Reviewerly/exHarmony huggingface dataset:
Using Depth Cameras for Recognition and Segmentation of Hand Gestures

In recent years, in combination with technological advances, new paradigms of interaction with the user have emerged. This has motivated the industry to create increasingly powerful and accessible natural user interface devices. In particular, depth cameras have achieved high levels of user adoption. These devices include the Microsoft Kinect, the Intel RealSense, and the Leap Motion Controller. This type of device facilitates the acquisition of data in human activity recognition. Hand gestures can be static or dynamic, depending on whether they present movement in the image sequences. Hand gesture recognition enables human-computer interaction (HCI) system developers to create more immersive, natural, and intuitive experiences and interactions. However, this task is not easy. That is why, in the academy, this problem has been addressed using machine learning techniques. The experiments carried out have shown very encouraging results indicating that the choice of this type of architecture allows obtaining an excellent efficiency of parameters and prediction times. It should be noted that the tests are carried out on a set of relevant data from the area. Based on this, the performance of this proposal is analysed about different scenarios such as lighting variation or camera movement, different types of gestures, and sensitivity or bias by people, among others. In this article, we will look at how infrared camera images can be used to segment, classify, and recognise one-handed gestures in a variety of lighting conditions. A standard webcam was modified, and an infrared filter was added to the lens to create the infrared camera. The scene was illuminated by additional infrared LED structures, allowing it to be used in various lighting conditions.
Privacy-Preserving Proximity Detection Framework for Location-Based Services

With the popularization of mobile communication and sensing equipment, as well as the rapid development of location-aware technology and wireless communication technology, LBSs(Location-based services) bring convenience to people’s lives and enable people to arrange activities more efficiently and reasonably. It can provide more flexible LBS proximity detection query, which has attracted widespread attention in recent years. However, the development of proximity detection query still faces many severe challenges including query information privacy. For example, when users want to ensure their location privacy and data security, they can get more secure location-based services. In this article, we propose an efficient and privacy-protecting proximity detection framework based on location services: PD(Proximity Detection). Through PD, users can query the range of arbitrary polygons and obtain accurate LBS results. Specifically, based on homomorphic encryption technology, an efficient PRQ(polygon range query) algorithm is constructed. With the help of PRQ, PD, you can obtain accurate polygon range query results through the encryption request and the services provided by the LAS(LBS Agent Server) and the CS(Cloud Server). In addition, the query privacy of the queryer and the information of the data provider are protected. The correctness proof and performance analysis show that the scheme is safe and feasible. Therefore, our scheme is suitable for many practical applications.
Incomplete multi-view clustering via independent self-representation learning

Incomplete multi-view clustering is a learning paradigm that combines heterogeneous and incomplete characteristics of multi-view data to obtain data structure and improve clustering performance. In practical application, each view will also be affected by missing values and outliers, in addition to missing some complete samples, which makes most traditional incomplete multi-view clustering methods ineffective. An incomplete multi-view clustering method based on independent self-presentation learning is proposed to solve the above issue. The proposed method fills in the missing features and learns the unique self-presentation matrix of each view using self-representation reconstruction. Then, to better mine the structure, low-rank constraints are added to the self-presentation matrix, and the diversity among different views is measured by introducing the Hilbert-Schmidt independence criterion. Experimental results on multiple data sets show that the proposed method can achieve better clustering than other advanced methods in most cases.
Kodiak@ALQAC2021: Deep Learning for Vietnamese Legal Information Processing

In this paper, we propose deep learning based methods for addressing the problems of legal text processing in the Automated Legal Question Answering Competition (ALQAC 2021). The competition consists of three challenging tasks based on well-known statute laws in Vietnamese and Thai Language. We participated in three tasks related to the Vietnamese statute law, including the legal document retrieval (Task 1), the legal textual entailment (Task 2), and the legal question answering (Task 3). In Task 1, we combine semantic and lexical scores to identify relevant articles. In Tasks 2&3, we fine-tune pretrained models for the Vietnamese language in order to classify proper labels. The experimental results demonstrate both the difficulties and potentials associated with these approaches.
StereoCNC: A Stereovision-guided Robotic Laser System

