OpenRaiser/Intern-Atlas huggingface dataset:
Gender Disparities in Contributions, Leadership, and Collaboration: An Exploratory Study on Software Systems Research
Venue: arXiv cs.SE 2025
Year: 2025

Gender diversity enhances research by bringing diverse perspectives and innovative approaches. It ensures equitable solutions that address the needs of diverse populations. However, gender disparity persists in research where women remain underrepresented, which might limit diversity and innovation. Many even leave scientific careers as their contributions often go unnoticed and undervalued. Therefore, understanding gender-based contributions and collaboration dynamics is crucial to addressing this gap and creating a more inclusive research environment. In this study, we analyzed 2,000 articles published over the past decade in the Journal of Systems and Software (JSS). From these, we selected 384 articles that detailed authors' contributions and contained both female and male authors to investigate gender-based contributions. Our contributions are fourfold. First, we analyzed women's engagement in software systems research. Our analysis showed that only 32.74% of the total authors are women and female-led or supervised studies were fewer than those of men. Second, we investigated female authors' contributions across 14 major roles. Interestingly, we found that women contributed comparably to men in most roles, with more contributions in conceptualization, writing, and reviewing articles. Third, we explored the areas of software systems research and found that female authors are more actively involved in human-centric research domains. Finally, we analyzed gender-based collaboration dynamics. Our findings revealed that female supervisors tended to collaborate locally more often than national-level collaborations. Our study highlights that females' contributions to software systems research are comparable to those of men. Therefore, the barriers need to be addressed to enhance female participation and ensure equity and inclusivity in research.
Parameterized Complexity of (d,r)-Domination via Modular Decomposition
Venue: arXiv cs.CC 2025
Year: 2025

With the rise of social media, misinformation has significant negative effects on the decision-making of individuals, organizations and communities within society. Identifying and mitigating the spread of fake information is a challenging issue. We consider a generalization of the Domination problem that can be used to detect a set of individuals who, through an awareness process, can prevent the spreading of fake narratives. The considered problem, named \textsc{$(d,r)$-Domination} generalizes both distance and multiple domination. We study the parameterized complexity of the problem according to standard and structural parameters. We give fixed-parameter algorithms as well as polynomial compressions/kernelizations for some variants of the problem and parameter combinations.
Robustness-enhanced Myoelectric Control with GAN-based Open-set Recognition
Venue: arXiv cs.CV 2025
Year: 2025

Electromyography (EMG) signals are widely used in human motion recognition and medical rehabilitation, yet their variability and susceptibility to noise significantly limit the reliability of myoelectric control systems. Existing recognition algorithms often fail to handle unfamiliar actions effectively, leading to system instability and errors. This paper proposes a novel framework based on Generative Adversarial Networks (GANs) to enhance the robustness and usability of myoelectric control systems by enabling open-set recognition. The method incorporates a GAN-based discriminator to identify and reject unknown actions, maintaining system stability by preventing misclassifications. Experimental evaluations on publicly available and self-collected datasets demonstrate a recognition accuracy of 97.6\% for known actions and a 23.6\% improvement in Active Error Rate (AER) after rejecting unknown actions. The proposed approach is computationally efficient and suitable for deployment on edge devices, making it practical for real-world applications.
Choose Your Explanation: A Comparison of SHAP and GradCAM in Human Activity Recognition
Venue: arXiv cs.LG 2025
Year: 2025

