pwc-archive/papers-with-abstracts huggingface dataset:
Fast geometrical extraction of nearest neighbors from multi-dimensional data
Authors: Yasir Aziz, Kashif Hussain Memon
Date: 2023-04-01 00:00:00
Proceeding: Pattern Recognition Journal 2023 4

K-Nearest Neighbor (KNN) algorithm plays a significant role in various fields of data science and machine learning. Most variants of the KNN algorithm involve distance computations and a parameter (K) that represents the required number of neighbors. The recent research regarding distance computations and finding the optimal value of K have made neighborhood extraction a slow process. This research presents a fast geometrical approach for neighborhood extraction from multi-dimensional data. Instead of distance computations, the proposed algorithm creates a geometrical shape based on the number of features of data. This geometrical shape encompasses the reference data point and the neighboring points. The pro- posed algorithm’s efficiency of time, classification, and hashing are evaluated and compared with existing state-of-the-art algorithms.
Self-Organizing Maps of Unbiased Ligand-Target Binding Pathways and Kinetics
Authors: Lara Callea, Camilla Caprai, Laura Bonati, Toni Giorgino, Stefano Motta
Date: 2024-09-19 00:00:00
Tasks: Drug Design, Drug Discovery, Specificity

The interpretation of ligand-target interactions at atomistic resolution is central to most efforts in computational drug discovery and optimization. However, the highly dynamic nature of protein targets, as well as possible induced fit effects, makes difficult to sample many interactions effectively with docking studies or even with large-scale molecular dynamics (MD) simulations. We propose a novel application of Self-Organizing Maps (SOM) to address the sampling and dynamic mapping tasks, particularly in cases involving ligand flexibility and induced fit. The SOM approach offers a data-driven strategy to create a map of the interaction process and pathways based on unbiased MD. Furthermore, we show how the preliminary SOM mapping is complementary to kinetic analysis, both with the employment of network-based approaches and Markov State Models (MSM). We demonstrate the method by comprehensively mapping a large dataset of 640 {\mu}s of unbiased trajectories sampling the recognition process between the phosphorylated YEEI peptide and its high-specificity target Lck-SH2. The integration of SOM into unbiased simulation protocols significantly advances our understanding of the ligand binding mechanism. This approach serves as a potent tool for mapping intricate ligand-target interactions with unprecedented detail, thereby enhancing the characterization of kinetic properties crucial to drug design.
When does cyclic dominance lead to stable spiral waves?
Authors: Bartosz Szczesny, Mauro Mobilia, Alastair M. Rucklidge
Date: 2012-10-31 00:00:00
Tasks: Diversity

Species diversity in ecosystems is often accompanied by the self-organisation of the population into fascinating spatio-temporal patterns. Here, we consider a two-dimensional three-species population model and study the spiralling patterns arising from the combined effects of generic cyclic dominance, mutation, pair-exchange and hopping of the individuals. The dynamics is characterised by nonlinear mobility and a Hopf bifurcation around which the system's phase diagram is inferred from the underlying complex Ginzburg-Landau equation derived using a perturbative multiscale expansion. While the dynamics is generally characterised by spiralling patterns, we show that spiral waves are stable in only one of the four phases. Furthermore, we characterise a phase where nonlinearity leads to the annihilation of spirals and to the spatially uniform dominance of each species in turn. Away from the Hopf bifurcation, when the coexistence fixed point is unstable, the spiralling patterns are also affected by nonlinear diffusion.
含分布式能源配电网中考虑供电可靠性的混合储能优化配置
Authors: 赵宇
Date: 2024-04-03 00:00:00
Proceeding: 智慧电力 2024 4

