Object hallucination remains a critical challenge in Large Vision-Language Models (LVLMs), where models generate content inconsistent with visual inputs. Existing language-decoder based mitigation approaches often regulate visual or textual attention independently, overlooking their interaction as two key causal factors. To address this, we propose Owl (Bi-mOdal attention reWeighting for Layer-wise hallucination mitigation), a causally-grounded framework that models hallucination process via a structural causal graph, treating decomposed visual and textual attentions as mediators. We introduce VTACR (Visual-to-Textual Attention Contribution Ratio), a novel metric that quantifies the modality contribution imbalance during decoding. Our analysis reveals that hallucinations frequently occur in low-VTACR scenarios, where textual priors dominate and visual grounding is weakened. To mitigate this, we design a fine-grained attention intervention mechanism that dynamically adjusts token- and layer-wise attention guided by VTACR signals. Finally, we propose a dual-path contrastive decoding strategy: one path emphasizes visually grounded predictions, while the other amplifies hallucinated ones -- letting visual truth shine and hallucination collapse. Experimental results on the POPE and CHAIR benchmarks show that Owl achieves significant hallucination reduction, setting a new SOTA in faithfulness while preserving vision-language understanding capability. Our code is available at https://github.com/CikZ2023/OWL

Identifying individual animals at different points in space and time is vital for effective wildlife monitoring and biodiversity conservation. While existing computer vision methods have shown promise in re-identifying animals, their capability in Animal Re-Identification (Animal ReID) remains restricted by the inherent visual variations, specifically high intra- and low inter-identity variations. High intra-identity variations refer to high visual diversity within the same individual due to pose or form changes and occlusions, and low inter-identity variations refer to subtle visual differences between distinct individuals due to fine-grained appearances. To address these challenges, we propose the Clip-based Animal RE-identification (CARE) framework, which leverages the image-conditioned textual description generation and individual-level semantic feature alignment, mitigating the negative impacts of visual variations in Animal ReID. Crucially, we have packaged CARE into a stand-alone toolkit and piloted it with stakeholders, facilitating real-world wildlife monitoring for biodiversity conservation. Extensive experiments on benchmark and in-the-wild datasets further demonstrate that CARE consistently outperforms state-of-the-art methods, validating its effectiveness in Animal ReID. Explore more about CARE at https://ml4sg.auckland.ac.nz/animal-re-identification-model/.

In today's data-driven landscape, intelligent systems must continuously learn from evolving data streams while preserving previously acquired knowledge. Continual learning (CL) has emerged as a key paradigm to meet this need, offering the potential to build adaptable and resilient AI systems. Despite its promise, CL faces core challenges such as catastrophic forgetting (CF), which occurs when learning new tasks degrades prior knowledge. These challenges have spurred rapid growth in CL research and method development. This paper presents a systematic literature review (SLR) of CL research published from 2010 to 2025. We adopt a reproducible methodology that combines automated searches across five major databases with manual searches from 13 key venues, resulting in a curated set of 189 peer-reviewed papers. Our review fills key gaps in earlier surveys by unifying fragmented classifications, probing overlooked causes of catastrophic forgetting, and advancing a more principled theoretical framing of CL challenges beyond empirical benchmarking. We contribute by offering (1) an updated, comprehensive overview of CL research; (2) refined taxonomies of learning methods and settings; (3) an analysis of the structural and functional causes of CF; and (4) the identification of open theoretical questions and emerging research directions. We provide an open-source repository of the reviewed literature and associated resources to support ongoing work: https://github.com/yyy24601/awesome-continual-learning.

Trajectory anomaly detection is critical in trajectory data mining. The objective is to identify abnormal movements of objects. Most existing trajectory anomaly detection methods focus on determining whether an entire trajectory is anomalous, lacking the ability to identify the exact anomalous sub-trajectories. Although recent research has started addressing anomalous sub-trajectories detection, these methods fail to extract the specific route pattern for the target trajectory. As a result, they struggle to identify anomalous sub-trajectories when the same sub-trajectory is regarded as normal in other routes. To overcome these limitations, we propose a Route-Enhanced Conditional Anomalous Sub-Trajectory detection model (RECAST). RECAST has two innovative components: (1) a Route Discovery Network (RDN) that extracts the normal route pattern of the given trajectory; (2) a Conditional Anomalous Sub-trajectory Detection (CASD) network that detects anomalies conditioned on the estimated route patterns. Our design enables RECAST to identify sub-trajectories as anomalous even if they are normal in other routes, as long as they are unlikely to occur in the route of the given trajectory. We evaluate the effectiveness and efficiency of RECAST using two real-world datasets. The results demonstrate that our method outperforms the state-of-the-art methods in detection accuracy with competitive runtime efficiency1.

Recent research has demonstrated the effectiveness of knowledge distillation in Domain Generalization. However, existing approaches often overlook domain-specific knowledge and rely on an offline distillation strategy, limiting the effectiveness of knowledge transfer. To address these limitations, we propose Balanced Online knowLedge Distillation (BOLD). BOLD leverages a multi-domain expert teacher model, with each expert specializing in a specific source domain, enabling the student to distill both domain-invariant and domain-specific knowledge. We incorporate the Pareto optimization principle and uncertainty weighting to balance these two types of knowledge, ensuring simultaneous optimization without compromising either. Additionally, BOLD employs an online knowledge distillation strategy, allowing the teacher and student to learn concurrently. This dynamic interaction enables the teacher to adapt based on student feedback, facilitating more effective knowledge transfer. Extensive experiments on seven benchmarks demonstrate that BOLD outperforms state-of-the-art methods. Furthermore, we provide theoretical insights that highlight the importance of domain-specific knowledge and the advantages of uncertainty weighting.

Abstract Bias in machine learning models remains a critical challenge, particularly in datasets with numeric features where discrimination may be subtle and hard to detect. Existing fairness frameworks rely on expert knowledge of marginalized groups, such as specific racial groups, and categorical features defining them. Furthermore, most frameworks evaluate bias in models rather than datasets, despite the fact that model bias can often be traced back to dataset shortcomings. Our research aims to remedy this gap by capturing dataset flaws in a set of meta-features at the dataset level, and to warn practitioners of bias risk when using such datasets for model training. We neither restrict the feature type nor expect domain knowledge. To this end, we develop methods to synthesize biased datasets and extend current fairness metrics to continuous features in order to quantify dataset-level discrimination risks. Our approach constructs a meta-database of diverse datasets, from which we derive transferable meta-features that capture dataset properties indicative of bias risk. Our findings demonstrate that dataset-level characteristics can serve as cost-effective indicators of bias risk, providing a novel method for data auditing that does not rely on expert knowledge. This work lays the foundation for early-warning systems, moving beyond model-focused assessments toward a data-centric approach.

