Reviewerly/exHarmony huggingface dataset:
ROS Based Obstacle Avoidance Motion Planning of UR5 Manipulator

The main work of the paper focuses on kinematic analysis modeling and obstacle avoidance motion planning based on a UR5 manipulator. It uses the software package MoveIt to generate various configuration files and startup files required for motion planning on Robot Operating System (ROS) platform. Through plug-ins of Python and Rviz, it accesses MoveIt and generates an obstacle environment. Then, it calls the Open Motion Planning Library to plan the motion of the UR5 manipulator, which is frequently used in both academic and industrial fields. The paper analyzes the widely used Rapidly-exploring Random Trees (RRT) algorithm and the improved RRT-connect algorithm by conducting a couple of motion planning experiments in an obstacle environment. It illustrates the experimental results of two different algorithms and compares their respective performances. Therefore, it sorts out the experiment process and the experimental results to provide ideas for the further improvement of motion planning of UR5 or other manipulators.
Understanding How to Support Remote Co-Design with a Conceptual Modular Shape-Changing Interface Toolkit

Remote collaboration is a challenge for physical product designers, and especially limits the mutual communication of the “thinking through prototyping” approach. Inspired by the NURBSforms and self-shape sensing technologies, we investigate how to support remote co-design with a conceptual modular shape-changing interface toolkit. By using stop-motion videos and low-fi non-electronic prototypes as probes, the concept was evaluated through on-line questionnaires and workshops. The result shows its potential especially in the exploration phase of remote co-design.
Which Dependency was Updated? Exploring Who Changes Dependencies in npm packages

Nowadays, software development increasingly depends on third-party library packages to reuse functionality and save the costs of building themselves. Since dependency is constantly evolving, developers struggle to update dependencies. In this work, we take the first exploration into the responsibility of updating a dependency. Analyzing 89,393 npm packages, we mine the repositories to understand who is the person responsible (i.e., dependency author) for the library update and whether or not the spread of responsibility of updating has an impact on what libraries will get updated. Our results show that 64.24% packages have only one dependency author who is responsible for the dependency. Furthermore, the number of dependency authors correlates with dependency changes, hinting that updating dependencies correlates with having more responsible developers. Lastly, we find that npm packages with just a single dependency author update different libraries compared to those with more dependency authors.
Detection of key texts from Tweets in port systems

Social media data is a rich source of information to analyze and detect potential problems from people. This work extracts Twitter&#x2019;s data related to two complex logistics companies to identify words, which can affect the strategy of ports. These words should enhance the business decision-making process to reduce social risks. The literature review suggested that TF-IDF and Latent Dirichlet Allocation can analyze the case studies. The results show that social networks can be linked to sustainable business aspects such as port planning and operations, information management, city, pandemic, ocean, culture, and people&#x2019;s beliefs.
Performance of Federated Learning with Local Differential Privacy: Federation or Individualƒ

Federated Learning (FL) [1] , a new form of distributed machine learning, has attracted much attention as a technique for privacy-preserving big data analysis. Because FL does not disclose raw data outside the device, it can improve data privacy compared to the method aggregating raw data in the cloud.
Information Theoretic-based Feature Selection (IFS) for Machine Learning

Mutual Information (MI) has been a popular choice for the selection criterion function of the feature selection techniques. However, only a handful of methods of formulating MI estimator for regression tasks exist, despite the existence of numerous methods of MI estimation for classification tasks in the literature. This work proposed a feature selection method based on mutual information for both classification and regression tasks. Unlike the existing MI estimator for regression tasks which are widely non-parametric, the proposed method provides a suitable way to estimate MI based on conventional kernel density estimation numerically. Two different experiments were conducted to evaluate the proposed IFS method. The first test is designed to assess the correctness of the formulated MI estimator by comparing it with various existing benchmarks. The second experiment evaluates the proposed method using natural domain data. Overall results obtained from these experiments show that the proposed method was successfully able to select relevant features.
An intelligent method of violation identification for power operation

Attitude analysis is widely used in various fields, but there is a lack of power operation behavior analysis. In order to realize the violation detection in the process of power operation, ROS and lidar information is used for auxiliary positioning, and OpenPose is used for skeleton extraction and detection. This paper proposes to detect the posture of power operators based on OpenPose, laying a foundation for the violation analysis of power operators. Finally, the algorithm is deployed on the edge processor Jetson Xavier NX. Experimental results show that the algorithm can perform pose detection analysis in the operation process of power operators and meet the requirements of subsequent violation analysis.
Massive High-dimensional Image Processing System Based on Big Data Technology

