• Introduced a principled definition of an uncertain decision boundary for BERT-based classifiers using a discriminative confidence margin, enabling systematic identification of ambiguous predictions. • Integrated word–class probability embeddings (WCPE) to enrich document representations with explicit class-discriminative signals, particularly effective for uncertain and noisy medical texts. • Proposed a two-stage, uncertainty-aware decision framework that routes confident samples to a baseline BERT model and uncertain samples to a WCPE-enhanced variant, improving robustness under ambiguity. • Developed a data-driven strategy for optimizing the uncertainty threshold (UncertainT), balancing coverage of ambiguous samples and overall classification accuracy. • Established the general applicability of the proposed uncertainty-aware decision framework across multiple datasets and pretrained backbones, while revealing that statistically significant improvements emerge selectively depending on dataset ambiguity and backbone pretraining. Bidirectional Encoder Representations from Transformers (BERT) has achieved state-of-the-art performance in Natural Language Processing (NLP) tasks but struggles with uncertainty management in multi-class medical text classification, where overlapping categories and domain-specific critical terms pose challenges. BERT’s attention mechanism may distribute focus across irrelevant contextual patterns, reducing its ability to prioritize medically significant terms. To address these limitations, we propose a two-stage decision-making framework incorporating an “uncertain boundary” mechanism to separate high-confidence (“certain”) and low-confidence (“uncertain”) cases based on an optimized uncertainty threshold. A baseline BERT model processes high-confidence cases, while uncertain cases are handled by an enhanced BERT model integrating Word-Class Probabilistic Embedding (WCPE) to improve domain-specific representation learning. We evaluate our framework across three datasets-Ohsumed, PubMed, and Drug Review and further examine its robustness under multiple pretrained language model settings. Using standard BERT as the primary backbone, results show consistent accuracy improvements from 0.770 to 0.773 on Ohsumed ( p < 0.05), 0.973 to 0.975 on PubMed ( p < 0.1), and 0.596 to 0.609 on Drug Review ( p < 0.05) with stronger gains on uncertain subsets. These findings show that confidence-based partitioning and model specialization enhance BERT’s classification performance while effectively handling uncertainty in medical NLP.

Outlier detection is extensively adopted in machine learning applications to identify rare but significant objects in a data distribution that deviate from the majority of objects. However, none of the existing outlier definitions use the available contextual label information in a dataset to improve the identification of outliers, though it could improve the performance of data analysis. In this study, we propose a novel definition for outliers considering the available contextual label information along with a method to remove outliers based on the proposed definition to improve classification performance and granulation purity. The experimental results on eight public datasets show that the removal of outliers using the novel method highly improves the classification accuracy with Support Vector Machine, k-Nearest Neighbors, and Classification and Regression Tree algorithms while identifying 100% pure subclasses of the labelled major classes. This method is beneficial in identifying the outliers automatically when the data contains classification information about a specific application instead of outlierness information.

Deep learning has emerged as a powerful tool in various domains, revolutionising machine learning research. However, one persistent challenge is the scarcity of labelled training data, which hampers the performance and generalisation of deep learning models. To address this limitation, researchers have developed innovative methods to overcome data scarcity and enhance deep model learning capabilities. Two prevalent techniques that have gained significant attention are transfer learning and self-supervised learning. Transfer learning leverages knowledge learned from pre-training on a large-scale dataset, such as ImageNet, and applies it to a target task with limited labelled data. This approach allows models to benefit from the learned representations and effectively transfer knowledge to new tasks, resulting in improved learning performance and generalisation. On the other hand, self-supervised learning focuses on training models using pretext tasks that do not require manual annotation, allowing them to learn valuable representations from large amounts of unlabelled data. These learned representations can then be fine-tuned for downstream tasks, mitigating the need for extensive labelled data. In recent years, transfer and self-supervised learning have found applications in various fields, including medical image processing, video recognition, and natural language processing. These approaches have demonstrated remarkable achievements, enabling breakthroughs in areas such as disease diagnosis, object recognition, and language understanding. However, while these methods offer numerous advantages, they also have limitations. For example, transfer learning may face domain mismatch issues between the pre-training and target domains, while self-supervised learning requires careful design of pretext tasks to ensure meaningful representations. This review paper explores the recent applications of these pre-training methods in various fields within the past three years. It delves into the advantages and limitations of each approach, assesses the performance of models employing these techniques, and identifies potential directions for future research. By providing a comprehensive review of current pre-training methods, this article offers guidance for selecting the best technique for specific deep learning applications to address the data scarcity issue.

