OpenRaiser/Intern-Atlas huggingface dataset:
Hunting DeFi Vulnerabilities via Context-Sensitive Concolic Verification
Venue: arXiv cs.SE 2024
Year: 2024

Decentralized finance (DeFi) is revolutionizing the traditional centralized finance paradigm with its attractive features such as high availability, transparency, and tamper-proofing. However, attacks targeting DeFi services have severely damaged the DeFi market, as evidenced by our investigation of 80 real-world DeFi incidents from 2017 to 2022. Existing methods, based on symbolic execution, model checking, semantic analysis, and fuzzing, fall short in identifying the most DeFi vulnerability types. To address the deficiency, we propose Context-Sensitive Concolic Verification (CSCV), a method of automating the DeFi vulnerability finding based on user-defined properties formulated in temporal logic. CSCV builds and optimizes contexts to guide verification processes that dynamically construct context-carrying transition systems in tandem with concolic executions. Furthermore, we demonstrate the effectiveness of CSCV through experiments on real-world DeFi services and qualitative comparison. The experiment results show that our CSCV prototype successfully detects 76.25% of the vulnerabilities from the investigated incidents with an average time of 253.06 seconds.
A Phone-based Distributed Ambient Temperature Measurement System with An Efficient Label-free Automated Training Strategy
Venue: arXiv cs.LG 2024
Year: 2024

Enhancing the energy efficiency of buildings significantly relies on monitoring indoor ambient temperature. The potential limitations of conventional temperature measurement techniques, together with the omnipresence of smartphones, have redirected researchers'attention towards the exploration of phone-based ambient temperature estimation methods. However, existing phone-based methods face challenges such as insufficient privacy protection, difficulty in adapting models to various phones, and hurdles in obtaining enough labeled training data. In this study, we propose a distributed phone-based ambient temperature estimation system which enables collaboration among multiple phones to accurately measure the ambient temperature in different areas of an indoor space. This system also provides an efficient, cost-effective approach with a few-shot meta-learning module and an automated label generation module. It shows that with just 5 new training data points, the temperature estimation model can adapt to a new phone and reach a good performance. Moreover, the system uses crowdsourcing to generate accurate labels for all newly collected training data, significantly reducing costs. Additionally, we highlight the potential of incorporating federated learning into our system to enhance privacy protection. We believe this study can advance the practical application of phone-based ambient temperature measurement, facilitating energy-saving efforts in buildings.
Sisu: Decentralized Trustless Bridge For Full Ethereum Node
Venue: arXiv cs.CR 2024
Year: 2024

In this paper, we present a detailed approach and implementation to prove Ethereum full node using recursive SNARK, distributed general GKR and Groth16. Our protocol's name is Sisu whose architecture is based on distributed Virgo in zkBridge with some major improvements. Besides proving signature aggregation, we provide solutions to 2 hard problems in proving Ethereum full node: 1) any public key is valid under previous beacon state and 2) all public keys are pairwise distinct. Our solution does not require worker-to-worker communication and therefore reduce total worker-to-worker network traffic from terabyte of data to zero compared to zkBridge. This makes our approach suitable for emerging distributed prover markets and more decentralized compared to zkBridge. Our design is highly parallelable and capable of running on GPU for most parts.
Comprehensive Survey of Model Compression and Speed up for Vision Transformers
Venue: arXiv cs.CV 2024
Year: 2024

Vision Transformers (ViT) have marked a paradigm shift in computer vision, outperforming state-of-the-art models across diverse tasks. However, their practical deployment is hampered by high computational and memory demands. This study addresses the challenge by evaluating four primary model compression techniques: quantization, low-rank approximation, knowledge distillation, and pruning. We methodically analyze and compare the efficacy of these techniques and their combinations in optimizing ViTs for resource-constrained environments. Our comprehensive experimental evaluation demonstrates that these methods facilitate a balanced compromise between model accuracy and computational efficiency, paving the way for wider application in edge computing devices.
Advancing Long-Term Multi-Energy Load Forecasting with Patchformer: A Patch and Transformer-Based Approach
Venue: arXiv cs.LG 2024
Year: 2024

In the context of increasing demands for long-term multi-energy load forecasting in real-world applications, this paper introduces Patchformer, a novel model that integrates patch embedding with encoder-decoder Transformer-based architectures. To address the limitation in existing Transformer-based models, which struggle with intricate temporal patterns in long-term forecasting, Patchformer employs patch embedding, which predicts multivariate time-series data by separating it into multiple univariate data and segmenting each of them into multiple patches. This method effectively enhances the model's ability to capture local and global semantic dependencies. The numerical analysis shows that the Patchformer obtains overall better prediction accuracy in both multivariate and univariate long-term forecasting on the novel Multi-Energy dataset and other benchmark datasets. In addition, the positive effect of the interdependence among energy-related products on the performance of long-term time-series forecasting across Patchformer and other compared models is discovered, and the superiority of the Patchformer against other models is also demonstrated, which presents a significant advancement in handling the interdependence and complexities of long-term multi-energy forecasting. Lastly, Patchformer is illustrated as the only model that follows the positive correlation between model performance and the length of the past sequence, which states its ability to capture long-range past local semantic information.
Conversations as a Source for Teaching Scientific Concepts at Different Education Levels
Venue: arXiv cs.CL 2024
Year: 2024

