Optimalisasi Pemahaman Dasar-dasar Fotografi melalui Model Pembelajaran Berbasis Masalah (PBL) dengan Pendekatan Pembelajaran Berdiferensiasi dan Quizziz di SMKIT Robbani Singosari

Abstract: This research investigates the effectiveness of implementing the Differentiated Learning Approach and the Quizziz platform to enhance the understanding of the fundamentals of photography at the Robbani Singosari Integrated Islamic Elementary School (SDIT) level. Challenges in photography learning, such as passive student engagement, an inadequately conducive learning environment, difficulty comprehending the material, monotonous teaching methods, and the incongruity of media with student characteristics, serve as the backdrop for this study. The incorporation of the Differentiated Learning Approach facilitates the adjustment of material to accommodate diverse learning styles, addressing the challenges encountered in the learning process. Additionally, the study centers on the utilization of Quizziz as an evaluative tool, revealing students' positive responses to the engaging and interactive learning experience facilitated by the platform. The research findings underscore the substantial role of the Differentiated Learning Approach in augmenting learning efficacy, particularly in surmounting obstacles to material comprehension and establishing an inclusive learning milieu. The employment of Quizziz as an assessment tool not only validates its effectiveness in gauging student comprehension but also introduces an entertaining and interactive facet that enriches the overall learning experience. This study aligns with prior research affirming the constructive influence of the Differentiated Learning Approach and the amalgamation of online platforms in fostering heightened student interest and engagement. Implications of these findings encompass recommendations for refining learning strategies at the elementary school level, especially within the context of photography education. The integration of the Differentiated Learning Approach and Quizziz is anticipated to make a significant contribution to enhancing student comprehension and cultivating a more gratifying learning experience in the current digital era.


Cryptanalysis of efficient semi-quantum secret sharing protocol using single particles

Abstract: Abstract In the paper[Chin. Phys. B 32 (2023) 070308], Xing et al . proposed a semi-quantum secret sharing protocol by using single particles. We study the security of the proposed protocol and find that it is not secure, that is, the three dishonest agents, Bob, Charlie and Emily can collude to obtain Alice’s secret without the help of David.


Enhancing Cognitive and Readiness Skills: An Integrated Assessment of Perceptual and Memory Abilities for SLD students

Abstract: Abstract: This study explores how cognitive and readiness skills in students with Specific Learning Disabilities (SLD) can be improved through an integrated assessment of their perceptual and memory abilities. SLD students often struggle with processing information, retaining knowledge, and preparing for tasks in academic environments. Recognizing the crucial link between perceptual and memory abilities is essential for tailoring interventions to support these students effectively. Utilizing a comprehensive assessment framework, the research aims to elucidate how perceptual and memory abilities influence cognitive processing and readiness skills in SLD students. It employs a variety of methodologies, including traditional psychometric measures and innovative approaches such as virtual reality simulations and cognitive task analyses, to offer a holistic perspective By integrating multiple methodologies, the study seeks to uncover the intricate interplay between perceptual and memory abilities and their impact on cognitive readiness among SLD students. The findings are expected to guide the development of targeted interventions and educational practices tailored to their unique needs. The implications of this research extend beyond academia, potentially informing the design of training programs and cognitive enhancement interventions for SLD students. By addressing their specific challenges, the study aims to foster inclusivity and equity in education. This research highlights the importance of assessing perceptual and memory abilities in SLD students to enhance their cognitive readiness and academic outcomes. By identifying factors contributing to their cognitive challenges, educators and practitioners can devise more effective strategies to support their learning and development.


Assistance in Preparing Final Reports for Educational Internship Students at SMA Negeri 01 Buay Madang

Abstract: Assistance in making final reports for educational internship students at SMA Negeri 01 Buay Madang includes several key points. Students who undertake educational internships at these schools have valuable experience in observing, interacting, and contributing to the educational environment. The final report reflects students' in-depth understanding of the learning process, school management, and the dynamics of teacher-student relationships at the high school. In the report, students present an evaluation of learning methods, challenges faced, and recommendations for improvement. Apart from that, student assistance also includes understanding the school curriculum and student involvement in extracurricular activities. In conclusion, the intern students succeeded in integrating educational theory with practice in the field, proving their professional growth and contribution to educational development at SMA Negeri 01 Buay Madang.


Impact of Motivation on Employee Performance of Selected Real Estate Firms in Abuja

Abstract: This study examined how motivation affected worker performance in selected Abuja real estate companies. The research design employed in the study is a survey and data were collected through the primary source where 66 structured questionnaires were distributed among the staff of five real estate companies in Abuja which include Osakwe & Associates and Wale Adewusi Associate. Data collected were analyzed using multiple regression analysis. The findings showed that employee motivation has a favorable and significant impact on performance in terms of job efficiency in Abuja real estate. Other findings included the fact that job security has a favorable yet negligible effect on the performance in terms of work efficiency in Abuja real estate. Salary and wages also have a positive and large impact on performance in terms of work efficiency in Abuja real estate, as well as rewards, which have a favorable and big impact on both. According to the report, real estate companies in Abuja should keep up their efforts to motivate staff members by guaranteeing their jobs, paying them a living wage, and rewarding them because doing so improves productivity.


Adaptive and Robust Following of 3D Paths by Holonomic Manipulator

Abstract: This paper addresses the problem of following three‐ dimensional path by holonomic manipulator with para‐ metric or structural uncertainty in the dynamics. De‐ scription of the manipulator relative to a desired three‐ dimensional path was presented. The path is parame‐ terized orthogonally to the Serret‐Frenet frame which is moving along the curve. The adaptive and robust control laws for stationary manipulator which ensures realiza‐ tion of the task were specified. Theoretical considerations are supported by the results of computer simulations con‐ ducted for RTR manipulator.


Skeleton-based Human Activity Classification in Sparse Image Sequences

Abstract: Research results on human activity classification in video are described, based on initial human skeleton estimation in video frames. Both single person actions and two-person interactions are considered. The initial skeleton data is estimated in selected video frames by OpenPose, HRNet or other dedicated library. Important contributions of presented work are computational steps of skeleton tracking and -refinement, and relational feature extraction from pairs of skeleton joints. It is shown, that this feature engineering significantly increases the classification accuracy. Regarding the final neural network encoder-classifier, two different architectures are designed and tested. The first solution is a lightweight MLP network, implementing the idea of a "mixture of pose experts". Several pose classifiers (experts) are trained independently on different time periods (snapshots) of single-person visual actions (or 2-person interactions), while the final classification is a time-related pooling of weighted expert classifications. All pose experts use the same deep encoding network. The second (middle weight) solution is based on a LSTM network.Both solutions are trained and tested on the action set of the well-known NTU RGB+D dataset, although only 2D data are used.Our results show comparable performance with some of the best reported STM- and CNN-based classifiers for this dataset. We conclude that by reducing the noise of skeleton data, highly successful lightweight- and midweight-approaches to visual activity recognition in image sequences can be achieved.


Multimodal Robot Programming Interface Based On RGB-D Perception and Neural Scene Understanding Modules

Abstract: In this paper, we propose a system for natural and intuitive interaction with the robot. Its purpose is to allow a person with no specialized knowledge and no training in robot programming to program a robotic arm.We utilize data from the RGB-D camera to segment the scene and detect objects. We also estimate the configuration of the operator's hand and the position of the visual marker to determine the intentions of the operator and the actions of the robot. To this end, we utilize trained neural networks and operations on the input point clouds. Also, voice commands are used to define or trigger the execution of the motion. Finally, we performed a set of experiments to show the properties of the proposed system.


The Development of Teacher Training Curriculum for Organizing of Learning Experiences to Enhance Social Development for Persons with Autism Spectrum Disorder

Abstract: The present study sought to (1) assess the needs in the development of the teacher training curriculum on providing learning experiences for promoting social development among persons with autism spectrum disorder (ASD), (2) develop the curriculum, and (3) examine its effectiveness. A mixed-method approach and research and development were adopted. The data were collected during July 2022–April 2023. The results are reported: 1. A review of documents, literature and related studies showed that teacher preparation was required for promoting persons with ASD’s social development. Additionally, the survey indicated that the school directors’ primary need for self-improvement was the frequency of attending training programs (PNIModified = 0.54), while that of the teachers was knowledge and understanding of promoting social development (PNIModified = 0.21). Also, the school directors’ strongest need in providing learning experiences was continuous promotion of social development (PNIModified = 0.47), and that of the teachers was implementation of learning activities based on lessons plans (PNIModified = 0.21). 2. The developed curriculum comprised eight components: rationale, objectives of the curriculum, contents, training activities and methods, training materials, durations, venues, and measurement and evaluation. Concerning its quality, the curriculum achieved a high level of suitability (x = 4.38, S.D. = 0.16). 3. After the curriculum implementation, the teachers achieved higher post-training scores (x = 22) than their pre-training scores (x = 17.20) and were most satisfied (x = 4.71, S.D. = 0.08). Provision of learning experiences achieved a mean score of 84.67%.


COVID-19 Collectivism

Abstract: Abstract This chapter explores how efforts to address COVID-19 in the years to come will require concern for the public good and a willingness to engage in collective activities toward better therapeutics and social supports for those affected. As an example, the chapter cites a slogan used by the Long COVID Alliance, “Nothing about us, without us.”


Can Contact Tracing Work Here?