Traditional ecological survey methods are expensive and time-consuming as they typically involve a domain expert in the field. As a result of this, continuous audio recordings are playing an ever more critical role in conservation and biodiversity monitoring. However, listening to these recordings is often infeasible, as they can be thousands of hours long. The knowledge of domain experts can be efficiently leveraged using visualization. Traditionally, spectrograms are used to visualize audio data. However, there is a limit to the duration of audio that can fit on a computer screen as a spectrogram. Several techniques have been adapted to overcome this. Each of these techniques has its own set of advantages and disadvantages that make it ideal for some situations but unsuitable for others. In this paper, we propose a novel visualization based on embeddings produced by Frequency Preserving Autoencoders. We evaluate by visually comparing with traditional spectrogram and spectral index false-color spectrogram, using human-generated annotations as a baseline. We find that calls from some species are more consistently visible in our autoencoder-based visualization, while others are more clearly visible in the alternative visualizations. This novel visualization presents a new opportunity to assist ecologists in identifying bird calls in long-duration audio, if calls are difficult to identify using other methods.

Given the tremendous potential and influence of artificial intelligence (AI) and algorithmic decision‐making (DM), these systems have found wide‐ranging applications across diverse fields, including education, business, healthcare industries, government, and justice sectors. While AI and DM offer significant benefits, they also carry the risk of unfavourable outcomes for users and society. As a result, ensuring the safety, reliability, and trustworthiness of these systems becomes crucial. This article aims to provide a comprehensive review of the synergy between AI and DM, focussing on the importance of trustworthiness. The review addresses the following four key questions, guiding readers towards a deeper understanding of this topic: (i) why do we need trustworthy AI? (ii) what are the requirements for trustworthy AI? In line with this second question, the key requirements that establish the trustworthiness of these systems have been explained, including explainability, accountability, robustness, fairness, acceptance of AI, privacy, accuracy, reproducibility, and human agency, and oversight. (iii) how can we have trustworthy data? and (iv) what are the priorities in terms of trustworthy requirements for challenging applications? Regarding this last question, six different applications have been discussed, including trustworthy AI in education, environmental science, 5G‐based IoT networks, robotics for architecture, engineering and construction, financial technology, and healthcare. The review emphasises the need to address trustworthiness in AI systems before their deployment in order to achieve the AI goal for good. An example is provided that demonstrates how trustworthy AI can be employed to eliminate bias in human resources management systems. The insights and recommendations presented in this paper will serve as a valuable guide for AI researchers seeking to achieve trustworthiness in their applications.

People with intellectual disability (ID) deserve appropriate access to information online. A vast amount of information on the Internet is written text in the form of articles, and it is often said that summarising these texts could enhance their accessibility. This qualitative research investigates how people with ID prefer to gather information from articles, either in their original form or automatically summarised. The researchers observed the choices and strategies of 10 participants with ID through the reading process, and conducted contextual interviews to understand their preferences, attitudes and the difficulties they faced. The study found that the length of the article is only secondary to the relevance of the article to the person's interests. Summarised articles were found easier to read and having familiar words and images to supplement the text can help people with intellectual disability understand what an article is about.

