OpenRaiser/Intern-Atlas huggingface dataset:
dabih -- encrypted data storage and sharing platform
Venue: arXiv cs.CR 2024
Year: 2024

Background: The secure management of sensitive clinical data, particularly human genomics data, has become a critical requirement in modern biomedical research. Although the necessary software and algorithms are readily available, their use by non-IT experts poses significant challenges. Methods: We developed dabih, an open-source web application specifically designed to facilitate user-friendly encrypted data management. dabih enables web-based uploading, storing, sharing, and downloading of sensitive data in any format. Its approach to data security involves a two-stage envelope encryption process. We combine symmetric-key encryption for data and public-key encryption as key encapsulation mechanism. The private key necessary for decrypting the data remains exclusively on the owner's device. Thus, accessing data is impossible without explicit permission from the keyholder. Results: dabih is available open-source on GitHub https://github.com/spang-lab/dabih, as ready to use containers on docker hub and includes a command line interface and a graphical bulk upload tool as pre-built binaries. Documentation is available as part of the web application. Conclusions: dabih enables everyone to use strong cryptography for their data, while being just as simple to use as other, non-encrypted, data storage solutions. All the cryptography occurs seamlessly in the background as users interact with a secure web portal, simply by dragging and dropping files.
AdaSem: Adaptive Goal-Oriented Semantic Communications for End-to-End Camera Relocalization
Venue: arXiv cs.IT 2024
Year: 2024

Recently, deep autoencoders have gained traction as a powerful method for implementing goal-oriented semantic communications systems. The idea is to train a mapping from the source domain directly to channel symbols, and vice versa. However, prior studies often focused on rate-distortion tradeoff and transmission delay, at the cost of increasing end-to-end complexity and thus latency. Moreover, the datasets used are often not reflective of real-world environments, and the results were not validated against real-world baseline systems, leading to an unfair comparison. In this paper, we study the problem of remote camera pose estimation and propose AdaSem, an adaptive semantic communications approach that optimizes the tradeoff between inference accuracy and end-to-end latency. We develop an adaptive semantic codec model, which encodes the source data into a dynamic number of symbols, based on the latent space distribution and the channel state feedback. We utilize a lightweight model for both transmitter and receiver to ensure comparable complexity to the baseline implemented in a real-world system. Extensive experiments on real-environment data show the effectiveness of our approach. When compared to a real implementation of a client-server camera relocalization service, AdaSem outperforms the baseline by reducing the end-to-end delay and estimation error by over 75% and 63%, respectively.
Morphology and Syntax of the Tamil Language
Venue: arXiv cs.CL 2024
Year: 2024

This paper provides an overview of the morphology and syntax of the Tamil language, focusing on its contemporary usage. The paper also highlights the complexity and richness of Tamil in terms of its morphological and syntactic features, which will be useful for linguists analysing the language and conducting comparative studies. In addition, the paper will be useful for those developing computational resources for the Tamil language. It is proven as a rule-based morphological analyser cum generator and a computational grammar for Tamil have already been developed based on this paper. To enhance accessibility for a broader audience, the analysis is conducted without relying on any specific grammatical formalism.
Cross-lingual neural fuzzy matching for exploiting target-language monolingual corpora in computer-aided translation
Venue: arXiv cs.CL 2024
Year: 2024

Computer-aided translation (CAT) tools based on translation memories (MT) play a prominent role in the translation workflow of professional translators. However, the reduced availability of in-domain TMs, as compared to in-domain monolingual corpora, limits its adoption for a number of translation tasks. In this paper, we introduce a novel neural approach aimed at overcoming this limitation by exploiting not only TMs, but also in-domain target-language (TL) monolingual corpora, and still enabling a similar functionality to that offered by conventional TM-based CAT tools. Our approach relies on cross-lingual sentence embeddings to retrieve translation proposals from TL monolingual corpora, and on a neural model to estimate their post-editing effort. The paper presents an automatic evaluation of these techniques on four language pairs that shows that our approach can successfully exploit monolingual texts in a TM-based CAT environment, increasing the amount of useful translation proposals, and that our neural model for estimating the post-editing effort enables the combination of translation proposals obtained from monolingual corpora and from TMs in the usual way. A human evaluation performed on a single language pair confirms the results of the automatic evaluation and seems to indicate that the translation proposals retrieved with our approach are more useful than what the automatic evaluation shows.
Deep Learning-based Group Causal Inference in Multivariate Time-series
Venue: arXiv cs.LG 2024
Year: 2024

