pwc-archive/papers-with-abstracts huggingface dataset:
Solving the Model Unavailable MARE using Q-Learning Algorithm
Authors: Fei Yan, Jie Gao, Tao Feng, Jianxing Liu
Date: 2024-07-18 00:00:00
Tasks: Q-Learning

In this paper, the discrete-time modified algebraic Riccati equation (MARE) is solved when the system model is completely unavailable. To achieve this, firstly a brand new iterative method based on the standard discrete-time algebraic Riccati equation (DARE) and its input weighting matrix is proposed to solve the MARE. For the single-input case, the iteration can be initialized by an arbitrary positive input weighting if and only if the MARE has a stabilizing solution; nevertheless a pre-given input weighting matrix of a sufficiently large magnitude is used to perform the iteration for the multi-input case when the characteristic parameter belongs to a specified subset. Benefit from the developed specific iteration structure, the Q-learning (QL) algorithm can be employed to subtly solve the MARE where only the system input/output data is used thus the system model is not required. Finally, a numerical simulation example is given to verify the effectiveness of the theoretical results and the algorithm.
Heart-Mind Harmony: Predicting Stress from Heart Rate
Authors: Aravind M S, Sri Bhavan Prakath, Tarunika R, Dr M.Srividya, Dr M.Marimuthu and  Dr S.GayathriDevi
Date: 2024-07-18 00:00:00
Proceeding: GRENZE International Journal of Engineering and Technology 2024 7

stress is a pervasive difficulty with some distance-attaining fitness implications. Early 
detection of pressure is important for well timed intervention and mitigation. in this research, we 
discover the feasibility of predicting stress degrees utilizing a rich dataset of coronary heart rate 
functions. This dataset includes a big range of coronary heart charge parameters, along with 
mean RR intervals, widespread deviations, spectral additives, and nonlinear dynamics, amongst 
others. each facts entry is associated with a completely unique affected person identifier, taking 
into account personalized analysis. The primary objective of this observe is to broaden a 
predictive model able to accurately discerning whether or not an man or woman is experiencing 
stress based on their coronary heart charge traits. machine learning strategies will be hired to 
extract significant patterns and relationships from the dataset. Key capabilities of interest 
encompass metrics like RMSSD (Root imply rectangular of Successive RR c programming 
language variations), LF/HF (Ratio of Low Frequency to excessive Frequency), and sample 
entropy (Sampen), which capture both linear and nonlinear components of coronary heart 
charge variability. moreover, demographic and contextual records, consisting of affected person 
circumstance and coronary heart charge, could be integrated into the predictive models to 
enhance their accuracy and practicality. This examine pursuits to make a contribution to the 
improvement of non-invasive and sensible stress detection methods that may be included into 
wearable gadgets or faraway tracking systems. these gear have the potential to assist individuals 
in managing pressure and healthcare professionals in delivering timely interventions. The effects 
may additionally pave the way for customized stress management strategies based totally on an 
person's heart rate profile. in the end, the findings of this research may additionally have broader 
implications for improving the satisfactory of existence and basic properly-being of individuals 
via permitting early pressure detection and intervention thru the analysis of heart price statistics.
A fast density peak clustering based particle swarm optimizer for dynamic optimization
Authors: Fei Li a, C, ∗, Qiang Yue a, B, Yuanchao Liu d, Haibin Ouyang e, Fangqing Gu f
Date: 2024-02-01 00:00:00
Proceeding: Expert Systems with Applications 2024 2
Tasks: Clustering, Diversity