Explaining machine learning (ML) models using eXplainable AI (XAI) techniques has become essential to make them more transparent and trustworthy. This is especially important in high-stakes domains like healthcare, where understanding model decisions is critical to ensure ethical, sound, and trustworthy outcome predictions. However, users are often confused about which explanability method to choose for their specific use case. We present a comparative analysis of widely used explainability methods, Shapley Additive Explanations (SHAP) and Gradient-weighted Class Activation Mapping (Grad-CAM), within the domain of human activity recognition (HAR) utilizing graph convolutional networks (GCNs). By evaluating these methods on skeleton-based data from two real-world datasets, including a healthcare-critical cerebral palsy (CP) case, this study provides vital insights into both approaches' strengths, limitations, and differences, offering a roadmap for selecting the most appropriate explanation method based on specific models and applications. We quantitatively and quantitatively compare these methods, focusing on feature importance ranking, interpretability, and model sensitivity through perturbation experiments. While SHAP provides detailed input feature attribution, Grad-CAM delivers faster, spatially oriented explanations, making both methods complementary depending on the application's requirements. Given the importance of XAI in enhancing trust and transparency in ML models, particularly in sensitive environments like healthcare, our research demonstrates how SHAP and Grad-CAM could complement each other to provide more interpretable and actionable model explanations.
Electric Vehicle Charging Stations Placement Optimization in Vietnam Using Mixed-Integer Nonlinear Programming Model
Venue: arXiv cs.CE 2025
Year: 2025

Vietnam is viewed as one of the promising markets for electric vehicles (EVs), especially automobiles, when it is predicted to reach 1 million in 2028 and 3.5 million in 2040. However, the lack of charging station infrastructure has hindered the growth rate of EVs in this country. This study aims to propose an optimization model using Mixed-Integer Nonlinear Programming to implement an optimal location strategy for EVs charging stations in Ho Chi Minh City. The problem is solved by Gurobi using the Brand-and-Cut method. There are two perspectives, including Charging Station Operators and EV users. In addition, 7 kinds of costs are considered. From 1509 Point of Interest and 199 residential areas, 134 POIs were chosen with 923 charging stations to fully satisfy the customer demand. Furthermore, the effectiveness of the proposed model is proved by a minor MIP Gap and running in a short time with full feasibility.
Mamba2D: A Natively Multi-Dimensional State-Space Model for Vision Tasks
Venue: arXiv cs.CV 2025
Year: 2025

State-Space Models (SSMs) have recently emerged as a powerful and efficient alternative to the long-standing transformer architecture. However, existing SSM conceptualizations retain deeply rooted biases from their roots in natural language processing. This constrains their ability to appropriately model the spatially-dependent characteristics of visual inputs. In this paper, we address these limitations by re-deriving modern selective state-space techniques, starting from a natively multidimensional formulation. Currently, prior works attempt to apply natively 1D SSMs to 2D data (i.e. images) by relying on arbitrary combinations of 1D scan directions to capture spatial dependencies. In contrast, Mamba2D improves upon this with a single 2D scan direction that factors in both dimensions of the input natively, effectively modelling spatial dependencies when constructing hidden states. Mamba2D shows comparable performance to prior adaptations of SSMs for vision tasks, on standard image classification evaluations with the ImageNet-1K dataset. Source code is available at https://github.com/cocoalex00/Mamba2D.
HashEvict: A Pre-Attention KV Cache Eviction Strategy using Locality-Sensitive Hashing
Venue: arXiv cs.LG 2025
Year: 2025

Transformer-based large language models (LLMs) use the key-value (KV) cache to significantly accelerate inference by storing the key and value embeddings of past tokens. However, this cache consumes significant GPU memory. In this work, we introduce HashEvict, an algorithm that uses locality-sensitive hashing (LSH) to compress the KV cache. HashEvict quickly locates tokens in the cache that are cosine dissimilar to the current query token. This is achieved by computing the Hamming distance between binarized Gaussian projections of the current token query and cached token keys, with a projection length much smaller than the embedding dimension. We maintain a lightweight binary structure in GPU memory to facilitate these calculations. Unlike existing compression strategies that compute attention to determine token retention, HashEvict makes these decisions pre-attention, thereby reducing computational costs. Additionally, HashEvict is dynamic - at every decoding step, the key and value of the current token replace the embeddings of a token expected to produce the lowest attention score. We demonstrate that HashEvict can compress the KV cache by 30%-70% while maintaining high performance across reasoning, multiple-choice, long-context retrieval and summarization tasks.
Neonpool: Reimagining cryptocurrency transaction pools for lightweight clients and IoT devices
Venue: arXiv cs.CR 2025
Year: 2025

