OpenRaiser/Intern-Atlas huggingface dataset:
Independent Range Sampling on Interval Data (Longer Version)
Venue: arXiv cs.DB 2024
Year: 2024

Many applications require efficient management of large sets of intervals because many objects are associated with intervals (e.g., time and price intervals). In such interval management systems, range search is a primitive operator for retrieving and analysis tasks. As dataset sizes are growing nowadays, range search results are also becoming larger, which may overwhelm users and incur long computation time. Because applications are usually satisfied with a subset of the result set, it is desirable to efficiently obtain only small samples from the result set.We therefore address the problem of independent range sampling on interval data, which outputs $s$ random samples that overlap a given query interval and are independent of the samples of all previous queries. To efficiently solve this problem theoretically and practically, we propose a variant of an interval tree, namely the augmented interval tree (or AIT), and we show that there exists an exact algorithm that needs $O(n \log n)$ space and $O(\log^{2} n + s)$ time, where $n$ is the dataset size. The simple structure of an AIT provides flexible extensions: (i) its time and space complexities respectively become $O(\log^{2} n + s)$ expected and $O(n)$ by bucketing intervals and (ii) it can deal with weighted intervals and outputs $s$ weighted random samples in $O(\log^{2} n+s\log n)$ time. We conduct extensive experiments on real datasets, and the results demonstrate that our algorithms significantly outperform competitors.
Accuracy Evaluation of a Lightweight Analytic Vehicle Dynamics Model for Maneuver Planning
Venue: arXiv cs.RO 2024
Year: 2024

Models for vehicle dynamics play an important role in maneuver planning for automated driving. They are used to derive trajectories from given control inputs, or to evaluate a given trajectory in terms of constraint violation or optimality criteria such as safety, comfort or ecology. Depending on the computation process, models with different assumptions and levels of detail are used; since maneuver planning usually has strong requirements for computation speed at a potentially high number of trajectory evaluations per planning cycle, most of the applied models aim to reduce complexity by implicitly or explicitly introducing simplifying assumptions. While evaluations show that these assumptions may be sufficiently valid under typical conditions, their effect has yet to be studied conclusively. We propose a model for vehicle dynamics that is convenient for maneuver planning by supporting both an analytic approach of extracting parameters from a given trajectory, and a generative approach of establishing a trajectory from given control inputs. Both applications of the model are evaluated in real-world test drives under dynamic conditions, both on a closed-off test track and on public roads, and effects arising from the simplifying assumptions are analyzed.
GPS-IDS: An Anomaly-based GPS Spoofing Attack Detection Framework for Autonomous Vehicles
Venue: arXiv cs.CR 2024
Year: 2024

Autonomous Vehicles (AVs) heavily rely on sensors and communication networks like Global Positioning System (GPS) to navigate autonomously. Prior research has indicated that networks like GPS are vulnerable to cyber-attacks such as spoofing and jamming, thus posing serious risks like navigation errors and system failures. These threats are expected to intensify with the widespread deployment of AVs, making it crucial to detect and mitigate such attacks. This paper proposes GPS Intrusion Detection System, or GPS-IDS, an Anomaly-based intrusion detection framework to detect GPS spoofing attacks on AVs. The framework uses a novel physics-based vehicle behavior model where a GPS navigation model is integrated into the conventional dynamic bicycle model for accurate AV behavior representation. Temporal features derived from this behavior model are analyzed using machine learning to detect normal and abnormal navigation behaviors. The performance of the GPS-IDS framework is evaluated on the AV-GPS-Dataset -- a GPS security dataset for AVs comprising real-world data collected using an AV testbed, and simulated data representing urban traffic environments. To the best of our knowledge, this dataset is the first of its kind and has been publicly released for the global research community to address such security challenges.
PromptMind Team at MEDIQA-CORR 2024: Improving Clinical Text Correction with Error Categorization and LLM Ensembles
Venue: arXiv cs.CL 2024
Year: 2024

