Reviewerly/exHarmony huggingface dataset:
Decoupling Classification and Localization for Single Stage 3D Vehicle Detection from Point Clouds

The single shared head detector is mostly used for both classification and regression tasks in object detection networks. However, the two tasks focus on different features, and the features extracted from a single head may inhibit each other’s expression. In 2D detection, a decoupled representation with multi-head structure works better than the shared one for both tasks in the R-CNN [1] blocks. Since the RCNN structure is not available for single-stage detectors, the decoupling must be implemented in a slightly different way. Therefore, we propose a double-head structure based on single-stage point cloud object detection, with two decoupled heads to describe the classification and regression tasks in the high-level feature map, respectively. One of the heads is used to extract classification information, while the other one is used for regression information. In particular, the regression information also contributes to the classification task, and we denote the category result by the union of the classification values predicted by both the regression head and the classification head. Our approach can improve the AP of the baseline in the car detection task on the kitti dataset.
Learn to Path: Using neural networks to predict Dubins path characteristics for aerial vehicles in wind

For asymptotically optimal sampling-based path planners such as RRT*, path quality improves as the number of samples added to the motion tree increases. However, each additional sample requires a nearest-neighbor search. Calculating state transition costs can be particularly difficult in cases with complex dynamics such as aerial vehicles in non-isotropic cost fields like wind. Computationally costly nearest neighbor searches increase the time required to add new samples to the search tree, thereby reducing the likelihood of finding low-cost paths in a given computational time. In this paper, we propose the use of a lightweight neural network to approximate nearest neighbor cost calculations. The network approach uses a low-dimensional encoding of the cost space along with a start and goal query pair and returns an estimate of the path cost that can be used for nearest neighbor and path validity estimation. We demonstrate our method for a Dubins airplane model in a 3D wind field and show that the network method achieves equivalent path lengths as an existing iterative solver 32% faster and, when given the same search time, up to 10.8% shorter.
Research on Recognition Algorithm of Scattered Rivets Based on Feature in Robot Automatic Nail Feeding System

Robot vision-based automatic nail feeding system is a research hotspot in the field of automatic drilling and riveting for aircraft assembly. As for traditional robot vision-based automatic nail feeding system, it is difficult to recognize scattered rivets. This paper proposes the feature-based algorithm for scattered rivets recognition for monocular vision-based nail feeding system. Firstly, the image of scattered rivets is pre-processed and the division method of maximum entropy threshold is employed to divide the reflective band of rivet. Then, through morphological filtering and other operations to get the size of the reflective band. Finally, compare the size of the reflective tape with the size characteristics of the rivet itself, and finally achieve the purpose of rivet identification.in this paper has a strong robustness for the blocking state and brightness variation of scattered rivets. Besides, it has scale invariance and rotational invariance. This algorithm has a quick speed and high recognition rate and accuracy, and meets the requirement for online rivet recognition of robot vision-based automatic nail feeding system.
Text Summarization for Information of Famous Indian Historical Monuments

A large amount of information is available on the Internet, however sometimes it is required to obtain small amount of information in shorter period of time. For this, systems make use of text summarization which is an application of Natural Language Processing. Text summarization is the process of shortening of data computationally, to generate a summary that will contain relevant information and salient features from original text. In this paper, a text summarization technique is proposed to acquire information about famous Indian historical monuments. Using the proposed model, users are able to get information about Indian historical monuments in the form of a summary. The proposed system works as a query-based text summarizer which uses latent semantic analysis and vector space model in order to search answers to the basic queries about the monument. The data which is to be summarized is generated by the algorithm itself using web scraping. As a performance parameter ROUGE score is used. The proposed approach is tested and validated for various Indian historical monuments and satisfactory results are obtained.
A Comparative Study of Various Apriori and FP-Growth Tree-Based Incremental Mining Methods

Association rule mining is a data mining task to find the frequent patterns in the stored databases. But with the change in time, the databases are getting updated. Each time either some new data instances are added and some obsolete data instances are deleted. As a result, traditional association mining is not much used in real-world applications. So, mining these changing databases is a big challenge in front of practitioners. This led to the emerging of a new data mining task, i.e., incremental mining. The incremental frequent pattern mining is a data mining task in which new rules are generated only on the updated database and previously generated mining rules to get the desired information. In this paper, we have reviewed various incremental mining methods based on the two major traditional association rule mining approaches, i.e., Apriori and FP-growth tree. A study-based comparison of all methods is also represented with the major key findings. At last, various challenges and future scope in incremental frequent mining are discussed.
Conceptualization of a Dialectic Between an Internet of Things System and Cultural Heritage

