pwc-archive/papers-with-abstracts huggingface dataset:
HW-TSC's Submission to the CCMT 2024 Machine Translation Tasks
Authors: Zhanglin Wu, Yuanchang Luo, Daimeng Wei, Jiawei Zheng, Bin Wei, Zongyao Li, Hengchao Shang, Jiaxin Guo, Shaojun Li, Weidong Zhang, Ning Xie, Hao Yang
Date: 2024-09-23 00:00:00
Tasks: Automatic Post-Editing, Ensemble Learning, Language Modeling, Language Modelling, Large Language Model, Machine Translation, NMT, Translation

This paper presents the submission of Huawei Translation Services Center (HW-TSC) to machine translation tasks of the 20th China Conference on Machine Translation (CCMT 2024). We participate in the bilingual machine translation task and multi-domain machine translation task. For these two translation tasks, we use training strategies such as regularized dropout, bidirectional training, data diversification, forward translation, back translation, alternated training, curriculum learning, and transductive ensemble learning to train neural machine translation (NMT) models based on the deep Transformer-big architecture. Furthermore, to explore whether large language model (LLM) can help improve the translation quality of NMT systems, we use supervised fine-tuning to train llama2-13b as an Automatic post-editing (APE) model to improve the translation results of the NMT model on the multi-domain machine translation task. By using these plyometric strategies, our submission achieves a competitive result in the final evaluation.
A-VL: Adaptive Attention for Large Vision-Language Models
Authors: Junyang Zhang, Mu Yuan, Ruiguang Zhong, Puhan Luo, Huiyou Zhan, Ningkang Zhang, Chengchen Hu, Xiangyang Li
Date: 2024-09-23 00:00:00
Tasks: Language Modeling, Language Modelling

The Large Vision-Language Model (LVLM) integrates computer vision and natural language processing techniques, offering substantial application potential. However, these models demand extensive resources during inference. Adaptive attention techniques can dynamically reduce computational redundancy and thus improve efficiency. Although current adaptive attention methods significantly reduce the memory requirements of Transformer-based language models, they are not tailored for LVLMs. We observe that LVLMs generate responses from both remote image tokens and local text tokens, and different modalities have different attention patterns. This observation inspires us to manage the attention for each modality separately. Specifically, for visual input, we store the cache of potentially useful information but only compute the most critical parts. For language input, we care more about local information. Based on our observation and analysis of vision-language attention patterns, we develop A-VL, a plug-and-play adaptive attention tailored for LVLM inference. Extensive evaluations on three vision-language tasks and five datasets show the effectiveness of our designs. Our approach A-VL outperforms existing adaptive attention methods in reducing memory usage and computational load without compromising performance.
TS-HTFA: Advancing Time Series Forecasting via Hierarchical Text-Free Alignment with Large Language Models
Authors: Pengfei Wang, Huanran Zheng, Qi'ao Xu, Silong Dai, Yiqiao Wang, Wenjing Yue, Wei Zhu, Tianwen Qian, Xiaoling Wang
Date: 2024-09-23 00:00:00
Tasks: Contrastive Learning, cross-modal alignment, Knowledge Distillation, Time Series, Time Series Analysis, Time Series Forecasting, Triplet, Word Embeddings

Given the significant potential of large language models (LLMs) in sequence modeling, emerging studies have begun applying them to time-series forecasting. Despite notable progress, existing methods still face two critical challenges: 1) their reliance on large amounts of paired text data, limiting the model applicability, and 2) a substantial modality gap between text and time series, leading to insufficient alignment and suboptimal performance. In this paper, we introduce \textbf{H}ierarchical \textbf{T}ext-\textbf{F}ree \textbf{A}lignment (\textbf{TS-HTFA}), a novel method that leverages hierarchical alignment to fully exploit the representation capacity of LLMs while eliminating the dependence on text data. Specifically, we replace paired text data with adaptive virtual text based on QR decomposition word embeddings and learnable prompt. Furthermore, we establish comprehensive cross-modal alignment at three levels: input, feature, and output. Extensive experiments on multiple time-series benchmarks demonstrate that HTFA achieves state-of-the-art performance, significantly improving prediction accuracy and generalization.
Multi-Modal Generative AI: Multi-modal LLM, Diffusion and Beyond
Authors: Hong Chen, Xin Wang, Yuwei Zhou, Bin Huang, YiPeng Zhang, Wei Feng, Houlun Chen, Zeyang Zhang, Siao Tang, Wenwu Zhu
Date: 2024-09-23 00:00:00
Tasks: Language Modelling, Large Language Model, Mixture-of-Experts, Video Generation

