Miracle-dz/UniFAR-FTUs huggingface dataset:
Dealing with missing sensor values in predicting shellfish farm closure

Shellfish farms need to be closed from harvesting when the water body is contaminated to avoid a serious health hazard. We have designed a sensor network framework to monitor a number of water quality variables to check the health of shellfish farms and predict closure if hazardous. Because of the uncertainty associated with the data acquisition process, a full set of sensor values are not always available for decision making purposes. The prediction system thus needs to deal with missing values. Statistical approaches are commonly used to generate an artificial value to approximate a missing sensor reading and predictions are made on the then complete set of sensor values. In this paper we present a new method that is capable of making predictions without making artificial approximations of missing values. The idea is to train a set of classifiers on different subsets of sensor values. Given a set of available sensor values, a prediction is made by the classifier trained on the corresponding set of sensor values. We have evaluated the system on the data obtained from a number of shellfish farms in Tasmania. Experimental results demonstrate that the proposed method to deal with missing values can predict closures with high accuracy.
Summarizing popular music via structural similarity analysis

We present a framework for summarizing digital media based on structural analysis. Though these methods are applicable to general media, we concentrate here on characterizing the repetitive structure in popular music. In the first step, a similarity matrix is calculated from interframe spectral similarity. Segment boundaries, such as verse-chorus transitions, are found by correlating a kernel along the diagonal of the matrix. Once segmented, spectral statistics of each segment are computed. In the second step, segments are clustered, based on the pairwise similarity of their statistics, using a matrix decomposition. Finally, the audio is summarized by combining segments representing the clusters most frequently repeated throughout the piece. We present results on a small corpus showing more than 90% correct detection of verse and chorus segments.
Phase transitions of extremal cuts for the configuration model

The $k$-section width and the Max-Cut for the configuration model are shown to exhibit phase transitions according to the values of certain parameters of the asymptotic degree distribution. These transitions mirror those observed on Erdős-Renyi random graphs, established by Luczak and McDiarmid (2001), and Coppersmith et al. (2004), respectively.
An Efficient Pedestrian Detection Approach Using a Novel Split Function of Hough Forests

In pedestrian detection applications, one of the most popular frameworks that has received extensive attention in recent years is widely known as a ‘Hough forest’ (HF). To improve the accuracy of detection, this paper proposes a novel split function to exploit the statistical information of the training set stored in each node during the construction of the forest. The proposed split function makes the trees in the forest more robust to noise and illumination changes. Moreover, the errors of each stage in the training forest are minimized using a global loss function to support trees to track harder training samples. After having the forest trained, the standard HF detector follows up to search for and localize instances in the image. Experimental results showed that the detection performance of the proposed framework was improved significantly with respect to the standard HF and alternating decision forest (ADF) in some public datasets.
Gesture-based teleoperation using a holonomic robot

This work presents the development and integration of an user interface (UI) framework based on various current input devices that take advantage of our ergonomics. The purpose is to teleoperate a holonomic robot using upper member gestures and postures for studying the suitable of such interfaces when programming and interacting with a mobile robot. As performance vary from UI to UI the framework is focused to be used as a complementary industrial or didactic tool thus, changing how inexperience users tackle their first impressions when working with mobile robots while performing simple gesture-based teleoperation tasks.
Decayed DivRank for Guided Summarization

Guided summarization is essentially an aspect-based multi-document summarization, where aspects can be taken as specified queries in summarization. We proposed a novel ranking algorithm, Decayed DivRank (DDRank) for guided summarization tasks of TAC2011. DDRank can address relevance, importance, diversity, and novelty simultaneously through a decayed vertex-reinforced random walk process in sentence ranking. With sentence ranked by DDRank, top-ranked sentences are supposed to be able to present aspect-related, important, diverse, and novel information contained in documents. Hence, we can select the top-ranked sentences as summary candidates directly. Aspects are approximately obtained by pLSA, and are adopted to generate sentence prior for DDRank in our approach.
Exploring the Effects of Genetic Variants on Clinical Profiles of Parkinson’s Disease Assessed by the Unified Parkinson’s Disease Rating Scale and the Hoehn–Yahr Stage

