A gloss composition and context clustering based distributed word sense representation model

In recent years, there has been an increasing interest in learning a distributed representation of word sense. Traditional context clustering based models usually require careful tuning of model parameters, and typically perform worse on infrequent word senses. This paper presents a novel approach which addresses these limitations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned representations outperform the publicly available embeddings on half of the metrics in the word similarity task, 6 out of 13 sub tasks in the analogical reasoning task, and gives the best overall accuracy in the word sense effect classification task, which shows the effectiveness of our proposed distributed distribution learning model.

Towards a multilevel model of the high performance work systems–workplace safety relationship

Human Resource (HR) systems and practices generally referred to as High Performance Work Practices (HPWPs), (Huselid, 1995) (sometimes termed High Commitment Work Practices or High Involvement Work Practices) have attracted much research attention in past decades. Although many conceptualizations of the construct have been proposed, there is general agreement that HPWPs encompass a bundle or set of HR practices including sophisticated staffing, intensive training and development, incentive-based compensation, performance management, initiatives aimed at increasing employee participation and involvement, job safety and security, and work design (e.g. Pfeffer, 1998). It is argued that these practices either directly and indirectly influence the extent to which employees’ knowledge, skills, abilities, and other characteristics are utilized in the organization. Research spanning nearly 20 years has provided considerable empirical evidence for relationships between HPWPs and various measures of performance including increased productivity, improved customer service, and reduced turnover (e.g. Guthrie, 2001; Belt & Giles, 2009). With the exception of a few papers (e.g., Laursen &Foss, 2003), this literature appears to lack focus on how HPWPs influence or foster more innovative-related attitudes and behaviours, extra role behaviors, and performance. This situation exists despite the vast evidence demonstrating the importance of innovation, proactivity, and creativity in its various forms to individual, group, and organizational performance outcomes. Several pertinent issues arise when considering HPWPs and their relationship to innovation and performance outcomes. At a broad level is the issue of which HPWPs are related to which innovation-related variables. Another issue not well identified in research relates to employees’ perceptions of HPWPs: does an employee actually perceive the HPWP –outcomes relationship? No matter how well HPWPs are designed, if they are not perceived and experienced by employees to be effective or worthwhile then their likely success in achieving positive outcomes is limited. At another level, research needs to consider the mechanisms through which HPWPs influence –innovation and performance. The research question here relates to what possible mediating variables are important to the success or failure of HPWPs in impacting innovative behaviours and attitudes and what are the potential process considerations? These questions call for theory refinement and the development of more comprehensive models of the HPWP-innovation/performance relationship that include intermediate linkages and boundary conditions (Ferris, Hochwarter, Buckley, Harrell-Cook, & Frink, 1999). While there are many calls for this type of research to be made a high priority, to date, researchers have made few inroads into answering these questions. This symposium brings together researchers from Australia, Europe, Asia and Africa to examine these various questions relating to the HPWP-innovation-performance relationship. Each paper discusses a HPWP and potential variables that can facilitate or hinder the effects of these practices on innovation- and performance- related outcomes. The first paper by Johnston and Becker explores the HPWPs in relation to work design in a disaster response organization that shifts quickly from business as usual to rapid response. The researchers examine how the enactment of the organizational response is devolved to groups and individuals. Moreover, they assess motivational characteristics that exist in dual work designs (normal operations and periods of disaster activation) and the implications for innovation. The second paper by Jørgensen reports the results of an investigation into training and development practices and innovative work behaviors (IWBs) in Danish organizations. Research on how to design and implement training and development initiatives to support IWBs and innovation in general is surprisingly scant and often vague. This research investigates the mechanisms by which training and development initiatives influence employee behaviors associated with innovation, and provides insights into how training and development can be used effectively by firms to attract and retain valuable human capital in knowledge-intensive firms. The next two papers in this symposium consider the role of employee perceptions of HPWPs and their relationships to innovation-related variables and performance. First, Bish and Newton examine perceptions of the characteristics and awareness of occupational health and safety (OHS) practices and their relationship to individual level adaptability and proactivity in an Australian public service organization. The authors explore the role of perceived supportive and visionary leadership and its impact on the OHS policy-adaptability/proactivity relationship. The study highlights the positive main effects of awareness and characteristics of OHS polices, and supportive and visionary leadership on individual adaptability and proactivity. It also highlights the important moderating effects of leadership in the OHS policy-adaptability/proactivity relationship. Okhawere and Davis present a conceptual model developed for a Nigerian study in the safety-critical oil and gas industry that takes a multi-level approach to the HPWP-safety relationship. Adopting a social exchange perspective, they propose that at the organizational level, organizational climate for safety mediates the relationship between enacted HPWS’s and organizational safety performance (prescribed and extra role performance). At the individual level, the experience of HPWP impacts on individual behaviors and attitudes in organizations, here operationalized as safety knowledge, skills and motivation, and these influence individual safety performance. However these latter relationships are moderated by organizational climate for safety. A positive organizational climate for safety strengthens the relationship between individual safety behaviors and attitudes and individual-level safety performance, therefore suggesting a cross-level boundary condition. The model includes both safety performance (behaviors) and organizational level safety outcomes, operationalized as accidents, injuries, and fatalities. The final paper of this symposium by Zhang and Liu explores leader development and relationship between transformational leadership and employee creativity and innovation in China. The authors further develop a model that incorporates the effects of extrinsic motivation (pay for performance: PFP) and employee collectivism in the leader-employee creativity relationship. The papers’ contributions include the incorporation of a PFP effect on creativity as moderator, rather than predictor in most studies; the exploration of the PFP effect from both fairness and strength perspectives; the advancement of knowledge on the impact of collectivism on the leader- employee creativity link. Last, this is the first study to examine three-way interactional effects among leader-member exchange (LMX), PFP and collectivism, thus, enriches our understanding of promoting employee creativity. In conclusion, this symposium draws upon the findings of four empirical studies and one conceptual study to provide an insight into understanding how different variables facilitate or potentially hinder the influence various HPWPs on innovation and performance. We will propose a number of questions for further consideration and discussion. The symposium will address the Conference Theme of ‘Capitalism in Question' by highlighting how HPWPs can promote financial health and performance of organizations while maintaining a high level of regard and respect for employees and organizational stakeholders. Furthermore, the focus on different countries and cultures explores the overall research question in relation to different modes or stages of development of capitalism.

