Reviewerly/exHarmony huggingface dataset:
Rasionalisasi Pos Curah Hujan Menggunakan Metode Kagan di DAS Ciliwung untuk Operasi Bendungan Ciawi dan Sukamahi

Hydrological stations are an important element in order to provide preliminary information to dam managers in planning and managing dams. Rainfall data generated from rainfall stations is one of the inputs in hydrological analysis that can be used in dam management. To support the hydrological analysis, an appropriate network of rainfall stations is needed, both in terms of the number and location of rainfall stations. Therefore, it is necessary to study the rationalization of the hydrological station network within the Ciliwung Watershed, so that it can be utilized in dam operations. The purpose of this study is to obtain an efficient and effective network of rainfall stations in the Ciliwung watershed to support the operation of the Ciawi and Sukamahi Dams. In this case, the Kagan method was used in the analysis of the rationalization of the rainfall station network by producing the optimum number of rainfall stations in the Ciliwung watershed, that is 29 rainfall stations. Based on these results, the number of rainfall stations in the Ciliwung watershed has met in terms of number, but the location point needs to be adjusted to the Kagan triangle.
The Use of Cybernetic Systems Based on Artificial Intelligence as Support for the Decision-Making Process in the Military Field

Abstract In the future, at the strategic, operational, and tactical levels of warfare, replacing human will and human decision with autonomous Artificial Intelligence (AI) weapon systems becomes every day a high possible scenario. These systems are already in use and have the ability to successfully deal with threats faster than humans. Countering AI weapon systems will represent a great challenge in a future war. Both military and commercial robots will in the future incorporate “artificial intelligence” (AI) that could make them capable of performing tasks and missions on their own. In the military context, this gives rise to a debate as to whether such robots should be allowed to carry out missions, especially if there is a possibility that any human life may be in the line of fire. Thus, it can be stated that unconventional knowledge will be required for successfully defeat of such a weapon system.
ANALYSIS OF USER BEHAVIOR AND INFORMATION NEEDS IN PUBLIC LIBRARY IN DEPOK

This Study discusses the Relation Betwwen Information Seeking Behavior And User Information Needs (Correlation Study on Information Seeking Behavior And User Information Needs in Depok Public Library). The purpose of this study was to determine the significance between the initiation, topic selection, exploration, focus formulation, collection, &amp; presentation stages in information-seeking behavior by users and the information needs of users at the Depok City Public Library. The method used is a quantitive method with the Spearman Rank correlation test. In measuring information needs the author uses the theory of Kart, Gurevitch, and Haas. Data collection techniques used this study include observation, literature study, and survey. The research population is the users of the Depok City Public Library for the period April-May 2022. The sample in this study uses an accidental sampling teachnique and totals 101 people. The results of this study show the significance between the initiation, topic selection, exploration, focus formulation, collection, &amp; presentation stages in information-seeking behavior by users and the information needs of users at the Depok City Public Library.&#x0D; Keywords: Information seeking behavior, information needs, library
PERSEPSI GURU DAN PUSTAKAWAN SLB NEGERI 1 SUNGAI PAGU TERHADAP DEPRIVASI INFORMASI BAGI SISWA TULI

This study aims to explain the views or perceptions of teachers and librarians as well as the responses of Deaf students at SLB Negeri 1 Sungai Pagu to the deprivation of information access. The type of this research is qualitative using descriptive method. Data was collected through observation, interviews, and questionnaires. These data were obtained from filling out questionnaires to six respondents at random and the results of interviews with school principals, librarians, teachers who had served as librarians, and six Deaf students from various levels. The results obtained are that some teachers at SLB Negeri 1 Sungai Pagu only understand this problem after receiving a simple explanation and in practice, the application of ethics to interacting with Deaf students is not optimal. In perception the librarian actually understands this problem, but she finds it difficult to overcome information deprivation because of the limited collection, access and facilities available in the library space. Based on the point of view of Deaf students, the lack of references to Deaf culture greatly impacts the low level of information absorption among Deaf students themselves.
Thermal Comfort of Ad-Du’a Mousque in Bandar Lampung City, Indonesia

The mosque is a place of worship for Muslims. Worship here is not merely a place to pray and recite the Koran, but activities that can get rewards and intercession in this world and the hereafter such as lectures, studies, discussions, and other positive things. The thermal in the room greatly affects the occupants and visitors. This study aims to determine the thermal at the Ad-Du’a Mosque. The method used is by measuring directly and gradually. The instrument used is thermohygrometers and anemometers. Thermohygrometer is used for measuring temperature and humidity. While, anemometer is used for measuring air velocity. The study was conducted at 5 points, 4 points in the room and 1 point on the terrace which is inside or on the terrace using artificial air conditioning (AC). The research results show that the thermal comfort inside the mosque does not meet SNI 03-6572-2001 and ASHRAE 55. The thermal comfort outside the mosque does not meet SNI 03-6572-2001, but meets ASHRAE 55.
Performance Evaluation of Various Solar Forecasting Models for Structural &amp; Endogenous Datasets

