LEVERAGING CLOUD TECHNOLOGIES AND SERVERLESS ARCHITECTURE FOR EFFICIENT WEB DEVELOPMENT: A CASE STUDY FROM REAL-WORLD APPLICATION

Abstract: This article provides a review of modern cloud solutions and serverless architecture using Backend as a Service (BaaS) and Function as a Service (FaaS) architecture as an example. In the scope of the article, a parallel is drawn between the consistently growing computing power and cloud technologies' growing popularity and availability for business. The results of an analytical review include a list of the most popular cloud providers from leading corporations and a comparison of low-level and high-level cloud technologies. The advantages and disadvantages of Google Cloud Platform (GCP) and Google Firebase are presented, where GCP is a low-level cloud provider and Firebase is a high-level cloud provider. The importance of understanding the context and specifics of the project when choosing cloud project solutions is emphasized which helps to maintain a balance between flexibility and development efficiency. The study introduces the practical utilization of the Supabase cloud platform for the development of a modern web application. The article convincingly proves the actuality of using Supabase for the development of an information system to optimize the modern personnel recruitment process, indicating specific advantages. An example of Supabase Edge Functions usage to generate feedback using the OpenAI Completions API and the Deno software platform is presented. The article convincingly proves that the use of cloud technologies is a modern strategy for building flexible, efficient, and scalable information systems. The advantages of the usage of the provision of infrastructure provided by world industry leaders are summarized.


PERSONALIZATION OF VISUAL CONTENT OF INTERACTIVE ART IN AUGMENTED REALITY BASED ON INDIVIDUAL USER PREFERENCES

Abstract: Topicality. In connection with the development of AR technologies and their use in interactive art, there is a growing need to develop methods of personalizing visual content, focused on the individual preferences of users. Research methods. Neural collaborative filtering method, generalized matrix factorization method, mood analysis on video. The purpose of the article: Researching the possibilities of improving the personalization of visual content in interactive art by evaluating the emotional reactions of users and their implicit feedback. The results obtained. The application of neural collaborative filtering and generalized matrix factorization to create adapted visual content in interactive art in AR was considered, which will significantly increase the relevance and immersion of users in interactive works. Conclusion. The considered approach can be used to improve immersiveness and personalization during user interaction with interactive art in AR.


Abstract: Article The Approach to AI Regulation for the Global South — The difference in balancing two regulatory approaches to AI was published on February 1, 2024 in the journal Computer Law Review International (volume 25, issue 1).


Virtual Reality: Characteristics and application in anxiety disorders and other clinical settings

Abstract: Virtual Reality (VR) technology places users in a computer-generated three-dimensional environment where they experience a variety of visual and auditory cues related to their clinical needs. Despite the considerable growth of scientific knowledge in this field, the application of this knowledge to clinical practice has been slow. The purpose of this paper is to synthesize the evidence related to the application of VRET interventions in the treatment of various disorders such as eating disorders, or even psychotic disorders and addictions, dwelling on anxiety disorders, in adults and in developmental age. VR technologies make it possible to create and replicate dangerous and impossible situations in the real world by allowing the precise acquisition of data, facilitating a more careful evaluation of rehabilitation progress; moreover, by exploiting the features of immersiveness and presence, it is possible to virtually recreate the perfect clinical setting for each intervention.


- Evaluation of an Open Access Version of the Trier Social Stress Test in Virtual Reality

Abstract: The Trier Social Stress Test (TSST) is one of the most frequently used psychosocial stressors under laboratory conditions. However, the implementation causes significant costs and is sensitive towards protocol variations, for instance the conduct of the judges. Therefore, in recent years, there has been growing interest in adapting the TSST in virtual reality (VR) to increase the standardization and economy of the procedure. This study aimed to validate a freely accessible version of the TSST-VR. Heart rate, salivary cortisol, and subjective stress ratings were measured in fifty male participants randomly assigned to the TSST-VR or a control procedure in VR. The analysis revealed a significant rise in subjective stress as well as in salivary cortisol in the stress condition compared to the control group indicating a significant induction of stress. Based on these findings, we conclude that the Open TSST-VR provides a suitable tool for eliciting psychosocial stress. In addition, this open access version offers various options to customize the VR environment to individual needs and enables collaborations between research groups.


Banking and Debunking: Applying Freirean Theory to the Educational Challenges of Conspiracy Culture

Abstract: Abstract The rise of conspiracy culture and the growing influence of conspiracy theories have attracted the attention of scholars from a range of fields. In recent years, Daniel Jolley, Asbjørn Dyrendal and others have noted the prevalence of conspiracy theories amongst adolescent schoolchildren in Scandinavia and the UK. This article draws on Paulo Freire’s concept of the ‘banking model’ of education to make the case against a ‘debunking’ approach to anti-conspiracist education. It argues that conspiracism should be understood as a feature of our culture and as such it should be interrogated rather than dismissed by educators who are engaged in the process of educating for democracy.


UCGAN: a contrastive learning-based unaligned image translation model with importance reweighting

Abstract: Image-to-image translation with unaligned domains is a seminal work that is capable of automatically selecting suitable image of unaligned dataset for translation. Unaligned datasets often contain irrelevant images that disrupt translation. However, the accuracy of selecting appropriate images is an issue that needs to be urgently improved. To improve the grasp of image features and enhance the performance of unaligned image-to-image translation without changing much semantic information, we propose a novel framework unaligned image-to-image translation model based on contrastive learning and generative adversarial networks (UCGAN), which can focus on both local and global features of the image. Inspired by contrastive learning, here we propose the patch-by-patch feature contrastive (PPFC). PPFC reuses the encoder of the generator to extract multi-layer features of different image patches. We then normalize the various features using L1-norm instead of L2-norm. To further improve translation performance, we also set up paired feature comparisons. We compare our proposed method UCGAN with state-of-the-art algorithms and the experimental results have demonstrated the superiority and practicality of our method.


Quantifying the Impact of Motions on Human Aiming Performance: Evidence from Eye Tracking and Bio-Signals

Abstract: Working on a moving platform can significantly impede human performance. Previous studies on moving vehicles have often focused on the overall impact on general task performance, whereas our study's emphasis is on precise hand movements, exploring the interaction between body motion and the escalation of task difficulty. We recruited 28 participants to engage in reciprocal aiming tasks, following Paul Fitts's setting, under both in-motion and stationary conditions. The task index of difficulty (ID) was manipulated by varying the width of the targets and the distance between the targets. We measured participants' movement time (MT), performance errors, and monitored their eye movements using an eye-tracking device, heart rate (HR), and respiration rate (RR) during the tasks. The measured parameters were compared across two experimental conditions and three ID levels. Compared to the stationary conditions, the in-motion conditions degraded human aiming performance, resulting in significantly prolonged MT, increased errors, and longer durations of eye fixations and saccades. Furthermore, HR and RR increased under the in-motion conditions. Linear relationships between MT and ID exhibited steeper slopes under the in-motion conditions compared to the stationary conditions. This study builds a foundation for us to explore the control mechanisms of individuals working in dynamic and demanding environments, such as pilots in airplanes and paramedics in ambulances.


Use of VR in Higher Education: A Systematic Review (2014-2023)

Abstract: With the ongoing advancement of intelligent information technology, virtual reality (VR) higher education, during this new era of reform, the incorporation of VR technology has led to an evolution of teaching methods. VR technology has been integrated into education and teaching, for example through virtual laboratories, digital environments and simulation-based training, thus providing higher education institutions with diverse teaching approaches and developmental opportunities.This study systematically categorises the utilisation of VR technology in tertiary education through the analysis of two databases, Web of Science (WoS) and Scopus.The systematic review process was conducted by referring to PRISMA (Preferred Reporting Items forSystematic Review and Meta-Analyses) as a guide, and 29 articles were extracted from the 478 articles for the period 2014 to 2023, taking into account exclusion and inclusion criteria.Firstly, the review revealed significant benefits of using VR applications in higher education, which were categorised into three categories. Secondly, the review examined challenges related to the use of VR in higher education content and categorised them into five categories.Overall, this review consolidates research discoveries and pragmatic insights to offer a refined comprehension of the present condition of VR in advanced education. Moreover, it provides recommendations for future development, which holds significant value for the effective use of VR technology in higher education.


PONG: A computational model of visual word recognition through bihemispheric activation.

Abstract: Orthographic processing is an open problem. Decades of visual word recognition research have fueled the development of various theoretical frameworks. Although these frameworks have had good explanatory power, various recent results cannot be satisfactorily captured in any model. In order to account for old and new phenomena alike, here I present a new theory of how the brain computes letter positions. According to PONG (which describes the Positional Ordering of N-Grams), each hemisphere of the brain comprises a set of mono- and multigram detectors. The crux is that the detectors for a given N-gram are activated to different extents in their respective hemispheres, depending on where in the visual field the N-gram is located. This differential activity allows the brain to estimate the leftness or rightness of that N-gram, whereby word activation is a function of the N-gram's identity plus its laterality relative to that of other activated N-grams. Simulations with PONG suggest that the framework effectively accounts for classic phenomena, as well as newer phenomena and cross-linguistic differences that cannot be explained by other models. I also reflect on the neurophysiological plausibility of the model and avenues for future inquiry. (PsycInfo Database Record (c) 2024 APA, all rights reserved).


