Miracle-dz/UniFAR-FTUs huggingface dataset:
Lifetime Enhancement in WSNs using Balanced Sensor Allocation to Cluster Heads

A wireless sensor network (WSN) consists of a large number of small sensors with limited energy. Prolonged network lifetime, scalability, and load balancing are important requirements for many WSN applications. Clustering sensor nodes is an effective technique for achieving these goals. In this work, we propose a new energy-efficient approach for clustering nodes in WSNs. Based on this approach, we propose a two phase algorithm; in its first phase, cluster heads are selected according to the Sebestyen algorithm. In the second phase, nodes are allocated to the cluster heads so balanced from number of membership view. Two criteria are considered in this allocation. The first criterion is the Euclidean distance between nodes and cluster heads, and the second one is the node degree. The simulation results demonstrate that our proposed algorithm enhances the WSN lifetime significantly in comparison to the weight-based clustering protocols and original Sebestyen algorithm.
Classifying Affective Haptic Stimuli through Gender-Specific Heart Rate Variability Nonlinear Analysis

This study reports on how velocity and force levels of caress-like haptic stimuli can elicit different emotional responses, which can be identified through the analysis of Autonomic Nervous System (ANS) dynamics. Affective stimuli were administered on the forearm of 32 healthy volunteers (16 women) through a haptic device with two levels of force, 2 N and 6 N, and two levels of velocity, 9.4 mm/s and 37 mm/s. ANS dynamics was estimated through Heart Rate Variability (HRV) linear and nonlinear analysis on recordings gathered before and after each stimulus. To this extent, we here propose and assess novel features from HRV symbolic analysis and Lagged Poincaré Plot. Classification was performed following a leave-one-subject-out procedure on nonlinear support vector machines. Pattern classification was split according to gender, significantly improving accuracies of recognition with respect to a “all-subjects” classification. Caressing force and velocity levels were recognized with up to 80 percent accuracy for men, and up to 84.38 percent for women. Our results demonstrate that changes in ANS control on cardiovascular dynamics, following emotional changes induced by caress-like haptic stimuli, can be effectively recognized by the proposed computational approach, considering that they occur in a gender-specific and nonlinear manner.
Students’ Acceptance of Online Courses: A test of UTAUT with Eco friendliness in the context of Sri Lankan Higher Education

The study systematically assessed the validity of unified theory of acceptance and use of technology (UTAUT with independent construct Eco-friendliness in a voluntary environment with respect to Sri Lankan higher education. State university students, who are partaking online learning experience, have considered, as the target population of the research and the model tested with a sample of 348 respondents. A questionnaire with 5-point likert scale has used to obtain data. The regression analysis emphasized the relationships demarcated in the theoretical model of the study. Consequently, the hypotheses substantiated, by highlighting the relationship among antecedents and dependent variable acceptance. The variables, which are greatly influential to augment the level of acceptance of online courses, were filtered, in order to take management decisions.
DOMAIN DECOMPOSITION METHODS WITH GRAPH CUTS ALGORITHMS FOR IMAGE SEGMENTATION

Recently, it is shown that graph cuts algorithms can be used to solve some variational image restoration problems, especially connected with noise removal and segmentation. For very large size problems, the cost for memory and computation increase dramatically. We propose a domain decomposition method with graph cuts algorithms. We show that the new approach is cost eﬀective both for memory and computation. Experiments with large size 2D and 3D data are supplied to show the eﬃciency of the algorithms.
Synthesizing Expressive Speech to Convey Focus using a Perturbation Model for Computer-Aided Pronunciation Training

We present a perturbation model that can modify the acoustic features of neutral speech in order to synthesize focus for certain words. In doing so, we can generate expressive speech output that highlights important speech segments to attract the listener’s attention. The ultimate objective is to synthesize corrective feedback in a computer-aided pronunciation training (CAPT) system. This work involves the design and collection of a speech corpus, whose text prompts contain focus words. Each prompt is recorded twice – a neutral production followed by an expressive one where specific words are highlighted with focus. The phones in these recordings are modeled in six different classes, based on their relations with stressed syllables in focus words. Phone boundaries are obtained automatically by forced alignment with an automatic speech recognizer. Acoustic features of the phones, relating to f0, energy and duration, are extracted. Features that have highest correlation with the phone classes, as well as low variances, are incorporated into the perturbation model. The model is applied to neutral recordings of 20 test sentences. Results from a listening test show that the 13 subjects can identify the focus words with an accuracy of over 98%. The perceived degree of focus in the identified words achieves a mean score of 4.5 in a five-point Likert scale.
Dynamic contracts when agent's quality is unknown