The forecasting of solar irradiation with high precision is critical for fulfilling electricity demand. The dataset used to train the learning-based models has a direct impact on the model’s prediction accuracy. This work evaluates the impact of two types of datasets: structural and endogenous datasets over the prediction accuracy of different solar forecasting models (five variants of artificial neural network (ANN) based models, Support vector machine (SVM), Linear Regression, Bagged and Boosted Regression tree). The issue of variability estimation is also explored in the paper in order to choose the best model for a given dataset. The performance of the models is assessed using two essential error metrics: mean absolute percentage error (MAPE) and root mean square error (RMSE). The results shows that the MAPE and RMSE for structural data vary from 1.99% to 29.73% and 23.39 W/m2 to 165.21 W/m2, respectively, whereas these errors for endogenous dataset ranges from 1.98% to 31.19% and 23.64 W/m22 to 152.56 W/m22. Moreover, these findings, together with the data variability findings, suggest that SVM is the best model for all forms of data variability, whereas CFNN may be employed for greater variability.
Free Speech, But …

Introduces the concept of freedom of speech and the legal and social constraints on speech. Free speech is often framed as absolute, but in practice our speech is limited by laws – bans on obscenity, threats, even entire languages. Other social forces hedge our speech as well: parents and teachers attempt to steer our speech; employers tell us what to say and what not to say; and groups of friends set norms and exact punishments for speech behavior in social settings. In all, as speakers and writers we contend with explicit and implicit rules about what we can and cannot say.
Motion Capture and Frame Extraction from Video Using OpenCV

Motion sensors have been used for a long time for e.g. as security systems installed at home which is good at detecting motion of large objects such as people &amp; animal. While these sensors trip security systems and alert the owner about intrusion they do not capture any data about the event. Additional systems such as cameras are needed to record evidence about the events that transpire. This paper introduces an technique which can be used by cameras to detect motion from video so as to alert security and to record evidence by highlighting exactly what has changed in the video thus eliminating the need for motion sensors entirely. It shows how parameters can be tuned in the algorithm to detect specific kinds of motion so that this technology can be used for more than just security such as monitoring machinery where certain variables need to be recorded at key moments such as when it makes rapid movements.
Ethical Planning with Multiple Temporal Values

This paper describes an approach to ethical planning in robotics that combines classical planning, Linear Temporal Logic and lexicographically ordered value sets. We also discuss the features and limitations of the model, with particular attention to its philosophical implications.
Covid-19 Contactless Remedies for Students in Educational Institutes

As we all know fingerprint recognition is one of the secure and accurate Biometric Technologies. If think about it in deep even with the Biometric system the virus can be spread during these situations. To overcome this, we need to come up with a secure and contactless way of authentication. So, let’s update to some contactless remedies like Iris authentication which are unique for every individual and they don’t need to have any physical contact. So, we can use this Iris detection for a secure and contactless authentication system. The main aim of this research is to provide contactless remedies for students in Educational institutes like Smart Locking system, Attendance management system, and Library Transaction by using their Iris authentication and Face Recognition. Coming to the outline of the attendance management system, we will first collect the data from the Kaggle repository. Next, we split the data into training and testing, then we will train the data using transfer learning techniques and test the model by using test data. Finally, we integrated the trained model with the flask. If the Iris matches then the attendance of a particular person will be posted. If not matched then we train the model by adding new person’s data. For the construction of modern electronic security systems, real-time face recognition is crucial. Face detection, feature extraction, and face recognition are the three procedures involved. After recognizing the face, it will check whether the person’s face matches the collected database. If it matches it will show the person’s name, the number of books he took, and what those books are for Library transactions and in the same way the locker will be open if the person’s data is matched. The proposed methods are secure and unique contactless ways of authentication for every individual. So, we can use these detection and authentication systems for secure and contactless applications. It can be successfully used for students in Educational institutes like Smart Locking system, Attendance management system, and Library Transaction by using their Iris authentication and Face Recognition. The Covid-19 infection in society will undoubtedly decline if the proposed argument is implemented.
A Cloud Based Approach for the Classification of Oral Cancer Using CNN and RCNN

