datasets. Thermal IR cameras are more robust against such conditions. Therefore, the usage of thermal imagery in real-world applications can be useful. Unsupervised domain adaptation (UDA) allows transferring information from a source domain to a fully unlabeled target domain. Despite substantial improvements in UDA, the performance gap between UDA and its supervised learning counterpart remains significant. By picking a small number of target samples to annotate and using them in training, active domain adaptation tries to mitigate this gap with minimum annotation expense. We propose an active domain adaptation method in order to examine the efficiency of combining the visible spectrum and thermal imagery modalities. When the domain gap is considerably large as in the visible-to-thermal task, we may conclude that the methods without explicit domain alignment cannot achieve their full potential. To this end, we propose a spectral transfer guided active domain adaptation method to select the most informative unlabeled target samples while aligning source and target domains. We used the large-scale visible spectrum dataset MS-COCO as the source domain and the thermal dataset FLIR ADAS as the target domain to present the results of our method. Extensive experimental evaluation demonstrates that our proposed method outperforms the state-of-the-art active domain adaptation methods. The code and models are publicly available.
Scrumlity: A Quality User Story Framework

Although Scrum presents several advantages, process and final product quality continue to be Scrum’s main challenges. The quality assessment should be an essential activity in the software development process. Several authors have attempted to improve the quality of Scrum projects, changing some aspects of the framework, such as including new quality practices, a quality role, and quality processes. However, the quantification of quality is still a challenge. For that reason, the authors proposed a framework called Scrumlity, which was defined in a previous study. This framework proposes a change to Scrum, including a quality role and some artifacts to evaluate quality through a complete execution of a Sprint. In this study, the authors add a User Story Quality assessment to the framework. The User Story Quality Assessment includes over 250 analyzed User Stories. Results obtained after this experiment indicate the importance of executing a User Story Quality Assessment and that Scrum Team members are willing to accept adding this to the framework.
Web-Based Rental House Smart Finder using Rapid Application Development basis for Evaluation of ISO 205010

Businesses including the promotion of rental properties, utilized the advantage of using technologies to attract more clients. Most of them use a website to easily advertised their properties online. Hence, there were lots of existing system developed to find hotels and apartments online, without considering other affordable rental houses, such as boarding houses and bed spacers. Aside from that, people in Bansud and Bongabong Oriental Mindoro, can’t easily find a rental house with the reason of lack of details regarding the location of each rental house and couldn’t find an affordable rental house immediately. From that, A Web-Based Rental House Smart Finder using Rapid Application Development basis for Evaluation of ISO 205010 was developed to assist the clients in finding affordable variations of rental houses such as boarding house, dormitories, hotels and others. The system can also provide necessary information of specific rental house properties and even gave an alternative option by providing near rental property listings using kilometer distance. The system can also accept reservation request from the clients. On the other hand, the Rapid Application Development was used in building the project for quick continuous development and testing during the project’s software development lifecycle. The RAD strategy allows to investigate a working model as in front of schedule as could be normal considering the present situation if the frameworks meet the necessities required, and propose imperative changes. The ISO/IEC 25010 software quality standards were applied in the evaluation to determine the functional suitability, performance efficiency, usability, reliability, security, maintainability and portability of the system. The overall mean of the ISO/IEC 25010 evaluation result was 3.67, which was regarded as very effective, indicating that the evaluators were satisfied by giving positive feedback. Some of the evaluators suggested to have a mobile application for accessibility.
AN ANALYSIS OF THE THAILAND TEACHERS WAYS IN USING LASAGNA MILKSHAKES SONG TO TEACH VOCABULARY FOR YOUNG LEARNERS

The purpose of the study was to know the way the Thailand teachers uses Lasagna milkshakes song to teach vocabulary for young learners. This study employed descriptive qualitative design by using observation sheet and video recording as the instruments. The sample of the study was the sixth English teacher of Samakkee Islam Wittaya School. The finding of the study was that the English teacher used four ways to teach vocabulary: introducing, modelling, practicing, and applying. Therefore, the researcher concluded that the teacher used introducing, modelling, practicing, and applying the ways when she taught English by using Lasagna Milkshakes Song to students.
THE USE OF KAHOOT APPLICATION ON STUDENTS VOCABULARY MASTERY

