DynamicFL: Balancing Communication Dynamics and Client Manipulation for Federated Learning

Federated Learning (FL) is a distributed machine learning (ML) paradigm, aiming to train a global model by exploiting the decentralized data across millions of edge devices. Compared with centralized learning, FL preserves the clients' privacy by refraining from explicitly downloading their data. However, given the geo-distributed edge devices (e.g., mobile, car, train, or subway) with highly dynamic networks in the wild, aggregating all the model updates from those participating devices will result in inevitable long-tail delays in FL. This will significantly degrade the efficiency of the training process. To resolve the high system heterogeneity in time-sensitive FL scenarios, we propose a novel FL framework, DynamicFL, by considering the communication dynamics and data quality across massive edge devices with a specially designed client manipulation strategy. \ours actively selects clients for model updating based on the network prediction from its dynamic network conditions and the quality of its training data. Additionally, our long-term greedy strategy in client selection tackles the problem of system performance degradation caused by short-term scheduling in a dynamic network. Lastly, to balance the trade-off between client performance evaluation and client manipulation granularity, we dynamically adjust the length of the observation window in the training process to optimize the long-term system efficiency. Compared with the state-of-the-art client selection scheme in FL, \ours can achieve a better model accuracy while consuming only 18.9\% -- 84.0\% of the wall-clock time. Our component-wise and sensitivity studies further demonstrate the robustness of \ours under various real-life scenarios

Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems

Artificial Intelligence (AI) systems such as autonomous vehicles, facial recognition, and speech recognition systems are increasingly integrated into our daily lives. However, despite their utility, these AI systems are vulnerable to a wide range of attacks such as adversarial, backdoor, data poisoning, membership inference, model inversion, and model stealing attacks. In particular, numerous attacks are designed to target a particular model or system, yet their effects can spread to additional targets, referred to as transferable attacks. Although considerable efforts have been directed toward developing transferable attacks, a holistic understanding of the advancements in transferable attacks remains elusive. In this paper, we comprehensively explore learning-based attacks from the perspective of transferability, particularly within the context of cyber-physical security. We delve into different domains -- the image, text, graph, audio, and video domains -- to highlight the ubiquitous and pervasive nature of transferable attacks. This paper categorizes and reviews the architecture of existing attacks from various viewpoints: data, process, model, and system. We further examine the implications of transferable attacks in practical scenarios such as autonomous driving, speech recognition, and large language models (LLMs). Additionally, we outline the potential research directions to encourage efforts in exploring the landscape of transferable attacks. This survey offers a holistic understanding of the prevailing transferable attacks and their impacts across different domains

PhantomSound: Black-Box, Query-Efficient Audio Adversarial Attack via Split-Second Phoneme Injection

In this paper, we propose PhantomSound, a query-efficient black-box attack toward voice assistants. Existing black-box adversarial attacks on voice assistants either apply substitution models or leverage the intermediate model output to estimate the gradients for crafting adversarial audio samples. However, these attack approaches require a significant amount of queries with a lengthy training stage. PhantomSound leverages the decision-based attack to produce effective adversarial audios, and reduces the number of queries by optimizing the gradient estimation. In the experiments, we perform our attack against 4 different speech-to-text APIs under 3 real-world scenarios to demonstrate the real-time attack impact. The results show that PhantomSound is practical and robust in attacking 5 popular commercial voice controllable devices over the air, and is able to bypass 3 liveness detection mechanisms with >95% success rate. The benchmark result shows that PhantomSound can generate adversarial examples and launch the attack in a few minutes. We significantly enhance the query efficiency and reduce the cost of a successful untargeted and targeted adversarial attack by 93.1% and 65.5% compared with the state-of-the-art black-box attacks, using merely ~300 queries (~5 minutes) and ~1,500 queries (~25 minutes), respectively.Comment: RAID 202

The simultaneous calcination/sulfation reaction of limestone under oxy-fuel CFB conditions

Using a customized thermogravimetric analyzer, the characteristics of the simultaneous calcination/sulfation reaction of limestone (the simultaneous reaction) under oxy-fuel circulating fluidized bed (CFB) boiler conditions were investigated. The results were compared with the calcination-then-sulfation reaction (the sequential reaction) that has been widely adopted by previous investigators. The sample mass in the simultaneous reaction was higher than that in the sequential reaction. With the increase of SO2 concentration (0–0.9%), the mass difference between the two reaction scenarios increased; while with the increase of temperature (890–950 °C), the difference became smaller. Calcination in the presence of SO2 was slower than that without SO2. With the increase of SO2 concentration, the pore volume of the calcined CaO decreased, and the effectiveness factors of the calcination reaction also declined. This indicates when CaSO4 forms, the pores in CaO were filled or blocked, thus increasing the internal resistance to CO2. Because the simultaneous process is the real one in CFB boilers, and it shows different characteristics from the sequential reaction, all investigations of CaO sulfation in CFB should follow this approach. Also in this work, the effects of SO2 concentration, temperature and H2O on the simultaneous reaction were studied. The sulfation ratio in the simultaneous reaction increased with higher SO2 concentration. Compared with that in the absence of H2O, 8% H2O in flue gas significantly improved sulfation. In the tested range (890–950 °C), the optimum temperature for sulfation was around 890 °C. The sulfation rate in the mass-loss stage was higher than that in the fast sulfation stage, which is likely due to the continuous generation of nascent CaO in this stage

