Reviewerly/exHarmony huggingface dataset:
Data Management System for Photovoltaic Solar Environment

Abstract As communication environments and Internet-of-Things technologies evolve, smart grid is being studied for collecting and predicting facility data in real time. In this paper, we propose a management system that collects the data of a photovoltaic system and predicts the amount of power generation. For this purpose, we designed an environment that can collect solar data, adjacent environment data, and facility information in real time. In addition, to expand the limits of previous power generation prediction studies, a power generation prediction experiment was conducted using recurrent neural network-based methods. The experimental results are compared with those of previous studies.
Texture Traits with Uniform-Quantization in Handwriting Documents for Digital Forensics Investigation

Abstract In crime and falsification related to documents are often difficult to verify orauthenticated by the authorities. This is the basis of research to develop a system in assisting digital forensics to investigate and seek the truth of the evidence in the form of digital handwritten documents. The steps that being taken by researchers are collecting and digitizing the documents into image form, converting color from RGB to greyscale, separate the object through thresholding, color histogram, uniform quantization from 256 to 128 of greylevel, texture feature extraction ie variance, skew, relative smoothness, entropy and mean, normalize the feature value and similarity measures through Euclidean distance calculations. From the results of testing of 10 data that has been matched with 20 training data, 6 documents successfully recognized correctly the authenticity of the document owner. Thus, the system in this study produces an accuracy of 60% and can be used to assist digital forensics in analyzing the authenticity of handwritten documents.
Graphical user interfaces in the X stack

“XML Everywhere” isn't just a slogan: it actually works, up and down the XML application stack. Recent developments, such as the inclusion of custom elements in HTML5, allow the declarative approach of XML to come into the browser/server interaction. XForms, supported by SVG and CSS, can serve as the basis for a graphical user interface. A custom WebSocket element can support client-to-client and server-push communication of XML data. Applications of State Chart XML (SCXML) mean that the “XML Everywhere” approach can be extended all the way to models of operations in an application. Interactive games offer living proof of the stack.
PENERAPAN KNOWLEDGE MANAGEMENT SYSTEM SALES AND CUSTOMER CARE PADA PT SATRIA MEDIKANTARA PALEMBANG

PT Satria Medikantara Palembang has a great desire to apply knowledge management system, therefore the documentation of knowledge and its utilization needs to be well managed in the context of performance improvement. The implementation of knowledge management in PT Satria Medikantara Palembang is considered very good and can have a positive impact for the quality of employees. Where every employee can store and document and share knowledge owned, so that other employees can access, even learn and discuss with other employees based on the knowledge posted. Then when needed a knowledge, it is very easy to find in the database searching feature in the web based knowledge management system at PT Satria Medikantara Palembang. The methodology used in this study refers to the methodology of knowledge management system life cycle developed by Awad and Ghaziri (2010), and this system will be based on web using PHP programming language
Region-Based CNNs for Pedestrian Gender Recognition in Visual Surveillance Environments

Inferring soft biometric labels in totally uncontrolled outdoor environments, such as surveillance scenarios, remains a challenge due to the low resolution of data and its covariates that might seriously compromise performance (e.g., occlusions and subjects pose). In this kind of data, even state-of-the-art deep-learning frameworks (such as ResNet) working in a holistic way, attain relatively poor performance, which was the main motivation for the work described in this paper. In particular, having noticed the main effect of the subjects’ “pose” factor, in this paper we describe a method that uses the body keypoints to estimate the subjects pose and define a set of regions of interest (e.g., head, torso, and legs). This information is used to learn appropriate classification models, specialized in different poses/body parts, which contributes to solid improvements in performance. This conclusion is supported by the experiments we conducted in multiple real-world outdoor scenarios, using the data acquired from advertising panels placed in crowded urban environments.
Extending Vocabularies: The Rack and the Weeds

In its simplest form a vocabulary is simply a set of words and phrases with predefined meanings. In this paper the term is used to mean a controlled vocabulary and, in particular, a controlled vocabulary in the context of computer markup languages such as XML or JSON or SGML. Vocabularies are created in specific contexts and for specific purposes. Like all human constructs they are flawed and need to be repaired and changed over time; as people use vocabularies they also gain understanding of the limitations in them and often want to extend them. Understanding these processes involves an understanding of the human needs involved: the social contexts in which people interact with and around the vocabularies. This paper characterizes some of these contexts and their properties, and in the light of this characterization describes changes to vocabularies, both successful and unsuccessful.
Stochastic Gradient Annealed Importance Sampling for Efficient Online Marginal Likelihood Estimation

