OpenRaiser/Intern-Atlas huggingface dataset:
Solving a Real-World Optimization Problem Using Proximal Policy Optimization with Curriculum Learning and Reward Engineering
Venue: arXiv cs.LG 2024
Year: 2024

We present a proximal policy optimization (PPO) agent trained through curriculum learning (CL) principles and meticulous reward engineering to optimize a real-world high-throughput waste sorting facility. Our work addresses the challenge of effectively balancing the competing objectives of operational safety, volume optimization, and minimizing resource usage. A vanilla agent trained from scratch on these multiple criteria fails to solve the problem due to its inherent complexities. This problem is particularly difficult due to the environment's extremely delayed rewards with long time horizons and class (or action) imbalance, with important actions being infrequent in the optimal policy. This forces the agent to anticipate long-term action consequences and prioritize rare but rewarding behaviours, creating a non-trivial reinforcement learning task. Our five-stage CL approach tackles these challenges by gradually increasing the complexity of the environmental dynamics during policy transfer while simultaneously refining the reward mechanism. This iterative and adaptable process enables the agent to learn a desired optimal policy. Results demonstrate that our approach significantly improves inference-time safety, achieving near-zero safety violations in addition to enhancing waste sorting plant efficiency.
LightFAt: Mitigating Control-flow Explosion via Lightweight PMU-based Control-flow Attestation
Venue: arXiv cs.CR 2024
Year: 2024

With the continuous evolution of computational devices, more and more applications are being executed remotely. The applications operate on a wide spectrum of devices, ranging from IoT nodes with low computational capabilities to large cloud providers with high capabilities. Remote execution often deals with sensitive data or executes proprietary software. Hence, the challenge of ensuring that the code execution will not be compromised rises. Remote Attestation deals with this challenge. It ensures the code is executed in a non-compromised environment by calculating a potentially large sequence of cryptographic hash values. Each hash calculation is computationally intensive and over a large sequence the overhead becomes extremely high. In this work, we propose LightFAt: a Lightweight Control Flow Attestation scheme. Instead of relying on the expensive cryptographic hash calculation, LightFAt leverages the readings from the processor's Performance Monitor Unit (PMU) in conjunction with a lightweight unsupervised machine learning (ML) classifier to detect whether a target application's control flow is compromised, hence improving the system's security. On the verifier's side, LightFAt reaches a detection accuracy of over 95%, with low false-negative and false-positive rates.
Interpolant Existence is Undecidable for Two-Variable First-Order Logic with Two Equivalence Relations
Venue: arXiv cs.LO 2024
Year: 2024

The interpolant existence problem (IEP) for a logic L is to decide, given formulas P and Q, whether there exists a formula I, built from the shared symbols of P and Q, such that P entails I and I entails Q in L. If L enjoys the Craig interpolation property (CIP), then the IEP reduces to validity in L. Recently, the IEP has been studied for logics without the CIP. The results obtained so far indicate that even though the IEP can be computationally harder than validity, it is decidable when L is decidable. Here, we give the first examples of decidable fragments of first-order logic for which the IEP is undecidable. Namely, we show that the IEP is undecidable for the two-variable fragment with two equivalence relations and for the two-variable guarded fragment with individual constants and two equivalence relations. We also determine the corresponding decidable Boolean description logics for which the IEP is undecidable.
Unblind Text Inputs: Predicting Hint-text of Text Input in Mobile Apps via LLM
Venue: arXiv cs.HC 2024
Year: 2024

Mobile apps have become indispensable for accessing and participating in various environments, especially for low-vision users. Users with visual impairments can use screen readers to read the content of each screen and understand the content that needs to be operated. Screen readers need to read the hint-text attribute in the text input component to remind visually impaired users what to fill in. Unfortunately, based on our analysis of 4,501 Android apps with text inputs, over 0.76 of them are missing hint-text. These issues are mostly caused by developers' lack of awareness when considering visually impaired individuals. To overcome these challenges, we developed an LLM-based hint-text generation model called HintDroid, which analyzes the GUI information of input components and uses in-context learning to generate the hint-text. To ensure the quality of hint-text generation, we further designed a feedback-based inspection mechanism to further adjust hint-text. The automated experiments demonstrate the high BLEU and a user study further confirms its usefulness. HintDroid can not only help visually impaired individuals, but also help ordinary people understand the requirements of input components. HintDroid demo video: https://youtu.be/FWgfcctRbfI.
Usage of OpenAlex for creating meaningful global overlay maps of science on the individual and institutional levels
Venue: arXiv cs.DL 2024
Year: 2024

