Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparative Study on Feature Selection Using Data Mining Tools

Clustering is a important technique of data mining. Clustering is an unsupervised learning problem that group objects based upon distance or similarity. Each group is known as a cluster. In this paper we make use of a large database 'Cardiology Dataset' containing 14 attributes and 303 instances to perform Feature Selection on K- means algorithm. We compared the results of simple clustering technique and clustering (K-means) with feature selection for Cardiology dataset, based upon various parameter using WEKA (Waikato Environment for Knowledge Analysis) and TANAGRA data mining tools. The results of the experiment show that clustering with feature selection give promising results on WEKA with utmost accuracy rate and robustness.
THE JOURNAL OF TEACHING ENGLISH FOR SPECIFIC AND ACADEMIC PURPOSES

Technology is inevitably connected to the teaching/learning process. It helps instructors, but at the same time, technology transfers responsibility for learning to students. Students can guide their learning at their own pace, direct their progress and have access to course content by participating in online/hybrid learning. That is why digital skills are essential in today’s education and society. But, what digital skills are needed to learn and thrive today? How about the skills needed to encourage engagement and motivation in learning? Digital literacy and confidence for everyone involved in education have become more vital than ever. This paper describes the results of a one-semester English for Information Technology course at the South East European University in Macedonia to determine if the core curriculum items provide motivation for students engaged in online/hybrid class. Also, it determines whether the students have the capabilities and skills to participate entirely in the digital society. The hybrid model discussed in this paper is the concurrent teaching-learning model. In this model, the author taught some students who were in person with her in the physical classroom, and at the same time, the teacher’s instruction was being streamed live through Google Meet with other students logged in at home. The teaching/learning process results will show why it is necessary to change our classroom practices in the modern education system.
Geochemistry of the Zhibo submarine intermediate‐mafic volcanic rocks and associated iron ores, Western Tianshan, Northwest China: Implications for ore genesis

The Zhibo iron deposit is hosted in Carboniferous submarine volcanic rocks in Western Tianshan, NW China. A series of magnetite‐bearing intermediate‐mafic volcanic rocks are recognized in the periphery of the Zhibo ore district. Most of these volcanic rocks formed at 314 ± 2 Ma, possess tholeiitic–calc‐alkaline affinities, and display remarkable negative Nb, Ta, and Ti anomalies on primitive mantle‐normalized incompatible element diagrams. These features, together with those of their relatively complete rock assemblages and Th/Yb versus Nb/Yb diagrams, are indicative of their formation in an active continental margin arc setting. The wide compositional spectrum of SiO2 values ranging from 47.11 to 62.75 wt. % and lower Mg# values (55–63) of basalts suggest that the Zhibo intermediate‐mafic volcanic rocks may have experienced magmatic differentiation. Their (Th/Ta)PM > 1, (La/Nb)PM > 1, Nb/Ta (11‐16), and Th/Ce (0.06‐0.23) values suggest that the source of these intermediate‐mafic volcanic rocks was significantly contaminated by crustal materials. The magnetites in the iron ore have lower contents of Al, Mn, Ti, and V, indicating that the mineralization of magnetite in the iron ore occurred under lower temperature and higher oxygen fugacity conditions than those in the intermediate‐mafic volcanic rocks. In addition, the magnetites in the Zhibo iron ores have lower contents of compatible elements (e.g., Ti, V, Mn, Co, Cr, and Zn) than those of the magnetite in the intermediate‐mafic volcanic rocks, suggesting that the Zhibo magnetites crystallized from late‐stage, residual iron‐rich magmatic melts/magmatic‐hydrothermal fluids. In addition, the textures of the volcanic rocks suggest that iron have ever enriched in the residual melt during the magmatic stage, and the iron‐rich fragments in andesitic volcaniclastic rocks indicate that the ore‐forming material was a high‐salinity fluid‐bearing iron‐rich melt. In combination of available information, including field observations and geochemical analyses, we interpret that the Zhibo iron deposit is magmatic‐hydrothermal in origin.
Semantic Lexicon Acquisition for Learning Parsers

