OpenRaiser/Intern-Atlas huggingface dataset:
Federated Bayesian Deep Learning: The Application of Statistical Aggregation Methods to Bayesian Models
Venue: arXiv cs.LG 2024
Year: 2024

Federated learning (FL) is an approach to training machine learning models that takes advantage of multiple distributed datasets while maintaining data privacy and reducing communication costs associated with sharing local datasets. Aggregation strategies have been developed to pool or fuse the weights and biases of distributed deterministic models; however, modern deterministic deep learning (DL) models are often poorly calibrated and lack the ability to communicate a measure of epistemic uncertainty in prediction, which is desirable for remote sensing platforms and safety-critical applications. Conversely, Bayesian DL models are often well calibrated and capable of quantifying and communicating a measure of epistemic uncertainty along with a competitive prediction accuracy. Unfortunately, because the weights and biases in Bayesian DL models are defined by a probability distribution, simple application of the aggregation methods associated with FL schemes for deterministic models is either impossible or results in sub-optimal performance. In this work, we use independent and identically distributed (IID) and non-IID partitions of the CIFAR-10 dataset and a fully variational ResNet-20 architecture to analyze six different aggregation strategies for Bayesian DL models. Additionally, we analyze the traditional federated averaging approach applied to an approximate Bayesian Monte Carlo dropout model as a lightweight alternative to more complex variational inference methods in FL. We show that aggregation strategy is a key hyperparameter in the design of a Bayesian FL system with downstream effects on accuracy, calibration, uncertainty quantification, training stability, and client compute requirements.
From Hardware Fingerprint to Access Token: Enhancing the Authentication on IoT Devices
Venue: arXiv cs.CR 2024
Year: 2024

The proliferation of consumer IoT products in our daily lives has raised the need for secure device authentication and access control. Unfortunately, these resource-constrained devices typically use token-based authentication, which is vulnerable to token compromise attacks that allow attackers to impersonate the devices and perform malicious operations by stealing the access token. Using hardware fingerprints to secure their authentication is a promising way to mitigate these threats. However, once attackers have stolen some hardware fingerprints (e.g., via MitM attacks), they can bypass the hardware authentication by training a machine learning model to mimic fingerprints or reusing these fingerprints to craft forge requests. In this paper, we present MCU-Token, a secure hardware fingerprinting framework for MCU-based IoT devices even if the cryptographic mechanisms (e.g., private keys) are compromised. MCU-Token can be easily integrated with various IoT devices by simply adding a short hardware fingerprint-based token to the existing payload. To prevent the reuse of this token, we propose a message mapping approach that binds the token to a specific request via generating the hardware fingerprints based on the request payload. To defeat the machine learning attacks, we mix the valid fingerprints with poisoning data so that attackers cannot train a usable model with the leaked tokens. MCU-Token can defend against armored adversary who may replay, craft, and offload the requests via MitM or use both hardware (e.g., use identical devices) and software (e.g., machine learning attacks) strategies to mimic the fingerprints. The system evaluation shows that MCU-Token can achieve high accuracy (over 97%) with a low overhead across various IoT devices and application scenarios.
Introduction to Human-Robot Interaction: A Multi-Perspective Introductory Course
Venue: arXiv cs.RO 2024
Year: 2024

In this paper I describe the design of an introductory course in Human-Robot Interaction. This project-driven course is designed to introduce undergraduate and graduate engineering students, especially those enrolled in Computer Science, Mechanical Engineering, and Robotics degree programs, to key theories and methods used in the field of Human-Robot Interaction that they would otherwise be unlikely to see in those degree programs. To achieve this aim, the course takes students all the way from stakeholder analysis to empirical evaluation, covering and integrating key Qualitative, Design, Computational, and Quantitative methods along the way. I detail the goals, audience, and format of the course, and provide a detailed walkthrough of the course syllabus.
Enhancing Testing at Meta with Rich-State Simulated Populations
Venue: arXiv cs.SE 2024
Year: 2024

This paper reports the results of the deployment of Rich-State Simulated Populations at Meta for both automated and manual testing. We use simulated users (aka test users) to mimic user interactions and acquire state in much the same way that real user accounts acquire state. For automated testing, we present empirical results from deployment on the Facebook, Messenger, and Instagram apps for iOS and Android Platforms. These apps consist of tens of millions of lines of code, communicating with hundreds of millions of lines of backend code, and are used by over 2 billion people every day. Our results reveal that rich state increases average code coverage by 38\%, and endpoint coverage by 61\%. More importantly, it also yields an average increase of 115\% in the faults found by automated testing. The rich-state test user populations are also deployed in a (continually evolving) Test Universe; a web-enabled simulation platform for privacy-safe manual testing, which has been used by over 21,000 Meta engineers since its deployment in November 2022.
Radon mitigation by soil depressurisation case study: radon concentration and pressure field extension monitoring in a pilot house in Spain
Venue: arXiv cs.CE 2024
Year: 2024

