Reviewerly/exHarmony huggingface dataset:
Load Balancing in Large Scale Bayesian Inference

We present a novel strategy to improve load balancing for large scale Bayesian inference problems. Load imbalance can be particularly destructive in generation based uncertainty quantification (UQ) methods since all compute nodes in a large-scale allocation have to synchronize after every generation and therefore remain in an idle state until the longest model evaluation finishes. Our strategy relies on the concurrent scheduling of independent Bayesian inference experiments while sharing a group of worker nodes, reducing the destructive effects of workload imbalance in population-based sampling methods.
The RML Ontology: A Community-Driven Modular Redesign After a Decade of Experience in Mapping Heterogeneous Data to RDF

Abstract The Relational to RDF Mapping Language (R2RML) became a W3C Recommendation a decade ago. Despite its wide adoption, its potential applicability beyond relational databases was swiftly explored. As a result, several extensions and new mapping languages were proposed to tackle the limitations that surfaced as R2RML was applied in real-world use cases. Over the years, one of these languages, the RDF Mapping Language (RML), has gathered a large community of contributors, users, and compliant tools. So far, there has been no well-defined set of features for the mapping language, nor was there a consensus-marking ontology. Consequently, it has become challenging for non-experts to fully comprehend and utilize the full range of the language’s capabilities. After three years of work, the W3C Community Group on Knowledge Graph Construction proposes a new specification for RML. This paper presents the new modular RML ontology and the accompanying SHACL shapes that complement the specification. We discuss the motivations and challenges that emerged when extending R2RML, the methodology we followed to design the new ontology while ensuring its backward compatibility with R2RML, and the novel features which increase its expressiveness. The new ontology consolidates the potential of RML, empowers practitioners to define mapping rules for constructing RDF graphs that were previously unattainable, and allows developers to implement systems in adherence with [R2]RML. Resource type : Ontology/ License : CC BY 4.0 International DOI : 10.5281/zenodo.7918478 / URL : http://w3id.org/rml/portal/
Deep quality assessment toward defogged aerial images

In order to improve the visual appearance of defogged of aerial images, in this work, a novel defogging algorithm based on conditional generative adversarial network is proposed. More specifically, the training process is carried out through an end-to-end trainable deep neural network. In detail, we upgrade the traditional adversarial loss function by incorporating an L1-regularized gradient to encode a rich set of detailed visual information inside each aerial image. In practice, to our best knowledge, existing image quality assessment algorithms might have deviation and supersaturation distortion on aerial images. To alleviate this problem, we leverage a random forest classification model to learn the mapping relationship between aerial image features and the quality ranking results. Subsequently, we transform the objective of defogged image quality assessment into a classification problem. Comprehensive experimental results on our compiled fogged aerial images quality data set have clearly demonstrated the effectiveness of our proposed algorithm.
Degraded Document Enhancement through Binarization Techniques

Enhancement of degraded documents is one of the significant and challenging research areas. In recent years, several binarization methods are proposed and presented for the improvement of degraded documents, but, most of them are not appropriate for all kinds of degradation. In this paper, we have described some state-of-the-art binarization techniques and compared their performances using DIBCO 2016 to DIBCO 2018 databases. In addition, we briefly discussed about the challenges and possible future works of image binarization.
Improving Data Quality for Regression Test Selection by Reducing Annotation Noise

Big data and machine learning models have been increasingly used to support software engineering processes and practices. One example is the use of machine learning models to improve test case selection in continuous integration. However, one of the challenges in building such models is the identification and reduction of noise that often comes in large data. In this paper, we present a noise reduction approach that deals with the problem of contradictory training entries. We empirically evaluate the effectiveness of the approach in the context of selective regression testing. For this purpose, we use a curated training set as input to a tree-based machine learning ensemble and compare the classification precision, recall, and f-score against a non-curated set. Our study shows that using the noise reduction approach on the training instances gives better results in prediction with an improvement of 37% on precision, 70% on recall, and 59% on f-score.
A Wide Area Multiview Static Crowd Estimation System Using UAV and 3D Training Simulator

