We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Mobile edge computing (MEC) is an emergent architecture where cloud computing services are extended to the edge of networks leveraging mobile base stations. As a promising edge technology, it can be applied to mobile, wireless, and wireline scenarios, using software and hardware platforms, located at the network edge in the vicinity of end-users. MEC provides seamless integration of multiple application service providers and vendors toward mobile subscribers, enterprises, and other vertical segments. It is an important component in the 5G architecture which supports variety of innovative applications and services where ultralow latency is required. This paper is aimed to present a comprehensive survey of relevant research and technological developments in the area of MEC. It provides the definition of MEC, its advantages, architectures, and application areas; where we in particular highlight related research and future directions. Finally, security and privacy issues and related existing solutions are also discussed.

/pdf/mobile-edge-computing-a-survey-3eu2fzhuxk.pdf

Mobile Edge Computing: A Survey

Despite the increasing usage of cloud computing, there are still issues unsolved due to inherent problems of cloud computing such as unreliable latency, lack of mobility support and location-awareness. Fog computing can address those problems by providing elastic resources and services to end users at the edge of network, while cloud computing are more about providing resources distributed in the core network. This survey discusses the definition of fog computing and similar concepts, introduces representative application scenarios, and identifies various aspects of issues we may encounter when designing and implementing fog computing systems. It also highlights some opportunities and challenges, as direction of potential future work, in related techniques that need to be considered in the context of fog computing.

https://www.cs.wm.edu/~liqun/paper/mobidata15-fog.pdf

A Survey of Fog Computing: Concepts, Applications and Issues

Internet-of-Things (IoT) are everywhere in our daily life. They are used in our homes, in hospitals, deployed outside to control and report the changes in environment, prevent fires, and many more beneficial functionality. However, all those benefits can come of huge risks of privacy loss and security issues. To secure the IoT devices, many research works have been conducted to countermeasure those problems and find a better way to eliminate those risks, or at least minimize their effects on the user’s privacy and security requirements. The survey consists of four segments. The first segment will explore the most relevant limitations of IoT devices and their solutions. The second one will present the classification of IoT attacks. The next segment will focus on the mechanisms and architectures for authentication and access control. The last segment will analyze the security issues in different layers.

A Survey on Security and Privacy Issues in Internet-of-Things

In an augmented reality (AR) application, placing labels in a manner that is clear and readable without occluding the critical information from the real-world can be a challenging problem. This paper introduces a label placement technique for AR used in street view scenarios. We propose a semantic-aware task-specific label placement method by identifying potentially important image regions through a novel feature map, which we refer to as guidance map. Given an input image, its saliency information, semantic information and the task-specific importance prior are integrated into the guidance map for our labeling task. To learn the task prior, we created a label placement dataset with the users' labeling preferences, as well as use it for evaluation. Our solution encodes the constraints for placing labels in an optimization problem to obtain the final label layout, and the labels will be placed in appropriate positions to reduce the chances of overlaying important real-world objects in street view AR scenarios. The experimental validation shows clearly the benefits of our method over previous solutions in the AR street view navigation and similar applications.

Semantic-Aware Label Placement for Augmented Reality in Street View.

The augmented reality (AR) Metaverse environment combines the physical and virtual world together. Privacy is a major concern in AR since the cameras use to capture the physical world can also capture other images that may potentially violate user or by-stander privacy. Advances in deep learning to process images and videos have exacerbated such privacy risks. This paper presents a new technique to protect privacy in AR systems by combining the idea of visual saliency together with privacy-sensitive object detection. We show that our technique is able to provide additional context to a given image to better balance between privacy and overall usability of the system.

https://dl.acm.org/doi/pdf/10.1145/3597063.3597358

Saliency-Aware Privacy Protection in Augmented Reality Systems

Many critical applications rely on cameras to capture video footage for analytical purposes. This has led to concerns about these cameras accidentally capturing more information than is necessary. In this paper, we propose a deep learning approach towards protecting privacy in camera-based systems. Instead of specifying specific objects (e.g. faces) are privacy sensitive, our technique distinguishes between salient (visually prominent) and non-salient objects based on the intuition that the latter is unlikely to be needed by the application.

Deep Learning Approach Protecting Privacy in Camera-Based Critical Applications.

The search for smart contract source codes has drawn research attention to fulfill developers’ and researchers’ needs. Yet, the existing studies are not mature enough to address smart contracts’ technical properties and functionalities. This paper proposes a system to improve the naive search for smart contract codes; for example, Etherscan has one keyword search feature without regard to the contract structure. We consider clustering smart contracts based on developers’ preferences, which increases the probability that the resulting source codes match developers’ needs. Our experimental results show a significant improvement in the complexity of the retrieved source codes of smart contracts compared with the baseline scenario using blockchain search engines (e.g., Etherscan). Our solution reduces the number of retrieved smart contract codes the developer has to check if the codes match her/his needs by 94%, 88%, 82%, or 98%, depending on the user’s search preferences. 

Improving smart contract search by semantic and structural clustering for source codes

Naturalistic Action Test (NAT) is a diagnostic tool that involves a participant completing a common, everyday task under the observation of a trained clinician The clinician can identify and quantify the severity of an individuals cognitive impairments based on the his or her actions while carrying out the task Individuals with cognitive impairments have been shown to commit errors such as performing an incorrect sequence of steps when completing a task at greater rates than individuals without cognitive impairments This paper describes our initial experiences in developing a wearable computing-based system to support NATs The system’s objective is to eventually help the clinician streamline the analysis of NATs by processing of the smartwatch collected sensor values to try and identify episodes that resemble errors

Chiu C. Tan

Papers

Semantic-Aware Label Placement for Augmented Reality in Street View.

Saliency-Aware Privacy Protection in Augmented Reality Systems

Deep Learning Approach Protecting Privacy in Camera-Based Critical Applications.

Improving smart contract search by semantic and structural clustering for source codes

Wearable Computing Support for Objective Assessment of Function in Older Adults