This paper describes a system, Wolfie (WOrd Learning From Interpreted Examples), that learns a semantic lexicon from a corpus of sentences paired with representations of their meaning. The lexicon learned consists of words paired with representations of their meaning, and allows for both synonymy and polysemy. Wolfie is part of an integrated system that learns to parse novel sentences into their meaning representations. Experimental results are presented that demonstrate Wolfie's ability to learn useful lexicons for a realistic domain. The lexicons learned by Wolfie are also compared to those learned by another lexical acquisition system, that of Siskind, 1996].
High throughput measurement of Arabidopsis thaliana fitness traits using transfer learning

Revealing the genetic effects of phenotypic variation is frequently challenging because of genetic redundancy, condition-specific requirements for genes, and/or changes in physiology or development that are too subtle to detect. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such as Arabidopsis thaliana. Here, we evaluated the performance of an image segmentation-based (ImageJ) and a transfer learning-based approach where a pre-trained model for detecting unrelated objects was repurposed for measuring two Arabidopsis fitness traits: seed and fruit counts. Although ImageJ was straightforward to use, the Pearson’s Correlation Coefficient (r) between true and predicted seed counts was only 0.92 because seeds touching each other were undercounted. In contrast, the transfer learning-based approach yielded near perfect seed counts (r=0.9998). Using the latter approach, we were also able to detect and count fruits (r=0.99), although undercounting remains an issue for images with large numbers of fruits. Through data augmentation, we generated images of different resolutions, contrasts, brightness, and blurriness and used them to establish final models robust to differences in image properties. Beyond providing models to facilitate the investigation of Arabidopsis fitness traits, this study exemplifies how pre-trained models for different purposes can be reused for plant biology applications through transfer learning and machine vision.
The TCP Control Block Interdependencein Fixed Networks — New PerformanceResults

In a today’s internetendsystem,thecongestionandflow controlof every TCPconnection is doneseparately . This meansthat every TCP senderof the TCP connectionsof an end systemhasto determinecurrentinformationaboutthenetwork for itself andindependently from otherTCPsenders. Dueto TCP’scongestioncontrolandtimer management, i.e., the slow start algorithm and timeout timer calculation,this can lead to suboptimalnetwork utilization. In addition,separatecontrol reducesfairnessbetweensimultaneousTCPconnections.Both effectsarecausedby several TCP senders’differentperceptionof current network conditions. Therefore,it mightbeaninterestingideato reusenetwork informationin anendsystem: informationcollectedby existing TCPconnectionscouldbeusedto initialize controlvariablesof new TCPconnectionswith moreup-to-datevalues.This canimprove theoverall network utilizationof andthefairnessbetweentheTCPconnectionsof anendsystem. Onesuchnetwork informationreuseapproachis theTCPcontrolblock interdependence (TCBI) [1]. In this paper , the performanceof two different TCBI control algorithmsis investigatedandcomparedto standardTCP by simulationsin a fixed network scenario. Sincemoreandmoreendsystemsareconnectedto theinternetvia wirelessLANs, alsothe influenceof packet lossesin the last hopof a TCP connectionon theperformanceof the TCBI controlalgorithmsis considered.
Power minimization in a backlit TFT-LCD display by concurrent brightness and contrast scaling

This paper presents a concurrent brightness and contrast scaling (CBCS) technique for a cold cathode fluorescent lamp (CCFL) back/it TFT-LCD display. The proposed technique aims at conserving power by reducing the backlight illumination while retaining the image fidelity through preservation of the image contrast. First, we explain how CCFL works and show how to model the non-linearity between its backlight illumination and power consumption. Next, we propose the contrast distortion metric to quantify the image quality loss after backlight scaling. Finally, we formulate and optimally solve the CBCS optimization problem subject to contrast distortion. Experimental results show that an average of 3.7X power saving can be achieved with only 10% of contrast distortion.
Human Motion: Understanding, Modelling, Capture, and Animation (Computational Imaging and Vision)

