OpenRaiser/Intern-Atlas huggingface dataset:
When Copilot Becomes Autopilot: Generative AI's Critical Risk to Knowledge Work and a Critical Solution
Venue: arXiv cs.HC 2024
Year: 2024

Generative AI, with its tendency to "hallucinate" incorrect results, may pose a risk to knowledge work by introducing errors. On the other hand, it may also provide unprecedented opportunities for users, particularly non-experts, to learn and apply advanced software features and greatly increase the scope and complexity of tasks they can successfully achieve. As an example of a complex knowledge workflow that is subject to risks and opportunities from generative AI, we consider the spreadsheet. AI hallucinations are an important challenge, but they are not the greatest risk posed by generative AI to spreadsheet workflows. Rather, as more work can be safely delegated to AI, the risk is that human critical thinking -- the ability to holistically and rigorously evaluate a problem and its solutions -- is degraded in the process. The solution is to design the interfaces of generative AI systems deliberately to foster and encourage critical thinking in knowledge work, building primarily on a long history of research on critical thinking tools for education. We discuss a prototype system for the activity of critical shortlisting in spreadsheets. The system uses generative AI to suggest shortlisting criteria and applies these criteria to sort rows in a spreadsheet. It also generates "provocations": short text snippets that critique the AI-generated criteria, highlighting risks, shortcomings, and alternatives. Our prototype opens up a rich and completely unexplored design space of critical thinking tools for modern AI-assisted knowledge work. We outline a research agenda for AI as a critic or provocateur, including questions about where and when provocations should appear, their form and content, and potential design trade-offs.
GIRAFE: Glottal Imaging Dataset for Advanced Segmentation, Analysis, and Facilitative Playbacks Evaluation
Venue: arXiv cs.CV 2024
Year: 2024

The advances in the development of Facilitative Playbacks extracted from High-Speed videoendoscopic sequences of the vocal folds are hindered by a notable lack of publicly available datasets annotated with the semantic segmentations corresponding to the area of the glottal gap. This fact also limits the reproducibility and further exploration of existing research in this field. To address this gap, GIRAFE is a data repository designed to facilitate the development of advanced techniques for the semantic segmentation, analysis, and fast evaluation of High-Speed videoendoscopic sequences of the vocal folds. The repository includes 65 high-speed videoendoscopic recordings from a cohort of 50 patients (30 female, 20 male). The dataset comprises 15 recordings from healthy controls, 26 from patients with diagnosed voice disorders, and 24 with an unknown health condition. All of them were manually annotated by an expert, including the masks corresponding to the semantic segmentation of the glottal gap. The repository is also complemented with the automatic segmentation of the glottal area using different state-of-the-art approaches. This data set has already supported several studies, which demonstrates its usefulness for the development of new glottal gap segmentation algorithms from High-Speed-Videoendoscopic sequences to improve or create new Facilitative Playbacks. Despite these advances and others in the field, the broader challenge of performing an accurate and completely automatic semantic segmentation method of the glottal area remains open.
Till the Layers Collapse: Compressing a Deep Neural Network through the Lenses of Batch Normalization Layers
Venue: arXiv cs.LG 2024
Year: 2024

Today, deep neural networks are widely used since they can handle a variety of complex tasks. Their generality makes them very powerful tools in modern technology. However, deep neural networks are often overparameterized. The usage of these large models consumes a lot of computation resources. In this paper, we introduce a method called \textbf{T}ill the \textbf{L}ayers \textbf{C}ollapse (TLC), which compresses deep neural networks through the lenses of batch normalization layers. By reducing the depth of these networks, our method decreases deep neural networks' computational requirements and overall latency. We validate our method on popular models such as Swin-T, MobileNet-V2, and RoBERTa, across both image classification and natural language processing (NLP) tasks.
Leveraging Color Channel Independence for Improved Unsupervised Object Detection
Venue: arXiv cs.CV 2024
Year: 2024