Causal inference in a nonlinear system of multivariate timeseries is instrumental in disentangling the intricate web of relationships among variables, enabling us to make more accurate predictions and gain deeper insights into real-world complex systems. Causality methods typically identify the causal structure of a multivariate system by considering the cause-effect relationship of each pair of variables while ignoring the collective effect of a group of variables or interactions involving more than two-time series variables. In this work, we test model invariance by group-level interventions on the trained deep networks to infer causal direction in groups of variables, such as climate and ecosystem, brain networks, etc. Extensive testing with synthetic and real-world time series data shows a significant improvement of our method over other applied group causality methods and provides us insights into real-world time series. The code for our method can be found at:https://github.com/wasimahmadpk/gCause.
Ask the experts: sourcing high-quality datasets for nutritional counselling through Human-AI collaboration
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs), with their flexible generation abilities, can be powerful data sources in domains with few or no available corpora. However, problems like hallucinations and biases limit such applications. In this case study, we pick nutrition counselling, a domain lacking any public resource, and show that high-quality datasets can be gathered by combining LLMs, crowd-workers and nutrition experts. We first crowd-source and cluster a novel dataset of diet-related issues, then work with experts to prompt ChatGPT into producing related supportive text. Finally, we let the experts evaluate the safety of the generated text. We release HAI-coaching, the first expert-annotated nutrition counselling dataset containing ~2.4K dietary struggles from crowd workers, and ~97K related supportive texts generated by ChatGPT. Extensive analysis shows that ChatGPT while producing highly fluent and human-like text, also manifests harmful behaviours, especially in sensitive topics like mental health, making it unsuitable for unsupervised use.
Autonomous Multiple-Trolley Collection System with Nonholonomic Robots: Design, Control, and Implementation
Venue: arXiv cs.RO 2024
Year: 2024

The intricate and multi-stage task in dynamic public spaces like luggage trolley collection in airports presents both a promising opportunity and an ongoing challenge for automated service robots. Previous research has primarily focused on handling a single trolley or individual functional components, creating a gap in providing cost-effective and efficient solutions for practical scenarios. In this paper, we propose a mobile manipulation robot incorporated with an autonomy framework for the collection and transportation of multiple trolleys that can significantly enhance operational efficiency. We address the key challenges in the trolley collection problem through the novel design of the mechanical system and the vision-based control strategy. We design a lightweight manipulator and docking mechanism, optimized for the sequential stacking and transportation of multiple trolleys. Additionally, based on the Control Lyapunov Function and Control Barrier Function, we propose a novel vision-based control with the online Quadratic Programming which significantly improves the accuracy and efficiency of the collection process. The practical application of our system is demonstrated in real world scenarios, where it successfully executes multiple-trolley collection tasks.
Centralized vs. Decoupled Dual-Arm Planning Taking into Account Path Quality
Venue: arXiv cs.RO 2024
Year: 2024

The aim of coordinated planning is to avoid robot-to-robot collisions in a multi-robot system, and there are two standard solution approaches: centralized planning and decoupled planning. Our first contribution is a decoupled planning approach that ensures C2-continuous control commands with zero velocities at the start and goal. We benchmark our decoupled approach with a centralized approach. Contrary to literature, we show that for a standard motion planning pipeline, such as the one used by MoveIt!, centralized planning is superior to decoupled planning in dual-arm manipulation: It has a lower computation time and a higher robustness. Our second contribution is an optimization that minimizes the rotational motion of an end-effector while considering obstacle avoidance. We derive the analytic gradients of this optimization problem, making the algorithm suitable for online motion planning. Our optimization extends an existing path quality improvement method. Integrating it into our decoupled approach overcomes its shortcomings and provides a motion planning pipeline that is robust at up to 99.9% with a planning time of less than 1s and that computes high-quality paths.
Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems
Venue: arXiv cs.IR 2024
Year: 2024

The hyperparameters of recommender systems for top-n predictions are typically optimized to enhance the predictive performance of algorithms. Thereby, the optimization algorithm, e.g., grid search or random search, searches for the best hyperparameter configuration according to an optimization-target metric, like nDCG or Precision. In contrast, the optimized algorithm, internally optimizes a different loss function during training, like squared error or cross-entropy. To tackle this discrepancy, recent work focused on generating loss functions better suited for recommender systems. Yet, when evaluating an algorithm using a top-n metric during optimization, another discrepancy between the optimization-target metric and the training loss has so far been ignored. During optimization, the top-n items are selected for computing a top-n metric; ignoring that the top-n items are selected from the recommendations of a model trained with an entirely different loss function. Item recommendations suitable for optimization-target metrics could be outside the top-n recommended items; hiddenly impacting the optimization performance. Therefore, we were motivated to analyze whether the top-n items are optimal for optimization-target top-n metrics. In pursuit of an answer, we exhaustively evaluate the predictive performance of 250 selection strategies besides selecting the top-n. We extensively evaluate each selection strategy over twelve implicit feedback and eight explicit feedback data sets with eleven recommender systems algorithms. Our results show that there exist selection strategies other than top-n that increase predictive performance for various algorithms and recommendation domains. However, the performance of the top ~43% of selection strategies is not significantly different. We discuss the impact of our findings on optimization and re-ranking in recommender systems and feasible solutions.
Monitoring the development of CFD applications on unstable HPC platforms
Venue: arXiv cs.DC 2024
Year: 2024

