Reviewerly/exHarmony huggingface dataset:
EDVAM: a 3D eye-tracking dataset for visual attention modeling in a virtual museum

Predicting visual attention facilitates an adaptive virtual museum environment and provides a context-aware and interactive user experience. Explorations toward development of a visual attention mechanism using eye-tracking data have so far been limited to 2D cases, and researchers are yet to approach this topic in a 3D virtual environment and from a spatiotemporal perspective. We present the first 3D Eye-tracking Dataset for Visual Attention modeling in a virtual Museum, known as the EDVAM. In addition, a deep learning model is devised and tested with the EDVAM to predict a user’s subsequent visual attention from previous eye movements. This work provides a reference for visual attention modeling and context-aware interaction in the context of virtual museums.
Verification of De-Identification Techniques for Personal Information Using Tree-Based Methods with Shapley Values

With the development of big data and cloud computing technologies, the importance of pseudonym information has grown. However, the tools for verifying whether the de-identification methodology is correctly applied to ensure data confidentiality and usability are insufficient. This paper proposes a verification of de-identification techniques for personal healthcare information by considering data confidentiality and usability. Data are generated and preprocessed by considering the actual statistical data, personal information datasets, and de-identification datasets based on medical data to represent the de-identification technique as a numeric dataset. Five tree-based regression models (i.e., decision tree, random forest, gradient boosting machine, extreme gradient boosting, and light gradient boosting machine) are constructed using the de-identification dataset to effectively discover nonlinear relationships between dependent and independent variables in numerical datasets. Then, the most effective model is selected from personal information data in which pseudonym processing is essential for data utilization. The Shapley additive explanation, an explainable artificial intelligence technique, is applied to the most effective model to establish pseudonym processing policies and machine learning to present a machine-learning process that selects an appropriate de-identification methodology.
Scientific and Pedagogical Community in Russian Universities in the Context of the Program «Priority-2030» Implementation: Problems and Prospects

The article presents a sociological view on the current problems of the scientific and pedagogical community (SPC) of universities participating in the strategic academic leadership program «Priority-2030». The main attention is paid to the characteristics of the trends in the development of the SPC, its contradictions and the possibilities of overcoming them. A special place is given to the sociological analysis of university management as a factor of scientific productivity of the SPC. The interaction of the SPC and university management is considered in the context of the goals of the Priority- 2030 program. The necessity of developing and implementation new principles of human capital management of SPC into university practices is substantiated. Among them are a differentiated approach to assessing the scientific activity of individual groups of SPCs; flexible restructuring of the main types of their professional activities; ensuring the high status of SPCs demonstrating major scientific achievements; overcoming anomalies and imitations in the activities of SPCs. The empirical basis of the article is the results of interviews with representatives of the SPC, analysis of documents, statistical data reflecting the development of Russian higher education in the XXI century.
A Bio-Inspired Multi-Population-Based Adaptive Backtracking Search Algorithm

Backtracking search algorithm (BSA) is a nature-based optimization technique extensively used to solve various real-world global optimization problems for the past few years. The present work aims to introduce an improved BSA (ImBSA) based on a multi-population approach and modified control parameter settings to apprehend an ensemble of various mutation strategies. In the proposed ImBSA, a new mutation strategy is suggested to enhance the algorithm's performance. Also, for all mutation strategies, the control parameters are updated adaptively during the algorithm's execution. Extensive experiments have been performed on CEC2014 and CEC2017 single-objective benchmark functions, and the results are compared with several state-of-the-art algorithms, improved BSA variants, efficient differential evolution (DE) variants, particle swarm optimization (PSO) variants, and some other hybrid variants. The nonparametric Friedman rank test has been conducted to examine the efficiency of the proposed algorithm statistically. Moreover, six real-world engineering design problems have been solved to examine the problem-solving ability of ImBSA. The experimental results, statistical analysis, convergence graphs, complexity analysis, and the results of real-world applications confirm the superior performance of the suggested ImBSA.
MEC and Blockchain-Enabled Energy-Efficient Internet of Vehicles Based on A3C Approach

