Reviewerly/exHarmony huggingface dataset:
A Full Dive into Realizing the Edge-enabled Metaverse: Visions, Enabling Technologies,and Challenges

Dubbed "the successor to the mobile Internet", the concept of the Metaverse has grown in popularity. While there exist lite versions of the Metaverse today, they are still far from realizing the full vision of an immersive, embodied, and interoperable Metaverse. Without addressing the issues of implementation from the communication and networking, as well as computation perspectives, the Metaverse is difficult to succeed the Internet, especially in terms of its accessibility to billions of users today. In this survey, we focus on the edge-enabled Metaverse to realize its ultimate vision. We first provide readers with a succinct tutorial of the Metaverse, an introduction to the architecture, as well as current developments. To enable ubiquitous, seamless, and embodied access to the Metaverse, we discuss the communication and networking challenges and survey cutting-edge solutions and concepts that leverage next-generation communication systems for users to immerse as and interact with embodied avatars in the Metaverse. Moreover, given the high computation costs required, e.g., to render 3D virtual worlds and run data-hungry artificial intelligence-driven avatars, we discuss the computation challenges and cloud-edge-end computation framework-driven solutions to realize the Metaverse on resource-constrained edge devices. Next, we explore how blockchain technologies can aid in the interoperable development of the Metaverse, not just in terms of empowering the economic circulation of virtual user-generated content but also to manage physical edge resources in a decentralized, transparent, and immutable manner. Finally, we discuss the future research directions towards realizing the true vision of the edge-enabled Metaverse.
DualConv: Dual Convolutional Kernels for Lightweight Deep Neural Networks

CNN architectures are generally heavy on memory and computational requirements which makes them infeasible for embedded systems with limited hardware resources. We propose dual convolutional kernels (DualConv) for constructing lightweight deep neural networks. DualConv combines 3$\times$3 and 1$\times$1 convolutional kernels to process the same input feature map channels simultaneously and exploits the group convolution technique to efficiently arrange convolutional filters. DualConv can be employed in any CNN model such as VGG-16 and ResNet-50 for image classification, YOLO and R-CNN for object detection, or FCN for semantic segmentation. In this paper, we extensively test DualConv for classification since these network architectures form the backbones for many other tasks. We also test DualConv for image detection on YOLO-V3. Experimental results show that, combined with our structural innovations, DualConv significantly reduces the computational cost and number of parameters of deep neural networks while surprisingly achieving slightly higher accuracy than the original models in some cases. We use DualConv to further reduce the number of parameters of the lightweight MobileNetV2 by 54% with only 0.68% drop in accuracy on CIFAR-100 dataset. When the number of parameters is not an issue, DualConv increases the accuracy of MobileNetV1 by 4.11% on the same dataset. Furthermore, DualConv significantly improves the YOLO-V3 object detection speed and improves its accuracy by 4.4% on PASCAL VOC dataset.
3-D Instance Segmentation of MVS Buildings

We present a novel 3D instance segmentation framework for Multi-View Stereo (MVS) buildings in urban scenes. Unlike existing works focusing on semantic segmentation of urban scenes, the emphasis of this work lies in detecting and segmenting 3D building instances even if they are attached and embedded in a large and imprecise 3D surface model. Multi-view RGB images are first enhanced to RGBH images by adding a heightmap and are segmented to obtain all roof instances using a fine-tuned 2D instance segmentation neural network. Instance masks from different multi-view images are then clustered into global masks. Our mask clustering accounts for spatial occlusion and overlapping, which can eliminate segmentation ambiguities among multi-view images. Based on these global masks, 3D roof instances are segmented out by mask back-projections and extended to the entire building instances through a Markov random field optimization. A new dataset that contains instance-level annotation for both 3D urban scenes (roofs and buildings) and drone images (roofs) is provided. To the best of our knowledge, it is the first outdoor dataset dedicated to 3D instance segmentation with much more annotations of attached 3D buildings than existing datasets. Quantitative evaluations and ablation studies have shown the effectiveness of all major steps and the advantages of our multi-view framework over the orthophoto-based method.
Czech Grammar Error Correction with a Large and Diverse Corpus

We introduce a large and diverse Czech corpus annotated for grammatical error correction (GEC) with the aim to contribute to the still scarce data resources in this domain for languages other than English. The Grammar Error Correction Corpus for Czech (GECCC) offers a variety of four domains, covering error distributions ranging from high error density essays written by non-native speakers, to website texts, where errors are expected to be much less common. We compare several Czech GEC systems, including several Transformer-based ones, setting a strong baseline to future research. Finally, we meta-evaluate common GEC metrics against human judgements on our data. We make the new Czech GEC corpus publicly available under the CC BY-SA 4.0 license at http://hdl.handle.net/11234/1-4639 .
The Effect of The Use of Multimedia Flip Book With the Flipped Classroom Approach in Vocational School

