pwc-archive/papers-with-abstracts huggingface dataset:
Cost-Aware Query Policies in Active Learning for Efficient Autonomous Robotic Exploration
Authors: Sapphira Akins, Hans Mertens, Frances Zhu
Date: 2024-10-31 00:00:00
Tasks: Active Learning, Decision Making, regression

In missions constrained by finite resources, efficient data collection is critical. Informative path planning, driven by automated decision-making, optimizes exploration by reducing the costs associated with accurate characterization of a target in an environment. Previous implementations of active learning did not consider the action cost for regression problems or only considered the action cost for classification problems. This paper analyzes an AL algorithm for Gaussian Process regression while incorporating action cost. The algorithm's performance is compared on various regression problems to include terrain mapping on diverse simulated surfaces along metrics of root mean square error, samples and distance until convergence, and model variance upon convergence. The cost-dependent acquisition policy doesn't organically optimize information gain over distance. Instead, the traditional uncertainty metric with a distance constraint best minimizes root-mean-square error over trajectory distance. This studys impact is to provide insight into incorporating action cost with AL methods to optimize exploration under realistic mission constraints.
NeuroSym-BioCAT: Leveraging Neuro-Symbolic Methods for Biomedical Scholarly Document Categorization and Question Answering
Authors: Parvez Zamil, Gollam Rabby, Md. Sadekur Rahman, Sören Auer
Date: 2024-10-29 00:00:00
Tasks: Question Answering

The growing volume of biomedical scholarly document abstracts presents an increasing challenge in efficiently retrieving accurate and relevant information. To address this, we introduce a novel approach that integrates an optimized topic modelling framework, OVB-LDA, with the BI-POP CMA-ES optimization technique for enhanced scholarly document abstract categorization. Complementing this, we employ the distilled MiniLM model, fine-tuned on domain-specific data, for high-precision answer extraction. Our approach is evaluated across three configurations: scholarly document abstract retrieval, gold-standard scholarly documents abstract, and gold-standard snippets, consistently outperforming established methods such as RYGH and bio-answer finder. Notably, we demonstrate that extracting answers from scholarly documents abstracts alone can yield high accuracy, underscoring the sufficiency of abstracts for many biomedical queries. Despite its compact size, MiniLM exhibits competitive performance, challenging the prevailing notion that only large, resource-intensive models can handle such complex tasks. Our results, validated across various question types and evaluation batches, highlight the robustness and adaptability of our method in real-world biomedical applications. While our approach shows promise, we identify challenges in handling complex list-type questions and inconsistencies in evaluation metrics. Future work will focus on refining the topic model with more extensive domain-specific datasets, further optimizing MiniLM and utilizing large language models (LLM) to improve both precision and efficiency in biomedical question answering.
Demo-Craft: Using In-Context Learning to Improve Code Generation in Large Language Models
Authors: Nirmal Joshua Kapu, Mihit Sreejith
Date: 2024-10-30 00:00:00
Tasks: Code Generation, HumanEval, In-Context Learning, mbpp

Generating executable code from natural language instructions using Large Language Models (LLMs) poses challenges such as semantic ambiguity and understanding taskspecific contexts. To address these issues, we propose a system called DemoCraft, which enhances code generation by leveraging in-context learning and demonstration selection, combined with latent concept learning. Latent concept learning introduces additional concept tokens, which are trainable embeddings that capture task-specific knowledge. We then test our system on two major datasets: MBPP and Humaneval. Our experimental results demonstrate that the proposed system achieves an approximate 2x increase in the pass@k metric compared to baseline models. Furthermore, we introduce two novel evaluation metrics: correctness@k and similarity@k. Our empirical studies indicate that our system attains nearly a 3x improvement in these metrics as well.
Dynamic AES Encryption and Blockchain Key Management: A Novel Solution for Cloud Data Security
Authors: MOHAMMED Y. SHAKOR 1, 2, MUSTAFA IBRAHIM KHALEEL 2, MEJDL SAFRAN 3, SULTAN ALFARHOOD 3, AND MICHELLE ZHU 4, (Member, IEEE)
Date: 2024-08-01 00:00:00
Proceeding: 3 february 2024 8
Tasks: Cloud Computing, Management