Abstract: Abstract The chapter discusses the shortcomings of contact tracing in the United States during the pandemic. The chapter identifies how lack of testing capacity, workforce limitations, and lack of trust in public health services all contributed to these shortcomings. It suggests the importance of addressing these issues to shore up testing infrastructure in the event of future pandemics.


Collective Management of Copyright and Neighbouring Rights: Current Status and Prospects

Abstract: The article examines the current issues of the institute of collective management of copyright and related rights. The author substantiates the agency nature of legal relations arising from an agreement between copyright holders and collective management organisations. It argues in favour of necessity to extend the institute of collective management to such ways of using musical and musical-dramatic works as their inclusion in a complex object and making them publicly available. The judicial practice according to which allows to compel the user to conclude a remuneration agreement with an accredited collective management organisation is critically evaluated. Some changes to the existing model of participation of collective management organisations in the protection of copyright and related rights are proposed.


Exploration of factors predicting the accuracy rate of the English reading section of the College Scholastic Ability Test using the decision tree model.

Abstract: The study aimed to identify factors that could affect the accuracy rate of the English section of the College Scholastic Ability Test (CSAT). A total of 45 variables related to the passages and questions that influence the rate of accuracy were included. The decision tree regression model was used to find out the possible variables and analyze the impact of each factor. The decision tree model revealed that the question type had the greatest impact on the accuracy rate of the English reading comprehension test, followed by the points of the question, adjective variation (ADJV), the number of dependent clauses per T-unit (DC_T), the number of clauses per T-unit (C_T), word count (nwords), and topic. In conclusion, this study shows that question-related factors have a greater impact on the accuracy rate than passage-related factors. Furthermore, this study provides implications for teaching and learning related to English reading in the College Scholastic Ability Test.


Informal digital learning of English and L2 willingness to communicate of Korean secondary learners: The mediating role of English as an international language

Abstract: Informal Digital Learning of English (IDLE) is often associated with Willingness to Communicate in a second language (L2 WTC). This study aims to examine whether English as an International Language (EIL) perceptions mediate the relationship between IDLE and L2 WTC of learners. Survey data was collected from 206 secondary school students in Korea. The questionnaire of IDLE activities asked whether they watch videos in English (Receptive IDLE) and communicate with others in English (Productive IDLE) through social media like YouTube and Instagram. L2 WTC survey asked how participants were willing to speak in English in the classroom. EIL perception scale examined the perception of the status of English, the variety of English speakers’ accents, and communication strategies. The results found that EIL perception partially mediated the relationship between IDLE and L2 WTC. The findings suggest that learners who participate in IDLE activities have more positive EIL perceptions, which leads to a higher level of WTC. The present study has pedagogical implications for teachers to encourage secondary school students to engage more frequently in IDLE activities autonomously which may enhance positive attitudes toward the varieties of English and the level of L2 WTC i n t he c lassroom.


A case study of teaching English poetry through EdTech tools with learner-centered approach: Focusing on fostering pre-service English teachers’ collaborative communication, creative thinking and digital literacy

Abstract: This study explores the integration of Educational Technology (EdTech) in a learner-centered approach to teaching English poetry, focusing on collaborative communication, creative thinking, and digital literacy, which are the competencies that students need to thrive, outlined by the Ministry of Education of the Republic of Korea. The research involved 17 participants from a “Teaching English Poetry” class. Prior to the midterm, the researcher demonstrated the use of various EdTech tools for teaching English poetry. After the midterm, pre-service English teachers were required to conduct micro-teaching using at least three different EdTech tools. Surveys based on the Technological Pedagogical Content Knowledge (TPACK) framework were used to measure how teacher’s TPACK capabilities affect students’ collaborative communication, creative thinking, and digital literacy. The results indicate a significant improvement in students’ engagement and comprehension of poetry, facilitated by the diverse capabilities of EdTech tools. These tools also promoted effective teamwork and problem-solving, enhancing students’ collaboration competencies. In conclusion, this study underscores the importance of EdTech tools in enriching the teaching and learning of English poetry, particularly in cultivating creativity, collaboration, and digital literacy. By fostering an environment conducive to collaboration and interactive learning, EdTech not only boosts student motivation but also enhances their comprehension and appreciation of English poetry.


Machine learning-based item difficulty prediction model using item text features

Abstract: This study investigates the difficulty of English reading passages in the College Scholastic Ability Test (CSAT) in South Korea, analyzing corpus linguistic features from 2015 to 2023 using Coh-Metrix software. A notable innovation of this research is the application of the Random Forest algorithm, which overcomes the limitations of traditional regression analysis, such as multicollinearity and overfitting. Focusing on textual complexity, the analysis utilizes 108 explanatory variables to predict the difficulty of CSAT English items. Rigorous methods, including grid search and cross-validation, were employed to ensure the reliability of the results. The Random Forest regression model demonstrated high predictive accuracy. Crucial variables influencing the difficulty of items were identified, providing valuable insights for educators and policymakers to improve CSAT preparation strategies. This research marks a significant advancement in language assessment by combining corpus analysis with machine learning algorithms. It not only enhances the prediction of test difficulty but also supports students and teachers with effective, tailored preparation strategies to address the varied challenges encountered in English public education.


Exploring the chronostratigraphy of a Bronze Age settlement through core drilling

Abstract: Abstract Core drilling has been used successfully in archaeology for a variety of applications, including mapping stratigraphy across large areas, estimating feature depth and content, understanding site formation processes, as well as complementing information obtained through surface collection and geophysical survey. In this paper, we provide an overview of the first core drilling campaign carried out in 2021 on the outer settlement of the Bronze Age tell from Toboliu in western Romania. The overall aim of this coring program was to gather information regarding the nature of various features within the settlement, with a focus on the anomalies visible in the geomagnetic site plan. Based on 165 drilled cores, a reconstruction of the site’s stratigraphy is presented in this paper. This reconstruction was controlled and confirmed by excavations in one of the coring transects where the stratigraphic interpretation of the cores could be linked with the excavated stratigraphy. Additionally, samples for radiocarbon dating were collected from several cores. Based on this data, we discuss preliminary insights into possible house locations and preservation issues, as well as the chronological development and occupation sequence of this complex site.


Using OSSEs to Evaluate GXS Impact in the Context of International Coordination

Abstract: Abstract The Geostationary eXtended Observations (GeoXO) program plans to include a hyperspectral infrared (IR) sounder on its central satellite, expected to launch in the mid-2030s. As part of the follow-on to the GOES program, the NOAA/NASA GeoXO Sounder (GXS) instrument will join several international counterparts in a geostationary orbit. In preparation, the NASA Global Modeling and Assimilation Office (GMAO) assessed the potential effectiveness of GXS both as a single GEO IR sounder and as part of a global ring that includes international partners. Using a global observing system simulation experiment (OSSE) framework, GXS was assessed from a numerical weather prediction (NWP) perspective. Evaluation of the ability of GXS, both alone and as part of a global ring ofGEOsounders, to improveweather prediction of thermodynamic variables was performed globally and regionally. GXS dominated regional analysis and forecast improvements, and contributed significantly to global increases in forecast skill relative to a Control. However, more sustained global improvements, on the order of 4 days, relied on international partnerships. Additionally, GXS showed the capability to improve hurricane forecast track errors on the timescales necessary for evacuation warnings. The FSOI metric over CONUS showed that the GXS observations provided the largest radiance impact on the moist energy error norm reduction. The high temporal resolution atmospheric profile information over much of the western hemisphere from GXS provides an opportunity to improve the representation of weather systems and their forecasts.


A Story-Based Random Structure Modeling Method for Regional Buildings Earthquake Loss Estimation

Abstract: To estimate earthquake losses for regional buildings, it is important to consider the varying seismic performances of different types of buildings. To achieve this, a dynamic elastic–plastic analysis based on detailed structural modeling is more accurate than the capacity spectrum method based on a single degree of freedom to obtain the response and damage of various structures under seismic activity and provide a more precise estimate of earthquake losses for buildings. Detailed information about the building facilities is necessary to create a fine structural model. However, obtaining precise actual structural details can be challenging with existing methods, especially when there are a large number of buildings to consider. This paper proposes a new method called story-based random structure (SBRS) modeling to address this issue, which is based on the common and reasonable layout of the structure. The process involves choosing design parameters that can represent the structural arrangement of a building as variables. These parameters include the materials’ type and strength, proportions, the components’ size, and other relevant factors. The values for these variables are determined based on engineering experience and design specifications. The range of values for these variables is also determined to ensure that all design requirements are met. Finally, the Latin Hypercubic Sampling (LHS) method is used to randomly sample and combine the variables, establishing a detailed structural model of the building with structural uncertainty. According to the results of the analysis, this method can simulate the structure and component information of a building by using a probabilistic approach, even without knowing the specific structural design. This method can be based on a small amount of readily available building information and solves the problem of the rapid and refined modeling of a large number of buildings at the regional scale. It also makes it possible to estimate the earthquake loss of regional buildings based on their seismic capacity.


Application of Autonomous Driving Algorithm Based on HSV and Yolov5 in Micro Intelligent Vehicles

Abstract: Artificial intelligence driving technology has become the main direction of technological progress in intelligent vehicles. To address the issue of road recognition and traffic sign recognition response in the field of intelligent vehicle autonomous driving, a vehicle autonomous driving algorithm based on HSV road detection and yolov5 traffic sign recognition is proposed and applied to micro intelligent vehicles. The hardware of the mini smart car is controlled by NVIDIA Jetson AGX Orin, and the software method is embedded in the Ubuntu system. The yolov5 neural network is used to solve the classification and location problems of traffic signs. The results of experimental testing prove that micro intelligent vehicles can drive according to regulations on simulated roads and accurately recognize road signs, and can make corresponding actions accurately. The research results provide certain reference value for the application and development of vehicle autonomous driving.


