OpenRaiser/Intern-Atlas huggingface dataset:
Unification of Secret Key Generation and Wiretap Channel Transmission
Venue: arXiv cs.IT 2024
Year: 2024

This paper presents further insights into a recently developed round-trip communication scheme called ``Secret-message Transmission by Echoing Encrypted Probes (STEEP)''. A legitimate wireless channel between a multi-antenna user (Alice) and a single-antenna user (Bob) in the presence of a multi-antenna eavesdropper (Eve) is focused on. STEEP does not require full-duplex, channel reciprocity or Eve's channel state information, but is able to yield a positive secrecy rate in bits per channel use between Alice and Bob in every channel coherence period as long as Eve's receive channel is not noiseless. This secrecy rate does not diminish as coherence time increases. Various statistical behaviors of STEEP's secrecy capacity due to random channel fading are also illustrated.
Prediction of Wort Density with LSTM Network
Venue: arXiv cs.LG 2024
Year: 2024

Many physical target values in technical processes are error-prone, cumbersome, or expensive to measure automatically. One example of a physical target value is the wort density, which is an important value needed for beer production. This article introduces a system that helps the brewer measure wort density through sensors in order to reduce errors in manual data collection. Instead of a direct measurement of wort density, a method is developed that calculates the density from measured values acquired by inexpensive standard sensors such as pressure or temperature. The model behind the calculation is a neural network, known as LSTM.
Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learning
Venue: arXiv cs.LG 2024
Year: 2024

Decentralized and lifelong-adaptive multi-agent collaborative learning aims to enhance collaboration among multiple agents without a central server, with each agent solving varied tasks over time. To achieve efficient collaboration, agents should: i) autonomously identify beneficial collaborative relationships in a decentralized manner; and ii) adapt to dynamically changing task observations. In this paper, we propose DeLAMA, a decentralized multi-agent lifelong collaborative learning algorithm with dynamic collaboration graphs. To promote autonomous collaboration relationship learning, we propose a decentralized graph structure learning algorithm, eliminating the need for external priors. To facilitate adaptation to dynamic tasks, we design a memory unit to capture the agents' accumulated learning history and knowledge, while preserving finite storage consumption. To further augment the system's expressive capabilities and computational efficiency, we apply algorithm unrolling, leveraging the advantages of both mathematical optimization and neural networks. This allows the agents to `learn to collaborate' through the supervision of training tasks. Our theoretical analysis verifies that inter-agent collaboration is communication efficient under a small number of communication rounds. The experimental results verify its ability to facilitate the discovery of collaboration strategies and adaptation to dynamic learning scenarios, achieving a 98.80% reduction in MSE and a 188.87% improvement in classification accuracy. We expect our work can serve as a foundational technique to facilitate future works towards an intelligent, decentralized, and dynamic multi-agent system. Code is available at https://github.com/ShuoTang123/DeLAMA.
Solving the p-Riccati Equations and Applications to the Factorisation of Differential Operators
Venue: arXiv cs.SC 2024
Year: 2024

The solutions of the equation f^{ (p--1) }+ f^p = h^p in the unknown function f overan algebraic function field of characteristic p are very closely linked to the structure and fac-torisations of linear differential operators with coefficients in function fields of characteristic p.However, while being able to solve this equation over general algebraic function fields is necessaryeven for operators with rational coefficients, no general resolution method has been developed.We present an algorithm for testing the existence of solutions in polynomial time in the ``size''of h and an algorithm based on the computation of Riemann-Roch spaces and the selection ofelements in the divisor class group, for computing solutions of size polynomial in the ``size'' of hin polynomial time in the size of h and linear in the characteristic p, and discuss its applicationsto the factorisation of linear differential operators in positive characteristic p.
Tail Optimality and Performance Analysis of the Nudge-M Scheduling Algorithm
Venue: arXiv cs.PF 2024
Year: 2024