Through the development of an Internet of Things system, it will be possible to recognize how technology has the ability to expand objects, moments, knowledge, stories, and places. All these components are part of cultural heritage, and its amplification is what it is intended with the development of an IoT system, also using georeferencing and augmented reality technologies. The article establishes the relationship that it is possible to create between the cultural heritage of a village located in the interior center of Portugal, named Amiais, and the technological development of an IoT system, which then promotes cultural heritage and contributes to the evolution of the territory as smart. For this purpose, ethnographic research and the survey of the local cultural heritage are used and, subsequently, the way in which technology-based systems impact the territory and people. It was of great relevance the contact of the project team with Amiais’ inhabitants, as well as with tourists and stakeholders. But it was also determinant the contact with the territory and with the elements of cultural heritage.
Conflicts in Metacommunication Messages in Alternating User and Designer Roles

The recent growth in the use of customizable software and systems by users with little or no specific knowledge in the field of computing has encouraged collaboration between users and user adherence to a role previously assigned to the designer. The alternation of the subject in the roles of user and designer may cause conflicts in the interpretation of metacommunication messages. This research sought to analyze these conflicts during the interaction on a video sharing platform, YouTube, using the principles of Hegelian Dialectic. For this, a case study was carried out with the methods of Semiotic Engineering, the Semiotic Inspection Method (SIM), and the Communicability Evaluation Method (CEM), followed by a dialectical analysis of the results. The results obtained opens reflections on possible conflicts of interpretation in the metacommunication messages of the interface when the user alternates between the roles of designer and user at the time of interaction and, with this, facilitate the elaboration and evaluation of interfaces more adequate to the requirements determined in the pre-production.
Imaging Spectroscopy Applied to Mineral Mapping Over Large Areas: Impact of Residual Atmospheric Artefacts in Reflectance Spectra on Mineral Identification and Mapping

Around the world, imaging spectroscopy (hyperspectral imaging) has been successfully applied to mapping surface minerals over small areas. In this paper, we examine one challenge to reliably mapping surface mineral composition over large areas: the impacts of atmospheric artefacts in reflectance spectra on mineral identification. To examine these impacts, we use a set of Airborne Visible and Infrared Imaging Spectrometer-Classic (AVIRISc) data collected over ~30,000 sq. km of southern California (SoCal) in 2018. The AVIRISc2018 SoCal reflectance data showed residual atmospheric contamination in wavelength regions at the edges of strong water vapor absorption features: 1.44-1.5 µm, 1.75-1.8 µm, and 2.35-2.5 µm. These three regions of atmospheric residuals affect identification and discrimination of mineral absorption features, especially when mineral abundances are low and spectral features are weak. Of these three regions, improving atmospheric correction of imaging spectrometer data in the 2.35-2.5 µm region will result in the most enhancement in mineral identification, discrimination, and mapping. An additional region near 2.2 µm affects phyllosilicate mineral identification. The cause of the distorted reflectance in this region is still being investigated.
Sentiment Analysis using Transformers and Machine Learning Models

Socia1 media is an online network environment where internet users express their positive or negative opinions about any subject, business, product, or situation. It is an environment where users can view and share relevant content, articles, news, thoughts, daily events and all kinds of visual and audio materials thanks to fast access. Social media sentiment analysis is a popular field for various industries and academic studies. These studies, also known as idea mining, are carried out to classify the general feeling in a text. Studies have been conducted on this subject with machine learning models in big data and natural language processing. In our research, sentiment analysis will be shown on text data sets with machine learning models and transformers.
A Shallow–Deep Feature Fusion Method for Pedestrian Detection

In this paper, a shallow–deep feature fusion (SDFF) method is developed for pedestrian detection. Firstly, we propose a shallow feature-based method under the ACF framework of pedestrian detection. More precisely, improved Haar-like templates with Local FDA learning are used to filter the channel maps of ACF such that these Haar-like features are able to improve the discriminative power and therefore enhance the detection performance. The proposed shallow feature is also referred to as weighted subset-haar-like feature. It is efficient in pedestrian detection with a high recall rate and precise localization. Secondly, the proposed shallow feature-based detection method operates as a region proposal. A classifier equipped with ResNet is then used to refine the region proposals to judge whether each region contains a pedestrian or not. The extensive experiments evaluated on INRIA, Caltech, and TUD-Brussel datasets show that SDFF is an effective and efficient method for pedestrian detection.
An infrared / Radar fusion tracking algorithm based on adaptive fault tolerant filtering