The problem encountered in learning operating system teaching materials majoring in network computer engineering is the lack of creativity in students' thinking skills so that they experience obstacles in the practical process. This study used a quasi-experimental design with a pretest-posttest Control Group Design. Data collection techniques using tests. The pre-test was carried out at the beginning to see the students' initial abilities before being given treatment, the post-test was carried out to test the effectiveness or compliance of the application of the treatment. In this study, the data analysis technique used 3 statistical tests, (1) normality test, (2) homogeneity test and (3) hypothesis testing. Through the use of hypothesis testing to show the results of H0 is rejected and H1 is accepted with a significance level of 5% so that the value is (P &lt;0.05). The conclusion is that the use of the Flip book Multimedia with the Flipped Classroom approach is able to improve the thinking skills of students majoring in network computer engineering in the operating system subject. So that later research can be applied to other subjects to help the learning process and increase the creativity of vocational high school students.
Image Denoising with Adaptive Weighted Graph Filtering

Graph filtering, which is founded on the theory of graph signal processing, is proved as a useful tool for image denoising. Most graph filtering methods focus on learning an ideal lowpass filter to remove noise, where clean images are restored from noisy ones by retaining the image components in low graph frequency bands. However, this lowpass filter has limited ability to separate the low-frequency noise from clean images such that it makes the denoising procedure less effective. To address this issue, we propose an adaptive weighted graph filtering (AWGF) method to replace the design of traditional ideal lowpass filter. In detail, we reassess the existing low-rank denoising method with adaptive regularizer learning (ARLLR) from the view of graph filtering. A shrinkage approach subsequently is presented on the graph frequency domain, where the components of noisy image are adaptively decreased in each band by calculating their component significances. As a result, it makes the proposed graph filtering more explainable and suitable for denoising. Meanwhile, we demonstrate a graph filter under the constraint of subspace representation is employed in the ARLLR method. Therefore, ARLLR can be treated as a special form of graph filtering. It not only enriches the theory of graph filtering, but also builds a bridge from the low-rank methods to the graph filtering methods. In the experiments, we perform the AWGF method with a graph filter generated by the classical graph Laplacian matrix. The results show our method can achieve a comparable denoising performance with several state-of-the-art denoising methods.
A Technique to Detect Copy-Move Forgery using Enhanced SURF

The field of digital imaging has advanced in recent years with increase of various digital gadgets and applications associated to it. With easily availability of image editing softwares which are either free of cost or budget friendly, image can be effortlessly tampered by the means of making forgery. This has led to increase in crime related to various image processing and computer vision applications. To combat with such forgeries digital forensic provide scientific techniques to identify whether image is original or forged. The proposed work implemented a image forgery check system based on SURF features. This is a pixel based technique where after preprocessing the images, relevant features are extracted and compared with a defined estimated threshold value. Based on the demonstrated results it is decided whether the image has been forged or not and if it is, then the area where tampering has been done is displayed as a forged part. The proposed algorithm is tested using open source CASIA image dataset. Also, the presented result shows that SURF feature based authentication provide forgery detection accuracy of 97%. The results are compared with other techniques in similar domain to prove the novelty of the work.
Detecting concept drift using HEDDM in data stream

In evolving data stream, when its concept undergoes a change it is known as concept drift. Detecting concept drift and handling it is a challenging task in data stream mining. If an algorithm is not adapted to concept drift, then it directly affects its performance. A number of algorithms have been developed to handle concept drift, but they are not suited for both sudden concept drift and gradual concept drift. Thus, there is a demand for an algorithm that can react to both the types of concept drift as well as incur less computational cost. A new approach hybrid early drift detection method (HEDDM) has been proposed for drift detection, which works with an ensemble method to improve the performance.
New Network Based on D-Linknet and Densenet for High Resolution Satellite Imagery Road Extraction

