The relevance of shared experiences : a multi-level study of collective efficacy as a moderator of job control in the stressor-strain relationship

Karasek's Job Demand-Control model proposes that control mitigates the positive effects of work stressors on employee strain. Evidence to date remains mixed and, although a number of individual-level moderators have been examined, the role of broader, contextual, group factors has been largely overlooked. In this study, the extent to which control buffered or exacerbated the effects of demands on strain at the individual level was hypothesized to be influenced by perceptions of collective efficacy at the group level. Data from 544 employees in Australian organizations, nested within 23 workgroups, revealed significant three-way cross-level interactions among demands, control and collective efficacy on anxiety and job satisfaction. When the group perceived high levels of collective efficacy, high control buffered the negative consequences of high demands on anxiety and satisfaction. Conversely, when the group perceived low levels of collective efficacy, high control exacerbated the negative consequences of high demands on anxiety, but not satisfaction. In addition, a stress-exacerbating effect for high demands on anxiety and satisfaction was found when there was a mismatch between collective efficacy and control (i.e. combined high collective efficacy and low control). These results provide support for the notion that the stressor-strain relationship is moderated by both individual- and group-level factors.

The ranking based constrained document clustering method and its application to social event detection

With the growing size and variety of social media files on the web, it’s becoming critical to efficiently organize them into clusters for further processing. This paper presents a novel scalable constrained document clustering method that harnesses the power of search engines capable of dealing with large text data. Instead of calculating distance between the documents and all of the clusters’ centroids, a neighborhood of best cluster candidates is chosen using a document ranking scheme. To make the method faster and less memory dependable, the in-memory and in-database processing are combined in a semi-incremental manner. This method has been extensively tested in the social event detection application. Empirical analysis shows that the proposed method is efficient both in computation and memory usage while producing notable accuracy.

Relevation! : an open source system for information retrieval relevance assessment

Relevation! is a system for performing relevance judgements for information retrieval evaluation. Relevation! is web-based, fully configurable and expandable; it allows researchers to effectively collect assessments and additional qualitative data. The system is easily deployed allowing assessors to smoothly perform their relevance judging tasks, even remotely. Relevation! is available as an open source project at: http://ielab.github.io/relevation.

Tensor-based item recommendation using probabilistic ranking in social tagging systems

A common problem with the use of tensor modeling in generating quality recommendations for large datasets is scalability. In this paper, we propose the Tensor-based Recommendation using Probabilistic Ranking method that generates the reconstructed tensor using block-striped parallel matrix multiplication and then probabilistically calculates the preferences of user to rank the recommended items. Empirical analysis on two real-world datasets shows that the proposed method is scalable for large tensor datasets and is able to outperform the benchmarking methods in terms of accuracy.

An analysis of ranking principles and retrieval strategies

The assumptions underlying the Probability Ranking Principle (PRP) have led to a number of alternative approaches that cater or compensate for the PRP’s limitations. All alternatives deviate from the PRP by incorporating dependencies. This results in a re-ranking that promotes or demotes documents depending upon their relationship with the documents that have been already ranked. In this paper, we compare and contrast the behaviour of state-of-the-art ranking strategies and principles. To do so, we tease out analytical relationships between the ranking approaches and we investigate the document kinematics to visualise the effects of the different approaches on document ranking.

When two is better than one : a study of ranking paradigms and their integrations for subtopic retrieval

In this paper, we consider the problem of document ranking in a non-traditional retrieval task, called subtopic retrieval. This task involves promoting relevant documents that cover many subtopics of a query at early ranks, providing thus diversity within the ranking. In the past years, several approaches have been proposed to diversify retrieval results. These approaches can be classified into two main paradigms, depending upon how the ranks of documents are revised for promoting diversity. In the first approach subtopic diversification is achieved implicitly, by choosing documents that are different from each other, while in the second approach this is done explicitly, by estimating the subtopics covered by documents. Within this context, we compare methods belonging to the two paradigms. Furthermore, we investigate possible strategies for integrating the two paradigms with the aim of formulating a new ranking method for subtopic retrieval. We conduct a number of experiments to empirically validate and contrast the state-of-the-art approaches as well as instantiations of our integration approach. The results show that the integration approach outperforms state-of-the-art strategies with respect to a number of measures.

