Reviewerly/exHarmony huggingface dataset:
Multiple Object Detection on Surveillance Videos for Improving Accuracy Using Enhanced Faster R-CNN

Computer vision is a dynamic and rapidly evolving field within the broader domain of artificial intelligence. Within surveillance monitoring systems, one of the central tasks is object detection, which involves identifying and localizing objects of interest in video sequences to provide safety and security of the people. Detection of multiple objects is a challenging task in video sequences which interprets less accuracy and false Bounding box regression. In this paper, enhanced faster R-CNN model is proposed and trained to compute regional proposal through Convolutional layers on the different scene of the sequences in term of lighting, motion capture related to spatial analysis. These enhancements could encompass architectural improvements, novel training strategies, or the incorporation of additional data sources to improve the model's overall performance. Proposed model is experimented on pedestrian video gives an improved accuracy detection rate than single detector techniques.
EADS: Edge-assisted and dual similarity loss for unpaired infrared-to-visible video translation

In extreme environments where visible imaging is limited, infrared imaging is often used to assist imaging. However, infrared images lack detailed semantic information and have low contrast, and may not be suitable for direct observation by humans or for practical tasks. Therefore, overcoming the significant differences between the two modalities and realizing the transfer of infrared to visible videos will help to better utilize infrared images. Based on this, we proposed a one side end-to-end infrared-to-visible video translation framework, EADS, that uses our edge-assisted generation and dual similarity loss to preserve the scene structure information to the maximum extent and realize the translation of infrared videos into realistic, detailed, and temporally and spatially coherent visible light videos. Experiments show that our translated videos can be used in tasks such as object detection and image fusion.
A generalized fuzzy clustering framework for incomplete data by integrating feature weighted and kernel learning

Missing data presents a challenge to clustering algorithms, as traditional methods tend to pad incomplete data first before clustering. To combine the two processes of padding and clustering and improve the clustering accuracy, a generalized fuzzy clustering framework is proposed based on optimal completion strategy (OCS) and nearest prototype strategy (NPS) with four improved algorithms developed. Feature weights are introduced to reduce outliers’ influence on the cluster centers, and kernel functions are used to solve the linear indistinguishability problem. The proposed algorithms are evaluated regarding correct clustering rate, iteration number, and external evaluation indexes with nine datasets from the UCI (University of California, Irvine) Machine Learning Repository. The results of the experiment indicate that the clustering accuracy of the feature weighted kernel fuzzy C-means algorithm with NPS (NPS-WKFCM) and feature weighted kernel fuzzy C-means algorithm with OCS (OCS-WKFCM) under varying missing rates is superior to that of seven conventional algorithms. Experiments demonstrate that the enhanced algorithm proposed for clustering incomplete data is superior.
Digital patient experience: understanding, improvement, and evaluation from a human-centered design perspective

New information and communication technologies have been introduced to transform healthcare into digitalization (Gopal et al., 2019). Digital health offers benefits but also risks (Perakslis et al., 2023). It must be clinically effective, safe, and meet the needs of the people that will use it and be treated by it to ensure high-quality healthcare delivery. An increasing number of healthcare organisations have invested in “human centred” methods from the field of design (Erwin & Krishnan, 2016a, 2016b). Design-led approaches are shaping future healthcare (Groeneveld et al., 2018; Tsekleves & Cooper, 2017) and have the potential to provide more effective care (Kolko, 2015). Nevertheless, designing and implementing these technology-involved care that focuses on the needs and values of the stakeholders require a holistic approach (Melles et al., 2021) and pose challenges to the human factors and ergonomics discipline (Carayon et al., 2020). Innovative approaches are needed for understanding, improving, and evaluating the digital patient experiences (Wang, Giunti, Melles, & Goossens, 2022; Wang, Giunti, Melles, Goossens, et al., 2022) and working at multiple interfaces in healthcare (Carayon et al., 2020). The related PhD research aims to improve the quality of care in digital health by defining, evaluating, and improving the digital patient experience. This PhD research contains five studies: a literature review study, an interview study, two surveys, and one mixed-methods study. Figure 1 presents the progress of the related PhD research. The literature review and interview studies are almost finished. One journal paper is published, and two conference papers are accepted. Others are in progress; for example, the two survey studies are in the data collection stage, and the mixed methods study is in planning. We identified detailed information, such as influencing factors, evaluation considerations, and design workflows, from our previous studies for designing digital health that improves the digital patient experience. To transform this information into explicit knowledge, the development of design and evaluation guidelines is the intended deliverable of this research.
Recording and Reporting Information System for Maternal Health Monitoring (Literature Review)