CNN (Convolutional Neural Networks) has been proved to be an effective method for road extraction in remote sensing fields recently. D-LinkNet based on LinkNet adopted consecutive dilation convolution with different expanding rate to enlarge the receptive field without reducing the resolution of the feature-maps so that had an outstanding performance in high resolution satellite imagery road extraction. However, too many parameters make some inadequacies for D-LinkNet, which introduced LinkNet as its backbone with ResNet construction. Focused on this problem, this paper proposed a new network to improve D-LinkNet: (1) Rebuild D-LinkNet by applying DenseNet as its backbone instead of ResNet; (2) Replacing initial block with stem block in the beginning of the network. The accuracy of road extraction results from the new network which was trained with our own training dataset after data augmentation was evaluated with IoU scores. The experimental results show that the proposed network has a higher IoU scores than D-LinkNet with less parameters.
A Secure and Robust Digital Image Watermarking Techniques

This paper speaks to various Digital Watermarking systems which allows a person to include unseen copyright see just as some other confirmation messages to pictures with the goal that it very well may be shielded from the unapproved get to. The principle point of paper is to investigate the examination of different digital picture watermarking schemes and it likewise exhibits that all these watermarking strategies gives picture watermarking full protection and sensible limit. Here hybrid Image watermarking procedure which takes the benefits of various transforms like DWT, DCT, SVD and Arnold Transform, which improves greater security and gives heartiness to the watermark. In this paper technique, picture is separated into a few gatherings of edges, and one of the casings is chosen where watermark will be implanted. Before embed watermark in a choose casing it will be pre-handled with Arnold Transform which will give security to it.
CBlockSim: A Modular High-Performance Blockchain Simulator

To avoid the inconvenience of the deployment of large-scale blockchains, blockchain simulators are used to facilitate blockchain design and implementation. We evaluate state-of-the-art simulators and find that they suffer from low performance and scalability. To build a more general and faster blockchain simulator, we extend an existing blockchain simulator. We add a network module integrated with a network topology generation algorithm and a block propagation algorithm to simulate the block propagation efficiently. We design a binary transaction pool structure and adopt bitwise operations to accelerate the simulation and reduce memory usage. Moreover, we modularize the simulator based on five primary blockchain processes. Significant blockchain elements are implemented in individual modules and can be combined flexibly to simulate different types of blockchains. Experiments demonstrate that the new simulator reduces the simulation time by an order of magnitude and improves scalability, enabling us to simulate more than ten thousand nodes.
EOG-Based Human–Computer Interface: 2000–2020 Review

Electro-oculography (EOG)-based brain-computer interface (BCI) is a relevant technology influencing physical medicine, daily life, gaming and even the aeronautics field. EOG-based BCI systems record activity related to users' intention, perception and motor decisions. It converts the bio-physiological signals into commands for external hardware, and it executes the operation expected by the user through the output device. EOG signal is used for identifying and classifying eye movements through active or passive interaction. Both types of interaction have the potential for controlling the output device by performing the user's communication with the environment. In the aeronautical field, investigations of EOG-BCI systems are being explored as a relevant tool to replace the manual command and as a communicative tool dedicated to accelerating the user's intention. This paper reviews the last two decades of EOG-based BCI studies and provides a structured design space with a large set of representative papers. Our purpose is to introduce the existing BCI systems based on EOG signals and to inspire the design of new ones. First, we highlight the basic components of EOG-based BCI studies, including EOG signal acquisition, EOG device particularity, extracted features, translation algorithms, and interaction commands. Second, we provide an overview of EOG-based BCI applications in the real and virtual environment along with the aeronautical application. We conclude with a discussion of the actual limits of EOG devices regarding existing systems. Finally, we provide suggestions to gain insight for future design inquiries.
ASM2TV: An Adaptive Semi-supervised Multi-Task Multi-View Learning Framework for Human Activity Recognition

Many real-world scenarios, such as human activity recognition (HAR) in IoT, can be formalized as a multi-task multi-view learning problem. Each specific task consists of multiple shared feature views collected from multiple sources, either homogeneous or heterogeneous. Common among recent approaches is to employ a typical hard/soft sharing strategy at the initial phase separately for each view across tasks to uncover common knowledge, underlying the assumption that all views are conditionally independent. On the one hand, multiple views across tasks possibly relate to each other under practical situations. On the other hand, supervised methods might be insufficient when labeled data is scarce. To tackle these challenges, we introduce a novel framework ASM2TV for semi-supervised multi-task multi-view learning. We present a new perspective named gating control policy, a learnable task-view-interacted sharing policy that adaptively selects the most desirable candidate shared block for any view across any task, which uncovers more fine-grained task-view-interacted relatedness and improves inference efficiency. Significantly, our proposed gathering consistency adaption procedure takes full advantage of large amounts of unlabeled fragmented time-series, making it a general framework that accommodates a wide range of applications. Experiments on two diverse real-world HAR benchmark datasets collected from various subjects and sources demonstrate our framework's superiority over other state-of-the-arts. Anonymous codes are available at https://github.com/zachstarkk/ASM2TV.
A deep transfer regression method based on seed replacement considering balanced domain adaptation

