Reviewerly/exHarmony huggingface dataset:
A study of machine translation for Vietnamese and Korean on the TED Talks 2020 corpus

Vietnam has achieved impressive economic growth in the last two decades. It becomes a worth investing country in the area. Consequently, the need of understanding foreign investors from different countries (S. Korea in specific) is an essential issue. Therefore, building an automatic machine translation system with high precision is a necessary solution, especially during the COVID-19 pandemic, where keeping distance is the best way to avoid spreading the virus. As a result, this research presents some experimental results on the TED Talks 2020 dataset for the task Korean - Vietnamese and Vietnamese - Korean machine translation with the purpose of providing an overview of the dataset and a deep learning machine translation model for the problem.
E-government Public Complaints Text Classification Using Particle Swarm Optimization in Naive Bayes Algorithm

A web-based online complaint portal is one of the e-government public services. The complaint's content must be categorised in order for it to be transmitted to the appropriate agency swiftly and properly. The most often used standard classification algorithms are the Naive Bayes Classifier (NBC) and k-Nearest Neighbor (k-NN), both of which classify just one label and must be tuned. The purpose of this project is to categorize complaint messages that include several labels simultaneously using NBC tuned for particle swarm optimization (PSO). The data source is the Open Data Jakarta and is partitioned into 70% training data and 30% test data for classification into seven labels. The NBC and k-NN algorithms are used to compare PSO's optimization performance. Cross-validation ten times revealed that optimizing NBC with PSO obtained an accuracy of 88.16%, much superior than k-NN at 83% and NBC at 70.57%. This optimization approach may be used to improve the efficacy of community-based e-government services.
Designing TehNusa Mobile Based Application Using Design Sprint Method

Indonesia is a country where most of the population work in the agricultural and plantation sectors. Both sectors are significant for the economy of Indonesia because it contributes to the increase in Gross Domestic Product (GDP). Such as the tea plantation sub-sector, which is one of the export commodities in Indonesia. However, several problems occur to the tea farmers in Indonesia. These problems influence decreasing in tea quality and farmers' welfare in Indonesia. For that reason, TehNusa is created to help solve the issues. TehNusa can be utilized to buy and sell harvested or processed tea. Another hand, TehNusa also has other functions to develop the quality and ability of the community in caring for and managing processed tea. Another side, BUMDes will play a role by managing and developing village prospects and tea farmers to minimize gaps. The design of the TehNusa application is created using the design sprint methodology and for the design validation, we're using the system usability scale methodology. The design of the TehNusa application can be made within 40 working hours, and the test result of design validation gets a conclusion of 81 points or equivalent to grade B. The TehNusa application is rated by users as being able to help make it easier to solve problems. In addition, further research needs to be carried out to determine the implementation of making applications and building business cooperation with BUMDes in Indonesian villages that have skilled farmers and have the potential to become processed tea producers.
Drone Flight Logs Sequence Mining

Data mining techniques in analyzing log data can discover a useful pattern which then is used to infer knowledge. Interesting patterns in log data can help the stakeholder to take action to diagnose a problem or improve the running system. Drone as one loT device, which consists of subsystems working together, also implements a logging mechanism. While a drone is flying, event-related logs are written into specific log files. These files contain precious information in case of incident happens to the drone. Assuming that the integrity of the log files is guaranteed, the investigator can find useful patterns and help conclude the incidents. To this end, this paper studies the sequence mining approach to discover some pre-defined incident-related events. As this is an initial study, the main contribution of this paper is the domain adaptation and modeling of the flight logs into a sequence database. After experimenting, we conclude that the modeling procedure is an essential step in conducting sequence mining. Frequency-oriented techniques are not suitable for small sequence databases, as the found patterns tend to have less critical events. Finally, two potential future directions are elaborated.
Utilizing Topic Modelling in Customer Product Review for Classifying Baby Product

E-commerce is growing at a breakneck pace. As a result, online shopping has increased, which has increased online product reviews. Often, we come across Amazon products with thousands of reviews, and if we look closely we discover that some of them are completely unrelated to the product. In this study, we conducted research on how product review classification can assist in resolving the issue of comments on incorrect items. The method used in this research consists of 4 steps which are, data acquisition, data pre-processing, topic modeling, and text classification. Where Latent Dirichlet Allocation (LDA) was used as our topic modeling technique, and for text classification we used Support Vector Machine (SVM), Logistic Regression, and Multi-Layer Perceptron (MLP) classifiers. We found out that by combining both topic modeling and text classification, a powerful tool for handling this kind of problem was developed. Adding the topic modeling can improve the model's accuracy performance from 0.61 to 0.78. So, we can conclude that the topic modeling was useful in classifying the product reviews.
An investigation of ML techniques to detect Phishing Websites by complexity reduction