A one-year monitoring study was conducted in a pilot house with high radon levels to investigate the ability and efficiency of radon mitigation by soil depressurisation (SD) both active and passive. The study included monitoring of radon concentration, pressure field extension (pfe) under the slab and some atmospheric parameters for different testing phases. Periods in which the house remained closed to foster radon accumulation were alternated with phases of active and passive soil depressurisation under different conditions. The behaviour of the radon concentration in the pilot house was analysed along with the influence of atmospheric variables, significant correlations were found for the radon concentration with atmospheric pressure, outdoor temperature and wind. From the pfe analysis it was proven that the pressure drop with distance from the suction point of the SD system is proportional to the depressurisation generated. It was found also that the permeability characterisation of the pilot house agrees with the literature about granular fill materials characterisation for radon SD systems across Europe. Radon reductions in excess of 85% were achieved for the different testing phases in all cases. Finally, from the results it was stated that a fan power of 23 W is sufficient to ensure radon reductions over 85% for dwellings with similar aggregate layer and soil permeability.
Detection of Opioid Users from Reddit Posts via an Attention-based Bidirectional Recurrent Neural Network
Venue: arXiv cs.CL 2024
Year: 2024

The opioid epidemic, referring to the growing hospitalizations and deaths because of overdose of opioid usage and addiction, has become a severe health problem in the United States. Many strategies have been developed by the federal and local governments and health communities to combat this crisis. Among them, improving our understanding of the epidemic through better health surveillance is one of the top priorities. In addition to direct testing, machine learning approaches may also allow us to detect opioid users by analyzing data from social media because many opioid users may choose not to do the tests but may share their experiences on social media anonymously. In this paper, we take advantage of recent advances in machine learning, collect and analyze user posts from a popular social network Reddit with the goal to identify opioid users. Posts from more than 1,000 users who have posted on three sub-reddits over a period of one month have been collected. In addition to the ones that contain keywords such as opioid, opiate, or heroin, we have also collected posts that contain slang words of opioid such as black or chocolate. We apply an attention-based bidirectional long short memory model to identify opioid users. Experimental results show that the approaches significantly outperform competitive algorithms in terms of F1-score. Furthermore, the model allows us to extract most informative words, such as opiate, opioid, and black, from posts via the attention layer, which provides more insights on how the machine learning algorithm works in distinguishing drug users from non-drug users.
An IoT system for a smart campus: Challenges and solutions illustrated over several real-world use cases
Venue: arXiv cs.CY 2024
Year: 2024

This article discusses the development of an IoT system for monitoring and controlling various devices and systems from different vendors. The authors considered key challenges in IoT projects, such as interoperability and integration, scalability, and data storage, processing, and visualization, during the design and deployment phases. In addition to these general challenges, the authors also delve into the specific integration challenges they encountered. Various devices and systems were integrated into the system and five real-world scenarios in a university campus environment are used to illustrate the challenges encountered. The scenarios involve monitoring various aspects of a university campus environment, including air quality, environmental parameters, energy efficiency, solar photovoltaic energy, and energy consumption. The authors analyzed data and CPU usage to ensure that the system could handle the large amount of data generated by the devices. The platform developed uses open source projects such as Home Assistant, InfluxDB, Grafana, and Node-RED. All developments have been published as open source in public repositories. In conclusion, this work highlights the potential and feasibility of IoT systems in various real-world applications, the importance of considering key challenges in IoT projects during the design and deployment phases, and the specific integration challenges that may be encountered.
I would love this to be like an assistant, not the teacher: a voice of the customer perspective of what distance learning students want from an Artificial Intelligence Digital Assistant
Venue: arXiv cs.CY 2024
Year: 2024

With the release of Generative AI systems such as ChatGPT, an increasing interest in using Artificial Intelligence (AI) has been observed across domains, including higher education. While emerging statistics show the popularity of using AI amongst undergraduate students, little is yet known about students' perceptions regarding AI including self-reported benefits and concerns from their actual usage, in particular in distance learning contexts. Using a two-step, mixed-methods approach, we examined the perceptions of ten online and distance learning students from diverse disciplines regarding the design of a hypothetical AI Digital Assistant (AIDA). In the first step, we captured students' perceptions via interviews, while the second step supported the triangulation of data by enabling students to share, compare, and contrast perceptions with those of peers. All participants agreed on the usefulness of such an AI tool while studying and reported benefits from using it for real-time assistance and query resolution, support for academic tasks, personalisation and accessibility, together with emotional and social support. Students' concerns related to the ethical and social implications of implementing AIDA, data privacy and data use, operational challenges, academic integrity and misuse, and the future of education. Implications for the design of AI-tailored systems are also discussed.
LLMs-based Few-Shot Disease Predictions using EHR: A Novel Approach Combining Predictive Agent Reasoning and Critical Agent Instruction
Venue: arXiv cs.CL 2024
Year: 2024