Many genetic variants have been linked to familial or sporadic Parkinson’s disease (PD), among which those identified in PARK16, BST1, SNCA, LRRK2, GBA and MAPT genes have been demonstrated to be the most common risk factors worldwide. Moreover, complex gene-gene and gene-environment interactions have been highlighted in PD pathogenesis. Compared to studies focusing on the predisposing effects of genes, there is a relative lack of research investigating how these genes and their interactions influence the clinical profiles of PD. In a cohort consisting of 2,011 Chinese Han PD patients, we selected 9 representative variants from the 6 above-mentioned common PD genes to analyze their main and epistatic effects on the Unified Parkinson’s Disease Rating Scale (UPDRS) and the Hoehn and Yahr (H-Y) stage of PD. With multiple linear regression models adjusting for medication status, disease duration, gender and age at onset, none of the variants displayed significant main effects on UPDRS or the H-Y scores. However, for gene-gene interaction analyses, 7 out of 37 pairs of variants showed significant or marginally significant associations with these scores. Among these, the GBA rs421016 (L444P)×LRRK2 rs33949390 (R1628P) interaction was consistently significant in relation to UPDRS III and UPDRS total (I+II+III), even after controlling for the family-wise error rate using False Discovery Rate (FDR-corrected p values are 0.0481 and 0.0070, respectively). Although the effects of the remaining pairs of variants did not survive the FDR correction, they showed marginally significant associations with either UPDRS or the H-Y stage (raw p<0.05). Our results highlight the importance of epistatic effects of multiple genes on the determination of PD clinical profiles and may have implications for molecular classification and personalized intervention of the disease.
Modeling and performance analysis of neural network based fuel cell driven electric traction system

The paper is focused on design, modeling and simulation of the Neural Network (NN) based Proton Exchange Membrane (PEM) fuel cell driven electric vehicle system. The Multi Layer Perceptron (MLP) NN model is used to predict the output voltage of the PEM fuel cell for a predefined vehicle driving cycle pattern. The optimality of neural network is investigated with the four different training algorithms along with the varying hidden neurons. The performance comparison of the neural network is carried out to select the optimum network model in terms of error minimization values and convergence rate. The optimum network which is chosen is used to develop a neural network based PEM fuel cell driven electric vehicle that incorporates the modeling of neural network based fuel cell, DC-DC converter and vehicle dynamics. The modeling and simulation work is carried out using MATLAB/Simulink in order to verify the reliability of the electric vehicle performance by applying three different modified standard driving cycle (M-NEDC/M-UDDS/M-US06) patterns. The simulation results obtained from the developed electric vehicle model are used to evaluate the vehicle performance in terms of amount of fuel consumption, maximum distance coverage and power flow within the vehicle system.
Testing the mapping between redshift and cosmic scale factor

The canonical redshift−scale factor relation, 1/a = 1 + z, is a key element in the standardCDM model of the big bang cosmology. Despite its fundamental role, this relation has not yet undergone any observational tests since Lemaˆitre and Hubble established the expansion of the Universe. It is strictly based on the assumption of the Friedmann-Lemaˆitre-Robertson-Walker metric describing a locally homogeneous and isotropic universe and that photons move on null geodesics of the metric. Thus any violation of this assumption, within general relativity or modified gravity, can yield a different mapping between the model redshift z = 1/a − 1 and the actually observed redshift zobs, i.e. zobs 6 z. Here we perform a simple test of consistency for the standard redshift−scale factor relation by determining simultaneous observational constraints on the concordanceCDM cosmological parameters and a generalized redshift mapping z = f(zobs). Using current baryon acoustic oscillations (BAO) and Type Ia supernova (SN) data we demonstrate that the generalized redshift mapping is strongly degenerated with dark energy. Marginalization over a class of monotonic functions f(zobs) changes substantially degeneracy between matter and dark energy density: the density parameters become anticorrelated with nearly vertical axis of degeneracy. Furthermore, we show that current SN and BAO data, analysed in a framework with the generalized redshift mapping, do not constrain dark energy unless the BAO data include the measurements from the Lyα forest of high-redshift quasars.
A State‐Space Approach for Deriving Bridge Displacement from Acceleration

Abstract: The dynamic response (i.e., acceleration and displacement) of a bridge under vehicular load is an important component of design and evaluation. Field measurement of girder displacement, however, is generally nontrivial. Traditional sensors often require a stationary reference such as temporary scaffolds or a suspended cable. In either form, there are added costs, restrictions, and labor. As a result, there are both economic and practical incentives for developing methods that can use an accelerometer to measure both acceleration and displacement. One difficulty of this, however, is the presence of small low‐frequency errors in the measured signal, which become sufficiently large through successive integrations and lead to a significantly distorted displacement profile. The objective of this article is to examine an analytical model based on the state‐space approach for minimizing such errors and to compare results with two time domain correction methods. Field measurements from a three‐span continuous bridge are used to assess the accuracy of each routine.
Quasi-crowdsourcing testing for educational projects

