Welcome to ICCSEA 2024

14th International Conference on
Computer Science, Engineering and
Applications (ICCSEA 2024)

November 16 ~ 17, 2024, Zurich, Switzerland



Accepted Papers
Inclusivity in Large Language Models: Personality Traits and Gender Bias in Scientific Abstracts

Naseela Pervez1 and Alexander J. Titus1,2,3, 1Information Sciences Institute, University of Southern California, 2Iovine and Young Academy, University of Southern California, 3In Vivo Group

ABSTRACT

Large language models (LLMs) are increasingly utilized to assist in scientific and academic writing, helping authors enhance the coherence of their articles. Previous studies have highlighted stereotypes and biases present in LLM outputs, emphasizing the need to evaluate these models for their alignment with human narrative styles and potential gender biases. In this study, we assess the alignment of three prominent LLMs—Claude 3 Opus, Mistral AI Large, and Gemini 1.5 Flash—by analyzing their performance on benchmark text-generation tasks for scientific abstracts. We employ the Linguistic Inquiry and Word Count (LIWC) framework to extract lexical, psychological, and social features from the generated texts. Our findings indicate that, while these models generally produce text closely resembling human-authored content, variations in stylistic features suggest significant gender biases. This research highlights the importance of developing LLMs that maintain a diversity of writing styles to promote inclusivity in academic discourse.

Keywords

Large Language Models (LLMs), Text Generation, Gender Bias, Linguistic Inquiry and Word Count (LIWC), Computational Linguistics.


Hyperparameter Optimization for Search Relevance in E-commerce

Manuel Dalcastagn´e and Giuseppe Di Fabbrizio, VUI, Inc., Boston, USA

ABSTRACT

The configuration of retrieval and ranking strategies in search engines is traditionally done manually by search experts in a time-consuming and often irreproducible process. A typical use case is field boosting in keyword-based search, where the weights of different fields are tuned in an endless trial-and-error process to obtain what seems to be the best possible results on a small set of manually picked user queries that do not always generalize as expected. Hyperparameter optimization (HPO) methods can be employed to automatically tune search engines and solve these problems. To the best of our knowledge, there has been little work in the research community regarding the application of HPO to search relevance in e-commerce. This study demonstrates the effectiveness of HPO techniques for search relevance in e-commerce and provides insights into the impact of field boosting, retrieval query structure, and query understanding on relevance. Differential evolution (DE) optimization achieves up to 13% improvement in terms of NDCG@10 over baseline search configurations on a publicly available dataset. Also, we provide guidelines on the application of HPO to search relevance in e-commerce, addressing the characteristics of search spaces, the multifidelity of objective functions, and the use of more than one metric for multi-objective optimization.

Keywords

Hyperparameter optimization, differential evolution, e-commerce search relevance optimization.


Scalable Query Understanding for E-commerce: an Ensemble Architecture With Graph-based Optimization

Manuel Dalcastagn´e and Giuseppe Di Fabbrizio, VUI, Inc., Boston, USA

ABSTRACT

Query understanding is a critical component of e-commerce platforms, enabling accurate interpretation of users’ intents and efficient retrieval of relevant products. This paper presents a study on scalable query understanding techniques applied to a real use case in the e-commerce grocery domain. We propose a novel architecture that combines deep learning models with traditional ML models to capture query nuances and provide robust performance. Our model ensemble approach aims to capture the nuances of user queries and provide robust performance across various query types and categories. We conduct experiments on real-life datasets and demonstrate the effectiveness of our proposed solution in terms of accuracy and scalability. An optimized graphbased architecture using Ray enables efficient processing of high-volume traffic. The experimental results highlight the benefits of combining diverse models.

Keywords

Query classification, query understanding, distributed and scalable machine learning.


Distributed Blockchain-based Firmware Update Architecture for Iot Environments

Jes´us Rugarc1, Santiago Figueroa-Lorenzo2,3, Saioa Arrizabalaga2,3, and Nasibeh Mohammadzadeh2, 1University of the Basque Country UPV/EHU, Donostia / San Sebasti´an-20018, Spain, 2CEIT-Basque Research and Technology Alliance (BRTA), Donostia / San Sebasti´an-20018, Spain, 3School of Engineering, University of Navarra, Tecnun, Donostia / San Sebasti´an-20018, Spain

ABSTRACT

The Internet of Things (IoT) is one of the most rapidly expanding fields of technology. IoT devices often have limited capabilities when it comes to security, and have been shown to have vulnerabilities that are often exploited by malicious agents. To fix those vulnerabilities, firmware updates are often needed. The process, however, can also be vulnerable. A secure update mechanism is needed to create a more secure IoT environment. This paper proposes a secure distributed IOT firmware update solution using Hyperledger Fabric Blockchain and IPFS based on the RFC 9019 and previously proposed frameworks, contributing with a strong manifest format and defining authentication and verification procedures. More importantly, we provide a public implementation on which performance tests were made, demonstrating the promising feasibility of using distributed ledger technologies for this problem.

Keywords

IoT, Hyperledger Fabric Blockchain, Security, Distributed solution, Firmware update.


Clustering Solidity Smart Contracts by Similarity

Ansumana F Jadama and Aditya Dilip Thakur, Faculty of Computer Science, University of New Brunswick Fredericton, NB, Canada

ABSTRACT

This paper addresses the challenging task of clustering source code files within Ethereum smart contracts. The intricate structure of these files, encompassing contracts, interfaces, and libraries, presents significant challenges in identifying syntactic similarities. Our methodology employs a detailed analysis of structural, behavioral, and contextual characteristics, integrating both syntactic and semantic features. The objective is to effectively cluster source code files, thereby facilitating a deeper understanding and systematic categorization of smart contracts. This comprehensive approach aims to enhance insights into the architectural patterns and functionalities of blockchain applications, supporting improved governance and management of these systems.

Keywords

Smart Contracts, Blockchain, Source Code Clustering, Syntactic Similarity, Semantic Features.


Development of a Co-design Architecture (Hardware/software) for Real-time Video Encryption Based Chaos

SID Hichem1 and AZZAZ Mohamed Salah1, SADOUDI Said2, 1Electronic and Digital Systems Laboratory, EMP, Algiers, Algeria, 2Telecommunications Laboratory, EMP, Algiers, Algeria

ABSTRACT

The article presents a novel Codesign Architecture (Hardware/Software) for Real-Time Video Encryption based on Chaos. It features an auto-switched Hybrid Chaotic Key Generator integrated into a flowsymmetric cryptosystem for encrypting video streams. Using the Genesys-2 FPGA platform and Pmod CAM-OV7670 camera, the system ensures synchronized key parameters for accurate decryption. The architecture addresses key availability challenges while balancing security, performance, hardware resources and a high level of security of the real-time video stream. Experimental results demonstrate its efficacy for efficient embedded ciphering communication systems specially for real-time video stream.

Keywords

Video, DSP, Chaos, Key Generator, RNG, Cryptography, NIST, Xilinx, Vivado, FPGA, Embeded system Genesys 2, VHDL, real-time, Vernam OTP, symmetric flow, synchronisation.