Composite SaaS scaling in cloud computing using a hybrid genetic algorithm

A Software-as-a-Service or SaaS can be delivered in a composite form, consisting of a set of application and data components that work together to deliver higher-level functional software. Components in a composite SaaS may need to be scaled – replicated or deleted, to accommodate the user’s load. It may not be necessary to replicate all components of the SaaS, as some components can be shared by other instances. On the other hand, when the load is low, some of the instances may need to be deleted to avoid resource underutilisation. Thus, it is important to determine which components are to be scaled such that the performance of the SaaS is still maintained. Extensive research on the SaaS resource management in Cloud has not yet addressed the challenges of scaling process for composite SaaS. Therefore, a hybrid genetic algorithm is proposed in which it utilises the problem’s knowledge and explores the best combination of scaling plan for the components. Experimental results demonstrate that the proposed algorithm outperforms existing heuristic-based solutions.

Agility in construction

Agile Project Management (APM) is a human-centred method for increasing customer-perceived value in a reliable manner. It has been proven to be particularly suited to creative projects such as Information Systems (IS) development and new product development in the automotive industry (this is in contrast to ‘lean production’ which has proved so useful to automotive production waste elimination). Construction is similarly largely a creative industry and might usefully adopt APM to improve its own reliable value delivery, rather than solely following the industrial trend of lean production. This paper describes APM, comparing it with two prominent lean construction initiatives, and then assesses by phase the potential for any impact of APM in construction. In conclusion: APM would have benefits for all phases of construction, particularly in planning and design, but its adoption for actual construction would generally be disrupted because of the lack of a coherent, well trained and trusted workforce.

Assessing the threshold temperatures among different age and cause-of-deaths

The relationship between temperature and mortality is non-linear and the effect estimates depend on the threshold temperatures selected. However, little is known about whether threshold temperatures differ with age or cause of deaths in the Southern Hemisphere. We conducted polynomial distributed lag non-linear models to assess the threshold temperatures for mortality from all ages (Dall), aged from 15 to 64 (D15-64), 65- 84(D65-84), ≥85 years (D85+), respiratory (RD) and cardiovascular diseases (CVD) in Brisbane, Australia, 1996–2004. We examined both hot and cold thresholds, and the lags of up to 15 days for cold effects and 3 days for hot effects. Results show that for the current day, the cold threshold was 20°C and the hot threshold was 28°C for the groups of Dall, D15-64 and D85+. The cold threshold was higher (23°C) for the group of D65-84 and lower (21°C) for the group of CVD. The hot threshold was higher (29°C) for the group of D65-84 and lower (27°C) for the group of RD. Compared to the current day, for the cold effects of up to 15-day lags, the threshold was lower for the group of D15-64, and the thresholds were higher for the groups of D65-84, D85+, RD and CVD; while for the hot effects of 3-day lags, the threshold was higher for the group of D15-64 and the thresholds were lower for the groups of D65-84 and RD. Temperature thresholds appeared to differ with age and death categories. The elderly and deaths from RD and CVD were more sensitive to temperature stress than the adult group. These findings may have implications in the assessment of temperature-related mortality and development of weather/health warning systems.

Agent-based modelling simulation case study : assessment of airport check-in and evacuation process by considering group travel behaviour of air passengers

Passenger experience has become a major factor that influences the success of an airport. In this context, passenger flow simulation has been used in designing and managing airports. However, most passenger flow simulations failed to consider the group dynamics when developing passenger flow models. In this paper, an agent-based model is presented to simulate passenger behaviour at the airport check-in and evacuation process. The simulation results show that the passenger behaviour can have significant influences on the performance and utilisation of services in airport terminals. The model was created using AnyLogic software and its parameters were initialised using recent research data published in the literature.

Agent-based application on different boarding strategies

In this paper we implemented six different boarding strategies (Wilma, Steffen, Reverse Pyramid, Random, Blocks and By letter) in order to minimize boarding time and turnaround time for Boeing 777 and Airbus 380 aircrafts by using Agent-based modelling approach. In the simulation, we divided passengers into six different categories which are group size more than 5 people, passengers with child, gold members, first class passengers, business class passengers and economy class passengers. Results from the simulation demonstrates Reverse Pyramid method is the best boarding method for Boeing 777 and Steffen method is the best boarding method for Airbus 380.

Effects of plasterboard joints on the fire performance of cold-formed steel walls

This paper presents the effect of plasterboard joints on the fire performance of cold-formed steel walls. Plasterboard joints are unavoidable. However, they can be arranged in a way that they do not significantly influence the fire performance of cold-formed steel walls. Hence a research study into the effects of plasterboard joints on the fire performance of plasterboard lined cold-formed steel walls was undertaken using both full-scale fire tests and numerical studies. In this study a back-blocking technique was used to eliminate the plasterboard joints being located over the studs. Instead plasterboard joints were used between studs with 150 mm wide plasterboards as back-blocks. Both experimental and numerical results from this study show that the fire resistance rating of single plasterboard lined cold-formed steel walls can be increased by 25% through the use of a back-blocking joint arrangement in comparison to the traditional plasterboard joint arrangement over the studs.