The Maternal and Child Health (MCH) program is a health program in Indonesia that aims to improve the health and welfare of mothers and children as stated in Permenkes number 4 of 2019. To support the implementation of the program requires technology in the form of information systems. Information systems are designed to help health workers manage maternal and child health more effectively. Delays in data collection, incomplete filling and long time in presenting data are one of the reasons why a maternal health information system is needed to run optimally. The purpose of this study was to describe the recording and reporting of maternal health monitoring activities. The method used is a literature review based on the PRISMA flow. The results of the health information system research are able to provide the right, accurate and up-to-date quality of information data so as to improve the efficiency and effectiveness of services and facilitate evaluation and help health workers manage maternal health more effectively. The running of an information system will depend heavily on human resources to utilize the information system and produce records and reports to improve the performance of users of the system
The Effectiveness of Interactive Reading Models in Improving Early Students Language Skills

This study aims to examine the effectiveness of interactive reading models in improving early childhood receptive and productive language skills. The interactive reading model used in this study consists of the traditional, the attention-focused, and the mind-map interactive reading models. This study used an experimental research method with a pre-posttest design. The sample in this study was 500 participants, with a composition of 150 people each in the traditional, focused attention, mind-map methods, with an age range of 4-6 years from 5 early childhood education schools. The results showed that the three interactive reading models had the same effect and there was no significant difference in the contribution of the three models. It was noted that the three interactive reading models significantly improve early childhood language skills. This improvement was noted in the child's receptive vocabulary, reading, storytelling and listening abilities. The strategies used in the interactive reading model pay attention to several aspects, including semantics and word repetition. The semantic aspect makes students motivated to classify meanings that make students able to gain a lot of new vocabulary mastery. This strategy is carried out in every vocabulary teaching. From the result, it was concluded that the three interactive reading models contribute significantly to reading ability. This implied that combining reading models in improving early childhood language skills is effective over the other strategies.
Characterizing arbitrary quantum networks in the noisy intermediate-scale quantum era

Quantum networks, as a playground for various quantum technologies, describe the distribution of quantum sources represented by edges to different parties represented by nodes in the network. Here, the author introduces a systematic approach to characterize arbitrary quantum networks in the noisy intermediate-scale quantum era, which can be noisy, intermediate-scale, random, and sparse.
Detecting Risky Authentication Using the OpenID Connect Token Exchange Time

With the rise in sophisticated cyber threats, traditional authentication methods are no longer sufficient. Risk-based authentication (RBA) plays a critical role in the context of the zero trust framework-a paradigm shift that assumes no trust within or outside the network. This research introduces a novel proposal as its core: utilization of the time required by OpenID Connect (OIDC) token exchanges as a new RBA feature. This innovative approach enables the detection of tunneled connections without any intervention from the user's browser or device. By analyzing the duration of OIDC token exchanges, the system can identify any irregularities that may signify unauthorized access attempts. This approach not only improves upon existing RBA frameworks but is also in alignment with the broader movement toward intelligent and responsive security systems.
Application of the Clustering Algorithm for the Classification of General Criminal Cases at the Binjai District Attorney's Office

The Binjai District Attorney's Office in carrying out its duties and functions, one of which handles general crimes, where so far the SPDP (Warranty to Commence Investigation) from the police that has entered the Binjai District Prosecutor's Office amounted to approximately 50 (fifty) cases each month. This amount consists of several types of general criminal cases. It is known that the types of general criminal cases amount to approximately 215 (two hundred and fifteen) types of cases, from this data, a method of classifying/clustering is needed from the types of cases that exist each month so that the data can be processed so as to produce the highest, moderate and highest scores. the lowest value of a type of case. The Binjai District Attorney's Office often receives requests for data from other ministries or agencies such as the BPS (Central Statistics Agency), the National Commission on Women and the National Commission on Children in the form of data recapitulation of crimes against women and children as perpetrators of crimes. The Binjai District Attorney's Office has a case handling system where the recapitulation cannot be taken directly but instead collects data manually, because the existing case handling system does not have the recapitulation as requested.The application of clustering has been carried out by many previous researchers. Among them, the K-Means Clustering Algorithm Analysis Mapping the Number of Crimes. The research was carried out using a data mining model in classifying illegal fishing with the K-Means algorithm analysis by determining the shortest distance using the eulclidean distance, more optimal than using the mahattan distance and chbchep distance in classifying student achievement, determining the centroid (central point) in the early stages of the algorithm K-Means is very influential on cluster results as the results of tests carried out using 267 records with different centroids produce different cluster results as well, a clustering model is obtained that can be used for illegal fishing in decision making for illegal fishing crimes high, medium, moderate .
English for Academic Purposes (EAP) Students’ Approaches to Learning in Responding to an Annotation Task