Recently it was shown that the response time of First-Come-First-Served (FCFS) scheduling can be stochastically and asymptotically improved upon by the {\it Nudge} scheduling algorithm in case of light-tailed job size distributions. Such improvements are feasible even when the jobs are partitioned into two types and the scheduler only has information about the type of incoming jobs (but not their size). In this paper we introduce Nudge-$M$ scheduling, where basically any incoming type-1 job is allowed to pass any type-2 job that is still waiting in the queue given that it arrived as one of the last $M$ jobs. We prove that Nudge-$M$ has an asymptotically optimal response time within a large family of Nudge scheduling algorithms when job sizes are light-tailed. Simple explicit results for the asymptotic tail improvement ratio (ATIR) of Nudge-$M$ over FCFS are derived as well as explicit results for the optimal parameter $M$. An expression for the ATIR that only depends on the type-1 and type-2 mean job sizes and the fraction of type-1 jobs is presented in the heavy traffic setting. The paper further presents a numerical method to compute the response time distribution and mean response time of Nudge-$M$ scheduling provided that the job size distribution of both job types follows a phase-type distribution (by making use of the framework of Markov modulated fluid queues with jumps).
Authorship and the Politics and Ethics of LLM Watermarks
Venue: arXiv cs.CY 2024
Year: 2024

Recently, watermarking schemes for large language models (LLMs) have been proposed to distinguish text generated by machines and by humans. The present paper explores philosophical, political, and ethical ramifications of implementing and using watermarking schemes. A definition of authorship that includes both machines (LLMs) and humans is proposed to serve as a backdrop. It is argued that private watermarks may provide private companies with sweeping rights to determine authorship, which is incompatible with traditional standards of authorship determination. Then, possible ramifications of the so-called entropy dependence of watermarking mechanisms are explored. It is argued that entropy may vary for different, socially salient groups. This could lead to group dependent rates at which machine generated text is detected. Specifically, groups more interested in low entropy text may face the challenge that it is harder to detect machine generated text that is of interest to them.
BEV$^2$PR: BEV-Enhanced Visual Place Recognition with Structural Cues
Venue: arXiv cs.CV 2024
Year: 2024

In this paper, we propose a new image-based visual place recognition (VPR) framework by exploiting the structural cues in bird's-eye view (BEV) from a single monocular camera. The motivation arises from two key observations about place recognition methods based on both appearance and structure: 1) For the methods relying on LiDAR sensors, the integration of LiDAR in robotic systems has led to increased expenses, while the alignment of data between different sensors is also a major challenge. 2) Other image-/camera-based methods, involving integrating RGB images and their derived variants (eg, pseudo depth images, pseudo 3D point clouds), exhibit several limitations, such as the failure to effectively exploit the explicit spatial relationships between different objects. To tackle the above issues, we design a new BEV-enhanced VPR framework, namely BEV$^2$PR, generating a composite descriptor with both visual cues and spatial awareness based on a single camera. The key points lie in: 1) We use BEV features as an explicit source of structural knowledge in constructing global features. 2) The lower layers of the pre-trained backbone from BEV generation are shared for visual and structural streams in VPR, facilitating the learning of fine-grained local features in the visual stream. 3) The complementary visual and structural features can jointly enhance VPR performance. Our BEV$^2$PR framework enables consistent performance improvements over several popular aggregation modules for RGB global features. The experiments on our collected VPR-NuScenes dataset demonstrate an absolute gain of 2.47% on Recall@1 for the strong Conv-AP baseline to achieve the best performance in our setting, and notably, a 18.06% gain on the hard set. The code and dataset will be available at https://github.com/FudongGe/BEV2PR.
MedKP: Medical Dialogue with Knowledge Enhancement and Clinical Pathway Encoding
Venue: arXiv cs.CL 2024
Year: 2024