Single-loss hash image retrieval method based on improved visual transformer

Abstract: Deep hashing methods have gained popularity in image retrieval due to their advantages such as low storage requirements and high efficiency. However, existing deep hashing methods for large-scale image retrieval tasks suffer from issues including low discriminative power of binary hash codes, difficult optimization of losses, and low retrieval accuracy. This paper proposes a single-loss hash image retrieval method based on an improved visual transformer to address these issues. The proposed method utilizes a pre-trained Vision Transformer (ViT) on ImageNet as the backbone network, augmented with a hash coding layer to extract image features more comprehensively. Additionally, we design a single learning objective loss function that addresses the discriminative power of hash codes and quantization errors, thereby eliminating the complexity of adjusting various loss weights. Experimental evaluations on ImageNet100, NUS-WIDE, CIFAR10, and MS-COCO datasets demonstrate the superior performance of the proposed method compared to contemporary methods, indicating its adaptability to diverse data.


A Machine Learning Approach to Improve the Usability of Severe Thunderstorm Wind Reports

Abstract: Abstract Many concerns are known to exist with thunderstorm wind reports in the National Center for Environmental Information Storm Events Database , including the overestimation of wind speed, changes in report frequency due to population density, and differences in reporting due to damage tracers. These concerns are especially pronounced with reports that are not associated with a wind speed measurement, but are estimated, which make up almost 90% of the database. We have used machine learning to predict the probability that a severe wind report was caused by severe intensity wind, or wind ≥ 50 kt. A total of six machine learning models were trained on 11 years of measured thunderstorm wind reports, along with meteorological parameters, population density, and elevation. Objective skill metrics such as the area under the ROC curve (AUC), Brier score, and reliability curves suggest that the best performing model is the stacked generalized linear model, which has an AUC around 0.9 and a Brier score around 0.1. The outputs from these models have many potential uses such as forecast verification and quality control for implementation in forecast tools. Our tool was evaluated favorably at the Hazardous Weather Testbed Spring Forecasting Experiments in 2020, 2021, and 2022.


Software Effort Estimation using Machine Learning Algorithms

Abstract: Effort estimation is a crucial aspect of software development, as it helps project managers plan, control, and schedule the development of software systems. This research study compares various machine learning techniques for estimating effort in software development, focusing on the most widely used and recent methods. The paper begins by highlighting the significance of effort estimation and its associated difficulties. It then presents a comprehensive overview of the different categories of effort estimation techniques, including algorithmic, model-based, and expert-based methods. The study concludes by comparing methods for a given software development project. Random Forest Regression algorithm performs well on the given dataset tested along with various Regression algorithms, including Support Vector, Linear, and Decision Tree Regression. Additionally, the research identifies areas for future investigation in software effort estimation, including the requirement for more accurate and reliable methods and the need to address the inherent complexity and uncertainty in software development projects. This paper provides a comprehensive examination of the current state-of-the-art in software effort estimation, serving as a resource for researchers in the field of software engineering.


Tunable photon-photon correlations in waveguide QED systems with giant atoms

Abstract: We investigate the scattering processes of two photons in a one-dimensional waveguide coupled to two giant atoms. By adjusting the accumulated phase shifts between the coupling points, we are able to effectively manipulate the characteristics of these scattering photons. Utilizing the Lippmann-Schwinger formalism, we derive analytical expressions for the wave functions describing two-photon interaction in separate, braided, and nested configurations. Based on these wave functions, we also obtain analytical expressions for the incoherent power spectra and second-order correlation functions. In contrast to small atoms, the incoherent spectrum, which is defined by the correlation of the bound state, can exhibit more tunability due to the phase shifts. Additionally, the second-order correlation functions in the transmission and reflection fields could be tuned to exhibit either bunching or antibunching upon resonant driving. These unique features offered by the giant atoms in waveguide QED could benefit the generation of nonclassical itinerant photons in quantum networks.


One-step parity measurement of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> cat-state qubits via reverse engineering and optimal control

Abstract: In this paper, a one-step protocol is proposed for the parity measurement of $N$ cat-state qubits which are encoded on the cat states of the modes in superconducting Kerr-nonlinear cavities. The parity measurement is performed with the help of an auxiliary qutrit. Especially, the auxiliary qutrit can (cannot) be excited to the higher-energy levels when the cat-state qubits are in the even- (odd-) parity state. By designing the Rabi frequency of the classical fields via reverse engineering and optimal control, the qutrit is driven to an excited dressed state in the even-parity case, which is robust to the systematic errors of the qutrit-cavity coupling strengths. Accordingly, the parity of the cat-state qubits can be distinguished with high accuracy by measuring the final population of the ground state of the auxiliary qutrit. Numerical simulations also show that the protocol is insensitive to the systematic errors of the classical fields, the inhomogeneity of the coupling strengths, intercavity cross talk, unwanted qutrit transitions, and decoherence. Therefore, the protocol may provide an effective approach for parity measurement of multiple cat-state qubits.


Artificial Intelligence Based Virtual Assistant for Vision Impaired Person

Abstract: Abstract: This project is a brilliant example of how technology can be harnessed for a profoundly positive purpose, enhancing the quality of life for those who may face challenges in certain aspects of daily living. It's about creating a tool that complements and supports individuals rather than substituting human interaction or assistance. That sounds like an incredible initiative! Combining technologies like object detection through YOLO algorithms, speech synthesis via text-to-speech, and incorporating them into smart glasses could significantly enhance the independence and mobility of visually impaired individuals. These glasses could provide real-time information about their surroundings, enabling them to navigate and interact more confidently with their environment.


A Stretchable Breathable Washable All-Textile Smart Wearable Keyboard

Abstract: Abstract The development of comfortable and lightweight wearable devices is an emerging technological goal in human-computer interaction. Conductive textiles attract a great deal of interest as versatile materials for wearable devices. It can naturally integrate into our daily clothing, making wearable devices more comfortable and convenient. However, developing all-textile wearable devices remains an important challenge. And the wiring of wearable devices in practical applications is also a thorny issue. In this study, a novel wearable smart keyboard is proposed. Benefit from the ingenious structural design of double-faced effect functional textiles and 3D spacer textiles, the smart keyboard achieves all-textile structure and the wires can also be hidden inside the 3D spacer textile. The smart keyboard is stretchable, breathable, washable and can be integrated with daily clothing by sewing. In addition to the functions of traditional keyboards, smart keyboards also have the ability to detect tapping force which lays the foundation for future intelligent applications, such as tracking the tapping force of typewriting to enhance security.The application examples of integrating the smart keyboard into lab-coat for typewriting are demonstrated. This study shows that the novel smart keyboard has great potential in the fields of wearable devices and human-computer interface.&#xD;


The Design and Development of Hands-on Activities for Digital Forensics Education

Abstract: It has been widely admitted by researchers and educators that hands-on activities are a core component in digital forensics education to help students gain practical skills that are needed in real-world forensic investigations. However, it is not clear in existing works about what kinds of hands-on activities are recommended to be integrated into a digital forensics course and how to design and develop them. In our teaching practice, hands-on activities for a digital forensics course are designed in three categories: 1) activities that assist students in learning how to use common digital forensics tools; 2) activities that help students gain in-depth understanding of the basic concepts and fundamental knowledge that are presented in class lectures; 3) activities that promote students the development of mindsets and data analytical skills that are needed for a digital forensic investigator. Various formats are employed to develop these hands-on exercises in different categories. The educational objectives and student learning outcomes map well to the CAE-CD (Centers of Academic Excellence - Cyber Defense) outcomes by completing their forensic knowledge units. In this paper, we share our idea and experience to design and implement such hands-on assignments in each category for meeting specific educational objectives. Sample exercises are briefly described to explain our idea in each category. Open source tools and data sets are introduced for references. Experiences, lessons, and sample feedback from students are discussed. Our results will provide a point of reference for those who teach digital forensics courses at a college or university, or are developing a digital forensic curriculum.


Matting Algorithm with Improved Portrait Details for Images with Complex Backgrounds

Abstract: With the continuous development of virtual reality, digital image applications, the required complex scene video proliferates. For this reason, portrait matting has become a popular topic. In this paper, a new matting algorithm with improved portrait details for images with complex backgrounds (MORLIPO) is proposed. This work combines the background restoration module (BRM) and the fine-grained matting module (FGMatting) to achieve high-detail matting for images with complex backgrounds. We recover the background by inputting a single image or video, which serves as a priori and aids in generating a more accurate alpha matte. The main framework uses the image matting model MODNet, the MobileNetV2 lightweight network, and the background restoration module, which can both preserve the background information of the current image and provide a more accurate prediction of the alpha matte of the current frame for the video image. It also provides the background prior of the previous frame to predict the alpha matte of the current frame more accurately. The fine-grained matting module is designed to extract fine-grained details of the foreground and retain the features, while combining with the semantic module to achieve more accurate matting. Our design allows training on a single NVIDIA 3090 GPU in an end-to-end manner and experiments on publicly available data sets. Experimental validation shows that our method performs well on both visual effects and objective evaluation metrics.


How Interpretable Is “Interpretable” Machine Learning?