Active Learning Experiences by Learning with "Buildings Around" Program

ABSTRACT For Civil engineering students, during the general learning in school, they can seldom meet opportunities to participate in the construction of whole projects in the real process. To make a situation for design ability according to Standards 5、6 and 8 in CDIO, the traditional engineering laboratory was moved to the campus in a program named "Learning with Buildings Around" in a course "Architectural Design and Construction" for Civil Engineering in Shantou University. The buildings in campus such as Library, teaching buildings, dormitory have been the Learning objects. Students explored these existing buildings on their space, function, composition, materials and constructing. The exploration practices constitute the students understanding of the buildings and stimulate students desire to create new buildings. This paper focuses on introducing the implementation of the program. Also a discussion on teachers' guide work and the learning outcomes are included as well as some improvement suggestions to active and experiential learning for the course

VSMask: Defending Against Voice Synthesis Attack via Real-Time Predictive Perturbation

Deep learning based voice synthesis technology generates artificial human-like speeches, which has been used in deepfakes or identity theft attacks. Existing defense mechanisms inject subtle adversarial perturbations into the raw speech audios to mislead the voice synthesis models. However, optimizing the adversarial perturbation not only consumes substantial computation time, but it also requires the availability of entire speech. Therefore, they are not suitable for protecting live speech streams, such as voice messages or online meetings. In this paper, we propose VSMask, a real-time protection mechanism against voice synthesis attacks. Different from offline protection schemes, VSMask leverages a predictive neural network to forecast the most effective perturbation for the upcoming streaming speech. VSMask introduces a universal perturbation tailored for arbitrary speech input to shield a real-time speech in its entirety. To minimize the audio distortion within the protected speech, we implement a weight-based perturbation constraint to reduce the perceptibility of the added perturbation. We comprehensively evaluate VSMask protection performance under different scenarios. The experimental results indicate that VSMask can effectively defend against 3 popular voice synthesis models. None of the synthetic voice could deceive the speaker verification models or human ears with VSMask protection. In a physical world experiment, we demonstrate that VSMask successfully safeguards the real-time speech by injecting the perturbation over the air

Long non-coding RNA SNHG7 promotes malignant melanoma progression through negative modulation of miR-9

Long non-coding small nucleolar RNA host gene 7 (lncRNA SNHG7) was verified to act as an onco- gene in human cancers. Nevertheless, the role of SNHG7 in malignant melanoma remains elusive. The present study showed an increase of SNHG7 expression in malignant melanoma tissues and cell lines. Besides, SNHG7 knockdown inhibited proliferation and migration in malignant melanoma cells. Bioinformatics analysis demonstrated that SNHG7 functions as a molecular sponge for miR-9 in biological behavior of melanoma cells. And miR-9 could inhibit the expression of PI3KR3 by binding with the 3’ -UTR. Furthermore, PI3KR3, pAKT, cyclin D1 and Girdin expression was down-regulated after SNHG7 knockdown by siRNA. In addition, SNHG7 knockdown decreased xenograft growth in vivo. Taken together, this research demonstrated that SNHG7 was an oncogene in malignant melanoma, providing a novel insight for the pathogenesis and new potential therapeutic target for malignant melanoma

NOB suppression in pilot-scale mainstream nitritation-denitritation system coupled with MBR for municipal wastewater treatment

The high energy consumption associated with biological treatment of municipal wastewater is posing a serious impact and challenge on the current global wastewater industry and is also inevitably linked to the issue of global climate change. To tackle such an emerging situation, this study aimed to develop strategies to effectively suppress nitrite oxidizing bacteria (NOB) in pilot-scale mainstream nitritation-denitritation system coupled with MBR for municipal wastewater treatment. The results showed that stable nitrite shunt was achieved, while more than 90% of COD and NH4+-N removal were obtained via nitritation-denitritation in the pilot plant fed with real municipal wastewater. Through adjusting aeration intensity in MBR in combination with the integrated control of dissolved oxygen (DO), sludge retention time (SRT) and sludge return ratio, NOB was successfully suppressed with a nitrite accumulation rate (NAR) of more than 80%

Effect of Graphene Oxide Treatment on the Properties of Cellulose Nanofibril Films Made of Banana Petiole Fibers

In this study, banana petiole-based cellulose nanofibril (CNF) films treated with graphene oxide (GO) were prepared and evaluated by means of Fourier-transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), dynamic mechanical analysis (DMA), and thermogravimetric analysis (TGA). Tensile strengths (TS), dynamic mechanical properties, and thermal stabilities of the films were affected positively when the GO loading was less than 4.4 wt%. From these results, FTIR spectra, and SEM analyses, a strong coupling between the GO and the cellulose matrix could be concluded at lower GO loadings. The TGA and DMA results also suggested that the CNF film treated with 4.4 wt% GO had more char residue, better thermal stability, higher storage modulus, and higher retention ratio when compared to that without treatment. This work provides a new approach for more effective utilization of banana petiole as a feedstock for CNF and GO/CNF composites

Fragmentation of Severely Encrusted Ureteral Stent Indwelled for 4 Years in a Boy

Four years ago, a 9-year-old boy received percutaneous nephrolithotomy (PCNL) and a 5F DJS was placed thereafter. The DJS was neglected until it caused serious complications including encrustation involving the whole stent, a 5-cm-diameter vesical stone, fragmentation of DJS, and serious urinary tract infection. For this rare and complex case of pediatric lithotomy, we combined PCNL with the suprapubic cystolithotomy for complete removal of the encrusted stent and associated stones without any complications and the patient was rendered stone- and stent-free safely