The study of human motion dates back more than 2000 years. With the event of information technology, new areas have been added to this field. Research using computer vision and computer graphics contributes to a transformation of biomechanics into a discipline that now applies computing technology throughout; on the other hand, computer vision and computer graphics also benefit from defining goals aimed at solving problems in biomechanics. Besides interactions, all three areas also developed their own inherent research dynamics towards studying human motion. Researchers from all three of these areas have contributed to this book to promote the establishment of human motion research as a multi-facetted discipline and to improve the exchange of ideas and concepts between these three areas. Some chapters review the state of the art whilst others report on leading edge research results, with applications in medicine, sport science, cinematography and robotics.
Signal Processing: Image Communication

Low-resolution images present challenges to a variety of object recognition problems in a variety of surveillance and navigation applications. In recent years, deep learning has advanced the state of the art in image super-resolution (SR) in terms of pixel domain peak signal to noise ratio (PSNR)/ mean square error (MSE). Inspired by the recent advances of deep convolutional neural networks in general image SR tasks, we develop a computer vision task-driven image SR solution by learning super-resolved gradient images using multiple convolutional neural networks for different scales. Recovering super-resolved gradient images at multiple scales, enables the system to recover more information useful for high level vision tasks than simply SR in the pixel domain. In particular, we propose a residual learning framework to perform image SR in the Difference of Gaussian (DOG) domain. The trained residual network models are then adapted to drive a widely adopted key point algorithm for image recognition, i.e. the SIFT detection and matching. Experimental results show that the proposed approach can significantly improve the SIFT keypoints repeatability compared to the state of the art in pixel domain image SR solutions.
Power-line Communication based Digital Home-Network Technology

We propose a method of using the three dimensional characteristic information to classify the front environment in travelling by using the images captured by a CCD camera equipped on a mobile robot. So, this paper proposes the method of deciding the travelling direction of a mobile robot with using input images based upon the suggested algorithm by preprocessing, and verified the validity of the image information which are detected as obstacles by the analysis through neural network. 키워드 mobile robot, neural network, obstacle avoidance, 3D, detection
1 Title : Towards an automated comparison of OpenStreetMap with authoritative road datasets

OpenStreetMap (OSM) is an extraordinarily large and diverse spatial database of the world. Road networks are amongst the most frequently occurring spatial content within the OSM database. These road network representations are usable in many applications. However the quality of these representations can vary between locations. Comparing OSM road networks with authoritative road datasets for a given area or region is an important task in assessing OSM's fitness for use for applications such as routing and navigation. Comparisons such as these can be technically challenging and no software implementation exists which facilitates such comparisons easily and automatically. In this paper we develop and propose a flexible methodology for comparing the geometry of OSM road network data with other road datasets. Quantitative measures for the completeness and spatial accuracy of OSM are computed including the compatibility of OSM road data with other map databases. Our methodology provides users with significant flexibility in how they can adjust the parameterisation to suit their needs. This software implementation is exclusively built on open source software and a significant degree of automation is provided for these comparisons. This software can subsequently be extended and adapted for comparison between OSM and other external road datasets. This is the pre-peer reviewed version of the following article: “Brovelli M. A. , Minghini M. , Molinari M. and Mooney P. (2016) Towards an automated comparison of OpenStreetMap with authoritative road datasets. Transactions in GIS, DOI: 10.1111/tgis.12182” , which has been published in final form at http://onlinelibrary.wiley. com/doi/10.1111/tgis.12182/full.
SITS: A Hierarchical Nonparametric Model using Speaker Identity for Topic Segmentation in Multiparty Conversations

