Beyond e-commerce: an entrepreneurial business modelling method for profitable e-venturing

E- business is used as a term that embraces e-commerce, its commercial exchange or transaction component. Both are subsets of a larger concept, e-venturing. E-business is a major disruptive innovation that is rapidly changing many of the accepted norms of effective management. So the paper revisits and reassesses several established principles of economics, strategy and entrepreneurship to place them in the context of the forces driving the emerging e-business economy. Entrepreneurship is applied as a 'framework enrichener', model-building tool and critical organisational behaviour to guide integration of e-business strategy into the total organisational strategy of a profit-seeking firm This permits development of a new business modelling process, labelled 'map and locate', that adapts a combination of entrepreneurial and strategic imperatives to the internet environment. The process assumes that value-provision, competitive distinction and profitability are the three essentials of any successful e-business strategy design and execution. Apart from its general conceptual role of linking strategic and entrepreneurial thinking, the 'map and locate' modelling process can be used as a practical tool for specific performance in a variety of circumstances. It focuses on learning and the development of metrics useful for measuring progress towards achievement of target outcomes.

The efficacy of partnership evaluation and its impact on alliance transformation : a case study 12 months post evaluation

Partnerships involving higher education, governments, and industry have been recognised as important vehicles for engaging community, leveraging knowledge, and sharing potential resources. The critical need for these partnerships in rural and regional locations has been of particular note. Partnership evaluation can serve a critical function of informing continuous improvement and may therefore assist the evaluated agencies to work towards responsive transformational change. The ability for a partnership to adapt and change may aid in their sustainability. Despite the potentially important role of partnership evaluation, the development of tools that measure partnership are at an early stage. Partnership evaluation is rarely reflected upon in the published literature. Moreover, benefits and reflections of the efficacy of evaluations 12 months post analysis is rare in the published literature. Therefore, a brief review of partnership approaches and measurement tools are presented. The purpose of this paper is to reflect upon the efficacy of an evaluation conducted 12 months previously of a partnership between Deakin University, the Department of Health and Department of Human Services (Barwon South West Region), known as the Deakin/DH/DHS Strategic Alliance. This case study reviews several tools/metrics utilised. The efficacy of the evaluation tools is discussed. Those metrics, underlying the tools which contributed to positive change in partnerships are discussed.

Virtual backbone routing structures in wireless ad-hoc networks

In this paper we studied several virtual routing structures (ring, ring-tree, cord, and mesh) which are scalable, independent on addresses, based on local information and partial global information for routing packets. These virtual routing structures are built on the top of the backbone nodes which are selected by considering power, connections, and immobility metrics.Our experimental results on the ns2 simulator and both TelosBand MicaZ sensor nodes tested platform prove that the virtual backbone structures are superior to the existing routing schemes and the different virtual structures fit in with the different physical scenarios.

Towards autonomous image fusion

Mobile robots are providing great assistance operating in hazardous environments such as nuclear cores, battlefields, natural disasters, and even at the nano-level of human cells. These robots are usually equipped with a wide variety of sensors in order to collect data and guide their navigation. Whether a single robot operating all sensors or a swarm of cooperating robots operating their special sensors, the captured data can be too large to be transferred across limited resources (e.g. bandwidth, battery, processing, and response time) in hazardous environments. Therefore, local computations have to be carried out on board the swarming robots to assess the worthiness of captured data and the capacity of fused information in a certain spatial dimension as well as selection of proper combination of fusion algorithms and metrics. This paper introduces to the concepts of Type-I and Type-II fusion errors, fusion capacity, and fusion worthiness. These concepts together form the ladder leading to autonomous fusion systems.

Malware variant detection using similarity search over sets of control flow graphs

Static detection of polymorphic malware variants plays an important role to improve system security. Control flow has shown to be an effective characteristic that represents polymorphic malware instances. In our research, we propose a similarity search of malware using novel distance metrics of malware signatures. We describe a malware signature by the set of control flow graphs the malware contains. We propose two approaches and use the first to perform pre-filtering. Firstly, we use a distance metric based on the distance between feature vectors. The feature vector is a decomposition of the set of graphs into either fixed size k-sub graphs, or q-gram strings of the high-level source after decompilation. We also propose a more effective but less computationally efficient distance metric based on the minimum matching distance. The minimum matching distance uses the string edit distances between programs' decompiled flow graphs, and the linear sum assignment problem to construct a minimum sum weight matching between two sets of graphs. We implement the distance metrics in a complete malware variant detection system. The evaluation shows that our approach is highly effective in terms of a limited false positive rate and our system detects more malware variants when compared to the detection rates of other algorithms.

