pwc-archive/papers-with-abstracts huggingface dataset:
SCCD: A Session-based Dataset for Chinese Cyberbullying Detection
Authors: Qingpo Yang, Yakai Chen, Zihui Xu, Yu-Ming Shang, Sanchuan Guo, Xi Zhang
Date: 2025-01-25 00:00:00

The rampant spread of cyberbullying content poses a growing threat to societal well-being. However, research on cyberbullying detection in Chinese remains underdeveloped, primarily due to the lack of comprehensive and reliable datasets. Notably, no existing Chinese dataset is specifically tailored for cyberbullying detection. Moreover, while comments play a crucial role within sessions, current session-based datasets often lack detailed, fine-grained annotations at the comment level. To address these limitations, we present a novel Chinese cyber-bullying dataset, termed SCCD, which consists of 677 session-level samples sourced from a major social media platform Weibo. Moreover, each comment within the sessions is annotated with fine-grained labels rather than conventional binary class labels. Empirically, we evaluate the performance of various baseline methods on SCCD, highlighting the challenges for effective Chinese cyberbullying detection.
AKVQ-VL: Attention-Aware KV Cache Adaptive 2-Bit Quantization for Vision-Language Models
Authors: Zunhai Su, Wang Shen, Linge Li, Zhe Chen, Hanyu Wei, Huangqi Yu, Kehong Yuan
Date: 2025-01-25 00:00:00
Tasks: Quantization

Vision-language models (VLMs) show remarkable performance in multimodal tasks. However, excessively long multimodal inputs lead to oversized Key-Value (KV) caches, resulting in significant memory consumption and I/O bottlenecks. Previous KV quantization methods for Large Language Models (LLMs) may alleviate these issues but overlook the attention saliency differences of multimodal tokens, resulting in suboptimal performance. In this paper, we investigate the attention-aware token saliency patterns in VLM and propose AKVQ-VL. AKVQ-VL leverages the proposed Text-Salient Attention (TSA) and Pivot-Token-Salient Attention (PSA) patterns to adaptively allocate bit budgets. Moreover, achieving extremely low-bit quantization requires effectively addressing outliers in KV tensors. AKVQ-VL utilizes the Walsh-Hadamard transform (WHT) to construct outlier-free KV caches, thereby reducing quantization difficulty. Evaluations of 2-bit quantization on 12 long-context and multimodal tasks demonstrate that AKVQ-VL maintains or even improves accuracy, outperforming LLM-oriented methods. AKVQ-VL can reduce peak memory usage by 2.13x, support up to 3.25x larger batch sizes and 2.46x throughput.
MDEval: Evaluating and Enhancing Markdown Awareness in Large Language Models
Authors: Zhongpu Chen, Yinfeng Liu, Long Shi, Zhi-Jie Wang, Xingyan Chen, Yu Zhao, Fuji Ren
Date: 2025-01-25 00:00:00

Large language models (LLMs) are expected to offer structured Markdown responses for the sake of readability in web chatbots (e.g., ChatGPT). Although there are a myriad of metrics to evaluate LLMs, they fail to evaluate the readability from the view of output content structure. To this end, we focus on an overlooked yet important metric -- Markdown Awareness, which directly impacts the readability and structure of the content generated by these language models. In this paper, we introduce MDEval, a comprehensive benchmark to assess Markdown Awareness for LLMs, by constructing a dataset with 20K instances covering 10 subjects in English and Chinese. Unlike traditional model-based evaluations, MDEval provides excellent interpretability by combining model-based generation tasks and statistical methods. Our results demonstrate that MDEval achieves a Spearman correlation of 0.791 and an accuracy of 84.1% with human, outperforming existing methods by a large margin. Extensive experimental results also show that through fine-tuning over our proposed dataset, less performant open-source models are able to achieve comparable performance to GPT-4o in terms of Markdown Awareness. To ensure reproducibility and transparency, MDEval is open sourced at https://github.com/SWUFE-DB-Group/MDEval-Benchmark.
Federated Retrieval Augmented Generation for Multi-Product Question Answering
Authors: Parshin Shojaee, Sai Sree Harsha, Dan Luo, Akash Maharaj, Tong Yu, Yunyao Li
Date: 2025-01-25 00:00:00
Tasks: Question Answering, RAG, Retrieval, Retrieval-augmented Generation