Nowadays, the rise of the Internet of Vehicles (IoV) has led to the rapid development of smart transportation. To increase the computing capacity of mobile vehicles and decrease the content delivery latency of suppliers, mobile edge computing (MEC) is considered as an indispensable solution. However, there are some essential issues to be considered: 1) security and privacy of data transmission, and 2) reasonable resource allocation for collaborative computing and caching. In this paper, to solve above issues, blockchain technology is adopted to ensure reliable transmission and interaction of data. Meanwhile, we develop an intelligent resource framework about computing and caching for blockchain-enabled MEC systems in IoV. Through jointly considering and optimizing offloading decision of computation task carried by vehicle, caching decision, the number of offloaded consensus nodes, block interval and block size, the energy consumption and computation overheads can be decreased, and the data throughput of the blockchain can be increased significantly. Moreover, the proposed optimization problem is modeled and formulated as a Markov decision process. Facing the complexity and dynamic of resource allocation, the asynchronous advantage actor-critic approach is considered and applied to solve the optimization problem. Experiment results demonstrate that the advantages of the proposed optimization scheme are obvious compared with other existing schemes.
First Gradually, Then Suddenly: Understanding the Impact of Image Compression on Object Detection Using Deep Learning

Video surveillance systems process high volumes of image data. To enable long-term retention of recorded images and because of the data transfer limitations in geographically distributed systems, lossy compression is commonly applied to images prior to processing, but this causes a deterioration in image quality due to the removal of potentially important image details. In this paper, we investigate the impact of image compression on the performance of object detection methods based on convolutional neural networks. We focus on Joint Photographic Expert Group (JPEG) compression and thoroughly analyze a range of the performance metrics. Our experimental study, performed over a widely used object detection benchmark, assessed the robustness of nine popular object-detection deep models against varying compression characteristics. We show that our methodology can allow practitioners to establish an acceptable compression level for specific use cases; hence, it can play a key role in applications that process and store very large image data.
Evacuation Behavior and Information Needs of Wellington, Aotearoa New Zealand Residents Following the 5 March 2021 Mw 7.3 East Cape Earthquake

Abstract At 2.27 a.m., on 5 March 2021, an Mw 7.3 earthquake occurred approximately 100 km off the East Cape of the North Island of Aotearoa New Zealand. This earthquake was felt across the entire country, including in the capital city of Wellington and the surrounding region. Previous work reports varied levels of tsunami evacuation behavior for natural warnings from earthquake shaking. To further explore this behavior, we surveyed residents of the Wellington region about their evacuation decisions following the 5 March East Cape earthquake, their motivations for evacuating or not evacuating, and their immediate information needs. Approximately one-third of those who felt the earthquake, and who were in a tsunami hazard zone, self-evacuated, similar to previous findings following a larger, closer earthquake in 2016. Reasons for evacuating immediately were evenly split over specific awareness of the National Emergency Management Agency’s “Long or strong, get gone” tsunami evacuation advice, general knowledge that earthquakes can trigger tsunami, and social influences. Among those who did not evacuate, common reasons included not receiving an official warning and not believing that the earthquake was strong enough to generate a tsunami. Most people sought information from GeoNet, an official provider of earthquake information, though civil defense and emergency management sources were less used than mainstream news and social media. Along with information about tsunami risk, people tended to seek or want general information about earthquake characteristics such as location and size. Overall, the findings suggest that current public education efforts to encourage people to recognize and act on natural warnings (i.e., earthquake shaking) rather than waiting for official advice should be continued, and that consideration should be given to encouraging the use of official information sources.
Working Notes of the Workshop Arabic Misogyny Identification (ArMI-2021)

This paper provides an overview of the first shared task on misogyny identification in Arabic tweets. Arabic Misogyny Identification task (ArMI) is introduced within the Hate Speech and Offensive Content detection (HASOC) track at FIRE-2021. The ArMI task combines two related classification subtasks: a main binary classification subtask for detecting the presence of misogynistic language, and a fine-grained multi-class classification subtask for identifying seven misogynistic behaviors found in misogynistic contents of 9,833 Arabic/dialectal tweets1. The systems introduced by the participants employed various methods including feature-based, neural networks using either classical machine learning techniques, ensemble methods or transformers. The best performing system achieved an F-measure of 91.4% and 66.5% for subtask A and subtask B, respectively.
Encryption-then-Compression System for Cloud-based Medical Image Services

In the recent years, encryption-then-compression (EtC) algorithms are becoming popular for multimedia applications due to their low computational requirements and format compliancy with the JPEG image standard. In this study, we first extend the applications of EtC algorithms to medical image processing domain. Then, given importance of medical data, we propose a method to improve the security performance of the conventional EtC schemes with acceptable degradation in the compression savings but without compromising quality of the recovered images. We have implemented a deep learning (DL) model for tuberculosis (TB) screening in Montgomery datasets. Our analysis shows that the recovered images are of high quality and has no adverse effect on the accuracy of the DL model for TB detection. The proposed method preserves the crucial information in the image necessary for correct diagnosis while significantly reducing the image size and providing security to enable telemedicine application of eHealth services.
A New Adaptive Attack on SIDH