The application of NLP in information retrieval

Abstract: The field of Natural Language Processing (NLP) has experienced impressive advancements and has found diverse applications. This paper presents a comprehensive review of the development of NLP in the field of information retrieval. It explores different stages of NLP techniques and methods, including keyword matching, rule-based approaches, statistical methods, and the utilization of machine learning and deep learning technologies. Furthermore, the paper provides detailed insights into the specific applications of NLP in domains such as academic information retrieval, medical information retrieval, travel information retrieval, and e-commerce information retrieval. It analyzes the current state of NLP applications in these domains, highlights their advantages, and discusses their associated limitations. Finally, the paper emphasizes the continuous advancement of the NLP field, with a particular focus on semantic understanding, personalized retrieval, and multimodal information retrieval, to better adapt to diverse data types and user requirements. The paper concludes by summarizing the main points discussed and providing future directions.


Demixing microwave signals using system-on-chip photonic processor

Abstract: Abstract The integrated photonic processor, co-packaged with electronic peripherals, is proposed for blind source separation of microwave signals, which separates signal-of-interest from dynamic interference with real-time adaptability.


‘Education is possible in any situation’ — what I’ve learnt from teaching in Kyiv amid a war

Abstract: Ukraine’s universities have adapted by blending innovative forms of remote learning. Lessons from this experiment are relevant to the rest of the world. Ukraine’s universities have adapted by blending innovative forms of remote learning. Lessons from this experiment are relevant to the rest of the world.


Three-dimensional Image Authentication from Multi-view Images

Abstract: Three-dimensional (3D) optical authentication is important for modern information security. Existing 3D optical authentication methods rely on integral imaging devices, necessitating meticulous calibration and incurring high transmission overhead. To streamline the acquisition of 3D information, this paper introduces a novel 3D optical authentication approach, to the best of our knowledge, based on the construction of 3D data from multi-view images. The proposed method simplifies 3D projection by generating fixed-viewpoint elemental images, eliminating the need for additional viewpoint information during transmission and authentication. Compressed sensing is used for compression during transmission, and a deep learning network is designed for 3D reconstruction, enhancing the recovery. Experimental outcomes confirm the efficiency of our proposed approach for 3D authentication across diverse datasets.


Application of the Savitzky-Golay filter in multi-spectral signal processing

Abstract: Multi-spectral signals are the result of the interaction between electromagnetic energy and the test material, which is then displayed by the signal fluctuation pattern of the test material. Signal fluctuations are inaccuracies in the peak amplitude of a signal caused by noise in the data. This fluctuation pattern reflects the properties of the test material, especially in this case H2O. To overcome this problem, it is necessary to use the right filter to smooth the signal and reduce the noise in the data so that the fluctuation pattern obtained is clearer and more accurate. This research involves the segmentation of HF fluctuation patterns, followed by the application of a Savitzky-Golay filter for signal smoothing. Signal quality is assessed objectively by calculating the Signal to Noise Ratio (SNR) and Mean Square Error (MSE). The research results show that the Savitzky-Golay filter succeeded in reducing noise and producing clearer fluctuation patterns. The SNR value varies, with the largest value reaching 16.6146 dB, and the smallest value being 3.0171 dB. This research contributes to a new method, namely the Savitzky-Golay adaptive filter, to identify multi-spectral signal fluctuation patterns more effectively, thereby enabling more accurate identification of fluctuation patterns. Apart from that, this research also provides insight into the characteristics of H2O which can be identified through fluctuation patterns, especially in certain segments with high amplitude. This method has potential for applications in various fields, especially in precise multi-spectral signal analysis.


Insights into Teaching Reading Skill Using Advertisement Media (A Case Study)

Abstract: In the current professional and educational environment, students must enhance their language abilities to capitalize on career and academic opportunities. Although reading is a crucial skill for youngsters, many struggle to comprehend, understand, and assimilate the content. This study investigates an interactive supplementary tool that enhances reading proficiency and facilitates students' comprehension of the text. During the academic year 2022–2023, a cohort of 120 female students was surveyed over the course of two semesters. The data collection methods used in this study consisted of a questionnaire that examined students' perspectives on advertising materials utilized in reading skills classrooms, along with a semi-structured questionnaire administered at the end of each unit. The results of this study suggest that the usage of commercial advertising improved students' reading ability, making it a great resource for teaching language skills.


Ensemble and Pre-Training Approach for Echo State Network and Extreme Learning Machine Models

Abstract: The echo state network (ESN) is a recurrent neural network that has yielded state-of-the-art results in many areas owing to its rapid learning ability and the fact that the weights of input neurons and hidden neurons are fixed throughout the learning process. However, the setting procedure for initializing the ESN's recurrent structure may lead to difficulties in designing a sound reservoir that matches a specific task. This paper proposes an improved pre-training method to adjust the model's parameters and topology to obtain an adaptive reservoir for a given application. Two strategies, namely global random selection and ensemble training, are introduced to pre-train the randomly initialized ESN model. Specifically, particle swarm optimization is applied to optimize chosen fixed and global weight values within the network, and the reliability and stability of the pre-trained model are enhanced by employing the ensemble training strategy. In addition, we test the feasibility of the model for time series prediction on six benchmarks and two real-life datasets. The experimental results show a clear enhancement in the ESN learning results. Furthermore, the proposed global random selection and ensemble training strategies are also applied to pre-train the extreme learning machine (ELM), which has a similar training process to the ESN model. Numerical experiments are subsequently carried out on the above-mentioned eight datasets. The experimental findings consistently show that the performance of the proposed pre-trained ELM model is also improved significantly. The suggested two strategies can thus enhance the ESN and ELM models' prediction accuracy and adaptability.


Using Metaverse-based Learning as a Strategy to Improve Higher Education: A Systematic Review (2019-2023)

Abstract: With the leap of modern technology, metaverse-based learning is booming. It breaks through the limitations of space and time and promotes the sharing of high-quality educational resources. Among them, user attitudes of using metaverse-based learning technology in higher education is one of the most important contents. Because students of every major are involved in improving their professional ability, and not everyone can experience a professional environment, metaverse-based learning plays an important role in higher education and professional training. Improving teaching and learning quality of higher education institutions in the form of metaverse-based learning is one of the crucial factors to ensure its sustainable development. However, the studies on metaverse-based learning are insufficient though it has great significance in the higher education area. By using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method, this study takes past articles as the research objects, and one main database named Scopus with one supporting database named Web of Science (WoS) was used. A total of 12 articles were selected and 3 themes emerged from it, namely learning acceptance construction, learning environment construction, and learning assessment construction. Last, some points were implemented in the study which can be the keystone of future research.


Identification of Multi-Mineral-Species Geochemical Anomalies using BME and the Spectrum Separable Module-constrained Variational Autoencoder

Abstract: This study presented a dual-drive multi-mineral-species anomaly detection system which involves the combined use of BME (Bayesian Maximum Entropy), SSM (Spectral Separable Module), High-Order Factor Analysis, a geologically constrained loss function, as well as a mixed Gaussian distribution-based thresholding algorithm, attaching to a deep CAE (Convolutional Auto-Encoder). We have achieved several firsts rarely considered in the existing literature, e.g., previous works focus mainly on separating single-mineral-species but multi-elemental anomalies, while we attempted to recognize the multi-mineral-species anomalies; previous works pay more attention to data, while we answered how to discover the ore-related correlations hidden within the input data; previous works fail to integrate soft data in a quantitative fashion, while we achieved that by capitalizing on BME and the stratigraphic combination entropy. A series of comparative experiments have demonstrated its advantages over other state-of-the-art approaches. Finally, we obtained a mineral-occurrence identification rate ( δ ) ranging from 36.87% to 61.46% versus the anomaly area ranging from 33.75% to 55.38% for each metalliferous anomaly division.


Exploring the Impact and Factors to Consider in Higher Education Practice: A Study in Reference to Generative Artificial Intelligence

Abstract: This study examines the impact of generative artificial intelligence (GAI) on higher education, focusing on the experiences of international students. It highlights the challenges of recognizing AI content and the potential for unfair charges against students. The essay also addresses biases in AI models and the need for justice and equity in AI judgments. It advocates for a well-rounded strategy that addresses both potential and problems, emphasizing AI literacy and ethical considerations. The essay incorporates the AI competency framework to ensure fair use of AI in the classroom.


Reflection of Federal Data Protection Standards on Cloud Governance

Abstract: As demand for more storage and processing power increases rapidly, cloud services in general are becoming more ubiquitous and popular. This, in turn, is increasing the need for developing highly sophisticated mechanisms and governance to reduce data breach risks in cloud-based infrastructures. Our research focuses on cloud governance by harmoniously combining multiple data security measures with legislative authority. We present legal aspects aimed at the prevention of data breaches, as well as the technical requirements regarding the implementation of data protection mechanisms. Specifically, we discuss primary authority and technical frameworks addressing least privilege in correlation with its application in Amazon Web Services (AWS), one of the major Cloud Service Providers (CSPs) on the market at present.


