11,267 research outputs found

    A Text-Mining Approach to Explain Unwanted Behaviours

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    Real-time classification of malicious URLs on Twitter using Machine Activity Data

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    Massive online social networks with hundreds of millions of active users are increasingly being used by Cyber criminals to spread malicious software (malware) to exploit vulnerabilities on the machines of users for personal gain. Twitter is particularly susceptible to such activity as, with its 140 character limit, it is common for people to include URLs in their tweets to link to more detailed information, evidence, news reports and so on. URLs are often shortened so the endpoint is not obvious before a person clicks the link. Cyber criminals can exploit this to propagate malicious URLs on Twitter, for which the endpoint is a malicious server that performs unwanted actions on the person’s machine. This is known as a drive-by-download. In this paper we develop a machine classification system to distinguish between malicious and benign URLs within seconds of the URL being clicked (i.e. ‘real-time’). We train the classifier using machine activity logs created while interacting with URLs extracted from Twitter data collected during a large global event – the Superbowl – and test it using data from another large sporting event – the Cricket World Cup. The results show that machine activity logs produce precision performances of up to 0.975 on training data from the first event and 0.747 on a test data from a second event. Furthermore, we examine the properties of the learned model to explain the relationship between machine activity and malicious software behaviour, and build a learning curve for the classifier to illustrate that very small samples of training data can be used with only a small detriment to performance

    Psychological elements explaining the consumer's adoption and use of a website recommendation system: A theoretical framework proposal

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    The purpose of this paper is to understand, with an emphasis on the psychological perspective of the research problem, the consumer's adoption and use of a certain web site recommendation system as well as the main psychological outcomes involved. The approach takes the form of theoretical modelling. Findings: A conceptual model is proposed and discussed. A total of 20 research propositions are theoretically analyzed and justified. Research limitations/implications: The theoretical discussion developed here is not empirically validated. This represents an opportunity for future research. Practical implications: The ideas extracted from the discussion of the conceptual model should be a help for recommendation systems designers and web site managers, so that they may be more aware, when working with such systems, of the psychological process consumers undergo when interacting with them. In this regard, numerous practical reflections and suggestions are presented

    Simulated testing of an adaptive multimedia information retrieval system

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    The Semantic Gap is considered to be a bottleneck in image and video retrieval. One way to increase the communication between user and system is to take advantage of the user's action with a system, e.g. to infer the relevance or otherwise of a video shot viewed by the user. In this paper we introduce a novel video retrieval system and propose a model of implicit information for interpreting the user's actions with the interface. The assumptions on which this model was created are then analysed in an experiment using simulated users based on relevance judgements to compare results of explicit and implicit retrieval cycles. Our model seems to enhance retrieval results. Results are presented and discussed in the final section

    Gendering, courtship and pay equality: developing attraction theory to understand work-life balance and entrepreneurial activity

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    Objectives: This paper examines one of the most intractable problems of the last 40 years: the difficulty in closing the pay inequality gap. Current wisdom is that the pay gap exists because of men's power to control the workplace, and men's dominant position in society generally. This paper examines an emergent literature on matriarchal power structures and proposes Attraction Theory as a holistic framework. Prior Work: This paper acknowledges a range of feminist literature that examines the underlying social relations and power structures that impact on pay differentials. This is critiqued on the basis of findings from courtship research as well as studies emerging from liberal / progressive writers in the men's movement. Approach: This paper is conceptual, using an inter-disciplinary understanding of social processes to critically appraise both the dominant discourse on equal pay and its emergent alternative. Attraction Theory is presented as a framework for exploring a complex discourse that unequal pay exists both because of men's power to control the workplace and women's power to control courtship and family life. Implications: Tackling pay inequality and work-life balance issues by focussing on power sharing in the workplace represents only a partial policy solution. Further progress depends on power-sharing in parental rights through academic recognition and political action to tackle negative stereotypes that impact on men during romantic courtship, conception, birth and divorce. Value: The value of the paper lies in the originality of the analysis and the range of insights that Attraction Theory provides into societal dynamics that impact on equal pay. The identification of paradoxes in the dominant discourse opens up new avenues for research and policy development on work-life balance. Whether these will close the pay gap is unclear, but it would advance equality and diversity goals by creating confidence that consensual choices rather the institutional inequalities perpetuate any remaining inequalities reported in statistics.</p

