Reviewerly/exHarmony huggingface dataset:
A Data Sampling and Two-Stage Convolution Neural Network for IoT Devices Identification

The rapid development of Internet of Things (IoT) enables emerging user services and applications to improve life quality. However, the presence of rogue IoT devices can result in the vulnerabilities that hurt users. In order to address this threat, organizations often apply security policies in which only the connection of white-listed IoT devices is permitted. To obtain that goal, organizations must be able to identify the IoT devices connected to their networks and, more specifically, to identify connected IoT devices that are not in the white-list (unknown devices). However, for new/unknown devices, it is often difficult to collect enough data samples to train an effective detection model. To address this problem, we propose a model that combines a data sampling technique with a two-stage Convolutional Neural Network to identify IoT devices. The proposed model is called DS-2CNN. DS-2CNN can accurately identify the IoT devices with very little training samples, thus allow the model to early identify unknown IoT devices. We have carried out the extensive experiments on the imbalanced dataset, i.e., the IoT Trace Dataset including 22 IoT devices. The experimental results have shown that DS-2CNN can significantly enhance the accuracy in identifying IoT devices comparing to a recent proposed model.
Data Augmentation Methods for Semantic Similarity Detection in Vietnamese Questionnaire

Recently, applying Data Augmentation in low resource languages has emerged as an obvious remedy for assisting deep learning models in achieving the state-of-the-art performance. Yet, there have not been many studies conducted for Vietnamese - considered as a low resource language. This paper proposes two new text augmentation methods for detecting semantic similarity between a pair of questions, named Synonym Replacement and Definition Replacement. The two methods exploit the shared keywords between questions to expand the dataset while guaranteeing the meaning preservation of the original sentences. Experimental results show that both approaches improve the performance of BERT and PhoBERT models. Significantly, the BERT with datasets enriched by Synonym Replacement gains 0.91 in accuracy and F1-score for Similarity Detection task. It implies that the offered methods can be adapted to optimize the efficacy of other natural language processing tasks.
Proposing Algorithm for Warning Minimum Distance Violation and Tracking Covid-19 Object Using YOLOV4 Model

Currently, the prevention of the spread of the Covid-19 epidemic is still a matter of concern with many new variants that are more infectious and making it more difficult to prevent it. In addition, several respiratory viral diseases such as influenza A, monkeypox, etc. help promote the management and prevention of epidemics. The paper presents the system using the YOLOV4 object recognition model to identify human objects from videos extracted. To increase accuracy with the desired context, we build a dataset of people and perform training on them. We use the Euclidean algorithm to calculate the distance between bounding box pairs. We then use a physical distance that approximates the pixel and set a threshold. It is possible to determine who has violated the minimum social distance threshold. In addition, we apply a tracking algorithm to be able to detect and trace those who have been in close contact with the cases to be monitored. The system has been performed on video and the accuracy of the model is up to 95.6%.
Post Processing Algorithm for Background Subtraction Model based on Entropy Approximation and Style Transfer Neural Network

Background subtraction which aims to detect foreground masks is a fundamental task in surveillance systems. On-going research in background subtraction models has shown that background modelings are a highly robust technique to extract regions of interest, known as foregrounds. However, these background modeling techniques still failed to reproduce backgrounds correctly. Therefore, our research focuses on background correction by filling the missing information in the reproduced background image. In this paper, we proposed a post-processing method to correct a constructed background after a novel framework of a tensor-driven GMM for background modeling. Our proposed method which is based on the entropy approximation not only improves the F-score evaluation of the TensorMOG modeling but also can be competitive with state-of-the-art models in terms of processing time.
Applying Rule Post-Pruning to Condition-based Tree for Generating Concise Rules - a Simple, but Effective Method

The big problem with associative classifiers is that they contain a large number of rules that make them difficult to interpret. To deal with this challenge, The authors in work [8] have represented this set ofrules as a tree model, namely condition-based tree (CBT), and propose an algorithm to convert this tree into a more concise rule set. In this paper, the authors inherit the idea of [8] and perform fine-turning on CBT by applying Rule Post-Pruning before converting this tree into rule set. Experimental results show that the proposed classifier has generated a small number of association rules and these rules give high accuracy compared to well-known associative classifiers.
Automatic 3D Hand Pose Estimation Based on YOLOv7 and HandFoldingNet from Egocentric Videos

