Reviewerly/exHarmony huggingface dataset:
Implementation of Autoregressive Integrated Moving Average for Tourism Recommendation in Pamijahan Area Based on Internet of Things

Pamijahan is one of the sub-districts located in Bogor regency, West Java. Pamijahan has a variety of tourist destinations, one of which is the Cigamea waterfall, Cikuluwung waterfall, and the Seribu watering problem when visiting tourists, one of them is the weather factor such as rainy weather where Bogor has a highland area so very big influence on the variation of rain there and visitors have difficulty in choosing tourist sites with sunny conditions, the solution to the problem above is to predict the weather during rainy weather in the tourist areas of Cigamea waterfall, Cikuluwung waterfall, and Seribu waterfall using the Autoreggressive Integrated Moving Average method as weather prediction process uses Time Series data with the help of the Internet of Things tool in data retrieval, the weather prediction testing process uses the Autoreggressive Integrated Moving Average method by calculating the value of MSE (Mean Square Error) and MAPE (Mean Absolute Presentage Error) as minimum as possible to see the level of accuracy of the weather prediction process at each designated tourist site. The results of this study indicate that weather forecasting in Cigamea, Cikuluwung and Seribu waterfalls shows the smallest MSE (Mean Square Error) value, the waterfall that has the smallest MSE value, the Seribu waterfall with the Mean Square Error value of the temperature sensor 0.06, Air Humidity 0.59, and Light Intensity 13.30, so that tourist attractions that can be used as tourist attractions for visitors are the Seribu Waterfall.
Real-Time Computer Vision for Transportation Safety using Deep Learning and IoT

With the advancements in technology and application orientations, the field of computer vision is evolving at a rapid rate. Real-Time systems capable of detecting objects with high precision has been a spotlighted field of research. Autonomous traffic systems, self-driving cars and advanced driver assistance systems (ADAS) are the areas where computer vision plays an extensive role. This paper employs YOLO v4 deep learning based computer vision system for detecting vehicles, animals and pedestrians in real-time with high precision scores. The system is based on cross stage partial (CSPDarknet53) convolutional neural network serving as the backbone network for object detection. For precise detection of the entities, 16,300 image sequences were utilized for training the algorithm. For fast computation and detection, the algorithm was deployed on NVIDIA GTX 1060 GPU yielding into effective frames per second processing capabilities. The algorithm was trained for cars, bikes, pedestrians, cows and dogs yielding precision scores of 0.9912, 0.9868, 0.9783, 0.9770 and 0.9726 respectively. Also, the system alerts the vehicle in case of pothole areas nearby to ensure safety. Survey-based pothole and animal hotspots were collected and stored in the server geo-database. The vehicle approaching the hotspot triggers the alert system with acoustic feedback. The deployed real-time computer vision system serves as an effective system for real-time transportation entities detection applications laying a solid foundation for intelligent transportation systems, autonomous vehicles and advanced traffic applications.
Adversarial Attacks on Deep Neural Network-based Power System Event Classification Models

Online event classification is essential to strengthening the reliability of the power transmission system. Recently, deep learning based methods have achieved great success in numerous domains such as computer vision and natural language processing. Researchers began to adopt deep learning based methods to solve the power system event identification problem and achieved effective results. However, these previous works do not consider that deep learning models are vulnerable to adversarial attacks, potentially influencing real-world applications' reliability. In this paper, we adopt several adversarial attack mechanisms by adding tailored noise signal to the input Phasor Measurement Units (PMU) time series and make the deep learning model misclassify the power system event. This numerical study discloses that current state-of-the-art deep learning based power system event classifiers are extremely vulnerable to adversarial attacks, which may jeopardize the reliability of the power transmission system.
Path Planning and Smoothing in Maze Exploration Using Virtual Mobile Robot-Based Modified Probabilistic Road Map

Planning an efficient and smooth path has become a recurring issue supporting mobile robot navigation. In the case of maze exploration, path planning must provide the shortest and easiest path to be followed by a mobile robot. This paper aims to develop a path-planning method connecting several target positions with a smooth path. This paper proposes a modified probabilistic road map with virtual mobile robot path smoothing. A new approach to producing random nodes that differ from previous path planning is presented here. A virtual non-holonomic mobile robot follows the sharp turn path generated by the proposed method. The trajectory left by the virtual robot results in a smooth path connecting some desired destinations. The magnitude of the distance of the proposed path planning and smoothing was more efficient than the path of previous research, with an average value of 18.72%. The virtual mobile robot-based modified probabilistic road map successfully improved the smoothness of the path.
Intelligent segmentation of subjective question area by removing template in intelligent marking