Abstract: The modern machine learning (ML) revolution has led to ML systems deployed at scale for the first time, substantially impacting society beyond computer science experts. It is critical that these systems are integrated appropriately with minimal negative side effects, and an important component of this goal is identifying whether these tools are interpretable by humans. Formal methods is a computer science discipline that may address this problem via mathematically grounded techniques for specifying and verifying the desired operation of software and hardware systems. In the ML context, ML systems may be designed to output a set of formal specifications that describe precisely what they learned, which can then be translated into a set of natural language rules. A widespread belief in the formal methods community is that the use of their approaches in machine learning lends interpretability to systems, making their reasoning understandable by humans. We evaluate the claim that formal specifications are interpretable by humans via an experiment with sixty-two participants. We describe the format of the study, provide statistical analysis of the results, and recommend modifications to researchersâ claims and ergonomic improvements to data presentation and validation training.


Deep learning assisted solar forecasting for battery swapping stations

Abstract: For the development of renewable energy generation as a viable alternative to fossil fuel-based generation, solar power has received widespread acceptance. An increase in solar power usage can mitigate the impact of climate change, energy demand, and cost-effective dispatch. To use solar power efficiently and ensure grid consistency, reliable and accurate forecasting of information becomes very crucial. Hence, the main purpose of this work is to get the best suitable solar irradiance forecasting model and to implement the forecasted solar power to the designed hybrid battery swapping stations. Each year, new techniques and approaches are used to improve the model accuracy with the critical aim of lowering the variability of predictions. This paper takes a close look at different deep-learning methods to predict solar irradiance for a certain period of time. The real-time time series data is used, and for analyzing the time series data, the statistical ARIMA model, LSTM-based RNN technique, and Dual Attention-based Recurrent Neural network have been used. These models are implemented using Jupyter Notebook with Python programming language. To analyze the efficiency and evaluate the performance of real-time data of the models, a comparative study has been done on the different error metrics. This paper reveals that the LSTM model is providing the best solar power forecasting with the least error metrics like MSE of .0091, MAE of .0525, RMSE of .0953, and MSLE of .0047.


High-level noise load identification based on Chebyshev polynomial piecewise fitting

Abstract: When the telemetry data noise level is high, the traditional integral moving average processing model has a general effect on noise reduction. In order to improve the noise reduction ability and obtain higher load identification accuracy, a new noise reduction model is constructed based on the Chebyshev orthogonal polynomial and the idea of piecewise overlap. The impact load is simulated by half sine wave. Under the condition of 15 % noise, the error values of load identification results are 1.94 % and 0.76 % respectively after the treatment of integral sliding noise reduction model and Chebyshev noise reduction model. In contrast, the new Chebyshev noise reduction model under high-level noise conditions has better accuracy and wider application.


Webshell combination detection method based on Naïve Bayes and decision tree

Abstract: The paper aims to enhance the recall rate of Webshell detection methods based on machine learning algorithms. It covers a broad research scope on Webshells, with a focus on Webshells as the research object. The research employs a combination of the Naive Bayes and Decision Tree methods, extracting and analyzing static features of Webshells. Significant conclusions regarding the improvement of recall rates are drawn from this analysis. Experimental validation conducted on publicly available datasets demonstrates that the proposed method significantly increases the recall rate of the Webshell detection model while maintaining a low false-positive rate. This improvement holds crucial significance for enhancing the security of web applications and servers, offering a feasible research approach in the field of Webshell detection.


Aging with robots: a brief review on eldercare automation

Abstract: Abstract Robotic solutions designed to cater to the needs of the elderly, commonly known as eldercare robots or nursing robots, hold the promise of delivering intelligent elderly care, alleviating societal caregiving pressures, and reducing financial burdens on nations. Scholars across various disciplines have delved into the realm of eldercare robotics from different perspectives. Four types of robots at the design level are currently used in the elderly care industry: anthropomorphic, zoomorphic, cartoon, and mechanical-functional. They can play such roles as assistants, companions, and even surrogate pets, providing support for the elderly’s daily routines, cognitive enhancement, and emotional well-being. Acceptance of eldercare robots hinges on three key factors: technical attributes, user-specific characteristics, and the surrounding social environment. The utilization of eldercare robots has the potential to positively impact various aspects of the elderly population, such as their physiological health, cognitive abilities, psychological well-being, and social interactions. However, it can also lead to social isolation, reduced autonomy, increased sense of objectification, blurred responsibility attribution, and tendencies towards deceptive and childish behavior. Additionally, eldercare robots also influence healthcare professionals in terms of workload, working conditions, job satisfaction and sense of purpose, both positively and negatively. This paper examines these research findings within the context of theories in communication, technology ethics, and medical ethics, shedding light on the multifaceted landscape of eldercare robotics.


A dependence graph pattern mining method for processor performance analysis

Abstract: As the complexity of processor microarchitecture and applications increases, obtaining performance optimization knowledge, such as critical dependent chains, becomes more challenging. To tackle this issue, this paper employs pattern mining methods to analyze the critical path of processor micro-execution dependence graphs. We propose a high average utility pattern mining algorithm called Dependence Graph Miner (DG-Miner) based on the characteristics of dependence graphs. DG-Miner overcomes the limitations of current pattern mining algorithms for dependence graph pattern mining by offering support for variable utility, candidate generation using endpoint matching, the adjustable upper bound, and the concise pattern judgment mechanism. Experiments reveal that, compared with existing upper bound candidate generation methods, the adjustable upper bound reduces the number of candidate patterns by 28.14% and the running time by 27% on average. The concise pattern judgment mechanism enhances the conciseness of mining results by 16.31% and reduces the running time by 39.82%. Furthermore, DG-Miner aids in identifying critical dependent chains, critical program regions, and performance exceptions.


Modeling arithmetic systems of elliptic curve cryptography using Microsoft Excel VBA

Abstract: Purpose. This study aims to develop a new teaching module to illustrate the arithmetic systems of Elliptic Curve Cryptography, a powerful yet simple algorithm for information security, by exploring the capability of the Visual Basic Applications of Microsoft Excel in user friendly way. Methodology. The research is performed using research and development approach, which is divided into five steps utilizing VBA features of Microsoft Excel. It starts with modeling arithmetic in Microsoft Excel spreadsheet, then testing the validity through calculation and setup of the actual arithmetic of Elliptic Curve Cryptography using VBA Excel, before performing the test of the VBA application and finally visualizes the results in graphical mode. Findings. Novel teaching software based on of Microsoft Excel Visual Basic Applications is produced that is able to simulate arithmetic system behind Elliptic Curve Cryptography in an easy way for students. Originality. To the best of the authors’ knowledge, this is the first simulation based on Excel VBA to illustrate the arithmetic systems of Elliptic Curve Cryptography for teaching purposes. Practical value. In general, mastering cryptography will need a steep learning curve; however, using Microsoft Excel as a simulation platform will accelerate learning. The main practical value is the ease of Microsoft Excel, which will turn cryptography learning which was commonly very difficult for student to become easier and user friendly.


Petrography and Heavy Minerals as Tools for Provenance Identification of the Injana Sandstone (Upper Miocene), Al-Habbaniyah City, Anbar Governorate, Iraq

Abstract: This study deals with the petrography and heavy mineral analysis of the Injana sandstone at Al-Habbaniyah City. Thirteen samples are collected from the Injana Formation. A petrographic study is achieved on ten samples while all samples are analyzed for heavy minerals. The petrographic analysis showed that the common components of the Injana sandstone are rock fragments, especially sedimentary fragments. Quartz grains (monocrystalline and polycrystalline) and feldspars (k-feldspar and plagioclase), bounded by matrix and carbonate cement. The suggested provenance for the Injana Formation is mainly sedimentary and igneous sources and the less dominant metamorphic source. These sandstones are considered to be mineralogically submature-immature and classified as litharenites. Heavy minerals analysis showed two sets of minerals including opaque and transparent minerals, the opaque minerals are significant components of the heavy mineral assemblages, then chlorite, epidotes, amphiboles, garnet, pyroxenes (ortho and clino), zircon, tourmaline, biotite, muscovite, rutile, and others. This assemblage implies that the dense minerals are typically found in basic igneous and metamorphic rocks, with less frequent occurrences in acidic igneous and reworked sediments. According to QLF and QmFLt classifications, the tectonic provenance of the Injana sandstone is described as recycled orogen. The MF-MT-GM classification showed that the sandstone of Injana is derived primarily from active continental margins. The result of maturity MI and ZTR revealed low values of maturity, the MI value ranges between 0.6 to 1.5 with an average of 0.9 and ZTR Maturity ranges between 0.7% and 9% with an average of 3.9%, these low values of maturity indicate sub-mature to immature sandstone.


Applying the world's advanced models of cloud-based e-learning training to foreign language teaching at Vietnam universities today

Abstract: E-learning is a common inevitable trend of the world education in the context of globalization, especially after the Covid-19 epidemic had broken out and has not yet completely ended. Currently, cloud-based training is growing more and more strongly in the background of the 4.0 technology revolution. Recognizing the importance of that issue, this paper studies some typical cloud-based e-learning training models in the world, and applies them to foreign language teaching at Vietnamese universities nowadays.