Object-centric architectures can learn to extract distinct object representations from visual scenes, enabling downstream applications on the object level. Similarly to autoencoder-based image models, object-centric approaches have been trained on the unsupervised reconstruction loss of images encoded by RGB color spaces. In our work, we challenge the common assumption that RGB images are the optimal color space for unsupervised learning in computer vision. We discuss conceptually and empirically that other color spaces, such as HSV, bear essential characteristics for object-centric representation learning, like robustness to lighting conditions. We further show that models improve when requiring them to predict additional color channels. Specifically, we propose to transform the predicted targets to the RGB-S space, which extends RGB with HSV's saturation component and leads to markedly better reconstruction and disentanglement for five common evaluation datasets. The use of composite color spaces can be implemented with basically no computational overhead, is agnostic of the models' architecture, and is universally applicable across a wide range of visual computing tasks and training types. The findings of our approach encourage additional investigations in computer vision tasks beyond object-centric learning.
Prompt-A-Video: Prompt Your Video Diffusion Model via Preference-Aligned LLM
Venue: arXiv cs.CV 2024
Year: 2024

Text-to-video models have made remarkable advancements through optimization on high-quality text-video pairs, where the textual prompts play a pivotal role in determining quality of output videos. However, achieving the desired output often entails multiple revisions and iterative inference to refine user-provided prompts. Current automatic methods for refining prompts encounter challenges such as Modality-Inconsistency, Cost-Discrepancy, and Model-Unaware when applied to text-to-video diffusion models. To address these problem, we introduce an LLM-based prompt adaptation framework, termed as Prompt-A-Video, which excels in crafting Video-Centric, Labor-Free and Preference-Aligned prompts tailored to specific video diffusion model. Our approach involves a meticulously crafted two-stage optimization and alignment system. Initially, we conduct a reward-guided prompt evolution pipeline to automatically create optimal prompts pool and leverage them for supervised fine-tuning (SFT) of the LLM. Then multi-dimensional rewards are employed to generate pairwise data for the SFT model, followed by the direct preference optimization (DPO) algorithm to further facilitate preference alignment. Through extensive experimentation and comparative analyses, we validate the effectiveness of Prompt-A-Video across diverse generation models, highlighting its potential to push the boundaries of video generation.
Tokenisation is NP-Complete
Venue: arXiv cs.DS 2024
Year: 2024

In this work, we prove the NP-completeness of two variants of tokenisation, defined as the problem of compressing a dataset to at most $δ$ symbols by either finding a vocabulary directly (direct tokenisation), or selecting a sequence of merge operations (bottom-up tokenisation).
Learning-by-teaching with ChatGPT: The effect of teachable ChatGPT agent on programming education
Venue: arXiv cs.CY 2024
Year: 2024

This study investigates the potential of using ChatGPT as a teachable agent to support students' learning by teaching process, specifically in programming education. While learning by teaching is an effective pedagogical strategy for promoting active learning, traditional teachable agents have limitations, particularly in facilitating natural language dialogue. Our research explored whether ChatGPT, with its ability to engage learners in natural conversations, can support this process. The findings reveal that interacting with ChatGPT improves students' knowledge gains and programming abilities, particularly in writing readable and logically sound code. However, it had limited impact on developing learners' error-correction skills, likely because ChatGPT tends to generate correct code, reducing opportunities for students to practice debugging. Additionally, students' self-regulated learning (SRL) abilities improved, suggesting that teaching ChatGPT fosters learners' higher self-efficacy and better implementation of SRL strategies. This study discussed the role of natural dialogue in fostering socialized learning by teaching, and explored ChatGPT's specific contributions in supporting students' SRL through the learning by teaching process. Overall, the study highlights ChatGPT's potential as a teachable agent, offering insights for future research on ChatGPT-supported education.
GLARE: Google Apps Arabic Reviews Dataset
Venue: arXiv cs.CL 2024
Year: 2024