Smartphone-based digital government model:the case of the beyaz masa (white table) app in Turkey

M-Government services are now at the forefront of both user expectations and technology capabilities. Within the current setting, there is growing evidence that interoperability is becoming a key issue towards service sustainability. Thus, the objective of this chapter is to highlight the case of "Beyas Masa" - a Turkish application for infrastructure repair services. This application requires different stakeholders from different cultural background and geographically dispersed regions to work together. The major aim of this chapter to showcase experiences in as far as implementation and adoption of m-Government is concerned in the case of Turkey. The study utilizes the co-creation literature to investigate the factors influencing successful implementation of the Beyas Masa. This study reveals that initiatives are fragmented due to differences in the characteristics of the targeted audience, the marketing strategy, technology supply, distribution, and media utilized to promote its awareness. The chapter posits that in order to have affluent m-Government implementation in Turkey, it is important that many of the standalone applications are integrated to encourage interoperability and that socio-cultural behaviours should be re-shaped to encourage active engagement and interactive government service provisions that unlock the power of ICT.

Bagging model with cost sensitive analysis on diabetes data

Diabetes patients might suffer from an unhealthy life, long-term treatment and chronic complicated diseases. The decreasing hospitalization rate is a crucial problem for health care centers. This study combines the bagging method with base classifier decision tree and costs-sensitive analysis for diabetes patients' classification purpose. Real patients' data collected from a regional hospital in Thailand were analyzed. The relevance factors were selected and used to construct base classifier decision tree models to classify diabetes and non-diabetes patients. The bagging method was then applied to improve accuracy. Finally, asymmetric classification cost matrices were used to give more alternative models for diabetes data analysis.

A new unsupervised convolutional neural network model for Chinese scene text detection

As one of the most popular deep learning models, convolution neural network (CNN) has achieved huge success in image information extraction. Traditionally CNN is trained by supervised learning method with labeled data and used as a classifier by adding a classification layer in the end. Its capability of extracting image features is largely limited due to the difficulty of setting up a large training dataset. In this paper, we propose a new unsupervised learning CNN model, which uses a so-called convolutional sparse auto-encoder (CSAE) algorithm pre-Train the CNN. Instead of using labeled natural images for CNN training, the CSAE algorithm can be used to train the CNN with unlabeled artificial images, which enables easy expansion of training data and unsupervised learning. The CSAE algorithm is especially designed for extracting complex features from specific objects such as Chinese characters. After the features of articficial images are extracted by the CSAE algorithm, the learned parameters are used to initialize the first CNN convolutional layer, and then the CNN model is fine-Trained by scene image patches with a linear classifier. The new CNN model is applied to Chinese scene text detection and is evaluated with a multilingual image dataset, which labels Chinese, English and numerals texts separately. More than 10% detection precision gain is observed over two CNN models.

