Reviewerly/exHarmony huggingface dataset:
Recognition of Train Driver's Attention Using Haar Cascade

Driving a train is a very responsible task as it involves the safety and security of the train passengers. Though, railway department assures the presence of two drivers in the main cockpit of a train, but any human error could result in catastrophic fatalities. In order to ensure the attention of a rail driver, an indigenous solution is developed using a Raspberry Pi 3 B+, a webcam and Open CV libraries for the detection of driver's attentiveness. The algorithm works on the basics of Haar Cascade Classifier for capturing the eyes movement, which performs its operation using the binary classifier. Therefore, if the eyelids are close (i.e., the statement of the binary classifier is true) for more than 15 seconds, an alert will be sent to the railway control room regarding the driver's drowsiness, resulting the mitigation of disastrous outcome by ensuring the necessarily and timely measures. To validate the instrumentation, it was compared with Haar classifier along with a survey of the train drivers regarding the effectiveness of the developed prototype. From that survey, 70% of the drivers were satisfied with the effectiveness of the developed rail driver attention detection system.
Haar Filter Hardware Architecture for the Accuracy Improvement of Stereo Vision Systems

In stereo vision systems, mismatching can occur frequently in specific regions containing noise and high-frequency components, such as checkered patterns. Therefore, this paper proposes a preprocessing method and architecture based on a 2-D Haar filter to improve the matching accuracy of disparity map by reducing the high-frequency and noise components. In this paper, the disparity map is computed by the semi-global matching (SGM) method, and separable weighted median filter is adopted as postprocessing. The SGM with proposed method reduces the average number of mismatching pixels by 3.5233% in non-occlusion condition and 3.2647% in occlusion condition compared to that with a 2-D Gaussian filter when using the KITTI 2015 stereo dataset. The proposed method is suitable for embedded stereo vision systems that requires high matching accuracy with reasonable resource overhead.
Artificial Intelligence Systems in the Decision-Making Process

The theory and practice of management has long been looking for methods and tools enabling the implementation of management functions in an organization based on the decision-making process based on modern information technologies. ICT has been successfully supporting management processes in organizations for many years, including in the form of integrated management information systems (eg MRP or ERP classes), decision support systems (SWD) or systems known as Business Intelligence for some time. The essence and purpose of the chapter is therefore to identify the impact of AI on the decision-making process based on the theory of fuzzy sets and fuzzy logic explaining the ways in which decision systems operate.
Landing Probabilities of Random Walks for Seed-Set Expansion in Hypergraphs

The landing probability of a vertex in a hypergraph is the probability of a random walk ending at the vertex after making a prescribed number of steps. Landing probabilities are of importance for a number of learning tasks on hypergraphs, including higher-order PageRanks and (local) community detection. We perform the first mean-field study of landing probabilities of random walks on hypergraphs and examine clique-expansion and tensor-based methods. In particular, we evaluate the mean-field characteristics of the two methods over a class of random hypergraph models for the task of seed-set community expansion. We determine parameter regimes in which one method outperforms the other and propose a new hybrid expansion method termed “partial clique-expansion” to reduce the projection distortion and reduce the complexity of tensor-based methods on partially expanded hypergraphs.
Operating Status Diagnosis of Power Equipment Based on Rule Engine

During the daily inspection and maintenance of power equipment, the operation and maintenance department has accumulated a large number of equipment operating status documents. The operating status document records defect phenomena, diagnosis, and solutions, etc. Therefore, such documents can provide reference basis and guidance for daily defect diagnosis. However, due to the complex operating status of the equipment and the inconsistent documentation method, status documents still cannot efficiently provide sufficient reference. In order to realize equipment diagnosis based on status documents, this paper developed and implemented a knowledge discovery system after analyzing the characteristics of the power equipment operating status documents. This system adopted the Drools rule engine technology. Firstly, it established the abnormal rules of power equipment operating status indicators, then classified the key information in the documents, and finally realized the accurate judgment of power equipment defects based on the status documents based on the Drools rule engine. The diagnostic accuracy rate reached 75.23%.
The parallel optimization based on the PVS algorithm and research on the evaluation function in the Game of the Amazons

The PVS search function, as a current mainstream and efficient algorithm, has been widely used in various kinds of chess program. We applied the parallel search function based on the PVS and improved the running speed of the program. At the same time, we also did some research and experiments on the evaluation function of Amazon chess which provided a set of available Amazon evaluation functions and parameter adjustment results for reference.
Insights into Artificial Neural Network techniques, and its Application in Steganography