In today's digital age, one of the predominant causes of the security breaches is phishing web sites that disguise them-selves as legitimate web sites and trick unsuspecting users into revealing sensitive information. With the proliferation of high-speed internet and the popularization of IT education, there is an increase in unscrupulous actors on the web who are always ready to counterfeit a legitimate website and use it to deceive and ma-nipulate users. Software and non-software-based techniques have been used to try to unmask the phishers. Phishing web sites have many characteristics in them. Thus, classifying and detecting those is unavoidably time-consuming and complex. Our research analyzed several hybrid machine learning models, including a bespoke preprocessing step of reducing minimally correlated features and then training with four boosting algorithms and three SVM models for classification. These models have also been trained after hyperparameter tuning. Among the investigated models, XGBoost brought the highest accuracy of 97.0455% after the hyperparameter tuning.
Quantum Method for Scaling the Finite Element Based Quantum Solutions of Electromagnetic Problems

Quantum computing is a potential approach for addressing computational challenges in solving electromagnetic problems. A quantum method, called the Harrow/Hassidim /Lloyd (HHL) algorithm has recently been applied to solve the equations from finite-element-method (FEM) formulation of electromagnetic problems. Here we address a further step in this direction. Since quantum state vectors always have unit norm, the solution vector from HHL is only proportional to the final electromagnetic solutions. The magnitude of the final electromagnetic solution vector remains unavailable from HHL In this paper, a new quantum formulation is proposed to find the magnitude of the final electromagnetic solutions. The proposed method can be used to scale the HHL results to the correct magnitude of electromagnetic solutions. In this way, the proposed method is a further advance in making the quantum computing more effective in solving electromagnetic problems.
Improved Poisson MAP Algorithm for Better Image Deconvolution

Captured images are usually obtained unclear and blurry. Image deconvolution algorithms are normally applied to get clearer images from their unclear versions. Research related to this field has tremendously grown in the past years due to the increased demand for top-quality images. The maximum a posteriori (MAP) concept has been used previously for image deconvolution. Accordingly, the Poisson MAP is a simple structure iterative algorithm that was proposed for image deconvolution. Iterative means that it needs many repetitions to deliver the output image. The repetitive procedure consumes time and involves more computational costs that can be avoided. Therefore, this algorithm is modified by utilizing two non-complex acceleration factors so that the output is obtained by using fewer iterations, making the algorithm runs faster. The improved algorithm is tested intensively with various unclear images, as well as it is compared with its original version, and the outcomes are evaluated using two evaluation methods (i.e., gradient information with features (GI-F) and run-time). From performing various tryouts, the original algorithm required an average of 36 iterations and an average runtime of 0.49 seconds, while the proposed algorithm required an average of 15 iterations and an average runtime of 0.28 seconds to produce better quality results. Hence, the proposed algorithm has shown better performances than its original version by providing better-acutance results rapidly.
Interior Design Studio in the New Normal Era: A Virtual Reality Case Study

This paper presented the case study of using Virtual Reality (VR) to elevate interest, engagement, and creativity in an interior design studio in the new normal era. Via the case-study approach, the author studied the VR experience of 13 students using Gravity Sketch in Meta/Oculus Quest 2 headsets to design a custom luminaire. Results showed an overall high interest and engagement regarding the VR experience, a moderate increase in novelty, and a significant increase in style for creativity.
An On-chip Accelerator with Hybrid Machine Learning for Modulation Classification of Radio Frequency Signals

We present a hybrid radio frequency machine learning (RFML) model that couples Short-time Fourier Transform with Convolutional Neural Network (STFT-CNN) for Automatic Modulation Classification (AMC). The simulation on RadioML2016.10a show 77.9% average accuracy for 0dB or higher Signal to Noise ratio with 16-bit fixed-point operation. An on-chip accelerator for STFT-CNN, designed and synthesized in 28nm CMOS, shows 7× lower power, 2× lower processing time, and 7.5× lower memory than a time-domain CNN accelerator with 32-bit floating point operation.
A Real-time Distributed Multi-camera Multi-object Tracking System

