Reviewerly/exHarmony huggingface dataset:
Research on the Storage and Sharing Model of Electronic License Based on Blockchain

Traditional electronic license management system has some problems, such as data fraud and difficulty in sharing. To solve these problems, a blockchain-based electronic license storage and sharing model are proposed. Firstly, build alliance chain, and use the characteristics of blockchain to ensure the authenticity of electronic license information. Secondly, the El Gamal digital signature scheme is improved. Finally, the PBFT consensus algorithm is improved. The analysis and results show that the model can effectively and quickly realize the secure storage and sharing of electronic licenses.
A Posteriori Model Of Professional Development Of Students In Extracurricular Activities

The role of practical training of university students is increasing in connection with the global challenges in the labor market. The article analyzes the problems that arise in the world and domestic labor market and suggests an effective mechanism for solving them. It is students’ practice-oriented training in extracurricular activities of the university. The analysis of scientific research on the role and place of extracurricular activities in students’ professional and personal development allowed us to clarify the basic concepts of the study and build a posteriori model of students’ professional training in extracurricular activities, consisting of several components: target, activity, evaluating and effective. The principles of university students’ extracurricular activities organization, revealed in the work, helped to identify the main ideas in implementation of the designed model in practice of the university. The article is addressed to teachers of higher education, organizers and participants of extracurricular activities interested in effective training of future university graduates.
Towards more realism? Challenging the aesthetization of pregnant bodies on social media

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Facial recognition with mask during pandemic period by big data technical of GMM

At this pandemic period, for the safety demand of emigration, footprint tracking of disease carrier, pandemic control…etc., it is urgent as well as important to do an automatic recognition of a person with mask. This study uses Mel-frequency Cep-strum technic to simulate and extract human features; uses big data technician of supervising learning method and VQGMM to find out the impact factors of human features that affecting human recognition hit rate. This study using same algorithm to do four time of testing with mask and without mask. The study result show, after supervising training, the testing result of the people with mask is better than without mask which gave evidence of the algorithms of this study is robust.
Differential Privacy Image Publishing based on NMF and SVD

Personal privacy has become one of the hot topics in the era of big data. Differential privacy protection technology has strong protection ability and relies on rigorous statistical model, which makes it widely studied and applied. For privacy protection in gray face image publishing, SRA and ESRA methods have been proposed, but they are not robust enough and prone to damage the image. Therefore, this paper proposes face image publishing algorithms NSRA and NESRA based on the combination of NMF and SVD matrix decomposition under heuristic and exponential mechanism. Based on the experimental comparison of single image data availability and real face database recognition accuracy, both NSRA and NESRA algorithms are better than SRA and ESRA algorithms, and the image stability is stronger. Compared with SRA method and ESRA method, this algorithm has better usability and robustness, and can be more effectively used in social platforms and medical systems.
Analyses of Hate and Non-Hate Expressions during Election using NLP

Social media platforms such as Twitter were able to connect people around the world closer by enabling them to actively connect and communicate as well as exchange ideas on social issues; however, it has also become niche for misinformation and hate speech which does not only affects peoples' perception of truth but can also lead to hate crimes as studies confirmed. Recent works in text classification has proved pivotal in detecting hate speech but is continued to be limited by some problems. Given this, the study aimed to further analyze the composition of hate expressions using the collection of tweets posted during the 2016 Philippine Presidential Election. Word bubbles were generated to identify word similarity and differences between hate and non-hate. Principal Component Analysis with K-means clustering was used to further confirm clustering of hate targets against the grouping from annotation. Results showed that both hate, and non-hate tweets have around 55% different terms while the clustering of hate targets based on annotation does not complement the groupings generated in terms of nearest neighbors. Lastly, lexical diversity reveals significant difference between hate classes and targets which suggest that it can be potentially used as a feature to improve hate speech classification.
Improving Effectiveness of STEM Education in Covid-19 Pandemic – A Pilot Study

Technology has evolved immensely over the past few decades. It has also created a huge impact over the STEM education. The methods and tools for education are reforming day to day to enhance the quality of teaching and learning. Recently, COVID-19 pandemic has totally changed the entire world due to its impact on almost all sectors including education. To provide a high standard education to students, it is necessary to innovate some new technology driven strategies and methods. Student fraternity faces many difficulties in present online education system. Some measures are to be taken to overcome these problems. Henceforth, in this work, several techniques have been discussed towards the enhancement of online education system. Approaches involving applications of Artificial Intelligence, Machine Learning and Big Data are addressed here. The technology impact on online STEM education in pandemic is assessed through a student feedback analysis. The proposed techniques in this paper could be used as a pilot study to improve the effectiveness of STEM education in pandemic.
A Transformer Architecture with Adaptive Attention for Fine-Grained Visual Classification