The objectivity of this research is to know the effect of using Kahoot application on students vocabulary mastery. The research was conducted to the second grade of Junior High School. Quantitative approach with quasi-experimental method and chose only post-test design. The approach used in this research. There were 38 students in each class for class J as a control group and class K as experimental group. The data were taken from post-test and were analyzed using t-test formula. According to the data calculation, the writer found that the average score of the experimental class is higher than the control class. Based on the statistical results of the t test, it was obtained that the significance value is 0.056 score with the significance value 0.05 (taken from the significance level 5%). It can be concluded that the use of Kahoot application has no effect on students' vocabulary mastery.Keywords: Application, Kahoot, Vocabulary Mastery.
Classification of Sign Language in Real Time Using Convolutional Neural Network

Communication between people is essential for daily life activities. However, humans are created with their own strengths and weaknesses. One of them is the difficulty of communication and interaction for people with hearing and speech impairments. Sign language is a language for people who have difficulty hearing and speaking. However, sign language is not popular in society, and people who have it will have more difficulties. This research aims to classify hand gestures of sign language into letters using a convolutional neural network (CNN). The dataset is obtained from Kaggle, with a total of 34,627 data divided by the ratio of training and testing data of 80:20. From the test results, the letters of the alphabet that can be translated are: A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, S, T, U, V, W, X, Y, and Z. Furthermore, validation accuracy is obtained. In this study, a very high validation accuracy was obtained. The easiest letters to guess are V and N, while the most difficult letters to guess are n, c, j, and z. With different preprocessing, the loss value can be reduced, giving a higher accuracy of 95.4%.
A NOVEL DEEP LEARNING MODEL FOR AUTOMATED CRIME PREDICTION

Smart Policing is the need of the hour as exponentially increase in crime rates is observed across the country, to intelligently deploying forces / rescue resources in preventing comes from happening and to minimize the effect of crimes. A crime is defined as a deliberate act that results in bodily or psychological harm as well as property damage or loss. Law enforcing agencies strive to come up with efficient prevention measures since the amount and variety of criminal acts are growing at an alarming rate. As technology scales up, the criminal activities are absolutely alarming, leaving the conventional criminal identification techniques incompetent. Today, the Police personnel across each country immensely reliant on digital information such as images, emails, video and etc. of crime scenes to accelerate investigation. It is urgent to devise technologies to achieve better system modelling that use machine learning algorithms in order to assist for easy and quick prediction of crimes. This article proposes a model that has convolutional neural networks, Long Short Term Memory (LSTM) and Multiplicative Attention. Simulation results of the proposed idea exhibit impressive prediction when conventional models such as Deep Learning for Real-Time Crime Forecasting (DLRTCF), Linear Regression (LR), Additive Regression (AR) and Decision Stump (DS) respectively. The prediction occurs at an average processing time between 1150ms and 2118ms which is highly significant.
Developing a blended learning curriculum using a digital notebook application for a surgical nursing practicum: The ADDIE model

Background: The COVID-19 pandemic has presented challenges to nursing practice globally. However, utilizing digital notebook applications to support nursing student practices may be an effective tool for surgical nursing practicum. Objective: This study aimed to design a blended learning curriculum for surgical nursing practicum, utilizing a digital notebook platform. Methods: This study applied the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model to create a blended learning curriculum for surgical nursing practicum, employing the OneNote application platform. The study included three experts and 90 third-year nursing students who evaluated the model. The study was conducted from July 2021 to March 2022 at the Faculty of Nursing, Chiang Mai University, Thailand. Data were analyzed using descriptive statistics. Results: The experts rated the model highly (Mean = 4.33, SD = 0.57). The satisfaction level of the students with the blended learning curriculum using a digital notebook application was also high (Mean = 4.88, SD = 0.31). Conclusion: The blended learning curriculum using a digital notebook application for surgical nursing practicum was satisfactory for learners. The results from this research can be applied in online learning or incorporated into nursing clinical practicum curricula during and even post-pandemic. The study results may also serve as an example or a piece of basic information to further develop an advanced online platform for teaching learning, either in Thailand or globally.