Research on Speech Emotion Recognition Analysis Based on Deep Learning

Abstract: Speech is a common way of communication in human daily life, which is very natural and easy to understand. In the process of communication, one will also express their emotions to the other party. Through emotional expression, the distance between people is smaller and communication is more convenient. Speech emotion recognition is also the key to human-computer emotional interaction, and effective recognition of speech emotions can improve speech intelligibility. The number of speech datasets is usually large, with several G or even larger. This article proposes a method of preprocessing the speech dataset to extract feature data folders, and then using the feature data folder directory as input to train the model. This method helps to improve the efficiency, repeatability, and manageability of data processing.


Obtaining Lower Query Complexities Through Lightweight Zeroth-Order Proximal Gradient Algorithms

Abstract: Abstract Zeroth-order (ZO) optimization is one key technique for machine learning problems where gradient calculation is expensive or impossible. Several variance, reduced ZO proximal algorithms have been proposed to speed up ZO optimization for nonsmooth problems, and all of them opted for the coordinated ZO estimator against the random ZO estimator when approximating the true gradient, since the former is more accurate. While the random ZO estimator introduces a larger error and makes convergence analysis more challenging compared to coordinated ZO estimator, it requires only O(1) computation, which is significantly less than O(d) computation of the coordinated ZO estimator, with d being dimension of the problem space. To take advantage of the computationally efficient nature of the random ZO estimator, we first propose a ZO objective decrease (ZOOD) property that can incorporate two different types of errors in the upper bound of convergence rate. Next, we propose two generic reduction frameworks for ZO optimization, which can automatically derive the convergence results for convex and nonconvex problems, respectively, as long as the convergence rate for the inner solver satisfies the ZOOD property. With the application of two reduction frameworks on our proposed ZOR-ProxSVRG and ZOR-ProxSAGA, two variance-reduced ZO proximal algorithms with fully random ZO estimators, we improve the state-of-the-art function query complexities from Omindn1/2ε2,dε3 to O˜n+dε2 under d&gt;n12 for nonconvex problems, and from Odε2 to O˜nlog1ε+dε for convex problems. Finally, we conduct experiments to verify the superiority of our proposed methods.


Language Learning Strategies Used by Malaysian ESL Students to Improve English Communication: A Systematic Literature Review

Abstract: Communication in English has long been recognised as one of the most challenging skills for pupils learning the language. Many studies in recent years have also emphasised the relevance of English communication abilities. With the advancement of technology in our current day, kids may learn communication skills through a variety of tools. As a result, determining the learning approaches used by pupils to build their English communication talents is crucial. A systematic literature review is undertaken in this study to investigate the prevailing language learning approaches utilised by Malaysian pupils acquiring English as a Second Language (ESL). Four databases were utilised to obtain the publications that were analysed: Scopus, Science Direct, Web of Science (WoS), and Google Scholar. The articles that met the eligibility criteria were published between 2020-2023. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) is the methodology utilised in this review. Metacognitive and cognitive processes were the most frequently utilised methods for improving speaking abilities, followed by compensatory and social approaches, according to the data. Memory and emotive methods, on the other hand, were the least used by pupils. The new study might assist teachers in selecting the best teaching method for future modern classrooms. The applicability of learning strategies for different learning skills may be extensively studied in future studies.


Pengaruh Model Pembelajaran Berbasis Masalah pada Kemampuan Literasi Ilmiah Siswa dalam Materi Transfer Panas Kelas V di Sekolah Dasar

Abstract:
Thesis This aim For know influence of learning models based problem to ability literacy participant educate on the material displacement heat fifth grade school base . As for the background study This based on results interviews and observations carried out , where ability literacy science participant still studying in class V spelled out low as well as lack of understanding
 teacher towards various various learning models . Study This carried out at SDN Banjarkemantren 2 in the even semester year 2022/2023 teachings . Method research used _ is Pre Experimental Design with One Group Pretest-Posttest Design type . Sample in study is participant educate VA class totaling 31 participants educate . Instruments used _ form instrument test form question choice double as well as sheet observation teacher and participant activities students , data obtained from test choice double and observation related with ability literacy science . Data analysis used the normality test and t-test. Analysis results show that the data is normally distributed because sig value . > 0.05 and in the t- test with using paired sample t-test results sig value <0.05 at level significance 5% then can it is concluded that h0 is rejected and ha is accepted . This matter show that application of learning models based problem give influence to ability literacy science participant educate on the material displacement heat in class V school base


Digital Forensics and Windows Sandbox as Anti-forensics tool

Abstract: Digital forensics is facing new challenges with rise in new anti-forensics techniques and tools including virtualization. Virtualization can be used as shield against different types of attacks, at the same time it can be leveraged by attackers as anti-forensics tool. Forensic investigators face enormous challenges while collecting the digital evidences in case where virtualization is used by an attacker. Virtualization comes in different forms, one of the difficulty form is light weight virtualization. Microsoft windows operating system offers sandbox light weight virtualization. Microsoft windows sandbox is an isolated testing environment to run programs or open files without affecting the application, system, or platform on which they run. After closing the sandbox nothing persists on the device, everything is discarded. This paper reveals the anti-forensics capabilities of sandbox and possible solutions to collect the forensics artefacts using windows registry. Registry analysis revealed that only use of sandbox on host operating system is discoverable and activities and data inside the sandbox are discarded permanently.


ASSESSING THE EFFECTIVENESS OF CURRENT CYBERSECURITY REGULATIONS AND POLICIES IN THE US

Abstract: This article assesses the effectiveness of current cybersecurity regulations and policies in the United States amidst the escalating frequency and sophistication of cyber threats. The focus is on the comprehensive framework established by the U.S. government, with a spotlight on the National Institute of Standards and Technology (NIST) Cybersecurity Framework and key regulations such as HIPAA, GLBA, FISMA, CISA, CCPA, and the DOD Cybersecurity Maturity Model Certification. The study evaluates the impact of these regulations on different sectors and analyzes trends in cybercrime data from 2000 to 2022. The findings highlight the challenges, successes, and the need for continuous adaptation in the face of evolving cyber threats


Exploring the Effectiveness of Using Instruction Analysis Criteria to Cultivate Music Teaching Competency of Preservice Elementary School Teachers

Abstract:
Objectives The purpose of this study was to promote preservice elementary school teachers’ participatory understanding of the planning and execution of music lessons by using instruction analysis criteria, and to cultivate their music teaching competency.
 Methods The study was conducted to reexamine the concepts and elements of music teaching competency through a literature review. Drawing upon existing research related to lesson analysis and music lesson evaluation, criteria for analyzing music lesson applicable to preservice elementary school teachers were extracted. The analysis criteria, comprising three domains (planning, instruction, reflection) with 14 components and 42 indicators, were applied to relevant courses. Additionally, surveys and small group interviews were conducted with participants to analyze their reactions to the criteria's utilization and assess its effecti Conclusions The usefulness, applicability, and effectiveness of the criteria for analyzing music instruction were confirmed. Above all, the enthusiastic response and continued interest of preservice elementary school teachers who participated in this study regarding the utilization of the analysis criteria suggest the need for more active support in the design and implementation of music lessons in elementary teacher training programs.


Ecological pollution features and health risk exposure to heavy metals via street dust and topsoil from Nkpor and Onitsha in Anambra, Nigeria

Abstract: The manuscript presents the investigation results on the pollution and risk of metal mines, and it is considered an important report on environmental pollution near mines in Nigeria, with archival value. The research involved soil sampling and heavy metal analysis for about 12 months in three metal mines. Based on these results, the paper provides information on pollution levels and hazards using well-known methods like pollution and ecological risk indexes. The increasing population in urban communities attracted by various industrial, economic and social activities causes contamination of atmospheric environment that can affect human health. We investigated heavy metal distributions, correlation coefficient among elements, ecological indices and probable health risk assessment in street dust and topsoil from Nkpor and Onitsha urban suburb, Nigeria. The mean concentration of heavy metals in car dust from Onitsha and Nkpor suburb follows thus: Fe > Mn > Cu > As > Pb > Ni > Cr. The decreasing trend of heavy metal in rooftop dust from both area: Fe > Mn > Cu > Pb > As > Ni > Cr whereas metal contents in topsoil were: Fe > Mn > Cu > Pb > Ni > Cr > As for both areas. The degree of pollution indices was characterized by contamination factor (CF), geo-accumulation factor (I-geo), pollution load index (PLI), Nemerow (PN), ecological and potential ecological risk index (ER and PERI) which indicated low pollution in the urban street environment. The results of Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) showed that the estimated heavy metals displayed sources from atmospheric deposition, natural origin and anthropogenic sources. Risk assessment revealed that ingestion of dust and soil was the significant route for heavy metals exposure to the populace followed by inhalation, then dermal contact. Considering all factors, non-cancer risk was more prominent in children than adults and no significant health hazard could be attributed to both aged groups as of the period of study except for As and Ni that needs constant monitoring to avoid exceeding organ damaging threshold limit of 1 × 10<sup>-4</sup>.