This paper describes our approach to the MEDIQA-CORR shared task, which involves error detection and correction in clinical notes curated by medical professionals. This task involves handling three subtasks: detecting the presence of errors, identifying the specific sentence containing the error, and correcting it. Through our work, we aim to assess the capabilities of Large Language Models (LLMs) trained on a vast corpora of internet data that contain both factual and unreliable information. We propose to comprehensively address all subtasks together, and suggest employing a unique prompt-based in-context learning strategy. We will evaluate its efficacy in this specialized task demanding a combination of general reasoning and medical knowledge. In medical systems where prediction errors can have grave consequences, we propose leveraging self-consistency and ensemble methods to enhance error correction and error detection performance.
Investigating the 'Autoencoder Behavior' in Speech Self-Supervised Models: a focus on HuBERT's Pretraining
Venue: arXiv cs.CL 2024
Year: 2024

Self-supervised learning has shown great success in Speech Recognition. However, it has been observed that finetuning all layers of the learned model leads to lower performance compared to resetting top layers. This phenomenon is attributed to the ''autoencoder'' behavior: top layers contain information closer to the input and are less suitable for tasks that require linguistic information, such as Speech Recognition.To better our understanding of this behavior, we propose to study the evolution of high-level information within the model during pretraining. We focus on the HuBERT model, which exhibits a less pronounced ''autoencoder'' behavior. By experimentally exploring various factors that may have an impact, we aim to improve the training procedure and enhance the top layers of HuBERT for high-level tasks.Furthermore, our experiments demonstrate that these improvements in the training procedure result in faster convergence and competitive performance on downstream tasks.
Is Less More? Quality, Quantity and Context in Idiom Processing with Natural Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Compositionality in language models presents a problem when processing idiomatic expressions, as their meaning often cannot be directly derived from their individual parts. Although fine-tuning and other optimization strategies can be used to improve representations of idiomatic expressions, this depends on the availability of relevant data. We present the Noun Compound Synonym Substitution in Books - NCSSB - datasets, which are created by substitution of synonyms of potentially idiomatic English noun compounds in public domain book texts. We explore the trade-off between data quantity and quality when training models for idiomaticity detection, in conjunction with contextual information obtained locally (from the surrounding sentences) or externally (through language resources). Performance on an idiomaticity detection task indicates that dataset quality is a stronger factor for context-enriched models, but that quantity also plays a role in models without context inclusion strategies.
Analysing Cross-Speaker Convergence in Face-to-Face Dialogue through the Lens of Automatically Detected Shared Linguistic Constructions
Venue: arXiv cs.CL 2024
Year: 2024

Conversation requires a substantial amount of coordination between dialogue participants, from managing turn taking to negotiating mutual understanding. Part of this coordination effort surfaces as the reuse of linguistic behaviour across speakers, a process often referred to as alignment. While the presence of linguistic alignment is well documented in the literature, several questions remain open, including the extent to which patterns of reuse across speakers have an impact on the emergence of labelling conventions for novel referents. In this study, we put forward a methodology for automatically detecting shared lemmatised constructions -- expressions with a common lexical core used by both speakers within a dialogue -- and apply it to a referential communication corpus where participants aim to identify novel objects for which no established labels exist. Our analyses uncover the usage patterns of shared constructions in interaction and reveal that features such as their frequency and the amount of different constructions used for a referent are associated with the degree of object labelling convergence the participants exhibit after social interaction. More generally, the present study shows that automatically detected shared constructions offer a useful level of analysis to investigate the dynamics of reference negotiation in dialogue.
Rethinking the adaptive relationship between Encoder Layers and Decoder Layers
Venue: arXiv cs.CL 2024
Year: 2024