Crowd size estimation is a challenging problem, especially when the crowd is spread over a significant geographical area. It has applications in monitoring of rallies and demonstrations and in calculating the assistance requirements in humanitarian disasters. Therefore, accomplishing a crowd surveillance system for large crowds constitutes a significant issue. UAV-based techniques are an appealing choice for crowd estimation over a large region, but they present a variety of interesting challenges, such as integrating per-frame estimates through a video without counting individuals twice. Large quantities of annotated training data are required to design, train, and test such a system. In this paper, we have first reviewed several crowd estimation techniques, existing crowd simulators and data sets available for crowd analysis. Later, we have described a simulation system to provide such data, avoiding the need for tedious and error-prone manual annotation. Then, we have evaluated synthetic video from the simulator using various existing single-frame crowd estimation techniques. Our findings show that the simulated data can be used to train and test crowd estimation, thereby providing a suitable platform to develop such techniques. We also propose an automated UAV-based 3D crowd estimation system that can be used for approximately static or slow-moving crowds, such as public events, political rallies, and natural or man-made disasters. We evaluate the results by applying our new framework to a variety of scenarios with varying crowd sizes. The proposed system gives promising results using widely accepted metrics including MAE, RMSE, Precision, Recall, and F1 score to validate the results.
An Approach to Dynamic Gesture Recognition Based on Instantaneous Posture

Dynamic gesture recognition is a challenging problem. Existing algorithms often input gesture data indiscriminately for recognition, which further increases the difficulty of recognition and reduces the accuracy and efficiency of recognition. In this paper, a dynamic gesture recognition method is proposed by searching the effective instantaneous posture in dynamic gestures. To a certain extent, the invalid gesture data is filtered, and an effective gesture feature description is established for real-time gesture recognition. Through the design of comparative experiments, the proposed algorithm is verified to be better than the sliding time window algorithm in recognition accuracy and efficiency.
Internet and Telecommunication Fraud Prevention Analysis based on Deep Learning

In recent years, contactless fraud crimes via telecommunication and Internet have grown rapidly. Meanwhile, the rate of solved criminal cases is much lower, which is mainly due to two reasons. Firstly, the definition of risk factors in the field of new Internet and telecommunication fraud crime is not comprehensive, resulting in the problem not being well defined. Secondly, Internet fraud crime information is mostly recorded using natural language with huge volume, and there is a lack of automated and intelligent way to deeply analyze and extract the risk factor. To better analyze the Internet and telecommunication fraud crime to help solve more cases, in this paper, we propose a new Internet and telecommunication fraud crime risk factor extraction system. After studying the existing related research, we propose a novel risk factor extraction technology based on BERT. This novel technology can gracefully deal with multi-sources and heterogeneous data problems during the extraction of risk factors in multiple dimensions; meanwhile, it can significantly reduce the need for computation resources and improve the online serving performance. After experimentation, this technique can significantly reduce training time by 60%-70%, and meanwhile, it can reduce the computation resources by 80% and improve serving performance by 5 times during serving. In our approach, we propose a novel approach to set sample weight and loss weight based on data characteristics and data distribution during model training, which can significantly improve extraction precision. With adjusting the sample weight during model training, we can get 1.56% precision improved. Moreover, setting the loss weight during model training, the precision can be improved by 1.63% compared to baseline mode.
Behavior-Aware Network Segmentation using IP Flows

Network segmentation is a powerful tool for network defense. In contemporary complex, dynamic, and multilayer networks, network segmentation suffers from lack of visibility into processes in the network, which results in less strict segment definition and loosen network security. Moreover, the dynamics of the networks makes the manual identification of network segments nearly impossible. In this paper, we inspect the possibilities of the behavior-aware network segmentation using IP flows and machine learning approaches that would enable to identify segments automatically, even in a complex network. We evaluate the suitability of clustering algorithms for identification of behavior-consistent segments in a network. We show that the clustering algorithms can identify relevant behavior-consistent clusters that overlap with those identified manually by experts. Apart from the segment identification, we investigate the other essential task of network segmentation process: assignment of an unknown host to an existing segment. We evaluate the performance of four different classification mechanisms on a real-world dataset. We show that it is possible to assign an unknown host to an appropriate network segment with up to 92% precision. Moreover, we release the whole dataset and experiment steps available for public use.
Exposing parallelism of discrete radon transform

Discrete Radon Transform, DRT, is an integral transform that computes the complete set, in terms of slope and intercepts, of line integrals through a two-dimensional domain. It exhibits linearithmic computational complexity and avoids the usage of real numbers thanks to a divide and conquer, or multiscale, approach with a loose definition of discrete lines. Even so, a careful inspection of multiscale definition of DRT, introduced by several authors on late 90s, has allowed us to expose additional levels of fine and coarse parallelism, which is beneficial for the efficient detection of line patterns in signals at high rates even on mobile devices. A parallel oriented algorithm is provided, and the speedups obtained are compared with alternative algorithms that compute Radon transform, and non-parallel version of multiscale DRT. Additionally, we provide clues to an efficient implementation in Halide language that is portable to desktop and mobile processors.
Development of a Novel System for Speaker Verification