The Apple Vision Pro as a Neurosurgical Planning Tool: A Case Report

Abstract: With its recent release, the Apple Vision Pro (Apple Inc., Cupertino, CA) represents a promising technological advancement of mixed reality in the field of neurosurgery and medicine more broadly. With all new technologies, it is critical to facilitate early use and assessment of the technology to facilitate adoption by the larger medical community. A 44-year-old female with a history of ruptured intracranial aneurysm status post anterior communicating artery aneurysm clipping presented with worsened confusion and intermittent headache. CT imaging revealed evidence of hydrocephalus due to the malfunction of a previous right parietal ventriculoperitoneal (VP) shunt. Prior to the case, the Apple Vision Pro was used in the operating room to visualize and interact with a 3D model of the patient's anatomy for the patient undergoing a VP shunt placement. A visualization of the 3D model through the headset was used to plan the approach and entry point. At the conclusion of the procedure, all clinicians and operating staff who used the technology for planning completed a survey about their initial impressions of the headset. Overall, users felt the 3D models felt realistic (4.5/5), that the display of the user's real-world view felt natural (4.3/5), and that the headset did not cause eye strain or fatigue (4.5/5). The majority of users responded that they would continue to use the headset for cases (4/5). This represents one of the first known clinical uses of the Apple Vision Pro. It is a cutting-edge technology that will likely provide immense value for healthcare providers as it becomes more integrated into clinical care.


Design and Implementation of Mobile Application for CNN-Based EEG Identification of Autism Spectrum Disorder

Abstract: Autism spectrum disorder (ASD) is a disorder of the nervous system from birth and during infancy. This disorder affects children's development, making it difficult for nerve function to develop, and causes the child concerned to have difficulty in fostering social relationships. Early detection of children with ASD is needed so that treatment is fast and on target. Currently, facilities and research on early diagnosis of ASD patients through EEG signals are still very few, requiring much cost and more effort to analyze EEG signals in examinations related to ASD detection cases. This study proposes a mobile phone application that can distinguish people living with ASD and normal data signals based on asynchronous EEG brain signals. This research also produces a preprocessing algorithm and BCI2000 EEG data signal so that it can be automated using Python. This research also produces an output model, namely the Deep Learning Convolutional Neural Network, which is deployed using Python-Flask so that the diagnosis of EEG signals with ASD and normal patients can be used on various platforms through restAPI. This research is also expected to help the community and support the diagnosis of ASD sufferers so that they can be handled appropriately. Data for ASD sufferers and normal data were correctly classified into the appropriate class. Handling this disease requires close and integrated cooperation, so this ASD classification will be very helpful for patients and can make a diagnosis in a faster time, enabling patients to receive targeted treatment and therapy.


Generative AI – The Revolutionizing Virtual Agents in Health Care

Abstract: The world of health insurance and Medicare has traditionally been perceived as complex and difficult to navigate. Fortunately, the application of Generative AI to virtual agents has begun to transform the industry. Large language and image, AI models, also known as generative AI or foundation models, have opened up new prospects for organizations and people involved in content creation. Once trained, a generative model can be "fine-tuned" for a certain content domain with far less data.


A Critical Review: Technology as Learning Media in Teaching Reading

Abstract: Reading is a language skill that makes it possible to understand information clearly, this skill is one of the basic skills that allows students to improve their performance. Technology is something interesting for students at this time, especially for the Generation Z students who were born around the year of 1995-2012. The purpose of this article is to do a Systematic Literature Review (SLR) on technology as learning media in teaching reading. This article will provide research on technology as a learning medial for teaching reading from 2015 to 2022 across several countries, primarily focusing on high school students. Various data collection methods were used, including questionnaires, interviews, tests, and observations. 14 studies examined the effectiveness of technology in teaching reading, highlighting its importance in creating engaging educational environments and improving reading skills through personalized learning experiences. These studies emphasized the transformative impact of technology-based tools, particularly apps, in enhancing reading skill development. Early reading media based on technology, incorporating visuals, animations, audio, and interactive quizzes, were found to enhance learning experiences, making reading more enjoyable and effective.


Use of museum pedagogics in the training of teachers of social sciences

Abstract: The article defines museum pedagogy as an interdisciplinary educational technology that develops in the field of academic and non-formal education, covering various forms of communication, current educational programs and cultural practices and is a basic element of the program-targeted management of the activities of applicants and scientific-pedagogical workers in the museum-pedagogical environment , a means of its practical implementation, a kind of activity plan, in which the connections of the defined goal and the mechanisms of its achievement are clearly established; the general principles that must be relied upon in the application of museum pedagogy in the process of training a social science teacher are outlined; the concept of "museum resource" is presented as the ability to increase the power of professional and pedagogical potential, to reproduce and deepen the content of educational programs in the exposition, to be accessible and suitable for implementation in the conditions of the educational process, understandable for the majority of consumers of museum information, important components of which contribute to professional training future history teacher, there are: museum-pedagogical information, which contains news about specific museum-pedagogical and educational programs and their specifics; educational and methodological support of programs; forms and methods of pedagogical activity in the museum environment; results of the pedagogical process and results of research and experimental work; expert assessments of specialists, etc.; "museum environment" as a certain active space for learning and educating students of pedagogical specialties and a perfect means of implementing productive changes in teacher training, which ensures direct "communication" with original museum objects and active participation in various types of activities, organized on the basis of museums, the ability to awaken in future teachers' desire for professional and personal self-realization, to form stable humanistic orientations, a conscious positive attitude towards society, children, pedagogical activity and oneself, which causes deeper personal professionalization and creates conditions for correction of professional growth; directions of work with student audiences, organizational forms, methods, techniques of use in the educational activity of museums are substantiated; it is proposed to include in the educational and professional training program of historians the educational component "Fundamentals of museum and archive work" and to create a complex of methodical support for the proposed discipline


Digital transformation of visual collaboration in the educational environment

Abstract:
The article delves into the significance of digital transformation in the educational environment through the application of the innovative tool Miro for conceptual visualization and collaborative work. A detailed analysis of Miro's functionality in the educational context reveals its potential to support interactive education and stimulate active participation in the educational process. The specification of Miro's role in the educational space particularly emphasizes its potential for excellent implementation and support of educational programs at V.G. Korolenko Poltava National Pedagogical University.
 It is determined that the use of this tool can illuminate new horizons and enhance the efficiency of the educational process in the university environment. Analyzing the possibilities of Miro, attention is given to the adaptability and flexibility of the tool to the individual needs of university programs, ensuring its excellent implementation in various disciplines and scientific fields.
 The crucial aspect lies in its interactive nature, enabling collaborative and effective work on tasks and projects. Through the research, it has been determined that Miro can contribute to enhancing interaction among participants in the educational process by simplifying the collaborative creation and discussion of materials. The analysis regarding the utilization of Miro in the educational process has revealed a range of advantages and challenges. These have been taken into account in the development of an effective life hack for future teachers, aimed at enhancing their professional preparation.
 The article proposes a comprehensive approach to studying and implementing digital tools in the educational process, revealing the perspectives of their utilization in the educational environment and providing specific recommendations for their effective application


The Correlation between Grammar Usage and Indonesian – English Translation Ability of English Study Program Students

Abstract: Grammar is an important component of a language that contains rules on the sentence structure construction and meaning system of the language. The grammar usage has a contribution in the language learning process, one of which is in translation. This research aimed to discover the correlation between grammar usage and Indonesian – English translation ability of the fourth-semester English Study Program students. The instruments of the research were sentence completion and error recognition questions for English grammar test and Indonesia – English translation essay for translation test. The statistical analysis using Pearson product-moment formula was applied to analyze the correlation between grammar usage and Indonesian – English translation ability of the fourth-semester students. The result of data analysis revealed that the Pearson correlation value r-counted of 0.837 was higher than r-table of 0.320 at the level of confidence 0.05 which meant that alternative hypothesis (Ha) was accepted and of null hypothesis (H0) was rejected. The research findings revealed that there existed a significant correlation between English grammar usage and Indonesian – English translation ability the fourth-semester English Study Program students of Universitas Riau. It is implied that the proficient English grammar students displayed commensurate in Indonesian – English translation ability while those who lack of grammar usage ability found difficulty in translation tasks. This research highlighted the critical role of grammar in shaping translation of the fourth-semester students of Universitas Riau English Study Program.


Innovative Practices in Teachers’ and Students’ Trainings

Abstract: The present work is dedicated to innovative educational practices, that are based on teaching and learning through the methods of science, technology, engineering and mathematics (STEM), combined with the use of various digital tools. These project activities are suitable for implementation in the learning process both in face-to-face and distance learning. They enable the development of communication and collaboration skills, critical thinking and problem solving, creativity and innovation, as well as the creation of digital content. The described practices and digital tools are used in the preparation of teachers in various qualification courses conducted by the Department for Qualification and Professional Development of Pedagogical Specialists at University of Plovdiv “Paisii Hilendarski”. They are used and implemented also in education of students in lower-secondary schools (5th – 7th grade). The teaching practices are based on the results of a three-year study of already trained Bulgarian teachers, who try them daily in the learning process and notice their positive impact on students. The article describes a realized STEM project, named “European countries”.


The Paradigm of Education through Leisure Activities in the Boarding School: Teacher’s Viewpoints

Abstract: We often look at leisure time in different spheres of life by default and not so much importance. The aim of the paper is to consider and observe the views of boarding school teachers towards the paradigm of education through leisure activities in the boarding school. The obtained findings indicate that teachers perceive the importance of leisure activities, but that a large number of them estimate that there is not enough developed competence for the specific section they lead in the boarding school. When it comes to cooperation with colleagues in the direction of improving leisure time, the teachers’ opinions are divided, where there are answers that reflect good cooperation, but there are also answers from teachers that suggest that cooperation is bad or non-existent.


