arXiv subjects

Jinfeng Li

Publications and source records attributed to Jinfeng Li.

47 records · Page 3Linked to original sources

arXiv2020

COVID-19 Contact-tracing Apps: a Survey on the Global Deployment and Challenges

To address the massive spike in uncertainties triggered by the coronavirus disease (COVID-19), there is an ever-increasing number of national governments that are rolling out contact-tracing Apps to aid the containment of the virus. The first hugely contentious issue facing the Apps is the deployment framework, i.e. centralized or decentralized. Based on this, the debate branches out to the corresponding technologies that underpin these architectures, i.e. GPS, QR codes, and Bluetooth. This work conducts a pioneering review of the above scenarios and contributes a geolocation mapping of the current deployment. The Apps vulnerabilities and the directions of research are identified, with a special focus on the Bluetooth-inspired decentralized paradigm.

cs.CR↗

arXiv2020

A Novel Twitter Sentiment Analysis Model with Baseline Correlation for Financial Market Prediction with Improved Efficiency

A novel social networks sentiment analysis model is proposed based on Twitter sentiment score (TSS) for real-time prediction of the future stock market price FTSE 100, as compared with conventional econometric models of investor sentiment based on closed-end fund discount (CEFD). The proposed TSS model features a new baseline correlation approach, which not only exhibits a decent prediction accuracy, but also reduces the computation burden and enables a fast decision making without the knowledge of historical data. Polynomial regression, classification modelling and lexicon-based sentiment analysis are performed using R. The obtained TSS predicts the future stock market trend in advance by 15 time samples (30 working hours) with an accuracy of 67.22% using the proposed baseline criterion without referring to historical TSS or market data. Specifically, TSS's prediction performance of an upward market is found far better than that of a downward market. Under the logistic regression and linear discriminant analysis, the accuracy of TSS in predicting the upward trend of the future market achieves 97.87%.

cs.SI↗

arXiv2020

Enhancing Review Comprehension with Domain-Specific Commonsense

Review comprehension has played an increasingly important role in improving the quality of online services and products and commonsense knowledge can further enhance review comprehension. However, existing general-purpose commonsense knowledge bases lack sufficient coverage and precision to meaningfully improve the comprehension of domain-specific reviews. In this paper, we introduce xSense, an effective system for review comprehension using domain-specific commonsense knowledge bases (xSense KBs). We show that xSense KBs can be constructed inexpensively and present a knowledge distillation method that enables us to use xSense KBs along with BERT to boost the performance of various review comprehension tasks. We evaluate xSense over three review comprehension tasks: aspect extraction, aspect sentiment classification, and question answering. We find that xSense outperforms the state-of-the-art models for the first two tasks and improves the baseline BERT QA model significantly, demonstrating the usefulness of incorporating commonsense into review comprehension pipelines. To facilitate future research and applications, we publicly release three domain-specific knowledge bases and a domain-specific question answering benchmark along with this paper.

cs.CL↗

arXiv2019

SirenAttack: Generating Adversarial Audio for End-to-End Acoustic Systems

Despite their immense popularity, deep learning-based acoustic systems are inherently vulnerable to adversarial attacks, wherein maliciously crafted audios trigger target systems to misbehave. In this paper, we present SirenAttack, a new class of attacks to generate adversarial audios. Compared with existing attacks, SirenAttack highlights with a set of significant features: (i) versatile -- it is able to deceive a range of end-to-end acoustic systems under both white-box and black-box settings; (ii) effective -- it is able to generate adversarial audios that can be recognized as specific phrases by target acoustic systems; and (iii) stealthy -- it is able to generate adversarial audios indistinguishable from their benign counterparts to human perception. We empirically evaluate SirenAttack on a set of state-of-the-art deep learning-based acoustic systems (including speech command recognition, speaker recognition and sound event classification), with results showing the versatility, effectiveness, and stealthiness of SirenAttack. For instance, it achieves 99.45% attack success rate on the IEMOCAP dataset against the ResNet18 model, while the generated adversarial audios are also misinterpreted by multiple popular ASR platforms, including Google Cloud Speech, Microsoft Bing Voice, and IBM Speech-to-Text. We further evaluate three potential defense methods to mitigate such attacks, including adversarial training, audio downsampling, and moving average filtering, which leads to promising directions for further research.

cs.CR↗

arXiv2019

Subjective Databases

Online users are constantly seeking experiences, such as a hotel with clean rooms and a lively bar, or a restaurant for a romantic rendezvous. However, e-commerce search engines only support queries involving objective attributes such as location, price, and cuisine, and any experiential data is relegated to text reviews. In order to support experiential queries, a database system needs to model subjective data and also be able to process queries where the user can express varied subjective experiences in words chosen by the user, in addition to specifying predicates involving objective attributes. This paper introduces Opine, a subjective database system that addresses these challenges. We introduce a data model for subjective databases. We describe how Opine translates subjective queries against the subjective database schema, which is done by matching the user query phrases to the underlying schema. We also show how the experiential conditions specified by the user can be combined and the results aggregated and ranked. We demonstrate that subjective databases satisfy user needs more effectively and accurately than alternative techniques through experiments with real data of hotel and restaurant reviews.

cs.DB↗

arXiv2019

Voyageur: An Experiential Travel Search Engine

We describe Voyageur, which is an application of experiential search to the domain of travel. Unlike traditional search engines for online services, experiential search focuses on the experiential aspects of the service under consideration. In particular, Voyageur needs to handle queries for subjective aspects of the service (e.g., quiet hotel, friendly staff) and combine these with objective attributes, such as price and location. Voyageur also highlights interesting facts and tips about the services the user is considering to provide them with further insights into their choices.

