Reviewerly/exHarmony huggingface dataset:
We Won’t Get Fooled Again: When Performance Metric Malfunction Affects the Landscape of Hyperparameter Optimization Problems

Hyperparameter optimization (HPO) is a well-studied research field. However, the effects and interactions of the components in an HPO pipeline are not yet well investigated. Then, we ask ourselves: Can the landscape of HPO be biased by the pipeline used to evaluate individual configurations? To address this question, we proposed to analyze the effect of the HPO pipeline on HPO problems using fitness landscape analysis. Particularly, we studied over 119 generic classification instances from either the DS-2019 (CNN) and YAHPO (XGBoost) HPO benchmark data sets, looking for patterns that could indicate evaluation pipeline malfunction, and relate them to HPO performance. Our main findings are: (i) In most instances, large groups of diverse hyperparameters (i.e., multiple configurations) yield the same ill performance, most likely associated with majority class prediction models (predictive accuracy) or models unable to attribute an appropriate class to observations (log loss); (ii) in these cases, a worsened correlation between the observed fitness and average fitness in the neighborhood is observed, potentially making harder the deployment of local-search-based HPO strategies. (iii) these effects are observed across different HPO scenarios (tuning CNN or XGBoost algorithms). Finally, we concluded that the HPO pipeline definition might negatively affect the HPO landscape.
Performance Evaluation of Smart Flower Optimization Algorithm Over Industrial Non-convex Constrained Optimization Problems

The large number of dimensions, complexity and constraints of real world problems is the deriving force to assess the algorithms in diverse applications scenarios. Over a past decade, nature inspired algorithm are seen as potential solutions to solve continuous optimization problems. These are non-deterministic algorithms with proven abilities to find near optimal solutions. However, according to not a single free lunch theorem, nor an any algorithm is suitable for solving all kinds of problems. This work present performance evaluation of recent meta-heuristic algorithm that is Sun Flower Optimization Algorithm (SFOA) over Non-Convex Constrained Optimization problems industrial problems from domains like mechanical engineering, chemical processes and Process synthesis and design problems. SFOA is compared with IUDE, MAgES, iLSHADE 424 for 15 problems with varied number of equality and inequality constraints. Results reveal that SFOA performs better for two out of four industrial chemical problems and five out of six process design and synthesis problems. Problems by mechanical engineering, IUDE, MAgES, iLSHADE 424 outperform SFOA for 4 out of five problems.
Securing Advanced Metering Infrastructure Using Blockchain for Effective Energy Trading

To effectively regulate the energy supply, Advanced Metering Infrastructure (AMI) deployment is gaining momentum in various parts of the world. Energy distributors, service providers, and consumers must work together to address several issues. All the transactions need to be documented properly and securely. Third parties can be trusted in those transactions by using blockchain. With the deployment of Advanced Metering Infrastructure (AMI) and distributed ledgers, blockchain may aid in safeguarding and facilitating the movement of data. This paper discusses the viability of utilizing blockchain for advanced metering infrastructure, along with the security risks and threat landscape. For the exchange of energy, we have proposed an Ethereum-based blockchain model, and results are discussed for the scenario when an independent house’s smart meter and distribution system operator trades energy.
Effect of women’s political inclusion on the level of infrastructures in Africa

The need for gender inclusion was highlighted as the fifth sustainable development goal (SDG) (i.e. SDG5) and policies have been gearing towards attaining this objective and its subsequent effect on macroeconomic outcomes. Equally, the demonstrated trend of infrastructures in Africa in terms of stocks and future need is unique compared to the rest of the world. The objective of this study is therefore to empirically examine the effect of women's political inclusion on infrastructural development in Africa. The results through the system GMM and Quantile Regression techniques show that women's political inclusion enhances infrastructural development in Africa. The result is robust across different measures of infrastructures and political inclusion. Besides, the positive relationship is maintained across income groups, levels of political stability and export structure. However, the effect is not significant in countries with infrastructural scores around the extreme quantiles. The results of the study recommend African policy makers to prioritise the inclusion of women in the political agenda as one of the strategies towards the development of infrastructures. This could come through the putting in place of laws that favour women's participation in politics. Moreover, the countries should ratify international conventions that favour gender inclusion.
A Real-Time Face Recognition Attendance Using Machine Learning