This article explores the adaptive relationship between Encoder Layers and Decoder Layers using the SOTA model Helsinki-NLP/opus-mt-de-en, which translates German to English. The specific method involves introducing a bias-free fully connected layer between the Encoder and Decoder, with different initializations of the layer's weights, and observing the outcomes of fine-tuning versus retraining. Four experiments were conducted in total. The results suggest that directly modifying the pre-trained model structure for fine-tuning yields suboptimal performance. However, upon observing the outcomes of the experiments with retraining, this structural adjustment shows significant potential.
BeACONS: A Blockchain-enabled Authentication and Communications Network for Scalable IoV
Venue: arXiv cs.DC 2024
Year: 2024

This paper introduces a novel blockchain-enabled authentication and communications network for scalable Internet of Vehicles, which aims to bolster security and confidentiality, diminish communications latency, and reduce dependence on centralised infrastructures like Certificate Authorities and Public Key Infrastructures by leveraging Blockchain-enabled Domain Name Services and Blockchain-enabled Mutual Authentication. The proposed network is structured into a primary layer, consisting of Road Side Units and edge servers as servers of Blockchain-enabled Domain Name Services for managing inter-vehicle communications identities, and a sub-layer within each vehicle for intra-vehicle communications via the Blockchain-enabled Mutual Authentication Protocol. This design facilitates secure connections across vehicles by coordinating between the layers, significantly improving communications security and efficiency. This study also evaluates Road Side Unit availability against the random distribution of Road Side Units along the route of different vehicles. The proposed model presents a novel pathway towards a decentralised, secure, and efficient Internet of Vehicles ecosystem, contributing to the advancement of autonomous and trustworthy vehicular networks.
Promoting AI Equity in Science: Generalized Domain Prompt Learning for Accessible VLM Research
Venue: arXiv cs.CV 2024
Year: 2024

Large-scale Vision-Language Models (VLMs) have demonstrated exceptional performance in natural vision tasks, motivating researchers across domains to explore domain-specific VLMs. However, the construction of powerful domain-specific VLMs demands vast amounts of annotated data, substantial electrical energy, and computing resources, primarily accessible to industry, yet hindering VLM research in academia. To address this challenge and foster sustainable and equitable VLM research, we present the Generalized Domain Prompt Learning (GDPL) framework. GDPL facilitates the transfer of VLMs' robust recognition capabilities from natural vision to specialized domains, without the need for extensive data or resources. By leveraging small-scale domain-specific foundation models and minimal prompt samples, GDPL empowers the language branch with domain knowledge through quaternion networks, uncovering cross-modal relationships between domain-specific vision features and natural vision-based contextual embeddings. Simultaneously, GDPL guides the vision branch into specific domains through hierarchical propagation of generated vision prompt features, grounded in well-matched vision-language relations. Furthermore, to fully harness the domain adaptation potential of VLMs, we introduce a novel low-rank adaptation approach. Extensive experiments across diverse domains like remote sensing, medical imaging, geology, Synthetic Aperture Radar, and fluid dynamics, validate the efficacy of GDPL, demonstrating its ability to achieve state-of-the-art domain recognition performance in a prompt learning paradigm. Our framework paves the way for sustainable and inclusive VLM research, transcending the barriers between academia and industry.
The impact of Compositionality in Zero-shot Multi-label action recognition for Object-based tasks
Venue: arXiv cs.CV 2024
Year: 2024

Addressing multi-label action recognition in videos represents a significant challenge for robotic applications in dynamic environments, especially when the robot is required to cooperate with humans in tasks that involve objects. Existing methods still struggle to recognize unseen actions or require extensive training data. To overcome these problems, we propose Dual-VCLIP, a unified approach for zero-shot multi-label action recognition. Dual-VCLIP enhances VCLIP, a zero-shot action recognition method, with the DualCoOp method for multi-label image classification. The strength of our method is that at training time it only learns two prompts, and it is therefore much simpler than other methods. We validate our method on the Charades dataset that includes a majority of object-based actions, demonstrating that -- despite its simplicity -- our method performs favorably with respect to existing methods on the complete dataset, and promising performance when tested on unseen actions. Our contribution emphasizes the impact of verb-object class-splits during robots' training for new cooperative tasks, highlighting the influence on the performance and giving insights into mitigating biases.
eScope: A Fine-Grained Power Prediction Mechanism for Mobile Applications
Venue: arXiv cs.DC 2024
Year: 2024

