Reviewerly/exHarmony huggingface dataset:
Generating Domain and Pose Variations between Pair of Cameras for Person Re-Identification

Person re-identification (re-id) remains an important task that aims to retrieve a person's images from an image dataset, given a probe image. The lack of cross-view (pose variations) training data and significant intra-class (domain) variations across different cameras make re-id more challenging. To solve these issues, this work proposes a Domain and Pose Invariant Generative Adversarial Network (DPI-GAN) to generate images for both domain and pose variations capture. It is based on a CycleGAN structure in which the generator networks are conditioned on a new pose. Identity and pose discriminators networks are used to monitor the image generation process. These generated images are used for learning domain and pose invariant features to improve the performance of person re-identification.
Subjective Analysis of an End-to-end Streaming System

Measuring Quality of Experience (QoE) and integrating these measurements into video streaming algorithms is a multi-faceted problem that fundamentally requires the design of comprehensive subjective QoE databases and metrics. To achieve this goal, we have recently designed the LIVE-NFLX-II database, a highly-realistic database which contains subjective QoE responses to various design dimensions, such as bitrate adaptation algorithms, network conditions and video content. Our database builds on recent advancements in content-adaptive encoding and incorporates actual network traces to capture realistic network variations on the client device. Using our database, we study the effects of multiple streaming dimensions on user experience and evaluate video quality and quality of experience models. We believe that the tools introduced here will help inspire further progress on the development of perceptuallyoptimized client adaptation and video streaming strategies.
A Hidden Markov Model-Based Method for Virtual Machine Anomaly Detection

The normal operation of virtual machine is a necessity for supporting cloud service. Motivated by the great desire of automated abmornal operation detection, this paper proposes a Hidden Markov Model-based method to conduct anomaly detection of virtual machine. This model can depict normal outline base of virtual machine operation and detect system outliers through calculating non-match rate. Through verifying the method in a real distributed environment, experiment results indicate that this method has 1.1%–4.9% better detection accuracy compared with two leading benchmarks with a much better efficiency.
A SURVEY ON INTEREST AND READING OF EFL STUDENTS

Reading plays important skills in language in developing the students’ success in their learning. This study aims to find out if EFL students preferred reading skill. It also intends to determine their interest in learning English in general. It employed a descriptive statistics technique. Data were collected from questionnaires and observation. The result showed that the interest and reading are somewhat varied. The majority of EFL students have no interest in reading other sources like newspaper and magazines and the rest have little interest in reading other sources in general. It also indicated that it is contrary to their statements stating that reading is important to their future. It is concluded that teachers and parents should encourage the students to like reading as well as providing a wide range of reading books so that they may have various sources to read. It could be interesting to consider the awareness of the students. Teachers are then to build and motivate the students’ interest in reading.
Computer-Aided Porous Materials Description Based on Morphological Spectra

Research & Development in Material Science Computer-Aided Porous Materials Description Based on Morphological Spectra Juliusz L Kulikowski* Nalecz Institute of Biocybernetics and Biomedical Engineering PAS, Poland *Corresponding author:Juliusz L Kulikowski, Nalecz Institute of Biocybernetics and Biomedical Engineering PAS, Warsaw, Poland Submission: August 30, 2019;Published: September 10, 2019 DOI: 10.31031/RDMS.2019.11.000769 ISSN: 2576-8840 Volume11 Issue4
MENINGKATKAN PRESTASI BELAJAR DENGAN METODE JIGSAW DALAM PEMBELAJARAN IPS POKOK BAHASAN PELESTARIAN MAKHLUK HIDUP PADA KELAS V SEMESTER I SD NEGERI BLOTONGAN 01 SALATIGA

The purpose of this study was to describe the application of the 2013 Curriculum Based Jigsaw learning technique and improve student learning achievement. The subjects used were the fifth grade students of Blotongan District Elementary School 01 Salatiga 1st Semester 2019/2020 Year with a total of 25 students. The technique used in this study is class action research or PTK which consists of two cycles. Each cycle consists of the stages of planning, action, observation and reflection. Data retrieval used is a test in the form of test questions and non-tests in the form of observation or direct observation. Data analysis was performed by quantitative techniques. In the first cycle the final score achieved was an average of 71.28. In the second cycle the number of final grades managed to rise with an average value of 79.32. Thus there was an increase of 8.04. Based on the analysis of the results of the study, the Jigsaw method succeeded in increasing the learning achievement of students in the first cycle of 72.35% and the second cycle of 82.50%. The application of the jigsaw learning method is proven to increase the enthusiasm and results of student learning achievemen
A neural network construction for pattern recognition problems with standard information on the basis of the algorithms of model with piecewise linear surfaces and parameters

