DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences

Identification of drug-target interactions (DTIs) plays a key role in drug discovery. The high cost and labor-intensive nature of in vitro and in vivo experiments have highlighted the importance of in silico-based DTI prediction approaches. In several computational models, conventional protein descriptors are shown to be not informative enough to predict accurate DTIs. Thus, in this study, we employ a convolutional neural network (CNN) on raw protein sequences to capture local residue patterns participating in DTIs. With CNN on protein sequences, our model performs better than previous protein descriptor-based models. In addition, our model performs better than the previous deep learning model for massive prediction of DTIs. By examining the pooled convolution results, we found that our model can detect binding sites of proteins for DTIs. In conclusion, our prediction model for detecting local residue patterns of target proteins successfully enriches the protein features of a raw protein sequence, yielding better prediction results than previous approaches.Comment: 26 pages, 7 figure

Analysis of Trust in the E-Commerce Adoption

Understanding user acceptance of the Internet, especially the usage intention of virtual communities, is important in explaining the fact that virtual communities have been growing at an exponential rate in recent years. This paper studies the trust of virtual communities to better understand and manage the activities of E-commerce. A theoretical model proposed in this paper is to clarify the factors as they are related to the Technology Acceptance Model. In particular the relationship between trust and Intentions is hypothesized. Using the Technology Acceptance Model, this research showed that the importance of trust in virtual communities. According to the research, different ways of stimulating the members are necessary in order to facilitate participation in activities of virtual communities. The effect of trust in members on intention to use is stronger than that of trust in service providers. The intention to purchase is more sensitive to trust in service providers than trust in members

시스템온칩 상에서의 효율적이고 실용적인 보안 모니터링을 위한 응용 특화 하드웨어 모듈

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. 백윤흥.Many researchers have proposed the concept of security monitoring, which watches the execution behavior of a program (e.g, control-flow or data-flow) running on the machine to find the existence of malicious attacks. Among the proposed approaches in the literature, software-based works are known to be relatively easy to be adopted to the commercial products, but may incur tremendous runtime overhead. Although many hardware-based solutions provide high performance, the inherent problem of them is that they usually mandate drastic change to the internal processor architecture. More recent ones to minimize the change have proposed external devices for security monitoring. However, these approaches intrinsically suffer from the high overhead to communicate with their external devices. Consequently, they either significantly lose performance, or inevitably make invasive modifications to the processor inside. In this thesis, I propose several approaches for efficient security monitoring, where external hardware engines conduct the task of monitoring. The main priority in desinging the engines is not to require any modification in the host processor core internal. Thus, the engines introduced in this thesis are designed as external hardware modules and integrated to the host processor using the existing interface in the system. Complying with the rule, I explored the architectural design space for the engine and in ths thesis, three types of such approaches will be presented. Starting from the hardware engine that utilizes only the system bus, I will introduce the final solution that exploits the debug interface of the commercial processor. From the design exploration, this thesis shows various design decisions that can be applied in the current commercial platforms.Chapter 1 Introduction 1 Chapter 2 Implementing an Application Specific Instructionset Processor for System Level Dynamic Program Analysis Engines 6 2.1 Introduction 6 2.2 Backgrounds 11 2.2.1 Understanding Tag-based DPA Techniques 11 2.2.2 DPA Execution on a System-Level Hardware Engine 12 2.3 System-Level Programmable DPA Engine for Extendibility 14 2.3.1 Overall System Design with PAU 14 2.3.2 Execution Trace Communication 17 2.3.3 Synchronization and Multi-threading Support 18 2.4 Tag Processing Core 20 2.4.1 TPC Instruction-Set Architecture 20 2.4.2 TPC Microarchitecture 25 2.5 Case Studies 27 2.5.1 Case Study 1 : DIFT for Data Leak Prevention 27 2.5.2 Case Study 2 : Uninitialized Memory Checking 33 2.5.3 Case Study 3 : Bound Checking 36 2.6 Implementing Optimizations for DIFT with TPC 38 2.6.1 Function Level Tag Propagation Optimization 40 2.6.2 Block Level Tag Propagation Optimization 42 2.7 Experiment 45 2.7.1 Prototype System 45 2.7.2 Synthesis Results 46 2.7.3 Performance Evaluation 47 2.8 Related Works 53 2.9 Chapter Summary 58 Chapter 3 A Practical Solution to Detect Code Reuse Attacks on ARM Mobile Devices using an On-chip Debug Module 60 3.1 Introduction 60 3.2 Related Work and Assumptions 65 3.2.1 Related Work 65 3.2.2 Threat Model and Assumptions 67 3.3 Architecture for ROP Detection 68 3.3.1 Branch Trace Analyzer 70 3.3.2 Shadow Call Stack 72 3.4 Meta-data Construction 74 3.4.1 Meta-data Structure 75 3.4.2 Using Meta-data for ROP Monitoring 78 3.5 Experimental Result 79 3.6 Chapter Summary 82 Chapter 4 Efficient Security Monitoring with Core Debug Interface in an Embedded Processor 84 4.1 Introduction 84 4.2 Background 86 4.2.1 Control Flow Integrity Checking for Detecting Code Reuse Attacks 86 4.2.2 Core Debug Interface 87 4.3 Our Framework 88 4.3.1 Overall Architecture 89 4.3.2 CDI Filter and Trace FIFO 90 4.3.3 Monitor Engine 91 4.4 Bulding a DIFT Engine for CDI 91 4.4.1 DIFT on Our Framework 92 4.4.2 Design of our DIFT Engine 94 4.5 Implementing a CRA Detection with CDI 98 4.5.1 Branch Regulation on Our Framework 98 4.5.2 Design of our CRA Detection Engine 100 4.6 Experiment 105 4.6.1 Prototype and Synthesis Result 105 4.6.2 Experimental Results for DIFT 106 4.6.3 Experimental Results for Branch Regulation 110 4.7 Related Work 111 4.8 Chapter Summary 114 Chapter 5 Conculsion 116 Bibliography 118 초록 132Docto

Stock market reaction to information technology investments: Towards an explanatory model

Online discussion forum, which plays an important role in online criticism, provides useful information such as online commentaries generated by other users. The paper uses regulatory focus theory to explain how online commentaries are processed differently depending on the user’s information processing style and how each self-regulatory mode moderates the impact of online commentaries on one’s overall evaluation of information. The study produces three major findings: (1) Promotion-focused users are more likely to distort online information than prevention-focused users do, (2) With hedonic information, information distortion will be stronger for promotion-focused users as compared to prevention-focused users, (3) With utilitarian information, information distortion will be stronger for prevention-focused people as compared to promotion-focused users. These finding have implications for online discussion forums in terms of how to manage users effectively and also how to prevent unintended criticism