Reviewerly/exHarmony huggingface dataset:
Evaluation of Membership Inference Attack Against Federated Learning With Differential Privacy on Edge Devices

Federated learning (FL) is a distributed deep learning technique in which training parties are split into a server and clients. Each client trains a model with its own training data and only sends the model to the server. Here, the clients do not share training data with each other, including private information. While this may be beneficial for data privacy, there is the growing threat of membership inference attacks (MIAs), which reveal private information in the training data from the model. FL with differential privacy (DP) is one of the countermeasures against MIAs. In this paper, we evaluated the relationship between the robustness against an MIA and the amount of noise added by DP in FL with varying calculation precisions, i.e., IEEE754 32-bit floating-point, IEEE754 16-bit (half-precision) floating-point, and bfloat16. The latter two conditions were implemented assuming that the training process of the client is performed on edge devices with limited computational resources. Our experimental results showed that there was a trade-off between defense and model accuracy. Furthermore, we found that the half-precision floating-point calculations do not cause significant degradation in the model accuracy even when used with DP.
Ensemble 3D-Gacha-AR Toy: A Toy Set of Augmented Reality on 3D-Printed Gachapon

We introduce a novel plaything that offers players distinct augmented reality (AR) interactive experiences when scanning an assemblage of figurines, in the context of playing with a set of characters. The concept of gachapon motivate the players need to acquire the complete collection in order to access the undisclosed narratives. Players can engage in enjoyable interactions by simply scanning by their smart devices to the 3D fabricated models. This approach enhances the intrinsic value of the collectible figurine toy. Furthermore, the physical models can be reproduced through a 3D printer, affording players the opportunity to produce replicas in elsewhere, while also permitting customization of size and desired decorative coloration, according to individual preferences.
Research on Action Proposal Generation Based on Contexts for Medical Operation Videos

Action proposal generation is to locate the temporal boundaries of action segments in a video. In this paper, LGCT network is proposed to solve the problems of dense action segments, strong correlation of action and fuzzy boundary in medical operation video. LGCT is based on the DETR with two improvements: (1) To address the issue of low proposal recall accuracy caused by weak contextual interactivity in the temporal dimension of video features, LGBlock (Local and Global Block) is introduced in the encoding position of the DETR to establish the association of the temporal context; (2) To address the issue of incomplete action proposals caused by blurred action boundaries, a complete prediction head based on background context is proposed, which introduces adjacent background information of proposals to predict complete scores to stabilize the pipeline of proposal generation. This paper conducts experimental exploration on the THUMOS14, ActivityNet1.3 and Medical-74 datasets. The entire model can be trained end-to-end and the generated proposals do not require any post-processing operations. The test metrics AR@500 for proposals reaches 62.29% and 75.31% in THUMOS14 and Medical-74 respectively, and AR@1 reaches 33.13% in ActivityNet1.3. Meanwhile, after introducing post-processing operations, AR@500 reaches 62.96% and 75.40% in THUMOS14 and Medical-74, and AR@1 reaches 33.21% in ActivityNet1.3.
Robust small area estimation for unit level model with density power divergence

Unit level model is one of the classical models in small area estimation, which plays an important role with unit information data. Empirical Bayesian(EB) estimation, as the optimal estimation under normal assumption, is the most commonly used parameter estimation method in unit level model. However, this kind of method is sensitive to outliers, and EB estimation will lead to considerable inflation of the mean square error(MSE) when there are outliers in the responses y ij . In this study, we propose a robust estimation method for the unit-level model with outliers based on the minimum density power divergence. Firstly, by introducing the minimum density power divergence function, we give the estimation equation of the parameters of the unit level model, and obtain the asymptotic distribution of the robust parameters. Considering the existence of tuning parameters in the robust estimator, an optimal parameter selection algorithm is proposed. Secondly, empirical Bayesian predictors of unit and area mean in finite populations are given, and the MSE of the proposed robust estimators of small area means is given by bootstrap method. Finally, we verify the superior performance of our proposed method through simulation data and real data. Through comparison, our proposed method can can solve the outlier situation better.
An approach for assisting diagnosis of Alzheimer's disease based on natural language processing

