Miracle-dz/UniFAR-FTUs huggingface dataset:
A Wearable IMU System for Flexible Teleoperation of a Collaborative Industrial Robot

Increasing the accessibility of collaborative robotics requires interfaces that support intuitive teleoperation. One possibility for an intuitive interface is offered by wearable systems that measure the operator’s movement and use the information for robot control. Such wearable systems should preserve the operator’s movement capabilities and, thus, their ability to flexibly operate in the workspace. This paper presents a novel wireless wearable system that uses only inertial measurement units (IMUs) to determine the orientation of the operator’s upper body parts. An algorithm was developed to transform the measured orientations to movement commands for an industrial collaborative robot. The algorithm includes a calibration procedure, which aligns the coordinate systems of all IMUs, the operator, and the robot, and the transformation of the operator’s relative hand motions to the movement of the robot’s end effector, which takes into account the operator’s orientation relative to the robot. The developed system is demonstrated with an example of an industrial application in which a workpiece needs to be inserted into a fixture. The robot’s motion is compared between the developed system and a standard robot controller. The results confirm that the developed system is intuitive, allows for flexible control, and is robust enough for use in industrial collaborative robotic applications.
Integrating Normal Vector Features into an Atrous Convolution Residual Network for LiDAR Point Cloud Classification

LiDAR point clouds are rich in spatial information and can effectively express the size, shape, position, and direction of objects; thus, they have the advantage of high spatial utilization. The point cloud focuses on describing the shape of the external surface of the object itself and will not store useless redundant information to describe the occupation. Therefore, point clouds have become the research focus of 3D data models and are widely used in large-scale scene reconstruction, virtual reality, digital elevation model production, and other fields. Since point clouds have various characteristics, such as disorder, density inconsistency, unstructuredness, and incomplete information, point cloud classification is still complex and challenging. To realize the semantic classification of LiDAR point clouds in complex scenarios, this paper proposes the integration of normal vector features into an atrous convolution residual network. Based on the RandLA-Net network structure, the proposed network integrates the atrous convolution into the residual module to extract global and local features of the point clouds. The atrous convolution can learn more valuable point cloud feature information by expanding the receptive field. Then, the point cloud normal vector is embedded in the local feature aggregation module of the RandLA-Net network to extract local semantic aggregation features. The improved local feature aggregation module can merge the deep features of the point cloud and mine the fine-grained information of the point cloud to improve the model’s segmentation ability in complex scenes. Finally, to resolve the imbalance of the distribution of the various categories of point clouds, the original loss function is optimized by adopting a reweighted method to prevent overfitting so that the network can focus on small target categories in the training process to effectively improve the classification performance. Through the experimental analysis of a Vaihingen (Germany) urban 3D semantic dataset from the ISPRS website, it is verified that the proposed algorithm has a strong generalization ability. The overall accuracy (OA) of the proposed algorithm on the Vaihingen urban 3D semantic dataset reached 97.9%, and the average reached 96.1%. Experiments show that the proposed algorithm fully exploits the semantic features of point clouds and effectively improves the accuracy of point cloud classification.
Design of Optimized Coded LFM Waveform for Spectrum Shared Radar System

To meet the increasing demands for remote sensing, a number of radar systems using Linear Frequency Modulation (LFM) waveforms have been deployed, causing the problem of depleting frequency resources. To address this problem, several researchers have proposed the Spectrum Shared Radar System (SSRS) in which multiple radars share the same frequency band to transmit and receive their own signals. To mitigate the interferences caused by the signal transmission by other radars, SSRS employs orthogonal waveforms that inherit the orthogonality of the waveforms from orthogonal codes. However, the inherited orthogonality of the codes is significantly reduced when incorporating LFM waveforms with the codes. To solve this problem, in this paper, we propose a novel but simple scheme for generating a set of optimized coded LFM waveforms via new optimization framework. In the optimization framework, we minimize the weighted sum of autocorrelation sidelobe peaks (ASP) and cross-correlation peaks (CP) of the coded LFM waveforms to maximize the orthogonality of the waveforms. Through computer simulations, we show that the waveforms generated by the proposed scheme outperform the waveforms created by previous proposals in terms of ASP and CP.
Automatic Point Cloud Registration for Large Outdoor Scenes Using a Priori Semantic Information