Continuous audio recordings are playing an ever more important role in conservation and biodiversity monitoring, however, listening to these recordings is often infeasible, as they can be thousands of hours long. Automating analysis using machine learning is in high demand. However, these algorithms require a feature representation. Several methods for generating feature representations for these data have been developed, using techniques such as domain-specific features and deep learning. However, domain-specific features are unlikely to be an ideal representation of the data and deep learning methods often require extensively labeled data.In this paper, we propose a method for generating a frequency-preserving autoencoder-based feature representation for unlabeled ecological audio. We evaluate multiple frequency-preserving autoencoder-based feature representations using a hierarchical clustering sample task. We compare this to a basic autoencoder feature representation, MFCC, and spectral acoustic indices. Experimental results show that some of these non-square autoencoder architectures compare well to these existing feature representations.This novel method for generating a feature representation for unlabeled ecological audio will offer a fast, general way for ecologists to generate a feature representation of their audio, which does not require extensively labeled data.

Online videos are a medium of choice for young adults to access or receive information, and recent work has highlighted that it is a particularly effective medium for adults with intellectual disability, by its visual nature. Reflecting on a case study presenting fieldwork observations of how adults with intellectual disability engage with videos on the Youtube platform, we propose a framework to define and evaluate the accessibility of such large video repositories, from an informational perspective. The proposed framework nuances the concept of information accessibility from that of the accessibility of information access interfaces themselves (generally catered for under web accessibility guidelines), or that of the documents (generally covered in general accessibility guidelines). It also includes a notion of search (or browsing) accessibility, which reflects the ability to reach the document containing the information. In the context of large information repositories, this concept goes beyond how the documents are organized into how automated processes (browsing or searching) can support users. In addition to the framework we also detail specifics of document accessibility for videos. The framework suggests a multi-dimensional approach to information accessibility evaluation which includes both cognitive and sensory aspects. This framework can serve as a basis for practitioners when designing video information repositories accessible to people with intellectual disability, and extends on the information presentation guidelines such as suggested by the WCAG.

This paper presents findings from a set of 'magic machines' workshops with newly arrived Iraqi refugees in Australia. The aim was to allow a broad range of response in designing innovative and creative technologies that can help refugees deal with specific challenges. To bring the 'future' into the present and to understand their needs and experiences, we asked 12 participants to create low-fi objects from different materials and to enact them in different scenarios. The magic machines workshops helped access refugees' voices and provided future contexts for them to deal with their challenges. The data analysis of the two workshops revealed three broad themes: information provision barriers, security and ethical challenges, and mistrust and cultural aspects. Our findings show that adopting a speculative design approach has encouraged refugees to have a strong voice- creatively articulated in the form of a set of magic machines. The study offers insights into refugees' perceptions of the future and current technology. It also informs policymakers of the issues around current policy hurdles newcomer refugees face in their settlement in the host community.

One of the main reasons for death among women is breast cancer. The traditional diagnosis process of breast cancer is time-consuming and expensive. Also, an early cancer diagnosis may reduce the breast cancer death rate. With the help of computer-aided diagnosis system, the efficiency increased and the cost is reduced of the cancer diagnosis. Traditional classification methodologies are based on feature extraction techniques. Currently, deep learning techniques have become the alternative solution for diagnosis and overcame the problems of the handcrafted features methods. Increasing the depth in a deep convolutional neural network makes the network suffer from gradient vanishing problems, which are not caused by overfitting but instead by an increase in depth. Therefore, our proposed network is designed based on the idea of increasing the width of the network. A novel two-branch deep convolutional neural network is proposed for the classification of histopathological breast images. The proposed network is trained on the dataset of ICIAR-2018 to classify images into four classes; invasive carcinoma, in situ carcinoma, benign lesion, and normal tissue image. The proposed network is beneficial for gradient propagation as the error can be back-propagated through multiple paths. It also helps to combine different levels of features at each step of the network since it is a two-branch network. The proposed network is superior to the existing methods by achieving a patch-wise classification accuracy of 83.6% and image-wise classification accuracy of 91.3% on the divided part from the training set. Moreover, we have achieved an image-wise classification accuracy of 89.4% on the unseen test images of ICIAR-2018.

