Reviewerly/exHarmony huggingface dataset:
People Identification based on Geometric Face Features for Cloud Services

Recently, the services provided by cloud computing play an important role in many areas of life. The security component represents the major challenge that hinders the adoption of cloud services in banking, healthcare and other transactions. In this paper, secure identification approach is proposed to use cloud services by recognizing authorized persons based on facial features. In the proposed approach, the face image is sent to the cloud side without any processing. When it is received in the cloud side, the image is processed and the geometric features are extracted using moment features extractor. The features of the face are used during the matching phase by using two different classifiers to make the final decision to grant authorization to access cloud services. The experimental results showed the efficiency and accuracy of the proposed approach. The performance is test using many facial images with online applications. Where an overall accuracy level of approximately 96% was obtained.
KAJIAN LITERATUR TENTANG FUNGSI MANAJEMEN BAGI KURIKULUM PENDIDIKAN ANAK USIA DINI

Early childhood education management is an effort to organize the process of early childhood education (PAUD) in order to achieve the goal of helping children grow and develop. The purpose of PAUD or early childhood education is to provide the best education for children so that it helps their growth and development. One of the things that is important in management is the function of management itself. This research is a literature research with procedures: (1) selecting a topic, (2) exploring information from the selected topic, (3) determining the research focus from the results of information exploration, (4) reading sources relevant to the research focus, (5) making and (6) processing research notes, and (7) compiling reports in the form of research articles. Research data was obtained by utilizing secondary data sources such as books, scientific articles, and websites. Data collection used documentation techniques by utilizing the researcher as a literature research with procedures: (1) selecting a topic, (2) exploring information from the selected topic, (3) determining the research focus from the results of information exploration, (4) reading sources relevant to the research focus, (5) making and (6) processing research notes, and (7) compiling reports in the form of research articles. Research data was obtained by utilizing secondary data sources such as books, scientific articles, and websites. Data collection used documentation techniques by utilizing the researcher as a ning, 2) Organizing early childhood education, 3) Early childhood education learning patterns, 4) Early childhood education evaluation system. So the management function has an important role in the development of the PAUD curriculum. Management functions such as planning, organizing, controlling, and evaluating are needed to develop, implement, and evaluate the PAUD curriculum effectively and efficiently. Planning functions to determine the direction and objectives of the PAUD curriculum, organizing functions to manage resources and determine the roles and responsibilities of each party involved, control functions to ensure that the PAUD curriculum is implemented properly according to plan, and evaluation functions to evaluate the effectiveness of the PAUD curriculum and make repairs if necessary.
INVESTIGATING THE FACTORS AFFECTING STUDENTS’ PROBLEMS IN SPEAKING PERFORMANCE (A CASE STUDY IN ENGLISH EDUCATION DEPARTMENT OF WIRALODRA UNIVERSITY)

Speaking is considered the most challenging skill in language learning, especially in an academic speaking context. It is uncommon for the students to find the problems and need more effort to have a good speaking performance. Therefore, this research aimed to investigate the factors affecting students’ problems in speaking performance. This research investigates performance conditions and affective factors mentioned by Tuan &amp; Mai (2015). This research was attended by 20 students of the 4th semester in the English Education Department of Wiralodra University. The instrument of this research used a questionnaire and interview. Twenty students participated in the questionnaire, and six out of twenty students participated in the interview. The data was analyzed by using dichotomous questions analysis. The results revealed that performance conditions and affective factors affect the students’ problems in speaking performance. The results indicate that planning was the major frequency in performance conditions, followed by support, the standard of performance, and time pressure. At the same time, self-confidence and anxiety became the major categories of affective factors and were followed by the motivation category. The writer suggests further research to investigate another factor affecting students’ problems in speaking performance with different focuses in terms of participants' scale, students’ level, and kind of speaking performance.
Explanation-Based Tool for Helping Data Producers to Reduce Privacy Risks

This paper demonstrates the interactive user interface PrivEx that helps data producers to reduce privacy risks raised by data collection from service providers in the Semantic Web of Things. PrivEx provides several types of support to data producers in their management of the tension between the privacy risks and the utility of the data they accept to publish. PrivEx is grounded on the formal framework presented in [1] for detecting automatically privacy risks raised by utility queries. In the demonstration, we will illustrate the functionalities of PrivEx, on a smart meter scenario inspired by a real-world use case providing time series of electrical consumptions of different customers associated with some metadata on their demographics, home sizes and equipment.
Toward biologically plausible artificial vision

