Reviewerly/exHarmony huggingface dataset:
Adaptive Scheduling Algorithm Based Task Loading in Cloud Data Centers

Cloud computing is a global storage framework whereby users use the tools, including the computation, storage, network, etc., that are provided automatically. The computational has led the cloud data centers to be stored in digital services that many consumers distribute. The biggest problem with cloud data centers is handling the millions of users’ continuous proposals. Therefore, in this paper, Adaptive Scheduling Algorithm Based Task Loading (ASA-TL) has been proposed to manage cloud data centers’ task to be stored in digital devices. ASA is implemented in which the task is shared between all the current virtual servers,and the cloud data are protected from overloading. The data assignment is made by taking account of the importance and status of the digital device that helps to assign task fairly and use them efficiently. TL manages these requests efficiently, and the task input must be equally and reliably allocated between various processors. The experimental result suggests that ASA-TL achieves the highest performance assessment measurements, including response time, processing time in the data center and overall expense.
Subsystem for Building a Digital Twin of the Main and Auxiliary Equipment of Thermal Scheme of Thermal Power Plant

Digital twins are currently one of the main tools for improving the operational efficiency of the main and auxiliary equipment. In the study, digital twins are used as a basis in predictive analytics systems. The comparison of the current measurement information coming from the sensors with the results calculated in the digital twins of the equipment units provides identification of the current technical condition. In this chapter, we consider one of the possible approaches to implementing the architecture of a software product for building a digital model of a thermal power plant thermal circuit—the allocation of layers. It describes the advantages and disadvantages of this method in designing, changing the source program code, and correcting the internal structure of software—refactoring. The main advantage of the method is scalability since thermal circuits can contain a large number of pieces of equipment. A three-level architectural solution of the digital twin construction subsystem has been developed. A fragment of a database accumulating the required data for obtaining calculated data using a digital double of the thermal circuit of a power plant is presented. It also shows the implementation of one of the abstract layers of the architecture—the database management system layer. The obtained results allow us to assert the effectiveness of the layers method application for use as part of predictive analytics systems using the concept of the main and auxiliary equipment digital twins of energy facilities.
Adaptive Affinity for Associations in Multi-Target Multi-Camera Tracking

Data associations in multi-target multi-camera tracking (MTMCT) usually estimate affinity directly from re-identification (re-ID) feature distances. However, we argue that it might not be the best choice given the difference in matching scopes between re-ID and MTMCT problems. Re-ID systems focus on global matching, which retrieves targets from all cameras and all times. In contrast, data association in tracking is a local matching problem, since its candidates only come from neighboring locations and time frames. In this paper, we design experiments to verify such misfit between global re-ID feature distances and local matching in tracking, and propose a simple yet effective approach to adapt affinity estimations to corresponding matching scopes in MTMCT. Instead of trying to deal with all appearance changes, we tailor the affinity metric to specialize in ones that might emerge during data associations. To this end, we introduce a new data sampling scheme with temporal windows originally used for data associations in tracking. Minimizing the mismatch, the adaptive affinity module brings significant improvements over global re-ID distance, and produces competitive performance on CityFlow and DukeMTMC datasets.
P2P Traffic Identification Using Machine Learning and Feature Selection Techniques

Classification of Internet traffic based on its applications is significantly important for better network management. The existing approaches for P2P traffic identification are port-based, payload-based, behaviour-based, and heuristic-based. In the present scenario, the method based on intrinsic techniques is not efficient to classify Internet traffic due to the use of dynamic port numbers, masquerading techniques and low accuracy. Therefore, the research is moved to the development of hybrid approaches. In this paper, hybrid approaches are proposed for the classification of Internet traffic into P2P and non-P2P traffic. The proposed approaches use machine learning algorithms with feature selection techniques such as analysis of variance and principal component analysis. Port-based labelling of data is used for the training of classifiers in the proposed system. The proposed approaches are investigated on standard UNIBS dataset, and the simulation and modelling are performed in python environment. The comparative study of the proposed hybrid approaches shows that random forest with ANOVA outperforms other approaches presented in this paper, resulting in 96.06% accuracy. The proposed approach provides better accuracy as compared to earlier reported similar approaches in the literature.
Advertisement Click Fraud Detection Using Machine Learning Algorithms

