Miracle-dz/UniFAR-FTUs huggingface dataset:
Maximum Likelihood Estimation of Gaussian Mixture Models Using PSO for Image Segmentation

Gaussian mixture model-based clustering algorithm is one of the advanced techniques applied to enhance the image segmentation performance. However, segmentation process is still encountering some critical difficulties: the model is quite sensitive to initialization, and easily gets trapped in local maxima. To address these problems in image segmentation, we proposed a novel clustering algorithm employing the arbitrary covariance matrices that uses particle swarm optimization for the estimation of Gaussian Mixture Models. Such model can be able to prevent the effective use of population-base algorithms during clustering, and the arbitrary covariance matrices allow independently updating individual parameters, while retaining the validity of the matrix. Then we present the solution that involves an optimization formulation to identify the correspondence between different parameter orderings of candidate solutions. The experimental results show that our method provides a simple segmentation process and the better quality of segmented images comparing to other methods. Furthermore, our method would provide an advanced technique for multi-dimensional image analysis and computer vision systems that can apply for various science and technology sector.
Licensed under Creative Commons Attribution Cc by Sleep Scheduling for Critical Event Monitoring in Wireless Sensor Networks

Wireless sensor network have used in many applications like forest fire detection, earth monitoring, air monitoring etc. When a critical event happen in the monitoring area it is find out by a sensor node. In this paper, we focus on critical event monitoring in wireless sensor networks (WSNs), where only a small number of packets need to be transmitted most of the time. When a critical event occurs, an alarm message should be broadcast to the entire network as soon as possible. To prolong the network lifetime, some sleep scheduling methods are always employed in WSNs, resulting in significant broadcasting delay, especially in large scale WSNs. In this paper, we propose a novel sleep scheduling method to reduce the delay of alarm broadcasting from any sensor node in WSNs. Specifically, we design two determined traffic paths for the transmission of alarm message, and level-by-level offset based wake-up pattern according to the paths, respectively. When a critical event occurs, an alarm is quickly transmitted along one of the traffic paths to a center node, and then it is immediately broadcast by the center node along another path without collision. Introduction Wireless Sensor Networks (WSN) is a collection of spatially deployed wireless sensors by which to monitor various changes of environmental conditions (e.g. forest fire, air pollutant concentration and object moving) in a collaborative manner without relying on any underlying infrastructure support. Recently, a number of research efforts have been made to develop sensor hardware and network architecture in order to effectively deploy WSNs for a variety of applications. Many network parameters such as sensing range, transmission range and node density have to be carefully considered at the network design stage, according to specific applications. To achieve this, it is critical to capture the impacts of network parameters on network performance with respect to application specifications. Since a distributed network has multiple nodes and services many messages and each node is a shared resource, many decisions must be made. There may be multiple paths from the source to destination. Therefore, message routing is an important topic. The main performance measures affected by the routing scheme are throughput (quality of service) and average packet delay (quantity of service).
Complex-Vector Time-Delay Control of Power Converters

Precise controlling of current produced by power converters is an important topic that has attracted interests over the last few decades. With the recent proliferation of grid-tied converters where the control of power flow is indirectly governed by the accuracy of current tracking, motivation to develop dynamically fast and accurate current controllers is even more intensive with more features expected to be embedded within a single control module. Believing in its continual importance, this paper contributes by proposing a complex-vector time-delay control scheme that can achieve high tracking precision and disturbance rejection. In principle, the proposed scheme can either be implemented solely in the stationary frame or in a ldquomixedrdquo stationary and synchronous frame, termed as mixed frame in the paper. Regardless of the frame orientation chosen, the scheme always exhibits ease of implementation since only a small amount of memory space for storing time-delayed values and simple arithmetic computations are needed for its physical realization. In addition to that, other advantages of the scheme include its abilities to compensate for negative-sequence, load and grid harmonic components using a set of load-matching control characteristics that are less sensitive to external noise interferences. These added features, complementing the basic requirement of fast and accurate fundamental positive-sequence tracking, render the proposed scheme as an attractive alternative for high-end converter control. Lastly, for proving its practicality, experimental testing of the scheme is performed digitally using a commercial grid converter with some informative results captured and compared with those of an existing scheme.
Feeding back learning resources repurposing patterns into the “information loop”: opportunities and challenges

