Reviewerly/exHarmony huggingface dataset:
Designing Augmented Reality for Malay Cultural Artifact Using Rapid Application Development

This project is intended to use Augmented Reality (AR) with an interactive element such as digital storytelling by giving users a new whole experience and exposure with the AR technology on the prominent artifacts of the Islamic arts complex. The objectives of this project are to identify the requirement, to design and develop the augmented reality for Malay cultural artifact called Keris (a local weapon) for a local museum in Malaysia by using the adapted version of Rapid Application Development (RAD) methodology. The marker-based technique provides advanced object tracking that will overlay with the physical world is one of the techniques used to fulfill the solution to the problems and this method also become one of the motivations in developing this project. This project will give an advantage to many parties especially for the museum as an added value to promote the artifacts and attract visitors to visit the museum. Future works may include the enhancement of the application is by adding more artifacts that can be chosen in the application, improve the user interface, provide dual language, and put the message panel if the user has reached the end of the digital storytelling. In conclusion, the application may have the ability to exposed AR technology as a new platform for tourism in Malaysia and capture user's attention on the local artifacts.
Using the project method as a means of developing the creative potential of primary school children in Ukraine

This article analyses the features of innovative curriculum design and the associated research activities of primary school students in Ukraine. It is emphasised that the priority is the formation of ‘meta-subject results’ in junior high school students, which can be seen as universal learning activities that will ensure, to a large extent, the success of learning at the next levels of education. Our research has shown the undeniable effectiveness of innovative curriculum design allied to pupil research activities based on project work when compared with traditional methodological approaches to learning. We argue that the increasing demand to develop key competencies in education gives a heightened need for these pedagogical approaches. Moreover, the introduction of project work allows the teacher to form a pedagogical situation in which the student formulates a goal and achieves it whilst developing the key skills that will underpin their wider education.
Action-driven contrastive representation for reinforcement learning

In reinforcement learning, reward-driven feature learning directly from high-dimensional images faces two challenges: sample-efficiency for solving control tasks and generalization to unseen observations. In prior works, these issues have been addressed through learning representation from pixel inputs. However, their representation faced the limitations of being vulnerable to the high diversity inherent in environments or not taking the characteristics for solving control tasks. To attenuate these phenomena, we propose the novel contrastive representation method, Action-Driven Auxiliary Task (ADAT), which forces a representation to concentrate on essential features for deciding actions and ignore control-irrelevant details. In the augmented state-action dictionary of ADAT, the agent learns representation to maximize agreement between observations sharing the same actions. The proposed method significantly outperforms model-free and model-based algorithms in the Atari and OpenAI ProcGen, widely used benchmarks for sample-efficiency and generalization.
A model for predicting crimes using big data and <scp>neural‐fuzzy</scp> networks

Abstract Big data analytics is important for identifying and analyzing different patterns, relations, and trends within a large volume of data such as crime prediction. In this paper, we propose a new model for predicting crimes using neural‐fuzzy model. For this purpose, time of occurrence and longitude and latitude of the crime scene are used. We use k‐means algorithms to cluster crime records based on their locations. The crime pattern of each cluster was learnt by its own neural‐fuzzy network, in addition using shuffled frog leaping algorithm to determine the optimal cluster radius. The accuracy and Mean Absolute Error and Root Mean Squared Error of this method were compared with: Gaussian Process Regression, Classification and Regression Tree, and Artificial Neural Network. The experiment results showed that the proposed model was the most accurate model and had the least error to predict the occurrence of crimes so can be used as an effective tool in real‐world applications.
Intelligent Blacklist Security System for Protecting Spammer in Corporate Email Solution: A Case of Corporate Email Service Provider in Thailand

In the present, there’re billions of information transactions around the world each day including email. Technology Land Co., Ltd. (TL) is the email service provider organization that provides the email domain name to the client’s organization. However, there have been problems that occurred in the TL email service provider (TLESP) system which must be solved immediately to solve the existed problems and prevent further damage. Additionally, the improved TLESP system has used artificial intelligence (AI) as the core for email spamming, viruses, hackers, and unusual behaviour detection. Also, the advanced TL’s email system incoming process and outgoing process security have been explained together with system results and user benefits after the improvement of the system in this study.
Bronchial orifice tracking-based branch level estimation for bronchoscopic navigation

This paper describes a branching level estimation method using the tracking result of the bronchial orifice structure in branches. Since the bronchus has a tree-like structure with many branches, it would be beneficial to physicians if the location of the bronchoscope in branches is provided. Hence, the estimation of branching level is the core work of coarse bronchoscope tracking-based navigation bronchoscopy. Previous method used the changes in the number of the bronchial orifice (BO) and the camera moving direction for the branching level estimation, which cannot observe the changes of each BO region. Therefore, we extract BO regions using a virtual depth image from deep learning and track these regions among real bronchoscope images. The branching level is estimated based on the results of BO tracking. Experimental results showed that the average accuracy of the branching level estimation is 92.1 %.
Orthogonal positioning attention deep supervision network for image segmentation in automatic breast ultrasound