Managing the limited energy on mobile platforms executing long-running, resource intensive streaming applications requires adapting an application's operators in response to their power consumption. For example, the frame refresh rate may be reduced if the rendering operation is consuming too much power. Currently, predicting an application's power consumption requires (1) building a device-specific power model for each hardware component, and (2) analyzing the application's code. This approach can be complicated and error-prone given the complexity of an application's logic and the hardware platforms with heterogeneous components that it may execute on. We propose eScope, an alternative method to directly estimate power consumption by each operator in an application. Specifically, eScope correlates an application's execution traces with its device-level energy draw. We implement eScope as a tool for Android platforms and evaluate it using workloads on several synthetic applications as well as two video stream analytics applications. Our evaluation suggests that eScope predicts an application's power use with 97% or better accuracy while incurring a compute time overhead of less than 3%.
Deceptive, Disruptive, No Big Deal: Japanese People React to Simulated Dark Commercial Patterns
Venue: arXiv cs.HC 2024
Year: 2024

Dark patterns and deceptive designs (DPs) are user interface elements that trick people into taking actions that benefit the purveyor. Such designs are widely deployed, with special varieties found in certain nations like Japan that can be traced to global power hierarchies and the local socio-linguistic context of use. In this breaking work, we report on the first user study involving Japanese people (n=30) experiencing a mock shopping website injected with simulated DPs. We found that Alphabet Soup and Misleading Reference Pricing were the most deceptive and least noticeable. Social Proofs, Sneaking in Items, and Untranslation were the least deceptive but Untranslation prevented most from cancelling their account. Mood significantly worsened after experiencing the website. We contribute the first empirical findings on a Japanese consumer base alongside a scalable approach to evaluating user attitudes, perceptions, and behaviours towards DPs in an interactive context. We urge for more human participant research and ideally collaborations with industry to assess real designs in the wild.
PromptMind Team at EHRSQL-2024: Improving Reliability of SQL Generation using Ensemble LLMs
Venue: arXiv cs.DB 2024
Year: 2024

This paper presents our approach to the EHRSQL-2024 shared task, which aims to develop a reliable Text-to-SQL system for electronic health records. We propose two approaches that leverage large language models (LLMs) for prompting and fine-tuning to generate EHRSQL queries. In both techniques, we concentrate on bridging the gap between the real-world knowledge on which LLMs are trained and the domain specific knowledge required for the task. The paper provides the results of each approach individually, demonstrating that they achieve high execution accuracy. Additionally, we show that an ensemble approach further enhances generation reliability by reducing errors. This approach secured us 2nd place in the shared task competition. The methodologies outlined in this paper are designed to be transferable to domain-specific Text-to-SQL problems that emphasize both accuracy and reliability.
Lollipop: SVM Rollups on Solana
Venue: arXiv cs.DC 2024
Year: 2024

We present a formal specification for the implementation of Solana virtual machine (SVM) rollups deployed on top of the Solana Layer 1 (L1) blockchain. We further discuss our motivation, implementation, design decisions, limitations, and preliminary results. Overall, this paper is intended to serve as an initial introduction to building such system(s) on top of the Solana L1 blockchain, but does not represent an absolute. Lastly, we comment discuss on extensions of this specification to support SVM rollups on other well-established L1 blockchains systems such as Ethereum.
Challenges in Deploying Long-Context Transformers: A Theoretical Peak Performance Analysis
Venue: arXiv cs.LG 2024
Year: 2024