In reading, students should pay close attention to the purpose of reading because this will help them to identify appropriate strategies. Similarly, students in the English for Academic Purposes (EAP) classroom should know why and how to read, as academic reading goes beyond simply understanding a text. Hence, understanding students’ approaches to learning in the EAP classroom is important because it provides insights to instructors on their motives for learning and the strategies they use to learn. This study used the qualitative method to explore students’ approaches to learning in responding to an annotation task in the EAP classroom. A delayed stimulated recall interview and students’ written samples were used for data acquisition. The data were analysed using thematic analysis. The findings showed how the students read, made sense of reading materials, and responded to the task. Moreover, the findings also showed that the students adopted a surface motive in learning and a mix of surface and deep strategies in responding to the task.
Meningkatkan Keterampilan Menyimak Cerita Fabel melalui Model Paired Storytelling melalui Media Wayang Kartun di Kelas II Mi BPPKI Cisaat dengan Teknik Project Base Learning (PJBL)

Based on the results of observations, the researchers observed that in Grade II MI BPPI Cisaat when the teacher explained in front of the class the students were less enthusiastic and tended to be busy with their own activities so that students became unfocused and unable to concentrate in listening to learning. Therefore, this study aims to improve the skills of listening to fables through the Paired Storytelling model of wayang cartoon media with the Project Base Learning (PjBL) technique in class II MI BPPI Cisaat. The subjects of this study consisted of 12 male students and 14 female students with a total of 26 students. Instruments used in data collection using the method of observation and written tests. The results showed that there was an increase in students' listening skills. This is evidenced by the average class score in the first cycle of 69 with the listening skill mastery criteria of 70.91% (enough category) and the completeness of learning outcomes of 61.5% (incomplete category). In the second cycle, using the Paired Storytelling model through wayang cartoon media using the Project Base Learning (PjBL) technique, the class average was 82. The criteria for mastery of listening skills were 81.73% (good category) with a completeness percentage of 84.6% (complete category). Based on observations of the results of the first and second cycles, it showed that there was a significant increase in students' skills in listening to fables in grade II MI BPPI Cisaat.
Re-Identification in Video Surveillance Systems Considering Appearance Changes

Human behavior analysis and visual anomaly detection are important applications in fields such as video surveillance, security systems, intelligent houses, and elderly care. People re-identification is one of the main steps in a surveillance system that directly affects system performance; and variations in appearance, pose and scene illumination may be challenging issues for such system. Previous re-identification approaches faced limitations while considering appearance changes in their tracking task. This paper proposes a new approach for people re-identification using a descriptor which is robust to appearance changes. In our proposed method the enhanced Gaussian of Gaussian (GOG) and the Hierarchical Gaussian Descriptors (HGDs) are employed to extract feature vectors from images. Experimental results on a number of commonly used people re-identification databases imply the superiority of the proposed approach in people re-identification compared to other existing approaches.
Travel Umroh dalam Pengembangan Peradaban Islam di Kota Medan

Umrah travel is a container that can make it easier for Muslims to go to the baitullah, in the city of Medan itself Umrah travel already has so many agencies, with various kinds of price offers. In line with the development of the business and business world in Indonesia, the development of the Umrah and Hajj services sector is also very well developed, due to the fact that the waiting lines for performing the Hajj have been for more than 10 years. The analysis of Umrah travel is reviewed against the large number of pilgrims and the enthusiasm of the Muslims to participate. The relationship between Umrah travel and the development of Islamic civilization has the same path. Keeping up with the times, the current travel promotion system relies on information media, so that all the latest news is quickly conveyed to pilgrims or prospective pilgrims.&#x0D; Keywords: travel, umroh, development, Islam, civilization.
Pengaruh Indeks Dow Jones, Suku Bunga The Fed, Dan Bitcoin terhadap IHSG

This research was conducted to examine the effect of the Dow Jones Index, The Fed Interest Rate, and Bitcoin on the Indonesian Composite Stock Price Index (IHSG). The data used is secondary data taken based on closing prices and monthly fluctuations from financial websites. The population is in the form of time series data for 58 months starting from 1 June 2018 to 31 March 2023 using a saturated sample. Data analysis in the study used the classical assumption test (multicollinearity, heteroscedasticity, autocorrelation, normality), multiple linear regression, determinant coefficient, and T test. The results showed that the Dow jones index had a positive effect on the JCI, The Fed's interest rate had a positive effect on the JCI, and Bitcoin has no effect on the JCI. The test results stated that the Dow Jones Index had a positive influence on the JCI, The Fed's Interest Rate had a positive influence on the JCI, and Bitcoin had no influence on the JCI.&#x0D; Keywords: Bitcoins; Dow Jones; IHSG; The Fed
Research on Improved Microscope Calibration Method Based on Coplanar Points

