---
_id: '54548'
author:
- first_name: Raphael Patrick
full_name: Prager, Raphael Patrick
last_name: Prager
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: Prager RP, Trautmann H. Exploratory Landscape Analysis for Mixed-Variable Problems.
IEEE Transactions on Evolutionary Computation. Published online 2024:1-1.
doi:10.1109/TEVC.2024.3399560
apa: Prager, R. P., & Trautmann, H. (2024). Exploratory Landscape Analysis for
Mixed-Variable Problems. IEEE Transactions on Evolutionary Computation,
1–1. https://doi.org/10.1109/TEVC.2024.3399560
bibtex: '@article{Prager_Trautmann_2024, title={Exploratory Landscape Analysis for
Mixed-Variable Problems}, DOI={10.1109/TEVC.2024.3399560},
journal={IEEE Transactions on Evolutionary Computation}, author={Prager, Raphael
Patrick and Trautmann, Heike}, year={2024}, pages={1–1} }'
chicago: Prager, Raphael Patrick, and Heike Trautmann. “Exploratory Landscape Analysis
for Mixed-Variable Problems.” IEEE Transactions on Evolutionary Computation,
2024, 1–1. https://doi.org/10.1109/TEVC.2024.3399560.
ieee: 'R. P. Prager and H. Trautmann, “Exploratory Landscape Analysis for Mixed-Variable
Problems,” IEEE Transactions on Evolutionary Computation, pp. 1–1, 2024,
doi: 10.1109/TEVC.2024.3399560.'
mla: Prager, Raphael Patrick, and Heike Trautmann. “Exploratory Landscape Analysis
for Mixed-Variable Problems.” IEEE Transactions on Evolutionary Computation,
2024, pp. 1–1, doi:10.1109/TEVC.2024.3399560.
short: R.P. Prager, H. Trautmann, IEEE Transactions on Evolutionary Computation
(2024) 1–1.
date_created: 2024-06-03T06:16:33Z
date_updated: 2024-06-03T06:17:13Z
department:
- _id: '819'
doi: 10.1109/TEVC.2024.3399560
keyword:
- Optimization
- Evolutionary computation
- Benchmark testing
- Hyperparameter optimization
- Portfolios
- Extraterrestrial measurements
- Dispersion
- Exploratory landscape analysis
- mixed-variable problem
- mixed search spaces
- automated algorithm selection
language:
- iso: eng
page: 1-1
publication: IEEE Transactions on Evolutionary Computation
status: public
title: Exploratory Landscape Analysis for Mixed-Variable Problems
type: journal_article
user_id: '15504'
year: '2024'
...
---
_id: '56221'
author:
- first_name: Angel E.
full_name: Rodriguez-Fernandez, Angel E.
last_name: Rodriguez-Fernandez
- first_name: Lennart
full_name: Schäpermeier, Lennart
last_name: Schäpermeier
- first_name: Carlos
full_name: Hernández, Carlos
last_name: Hernández
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Oliver
full_name: Schütze, Oliver
last_name: Schütze
citation:
ama: Rodriguez-Fernandez AE, Schäpermeier L, Hernández C, Kerschke P, Trautmann
H, Schütze O. Finding ϵ-Locally Optimal Solutions for Multi-Objective Multimodal
Optimization. IEEE Transactions on Evolutionary Computation. Published
online 2024:1-1. doi:10.1109/TEVC.2024.3458855
apa: Rodriguez-Fernandez, A. E., Schäpermeier, L., Hernández, C., Kerschke, P.,
Trautmann, H., & Schütze, O. (2024). Finding ϵ-Locally Optimal Solutions for
Multi-Objective Multimodal Optimization. IEEE Transactions on Evolutionary
Computation, 1–1. https://doi.org/10.1109/TEVC.2024.3458855
bibtex: '@article{Rodriguez-Fernandez_Schäpermeier_Hernández_Kerschke_Trautmann_Schütze_2024,
title={Finding ϵ-Locally Optimal Solutions for Multi-Objective Multimodal Optimization},
DOI={10.1109/TEVC.2024.3458855},
journal={IEEE Transactions on Evolutionary Computation}, author={Rodriguez-Fernandez,
Angel E. and Schäpermeier, Lennart and Hernández, Carlos and Kerschke, Pascal
and Trautmann, Heike and Schütze, Oliver}, year={2024}, pages={1–1} }'
chicago: Rodriguez-Fernandez, Angel E., Lennart Schäpermeier, Carlos Hernández,
Pascal Kerschke, Heike Trautmann, and Oliver Schütze. “Finding ϵ-Locally Optimal
Solutions for Multi-Objective Multimodal Optimization.” IEEE Transactions on
Evolutionary Computation, 2024, 1–1. https://doi.org/10.1109/TEVC.2024.3458855.
ieee: 'A. E. Rodriguez-Fernandez, L. Schäpermeier, C. Hernández, P. Kerschke, H.