Online advertising has become one of the important funding models to support websites. Advertisers pay the publisher a set amount of money for each click on their ad (which leads to the advertiser’s website). Because Internet advertising involves such large sums of money, malevolent parties try to obtain an unfair edge. To raise illegally money, attackers use “click fraud” in which a person will repeatedly click on a specific link with the motive to earn money illegally. Click fraud occurs when a script exploits Internet marketers by repeatedly clicking on a pay-per-click (PPC) advertisement to generate fake charges. There are several models to detect click fraud whenever a person or a computer program clicks on a specific link to check if a person is legal or illegal by calculating click through rate to provide security. This paper proposed a machine learning approach to identify the false click by users for differentiating illegal users from original users for this logistic regression and Gaussian naive Bayes classifier algorithms are used to classify clicks with the respective results of 99.76% for logistic regression and 99.23% for Gaussian naive Bayes.
Quickest Change Detection in Anonymous Heterogeneous Sensor Networks

The problem of quickest change detection (QCD) in anonymous heterogeneous sensor networks is studied. There are <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> heterogeneous sensors and a fusion center. The sensors are clustered into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> groups, and different groups follow different data-generating distributions. At some unknown time, an event occurs in the network and changes the data-generating distribution of the sensors. The goal is to detect the change as quickly as possible, subject to false alarm constraints. The anonymous setting is studied, where at each time step, the fusion center receives <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> unordered samples, and the fusion center does not know which sensor each sample comes from, and thus does not know its exact distribution. A simple optimality proof is first derived for the mixture likelihood ratio test, which was constructed and proved to be optimal for the non-sequential anonymous setting in (Chen <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2019). For the QCD problem, a mixture CuSum algorithm is further constructed, and is further shown to be optimal under Lorden’s criterion. For large networks, a computationally efficient test is proposed and a novel theoretical characterization of its false alarm rate is developed. Numerical results are provided to validate the theoretical results.
Day-ahead Power Forecasting Model for a Photovoltaic Plant in Macao Based on Weather Classification Using SVM/PCC/LM-ANN

With the growing demand for clean energy, the world's installed solar energy capacity has increased substantially in the last few years. But the power output of photovoltaic (PV) panels varies greatly under different weather conditions. To improve PV power stations' prediction accuracy, this paper designs a forecasting system that uses artificial neural networks (ANNs) optimized by Levenberg–Marquardt (LM) algorithm based on weather classification for 1-day ahead hourly forecasting. In this process, first, the weather is divided into three types: A - sunny, B - cloudy and C - rainy by using support vector machine (SVM) method. Then, the correlation between meteorological factors and PV power output is analyzed through Pearson correlation coefficient (PCC) method in order to select the forecasting model's input. Finally, the corresponding LM-ANN forecasting sub-models are established under each weather type. After applying the trained three sub-models to a small PV power plant in Macao, the results prove that the proposed forecasting system achieves better prediction effect than traditional backpropagation ANN model.
Tesseract OCR for Hindi Typewritten Documents

The development of Optical Character Recognition (OCR) in Indian text is an active area of research today. The presence of a large number of characters in a set of alphabets, their complex combinations is a major challenge for the OCR designer. This OCR is giving very good results even for Hindi documents that are machine printed. But, it is not giving good results for typewriter typed Hindi documents. Even there is no OCR that is available to date which is capable of recognizing text from typewriter typed Hindi documents. This paper introduces an automated training data framework, provided only with labelled text images, thus removing the need for manually generated text. In contrast to the training images and images text as ground-truth, this approach is based on the random, rule-based generation of meaningless text in an image file and their ground-truth text file. In this paper we describe a dataset of typewriter type Hindi documents and Ground truth (GT) typewritten Hindi text images paired with their transcription. Typewriter typed documents can be incorporated into the repository of Plagiarism detection tools because text cannot be recognized by any OCR. Thus, the functionality of the existing OCR needs to be extended for recognizing typewriter typed documents.
Neuro-Fuzzy Learning in Context Educative

Technological development, teaching models more flexible, determining birth of new society which can define “learning society It is society that requires renewal in ways to transmit and acquire knowledge, and teachers role in process formative as well as process evaluation change radically. In this paper have analyzed validation technical of learners in training, in terms of knowledge, applying neuro-fuzzy logic in order to reduce errors due to teacher subjectivity.
Cross-Modal Knowledge Distillation for Depth Privileged Monocular Visual Odometry