Quilty-Dunn et al. argue that deep convolutional neural networks (DCNNs) optimized for image classification exemplify structural disanalogies to human vision. A different kind of artificial vision - found in reinforcement-learning agents navigating artificial three-dimensional environments - can be expected to be more human-like. Recent work suggests that language-like representations substantially improves these agents' performance, lending some indirect support to the language-of-thought hypothesis (LoTH).
Symmetrical SyncMap for imbalanced general chunking problems

Recently, SyncMap pioneered an approach to learn complex structures from sequences as well as adapt to any changes in underlying structures. This is achieved by using only nonlinear dynamical equations inspired by neuron group behaviors, i.e., without loss functions. Here we propose Symmetrical SyncMap that goes beyond the original work to show how to create dynamical equations and attractor-repeller points which are stable over the long run, even dealing with imbalanced continual general chunking problems (CGCPs). The main idea is to apply equal updates from negative and positive feedback loops by symmetrical activation. We then introduce the concept of memory window to allow for more positive updates. Our algorithm surpasses or ties other unsupervised state-of-the-art baselines in all 12 imbalanced CGCPs with various difficulties, including dynamically changing ones. To verify its performance in real-world scenarios, we conduct experiments on several well-studied structure learning problems. The proposed method surpasses substantially other methods in 3 out of 4 scenarios, suggesting that symmetrical activation plays a critical role in uncovering topological structures and even hierarchies encoded in temporal data.
An Inspection Technique Using Fit Clearance Based on Microscopic Vision in Precision Assembly

Inspection is a crucial process to ensure product quality. In the precision assembly of an optic-mechanical device, a part with micro multi-section arcs needs to be inspected and assembled into another part. Actually, because of machining errors, including dimensional and geometric shapes, can lead to complex deformation modes for parts with micro multi-section arcs, posing challenges to their inspection. Furthermore, inconsistencies in feature images in microscopic vision may complicate the extraction of the Region of Interest (ROI). To address these issues, this paper proposes an ROI extraction method based on the CAD model for rough positioning of feature points and connected region detection for refinement. Subsequently, based on feature points, the CAD model is used again to obtain the ROI. For inspection purposes, this paper proposes a method suitable for micro multi-section arcs based on assembly fit requirements. Experimental testing was performed on parts with eight-section arcs and mirrors to verify the effectiveness of the proposed method. This method provides a suitable solution for the inspection of micro multi-section arcs in precision assembly with the potential to improve the accuracy of the inspection results.
Knowledge Graph Based Augmented Reality Work Instruction for Wire Harness Final Assembly on Formboard

The final assembly of wire harness on formboard is an important phase for harness development of modern mechatronic products. Process visualization based on augmented reality (AR) can superimposes the information in front of the workers' view to facilitate their work. An framework for wire harness final assembly (WHFA) based on AR visualization is proposed in this paper. The assembly process information and its visualization format are studied in this paper for wire harness final assembly. A unified description of assembly process is presented based on knowledge graph method, which provides a formal framework for consistent modeling of the wire harness data for information integration. In order to assistant the wire harness assembly tasks, different visual assets are design to deliver the required assembly information. The visual assets can help the workers to localize the object/position, understand the operating procedure and display the important digital number. A XML file which are generated from the knowledge graph is used to describe the wire harness assembly step and the corresponding AR visualization format for shop floor work instruction on formboard. An experimental AR based wire harness final assembly instruction system is developed. Examples are given to show the augmented assembly information instruction of a wire harness final assembly on formboard by using a common monitor display at a fixed workstation with a static overhead camera.
Data class-specific all-optical transformations using diffractive computing

We present data class-specific transformation diffractive networks that all-optically perform different preassigned transformations for different input data classes. The visual information encoded in the amplitude, phase, or intensity channel of the input field is all-optically processed and transformed/encrypted by the diffractive network. The amplitude or intensity of the resulting field approximates the transformed/encrypted input information using the transformation matrix specifically assigned for that data class. We experimentally validated this class-specific transformation framework by designing and fabricating two diffractive networks at 1550nm and 0.75mm wavelengths. The presented framework provides a fast, secure, and energy-efficient solution to data encryption applications.
All-optical Computation of a large group of linear transformations using a broadband diffractive optical network

