OpenRaiser/Intern-Atlas huggingface dataset:
MathDSL: A Domain-Specific Language for Concise Mathematical Solutions Via Program Synthesis
Venue: arXiv cs.LG 2024
Year: 2024

We present MathDSL, a Domain-Specific Language (DSL) for mathematical equation solving, which, when deployed in program synthesis models, outperforms state-of-the-art reinforcement-learning-based methods. We also introduce a quantitative metric for measuring the conciseness of a mathematical solution and demonstrate the improvement in the quality of generated solutions compared to other methods. Our system demonstrates that a program synthesis system (DreamCoder) using MathDSL can generate programs that solve linear equations with greater accuracy and conciseness than using reinforcement learning systems. Additionally, we demonstrate that if we use the action spaces of previous reinforcement learning systems as DSLs, MathDSL outperforms the action-space-DSLs. We use DreamCoder to store equation-solving strategies as learned abstractions in its program library and demonstrate that by using MathDSL, these can be converted into human-interpretable solution strategies that could have applications in mathematical education.
Broadcast Product: Shape-aligned Element-wise Multiplication and Beyond
Venue: arXiv cs.LG 2024
Year: 2024

We propose a new operator defined between two tensors, the broadcast product. The broadcast product calculates the Hadamard product after duplicating elements to align the shapes of the two tensors. Complex tensor operations in libraries like \texttt{numpy} can be succinctly represented as mathematical expressions using the broadcast product. Finally, we propose a novel tensor decomposition using the broadcast product, highlighting its potential applications in dimensionality reduction.
BioZero: An Efficient and Privacy-Preserving Decentralized Biometric Authentication Protocol on Open Blockchain
Venue: arXiv cs.CR 2024
Year: 2024

Digital identity plays a vital role in enabling secure access to resources and services in the digital world. Traditional identity authentication methods, such as password-based and biometric authentications, have limitations in terms of security, privacy, and scalability. Decentralized authentication approaches leveraging blockchain technology have emerged as a promising solution. However, existing decentralized authentication methods often rely on indirect identity verification (e.g. using passwords or digital signatures as authentication credentials) and face challenges such as Sybil attacks. In this paper, we propose BioZero, an efficient and privacy-preserving decentralized biometric authentication protocol that can be implemented on open blockchain. BioZero leverages Pedersen commitment and homomorphic computation to protect user biometric privacy while enabling efficient verification. We enhance the protocol with non-interactive homomorphic computation and employ zero-knowledge proofs for secure on-chain verification. The unique aspect of BioZero is that it is fully decentralized and can be executed by blockchain smart contracts in a very efficient way. We analyze the security of BioZero and validate its performance through a prototype implementation. The results demonstrate the effectiveness, efficiency, and security of BioZero in decentralized authentication scenarios. Our work contributes to the advancement of decentralized identity authentication using biometrics.
Drone Stereo Vision for Radiata Pine Branch Detection and Distance Measurement: Integrating SGBM and Segmentation Models
Venue: arXiv cs.CV 2024
Year: 2024

Manual pruning of radiata pine trees presents significant safety risks due to their substantial height and the challenging terrains in which they thrive. To address these risks, this research proposes the development of a drone-based pruning system equipped with specialized pruning tools and a stereo vision camera, enabling precise detection and trimming of branches. Deep learning algorithms, including YOLO and Mask R-CNN, are employed to ensure accurate branch detection, while the Semi-Global Matching algorithm is integrated to provide reliable distance estimation. The synergy between these techniques facilitates the precise identification of branch locations and enables efficient, targeted pruning. Experimental results demonstrate that the combined implementation of YOLO and SGBM enables the drone to accurately detect branches and measure their distances from the drone. This research not only improves the safety and efficiency of pruning operations but also makes a significant contribution to the advancement of drone technology in the automation of agricultural and forestry practices, laying a foundational framework for further innovations in environmental management.
MUSE: Integrating Multi-Knowledge for Knowledge Graph Completion
Venue: arXiv cs.CL 2024
Year: 2024