Most self-supervised monocular visual odometry (VO) suffer from the scale ambiguity problem. A promising way to address this problem is to introduce additional information for training. In this work, we propose a new depth privileged framework to learn a monocular VO. It assumes that sparse depth is provided during training time but not available at the test stage. To make full use of the privileged depth information, we propose a cross-modal knowledge distillation method, which utilizes a well-trained visual-lidar odometry (VLO) as a teacher to guide the training of the VO network. Knowledge distillation is conducted at both output and hint levels. Besides, a distillation condition check is also designed to leave out the noise that may be contained in the teacher’s predictions. Experiments on the KITTI odometry benchmark show that the proposed method produces accurate pose estimation results with a recovered actual scale. It also outperforms most stereo privileged monocular VOs.
A CVSS-based Vulnerability Assessment Method for Reducing Scoring Error

Vulnerability assessment is one of the important topics in cyberspace security, which mainly includes threat assessment, risk level assessment, vulnerability rating score, etc. CVSS (Common Vulnerability Scoring System) is a commonly used vulnerability assessment method in academia and industry. However, CVSS has the following problems. First, CVSS is not very versatile in practical scoring. The value of the metrics will be different depending on the people with different domain knowledge and different working experience, which will lead to error in the final score. Second, the weight of CVSS metrics is more subjective. To reduce scoring error, in this paper, we propose a CVSS-based vulnerability assessment method to reduce the dimension of vulnerability metrics. This method contains a vulnerability evaluation model based on decision tree, which can reduce the error caused by manual scoring. The experiments show that the reduction of vulnerability metrics leads to a reduction in scoring error.
A Blockchain-Based Framework for Secure Storage and Sharing of Resumes

In response to problems in the centralized storage of personal resumes on third-party recruitment platforms, such as inadequate privacy protection, inability of individuals to accurately authorize downloads, and inability to determine who downloaded the resume and when, this study proposes a blockchain-based framework for secure storage and sharing of resumes.Users can employ an authorized access mechanism to protect their privacy rights.The proposed framework uses smart contracts, interplanetary file system, symmetric encryption, and digital signatures to protect, verify, and share resumes.Encryption keys are split and stored in multiple depositories through secret-sharing technology to improve the security of these keys.Corresponding key escrow incentives are implemented using smart contracts to automatically verify the correctness of keys and encourage the active participation of honest key escrow parties.This framework combines blockchain and searchable symmetric encryption technology to realize multikeyword search using inverted indexing and Bloom filters and verify search results on the chain.Escrow search service fees are charged through contracts.Only after the search results are verified can the search service provider obtain the search fee, thus ensuring fair and efficient search for encrypted resumes.The framework is decentralized, secure, and tamper-evident, and achieves controlled sharing while protecting personal privacy and information security.
Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection

Recent studies on learning with noisy labels have shown remarkable performance by exploiting a small clean dataset. In particular, model agnostic meta-learning-based label correction methods further improve performance by correcting noisy labels on the fly. However, there is no safeguard on the label miscorrection, resulting in unavoidable performance degradation. Moreover, every training step requires at least three back-propagations, significantly slowing down the training speed. To mitigate these issues, we propose a robust and efficient method, FasTEN, which learns a label transition matrix on the fly. Employing the transition matrix makes the classifier skeptical about all the corrected samples, which alleviates the miscorrection issue. We also introduce a two-head architecture to efficiently estimate the label transition matrix every iteration within a single back-propagation, so that the estimated matrix closely follows the shifting noise distribution induced by label correction. Extensive experiments demonstrate that our FasTEN shows the best performance in training efficiency while having comparable or better accuracy than existing methods, especially achieving state-of-the-art performance in a real-world noisy dataset, Clothing1M.
Abstractions for Visualizing Preferences in Group Decisions

