Reviewerly/exHarmony huggingface dataset:
NITG Chain: A Scalable, Private and Permissioned Blockchain with Proof of Reputation Consensus Method

Blockchain has grown for various IT applications in different domains. A private and permissioned blockchain has high potential in business to business applications. However, scalability is a major hindrance in wide adoption of blockchain. In literature, solutions such as on-chain, off-chain, side chain, etc. have been reported to address the scalability issue in blockchain. However, still there is a room for further improvements. In this paper, we design NITG Chain (NITGoa Chain), a scalable, private and permissioned blockchain for business to business applications. Here, a node can be a part of NITG Chain network through mutual authentication and handshaking with its peer node. NITG Chain applies Proof of Reputation consensus method, in which only authorized and reputed nodes are allowed to mine the block. A block created by any authorized node is verified by other authority nodes and appended to NITG Chain after receiving and verifying the confirmation from 2/3 of authority nodes. The selection of authority node for block creation is done based on its liveliness and reputation. NITG Chain ensures blockchain reliability, security from blockchain fork and affordable throughput and scalability. The functional and performance validation of NITG Chain is done using a testbed at NIT Goa by applying different size of transactions and blocks. From the experimental results, it is observed that NITG Chain achieves throughput 712 tps on average, while generating a block at an interval of 10 s.
Research on Positioning of Mobile Robot with Manipulator Arm for Machining of Large Cabin Support in Original Position

The processing of large-scale cabin support parts for manned spaceflight requires repeated installation, processing and measurement, resulting in low processing efficiency and poor processing accuracy. This paper proposes a method of using a mobile processing robot to complete the processing of bracket parts, and studies the difficult problem of station planning in this method. This paper establishes the coordinate system and coordinate transformation system of each object in the processing process, rasterizes the working space of the processing cabin, and uses the inverse solution of the robot to filter out the feasible station area in the working space, and through the type of support that can be processed The optimal Z-direction feasible site hierarchy is selected with the number of site points, and finally the smallest site set is selected in the optimal hierarchy to complete the site planning of the mobile robot. In this paper, we select a mobile robot with a KUKA manipulator to randomly process a space capsule for experimental verification. The results show that this method can effectively improve the processing efficiency of the capsule bracket.
Online and offline mixed teaching design of French based on big data fuzzy K-means clustering

The mixed online and offline education in college French teaching not only caters to the current learning habits of young students, but also further enriches students' learning styles and expands new ideas for educational diversification. Aiming at the problem of inaccurate classification of big data information in traditional French teaching ability evaluation algorithm, a French teaching ability evaluation algorithm based on big data fuzzy K-means clustering and information fusion is proposed. Firstly, the fuzzy K-means clustering algorithm of big data is analyzed, and the online and offline learning behavior model and behavior analysis platform of students are established. In addition, students' learning behavior is taken as the basis, and it is used as the source of big data to realize prediction analysis and example verification. On this basis, do further big data mining, master students' online learning habits, provide better data guidance for mixed teaching, and achieve better teaching effect.
A 3D Dubins Curve Constructing Method Based on Particle Swarm Optimization

The navigation error of aircraft increases in task. Aircraft has to correct the navigation error under structure constraints to avoid path deviation caused by navigation error. Aircraft path planning with navigation correction under the turning radius constraint is a challenge for traditional path planning methods. In this paper, we propose a 3D Dubins curve constructing method which can draw a smooth path in 3D space for the aircraft, next we extend Dynamic Programming for Navigation Error Correction method by 3D Dubins curves to abtain a feasible path under the constraints of turning radius, and then we improve particle swarm optimization method to compute an almost optimal Dubins curve. Finally our algorithm return a feasible smooth path with approximately the optimal length for the path planning problem with navigation correction under the turning radius constraint.
Computer Vision Based Mouse With Color Detection Technique

In this research paper, we have used computer vision technology for the development of interaction between computers and humans. This technology can act as an interface between computers and humans. Nowadays, everyone uses mouse and keyboards to interact with computers. This traditional approach of interaction is costly as well as not a user-friendly approach. In this paper, we propose a computer vision-based mouse with a color detection technique that uses a web camera of our computer to detect the color pointer and accordingly it perform the various mouse functions on the screen. This developed approach of performing different mouse functions can reduce the hardware cost of the computer and make it user-friendly for everyone.
Metric for Comparison of Graph-Theoretic Models of the Same Dimension with Ordered Vertices