As an important and fundamental step in 3D reconstruction, point cloud registration aims to find rigid transformations that register two point sets. The major challenge in point cloud registration techniques is finding correct correspondences in the scenes that may contain many repetitive structures and noise. This paper is primarily concerned with improving registration using a priori semantic information in the search for correspondences. In particular, we present a new point cloud registration pipeline for large, outdoor scenes that takes advantage of semantic segmentation. Our method consisted of extracting semantic segments from point clouds using an efficient deep neural network, then detecting the key points of the point cloud and using a feature descriptor to get the initial correspondence set, and, finally, applying a Random Sample Consensus (RANSAC) strategy to estimate the transformations that align segments with the same labels. Instead of using all points to estimate a global alignment, our method aligned two point clouds using transformations calculated by each segment with the highest inlier ratio. We evaluated our method on the publicly available Whu-TLS registration data set. These experiments demonstrate how a priori semantic information improves registration in terms of precision and speed.
Kinematics Adaptation and Inter-Limb Symmetry during Gait in Obese Adults

The main purpose of this study is to characterize lower limb joint kinematics during gait in obese individuals by analyzing inter-limb symmetry and angular trends of lower limb joints during walking. To this purpose, 26 obese individuals (mean age 28.5 years) and 26 normal-weight age- and sex-matched were tested using 3D gait analysis. Raw kinematic data were processed to derive joint-specific angle trends and angle-angle diagrams (synchronized cyclograms) which were characterized in terms of area, orientation and trend symmetry parameters. The results show that obese individuals exhibit a kinematic pattern which significantly differs from those of normal weight especially in the stance phase. In terms of inter-limb symmetry, higher values were found in obese individuals for all the considered parameters, even though the statistical significance was detected only in the case of trend symmetry index at ankle joint. The described alterations of gait kinematics in the obese individuals and especially the results on gait asymmetry are important, because the cyclic uneven movement repeated for hours daily can involve asymmetrical spine loading and cause lumbar pain and could be dangerous for overweight individuals.
Wave Peel Tracking: A New Approach for Assessing Surf Amenity and Analysis of Breaking Waves

The creation and protection of surfing breaks along populated coastlines have become a consideration for many councils and governments as surfing breaks are a major driver of tourism. To assess the surf amenity of surfing breaks, a quantitative and objective assessment method is required. A new wave peel tracking (WPT) method has been developed using a shore-based camera to assess surf amenity by measuring and quantifying potential surfing ride rate, length, duration, speed and direction on a wave-by-wave basis. The wave peel (or “curl” below the wave peak) is the optimal surfing region on a wave, and each wave peel track represents a surfable ride. Wave peel regions are identified, classified and tracked using traditional and machine learning-based computer vision techniques. The methodology is validated by comparing the rectified wave peel tracks with GPS-measured tracks from surfers in the wave peel regions. The WPT methodology is evaluated with data from a reef and adjacent natural beach at the Gold Coast, Australia. The reef produced longer ride lengths than the nearshore region and showed a consistent breaking location along the reef crest. Spatial maps of the wave peel tracks show the influence of tides on the wave breaking patterns and intensity. The WPT algorithm provides a robust, automated method for quantifying surf amenity to provide baseline data for surf break conservation. The methodology has potential uses to verify numerical modelling of surf breaks and to assess the impact of coastal development on surf breaks.
Explaining Drug-Discovery Hypotheses Using Knowledge-Graph Patterns

Drug discovery is an important process used by biomedical experts to identify potential treatments for diseases. In its traditional form, the process requires significant expert time and manual effort. By encoding a wealth of information about relationships between drugs and diseases, modern large-scale biomedical knowledge graphs provide excellent opportunities to accelerate drug discovery, by automating aspects of the process. One opportunity is to use explainable fact-checking tools to generate explanations for hypothesized drug-disease treatment relationships in a given knowledge graph, with a reliability score assigned to each explanation. The explanations and their scores can then be used by experts to determine which drug-disease pairs to consider for clinical trials.In our collaboration with a biomedical team, we have found that existing explainable fact-checking tools are not necessarily helpful in drug discovery, as their explanation formats and evaluation metrics do not match well the requirements of scientific discovery in the biomedical domain. To address these challenges in using fact-checking tools in drug discovery, we introduce a scalable automated approach for generating explanations that are modeled after existing biomedical concepts and supplemented with data-supported evaluation metrics. Our explanations are based on knowledge-graph patterns, which are readily understood by biomedical experts. Our experimental results suggest that our proposed metrics are accurate and useful on largescale biomedical knowledge graphs, and our explanations are understandable and reasonable to experts doing drug discovery.
An Overlapping Community Detection Algorithm for Label Propagation Based on Node Influence