Introduction Alzheimer's Disease (AD) is a common dementia which affects linguistic function, memory, cognitive and visual spatial ability of the patients. Language is proved to have the relationship with AD, so the time that AD can be diagnosed in a doctor's office is coming. Methods In this study, the Pitt datasets are used to detect AD which is balanced in gender and age. First bidirectional Encoder Representation from Transformers (Bert) pretrained model is used to acquire the word vector. Then two channels are constructed in the feature extraction layer, which is, convolutional neural networks (CNN) and long and short time memory (LSTM) model to extract local features and global features respectively. The local features and global features are concatenated to generate feature vectors containing rich semantics, which are sent to softmax classifier for classification. Results Finally, we obtain a best accuracy of 89.3% which is comparative compared to other studies. In the meanwhile, we do the comparative experiments with TextCNN and LSTM model respectively, the combined model manifests best and TextCNN takes the second place. Discussion The performance illustrates the feasibility to predict AD effectively by using acoustic and linguistic datasets.
PELATIHAN DAN PENDAMPINGAN DASAR PEMROGRAMAN MATLAB (MATRIX LABORATORY) DI SMK PGRI BANJARBARU

SMK PGRI BANJARBARU is one of the private schools located on the Loktabat rubber plantation road, Banjarbaru City which has quite good resources, both teachers, staff and students. At this time, the knowledge regarding mathematical programming for teachers and students at SMK PGRI BANJARBARU is only limited to learning knowledge, without any skills, for this reason it is necessary to provide training and mentoring in mathematical programming, one of which is using MATLAB software, which must make programming material an open material. given to the students. Programming languages taught. teachers to students now are HTML and JavaScript. Then, the school switched to Pascal recently, but there are no reports regarding student performance. Based on information and requests from the Principal, Mr. Setiawan S.E, during a field study visit in February 2023, asked the proposing team to provide Matlab programming training for students and teachers at his school. A preliminary survey has also been carried out by the implementing team at this school. This school has provided electricity facilities and has a number of computers. This really supports the implementation of the activities in question. This is what underlies us to carry out community service with the title Basic training and assistance for programming matlab teachers at SMK PGRI BANJARBARU. The results of knowledge regarding Matlab training and assistance increased as seen by the students' average score of 92.5
Enjeux discursifs de la forme « ok émoji(s) » dans les tweets figuratifs

This paper is part of the ok project led by the DisCo research group. In this study, the tools of Digital Discourse Analysis are used to examine the linguistic challenges of the simultaneous use on Twitter of ok as a syntactic stuctutre and the emoji(s) as an iconic entity. The aim of the analysis in this article is to evaluate the sense-making that occurs through the conjoint presence of these two distinct discursive parameters within the same tweet.
Biphoton routing in few-emitter chiral waveguide quantum electrodynamics ladders

We study the problem of two-photon routing in waveguide QED ladders where a few two-level quantum emitters (QEs) are simultaneously coupled with two chiral waveguides. We analyze the routing probability in two regimes, namely, under a purely plane wave approximation (scattering case) and in the presence of photon-photon bound state formation. Within the scattering case, we examine the two-photon routing in the presence of up to five QEs, considering two possibilities separately: ideal-symmetric coupling and the critical coupling scenario. We examine the photon routing up to the two QEs for the bound state situation and compare the photon redirection efficiency with the corresponding scattering case. Our findings show the potential of utilizing chiral light-matter interactions in multi-photon and multi-emitter-based quantum networking protocols where interlinking among spatially distant nodes is required.
Pelatihan Penulisan Artikel Ilmiah dan Manajemen Referensi bagi Dosen Institut Ilmu Kesehatan Pelamonia

In Indonesia, one of the benchmarks for lecturer performance can be noticed from their publications. Therefore, it is important for lecturers to continue improving their knowledge and skills in writing a scientific article such as reference management skills. Prior to the service activities, an analysis of problems related to writing a scientific article was carried out. The service activity began with a pre-test to measure the extent of the participants' understanding in writing a scientific article and reference management. Furthermore, two trainings namely scientific article writing training and reference management training were presented by the speakers. After the training, a discussion session was held between the participants and team. The activity ends with a post-test to measure the extent to which the participants' knowledge and skills have increased in scientific article writing and reference management. Reflection on the implementation of this program is carried out by team leader and team to review all the benefits and weakness of this program and its implementation. Partners in this service are Lecturers of Institut Ilmu Kesehatan Pelamonia with 46 participants. The results achieved in this service are: before the presentation of the material, 75% of the participants still lacked in understanding regarding the writing a scientific article in Reputable International Journals. After the presentation of the material, 93.5% of the participants had understood the writing a scientific article in Reputable International Journals.
Conclusion: an insight into the recent developments and future trends in XAI

