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Author(s):  
Elvys Linhares Pontes ◽  
Luis Adrián Cabrera-Diego ◽  
Jose G. Moreno ◽  
Emanuela Boros ◽  
Ahmed Hamdi ◽  
...  

AbstractDigital libraries have a key role in cultural heritage as they provide access to our culture and history by indexing books and historical documents (newspapers and letters). Digital libraries use natural language processing (NLP) tools to process these documents and enrich them with meta-information, such as named entities. Despite recent advances in these NLP models, most of them are built for specific languages and contemporary documents that are not optimized for handling historical material that may for instance contain language variations and optical character recognition (OCR) errors. In this work, we focused on the entity linking (EL) task that is fundamental to the indexation of documents in digital libraries. We developed a Multilingual Entity Linking architecture for HIstorical preSS Articles that is composed of multilingual analysis, OCR correction, and filter analysis to alleviate the impact of historical documents in the EL task. The source code is publicly available. Experimentation has been done over two historical documents covering five European languages (English, Finnish, French, German, and Swedish). Results have shown that our system improved the global performance for all languages and datasets by achieving an F-score@1 of up to 0.681 and an F-score@5 of up to 0.787.


Author(s):  
Jens Trautmann ◽  
Arthur Beckers ◽  
Lennert Wouters ◽  
Stefan Wildermann ◽  
Ingrid Verbauwhede ◽  
...  

Locating a cryptographic operation in a side-channel trace, i.e. finding out where it is in the time domain, without having a template, can be a tedious task even for unprotected implementations. The sheer amount of data can be overwhelming. In a simple call to OpenSSL for AES-128 ECB encryption of a single data block, only 0.00028% of the trace relate to the actual AES-128 encryption. The rest is overhead. We introduce the (to our best knowledge) first method to locate a cryptographic operation in a side-channel trace in a largely automated fashion. The method exploits meta information about the cryptographic operation and requires an estimate of its implementation’s execution time.The method lends itself to parallelization and our implementation in a tool greatly benefits from GPU acceleration. The tool can be used offline for trace segmentation and for generating a template which can then be used online in real-time waveformmatching based triggering systems for trace acquisition or fault injection. We evaluate it in six scenarios involving hardware and software implementations of different cryptographic operations executed on diverse platforms. Two of these scenarios cover realistic protocol level use-cases and demonstrate the real-world applicability of our tool in scenarios where classical leakage-detection techniques would not work. The results highlight the usefulness of the tool because it reliably and efficiently automates the task and therefore frees up time of the analyst.The method does not work on traces of implementations protected by effective time randomization countermeasures, e.g. random delays and unstable clock frequency, but is not affected by masking, shuffling and similar countermeasures.


Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7619
Author(s):  
Jelle De Bock ◽  
Steven Verstockt

Video-based trajectory analysis might be rather well discussed in sports, such as soccer or basketball, but in cycling, this is far less common. In this paper, a video processing pipeline to extract riding lines in cyclocross races is presented. The pipeline consists of a stepwise analysis process to extract riding behavior from a region (i.e., the fence) in a video camera feed. In the first step, the riders are identified by an Alphapose skeleton detector and tracked with a spatiotemporally aware pose tracker. Next, each detected pose is enriched with additional meta-information, such as rider modus (e.g., sitting on the saddle or standing on the pedals) and detected team (based on the worn jerseys). Finally, a post-processor brings all the information together and proposes ride lines with meta-information for the riders in the fence. The presented methodology can provide interesting insights, such as intra-athlete ride line clustering, anomaly detection, and detailed breakdowns of riding and running durations within the segment. Such detailed rider info can be very valuable for performance analysis, storytelling, and automatic summarization.