The work is dedicated to methods of comparison and classification of graph-theoretic models which are known within the direction of graph matching. It contains an overview of metrics for comparing graphs based on a maximum common subgraph. A modification of the measure based on a maximum common subgraph is proposed, which takes into account the ordering of vertices (each vertex is associated with its unique serial number). If the graphs have the same dimension (i.e. same number of vertices) it is shown that this measure satisfies all the properties of the metric (nonnegativity, identity, symmetry, triangle inequality). It is assumed that this metric can be used to solve the problem of text attribution.
Research on Motion State Recognition of Random Forest Based on Bayesian Optimization

In order to improve the accuracy of recognition of human motion states by smart phones, a deep recognition method based on Bayesian optimized Random Forest (B-RF) is proposed. Firstly, the sensor data input format is standardized by using time coordinate labels; Secondly, k-nearest neighbor and moving time window are used to expanding and de-noising the data of acceleration sensor, gyroscope, barometer, sensitometer and distance sensor. Finally, data mining is performed on sensor data in Bayesian-optimized random forests, and human motion states are classified. Experiments show that the recognition accuracy of the method can reach 97.26% in the training of multi person model, which can improve the recognition rate of motion state.
Randomize the Future: Asymptotically Optimal Locally Private Frequency Estimation Protocol for Longitudinal Data

Longitudinal data tracking under Local Differential Privacy (LDP) is a challenging task. Baseline solutions that repeatedly invoke a protocol designed for one-time computation lead to linear decay in the privacy or utility guarantee with respect to the number of computations. To avoid this, the recent approach of Erlingsson et al. (2020) exploits the potential sparsity of user data that changes only infrequently. Their protocol targets the fundamental problem of frequency estimation for longitudinal binary data, with l∞ error of O ((1 / ε) ⋅ (log d)3/2 ⋅ k ⋅ √ n ⋅ log (d / β)), where ε is the privacy budget, d is the number of time periods, k is the maximum number of changes of user data, and β is the failure probability. Notably, the error bound scales polylogarithmically with d, but linearly with k.
An Efficient Search Method Using Features to Match Joint Keywords on Encrypted Cloud Data

With the continuous improvement of the security of cloud storage, more users upload private data to the cloud. However, a large number of encrypted data using independent keywords to create indexes not only directly increase the storage overhead, but also lead to the decline of search efficiency. Therefore, this paper proposes an efficient search method using features to match joint keywords (FMJK) on encrypted cloud data. This method proposes that each <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> keywords are randomly selected from the non-duplicated keywords, which are extracted from the documents of the data owner, to generate a joint keyword, and all joint keywords form a keyword dictionary. Each joint keyword is matched with the feature of the document and the query keyword respectively, and the result obtained by the former is regarded as a dimension of the document index, while the result obtained by the latter is regarded as a dimension of the query trapdoor. Finally, the BM25 algorithm is used to calculate the inner product of the document index and the trapdoor, and then sort them and the top k results are returned. Theoretical analysis and experimental results show that the proposed method is more feasible and more effective than the compared schemes.
Ontological representation of image processing methods for coronal hole material segmentation centered on physical restrains using multi-modal scheme

The manuscripts demonstrates the consequences on the way to advance in addition authenticate image processing techniques intended for deciding the finest physical models centered happening on solar image surveillances. This technique comprises of choosing the physical models centered proceeding on by means of coronal holes extracted from the images. Eventually, the objective is on the way to custom physical models in order to forecast geomagnetic storms. For coronal hole segmentation it is centered on physical restraints progressing a multi-modal scheme which customs segmentation maps commencing from three diversified methods in order to initialize a level-set technique which progresses the preliminary coronal hole segmentation towards the magnetic boundary. These coronal holes are clustered, mapped and validated. The suggested multi-modal segmentation technique vitally outstripped SegNet, U-net, Henney- Harvey, and FCN by giving precise boundary recognition. This suggested techniques gives 95.5% map classification precisely. An ontological representation IPCHSonto of the image processing method for Coronal Hole Segmentation have been built using protégé tool. This ontology is précised in formal languages by means of a well-defined semantics. Ontologies remain built upon a segmented understanding within a community.
TEA-Z: A Tiny and Efficient Architecture Based on <i>Z</i> Channel for Image Watermarking and Its Versatile Hardware Implementation