With appropriate data selection and training techniques, Large Language Models (LLMs) have demonstrated exceptional success in various medical examinations and multiple-choice questions. However, the application of LLMs in medical dialogue generation-a task more closely aligned with actual medical practice-has been less explored. This gap is attributed to the insufficient medical knowledge of LLMs, which leads to inaccuracies and hallucinated information in the generated medical responses. In this work, we introduce the Medical dialogue with Knowledge enhancement and clinical Pathway encoding (MedKP) framework, which integrates an external knowledge enhancement module through a medical knowledge graph and an internal clinical pathway encoding via medical entities and physician actions. Evaluated with comprehensive metrics, our experiments on two large-scale, real-world online medical consultation datasets (MedDG and KaMed) demonstrate that MedKP surpasses multiple baselines and mitigates the incidence of hallucinations, achieving a new state-of-the-art. Extensive ablation studies further reveal the effectiveness of each component of MedKP. This enhancement advances the development of reliable, automated medical consultation responses using LLMs, thereby broadening the potential accessibility of precise and real-time medical assistance.
Self-Sovereign Identity for Electric Vehicle Charging
Venue: arXiv cs.CR 2024
Year: 2024

Electric Vehicles (EVs) are more and more charged at public Charge Points (CPs) using Plug-and-Charge (PnC) protocols such as the ISO 15118 standard which eliminates user interaction for authentication and authorization. Currently, this requires a rather complex Public Key Infrastructure (PKI) and enables driver tracking via the included unique identifiers. In this paper, we propose an approach for using Self-Sovereign Identities (SSIs) as trusted credentials for EV charging authentication and authorization which overcomes the privacy problems and the issues of a complex centralized PKI. Our implementation shows the feasibility of our approach with ISO 15118. The security and privacy of the proposed approach is shown in a formal analysis using the Tamarin prover.
Optimal Bounds for Distinct Quartics
Venue: arXiv cs.DS 2024
Year: 2024

A fundamental concept related to strings is that of repetitions. It has been extensively studied in many versions, from both purely combinatorial and algorithmic angles. One of the most basic questions is how many distinct squares, i.e., distinct strings of the form $UU$, a string of length $n$ can contain as fragments. It turns out that this is always $\mathcal{O}(n)$, and the bound cannot be improved to sublinear in $n$ [Fraenkel and Simpson, JCTA 1998]. Several similar questions about repetitions in strings have been considered, and by now we seem to have a good understanding of their repetitive structure. For higher-dimensional strings, the basic concept of periodicity has been successfully extended and applied to design efficient algorithms -- it is inherently more complex than for regular strings. Extending the notion of repetitions and understanding the repetitive structure of higher-dimensional strings is however far from complete. Quartics were introduced by Apostolico and Brimkov [TCS 2000] as analogues of squares in two dimensions. Charalampopoulos, Radoszewski, Rytter, Waleń, and Zuba [ESA 2020] proved that the number of distinct quartics in an $n\times n$ 2D string is $\mathcal{O}(n^2 \log^2 n)$ and that they can be computed in $\mathcal{O}(n^2 \log^2 n)$ time. Gawrychowski, Ghazawi, and Landau [SPIRE 2021] constructed an infinite family of $n \times n$ 2D strings with $Ω(n^2 \log n)$ distinct quartics. This brings the challenge of determining asymptotically tight bounds. Here, we settle both the combinatorial and the algorithmic aspects of this question: the number of distinct quartics in an $n\times n$ 2D string is $\mathcal{O}(n^2 \log n)$ and they can be computed in the worst-case optimal $\mathcal{O}(n^2 \log n)$ time.
Performance of Expansive Soil Stabilized with Bamboo Charcoal, Quarry Dust, and Lime for Use as Road Subgrade Material
Venue: arXiv cs.CE 2024
Year: 2024

