Papers | Parallel Computing

@inproceedings{23:mittone:multiview,

title = {Distributed Edge Inference: an Experimental Study on Multiview Detection},

author = {Gianluca Mittone and Giulio Malenza and Marco Aldinucci and Robert Birke},

url = {https://iris.unito.it/handle/2318/1950083},

doi = {10.1145/3603166.3632561},

year  = {2024},

date = {2024-12-01},

booktitle = {Proc. of the 16th IEEE/ACM Intl. Conference on Utility and Cloud Computing Companion (UCC)},

volume = {30},

pages = {1-6},

publisher = {ACM},

address = {Taormina, Italy},

institution = {Computer Science Department, University of Torino},

abstract = {Computing is evolving rapidly to cater to the increasing demand for sophisticated services, and Cloud computing lays a solid foundation for flexible on-demand provisioning. However, as the size of applications grows, the centralised client-server approach used by Cloud computing increasingly limits the applications' scalability. To achieve ultra-scalability, cloud/edge/fog computing converges into the compute continuum, completely decentralising the infrastructure to encompass universal, pervasive resources. The compute continuum makes devising applications benefitting from this complex environment a challenging research problem. We put the opportunities the compute continuum others to the test through a real-world multi-view detection model (MvDet) implemented with the FastFL C/C++ high-performance edge inference framework. Computational performance is discussed considering many experimental scenarios, encompassing different edge computational capabilities and network bandwidths. We obtain up to 1.92x speedup in inference time over a centralised solution using the same devices.},

note = {eupilot, icsc},

keywords = {ai, eupilot, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{24:casella:fedrec,

title = {Federated Learning in a Semi-Supervised Environment for Earth Observation Data},

author = {Bruno Casella and Alessio Barbaro Chisari and Marco Aldinucci and Sebastiano Battiato and Mario Valerio Giuffrida},

url = {https://iris.unito.it/retrieve/a798d7b8-6b98-48c2-92f4-327d2aaa8788/ES2024-214.pdf},

doi = {10.14428/esann/2024.es2024-214},

year  = {2024},

date = {2024-10-01},

booktitle = {Proceedings of the 32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN},

address = {Bruges, Belgium},

abstract = {We propose FedRec, a federated learning workflow taking advantage of unlabelled data in a semi-supervised environment to assist in the training of a supervised aggregated model. In our proposed method, an encoder architecture extracting features from unlabelled data is aggregated with the feature extractor of a classification model via weight averaging. The fully connected layers of the supervised models are also averaged in a federated fashion. We show the effectiveness of our approach by comparing it with the state-of-the-art federated algorithm, an isolated and a centralised baseline, on novel cloud detection datasets.},

keywords = {ai, epi, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{24:casella:frocks,

title = {Federated Time Series Classification with ROCKET features},

author = {Bruno Casella and Jakobs Matthias and Marco Aldinucci and Sebastian Buschjager},

url = {https://iris.unito.it/retrieve/51b63fc1-3e22-4ad4-8926-84af69cde739/ES2024-61.pdf},

doi = {10.14428/esann/2024.es2024-61},

year  = {2024},

date = {2024-10-01},

booktitle = {Proceedings of the 32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN},

address = {Bruges, Belgium},

abstract = {This paper proposes FROCKS, a federated time series classification method using ROCKET features. Our approach dynamically adapts the models’ features by selecting and exchanging the best-performing ROCKET kernels from a federation of clients. Specifically, the server gathers the best-performing kernels of the clients together with the associated model parameters, and it performs a weighted average if a kernel is best-performing for more than one client. We compare the proposed method with state-of-the-art approaches on the UCR archive binary classification datasets and show superior performance on most datasets.},

keywords = {ai, epi, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{24:esann:fonio:fedhp,

title = {FedHP: Federated Learning with Hyperspherical Prototypical Regularization},

author = {Samuele Fonio and Mirko Polato and Roberto Esposito},

url = {https://www.esann.org/sites/default/files/proceedings/2024/ES2024-183.pdf},

year  = {2024},

date = {2024-10-01},

booktitle = {32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, (ESANN)},

address = {Bruges, Belgium},

abstract = {This paper presents FedHP, an algorithm that amalgamates federated learning, hyperspherical geometries, and prototype learning. Federated Learning (FL) has garnered attention as a privacy-preserving method for constructing robust models across distributed datasets. Traditionally, FL involves exchanging model parameters to uphold data privacy; however, in scenarios with costly data communication, exchanging large neural net- work models becomes impractical. In such instances, prototype learning provides a feasible solution by necessitating the exchange of a few class prototypes instead of entire deep learning models. Motivated by these considerations, our approach leverages recent advancements in prototype learning, particularly the benefits offered by non-Euclidean geometries. Alongside introducing FedHP, we provide empirical evidence demonstrat- ing its comparable performance to other state-of-the-art approaches while significantly reducing communication costs.},

keywords = {ai, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{24:fm:swirl,

title = {Introducing SWIRL: An Intermediate Representation Language for Scientific Workflows},

author = {Iacopo Colonnelli and Doriana Medić and Alberto Mulone and Viviana Bono and Luca Padovani and Marco Aldinucci},

editor = {André Platzer and Kristin Yvonne Rozier and Matteo Pradella and Matteo Rossi},

url = {https://iris.unito.it/retrieve/b39a6f09-a8d3-4974-abf6-c109916694fa/PDFEditoriale.pdf},

doi = {10.1007/978-3-031-71162-6_12},

year  = {2024},

date = {2024-09-01},

booktitle = {Formal Methods. FM 2024},

volume = {14933},

pages = {226–244},

publisher = {Springer Nature Switzerland},

address = {Milano, Italy},

series = {Lecture Notes in Computer Science},

abstract = {In the ever-evolving landscape of scientific computing, properly supporting the modularity and complexity of modern scientific applications requires new approaches to workflow execution, like seamless interoperability between different workflow systems, distributed-by-design workflow models, and automatic optimisation of data movements. In order to address this need, this article introduces SWIRL, an intermediate representation language for scientific workflows. In contrast with other product-agnostic workflow languages, SWIRL is not designed for human interaction but to serve as a low-level compilation target for distributed workflow execution plans. The main advantages of SWIRL semantics are low-level primitives based on the send/receive programming model and a formal framework ensuring the consistency of the semantics and the specification of translating workflow models represented by Directed Acyclic Graphs (DAGs) into SWIRL workflow descriptions. Additionally, SWIRL offers rewriting rules designed to optimise execution traces, accompanied by corresponding equivalence. An open-source SWIRL compiler toolchain has been developed using the ANTLR Python3 bindings.},

keywords = {eupex, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{24:madrid:hipes,

title = {A Fault Tolerance mechanism for Hybrid Scientific Workflows},

author = {Alberto Mulone and Doriana Medić and Marco Aldinucci},

year  = {2024},

date = {2024-08-01},

booktitle = {1st workshop about High-Performance e-Science (HiPES)},

address = {Madrid, Spain},

abstract = {In large distributed systems, failures are a daily event occurring frequently, especially with growing numbers of computation tasks and locations on which they are deployed. The advantage of representing an application as a workflow is possibility to utilize the Workflow Management Systems which are reliable systems guaranteeing the correct execution of the application and providing the features such as portability, scalability, and fault tolerance. Over recent years, the emergence of hybrid workflows has posed new and intriguing challenges by increasing the possibility of distributing computations involving heterogeneous and independent environments. As a consequence, the number of possible points of failure in the execution augmented, creating different important challenges interesting to study.},

keywords = {eupex, icsc, streamflow},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{24:betianu:llm,