Multi-modal generative AI has received increasing attention in both academia and industry. Particularly, two dominant families of techniques are: i) The multi-modal large language model (MLLM) such as GPT-4V, which shows impressive ability for multi-modal understanding; ii) The diffusion model such as Sora, which exhibits remarkable multi-modal powers, especially with respect to visual generation. As such, one natural question arises: Is it possible to have a unified model for both understanding and generation? To answer this question, in this paper, we first provide a detailed review of both MLLM and diffusion models, including their probabilistic modeling procedure, multi-modal architecture design, and advanced applications to image/video large language models as well as text-to-image/video generation. Then, we discuss the two important questions on the unified model: i) whether the unified model should adopt the auto-regressive or diffusion probabilistic modeling, and ii) whether the model should utilize a dense architecture or the Mixture of Experts(MoE) architectures to better support generation and understanding, two objectives. We further provide several possible strategies for building a unified model and analyze their potential advantages and disadvantages. We also summarize existing large-scale multi-modal datasets for better model pretraining in the future. To conclude the paper, we present several challenging future directions, which we believe can contribute to the ongoing advancement of multi-modal generative AI.
Analogous Alignments: Digital "Formally" meets Analog
Authors: Hansa Mohanty, Deepak Narayan Gadde
Date: 2024-09-23 00:00:00

The complexity of modern-day System-on-Chips (SoCs) is continually increasing, and it becomes increasingly challenging to deliver dependable and credible chips in a short time-to-market. Especially, in the case of test chips, where the aim is to study the feasibility of the design, time is a crucial factor. Pre-silicon functional verification is one of the main contributors that makes up a large portion of the product development cycle. Verification engineers often loosely verify test chips that turn out to be non-functional on the silicon, ultimately resulting in expensive re-spins. To left-shift the verification efforts, formal verification is a powerful methodology that aims to exhaustively verify designs, giving better confidence in the overall quality. This paper focuses on the pragmatic formal verification of a mixed signal Intellectual Property (IP) that has a combination of digital and analog blocks. This paper discusses a novel approach of including the analog behavioral model into the formal verification setup. Digital and Analog Mixed-Signal (AMS) designs, which are fundamentally different in nature, are integrated seamlessly in a formal verification setup, a concept that can be referred to as "Analogous Alignments". Our formal setup leverages powerful formal techniques such as FPV, CSR verification, and connectivity checks. The properties used for FPV are auto-generated using a metamodeling framework. The paper also discusses the challenges faced especially related to state-space explosion, non-compatibility of formal with AMS models, and techniques to mitigate them such as k-induction. With this verification approach, we were able to exhaustively verify the design within a reasonable time and with sufficient coverage. We also reported several bugs at an early stage, making the complete design verification process iterative and effective.
Acting for the Right Reasons: Creating Reason-Sensitive Artificial Moral Agents
Authors: Kevin Baum, Lisa Dargasz, Felix Jahn, Timo P. Gros, Verena Wolf
Date: 2024-09-23 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning

We propose an extension of the reinforcement learning architecture that enables moral decision-making of reinforcement learning agents based on normative reasons. Central to this approach is a reason-based shield generator yielding a moral shield that binds the agent to actions that conform with recognized normative reasons so that our overall architecture restricts the agent to actions that are (internally) morally justified. In addition, we describe an algorithm that allows to iteratively improve the reason-based shield generator through case-based feedback from a moral judge.
SPformer: A Transformer Based DRL Decision Making Method for Connected Automated Vehicles
Authors: Ye Han, Lijun Zhang, Dejian Meng, Xingyu Hu, Yixia Lu
Date: 2024-09-23 00:00:00
Tasks: Autonomous Driving, Decision Making, Deep Reinforcement Learning

In mixed autonomy traffic environment, every decision made by an autonomous-driving car may have a great impact on the transportation system. Because of the complex interaction between vehicles, it is challenging to make decisions that can ensure both high traffic efficiency and safety now and futher. Connected automated vehicles (CAVs) have great potential to improve the quality of decision-making in this continuous, highly dynamic and interactive environment because of their stronger sensing and communicating ability. For multi-vehicle collaborative decision-making algorithms based on deep reinforcement learning (DRL), we need to represent the interactions between vehicles to obtain interactive features. The representation in this aspect directly affects the learning efficiency and the quality of the learned policy. To this end, we propose a CAV decision-making architecture based on transformer and reinforcement learning algorithms. A learnable policy token is used as the learning medium of the multi-vehicle joint policy, the states of all vehicles in the area of interest can be adaptively noticed in order to extract interactive features among agents. We also design an intuitive physical positional encodings, the redundant location information of which optimizes the performance of the network. Simulations show that our model can make good use of all the state information of vehicles in traffic scenario, so as to obtain high-quality driving decisions that meet efficiency and safety objectives. The comparison shows that our method significantly improves existing DRL-based multi-vehicle cooperative decision-making algorithms.
ChatGPT as a Solver and Grader of Programming Exams written in Spanish
Authors: Pablo Fernández-Saborido, Marcos Fernández-Pichel, David E. Losada
Date: 2024-09-23 00:00:00

Evaluating the capabilities of Large Language Models (LLMs) to assist teachers and students in educational tasks is receiving increasing attention. In this paper, we assess ChatGPT's capacities to solve and grade real programming exams, from an accredited BSc degree in Computer Science, written in Spanish. Our findings suggest that this AI model is only effective for solving simple coding tasks. Its proficiency in tackling complex problems or evaluating solutions authored by others are far from effective. As part of this research, we also release a new corpus of programming tasks and the corresponding prompts for solving the problems or grading the solutions. This resource can be further exploited by other research teams.
Evaluating ML Robustness in GNSS Interference Classification, Characterization \& Localization
Authors: Lucas Heublein, Tobias Feigl, Thorsten Nowak, Alexander Rügamer, Christopher Mutschler, Felix Ott
Date: 2024-09-23 00:00:00

Jamming devices present a significant threat by disrupting signals from the global navigation satellite system (GNSS), compromising the robustness of accurate positioning. The detection of anomalies within frequency snapshots is crucial to counteract these interferences effectively. A critical preliminary measure involves the reliable classification of interferences and characterization and localization of jamming devices. This paper introduces an extensive dataset compromising snapshots obtained from a low-frequency antenna, capturing diverse generated interferences within a large-scale environment including controlled multipath effects. Our objective is to assess the resilience of ML models against environmental changes, such as multipath effects, variations in interference attributes, such as the interference class, bandwidth, and signal-to-noise ratio, the accuracy jamming device localization, and the constraints imposed by snapshot input lengths. By analyzing the aleatoric and epistemic uncertainties, we demonstrate the adaptness of our model in generalizing across diverse facets, thus establishing its suitability for real-world applications. https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/controlled_low_frequency
Log-normal Mutations and their Use in Detecting Surreptitious Fake Images
Authors: Ismail Labiad, Thomas Bäck, Pierre Fernandez, Laurent Najman, Tom Sander, Furong Ye, Mariia Zameshina, Olivier Teytaud
Date: 2024-09-23 00:00:00