Abstract Deep Steganography is a data concealment technology that uses artificial intelligence (AI) to automate the process of hiding and extracting information through layers of training. It enables for the automated generation of a cover depending on the concealed message. Previously, the technique depended on the existing cover to hide data, which limited the number of Steganographic characteristics available. Artificial intelligence and deep learning techniques have been used to steganography recently and the results are satisfactory. Although neural networks have demonstrated their ability to imitate human talents, it is still too early to draw comparisons between people and them. To improve their capabilities, neural networks are being employed in a number of disciplines, including steganography. Recurrent Neural Networks (RNN) is a widely used technology that automatically creates Stego-text regardless of payload volume. The features are extracted using a convolution neural network (CNN) based on the image. Perceptron, Multi-Layer Perceptron (MLP), Feed Forward Neural Network, Long Short Term Memory (LSTM) networks, and others are examples of this. In this research, we looked at all of the neural network approaches for Steganographic purposes in depth. This article also discusses the problems that each technology faces, as well as potential solutions.
Determination of Step Check Quality Control Thresholds on Air Temperature Data at South Tangerang Climatological Station

Abstract All data, including air temperature data, must be verified by conducting quality control using the step check method. Step check quality control is carried out by looking at the difference of a parameter in a certain period compared to the threshold value that was already determined. Therefore before carrying out step check quality control, it is necessary to determine the ceiling and floor boundaries of the difference in air temperature data every hour. The data used in this study are hourly air temperature data and hourly present weather data from weather observations at the South Tangerang Climatological Station during 2016 - 2020. In determining the threshold for air temperature step check quality control, the air temperature data is paired with weather condition data to obtain a threshold value according to rain and no rain conditions. The threshold conducted in this study is based on a check for unusual climatological values, where the limits for an unusual and impossible jump in hourly air temperature changes are determined based on a certain percentage of the data distribution. This study uses percentile analysis to determine the threshold, where 5% in the lower and upper part of the data distribution are used as the threshold. The results show various thresholds every hour. The increase in temperature dominates the changes of hourly air temperature in no-rain conditions. The highest threshold for temperature increase occurs at 00.00 – 01.00 UTC at 3.2°C and continues to decrease over time. The highest threshold for temperature decrease occurs at 09.00 UTC - 10.00 UTC at 2.2°C. In rain conditions, the increase in temperature can still occur. However, the decrease in temperature mainly occurs. The highest threshold for temperature increase during rainy conditions is 1.8°C at 01.00 - 02.00 UTC, while the highest threshold for the temperature decrease is 5.8°C at 06.00 UTC – 07.00 UTC. With these results, observers can first carry out quality control with the Step Check method before filling in the data into the system database. Thus, any suspect data either from reading errors or tool errors can be minimized and finally produce a valid dataset.
Design and Development of Teaching System for Manipulator Simulation Control

The Manipulator is widely used in the current industrial field, and its teaching has a strong theoretical and practical nature. However, traditional theoretical teaching is often out of touch with practice, resulting in poor teaching effects. This paper develops a teaching software system for Manipulator simulation control, provides an interactive and visual learning platform for Manipulator principles, and deepens its understanding of teaching content through PBL teaching, reduces the difficulty of course learning, and improves students Interest in learning.
Method for determining the authenticity of a document based on computer colorimetry

Determining the authenticity of documents is an important task when conducting investigative measures. In practice, for the examination of documents to determine the prescription of their creation, a spectral method is used, in which it is necessary to make clippings from the document under study, which leads to damage to the document under study, and also requires the use of expensive equipment, which affects the price of the examination of the document. The article describes a method for determining the authenticity of a document based on computer colorimetry, which does not require expensive equipment for conducting an examination. This method for determining the authenticity of a document uses a change in the color of the strokes of a ballpoint pen over time, which is caused by the drying of the solvent in the ink of the pen, and also lacks the disadvantage of the need to damage the document under study.
Multimodal Feature Analysis for Precise Human Hand Gesture Recognition

Recognizing human hand gestures through digital inputs is becoming the feed for human-controlled machines and action-based systems. Detecting the patterns of the human hand faces a variety of issues such as classification and correlation due to the time and physical structures. To leverage the success rate of gesture identification, this paper presents multimodal feature analysis through precise feature classification and analysis. Based on the features, the coordinates are segregated to detect the precise correlation with the existing dataset, to improve the rate of success. The proposed method relies on pixel extraction from the classified features in a recurrent manner, to achieve a better success rate over the varying classification instances and inputs. The experimental results prove the consistency of the proposed analysis method by achieving less recognition time.
Component-based Analysis of Dynamic Search Performance

