Reviewerly/exHarmony huggingface dataset:
A Generative Approach to Open Set Recognition Using Distance-Based Probabilistic Anomaly Augmentation

Machine learning (ML) algorithms that are used in decision support (DS) and autonomous systems commonly train on labeled categorical samples from a closed set. This, however, poses a problem for deployed DS and autonomous systems when they encounter an anomalous pattern that did not originate from the closed set distribution used for training. In this case, the ML algorithm that was trained only on closed set samples may erroneously identify an anomalous pattern as having originated from one of the categories in the closed set, sometimes with very high confidence. In this paper, we consider the problem of unknown pattern recognition from a generative perspective in which additional synthetic training samples that represent anomalies are added to the training data. These synthetic samples are generated to optimally balance the desire to place anomalies all along the boundary of the training set in feature space, while not adversely effecting core classification performance on the test set. We demonstrate the efficacy of distance-based probabilistic anomaly augmentation (DPAA) that is proposed in this paper for a diverse set of applications such as character recognition and intrusion detection, and compare its combined classification and identification performance to both recent open set and more traditional novelty detection approaches.
Designing a Blockchain Data Storage System Using Ethereum Architecture and Peer-to-Peer InterPlanetary File System (IPFS)

In this study, a higher level of security was implemented by using InterPlanetary File System (IPFS) based peer-to-peer data storage on an Ethereum blockchain. IPFS is used as an image data storage medium that generates an IPFS hash, or known as content identifier (CID). The CID is used to access image data on the IPFS, which can be done with the help of an IPFS gateway. The CID is sent into the blockchain using a smart contract. The results of the study demonstrates that the use of IPFS as additional security on a blockchain system can be done. The size of file sent using IPFS proportionally affects the time it takes for IPFS to store the file. The larger the file size, the more time it will take. For each additional 1 MB file, there is an approximately 5% increase in access time. The distance from the IPFS gateway will also affect file access times in which, the farther the IPFS gateway, the more time it takes. If the IPFS gateway is at a distance of 1,000 km, there will be a decrease in access time of about 5%. The distance between the IPFS gateway and the accessor also affects success with accessing data. For the most distant gateway, i.e. the United States, there was even an access failure of 24%.
De Bruijn-Based and k-Ary n-Cube-Based Algebraic Models in Fog Environments

In order to deal with offloading tasks, fog computing involves a set of distributed hosts, being interconnected according to a certain switching topology, upon which overlay networks may be configured. These type of logical networks will be adapted to fit the needs of different kinds of application flows, resulting in diverse sorts of traffic, each one with its own constraints regarding quality of service. In this context, de Bruijn graphs are reviewed as a feasible technology to be employed for traffic forwarding, and furthermore, k-ary n-cubes are also presented as an alternative option. Additionally, the role of de Bruijn shapes for dealing with position location in different dimensions is also discussed.
The publics of design: Challenges for design research and practice

The notion of ‘publics’ has emerged as a frame for rethinking the relationships between design practices and the communities affected by design. This paper explores two facets of the intersection of design and publics. First, we examine the notion of designing for public interests with an understanding of publics as multiple, conflicting and varied. Second, with reference to the Australian context, we reflect on different ways design communities can identify and support the formation of publics as a pivotal next step to more effectively serve its own interests and values, including the urgent enactment of social change. Our discussion identifies four sets of tensions that we offer as provocations for constructive and ongoing engagement by the design community at large to inform the process of designing for our own publics.
Market-Aware Dynamic Person-Job Fit with Hierarchical Reinforcement Learning

Person-Job Fit (PJF) is the core of online recruitment. Several recent methods took PJF as a preference-learning problem, and tried to learn two-sided preferences from their historical behaviors. However, they ignored users' interactive feedbacks (accepted or rejected) received from the other side which may change users' preferences. In addition, they neglected the status of local market which may affect the final matching results of person-job pairs. To solve these issues, we propose a market-aware dynamic PJF method with hierarchical reinforcement learning (HIRE). We design a two-level hierarchy of reinforcement learning policies. Two low-level policies aim to learn the dynamic preferences of both persons and jobs with consideration of interactive feedbacks, and a high-level policy aims to learn the optimal dynamic matching strategy with consideration of local market state. Extensive experiments on two real-world datasets show the effectiveness of HIRE compared with the state-of-the-art.
Overlapping Cell Segmentation with Depth Information