Systematic Design of a Natural Sciences Collections Digitisation Dashboard

Abstract: This paper describes the design and build of a pilot Natural Sciences Collections Digitisation Dashboard (CDD). The CDD will become a key service for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo) and aims to improve the discoverability of natural science collections (NSCs) held in European institutions, both digitised and undigitised. Furthermore, it will serve as a dynamic visual assessment tool for strategic decision-making, including the prioritisation of digitisation. The CDD pilot includes high-level information from nine European NSCs, covering the number of objects, taxonomic scope, storage type, chronostratigraphy (Earth Science Collections), geographical region and level of detail in digitisation. This information is structured through a standardised Collection Classification Scheme, which uses high-level categorisation to describe physical natural science collections.


The Correlation between Personality Type, Learning Motivation, and Ability to Read Poetry of Indonesian 10th Grade Students

Abstract: The conditions of human personality and motivation significantly influence all activities undertaken, including during the learning process. The ability to recite poetry is one of the learning achievements in the Indonesian language subject in the realm of literature. This research aims to identify the conditions of personality types, learning motivation, and the ability to recite literary poetry in vocational school (SMK) students. In addition to describing each component, these three variables will be interconnected to determine the strength of the relationships that occur. The research method used is a quantitative method with a correlational research design. The population used consists of 10th-grade students at SMKN 12 Surabaya, with the research sample being 32 students from the X-2 class majoring in Creative Metal Crafts and Jewelry. Data collection is carried out using questionnaire and observation techniques. After the data is collected, a multiple correlation analysis is performed with the aim of demonstrating the relationship between the three research variables. The results of this research include: 1) The personality type of X-2 students majoring in Creative Metal Crafts and Jewelry at SMKN 12 Surabaya tends to be introverted; 2) The learning motivation of X-2 students majoring in Creative Metal Crafts and Jewelry at SMKN 12 Surabaya is in the Very Low category; 3) The ability to recite poetry of X-2 students majoring in Creative Metal Crafts and Jewelry at SMKN 12 Surabaya is classified as insufficient or falls into category D; 4) The personality type (X1) and learning motivation (X2) variables together have a positive and significant correlation in the Moderate category with the ability to recite poetry variable (Y), with a correlation coefficient value of 0.526.


Objective assessment of learning outcomes: Peculiarities of design and application of assessment tools in modern conditions of digital development

Abstract: Introduction. The article deals with the problem of designing and application of assessment tools in the context of objective assessment of learning outcomes in higher education. The aim of the article is to identify the peculiarities of design and application of assessment tools in modern conditions of digital development for the objective assessment of learning outcomes. Materials and Methods. In order to achieve the purpose of the research, the authors used theoretical (analysis of theoretical sources) and empirical (survey of 222 teachers) methods. The data obtained were subjected to quantitative and qualitative analysis. Results. The authors analyzed the approaches to the interpretation of assessment procedures and means in theoretical studies and regulatory documents. The main results of the study allowed to identify a number of problems in the development of assessment tools banks: substitution of the subject of the assessment for the knowledge component; orientation to the development of assessment materials at the discipline level; assessment of the validity and reliability of the designed assessment tools; assessment of teachers’ labor input; the ratio of the used template assessment tools and practical tasks; difficulties in the design of interdisciplinary assessment tools and competence assessment in general; underestimation of the potential of digital environment in the development of assessment tools. Evaluating the problems of designing and applying assessment tools by teachers depended on the subject area they teach and their experience of participation in the development of assessment tools at various levels. The dependence on teachers’professional experience and the position held has not been revealed. Conclusions. Based on the results of the study, conclusions about the types of problems in designing and application of assessment tools in modern conditions are drawn. Practical recommendations are provided.


Influence of Reading Books on Psychological Wellbeing and Decision Making among Higher Secondary Students

Abstract: The study aims to find out the influence of books reading on Psychological Wellbeing and Decision Making of higher secondary students. Convenient random sampling technique is used for the selection of the sample. A total of 1269 higher secondary students are selected from the city of Chennai. Scale of Psychological wellbeing and Decision Making Scale were administered for the present study to collect data. Results are statistically analyzed through Critical ratio and correlation. Present research concludes that books reading enhances Psychological Wellbeing and decisions making skills among higher secondary students.


PENGARUH PENGGUNAAN MEDIA KOMIK TERHADAP KEMAMPUAN MENULIS KALIMAT PADA KELAS III SD INPRES 15 KABUPATEN SORONG

Abstract: Learning media is one of the tools used to achieve learning. It is kind of visual or audio. Learning media is easy to understand the material from teacher’s explanation. This research is quantitative method with pre-experimental design. The sample of this research is the third students and the total number is 20 students. To analyse the data is used SPSS application to calculate the result of pre-test and post-test. Based on the result, the average of post-test score is 87.30 with the standard deviation 4.74 and the average of pre-test score is 72.75 with standard deviation 6.27. It means that those scores are calculated statistically to get the score of ttable 0.10 and the significant of paired samples test is 0.00 < 0.05, it indicates that there is an improvement of experimental class that uses comic media in writing sentence. The use of comic media can help the student in writing sentence and students motivate in learning process.


BLOCKCHAIN, A STRATEGIC TECHNOLOGY TO BE ADOPTED BY PUBLIC AND PRIVATE COMPANIES FOR THE BENEFIT OF BRAZILIAN SOCIETY

Abstract: Purpose: Highlight the strategies used by organizations when using blockchain, the problems and difficulties they face, and how the use of blockchain technologies can help public and private Brazilian companies, understanding their concepts and importance. Theoretical Framework: Blockchain technology emerges to allow transactions to be more secure and transparent and is very important for organizations to make their processes more agile. However, to implement it, it is important to use strategies. Design/Methodology/Approach: Important aspects for the implementation and use of blockchain by organizations, challenges, benefits, essentiality, and the need to obtain this knowledge were analyzed, therefore, four public and private Brazilian companies were analyzed, where a bibliographic review was used and, therefore, exploratory research. Findings: Strategies were identified, such as storing and managing data with blockchain and using cryptocurrencies as a form of payment. The challenges to be overcome are usability, scalability, environmental impact, security, and crime. Public and private companies benefit from lower costs, agility, efficiency, shorter transaction times, customer attraction and greater engagement. Research, Practical & Social Implications: The research was relevant, and the objectives were met, the implications identified proved to be beneficial for companies and society. Originality/Value: The use of blockchain by organizations to address problems and increase business scalability is essential, as it brings greater capacity to control operations in an integrated manner with better communication and greater transparency towards society.


Bio-Inspired Watermarking Method for Authentication of Fundus Images in Computer-Aided Diagnosis of Retinopathy

Abstract: Nowadays, medical imaging has become an indispensable tool for the diagnosis of some pathologies and as a health prevention instrument. In addition, medical images are transmitted over all types of computer networks, many of them insecure or susceptible to intervention, making sensitive patient information vulnerable. Thus, image watermarking is a popular approach to embed copyright protection, Electronic Patient Information (EPR), institution information, or other digital image into medical images. However, in the medical field, the watermark must preserve the quality of the image for diagnosis purposes. In addition, the inserted watermark must be robust both to intentional and unintentional attacks, which try to delete or weaken it. This work presents a bio-inspired watermarking algorithm applied to retinal fundus images used in computer-aided retinopathy diagnosis. The proposed system uses the Steered Hermite Transform (SHT), an image model inspired by the Human Vision System (HVS), as a spread spectrum watermarking technique, by leveraging its bio-inspired nature to give imperceptibility to the watermark. In addition, the Singular Value Decomposition (SVD) is used to incorporate the robustness of the watermark against attacks. Moreover, the watermark is embedded into the RGB fundus images through the blood vessel patterns extracted by the SHT and using the luma band of Y’CbCr color model. Also, the watermark was encrypted using the Jigsaw Transform (JST) to incorporate an extra level of security. The proposed approach was tested using the image public dataset MESSIDOR-2, which contains 1748 8-bit color images of different sizes and presenting different Diabetic Retinopathy (DR). Thus, on the one hand, in the experiments we evaluate the proposed bio-inspired watermarking method over the entire MESSIDOR-2 dataset, showing that the embedding process does not affect the quality of the fundus images and the extracted watermark, by obtaining average Peak Signal-to-Noise Ratio (PSNR) values higher to 53 dB for the watermarked images and average PSNR values higher to 32 dB to the extracted watermark for the entire dataset. Also, we tested the method against image processing and geometric attacks successfully extracting the watermarking. A comparison of the proposed method against state-of-the-art was performed, obtaining competitive results. On the other hand, we classified the DR grade of the fundus image dataset using four trained deep learning models (VGG16, ResNet50, InceptionV3, and YOLOv8) to evaluate the inference results using the originals and marked images. Thus, the results show that DR grading remains both in the non-marked and marked images.


Ethics: What is the Brazilian Software Engineering Research Scenario?

Abstract: Background: Ethics is the theory or science of the moral behavior of humans in society. Traditionally, we value “unethical” actions that go against determining morality in a specific context. One of the sub-domains of Ethics is Computational Ethics, which deals with ethical dilemmas that are strictly related to computational issues. Dilemmas in this area involve privacy, improper access, intellectual property, digital norms and laws, power, socio-technical aspects (such as gender discrimination), and robotics, among others. In this context, “Software Engineering” and “Software” are different objects. Engineering is an act, a practice, as also coding, programming, and software reuse. As with any act, moral and subsequent ethical considerations are appropriate. We characterize software as an object of concrete reality, as a sociotechnical system formed by a technical artifact, human aspect, and procedural aspect. This assumption will form the main base discussion of ethics and morals in Software Engineering in this paper. Objective: The goal of this paper is to unveil the Brazilian Software Engineering ethics panorama. Method: We follow the rigor of, and inspired by, a Systematic Literature Review (SLR) protocol to answer the question: how does ethics explicitly permeate the Brazilian Software Engineering publications between the last thirteen years (2010 and 2022)? Results: After analyzing 1529 papers through the research protocol, 175 (≈11%) presented some explicit occurrence of ethical aspects. The occurrence was relevant in only 7 papers (≈0.4%), exposing a shallow scenario on ethical or moral aspects. Conclusions: If Ethics is a topic considered important to deliberate, research or discuss, this did not occur significantly in the Brazilian Software Engineering research scenario since 2010. With this result in mind, we discussed parallel terms and concepts to enrich the contribution of the qualitative synthesis.


