Reviewerly/exHarmony huggingface dataset:
Scene-Adaptive Real-Time Fast Dehazing and Detection in Driving Environment

Real-time and effective dehazing is crucial to ensure safe and smooth operations of driving in foggy conditions. In this paper, a novel vision-based scene-adaptive real-time dehazing method for continuous video frames is developed, and the enhanced frames are fed into a detector in order to assess the value of image enhancement for detection tasks. The defogging stage is established by importing scene adaptation in order to improve the effectiveness of continuous frames defogging. Considering the distribution of atmospheric light intensity within the frame, the positions of the frame’s key points are determined in order to distinguish the scene differences between frames. Then, an algorithm is proposed for the inter-frame perception of atmospheric light intensity based on key points (“inverted triangle”), which employs a dynamic updating to adjust the estimated update frequency of atmospheric light intensity in time based on the scene change. Further, frames are inverted to estimate the transmission map based on the scattering model, which can quickly recover free of fog. Finally, the adaptive region of interest is set based on the obtained key point of frames, and the enhanced image is fed to the detector. The experimental results indicate that the proposed method can effectively handle sudden scene changes, as the defogging accuracy can reach 98.58%, the average defogging time of each frame is only a few milliseconds, and the detection accuracy can be improved by 4.11%.
Electromyography Based Gesture Decoding Employing Few-Shot Learning, Transfer Learning, and Training From Scratch

Over the last decade several machine learning (ML) based data-driven approaches have been used for Electromyography (EMG) based control of prosthetic hands.However, the performance of EMG-based frameworks can be affected by: i) the onset of fatigue due to long data collection sessions, ii) musculoskeletal differences between individuals, and iii) sensor position drifting between different sessions with the same user.To evaluate these aspects, in this work, we compare the performance of EMG-based hand gesture decoding models developed using three approaches.This comparison allows for future works in EMG-based Human-Machine Interfaces development to make more informed ML decisions.First, we trained from scratch a Transformer-based architecture, called Temporal Multi-Channel Vision Transformer (TMC-ViT).For our second approach, we utilized a pre-trained and fine-tuned TMC-ViT model (a transfer learning approach).Finally, for our third approach, we developed a Prototypical Network (a fewshot learning approach).The models are trained in a subject-specific and subject-generic manner for eight subjects and validated employing the 10-fold cross-validation procedure.This study shows that training a deep learning decoding model from scratch in a subject-specific manner leads to higher decoding accuracies when a larger dataset is available.For smaller datasets, subject-generic models, or inter-session models, the few-shot learning approach produces more robust results with better performance, and is more suited to applications where long data collection scenarios are not possible, or where multiple users are intended for the interface.Our findings show that the few-shot learning approach can outperform training a model from scratch in different scenarios.
A Comparison of Automated Journal Recommender Systems

Choosing the right journal for an article can be a challenge. Automated manuscript matching can help authors with the decision by recommending suitable journals based on user-defined criteria. Several approaches for efficient matching have been proposed in the research literature. However, only a few actual recommender systems are available for end users. In this paper, we present an overview of available services and compare their key characteristics such as input values, functionalities, and privacy. We conduct a quantitative analysis of their recommendation results: (a) examining the overlap in the results and pointing out the similarities among them; (b) evaluating their quality with a comparison of their accuracy. Due to the providers' lack of transparency about the used technologies, the results cannot be easily interpreted. This highlights the need for openness about the used algorithms and data sets.
BIP! NDR (NoDoiRefs): A Dataset of Citations from Papers Without DOIs in Computer Science Conferences and Workshops

In the field of Computer Science, conference and workshop papers serve as important contributions, carrying substantial weight in research assessment processes, compared to other disciplines. However, a considerable number of these papers are not assigned a Digital Object Identifier (DOI), hence their citations are not reported in widely used citation datasets like OpenCitations and Crossref, raising limitations to citation analysis. While the Microsoft Academic Graph (MAG) previously addressed this issue by providing substantial coverage, its discontinuation has created a void in available data. BIP! NDR aims to alleviate this issue and enhance the research assessment processes within the field of Computer Science. To accomplish this, it leverages a workflow that identifies and retrieves Open Science papers lacking DOIs from the DBLP Corpus, and by performing text analysis, it extracts citation information directly from their full text. The current version of the dataset contains more than 510K citations made by approximately 60K open access Computer Science conference or workshop papers that, according to DBLP, do not have a DOI.
Investigating the Relation Between Authors’ Academic Age and Their Citations