A design model is the abstract representation of an actual process or software product. Although some software faults can be found by diagnosing design models before implementation, repairing the design models is time-consuming to software developers. To achieve faster software development, this paper introduces an automated approach to generally repair design models diagnosed by model checking. Model checkers are used to detect faults such as unreachable goals and violated properties in design models. Such faults are eliminated in parallel by insertion, modification and deletion operators found by constraint solving and predictive models. The outcomes of model repair are evaluated using the ISO/IEC 25010 software quality metrics. Experimental results have demonstrated that the proposed approach can eliminate unreachable goals and invariant violations in various design models while preserving their model quality. The effectiveness and performance of such design model repair processes depend mainly on the complexity of design model, the efficiency of constraint solver and the accuracy of predictive model. This study indicates that model-driven software development can be more efficient by automating model diagnosis, fault elimination and quality evaluation. • The repair of software design models is automated by formal methods. • Model checking simultaneously uncovers multiple faults in design models. • Model faults are eliminated by insertion, modification and deletion operators. • Predictions and constraint solving improve the correctness of repair. • Automated model diagnosis and repair preserve the quality of design models.

Deep learning methods often suffer performance degradation due to domain shift, where discrepancies exist between training and testing data distributions. Domain generalization mitigates this problem by leveraging information from multiple source domains to enhance model generalization capabilities for unseen domains. However, existing domain generalization methods typically present examples to the model in a random manner, overlooking the potential benefits of structured data presentation. To bridge this gap, we propose a novel learning strategy, Symmetric Self-Paced Learning (SSPL), for domain generalization. SSPL consists of a Symmetric Self-Paced training scheduler and a Gradient-based Difficulty Measure (GDM). Specifically, the proposed training scheduler initially focuses on easy examples, gradually shifting emphasis to harder examples as training progresses. GDM dynamically evaluates example difficulty through the gradient magnitude with respect to the example itself. Experiments across five popular benchmark datasets demonstrate the effectiveness of the proposed learning strategy.

Continual Learning (CL) methods enable models to learn new tasks without forgetting previously learned ones. Catastrophic Forgetting (CF) occurs when the parameters of a neural network are updated for a new task, causing the model to lose performance on tasks it has previously learned. To mitigate CF, parameter isolation methods use a “task mask” to allocate a subset of weights to each task; these weights are typically frozen to preserve task performance. However, frozen weights can limit positive backward transfer, which is the beneficial reuse of knowledge from new tasks to improve the accuracy of previously learned tasks. To address this gap, we introduce LEarning AFter learning (LEAF), a novel CL method that enables positive backward transfer by dynamically updating frozen task masks based on gradient updates that signal sufficient backward knowledge transfer. This mechanism allows for selective integration of new knowledge without sacrificing previously acquired knowledge. Our experiments show that LEAF surpasses existing state-of-the-art methods in terms of accuracy while maintaining comparable memory and runtime efficiencies. Moreover, it outperforms other backward transfer techniques in improving the accuracy of a prioritized task. Our code is available at https://github.com/wernse/LEAF.

Despite impressive performance on language modelling and complex reasoning tasks, Large Language Models (LLMs) fall short on the same tasks in uncommon settings or with distribution shifts, exhibiting a lack of generalisation ability.By contrast, systems such as causal models, that learn abstract variables and causal relationships, can demonstrate increased robustness against changes in the distribution.One reason for this success is the existence and use of Independent Causal Mechanisms (ICMs) representing high-level concepts that only sparsely interact.In this work, we apply two concepts from causality to learn ICMs within LLMs.We develop a new LLM architecture composed of multiple sparsely interacting language modelling modules.We show that such causal constraints can improve out-ofdistribution performance on abstract and causal reasoning tasks.We also investigate the level of independence and domain specialisation and show that LLMs rely on pre-trained partially domain-invariant mechanisms resilient to finetuning.

Traditional deep learning models often struggle in few-shot learning scenarios, where limited labeled data is available. While the Contrastive Language-Image Pre-training (CLIP) model demonstrates impressive zero-shot capabilities, its performance in few-shot scenarios remains limited. Existing methods primarily aim to leverage the limited labeled dataset, but this offers limited potential for improvement. To overcome the limitations of small datasets in few-shot learning, we introduce a novel framework, SSAT-Adapter, that leverages CLIP's language understanding to generate informative auxiliary tasks and improve CLIP's performance and adaptability in few-shot settings. We utilize CLIP's language understanding to create decision-boundary-focused image latents. These latents form auxiliary tasks, including inter-class instances to bridge CLIP's pre-trained knowledge with the provided examples, and intra-class instances to subtly expand the representation of target classes. A self-paced training regime, progressing from easier to more complex tasks, further promotes robust learning. Experiments show our framework outperforms the state-of-the-art online few-shot learning method by an average of 2.2% on eleven image classification datasets. Further ablation studies on various tasks demonstrate the effectiveness of our approach to enhance CLIP's adaptability in few-shot image classification.

In the face of rising surface temperatures from climate change, impacting biodiversity, extreme weather events, and agricultural productivity, understanding the drivers behind temperature changes is imperative. Traditional global climate models (GCMs) are computationally expensive, limiting their applicability, while machine learning approaches, though promising, face interpretability challenges due to their "black box" nature, especially in a dynamic setting where the data is constantly evolving. We propose DUO, a framework to identify shifts in important features and feature combinations as the data distribution changes over time. Our model independently assesses the importance of features and their interactions while also evaluating their relevance when combined with additional features, contributing to the target class. As a case study, we apply DUO to assess the shifts in climate drivers for station-level temperatures in six locations across New Zealand from 1980 to 2020, we identify specific humidity, geopotential height, and air temperature at high atmospheric pressure levels as the most important features for describing temperature variability. By revealing how climate drivers change over time, DUO contributes to a deeper understanding of temperature change patterns, enabling practitioners to develop targeted and adaptive mitigation strategies.

Federated learning (FL) is a popular approach to facilitate privacy-aware machine learning since it allows multiple clients to collaboratively train a global model without granting others access to their private data. It is, however, known that FL can be vulnerable tomembership inference attacks(MIAs), where the training records of the global model can be distinguished from the testing records. Surprisingly, research focusing on the investigation of the source inference problem appears to be lacking. We also observe that identifying a training record's source client can result in privacy breaches extending beyond MIAs. For example, consider an FL application where multiple hospitals jointly train a COVID-19 diagnosis model, membership inference attackers can identify the medical records that have been used for training, and any additional identification of the source hospital can result the patient from the particular hospital more prone to discrimination. Seeking to contribute to the literature gap, we take the first step to investigate source privacy in FL. Specifically, we propose a new inference attack (hereafter referred to assource inference attack– SIA), designed to facilitate an honest-but-curious server to identify the training record's source client. The proposed SIAs leverage the Bayesian theorem to allow the server to implement the attack in a non-intrusive manner without deviating from the defined FL protocol. We then evaluate SIAs in three different FL frameworks to show that in existing FL frameworks, the clients sharing gradients, model parameters, or predictions on a public dataset will leak such source information to the server. We also conduct extensive experiments on various datasets to investigate the key factors in an SIA. The experimental results validate the efficacy of the proposed SIAs, e.g., an attack success rate of 67.1% (baseline 10%) can be achieved when the clients share model parameters with the server. Comprehensive ablation studies demonstrate that the success of an SIA is directly related to the overfitting of the local models.

