Reviewerly/exHarmony huggingface dataset:
Building an All-Round and Three-Dimensional Ideological Education System for Chemical Experiment Teaching Relying on the National First-Class Chemical Experiment Course

Basic chemical experiment (I)" in Xiamen University, a course set for chemistry freshmen, is awarded national first-class off-line course in 2020.A characteristic educational system focused on both virtue and talent is formed in experimental teaching content, process, service, and laboratory construction and management.This article introduces a way for building an all-round and three-dimensional ideological education system relied on the first-class course.The aim is to provide reference experience in basic experiment teaching, not only for the enrichment of the ideological and political education content, but also for the innovation of the form of the ideological and political education.
Data Analysis and Exploration with Visual Approaches

Abstract A comprehensive study of the Earth system and its related processes requires a holistic examination and understanding of multidimensional data acquired with a large number of different sensors or produced by various models. To this end, the Digital Earth project developed a set of software solutions to study environmental data sets using visual approaches. In the following chapter, we present three data visualization products developed to deal with the challenges of the analysis and exploration of environmental data.
Fenet: Feature Enhancement Network for Arbitrary Direction Text Detection

With the development of deep learning, scene text detection becomes a frequently included task in wide range of applications in areas, such as automatic driving, scene text translation, and scene semantic understanding, etc. Existing frameworks usually encode the text candidates independently by using some hand-crafted features or deep convolution in series (e.g., ResNet, VGGNet, etc). These methods encounter some difficulties, such as the variety of text shapes, complex backgrounds, and only relatively long information can be remembered, etc. Instead, our proposed network, named as Feature Enhancement Network (FENet), can effectively deal with the arbitrary direction text detection by integrating attention mechanism and Bi-directional Long Short-Term Memory (BLSTM). There are three key characteristics in our method: (i) The feature maps is recurrently strengthened by integrating attention mechanism based CBAM module and BLSTM-based sequence feature extraction module. (ii) Updating the components of the bottleneck layer of ResNet for facilitating the network micro-batch training. (iii) The learnability of the network is enhanced by introducing the residual network structure in the BLSTM-based sequence feature extraction module. Extensive experiments have been carried out on five public datasets, i.e, ICDAR 2013, ICDAR 2015, MSRA-TD500, SCUT-CTW1500 and ICDAR2017 -MLT, the experiment results demonstrate that the proposed method achieves the promissing results. We will release code at the website https://github.com/lyy0117 to facilitate community research in future.
Digitalizing education as an educational tool for teaching in extraordinary circumstances

This paper will analyze the implementation of video lectures as a new form of teaching material which could play an important role in situations when it is necessary to adopt online education, as was the case during the global pandemic caused by the SARS-COV-2 virus. In addition to video lectures, our students were given access to a course book and PowerPoint presentations which included voice recordings of the instructor. The students took part in a survey to identify the form of teaching they found to be most useful. The results were analyzed in this paper, and a classification made in an attempt to answer the question of whether video presentations are a sound investment. The prioritization of the criteria on which to base the evaluation of the educational methods included in the teaching process will be carried out using the PIvot Pairwise RElative Criteria Importance Assessment - the PIPRECIA method.
VLSI Implementation of Lifting Based 3-DDWT

This paper proposes an inexperienced shape for lifting based totally absolutely 3-d DWT Lifting based totally absolutely for video/ picture graph signal using parallel pipeline approach. The essential intention of this paper is to lessen the important course get rid of in computing the 9/7 lossy lifting steps with reduced clock cycles. The shape consists of row, column and temporal processors and video frames are processed with inside the utilization of separability and cyclic symmetry property. The novelty of this approach is with the useful resource of the usage of using flipping form for adders and converting multipliers with the useful resource of the usage of shift and add operations. These benefits of the proposed approach for low latency, power consumption, and immoderate throughput over many modern-day architectures. To validate this model, the shape is being coded in Verilog HDL and completed with inside the utilization of Filing IS 14.7xc7a100t-3-csg324 FPGA. The basic overall performance of this shape of 3D lifting DWT processor achieves a tempo of at least 373 MHZ with low power dissipation, making it suitable for real time immoderate tempo video applications.
A new look at Bayesian uncertainty

The aim of this paper is to present an alternative perspective on the foundations of the Bayesian approach when considering uncertainty. We take the view that uncertainty is caused by the missing data rather than having only a finite set of data. This turns out to be an important distinction. The missing here refers to what would need to be seen for the parameter or statistic of interest to be fully and uniquely specified. The connection to the traditional view of Bayes can be understood via an obscure result of Doob in the 1940s; the point being that if the conditional distribution for the missing observations given the observed data is constructed in a particular way, then one can recover the traditional Bayesian posterior distribution. However, there is flexibility with how the missing data can be modeled given the observations and this forms the basis of the paper and the new ideas.
Semi-supervised Adaptive Method for Human Activities Recognition (HAR)

