The impact of computer display height and desk design on 3D posture during information technology work by young adults

Computer display height and desk design to allow forearm support are two critical design features of workstations for information technology tasks. However there is currently no 3D description of head and neck posture with different computer display heights and no direct comparison to paper based information technology tasks. There is also inconsistent evidence on the effect of forearm support on posture and no evidence on whether these features interact. This study compared the 3D head, neck and upper limb postures of 18 male and 18 female young adults whilst working with different display and desk design conditions. There was no substantial interaction between display height and desk design. Lower display heights increased head and neck flexion with more spinal asymmetry when working with paper. The curved desk, designed to provide forearm support, increased scapula elevation/protraction and shoulder flexion/abduction.

Phenomenological inquiry into the experience of web project managers

The advent of the Internet and the World Wide Web has been instrumental in bringing about the growth in the implementation of web-based information systems (WBIS). Such systems are designed with the aim of improving productivity, data accuracy, and the reduction of paperwork and administrative overheads. Moreover, unlike their conventional non-web-based predecessors, the WBIS are commonly aimed at users who are casual and untrained, geographically distributed and non-homogenous. The dissemination of WBIS necessitates additional infrastructure support in the form of a security system, workflow and transaction management, and web administration. WBIS are commonly developed using an evolutionary approach, whereby the version of the application, acquired from the vendor, is first deployed as a pilot, in order to gather feedback from the target users before the evolutionary cycles commence. While a number of web development methodologies have been proposed by existing research, there is a dearth of empirical evidence that elucidates the experiences of project initiators in pursuing the evolution of web services, a process that undoubtedly involves dealing with stakeholder issues. This research project presents a phenomenological investigation of the experiences of project managers with the implementation of web-based employee service systems (ESS), a domain that has witnessed a sharp growth in Australia in recent times. However, the project managers’ rich, multidimensional account of their experiences with the implementation of ESS revealed the social obstacles and fragility of intra-organizational relationships that demanded a cautious and tactful approach. Thus, the study provides a socio-organizational perspective to web projects in contrast to the functionalist paradigm of current web development methodologies. The research also confirms that consideration of the concerns of stakeholders by project managers is crucial to the successive cycles of ESS evolution. Project managers address stakeholder concerns by pursuing actions that are aimed at encouraging ESS usage, but at the same time, such actions can have consequences necessitating subsequent iterations of system enhancement and improvement. Finally, the research also discovered that despite the different socio-political climate prevalent in various organizations, in which ESS are being implemented, the experiences of project managers in dealing with stakeholder concerns can be captured and independently confirmed in terms of their perceived relevance and usefulness in problem-solving within the application domain.

Internet traffic classification using machine learning : a token-based approach

Due to the increasing unreliability of traditional port-based methods, Internet traffic classification has attracted a lot of research efforts in recent years. Quite a lot of previous papers have focused on using statistical characteristics as discriminators and applying machine learning techniques to classify the traffic flows. In this paper, we propose a novel machine learning based approach where the features are extracted from packet payload instead of flow statistics. Specifically, every flow is represented by a feature vector, in which each item indicates the occurrence of a particular token, i.e.; a common substring, in the payload. We have applied various machine learning algorithms to evaluate the idea and used different feature selection schemes to identify the critical tokens. Experimental result based on a real-world traffic data set shows that the approach can achieve high accuracy with low overhead.

Supporting emotion communication in information systems

Society is becoming increasingly reliant on Information Systems to meet its everyday communication requirements, yet many current implementations lack support for important conversational cues. One such cue is emotion communication. Emotion communication carries with it many signals that affect our behaviour, the interpretation of the message and provide a catalyst to other forms of communication such as empathy and the formation of social ties. Emotion itself can affect the very decision to communicate, or the way in which one may respond to a given communication. To explore the ways in which systems may better support emotion communication between members of a social group, a cloud-based information system was developed and trialled which both large and small groups. This paper presents results on how Information Systems can best support emotion communication in social groups.

Internet cognitive behavioural therapy for panic disorder : does the inclusion of stress management information improve end-state functioning?

