Reviewerly/exHarmony huggingface dataset:
Autonomous Racing Vehicle

F1TENTH is a competition that combines autonomous driving and robotics. It encourages students to build software for 1/10th scale autonomous vehicles. This paper proposes a software stack for F1TENTH competition. The 5 subsystems in the software stack decide how the car will behave. The perception system will collect all the information regarding the surroundings of the car. The mapping system will create a global map based on the data from perception. The localization system will localize the car and provide the position of the car in the global map. The planning and controls subsystems will decide how the car will move around in the map. Camera was used as the main sensor for recording the environment. RTAB-Map was used for mapping and localization of the map. PID and pure pursuit was used for the controls of the car. This paper also discusses how the hardware was modified for testing of the said software stack.
Factors Affecting the Adoption of Augmented Reality (Ar) Technology as a Marketing Strategy in the Fashion Retailing Sector of Sri Lanka

The purpose of this paper is to investigate the factors affecting the adoption of Augmented Reality (AR) technology as a marketing strategy in the fashion retailing sector of Sri Lanka. The study developed its conceptual framework based on the Technology-Organization-Environment (TOE) theory where a structured online questionnaire was distributed among 179 fashion retailors that conduct businesses online on social media platforms (Facebook). The multiple regression technique was employed to analyze data with the use of SPSS software. The study findings revealed a significant relationship between the perceived benefits, technology complexity, IT infrastructure capability, financial readiness and AR adoption whereas a non-significant relationship was observed between AR adoption of competitors and government policies with AR technology adoption in the fashion retailing sector of Sri Lanka. The study results are imperative for the marketers to understand the factors affecting AR technology adoption from a company perspective to develop strategies and introduce mobile applications incorporating AR technology. Further, this is the first kind of study to examine the factors affecting AR adoption in the Sri Lankan context from the company perspective. Moreover, studies could incorporate different theories to extend the framework where the study can be extended to other sectors such as tourism, learning, supermarkets etc.
Model Free Safe Control for Reinforcement Learning in a Clustered Dynamic Environment

While reinforcement learning (RL) has been wildly used in continuous control tasks with impressive performance, there are two main challenges in RL development: continuously satisfying the safety constraints in a clustered dynamic environment and lacking the explicit analytical models of dynamic systems for typical safeguard algorithms in RL training. This paper proposed a model-free safe control strategy to safeguard the RL agent in a clustered dynamic environment. By extending and applying the adaptive momentum boundary approximating (AdamBA) method to monitor and modify the RL nominal controls in a clustered dynamic environment, experimental results have shown the better safeguard performance of the proposed algorithm than other safe RL methods. The proposed algorithm could be easily extended to other RL algorithm with black-box implicit analytical model of dynamic systems.
New Age Technology and Geospatial Data Adoption in Rural Areas to Solve Inefficiencies in Implementation of Welfare Schemes

Technology has developed rapidly in the past few decades, yet there is a lacuna in using new technologies for rural development. In this paper, the Author wants to review the existing methods of implementation of Government Welfare Schemes by taking Udigala Gram Panchayat of Chamarajanagar District in Karnataka as the study area and model a smart Action plan for the area using the Web and Geospatial Technologies. The geographical Information System can play an important role in helping the Village Administration in the implementation of Poverty Alleviation schemes and other schemes such as the Mahatma Gandhi National Rural Employment Guarantee Act. Spatial Analysis can help a in solving few of the deeply entrenched problems in administrative setups. The Paper will also look at how the integration of GIS and Web Technologies can help in deciding the building of the required infrastructure required for the development of the Area and it will also look at proposing a way to solve the problem of identification of beneficiaries for target implementation of schemes. The paper will also suggest issues and problems regarding the usage of Geospatial data. The higher administration generally is not aware of the local resources and infrastructure needed in the village. A pool of projects will be created for the Village under study taking into aspect different data.
A Fall Detection Algorithm Based on Improved MobileNetV2

With the aggravation of the current social aging problem, the health problems of the elderly living alone have become the focus of attention in the medical and health field. Due to a large number of network parameters, the current deep learning-based fall detection algorithm takes a long time to train. To make the algorithm meet the demands of both lightweight and high accuracy, a new method for fall detection is proposed in this article. We improve the block of MobileNetV2 by means of adding channel attention and spatial attention mechanisms before channel convolution and spatial convolution in the block, respectively. The above operation can improve the performance of capturing essential information without additional network computation, which enhances the effectiveness of network feature extraction and detection performance. The computer simulation shows that the detection accuracy of the proposed algorithm on the public datasets Le2i and UR is 98.8% and 99.7%, respectively, which can accurately detect the falling behavior in the image frame column.
Factory Worker Safety Mask Monitoring System Based on Deep Learning and AI Edge Computing