With the advancement of artificial intelligence technology, analysis of cervical cells with the aid of the cervical cancer auxiliary diagnosis system can improve the accuracy and efficiency of cervical cancer screening. In order to better complete the overlapping cell segmentation in cervical cell samples, this paper proposes a segmentation algorithm for cytoplasm based on depth information. The segmentation algorithm based on depth information can make full use of the depth information in the stacked cervical cell samples, so as to better complete the overlapping cell segmentation. The experimental results show that, based on the ISBI2015 public data set, the cell segmentation algorithm based on depth information proposed in this paper is higher than other current methods in terms of positive predictive value, negative predictive value, precision rate and recall rate.
Fuzzt Set-Based Kernel Extreme Learning Machine Autoencoder for Multi-Label Classification

The multi-label learning algorithm based on an extreme learning machine has the advantage of high efficiency and generalization ability, but its classification ability is weak due to ignoring the correlation between features and labels. Accordingly, in this paper, the fuzzy set-based kernel extreme learning machine autoencoder for multi-label classification (KELM-AE-fuzzy) is proposed. Firstly, the correlation between features and labels is analyzed based on fuzzy set theory, and the correlation label membership matrix and label completion matrix are constructed. Then, the kernel extreme learning machine autoencoder is used to fuse the correlation label membership matrix with the original feature space and generate the reconstructed feature space. Eventually, kernel extreme learning machine (KELM) is used as a classifier, where the label matrix is used with the label completion matrix. Comparative experiments on several multi-label datasets demonstrate that KELM-AE-fuzzy outperforms other multi-label algorithms, and the effectiveness of the proposed algorithm is verified.
Invertible Image Dataset Protection

The security of data storage is a big issue for companies. They must take effective steps to prevent valuable image datasets from being stolen for illegal commercial purposes. While data encryption is a common solution, it drastically down-grades the visual quality and therefore forbids common yet trivial use such as eye-checking without a decryption. We present a novel solution for dataset protection in this scenario by robustly and reversibly transform the images into adver-sarial images. An invertible Image Dataset Protection NET-work (IDP-Net) is developed to introduce slight and acceptable changes to the images within the dataset. The protected images can be published and circulated on the social networks instead of their original version. Malicious attackers can only observe the images but cannot train pirated models based on them. Meanwhile, IDP-Net ensures the performance of au-thorized models, namely, trusted users can revert the protection and retrieve the protected images to their original version. Therefore, the dataset can be stored within the pro-tected version alone to ensure safety. Extensive experiments demonstrate that IDP-Net can better protect the security of image dataset against defensive methods compared to previ-ous methods. Besides, the introduced distortion is acceptable and the original images can be reconstructed nearly error-free.
A Decentralised Mechanism for Secure Cloud Computing Transactions

In this paper, we present an innovative mechanism for transaction processing for big data in Cloud Computing context. We focus on data and information security in proposing the decentralised mechanism. Traditional transaction processing, if monitored, can potentially help an attacker to construct context of the information stored by the client to subsequently determine of value of the client's digital asset. In the proposed mechanism, we take a de centralised and collaborative approach both in terms of implementations and operations of transactions between end-users and Cloud servers. In the proposed transaction mechanism, the total picture of a transaction is not recoverable by unwanted third parties and thus the information on data processed by a transaction may not become a knowledge of an eavesdropper. The proposed mechanism is built upon and motivated by the principles of Ki-Ngā-KNgōpuku - a de centralised and distributed computing security model that employs scattered and redundant de centralised processing.
Grid Defect Data Completion Based on Generative Adversarial Imputation Nets

With the in-depth development of grid intelligence, the data in the power system becomes more and more important. However, in the environment of high magnetoelectric interference in the power system, data collection and transmission are missing due to equipment failure and communication interference. In this case, it is necessary to complete the data processing. The traditional reconstruction method ignores the inherent relationship between the data and the system, which reduces the accuracy. This paper proposes a method of power system data completion based on Generative Adversarial Imputation Nets. This article first introduces the characteristics of GAN and power system missing data to prepare for data completion; then introduces the Generative Adversarial Imputation Nets algorithm; finally designs the data completion algorithm of the power system to realize the data completion function. The feasibility of the algorithm is verified on the python platform.
An Integrated Model of Digitalisation-Related Competencies in Teacher Education