2021 ◽  
Vol 39 (4) ◽  
pp. 1-44
Author(s):  
Johannes Kiesel ◽  
Lars Meyer ◽  
Martin Potthast ◽  
Benno Stein

The exchange of meta-information has always formed part of information behavior. In this article, we show that this rule also extends to conversational search. Information about the user’s information need, their preferences, and the quality of search results are only some of the most salient examples of meta-information that are exchanged as a matter of course in a search conversation. To understand the importance of meta-information for conversational search, we revisit its definition and survey how meta-information has been taken into account in the past in information retrieval. Meta-information has gone by many names, about which a concise overview is provided. An in-depth analysis of the role of meta-information in search and conversation theories reveals that they provide significant support for the importance of meta-information in conversational search. We further identify conversational search datasets are suitable for a deeper inspection with regard to meta-information, namely, Spoken Conversational Search and Microsoft Information-Seeking Conversations. A quantitative data analysis demonstrates the practical significance of meta-information in information-seeking conversations, whereas a qualitative analysis shows the effects of exchanging different types. Finally, we discuss practical applications and challenges of meta-information in conversational search, including a case study of VERSE, an existing search system for the visually impaired.


2021 ◽  
Author(s):  
Anastasia Malysheva ◽  
Alexey Tikhonov ◽  
Ivan P. Yamshchikov

Narrative generation and analysis are still on the fringe of modern natural language processing yet are crucial in a variety of applications. This paper proposes a feature extraction method for plot dynamics. We present a dataset that consists of the plot descriptions for thirteen thousand TV shows alongside meta-information on their genres and dynamic plots extracted from them. We validate the proposed tool for plot dynamics extraction and discuss possible applications of this method to the tasks of narrative analysis and generation.


2021 ◽  
Author(s):  
Ines Reinecke ◽  
Michéle Zoch ◽  
Christian Reich ◽  
Martin Sedlmayr ◽  
Franziska Bathelt

OHDSI, a fast growing open-science research community seeks to enable researchers from around the globe to conduct network studies based on standardized data and vocabularies. There is no comprehensive review of publications about OHDSI’s standard: the OMOP Common Data Model and its usage available. In this work we aim to close this gap and provide a summary of existing publications including the analysis of its meta information such as the choice of journals, journal types, countries, as well as an analysis by topics based on a title and abstract screening. Since 2016, the number of publications has been constantly growing and the relevance of the OMOP CDM is increasing in terms of multi-country studies based on observational patient data.


2021 ◽  
Author(s):  
Wenying Duan ◽  
Xiaoxi He ◽  
Zimu Zhou ◽  
Hong Rao ◽  
Lothar Thiele

2021 ◽  
Vol 26 (6) ◽  
Author(s):  
Christoph Laaber ◽  
Mikael Basmaci ◽  
Pasquale Salza

AbstractSoftware benchmarks are only as good as the performance measurements they yield. Unstable benchmarks show high variability among repeated measurements, which causes uncertainty about the actual performance and complicates reliable change assessment. However, if a benchmark is stable or unstable only becomes evident after it has been executed and its results are available. In this paper, we introduce a machine-learning-based approach to predict a benchmark’s stability without having to execute it. Our approach relies on 58 statically-computed source code features, extracted for benchmark code and code called by a benchmark, related to (1) meta information, e.g., lines of code (LOC), (2) programming language elements, e.g., conditionals or loops, and (3) potentially performance-impacting standard library calls, e.g., file and network input/output (I/O). To assess our approach’s effectiveness, we perform a large-scale experiment on 4,461 Go benchmarks coming from 230 open-source software (OSS) projects. First, we assess the prediction performance of our machine learning models using 11 binary classification algorithms. We find that Random Forest performs best with good prediction performance from 0.79 to 0.90, and 0.43 to 0.68, in terms of AUC and MCC, respectively. Second, we perform feature importance analyses for individual features and feature categories. We find that 7 features related to meta-information, slice usage, nested loops, and synchronization application programming interfaces (APIs) are individually important for good predictions; and that the combination of all features of the called source code is paramount for our model, while the combination of features of the benchmark itself is less important. Our results show that although benchmark stability is affected by more than just the source code, we can effectively utilize machine learning models to predict whether a benchmark will be stable or not ahead of execution. This enables spending precious testing time on reliable benchmarks, supporting developers to identify unstable benchmarks during development, allowing unstable benchmarks to be repeated more often, estimating stability in scenarios where repeated benchmark execution is infeasible or impossible, and warning developers if new benchmarks or existing benchmarks executed in new environments will be unstable.