Knowledge Graph Completion (KGC) aims to predict the missing [relation] part of (head entity)--[relation]->(tail entity) triplet. Most existing KGC methods focus on single features (e.g., relation types) or sub-graph aggregation. However, they do not fully explore the Knowledge Graph (KG) features and neglect the guidance of external semantic knowledge. To address these shortcomings, we propose a knowledge-aware reasoning model (MUSE), which designs a novel multi-knowledge representation learning mechanism for missing relation prediction. Our model develops a tailored embedding space through three parallel components: 1) Prior Knowledge Learning for enhancing the triplets' semantic representation by fine-tuning BERT; 2) Context Message Passing for enhancing the context messages of KG; 3) Relational Path Aggregation for enhancing the path representation from the head entity to the tail entity. The experimental results show that MUSE significantly outperforms other baselines on four public datasets, achieving over 5.50% H@1 improvement and 4.20% MRR improvement on the NELL995 dataset. The code and datasets will be released via https://github.com/SUSTech-TP/ADMA2024-MUSE.git.
Pixel-Space Post-Training of Latent Diffusion Models
Venue: arXiv cs.CV 2024
Year: 2024

Latent diffusion models (LDMs) have made significant advancements in the field of image generation in recent years. One major advantage of LDMs is their ability to operate in a compressed latent space, allowing for more efficient training and deployment. However, despite these advantages, challenges with LDMs still remain. For example, it has been observed that LDMs often generate high-frequency details and complex compositions imperfectly. We hypothesize that one reason for these flaws is due to the fact that all pre- and post-training of LDMs are done in latent space, which is typically $8 \times 8$ lower spatial-resolution than the output images. To address this issue, we propose adding pixel-space supervision in the post-training process to better preserve high-frequency details. Experimentally, we show that adding a pixel-space objective significantly improves both supervised quality fine-tuning and preference-based post-training by a large margin on a state-of-the-art DiT transformer and U-Net diffusion models in both visual quality and visual flaw metrics, while maintaining the same text alignment quality.
DualCoTs: Dual Chain-of-Thoughts Prompting for Sentiment Lexicon Expansion of Idioms
Venue: arXiv cs.CL 2024
Year: 2024

Idioms represent a ubiquitous vehicle for conveying sentiments in the realm of everyday discourse, rendering the nuanced analysis of idiom sentiment crucial for a comprehensive understanding of emotional expression within real-world texts. Nevertheless, the existing corpora dedicated to idiom sentiment analysis considerably limit research in text sentiment analysis. In this paper, we propose an innovative approach to automatically expand the sentiment lexicon for idioms, leveraging the capabilities of large language models through the application of Chain-of-Thought prompting. To demonstrate the effectiveness of this approach, we integrate multiple existing resources and construct an emotional idiom lexicon expansion dataset (called EmoIdiomE), which encompasses a comprehensive repository of Chinese and English idioms. Then we designed the Dual Chain-of-Thoughts (DualCoTs) method, which combines insights from linguistics and psycholinguistics, to demonstrate the effectiveness of using large models to automatically expand the sentiment lexicon for idioms. Experiments show that DualCoTs is effective in idioms sentiment lexicon expansion in both Chinese and English. For reproducibility, we will release the data and code upon acceptance.
Open Digital Rights Enforcement Framework (ODRE): from descriptive to enforceable policies
Venue: arXiv cs.CR 2024
Year: 2024

From centralised platforms to decentralised ecosystems, like Data Spaces, sharing data has become a paramount challenge. For this reason, the definition of data usage policies has become crucial in these domains, highlighting the necessity of effective policy enforcement mechanisms. The Open Digital Rights Language (ODRL) is a W3C standard ontology designed to describe data usage policies, however, it lacks built-in enforcement capabilities, limiting its practical application. This paper introduces the Open Digital Rights Enforcement (ODRE) framework, whose goal is to provide ODRL with enforcement capabilities. The ODRE framework proposes a novel approach to express ODRL policies that integrates the descriptive ontology terms of ODRL with other languages that allow behaviour specification, such as dynamic data handling or function evaluation. The framework includes an enforcement algorithm for ODRL policies and two open-source implementations in Python and Java. The ODRE framework is also designed to support future extensions of ODRL to specific domain scenarios. In addition, current limitations of ODRE, ODRL, and current challenges are reported. Finally, to demonstrate the enforcement capabilities of the implementations, their performance, and their extensibility features, several experiments have been carried out with positive results.
Estimating The Carbon Footprint Of Digital Agriculture Deployment: A Parametric Bottom-Up Modelling Approach
Venue: arXiv cs.CY 2024
Year: 2024