Group decision making occurs when individuals collectively choose from a set of alternatives based on individual preferences. In these ubiquitous situations, it can be helpful for decision makers to visually model and compare stakeholder preferences in order to better understand others' points of view and reach consensus. Although a number of collaboration support tools allow preference inspection in some form, they are rarely based on a comprehensive understanding of the needs of group decision makers. The goal of our work is to study these demands, develop abstractions to model them, and create a framework to inform the design and assessment of existing and future tools. First, guided by decision analysis theory, we examine a diverse set of group decision making scenarios, characterizing variations in problem formulation, analysis goals, and situational features. Second, we amalgamate these findings into data and task abstractions that can be used to relate specific scenarios to the language of visualization. Finally, we use this framework to assess existing preference visualization tools in order to shed light on areas for future work in supporting group decision making.
Small scale dog face detection using improved Faster RCNN

In order to achieve fast localization and detection of dog face in intelligent dog management system, a dog face detection algorithm based on improved Faster RCNN was proposed. To obtain the feature extraction backbone network suitable for small size targets, the algorithm extracts the features of the detection target by constructing the residual convolution network, and fuses the features of different layers of the convolution network with the feature pyramid structure. Then, the region proposal network is used to generate and filter the possible regions of the target, and the bilinear interpolation method is used to calculate the bounding box coordinate values to achieve accurate location. Using Tsinghua dog image dataset to train the model, an improved Faster RCNN model adapted to small scale targets is obtained. The experimental results show that the improved method proposed in this paper maintains high precision in the detection of small size dog face, and can realize the detection of different scale objects, which has certain engineering practical value.
Towards Emotional Intelligence: Analysis of Static Facial Features in LinkedIn Profile Pictures

In an attempt to distil information conveyed by LinkedIn users’ pictures, in addition to the usual information provided in their profiles, we explored a large public dataset of 10,610 Australian LinkedIn users. The dataset contained in excess of 50 parameters, of which 22 were dedicated to picture features. The study confirmed that K-means clustering and Principal Component Analysis (PCA) are viable techniques for the classification of users, based on facial feature extraction and analysis. Furthermore, the study demonstrated that reduction in feature dimensionality, using PCA, yielded a significant improvement in computational time and resource consumption.
Planning Secure Consumption: Food Safety Using Blockchain

Agriculture is the foremost form of sustenance for all living beings on the planet. Food supply chain management is the flow of agricultural products from farms, harvesters, processing, distributing, retail and finally consumers. It is a sequence that starts from food production till final consumption. The existing traceability of the food supply chain isn’t entirely reliable and the authentic city of each stakeholder cannot be checked manually as it is a time-taking. Instead of a centralized source, a transparent and decentralized process can be adapted through Blockchain Technology to trace the food back to its source. Blockchain is a peer-to-peer network that helps keep the data in the ledger tamper-proof. It would be a way to prevent serious health risks and be versed in the origin, transport and sale of the products that consumers buy from stakeholders. In this paper, we propose a traceable food-safety with Blockchain Technology & Smart Contracts which would make it transparent and tamper-proof. Through the proposed solution, a long-lasting trust is established among the consumers and the stakeholders to make it reliable, contamination-free and traceable. The entire supply chain, right from the fields to the kitchen, will be closely monitored which leaves room for no mistakes.
ARChem: Augmented Reality Based Chemistry LAB Simulation for Teaching and Assessment

This paper introduces an Augmented Reality (AR)-based chemistry (ARChem) lab platform for teaching and assessment. The current in-class lab teaching has several drawbacks such as the high cost of lab materials, physical constraints in lab sizes, and exposure to hazardous materials. Besides, with Covid-19, access to the in-person labs and materials is significantly restricted. This restriction hampers regular lab instruction, self-learning, and hands-on experience. Our ARChem platform stems from the motivation of delivering a similar experience to regular instruction. In addition, ARChem aims to promote learning and advancement by incorporating gaming elements. Also, assessment and evaluation are provided for self-improvement. Our ARChem in-class chemistry lab uses a physical environment and virtual lab equipment and fluids. The environment is based on the AR technique where virtual lab equipment and materials (e.g. fluids and supplies) are overlaid on the physical environment using only mobile devices. To do this efficiently, ARChem provides computationally optimal methods such as simulating multiple bimanual chemistry reactions, low-cost fluid physics in containers, and realistic visualization. We enable a realistic lab environment with minimal hardware constraints attaining high simulation and visual frame rates. This simulation efficiency makes ARChem compatible with a wide range of low-end mobile devices and is accessible to any student. Our ultimate goal is to eliminate the drawbacks of current in-class teaching with a simulation environment and improve remote and asynchronous learning experience especially during an outbreak like Covid-19.