Multi-camera Multi-object Tracking (MCMOT) is a challenging problem. Most of the existing methods use a centralized architecture to achieve high tracking accuracy. However, as the number of devices increases, the communication and computing needs of a centralized architecture will grow superlinearly, making it difficult to use in city-scale scenarios. In this paper, we propose a real-time distributed MCMOT system, Uni-ID, which ensures that the communication and computing costs of each device remain almost constant as the number of devices increases. Uni-ID uses the existing single camera detection and tracking architecture and adds a feature extraction network between the detection and tracking phases. By broadcasting the extracted features, the device can reach a consensus on the object so that an object has the same ID in different devices. The system has been tested with real-device recorded data and achieved state-of-the-art performance
Multi-stream Multi-attention Deep Neural Network for Context-Aware Human Action Recognition

Technological innovations in deep learning models have enabled reasonably close solutions to a wide variety of computer vision tasks such as object detection, face recognition, and many more. On the other hand, Human Action Recognition (HAR) is still far from human-level ability due to several challenges such as diversity in performing actions. Due to data availability in multiple modalities, HAR using video data recorded by RGB-D cameras is frequently used in current research. This paper proposes an approach for recognizing human actions using depth and skeleton data captured using the Kinect depth sensor. Attention modules have been introduced in recent years to assist in focusing on the most important features in computer vision tasks. This paper proposes a multi-stream deep learning model with multiple attention blocks for HAR. At first, the depth and skeletal modalities' action data are represented using two distinct action descriptors. Each generates an image from the action data gathered from numerous frames. The proposed deep learning model is trained using these descriptors. Additionally, we propose a set of score fusion techniques for accurate HAR using all the features and trained CNN + LSTM streams. The proposed method is evaluated on two benchmark datasets using well known cross-subject evaluation protocol. The proposed technique achieved 89.83% and 90.7% accuracy on the MSRAction3D and UTDMHAD datasets, respectively. The experimental results establish the validity and effectiveness of the proposed model.
Identifying Deepfake Faces with ResNet50-Keras using Amazon EC2 DL1 Instances powered by Gaudi Accelerators from Habana Labs

With the emergence of deepfake technology, it has become exponentially more difficult to identify real content from the artificially generated on social media. To counter the ill-effects of online deepfake content, we propose an application to predict if a given facial image is real or generated virtually via deepfake technology. We train a custom ResNet50-Keras model on the new AWS EC2 DL1 Instances powered by Gaudi accelerators developed by Habana Labs (an Intel company) and integrate the instance with Amazon's S3 bucket. With the model saved as an h5 file, we make a RestAPI using FastAPI. The API takes an input image, converts it into a JSON request, and passes it to the backend. Faces extracted from these images are passed through various pre-processing methods and finally to the model that classifies them to be either a deepfake or not and generates a face mesh accordingly. The model combines the processed faces into a single image sent back as a response to the client that changes the stateHooks to display the desired result. Further, we have summarized the results obtained when detecting such manipulated images.
Parent Coin based Cryptocurrency Price Prediction using Regression Techniques

Cryptocurrencies are becoming increasingly popular day by day among people. It is providing more features than traditional banking or money can provide, hence attracting investors all around the world. But people are still hesitant to use this technology because of the high price volatility are variable markets. The majority of the forecasting solutions reported have some level of error and cannot predict the price accurately due to randomness. The proposed model consists of using parent coins as a parameter for price prediction to overcome market volatility. Moving average is used as a data preprocessing technique for the effective prediction of Litecoin and Dogecoin prices. Also, in this, we are using Decision Tree, Random Forest, Extra- Tree-Regressor and Ridge regressor models for predicting the close price of cryptocurrencies. From the result, we can see that our proposed model performs better than predicting without using the parent coin feature.
A Research on Learning Community in Robotics Education in High Schools under the Grounded Theory

The objective of this study is to reveal the features of learning communities in robotics education with the aim of providing a reference for the better establishment of robotics education learning teams and the development of robotics education in primary and secondary schools. Based on the method of grounded theory, this study coded semi-structured interviews with 15 high school students using the qualitative research analysis software NVIVO to form 66 concepts, 21 original categories, 9 main categories, and 4 core categories to analyse the individual expectations, shared vision, membership structure, learning styles, and learning effectiveness of student learning communities in robotics education.
Patient-specific Model Generation using Multiview 3D Reconstruction for Hyperthermia Treatment Planning

Hyperthermia treatment of locally advanced mouth and oral cancers requires three-dimensional model of the patient, mostly emphasizing nasal throat region for treatment planning. 3D patient-specific model is crucial to plan the procedure for effective and localized thermal therapy. An image-based reconstruction technique, structure from motion (SfM) is a cost effective, flexible and efficient solution for this end application. Although SfM provides various algorithms and pipelines, it is important to select the optimum image capturing technique and effective pipeline to obtain robust 3D image reconstruction. In this work, we present the evaluation and analysis of effective hand-held image capturing techniques and performance of few open-source reconstruction pipelines tested on custom synthetic and real world captured data.
Performance Evaluation of Navigation Schemes for Autonomous Exploration of a Secondary Explorer Rover in a Martian Lava Cave