Trautmann, and O. Schütze, “Finding ϵ-Locally Optimal Solutions for Multi-Objective
Multimodal Optimization,” IEEE Transactions on Evolutionary Computation,
pp. 1–1, 2024, doi: 10.1109/TEVC.2024.3458855.'
mla: Rodriguez-Fernandez, Angel E., et al. “Finding ϵ-Locally Optimal Solutions
for Multi-Objective Multimodal Optimization.” IEEE Transactions on Evolutionary
Computation, 2024, pp. 1–1, doi:10.1109/TEVC.2024.3458855.
short: A.E. Rodriguez-Fernandez, L. Schäpermeier, C. Hernández, P. Kerschke, H.
Trautmann, O. Schütze, IEEE Transactions on Evolutionary Computation (2024) 1–1.
date_created: 2024-09-24T08:01:14Z
date_updated: 2024-09-24T08:01:47Z
doi: 10.1109/TEVC.2024.3458855
keyword:
- Optimization
- Evolutionary computation
- Approximation algorithms
- Benchmark testing
- Vectors
- Surveys
- Pareto optimization
- multi-objective optimization
- evolutionary computation
- multimodal optimization
- local solutions
language:
- iso: eng
page: 1-1
publication: IEEE Transactions on Evolutionary Computation
status: public
title: Finding ϵ-Locally Optimal Solutions for Multi-Objective Multimodal Optimization
type: journal_article
user_id: '15504'
year: '2024'
...
---
_id: '56581'
abstract:
- lang: eng
text: 'In recent years, there has been a surge in natural language processing research
focused on low-resource languages (LrLs), underscoring the growing recognition
that LrLs deserve the same attention as high-resource languages (HrLs). This shift
is crucial for ensuring linguistic diversity and inclusivity in the digital age.
Despite Indonesian ranking as the 11th most spoken language globally, it remains
under-resourced in terms of computational tools and datasets. Within the semantic
web domain, Entity Linking (EL) is pivotal, linking textual entity mentions to
their corresponding entries in knowledge bases. This process is foundational for
advanced information extraction tasks, including relation extraction and event
detection. To bolster EL research in Indonesian, we introduce IndEL, the first
benchmark dataset tailored for both general and specific domains. IndEL was manually
curated using Wikidata, adhering to a rigorous set of annotation guidelines. We
used two Named Entity Recognition (NER) benchmark datasets for entity extraction:
NER UI for the general domain and IndQNER for the specific domain. IndQNER focused
on entities from the Indonesian translation of the Quran. IndEL comprises 4765
entities in the general domain and 2453 in the specific domain. Using the GERBIL
framework, we use IndEL to evaluate the performance of various EL systems, such
as Babelfy, DBpedia Spotlight, MAG, OpenTapioca, and WAT. Our further investigation
reveals that within Wikidata, a significant number of NIL entities remain unlinked
due to the limited number of Indonesian labels and the use of acronyms. Especially
in the specific domain, transliteration and translation processes performed to
create the Indonesian translation of the Quran contribute to the presence of entities
in a descriptive form and as synonyms.'
author:
- first_name: Ria Hari
full_name: Gusmita, Ria Hari
id: '71039'
last_name: Gusmita
- first_name: Muhammad Faruq Amiral
full_name: Abshar, Muhammad Faruq Amiral
last_name: Abshar
- first_name: Diego
full_name: Moussallem, Diego
id: '71635'
last_name: Moussallem
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'Gusmita RH, Abshar MFA, Moussallem D, Ngonga Ngomo A-C. IndEL: Indonesian
Entity Linking Benchmark Dataset for General and Specific Domains. In: Lecture
Notes in Computer Science. Springer Nature Switzerland; 2024. doi:10.1007/978-3-031-70239-6_34'
apa: 'Gusmita, R. H., Abshar, M. F. A., Moussallem, D., & Ngonga Ngomo, A.-C.
(2024). IndEL: Indonesian Entity Linking Benchmark Dataset for General and Specific
Domains. In Lecture Notes in Computer Science. The 29th Annual International
Conference on Natural Language & Information Systems (NLDB 2024), Turin, Italy.
Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-70239-6_34'
bibtex: '@inbook{Gusmita_Abshar_Moussallem_Ngonga Ngomo_2024, place={Cham}, title={IndEL:
Indonesian Entity Linking Benchmark Dataset for General and Specific Domains},
DOI={10.1007/978-3-031-70239-6_34},
booktitle={Lecture Notes in Computer Science}, publisher={Springer Nature Switzerland},
author={Gusmita, Ria Hari and Abshar, Muhammad Faruq Amiral and Moussallem, Diego
and Ngonga Ngomo, Axel-Cyrille}, year={2024} }'
chicago: 'Gusmita, Ria Hari, Muhammad Faruq Amiral Abshar, Diego Moussallem, and
Axel-Cyrille Ngonga Ngomo. “IndEL: Indonesian Entity Linking Benchmark Dataset
for General and Specific Domains.” In Lecture Notes in Computer Science.
Cham: Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-70239-6_34.'
ieee: 'R. H. Gusmita, M. F. A. Abshar, D. Moussallem, and A.-C. Ngonga Ngomo, “IndEL:
Indonesian Entity Linking Benchmark Dataset for General and Specific Domains,”
in Lecture Notes in Computer Science, Cham: Springer Nature Switzerland,
2024.'
mla: 'Gusmita, Ria Hari, et al. “IndEL: Indonesian Entity Linking Benchmark Dataset
for General and Specific Domains.” Lecture Notes in Computer Science, Springer
Nature Switzerland, 2024, doi:10.1007/978-3-031-70239-6_34.'
short: 'R.H. Gusmita, M.F.A. Abshar, D. Moussallem, A.-C. Ngonga Ngomo, in: Lecture
Notes in Computer Science, Springer Nature Switzerland, Cham, 2024.'
conference:
end_date: 2024-06-27
location: Turin, Italy
name: The 29th Annual International Conference on Natural Language & Information
Systems (NLDB 2024)
start_date: 2024-06-25
date_created: 2024-10-10T14:29:08Z
date_updated: 2024-10-14T19:22:16Z
doi: 10.1007/978-3-031-70239-6_34
keyword:
- entity linking benchmark dataset
- Indonesian
- general and specific domains
language:
- iso: eng
place: Cham
publication: Lecture Notes in Computer Science
publication_identifier:
isbn:
- '9783031702389'
- '9783031702396'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer Nature Switzerland
related_material:
link:
- relation: confirmation
url: https://link.springer.com/chapter/10.1007/978-3-031-70239-6_34
status: public
title: 'IndEL: Indonesian Entity Linking Benchmark Dataset for General and Specific
Domains'
type: book_chapter
user_id: '71039'
year: '2024'
...
---
_id: '46572'
abstract:
- lang: eng
text: Indonesian is classified as underrepresented in the Natural Language Processing
(NLP) field, despite being the tenth most spoken language in the world with 198
million speakers. The paucity of datasets is recognized as the main reason for
the slow advancements in NLP research for underrepresented languages. Significant
attempts were made in 2020 to address this drawback for Indonesian. The Indonesian
Natural Language Understanding (IndoNLU) benchmark was introduced alongside IndoBERT
pre-trained language model. The second benchmark, Indonesian Language Evaluation
Montage (IndoLEM), was presented in the same year. These benchmarks support several
tasks, including Named Entity Recognition (NER). However, all NER datasets are
in the public domain and do not contain domain-specific datasets. To alleviate
this drawback, we introduce IndQNER, a manually annotated NER benchmark dataset
in the religious domain that adheres to a meticulously designed annotation guideline.
Since Indonesia has the world’s largest Muslim population, we build the dataset
from the Indonesian translation of the Quran. The dataset includes 2475 named
entities representing 18 different classes. To assess the annotation quality of
IndQNER, we perform experiments with BiLSTM and CRF-based NER, as well as IndoBERT
fine-tuning. The results reveal that the first model outperforms the second model
achieving 0.98 F1 points. This outcome indicates that IndQNER may be an acceptable
evaluation metric for Indonesian NER tasks in the aforementioned domain, widening
the research’s domain range.
author:
- first_name: Ria Hari
full_name: Gusmita, Ria Hari
id: '71039'
last_name: Gusmita
- first_name: Asep Fajar
full_name: Firmansyah, Asep Fajar
id: '76787'
last_name: Firmansyah
- first_name: Diego
full_name: Moussallem, Diego
id: '71635'
last_name: Moussallem
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'Gusmita RH, Firmansyah AF, Moussallem D, Ngonga Ngomo A-C. IndQNER: Named
Entity Recognition Benchmark Dataset from the Indonesian Translation of the Quran.
In: Natural Language Processing and Information Systems. Springer Nature
Switzerland; 2023. doi:10.1007/978-3-031-35320-8_12'
apa: 'Gusmita, R. H., Firmansyah, A. F., Moussallem, D., & Ngonga Ngomo, A.-C.