2021 ◽  
Vol 9 (8) ◽  
pp. 94
Author(s):  
Yuxin Shen ◽  
Minn N. Yoon ◽  
Silvia Ortiz ◽  
Reid Friesen ◽  
Hollis Lai

A web-based image classification tool (DiLearn) was developed to facilitate active learning in the oral health profession. Students engage with oral lesion images using swipe gestures to classify each image into pre-determined categories (e.g., left for refer and right for no intervention). To assemble the training modules and to provide feedback to students, DiLearn requires each oral lesion image to be classified, with various features displayed in the image. The collection of accurate meta-information is a crucial step for enabling the self-directed active learning approach taken in DiLearn. The purpose of this study is to evaluate the classification consistency of features in oral lesion images by experts and students for use in the learning tool. Twenty oral lesion images from DiLearn’s image bank were classified by three oral lesion experts and two senior dental hygiene students using the same rubric containing eight features. Classification agreement among and between raters were evaluated using Fleiss’ and Cohen’s Kappa. Classification agreement among the three experts ranged from identical (Fleiss’ Kappa = 1) for “clinical action”, to slight agreement for “border regularity” (Fleiss’ Kappa = 0.136), with the majority of categories having fair to moderate agreement (Fleiss’ Kappa = 0.332–0.545). Inclusion of the two student raters with the experts yielded fair to moderate overall classification agreement (Fleiss’ Kappa = 0.224–0.554), with the exception of “morphology”. The feature of clinical action could be accurately classified, while other anatomical features indirectly related to diagnosis had a lower classification consistency. The findings suggest that one oral lesion expert or two student raters can provide fairly consistent meta-information for selected categories of features implicated in the creation of image classification tasks in DiLearn.


Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1786
Author(s):  
Cezara Benegui ◽  
Radu Tudor Ionescu

In this paper, we propose an enhanced version of the Authentication with Built-in Camera (ABC) protocol by employing a deep learning solution based on built-in motion sensors. The standard ABC protocol identifies mobile devices based on the photo-response non-uniformity (PRNU) of the camera sensor, while also considering QR-code-based meta-information. During registration, users are required to capture photos using their smartphone camera. The photos are sent to a server that computes the camera fingerprint, storing it as an authentication trait. During authentication, the user is required to take two photos that contain two QR codes presented on a screen. The presented QR code images also contain a unique probe signal, similar to a camera fingerprint, generated by the protocol. During verification, the server computes the fingerprint of the received photos and authenticates the user if (i) the probe signal is present, (ii) the metadata embedded in the QR codes is correct and (iii) the camera fingerprint is identified correctly. However, the protocol is vulnerable to forgery attacks when the attacker can compute the camera fingerprint from external photos, as shown in our preliminary work. Hence, attackers can easily remove their PRNU from the authentication photos without completely altering the probe signal, resulting in attacks that bypass the defense systems of the ABC protocol. In this context, we propose an enhancement to the ABC protocol, using motion sensor data as an additional and passive authentication layer. Smartphones can be identified through their motion sensor data, which, unlike photos, is never posted by users on social media platforms, thus being more secure than using photographs alone. To this end, we transform motion signals into embedding vectors produced by deep neural networks, applying Support Vector Machines for the smartphone identification task. Our change to the ABC protocol results in a multi-modal protocol that lowers the false acceptance rate for the attack proposed in our previous work to a percentage as low as 0.07%. In this paper, we present the attack that makes ABC vulnerable, as well as our multi-modal ABC protocol along with relevant experiments and results.


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