One of the key tasks for analyzing conversational data is segmenting it into coherent topic segments. However, most models of topic segmentation ignore the social aspect of conversations, focusing only on the words used. We introduce a hierarchical Bayesian nonparametric model, Speaker Identity for Topic Segmentation (SITS), that discovers (1) the topics used in a conversation, (2) how these topics are shared across conversations, (3) when these topics shift, and (4) a person-speciﬁc tendency to introduce new topics. We evaluate against current unsupervised segmentation models to show that including person-speciﬁc information improves segmentation performance on meeting corpora and on political debates. Moreover, we provide evidence that SITS captures an individual’s tendency to introduce new topics in political contexts, via analysis of the 2008 US presidential debates and the television program Crossﬁre.
Training-free compressed sensing for wireless neural recording

Signal compression is crucial for resource-constrained wireless neural recording applications with limited data bandwidth, and Compressed Sensing (CS) has successfully demonstrated its potential in this field. However, the conventional CS approaches rely on data-dependent and computationally intensive dictionary learning processes to find out the sparse representation of neural signals, and dictionary re-training is inevitable during real experiments. This paper proposes a training-free CS approach for wireless neural recording. By adopting the analysis model to enforce the signal sparsity and constructing a multi-order difference matrix as the analysis operator, it avoids the dictionary learning procedure and reduces the need for previously acquired data and computational complexity. In addition, a group weighted analysis 11-minimization method is developed to recover the neural signals. Experimental results reveal that the proposed approach outperforms the state-of-the-art CS methods for wireless neural recording.
El Capital Intelectual: Una Herramienta Clave en la Gestión Administrativa de "La Unidad Central del Valle del Cauca-UCEVA" (The Intellectual Capital: A Key Tool in Administrative Management "Central Unit Cauca Valley UCEVA"

The objective of this research was to characterize the intellectual capital as a key tool in the administration of the UCEVA through a proposed measuring instrument of intellectual capital that allows UCEVA directives define strategies that will enable their integration into administrative management, proposal arises from the concept of the strategic direction of the balanced scorecard (CMI) and components and relationships Intellectus Model diagnosis made from each of the components of intellectual capital and through UCEVA the case study method was studied and analyzed mixed institution. Data were collected through surveys that reveal human capital skills and learn about the different relationships that the organization has with its students and graduates. Direct observation was used in X El Capital Intelectual: Una Herramienta Clave en la Gestion Administrativa de “La Unidad Central del Valle del Cauca-UCEVA” the characterization and determination of formal knowledge, the experience of human capital and structural capital
Inter-ping sound field from a simulated mid-frequency active sonar, and its implication to marine mammal tonal masking

This study investigates the characteristics of the sound field during the 2013 and 2014 Southern California Behavioral Response Study. Acoustic data were collected before, during, and after simulated mid-frequency active sonar (MFAS) playbacks. A numerical approach, incremental computation method, is developed to quantify the inter-ping sound field during the simulated MFAS playback. Comparisons are made between inter-ping sound field and natural background in three distinctive frequency bands: low-frequency ( 4.5 kHz) bands. The results show significantly elevated sound pressure levels (SPLs) in the inter-ping interval of the MFAS-frequency band when compared to natural background levels before and after playbacks. No difference is observed between inter-ping SPLs and natural background levels in the low- and high-frequency bands. In addition, the duration of elevated inter-ping sound field depends on the MFAS source distance. The prolonged eleva.
The ARNN model relativises P = NP and P (cid:54) = NP

In this paper we prove that the relations P = NP and P (cid:54) = NP relativise to the deterministic / non-deterministic artiﬁcial recurrent neural net ( ARNN ) with real weights (informally considered as oracles in [1] and [2]). Although, in the nineties, a dozen of papers were written on the ARNN model, some introducing computation via neural nets with real weights and some introducing non-deterministic and stochastic neural nets, it seems that no one noticed such a relativisation, which is also true of many other non-standard models of computation involving real-valued parameters.
On the Capacity of the Discrete-Time Poisson Channel