Determinants of second row passenger comfort

China is becoming an increasingly important automotive market. Customer’s vehicle usage, preferences and requirements differ from traditional western markets in a number of aspects – rear seat usage rates are higher, vehicles are used for business purposes as well as for private transport and rear seat usage is generally more important to Chinese customers compared to their western counterparts. The purpose of this project is to dimension and investigate these differences from an ergonomics perspective and use these results to guide the design of future products. The focus for this project will be specific to vehicles in the CD segment. More specifically, this project focuses on the second row ‘ambience’. Ambience refers to the global feeling perceived by second row passengers, and the main factors contributing to ambience are: ingress and egress comfort, seat comfort, roominess, and ease of use of the controls. In order to investigate the aforementioned parameters, an experimental study has been conducted in Shanghai, China. This experiment involved 80 healthy Chinese CD- and D-car customers. These subjects were asked to evaluate different features present in the second row environment of three different cars: A Ford Mondeo, Toyota Camry and Mercedes S-class. Various data has been collected during this experiment: First, the anthropometric dimensions of the subjects have been measured. The subjects were also asked to fill a questionnaire about demographics, their own car usage, and their perception of a various number of features present in the three tested cars. A great amount of technical data was also collected. The first part of this report presents the results given by the questionnaires. It includes Chinese demographics, vehicle usage habits, and the subjective perception of the features present in the tested cars. It also presents the results of the anthropometric measurements. This gives a first insight into Chinese customers’ habits and preferences. The second part deals with the technical data recorded during the experiment: second row seat adjustment ranges, roominess, optimal location of controls, and pressure mapping analysis. Analysis of technical data allows a deeper understanding of the factors contributing to comfort and ambience perception. Using the technical data together with the comfort ratings given by the subjects in the questionnaire, recommendations on several design parameters were provided. Finally, an experimental study of car ingress-egress has been conducted in a University laboratory controlled environment. During this study, the ingress and egress motion of 20 customers from Chinese origin was recorded using a motion capture system. The last part of this report presents the protocol and data processing that led to building an ingress-egress motion database that was provided to Ford.

Enterprise resource planning or enterprise resource plotting? Technological, organisational and inter-organisational aspects of enterprise resource planning package adoption and diffusion

Those in organisations tend to adopt new technologies as a way to improve their functions, reduce cost and attain best practices. Thus, technology promoters (or vendors) work along those lines in order to convince adopters to invest in those technologies and develop their own organisations profit in return. The possible resultant ‘conflicts of interest’ makes the study of reasons behind IT diffusion and adoption an interesting subject. In this paper we look at IT diffusion and adoption in terms of technology (system features), organisational aspects (firm level characteristics) and inter-organisational aspects (market dynamics) in order to see who might be the real beneficiaries of technology adoption. We use ERP packages as an example of an innovation that has been widely diffused and adopted for the last 10 years. We believe that our findings can be useful to those adopting ERP packages as it gives them a wider view of the situation.

Injury narrative text classification : a preliminary study

Description of a patient's injuries is recorded in narrative text form by hospital emergency departments. For statistical reporting, this text data needs to be mapped to pre-defined codes. Existing research in this field uses the Naïve Bayes probabilistic method to build classifiers for mapping. In this paper, we focus on providing guidance on the selection of a classification method. We build a number of classifiers belonging to different classification families such as decision tree, probabilistic, neural networks, and instance-based, ensemble-based and kernel-based linear classifiers. An extensive pre-processing is carried out to ensure the quality of data and, in hence, the quality classification outcome. The records with a null entry in injury description are removed. The misspelling correction process is carried out by finding and replacing the misspelt word with a soundlike word. Meaningful phrases have been identified and kept, instead of removing the part of phrase as a stop word. The abbreviations appearing in many forms of entry are manually identified and only one form of abbreviations is used. Clustering is utilised to discriminate between non-frequent and frequent terms. This process reduced the number of text features dramatically from about 28,000 to 5000. The medical narrative text injury dataset, under consideration, is composed of many short documents. The data can be characterized as high-dimensional and sparse, i.e., few features are irrelevant but features are correlated with one another. Therefore, Matrix factorization techniques such as Singular Value Decomposition (SVD) and Non Negative Matrix Factorization (NNMF) have been used to map the processed feature space to a lower-dimensional feature space. Classifiers with these reduced feature space have been built. In experiments, a set of tests are conducted to reflect which classification method is best for the medical text classification. The Non Negative Matrix Factorization with Support Vector Machine method can achieve 93% precision which is higher than all the tested traditional classifiers. We also found that TF/IDF weighting which works well for long text classification is inferior to binary weighting in short document classification. Another finding is that the Top-n terms should be removed in consultation with medical experts, as it affects the classification performance.