This paper proposes an End-to-End stereovision-guided laser surgery system that can conduct laser ablation on targets selected by human operators in the color image, referred as StereoCNC. Two digital cameras are integrated into a previously developed robotic laser system to add a color sensing modality and formulate the stereovision. A calibration method is implemented to register the coordinate frames between stereo cameras and the laser system, modelled as a 3D-to-3D least-squares problem. The calibration reprojection errors are used to characterize a 3D error field by Gaussian Process Regression (GPR). This error field can make predictions for new point cloud data to identify an optimal position with lower calibration errors. A stereovision-guided laser ablation pipeline is proposed to optimize the positioning of the surgical site within the error field, which is achieved with a Genetic Algorithm search; mechanical stages move the site to the low-error region. The pipeline is validated by the experiments on phantoms with color texture and various geometric shapes. The overall targeting accuracy of the system achieved an average RMSE of 0.13 ± 0.02 mm and maximum error of 0.34 ± 0.06 mm, as measured by pre- and post-laser ablation images. The results show potential applications of using the developed stereovision-guided robotic system for superficial laser surgery, including dermatologic applications or removal of exposed tumorous tissue in neurosurgery.
GAN based indian sign language synthesis

Controllable visual reproduction of sign language, termed Sign Language Synthesis (SLS), is a major and challenging task in sign language processing. Traditional methods have used computer animation to perform this task, but these have faced several limitations. Animation usually requires expensive equipment to perform motion capture and intensive manual oversight to ensure accuracy. Recently, Generative Adversarial Networks (GANs) have had very promising results in pose and motion transfer. This has been explored as a SLS method in Stoll, et al. [22] and related work. However, this work has required datasets with manual annotation, both in the form of requiring a corpus of manually selected "good" hand poses, as well a large corpus of videos of continuous signing which are annotated for the sequence of signs appearing in them. Most sign languages, however, face a dearth in data, especially annotated data, and this is the case for Indian Sign Language (ISL). In this paper, we present a method for overcoming this issue in the first GAN based SLS model created specifically for ISL. We use a combination of separate generators for the hand and body to overcome the problem of requiring hand-picked "good" hand images from training videos. We further refine the output with another network to remove the artefacts appearing due to combining separate GAN outputs. We also experiment with creating continuous sign language output without requiring an annotated corpus, by stitching together individual signs obtained from a publicly available video lexicon of ISL. We show our model performs competitively in these tasks in both quantitative measures as well as in human perception tests.
Camouflaged Adversarial Attack on Object Detector

The existence of physical-world adversarial examples such as adversarial patches proves the vulnerability of real-world deep learning systems. Therefore, it is essential to develop efficient adversarial attack algorithms to identify potential risks and build a robust system. The patch-based physical adversarial attack has shown its effectiveness in attacking neural network-based object detectors. However, the generated patches are quite perceptible for humans, violating the fundamental assumption of adversarial examples. In this work, we present task-specific loss functions that can generate imperceptible adversarial patches based on camouflaged patterns. First, we propose a constrained optimization method with two camouflage assessment metrics to quantify camouflage performance. Then, we show the regularization with those metrics can help generate the adversarial patches based on camouflage patterns. Furthermore, we validate our methods with various experiments and show that we can generate natural-style camouflaged adversarial patches with comparable attack performance.
Multi-sensor Area Deployment Optimization Method Based on Improved Cuckoo Search Algorithm

Aiming at the problem that the detection range of sensors in a specific area on the sea cannot meet the coverage requirements, a multi-sensor area deployment detection blind compensation model is proposed. On the basis of the sensor detection model, the sensor area coverage model is established with the coverage area as an indicator; the sensor movement cost model is established according to the movement of non-fixed sensors (the sensor is deployed on a mobile platform); Draw lessons from the theory of electric charges being forced in the electric field the cuckoo algorithm (CS) is improved according to the detection model, and a multi-sensor area deployment detection blind compensation method based on the improved cuckoo search algorithm (R-CS) is proposed. Comparing the R-CS algorithm with the basic CS algorithm, the simulation results show that the improved algorithm has improved the convergence speed and accuracy.
Rapid Estimation of Earthquake Magnitude and Source Parameters Using Genetic Algorithms

To minimize the impacts of large losses and optimize the emergency response when a large earthquake occurs, an accurate early warning of an earthquake or tsunami is crucial. One important parameter that can provide an accurate early warning is the earthquake’s magnitude. This study proposes a method for estimating the magnitude, and some of the source parameters, of an earthquake using genetic algorithms (GAs). In this study, GAs were used to perform an inversion of Okada’s model from earthquake displacement data. In the first stage of the experiment, the GA was used to inverse the displacement calculated from the forward calculation in Okada’s model. The best performance of the GA was obtained by tuning the hyperparameters to obtain the most functional configuration. In the second stage, the inversion method was tested on GPS time series data from the 2011 Tohoku Oki earthquake. The earthquake’s displacement was first estimated from GPS time series data using a detection and estimation formula from previous research to calculate the permanent displacement value. The proposed method can estimate an earthquake’s magnitude and four source parameters (i.e., length, width, rake, and slip) close to the real values with reasonable accuracy.
Application Research on Roller Skater Detection, Tracking, and Trajectory Prediction Based on Video Stream