Open conversations are one of the most engaging forms of teaching. However, creating those conversations in educational software is a complex endeavor, especially if we want to address the needs of different audiences. While language models hold great promise for educational applications, there are substantial challenges in training them to engage in meaningful and effective conversational teaching, especially when considering the diverse needs of various audiences. No official data sets exist for this task to facilitate the training of language models for conversational teaching, considering the diverse needs of various audiences. This paper presents a novel source for facilitating conversational teaching of scientific concepts at various difficulty levels (from preschooler to expert), namely dialogues taken from video transcripts. We analyse this data source in various ways to show that it offers a diverse array of examples that can be used to generate contextually appropriate and natural responses to scientific topics for specific target audiences. It is a freely available valuable resource for training and evaluating conversation models, encompassing organically occurring dialogues. While the raw data is available online, we provide additional metadata for conversational analysis of dialogues at each level in all available videos.
Spiral of Silence: How is Large Language Model Killing Information Retrieval? -- A Case Study on Open Domain Question Answering
Venue: arXiv cs.IR 2024
Year: 2024

The practice of Retrieval-Augmented Generation (RAG), which integrates Large Language Models (LLMs) with retrieval systems, has become increasingly prevalent. However, the repercussions of LLM-derived content infiltrating the web and influencing the retrieval-generation feedback loop are largely uncharted territories. In this study, we construct and iteratively run a simulation pipeline to deeply investigate the short-term and long-term effects of LLM text on RAG systems. Taking the trending Open Domain Question Answering (ODQA) task as a point of entry, our findings reveal a potential digital "Spiral of Silence" effect, with LLM-generated text consistently outperforming human-authored content in search rankings, thereby diminishing the presence and impact of human contributions online. This trend risks creating an imbalanced information ecosystem, where the unchecked proliferation of erroneous LLM-generated content may result in the marginalization of accurate information. We urge the academic community to take heed of this potential issue, ensuring a diverse and authentic digital information landscape.
Subsequences With Generalised Gap Constraints: Upper and Lower Complexity Bounds
Venue: arXiv cs.DS 2024
Year: 2024

For two strings u, v over some alphabet A, we investigate the problem of embedding u into w as a subsequence under the presence of generalised gap constraints. A generalised gap constraint is a triple (i, j, C_{i, j}), where 1 <= i < j <= |u| and C_{i, j} is a subset of A^*. Embedding u as a subsequence into v such that (i, j, C_{i, j}) is satisfied means that if u[i] and u[j] are mapped to v[k] and v[l], respectively, then the induced gap v[k + 1..l - 1] must be a string from C_{i, j}. This generalises the setting recently investigated in [Day et al., ISAAC 2022], where only gap constraints of the form C_{i, i + 1} are considered, as well as the setting from [Kosche et al., RP 2022], where only gap constraints of the form C_{1, |u|} are considered. We show that subsequence matching under generalised gap constraints is NP-hard, and we complement this general lower bound with a thorough (parameterised) complexity analysis. Moreover, we identify several efficiently solvable subclasses that result from restricting the interval structure induced by the generalised gap constraints.
Robust Noisy Label Learning via Two-Stream Sample Distillation
Venue: arXiv cs.CV 2024
Year: 2024

Noisy label learning aims to learn robust networks under the supervision of noisy labels, which plays a critical role in deep learning. Existing work either conducts sample selection or label correction to deal with noisy labels during the model training process. In this paper, we design a simple yet effective sample selection framework, termed Two-Stream Sample Distillation (TSSD), for noisy label learning, which can extract more high-quality samples with clean labels to improve the robustness of network training. Firstly, a novel Parallel Sample Division (PSD) module is designed to generate a certain training set with sufficient reliable positive and negative samples by jointly considering the sample structure in feature space and the human prior in loss space. Secondly, a novel Meta Sample Purification (MSP) module is further designed to mine adequate semi-hard samples from the remaining uncertain training set by learning a strong meta classifier with extra golden data. As a result, more and more high-quality samples will be distilled from the noisy training set to train networks robustly in every iteration. Extensive experiments on four benchmark datasets, including CIFAR-10, CIFAR-100, Tiny-ImageNet, and Clothing-1M, show that our method has achieved state-of-the-art results over its competitors.
CoTAR: Chain-of-Thought Attribution Reasoning with Multi-level Granularity
Venue: arXiv cs.CL 2024
Year: 2024