Restricted dissimilarity functions and penalty functions

In this work we introduce the definition of restricted dissimilarity functions and we link it with some other notions, such as metrics. In particular, we also show how restricted dissimilarity functions can be used to build penalty functions.

Subsequence frequency measurement and its impact on knowledge discovery in single sequences

Subsequence frequency measurement is a basic and essential problem in knowledge discovery in single sequences. Frequency based knowledge discovery in single sequences tends to be unreliable since different resulting sets may be obtained from a same sequence when different frequency metrics are adopted. In this chapter, we investigate subsequence frequency measurement and its impact on the reliability of knowledge discovery in single sequences. We analyse seven previous frequency metrics, identify their inherent inaccuracies, and explore their impacts on two kinds of knowledge discovered from single sequences, frequent episodes and episode rules. We further give three suggestions for frequency metrics and introduce a new frequency metric in order to improve the reliability. Empirical evaluation reveals the inaccuracies and verifies our findings.

Low-SES students wrongly classified, but who cares?

Equity experts agree with research findings that the metrics for measuring socioeconomic status (SES) are problematic. But they disagree that it really matters.

An iterative approach of protein function prediction

Background: Current approaches of predicting protein functions from a protein-protein interaction (PPI) dataset are based on an assumption that the available functions of the proteins (a.k.a. annotated proteins) will determine the functions of the proteins whose functions are unknown yet at the moment (a.k.a. un-annotated proteins). Therefore, the protein function prediction is a mono-directed and one-off procedure, i.e. from annotated proteins to un-annotated proteins. However, the interactions between proteins are mutual rather than static and mono-directed, although functions of some proteins are unknown for some reasons at present. That means when we use the similarity-based approach to predict functions of un-annotated proteins, the un-annotated proteins, once their functions are predicted, will affect the similarities between proteins, which in turn will affect the prediction results. In other words, the function prediction is a dynamic and mutual procedure. This dynamic feature of protein interactions, however, was not considered in the existing prediction algorithms.

Results: In this paper, we propose a new prediction approach that predicts protein functions iteratively. This iterative approach incorporates the dynamic and mutual features of PPI interactions, as well as the local and global semantic influence of protein functions, into the prediction. To guarantee predicting functions iteratively, we propose a new protein similarity from protein functions. We adapt new evaluation metrics to evaluate the prediction quality of our algorithm and other similar algorithms. Experiments on real PPI datasets were conducted to evaluate the effectiveness of the proposed approach in predicting unknown protein functions.

Conclusions: The iterative approach is more likely to reflect the real biological nature between proteins when predicting functions. A proper definition of protein similarity from protein functions is the key to predicting functions iteratively. The evaluation results demonstrated that in most cases, the iterative approach outperformed non-iterative ones with higher prediction quality in terms of prediction precision, recall and F-value.

Identifying film takes for cinematic analysis

In this paper, we focus on the ‘reverse editing’ problem in movie analysis, i.e., the extraction of film takes, original camera shots that a film editor extracts and arranges to produce a finished scene. The ability to disassemble final scenes and shots into takes is essential for nonlinear browsing, content annotation and the extraction of higher order cinematic constructs from film. In this work, we investigate agglomerative hierachical clustering methods along with different similarity metrics and group distances for this task, and demonstrate our findings with 10 movies.

A generalised cost-aware caching scheme for caching continuous media objects in best-effort network environments

In this paper, we investigate the potential of caching to improve QoS in the context of continuous media applications over wired best-effort networks. We propose the use of a flexible caching scheme, called GD-Multi in caching continuous media (CM) objects. An important novel feature of our scheme is the provision of user or system administrator inputs in determining the cost function. Based on the proposed flexible cost function, Multi, an improvised Greedy Dual (GD) replacement algorithm called GD-multi (GDM) has been developed for layered multi-resolution multimedia streams. The proposed Multi function takes receiver feedback into account. We investigate the influence of parameters such as loss rate, jitter, delay and area in determining a proxy’s cache contents so as to enhance QoS perceived by clients. Simulation studies show improvement in QoS perceived at the clients in accordance to supplied optimisation metrics. From an implementation perspective, signalling requirements for carrying QoS feedback are minimal and fully compatible with existing RTSP-based Internet applications.

Utility of short-term evaluation of presbyopic contact lens performance

Objectives: To establish if evaluations of multifocal contact lens performance conducted at dispensing are representative of behavior after a moderate adaptation period.