Solving Problems – Models, Strategies and Potential Implementation in Mathematics Education of 9 – 10-Year-Old Students

Abstract: The recent reforms in the educational system in Bulgaria aimed to provide the development of transversal skills and competences established in the state educational standards and curricula in all academic disciplines at primary school. Mathematics is the discipline that develops abstract-logical connections and makes sense of the world in a symbolic way, while building up the ability of students to cope with tasks in unfamiliar situations, in other words – to solve problems. The present paper attempts to investigate the theory of solving problems in mathematics at primary school age searching for its potential implementation. Methods of the study include theoretical research in the field of problem solving, covering its levels, models and strategies for its application, as well as content analysis of educational documentation for the 3rd grade. As a result of the study, relevant models and strategies are presented that have the potential to develop students’ problem-solving skills, revealing the need for increasing the number of tasks in the textbooks, which provide opportunities for problematization.


Strategies for Cultivating Students' Intercultural Communication Skills in Higher Vocational English Education

Abstract: Under the background of globalization and the informationization era, higher vocational English education is facing unprecedented challenges and opportunities. The cultivation of intercultural communicative competence has become a hot topic in the field of education, especially for higher vocational students, this competence is not only related to the effect of language learning but also directly affects the competitiveness and adaptability in the internationalized workplace in the future. At present, the following major problems exist in the cultivation of students' intercultural communication competence in higher vocational English education, the disconnection between teaching materials and actual intercultural communication requirements, the limitations of teachers' intercultural teaching awareness and methods, the insufficiency of students' intercultural experience and awareness, and the insufficiency of the school's educational resources and platforms. Based on this, this paper aims to discuss how to effectively improve students' intercultural communication competence in higher vocational English education, analyze the problems existing in the current educational practice, and propose corresponding improvement strategies.


Technology-mediated education for marginalized populations: financial literacy gap

Abstract: A person's financial attitude includes thoughts, attitudes, and confidence in managing money. A positive financial attitude is crucial for responsible financial behavior and long-term well-being. Financial literacy includes behavior and financial consequences, not just information and abilities. The themes above are linked to financial behavior, including saving, spending, debt management, and investment choices. Technology in financial education may reduce financial literacy gaps in emerging nations. Technology-mediated financial education is scalable, cost-effective, adaptable, and can reach populations with inadequate financial services, according to this study. Financial literacy research should provide empirically supported distribution strategies based on target population characteristics and contextual needs. Computer literacy, language, and cultural appropriateness issues require more research. Evaluating digital inclusion and divide reduction efforts is crucial. However, this system has unique challenges. An extensive literature review over the previous decade was conducted using "ABIInform," "EBSO Host," "Emerald," "Google Scholar," "Science Direct," "ProQuest," "Web of Science," and "ERIC." The study used "financial literacy," "technology-mediated," "financial education," and "developing countries." Insufficient infrastructure, access, linguistic and cultural barriers, and community alienation are the impediments. Insignificant. This study critically reviews technology-mediated financial education literature in developing nations. A complete assessment of long-term consequences, a comparative examination of distribution techniques, and a research methodology are needed. This study focuses on how the digital divide affects underprivileged communities' financial literacy. Due to the need for more knowledge, more research is needed on how limited technological access affects financial understanding. Addressing this deficit requires more investigation.


Semantic Model for Fragment of Hindi (Part 1)

Abstract: This paper proposes a formal model for syntactic and semantic analysis for the Hindi language using context-free grammar. In this paper, we developed a syntactic parser that generates syntactic trees for Hindi sentences based on rules of propositional logic, and gender conventions. The context-free rules we have written follow a top-down approach with a sentence that goes on self-arrangement. A set of experiments were run based on the corpus we have created, and significant results are presented in this paper. In addition to the above, the model characterizes lexical items in terms of individuals and sets for the syntactic distribution for well-formed formulas.


Evaluation of SIAKAD ITSK RS dr. Soepraoen using Technology Acceptance Model (TAM)

Abstract: Institut Teknologi, Sains, dan Kesehatan (ITSK) RS dr. Soepraoen is one of the higher education institutions that implements academic information systems in the process of providing education. The academic information system (SIAKAD) involves many users and many data processings, so there is possibility of problems with network access. Therefore, an evaluation is needed to determine user’s acceptances of the performance of SIAKAD which has been used so far at ITSK. The evaluation model proposed in this research is the Technology Acceptance Model (TAM), which is a model that provides perceptions based on the experiences experienced by users. This research used a questionnaire instrument which was distributed to all SIAKAD ITSK users. The expected result is a positive percentage that will describe the correct system performance and a negative percentage that can be an input for further improvements to SIAKAD ITSK.


Development of Basic Finger Shapes Indonesian Traditional Dance Application Using Color-Based Hand Gesture Recognition

Abstract: UNESCO stated that Indonesian traditional dance is a world cultural heritage that must be preserved and maintained. Efforts made by the government to preserve traditional dance are still not successful. Research shows that students have difficulty in learning traditional Indonesian dance exercises. Therefore, this study develops a solution that focuses on the difficulty of learning the basic finger postures in traditional Indonesian dances. This is important because fingers are instruments in dancing that have meaningful forms, such as Closed Urang Chopsticks, Nyempurit, Ngruji, Open Urang Chopsticks, Ke-pelan, Baya Mangap, and Ngithing. Learning the practice of basic finger postures aims to provide an independent learning experience through repetitive movements to strengthen or strengthen. This research has succeeded in making a real-time Color-Based Hand Gesture Recognition application for reliable Android devices. This is evidenced by applications that can function well in test scenarios with varying lighting and background conditions. Finally, students can learn the hand gestures of traditional Indonesian dances independently with ease. In addition, the application of the results of this research also contributes to becoming a learning medium in the current era of Blended Learning. Suggestions for future research are to increase the accuracy of Nyempurit and Ngithing attitudes.


Mathematical modelling and analysis of Kidnapping dynamics

Abstract: The problem of kidnapping as a social menace to a society is increasing in some African countries such as Nigeria. We therefore proposed a new deterministic mathematical model for the dynamics of Kidnapping in a community. This menace is considered like a two strains communicable disease with kidnapping propagation mission by kidnappers as one strain and adoption mission for kidnapped victims as the other strain to assess the impact of super-infection. The model exhibits four equilibrium points each of which is unique and asymptotically stable both locally and globally under certain conditions. We obtain the kidnapping propagation number where is the propagation number associated with strain . Another important threshold parameters associated with respective strains 1 and 2 are and . Indeed, we show that at most one strain invades the population if one of these parameters is less than unity. while the two strains coexist at endemic state when both and are greater than unity. The global stability results of the model equilibria are established by numerical simulations. This simulations results indicate that the super-infection destabilizes the coexistence equilibrium.


Sosialisasi Pembuatan Media Pembelajaran Problem Based Learning (PBL) untuk Meningkatkan Kualitas Pembelajaran Guru SD Negeri 057761 Kwala Gebang

Abstract: Preparing a golden generation for the future that can compete in the progress of the times can be done through good education. So that education is a forum for students to be able to develop all the skills they have. As time progresses, technological progress also develops. The rapid development of technology also has an impact on the world of education. Learning activities cannot be separated from methods, models, teaching aids and also media. Teachers can use learning media as a means of conveying knowledge to their students.


Using Digital Literacy Components to Develop ESL Learners’ Digital Mastery

Abstract: The capacity to locate, assess, and communicate information via keyboards or digital media platforms is known as digital literacy. Utilizing information and communication technology to produce, assess, and distribute information requires a combination of technical and cognitive skills. Some ESL learners are still struggling when they are using the media online to search for articles and journals. In addition, there are still a lot of students that are unsure about how to use digital technology for their education. ESL learners must be proactive and digitally knowledgeable in this age of technology to successfully choose and organize their search results. Without these digital abilities, ESL Malaysian students would not be able to utilize a variety of ICT platforms for information access and processing for academic needs, as well as subsequently for career needs, specifically to fulfil the demands of the Industrial Revolution 4.0 (IR 4.0). This study explores the development of digital mastery using the digital literacy components. The study employed a survey research design. An online questionnaire was distributed to ESL learners in a selected university. A cluster sampling was used in selecting the samples. A sample of 186 university students were involved in this study. Descriptive and inferential statistics were used to analyze the quantitative data collected. Data was analyzed using SPSS version 27 for the questionnaire. In brief, the study contributes to a new model related to digital mastery for ESL students in higher education.


Microfacies analysis and depositional environment of Shiranish Formation in Selected Sections, Kurdistan Region, NE Iraq

Abstract: Shiranish Formation in both Smaquli and Rawanduz sections is composed of an alternation of marly limestones, limestone and marl beds with fissile shale beds. Depend on color, lithology and other sedimentological criteria during field observations of the studied sections, the Shiranish Formation is divided lithologically into lower, middle and upper units. Planktonic Foraminiferal Lime Mudstone Microfacies (Shf1), Lime Wackestone (Shf2) and Planktonic Foraminiferal Lime Packstone Microfacies (Shf3) have been defined as three distinct microfacies with four submicrofacies ((Keeled Planktonic Foraminiferal Lime Wackestone submicrofacies (Shf2b), Globular Chamber Planktonic Foraminiferal Lime Wackestone submicrofacies (Shf2a), Bioclastic Lime Wackestone submicrofacies (Shf2c), Planktonic Foraminiferal Lime Wackestone submicrofacies (Shf2d)). The major microfacies in both sections was lime wackestone which is dominated in middle and upper units in both sections. Sedimentological and paleontological evidences suggest that the Formation was deposited in the outer shelf setting and progressed to the upper bathyal setting, in addition to that the microfacies changed from packstone to mudstone meaning deeping upward sequence.