In order to overcome the difficulty of massive high-dimensional image processing, a massive high-dimensional image processing system based on big data technology is proposed in this paper. The system fully combines the big data technology, analyzes the differences between the current massive high-dimensional image processing work and the traditional image processing work, and discusses the difficulties of the current massive high-dimensional image processing work. Based on this, the system also includes parallel image processing model and low delay dynamic processing model, which greatly improves the accuracy and efficiency of static image processing and dynamic image processing. The results demonstrate that the system has strong feasibility, and can indeed process large-scale high-dimensional image data and save the time and cost of data processing.
Few-Shot Object Detection of Drones

Nowadays, object detection has experienced great progress, especially in small target detection. And the drone has the characteristics of small size, various shapes, and flexible flight. So how to detect the drone based on deep learning is a topic worthy of study. This paper aims to build sample data sets of the drone by means of the data augmentation and improve the detection accuracy of the small drone based on the You Only Look Once v5s (YOLOv5s). This model is adopted in the study since it can achieve a good trade-off between accuracy and speed. In the paper, transfer learning is used to solve the problem caused by small data set. 245 Images which are trained and tested in this study are from the Internet. The experimental results show this proposed method can achieve 96.8% mean average precision(mPA50) and accurately detect the moving small drone in the video.
Methods of acceleration for feature extractions in medical imaging using GPU processing

Nowadays, computers can scale the workload of domain experts in many medical areas and even outmatch the human interpretation of data by extracting the important information using specific analysis methods. This information can be presented in a perceivable way to the medical experts through visualization methods. The present work contribute to medical image analysis and processing with applications in pulmonary oncology. Our contribution consists in new approaches of accelerating the investigation of radiographic images which performs processing by applying feature extraction algorithms as Canny Edge Detection and Hough Transform for lines and circles. Our research proposes new ways to reduce the computing times of Canny Edge, Hough Transform with circle and lines algorithms, either through parallel implementations with CUDA and based on the particularities of combining CUDA shared memory and texture rebinding memory for the investigated images. Our result demonstrates that the proposed strategy is time efficient by reducing the computation times between 75&#x0025; and 99&#x0025;.
Blockchain and Austrian Economics

In this chapter, the basic historical origins, research methods and influence of the Austrian School of Economics on digital economics will be discussed in detail, especially as the inner logic of this influence is explored through the study of the blockchain economic paradigm.
Forensic Digital Data Tamper Detection Using Image Steganography and S-Des

In this current age, stakeholders exchange legal documents, as well as documents that are official, sensitive and confidential via digital channels[1]. To securely communicate information between stakeholders is not an easy task considering the intentional or unintentional changes and possible attacks that can occur during communication. This paper focuses on protecting and securing data by hiding the data using steganography techniques, after encrypting the data to avoid unauthorized changes or modification made by adversaries to the data through using the Simplified Data Encryption Technique. By leveraging on these two approaches, secret data security intensifies to two levels and a steganography image of high quality is attained. Cryptography converts plaintext into cipher text (unreadable text); whereas steganography is the technique of hiding secret messages in other messages. First encryption of data is done using the Simplified Data Encryption Standard (S-DES) algorithm after which the message encrypted is embedded in the cover image by means of the Least Significant Bit (LSB) approach.
Unique insights

Early Years EducatorVol. 23, No. 8 EditorialUnique insightsAngelica CelinskaAngelica CelinskaEditorSearch for more papers by this authorAngelica CelinskaPublished Online:9 Feb 2022https://doi.org/10.12968/eyed.2022.23.8.5AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetails 2 March 2022Volume 23Issue 8ISSN (print): 1465-931XISSN (online): 2052-4617 Metrics History Published online 9 February 2022 Published in print 2 March 2022 Information© MA Education LimitedPDF download
Multi-sensor Fusion-Based Object Detection Implemented on ROS