In many search scenarios, such as exploratory, comparative, or survey-oriented search, users interact with dynamic search systems to satisfy multi-aspect information needs. These systems utilize different dynamic approaches that exploit various user feedback granularity types. Although studies have provided insights about the role of many components of these systems, they used black-box and isolated experimental setups. Therefore, the effects of these components or their interactions are still not well understood. We address this by following a methodology based on Analysis of Variance (ANOVA). We built a Grid Of Points that consists of systems based on different ways to instantiate three components: initial rankers, dynamic rerankers, and user feedback granularity. Using evaluation scores based on the TREC Dynamic Domain collections, we built several ANOVA models to estimate the effects. We found that (i) although all components significantly affect search effectiveness, the initial ranker has the largest effective size, (ii) the effect sizes of these components vary based on the length of the search session and the used effectiveness metric, and (iii) initial rankers and dynamic rerankers have more prominent effects than user feedback granularity. To improve effectiveness, we recommend improving the quality of initial rankers and dynamic rerankers. This does not require eliciting detailed user feedback, which might be expensive or invasive.
3D Real-Scene Model of Oblique Photography Based on Retinex

The traditional 3D modeling adopts the method of manual field measurement data, which meets the accuracy required by production and construction, but the efficiency of data acquisition is too low. In order to improve the overall work efficiency, the UAV oblique photography is used in the 3D modeling. It not only liberates technicians from the hard field work, greatly reduces the field work workload, but also can quickly build 3D real-scene model, shorten the drawing period, reduce the production cost, which has a positive role in promoting the development of geographic information industry. However, in the process of UAV tilt photography, the image effect is often not very ideal due to the influence of bad weather such as uncertain rain or shine and wind and fog, and uneven illumination phenomena such as underexposure, overexposure and shadow appear, which directly affect the quality of the 3D real-scene model in the later stage. In order to eliminate this effect, Retinex method in image enhancement technology is applied to preprocess the oblique photography image to ensure the image quality, which lays a foundation for the quality of the subsequent 3D real-scene model.
KNN-Attention-CNN Model for Text Emotion Classification

Convolutional Neural Network (CNN) shows superior performance in the field of emotion classification, but existing approaches input single text matrix to CNN, which are the lack of considering similar texts in training dataset, and effect on the overall classification performance. In this paper, we put forward a kind of combining KNN algorithm which is used to extract the weighted text matrices, self-attention mechanism to obtain the fusion attention text matrices, and CNN text emotion classification model, and we dub it KNN-Attention-CNN. First, KNN algorithm is used in the pre-processing part to extract the similar features of training text dataset and obtain the weighted text matrix. Then the self-attention mechanism is introduced to extract the emotional features with the remote dependence relationship, and finally the classification of the emotional features is implemented through the convolutional neural network. The experimental results are conducted on three real data sets show that the proposed KNN-Attention-CNN model are superior to ordinary CNN, and the accuracy is 3.12% higher than CNN. The comparison between KNN-Attention-CNN and five other baseline models verifies the promising performance of KNN-Attention-CNN. We change the length of the input sentences and K value in KNN algorithm respectively, affirm that the addition of the self-attention mechanism has a more obvious effect on the emotional classification on long sentences, and the appropriate K value can optimize the classification performance.
Visual Integration of Mental Health Information of College Students Based on Fuzzy-Grey

College Students' mental health information resources involve a wide range and a large amount of information, resulting in the time-consuming and laborious query of information and low operation efficiency of the existing information resource integration platform. In order to solve the above problems, a visual integration method of College Students' mental health information based on Fuzzy Grey is designed. The visual integration design of mental health information is carried out based on nonlinear mapping algorithm and off-line chaotic model mapping algorithm, which realizes the collection, screening and integration of information resources. Experiments show that the mental health information resource integration platform based on Fuzzy Grey technology has excellent performance
Stack LSTM for Chinese Image Captioning

Image captioning has attracted considerable attention in recent years. However, little work has been done for Chinese image captioning which has unique cultural characteristics and wording requirements. This paper studies how to generate more accurate Chinese image captions. We propose a novel Chinese image captioning model, which uses the pre-trained ResNet50 to extract the visual information of the image and a double-layer LSTM to predict each Chinese word. Applying this approach to Chinese image captioning, we obtained the better results on the AIC-ICC dataset compared with other image captioning algorithms, the method proposed in this paper greatly improves the evaluation performances and achieves BLEU-4 /CIDEr scores of 39.9/121.7, respectively. The actual generation results also show that the model can generate accurate, diverse and vivid Chinese caption of images.
DEVELOPING AR INTERFACE FOR INTERIOR MODELLING FROM 3D SENSOR

