pwc-archive/papers-with-abstracts huggingface dataset:
EduAgent: Generative Student Agents in Learning
Authors: Songlin Xu, Xinyu Zhang, Lianhui Qin
Date: 2024-03-23 00:00:00

Student simulation in online education is important to address dynamic learning behaviors of students with diverse backgrounds. Existing simulation models based on deep learning usually need massive training data, lacking prior knowledge in educational contexts. Large language models (LLMs) may contain such prior knowledge since they are pre-trained from a large corpus. However, because student behaviors are dynamic and multifaceted with individual differences, directly prompting LLMs is not robust nor accurate enough to capture fine-grained interactions among diverse student personas, learning behaviors, and learning outcomes. This work tackles this problem by presenting a newly annotated fine-grained large-scale dataset and proposing EduAgent, a novel generative agent framework incorporating cognitive prior knowledge (i.e., theoretical findings revealed in cognitive science) to guide LLMs to first reason correlations among various behaviors and then make simulations. Our two experiments show that EduAgent could not only mimic and predict learning behaviors of real students but also generate realistic learning behaviors of virtual students without real data.
Machine Learning-based Approach for Ex-post Assessment of Community Risk and Resilience Based on Coupled Human-infrastructure Systems Performance
Authors: Xiangpeng Li, Ali Mostafavi
Date: 2024-03-24 00:00:00

There is a limitation in the literature of data-driven analyses for the ex-post evaluation of community risk and resilience, particularly using features related to the performance of coupled human-infrastructure systems. To address this gap, in this study we created a machine learning-based method for the ex-post assessment of community risk and resilience and their interplay based on features related to the coupled human-infrastructure systems performance. Utilizing feature groups related to population protective actions, infrastructure/building performance features, and recovery features, we examined the risk and resilience performance of communities in the context of the 2017 Hurricane Harvey in Harris County, Texas. These features related to the coupled human-infrastructure systems performance were processed using the K-means clustering method to classify census block groups into four distinct clusters then, based on feature analysis, these clusters were labeled and designated into four quadrants of risk-resilience archetypes. Finally, we analyzed the disparities in risk-resilience status of spatial areas across different clusters as well as different income groups. The findings unveil the risk-resilience status of spatial areas shaped by their coupled human-infrastructure systems performance and their interactions. The results also inform about features that contribute to high resilience in high-risk areas. For example, the results indicate that in high-risk areas, evacuation rates contributed to a greater resilience, while in low-risk areas, preparedness contributed to greater resilience.
Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length
Authors: Katerina Hlavackova-Schindler, Anna Melnykova, Irene Tubikanec
Date: 2023-09-05 00:00:00
Tasks: Causal Inference, Model Selection

Multivariate Hawkes processes (MHPs) are versatile probabilistic tools used to model various real-life phenomena: earthquakes, operations on stock markets, neuronal activity, virus propagation and many others. In this paper, we focus on MHPs with exponential decay kernels and estimate connectivity graphs, which represent the Granger causal relations between their components. We approach this inference problem by proposing an optimization criterion and model selection algorithm based on the minimum message length (MML) principle. MML compares Granger causal models using the Occam's razor principle in the following way: even when models have a comparable goodness-of-fit to the observed data, the one generating the most concise explanation of the data is preferred. While most of the state-of-art methods using lasso-type penalization tend to overfitting in scenarios with short time horizons, the proposed MML-based method achieves high F1 scores in these settings. We conduct a numerical study comparing the proposed algorithm to other related classical and state-of-art methods, where we achieve the highest F1 scores in specific sparse graph settings. We illustrate the proposed method also on G7 sovereign bond data and obtain causal connections, which are in agreement with the expert knowledge available in the literature.
Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control
Authors: Nate Rahn, Pierluca D'Oro, Harley Wiltzer, Pierre-Luc Bacon, Marc G. Bellemare
Date: 2023-09-26 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning

Deep reinforcement learning agents for continuous control are known to exhibit significant instability in their performance over time. In this work, we provide a fresh perspective on these behaviors by studying the return landscape: the mapping between a policy and a return. We find that popular algorithms traverse noisy neighborhoods of this landscape, in which a single update to the policy parameters leads to a wide range of returns. By taking a distributional view of these returns, we map the landscape, characterizing failure-prone regions of policy space and revealing a hidden dimension of policy quality. We show that the landscape exhibits surprising structure by finding simple paths in parameter space which improve the stability of a policy. To conclude, we develop a distribution-aware procedure which finds such paths, navigating away from noisy neighborhoods in order to improve the robustness of a policy. Taken together, our results provide new insight into the optimization, evaluation, and design of agents.
Evolving Genetic Programming Tree Models for Predicting the Mechanical Properties of Green Fibers for Better Biocomposite Materials
Authors: Faris M. AL-Oqla, Hossam Faris, Maria Habib, Pedro A. Castillo-Valdivieso
Date: 2024-02-20 00:00:00