With the continuous progress of science and technology, the sport of roller skating has developed rapidly and the technical level of the game has become higher and higher. Its sport performance has been rapidly improved. However, China’s roller skating is relatively late, and there is still a certain gap compared with many Western developed countries. In order to improve the performance of China’s roller skating, this study takes the representative Chinese and foreign excellent speed skaters as the research object and compares the sprinting technology of Chinese and foreign excellent speed skaters by using image measurement and image analysis to obtain the kinematic parameters and data of the athletes’ sprinting technology in the competition state. In view of the problem that the current video target tracking algorithm is easy to follow multiple targets, a video multiobject detection and tracking algorithm with improved tracking learning detection (TLD) is studied with the skater in the video as the research object. For the lost target, the prediction function of Kalman filter algorithm is used to track the trajectory of the typical target in the video, and the trajectory tracked by Kalman filter algorithm is used to compensate the lost part of TLD algorithm, so as to obtain the complete trajectory of the typical target in the video to improve the accuracy of video multiobject tracking. Since the existing trajectory prediction algorithms have the limitation of poor accuracy, a social-long short-term memory (Social-LSTM) network-based video typical target trajectory prediction algorithm is proposed to predict the trajectory sequences of typical targets to be detected by incorporating the contextual environment information and the interaction relationship between multiple target trajectories into the Social-LSTM network. The simulation results show that the proposed trajectory prediction algorithm outperforms the traditional LSTM algorithm, Hidden Markov Model Algorithm, and Hybrid Gaussian model algorithm, which is helpful to improve the accuracy of video roller skater target trajectory prediction, and the tracking success rate is 0.98.
Code Component Retrieval Using Code2Vec

The system helps to gather the code relevant compound by using code2vec tokenization concept. This research work has used a embedding code, which go through code2vec model. In this model code components are converted to tokens and give a numeric representation using code embedding technique. It retrieves the most accurate code and obtains the code component by their prediction score. Here, the score is calculated by using cosine similarity for obtaining the best results from the dataset.
Generative Adverserial Analysis of Phishing Attacks on Static and Dynamic Content of Webpages

In this paper, we studied the phishing problem from data analytics and AI-powered adversarial attacks perspectives. We evaluated several static and dynamic features that can be used as good models' predictors. Unlike the URL, which can be easily crafted to evade detection, the page static and dynamic content cannot be easily changed without changing what is presented to the potential victim. We evaluated several conventional and ensemble-based models and reported the best models and settings of models that showed high prediction accuracy. We then analyzed the feasibility of evading phishing classifiers by perturbing static and dynamic features using AI generative models then test both conventional and ensemble classifiers. Our results shows that the analysis of static and dynamic features of web pages has good potential in the area adversarial learning to generate phishing attacks. The results yield that it is more challenging to evade phishing classifiers relying on dynamic content features, which offers a good level of robustness against evasion tactics.
Intelligent Evaluation and Reflection Model Applied in Internet Smart Teaching Tools by Computer Remote Video Technology

To prevent and control the outbreak of coronavirus disease, in the spring of 2020 the Ministry of Education in China announced an initiative for universities to postpone the back-to-school season and start nationwide online teaching. This paper first summarizes the literature review of research on online teaching at home and abroad. Then it introduces the application of smart teaching tool Rain Classroom in College English course at Huazhong Agricultural University and the teaching design before, during, and after class. After comparative teaching experiments, empirical data suggest that students achieved notable progress in academic performance in classes using smart teaching tools. In the questionnaire survey, students show high level of adaptability and satisfaction to the teaching model and learning outcomes. Thus it can be concluded that smart teaching tools guaranteed the high quality of online teaching during the coronavirus pandemic. At last, the author proposes suggestions on the R& D of smart teaching tools and faculty teaching development. It is hoped that this paper can provide reference for future research on online teaching.
Landmine Detection Using A Mobile Robot