The transaction pool plays a critical role in processing and disseminating transactions in cryptocurrency networks. However, increasing transaction loads strain the resources of full node deployments. We present Neonpool, an innovative transaction pool optimization using bloom filter variants, which reduces the memory footprint of the transaction pool to a fraction. Implemented in C++ and benchmarked using a unique Bitcoin and Ethereum dataset, our solution verifies and forwards transactions with over 99.99\% accuracy and does not necessitate a hard fork. Neonpool is ideally suited for lightweight cryptocurrency clients and for resource-constrained devices such as browsers, systems-on-a-chip, mobile or IoT devices.
Optimizing Queries with Many-to-Many Joins
Venue: arXiv cs.DB 2025
Year: 2025

As database query processing techniques are being used to handle diverse workloads, a key emerging challenge is how to efficiently handle multi-way join queries containing multiple many-to-many joins. While uncommon in traditional enterprise settings that have been the focus of much of the query optimization work to date, such queries are seen frequently in other contexts such as graph workloads. This has led to much work on developing join algorithms for handling cyclic queries, on compressed (factorized) representations for more efficient storage of intermediate results, and on use of semi-joins or predicate transfer to avoid generating large redundant intermediate results. In this paper, we address a core query optimization problem in this context. Specifically, we introduce an improved cost model that more accurately captures the cost of a query plan in such scenarios, and we present several optimization algorithms for query optimization that incorporate these new cost functions. We present an extensive experimental evaluation, that compares the factorized representation approach with a full semi-join reduction approach as well as to an approach that uses bitvectors to eliminate tuples early through sideways information passing. We also present new analyses of robustness of these techniques to the choice of the join order, potentially eliminating the need for more complex query optimization and selectivity estimation techniques.
Temporal Explorability Games
Venue: arXiv cs.GT 2025
Year: 2025

Temporal graphs extend ordinary graphs with discrete time that affects the availability of edges. We consider solving games played on temporal graphs where one player aims to explore the graph, i.e., visit all vertices. The complexity depends majorly on two factors: the presence of an adversary and how edge availability is specified. We demonstrate that on static graphs, where edges are always available, solving explorability games is just as hard as solving reachability games. In contrast, on temporal graphs, the complexity of explorability coincides with generalized reachability (NP-complete for one-player and PSPACE- complete for two player games). We further show that if temporal graphs are given symbolically, even one-player reachability and thus explorability and generalized reachability games are PSPACE-hard. For one player, all these are also solvable in PSPACE and for two players, they are in PSPACE, EXP and EXP, respectively.
EMPRA: Embedding Perturbation Rank Attack against Neural Ranking Models
Venue: arXiv cs.IR 2025
Year: 2025

Recent research has shown that neural information retrieval techniques may be susceptible to adversarial attacks. Adversarial attacks seek to manipulate the ranking of documents, with the intention of exposing users to targeted content. In this paper, we introduce the Embedding Perturbation Rank Attack (EMPRA) method, a novel approach designed to perform adversarial attacks on black-box Neural Ranking Models (NRMs). EMPRA manipulates sentence-level embeddings, guiding them towards pertinent context related to the query while preserving semantic integrity. This process generates adversarial texts that seamlessly integrate with the original content and remain imperceptible to humans. Our extensive evaluation conducted on the widely-used MS MARCO V1 passage collection demonstrate the effectiveness of EMPRA against a wide range of state-of-the-art baselines in promoting a specific set of target documents within a given ranked results. Specifically, EMPRA successfully achieves a re-ranking of almost 96% of target documents originally ranked between 51-100 to rank within the top 10. Furthermore, EMPRA does not depend on surrogate models for adversarial text generation, enhancing its robustness against different NRMs in realistic settings.
Enhancing Contrastive Learning Inspired by the Philosophy of "The Blind Men and the Elephant"
Venue: arXiv cs.CV 2025
Year: 2025