Systems for managing attendance are crucial for all organizations. One of the most often utilized biometrics for verifying human identity is the face. Most businesses in Rwanda utilize logbooks, cards, or fingerprints to track employees' attendance at work. However, because of COVID-19, which is an infectious disease, attendance has been discontinued due to concerns over its spread. This study describes real-time Face recognition attendance using Machine Learning, an Internet of Things (IoT)-based biometric face recognition solution. To capture the live streaming video, a high-quality camera with a Sony IMX477 sensor and a 16mm 10MP Telephoto lens connected to a Raspberry Pi 4 Model B, can send frames at a time to the cloud. The pre-trained FaceNet model is employed by the system to extract features from a face image after using MTCNN (Multi-Task Cascaded Convolutional Neural Networks) to recognize facial landmarks on images. Real-time image processing is done in the cloud, and attendance is recorded on a dashboard that is accessible from anywhere. The system sends the email to the employee using SMTP Protocol in case of arriving late /absent without permission. The result reveals that the system is safe, dependable, trustworthy, and does not require physical touch.
The Role of Mobile Technologies in Blended Learning Environments

Mobile technologies exist in a wide spectrum and have a great impact on today’s world. One of the reasons of mobile technologies’ becoming a vital part of everyday life may be various features they possess such as easy access to content with no time and limit boundaries. This has brought up the question whether they may assist language learning. Considering Web 2.0 tools in the context of mobile technologies, for this study a well-known Web 2.0 tool has been chosen to investigate its effects, Duolingo, on students’ academic performance when used in blended learning settings to support formal language education. The academic performance levels of students are determined before and after the intervention in a high school in accordance with an instructional design prepared to support students in and out of school hours. Data were analyzed by using descriptive analyses techniques. The results of the study are discussed considering the significant difference in students’ academic performance after the intervention. It can be concluded that there are possible advantages of integrating a Web 2.0 tool in blended learning contexts.
Continuous Latent Spaces Sampling for Graph Autoencoder

This paper proposes colaGAE, a self-supervised learning framework for graph-structured data. While graph autoencoders (GAEs) commonly use graph reconstruction as a pretext task, this simple approach often yields poor model performance. To address this issue, colaGAE employs mutual isomorphism as a pretext task for a continuous latent space sampling GAE (colaGAE). The central idea of mutual isomorphism is to sample from multiple views in the latent space and reconstruct the graph structure, with significant improvements in terms of the model’s training difficulty. To investigate whether continuous latent space sampling can enhance GAEs’ learning of graph representations, we provide both theoretical and empirical evidence for the benefits of this pretext task. Theoretically, we prove that mutual isomorphism can offer improvements with respect to the difficulty of model training, leading to better performance. Empirically, we conduct extensive experiments on eight benchmark datasets and achieve four state-of-the-art (SOTA) results; the average accuracy rate experiences a notable enhancement of 0.3%, demonstrating the superiority of colaGAE in node classification tasks.
Acknowledgment of patient in sense behaviors using bidirectional <scp>ConvLSTM</scp>

Abstract Recognizing patient activity in real‐time from video or images collected by a CCTV camera available in the hospital during a Covid‐19 situation has proven challenging. The dilemma of patient activity recognition is identifying and recognizing a patient's various actions in a series of videos. The process presented in our paper needs to achieve unrestricted, generic behavior in videos. Detecting events in any video is often difficult because we use Bidirectional ConvLSTM to create a robust patient in the sense behaviors (PSB) framework capable of eliminating certain barriers. To begin this paper by proposing a new Bidirectional ConvLSTM for establishing a stable PSB scheme. Our proposed model is capable of accurately predicting patient's behaviors like seated, standing, and so on. Using Bidirectional ConvLSTM, learning information from a pre‐trained model is an excellent place to start for rapidly developing a new PSB system using a current PSB database, as both the source and target datasets are critical. All parameters are frozen in a pre‐trained PSB device. Then, using the UCI and HMDB51 dataset to train the model, variables and local relations are progressively fixed. A novel PSB framework is developed using the target dataset. Relevant tests are conducted using commonly used research indices to assess prediction precision accuracy. They acknowledge six patient's behavior with a weighted accuracy rate of 92%. For recognizing novel activity, laying, the precision of a corresponding prediction is the best, 91%, of all six test results. The proposed work uses bidirectional ConvLSTM with modified activation layers to sense the patients' behavior. This article may be a patient activity recognition system to identify an individual. It takes a clip of COVID‐19 patients as input and looks for matches inside the hold‐on images.
Real-Time Detection of Unrecognized Objects in Logistics Warehouses Using Semantic Segmentation

Pallet detection and tracking using computer vision is challenging due to the complexity of the object and its contents, lighting conditions, background clutter, and occlusions in industrial areas. Using semantic segmentation, this paper aims to detect pallets in a logistics warehouse. The proposed method examines changes in image segmentation from one frame to the next using semantic segmentation, taking into account the position and stationary behavior of newly introduced objects in the scene. The results indicate that the proposed method can detect pallets despite the complexity of the object and its contents. This demonstrates the utility of semantic segmentation for detecting unrecognized objects in real-world scenarios where a precise definition of the class cannot be given.
Structure markers