When factory workers work on a daily basis, safety accidents may occur due to goggles and protective masks not being worn or not being worn in a standardized manner. Such accidents put workers’ lives and health at risk, damage factory equipment, and can cause serious fires and injuries. The system uses Artificial Intelligence (AI) and AI edge computing to monitor workers’ protective equipment, using deep learning to train a suitable model, which is remotely transmitted to the AI edge computing module Jetson Nano via Secure Shell (SSH) protocol, and finally the AI edge computing module determines the workers’ protective equipment wearing situation. The AI edge computing module provides some practical value for the prevention of factory safety accidents, and also provides a reliable basis for the establishment of smart factories.
Pandemic Genres: Processing the COVID-19 Pandemic through Electronic Literature

This essay surveys works of electronic literature and digital art initiated in the earliest months of the pandemic that are reflective the specific conditions and anxieties of the period. Here, we offer critical readings of these works to provide a better understanding of how electronic literature and digital art were used to process the experience and communicate the experience of the COVID-19 pandemic. Through an analysis of 13 works by 18 authors, certain traits and commonalities are identified as characteristic of a period-specific genre of COVID E-Lit. These include: an impulse towards the post-digital with crossovers both to analog artistic practice and forms such as net art more common to the early web; a focus during the periods of lockdown on domestic, local, and interior environments; digital takes on a chronicle mode of storytelling familiar from prior pandemic periods; meditation on the loss and substitution of shared public space; use of text generation to represent repetitive and interminable experiences of the pandemic; consideration of the virus itself as a language and on language as a manifestation of power and control; the influence of ubiquitous visualizations and statistical representations of the pandemic; and a desire to wrestle with the implications of the massive cultural shift to digital platforms that took place during the pandemic.
A boosted co‐training method for class‐imbalanced learning

Abstract Class imbalance learning (CIL) has become one of the most challenging research topics. In this article, we propose a Boosted co‐training method to modify the class distribution so that traditional classifiers can be readily adapted to imbalanced datasets. This article is among the first to utilize pseudo‐labelled data of co‐training to enlarge the training set of minority classes. Compared with existing oversampling methods which generate minority samples based on labelled data, the proposed method has the ability to learn from unlabelled data and then decrease the risk of overfitting. Furthermore, we propose a boosting‐style technique which implicitly modifies the class distribution and combines it with co‐training to alleviate the bias towards majority classes. Finally, we collect two series of classifiers generated during Boosted co‐training to build an ensemble for the classification. It further improves the CIL performance by leveraging the strength of ensemble learning. By taking advantage of the diversity of co‐training, we also contribute a new approach to generating base classifiers for ensemble learning. The proposed method is compared with eight state‐of‐the‐art CIL methods on a variety of benchmark datasets. Measured by G‐Mean, F‐Measure, and AUC, Boosted co‐training achieves the best performances and average ranks on 18 benchmark datasets. The experimental results demonstrate the significant superiority of Boosted co‐training over other CIL methods.
A Short History of Xerox' Darwin Toolset for Programming Real-Time Machine Control Systems

In the 1970s, the Xerox Corporation located its development and manufacturing facilities in and around Rochester, New York [East Coast] and El Segundo, California [West Coast]. These had teams dedicated to providing programming tools for development engineers, who were located primarily on the East Coast, to use in creating control code for new products. This article delves into the history of those tools, primarily into the original, ground-breaking, high-level language called Shadow.
Sharpness-Aware Minimization Leads to Better Robustness in Meta-learning

Transforming few-shot learning into meta-learning is an important way to narrow the gap between human ability and machine learning. In this paper, we study the adversarial robustness of meta-learning model and propose Defending R2D2 algorithm (DeR2D2) to resist attacks. We pay more attention to the two problems of adversarial meta-learning: the high training cost and the significant decrease of classification accuracy on clean samples. First, we demonstrate that the introduction of adversarial samples in R2D2 training can improve its adversarial robustness. Second, we choose Randomized Fast Gradient Sign Method (R+FGSM) instead of Projected Gradient Descent (PGD) as the adversarial training method, which significantly reduces the training cost. Finally, due to the Sharpness-Aware Minimization (SAM), our method further reduces adversarial training time and significantly improves the classification accuracy on clean samples. In addition, we verify that in most cases, DeR2D2 also has a strong ability to defend against attacks.
Multi-Scale Aggregation Stereo Matching Network Based on Dense Grouping Atrous Convolution