LPA algorithm is an effective community detection algorithm. based on the original algorithm, a new algorithm based on node importance, similarity between nodes, influence of adjacent nodes and label propagation strategy is proposed in view of the randomness and instability of overlapping community detection in LPA algorithm. First, the importance of nodes, the similarity between nodes, the influence of adjacent nodes are calculated, and the label set of nodes is generated according to the calculation results. Through the label update strategy, the attribution coefficient of each node to the community is calculated, and the updated label set is iterated continuously. The results show that the algorithm has near linear time complexity and can effectively improve the accuracy and stability of large-scale overlapping community detection.
The Proficient ML method for Vehicle Detection and Recognition in Video Sequence

Deep Learning has become a highly strong technique and capacity to manage enormous volumes of data over the last few decades. Convolutional Neural Networks are a type of deep neural network that is quite popular. Traffic difficulties are becoming more prevalent as the number of people and cars on the road grows. Machine learning is currently being used to tackle some of these issues, including precise bridge and highway toll lanes, parking lot management, and traffic prevention. This research will look at how deep learning may be used to monitor cars and recognize license plates. This research implemented the CNN, Connected Component Analysis, and segmentation technique for vehicle detection and vehicle number plate detection. The proposed model gives the fundamentals of current neural networks and how they interact with computer vision applications. It can increase the accuracy of the proposed model with a pre-trained model by employing the key components of neural networks. Finally, applying the CNN model obtained more accuracy in the character recognition phase.
Diabetic Retinopathy Detection with Feature Enhancement and Deep Learning

Diabetic Retinopathy is a medical condition which occurs in people having diabetes. Both type 1 and type 2 diabetes patients are affected by this disease. The major problem is there are no significant symptoms of this disease at its early stages for it to be identified and treated, only when it gets worse it starts to show symptoms. Primarily, there are two major types of diabetic retinopathy which are non-proliferative diabetic retinopathy and proliferative diabetic retinopathy. Non-proliferative diabetic retinopathy is the early stages of the disease which when identified can be treated. Proliferative diabetic retinopathy is the later stage of the disease which when identified is difficult to treat. This disease is both time taking and prone to errors when manually examined by doctors. Deep learning has been in existence for a while and has been efficient in analysing medical images. Convolutional Neural Networks (CNNs) are used for feature extraction from the image and difference of gaussians algorithm is used in improving feature extraction. The dataset used is from Kaggle containing around 35126 images. There are some deep learning models for this problem statement but they don’t predict the disease flawlessly. Three convolutional neural networks are trained, validated and tested on the dataset. These models perform better than the existing models in predicting diabetic retinopathy on the Kaggle dataset.
AI Based Classification Framework For Cancer Detection Using Brain MRI Images

Brain imaging technologies plays an important role in medical diagnosis by providing new views of the brain anatomy giving greater insight into brain condition and functions. Image processing is used in the area of medical science to assist the early detection and treatment of life-critical illness. In this paper, cancer detection based on the brain magnetic resonance imaging (MRI) images using a combination of convolutional neural network (CNN) and sparse stacked auto encoder is presented. This combination is found to provide a significant effect in improving the accuracy and effectiveness of the classification process. The proposed method is coded in MATLAB and verified with the dataset consisting of 120 MRI images. The results obtained had shown that the proposed classifier is very much effective in classifying and grading the brain tumor MRI images.
Fingerprint Enhancement using Iterative Contextual Filtering for Fingerprint Matching

Fingerprint matching depends on the quality of the fingerprint images. When fingerprint image quality is low, it can degrade the performance of fingerprint matching significantly. Fingerprint images are often contaminated by noise. Therefore, image quality is crucial for fingerprint matching. In this paper, an image enhancement algorithm in which contextual filtering is applied iteratively to a fingerprint image has been proposed. The main idea of the algorithm is to iterate the output of the Gabor filter to get better enhancement and matching performance. The result of the algorithm has five filtered images due to five times iteration. It showed that the proposed method is significantly better based on Equal Error Rate (EER) compared to the Gabor filter and the modified Gabor filter. The proposed method surpassed the Gabor filter by 3.08 % and the modified Gabor filter by 2.95 %.
Towards Selecting Informative Content for Cyber Threat Intelligence

Nowadays, there is an increasing need for cyber security professionals to make use of tools that automatically extract Cyber Threat Intelligence (CTI) relying on information collected from relevant blogs and news sources that are publicly available. When such sources are used, an important part of the CTI extraction process is content selection, in which pages that do not contain CTI-related information should be filtered out. For this task, we apply supervised machine learning-based text classification techniques, trained on a new dataset created for the purposes of this work. Furthermore, we show in practice the importance of a good content selection process in a commonly used CTI extraction pipeline, by inspecting the results of the Named Entity Recognition (NER) process that normally follows.
A tree-based machine learning methodology to automatically classify software vulnerabilities