The SIDH key exchange is the main building block of SIKE, the only isogeny based scheme involved in the NIST standardization process. In 2016, Galbraith et al. presented an adaptive attack on SIDH. In this attack, a malicious party manipulates the torsion points in his public key in order to recover an honest party’s static secret key, when having access to a key exchange oracle. In 2017, Petit designed a passive attack (which was improved by de Quehen et al. in 2020) that exploits the torsion point information available in SIDH public key to recover the secret isogeny when the endomorphism ring of the starting curve is known. In this paper, firstly, we generalize the torsion point attacks by de Quehen et al. Secondly, we introduce a new adaptive attack vector on SIDH-type schemes. Our attack uses the access to a key exchange oracle to recover the action of the secret isogeny on larger subgroups. This leads to an unbalanced SIDH instance for which the secret isogeny can be recovered in polynomial time using the generalized torsion point attacks. Our attack is different from the GPST adaptive attack and constitutes a new cryptanalytic tool for isogeny based cryptography. This result proves that the torsion point attacks are relevant to SIDH (Disclaimer: this result is applicable to SIDH-type schemes only, not to SIKE.) parameters in an adaptive attack setting. We suggest attack parameters for some SIDH primes and discuss some countermeasures.
An Evolutive Frequent Pattern Tree-based Incremental Knowledge Discovery Algorithm

To understand current situation in specific scenarios, valuable knowledge should be mined from both historical data and emerging new data. However, most existing algorithms take the historical data and the emerging data as a whole and periodically repeat to analyze all of them, which results in heavy computation overhead. It is also challenging to accurately discover new knowledge in time, because the emerging data are usually small compared to the historical data. To address these challenges, we propose a novel knowledge discovery algorithm based on double evolving frequent pattern trees that can trace the dynamically evolving data by an incremental sliding window. One tree is used to record frequent patterns from the historical data, and the other one records incremental frequent items. The structures of the double frequent pattern trees and their relationships are updated periodically according to the emerging data and a sliding window. New frequent patterns are mined from the incremental data and new knowledge can be obtained from pattern changes. Evaluations show that this algorithm can discover new knowledge from evolving data with good performance and high accuracy.
Comparative study of Arabic text classification using feature vectorization methods

Arabic Text Classification (ATC) also known Arabic text categorization is the task of assigning categories to Arabic documents based on their contents. It is mostly used for sentiment analysis, detecting trends in customer feedback, spam detection and topic labeling. This paper presents an empirical study of five classification models using two Arabic datasets cnn_arabic and osac_uft8. These algorithms are Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN) and Logistic Regression (LR). Three feature vectorization methods were applied to convert text into numeric vectors word count, Terms Frequency-Inverse Document Frequency (TF-IDF) and word embedding using word2vec. For the applied feature vectorization techniques, the experiment shows that the classifiers SVM and LR score the highest performance followed by RF, KNN and DT. Besides, the experiment shows that feature vectorization methods and dataset size have high impact on the performance of the algorithms RF, KNN and DT, while SVM and LR maintain stable outcomes.
Selecting and combining complementary feature representations and classifiers for hate speech detection

Hate speech is a major issue in social networks due to the high volume of data generated daily. Recent works demonstrate the usefulness of machine learning (ML) in dealing with the nuances required to distinguish between hateful posts from just sarcasm or offensive language. Many ML solutions for hate speech detection have been proposed by either changing how features are extracted from the text or the classification algorithm employed. However, most works consider only one type of feature extraction and classification algorithm. This work argues that a combination of multiple feature extraction techniques and different classification models is needed. We propose a framework to analyze the relationship between multiple feature extraction and classification techniques to understand how they complement each other. The framework is used to select a subset of complementary techniques to compose a robust multiple classifiers system (MCS) for hate speech detection. The experimental study considering four hate speech classification datasets demonstrates that the proposed framework is a promising methodology for analyzing and designing high-performing MCS for this task. MCS system obtained using the proposed framework significantly outperforms the combination of all models and the homogeneous and heterogeneous selection heuristics, demonstrating the importance of having a proper selection scheme. Source code, figures and dataset splits can be found in the GitHub repository: https://github.com/Menelau/Hate-Speech-MCS.
Automating Developer Chat Mining