title = {DALLMi: Domain Adaption for LLM-based Multi-label Classifier},

author = {Miruna Bețianu and Abele Mălan and Marco Aldinucci and Robert Birke and Lydia Chen},

editor = {De-Nian Yang and Xing Xie and Vincent S. Tseng and Jian Pei and Jen-Wei Huang and Jerry Chun-Wei Lin},

url = {https://hdl.handle.net/2318/1976672},

doi = {10.1007/978-981-97-2259-4_21},

year  = {2024},

date = {2024-05-01},

booktitle = {Proceedings of the 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining},

volume = {14647},

pages = {277–289},

publisher = {Springer},

address = {Taipei, Taiwan},

series = {Lecture Notes in Computer Science},

abstract = {Large language models (LLMs) increasingly serve as the backbone for classifying text associated with distinct domains and simultaneously several labels (classes). When encountering domain shifts, e.g., classifier of movie reviews from IMDb to Rotten Tomatoes, adapting such an LLM-based multi-label classifier is challenging due to incomplete label sets at the target domain and daunting training overhead. The existing domain adaptation methods address either image multi-label classifiers or text binary classifiers. In this paper, we design DALLMi, Domain Adaptation Large Language Model interpolator, a first-of-its-kind semi-supervised domain adaptation method for text data models based on LLMs, specifically BERT. The core of DALLMi is the novel variation loss and MixUp regularization, which jointly leverage the limited positively labeled and large quantity of unlabeled text and, importantly, their interpolation from the BERT word embeddings. DALLMi also introduces a label-balanced sampling strategy to overcome the imbalance between labeled and unlabeled data. We evaluate DALLMi against the partial-supervised and unsupervised approach on three datasets under different scenarios of label availability for the target domain. Our results show that DALLMi achieves higher mAP than unsupervised and partially-supervised approaches by 19.9% and 52.2%, respectively.},

keywords = {ai, eupilot, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{24:chen:llm,

title = {On Dark Knowledge for Distilling Generators},

author = {Chi Hong and Robert Birke and Pin-Yu Chen and Lydia Chen},

editor = {De-Nian Yang and Xing Xie and Vincent S. Tseng and Jian Pei and Jen-Wei Huang and Jerry Chun-Wei Lin},

url = {https://hdl.handle.net/2318/1976671},

doi = {10.1007/978-981-97-2253-2_19},

year  = {2024},

date = {2024-05-01},

booktitle = {Proceedings of the 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining},

volume = {14646},

pages = {235–247},

publisher = {Springer},

address = {Taipei, Taiwan},

series = {Lecture Notes in Computer Science},

abstract = {Knowledge distillation has been applied on generative models, such as Variational Autoencoder (VAE) and Generative Adversarial Networks (GANs). To distill the knowledge, the synthetic outputs of a teacher generator are used to train a student model. While the dark knowledge, i.e., the probabilistic output, is well explored in distilling classifiers, little is known about the existence of an equivalent dark knowledge for generative models and its extractability. In this paper, we derive the first kind of empirical risk bound for distilling generative models from a Bayesian perspective. Through our analysis, we show the existence of the dark knowledge for generative models, i.e., Bayes probability distribution of a synthetic output from a given input, which achieves lower empirical risk bound than merely using the synthetic output of the generators. Furthermore, we propose a Dark Knowledge based Distillation , DKtill, which trains the student generator based on the (approximate) dark knowledge. Our extensive evaluation on distilling VAE, conditional GANs, and translation GANs on Facades and CelebA datasets show that the FID of student generators trained by DKtill combining dark knowledge are lower than student generators trained only by the synthetic outputs by up to 42.66%, and 78.99%, respectively.},

keywords = {ai, epi, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{24:casella:sgx,

title = {A Performance Analysis for Confidential Federated Learning},

author = {Bruno Casella and Iacopo Colonnelli and Gianluca Mittone and Robert Birke and Walter Riviera and Antonio Sciarappa and Carlo Cavazzoni and Marco Aldinucci},

url = {https://iris.unito.it/retrieve/b5877a97-2d8d-4e95-8791-0aa4a1b953b3/DLSP___CONFIDENTIAL_FL.pdf},

doi = {10.1109/SPW63631.2024.00009},

year  = {2024},

date = {2024-05-01},

booktitle = {Proceedings of the 2024 Deep Learning Security and Privacy Workshop, IEEE Symposium on Security and Privacy 2024},

address = {San Francisco, CA},

abstract = {Federated Learning (FL) has emerged as a solution to preserve data privacy by keeping the data locally on each participant's device. However, FL alone is still vulnerable to attacks that can cause privacy leaks. Therefore, it becomes necessary to take additional security measures at the cost of increasing runtimes. The Trusted Execution Environment (TEE) approach promises to offer the highest degree of security during execution. However, TEEs suffer from memory limits which prevent safe end-to-end FL training of modern deep models. State-of- the-art approaches limit secure training to selected layers, failing to avert the full spectrum of attacks or adopt layer-wise training affecting model performance. We benchmark the usage of a library OS (LibOS) to run the full, unmodified end-to-end FL training inside the TEE. We extensively evaluate and model the overhead of the different security mechanisms needed to protect the data and model during computation (TEE), communication (TLS), and storage (disk encryption). The obtained results across three datasets and two models demonstrate that LibOSes are a viable way to seamlessly inject security into FL with limited overhead (at most 2x), offering valuable guidance for researchers and developers aiming to apply FL in data-security-focused contexts.},

keywords = {ai, confidential, epi, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{24:pdp:karmarkar,

title = {Benchmarking Parallelization Models through Karmarkar Interior-point method},

author = {Marco Edoardo Santimaria and Samuele Fonio and Giulio Malenza and Iacopo Colonnelli and Marco Aldinucci},

editor = {Horacio González-Vélez Adriana E. Chis},

url = {https://hdl.handle.net/2318/1964571},

doi = {10.1109/PDP62718.2024.00010},

issn = {2377-5750},

year  = {2024},

date = {2024-03-01},

booktitle = {Proc. of 32nd Euromicro intl. Conference on Parallel, Distributed and Network-based Processing (PDP)},

pages = {1-8},

publisher = {IEEE},

address = {Dublin, Ireland},

abstract = {Optimization problems are one of the main focus of scientific research. Their computational-intensive nature makes them prone to be parallelized with consistent improvements in performance. This paper sheds light on different parallel models for accelerating Karmarkar's Interior-point method. To do so, we assess parallelization strategies for individual operations within the aforementioned Karmarkar's algorithm using OpenMP, GPU acceleration with CUDA, and the recent Parallel Standard C++ Linear Algebra library (PSTL) executing both on GPU and CPU. Our different implementations yield interesting benchmark results that show the optimal approach for parallelizing interior point algorithms for general Linear Programming (LP) problems. In addition, we propose a more theoretical perspective of the parallelization of this algorithm, with a detailed study of our OpenMP implementation, showing the limits of optimizing the single operations},

keywords = {HPC, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Malenza_P3HPC_24,

title = {Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study},

author = {Giulio Malenza and Valentina Cesare and Marco Edoardo Santimaria and Robert Birke and Alberto Vecchiato and Ugo Becciani and Marco Aldinucci},

url = {https://conferences.computer.org/sc-wpub/pdfs/SC-W2024-6oZmigAQfgJ1GhPL0yE3pS/555400b152/555400b152.pdf},