(2023). IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian
Translation of the Quran. In Natural Language Processing and Information Systems.
International Conference on Applications of Natural Language to Information Systems
(NLDB) 2023, Derby, UK. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-35320-8_12'
bibtex: '@inbook{Gusmita_Firmansyah_Moussallem_Ngonga Ngomo_2023, place={Cham},
title={IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian
Translation of the Quran}, DOI={10.1007/978-3-031-35320-8_12},
booktitle={Natural Language Processing and Information Systems}, publisher={Springer
Nature Switzerland}, author={Gusmita, Ria Hari and Firmansyah, Asep Fajar and
Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}, year={2023} }'
chicago: 'Gusmita, Ria Hari, Asep Fajar Firmansyah, Diego Moussallem, and Axel-Cyrille
Ngonga Ngomo. “IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian
Translation of the Quran.” In Natural Language Processing and Information Systems.
Cham: Springer Nature Switzerland, 2023. https://doi.org/10.1007/978-3-031-35320-8_12.'
ieee: 'R. H. Gusmita, A. F. Firmansyah, D. Moussallem, and A.-C. Ngonga Ngomo, “IndQNER:
Named Entity Recognition Benchmark Dataset from the Indonesian Translation of the Quran,”
in Natural Language Processing and Information Systems, Cham: Springer
Nature Switzerland, 2023.'
mla: 'Gusmita, Ria Hari, et al. “IndQNER: Named Entity Recognition Benchmark Dataset
from the Indonesian Translation of the Quran.” Natural Language Processing
and Information Systems, Springer Nature Switzerland, 2023, doi:10.1007/978-3-031-35320-8_12.'
short: 'R.H. Gusmita, A.F. Firmansyah, D. Moussallem, A.-C. Ngonga Ngomo, in: Natural
Language Processing and Information Systems, Springer Nature Switzerland, Cham,
2023.'
conference:
end_date: 2023-06-23
location: Derby, UK
name: International Conference on Applications of Natural Language to Information
Systems (NLDB) 2023
start_date: 2023-06-21
date_created: 2023-08-17T12:41:45Z
date_updated: 2024-11-19T15:41:34Z
department:
- _id: '34'
- _id: '574'
doi: 10.1007/978-3-031-35320-8_12
keyword:
- NER benchmark dataset
- Indonesian
- specific domain
language:
- iso: eng
place: Cham
publication: Natural Language Processing and Information Systems
publication_identifier:
isbn:
- '9783031353192'
- '9783031353208'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer Nature Switzerland
related_material:
link:
- relation: confirmation
url: https://link.springer.com/chapter/10.1007/978-3-031-35320-8_12
status: public
title: 'IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian Translation
of the Quran'
type: book_chapter
user_id: '71039'
year: '2023'
...
---
_id: '32409'
abstract:
- lang: eng
text: 'Context: Cryptographic APIs are often misused in real-world applications.
Therefore, many cryptographic API misuse detection tools have been introduced.
However, there exists no established reference benchmark for a fair and comprehensive
comparison and evaluation of these tools. While there are benchmarks, they often
only address a subset of the domain or were only used to evaluate a subset of
existing misuse detection tools. Objective: To fairly compare cryptographic API
misuse detection tools and to drive future development in this domain, we will
devise such a benchmark. Openness and transparency in the generation process are
key factors to fairly generate and establish the needed benchmark. Method: We
propose an approach where we derive the benchmark generation methodology from
the literature which consists of general best practices in benchmarking and domain-specific
benchmark generation. A part of this methodology is transparency and openness
of the generation process, which is achieved by pre-registering this work. Based
on our methodology we design CamBench, a fair "Cryptographic API Misuse Detection
Tool Benchmark Suite". We will implement the first version of CamBench limiting
the domain to Java, the JCA, and static analyses. Finally, we will use CamBench
to compare current misuse detection tools and compare CamBench to related benchmarks
of its domain.'
author:
- first_name: Michael
full_name: Schlichtig, Michael
id: '32312'
last_name: Schlichtig
orcid: 0000-0001-6600-6171
- first_name: Anna-Katharina
full_name: Wickert, Anna-Katharina
last_name: Wickert
- first_name: Stefan
full_name: Krüger, Stefan
last_name: Krüger
- first_name: Eric
full_name: Bodden, Eric
id: '59256'
last_name: Bodden
orcid: 0000-0003-3470-3647
- first_name: Mira
full_name: Mezini, Mira
last_name: Mezini
citation:
ama: Schlichtig M, Wickert A-K, Krüger S, Bodden E, Mezini M. CamBench -- Cryptographic
API Misuse Detection Tool Benchmark Suite.; 2022. doi:10.48550/ARXIV.2204.06447
apa: Schlichtig, M., Wickert, A.-K., Krüger, S., Bodden, E., & Mezini, M. (2022).