The paper outlines a model for framing the representation and treatment of information gathered from the reuse and repurposing of learning resources from distributed repositories. The model takes into account as sources of information both static user-edited or automatically generated metadata fields and the emerging, dynamic information clouds that surrounds a learning resource when users comment on it, tags it, or explicitly links it to other learning resources. By coordinating these separate information layers, the advantages that can be achieved are reducing the semantic gap occurring when unanticipated contexts of use are to be described by resorting only to predefined vocabularies; and improvements in the relevance of the retrieved resources after a query. To achieve this “coordination” it is proposed that the textual descriptions of the repurposing activity with respect to the intended learning outcomes and pedagogical strategies are fed to a dynamic unsupervised classification method that operates on the above mentioned information spaces, and that supports exploratory search by suggesting associations. It is argued that the proposed analogical retrieval, as opposed to standard query matching, is more fit to tracking the loci of innovation and sustaining the formation of best practices in the community.
On feedback target control for uncertain discrete-time systems through polyhedral techniques

Problems of feedback target control for linear and bilinear dynamical discrete-time systems under uncertainties and state constraints are considered. There are known approaches to solving problems of this kind, including ones for differential systems, based on construction of solvability tubes (Krasovskii’s bridges). Since practical construction of such tubes may be cumbersome, different numerical methods were devised. Among them constructive computation schemes for linear systems based on the ellipsoidal techniques were proposed (see, for example, [1,2]) and then expanded to the polyhedral techniques [3,4]. Such methods are ideologically close to interval analysis. Their main advantage is that they allow to find solutions by rather simple means.
Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter

An analytic solution to the multi-target Bayes recursion known as the δ-Generalized Labeled Multi-Bernoulli ( δ-GLMB) filter has been recently proposed by Vo and Vo in [“Labeled Random Finite Sets and Multi-Object Conjugate Priors,” IEEE Trans. Signal Process. , vol. 61, no. 13, pp. 3460-3475, 2014]. As a sequel to that paper, the present paper details efficient implementations of the δ-GLMB multi-target tracking filter. Each iteration of this filter involves an update operation and a prediction operation, both of which result in weighted sums of multi-target exponentials with intractably large number of terms. To truncate these sums, the ranked assignment and K-th shortest path algorithms are used in the update and prediction, respectively, to determine the most significant terms without exhaustively computing all of the terms. In addition, using tools derived from the same framework, such as probability hypothesis density filtering, we present inexpensive (relative to the δ-GLMB filter) look-ahead strategies to reduce the number of computations. Characterization of the L1-error in the multi-target density arising from the truncation is presented.
A Forecasting Methodology Using Support Vector Regression and Dynamic Feature Selection

Various techniques have been proposed to forecast a given time series. Models from the ARIMA family have been successfully used, as well as regression approaches based on e.g. linear, non-linear regression, neural networks, and Support Vector Regression. What makes the difference in many real-world applications, however, is not the technique but an appropriate forecasting methodology. Here, we propose such a methodology for the regression-based forecasting approach. A hybrid system is presented that iteratively selects the most relevant features and constructs the regression model optimizing its parameters dynamically. We develop a particular technique for feature selection as well as for model construction. The methodology, however, is a generic one providing the opportunity to employ alternative approaches within our framework. The application to several time series underlines its usefulness.
New Approach to Spectral Analysis of High Bit Rate PCM Signals

—Pulse code modulation is a widespread technique in digital communication with significant impact on existing modern and proposed future communication technologies. Its widespread utilization is due to its simplicity and attractive spectral characteristics. In this paper, we present a new approach to the spectral analysis of PCM signals using Riemann-Stieltjes integrals, which is very accurate for high bit rates. This approach can serve as a model for similar spectral analysis of other competing modulation schemes.
Assessing the quality and knowledge contribution of mis quarterly: A citation analysis

This paper employs citation analysis to investigate empirically the influence of MIS Quarterly on both IS and other literature. Specifically, we examine the impact of source article category, method type, and research area on article citation rates. Our results reveal that the citation-based quality indices of MISQ have been improving over the years. In addition, among the six categories of MISQ source articles, the methodological articles on average receive the most citings per article. Moreover, of the source articles employing different research methods, surveys, case studies, and lab experiments are more likely to be cited. Among source articles addressing different research areas, those focusing on IT and individuals receive the most citings per article. Finally, our analysis also shows that MISQ articles are well-cited by researchers from both IS and other disciplines, implying that IS does make knowledge contribution to other disciplines.
From climate change victims to climate change actors: The role of eco-parenting in building mitigation and adaptation capacities in children

Abstract Studies have acknowledged children not as climate change victims only, but also as climate change actors. However, only a few have addressed the preparatory stages of children as climate change actors. However, the few studies that addressed these preparatory stages gave attention only to the roles of schools and government agencies without adequate attention to that of parents. This article therefore attempts to discuss the role of eco-parenting in building mitigation and adaptation capacities in children. Relying on the authoritative parenting model, the article avers that eco-parenting can have significant effects on children's ability to protect the environment, mitigate, and adapt to climate change impacts.
Decomposing Funding Ratio Risk: Providing Pension Funds with Key Insights into Their Liabilities Hedge Mismatch and Other Factor Exposures