Machine Physics and Performance

Abstract: Advanced Series on Directions in High Energy PhysicsThe High Luminosity Large Hadron Collider, pp. 121-164 (2024) Open AccessChapter 5: Machine Physics and PerformanceG. Arduini, R. De Maria, M. Giovannozzi, G. Iadarola, E. Métral, Y. Papaphilippou, and R. TomásG. ArduiniCERN, BE Department, Genève 23, CH-1211, Switzerland, R. De MariaCERN, BE Department, Genève 23, CH-1211, Switzerland, M. GiovannozziCERN, BE Department, Genève 23, CH-1211, Switzerland, G. IadarolaCERN, BE Department, Genève 23, CH-1211, Switzerland, E. MétralCERN, BE Department, Genève 23, CH-1211, Switzerland, Y. PapaphilippouCERN, BE Department, Genève 23, CH-1211, Switzerland, and R. TomásCERN, BE Department, Genève 23, CH-1211, Switzerlandhttps://doi.org/10.1142/9789811278952_0005Cited by:0 (Source: Crossref) PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: The main beam and machine parameter choices and the underlying beam dynamics considerations are reviewed together with the main challenges and expected performance. FiguresReferencesRelatedDetails Recommended The High Luminosity Large Hadron ColliderMetrics History Information© Oliver S Brüning and Lucio RossiThis is an open access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC BY) License.PDF download


Higher Education Language Translation Practices in North Sumatra: Case Study of Lecturer and Student Perceptions

Abstract: This research explores the multifaceted concept of translanguaging in the context of teaching English as a foreign language in three universities in North Sumatra. Translanguaging, a term encompassing various aspects of multilingualism, is examined as a pedagogical approach that utilizes multiple languages within a classroom setting. The study investigates translanguaging practices among lecturers and aims to address three key research questions: the types of translanguaging practices, perceptions of lecturers and students, and the motivations behind incorporating translanguaging in teaching and learning activities. The research method is a qualitative method with a case study approach model focusing on the exploration of a "bounded system" on one specific case or on some cases in detail by exploring data in depth. Through a combination of surveys and interviews with lecturers, the study identifies eight motivations for the intentional use of translanguaging practices. These motivations include adjusting to students' proficiency levels, clarification, and emphasis, translanguaging as an academic strategy, providing challenges for student growth, considering diverse student demographics, expressing firmness and solidarity, addressing real lexical needs in communication, and expressing group identity. The findings reveal positive responses to translanguaging from both lecturers and students, highlighting its versatility in meeting educational objectives and fostering effective communication in diverse learning environments. The motivations align with existing literature, emphasizing the adaptability and significance of translanguaging in enriching the teaching and learning experience. The study concludes with several insights into translanguaging practices in North Sumatra's universities and recommends possibilities for future research and practical implementation. The proposed recommendations include professional development for lecturers, curriculum design, student training, and calls for more studies in tranlanguaging. Overall, this research contributes to the understanding of translanguaging motivations and offers practical suggestions for enhancing its integration in higher education institutions.


Interactive CBIR System for Various Similarity Metrics Based on Colour Content of Image

Abstract: The ‘Content Based Image Retrieval’ (CBIR) is a well-known image retrieval system. It has become more popular in recent decades due to its large image datasets. This paper provides a detailed interactive CBIR system for comparing various similarity metrics. Similarity metrics are usually distance metrics that can measure the closeness of the image features. Image retrieval is implemented as user interactive, where users can select various similarity measures for comparison. This interactive or graphical system allows six distance metrics on retinal image retrieval. The reported results confirm that the implemented system performs well.


The Effectiveness of English in Supporting Communication with Customers to Provide the Best Service in the World of Work

Abstract: This research aims to determine the description of workers' ability to speak English, English is needed to communicate via online media/internet both with fellow actors and with consumers consisting of domestic consumers and tourists. The total sample technique used in this research was 70 people. The description of the characteristics of MSME actors consists of age, education, length of business, and media ownership, while the characteristics of MSMEs are presented based on micro, small and medium business scale. English language skills are measured based on the ability to read, understand and interpret messages on social media. Based on the formulation of research objectives and the results of data analysis tests, it can be concluded that the description of the characteristics of MSME actors mostly includes those of productive age (80%), most of the respondents are male (40%), the education of MSME actors is dominated by undergraduate graduates (60%), and has Most have more than 5 years of experience and most have more than 1(77.14%) media experience. The characteristics of business scale include small scale (50%), only 1.43% of medium scale ones were in the research sample. The description of the English language skills of MSME players is quite good with an average score of 3.32, while the ability to use media has a better average score than English language skills, namely 3.42. The results of the correlational analysis show that there is a very real positive relationship between English language ability and media use ability, which is expressed by an r value of 0.993, meaning that the higher the English language ability, the higher the media use ability.


The dependence of the effectiveness of neural networks for recognizing human voice on language

Abstract: This study examines the effectiveness of neural network architectures (multilayer perceptron MLP, convolutional neural network CNN, recurrent neural network RNN) for human voice recognition, with an emphasis on the Kazakh language. Problems related to language, the difference between speakers, and the influence of network architecture on recognition accuracy are considered. The methodology includes extensive training and testing, studying the accuracy of recognition in different languages, and different sets of data on speakers. Using a comparative analysis, this study evaluates the performance of three architectures trained exclusively in the Kazakh language. The testing included statements in Kazakhs and other languages, while the number of speakers varied to assess its impact on recognition accuracy. During the study, the results showed that CNN neural networks are more effective in recognizing human voice than RNN and MLP. Also, it was found that the CNN has a higher accuracy in recognizing the human voice in the Kazakh language, both for a small and for a large number of announcers. For example, for 20 speakers, the recognition error in Russian was 21.86 %, whereas in Kazakhs it was 10.6 %. A similar trend was observed for 80 speakers: 16.2 % Russians and 8.3 % Kazakhs. It can also be argued that learning one language does not guarantee high recognition accuracy in other languages. Therefore, the accuracy of human voice recognition by neural networks depends significantly on the language in which training is conducted. In addition, this study highlights the importance of different sets of speaker data to achieve optimal results. This knowledge is crucial for advancing the development of reliable human voice recognition systems that can accurately identify different human voices in different language contexts


Indonesian Twitter Users’ Language Attitude Towards English-Indonesian Code-mixing

Abstract: Social media users need language as a tool to communicate, both in verbal and written form. Over time, the language used in social media also shows some significant growth. Talking about language in social media, there is an interesting phenomenon recently on social media that can be reached within the internet, namely the code-mixing of Indonesian and English (which in Indonesian are also popularly known as Jaksel language, Keminggris, Kemlondo, Indoglish, Englonesian, or Bahasa Gado-gado). This study is aimed to find out the attitudes of 500 Indonesian Twitter users towards English-Indonesian code-mixing with a questionnaire. This study uses a descriptive-quantitative approach. For the result of each aspect, it shows a quite positive indication: Cognitive aspect 2.92, Affective aspect 2.75, and Conative 2.79. If the three aspects being summed up, also indicated a quite positive attitude towards the code-mixing with point or score 2.82. The attitudes come from many reasons and factors, such as the need for understandability in communication, language prestige, prepossessing or attracting attention, confidence, and language aptitude.


CHARACTERISTICS OF LONELINESS EXPERIENCE AMONG ADOLESCENTS WITH DIFFERENT LEVELS OF SELF-REGULATION

Abstract: The paper examines the characteristics of adolescents’ loneliness experience via the lens of individual differences in self-regulation. The study included 82 participants aged 13 to 14. The levels of self-regulation were identified by means of cluster analysis. To investigate the specifics of loneliness experience in the groups of respondents with the outlined levels of self-regulation, a Kruskal–Wallis H test was used. The study found statistically significant differences in the characteristics of loneliness experience among adolescent respondents with high, medium, and low levels of self-regulation.


A SMARTPHONE IN A STUDENT’S HANDS: PROS AND CONS

Abstract: This publication presents the results obtained as part of the search (preparatory) stage of an experiment on the problem of using mobile phones in the educational process. At this stage, the authors summarised the domestic and foreign experience and carried out an individual analysis of their own pedagogical work, including: an interview with the 1st–4th-year university students (“04.03.01 Chemistry” field of study); identification of possible ways for students to use phones for educational purposes; trial inclusion of tasks requiring the use of a smartphone when mastering certain academic disciplines. According to the research, positive and negative aspects of the use of a mobile phone in the educational process were identified, and a number of contradictions related to the ambiguity of understanding the problem under study were revealed


THE MUTUAL INFLUENCE OF PSYCHOMOTOR ACTIVITY AND COGNITIVE DEVELOPMENT OF PRESCHOOL CHILDREN IN THE INFORMATION AND EDUCATION ENVIRONMENT

Abstract: The purpose of the study is to analyse the influence of the emotional and affective content of pedagogical influences transmitted by means of information technologies on the psychomotor development of an older preschool child. The relevance of the study is due to the importance of psychomotor development in childhood, which is the basis of a child’s mental development at school age. The pedagogical experiment involved 26 children aged 5 ± 0.4 years, divided into CG, EG1 and EG2 groups. During the period of implementation of psychomotor practices (3 months), the participants of the CG did not participate in them. Motor exercises with EG1 children using an educational computer game were conducted once a week, and with EG2 children twice a week. The comparison of the results obtained during the implementation of the pedagogical experiment, at the stages before and after its organisation, made it possible to conclude about the effectiveness of systematisation of psychomotor activity using information technology tools for the development of children’s cognitive abilities.