CamBench -- Cryptographic API Misuse Detection Tool Benchmark Suite. https://doi.org/10.48550/ARXIV.2204.06447
bibtex: '@book{Schlichtig_Wickert_Krüger_Bodden_Mezini_2022, title={CamBench --
Cryptographic API Misuse Detection Tool Benchmark Suite}, DOI={10.48550/ARXIV.2204.06447},
author={Schlichtig, Michael and Wickert, Anna-Katharina and Krüger, Stefan and
Bodden, Eric and Mezini, Mira}, year={2022} }'
chicago: Schlichtig, Michael, Anna-Katharina Wickert, Stefan Krüger, Eric Bodden,
and Mira Mezini. CamBench -- Cryptographic API Misuse Detection Tool Benchmark
Suite, 2022. https://doi.org/10.48550/ARXIV.2204.06447.
ieee: M. Schlichtig, A.-K. Wickert, S. Krüger, E. Bodden, and M. Mezini, CamBench
-- Cryptographic API Misuse Detection Tool Benchmark Suite. 2022.
mla: Schlichtig, Michael, et al. CamBench -- Cryptographic API Misuse Detection
Tool Benchmark Suite. 2022, doi:10.48550/ARXIV.2204.06447.
short: M. Schlichtig, A.-K. Wickert, S. Krüger, E. Bodden, M. Mezini, CamBench --
Cryptographic API Misuse Detection Tool Benchmark Suite, 2022.
date_created: 2022-07-25T07:56:59Z
date_updated: 2022-07-25T10:23:44Z
department:
- _id: '76'
doi: 10.48550/ARXIV.2204.06447
keyword:
- cryptography
- benchmark
- API misuse
- static analysis
language:
- iso: eng
related_material:
link:
- relation: confirmation
url: https://arxiv.org/abs/2204.06447
status: public
title: CamBench -- Cryptographic API Misuse Detection Tool Benchmark Suite
type: misc
user_id: '32312'
year: '2022'
...
---
_id: '15838'
abstract:
- lang: eng
text: In the field of software analysis a trade-off between scalability and accuracy
always exists. In this respect, Android app analysis is no exception, in particular,
analyzing large or many apps can be challenging. Dealing with many small apps
is a typical challenge when facing micro-benchmarks such as DROIDBENCH or ICC-BENCH.
These particular benchmarks are not only used for the evaluation of novel tools
but also in continuous integration pipelines of existing mature tools to maintain
and guarantee a certain quality-level. Considering this latter usage it becomes
very important to be able to achieve benchmark results as fast as possible. Hence,
benchmarks have to be optimized for this purpose. One approach to do so is app
merging. We implemented the Android Merge Tool (AMT) following this approach and
show that its novel aspects can be used to produce scaled up and accurate benchmarks.
For such benchmarks Android app analysis tools do not suffer from the scalability-accuracy
trade-off anymore. We show this throughout detailed experiments on DROIDBENCH
employing three different analysis tools (AMANDROID, ICCTA, FLOWDROID). Benchmark
execution times are largely reduced without losing benchmark accuracy. Moreover,
we argue why AMT is an advantageous successor of the state-of-the-art app merging
tool (APKCOMBINER) in analysis lift-up scenarios.
author:
- first_name: Felix
full_name: Pauck, Felix
id: '22398'
last_name: Pauck
- first_name: Shikun
full_name: Zhang, Shikun
last_name: Zhang
citation:
ama: 'Pauck F, Zhang S. Android App Merging for Benchmark Speed-Up and Analysis
Lift-Up. In: 2019 34th IEEE/ACM International Conference on Automated Software
Engineering Workshop (ASEW). ; 2019. doi:10.1109/asew.2019.00019'
apa: Pauck, F., & Zhang, S. (2019). Android App Merging for Benchmark Speed-Up
and Analysis Lift-Up. In 2019 34th IEEE/ACM International Conference on Automated
Software Engineering Workshop (ASEW). https://doi.org/10.1109/asew.2019.00019
bibtex: '@inproceedings{Pauck_Zhang_2019, title={Android App Merging for Benchmark
Speed-Up and Analysis Lift-Up}, DOI={10.1109/asew.2019.00019},
booktitle={2019 34th IEEE/ACM International Conference on Automated Software Engineering
Workshop (ASEW)}, author={Pauck, Felix and Zhang, Shikun}, year={2019} }'
chicago: Pauck, Felix, and Shikun Zhang. “Android App Merging for Benchmark Speed-Up
and Analysis Lift-Up.” In 2019 34th IEEE/ACM International Conference on Automated
Software Engineering Workshop (ASEW), 2019. https://doi.org/10.1109/asew.2019.00019.
