Reviewerly/exHarmony huggingface dataset:
A comparative study of syllables and character level N-grams for Dravidian multi-script and code-mixed offensive language identification

Curfews and lockdowns around the world in the Covid-19 era have increased the usage of the internet drastically and accordingly the amount of data shared on social media. In addition to using social media for sharing useful information, some miscreants are using the power of social media to spread hate speech and offensive content. Filtering the offensive language content manually is a laborious task due to the huge volume of data. Further, rapid developments in hardware and software technology have provided opportunities for users to post their comments not only in English but also in their native language scripts. However, based on the ease of Roman script usage, social media users specifically in multilingual countries like India, prefer to comment in code-mixed and multi-script texts. The typical systems that are employed to process and analyze monolingual texts are usually not appropriate for these kinds of texts. Further, as these texts do not adhere to the rules and regulations of any language to frame the words and sentences, the complexity of analyzing such texts increases. The novelty of the present study is to address the Offensive Language Identification (OLI) task in code-mixed and multi-script texts, this paper proposes to use relevant syllable and character n-grams features to train Machine Learning (ML) classifiers. The performance of the proposed models is evaluated on three Dravidian language pairs, namely: Malayalam-English, Tamil-English, and Kannada-English. The performances of ML classifiers prove the effectiveness of syllable and character n-grams features for code-mixed and multi-script texts analysis.
Multiple Object Tracking by Joint Head, Body Detection and Re-Identification

Multi-object tracking (MOT) is an important problem in computer vision which has a wide range of applications. Formulating MOT as multi-task learning of object detection and re-Identification (re-ID) in a single network is appealing since it achieves real-time but effective inference on detection and tracking. However, in crowd scenes, the existing MOT methods usually fail to locate occluded objects, which also results in bad effects on the re-ID task. To solve people tracking in crowd scenes, we present a model called HBR (Head-Body-ReID Joint Tracking) to jointly formulates head detection, body detection and re-ID tasks into an uniform framework. Human heads are hardly affected by occlusions in crowd scenes, and they can provide informative clues for whole body detection. The experimental results on MOT17 and MOT20 show that our proposed model performs better than the state-of-the-arts.
Model Predictive 6D Image-Based Visual Servoing for 3C Products Assembly

The application of visual servoing in robot assembly is very promising. In this paper, the 6-degree-of-freedom (DOF) image-based visual servoing (IBVS) control based on model predictive control (MPC) is implemented for 3C products assembly. Since the classical IBVS is calculated by multiplying the error times the pseudoinverse of an interaction matrix, it cannot handle constraints. Hence, the collision encountered in the process and the feature points out of the visual range cannot be solved well. Therefore, based on MPC, which can solve the servoing problems by transforming them into constrained optimization, this paper proposes a new MPC for the 3C products assembly system. In the new MPC, we estimate the depth information through image information, to solve the problem of deep collision, and propose a new cost function, which can better solve the problem of the system falling into a local optimal solution. Finally, the simulation verified in the ROS-Gazebo simulation results shows that the new control method is feasible and the control performance is more satisfactory.
Dual Path DNN Based Heterogenous Reference Image Quality Assessment via Decoupling the Quality Difference and Content Difference

Blind image quality assessment (BIQA) is a fundamental yet challenging task in the field of low-level computer vision. The difficulty is particularly due to the limited information, for which the corresponding reference for comparison is typically absent. In order to improve the accuracy and generalization ability of BIQA metrics, our work proposes a dual-path deep neural network (DNN) based heterogenous reference BIQA framework in which an arbitrarily selected pristine image is employed to provide important prior quality information for the IQA framework. The proposed IQA metric is still ‘blind’ since the corresponding reference image is unseen, but our metric could obtain more prior quality information than previous work with the help of heterogenous reference. Experimental results indicate that our proposed BIQA framework is as competitive as state-of-the-art BIQA models.
Engineering Drawing Manager: A Smart Data Extractor and Management Software

Engineering drawings play an important role in fields such as architecture, industrial engineering, and electric engineering, within which tables contain essential data and structures. However, most engineering drawings exist in the form of scanned PDFs or images, which is inconvenient for data management and storage, especially for table information. Also, many industries are in urgent need of data management software for engineering drawings to improve the degree of digital preservation and management. To this end, a software, ED Manager which is based on the fusion of deep learning and traditional image processing, is presented to detect the position and structure of the table, split and recognize characters, and reconstruct the table in a digital form. Further, we extract crucial information and develop a user interface and database to construct a comprehensive model that fits most engineering drawings. Our software can accurately locate tables for various complex drawings, extract structured information from tables, and build a better data management software for engineering drawings.
Augmented Graph Attention with Temporal Gradation and Reorganization for Human Motion Prediction