With the rapid development of computer technology and communication technology, computers have been applied to all areas of people's lives. In the field of education, it is a necessary function for the intellectualization of the education system to realize the automatic marking of examination papers by computer. In intelligent marking, the traditional method is used to segment the subjective question area of the test paper, which can not effectively segment different types of answer sheets and has the defects of low segmentation accuracy. The edge detection method is used to correct the subjective question area of the answer sheet, and the boundary box of the subjective question part is accurately screened by positioning the top, left, and bottom positioning lines of the answer sheet to complete the segmentation of the subjective question area in marking. The experimental results show that the correct rate of the new method is more than 95%, which is much higher than that of the traditional method. The new method can be widely used in intelligent marking work, and can effectively improve the efficiency of intelligent marking.
Double Auction Mechanism for Cooperative Swarm Learning in Internet of Vehicles

Federated Learning (FL) plays a prominent role for realizing distributed machine learning in the Internet of Vehicles (IoV). However, the clients still need to transfer their local models to the central coordinator in FL, which brings redundant communication overhead. In Swarm Learning (SL), there exists no central coordinator and all joint members can merge others’ parameters through a swarm network. This paper proposes a cooperative SL framework that improves training efficiency and security. To incentivize vehicle users (VUs) to cooperatively train, we further design an iterative double auction based SL scheme to maximize the total social welfare and stimulate VUs with good channel states and sufficient resources to participate in SL. The extensive experiment results show the effectiveness of the proposed scheme.
PaCHash: Packed and Compressed Hash Tables

We introduce PaCHash, a hash table that stores its objects contiguously in an array without intervening space, even if the objects have variable size. In particular, each object can be compressed using standard compression techniques. A small search data structure allows locating the objects in constant expected time. PaCHash is most naturally described as a static external hash table where it needs a constant number of bits of internal memory per block of external memory. Here, in some sense, PaCHash beats a lower bound on the space consumption of k-perfect hashing. An implementation for fast SSDs needs about 5 bits of internal memory per block of external memory, requires only one disk access (of variable length) per search operation, and has small internal search overhead compared to the disk access cost. Our experiments show that it has lower space consumption than all previous approaches even when considering objects of identical size.
A Certificateless-Based Blind Signature Scheme with Message Recovery

Blind signature is a digital signature technology that enables the signature requester to obtain the valid signature of the signer without revealing the identity and content of the message to the signer. It is widely used in electronic cash, electronic voting and other fields. The message recovery technology is a technology that can hide the message in the signature, and only pass the signature without passing the message when it is delivered. In this paper, a blind signature scheme based on certificateless message recovery is proposed based on the combination of blind signature and message recovery technology, and based on certificateless public key cryptosystem. The signer can sign the blinded message under the certificateless cryptosystem, and the final blind signature result can only transmit the signature to reduce the conmmunication bandwidth. Security analysis shows that the scheme satisfies correctness, blindness, and unforgeability.
Utility of Imprint Cytology in Intraoperative Diagnosis of Ovarian Neoplasm

Background: The ovaries are female reproductive organs and at the same time, the site of origin of some of the most complex as well as lethal neoplasms. Aims: The present study was conducted to assess whether imprint cytology can help in rapid diagnosis of ovarian neoplasm and thus facilitate individualized treatment. Materials and Methods: This prospective study was carried out in the Department of Pathology in a tertiary care hospital in collaboration with Department of Gynaecology after taking Institutional ethical committee approval. Fifty patients, attending Gynaecology and Obstetrics OPD &amp; indoor and diagnosed clinically &amp; radiologically (USG) as cases of ovarian tumour was selected. After taking valid consent from the patients, a detailed history was taken and clinical examination was done preoperatively. Imprint smears were made intraoperatively from fresh samples and stained with M.G.G. stain for air dried smears and Papanicolaou stain for alcohol fixed smears. Results: Out of total 50 cases, 32(64%) was epithelial tumours and 18(36%) non-epithelial tumour cases. Age group varied from 5 to 62 years. Overall sensitivity in our study to diagnose benign and malignant ovarian tumours by imprint cytology is 94% and the specificity is 74% with positive and negative predictive value of 63% and 96% respectively. Diagnostic accuracy was 78% with high statistical significance (p&lt;0.001). Conclusion: By taking samples from a variety of representative tumour areas, the accuracy of this method can be improved. Imprint cytology's accuracy is on par with that of frozen section histology. Therefore, extensive use of imprint cytology should be made in the management of patients with ovarian neoplasia.
Missing Fault Data Processing Method Based On Improved Harmony Search Algorithm