3D hand pose estimation problem still contains many challenges such as high degree-of-freedom (high-DOF) of 3D point cloud data, the obscured data, the loss of depth image data, especially the data obtained from the first-person viewpoint. In this paper, we propose an automated method for 3D hand pose estimation on detectable hand point cloud data collected from Egocentric vision. Our approach is the combination of YOLOv7 and HoldFoldingNet which are the current highest performing CNNs for hand detection and 3D hand pose estimation problem. We evaluate two stages in the proposed method on the FPHAB dataset, (1) the first stage is the evaluation of hand detection based on the pre-trained model of YOLOv7 and its variants, and (2) the second stage is to evaluate the result of 3D hand estimation on the HoldFoldingNet with the annotation box and the detected box and compare them with the state-of-the-art methods. The result of detecting hand action is the lowest (P=97% - thres <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IOU</inf> = 0.95). The 3D hand pose estimation results using HoldFoldingNet on the FPHAB dataset have the lowest error of 19.98mm, 20.4mm, respectively when testing on the annotation box and the detected box of the 5th configuration,
RTHG: Towards Real- Time Head Detection And Gaze Estimation

As an important way of understanding human in-tentions, gaze estimation has always been a research hotspot in the field of human-robot interaction. Most studies now estimate gaze direction by analyzing head features and head detection is required before gaze estimation. For these two sequential tasks, the current research usually adopts two different networks, which increases the memory occupation of the graphics card and is not easy to deploy on the edge device. In this paper, we propose a unified network for simultaneous head detection and gaze estimation, unifying these two tasks into a multi-task learning model. In this network framework, head detection and gaze estimation share the same set of features, which enables them to promote each other to improve detection accuracy. We evaluated our model on gaze360 dataset and the gaze error dropped to 19.62 degrees while running at 23 fps.
A non-destructive inspection method for electronic packaging reliability incorporating mechanical and thermal information

Electronic components may fail because of non-uniform thermal load, different Coefficients of Thermal Expansion (CTE) among packaging materials, or solder curing shrinkage. Common failures include substrate warpage, structure delamination, and cracking, which reflect the signification of thermal deformation detection for packaging structures. To illustrate the thermo mechanical properties of electronic packages, a non-destructive inspection method combining the projected speckle three-dimensional digital image correlation (3D DIC) technique and infrared thermography (IRT) is proposed. The temperature of each point on the sample surface is obtained by camera calibration, thereby realizing the coupling of mechanical and thermal information. This method can measure out-of-plane displacement without contaminating the sample and adjust the projected speckle flexibly in different applied situations. The inspection system is validated by displacement measurement and chip-heating experiments, and the standard deviation of displacement can be controlled within 1µm.
BERT-TRIPLE: Using BERT for Extracting Triples from Vietnamese Sentences

A triple consists of a head entity, relation, and tail entity. Triple is a key component of the knowledge graph. Extracting the triples from a sentence in the Vietnamese corpus is an interesting problem in building knowledge graphs and generating questions for the question answering systems. The previous works mostly have been done by using rules. Little research has applied machine learning models to extract triples from a given sentence. In this paper, we proposed a model named BERT-TRIPLE that is based on the BERT model to extract triples from the Vietnamese corpus. In particular, we performed two phases that were annotation and training. In the annotation phase, our method annotates word tokens in sentences. In the training phase, we fine-tuned the BERT model to train the labeled dataset for predicting triples in Vietnamese sentences. After collecting the triples of Vietnamese text, we generate questions for Question Answering systems. We experiment to prove the precision of our proposed method on the Vietnamese Wikipedia corpus. Our model improved F1 score better than VnCoreNLP and underthesea models with 12% and 17% respectively. Also, our model was more efficient than the Text-to-Text Transfer Transformer (T5) model in the question generation task.
An Approach to Recommend Fishing Location and Forecast Fish Production by Using Big Data Analysis and Distributed Deep Learning

In fishing industry, fish populations can move over a vast sea and thus boats often search for days or weeks before making a catch. Given the excessive CO2 emissions from vessels and rising fuel cost, it is important to optimize commercial fishing activities by reducing un-necessary searching period. This study proposes a hybrid recommendation system for predicting the best locations for catching specific fish species and experimented various deep learning based multi-variate time series models to forecast the gross weight of two important species of Norwegian fishery: haddock and mackerel. To ensure the practicality, both recommendation system models and time series forecasting models are trained and deployed using Apache Spark and BigDL, which are frameworks for big data processing and distributed deep learning training. The proposed hybrid recommendation model achieve good performance with RMSE of 0.4933. Deep learning based models are also shown to achieve high performance on forecasting fish gross weight. Through this study, it is revealed that datetime and environmental features can play important roles for building sustainable commercial fishing plan.
Aspect-Category-Opinion-Sentiment Extraction Using Generative Transformer Model

