OpenRaiser/Intern-Atlas huggingface dataset:
A multi-dimensional analysis of usage counts, Mendeley readership, and citations for journal and conference papers
Venue: arXiv cs.DL 2024
Year: 2024

This study analyzed 16,799 journal papers and 98,773 conference papers published by IEEE Xplore in 2016 to investigate the relationships among usage counts, Mendeley readership, and citations through descriptive, regression, and mediation analyses. Differences in the relationship among these metrics between journal and conference papers are also studied. Results showed that there is no significant difference between journal and conference papers in the distribution patterns and accumulation rates of the three metrics. However, the correlation coefficients of the interrelationships between the three metrics were lower in conference papers compared to journal papers. Secondly, funding, international collaboration, and open access are positively associated with all three metrics, except for the case of funding on the usage metrics of conference papers. Furthermore, early Mendeley readership is a better predictor of citations than early usage counts and performs better for journal papers. Finally, we reveal that early Mendeley readership partially mediates between early usage counts and citation counts in the journal and conference papers. The main difference is that conference papers rely more on the direct effect of early usage counts on citations. This study contributes to expanding the existing knowledge on the relationships among usage counts, Mendeley readership, and citations in journal and conference papers, providing new insights into the relationship between the three metrics through mediation analysis.
Exploring Color Invariance through Image-Level Ensemble Learning
Venue: arXiv cs.CV 2024
Year: 2024

In the field of computer vision, the persistent presence of color bias, resulting from fluctuations in real-world lighting and camera conditions, presents a substantial challenge to the robustness of models. This issue is particularly pronounced in complex wide-area surveillance scenarios, such as person re-identification and industrial dust segmentation, where models often experience a decline in performance due to overfitting on color information during training, given the presence of environmental variations. Consequently, there is a need to effectively adapt models to cope with the complexities of camera conditions. To address this challenge, this study introduces a learning strategy named Random Color Erasing, which draws inspiration from ensemble learning. This strategy selectively erases partial or complete color information in the training data without disrupting the original image structure, thereby achieving a balanced weighting of color features and other features within the neural network. This approach mitigates the risk of overfitting and enhances the model's ability to handle color variation, thereby improving its overall robustness. The approach we propose serves as an ensemble learning strategy, characterized by robust interpretability. A comprehensive analysis of this methodology is presented in this paper. Across various tasks such as person re-identification and semantic segmentation, our approach consistently improves strong baseline methods. Notably, in comparison to existing methods that prioritize color robustness, our strategy significantly enhances performance in cross-domain scenarios. The code available at \url{https://github.com/layumi/Person\_reID\_baseline\_pytorch/blob/master/random\_erasing.py} or \url{https://github.com/finger-monkey/Data-Augmentation}.
Cross-lingual Editing in Multilingual Language Models
Venue: arXiv cs.CL 2024
Year: 2024

The training of large language models (LLMs) necessitates substantial data and computational resources, and updating outdated LLMs entails significant efforts and resources. While numerous model editing techniques (METs) have emerged to efficiently update model outputs without retraining, their effectiveness in multilingual LLMs, where knowledge is stored in diverse languages, remains an underexplored research area. This research paper introduces the cross-lingual model editing (\textbf{XME}) paradigm, wherein a fact is edited in one language, and the subsequent update propagation is observed across other languages. To investigate the XME paradigm, we conducted experiments using BLOOM, mBERT, and XLM-RoBERTa using the two writing scripts: \textit{Latin} (English, French, and Spanish) and \textit{Indic} (Hindi, Gujarati, and Bengali). The results reveal notable performance limitations of state-of-the-art METs under the XME setting, mainly when the languages involved belong to two distinct script families. These findings highlight the need for further research and development of XME techniques to address these challenges. For more comprehensive information, the dataset used in this research and the associated code are publicly available at the following URL\url{https://github.com/lingo-iitgn/XME}.
Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recency
Venue: arXiv cs.IR 2024
Year: 2024

This paper explores the biases in ChatGPT-based recommender systems, focusing on provider fairness (item-side fairness). Through extensive experiments and over a thousand API calls, we investigate the impact of prompt design strategies-including structure, system role, and intent-on evaluation metrics such as provider fairness, catalog coverage, temporal stability, and recency. The first experiment examines these strategies in classical top-K recommendations, while the second evaluates sequential in-context learning (ICL). In the first experiment, we assess seven distinct prompt scenarios on top-K recommendation accuracy and fairness. Accuracy-oriented prompts, like Simple and Chain-of-Thought (COT), outperform diversification prompts, which, despite enhancing temporal freshness, reduce accuracy by up to 50%. Embedding fairness into system roles, such as "act as a fair recommender," proved more effective than fairness directives within prompts. Diversification prompts led to recommending newer movies, offering broader genre distribution compared to traditional collaborative filtering (CF) models. The second experiment explores sequential ICL, comparing zero-shot and few-shot ICL. Results indicate that including user demographic information in prompts affects model biases and stereotypes. However, ICL did not consistently improve item fairness and catalog coverage over zero-shot learning. Zero-shot learning achieved higher NDCG and coverage, while ICL-2 showed slight improvements in hit rate (HR) when age-group context was included. Our study provides insights into biases of RecLLMs, particularly in provider fairness and catalog coverage. By examining prompt design, learning strategies, and system roles, we highlight the potential and challenges of integrating LLMs into recommendation systems. Further details can be found at https://github.com/yasdel/Benchmark_RecLLM_Fairness.
Catch the Butterfly: Peeking into the Terms and Conflicts among SPDX Licenses
Venue: arXiv cs.SE 2024
Year: 2024