This paper presents an improved harmony search algorithm (OP-HSA) for missing fault data processing, to find a relatively optimal missing data imputation method from the alternatives. In real life, data is usually missing due to equipment failure or staff negligence, and researchers will preprocess the missing data before using it for work. There are more than one hundred methods to impute missing data, it is not easy to find the right imputation method for missing data. Selecting the right data imputation method can get twice the result with half the effort. To solve this challenge, this paper proposes OP-HSA. In the experiment of this paper, the data processed by different imputation methods are fitted with the original dataset and the similarity error of the dataset is compared. The results show that the improved harmony search algorithm can find a more appropriate data imputation method.
JFeature: Know Your Corpus

Software corpora are crucial for evaluating research artifacts and ensuring repeatability of outcomes. Corpora such as DaCapo and Defects4J provide a collection of real-world open-source projects for evaluating the robustness and performance of software tools like static analysers. However, what do we know about these corpora? What do we know about their composition? Are they really suited for our particular problem? We developed JFEATURE, an extensible static analysis tool that extracts syntactic and semantic features from Java programs, to assist developers in answering these questions. We demonstrate the potential of JFEATURE by applying it to four widely-used corpora in the program analysis area, and we suggest other applications, including longitudinal studies of individual Java projects and the creation of new corpora.
Representation of Soccer Robotics in The Fastest Trajectory Tracking

Estimating the robot’s movement toward the target will be more optimal if it considers the shortest path and still considers the obstacles to that target. Soccer robot games have a dynamic object movement activity, so soccer robots must always compare the difference in distance and obstructed paths. Dijkstra’s algorithm, an optimization algorithm for determining the shortest path of a track, can be applied in a dynamic environment by considering the distance and weight of the track so that the robot can choose a more effective path. The application of this method to the soccer robot game is more effective than the obstacle avoidance method without regard to several environmental factors. This method can prevent the robot from being trapped in an impassable path so that the robot can optimize travel time to the target point. The test results show that the distance to the target of 0.85 m in the presence of several obstacles can be reached in the path of 0.87 m, with a speed of 0.08 m/s and within 12 s.
Face Detection in Still Image using SSD MobileNet V2 and Geometrical Algorithm

Face detection plays an important role in today’s technological advancement as it is used in various applications in multiple fields. Many techniques and frameworks have been proposed throughout the decades, utilising different methods that range from algorithms to machine learning and even deep learning. In this paper, a face detection framework that utilises a two-step method was proposed to overcome over-detection in still images containing face images and misdetection in non-face images. The two-step method consists of feature detection using SSD MobileNet V2 and a geometrical algorithm for detecting the face region in the given image. The proposed method was compared with the state-of-the-art face detection algorithms such as MTCNN, Dlib face detector, and OpenCV optimised Haar Classifier. The proposed method performed well in the testing dataset, with 91.5% prediction accuracy. However, it fell short in terms of prediction accuracy performance against the MTCNN and Dlib but performed better in reducing the number of over-detection and misdetection. Hence, achieving the target of the proposed framework.
Measuring the impact of CSR practices on customer satisfaction during pandemic: a quantitative study in the Philippines

Purpose This study aims to analyze how consumers perceive the corporate social responsibility (CSR) practices carried out by a local water district in Angeles City, Philippines. The main objective of the study is to investigate how CSR practices influence customer satisfaction. To achieve this, several pieces of literature were mentioned to prove that CSR has an important role to its stakeholders. Design/methodology/approach The study used a descriptive and quantitative approach to test the hypotheses. The participants of the study were the residents of the top three barangays in Angeles City, Philippines, in terms of the most consumed water supply, namely: Cut-cut, Pampang and Anunas. Using the Raosoft sample size calculator, the computed sample size was 382 respondents and distributed using stratified sampling. Methodologically, the study used statistical treatment to test all the variables and validated the instrument. Findings The result of the study shows that CSR practices significantly impact customer satisfaction. In the test of the degree of relationship between the dimensions of CSR and customer satisfaction, it was found that there is a significant relationship between the two variables. Research limitations/implications The future study may explore the relationship between CSR and the resource-based view theory of the firm. The resource-based perspectives are useful to understand why firms engage in CSR activities and disclosure. From a resource-based perspective, CSR is seen as providing internal or external benefits, or both. Practical implications The water district must promote social welfare and behave as good corporate citizens; they must spend the resources allocated to CSR initiatives in ways that yield optimum benefits to society as well as to the stakeholders of the company. Social implications The paper points out that the water supply industry is one of the most regulated sectors, as the operation requirements are tighter than any other activity. The authors emphasize that water companies must assume special responsibility because their activities are directly related to the use of natural resources, environmental pollution, and public health. Originality/value This study used a descriptive-explanatory strategy to determine the significant variables using PLS-SEM. This paper addressed how consumers perceive the CSR practices carried out by the local water district in Angeles City, Philippines.
Digital Reading Check‐ins: Supporting Independent Digital Reading