We solve a long-term contracting problem with symmetric uncertainty about the agent's quality, and a hidden action of the agent. As information about quality accumulates, incentives become easier to provide because the agent has less room to manipulate the principal's beliefs. This result is opposite to that in the literature on "career concerns" in which incentives via short-term contracts become harder to provide as the agent's quality is revealed over time.
A Novel Reduction Method Based On Attributes Similarity In Chinese Traditional Medicine Prescriptions

According to the similarity among data attributes, this paper proposes a reduction method of high dimensional data. Different from the existed algorithms, this method takes attributes as basic vectors in high dimensions space, and data tuples as vector sum of attributes vectors. With the transcendental concept similar information between attributes, the weight computing is defined as formulas of attribute vectors and their projects on each other, and the final result is gotten from three simplifying algorithms which are proposed in this paper. The paper analyzes the new method, and compares reduced results between different algorithms. This method is successfully applied in automatic induction of Chinese traditional medicine prescription. The extension experiments prove the validity of the method.
The application of improved decision tree algorithm in the electric power marketing

Data mining technology has been widely studied and applied since its inception, the researches on C4.5 algorithm in the aspects of data classification technology have comparative matured, but the application of electric power marketing data mining aspects is relatively less. Decision tree classification technology was discussed in this paper, and detailed introduced an improved C4.5 decision tree classification algorithm, and then used the actual electric power marketing data as the basis for data mining. Through analysis of the results of data mining, which show that the decision tree algorithm is efficient and accurate, is the preferred tool of electric power data mining. The decision tree algorithm is used in electric power enterprises, mines out marketing knowledge of behind the hidden electric power data, so as to provide help for electric power enterprises operation and decision-making.
Empirical Bayes Forecasts of One Time Series Using Many Predictors

We consider both frequentist and empirical Bayes forecasts of a single time series using a linear model with T observations and K orthonormal predictors. The frequentist formulation considers estimators that are equivariant under permutations (reorderings) of the regressors. The empirical Bayes formulation (both parametric and nonparametric) treats the coefficients as i.i. d. and estimates their prior. Asymptotically, when K is proportional to T the empirical Bayes estimator is shown to be: (i) optimal in Robbins' (1955, 1964) sense; (ii) the minimum risk equivariant estimator; and (iii) minimax in both the frequentist and Bayesian problems over a class of nonGaussian error distributions. Also, the asymptotic frequentist risk of the minimum risk equivariant estimator is shown to equal the Bayes risk of the (infeasible subjectivist) Bayes estimator in the Gaussian case, where the 'prior' is the weak limit of the empirical cdf of the true parameter values. Monte Carlo results are encouraging. The new estimators are used to forecast monthly postwar U.S. macroeconomic time series using the first 151 principal components from a large panel of predictors.
Local binary pattern feature vector extraction with CNN

In this paper we present a novel approach for implementing a local binary pattern (LBP) based feature extraction system with cellular nonlinear networks (CNNs). The LBP methodology is based on transforming local binary features of an image into micro-patterns that can be used to, for example, moving object detection and face recognition and detection. We show how the LBP feature vectors can be produced using the standard CNN. Also, we show how simple modifications to the standard CNN cell can be used to make the processing of the LBPs more effective. An analog readout scheme is described and the effect of the analog readout on face recognition accuracy is simulated. The simulations are performed using the standard FERET (the facial recognition technology) database.
A comparison of classifiers for activity recognition using multiple accelerometer-based sensors

Physical activity has a positive impact on people's well-being and it can decrease the occurrence of chronic disease. To date, there has been a substantial amount of research studies, which focus on activity recognition using accelerometer and gyroscope-based sensors. However, many of these studies adopt a single sensor approach and focus on proposing novel features combined with complex classifiers to improve the overall recognition accuracy. In addition, the implementation of the advance feature extraction algorithms and the complex classifiers may exceed the computing ability of most current ambulatory monitoring sensor platforms. This study proposes a method to adopt multiple accelerometer-based sensors on different body locations to cope with this challenge. The objective of this method is to achieve higher recognition accuracy with “light-weight” signal processing algorithms. For choosing the suitable classifier for this multi-sensor system, a comparison of the popular classifiers is presented with the same settings. Eight subjects were recruited to perform eight normal scripted activities in different life scenarios, and each repeated three times. Thus a total of 192 activities were recorded. These activities, then, were segmented and annotated in the laboratory. The collected dataset was used to compare and analyze the following classifiers: the Naïve Bayes classifier, the Decision Tree classifier, the Artificial Neural Networks classifier, the K-Nearest Neighbor classifier and The Support Vector Machines classifier. The comparison focuses on both recognition accuracy and execution time.
Modelling and Inference for Networks with Repairable Redundant Subsystems