Abstract This paper aims to show the process of Augmented Reality (AR) development representing existing building interior from a 3D sensor. These days, AR is becoming well known and widely used in various type of areas such as in gaming industry, medical training, and education. Due to its advantages, AR development for building interior is also necessity, specifically to assist people in understanding the scene more, in Architectural, Engineering, Construction / Facility Management (AEC/FM) applications as well as businesses like interior designing and household marketing. With respect to current situation of COVID-19 pandemic that has restricted travelling makes the usage of AR very beneficial and it could assist people to visualize the interior that needs to be visited. With the aid of suitable sensors like 3D sensor, the development of AR representing existing building interior which is usually full of clutter and occlusion could be eased.
Expert System for Legal Consultation of Song Royalty with Iterative Dichotomiser 3 Algorithm

Determining the parties which are entitled to receive royalty for a commercial product such as music and songs is getting more complex in today’s world. It is necessary to acknowledge the moral and economic rights of the persons involved in creation of such objects, and therefore the need to register the copyright of a song is a must. In reality, many factors aside from the legal basis may affect to whom the rights will fall to. Therefore this study designs an expert system which help to suggest if a person is entitled to receive the royalty of music and registered in Indonesian Directorate General of Intellectual Property (DJKI), and therefore they can make a legal act if deemed necessary. The expert system employs predicate calculus and Bayesian theorem to construct the decision rules, and Iterative Dichotomiser 3 (ID3) as a production rule to calculate information gain from the decision tree. Experiment conducted with 120 song data samples shown the accuracy rate of 91.67% of suggesting the entitled parties for the royalty of a song. Thus this system may help parties in concern to ensure their claim on the moral and economic rights of the song piece.
Design of a Cloud-based Paging Intelligent Classroom Recording and Broadcasting System

After the epidemic, online and offline mixed teaching will become a norm, and video teaching will be the most commonly used online teaching mode. The fragmented way of paging recording and broadcasting designed by this system can solve the traditional misreading in the recording of the whole text or need a lot of editing work because of being interrupted, which can greatly improve the efficiency of recording classes. This system adopts B/S mode, chooses SpringBoot and SSM-(Spring_SpringMVC_Mybatis) framework and, Spring Cloud microservice framework. Using document cutting, stroke track monitoring, stroke track restoration, progress bar jump and multi-version recording algorithms, the text is paginated and recorded, and the recorded course is developed twice. It supports online and offline playback of recorded courses and saves most traffic mode. It is a real online recording and teaching synchronization of the Internet teaching platfom.
RMF-Net: Improving Object Detection with Multi-Scale Strategy

We propose a target detection network (RMF-Net) based on the multi-scale strategy to solve the problems of large differences in the detection scale and mutual occlusion, which result in inaccurate locations. A multi-layer feature fusion module and multi-expansion dilated convolution pyramid module were designed based on the ResNet-101 residual network. The ability of the network to express the multi-scale features of the target could be improved by combining the shallow and deep features of the target and expanding the receptive field of the network. Moreover, RoI Align pooling was introduced to reduce the low accuracy of the anchor frame caused by multiple quantizations for improved positioning accuracy. Finally, an AD-IoU loss function was designed, which can adaptively optimise the distance between the prediction box and real box by comprehensively considering the overlap rate, centre distance, and aspect ratio between the boxes and can improve the detection accuracy of the occlusion target. Ablation experiments on the RMF-Net model verified the effectiveness of each factor in improving the network detection accuracy. Comparative experiments were conducted on the Pascal VOC2007 and Pascal VOC2012 datasets with various target detection algorithms based on convolutional neural networks. The results demonstrated that RMF-Net exhibited strong scale adaptability at different occlusion rates. The detection accuracy reached 80.4% and 78.5% respectively.
Designing Obstacle Reminder for Safe AR Navigation

AR navigation has been widely used in mobile devices. However, users sometimes immerse in the navigation interface and ignore probable dangers in the real environment. It is necessary to remind users of potential dangers and avoid accidents. Most of existing works focus on how to effectively guide users in AR but few concern about design of danger reminder. In this paper, we build a virtual AR navigation system and compare user experience on different types of obstacle reminder. Furthermore, we compare the influence of color, motion and appearance distance on effectiveness of the reminder. Results show that red color and bi-color are more obvious than blue color for reminder. Motion such as flickering effect helps enhance remind effectiveness.