This paper presents a model of digitalisation-related competencies for teacher education, developed by a working group on digitalisation in teacher education (WG DidL) at the University of Duisburg-Essen. Currently, there are various models available that outline the competencies teachers should develop for being equipped to work in a digital world. These approaches often mention various, widely applicable digitalisation-related competences that teachers are meant to acquire, or they are based on a limited or only implicit understanding of digitalisation. The aim of the presented model is to contribute to the discussion of how to best integrate existing models. It is based on an integrated understanding of digitalisation-related competencies that encompass teaching and learning with digital media, as well as learning about digitalisation as a subject matter in its own right. At the center of the model are generally formulated competency goals for teaching and learning, for professional engagement, and for reflective, critical-constructive teaching practice. The potential for achieving these goals is then illustrated by means of interdisciplinary and/or subject-specific examples. In this way, the model can also be applied to specific subject areas and to their teaching methodologies.
The Peculiarities of Teaching English as a Foreign Language Course

Context. Research stresses out that teaching English in universities of culture has specific features in terms of vocabulary, grammar and developed skills. Purpose or goal. The purpose of this paper is to outline the three-stage model of teaching English to art students. As the curriculum presupposes studying English for both general and special purposes, as well as mastering skills essential for corresponding professional communication sphere, it influences the content of the studied material and the structure of the academic process. The description of the highlighted stages enables specialists to apply the results for experimental purposes. Approach. The level of our students’ English language proficiency ranges from A2 (pre-intermediate) to B1 (intermediate) according to the Common European Framework of Reference for Languages (2002). We focus our study on learners whose language skills are of A2 level. Throughout the course, we tested the development of skills (Writing, Use of English, etc.) once a month 3 times. We name the benefits of the course for art students in this article. Actual or anticipated outcomes. The analysis revealed that the group with learning outperformed the control groups. While the results were encouraging, continued improvement is indicated and discussed. Conclusions. We recommend conducting investigations with control groups and experimental, in both sampling and duration, not only on writing and reading skills, but also on grammar, reading comprehension and other language aspects to corroborate the current findings and increase their potential generalizability.
RONI-Based Medical Image Watermarking Using DWT and LSB Algorithms

In recent years, hiding information in medical images is the largest usage to secure this information or garnet the integrity of the owner, the embedding can distort the medical image and change the necessary health patient information. In this paper, we propose a robust method for medical image watermarking to secure patient data when it transmits in a non-secure channel. First of all, the original image is filtered by a sharpening filter for enhanced contrast then separated into two regions using snake segmentation. The embedding mark (Electronic Patient Record) is added to the frequency domain after applying Discrete Wavelet Transform (DWT) on the region of non-interest (RONI) using the last signification bit (LSB). This region has a predominantly black background; the region of interest (ROI) has the necessary patient information. This method preserves a high-quality watermarked image, and it improves imperceptibility, security, and authentication. Our method is evaluated by Pick Signal to Noise Ratio (PSNR = 46.4039 for 512 * 512 bits image size), SNR, NC, and histogram analysis, and it's compared with existing schemes.
Real-Time Interaction Tools in Virtual Classroom Systems

For years our research goal is how to integrate ICT in teaching process to activate and involve our students and get better and better results. We implemented a Classroom Response System which fitted perfectly to our university requirements. During the emergency situation caused by COVID-19 most of the education changed to remote teaching choosing between hybrid, online synchron or online mode. In the time of writing the paper we already started the third semester of emergency situation. Now we got to know thoroughly different IT tools and experiences how to enhance our education quality which key factor is activity. In this paper we should like to focus on interaction possibilities in virtual classroom systems and present some benefits of E-Lection system implemented by us. Online synchron teaching period with students' feedbacks lead us to plan new additional features to our system. This newness will produce a more useable, complex, and integrated system to support both students and professors' requirements.
Multiple Object Detection using Deep Learning

In the cutting edge and quick world, finding a precise and proficient object recognition for headway in Computer vision frameworks has been integral part. With the emerge offaster (due to faster and better hardware to) and increasingly accurate deep learning techniques the prediction for finding a good and accurate model has been justified. In this undertaking, we are actually aiming to incorporate best possible technique to tackle object detection with goal of achieving higher accuracy in our Multiple Object Detection project. The major motivation in our task was to making tremendous contribution to increase the accuracy of the object detection. In this venture, we utilize four models to tackle the problem, mainly Mask- RCNN (Mask Region Convolution Neural Network), Faster-RCNN (Faster Region Convolution Neural Network), SSD (Single Shot MultiBox Detection) and YOLO (You look only once) trained on the most publicly and freely accessible dataset (MS-COCO).
Internal Validity Index for Fuzzy Clustering Based on Relative Uncertainty