We consider the problem of modelling the reliability of a network of subsystems where each subsystem has redundancy and is repairable. The motivation for this work is large-scale telecommunications networks. The time to failure of the subsystem hardware is modeled by an appropriate Markov process and is hence a phase-type distribution. The network structure defines a failure rule in terms of the states of the subsystems, allowing computation by Monte Carlo simulation of the time to failure distribution for the network. This is illustrated by some simple examples. When data on the reliability of the subsystems are available, this can be incorporated via a Bayesian inference approach to modify the prediction of network reliability.
Fetal Eye Movements on Magnetic Resonance Imaging

Objectives Eye movements are the physical expression of upper fetal brainstem function. Our aim was to identify and differentiate specific types of fetal eye movement patterns using dynamic MRI sequences. Their occurrence as well as the presence of conjugated eyeball motion and consistently parallel eyeball position was systematically analyzed. Methods Dynamic SSFP sequences were acquired in 72 singleton fetuses (17–40 GW, three age groups [17–23 GW, 24–32 GW, 33–40 GW]). Fetal eye movements were evaluated according to a modified classification originally published by Birnholz (1981): Type 0: no eye movements; Type I: single transient deviations; Type Ia: fast deviation, slower reposition; Type Ib: fast deviation, fast reposition; Type II: single prolonged eye movements; Type III: complex sequences; and Type IV: nystagmoid. Results In 95.8% of fetuses, the evaluation of eye movements was possible using MRI, with a mean acquisition time of 70 seconds. Due to head motion, 4.2% of the fetuses and 20.1% of all dynamic SSFP sequences were excluded. Eye movements were observed in 45 fetuses (65.2%). Significant differences between the age groups were found for Type I (p = 0.03), Type Ia (p = 0.031), and Type IV eye movements (p = 0.033). Consistently parallel bulbs were found in 27.3–45%. Conclusions In human fetuses, different eye movement patterns can be identified and described by MRI in utero. In addition to the originally classified eye movement patterns, a novel subtype has been observed, which apparently characterizes an important step in fetal brainstem development. We evaluated, for the first time, eyeball position in fetuses. Ultimately, the assessment of fetal eye movements by MRI yields the potential to identify early signs of brainstem dysfunction, as encountered in brain malformations such as Chiari II or molar tooth malformations.
Combined Inactivation of pRB and Hippo Pathways Induces Dedifferentiation in the Drosophila Retina

Functional inactivation of the Retinoblastoma (pRB) pathway is an early and obligatory event in tumorigenesis. The importance of pRB is usually explained by its ability to promote cell cycle exit. Here, we demonstrate that, independently of cell cycle exit control, in cooperation with the Hippo tumor suppressor pathway, pRB functions to maintain the terminally differentiated state. We show that mutations in the Hippo signaling pathway, wts or hpo, trigger widespread dedifferentiation of rbf mutant cells in the Drosophila eye. Initially, rbf wts or rbf hpo double mutant cells are morphologically indistinguishable from their wild-type counterparts as they properly differentiate into photoreceptors, form axonal projections, and express late neuronal markers. However, the double mutant cells cannot maintain their neuronal identity, dedifferentiate, and thus become uncommitted eye specific cells. Surprisingly, this dedifferentiation is fully independent of cell cycle exit defects and occurs even when inappropriate proliferation is fully blocked by a de2f1 mutation. Thus, our results reveal the novel involvement of the pRB pathway during the maintenance of a differentiated state and suggest that terminally differentiated Rb mutant cells are intrinsically prone to dedifferentiation, can be converted to progenitor cells, and thus contribute to cancer advancement.
Some empirical evidence on business-IT alignment processes in the public sector: A case study report

An empirical study that explores business-IT alignment processes in a networked organization among the province Overijssel, the municipalities Zwolle and Enschede, the water board district Regge & Dinkel and Royal Grolsch N.V. in The Netherlands, is summarized in this report. The aim of the study was to identify processes that contribute to improve such alignment. This study represents a continuation of previous validation efforts that help us to confirm the business-IT alignment process areas that should ultimately be included in the ICoNOs MM. Evidence was sought for the alignment of business and IT through the use of information systems to support the requirements of the organization in a specific project. The results of this study in the public sector also are relevant to the private sector where (i) business-IT alignment plays an increasingly valuable role, and (ii) the characteristics of collaborative networked organizations are present.
Digital data in New Zealand schools: Policy reform and school leadership