The Influence of STAD Method Towards Students Writing Ability in Recount Text

Abstract: The purpose of this research is to determine the influence of learning using the STAD method on students' writing ability. The researchers used quantitative and experimental research methodology. The researchers used two classes as samples, which consisted of an experimental and a control class. Students in the experimental class were provided with instruction with the STAD method, and students in the control class comprised instructed using conventional methods. In collecting data, the researchers used written tests.


Influence of Physical Education Courses on Soft Skills Development of College Students Under the Concept of Outdoor Education

Abstract: In recent years, there has been increasing attention on the development of soft skills among college students. This study aims to explore the influence of physical education courses on the development of soft skills, particularly under the concept of outdoor education. A qualitative research approach was adopted, involving interviews and observations of college students who participated in outdoor physical education courses. The results indicate that outdoor education in physical education courses can effectively enhance college students’ soft skills, including communication, teamwork, leadership, problem-solving, and adaptability. The findings highlight the significance of incorporating outdoor education into the physical education curriculum to foster students’ holistic development.


The Effect of Health Education With Educational Video on Knowledge and Attitude of Community About Basic Life Support

Abstract: Introduction: Quick and appropriate help when met an unconscious person is important to providing Basic Life Support (BLS). Knowledge and Attitude about BLS are very important in order to provide quick assistance of cardiac arrest. The purpose of this study was to analyze the effect of providing health education with video on people's knowledge and Attitude about BLS. Methods: The study used a quasy-experimental design by intervening in a group and then observed. This research was conducted on the community who became Kader in Perak, Surabaya. Total sample of 64 respondents obtained through total sampling technique. The independent variable is video-based health education. Dependent variable is knowledge and attitude about BLS. Data were collected using a questionnaire and analyzed by the Wilcoxon sign test and Mann whitney u test with a significance of p < 0.05. Results: The results showed an increase in knowledge and Attitude in the intervention group after being given educational videos with (p = 0.001) and Attitude with (p = 0.006). The results of the test with Mann Whitney showed that there was an average difference between the intervention group and the control group with significant results in knowledge (p=0.000), attitude (p=0.000). Conclusion: To improve people's knowledge about basic life support can be obtained from electronic media regarding knowledge about basic life support. It is hoped that it can provide knowledge of basic life support and can apply or assist in life-threatening emergencies in the home environment. It is hoped that the results of this research can be used as a reference for further research by adding other variables and before the research, researchers are expected to prepare research instruments according to the standards of the instruments that will be used, such as: phantom CPR according to standards.


Prediction of Customer Engagement Response to E-wallet Content Based on Machine Learning Using Combined E-Wallet Dataset and Individual E-Wallet Dataset

Abstract: Objectives: The COVID pandemic proved that humans are not hesitant to adopt technology in the banking industry, especially as a method of payment. In Indonesia, this fact is demonstrated by the value of e-wallet transactions in 2021 which reached 18,5 billion USD. The implementation of QRIS (Quick Response Code Indonesian Standard) in the payment systems of e-wallets compels every e-wallet provider to intensify their marketing activities to overcome competitors. Previous studies on this topic primarily focused on factors that influence customer engagement on social media using traditional approaches, such as interviews and statistical methods to process the data. This research aims to overcome the limitations of previous research by using machine learning methods in predicting the customer engagement type of primary data collected directly from social media in this case Twitter.Methodology: In this paper, we propose the application of machine learning methods such as XGBoost, Random Forest, Decision Tree, and KNN to predict the most likely engagement type of a tweet related to e-wallet content using 15.756 data which are directly collected from Twitter.Finding: This research successfully found that XGBoost and KNN are the machine learning algorithms that perform best and the results in prediction from using the combined dataset and the individual e-wallet brands dataset are similar.Conclusion: Even though the prediction accuracy in this research is good, this research still has many limitations. Thus, future research in the same field would benefit from a larger amount of data to accommodate machine learning algorithms that are more complex like deep learning.


Pengembangan E-Book Interaktif Berbasis Kearifan Lokal untuk Pembelajaran Keterampilan Membaca Teks Berita SMPN 34 Padang

Abstract: The background to this research is that technological developments are increasingly rapid in human life in various fields, one of which is education. This type of research uses Research and Development (R&D). The development model used in this research is a 4-D model consisting of four stages, namely: 1) define, 2) design, 3) develop, and 4) disseminate. This research used a questionnaire instrument data collection technique which was distributed to class VIII students of SMP Negeri 34 Padang. Based on the results of the student needs questionnaire analysis, the results showed that 83% of students needed digital learning media. There are 3 aspects in the research questionnaire, namely student needs in the learning process aspect which obtained a percentage of 84%, student needs in the learning media aspect 84%, and student needs in the teaching material content aspect 80%. The average percentage result for all aspects is 83%, so it falls into the "very feasible" category. From the results of the analysis of the student needs questionnaire, it was concluded that researchers needed to develop learning media in the form of interactive e-books based on local wisdom.


improvement of signal communication for a foraging task using evolutionary robotics

Abstract: Communication systems represent an evolutionary advantage for a group of robots solving coordinated tasks. In the field of evolutionary robotics, the emergence and establishment of communication are regulated by different variables. These systems are a tool produced by artificial evolution for the exchange of personal and environmental information. Since evolutionary processes involve multi-objective evolutionary methods, it is important to study all the mechanisms that affect the emergence and establishment of communication systems. One of these variables is the evolutionary advantage of emerging signals. In this article it is assumed that the signals appear because they are evolutionarily useful in solving a task. Emitter and receiver generate a process of conceptualization, which makes the signals associated with a meaning for the community. In this way an experiment is adapted to the FARSA simulator and the MARXBOT robot. This experiment consists of a group of robots that spend as much time as possible in a food zone and avoid a poisoned zone. Under normal conditions, robots tend to point to the food zone to attract the rest of the population. When the evolutionary importance of pointing out the food zone is reduced, the signals arise in different situations such as the presence of nearby objects and the poisoned zone. Communication is configured with the LED rings and linear cameras of the robots. In addition, an ethological method is adapted to quantify the behavioral effects of experimental manipulation.


Give Your Project a Name

Abstract: While some people are driven by infinite backlogs and iteration, others prefer launches and deadlines. Over the years, I have found certain milestones to be instrumental in creating a cohesive team with strong morale. When people have to work together to get through a challenging task, reaching those milestones bring them together.


Sistem Smart Garden Tanaman Bayam Menggunakan Metode Fuzzy Logic

Abstract: Treatment of Spinach, even though it has been given fertilizer, also keep on eye to monitor lots of water when watering. If the soil is too wet it will interfere with the absorption of nutrients needed for spinach growth. The use of technology makes it easy to monitor and control the condition of spinach plants in real time. Good soil moisture for spinach plants is in the range of 45% equivalent to a voltage value of 2.78 Volts to 60% which is equivalent to a voltage value of 2.03V. Meanwhile, soil that is suitable for growing spinach is soil that has a pH between 6-7. Mamdani fuzzy logic method is applied to a microcontroller as a reference in making decisions to regulate the speed of the water pump. There are five levels of pump speed for automatic watering of spinach from very fast to very slow to maintain stable soil moisture. Based on the test results, it is proven that the system can work correctly using Mamdani fuzzy with an error of 0.023%.


EVALUATION OF THE QUALITY OF IMPLEMENTATION OF STUDENT-CENTERED EDUCATION TECHNOLOGIES IN A HIGHER EDUCATIONAL INSTITUTION

Abstract: The article considers the concept of student-centered education as an educational concept. The set of components characterising the concept of student-centered education is specified. The way of formation and development of the concept of student-centered education is described. The authors consider the influence of student-centered education on the transformation in the relations between the subjects of the educational process, within which students acquire enhanced opportunities in managing the individual educational process, while the teacher’s role acquires the nature of tutorship. The article also presents the results of the student survey “The Assessment of Student Satisfaction with the Quality of Implementation of Student-Centered Education Technologies at University” and makes corresponding conclusions.