Previous research has established Internet-based cognitive behavioural therapy (CBT) for panic disorder (PD) as effective in reducing panic severity and frequency. There is evidence, however, that such programs are less effective at improving overall end-state functioning, defined by a PD clinician severity rating of ≤2 and panic free. In order to test the effect on end-state functioning of the incorporation of stress management material within a CBT program for PD, 32 people with PD were randomised to either Internet-based CBT (PO1), Internet-based CBT plus stress management (PO2) or an Internet-based information-only control condition (IC). Both CBT treatments were more effective at posttreatment assessment than the control condition in reducing PD severity, panic and agoraphobia-related cognition, negative affect and self-ratings of health. PO2 was more effective than PO1 at posttreatment assessment on PD severity and general anxiety, although at 3-month follow-up these differences were no longer apparent. This study provides further support for the efficacy of Internet-based CBT for PD and suggests that although the incorporation of stress management material confers short-term advantages over a standard program, it is not associated with any longer term improvements on panic severity and related cognitions, negative affect, general wellbeing and end-state functioning.

Classification and optimization of product review information using soft computing models

A soft computing framework to classify and optimize text-based information extracted from customers' product reviews is proposed in this paper. The soft computing framework performs classification and optimization in two stages. Given a set of keywords extracted from unstructured text-based product reviews, a Support Vector Machine (SVM) is used to classify the reviews into two categories (positive and negative reviews) in the first stage. An ensemble of evolutionary algorithms is deployed to perform optimization in the second stage. Specifically, the Modified micro Genetic Algorithm (MmGA) optimizer is applied to maximize classification accuracy and minimize the number of keywords used in classification. Two Amazon product reviews databases are employed to evaluate the effectiveness of the SVM classifier and the ensemble of MmGA optimizers in classification and optimization of product related keywords. The results are analyzed and compared with those published in the literature. The outputs potentially serve as a list of impression words that contains useful information from the customers' viewpoints. These impression words can be further leveraged for product design and improvement activities in accordance with the Kansei engineering methodology.

Cluster analysis and optimization in color-based clustering for image abstract

Cluster analysis has been identified as a core task in data mining. What constitutes a cluster, or a good clustering, may depend on the background of researchers and applications. This paper proposes two optimization criteria of abstract degree and fidelity in the field of image abstract. To satisfy the fidelity criteria, a novel clustering algorithm named Global Optimized Color-based DBSCAN Clustering (GOC-DBSCAN) is provided. Also, non-optimized local color information based version of GOC-DBSCAN, called HSV-DBSCAN, is given. Both of them are based on HSV color space. Clusters of GOC-DBSCAN are analyzed to find the factors that impact on the performance of both abstract degree and fidelity. Examples show generally the greater the abstract degree is, the less is the fidelity. It also shows GOC-DBSCAN outperforms HSV-DBSCAN when they are evaluated by the two optimization criteria.

Fuzzy-based feature and instance recovery

The severe class distribution shews the presence of underrepresented data, which has great effects on the performance of learning algorithm, is still a challenge of data mining and machine learning. Lots of researches currently focus on experimental comparison of the existing re-sampling approaches. We believe it requires new ways of constructing better algorithms to further balance and analyse the data set. This paper presents a Fuzzy-based Information Decomposition oversampling (FIDoS) algorithm used for handling the imbalanced data. Generally speaking, this is a new way of addressing imbalanced learning problems from missing data perspective. First, we assume that there are missing instances in the minority class that result in the imbalanced dataset. Then the proposed algorithm which takes advantages of fuzzy membership function is used to transfer information to the missing minority class instances. Finally, the experimental results demonstrate that the proposed algorithm is more practical and applicable compared to sampling techniques.

Statistical detection of online drifting twitter spam

Spam has become a critical problem in online social networks. This paper focuses on Twitter spam detection. Recent research works focus on applying machine learning techniques for Twitter spam detection, which make use of the statistical features of tweets. We observe existing machine learning based detection methods suffer from the problem of Twitter spam drift, i.e., the statistical properties of spam tweets vary over time. To avoid this problem, an effective solution is to train one twitter spam classifier every day. However, it faces a challenge of the small number of imbalanced training data because labelling spam samples is time-consuming. This paper proposes a new method to address this challenge. The new method employs two new techniques, fuzzy-based redistribution and asymmetric sampling. We develop a fuzzy-based information decomposition technique to re-distribute the spam class and generate more spam samples. Moreover, an asymmetric sampling technique is proposed to re-balance the sizes of spam samples and non-spam samples in the training data. Finally, we apply the ensemble technique to combine the spam classifiers over two different training sets. A number of experiments are performed on a real-world 10-day ground-truth dataset to evaluate the new method. Experiments results show that the new method can significantly improve the detection performance for drifting Twitter spam.