Digitalization appears as a lever to enhance agriculture sustainability. However, existing works on digital agriculture's own sustainability remain scarce, disregarding the environmental effects of deploying digital devices on a large-scale. We propose a bottom-up method to estimate the carbon footprint of digital agriculture scenarios considering deployment of devices over a diversity of farm sizes. It is applied to two use-cases and demonstrates that digital agriculture encompasses a diversity of devices with heterogeneous carbon footprints and that more complex devices yield higher footprints not always compensated by better performances or scaling gains. By emphasizing the necessity of considering the multiplicity of devices, and the territorial distribution of farm sizes when modelling digital agriculture deployments, this study highlights the need for further exploration of the first-order effects of digital technologies in agriculture.
Leveraging Semantic and Geometric Information for Zero-Shot Robot-to-Human Handover
Venue: arXiv cs.RO 2024
Year: 2024

Human-robot interaction (HRI) encompasses a wide range of collaborative tasks, with handover being one of the most fundamental. As robots become more integrated into human environments, the potential for service robots to assist in handing objects to humans is increasingly promising. In robot-to-human (R2H) handover, selecting the optimal grasp is crucial for success, as it requires avoiding interference with the humans preferred grasp region and minimizing intrusion into their workspace. Existing methods either inadequately consider geometric information or rely on data-driven approaches, which often struggle to generalize across diverse objects. To address these limitations, we propose a novel zero-shot system that combines semantic and geometric information to generate optimal handover grasps. Our method first identifies grasp regions using semantic knowledge from vision-language models (VLMs) and, by incorporating customized visual prompts, achieves finer granularity in region grounding. A grasp is then selected based on grasp distance and approach angle to maximize human ease and avoid interference. We validate our approach through ablation studies and real-world comparison experiments. Results demonstrate that our system improves handover success rates and provides a more user-preferred interaction experience. Videos, appendixes and more are available at https://sites.google.com/view/vlm-handover/.
Verifying Randomized Consensus Protocols with Common Coins
Venue: arXiv cs.DC 2024
Year: 2024

Randomized fault-tolerant consensus protocols with common coins are widely used in cloud computing and blockchain platforms. Due to their fundamental role, it is vital to guarantee their correctness. Threshold automata is a formal model designed for the verification of fault-tolerant consensus protocols. It has recently been extended to probabilistic threshold automata (PTAs) to verify randomized fault-tolerant consensus protocols. Nevertheless, PTA can only model randomized consensus protocols with local coins. In this work, we extend PTA to verify randomized fault-tolerant consensus protocols with common coins. Our main idea is to add a process to simulate the common coin (the so-called common-coin process). Although the addition of the common-coin process destroys the symmetry and poses technical challenges, we show how PTA can be adapted to overcome the challenges. We apply our approach to verify the agreement, validity and almost-sure termination properties of 8 randomized consensus protocols with common coins.
Convolutional Signal Propagation: A Simple Scalable Algorithm for Hypergraphs
Venue: arXiv cs.LG 2024
Year: 2024

Last decade has seen the emergence of numerous methods for learning on graphs, particularly Graph Neural Networks (GNNs). These methods, however, are often not directly applicable to more complex structures like bipartite graphs (equivalent to hypergraphs), which represent interactions among two entity types (e.g. a user liking a movie). This paper proposes Convolutional Signal Propagation (CSP), a non-parametric simple and scalable method that natively operates on bipartite graphs (hypergraphs) and can be implemented with just a few lines of code. After defining CSP, we demonstrate its relationship with well-established methods like label propagation, Naive Bayes, and Hypergraph Convolutional Networks. We evaluate CSP against several reference methods on real-world datasets from multiple domains, focusing on retrieval and classification tasks. Our results show that CSP offers competitive performance while maintaining low computational complexity, making it an ideal first choice as a baseline for hypergraph node classification and retrieval. Moreover, despite operating on hypergraphs, CSP achieves good results in tasks typically not associated with hypergraphs, such as natural language processing.
SLO-Aware Task Offloading within Collaborative Vehicle Platoons
Venue: arXiv cs.DC 2024
Year: 2024