The precise segmentation of breast tumors is of great significance to the clinical diagnosis of breast cancer. Automatic breast ultrasound (ABUS) is a low-cost medical imaging method. However, one breast scan will produce hundreds of three-dimensional ABUS slices. Manual screening for abnormalities is not only time-consuming, but also heavily relies on the doctor's experience. In this study, a new network with Orthogonal Positioning Attention Deep Supervision Network (OPADSN) was proposed for breast cancer tumor segmentation. OPADSN adopts an orthogonal strategy, sending the input to the horizontal axis and the vertical axis to output features in two independent directions. Orthogonal strategy not only retains more location information, but also helps the model locate and identify the target of interest more accurately. In addition, this article introduces the Original Resolution Subnetwork Network (ORSNet) into the context module for the first time to form the Original Resolution Subnetwork Enhancement Context Attention (ORS-CA). ORSNet can solve the problem of edge blur caused by downsampling, thereby generating high-resolution spatial features. Repeated use of the ORS-CA module can establish the connection between the breast tumor area and the boundary clues, making the tumor contour easier to judge. Due to this iterative interaction mechanism between regions and borders, OPADSN can correct different regions in the prediction results. This article conducted an experiment on a dataset of 783 two-dimensional breast ultrasound images. The experimental results show that the OLADSN method achieves 93.1% segmentation accuracy in Mean Dice, which is a satisfactory result.
DNN-based Human Activity Recognition by Learning Initial Motion Data for Virtual Multi-Sports

This paper proposes a new deep neural network-based approach of human activity recognition for virtual multi-sports. The proposed approach acquires initial motion data of sports balls, not full motion data of human activities, with a single high-speed camera. Then, a deep neural network model is trained to recognize corresponding human activities, such as baseball batting, soccer kicking, golf swing, and so on, by learning those initial motion data. The proposed approach is very efficient and effective for practical applications of virtual multi-sports. In practice, the proposed approach was successfully applied to a unified virtual multi-sport platform. The effectiveness and efficiency of proposed approach was verified with real experimental results.
Graph regression for pressure peak prediction in fracturing processes

In the oil and gas industrial field, fracturing construction technology is commonly used for increasing oil–gas production. A major concern in fracturing construction is whether the pressure at the wellhead will exceed safety threshold, when a large amount of sand-containing fluid is periodically injected into a well. To avoid accidents like blowout caused by extreme wellhead pressure, it is critically important to monitor pressure change in real time. In this work we propose to predict the pressure peaks during each fracturing period, for better designing future fracturing strategy. Towards this end, we present a novel non-parametric graph regression method, which is able to model the correlation of historical pressure peaks and learn the features of fracturing cycles via Laplacian-smoothness based graph learning. whereby the periodic fracturing signals, namely, fracking fluid concentration sequence, together with peaks, are able to be encoded in a latent Euclidean space by Laplacian-smoothness inspired graph learning. Then we introduce non-parametric linear regression for peak prediction based on the most similar nodes (e.g. peaks) w.r.t the node in the predicting period. Meanwhile, the current graph is updated using the predicted peak value. In the graph update, we particularly introduce a node forgetting mechanism to control the graph scale and to reduce the computational complexity, so as to achieve rapid prediction of construction operations. We conduct extensive experiments on real-world datasets from a well. The experimental results demonstrate that the proposed method can effectively predict the oil pressure peak and significantly outperform the state-of-the-art models.
Radioactive Waste Barrel Localization Based on Concentric Circle Detection with Edge Pre-Classification and Randomized Hough Transform

In this paper, we propose an effective method to locate radioactive waste barrel with imperfect concentric circle edges. First, this method estimate the rough centers of circles quickly based on lookup table and bitwise accumulator. Based on the rough centers and the gradient of edges, the candidate edges of circles are collected and pre-classified into overlapped detection domains. After the edge classification, the best centers of each domain are voted by Randomized Hough Transform. According to the votes of the best centers, the true center of concentric circles is determined by the centers with the highest concentricity. We experimentally compare the performance of the proposed method with RANSAC and LUT-BCA. The results show that proposed method is fast, accurate and robust.
α-Completely Regular and Almost α-Completely Regular Spaces