Mathur, Pranay Kumar, Ravish Jain, Rahul3D Perception of the environment in real-time is a critical aspect for object detection, obstacle avoidance, and classification in autonomous vehicles. This paper proposes a novel 3D object classifier that can exploit data from a LIDAR, a RADAR, and a monocular camera image after orthogonal projection. To achieve this, a learnable architecture is designed end-to-end, which fuses the detection results from multiple sensor modalities initially and exploits continuous convolution subsequently to achieve the desired levels of accuracy. An adaptive algorithm for prediction in real-time is used to automatically increase weightage to prediction results from a particular sensor modality which aids in keeping accuracy invariant to scene changes. To prevent the bias, we are using a training strategy which provides attention to the specific type of sensor. This strategy is inspired by dropout. The entire algorithm has been implemented on the Robot Operating System to make it easier to deploy and transfer. We have experimentally evaluated our method on the NuScenes dataset.
Development of The Educational Tool For Optimized Algorithm Model Using MATLAB GUI

Optimization algorithms are mathematical functions that are widely used to solve objective function problems through maximization or minimization. Typically, students at the beginner level face difficulties in understanding the complex mathematical solutions used in optimization algorithms. This paper presents an educational graphical user interface (GUI) for simulation of various topology optimization algorithms that users can use to explore the behavior of local search (LS), tabu search (TS) and particle swarm optimization (PSO) algorithms. The GUI is created using the MATLAB GUIDE tool, which acts as a front-end interface that can be used for teaching as well as learning. Benchmark functions such as the De Jong first function (DF), the Griewank function (GF), the Himmelblau function (HF) and the Periodic function (PF) were used to validate the investigated optimization algorithms. The developed GUI has features where the user can enter functions, define boundary limits, specify solver parameters and select the type of output display. The results of the instance HF function shown using the GUI optimization tool are in good agreement with the benchmark function.
JAVRIS: Joint Artificial Visual Prediction and Control for Remote-(Robot) Interaction Systems

In recent years, robots are taking on more and more tasks throughout many different application domains. Many of those robots work fully automatically and without human intervention. However, more complex tasks still require to be done by a human in remote control. One example would be robot remote surgery. Remote controlling a robot requires ultra- low latency on the communication, to allow fast and precise movements. To make this possible with today’s systems, the human operator must be in the same room. Enabling the user to operate from anywhere around the world, would bring a huge benefit as travels for highly trained experts can be minimized.In order to allow wider distances between the user and robot, we propose an AI-based prediction. Predicting the robots behavior can generate negative latency, which improves the precision of control. In large-scale communication networks, the main part of experienced latency comes from the propagation delay and is therefore not avoidable. Predicting upcoming data before it actually arrives, can create a zero-latency experience for the human operator. To prove this idea in the context of remote robot control, we propose JAVRIS. Using the CMIYC demonstrator, JAVRIS improved the score of an inexperienced user by over 1000%.
The Cleisthenes Protocol: A Fair Governance-Based Democratic Consensus Algorithm

Due to the high interest in the past few years, blockchain technology benefited from a fast increase in adoption rate as well as fast evolution. One of the crucial points in the blockchain space, the consensus mechanism, generally still suffers from lack of development, thus decreasing the adoption of the technology in big scale projects and daily use. Older-established consensus algorithms, such as Proof of Work (PoW) and Proof of Stake (PoS), suffer from various flaws, ranging from the high costs of maintaining a node in the network and generating new blocks, to the risk of being affected by a series of attacks, as well potential of network monopoly by various entities. This paper proposes a model of a democratically-governed consensus algorithm that is focused on a fair distribution of assets, with a fast and secure way of validating and distributing the blocks, while being impervious to a sizeable range of attacks.
Sentiment Analysis from Stock Market News in Romanian using Chaos Game Representation

A recently proposed methodology for authorship attribution is adapted in the current work for sentiment analysis. Furthermore, it is applied here for a non-English language, i.e. for Romanian. The procedure works at the character level, hence it does not depend on the language, although it is designed only for the languages that use the Latin alphabet. The data set used is taken from financial market news and it contains paragraphs that refer to two particular companies. In order to establish the ground truth for the sentiment scores, the text is translated into English and the sentiment analysis tool VADER is further used. The aim of the methodology is to build a regression model that fits the initial paragraphs with text in Romanian to the scores established by VADER and the results are encouraging.
The Mastery of the Evolution of Human Competence for Optimal Planning of Human Resource Allocation: Case Study

The mastery of human competence is a real lever for optimizing industrial performance. In this article, we aimed to model the development of competences of operators for a company with a large capacity oriented operators. This modeling allowed us to solve several problems related to the estimation of the evolution of the competence of production operators in the planning phase of allocation of resources.
A Proposed Framework for Tracking and Identifying Users of Web Surfing Sessions

