Publications (277)
ARTICLE
Détection de communautés dans les graphes de connaissance d'activités
Marthe Désirée Olivia HABACK , Serge SONFACK SOUNCHIO , Orlane SONKENG TSAFACK , Halguièta NASSA TRAWINA , Ho Tuong Vinh
La gestion des connaissances produites au cours des activités au sein des organisations joue un rôle important dans leur développement et leur succès. La formalisation de ces connaissances sous forme de graphe de connaissance d’activités (Activity Knowledge Graph : AKG) permet de représenter et de réutiliser ces connaissances pour résoudre des(...)
Graphe de connaissance, graphe de connaissance d’activité, détection de communauté, algorithme de Louvain
ARTICLE
How Are We Detecting Inconsistent Method Names? An Empirical Study from Code Review Perspective
Kisub Kim, Xin Zhou, Dongsun Kim, Julia Lawall, Kui Liu, Tegawende F. Bissyande, Jacques Klein, Jaekwon Lee, David Lo
Proper naming of methods can make program code easier to understand, and thus enhance software maintainability. Yet, developers may use inconsistent names due to poor communication or a lack of familiarity with conventions within the software development lifecycle. To address this issue, much research effort has been invested into building aut(...)
Inconsistent
ARTICLE
LLM for Mobile: An Initial Roadmap
Daihang Chen, Yonghui Liu, Mingyi Zhou, Yanjie Zhao, Haoyu Wang, Shuai Wang, Xiao Chen, Tegawendé F. Bissyandé, Jacques Klein, Li Li
When mobile meets LLMs, mobile app users deserve to have more intelligent usage experiences. For this to happen, we argue that there is a strong need to apply LLMs for the mobile ecosystem. We therefore provide a research roadmap for guiding our fellow researchers to achieve that as a whole. In this roadmap, we sum up six directions that we be(...)
LLM
ARTICLE
Just-in-Time Detection of Silent Security Patches
Xunzhu Tang, Kisub Kim, Saad Ezzini, Yewei Song, Haoye Tian, Jacques Klein, Tegawende Bissyande
Open-source code is pervasive. In this setting, embedded vulnerabilities are spreading to downstream software at an alarming rate. Although such vulnerabilities are generally identified and addressed rapidly, inconsistent maintenance policies can cause security patches to go unnoticed. Indeed, security patches can be silent, i.e., they do not(...)
Patches
ARTICLE
Performance of the Large Language Models in African rheumatology: a diagnostic test accuracy study of ChatGPT-4, Gemini, Copilot, and Claude artificial intelligence.
Yannick Laurent Tchenadoyo Bayala, Wendlassida Joelle Stéphanie Zabsonré/Tiendrebeogo, Dieu- Donné Ouedraogo , Fulgence Kaboré , Charles Sougué , Aristide Relwendé Yameogo , Wendlassida Martin Nacanabo , Ismael Ayouba Tinni, Aboubakar Ouedraogo and Yamyellé Enselme Zongo.
Background: Artificial intelligence (AI) tools, particularly Large Language Models (LLMs), are revolutionizing medical practice, including rheumatology. However, their diagnostic capabilities remain underexplored in the African context. To assess the diagnostic accuracy of ChatGPT-4, Gemini, Copilot, and Claude AI in rheumatology within an Afr(...)
Africa; Artificial intelligence; Diagnostic accuracy; Large Language Models; Rheumatology
ARTICLE
A large-scale LPWAN Architecture for Multimedia Data Collection in High-security Challenge Areas
OUATTARA Yacouba, PODA Pasteur et TRAORE Mamouta
The Internet of Things (IoT) is rapidly expanding, including in conflict zones where security is
critical. Sound signals used for intelligence in the Middle East, combined with Sahel challenges from poor
infrastructure, emphasize the need for better data collection. This paper proposes a secure IoT architecture
using Low Power Wide Area Net(...)
LPWAN, LoRaWAN, Architecture, Design, Terrorism, Steganography, Military.
ARTICLE
Temporal-Incremental Learning for Android Malware Detection
Tiezhu Sun, Nadia Daoudi, Weiguo Pian, Kisub Kim, Kevin Allix, Tegawendé F. Bissyandé, Jacques Klein
Malware classification is a specific and refined task within the broader malware detection problem. Effective classification aids in understanding attack techniques and developing robust defenses, ensuring application security and timely mitigation of software vulnerabilities. The dynamic nature of malware demands adaptive classification techn(...)
