Reviewerly/exHarmony huggingface dataset:
Rotated and Masked Image Modeling: A Superior Self-Supervised Method for Classification

Mask image modeling (MIM) has performed excellently as a transformer-based self-supervised method via random masking and reconstruction. However, since the unmasked image patches are non-participation in the loss computation, MIM cannot effectively utilize the data and waste much computation. This drawback usually limits the learning ability of the pre-training model when pre-training on small-scale datasets. To solve this problem, we propose a novel self-supervised learning method for small-scale datasets called RotMIM. Unlike MIM, RotMIM has a different pretext task: recognizing the rotation angle that is applied to the unmasked patches. RotMIM can fully utilize data and provide a stronger self-supervised signal. Moreover, to fit RotMIM, we propose a data augmentation method called FeaMix. Our proposal ensures that the mixing area with RotMIM understands that each basic unit of semantic information in an image has the same size. This consistency guarantees clean tokenization during fine-tuning after pre-training. Our proposals outperform state-of-the-art self-supervised methods on three popular datasets, Mini-ImageNet, Caltech256, and Cifar100.
Minimum Error Entropy Estimation Under Contaminated Gaussian Noise

It is shown that R´enyi's entropy of a Gaussian mixture with entropic index α ∊ (1,∞] is upper-bounded by the cluster with minimum variance. This basic idea leads to a clean worst-case formulation of the minimum error entropy principle in the context of linear multi-sensor fusion by using a largely contaminated Gaussian distribution to model sensor errors with outliers. The obtained entropic best linear unbiased estimator leads to an operational interpretation in terms of a precision/reliability trade-off, it resonates closely with model order selection methods, and it provides a possible information theoretic root to sparsity-promoting regularization.
Feature Differentiation Reconstruction Network for Weakly-Supervised Video Anomaly Detection

Recent research into video anomaly detection under weakly supervised settings has made significant progress in identifying anomalies with only coarse-grained annotations. Mainstream weakly supervised methods improve detection performance by generating high-quality pseudo labels for video segments. However, these pseudo-label-based methods have been ordinarily hindered by manually-set constraint rules as the bottleneck. In this paper, we propose the Feature Differentiation Reconstruction Network (FDR-Net), which no longer relies on pseudo labels and instead uses a differential reconstruction strategy to improve the discriminability of the representation. Concretely, video features are first randomly masked out and then reconstructed with distinct targets for normal and abnormal videos during the differential reconstruction process. Besides, we also introduce a dense transformer-based encoder to refine spatial-temporal relationships among video segments. Comprehensive experiments on ShanghaiTech demonstrate the superior performance of our model.
Historical Process and Theoretical Foundations of Metaverse Starting from Web 1.0

The World Wide Web has come a long way since its emergence, from a small impact on our daily lives to a vast universe that accommodates all of humanity today. This study delves into the historical and theoretical foundations of the web, which is preparing to present a world that will cover our five senses. The chapter begins by explaining the difference between the Internet and the web and goes on to examine the transition process from Web 1.0, which provided information connections, to Web 2.0 technologies that focus on interaction. The chapter continues by discussing Web 3.0 and its decentralized protocols, new technologies, and algorithms. It also investigates the commercial, cultural, and social implications of next-generation web technologies such as artificial intelligence, augmented reality, three-dimensional universes, and semantic networks. To investigate these effects, the concepts of blockchain, NFTs, tokens, and the Metaverse are used. In summary, the goal of the study is to provide a comprehensive understanding of the evolution of the web and the impact of new technologies on our daily lives.
Evaluation of the Metaverse Universe in Light of Psychology and Sociology

The concept of Metaverse has become a popular field in today’s digital world, which is both intriguing in large circles, and where considerable research and developments are being performed and followed Metaverse, which heralds the transition from Web 2.0 to Web 3.0, includes many new technologies such as augmented reality, virtual reality, mixed reality, and NFTs. Simply, the Metaverse can be thought of as a “virtual twin” of the world we live in at abstract level. This twin world offers many services to its users, such as economic, cultural, and communication systems, akin to those found in our own reality. The most significant difference is that in the Metaverse universe, individuals form their own avatar according to their preferences and perform their interpersonal and social relations through it. The fact that the Metaverse universe allows us to differentiate the self-presentation in shape allows users to experience desired emotions such as feeling more valuable, being approved by society, and being able to socialize according to their wishes. In addition, by means of avatars, individuals form a digital realm that allows them to realize new socialization processes in terms of their own status, socioeconomic class, identity, and image. The fact that users have the opportunity to experience anything they have in the physical world in a way that is close to reality in the Metaverse also provides many effects in the field of psychology such as grief, crisis, trauma processes, interpersonal relationships, changes in self-perception and self-esteem, real-life and virtual life balance, close relationship experiences. In sociological terms, Metaverse can be evaluated in terms of various criteria such as socialization processes, globalization, and class. Within the scope of all these reasons, in this section, it is aimed to examine the paradigm shifts that the concept of the Metaverse, which has revolutionized many fields, has revealed in terms of psychology and sociology sciences.
The Extended Reality Technology and Its Utilization in Metaverse