One of the most important elements in reducing cancer mortality rates is early identification. Oral cancer, for example, might be screened (much like squamous cell carcinoma.) by taking Papanicolaou test any dentist doctor. The only disadvantage is that manual processing of the vast amounts of data acquired by us is relatively expensive. CNN showed promise in differentiating among cancer and non-cancer cells which is allowing for fast automated cancer screening data processing. We look into different architectures that mainly focus on using texture information to classify cytological malignancy, driven by research which show chromatin texture is one of the most important discriminative variables for that reason. According to the findings, local binary patterns (lbps) in spire CNN classifiers outperform general-purpose CNN’S. This is true when varying different levels of data augmentation and pre-training are taken into account. We also created an interface for the application using the flask framework and deployed in to the AWS cloud for easy access from anywhere.
Design and Development of an Efficient and Intelligent Weather Forecasting App

Weather Forecasting App is a based-on web application that provide the exact the weather data of user’s location. In the proposed web application, there are many parameters used like humidity, wind pressure, wind speed, temperature, sunrise and sunset of and area. In this we can give exact location of a hilly area and forestry area. In this web app we have used API in JavaScript to fetch all the weather’s parameters data. In the proposed app open weather map API being use to fetch the weather data for the user’s location.
A Review on Intelligent Health Care System Using Learning Methods

All organizations that are striving for healthy living must establish and maintain updated diet regimens. A health care recommendation system uses data to give medical advice. Machine Learning analyses data to predict future requests and improve healthcare management. An intelligent health care system needs to be built using machine learning and blockchain for health care recommendation systems. This paper discusses in detail the various systems for providing high quality health services. The existing systems that are discussed are Distributed Ledger Technology (DLT), swarm intelligence learning-based systems, adaptive systems, and deep learning systems. Furthermore, various techniques, advantages, disadvantages, tools used, and their accuracy are compared and contrasted. From the studies, it is clear that block chain technology with deep learning techniques provides better accuracy than other conventional methods.
DI-Gesture: Domain-Independent and Real-Time Gesture Recognition with Millimeter-Wave Signals

Human gesture recognition using millimeter wave (mmWave) signals provides attractive applications including smart home and in-car interfaces. While existing works achieve promising performance under controlled settings, practical applications are still limited due to the need for intensive data collection, extra training efforts when adapting to new domains (i.e. environments, persons and locations) and poor performance for real-time recognition. In this paper, we propose DI-Gesture, a domain-independent and real-time mmWave gesture recognition system. Specifically, we first derive the signal variation corresponding to human gestures with spatial-temporal processing. To enhance the robustness of the system and reduce data collecting efforts, we design a data augmentation framework based on the correlation between signal patterns and gesture variations. Furthermore, we propose a dynamic window mechanism to perform gesture segmentation automatically and accurately, thus enabling real-time recognition. Finally, we build a lightweight neural network to extract spatial-temporal information from the data for gesture classification. Extensive experimental results show DI-Gesture achieves an average accuracy of 97.92%, 99.18% and 98.76% for new users, environments and locations, respectively. In real-time scenario, the accuracy of DI-Gesture reaches over 97% with an average inference time of 2.87ms, which demonstrates the superior robustness and effectiveness of our system.
Effects of Quantization on Federated Learning with Local Differential Privacy

Federated learning (FL) enables large-scale machine learning with user data privacy due to its decentralized structure. However, the user data can still be inferred via the shared model updates. To strengthen the privacy, we consider FL with local differential privacy (LDP). One of the challenges in FL is its huge communication cost caused by iterative transmissions of model updates. It has been relieved by quantization in the literature, however, there have been not many works that consider its effect on LDP and the unboundedness of the randomized model updates. We propose a communication-efficient FL algorithm with LDP that uses a Gaussian mechanism followed by quantization and the Elias-gamma coding. A novel design of the algorithm guarantees LDP even after the quantization. Under the proposed algorithm, we provide a trade-off analysis of privacy and communication costs theoretically: quantization reduces the communication costs but requires a larger perturbation to enable LDP. Experimental results show that the accuracy is mostly affected by the noise from LDP mechanisms, and it becomes enhanced when the quantization error is larger. Nonetheless, our experimental results enabled LDP with a significant compression ratio and only a slight reduction of accuracy in return. Furthermore, the proposed algorithm outperforms another algorithm with a discrete Gaussian mechanism under the same privacy budget and communication costs constraints in the experiments.
Research and Design of College English Online Teaching based on Computer Aided Instruction Resource Library