Recent advancements in Large Language Models and Retrieval-Augmented Generation have boosted interest in domain-specific question-answering for enterprise products. However, AI Assistants often face challenges in multi-product QA settings, requiring accurate responses across diverse domains. Existing multi-domain RAG-QA approaches either query all domains indiscriminately, increasing computational costs and LLM hallucinations, or rely on rigid resource selection, which can limit search results. We introduce MKP-QA, a novel multi-product knowledge-augmented QA framework with probabilistic federated search across domains and relevant knowledge. This method enhances multi-domain search quality by aggregating query-domain and query-passage probabilistic relevance. To address the lack of suitable benchmarks for multi-product QAs, we also present new datasets focused on three Adobe products: Adobe Experience Platform, Target, and Customer Journey Analytics. Our experiments show that MKP-QA significantly boosts multi-product RAG-QA performance in terms of both retrieval accuracy and response quality.
The Muddy Waters of Modeling Empathy in Language: The Practical Impacts of Theoretical Constructs
Authors: Allison Lahnala, Charles Welch, David Jurgens, Lucie Flek
Date: 2025-01-24 00:00:00

Conceptual operationalizations of empathy in NLP are varied, with some having specific behaviors and properties, while others are more abstract. How these variations relate to one another and capture properties of empathy observable in text remains unclear. To provide insight into this, we analyze the transfer performance of empathy models adapted to empathy tasks with different theoretical groundings. We study (1) the dimensionality of empathy definitions, (2) the correspondence between the defined dimensions and measured/observed properties, and (3) the conduciveness of the data to represent them, finding they have a significant impact to performance compared to other transfer setting features. Characterizing the theoretical grounding of empathy tasks as direct, abstract, or adjacent further indicates that tasks that directly predict specified empathy components have higher transferability. Our work provides empirical evidence for the need for precise and multidimensional empathy operationalizations.
A review of annotation classification tools in the educational domain
Authors: Joaquín Gayoso-Cabada, Antonio Sarasa-Cabezuelo, José-Luis Sierra
Date: 2025-01-24 00:00:00
Tasks: Classification

An annotation consists of a portion of information that is associated with a piece of content in order to explain something about the content or to add more information. The use of annotations as a tool in the educational field has positive effects on the learning process. The usual way to use this instrument is to provide students with contents, usually textual, with which they must associate annotations. In most cases this task is performed in groups of students who work collaboratively. This process encourages analysis and understanding of the contents since they have to understand them in order to annotate them, and also encourages teamwork. To facilitate its use, computer applications have been devel-oped in recent decades that implement the annotation process and offer a set of additional functionalities. One of these functionalities is the classification of the annotations made. This functionality can be exploited in various ways in the learning process, such as guiding the students in the annotation process, providing information to the student about how the annotation process is done and to the teacher about how the students write and how they understand the content, as well as implementing other innovative educational processes. In this sense, the classification of annotations plays a critical role in the application of the annotation in the educational field. There are many studies of annotations, but most of them consider the classification aspect marginally only. This paper presents an initial study of the classification mech-anisms used in the annotation tools, identifying four types of cases: absence of classification mechanisms, classification based on pre-established vocabularies, classification based on extensible vocabularies, and classification based on struc-tured vocabularies.
Runtime Analysis of the Compact Genetic Algorithm on the LeadingOnes Benchmark
Authors: Marcel Chwiałkowski, Benjamin Doerr, Martin S. Krejca
Date: 2025-01-27 00:00:00