The widespread adoption of third-party libraries (TPLs) in software development has accelerated the creation of modern software. However, this convenience comes with potential legal risks. Developers may inadvertently violate the licenses of TPLs, leading to legal issues. While existing studies have explored software licenses and potential incompatibilities, these studies often focus on a limited set of licenses or rely on low-quality license data, which may affect their conclusions. To address this gap, there is a need for a high-quality license dataset that encompasses a broad range of mainstream licenses to help developers navigate the complex landscape of software licenses, avoid potential legal pitfalls, and guide solutions for managing license compliance and compatibility in software development. To this end, we conduct the first work to understand the mainstream software licenses based on term granularity and obtain a high-quality dataset of 453 SPDX licenses with well-labeled terms and conflicts. Specifically, we first conduct a differential analysis of the mainstream platforms to understand the terms and attitudes of each license. Next, we propose a standardized set of license terms to capture and label existing mainstream licenses with high quality. Moreover, we include copyleft conflicts and conclude the three major types of license conflicts among the 453 SPDX licenses. Based on these, we carry out two empirical studies to reveal the concerns and threats from the perspectives of both licensors and licensees. One study provides an in-depth analysis of the similarities, differences, and conflicts among SPDX licenses, revisits the usage and conflicts of licenses in the NPM ecosystem, and draws conclusions that differ from previous work. Our studies reveal some insightful findings and disclose relevant analytical data, which set the stage for further research.
G.O.G: A Versatile Gripper-On-Gripper Design for Bimanual Cloth Manipulation with a Single Robotic Arm
Venue: arXiv cs.RO 2024
Year: 2024

The manipulation of garments poses research challenges due to their deformable nature and the extensive variability in shapes and sizes. Despite numerous attempts by researchers to address these via approaches involving robot perception and control, there has been a relatively limited interest in resolving it through the co-development of robot hardware. Consequently, the majority of studies employ off-the-shelf grippers in conjunction with dual robot arms to enable bimanual manipulation and high dexterity. However, this dual-arm system increases the overall cost of the robotic system as well as its control complexity in order to tackle robot collisions and other robot coordination issues. As an alternative approach, we propose to enable bimanual cloth manipulation using a single robot arm via novel end effector design -- sharing dexterity skills between manipulator and gripper rather than relying entirely on robot arm coordination. To this end, we introduce a new gripper, called G.O.G., based on a gripper-on-gripper structure where the first gripper independently regulates the span, up to 500mm, between its fingers which are in turn also grippers. These finger grippers consist of a variable friction module that enables two grasping modes: firm and sliding grasps. Household item and cloth object benchmarks are employed to evaluate the performance of the proposed design, encompassing both experiments on the gripper design itself and on cloth manipulation. Experimental results demonstrate the potential of the introduced ideas to undertake a range of bimanual cloth manipulation tasks with a single robot arm. Supplementary material is available at https://sites.google.com/view/gripperongripper.
DRAT Proofs of Unsatisfiability for SAT Modulo Monotonic Theories
Venue: arXiv cs.LO 2024
Year: 2024

Generating proofs of unsatisfiability is a valuable capability of most SAT solvers, and is an active area of research for SMT solvers. This paper introduces the first method to efficiently generate proofs of unsatisfiability specifically for an important subset of SMT: SAT Modulo Monotonic Theories (SMMT), which includes many useful finite-domain theories (e.g., bit vectors and many graph-theoretic properties) and is used in production at Amazon Web Services. Our method uses propositional definitions of the theory predicates, from which it generates compact Horn approximations of the definitions, which lead to efficient DRAT proofs, leveraging the large investment the SAT community has made in DRAT. In experiments on practical SMMT problems, our proof generation overhead is minimal (7.41% geometric mean slowdown, 28.8% worst-case), and we can generate and check proofs for many problems that were previously intractable.
Demonstration of Cooperative Transport Interface using open-source 5G OpenRAN and virtualised PON network
Venue: arXiv cs.NI 2024
Year: 2024

