Applications in real life are composed of different kinds of network systems; these networks may be interfered by uncontrollable or unpredictable disruptive events involving natural disasters, human errors, evil-intentioned attacks, or other disturba...
Social networks (SNs) and many other industrial types of networks, structured by many nodes and relationships between nodes, have become an integral part of our daily lives. A binary-state network (BN) is often used to model structures and applicatio...
The binary-state network, which is fundamental to several modern systems, only operates in two states: operational or inoperable. Network reliability is crucial in its planning, design, and evaluation, with the minimal cut (MC) being a cornerstone fo...
In this paper, we study the statistical properties of the stationary firing-rate states of a neural network model with quenched disorder. The model has arbitrary size, discrete-time evolution equations and binary firing rates, while the topology and...
A state and fault estimator is designed in this paper for nonlinear complex networks using binary encoding schemes subject to parameter uncertainties, randomly switching couplings, randomly occurring deception attacks and bounded stochastic noises. A...
The nature and characteristics of free-burning wildland fires have significant economic, safety, and environmental impacts. Additionally, the increase in global warming has led to an increase in the number and severity of wildfires. Hence, there is a...
The focus of this work is to design a deeply quantized anomaly detector of oil leaks that may happen at the junction between the wind turbine high-speed shaft and the external bracket of the power generator. We propose a block-based binary shallow ec...
This paper is dedicated to dealing with the design issue of a non-fragile state estimator for a type of nonlinear complex network subject to random couplings and random multiple time delays under binary encoding schemes (BESs). The BESs are put into...
This study proposes a simple correlation for approximating hydrogen solubility in biomaterials as a function of pressure and temperature. The pre-exponential term of the proposed model linearly relates to the pressure, whereas the exponential term is...
Land use and land cover change (LUCC) is an important issue in global environmental change and sustainable development, yet spatial simulation of LUCC remains challenging due to the land use system complexity. The cellular automata (CA) model plays a...
Samantaray et al [...]
Binary neural networks (BNNs) can substantially accelerate a neural network’s inference time by substituting its costly floating-point arithmetic with bit-wise operations. Nevertheless, state-of-the-art approaches reduce the efficiency of the d...
This paper proposes an event-triggered state estimation method for parameter-uncertain systems with a binary encoding transmission scheme. Firstly, a binary encoding transmission scheme is introduced between the state estimator and the system to impr...
The purpose of this reply is to address the comments given by Kelly et al. on our original paper “Unique Brain Network Identification Number for Parkinson’s and Healthy Individuals using Structural MRI”. We agree to the inadvertent...
In complex dynamical networks, pinning control techniques are often applied to control a small fraction of the nodes in order to stabilize the network with reduced control effort and energy, facilitating adequate development of the complex network. S...
The use of natural language processing to analyze binary data is a popular research topic in malware analysis. Embedding binary code into a vector is an important basis for building a binary analysis neural network model. Current solutions focus on e...
This paper proposes the “ultimate compression” method as a solution to the expansive computation and high storage costs required by state-of-the-art neural network models in inference. Our approach uniquely combines tensor decomposition t...
The promoter state of a gene and its expression levels are modulated by the amounts of transcription factors interacting with its regulatory regions. Hence, one may interpret a gene network as a communicating system in which the state of the promoter...
Transformer is a powerful model widely used in artificial intelligence applications. It contains complex structures and has extremely high computational requirements that are not suitable for embedded intelligent sensors with limited computational re...
We focus on the problem that the Grover algorithm is not suitable for the completely unknown proportion of target solutions. Considering whether the existing quantum classifier used by the current quantum neural network (QNN) to complete the classifi...
Binary code similarity measurement is a popular research area in binary analysis with the recent development of deep learning-based models. Current state-of-the-art methods often use the pre-trained language model (PTLM) to embed instructions into ba...
Binary neural networks (BNNs) are an extremely promising method for reducing deep neural networks’ complexity and power consumption significantly. Binarization techniques, however, suffer from ineligible performance degradation compared to thei...
Recently, new variants of non-systematic satisfiability logic were proposed to govern Discrete Hopfield Neural Network. This new variant of satisfiability logical rule will provide flexibility and enhance the diversity of the neuron states in the Dis...
Deep neural networks (DNNs) could be affected by the regression level of learning frameworks and challenging changes caused by external factors; their deep expressiveness is greatly restricted. Inspired by the fine-tuned DNNs with sensitivity dispari...
Binary code similarity detection is used to calculate the code similarity of a pair of binary functions or files, through a certain calculation method and judgment method. It is a fundamental task in the field of computer binary security. Traditional...
Phasor Measurement Units (PMUs) are the backbone of smart grids that are able to measure power system observability in real-time. The deployment of synchronized sensors in power networks opens up the advantage of real-time monitoring of the network s...
