There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Data Mining Practical Machine Learning Tools and Techniques

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

Appending digital signatures and certificates to messages guarantee data integrity and ensure non-repudiation, but do not identify greedy authenticated nodes. Trust evolves if some reputable and trusted node verifies the node, data and evaluates the trustworthiness of the node using an accurate metric. But, even if the verifying party is a trusted centralized party, there is opacity and obscurity in computed reputation rating. The trusted party maps it with the node's identity, but how is it evaluated and what inputs derive the reputation rating remains hidden, thus concealment of transparency leads to privacy. Besides, the malevolent nodes might collude together for defamatory actions against reliable nodes, and eventually bad mouth these nodes or praise malicious nodes collaboratively. Thus, we cannot always assume the fairness of the nodes as the rating they give to any node might not be a fair one. In this paper, we propose a smart contract-based approach to update and query the reputation of nodes, stored and maintained by IPFS distributed storage. The use case particularly deals with an emergency scenario, dealing against colluding attacks. Our scheme is implemented using MATLAB simulation. The results show how smart contracts are capable of accurately identifying trustworthy nodes and record the reputation of a node transparently and immutably.

Trust and Reputation in Vehicular Networks: A Smart Contract-Based Approach

Radio-frequency identification (RFID) is an automatic identification system which has become a hot topic in the fields of manufacturing, logistics, and so on. The purpose of this research is to propose a methodology for designing the RFID network planning problem (RNP) for application in the Mackay Memorial Hospital in Hsinchu, Taiwan. In this study, the RFID network is first considered as a grid and divided into several small squares. A soft computing methodology called FKB-SSO is proposed to solve the RNP problem based on simplified swarm optimization (SSO) by integrating k-means, fuzzy adaptive resonance theory (fuzzy-ART), and binary search. The proposed FKB-SSO will provide the basis for strategic decisions in constructing the RFID network to reduce the number of RFID readers with a minimal budget under the constraint of 100% coverage rate. The proposed FKB-SSO is more efficient than PSO and experts' manual solution in both run time and solution quality.

A radio frequency identification network design methodology for the decision problem in Mackay Memorial Hospital based on swarm optimization

This paper proposed a method for discriminating pedestrians from rigid objects in a video. The methodis a motion-based recognition of moving objects. This method is motivated by the assumptions that human beings are non-rigid and their movements are periodic. Moving objects and their skeletons are extracted. The motion cue is determined by the angle formed by the centroid point and the two bottom end points at object’s skeleton. The histogram of the cue over a time period is used to determine if the object is pedestrian or not. This cue does not require any pre-built models. Neither does it need Fourier Transform to obtain the cycle of the objects. The proposed method is computation inexpensive, and it can be used for realtime video surveillance.

Motion Based Pedestrian Recognition

Security and privacy of the users have become significant concerns due to the involvement of the Internet of things (IoT) devices in numerous applications. Cyber threats are growing at an explosive pace making the existing security and privacy measures inadequate. Hence, everyone on the Internet is a product for hackers. Consequently, Machine Learning (ML) algorithms are used to produce accurate outputs from large complex databases, where the generated outputs can be used to predict and detect vulnerabilities in IoT-based systems. Furthermore, Blockchain (BC) techniques are becoming popular in modern IoT applications to solve security and privacy issues. Several studies have been conducted on either ML algorithms or BC techniques. However, these studies target either security or privacy issues using ML algorithms or BC techniques, thus posing a need for a combined survey on efforts made in recent years addressing both security and privacy issues using ML algorithms and BC techniques. In this paper, we provide a summary of research efforts made in the past few years, starting from 2008 to 2019, addressing security and privacy issues using ML algorithms and BCtechniques in the IoT domain. First, we discuss and categorize various security and privacy threats reported in the past twelve years in the IoT domain. Then, we classify the literature on security and privacy efforts based on ML algorithms and BC techniques in the IoT domain. Finally, we identify and illuminate several challenges and future research directions in using ML algorithms and BC techniques to address security and privacy issues in the IoT domain.

Security & Privacy in IoT Using Machine Learning & Blockchain: Threats & Countermeasures.

Fractal image compression is an efficient and effective technique in image coding. This paper presents a novel interactive progressive fractal decoding method, with which the compressed file can be transmitted incrementally and reconstructed progressively at users side. It requires no modification to either encoder or decoder of any fractal image compression algorithm. In addition, it also provides the user-controlled decoding procedure with the inherited fractal fast decoding feature. The experimental results illustrated that the proposed progressive decoding method can be applied directly to various fractal compression techniques and especially favourable to applications where the transmission bandwidth is of great concern.

Xiangjian He

Papers

Trust and Reputation in Vehicular Networks: A Smart Contract-Based Approach

A radio frequency identification network design methodology for the decision problem in Mackay Memorial Hospital based on swarm optimization

Motion Based Pedestrian Recognition

Security & Privacy in IoT Using Machine Learning & Blockchain: Threats & Countermeasures.

A Novel Interactive Progressive Decoding Method for Fractal Image Compression