Abstract In this paper we revisit the association of types of structure with modes of meaning in systemic functional linguistics (SFL). Focusing mainly on nominal group grammar, we argue that the association of experiential structure with non-recursive systems realised by multivariate structures and logical structure with recursive systems realised by univariate structures needs to be relaxed – in recognition of dependency structures consisting of two elements in a head/dependent relation. We refer to such structures as subjacency duplexes and explore their potential for the analysis of what are often dismissed in SFL as structure markers – adpositions, linkers and binders in particular.
Evaluation of the School Literacy Movement Program in Senior High School

The government launched a school literacy program at the education level to increase students' interest in reading and writing. This study was conducted to analyze how the literacy program works. This research is using CIPP evaluation model. The method used in this study is a qualitative research method with an evaluation research design by conducting observations, interviews, and examining program implementation documents. Sources of information were obtained through interviews with principals, educators, and students. The research results on the context component show that the CIPP of the School Literacy Movement of senior high school was successfully implemented. The literacy program is implemented with stages 1) Habituation of reading non-lesson books for 15 minutes before learning begins. 2) Learning with foreign language book reading activities, be it English, French, and Japanese books. 3) Development by requiring grade XII students to write a novel with a target of 1 school year which will be printed in April. These three stages follow the School Literacy Movement (GLS) guidelines.
Realization and Optimization of Combinational Circuits Using Simulated Annealing and Partitioning Approach

Combinational logic circuits (CLCs) are basic building blocks of a system and optimization of these circuits in terms of reduced gates, transistors, or levels will lead to reduced area on chip, reduced power, and improved speed. Simulated annealing (SA) is a thermo-inspired metaheuristic used for solving various engineering and non-engineering problems. SA is also used for the realization and optimization of CLCs. Circuits with a large number of inputs and outputs require more generations for realization. Realization of the optimal circuit with fewer generations is desired as realization time increases with increase in the number of generations. In this paper, an attempt is made to realize circuits using population-based SA with fewer generations. SA with partitioning approach is proposed in this paper for circuits that could not be realized with fewer preset generations. To evaluate the performance of the proposed work, benchmark circuits from LGSynth'91 are considered, and it is observed that the success rate improved and realization time reduced with the proposed partitioning approach. During the evaluation, it is also observed that the gate count was reduced by 2.5–77.39% and the transistor count was reduced by 7.69–95.53% on using proposed work with fewer generations over circuits reported in the literature.
Integrative Review of International Publications about Open Banking

Open Banking is an emerging and relevant topic in the banking industry that has been explored worldwide.This refers to an initiative aimed at promoting the integration of banking systems through an open platform, allowing different financial institutions to share their clients' data and services in a secure and standardized manner.Given the relevance of the subject, there is a need to understand scientific publications on the topic and highlight their contributions to the Science of Administration.In this sense, this article aims to conduct an integrative analysis of the literature on Open Banking.This work involved a structured process of selection and integrative analysis based on four databases, which resulted in 425 works.These studies underwent a filtering process, resulting in a portfolio of 26 articles aligned with the subject and representing the current state of the topic from the researcher's perspective.The results indicate that the studies are focused on topics such as system integration, data security, and operational models, mainly related to the initial phases of Open Banking implementation.Additionally, the study highlights the lack and need for studies focused on corporate and business aspects, such as competition strategies, management models, and strategic positioning in this new market.
Manipulation of single stored-photon with microwave field based on Rydberg polariton

We demonstrate a coherent microwave manipulation of a single optical photon based on a single Rydberg excitation in an atomic ensemble. Due to the strong nonlinearities in a Rydberg blockade region, a single photon can be stored in the formation of Rydberg polariton using electromagnetically induced transparency (EIT). The manipulation of the stored single photon is performed by applying a microwave field that resonantly couples the nS1/2 and nP3/2, while the coherent readout is performed by mapping the excitation into a single photon. We achieve a single photon source with g(2)(0) = 0.29 ± 0.08 at 80S1/2 without applying microwave fields. By implementing the microwave field during the storage time and retrieval process, we show the Rabi oscillation and modulation of stored photons that can be controlled to retrieve early or late. Rapid modulation frequencies up to 50 MHz can be obtained. Our experimental observations can be well explained via numerical simulations based on an improved superatom model accounting for the dipole-dipole interactions in a Rydberg EIT medium. Our work provides a way to manipulate the stored photons by employing the microwave field, which is significant for developing quantum technologies.
An Automated Method of 3D Facial Soft Tissue Landmark Prediction Based on Object Detection and Deep Learning

