Reviewerly/exHarmony huggingface dataset:
Agile Scrum in Monitoring DepEd's Programs, Projects and Activities (PPA)

Project and organizational success lie in the process model being used. In fact, Whiting, et al. in their study express that a formal quality assessment is facilitated by using a structured tool, a convenient standardized way to assess key components of quality that has been considered by an organization in achieving success. Department of Education, a large governing body had set its focus on, not only in the instruction but also in other aspects, promoting holistic development of learners. These are drafted into a School Improvement Plan (SIP), a consensus consolidated from master teachers of different subject areas and coordinators of different programs. With its broad scope, this poses a problem during the implementation such as fragmentation of monitoring and evaluation. This paper had evidently showed the positive impact of Agile Scrum that is the reason why the author had chosen such a methodology. It has been proven with other related studies that such methodology is effective not just for software development but also in academe and even in construction. The adoption of Agile Scrum as a proposed process model was customized in the context of monitoring DepEd's programs, project, and activities (PPA's).
A method for dynamic 3D shape measurements based on multiple-shot FTP and motion compensation

Fringe projection profilometry (FPP) has attracted considerable interests for addressing the challenge of measuring three-dimension (3D) shapes of moving objects. Compared with phase shift profilometry (PSP) which requires the capture of multiple fringe patterns and is thus only suitable for static objects, Fourier transform profilometry (FTP) is less sensitive to motion-induced errors. However, FTP is prone to the influence of background lights and variations of the surface reflectivity, which may result in less accurate measurements. There are studies aimed to reduce the measurement errors with FTP using more sophisticated processing of the fringe patterns. However, existing works focus on schemes based on single images and the correlation of the dynamic 3D shapes is largely unexplored. In this work, we present a new method that refines FTP-based dynamic shape measurements. Assuming 3D rigid movements of the targets, we propose to utilize knowledge of the motion parameters and combine the multiple height maps obtained from several FTP measurements after compensating the motion effect. Approaches for automatically combining the height information are studied. It is observed that the measurement accuracy can be improved using the proposed method and the influence due to ambient lights and reflectivity variations can be suppressed. Computer simulations are performed to verify the effectiveness of the proposed method. The proposed method can also be integrated into other FPP systems to improve the performance for dynamic object measurements.
Relationships among information items of meeting minutes in a minutes management system MRS

This paper describes two types of relationships of some information items in a collection of minutes of meetings. The relationships between persons and themes, and ones between two subjects in the minutes are dealt with, which are essential items to analyze minutes. Meeting minutes record reports and discussions with respect to certain themes in meetings. These reports and discussions are arranged into suitable subjects with appropriate headings. To develop a system which analyzes the collection of meeting minutes is required. Then, to show relationships among information items described in the collection of minutes is one of important problems. By complying with this requirement, it is able to find facts which are not found so far, and to assist users to interpret the collection of minutes. In order to understand the minutes, some information items play important roles. In especial, it is necessary to make clear two types of relationships in a collection of minutes. They are relationships between members of a meeting and themes which the members are interested in, and between two subjects described in two minutes for consecutive meetings, which discussed on similar themes. Then, themes are treated as a collection of named entities and keywords. The former is implemented based on latent semantic indexing. The latter is realized by relating two subjects in different consequent minutes from a viewpoint that two subjects treated similar themes. The similarities between two subjects are measured base on occurrences of named entities and keywords. By realizing these mechanisms, intuitive understandability of the relationships among some information items is improved for interpreting a set of minutes, which are persons, named entities and subjects.
Distributed weighted fusion robust white noise deconvolution estimators for multisensor systems with mixed uncertainties

This paper addresses the design of robust scalars-weighted fusion time-varying white noise deconvolution estimators (WNDEs) for uncertain multisensor systems with mixed uncertainties including uncertain-variance multiplicative noises in measurement matrix, missing measurements, and uncertain-variance linearly correlated measurement and process white noises. By introducing the fictitious noise, the considered system is converted into one with only uncertain noise variances. According to the minimax robust estimation principle, based on the worst-case systems with the conservative upper bounds of noise variances, using the optimal fusion criterion with scalar weights, the robust scalars-weighted fusion time-varying WNDEs (filter and smoother) are presented in a unified framework. Their robustness is proved by using a combination method which consists of Lyapunov equation approach, augmented noise approach, and decomposition approach of non-negative definite matrix, such that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. The accuracy relations between the robust local and fused time-varying WNDEs are proved. A simulation example with respect to the IS-136 multisensor communication systems shows the effectiveness and correctness of the proposed results.
Impact of Artificial Intelligence Using Multilevel Inverters for the Evolution the Performance of Induction Machine

