The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

Silver nanoparticles (AgNPs) can be synthesized from a variety of techniques including physical, chemical and biological routes. They have been widely used as nanomaterials for manufacturing cosmetic and healthcare products, antimicrobial textiles, wound dressings, antitumor drug carriers, etc. due to their excellent antimicrobial properties. Accordingly, AgNPs have gained access into our daily life, and the inevitable human exposure to these nanoparticles has raised concerns about their potential hazards to the environment, health, and safety in recent years. From in vitro cell cultivation tests, AgNPs have been reported to be toxic to several human cell lines including human bronchial epithelial cells, human umbilical vein endothelial cells, red blood cells, human peripheral blood mononuclear cells, immortal human keratinocytes, liver cells, etc. AgNPs induce a dose-, size- and time-dependent cytotoxicity, particularly for those with sizes ≤10 nm. Furthermore, AgNPs can cross the brain blood barrier of mice through the circulation system on the basis of in vivo animal tests. AgNPs tend to accumulate in mice organs such as liver, spleen, kidney and brain following intravenous, intraperitoneal, and intratracheal routes of administration. In this respect, AgNPs are considered a double-edged sword that can eliminate microorganisms but induce cytotoxicity in mammalian cells. This article provides a state-of-the-art review on the synthesis of AgNPs, and their applications in antimicrobial textile fabrics, food packaging films, and wound dressings. Particular attention is paid to the bactericidal activity and cytotoxic effect in mammalian cells.

Bactericidal and Cytotoxic Properties of Silver Nanoparticles.

Solid composite electrolytes (SCEs) that combine the advantages of solid polymer electrolytes (SPEs) and inorganic ceramic electrolytes (ICEs) present acceptable ionic conductivity, high mechanical strength, and favorable interfacial contact with electrodes, which greatly improve the electrochemical performance of all-solid-state batteries compared to single SPEs and ICEs. However, there are many challenges to overcome before the practical application of SCEs, including the low ionic conductivity less than 10-3 S cm-1 at ambient temperature, poor interfacial stability, and high interfacial resistance, which greatly restrict the room temperature performance. Herein, the advances of SCEs applied in all-solid-state lithium batteries are presented, including the Li ion migration mechanism of SCEs, the strategies to enhance the ionic conductivity of SCEs by various morphologies of ICEs, and construction methods of the low resistance and stable interfaces of SCEs with both cathode and anode. Finally, some typical applications of SCEs in lithium batteries are summarized and future development directions are prospected. This work presents how it is quite significant to further enhance the ionic conductivity of SCEs by developing the novel SPEs with the special morphology of ICEs for advanced all-solid-state lithium batteries.

https://onlinelibrary.wiley.com/doi/epdf/10.1002/advs.201903088

Progress and Perspective of Ceramic/Polymer Composite Solid Electrolytes for Lithium Batteries

Safety is the most important requirement for autonomous vehicles; hence, the ultimate challenge of designing an edge computing ecosystem for autonomous vehicles is to deliver enough computing power, redundancy, and security so as to guarantee the safety of autonomous vehicles. Specifically, autonomous driving systems are extremely complex; they tightly integrate many technologies, including sensing, localization, perception, decision making, as well as the smooth interactions with cloud platforms for high-definition (HD) map generation and data storage. These complexities impose numerous challenges for the design of autonomous driving edge computing systems. First, edge computing systems for autonomous driving need to process an enormous amount of data in real time, and often the incoming data from different sensors are highly heterogeneous. Since autonomous driving edge computing systems are mobile, they often have very strict energy consumption restrictions. Thus, it is imperative to deliver sufficient computing power with reasonable energy consumption, to guarantee the safety of autonomous vehicles, even at high speed. Second, in addition to the edge system design, vehicle-to-everything (V2X) provides redundancy for autonomous driving workloads and alleviates stringent performance and energy constraints on the edge side. With V2X, more research is required to define how vehicles cooperate with each other and the infrastructure. Last, safety cannot be guaranteed when security is compromised. Thus, protecting autonomous driving edge computing systems against attacks at different layers of the sensing and computing stack is of paramount concern. In this paper, we review state-of-the-art approaches in these areas as well as explore potential solutions to address these challenges.

Edge Computing for Autonomous Driving: Opportunities and Challenges

Hydrogen atom makes graphene magnetic Graphene has many extraordinary mechanical and electronic properties, but it's not magnetic. To make it so, the simplest strategy is to modify its electronic structure to create unpaired electrons. Researchers can do that by, for example, removing individual carbon atoms or adsorbing hydrogen onto graphene. This has to be done in a very controlled way because of a peculiarity of the graphene's crystal lattice, which consists of two sublattices. Gonzales-Herrero et al. deposited a single hydrogen atom on top of graphene and used scanning tunneling microscopy to detect magnetism on the sublattice lacking the deposited atom (see the Perspective by Hollen and Gupta). Science, this issue p. 437; see also p. 415 Scanning tunneling microscopy shows that a hydrogen atom deposited on graphene makes the complementary sublattice magnetic. [Also see Perspective by Hollen and Gupta] Isolated hydrogen atoms absorbed on graphene are predicted to induce magnetic moments. Here we demonstrate that the adsorption of a single hydrogen atom on graphene induces a magnetic moment characterized by a ~20–millielectron volt spin-split state at the Fermi energy. Our scanning tunneling microscopy (STM) experiments, complemented by first-principles calculations, show that such a spin-polarized state is essentially localized on the carbon sublattice opposite to the one where the hydrogen atom is chemisorbed. This atomically modulated spin texture, which extends several nanometers away from the hydrogen atom, drives the direct coupling between the magnetic moments at unusually long distances. By using the STM tip to manipulate hydrogen atoms with atomic precision, it is possible to tailor the magnetism of selected graphene regions.