This research explores the recognition of choruses of two co-existing sibling frog species in long-duration field recordings using false-colour spectrograms and acoustic indices. Acid frogs are a group of endemic frogs that are particularly sensitive to habitat change and competition from other species. The Wallum Sedgefrog (Litoria olongburensis) is the most threatened acid frog species facing habitat loss and degradation across much of their distribution, in addition to further pressures associated with anecdotally-recognised competition from their sibling species, the Eastern Sedgefrogs (Litoria fallax). Monitoring the calling behaviours of these two species is essential for informing L. olongburensis management and protection, and for obtaining ecological information about the process and implications of their competition. Considering the cryptic nature of L. olongburensis and the sensitivity of their habitat to human disturbance, passive acoustic monitoring is a suitable method for monitoring this species. However, manually processing the large quantities of acoustic data collected using these methods is time-consuming and not feasible in the long-term. Therefore, there is a high demand for automated acoustic recognition tools to efficiently search months of recordings and identify target species. Our research provides more insight on how to choose acoustic features that efficiently recognise species from large-scale field-collected recordings at a larger scale. The experimental results show that these techniques are useful in identifying choruses of the two competitive frog species with an accuracy of 76.7% on identifying four acoustic patterns (whether the two species occurred).

SOPARC is a widely used method for collecting data about park-based physical activity, however there have been no interactive tools designed for visualising this data. Due to this many researchers using the SOPARC method use tables, bar graphs, or other static methods to visualise the collected data. Many of these methods result in highly obfuscated visualisations. The aim of this research is to design an interactive tool for visualising SOPARC data that can produce user customised output according to their needs, is easy to use, allows for rapid differentiation of distinct areas, and allows for numerical results to be shown. An interactive visualisation tool has been designed and developed that uses a map-based approach, with colour and numerical overlays. This interactive visualisation tool will provide a new, more transparent way to make sense of SOPARC data that will enhance the decision making process for parks and related policies.

Supervised learning algorithms employ labeled training data for classification purposes while obtaining labeled data for large datasets is costly and time consuming. Semi-supervised learning algorithms, on the contrary, use a small set of labeled data and a large set of unlabeled data to improve predication performance and thus may be a good alternative to supervised learning algorithms for large text datasets. Although many semi-supervised learning algorithms have been proposed in the data science literature, most of these algorithms are not feasible for discrete and unstructured text data.

Passive acoustic recording has great potential for monitoring soniferous endangered and cryptic species. However, this approach requires analysis of long duration environmental acoustic recordings that span months or years. There is a variety of approaches to analysing acoustic data. However, it is unclear which approaches are best suited for monitoring of endangered species in the wild. Specifically, this study is undertaking acoustic monitoring of the critically endangered White-bellied Heron (Ardea insignis) in Bhutan. Four different acoustic analysis methods are investigated in terms of their detection accuracy, involvement of human experts, and overall utility to ecologists for target species monitoring work. Our experimental results show that human pattern detection using a visualization technique has detection performance on par with a cluster-based recogniser, while a machine learning classifier implemented using the same acoustic features suffers from very low precision. Further, specific cases of false positives and false negatives by the different methods are investigated and discussed in terms of their overall utility for ecological monitoring. Based on our experimental results, we demonstrate how an integrated semi-automated approach of human visual pattern analysis with a recogniser is a robust system for acoustic monitoring of target species.

Acoustic classification of frogs has received increasing attention for its promising application in ecological studies. Various studies have been proposed for classifying frog species, but most recordings are assumed to have only a single species. In this study, a method to classify multiple frog species in an audio clip is presented. To be specific, continuous frog recordings are first cropped into audio clips (10 seconds). Then, various time-frequency representations are generated for each 10-s recording. Next, instead of using traditional hand-crafted features, various features are extracted using pre-trained networks using three time-frequency representations: Fast-Fourier spectrogram, Constant-Q transform spectrogram, and Gammatone-like spectrogram. Finally, a binary relevance based multi-label classification approach is proposed to classify simultaneously vocalizing frog species with our proposed features. Our proposed method is verified using eight frog species widely distributed in Queensland, Australia. The results show that the proposed features extracted via pre-trained networks can achieve better classification performance when compared to hand-crafted features for classifying multiple simultaneously vocalizing species.