In the context of autonomous vehicles (AVs), offloading is essential for guaranteeing the execution of perception tasks, e.g., mobile mapping or object detection. While existing work focused extensively on minimizing inter-vehicle networking latency through offloading, other objectives become relevant in the case of vehicle platoons, e.g., energy efficiency or data quality for heavy-duty or public transport. Therefore, we aim to enforce these Service Level Objectives (SLOs) through intelligent task offloading within AV platoons. We present a collaborative framework for handling and offloading services in a purely Vehicle-to-Vehicle approach (V2V) based on Bayesian Networks (BNs). Each service aggregates local observations into a platoon-wide understanding of how to ensure SLOs for heterogeneous vehicle types. With the resulting models, services can proactively decide to offload if this promises to improve global SLO fulfillment. We evaluate the approach in a real-case setting, where vehicles in a platoon continuously (i.e., every 500 ms) interpret the SLOs of three actual perception services. Our probabilistic, predictive method shows promising results in handling large AV platoons; within seconds, it detects and resolves SLO violations through offloading.
Demystifying Privacy in 5G Stand Alone Networks
Venue: arXiv cs.NI 2024
Year: 2024

Ensuring user privacy remains critical in mobile networks, particularly with the rise of connected devices and denser 5G infrastructure. Privacy concerns have persisted across 2G, 3G, and 4G/LTE networks. Recognizing these concerns, the 3rd Generation Partnership Project (3GPP) has made privacy enhancements in 5G Release 15. However, the extent of operator adoption remains unclear, especially as most networks operate in 5G Non Stand Alone (NSA) mode, relying on 4G Core Networks. This study provides the first qualitative and experimental comparison between 5G NSA and Stand Alone (SA) in real operator networks, focusing on privacy enhancements addressing top eight pre-5G attacks based on recent academic literature. Additionally, it evaluates the privacy levels of OpenAirInterface (OAI), a leading open-source software for 5G, against real network deployments for the same attacks. The analysis reveals two new 5G privacy vulnerabilities, underscoring the need for further research and stricter standards.
Optimal Sensitivity Oracle for Steiner Mincut
Venue: arXiv cs.DS 2024
Year: 2024

Let $G=(V,E)$ be an undirected weighted graph on $n=|V|$ vertices and $S\subseteq V$ be a Steiner set. Steiner mincut is a well-studied concept, which provides a generalization to both (s,t)-mincut (when $|S|=2$) and global mincut (when $|S|=n$). Here, we address the problem of designing a compact data structure that can efficiently report a Steiner mincut and its capacity after the failure of any edge in $G$; such a data structure is known as a \textit{Sensitivity Oracle} for Steiner mincut. In the area of minimum cuts, although many Sensitivity Oracles have been designed in unweighted graphs, however, in weighted graphs, Sensitivity Oracles exist only for (s,t)-mincut [Annals of Operations Research 1991, NETWORKS 2019, ICALP 2024], which is just a special case of Steiner mincut. Here, we generalize this result to any arbitrary set $S\subseteq V$. 1. Sensitivity Oracle: Assuming the capacity of every edge is known, a. there is an ${\mathcal O}(n)$ space data structure that can report the capacity of Steiner mincut in ${\mathcal O}(1)$ time and b. there is an ${\mathcal O}(n(n-|S|+1))$ space data structure that can report a Steiner mincut in ${\mathcal O}(n)$ time after the failure of any edge in $G$. 2. Lower Bound: We show that any data structure that, after the failure of any edge, can report a Steiner mincut or its capacity must occupy $Ω(n^2)$ bits of space in the worst case, irrespective of the size of the Steiner set. The lower bound in (2) shows that the assumption in (1) is essential to break the $Ω(n^2)$ lower bound on space. For $|S|=n-k$ for any constant $k\ge 0$, it occupies only ${\mathcal O}(n)$ space. So, we also present the first Sensitivity Oracle occupying ${\mathcal O}(n)$ space for global mincut.
PEDRO: Parameter-Efficient Fine-tuning with Prompt DEpenDent Representation MOdification
Venue: arXiv cs.CL 2024
Year: 2024