doi = {10.1109/SCW63240.2024.00157},

isbn = {979-8-3503-5554-3},

year  = {2024},

date = {2024-01-01},

booktitle = {SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis},

pages = {1152-1163},

publisher = {IEEE},

abstract = {Applications that analyze data from modern scientific experiments will soon require a computing capacity of ExaFLOPs. The current trend to achieve such performance is to employ GPU-accelerated supercomputers and design applications to optimally exploit this hardware. Since each supercomputer is typically a one-off project, the necessity of having computational languages portable across diverse CPU and GPU architectures without performance losses is increasingly compelling. Here, we study the performance portability of the LSQR algorithm as found in the AVU-GSR code of the ESA Gaia mission. This code computes the astrometric parameters of the ~108 stars in our Galaxy. The LSQR algorithm is widely used across a broad range of high-performance computing (HPC) applications, elevating the study's relevance beyond the astrophysical domain. We developed different GPU-accelerated ports based on CUDA, C++ PSTL, SYCL, OpenMP, and HIP. We carefully verified the correctness of each port and tuned them to five different GPU-accelerated platforms from NVIDIA and AMD to evaluate the performance portability (PP) in terms of the harmonic mean of the application's performance efficiency across the tested hardware. HIP was demonstrated to be the most portable solution with a 0.94 average PP across the tested problem sizes, closely followed by SYCL coupled with AdaptiveCpp (ACPP) with 0.93. If we only consider NVIDIA platforms, CUDA would be the winner with 0.97. The tuning-oblivious C++ PSTL achieves 0.62 when coupled with vendor-specific compilers.},

keywords = {eupex, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Antelmi_BigHPC_24,

title = {High-Performance Computation on a Rust-based distributed ABM engine},

author = {Daniele De Vinco and Andrea Tranquillo and Alessia Antelmi and Carmine Spagnuolo and Vittorio Scarano},

editor = {Alessia Antelmi and Emanuele Carlini and Patrizio Dazzi},

url = {https://ceur-ws.org/Vol-3785/paper124.pdf},

year  = {2024},

date = {2024-01-01},

booktitle = {Proceedings of BigHPC2024: Special Track on Big Data and High-Performance Computing, co-located with the 3textsuperscriptrd Italian Conference on Big Data and Data Science, ITADATA2024},

volume = {3785},

publisher = {CEUR-WS.org},

series = {CEUR Workshop Proceedings},

abstract = {An agent-based model (ABM) is a computational model for simulating autonomous agents' actions and interactions to understand a system's behavior and what governs its outcomes. When the data or number of agents grow or multiple runs are necessary, agent-based simulations are generally computationally costly. Therefore, adopting different computing paradigms, such as the distributed one, is essential to manage long-running simulations. The main problem with this approach is finding a way to distribute and balance the simulation field so that the agents can move from one machine to another with the least amount of synchronization overhead. Based on our experiences, we present a Rust-based ABM engine capable of distributing models on high-performance computing resources, gaining remarkable speedup against the sequential version.},

keywords = {analytics, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{harrak2024fedsurvboost,

title = {Federated AdaBoost for Survival Analysis},

author = {Oussama Harrak and Bruno Casella and Samuele Fonio and Piero Fariselli and Gianluca Mittone and Tiziana Sanavia and Marco Aldinucci},

year  = {2024},

date = {2024-01-01},

booktitle = {Proceedings of the ECML-PKDD Workshop, 2nd workshop on advancements in Federated Learning},

address = {Vilnius, Lithuania},

abstract = {This work proposes FedSurvBoost, a federated learning pipeline for survival analysis based on the AdaBoost.F algorithm, which iteratively aggregates the best local weak hypotheses. Our method extends AdaBoost.F by removing the dependence on the number of classes coefficient from the computation of the weights of the best model. This makes it suitable for regression tasks, such as survival analysis. We show the effectiveness of our approach by comparing it with state-of-the-art methods, specifically developed for survival analysis problems, on two common survival datasets. Our code is available at https://github.com/oussamaHarrak/FedSurvBoost.},

keywords = {epi, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{24:garcia:itadata,

title = {Assessing Large Language Models Inference Performance on a 64-core RISC-V CPU with Silicon-Enabled Vectors},

author = {Adriano Marques Garcia and Giulio Malenza and Robert Birke and Marco Aldinucci},

editor = {Alessia Antelmi and Emanuele Carlini and Patrizio Dazzi},

url = {https://iris.unito.it/retrieve/1540f675-5e88-4f57-95e7-df8e0fe5f1df/paper110.pdf},

year  = {2024},

date = {2024-01-01},

booktitle = {Proceedings of BigHPC2024: Special Track on Big Data and High-Performance Computing, co-located with the 3textsuperscriptrd Italian Conference on Big Data and Data Science, ITADATA2024},

volume = {3785},

pages = {1-9},

publisher = {CEUR-WS.org},

address = {Pisa, Italy},

series = {CEUR Workshop Proceedings},

abstract = {The rising usage of compute-intensive AI applications with fast response time requirements, such as text generation using large language models, underscores the need for more efficient and versatile hardware solutions. This drives the exploration of emerging architectures like RISC-V, which has the potential to deliver strong performance within tight power constraints. The recent commercial release of processors with RISC-V Vector (RVV) silicon-enabled extensions further amplifies the significance of RISC-V architectures, offering enhanced capabilities for parallel processing and accelerating tasks critical to large language models and other AI applications. This work aims to evaluate the BERT and GPT-2 language models inference performance on the SOPHON SG2042 64-core RISC-V architecture with silicon-enabled RVV v0.7.1. We benchmarked the models with and without RVV, using OpenBLAS and BLIS as BLAS backends for PyTorch to enable vectorization. Enabling RVV in OpenBLAS improved the inference performance by up to 40% in some cases.},

keywords = {eupilot, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{24:brescia:itadata,

title = {Performance Analysis on DNA Alignment Workload with Intel SGX Multithreading},

author = {Lorenzo Brescia and Iacopo Colonnelli and Marco Aldinucci},

editor = {Alessia Antelmi and Emanuele Carlini and Patrizio Dazzi},

url = {https://ceur-ws.org/Vol-3785/paper107.pdf},

year  = {2024},

date = {2024-01-01},

booktitle = {Proceedings of BigHPC2024: Special Track on Big Data and High-Performance Computing, co-located with the 3textsuperscriptrd Italian Conference on Big Data and Data Science, ITADATA2024},

volume = {3785},

publisher = {CEUR-WS.org},

series = {CEUR Workshop Proceedings},

abstract = {Data confidentiality is a critical issue in the digital age, impacting interactions between users and public services and between scientific computing organizations and Cloud and HPC providers. Performance in parallel computing is essential, yet techniques for establishing Trusted Execution Environments (TEEs) to ensure privacy in remote environments often negatively impact execution time. This paper aims to analyze the performance of a parallel bioinformatics workload for DNA alignment (Bowtie2) executed within the confidential enclaves of Intel SGX processors. The results provide encouraging insights regarding the feasibility of using SGX-based TEEs for parallel computing on large datasets. The findings indicate that, under conditions of high parallelization and with twice as many threads, workloads executed within SGX enclaves perform, on average, 15% faster than non-confidential execution. This empirical demonstration supports the potential of SGX-based TEEs to effectively balance the need for privacy with the demands of high-performance computing.},

keywords = {confidential, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Antelmi_KDD_2024,

title = {A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide},

author = {Sunwoo Kim and Soo Yong Lee and Yue Gao and Alessia Antelmi and Mirko Polato and Kijung Shin},