In many cases, adversarial attacks are based on specialized algorithms specifically dedicated to attacking automatic image classifiers. These algorithms perform well, thanks to an excellent ad hoc distribution of initial attacks. However, these attacks are easily detected due to their specific initial distribution. We therefore consider other black-box attacks, inspired from generic black-box optimization tools, and in particular the log-normal algorithm. We apply the log-normal method to the attack of fake detectors, and get successful attacks: importantly, these attacks are not detected by detectors specialized on classical adversarial attacks. Then, combining these attacks and deep detection, we create improved fake detectors.
Boosting Healthcare LLMs Through Retrieved Context
Authors: Jordi Bayarri-Planas, Ashwin Kumar Gururajan, Dario Garcia-Gasulla
Date: 2024-09-23 00:00:00
Tasks: Benchmarking, Multiple-choice, Question Answering, Retrieval

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing, and yet, their factual inaccuracies and hallucinations limits their application, particularly in critical domains like healthcare. Context retrieval methods, by introducing relevant information as input, have emerged as a crucial approach for enhancing LLM factuality and reliability. This study explores the boundaries of context retrieval methods within the healthcare domain, optimizing their components and benchmarking their performance against open and closed alternatives. Our findings reveal how open LLMs, when augmented with an optimized retrieval system, can achieve performance comparable to the biggest private solutions on established healthcare benchmarks (multiple-choice question answering). Recognizing the lack of realism of including the possible answers within the question (a setup only found in medical exams), and after assessing a strong LLM performance degradation in the absence of those options, we extend the context retrieval system in that direction. In particular, we propose OpenMedPrompt a pipeline that improves the generation of more reliable open-ended answers, moving this technology closer to practical application.
Automatic Feature Learning for Essence: a Case Study on Car Sequencing
Authors: Alessio Pellegrino, Özgür Akgün, Nguyen Dang, Zeynep Kiziltan, Ian Miguel
Date: 2024-09-23 00:00:00
Tasks: Language Modeling, Language Modelling

Constraint modelling languages such as Essence offer a means to describe combinatorial problems at a high-level, i.e., without committing to detailed modelling decisions for a particular solver or solving paradigm. Given a problem description written in Essence, there are multiple ways to translate it to a low-level constraint model. Choosing the right combination of a low-level constraint model and a target constraint solver can have significant impact on the effectiveness of the solving process. Furthermore, the choice of the best combination of constraint model and solver can be instance-dependent, i.e., there may not exist a single combination that works best for all instances of the same problem. In this paper, we consider the task of building machine learning models to automatically select the best combination for a problem instance. A critical part of the learning process is to define instance features, which serve as input to the selection model. Our contribution is automatic learning of instance features directly from the high-level representation of a problem instance using a language model. We evaluate the performance of our approach using the Essence modelling language with a case study involving the car sequencing problem.
Goal-based Neural Physics Vehicle Trajectory Prediction Model
Authors: Rui Gan, Haotian Shi, Pei Li, Keshu Wu, Bocheng An, Linheng Li, Junyi Ma, Chengyuan Ma, Bin Ran
Date: 2024-09-23 00:00:00
Tasks: Autonomous Driving, model, Prediction, Trajectory Prediction

Vehicle trajectory prediction plays a vital role in intelligent transportation systems and autonomous driving, as it significantly affects vehicle behavior planning and control, thereby influencing traffic safety and efficiency. Numerous studies have been conducted to predict short-term vehicle trajectories in the immediate future. However, long-term trajectory prediction remains a major challenge due to accumulated errors and uncertainties. Additionally, balancing accuracy with interpretability in the prediction is another challenging issue in predicting vehicle trajectory. To address these challenges, this paper proposes a Goal-based Neural Physics Vehicle Trajectory Prediction Model (GNP). The GNP model simplifies vehicle trajectory prediction into a two-stage process: determining the vehicle's goal and then choosing the appropriate trajectory to reach this goal. The GNP model contains two sub-modules to achieve this process. The first sub-module employs a multi-head attention mechanism to accurately predict goals. The second sub-module integrates a deep learning model with a physics-based social force model to progressively predict the complete trajectory using the generated goals. The GNP demonstrates state-of-the-art long-term prediction accuracy compared to four baseline models. We provide interpretable visualization results to highlight the multi-modality and inherent nature of our neural physics framework. Additionally, ablation studies are performed to validate the effectiveness of our key designs.
Chattronics: using GPTs to assist in the design of data acquisition systems
Authors: Jonathan Paul Driemeyer Brown, Tiago Oliveira Weber
Date: 2024-09-23 00:00:00