Global overlay maps of science use base maps that are overlaid by specific data (from single researchers, institutions, or countries) for visualizing scientific performance such as field-specific paper output. A procedure to create global overlay maps using OpenAlex is proposed. Six different global base maps are provided. Using one of these base maps, example overlay maps for one individual (the first author of this paper) and his research institution are shown and analyzed. A method for normalizing the overlay data is proposed. Overlay maps using raw overlay data display general concepts more pronounced than their counterparts using normalized overlay data. Advantages and limitations of the proposed overlay approach are discussed.
Adaptive Affinity-Based Generalization For MRI Imaging Segmentation Across Resource-Limited Settings
Venue: arXiv cs.CV 2024
Year: 2024

The joint utilization of diverse data sources for medical imaging segmentation has emerged as a crucial area of research, aiming to address challenges such as data heterogeneity, domain shift, and data quality discrepancies. Integrating information from multiple data domains has shown promise in improving model generalizability and adaptability. However, this approach often demands substantial computational resources, hindering its practicality. In response, knowledge distillation (KD) has garnered attention as a solution. KD involves training light-weight models to emulate the behavior of more resource-intensive models, thereby mitigating the computational burden while maintaining performance. This paper addresses the pressing need to develop a lightweight and generalizable model for medical imaging segmentation that can effectively handle data integration challenges. Our proposed approach introduces a novel relation-based knowledge framework by seamlessly combining adaptive affinity-based and kernel-based distillation through a gram matrix that can capture the style representation across features. This methodology empowers the student model to accurately replicate the feature representations of the teacher model, facilitating robust performance even in the face of domain shift and data heterogeneity. To validate our innovative approach, we conducted experiments on publicly available multi-source prostate MRI data. The results demonstrate a significant enhancement in segmentation performance using lightweight networks. Notably, our method achieves this improvement while reducing both inference time and storage usage, rendering it a practical and efficient solution for real-time medical imaging segmentation.
Continual Learning of Numerous Tasks from Long-tail Distributions
Venue: arXiv cs.LG 2024
Year: 2024

Continual learning, an important aspect of artificial intelligence and machine learning research, focuses on developing models that learn and adapt to new tasks while retaining previously acquired knowledge. Existing continual learning algorithms usually involve a small number of tasks with uniform sizes and may not accurately represent real-world learning scenarios. In this paper, we investigate the performance of continual learning algorithms with a large number of tasks drawn from a task distribution that is long-tail in terms of task sizes. We design one synthetic dataset and two real-world continual learning datasets to evaluate the performance of existing algorithms in such a setting. Moreover, we study an overlooked factor in continual learning, the optimizer states, e.g. first and second moments in the Adam optimizer, and investigate how it can be used to improve continual learning performance. We propose a method that reuses the optimizer states in Adam by maintaining a weighted average of the second moments from previous tasks. We demonstrate that our method, compatible with most existing continual learning algorithms, effectively reduces forgetting with only a small amount of additional computational or memory costs, and provides further improvements on existing continual learning algorithms, particularly in a long-tail task sequence.
AQuA -- Combining Experts' and Non-Experts' Views To Assess Deliberation Quality in Online Discussions Using LLMs
Venue: arXiv cs.CL 2024
Year: 2024

Measuring the quality of contributions in political online discussions is crucial in deliberation research and computer science. Research has identified various indicators to assess online discussion quality, and with deep learning advancements, automating these measures has become feasible. While some studies focus on analyzing specific quality indicators, a comprehensive quality score incorporating various deliberative aspects is often preferred. In this work, we introduce AQuA, an additive score that calculates a unified deliberative quality score from multiple indices for each discussion post. Unlike other singular scores, AQuA preserves information on the deliberative aspects present in comments, enhancing model transparency. We develop adapter models for 20 deliberative indices, and calculate correlation coefficients between experts' annotations and the perceived deliberativeness by non-experts to weigh the individual indices into a single deliberative score. We demonstrate that the AQuA score can be computed easily from pre-trained adapters and aligns well with annotations on other datasets that have not be seen during training. The analysis of experts' vs. non-experts' annotations confirms theoretical findings in the social science literature.
Robust Pushing: Exploiting Quasi-static Belief Dynamics and Contact-informed Optimization
Venue: arXiv cs.RO 2024
Year: 2024

