TOWARD HIGHLY SECURE AND AUTONOMIC COMPUTING SYSTEMS: A HIERARCHICAL APPROACH

The overall objective of this research project is to develop novel architectural techniques as well as system software to achieve a highly secure and intrusion-tolerant computing system. Such system will be autonomous, self-adapting, introspective, with self-healing capability under the circumstances of improper operations, abnormal workloads, and malicious attacks. The scope of this research includes: (1) System-wide, unified introspection techniques for autonomic systems, (2) Secure information-flow microarchitecture, (3) Memory-centric security architecture, (4) Authentication control and its implication to security, (5) Digital right management, (5) Microarchitectural denial-of-service attacks on shared resources. During the period of the project, we developed several architectural techniques and system software for achieving a robust, secure, and reliable computing system toward our goal

A Temporal Usage Pattern-based Tag Recommendation Approach

While social tagging can benefit Internet users managing their resources, it suffers the problems such as diverse and/or unchecked vocabulary and unwillingness to tag. Use of freely new tags and/or reuse of frequent tags have degraded coherence of corresponding resources of each tag that further frustrates people in retrieving information due to cognitive dissonance. Tag recommender systems can recommend users the most relevant tags to the resource they intend to annotate, and drastically transfer the tagging process from generation to recognition to reduce user’s cognitive effort and time. Prior research on tag recommendation has addressed the time-dependence issues of tags by applying a time decaying measure to determine the recurrence probability of a tag according to its recency instead of its usage pattern. In response, this study intends to propose the temporal usage pattern-based tag recommendation technique to consider the usage patterns and temporal characteristic of tags for making recommendations

The Value of Academic Directors to Stakeholders: Evidence on Corporate Social Responsibility Reporting

This study explores the regulatory setting in Taiwan and examines the association between academic directors and corporate social responsibility (CSR) reporting. We find that firms with academic directors on the board are more likely to issue a stand-alone CSR report and obtain third-party assurance on their CSR reports. We also find a positive association between CSR reporting and academic directors with industry expertise. Further cross-sectional analyses indicate that the positive relation between academic directors (and their industry expertise) and CSR reporting is stronger in firms with higher growth, greater institutional ownership, and lower control-ownership divergence. Our findings that the presence of academic directors can promote better sustainability reporting suggest that academic directors contribute not only to shareholder value but also to wider stakeholder interests

Nanotargeted Radionuclides for Cancer Nuclear Imaging and Internal Radiotherapy

Current progress in nanomedicine has exploited the possibility of designing tumor-targeted nanocarriers being able to deliver radionuclide payloads in a site or molecular selective manner to improve the efficacy and safety of cancer imaging and therapy. Radionuclides of auger electron-, α-, β-, and γ-radiation emitters have been surface-bioconjugated or after-loaded in nanoparticles to improve the efficacy and reduce the toxicity of cancer imaging and therapy in preclinical and clinical studies. This article provides a brief overview of current status of applications, advantages, problems, up-to-date research and development, and future prospects of nanotargeted radionuclides in cancer nuclear imaging and radiotherapy. Passive and active nanotargeting delivery of radionuclides with illustrating examples for tumor imaging and therapy are reviewed and summarized. Research on combing different modes of selective delivery of radionuclides through nanocarriers targeted delivery for tumor imaging and therapy offers the new possibility of large increases in cancer diagnostic efficacy and therapeutic index. However, further efforts and challenges in preclinical and clinical efficacy and toxicity studies are required to translate those advanced technologies to the clinical applications for cancer patients

A Temporal Frequent Itemset-Based Clustering Approach For Discovering Event Episodes From News Sequence

When performing environmental scanning, organizations typically deal with a numerous of events and topics about their core business, relevant technique standards, competitors, and market, where each event or topic to monitor or track generally is associated with many news documents. To reduce information overload and information fatigues when monitoring or tracking such events, it is essential to develop an effective event episode discovery mechanism for organizing all news documents pertaining to an event of interest. In this study, we propose the time-adjoining frequent itemset-based event-episode discovery (TAFIED) technique. Based on the frequent itemset-based hierarchical clustering (FIHC) approach, our proposed TAFIED further considers the temporal characteristic of news articles, including the burst, novelty, and temporal proximity of features in an event episode, when discovering event episodes from the sequence of news articles pertaining to a specific event. Using the traditional feature-based HAC, HAC with a time-decaying function (HAC+TD), and FIHC techniques as performance benchmarks, our empirical evaluation results suggest that the proposed TAFIED technique outperforms all evaluation benchmarks in cluster recall and cluster precision

