Reviewerly/exHarmony huggingface dataset:
Pareto-optimal Defenses for the Web Infrastructure: Theory and Practice

The integrity of the content a user is exposed to when browsing the web relies on a plethora of non-web technologies and an infrastructure of interdependent hosts, communication technologies, and trust relations. Incidents like the Chinese Great Cannon or the MyEtherWallet attack make it painfully clear: the security of end users hinges on the security of the surrounding infrastructure: routing, DNS, content delivery, and the PKI. There are many competing, but isolated proposals to increase security, from the network up to the application layer. So far, researchers have focused on analyzing attacks and defenses on specific layers. We still lack an evaluation of how, given the status quo of the web, these proposals can be combined, how effective they are, and at what cost the increase of security comes. In this work, we propose a graph-based analysis based on Stackelberg planning that considers a rich attacker model and a multitude of proposals from IPsec to DNSSEC and SRI. Our threat model considers the security of billions of users against attackers ranging from small hacker groups to nation-state actors. Analyzing the infrastructure of the Top 5k Alexa domains, we discover that the security mechanisms currently deployed are ineffective and that some infrastructure providers have a comparable threat potential to nations. We find a considerable increase of security (up to 13% protected web visits) is possible at a relatively modest cost, due to the effectiveness of mitigations at the application and transport layer, which dominate expensive infrastructure enhancements such as DNSSEC and IPsec.
Modeling difficulties in creating conceptual data models

Abstract Conceptual modeling is a learning task essential to students of computer science, software engineering and related programs. Construed as a complex task, surprisingly little is known about the actual act of conceptual modeling, and about modeling difficulties learners experience. Combining complementary modes of observation of learners’ modeling processes, we study modeling difficulties encountered while performing a data modeling task. Using the concept of cognitive breakdown, we analyze audiovisual protocols of the individual modelers’ modeling processes, recordings of their interactions with the employed modeling software tool and survey data of modelers about their perception of encountered modeling difficulties. In an exploratory study and a follow-up study, we identify eight types of modeling difficulties related to modeling entity types, generalization hierarchies, relationship types, attributes and cardinalities. The identified types of modeling difficulties contribute to a better and more complete understanding of data modeling processes intended to inform design science research on modeling assistance for data modelers at different stages of their learning and mastering of conceptual data modeling.
A Scalable Analytics Pipeline for COVID-19 Face Mask Surveillance

The COVID-19 coronavirus pandemic still causes a global health crisis. An effective protection method is using a face mask in public areas, according to the World Health Organization (WHO). Computer vision systems can be allies in monitoring public areas where the face mask is mandatory. However, face mask detection is challenging due to many factors, including diversity of people, facial features, head accessories, mask design, image position, and lighting changes. To tackle these issues, we present the following contributions: a new balanced face mask dataset named UTFPR-FMD1, consisting of 61,430 images splitted into “face” and “mask” classes; a transfer learning classification model for computer vision tasks, trained with our dataset; a new processing pipeline that allows face mask detection in video streams. Unlike available public datasets with imbalanced class distributions, the UTFPR-FMD1 contains images from different people, gender, and ages to minimize the training difficulty of deep learning models. We introduced a new measure to select valid images to perform inferences. Experimental results show the effectiveness of our model, outperforming the state-of-art methods for face mask detection tasks. Additionally, and different from other authors, we also present qualitative results. The system can detect heads with up to 60 degrees of rotation and process up to 10 FPS. In future work, we will deploy the current framework into production, perform tests in a near real-time environment, and extend it to process multiple video streams.
Adaptive behavior in optimal sequential search.

Sequential decision making-making a decision where available options are encountered successively-is a hallmark of everyday life.Such decisions require deciding to accept or reject an alternative without knowing potential future options.Prior work focused on understanding choice behavior by developing decision models that capture human choices in such tasks.We investigated people's adaptive behavior in changing environments in light of their cognitive strategies.We present two studies in which we modified (a) outcome variance and (b) the time horizon and provide empirical evidence that people adapt to both context manipulations.Furthermore, we apply a recently developed threshold model of optimal stopping to our data to disentangle different cognitive processes involved in optimal stopping behavior.The results from Study 1 show that participants adaptively scaled the values of the sampling distribution to its variance, suggesting that the value of an option is perceived in relative rather than absolute terms.The results from Study 2 suggest that increasing the time horizon decreases the initial acceptance level, but less strongly than would be optimal.Furthermore, for longer sequences, participants more weakly adjusted this acceptance threshold over time than for shorter sequences.Further correlations between individual estimates in each condition indicate that individual differences between the participants' thresholds remain fairly stable between the conditions, pointing toward an additive effect of our manipulations.
A State-of-the-Art Survey on Various Domains of Multi-Agent Systems and Machine Learning