In this paper, based on the fusion tracking of infrared and radar, a federated kalman filtering algorithm based on adaptive fault tolerance is designed. According to the observation equation of infrared and radar, a distributed fusion federated kalman filtering structure is established by using three infrared sensors and three radar sensors. The sub nodes filter their detection information respectively, and the estimation results are obtained. After that, the sub nodes filter results are sent into their part of fault-tolerant judgment respectively, then we can get the weight information of each sub node, thus it can determine the degree of dependence of each node, and the filtering results of all sub nodes are fused into calculation by their weight. The final simulation shows that the algorithm can isolate the interference in the multi-mode fusion tracking process effectively , and get a stable state estimation output.
Benchmarking Apache Beam for IoT Applications

The pervasiveness of computational and communication devices, coupled with innovative Internet of Things (IoT) application scenarios, led to a massive increase in the number of available data sources and the volume of data produced per unit of time. This contributed to the emergence of many open-source streaming data processing systems of different characteristics and diverging performances in specific usage scenarios. The heterogeneity of platforms, programming languages, and models used in such systems resulted in the prohibitively complex effort required to quickly and efficiently test their suitability for specific use cases. Apache Beam framework aims to introduce the unifying programming model for data processing systems, tackling the heterogeneity problem and allowing for fast testing of performance in specific usage scenarios and migration between different platforms. In order to test the maturity of the Apache Beam framework and its performance in processing data from the IoT domain, we constructed a benchmarking environment employing data sets with significant spatio-temporal properties along with a representative set of streaming operations for such data. We used Apache Kafka as data source and result collection within different computational resource configurations hosting execution engines. For each combination of the computational resource configuration, execution engine, and streaming operation, we measured and compared performance in terms of average throughput and its variance. The results show that the evaluated Flink and Spark runners, deployed on the single machine, manifest the law of diminishing returns rather quickly with regards to the number of available cores. While Spark runner core throughput significantly outperforms Flink runner's core throughput, Spark runner's system throughput is consistently lower than Flink ones.
Analysis of Beginning Reading Ability in Elementary School

This research was motivated by the fact that in the first grade of SDN Keret Krembung there were students who experienced the ability to read early. Therefore, the researcher wants to analyze the abilities experienced by students in beginning reading. The purpose of this research is to find out what is being faced by class I students so that they experience early reading skills. This study uses a qualitative research approach, this research has a special approach in the field, this study uses 2 student subjects. Data was collected using observation, interviews, and documentation. Test the validity of the data is done by triangulation techniques or methods. The results showed that the abilities experienced by first-class students in reading the beginning of SDN Keret Krembung were: (1) they could pronounce the letters of the alphabet, (2) they could identify letters, (3) they could recognize vowels, (4) they could distinguish between letters and letters. letter.
Learning Strategies at During the Covid-19 Pandemic at SD Muhammadiyah 5 Porong

This research examines various aspects, including: student learning activities, problems during learning, to learning solutions at SD Muhammadiyah 5 Porong in the midst of the Covid-19 pandemic. In the research process using data collection techniques by means of interview, observation, photos and videos when the research process was carried out. This study aims to obtain information regarding the strategies being carried out at SD Muhammadiyah 5 Porong in learning in the midst of the Covid-19 pandemic. This research method uses descriptive qualitative research methods. The existence of the Covid-19 pandemic has resulted in the teaching and learning process now being shifted to online learning. Online learning is a solution to solving educational problems due to the current Covid-19 pandemic. The offline method is also applied through face-to-face learning and continues to carry out obligations by complying with health protocols. Such is the learning conducted at SD Muhammadiyah 5 Porong. By using the online method and the offline method which is applied at SD Muhammadiyah 5 Porong amidst the current learning problems with the state of Indonesia affected by Covid-19.
RETRACTED: Research on Virtual Reality Experience Design System Based on Artificial Intelligence Technology and Mental Model

Abstract Mental model is the development of human brain thinking. In daily life, it is expressed as the representation of surrounding objects and people in the environment, and it can also be expressed as a stable representation or image of the human brain for permanent memory. As a collection of internal factors, it helps to understand people’s motivations, thought processes, emotions, needs, etc. The mental model combined with the virtual reality system technology meets the user’s thinking mode, and the product will have an excellent user experience. This article mainly introduces the analysis and research of virtual reality experience design system based on mental model. This paper uses the analysis and research of the virtual reality experience design system based on the mental model, uses the virtual reality interactive system to design, and reasonably analyzes the feasibility of the virtual reality interface model. The human-computer interaction in virtual reality and the presentation effect of virtual scenes are the two most important aspects of virtual reality experience. The experimental results of this paper show that the analysis and research of the virtual reality experience design system based on the mental model has increased the sense of virtual reality experience by 16%. The limitations of the analysis of the virtual reality experience design system of the mental model are good for virtual reality applications. The methods and approaches of human-computer interaction are analyzed, discussed and summarized, so as to enrich the academic research results.
Analysis of abnormal running behavior of mechanical operators based on sensor detection

