Development of domestic and international computer forensics

With the increasing of computer crime, instant emergence of new digital product, new computer technology and computer forensics technology is promoted, developed constantly. This paper described basic information/content of computer forensics, and elaborated the development of current computer forensics at domestic and overseas market. The trends of computer forensics are pointed out and recent hot topics of research are introduced. © 2011 IEEE.published_or_final_versionThe 7th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2011), Dalian, China, 14-16 October 2011. In Proceedings of the 7th IIH-MSP, 2011, p. 388-39

A totally Self-Checking Dynamic Asynchronous Datapath

This paper investigates the inherent totally self-checking (TSC) property of one type of dynamic asynchronous datapath based on Differential Cascode Voltage Logic (DCVSL). As a result, a totally self-checking dynamic asynchronous datapath architecture is proposed. It is simpler than other similar approaches and represents a new approach to fault tolerant design.published_or_final_versio

Learning domain-specific sentiment lexicon with supervised sentiment-aware LDA

Frontiers in Artificial Intelligence and Applications, v. 263 entitled: ECAI 2014: 21st European Conference on Artificial Intelligence, 18-22 August 2014, Prague, Czech Republic - Including Prestigious Applications of Intelligent Systems (PAIS 2014)Analyzing and understanding people's sentiments towards different topics has become an interesting task due to the explosion of opinion-rich resources. In most sentiment analysis applications, sentiment lexicons play a crucial role, to be used as metadata of sentiment polarity. However, most previous works focus on discovering general-purpose sentiment lexicons. They cannot capture domain-specific sentiment words, or implicit and connotative sentiment words that are seemingly objective. In this paper, we propose a supervised sentiment-aware LDA model (ssLDA). The model uses a minimal set of domain-independent seed words and document labels to discover a domain-specific lexicon, learning a lexicon much richer and adaptive to the sentiment of specific document. Experiments on two publicly-available datasets (movie reviews and Obama-McCain debate dataset) show that our model is effective in constructing a comprehensive and high-quality domain-specific sentiment lexicon. Furthermore, the resulting lexicon significantly improves the performance of sentiment classification tasks. © 2014 The Authors and IOS Press.published_or_final_versio

Human infection with a novel avian influenza A(H5N6) virus

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A topic model for building fine-grained domain-specific emotion lexicon

Emotion lexicons play a crucial role in sentiment analysis and opinion mining. In this paper, we propose a novel Emotion-aware LDA (EaLDA) model to build a domainspecific lexicon for predefined emotions that include anger, disgust, fear, joy, sadness, surprise. The model uses a minimal set of domain-independent seed words as prior knowledge to discover a domainspecific lexicon, learning a fine-grained emotion lexicon much richer and adaptive to a specific domain. By comprehensive experiments, we show that our model can generate a high-quality fine-grained domain-specific emotion lexicon. © 2014 Association for Computational Linguistics.published_or_final_versio

LCCT: a semisupervised model for sentiment classification

Conference Theme: Human Language TechnologiesAnalyzing public opinions towards products, services and social events is an important but challenging task. An accurate sentiment analyzer should take both lexicon-level information and corpus-level information into account. It also needs to exploit the domain-specific knowledge and utilize the common knowledge shared across domains. In addition, we want the algorithm being able to deal with missing labels and learning from incomplete sentiment lexicons. This paper presents a LCCT (Lexicon-based and Corpus-based, Co-Training) model for semi-supervised sentiment classification. The proposed method combines the idea of lexicon-based learning and corpus-based learning in a unified co-training framework. It is capable of incorporating both domain-specific and domain-independent knowledge. Extensive experiments show that it achieves very competitive classification accuracy, even with a small portion of labeled data. Comparing to state-of-the-art sentiment classification methods, the LCCT approach exhibits significantly better performances on a variety of datasets in both English and Chinese. © 2015 Association for Computational Linguisticspublished_or_final_versio