Dynamic optimization problems (DOPs) are optimization problems with time evolution characteristics. In this
type of problem, the decision variables and the state variables change over time and produce results that
will impact the future. To effectively address DOPs, this paper proposes a fast density peak clustering based
particle swarm optimizer for dynamic optimization (DPCPSO). The main innovations of DPCPSO contain three
critical components. First, a fast density peak clustering is applied to create multiple sub-populations, which
can help the algorithm locate peaks. Second, stagnation detection is used to tackle the loss of diversity. Third,
an optimal particle calibration strategy which can find the optimal solution quickly in a changing environment
is proposed in response to environmental changes. Moreover, the hill climbing method is applied to help the
memory quickly locate new peaks if the environment changes. The performance of our proposed algorithm
has been tested on Mobile Peak Benchmark (MPB), Generalized Dynamic Benchmark Generator (GDBG) and
Generalized Moving Peaks Benchmark (GMPB) problems and compared with seven state-of-the-art dynamic
optimization algorithms. The experimental results validate the proposed algorithm performed competitively
while solving DOPs.
Idle is the New Sleep: Configuration-Aware Alternative to Powering Off FPGA-Based DL Accelerators During Inactivity
Authors: Chao Qian, Christopher Cichiwskyj, Tianheng Ling, Gregor Schiele
Date: 2024-06-28 00:00:00

In the rapidly evolving Internet of Things (IoT) domain, we concentrate on enhancing energy efficiency in Deep Learning accelerators on FPGA-based heterogeneous platforms, aligning with the principles of sustainable computing. Instead of focusing on the inference phase, we introduce innovative optimizations to minimize the overhead of the FPGA configuration phase. By fine-tuning configuration parameters correctly, we achieved a 40.13-fold reduction in configuration energy. Moreover, augmented with power-saving methods, our Idle-Waiting strategy outperformed the traditional On-Off strategy in duty-cycle mode for request periods up to 499.06 ms. Specifically, at a 40 ms request period within a 4147 J energy budget, this strategy extends the system lifetime to approximately 12.39x that of the On-Off strategy. Empirically validated through hardware measurements and simulations, these optimizations provide valuable insights and practical methods for achieving energy-efficient and sustainable deployments in IoT.
No Questions Asked: Effects of Transparency on Stablecoin Liquidity During the Collapse of Silicon Valley Bank
Authors: Walter Hernandez Cruz, Jiahua Xu, Paolo Tasca, Carlo Campajola
Date: 2024-07-16 00:00:00

Fiat-pegged stablecoins are by nature exposed to spillover effects during market turmoil in Traditional Finance (TradFi). We observe a difference in TradFi market shocks impact between various stablecoins, in particular, USD Coin (USDC) and Tether USDT (USDT), the former with a higher reporting frequency and transparency than the latter. We investigate this, using top USDC and USDT liquidity pools in Uniswap, by adapting the Marginal Cost of Immediacy (MCI) measure to Uniswap's Automated Market Maker, and then conducting Difference-in-Differences analysis on MCI and Total Value Locked (TVL) in USD, as well as measuring liquidity concentration across different providers. Results show that the Silicon Valley Bank (SVB) event reduced USDC's TVL dominance over USDT, increased USDT's liquidity cost relative to USDC, and liquidity provision remained concentrated with pool-specific trends. These findings reveal a flight-to-safety behavior and counterintuitive effects of stablecoin transparency: USDC's frequent and detailed disclosures led to swift market reactions, while USDT's opacity and less frequent reporting provided a safety net against immediate impacts.
International Trade Flow Prediction with Bilateral Trade Provisions
Authors: Zijie Pan, Stepan Gordeev, Jiahui Zhao, Ziyi Meng, Caiwen Ding, Sandro Steinbach, Dongjin Song
Date: 2024-06-23 00:00:00
Tasks: Prediction, Variable Selection

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.
Form-Finding and Physical Property Predictions of Tensegrity Structures Using Deep Neural Networks
Authors: Muhao Chen, Jing Qin
Date: 2024-06-15 00:00:00
Tasks: Form