Cane-toads (Bufo marinus) are introduced to Australia to negate the insect pests. However, cane-toad population is out of control, and has highly affected native frog species for its great reproductive capacity. Therefore, it is necessary to monitor cane-toads and control their population. Recent advances in acoustic sensors make it possible to monitor cane-toads over long periods. In this paper, we present a novel approach to analyze cane-toad calls for long-term monitoring. Specifically, recorded acoustic data is first transformed into spectrogram using short-time Fourier transform. Then, cane-toad calls are recognized with an oscillation detector. Next, recognized cane-toad calls are annotated for further analysis. Initial analysis of our method demonstrates promising results, with 71.36% true positive rate. With the oscillation detector, cane-toad calling activity is investigated over the whole year.

Avian species richness surveys, which measure the total number of unique avian species, can be conducted via remote acoustic sensors. An immense quantity of data can be collected, which, although rich in useful information, places a great workload on the scientists who manually inspect the audio. To deal with this big data problem, we calculated acoustic indices from audio data at a one-minute resolution and used them to classify one-minute recordings into five classes. By filtering out the non-avian minutes, we can reduce the amount of data by about 50% and improve the efficiency of determining avian species richness. The experimental results show that, given 60 one-minute samples, our approach enables to direct ecologists to find about 10% more avian species.

Frog protection has become increasingly essential due to the rapid decline of its biodiversity. Therefore, it is valuable to develop new methods for studying this biodiversity. In this paper, a novel feature extraction method is proposed based on perceptual wavelet packet decomposition for classifying frog calls in noisy environments. Pre-processing and syllable segmentation are first applied to the frog call. Then, a spectral peak track is extracted from each syllable if possible. Track duration, dominant frequency and oscillation rate are directly extracted from the track. With k-means clustering algorithm, the calculated dominant frequency of all frog species is clustered into k parts, which produce a frequency scale for wavelet packet decomposition. Based on the adaptive frequency scale, wavelet packet decomposition is applied to the frog calls. Using the wavelet packet decomposition coefficients, a new feature set named perceptual wavelet packet decomposition sub-band cepstral coefficients is extracted. Finally, a k-nearest neighbour (k-NN) classifier is used for the classification. The experiment results show that the proposed features can achieve an average classification accuracy of 97.45% which outperforms syllable features (86.87%) and Mel-frequency cepstral coefficients (MFCCs) feature (90.80%).

Frogs have received increasing attention due to their effectiveness for indicating the environment change. Therefore, it is important to monitor and assess frogs. With the development of sensor techniques, large volumes of audio data (including frog calls) have been collected and need to be analysed. After transforming the audio data into its spectrogram representation using short-time Fourier transform, the visual inspection of this representation motivates us to use image processing techniques for analysing audio data. Applying acoustic event detection (AED) method to spectrograms, acoustic events are firstly detected from which ridges are extracted. Three feature sets, Mel-frequency cepstral coefficients (MFCCs), AED feature set and ridge feature set, are then used for frog call classification with a support vector machine classifier. Fifteen frog species widely spread in Queensland, Australia, are selected to evaluate the proposed method. The experimental results show that ridge feature set can achieve an average classification accuracy of 74.73% which outperforms the MFCCs (38.99%) and AED feature set (67.78%).