State-of-the-art performance in QA tasks is currently achieved by systems employing Large Language Models (LLMs), however these models tend to hallucinate information in their responses. One approach focuses on enhancing the generation process by incorporating attribution from the given input to the output. However, the challenge of identifying appropriate attributions and verifying their accuracy against a source is a complex task that requires significant improvements in assessing such systems. We introduce an attribution-oriented Chain-of-Thought reasoning method to enhance the accuracy of attributions. This approach focuses the reasoning process on generating an attribution-centric output. Evaluations on two context-enhanced question-answering datasets using GPT-4 demonstrate improved accuracy and correctness of attributions. In addition, the combination of our method with finetuning enhances the response and attribution accuracy of two smaller LLMs, showing their potential to outperform GPT-4 in some cases.
Simple $k$-crashing Plan with a Good Approximation Ratio
Venue: arXiv cs.DS 2024
Year: 2024

In project management, a project is typically described as an activity-on-edge network (AOE network), where each activity / job is represented as an edge of some network $N$ (which is a DAG). Some jobs must be finished before others can be started, as described by the topology structure of $N$. It is known that job $j_i$ in normal speed would require $b_i$ days to be finished after it is started. Given the network $N$ with the associated edge lengths $b_1,\ldots,b_m$, the duration of the project is determined, which equals the length of the critical path (namely, the longest path) of $N$. To speed up the project (i.e. reduce the duration), the manager can crash a few jobs (namely, reduce the length of the corresponding edges) by investing extra resources into that job. However, the time for completing $j_i$ has a lower bound due to technological limits -- it requires at least $a_i$ days to be completed. Moreover, it is expensive to buy resources. Given $N$ and an integer $k\geq 1$, the $k$-crashing problem asks the minimum amount of resources required to speed up the project by $k$ days. We show a simple and efficient algorithm with an approximation ratio $\frac{1}{1}+\ldots+\frac{1}{k}$ for this problem. We also study a related problem called $k$-LIS, in which we are given a sequence $ω$ of numbers and we aim to find $k$ disjoint increasing subsequence of $ω$ with the largest total length. We show a $(1-\frac{1}{e})$-approximation algorithm which is simple and efficient.
An Enhanced Differential Grouping Method for Large-Scale Overlapping Problems
Venue: arXiv cs.NE 2024
Year: 2024

Large-scale overlapping problems are prevalent in practical engineering applications, and the optimization challenge is significantly amplified due to the existence of shared variables. Decomposition-based cooperative coevolution (CC) algorithms have demonstrated promising performance in addressing large-scale overlapping problems. However, current CC frameworks designed for overlapping problems rely on grouping methods for the identification of overlapping problem structures and the current grouping methods for large-scale overlapping problems fail to consider both accuracy and efficiency simultaneously. In this article, we propose a two-stage enhanced grouping method for large-scale overlapping problems, called OEDG, which achieves accurate grouping while significantly reducing computational resource consumption. In the first stage, OEDG employs a grouping method based on the finite differences principle to identify all subcomponents and shared variables. In the second stage, we propose two grouping refinement methods, called subcomponent union detection (SUD) and subcomponent detection (SD), to enhance and refine the grouping results. SUD examines the information of the subcomponents and shared variables obtained in the previous stage, and SD corrects inaccurate grouping results. To better verify the performance of the proposed OEDG, we propose a series of novel benchmarks that consider various properties of large-scale overlapping problems, including the topology structure, overlapping degree, and separability. Extensive experimental results demonstrate that OEDG is capable of accurately grouping different types of large-scale overlapping problems while consuming fewer computational resources. Finally, we empirically verify that the proposed OEDG can effectively improve the optimization performance of diverse large-scale overlapping problems.
AllTheDocks road safety dataset: A cyclist's perspective and experience
Venue: arXiv cs.MM 2024
Year: 2024

Active travel is an essential component in intelligent transportation systems. Cycling, as a form of active travel, shares the road space with motorised traffic which often affects the cyclists' safety and comfort and therefore peoples' propensity to uptake cycling instead of driving. This paper presents a unique dataset, collected by cyclists across London, that includes video footage, accelerometer, GPS, and gyroscope data. The dataset is then labelled by an independent group of London cyclists to rank the safety level of each frame and to identify objects in the cyclist's field of vision that might affect their experience. Furthermore, in this dataset, the quality of the road is measured by the international roughness index of the surface, which indicates the comfort of cycling on the road. The dataset will be made available for open access in the hope of motivating more research in this area to underpin the requirements for cyclists' safety and comfort and encourage more people to replace vehicle travel with cycling.
Robotic Sensor Network: Achieving Mutual Communication Control Assistance With Fast Cross-Layer Optimization
Venue: arXiv cs.RO 2024
Year: 2024