针对分布式能源（DERs）接入对配电网供电可靠性带来的不确定性问题，提出了计及供电可靠性的配电
网混合储能系统（HESS）优化配置双层模型。首先，提取了5种负荷用户典型负荷时序曲线和风电、光伏机组典
型出力时序曲线，并且基于序贯蒙特卡洛法建立了考虑DERs孤岛、HESS随机故障及运行策略的配电网供电可
靠性仿真模型；其次，针对配电网故障情况下HESS运行优化问题，提出故障停电场景下HESS多目标优化配置
双层模型以及考虑HESS容量分点和功率分点的充放电策略。最后，以改进IEEE-31节点系统进行算例验证，结
果显示所提模型可使配电网综合供电可靠性提升0.118%，且DERs消纳量显著提高30%以上。
The Gaussian Discriminant Variational Autoencoder (GdVAE): A Self-Explainable Model with Counterfactual Explanations
Authors: Anselm Haselhoff, Kevin Trelenberg, Fabian Küppers, Jonas Schneider
Date: 2024-09-19 00:00:00
Tasks: Classification, counterfactual, Counterfactual Explanation, Explainable Artificial Intelligence (XAI), Explainable Models, Riemannian optimization

Visual counterfactual explanation (CF) methods modify image concepts, e.g, shape, to change a prediction to a predefined outcome while closely resembling the original query image. Unlike self-explainable models (SEMs) and heatmap techniques, they grant users the ability to examine hypothetical "what-if" scenarios. Previous CF methods either entail post-hoc training, limiting the balance between transparency and CF quality, or demand optimization during inference. To bridge the gap between transparent SEMs and CF methods, we introduce the GdVAE, a self-explainable model based on a conditional variational autoencoder (CVAE), featuring a Gaussian discriminant analysis (GDA) classifier and integrated CF explanations. Full transparency is achieved through a generative classifier that leverages class-specific prototypes for the downstream task and a closed-form solution for CFs in the latent space. The consistency of CFs is improved by regularizing the latent space with the explainer function. Extensive comparisons with existing approaches affirm the effectiveness of our method in producing high-quality CF explanations while preserving transparency. Code and models are public.
The Unseen AI Disruptions for Power Grids: LLM-Induced Transients
Authors: Yuzhuo Li, Mariam Mughees, Yize Chen, Yunwei Ryan Li
Date: 2024-09-09 00:00:00

Recent breakthroughs of large language models (LLMs) have exhibited superior capability across major industries and stimulated multi-hundred-billion-dollar investment in AI-centric data centers in the next 3-5 years. This, in turn, bring the increasing concerns on sustainability and AI-related energy usage. However, there is a largely overlooked issue as challenging and critical as AI model and infrastructure efficiency: the disruptive dynamic power consumption behaviour. With fast, transient dynamics, AI infrastructure features ultra-low inertia, sharp power surge and dip, and a significant peak-idle power ratio. The power scale covers from several hundred watts to megawatts, even to gigawatts. These never-seen-before characteristics make AI a very unique load and pose threats to the power grid reliability and resilience. To reveal this hidden problem, this paper examines the scale of AI power consumption, analyzes AI transient behaviour in various scenarios, develops high-level mathematical models to depict AI workload behaviour and discusses the multifaceted challenges and opportunities they potentially bring to existing power grids. Observing the rapidly evolving machine learning (ML) and AI technologies, this work emphasizes the critical need for interdisciplinary approaches to ensure reliable and sustainable AI infrastructure development, and provides a starting point for researchers and practitioners to tackle such challenges.
RTLRewriter: Methodologies for Large Models aided RTL Code Optimization
Authors: Xufeng Yao, Yiwen Wang, Xing Li, Yingzhao Lian, Ran Chen, Lei Chen, Mingxuan Yuan, Hong Xu, Bei Yu
Date: 2024-09-04 00:00:00
Tasks: Benchmarking