Bioacoustic monitoring has become a significant research topic for species diversity conservation. Due to the development of sensing techniques, acoustic sensors are widely deployed in the field to record animal sounds over a large spatial and temporal scale. With large volumes of collected audio data, it is essential to develop semi-automatic or automatic techniques to analyse the data. This can help ecologists make decisions on how to protect and promote the species diversity. This paper presents generic features to characterize a range of bird species for bird vocalisation retrieval. In the implementation, audio recordings are first converted to spectrograms using short-time Fourier transform, then a ridge detection method is applied to the spectrogram for detecting points of interest. Based on the detected points, a new region representation are explored for describing various bird vocalisations and a local descriptor including temporal entropy, frequency bin entropy and histogram of counts of four ridge directions is calculated for each sub-region. To speed up the retrieval process, indexing is carried out and the retrieved results are ranked according to similarity scores. The experimental results show that our proposed feature set can achieve 0.71 in term of retrieval success rate which outperforms spectral ridge features alone (0.55) and Mel frequency cepstral coefficients (0.36).

The mean shift tracker has achieved great success in visual object tracking due to its efficiency being nonparametric. However, it is still difficult for the tracker to handle scale changes of the object. In this paper, we associate a scale adaptive approach with the mean shift tracker. Firstly, the target in the current frame is located by the mean shift tracker. Then, a feature point matching procedure is employed to get the matched pairs of the feature point between target regions in the current frame and the previous frame. We employ FAST-9 corner detector and HOG descriptor for the feature matching. Finally, with the acquired matched pairs of the feature point, the affine transformation between target regions in the two frames is solved to obtain the current scale of the target. Experimental results show that the proposed tracker gives satisfying results when the scale of the target changes, with a good performance of efficiency.

Corner detection has shown its great importance in many computer vision tasks. However, in real-world applications, noise in the image strongly affects the performance of corner detectors. Few corner detectors have been designed to be robust to heavy noise by now, partly because the noise could be reduced by a denoising procedure. In this paper, we present a corner detector that could find discriminative corners in images contaminated by noise of different levels, without any denoising procedure. Candidate corners (i.e., features) are firstly detected by a modified SUSAN approach, and then false corners in noise are rejected based on their local characteristics. Features in flat regions are removed based on their intensity centroid, and features on edge structures are removed using the Harris response. The detector is self-adaptive to noise since the image signal-to-noise ratio (SNR) is automatically estimated to choose an appropriate threshold for refining features. Experimental results show that our detector has better performance at locating discriminative corners in images with strong noise than other widely used corner or key point detectors.

Raven and Song Scope are two, state-of-the-art automated sound analysis tools, based on machine learning techniques for detection of species vocalisations. Individually, these systems have been the subject of a number of reviews; however, to date there have been no comparisons made of their relative performance. This paper compares the tools based on six aspects: theory, software interface, ease of use, detection targets, detection accuracy, and potential applications. Examining these tools, we identified that they fail to detect both syllables and call structures, since Raven only aims to detect syllables while Song Scope targets call structures. Therefore, a Timed Probabilistic Automata (TPA) system is proposed which separates syllables and clusters them into complex structures.

We present a novel approach for multi-object detection in aerial videos based on tracking. The proposed method mainly involves four steps. Firstly, both the motion history image and the tracking trajectory are employed to extract candidate target regions. Secondly, the spatial-temporal saliency is used to detect moving objects in the candidate regions. Thirdly, the previous detected objects are tracked by mean shift in the current frame. And finally, the detection results are fused with the tracking results to get refined detection results, in turn the modified detection results are used to update the tracking models. The proposed algorithm is evaluated on VIVID aerial videos, and the results show that our approach can reliably detect moving objects even in challenging situations. Meanwhile, the proposed method can process videos in real time, without the effect of time delay.

Acoustic sensors play an important role in augmenting the traditional biodiversity monitoring activities carried out by ecologists and conservation biologists. With this ability however comes the burden of analysing large volumes of complex acoustic data. Given the complexity of acoustic sensor data, fully automated analysis for a wide range of species is still a significant challenge. This research investigates the use of citizen scientists to analyse large volumes of environmental acoustic data in order to identify bird species. Specifically, it investigates ways in which the efficiency of a user can be improved through the use of species identification tools and the use of reputation models to predict the accuracy of users with unidentified skill levels. Initial experimental results are reported.