This work is aimed at studying some of the characterizations of α-completely regular and almost α-completely regular spaces through the new concept of α-zero sets and α-continuous functions. 2010 Mathematics Subject Classification: primary 05C38, 15A15; Secondary 05A15, 15A18.
Çevrimiçi Kaynakları Kullanarak İngilizce Eşdizimliliklerin Veri Güdümlü ve Tümdengelimsel Öğrenimi: Yakınsak Karma Yöntemler Çalışması

This convergent mixed methods study aims to compare effectiveness of using three online consultation resources entailing deductive vs. data-driven learning on learning and retention of 18 verb-noun collocations by EFL learners. The participants (N=45) randomly assigned to treatment conditions consulted three different online resources in different orders to complete the same online error correction tasks and gap-fill exercises in three sessions. The participants were given the Vocabulary Size Test (VST), and a productive collocation translation test (CTT) as the pretest, the posttest and the retention test. A sub-set of the participants also performed think aloud protocols during the treatment. After the treatment, the participants were given the CTT and responded a rating scale and an open-ended question. The results indicated that all of the resources led to significantly higher learning and retention rates with no significant differences among the resources. It was also found that the participants rate the resources differently and go through different cognitive processes when consulting resources. The VST scores positively correlated with the posttest scores, suggesting that participants’ vocabulary sizes can be a moderating variable. The findings are discussed based on previous research and within the framework of data-driven learning.
A hybrid acoustic model based on PDP coding for resolving articulation differences in low-resource speech recognition

Nowadays, low-resource automatic speech recognition (ASR) is a challenging task. The traditional low-resource automatic speech recognition methods failed to capture pronunciation variations and did not have sufficient phone frame alignment capabilities. Some studies have found that pronunciation variations are mainly reflected in the distribution of resonance peaks for vowels and compound vowels and are particularly prominent in spectrograms. Inspired by this idea, we combine it with deep learning techniques and propose a hybrid acoustic model to address the difficulty of capturing pronunciation variation in low-resource ASR. We introduce a pronunciation difference processing (PDP) block to capture resonance peak variations. And we add an improved GRU network at the back end of the model to enhance the alignment of phone frame states. At the same time, we introduce a multi-head attention to combines coarse and fine-grained features of the audio and spectrum to highlights differences in resonant peaks. Finally, we analyzed the effect of different structure parameters and coding positions for the results. Our method was evaluated on the Aidatatang and IBAN datasets. Among them, the results show that adding the PDP module respectively reduces 1.84%, 0.26%WER and 5.2%, 4.3%SER as compared to the baseline mainstream model. After adding the improved GRU, the results show that adding the PDP module respectively reduces 1.92%, 0.38%WER and 5.6%, 4.4 %SER. At the same time, after we introduced multi-head attention, the results show that adding the PDP module respectively reduces 2.33 %,0.45%WER and 6.0%, 4.8 %SER.
To Be Human-like or Machine-like? An Empirical Research on User Trust in AI Applications in Service Industry

This research investigates user trust in artificial intelligence (AI) applications based on empirical analysis of intelligent service robots in service industry. We examined the impacts of human-like design of AI robots on intelligence and anthropomorphism perceived by users. The relationships between perceived intelligence, perceived anthropomorphism, and level of trust were also tested. An online survey with a between-subject design was conducted to collect data. The results indicate that robots with physical human-like appearance were perceived lower level of anthropomorphism and intelligence, and interaction function design on robots does not significantly increase those perceptions. Moreover, the significant effect of perceived intelligence on perceived anthropomorphism was confirmed, and both perceptions of users exert positive effect on their trust in AI robots.
Adoption of Blockchain Integrated with Machine Learning in Intelligent Transportation System

An intelligent transportation System (ITS) is an unconventional application that aims to provide traffic congestion information and emergency information to other vehicles. In ITS, the compilation and distribution of traffic event information by vehicles are of the most extreme significance. To establish an effective data-sharing network in ITS, the vehicles need to share the data in a protected form in real-time. Conventional ITS system includes data streaming from On-Board Unit (OBU) sensors through Road Side Units (RSU) to centralized cloud servers. Nevertheless, it is still a challenge to distribute essential event information in a selected area under the dynamic ITS environment and in the existence of a malicious vehicle. The current fundamental issues which emerge include privacy concerns due to third party management of cloud servers, single point of failure which are a bottleneck in data flow among the vehicles.
Calibration of cine MRI segmentation probability for uncertainty estimation using a multi-task cross-task learning architecture