Non-prehensile manipulation such as pushing is typically subject to uncertain, non-smooth dynamics. However, modeling the uncertainty of the dynamics typically results in intractable belief dynamics, making data-efficient planning under uncertainty difficult. This article focuses on the problem of efficiently generating robust open-loop pushing plans. First, we investigate how the belief over object configurations propagates through quasi-static contact dynamics. We exploit the simplified dynamics to predict the variance of the object configuration without sampling from a perturbation distribution. In a sampling-based trajectory optimization algorithm, the gain of the variance is constrained in order to enforce robustness of the plan. Second, we propose an informed trajectory sampling mechanism for drawing robot trajectories that are likely to make contact with the object. This sampling mechanism is shown to significantly improve chances of finding robust solutions, especially when making-and-breaking contacts is required. We demonstrate that the proposed approach is able to synthesize bi-manual pushing trajectories, resulting in successful long-horizon pushing maneuvers without exteroceptive feedback such as vision or tactile feedback. We furthermore deploy the proposed approach in a model-predictive control scheme, demonstrating additional robustness against unmodeled perturbations.
Efficient Multi-Vector Dense Retrieval Using Bit Vectors
Venue: arXiv cs.IR 2024
Year: 2024

Dense retrieval techniques employ pre-trained large language models to build a high-dimensional representation of queries and passages. These representations compute the relevance of a passage w.r.t. to a query using efficient similarity measures. In this line, multi-vector representations show improved effectiveness at the expense of a one-order-of-magnitude increase in memory footprint and query latency by encoding queries and documents on a per-token level. Recently, PLAID has tackled these problems by introducing a centroid-based term representation to reduce the memory impact of multi-vector systems. By exploiting a centroid interaction mechanism, PLAID filters out non-relevant documents, thus reducing the cost of the successive ranking stages. This paper proposes ``Efficient Multi-Vector dense retrieval with Bit vectors'' (EMVB), a novel framework for efficient query processing in multi-vector dense retrieval. First, EMVB employs a highly efficient pre-filtering step of passages using optimized bit vectors. Second, the computation of the centroid interaction happens column-wise, exploiting SIMD instructions, thus reducing its latency. Third, EMVB leverages Product Quantization (PQ) to reduce the memory footprint of storing vector representations while jointly allowing for fast late interaction. Fourth, we introduce a per-document term filtering method that further improves the efficiency of the last step. Experiments on MS MARCO and LoTTE show that EMVB is up to 2.8x faster while reducing the memory footprint by 1.8x with no loss in retrieval accuracy compared to PLAID.
Conifer: Improving Complex Constrained Instruction-Following Ability of Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

The ability of large language models (LLMs) to follow instructions is crucial to real-world applications. Despite recent advances, several studies have highlighted that LLMs struggle when faced with challenging instructions, especially those that include complex constraints, hindering their effectiveness in various tasks. To address this challenge, we introduce Conifer, a novel instruction tuning dataset, designed to enhance LLMs to follow multi-level instructions with complex constraints. Utilizing GPT-4, we curate the dataset by a series of LLM-driven refinement processes to ensure high quality. We also propose a progressive learning scheme that emphasizes an easy-to-hard progression, and learning from process feedback. Models trained with Conifer exhibit remarkable improvements in instruction-following abilities, especially for instructions with complex constraints. On several instruction-following benchmarks, our 7B model outperforms the state-of-the-art open-source 7B models, even exceeds the performance of models 10 times larger on certain metrics. All the code and Conifer dataset are available at https://www.github.com/ConiferLM/Conifer.
I-Design: Personalized LLM Interior Designer
Venue: arXiv cs.AI 2024
Year: 2024

Interior design allows us to be who we are and live how we want - each design is as unique as our distinct personality. However, it is not trivial for non-professionals to express and materialize this since it requires aligning functional and visual expectations with the constraints of physical space; this renders interior design a luxury. To make it more accessible, we present I-Design, a personalized interior designer that allows users to generate and visualize their design goals through natural language communication. I-Design starts with a team of large language model agents that engage in dialogues and logical reasoning with one another, transforming textual user input into feasible scene graph designs with relative object relationships. Subsequently, an effective placement algorithm determines optimal locations for each object within the scene. The final design is then constructed in 3D by retrieving and integrating assets from an existing object database. Additionally, we propose a new evaluation protocol that utilizes a vision-language model and complements the design pipeline. Extensive quantitative and qualitative experiments show that I-Design outperforms existing methods in delivering high-quality 3D design solutions and aligning with abstract concepts that match user input, showcasing its advantages across detailed 3D arrangement and conceptual fidelity.
MODNO: Multi Operator Learning With Distributed Neural Operators
Venue: arXiv cs.LG 2024
Year: 2024

