Miracle-dz/UniFAR-FTUs huggingface dataset:
Vegetation community and sediment dynamics at Ukerebagh Island, Tweed Heads

An increasing trend of mangrove expansion into saltmarsh communities has been observed in south-eastern Australia as part of a worldwide trend of woody vegetation encroachment on grasslands (Saintilan & Rogers 2013). Changes in coastal wetland vegetation communities as a result of sea-level rise are likely to alter ‘blue carbon’ sequestration rates, depending on sediment supply, primary productivity and the inherent ability of vegetation to adjust to regional hydrologic and geomorphic changes. The rate of sediment supply, both mineral and organic, many not meet the requirements to fill ‘accommodation space’ created by relative sealevel rise (Woodroffe et al. 2016). Changes in hydrological and sediment supply characteristics may be influenced by anthropogenic modifications to the estuary, including channel dredging, ocean breakwall construction, and land use change. This study investigated multi-decadal sedimentation rate changes and longterm vegetation community evolution at Ukerebagh Island, a coastal wetland within the sub-tropical Tweed River estuary on the NSW-Queensland border. 210Pb/137Cs analysis indicated that considerable acceleration of sedimentation rates has occurred throughout the latter part of the 20th century, in line with recorded sea level rise. However, any contributing effects of the operation of the Tweed River Sand Bypass, a sand pump assisting longshore drift, cannot be distinguished from sea-level rise related increases in sedimentation. Down-core variation in carbon stable isotope (δ13C) values were used to infer shifts between C3 (mangrove) and C4 (saltmarsh – Sporobolus virginicus) communities. Sediments in mangrove communities indicated the continuous influence of mangroves on soil carbon, while saltmarsh sediments displayed a marked shift between C3 and C4 signatures, suggesting the former presence of either mangroves or C3 saltmarsh species. Mixed cores exhibited an intermediate δ13C signature, indicating the joint contribution of mangrove and saltmarsh to soil organic carbon. Variations in δ13C correspond to time series analysis of vegetation distribution by Saintilan (1998) which indicated relatively recent mangrove expansion. Observed changes in soil organic carbon content primarily related to decomposition over time. However, total carbon store values did not differ significantly between vegetation types, which may be related to differing accumulation time periods, or the methodological approach adopted. Additionally, new vegetation community mapping indicated that previously observed patterns of mudflat formation and landwards mangrove transgression are continuing, suggesting that while carbon sequestration levels may increase with projected sea-level rise, the long-term resilience and provision of ecosystem services by saltmarsh may be limited.
A Robust Self-Constructing Normalized Gaussian Network for Online Machine Learning

Graduate School of Information Science and Technology Division of Computer Science and Information Technology Doctor of Philosophy A Robust Self-Constructing Normalized Gaussian Network for Online Machine Learning by Jana Cathrin BACKHUS In this thesis, I aim to improve the robustness and applicability of Normalized Gaussian networks (NGnet) in the context of online machine learning tasks. A challenging problem in online machine learning is the limited domain knowledge provoked by restricted prior knowledge, while additional information are received only sequentially over time. The limited domain knowledge makes the application of artificial neural networks (ANN) more difficult, and major challenges include negative interference and the selection of an accurate model complexity. In this thesis, I consider these challenges in regard to the NGnet, which belongs to a group of ANNs that possess a certain grade of robustness against negative interference due to the local properties of their network architecture. Yet, further improvements of robustness are necessary in regard to the ANN’s learning algorithm and model complexity selection. A recently proposed learning algorithm with localized forgetting provides robustness against negative interference, but it is not applicable over the full numerical range of an implied discount factor. Also, dynamic model selection was yet to be considered. Therefore, I revise the localized forgetting approach and adapt dynamic model selection to it in a self-constructing manner. Dynamic model selection has been considered for an earlier learning algorithm of the NGnet with global forgetting, which however shows a non-robust behavior in negative interference prone environments. Then, I propose localization of some of the model selection mechanisms to improve their robustness and add a new merge manipulation to deal with model redundancies. The effectiveness of the proposed method is compared with earlier learning approaches of the NGnet for several experiments. The proposed method possesses robust and favorable performance in the different tested learning environments, making it the better alternative when applied to online learning tasks with proneness to negative interference.
Network-aware Web Browsing on Heterogeneous Mobile Systems