A CRITICAL ANALYSIS AND REVIEW OF THE CONTROLLER PLACEMENT PROBLEM IN 5G SDN SECURED NETWORKS. OPEN ISSUES AND POSSIBLE SOLUTIONS

Abstract: This paper attempts a critical analysis of the Controller Placement Problem (CPP) in Software-Defined Networking (SDN) environments, identifies open issues and exploring potential solutions. The strategies to solve the CPP are examined aiming to enhance network performance, scalability, and resilience. SDN is a powerful technology used more and more in different networks. In particular the SDN control is a strong candidate proposed in 5G architectures. This study considers various aspects of CPP, including latency, reliability, and effective load distribution, which are also important in the context of 5G networks. The analysis reviews existing literature, revealing gaps in current strategies, particularly in addressing the dynamic nature of 5G architectures. Special attention is given to the multi-Controller contexts encountered in large networks, where Multi-controller Placement Problem (MCPP) emerges. This overview not only underscores the criticality of CPP in 5G SDN but also outlines some future research directions by our original proposal for a generic architecture for dynamic optimization in CPP.


Effects of Laughter Yoga on Premenstrual Symptoms.

Abstract: Premenstrual syndrome (PMS) is associated with a group of emotional, behavioral, and somatic symptoms that occur during the menstrual cycle. Laughter yoga involves a combination of laughter exercises and breathing techniques derived from more traditional yoga practice. No previous studies have examined the effects of laughter yoga on the symptoms of PMS.The study intended to assess the effectiveness of laughter yoga in coping with the premenstrual symptoms of women.The research team performed a pretest and posttest, quasi-experimental study.The study took place in the nursing and midwifery departments at Marmara University, a state university located in Istanbul, Turkey.Participants were 80 female students in those departments at the university between February 2019 and May 2020 who had PMS.The research team divided participants into two groups based on their departments: (1) an intervention group, the laughter yoga group, with 32 participants who took part in a twice-weekly laughter yoga program that included a weekly 30-min group session consisting of laughter and deep breathing, and (2) a control group with 48 participants.The research team collected the data using the Demographic and Descriptive Information Form (DDIF), Premenstrual Syndrome Symptom Scale (PMSS), and Visual Analogue Scale (VAS).Between baseline and postintervention, the laughter yoga group's mean scores for the PMSS subdimensions depressive affection (P = .00), anxiety (P = .04), fatigue (P = .00), irritability (P = .01), depressive thoughts (P = .03), pain (P = .002), changes in sleep (P = .004), and bloating (P = .001) significantly decreased. The laughter yoga group's scores for fatigue (P = .03) and pain (P = .001) were significantly lower than those of the control group postintervention.Laughter yoga is a noninvasive complementary therapy that clinicians can use to reduce the PMS symptoms.


Foreword

Abstract: Governments, businesses, and individuals worldwide continue to face significant threats from cyber security breaches. The demand for cyber security professionals has grown substantially in recent years, and this trend is expected to continue, resulting in labour shortages in several countries. The first step in addressing the scarcity of skilled workers in the cyber security sector is to understand the supply and demand dynamics of cyber security skills. Governments and organisations can utilise this information to pinpoint vulnerabilities and determine areas requiring additional resources. From the demand side, analysing job postings can help identify trends in demand for cyber security professionals and the skills needed to strengthen organisations' cyber security. Simultaneously, studying cyber security education and training programs offers insights into the evolving development of the cyber security workforce, shedding light on potential mismatches between supply and demand.


Regional Clustering Based on Types of Non-Communicable Diseases Using k-Means Algorithm

Abstract: Noncommunicable diseases (NCDs) have become a global threat to public health, necessitating a comprehensive understanding of their geographic and epidemiological distribution in order to devise appropriate interventions. The objective of this study is to clustering areas of Banten Province based on NCDS profiles using the unsupervised learning technique. The method used in this study is the k-means algorithm for grouping types of non-communicable diseases based on region. The processing and normalisation of NCDS prevalence data from various health sources preceded cluster analysis using the k-means clustering algorithm. This research is categorised into two scenarios: the first involves the clustering of data obtained from outlier analysis, while the second scenario excludes any outliers. The objective is to observe disparities in regional clustering outcomes by categorising non-communicable diseases according to these two scenarios. The silhouette index is used to determine the validity of cluster results. These findings are analysed in depth to determine the geographic and socioeconomic patterns associated with each cluster's NCDS profile. Based on the mean silhouette index value of 0.812, the results indicate that the sum of k = 2 in the k-means algorithm is the optimal cluster result in this case. Five non-communicable diseases, namely diabetes, hypertension, obesity, stroke, and cataracts, necessitate significant focus in the first cluster (C1), where 202 regions were grouped. Six regions belong to the second cluster (C2), which includes areas that are not only susceptible to the five non-communicable diseases in cluster C1 but also to breast cancer, cervical cancer, heart disease, chronic obstructive pulmonary disease (COPD), and congenital deafness.


Urban Heat Island Literacy Among Geography Education Students of Universitas Muhammadiyah Surakarta

Abstract: Geography Education students are expected to have literacy about the phenomena around them, one of which is Urban Heat Island (UHI) literacy. It is appropriate by the mission of Geography Education at the Muhammadiyah University of Surakarta (UMS) is to produce geography educators with a view on disaster mitigation. This study aims to determine the UHI literacy of students in 7th semester, Deparment Geography Education, Faculty of Teacher Training and Education, Universitas Muhammadiyah Surakarta. This research is a quantitative descriptive study with survey methods. The study using purposive random sampling of 80 respondents and as many as 54 collage students were selected as samples. The UHI literacy research instrument was developed based on the OECD's International Program for Student Assessment (PISA) in 1999. Based on the indicators in scientific literacy which are divided into three categories, the results are obtained: 1) Indicators of UHI's concepts show that high categories are middle class 20.4 % and the majority of students at a high level with 79.6%; 2) Indicators of UHI's processes obtained results with 1.8 % of students at a low level, 20.4 % of students at a medium level, and 78.8 % at a high level; 3) UHI's situation indicators showed that the high category was 93% and moderate was 7%. The conclusion shows that the UHI literacy of students is relatively high, indicating that curriculum based on disaster insight has been doing well, but further research is needed on other disaster insights and analysis of factors which affect the results above.Keywords: Urban Heat Island, Urban Heat Island Literacy, Curriculum


Expert System for Diagnosing Corn Plant Diseases Using the Web-Based Certainty Factor Method

Abstract: In the process of corn plant growth, it often faces the threat of various diseases that can hinder optimal growth. Dealing with this issue often requires the presence of a plant expert, such as a field agricultural extension officer. However, limitations in the human resources of agricultural extension officers, time constraints, and geographical distances often make it difficult for farmers to get direct assistance from experts. To overcome these constraints, a system capable of addressing issues related to corn plant diseases, similar to the role of an expert, is needed. In this study, the Certainty Factor method is employed, suitable for an expert system that measures the level of uncertainty, and through testing with 28 sample data cases, the accuracy level of this system reaches 90%.


Perbandingan Algoritma Winnowing dan Algoritma Rabin-Karp pada Aplikasi Pendeteksi Kesamaan Dokumen Skripsi

Abstract: Plagiarism is often found in the academic world and is considered a serious violation because it involves the appropriation of ideas, opinions, or writings of others. A thesis is the final work of a student that must meet scientific standards and be tailored to the field of study. It is important to check the similarity of the thesis using a web-based sistem to prevent plagiarism and ensure academic integrity. This sistem will be developed as a web-based platform with the aim of comparing the Winnowing algorithm and the Rabin-Karp algorithm in checking the similarity of thesis/final project texts with existing thesis data. In principle, both methods involve searching for strings using hashing functions to compare the sought string (m) with the compared string (n) by comparing the results of the hashing function used. However, the Winnowing algorithm differs in that it does not use all hash values from each formed set of grams. The hash values formed in the previous stage will be divided into a window of size (w). In this research, the sistem testing uses data from Computer Engineering students at Halu Oleo University to facilitate checking the plagiarism level of theses using the Rabin-Karp and Winnowing algorithms. In this study, the Rabin-Karp and Winnowing algorithms have been implemented successfully in the plagiarism checking sistem for students' theses. The test results for the comparison of the Winnowing and Rabin-Karp algorithms in terms of processing time show that the Rabin-Karp algorithm takes 1.509 seconds, while the Winnowing algorithm takes 1.508 seconds. Subsequent testing using Normalized Mean Absolute Error (NMAE) reveals that the Rabin-Karp algorithm has an absolute error value of 0.1829, while the Winnowing algorithm has a value of 0.0194. Therefore, based on the NMAE test, the Winnowing algorithm performs better than the Rabin-Karp algorithm.