Electronic health records (EHRs) contain valuable patient data for health-related prediction tasks, such as disease prediction. Traditional approaches rely on supervised learning methods that require large labeled datasets, which can be expensive and challenging to obtain. In this study, we investigate the feasibility of applying Large Language Models (LLMs) to convert structured patient visit data (e.g., diagnoses, labs, prescriptions) into natural language narratives. We evaluate the zero-shot and few-shot performance of LLMs using various EHR-prediction-oriented prompting strategies. Furthermore, we propose a novel approach that utilizes LLM agents with different roles: a predictor agent that makes predictions and generates reasoning processes and a critic agent that analyzes incorrect predictions and provides guidance for improving the reasoning of the predictor agent. Our results demonstrate that with the proposed approach, LLMs can achieve decent few-shot performance compared to traditional supervised learning methods in EHR-based disease predictions, suggesting its potential for health-oriented applications.
Vi-Mistral-X: Building a Vietnamese Language Model with Advanced Continual Pre-training
Venue: arXiv cs.CL 2024
Year: 2024

The advancement of Large Language Models (LLMs) has significantly transformed the field of natural language processing, although the focus on English-centric models has created a noticeable research gap for specific languages, including Vietnamese. To address this issue, this paper presents vi-mistral-x, an innovative Large Language Model designed expressly for the Vietnamese language. It utilizes a unique method of continual pre-training, based on the Mistral architecture, which incorporates grouped-query attention and sliding window attention techniques. This model, vi-Mistral-X, marks a significant step forward in improving the understanding and generation of the Vietnamese language. It introduces an additional phase of continual pre-training, specifically adapted for Vietnamese, enhancing the model's capability in understanding complex language nuances and generating accurate, context-aware Vietnamese text. Through comprehensive testing on various benchmarks, vi-mistral-x has shown to outperform existing Vietnamese LLMs in several key areas, including text classification, question answering, and text generation. Particularly, in the Vietnamese Multitask Language Understanding (VMLU) benchmark, vi-mistral-x sets a new standard, outperforming other available models significantly. This paper highlights the critical role of continual pre-training in advancing language-specific LLMs and opens new avenues for the development of multilingual models. We aim for vi-mistral-x to not just be an important asset for processing the Vietnamese language but also to encourage more advancements in creating large language models for languages that are less represented.
Navigating Fairness: Practitioners' Understanding, Challenges, and Strategies in AI/ML Development
Venue: arXiv cs.CY 2024
Year: 2024

The rise in the use of AI/ML applications across industries has sparked more discussions about the fairness of AI/ML in recent times. While prior research on the fairness of AI/ML exists, there is a lack of empirical studies focused on understanding the perspectives and experiences of AI practitioners in developing a fair AI/ML system. Understanding AI practitioners' perspectives and experiences on the fairness of AI/ML systems are important because they are directly involved in its development and deployment and their insights can offer valuable real-world perspectives on the challenges associated with ensuring fairness in AI/ML systems. We conducted semi-structured interviews with 22 AI practitioners to investigate their understanding of what a 'fair AI/ML' is, the challenges they face in developing a fair AI/ML system, the consequences of developing an unfair AI/ML system, and the strategies they employ to ensure AI/ML system fairness. We developed a framework showcasing the relationship between AI practitioners' understanding of 'fair AI/ML' system and (i) their challenges in its development, (ii) the consequences of developing an unfair AI/ML system, and (iii) strategies used to ensure AI/ML system fairness. By exploring AI practitioners' perspectives and experiences, this study provides actionable insights to enhance AI/ML fairness, which may promote fairer systems, reduce bias, and foster public trust in AI technologies. Additionally, we also identify areas for further investigation and offer recommendations to aid AI practitioners and AI companies in navigating fairness.
Increasing retrofit device adoption in social housing: evidence from two field experiments in Belgium
Venue: arXiv cs.CY 2024
Year: 2024