This paper introduces GLARE an Arabic Apps Reviews dataset collected from Saudi Google PlayStore. It consists of 76M reviews, 69M of which are Arabic reviews of 9,980 Android Applications. We present the data collection methodology, along with a detailed Exploratory Data Analysis (EDA) and Feature Engineering on the gathered reviews. We also highlight possible use cases and benefits of the dataset.
Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

With the rapid advancement of Large Language Models (LLMs), significant safety concerns have emerged. Fundamentally, the safety of large language models is closely linked to the accuracy, comprehensiveness, and clarity of their understanding of safety knowledge, particularly in domains such as law, policy and ethics. This factuality ability is crucial in determining whether these models can be deployed and applied safely and compliantly within specific regions. To address these challenges and better evaluate the factuality ability of LLMs to answer short questions, we introduce the Chinese SafetyQA benchmark. Chinese SafetyQA has several properties (i.e., Chinese, Diverse, High-quality, Static, Easy-to-evaluate, Safety-related, Harmless). Based on Chinese SafetyQA, we perform a comprehensive evaluation on the factuality abilities of existing LLMs and analyze how these capabilities relate to LLM abilities, e.g., RAG ability and robustness against attacks.
Context-DPO: Aligning Language Models for Context-Faithfulness
Venue: arXiv cs.CL 2024
Year: 2024

Reliable responses from large language models (LLMs) require adherence to user instructions and retrieved information. While alignment techniques help LLMs align with human intentions and values, improving context-faithfulness through alignment remains underexplored. To address this, we propose $\textbf{Context-DPO}$, the first alignment method specifically designed to enhance LLMs' context-faithfulness. We introduce $\textbf{ConFiQA}$, a benchmark that simulates Retrieval-Augmented Generation (RAG) scenarios with knowledge conflicts to evaluate context-faithfulness. By leveraging faithful and stubborn responses to questions with provided context from ConFiQA, our Context-DPO aligns LLMs through direct preference optimization. Extensive experiments demonstrate that our Context-DPO significantly improves context-faithfulness, achieving 35% to 280% improvements on popular open-source models. Further analysis demonstrates that Context-DPO preserves LLMs' generative capabilities while providing interpretable insights into context utilization. Our code and data are released at https://github.com/byronBBL/Context-DPO
Eliciting Causal Abilities in Large Language Models for Reasoning Tasks
Venue: arXiv cs.CL 2024
Year: 2024

Prompt optimization automatically refines prompting expressions, unlocking the full potential of LLMs in downstream tasks. However, current prompt optimization methods are costly to train and lack sufficient interpretability. This paper proposes enhancing LLMs' reasoning performance by eliciting their causal inference ability from prompting instructions to correct answers. Specifically, we introduce the Self-Causal Instruction Enhancement (SCIE) method, which enables LLMs to generate high-quality, low-quantity observational data, then estimates the causal effect based on these data, and ultimately generates instructions with the optimized causal effect. In SCIE, the instructions are treated as the treatment, and textual features are used to process natural language, establishing causal relationships through treatments between instructions and downstream tasks. Additionally, we propose applying Object-Relational (OR) principles, where the uncovered causal relationships are treated as the inheritable class across task objects, ensuring low-cost reusability. Extensive experiments demonstrate that our method effectively generates instructions that enhance reasoning performance with reduced training cost of prompts, leveraging interpretable textual features to provide actionable insights.
Scalable and low-cost remote lab platforms: Teaching industrial robotics using open-source tools and understanding its social implications
Venue: arXiv cs.RO 2024
Year: 2024

With recent advancements in industrial robots, educating students in new technologies and preparing them for the future is imperative. However, access to industrial robots for teaching poses challenges, such as the high cost of acquiring these robots, the safety of the operator and the robot, and complicated training material. This paper proposes two low-cost platforms built using open-source tools like Robot Operating System (ROS) and its latest version ROS 2 to help students learn and test algorithms on remotely connected industrial robots. Universal Robotics (UR5) arm and a custom mobile rover were deployed in different life-size testbeds, a greenhouse, and a warehouse to create an Autonomous Agricultural Harvester System (AAHS) and an Autonomous Warehouse Management System (AWMS). These platforms were deployed for a period of 7 months and were tested for their efficacy with 1,433 and 1,312 students, respectively. The hardware used in AAHS and AWMS was controlled remotely for 160 and 355 hours, respectively, by students over a period of 3 months.
Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

