Reviewerly/exHarmony huggingface dataset:
Research and Implementation of Intelligent Platform for Targeted Employment Poverty Alleviation Based On Cloud Computing and Big Data

With the development of Internet technology, big data technology provides technical support for China’s targeted poverty alleviation. The key to targeted poverty alleviation lies in providing employment. Employment poverty alleviation is the most direct, realistic and effective way for poor workers to get rid of poverty. How to establish an information communication platform between the poor and enterprises is a key problem to be solved urgently. This paper uses cloud computing, big data and intelligent decision making technology to develop the big data “Internet plus” platform for precision employment and poverty alleviation. It has solved the problems of asymmetric information among enterprises, the poor and government departments, lack of effective supply and demand docking platform, unbalanced regional distribution of employment posts and labor force, inaccurate employment services, long employment chain and difficult tracking services. This paper describes in detail the architecture design, targeted employment framework process, deployment and implementation for the platform adopting scrum and continuous integration development method. The platform has been successfully online, exploring employment poverty alleviation for about one year, successfully solved the employment of nearly 2,000 poor labor forces and 5,000 needy students in secondary vocational and higher vocational education, cooperated with more than 100 large-scale well-known enterprises in manufacturing, logistics and service industry, formed a systematic targeted employment poverty alleviation model, scheme and experience. Compared with the existing systems in the literature, the advantages and transformation effect of this platform are remarkable. The platform construction experience of this paper can provide reference for the application of big data, cloud computing and other technologies in other fields of national construction.
An internet traffic classification method based on echo state network and improved salp swarm algorithm

Internet traffic classification is fundamental to network monitoring, service quality and security. In this paper, we propose an internet traffic classification method based on the Echo State Network (ESN). To enhance the identification performance, we improve the Salp Swarm Algorithm (SSA) to optimize the ESN. At first, Tent mapping with reversal learning, polynomial operator and dynamic mutation strategy are introduced to improve the SSA, which enhances its optimization performance. Then, the advanced SSA are utilized to optimize the hyperparameters of the ESN, including the size of the reservoir, sparse degree, spectral radius and input scale. Finally, the optimized ESN is adopted to classify Internet traffic. The simulation results show that the proposed ESN-based method performs much better than other traditional machine learning algorithms in terms of per-class metrics and overall accuracy.
Increasing the interoperability of snow/ice hyperspectral observations

This study aims to set up a metadata profile useful for preparing an interoperable dataset containing snow and ice hyperspectral measurements. The proposed Snow and Ice Spectral Library (SISpec) scheme was prepared for sharing a data collection focused on Antarctica, including 70 observations. Following the perspective to grant “open access” to such a dataset, we found a compromise between the ERC (European Research Council) guidelines, the FAIR (Findability, Accessibility, Interoperability, and Reuse) Data principles defined by the RDA (Research Data Alliance), and the GEO (Group on Earth Observation) Data Sharing Principles. The ISO (International Organization for Standardization) standard 19115 was chosen as the standard framework for describing SISpec. When the available metadata scheme was not sufficient or suitable, metadata extensions or new detailed metadata components were created to be compliant with the ISO 19115 standard. We also considered the INSPIRE (Infrastructure for Spatial Information in Europe) requirements and the result is a metadata model that can be useful to share SISpec metadata both in the European and international contexts. Particularly detailed metadata sections and elements were created for describing spectral signatures and microphysical snow parameters.
Efficient quantum private comparison protocol based on one direction discrete quantum walks on the circle

We propose an efficient quantum private comparison protocol firstly based on one direction quantum walks. With the help of one direction quantum walk, we develop a novel method that allows the semi-honest third party to set a flag to judge the comparing result, which improves the qubit efficiency and the maximum quantity of the participants’ secret messages. Besides, our protocol can judge the size of the secret messages, not only equality. Furthermore, the quantum walks particle is disentangled in the initial state. It only requires a quantum walks operator to move, making our proposed protocol easy to implement and reducing the quantum resources. Through security analysis, we prove that our protocol can withstand well-known attacks and brute-force attacks. Analyses also reveal that our protocol is correct and practical.
Safeguard and Preventive Measures of Natural Disasters Using Early Warning Systems—A Comprehensive Review