Time cumulative complexity modeling and analysis for space-based networks

In this paper, the notion of the cumulative time varying graph (C-TVG) is proposed to model the high dynamics and relationships between ordered static graph sequences for space-based information networks (SBINs). In order to improve the performance of management and control of the SBIN, the complexity and social properties of the SBIN's high dynamic topology during a period of time is investigated based on the proposed C-TVG. Moreover, a cumulative topology generation algorithm is designed to establish the topology evolution of the SBIN, which supports the C-TVG based complexity analysis and reduces network congestions and collisions resulting from traditional link establishment mechanisms between satellites. Simulations test the social properties of the SBIN cumulative topology generated through the proposed C-TVG algorithm. Results indicate that through the C-TVG based analysis, more complexity properties of the SBIN can be revealed than the topology analysis without time cumulation. In addition, the application of attack on the SBIN is simulated, and results indicate the validity and effectiveness of the proposed C-TVG and C-TVG based complexity analysis for the SBIN.

Culture-based fisheries: an underutilised opportunity in aquaculture development

The reasons for considering culture-based fisheries as an aquaculture practice are presented. The need to develop culture-based fisheries, which are basically a nonconsumptive user of water compared to conventional aquaculture practices such as pond culture, to augment the aquatic food supplies, in the wake of increasing consumption of aquatic food, dwindling catches from the wild, and the decreasing rate of growth of conventional aquaculture is evaluated.

The well-documented culture-based fishery practices of a number of countries are reviewed, and the development of these is traced. It is suggested that successful practices occur in mainland China, where the production from culture-based fishery in reservoirs is estimated to be 1,165,075 tons (from a total area of 1,567,971 ha), approximating 743 kg ha⁻¹ year⁻¹, and which is reputed to have recorded a yearly growth of 52% from 1979 to 1997. General features of culture-based fisheries and factors responsible for increasing yields are discussed, and the importance of such practices to rural communities in developing countries is emphasised. The constraints facing the development of the practices and the importance of overcoming such constraints, especially in the wake of the increasing challenges the conventional aquaculture industry is encountering, are evaluated.

Development of a multimedia data acquisition toolkit and its application for exchanging demolished materials

In the development of a web-based information system such as a demolition material management system, a great amount of diversified information on projects should be acquired from particular users located with various computer platforms. This issue is difficult to handle using the limited HTTP form submission, which could lead to inaccuracy of the information and inefficiency of the whole system. This paper describes a web-based graphical user interfaced, dynamic and distributed multimedia data acquisition mechanism, which accepts users' drawings and retrieval information from the canvas and stores the multimedia data on a server for further usages. Furthermore, techniques and principles needed to construct such a multimedia data acquisition tool are addressed in detail. The application of this distributed multimedia tool in developing a web-based demolition material management system is also described.

Agents for invocation of web services

Web services are becoming popular and widely accepted on the Internet. UDDI is the standard for publishing and discovery of web services. In this paper, we investigate semantics description of web services based on domain ontology; based on this language, we propose an architecture for invoking agents to consume services within the UDDI registry. The semantics service description language together with agent creation
architecture provides a new way to discover and utilise published web services. This method is flexible and extendable to accomplish complex web service requests.

Using autonomous mobile agents for efficient data collection in sensor networks

Sensor networks are emerging as the new frontier in sensing technology, however there are still issues that need to be addressed. Two such issues are data collection and energy conservation. We consider a mobile robot, or a mobile agent, traveling the network collecting information from the sensors themselves before their onboard memory storage buffers are full. A novel algorithm is presented that is an adaptation of a local search algorithm for a special case of the Asymmetric Traveling Salesman Problem with Time-windows (ATSPTW) for solving the dynamic scheduling problem of what nodes are to be visited so that the information collected is not lost. Our algorithms are given and compared to other work.