Unsupervised clustering and clustering validity are used as essential instruments of data analytics.Despite clustering being realized under uncertainty, validity indices do not deliver any quantitative evaluation of the uncertainties in the suggested partitionings.Also, validity measures may be biased towards the underlying clustering method.Moreover, neglecting a confidence requirement may result in over-partitioning.In the absence of an error estimate or a confidence parameter, probable clustering errors are forwarded to the later stages of the system.Whereas, having an uncertainty margin of the projected labeling can be very fruitful for many applications such as machine learning.Herein, the validity issue was approached through estimation of the uncertainty and a novel low complexity index proposed for fuzzy clustering.It involves only uni-dimensional membership weights, regardless of the data dimension, stipulates no specific distribution, and is independent of the underlying similarity measure.Inclusive tests and comparisons returned that it can reliably estimate the optimum number of partitions under different data distributions, besides behaving more robust to over partitioning.Also, in the comparative correlation analysis between true clustering error rates and some known internal validity indices, the suggested index exhibited the highest strong correlations.This relationship has been also proven stable through additional statistical acceptance tests.Thus the provided relative uncertainty measure can be used as a probable error estimate in the clustering as well.Besides, it is the only method known that can exclusively identify data points in dubiety and is adjustable according to the required confidence level.
Code Injection Prevention in Content Management Systems Using Machine Learning

Web content management system is a software content management system (CMS) specifically designed for hosting web applications. They make it easier to author, collaborate, and administer websites without the need of complex programming languages. Even though they score very high marks on the usability front, they are very vulnerable to security exploits. In our previous work (Kavithamani et al. in Intelligence in big data technologies—beyond the hype. Advances in intelligent systems and computing. Springer, Singapore, 2021 [1]), we analyzed and presented various security vulnerabilities of a WordPress-based Web Content Management System. We focused on one such malware attack from the origin of the attack to how it exploits the CMS application, and the malware’s regeneration capabilities. Majority of the attacks that were able to breach the security were Code Injection attacks. In this work, we narrow our focus to preventing this type of Code Injection vulnerability using machine learning. We also discuss the data gathering approach we deployed using Honeypots to gather malicious Code Injection attempts. We explain the different approaches used in data mining, feature extraction, feature selection and the results from the machine learning model.
The influence of Artificial intelligence on the right to freedom of expression

The right to freedom of thought and expression represents one of the fundamental principles of a democratic and civilized society. The Internet has become the most important communication medium through which the individuals exercise their right to seek, receive and impart information and ideas of any kind, regardless of any frontiers. Various technologies have been used to enable an online communication, while today artificial intelligence systems are deployed in every corner of the Internet, providing information dissemination and communication. The application of the artificial intelligence systems is based on generating, collecting, and processing a large quantity of personal data with the aim of profiling users and predicting their future behaviour. This can have serious consequences for the right to freedom of expression. Through the content personalization on online platforms, particularly on social networks and search engines, the artificial intelligence systems choose the content that users can see and the order in which they see it, leaving them in the so-called 'filter bubbles'. Artificial intelligence systems also moderate the content, removing the one which does not comply with the rules of the online platforms, and, temporarily or permanently, blocking the users who violate the community rules, raising thus the issues of legality, legitimacy and proportionality of the decisions made by artificial intelligence.
Metamorphic Testing for Autonomous Driving Systems in Fog based on Quantitative Measurement

Fog is one of the major disaster weather affecting traffic safety. For autonomous driving system in foggy, the image is captured by the camera that will have some problems, which will make the autonomous driving system easily affected. Therefore, it is worth to do further research on how and to what extent foggy weather affects autonomous driving systems. This paper proposes a test method, that can quantify the influence of fog for the autonomous driving system whose output is steering angle. The method relies on the metamorphic testing and proposes the metamorphic relations from the density and direction of fog to determine whether the autonomous driving system predicts inconsistent with driving behavior. In this paper, the density of the fog is divided into 5 levels according to GB/T 27964–2011, and the direction of the fog is divided into 9 directions according to the regional information, and the fog scene images with different densities and directions are simulated using OpenCV and CycleGAN. The results of relevant experiments carried out for three different autonomous driving systems show that the method can quantitatively and effectively detect a large number of failures in the autonomous driving system. Specifically, the autonomous driving system is rarely affected by the direction of fog, but the number of failures in the autonomous driving system is positively correlated with the density of fog, and it is more likely to fail in the case of dense fog.