/pdf/atomic-scale-control-of-graphene-magnetism-by-using-hydrogen-53d9rq6gri.pdf

Atomic-scale control of graphene magnetism by using hydrogen atoms

The invention provides a flow modeling method which realizes automatic processing of drag operation on a flow chart display zone, wherein the flow chart display zone is provided with a layout editor, an event monitor and a flow database. The method comprises the following steps: after a drag operation sent by a user is monitored, the position of the newly added action related with the drag operation is calculated; the newly added action overlaps with the prior action in the position, and a new branch containing the newly added action is added on the branch where the prior action is positioned; the layout of the flow chart is adjusted in the direction of longitudinal coordinates, a starting node of a parallel control flow corresponding to the newly added branch is used as the center in theadjustment process, and the longitudinal coordinates of all branches in the parallel control flow are calculated, therefore, all branches are symmetrical to the center; and the direction of the longitudinal coordinates is the normal direction of the newly added branch. The invention has the advantages that the flow modeling process is simple and convenientand has few errors; and the flow modelingefficiency is high.

Flow modeling method

The need for power-aware computing has become increasingly apparent. Common power-aware platforms have placed the burden of optimizing energy consumption on the programmer. In many cases this is a complex task which requires more time from the programmer than is acceptable. Hence, auto-tuning for power-aware computing has been proposed to alleviate the programmer from this task. Previous research has been focusing on automatic tuning of individual applications. However, there has been little work that tunes multiple programs across an entire platform. The Single-Chip Cloud Computer (SCC) is an experimental processor created by Intel Labs. In this paper, we present a method that extends auto-tuning to consider the multi-programmed workload across the entire many-core platform of SCC. Using an algorithm based on Differential Evolution, we were able to reduce the energy-delay product of the workload by 58.5%.

/pdf/auto-tuning-multi-programmed-workload-on-the-scc-1j0g825o8a.pdf

Auto-tuning multi-programmed workload on the SCC

Integrate multifunctional ionic sieve lithiated X zeolite-ionic liquid electrolyte for solid-state lithium metal batteries with ultralong lifespan

In the past 30 years, more and more information is stored in digitized format due to the rapid development of computer and communication technology. However, without good protection scheme, data will be hacked and cracked by the malicious intruders. A good security mechanism will keep information secrecy and integrity. However, cryptography algorithms are extremely expensive in terms of execution time because many arithmetic and logic operations are executed in the encryption/decryption process to make data not easily cracked. Huge amount of data also are transmitted between the CPU and memory. Using a traditional general-purpose processor would not be efficient for this scenario. Thus, the performance enhancement for the security system is crucial in modern world.

/pdf/hardware-assisted-security-mechanism-the-acceleration-of-44gd24gwjl.pdf

Hardware-assisted security mechanism: The acceleration of cryptographic operations with low hardware cost

In recent years, scale, frequency and complexity of cyber-attacks have been continuously on the rise. As a result, it has significantly impacted our daily lives and society as a whole. Never before have we had such an urgent need to defend against cyber-attacks. Previous studies suggest that it is possible to detect rootkits and control-flow attacks with high accuracy using information collected from hardware level. For data-only exploits, however, where the control-flow of the victim application is strictly conserved while its behavior may only be slightly modified, high accuracy detection is much more difficult to achieve. In this study, we propose the use of low-level hardware information collected as a short time series for the detection of data-only malware attacks. We employed several representative classification algorithms, e.g., linear regression (LR), autoencoder (AE), stacked denoising autoencoder (SDA), and echo state network (ESN). We build one-class classifiers that either use individual samples collected via monitoring hardware-level events or use multiple samples of hardware events collected at different time during execution, but all with only the knowledge from regular behavior. Using several real-life attacks as case studies, we examined their detection accuracy when confronted with malicious behavior. Our experimental results show that our SDA- and ESN-based approaches can achieve an average detection accuracy of 97.75% and 98.36% for the exploits studied, respectively. Our study suggests that when the hardware events are monitored at different time spots during the execution of the vulnerable application, our SDA- and ESN-based approaches have the potential to boost the detection accuracy for data exploits.

Chen Liu

Papers

Flow modeling method

Auto-tuning multi-programmed workload on the SCC

Integrate multifunctional ionic sieve lithiated X zeolite-ionic liquid electrolyte for solid-state lithium metal batteries with ultralong lifespan

Hardware-assisted security mechanism: The acceleration of cryptographic operations with low hardware cost

Detecting Data Exploits Using Low-level Hardware Information: A Short Time Series Approach