A Low-cost and Portable Active Noise Control Unit

The objective of this research is to employ cutting-edge active noise control methodologies in order to mitigate the noise emissions produced by electrical appliances, such as a coffee machine. The algorithm utilized in this study is the modified Filtered-X Least Mean Square (FXLMS) algorithm. This algorithm aims to generate an anti-noise waveform by utilizing measurements from both the reference microphone and the error microphone. The desired outcome of this approach is to achieve a residual noise level of zero. The primary difficulty lies in conducting the experiment in an open space setting, as conventional active noise control systems are designed to function within enclosed environments, such as closed rooms or relatively confined spaces like the volume inside headphones. A validation test bench is established, employing the Sigma Studio software to oversee the entire system, with the ADAU1452 digital signal processor being chosen. This study presents an introduction to different Active Noise Control systems and algorithms, followed by the execution of simulations for representative techniques. Subsequently, this section provides a comprehensive account of the procedures involved in executing the experiments, followed by an exploration of potential avenues for further research.Comment: A final year project report presented to the Nanyang Technological Universit

Stop sending me messages!: The negative impact of persuasive messages on green transportation

Persuasive information and communication technology has been used to persuade people to choose fuel-efficient transportation (i.e., green transportation), for example, by sending messages to the public. Many factors may influence the effect of such messages. In this exploratory we report a social experiment, in which participants received persuasive messages from social and non-social approaches. To our surprise, results seem to show a negative impact on green transportation, meaning participants receiving the messages used less green transportation modes. This suggests that messages may not be as an effective way to persuade the public as many organizations’ practice assumes and other persuasive techniques such as real-time feedback and awareness raising techniques may be needed in causing the desired changes

Neural Architectural Backdoors

This paper asks the intriguing question: is it possible to exploit neural architecture search (NAS) as a new attack vector to launch previously improbable attacks? Specifically, we present EVAS, a new attack that leverages NAS to find neural architectures with inherent backdoors and exploits such vulnerability using input-aware triggers. Compared with existing attacks, EVAS demonstrates many interesting properties: (i) it does not require polluting training data or perturbing model parameters; (ii) it is agnostic to downstream fine-tuning or even re-training from scratch; (iii) it naturally evades defenses that rely on inspecting model parameters or training data. With extensive evaluation on benchmark datasets, we show that EVAS features high evasiveness, transferability, and robustness, thereby expanding the adversary's design spectrum. We further characterize the mechanisms underlying EVAS, which are possibly explainable by architecture-level ``shortcuts'' that recognize trigger patterns. This work raises concerns about the current practice of NAS and points to potential directions to develop effective countermeasures

Audio is all in one: speech-driven gesture synthetics using WavLM pre-trained model

The generation of co-speech gestures for digital humans is an emerging area in the field of virtual human creation. Prior research has made progress by using acoustic and semantic information as input and adopting classify method to identify the person's ID and emotion for driving co-speech gesture generation. However, this endeavour still faces significant challenges. These challenges go beyond the intricate interplay between co-speech gestures, speech acoustic, and semantics; they also encompass the complexities associated with personality, emotion, and other obscure but important factors. This paper introduces "diffmotion-v2," a speech-conditional diffusion-based and non-autoregressive transformer-based generative model with WavLM pre-trained model. It can produce individual and stylized full-body co-speech gestures only using raw speech audio, eliminating the need for complex multimodal processing and manually annotated. Firstly, considering that speech audio not only contains acoustic and semantic features but also conveys personality traits, emotions, and more subtle information related to accompanying gestures, we pioneer the adaptation of WavLM, a large-scale pre-trained model, to extract low-level and high-level audio information. Secondly, we introduce an adaptive layer norm architecture in the transformer-based layer to learn the relationship between speech information and accompanying gestures. Extensive subjective evaluation experiments are conducted on the Trinity, ZEGGS, and BEAT datasets to confirm the WavLM and the model's ability to synthesize natural co-speech gestures with various styles.Comment: 10 pages, 5 figures, 1 tabl

Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks

Pre-trained language models (PLMs) have demonstrated remarkable performance as few-shot learners. However, their security risks under such settings are largely unexplored. In this work, we conduct a pilot study showing that PLMs as few-shot learners are highly vulnerable to backdoor attacks while existing defenses are inadequate due to the unique challenges of few-shot scenarios. To address such challenges, we advocate MDP, a novel lightweight, pluggable, and effective defense for PLMs as few-shot learners. Specifically, MDP leverages the gap between the masking-sensitivity of poisoned and clean samples: with reference to the limited few-shot data as distributional anchors, it compares the representations of given samples under varying masking and identifies poisoned samples as ones with significant variations. We show analytically that MDP creates an interesting dilemma for the attacker to choose between attack effectiveness and detection evasiveness. The empirical evaluation using benchmark datasets and representative attacks validates the efficacy of MDP.Comment: Accepted by NeurIPS'2

An Embarrassingly Simple Backdoor Attack on Self-supervised Learning

As a new paradigm in machine learning, self-supervised learning (SSL) is capable of learning high-quality representations of complex data without relying on labels. In addition to eliminating the need for labeled data, research has found that SSL improves the adversarial robustness over supervised learning since lacking labels makes it more challenging for adversaries to manipulate model predictions. However, the extent to which this robustness superiority generalizes to other types of attacks remains an open question. We explore this question in the context of backdoor attacks. Specifically, we design and evaluate CTRL, an embarrassingly simple yet highly effective self-supervised backdoor attack. By only polluting a tiny fraction of training data (<= 1%) with indistinguishable poisoning samples, CTRL causes any trigger-embedded input to be misclassified to the adversary's designated class with a high probability (>= 99%) at inference time. Our findings suggest that SSL and supervised learning are comparably vulnerable to backdoor attacks. More importantly, through the lens of CTRL, we study the inherent vulnerability of SSL to backdoor attacks. With both empirical and analytical evidence, we reveal that the representation invariance property of SSL, which benefits adversarial robustness, may also be the very reason making \ssl highly susceptible to backdoor attacks. Our findings also imply that the existing defenses against supervised backdoor attacks are not easily retrofitted to the unique vulnerability of SSL.Comment: The 2023 International Conference on Computer Vision (ICCV '23

On the Security Risks of Knowledge Graph Reasoning

Knowledge graph reasoning (KGR) -- answering complex logical queries over large knowledge graphs -- represents an important artificial intelligence task, entailing a range of applications (e.g., cyber threat hunting). However, despite its surging popularity, the potential security risks of KGR are largely unexplored, which is concerning, given the increasing use of such capability in security-critical domains. This work represents a solid initial step towards bridging the striking gap. We systematize the security threats to KGR according to the adversary's objectives, knowledge, and attack vectors. Further, we present ROAR, a new class of attacks that instantiate a variety of such threats. Through empirical evaluation in representative use cases (e.g., medical decision support, cyber threat hunting, and commonsense reasoning), we demonstrate that ROAR is highly effective to mislead KGR to suggest pre-defined answers for target queries, yet with negligible impact on non-target ones. Finally, we explore potential countermeasures against ROAR, including filtering of potentially poisoning knowledge and training with adversarially augmented queries, which leads to several promising research directions.Comment: In proceedings of USENIX Security'23. Codes: https://github.com/HarrialX/security-risk-KG-reasonin

Low expression of Notch1 may be associated with acute myocardial infarction

BackgroundThe transmembrane protein Notch1 is associated with cell growth, development, differentiation, proliferation, apoptosis, adhesion, and the epithelial mesenchymal transition. Proteomics, as a research method, uses a series of sequencing techniques to study the composition, expression levels, and modifications of proteins. Here, the association between Notch1 and acute myocardial infarction (AMI) was investigated using proteomics, to assess the possibility of using Notch1 as a biomarker for the disease.MethodsFifty-five eligible patients with AMI and 74 with chronic coronary syndrome (CCS) were enrolled, representing the experimental and control groups, respectively. The mRNA levels were assessed using RT-qPCR and proteins were measured using ELISA, and the results were compared and analyzed.ResultsNotch1 mRNA levels were 0.52 times higher in the peripheral blood mononuclear cells of the AMI group relative to the CCS group (p &lt; 0.05) while Notch1 protein levels were 0.63 times higher in peripheral blood plasma in AMI patients (p &lt; 0.05). Notch1 levels were not associated with older age, hypertension, smoking, high abdominal-blood glucose, high total cholesterol, and high LDL in AMI. Logistic regression indicated associations between AMI and reduced Notch1 expression, hypertension, smoking, and high fasting glucose.ConclusionsNotch1 expression was reduced in the peripheral blood of patients with AMI relative to those with CCS. The low expression of Notch1 was found to be an independent risk factor for AMI and may thus be an indicator of the disease

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve