Reviewerly/exHarmony huggingface dataset:
Role of Information Technology in Education System

In the modern era of technology teaching has been much interesting and involves people of all age groups to understand the fundamentals and concepts of the phenomenon, which occurs. Technology plays a major role in imparting the knowledge as well as to practice the experiments in a virtual laboratory environment. Infrastructure of the laboratory is the key factor for making students involved in learning physics. Information Technology plays a huge contribution in making various complex calculations and deriving the necessary results. Information Technology Infrastructure helps students to use virtual infrastructure wherein the students can use the hardware as well as software configuration and can run numerous software on the virtual machine. Virtual machines provide the hardware configuration as per the necessity at a minimal cost so that students do not have to spend a huge amount from their pockets to upgrade their computer system. This paper describes the virtual infrastructure, which can be used to obtain the required hardware configuration to run the software online and also for online education.
Extracting the Sparse Longest Common Prefix Array from the Suffix Binary Search Tree

Given a text T of length n, the sparse suffix sorting problem asks for the lexicographic order of suffixes starting at m selectable text positions P. The suffix binary search tree [Irving and Love, JDA’03] is a dynamic data structure that can answer this problem dynamically in the sense that insertions and deletions of positions in P are allowed. While a standard binary search tree on strings needs to store two longest-common prefix (LCP) values per node for providing the same query bounds, each suffix binary search tree node only stores a single LCP value and a bit flag. Its tree topology induces the sorting of the m suffixes by an Euler tour in \({{\mathcal {O}}}(m)\) time. However, it has not been addressed how to compute the lengths of the longest common prefixes of two suffixes with neighboring ranks with this data structure. We show that we can compute these lengths again by an Euler tour in \({{\mathcal {O}}}(m)\) time.
Education and self-education in the optics of tutoring approach

This article discusses the problem of comprehension of self-education and education in tutoring pedagogy. The matter is that if student's personality and different development trajectories of different personality types are recognized in education, it is difficult to imagine education without self-education. As empirical material, the author cites two cases from his personal experience; analyzes the establishment and development of personality in culture, claiming that the problematic situations are the source of its development. Description is given to the key problematic situations that determine the evolution of a modern individual in phylogenies and ontogenesis. The author distinguishes between spontaneous development, evolution of personality, and conscious work, the goal of which is education or self-education. The article discusses the peculiarities of tutoring perspective on education and self-education, emphasizing that tutoring efforts are aimed at formation of favorable environment for person&amp;rsquo;s self-development and evolution, rather than designing an individual based on the pedagogical patterns.&amp;nbsp; At the same time, the tutor strives to create a culturally congruent and humanistic environment. The paper reviews a complicated problem of where a tutor as a pedagogue should lead his student and what does &amp;ldquo;lead&amp;rdquo; means in educational practice. The conclusion is made that tutoring approach is quite unusual in the aspect the it does not set particular pedagogical contents and goals, but rather points at the principles that should be jointly implemented by the tutor and his students. The author considers the three hypostases of a person for the tutor to focus on.
Logarithmic Retinex Decomposition-Aided Convolutional Neural Networks for Low-Light Image Enhancement

Maritime accidents kill thousands of lives every year because of the difficulty in rescuing. Usually, the weather conditions at sea are very complicated, especially at midnight. The images captured by the monitor often suffer from low visibility under this circumstance, which gives rescuers a great challenge to seek survivors. Existing image enhancement methods are not suitable for the offshore environment. We propose an image enhancement model to restore the image from the low-light condition to solve this problem. We found that if we transform an image into a logarithmic image, it can provide more details in darkness, and we decide to make better use of this information because the survivors’ bodies may be hidden in darkness. We build networks based on previous Retinex-based network researches and use the logarithmic image as the original dataset. The De-Net decomposes the image into the reflectance map and illumination map for further adjustment. The recovered logarithmic image will turn back to the output image after the I-Net and R-Net. Experiments have been implemented on synthetic and realistic low-light images to verify the effectiveness of our method. Experimental results have illustrated that it is widely applicable to maritime low-light images.
Research on Unbalanced Sample Segmentation of Remote Sensing Image

Abstract This article proposes a framework for the unbalanced sample segmentation of remote sensing images on an open data set. For the few sample categories, improved by converting it into binary segmentation and combining with weighted cross-entropy loss, and then merged with the segmentation result of the sufficient sample categories. Finally, the mIoU (mean Intersection of Union) of the 8 categories is increased by 4.5% compared to the results before the improvement, especially, the results of the few sample category road and grassland are increased by 10.2% and 9.7%. The experiments show that the framework can greatly improve the segmentation performance of the few sample categories, and have a good guiding significance for the problem of multiclass unbalanced sample segmentation.
Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period