Advanced modern technology and industrial sustainability theme have contributed implementing composite materials for various industrial applications. Green composites are among the desired alternatives for the green products. However, to properly control the performance of the green composites, predicting their constituents properties are of paramount importance. This work presents an innovative evolving genetic programming tree models for predicting the mechanical properties of natural fibers based upon several inherent chemical and physical properties. Cellulose, hemicellulose, lignin and moisture contents as well as the Microfibrillar angle of various natural fibers were considered to establish the prediction models. A one-hold-out methodology was applied for training/testing phases. Robust models were developed to predict the tensile strength, Young's modulus, and the elongation at break properties of the natural fibers. It was revealed that Microfibrillar angle was dominant and capable of determining the ultimate tensile strength of the natural fibers by 44.7% comparable to other considered properties, while the impact of cellulose content in the model was only 35.6%. This in order would facilitate utilizing artificial intelligence in predicting the overall mechanical properties of natural fibers without experimental efforts and cost to enhance developing better green composite materials for various industrial applications.
Personality-affected Emotion Generation in Dialog Systems
Authors: Zhiyuan Wen, Jiannong Cao, Jiaxing Shen, Ruosong Yang, Shuaiqi Liu, Maosong Sun
Date: 2024-04-03 00:00:00

Generating appropriate emotions for responses is essential for dialog systems to provide human-like interaction in various application scenarios. Most previous dialog systems tried to achieve this goal by learning empathetic manners from anonymous conversational data. However, emotional responses generated by those methods may be inconsistent, which will decrease user engagement and service quality. Psychological findings suggest that the emotional expressions of humans are rooted in personality traits. Therefore, we propose a new task, Personality-affected Emotion Generation, to generate emotion based on the personality given to the dialog system and further investigate a solution through the personality-affected mood transition. Specifically, we first construct a daily dialog dataset, Personality EmotionLines Dataset (PELD), with emotion and personality annotations. Subsequently, we analyze the challenges in this task, i.e., (1) heterogeneously integrating personality and emotional factors and (2) extracting multi-granularity emotional information in the dialog context. Finally, we propose to model the personality as the transition weight by simulating the mood transition process in the dialog system and solve the challenges above. We conduct extensive experiments on PELD for evaluation. Results suggest that by adopting our method, the emotion generation performance is improved by 13% in macro-F1 and 5% in weighted-F1 from the BERT-base model.
Is Complexity an Illusion?
Authors: Michael Timothy Bennett
Date: 2024-03-31 00:00:00

Simplicity is held by many to be the key to general intelligence. Simpler models tend to "generalise", identifying the cause or generator of data with greater sample efficiency. The implications of the correlation between simplicity and generalisation extend far beyond computer science, addressing questions of physics and even biology. Yet simplicity is a property of form, while generalisation is of function. In interactive settings, any correlation between the two depends on interpretation. In theory there could be no correlation and yet in practice, there is. Previous theoretical work showed generalisation to be a consequence of "weak" constraints implied by function, not form. Experiments demonstrated choosing weak constraints over simple forms yielded a 110-500% improvement in generalisation rate. Here we show that all constraints can take equally simple forms, regardless of weakness. However if forms are spatially extended, then function is represented using a finite subset of forms. If function is represented using a finite subset of forms, then we can force a correlation between simplicity and generalisation by making weak constraints take simple forms. If function is determined by a goal directed process that favours versatility (e.g. natural selection), then efficiency demands weak constraints take simple forms. Complexity has no causal influence on generalisation, but appears to due to confounding.
Content Knowledge Identification with Multi-Agent Large Language Models (LLMs)
Authors: Kaiqi Yang, Yucheng Chu, Taylor Darwin, Ahreum Han, Hang Li, Hongzhi Wen, Yasemin Copur-Gencturk, Jiliang Tang, Hui Liu
Date: 2024-03-22 00:00:00
Tasks: Diversity

Teachers' mathematical content knowledge (CK) is of vital importance and need in teacher professional development (PD) programs. Computer-aided asynchronous PD systems are the most recent proposed PD techniques, which aim to help teachers improve their PD equally with fewer concerns about costs and limitations of time or location. However, current automatic CK identification methods, which serve as one of the core techniques of asynchronous PD systems, face challenges such as diversity of user responses, scarcity of high-quality annotated data, and low interpretability of the predictions. To tackle these challenges, we propose a Multi-Agent LLMs-based framework, LLMAgent-CK, to assess the user responses' coverage of identified CK learning goals without human annotations. By taking advantage of multi-agent LLMs in strong generalization ability and human-like discussions, our proposed LLMAgent-CK presents promising CK identifying performance on a real-world mathematical CK dataset MaCKT. Moreover, our case studies further demonstrate the working of the multi-agent framework.
Computation Offloading for Multi-server Multi-access Edge Vehicular Networks: A DDQN-based Method
Authors: Siyu Wang, Bo Yang, Zhiwen Yu, Xuelin Cao, Yan Zhang, Chau Yuen
Date: 2024-02-21 00:00:00
Tasks: Decision Making, Edge-computing, Scheduling

