Deployment simulation research of new spinning space web

For capturing large space debris and other non-cooperative targets, a new double rotational space web system is proposed in this paper, and the deployment process of space web system is analyzed with kinematic modeling methods. After explaining the design of the rotational web system and folding, unfolding strategies, a simplified kinematic model of the space web system is established to simulate the first stage of the rotational web deployment by the macro command in ADAMS, which focus on the modeling and coiling of the flexible tether, and the influence of space web deployment about the angular velocity and mass of the central rigid body, the mass of corner masses. Kinematic simulation results show that the spinning space web can deploy successfully and stably in the first stage by applying appropriate kinematic parameters, and the displacement out of the plane is very small

Common genetic variants in ADCY5 and gestational glycemic traits.

Two meta-analysis of genome wide association studies identified two variants at adenylate cyclase 5 (ADCY5) associated with type 2 diabetes mellitus, fasting and 2-hour glucose in non-pregnant individuals of European descent. The objective of our study was to explore the role of common variants in ADCY5 on gestational glycemic traits, including plasma glucose, insulin values, β cell function and insulin resistance in the fasted state as well as plasma glucose 1 hour after a 50-gram glucose challenge test among Chinese Han women. Homoeostasis model assessment (HOMA) was used to quantify β cell function (HOMA1-β and HOMA2-β) and insulin resistance (HOMA1-IR and HOMA2-IR). Thirty-five single nucleotide polymorphisms (SNPs) in ADCY5 were genotyped in 929 unrelated Chinese Han women with singleton pregnancies. Three SNPs (rs6797915, rs9856662 and rs9875803) displayed evidence for association with plasma glucose 1 hour after a 50-gram glucose challenge test (P = 0.042, 0.018 and 0.018, respectively), one (rs6777397) displayed evidence for association with HOMA1-β (P = 0.014), and one (rs6762009) displayed evidence for association with HOMA1-IR (P = 0.033). These results provide additional insight into the effects of genetic variation within ADCY5 in glucose metabolism, especially during pregnancy and in non-European descent populations

The audio auditor: user-level membership inference in Internet of Things voice services

With the rapid development of deep learning techniques, the popularity of voice services implemented on various Internet of Things (IoT) devices is ever increasing. In this paper, we examine user-level membership inference in the problem space of voice services, by designing an audio auditor to verify whether a specific user had unwillingly contributed audio used to train an automatic speech recognition (ASR) model under strict black-box access. With user representation of the input audio data and their corresponding translated text, our trained auditor is effective in user-level audit. We also observe that the auditor trained on specific data can be generalized well regardless of the ASR model architecture. We validate the auditor on ASR models trained with LSTM, RNNs, and GRU algorithms on two state-of-the-art pipelines, the hybrid ASR system and the end-to-end ASR system. Finally, we conduct a real-world trial of our auditor on iPhone Siri, achieving an overall accuracy exceeding 80%. We hope the methodology developed in this paper and findings can inform privacy advocates to overhaul IoT privacy

A Transaction Model and Profit Allocation Method of Multiple Energy Storage Oriented to Versatile Regulation Demand

This study proposes a day-ahead transaction model that combines multiple energy storage systems (ESS), including a hydrogen storage system (HSS), battery energy storage system (BESS), and compressed air energy storage (CAES). It is catering to the trend of a diversified power market to respond to the constraints from the insufficient flexibility of a high-proportion renewable energy system (RES). The model is a double-layer game based on the Nash–Stackelberg–cooperative (N–S–C) game. Multiple users in the upper layer form the Nash game with the goal of maximizing their own benefits, while the multiple ESSs in the lower layer form a cooperative game with the goal of maximizing the overall benefits; the two layers form a Stackelberg game. Moreover, an allocation mechanism is proposed to balance the overall and individual rationality and promote the sustainable development of multiple ESSs, considering the operational characteristics. A numerical simulation is carried out using the rationality and effectiveness of the proposed model, which is based on data from the renewable energy gathering area in northwest China. The results show that this strategy shortens the energy storage payback period and improves the energy storage utilization. The simulation results indicate that small-scale energy storage with a rated power of less than 18 MWh does not have a price advantage, indicating the need to improve the configuration capacity of energy storage in the future from decentralized energy storage to independent/shared energy storage