Methods: Eighty-eight presbyopic subjects, across four clinical sites, wore each of four multifocal soft contact lenses (ACUVUE BIFOCAL, Focus Progressives, Proclear Multifocal, and SofLens Multifocal) for 4 days of daily wear. Comprehensive performance assessments were conducted at dispensing and after 4 days wear and included the following objective metrics: LogMAR acuity (contrast, 90% and 10%; illumination, 250 and 10 cd/m2; distance, 6 m, 100 cm, and 40 cm), stereopsis (RANDOT), reading critical print size and maximum speed and range of clear vision at near. Subjective assessments were made, with 100-point numerical rating scales, of comfort, ghosting (distance, near), visual quality (distance, intermediate, and near), and the appearance of haloes. At two sites, subjects (n = 39) also rated visual fluctuation (distance, intermediate, and near), facial recognition, and overall satisfaction.

Results: Among the objective variables, significant differences (paired t test, P<0.05) between dispensing and 4 days were found only for range of clear vision at near (2.9 ± 2.0 cm; mean difference ± standard deviation) and high contrast near acuity in low illumination (-0.013 ± 0.011 LogMAR). With the exception of insertion comfort, all subjective variables showed significant decrements over the same period. Overall satisfaction declined by an average of 10.9 ± 5.1 points.

Conclusions: Early assessment is relatively unrepresentative of performance later on during multifocal contact lens wear. Acuity based measures of vision remain substantially unchanged over the medium term, apparently because these metrics are insensitive indicators of performance compared with subjective alternatives.

On Operating Strategies of the Fuzzy Artmap Neural Network: A Comparative Study

In this paper, the effectiveness of three different operating strategies applied to the Fuzzy ARTMAP (FAM) neural network in pattern classification tasks is analyzed and compared. Three types of FAM, namely average FAM, voting FAM, and ordered FAM, are formed for experimentation. In average FAM, a pool of the FAM networks is trained using random sequences of input patterns, and the performance metrics from multiple networks are averaged. In voting FAM, predictions from a number of FAM networks are combined using the majority-voting scheme to reach a final output. In ordered FAM, a pre-processing procedure known as the ordering algorithm is employed to identify a fixed sequence of input patterns for training the FAM network. Three medical data sets are employed to evaluate the performances of these three types of FAM. The results are analyzed and compared with those from other learning systems. Bootstrapping has also been used to analyze and quantify the results statistically. [ABSTRACT FROM AUTHOR].

Impact of personalized recommendation and social comparison on learning behaviours and outcomes

ELearning suffers from the lack of face-to-face interaction and can deprive learners from the benefits of social interaction and comparison. In this paper we present the results of a study conducted for the impact of social comparison. The study was conducted by collecting students’ engagement with an eLearning tool, the attendance, and grades scored by students at specific milestones and presented these metrics to students as feedback using Kiviat charts. The charts were complemented with appropriate recommendations to allow them to adapt their study strategy and behaviour. The study spanned over 4 semesters (2 with and 2 without the Kiviats) and the results were analysed using paired T tests to test the pre and post results on topics covered by the eLearning tool. Survey questionnaires were also administered at the end for qualitative analysis. The results indicated that the Kiviat feedback with recommendation had positive impact on learning outcomes and attitudes.

Health information technology governance : a perspective on investment decision processes

Realizing value from IT investments continues to be a challenge for most healthcare organizations. IT governance (ITG) is envisaged to solve many of these challenges. ITG is the practice that establishes accountability framework for IT investments by allocating decision rights among major participants involved in IT decision processes. As ITG is relatively new in healthcare industry, it is expected that knowledge about how healthcare organizations govern their IT decisions is limited. This research aims to extend this knowledge and to assist both researchers and professionals by providing insights on how IT decisions are made and governed in healthcare organizations (HOs). This research adopts case-study methodology to investigate IT governance in two distinctly different HOs. The research findings indicate that HOs implement ITG to achieve alignment between business objectives and IT. Both HOs set up a five-stage IT decision process to identify, evaluate and prioritize IT investment ideas. They also established generic committee-structures that clearly defined roles and decision authorities to govern such process. It is suggested here that ITG in HOs is heavily influenced by strategic priorities, organizational structure, governance experience and governmental initiatives. Effective ITG in HOs is challenged by IT alignment, policy government, involvement of healthcare executives, and lack of business metrics to justify and evaluate decisions. The research proposes recommendations to address these challenges.