With the development of deep transfer learning, the generalization abilities of models in similar scenarios have been significantly improved. However, for regression tasks, either the marginal distribution or the conditional distribution is usually ignored. In addition, initiative regarding the representation and learning of domain knowledge is lacking due to the reliance on the loss function. A deep transfer regression method based on seed replacement considering balanced domain adaptation, called DTRSR, is proposed in this work. DTRSR is composed of four parts: structure freezing and parameter transfer, deep feature extraction, seed replacement and a fusion loss function. First, domain knowledge is captured at the model level through structure freezing and parameter transfer. Second, seed replacement is used for knowledge learning in the source and target domains at the data level. Finally, a fusion loss function considering balanced distribution adaptation is constructed to acquire domain knowledge at the loss level. In summary, domain knowledge is sufficiently learned through DTRSR. In addition, seed replacement improves the initiative of knowledge learning instead of relying only on the loss function to learn automatically. DTRSR is compared on three datasets, namely, Tool Wear, Battery Capacity and Robot Machining Errors, with nine other methods. The proposed method achieves excellent performance on most tasks, which validates its effectiveness and great potential in regression tasks.
SpADe: Multi-Stage Spam Account Detection for Online Social Networks

In recent years, Online Social Networks (OSNs) have radically changed the way people communicate.The most widely used platforms, such as Facebook, Youtube, and Instagram, claim more than one billion monthly active users each.Beyond these, news-oriented micro-blogging services, e.g., Twitter, are daily accessed by more than 120 million users sharing contents from all over the world.Unfortunately, legitimate users of the OSNs are mixed with malicious ones, which are interested in spreading unwanted, misleading, harmful, or discriminatory content.Spam detection in OSNs is generally approached by considering the characteristics of the account under analysis, its connection with the rest of the network, as well as data and metadata representing the content shared.However, obtaining all this information can be computationally expensive, or even unfeasible, on massive networks.Driven by these motivations, in this article we propose SpADe, a multi-stage Spam Account Detection algorithm with reject option, whose purpose is to exploit less costly features at the early stages, while progressively extracting more complex information only for those accounts that are difficult to classify.Experimental evaluation shows the effectiveness of the proposed algorithm compared to single-stage approaches, which are much more complex in terms of features processing and classification time.
Comparative analysis of online dictionaries in the context of the digital transformation of education

The article is devoted to a comparative analysis of popular online dictionaries and an overview of the main tools of these resources to study a language. The use of dictionaries in learning a foreign language is an important step to understanding the language. The effectiveness of this process increases with the use of online dictionaries, which have a lot of tools for improving the educational process. Based on the Alexa Internet resource it was found the most popular online dictionaries: Cambridge Dictionary, Wordreference, Merriam–Webster, Wiktionary, TheFreeDictionary, Dictionary.com, Glosbe, Collins Dictionary, Longman Dictionary, Oxford Dictionary. As a result of the deep analysis of these online dictionaries, we found out they have the next standard functions like the word explanations, transcription, audio pronounce, semantic connections, and examples of use. In propose dictionaries, we also found out the additional tools of learning foreign languages (mostly English) that can be effective. In general, we described sixteen functions of the online platforms for learning that can be useful in learning a foreign language. We have compiled a comparison table based on the next functions: machine translation, multilingualism, a video of pronunciation, an image of a word, discussion, collaborative edit, the rank of words, hints, learning tools, thesaurus, paid services, sharing content, hyperlinks in a definition, registration, lists of words, mobile version, etc. Based on the additional tools of online dictionaries we created a diagram that shows the functionality of analyzed platforms.
Features of design of digital aids for training students with autistic disorders

In accordance with the aims of the paper, it is covered essential peculiarities of the design of digital aids for ASD-students. They are distinguished coming from the analysis of common learning and social difficulties inherent to the trainees with autistic disorders; needs for their speaking habits development; advantages of special digital support in terms of facilitating ASD-students’ training. The distinguished features essential in the process of the digital aids design are demonstrated on the example of the development of an e-simulator for young ASD-students’ speech encouragement. The main stages of its design and functionality are characterised. It is emphasised in conclusion that the developed digital simulator due to its functionality helps to overcome a number of problems faced by young ASD-students. The problems are specifed.