The COVID-19 pandemic situation had imposed unexpected changes in our day-to-day life, especially in terms of how we work and how we learn. Before COVID-19, e-learning methodologies were embraced only by inquisitive learners and by few corporate companies and educational institutions to disseminate knowledge through an alternative mode of content delivery. Although e-learning offers great flexibility to the learners to learn at their own convenient time and pace, by accessing the content via any smart device irrespective of their geographical location, it has got some limitations too due to which most of the teachers/students, as well as the educational institutions, prefer the traditional method of teaching/learning over online learning. In this paper, exclusive sentiment analysis has been carried out to assess the perception of the students on the adoption of online learning using a dataset that has been collected during the pre-COVID-19 pandemic period. The results of the analysis demonstrate that online learning was not as popular and widely adopted among the student's fraternity as it is during this COVID-19 pandemic period.
New Directions in Traffic Control Analysis through Video Surveillance

Traffic management is an increasing problem in both cities and sub urban areas. Authority people involved in traffic management system spend much of time in controlling traffic at junctions. With the advances in technology, monitoring traffic through image processing and video surveillance techniques became the researchers’ attention. These techniques help us in controlling traffic as well as to identification of kamikaze drivers and speed violators. The key focus of this research is to do traffic analysis using video surveillance to detect speedy drivers. A wide range of traffic parameters such as flow of traffic, speed of vehicles and vehicle registration number are the major components involved in this research. In this paper, traffic analysis is carried out based on streaming video data with YOLO tool. In this paper an eco system is developed for object detection, vehicle number detection and the speed of the vehicle using computer vision algorithms. With the application tool developed, traffic control authority people can warn the speedy drivers on the fly.
Description of Dressed-Photon Dynamics and Extraction Process

Several interesting physical phenomena and industrial applications explained by the dressed photon have been reported in recent years. These require a novel concept in an off-shell science that deviates from the conventional optics, satisfying energy and momentum conservation laws. In this paper, starting from an original model that captures dressed-photon characteristics phenomenologically, the dynamics of the dressed photon in a nanomatter system and the mechanism for extracting internal degrees of freedom of the dressed photon to an external space have been examined by theoretical and numerical approaches. Our proposal is that basis states of the dressed photon can be transformed to the form that reflects the spatial distribution of the dressed-photon steady state in the system, and some of basis states with predetermined spatial distribution can relate to the dissipation components in the external space by means of the renormalization technique. From the results of numerical simulation, it is found that quasi-static states are regarded as the photon with light mass or massless, and the extraction of active states strongly affects the spatial distribution in a new steady state. The concept for extracting dressed-photon energy to an external space will contribute to a detailed understanding of dressed-photon physics and future industrial applications.
The Extreme Value Evolving Predictor in Multiple Time Series Learning

This paper extends the evolving fuzzy-rule-based algorithm denoted Extreme Value evolving Predictor (EVeP) to deal with multivariate time series. EVeP offers a statistically well-founded approach to the online definition of the fuzzy granules at the antecedent and consequent parts of evolving fuzzy rules. The interplay established by these granules is used to formulate a regularized multitask learning problem which employs a sparse graph of the structural relationship promoted by the rules. With this multitask strategy, the Takagi-Sugeno consequent terms of the rules are then properly determined. In this extended version, called Extreme Value evolving Predictor in Multiple Time Series Learning (EVeP_MTSL), we propose an approach that resorts to the similarity degree among the time series. The similarity is calculated by the distance correlation statistical measure extracted from a sliding window of data points belonging to the multiple time series. Noticing that each fuzzy rule is part of a specific time series predictor, the new unified model called EVeP_MTSL updates the sparse graph by composing the relationship established by each pair of fuzzy rules (already provided by EVeP) with the similarity degree of their corresponding time series. We are then exploring not only the current interplay of the multiple rules that compose each evolving predictor, but also the current correlation of the multiple time series being simultaneously predicted. Two computational experiments reveal the superior performance of EVeP_MTSL when compared with other contenders devoted to online multivariate time series prediction.
IV-WA: An Information Visual Tool with WebAR