University of Glasgow at ImageCLEFPhoto 2009 : optimising similarity and diversity in image retrieval

In this paper we describe the approaches adopted to generate the runs submitted to ImageCLEFPhoto 2009 with an aim to promote document diversity in the rankings. Four of our runs are text based approaches that employ textual statistics extracted from the captions of images, i.e. MMR [1] as a state of the art method for result diversification, two approaches that combine relevance information and clustering techniques, and an instantiation of Quantum Probability Ranking Principle. The fifth run exploits visual features of the provided images to re-rank the initial results by means of Factor Analysis. The results reveal that our methods based on only text captions consistently improve the performance of the respective baselines, while the approach that combines visual features with textual statistics shows lower levels of improvements.

Why assessing relevance in medical IR is demanding

This study investigates if and why assessing relevance of clinical records for a clinical retrieval task is cognitively demanding. Previous research has highlighted the challenges and issues information retrieval systems are faced with when determining the relevance of documents in this domain, e.g., the vocabulary mismatch problem. Determining if this assessment imposes cognitive load on human assessors, and why this is the case, may shed lights on what are the (cognitive) processes that assessors use for determining document relevance (in this domain). High cognitive load may impair the ability of the user to make accurate relevance judgements and hence the design of IR mechanisms may need to take this into account in order to reduce the load.

Perceptions of document relevance

This article presents a study of how humans perceive and judge the relevance of documents. Humans are adept at making reasonably robust and quick decisions about what information is relevant to them, despite the ever increasing complexity and volume of their surrounding information environment. The literature on document relevance has identified various dimensions of relevance (e.g., topicality, novelty, etc.), however little is understood about how these dimensions may interact. We performed a crowdsourced study of how human subjects judge two relevance dimensions in relation to document snippets retrieved from an internet search engine. The order of the judgment was controlled. For those judgments exhibiting an order effect, a q–test was performed to determine whether the order effects can be explained by a quantum decision model based on incompatible decision perspectives. Some evidence of incompatibility was found which suggests incompatible decision perspectives is appropriate for explaining interacting dimensions of relevance in such instances.

Clinicopathological relevance of BRAF mutations in human cancer

BRAF represents one of the most frequently mutated protein kinase genes in human tumours. The mutation is commonly tested in pathology practice. BRAF mutation is seen in melanoma, papillary thyroid carcinoma (including papillary thyroid carcinoma arising from ovarian teratoma), ovarian serous tumours, colorectal carcinoma, gliomas, hepatobiliary carcinomas and hairy cell leukaemia. In these cancers, various genetic aberrations of the BRAF proto-oncogene, such as different point mutations and chromosomal rearrangements, have been reported. The most common mutation, BRAF V600E, can be detected by DNA sequencing and immunohistochemistry on formalin fixed, paraffin embedded tumour tissue. Detection of BRAF V600E mutation has the potential for clinical use as a diagnostic and prognostic marker. In addition, a great deal of research effort has been spent in strategies inhibiting its activity. Indeed, recent clinical trials involving BRAF selective inhibitors exhibited promising response rates in metastatic melanoma patients. Clinical trials are underway for other cancers. However, cutaneous side effects of treatment have been reported and therapeutic response to cancer is short-lived due to the emergence of several resistance mechanisms. In this review, we give an update on the clinical pathological relevance of BRAF mutation in cancer. It is hoped that the review will enhance the direction of future research and assist in more effective use of the knowledge of BRAF mutation in clinical practice.