Multi-agent systems (MASs) are defined as a group of interacting entities or agents sharing a common environment that changes over time, with capabilities of perception and action, and the mechanisms for their coordination provide a modern perspective on systems that traditionally were regarded as centralized. The main characteristics of agents are learning and adaptation. In the last few years, MASs have received tremendous attention from scholars in different fields. However, there are still challenges faced by MASs and their integration with machine learning (ML) methods. The primary goal of the study is to provide a broad review of the current developments in the field of MASs combined with ML methods. First, we present features of MASs considering the ML perspective. Second, we provide a classification of applications of MASs combined with ML methods. Third, we present a density map of applications in E-learning, manufacturing, and commerce. We expect this study to serve as a comprehensive resource for researchers and practitioners in the area.
Structurization of educational expedition studies in the form of taxonomies

High volume and low level of data structuring cause problems with data processing nowadays. The scope of perspective to provide such structuring is scientific studies because, in most cases, they provide predicted measurement parameters, and it’s easy to structure. As a result, the parameters that will be measured can be defined and forms prepared for completion during studies. As a result, the parameters that will be measured can be defined and forms prepared for completion during shipping. This study corroborates how to handle this data in ontologies, providing structuring and interoperability of data. The case of the use of such an approach based on the expedition of real-life environmental study during Junior Academy of Sciences of Ukraine expedition is described. The approach uses Excel-based templates to be filled during the measurement of parameters. Each line in such an array represents the location or name of the search subject. An extra column is dedicated to geolocation insertion. After providing the research, such files are uploaded into the Polyhedron system, and ontology is generated. These ontologies are stored and integrated into ArcGis and can be used to present their own findings and provide basic searches by other researchers. The process of integrating a few ontologies that used a proposed template from different expeditions is described. Additional, as such studies are also used IMRAD, it is possible to use the approach of using IMRAD as the core of the expedition result’s graph and use the expedition studies in the same structure as other.
Stability of Algorithms in Statistical Modeling

In this paper, we investigate algorithms stability for calculation of multidimensional integrals using the statistical modeling methods. We considered issues of the algorithms optimization and we give sufficient conditions for the stability. We apply our approach to both calculation of integral from the regression function and the moments integral calculation. In all our numerical experiences, we used the mt19937 pseudorandom number generator.
Usability Testing of Mixed Reality Scenarios: A Hands-on Report

We would like to share our insights in designing, preparing, preforming, and analyzing usability tests for multiple connected augmented reality and virtual reality applications as well as traditional mobile applications developed for a multimodal screening tool. This screening tool is under development at the University of Applied Sciences FH JOANNEUM in Graz, Austria. Several researchers from the departments of health studies and applied computer sciences are working closely together to establish a tool for early diagnosis of cognitive impairments to contribute to the management of dementia. The usability of this screening tool was evaluated by ten therapists paired with ten clients as testing group 1 and two usability experts in a separate test (group 2). In this chapter, we would like to describe why we use observed summative evaluation using the co-discovery method followed by post-task questionnaires for the first testing group. We are going to discuss the reasons for performing the cognitive walkthrough method as co-discovery with usability experts of testing group two as well. Furthermore, we describe how we use camera recordings (traditional cameras, 360-degree cameras), screen recording, and special tailor-made software to experience the screening process through the user’s eyes.
ON COMBINATORIAL ENTROPY OF SOURCES OF BINARY INFORMATION

The paper considers the question of converting the entropy of a probabilistic information source generating binary sequences into combinatorial forms of its two constituent sources. One of them is basic, and the second has a conditional entropy with respect to the first. It is shown that together such sources make it possible to optimally encode information using numbering at the efficiency level of known methods of optimal coding. In terms of their functions, they are universal and can solve all the problems that are solved by the methods of coding probabilistic sources. However, unlike them, they do not require statistical tests before optimal coding, or they are carried out to a much lesser extent. They also simplify the calculation of the entropy of a probabilistic source of information, which is useful in solving many problems where it is necessary to know the value of the entropy of the source, for example, in error-correcting coding.
Deriving instrumental point spread functions from partially occulted images