Early marker-based mobile augmented reality technology relies on special patterns and cannot be applied to tracking natural feature objects. At present, more and more applications are prone to track natural feature objects, which gradually advances the natural features-based development of mobile augmented reality. However, current mobile augmented reality applications depend heavily on open-source software development kits like AR- Toolkit which only recognize a single object rather than multiple natural feature objects. Inspired by employing a vocabulary tree model to retrieve query sentences from an article, this paper proposes a multiple targets retrieval strategy called Pruning Inverse Visual Vocabulary Tree (PIVVT), which converts the feature matching into an N-ary tree searching feeding the real-time. Due to the instability of traditional vocabulary tree construction, which leads to insufficient storage, low retrieval speed, and accuracy, we reverse tree construction to fill up the entire tree from the bottom to the top to forms a full binary tree with the least amount of the storage, which builds a stable vocabulary tree. Finally, the test results prove that the strategy can improve the speed and accuracy of multiple natural feature target retrieval in mobile augmented reality.
Research on Pedestrian Detection Method Based on Receptive Field Module

With the continuous development of technology, autonomous driving has become a popular subject of discussion in the field of artificial intelligence. Accurate detection of passing pedestrians has been identified as an important problem in unmanned driving. Given this, this paper proposes an improved Single Shot MultiBox Detector (SSD) algorithm. The algorithm is improved from two aspects. Firstly, a Receptive Field Block (RFB) module is assembled to the top of the SSD to enhance the feature extraction capability and robustness of the model. Secondly, an Efficient Channel Attention (ECA) module is added to improve the performance of the deep neural network and enhance its feature representation capability. The experimental results on the PASCAL VOC 2007 (VOC) dataset show that the proposed algorithm can significantly improve the accuracy and speed of pedestrian detection compared with the traditional SSD algorithm.
A Broker Approach in Elevation of Potential Service Provider for Consumer Preferences with Utility Function in Cloud Environment

Cloud Computing is a looming and auspicious outcome which offers optimal resource utilization on demand. Elasticity is the feature available in cloud computing which compliments to the Consumer side requests dynamically. The tendency of elasticity exhibits the characteristics of provisioning and de provisioning of resource utilities in an autonomic manner. An SLA with identified SLO's must have elasticity which is really one of the qualitative terms for the recent analysts. The delegate responsible role of broker utilized in selection of suitable SaaS provider with a Distinctive utility algorithm where this utility algorithm endeavors performance compared to the currently prevailing utility driven algorithm. The outcome dataset of this SaaS provider selection algorithm exhibits minimum variance, in the order of Service provider selection, towards customer QoS Preferences.
Differential Privacy in NoSQL Systems

With the advent of the volume, velocity and variety of data in today’s day and age, big data has become the norm in many places. Data at rest, the same data that is fed to the analyst of a company is at most times, not encrypted and depends on the fact that a person can be trusted by the company. Our contribution is implementing differential privacy in a NoSQL database while combating the few drawbacks in differential privacy with techniques such as cluffering (clustering and shuffling) and k-Anonymization. We implement an end to end implementation in a NoSQL database, MongoDB to promote privacy by perturbation using differential privacy which has been shown to be one of the best ways to do so. A generic system gets formed where local differential privacy is enforced before sending the sensitive database for analysis and enforcing differential privacy concepts on aggregate queries ensuring that no particular individual gets exposed in the process. We further provide the basis for implementing other aggregates apart from the ones we implemented and for complex queries that are not yet supported while the core concepts remain the same.
Hybrid Attention Cascaded U-net For Building Extraction from Aerial Images

Building extraction from aerial images is an effective way to monitor the urban changes and make city planning. To detect more easily missed small objects and improve accuracy, this paper proposes a simple but powerful deep neural network named Hybrid Attention Cascaded U-net (HAC U-net). In the new model, attention unit (AU) is designed to take the place of some skip connections between encoder and decoder. Receptive fields of different sizes are captured by AU. Through this replacement, more spatial and contextual information on different scales can be obtained. HAC U-net combines two encoder-decoder components to form a cascaded framework. Such cascaded architecture can deepen the depth of network and abstract advanced features. HAC U-net is the first model to flexibly decompose large-kernel convolutions, use residual connection internally and AU at different stages. Therefore, memory consumption is greatly reduced without significantly influencing the accuracy. Experimental results show that HAC U-net outperforms other baseline models with an accuracy of 93.90% and intersection over union (IoU) of 61.92%.
A novel k-mean clustering based graph cut for brain MR image segmentation