url = {https://doi.org/10.1145/3637528.3671457},

doi = {10.1145/3637528.3671457},

isbn = {9798400704901},

year  = {2024},

date = {2024-01-01},

booktitle = {Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},

pages = {6534–6544},

publisher = {Association for Computing Machinery},

address = {Barcelona, Spain},

series = {KDD '24},

abstract = {Higher-order interactions (HOIs) are ubiquitous in real-world complex systems and applications. Investigation of deep learning for HOIs, thus, has become a valuable agenda for the data mining and machine learning communities. As networks of HOIs are expressed mathematically as hypergraphs, hypergraph neural networks (HNNs) have emerged as a powerful tool for representation learning on hypergraphs. Given the emerging trend, we present the first survey dedicated to HNNs, with an in-depth and step-by-step guide. Broadly, the present survey overviews HNN architectures, training strategies, and applications. First, we break existing HNNs down into four design components: (i) input features, (ii) input structures, (iii) message-passing schemes, and (iv) training strategies. Second, we examine how HNNs address and learn HOIs with each of their components. Third, we overview the recent applications of HNNs in recommendation, bioinformatics and medical science, time series analysis, and computer vision. Lastly, we conclude with a discussion on limitations and future directions.},

keywords = {ai, analytics, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{24:queyrut:srds,

title = {CLUES: Collusive Theft of Conditional Generative Adversarial Networks},

author = {Simon Queyrut and Robert Birke and Pascal Felber and Valerio Schiavon},

year  = {2024},

date = {2024-01-01},

booktitle = {43rd International Symposium on Reliable Distributed Systems SRDS},

keywords = {ai, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{23:brescia:wide,

title = {Secure Generic Remote Workflow Execution with TEEs},

author = {Lorenzo Brescia and Marco Aldinucci},

doi = {10.1145/3642978.3652834},

year  = {2024},

date = {2024-01-01},

booktitle = {Proc. of the 2nd Workshop on Workflows in Distributed Environments (WiDE)},

pages = {8-13},

publisher = {ACM},

address = {Athens, Greece},

abstract = {In scientific environments, the frequent need to process substantial volumes of data poses a common challenge. Individuals tasked with executing these computations frequently encounter a deficit in local computational resources, leading them to opt for the facilities of a Cloud Service Provider (CSP) for data processing. However, the data subjected to these calculations may be subject to confidentiality constraints. This paper introduces a proof-of-concept framework that leverages Gramine LibOS and Intel SGX, enabling the protection of generic remote workflow computations through SGX enclaves as Trusted Execution Environments (TEEs). The framework entails the delineation of user and CSP behavior and has been implemented using Bash scripts. Furthermore, an infrastructure has been designed for the Data Center Attestation Primitives (DCAP) remote attestation mechanism, wherein the user gains trust in the proper instantiation of the enclave within the CSP. To assess the framework efficacy, it has been tested on two distinct workflows, one trivial and the other involving real-world bioinformatics applications for processing DNA data. The performance study revealed that the framework incurred an acceptable overhead, ranging from a factor of x1.4 to x1.8 compared to unsafe execution practice.},

howpublished = {Proceedings of the 2nd Workshop on Workflows in Distributed Environments},

keywords = {confidential, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Antelmi_WebSci_2024,

title = {Deciphering Conversational Networks: Stance Detection via Hypergraphs and LLMs},

author = {Daniele De Vinco and Alessia Antelmi and Carmine Spagnuolo and Luca Maria Aiello},

url = {https://doi.org/10.1145/3630744.3658418},

doi = {10.1145/3630744.3658418},

isbn = {9798400704536},

year  = {2024},

date = {2024-01-01},

booktitle = {Companion Publication of the 16th ACM Web Science Conference},

pages = {3–4},

publisher = {Association for Computing Machinery},

address = {Stuttgart, Germany},

series = {Websci Companion '24},

abstract = {Understanding the structural and linguistic properties of conversational data in social media is crucial for extracting meaningful insights to understand opinion dynamics, (mis-)information spreading, and the evolution of harmful behavior. Current state-of-the-art mathematical frameworks, such as hypergraphs and linguistic tools, such as large language models (LLMs), offer robust methodologies for modeling high-order group interactions and unprecedented capabilities for dealing with natural language-related tasks. In this study, we propose an innovative approach that blends these worlds by abstracting conversational networks via hypergraphs and analyzing their dynamics through LLMs. Our aim is to enhance the stance detection task by incorporating the high-order interactions naturally embedded within a conversation, thereby enriching the contextual understanding of LLMs regarding the intricate human dynamics underlying social media data.},

keywords = {analytics, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Antelmi_WAW_2024,

title = {HypergraphRepository: A Community-Driven and Interactive Hypernetwork Data Collection},

author = {Alessia Antelmi and Daniele De Vinco and Carmine Spagnuolo},

editor = {Megan Dewar and Bogumił Kamiński and Daniel Kaszyński and Łukasz Kraiński and Paweł Prałat and François Théberge and Małgorzata Wrzosek},

doi = {10.1007/978-3-031-59205-8_11},

isbn = {978-3-031-59205-8},

year  = {2024},

date = {2024-01-01},

booktitle = {Modelling and Mining Networks},

pages = {159–173},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {Hypergraph research has been thriving over the past few years, with a growing interest in a plethora of domains. Despite this remarkable surge, the lack of a comprehensive platform for searching and downloading diverse and well-curated datasets poses a significant obstacle to the continued advancement of the field. This absence hinders the ability of researchers and practitioners to validate and benchmark their hypergraph algorithms and models effectively.},

keywords = {analytics, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{23:hipc:capio,

title = {CAPIO: a Middleware for Transparent I/O Streaming in Data-Intensive Workflows},

author = {Alberto Riccardo Martinelli and Massimo Torquati and Marco Aldinucci and Iacopo Colonnelli and Barbara Cantalupo},

url = {https://iris.unito.it/retrieve/27380f37-0978-409e-a9d8-2b5e95a4bb85/CAPIO-HiPC23-preprint.pdf},

doi = {10.1109/HiPC58850.2023.00031},

year  = {2023},

date = {2023-12-01},

booktitle = {2023 IEEE 30th International Conference on High Performance Computing, Data, and Analytics (HiPC)},

publisher = {IEEE},

address = {Goa, India},

abstract = {With the increasing amount of digital data available for analysis and simulation, the class of I/O-intensive HPC workflows is fated to quickly expand, further exacerbating the performance gap between computing, memory, and storage technologies. This paper introduces CAPIO (Cross-Application Programmable I/O), a middleware capable of injecting I/O streaming capabilities into file-based workflows, improving the computation-I/O overlap without the need to change the application code. The contribution is twofold: 1) at design time, a new I/O coordination language allows users to annotate workflow data dependencies with synchronization semantics; 2) at run time, a user-space middleware automatically and transparently to the user turns a workflow batch execution into a streaming execution according to the semantics expressed in the configuration file. CAPIO has been tested on synthetic benchmarks simulating typical workflow I/O patterns and two real-world workflows. Experiments show that CAPIO reduces the execution time by 10% to 66% for data-intensive workflows that use the file system as a communication medium.},

keywords = {admire, capio, eupex, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{WORKS2023,

title = {A Systematic Mapping Study of Italian Research on Workflows},

author = {Marco Aldinucci and Elena Maria Baralis and Valeria Cardellini and Iacopo Colonnelli and Marco Danelutto and Sergio Decherchi and Giuseppe Di Modica and Luca Ferrucci and Marco Gribaudo and Francesco Iannone and Marco Lapegna and Doriana Medic and Giuseppa Muscianisi and Francesca Righetti and Eva Sciacca and Nicola Tonellotto and Mauro Tortonesi and Paolo Trunfio and Tullio Vardanega},

url = {https://doi.org/10.1145/3624062.3624285},

doi = {10.1145/3624062.3624285},

year  = {2023},

date = {2023-11-01},

booktitle = {Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis, SC-W 2023},