In the paper for recognition problems with standard information on the basis of the operator approach and the model with parameters and piecewise linear surface has been constructed a neural network which reproduced of computations performs by the correct algorithm.
Classification of BINUS University Student Organizations’ Logos from the Perspective of Visual Construction

This research focuses on the classification of BINUS University student organizations’ logo using textual qualitative approach. It aims at analyzing how they chose the shape of the logo in order to express their identity. This research is framed by three significant focal points in view of its construction namely, the combination of picture mark and letter mark, the integration of picture mark within letter mark or vice versa, and letter mark only. The result of this research shows that the officials of the organizations tend to choose logo that has aesthetic quality in simple visual form rather than complicated one. This research also closely observes the making process of the logo and its classification up to its final appointment as the official logo of the student organizations.
Optimized Scheduling Approach for Scientific Applications Based on Clustering in Cloud Computing Environment

Cloud Computing refers to the use of the computing capabilities of remote computers, where the user has considerable computing power without having powerful units. Scientific applications, usually represented as Directed Acyclic Graphs (DAGs), are an important class of applications that lead to challenging problems for resource management in distributed computing. With the advent of Cloud Computing, particularly the IaaS offers for on demand virtual machines leasing, multiple jobs execution, consisting of a large number of DAGs, needs an elaborated scheduling and resource provisioning policies, for efficient use of resources. Only few works exists that consider this problem in the context of clouds environment. In goal of optimization and fault tolerance, DAGs applications are generally partitioned into multiple parallel DAGs using clustering algorithm and assigned to VM (Virtual Machine) resources independently. In this work, we investigate through simulation, the impact of clustering for both provisioning and scheduling policies in the total makespan and financial costs for execution of user's application. We implemented four scheduling policies well-known in grid computing systems, and adapted clustering algorithm to our resource management policy that leases and destroys dynamically VMs. We show that dynamic policies can achieve equal or even better performance than static management policies.
Bank Privatization in Sub-Saharan Africa: The Case of Nigeria Deposit Money Banks (1980 – 2015)

IntroductionThe ultimate goal of any credible and legitimate government is to ensure sustained improvement in the standard of living of the citizenry.This was clearly demonstrated when economic depression, World War II, and the final breakup of colonial empires pushed government into a more active role including ownership of production and provision of all types of goods and services.These are examples of the direct or indirect involvement of government in an economy to correct externalities; ensure equitable distribution of income as well as allocation of goods and services (Ajibola, 2008; Asaolu, 2015; Dornigie, 2012; Duru, 2000;Megginson & Netter, 2001).Drawing on the successful experience of most developed economies, many African countries at independence adopted a statist approach to economic development, with government being the major player.It is estimated that about three thousand enterprises are fully or partially controlled by governments in Africa.In Nigeria, the size of government in the economy had grown sharply between independence in 1960 and the era of structural adjustment in the 1980s.Nigerian economy from colonial history through independence and up till today has been characterized by huge government investments in public enterprises thus, making government a major shareholder (Dornigie, 2012; Duru, 2000).Available evidences suggest that this public-dominated process has not achieved the desired impact in most African countries coupled with the development in the globalized world (El-Rufai, 2000;Usman, 2000).These have precipitated the clamour for comprehensive national economic reforms that will facilitate efficient macroeconomic management such as privatization, commercialization and liberalization with more emphasis on privatization, (El-Rufai, 2000;Usman, 2000).In Nigeria, the performance of the public sector was not accompanied by sustainable economic growth rate anticipated by policy makers or planners.There have been slowdowns especially in economic activities coupled with worldwide economic recession (El-Rufai, 2000;Usman, 2000).Privatization is a common process the world over or put differently, evolution of a New World Economic Order.This process is part of a wider concept known as new "Public Management." which is characterized by the liberalization or deregulation of economic activities.This is aimed at achieving efficiency and effectiveness in resource allocation and utilization of the public sector by the introduction of a set according to the geographical area.It is also the relieving of the government shareholding to the public and / or private sector so as to boost the performance of the organizations
The Impact of a Creolized Text Non-verbal and Verbal Parts on its Perception (an Experimental Study)

The paper analyzes the impact of creolized text verbal and non-verbal parts on the perception of its meaning. The perception facreolized text is dualistic: the non-verbal part (image) is identified with visual recollection images while the verbal part (text) is identified with verbal models reflecting nonlingual consciousness images as well. The perception of acreolized text perception was analyzed with Osgood’s semantic differential method which enables the identification of sense perception results involving both genetically translated conscious images (images of color) and conscious images developed in one’s culture acquisition. The applied method reveals denotative as well as axiological characteristics of creolized text content. The resulting semantic spaces demonstrate the impact patterns of the image and text parts on the creolized text understanding.
Critical Discourse Analysis On �Spiderman�

Talking about CDA or �Critical Discourse Analysis� means talking about power domination, ideologies and concepts employ in certain community. The reflection of power, ideologies, concepts or event traditional values can be found easily nowadays in the entertainment section with products like movies, songs, advertising, and many more. Since the values are packed with attractive features, some people are allured with the features and forget the power that drives some people to create the products which enable them to portray their beliefs and values. This research is trying to reveal the critical aspect of the movie �Spiderman� (2002) which contain Christian beliefs, bias concepts in American�s humanity and heroism. The method employs in the research is descriptive analysis based on the data taken from the scenes of the movie, tagline, dialogues. The result shows that through this movie, American unveils the ideology of liberalism covered with the values of humanity and heroism to drive people into the hegemony: American is the only power domination and the source of great heroes.
MODELLING OF PARTIAL DISCHARGE ANALYSIS SYSTEM USING WAVELET TRANSFORM DENOISING TECHNIQUE IN LABVIEW ENVIRONMENT

Partial discharge (PD) measurement is an essential to detect and diagnose the existence of the PD. However, this measurement has faced noise disturbance in industrial environments. Thus, PD analysis system using discrete wavelet transform (DWT) denoising technique via Laboratory Virtual Instrument Engineering Workbench (LabVIEW) software is proposed to distinguish noise from the measured PD signal. In this work, the performance of denoising process is analyzed based on calculated mean square error (MSE) and signal to noise ratio (SNR). The result is manipulated based on Haar, Daubechies, Coiflets, Symlets and Biorthogonal type of mother wavelet with different decomposition levels. From the SNR results, all types of the mother wavelet are suitable to be used in denoising technique since the value of SNR is in large positive value. Therefore, further studies were conducted and found out that db14, coif3, sym5 and bior5.5 wavelets with least MSE value are considered good to be used in the denoising technique. However, bior5.5 wavelet is proposed as the most optimum mother wavelet due to consistency of producing minimum value of MSE and followed by db14.
Testing collinear factorization in a spectator model with mass corrections

In perturbative QCD, the masses of the hadrons involved in high energy reactions can usually be neglected.However, in the case of Kaon production in electron-proton collisions at low (and not so low) beam energies this may not be a good approximation.In particular, a recent proposal to include hadron masses in theoretical calculations shows how these Hadron Mass Corrections (HMCs) can explain a large discrepancy observed in measurements performed at the HERMES and COMPASS experiments.In this talk, we present preliminary results of a spectator model calculation designed to test the range of validity of the approximations needed in the proposed factorization scheme.We focus on inclusive DIS scattering as a first step towards the analysis of HMCs in semi-inclusive processes.
A Multi-feature Fusion and SSAE-Based Deep Network for Image Semantic Recognition

Images are powerful tools with which to convey human emotions, with different images stimulating diverse emotions. Numerous factors affect the emotions stimulated by the image, and many researchers have previously focused on low-level features such as color, texture and so on. Inspired by the successful use of deep convolutional neural networks (CNN) in the visual recognition field, we used a data augmentation method for small data sets to gain the sufficient number of the training dataset. In this paper, we use low-level features (color and texture features) of the image to assist the extraction of advanced features (image object category features and deep emotion features of images), which are automatically learned by deep networks, to obtain more effective image sentiment features. Then, we use the stack sparse auto-encoding network to recognize the emotions evoked by the image. Finally, high-level semantic descriptive phrases including image emotions and objects are output. Our experiments are carried out on the IAPS and GAPED data sets of the dimension space and the artphoto data set of the discrete space. Compared with the traditional manual extraction methods and other existing models, our method is superior to in terms of test performance.
TINGKAT LITERASI MEDIA BERBASIS KOMPETENSI INDIVIDU DI KALANGAN SISWA SMA NEGERI 17 MAKASSAR

Nowadays the mass media is experiencing very rapid development. Various positive and negative impacts certainly cannot be ignored. This impact hits parents, adults, teenagers and children. Adolescents are a group that is vulnerable to the negative effects of mass media by which media literacy movements emerge as solutive actions over concerns about negative media influences. The ability of media literacy is very necessary so that the media can be used intelligently and wisely. The purpose of this study are: (1) To determine the level of technical skills (technical expertise) of SMA Negeri 17 Makassar students in accessing computer and internet media; (2) To describe the level of critical understanding (critical understanding) of SMA Negeri 17 Makassar students in analyzing and evaluating computer and internet media content; (3) To understand the level of communicative abilities (communication skills) of SMA Negeri 17 Makassar students in establishing social relations through internet media and creating creative internet media content; (4) To find out the level of media literacy of SMA 17 Makassar students. The results of this study indicate that cumulatively the level of technical skill ability is at 99.5%, the level of critical understanding capability is at 74.7%, the level of communicative abilities is at a value of 110.3%. By referring to the values above, the level of media literacy of Makassar 17 Public High School students is 94.83% or at the Medium level.
Deep Q-Network Based Power Allocation Meets Reservoir Computing in Distributed Dynamic Spectrum Access Networks