Camera calibration is the premise and basis for computer vision to obtain three-dimensional space information. Its calibration accuracy directly affects the accuracy of three-dimensional measurement and the results of three-dimensional reconstruction. However, the stereo light microscope has the problems of small depth of field and complex optical path, and the traditional macroscopic calibration method cannot be applied to microscopic calibration. In order to solve this problem, this paper proposes an improved microscope calibration method based on coplanar points. Firstly, the method of varying focal length is used to obtain the camera principal point coordinates. By shooting the same target point at different magnifications, the image coordinates are obtained respectively, then the least square method is used to fit the line, and the intersection of the fitted line is the principal point of the image. Then, based on the coplanar point method, the analytical solution of the camera scale factor is obtained, which improves the robustness of the initial value estimation of the internal parameters. Finally, the initial value estimation of internal parameters is optimized by nonlinear least square method, and the tangential distortion is considered as a factor of nonlinear optimization, so as to obtain accurate microscope calibration parameters. The experimental results show that compared with the traditional Zhang’s calibration method, the improved microscope calibration method based on coplanar points has higher calibration accuracy and is suitable for microscope calibration.
Scrum Maturity Level Evaluation and Improvement Recommendation: Case Study on ABC Application

Bank XYZ, one of the digital banking in Indonesia, has a digital product ABC for customers to complete online banking transactions. Bank XYZ uses Scrum as the methodology to develop ABC. Several problems were found in the process related to the delay in the release process. The achievement of sprint goals from May to December 2021 is only 6%. This fact allegedly caused some frequent release delays. To resolve the root causes, mixed-method research was conducted to provide recommendations for improving the implementation of Scrum. The Scrum Maturity Model questionnaires were distributed to several Scrum teams, followed by interviews with several roles that were used to validate the results. The key process area rating formula of the Agile maturity model was used to decide the maturity level. After the maturity level result was obtained, recommendation practices were generated from the not well-implemented practice. This case-based research shows that Bank XYZ reached maturity level 2 for ABC development. Bank XYZ has implemented 78 out of 79 practices, however, 28 practices need improvement and one practice needs to be applied. Objectives of maturity levels group recommendation practices. The combination of Scrum best practices and empirical practices from previous research generated those practices. This research was intended to give general recommendations on how to improve Scrum implementation and on how to resolve release time problems by enhancing Scrum in Bank XYZ empirically.
Automatically Defining Game Action Spaces for Exploration Using Program Analysis

The capability to automatically explore different possible game states and functionality is valuable for the automated test and analysis of computer games. However, automatic exploration requires an exploration agent to be capable of determining and performing the possible actions in game states, for which a model is typically unavailable in games built with traditional game engines. Therefore, existing work on automatic exploration typically either manually defines a game's action space or imprecisely guesses the possible actions. In this paper we propose a program analysis technique compatible with traditional game engines, which automatically analyzes the user input handling logic present in a game to determine a discrete action space corresponding to the possible user inputs, along with the conditions under which the actions are valid, and the relevant user inputs to simulate on the game to perform a chosen action. We implemented a prototype of our approach capable of producing the action spaces of Gym environments for Unity games, then evaluated the exploration performance enabled by our technique for random exploration and exploration via curiosity-driven reinforcement learning agents. Our results show that for most games, our analysis enables exploration performance that matches or exceeds that of manually engineered action spaces, and the analysis is fast enough for real time game play.
The Five-Dollar Model: Generating Game Maps and Sprites from Sentence Embeddings

The five-dollar model is a lightweight text-to-image generative architecture that generates low dimensional images or tile maps from an encoded text prompt. This model can successfully generate accurate and aesthetically pleasing content in low dimensional domains, with limited amounts of training data. Despite the small size of both the model and datasets, the generated images or maps are still able to maintain the encoded semantic meaning of the textual prompt. We apply this model to three small datasets: pixel art video game maps, video game sprite images, and down-scaled emoji images and apply novel augmentation strategies to improve the performance of our model on these limited datasets. We evaluate our models' performance using cosine similarity score between text-image pairs generated by the CLIP VIT-B/32 model to demonstrate quality generation.