This work proposes a novel framework, Uncertainty-Guided Cross Attention Ensemble Mean Teacher (UG-CEMT), for achieving state-of-the-art performance in semi-supervised medical image segmentation. UG-CEMT leverages the strengths of co-training and knowledge distillation by combining a Cross-attention Ensemble Mean Teacher framework (CEMT) inspired by Vision Transformers (ViT) with uncertainty-guided consistency regularization and Sharpness-Aware Minimization emphasizing uncertainty. UG-CEMT improves semi-supervised performance while maintaining a consistent network architecture and task setting by fostering high disparity between sub-networks. Experiments demonstrate significant advantages over existing methods like Mean Teacher and Cross-pseudo Supervision in terms of disparity, domain generalization, and medical image segmentation performance. UG-CEMT achieves state-of-the-art results on multi-center prostate MRI and cardiac MRI datasets, where object segmentation is particularly challenging. Our results show that using only 10\% labeled data, UG-CEMT approaches the performance of fully supervised methods, demonstrating its effectiveness in exploiting unlabeled data for robust medical image segmentation. The code is publicly available at \url{https://github.com/Meghnak13/UG-CEMT}
Recovering WPA-3 Network Password by Bypassing the Simultaneous Authentication of Equals Handshake using Social Engineering Captive Portal
Venue: arXiv cs.CR 2024
Year: 2024

Wi-Fi Protected Access 3 (WPA3) is the accepted standard for next generation wireless security. WPA3 comes with exciting new features that allows for increased security of Wi-Fi networks. One such feature is the Simultaneous Authentication of Equals (SAE) which is a protocol whereby passphrases are hashed using a Password Authenticated Key Exchange with keys from both the Access Point and the Client making the password resistant to offline dictionary attacks. (Harkins, Dan. 2019) This objective of this research paper seeks to bypass WPA3-SAE to acquire network password via Man-in-the-Middle attack and Social Engineering. This method can prove to be useful given that majority of network attacks stem from social engineering. For this research we would be looking at the security of WPA3 personal transition mode and capture the network password via a captive portal. Breaching the WPA3 network can be possible by building on various security flaws that was disclosed on WPA3 in 2021. Due to the discovery of Dragonblood downgrade attacks disclosed in 2019, identified that WPA2/3Handshakes could be acquired. A Man in the Middle attack proposed set up is carried out by using race conditions to deauthentication WPA3 network and then using a Raspberry Pi to spawn a rouge WPA3 network. As such, the handshake acquired can then be utilized as to verify the password that would be entered in the captive portal of the rouge WPA3 network. This research identified that the Password was able to be recovered from Social Engineering Captive Portal when Protected Management Frames are not implemented. This research also indicates that some devices are not able to connect to a WPA 3 transition network which is contradicting the Wi-Fi Alliance claim that it is backwards compatible with WPA2.
A Retrieval-Augmented Generation Framework for Academic Literature Navigation in Data Science
Venue: arXiv cs.IR 2024
Year: 2024

In the rapidly evolving field of data science, efficiently navigating the expansive body of academic literature is crucial for informed decision-making and innovation. This paper presents an enhanced Retrieval-Augmented Generation (RAG) application, an artificial intelligence (AI)-based system designed to assist data scientists in accessing precise and contextually relevant academic resources. The AI-powered application integrates advanced techniques, including the GeneRation Of BIbliographic Data (GROBID) technique for extracting bibliographic information, fine-tuned embedding models, semantic chunking, and an abstract-first retrieval method, to significantly improve the relevance and accuracy of the retrieved information. This implementation of AI specifically addresses the challenge of academic literature navigation. A comprehensive evaluation using the Retrieval-Augmented Generation Assessment System (RAGAS) framework demonstrates substantial improvements in key metrics, particularly Context Relevance, underscoring the system's effectiveness in reducing information overload and enhancing decision-making processes. Our findings highlight the potential of this enhanced Retrieval-Augmented Generation system to transform academic exploration within data science, ultimately advancing the workflow of research and innovation in the field.
An Explorative Study of Pig Butchering Scams
Venue: arXiv cs.CR 2024
Year: 2024