ieee: F. Pauck and S. Zhang, “Android App Merging for Benchmark Speed-Up and Analysis
Lift-Up,” in 2019 34th IEEE/ACM International Conference on Automated Software
Engineering Workshop (ASEW), 2019.
mla: Pauck, Felix, and Shikun Zhang. “Android App Merging for Benchmark Speed-Up
and Analysis Lift-Up.” 2019 34th IEEE/ACM International Conference on Automated
Software Engineering Workshop (ASEW), 2019, doi:10.1109/asew.2019.00019.
short: 'F. Pauck, S. Zhang, in: 2019 34th IEEE/ACM International Conference on Automated
Software Engineering Workshop (ASEW), 2019.'
date_created: 2020-02-06T17:06:51Z
date_updated: 2022-01-06T06:52:38Z
ddc:
- '004'
department:
- _id: '77'
doi: 10.1109/asew.2019.00019
file:
- access_level: closed
content_type: application/pdf
creator: fpauck
date_created: 2020-02-06T17:09:45Z
date_updated: 2020-02-06T17:09:45Z
file_id: '15839'
file_name: AMT_final.pdf
file_size: 644517
relation: main_file
file_date_updated: 2020-02-06T17:09:45Z
has_accepted_license: '1'
keyword:
- Program Analysis
- Android App Analysis
- Taint Analysis
- App Merging
- Benchmark
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '12'
name: SFB 901 - Subproject B4
publication: 2019 34th IEEE/ACM International Conference on Automated Software Engineering
Workshop (ASEW)
publication_identifier:
isbn:
- '9781728141367'
publication_status: published
status: public
title: Android App Merging for Benchmark Speed-Up and Analysis Lift-Up
type: conference
user_id: '477'
year: '2019'
...
---
_id: '56579'
abstract:
- lang: eng
text: Question answering engines have become one of the most popular type of applications
driven by Semantic Web technologies. Consequently, the provision of means to quantify
the performance of current question answering approaches on current datasets has
become ever more important. However, a large percentage of the queries found in
popular question answering benchmarks cannot be executed on current versions of
their reference dataset. There is a consequently a clear need to curate question
answering benchmarks periodically. However, the manual alteration of question
answering benchmarks is often error-prone. We alleviate this problem by presenting
QUANT, a novel framework for the creation and curation of question answering benchmarks.
QUANT sup-ports the curation of benchmarks by generating smart edit suggestions
for question-query pair and for the corresponding metadata. In addition, our framework
supports the creation of new benchmark entries by pro-viding predefined quality
checks for queries. We evaluate QUANT on 653questions obtained from QALD-1 to
QALD-8 with 10 users. Our results show that our framework generates reliable suggestions
and can reduce the curation effort for QA benchmarks by up to 91%.
author:
- first_name: Ria Hari
full_name: Gusmita, Ria Hari
id: '71039'
last_name: Gusmita
- first_name: Rricha
full_name: Jalota, Rricha
last_name: Jalota
- first_name: Daniel
full_name: Vollmers, Daniel
last_name: Vollmers
- first_name: Jan
full_name: Reineke, Jan
last_name: Reineke
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
- first_name: Ricardo
full_name: Usbeck, Ricardo
last_name: Usbeck
citation:
ama: 'Gusmita RH, Jalota R, Vollmers D, Reineke J, Ngonga Ngomo A-C, Usbeck R. QUANT
- Question Answering Benchmark Curator. In: Acosta M, Cudr{\’e}-Mauroux P, Maleshkova
M, Pellegrini T, Sack H, Sure-Vetter Y, eds. Semantic Systems. The Power of
AI and Knowledge Graphs. Springer International Publishing; 2019:343--358.
doi:10.1007/978-3-030-33220-4_25'
apa: Gusmita, R. H., Jalota, R., Vollmers, D., Reineke, J., Ngonga Ngomo, A.-C.,
& Usbeck, R. (2019). QUANT - Question Answering Benchmark Curator. In M. Acosta,
P. Cudr{\’e}-Mauroux, M. Maleshkova, T. Pellegrini, H. Sack, & Y. Sure-Vetter
(Eds.), Semantic Systems. The Power of AI and Knowledge Graphs (pp. 343--358).
