Reviewerly/exHarmony huggingface dataset:
Hybrid Segmentation of Malaria-Infected Cells in Thin Blood Slide Images

Malaria is a hazardous disease responsible for nearly 400 to 1000 deaths annually in India. The conventional technique to diagnose malaria is through microscopy. It takes a few hours by for an expert to examine and diagnose malarial parasites in the blood smear. The diagnosis report may vary when the blood smears are analyzed by different experts. In proposed work, an image processing based robust algorithm is designed to diagnose malarial parasites with minimal intervention of an expert. Initially, the images are enhanced by extracting hue, saturation and intensity planes followed by histogram equalization. After preprocessing, a median filter is employed to eliminate the noise from the images. After the preprocessing, segmentation of malaria parasite is achieved using k-means clustering to get the clear vision of the region of interest. The clustering is followed by region growing area extraction to remove the unwanted area from the segmented image. The second part deals with counting the number of infected RBCs. The method uses roundness detection for the calculation of the infected RBCs. The experiments give encouraging results for the saturation plane of HSI color space and segmentation accuracy by up to 94%.
SISTEM MANAJEMEN PENGETAHUAN PERLINDUNGAN ANAK (STUDI KASUS: SAKTI PEKSOS DI KEMENTERIAN SOSIAL)

In 2014, The Ministry of Social Affairs ofThe Republic of Indonesia (MOSA) send 670 social workers, called “Sakti Peksos” to all provinces in Indonesia. Sakti Peksos, which stands for “Satuan Bakti Pekerja Sosial” is the social workers who assist the Child Welfare Program in Indonesia or known as “PKSA”. To assist the children’s case, the social workers need many practice knowledge of how to deal with the children’s issues. Therefore, it is beneficial to manage the existing information by developing knowledge management system. Through the application based web program, this knowledge management system will develop all the Sakti Peksos’s knowledge. This web aims at organizing all the informations that they have so that it can be shared to other Sakti Peksos. The purpose of this study was to develop a Child Protection Knowledge Management System for Sakti Peksos in the Ministry of Social Affairs. This research uses Knowledge Management System Life Cycle methodology which is adopted from award and ghaziri methodology. This research involves four steps; gathering the knowledge, designing the brueprint of knowledge management system, helding verification as well as validation process, and implementing knowledge management system. The result of this research is a web based child protection knowledge management system, equipped with CakePHP framework and MySQL as Relational Database Management System (RDBMS).
Codebooks from generalized bent <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e146" altimg="si4.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">Z</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msub></mml:math>-valued quadratic forms

Codebooks with small inner-product correlation have applications in unitary space–time modulations, multiple description coding over erasure channels, direct spread code division multiple access communications, compressed sensing, and coding theory. It is interesting to construct codebooks (asymptotically) achieving the Levenshtein bound. This paper presents a class of generalized bent Z4-valued quadratic forms, which contains functions proposed by Heng and Yue (2017). Using these generalized bent Z4-valued quadratic forms, we construct optimal codebooks achieving the Levenshtein bound. These codebooks have parameters (22m+2m,2m) and alphabet size 6.
Vergleich der menschlichen, maschinellen und Post-Editing-Übersetzung aus dem Slowakischen ins Deutsche mittels automatischer Evaluation

Summary The aim of the study is to compare translation quality and effectiveness (translator productivity) using measures of the automatic evaluation of machine translation output. The examined translation(s) was a legal text, translated from Slovak (mother tongue) into German. We distinguish human translation (HT), machine translation (MT) and post-edited MT (PEMT). For the evaluation we used our own tool, wherein were implemented the metrics of automatic MT evaluation. Analysis of Variance and Multiply Comparisons (Tukey HSD test) were applied to textual data. The results proved that in terms of productivity, regarding the expert and terminological knowledge of human translators, post-editing of MT is more effective than HT. On the other hand, in terms of quality (sentence structure or stylistics), regardless of time, HT is of a higher quality then MT, but similar to PEMT. The post-editors were affected by grammar and by stylistics of MT output, but post-edited a larger volume of MT than human translators translated.
Intellectual structure and evolution patterns of archival information resource research in China