cs.DB↗

arXiv2018

TextBugger: Generating Adversarial Text Against Real-world Applications

Deep Learning-based Text Understanding (DLTU) is the backbone technique behind various applications, including question answering, machine translation, and text classification. Despite its tremendous popularity, the security vulnerabilities of DLTU are still largely unknown, which is highly concerning given its increasing use in security-sensitive applications such as sentiment analysis and toxic content detection. In this paper, we show that DLTU is inherently vulnerable to adversarial text attacks, in which maliciously crafted texts trigger target DLTU systems and services to misbehave. Specifically, we present TextBugger, a general attack framework for generating adversarial texts. In contrast to prior works, TextBugger differs in significant ways: (i) effective -- it outperforms state-of-the-art attacks in terms of attack success rate; (ii) evasive -- it preserves the utility of benign text, with 94.9\% of the adversarial text correctly recognized by human readers; and (iii) efficient -- it generates adversarial text with computational complexity sub-linear to the text length. We empirically evaluate TextBugger on a set of real-world DLTU systems and services used for sentiment analysis and toxic content detection, demonstrating its effectiveness, evasiveness, and efficiency. For instance, TextBugger achieves 100\% success rate on the IMDB dataset based on Amazon AWS Comprehend within 4.61 seconds and preserves 97\% semantic similarity. We further discuss possible defense mechanisms to mitigate such attack and the adversary's potential countermeasures, which leads to promising directions for further research.

cs.CR↗

arXiv2018

Norm-Ranging LSH for Maximum Inner Product Search

Neyshabur and Srebro proposed Simple-LSH, which is the state-of-the-art hashing method for maximum inner product search (MIPS) with performance guarantee. We found that the performance of Simple-LSH, in both theory and practice, suffers from long tails in the 2-norm distribution of real datasets. We propose Norm-ranging LSH, which addresses the excessive normalization problem caused by long tails in Simple-LSH by partitioning a dataset into multiple sub-datasets and building a hash index for each sub-dataset independently. We prove that Norm-ranging LSH has lower query time complexity than Simple-LSH. We also show that the idea of partitioning the dataset can improve other hashing based methods for MIPS. To support efficient query processing on the hash indexes of the sub-datasets, a novel similarity metric is formulated. Experiments show that Norm-ranging LSH achieves an order of magnitude speedup over Simple-LSH for the same recall, thus significantly benefiting applications that involve MIPS.

cs.LG↗

arXiv2018

Scanning Microwave Microscopy of Vital Mitochondria in Respiration Buffer

We demonstrate imaging using scanning microwave microscopy (SMM) of vital mitochondria in respiration buffer. The mitochondria are isolated from cultured HeLa cells and tethered to a solid graphene support. The mitochondria are kept vital (alive) using a respiration buffer, which provides nutrients to sustain the Krebs cycle. We verify that the mitochondria are "alive" by measuring the membrane potential using a voltage sensitive fluorescent dye (TMRE). The organelles are measured capacitively at 7 GHz. Several technical advances are demonstrated which enable this work: 1) The SMM operates in an electrophysiologically relevant liquid (hence conducting) environment; 2) The SMM operates in tapping mode, averaging the microwave reflection measurement over many tapping periods; 3) A tuned reflectometer enables increased sensitivity; 4) Variable frequencies up to 18 GHz are used; 5) In contrast with traditional matching/resonant methods that exhibit high quality factor that fail in the presence of liquids, interferometric/tuned reflectometer gives the possibility to adjust the quality factor or sensitivity even in the presence of the liquid.

physics.bio-ph↗

arXiv2016

Efficient Processing of Reachability and Time-Based Path Queries in a Temporal Graph

A temporal graph is a graph in which vertices communicate with each other at specific time, e.g., $A$ calls $B$ at 11 a.m. and talks for 7 minutes, which is modeled by an edge from $A$ to $B$ with starting time "11 a.m." and duration "7 mins". Temporal graphs can be used to model many networks with time-related activities, but efficient algorithms for analyzing temporal graphs are severely inadequate. We study fundamental problems such as answering reachability and time-based path queries in a temporal graph, and propose an efficient indexing technique specifically designed for processing these queries in a temporal graph. Our results show that our method is efficient and scalable in both index construction and query processing.

cs.DB↗

arXiv2016

MeV Argon ion beam generation with narrow energy spread

Laser driven particle acceleration has shown remarkable progresses in generating multi-GeV electron bunches and 10s of MeV ion beams based on high-power laser facilities. Intense laser pulse offers the acceleration field of 1012 Volt per meter, several orders of magnitude larger than that in conventional accelerators, enabling compact devices. Here we report that a highly-collimated argon ion beam with narrow energy spread is produced by irradiating a 45-fs fully-relativistic laser pulse onto an argon cluster target. The highly-charged (Argon ion with charge state of 16+) heavy ion beam has a minimum absolute energy spread of 0.19 MeV per nucleon at the energy peak of 0.39 MeV per nucleon. we identify a novel scheme from particle-in-cell simulations that greatly reduces the beam energy spread. The laser-driven intense plasma wakefield has a strong modulation on the ion beam in a way that the low energy part is cut off. The pre-accelerated argon ion beam from Coulomb explosion thus becomes more mono-energetic and collimated.

physics.plasm-ph↗