In the design of tensegrity structures, traditional form-finding methods utilize kinematic and static approaches to identify geometric configurations that achieve equilibrium. However, these methods often fall short when applied to actual physical models due to imperfections in the manufacturing of structural elements, assembly errors, and material non-linearities. In this work, we develop a deep neural network (DNN) approach to predict the geometric configurations and physical properties-such as nodal coordinates, member forces, and natural frequencies-of any tensegrity structures in equilibrium states. First, we outline the analytical governing equations for tensegrity structures, covering statics involving nodal coordinates and member forces, as well as modal information. Next, we propose a data-driven framework for training an appropriate DNN model capable of simultaneously predicting tensegrity forms and physical properties, thereby circumventing the need to solve equilibrium equations. For validation, we analyze three tensegrity structures, including a tensegrity D-bar, prism, and lander, demonstrating that our approach can identify approximation systems with relatively very small output errors. This technique is applicable to a wide range of tensegrity structures, particularly in real-world construction, and can be extended to address additional challenges in identifying structural physics information.
To Trade Or Not To Trade: Cascading Waterfall Round Robin Rebalancing Mechanism for Cryptocurrencies
Authors: Ravi Kashyap
Date: 2024-05-17 00:00:00

We have designed an innovative portfolio rebalancing mechanism termed the Cascading Waterfall Round Robin Mechanism. This algorithmic approach recommends an ideal size and number of trades for each asset during the periodic rebalancing process, factoring in the gas fee and slippage. The essence of the model we have created gives indications regarding whether trades should be made on individual assets depending on the uncertainty in the micro - asset level characteristics - and macro - aggregate market factors - environments. In the hyper-volatile crypto market, our approach to daily rebalancing will benefit from volatility. Price movements will cause our algorithm to buy assets that drop in prices and sell as they soar. In fact, the buying and selling happen only when certain boundaries are crossed in order to weed out any market noise and ensure sound trade execution. We have provided several numerical examples to illustrate the steps - including the calculation of several intermediate variables - of our rebalancing mechanism. The Algorithm we have developed can be easily applied outside blockchain to investment funds across all asset classes at any trading frequency and rebalancing duration. Shakespeare As A Crypto Trader: To Trade Or Not To Trade, that is the Question, Whether an Optimizer can Yield the Answer, Against the Spikes and Crashes of Markets Gone Wild, To Quench One's Thirst before Liquidity Runs Dry, Or Wait till the Tide of Momentum turns Mild.
SemiAdv: Query-Efficient Black-Box Adversarial Attack with Unlabeled Images
Authors: Mingyuan Fan, Yang Liu, Cen Chen, Ximeng Liu
Date: 2024-07-13 00:00:00
Tasks: Adversarial Attack

Adversarial attack has garnered considerable attention due to its profound implications for the secure deployment of robots in sensitive security scenarios. To potentially push for advances in the field, this paper studies the adversarial attack in the black-box setting and proposes an unlabeled data-driven adversarial attack method, called SemiAdv. Specifically, SemiAdv achieves the following breakthroughs compared with previous works. First, by introducing the semi-supervised learning technique into the adversarial attack, SemiAdv substantially decreases the number of queries required for generating adversarial samples. On average, SemiAdv only needs to query a few hundred times to launch an effective attack with more than 90% success rate. Second, many existing black-box adversarial attacks require massive labeled data to mitigate the difference between the local substitute model and the remote target model for a good attack performance. While SemiAdv relaxes this limitation and is capable of utilizing unlabeled raw data to launch an effective attack. Finally, our experiments show that SemiAdv saves up to 12x query accesses for generating adversarial samples while maintaining a competitive attack success rate compared with state-of-the-art attacks.
Was it Slander? Towards Exact Inversion of Generative Language Models
Authors: Adrians Skapars, Edoardo Manino, Youcheng Sun, Lucas C. Cordeiro
Date: 2024-07-10 00:00:00