Web browsing is an important application domain. However, it imposes a significant burden on battery-powered mobile devices. While heterogeneous multi-cores offer the potential for energy-efficient mobile web browsing, existing web browsers fail to exploit the heterogeneous hardware because they are not tuned for typical networking environments and web workloads, and there are few existing efforts that try to lower the energy consumption of web browsing for heterogeneous mobile platforms. Our work introduces a better way to optimize web browsing on heterogeneous mobile devices. We achieve this by developing a machine learning based approach to predict which of the CPU cores to use and the operating frequencies of CPU and GPU. We do so by first learning, offline , a set of predictive models for a range of networking environments. We then choose a learnt model at runtime to predict the optimal processor configuration. The prediction is based on the web content, the network status and the optimization goal. We evaluate our approach by applying it to the open-source Chromium browser and testing it on two representative heterogeneous mobile multi-cores platforms. We apply our approach to the top 1000 popular websites across seven typical networking environments. Our approach achieves over 80% of best available performance. We obtain, on average, over 17% (up to 63%), 31% (up to 88%), and 30% (up to 91%) improvement respectively for load time, energy consumption and the energy delay product, when compared to two state-of-the-art mobile web browsing schedulers.
The University of Illinois' Graduate School of Library and Information Science at TREC 2013

The University of Illinois’ Graduate School of Library and Information Science (uiucGSLIS) participated in TREC’s knowledge base acceleration (KBA) track in 2013. Specifically, we submitted runs for the cumulative citation recommendation (CCR) task. CCR is a type of document filtering. The use-case is that our user U is an editor of a node T in a knowledge base such as Wikipedia (for example, the entry for blogger Jamie Parsley (http://en.wikipedia.org/wiki/Jamie Parsley). Given an incoming stream of documents D,the system must emit a binary route/not-route decision for each document Di in real time. In this case, the binary decision signals whether we should recommend Di to U as a source of information for editing the entity T ’s page. In other words, our goal is to monitor D, signaling to U when we find a document that contains “edit-worthy” information regarding the entity T .
PERFORMANCE EVALUATION OF UMTS UNDER OSPF, RIP AND IGRP

The concept of communication and information exchange has been revolutionized after the growth of computers. Development in computers’ world, especially the Internet has made network performance a burning issue. Now we are in the scenario in which everyone is leading to the best performance. Now the things get changed when the UMTS (Universal Mobile Telecommunication System) comes into existence. In this chapter we examine the performance of UMTS under the RIP (Routing Information Protocol), OSPF(Open Shortest Path First) and IGRP(Interior Gateway Routing Protocol). The paper demonstrates the efficiency and effectiveness of UMTS network under these protocol and we will be able to find out which one among them is best suited and provide better quality. Keywords— UMTS, RIP, IGRP, OSPF, Protocol, Performance, OPNET
Hidden in Plain Sight Classifying Emails Using Embedded Image Contents

A vast majority of the emails received by people today are machine-generated by businesses communicating with consumers. While some emails originate as a result of a transaction (e.g., hotel or restaurant reservation confirmations, online purchase receipts, ship-ping notifications, etc.), a large fraction are commercial emails promoting an offer (a special sale, free shipping, available for a limited time, etc.). The sheer number of these promotional emails makes it difficult for users to read all these emails and decide which ones are actually interesting and actionable. In this paper, we tackle the problem of extracting information from commercial emails promoting an offer to the user. This information enables an email platform to build several new experiences that can unlock the value in these emails without the user having to navigate and read all of them. For instance, we can highlight offers that are expiring soon, or display a notification when there’s an unexpired offer from a merchant if your phone recognizes that you are at that merchant’s store. A key challenge in extracting information from such commercial emails is that they are often image-rich and contain very little text. Training a machine learning (ML) model on a rendered image-rich email and applying it to each incoming email can be prohibitively expensive. In this paper, we describe a cost-effective approach for extracting signals from both the text and image content of commercial emails in the context of Gmail, an email platform that serves over a billion users around the world. The key insight is to leverage the template structure of emails, and use off-the-shelf OCR techniques to obtain the text from images to augment the existing text features offline . Compared to a text-only approach, we show that we are able to identify 9.12% more email templates corresponding to ~5% more emails being identified as offers. Interestingly, our analysis shows that this 5% improvement in coverage is across the board, irrespective of whether the emails were sent by large mer-chants or small local merchants, allowing us to deliver an improved experience for everyone.
TRUMMAR - A Trust Model for Mobile Agent Systems Based on Reputation

In this paper we present TRUMMAR, a reputation-based trust model that mobile agent systems can use to protect agents from malicious hosts. TRUMMAR is unique in being a truly comprehensive model since it accounts, in a unified framework, for a multitude of concepts such as prior-derived reputation, first impression, loss of reputation information with time, hierarchy of host systems (neighbors, friends, and strangers), and the inclusion of interaction results in reputation calculation. TRUMMAR is also general enough to be applied to any distributed system. We show simulation results that verify the correctness of this model and the effects of its various parameters.
Sea Surface Temperature Prediction Approach Based on 3D CNN and LSTM with Attention Mechanism

