Reviewerly/exHarmony huggingface dataset:
Sensor based solar and wind energy potential and future forecasting in India

In this world needs to consider the elective asset of the ability of the work which is separated from traditional vivacity assets (biogas, coal and so on). The main valuable things in the world are done by solar energy & huge capability of environmentally friendly power. India has a tremendous potential for creating green power from the environmentally friendly power sources. For advance development of power production in from renewable energies, the public sectors in India try in their best to set up a strong alternative resource. Prime minister was announced a scheme for renewable energy which has framed by Jawaharlal Nehru National Solar scheme. This scheme has set a good objective to build up 25,000 MW projects in various sites in India like some unique steps following as (a) lifelong strategy; (b) enormous scope organization objectives; (c) management (d) homegrown creation in crude materials segments, therefore to expert in the framework similarity by 2024. In this study plan is help for improvement the target and. A solar energy preliminary diagram is given by our paper. It surveys the present status of solar energy as far as present limit, alongside authentic patterns of sun powered energy and future capability of an alternate type of renewable energy.
Impact of Modularization and Coupling on the Complexity of Industrial Control and Automation Systems

Coupling could be an arbitrary property to Industrial Automation and Control Systems (ICAS) with a significant impact on complexity. Different software design approaches may result in coupling between software modules that either increase or decrease the overall system complexity. This paper proposes using the Cynefin Framework to guide the software design patterns used during the high-level design of software architecture, with the intent of reducing components coupling and overall system complexity.
Wavelet Transformation and Local Binary Pattern for Data Augmentation in Deep Learning-based Face Recognition

Facial recognition is used in many fields such as verification, communication, and other fields. The success of facial analysis is influenced by noise from environmental factors such as illumination, expression, posture, occlusion, and others. Therefore, a solid and effective analytical model is needed to overcome noise using feature extraction and deep learning. This study compares the effect of various wavelet transform methods and local binary pattern (LBP) on facial recognition as additional data on the CNN architecture and the pre-trained VGG16 model on the Yale-B facial recognition dataset. The results showed that the LBP and IDWT features of discrete wavelet transform (DWT) as augmented data resulted in the highest accuracy value of 99.69%.
A Web Scraping based approach for data research through social media: An Instagram case

This paper presents a Web Scraping based approach to solve the data extraction problem in Instagram. Data from Instagram can be helpful in different research contexts. We describe the theoretical characterization of our proposal and its implementation in a real-case scenario. Also, we provide preliminary results and potential implementation challenges. The main contribution of our work is an instrument for research applying social media data analytics. Thus, researchers can take advantage of Instagram as a rich and dynamic data source.
PointFix: Learning to Fix Domain Bias for Robust Online Stereo Adaptation

Online stereo adaptation tackles the domain shift problem, caused by different environments between synthetic (training) and real (test) datasets, to promptly adapt stereo models in dynamic real-world applications such as autonomous driving. However, previous methods often fail to counteract particular regions related to dynamic objects with more severe environmental changes. To mitigate this issue, we propose to incorporate an auxiliary point-selective network into a meta-learning framework, called PointFix, to provide a robust initialization of stereo models for online stereo adaptation. In a nutshell, our auxiliary network learns to fix local variants intensively by effectively back-propagating local information through the meta-gradient for the robust initialization of the baseline model. This network is model-agnostic, so can be used in any kind of architectures in a plug-and-play manner. We conduct extensive experiments to verify the effectiveness of our method under three adaptation settings such as short-, mid-, and long-term sequences. Experimental results show that the proper initialization of the base stereo model by the auxiliary network enables our learning paradigm to achieve state-of-the-art performance at inference.
Human Perception Measurement by Electroencephalography for Facial Image Compression

Facial images are the main focused contents in video conferences and many other applications. Therefore, it turns to a critical issue to measure and maintain the perceptual quality of facial images if transmitted over a bandwidth-limited communication system. In this letter, we propose a regional distortion perceptual threshold measurement model based on electroencephalography (EEG) to establish the relationship between image quality and human perception. Then, a facial image compression method is presented based on the model to improve the perceptual quality. Specifically, we construct a facial image dataset with regional distortion using the better portable graphics (BPG) compression. With the dataset, we design an EEG experiment and collect the brain responses to measure the human perception on regional distortions. By this method, a regional distortion perceptual threshold map (RDPTM) is constructed to guide the data rate allocation process for different regions of facial images. The experimental results show that our method can measure the human perception of regional distortion using EEG and improve the image perceptual quality by data rate allocation based on the RDPTM.
Polarization Diverse True Heterodyne Receiver Architecture for Continuous Variable Quantum Key Distribution

