Distributed denial-of-service is one kind of the most highlighted and most important attacks of today's cyberworld. With simple but extremely powerful attack mechanisms, it introduces an immense threat to current Internet community. In this article, we present a comprehensive survey of distributed denial-of-service attack, prevention, and mitigation techniques. We provide a systematic analysis of this type of attacks including motivations and evolution, analysis of different attacks so far, protection techniques and mitigation techniques, and possible limitations and challenges of existing research. Finally, some important research directions are outlined which require more attentions in near future to ensure successful defense against distributed denial-of-service attacks.

No abstract

All‐solid‐state lithium metal batteries (ASSLMBs) stand out for the next generation of energy storage system. However, the further realization is severely hampered by the lithium dendrite formation in solid state electrolytes (SSEs), by mechanisms that remain controversial. Herein, with the aid of experimental and theoretical approaches, the origin of dendrite formation in representative LiBH4 SSE, which is thermodynamically stable with the Li metal, suppressing the side reaction between Li and SSE is elucidated. It is demonstrated that upon diffusion, Li+ encounters an electron, and is subsequently reduced to Li0 within the grain boundary/pore of SSE, eventually leading to short circuit. Thus, introducing LiF with the ability of interstitial filling and low electronic conductivity into SSE is the effective countermeasure, and as expected, with the addition of LiF, the critical current density (CCD) increases by 235% compared to the value of pure LiBH4. The TiS2|LiBH4–LiF|Li ASSLMBs manifest a reversible capacity of 137 mAh g−1 at 0.4 C upon 60 cycles. These findings not only unravel critical issues in Li dendrite formation in SSE, but also propose the countermeasure.

This article proposes a novel framework for the real-time capture, assessment, and visualization of ballet dance movements as performed by a student in an instructional, virtual reality (VR) setting. The acquisition of human movement data is facilitated by skeletal joint tracking captured using the popular Microsoft (MS) Kinect camera system, while instruction and performance evaluation are provided in the form of 3D visualizations and feedback through a CAVE virtual environment, in which the student is fully immersed. The proposed framework is based on the unsupervised parsing of ballet dance movement into a structured posture space using the spherical self-organizing map (SSOM). A unique feature descriptor is proposed to more appropriately reflect the subtleties of ballet dance movements, which are represented as gesture trajectories through posture space on the SSOM. This recognition subsystem is used to identify the category of movement the student is attempting when prompted (by a virtual instructor) to perform a particular dance sequence. The dance sequence is then segmented and cross-referenced against a library of gestural components performed by the teacher. This facilitates alignment and score-based assessment of individual movements within the context of the dance sequence. An immersive interface enables the student to review his or her performance from a number of vantage points, each providing a unique perspective and spatial context suggestive of how the student might make improvements in training. An evaluation of the recognition and virtual feedback systems is presented.

Pressure‐induced polymerization (PIP) of aromatics is a novel method for constructing sp3‐carbon frameworks, and nanothreads with diamond‐like structures were synthesized by compressing benzene and its derivatives. Here by compressing a benzene‐hexafluorobenzene cocrystal (CHCF), H‐F‐substituted graphane with a layered structure in the PIP product was identified. Based on the crystal structure determined from the in situ neutron diffraction and the intermediate products identified by gas chromatography‐mass spectrum, we found that at 20 GPa CHCF forms tilted columns with benzene and hexafluorobenzene stacked alternatively, and leads to a [4+2] polymer, which then transforms to short‐range ordered H‐F‐substituted graphane. The reaction process involves [4+2] Diels–Alder, retro‐Diels–Alder, and 1‐1′ coupling reactions, and the former is the key reaction in the PIP. These studies confirm the elemental reactions of PIP of CHCF for the first time, and provide insight into the PIP of aromatics.

In recent years, the technology about IoT (Internet of Things) has been applied into finance domain, and the generated data, such as the real-time data of chattel mortgage supervision with GPS, sensors, network cameras, mobile devices, etc., has been used to improve the capability of financial credit risk management of bank loans. Financial credit risk is by far one of the most significant risks that commercial banks have to face, however, when confronting to the massively growing financial data from multiple sources including Internet, mobile networks or IoT, traditional statistical models and neural network models might not operate fairly or accurately enough for credit risk assessment with those diverse data. Hence, there is a practical need to establish more powerful risk prediction models with artificial intelligence based on big data analytics to predict default behaviors with better accuracy and capacity. In this article, a big data mining approach of Particle Swarm Optimization (PSO) based Backpropagation (BP) neural network is proposed for financial risk management in commercial banks with IoT deployment, which constructs a nonlinear parallel optimization model with Apache Spark and Hadoop HDFS techniques on the dataset of on-balance sheet item and off-balance sheet item. The experiment results indicate that this parallel risk management model has fast convergence rate and powerful predictive capacity, and performs efficiently in screening default behaviors. In the meanwhile, the distributed implementation on big data clusters largely reduces the processing time of model training and testing. INDEX TERMS Big data, artificial intelligence, financial risk management, Internet of Things, particle swarm optimization, BP neural network.

No abstract

The proposed framework offers real-time analysis and visualization of ballet movements performed in a virtual reality environment. Students receive quantitative assessmentsdelivered using concurrent, localized visualizations-and a performance score based on incremental dynamic time warping.