The key to image depth estimation is to accurately find corresponding points between the left and right images. A binocular camera can directly estimate the depth of the left and right image range, which completely avoids the requirement of target recognition accuracy in a monocular depth estimation. However, it is difficult for binocular stereo matching to accurately segment objects and find matching points in the ill-posed areas (weak texture, deformation, object edge, etc.) of the left and right images. In the semantic segmentation task, atrous convolution is used to solve the contradiction between the receptive field and the segmentation accuracy. This research focused on balancing the impact of holes on the segmentation task. In addition, in order to solve the issue where matching points in the ill-posed regions of left and right images are affected by noise, we used a 3D convolution to aggregate the cost volume to obtain better accuracy. However, the 3D convolution method is prone to mismatching in ill-posed areas of the image. To tackle the problems above, we proposed a dense grouping atrous convolution spatial pyramid pooling (DenseGASPP) method. The feature of the DenseGASPP method is that there is a dense connection between the group atrous convolutions to fully integrate feature information. This method can expand the receptive field and balance the effect of holes on the segmentation task. Moreover, we introduced multi-scale cost aggregation into our method, which uses the repeated exchange of information between cost volumes of different scales to obtain rich contextual information and reduce the mismatching of the network. To evaluate the performance of our method, we conducted several groups of typical algorithm experiments on the scene flow and KITTI 2015 standard datasets. From the results, our model achieves better performance, reducing the EPE from 1.09 to 0.67, which improves the mismatching ability of binocular depth estimation algorithm in ill-posed regions.
Trajectory Planning in Robot Joint Space Based on Improved Quantum Particle Swarm Optimization Algorithm

Trajectory planning is a crucial step in controlling robot motion. The efficiency and accuracy of trajectory planning directly impact the real-time control and accuracy of robot motion. The robot’s trajectory is mapped to the joint space, and a mathematical model of trajectory planning is established to meet physical constraints during motion and avoid joint coupling problems. To enhance convergence speed and avoid local optima, an improved quantum particle swarm optimization algorithm is proposed and applied to solve the mathematical model of robot trajectory planning. The trajectory planning in robot joint space is then tested based on the improved quantum particle swarm optimization algorithm. The results demonstrate that this method can replace the traditional trajectory planning algorithms and offers higher accuracy and efficiency.
Dynamic displacement measurement of hoisting rope in lateral and longitudinal direction by improving Lucas–Kanade algorithm

The hoisting rope has complex dynamic characteristics under external excitation during operation, characterized by large longitudinal movement and minor lateral oscillation. However, there are no appropriate approaches to acquire the dynamic displacement of the rope currently. In this study, a fast vision-based measurement framework, including initial value forecast (IVF) and target tracking based on Lucas–Kanade (LK) algorithm, called IVF-LK, is proposed to obtain the lateral-longitudinal displacement of the rope. Firstly, an IVF method is proposed to obtain the initial value of iteration, overcoming the problem of non-convergence of LK algorithm under large displacement. Secondly, a parameter update strategy makes the convergence speed of LK significantly improved. Then, the performance of IVF-LK algorithm was tested in the experimental bench, which reveal its superior capacity in rope-structure dynamic measurement both in efficiency and precision. Finally, a field experiment was implemented to analyze the dynamic displacement of the hoisting rope.
Secure IoT-Based Physical Training Network

Wearables are used by sports professionals to guide their pupils. They drop their suggestions which are later accessed by the students. The previous investigation was about the relationships among monitoring proficiency. The crucial elements considered are sports exercises and physical workouts. The approach manages important details from IoT-based wearable widgets. The suggested approach has gained tremendous biological action monitoring corresponding to the traditional techniques. It can assist researchers and enhance the associated characteristics of capabilities with heightened precision percentages. The recommendation suggests a security procedure to ensure the protection of the data stored and displayed. The suggestion makes double authentication. The first authentication gives authorization to his data. The second authentication gives the trainer to give his suggestion and the student to give his feedback on the suggestion. The first authorization enhances security by 8.24% and the second authorization improves protection by 9.91% compared to the previous contribution.
Breast Cancer Classification Using Deep Learning

Breast cancer (breast carcinoma) is the most common type of cancer in women, and it is the most dangerous cancer, together with lung cancer. Early detection of this type of cancer is crucial to reduce the mortality rate since breast cancer is often treatable when it is diagnosed early. Cancer starts from a benign state, and without appropriate treatment at the early stages, it becomes malignant. A common way to detect breast cancer is histological biopsy evaluation. Deep learning, as one of the currently most popular computer science research trends, improves neural networks, which have more and deeper layers allowing higher abstraction levels and more accurate data analysis. AI and machine learning have gained a lot of popularity and acceptance in recent years. Here are the top path breaking applications of deep learning in healthcare. Although deep convolutional neural networks as a deep learning algorithm has recently achieved promising results in data analysis, the authors propose a new deep CNN architecture for the classification of breast tumours in ultrasound images.
Cyber-Physical Systems Based on Marble Marcher Using Artificial Intelligence With IoT

