Migrating Fortran code to C++ is a common task for many scientific computing teams, driven by the need to leverage modern programming paradigms, enhance cross-platform compatibility, and improve maintainability. Automating this translation process using large language models (LLMs) has shown promise, but the lack of high-quality, specialized datasets has hindered their effectiveness. In this paper, we address this challenge by introducing a novel multi-turn dialogue dataset, Fortran2CPP, specifically designed for Fortran-to-C++ code migration. Our dataset, significantly larger than existing alternatives, is generated using a unique LLM-driven, dual-agent pipeline incorporating iterative compilation, execution, and code repair to ensure high quality and functional correctness. To demonstrate the effectiveness of our dataset, we fine-tuned several open-weight LLMs on Fortran2CPP and evaluated their performance on two independent benchmarks. Fine-tuning on our dataset led to remarkable gains, with models achieving up to a 3.31x increase in CodeBLEU score and a 92\% improvement in compilation success rate. This highlights the dataset's ability to enhance both the syntactic accuracy and compilability of the translated C++ code. Our dataset and model have been open-sourced and are available on our public GitHub repository\footnote{\url{https://github.com/HPC-Fortran2CPP/Fortran2Cpp}}.

With Large Language Models (LLMs) recently demonstrating impressive proficiency in code generation, it is promising to extend their abilities to Hardware Description Language (HDL). However, LLMs tend to generate single HDL code blocks rather than hierarchical structures for hardware designs, leading to hallucinations, particularly in complex designs like Domain-Specific Accelerators (DSAs). To address this, we propose HiVeGen, a hierarchical LLM-based Verilog generation framework that decomposes generation tasks into LLM-manageable hierarchical submodules. HiVeGen further harnesses the advantages of such hierarchical structures by integrating automatic Design Space Exploration (DSE) into hierarchy-aware prompt generation, introducing weight-based retrieval to enhance code reuse, and enabling real-time human-computer interaction to lower error-correction cost, significantly improving the quality of generated designs.

Adversarial examples represent a serious issue for the application of machine learning models in many sensitive domains. For generating adversarial examples, decision based black-box attacks are one of the most practical techniques as they only require query access to the model. One of the most recently proposed state-of-the-art decision based black-box attacks is Triangle Attack (TA). In this paper, we offer a high-level description of TA and explain potential theoretical limitations. We then propose a new decision based black-box attack, Triangle Attack with Reinforcement Learning (TARL). Our new attack addresses the limits of TA by leveraging reinforcement learning. This creates an attack that can achieve similar, if not better, attack accuracy than TA with half as many queries on state-of-the-art classifiers and defenses across ImageNet and CIFAR-10.

Private inference (PI) has emerged as a promising solution to execute computations on encrypted data, safeguarding user privacy and model parameters in edge computing. However, existing PI methods are predominantly developed considering constant resource constraints, overlooking the varied and dynamic resource constraints in diverse edge devices, like energy budgets. Consequently, model providers have to design specialized models for different devices, where all of them have to be stored on the edge server, resulting in inefficient deployment. To fill this gap, this work presents AdaPI, a novel approach that achieves adaptive PI by allowing a model to perform well across edge devices with diverse energy budgets. AdaPI employs a PI-aware training strategy that optimizes the model weights alongside weight-level and feature-level soft masks. These soft masks are subsequently transformed into multiple binary masks to enable adjustments in communication and computation workloads. Through sequentially training the model with increasingly dense binary masks, AdaPI attains optimal accuracy for each energy budget, which outperforms the state-of-the-art PI methods by 7.3\% in terms of test accuracy on CIFAR-100. The code of AdaPI can be accessed via https://github.com/jiahuiiiiii/AdaPI.

This paper presents a novel methodology for predicting international bilateral trade flows, emphasizing the growing importance of Preferential Trade Agreements (PTAs) in the global trade landscape. Acknowledging the limitations of traditional models like the Gravity Model of Trade, this study introduces a two-stage approach combining explainable machine learning and factorization models. The first stage employs SHAP Explainer for effective variable selection, identifying key provisions in PTAs, while the second stage utilizes Factorization Machine models to analyze the pairwise interaction effects of these provisions on trade flows. By analyzing comprehensive datasets, the paper demonstrates the efficacy of this approach. The findings not only enhance the predictive accuracy of trade flow models but also offer deeper insights into the complex dynamics of international trade, influenced by specific bilateral trade provisions.