Adaptive Transaction Scheduling for Transactional Memory Systems

Transactional memory systems are expected to enable parallel programming at lower programming complexity, while delivering improved performance over traditional lock-based systems. Nonetheless, we observed that there are situations where transactional memory systems could actually perform worse, and that these situations will actually become dominant in future workloads as more and larger-scale trans- actional memory systems are available. Transactional memory systems can excel locks only when the executing workloads contain sufficient parallelism. When the workload lacks the inherent parallelism, blindly launching excessive transactions can adversely result in performance degradation. To quantita- tively demonstrate the issues, we introduce the concept of effective transactions in this paper. We show that the effectiveness of a transaction is closely related to a dynamic quantity we call contention inten- sity. By limiting the contention intensity below the desired level, we can significantly increase transaction effectiveness. Increased effectiveness directly increases the overall performance of a transactional memory system. Based on our study, we implemented a transaction scheduler which not only guarantees that hard- ware transactional memory systems perform better than locks, but also significantly improves performance for both the hardware and software transactional memory systems

EXPLORING E-LEARNING BEHAVIOR THROUGH LEARNING DISCOURSES

As many studies predict e-learning behaviors through intention, few of them investigate user’s learning behaviors directly. In addition to intention, individual’s e-learning behaviors may be influenced by technology readiness and group influences, such as social identity and social bond. This research-in-progress study explores how e-learning behaviors vary with intention, technology readiness, social identity and social bond. Our investigation was based on analyzing the speech acts embedded in fourteen learners’ online discourses in an eighteen-week e-learning course. We then compared how speech acts varied among groups with different degree of intention, technology readiness, social identity, and social bond. Our findings contribute e-learning research by clarifying how intention, technology readiness, social identity, and social bond influence learning behaviors in e-learning context

Production of N-acetyl-D-neuraminic acid using two sequential enzymes overexpressed as double-tagged fusion proteins

<p>Abstract</p> <p>Background</p> <p>Two sequential enzymes in the production of sialic acids, N-acetyl-D-glucosamine 2-epimerase (GlcNAc 2-epimerase) and <it>N</it>-acetyl-D-neuraminic acid aldolase (Neu5Ac aldolase), were overexpressed as double-tagged gene fusions. Both were tagged with glutathione S-transferase (GST) at the N-terminus, but at the C-terminus, one was tagged with five contiguous aspartate residues (5D), and the other with five contiguous arginine residues (5R).</p> <p>Results</p> <p>Both fusion proteins were overexpressed in <it>Escherichia coli </it>and retained enzymatic activity. The fusions were designed so their surfaces were charged under enzyme reaction conditions, which allowed isolation and immobilization in a single step, through a simple capture with either an anionic or a cationic exchanger (Sepharose Q or Sepharose SP) that electrostatically bound the 5D or 5R tag. The introduction of double tags only marginally altered the affinity of the enzymes for their substrates, and the double-tagged proteins were enzymatically active in both soluble and immobilized forms. Combined use of the fusion proteins led to the production of <it>N</it>-acetyl-D-neuraminic acid (Neu5Ac) from <it>N</it>-acetyl-D-glucosamine (GlcNAc).</p> <p>Conclusion</p> <p>Double-tagged gene fusions were overexpressed to yield two enzymes that perform sequential steps in sialic acid synthesis. The proteins were easily immobilized via ionic tags onto ionic exchange resins and could thus be purified by direct capture from crude protein extracts. The immobilized, double-tagged proteins were effective for one-pot enzymatic production of sialic acid.</p

Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systems

Online personalized recommendation services are generally hosted in the cloud where users query the cloud-based model to receive recommended input such as merchandise of interest or news feed. State-of-the-art recommendation models rely on sparse and dense features to represent users' profile information and the items they interact with. Although sparse features account for 99% of the total model size, there was not enough attention paid to the potential information leakage through sparse features. These sparse features are employed to track users' behavior, e.g., their click history, object interactions, etc., potentially carrying each user's private information. Sparse features are represented as learned embedding vectors that are stored in large tables, and personalized recommendation is performed by using a specific user's sparse feature to index through the tables. Even with recently-proposed methods that hides the computation happening in the cloud, an attacker in the cloud may be able to still track the access patterns to the embedding tables. This paper explores the private information that may be learned by tracking a recommendation model's sparse feature access patterns. We first characterize the types of attacks that can be carried out on sparse features in recommendation models in an untrusted cloud, followed by a demonstration of how each of these attacks leads to extracting users' private information or tracking users by their behavior over time