The process of generating data such as images is controlled by independent and unknown factors of variation. The retrieval of these variables has been studied extensively in the disentanglement, causal representation learning, and independent component analysis fields. Recently, approaches merging these domains together have shown great success. Instead of directly representing the factors of variation, the problem of disentanglement can be seen as finding the interventions on one image that yield a change to a single factor. Following this assumption, we introduce a new method for disentanglement inspired by causal dynamics that combines causality theory with vector-quantized variational autoencoders. Our model considers the quantized vectors as causal variables and links them in a causal graph. It performs causal interventions on the graph and generates atomic transitions affecting a unique factor of variation in the image. We also introduce a new task of action retrieval that consists of finding the action responsible for the transition between two images. We test our method on standard synthetic and real-world disentanglement datasets. We show that it can effectively disentangle the factors of variation and perform precise interventions on high-level semantic attributes of an image without affecting its quality, even with imbalanced data distributions.

The Postgraduage Certificate in Information Technology at the University of Auckland is a bootcamp-style transition programme for students without prior programming experience. The programme's part-time variant, designed for working professionals, has traditionally been offered with intensive evening classes, with extensive tutor and instructor support. Spurred on by the COVID-19 pandemic, our University has moved this part-time variant fully online. Benefits of the online offering include a wider, more inclusive audience and flexibility of student learning schedules. However, it has introduced several pedagogical challenges, including but not limited to how instructors can continue to provide meaningful support, feedback, and student engagement in an online environment. In this paper, we reflect on our experience, analyse qualitative and quantitative feedback such as student evaluations and grades, and provide recommendations for those wishing to undertake a similar transformation in the future.

Summary Recommender systems are important applications in big data analytics because accurate recommendation items or high‐valued suggestions can bring high profit to both commercial companies and customers. To make precise recommendations, a recommender system often needs large and fine‐grained data for training. In the current big data era, data often exists in the form of isolated islands, and it is difficult to integrate the data scattered due to privacy security concerns. Moreover, privacy laws and regulations make it harder to share data. Therefore, designing a privacy‐preserving recommender system is of paramount importance. Existing privacy‐preserving recommender system models mainly adapt cryptography approaches to achieve privacy preservation. However, cryptography approaches have heavy overhead when performing encryption and decryption operations and they lack a good level of flexibility. In this paper, we conduct privacy analysis on the existing locality sensitive hashing (LSH) approach based privacy‐preserving recommender system and show how an attacker can retrieve user's information under such a recommender system. Given such privacy risks, we propose differentially private LSH approach to build recommender system that can offer differential privacy guarantees for users. Our proposed efficient and scalable federated recommender system can make full use of multiple source data from different data owners while guaranteeing privacy preservation of users' data in contributing parties. Extensive experiments on real‐world benchmark datasets show that our approach can achieve both high time efficiency and accuracy under small privacy budgets.

Summary The automation of programming, which lies at the intersection of software engineering and artificial intelligence, enables machines to automatically generate programs that satisfy given requirements. In the context of B formal design modeling, one of the challenges is the refactoring of substitutions in design specifications, which often uses state transitions to describe how program or system statuses change during execution. This paper proposes a condition and substitution refactoring algorithm for the B formal specification language. The aim of the work is to automatically derive B operational predicates based on given transitions. The work has been extremely useful to machine‐driven formal design model repair as well as automated design specification generation. Given a set of state transitions, common relations of their state variables can be discovered and clustered into a number of classes. These relations can be further used to synthesize substitutions that derive new states from existing states. To restrict application domains of the synthesized substitutions, conditions that guard these substitutions are generated using first‐order logic. We have implemented the proposed algorithm as an extension to the ProB model checker. Experiments were conducted based on the B model public dataset. The evaluation results demonstrated that our solution is able to synthesize conditions and substitutions for various sets of state transitions in a wide range of B models.

Pollution from wood burners has profound health implications for the general population. Typically, monitoring the level of airborne particulate matter, PM2.5, in these areas often requires making inferences about missing or corrupted readings. Air Quality inference in these cases often poses critical challenges. The factors can evolve over time, changing the distribution of data. Such changes in the distribution of data are known as concept drift. Moreover, air pollution inference for a location typically would require historical data to be collected for the location. We investigate five air quality studies in New Zealand rural towns. We explore two different research problems: (1) an adaptive recurrent drift algorithm to model recurrence patterns in PM2.5levels for a town with the ability to recover after accuracy deterioration after a concept drift using an adaptive recurrent drift algorithm, and (2) transfer learning for the data stream whereby we reuse a pre-trained air pollution inference model from a town as the starting point for an air pollution inference model on another town. We further investigate the relationship between the changes we detected and changes within the prediction horizon. We showed that the average accuracy of the air quality inference for the five towns is between 70% and 94% using the recurrent drift algorithm. We also show that transfer learning was advantageous between two of the five towns.

In the online data stream environment, a model is typically not effective until a sufficient number of data instances have been seen. One solution to alleviate this issue is using model transfer. A major gap in current research is that they do not address the cost-effectiveness of model transfer in the online context, where processing time is crucial as data instances continuously arrive at high-speed. Model transfer in data streams involves continuously adapting the model to new data in the target stream. Suppose the target stream contains hard-to-learn patterns not covered by the transferred model. In that case, adaptation may incur extra processing time with little accuracy performance gains compared to simply building a new model for the target stream from scratch. Therefore, considering the cost of model transfer and target model construction is essential to balance the accuracy performance gains against computation cost for cost-effective model transfer. To address this gap, we propose a framework called OPERA (Online Transfer using Phantom Tree for Real-Time Adaptation), that orchestrates transfer learning based on the accuracy gain and runtime tradeoffs between transferring and adapting a source model, versus no transfer and constructing a new model for the target stream. We conduct extensive empirical studies to show that our framework can balance accuracy performance gains and runtime for cost-effective transfer learning in the data stream environment.