Contrastive learning is a prevalent technique in self-supervised vision representation learning, typically generating positive pairs by applying two data augmentations to the same image. Designing effective data augmentation strategies is crucial for the success of contrastive learning. Inspired by the story of the blind men and the elephant, we introduce JointCrop and JointBlur. These methods generate more challenging positive pairs by leveraging the joint distribution of the two augmentation parameters, thereby enabling contrastive learning to acquire more effective feature representations. To the best of our knowledge, this is the first effort to explicitly incorporate the joint distribution of two data augmentation parameters into contrastive learning. As a plug-and-play framework without additional computational overhead, JointCrop and JointBlur enhance the performance of SimCLR, BYOL, MoCo v1, MoCo v2, MoCo v3, SimSiam, and Dino baselines with notable improvements.
Quantum $(r,δ)$-locally recoverable codes
Venue: arXiv cs.IT 2025
Year: 2025

Classical $(r,δ)$-locally recoverable codes are designed for avoiding loss of information in large scale distributed and cloud storage systems. We introduce the quantum counterpart of those codes by defining quantum $(r,δ)$-locally recoverable codes which are quantum error-correcting codes capable of correcting $δ-1$ qudit erasures from sets of at most $r+ δ-1$ qudits. We give a necessary and sufficient condition for a quantum stabilizer code $Q(C)$ to be $(r,δ)$-locally recoverable. Our condition depends only on the puncturing and shortening at suitable sets of both the symplectic self-orthogonal code $C$ used for constructing $Q(C)$ and its symplectic dual $C^{\perp_s}$. When $Q(C)$ comes from a Hermitian or Euclidean dual-containing code, and under an extra condition, we show that there is an equivalence between the classical and quantum concepts of $(r,δ)$-local recoverability. A Singleton-like bound is stated in this case and examples attaining the bound are given.
Large Language Model Can Be a Foundation for Hidden Rationale-Based Retrieval
Venue: arXiv cs.IR 2025
Year: 2025

Despite the recent advancement in Retrieval-Augmented Generation (RAG) systems, most retrieval methodologies are often developed for factual retrieval, which assumes query and positive documents are semantically similar. In this paper, we instead propose and study a more challenging type of retrieval task, called hidden rationale retrieval, in which query and document are not similar but can be inferred by reasoning chains, logic relationships, or empirical experiences. To address such problems, an instruction-tuned Large language model (LLM) with a cross-encoder architecture could be a reasonable choice. To further strengthen pioneering LLM-based retrievers, we design a special instruction that transforms the retrieval task into a generative task by prompting LLM to answer a binary-choice question. The model can be fine-tuned with direct preference optimization (DPO). The framework is also optimized for computational efficiency with no performance degradation. We name this retrieval framework by RaHoRe and verify its zero-shot and fine-tuned performance superiority on Emotional Support Conversation (ESC), compared with previous retrieval works. Our study suggests the potential to employ LLM as a foundation for a wider scope of retrieval tasks. Our codes, models, and datasets are available on https://github.com/flyfree5/LaHoRe.
Task-Driven Co-Design of Mobile Manipulators
Venue: arXiv cs.RO 2025
Year: 2025

Recent interest in mobile manipulation has resulted in a wide range of new robot designs. A large family of these designs focuses on modular platforms that combine existing mobile bases with static manipulator arms. They combine these modules by mounting the arm in a tabletop configuration. However, the operating workspaces and heights for common mobile manipulation tasks, such as opening articulated objects, significantly differ from tabletop manipulation tasks. As a result, these standard arm mounting configurations can result in kinematics with restricted joint ranges and motions. To address these problems, we present the first Concurrent Design approach for mobile manipulators to optimize key arm-mounting parameters. Our approach directly targets task performance across representative household tasks by training a powerful multitask-capable reinforcement learning policy in an inner loop while optimizing over a distribution of design configurations guided by Bayesian Optimization and HyperBand (BOHB) in an outer loop. This results in novel designs that significantly improve performance across both seen and unseen test tasks, and outperform designs generated by heuristic-based performance indices that are cheaper to evaluate but only weakly correlated with the motions of interest. We evaluate the physical feasibility of the resulting designs and show that they are practical and remain modular, affordable, and compatible with existing commercial components. We open-source the approach and generated designs to facilitate further improvements of these platforms.
The Good, the Bad, and the (Un)Usable: A Rapid Literature Review on Privacy as Code
Venue: arXiv cs.SE 2025
Year: 2025