In the rapidly evolving realm of cloud computing security, this paper introduces an innovative solution to address persistent challenges. The proliferation of cloud technology has brought forth heightened concerns regarding data security, necessitating novel approaches to safeguarding sensitive information. The issue centers on the vulnerability of cloud-stored data, usually necessitating enhanced encryption and key management strategies. Traditional methods usually fall short in mitigating risks associated with compromised encryption keys and centralized key storage. To combat these challenges, our proposed solution encompasses a two-phase approach. In the first phase, dynamic Advanced Encryption Standard (AES) keys are generated, ensuring each file’s encryption with a unique and ever-changing key. This approach significantly enhances file-level security, curtailing an attacker’s ability to decrypt multiple files even if a key is compromised. The second phase introduces blockchain technology, where keys are securely stored with accompanying metadata, bolstering security and data integrity. Elliptic Curve Cryptography (ECC) public key encryption enhances security during transmission and storage, while also facilitating secure file sharing. In conclusion, this comprehensive approach enhances cloud security, providing robust encryption, decentralized key management, and protection against unauthorized access. Its scalability and adaptability make it a valuable asset in contemporary cloud security paradigms, assuring users of data security in the cloud.
Enhancing LMMSE Performance with Modest Complexity Increase via Neural Network Equalizers
Authors: Vadim Rozenfeld, Dan Raphaeli, Oded Bialer
Date: 2024-11-03 00:00:00

The BCJR algorithm is renowned for its optimal equalization, minimizing bit error rate (BER) over intersymbol interference (ISI) channels. However, its complexity grows exponentially with the channel memory, posing a significant computational burden. In contrast, the linear minimum mean square error (LMMSE) equalizer offers a notably simpler solution, albeit with reduced performance compared to the BCJR. Recently, Neural Network (NN) based equalizers have emerged as promising alternatives. Trained to map observations to the original transmitted symbols, these NNs demonstrate performance similar to the BCJR algorithm. However, they often entail a high number of learnable parameters, resulting in complexities comparable to or even larger than the BCJR. This paper explores the potential of NN-based equalization with a reduced number of learnable parameters and low complexity. We introduce a NN equalizer with complexity comparable to LMMSE, surpassing LMMSE performance and achieving a modest performance gap from the BCJR equalizer. A significant challenge with NNs featuring a limited parameter count is their susceptibility to converging to local minima, leading to suboptimal performance. To address this challenge, we propose a novel NN equalizer architecture with a unique initialization approach based on LMMSE. This innovative method effectively overcomes optimization challenges and enhances LMMSE performance, applicable both with and without turbo decoding.
Multi-scale feature reconstruction network for industrial anomaly detection
Authors: Ehtesham Iqbal, Samee Ullah Khan, Sajid Javed, Brain Moyo, Yahya Zweiri, Yusra Abdulrahman
Date: 2024-10-23 00:00:00
Proceeding: Knowledge-Based Systems 2024 10
Tasks: Anomaly Detection, Unsupervised Anomaly Detection

Unsupervised anomaly detection techniques, which operate without prior knowledge of anomalies, have garnered significant attention in industrial inspection due to their adaptability and generalization. Therefore, knowledge-based computer vision techniques have been broadly applied to identify unusual image patterns. However, real-time industrial applications present challenges such as limited anomalous samples, inadequate defect knowledge, and complex background textures. These factors lead to difficulties in accurately identifying defect regions, and conventional auto-encoder networks often struggle to overcome these issues.To address these limitations, we propose a multi-scale feature reconstruction (MSFR) network specifically designed for domain shift scenarios. Our approach employs a pyramidal vision transformer network (PVTN) to reconstruct multi-scale feature maps, capturing discriminative features at various scales. Additionally, a pre-trained module extracts multi-level features at the same scale, and a dedicated feature matching module enhances accuracy by improving the alignment probability between features. The MSFR strategy surpasses conventional auto-encoders by filtering pixel-level information at multiple depths. Empirical evaluations were conducted using benchmark datasets such as MVTec AD and AeBAD-S. Furthermore, an extensive ablation study demonstrates the effectiveness and viability of the proposed MSFR approach for industrial anomaly detection tasks. The experimental results show that the proposed model significantly outperforms recent approaches, making it highly suitable for real-world industrial applications, particularly in manufacturing.
Interpret and Control Dense Retrieval with Sparse Latent Features
Authors: Hao Kang, Tevin Wang, Chenyan Xiong
Date: 2024-10-17 00:00:00
Tasks: Retrieval

Dense embeddings deliver strong retrieval performance but often lack interpretability and controllability. This paper introduces a novel approach using sparse autoencoders (SAE) to interpret and control dense embeddings via the learned latent sparse features. Our key contribution is the development of a retrieval-oriented contrastive loss, which ensures the sparse latent features remain effective for retrieval tasks and thus meaningful to interpret. Experimental results demonstrate that both the learned latent sparse features and their reconstructed embeddings retain nearly the same retrieval accuracy as the original dense vectors, affirming their faithfulness. Our further examination of the sparse latent space reveals interesting features underlying the dense embeddings and we can control the retrieval behaviors via manipulating the latent sparse features, for example, prioritizing documents from specific perspectives in the retrieval results.
Enhancing Diversity in Bayesian Deep Learning via Hyperspherical Energy Minimization of CKA
Authors: David Smerkous, Qinxun Bai, Fuxin Li
Date: 2024-10-31 00:00:00
Tasks: Deep Learning, Diversity, Outlier Detection, Uncertainty Quantification

Particle-based Bayesian deep learning often requires a similarity metric to compare two networks. However, naive similarity metrics lack permutation invariance and are inappropriate for comparing networks. Centered Kernel Alignment (CKA) on feature kernels has been proposed to compare deep networks but has not been used as an optimization objective in Bayesian deep learning. In this paper, we explore the use of CKA in Bayesian deep learning to generate diverse ensembles and hypernetworks that output a network posterior. Noting that CKA projects kernels onto a unit hypersphere and that directly optimizing the CKA objective leads to diminishing gradients when two networks are very similar. We propose adopting the approach of hyperspherical energy (HE) on top of CKA kernels to address this drawback and improve training stability. Additionally, by leveraging CKA-based feature kernels, we derive feature repulsive terms applied to synthetically generated outlier examples. Experiments on both diverse ensembles and hypernetworks show that our approach significantly outperforms baselines in terms of uncertainty quantification in both synthetic and realistic outlier detection tasks.
Towards Intelligent Augmented Reality (iAR): A Taxonomy of Context, an Architecture for iAR, and an Empirical Study
Authors: Shakiba Davari, Daniel Stover, Alexander Giovannelli, Cory Ilo, Doug A. Bowman
Date: 2024-11-04 00:00:00

Recent advancements in Augmented Reality (AR) research have highlighted the critical role of context awareness in enhancing interface effectiveness and user experience. This underscores the need for intelligent AR (iAR) interfaces that dynamically adapt across various contexts to provide optimal experiences. In this paper, we (a) propose a comprehensive framework for context-aware inference and adaptation in iAR, (b) introduce a taxonomy that describes context through quantifiable input data, and (c) present an architecture that outlines the implementation of our proposed framework and taxonomy within iAR. Additionally, we present an empirical AR experiment to observe user behavior and record user performance, context, and user-specified adaptations to the AR interfaces within a context-switching scenario. We (d) explore the nuanced relationships between context and user adaptations in this scenario and discuss the significance of our framework in identifying these patterns. This experiment emphasizes the significance of context-awareness in iAR and provides a preliminary training dataset for this specific Scenario.
Slicing for AI: An Online Learning Framework for Network Slicing Supporting AI Services
Authors: Menna Helmy, Alaa Awad Abdellatif, Naram Mhaisen, Amr Mohamed, Aiman Erbad
Date: 2024-10-20 00:00:00