A natural disaster is a progression of connected or unconnected events in nature that generates collapse, eradication, and losses of livelihood, life, property, and many more with extreme destruction, which is not caused or controlled by humans. Natural disasters affect natural resources and human-made property; therefore, to minimize the losses caused by natural disasters to save property, resources, and life. Addressing these problems becomes essential; hence, disaster management decides to adopt early warning system (EWS) to anticipate disaster to evacuating, safeguarding, and preventing humans as soon as possible. This paper presents the role of early warning systems in major natural disasters such as earthquakes, tsunamis, landslides, volcanos, wildfires, and epidemics. EWS attempts to reduce the risk of disasters by providing adequate and timely information by adopting numerous processes and tools to produce warning data through technical monitoring. Consequently, it generates awareness among the public for forecasting and predicting disaster, together with it has potentiality and reliability to operate 24 h a day. In the present day, many countries remarkably minimize the number of deaths by adopting EWS, but preventing infrastructure is still questionable for sudden disaster; however, moderate outbreak disasters can contribute considerable time to build up barriers and retrofitting buildings. EWS does not deliver satisfactory outcomes unless early action and communication are performed flawlessly; furthermore, warnings are still not efficiently communicated in developing countries and some developed countries due to not being technically vital in understanding the nature, locations, and frequencies. This proposed article provides detailed insight into the natural disasters using EWS for all major disasters and its evolution. Progress on the various aspects of EWS and its operation is reviewed in detail along with future scope and recommendations.
Design of Robotic Snake with ESP 32 CAM and Arduino

The present study proposes a robotic snake design using ESP 32 CAM and arduino. The robotic snake with an extended reach with locomotion has wide applications in surveillance, maintenance, rescue and spying through a narrow passage and in an adverse environment etc. The proposed design of robot is a bio-mimic camouflage of snake. The prototype of the robot is made which consists of one head unit followed by five cells that are capable of moving through any narrow passage. The head unit comprises of arduino based centralized controlling unit and ESP 32 CAM module. The camera module, video streams the front view of the robot using the remotely located control panel. Based on the front view of the robot, an operator can control the locomotion of the robot via the arduino of the central control unit. The proposed design has many real-life applications.
Rapid On-Site Evaluation Technical Exercise Using Digital Pathology: Peer Comparison in an External Quality Assurance Setting

The Royal College of Pathologists of Australasia Quality Assurance Programs (RCPAQAP) is a world leader in the provision of external quality assurance (EQA) for pathology laboratories. The development and delivery of new programmes are designed to meet the evolving needs of our participants and pathology practice. A cytopathology technical programme was established in 2018 to provide proficiency testing in routine cytopreparatory techniques. The cytopathology technical pilot focused on standard operating procedures and laboratory performance during rapid on-site evaluation (ROSE). The exercise aimed to assess the diagnostic adequacy and provide technical recommendations on each of the whole slide images from four fine-needle aspiration biopsy (FNAB) samples of the thyroid in a setting of ROSE.Four whole slide imaging cases, each comprising one Diff-QuikTM stained preparation of an FNAB of the thyroid, were provided to all laboratories currently enrolled in the RCPAQAP Cytopathology diagnostic programmes. Each scanned image represented one slide from the first pass collected by a radiologist under ultrasound guidance. Participating laboratories were encouraged to distribute the exercise to individual staff members who attend ROSE procedures. A laboratory practice questionnaire was also conducted.There were a total of 186 submissions received for the assessment across the four virtual cases provided. Although the assessment of on-site adequacy by participants showed high concordance across some of the cases, there was variation in the initial diagnostic evaluation and technical recommendation.ROSE has been adopted as a standard procedure in many laboratories worldwide. A peer comparison of participating laboratories in an EQA exercise has highlighted ROSE as susceptible to variation across standard operating procedures, the provision of diagnostic adequacy, diagnostic feedback to the attending clinician, and technical recommendation.
AG-Net: An Advanced General CNN Model for Steganalysis

Steganography has made great progress over the past few years due to the advancement of deep convolutional neural networks (DCNNs), which have been successfully used to multi-domains.Correspondingly the performance of steganalysis models inevitably encounters a bottleneck since the CNN based steganography models perform better.In this paper, we propose an Advanced General convolutional neural Network (AG-Net) for steganalysis to deal with this problem.We firstly design a confrontation module to extract and compare features of cover and stego images, which are captured from an unknown steganography network.Then, we construct the association between two adjacent confrontation modules according to the feature comparison of the previous module, to accumulate the differences of mid-and highlevel features between the cover and stego images.Thirdly, we deliver the loss of the last confrontation module to a softmax layer after batch normalization and scalarization, to classify and detect stego images.Extensive experiments and evaluations demonstrate that the proposed AG-Net can achieve promising performance in response to different challenging steganographic algorithms.
Comparison of stress separation procedures. experiments versus theoretical formulation