The reasonableness and logical AI have started expanding considerably by both examination of local area and industry. Clarifying the capacity of artificial intelligence (AI) gives numerous subjects of dynamic examination by the need of passing on well-being and trust to clients in the "how" and "why" of computerized decision-production in various applications like independent driving, clinical determination, or banking and money. In this chapter, we present a chronicled point of view of explainable AI (XAI). The calculations utilized in AI can be separated into white-box and discovery AI [machine learning (ML)] calculations. White-box models are ML models that give results that are reasonable to specialists in the space. Black-box models, then again, are amazingly difficult to clarify and can barely be seen even by space experts. XAI calculations are considered to follow the three standards of straightforwardness, interpretability, and logic. We examine how reasonableness was mostly imagined before, how it is perceived in the present, and how it very well may be perceived later on. We close the chapter by proposing measures for clarifications that we accept will assume an essential part in the advancement of human-justifiable logical frameworks.
ОСОБЛИВОСТІ ІНСТИТУЦІОНАЛІЗАЦІЇ ПРОФЕСІЙНОГО РОЗВИТКУ ВИКЛАДАЧА В СИСТЕМІ БЕЗПЕРЕРВНОЇ ПЕДАГОГІЧНОЇ ОСВІТИ (ПОЧАТОК ХХІ СТОЛІТТЯ)

The article analyzes modern problems of distance teaching of physics usinginformation and communication technologies during distance learning.Some aspects of the use of information resources, software tools duringonline education in both higher and secondary schools are considered.Since the task of the education system is to provide high-quality knowledge,the acquisition of competencies by students, the information and materialand technical support must meet special requirements. The work examinesthe availability of Ukrainian resources in the information space that can beused in the educational process, in particular, such as literature, videomaterials of physical phenomena, experiments, the availability of onlinelaboratories for higher education institutions, as well as the possibility ofusing these resources by students and pupils. This analysis will make itpossible to identify problematic issues that arise during online learning, toconsider and develop measures to improve the use of information andcommunication technologies in the educational process, considering thetechnical equipment of students and school pupils, to develop didactic,informational, software tools and materials, to fill the information space withaccessible means of online learning. Possible ways of improvingeducational work in the direction of modern requirements of educationtransformation, when a student or a student of higher education becomes asubject of the educational process, understands the need for constant selfdevelopmentand self-realization in the profession and life in a democraticsociety, are explored, an analysis of available Internet resources,information and communication tools, and material and technical supportthat allow distance learning to be carried out online is discussed.
THE INFLUENCE OF DISTANCE EDUCATION ON THE DEVELOPMENT OF PROFESSIONAL COMPETENCES OF FUTURE CHEMISTRY TEACHERS

The article highlights the influence of distance education on thedevelopment of professional competences of future chemistryteachers. It was found that the development of professionalcompetences of future chemistry teachers at the Department ofChemistry and Chemistry Education of Bohdan KhmelnytskyMelitopol State Pedagogical University is realized in the contextof studying such academic disciplines as “General and InorganicChemistry”, “Inorganic synthesis”, “Organic Chemistry”, “Physicaland Colloid Chemistry”, “Organic synthesis”, “Nanochemistry”,“Biochemistry”, etc. Studying of academic disciplines in thedistance format is realized through the system of the Centre ofDistance Educational Technologies at Bohdan KhmelnytskyMelitopol State Pedagogical University.It was found that the use of virtual chemistry laboratories whilestudying chemistry disciplines in the conditions of distancelearning is especially relevant when the educational process isrealized online with the increased amount of self-study learningmaterial, lack of necessary equipment, reagents and possibilitiesfor teachers to carry out and demonstrate laboratory experiments.Carrying out a chemistry experiment as a specific method oflearning in the course of studying all components of theeducational program is an important condition for the formationand development of the professional competences of the futurechemistry teacher.It was determined that during distance learning at university,where a professional training of future chemistry teachers isrealized, it is necessary to develop courses in the field ofchemistry based on the blended scheme, which includesinformative computer materials, such as lectures, tests, webinars,etc. It was found that the high level of professional competencesof future chemistry teachers allows us to draw a conclusion ontheir work by specialty which guarantees successful work of achemistry teacher.
With or Without Emojis? A Conceptual Paper on Food Vendor Instagram Caption towards Customer Engagement