The forthcoming 6G networks will embrace a new realm of AI-driven services that requires innovative network slicing strategies, namely slicing for AI, which involves the creation of customized network slices to meet Quality of service (QoS) requirements of diverse AI services. This poses challenges due to time-varying dynamics of users' behavior and mobile networks. Thus, this paper proposes an online learning framework to optimize the allocation of computational and communication resources to AI services, while considering their unique key performance indicators (KPIs), such as accuracy, latency, and cost. We define a problem of optimizing the total accuracy while balancing conflicting KPIs, prove its NP-hardness, and propose an online learning framework for solving it in dynamic environments. We present a basic online solution and two variations employing a pre-learning elimination method for reducing the decision space to expedite the learning. Furthermore, we propose a biased decision space subset selection by incorporating prior knowledge to enhance the learning speed without compromising performance and present two alternatives of handling the selected subset. Our results depict the efficiency of the proposed solutions in converging to the optimal decisions, while reducing decision space and improving time complexity.
KeyInst: Keyword Instruction for Improving SQL Formulation in Text-to-SQL
Authors: Xiping Liu, Zhao Tan
Date: 2024-10-18 00:00:00
Tasks: Natural Language Queries, SQL Parsing, Text to SQL, Text-To-SQL

Text-to-SQL parsing involves the translation of natural language queries (NLQs) into their corresponding SQL commands. A principal challenge within this domain is the formulation of SQL queries that are not only syntactically correct but also semantically aligned with the natural language input. However, the intrinsic disparity between the NLQ and the SQL poses a significant challenge. In this research, we introduce Keyword Instruction (KeyInst), a novel method designed to enhance SQL formulation by Large Language Models (LLMs). KeyInst essentially provides guidance on pivotal SQL keywords likely to be part of the final query, thus facilitates a smoother SQL query formulation process. We explore two strategies for integrating KeyInst into Text-to-SQL parsing: a pipeline strategy and a single-pass strategy. The former first generates KeyInst for question, which are then used to prompt LLMs. The latter employs a fine-tuned model to concurrently generate KeyInst and SQL in one step. We developed StrucQL, a benchmark specifically designed for the evaluation of SQL formulation. Extensive experiments on StrucQL and other benchmarks demonstrate that KeyInst significantly improves upon the existing Text-to-SQL prompting techniques.
FIRE: Fact-checking with Iterative Retrieval and Verification
Authors: Zhuohan Xie, Rui Xing, Yuxia Wang, Jiahui Geng, Hasan Iqbal, Dhruv Sahnan, Iryna Gurevych, Preslav Nakov
Date: 2024-10-17 00:00:00
Tasks: Claim Verification, Fact Checking, Language Modeling, Language Modelling, Large Language Model, Retrieval

Fact-checking long-form text is challenging, and it is therefore common practice to break it down into multiple atomic claims. The typical approach to fact-checking these atomic claims involves retrieving a fixed number of pieces of evidence, followed by a verification step. However, this method is usually not cost-effective, as it underutilizes the verification model's internal knowledge of the claim and fails to replicate the iterative reasoning process in human search strategies. To address these limitations, we propose FIRE, a novel agent-based framework that integrates evidence retrieval and claim verification in an iterative manner. Specifically, FIRE employs a unified mechanism to decide whether to provide a final answer or generate a subsequent search query, based on its confidence in the current judgment. We compare FIRE with other strong fact-checking frameworks and find that it achieves slightly better performance while reducing large language model (LLM) costs by an average of 7.6 times and search costs by 16.5 times. These results indicate that FIRE holds promise for application in large-scale fact-checking operations. Our code is available at https://github.com/mbzuai-nlp/fire.git.
Proactive Detection and Calibration of Seasonal Advertisements with Multimodal Large Language Models
Authors: Hamid Eghbalzadeh, Shuai Shao, Saurabh Verma, Venugopal Mani, Hongnan Wang, Jigar Madia, Vitali Karpinchyk, Andrey Malevich
Date: 2024-10-16 00:00:00
Tasks: Knowledge Distillation

