Reviewerly/exHarmony huggingface dataset:
Development of a Digital Twin of Reservoir Filling by Gas Mixture Component

The subject of research is a development of a digital twin of the process of filling the reservoir with gas mixture components. The problem is motivated by the concept of digitalization of modern production and is considered in relation to the researched technology of thermal pulse processing. The features of the operation of the fuel mixture generator according to the method of critical holes are determined and the corresponding determining equations for the dosing of gas mixture components and the reservoir filling time is given. It analyzed the modes of operation of the digital twin and selected the settings of the model of operation of the digital twin, which ensure a rational reproduction of the gas-dynamic non-stationary process of filling the tank with a component of the gas mixture. A gas-dynamic unsteady flow model was built and a numerical study was carried out using the ANSYS Fluent software. A reduced-order model (ROM mod-el of ANSYS Fluent) was developed and used in ANSYS Twin Builder for the construction of a digital twin. Developed examples of digital twins of the system of filling the tank with a gas mixture using standard elements of the Twin Builder and Modelica libraries.
Weakly Supervised Patchwise CT Liver Segmentation

Accurate segmentation of medical images in magnetic resonance imaging and computed tomography images, is helpful for many diagnosis tasks. However, in semantic obtain densely annotated masks are expensive. To tackle this problem, our method learns to segment by only leveraging CAMs and automatically generated bounding boxes priors in an iterative supervision framework. To fully use available scribbles to reach the limited pixel-level annotations, we present a novel approach, which consists of two components, refinement in prediction step and boundary regression optimization. Specifically, we adopt an iterative process and features enhancement technique to refine prediction. In the boundary regression component, to guide the segmentation network in localization boundaries between regions of object to segment we employ fine grained representation of feature maps. Experimental results on the CHAOS challenge demonstrate the proposed model achieves mIoU of 88.7% setting up the new state-of-the-art.
Using Clinical Data and Deep Features in Renal Pathologies Classification

The identification and categorization of glomerular diseases are crucial in the diagnosis of numerous renal diseases. This research carried out a comprehensive investigation to establish the optimal group of characteristics for depicting glomerular pathological images. Our feature extraction methodology includes clinical data and deep features. In addition, we compared four classifiers to support the specialist defining a renal pathology diagnosis. The study found that by combining clinical information with deep features, a high level of accuracy (95.98%) and agreement (98.93%) was achieved using the Random Forest classifier. This suggests that the combination of these two types of data makes it easier to correctly classify different diseases.
Continuous Authentication of Tablet Users Using Raw Swipe Characteristics: Tree Based Approaches

In terms of portability and convenience of usage, tablets are user-friendly devices next to smartphones. Operated through their relatively bigger sized touchscreens with respect to smartphones, these devices have found applications in the education sector, conducting surveys, biological and medical areas and as security control devices, to include a few. Albeit being convenient, the security of these devices can be easily compromised despite being equipped with some primary authentication mechanism. This paper explores secondary security measures that can complement the primary authentication mechanism using unprocessed swiping activity data from the user. Keeping in view the fact that research works conducted in this domain being few in numbers, this paper proposes a framework for continuously authenticating a tablet’s user. Implemented on a contemporary dataset that include both smartphone and tablet swipe vectors this tree based framework exhibited acceptable authentication performance compared to other such works. This was achieved without requiring the data to be preprocessed or scaled and based on single user swipes.
Generating Cyclic Rotation Gray Codes for Stamp Foldings and Semi-meanders

We present a simple algorithm that generates cyclic rotation Gray codes for stamp foldings and semi-meanders, where consecutive strings differ by a stamp rotation. These are the first known Gray codes for stamp foldings and semi-meanders, and we thus solve an open problem posted by Sawada and Li in [Electron. J. Comb. 19(2), 2012]. The algorithm generates each stamp folding and semi-meander in constant amortized time and O(n)-amortized time per string respectively, using a linear amount of memory.
Human Activity Recognition in a Thermal Spectrum for an Intelligent Video Surveillance Application

In this paper, we introduce a new method for Human Activity Recognition (HAR) from thermal video sequences to understand the behaviour of individuals in a scene. The proposed method applies a ConvLSTM model on the moving regions to identify the class of activity. Such process makes use of spatial and temporal information of the thermal video and provides an important gain in accuracy and time computation. From the experimental study that has been carried out, it is worth pointing out that incorporating data augmentation and temporal (motion) information helps to improve the model performance.
SP2P-MAKA: Smart Contract Based Secure P2P Mutual Authentication Key Agreement Protocol for Intelligent Energy System