The compact genetic algorithm (cGA) is one of the simplest estimation-of-distribution algorithms (EDAs). Next to the univariate marginal distribution algorithm (UMDA) -- another simple EDA -- , the cGA has been subject to extensive mathematical runtime analyses, often showcasing a similar or even superior performance to competing approaches. Surprisingly though, up to date and in contrast to the UMDA and many other heuristics, we lack a rigorous runtime analysis of the cGA on the LeadingOnes benchmark -- one of the most studied theory benchmarks in the domain of evolutionary computation. We fill this gap in the literature by conducting a formal runtime analysis of the cGA on LeadingOnes. For the cGA's single parameter -- called the hypothetical population size -- at least polylogarithmically larger than the problem size, we prove that the cGA samples the optimum of LeadingOnes with high probability within a number of function evaluations quasi-linear in the problem size and linear in the hypothetical population size. For the best hypothetical population size, our result matches, up to polylogarithmic factors, the typical quadratic runtime that many randomized search heuristics exhibit on LeadingOnes. Our analysis exhibits some noteworthy differences in the working principles of the two algorithms which were not visible in previous works.
A Benchmarking Environment for Worker Flexibility in Flexible Job Shop Scheduling Problems
Authors: David Hutter, Thomas Steinberger, Michael Hellwig
Date: 2025-01-27 00:00:00
Tasks: Benchmarking, Evolutionary Algorithms, Job Shop Scheduling, Scheduling

In Production Scheduling, the Flexible Job Shop Scheduling Problem (FJSSP) aims to optimize a sequence of operations and assign each to an eligible machine with varying processing times. For integration of the workforce, each machine also requires a worker to be present to process an operation which additionally affects the processing times. The resulting problem is called Flexible Job Shop Scheduling Problem with Worker Flexibility (FJSSP-W). The FJSSP has been approached with various problem representations, including Mixed Integer Linear Programming (MILP), Constrained Programming (CP), and Simulation-based Optimization (SBO). In the latter area in particular, there exists a large number of specialized Evolutionary Algorithms (EA) like Particle Swarm Optimization (PSO) or Genetic Algorithms (GA). Yet, the solvers are often developed for single use cases only, and validated on a few selected test instances, let alone compared with results from solvers using other problem representations. While suitable approaches do also exist, the design of the FJSSP-W instances is not standardized and the algorithms are hardly comparable. This calls for a systematic benchmarking environment that provides a comprehensive set of FJSSP(-W) instances and supports targeted algorithm development. It will facilitate the comparison of algorithmic performance in the face of different problem characteristics. The present paper presents a collection of 402 commonly accepted FJSSP instances and proposes an approach to extend these with worker flexibility. In addition, we present a detailed procedure for the evaluation of scheduling algorithms on these problem sets and provide suitable model representations for this purpose. We provide complexity characteristics for all presented instances as well as baseline results of common commercial solvers to facilitate the validation of new algorithmic developments.
PSO and the Traveling Salesman Problem: An Intelligent Optimization Approach
Authors: Kael Silva Araújo, Francisco Márcio Barboza
Date: 2025-01-25 00:00:00
Tasks: Combinatorial Optimization, Traveling Salesman Problem