While deep learning has shown potential in solving a variety of medical image analysis problems including segmentation, registration, motion estimation, etc., their applications in the real-world clinical setting are still not affluent due to the lack of reliability caused by the failures of deep learning models in prediction. Furthermore, deep learning models need a large number of labeled datasets. In this work, we propose a novel method that incorporates uncertainty estimation to detect failures in the segmentation masks generated by CNNs. Our study further showcases the potential of our model to evaluate the correlation between the uncertainty and the segmentation errors for a given model. Furthermore, we introduce a multi-task cross-task learning consistency approach to enforce the correlation between the pixel-level (segmentation) and the geometric-level (distance map) tasks. Our extensive experimentation with varied quantities of labeled data in the training sets justifies the effectiveness of our model for the segmentation and uncertainty estimation of the left ventricle (LV), right ventricle (RV), and myocardium (Myo) at end-diastole (ED) and end-systole (ES) phases from cine MRI images available through the MICCAI 2017 ACDC Challenge Dataset. Our study serves as a proof-of-concept of how uncertainty measure correlates with the erroneous segmentation generated by different deep learning models, further showcasing the potential of our model to flag low-quality segmentation from a given model in our future study.
Investigation of adversarial robust training for establishing interpretable CNN-based numerical observers

The use of convolutional neural networks (CNNs) for establishing anthropomorphic numerical observers (ANOs) is being actively explored. In these data-driven approaches, CNNs are trained in a standard supervised way with human-labeled training data; hence, the anthropomorphic component of this procedure resides only in the training labels. However, it is well-known that such traditionally trained CNNs can rely on image features that are highly specific to the training distribution and may not align with features exploited by human perception. While being able to predict human observer performance under certain specified conditions, traditionally-trained CNNs lack the interpretability and robustness that may be desired for an ANO. To address this, in this work we investigate the use of an adversarial robust training strategy for training CNN-based observers. As recently demonstrated in the computer vision literature, this training strategy can result in CNNs that exploit more human-interpretable features than would be employed by a standard CNN. Robustly trained CNNs are systematically investigated for performing a signal-known-exactly (SKE) and background-known-statistically (BKS) binary detection task. Additionally, a differential evolution-based optimization procedure is developed to establish robustly trained CNNs that achieve a specified performance, which may provide a new approach to establishing ANOs.
Unsupervised Video Anomaly Detection in Traffic and Crowded Scenes

In this paper, we propose a scene-independent robust unsupervised video anomaly detection method based on future frame prediction as a breakthrough and better video anomaly detection technique. Most conventional methods evaluate and develop a static camera and dashcam approach as independent tasks, and no method has been proposed that is independent of the capture conditions. The proposed method introduces a frame-wide future prediction-based spatio-temporal adversarial networks that can handle arbitrary series lengths to cope with various scenes. The noise in the prediction error caused by constant background changes is improved by weighting the regions of interest for the discriminator of the generative adversarial networks (GANs). This framework can be applied to all cases regardless of the scene environment. Experiments on public datasets of general traffic scenes and crowded scenes confirm the superiority of the proposed method over current state-of-the-art methods.
Design of Metaheuristic Feature Selection with Deep Learning Based Credit Card Fraud Detection Model

Credit cards are highly preferable method for users to make transactions online and of fline. Due to the everincreasing demand for new payment methods and digital transactions, possible credit card attacks are also raised. The massive quantity of credit card data makes it necessary to develop effective feature selection approaches to reduce the computation complexity and improve classification accuracy. Earlier studies have mainly concentrated on the development of feature selection based classification models for credit card fraud detection. In this aspect, this study focuses on the design of binary emperor penguin optimization (BEPO) based feature selection with optimal gated recurrent unit (OGRU), called BEPO-OGRU technique for credit card fraud detection. The goal of the BEPO-OGRU technique is to detect and classify possible credit card frauds. In addition, the BEPO-OGRU technique involves the design of BEPO based feature selection technique to choose an optimum set of features. Moreover, the OGRU based credit card classification model is derived in which the hyperparameters of the GRU are optimally chosen by the use of Harris Hawks optimization (HHO) algorithm, shows the novelty of the work. A wide range of simulations ensured the betterment of the BEPO-OGRU technique with the improved accuracy of 94.78% and 94.16% on the German Credit dataset and Credit Card Fraud Detection dataset.
Leveraging Human-Agent Collaboration for Multimodal Task Guidance with Concurrent Authoring Capabilities

Instructive assistance systems are applied to assist users in performing a task by providing instructions. These systems are especially relevant for industrial workers and service technicians who must cope with the increasing task variety and complexity that resulted from the trend of applying cyber-physical production systems and small lot sizes. While current research mainly focuses on assembly work and the application of augmented reality, there is a lack of research for these systems for adapting to the challenging conditions of maintenance environments and for providing concurrent authoring capabilities. Within this PhD project, we aim to design a multimodal conversational agent as an instructive assistance system with authoring capabilities and evaluate the system in three different scenarios to close this research gap.