We present a polarization diverse receiver architecture for continuous variables quantum key distribution. We demonstrate experimentally that our system is capable of reliably achieving theoretical security against collective attacks, even under very adverse random polarization drift scenarios, and is capable of functioning indefinitely without any user input. We show that, in a similar scenario, a standard CV-QKD system without polarization diversity cannot distribute keys securely. Our receiver architecture forgoes the need for any polarization matching calibrations or feedback loops and is usable in any locally generated local oscillator key distribution system. A theoretical study on the security impact of polarization drift is presented, based on which we show that, when using our polarization diverse receiver, it is possible to achieve the performance of a channel with zero polarization drift.
An application of a virus optimization algorithm to the problem of computing binary self-dual and LCD codes

In this paper, we employ a virus optimization (VO) algorithm, which is one of the metaheuristic optimization techniques, and a known construction method to compute many new binary [72, 36, 12] self-dual codes and optimal/near-optimal linear complementary dual (LCD) codes.In particular, we obtain 39 Type I and 19 Type II codes of length 72, with parameters in their weight enumerators that were not known in the literature before, and 85 new binary LCD codes that are either optimal or near-optimal.We also present the generator matrix of extended Golay code [24, 12,8] by a cyclic group matrix ring element.
Fall-Sense: An Enhanced Sensor System to Predict and Detect Elderly Falls using IoMT

Falls are the greatest cause of death in elderly people. Once falling occurs, it is challenging to control the frequency of occurrences of falls. There is a possibility of reducing the frequency of occurrences of falls by monitoring and predicting the falls. With that, having a device or a system to predict the occurrences of falls can be a huge advancement in the wearable technology.
Referring Object Manipulation of Natural Images with Conditional Classifier-Free Guidance

We introduce the problem of referring object manipulation (ROM), which aims to generate photo-realistic image edits regarding two textual descriptions: 1) a text referring to an object in the input image and 2) a text describing how to manipulate the referred object. A successful ROM model would enable users to simply use natural language to manipulate images, removing the need for learning sophisticated image editing software. We present one of the first approach to address this challenging multi-modal problem by combining a referring image segmentation method with a text-guided diffusion model. Specifically, we propose a conditional classifier-free guidance scheme to better guide the diffusion process along the direction from the referring expression to the target prompt. In addition, we provide a new localized ranking method and further improvements to make the generated edits more robust. Experimental results show that the proposed framework can serve as a simple but strong baseline for referring object manipulation. Also, comparisons with several baseline text-guided diffusion models demonstrate the effectiveness of our conditional classifier-free guidance technique.
FAIR research data integration in CRIS at FAU Erlangen-Nürnberg

CRIS systems are an important resource about the university's scientists, research projects and publications. The functionality of FAU's CRIS was constantly extended by several additional entity types in the last years, such as affiliations, research infrastructure, internal applications and more. Most of them go beyond standards like CERIF or ”Kerndatensatz Forschung”, but follow their general principles. In this paper, we present the current state of our approach in supporting FAIR research data management in our CRIS. We achieve synergy effects between research data and research information, since some parts of research data's meta data are already contained in the CRIS.
ZCC: Mitigating Double-spending Attacks in Micropayment Bitcoin Transactions

Bitcoin is one of the popular cryptocurrencies but it has many pitfalls due to its slow payment nature. The average confirmation time of a Bitcoin transaction is ten minutes. Block propagation, validation, and mining are time-consuming tasks in the Bitcoin network. They increase confirmation delay, which becomes a large overhead, especially for micropayment transactions. In a micropayment transaction, the buyer expects quick service and the seller expects fast payment. Another downside of excess delay is that attackers get better chances to perform successful double-spending attacks. In this paper, we propose a protocol named ZCC that not only provides quick service to users but also guarantees payment to sellers. In ZCC, a group of full nodes acts as an approval committee that commits the user's ZCC transactions based on majority approvals obtained from group members. In the ZCC scheme, mining time is much less compared to the mining time of Bitcoin transactions because of lower difficulty targets. We analyze total acceptance time and the probability of a double-spending attack in the ZCC scheme. Also, we evaluate the performance of ZCC using the BlockSim simulator for the different transaction and block-related parameters, and observe substantially improved performance for the ZCC scheme.
Generative Subgraph Contrast for Self-Supervised Graph Representation Learning