Recently issued data privacy regulations like GDPR (General Data Protection Regulation) grant individuals the right to be forgotten. In the context of machine learning, this requires a model to forget about a training data sample if requested by the data owner (i.e., machine unlearning). As an essential step prior to machine unlearning, it is still a challenge for a data owner to tell whether or not her data have been used by an unauthorized party to train a machine learning model. Membership inference is a recently emerging technique to identify whether a data sample was used to train a target model, and seems to be a promising solution to this challenge. However, straightforward adoption of existing membership inference approaches fails to address the challenge effectively due to being originally designed for attacking membership privacy and suffering from several severe limitations such as low inference accuracy on well-generalized models. In this paper, we propose a novel membership inference approach inspired by the backdoor technology to address the said challenge. Specifically, our approach of Membership Inference via Backdooring (MIB) leverages the key observation that a backdoored model behaves very differently from a clean model when predicting on deliberately marked samples created by a data owner. Appealingly, MIB requires data owners' marking a small number of samples for membership inference and only black-box access to the target model, with theoretical guarantees for inference results. We perform extensive experiments on various datasets and deep neural network architectures, and the results validate the efficacy of our approach, e.g., marking only 0.1% of the training dataset is practically sufficient for effective membership inference.

Automated model repair techniques enable machines to synthesise patches that ensure models meet given requirements. B-repair, which is an existing model repair approach, assists users in repairing erroneous models in the B formal method, but repairing large models is inefficient due to successive applications of repair. In this work, we improve the performance of B-repair using simultaneous modifications, repair refactoring, and better classifiers. The simultaneous modifications can eliminate multiple invariant violations at a time so the average time to repair each fault can be reduced. Further, the modifications can be refactored to reduce the length of repair. The purpose of using better classifiers is to perform more accurate and general repairs and avoid inefficient brute-force searches. We conducted an empirical study to demonstrate that the improved implementation leads to the entire model process achieving higher accuracy, generality, and efficiency.

In many real-world applications, data are often produced in the form of streams. Consider, for example, data produced by sensors. In data streams there can be concept drift where the distribution of the data changes. When we deal with multiple streams from the same domain, concepts that have occurred in one stream may occur in another. Therefore, being able to reuse knowledge across multiple streams can help models recover from concept drifts more quickly. A major challenge is that these data streams may be only partially identical and a direct adoption would not suffice. In this paper, we propose a novel framework to transfer both identical and partially identical concepts across different streams. In particular, we propose a new technique called Adaptive Online TrAdaBoost that tunes weight adjustments during boosting based on model performance. The experiments on synthetic data verify the desired properties of the proposed method, and the experiments on real-world data show the better performance of the method for data stream mining compared with its baselines.

Federated learning (FL) has emerged as a promising privacy-aware paradigm that allows multiple clients to jointly train a model without sharing their private data. Recently, many studies have shown that FL is vulnerable to membership inference attacks (MIAs) that can distinguish the training members of the given model from the non-members. However, existing MIAs ignore the source of a training member, i.e., the information of the client owning the training member, while it is essential to explore source privacy in FL beyond membership privacy of examples from all clients. The leakage of source information can lead to severe privacy issues. For example, identification of the hospital contributing to the training of an FL model for the COVID-19 pandemic can render the owner of a data record from this hospital more prone to discrimination if the hospital is in a high risk region. In this paper, we propose a new inference attack called source inference attack (SIA), which can derive an optimal estimation of the source of a training member. Specifically, we innovatively adopt the Bayesian perspective to demonstrate that an honest-but-curious server can launch an SIA to steal non-trivial source information of the training members without violating the FL protocol. The server leverages the prediction loss of local models on the training members to achieve the attack effectively and non-intrusively. We conduct extensive experiments on one synthetic and five real datasets to evaluate the key factors in an SIA, and the results show the efficacy of the proposed source inference attack.

Membership inference attacks on a machine learning model aim to determine whether a given data record is a member of the training set. They pose severe privacy risks to individuals, e.g., identifying an individual's participation in a hospital's health analytic training set reveals that this individual was once a patient in that hospital. Adversarial regularization (AR) is one of the state-of-the-art defense methods that mitigate such attacks while preserving a model's prediction accuracy. AR adds membership inference attacks as a new regularization term to the target model during the training process. It is an adversarial training algorithm that is trained on a defended model which is essentially the same as training the generator of generative adversarial networks (GANs). We observe that many GAN variants are able to generate higher quality samples and offer more stability during the training phase than GANs. However, whether these GAN variants are available to improve the effectiveness of AR has not been investigated. In this paper, we propose an enhanced adversarial regularization (EAR) method based on Least Square GANs (LSGANs). The new EAR surpasses the existing AR in offering more powerful defensive ability while preserving the same prediction accuracy of the protected classifiers. We systematically evaluate EAR on five datasets with different target classifiers under four different attack methods and compare it with four other defense methods. We experimentally show that our new method performs the best among other defense methods.

Most machine learning algorithms rely on a set of hyperparameters, which are tuned either by domain experts or using automated methods. In the context of data streams, the complexity of this task is compounded by the need for tuning every time a concept drift occurs. The problem of automated tuning may lead to classification performance improvements. Current methods optimise the hyperparameters from scratch every time concept drift is detected. Additionally, meta-learning techniques developed for static machine learning tasks allow pre-built knowledge to adapt faster to new tasks. We propose a meta-learning approach for automated tuning in evolving data streams, specifically for tuning parameters in Adaptive Random Forest (ARF) and parameters for several drift detectors. We compare our approach against non-adaptive methods on both synthetic and real-world datasets. Empirical results show that our approach makes a compromises between predictive performance and resource consumption for ARF, and between true positives and false positives for drift detectors.

Concept drift detection is used to signal to a learning algorithm that there has been a change in the underlying distribution of the data stream. However, there is a delay in detecting the actual drifts, leading to performance loss between the start of the drift and the detection point. There are two major challenges in reducing such performance loss, specifically the difficulty in anticipating the location of the next drift point and determining the exact concept that will appear for timely concept adaptation. In this research, we leverage concept recurrences in data streams. We proposed a framework called Nacre, which can perform proactive drift detection and online updates to allow for smooth adaptation of concept drifts. We present a novel technique, called drift coordinator, that anticipates the next drift point and assesses the incoming concept. This will ultimately increase accuracy in the classification performance. We demonstrate that our method is able to learn and predict drift trends in streams with recurring drifts. This allows the anticipation of future changes which enables users and detection methods to be more proactive. We empirically show that our technique outperforms baselines in terms of accuracy, kappa, accuracy gain per drift and cumulative accuracy gain on both synthetic and real-world datasets.

The recommender system is an important application in big data analytics because accurate recommendation items or high-valued suggestions can bring high profit to both commercial companies and customers. To make precise recommendations, a recommender system often needs large and fine-grained data for training. In the current big data era, data often exist in the form of isolated islands, and it is difficult to integrate the data scattered due to privacy security concerns. Moreover, privacy laws and regulations make it harder to share data. Therefore, designing a privacy-preserving recommender system is of paramount importance. Existing privacy-preserving recommender system models mainly adapt cryptography approaches to achieve privacy preservation. However, cryptography approaches have heavy overhead when performing encryption and decryption operations and they lack a good level of flexibility. In this paper, we propose a Locality Sensitive Hashing (LSH) based approach for federated recommender system. Our proposed efficient and scalable federated recommender system can make full use of multiple source data from different data owners while guaranteeing preservation of privacy of contributing parties. Extensive experiments on real-world benchmark datasets show that our approach can achieve both high time efficiency and accuracy under small privacy budgets.