The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem that aims to find the shortest possible route that visits each city exactly once and returns to the starting point. This paper explores the application of Particle Swarm Optimization (PSO), a population-based optimization algorithm, to solve TSP. Although PSO was originally designed for continuous optimization problems, this work adapts PSO for the discrete nature of TSP by treating the order of cities as a permutation. A local search strategy, including 2-opt and 3-opt techniques, is applied to improve the solution after updating the particle positions. The performance of the proposed PSO algorithm is evaluated using benchmark TSP instances and compared to other popular optimization algorithms, such as Genetic Algorithms (GA) and Simulated Annealing (SA). Results show that PSO performs well for small to medium-sized problems, though its performance diminishes for larger instances due to difficulties in escaping local optima. This paper concludes that PSO is a promising approach for solving TSP, with potential for further improvement through hybridization with other optimization techniques.
EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement Learning
Authors: Bowen Zheng, Ran Cheng, Kay Chen Tan
Date: 2025-01-25 00:00:00
Tasks: Benchmarking, Evolutionary Algorithms, GPU, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Evolutionary Reinforcement Learning (EvoRL) has emerged as a promising approach to overcoming the limitations of traditional reinforcement learning (RL) by integrating the Evolutionary Computation (EC) paradigm with RL. However, the population-based nature of EC significantly increases computational costs, thereby restricting the exploration of algorithmic design choices and scalability in large-scale settings. To address this challenge, we introduce $\texttt{$\textbf{EvoRL}$}$, the first end-to-end EvoRL framework optimized for GPU acceleration. The framework executes the entire training pipeline on accelerators, including environment simulations and EC processes, leveraging hierarchical parallelism through vectorization and compilation techniques to achieve superior speed and scalability. This design enables the efficient training of large populations on a single machine. In addition to its performance-oriented design, $\texttt{$\textbf{EvoRL}$}$ offers a comprehensive platform for EvoRL research, encompassing implementations of traditional RL algorithms (e.g., A2C, PPO, DDPG, TD3, SAC), Evolutionary Algorithms (e.g., CMA-ES, OpenES, ARS), and hybrid EvoRL paradigms such as Evolutionary-guided RL (e.g., ERL, CEM-RL) and Population-Based AutoRL (e.g., PBT). The framework's modular architecture and user-friendly interface allow researchers to seamlessly integrate new components, customize algorithms, and conduct fair benchmarking and ablation studies. The project is open-source and available at: https://github.com/EMI-Group/evorl.
Hybrid Firefly-Genetic Algorithm for Single and Multi-dimensional 0-1 Knapsack Problems
Authors: Aswathi Malanthara, Ishaan R Kale
Date: 2024-12-31 00:00:00
Tasks: Computational Efficiency

This paper addresses the challenges faced by algorithms, such as the Firefly Algorithm (FA) and the Genetic Algorithm (GA), in constrained optimization problems. While both algorithms perform well for unconstrained problems, their effectiveness diminishes when constraints are introduced due to limitations in exploration, exploitation, and constraint handling. To overcome these challenges, a hybrid FAGA algorithm is proposed, combining the strengths of both algorithms. The hybrid algorithm is validated by solving unconstrained benchmark functions and constrained optimization problems, including design engineering problems and combinatorial problems such as the 0-1 Knapsack Problem. The proposed algorithm delivers improved solution accuracy and computational efficiency compared to conventional optimization algorithm. This paper outlines the development and structure of the hybrid algorithm and demonstrates its effectiveness in handling complex optimization problems.
A survey on pioneering metaheuristic algorithms between 2019 and 2024
Authors: Tansel Dokeroglu, Deniz Canturk, Tayfun Kucukyilmaz
Date: 2024-12-28 00:00:00
Tasks: Survey

This review examines over 150 new metaheuristics of the last six years (between 2019 and 2024), underscoring their profound influence and performance. Over the past three decades, more than 500 new metaheuristic algorithms have been proposed, with no slowdown in sight. An overwhelming abundance that complicates the process of selecting and assessing the most effective solutions for complex optimization challenges. Our evaluation centers on pivotal criteria, including annual citation metrics, the breadth of the addressed problem types, source code availability, user friendly parameter configurations, innovative mechanisms and operators, and approaches designed to mitigate traditional metaheuristic issues such as stagnation and premature convergence. We further explore recent high impact applications of the past six years' most influential 23 metahueristic algorithms, shedding light on their advantages and limitations, while identifying challenges and potential avenues for future research.
Equation discovery framework EPDE: Towards a better equation discovery
Authors: Mikhail Maslyaev, Alexander Hvatov
Date: 2024-12-28 00:00:00
Tasks: Equation Discovery