Two-way recommendation methods for social networks

In this work, we present the challenges associated with the two-way recommendation methods in social networks and the solutions. We discuss them from the perspective of community-type social networks such as online dating networks.

Clustering and labeling a web scale document collection using Wikipedia clusters

Clustering is an important technique in organising and categorising web scale documents. The main challenges faced in clustering the billions of documents available on the web are the processing power required and the sheer size of the datasets available. More importantly, it is nigh impossible to generate the labels for a general web document collection containing billions of documents and a vast taxonomy of topics. However, document clusters are most commonly evaluated by comparison to a ground truth set of labels for documents. This paper presents a clustering and labeling solution where the Wikipedia is clustered and hundreds of millions of web documents in ClueWeb12 are mapped on to those clusters. This solution is based on the assumption that the Wikipedia contains such a wide range of diverse topics that it represents a small scale web. We found that it was possible to perform the web scale document clustering and labeling process on one desktop computer under a couple of days for the Wikipedia clustering solution containing about 1000 clusters. It takes longer to execute a solution with finer granularity clusters such as 10,000 or 50,000. These results were evaluated using a set of external data.

Classification of inventory data suit for managing Australian railway bridges

With a focus to optimising the life cycle performance of Australian Railway bridges, new bridge classification and environmental classification systems are proposed. The new bridge classification system is mainly to facilitate the implementation of novel Bridge Management System (BMS) which optimise the life cycle cost both at project level and network level while environment classification is mainly to improve accuracy of Remaining Service Potential (RSP) module of the proposed BMS. In fact, limited capacity of the existing BMS to trigger the maintenance intervention point is an indirect result of inadequacies of the existing bridge and environmental classification systems. The proposed bridge classification system permits to identify the intervention points based on percentage deterioration of individual elements and maintenance cost, while allowing performance based rating technique to implement for maintenance optimisation and prioritisation. Simultaneously, the proposed environment classification system will enhance the accuracy of prediction of deterioration of steel components.

Using viewer’s facial expression and heart rate for sports video highlights detection

Viewer interests, evoked by video content, can potentially identify the highlights of the video. This paper explores the use of facial expressions (FE) and heart rate (HR) of viewers captured using camera and non-strapped sensor for identifying interesting video segments. The data from ten subjects with three videos showed that these signals are viewer dependent and not synchronized with the video contents. To address this issue, new algorithms are proposed to effectively combine FE and HR signals for identifying the time when viewer interest is potentially high. The results show that, compared with subjective annotation and match report highlights, ‘non-neutral’ FE and ‘relatively higher and faster’ HR is able to capture 60%-80% of goal, foul, and shot-on-goal soccer video events. FE is found to be more indicative than HR of viewer’s interests, but the fusion of these two modalities outperforms each of them.

Medical semantic similarity with a neural language model

Advances in neural network language models have demonstrated that these models can effectively learn representations of words meaning. In this paper, we explore a variation of neural language models that can learn on concepts taken from structured ontologies and extracted from free-text, rather than directly from terms in free-text. This model is employed for the task of measuring semantic similarity between medical concepts, a task that is central to a number of techniques in medical informatics and information retrieval. The model is built with two medical corpora (journal abstracts and patient records) and empirically validated on two ground-truth datasets of human-judged concept pairs assessed by medical professionals. Empirically, our approach correlates closely with expert human assessors ($\approx$ 0.9) and outperforms a number of state-of-the-art benchmarks for medical semantic similarity. The demonstrated superiority of this model for providing an effective semantic similarity measure is promising in that this may translate into effectiveness gains for techniques in medical information retrieval and medical informatics (e.g., query expansion and literature-based discovery).

Exploiting inference from semantic annotations for information retrieval

The increasing amount of information that is annotated against standardised semantic resources offers opportunities to incorporate sophisticated levels of reasoning, or inference, into the retrieval process. In this position paper, we reflect on the need to incorporate semantic inference into retrieval (in particular for medical information retrieval) as well as previous attempts that have been made so far with mixed success. Medical information retrieval is a fertile ground for testing inference mechanisms to augment retrieval. The medical domain offers a plethora of carefully curated, structured, semantic resources, along with well established entity extraction and linking tools, and search topics that intuitively require a number of different inferential processes (e.g., conceptual similarity, conceptual implication, etc.). We argue that integrating semantic inference in information retrieval has the potential to uncover a large amount of information that otherwise would be inaccessible; but inference is also risky and, if not used cautiously, can harm retrieval.