In speaker Verification we are verifying the person by extracting the features from his/her sound and then classifying to prove that is he/her is valid speaker or not, who is claiming that he is. This is the most useful and well-known biometric recognition techniques in those fields and area in which security is our first priority. In this paper we have designed a speaker verification system which is giving good results when we mutate our classifier up to 50 thousand time. We take voice samples of different speaker and extract characteristics by Mel frequency Cepstrum coefficient (MFCC) and classify the features through Cartesian genetic programing evolved artificial neural network (CGPANN). The designed system has better accuracy than existing system for speaker verification.
Implementation of Generalized Regression Neural Network (GRNN) for Solar Panel Power Estimation

An efficient algorithm to characterize the current-voltage (IV) curve of photovoltaic (PV) modules under different operating conditions is necessary for solar power estimation and some operational stability. In this paper, the developed algorithm utilizes a generalized regression neural network (GRNN) as a power estimator for solar panels. The proposed model uses the seven input variables namely, the IV characteristic curve, weather condition, and temperature parameters from the testbed solar panel modules of Cheongju University dated August 2019 to July 2020. The dataset is divided into three sections as training, validation, and testing set to 60%, 20%, and 20 %, respectively. The simulation provides comparative results for the actual and predicted power. Moreover, GRNN shows a better predicted power output compared to a feed-forward neural network (FFNN). The regression value results in a much nearer to 1 that fits the dataset. The correlation coefficient is 0.9961 which identifies the provided dataset is on the line of best fit.
Visual Design Thinking for Public Education: Improving knowledge development and exchange via transformative pictogram design

How might the exchange and development of knowledge improve through a critical examination of pictogram-based methods of information visualization? In this article, we investigate the International System of TYpographic Picture Education(ISOTYPE), an influential model of pictorial diagram design theory and practice. Given ISOTYPE's continuing impact on information design, we use it as a critical case study to assess the potential and limitations of pictorial diagrams(PD) for knowledge development and exchange. The goal of this study is not to evaluate artistic quality, style, or designer talent; rather, our focus is on analysis of the learning functions of pictograms and their potential for knowledge transmission and supporting reasoning behaviour among diverse audiences. We explore the different features of a pictogram, and how these features might support knowledge development through diagrammatic reasoning(DR). Three key questions are posed: 1. What are the advantages and disadvantages of pictograms for promoting learning and reasoning behaviour versus more abstract or representational visual information design? 2. What are the criteria for choosing the visual characteristics of pictograms? 3. How can the visual characteristics of pictograms be evaluated and revised base on the goal of supporting learning and reasoning behaviour?
Exploring the Role of Chatbots and Messaging Applications in Higher Education: A Teacher’s Perspective

The use of new technologies such as messaging applications and chatbots in higher education is rapidly growing in Western countries. This entails a careful consideration of the potential opportunities and/or challenges of adopting these tools. Hence, a comprehensive examination of the teachers' opinions and needs in this discipline can shed light on the effective ways of enhancing students' learning and boosting their progress. In this contribution, we have surveyed the opinions of instructors based in Spain (mainly) and Spanish-speaking countries. Specifically, we aimed to collect teachers' feedback about their opinions regarding the introduction of the messaging platforms and chatbots in their classes, understand their needs and to collect information about the various educational use cases where these tools are valuable. In addition, an analysis of how and when teachers' opinions towards the use of these tools can vary across gender, experience, and their discipline of specialization is presented. The key findings of this study highlight the factors that can contribute to the advancement of the adoption of messaging platforms and Chabots in higher education institutions to achieve the desired learning outcomes.
What Do Untargeted Adversarial Examples Reveal in Medical Image Segmentation?

Recent literature point out overconfidence problems in DNNs which is demonstrated as biased confidences in false predictions in medical image segmentation tasks regardless of the ground truth. To explore and identify the uncertain regions, we propose a post-training method with untargeted adversarial examples where the input image is iteratively perturbed in a direction that maximizes the loss of original and perturbed prediction. The perturbed predictions from these adversarial examples can be seen as unstable areas in terms of input variability; we theoretically observe that the gradient of negative class confidence in terms of input image plays a key role for perturbed outputs, and empirically show that a small adversarial perturbation can help find hidden regions in the output segmentation maps. Compared to previous methods for uncertainty estimation, our method yields competitive results for uncertain region findings on medical image datasets while only requiring one extra inference from a pre-trained model and short iteration of attack. We expect our novel findings can provide insights for future medical image segmentation problems where detection of subtle variations (e.g., lesions) are required.
A Boat Is a Boat Is a Boat…Unless It Is a Horse – Rethinking the Role of Typology