The point spread function (PSF) of an imaging system describes the response of the system to a point source. Accurately determining the PSF enables one to correct for the combined effects of focusing and scattering within the imaging system and, thereby, enhance the spatial resolution and dynamic contrast of the resulting images. We present a semi-empirical semi-blind methodology to derive a PSF from partially occulted images. We partition the two-dimensional PSF into multiple segments, set up a multilinear system of equations, and directly fit the system of equations to determine the PSF weight in each segment. The algorithm is guaranteed to converge toward the correct instrumental PSF for a large class of occultations, does not require a predefined functional form of the PSF, and can be applied to a large variety of partially occulted images, such as within laboratory settings, regular calibrations within a production line or in the field, astronomical images of distant clusters of stars, or partial solar eclipse images. We show that the central weight of the PSF, which gives the percentage of photons that are not scattered by the instrument, is accurate to better than 1.2%. The mean absolute percentage error between the reconstructed and true PSF is usually between 0.5 and 5% for the entire PSF, between 0.5 and 5% for the PSF core, and between 0.5 and 3% for the PSF tail.
Clustering multivariate count data via Dirichlet-multinomial network fusion

Clustering of multivariate count data has widespread applications in areas such as text analysis and microbiome studies. The need to account for overdispersion generally results in a nonconvex loss function, which does not fit into the existing convex clustering framework. Moreover, prior knowledge of a network over the samples, often available from citation or similarity relationships, is not taken into account. We introduce Dirichlet-multinomial network fusion (DMNet) for clustering multivariate count data, which models the samples via Dirichlet-multinomial distributions with individual parameters and employs a weighted group L1 fusion penalty to pursue homogeneity over a prespecified network. To circumvent the nonconvexity issue, we present two exponential family approximations to the Dirichlet-multinomial distribution, which are amenable to efficient optimization and theoretical analysis. We derive an ADMM algorithm and establish nonasymptotic error bounds for the proposed methods. Our bounds involve a trade-off between the connectivity of the network and its fidelity to the true parameter. The usefulness of our methods is illustrated through simulation studies and two text clustering applications.
Multi-Task Assignment Method of the Cloud Computing Platform Based on Artificial Intelligence

To realize load balancing of cloud computing platforms in big data processing, the method of finding the optimal load balancing physical host in the algorithm cycle is adopted at present. This optimal load balancing strategy that overly focuses on the current deployment problem has certain limitations. It will make the system less efficient and the user's waiting time unnecessarily prolonged. This paper proposes a task assignment method for long-term resource load balancing of cloud platforms based on artificial intelligence and big data (TABAI). The maximum posterior probability for each physical host is calculated using Bayesian theory. Euler's formula is used to calculate the similarity between the host with the largest posterior probability and other hosts as a threshold. The hosts are classified according to the threshold to determine the optimal cluster and then form the final set of candidate physical hosts. It improves the resource utilization and external service capability of the cloud platform by combining cluster analysis with Bayes' theorem to achieve global load balancing in the time dimension. The experimental results show that: TABAI has a smaller processing time than the traditional load balancing multi-task assignment method. When the time is >600 s, the standard deviation of TABAI decreases to a greater extent, and it has stronger external service capabilities.
Light field reconstruction in angular domain with multi-models fusion through representation alternate convolution

To alleviate the spatial-angular trade-off in sampled light fields (LFs), LF super-resolution (SR) has been studied. Most of the current LFSR methods only concern limited relations in LFs, which leads to the insufficient exploitation of the multi-dimensional information. To address this issue, we present a multi-models fusion framework for LFSR in this paper. Models embodying LF from distinct aspects are integrated to constitute the fusion framework. Therefore, the number and the arrangement of these models together with the depth of each model determine the performance of the framework; we make the comprehensive analysis on these factors to reach the best SR result. However, models in the framework are isolated to each other as the unique inputs are required. To tackle this issue, the representation alternate convolution (RAC) is introduced. As the fusion is conducted successfully through the RAC, the multi-dimensional information in LFs is fully exploited. Experimental results demonstrate that our method achieves superior performance against state-of-the-art techniques quantitatively and qualitatively.
Target detection models based on deep learning