Training large language models (LLMs) requires a substantial investment of time and money. To get a good return on investment, the developers spend considerable effort ensuring that the model never produces harmful and offensive outputs. However, bad-faith actors may still try to slander the reputation of an LLM by publicly reporting a forged output. In this paper, we show that defending against such slander attacks requires reconstructing the input of the forged output or proving that it does not exist. To do so, we propose and evaluate a search based approach for targeted adversarial attacks for LLMs. Our experiments show that we are rarely able to reconstruct the exact input of an arbitrary output, thus demonstrating that LLMs are still vulnerable to slander attacks.
AI AI Bias: Large Language Models Favor Their Own Generated Content
Authors: Walter Laurito, Benjamin Davis, Peli Grietzer, Tomáš Gavenčiak, Ada Böhm, Jan Kulveit
Date: 2024-07-09 00:00:00
Tasks: Experimental Design

Are large language models (LLMs) biased towards text generated by LLMs over text authored by humans, leading to possible anti-human bias? Utilizing a classical experimental design inspired by employment discrimination studies, we tested widely-used LLMs, including GPT-3.5 and GPT4, in binary-choice scenarios. These involved LLM-based agents selecting between products and academic papers described either by humans or LLMs under identical conditions. Our results show a consistent tendency for LLM-based AIs to prefer LLM-generated content. This suggests the possibility of AI systems implicitly discriminating against humans, giving AI agents an unfair advantage.
Limits to Predicting Online Speech Using Large Language Models
Authors: Mina Remeli, Moritz Hardt, Robert C. Williamson
Date: 2024-07-08 00:00:00

We study the predictability of online speech on social media, and whether predictability improves with information outside a user's own posts. Recent theoretical results suggest that posts from a user's social circle are as predictive of the user's future posts as that of the user's past posts. Motivated by the success of large language models, we empirically test this hypothesis. We define predictability as a measure of the model's uncertainty, i.e., its negative log-likelihood on future tokens given context. As the basis of our study, we collect 10M tweets for ``tweet-tuning'' base models and a further 6.25M posts from more than five thousand X (previously Twitter) users and their peers. Across four large language models ranging in size from 1.5 billion to 70 billion parameters, we find that predicting a user's posts from their peers' posts performs poorly. Moreover, the value of the user's own posts for prediction is consistently higher than that of their peers'. We extend our investigation with a detailed analysis on what's learned in-context and the robustness of our findings. From context, base models learn to correctly predict @-mentions and hashtags. Moreover, our results replicate if instead of prompting the model with additional context, we finetune on it. Across the board, we find that predicting the posts of individual users remains hard.
Large Language Model Agents for Improving Engagement with Behavior Change Interventions: Application to Digital Mindfulness
Authors: Harsh Kumar, Suhyeon Yoo, Angela Zavaleta Bernuy, Jiakai Shi, Huayin Luo, Joseph Williams, Anastasia Kuzminykh, Ashton Anderson, Rachel Kornfield
Date: 2024-07-03 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Although engagement in self-directed wellness exercises typically declines over time, integrating social support such as coaching can sustain it. However, traditional forms of support are often inaccessible due to the high costs and complex coordination. Large Language Models (LLMs) show promise in providing human-like dialogues that could emulate social support. Yet, in-depth, in situ investigations of LLMs to support behavior change remain underexplored. We conducted two randomized experiments to assess the impact of LLM agents on user engagement with mindfulness exercises. First, a single-session study, involved 502 crowdworkers; second, a three-week study, included 54 participants. We explored two types of LLM agents: one providing information and another facilitating self-reflection. Both agents enhanced users' intentions to practice mindfulness. However, only the information-providing LLM, featuring a friendly persona, significantly improved engagement with the exercises. Our findings suggest that specific LLM agents may bridge the social support gap in digital health interventions.
OxonFair: A Flexible Toolkit for Algorithmic Fairness
Authors: Eoin Delaney, Zihao Fu, Sandra Wachter, Brent Mittelstadt, Chris Russell
Date: 2024-06-30 00:00:00
Tasks: Articles, Binary Classification, Fairness