This paper presents a study of Impact of artificial intelligence using multilevel inverters for the evolution the performance of induction machine. Artificial intelligence it is a scientific discipline related to the processing of knowledge and reasoning in order to allow a machine to perform functions normally associated with the human being. Artificial intelligence tries to reproduce cognitive processes for the purpose of carrying out ‘smart’ actions; it is like ‘building computer programs that perform tasks that are currently performed more satisfactorily by human beings because they require high-level mental processes. This work presents a detailed analysis of the comparative advantage of two-level Inverter and the Three Level Diode Clamped Inverter fed Induction Motor. The comparison is based on the evaluation of harmonic distortion THD, on the other hand and to show the performance of the proposed Fuzzy Mode Controller, has been compared with the classical PI control method.
Feature-Opinion Co-extraction Based Upon Genuine Score Analysis

In the recent few years, the Internet has become more user-centric. People prefer to use it for sharing their opinion through different social media. This, in turn, leads to a tremendous increase in the volume of user-generated content. Mining this users’ opinion base is useful from the perspective of buyers as well as sellers. It involves the task of textual analysis. The purpose of this paper is to co-extract the candidate feature-opinion (FO) pairs and find out genuine pairs out of it. In this paper, we focused on rule-based or supervised approach for co-extracting FO pairs. Our main contribution is that we proposed a statistical technique to grab genuine FO pairs. It is a mathematical blend of regularity score (representing an association of an FO pair with the document) and opinion association score (representing an interassociation between the components of FO pair itself). We followed a loosely constrained information extraction unit to design a domain-independent system. To calculate the score values, the candidate components are modeled by bipartite graph and treated by HITS algorithm. For the experiment purpose, we used Opinosis Dataset 1.0. The experimental analysis shows that our mathematical model provides a better result than the existing approach.
Samples Selective Updating Mechanism for Object Tracking

In the issue of object tracking, tracking method probably receives wrong position of an object due to the complex environment conditions in a real scenario, such as object occlusion, illumination change, and object deformation. For discriminative model tracking method, this wrong position information may results in tracking failure. To tackle the wrong tracking problems, we revisit the efficient convolution operators (ECO) method and introduce a samples selective updating mechanism. This updating mechanism employs cascaded screening method which can eliminate the wrong tracking output and protect the training set from being contaminated. The experiments are performed on OTB100 benchmark. And the test shows that our tracker achieves the OPE score of 93.5% and improves 1.19% compares with ECO.
Dimensionality Reduction With Multi-Fold Deep Denoising Autoencoder

Natural data erupting directly out of various data sources, such as text, image, video, audio, and sensor data, comes with an inherent property of having very large dimensions or features of the data. While these features add richness and perspectives to the data, due to sparsity associated with them, it adds to the computational complexity while learning, unable to visualize and interpret them, thus requiring large scale computational power to make insights out of it. This is famously called “curse of dimensionality.” This chapter discusses the methods by which curse of dimensionality is cured using conventional methods and analyzes its performance for given complex datasets. It also discusses the advantages of nonlinear methods over linear methods and neural networks, which could be a better approach when compared to other nonlinear methods. It also discusses future research areas such as application of deep learning techniques, which can be applied as a cure for this curse.
Modified Angstrom Solar Radiation Model for Lagos Nigeria

Abstract Solar energy generation in tropical climate system is said to be a viable project because of the availability of solar radiation. The use of solar photovoltaic (PV) panel in the research area is encouraging at the moment but the issue has been that the lifespan of the PV panel is somewhat very low. This research aims to determine the solar radiation model for Lagos to enhance the optimization of the production of solar PV panels for Lagos Nigeria. This would enhance higher patronage in the commercial hub of Nigeria.
An Improved Forgery Detection Method for Images

Abstract A forgery image detection system is introduced to detect the forgery in images using global, local and pixel based features. Global features will consider the whole images which include luminance and chrominance features. These global features can be extracted by using Zernike moments. The local feature consists of descriptors of multiple interest points. Image authentication is done by using hash method. The set of images will be trained in the database earlier itself. Then the test image and reference image will be compared based on pixels. By analyzing the hash distance the system can identify the test image is forged or not. As an improvement, pixel based feature extraction is used. Pixel based feature extraction is carried out by using supervised classification algorithm.
Measurements of Accuracy in Biostatistics