The increasing number of authors and consequent publications in computer science can cause some pitfalls, such as understanding the use and fairness of quality indicators for assessing research. In this preliminary work, we aim to examine whether there is a correlation between the citation count and the number of authors contributing to a paper as well as their academic ages. Additionally, we shed light on highly cited papers and compare their authors. For this purpose, we investigate authors' characteristics by conducting data analyses based on a dataset of four prestigious software-engineering-related conferences comprising 5,143 papers and their authors. Our results indicate that the number of authors does not connect to the citation count, but the current academic age of the authors does. We also found that 98% of the highly cited main-track papers had a contribution from at least one senior researcher, whereas none of these papers was written by a junior researcher alone. These first results are a step towards more in-depth research concerning the fair evaluation of computer-science researchers—specifically regarding juniors and their inclusion.
Detection of Redacted Text in Legal Documents

We present a technique for automatically detecting redacted text in legal documents, using a combination of Optical Character Recognition (OCR) and morphological operations from the Computer Vision domain, allowing us to detect a wide variety of different types of redaction blocks with little to no training data. As this is a segmentation task, we evaluate our technique using the Panoptic Quality methodology, with the algorithm obtaining F1 scores of 0.79, 0.86 and 0.76 on black, colored and outlined redaction blocks respectively, and an F1 score of 0.62 for gray blocks. The total running time of the algorithm is two seconds on average measured on a thousand pages from a government supplier, with roughly $$98\%$$ of this time being used by Tesseract and the conversion from PDF to PNG, and $$2\%$$ by the detection algorithm. Detecting text redaction at scale thus is feasible, allowing a more or less objective measurement of this practice.The redacted text detection code and the manually labelled dataset created for evaluation is released via Github.
Tracing Data Footprints: Formal and Informal Data Citations in the Scientific Literature

Data citation has become a prevalent practice within the scientific community, serving the purpose of facilitating data discovery, reproducibility, and credit attribution. Consequently, data has gained significant importance in the scholarly process. Despite its growing prominence, data citation is still at an early stage, with considerable variations in practices observed across scientific domains. Such diversity hampers the ability to consistently analyze, detect, and quantify data citations. We focus on the European Marine Science (MES) community to examine how data is cited in this specific context. We identify four types of data citations: formal, informal, complete, and incomplete. By analyzing the usage of these diverse data citation modalities, we investigate their impact on the widespread adoption of data citation practices.
Readability Measures as Predictors of Understandability and Engagement in Searching to Learn

Search engines have become essential tools for learning, providing access to vast amounts of educational resources. However, selecting the most suitable resources from numerous options can be challenging for learners. While search engines primarily rank resources based on topical relevance, factors like understandability and engagement are crucial for effective learning as well. Understandability, a key aspect of text, is often associated with readability. This study evaluates eight commonly used readability measures to determine their effectiveness in predicting understandability, engagement, topical relevance, and user-assigned ranks. The empirical evaluation employs a survey-based methodology, collecting explicit relevance feedback from participants regarding their preferences for learning from web pages. The relevance data was then analyzed concerning the readability measures. The findings highlight that readability measures are not only reliable predictors of understandability but also of engagement. Specifically, the FKGL and GFI measures demonstrate the highest and most consistent correlation with perceived understandability and engagement. This research provides valuable insights for selecting effective readability measures to tailor search results to the users' learning needs.
How to Cite a Web Ranking and Make it FAIR

Citing data is crucial for acknowledging and recognizing the contributions of experts, scientists, and institutions in creating and maintaining high-quality datasets. It ensures proper attribution and supports reproducibility in scientific research. While data citation methods have focused on structured or semi-structured datasets, there is a need to address the citation of web rankings. Web rankings are significant in scientific literature, information articles, and decision-making processes. However, citing web rankings presents challenges due to their dynamic nature. In response, we introduce a new "ranking citation" model and the Unipd Ranking Citation tool, designed to generate persistent and machine-readable citations, enhancing reproducibility and accountability in scientific research and general contexts. It is a user-friendly, open-source Chrome extension that employs ontology and RDF graphs for machine understanding and future reconstruction of rankings.
A Robust Approach for Hybrid Personalized Recommender Systems

The personalization of services for users is one of the most crucial objectives of digital platforms. This objective is accomplished by integrating automated recommendation components into information systems. The increasing computational power and storage capacity available today have opened up opportunities to deploy a combination of diverse approaches to enhance the accuracy of the recommendation process. Compared to previous research, the distinguishing feature of this study is the introduction of an approach that combines not only computational aspects but also data types. In terms of computation, our approach integrates both item-based and user-based recommendations. Regarding data type, we utilize all three common data types, i.e., user ratings, user reviews, and user interactions, to learn user preferences for recommendations. This comprehensive combination has demonstrated its effectiveness through experiments.
Large Synthetic Data from the ar$$\mathrm {\chi }$$iv for OCR Post Correction of Historic Scientific Articles