In today's world, where digital changes and transitions are experienced with the developments in technology, many inventions and innovative applications enter daily life. The extended reality technology, which covers virtual reality, augmented reality, and mixed reality technologies, is among these innovative applications. These technologies not only change the way individuals get information, rejoice, make decisions, and act but also enable the creation of a participatory experience. For this reason, the extended reality technology, which has been increasingly used in many fields and disciplines, is one of the significant building blocks of the widely spoken Metaverse. Based on the convergence of technologies that enable multi-sensory interactions with extended reality technology, digital objects, and people, Metaverse is defined as a network of immersive environments with interconnected social networks on permanent multi-user platforms. Accordingly, in this network, it is possible to travel, work together, play games, shop, and engage in educational activities with avatars and digital twins created and controlled by users. In this study, extended reality technology, which is one of the significant building blocks of Metaverse, and the use of this technology in Metaverse were discussed. In this regard, the study, which has been planned as a descriptive study, includes literature analysis and definitions and explanations about extended reality technology and Metaverse; Afterward, the use of extended reality technology in Metaverse was explained by supporting it with current examples.
Evaluation of Open Source, Open Data Sharing, and Data Anonymization Concepts in the Development of the Metaverse

As the Metaverse concept becomes widespread in our lives, people are expected to be included in an artificial physical environment, and a new reality phenomenon will emerge in the virtual universe and the existing universe. In this context, the Metaverse concept is an abstract concept used to describe a digital environment to influence people's physical world and access its content. The information to be included in the virtual universe will be formed by processing the data obtained from individuals, and this situation is expected to reveal many paradigm shifts, especially in the concept of data. Compared to the data in today's social media usage, the Metaverse will be a greater power in analyzing human behavior, since the data on the Metaverse contains a deeper and wider range of data sources than open sources because participation in the Metaverse system involves the collection of unprecedented volumes and types of personal data. Along with the virtual universe, many situations such as storing sensitive biometric and physiological data and transferring virtual payments between platforms, will increase the possibility of malware attacks and data breaches, and data security policies will be reassessed. For this reason, in parallel with the privacy concerns that will increase with the spread of the Metaverse concept, it is expected that data anonymization will become more important and the open data policy of companies that manage the Metaverse will be questioned. Within the scope of all these reasons, in this section, it is aimed to examine the changes caused by the Metaverse concept, which has revolutionized information technologies today, where data is the greatest power in accessing information, in terms of open source, open data sharing, and data anonymization.
The Role of Artificial Intelligence and Robotic Solution Technologies in Metaverse Design

Metaverse is designed as a time and space-independent environment where virtual and reality will be intertwined, real-time and multi-user, human–computer–robot interactions will be possible. Metaverse design needs to be handled together with the internet, social networks, computer games, virtual reality glasses, augmented reality software, the internet of things, wearable devices, 5G/6G internet infrastructure, cryptocurrencies, artificial intelligence, and robotics technologies. The Metaverse in design is a concept that covers every field, from education to art, from commerce to health, and from games to entertainment. Platforms, where many people of different languages, religions, races, and ages can interact simultaneously with avatars, AR/VR, smart devices, and wearables, have the potential to generate enormous amounts of data. The analysis of this produced data with advanced artificial intelligence techniques has critical importance in terms of both the ecosystem’s continuity and the user experience’s improvement. Artificial intelligence and robotic technologies act as a bridge between the real and virtual worlds in Metaverse platforms. To provide communication between human–avatar, avatar–avatar, human–robot, and robot–avatar, natural language recognition, voice recognition, voice-to-text conversion, and text-to-speech conversion tasks can be performed with artificial intelligence technologies. Metaverse platforms, where individuals in the real world can control their avatars or robots in the virtual world with various hardware, communicate with others, and produce rich digital content, also promise to make their economies. However, such a Metaverse ecosystem has not been created, as the necessary technology and infrastructure are not yet available. In addition, the hardware required for users to enter these Metaverse platforms is limited in number, quite expensive, cumbersome, and unsuitable for long-term use. For this reason, technology companies and social media giants are investing heavily to create their Metaverse ecosystems. Therefore, the future of the internet is shaped in parallel with the development of infrastructure and technology.
Collaborative Robot-Oriented Joint Real-Time Control Based on Heterogeneous Embedded Platform