Concept drift detection refers to the process of detecting changes in the underlying distribution of data. Interest in the data stream mining community has increased, because of their role in improving the performance of online learning algorithms. Over the years, a myriad of drift detection methods have been proposed. However, most of these methods are single detectors, which usually work well only with a single type of drift. In this research, we propose a semi-supervised locally-weighted ensemble detector (SLED), where the relative performance among its base detectors is characterized by a set of weights learned in a semi-supervised manner. The aim of this technique is to effectively deal with both abrupt and gradual concept drifts. In our experiments, SLED is configured with ten well-known drift detectors. To evaluate the performance of SLED, we compare it with single detectors as well as state-of-the-art ensemble methods on both synthetic and real-world datasets using different performance measures. The experimental results show that SLED has fewer false positives, higher precision, and higher Matthews correlation coefficient while maintaining reasonably good performance for other measures.

in their accompanying referral documents, which contain a mix of free text and structured data. By training a model to predict triage decisions from these referral documents, we can partially automate the triage process, resulting in more efficient and systematic triage decisions. One of the difficulties of this task is maintaining robustness against changes in triage priorities due to changes in policy, funding, staff, or other factors. This is reflected as changes in relationship between document features and triage labels, also known as concept drift. These changes must be detected so that the model can be retrained to reflect the new environment. We introduce a new concept drift detection algorithm for this domain called calibrated drift detection method (CDDM). We evaluated CDDM on benchmark and synthetic medical triage datasets, and find it competitive with state-of-the-art detectors, while also being less prone to false positives from feature drift.

The B method has facilitated the development of software by specifying the design of software as abstract machines and formally verifying the correctness of the abstract machines. The quality of B abstract machines can significantly impact the quality of final software products. In this paper, we propose a set of criteria for measuring the quality of B abstract machines based on ISO/IEC 25010, which is one of the latest international standards for evaluating software quality in software engineering. These criteria evaluate abstract machines using a number of general-purpose and domain-independent equations and model checking techniques, so that the quality of abstract machines can be quantified as vectors. The proposed criteria are implemented as a B model quality evaluator, and they are explained and justified using a number of examples.

This paper proposes a probabilistic reachability repair solution that enables abstract machines to automatically evolve and satisfy desired requirements. The solution is a combination of the B-method, machine learning and program synthesis. The B-method is used to formally specify an abstract machine and analyse the reachability of the abstract machine. Machine learning models are used to approximate features hidden in the semantics of the abstract machine. When the abstract machine fails to reach a desired state, the machine learning models are used to discover missing transitions to the state. Inserting the discovered transitions into the original abstract machine will lead to a repaired abstract machine that is capable of achieving the state. To obtain the repaired abstract machine, a set of insertion repairs are synthesised from the discovered transitions and are simplified using context-free grammars. Experimental results reveal that the reachability repair solution is applicable to a wide range of abstract machines and can accurately discover transitions that satisfy the requirements of reachability. Moreover, the results demonstrate that random forests are efficient machine learning models on transition discovery tasks. Additionally, we argue that the automated reachability repair process can improve the efficiency of software development.

Network embedding learns the vector representations of nodes. Most real world networks are heterogeneous and evolve over time. There are, however, no network embedding approaches designed for dynamic heterogeneous networks so far. Addressing this research gap is beneficial for analyzing and mining real world networks. We develop a novel representation learning method, change2vec, which considers a dynamic heterogeneous network as snapshots of networks with different time stamps. Instead of processing the whole network at each time stamp, change2vec models changes between two consecutive static networks by capturing newly-added and deleted nodes with their neighbour nodes as well as newly-formed or deleted edges that caused core structural changes known as triad closure or open processes. Change2vec leverages metapath based node embedding and change modeling to preserve both heterogeneous and dynamic features of a network. Experimental results show that change2vec outperforms two state-of-the-art methods in terms of clustering performance and efficiency.

The reasons for concept drift in a data stream can vary widely, from deterioration of a machine to a change in peoples' buying patterns. In order to effectively detect concept drifts, most predictive stream mining systems contain a drift detector that monitors and signals concept drifts. However, few of these systems are designed to find drifts in transactional datasets, which have unlabelled data. Transactional datasets describe events, such as orders or payments, which are traditionally analysed using association rules. In this paper, we propose a novel drift detection technique, ProChange, that has two parts. The first part is a drift detector, VR-Change, that finds both real and virtual drifts in unlabelled transactional data streams using the Hellinger distance. The second part is a drift predictor, which models the volatility of drifts using a probabilistic network to predict the location of future drifts. Using the predictor, we can dynamically adapt the confidence threshold, enabling VR-Change to be more sensitive around potential future drift points. We evaluated the performance of ProChange by comparing it against traditional detectors showing that it detects both real and virtual drifts effectively and efficiently in terms of accuracy.

The B-method provides facilities for the design, development and automated verification of software systems, but the repair of faulty abstract machines of B is still a manual task. This paper proposes B-repair, a method that supports the computer-assisted repair of faulty abstract machines. Based on model checking, the B-repair system can generate possible repairs, evaluate the repairs and apply the repairs to faulty B machines. The generation of repairs is based on two formal templates, namely isolation and revision. Moreover, the evaluation of repairs is based on machine learning techniques, such as logistic models, residual neural networks and random forests. The machine learning models learn from the state graph of the original abstract machine, which are used to estimate the quality of repairs. Users are able to select satisfactory repairs and apply the selected repairs to the original machine. Using such a computer-assisted methodology, the B-repair method improves the efficiency of software development using the B-method. The B-repair method has been validated using different real-world models, and the validation result has shown that B-repair can suggest a number of repairs with realistic meanings.

Concept drifts cause data streams to change over time, making classification harder. Drift detection signals when change occurs so remedial action can be taken. However, existing approaches do not allow drift detectors to improve drift detection by using patterns associated with drift across multiple stream metadata. Metadata Drift Predictor (MDP) is a novel framework which allows this by adapting drift sensitivity mid-stream to reduce errors in drift detection. MDP clusters metadata patterns at drift points and uses recurring patterns to encourage drift detection where metadata looks similar to past drift points and/or discourage drift detection where metadata looks different from past drift points. It avoids relying on statistical assumptions of underlying drift patterns. Our experiments demonstrate that MDP works consistently across traditional stream classifiers and drift detectors to reduce delay and false positive drift detection and/or reduce false negative drift detection. We show that MDP can be used to reduce drifts detected in real-world datasets while improving the accuracy that each detected drift provides when compared to other popular meta-learning frameworks.