Application of scientific and technical means in criminal proceedings in the Republic of Kazakhstan

Abstract: Relevance. The relevance of the study stems from the fact that the rapid development of new technologies and their introduction into all spheres of social life opens up great prospects for the use of scientific and technological progress in criminal proceedings to ensure full and transparent investigation of various types of crime. Purpose. The purpose of the study is to provide a comprehensive and in-depth examination of the various scientific and technical tools in criminal proceedings, to analyze the effectiveness and expediency of the use of certain technical tools, to study the experience of other countries and to carry out a comparative analysis. Methodology. The formal-legal method led the way, as it enabled a consistent study of the various new technologies used in criminal proceedings and the study of the legal provisions governing the use of such tools. Results. The outcome of the research is a comprehensive study of the most popular scientific and technological means used at all stages of criminal proceedings and by all law enforcement agencies; negative and positive aspects of the use of certain types of scientific and technological means in investigation and prosecution were examined, the experience of using the latest technologies in other countries was examined and a comparative legal analysis was carried out. In addition, the legal framework, which sets out the basic provisions for the use of technical means in the investigation of crimes in different countries, was examined. Conclusions. The practical significance of the research lies in the fact that understanding the specifics of the application of scientific and technical means will help to use them more effectively and reasonably in the process of criminal investigation and court proceedings.


Heat Transfer-Inspired Network for Image Super-Resolution Reconstruction

Abstract: Image super-resolution (SR) is a critical image preprocessing task for many applications. How to recover features as accurately as possible is the focus of SR algorithms. Most existing SR methods tend to guide the image reconstruction process with gradient maps, frequency perception modules, etc. and improve the quality of recovered images from the perspective of enhancing edges, but rarely optimize the neural network structure from the system level. In this article, we conduct an in- depth exploration for the inner nature of the SR network structure. In light of the consistency between thermal particles in the thermal field and pixels in the image domain, we propose a novel heat-transfer-inspired network (HTI-Net) for image SR reconstruction based on the theoretical basis of heat transfer. With the finite difference theory, we use a second-order mixed-difference equation to redesign the residual network (ResNet), which can fully integrate multiple information to achieve better feature reuse. In addition, according to the thermal conduction differential equation (TCDE) in the thermal field, the pixel value flow equation (PVFE) in the image domain is derived to mine deep potential feature information. The experimental results on multiple standard databases demonstrate that the proposed HTI-Net has superior edge detail reconstruction effect and parameter performance compared with the existing SR methods. The experimental results on the microscope chip image (MCI) database consisting of realistic low-resolution (LR) and high-resolution (HR) images show that the proposed HTI-Net for image SR reconstruction can improve the effectiveness of the hardware Trojan detection system.


Prompting Is All You Need: Automated Android Bug Replay with Large Language Models

Abstract: Bug reports are vital for software maintenance that allow users to inform developers of the problems encountered while using the software. As such, researchers have committed considerable resources toward automating bug replay to expedite the process of software maintenance. Nonetheless, the success of current automated approaches is largely dictated by the characteristics and quality of bug reports, as they are constrained by the limitations of manually-crafted patterns and pre-defined vocabulary lists. Inspired by the success of Large Language Models (LLMs) in natural language understanding, we propose AdbGPT, a new lightweight approach to automatically reproduce the bugs from bug reports through prompt engineering, without any training and hard-coding effort. AdbGPT leverages few-shot learning and chain-of-thought reasoning to elicit human knowledge and logical reasoning from LLMs to accomplish the bug replay in a manner similar to a developer. Our evaluations demonstrate the effectiveness and efficiency of our AdbGPT to reproduce 81.3% of bug reports in 253.6 seconds, outperforming the state-of-the-art baselines and ablation studies. We also conduct a small-scale user study to confirm the usefulness of AdbGPT in enhancing developers' bug replay capabilities.


Markov Subsampling Based on Huber Criterion

Abstract: Subsampling is an important technique to tackle the computational challenges brought by big data. Many subsampling procedures fall within the framework of importance sampling, which assigns high sampling probabilities to the samples appearing to have big impacts. When the noise level is high, those sampling procedures tend to pick many outliers and thus often do not perform satisfactorily in practice. To tackle this issue, we design a new Markov subsampling strategy based on Huber criterion (HMS) to construct an informative subset from the noisy full data; the constructed subset then serves as refined working data for efficient processing. HMS is built upon a Metropolis-Hasting procedure, where the inclusion probability of each sampling unit is determined using the Huber criterion to prevent over scoring the outliers. Under mild conditions, we show that the estimator based on the subsamples selected by HMS is statistically consistent with a sub-Gaussian deviation bound. The promising performance of HMS is demonstrated by extensive studies on large-scale simulations and real data examples.


Stage-Wise Magnitude-Based Pruning for Recurrent Neural Networks

Abstract: A recurrent neural network (RNN) has shown powerful performance in tackling various natural language processing (NLP) tasks, resulting in numerous powerful models containing both RNN neurons and feedforward neurons. On the other hand, the deep structure of RNN has heavily restricted its implementation on mobile devices, where quite a few applications involve NLP tasks. Magnitude-based pruning (MP) is a promising way to address such a challenge. However, the existing MP methods are mostly designed for feedforward neural networks that do not involve a recurrent structure, and, thus, have performed less satisfactorily on pruning models containing RNN layers. In this article, a novel stage-wise MP method is proposed by explicitly taking the featured recurrent structure of RNN into account, which can effectively prune feedforward layers and RNN layers, simultaneously. The connections of neural networks are first grouped into three types according to how they are intersected with recurrent neurons. Then, an optimization-based pruning method is applied to compress each group of connections, respectively. Empirical studies show that the proposed method performs significantly better than the commonly used RNN pruning methods; i.e., up to 96.84% connections are pruned with little or even no degradation of precision indicators on the testing datasets.


Implementing Graph-Theoretic Feature Selection by Quantum Approximate Optimization Algorithm

Abstract: Feature selection plays a significant role in computer science; nevertheless, this task is intractable since its search space scales exponentially with the number of dimensions. Motivated by the potential advantages of near-term quantum computing, three graph-theoretic feature selection (GTFS) methods, including minimum cut (MinCut)-based, densest k -subgraph (DkS)-based, and maximal-independent set/minimal vertex cover (MIS/MVC)-based, are investigated in this article, where the original graph-theoretic problems are naturally formulated as the quadratic problems in binary variables and then solved using the quantum approximate optimization algorithm (QAOA). Specifically, three separate graphs are created from the raw feature set, where the vertex set consists of individual features and pairwise measure describes the edge. The corresponding feature subset is generated by deriving a subgraph from the established graph using QAOA. For the above three GTFS approaches, the solving procedure and quantum circuit for the corresponding graph-theoretic problems are formulated with the framework of QAOA. In addition, those proposals could be employed as a local solver and integrated with the Tabu search algorithm for solving large-scale GTFS problems utilizing limited quantum bit resource. Finally, extensive numerical experiments are conducted with 20 publicly available datasets and the results demonstrate that each model is superior to its classical scheme. In addition, the complexity of each model is only O(p n2) even in the worst cases, where p is the number of layers in QAOA and n is the number of features.


ReCNAS: Resource-Constrained Neural Architecture Search Based on Differentiable Annealing and Dynamic Pruning

Abstract: The differentiable neural architecture search (NAS) framework has obtained extensive attention and achieved remarkable performance due to its search efficiency. However, most existing differentiable NAS methods still suffer from issues of model collapse, degenerated search-evaluation correlation, and inefficient hardware deployment, which causes the searched architectures to be suboptimal in accuracy and cannot meet different computation resource constraints (e.g., FLOPs and latency). In this article, we propose a novel resource-constrained NAS (ReCNAS) method, which can efficiently search high-performance architectures that satisfy the given constraints, and deal with the issues observed in previous differentiable NAS methods from three aspects: search space, search strategy, and resource adaptability. First, we introduce an elastic densely connected layerwise search space, which decouples the architecture depth representation from the search of candidate operations to alleviate the aggregation of skip connections and architecture redundancies. Second, a scheme of group annealing and progressive pruning is proposed to improve the efficiency and bridge the search-evaluation gap, which steadily forces the architecture parameters close to binary distribution and progressively prunes the inferior operations. Third, we present a novel resource-constrained architecture generation method, which prunes the redundant channel throughout the search based on dynamic programming, making the searched architecture scalable to different devices and requirements. Extensive experimental results demonstrate the efficiency and search stability of our ReCNAS, which is capable of discovering high-performance architectures on different datasets and tasks, surpassing other NAS methods, while tightly meeting the target resource constraints without any tuning required. Besides, the searched architectures show strong generalizability to other complex vision tasks.


Nonlinear topological symmetry protection in a dissipative system

Abstract: We report an experimental and theoretical investigation of a system whose dynamics is dominated by an intricate interplay between three key concepts of modern physics: topology, nonlinearity, and spontaneous symmetry breaking. The experiment is based on a two-mode coherently-driven optical resonator in which photons interact through the Kerr nonlinearity. In presence of a phase defect between the modes, a nonlinear attractor develops, which confers a synthetic M\"obius topology to the modal structure of the system. That topology is associated with an inherently protected exchange symmetry between the modes, enabling the realization of spontaneous symmetry breaking in ideal, bias-free, conditions without any fine tuning of parameters. The dynamic manifests itself by a periodic alternation of the modes from one resonator roundtrip to the next reminiscent of period-doubling. This extends to a range of localized structures in the form of domain walls, bright solitons, and breathers, which have all been observed with remarkable long term stability. A rigorous testing of the randomness of the symmetry-broken state selection statistics has also confirmed the robustness of the exchange symmetry in our experiment. Our results and conclusions are supported by an effective Hamiltonian model explaining the symmetry protection in our system. The model also shows that our work has relevance to other systems of interacting bosons and to the Floquet engineering of quantum matter. Our work could also be beneficial to the implementation of coherent Ising machines.