On the Capacity of the Discrete-Time Poisson Channel Student: Chen Wei-Hsiang Advisor: Prof. Stefan M. Moser Institute of Communication Engineering National Chiao Tung University The capacity of the discrete time Poisson channel is studied. This channel law describes optical communication with a direct-detection receiver, where the output models the receiving photons due to the transmitted laser signal and due to surrounding light. For battery issues and safety reasons the inputs are simultaneously constrained on both their average and peak power. We try to find an upper and a lower bound on the capacity. Applying some recent results on the Poisson distribution and a dual expression of channel capacity, the bounds are partially analytic and partially numerical, where the computation complexity is very low. The gap between our bounds is not large, i.e. we ave succeeded in narrowing the range of the capacity.
ICAT: A Novel Algorithm to Robustly Identify Cell States 1 Following Perturbations in Single Cell Transcriptomes 2

The detection of distinct cellular identities is central to the analysis of single-cell RNA 25 sequencing experiments. However, in perturbation experiments, current methods typically fail to 26 correctly match cell states between conditions or erroneously remove population substructure. 27 Here we present the novel, unsupervised algorithm ICAT that employs self-supervised feature 28 weighting and semi-supervised clustering to accurately resolve cell states across 29 heterogeneous conditions. Using simulated and various real datasets, we show ICAT is superior 30 in identifying and resolving cell states compared to current integration workflows. ICAT is robust 31 to low signal strength, high perturbation severity, and disparate cell type proportions. We 32 empirically validate ICAT in a developmental model and find that only ICAT identifies a 33 perturbation-unique cellular response. Taken together, our results demonstrate that ICAT offers 34 a significant improvement in defining cellular responses to perturbation in single-cell RNA 35 sequencing data. 36
An Energy-Aware On-Demand Clustering Routing Algorithm for Ad Hoc Networks

The more the number of nodes is, the worse the performance of the flat-based routing protocol is in the wireless ad hoc network. And the clustering algorithm based on maximal connectivity ignores the equipoise of network energy, and it affects network lifetime. So, this paper brings forward a self-maintenance clustering algorithm, which takes on-demand flat-based routing mechanism, remnant energy of nodes and their maximal connectivity into account. These simulation results show its validity, and this algorithm improves the scalability, enhances the network performance and prolongs the existence time of the network.
Nonlinear principal component analysis by learning nerve fields united by inhibitory connections

The authors propose a model with feedforward and a recurrent self-organizing maps model which can extract nonlinear principal components. This model consists of several learning nerve fields that are mutually inhibiting each other, and is an extension of the self-organizing overlapping maps model proposed previously by the authors. The feedforward model is sufficient for nonlinear principal component analysis, but the recurrent model with symmetrical inhibitory connections between nerve fields has additional interesting characteristics. © 2004 Wiley Periodicals, Inc. Syst Comp Jpn, 35(3): 68–78, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.1230
Low Phosphorus Availability Decreases Susceptibility of Tropical Primary Productivity to Droughts

Large uncertainties in the susceptibility of tropical forest productivity to precipitation changes hamper climate change projection. Interactions between the availabilities of water and phosphorus could theoretically either increase or decrease the susceptibility of tropical gas exchange to variation of precipitation. The inclusion of phosphorus‐water interactions in a land surface model reduces the coefficient of variance, a measure of variability, of biweekly gross primary productivity by a factor of 1.5–2.3 at three tropical forest sites in Brazil, bringing it closer to estimates from eddy covariance measurements and remote sensing. Soil drought conditions are attenuated due to 8–30% lower water consumption during wet periods in presence of phosphorus limitation. When soils are dry, plant phosphorus acquisition is impaired by reduced ion mobility, despite an increase in net phosphorus mineralization. We conclude that water‐phosphorus interactions cannot be omitted in analysis of the resilience of tropical ecosystems to precipitation changes.