Designing and Evaluating an Automatic Forensic Model for Fast Response of Cross-border E-commerce Security Incidents

The rapid development of cross-border e-commerce over the past decade has accelerated the integration of the global economy. At the same time, cross-border e-commerce has increased the prevalence of cybercrime, and the future success of e-commerce depends on enhanced online privacy and security. However, investigating security incidents is time- and cost-intensive as identifying telltale anomalies and the source of attacks requires the use of multiple forensic tools and technologies and security domain knowledge. Prompt responses to cyber-attacks are important to reduce damage and loss and to improve the security of cross-border e-commerce. This article proposes a digital forensic model for first incident responders to identify suspicious system behaviors. A prototype system is developed and evaluated by incident response handlers. The model and system are proven to help reduce time and effort in investigating cyberattacks. The proposed model is expected to enhance security incident handling efficiency for cross-border e-commerce.

Deep Neural Network–based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions

Internet-enabled smartphones and ultra-wide displays are transforming a variety of visual apps spanning from on-demand movies and 360° videos to video-conferencing and live streaming. However, robustly delivering visual content under fluctuating networking conditions on devices of diverse capabilities remains an open problem. In recent years, advances in the field of deep learning on tasks such as super-resolution and image enhancement have led to unprecedented performance in generating high-quality images from low-quality ones, a process we refer to as neural enhancement. In this article, we survey state-of-the-art content delivery systems that employ neural enhancement as a key component in achieving both fast response time and high visual quality. We first present the components and architecture of existing content delivery systems, highlighting their challenges and motivating the use of neural enhancement models as a countermeasure. We then cover the deployment challenges of these models and analyze existing systems and their design decisions in efficiently overcoming these technical challenges. Additionally, we underline the key trends and common approaches across systems that target diverse use-cases. Finally, we present promising future directions based on the latest insights from deep learning research to further boost the quality of experience of content delivery systems.

Designing and Evaluating an Automatic Forensic Model for Fast Response of Cross-Border E-Commerce Security Incidents

The rapid development of cross-border e-commerce over the past decade has accelerated the integration of the global economy. At the same time, cross-border e-commerce has increased the prevalence of cybercrime, and the future success of e-commerce depends on enhanced online privacy and security. However, investigating security incidents is time- and cost-intensive as identifying telltale anomalies and the source of attacks requires the use of multiple forensic tools and technologies and security domain knowledge. Prompt responses to cyber-attacks are important to reduce damage and loss and to improve the security of cross-border e-commerce. This article proposes a digital forensic model for first incident responders to identify suspicious system behaviors. A prototype system is developed and evaluated by incident response handlers. The model and system are proven to help reduce time and effort in investigating cyberattacks. The proposed model is expected to enhance security incident handling efficiency for cross-border e-commerce.

Nonlinear co-generation of graphene plasmons for optoelectronic logic gates

Surface plasmons in graphene provide a compelling strategy for advanced photonic technologies thanks to their tight confinement, fast response and tunability. Recent advances in the field of all-optical generation of graphene’s plasmons in planar waveguides offer a promising method for high-speed signal processing in nanoscale integrated optoelectronic devices. Here, we use two counter propagating frequency combs with temporally synchronized pulses to demonstrate deterministic all-optical generation and electrical control of multiple plasmon polaritons, excited via difference frequency generation (DFG). Electrical tuning of a hybrid graphene-fiber device offers a precise control over the DFG phase-matching, leading to tunable responses of the graphene’s plasmons at different frequencies and provides a powerful tool for high-speed logic operations. Our results offer new insights for plasmonics on hybrid photonic devices based on layered materials and pave the way to high-speed integrated optoelectronic computing circuits.

Thin MAPb0.5Sn0.5I3 Perovskite Single Crystals for Sensitive Infrared Light Detection

Metal halide perovskite single-crystal detectors have attracted increasing attention due to the advantages of low noise, high sensitivity, and fast response. However, the narrow photoresponse range of widely investigated lead-based perovskite single crystals limit their application in near-infrared (NIR) detection. In this work, tin (Sn) is incorporated into methylammonium lead iodide (MAPbI3) single crystals to extend the absorption range to around 950 nm. Using a space-confined strategy, MAPb0.5Sn0.5I3 single-crystal thin films with a thickness of 15 μm is obtained, which is applied for sensitive NIR detection. The as-fabricated detectors show a responsivity of 0.514 A/W and a specific detectivity of 1.4974×1011 cmHz1/2/W under 905 nm light illumination and –1V. Moreover, the NIR detectors exhibit good operational stability (∼30000 s), which can be attributed to the low trap density and good stability of perovskite single crystals. This work demonstrates an effective way for sensitive NIR detection.