Register Transfer Level (RTL) code optimization is crucial for enhancing the efficiency and performance of digital circuits during early synthesis stages. Currently, optimization relies heavily on manual efforts by skilled engineers, often requiring multiple iterations based on synthesis feedback. In contrast, existing compiler-based methods fall short in addressing complex designs. This paper introduces RTLRewriter, an innovative framework that leverages large models to optimize RTL code. A circuit partition pipeline is utilized for fast synthesis and efficient rewriting. A multi-modal program analysis is proposed to incorporate vital visual diagram information as optimization cues. A specialized search engine is designed to identify useful optimization guides, algorithms, and code snippets that enhance the model ability to generate optimized RTL. Additionally, we introduce a Cost-aware Monte Carlo Tree Search (C-MCTS) algorithm for efficient rewriting, managing diverse retrieved contents and steering the rewriting results. Furthermore, a fast verification pipeline is proposed to reduce verification cost. To cater to the needs of both industry and academia, we propose two benchmarking suites: the Large Rewriter Benchmark, targeting complex scenarios with extensive circuit partitioning, optimization trade-offs, and verification challenges, and the Small Rewriter Benchmark, designed for a wider range of scenarios and patterns. Our comparative analysis with established compilers such as Yosys and E-graph demonstrates significant improvements, highlighting the benefits of integrating large models into the early stages of circuit design. We provide our benchmarks at https://github.com/yaoxufeng/RTLRewriter-Bench.
AIvril: AI-Driven RTL Generation With Verification In-The-Loop
Authors: Mubashir ul Islam, Humza Sami, Pierre-Emmanuel Gaillardon, Valerio Tenace
Date: 2024-09-03 00:00:00
Tasks: Code Generation

Large Language Models (LLMs) are computational models capable of performing complex natural language processing tasks. Leveraging these capabilities, LLMs hold the potential to transform the entire hardware design stack, with predictions suggesting that front-end and back-end tasks could be fully automated in the near future. Currently, LLMs show great promise in streamlining Register Transfer Level (RTL) generation, enhancing efficiency, and accelerating innovation. However, their probabilistic nature makes them prone to inaccuracies - a significant drawback in RTL design, where reliability and precision are essential. To address these challenges, this paper introduces AIvril, an advanced framework designed to enhance the accuracy and reliability of RTL-aware LLMs. AIvril employs a multi-agent, LLM-agnostic system for automatic syntax correction and functional verification, significantly reducing - and in many cases, completely eliminating - instances of erroneous code generation. Experimental results conducted on the VerilogEval-Human dataset show that our framework improves code quality by nearly 2x when compared to previous works, while achieving an 88.46% success rate in meeting verification objectives. This represents a critical step toward automating and optimizing hardware design workflows, offering a more dependable methodology for AI-driven RTL design.
Applying Action Masking and Curriculum Learning Techniques to Improve Data Efficiency and Overall Performance in Operational Technology Cyber Security using Reinforcement Learning
Authors: Alec Wilson, William Holmes, Ryan Menzies, Kez Smithson Whitehead
Date: 2024-09-13 00:00:00

In previous work, the IPMSRL environment (Integrated Platform Management System Reinforcement Learning environment) was developed with the aim of training defensive RL agents in a simulator representing a subset of an IPMS on a maritime vessel under a cyber-attack. This paper extends the use of IPMSRL to enhance realism including the additional dynamics of false positive alerts and alert delay. Applying curriculum learning, in the most difficult environment tested, resulted in an episode reward mean increasing from a baseline result of -2.791 to -0.569. Applying action masking, in the most difficult environment tested, resulted in an episode reward mean increasing from a baseline result of -2.791 to -0.743. Importantly, this level of performance was reached in less than 1 million timesteps, which was far more data efficient than vanilla PPO which reached a lower level of performance after 2.5 million timesteps. The training method which resulted in the highest level of performance observed in this paper was a combination of the application of curriculum learning and action masking, with a mean episode reward of 0.137. This paper also introduces a basic hardcoded defensive agent encoding a representation of cyber security best practice, which provides context to the episode reward mean figures reached by the RL agents. The hardcoded agent managed an episode reward mean of -1.895. This paper therefore shows that applications of curriculum learning and action masking, both independently and in tandem, present a way to overcome the complex real-world dynamics that are present in operational technology cyber security threat remediation.
Generated Data with Fake Privacy: Hidden Dangers of Fine-tuning Large Language Models on Generated Data
Authors: Atilla Akkus, Masoud Poorghaffar Aghdam, Mingjie Li, Junjie Chu, Michael Backes, Yang Zhang, Sinem Sav
Date: 2024-09-12 00:00:00
Tasks: Synthetic Data Generation