In recent years, adverse market conditions have demolished the funding status of many defined benefit (DB) pension plans, highlighting the need for better risk management. In this article, the authors propose a novel framework to decompose the risk of DB pension plans, which differs from earlier work in two fundamental ways. First, while others focused on surplus risk, the authors give sound reasons to focus on funding-ratio risk instead. Second, the authors include a special mismatch factor to measure the sensitivity of the funding ratio to changes in the value of liabilities. They illustrate their framework with a case study based on an actual DB pension fund and decompose its funding-ratio risk into mismatch risk and other factor exposures dealing with real interest rates, inflation, and economic growth risks.
The Design of Terminal Sliding Controller of Two-Link Flexible Manipulators

A terminal sliding mode controller is proposed for a two-link flexible manipulator to address its non-minimum phase characteristics using the output redefinition method. The manipulator system is decomposed into two parts by input-output linearization, namely an input-output subsystem and the zero dynamics respectively. Then a terminal sliding mode control strategy is designed to make the input-output subsystem converge to zeros in finite time. The relationship between the eigenvalues of the zero dynamics and the parameters of the redefined output is obtained. Moreover, the zero dynamics can be asymptotical stable at equilibrium point by choosing suitable controller' parameters. The whole original two-link flexible manipulator control system is guaranteed to be asymptotically stable and the control design procedure is simple and easy to implement. Simulation results are presented to validate the design.
Inoculum Size of Clostridium botulinum 56A Spores Influences Time-to-Detection and Percent Growth-Positive Samples

The influence of inoculum size on the growth kinetics of Clostridium botulinum 56A and percentage of growth-positive samples was studied in a complete factorial design with factors of inoculum size (1, 100, or 10,000 spores), pH, and sodium-chloride concentration. Growth was followed hourly as change in A 620 . Polynomial regres- sion was used to analyze the data. The time-to-detection and percent growth-positive samples were significantly affected by inoculum size and its quadratic term. When inoculum size increased from 1 to 100 spores/sample, the percent growth-positive samples increased, and the time-to-detection decreased. When the inoculum was 1000 spores/sample or higher, there was little additional effect. Inoculum size might influence results through simple probability or quorum sensing. The maximum growth rate was independent of inoculum levels.
Automatic detection of rumoured tweets and finding its origin

The number of people using social networking sites such as Twitter is exponentially increasing every day. These sites not only act as a platform for staying connected with friends and exchanging opinions and ideologies, but also help to share and disseminate information. Important events in the real world cause a huge flood of tweets on Twitter. During many such events, Twitter has been used to spread rumours and cause havoc and panic among the people, thus worsening the situation. In this project, we try to automatically detect the rumours spreading on Twitter and identify its source. Using some of the rumoured tweets posted during the London Riots in 2011 and some non-rumoured tweets, we trained a classifier. Our classifier correctly classifies the tweets with high accuracy. We show that, in rumour detection, the information propagated by the user becomes more important than the identity of the user by showing that tweet-based features play a much higher and significant role than user-based features. We propose an algorithm to find the origin of the rumoured tweets i.e. obtain the account information of the user who first started spreading rumours on Twitter.
A criterion for adaptive autoregressive models

A criterion, similar to the information criterion of a stationary autoregressive (AR) model, is introduced for an adaptive (non-stationary) autoregressive model. It is applied to nonstationary EEG data. It is shown that the criterion can be used to determine the update coefficient, the model order and the estimation algorithm.
Analysis of robot navigation schemes using Rantzer’s Dual Lyapunov Theorem

When robots are driven by the negative gradient of a potential field that consists of the sum of an attractive and a repulsive term, convergence to the desired configuration cannot be guaranteed by traditional Lyapunov techniques. In this paper, sufficient conditions for convergence of such systems are provided instead with the use of Rantzer's dual Lyapunov theorem. In particular, a condition that involves the trace of the Hessian matrix of the potential function is derived and then applied to the cases of navigation of a single robot and of multi-robot formation stabilization. The main result of the paper states that a sufficient condition for convergence to a desired configuration in both cases is that the attractive potential admits a sufficiently large gain. A lower bound on the attractive potential is computed. Computer simulations that support the new results are provided.
Application of Fitts' law to eye gaze interaction

An experiment is described comparing the performance of an eye tracker and a mouse in a simple pointing task. Subjects had to make rapid and accurate horizontal movements to targets that were vertical ribbons located at various distances from the cursor's starting position. The dwell-time protocol was used for the eye tracker to make selections. Movement times were shorter for the mouse than for the eye tracker. Fitts' Law model was shown to predict movement times using both interaction techniques equally well. The model is thus seen to be a potential contributor to design of modern multimodal human-computer interfaces.
Sensitization by 5-Azacytidine toward Death Receptor-Induced Hepatic Apoptosis