Robotic sensor network (RSN) is an emerging paradigm that harvests data from remote sensors adopting mobile robots. However, communication and control functionalities in RSNs are interdependent, for which existing approaches become inefficient, as they plan robot trajectories merely based on unidirectional impact between communication and control. This paper proposes the concept of mutual communication control assistance (MCCA), which leverages a model predictive communication and control (MPC2) design for intertwined optimization of motion-assisted communication and communication-assisted collision avoidance. The MPC2 problem jointly optimizes the cross-layer variables of sensor powers and robot actions, and is solved by alternating optimization, strong duality, and cross-horizon minorization maximization in real time. This approach contrasts with conventional communication control co-design methods that calculate an offline non-executable trajectory. Experiments in a high-fidelity RSN simulator demonstrate that the proposed MCCA outperforms various benchmarks in terms of communication efficiency and navigation time.
Characterizing Polkadot's Transactions Ecosystem: methodology, tools, and insights
Venue: arXiv cs.CR 2024
Year: 2024

The growth potential of a crypto(currency) project can be measured by the use cases spurred by the underlying technology. However, these projects are usually distributed, with a weak feedback schemes. Hence, a metric that is widely used as a proxy for their healthiness is the number of transactions and related volumes. Nevertheless, such a metric can be subject to manipulation (the crypto market being an unregulated one magnifies such a risk). To address the cited gap we design a comprehensive methodology to process large cryptocurrency transaction graphs that, after clustering user addresses of interest, derives a compact representation of the network that highlights clusters interactions. To show the viability of our solution, we bring forward a use case centered on Polkadot, which has gained significant attention in the digital currency landscape due to its pioneering approach to interoperability and scalability. However, little is known about how many and to what extent its wide range of enabled use cases have been adopted by end-users so far. The answer to this type of question means mapping Polkadot (or any analyzed crypto project) on a palette that ranges from a thriving ecosystem to a speculative coin without compelling use cases. Our findings demonstrate that crypto exchanges exert considerable influence on the Polkadot network, owning nearly 40% of all addresses in the ledger and absorbing at least 80% of all transactions. In addition, the high volume of inter-exchange transactions (> 20%) underscores the strong interconnections among just a couple of prominent exchanges, prompting further investigations into the behavior of these actors to uncover potential unethical activities, such as wash trading. These results, while characterized by a high level of scalability and adaptability, are at the same time immune from the drawbacks of currently used metrics.
Generative AI for Advanced UAV Networking
Venue: arXiv cs.NI 2024
Year: 2024

With the impressive achievements of chatGPT and Sora, generative artificial intelligence (GAI) has received increasing attention. Not limited to the field of content generation, GAI is also widely used to solve the problems in wireless communication scenarios due to its powerful learning and generalization capabilities. Therefore, we discuss key applications of GAI in improving unmanned aerial vehicle (UAV) communication and networking performance in this article. Specifically, we first review the key technologies of GAI and the important roles of UAV networking. Then, we show how GAI can improve the communication, networking, and security performances of UAV systems. Subsequently, we propose a novel framework of GAI for advanced UAV networking, and then present a case study of UAV-enabled spectrum map estimation and transmission rate optimization based on the proposed framework to verify the effectiveness of GAI-enabled UAV systems. Finally, we discuss some important open directions.
HiGraphDTI: Hierarchical Graph Representation Learning for Drug-Target Interaction Prediction
Venue: arXiv cs.LG 2024
Year: 2024

The discovery of drug-target interactions (DTIs) plays a crucial role in pharmaceutical development. The deep learning model achieves more accurate results in DTI prediction due to its ability to extract robust and expressive features from drug and target chemical structures. However, existing deep learning methods typically generate drug features via aggregating molecular atom representations, ignoring the chemical properties carried by motifs, i.e., substructures of the molecular graph. The atom-drug double-level molecular representation learning can not fully exploit structure information and fails to interpret the DTI mechanism from the motif perspective. In addition, sequential model-based target feature extraction either fuses limited contextual information or requires expensive computational resources. To tackle the above issues, we propose a hierarchical graph representation learning-based DTI prediction method (HiGraphDTI). Specifically, HiGraphDTI learns hierarchical drug representations from triple-level molecular graphs to thoroughly exploit chemical information embedded in atoms, motifs, and molecules. Then, an attentional feature fusion module incorporates information from different receptive fields to extract expressive target features.Last, the hierarchical attention mechanism identifies crucial molecular segments, which offers complementary views for interpreting interaction mechanisms. The experiment results not only demonstrate the superiority of HiGraphDTI to the state-of-the-art methods, but also confirm the practical ability of our model in interaction interpretation and new DTI discovery.