Contrastive learning has shown great promise in the field of graph representation learning. By manually constructing positive/negative samples, most graph contrastive learning methods rely on the vector inner product based similarity metric to distinguish the samples for graph representation. However, the handcrafted sample construction (e.g., the perturbation on the nodes or edges of the graph) may not effectively capture the intrinsic local structures of the graph. Also, the vector inner product based similarity metric cannot fully exploit the local structures of the graph to characterize the graph difference well. To this end, in this paper, we propose a novel adaptive subgraph generation based contrastive learning framework for efficient and robust self-supervised graph representation learning, and the optimal transport distance is utilized as the similarity metric between the subgraphs. It aims to generate contrastive samples by capturing the intrinsic structures of the graph and distinguish the samples based on the features and structures of subgraphs simultaneously. Specifically, for each center node, by adaptively learning relation weights to the nodes of the corresponding neighborhood, we first develop a network to generate the interpolated subgraph. We then construct the positive and negative pairs of subgraphs from the same and different nodes, respectively. Finally, we employ two types of optimal transport distances (i.e., Wasserstein distance and Gromov-Wasserstein distance) to construct the structured contrastive loss. Extensive node classification experiments on benchmark datasets verify the effectiveness of our graph contrastive learning method. Source code is available at https://github.com/yh-han/GSC.git .
ChaCo: Character Contrastive Learning for Handwritten Text Recognition

Current mainstream text recognition models rely heavily on large-scale data, requiring expensive annotations to achieve high performance. Contrast-based self-supervised learning methods aimed at minimizing distances between positive pairs provide a nice way to alleviate this problem. Previous studies are implemented from the perspective of words, taking the entire word image as model input. But characters are actually the basic elements of words, so in this paper, we implement contrastive learning from another perspective, i.e., the perspective of characters. Specifically, a simple yet effective method, termed ChaCo, is proposed, which takes the characters and strokes (called a character unit) cropped from the word image as model input. However, in the commonly used random cropping approach, the positive pairs may contain completely different characters, in which case it is unreasonable to minimize the distance between positive pairs. To address this issue, we introduce a Character Unit Cropping Module (CUCM) to ensure the positive pairs contain the same characters by constraining the selection region of the positive sample. Experiments show that our proposed method can achieve much better representation quality than previous methods while requiring fewer computation resources. Under the semi-supervised setting, ChaCo can achieve promising performance with an accuracy improvement of 13.1 points on the IAM dataset.
Analysis of Financial Data Risk and Network Information Security by Blockchain Technology and Edge Computing

To solve the problem of computer network security, this study creatively establishes a new information security (IS) strategy recommendation system and a cooperative intrusion detection system (CIDS) based on blockchain (BC) and edge computing (EC) technologies. The innovation lies in the proposal of the real-time CIDS method. First, the related technologies and theories of the Bayesian network, deep learning (DL), and neural network (NN) are analyzed. Second, a real-time IS strategy recommendation system is constructed using NNs and expert systems, and CIDS is built on BC and DL networks. Finally, experiments compare recurrent NN and long-short term memory (LSTM). The research results reveal that the accuracy of LSTM can reach about 93%. The comparison between the single intrusion detection system (SIDS) and CIDS denotes that CIDS is better than SIDS in detection accuracy, and the specific performance is 97.54% > 91%. The proposed model based on business continuity management can help enterprises better protect financial data and establish a complete protection system of network information security (NIS). In this study, a CIDS with good performance is obtained, which lays a foundation for the further development of the NIS.
A Simple Convolutional Neural Network for Small Sample Multi-lingual Offline Handwritten Signature Recognition

Based on convolutional neural network, a simpler network for offline handwritten signature recognition is proposed in this paper. A total of 7,200 handwritten signature images in Chinese, Uyghur, and Kazakh languages were collected from 300 volunteers to establish an offline multi-lingual handwritten signature database. The signature images were randomly divided into training, verification, and testing sets. The average accuracy of five experiments was taken as the recognition result. Because it is difficult to collect a large number of signatures at one time, many experiments have been carried out on a multi-lingual signature database and CEDAR database with the training samples are 6, 12, and 18 respectively. Experimental results show that this method is an effective signature recognition method, and the effect is better than the existing network when there are few training samples.
A Motion-based Measurement Method for Monocular Vision System

Monocular vision system is very commonly used in robots. But it is difficult for a monocular vision system to obtain the depth information of object, which results in the failure of 3D (three dimensional) measurement. In this paper, a motion-based measurement method is proposed for a monocular vision system consisting of yawing camera. The depth of object is estimated with the motion increment based on the interaction matrix when the camera translates with the moving platform. Then the object's X- and Y- coordinates are computed with the depth and its imaging coordinates. In the two typical cases of front view and side view, the errors caused by different variables are analyzed. The error in the motion increment is always an important error source. The experimental results verify the effectiveness of the proposed measurement method.
CircleSnake: Instance Segmentation with Circle Representation