We demonstrate a real-time, converged 5G-PON through the Cooperative Transport Interface, synchronising 5G and PON-DBA upstream schedulers. This innovative approach, implemented using 5G and PON open network implementations, significantly enhances network resource allocation, reducing latency.
Removal then Selection: A Coarse-to-Fine Fusion Perspective for RGB-Infrared Object Detection
Venue: arXiv cs.CV 2024
Year: 2024

In recent years, object detection utilizing both visible (RGB) and thermal infrared (IR) imagery has garnered extensive attention and has been widely implemented across a diverse array of fields. By leveraging the complementary properties between RGB and IR images, the object detection task can achieve reliable and robust object localization across a variety of lighting conditions, from daytime to nighttime environments. Most existing multi-modal object detection methods directly input the RGB and IR images into deep neural networks, resulting in inferior detection performance. We believe that this issue arises not only from the challenges associated with effectively integrating multimodal information but also from the presence of redundant features in both the RGB and IR modalities. The redundant information of each modality will exacerbates the fusion imprecision problems during propagation. To address this issue, we draw inspiration from the human brain's mechanism for processing multimodal information and propose a novel coarse-to-fine perspective to purify and fuse features from both modalities. Specifically, following this perspective, we design a Redundant Spectrum Removal module to remove interfering information within each modality coarsely and a Dynamic Feature Selection module to finely select the desired features for feature fusion. To verify the effectiveness of the coarse-to-fine fusion strategy, we construct a new object detector called the Removal then Selection Detector (RSDet). Extensive experiments on three RGB-IR object detection datasets verify the superior performance of our method.
FinLLMs: A Framework for Financial Reasoning Dataset Generation with Large Language Models
Venue: arXiv cs.AI 2024
Year: 2024

Large Language models (LLMs) usually rely on extensive training datasets. In the financial domain, creating numerical reasoning datasets that include a mix of tables and long text often involves substantial manual annotation expenses. To address the limited data resources and reduce the annotation cost, we introduce FinLLMs, a method for generating financial question-answering data based on common financial formulas using Large Language Models. First, we compile a list of common financial formulas and construct a graph based on the variables these formulas employ. We then augment the formula set by combining those that share identical variables as new elements. Specifically, we explore formulas obtained by manual annotation and merge those formulas with shared variables by traversing the constructed graph. Finally, utilizing GPT-3.5, we generate financial question-answering data that encompasses both tabular information and long textual content, building on the collected formula set. Our experiments demonstrate that synthetic data generated by FinLLMs effectively enhances the performance of several large-scale numerical reasoning models in the financial domain, outperforming two established benchmark financial question-answering datasets.
Ethical Artificial Intelligence Principles and Guidelines for the Governance and Utilization of Highly Advanced Large Language Models
Venue: arXiv cs.CY 2024
Year: 2024

Given the success of ChatGPT, LaMDA and other large language models (LLMs), there has been an increase in development and usage of LLMs within the technology sector and other sectors. While the level in which LLMs has not reached a level where it has surpassed human intelligence, there will be a time when it will. Such LLMs can be referred to as advanced LLMs. Currently, there are limited usage of ethical artificial intelligence (AI) principles and guidelines addressing advanced LLMs due to the fact that we have not reached that point yet. However, this is a problem as once we do reach that point, we will not be adequately prepared to deal with the aftermath of it in an ethical and optimal way, which will lead to undesired and unexpected consequences. This paper addresses this issue by discussing what ethical AI principles and guidelines can be used to address highly advanced LLMs.
Hybrid Online Certificate Status Protocol with Certificate Revocation List for Smart Grid Public Key Infrastructure
Venue: arXiv cs.CR 2024
Year: 2024

Hsu et al. (2022) proposed a cryptographic scheme within the public key infrastructure to bolster the security of smart grid meters. Their proposal involved developing the Certificate Management over CMS mechanism to establish Simple Certificate Enrollment Protocol and Enrollment over Secure Transport protocol. Additionally, they implemented Online Certificate Status Protocol (OCSP) services to independently query the status of certificates. However, their implementation featured a single OCSP server handling all query requests. Considering the typical scenario in smart grid PKI environments with over tens of thousands of end-meters, we introduced a Hybrid Online Certificate Status Protocol mechanism. This approach decreases demand of query resources from the client to OCSP servers collaborating with Certificate Revocation Lists. Our simulations, mimicking meter behavior, demonstrated increased efficiency, creating a more robust architecture tailored to the smart grid meter landscape.
Help Me Reflect: Leveraging Self-Reflection Interface Nudges to Enhance Deliberativeness on Online Deliberation Platforms
Venue: arXiv cs.HC 2024
Year: 2024