Privacy and security are central to the design of information systems endowed with sound data protection and cyber resilience capabilities. Still, developers often struggle to incorporate these properties into software projects as they either lack proper cybersecurity training or do not consider them a priority. Prior work has tried to support privacy and security engineering activities through threat modeling methods for scrutinizing flaws in system architectures. Moreover, several techniques for the automatic identification of vulnerabilities and the generation of secure code implementations have also been proposed in the current literature. Conversely, such as-code approaches seem under-investigated in the privacy domain, with little work elaborating on (i) the automatic detection of privacy properties in source code or (ii) the generation of privacy-friendly code. In this work, we seek to characterize the current research landscape of Privacy as Code (PaC) methods and tools by conducting a rapid literature review. Our results suggest that PaC research is in its infancy, especially regarding the performance evaluation and usability assessment of the existing approaches. Based on these findings, we outline and discuss prospective research directions concerning empirical studies with software practitioners, the curation of benchmark datasets, and the role of generative AI technologies.
Interact with me: Joint Egocentric Forecasting of Intent to Interact, Attitude and Social Actions
Venue: arXiv cs.CV 2025
Year: 2025

For efficient human-agent interaction, an agent should proactively recognize their target user and prepare for upcoming interactions. We formulate this challenging problem as the novel task of jointly forecasting a person's intent to interact with the agent, their attitude towards the agent and the action they will perform, from the agent's (egocentric) perspective. So we propose \emph{SocialEgoNet} - a graph-based spatiotemporal framework that exploits task dependencies through a hierarchical multitask learning approach. SocialEgoNet uses whole-body skeletons (keypoints from face, hands and body) extracted from only 1 second of video input for high inference speed. For evaluation, we augment an existing egocentric human-agent interaction dataset with new class labels and bounding box annotations. Extensive experiments on this augmented dataset, named JPL-Social, demonstrate \emph{real-time} inference and superior performance (average accuracy across all tasks: 83.15\%) of our model outperforming several competitive baselines. The additional annotations and code will be available upon acceptance.
Beyond Partisan Leaning: A Comparative Analysis of Political Bias in Large Language Models
Venue: arXiv cs.CY 2025
Year: 2025

As large language models (LLMs) become increasingly embedded in civic, educational, and political information environments, concerns about their potential political bias have grown. Prior research often evaluates such bias through simulated personas or predefined ideological typologies, which may introduce artificial framing effects or overlook how models behave in general use scenarios. This study adopts a persona-free, topic-specific approach to evaluate political behavior in LLMs, reflecting how users typically interact with these systems-without ideological role-play or conditioning. We introduce a two-dimensional framework: one axis captures partisan orientation on highly polarized topics (e.g., abortion, immigration), and the other assesses sociopolitical engagement on less polarized issues (e.g., climate change, foreign policy). Using survey-style prompts drawn from the ANES and Pew Research Center, we analyze responses from 43 LLMs developed in the U.S., Europe, China, and the Middle East. We propose an entropy-weighted bias score to quantify both the direction and consistency of partisan alignment, and identify four behavioral clusters through engagement profiles. Findings show most models lean center-left or left ideologically and vary in their nonpartisan engagement patterns. Model scale and openness are not strong predictors of behavior, suggesting that alignment strategy and institutional context play a more decisive role in shaping political expression.