The fine-grained visual classification (FGVC) problem is to classify different subclasses in same superclass. Due to the similarity between subclasses, the problem requires capturing fine-grained discriminative features. Although current approaches are able to extract more fine-grained features by designing complex feature extraction modules, the excessive focus on discriminative features results in ignoring massive global feature information and reducing the ability of resisting background noise. This paper propose a transformer architecture based on vision transformer (ViT) with adaptive attention (TransAA). To optimize the attention of ViT, we design two modules. An attention-weakening module is designed to enforce the model to capture more feature information, and an attention-enhancement module is designed to enhance the extraction ability of the critical features. Otherwise, we introduce a sample weighting loss function in the training process to adaptively adjust both weakening and enhancement processes. The performance of the TransAA is demonstrated on three benchmark fine-grained datasets.
Policies and Strategic Issues in Promoting Rapid Industrial Development in Nigeria

This concluding chapter highlights some of the key policies and strategies which ought to be pursued by government in order to achieve the goal of rapid industrial development in Nigeria. This is considered to be of paramount importance because without such a well defined and articulated framework, policy formulation and execution are likely to be opportunistic without any underlying strategic focus or commitment to long term goals.
NURBS-based trajectory design for motion planning in a multi-obstacle environment

This work presents a novel solution for the satisfaction of continuous-time constraints through the use of NURBS curves. Exploiting the NURBS functions properties (local support, convexity and positivity) and applying knot refinement strategies, we improve on the sufficient conditions which guarantee obstacle avoidance and the validation of velocity bounds. We show that feasibility and performance are significantly improved with only a moderate (and controllable) increase in complexity. We highlight the approach for off-line trajectory design with obstacle(s) avoidance.
Hate Speech Detection on Social Media Using Graph Convolutional Networks

Detection of hateful content on Twitter has become the need of the hour. Hate detection is challenging primarily due to the subjective definition of “hateful”. In the absence of context, text content alone is often not sufficient to detect hate. In this paper, we propose a framework that combines content with context, to detect hate. The framework takes into account (a) textual features of the content and (b) unified features of the author to detect hateful content. We use a Variational Graph Auto-encoder (VGAE) to jointly learn the unified features of authors using a social network, content produced by the authors, and their profile information. To accommodate emerging and future language models, we develop a flexible framework that incorporates any text encoder as a plug-in to obtain the textual features of the content. We empirically demonstrate the performance and utility of the framework on two diverse datasets from Twitter.
Multimodal Sentiment Analysis Based on Attention Mechanism and Tensor Fusion Network

In recent years, an increasing number of people have indicated their inclination to express their feelings and opinions in the form of text and pictures on social media. Thus, the amount of multimodal data with text and pictures as the main content is increasing. By analyzing the sentiment such multimodal data, people’s attitudes and opinions can be understood. To solve the problem of information redundancy in the multimodal sentiment classification task, first, an image feature extraction model is established based on an attention neural network, which highlights the key areas of the image sentiment information. Second, the tensor product of the text and image mode is used as the joint feature expression of the multimodal data by using the tensor fusion method. After that, a specific information extraction module is designed to extract the fusion features and eliminate the redundant information in the joint features. Results of the experiments performed on two real Twitter image and text datasets demonstrate that the proposed model can outperform the existing models in classifying sentiments.
Computer-Assisted Heuristic Evaluation of Data Visualization

Heuristic evaluation has been an important part of data visualization. Many heuristic rules and guidelines for evaluating data visualization have been proposed and reviewed. However, applying heuristic evaluation in practice is not trivial. First, the heuristic rules are discussed in different publications across different disciplines. There is no central repository of heuristic rules for data visualization. There are no consistent guidelines on how to apply them. Second, it is difficult to find multiple experts who are knowledgeable about the heuristic rules, their pitfalls, and counterpoints. To address this issue, we present a computer-assisted heuristic evaluation method for data visualization. Based on this method, we developed a Python-based tool for evaluating plots created by the visualization tool Plotly. Recent advances in declarative data visualization libraries have made it feasible to create such a tool. By providing advice, critiques, and recommendations, this tool serves as a knowledgeable virtual assistant to help data visualization developers evaluate their visualizations as they code.
Multi-robot Path Planning Based on Spatio-Temporal Information in Large-scale Unknown Environment