We explore the parallel information processing capacity of a broadband diffractive optical network and demonstrate that a single diffractive network could perform a large group of arbitrarily-selected, complex-valued linear transformations between its input and output fields-of-view at different wavelengths, accessed sequentially or simultaneously. Through deep learning-based training of the thickness values of its diffractive features, we demonstrate that a wavelength-multiplexed diffractive processor can implement W>180 complex-valued linear transformations with a negligible error when its number of trainable diffractive features approaches 2W×I×O, where I and O refer to the number of input and output pixels, respectively.
All-optical quantitative phase imaging through random diffusers using a diffractive network

We present a novel approach to perform quantitative phase imaging (QPI) through random phase diffusers using a diffractive neural network consisting of successive diffractive layers optimized using deep learning. This diffractive network is trained to convert the phase information of samples positioned behind random diffusers into intensity variations at the output, enabling all-optical phase recovery and quantitative phase imaging of objects hidden by unknown random diffusers. Unlike traditional digital image reconstruction methods, our all-optical diffractive processor does not require external power beyond the illumination beam and operates at the speed of light propagation.
Time-lapse image classification using a diffractive optical network

We present a time-lapse approach for image classification that significantly improves the inference of a standalone diffractive optical network. This approach utilizes the information diversity derived from controlled or random lateral displacements of the objects relative to a diffractive optical network, over a finite integration time at the image sensor, to enhance its generalization and statistical inference performance. By employing this time-lapse training and inference, we achieved a numerical blind testing accuracy of 62.03% on grayscale CIFAR-10 images, which represents the highest classification accuracy for this dataset achieved so far using a single diffractive network.
Nonlinear spatiotemporal optical computing

In the field of machine learning, large datasets are essential for heavy tasks. However, the performance of power-hungry processors is limited by the data transfer to and from memory. Optical computing has been gaining interest as a means of high-speed computation, and here we present an optical computing framework called scalable optical learning operator based on spatiotemporal effects in multimode fibers. This framework is capable of performing various learning tasks, such as classifying COVID-19 X-ray lung images, speech recognition, and age prediction from face images. Our approach addresses the energy scaling problem without compromising speed by leveraging the simultaneous, linear and nonlinear interaction of spatial modes as a computation engine. Our experiments demonstrate the accuracy of our method comparable to a digital implementation.
The impact of artificial intelligence on modern education: prospects and challenges

This paper examines the intricate links between artificial intelligence (AI) and education, delving into both theoretical and practical aspects while evaluating possible ramifications for labor market dynamics, professional activity, and wider educational paradigms. Our research methodology involved analyzing relevant scientific literature, classifying data, consulting with subject matter experts, and synthesizing the results. Our research suggests that AI has the ability to greatly improve pedagogical processes, personalize learning experiences to meet individual student needs, and successfully address the time and financial limitations that are inherent in traditional educational models. However, our study also reveals challenges related to data confidentiality, potential plagiarism and fraud associated with AI use, and socioeconomic disparities resulting from unequal technology access. Additionally, we identified a significant gap in current AI usage standards legislation. It is essential for researchers, educators, and policymakers to recognize the potential risks of AI implementation in educational settings and proactively develop strategies that prioritize ethics, safety, and effectiveness. With labor market trends favoring specialists knowledgeable in utilizing AI tools, a consequent change in curricula is expected. In response to our findings, we recommend the creation of new academic disciplines that concentrate on the cultivation of AI expertise; the establishment of comprehensive national AI strategies; the crafting of retraining roadmaps for those who may be affected by AI automation; the inclusion of online AI courses in existing educational programs; and the promotion of grant funding for future AI research. Our future research will concentrate on reducing the potential negative impacts of integrating AI into educational systems.
Welcome to the 8th International Workshop on Empirical Requirements Engineering (EmpiRE 2023)

A message from the workshop chairs of the 8th International Workshop on Empirical Requirements Engineering, co-located with the 31st IEEE International Requirements Engineering Conference (RE 2023) Hannover, Germany, September 4–8, 2023.
A Novel Software Tool for Fast Multiview Visualization of High-Dimensional Datasets