Dynamic spectrum access (DSA) is regarded as one of the key enabling technologies for future communication networks. In this paper, we introduce a power allocation strategy for distributed DSA networks using a powerful machine learning tool, namely deep reinforcement learning. The introduced power allocation strategy enables DSA users to conduct power allocation in a distributed fashion without relying on channel state information and cooperations among DSA users. Furthermore, to capture the temporal correlation of the underlying DSA network environments, the reservoir computing, a special class of recurrent neural network, is employed to realize the introduced deep reinforcement learning scheme. The combination of reservoir computing and deep reinforcement learning significantly improves the efficiency of the introduced resource allocation scheme. Simulation evaluations are conducted to demonstrate the effectiveness of the introduced power allocation strategy.
Disambiguation-free partial label learning

Partial label learning is an important weakly supervised machine learning framework. In partial label learning, each object is described by a single instance in the input space; however, in the output space, it is associated with a set of candidate labels among which only one is valid. An intuitive strategy is to disambiguate candidate labels, but this strategy tends to be misled by false positive labels; therefore, new disambiguation-free approaches need to be considered. In this paper, several algorithms are reviewed from the perspective of disambiguation and disambiguation-free strategies. First, the problem definition on partial label learning and its relationship with other related learning frameworks are given. Second, several representative partial label learning algorithms via the disambiguation strategy are introduced. Third, two of our proposed disambiguation-free algorithms are presented. Finally, the summary of this paper is given and possible future investigations on partial label learning are briefly discussed.
PENGARUH KUALITAS SDM, KOMITMEN ORGANISASI, PEMANFAATAN TEKNOLOGI INFORMASI PADA EFEKTIVITAS PENERAPAN SAP BERBASIS AKRUAL

Abstract: Effect of Quality of Human Resources, Organizational Commitment, Use ofInformation Technology on the Effectiveness of Accrual-Based SAP Implementation. Basedon knowing the influence of the quality of human resources, organizational commitment, andthe application of information technology on the effectiveness of applying accrual-basedgovernment accounting standards to SKPD Karangasem Regency. As many as 134 civilservants in the financial section are the population as well as samples from this study becausethe sampling technique used is saturated samples. multiple linear regression is the analysistechniques that used in this study,and the results show that the quality of HR, organizationalcommitment and the application of information technology have a positive influence on theeffectiveness of accrual-based SAP implementation in the SKPD of Karangasem Regency.
Introducing AnomDB: An Unsupervised Anomaly Detection Method for CNC Machine Control Data

We propose the application of unsupervised machine learning to automatically detect anomalous behavior preceding machine failure. We achieve this through an approach that utilizes Principal Component Analysis (PCA), latent feature extraction, and Density Based Scanning of Applications with Noise (DBSCAN). We call this method AnomDB. Time series data collected from Computer Numerically Controlled (CNC) machines may benefit from this technique due to its ability to consolidate noisy, multivariate data from CNC controls and detect anomalies without reliance on periodicity of signal. We perform experiments on CNC machine control data to demonstrate the effectiveness of this method in discovering anomalies over other commonly used methods of anomaly detection such as IQR and k-means clustering. We show the effectiveness of this method on a representative example in an actual machine shop, and then on a series of real machining data with synthetic anomalies injected.
Face Recognition by the Construction of Matching Cliques of Points

This paper addresses the problem of face recognition using a graphical representation to identify structure that is common to pairs of images. Matching graphs are constructed where nodes correspond to local brightness gradient directions and edges are dependent on the relative orientation of the nodes. Similarity is determined from the size of maximal matching cliques in pattern pairs. The method uses a single reference face image to obtain recognition without a training stage. Results on samples from MegaFace obtain a 100% correct recognition result.
On some properties of ML and REML estimators in mixed normal models with two variance components

In the paper, the problem of estimation of variance components σ 2 1 and σ 2 2 by using the ML-method and REML-method in a normal mixed linear model N Y, E(Y ) = Xβ, Cov(Y ) = σ 2 1 V + σ 2 2 I n is considered.This paper deal with properties of estimators of variance components, particularly when an explicit form of these estimators is unknown.The conditions when the ML and REML estimators can be expressed in explicit forms are given, too.The simulation study for one-way classification unbalanced random model together with a new proposition of approximation of expectation and variances of ML and REML estimators are shown.Numerical calculations with reference to the generalized Fisher's information are also given.