Due to their substantial sizes, large language models (LLMs) are typically deployed within a single-backbone multi-tenant framework. In this setup, a single instance of an LLM backbone must cater to multiple users or tasks through the application of various parameter-efficient fine-tuning (PEFT) models. Despite the availability of numerous effective PEFT techniques such as LoRA, there remains a need for a PEFT approach that achieves both high efficiency during inference and competitive performance on downstream tasks. In this research, we introduce a new and straightforward PEFT methodology named \underline{P}rompt D\underline{E}pen\underline{D}ent \underline{R}epresentation M\underline{O}dification (PEDRO). The proposed method involves integrating a lightweight vector generator into each Transformer layer, which generates vectors contingent upon the input prompts. These vectors then modify the hidden representations created by the LLM through a dot product operation, thereby influencing the semantic output and generated content of the model. Extensive experimentation across a variety of tasks indicates that: (a) PEDRO surpasses recent PEFT benchmarks when using a similar number of tunable parameters. (b) Under the single-backbone multi-tenant deployment model, PEDRO exhibits superior efficiency compared to LoRA, indicating significant industrial potential.
ReThink: Reveal the Threat of Electromagnetic Interference on Power Inverters
Venue: arXiv cs.CR 2024
Year: 2024

With the boom of renewable energy sources (RES), the number of power inverters proliferates. Power inverters are the key electronic devices that transform the direct current (DC) power from RES to the alternating current (AC) power on the grids, and their security can affect the stable operation of RES and even power grids. This paper analyzes the security of photovoltaic (PV) inverters from the aspects of internal sensors since they serve as the foundation for safe power conversion. We discover that both the embedded current sensors and voltage sensors are vulnerable to electromagnetic interference (EMI) of 1 GHz or higher, despite electromagnetic compatibility (EMC) countermeasures. Such vulnerabilities can lead to incorrect measurements and deceiving the control algorithms, and we design ReThink that could produce three types of consequences on PV inverters by emitting carefully crafted EMI, i.e., Denial of Service (DoS), damaging inverters physically or damping the power output. We successfully validate these consequences on 5 off-the-shelf PV inverters, and even in a real-world microgrid, by transmitting EMI signals at a distance of 100-150cm and a total power within 20W. Our work aims to raise awareness of the security of power electronic devices of RES, as they represent an emerging Cyber-Physical attack surface to the future RES-dominated grid. Finally, to cope with such threats, we provide hardware and software-based countermeasures.
Multi-UAV Enabled MEC Networks: Optimizing Delay through Intelligent 3D Trajectory Planning and Resource Allocation
Venue: arXiv cs.MA 2024
Year: 2024

Mobile Edge Computing (MEC) reduces the computational burden on terminal devices by shortening the distance between these devices and computing nodes. Integrating Unmanned Aerial Vehicles (UAVs) with enhanced MEC networks can leverage the high mobility of UAVs to flexibly adjust network topology, further expanding the applicability of MEC. However, in highly dynamic and complex real-world environments, it is crucial to balance task offloading effectiveness with algorithm performance. This paper investigates a multi-UAV communication network equipped with edge computing nodes to assist terminal users in task computation. Our goal is to reduce the task processing delay for users through the joint optimization of discrete computation modes, continuous 3D trajectories, and resource assignment. To address the challenges posed by the mixed action space, we propose a Multi-UAV Edge Computing Resource Scheduling (MUECRS) algorithm, which comprises two key components: 1) trajectory optimization, and 2) computation mode and resource management. Experimental results demonstrate our method effectively designs the 3D flight trajectories of UAVs, enabling rapid terminal coverage. Furthermore, the proposed algorithm achieves efficient resource deployment and scheduling, outperforming comparative algorithms by at least 16.7%, demonstrating superior adaptability and robustness.