Habitation in mars is one of the most ambitious goals of the 21st century as our permanent habitat, the earth is slowly losing its habitation qualities due to different disasters, pollution and rise of temperature. But there include several major challenges before we can make mars a habitable planet. Radiation, dusty storms, meteorites etc. makes the Martian surface one of the difficult places to live. The caves in the mars can be a viable solution to this problem as they can give protection against these threats. Lava caves are of particular interest in these case as these caves is similar to the lava caves found on earth. This research presents the design and performance evaluation of an autonomous secondary explorer rover in lava tubes of Mars. 3D & 2D mapping was done in a simulated environment and the performance of various path planning algorithms was evaluated in the lava cave. The experiment results show global path planners, especially A* can perform well if the entrance and exit are well defined while local path planners need human interventions several times even though they can avoid sudden dangers or landscape change inside the cave.
Robot Path Planning by Using Improved A* Algorithm and Dynamic Window Method

With the market for automated robots expanding, path planning in automated robots has become an important research topic. In this paper, a hybrid algorithm based on the improved A* algorithm and dynamic window method is presented to meet the needs of global optimal and real-time barrier avoidance. The computational model of the proposed algorithm is as follows. First, the traditional A* algorithm is improved; the improved A* algorithm can determine a globally optimal path for the robot. Second, the local path planning algorithm based on the dynamic window method can circumvent obstacles that appear in the robot's travel path in real time, effectively resolving the problem encountered in traditional robot path planning wherein obstacles cannot be avoided dynamically. The experimental results show that the combination of the A* algorithm and dynamic window method can improve the path planning of robots in complex dynamic environments.
Research on National Common Language Learning Intervention for Young Children in Western Ethnic Areas Supported by AR Technology

This study selected 20 Tibetan children in a large class as the research object to explore a more effective teaching model to promote children's learning of the national common language. The research results are analyzed by SPSS data and it is found that under the AR teaching model, children's national common language learning has significant advantages in terms of understanding and language expression ability. It is suggested that teachers in the ethnic minority areas in western China guide children to carry out teaching activities with the help of AR resources on the basis of the original national common language teaching activities.
Molecular-InChI: Automated Recognition of Optical Chemical Structure

With the advent of a new era dominated by digital media and publications in recent years, the importance of striking a balance between traditional and new modes of operation has become increasingly apparent. It has been standard practice in the field of chemistry for decades to express chemical compounds using their structural forms, referred to as the Skeletal formula. In this research, we tried to interpret these old chemical structure images, extracted from old literature, to transform pictures back to the underlying chemical structure labeled as InChI text using a huge set of synthetic image data produced by Bristol-Myers Squibb. In this paper, we propose an improved synthetic data and an Encoder-Decoder-based deep learning-based model to automatically represent these molecular images into their underlying InChI representation.
Algorithm for Counting the Number of Students in Class Based on Mask-RCNN Optimization

In order to solve the problems of low efficiency, small scale, and data reliability in the traditional manual counting method, a method using mask region convolutional neural network (Mask R-CNN) to automatically calculate attendance was proposed. In order to extract deeper image information, the feature extraction network is designed as ResNet101, and feature map fusion is performed on multi-level feature maps. In order to make up for the lack of recognition of the objects whose body parts are occluded, the Mask R-CNN algorithm is used for the second recognition. The experimental results on the self-built classroom monitoring screenshot data set show that compared with the method of directly using the Mask R-CNN algorithm for recognition, the secondary recognition method can identify more targets and improve the accuracy of identifying the number of people.
Structural Design and Motion Analysis of a Rehabilitation Wheelchair for Posture Conversion during Sit to Stand Transfer

Rehabilitation wheelchair for posture conversion during sit to stand transfer is of great significance in rehabilitation training. Based on the basic form and evolution form of linkage mechanism, the combination of motion mechanism for a rehabilitation wheelchair is completed, and the motion size of the mechanism is determined by CAD software. Then, on the basis of the motion mechanism of rehabilitation wheelchair, the mechanical structure is designed. The motion analysis of the chair surface is carried out by using path tracking method in SolidWorks, and the tracking track of the chair surface is obtained. The calculation and simulation results show that the designed rehabilitation wheelchair conforms to the basic trajectory posture conversion during sit to stand transfer, and the movement is stable that can meets the requirements.