Machine learning–based cyber attacks targeting on controlled information: a survey

Stealing attack against controlled information, along with the increasing number of information leakage incidents, has become an emerging cyber security threat in recent years. Due to the booming development and deployment of advanced analytics solutions, novel stealing attacks utilize machine learning (ML) algorithms to achieve high success rate and cause a lot of damage. Detecting and defending against such attacks is challenging and urgent so that governments, organizations, and individuals should attach great importance to the ML-based stealing attacks. This survey presents the recent advances in this new type of attack and corresponding countermeasures. The ML-based stealing attack is reviewed in perspectives of three categories of targeted controlled information, including controlled user activities, controlled ML model-related information, and controlled authentication information. Recent publications are summarized to generalize an overarching attack methodology and to derive the limitations and future directions of ML-based stealing attacks. Furthermore, countermeasures are proposed towards developing effective protections from three aspects -- detection, disruption, and isolation.Comment: Published in ACM Computing Survey

No-Label User-Level Membership Inference for ASR Model Auditing

With the advancement of speech recognition techniques, AI-powered voice assistants become ubiquitous. However, it also increases privacy concerns regarding users’ voice recordings. User-level membership inference detects whether a service provider misused users’ audio to build its Automatic Speech Recognition (ASR) model without users’ consent. Previous research assumes the model’s outputs, including its label (i.e., transcription) and confidence score, are available for security auditing. However, the model’s outputs are unavailable in many real-world cases, i.e., no-label black-box scenarios, which is a big challenge. We propose a substitute model analysis to transfer the knowledge of the service system to that of its built-in ASR model’s behavior with semantic analysis techniques. Based on this analysis, our auditor can determine the user-level membership with high accuracy (∼ 80%) by utilizing a shadow system technique and a gap inference method. The gap inference-based auditor is generic and independent of ASR models

The Audio Auditor: User-Level Membership Inference in Internet of Things Voice Services

With the rapid development of deep learning techniques, the popularity of voice services implemented on various Internet of Things (IoT) devices is ever increasing. In this paper, we examine user-level membership inference in the problem space of voice services, by designing an audio auditor to verify whether a specific user had unwillingly contributed audio used to train an automatic speech recognition (ASR) model under strict black-box access. With user representation of the input audio data and their corresponding translated text, our trained auditor is effective in user-level audit. We also observe that the auditor trained on specific data can be generalized well regardless of the ASR model architecture. We validate the auditor on ASR models trained with LSTM, RNNs, and GRU algorithms on two state-of-the-art pipelines, the hybrid ASR system and the end-to-end ASR system. Finally, we conduct a real-world trial of our auditor on iPhone Siri, achieving an overall accuracy exceeding 80%. We hope the methodology developed in this paper and findings can inform privacy advocates to overhaul IoT privacy

Modulating Smart Mechanoluminescent Phosphors for Multistimuli Responsive Optical Wood

Abstract Mechanoluminescence is a smart light‐emitting phenomenon in which applied mechanical energy is directly converted into photon emissions. In particular, mechanoluminescent materials have shown considerable potential for applications in the fields of energy and sensing. This study thoroughly investigates the mechanoluminescence and long afterglow properties of singly doped and codoped Sr2MgSi2O7(SMSO) with varying concentrations of Eu2+ and Dy3+ ions. Subsequently, a comprehensive analysis of its multimode luminescence properties, including photoluminescence, mechanoluminescence, long afterglow, and X‐ray‐induced luminescence, is conducted. In addition, the density of states mapping is acquired through first‐principles calculations, confirming that the enhanced mechanoluminescence properties of SMSO primarily stem from the deep trap introduced by Dy3+. In contrast to traditional mixing with Polydimethylsiloxane, in this study, the powders are incorporated into optically transparent wood to produce a multiresponse with mechanoluminescence, long afterglow, and X‐ray‐excited luminescence. This structure is achieved by pretreating natural wood, eliminating lignin, and subsequently modifying the wood to overall modification using various smart phosphors and epoxy resin composites. After natural drying, a multifunctional composite wood structure with diverse luminescence properties is obtained. Owing to its environmental friendliness, sustainability, self‐power, and cost‐effectiveness, this smart mechanoluminescence wood is anticipated to find extensive applications in construction materials and energy‐efficient displays