A real-time joint controller oriented on collaborative robots (co-robots) using an embedded multi-core heterogeneous development board is proposed in this paper. The co-robot control system is composed of an ARM core running Linux-RT and a DSP running a real-time RTOS system, which ensures that the system kernel’s real-time performance is achieved. The inter-core communication is accomplished using the IPC component provided by Texas Instruments. The co-robot control tasks are assigned to different processors based on their characteristics. Based on this multi-core heterogeneous co-robot control system architecture, a joint compliance control algorithm based on admittance control was designed and discretized. The robot joint compliance control experiment was carried out on a single-joint co-robot platform. The results indicate that good performance on real-time, fast response, and compliance collaboration were achieved by the multi-core heterogeneous co-robot joint controller.
The Tourism Sector in Metaverse: Virtual Hotel and Applications

The existence of technological innovations and the transition from the physical world to the virtual world form the basis for research beyond the universe. This is how the Metaverse concept can be defined as beyond the universe. In the Metaverse world, people can create an avatar with the characters they create and with the presence of smart technologies like virtual glasses, smart consoles, etc. They can also travel in this world with their avatars. The physical world's needs can be met as well as those of the virtual world. In the Metaverse world, people will be able to benefit from hotel businesses to meet the accommodation needs of their avatars. Virtual hotels and applications can also be found in the Metaverse world. The purpose of this research consists of constructing a virtual hotel in the tourism sector of the Metaverse world and determining what the applications of this hotel can be. Within the scope of the research, how to structure the virtual hotel application that can be created in the Metaverse world and its advantages and risks in terms of tourism businesses are conceptually examined. Document analysis and case studies are used as research methods. As a result of the research, it is stated what virtual hotels and applications can be in the context of the tourism sector in the Metaverse, how businesses and guests who use the applications can benefit from these applications, and what alternatives can be related to tourism activities in the Metaverse in future studies from a broad perspective.
Non-fungible Tokens (NFT), Virtual Real Estate and Digital Assets, Cultural and Artistic Works

Working mainly on the Ethereum blockchain, Non-Fungible Tokens (NFTs) are, in their simplest form, digital certificates with unique code and metadata belonging to digital assets. NFTs can be used, traded, gained, or lost in value. Most audiovisual artifacts such as images, sounds, games, videos, digital artworks, and even digital posts (for example, tweets) can be NFT. NFT is not a currency like cryptocurrencies, as the name suggests, they are non-fungible. Any currency (for example, dollar or bitcoin) is the same everywhere in the world (Bitcoin which is belonged to a Bitcoin owner in Turkey, and Bitcoin which is belonged to a Bitcoin owner in America are the same). However, NFTs are digital representations of the asset they represent and are unique. It is often discussed whether NFT is a temporary trend or a new technology that will revolutionize art, culture, and intellectual property rights. Along with these discussions, it is a fact that the NFT market has grown exponentially. NFTs improve the marketing processes of cultural and artistic works, make them more accessible, may eliminate intermediaries, and facilitate traceability. In this study, the development, features, and ecosystem of NFTs, which are becoming popular day by day and an important element of Web 3.0, are examined in detail and tried to provide the reader with a holistic and objective perspective on NFTs.
3D Human Pose Estimation in Video for Human-Computer/Robot Interaction

3D human pose estimation is widely used in motion capture, human-computer interaction, virtual character driving and other fields. The current 3D human pose estimation has been suffering from depth blurring and self-obscuring problems to be solved. This paper proposes a human pose estimation network in video based on a 2D lifting to 3D approach using transformer and graph convolutional network(GCN), which are widely used in natural language processing. We use transformer to obtain sequence features and use graph convolution to extract features between local joints to get more accurate 3D pose coordinates. In addition, we use the proposed 3D pose estimation network for animated character motion generation and robot motion following and design two systems of human-computer/robot interaction (HCI/HRI) applications. The proposed 3D human pose estimation network is tested on the Human3.6M dataset and outperforms the state-of-the-art models. Both HCI/HRI systems are designed to work quickly and accurately by the proposed 3D human pose estimation method.
Efficient Autonomous Exploration of Unknown Environment Using Regions Segmentation and VRP

