Aquaporin (AQP) proteins have been shown to transport water and other small molecules through biological membranes, which is crucial for plants combat stress caused by drought. However, the precise role of AQPs in drought response not completely understood plants. In this study, a PIP2 subgroup gene AQP, designated as TaAQP7, was cloned characterized from wheat. Expression TaAQP7-GFP fusion protein revealed its localization plasma membrane. TaAQP7 exhibited high channel activity Xenopus...

Smart contracts, one of the success stories in blockchain 2.0, have been widely utilized a broad range applications, including those involving Internet Things (IoT). Given fast-pace nature topic, it can be challenging for research community to keep track latest advances. Hence, this article, we perform comprehensive, in-depth review known security challenges (e.g., inherently vulnerable particularities, programming vulnerabilities, and attacks) potential opportunities associated with...

Type-2 fuzzy logic system (FLS) cascaded with neural network, type-2 network (T2FNN), is presented in this paper to handle uncertainty dynamical optimal learning. A T2FNN consists of a linguistic process as the antecedent part, and two-layer interval consequent part. general computational-intensive due complexity type 2 1 reduction. Therefore, adopted simplify computational process. The training algorithm for part first developed. stable left right learning rates sense maximum error...

With the increasing popularity of location-based services (LBSs), it is paramount importance to preserve one's location privacy. The commonly used privacy preserving approach, k-anonymity, strives aggregate queries k nearby users within a so-called cloaked region via trusted third-party anonymizer. As such, probability identify every user involved no more than 1/k, thus offering preservation for users. One inherent limitation however, that all are assumed be and report their real locations....

Social media networks have shown rapid growth in the past, and massive social data are generated which can reveal behavior or emotion propensities of users. Numerous researchers leverage machine learning technology to build analytic models detect abnormal behaviors mental illnesses from effectively. Although only public prediction interfaces models, general, these may leak information about individual records on were trained. Knowing a certain user's record was used train model breach user...

Machine learning techniques have been widely applied to various applications. However, they are potentially vulnerable data poisoning attacks, where sophisticated attackers can disrupt the procedure by injecting a fraction of malicious samples into training dataset. Existing defense against attacks largely attack-specific: designed for one specific type but do not work other types, mainly due distinct principles follow. Yet few general strategies developed. In this paper, we propose De-Pois,...

Federated learning (FL) has recently emerged as a promising distributed machine (ML) paradigm. Practical needs of the "right to be forgotten" and countering data poisoning attacks call for efficient techniques that can remove, or unlearn, specific training from trained FL model. Existing unlearning in context ML, however, are no longer effect FL, mainly due inherent distinction way how ML learn data. Therefore, enable removal models remains largely under-explored. In this paper, we take...

One of the major applications wireless sensor networks (WSNs) is navigation service for emergency evacuation, goal which to assist people in escaping from a hazardous region safely and quickly when an occurs. Most existing solutions focus on finding safest path each person, while ignoring possible large detours congestions caused by plenty rushing exit. In this paper, we present CANS, C ongestion-Adaptive small stretch Navigation algorithm with WSNs. Specifically, CANS leverages idea level...

Device-free localization of objects not equipped with RF radios is playing a critical role in many applications. This paper presents LIFS, Low human-effort, device-free system fine-grained subcarrier information, which can localize target accurately without offline training. The basic idea simple: channel state information (CSI) sensitive to target's location and thus the be localized by modelling CSI measurements multiple wireless links. However, due rich multipath indoors, easily modelled....

Filter bank multicarrier (FBMC) modulation is a promising candidate method for future communication systems. However, FBMC systems cannot directly use channel estimation methods proposed orthogonal frequency-division multiplexing due to its inherent imaginary interference. In this letter, we propose and equalization scheme based on deep learning (DL-CE) the DL-CE scheme, state information constellation demapping are learned by neural networks model, then distorted frequency-domain sequences...

Federated Learning (FL) is a new computing paradigm in privacy-preserving Machine (ML), where the ML model trained decentralized manner by clients, preventing server from directly accessing privacy-sensitive data clients. Unfortunately, recent advances have shown potential risks for user-level privacy breaches under cross-silo FL framework. In this paper, we propose addressing issue using three-plane framework to secure FL, taking advantage of Local Differential Privacy (LDP) mechanism. The...

Over the past years, emergence of intelligent networks empowered by machine learning techniques has brought great facilitates to different aspects human life. However, using in also presents potential security and privacy threats. A common practice is so-called poisoning attacks where malicious users inject fake training data with aim corrupting learned model. In this survey, we comprehensively review existing as well countermeasures for first time. We emphasize compare principles formal...

Non-intrusive load monitoring (NILM) provides fine-grained consumption information at the appliance level by analyzing terminal voltage and total current measured. It shows prospective applications in demand side management, such as response, energy efficiency, home management system. However, most cutting-edge NILM models have a critical assumption that switching events are triggered known appliances training set, which may be unrealistic. In reality, new constantly being added, reducing...

Indoor localization plays an important role as the basis for a variety of mobile applications, such navigating, tracking, and monitoring in indoor environments. However, many systems cause potential privacy leakage data transmission between users server (LS). Unfortunately, there has been little research done on issue, existing privacy-preserving solutions are algorithm-driven, each designed specific algorithms, which hinders their wide-scale adoption. Furthermore, they mainly focus users'...

With the increasing popularity of location-based services (LBS), how to preserve one's location privacy has become a key issue be concerned. The commonly used approach k-anonymity, originally designed for protecting user's snapshot privacy, inherently fails user from location-dependent attacks (LDA) that include maximum movement boundary (MMB) and arrival (MAB) attacks, when continuously requests LBS. This paper presents RobLoP, robust preserving algorithm against LDA in continuous LBS...

Recently, edge computing has attracted significant interest due to its ability extend cloud utilities and services the network with low response times communication costs. In general, requires mobile users upload their raw data a centralized server for further processing. However, these usually contain sensitive information about that do not want reveal, such as sexual orientation, political stance, health status, service access history. The transmission of user increases leakage risk...

Machine learning (ML) has achieved huge success in recent years, but is also vulnerable to various attacks. In this article, we concentrate on membership inference attacks and propose Aster, which merely requires the target model's black-box API a data sample determine whether was used train given ML model or not. The key idea of Aster that training fully trained usually lower prediction sensitivities compared with non-training (i.e., testing data). Less sensitivity means when perturbing...

With the rapid development of large urban agglomerations and increasing complexity roads, high-precision positioning vehicles has become cornerstone for application vehicle core technologies such as automatic driving. The real-time accuracy satellite navigation is easily affected by canyons, its stability poor; thus, how to use information internet achieve fusion a difficult problem multivehicle cooperative positioning. Aiming at this problem, paper proposes 3D algorithm based on geometric...