The idea of crowdsourcing tasks in software engineering, especially software testing, has gained popularity in recent years. Crowdsourcing testing and educational projects are natural complementary. One of the challenges of crowdsourcing testing is to find a number of qualified workers with low cost. Students in software engineering are suitable candidates for crowdsourcing testing. On the other hand, practical projects play a key role in software engineering education. In order to enhance educational project outcomes and achieve industrial-strength training, we need to provide the opportunity for students to be exposed to commercial software development. In this paper, we report a preliminary study on crowdsourcing testing for educational projects. We introduce three commercial software products as educational testing projects, which are crowdsourced by our teaching support system. We call this "Quasi-Crowdsourcing Test" (QCT) because the candidate workers are students, who have certain social relations. The investigation results are encouraging and show to be beneficial to both the students and industry in QCT projects.
Modeling for Investment Guidance as a Performance Pre-assessment Method of Enterprise Integration

Current enterprise performance research focuses on the post-assessment and is always lack of approaches about the necessary pre-assessment. In this paper a modeling method for investment guidance of enterprise integration is proposed, which is a significant pre-assessment method. Because of the numerous dynamic and subjective indicators of performance, modeling method presented in this paper is mainly based on the fuzzy rule for its natural advantages in this area. Firstly, considering the research requirements discussed in the literature review, the performance modeling framework for investment guidance is put forward grounded on the process model. Secondly, the original motivation to employ the fuzzy logic method is discussed and a theorem with logical criteria is suggested for conflict and redundancy validation of fuzzy rules which is necessary for performance modeling. Finally, the model for investment guidance is constructed and then a non-linear programming model is formulated to optimize the investment of enterprise integration.
Facial Expression Recognition for Security

: Facial expressions play an important role in interpersonal relations as well as for security purposes. The malicious intensions of a thief can be recognized with the help of his gestures, facial expressions being its major part. This is because humans demonstrate and convey a lot of evident information visually rather than verbally. Although humans recognize facial expressions virtually without effort or delay, reliable expression recognition by machine remains a challenge. A picture portrays much more than its equivalent textual description. Along this theory, we assert that although verbal and gestural methods convey valuable information, facial expressions are unparalleled in this regard. In sustenance to this idea, a facial expression is considered to consist of deformations of facial components and their spatial relations, along with changes in the pigmentation of the same. This paper envisages the detection of faces, localization of features thus leading to emotion recognition in images.
Harnessing Context to Support Proactive Behaviours

In order to realise an intimate and proactive personal assistant under the Weiser’s vision for ubiquitous computing environments, we propose the utilisation of context history together with user modelling and machine learning techniques. Our approach could support dynamic adaptations to changes of the user’s life style or changes in the situation itself by detecting patterns in a user’s behaviour. In addition, we propose the requirement of an explicit explanation about such dynamic adaptations to the user in order to encourage a trust relationship between the user and the context-aware system. In this paper, we describe appropriate scenarios that reveal the potential of our approach. In order to examine how dynamic adaptations can be implemented, three types of learning design are proposed and the implications of using these designs are discussed.
GOAFR plus-ABC: Geographic routing based on Adaptive Boundary Circle in MANETs

In mobile ad hoc networks, GOAFR+ (Greedy Other Adaptive Face Routing Plus) has a strong point that guarantees the packet delivery to the destination with low cost by combining greedy forwarding and face routing. In face routing mode, GOAFR+ uses the boundary circle to restrict a searching area. However, since the boundary circle is used without any location information of neighbors, it caused unnecessary expansion of boundary circle frequently. In this paper, we propose an efficient GOAFR+ based on adaptive boundary circle called GOAFR Plus-ABC (Greedy Other Adaptive Face Routing plus Adaptive Boundary Circle) to minimize expansion of boundary circle by using location information of neighbors in face routing mode. Simulation results show that our proposed scheme outperforms GOAFR+ up to 15% in terms of forwarding cost.
The Red Blood Cell and Nitric Oxide: Derived, Stimulated, or Both?

