Corporate governance, accounting and finance: a review

We review accounting and finance research on corporate governance (CG). In the course of our review, we focus on a particularly vexing issue, namely endogeneity in the relationships between CG and other matters of concern to accounting and finance scholars, and suggest ways to deal with it. Given the advent of large commercial CG databases, we also stress the importance of how CG is measured and in particular, the construction of CG indices, which should be sensitive to local institutional arrangements, and the need to capture both internal and external aspects of governance. The ‘stickiness’ of CG characteristics provides an additional challenge to CG scholars. Better theory is required, for example, to explain whether various CG practices substitute for each other or are complements. While a multidisciplinary approach to developing better theory is never without its difficulties, it could enrich the current body of knowledge in CG. Despite the vastness of the existing CG literature, these issues do suggest a number of avenues for future research.

Super-resolved faces for improved face recognition from surveillance video advances in biometrics

Characteristics of surveillance video generally include low resolution and poor quality due to environmental, storage and processing limitations. It is extremely difficult for computers and human operators to identify individuals from these videos. To overcome this problem, super-resolution can be used in conjunction with an automated face recognition system to enhance the spatial resolution of video frames containing the subject and narrow down the number of manual verifications performed by the human operator by presenting a list of most likely candidates from the database. As the super-resolution reconstruction process is ill-posed, visual artifacts are often generated as a result. These artifacts can be visually distracting to humans and/or affect machine recognition algorithms. While it is intuitive that higher resolution should lead to improved recognition accuracy, the effects of super-resolution and such artifacts on face recognition performance have not been systematically studied. This paper aims to address this gap while illustrating that super-resolution allows more accurate identification of individuals from low-resolution surveillance footage. The proposed optical flow-based super-resolution method is benchmarked against Baker et al.’s hallucination and Schultz et al.’s super-resolution techniques on images from the Terrascope and XM2VTS databases. Ground truth and interpolated images were also tested to provide a baseline for comparison. Results show that a suitable super-resolution system can improve the discriminability of surveillance video and enhance face recognition accuracy. The experiments also show that Schultz et al.’s method fails when dealing surveillance footage due to its assumption of rigid objects in the scene. The hallucination and optical flow-based methods performed comparably, with the optical flow-based method producing less visually distracting artifacts that interfered with human recognition.

Having a yarn about smoking : using action research to develop a 'no smoking' policy within an Aboriginal health organisation

Objectives: This article reports on a culturally appropriate process of development of a smoke-free workplace policy within the peak Aboriginal Controlled Community Health Organisation in Victoria, Australia. Smoking is acknowledged as being responsible for at least 20% of all deaths in Aboriginal communities in Australia, and many Aboriginal health workers smoke. Methods: The smoke-free workplace policy was developed using the iterative, discursive and experience-based methodology of Participatory Action Research, combined with the culturally embedded concept of ‘having a yarn’. Results: Staff members initially identified smoking as a topic to be avoided within workplace discussions. This was due, in part, to grief (everyone had suffered a smoking related bereavement). Further, there was anxiety that discussing smoking would result in culturally difficult conflict. The use of yarning opened up a safe space for discussion and debate,enabling development of a policy that was accepted across the organisation. Conclusions: Within Aboriginal organisations, it is not sufficient to focus on the outcomes of policy development. Rather, due attention must be paid to the process employed in development of policy, particularly when that policy is directly related to an emotionally and communally weighted topic such as smoking.

Compilation of somatic mutations of the CDKN2 gene in human cancers : non-random distribution of base substitutions