Online chatrooms are gaining popularity as a communication channel between widely distributed developers of Open Source Software (OSS) projects. Most discussion threads in chatrooms follow a Q&A format, with some developers (askers) raising an initial question and others (respondents) joining in to provide answers. These discussion threads are embedded with rich information that can satisfy the diverse needs of various OSS stakeholders. However, retrieving information from threads is challenging as it requires a thread-level analysis to understand the context. Moreover, the chat data is transient and unstructured, consisting of entangled informal conversations. In this paper, we address this challenge by identifying the information types available in developer chats and further introducing an automated mining technique. Through manual examination of chat data from three chatrooms on Gitter, using card sorting, we build a thread-level taxonomy with nine information categories and create a labeled dataset with 2,959 threads. We propose a classification approach (named F2CHAT) to structure the vast amount of threads based on the information type automatically, helping stakeholders quickly acquire their desired information. F2CHAT effectively combines handcrafted non-textual features with deep textual features extracted by neural models. Specifically, it has two stages with the first one leveraging the siamese architecture to pretrain the textual feature encoder, and the second one facilitating an in-depth fusion of two types of features. Evaluation results suggest that our approach achieves an average F1-score of 0.628, which improves the baseline by 57%. Experiments also verify the effectiveness of our identified non-textual features under both intra-project and cross-project validations.
Quantifying uncertainty in deep learning approaches to radio galaxy classification

ABSTRACT In this work we use variational inference to quantify the degree of uncertainty in deep learning model predictions of radio galaxy classification. We show that the level of model posterior variance for individual test samples is correlated with human uncertainty when labelling radio galaxies. We explore the model performance and uncertainty calibration for different weight priors and suggest that a sparse prior produces more well-calibrated uncertainty estimates. Using the posterior distributions for individual weights, we demonstrate that we can prune 30 per cent of the fully connected layer weights without significant loss of performance by removing the weights with the lowest signal-to-noise ratio. A larger degree of pruning can be achieved using a Fisher information based ranking, but both pruning methods affect the uncertainty calibration for Fanaroff–Riley type I and type II radio galaxies differently. Like other work in this field, we experience a cold posterior effect, whereby the posterior must be down-weighted to achieve good predictive performance. We examine whether adapting the cost function to accommodate model misspecification can compensate for this effect, but find that it does not make a significant difference. We also examine the effect of principled data augmentation and find that this improves upon the baseline but also does not compensate for the observed effect. We interpret this as the cold posterior effect being due to the overly effective curation of our training sample leading to likelihood misspecification, and raise this as a potential issue for Bayesian deep learning approaches to radio galaxy classification in future.
A privacy-preserving distributed platform for COVID-19 vaccine passports

Digital vaccination passports are being proposed by various governments internationally. Trust, scalability and security are all key challenges in implementing an online vaccine passport. Initial approaches attempt to solve this problem by using centralised systems with trusted authorities. However, sharing vaccine passport data between different organisations, regions and countries has become a major challenge. A platform for creating, storing and verifying digital COVID-19 vaccine certifications is presented, making use of InterPlanetary File System (IPFS) to guarantee that there is no single point of failure and to allow data to be securely distributed globally. Blockchain and smart contracts are also integrated into the platform to explicitly determine policies and log access rights to the passport data while ensuring all actions are audited and verifiably immutable. Our proposed platform realises General Data Protection Regulation (GDPR) requirements in terms of user consent, data encryption, data erasure and accountability obligations. We assess the scalability and performance of the platform using IPFS and Blockchain test networks.
Automatic Real-Time Medical Mask Detection Using Deep Learning to Fight COVID-19

The COVID-19 pandemic is a virus that has disastrous effects on human lives globally; still spreading like wildfire causing huge losses to humanity and economies.There is a need to follow few constraints like social distancing norms, personal hygiene, and masking up to effectively control the virus spread.The proposal is to detect the face frame and confirm the faces are properly covered with masks.By applying the concepts of Deep learning, the results obtained for mask detection are found to be effective.The system is trained using 4500 images to accurately judge and justify its accuracy.The aim is to develop an algorithm to automatically detect a mask, but the approach does not facilitate the percentage of improper usage.Accuracy levels are as low as 50% if the mask is improperly covered and an alert is raised for improper placement.It can be used at traffic places and social gatherings for the prevention of virus transmission.It works by first locating the region of interest by creating a frame boundary, then facial points are picked up to detect and concentrate on specific features.The training on the input images is performed using different epochs until the artificial face mask detection dataset is created.The system is implemented using Tensor-Flow with OpenCV and Python using a Jupyter Notebook simulation environment.The training dataset used is collected from a set of diverse open-source datasets with filtered images available at Kaggle Medical Mask Dataset by Mikolaj Witkowski, Kera, and Prajna Bhandary.To simulate MobilNetV2 classifier is used to load and pre-process the image dataset for building a fully connected head.The objective is to assess the accuracy of the identification, measuring the efficiency and effectiveness of algorithms for precision, recall, and F1 score.