The physical object or robotic arm movement in cyber physical system controlled through artificial intelligence algorithms impact on smart devices through marble marcher game is proposed in this work. The cyber physical object movement is framed from the fractal surface which can produce any difficult terrain producing more input for intelligent algorithms. Four different surfaces are framed from the parameters such as rows, columns, foundation functions and peak values. The surface governing equations split the surface as side of cubes controlling the six different geometric planes for extracting more data. These data used as input for taking decision done by intelligent algorithm from core input from the pixel-by-pixel analysis of the smart device input. The coordinate transformation produces more control variable for different factors and accuracy parameters. The simulation model developed in MATLAB language and the results shows that the proposed system enhances the data control and transfer with minimum hardware requirement.
A multi-agent reinforcement learning algorithm with the action preference selection strategy for massive target cooperative search mission planning

Target search is widely applied in military reconnaissance, geological exploration and personnel search and rescue. Most target search algorithms perform well in single target search but are inefficient or even ineffective in multi-target search. To solve the multi-target search problem in an uncertain environment, this paper constructs a multi-agent massive target cooperative search mission planning model and proposes an improved reinforcement learning algorithm using the action preference selection strategy. Based on the Reinforce algorithm, this algorithm solves the problem of invalid searches within the stochastic strategy by changing the preferred action selection method. The proposed method improves the efficiency of multiple agents in the search for targets without collision using a cooperative mechanism and reward rules based on the odor effect. Simulation experiments are conducted in three aspects to verify the effectiveness and robustness of the improved algorithm and compare it with other reinforcement learning algorithms in the field of multi-agent learning. The results demonstrate that the improved algorithm has obvious advantages in terms of mission success rate, target search rate and average search time, and the movement trajectory of multiple agents is more concise.
Questioning the ability of feature-based explanations to empower non-experts in robo-advised financial decision-making

Robo-advisors are democratizing access to life-insurance by enabling fully online underwriting. In Europe, financial legislation requires that the reasons for recommending a life insurance plan be explained according to the characteristics of the client, in order to empower the client to make a “fully informed decision”. In this study conducted in France, we seek to understand whether legal requirements for feature-based explanations actually help users in their decision-making. We conduct a qualitative study to characterize the explainability needs formulated by non-expert users and by regulators expert in customer protection. We then run a large-scale quantitative study using Robex, a simplified robo-advisor built using ecological interface design that delivers recommendations with explanations in different hybrid textual and visual formats: either “dialogic”—more textual—or “graphical”—more visual. We find that providing feature-based explanations does not improve appropriate reliance or understanding compared to not providing any explanation. In addition, dialogic explanations increase users’ trust in the recommendations of the robo-advisor, sometimes to the users’ detriment. This real-world scenario illustrates how XAI can address information asymmetry in complex areas such as finance. This work has implications for other critical, AI-based recommender systems, where the General Data Protection Regulation (GDPR) may require similar provisions for feature-based explanations.
Performance of Event Operation Division Employees Towards the Operational Implementation of Phitagoras Global Duta Online Events During the New Normal Period

The New Normal scenario is carried out by considering regional preparedness and the results of the Epidemiology study in the related area.However, the current condition remains a priority for the Ministry of Tourism and Creative Economy (Kemenparekraf) to be able to organize and integrate online and offline events during the New Normal period.In addition to compiling protocols for implementing post-pandemic event activities, the Ministry of Tourism and Creative Economy collaborates with industry players to encourage increased industrial capacity, infrastructure, and technology net new habits exist initiatives.For this reason, new habits exist in the current New Normal era, especially in the Online Event Industry.PT.Phitagoras Global Duta is one company that provides Certification Training services.The In the normal, average activities are held online or virtual.The purpose of this research is to find out how the performance of the EO division in the operation of organizing Online Events during the New Normal and to find out how the EO Division is in overcoming the problems that exist in Online Events during the New Normal, based on employee performance indicators, namely Quantity, Quality, Timeliness.and Effectiveness.This study uses a qualitative descriptive method and sampling chase the queueing tissuing purposive sampling by interviewing four people as resource persons.Based on the results of research and views with related sources, the problems that exist in the EO Division's Employee Performance in organizing online events at PT. Phitagoras Global Duat is from the timeliness and quality of the EO team that has not been fulfilled.