Human motion prediction has progressed leap-forward by propulsion of many prior excellent work since its great significance for the promotion of various artificial intelligence applications. How to extract historical spatio-temporal feature better to reduce the discontinuity and long-term error accumulation of prediction motion are still the main challenge of current literature. In this work, a novel augmented graph attention with temporal gradation and reorganization method is proposed, which combines channel attention with graph attention and temporal convolution to be a integrated block for the first time on human motion prediction. The block learns ‘what’, ‘where’ and ‘when’ while capturing the spatial structure of human skeleton in the channel-spatial axes and temporal information in the sequential dimension, respectively. Furthermore, the mechanism of temporal gradation and reorganization can retain complicated and high dynamic temporal information effectively without selection of convolutional kernel size. Our experiments on Human3.6M datasets show that the proposed network performs higher prediction accuracy compared with state-of-the-art methods.
MIA-Net: An Improved U-Net for Ammunition Segmentation

The task of destroying all types of scrapped ammunitions has always been extremely dangerous. Effective ammunition image segmentation algorithms can assist in reducing the risk of destruction missions. According to the uniqueness of the ammunition image segmentation task, we proposed an effective method for ammunition image segmentation named MIA-Net based on U-Net deep learning model. The Multi-scale Input Module (MIM) is used to supervise and revise the model at multiple levels, and the Weighted Attention Module (WAM) is used to represent features of different channels. The learning of multi-layer information is weighted and fused, which greatly increases the performance of the segmentation network model. Experiments demonstrate the effectiveness of the proposed method.
A Novel Preprocessing Approach with Soft Voting for Hand Gesture Recognition with A-Mode Ultrasound Sensing

To explore the potential of gesture recognition based on the A-mode ultrasound (AUS) interface in human-computer interaction (HCI), according to the characteristics of AUS signal, a novel preprocessing approach is designed, feature extraction is performed by the window analysis method, and four methods, Linear Discriminant Analysis (LDA), k-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Artificial Neural Network (ANN) for classification. The experimental results show that the single feature with the best results can achieve 91.63% accuracy on KNN. Meanwhile, by feature combination, we can achieve 91.91% accuracy on the KNN classifier, it is 3.60% higher than the highest recognition rate of 88.03% among linear fitting features, called KB features. Further, we learn the integration of soft voting for four classifiers, LDA, KNN, SVM, and ANN, and achieve the highest recognition rate of 92.32% on single features and can achieve 93.09% decoding rate on combined features, which is 4.01% higher than 89.08% among KB features with the soft voting method. The experimental results show that AUS has outstanding performance in gesture decoding.
An Improved Off-Policy Actor-Critic Algorithm with Historical Behaviors Reusing for Robotic Control

When the robot uses reinforcement learning (RL) to learn behavior policy, the requirement of RL algorithm is that it can use limited interactive data to learn the relatively optimal policy model. In this paper, we present an off-policy actor-critic deep RL algorithm based on maximum entropy RL framework. In policy improvement step, an off-policy likelihood ratio policy gradient method is derived, where the actions are sampled simultaneously from the current policy model and the experience replay buffer according to the sampled states. This method makes full use of the past experience. Moreover, we design an unified critic network, which can simultaneously approximate the state-value and action-value functions. On a range of continuous control benchmarks, the results show that our method outperforms the state-of-the-art soft actor-critic (SAC) algorithm in stability and asymptotic performance.
Two-Stream Adaptive Weight Convolutional Neural Network Based on Spatial Attention for Human Action Recognition

There is the problem of strong spatio-temporal complexity of actions and low differentiation of similar action features in Action Recognition. Unimodal input, such as RGB image, can provide rich appearance and context information, but background motion and object occlusion make it difficult to extract human motion information. Multimodal-based human action recognition methods can solve these problems. However, the problems of inadequate fusion of multimodal features lead to poor algorithm robustness and insufficient consideration of modal differences in existing multimodal-based human action recognition methods. We proposed a two-stream adaptive weight convolutional neural network based on spatial attention for human action recognition, SA-AWCNN, to achieve cross-modality feature complementarity. This method constructs a local feature interaction module from depth to RGB to effectively improve the network modal information interaction ability by using the complementarity between modes. At the same time, the spatial attention module is introduced to strengthen the spatial dimension feature information, and the effectiveness of network feature extraction is improved without increasing network parameters. Experiments show that the proposed method is effective in completing human action recognition tasks. The accuracy of our method on NTU RGB+ D dataset and SBU Kinect interaction dataset reaches 91.85% and 94.30%.
Revised Discrete Control Barrier Functions for Safe Control of a Redundant DoF Manipulator