Ferroelectric-based synapses and neurons for neuromorphic computing

The shift towards a distributed computing paradigm, where multiple systems acquire and elaborate data in real-time, leads to challenges that must be met. In particular, it is becoming increasingly essential to compute on the edge of the network, close to the sensor collecting data. The requirements of a system operating on the edge are very tight: power efficiency, low area occupation, fast response times, and on-line learning. Brain-inspired architectures such as Spiking Neural Networks (SNNs) use artificial neurons and synapses that simultaneously perform low-latency computation and internal-state storage with very low power consumption. Still, they mainly rely on standard complementary metal-oxide-semiconductor (CMOS) technologies, making SNNs unfit to meet the aforementioned constraints. Recently, emerging technologies such as memristive devices have been investigated to flank CMOS technology and overcome edge computing systems' power and memory constraints. In this review, we will focus on ferroelectric technology. Thanks to its CMOS-compatible fabrication process and extreme energy efficiency, ferroelectric devices are rapidly affirming themselves as one of the most promising technology for neuromorphic computing. Therefore, we will discuss their role in emulating neural and synaptic behaviors in an area and power-efficient way.

Electrospun ZnSnO3/ZnO Composite Nanofibers and Its Air-Sensitive Properties

In this work, a novel heterojunction based on ZnSnO3/ZnO nanofibers was prepared using electrospinning method. The crystal, structural and surface compositional properties of sample based on ZnSnO3 and ZnSnO3/ZnO composite nanofibers were investigated by X-ray diffractometer (XRD), Scanning electron microscope (SEM), X-ray photoelectron spectrometer (XPS) and Brunauer-Emmett-Teller (BET). Compared to pure ZnSnO3 nanofibers, the ZnSnO3/ZnO heterostructure nanofibers display high sensitivity and selectivity response with fast response towards ethanol gas at low operational temperature. The sensitivity response of sensor based on ZnSnO3/ZnO composite nanofibers were 19.6 towards 50 ppm ethanol gas at 225°C, which was about 1.5 times superior than that of pure ZnSnO3 nanofibers, which can be owed mainly to the presence of oxygen vacancies and the synergistic effect between ZnSnO3 and ZnO.

Ultrasensitive dynamic light scattering immunosensing platform for NT-proBNP detection using boronate affinity amplification

Herein, we reported a new dynamic light scattering (DLS) immunosensing technology for the rapid and sensitive detection of glycoprotein N-terminal pro-brain natriuretic peptide (NT-proBNP). In this design, the boronate affinity recognition based on the interaction of boronic acid ligands and cis-diols was introduced to amplify the nanoparticle aggregation to enable highly sensitive DLS transduction, thereby lowering the limit of detection (LOD) of the methodology. After covalently coupling with antibodies, magnetic nanoparticles (MNPs) were employed as the nanoprobes to selectively capture trace amount of NT-proBNP from complex samples and facilitate DLS signal transduction. Meanwhile, silica nanoparticles modified with phenylboronic acid (SiO2@PBA) were designed as the crosslinking agent to bridge the aggregation of MNPs in the presence of target NT-proBNP. Owing to the multivalent and fast affinity recognition between NT-proBNP containing cis-diols and SiO2@PBA, the developed DLS immunosensor exhibited charming advantages over traditional immunoassays, including ultrahigh sensitivity with an LOD of 7.4 fg mL−1, fast response time (< 20 min), and small sample consumption (1 μL). The DLS immunosensor was further characterized with good selectivity, accuracy, precision, reproducibility, and practicability. Collectively, this work demonstrated the promising application of the designed boronate affinity amplified-DLS immunosensor for field or point-of-care testing of cis-diol-containing molecules. Graphical Abstract

Self-Evolutionary Neuron Model for Fast-Response Spiking Neural Networks

We propose two simple and effective spiking neuron models to improve the response time of the conventional spiking neural network. The proposed neuron models adaptively tune the presynaptic input current depending on the input received from its presynapses and subsequent neuron firing events. We analyze and derive the firing activity homeostatic convergence of the proposed models. We experimentally verify and compare the models on MNIST handwritten digits and FashionMNIST classification tasks. We show that the proposed neuron models significantly increase the response speed to the input signal.