With the popularity of smart phones and digital cameras, authentication and protection of digital images have become important to every producer and consumer. Although many digital watermarking methods have been proposed to mitigate the growing problem of online piracy, their efficient deployment for real-time processing in hardware lacks sufficient attention. Hence, with the first introduction of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Z}$ </tex-math></inline-formula> channel, this article advances a tiny and efficient architecture named TEA-Z to implement digital watermarking for color images. Avoiding complex computing units with the general color space conversion (GCSC) and simplified discrete cosine transform (DCT), TEA-Z can better achieve the design goals of low complexity, high efficiency, and easy deployment compared with the latest methods. For FPGA implementation, TEA-Z can achieve the maximum frame rate of 3150 frames/s for 256 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 256 color images and peak energy efficiency of 4.13 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times \,\,\textbf {10}^{\textbf {5}}$ </tex-math></inline-formula> Mbps/W at the maximum frequency of 206.48 MHz. For ASIC implementation, the peak energy efficiency will be 2.40 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times \,\,\textbf {10}^{\textbf {7}}$ </tex-math></inline-formula> Mbps/W with the tiny area of 0.00356 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textbf {mm}^{\textbf {2}}$ </tex-math></inline-formula> and ultralow power of 0.2002 mW at the frequency of 200 MHz under 90-nm CMOS technology. Moreover, TEA-Z can provide various watermarking strategies with its configurations to meet different needs.
Usable Privacy and Security from the Perspective of Cognitive Abilities

Privacy, Information, and Cybersecurity (PICS) are related properties that have become a concern for more or less everyone. A large portion of the responsibility for PICS is put on the end-user, who is expected to adopt PICS tools, guidelines, and features to stay secure and maintain organizational security. However, the literature describes that many users do not adopt PICS tools and a key reason seems to be usability. This study acknowledges that the usability of PICS tools is a crucial concern and seeks to problematize further by adding cognitive ability as a key usability aspect. We argue that a user’s cognitive abilities determine how the user perceives the usability of PICS tools and that usability guidelines should account for varying cognitive abilities held by different user groups. This paper presents a case study with focus on how cognitive disabilities can affect the usability of PICS tools. Interviews with users with cognitive disabilities as well as usability experts, and experts on cognitive disabilities were conducted. The results suggest that many of the usability factors are shared by all users, cognitive challenges or not. However, cognitive challenges often cause usability issues to be more severe. Based on the results, several design guidelines for the usability of PICS tools are suggested.
A Comprehensive Emotion Recognition from Microblogs Using Deep Learning

Emotion is one of the fundamental features of human beings. With the advancement of the internet, social media platforms (e.g., Facebook, Twitter, Instagram) became an integral part of social interaction in daily life. Microblog is one of the popular platforms for sharing opinions and has become an important source for emotion research and analysis. Human holds different emotion states (e.g., Happy, Sad, Angry), but most existing studies are concerned with a few selected emotion categories. This study aims to develop a Comprehensive Emotion Recognition from Microblogs (CERM) system intelligently managing available microblog contents (texts, symbols) to recognize all the standard emotional categories: Anger, Fear, Joy, Love, Sad, Surprise, Disgust, and Neutral. As the use of emoticons (graphical symbols) is being popular in microblogs, the main attraction of the study is the management of both emoticon and text for better performance. From Twitter, various microblogs (i.e., tweets) samples are collected, processed, and used to classify all the standard emotion categories. The most popular deep learning methods, including Convolutional Neural Network and Long Short-Term Memory, are employed for emotion classification, and the best suited deep learning model is identified from the experiments. The proposed CERM system is confirmed to outperform related existing methods in comparison based on recognition accuracy.
Evidence Mechanism of Power Dispatching Instruction Based on Blockchain

With the development and application of energy Internet technology, the collaborative interaction of "source network, load and storage" has become the development trend of power grid dispatching.The large-scale access of renewable energy on the load side, the unified management of adjustable loads, and the participation of multiple parties in energy operations have put forward requirements for the safety, credibility, openness, and transparency of the load dispatching environment.Under the environment of carbon emission reduction, the paper proposed an architecture of the scheduling data blockchain, based on the in-depth study of blockchain.Moreover, smart contracts are used to realize the application scenario of load dispatching instruction evidence on the blockchain.The content and storage mode of scheduling instruction evidence on blockchain are studied.And different storage modes are adopted according to the actual needs.And the smart contract system realizes the evidence generation of power dispatching instruction.This is the basis for the normal circulation of power dispatching instruction evidence.The research significance of this paper is highlighted as follows.The data and information generated in the power dispatching process are stored as evidence.On the one hand, it can provide a basis for settlement between power production and dispatching companies and power users.On the other hand, it can prepare for distributed transactions in the power grid under the environment of carbon emission reduction.
Fuzzy Comprehensive Evaluation of Satisfaction of Network Teaching Platform