A myriad of factors affect large scale ads delivery systems and influence both user experience and revenue. One such factor is proactive detection and calibration of seasonal advertisements to help with increasing conversion and user satisfaction. In this paper, we present Proactive Detection and Calibration of Seasonal Advertisements (PDCaSA), a research problem that is of interest for the ads ranking and recommendation community, both in the industrial setting as well as in research. Our paper provides detailed guidelines from various angles of this problem tested in, and motivated by a large-scale industrial ads ranking system. We share our findings including the clear statement of the problem and its motivation rooted in real-world systems, evaluation metrics, and sheds lights to the existing challenges, lessons learned, and best practices of data annotation and machine learning modeling to tackle this problem. Lastly, we present a conclusive solution we took during this research exploration: to detect seasonality, we leveraged Multimodal LLMs (MLMs) which on our in-house benchmark achieved 0.97 top F1 score. Based on our findings, we envision MLMs as a teacher for knowledge distillation, a machine labeler, and a part of the ensembled and tiered seasonality detection system, which can empower ads ranking systems with enriched seasonal information.
RuleRAG: Rule-guided retrieval-augmented generation with language models for question answering
Authors: Zhongwu Chen, Chengjin Xu, Dingmin Wang, Zhen Huang, Yong Dou, Jian Guo
Date: 2024-10-15 00:00:00
Tasks: In-Context Learning, Instruction Following, Question Answering, RAG, Retrieval, Retrieval-augmented Generation

Retrieval-augmented generation (RAG) framework has shown promising potential in knowledge-intensive question answering (QA) by retrieving external corpus and generating based on augmented context. However, existing approaches only consider the query itself, neither specifying the retrieval preferences for the retrievers nor informing the generators of how to refer to the retrieved documents for the answers, which poses a significant challenge to the QA performance. To address these issues, we propose Rule-Guided Retrieval-Augmented Generation with LMs, which explicitly introduces symbolic rules as demonstrations for in-context learning (RuleRAG-ICL) to guide retrievers to retrieve logically related documents in the directions of rules and uniformly guide generators to generate answers attributed by the guidance of the same set of rules. Moreover, the combination of queries and rules can be further used as supervised fine-tuning data to update retrievers and generators (RuleRAG-FT) to achieve better rule-based instruction following capability, leading to retrieve more supportive results and generate more acceptable answers. To emphasize the attribution of rules, we construct five rule-aware QA benchmarks, including three temporal and two static scenarios, and equip RuleRAG with several kinds of retrievers and generators. Experiments demonstrate that training-free RuleRAG-ICL effectively improves the retrieval quality of +89.2% in Recall@10 scores and generation accuracy of +103.1% in exact match scores over standard RAG on average across the five benchmarks, and further fine-tuned RuleRAG-FT consistently yields more significant performance enhancement. Extensive analyses indicate that RuleRAG scales well with increasing numbers of retrieved documents and exhibits generalization ability for untrained rules.
Search Engines in an AI Era: The False Promise of Factual and Verifiable Source-Cited Responses
Authors: Pranav Narayanan Venkit, Philippe Laban, Yilun Zhou, Yixin Mao, Chien-Sheng Wu
Date: 2024-10-15 00:00:00
Tasks: Hallucination, Language Modeling, Language Modelling, Large Language Model

Large Language Model (LLM)-based applications are graduating from research prototypes to products serving millions of users, influencing how people write and consume information. A prominent example is the appearance of Answer Engines: LLM-based generative search engines supplanting traditional search engines. Answer engines not only retrieve relevant sources to a user query but synthesize answer summaries that cite the sources. To understand these systems' limitations, we first conducted a study with 21 participants, evaluating interactions with answer vs. traditional search engines and identifying 16 answer engine limitations. From these insights, we propose 16 answer engine design recommendations, linked to 8 metrics. An automated evaluation implementing our metrics on three popular engines (You.com, Perplexity.ai, BingChat) quantifies common limitations (e.g., frequent hallucination, inaccurate citation) and unique features (e.g., variation in answer confidence), with results mirroring user study insights. We release our Answer Engine Evaluation benchmark (AEE) to facilitate transparent evaluation of LLM-based applications.