The control barrier function provides an efficient tool to ensure the safety of Human-robot interaction. In this paper, we represent the barrier function in the discrete-time form together with the control Lyapunov function for safety operation in human-robot coexisting environment. And we apply it to the kinematic control of the redundant degree-of-freedom manipulator in the task space without disturbing the robot’s job. Specially, we revise the definition of the minimum distance from each link to the human so that no collision happens on any link of the robot. The effectiveness of the control method is verified by simulation of controlling a planar three-link manipulator.
An Autonomous Obstacle Avoidance Method for Dual-Arm Surgical Robot Based on the Improved Artificial Potential Field Method

Most surgical robots adopt a simple master-slave control method, and their forms are transitioning from non-autonomous to semi-autonomous states. Motion planning and obstacle avoidance are essential research directions. In this regard, an obstacle avoidance method for a dual-arm surgical robot based on an improved artificial potential field is proposed. First, improve the artificial potential field method. Considering the working posture of the dual-arm surgical robot in the limited workspace, respectively apply the gravitational potential field and the repulsive potential field to each joint to achieve precise control. Secondly, in terms of collision detection, use the convex hull algorithm to convex the model, and use the GJK algorithm to calculate the distance between the obstacle and the equivalent convex body of the manipulator. The distance threshold can be manually set to alert when a collision is imminent. Besides, the motion is steady by using the adaptive step size. An improved splicing path method is proposed to make the manipulator jump out of the local minimum and improve the planning efficiency. Finally, smooth the resulting path and ensure the manipulator doesn’t collide. Experiments show that this study can make the dual-arm surgical robot move relatively smoothly on avoiding obstacles, which provides a reference for the automation of surgical robots.
Super-Resolution Based on Degradation Learning and Self-attention for Small-Scale Pedestrian Detection

Recently the object detection algorithms have been widely used in various fields. In the highway monitoring scene, the performance of existing pedestrian detection algorithms degrade rapidly when the size of pedestrian decreased. To enhance the performance of detector, we designed a method named DSANet, which combines super-resolution with object detection algorithm, so that the detection network can capture more detailed features. Compared with the existing super-resolution algorithms, we integrate degeneration learning and self-attention module to make the super-resolution algorithm better fit the pedestrian detection. In particular, we introduce the MSAF module to fuse self attention information of different head numbers. The proposed super-resolution method provides better support for pedestrian detection. The experimental results show that the reconstructed SR image has richer detail features, which improves the accuracy of pedestrian detection.
Generative Unknown-Aware Object Detection

Building object detectors can identify unknown objects, which is critical yet underexplored. An additional discriminative inference pass is generally needed to generate final classification results for “unknown” class on the existing methods of unknown-aware object detection. But human beings can immediately identify unknown objects by themselves without other auxiliary materials. Hence, a stricter task called “Generative Unknown-Aware Object Detection” (G-UAOD) is proposed to constrain the prediction process in this paper. The setting of G-UAOD lays stress on identifying unknown objects by detectors themselves. For achieving this task, we first repair the serious issue of providing inconsistent labels for training by the proposed auto-labelling unknowns methodologies. The inconsistent labels come from the situation that proposals which highly overlap with the same unknown objects may are given distinct labels. Secondly, the overlooked components of known objects are capitalized. The rest proposals which appear strong possibilities that localize the components of known objects are reactivated as “unknown” class. Finally, only the classification loss of the unknown proposals is computed to learn the network but the regression loss of them is left out. Our work improves the U-Recall score remarkably compared to the previous best method while retains the competitive accuracy of detecting known objects.
A Monocular Vision-Based Human-Following Approach for Mobile Robots

The ability to recognize and follow human beings is considered to be a key technology for mobile robots. A vision-based following approach is proposed in this paper. It consists of two parts: the human detection part and the visual servoing part. In the human detection part, the image information is captured by the robot camera and processed by a neural network detection algorithm to extract human position and state information. In the visual servoing part, robot switches between three modes including seeking, following and stopping for human-following. Technically, an algorithm is proposed to calculate the angle between the robot and the human, which is used to control. The innovation of this paper is the use of a full vision-based approach to human-following, where the whole process only requires image information. A series of experiments were conducted to verify the effectiveness of the approach.
Topology Reconstruction of High-Reflective Surfaces Based on Multi-modality Data