As privacy-preserving becomes a pivotal aspect of deep learning (DL) development, multi-party computation (MPC) has gained prominence for its efficiency and strong security. However, the practice of current MPC frameworks is limited, especially when dealing with large neural networks, exemplified by the prolonged execution time of 25.8 seconds for secure inference on ResNet-152. The primary challenge lies in the reliance of current MPC approaches on additive secret sharing, which incurs significant communication overhead with non-linear operations such as comparisons. Furthermore, additive sharing suffers from poor scalability on party size. In contrast, the evolving landscape of MPC necessitates accommodating a larger number of compute parties and ensuring robust performance against malicious activities or computational failures. In light of these challenges, we propose SSNet, which for the first time, employs Shamir's secret sharing (SSS) as the backbone of MPC-based ML framework. We meticulously develop all framework primitives and operations for secure DL models tailored to seamlessly integrate with the SSS scheme. SSNet demonstrates the ability to scale up party numbers straightforwardly and embeds strategies to authenticate the computation correctness without incurring significant performance overhead. Additionally, SSNet introduces masking strategies designed to reduce communication overhead associated with non-linear operations. We conduct comprehensive experimental evaluations on commercial cloud computing infrastructure from Amazon AWS, as well as across diverse prevalent DNN models and datasets. SSNet demonstrates a substantial performance boost, achieving speed-ups ranging from 3x to 14x compared to SOTA MPC frameworks. Moreover, SSNet also represents the first framework that is evaluated on a five-party computation setup, in the context of secure DL inference.

With the rapid proliferation of textual data, predicting long texts has emerged as a significant challenge in the domain of natural language processing. Traditional text prediction methods encounter substantial difficulties when grappling with long texts, primarily due to the presence of redundant and irrelevant information, which impedes the model's capacity to capture pivotal insights from the text. To address this issue, we introduce a novel approach to long-text classification and prediction. Initially, we employ embedding techniques to condense the long texts, aiming to diminish the redundancy therein. Subsequently,the Bidirectional Encoder Representations from Transformers (BERT) embedding method is utilized for text classification training. Experimental outcomes indicate that our method realizes considerable performance enhancements in classifying long texts of Preferential Trade Agreements. Furthermore, the condensation of text through embedding methods not only augments prediction accuracy but also substantially reduces computational complexity. Overall, this paper presents a strategy for long-text prediction, offering a valuable reference for researchers and engineers in the natural language processing sphere.

Resistive Random Access Memory (ReRAM) has emerged as a promising platform for deep neural networks (DNNs) due to its support for parallel in-situ matrix-vector multiplication. However, hardware failures, such as stuck-at-fault defects, can result in significant prediction errors during model inference. While additional crossbars can be used to address these failures, they come with storage overhead and are not efficient in terms of space, energy, and cost. In this paper, we propose a fault protection mechanism that incurs zero space cost. Our approach includes: 1) differentiable structure pruning of rows and columns to reduce model redundancy, 2) weight duplication and voting for robust output, and 3) embedding duplicated most significant bits (MSBs) into the model weight. We evaluate our method on nine tasks of the GLUE benchmark with the BERT model, and experimental results prove its effectiveness.

The increasing use of Advanced Language Models (ALMs) in diverse sectors, particularly due to their impressive capability to generate top-tier content following linguistic instructions, forms the core of this investigation. This study probes into ALMs' deployment in electronic hardware design, with a specific emphasis on the synthesis and enhancement of Verilog programming. We introduce an innovative framework, crafted to assess and amplify ALMs' productivity in this niche. The methodology commences with the initial crafting of Verilog programming via ALMs, succeeded by a distinct dual-stage refinement protocol. The premier stage prioritizes augmenting the code's operational and linguistic precision, while the latter stage is dedicated to aligning the code with Power-Performance-Area (PPA) benchmarks, a pivotal component in proficient hardware design. This bifurcated strategy, merging error remediation with PPA enhancement, has yielded substantial upgrades in the caliber of ALM-created Verilog programming. Our framework achieves an 81.37% rate in linguistic accuracy and 62.0% in operational efficacy in programming synthesis, surpassing current leading-edge techniques, such as 73% in linguistic accuracy and 46% in operational efficacy. These findings illuminate ALMs' aptitude in tackling complex technical domains and signal a positive shift in the mechanization of hardware design operations.

Multi-Task Learning (MTL) involves developing a singular model, known as a multi-task model, to concurrently perform multiple tasks. While the security of single-task models has been thoroughly studied, multi-task models pose several critical security questions, such as 1) their vulnerability to single-task adversarial attacks, 2) the possibility of designing attacks that target multiple tasks, and 3) the impact of task sharing and adversarial training on their resilience to such attacks. This paper addresses these queries through detailed analysis and rigorous experimentation. First, we explore the adaptation of single-task white-box attacks to multi-task models and identify their limitations. We then introduce a novel attack framework, the Gradient Balancing Multi-Task Attack (GB-MTA), which treats attacking a multi-task model as an optimization problem. This problem, based on averaged relative loss change across tasks, is approximated as an integer linear programming problem. Extensive evaluations on MTL benchmarks, NYUv2 and Tiny-Taxonomy, demonstrate GB-MTA's effectiveness against both standard and adversarially trained multi-task models. The results also highlight a trade-off between task accuracy improvement via parameter sharing and increased model vulnerability due to enhanced attack transferability.