Using sensors and mobile devices integrated with hardware and software tools for Human Recognition Activities (HAR), is a growing scientific field, the analysis based on this information have promising benefits to detect regular and irregular behaviors in individuals during their daily activities. In this study, the Van Kasteren dataset was used for the experimental stage, and it all data was processed using the data mining classification methods: Decision Trees (DT), Support Vector Machines (SVM) and Naïve Bayes (NB). These methods were applied during the training and validation processes with the proposed methodology, and the results obtained showed that all these three methods were successful to identify the cluster associated to the activities contained in the Van Kasteren dataset. The Support Vector Machines (SVM) method showed the best results with the evaluation metrics: True Positive Rate (TPR) 99.2%, False Positive Rate (FPR) 0.6%, precision (99.2%), coverage (99.2%) and F-Measure (98.8%).
Federated Learning Using Variable Local Training for Brain Tumor Segmentation

The potential for deep learning to improve medical image analysis is often stymied by the difficulty in acquiring and collecting sufficient data to train models. One major barrier to data acquisition is the private and sensitive nature of the data in question, as concerns about patient privacy, among others, make data sharing between institutions difficult. Distributed learning avoids the need to share data centrally by training models locally. One approach to distributed learning is federated learning, where models are trained in parallel at local institutions and aggregated together into a global model. The 2021 Federated Tumor Segmentation (FeTS) challenge focuses on federated learning for brain tumor segmentation using magnetic resonance imaging scans collected from a real-world federation of collaborating institutions. We developed a federated training algorithm that uses a combination of variable local epochs in each federated round, a decaying learning rate, and an ensemble weight aggregation function. When testing on unseen validation data our model trained with federated learning achieves very similar performance (average DSC score of 0.674) to a central model trained on pooled data (average DSC score 0.685). When our federated learning algorithm was evaluated on unseen training and testing data, it achieved similar performances on the FeTS challenge leaderboards 1 and 2 (average DSC scores of 0.623 and 0.608, respectively). This federated learning algorithm offers an approach to training deep learning learning models without the need to share private and sensitive patient data.
Communication-Efficient Federated Learning With Gradual Layer Freezing

Federated learning (FL) is a collaborative, privacy-preserving method for training deep neural networks at the edge of the Internet of Things (IoT). Despite the many advantages, existing FL implementations suffer high communication costs that prevent adoption at scale. Specifically, the frequent model updates between the central server and the many end nodes are a source of channel congestion and high energy consumption. This letter tackles this aspect by introducing federated learning with gradual layer freezing (FedGLF), a novel FL scheme that gradually reduces the portion of the model sent back and forth, relieving the communication bundle yet preserving the quality of the training service. The results collected on two image classification tasks learned with different data distributions prove that FedGLF outperforms conventional FL schemes, with data volume savings ranging from 14% to 59% or up to 2.5% higher accuracy.
Adaptive Face Recognition Using Adversarial Information Network

In many real-world applications, face recognition models often degenerate when training data (referred to as source domain) are different from testing data (referred to as target domain). To alleviate this mismatch caused by some factors like pose and skin tone, the utilization of pseudo-labels generated by clustering algorithms is an effective way in unsupervised domain adaptation. However, they always miss some hard positive samples. Supervision on pseudo-labeled samples attracts them towards their prototypes and would cause an intra-domain gap between pseudo-labeled samples and the remaining unlabeled samples within target domain, which results in the lack of discrimination in face recognition. In this paper, considering the particularity of face recognition, we propose a novel adversarial information network (AIN) to address it. First, a novel adversarial mutual information (MI) loss is proposed to alternately minimize MI with respect to the target classifier and maximize MI with respect to the feature extractor. By this min-max manner, the positions of target prototypes are adaptively modified which makes unlabeled images clustered more easily such that intra-domain gap can be mitigated. Second, to assist adversarial MI loss, we utilize a graph convolution network to predict linkage likelihoods between target data and generate pseudo-labels. It leverages valuable information in the context of nodes and can achieve more reliable results. The proposed method is evaluated under two scenarios, i.e., domain adaptation across poses and image conditions, and domain adaptation across faces with different skin tones. Extensive experiments show that AIN successfully improves cross-domain generalization and offers a new state-of-the-art on RFW dataset.
Generation of Logical Function Libraries