We consider estimating the marginal likelihood in settings with independent and identically distributed (i.i.d.) data. We propose estimating the predictive distributions in a sequential factorization of the marginal likelihood in such settings by using stochastic gradient Markov Chain Monte Carlo techniques. This approach is far more efficient than traditional marginal likelihood estimation techniques such as nested sampling and annealed importance sampling due to its use of mini-batches to approximate the likelihood. Stability of the estimates is provided by an adaptive annealing schedule. The resulting stochastic gradient annealed importance sampling (SGAIS) technique, which is the key contribution of our paper, enables us to estimate the marginal likelihood of a number of models considerably faster than traditional approaches, with no noticeable loss of accuracy. An important benefit of our approach is that the marginal likelihood is calculated in an online fashion as data becomes available, allowing the estimates to be used for applications such as online weighted model combination.
Action Recognition Using Local Visual Descriptors and Inertial Data

Different body sensors and modalities can be used in human action recognition, either separately or simultaneously. Multi-modal data can be used in recognizing human action. In this work we are using inertial measurement units (IMUs) positioned at left and right hands with first person vision for human action recognition. A novel statistical feature extraction method was proposed based on curvature of the graph of a function and tracking left and right hand positions in space. Local visual descriptors have been used as features for egocentric vision. An intermediate fusion between IMUs and visual sensors has been performed. Despite of using only two IMUs sensors with egocentric vision, our classification result achieved is 99.61% for recognizing nine different actions. Feature extraction step could play a vital step in human action recognition with limited number of sensors, hence, our method might indeed be promising.
Entropic quantum machine

We study nanomachines whose relevant (effective) degrees of freedom $f\ensuremath{\gg}1$ but smaller than $f$ of proteins. In these machines, both the entropic and the quantum effects over the whole system play the essential roles in producing nontrivial functions. We therefore call them entropic quantum machines (EQMs). We propose a systematic protocol for designing the EQMs, which enables a rough sketch, accurate design of equilibrium states, and accurate estimate of response time. As an illustration, we design a novel EQM, which shows two characteristic shapes. One can switch from one shape to the other by changing temperature or by applying a pulsed external field. We discuss two potential applications of this example of an EQM.
KOMPARASI PENERAPAN BAHASA JURNALISTIK PORTAL BERITA DARING NASIONAL

Online media in the form of news portals always presents the latest news and is easily accessible when and where to keep abreast of the times. However, the language of the news that is applied in the online news portal is often not in accordance with the rules of the journalistic language that applies. The purpose of this study is to find out how comparisons of journalistic languages are applied to national online news portals, namely Antaranews.com and Kompas.com. This study uses the theory of the concept of Spelling and Indonesian Journal which is owned by Haris Sumadiria. The method used is kuanlitatif approach with content and description analysis. The results of the study found that the comparison of journalistic language applications between Antaranews.com was 51% while Kompas.com was 49%. Based on the results of this study, there are many indicators that are not found in both media, namely simple, concise, solid, straightforward, clear and clear. This comparison occurs because each media has the writing rules of each guide or white book. For further research to be more maximal in looking for more and wider references.
Random Forest Algorithm-Based Lightweight Comprehensive Evaluation for Wireless User Perception

The quality of wireless user perception for cells in a particular scenario is reflected on a set of indicators. Comprehensive evaluation of those cells is the base of network optimization for operators. Traditional methods use weighted sum of all indicators as the evaluation result. However, these indicators include some ineffective ones, which leads to an unconvincing evaluation result. To achieve a convincing and accurate result, we propose a lightweight comprehensive evaluation method. Firstly, indicator selection is implemented via random forest algorithm. Secondly, those selected indicators are weighted via entropy method. Finally, we compute the score of all cells with the weights. Experiment results are given to show that the cells with higher score perform better on all indicators, which is coincide with the actual situation. Hence, our proposed method is not only lightweight but also obtain more accurate result.
(1811-4823) Trend analysis in L-fuzzy contexts with absent values

Sometimes we have to work with L-fuzzy context sequences where one or more values are missing. These sequences can represent, among other things, the evolution in time of an L-fuzzy context. The studies of tendencies that wehave done so far used tools that are not valid when the L-fuzzy context has unknown values. In this work we address such situations and we propose new methods to tackle the problem. Besides, we use the study of tendencies to analyserelations between the objects and the attributes of L-fuzzy contexts and to replace the absent values taking into account the behaviour of the sequence.
Markup Vocabulary Customization

Introductory paragraph for the 2019 Balisage Symposium Proceedings
Creating Quality Methods

A method is the smallest block of code in an application. Methods can be reused, which helps prevent duplication. A method hides the details of an operation, so refactoring will not affect the calling code. This takes careful planning to achieve. In this chapter, we will discuss different techniques used to create quality methods: parameters, return values, guard clauses, length, comments, and nesting.
Information Seeking Behaviour: An overview