The CDKN2 gene, encoding the cyclin-dependent kinase inhibitor p16, is a tumour suppressor gene that maps to chromosome band 9p21-p22. The most common mechanism of inactivation of this gene in human cancers is through homozygous deletion; however, in a smaller proportion of tumours and tumour cell lines intragenic mutations occur. In this study we have compiled a database of over 120 published point mutations in the CDKN2 gene from a wide variety of tumour types. A further 50 deletions, insertions, and splice mutations in CDKN2 have also been compiled. Furthermore, we have standardised the numbering of all mutations according to the full-length 156 amino acid form of p16. From this study we are able to define several hot spots, some of which occur at conserved residues within the ankyrin domains of p16. While many of the hotspots are shared by a number of cancers, the relative importance of each position varies, possibly reflecting the role of different carcinogens in the development of certain tumours. As reported previously, the mutational spectrum of CDKN2 in melanomas differs from that of internal malignancies and supports the involvement of UV in melanoma tumorigenesis. Notably, 52% of all substitutions in melanoma-derived samples occurred at just six nucleotide positions. Nonsense mutations comprise a comparatively high proportion of mutations present in the CDKN2 gene, and possible explanations for this are discussed.

FGFR2 as a molecular target in endometrial cancer

Although molecularly targeted therapies have been effective in some cancer types, no targeted therapy is approved for use in endometrial cancer. The recent identification of activating mutations in fibroblast growth factor receptor 2 (FGFR2) in endometrial tumors has generated a new avenue for the development of targeted therapeutic agents. The majority of the mutations identified are identical to germline mutations in FGFR2 and FGFR3 that cause craniosynostosis and hypochondroplasia syndromes and result in both ligand-independent and ligand-dependent receptor activation. Mutations that predominantly occur in the endometrioid subtype of endometrial cancer, are mutually exclusive with KRAS mutation, but occur in the presence of PTEN abrogation. In vitro studies have shown that endometrial cancer cell lines with activating FGFR2 mutations are selectively sensitive to a pan-FGFR inhibitor, PD173074. Several agents with activity against FGFRs are currently in clinical trials. Investigation of these agents in endometrial cancer patients with activating FGFR2 mutations is warranted.

Leveraging Web 2.0 data for scalable semi-supervised learning of domain-specific sentiment lexicons

Since manually constructing domain-specific sentiment lexicons is extremely time consuming and it may not even be feasible for domains where linguistic expertise is not available. Research on the automatic construction of domain-specific sentiment lexicons has become a hot topic in recent years. The main contribution of this paper is the illustration of a novel semi-supervised learning method which exploits both term-to-term and document-to-term relations hidden in a corpus for the construction of domain specific sentiment lexicons. More specifically, the proposed two-pass pseudo labeling method combines shallow linguistic parsing and corpusbase statistical learning to make domain-specific sentiment extraction scalable with respect to the sheer volume of opinionated documents archived on the Internet these days. Another novelty of the proposed method is that it can utilize the readily available user-contributed labels of opinionated documents (e.g., the user ratings of product reviews) to bootstrap the performance of sentiment lexicon construction. Our experiments show that the proposed method can generate high quality domain-specific sentiment lexicons as directly assessed by human experts. Moreover, the system generated domain-specific sentiment lexicons can improve polarity prediction tasks at the document level by 2:18% when compared to other well-known baseline methods. Our research opens the door to the development of practical and scalable methods for domain-specific sentiment analysis.

Manufacturing Yield Improvement by Clustering

Dealing with product yield and quality in manufacturing industries is getting more difficult due to the increasing volume and complexity of data and quicker time to market expectations. Data mining offers tools for quick discovery of relationships, patterns and knowledge in large databases. Growing self-organizing map (GSOM) is established as an efficient unsupervised datamining algorithm. In this study some modifications to the original GSOM are proposed for manufacturing yield improvement by clustering. These modifications include introduction of a clustering quality measure to evaluate the performance of the programme in separating good and faulty products and a filtering index to reduce noise from the dataset. Results show that the proposed method is able to effectively differentiate good and faulty products. It will help engineers construct the knowledge base to predict product quality automatically from collected data and provide insights for yield improvement.

Activity modelling in crowded environments : a soft decision approach

Probabilistic topic models have recently been used for activity analysis in video processing, due to their strong capacity to model both local activities and interactions in crowded scenes. In those applications, a video sequence is divided into a collection of uniform non-overlaping video clips, and the high dimensional continuous inputs are quantized into a bag of discrete visual words. The hard division of video clips, and hard assignment of visual words leads to problems when an activity is split over multiple clips, or the most appropriate visual word for quantization is unclear. In this paper, we propose a novel algorithm, which makes use of a soft histogram technique to compensate for the loss of information in the quantization process; and a soft cut technique in the temporal domain to overcome problems caused by separating an activity into two video clips. In the detection process, we also apply a soft decision strategy to detect unusual events.We show that the proposed soft decision approach outperforms its hard decision counterpart in both local and global activity modelling.