Autonomous exploration of unknown environments is a critical application scenario in robotics. However, existing studies have strived to generate an efficient tour plan that enables the robots to fully explore the environment. This is due to the greedy strategies and the lack of utilizing the information of the unexplored area. To address the above issues, we propose a new exploration strategy. Our approach involves partitioning the unexplored area into distinct regions and utilizing their estimated workload and spatial structure to construct a region graph. Subsequently, a VRP (Vehicle Routing Problem) planner allocates regions to robots and generates a global tour for each robot in a coordinated manner, which is then refined and provided to the robot’s navigation module. Experimental results demonstrate that that our method outperforms existing approaches in terms of exploration speed, total distance, and overlap ratio.
Coordinated Passive Maneuvering Target Tracking by Multiple Underwater Vehicles Based on Asynchronous Sequential Filtering

As a hot topic in the development of marine science, underwater target tracking technology has been playing an important role since its birth. Passive sonar, one of the widely used sensor devices in tracking, collecting data without emitting energy signals, has good concealment. As a sensor carrier with superior performance, autonomous underwater vehicle (AUV) can be used to build an underwater multi-platform collaborative tracking system, to obtain composite observation data and improve tracking performance. In this paper, the problem of cooperative passive tracking of underwater maneuvering targets by multi-AUVs is studied. Aiming at the divergence problem of tracking maneuvering targets by a single AUV, a collaborative tracking system which has multi-AUVs, is formed. In view of the unavoidable asynchronous sampling problem among multi-AUVs, the asynchronous sequential filtering method is derived. In order to solve the prediction model distortion problem, the strong tracking theory is introduced. Therefore, combined with the Unscented-Kalman-Filter, an Asynchronous-Sequential-Strong-Tracking-Filter-Unscented-Kalman-Tracking algorithm (AS-STF-UKT) is proposed. In simulation scenario, it successfully solved the previous problems.
L-EfficientUNet: Lightweight End-to-End Monocular Depth Estimation for Mobile Robots

In order to solve the problems of monocular depth estimation based on deep learning, such as the amount of computation and parameters of deep network architecture is too large and difficult to be applied in engineering equipment, a lightweight end-to-end monocular depth estimation model is proposed. Firstly, an improved feature extraction module is designed based on migration learning, and the effects of scaling the model’s depth, width, and input image resolution on the model’s accuracy and computational resources are also considered. An optimized combination of model expansion is used to ensure the model’s accuracy and save computational resources simultaneously. Secondly, the fusion loss function is designed to fully consider the characteristics between the predicted value and the real value of the image, reduce the storage requirements and computational complexity of the model, and maintain the accuracy of the model in the reasoning phase. The experimental results on the NYU Depth-V2 dataset show that the proposed method has an average accuracy improvement of 0.086 with a threshold ratio of 1.25, and an average structural similarity improvement of 0.006 in depth maps. The method in this paper uses only 5.9M parametric quantities and 4.4G multiplicative computations, which are 16.982M and 20.544G lower than the comparative literatures. The method in this paper can be deployed on the mobile robot with Raspberry Pi 4 4 GB and achieve an inference speed of 5 Hz, which is 2.86 times faster than the comparative literatures.
Detection and Positioning of Workpiece Grinding Area in Dark Scenes with Large Exposure

Workpiece grinding is a crucial process in the smart manufacturing chain. In order to meet the requirements of industrial precision and relieve heavy work, researchers have developed a vision-based grinding robot. However, the problem of workpiece grinding area detection and positioning is difficult to be solved in dark scenes with large exposure. This paper proposes a method that fuses technologies such as processing of the image, coordinate and point cloud, which can accurately detect and locate the workpiece grinding area. Firstly, A method based on YOLOv7 and improved image preprocessing is used to detect labels of the grinding area. Secondly, A model for the prediction of the coordinates based on multiple linear regression was used to predict the coordinates of missing labels for the same grinding area. Finally, Data processing of the point cloud and transformation of the system of coordinates are used to achieve the acquisition of the coordinates of positioning vertices in the grinding area and conversion from the camera coordinate system to the world coordinate system. We used several sets of data for evaluation in our experiments, and the experimental results show that our proposed method can effectively detect the workpiece grinding area. At the same time, our method can also predict the coordinates of missing labels, which provides a more stable and reliable guarantee for industrial production.
Efficient and Hierarchical Quadrotor Planner for Fast Autonomous Flight

In this paper, we propose an effective and hierarchical planning framework for quadrotor fast autonomous flight. In the front-end, we propose an efficient guided sampling strategy by topology graph. In the back-end, the traditional trajectory optimization problems are formulated as minimising an objective function such as total energy cost while considering safety and dynamic feasibility constraints. However, the time characteristic is ignored. In this paper, the time optimal trajectory is studied, the time optimal trajectory problem is transformed to a convex optimization problem with a time optimal guidance trajectory based on the Pontryagin’s Minimum Principle. The simulation experimental results demonstrate the feasibility and validity of the proposed motion planning method.