Sea Surface Temperature (SST) is an important physical quantity of the ocean system. The accurate prediction of SST is essential for studying physical ocean phenomena and forecasting the ocean environment information. In this paper, a SST prediction approach based on 3-Diminsional Convolutional Neural Network (3D CNN) and Long Short-Term Memory (LSTM) network with attention mechanism is proposed, which considers the spatial correlation and temporal dependency of SST data. Firstly, the machine learning algorithm XGBoost is used to extract the long period time features of each SST data. Then the 3D CNN is used to capture the spatial correlations among SST field data composed of multiple observation points in a selected sea area, followed by the LSTM model to extract the time dependency features of the SST field time series data, and the attention mechanism is added to weight the output of each step of LSTM model to adjust the prediction results and improve the prediction accuracy of the approach. A series of experimental results show that the proposed approach has lower complexity, higher training efficiency and prediction accuracy, which is significantly better than the existing prediction models.
Genetic Algorithm Parameter Optimization using Taguchi Robust Design for Multi-response Optimization of Experimental and Historical Data

This paper presents a methodology for robust optimization of Genetic Algorithm (GA) involving complex interactions among the control parameters. Finding the Optimum GA parameters to solve an optimization problem for producing best results with least variability is still an open area of research. The proposed research approach primarily covers the robust optimization of Genetic Algorithm control parameters using Taguchi Design of Experiment (DOE) with a special set of L25 orthogonal array (OA). The experimental design and the study is conducted with MATLAB Genetic Algorithm internal control parameters using real-coded Genetic Algorithm fitness functions operates directly on real values of two different case studies. One of them is based on experimental data for the development of a new product and the other one is based on the historical data of an existing product under large scale complex manufacturing system. The performance characteristics (Best fitness value) of GA are evaluated. The main effect for signal-tonoise ratios (SNR), main effect for means, response tables and analysis of variance (ANOVA) are employed and optimum parameter settings are obtained. The confirmation experiments are carried out with the optimum setting parameters and the results are compared with the predicted & default setting values of GA. The experimental results show an increase of signal-to-noise ratio (SNR) by around 54 % and mean by 12 to 25 % from the default setting of GA to the optimum settings arrived during the experimental process, which are extremely significant. Further, cross over was found to be the most influential parameter for both the case studies, followed by other parameters like population size, selection process for case study-1 and mutation rate, population size for case study-2. It is evident that the GA performances are also sensitive to the objective function (transfer function in GA terms). The study clearly shows the robust optimization of GA parameters leading to best level of performance characteristics with least variability for the solution of real life optimization problem, using Taguchi Experimental Design Techniques.
A virtual reality game for cognitive impairment screening in elderly: a user perspective

—Today, there are 50 million people suffering from dementia worldwide, that is a new case every 3 seconds and more than 152 million cases expected in 2050. Aging related morbidity is a real social problem making screening a major challenge. Early diagnosis and management would improve the quality of life offered to the patient and those around him and reduce the economic and social consequences of dementia. The traditional paper-and-pencil approach does not sufficiently reflect the daily reality of the person and what they are able to accomplish. The lack of ecological validity is one of the main criticisms of this approach.
THE AT&T GERMAN TEXT-T REALISTIC LINGUISTIC

Like many current TTS systems the AT&T German text-tospeech system is based on the methods of unit selection and concatenative synthesis [1]. This paper highlights efforts to improve TTS quality by closely matching the speakers' original productions with linguistic descriptions. On the segmental level this is achieved by adjusting the speakers' individual productions to an established, general norm via strict monitoring and correspondingly by having the linguistic representations that control automatic alignment and TTS output, i.e. the recognition dictionary and letter-to-sound rules, reflect those original productions. The chosen standard represents a realistic form of spoken German, avoiding overly formal pronunciations. A perceptual comparison with a more traditional interpretation of German pronunciation demonstrates the positive effect of these measures on overall synthesis quality.
X-Spread-A software modeling approach of schema evolution propagation to XML documents

This paper presents X-Spread, a software modeling approach to propagation of XML schemata evolution to XML documents referring the modified schemata. This mechanism focuses on modifications of XML schemata on different distribution scenarios considering the possible distribution scenarios of schemata and documents as well, allowing for software engineers to focus on application design and not in the issue of XML documents adaptation. This mechanism is composed by change detection algorithms, responsible by the detection of differences between schemata, by storage of the different schema versions, by a revalidation process of existing documents, considering only modifications performed on parts of the schemata and by an adaptation process of documents considered invalid due to schema modifications, with the mechanism execution as a whole demanding no participation of the end user. This mechanism addresses semistructured artifacts distribution scenarios usually not addressed by papers about propagation of XML schema modifications to
Genome-wide synteny through highly sensitive sequence alignment: Satsuma