Arterial traffic congestion analysis using Bluetooth duration data

The aim of this study is to assess the potential use of Bluetooth data for traffic monitoring of arterial road networks. Bluetooth data provides the direct measurement of travel time between pairs of scanners, and intensive research has been reported on this topic. Bluetooth data includes “Duration” data, which represents the time spent by Bluetooth devices to pass through the detection range of Bluetooth scanners. If the scanners are located at signalised intersections, this Duration can be related to intersection performance, and hence represents valuable information for traffic monitoring. However the use of Duration has been ignored in previous analyses. In this study, the Duration data as well as travel time data is analysed to capture the traffic condition of a main arterial route in Brisbane. The data consists of one week of Bluetooth data provided by Brisbane City Council. As well, micro simulation analysis is conducted to further investigate the properties of Duration. The results reveal characteristics of Duration, and address future research needs to utilise this valuable data source.

A traffic simulation standard based on data marts

Traffic Simulation models tend to have their own data input and output formats. In an effort to standardise the input for traffic simulations, we introduce in this paper a set of data marts that aim to serve as a common interface between the necessaary data, stored in dedicated databases, and the swoftware packages, that require the input in a certain format. The data marts are developed based on real world objects (e.g. roads, traffic lights, controllers) rather than abstract models and hence contain all necessary information that can be transformed by the importing software package to their needs. The paper contains a full description of the data marts for network coding, simulation results, and scenario management, which have been discussed with industry partners to ensure sustainability.

The theories of reasoned action and planned behaviour applied to business decision : a selective annotated bibliography

Purpose: This paper provides a selective annotated bibliography that summarises journal articles which have employed either the theory of reasoned action or the theory of planned behaviour to circumstances which are relevant to business activities. Design/methodology/approach: Searches were conducted on the EBSCO Host and ProQuest databases to identify papers that had used either the theory of reasoned action or theory of planned behaviour in their methodology. The bibliography was separated into three categories- financial decision making, strategic decision making, and professional decision making. Implications: The information presented in this paper is intended to assist and facilitate further research by broadening the awareness of the literature and providing examples of the application of the theory as it has been employed in prior research.

The use of Twitter hashtags in the formation of ad hoc publics

As the use of Twitter has become more commonplace throughout many nations, its role in political discussion has also increased. This has been evident in contexts ranging from general political discussion through local, state, and national elections (such as in the 2010 Australian elections) to protests and other activist mobilisation (for example in the current uprisings in Tunisia, Egypt, and Yemen, as well as in the controversy around Wikileaks). Research into the use of Twitter in such political contexts has also developed rapidly, aided by substantial advancements in quantitative and qualitative methodologies for capturing, processing, analysing, and visualising Twitter updates by large groups of users. Recent work has especially highlighted the role of the Twitter hashtag – a short keyword, prefixed with the hash symbol ‘#’ – as a means of coordinating a distributed discussion between more or less large groups of users, who do not need to be connected through existing ‘follower’ networks. Twitter hashtags – such as ‘#ausvotes’ for the 2010 Australian elections, ‘#londonriots’ for the coordination of information and political debates around the recent unrest in London, or ‘#wikileaks’ for the controversy around Wikileaks thus aid the formation of ad hoc publics around specific themes and topics. They emerge from within the Twitter community – sometimes as a result of pre-planning or quickly reached consensus, sometimes through protracted debate about what the appropriate hashtag for an event or topic should be (which may also lead to the formation of competing publics using different hashtags). Drawing on innovative methodologies for the study of Twitter content, this paper examines the use of hashtags in political debate in the context of a number of major case studies.