The upcoming intelligent system which attempts to maximize energy efficiency is known as the Intelligent Energy System (IES). In which, prosumers could be either energy requester or responder, who dynamically adjust energy transfer on real-time basis, via a bi- directional channel. Even so, the widespread use of the IES raises several privacy and security concerns, such as centrally controlled administrative power, data alteration activities. Thus, the primary objective of our protocol is to address issues reading identity authentication exist in the IES. A trustworthy and reliable mutual authentication scheme is suggested by combining the Lattice-based Cryptosystem (LB) with blockchain based smart contract concept for P2P communication in IES. Blockchain concept is mainly used to minimize the negative impact of malicious validators existence in distributed system. To maintain the privacy of transferred data, an agreement session key is being generated between mutually authenticated peers. As a result, our suggested protocol works admirably with principle of integrity, security, and privacy. An informal security analysis is also performed by imposing the concept of hard assumption of SIS and ISIS challenges exist in LB cryptosystem.
Designing an incentive mechanism for information security policy compliance: An experiment

Much information security research focuses on policies firms could adopt to reduce or eliminate employees' violation behavior. However, current information security policies are based on increasingly outmoded models of compliance behavior. This paper proposes a novel behavioral-based mechanism that offers rewards and punishments to incentivize employees to take the time to protect a company's information assets. This new mechanism is grounded in insights from externality taxes and subsidies, as well as from behavioral economics, that specific incentives operationalized as monetary rewards and punishments effectively improve information security compliance. We also consider the importance of detection in implementing our mechanism. We conduct a set of laboratory experiments to study the impact of the rewards and punishments, as well as the importance of the probability of detection. Our results show clearly that rewards alone or a combination of rewards and punishments are effective in improving information security policy compliance in both high and low-detection environments, but punishments alone are not effective in either of our environments. In addition, a company's information security compensation plan is more likely to be effective in improving compliance if the company can more reliably detect violations. Overall, our study suggests that a compensation structure based on small and predictable financial rewards and punishments is likely more effective than the current punishment-focused approach.
Web Based Machine Learning Automated Pipeline

With the increasing volume, velocity, veracity, and variety of data, it has become critical to have efficient techniques and tools for managing and analyzing data in machine learning. Abstraction is a powerful concept that allows users to interact with machine learning algorithms without understanding their technical implementation details. In this project the user will provide the dataset in .csv format the dataset is then processed further to different machine learning preprocessing steps like removing unwanted columns, handling missing values, label encoding, outlier detection and removal, normalization, model building, model prediction, and the result can be downloaded as pdf, tracable pdf and CSV, this all processes gives a result of different model and their respective accuracy so that we can choose the best model for that particular dataset. tracable pdf will be containing all the timestamp of the processes done with their respective result, Apart from client-server model user is also provided a api so that all processes can be implemented in different platforms like c++, java, ruby etc. Overall, this paper highlights the critical role of abstraction in managing the complexity of data and machine learning algorithms, enabling more efficient and effective analysis of large and complex datasets.
Towards Better Bounds for Finding Quasi-Identifiers

We revisit the problem of finding small ε-separation keys introduced by Motwani and Xu (2008). In this problem, the input is a data set consisting of m-dimensional tuples {x1,x2,...,xn}. The goal is to find a small subset of coordinates that separates at least (1-ε)(n2) pairs of tuples. When n is large, they provided a fast algorithm that runs on Θ(m/ε) tuples sampled uniformly at random. We show that the sample size can be improved to Θ(m/√ε). Our algorithm also enjoys a faster running time.
A Query Language Perspective on Graph Learning

A key component of graph and relational learning methods is the computation of vector representations of the input graphs or relations. The starting point of this tutorial is that we model this computation as queries, mapping relational objects into the realm of real vector spaces. We then revisit recent works in the machine learning community on the expressive power of graph learning methods from this unifying query language perspective. Here, we consider the expressive power related to the discrimination of inputs and to the approximation power of functions. Finally, we argue that the bridge between graph learning and query languages opens many interesting avenues for further research.
Guaranteeing the Õ(AGM/OUT) Runtime for Uniform Sampling and Size Estimation over Joins

We propose a new method for estimating the number of answers OUT of a small join query Q in a large database D, and for uniform sampling over joins. Our method is the first to satisfy all the following statements.
RegRL-KG: Learning an L1 regularized reinforcement agent for keyphrase generation