QR-codes in English textbook: Improving listening and speaking skills

Abstract: This quantitative study examines the impact of implementing QR codes in English textbooks on students' listening and speaking skills. This study used a quasi-experimental design with pre-tests and post-tests within experimental and control classes to evaluate the effectiveness of QR-code content in improving language skills. Sixty students in an Islamic elementary school as the participants will be randomly assigned to an experimental class and a control class. The experimental class used an English textbook published by Erlangga, which is enriched with a QR code connected to multimedia resources for listening and interactive exercises for speaking practice. The control class used a traditional textbook without a QR code. The assessments for both groups will include pre-and post-assessments to evaluate the changes in their listening and speaking skills. The T-test is conducted to determine the significance of the improvement in the experimental class compared to the control class. The study revealed statistically significant improvements in listening and speaking skills among students exposed to QR-code content in English textbooks. AbstrakPenelitian kuantitatif ini bertujuan untuk menguji dampak penerapan kode QR dalam buku pelajaran Bahasa Inggris terhadap keterampilan mendengarkan dan berbicara siswa. Penelitian ini menggunakan desain kuasi-eksperimental dengan pre-test dan post-test dalam kelas eksperimen dan kontrol untuk mengevaluasi efektivitas konten kode QR dalam meningkatkan keterampilan berbahasa. 60 siswa di sebuah sekolah dasar Islam sebagai partisipan akan secara acak ditugaskan ke dalam kelas eksperimen dan kelas kontrol. Kelas eksperimen menggunakan buku pelajaran Bahasa Inggris yang diterbitkan oleh Erlangga yang diperkaya dengan kode QR yang terhubung dengan sumber daya multimedia untuk latihan mendengarkan dan latihan berbicara interaktif. Kelas kontrol menggunakan buku pelajaran tradisional tanpa kode QR. Evaluasi untuk kedua kelompok akan mencakup pre-test dan post-test untuk mengevaluasi perubahan dalam keterampilan mendengarkan dan berbicara mereka. Uji T dilakukan untuk menentukan signifikansi peningkatan dalam kelas eksperimen dibandingkan dengan kelas kontrol. Hasil dari penelitian ini menunjukkan adanya peningkatan yang signifikan secara statistik dalam keterampilan mendengarkan dan berbicara di antara siswa yang menggunakan konten kode QR dalam buku pelajaran Bahasa Inggris dari pada siswa yang pembelajarannya secara konvensional.Kata Kunci: Kode QR; Sekolah Dasar; Listening; Speaking


SOME TRENDS IN THE DEVELOPMENT OF DOMESTIC PREVENTIVE PEDAGOGY IN THE LATE 20th – EARLY 21st CENTURIES

Abstract:
The purpose of the article is to identify the state and some trends in the development
 of domestic preventive pedagogy in the late 20th – early 21st centuries on the basis of the publication activity in the format of this issue


The Effects of Remote Phonological Processing Intervention on Word Identification and Reading Fluency in Poor Readers

Abstract: Purpose: Communication technology and electronic devices are developing rapidly, and as many students have experienced remote education since COVID-19, the interest in the necessity and effects of telepractice has also grown in the field of speech-language pathology. Accordingly, this study aimed to investigate the effectiveness of telepractice for poor readers. Method: The subjects of the study were three 2nd-3rd grade students with poor reading. They were able to read words using the phonics principle but had difficulty reading words adapted to phonological processes. For these subjects, phonological processing intervention was conducted for 4 types of 7 coda rule, lenition, tensification, and aspiration. Both intervention and evaluation were conducted remotely using ZOOM. An intermittent multiple baseline design was applied to determine whether remote phonological processing intervention had a positive effect on the word identification of poor readers, and a pre-post design was applied to confirm improvements in reading fluency. Results: Results indicate that, following remote phonological processing intervention, all students demonstrated improvements in word identification scores compared to the baseline. Furthermore, their reading fluency scores exhibited enhancement in the post-test compared to the pre-test. Conclusion: In conclusion, the study demonstrates the effectiveness of remote phonological processing intervention for poor readers. We explored models and directions for future telepractice.


Estimasi Arah Robot Pembersih Panel surya Menggunakan Algoritma Line Detection Klasik

Abstract: Currently, solar panels are widely used in homes, offices, and buildings. Solar energy is converted into electrical energy by solar panels. The surface of solar panels determines how efficient they are; if the surface is dirty, the efficiency of solar panels will decrease. Therefore, they must be cleaned to maintain their effectiveness. Cleaning solar panels is one of the tasks that can be performed by an autonomous robot, which can move without the need for human control or remote control usage. To make the robot move autonomously, a camera is used to capture images of solar panels so that the robot can move on its own. The images captured by the camera will then be processed using a classic line detection algorithm (Hough line transform) to estimate the direction, in the form of the angle (theta), of the robot relative to the solar panel based on the lines detected by the algorithm. The direction of the robot relative to the solar panel determines the robot's future movement. The algorithm used (Hough line transform) has been able to estimate the direction of the robot quite well with a relatively fast processing time. Thus, it can be concluded that this research successfully determines the direction based on the available images


Optimization of use case point through the use of metaheuristic algorithm in estimating software effort

Abstract: Use Case Points estimation framework relies on the complexity weight parameters to estimate software development projects. However, due to the discontinue parameters, it lead to abrupt weight classification and results in inaccurate estimation. Several research studies have addressed these weaknesses by employing various approaches, including fuzzy logic, regression analysis, and optimization techniques. Nevertheless, the utilization of optimization techniques to determine use case weight parameter values has yet to be extensively explored, with the potential to enhance accuracy further. Motivated by this, the current research delves into various metaheuristic search-based algorithms, such as genetic algorithms, Firefly algorithms, Reptile search algorithms, Particle swarm optimization, and Grey Wolf optimizers. The experimental investigation was carried out using a Silhavy UCP estimation dataset, which contains 71 project data from three software houses and is publicly available. Furthermore, we compared the performance between models based on metaheuristic algorithms. The findings indicate that the performance of the Firefly algorithm outperforms the others based on five accuracy metrics: mean absolute error, mean balance relative error, mean inverted relative error, standardized accuracy, and effect size.


Digital Literacy: Fundamental Competence for Modern Society

Abstract: This research highlights the significance of digital literacy in our increasingly interconnected modern society through information technology. It addresses the primary challenges faced by modern society in managing information in the digital era, including the spread of fake news and data privacy breaches. The research aims to elucidate the contribution of digital literacy in addressing these challenges faced by modern society and identify its positive impacts in safeguarding data security, combating misinformation, and enabling active participation in the digital community. The research methodology employed a systematic literature review (SLR) to gather, evaluate, and synthesize literature related to modern society and digital literacy. The findings of the research reveal that digital literacy plays a pivotal role in assisting individuals in comprehending, filtering, and assessing information encountered online while safeguarding their privacy in the digital environment. In conclusion, digital literacy is not just a technical skill but also the key to responsibly and wisely harnessing digital opportunities.


Appropriate TPACK Components for Overcoming Misconceptions on Force Concept Inventory Test

Abstract: Misconception is one of the obstacles in the achievement of physics learning and must be immediately corrected so that there is no retention of misconceptions in subsequent knowledge. One way to prevent and reduce misconceptions that occur is with the teachers skill of Technological Pedagogical and Content Knowledge (TPACK). Teachers who integrate TPACK in learning can prevent misconceptions and find solutions to overcome the misconceptions. This study aims to determine the TPACK skill of teachers in overcoming misconceptions and the appropriate TPACK components to reduce misconceptions based on concepts on the three tier test of FCI (Force Concept Inventory). The research method used is mixed method with concurrent embedded design. The main data in this study is TPACK which is qualitative data and supporting data is students misconception which is quantitative data. The results showed a decrease in misconceptions after being given an understanding of the concept using the teacher's TPACK skill. The components used during the teacher's reduction of misconceptions based on the concepts in the FCI three tier test of FCI are TPACK and PCK components. So it can be concluded that the teacher's TPACK skill can reduce the misconceptions that occur in the three tier test of FCI


Good Communication Supports Pre-School Children's Ability and Its Application to Personal Hygiene Hand Washing in Kindergarten

Abstract: Infectious diseases in pre-school children are prone to occur because their growth and development age is in the pre-adolescent age range or pre-school age where this age requires more space for wider movement such as schools, large fields. Hand washing is one of the knowledge that needs to be applied correctly by school-age children to maintain hand hygiene. This knowledge apart from home needs to be emphasised from the teacher at the child's school. Children's dominant activities at school make school one of the places expected to provide knowledge about proper hand hygiene. This research is a quantitative study with an analytical descriptive approach and design and method used is aquasi experiment with a pre and post without control group design approach. This study is to measure the effect of good communication on the ability of pre-school children to wash their hands. Data was collected by video recording children while washing their hands before and after the communication intervention. Bivariate data analysis using non-parametric test with Wilcoxon test because the data distribution is not normal. The results showed 40 respondents z count value of -4.465 with a p-value of (0.000) < α (0.05, this indicates that there is a significant influence between the ability of children to wash their hands before the pretest before the treatment of good communication about hand washing on the posttest after the application of good communication treatment about hand washing. It can be concluded that there is an effect of good communication on the ability of pre-school children to wash their hands.


A New Integrated Approach for Cloud Service Composition and Sharing Using a Hybrid Algorithm

Abstract: The concept of a “Smart City” emphasizes the need to employ information and communication technologies to strengthen the quality, connectivity, and efficiency of various municipal services. Cloud computing and the Internet of Things are shaping future tech. Both ideas greatly impact smart city application and solution development. Cloud computing is amazing at managing and storing remote service access. Several companies have switched to cloud leasing to reduce local resource burden. Due to the intricacy and flexibility of cloud-maintained services, selecting jobs that best suit client needs should be optimized. Quality of service criteria for each cloud service are the best tools for choosing and optimizing cloud carriers. Genetic algorithms (GAs) and ant colony optimization (ACO) are combined to make cloud computing. It is discovered that the recommended ACO + GA obtains an accuracy of 82% when compared to existing methods of energy- and reliability-aware multiobjective optimization method and the hybrid cuckoo particles swarm, artificial bee colony optimization (CPS + ABCO) where accuracy is 68% and 75%, respectively.