    Serious Gaming Analytics: What StudentsÂŽ Log Files Tell Us about Gaming and Learning

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    In this paper we explore existing log files of the VIBOA environmental policy game. Our aim is to identify relevant player behaviours and performance patterns. The VIBOA game is a 50 hours master level serious game that supports inquiry-based learning: students adopt the role of an environmental consultant in the (fictitious) consultancy agency VIBOA, and have to deal with complex, multi-faceted environmental problems in an academic and methodologically sound way. A sample of 118 master students played the game. We used learning analytics to extract relevant data from the logging and find meaningful patterns and relationships. We observed substantial behavioural variability across students. Correlation analysis suggest a behavioural trade that reflects the rate of “switching” between different game objects or activities. We were able to establish a model that uses switching indicators as predictors for the efficiency of learning. Also we found slight evidence that students who display increased switching behaviours need more time to complete the games. We conclude the paper by critically evaluating our findings, making explicit the limitations of our study and making suggestions for future research that links together learning analytics and serious gaming

    Drug Reviews: Cross-condition and Cross-source Analysis by Review Quantification Using Regional CNN-LSTM Models

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    Pharmaceutical drugs are usually rated by customers or patients (i.e. in a scale from 1 to 10). Often, they also give reviews or comments on the drug and its side effects. It is desirable to quantify the reviews to help analyze drug favorability in the market, in the absence of ratings. Since these reviews are in the form of text, we should use lexical methods for the analysis. The intent of this study was two-fold: First, to understand how better the efficiency will be if CNN-LSTM models are used to predict ratings or sentiment from reviews. These models are known to perform better than usual machine learning models in the case of textual data sequences. Second, how effective is it to migrate such information extraction models across different drug review data sets and across different disease conditions. Therefore three experiments were designed, first, an In-domain experiment where train and test data are from the same dataset. Two more experiments were conducted to examine the migration capability of models, namely cross-data source, where train and test are from different sources and cross-disease condition model training, where train and test data belong to different disease conditions in the same dataset. The experiments were evaluated using popular metrics such as RMSE, MAE, R2 and Pearson’s coefficient and the results showed that the proposed deep learning regression model works less successfully when compared to the machine learning sentiment extraction models in the literature, which were done on the same datasets. But, this study contributes to the existing literature in the quantity of research work done and in quality of the model and also suggests the future researchers on how to improve. This work also addressed the shortcomings in the literature by introducin

    Development of an ontology for aerospace engine components degradation in service

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    This paper presents the development of an ontology for component service degradation. In this paper, degradation mechanisms in gas turbine metallic components are used for a case study to explain how a taxonomy within an ontology can be validated. The validation method used in this paper uses an iterative process and sanity checks. Data extracted from on-demand textual information are filtered and grouped into classes of degradation mechanisms. Various concepts are systematically and hierarchically arranged for use in the service maintenance ontology. The allocation of the mechanisms to the AS-IS ontology presents a robust data collection hub. Data integrity is guaranteed when the TO-BE ontology is introduced to analyse processes relative to various failure events. The initial evaluation reveals improvement in the performance of the TO-BE domain ontology based on iterations and updates with recognised mechanisms. The information extracted and collected is required to improve service k nowledge and performance feedback which are important for service engineers. Existing research areas such as natural language processing, knowledge management, and information extraction were also examined

    User-centred design for civil construction: optimising productivity by reducing safety and health risks associated with the operation and maintenance of on-road vehicles and mobile plant.

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    A range of productivity implications, injury and health risks are associated with the operation and maintenance of road construction equipment. Potential unwanted events giving rise to these risks include: slip, trips and falls from ground or at height; performance of hazardous manual tasks; exposure to heat, chemicals and whole body vibration; vehicle roll overs; and collisions. It may be possible to remove or reduce the risk of these events through improved design of the equipment and wider organisational systems. Design analysis techniques and a risk assessment tool (Design OMAT and EDEEP) were applied in the review of an asphalt job truck. Findings have led to preliminary design considerations for improvement and there are implications for organisational system change
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