Keyphrase generation (KG) aims at condensing the content from the source text to the target concise phrases. Though many KG algorithms have been proposed, most of them are tailored into deep learning settings with various specially designed strategies and may fail in solving the bias exposure problem. Reinforcement Learning (RL), a class of control optimization techniques, are well suited to compensate for some of the limitations of deep learning methods. Nevertheless, RL methods typically suffer from four core difficulties in keyphrase generation: environment interaction and effective exploration, complex action control, reward design, and task-specific obstacle. To tackle this difficult but significant task, we present RegRL-KG, including actor-critic based-reinforcement learning control and L1 policy regularization under the first principle of minimizing the maximum likelihood estimation (MLE) criterion by a sequence-to-sequence (Seq2Seq) deep learnining model, for efficient keyphrase generation. The agent utilizes an actor-critic network to control the generated probability distribution and employs L1 policy regularization to solve the bias exposure problem. Extensive experiments show that our method brings improvement in terms of the evaluation metrics on five scientific article benchmark datasets.
Active ordinal classification by querying relative information

Collecting and learning with auxiliary information is a way to further reduce the labeling cost of active learning. This paper studies the problem of active learning for ordinal classification by querying low-cost relative information (instance-pair relation information) through pairwise queries. Two challenges in this study that arise are how to train an ordinal classifier with absolute information (labeled data) and relative information simultaneously and how to select appropriate query pairs for querying. To solve the first problem, we convert the absolute and relative information into the class interval-labeled training instances form by introducing a class interval concept and two reasoning rules. Then, we design a new ordinal classification model for learning with the class interval-labeled training instances. For query pair selection, we specify that each query pair consists of an unlabeled instance and a labeled instance. The unlabeled instance is selected by a margin-based critical instance selection method, and the corresponding labeled instance is selected based on an expected cost minimization strategy. Extensive experiments on twelve public datasets validate that the proposed method is superior to the state-of-the-art methods.
Trajectory personalization privacy preservation method based on multi-sensitivity attribute generalization and local suppression

Fast-developing mobile location-aware services generate an enormous volume of trajectory data while adding value to people’s lives. However, trajectory data contains not only location information, but also sensitive personal information. If the original trajectory data is published directly, it could result in serious privacy leaks. Most of the existing privacy-preserving trajectory publishing methods only protect the location information or set the same privacy preservation levels for all moving objects. To meet the users’ personalized privacy requirements and ensure the utility of trajectory location and sensitive information, we propose a trajectory personalized privacy preservation method based on multi-sensitivity attribute generalization and local suppression. First, we set different security levels for each trajectory by calculating the correlation between sensitive attributes to establish a sensitive attribute classification tree. Second, we generalized sensitive attributes based on privacy preservation levels for each trajectory, the trajectory data still at risk of privacy leakage after generalization was locally suppressed. Finally, an anonymized trajectory dataset was generated. Experimental results on real datasets demonstrated that our method could improve data availability while preserving privacy.
Research on bionic flower interaction technology based on linkage mechanism

In this paper we have designed an interactive device of bionic flower based on linkage mechanism by studying the shape and structure of flowers. Firstly, in order to solve the problem of variable morphology of the bionic flower, we have built the internal skeletal structure of the bionic flower in accordance with the working principle of the hinge linkage mechanism; secondly, based on the internal skeletal structure, we have tested a variety of ductile materials, and identified the multifunctional paper material that can be shape-shifting and color-changing; thirdly, we have used the key technology of Arduino to build the intelligent sensors and hardware, and solved the opening and closing sequence and response range of multiplecluster interactive objects. This article research provides new ideas for the display of flower and plant science to the public from the perspective of bionics, and gives new application areas and cultural values to the interactive bionic design of plants and flowers.
Triple trustworthiness evaluation for knowledge graph of industrial domain

The knowledge graph is widely used in industrial fields due to its structural characteristics. In order to reduce the cost of wrong decision-making, it is more important for the industrial knowledge graph to guarantee the quality and comprehensiveness of knowledge. In order to obtain the trustworthiness information of triples in the graph, this paper proposed a model to evaluate triple trustworthiness for industrial knowledge graphs(TT-IKG) and obtains the final score of triples by fusing the output of three sub-modules that investigate the local confidence of triples, the confidence of schema-matching and the confidence of global paths. Experiments on the real industrial knowledge graph of enterprises verified the effectiveness of the model, and the experimental results on the error detection and graph completion tasks of the knowledge graph show that the effect is better than that of other models.
Traffic sign detection and recognition based on multi-size feature extraction and enhanced feature fusion module