We tackle the challenging tasks of monitoring on unstable HPC platforms the performance of CFD applications all along their development. We have designed and implemented a monitoring framework, integrated at the end of a CI-CD pipeline. Measures retrieved during the automatic execution of production simulations are analyzed within a visual analytics interface we developed, providing advanced visualizations and interaction. We have validated this approach by monitoring the CFD code Alya over two years, detecting and resolving issues related to the platform, and highlighting performance improvement.
Submodule approach to creative telescoping
Venue: arXiv cs.SC 2024
Year: 2024

This paper proposes ideas to speed up the process of creative telescoping, particularly when the telescoper is reducible. One can interpret telescoping as computing an annihilator $L \in D$ for an element $m$ in a $D$-module $M$. The main idea is to look for submodules of $M$. If $N$ is a non-trivial submodule of $M$, constructing the minimal operator $R$ of the image of $m$ in $M/N$ gives a right-factor of $L$ in $D$. Then $L = L' R$ where the left-factor $L'$ is the telescoper of $R(m) \in N$. To expedite computing $L'$, compute the action of $D$ on a natural basis of $N$, then obtain $L'$ with a cyclic vector computation. The next main idea is that when $N$ has automorphisms, use them to construct submodules. An automorphism with distinct eigenvalues can be used to decompose $N$ as a direct sum $N_1 \oplus \cdots \oplus N_k$. Then $L'$ is the LCLM (Least Common Left Multiple) of $L_1, \ldots, L_k$ where $L_i$ is the telescoper of the projection of $R(m)$ on $N_i$. An LCLM can greatly increase the degrees of coefficients, so $L'$ and $L$ can be much larger expressions than the factors $L_1,\ldots,L_k$ and $R$. Examples show that computing each factor $L_i$ and $R$ seperately can save a lot of CPU time compared to computing $L$ in expanded form with standard creative telescoping.
Incentivizing Secure Software Development: the Role of Voluntary Audit and Liability Waiver
Venue: arXiv cs.CR 2024
Year: 2024

Misaligned incentives in secure software development have long been the focus of research in the economics of security. Product liability, a powerful legal framework in other industries, has been largely ineffective for software products until recent times. However, the rapid regulatory responses to recent global cyber attacks by both the United States and the European Union, together with the (relative) success of the General Data Protection Regulation in defining both duty and standard of care for software vendors, may enable regulators to use liability to re-align incentives for the benefit of the digital society. Specifically, the recent United States National Cybersecurity Strategy suggests shifting responsibility for cyber incidents back to software vendors. In doing so, the strategy also puts forward the concept of the liability waiver: if a software company voluntarily undergoes and passes an IT security audit, its future product liability is (fully or partially) waived. In this paper, we analyze this audit scenario from the perspective of the software vendor and the auditor, respectively. From the vendor's view, this is formulated as a sequential decision problem: a vendor with a product or process needs to pass a mandatory audit to release the product onto the market; it is allowed to go through the audit repeatedly, and thus the vendor needs to determine what level of effort to put into the product following each failed test. We show that the optimal strategy for an opt-in vendor is to never quit and to exert cumulative investments in either a ``one-and-done'' or ``incremental'' manner. From the auditor's view, we examine what type of audit might be the most effective in incentivizing voluntary participation and, at the same time, a more desirable effort from the vendor. We also showed how dynamic audits can be exploited to increase the vendor's incentivizable investment.
Solving Continual Offline Reinforcement Learning with Decision Transformer
Venue: arXiv cs.LG 2024
Year: 2024

Continuous offline reinforcement learning (CORL) combines continuous and offline reinforcement learning, enabling agents to learn multiple tasks from static datasets without forgetting prior tasks. However, CORL faces challenges in balancing stability and plasticity. Existing methods, employing Actor-Critic structures and experience replay (ER), suffer from distribution shifts, low efficiency, and weak knowledge-sharing. We aim to investigate whether Decision Transformer (DT), another offline RL paradigm, can serve as a more suitable offline continuous learner to address these issues. We first compare AC-based offline algorithms with DT in the CORL framework. DT offers advantages in learning efficiency, distribution shift mitigation, and zero-shot generalization but exacerbates the forgetting problem during supervised parameter updates. We introduce multi-head DT (MH-DT) and low-rank adaptation DT (LoRA-DT) to mitigate DT's forgetting problem. MH-DT stores task-specific knowledge using multiple heads, facilitating knowledge sharing with common components. It employs distillation and selective rehearsal to enhance current task learning when a replay buffer is available. In buffer-unavailable scenarios, LoRA-DT merges less influential weights and fine-tunes DT's decisive MLP layer to adapt to the current task. Extensive experiments on MoJuCo and Meta-World benchmarks demonstrate that our methods outperform SOTA CORL baselines and showcase enhanced learning capabilities and superior memory efficiency.
Large Language Models Portray Socially Subordinate Groups as More Homogeneous, Consistent with a Bias Observed in Humans
Venue: arXiv cs.CL 2024
Year: 2024