Abstract Today, it is widely accepted that typology is a biased and inconsistent attempt to classify archaeological material based on the similarity of a predefined set of features. In this respect, machine learning (ML) works similar to typology. ML approaches are often deployed because it is thought that they reduce biases. However, biases are introduced into the process at many points, e.g., feature selection. In a project applying ML to Scandinavian rock art data, it was noticed that the algorithm struggles with classifying certain motifs correctly. This contribution discusses the consistency in applying biases by ML in contrast to the inconsistency of human classification. It is argued that it is necessary to bring machines and humans into a meaningful dialogue attempting to understand why apparent “misclassifications” happen. This is important to inform us about the classification output, our biases, and the rock art data, which are in themself inconsistent, ambiguous, and biased because they are the outcomes of human creativity. The human inconsistency is a necessary component because in rock art not everything that looks similar has a similar meaning.
Educational data brokers: using the walkthrough method to identify data brokering by edtech platforms

As a result of the growing commercial marketplace for teachers’ digital data, a new organization that includes educational data brokers has evolved. Educational data brokerage is relatively intangible due to the ease of de-identified data being collected and sold via educational technology. There is an urgent need to expose how the brokerage of educational data relates to the commercial mediation of consent and privacy in educational settings. It is difficult due to a lack of consistent terminology about organizations that buy and sell data. This paper offers an extensive analysis of the social learning platform Edmodo and provides evidence that justifies the term ‘educational data broker’. The results aim to provide new terminology to a largely obfuscated process in educational settings and bring to light a concrete example of brokerage activity focusing on teachers’ online activity.
Generating Questions on the Conversational Recommender System using Semantic Reasoning and Singular Value Decomposition

In some previous research, Conversational Recommender System (CRS) based on product functional requirements was developed. Product functional requirements are requirements from product usability point of view. For example, users want smartphones for browsing and playing games. So that users who do not understand the technical features of the product, can more easily express their needs. In this CRS, questions are generated using semantic reasoning. However, the functional requirements asked to the user are chosen from the candidate nodes randomly. The candidate nodes are a set of functional requirements questions that the user potentially likes. Thus, users will get questions about functional requirements that do not match their preferences. Thus, the system will give repeated questions and make the system inefficient. In this study, we overcome this problem by proposing a learning mechanism using Singular Value Decomposition (SVD) to generate questions. By using this SVD, we hope to shorten the number of interactions between the user and the system. The results of the evaluation show that SVD has accuracy and running time to generate questions that are more stable.
How to leverage intelligent agents and complex event processing to improve patient monitoring

Abstract This paper describes an intelligent ecosystem that can continuously monitor patients’ health conditions, whether at home, at work or during recreational activities, by leveraging a creative blend of wearable medical devices, intelligent agents (IA) and complex event processing (CEP). With the help of a smart application, linking wearable devices and the power of IA and CEP, patients will be constantly and actively supervised during their daily activities. This can even save their lives in case they experience sudden or gradual problems. Thanks to our system, patients with chronic illnesses that are not serious but potentially unstable will no longer overburden first aid services. This is also helpful in containing the spread of COVID-19. Specifically, in this paper, we focus on automatic monitoring of vital parameters, electrocardiogram analysis and psoriasis detection. Experimental results conducted on real patients show how promising our approach is.
Passive operating system fingerprinting revisited: Evaluation and current challenges

Fingerprinting a host's operating system is a very common yet precarious task in network, asset, and vulnerability management. Estimating the operating system via network traffic analysis may leverage TCP/IP header parameters or complex analysis of hosts' behavior using machine learning. However, the existing approaches are becoming obsolete as network traffic evolves which makes the problem still open. This paper discusses various approaches to passive OS fingerprinting and their evolution in the past twenty years. We illustrate their usage, compare their results in an experiment, and list challenges faced by the current fingerprinting approaches. The hosts' differences in network stack settings were initially the most important information source for OS fingerprinting, which is now complemented by hosts' behavioral analysis and combined approaches backed by machine learning. The most impactful reasons for this evolution were the Internet-wide network traffic encryption and the general adoption of privacy-preserving concepts in application protocols. Other changes, such as the increasing proliferation of web applications on handheld devices, raised the need to identify these devices in the networks, for which we may use the techniques of OS fingerprinting.