Synchrony of clinical and laboratory surveillance for influenza in Hong Kong

Background: Consultation rates of influenza-like illness (ILI) in an outpatient setting have been regarded as a good indicator of influenza virus activity in the community. As ILI-like symptoms may be caused by etiologies other than influenza, and influenza virus activity in the tropics and subtropics is less predictable than in temperate regions, the correlation between of ILI and influenza virus activity in tropical and subtropical regions is less well defined. Methodology and Principal Findings: In this study, we used wavelet analysis to investigate the relationship between seasonality of influenza virus activity and consultation rates of ILI reported separately by General Out-patient Clinics (GOPC) and General Practitioners (GP). During the periods 1998-2000 and 2002-2003, influenza virus activity exhibited both annual and semiannual cycles, with one peak in the winter and another in late spring or early summer. But during 2001 and 2004-2006, only annual cycles could be clearly identified. ILI consultation rates in both GOPC and GP settings share a similar non-stationary seasonal pattern. We found high coherence between ILI in GOPC and influenza virus activity for the annual cycle but this was only significant (P<0.05) during the periods 1998-1999 and 2002-2006. For the semiannual cycle high coherence (p<0.05) was also found significant during the period 1998-1999 and year 2003 when two peaks of influenza were evident. Similarly, ILI in GP setting is also associated with influenza virus activity for both the annual and semiannual cycles. On average, oscillation of ILI in GP and of ILI in GOPC preceded influenza virus isolation by approximately four and two weeks, respectively. Conclusions: Our findings suggest that consultation rates of ILI precede the oscillations of laboratory surveillance by at least two weeks and can be used as a predictor for influenza epidemics in Hong Kong. The validity of our model for other tropical regions needs to be explored. © 2008 Yang et al.published_or_final_versio

Polydimethylsiloxane-Embedded Conductive Fabric: Characterization and Application for Realization of Robust Passive and Active Flexible Wearable Antennas

© 2013 IEEE. We present our study on polydimethylsiloxane (PDMS)-embedded conductive fabric, which we propose as a simple yet effective solution to the challenging issue of poor PDMS-metal adhesion, allowing for a relatively easy realization of robust flexible antennas for wearable applications. The method combines the use of conductive fabric as a radiator with PDMS, which acts as the substrate and a protective encapsulation simultaneously. For the first time, a holistic study on the mechanical and electrical properties of the proposed combination of materials is presented thoroughly using a number of fabricated samples. As concept demonstrations, a microstrip patch and a reconfigurable patch antenna are fabricated using the proposed technique to validate the idea. The inclusion of a PDMS-ceramic composite as part of the antenna's substrate, which leads to over 50% reduction in the size compared with a pure PDMS, is also demonstrated to showcase further the versatility of the proposed technique. The fabricated antennas are tested in several wearable scenarios and consistent performance including reconfigurability is obtained even after the antennas are exposed to harsh environments, i.e., extreme bending and machine-washing

Environmental factors and mortality risks associated influenza

BACKGROUND AND AIMS: The subtropical and tropical regions exhibit a distinct seasonality of influenza incidence from the temperate regions, and the mechanism behind it remains unclear. Environmental factors have been related to the transmission and survival of influenza viruses but no studies have ever explored the role of environmental factors on regulating severity of influenza infection. METHODS: We applied a Poisson regression model to the mortality data of two Asian metropolitan cities located at the subtropical zone, Guangzhou and Hong Kong. Interaction between …postprin

The PB2 mutation with lysine at 627 enhances the pathogenicity of avian influenza (H7N9) virus which belongs to a non-zoonotic lineage

A novel avian-origin influenza A (H7N9) virus emerged in China in 2013 and has caused zoonotic disease in over 1123 persons with an overall mortality around 30%. Amino acid changes at the residues 591, 627 and 701 of polymerase basic protein 2 (PB2) have been found frequently in the human H7N9 isolates but not in viruses isolated from avian species. We have recently identified a cluster of H7N9 viruses in ducks which circulated in China prior to the first recognition of zoonotic disease in 2013. These duck viruses have genetic background distinct from the zoonotic H7N9 lineage. We found that the introduction of PB2 mutation with K at 627 but not K at 591 or N at 701 to the duck H7N9 virus led to increased pathogenicity in mice. We also found that the induction of pro-inflammatory cytokines including TNF-α, IP-10, MCP-1 and MIP-1α were associated with increased severity of infection. We conclude that introduction of the mammalian adaptation mutations into the PB2 gene of duck H7N9 viruses, which are genetically unrelated to the zoonotic H7N9 lineage, can also enhance pathogenicity in mice.published_or_final_versio