Expansive soils such as Black Cotton Soils (BCS) present significant challenges for road subgrade construction due to their high plasticity, swelling potential, and low strength. This study explores a triphasic stabilization method using Bamboo Charcoal (BC), Quarry Dust (QD), and Lime (L) to enhance the engineering properties of BCS for rural road applications. Initial soil characterization involved standard tests, including Atterberg limits, compaction, and Californian Bearing Ratio (CBR) assessments. The soil was treated with varying BC proportions (5% to 35% at 5% intervals) in the initial phase, leading to a progressive reduction in the Plasticity Index (PI) and swell index and an enhancement in the CBR up to 20% BC content. This further resulted in a soaked CBR value of 2.7%. In the second phase, additional treatment combined with BC and QD, incorporating diverse QD proportions (4% to 24%) relative to the optimal BC content. This further improved the CBR to 7.7% at 12% QD, but the PI exhibited a non-linear trend. Finally, 5% lime was introduced in the final phase. This minimized the PI to 11.2% and significantly increased the CBR to 19%. The optimal combination of 20% BC, 12% QD, and 5% Lime achieved optimal plasticity, compaction, and strength characteristics, demonstrating the viability of this approach for transforming BCS into a sustainable and cost-effective alternative for rural road subgrade construction.
Poisoning Programs by Un-Repairing Code: Security Concerns of AI-generated Code
Venue: arXiv cs.CR 2024
Year: 2024

AI-based code generators have gained a fundamental role in assisting developers in writing software starting from natural language (NL). However, since these large language models are trained on massive volumes of data collected from unreliable online sources (e.g., GitHub, Hugging Face), AI models become an easy target for data poisoning attacks, in which an attacker corrupts the training data by injecting a small amount of poison into it, i.e., astutely crafted malicious samples. In this position paper, we address the security of AI code generators by identifying a novel data poisoning attack that results in the generation of vulnerable code. Next, we devise an extensive evaluation of how these attacks impact state-of-the-art models for code generation. Lastly, we discuss potential solutions to overcome this threat.
The Ouroboros of Memristors: Neural Networks Facilitating Memristor Programming
Venue: arXiv cs.ET 2024
Year: 2024

Memristive devices hold promise to improve the scale and efficiency of machine learning and neuromorphic hardware, thanks to their compact size, low power consumption, and the ability to perform matrix multiplications in constant time. However, on-chip training with memristor arrays still faces challenges, including device-to-device and cycle-to-cycle variations, switching non-linearity, and especially SET and RESET asymmetry. To combat device non-linearity and asymmetry, we propose to program memristors by harnessing neural networks that map desired conductance updates to the required pulse times. With our method, approximately 95% of devices can be programmed within a relative percentage difference of +-50% from the target conductance after just one attempt. Our approach substantially reduces memristor programming delays compared to traditional write-and-verify methods, presenting an advantageous solution for on-chip training scenarios. Furthermore, our proposed neural network can be accelerated by memristor arrays upon deployment, providing assistance while reducing hardware overhead compared with previous works. This work contributes significantly to the practical application of memristors, particularly in reducing delays in memristor programming. It also envisions the future development of memristor-based machine learning accelerators.
Societal and scientific impact of policy research: A large-scale empirical study of some explanatory factors using Altmetric and Overton
Venue: arXiv cs.DL 2024
Year: 2024

This study investigates how scientific research influences policymaking by analyzing citations of research articles in policy documents (policy impact) for nearly 125,000 articles across 434 public policy journals. We reveal distinct citation patterns between policymakers and other stakeholders like researchers, journalists, and the public. News and blog mentions, social media engagement, and open access publications (excluding fully open access) significantly increase the likelihood of a research article being cited in policy documents. Conversely, articles locked behind paywalls and those published under the full open access model (based on Altmetric data) have a lower chance of being policy-cited. Publication year and policy type show no significant influence. Our findings emphasize the crucial role of science communication channels like news media and social media in bridging the gap between research and policy. Interestingly, academic citations hold a weaker influence on policy citations compared to news mentions, suggesting a potential disconnect between how researchers reference research and how policymakers utilize it. This highlights the need for improved communication strategies to ensure research informs policy decisions more effectively. This study provides valuable insights for researchers, policymakers, and science communicators. Researchers can tailor their dissemination efforts to reach policymakers through media channels. Policymakers can leverage these findings to identify research with higher policy relevance. Science communicators can play a critical role in translating research for policymakers and fostering dialogue between the scientific and policymaking communities.
Unprotected 4G/5G Control Procedures at Low Layers Considered Dangerous
Venue: arXiv cs.CR 2024
Year: 2024