Circle representation has recently been introduced as a “medical imaging optimized" representation for more effective instance object detection on ball-shaped medical objects. With its superior performance on instance detection, it is appealing to extend the circle representation to instance medical object segmentation. In this work, we propose CircleSnake, a simple end-to-end circle contour deformation-based segmentation method for ball-shaped medical objects. Compared to the prevalent DeepSnake method, our contribution is threefold: (1) We replace the complicated bounding box to octagon contour transformation with a computation-free and consistent bounding circle to circle contour adaption for segmenting ball-shaped medical objects; (2) Circle representation has fewer degrees of freedom (DoF = 2) as compared with the octagon representation (DoF = 8), thus yielding a more robust segmentation performance and better rotation consistency; (3) To the best of our knowledge, the proposed CircleSnake method is the first end-to-end circle representation deep segmentation pipeline method with consistent circle detection, circle contour proposal, and circular convolution. The key innovation is to integrate the circular graph convolution with circle detection into an end-to-end instance segmentation framework, enabled by the proposed simple and consistent circle contour representation. Glomeruli are used to evaluate the performance of the benchmarks. From the results, CircleSnake increases the average precision of glomerular detection from 0.559 to 0.614. The Dice score increased from 0.804 to 0.849. The code has been released: .
Anime Characters Generation with Generative Adversarial Networks

Automatic anime character generation has the potential to motivate professionals to create new characters while also lowering the cost of creating animation. Adversarial generative networks (GANs) have yielded impressive results in the field of image synthesis. There have been some attempts to apply the GAN model to produce anime character facial images. The increasing number of contributions in recent years requires a systematic summary and analysis of new findings to speed up future research. This paper presents a comprehensive analysis of the GAN-based generation of anime characters. This paper introduces the concept of generative adversarial networks at first, including the adversarial idea as well as its algorithms. Then, the author presents GANs’ application in anime character generation, including the ideas and new findings in this field. Finally, the author also offers a summary of the open challenges for anime character generation.
A Tourism Introduction Application Using Augmented Reality

Tourism is a journey from one place to another. Whether it is an individual, a group, or a company, participants on this trip are interested in mental balance, such as reducing stress, entertaining themselves, and refreshing. A tourist attraction is one of the products or advantages of an area, where the region can create income and attract tourists to their tourist destinations. One way to promote tourism more attractively is with augmented reality media. This tourism introduction application using Augmented reality technology aims to make it easier for tourists to get to know tourism with interactive media. This tourism introduction application is needed for promotional media, including video playback features of Augmented reality technology and information about tourism. Augmented Reality is a real object in an area map that will become a marker object by detailing the tourist plan. A scan can be carried out to display 2D images, text, audio, and video with the android platform so that it can make it easier for users to use it. This research aims to design and build a tourism introduction application with the Application of Augmented Reality Technology. This research uses the Multimedia Development Life cycle method, with six stages: concept, design, material collecting, assembly, testing, and distribution, with the testing method using alpha and beta tests. The results of this research are in the form of an Android-based tourism introduction augmented reality application. This application can give contributions to assist tourists in finding information about tourism in an area and help the Department of Tourism and Culture promote tourism in the region more attractively.
Analysis of Effective Online Learning Media during Covid-19 Pandemic (Study of Student's and Teacher's Perception)

The Focus of this research to determine how Effective is online learning during pandemic using Microsoft Teams and Zoom applications that used by Teacher and Student. The methodology is using Technology Acceptance Model (TAM) to research the effect of innovation on user behavior. This research emphasizes to analyze how effective is online learning during pandemic covid-19 and which application is most used for teaching purpose and learning for student during pandemic covid 19. Research method used for this research is quantitative technique by information assortment utilizing questionnaire from Google form. The data will be processes using SPSS. We expect for the result can be reference for the future so Teacher and student can understand which application most used during pandemic covid-19 for Teaching and learning during pandemic covid-19.
Round-Optimal Black-Box Secure Computation from Two-Round Malicious OT

We give round-optimal black-box constructions of two-party and multiparty protocols in the common random/reference string (CRS) model, with security against malicious adversaries, based on any two-round oblivious transfer (OT) protocol in the same model. Specifically, we obtain two types of results.