Object detection is one of the most basic and important tasks in the field of computer vision, and it is the foundation of high-level vision tasks such as behaviour recognition and human-computer interaction. With the development of deep learning technology, the accuracy and efficiency of target detection models have been greatly improved. Compared with traditional target detection algorithms, deep learning uses powerful hierarchical feature extraction and learning capabilities to make a breakthrough in the performance of target detection algorithms. At the same time, the emergence of large-scale data sets and the greatly improved computing power of graphics cards have also contributed to the vigorous development of this field. This article reviews the existing research results of target detection based on deep learning. This article first reviews the traditional target detection algorithm and its existing problems, then introduces the two-stage detection model and single-stage detection model under deep learning, and finally briefly summarizes the application scenarios of the target detection algorithm and summarizes the full text.
Small object detection in remote sensing images based on target cluster and multi-scale prediction

In recent years, object detection, as an important branch of computer vision, has made great progress and received attention from various fields. However, for remote sensing images with small size, arbitrary orientation and dense distribution characteristics, there are still big challenges. In this paper, we propose a multi-classification detection network for small and dense remote sensing small targets. A coarse-grained target cluster anchor frame is designed to delineate the range of target regions on remote sensing images, which effectively reduces the influence of the large-scale background on the detection network for the subsequent detection network. Meanwhile, according to the concept of multi-scale detection in SSD network, a dense convolution is applied to the multi-scale feature map after the backbone network to improve the features of small targets. Finally, the rotating anchor frame is used as the precise location of this detector, and the Focal LOSS function is used to mitigate the gradient disappearance problem in the regression process. Different panels in this model are extensively experimented on the remote sensing dataset DOTA and NWPU VHR-10 to get more excellent detection results.
Dynamic network representation learning based on temporal neighborhood aggregation

With the wide application of network data in many fields, network representation learning technology has become the focus of scholars' research. Especially in dynamic networks, how to combine network structure information with time information is the difficulty of current research. This paper proposes a dynamic network representation learning method (ESNA) based on temporal neighborhood aggregation. Further, We divide the influence of nodes on the network into local power and global power, learn the local influence of nodes on the network by using time difference attention mechanism aggregation in the temporal neighborhood, delimit the influence range of nodes on the global network by using random walk model outside the time series neighborhood, and finally fuse the local and global influence of nodes on the network based on word vector model to form a network embedded with time information. We experimented with real dynamic network data in the link prediction task, and the results showed the effectiveness of our method.
Trusted regulatory technology for blockchain data privacy protection

At present, blockchain users lack effective means of supervision and authorization for intermediaries at different levels, making user data privacy at risk of leakage. Aiming at the above problems, this paper studies the trusted supervision technology scheme suitable for blockchain data privacy protection, enriches and expands on the classic BIP32 protocol, and designs and implements a dynamic authorization strategy in the new protocol. Authorization policies make private data truly in the hands of users by changing and setting regulatory permissions. The experimental results and performance comparison show that the blockchain data privacy protection method proposed by this scheme is efficient, stable, and has strong practicability, which is suitable for data privacy protection scenarios of trusted supervision. At the same time, experiments show that with the increase of the encryption key depth, the supervision time increases linearly.
The application of the mask detection based on automatic machine learning

It is important to observe whether people wear masks in public due to the spread of Convid-19 in the world. In this work, I built the object detection model to classify whether people are wearing facial masks and wearing them correctly with Automated Machine Learning (AutoML) technology on easyDL platform. The whole process is completed automatically and it is easy to operate so that one does not have to be an expert of machine learning to train a qualified model for practical use. The dataset includes images and labels in XML format. I adjusted a few parameters such as the data-augmentation operator and the algorithm provided by the platform to choose. Finally, an acceptable result was achieved and I deployed the model on the web with a web framework called Flask. The model offers a relatively satisfactory result and can be successfully called by API provided by the platform.
TextC/R/RCNN for multi-label classification based ICD coding

The International Classification of Diseases (ICD) is a health care Classification system initiated by the World Health Organization. ICD codes are used to quantify important statistical data and facilitate the search for patient cohort with similar diagnosis. In addition, they are also of great value and significance as a means of standardized information exchange between hospitals. Manual ICD coding is a time-consuming and laborious work, now most people use machine/deep learning methods for automatic coding. TextCNN, TextRNN and TextRCNN have been the mainstream models of multilabel text classification task since they were proposed. In this paper, three language models are combined with ICD automatic coding task. The experimental results show that the three models have achieved good results. In addition to that, this paper demonstrates the limiting factors of the model performance through detailed experiments to guide the future work to make further progress.