Transformer-based long context generative models power emerging AI applications like hour-long video understanding and project-level coding agent. Deploying long context transformers (e.g., 100K to 10M tokens) is prohibitively expensive compared to short context (e.g., 4K tokens) model variants. Reducing the cost of long-context transformers is becoming a pressing research and engineering challenge starting from the year of 2024. This work describes a concurrent programming framework for quantitatively analyzing the efficiency challenges in serving multiple long-context requests under limited size of GPU high-bandwidth memory (HBM) regime. We give a detailed analysis of how all additional computational costs, compared to 4K context, trace back to \textit{one single source: the large size of the KV cache}. We use a 34B GPT-3.5 level model of 50K context on A100 NVLink as a running example, and describe how its large KV cache causes four types of deployment challenges: (1) prefilling long inputs takes much longer compute time and GPU memory than short inputs; (2) after prefilling, the large KV cache residing on the GPU HBM substantially restricts the number of concurrent users being served; (3) during decoding, repeatedly reading the KV cache from HBM to SM largely increases latency; (4) when KV cache memory overflows, swapping it from HBM to DDR causes significant context switching latency. We use this framework to analyze existing works and identify possibilities of combining them to build end-to-end systems. Overall, this work offers a foundational framework for analyzing long context transformer deployment and identifies directions towards reducing the inference cost of 1M context to be as cheap as 4K.
What is it for a Machine Learning Model to Have a Capability?
Venue: arXiv cs.AI 2024
Year: 2024

What can contemporary machine learning (ML) models do? Given the proliferation of ML models in society, answering this question matters to a variety of stakeholders, both public and private. The evaluation of models' capabilities is rapidly emerging as a key subfield of modern ML, buoyed by regulatory attention and government grants. Despite this, the notion of an ML model possessing a capability has not been interrogated: what are we saying when we say that a model is able to do something? And what sorts of evidence bear upon this question? In this paper, we aim to answer these questions, using the capabilities of large language models (LLMs) as a running example. Drawing on the large philosophical literature on abilities, we develop an account of ML models' capabilities which can be usefully applied to the nascent science of model evaluation. Our core proposal is a conditional analysis of model abilities (CAMA): crudely, a machine learning model has a capability to X just when it would reliably succeed at doing X if it 'tried'. The main contribution of the paper is making this proposal precise in the context of ML, resulting in an operationalisation of CAMA applicable to LLMs. We then put CAMA to work, showing that it can help make sense of various features of ML model evaluation practice, as well as suggest procedures for performing fair inter-model comparisons.
On Low Field Size Constructions of Access-Optimal Convertible Codes
Venue: arXiv cs.IT 2024
Year: 2024

Most large-scale storage systems employ erasure coding to provide resilience against disk failures. Recent work has shown that tuning this redundancy to changes in disk failure rates leads to substantial storage savings. This process requires code conversion, wherein data encoded using an $[n^{I\mskip-2mu},k^{I\mskip-2mu}]$ initial code has to be transformed into data encoded using an $[n^{F\mskip-2mu},k^{F\mskip-2mu}]$ final code, a resource-intensive operation. Convertible codes are a class of codes that enable efficient code conversion while maintaining other desirable properties. In this paper, we focus on the access cost of conversion (total number of code symbols accessed in the conversion process) and on an important subclass of conversions known as the merge regime (combining multiple initial codewords into a single final codeword). In this setting, explicit constructions are known for systematic access-optimal Maximum Distance Separable (MDS) convertible codes for all parameters in the merge regime. However, the existing construction for a key subset of these parameters, which makes use of Vandermonde parity matrices, requires a large field size making it unsuitable for practical applications. In this paper, we provide (1) sharper bounds on the minimum field size requirement for such codes, and (2) explicit constructions for low field sizes for several parameter ranges. In doing so, we provide a proof of super-regularity of specially designed classes of Vandermonde matrices that could be of independent interest.