In this paper, we investigate a multi-user offloading problem in the overlapping domain of a multi-server mobile edge computing system. We divide the original problem into two stages: the offloading decision making stage and the request scheduling stage. To prevent the terminal from going out of service area during offloading, we consider the mobility parameter of the terminal according to the human behaviour model when making the offloading decision, and then introduce a server evaluation mechanism based on both the mobility parameter and the server load to select the optimal offloading server. In order to fully utilise the server resources, we design a double deep Q-network (DDQN)-based reward evaluation algorithm that considers the priority of tasks when scheduling offload requests. Finally, numerical simulations are conducted to verify that our proposed method outperforms traditional mathematical computation methods as well as the DQN algorithm.
An Active Contour Model Using Matched Filter and Hessian Matrix for Retinal Vessels Segmentation
Authors: Mahtab Shabani, Hossein Pourghassem
Date: 2022-01-27 00:00:00
Proceeding: Turkish journal of electrical engineering and computer sciences 2022 1
Tasks: Diagnostic, Image Segmentation, Medical Image Analysis, Retinal Vessel Segmentation, Specificity, Vessel Detection

Medical image analysis, especially of the retina, plays an important role in diagnostic decision support tools. The properties of retinal blood vessels are used for disease diagnoses such as diabetes, glaucoma, and hypertension. There are some challenges in the utilization of retinal blood vessel patterns such as low contrast and intensity inhomogeneities. Thus, an automatic algorithm for vessel extraction is required. Active contour is a strong method for edge extraction. However, it cannot extract thin vessels and ridges very well. In this research, we propose an improved active contour method that uses discrete wavelet transform for energy minimization to solve this problem. The minimization formula terminates segmentation into two regions, foreground and background. We found out that foreground pixels are more important than background. Therefore, we change the minimization formulation in such a way that gives more weight to the foreground. The contour edge has orientation in each region. The wavelet terms in the minimization formula help to detect edge direction in horizontal, vertical, and diagonal orientations. Since bright and dark lesions do not have direction, these terms can decrease false vessel detection. The second part of the innovation is called the optimization process, which works instead of reinitialization. Sometimes, the evolution in the iterated process destroys the stability of the evolution. To prevent the destruction of the stability of evolution, we used an optimization process formula whose task is to keep contour on the edges of the image. The performance of the proposed algorithms is compared and analyzed on five databases. The values achieved are 94.3%, 73.36%, and 97.41% for accuracy, sensitivity, and specificity, respectively, on the DRIVE dataset, and the proposed algorithm is comparable to the state-of-the-art approaches.
AReputation-Based Mechanism for Transaction Processing in Blockchain Systems
Authors: Jiarui Zhang, Yukun Cheng, Xiaotie Deng
Date: 2022-10-10 00:00:00
Proceeding: journal 2022 10

Blockchain protocols require nodes to verify all received transactions before forwarding them. However, massive spam
 transactions cause the participants in blockchain systems to consume many resources in verifying and propagating transactions. This
 paper proposes a reputation-based mechanism to increase the efficiency of processing transactions by considering the reputations of
 the sending nodes. Reputations are in turn adjusted based on the quality of transaction processing. Our proposed reputation-based
 mechanism offers three main contributions. First, we modify the verification strategy so that nodes set a probability of verifying a
 received transaction considering the likelihood of it being spam: transactions from a node with a low reputation have a high probability of being verified. Second, we optimize the transaction forwarding protocol to reduce propagation delay by prioritizing forwarding
 transactions to reputable receivers. Third, we design a data request protocol that provides alternative data exchange methods for
 nodes with different reputations. A series of simulations demonstrate the performance of our reputation-based mechanism.
BoostTrack: boosting the similarity measure and detection confidence for improved multiple object tracking
Authors: Stanojevic Vukasin, Todorovic Branimir
Date: 2024-04-12 00:00:00
Proceeding: Machine Vision and Applications 2024 4
Tasks: Motion Compensation, Multi-Object Tracking, Multiple Object Tracking, Multiple People Tracking, Object Tracking