Altered JS-2 expression in colorectal cancers and its clinical pathological relevance

JS-2 is a novel gene located at 5p15.2 and originally detected in primary oesophageal cancer. There is no study on the role of JS-2 in colorectal cancer. The aim of this study is to determine the gene copy number and expression of JS-2 in a large cohort of patients with colorectal tumours and correlate these to the clinicopathological features of the cancer patients. We evaluated the DNA copy number and mRNA expression of JS-2 in 176 colorectal tissues (116 adenocarcinomas, 30 adenomas and 30 non-neoplastic tissues) using real-time polymerase chain reaction. JS-2 expression was also evaluated in two colorectal cancer cell lines and a benign colorectal cell line. JS-2 amplification was noted in 35% of the colorectal adenocarcinomas. Significant differences in relative expression levels for JS-2 mRNA between different colorectal tissues were noted (p = 0.05). Distal colorectal adenocarcinoma had significantly higher copy number than proximal adenocarcinoma (p = 0.005). The relative expression level of JS-2 was different between colonic and rectal adenocarcinoma (p = 0.007). Mucinous adenocarcinoma showed higher JS-2 expression than non-mucinous adenocarcinoma (p = 0.02). Early T-stage cancers appear to have higher JS-2 copy number and lower expression of JS-2 mRNA than later stage cancers (p = 0.001 and 0.03 respectively). Colorectal cancer cell lines showed lower expression of JS-2 than the benign colorectal cell line. JS-2 copy number change and expression were shown for the first time to be altered in the carcinogenesis of colorectal cancer. In addition, genetic alteration of JS-2 was found to be related to location, pathological subtypes and staging of colorectal cancer.

Optimism : what it is and its relevance in the school context

"For myself, I am an optimist - it does not seem to be much use to be anything else". Winston Churchill Optimism has its modern roots in philosophy dating back to the 17th century in the writings of philosophers such as Descartes and Voltaire (Domino & Conway, 2001). Previous to these philosophical writings, the concept of optimism was revealed in the teaching of many of the great spiritual traditions such as Buddhism and Christianity (Miller, Richards, & Keller, 2001). In the 20th century, optimism became defined in juxtaposition to pessimism, sometimes conceptualized as a bipolar unidimensional construct and by others as two related but separate constructs (Garber, 2000). Contemporary models (Scheier & Carver, 1985; Seligman, 1991) have increasingly focused on distinguishing optimism-pessimism as a general dispositional orientation, as described by expectancy theory, and as an explanatory process, described by explanatory style theory.

Utilizing voting systems for ranking user tweets

Twitter is a very popular social network website that allows users to publish short posts called tweets. Users in Twitter can follow other users, called followees. A user can see the posts of his followees on his Twitter profile home page. An information overload problem arose, with the increase of the number of followees, related to the number of tweets available in the user page. Twitter, similar to other social network websites, attempts to elevate the tweets the user is expected to be interested in to increase overall user engagement. However, Twitter still uses the chronological order to rank the tweets. The tweets ranking problem was addressed in many current researches. A sub-problem of this problem is to rank the tweets for a single followee. In this paper we represent the tweets using several features and then we propose to use a weighted version of the famous voting system Borda-Count (BC) to combine several ranked lists into one. A gradient descent method and collaborative filtering method are employed to learn the optimal weights. We also employ the Baldwin voting system for blending features (or predictors). Finally we use the greedy feature selection algorithm to select the best combination of features to ensure the best results.

Marx as a Critical Discourse Analyst : The Genesis of a Critical Method and its Relevance to the Critique of Global Capital (with Phil Graham)

In this paper we identify elements in Marx’s economic and political writings that are relevant to contemporary critical discourse analysis (CDA). We argue that Marx can be seen to be engaging in a form of discourse analysis. We identify the elements in Marx’s historical materialist method that support such a perspective, and exemplify these in longitudinal comparison of Marx’s texts.