The article deals with stress separation using different experimental techniques and their comparison to numerical and theoretical solutions. The method is applied to a keyhole sample coupon, to which measurements of fringe order, temperature or displacement were made using photo-elasticity, TSA (Thermo-elastic Stress Analysis) and DIC (Digital Image Correlation). The results are compared to FEM simulations (Finite Element Method) using the stress concentration factor (Kt) as a benchmark. Additionally, an Airy stress function is proposed and tested against obtained measurements. The comparison of Kt shows agreement among measurements as well as numerical and theoretical results. It is concluded that the presented method can be used for isotropic materials subjected to the plane stress, where stress variation through-thickness is negligible.
Quantum correlation measurement of laser power noise below shot noise

In this article, the quantum correlation measurement technique as a method of power noise monitoring is investigated. Its principal idea of correlating two photodetector signals is introduced and contrasted to the conventional approach, which uses only a single photodetector. We discuss how this scheme can be used to obtain power noise information below the shot noise of the detected beam and also below the electronic dark noise of the individual photodetectors, both of which is not possible with the conventional approach. Furthermore, experimental results are presented, that demonstrate a detection of technical laser power noise one order of magnitude below the shot noise of the detected beam.
Extensive review on IoT security challenges and LWC implementation on tiny hardware for node level security evaluation

Internet-of-Things (IoT) offers a novel intelligent ecosystem that consists of various physical objects interconnected via the internet, which draws the attention of researchers, academicians, and industrialists. Various physical objects are embedded systems that perform dedicated operations that include sensing, monitoring, and controls. Such connectivity of embedded system devices over the internet creates an intelligent mesh worldwide and makes city, industry, and human life entirely automated and intelligent. However, the existing embedded system with radio modules is battery operated, referred to as a low-resourced device. Further, it is expected that the device should consume less operational power. Tiny size devices are offering less memory which creates a resource-constrained environment. An efficient hardware implementation of security algorithms is challenging in a constrained environment that satisfies all performance metrics. Standard internet connectivity of all devices with new wireless paradigms (e.g. ZigBee, LoRa, Wi-Fi, SigFox, etc.) essentially needs to be scrutinized for secured data communication and other security flaws. The universal connection allows an adversary to access secured technology via vulnerable systems. Many researchers are analyzing IoT technologies in every possible aspect to provide an economically secured solution. Importing software-tested encryption standards on hardware with efficient results can produce reliable IoT nodes. In this paper, we present the overview of IoT infrastructure with supporting data communication protocols. Also, we discussed essential cryptographic design rationale to minimize overall structure with the importance of metrics. Environmental and implementation based challenges, trade-off, and importance of cryptography towards the development of secured IoT node with Light-Weight Cipher (LWC) ANU and PRESENT proof-of-concept for generic application is provided in this research.
An Emotion-Aware Persuasive Architecture to Support Challenging Classroom Situations

Nowadays, students face stressful situations that affect their mental health and negatively impact their academic performance. This situation has been exacerbated due to the COVID-19 pandemics. Teaching and learning process stakeholders face two main problems: the lack of awareness of the situations arising from students and the little available information that support the teaching activities. Here, we propose an emotion-aware architecture for persuasive systems in education, identifying challenging classroom situations and promoting mental health through multi-modal persuasive notifications. An important module is emotion inference, which reads real-time physiological data from students through the Empatica E4 wristband. Based on this architecture, we developed the Kusisqa persuasive system, to support students in regulating their negative emotions (They can all lead to stress) during challenging classroom situations. Preliminary results provide insights that teachers achieved an increased awareness of the situations of students and perceived the prototype tool as useful.
Trustworthy Software Development — practical view of security processes through MVP methodology

With the popularization of 5G, one of its characteristics supports a large amount of IoT devices and a lot of open sources will be introduced. Therefore, trustworthiness has become more important for users and developers. According to IBM Systems Sciences Institute, we understand that taking software security into consideration in the early stages of development will save 99% cost. Therefore, security by design can not only save development costs but also improve the overall security of the software.In this paper, we mainly focus on the trustworthiness aspect in the software development process based on ISO/IEC/IEEE15288, NIST800-160, DoD Enterprise DevSecOps Reference, and A Guide to the Project Management Body of Knowledge (PMBOK Guide) - Seventh Edition and the Standard for Project Management concept. A trustworthiness framework will be proposed to make software more reliable and trustworthy for users and allows enterprises and developers to control and test the trustworthiness of production. After that, the analysis of software security is provided. This platform will provide the guidance for industries easier to use and apply to their product.
MEViT: Motion Enhanced Video Transformer for Video Classification