Large language models (LLMs) have demonstrated significant success in various domain-specific tasks, with their performance often improving substantially after fine-tuning. However, fine-tuning with real-world data introduces privacy risks. To mitigate these risks, developers increasingly rely on synthetic data generation as an alternative to using real data, as data generated by traditional models is believed to be different from real-world data. However, with the advanced capabilities of LLMs, the distinction between real data and data generated by these models has become nearly indistinguishable. This convergence introduces similar privacy risks for generated data to those associated with real data. Our study investigates whether fine-tuning with LLM-generated data truly enhances privacy or introduces additional privacy risks by examining the structural characteristics of data generated by LLMs, focusing on two primary fine-tuning approaches: supervised fine-tuning (SFT) with unstructured (plain-text) generated data and self-instruct tuning. In the scenario of SFT, the data is put into a particular instruction tuning format used by previous studies. We use Personal Information Identifier (PII) leakage and Membership Inference Attacks (MIAs) on the Pythia Model Suite and Open Pre-trained Transformer (OPT) to measure privacy risks. Notably, after fine-tuning with unstructured generated data, the rate of successful PII extractions for Pythia increased by over 20%, highlighting the potential privacy implications of such approaches. Furthermore, the ROC-AUC score of MIAs for Pythia-6.9b, the second biggest model of the suite, increases over 40% after self-instruct tuning. Our results indicate the potential privacy risks associated with fine-tuning LLMs using generated data, underscoring the need for careful consideration of privacy safeguards in such approaches.
Semantic Interoperability on Blockchain by Generating Smart Contracts Based on Knowledge Graphs
Authors: William Van Woensel, Oshani Seneviratne
Date: 2024-09-11 00:00:00
Tasks: Code Generation, Decision Making, Knowledge Graphs

Background: Health 3.0 allows decision making to be based on longitudinal data from multiple institutions, from across the patient's healthcare journey. In such a distributed setting, blockchain smart contracts can act as neutral intermediaries to implement trustworthy decision making. Objective: In a distributed setting, transmitted data will be structured using standards (such as HL7 FHIR) for semantic interoperability. In turn, the smart contract will require interoperability with this standard, implement a complex communication setup (e.g., using oracles), and be developed using blockchain languages (e.g., Solidity). We propose the encoding of smart contract logic using a high-level semantic Knowledge Graph, using concepts from the domain standard. We then deploy this semantic KG on blockchain. Methods: Off-chain, a code generation pipeline compiles the KG into a concrete smart contract, which is then deployed on-chain. Our pipeline targets an intermediary bridge representation, which can be transpiled into a specific blockchain language. Our choice avoids on-chain rule engines, with unpredictable and likely higher computational cost; it is thus in line with the economic rules of blockchain. Results: We applied our code generation approach to generate smart contracts for 3 health insurance cases from Medicare. We discuss the suitability of our approach - the need for a neutral intermediary - for a number of healthcare use cases. Our evaluation finds that the generated contracts perform well in terms of correctness and execution cost ("gas") on blockchain. Conclusions: We showed that it is feasible to automatically generate smart contract code based on a semantic KG, in a way that respects the economic rules of blockchain. Future work includes studying the use of Large Language Models (LLM) in our approach, and evaluations on other blockchains.
NoPhish: Efficient Chrome Extension for Phishing Detection Using Machine Learning Techniques
Authors: Leand Thaqi, Arbnor Halili, Kamer Vishi, Blerim Rexha
Date: 2024-09-01 00:00:00

The growth of digitalization services via web browsers has simplified our daily routine of doing business. But at the same time, it has made the web browser very attractive for several cyber-attacks. Web phishing is a well-known cyberattack that is used by attackers camouflaging as trustworthy web servers to obtain sensitive user information such as credit card numbers, bank information, personal ID, social security number, and username and passwords. In recent years many techniques have been developed to identify the authentic web pages that users visit and warn them when the webpage is phishing. In this paper, we have developed an extension for Chrome the most favorite web browser, that will serve as a middleware between the user and phishing websites. The Chrome extension named "NoPhish" shall identify a phishing webpage based on several Machine Learning techniques. We have used the training dataset from "PhishTank" and extracted the 22 most popular features as rated by the Alexa database. The training algorithms used are Random Forest, Support Vector Machine, and k-Nearest Neighbor. The performance results show that Random Forest delivers the best precision.
AI Literacy for All: Adjustable Interdisciplinary Socio-technical Curriculum
Authors: Sri Yash Tadimalla, Mary Lou Maher
Date: 2024-09-02 00:00:00
Tasks: All