MOTIVATION Comparative genomics heavily relies on alignments of large and often complex DNA sequences. From an engineering perspective, the problem here is to provide maximum sensitivity (to find all there is to find), specificity (to only find real homology) and speed (to accommodate the billions of base pairs of vertebrate genomes). RESULTS Satsuma addresses all three issues through novel strategies: (i) cross-correlation, implemented via fast Fourier transform; (ii) a match scoring scheme that eliminates almost all false hits; and (iii) an asynchronous 'battleship'-like search that allows for aligning two entire fish genomes (470 and 217 Mb) in 120 CPU hours using 15 processors on a single machine. AVAILABILITY Satsuma is part of the Spines software package, implemented in C++ on Linux. The latest version of Spines can be freely downloaded under the LGPL license from http://www.broadinstitute. org/science/programs/genome-biology/spines/.
Retrieval of Spatial Join Pattern Instances from Sensor Networks ∗ †

We study the continuous evaluation of spatial join queries and extensions thereof, defined by interesting combinations of sensor readings (events) that co-occur in a spatial neighborhood. An example of such a pattern is " a high temperature reading in the vicinity of at least four high-pressure readings ". We devise protocols for 'in-network' evaluation of this class of queries, aiming at the minimization of power consumption. In addition, we develop cost models that suggest the appropriateness of each protocol, based on various factors, including selectivity of query elements, energy requirements for sensing, and network topology. Finally, we experimentally compare the effectiveness of the proposed solutions on an experimental platform that emulates real sensor networks.
Detection and Prevention of Black Hole Attack in MANET

The traditional AODV protocol is vulnerable to the well-known black hole attack. In case of multipath AODV, when one path is affected by blackhole attack, then source node can choose another alternative path. In this paper we propose a new type blackhole attack mainly targeting multipath AODV. To the best of our knowledge there is no blackhole attack which specifically targets multipath AODV. Broadly speaking, in the proposed blackhole attack, the attacker looks for nodes through which many alternative paths of multipath AODV passes through. Such nodes are selected and blackhole attack is launched targeting them, thereby damaging more number of paths in a single attempt.Further, the attacker selects only those genuine nodes through more than a threshold number of alternative paths pass through, thereby facilitating the attacker to use less number of nodes. So the attack scheme is power aware. Finally we also propose an IDS to detect the proposed energy aware blackhole attack. NS2 experimental results show the validity of the proposed attack.
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MicroReview Integration of omics data: how well does it work for bacteria?

2004). However, rather than ascribing the dynamic properties of the whole network to individual motifs, Dobrin et al . (2004) suggest a more complex model: single motifs would almost never occur in isolation but aggregate into homologous motif clusters that largely overlap with biological functions. In turn, these motif clusters coalesce into superclusters that deﬁne the global statistical properties of the whole network. Ma et al . (2004), who extended the transcriptional regulatory network of E. coli generated by Shen-Orr et al . (2002), found, however, that most of the motifs are connected to form a giant motif cluster instead of forming several small disconnected clusters. By relating the network topology with microarray data obtained in several conditions, Balazsi et al . (2005) were able to propose a model for transcriptional signal transduction in bacteria. Complex environmental signals would be decomposed by the cell into elementary perturbations processed by individual origons. These represent regulatory subnetworks that originate at distinct classes of sensor transcription factors, i.e. an environmental signal-affected transcriptional subnetwork consisting of a set of operons regulated directly or indirectly by a single transcription factor that is not regulated transcriptionally by any other transcription factor. The ﬁnal response develops by reassembling the elementary perturbations near the output of the network. These regulatory networks and their topological properties have been the subject of different evolutionary studies. Babu et al . (2006), for instance, analysed conservation patterns of the E. coli transcriptional network (Shen-Orr et al ., 2002) across 175 prokaryotic genomes. Natural selection appears to modify individual interactions to arrive at an optimal design for a given organism, rather than preserving whole blocks of pre-existing transcriptional interactions (Babu et al ., 2006). Orthologous genes can become part of a different motif type in the regulatory network when a speciﬁc transcription factor in the species of origin is lost or gained, often in adaptation to a new environment. This might explain why distantly related organisms, but with similar lifestyle, tend to conserve network motifs.