Over the years, several security vulnerabilities in the 3GPP cellular systems have been demonstrated in the literature. Most studies focus on higher layers of the cellular radio stack, such as the RRC and NAS, which are cryptographically protected. However, lower layers of the stack, such as PHY and MAC, are not as thoroughly studied, even though they are neither encrypted nor integrity protected. Furthermore, the latest releases of 5G significantly increased the number of low-layer control messages and procedures. The complexity of the cellular standards and the high degree of cross-layer operations, makes reasoning about security non-trivial, and requires a systematic analysis. We study the control procedures carried by each physical channel, and find that current cellular systems are susceptible to several new passive attacks due to information leakage, and active attacks by injecting MAC and PHY messages. For instance, we find that beamforming information leakage enables fingerprinting-based localization and tracking of users. We identify active attacks that reduce the users' throughput by disabling RF front ends at the UE, disrupt user communications by tricking other connected UEs into acting as jammers, or stealthily disconnect an active user. We evaluate our attacks against COTS UEs in various scenarios and demonstrate their practicality by measuring current operators' configurations across three countries. Our results show that an attacker can, among other things, localize users with an accuracy of 20 meters 96% of the time, track users' moving paths with a probability of 90%, reduce throughput by more than 95% within 2 seconds (by spoofing a 39 bits DCI), and disconnect users.
ACT-MNMT Auto-Constriction Turning for Multilingual Neural Machine Translation
Venue: arXiv cs.CL 2024
Year: 2024

Large language model (LLM) has achieved promising performance in multilingual machine translation tasks through zero/few-shot prompts or prompt-tuning. However, due to the mixture of multilingual data during the pre-training of LLM, the LLM-based translation models face the off-target issue in both prompt-based methods, including a series of phenomena, namely instruction misunderstanding, translation with wrong language and over-generation. For this issue, this paper introduces an \textbf{\underline{A}}uto-\textbf{\underline{C}}onstriction \textbf{\underline{T}}urning mechanism for \textbf{\underline{M}}ultilingual \textbf{\underline{N}}eural \textbf{\underline{M}}achine \textbf{\underline{T}}ranslation (\model), which is a novel supervised fine-tuning mechanism and orthogonal to the traditional prompt-based methods. In this method, \model automatically constructs a constrained template in the target side by adding trigger tokens ahead of the ground truth. Furthermore, trigger tokens can be arranged and combined freely to represent different task semantics, and they can be iteratively updated to maximize the label likelihood. Experiments are performed on WMT test sets with multiple metrics, and the experimental results demonstrate that \model achieves substantially improved performance across multiple translation directions and reduce the off-target phenomena in the translation.
Born to Run, Programmed to Play: Mapping the Extended Reality Exergames Landscape
Venue: arXiv cs.HC 2024
Year: 2024

Many people struggle to exercise regularly, raising the risk of serious health-related issues. Extended reality (XR) exergames address these hurdles by combining physical exercises with enjoyable, immersive gameplay. While a growing body of research explores XR exergames, no previous review has structured this rapidly expanding research landscape. We conducted a scoping review of the current state of XR exergame research to (i) provide a structured overview, (ii) highlight trends, and (iii) uncover knowledge gaps. After identifying 1318 papers in human-computer interaction and medical databases, we ultimately included 186 papers in our analysis. We provide a quantitative and qualitative summary of XR exergame research, showing current trends and potential future considerations. Finally, we provide a taxonomy of XR exergames to help future design and methodological investigation and reporting.
SPLADE-v3: New baselines for SPLADE
Venue: arXiv cs.IR 2024
Year: 2024

A companion to the release of the latest version of the SPLADE library. We describe changes to the training structure and present our latest series of models -- SPLADE-v3. We compare this new version to BM25, SPLADE++, as well as re-rankers, and showcase its effectiveness via a meta-analysis over more than 40 query sets. SPLADE-v3 further pushes the limit of SPLADE models: it is statistically significantly more effective than both BM25 and SPLADE++, while comparing well to cross-encoder re-rankers. Specifically, it gets more than 40 MRR@10 on the MS MARCO dev set, and improves by 2% the out-of-domain results on the BEIR benchmark.