Image Segmentation is an influential method to detect and distinguish the diseased and normal sections of an image. Image segmentation extracts the regions of interest for precise diagnosis of tumours and scheduling appropriate line of treatment. The distinct tumours in brain have varied outlies, position and intensity values. Consequently, it is difficult to develop a common technique for brain MRI segmentation. Moreover, the extraction of anomalies from the brain MRI turns out to be a challenging task. In this research, we have developed a self-regulating method for selection of seed points for partitioning the graph of MR images with tumor to attain graph cut segmentation. The proposes method overcomes the key problem of primary seed point selection by utilizing the balanced brain layout and combines k - mean clustering into graph cut for segmenting an image. The outcomes attained by the proposed technique facilitates improved segmentation of the diseased region.
Improving Efficient Neural Architecture Search Using Out-net

research-article Open Access Share on Improving Efficient Neural Architecture Search Using Out-net Authors: Cong Liu School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing,China, China School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing,China, ChinaView Profile , Qinghai Miao School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing,China, China School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing,China, ChinaView Profile , Min Huang School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing,China, China School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing,China, ChinaView Profile Authors Info & Claims ICCAI 2021: 2021 7th International Conference on Computing and Artificial IntelligenceApril 2021 Pages 266–271https://doi.org/10.1145/3467707.3467747Online:24 September 2021Publication History 0citation52DownloadsMetricsTotal Citations0Total Downloads52Last 12 Months52Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Sparse Representation Algorithm Based on Block LBP and Adaptive Weighting

To solve the problem of low recognition rate caused by occlusion, illumination and pose change in face image, a sparse representation algorithm based on block LBP and adaptive weighting (SBLAW) is proposed. Firstly, the face image is divided into equal blocks and LBP value of the sub-blocks is calculated, then the histogram of each sub-block is counted and weighted adaptively. Secondly, the image is divided into overlapping blocks, and the statistical histograms of the sub-blocks in the overlapping blocks is concatenated as the feature vector. Finally, the sparse representation classification method is used for face recognition. The experiments on the ORL, AR and FERET face databases show that the proposed method is robust to illumination and other factors, and the recognition rate is improved.
Research on Role Access Control Model Based on Identity Authentication

Abstract Cryptography and access control are the core technologies for a secure operating system. Cryptography can guarantee the confidentiality, integrity, availability and unforgeability of information in the access control system. In the identity-based cryptosystem, the user’s private key is centrally generated by the key distribution center, and key escrow is a major security risk. In view of this problem, it is proposed that multiple trusted entities participate in the key distribution scheme in a serial manner. At the same time, this paper adds a separate blind factor to each user to increase security of the scheme; it also introduces the concept of virtual key, which uses additivity of the key space in the pair-based encryption scheme. In key generation, a timestamp T is added, which enables one-time pad of the system, enhances security of the system, and eliminates collusion attacks by KGC and multiple KPA. Finally, the improved signcryption scheme is combined with the RBAC model, and the I_RBAC model is proposed, which enables identity authentication and information confidentiality of users in simple communication in the RBAC model. Finally, the efficiency and safety of this scheme are analyzed.
Geometric Models for Relevant Logics

Alasdair Urquhart’s work on models for relevant logics is distinctive in a number of different ways. One key theme, present in both his undecidability proof for the relevant logic R (Urquhart ) and his proof of the failure of interpolation in R (Urquhart ), is the use of techniques from geometry (Urquhart ). In this paper, inspired by Urquhart’s work, I explore ways to generate natural models of R \(^+\) from geometries, and different constraints that an accessibility relation in such a model might satisfy. I end by showing that a set of natural conditions on an accessibility relation, motivated by geometric considerations, is jointly unsatisfiable.
Detection of tumours from MRI scans using Segmentation techniques

Medical Image segmentation is complex and challenging task due to the distinct features of biomedical images. MRI scan is commonly used in treating brain, prostate, uterus cancer, ankle and foot cancers. The proposed research work utilizes the real medical images of uterus cancer, brain tumour and throat cancer images obtained from Sagar hospital. MRI images are likely to suffer from the noises such as Gaussian noise, salt pepper noise and speckle noise. Henceforth, noise removal is a very important task to make the image clear with high accuracy and suitable for further diagnosis process. In our paper, the proposed tumour image processing involves three stages 1) converting RGB images to grayscale, 2) pre-processing the images using Gaussian, median and anisotropic filters for noise removal and image enhancement 3) Tumour segmentation using watershed and active contour method. The proposed research work has compared the results of watershed and active contour method. Also, it leverages the ability to find the area and perimeter values of tumour, which indicates the size of brain tumours that provide more accuracy compared to other existing algorithms. Watershed algorithm gives better results, when compared to active contour method, since tumour area using watershed segmentation is more accurate than active contour method. Keywords-