Recently, there has been a strong demand for talented ICT (Information and Communication Technology) graduates in the software industry in New Zealand. To meet this demand, in 2015, the government of New Zealand provided funding for three new ICT Graduate Schools. The challenge for the schools was twofold: to provide a qualification for students transitioning into ICT and to prepare those with an ICT education for the workforce. Each of the Schools offer different programmes. We offer two postgraduate programmes for growing talent and knowledge to support the New Zealand's ICT sector. In this paper, we describe our experience with delivering one of the postgraduate programmes, the Postgraduate Certificate in Information Technology. The programme consists of two courses, Programming for Industry and Programming with Web Technologies. The courses focus on providing industry-focused education to students with non-ICT backgrounds for a career in IT. The programme has been running for 2 years and runs over one semester (12 weeks) in a "boot-camp" style, which has proved to be successful. In this paper, we provide an overview of the features of the programme, which incorporates industry-focused activities for building students' knowledge relevant to the software industry. Finally, we discuss key challenges and identify directions for improvement.

The emergence of New Data Sources (NDS) in healthcare is revolutionising traditional electronic health records in terms of data availability, storage, and access. Increasingly, clinicians are using NDS to build a virtual holistic image of a patients health condition. This research is focused on a review and analysis of the current legislation and privacy rules available for healthcare professionals. NDS in this project refers to and includes patient-generated health data, consumer device data, wearable health and fitness data, and data from social media. This project reviewed legal and regulatory requirements for New Zealand, Australia, the European Union, and the United States to establish the ground reality of existing mechanisms in place concerning the use of NDS. The outcome of our research is to recommend changes and enhancements required to better prepare for the ’tsunami’ of NDS and applications in the currently evolving data-driven healthcare area and precision or personalised health initiatives such as Precision Driven Health (PDH) in New Zealand.

This paper briefly reviews the construction of Ruby on Rails applications, identifies the pitfalls of existing tools, and proposes the design and development of a lean cross-platform desktop application, along with an evaluation of the prototype.

Instead of constructing complex declarative queries, many users prefer to write their programs using procedural code embedded with simple queries. Since many users are not expert programmers or the programs are written in a rush, these programs usually exhibit poor performance in practice and it is a challenge to automatically and efficiently optimize these programs. In this article, we present UniAD, which stands for Uni fied execution for Ad hoc Data processing, a system designed to simplify the programming of data processing tasks and provide efficient execution for user programs. We provide the background of program semantics and propose a novel intermediate representation, called Unified Intermediate Representation (UniIR), which utilizes a simple and expressive mechanism HOQ to describe the operations performed in programs. By combining both procedural and declarative logics with the proposed intermediate representation, we can perform various optimizations across the boundary between procedural and declarative code. We propose a transformation-based optimizer to automatically optimize programs and implement the UniAD system. The extensive experimental results on various benchmarks demonstrate that our techniques can significantly improve the performance of a wide range of data processing programs.

Model Driven Engineering (MDE), despite having many advantages, is often overlooked by programmers due to lack of proper understanding and training in the matter.This paper investigates the advantages and disadvantages of MDE and looks at research results showing the adoption rates of design models.In light of the findings, an educational tool, namely Lorini, was developed to provide automated code generation from the design models.The implemented tool consists in a plug-in for the Astah framework aimed at teaching Java programming to students through UML diagrams.It features instantaneous code generation from three types of UML diagrams, code-diagram matching, a feedback panel for error displays and on-the-fly compilation and execution of the resulting program.Evaluation of the tool indicated it to be successful with unique educational features and intuitive to use.

With the growing in size and complexity of modern computer systems, the need for improving the quality at all stages of software development has become a critical issue. The current software production has been largely dependent on manual code development. Despite the slow development process, the errors introduced by the programmers contribute to a substantial portion of defects in the final software product. Model-driven engineering (MDE), despite having many advantages, is often overlooked by programmers due to lack of proper understanding and training in the matter. This paper investigates the advantages and disadvantages of MDE and looks at research results showing the adoption rates of design models. It analyzes different tools used for automated code generation and displays the reasons that led to technical decisions such as the programming language or design model used. In light of the findings, an educational tool, namely Lorini, was developed to provide automated code generation from the design models. The implemented tool consists of a plug-in for the Astah framework aimed at teaching Java programming to students through UML diagrams. It features instantaneous code generation from three types of UML diagrams, code-diagram matching, a feedback panel for error displays and on-the-fly compilation and execution of the resulting program. We also explore the possibility of generating assertion constraints from the design model and use them to verify the implementation. Evaluation of the tool indicated it to be successful with unique educational features and intuitive to use.

Categories are the fundamental components of scientific knowledge and are used in every phase of the scientific process. However, they are often in a state of flux, with new observations, discoveries and changes in our conceptual understanding leading to the birth and death of categories, drift in their identities, as well as merging or splitting. Contemporary research tools rarely support such changes in operationalized categories, neglecting the problem of capturing and utilizing the knowledge lurking behind the process of change. This paper presents a tool -- AdvoCate1 -- that represents the dynamic nature of categories. It allows category evolution to be modelled, while maintaining a category versioning system that captures all the different versions of a category, along with the process of its exploration and evolution through use. This helps us to better understand and communicate different versions of categories and the reasons and decisions behind any changes they undergo. We demonstrate the usefulness of AdvoCate using examples of category evolution from a land cover mapping exercise.

Optimization based techniques have emerged as important methods to tackle the problems of efficiency and accuracy in data mining. One of the current application areas is outlier detection that has not been fully explored yet but has enormous potential. Web bots are an example of outliers, which can be found in the web usage analysis process. Web bot requests are different from a genuine web user as web bots crawl large numbers of pages in a very short time. If web bots remains undetected they can skew the analysis process which can result in incorrect patterns that can cause wrong decisions. In this paper we use one of the popular Swarm Intelligence (SI) based techniques called Particle Swarm Optimization (PSO) to detect web bots among genuine user requests. We use our Particle Swarm Optimization (PSO) based clustering algorithm, Hierarchical Particle Swarm Optimization based clustering (HPSO-clustering) to cluster the web-usage data and detect the abnormal behaviour caused by the web bots. We present the results of detection which shows that our proposed approach is capable of detecting such abnormal behaviour. We then compare the characteristics of the detected web bots with genuine web-users using cross validation.

Understanding the impacts of copyright is a challenge for the sharing and reuse of our research data. There is growing recognition of the problem, but the legal knowledge required to navigate through the minefield of restrictions and risks is often too difficult to uncover and understand. As of yet there are no appropriate tools to aid researchers, librarians and research policy makers. To address this gap we present Camden, an automated copyright reasoning tool designed to integrate into existing research workflows. At its core, Camden uses dynamically generated defeasible rules to reason over the legality of a situation of using, combining and publishing data, while additionally suggesting potential licenses by which to safely share derived research outputs. This functionality has been wrapped up into an embedded software library and offered as a web application. In this paper we introduce Camden, describe its model of computational reasoning and discuss how it can be included into existing and future eResearch tools and services.

