Online Deception Detection Refueled by Real World Data Collection

The lack of large realistic datasets presents a bottleneck in online deception detection studies. In this paper, we apply a data collection method based on social network analysis to quickly identify high-quality deceptive and truthful online reviews from Amazon. The dataset contains more than 10,000 deceptive reviews and is diverse in product domains and reviewers. Using this dataset, we explore effective general features for online deception detection that perform well across domains. We demonstrate that with generalized features - advertising speak and writing complexity scores - deception detection performance can be further improved by adding additional deceptive reviews from assorted domains in training. Finally, reviewer level evaluation gives an interesting insight into different deceptive reviewers' writing styles.Comment: 10 pages, Accepted to Recent Advances in Natural Language Processing (RANLP) 201

Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph

We argue that semantic meanings of a sentence or clause can not be interpreted independently from the rest of a paragraph, or independently from all discourse relations and the overall paragraph-level discourse structure. With the goal of improving implicit discourse relation classification, we introduce a paragraph-level neural networks that model inter-dependencies between discourse units as well as discourse relation continuity and patterns, and predict a sequence of discourse relations in a paragraph. Experimental results show that our model outperforms the previous state-of-the-art systems on the benchmark corpus of PDTB.Comment: Accepted by NAACL 201

W4IPS: A Web-based Interactive Power System Simulation Environment For Power System Security Analysis

Modern power systems are increasingly evolving Cyber-Physical Systems (CPS) that feature close interaction between Information and Communication Technology (ICT), physical and electrical devices, and human factors. The interactivity and security of CPS are the essential building blocks for the reliability, stability and economic operation of power systems. This paper presents a web-based interactive multi-user power system simulation environment and open source toolset (W4IPS) whose main features are a publish/subscribe structure, a real-time data sharing capability, role-based multi-user visualizations, distributed multi-user interactive controls, an easy to use and deploy web interface, and flexible and extensible support for communication protocols. The paper demonstrates the use of W4IPS features as an ideal platform for contingency response training and cyber security analysis, with an emphasis on interactivity and expandability. In particular, we present the use cases and the results of W4IPS in power system operation education and security analysis

Synthesis of Multifunctional Organoboron Compounds by Copper-Catalyzed Enantioselective Reactions:

Thesis advisor: Amir H. HoveydaChapter 1. We have developed a catalytic method for enantio- and SN2’-selective allylic substitution of commercially available diborylmethane to trisubstituted allylic phosphates (pin = pinacolato). The transformations are catalyzed by NHC–Cu complexes (NHC = N-heterocyclic carbene). Products bearing quaternary stereogenic carbon centers are obtained in up to 86% yield (after oxidation), >98:2 SN2’/SN2 selectivity and 95:5 enantiomeric ratio (e.r.). Chapter 2. We have developed a facile multicomponent catalytic process that begins with a chemo- and site-selective copper–hydride addition to allenyl-B(pin) followed by enantioselective conjugate addition of the resulting allylcopper intermediate to α,β-unsaturated malonate, generating products that contain a stereogenic center and an easily functionalizable alkenyl-B(pin) group in up to 84% yield, >98:2 E/Z selectivity and 96:4 enantiomeric ratio. The transformations are catalyzed by chiral Cu complexes derived from commercially available bisphosphines and CuCl.Thesis (MS) — Boston College, 2017.Submitted to: Boston College. Graduate School of Arts and Sciences.Discipline: Chemistry

A Two-Stage Training Framework for Joint Speech Compression and Enhancement

This paper considers the joint compression and enhancement problem for speech signal in the presence of noise. Recently, the SoundStream codec, which relies on end-to-end joint training of an encoder-decoder pair and a residual vector quantizer by a combination of adversarial and reconstruction losses,has shown very promising performance, especially in subjective perception quality. In this work, we provide a theoretical result to show that, to simultaneously achieve low distortion and high perception in the presence of noise, there exist an optimal two-stage optimization procedure for the joint compression and enhancement problem. This procedure firstly optimizes an encoder-decoder pair using only distortion loss and then fixes the encoder to optimize a perceptual decoder using perception loss. Based on this result, we construct a two-stage training framework for joint compression and enhancement of noisy speech signal. Unlike existing training methods which are heuristic, the proposed two-stage training method has a theoretical foundation. Finally, experimental results for various noise and bit-rate conditions are provided. The results demonstrate that a codec trained by the proposed framework can outperform SoundStream and other representative codecs in terms of both objective and subjective evaluation metrics. Code is available at \textit{https://github.com/jscscloris/SEStream}