Historical scientific articles often require Optical Character Recognition (OCR) to transform scanned documents into machine-readable text, a process that often produces errors. We present a pipeline for the generation of a synthetic ground truth/OCR dataset to correct the OCR results of the astrophysics literature holdings of the NASA Astrophysics Data System (ADS). By mining the ar $$\mathrm {\chi }$$ iv we create, to the authors' knowledge, the largest scientific synthetic ground truth/OCR post correction dataset of 203,354,393 character pairs. Baseline models trained with this dataset find the mean improvement in character and word error rates of 7.71% and 18.82% for historical OCR text, respectively. Interactive dashboards to explore the dataset are available online: https://readingtimemachine.github.io/projects/1-ocr-groundtruth-may2023 , and data and code, are hosted on GitHub: https://github.com/ReadingTimeMachine/ocr_post_correction .
Image Modification Modeled as a Storytelling Process

Digital libraries have focused on change to images from the perspectives of prevention and reversal. Since change is a required component of scholarship, we seek to adding the modeling of change to support its characterization. In this paper we discuss change to images in traditional media and propose a formal model of that change. The subject calls for a kaleidoscopic approach as tracking changes in images is an interesting exercise in storytelling, both when one looks at deliberately changing them with a purpose and at tracking past changes.
Known by the Company It Keeps: Proximity-Based Indexing for Physical Content in Archival Repositories

Despite the plethora of born-digital content, vast troves of important content remain accessible only on physical media such as paper or microfilm. The traditional approach to indexing undigitized content is using manually created metadata that describes it at some level of aggregation (e.g., folder, box, or collection). Searchers led in this way to some subset of the content often must then manually examine substantial quantities of physical media to find what they are looking for. This paper proposes a complementary approach, in which selective digitization of a small portion of the content is used as a basis for proximity-based indexing as a way of bringing the user closer to the specific content for which they are looking. Experiments with 35 boxes of partially digitized US State Department records indicate that box-level indexes built in this way can provide a useful basis for search.
Online Voting in Ontario Municipalities: A Standards-Based Review

Abstract Over two hundred municipalities now offer online voting in Ontario, Canada, representing one of the largest deployments of digital elections worldwide. Many have eliminated the paper ballot altogether. Despite this, no provincial or federal-level standards exist. This gap leaves local election officials to create and apply their own cybersecurity requirements with varying degrees of success. Until a standard can be developed and adopted, we turn to perhaps the most natural and immediate stand-in: The Council of Europe’s (CoE) standards for e-voting. We use this baseline to present the first standards-based analysis of online voting practices in Ontario. Our results find the province is broadly non-compliant , with only 14% of the CoE’s 49 standards and 93 implementation guidelines categorized as fully met. We summarize these differences and identify areas for improvement in the hope of underscoring the need for domestic e-voting standards.
Major Research Achievements

Taking the new theories, new structures, new methods and new technologies involved in cognitive computing as breakthrough points, the original innovations in scientific theory and experimental technology have been realized, which have driven the leap-forward development of basic theories of cognitive computing in China.
Coercion Mitigation for Voting Systems with Trackers: A Selene Case Study

Abstract An interesting approach to achieving verifiability in voting systems is to make use of tracking numbers. This gives voters a simple way of verifying that their ballot was counted: they can simply look up their ballot/tracker pair on a public bulletin board. It is crucial to understand how trackers affect other security properties, in particular privacy. However, existing privacy definitions are not designed to accommodate tracker-based voting systems. Furthermore, the addition of trackers increases the threat of coercion. There does however exist techniques to mitigate the coercion threat. While the term coercion mitigation has been used in the literature when describing voting systems such as Selene, no formal definition of coercion mitigation seems to exist. In this paper we formally define what coercion mitigation means for tracker-based voting systems. We model Selene in our framework and we prove that Selene provides coercion mitigation, in addition to privacy and verifiability.
Synthesizing Web Archive Collections into Big Data: Lessons from Mining Data from Web Archives