Fringe projection profilometry (FPP) performs poorly in the three-dimensional (3-D) topology reconstruction of high-reflective surfaces. The missing points are inevitable in the high-reflective regions in the point clouds reconstructed by the FPP. The existing research on the high-reflective problems cannot ensure the accuracy and efficiency of the reconstruction simultaneously. The photometric stereo (PS) technique is introduced into the system to provide additional normal information to solve the problem in this paper. The point clouds measured by the FPP sensor and the normal data measured by the PS sensor are combined into the multi-modality data. The fusion method is formulated as an optimization problem based on two constraints, i.e., the position constraint estimated by the point clouds and the direction constraint estimated by the normal data. Different values of the weight factor are specially designed in the optimization to deal with the constraint reliability difference in the high-reflective regions and the other regions. Not only the missing points in high-reflective regions are complementally reconstructed through the proposed fusion method, but also the accuracy of the reconstructed topology is optimized due to the introduction of the high-accuracy normal data. In addition, the proposed method only needs measuring once without repeated parameter adjustment or any human intervention, which improves the reconstruction efficiency. In the experiment of reconstructing a metal free-form surface, the deviation of the topology error between the proposed fusion method and a coordinate measuring machine is only 10 µm.
A History of Data Visualization &amp; Graphic Communication, by MichaelFriendly and HowardWainer, Harvard University Press, Cambridge MA: 308 pages including references and index. 2021. Reviewed by Malcolm James Ree, Our Lady of the Lake University

Personnel PsychologyEarly View BOOK REVIEW A History of Data Visualization & Graphic Communication, by Michael Friendly and Howard Wainer, Harvard University Press, Cambridge MA: 308 pages including references and index. 2021. Reviewed by Malcolm James Ree, Our Lady of the Lake University Malcolm James Ree, Corresponding Author Malcolm James Ree malcolmree@att.net orcid.org/0000-0003-3922-5269 Our Lady of the Lake University, San Antonio, Texas, USA Correspondence Malcolm James Ree, Our Lady of the Lake University, San Antonio, TX 78207, USA. Email: malcolmree@att.netSearch for more papers by this author Malcolm James Ree, Corresponding Author Malcolm James Ree malcolmree@att.net orcid.org/0000-0003-3922-5269 Our Lady of the Lake University, San Antonio, Texas, USA Correspondence Malcolm James Ree, Our Lady of the Lake University, San Antonio, TX 78207, USA. Email: malcolmree@att.netSearch for more papers by this author First published: 09 August 2022 https://doi.org/10.1111/peps.12540Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
Machine Learning Techniques to Diagnose and Treat Cancer Disease

Cancer is one of the most common diseases nowadays, and it is a very heterogeneous disease that consists of several different subtypes. According to data from the World Health Organization (WHO), this disease caused death to approximately 10 million people during 2020, and in the same period, 19.3 million new cases were identified. Breast cancer is the most common cancer diagnosed for women, and lung cancer is the most detected cancer in men. Artificial intelligence has many different applications, and specifically, machine learning techniques are used for detecting and treating cancer. The methods associated with machine learning are computer algorithms that are considering different types of logic, and therefore, those types can be classified into supervised, unsupervised, reinforcement learning, self-supervised learning, active learning, etc. The main purpose of this work is to review and evaluate the different techniques associated to machine learning used by medical professionals but also by researchers with the main objectives of detecting and treating cancer.
Digital Technology for Institutional Investors in the Environment of Industry 4.0

The chapter analyzes the main applications of digital technologies by institutional investors in the context of Industry 4.0 and developing amid global challenges (i.e., the COVID-19 pandemic). First of all, these are big data analysis; blockchain technologies; artificial intelligence; cybersecurity; robotization of financial, investment, and business processes; chatbots; mobile financial services; and cloud technologies. Among the digital technologies designed to assist investment, there are digital portfolio managers and robo-advisers, as well as specialist software for investment portfolios, personalized and aggregated financial and banking services, digital payments, risk assessment, RegTech, etc. The track record of institutional digitalization will help to streamline the operation of institutional investors and will enable them to determine more clearly the directions of developing and adjusting their economic policy in the context of Industry 4.0 and the nascent transition to Industry 5.0, thereby increasing their competitiveness and sustainability.