GeneGPT: Augmenting large language models with domain tools for improved access to biomedical information

Abstract: Abstract Motivation While large language models (LLMs) have been successfully applied to various tasks, they still face challenges with hallucinations. Augmenting LLMs with domain-specific tools such as database utilities can facilitate easier and more precise access to specialized knowledge. In this paper, we present GeneGPT, a novel method for teaching LLMs to use the Web APIs of the National Center for Biotechnology Information (NCBI) for answering genomics questions. Specifically, we prompt Codex to solve the GeneTuring tests with NCBI Web APIs by in-context learning and an augmented decoding algorithm that can detect and execute API calls. Results Experimental results show that GeneGPT achieves state-of-the-art performance on eight tasks in the GeneTuring benchmark with an average score of 0.83, largely surpassing retrieval-augmented LLMs such as the new Bing (0.44), biomedical LLMs such as BioMedLM (0.08) and BioGPT (0.04), as well as GPT-3 (0.16) and ChatGPT (0.12). Our further analyses suggest that: First, API demonstrations have good cross-task generalizability and are more useful than documentations for in-context learning; Second, GeneGPT can generalize to longer chains of API calls and answer multi-hop questions in GeneHop, a novel dataset introduced in this work; Finally, different types of errors are enriched in different tasks, providing valuable insights for future improvements. Availability The GeneGPT code and data are publicly available at https://github.com/ncbi/GeneGPT.


A Laplacian Pyramid Based Generative H&E Stain Augmentation Network

Abstract: Hematoxylin and Eosin (H&E) staining is a widely used sample preparation procedure for enhancing the saturation of tissue sections and the contrast between nuclei and cytoplasm in histology images for medical diagnostics. However, various factors, such as the differences in the reagents used, result in high variability in the colors of the stains actually recorded. This variability poses a challenge in achieving generalization for machine-learning based computer-aided diagnostic tools. To desensitize the learned models to stain variations, we propose the Generative Stain Augmentation Network (G-SAN) - a GAN-based framework that augments a collection of cell images with simulated yet realistic stain variations. At its core, G-SAN uses a novel and highly computationally efficient Laplacian Pyramid (LP) based generator architecture, that is capable of disentangling stain from cell morphology. Through the task of patch classification and nucleus segmentation, we show that using G-SAN-augmented training data provides on average 15.7% improvement in F1 score and 7.3% improvement in panoptic quality, respectively. Our code is available at https://github.com/lifangda01/GSAN-Demo.


Discovering Datasets in Unstructured Corpora: Discovering Use and Identifying New Opportunities

Abstract: Federal statistical agencies are keenly aware that scientific research is not the only way in which their data assets are used for evidence building. Entities around the world have been building services on top of curated corpuses of scientific research, to help provide insight as to the importance of the works in the collection. This also provides a much-needed framework for statistical agencies and other dataset creators to search and find the usages and impacts of those datasets. While the other papers in this special issue largely apply machine learning models to find datasets in an extensively curated corpus, this paper starts with a much less structured framework and examines the potential to discover how datasets are used in writing thatâs targeted at a broader base of users than scientific researchers, and describes the challenges and lessons learned from the exercise.


Bioframe: Operations on genomic intervals in pandas dataframes

Abstract: Genomic intervals are one of the most prevalent data structures in computational genome biology, and used to represent features ranging from genes, to DNA binding sites, to disease variants. Operations on genomic intervals provide a language for asking questions about relationships between features. While there are excellent interval arithmetic tools for the command line, they are not smoothly integrated into Python, one of the most popular general-purpose computational and visualization environments.Bioframe is a library to enable flexible and performant operations on genomic interval dataframes in Python. Bioframe extends the Python data science stack to use cases for computational genome biology by building directly on top of two of the most commonly-used Python libraries, NumPy and Pandas. The bioframe API enables flexible name and column orders, and decouples operations from data formats to avoid unnecessary conversions, a common scourge for bioinformaticians. Bioframe achieves these goals while maintaining high performance and a rich set of features.Bioframe is open-source under MIT license, cross-platform, and can be installed from the Python Package Index. The source code is maintained by Open2C on GitHub at https://github.com/open2c/bioframe.


Discriminative Regression With Adaptive Graph Diffusion

Abstract: In this article, we propose a new linear regression (LR)-based multiclass classification method, called discriminative regression with adaptive graph diffusion (DRAGD). Different from existing graph embedding-based LR methods, DRAGD introduces a new graph learning and embedding term, which explores the high-order structure information between four tuples, rather than conventional sample pairs to learn an intrinsic graph. Moreover, DRAGD provides a new way to simultaneously capture the local geometric structure and representation structure of data in one term. To enhance the discriminability of the transformation matrix, a retargeted learning approach is introduced. As a result of combining the above-mentioned techniques, DRAGD can flexibly explore more unsupervised information underlying the data and the label information to obtain the most discriminative transformation matrix for multiclass classification tasks. Experimental results on six well-known real-world databases and a synthetic database demonstrate that DRAGD is superior to the state-of-the-art LR methods.


Autoencoder in Autoencoder Networks

Abstract: Modeling complex correlations on multiview data is still challenging, especially for high-dimensional features with possible noise. To address this issue, we propose a novel unsupervised multiview representation learning (UMRL) algorithm, termed autoencoder in autoencoder networks (AE2-Nets). The proposed framework effectively encodes information from high-dimensional heterogeneous data into a compact and informative representation with the proposed bidirectional encoding strategy. Specifically, the proposed AE2-Nets conduct encoding in two directions: the inner-AE-networks extract view-specific intrinsic information (forward encoding), while the outer-AE-networks integrate this view-specific intrinsic information from different views into a latent representation (backward encoding). For the nested architecture, we further provide a probabilistic explanation and extension from hierarchical variational autoencoder. The forward-backward strategy flexibly addresses high-dimensional (noisy) features within each view and encodes complementarity across multiple views in a unified framework. Extensive results on benchmark datasets validate the advantages compared to the state-of-the-art algorithms.


Personalized Federated Few-Shot Learning

Abstract: Personalized federated learning (PFL) learns a personalized model for each client in a decentralized manner, where each client owns private data that are not shared and data among clients are non-independent and identically distributed (i.i.d.) However, existing PFL solutions assume that clients have sufficient training samples to jointly induce personalized models. Thus, existing PFL solutions cannot perform well in a few-shot scenario, where most or all clients only have a handful of samples for training. Furthermore, existing few-shot learning (FSL) approaches typically need centralized training data; as such, these FSL methods are not applicable in decentralized scenarios. How to enable PFL with limited training samples per client is a practical but understudied problem. In this article, we propose a solution called personalized federated few-shot learning (pFedFSL) to tackle this problem. Specifically, pFedFSL learns a personalized and discriminative feature space for each client by identifying which models perform well on which clients, without exposing local data of clients to the server and other clients, and which clients should be selected for collaboration with the target client. In the learned feature spaces, each sample is made closer to samples of the same category and farther away from samples of different categories. Experimental results on four benchmark datasets demonstrate that pFedFSL outperforms competitive baselines across different settings.


Best Practices of Engineering Education Internationalization in a Russian Top-20 University

Abstract: Top world universities apply complex internationalization strategies to keep a sustainable leadership confirmed by the global rankings such as QS, THE, ARWU, or to improve their position in these rankings.Russian universities are not an exception and the analysis of the websites of Russian Top-20 universities (according to the National Ranking of Russian Universities, 9 universities of this Top-20 list are engineering universities) demonstrates their strong internationalization infrastructure.Tracking success stories and best practices of such universities using accurate statistics is a useful tool for sharing their internationalization experiences, finding matching points for cooperation and predicting of their competitiveness potential in the global higher education community.


Altruistic Collaborative Learning

Abstract: This article proposes a new learning paradigm based on the concept of concordant gradients for ensemble learning strategies. In this paradigm, learners update their weights if and only if the gradients of their cost functions are mutually concordant in a sense given by paper. The objective of the proposed concordant optimization framework is robustness against uncertainties by postponing to a later epoch, the consideration of examples associated with discordant directions during a training phase. Concordance constrained collaboration is shown to be relevant, especially in intricate classification issues where exclusive class labeling involves information bias due to correlated disturbances affecting almost all training examples. The first learning paradigm applies on a gradient descent strategy based on allied agents, subjected to concordance checking before moving forward in training epochs. The second learning paradigm is related to multivariate dense neural matrix fusion, where the fusion operator is itself a learnable neural operator. In addition to these paradigms, this article proposes a new categorical probability transform to enrich the existing collection and propose an alternative scenario for integrating penalized SoftMax information. Finally, this article assesses the relevance of the above contributions with respect to several deep learning frameworks and a collaborative classification involving dependent classes.