We present OxonFair, a new open source toolkit for enforcing fairness in binary classification. Compared to existing toolkits: (i) We support NLP and Computer Vision classification as well as standard tabular problems. (ii) We support enforcing fairness on validation data, making us robust to a wide range of overfitting challenges. (iii) Our approach can optimize any measure based on True Positives, False Positive, False Negatives, and True Negatives. This makes it easily extensible and much more expressive than existing toolkits. It supports all 9 and all 10 of the decision-based group metrics of two popular review articles. (iv) We jointly optimize a performance objective alongside fairness constraints. This minimizes degradation while enforcing fairness, and even improves the performance of inadequately tuned unfair baselines. OxonFair is compatible with standard ML toolkits, including sklearn, Autogluon, and PyTorch and is available at https://github.com/oxfordinternetinstitute/oxonfair
"I understand why I got this grade": Automatic Short Answer Grading with Feedback
Authors: Dishank Aggarwal, Pritam Sil, Bhaskaran Raman, Pushpak Bhattacharyya
Date: 2024-06-30 00:00:00
Tasks: automatic short answer grading

In recent years, there has been a growing interest in using Artificial Intelligence (AI) to automate student assessment in education. Among different types of assessments, summative assessments play a crucial role in evaluating a student's understanding level of a course. Such examinations often involve short-answer questions. However, grading these responses and providing meaningful feedback manually at scale is both time-consuming and labor-intensive. Feedback is particularly important, as it helps students recognize their strengths and areas for improvement. Despite the importance of this task, there is a significant lack of publicly available datasets that support automatic short-answer grading with feedback generation. To address this gap, we introduce Engineering Short Answer Feedback (EngSAF), a dataset designed for automatic short-answer grading with feedback. The dataset covers a diverse range of subjects, questions, and answer patterns from multiple engineering domains and contains ~5.8k data points. We incorporate feedback into our dataset by leveraging the generative capabilities of state-of-the-art large language models (LLMs) using our Label-Aware Synthetic Feedback Generation (LASFG) strategy. This paper underscores the importance of enhanced feedback in practical educational settings, outlines dataset annotation and feedback generation processes, conducts a thorough EngSAF analysis, and provides different LLMs-based zero-shot and finetuned baselines for future comparison. The best-performing model (Mistral-7B) achieves an overall accuracy of 75.4% and 58.7% on unseen answers and unseen question test sets, respectively. Additionally, we demonstrate the efficiency and effectiveness of our ASAG system through its deployment in a real-world end-semester exam at a reputed institute.
Evaluation of Bias Towards Medical Professionals in Large Language Models
Authors: Xi Chen, Yang Xu, MingKe You, Li Wang, Weizhi Liu, Jian Li
Date: 2024-06-30 00:00:00

This study evaluates whether large language models (LLMs) exhibit biases towards medical professionals. Fictitious candidate resumes were created to control for identity factors while maintaining consistent qualifications. Three LLMs (GPT-4, Claude-3-haiku, and Mistral-Large) were tested using a standardized prompt to evaluate resumes for specific residency programs. Explicit bias was tested by changing gender and race information, while implicit bias was tested by changing names while hiding race and gender. Physician data from the Association of American Medical Colleges was used to compare with real-world demographics. 900,000 resumes were evaluated. All LLMs exhibited significant gender and racial biases across medical specialties. Gender preferences varied, favoring male candidates in surgery and orthopedics, while preferring females in dermatology, family medicine, obstetrics and gynecology, pediatrics, and psychiatry. Claude-3 and Mistral-Large generally favored Asian candidates, while GPT-4 preferred Black and Hispanic candidates in several specialties. Tests revealed strong preferences towards Hispanic females and Asian males in various specialties. Compared to real-world data, LLMs consistently chose higher proportions of female and underrepresented racial candidates than their actual representation in the medical workforce. GPT-4, Claude-3, and Mistral-Large showed significant gender and racial biases when evaluating medical professionals for residency selection. These findings highlight the potential for LLMs to perpetuate biases and compromise healthcare workforce diversity if used without proper bias mitigation strategies.