The study of operator learning involves the utilization of neural networks to approximate operators. Traditionally, the focus has been on single-operator learning (SOL). However, recent advances have rapidly expanded this to include the approximation of multiple operators using foundation models equipped with millions or billions of trainable parameters, leading to the research of multi-operator learning (MOL). In this paper, we present a novel distributed training approach aimed at enabling a single neural operator with significantly fewer parameters to effectively tackle multi-operator learning challenges, all without incurring additional average costs. Our method is applicable to various neural operators, such as Deep Operator Neural Networks (DON). The core idea is to independently learn the output basis functions for each operator using its dedicated data, while simultaneously centralizing the learning of the input function encoding shared by all operators using the entire dataset. Through a systematic study of five numerical examples, we compare the accuracy and cost of training a single neural operator for each operator independently versus training a MOL model using our proposed method. Our results demonstrate enhanced efficiency and satisfactory accuracy. Moreover, our approach illustrates that some operators with limited data can be more effectively constructed with the aid of data from analogous operators through MOL learning. This highlights another MOL's potential to bolster operator learning.
Automated Transparency: A Legal and Empirical Analysis of the Digital Services Act Transparency Database
Venue: arXiv cs.CY 2024
Year: 2024

The Digital Services Act (DSA) is a much awaited platforms liability reform in the European Union that was adopted on 1 November 2022 with the ambition to set a global example in terms of accountability and transparency. Among other obligations, the DSA emphasizes the need for online platforms to report on their content moderation decisions (`statements of reasons' - SoRs), which is a novel transparency mechanism we refer to as automated transparency in this study. SoRs are currently made available in the DSA Transparency Database, launched by the European Commission in September 2023. The DSA Transparency Database marks a historical achievement in platform governance, and allows investigations about the actual transparency gains, both at structure level as well as at the level of platform compliance. This study aims to understand whether the Transparency Database helps the DSA to live up to its transparency promises. We use legal and empirical arguments to show that while there are some transparency gains, compliance remains problematic, as the current database structure allows for a lot of discretion from platforms in terms of transparency practices. In our empirical study, we analyze a representative sample of the Transparency Database (131m SoRs) submitted in November 2023, to characterise and evaluate platform content moderation practices.
Artificial Cardiac Conduction System: Simulating Heart Function for Advanced Computational Problem Solving
Venue: arXiv cs.NE 2024
Year: 2024

This work proposes a novel bio-inspired metaheuristic called Artificial Cardiac Conduction System (ACCS) inspired by the human cardiac conduction system. The ACCS algorithm imitates the functional behaviour of the human heart that generates and sends signals to the heart muscle, initiating it to contract. Four nodes in the myocardium layer participate in generating and controlling heart rate, such as the sinoatrial, atrioventricular, bundle of His, and Purkinje fibres. The mechanism of controlling the heart rate through these four nodes is implemented. The algorithm is then benchmarked on 19 well-known mathematical test functions as it can determine the exploitation and exploration capability of the algorithm. The results are verified by a comparative study with Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), and Fast Evolutionary Programming (FEP). The algorithm undergoes a rigorous evaluation using the CEC-C06 2019 Benchmark Test Functions, illuminating its adeptness in both exploitation and exploration. Validation ensues through a meticulous comparative analysis involving the Dragonfly Algorithm (DA), WOA, PSO, Lagrange Elementary Optimization (Leo), and the Ant Nesting Algorithm (ANA). The results show that the ACCS algorithm can provide very competitive results compared to these well-known metaheuristics and other conventional methods.
Machine Learning Driven Global Optimisation Framework for Analog Circuit Design
Venue: arXiv cs.NE 2024
Year: 2024

We propose a machine learning-driven optimisation framework for analog circuit design in this paper. The primary objective is to determine the device sizes for the optimal performance of analog circuits for a given set of specifications. Our methodology entails employing machine learning models and spice simulations to direct the optimisation algorithm towards achieving the optimal design for analog circuits. Machine learning based global offline surrogate models, with the circuit design parameters as the input, are built in the design space for the analog circuits under study and is used to guide the optimisation algorithm, resulting in faster convergence and a reduced number of spice simulations. Multi-layer perceptron and random forest regressors are employed to predict the required design specifications of the analog circuit. Since the saturation condition of transistors is vital in the proper working of analog circuits, multi-layer perceptron classifiers are used to predict the saturation condition of each transistor in the circuit. The feasibility of the candidate solutions is verified using machine learning models before invoking spice simulations. We validate the proposed framework using three circuit topologies--a bandgap reference, a folded cascode operational amplifier, and a two-stage operational amplifier. The simulation results show better optimum values and lower standard deviations for fitness functions after convergence. Incorporating the machine learning-based predictions proposed in the optimisation method has resulted in the reduction of spice calls by 56%, 59%, and 83% when compared with standard approaches in the three test cases considered in the study.