This conceptual paper investigates the impact of emojis on customer engagement in food vendor Instagram captions. Emojis have gained traction in online communication, but their influence on customer engagement in the context of food vendors remains unexplored. Drawing on concepts of customer engagement, social media marketing and semiotics, the research explores how emojis capture attention, convey emotions, enhance communication, and foster customer engagement. Through qualitative interviews with food vendors and customers in Kulim, this research aims to identify patterns and themes related to the impact of emojis on customer engagement. The findings of this research are expected to contribute to the growing body of literature on social media marketing and stimulate further investigation into the utilization of emojis for enhancing customer engagement on social media, enabling food vendors to leverage emojis effectively in their Instagram captions as an effective strategy to cultivate stronger engagement.
An overview of past and present progressions in XAI

Deep learning has gained a huge commitment to the new headway in man-made brainpower. In contrast with conventional artificial intelligence (AI) techniques, for example, choice trees and backing vector machines, profound learning strategies have accomplished considerable improvement in different forecast assignments. Notwithstanding, deep neural networks (DNNs) are similarly powerless in clarifying their derivation cycles and eventual outcomes. In some certifiable applications, for example, business choice, process advancement, clinical determination, and venture suggestion, reasonableness and straightforwardness of our AI frameworks become especially fundamental for their clients, for individuals who are impacted by AI choices, and besides, for the scientists and designers who make the AI arrangements. This chapter gives an insight into explainable AI, the new and trending current technology used for diverse modern-day applications.
big S-Boxes for Deeply Improved Hill Image Coding

This paper introduces a novel approach to encrypt color images, utilizing multiple chaotic maps and working with any size pixel blocks. The encryption procedure commences by converting the original image into a vector, which is then divided into equal dimension subblocks. These blocks undergo a new Vigenère transformation, employing two large dynamic substitution tables interconnected by an enhanced Hill operation using a large invertible matrix generated from a pseudo-random table, controlled by another binary dynamics table. Additionally, the truncated block undergoes the same encryption process using another pseudo-random invertible matrix. To evaluate the effectiveness and resilience of our algorithm, we conducted numerous simulations on randomly selected images of varying sizes and formats from diverse databases.
Monocular Depth Estimation Algorithm Integrating Parallel Transformer and Multi-Scale Features

In the process of environmental perception, traditional CNN is often unable to effectively capture global context information due to its network structure, which leads to the problem of blurred edges of objects and scenes. Aiming at this problem, a self-supervised monocular depth estimation algorithm incorporating a Transformer is proposed. First of all, the encoder-decoder architecture is adopted. In the course of the encoding procedure, the input image generates images with different patch sizes but the same size. The multi-path Transformer network and single-path CNN network are used to extract global and local features, respectively, and feature fusion is achieved through interactive modules, which improves the network’s ability to acquire global information. Second, a multi-scale fusion structure of hierarchical features is designed to improve the utilization of features of different scales. Experiments for training the model were conducted using the KITTI dataset. The outcomes reveal that the proposed algorithm outperforms the mainstream algorithm. Compared with the latest CNN-Transformer algorithm, the proposed algorithm reduces the absolute relative error by 3.7% and the squared relative error by 3.9%.
Application of Unsupervised Machine Learning Algorithm in Patent Text Classification

In order to classify patent texts more accurately, this paper conducts an application study using unsupervised machine learning algorithms for patent text classification. This article classifies the characteristics of various text classification algorithms and utilizes unsupervised topic algorithms to detect research topics in massive patent texts, building and forming a research process and system platform for automatic text classification based on domain patent texts, in order to further mine important classification topics. Finally, an application test was conducted in the field of plants with Chinese characteristics.