Current drift detection techniques detect a change in distribution within a stream. However, there are no current techniques that analyze the change in the rate of these detected changes. We coin the term stream volatility, to describe the rate of changes in a stream. A stream has a high volatility if changes are detected frequently and has a low volatility if changes are detected infrequently. We are particularly interested in a volatility shift which is a change in the rate of change (e.g. From high volatility to low volatility). We introduce and define the concept of stream volatility, and propose a novel technique to detect volatility on data streams in the presence of concept drifts. In the experiments we show our algorithm to be both fast and efficient. We also propose a new algorithm for drift detection called SEED that is faster and more memory efficient than the existing state-of-the-art drift detection approach. A faster drift detection algorithm has a flow-on benefit to the subsequent volatility detection stage because both algorithms run concurrently on the data stream.

Recommender systems are highly vulnerable to attacks. Attackers who introduce biased ratings in order to affect recommendations, have been shown to be effective against collaborative filtering algorithms. In this paper, we study the use of statistical metrics to detect rating patterns of attackers. Two metrics, Rating Deviation from Mean Agreement (RDMA) and Degree of Similarity with Top Neighbors (DegSim), are used for analysing rating patterns between malicious profiles and genuine profiles in shilling attacks. Building upon this, we propose and evaluate an algorithm for detecting shilling attacks in recommender systems using a statistical approach. We look at two attack models: random attack and average attack. The experimental results show that our detection technique based on target item analysis is an effective approach in detecting shilling attacks for both the random and average attack model.

Recommender systems using Collaborative Filtering techniques are capable of make personalized predictions. However, these systems are highly vulnerable to profile injection attacks. Group attacks are attacks that target a group of items instead of one, and there are common attributes among these items. Such profiles will have a good probability of being similar to a large number of user profiles, making them hard to detect. We propose a novel technique for identifying group attack profiles which uses an improved metric based on Degree of Similarity with Top Neighbors (DegSim) and Rating Deviation from Mean Agreement (RDMA). We also extend our work with a detailed analysis of target item rating patterns. Experiments show that the combined methods can improve detection rates in user-based recommender systems.

It is important to provide efficient execution for ad-hoc data processing programs. In contrast to constructing complex declarative queries, many users prefer to write their programs using procedural code with simple queries. As many users are not expert programmers, their programs usually exhibit poor performance in practice and it is a challenge to automatically optimize these programs and efficiently execute the programs. In this paper, we present UniAD, a system designed to simplify the programming of data processing tasks and provide efficient execution for user programs. We propose a novel intermediate representation named UniQL which utilizes HOQs to describe the operations performed in programs. By combining both procedural and declarative logics, we can perform various optimizations across the boundary between procedural and declarative codes. We describe optimizations and conduct extensive empirical studies using UniAD. The experimental results on four benchmarks demonstrate that our techniques can significantly improve the performance of a wide range of data processing programs.

Recommender systems have become one of the necessary tools to help a web user find a potentially interesting resource based on their preferences. In implicit recommender systems, the recommendations are made based on the implicit information of the w

Recently, a number of semi-stream join algorithms have been published. The typical system setup for these consists of one fast stream input that has to be joined with a disk-based relation R. These semi-stream join approaches typically perform the join with a limited main memory partition assigned to them, which is generally not large enough to hold the whole relation R. We propose a caching approach that can be used as a front-stage for different semi-stream join algorithms, resulting in significant performance gains for common applications. We analyze our approach in the context of a seminal semi-stream join, MESHJOIN (Mesh Join), and provide a cost model for the resulting semi-stream join algorithm, which we call CMESHJOIN (Cached Mesh Join). The algorithm takes advantage of skewed distributions; this article presents results for Zipfian distributions of the type that appears in many applications.

Semantic Web Services (SWS) introduce a semantic layer to the current web infrastructure, enabling the automated processing of web tasks. Different frameworks have been proposed for designing SWS, however, there has been little research in the area of testing SWS. Generating test cases for SWS is challenging due to its dynamic nature and abstract views, evaluating the test cases is equally essential in order to ensure the quality of the test suite. In this paper, we propose a goal oriented generation and mutation based evaluation approach towards SWS testing. This way, the generation (model-based) and evaluation (code-based) procedures are totally independent to each other, which further provides a much accurate measurement on the results. In addition, a tool support has been developed to automate the major steps in the proposed solution.

Data clustering aims to group data based on similarities between the data elements. Recently, due to the increasing complexity and amount of heterogenous data, modeling of such data for clustering has become a serious challenge. In this paper we tackle the problem of modeling heterogeneous web usage data for clustering. The main contribution is a new similarity measure which we propose to cluster heterogeneous web usage data. We then use this similarity measure in our Particle Swarm Optimization (PSO) based clustering algorithm, Hierarchical Particle Swarm Optimization based clustering (HPSO-clustering). HPSO-clustering combines the qualities of hierarchical and partitional clustering to cluster data in a hierarchical agglomerative manner. We present the clustering results and explain the effects of the new similarity measure on inter-cluster and intra-cluster distances. These measures verify the applicability of the proposed similarity measure on web usage data.

Due to a marked increase in the number of web users and their activities, many application areas that use patterns generated from their activities has been proposed. Web-based implicit recommender systems are one such application. An implicit recommender system is a tool that helps guide a user to a particular web resource based on implicit data. Implicit data comes from the web users activities without their active participation. Building such a system is a complex process due to two reasons, there is a huge amount of data and the quality of the data is poor. In this research, we tackle the first problem of generating patterns efficiently for recommender system by proposing Hierarchical Particle Swarm Optimization based clustering (HPSO-clustering). HPSO-clustering is a clustering approach based on Particle Swarm Optimization which combines both the properties of hierarchical and partitional clustering. We grouped the users' session into different clusters. Recommendations for an active user are generated from these clusters. In this paper we report the results of accuracy of recommendations. We achieved an overall 60% to 65% of precision for an active user, while in some clusters the precision achieved was 100% when top 5 ranked recommendations were selected.

Outlier detection is an important field in data mining and knowledge discovery, which aims to identify abnormal observations in a large dataset. Common application areas of outlier detection are intrusion detection in computer networks, credit cards fraud detection, detecting abnormal changes in stock prices, and identifying abnormal health conditions. We propose the use of a novel swarm intelligence based clustering technique called Hierarchical Particle Swarm Optimization Based Clustering (HPSO-clustering) for outlier detection. The proposed technique is able to perform Hierarchical Agglomerative Clustering (HAC) as well as outlier detection. In the proposed approach a swarm of particles evolves through different stages to identify outliers and normal clusters. The experimentation of the proposed approach is performed on benchmark datasets which show that the efficiency of the approach is better than some other popular outlier detection techniques.