Web archives are sources of big data. When presenting human visitors with archived web pages, or mementos, web archives often apply user interface augmentations to assist them. Unfortunately, these augmentations present challenges for natural language processing, computer vision, and machine learning methods. Thus, big data researchers must apply special techniques to web archives when acquiring mementos. This paper details these techniques so that future projects can more easily create datasets and conduct research. We review 22 web archives and discuss the methods needed to re-synthesize a memento to something close to its original capture without augmentations. We close by discussing options for improving the state of memento sharing for big data efforts.
From ISAD(G) to Linked Data Archival Descriptions

Archives preserve materials that allow us to understand and interpret the past and think about the future. With the evolution of the information society, archives must take advantage of technological innovations and adapt to changes in the kind and volume of the information created. Semantic Web representations are appropriate for structuring archival data and linking them to external sources, allowing versatile access by multiple applications. ArchOnto is a new Linked Data Model based on CIDOC CRM to describe archival objects. ArchOnto combines specific aspects of archiving with the CIDOC CRM standard. In this work, we analyze the ArchOnto representation of a set of archival records from the Portuguese National Archives and compare it to their CIDOC CRM representation. As a result of ArchOnto's representation, we observe an increase in the number of classes used, from 20 in CIDOC CRM to 28 in ArchOnto, and in the number of properties, from 25 in CIDOC CRM to 28 in ArchOnto. This growth stems from the refinement of object types and their relationships, favouring the use of controlled vocabularies. ArchOnto provides higher readability for the information in archival records, keeping it in line with current standards.
OpenVoting: Recoverability from Failures in Dual Voting

Abstract In this paper we address the problem of recovery from failures without re-running entire elections when elections fail to verify. We consider the setting of dual voting protocols, where the cryptographic guarantees of end-to-end verifiable voting (E2E-V) are combined with the simplicity of audit using voter-verified paper records (VVPR). We first consider the design requirements of such a system and then suggest a protocol called OpenVoting , which identifies a verifiable subset of error-free votes consistent with the VVPRs, and the polling booths corresponding to the votes that fail to verify with possible reasons for the failures.
Verifiability Experiences in Ontario’s 2022 Online Elections

Abstract Despite being one of the biggest international users of online voting with two decades of use, Canada has tended to use non-verifiable online voting systems. This has prompted concern about the verification of election results and potential impacts on public and administrator confidence in elections and democracy. In the 2022 Ontario municipal elections, however, about 9% of municipalities offered the option of individual verifiability to online voters. This article draws upon the experiences of two local governments of different sizes, resources, capacity, and online voting histories - Ignace and Markham - and their vendors to understand the considerations and challenges that come with the introduction of verifiability mechanisms in local elections. We identify deterrents to implementation and possible solutions to see an increase in uptake and improve the integrity of local elections.
Aspect-Driven Structuring of Historical Dutch Newspaper Archives

Digital libraries oftentimes provide access to historical newspaper archives via keyword-based search. Historical figures and their roles are particularly interesting cognitive access points in historical research. Structuring and clustering news articles would allow more sophisticated access for users to explore such information. However, real-world limitations such as the lack of training data, licensing restrictions and non-English text with OCR errors make the composition of such a system difficult and cost-intensive in practice. In this work we tackle these issues with the showcase of the National Library of the Netherlands by introducing a role-based interface that structures news articles on historical persons. In-depth, component-wise evaluations and interviews with domain experts highlighted our prototype's effectiveness and appropriateness for a real-world digital library collection.
Deep Learning in Medical Image Analysis for Personalized Medicine

Deep learning (DL) has transformed the field of medical image processing, enabling unprecedented accuracy and efficiency in various applications. It has been widely utilized for medical image analysis of different anatomical regions. In this article, we provide an overview of commonly used deep learning methods, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). We briefly overview their applications in medical image analysis, such as image classification, object detection/localization, segmentation, generation, and registration. We also highlight the strengths and limitations of each method and identify the challenges that still need to be addressed, including the limited availability of annotated data, variability in medical images, and the interpretability issue. Finally, we discuss future research directions, including developing explainable deep learning methods and integrating multi-modal data.
Motion Saliency Detection using Depth Information for Human Action Recognition Applications

Video processing tasks require the analysis of an enormous amount of data. However, in most practical applications this analysis is not required on the whole frame or video but is limited to the regions where some action is taking place. If these regions can be identified with low effort, the total computation time required to process and analyze such videos can be drastically reduced. In this work a new motion saliency detection algorithm intended to be incorporated in a human action recognition pipeline is presented. The approach uses depth data to improve the performance of the difference of frames method which is computationally simple and efficient. Results show that the algorithm achieves a performance which is comparable to the state of the art while requiring a much lower computational time.