Handling unreliable detections and avoiding identity switches are crucial for the success of multiple object tracking (MOT). Ideally, MOT algorithm should use true positive detections only, work in real-time and produce no identity switches. To approach the described ideal solution, we present the BoostTrack, a simple yet effective tracing-by-detection MOT method that utilizes several lightweight plug and play additions to improve MOT performance. We design a detection-tracklet confidence score and use it to scale the similarity measure and implicitly favour high detection confidence and high tracklet confidence pairs in one-stage association. To reduce the ambiguity arising from using intersection over union (IoU), we propose a novel Mahalanobis distance and shape similarity additions to boost the overall similarity measure. To utilize low-detection score bounding boxes in one-stage association, we propose to boost the confidence scores of two groups of detections: the detections we assume to correspond to the existing tracked object, and the detections we assume to correspond to a previously undetected object. The proposed additions are orthogonal to the existing approaches, and we combine them with interpolation and camera motion compensation to achieve results comparable to the standard benchmark solutions while retaining real-time execution speed. When combined with appearance similarity, our method outperforms all standard benchmark solutions on MOT17 and MOT20 datasets. It ranks first among online methods in HOTA metric in the MOT Challenge on MOT17 and MOT20 test sets. We make our code available at https://github.com/vukasin-stanojevic/BoostTrack.
Cross-to-merge training with class balance strategy for learning with noisy labels
Authors: Qian Zhang, Yi Zhu, Ming Yang, Ge Jin, YingWen Zhu, Qiu Chen
Date: 2024-04-01 00:00:00
Proceeding: Expert Systems with Applications 2024 4
Tasks: Image Classification, Learning with noisy labels

The collection of large-scale datasets inevitably introduces noisy labels, leading to a substantial degradation in 
the performance of deep neural networks (DNNs). Although sample selection is a mainstream method in the field 
of learning with noisy labels, which aims to mitigate the impact of noisy labels during model training, the testing 
performance of these methods exhibits significant fluctuations across different noise rates and types. In this 
paper, we propose Cross-to-Merge Training (C2MT), a novel framework that is insensitive to the prior information in sample selection progress, enhancing model robustness. In practical implementation, using crossdivided training data, two different networks are cross-trained with the co-teaching strategy for several local 
rounds, subsequently merged into a unified model by performing federated averages on the parameters of two 
models periodically. Additionally, we introduce a new class balance strategy, named Median Balance Strategy 
(MBS), during the cross-dividing process, which evenly divides the training data into a labeled subset and an 
unlabeled subset based on the estimated loss distribution characteristics. Extensive experimental results on both 
synthetic and real-world datasets demonstrate the effectiveness of C2MT. The Code will be available at: https://g 
ithub.com/LanXiaoPang613/C2MT.
ST-LLM: Large Language Models Are Effective Temporal Learners
Authors: Ruyang Liu, Chen Li, Haoran Tang, Yixiao Ge, Ying Shan, Ge Li
Date: 2024-03-30 00:00:00
Tasks: MVBench, Reading Comprehension, Video-based Generative Performance Benchmarking, Video Question Answering, Video Understanding

Large Language Models (LLMs) have showcased impressive capabilities in text comprehension and generation, prompting research efforts towards video LLMs to facilitate human-AI interaction at the video level. However, how to effectively encode and understand videos in video-based dialogue systems remains to be solved. In this paper, we investigate a straightforward yet unexplored question: Can we feed all spatial-temporal tokens into the LLM, thus delegating the task of video sequence modeling to the LLMs? Surprisingly, this simple approach yields significant improvements in video understanding. Based upon this, we propose ST-LLM, an effective video-LLM baseline with Spatial-Temporal sequence modeling inside LLM. Furthermore, to address the overhead and stability issues introduced by uncompressed video tokens within LLMs, we develop a dynamic masking strategy with tailor-made training objectives. For particularly long videos, we have also designed a global-local input module to balance efficiency and effectiveness. Consequently, we harness LLM for proficient spatial-temporal modeling, while upholding efficiency and stability. Extensive experimental results attest to the effectiveness of our method. Through a more concise model and training pipeline, ST-LLM establishes a new state-of-the-art result on VideoChatGPT-Bench and MVBench. Codes have been available at https://github.com/TencentARC/ST-LLM.
Information retrieval: recent advances and beyond
Authors: Kailash Hambarde, Hugo Proença
Date: 2023-07-14 00:00:00
Proceeding: IEEE Access 2023 7
Tasks: Information Retrieval, Retrieval

This paper provides an extensive and thorough overview of the models and techniques utilized in the first and second stages of the typical information retrieval processing chain. Our discussion encompasses the current state-of-the-art models, covering a wide range of methods and approaches in the field of information retrieval. We delve into the historical development of these models, analyze the key advancements and breakthroughs, and address the challenges and limitations faced by researchers and practitioners in the domain. By offering a comprehensive understanding of the field, this survey is a valuable resource for researchers, practitioners, and newcomers to the information retrieval domain, fostering knowledge growth, innovation, and the development of novel ideas and techniques.