Deep learning has dominated the research field of traffic sign detection, but the traffic sign detection algorithms based on deep learning have difficulty in solving the two tasks of localization and classification simultaneously when performing traffic sign detection on realistic and complex traffic scene images, and the images or the types of traffic signs provided by the public dataset used by the relevant algorithm cannot meet the situations encountered in realistic traffic scenes.To solve the above problems, this paper creates a new road traffic sign dataset, and based on the YOLOv4 algorithm, designs a multi-size feature extraction module and an enhanced feature fusion module to improve the algorithm’s ability to locate and classify traffic signs simultaneously, in view of the complexity of realistic traffic scene images and the large variation of traffic sign sizes in the images. The experimental results on the newly created dataset show that the improved algorithm achieves 83.63% mean Average Precision (mAP), which is higher than several major object detection algorithms based on deep learning for the same type of task at present. The newly created dataset in this paper is publicly available at https://github.com/zhang1018/Traffic-sign-dataset-for-public.
Safe co-training for semi-supervised regression

Co-training is a popular semi-supervised learning method. The learners exchange pseudo-labels obtained from different views to reduce the accumulation of errors. One of the key issues is how to ensure the quality of pseudo-labels. However, the pseudo-labels obtained during the co-training process may be inaccurate. In this paper, we propose a safe co-training (SaCo) algorithm for regression with two new characteristics. First, the safe labeling technique obtains pseudo-labels that are certified by both views to ensure their reliability. It differs from popular techniques of using two views to assign pseudo-labels to each other. Second, the label dynamic adjustment strategy updates the previous pseudo-labels to keep them up-to-date. These pseudo-labels are predicted using the augmented training data. Experiments are conducted on twelve datasets commonly used for regression testing. Results show that SaCo is superior to other co-training style regression algorithms and state-of-the-art semi-supervised regression algorithms.
A novel fault tolerance based load balancing technique in cloud computing

Cloud computing has emerged as one of the most promising technologies for meeting rising computing needs. However, high-performance computing systems are more likely to fail due to the proliferation of components and servers. If a sub-system fails, the entire system may not be functional. In this regard, the occurrence of faults is tolerable using an efficient fault-tolerant method. Since cloud computing involves storing data on a remote network, system failures and congestion are the most common causes of faults. The paper presents a new approach to adopting a fault-tolerant mechanism that adaptively monitors health to detect faults, handles faults using a migration technique, and avoids network congestion. With the advantage of the Ant Colony Optimization (ACO) algorithm and active clustering, the load is distributed evenly in data centers. Simulation results indicate that our algorithm outperforms previous algorithms regarding total execution time and imbalance degree up to 10% and 17%, respectively.
Destructive computing with winner-lose-all competition in multi-layered neural networks

The present paper aims to propose a new learning method based on destructive computing, contrary to the conventional progressive computing or the steady-step learning. In spite of the existence of a large amount of biased or distorted information in inputs, the conventional learning methods fundamentally aim to gradually acquire information that is as faithful as possible to inputs, which has prevented us from acquiring intrinsic information hidden in the deepest level of inputs. At this time, it is permitted to suppose a leap to that level by changing information at hand not gradually but drastically. In particular, for the really drastic change of information, we introduce the winner-lose-all (WLA) to drastically destroy the supposedly most important information for immediately reaching or leaping to intrinsic information, hidden in complicated inputs. The method was applied to a target-marketing problem. The experimental results show that, with the new method, multi-layered neural networks had an ability to disentangle complicated network configurations into the simplest ones with simple and independent correlation coefficients between inputs and targets. This was realized by drastically changing the information content in the course of learning and, correspondingly, by mixing regular and irregular properties over connection weights.
Professional discourse of IT specialists

The article deals with the appearance in the language of a new special vocabulary used by IT specialists, and the features of its origin. The purpose of the study is to identify and analyze special vocabulary in professional discourse. The material for the study was 1167 lexical units, of which 582 in the Russian dictionary, 649 in the English-Russian dictionary. The study found that IT specialists operate with terms, professionalism, and slang. Slang carries the need to learn English and is an integral part of professional discourse. Five themes are identified that unite all professional concepts and the functions of a professional language are defined.