Sentiment analysis is one of Natural Language Processing's applications that aims to process and extract sentiment information quickly and effectively. To expand upon the previous triplet extraction, that being aspect-opinion-sentiment triplets, Aspect-Category-Opinion-Sentiment (ACOS) quadruple extraction was created. There are several methods to extract quadruples, albeit with several limitations, such as their effectiveness towards implicit information and their overall low-performance score. This paper proposes a method of using BART-Aspect-Based-Sentiment-Analysis (BARTABSA), a sentiment analysis model that aims to unify the previous Aspect Based Sentiment Analysis subtask - namely Aspect-Opinion pair extraction and Aspect-Opinion-Sentiment triplet extraction, and solve them without changing the core algorithm or adding other models to it - to solve the ACOS subtask. After some modification to the data and the model's outer layer, the result shows significant and promising improvements over previous results.
Low-Resource Speech Recognition Based on Transfer Learning

A lot of research aims to improve accuracy in end-to-end speech recognition, and achieves higher accuracy on various famous corpora. However, there are many languages which do not have enough data to build their speech recognition system in the world. The system often cannot get a reliable result and be used in the real-world. Therefore, how to build a robust low-resource speech recognition system is an important issue in speech recognition. In this paper, we use ESPnet toolkit to implement an end-to-end speech recognition model based on sequence-to-sequence architecture, and use Fairseq toolkit to implement an unsupervised pre-training model for assisted speech recognition. In addition, we use unlabeled speech data to help extract speech features, and transfer a speech recognition model with sufficient corpus to Hakka speech recognition with less corpus through transfer learning. Experimental results show that we establish a more robust low-resource Hakka speech recognition system.
An advanced algorithm for discrimination prevention in data mining

Data mining has been effectively utilized in huge expanded application areas. Associations overall go with significant business choices in light of mining of data. Discrimination in navigation and preservation of protection of data are a couple of significant difficulties in data mining. Discrimination is of two sorts, Direct Discrimination and Indirect Discrimination. Discrimination prevention includes two significant angles disclosure of direct and additionally roundabout discrimination and data transformation as alteration of separating rules without influencing data quality. Existing strategies for discrimination prevention utilize one of the three methodologies, in particular, the pre-process, in-process or post-process approach. Tests for the exhibition assessment of all calculations by fluctuating these info boundaries, each in turn to review and break down impact of these boundaries.
Using Blockchain and Artificial Intelligence to build a Job Recommendation System for Students in Information Technology

In the current era of rapidly developing 4.0 technology, jobs related to Information Technology are becoming more diverse and prosperous. That has less or more influenced the Information Technology students in determining the future job that is right for them. Based on this painful problem, we decided to build an application called SmartCV, which uses Smart Contract in Blockchain Technology to find jobs and connect with company recruiters for Information Technology students in Vietnam and the region. At the same time, we implemented a recommendation system using Machine Learning and Statistical Analysis algorithms to help them choose a suitable job based on their skills. Machine Learning algorithms included Feed Forward Neural Network (FNN), Sequential (KNF), Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), and Statistical Analysis algorithm included Logistic Regression (LR) using the Python programming language. This recommendation system was trained and tested using a dataset of IT jobs crawled on job search websites. We evaluated machine learning algorithms using accuracy, recall, precision, and micro-F1 to predict the most suitable job. The experimental results of predicting students' jobs demonstrate that the Convolutional Neural Network Model (CNN) gives the highest accuracy.
A Filter Approach Based on Binary Integer Programming for Feature Selection

Feature Selection (FS) aims to identify the most relevant information from the data in order to boost the overall performance of the learning model in terms of both accuracy and computational cost. Traditional FS methods rely on specific criteria to rank the features in order of their relevant scores and filter out low-ranking elements. However, the nature of these greedy approaches may suffer from the locally optimal choice. To tackle this problem, we propose a novel FS approach by reformulating the existing filter selection problem into an integer program which can be solved effectively by branch-and-cut algorithms to find the globally optimal solution (an optimal subset of features). Experiments on a wide range of benchmark datasets indicate that our proposed model outperforms existing state-of-the-art approaches in terms of considerably reducing the search space and computational resources.
A Blockchain-based approach and Attribute-based Encryption for Healthcare Record Data Exchange

Sharing medical data can help doctors to give a more rapid and accurate diagnosis of a patient's health problems. However, electronic healthcare records (EHRs) are also considered sensitive data, whose sharing may raise issues of security and privacy. Most current healthcare systems not only manage their data in centralized databases but also lack protection methods, which makes them vulnerable and targeted for cyberattacks. A vulnerable healthcare system then can lead to the leakage of its managed data and serious consequences. This study aims to improve the security and privacy of exchanging EHRs using blockchain technology, IPFS, and attribute-based encryption (ABE). More specifically, to overcome the weakness of centralized storage, the proposed approach uses decentralized databases in its design. We implement a permissioned blockchain network with multiple nodes to ensure the availability and secure data in case of incidents. Additionally, encryption that incorporates the owner's special attributes guarantees data privacy. The proposed system, called BABEHealth, is intended to improve the robustness of healthcare management systems and deal with known security flaws in existing systems for smart healthcare. To evaluate the performance of the proposed system, we consider the latency of transaction creation, the change in data size when applying encryption, and the encryption time with various data quantities.
Learning Robust Vehicle Navigation Policy Under Interference and Partial Sensor Failures