An integrated framework to assess financial reward systems in construction projects

Motivation is a major driver of project performance. Despite team member ability to deliver successful project outcomes if they are not positively motivated to pursue joint project goals, then performance will be constrained. One approach to improving the motivation of project organizations is by offering a financial reward for the achievement of set performance standards above a minimum required level. However, little investigation has been undertaken into the features of successful incentive systems as a part of an overall delivery strategy. With input from organizational management literature, and drawing on the literature covering psychological and economic theories of motivation, this paper presents an integrated framework that can be used by project organizations to assess the impact of financial reward systems on motivation in construction projects. The integrated framework offers four motivation indicators which reflect key theoretical concepts across both psychological and economic disciplines. The indicators are: (1) Goal Commitment, (2) Distributive Justice, (3) Procedural Justice, and (4) Reciprocity. The paper also interprets the integrated framework against the results of a successful Australian social infrastructure project case study and identifies key learning’s for project organizations to consider when designing financial reward systems. Case study results suggest that motivation directed towards the achievement of incentive goals is influenced not only by the value placed on the financial reward for commercial benefit, but also driven by the strength of the project initiatives that encourage just and fair dealings, supporting the establishment of trust and positive reciprocal behavior across a project team. The strength of the project relationships was found to be influenced by how attractive the achievement of the goal is to the incentive recipient and how likely they were to push for the achievement of the goal. Interestingly, findings also suggested that contractor motivation is also influenced by the fairness of the performance measurement process and their perception of the trustworthiness and transparency of their client. These findings provide the basis for future research on the impact of financial reward systems on motivation in construction projects. It is anticipated that such research will shed new light on this complex topic and further define how reward systems should be designed to promote project team motivation. Due to the unique nature of construction projects with high levels of task complexity and interdependence, results are expected to vary in comparison to previous studies based on individuals or single-entity organizations.

Sh-h-h-h : representations of perpetrators of sexual child abuse in picturebooks

Children’s picturebooks dealing with the topic of child sexual abuse first appeared in the early 1980s with the aim of addressing the need for age-appropriate texts to teach sexual abuse prevention concepts and to provide support for young children who may be at risk of or have already experienced sexual abuse. Despite the apparent potential of children’s picturebooks to convey child sexual abuse prevention concepts, very few studies have addressed the topic of child sexual abuse in children’s literature. Based on a larger study of 60 picturebooks about sexual child abuse published over the past 25 years, this paper critically examines eight picturebook representations of the perpetrators of sexual child abuse as a way to understand how potentially dangerous adults are explained to the young readers of these texts.

Psychological interventions for alcohol misuse among people with co-occurring depression or anxiety disorders: A systematic review

Abstract OBJECTIVE: Depression, anxiety and alcohol misuse frequently co-occur. While there is an extensive literature reporting on the efficacy of psychological treatments that target depression, anxiety or alcohol misuse separately, less research has examined treatments that address these disorders when they co-occur. We conducted a systematic review to determine whether psychological interventions that target alcohol misuse among people with co-occurring depressive or anxiety disorders are effective. DATA SOURCES: We systematically searched the PubMed and PsychINFO databases from inception to March 2010. Individual searches in alcohol, depression and anxiety were conducted, and were limited to 'human' published 'randomized controlled trials' or 'sequential allocation' articles written in English. STUDY SELECTION: We identified randomized controlled trials that compared manual guided psychological interventions for alcohol misuse among individuals with depressive or anxiety disorders. Of 1540 articles identified, eight met inclusion criteria for the review. DATA EXTRACTION: From each study, we recorded alcohol and mental health outcomes, and other relevant clinical factors including age, gender ratio, follow-up length and drop-out rates. Quality of studies was also assessed. DATA SYNTHESIS: Motivational interviewing and cognitive-behavioral interventions were associated with significant reductions in alcohol consumption and depressive and/or anxiety symptoms. Although brief interventions were associated with significant improvements in both mental health and alcohol use variables, longer interventions produced even better outcomes. CONCLUSIONS: There is accumulating evidence for the effectiveness of motivational interviewing and cognitive behavior therapy for people with co-occurring alcohol and depressive or anxiety disorders.