This paper presents a curriculum, "AI Literacy for All," to promote an interdisciplinary understanding of AI, its socio-technical implications, and its practical applications for all levels of education. With the rapid evolution of artificial intelligence (AI), there is a need for AI literacy that goes beyond the traditional AI education curriculum. AI literacy has been conceptualized in various ways, including public literacy, competency building for designers, conceptual understanding of AI concepts, and domain-specific upskilling. Most of these conceptualizations were established before the public release of Generative AI (Gen-AI) tools like ChatGPT. AI education has focused on the principles and applications of AI through a technical lens that emphasizes the mastery of AI principles, the mathematical foundations underlying these technologies, and the programming and mathematical skills necessary to implement AI solutions. In AI Literacy for All, we emphasize a balanced curriculum that includes technical and non-technical learning outcomes to enable a conceptual understanding and critical evaluation of AI technologies in an interdisciplinary socio-technical context. The paper presents four pillars of AI literacy: understanding the scope and technical dimensions of AI, learning how to interact with Gen-AI in an informed and responsible way, the socio-technical issues of ethical and responsible AI, and the social and future implications of AI. While it is important to include all learning outcomes for AI education in a Computer Science major, the learning outcomes can be adjusted for other learning contexts, including, non-CS majors, high school summer camps, the adult workforce, and the public. This paper advocates for a shift in AI literacy education to offer a more interdisciplinary socio-technical approach as a pathway to broaden participation in AI.
Agentic Society: Merging skeleton from real world and texture from Large Language Model
Authors: Yuqi Bai, Kun Sun, Huishi Yin
Date: 2024-09-02 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Recent advancements in large language models (LLMs) and agent technologies offer promising solutions to the simulation of social science experiments, but the availability of data of real-world population required by many of them still poses as a major challenge. This paper explores a novel framework that leverages census data and LLMs to generate virtual populations, significantly reducing resource requirements and bypassing privacy compliance issues associated with real-world data, while keeping a statistical truthfulness. Drawing on real-world census data, our approach first generates a persona that reflects demographic characteristics of the population. We then employ LLMs to enrich these personas with intricate details, using techniques akin to those in image generative models but applied to textual data. Additionally, we propose a framework for the evaluation of the feasibility of our method with respect to capability of LLMs based on personality trait tests, specifically the Big Five model, which also enhances the depth and realism of the generated personas. Through preliminary experiments and analysis, we demonstrate that our method produces personas with variability essential for simulating diverse human behaviors in social science experiments. But the evaluation result shows that only weak sign of statistical truthfulness can be produced due to limited capability of current LLMs. Insights from our study also highlight the tension within LLMs between aligning with human values and reflecting real-world complexities. Thorough and rigorous test call for further research. Our codes are released at https://github.com/baiyuqi/agentic-society.git
Adapting to the AI Disruption: Reshaping the IT Landscape and Educational Paradigms
Authors: Murat Ozer, Yasin Kose, Goksel Kucukkaya, Assel Mukasheva, Kazim Ciris
Date: 2024-09-01 00:00:00

Artificial intelligence (AI) signals the beginning of a revolutionary period where technological advancement and social change interact to completely reshape economies, work paradigms, and industries worldwide. This essay addresses the opportunities and problems brought about by the AI-driven economy as it examines the effects of AI disruption on the IT sector and information technology education. By comparing the current AI revolution to previous industrial revolutions, we investigate the significant effects of AI technologies on workforce dynamics, employment, and organizational procedures. Human-centered design principles and ethical considerations become crucial requirements for the responsible development and implementation of AI systems in the face of the field's rapid advancements. IT education programs must change to meet the changing demands of the AI era and give students the skills and competencies they need to succeed in a digital world that is changing quickly. In light of AI-driven automation, we also examine the possible advantages and difficulties of moving to a shorter workweek, emphasizing chances to improve worker productivity, well-being, and work-life balance. We can build a more incslusive and sustainable future for the IT industry and beyond, enhancing human capabilities, advancing collective well-being, and fostering a society where AI serves as a force for good by embracing the opportunities presented by AI while proactively addressing its challenges.