5-Azacytidine (5-aza-CR) is a DNA-hypomethylating antineoplastic agent used because of its inhibitory activity on DNA methyltransferases. Today, it is approved as an epigenetically active drug therapy for treatment of myelodysplastic disorders, with a contraindication as to pre-existing liver diseases. Because the mechanism of its hepatotoxicity is still unknown, we investigated the pharmacodynamic properties of 5-aza-CR with regard to death receptor/ligand-induced apoptosis and the mode of execution of cell death. In a time- and concentration-dependent manner, primary murine, human hepatocytes and HepG2 cells exposed to 5-aza-CR became highly sensitive toward cell death induced by CD95L, tumor necrosis factor (TNF)-related apoptosis-inducing ligand, or TNF. Cell death was characterized as apoptotic by membrane blebbing, chromatin condensation, and exposure of phosphatidylserine on the outer membrane. Neither 5-aza-2′-deoxycytidine nor the common DNA methyltransferase inhibitors S-(5′-adenosyl)-l-homocysteine or RG 108 showed any significant effects under these conditions. Despite the complete protection of HepG2 by high concentrations of the pan-caspase inhibitor N-benzyloxycarbonyl-Val-Ala-Asp(O-Me) fluoromethyl ketone (z-VAD-fmk), effector caspase-3/7 activity was completely abolished at approximately a 20-fold lower concentration of z-VAD-fmk. Under these conditions, the serine protease inhibitors N,α-tosyl-l-phenylalanine chloromethyl ketone, N,p-tosyl-l-lysine chloromethyl ketone, and 4-(2-aminoethyl)-benzenesulfonyl fluoride, respectively, conferred protection against death receptor ligands. We conclude that this caspase-independent apoptosis is executed by a yet-unidentified serine protease.
SLO-Aware Deployment of Web Applications Requiring Strong Consistency Using Multiple Clouds

Geographically dispersed cloud data centers (DCs) enable web application providers to improve their services' response time and availability by deploying application replicas in multiple DCs. To allow applications requiring strong consistency to be deployed in multiple clouds, industry and academia have developed various scalable database systems that can guarantee strong inter-DC consistency with alleviated network overhead. For applications using these database systems, it is essential to take both the network latencies to the end users and the communication overhead of the databases into account when selecting the hosting DCs. In this paper, we study how to identify the satisfactory deployment plan (hosting DCs and request routing) considering SLO satisfaction, migration cost, and operational cost for applications using these databases. The proposed approach involves two steps. First, it searches the deployment plan with minimum amount of SLO violations using genetic algorithm when the application is first migrated to the clouds. Then it continuously optimizes the deployment in a certain time interval according to the changing workload and the current deployment plan. We illustrate how our approach works for the applications using two databases (Cassandra and Galera Cluster), and demonstrate the effectiveness of our approach through simulation studies using settings of two example applications (TPC-W and Twissandra). Our solution is extensible to applications using other database systems that have similar properties.
Autophagy Signaling in Skeletal Muscle of Infarcted Rats

Background Heart failure (HF)-induced skeletal muscle atrophy is often associated to exercise intolerance and poor prognosis. Better understanding of the molecular mechanisms underlying HF-induced muscle atrophy may contribute to the development of pharmacological strategies to prevent or treat such condition. It has been shown that autophagy-lysosome system is an important mechanism for maintenance of muscle mass. However, its role in HF-induced myopathy has not been addressed yet. Therefore, the aim of the present study was to evaluate autophagy signaling in myocardial infarction (MI)-induced muscle atrophy in rats. Methods/Principal Findings Wistar rats underwent MI or Sham surgeries, and after 12 weeks were submitted to echocardiography, exercise tolerance and histology evaluations. Cathepsin L activity and expression of autophagy-related genes and proteins were assessed in soleus and plantaris muscles by fluorimetric assay, qRT-PCR and immunoblotting, respectively. MI rats displayed exercise intolerance, left ventricular dysfunction and dilation, thereby suggesting the presence of HF. The key findings of the present study were: a) upregulation of autophagy-related genes (GABARAPL1, ATG7, BNIP3, CTSL1 and LAMP2) was observed only in plantaris while muscle atrophy was observed in both soleus and plantaris muscles, and b) Cathepsin L activity, Bnip3 and Fis1 protein levels, and levels of lipid hydroperoxides were increased specifically in plantaris muscle of MI rats. Conclusions Altogether our results provide evidence for autophagy signaling regulation in HF-induced plantaris atrophy but not soleus atrophy. Therefore, autophagy-lysosome system is differentially regulated in atrophic muscles comprising different fiber-types and metabolic characteristics.