The internet is a very important tool used in our daily lives. Due to its benefit of easy transfer of information, most people have made the web a virtual place where they store various information and carryout daily commercial and personal activities. The volume of information deposited on the web, aids easy tracking of individuals because of the footprints they leave behind during various web sessions. Tracking users can be very helpful to both government and private organizations in identifying persons, their interests, and other traits they possess, which helps them target interventions accordingly. This paper discusses several mechanisms and web objects such as HTTP cookies, proxy servers, scripting etc. that can be used to track and uniquely identify users surfing the web. Related works to this paper are discussed and finally, a mechanism which combines the use of various web objects and protocols was proposed to accurately track and identify unique users during web surfing sessions. This proposed mechanism consists of three main steps; data retrieval, data analysis to identify the user and devices the user uses when surfing the web and finally summarization of the derived user information for clear and easy understanding
Improved PolarMask with Attention for Instance Segmentation

As an important field of computer vision, instance segmentation is mainly divided into one-stage segmentation methods and two-stage segmentation methods. PolarMask is a one-stage instance segmentation method, which has the advantages of simple structure and fast speed. However, its accuracy is not good enough. To optimize the accuracy of PolarMask, we firstly propose attention-based polar Intersection over Union (IoU) loss based on PolarMask, and then we replace Feature Pyramid Network (FPN) structure and IoU loss with FPN-involution, PAFPN, and CIoU loss, respectively. Through ablation study, it is proved the effect of the proposed attention-based Polar IoU loss and verified the effect of the replacement module in the model. Further, through combination experiments, we find the most efficient combination method on COCO minival dataset. Finally, we compare our method with others, and an Improved PolarMask method is obtained.
DetachMix: Color Channels Fusion for Training Object Detection Neural Networks

Pre-training strategies greatly improve various image classification model&#x0027;s accuracy. They have proved to be effective for guiding the model to attend on objects in complex samples, thereby letting the network perform better without adding extra inference cost. However, the training strategies and pipelines dramatically vary among different models, and they only change the complexity of the samples, but do not really combine the targets with the training process of the network. We therefore propose the DetachMix augmentation strategy, and it is divided into two steps: the first step is to segment the picture according to the color channels and train them separately on the network.The second step is to merge the first convolutional layer of the obtained weight files to replace the one obtained during normal training. The network model gained through DetachMix can not only combine images with network training, but also can be used in combination with other methods of processing samples (such as cutmix [1], Mixup [2], etc.). The method we proposed improves network performance by improving the network&#x0027;s ability to extract original semantic information from picture, and it is applicable to all models with the same reasoning time. We conducted a test on the collected Person Head State dataset and compared our method with the latest data processing methods. DetachMix can improve up to at most 8.3&#x0025; precision compared to state-of-the-art baselines,reached 91.43 &#x0025;.
Application of Data Augmentation of Rare Words Based on “Font-CycleGan” in Character Recognition

Given the low efficiency of massive image data processing in actual business departments, image character recognition OCR (optical character recognition) based on deep learning neural networks is not high. Based on the above background, a text image data enhancement method based on Font-CycleGan is proposed to improve text recognition accuracy. Our goal is to generate the data of specific fonts for the original hidden word text samples through the font library and then enhance the data of the hidden word text through CycleGan to enrich the database of the hidden word text to improve the performance of the classifier and the accuracy of text recognition. We evaluate and compare the impact of traditional character recognition and font-cyclgan based data to enhance character recognition accuracy. The results show that the improvement effect of this method is more significant.
Robust Hierarchical Data Association for Multiple Object Tracking Under Abrupt Appearance Changes

In automated traffic surveillance, the correct interpretation of a perceived scene from a stream of video data combined with CNN-based object detection is an emerging topic. To understand and predict the scene, detected vehicles must be re-identified in a stream of consecutive video frames, and their trajectories need to be reconstructed. Major challenges in video-based Multiple Object Tracking are abrupt appearance changes of an object, which e.g. are caused by perspective changes and mutual occlusion of objects. This contribution presents a hierarchical data association approach with error-compensating association rounds, which is combined with a model-based trajectory tracking approach. The benefit of the proposed approach is evaluated using Higher Order Tracking Accuracy.