Energy efficient technologies are particularly important for social housing settings: they offer the potential to improve tenants' wellbeing through monetary savings and comfort, while reducing emissions of entire communities. Slow uptake of innovative energy technology in social housing has been associated with a lack of trust and the perceived risks of adoption. To counteract both, we designed a communication campaign for a retrofit technology for heating including social norms for technology adoption and concretely experienced benefits. We report two randomized controlled trials (RCT) in two different social housing communities in Belgium. In the first study, randomization was on housing block level: the communication led to significant higher uptake rates compared to the control group, (b = 1.7, p = .024). In the second study randomization occurred on apartment level, again yielding a significant increase (b = 1.62, p = 0.02), when an interaction with housing blocks was considered. We discuss challenges of conducting randomized controlled trials in social housing communities.
Open Conversational LLMs do not know most Spanish words
Venue: arXiv cs.CL 2024
Year: 2024

The growing interest in Large Language Models (LLMs) and in particular in conversational models with which users can interact has led to the development of a large number of open-source chat LLMs. These models are evaluated on a wide range of benchmarks to assess their capabilities in answering questions or solving problems on almost any possible topic or to test their ability to reason or interpret texts. Instead, the evaluation of the knowledge that these models have of the languages has received much less attention. For example, the words that they can recognize and use in different languages. In this paper, we evaluate the knowledge that open-source chat LLMs have of Spanish words by testing a sample of words in a reference dictionary. The results show that open-source chat LLMs produce incorrect meanings for an important fraction of the words and are not able to use most of the words correctly to write sentences with context. These results show how Spanish is left behind in the open-source LLM race and highlight the need to push for linguistic fairness in conversational LLMs ensuring that they provide similar performance across languages.
SymboSLAM: Semantic Map Generation in a Multi-Agent System
Venue: arXiv cs.AI 2024
Year: 2024

Sub-symbolic artificial intelligence methods dominate the fields of environment-type classification and Simultaneous Localisation and Mapping. However, a significant area overlooked within these fields is solution transparency for the human-machine interaction space, as the sub-symbolic methods employed for map generation do not account for the explainability of the solutions generated. This paper proposes a novel approach to environment-type classification through Symbolic Simultaneous Localisation and Mapping, SymboSLAM, to bridge the explainability gap. Our method for environment-type classification observes ontological reasoning used to synthesise the context of an environment through the features found within. We achieve explainability within the model by presenting operators with environment-type classifications overlayed by a semantically labelled occupancy map of landmarks and features. We evaluate SymboSLAM with ground-truth maps of the Canberra region, demonstrating method effectiveness. We assessed the system through both simulations and real-world trials.
Privacy-Preserving End-to-End Spoken Language Understanding
Venue: arXiv cs.CR 2024
Year: 2024

Spoken language understanding (SLU), one of the key enabling technologies for human-computer interaction in IoT devices, provides an easy-to-use user interface. Human speech can contain a lot of user-sensitive information, such as gender, identity, and sensitive content. New types of security and privacy breaches have thus emerged. Users do not want to expose their personal sensitive information to malicious attacks by untrusted third parties. Thus, the SLU system needs to ensure that a potential malicious attacker cannot deduce the sensitive attributes of the users, while it should avoid greatly compromising the SLU accuracy. To address the above challenge, this paper proposes a novel SLU multi-task privacy-preserving model to prevent both the speech recognition (ASR) and identity recognition (IR) attacks. The model uses the hidden layer separation technique so that SLU information is distributed only in a specific portion of the hidden layer, and the other two types of information are removed to obtain a privacy-secure hidden layer. In order to achieve good balance between efficiency and privacy, we introduce a new mechanism of model pre-training, namely joint adversarial training, to further enhance the user privacy. Experiments over two SLU datasets show that the proposed method can reduce the accuracy of both the ASR and IR attacks close to that of a random guess, while leaving the SLU performance largely unaffected.
Efficiently Estimating Mutual Information Between Attributes Across Tables
Venue: arXiv cs.DB 2024
Year: 2024

Relational data augmentation is a powerful technique for enhancing data analytics and improving machine learning models by incorporating columns from external datasets. However, it is challenging to efficiently discover relevant external tables to join with a given input table. Existing approaches rely on data discovery systems to identify joinable tables from external sources, typically based on overlap or containment. However, the sheer number of tables obtained from these systems results in irrelevant joins that need to be performed; this can be computationally expensive or even infeasible in practice. We address this limitation by proposing the use of efficient mutual information (MI) estimation for finding relevant joinable tables. We introduce a new sketching method that enables efficient evaluation of relationship discovery queries by estimating MI without materializing the joins and returning a smaller set of tables that are more likely to be relevant. We also demonstrate the effectiveness of our approach at approximating MI in extensive experiments using synthetic and real-world datasets.