Android Malware
ARTICLE
Drop-in efficient self-attention approximation method
Damien François, Mathis Saillot, Jacques Klein, Tegawendé F. Bissyandé, Alexander Skupin
Transformers have achieved state-of-the-art performance in most common tasks to which they have been applied. Those achievements are attributed to the Self-Attention mechanism at their core. Self-Attention is understood to map the relationship between tokens of any given sequence. This exhaustive mapping incurs massive costs in memory and infe(...)
method
ARTICLE
Towards a Single-Sign-On Authentication Architecture Based on OpenID Connect Protocol and Blockchain Technology
Assane Ilboudo, Didier Bassole, Justin P. Kouraogo, Gouayon Koala, Oumarou Sie
OpenID Connect is a delegated authentication protocol used in web and mobile applications. It emphasizes the crucial role of third-party applications, also known as Relying Parties, which securely request information from an identity provider. In this article, we have proposed an approach based on the Ethereum blockchain to enhance the authent(...)
Authentication, Security, Blockchain, OpenID Connect
ARTICLE
A Low-Resource Language Translation: French to Mooré
Hamed Joseph Ouily, Aminata Sabané, Delwende Eliane Birba, Rodrique Kafando, Abdoul-Kader Kabore, Tegawendé F. Bissyandé
Natural Language Processing (NLP) is an exciting field of artificial intelligence with the goal of enabling machines to understand human language in a natural way. Neural Machine Translation (NMT) stands out as one of the most promising applications of NLP, offering the ability to effectively translate text from a source language to a target l(...)
Translation (biology), Linguistics, Resource (disambiguation), Computer science, Philosophy, Biology, Computer network
ARTICLE
5G NR Uplink Performance Optimization: A Comprehensive Study on PRACH and PUSCH Interference Management
Désiré GUEL, Flavien Hervé SOMDA, Boureima Zerbo, Oumarou Sié
The evolution of 5G New Radio (NR) technology offers unprecedented speeds, ultra-low latency, and the
capability to connect billions of devices. However, these advancements come with significant challenges,
particularly in managing interference during uplink communication. This study presents a comprehensive
investigation into the optimizat(...)
5G-NR , Physical Random Access Channel (PRACH), PUSCH, Intra/Inter-Cell Interference, Quality of Service (QoS), Block Error Rate (BLER)
ARTICLE
5G NR Uplink Performance Optimization A Comprehensive Study on PRACH and PUSCH Interference Management
Désiré Guel, P. Justin Kouraogo, Flavien Hervé Somda, Boureima Zerbo, Oumarou Sié
The evolution of 5G New Radio (NR) technology offers unprecedented speeds, ultra-low latency, and the capability to connect billions of devices. However, these advancements come with significant challenges, particularly in managing interference during uplink communication. This study presents a comprehensive investigation into the optimization(...)
5G-NR , Physical Random Access Channel (PRACH), PUSCH, Intra/Inter-Cell Interference, Quality of Service (QoS), Block Error Rate (BLER)
ARTICLE
Revisiting the Non-Determinism of Code Generation by the GPT-3.5 Large Language Model
Salimata Sawadogo, Aminata Sabané, Rodrique Kafando, Tegawendé F. Bissyandé
Despite recent advancements in Large Language Models (LLMs) for code generation, their inherent non-determinism remains a significant obstacle for reliable and reproducible software engineering research. Prior work has highlighted the high degree of variability in LLM-generated code, even when prompted with identical inputs. This non-determini(...)
LLM, TOT, GPT
ARTICLE
IoT Devices Security Improvement Based on Collaborative Intrusion Detection System and Blockchain Technology
Yempabou Yves Stéphans Loari; Didier Bassole; Leonard M. Sawadogo; Gouayon Koala; Oumarou Sié
IoT is one of today's major growth areas. With its low computing power and massively exchanged heterogeneous data, it is vulnerable to numerous at-tacks. In this paper, we proposed a collaborative system based on hybrid intrusion detection and blockchain technology to improve IoT devices security. Thus, we simulated a federated learning using(...)
IoT security, federated learning, CIDS, Blockchain
ARTICLE
Enriching automatic test case generation by extracting relevant test inputs from bug reports
Wendkûuni C. Ouédraogo, Laura Plein, Kader Kaboré, Andrew Habib, Jacques Klein, David Lo, Tegawendé F. Bissyandé
Abstract The quality of software is closely tied to the effectiveness of the tests it undergoes. Manual test writing, though crucial for bug detection, is time-consuming, which has driven significant research into automated test case generation. However, current methods often struggle to generate relevant inputs, limiting the effectiveness of(...)
Test (biology), Computer science, Natural language processing, Artificial intelligence, Data mining, Reliability engineering, Engineering, Geology