Biologically inspired features for scene classification in video surveillance

Inspired by human visual cognition mechanism, this paper first presents a scene classification method based on an improved standard model feature. Compared with state-of-the-art efforts in scene classification, the newly proposed method is more robust, more selective, and of lower complexity. These advantages are demonstrated by two sets of experiments on both our own database and standard public ones. Furthermore, occlusion and disorder problems in scene classification in video surveillance are also first studied in this paper. © 2010 IEEE.

Corporate identity at the stakeholder group level:a conceptual model of front-line employee corporate identity

There is a paucity of literature regarding the construction and operation of corporate identity at the stakeholder group level. This article examines corporate identity from the perspective of an individual stakeholder group, namely, front-line employees. A stakeholder group that is central to the development of an organization’s corporate identity as it spans an organization’s boundaries, frequently interacts with both internal and external stakeholders, and influences a firm’s financial performance by building customer loyalty and satisfaction. The article reviews the corporate identity, branding, services and social identity literatures to address how corporate identity manifests within the front-line employee stakeholder group, identifying what components comprise front-line employee corporate identity and assessing what contribution front-line employees make to constructing a strong and enduring corporate identity for an organization. In reviewing the literature the article develops propositions that, in conjunction with a conceptual model, constitute the generation of theory that is recommended for empirical testing.

Learning user and product distributed representations using a sequence model for sentiment analysis

In product reviews, it is observed that the distribution of polarity ratings over reviews written by different users or evaluated based on different products are often skewed in the real world. As such, incorporating user and product information would be helpful for the task of sentiment classification of reviews. However, existing approaches ignored the temporal nature of reviews posted by the same user or evaluated on the same product. We argue that the temporal relations of reviews might be potentially useful for learning user and product embedding and thus propose employing a sequence model to embed these temporal relations into user and product representations so as to improve the performance of document-level sentiment analysis. Specifically, we first learn a distributed representation of each review by a one-dimensional convolutional neural network. Then, taking these representations as pretrained vectors, we use a recurrent neural network with gated recurrent units to learn distributed representations of users and products. Finally, we feed the user, product and review representations into a machine learning classifier for sentiment classification. Our approach has been evaluated on three large-scale review datasets from the IMDB and Yelp. Experimental results show that: (1) sequence modeling for the purposes of distributed user and product representation learning can improve the performance of document-level sentiment classification; (2) the proposed approach achieves state-of-The-Art results on these benchmark datasets.

Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN

Different types of sentences express sentiment in very different ways. Traditional sentence-level sentiment classification research focuses on one-technique-fits-all solution or only centers on one special type of sentences. In this paper, we propose a divide-and-conquer approach which first classifies sentences into different types, then performs sentiment analysis separately on sentences from each type. Specifically, we find that sentences tend to be more complex if they contain more sentiment targets. Thus, we propose to first apply a neural network based sequence model to classify opinionated sentences into three types according to the number of targets appeared in a sentence. Each group of sentences is then fed into a one-dimensional convolutional neural network separately for sentiment classification. Our approach has been evaluated on four sentiment classification datasets and compared with a wide range of baselines. Experimental results show that: (1) sentence type classification can improve the performance of sentence-level sentiment analysis; (2) the proposed approach achieves state-of-the-art results on several benchmarking datasets.

Dynamic Bayesian network for semantic place classification in mobile robotics

In this paper, the problem of semantic place categorization in mobile robotics is addressed by considering a time-based probabilistic approach called dynamic Bayesian mixture model (DBMM), which is an improved variation of the dynamic Bayesian network. More specifically, multi-class semantic classification is performed by a DBMM composed of a mixture of heterogeneous base classifiers, using geometrical features computed from 2D laserscanner data, where the sensor is mounted on-board a moving robot operating indoors. Besides its capability to combine different probabilistic classifiers, the DBMM approach also incorporates time-based (dynamic) inferences in the form of previous class-conditional probabilities and priors. Extensive experiments were carried out on publicly available benchmark datasets, highlighting the influence of the number of time-slices and the effect of additive smoothing on the classification performance of the proposed approach. Reported results, under different scenarios and conditions, show the effectiveness and competitive performance of the DBMM.