This article aims at the students' satisfaction evaluation of the online teaching resource platform and the effectiveness of online courses. Taking the wisdom tree network teaching resource platform with the highest frequency selected by students as an example, it analyzes by using the fuzzy comprehensive evaluation method. The analysis shows that the overall performance of Guangxi University students’ satisfaction with the Wisdom Tree platform is "satisfied", but the coursework and course assessment methods and mechanisms of the Wisdom Tree platform need to be improved.
A Biaffine Attention-Based Approach for Event Factor Extraction

Event extraction is an important task under certain profession domains. CCKS 2021 holds a communication domain event extraction benchmark and we purposed an approach with the biaffine attention mechanism to finish the task. The solution combines the state-of-the-art BERT-like base models and the biaffine attention mechanism to build a two-stage model, one stage for event trigger extraction and another for event role extraction. Besides, we apply several strategies, ensemble multi models to retrieve the final predictions. Eventually our approach performs on the competition data set well with an F1-score of 0.8033 and takes the first place on the leaderboard.
A novel approach of classifying ABO blood group image dataset using deep learning algorithm

Deep learning is important in the medical profession, and it has a wide range of applications, including diagnosis, research, and so on. In imaging technology, classifying the medical images in an automatic way is onerous. In the proposed work, the ABO blood group identification using novel deep learning approach for enhancement of bio medical automation. The ABO blood group data set is developed and classify the blood group automatically using Convolute neural network (CNN) which is capable of extracting and learning features from medical image dataset. As a result, the proposed innovative CNN framework is used in the medical field to classify human blood classes. As a result, our proposed dataset is used to train the model and test the sample in order to identify blood group in the shortest time possible with a 96.7 percent accuracy. The results of the proposed model are compared to those of existing CNN models such as Alex net and Lenet5. The findings show that the proposed method is the most appropriate for classifying human blood groups in medical applications.
Quality Assessment of In-the-Wild Videos via Multi-Scale Spatio-Temporal Feature Extraction

No reference quality assessment of in-the-wild videos is a challenging task as the reference video is not available and in-the-wild videos usually contain uncertain and various distortion which is from different stages of video generation, such as shooting, compression, transmission, etc. In this work, We propose a no-reference quality assessment model for evaluating in-the-wild videos. Specifically, we design a method to extract the multi-scale spatial features and motion features of videos. Then we use two temporal models to establish the long-term dependencies of the features. We evaluate the proposed model on four publicly available in-the-wild datasets, and the experimental results show that the proposed model outperforms existing representative models on both individual datasets and cross datasets.
Exploration of comfort factors for virtual reality environments

The democratization of virtual reality with head-mounted displays has brought a tremendous number of possible applications.However, depending on the used content and device, the user's quality of experience (QoE) and comfort may be significantly altered.It is thus very important to understand and model the factors involved in this process.Many authors investigated the potential aspects leading to sickness or discomfort in immersive environments, each of them targeting a set of specific factors (often a single one).Our goal in this paper is to understand how QoE and comfort are impacted by the virtual environment, observer's motion, scene complexity and rendering quality, based on psychophysical experiments.Our study starts by identifying a set of factors potentially affecting QoE.A reliable and reproducible experimental protocol is proposed to characterize their impact on the user.Base on a statistical study of the results, a better understanding of the nature of impact is achieved.
The Data Control in the System of Residual Classes

This work discusses issues related to the development and application of methods for improving the efficiency of operation based on the use of the codes of the system of residual classes (SRC). The research is aimed at presents the methods of data operational control in the SRC. The main focus is given to the control methods based on the principle of nullification of numbers in the SRC. It is shown that the resulting redundancy with the introduction of one additional (control) base provides control and correction of errors in the process of performing operations. The algorithms developed and presented in the work allow modeling the data control unit for the practical implementation of the proposed methods. The analysis is performed and the calculated data of the implementation time of the control operation for the considered methods of nullification is obtained, and the conditional amount of the control system equipment is also calculated.