In the recent past, so-called pig-butchering scams are on the rise. This term is based on a translation of the Chinese term "Sha Zhu Pan", where scammers refer to victims as "pig" which are to be "fattened up before slaughter" so that scammer can siphon off as much monetary value as possible. In this type of scam, attackers perform social engineering tricks on victims over an extended period to build credibility or relationships. After a certain period, when victims transfer larger amounts of money to scammers, the fraudsters' platforms or profiles go permanently offline and the victims' money is lost. In this work, we provide the first comprehensive study of pig-butchering scams from multiple vantage points. Our study analyzes the direct victims' narratives shared on multiple social media platforms, public abuse report databases, and case studies from news outlets. Between March 2024 to October 2024, we collected data related to pig butchering scams from (i) four social media platforms comprised of more than 430,000 social media accounts and 770,000 posts; (ii) more than 3,200 public abuse reports narratives, and (iii) about 1,000 news articles. Through automated and qualitative evaluation, we provide an evaluation of victims of pig-butchering scams, finding 146 social media scammed users, 2,570 abuse reports narratives, and 50 case studies of 834 souls from news outlets. In total, we approximated losses of over \$521 million related to such scams. To complement this analysis, we performed a survey on crowdsourcing platforms with 584 users to broaden the insights on comparative analysis of pig-butchering scams with other types of scams. Our research highlights that these attacks are sophisticated and often require multiple entities, including policymakers and law enforcement, to work together alongside user education to create a proactive detection of such scams.
SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic Retrieval
Venue: arXiv cs.CL 2024
Year: 2024

Retrieval-Augmented Generation (RAG) systems have become pivotal in leveraging vast corpora to generate informed and contextually relevant responses, notably reducing hallucinations in Large Language Models. Despite significant advancements, these systems struggle to efficiently process and retrieve information from large datasets while maintaining a comprehensive understanding of the context. This paper introduces SKETCH, a novel methodology that enhances the RAG retrieval process by integrating semantic text retrieval with knowledge graphs, thereby merging structured and unstructured data for a more holistic comprehension. SKETCH, demonstrates substantial improvements in retrieval performance and maintains superior context integrity compared to traditional methods. Evaluated across four diverse datasets: QuALITY, QASPER, NarrativeQA, and Italian Cuisine-SKETCH consistently outperforms baseline approaches on key RAGAS metrics such as answer_relevancy, faithfulness, context_precision and context_recall. Notably, on the Italian Cuisine dataset, SKETCH achieved an answer relevancy of 0.94 and a context precision of 0.99, representing the highest performance across all evaluated metrics. These results highlight SKETCH's capability in delivering more accurate and contextually relevant responses, setting new benchmarks for future retrieval systems.
Non-Uniform Parameter-Wise Model Merging
Venue: arXiv cs.LG 2024
Year: 2024

Combining multiple machine learning models has long been a technique for enhancing performance, particularly in distributed settings. Traditional approaches, such as model ensembles, work well, but are expensive in terms of memory and compute. Recently, methods based on averaging model parameters have achieved good results in some settings and have gained popularity. However, merging models initialized differently that do not share a part of their training trajectories can yield worse results than simply using the base models, even after aligning their neurons. In this paper, we introduce a novel approach, Non-uniform Parameter-wise Model Merging, or NP Merge, which merges models by learning the contribution of each parameter to the final model using gradient-based optimization. We empirically demonstrate the effectiveness of our method for merging models of various architectures in multiple settings, outperforming past methods. We also extend NP Merge to handle the merging of multiple models, showcasing its scalability and robustness.