pages = {2065–2076},

publisher = {ACM},

address = {Denver, CO, USA},

abstract = {An entire ecosystem of methodologies and tools revolves around scientific workflow management. They cover crucial non-functional requirements that standard workflow models fail to target, such as interactive execution, energy efficiency, performance portability, Big Data management, and intelligent orchestration in the Computing Continuum. Characterizing and monitoring this ecosystem is crucial to develop an informed view of current and future research directions. This work conducts a systematic mapping study of the Italian workflow research community, collecting and analyzing 25 tools and 10 applications from several scientific domains in the context of the ``National Research Centre for HPC, Big Data, and Quantum Computing'' (ICSC). The study aims to outline the main current research directions and determine how they address the critical needs of modern scientific applications. The findings highlight a variegated research ecosystem of tools, with a prominent interest in advanced workflow orchestration and still immature but promising efforts toward energy efficiency.},

keywords = {icsc, jupyter-workflow, streamflow},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{23:zhao:fctgan,

title = {FCT-GAN: Enhancing Global Correlation of Table Synthesis via Fourier Transform},

author = {Zilong Zhao and Robert Birke and Lydia Y. Chen},

url = {https://iris.unito.it/retrieve/966ba767-dbbd-41e1-b4e3-7ab7ba09303f/FCT-GAN.pdf},

doi = {10.1145/3583780.3615202},

year  = {2023},

date = {2023-10-01},

booktitle = {32nd ACM International Conference on Information and Knowledge Management (CIKM '23)},

publisher = {ACM},

address = {Birmingham, United Kingdom},

abstract = {An alternative method for sharing knowledge while complying with strict data access regulations, such as the European General Data Protection Regulation (GDPR), is the emergence of synthetic tabular data. Mainstream table synthesizers utilize methodologies derived from Generative Adversarial Networks (GAN). Although several state-of-the-art (SOTA) tabular GAN algorithms inherit Convolutional Neural Network (CNN)-based architectures, which have proven effective for images, they tend to overlook two critical properties of tabular data: (i) the global correlation across columns, and (ii) the semantic invariance to the column order. Permuting columns in a table does not alter the semantic meaning of the data, but features extracted by CNNs can change significantly due to their limited convolution filter kernel size. To address the above problems, we propose FCT-GAN– the first conditional tabular GAN to adopt Fourier networks into table synthesis. FCT-GAN enhances permutation invariant GAN training by strengthening the learning of global correlations via Fourier layers. Extensive evaluation on benchmarks and real-world datasets show that FCT-GAN can synthesize tabular data with better (up to 27.8%) machine learning utility (i.e. a proxy of global correlations) and higher (up to 26.5%) statistical similarity to real data. FCT-GAN also has the least variation on synthetic data quality among 7 SOTA baselines on 3 different training-data column orders.},

keywords = {icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{23:esann:fonio,

title = {Hierarchical priors for Hyperspherical Prototypical Networks},

author = {Samuele Fonio and Lorenzo Paletto and Mattia Cerrato and Dino Ienco and Roberto Esposito},

url = {https://www.esann.org/sites/default/files/proceedings/2023/ES2023-65.pdf},

year  = {2023},

date = {2023-10-01},

booktitle = {31th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN},

address = {Bruges, Belgium},

abstract = {In this paper, we explore the usage of hierarchical priors to improve learning in contexts where the number of available examples is extremely low. Specifically, we consider a Prototype Learning setting where deep neural networks are used to embed data in hyperspherical geometries.In this scenario, we propose an innovative way to learn the prototypes by combining class separation and hierarchical information. In addition, we introduce a contrastive loss function capable of balancing the exploitation of prototypes through a prototype pruning mechanism. We compare the proposed method with state-of-the-art approaches on two public datasets.},

note = {In print},

keywords = {ai, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{23:iciap:fedmed:ws:fonio,

title = {Benchmarking Federated Learning Frameworks for Medical Imaging Tasks},

author = {Samuele Fonio},

editor = {G. L. Foresti and A. Fusiello and E. Hancock},

url = {https://link.springer.com/chapter/10.1007/978-3-031-51026-7_20},

doi = {10.1007/978-3-031-51026-7_20},

year  = {2023},

date = {2023-09-01},

booktitle = {Image Analysis and Processing - ICIAP 2023 Workshops. ICIAP 2023},

volume = {14366},

publisher = {Springer, Cham},

address = {Udine, Italy},

abstract = {This paper presents a comprehensive benchmarking study of various Federated Learning (FL) frameworks applied to the task of Medical Image Classification. The research specifically addresses the often neglected and complex aspects of scalability and usability in off-the-shelf FL frameworks. Through experimental validation using real case deployments, we provide empirical evidence of the performance and practical relevance of open source FL frameworks. Our findings contribute valuable insights for anyone interested in deploying a FL system, with a particular focus on the healthcare domain—an increasingly attractive field for FL applications.},

note = {In print},

keywords = {ai, eupilot, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{23:itadata:extabstract:mittone:fonio,

title = {Benchmarking Federated Learning Scalability},

author = {Gianluca Mittone and Samuele Fonio},

url = {https://hdl.handle.net/2318/1933852},

year  = {2023},

date = {2023-09-01},

booktitle = {Proceedings of the 2nd Italian Conference on Big Data and Data Science, ITADATA 2023, September 11-13, 2023},

publisher = {CEUR},

address = {Naples, Italy},

abstract = {Federated Learning (FL) is a widespread Machine Learning paradigm handling distributed Big Data. In this work, we demonstrate that different FL frameworks expose different scaling performances despite adopting the same technologies, highlighting the need for a more comprehensive study on the topic.},

keywords = {eupilot, HPC, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{23:hong:datafree,

title = {Exploring and Exploiting Data-Free Model Stealing},

author = {Chi Hong and Jiyue Huang and Robert Birke and Lydia Y. Chen},

url = {https://iris.unito.it/retrieve/ce44dec6-12c9-443d-99e7-f1141e50aa3a/Data-free%20Model%20Stealing.pdf},

doi = {10.1007/978-3-031-43424-2_2},

year  = {2023},

date = {2023-09-01},

booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)},

address = {Turin, Italy},

abstract = {Deep machine learning models, e.g., image classifier, are increasingly deployed in the wild to provide services to users. Adversaries are shown capable of stealing the knowledge of these models by sending inference queries and then training substitute models based on query results. The availability and quality of adversarial query inputs are undoubtedly crucial in the stealing process. The recent prior art demonstrates the feasibility of replacing real data by exploring the synthetic adversarial queries, so called data-free attacks, under strong adversarial assumptions, i.e., the deployed classier returns not only class labels but also class probabilities. In this paper, we consider a general adversarial model and propose an effective data-free stealing algorithm, Tandem-GAN, which not only explores synthetic queries but also explicitly exploits the high quality ones. The core of TandemGAN is composed of (i) substitute model which imitates the target model through synthetic queries and their inferred labels; and (ii) a tandem generator consisting of two networks, Gx and Ge, which first explores the synthetic data space via Gx and then exploits high-quality examples via Ge to maximize the knowledge transfer from the target to the substitute model. Our results on four datasets show that the accuracy of our trained substitute model ranges between 96-67% of the target model and outperforms the existing state-of-the-art data-free model stealing approach by up to 2.5X.},

keywords = {eupilot, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{23:mittone:mafl,

title = {Model-Agnostic Federated Learning},

author = {Gianluca Mittone and Walter Riviera and Iacopo Colonnelli and Robert Birke and Marco Aldinucci},