Multi-agent path planning is an important re-search direction in robotics due to its wide range of applications such as autonomous driving, automatic storage and so on. Off-the-shelf methods usually only consider the spatial information of the environment where the robot is located. Here we argue that the robot should use both spatial and temporal information to complete action decisions. To this end, We propose a novel multi-robot path planning model mainly composed of three modules: a Convolutional Neural Network (CNN) module, a Graph Neural Network (GNN) module and a Long short-term memory (LSTM) module. Specifically, CNN is responsible for extracting the local spatial features of each robot, and the GNN enables multiple robots to communicate their local spatial features with the neighbours. The LSTM completes the task of fusing temporal information. The combination of temporal and spatial information allows the robot to have a memory function when making behavioral decisions. We validated our approach in some complex environments, and the simulation results show that our approach has a better path planning performance than state of the arts.
Gabor Filter Incorporated CNN for Compression

Convolutional neural networks (CNNs) are remarkably successful in many computer vision tasks. However, the high cost of inference is problematic for embedded and real-time systems, so there are many studies on compressing the networks. On the other hand, recent advances in self-attention models showed that convolution filters are preferable to self-attention in the earlier layers, which indicates that stronger inductive biases are better in the earlier layers. As shown in convolutional filters, strong biases can train specific filters and construct unnecessarily filters to zero. This is analogous to classical image processing tasks, where choosing the suitable filters makes a compact dictionary to represent features. We follow this idea and incorporate Gabor filters in the earlier layers of CNNs for compression. The parameters of Gabor filters are learned through backpropagation, so the features are restricted to Gabor filters. We show that the first layer of VGG-16 for CIFAR-10 has 192 kernels/features, but learning Gabor filters requires an average of 29.4 kernels. Also, using Gabor filters, an average of 83% and 94% of kernels in the first and the second layer, respectively, can be removed on the altered ResNet-20, where the first five layers are exchanged with two layers of larger kernels for CIFAR-10.
Impact of blockchain technology on smart grids - A systematic literature review

As the energy systems transform and power distribution moves towards smart grids comprised of multiple distributed energy resources (DERs) such as renewable energy and battery storage, there is a major need to develop platforms that can decentralise energy distribution due to the shortcomings of a centralised distribution system such as high coordination costs and low efficiency. Since its inception in 2008, blockchain technology has found its application in every field from finance to healthcare and due to its decentralised nature and potential for secure transactions, researchers have also found various applications of blockchain in the energy sector. In this article, we review the impact of blockchain technology on smart grids and distributed energy resources. A systematic literature analysis of 72 research articles was conducted based on defined criteria, which helped in identifying the challenges, methodologies, current state of the art, research gaps and future directions for research. As a result, four key focus areas of blockchain energy research were identified as Transactive energy, Electric Vehicle Integration, Privacy and Security and Demand Response, as well as some other relatively new and unexplored areas. Further, we have highlighted the shortcomings of the existing research and the limitations of current systems that need to be overcome before blockchain technology can be implemented on a large scale.
Largest Ellipsoid Applications in Decentralized Multi-Agent State Estimation

When estimating the states of several agents that exchange information, it is common that the state errors of multiple agents will become correlated. When it is infeasible to keep track of this correlation, covariance intersection provides a tool to keep the state estimates from becoming overconfident. Covariance intersection (CI) can be very conservative however, and the information fusion community has developed a less conservative alternative known as largest ellipsoid (LE) estimation. Unlike CI, LE is only provably consistent in very specific situations, but has been demonstrated to be consistent in fusion applications. We adapt this method slightly to be usable in non-linear state estimation. This is demonstrated in two example problems; a line of cars that can range to each other and lose GPS while going through a tunnel, and a small aircraft swarm with inter-agent ranging and anchor nodes. In the car example we demonstrate LE achieving superior performance to CI while maintaining consistency. In the aircraft example we demonstrate consistent LE estimation with a more nonlinear problem, and show how LE estimation can be used to implement soft constraints with unknown dynamics.
Neural Network-based Soil Detection and Classification