Large language models (LLMs) are becoming pervasive in everyday life, yet their propensity to reproduce biases inherited from training data remains a pressing concern. Prior investigations into bias in LLMs have focused on the association of social groups with stereotypical attributes. However, this is only one form of human bias such systems may reproduce. We investigate a new form of bias in LLMs that resembles a social psychological phenomenon where socially subordinate groups are perceived as more homogeneous than socially dominant groups. We had ChatGPT, a state-of-the-art LLM, generate texts about intersectional group identities and compared those texts on measures of homogeneity. We consistently found that ChatGPT portrayed African, Asian, and Hispanic Americans as more homogeneous than White Americans, indicating that the model described racial minority groups with a narrower range of human experience. ChatGPT also portrayed women as more homogeneous than men, but these differences were small. Finally, we found that the effect of gender differed across racial/ethnic groups such that the effect of gender was consistent within African and Hispanic Americans but not within Asian and White Americans. We argue that the tendency of LLMs to describe groups as less diverse risks perpetuating stereotypes and discriminatory behavior.
Supporting Student Decisions on Learning Recommendations: An LLM-Based Chatbot with Knowledge Graph Contextualization for Conversational Explainability and Mentoring
Venue: arXiv cs.AI 2024
Year: 2024

Student commitment towards a learning recommendation is not separable from their understanding of the reasons it was recommended to them; and their ability to modify it based on that understanding. Among explainability approaches, chatbots offer the potential to engage the student in a conversation, similar to a discussion with a peer or a mentor. The capabilities of chatbots, however, are still not sufficient to replace a human mentor, despite the advancements of generative AI (GenAI) and large language models (LLM). Therefore, we propose an approach to utilize chatbots as mediators of the conversation and sources of limited and controlled generation of explanations, to harvest the potential of LLMs while reducing their potential risks at the same time. The proposed LLM-based chatbot supports students in understanding learning-paths recommendations. We use a knowledge graph (KG) as a human-curated source of information, to regulate the LLM's output through defining its prompt's context. A group chat approach is developed to connect students with human mentors, either on demand or in cases that exceed the chatbot's pre-defined tasks. We evaluate the chatbot with a user study, to provide a proof-of-concept and highlight the potential requirements and limitations of utilizing chatbots in conversational explainability.
SecPLF: Secure Protocols for Loanable Funds against Oracle Manipulation Attacks
Venue: arXiv cs.CR 2024
Year: 2024

The evolving landscape of Decentralized Finance (DeFi) has raised critical security concerns, especially pertaining to Protocols for Loanable Funds (PLFs) and their dependency on price oracles, which are susceptible to manipulation. The emergence of flash loans has further amplified these risks, enabling increasingly complex oracle manipulation attacks that can lead to significant financial losses. Responding to this threat, we first dissect the attack mechanism by formalizing the standard operational and adversary models for PLFs. Based on our analysis, we propose SecPLF, a robust and practical solution designed to counteract oracle manipulation attacks efficiently. SecPLF operates by tracking a price state for each crypto-asset, including the recent price and the timestamp of its last update. By imposing price constraints on the price oracle usage, SecPLF ensures a PLF only engages a price oracle if the last recorded price falls within a defined threshold, thereby negating the profitability of potential attacks. Our evaluation based on historical market data confirms SecPLF's efficacy in providing high-confidence prevention against arbitrage attacks that arise due to minor price differences. SecPLF delivers proactive protection against oracle manipulation attacks, offering ease of implementation, oracle-agnostic property, and resource and cost efficiency.
GATS: Gather-Attend-Scatter
Venue: arXiv cs.AI 2024
Year: 2024

As the AI community increasingly adopts large-scale models, it is crucial to develop general and flexible tools to integrate them. We introduce Gather-Attend-Scatter (GATS), a novel module that enables seamless combination of pretrained foundation models, both trainable and frozen, into larger multimodal networks. GATS empowers AI systems to process and generate information across multiple modalities at different rates. In contrast to traditional fine-tuning, GATS allows for the original component models to remain frozen, avoiding the risk of them losing important knowledge acquired during the pretraining phase. We demonstrate the utility and versatility of GATS with a few experiments across games, robotics, and multimodal input-output systems.