url = {https://doi.org/10.1007/978-3-031-39698-4_26},

doi = {10.1007/978-3-031-39698-4_26},

year  = {2023},

date = {2023-08-01},

booktitle = {Euro-Par 2023: Parallel Processing},

volume = {14100},

pages = {383–396},

publisher = {Springer},

address = {Limassol, Cyprus},

institution = {Computer Science Department, University of Torino},

abstract = {Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of Deep Neural Networks (DNNs). On the one hand, this allowed its development and widespread use as DNNs proliferated. On the other hand, it neglected all those scenarios in which using DNNs is not possible or advantageous. The fact that most current FL frameworks only allow training DNNs reinforces this problem. To address the lack of FL solutions for non-DNN-based use cases, we propose MAFL (Model-Agnostic Federated Learning). MAFL marries a model-agnostic FL algorithm, AdaBoost.F, with an open industry-grade FL framework: Intel OpenFL. MAFL is the first FL system not tied to any specific type of machine learning model, allowing exploration of FL scenarios beyond DNNs and trees. We test MAFL from multiple points of view, assessing its correctness, flexibility and scaling properties up to 64 nodes. We optimised the base software achieving a 5.5x speedup on a standard FL scenario. MAFL is compatible with x86-64, ARM-v8, Power and RISC-V.},

keywords = {ai, confidential, eupilot, icsc, riscv},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{23:risc-v-summit,

title = {Experimenting with PyTorch on RISC-V},

author = {Iacopo Colonnelli and Robert Birke and Marco Aldinucci},

url = {https://iris.unito.it/retrieve/429bf344-9090-42c3-809c-1b8ac320a930/2023-06-08-Iacopo-COLONNELLI-abstract.pdf},

year  = {2023},

date = {2023-06-01},

booktitle = {RISC-V Summit Europe 2023},

address = {Barcelona, Spain},

abstract = {RISC-V is an emerging instruction set architecture. Its modular and extensible open-source royalty-free design is increasingly attracting interest from both research and industry. Nowadays, different RISC-V-based boards can be bought off the shelf. However, software availability is equivalently vital in guaranteeing the RISC-V ecosystem's success. Here we contribute with the first publicly available port of PyTorch. PyTorch is one of the most popular Deep Learning libraries available today. As such, it is a crucial enabler in running state-of-the-art AI applications on RISC-V-based systems and a first step towards a fully democratic end-to-end codesign process.},

note = {Poster},

keywords = {eupilot, icsc, riscv},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{23:aldinucci:continuum,

title = {A Proposal for a Continuum-aware Programming Model: From Workflows to Services Autonomously Interacting in the Compute Continuum},

author = {Marco Aldinucci and Robert Birke and Antonio Brogi and Emanuele Carlini and Massimo Coppola and Marco Danelutto and Patrizio Dazzi and Luca Ferrucci and Forti Stefano and Hanna Kavalionak and Gabriele Mencagli and Matteo Mordacchin and Marcelo Pasin and Federica Paganelli and Massimo Torquati},

url = {https://iris.unito.it/retrieve/2ae13a33-5814-43da-8ea6-2d3e8b122384/Continuum-aware-PM.pdf},

doi = {10.1109/COMPSAC57700.2023.00287},

year  = {2023},

date = {2023-06-01},

booktitle = {2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)},

publisher = {IEEE},

address = {Turin, Italy},

abstract = {This paper proposes a continuum-aware programming model enabling the execution of application workflows across the compute continuum: cloud, fog and edge resources. It simplifies the management of heterogeneous nodes while alleviating the burden of programmers and unleashing innovation. This model optimizes the continuum through advanced development experiences by transforming workflows into autonomous service collaborations. It reduces complexity in positioning/interconnecting services across the continuum. A meta-model introduces high-level workflow descriptions as service networks with defined contracts and quality of service, thus enabling the deployment/management of workflows as first-class entities. It also provides automation based on policies, monitoring and heuristics. Tailored mechanisms orchestrate/manage services across the continuum, optimizing performance, cost, data protection and sustainability while managing risks. This model facilitates incremental development with visibility of design impacts and seamless evolution of applications and infrastructures. In this work, we explore this new computing paradigm showing how it can trigger the development of a new generation of tools to support the compute continuum progress.},

keywords = {icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{23:mittone:fl-riscv,

title = {Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning},

author = {Gianluca Mittone and Nicolò Tonci and Robert Birke and Iacopo Colonnelli and Doriana Medić and Andrea Bartolini and Roberto Esposito and Emanuele Parisi and Francesco Beneventi and Mirko Polato and Massimo Torquati and Luca Benini and Marco Aldinucci},

url = {https://dl.acm.org/doi/pdf/10.1145/3587135.3592211},

doi = {10.1145/3587135.3592211},

isbn = {979-8-4007-0140-5/23/05},

year  = {2023},

date = {2023-05-01},

booktitle = {20th ACM International Conference on Computing Frontiers (CF '23)},

publisher = {ACM},

address = {Bologna, Italy},

institution = {Computer Science Department, University of Torino},

abstract = {Decentralised Machine Learning (DML) enables collaborative machine learning without centralised input data. Federated Learning (FL) and Edge Inference are examples of DML. While tools for DML (especially FL) are starting to flourish, many are not flexible and portable enough to experiment with novel systems (e.g., RISC-V), non-fully connected topologies, and asynchronous collaboration schemes. We overcome these limitations via a domain-specific language allowing to map DML schemes to an underlying middleware, i.e. the FastFlow parallel programming library. We experiment with it by generating different working DML schemes on two emerging architectures (ARM-v8, RISC-V) and the x86-64 platform. We characterise the performance and energy efficiency of the presented schemes and systems. As a byproduct, we introduce a RISC-V porting of the PyTorch framework, the first publicly available to our knowledge.},

note = {https://arxiv.org/abs/2302.07946},

keywords = {ai, confidential, eupilot, HPC, icsc, riscv},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{23:mittone:dti,

title = {A Federated Learning Benchmark for Drug-Target Interaction},

author = {Gianluca Mittone and Filip Svoboda and Marco Aldinucci and Nicholas D. Lane and Pietro Lio},

url = {https://hdl.handle.net/2318/1898472},

doi = {10.1145/3543873.3587687},

isbn = {978-1-4503-9419-2/23/04},

year  = {2023},

date = {2023-04-01},

booktitle = {Companion Proceedings of the ACM Web Conference 2023 (WWW '23 Companion)},

publisher = {ACM},

address = {Austin, Texas},

institution = {Computer Science Department, University of Torino},

abstract = {Aggregating pharmaceutical data in the drug-target interaction (DTI) domain has the potential to deliver life-saving breakthroughs. It is, however, notoriously difficult due to regulatory constraints and commercial interests. This work proposes the application of federated learning, which we argue to be reconcilable with the industry's constraints, as it does not require sharing of any information that would reveal the entities' data or any other high-level summary of it. When used on a representative GraphDTA model and the KIBA dataset it achieves up to 15 percent improved performance relative to the best available non-privacy preserving alternative. Our extensive battery of experiments shows that, unlike in other domains, the non-IID data distribution in the DTI datasets does not deteriorate FL performance. Additionally, we identify a material trade-off between the benefits of adding new data, and the cost of adding more clients.},

note = {https://arxiv.org/abs/2302.07684},

keywords = {ai, confidential, eupilot, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Antelmi_CNA1_2023,

title = {Tell Me Who You Are and I Will Predict Your Vulnerability to Political Persuasion Techniques},

author = {Alessia Antelmi and Luca La Cava and Arianna Pera},

url = {https://iris.unito.it/bitstream/2318/1949370/1/_CNA__23__Personality_vs_propaganda.pdf},