Soil classification is the disintegration of soil sets to specific gatherings having like attributes and comparable behaviors. Practically many nations do product trading, in which those nations sending out higher horticulture products are especially rely upon the soil qualities. In this manner, soil quality recognition and classification are a lot of significant. Recognition of the soil kind assists with keeping away from horticultural product amount misfortune. This paper introduces a fully connected network (FCN), deep learning model-based recognition of the soil kinds. Soil classification incorporates steps like image acquisition, feature extraction, and classification. The proposed method produces an average accuracy of 97.2% with an average mean of 65.27 and average energy of 0.0298. The proposed model classifies peat, sandy Clay, Silty Sand, and Human clay soil types effectively. Keywords: Classification; Fully Connected Network; Deep Learning, Soil Detection, Soil Classification.
Cryptography Implementation for electronic voting security

Electronic voting is done by recording election data centrally and presenting it into fast digital information. E-voting is an internet technology, which aims to improve the performance of the voting process, and is expected to be a breakthrough in technological development that helps human work. Security constraints in e-voting include the distribution of voting, for example voters who distribute voting rights, are illegitimate and can abuse voters’ rights, because they know the voter’s account to access the system. This study implements cryptographic methods as a password security for voter accounts, and the results of voting, so that the secrecy of the voters’ data and the results of voting are maintained. RSA algorithm to implement cryptographic techniques based on two mathematical problems, namely factorization and modulo operations on composite numbers, which use two different keys for encryption and decryption processes. The public key is used encryption process to encode text, and the private key is used description process, namely translating ciphertext into plaintext.
HPSG-Inspired Joint Neural Constituent and Dependency Parsing in O($n^3$) Time Complexity

Constituent and dependency parsing, the two classic forms of syntactic parsing, have been found to benefit from joint training and decoding under a uniform formalism, inspired by Head-driven Phrase Structure Grammar (HPSG). We thus refer to this joint parsing of constituency and dependency as HPSG-like parsing. However, in HPSG-like parsing, decoding this unified grammar has a higher time complexity (<inline-formula><tex-math notation="LaTeX">$O(n^5)$</tex-math></inline-formula>) than decoding either form individually (<inline-formula><tex-math notation="LaTeX">$O(n^3)$</tex-math></inline-formula>) since more factors have to be considered during decoding. We thus propose an improved head scorer that helps achieve a novel performance-preserved parser in <inline-formula><tex-math notation="LaTeX">$O(n^3$</tex-math></inline-formula>) time complexity. Furthermore, on the basis of this proposed practical HPSG-like parser, we investigated the strengths of HPSG-like parsing and explored the general method of training an HPSG-like parser from only a constituent or dependency annotations in a multilingual scenario. We thus present a more effective, more in-depth, and general work on HPSG-like parsing.
FPGA Implementation and Comparative Analysis of DES and AES Encryption Algorithms Using Verilog File I/O Operations

Data communicated electronically is prone to attacks. Due to communication systems and wireless communications technology advancements, providing data security plays a vital role. Expanded interest for information security is an evident reality. The need to protect the critical network infrastructure and information systems has become important. Towards accomplishing higher security, cryptographic algorithms assume a significant part in the safeguarding of information from unapproved utilization. Due to the ability to provide faster and more customizable solutions, the field-programmable gate arrays (FPGAs) are suitable option to implement the cryptographic algorithms. In this paper, we have implemented the data encryption standard (DES) and advanced encryption standard (AES) encryption algorithms. Here, the input plain text data and the key input are read from a text file, and the encrypted output data is captured in a text file. The mentioned design is implemented in Verilog language using Libero SoC Design Suite, targeting the PolarFire FPGA family and the MPF500T-1FCG1152I device.
PV panel system modelling method based on neural network

In this paper, a nonlinear system of a photovoltaic (PV) module has been studied. Three types of Artificial Neural Network (ANN) time series are used for PV panel temperature prediction, these types are Nonlinear Autoregressive with External Input (NARX), Nonlinear Autoregressive (NAR) and Nonlinear Input-Output modelling methods. The input data parameters of the model were (ambient temperature, humidity, wind speed and irradiance), while the output data parameter was the PV panel temperature. All modelling techniques tested using Mean Square Error (MSE), and the results of them are compared with each other to conclude which method is the best. The results showed that NARX method documented the lowest MSE of 0.0557 while NAR and nonlinear input-output recorded 0.0560 and 1.5102 respectively.