Springer International Publishing. https://doi.org/10.1007/978-3-030-33220-4_25
bibtex: '@inbook{Gusmita_Jalota_Vollmers_Reineke_Ngonga Ngomo_Usbeck_2019, place={Cham},
title={QUANT - Question Answering Benchmark Curator}, DOI={10.1007/978-3-030-33220-4_25},
booktitle={Semantic Systems. The Power of AI and Knowledge Graphs}, publisher={Springer
International Publishing}, author={Gusmita, Ria Hari and Jalota, Rricha and Vollmers,
Daniel and Reineke, Jan and Ngonga Ngomo, Axel-Cyrille and Usbeck, Ricardo}, editor={Acosta,
Maribel and Cudr{\’e}-Mauroux, Philippe and Maleshkova, Maria and Pellegrini,
Tassilo and Sack, Harald and Sure-Vetter, York}, year={2019}, pages={343--358}
}'
chicago: 'Gusmita, Ria Hari, Rricha Jalota, Daniel Vollmers, Jan Reineke, Axel-Cyrille
Ngonga Ngomo, and Ricardo Usbeck. “QUANT - Question Answering Benchmark Curator.”
In Semantic Systems. The Power of AI and Knowledge Graphs, edited by Maribel
Acosta, Philippe Cudr{\’e}-Mauroux, Maria Maleshkova, Tassilo Pellegrini, Harald
Sack, and York Sure-Vetter, 343--358. Cham: Springer International Publishing,
2019. https://doi.org/10.1007/978-3-030-33220-4_25.'
ieee: 'R. H. Gusmita, R. Jalota, D. Vollmers, J. Reineke, A.-C. Ngonga Ngomo, and
R. Usbeck, “QUANT - Question Answering Benchmark Curator,” in Semantic Systems.
The Power of AI and Knowledge Graphs, M. Acosta, P. Cudr{\’e}-Mauroux, M.
Maleshkova, T. Pellegrini, H. Sack, and Y. Sure-Vetter, Eds. Cham: Springer International
Publishing, 2019, pp. 343--358.'
mla: Gusmita, Ria Hari, et al. “QUANT - Question Answering Benchmark Curator.” Semantic
Systems. The Power of AI and Knowledge Graphs, edited by Maribel Acosta et
al., Springer International Publishing, 2019, pp. 343--358, doi:10.1007/978-3-030-33220-4_25.
short: 'R.H. Gusmita, R. Jalota, D. Vollmers, J. Reineke, A.-C. Ngonga Ngomo, R.
Usbeck, in: M. Acosta, P. Cudr{\’e}-Mauroux, M. Maleshkova, T. Pellegrini, H.
Sack, Y. Sure-Vetter (Eds.), Semantic Systems. The Power of AI and Knowledge Graphs,
Springer International Publishing, Cham, 2019, pp. 343--358.'
conference:
end_date: 2019-09-12
location: Karlsruhe, Germany
name: SEMANTiCS 2019
start_date: 2019-09-09
date_created: 2024-10-10T14:25:00Z
date_updated: 2024-11-19T15:34:24Z
ddc:
- '004'
doi: 10.1007/978-3-030-33220-4_25
editor:
- first_name: Maribel
full_name: Acosta, Maribel
last_name: Acosta
- first_name: Philippe
full_name: Cudr{\'e}-Mauroux, Philippe
last_name: Cudr{\'e}-Mauroux
- first_name: Maria
full_name: Maleshkova, Maria
last_name: Maleshkova
- first_name: Tassilo
full_name: Pellegrini, Tassilo
last_name: Pellegrini
- first_name: Harald
full_name: Sack, Harald
last_name: Sack
- first_name: York
full_name: Sure-Vetter, York
last_name: Sure-Vetter
file:
- access_level: closed
content_type: application/pdf
creator: gusmita
date_created: 2024-11-19T15:28:45Z
date_updated: 2024-11-19T15:28:45Z
file_id: '57244'
file_name: Gusmita_et_al-2019-Semantic_Systems._The_Power_of_AI_and_Knowledge_Graphs,_15th_International....pdf
file_size: 1179910
relation: main_file
success: 1
file_date_updated: 2024-11-19T15:28:45Z
has_accepted_license: '1'
keyword:
- Benchmark
- Question answering
- Knowledge base
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://rdcu.be/d0KJc
oa: '1'
page: 343--358
place: Cham
publication: Semantic Systems. The Power of AI and Knowledge Graphs
publication_identifier:
eisbn:
- 978-3-030-33220-4
isbn:
- 978-3-030-33219-8
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer International Publishing
status: public
title: QUANT - Question Answering Benchmark Curator
type: book_chapter
user_id: '71039'
year: '2019'
...