year  = {2023},

date = {2023-01-01},

booktitle = {The 12th International Conference on Complex Networks and their Applications-Book of Abstracts},

abstract = {Given the evolving role of social media in political communication and the strategic use of these platforms by politicians to shape public opinion, research has commonly focused on investigating computational propaganda as a means for automated information diffusion. Focusing on a less explored yet promising line, we aim to assess political persuasion in digital contexts by introducing a computational framework that combines Natural Language Processing and Network Science methods to investigate the linkage between persuasion techniques on social media and personality traits of online political audiences. Our final goal is to enhance public awareness of political tactics and encourage critical thinking in response to the online spread of political information.},

keywords = {analytics, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Antelmi_CNA_2023,

title = {Finding Hidden Swingers in the 2022 Italian Elections Twitter Discourse},

author = {Alessia Antelmi and Luca La Cava and Arianna Pera},

url = {https://iris.unito.it/bitstream/2318/1949354/1/_CNA__23__TweetYourMind.pdf},

year  = {2023},

date = {2023-01-01},

booktitle = {The 12th International Conference on Complex Networks and their Applications-Book of Abstracts},

abstract = {The volume of the Italian online political discourse on social media has recently increased, but the coverage level does not compare with other Countries such as the US. Nonetheless, researchers focused on studying polarization and homophily with respect to political debates or investigating the role of populism in online engagement. In this research landscape, the analysis of political preference shifts through social media remains to be explored. We aim to bridge this gap by examining the Twitter discourse during the 2022 Italian general elections, with a specific emphasis on political "swingers". In particular, our findings indicate a stable political discourse in Italy, yet they also uncover a growing presence of political swingers willing to shift their support to significantly different factions.},

keywords = {analytics, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Antelmi_ITADATA_2023,

title = {The SWH-Analytics Framework},

author = {Alessia Antelmi and Massimo Torquati and Daniele Gregori and Francesco Polzella and Gianmarco Spinatelli and Marco Aldinucci},

editor = {Nicola Bena and Beniamino Di Martino and Antonio Maratea and Alessandro Sperduti and Emanuel Di Nardo and Angelo Ciaramella and Raffaele Montella and Claudio A. Ardagna},

url = {https://ceur-ws.org/Vol-3606/paper76.pdf},

year  = {2023},

date = {2023-01-01},

booktitle = {Proceedings of the 2nd Italian Conference on Big Data and Data Science (ITADATA 2023), Naples, Italy, September 11-13, 2023},

volume = {3606},

publisher = {CEUR-WS.org},

series = {CEUR Workshop Proceedings},

abstract = {The Software Heritage (SWH) dataset serves as a vast repository for open-source code, with the ambitious goal of preserving all publicly available open-source projects. Despite being designed to effectively archive project files, its size of nearly 1 petabyte presents challenges in efficiently supporting Big Data MapReduce or AI systems. To address this disparity and enable seamless custom analytics on the SWH dataset, we present the SWH-Analytics (SWHA) architecture. This development environment quickly and transparently runs custom analytic applications on open-source software data preserved over time by SWH.},

keywords = {admire, analytics, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{23:colonnelli:itadata,

title = {Workflow Models for Heterogeneous Distributed Systems},

author = {Iacopo Colonnelli},

editor = {Nicola Bena and Beniamino Di Martino and Antonio Maratea and Alessandro Sperduti and Emanuel Di Nardo and Angelo Ciaramella and Raffaele Montella and Claudio A. Ardagna},

url = {https://ceur-ws.org/Vol-3606/invited77.pdf},

year  = {2023},

date = {2023-01-01},

booktitle = {Proceedings of the 2nd Italian Conference on Big Data and Data Science (ITADATA 2023), Naples, Italy, September 11-13, 2023},

volume = {3606},

publisher = {CEUR-WS.org},

series = {CEUR Workshop Proceedings},

abstract = {This article introduces a novel hybrid workflow abstraction that injects topology awareness directly into the definition of a distributed workflow model. In particular, the article briefly discusses the advantages brought by this approach to the design and orchestration of large-scale data-oriented workflows, the current level of support from state-of-the-art workflow systems, and some future research directions.},

keywords = {across, eupex, icsc, jupyter-workflow, streamflow},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{23:casella:onchain,

title = {Predicting Cryptocurrencies Market Phases through On-Chain Data Long-Term Forecasting},

author = {Bruno Casella and Lorenzo Paletto},

url = {https://iris.unito.it/bitstream/2318/1902652/1/6.%20ICBC23%20-%20PREDICTING%20BTC.pdf},

doi = {https://doi.org/10.1109/ICBC56567.2023.10174989},

year  = {2023},

date = {2023-01-01},

booktitle = {Proceedings of the 2023 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), 1-5 May 2023, Dubai},

abstract = {Blockchain, the underlying technology of Bitcoin and several other cryptocurrencies, like Ethereum, produces a massive amount of open-access data that can be analyzed, providing important information about the network's activity and its respective token. The on-chain data have extensively been used as input to Machine Learning algorithms for predicting cryptocurrencies' future prices; however, there is a lack of study in predicting the future behaviour of on-chain data. This study aims to show how on-chain data can be used to detect cryptocurrency market regimes, like minimum and maximum, bear and bull market phases, and how forecasting these data can provide an optimal asset allocation for long-term investors.},

note = {https://ieeexplore.ieee.org/document/10174989},

keywords = {epi, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{23:casella:architecturalfedavg,

title = {Architecture-Based FedAvg for Vertical Federated Learning},

author = {Bruno Casella and Samuele Fonio},

url = {https://iris.unito.it/retrieve/173d9960-8531-419d-9bd5-5acce6694c4e/Aggregation%20Based%20VFL.pdf},

doi = {10.1145/3603166.3632559},

year  = {2023},

date = {2023-01-01},

booktitle = {Proceedings of the 3rd Workshop on Distributed Machine Learning for the Intelligent Computing Continuum (DML-ICC), IEEE/ACM UCC 2023, Taormina, Italy, 4 December 2023},

abstract = {Federated Learning (FL) has emerged as a promising solution to address privacy concerns by collaboratively training Deep Learning (DL) models across distributed parties. This work proposes an architecture-based aggregation strategy in Vertical FL, where parties hold data with different attributes but shared instances. Our approach leverages the identical architectural parts, i.e. neural network layers, of different models to selectively aggregate weights, which is particularly relevant when collaborating with institutions holding different types of datasets, i.e., image, text, or tabular datasets. In a scenario where two entities train DL models, such as a Convolutional Neural Network (CNN) and a Multi-Layer Perceptron (MLP), our strategy computes the average only for architecturally identical segments. This preserves data-specific features learned from demographic and clinical data. We tested our approach on two clinical datasets, i.e., the COVID-CXR dataset and the ADNI study. Results show that our method achieves comparable results with the centralized scenario, in which all the data are collected in a single data lake, and benefits from FL generalizability. In particular, compared to the non-federated models, our proposed proof-of-concept model exhibits a slight performance loss on the COVID-CXR dataset (less than 8%), but outperforms ADNI models by up to 12%. Moreover, communication costs between training rounds are minimized by exchanging only the dense layer parameters.},

note = {https://iris.unito.it/bitstream/2318/1949730/1/HALF_HVL_for_DML_ICC23___Taormina-2.pdf},

keywords = {ai, epi, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/ftfjp/AngeloBDF23,

title = {Gradual Guarantee for FJ with lambda-Expressions},

author = {Pedro Ângelo and Viviana Bono and Mariangiola Dezani-Ciancaglini and Mário Florido},