Given the scale and damage to human lives both physical and economic, landmine detection and inspection have been an important domain for human aid response. This process has led to the invention and development of various safe and effective methods for landmine detection, marking, and cleanup. But most often the tools used are expensive, costly to operate, and require skilled technicians. For a very long time, a large part of this process was carried by human personnel. These methods and very conventional and place humans and human life at risk. Operating with such conventional methods requires extensive training and caution. To minimize human interaction modern methods use mobile robots to detects the buried landmines. The mobile robot is equipped with different types of sensors for landmine detection and autonomous navigation. The paper presents the design and implementation of a simulated mobile robot that detects the buried landmines on the battlefield to reduce human intervention. The mobile robot has various sensors for obstacle avoidance and mine detection. The simulation and visualization are carried out in ROS Gazebo and RViz.
Automated Paraphrase Quality Assessment Using Language Models and Transfer Learning

Learning to paraphrase supports both writing ability and reading comprehension, particularly for less skilled learners. As such, educational tools that integrate automated evaluations of paraphrases can be used to provide timely feedback to enhance learner paraphrasing skills more efficiently and effectively. Paraphrase identification is a popular NLP classification task that involves establishing whether two sentences share a similar meaning. Paraphrase quality assessment is a slightly more complex task, in which pairs of sentences are evaluated in-depth across multiple dimensions. In this study, we focus on four dimensions: lexical, syntactical, semantic, and overall quality. Our study introduces and evaluates various machine learning models using handcrafted features combined with Extra Trees, Siamese neural networks using BiLSTM RNNs, and pretrained BERT-based models, together with transfer learning from a larger general paraphrase corpus, to estimate the quality of paraphrases across the four dimensions. Two datasets are considered for the tasks involving paraphrase quality: ULPC (User Language Paraphrase Corpus) containing 1998 paraphrases and a smaller dataset with 115 paraphrases based on children’s inputs. The paraphrase identification dataset used for the transfer learning task is the MSRP dataset (Microsoft Research Paraphrase Corpus) containing 5801 paraphrases. On the ULPC dataset, our BERT model improves upon the previous baseline by at least 0.1 in F1-score across the four dimensions. When using fine-tuning from ULPC for the children dataset, both the BERT and Siamese neural network models improve upon their original scores by at least 0.11 F1-score. The results of these experiments suggest that transfer learning using generic paraphrase identification datasets can be successful, while at the same time obtaining comparable results in fewer epochs.
Detection and recognition method for pointer-type meter based on deep learning

In order to free up human productivity and hand over the tedious and potentially dangerous task of reading numbers to machines, many experts and scholars have been working on meter recognition since the beginning of the 21st century. With the advent of deep learning techniques, gauge recognition has been greatly improved, but there are still many problems. In order to solve one of these problems, which requires manual input of scale position and scale value, this paper proposes a deep learning-based method for detecting and recognizing pointer-type gauges, which can extract the scale by introducing text detection and text recognition. Experiments show that this method can meet the requirements in terms of response time and accuracy even if it is fully automated.
Robust adaptive multi‐target tracking with unknown measurement and process noise covariance matrices

A robust adaptive probability hypothesis density (PHD) filter is proposed to address the degradation of PHD performance due to an unknown process noise and measurement noise covariance matrix. Therefore, the inverse Wishart distribution is introduced to model the prior distribution of process noise and measurement noise. Meanwhile, the multi-target posterior intensity is approximated as a mixture of the inverse Wishart distribution and Gaussian distribution. The closed solution of the robust PHD filter is derived by the variational Bayes approach. Simulation results show that the proposed algorithm outperforms the Gaussian-mixed PHD filter and the variational Bayesian PHD filter in terms of target number estimation accuracy and optimal sub-pattern assignment distance.
Analysis on Sentiment Analytics Using Deep Learning Techniques

Sentiment analytics is the process of applying natural language processing and methods for text-based information to define and extract subjective knowledge of the text. Natural language processing and text classifications can deal with limited corpus data and more attention has been gained by semantic texts and word embedding methods. Deep learning is a powerful method that learns different layers of representations or qualities of information and produces state-of-the-art prediction results. In different applications of sentiment analytics, deep learning methods are used at the sentence, document, and aspect levels. This review paper is based on the main difficulties in the sentiment assessment stage that significantly affect sentiment score, pooling, and polarity detection. The most popular deep learning methods are a Convolution Neural Network and Recurrent Neural Network. Finally, a comparative study is made with a vast literature survey using deep learning models.