The deliberative potential of online platforms has been widely examined. However, little is known about how various interface-based reflection nudges impact the quality of deliberation. This paper presents two user studies with 12 and 120 participants, respectively, to investigate the impacts of different reflective nudges on the quality of deliberation. In the first study, we examined five distinct reflective nudges: persona, temporal prompts, analogies and metaphors, cultural prompts and storytelling. Persona, temporal prompts, and storytelling emerged as the preferred nudges for implementation on online deliberation platforms. In the second study, we assess the impacts of these preferred reflectors more thoroughly. Results revealed a significant positive impact of these reflectors on deliberative quality. Specifically, persona promotes a deliberative environment for balanced and opinionated viewpoints while temporal prompts promote more individualised viewpoints. Our findings suggest that the choice of reflectors can significantly influence the dynamics and shape the nature of online discussions.
Recent Advances in Named Entity Recognition: A Comprehensive Survey and Comparative Study
Venue: arXiv cs.CL 2024
Year: 2024

Named Entity Recognition seeks to extract substrings within a text that name real-world objects and to determine their type (for example, whether they refer to persons or organizations). In this survey, we first present an overview of recent popular approaches, including advancements in Transformer-based methods and Large Language Models (LLMs) that have not had much coverage in other surveys. In addition, we discuss reinforcement learning and graph-based approaches, highlighting their role in enhancing NER performance. Second, we focus on methods designed for datasets with scarce annotations. Third, we evaluate the performance of the main NER implementations on a variety of datasets with differing characteristics (as regards their domain, their size, and their number of classes). We thus provide a deep comparison of algorithms that have never been considered together. Our experiments shed some light on how the characteristics of datasets affect the behavior of the methods we compare.
Cactus Representation of Minimum Cuts: Derandomize and Speed up
Venue: arXiv cs.DS 2024
Year: 2024

Given an undirected weighted graph with $n$ vertices and $m$ edges, we give the first deterministic $m^{1+o(1)}$-time algorithm for constructing the cactus representation of \emph{all} global minimum cuts. This improves the current $n^{2+o(1)}$-time state-of-the-art deterministic algorithm, which can be obtained by combining ideas implicitly from three papers [Karger JACM'2000, Li STOC'2021, and Gabow TALG'2016] The known explicitly stated deterministic algorithm has a runtime of $\tilde{O}(mn)$ [Fleischer 1999, Nagamochi and Nakao 2000]. Using our technique, we can even speed up the fastest randomized algorithm of [Karger and Panigrahi, SODA'2009] whose running time is at least $Ω(m\log^4 n)$ to $O(m\log^3 n)$.
Pruning for Protection: Increasing Jailbreak Resistance in Aligned LLMs Without Fine-Tuning
Venue: arXiv cs.LG 2024
Year: 2024

This paper investigates the impact of model compression on the way Large Language Models (LLMs) process prompts, particularly concerning jailbreak resistance. We show that moderate WANDA pruning can enhance resistance to jailbreaking attacks without fine-tuning, while maintaining performance on standard benchmarks. To systematically evaluate this safety enhancement, we introduce a dataset of 225 harmful tasks across five categories. Our analysis of LLaMA-2 Chat, Vicuna 1.3, and Mistral Instruct v0.2 reveals that pruning benefits correlate with initial model safety levels. We interpret these results by examining changes in attention patterns and perplexity shifts, demonstrating that pruned models exhibit sharper attention and increased sensitivity to artificial jailbreak constructs. We extend our evaluation to the AdvBench harmful behavior tasks and the GCG attack method. We find that LLaMA-2 is much safer on AdvBench prompts than on our dataset when evaluated with manual jailbreak attempts, and that pruning is effective against both automated attacks and manual jailbreaking on Advbench.
Artificial intelligence to automate the systematic review of scientific literature
Venue: arXiv cs.IR 2024
Year: 2024

Artificial intelligence (AI) has acquired notorious relevance in modern computing as it effectively solves complex tasks traditionally done by humans. AI provides methods to represent and infer knowledge, efficiently manipulate texts and learn from vast amount of data. These characteristics are applicable in many activities that human find laborious or repetitive, as is the case of the analysis of scientific literature. Manually preparing and writing a systematic literature review (SLR) takes considerable time and effort, since it requires planning a strategy, conducting the literature search and analysis, and reporting the findings. Depending on the area under study, the number of papers retrieved can be of hundreds or thousands, meaning that filtering those relevant ones and extracting the key information becomes a costly and error-prone process. However, some of the involved tasks are repetitive and, therefore, subject to automation by means of AI. In this paper, we present a survey of AI techniques proposed in the last 15 years to help researchers conduct systematic analyses of scientific literature. We describe the tasks currently supported, the types of algorithms applied, and available tools proposed in 34 primary studies. This survey also provides a historical perspective of the evolution of the field and the role that humans can play in an increasingly automated SLR process.