Following a brief introduction to properties of the red blood cell (RBC), this review will provide information pertaining to the role of the RBC as both a deliverer of nitric oxide (NO), and a stimulator of NO release via RBC- generated ATP. In addition, an evaluation of the potential downstream effects of RBC-derived ATP, via its ability to stimulate NO in other cell types, will be provided. Collectively, it is now apparent that the RBC is a determinant in functions other than oxygen delivery alone. The average human red blood cell (RBC) has a volume of approximately 87 femtoliters, is about 6-7 μm in diameter, and spends, on average 110-120 days in the human body before being metabolized by such organs as the spleen. RBCs also represent about 40-45% of the total blood volume in an adult human (5 L); thus, the average adult has about 2 L of RBCs. The RBC also has many unique features in comparison to other cell types in vivo in that it lacks a nucleus and has no mitochondria. However, the RBC is not completely void of cell machinery; indeed, it has a very active cell bilayer, possesses a glucose transporter (GLUT1), has many of the same ion pumps as other cells, and is even thought to participate in the overall control of blood flow.
A Cooperative Approach to Corporate Memory Modeling

Nowadays, the importance of Knowledge Management is growing in organizational contexts. Corporate Memory is an appropriate tool to represent organizational knowledge. This work presents an ontology-based approach to Corporate Memory modeling. In it, the members of an organization act as ‘knowledge builders’ and they construct the Corporate Memory cooperatively. Furthermore, the employees who take part of the Corporate Memory construction process are allowed to use their own terminology, even for requesting information about the Corporate Memory until a specific instant.
Modeling Inflation after the Crisis

In the United States, the rate of price inflation falls in recessions. Turning this observation into a useful inflation forecasting equation is difficult because of multiple sources of time variation in the inflation process, including changes in Fed policy and credibility. We propose a tightly parameterized model in which the deviation of inflation from a stochastic trend (which we interpret as long-term expected inflation) reacts stably to a new gap measure, which we call the unemployment recession gap. The short-term response of inflation to an increase in this gap is stable, but the long-term response depends on the resilience, or anchoring, of trend inflation. Dynamic simulations (given the path of unemployment) match the paths of inflation during post-1960 downturns, including the current one.
Global Exponential Stability Analysis of a Class of Dynamical Neural Networks

The problem of the global exponential stability of a class of Hopfield neural networks is considered. Based on nonnegative matrix theory, a sufficient condition for the existence, uniqueness and global exponential stability of the equilibrium point is presented. And the upper bound for the degree of exponential stability is given. Moreover, a simulation is given to show the effectiveness of the result.
Distributed nonlinear control of multi-agent systems with switching topologies

This paper studies the distributed nonlinear control of multi-agent systems with switching topologies for output agreement. A novel cyclic-small-gain approach is proposed. The crucial idea is to introduce a new dynamic mechanism to process the exchanged information between the agents, and to transform the distributed control problem into a stabilization problem for a dynamic network composed of input-to-output stable (IOS) subsystems. The desired distributed controller is designed based on IOS and cyclic-small-gain techniques. More interestingly, it is shown that the proposed method can be extended to distributed control design in the presence of disturbances.
End-to-End Instance Segmentation and Counting with Recurrent Attention

While convolutional neural networks have gained impressive success recently in solving structured prediction problems such as semantic segmentation, it remains a challenge to differentiate individual object instances in the scene. Instance segmentation is very important in a variety of applications, such as autonomous driving, image captioning, and visual question answering. Techniques that combine large graphical models with low-level vision have been proposed to address this problem; however, we propose an end-to-end recurrent neural network (RNN) architecture with an attention mechanism to model a human-like counting process, and produce detailed instance segmentations. The network is jointly trained to sequentially produce regions of interest as well as a dominant object segmentation within each region. The proposed model achieves state-of-the-art results on the CVPPP leaf segmentation dataset and KITTI vehicle segmentation dataset.
User individuality management in websites based on WAI-ARIA annotations and ontologies

In this paper, we describe a system for adapting websites based on an annotation approach. The system adapts annotated websites according to the user characteristics. The core of the system is composed of a repository containing a comprehensive set of adaptation techniques. 99 techniques have been identified and classified, 48 of them have been fully implemented in the system. This wide range of implemented techniques provides the system with the necessary mechanisms for adapting diverse aspects of website interfaces, such as the content to display, the layout and the structure. The techniques to apply for a given user are inferred according to the information stored in an ontology. This defines the bases for associating user characteristics with the adaptation techniques to apply for specific interaction elements annotated in a website. An annotation language based on the roles and properties of WAI-ARIA has been elaborated. Therefore, any website annotated in this language can be adapted. This adaptation system has been applied to two different websites and adapted interfaces have been obtained for three different user groups.
The Variation of Software Development Productivity 1995-2005

Software development has experienced remarkable progress in the past decade. However, due to the rising complexity and magnitude of the project the development productivity has not been consistently improved. By analyzing the latest ISBSG data repository with 4106 projects, we discovered that software development productivity has actually undergone irregular variations between the years 1995 and 2005. Considering the factors significant to the productivity, we found its variations are primarily caused by the variations of average team size and the unbalanced uses of the less productive language 3GL. Keywords—Productivity, Programming Languages, Software Engineering, Team Size.