| 1. |
- Bott, Lukas Thomas, et al.
(författare)
-
Coulomb dissociation of O-16 into He-4 and C-12
- 2023
-
Ingår i: NUCLEAR PHYSICS IN ASTROPHYSICS - X, NPA-X 2022. - : EDP Sciences. - 2100-014X. ; 279
-
Konferensbidrag (refereegranskat)abstract
- We measured the Coulomb dissociation of O-16 into He-4 and C-12 within the FAIR Phase-0 program at GSI Helmholtzzentrum fur Schwerionenforschung Darmstadt, Germany. From this we will extract the photon dissociation cross section O-16(alpha,gamma)C-12, which is the time reversed reaction to C-12(alpha,gamma)O-16. With this indirect method, we aim to improve on the accuracy of the experimental data at lower energies than measured so far. The expected low cross section for the Coulomb dissociation reaction and close magnetic rigidity of beam and fragments demand a high precision measurement. Hence, new detector systems were built and radical changes to the (RB)-B-3 setup were necessary to cope with the high-intensity O-16 beam. All tracking detectors were designed to let the unreacted O-16 ions pass, while detecting the C-12 and He-4.
|
|
| 2. |
- Abbasi, Rasha, et al.
(författare)
-
IceCube search for neutrinos from GRB 221009A
- 2023
-
Ingår i: Proceedings of 38th International Cosmic Ray Conference (ICRC 2023). - : Sissa Medialab Srl.
-
Konferensbidrag (refereegranskat)abstract
- GRB 221009A is the brightest Gamma Ray Burst (GRB) ever observed. The observed extremelyhigh flux of high and very-high-energy photons provide a unique opportunity to probe the predictedneutrino counterpart to the electromagnetic emission. We have used a variety of methods to searchfor neutrinos in coincidence with the GRB over several time windows during the precursor, promptand afterglow phases of the GRB. MeV scale neutrinos are studied using photo-multiplier ratescalers which are normally used to search for galactic core-collapse supernovae neutrinos. GeVneutrinos are searched starting with DeepCore triggers. These events don’t have directionallocalization, but instead can indicate an excess in the rate of events. 10 GeV - 1 TeV and >TeVneutrinos are searched using traditional neutrino point source methods which take into accountthe direction and time of events with DeepCore and the entire IceCube detector respectively. The>TeV results include both a fast-response analysis conducted by IceCube in real-time with timewindows of T0 − 1 to T0 + 2 hours and T0 ± 1 day around the time of GRB 221009A, as well asan offline analysis with 3 new time windows up to a time window of T0 − 1 to T0 + 14 days, thelongest time period we consider. The combination of observations by IceCube covers 9 ordersof magnitude in neutrino energy, from MeV to PeV, placing upper limits across the range forpredicted neutrino emission.
|
|
| 3. |
- Muñoz Sánchez, Ricardo, 1992, et al.
(författare)
-
Did the Names I Used within My Essay Affect My Score? Diagnosing Name Biases in Automated Essay Scoring
- 2024
-
Ingår i: Proceedings of the Workshop on Computational Approaches to Language Data Pseudonymization (CALD-pseudo 2024). - : Association for Computational Linguistics.
-
Konferensbidrag (refereegranskat)abstract
- Automated essay scoring (AES) of second-language learner essays is a high-stakes task as it can affect the job and educational opportunities a student may have access to. Thus, it becomes imperative to make sure that the essays are graded based on the students’ language proficiency as opposed to other reasons, such as personal names used in the text of the essay. Moreover, most of the research data for AES tends to contain personal identifiable information. Because of that, pseudonymization becomes an important tool to make sure that this data can be freely shared. Thus, our systems should not grade students based on which given names were used in the text of the essay, both for fairness and for privacy reasons. In this paper we explore how given names affect the CEFR level classification of essays of second language learners of Swedish. We use essays containing just one personal name and substitute it for names from lists of given names from four different ethnic origins, namely Swedish, Finnish, Anglo-American, and Arabic. We find that changing the names within the essays has no apparent effect on the classification task, regardless of whether a feature-based or a transformer-based model is used.
|
|
| 4. |
- Muñoz Sánchez, Ricardo, 1992, et al.
(författare)
-
Name Biases in Automated Essay Assessment
- 2024
-
Ingår i: The 28th International Congress of Onomastic Sciences (ICOS 28).
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Artificial intelligence is being deployed in high-stakes situations, such as automated grading of second language essays in proficiency assessment. While they can improve the opportunities students have (education, work opportunities, etc.), such systems often display human-like biases. Aldrin (2017) notes that human graders have a slight bias based on names appearing in essay texts. We aim to identify whether the same pattern holds in automated systems. In this study we aim to answer the following research questions: 1) Does changing given names inside a second language learner essay affect the way the text is graded? 2) How much does this differ between feature-based machine learning and deep learning? For this, we use a de-anonymized (i.e. original) version of the Swell-pilot corpus of second language Swedish learner essays (Volodina 2016), which consists of 502 essays annotated with CEFR levels as our source data. First, we compile four lists of given names inspired by those of Aldrin (2017): traditional Swedish names; modern Swedish names of Anglo-American origin; Finnish names (due to the close sociocultural links between both countries); and names of Arabic origin (the most prominent group of learners in the corpus). Second, we create a diagnostic dataset to identify biases in the classification task. We select SweLL-pilot essays in which a given name appears only once. Then, we generate an essay version for each name on the lists by substituting the name in the original text with one from the list. Third, we fine-tune a BERT (Devlin et al. 2019) model on the original SweLL-pilot data to predict the CEFR level of a given essay and compare it to an existing feature-based model (Pilan 2016). Finally, we test the two models and compare the equality of opportunity between the different given name groups on the diagnostic dataset.
|
|
| 5. |
|
|
| 6. |
- Pagnin, Elena, 1989, et al.