Batch normalization (BN) is crucial for achieving state-of-the-art binary neural networks (BNNs). Unlike full-precision neural networks, BNNs restrict activations to discrete values
In this paper, we present the multi-stage attentive network (MSAN), an efficient and good generalization performance convolutional neural network (CNN) architecture for motion deblurring. We build a multi-stage encoder–decoder network with self...
The occurrence of epileptiform discharges (ED) in electroencephalographic (EEG) recordings of patients with epilepsy signifies a change in brain dynamics and particularly brain connectivity. Transcranial magnetic stimulation (TMS) has been recently a...
28 April 2022
Wireless Sensor Networks (WSNs) are increasingly deployed in Internet of Things (IoT) systems for applications such as smart transportation, telemedicine, smart health monitoring and fall detection systems for the elderly people. Given that huge amou...
To address the problems of difficult online monitoring, low recognition efficiency and the subjectivity of work condition identification in mineral flotation processes, a foam flotation performance state recognition method is developed to improve the...
Coal remains one of the key raw materials used in the energy industry to generate electricity and heat. As a result, diagnostics of the combustion process is still an important topic of scientific research. Correct implementation of the process allow...
This paper introduces a novel inverse design framework that combines pixelated microstrip antenna modeling, convolutional neural network (CNN), and binary particle swarm optimization (BPSO) to automate the process of generating antenna structures fro...
With the emergence of big data, the efficiency of data querying and data storage has become a critical bottleneck in the remote sensing community. In this letter, we explore hash learning for the indexing of large-scale remote sensing images (RSIs) w...
In recent years, wireless sensor networks (WSNs) have become a crucial technology for infrastructure monitoring. To ensure the reliability of monitoring services, evaluating the network’s reliability is particularly important. Sensor nodes are...
The security of wireless sensor networks (WSN) has become a great challenge due to the transmission of sensor data through an open and wireless network with limited resources. In the paper, we discussed a lightweight security scheme to protect the co...
The widespread integration of third-party components (TPCs) in Internet of Things (IoT) firmware significantly increases the risk of software vulnerabilities, especially in resource-constrained devices deployed in sensitive environments. Binary Code...
The numerical solution of relativistic hydrodynamics equations in conservative form requires root-finding algorithms that invert the conservative-to-primitive variables map. These algorithms employ the equation of state of the fluid and can be comput...
The estimation of vessels’ centerlines is a critical step in assessing the geometry of the vessel, the topological representation of the vessel tree, and vascular network visualization. In this research, we present a novel method for obtaining...
In recent years, searching and retrieving relevant images from large databases has become an emerging challenge for the researcher. Hashing methods that mapped raw data into a short binary code have attracted increasing attention from the researcher....
With the increasing utilization of drones, the cyber security threats they face have become more prominent. Code reuse in the software development of drone systems has led to vulnerabilities in drones. The binary code similarity analysis method offer...
Human fall identification can play a significant role in generating sensor based alarm systems, assisting physical therapists not only to reduce after fall effects but also to save human lives. Usually, elderly people suffer from various kinds of dis...
Low rank and sparse representation (LRSR) with dual-dictionaries-based methods for detecting anomalies in hyperspectral images (HSIs) are proven to be effective. However, the potential anomaly dictionary is vulnerable to being contaminated by the bac...
Vulnerability prediction, in which static analysis is leveraged to predict the vulnerabilities of binary programs, has become a popular research topic. Traditional vulnerability prediction methods depend on vulnerability patterns, which must be prede...
Various aspects of task execution load balancing of Internet of Things (IoTs) networks can be optimised using intelligent algorithms provided by software-defined networking (SDN). These load balancing aspects include makespan, energy consumption, and...
The joint utilization of multi-source data is of great significance in geospatial observation applications, such as urban planning, disaster assessment, and military applications. However, this approach is confronted with challenges including inconsi...
Perception and motor interaction with physical surroundings can be analyzed by the changes in probability laws governing two possible outcomes of neuronal activity, namely the presence or absence of spikes (binary states). Perception and motor intera...
Resting-state functional magnetic resonance imaging (fMRI) with graph theoretical modeling has been increasingly applied for assessing whole brain network topological organization, yet its reproducibility remains controversial. In this study, we acqu...
This work proposes a method for hardware reduction in circuits of Mealy finite state machines (FSMs). The circuits are implemented as networks of interconnected look-up table (LUT) elements. The FSMs with twofold state assignment and encoding of outp...
We demonstrate experimentally that a phase-separating host solvent can be used to organize colloidal rods into different cluster and network states. The rods are silica sphero-cylinders which are preferentially wet by the water-rich phase of an oil–w...
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