Software vulnerabilities have become a major problem for the security analysts, since the number of new vulnerabilities is constantly growing. Thus, there was a need for a categorization system, in order to group and handle these vulnerabilities in a more efficient way. Hence, the MITRE corporation introduced the Common Weakness Enumeration that is a list of the most common software and hardware vulnerabilities. However, the manual task of understanding and analyzing new vulnerabilities by security experts, is a very slow and exhausting process. For this reason, a new automated classification methodology is introduced in this paper, based on the vulnerability textual descriptions from National Vulnerability Database. The proposed methodology, combines textual analysis and tree-based machine learning techniques in order to classify vulnerabilities automatically. The results of the experiments showed that the proposed methodology performed pretty well achieving an overall accuracy close to 80%.
Secure Communication in Smart Meters using Elliptic Curve Cryptography and Digital Signature Algorithm

With the advancement in the growth of Internet-of-Things (IoT), its number of applications has also increased such as in healthcare, smart cities, vehicles, industries, household appliances, and Smart Grids (SG). One of the major applications of IoT is the SG and smart meter which consists of a large number of internet-connected sensors and can communicate bi-directionally in real-time. The SG network involves smart meters, data collectors, generators, and sensors connected with the internet. SG networks involve the generation, distribution, transmission, and consumption of electrical power supplies. It consists of Household Area Network (HAN), and Neighborhood Area Network (NAN) for communication. Smart meters can communicate bidirectionally with consumers and provide real-time information to utility offices. But this communication channel is a wide-open network for data transmission. Therefore, it makes the SG network and smart meter vulnerable to outside hacker and various Cyber-Physical System (CPS) attacks such as False Data Injection (FDI), inserting malicious data, erroneous data, manipulating the sensor reading values. Here cryptography techniques can play a major role along with the private blockchain model for secure data transmission in smart meters. Hence, to overcome these existing issues and challenges in smart meter communication we have proposed a blockchain-based system model for secure communication along with a novel Advanced Elliptic Curve Cryptography Digital Signature (AECCDS) algorithm in Fog Computing (FC) environment. Here FC nodes will work as miners at the edge of smart meters for secure and real-time communication. The algorithm is implemented using iFogSim, Geth version 1.9. 25, Ganache, Truffle for compiling smart contracts, Anaconda (Python editor), and ATOM as language editor for the smart contracts.
FN-Net: A Deep Convolutional Neural Network for Fake News Detection

Information and communication technology has evolved rapidly over the past decades, with a substantial development being the emergence of social media. It is the new norm that people share their information instantly and massively through social media platforms. The downside of this is that fake news also spread more rapidly and diffuse deeper than before. This has caused a devastating impact on people who are misled by fake news. In the interest of mitigating this problem, fake news detection is crucial to help people differentiate the authenticity of the news. In this research, an enhanced convolutional neural network (CNN) model, referred to as Fake News Net (FN-Net) is devised for fake news detection. The FN-Net consists of more pairs of convolution and max pooling layers to better encode the high-level features at different granularities. Besides that, two regularization techniques are incorporated into the FN-Net to address the overfitting problem. The gradient descent process of FN-Net is also accelerated by the Adam optimizer. The empirical studies on four datasets demonstrate that FN-Net outshines the original CNN model.
Using Counterfactual Regret Minimization and Monte Carlo Tree Search for Cybersecurity Threats

Mitigating cyber threats requires adequate understanding of the attacker characteristics in particular their patterns. Such knowledge is essential in addressing the defensive measures that mitigate the attack. If the attacker enters in the network system, the game tree model generates resources by to counter such threat. This is done by altering the parity in the next game tree iteration which yield an adequate response to counter it. If an attacker enters a network system, and a game tree models the resources he must interface with, then that game tree can be altered, by changing the parity on the next to last iteration. This paper analyzes the sequence of patterns based on incoming attacks. The detection of attacker’s pattern and subsequent changes in iterations to counter threat can be viewed as adequate resource or know how in cyber threat mitigations It was realized that changing the game tree of the hacker deprives the attacker of network resources and hence would represent a defensive measure against the attack; that is changing varying or understanding attacker paths, creates an effective defensive measure to protect the system against the incoming threats. In this paper we analyze a unique combination of CFR and MCTS that attempts to detect the behavior of a hacker. Counterfactual Regret (CFR) is a game theory concept that helps identify patterns of attacks. The pattern recognition concept of Monte Carlo Tree Search (MCTS) is used in harmony with CFR in order to enhance the detection of attacks.