Due to the advantages in extracting the long-range dependencies, self-attention based transformers are widely used to model the spatio-temporal features for video classification, which achieves competitive performance compared to 3D CNNs. To reduce the computational complexity, existing methods divide the frames into patches and factorize the spatial and temporal domains. However, most existing methods globally connect the patches at the same position in different frames to extract the temporal features, and ignore the patch motion due to video objects moving, which might hurt the performance of transformers. This paper proposes a novel architecture called Motion Enhanced Video Transformer (MEViT) for video classification, which captures patch motion information via a new module named Motion self-attention. Different from existing self-attention operation on the temporal dimension, motion self-attention globally connects the query patch and the neighborhood patches in other frames along the temporal dimension when modelling the patch temporal dependencies. Furthermore, this paper also discusses how attention blocks are stacked and how to use the spatio-temporal feature to get the classification feature. Experiments on popular public datasets (including Kinetics-400/600 and Something-Something-v2) demonstrate that our MEViT model outperforms existing dominant video transformer models.
Predictive Analytics in Digital Education: Analysis and Evaluation of Students’ Learning Success

Problem and Aim. The article actualizes the problem of modeling the learning process within electronic educational environment, based on the use of educational analytics data. The purpose of the article is to reveal and substantiate the possibilities of using predictive analytics methods for analyzing and assessing the of students’ learning success based on their current educational results. Methodology. Theoretical, empirical and mathematical methods were used during the research. The construction of the predictive model was carried out on the basis of the technologies of intellectual analysis and processing of big data (Data Mining). Research results. The relevance of the use of methods for analyzing big data in the educational sphere for improving the quality and efficiency of the educational and pedagogical process has been substantiated. The definitions of the technology of data mining are given, its types are considered (descriptive, diagnostic, predictive and prescriptive). The machine learning methods used in predictive analytics algorithms are described. Examples of the using predictive analytics methods in various fields of activity are given. An algorithm for designing and developing a predictive model is described. On its basis, a predictive model is proposed for analyzing and assessing the success of student learning, implemented as part of an experimental study within training module “Optimization Methods”. To assess the quality of the model, the methods of linear and logistic regression, deep neural networks and a decision tree were applied. In conclusion, it is concluded that the development and implementation of a model based on a set of methods gives a synergistic effect – the ability to maximally take into account the peculiarities of a student’s work with educational material and the degree of its assimilation. And as a result, it allows you to increase the level of training of students, improve the quality of the educational material provided in an electronic environment, build an individual trajectory of learning and help eliminate the problem of student expulsion through timely action.
Web-based University Classroom Attendance System Based on Deep Learning Face Recognition

Nowadays, many attendance applications utilise biometric techniques such as the face, fingerprint, and iris recognition.Biometrics has become ubiquitous in many sectors.Due to the advancement of deep learning algorithms, the accuracy rate of biometric techniques has been improved tremendously.This paper proposes a web-based attendance system that adopts facial recognition using open-source deep learning pre-trained models.Face recognition procedural steps using web technology and database were explained.The methodology used the required pre-trained weight files embedded in the procedure of face recognition.The face recognition method includes two important processes: registration of face datasets and face matching.The extracted feature vectors were implemented and stored in an online database to create a more dynamic face recognition process.Finally, user testing was conducted, whereby users were asked to perform a series of biometric verification.The testing consists of facial scans from the front, right (30 -45 degrees) and left (30 -45 degrees).Reported face recognition results showed an accuracy of 92% with a precision of 100% and recall of 90%.
Theoretical and experimental study on a new reconstruction model based on geometric optics for a single-lens biprism-based stereovision system

Abstract In the present work, we report a stereovision system with one single camera. The configuration has only one single camera in front of which a biprism is placed. A new mathematical model based on optical geometrical optics is proposed for a single-lens biprism-based (SLBB) stereovision system in this paper. The path of each ray in three-dimensional (3D) space is tracked precisely, and the intersection of the rays is determined by an optimization method. Under the assumption of pinhole imaging, the 3D coordinate of the measuring point can be obtained through this model. Further, the model has been examined and verified by experiment. The experimental results show that the mathematical model can effectively measure the depth of the measuring points and the relative distance between measuring points. The deviation between the reconstruction results of the measuring point and the real 3D coordinate shows a certain regularity with the changing of the distance between the measuring point and the camera.