The issues of creating libraries of logical functions used at the design stages of electronic equipment using programmable logic integrated circuits are considered.An efficient algorithm is proposed that allows solving a larger range of issues and obtaining libraries of a larger size compared to the previously proposed algorithm.
Optimized Features Extraction and Recognition System

The Smart Attendance system using Real-Time Face Recognition helps handle every day-to-day student attendance issue. An individual will not just take the attendance but also automatically document the entry-exit time of the employees. In addition, it adds to the security of the workplace as the system can acknowledge who left the designated area and when accurately. The existing outdated approach is riddled with ambiguity, resulting in incorrect and wasteful marking attendance. One of the natural characteristics that may individually identify a person is a human face. This research paper provides an efficient attendance monitoring approach based on face detection and identification. In real-time, the system captures various facial feeds. In this approach, the instructor scans his RFID tag after authorization, system's camera captures a photo of the class, reading numerous faces from it and searching them in databases. If the students' faces are founded in an institute's database, then it marks the attendance and saves data into a database. The instructor also keeps track of the students' attendance. This approach decreases the number of proxies during attendance and hence the teacher's burden. And the system manages data in the form of spreadsheets.
Lwnet: A Light-Weight Network Combining Knowledge Distillation with Self-Supervision for Monocular Depth Estimation

Depth information is crucial to understand the structural information of the scene. The supervised monocular depth estimation has achieved satisfactory accuracy, however, it is demanding for scene data with ground-truth depth for training, which is difficult to be applied in most unfamiliar scenes. Considering that, self-supervised monocular depth estimation with only images has rapidly evolved into the important branch of depth estimation task. The existing high-precision self-supervised models with a great deal of parameters, which limits their real applications. To this end, we propose a light-weight monocular depth estimation network LWNet. It adopts MobileNetV3 as the encoder with two effective modules: (1) parameter-free channel attention(PCA) module, which captures the effective channel information of features in a parameter-free form and is more suitable for light-weight structures and (2) dense feature aggregation(DFA) layer, which strengthens the connection among features through residuals and improves the accuracy of lightweight models. Due to the limited number of light-weight network parameters, it is difficult to maximize its performance by training it with an end-to-end approach only. Thus, in this work, we utilize a joint training strategy of knowledge distillation and self-supervision to make full use of the training data to efficiently improve the prediction accuracy of LWNet. For the structure of the teacher model, we combine the transformer block with the dense connected prediction layer to constitute TDNet for supervising LWNet in the knowledge distillation training. Experiments show that LWNet with only 3.1M parameters is highly advantageous compared with other complex networks.
White Hat Security-An Overview of Penetration Testing Tools

Pentesting, commonly referred to as ethical or white hat hacking, is the focus of this thesis. Many people are interested in this topic, especially those who work in the field of business information technology. Knowing the finer points of pentesting is quite advantageous for sys admins, our crew has been wanting to learn it for some time. Learning about pentesting equips one with useful tools for managing and documenting the security of the system for which they are accountable. Learning penetration testing will help the authors of this thesis by providing a solid foundation of information relevant to the penetration testing sector, in addition to opening doors to a whole new world. The only topics covered in this thesis will be an introduction to pentesting tools and general pentesting concepts. Other facets of network testing and security will not be covered.
Fuzzy logics: a new method of predictions

The idea of fuzzy logic was first advanced by Dr. Lotfi Zadeh of the University of California at Berkeley in the 1960s. Dr. Zadeh was working on the problem of the computer's understanding of natural language. Natural language (like most other activities in life and indeed the universe) is not easily translated into the absolute terms of 0 and 1. (Whether everything is ultimately describable in binary terms is a philosophical question worth pursuing, but in practice much data we might want to feed into a computer are in some state in between and so, frequently, are the results of computing.) It may help to see fuzzy logic as the way reasoning really works, and binary or Boolean logic is simply a special case of it. Please note that with minor editing and manipulation, the materials presented in this chapter have been borrowed from the book published from Zohuri and Moghaddam with permission from both authors and publisher as well.
Python programming–driven artificial intelligence

Python has become one of the most popular platforms and the leading open-source programming language for artificial intelligence, natural language processing, machine learning, and deep learning. It was conceived in the late 1980s by Guido van Rossum at Centrum Wiskunde and Informatica (CWI) in the Netherlands, and its first version was released in 1991 (https://en.wikipedia.org/wiki/Python_%28programming_language%29). Van Rossum conceived Python as a successor to an earlier language developed at CWI with the intent to be a high-level, general-purpose, interpretive, user-based programming language rather than a systems-programming language. Thus, from the onset, Python was designed to be interactive, structured, and self-intuitive. The Python programming language is currently managed by the Python Software Foundation (PSF), a nonprofit organization, which also holds the intellectual property rights to this platform (https://www.python.org/psf/mission/). As part of its mission, PSF also manages the distribution of the platform and the compilations of all extensions to the codebase by soliciting crowd-sourced contributions from the broad community of Python users and support.
Artificial intelligence, machine learning, and deep learning driving big data