---
_id: '2432'
abstract:
- lang: eng
text: In this paper, we present the analysis of applications from the domain of
handheld and wearable computing. This analysis is the first step to derive and
evaluate design parameters for dynamically reconfigurable processors. We discuss
the selection of representative benchmarks for handhelds and wearables and group
the applications into multimedia, communications, and cryptography programs. We
simulate the applications on a cycle-accurate processor simulator and gather statistical
data such as instruction mix, cache hit rates and memory requirements for an embedded
processor model. A breakdown of the executed cycles into different functions identifies
the most compute-intensive code sections - the kernels. Then, we analyze the applications
and discuss parameters that strongly influence the design of dynamically reconfigurable
processors. Finally, we outline the construction of a parameterizable simulation
model for a reconfigurable unit that is attached to a processor core.
author:
- first_name: Rolf
full_name: Enzler, Rolf
last_name: Enzler
- first_name: Marco
full_name: Platzner, Marco
id: '398'
last_name: Platzner
- first_name: Christian
full_name: Plessl, Christian
id: '16153'
last_name: Plessl
orcid: 0000-0001-5728-9982
- first_name: Lothar
full_name: Thiele, Lothar
last_name: Thiele
- first_name: Gerhard
full_name: Tröster, Gerhard
last_name: Tröster
citation:
ama: 'Enzler R, Platzner M, Plessl C, Thiele L, Tröster G. Reconfigurable Processors
for Handhelds and Wearables: Application Analysis. In: Reconfigurable Technology:
FPGAs and Reconfigurable Processors for Computing and Communications III.
Vol 4525. Proc. SPIE. ; 2001:135-146. doi:10.1117/12.434376'
apa: 'Enzler, R., Platzner, M., Plessl, C., Thiele, L., & Tröster, G. (2001).
Reconfigurable Processors for Handhelds and Wearables: Application Analysis. In
Reconfigurable Technology: FPGAs and Reconfigurable Processors for Computing
and Communications III (Vol. 4525, pp. 135–146). https://doi.org/10.1117/12.434376'
bibtex: '@inproceedings{Enzler_Platzner_Plessl_Thiele_Tröster_2001, series={Proc.
SPIE}, title={Reconfigurable Processors for Handhelds and Wearables: Application
Analysis}, volume={4525}, DOI={10.1117/12.434376},
booktitle={Reconfigurable Technology: FPGAs and Reconfigurable Processors for
Computing and Communications III}, author={Enzler, Rolf and Platzner, Marco and
Plessl, Christian and Thiele, Lothar and Tröster, Gerhard}, year={2001}, pages={135–146},
collection={Proc. SPIE} }'
chicago: 'Enzler, Rolf, Marco Platzner, Christian Plessl, Lothar Thiele, and Gerhard
Tröster. “Reconfigurable Processors for Handhelds and Wearables: Application Analysis.”
In Reconfigurable Technology: FPGAs and Reconfigurable Processors for Computing
and Communications III, 4525:135–46. Proc. SPIE, 2001. https://doi.org/10.1117/12.434376.'
ieee: 'R. Enzler, M. Platzner, C. Plessl, L. Thiele, and G. Tröster, “Reconfigurable
Processors for Handhelds and Wearables: Application Analysis,” in Reconfigurable
Technology: FPGAs and Reconfigurable Processors for Computing and Communications
III, 2001, vol. 4525, pp. 135–146.'
mla: 'Enzler, Rolf, et al. “Reconfigurable Processors for Handhelds and Wearables:
Application Analysis.” Reconfigurable Technology: FPGAs and Reconfigurable
Processors for Computing and Communications III, vol. 4525, 2001, pp. 135–46,
doi:10.1117/12.434376.'
short: 'R. Enzler, M. Platzner, C. Plessl, L. Thiele, G. Tröster, in: Reconfigurable
Technology: FPGAs and Reconfigurable Processors for Computing and Communications
III, 2001, pp. 135–146.'
date_created: 2018-04-17T15:51:39Z
date_updated: 2022-01-06T06:56:17Z
department:
- _id: '518'
- _id: '78'
doi: 10.1117/12.434376
intvolume: ' 4525'
keyword:
- benchmark
page: 135-146
publication: 'Reconfigurable Technology: FPGAs and Reconfigurable Processors for Computing
and Communications III'
series_title: Proc. SPIE
status: public
title: 'Reconfigurable Processors for Handhelds and Wearables: Application Analysis'
type: conference
user_id: '24135'
volume: 4525
year: '2001'
...