To fulfill the increasing demand of business for the latest information, current data integration approaches are moving towards real-time updates. One important element in real-time data integration is the join of a continuous incoming data stream with a disk-based relation. In this paper we investigate a stream-based join algorithm, called mesh join (MESHJOIN), and propose an improved version called reduced MESHJOIN (R-MESHJOIN). Both algorithms tune the memory, allocating parts of the memory to key components. In MESHJOIN there is a dependency between the size of partitions in an internal queue for the stream data and the number of iterations required to bring the disk-based relation into memory. This dependency hampers the optimal distribution of memory among the join components. In particular the size of the disk-buffer varies with the size of the disk-based relation which is unnecessary. On the other hand the R-MESHJOIN algorithm removes this dependency. This enables an optimal distribution of available memory among the join components. In R-MESHJOIN a change in the size of the disk-based relation does not affect the size of the disk-buffer. An experimental study is conducted in order to validate the arguments.

Semistructured data has become more and more prominent in the fast growing areas of web information technology. XML has been used as a standard format for semistructured data in representing and exchanging information in various applications. However, the lack of formality and verification support in the design of a good semistructured data model may hinder its development. For example, redundant data in XML must be removed or minimized to avoid inconsistent and inefficient information processing. Normalization algorithms have been developed to overcome these problems by transforming the schema of a semistructured document into a better form. Therefore, it is essential to ensure that a transformed schema model preserves the same information that its original form holds. In this paper, we present an approach to investigate and verify the no-data-loss property of semistructured data normalization. We encode the verification criteria in the Semantic Web Rule Language (SWRL) and make use of its ontology reasoning engine to provide automated support for the checking process. In summary, our approach not only investigates the information preserving aspect of semistructured data normalization, but also provides a scalable and automated solution towards the problem.

The dramatic expansion of semistructured data has led to the development of database systems for manipulating the data. Despite its huge potential, there is still a lack of formality and verification support in the design of good semistructured databases. Like traditional database systems, developed semistructured database systems should contain minimal redundancies and update anomalies, in order to store and manage the data effectively. Several normalization algorithms have been proposed to satisfy these needs, by transforming the schema of the semistructured data into a better form. It is essential to ensure that the normalized schema remains semantically equivalent to its original form. In this paper, we present tool support for reasoning about the correctness of semistructured data normalization. The proposed approach uses the ORA-SS data modeling notation and defines its correctness criteria and rules in the PVS formal language. It further utilizes the PVS theorem prover to perform automated checking on the normalized schema, checking that functional dependencies are preserved, no data is lost and no spurious data is created. In summary, our approach not only investigates the characteristics of semistructured data normalization, but also provides a scalable and automated first step towards reasoning about the correctness of normalization algorithms on semistructured data.

Clustering is an important data mining task and has been explored extensively by a number of researchers for different application areas such as finding similarities in images, text data and bio-informatics data. Various optimization techniques have been proposed to improve the performance of clustering algorithms. In this paper we propose a novel algorithm for clustering that we call evolutionary particle swarm optimization (EPSO)-clustering algorithm which is based on PSO. The proposed algorithm is based on the evolution of swarm generations where the particles are initially uniformly distributed in the input data space and after a specified number of iterations; a new generation of the swarm evolves. The swarm tries to dynamically adjust itself after each generation to optimal positions. The paper describes the new algorithm the initial implementation and presents tests performed on real clustering benchmark data. The proposed method is compared with k-means clustering- a benchmark clustering technique and simple particle swarm clustering algorithm. The results show that the algorithm is efficient and produces compact clusters.

Over the past decade, a large number of deductive object-oriented database languages have been proposed. The earliest of these languages had few object-oriented features, and more and more features have systematically been incorporated in successive languages. However, a language with a clean logical semantics that naturally accounts for all the key object-oriented features, is still missing from the literature. This article takes us another step towards solving this problem. Two features that are currently missing are the encapsulation of rule-based methods in classes, and nonmonotonic structural and behavioral inheritance with overriding, conflict resolution and blocking. This article introduces the syntax of a language with these features. The language is restricted in the sense that we have omitted other object-oriented and deductive features that are now well understood, in order to make our contribution clearer. It then defines a class of databases, called well-defined databases , that have an intuitive meaning and develops a direct logical semantics for this class of databases. The semantics is based on the well-founded semantics from logic programming. The work presented in this article establishes a firm logical foundation for deductive object-oriented databases.

If XML is to play the critical role of the lingua franca for Internet data interchange that many predict, it is necessary to start designing and adopting benchmarks allowing the comparative performance analysis of the tools being developed and proposed. The effectiveness of existing XML query languages has been studied by many, with a focus on the comparison of linguistic features, implicitly reflecting the fact that most XML tools exist only on paper. In this paper, with a focus on efficiency and concreteness, we propose a pragmatic first step toward the systematic benchmarking of XML query processing platforms with an initial focus on the data (versus document) point of view. We propose XOO7, an XML version of the OO7 benchmark. We discuss the applicability of XOO7, its strengths, limitations and the extensions we are considering. We illustrate its use by presenting and discussing the performance comparison against XOO7 of three different query processing platforms for XML.

Over the past decade, a large number of deductive object-oriented database languages have been proposed. The earliest of these languages had few object-oriented features, and more and more features have systematically been incorporated in successive languages. However, a language with a clean logical semantics that naturally accounts for all the key object-oriented features, is still missing from the literature. Two features that are currently missing are the encapsulation of rule-based methods in classes, and nonmonotonic behavioral inheritance with overriding, conflict resolution and blocking. This paper introduces the syntax of a language with these features. It then defines a class of databases, called well-defined databases, that have an intuitive meaning and develops a direct logical semantics for this class of databases. The semantics is based on the well-founded semantics from logic programming. The work presented in this paper establishes a firm logical foundation for deductive object-oriented databases.

Semistructured data has become prevalent with the growth of the Internet. The development of new web applications that require efficient design and maintenance of large amounts of data makes it increasingly important to design "good" semistructured databases to prevent data redundancy and updating anomalies. However, it is not easy, even impossible, for current semistructured data models to capture the semantics traditionally needed for designing databases. In this paper, we show how an object-relationship-attribute model for semistructured data (ORA-SS) can facilitate the design of "good" semistructured databases. This is accomplished via the normalization of ORA-SS. An XML DTD or Schema generated from a normal form ORA-SS schema diagram has no undesirable redundancy, and thus no updating anomalies for the complying semistructured databases. The general design methodology and detailed steps for converting an ORA-SS schema diagram into a normal form ORA-SS schema diagram are presented. These steps can also be used as guidelines for designing semistructured databases using the ORA-SS model.

Computer science students benefit from working in teams. But working in teams is difficult and team skills are seldom taught. In this paper, we describe mechanisms we put in place for supporting team processes in our group project course. We evaluate the mechanisms and extract guidelines that are useful for supporting team dynamics.