Autonomous driving has been a research hotspot for several years. However, most researchers focus on the ideal driving condition, where the sensor data is always accurate and clean. In reality, the urban environment is complex and the sensor data can easily be disturbed, which can lead to failures of existing driving approaches. In this paper, we present a multi-sensor fusion based navigation framework to achieve robust navigation under interference and partial sensor failures, which implicitly learns the reliability of each input information and introduces graph neural networks to reconstruct features for robust navigation policy learning. Experiments on the high-fidelity CARLA platform demonstrate the effectiveness of our approach, especially our strong robustness against various disturbances on both the input point cloud data and the visual images.
MVRackLay: Monocular Multi-View Layout Estimation for Warehouse Racks and Shelves

In this paper, we propose and showcase, for the first time, monocular multi-view layout estimation for warehouse racks and shelves. Unlike typical layout estimation methods, MVRackLay estimates multi-layered layouts, wherein each layer corresponds to the layout of a shelf within a rack. Given a sequence of images of a warehouse scene, a dual-headed Convolutional-LSTM architecture outputs segmented racks, the front and the top view layout of each shelf within a rack. With minimal effort, such an output is transformed into a 3D rendering of all racks, shelves and objects on the shelves, giving an accurate 3D depiction of the entire warehouse scene in terms of racks, shelves and the number of objects on each shelf. MVRackLay generalizes to a diverse set of warehouse scenes with varying number of objects on each shelf, number of shelves and in the presence of other such racks in the background. Further, MVRackLay shows superior performance vis-a-vis its single view counterpart, RackLay [1] in layout accuracy, quantized in terms of the mean IoU and mAP metrics. We also showcase a multi-view stitching of the 3D layouts resulting in a representation of the warehouse scene with respect to a global reference frame akin to a rendering of the scene from a SLAM pipeline. To the best of our knowledge, this is the first such work to portray a 3D rendering of a warehouse scene in terms of its semantic components - Racks, Shelves and Objects - all from a single monocular camera.
A RRT-A* Path Planning Algorithm for Cable-driven Manipulators

Cable-driven manipulators (CDMs) are widely used for operations in confined spaces due to their slender bodies and multiple degrees of freedom (DOFs). To plan passable paths for them in narrow spaces, a rapidly exploring random tree (RRT) algorithm is often used. However, the cost of planning process are not considered in this method. In order to improve the quality of path planning of CDMs, this work optimizes a traditional RRT algorithm by fusing it with an A* algorithm. In the novel RRT-A* method, the RRT algorithm is used to generate feasible paths, the A* algorithm is used to estimate the cost and measure the selection of the traversal search of each feasible node of the path. Compared with the traditional RRT algorithm, the novel algorithm is better in some performances such as complex path, path redundancy and large random path angle. Simulation results show that this method can effectively reduce path cost and the number of nodes. For further validation, a 17 DOFs CDM prototype is conducted to move in multi-obstacle environments to test the proposed method.
Deepfakes Examiner: An End-to-End Deep Learning Model for Deepfakes Videos Detection

Deepfakes generation approaches have made it possible even for less technical users to generate fake videos using only the source and target images. Thus, the threats associated with deepfake video generation such as impersonating public figures, defamation, and spreading disinformation on media platforms have increased exponentially. The significant improvement in the deepfakes generation techniques necessitates the development of effective deepfakes detection methods to counter disinformation threats. Existing techniques do not provide reliable deepfakes detection particularly when the videos are generated using different deepfakes generation techniques and contain variations in illumination conditions and diverse ethnicities. Therefore, this paper proposes a novel hybrid deep learning framework, InceptionResNet-BiLSTM, that is robust to different ethnicities and varied illumination conditions, and able to detect deepfake videos generated using different techniques. The proposed InceptionResNet-BiLSTM consists of two components: customized InceptionResNetV2 and Bidirectional Long-Short Term Memory (BiLSTM). In our proposed framework, faces extracted from the videos are fed to our customized InceptionResNetV2 for extracting frame-level learnable features. The sequences of features are then used to train a temporally aware BiLSTM to classify between the real and fake video. We evaluated our proposed approach on the diverse, standard, and largescale FaceForensics++ (FF++) dataset containing videos manipulated using different techniques (i.e., DeepFakes, FaceSwap, Face2Face, FaceShifter, and NeuralTextures) and the FakeA VCeleb dataset. Our method achieved an accuracy greater than 90% on DeepFakes, FaceSwap, and Face2Face subsets. Performance and generalizability evaluation highlights the effectiveness of our method for detecting deepfake videos generated through different techniques on diverse FF++ and FakeA VCeleb datasets.