(författare)
-
HIKE: Walking the Privacy Trail
- 2018
-
Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. ; 11124 LNCS, s. 43-66
-
Konferensbidrag (refereegranskat)abstract
- We consider the problem of privacy-preserving processing of outsourced data in the context of user-customised services. Clients store their data on a server. In order to provide user-dependent services, service providers may ask the server to compute functions on the users’ data. We propose a new solution to this problem that guarantees data privacy (i.e., an honest-but-curious server cannot access plaintexts), as well as that service providers can correctly decrypt only –functions on– the data the user gave them access to (i.e., service providers learn nothing more than the result of user-selected computations). Our solution has as base point a new secure labelled homomorphic encryption scheme (LEEG). LEEG supports additional algorithms (FEET) that enhance the scheme’s functionalities with extra privacy-oriented fea- tures. Equipped with LEEG and FEET, we define HIKE: a lightweight protocol for private and secure storage, computation and disclosure of users’ data. Finally, we implement HIKE and benchmark its performances demonstrating its succinctness and efficiency.
|
|
| 7. |
- Sicilia, Miguel-Angel, et al.
(författare)
-
Navigating learning resources through linked data : A preliminary report on the re-design of organic.edunet
- 2011
-
Ingår i: Linked Learning 2011 eLearning Approaches for the Linked Data Age. - : Rheinisch-Westfaelische Technische Hochschule Aachen. ; , s. 1-8
-
Konferensbidrag (refereegranskat)abstract
- Learning objects repositories have grown and matured in the last years, being currently a cornerstone for open education. Several current systems are offering metadata openly through mainstream harvesting protocols or providing standardized query interfaces. Also, the use of standardized vocabularies or ontologies is becoming more common to provide a degree of semantic interoperability. However, learning object metadata is typically not linked across repositories, and it is not providing a way to navigate by using other sources of data available on the Web. The linked open data (LOD) approach provides the framework for the evolution of learning object repositories into a more flexible system of sharing learning resource metadata. This paper describes how linked data has been integrated in the design and redesign of the export mechanisms of Organic.Edunet, a federation of learning repositories in the domain of organic agriculture that uses an RDF store and several ontologies to browse and search resources. The paper focuses on how the existing search and semantic browsing mechanisms can benefit from the use of LOD across repositories.
|
|
| 8. |
- Szawerna, Maria Irena, et al.
(författare)
-
Detecting Personal Identifiable Information in Swedish Learner Essays
- 2024
-
Ingår i: Proceedings of the Workshop on Computational Approaches to Language Data Pseudonymization (CALD-pseudo 2024). - : Association for Computational Linguistics.
-
Konferensbidrag (refereegranskat)abstract
- Linguistic data can — and often does — contain PII (Personal Identifiable Information). Both from a legal and ethical standpoint, the sharing of such data is not permissible. According to the GDPR, pseudonymization, i.e. the replacement of sensitive information with surrogates, is an acceptable strategy for privacy preservation. While research has been conducted on the detection and replacement of sensitive data in Swedish medical data using Large Language Models (LLMs), it is unclear whether these models handle PII in less structured and more thematically varied texts equally well. In this paper, we present and discuss the performance of an LLM-based PII-detection system for Swedish learner essays.
|
|
| 9. |
- Szawerna, Maria Irena, et al.
(författare)
-
Pseudonymization Categories across Domain Boundaries
- 2024
-
Ingår i: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). - : ELRA and ICCL. - 9782493814104
-
Konferensbidrag (refereegranskat)abstract
- Linguistic data, a component critical not only for research in a variety of fields but also for the development of various Natural Language Processing (NLP) applications, can contain personal information. As a result, its accessibility is limited, both from a legal and an ethical standpoint. One of the solutions is the pseudonymization of the data. Key stages of this process include the identification of sensitive elements and the generation of suitable surrogates in a way that the data is still useful for the intended task. Within this paper, we conduct an analysis of tagsets that have previously been utilized in anonymization and pseudonymization. We also investigate what kinds of Personally Identifiable Information (PII) appear in various domains. These reveal that none of the analyzed tagsets account for all of the PII types present cross-domain at the level of detailedness seemingly required for pseudonymization. We advocate for a universal system of tags for categorizing PIIs leading up to their replacement. Such categorization could facilitate the generation of grammatically, semantically, and sociolinguistically appropriate surrogates for the kinds of information that are considered sensitive in a given domain, resulting in a system that would enable dynamic pseudonymization while keeping the texts readable and useful for future research in various fields.
|
|