Equation discovery methods hold promise for extracting knowledge from physics-related data. However, existing approaches often require substantial prior information that significantly reduces the amount of knowledge extracted. In this paper, we enhance the EPDE algorithm -- an evolutionary optimization-based discovery framework. In contrast to methods like SINDy, which rely on pre-defined libraries of terms and linearities, our approach generates terms using fundamental building blocks such as elementary functions and individual differentials. Within evolutionary optimization, we may improve the computation of the fitness function as is done in gradient methods and enhance the optimization algorithm itself. By incorporating multi-objective optimization, we effectively explore the search space, yielding more robust equation extraction, even when dealing with complex experimental data. We validate our algorithm's noise resilience and overall performance by comparing its results with those from the state-of-the-art equation discovery framework SINDy.
LPBSA: Enhancing Optimization Efficiency through Learner Performance-based Behavior and Simulated Annealing
Authors: Dana R. Hamad, Tarik A. Rashid
Date: 2024-12-23 00:00:00
Tasks: Navigate

This study introduces the LPBSA, an advanced optimization algorithm that combines Learner Performance-based Behavior (LPB) and Simulated Annealing (SA) in a hybrid approach. Emphasizing metaheuristics, the LPBSA addresses and mitigates the challenges associated with traditional LPB methodologies, enhancing convergence, robustness, and adaptability in solving complex optimization problems. Through extensive evaluations using benchmark test functions, the LPBSA demonstrates superior performance compared to LPB and competes favorably with established algorithms such as PSO, FDO, LEO, and GA. Real-world applications underscore the algorithm's promise, with LPBSA outperforming the LEO algorithm in two tested scenarios. Based on the study results many test function results such as TF5 by recording (4.76762333) and some other test functions provided in the result section prove that LPBSA outperforms popular algorithms. This research highlights the efficacy of a hybrid approach in the ongoing evolution of optimization algorithms, showcasing the LPBSA's capacity to navigate diverse optimization landscapes and contribute significantly to addressing intricate optimization challenges.
Optimizing LPB Algorithms using Simulated Annealing
Authors: Dana Rasul Hamad, Tarik A. Rashid
Date: 2024-12-22 00:00:00

Learner Performance-based Behavior using Simulated Annealing (LPBSA) is an improvement of the Learner Performance-based Behavior (LPB) algorithm. LPBSA, like LPB, has been proven to deal with single and complex problems. Simulated Annealing (SA) has been utilized as a powerful technique to optimize LPB. LPBSA has provided results that outperformed popular algorithms, like the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and even LPB. This study outlines the improved algorithm's working procedure by providing a main population and dividing it into Good and Bad populations and then applying crossover and mutation operators. When some individuals are born in the crossover stage, they have to go through the mutation process. Between these two steps, we have applied SA using the Metropolis Acceptance Criterion (MAC) to accept only the best and most useful individuals to be used in the next iteration. Finally, the outcomes demonstrate that the population is enhanced, leading to improved efficiency and validating the performance of LPBSA.
Neuromorphic Spiking Neural Network Based Classification of COVID-19 Spike Sequences
Authors: Taslim Murad, Prakash Chourasia, Sarwan Ali, Imdad Ullah Khan, Murray Patterson
Date: 2024-12-19 00:00:00

The availability of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) virus data post-COVID has reached exponentially to an enormous magnitude, opening research doors to analyze its behavior. Various studies are conducted by researchers to gain a deeper understanding of the virus, like genomic surveillance, etc, so that efficient prevention mechanisms can be developed. However, the unstable nature of the virus (rapid mutations, multiple hosts, etc) creates challenges in designing analytical systems for it. Therefore, we propose a neural network-based (NN) mechanism to perform an efficient analysis of the SARS-CoV-2 data, as NN portrays generalized behavior upon training. Moreover, rather than using the full-length genome of the virus, we apply our method to its spike region, as this region is known to have predominant mutations and is used to attach to the host cell membrane. In this paper, we introduce a pipeline that first converts the spike protein sequences into a fixed-length numerical representation and then uses Neuromorphic Spiking Neural Network to classify those sequences. We compare the performance of our method with various baselines using real-world SARS-CoV-2 spike sequence data and show that our method is able to achieve higher predictive accuracy compared to the recent baselines.