Due to the nature of biological and medical data, biostatistics has been playing an increasing role in a wide range of applications in biology and medicine. The aim of this article is to provide insights on some basic concepts and measurement procedures used in biostatistics. The emphasis has been placed on the application of biostatistics in the realm of classification aiming to design a good prediction model. Various statistical metrics and significance tests used to evaluate the performance of a predictor have been discussed. It has been highlighted that the interpretation of the values of these metrics should be cautious when applied to biological domain especially when dealing with highly imbalanced datasets.
High-Performance Computing Environment with Cooperation Between Supercomputer and Cloud

Due to the recent popularization of machine learning, such a deep reinforcement learning as AlphaGO has advanced to analyze large-scale data and is attracting great attention. In deep reinforcement learning, users evaluate many functions in large-scale computer environments, including supercomputer and cloud systems. Cloud services can provide computer resources based on the scale of the computer environment desired by users. On the other hand, in conventional large-scale computer environment that only consists of CPUs or GPUs, the processing time greatly increases according to the scale of the calculation processing. In this paper, we propose a high-performance computing environment for deep reinforcement learning that links supercomputer and cloud systems. Our proposed system can construct a high-performance computing environment based on the scale of the computing process by the cooperation of the supercomputing and cloud systems with short physical distance and short network distance. In our evaluation of deep reinforcement learning using our proposed system, we confirmed that computer resources can be effectively used by allocating suitable processing for the supercomputer and the cloud according to the usage situations of the CPU, the GPU, and the memory.
Why pushing a bell does not produce a sound

The difference between "beating" and "pushing" results in the perception that a push just makes the object move as a whole, while a beat produces also a sound. Through a detailed analysis of the physics underlying such everyday experiences, we identify the \emph{strength}, the \emph{duration} and the \emph{softness} of the applied contact force, as the main (measurable) characteristics that mark such difference. The strength determines the final velocity $\Delta v$ achieved by the body. The duration $2\tau$ compares to the time $\tau_\ell$ the sound takes to cross the body. The softness $\gamma$ (a positive exponent) results from the shape in time of the contact force. Those three elements enter the formula for the intensity of the sound produced. The relevant role of the softness is stressed and specific values are calculated for a thin metallic bar, chosen as the simplest possible model system.
Alternative models for the modified form of ridge regularized linear model in discovering Markov boundary

Abstract It has been proved that the modified form of ridge regularized linear model (MRRLM) can discover a subset of Markov boundary of the target variable under some constrained conditions. However, MRRLM cannot be applied to the data sets with collinear variables due to covariance matrix is employed. To develop a suitable alternative model for MRRLM, we study the relationships of discovery performance of Markov boundary among MRRLM, ridge regression linear model (RRLM), and LASSO combining with permutation test through empirical method. In addition, we also proposed a new NVRRLM to discover Markov boundary of the target variable. The experimental results show that: (1) On the binary data sets, MRRLM has a basically similar performance with LASSO and RRLM; (2) On the continuous data sets, MRRLM has a basically similar discovery performance with LASSO but has higher discovery performance than RRLM; (3) The new NVRRLM can replace MRRLM on the data sets with collinear variables. The above experimental results demonstrate NVRRLM can effectively deal with variable collinearity problems.
Recognition of Signals with Unknown Parameters

This paper deals with a new block scheme for digital modulation recognition with unknown parameters. Present methods together with their characteristic properties are described. The symbol rate estimation by using combined method is presented in more detail.
Image Defogging Combined with Compensation of High Lighted Areas

This paper proposes an improved dark channel prior defogging algorithm to improve the color distortion in the sky and other high lighted areas. Firstly, the dark channel prior is used to estimate the transmittance and the atmospheric light value. Then, guided filter is used to smooth the transmittance, and L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> gradient minimization filter is used for further refinement. Finally, the constructor method is used to compensate the transmittance to improve the color distortion in the high lighted area. Experimental results show that the method can accurately estimate the transmittance and enhance the contrast of haze removal images, the color distortion of high lighted areas is improved. The haze removal results obtained after haze removing are clearer and more adaptive.
On Accuracy of Approximation for the Resource Constrained Shortest Path Problem

The paper we considers the Resource Constrained Shortest Path problem (RCSP). This problem is NP- hard extension of a well-known shortest path problem in the directed graph G = (V;E). In the RCSP problem each arc e from E has a cost w(e) and additional weight functions ri(e); i = 1; : : : ; k, which specifying its requirements from a finite set of resource. A polynomial time ϵ-approximation algorithm RevTree based on node labeling method is presented in the paper. The main advantage of the RevTree algorithm over existing ones is its ability to produce ϵ approximation of the RCSP problem in O(jV j2) time. The present paper provides a proof of complexity and aproximation of RevTree algorithm
Optimal and Suboptimal Resource Allocation Techniques by Using Virtualmigration in Cloud Computing