Information can be defined as a collection of facts organized in such a way that they have additional value beyond the value of the facts themselves. In simple word “information” can be defined as a processed data. Information, in the most restricted technical sense; it is an ordered sequence of symbols that can be interpreted as a message. This study aims to propose an overview of the various models and to find out how models are related to each other. These will serve the information community to illustrate that a number of factors which were responsible for, and contribute to, the different forms of sustainability of digital information services.Keyword: Information Seeking Behaviour, Models, Types
An Elementary Approach to Convergence Guarantees of Optimization Algorithms for Deep Networks

We present an approach to obtain convergence guarantees of optimization algorithms for deep networks based on elementary arguments and computations. The convergence analysis revolves around the analytical and computational structures of optimization oracles central to the implementation of deep networks in machine learning software. We provide a systematic way to compute the smoothness constants that govern the convergence behavior of first-order optimization algorithms used to train deep networks. A diverse set of example components and architectures arising in modern deep networks intersperse the exposition to illustrate the approach.
Visualizing Deep Q-Learning to Understanding Behavior of Swarm Robotic System

Swarm robotic systems (SRS) are a type of multi-robot systems that consist of many homogeneous autonomous robots inspired by social insects. In our pervious study, we succeeded in developing end-to-end control policies for SRS using Deep Q-Network (DQN) algorithm. However, since DQN is totally a black box, it is difficult to understand what were learnt through the learning process. Therefore, in this paper, a novel method of visualizing the decision making process in the DQN is proposed by combining Deconvolutional Network (Deconvnet) and Gradient-weighted Class Activation Mapping (Grad-CAM). Then we show what are being preserved as the deep features and which part of input image is concerned to make an action decision. The proposed method is demonstrated by conducting the computer simulations of a round trip task, in which the swarm robots need to visit two different locations alternatively as many times as possible. The computer simulations might also be explained that the proposed method visualizes the policies learned by DQN.
Deep Feature Fusion for Breast Cancer Diagnosis on Histopathology Images

This paper presents a deep feature fusion method based on the concept of 'residual connection' of ResNet to effectively extract distinguishable features which help to improve the classification performance of the Breast cancer prediction on histopathology images. Specifically, we fuse the features extracted from different blocks of Inception-V3 to merge the features learned. The concatenated features are considered as rich information which could capture the deep features of the images. Three experiments were also conducted to investigate the three factors that may affect the classification performance: 1) Feature extractor or Fine-tuningƒ 2) Normalization vs. Non-normalization and 3) The effectiveness of our deep feature fusion method. The dataset used in this study includes 400 microscopy images collected from the ICIAR 2018 Grand Challenge on Breast Cancer histopathology images. The images are divided into 4 classes which indicate the aggressiveness levels of breast cancer, described as Normal (N), Benign (B), In Situ Carcinoma (IS) or Invasive Carcinoma (IV) according to the predominant cancer type in each image. Experimental results show that our proposed deep feature fusion method can achieve a very high classification accuracy with 95% in distinguishing 4 types of cancer classes and 97.5% for differentiating two combined groups of cancer, which are Carcinoma (N+B) and Non-carcinoma (IS+IV).
A Comparative Study of Crack Branching in Glass Using Photoelasticity, Digital Image Correlation and Digital Gradient Sensing Techniques

Fracture behavior of high-stiffness and low-toughness materials such as soda-lime glass typically involves crack branching, a phenomenon which has not been well-understood. Attempts to shed light on this issue via full-field optical investigation has been hampered by numerous spatio-temporal experimental challenges as crack speeds in these materials reach upwards of 1500 m/s accompanied by highly localized small deformations. In this work, three different optical techniques—transmission photoelasticity, 2D Digital Image Correlation (DIC) and transmission-mode Digital Gradient Sensing (DGS)—are separately implemented to comparatively assess their efficacy to study crack growth and branching in soda-lime glass plates. Each method is implemented in conjunction with ultrahigh-speed photography (1–2 Mfps), flash/pulse illumination and a modified-Hopkinson pressure bar on nominally identical wedge impact experiments. Visualization of results from these experiments along with preliminary observations on their pros and cons of each approach for this material system are presented.
Device Independent Quantum Key Distribution Using Three-Party Pseudo-Telepathy

Removing trustworthiness from the devices is the motivation towards device independent quantum key distribution (DI-QKD). The only assumption in this case is that the devices obey the laws of quantum mechanics and are spatially isolated from each other. The security of the protocol can be achieved by certain tests based on various statistical analysis. Recently, Vidick and Vazirani (VV) proposed a DI-QKD scheme (Phys. Rev. Lett., 2014) exploiting the CHSH game. In a similar direction, here we present a simple proposal that exploits the idea of multi-party pseudo-telepathy game to certify device independent security. The relative advantages of our protocol are also discussed.
Perancangan Aplikasi Media Pembelajaran Pengenalan Pahlawan Nasional untuk Meningkatkan Rasa Nasionalis Berbasis Android