In 1989, New Zealand started to follow an international trend of reforming education policy according to the neoliberal principles of competition, choice and self-managing schools. Since then, the increasing availability of digital data in schools has corresponded with the development of student achievement measurement tools and benchmarking of standards that enable comparison of schools and cohorts of students. More recently, national policy targets for student achievement have been introduced and form the basis of accountability measures. The article uses Hargreaves and Shirley’s ‘Four Ways’ characterisation of education policy change as a framework to examine the influence that national policy has had on the use of data, on power relations between schools and the national policymakers, and on the challenges faced by school leaders. Interviews in 16 schools explored the types of data available in each school, how they are used and how principals, as leaders in these self-managing schools, would like to be able to use the data. Two systemic influences explain the patterns found in the research. The first is the tension principals face between data required for accountability reporting and data needed for school-based decision-making. The second is the issue with regard to economies of scale and marketisation of education that affects equitable access to the knowledge, tools and expertise that enable effective data usage.
Speech Recognition in Machines 785 Speech Recognition in Machines

Over the past several decades, a need has arisen to enable humans to communicate with machines in order to control their actions or to obtain information. Initial attempts at providing human-machine communications led to the development of the keyboard, the mouse, the trackball, the touch-screen, and the joystick. However none of these communication devices provides the richness or the ease of use of speech, which has been the most natural form of communication between humans for tens of centuries. Hence a need has arisen to provide a voice interface between humans and machines. This need has been met, to a limited extent, by speech-processing systems that enable a machine to speak (speech synthesis systems) and that enable a machine to understand (speech recognition systems) human speech. We concentrate on speech recognition systems in this section. Speech recognition by machine refers to the capability of a machine to convert human speech to a textual form, providing a transcription or interpretation of everything the human speaks while the machine is listening. This capability is required for tasks in which the human is controlling the actions of the machine using only limited speaking capability, such as while speaking simple commands or sequences of words from a limited vocabulary (e.g., digit sequences for a telephone number). In the more general case, usually referred to as speech understanding, the machine need only reliably recognize a limited subset of the user input speech—namely, the parts of the speech that specify enough about the action requested so that the machine can either respond appropriately or initiate some action in response to what was understood. Speech recognition systems have been deployed in applications ranging from control of desktop computers, to tele-communication services, to business services. The earliest approaches to speech recognition were based on finding speech sounds and providing appropriate labels to these sounds. This is the basis of the acoustic-phonetic approach (Hemdal and Hughes 1967), which postulates that there exist finite, distinctive phonetic units (phonemes) in spoken language and that these units are broadly characterized by a set of acoustic properties that are manifest in the speech signal over time. Even though the acoustic properties of phonetic units are highly variable, both with speakers and with neighboring sounds (the so-called coarticulation effect), it is assumed in the acoustic-phonetic approach that the rules governing the variability are straightforward and can be readily learned (by a machine). The first …
Summary of Revision Title: Eecient Join-index-based Spatial-join Processing: a Clustering Approach

Comment: What are the complexities of the proposed algorithms (such as SC)? Response: We have added text summarizing an analysis of the computational complexity. This text appears in Section 2.2 (last paragraph) and Section 4.4 (last paragraph), and is reproduced in Appendix A of this document. We also added additional text and gure in the \Scope" (Section 1.3) to show the diierence between spatial-join and equi-join on linear keys. The text and gure are reproduced in Appendix B of this document. Comment: What is the cost of creating and maintaining join-indexes? Since the authors discuss spatial join computation, it is nice to see how the proposed set of algorithms compare with say R*-Tree (used for join processing). Response: We have added the following text in Section 1.3 in the paragraph titled \Scope:" \Readers interested in update problem of join-indices or in comparison of join-indices with other join-strategies are referred to 9]. Our focus is on join processing algorithms given a join-index. " 2 Reviewer II Comment: Authors do not present scenarios where the technique might underperform. Authors also do not discuss the cases where the algorithm's performance is likely to degrade. Response: Experimental result do show a few scenarios where proposed approach underperform. For example, note following text from rst paragraph of section 6: \The proposed AC and SC algorithms outperformed the traditional methods in our experiments with the Sequoia 2000 dataset, particularly when the size of the memory buuer was small (<10%), relative to the size of the spatial relations. " 1 The following text from Section 5.1. 1: \The OM method performs well with the 4 and 8 Kbytes page size. " And the following text from Section 5.1. 2: \The AC and SC perform better than all of the other methods when the buuer size is relatively low, e.g., 5 to 10 percent. The CO and OM methods do well with large buuer sizes. " Comment: I think the title is for more general than what the paper is about. The paper just attacks the problem of generating eecient page access sequence whereas the title gives out a impression that the paper is about the complete join processing. Response: We agree. We left \page scheduling" out of the title to keep title length reasonable. Abstract and Keywords are used to convey the focus on page access sequence. Note the following sentences in abstract: \The I/O cost of join computation using a join-index with …