The usefulness of Large Language Models (LLM) is being continuously tested in various fields. However, their intrinsic linguistic characteristic is still one of the limiting factors when applying these models to exact sciences. In this article, a novel approach to use General Pre-Trained Transformers to assist in the design phase of data acquisition systems will be presented. The solution is packaged in the form of an application that retains the conversational aspects of LLMs, in such a manner that the user must provide details on the desired project in order for the model to draft both a system-level architectural diagram and the block-level specifications, following a Top-Down methodology based on restrictions. To test this tool, two distinct user emulations were used, one of which uses an additional GPT model. In total, 4 different data acquisition projects were used in the testing phase, each with its own measurement requirements: angular position, temperature, acceleration and a fourth project with both pressure and superficial temperature measurements. After 160 test iterations, the study concludes that there is potential for these models to serve adequately as synthesis/assistant tools for data acquisition systems, but there are still technological limitations. The results show coherent architectures and topologies, but that GPTs have difficulties in simultaneously considering all requirements and many times commits theoretical mistakes.
MACeIP: A Multimodal Ambient Context-enriched Intelligence Platform in Smart Cities
Authors: Truong Thanh Hung Nguyen, Phuc Truong Loc Nguyen, Monica Wachowicz, Hung Cao
Date: 2024-09-23 00:00:00
Tasks: Asset Management, Cloud Computing, Management

This paper presents a Multimodal Ambient Context-enriched Intelligence Platform (MACeIP) for Smart Cities, a comprehensive system designed to enhance urban management and citizen engagement. Our platform integrates advanced technologies, including Internet of Things (IoT) sensors, edge and cloud computing, and Multimodal AI, to create a responsive and intelligent urban ecosystem. Key components include Interactive Hubs for citizen interaction, an extensive IoT sensor network, intelligent public asset management, a pedestrian monitoring system, a City Planning Portal, and a Cloud Computing System. We demonstrate the prototype of MACeIP in several cities, focusing on Fredericton, New Brunswick. This work contributes to innovative city development by offering a scalable, efficient, and user-centric approach to urban intelligence and management.
Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges
Authors: Harikha Manthena, Shaghayegh Shajarian, Jeffrey Kimmell, Mahmoud Abdelsalam, Sajad Khorsandroo, Maanak Gupta
Date: 2024-09-09 00:00:00
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), Malware Analysis, Malware Classification, Malware Detection, Survey

Machine learning (ML) has rapidly advanced in recent years, revolutionizing fields such as finance, medicine, and cybersecurity. In malware detection, ML-based approaches have demonstrated high accuracy; however, their lack of transparency poses a significant challenge. Traditional black-box models often fail to provide interpretable justifications for their predictions, limiting their adoption in security-critical environments where understanding the reasoning behind a detection is essential for threat mitigation and response. Explainable AI (XAI) addresses this gap by enhancing model interpretability while maintaining strong detection capabilities. This survey presents a comprehensive review of state-of-the-art ML techniques for malware analysis, with a specific focus on explainability methods. We examine existing XAI frameworks, their application in malware classification and detection, and the challenges associated with making malware detection models more interpretable. Additionally, we explore recent advancements and highlight open research challenges in the field of explainable malware analysis. By providing a structured overview of XAI-driven malware detection approaches, this survey serves as a valuable resource for researchers and practitioners seeking to bridge the gap between ML performance and explainability in cybersecurity.