The dangerous and harmful factors of machinery manufacturing mainly include human unsafe behavior and consciousness factors, unsafe state factors of equipment and adverse environmental factors. The characteristics and classification of abnormal behavior are analyzed, the detection methods of operators' abnormal running behavior are compared, and the discrimination basis of operators' abnormal running behavior is analyzed. The wearable inertial sensor is studied. The acceleration sensor is used to measure the acceleration of the measured object, and the gyroscope sensor can measure the angle of the human body relative to the ground. MPU9250 is selected as the measurement tool of acceleration and angle to identify the abnormal behavior of the human body, and cooperate with the single chip microcomputer, It is conducive to the detection and early warning of abnormal behavior of mechanical operators.
The Quantum Electromagnetism and Gravity Waves in Inflective Spaces with Topological Transformations of De-formations and Re-formations: Designing an Elementary Particle

The gravity waves, electromagnetic waves, and the most primitive elementary particle creation processes from completely null space are built by using the topological presentations, TPs with both event independent approach, EIA and process independent approach, PIA. The creation of gravity waves are explained by a building mechanism with the self-capability of elementary sets of thing spaces from single point spaces as a many-sided self-optimization process of the degenerations in single point space structures. The reason of invariancies are explained. The time is explained as a process dissipating an energy ingredient. Deriving the equations for waves and particles from the TPs and time energy in inflective spaces are worked for above said purposes.
The Upper Confidence Bound Algorithm: Bernoulli Noise

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SmartMeeting

Meetings are a necessary part of the operations of any institution, whether they are held online or in-person. However, meeting transcription and summarization are always painful requirements since they involve tedious human effort. This drives the need for automatic meeting transcription and summarization (AMTS) systems. A successful AMTS system relies on systematic integration of multiple natural language processing (NLP) techniques, such as automatic speech recognition, speaker identification, and meeting summarization, which are traditionally developed separately and validated offline with standard datasets. In this demonstration, we provide a novel productive meeting tool named SmartMeeting, which enables users to automatically record, transcribe, summarize, and manage the information in an in-person meeting. SmartMeeting transcribes every word on the fly, enriches the transcript with speaker identification and voice separation, and extracts essential decisions and crucial insights automatically. In our demonstration, the audience can experience the great potential of the state-of-the-art NLP techniques in this real-life application.
Semantic Labeling of Very High-Resolution Imagery by Leveraging Contextual Information with Optimized Non-Local Neural Network

Semantic labeling of very high-resolution (VHR) aerial imagery attempts to assign a specific label to every pixel. Resorting to the significant progress of semantic segmentation for natural images, the non-local neural networks express superior capability in capturing multi-scale contextual information, which is helpful for accurately labeling raw images. However, the computational complexity of non-local neural networks is criticized. In this paper, the optimized non-local block (ONN) is devised and embedded in the encoder stage to reduce the computational complexity in capturing contextual information. Besides, the DUpsampling is introduced to further boost the efficiency of decoder. Finally, the extensive experiments, including numerical metrics and visual inspection comparisons, are conducted on ISPRS benchmarks. The results indicate that the proposed method performs better than several SOTA methods both in accuracy and efficiency.
Embedding ROS and AI-Based Perception Capabilities in a Novel Low-Cost Assistive Robotic Platform

This paper describes how we developed a novel low-cost assistive robotic platform, with AI-based perception capabilities, able to navigate autonomously using Robot Operating System (ROS). The platform is a differential wheeled robot, equipped with two motors and encoders, which are controlled with an Arduino board. It also includes a Jetson Xavier processing board on which we deploy all AI processes, and the ROS architecture. As a result of the work, we have a fully functional platform, able to recognize actions online, and navigate autonomously through environments whose map has been preloaded.
The Resistible Rise of Cognitive Science

Cognitive Science could not have been what it is without Artificial Intelligence. This essay explore the development of CogSci, its oblivion of AI as its primary philosophical foundations, and the reduction of Artificial Intelligence to a mere technology emptied of its philosophical potential.