Abstract This paper discusses about data transmission between hot machines and warm machines. We employ a Main Cloud Server (MCS) for this purpose. MCS provides a variety of services. User have to give request to MCS and it will provide necessary service. User will be sends request to MCS for data upload and the request will be sent to CSP and that allocates job to Virtual Machines. We need to use two machines such as HOT MACHINES and WARM MACHINES. Hot machines can handle current job. Warm machines can be idle state until the job is assigned. Every machines will be having three virtual machines. The user can raise the service request CSP to access the resources found in cloud. So, the CSP provides approve or rejection is accept by CSP, then user can immediately access or download the resource from cloud server.
The Use of Audio Media to Improve Students Motivation in English Subject at Junior High School, Kupang District-East Nusa Tenggara

The learning process is an activity in applying the curriculum in an educational institution to affect the students' achievement. Some determinant factors which affecting the teaching and learning process are teaching methods and learning media which used to teach daily. Some problems were found in public school at Junior high school (SMPN) 3 Amabi Oefeto in Kupang district, East Nusa Tenggara province in academic year 2017/2018. One of these problems is the lack of students' participation in learning English. Information was gained from some students where some teachers were limited in creating an active learning and teaching process has been focused on teachers centred-learning. Besides, some teachers explain that English subject is a difficult subject and in daily communication, they have been using mother tongue to communicate with each other. Data also came from two English teachers by interview. The lack of facilitation affecting them in creating fun and active learning English. In adding, English book is the only media that teachers use. Teachers explained that students' motivation and achievement are not satisfying and not comparable with schools which have complete facilities.
Digital fundamental groups and edge groups of clique complexes

In previous work, we have defined—intrinsically, entirely within the digital setting—a fundamental group for digital images. Here, we show that this group is isomorphic to the edge group of the clique complex of the digital image considered as a graph. The clique complex is a simplicial complex and its edge group is well-known to be isomorphic to the ordinary (topological) fundamental group of its geometric realization. This identification of our intrinsic digital fundamental group with a topological fundamental group—extrinsic to the digital setting—means that many familiar facts about the ordinary fundamental group may be translated into their counterparts for the digital fundamental group: The digital fundamental group of any digital simple closed curve is $$\mathbb {Z}$$ ; a version of the Seifert–van Kampen Theorem holds for our digital fundamental group; every finitely presented group occurs as the (digital) fundamental group of some digital image. We also show that the (digital) fundamental group of every 2D digital image is a free group.
Logarithm Difference Based Illumination Invariant Feature for Severe Illumination Variation Face Recognition

In spired by the fact that a logarithm difference is the subtraction of two neighbor pixels, which may be a positive or negative numerical value, we divide a face local region into a positive region and a negative region, and the general logarithm difference model (GLDM) is developed by integrating positive and negative logarithm differences. Then, the multiscale logarithm difference edge-maps (MSLDE) [1] is employed as the test-bed, and the proposed GLDM is introduced into MSLDE to form General MSLDE (GMSLDE). Finally, the performance of GMSLDE is verified on the Extended Yale B and CMU PIE face databases with severe illumination variations. The experimental results indicate that the proposed GLDM can efficiently improve the performance of logarithm difference edge-map against severe illumination variations.
Road detection using cycle-consistent adversarial networks

Today, road detection is still a challenging task in intelligent driving. With the continuous improvement of computer vision, many methods of deep learning are used for road detection because they can achieve image features at a deeper level and discover road areas from raw RGB data. However, the method to detect the road areas accurately needs to be improved. We present a method that can extract the road area features and complete road detection tasks. Our method mainly includes the following points: (1) to introduce the cycle-consistent adversarial network to extract the road area features in a picture and complete image to image conversion and (2) to complete road detection by adding a new model and to improve the accuracy of detection. The results of our method are evaluated by uploading to the Karlsruhe Institute of Technology and Toyota Technological Institute road detection benchmark and named it as “road detection cycle-consistent adversarial networks.” Our method achieves an overall max <italic>F</italic>-measure of 88.63% and precision of 91.35%. In addition to high precision, our method also has a good robustness. Meanwhile, the accuracy for narrow road areas needs to be optimized in the future.