The impact of safety accidents is very serious, which will not only cause the loss of economic benefits of the project, but also harm the life safety of participants in the building site, and at the same time affect the image of construction-related enterprises and the growth of the industry.BIM as a global and digital technology, is regarded as a revolution in the building industry, which can effectively improve the building efficiency and quality.In this article, a risk assessment model of prefabricated buildings based on artificial neural network (ANN) and BIM is proposed, which can provide early warning and assessment of construction risks, so as to build a safe construction environment and provide theoretical and technical support for the design and construction of prefabricated buildings.The results show that, after many iterations, the accuracy of this method is 97.94%, which is 18.66% higher than that of particle swarm optimization (PSO), and the error is reduced by 33.75%.This algorithm solves the difficulties that traditional models are difficult to deal with highly nonlinear models and lack of adaptive ability.A reasonable safety assessment system can help constructors understand the safety management problems faced in the construction of prefabricated buildings and make accurate judgments.
A Literature Review on Agile Methodologies Quality, eXtreme Programming and SCRUM

Agile methodologies have become one of the most applied methods in the software development industry. However, agile methodologies face some challenges such as less documentation and wasting time considering changes. This review presents how the previous studies attempted to cover issues of agile methodologies and the modifications in the performance of agile methodologies. The paper also highlights unresolved issues to get the attention of developers, researchers, and software practitioners
Pengaruh Dukungan Keluarga terhadap Minat Baca Siswa Kelas IX MTs Nurul Islam Kota Kediri

This study aims to determine the effect of family support on reading interest in class IX MTs Nurul Islam, Kediri. This study uses a quantitative approach. The sample in this study were 57 respondents. The sampling technique uses non-probability sampling in the form of purposive sampling. The research data analysis was carried out using linear regression analysis. Based on the results of research on family support for reading interest in class IX MTs Nurul Islam, Kediri, namely: the level of family support is in the moderate category and the level of students' reading interest is in the moderate category. Based on the results of the regression analysis, a sufficient/moderate (positive) relationship was obtained between family support and reading interest with a coefficient of determination of 20.9%. Thus it can be said that the influence of reading interest is influenced by family support by 20.9%, while the remaining 79.1% is influenced by other factors.
Attitudes and Support Strategies of Parents to Promote English-Indonesian Bilingualism in Children

The goal of this study was to look into parents' attitudes toward encouraging English-Indonesian bilingualism in their children, as well as their support strategies for doing so. This study used a mixed-method approach to collect both quantitative and qualitative data. The quantitative data was collected using a questionnaire, while the qualitative data was collected during the interviews using open-ended questions. Twenty mothers who raise bilingual (English-Indonesian) children took part in the study. The researcher collected data using a questionnaire and an interview. The majority of parents prefer English-Indonesian bilingualism for their children, according to the findings of this study. By being actively involved in their children's language development, parents always make efforts to maintain their children's bilingual skills. The support systems that parents provide for their children, as well as the degree of support provided in English language learning, appear to differ. To make the learning process effective and successful, parents focus on their children's interests while developing Indonesian-English skills for them.
SCRIBO: A Graph Visualizer &amp; Analytical Tool

Data science is a very visual field of computer science where everything needs to be depicted graphically in order to derive new forms of data and conclusions from it. This paper introduces visualization techniques where graphs can be better rendered using advance techniques such as vertex and edge clipping against the view port, rendering a large drawing area by panning the view port to focus on a specific region, labelling vertex data with numeric , text and images and advanced analytic algorithms such as analyzing and finding the shortest path between two vertices using Dijkstra’s, Floyd, finding the minimum spanning tree using Prims and Kruskal’s and traversing all vertices in a graph using Breadth first search and Depth first search. Practically any issue if it can be condensed into a graph data structure format can be sent to this program and are graphically evaluated to discover new information. This paper explains the above-mentioned techniques in detail and how they can work together to create an advanced visualization tool for data scientists working with bidirectional weighted relational data that is graphs.
Bank Robbery Detection System Using Computer Vision

We propose a computer-vision-based detection and recognition system which can be used in the banks to detect the anomalous activity of bank robbery. We propose CCTV based robbery detection along with tracking of thieves. We have used computer vision to detect theft and robbers in CCTV footage, without the use of sensors. This system concentrates on object detection. This detection of bank robbery is done based on detecting components, which are prohibited by using in the banks and are common in robbery, like handguns, a person wearing a helmet or a ski mask which comes under the object detection. Apart from these, recognition is done on the human postures like raising hands and kneel down which comes under the posture detection. The security official will be notified about the suspicious event by using Real-time analysis of the movement of any human from CCTV footage and thus gives a chance to avert the same, so that necessary action will be taken by the authority and prevent threat to bank as well as to the human life presents there.