Abstract We know that teacher support during independent reading can greatly impact the growth and development of beginning readers. However, the rapid conversion to online learning in the 2020–2021 school year required teachers to rethink and retool traditional strategies to meet the needs of their online learners. This teaching and learning guide explores the use of a digital reading check‐in strategy to support beginning readers as they read independently on digital platforms. We explain how this strategy was implemented successfully in a first‐grade classroom and how it can be employed with any digital reading platform used in classrooms today.
Three-dimensional Reconstruction of Retinal Blood Vessels based on Binocular Vision

Due to the limited perception ability of the naked eye, it is difficult to accurately locate the micro scale retinal vessels, which is easy to cause organ damage in the process of surgery. Therefore, the retinal vascular reconstruction method based on binocular stereo vision is studied. Firstly, build a binocular vision detection system and then calibrate and correct the system. Secondly, filter and segment the collected 3D printed eyeball blood vessel model image, and calculate the parallax and depth map by constructing the error energy function. After that, the high-precision 3D point cloud model can be obtained. Finally, the error analysis of three-dimensional point cloud is carried out by binocular parallax principle. The experimental results show that this method can control the error within 0.5 mm and meet the requirements of retinal vascular surgery.
Marathi Sign Language Hand Gesture Recognition Using Accelerometer and 3D Printed Gloves

Due to the communication abilities impairment, deaf & dumb peoples are more or less isolated from mainstream societal activities. NGOs, social workers & Governments in India are putting efforts with multiple initiatives to increase the involvement of these peoples in the mainstream activities happening in the rest of world. This paper proposes to establish an alternative method to streamline the communications among normal & speech impaired persons by using smart hand glove system equipped with 3D accelerometer modules at the fingertips. The proposed system is delivering the measurement results in very proficient way. The experiments were carried out over 15 persons using the sign gestures, providing good co-relation between the Posed gestures versus system mapped gestures.
Driving UI State with Combine

Modern UIs have to respond to a multitude of input signals at the same time: users can communicate with an app using keyboard input, multitouch, physical gestures, and even voice commands. On top of this, applications might receive data from remote servers and local APIs.
A NEW QUASI-NEWTON METHOD FOR NON-LINEAR OPTIMIZATION PROBLEMS AND ITS APPLICATION IN ARTIFICIAL NEURAL NETWORKS (ANN)

The Quasi-Newton (QN) method is a widely used stationary iterative method for solving unconstrained optimization problems.One particular method within the Quasi-Newton family is the Symmetric Rank-One (SR1) method.In this research, we propose a new variant of the Quasi-Newton SR1 method that utilizes the Barzilai-Borwein step size.Our analysis demonstrates that the updated matrix resulting from the proposed method is both symmetric and positive definite.Additionally, our numerical experiments show that the proposed SR1 method, when combined with the PCG method, is effective in solving unconstrained optimization problems, as evidenced by its low number of iterations and function evaluations.Furthermore, we demonstrate that our proposed SR1 method is more efficient in solving large-scale problems with a varying number of variables compared to the original method.The numerical results of applying the new SR1 method to neural network problems also reveal its effectiveness.
PENGARUH MANAJEMEN KESISWAAN TERHADAP PRESTASI BELAJAR PESERTA DIDIK KELAS IX MAN 4 PIDIE JAYA

Learning outcomes are the main goals that must be achieved by learning activities. Learning outcomes are used to determine how well students understand and are able to understand the material. The achievement of student learning outcomes is related to cognitive, emotional, and psychomotor aspects. From this, it can be concluded that learning outcomes are abilities possessed by students after going through the learning process at school, related to their knowledge, and can be measured by measuring them in the form of numerical tests. Learning outcomes in the world of education are measured by assessments carried out after the learning process. The results of this study provide an overview of students' progress in participating in learning from time to time. The purpose of this study was to determine the effect of student management on the learning achievement of class IX students at MAN 4 Pidie Jaya. The populations in this study were students of class IX MAN 4 Pidie Jaya. The determination of the sample design used in this study was based on the purposive sampling method with 60 students obtained and then 35 students were drawn as samples. The data analysis method in this study uses simple linear regression to predict the effect of the independent variables. The results show that student management has a significant effect on learning achievement, which means student management also means the entire process of planned and considered activities, as well as continuous student development so that the learning process from student registration to withdrawal can be tracked effectively and efficiently.