Artificial Intelligence (AI) is one of those technologies that seem to be expanding outward in every direction, and this expansion is driven by sheer volume of data that we are encountering in our daily routine life in these days, where technology is deviating from its tradition format to the more modern world of electronic gadget. In today's modern technological world, we are accumulating so much data in real time with speed of electron through the world of Internet of Things (IoT), and it comes to us in Omni-Direction space. Dealing with these data in the form of structured and unstructured, we have no choice except to turn to AI for assistant, in order to stay on top of all the information that is collected from these data for us to have a knowledge that allows us to consequently have power of right and accurate decision-making in real time in our daily routine as stakeholder. However, comes with AI needs its two other integrated components, namely Machine Learning (ML) and Deep Learning (DL) to be more effective to us as human being, where we are relying on Artificial Intelligence to run our day-to-day operation in very resilience form.
Automatic Face Mask Detection on a Gates to Combat the Spread of Covid-19

The COVID-19 pandemic has spread across the globe, hitting almost every country. To stop the spread of the COVID-19 pandemic, this article introduces face mask detection on a gate to assure the safety of Instructors and students in both class and public places. This work aims to distinguish between faces with masks and without masks. A deep learning algorithm You Only Look Once (YOLO) V5 is used for face mask detection and classification. This algorithm detects the faces with and without masks using the video frames from the surveillance camera. The model trained on over 800 video frames. The sequence of a video frame for face mask detection is fed to the model for feature acquisition. Then the model classifies the frames as faces with a mask and without a mask. We used loss functions like Generalize Intersection of Union for abjectness and classification accuracy. The datasets used to train the model are divided as 80% and 20% for training and testing, respectively. The model has provided a promising result. The result found shows accuracy and precision of 95% and 96%, respectively. Results show that the model performance is a good classifier. The successful findings indicate the suggested work's soundness.
Digital healthcare data management using blockchain technology in genomics and COVID-19

From birth certificate until death certificate, a person accumulates healthcare data. Each time a person has contact with a healthcare professional some type of record is produced, be it paper, electronic, or both. In one year, a normal healthy American may visit the dentist twice, see a doctor for a wellness visit, call a physician for a bad cold, and refill a dozen allergy prescriptions. Each one of these encounters becomes a part of the many records stored somewhere in the archives of healthcare data. Much of the data is merely entered into a computer system so that the provider can receive payment, and then the data are added to a massive “warehouse.” Each one of these records can provide insight not only into an individual’s well-being but may, in fact, affect healthcare across similar groups based on age, gender, or location, and provide an analytic base to determine insurance rates, study disease trends, and modify treatment protocols. This administrative type of information claims and encounters eligibility and enrollment is still vastly underused. This is due in part to the massive amounts of administrative data, in part to their complexity, and in part to their misuse in the past. Studies of this type of data are only now beginning to receive proper time and attention due to Coronavirus disease-2019. The process of converting healthcare data into useful information is the focus of this book chapter.
Automatic Attendance Recording System Using Facial Recognition

The student attendance is collected either manually or digitally through an RFID card. Recently, some of the institutions adopted the use of dynamic QR codes for attendance monitoring. However, these systems are challenging to maintain and time-consuming. The student can easily manipulate signatures for attendance monitoring. Moreover, the QR code system requires the application on both the lecturer and student sides. The lecturer needs to set up the QR code while using the mobile app to use the system. Therefore, it is time-consuming and sacrifices several minutes at the beginning of each class. The student also sometimes has difficulty in using the app leading to manual data insertion. Therefore, this project aims to solve this issue by using the facial recognition system on a lecturer’s laptop to collect the attendances. This project requires the use of a webcam and MATLAB to perform facial image recognition. This work utilizes face detection algorithm in the MATLAB image processing toolbox to build a system that will detect and recognize the frontal faces of students in a classroom. Firstly, several images of students will be recorded in a database. Then, a GUI-based application automatically identifies a face and matches it with the database created. Lecturers will use the GUI on their laptops, and students will show their faces to the web camera for their attendance to be taken. The system was tested successfully where student face was successfully recognized and recorded in the attendance monitoring system. Only registered user faces will be detected.