日時: 6月10日(木)3限（13:30~15:00）

title: *** Millimeter Wave Vs. Microwave: Which Do Eavesdroppers Prefer? *** 

abstract: *** Hybrid communication systems, where millimeter wave (mmWave) links coexist with microwave links, have been regarded as a fundamental component in 5G systems, while, like any other wireless system, communications in these systems are vulnerable to eavesdropping attacks. In hybrid wireless systems, eavesdroppers can improve their eavesdropping effect by opportunistically selecting the wave (i.e., mmWave or microwave) to eavesdrop on, partitioning the network into an mmWave eavesdropping region and a microwave eavesdropping region. This paper therefore investigates the eavesdropping region characterization problem in hybrid wireless systems from the perspective of physical layer security. We first derive the secrecy outage probabilities (SOPs) of the network when eavesdroppers choose to eavesdrop on the mmWave and microwave, respectively. Using the ratio between the SOP of mmWave links and that of microwave links as the eavesdropping wave selection criterion, we determine the mmWave and microwave eavesdropping regions. Finally, we provide extensive numerical results to illustrate the eavesdropping regions under various settings of network parameters. *** 

language of the presentation: *** English *** 

Title: A High-performance ASIC-resistant Hardware Architecture for Cryptocurrency Mining  

Abstract: Blockchain is a technology using a decentralized network. Accordingly, its database stores information differently from a typical database. The data is stored in blocks that are chained together. Once the block is filled with data, it will be stored in the blockchain permanently if proof of consensus mechanism is accepted. Blockchain is usually applied for cryptocurrencies such as Bitcoin. Bitcoin uses the Proof of Work(PoW) consensus mechanism. Despite ensuring high security, PoW required huge computation power to mine a new block to lengthen the blockchain. Moreover, because of the simple mining hash algorithm, most Bitcoin miners are performed by ASICs. With the outstanding hash performance, ASICs make the miners' power not balance and tend to concentrate. Then, the Bitcoin mining difficulty will increase, and ASIC miners will take over the mining process. This may harm the blockchain network because the ASIC miners control and make any invalidated data. Hence, ASIC-resistant algorithms are invented to destroy the ASIC advantages. They make ASICs useless, inefficient, or high cost and risk. With high complexity, they bring obstacles for general-purpose GPUs and CPUs. This research will develop an optimized hardware architecture for an ASIC-resistant mining system on FPGA, a flexible, high-performance, and low-power hardware platform. We will prove that our proposed system has much higher energy efficiency for cryptography mining systems by evaluating power consumption and hash rate.  

Language of the presentation: English 

title: *** Automatically Classifying Self-Admitted Technical Debt in Build Systems *** 

abstract: *** Technical debt is a metaphor used to describe the situation in which long-term code quality is traded for short-term goals in software projects. In recent years, the concept of self-admitted technical debt (SATD) was proposed, which focuses on debt that is intentionally introduced and described by developers. Although prior work has made important observations about SATD in source code, little is known about SATD in build systems, i.e., the specifications that describe how source code is transformed into deliverables. Build systems often suffer massive maintenance activities during the development process. The part of these activities is produced by SATD, since SATD changes are more difficult to perform and SATD inevitably generates long-term maintenance problems from the short-term hack. Thus, I set out to better understand the characteristics of SATD in build systems. To do so, I classify SATD by locations, reasons, and purposes. To automate the detection of SATD reasons and purposes, I train classifiers to label comments according to the surrounding document content. My work presents the first step towards understanding SATD comment in build systems and opens up avenues for future work on tools to support SATD management in build systems. *** 

language of the presentation: *** English ***