The National Hero is a title given to Indonesian citizens who fought against invaders who died in defense of the nation and state of Indonesia, or produce extraordinary achievements and works for the development and progress of the nation and state of the Republic of Indonesia. This research is motivated by the development of multimedia technology in various fields including in the field of education. The problem is that education in Indonesia still uses many conventional methods, namely by listening to teacher explanations and using books. This method makes students easily bored. Therefore, a learning media is made using interactive multimedia technology based on Android, so students are more enthusiastic about learning. Along with the development of information technology, there are now many devices have been created to facilitate access to information. Information systems about the history of Indonesian Heroes today are still dominated by books. Therefore, researchers will create a learning application for the introduction of national heroes use the development of android-based technology that is growing rapidly nowadays. The purpose of making this learning media is to help students get to know National Heroes in Indonesia to improve memory and nationalist feelings of Tanjung Morawa SDN 104238 students which was developed with Adobe Flash and Android Smartphones.
Selecting Relevant Association Rules From Imperfect Data

Association Rule Mining (ARM) in the context of imperfect data (e.g. imprecise data) has received little attention so far despite the prevalence of such data in a wide range of real-world applications. In this work, we present an ARM approach that can be used to handle imprecise data and derive imprecise rules. Based on evidence theory and Multiple Criteria Decision Analysis, the proposed approach relies on a selection procedure for identifying the most relevant rules while considering information characterizing their interestingness. The several measures of interestingness defined for comparing the rules as well as the selection procedure are presented. We also show how a priori knowledge about attribute values defined into domain taxonomies can be used to (i) ease the mining process, and to (ii) help identifying relevant rules for a domain of interest. Our approach is illustrated using a concrete simplified case study related to humanitarian projects analysis.
SAML 2.0

The Security Assertion Markup Language (SAML) 2.01 is known for providing two important features, cross-domain single sign-on (SSO) and identity federation. SAML 2.0 has been adopted in many enterprise environments because it enabled the enterprise to have applications used by employees, customers, and partners delegate user authentication to a centralized enterprise identity provider. This gave the enterprise a central place to manage and control identities. If you are writing an application for large enterprise customers, they may expect you to support authentication using SAML 2.0 .
A Context-Aware and Self-Adaptation Strategy for Cloud Service Selection and Configuration in Run-Time

Day after day, the number of mobile applications deployed on cloud computing continues in increasing because o f smartphone capabilities improvement. Cloud computing has already succeeded in the web based application, for that reason, the demand for context aware services provided by cloud computing increases. To customize a cloud service that takes into account th e consumer requirements, which depend on information change, it brings to light many recent challenges to cloud computing about environment aware, location aware, time aware. The cloud provider, moreover, has to manage personalized applications and the con straints of mobile devices in matters of interaction abilities and communication restrictions. This paper proposes a strategy for selecting automatically an appropriate cloud environment that runs out whole requirements, defines a configuration for the ass ociated cloud environment and able to easily adapt to the change of the environment on either the user or the cloud side or both. This process builds on the principles of dynamic software product lines, Agent oriented software engineering, and the MAPE k m odel to select and configure cloud environments according to the consumer needs and the context change.
Context-Aware Superpixel and Bilateral Entropy—Image Coherence Induces Less Entropy

Superpixel clustering is one of the most popular computer vision techniques that aggregates coherent pixels into perceptually meaningful groups, taking inspiration from Gestalt grouping rules. However, due to brain complexity, the underlying mechanisms of such perceptual rules are unclear. Thus, conventional superpixel methods do not completely follow them and merely generate a flat image partition rather than hierarchical ones like a human does. In addition, those methods need to initialize the total number of superpixels, which may not suit diverse images. In this paper, we first propose context-aware superpixel (CASP) that follows both Gestalt grouping rules and the top-down hierarchical principle. Thus, CASP enables to adapt the total number of superpixels to specific images automatically. Next, we propose bilateral entropy, with two aspects conditional intensity entropy and spatial occupation entropy, to evaluate the encoding efficiency of image coherence. Extensive experiments demonstrate CASP achieves better superpixel segmentation performance and less entropy than baseline methods. More than that, using Pearson's correlation coefficient, a collection of data with a total of 120 samples demonstrates a strong correlation between local image coherence and superpixel segmentation performance. Our results inversely support the reliability of above-mentioned perceptual rules, and eventually, we suggest designing novel entropy criteria to test the encoding efficiency of more complex patterns.