Scatterplot is a popular technique for visualizing high-dimensional datasets by using linear and nonlinear dimension reduction methods. These methods map the original high-dimensional dataset onto scatterplot points directly by dimension reduction, and hence require a high computation cost. Despite many improvements in scatterplot visual effects, however, when the data volume is large, the data mapped onto scatterplot data points will overlap, resulting a low quality of visualization. In this paper, we propose a novel software tool that ensembles five integrated components for fast multiview visualization of high-dimensional datasets: sampling, dimension reduction, clustering, multiview collaborative analysis, and dimension re-arrangement. In our tool, while the sampling component reduces the sizes of the datasets applying the random sampling technique to gain a high visualization efficiency, dimension reduction reduces the dimensions of the datasets applying principal-component analysis to improve the visualization quality. Next, clustering discovers hidden information in the reduced dataset applying fuzzy c-mean clustering to display hidden patterns of the original datasets. Finally, multiview collaborative analysis enables users to analyse multidimensional datasets from different aspects at the same time by combining scatterplot and scatterplot matrices. To optimize the visualization effects, in the scatterplot matrices, we re-arrange their dimensions and adjust the positions of scatterplots so that similar scatterplot points are adjacent in positions. As the result, in comparison with the existing visualization tools that apply some of these techniques, our tool not only improves the efficiency of dimension reduction but also enhances the quality of visualization and enables more comprehensive analysis. We test our tool on different real datasets to demonstrate its effectiveness. The experimental results validate that our method is effective in both efficiency and quality of visualization.
A Sequential Decision Algorithm of Reinforcement Learning for Composite Action Space

It is the key research object of electronic warfare to use UAV( Unmanned Aerial Vehicle) clusters to carry out electronic countermeasure tasks. The UAV carries loads such as reconnaissance and interference at the same time, which makes it necessary to simultaneously decide multiple types of actions—namely, compound actions—which poses a challenge to intelligent decision-making algorithms. Considering the problem of action-space dimensional complexity and weak collaboration between decisions in multi-agent scenarios with composite actions, this study proposed a decision algorithm involving a multi-agent reinforcement-learning sequence, which combined joint composite actions into sequential decision, reducing the difficulty of a single decision and enhancing the collaboration between various agents and their individual decisions. Because long decision sequences required better depth modeling and had high variance, a DeLighT module was added to the naïve transformer model to increase the depth and baseline techniques, which were used to reduce the variance in the value estimation. The simulated results verified the effectiveness of the proposed algorithm in the UAV cooperative combat scenario, where each agent had a composite action space and showed better performance than the existing algorithms.
INTERACTIVE CHATBOTS: A WAY FOR THE DEVELOPMENT OF THE EDUCATIONAL SECTOR

In this day and age, Chatbots are becoming vital for many organizations and consortiums to negotiate with their customers. They are also being developed for educational Institutes to provide students of Institutes with services and support very quickly and efficiently. This paper provides the recapitulation of Chatbot usage and its primary aim of being designed for accessing information about an educational institute and assisting professors, students, and all other members who are in need of information and requires a speedy response from any corner of the world without being present in the Institute’s premises. Educational chatbots ameliorate profitability and correlation, and confine ambiguity from communications. It builds a between the Institute’s official site and its users. The Educational Chatbot has the potential to reply to any student’s, teacher’s, or other member's queries, which can be related to any information such as the Institute’s academics and admission process, assignments, research, administration, important events, etc. Chatbots guarantee that all the data will be made accessible to any individual day in and day out.
UAV GPS Spoofing Detection via Neural Generative One-Class Classification

Unmanned Aerial Vehicles (UAVs) are increasingly used for civilian applications but remain vulnerable to spoofing attacks due to their reliance on unencrypted Global Positioning System (GPS) signals. While traditional hardware-based detection methods are effective, they are often inflexible and costly. Through comprehensive simulations and comparative analysis, this study delves into the potential of neural generative one-class classifiers, particularly Generative Adversarial Networks (GANs), for GPS spoofing detection in civilian UAVs. Results indicate that these models provide consistent and computationally efficient one-class classification. Future research directions include exploring further extensions of these models and testing in real-world UAV scenarios.
The Spherical Retractable Bubble Space: An Egocentric Graph Visualization throughout a Retractable Visualization Space

In this paper, we present a new egocentric metaphor for graph visualization that consists in positioning a graph between two concentric spheres of different radii. It improves the expansion of nodes in space, contrary to 3D spatialization algorithms. The edge drawing is optimized by pushing all the edges into the area delimited by our two concentric spheres so that a user can move freely without being encumbered by edges. Our new metaphor also makes it possible to reduce the display angles in order to have a global view of the graph without leaving the egocentricity.