The application of object-based approaches to the problem of extracting vegetation information from images requires accurate delineation of individual tree crowns. This paper presents an automated method for individual tree crown detection and delineation by applying a simplified PCNN model in spectral feature space followed by post-processing using morphological reconstruction. The algorithm was tested on high resolution multi-spectral aerial images and the results are compared with two existing image segmentation algorithms. The results demonstrate that our algorithm outperforms the other two solutions with the average accuracy of 81.8%.

Multi-resolution modelling has become essential as modern 3D applications demand 3D objects with higher LODs (LOD). Multimodal devices such as PDAs and UMPCs do not have sufficient resources to handle the original 3D objects. The increased usage of collaborative applications has created many challenges for remote manipulation working with 3D objects of different quality. This paper studies how we can improve multi-resolution techniques by performing multi-edge decimation and using annotative commands. It also investigates how devices with poorer quality 3D object can participate in collaborative actions.

In a typical collaborative application, users contends for common resources by mutual exclusion. The introduction of multi-modal environment, however, introduced problems such as frequent dropping of connection or limited connectivity speed of mobile users. This paper target 3D resources which require additional considerations such as dependency of users' manipulation command. This paper introduces Dynamic Locking Synchronisation technique to enable seamless and collaborative environment for large number of user, by combining the contention-free concepts of locking mechanism and the seamless nature of lockless design.

The need for large scale environmental monitoring to manage environmental change is well established. Ecologists have long used acoustics as a means of monitoring the environment in their field work, and so the value of an acoustic environmental observatory is evident. However, the volume of data generated by such an observatory would quickly overwhelm even the most fervent scientist using traditional methods. In this paper we present our steps towards realising a complete acoustic environmental observatory - i.e. a cohesive set of hardware sensors, management utilities, and analytical tools required for large scale environmental monitoring. Concrete examples of these elements, which are in active use by ecological scientists, are also presented.

Much research on 3D graphics collaboration has focused on improving collaborative viewing or enabling simple manipulations. There are, however, two major problems which are not yet resolved in collaborative environments. Simultaneous manipulation of a common 3D object by users can cause conflicts, and the integrity of the source 3D object will be compromised during updates. 3D objects can be locked for exclusive use to prevent conflicts, but this requires users to wait for their turn in order to perform manipulations. The capability of seamless and simultaneous manipulation raises several challenges such as how to resolve manipulation conflict and to deliver manipulation commands to users.

In this demonstration, we will show how smartphones can be used as a platform for monitoring environmental change, particularly with respect to birdlife. We have researched and implemented a platform using Microsoft smartphones for remotely monitoring birds. The platform comprises smart-phones running a custom application for recording bird song. The application manages sensors in an autonomic fashion to ensure that they operate reliably for long periods of time in a power efficient manner.

Geographic Information System (GIS) technology has many applications and plays a vital role in the majority of the daily operations by government and public administration. However, due to its technical complexity and cost, communities lacking the expertise and resources cannot benefit from this technology. The OpenCIS project, a user‐friendly free Open Source GIS‐based Web Community Information System, seeks to address this issue by bridging the gap between a simple map viewer and full GIS, representing one of the first steps towards the goal of grassroots empowerment through GIS technology.

Many decisions need to be made based on imprecise or incomplete initial information. In such cases, decision makers are generally more interested in sets of the most promising solutions rather than the best single solution. Therefore, in contrast to conventional optimisation approaches that aim to find exact optimal points, we aim to find optimal ranges with variable satisfaction degrees. The paper presents a fuzzy-set-based approach for the representation and optimisation of practical problems with imprecise properties where evolutionary computation is used for obtaining fuzzy solutions through guided searching. The representation of fuzzy sets, its initialisation, crossover, mutation, and validation, the ranking approach for fuzzy objective values, and the propagation method of fuzzy information are discussed. Several examples for illustrating the fuzzy evolutionary optimisation approach are provided.