editor = {Aaron Tomb},

url = {https://doi.org/10.1145/3605156.3606453},

doi = {10.1145/3605156.3606453},

year  = {2023},

date = {2023-01-01},

booktitle = {Proceedings of the 25th ACM International Workshop on Formal Techniques for Java-like Programs, FTfJP 2023, Seattle, WA, USA, 18 July 2023},

pages = {32–38},

publisher = {ACM},

keywords = {admire, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{23:SAMOS,

title = {RISC-V-based Platforms for HPC: Analyzing Non-functional Properties for Future HPC and Big-Data Clusters},

author = {William Fornaciari and Federico Reghenzani and Federico Terraneo and Davide Baroffio and Cecilia Metra and Martin Omana and Josie E. Rodriguez Condia and Matteo Sonza Reorda and Robert Birke and Iacopo Colonnelli and Gianluca Mittone and Marco Aldinucci and Gabriele Mencagli and Francesco Iannone and Filippo Palombi and Giuseppe Zummo and Daniele Cesarini and Federico Tesser},

url = {https://iris.unito.it/retrieve/b627eab0-3aa1-4fd7-8685-f47c62c792b3/SAMOS_2023_CN_HPC_FL1.pdf},

doi = {10.1007/978-3-031-46077-7_26},

year  = {2023},

date = {2023-01-01},

booktitle = {Embedded Computer Systems: Architectures, Modeling, and Simulation - 23rd International Conference, SAMOS 2023},

address = {Samos, Greece},

abstract = {High-PerformanceComputing(HPC)haveevolvedtobeused to perform simulations of systems where physical experimentation is pro- hibitively impractical, expensive, or dangerous. This paper provides a general overview and showcases the analysis of non-functional properties in RISC-V-based platforms for HPCs. In particular, our analyses target the evaluation of power and energy control, thermal management, and reliability assessment of promising systems, structures, and technologies devised for current and future generation of HPC machines. The main set of design methodologies and technologies developed within the activ- ities of the Future and HPC & Big Data spoke of the National Centre of HPC, Big Data and Quantum Computing project are described along with the description of the testbed for experimenting two-phase cooling approaches.},

note = {icsc},

keywords = {icsc, riscv},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Antelmi_IC2S2_2023,

title = {Towards Unraveling Developers Communities in Stack Overflow and Reddit},

author = {Alessia Antelmi and Daniele De Vinco and Gennaro Cordasco and Carmine Spagnuolo},

url = {https://openreview.net/forum?id=WP5ZaAFP19},

year  = {2023},

date = {2023-01-01},

booktitle = {International Conference on Computational Social Science 2023},

abstract = {This work investigates the developers' behavior and community formation around the twenty most popular programming languages. We examined two consecutive years of programming-related questions from Stack Overflow and Reddit, performing a longitudinal study on users' posting activity and their high-order interaction patterns abstracted via hypergraphs. Our analysis highlighted crucial differences in how these QA platforms are utilized by their users. In line with previous literature, it emphasized the constant decline of Stack Overflow in favor of more community-friendly platforms, such as Reddit, which has been growing rapidly lately.},

keywords = {analytics, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Antelmi_TEL4FC_2023,

title = {Engagement in Open Data Workshops: The dark side of remote settings},

author = {Alessia Antelmi},

url = {https://link.springer.com/chapter/10.1007/978-3-031-42134-1_33},

year  = {2023},

date = {2023-01-01},

booktitle = {Methodologies and Intelligent Systems for Technology Enhanced Learning, 12th International Conference},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {The increasing availability of Open Data gives birth to a fertile field for interested stakeholders to create value out of them; however, limited technical expertise and poor awareness are crucial barriers to their exploitation. Because of these reasons, there is an urge for learners to acquire data and information literacy competencies, which are essential for 21st-century skills, and become familiar with available Open Data sources and their potential uses. To promote the dialogue around activities to boost recognition of Open Data and improve users' skills to work with them, we proposed a series of workshops to introduce Italian high school learners to searching for, authoring, and building effective communication based on Open Data. This article describes an ongoing activity and details its organization, reports preliminary results on learners' engagement, and discusses both challenges of the remote setting as well as promising learning outcomes.},

keywords = {analytics, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{23:medic:formal-model,

title = {Towards formal model for location aware workflows},

author = {Doriana Medić and Marco Aldinucci},

editor = {Hossain Shahriar and Yuuichi Teranishi and Alfredo Cuzzocrea and Moushumi Sharmin and Dave Towey and A. K. M. Jahangir Alam Majumder and Hiroki Kashiwazaki and Ji-Jiang Yang and Michiharu Takemoto and Nazmus Sakib and Ryohei Banno and Sheikh Iqbal Ahamed},

url = {https://iris.unito.it/retrieve/1f9f959c-cd88-4d9c-90ea-54f1c86a15bc/6210-medic.pdf},

doi = {10.1109/COMPSAC57700.2023.00289},

year  = {2023},

date = {2023-01-01},

booktitle = {47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023},

pages = {1864–1869},

publisher = {IEEE},

address = {Torino, Italy},

abstract = {Designing complex applications and executing them on large-scale topologies of heterogeneous architectures is becoming increasingly crucial in many scientific domains. As a result, diverse workflow modelling paradigms are developed, most of them with no formalisation provided. In these circumstances, comparing two different models or switching from one system to the other becomes a hard nut to crack. This paper investigates the capability of process algebra to model a location aware workflow system. Distributed π-calculus is considered as the base of the formal model due to its ability to describe the communicating components that change their structure as an outcome of the communication. Later, it is discussed how the base model could be extended or modified to capture different features of location aware workflow system. The intention of this paper is to highlight the fact that due to its flexibility, π-calculus, could be a good candidate to represent the behavioural perspective of the workflow system.},

keywords = {eupex, icsc, semantics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{23:mulone:wide:vcp,

title = {Porting the Variant Calling Pipeline for NGS data in cloud-HPC environment},

author = {Alberto Mulone and Sherine Awad and Davide Chiarugi and Marco Aldinucci},

editor = {Hossain Shahriar and Yuuichi Teranishi and Alfredo Cuzzocrea and Moushumi Sharmin and Dave Towey and A. K. M. Jahangir Alam Majumder and Hiroki Kashiwazaki and Ji-Jiang Yang and Michiharu Takemoto and Nazmus Sakib and Ryohei Banno and Sheikh Iqbal Ahamed},

url = {https://iris.unito.it/bitstream/2318/1919364/1/paper.pdf},

doi = {10.1109/COMPSAC57700.2023.00288},

year  = {2023},

date = {2023-01-01},

booktitle = {47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023},

pages = {1858–1863},

publisher = {IEEE},

address = {Torino, Italy},

abstract = {In recent years we have understood the importance of analyzing and sequencing human genetic variation. A relevant aspect that emerged from the Covid-19 pandemic was the need to obtain results very quickly; this involved using High-Performance Computing (HPC) environments to execute the Next Generation Sequencing (NGS) pipeline. However, HPC is not always the most suitable environment for the entire execution of a pipeline, especially when it involves many heterogeneous tools. The ability to execute parts of the pipeline on different environments can lead to higher performance but also cheaper executions. This work shows the design and optimization process that led us to a state-of-the-art Variant Calling hybrid workflow based on the StreamFlow Workflow Management System (WfMS). We also compare StreamFlow with Snakemake, an established WfMS targeting HPC facilities, observing comparable performance on single environments and satisfactory improvements with a hybrid cloud-HPC configuration.},

howpublished = {47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023},

keywords = {across, icsc, streamflow},

pubstate = {published},

tppubtype = {inproceedings}

}