Three-dimensional facial soft tissue landmark prediction is an important tool in dentistry, for which several methods have been developed in recent years, including a deep learning algorithm which relies on converting 3D models into 2D maps, which results in the loss of information and precision.This study proposes a neural network architecture capable of directly predicting landmarks from a 3D facial soft tissue model. Firstly, the range of each organ is obtained by an object detection network. Secondly, the prediction networks obtain landmarks from the 3D models of different organs.The mean error of this method in local experiments is 2.62±2.39, which is lower than that in other machine learning algorithms or geometric information algorithms. Additionally, over 72% of the mean error of test data falls within ±2.5 mm, and 100% falls within 3 mm. Moreover, this method can predict 32 landmarks, which is higher than any other machine learning-based algorithm.According to the results, the proposed method can precisely predict a large number of 3D facial soft tissue landmarks, which gives the feasibility of directly using 3D models for prediction.
ANALISIS DISPERSI EMISI CEROBONG PEMBANGKIT LISTRIK DI PT. INDONESIA POWER BALI PGU PESANGGARAN

Air as an important environmental component in life that needs to be maintained and improved in quality. The presence of human activity causes the composition of clean air to undergo changes. Air pollution is caused by several factors. One of them is the industrial activities of power plants that use fossil fuels and natural gas. This research was conducted to monitor the distribution of emissions produced by power plants in PT. Indonesia Power Bali PGU uses Geographic Information System (GIS) technology and is validated using the paired t-test method. The result is that the direction of distribution of each emission parameter (SO2, NO2 and Dust (TSP)) follows the wind direction according to data obtained from BMKG with a downward trend according to the distance traveled. This is because emissions are propagated by wind with dispersion variables at any given distance. The degree of validity of the modeling analysis with the results of ambient air quality measurements at each monitoring point of the SO2 and NO2 parameters is no difference between the results obtained. However, for dust parameters (TSP) there are significant differences. This is due to atmospheric conditions at the time of sampling, the location of the monitoring point surrounded by buildings, 1 sample point being on the side of the highway, and community activities around the monitoring point location.&#x0D; Keywords: Power Plant Emission; Air Quaity Control; Geographic Information System (GIS); Gaussian Disperssion
PRACTICES OF BENCHMARKING, VULNERABILITY IN THE COMPUTER VISION PIPELINE

Computer vision datasets have proved to be key instruments to interpret visual data. This article concentrates on benchmark datasets which are used to define a technical problem and provide with a common referent against which different solutions can be compared. Through three case studies, ImageNet, the Pilot Parliaments Benchmark dataset and VizWiz, the article analyzes how photography is mobilised to conceive interventions in computer vision. The benchmark is a privileged site where photographic curation is tactically performed to change the scale of visual perception, oppose racial and gendered discrimination or rethink image interpretation for visually impaired users. Through the elaboration of benchmarks, engineers create curatorial pipelines involving large chains of heterogeneous actors exploiting various photographic practices from amateur snapshot to political portraiture or photography made by blind users. The article contends that the mobilisation of photography in the benchmark goes together with a multifaceted notion of vulnerability. It analyzes how various forms of vulnerabilities and insecurities pertaining to users, software companies, or vision systems are framed and how benchmarks are conceived in response to them. Following the alliances that form around vulnerabilities, the text explores the potential and limits of the practices of benchmarking in computer vision.
VIDEO MOTION GRAPHIC PROFIL PERPUSTAKAAN UMUM DAERAH MR. BESAR MARTOKOESOEMO KOTA TEGAL SEBAGAI MEDIA PENGENALAN KEPADA MASYARAKAT KOTA TEGAL

As a Regional Library a source or center of literature in the city of Tegal should be able to deliver information and promotion only limited to print media and content design for social media needs, then there should be effective alternative media to increase promotion. The problems faced in this regard, in the form of advances in social media especially the younger generation, thus demanding the library in its introduction and in providing science-related information. This research aims to produce a profile video based on motion graphics to attract the reading interest of the Tegal community, especially the younger generation. The research methods used are R and D conducted by finding potentials and problems, literature review, collecting information, and observations for subsequent analysis to moving graphic design in the form of initial ideas and concepts, scripts and storyboards, character design, and animating, editing and music. Then it is validated through FGD to make improvements. The end result of this study is a profile video in the form of motion graphics with a flat style graphic style that aims to facilitate understanding and is expected to foster interest in reading about the people of Tegal and surrounding areas, especially the younger generation.