Purpose The purpose of this paper is to identify the leading topics and developmental trends of archival information resource research in China by visualizing the intellectual structure and evolution patterns of archival information resource research. Design/methodology/approach This study took China National Knowledge Infrastructure (CNKI) as the data source and extracted keywords from relevant articles in archival information resource research as the sample. First, the frequency and co-occurrence of keywords were calculated by using SCI2. Second, this study analyzed the co-word network indicators by using Pajek. Then, topic community detection was conducted by using a VOS viewer, as well as the visualization of intellectual structures. Next, this study developed a graphical mapping of the evolution of research topics over time by using Cortext. Findings The research topics of archival information resources in China were unbalanced but distinct. Researchers focus on the construction and utilization of archival information resource, which consist of five evident research directions. The phenomena of fusion and differentiation coexist in research topic evolution. There were both continuities of traditional research and innovations in emerging research. The archival information resource research tended to be systematized and extended, reflecting the vertical and horizontal extension of the research content. Originality/value Based on a large number of previous studies, this study adopted quantitative methods to reveal the intellectual structure and evolution patterns of archival information resource research in China, providing guidance for researchers and institutions to grasp research status and developmental trends.
A Structure of Semantic Service in a Distributed Knowledge Based System

The main difference in distributed systems based on knowledge is the use of the service approach and ontologies in knowledge engineering.Semantic service acts as a software agent that regulates the interaction of all components of the system.Semantic service provides: semantic principle, which defines a formal description of information and allows you to define the following characteristics of services: scalability, semantic interoperability, formal models of services and ontologies; the principle of decision making; the principle of distribution, which allows you to aggregate the capabilities of several computing objects through cooperation.The communication and collaboration mechanism of the distributed systems based on knowledge covers the message transfer between services, information exchange between semantic service and distributed knowledge bases, and the semantic service behaviors in the entire process from request submission, request handle, to result return.The knowledge base is formed from three types of ontologies.The rules for recognizing performance problems are converted into ontology and the system knowledge base is placed.The working memory of a semantic service contains facts that correspond to simple events, composite events, and identified problems and recommendations.The algorithm for using semantic service is discussed in the article.To assess the performance of the SS attached, create an information system of two components: event trace generator and event trace analyzer.
An Enhanced Plagiarism Detection Based on Syntactico-Semantic Knowledge

The issue of plagiarism in documents has been present for centuries. Yet, the widespread dissemination of information technology, including the internet, made plagiarism much easier. Consequently, methods and systems aiding in the detection of plagiarism have attracted much research within the last two decades. This paper introduces a plagiarism detection technique based on the semantic knowledge, notably semantic class and thematic role. This technique analyzes and compares text based on the semantic allocation for each term in the sentence. Semantic knowledge is superior in semantically generating arguments for each sentence. Weighting for each argument generated by semantic knowledge to study its behavior is also introduced in this paper. It was found that not all arguments affect the plagiarism detection process. In addition, experimental results on PAN13-14 ( http://pan.webis.de/ .) data sets revealed significant speed-up, which outperforms the recent methods for plagiarism detection in terms of Recall and Precision measure.
The Gold Tree: An Information System for Analyzing Academic Genealogy

Academic genealogy investigates the relationships between student researchers and academy professionals. In recent years, it proved to be a powerful technique to help analyze the spread of scientific knowledge. Tools that make to visualize these relationships among academics easier are potentially useful and have been proposed. This work specifies and describes the development of a Web information system for creating and visualizing academic genealogy trees from a set of metadata extracted and integrated from multiple sources. The proposed system allows a researcher to query and track information about his or her advisers and graduate students at any level. A case study was explored to validate the system using data from more than 570 thousand theses and dissertations.
Analysis of Spanish Radiometric Networks with the Novel Bias-Based Quality Control (BQC) Method

Different types of measuring errors can increase the uncertainty of solar radiation measurements, but most common quality control (QC) methods do not detect frequent defects such as shading or calibration errors due to their low magnitude. We recently presented a new procedure, the Bias-based Quality Control (BQC), that detects low-magnitude defects by analyzing the stability of the deviations between several independent radiation databases and measurements. In this study, we extend the validation of the BQC by analyzing the quality of all publicly available Spanish radiometric networks measuring global horizontal irradiance (9 networks, 732 stations). Similarly to our previous validation, the BQC found many defects such as shading, soiling, or calibration issues not detected by classical QC methods. The results questioned the quality of SIAR, Euskalmet, MeteoGalica, and SOS Rioja, as all of them presented defects in more than 40% of their stations. Those studies based on these networks should be interpreted cautiously. In contrast, the number of defects was below a 5% in BSRN, AEMET, MeteoNavarra, Meteocat, and SIAR Rioja, though the presence of defects in networks such as AEMET highlights the importance of QC even when using a priori reliable stations.
<i>Coding with XML for Efficiencies in Cataloging and Metadata</i>

Coding with XML for Efficiencies in Cataloging and Metadata is intended for library staff who are ready to integrate XML technologies into their work and is designed to be accessible even for those...
Real-Time Detection of DNS Exfiltration and Tunneling from Enterprise Networks

Enterprise networks constantly face the threat of valuable and sensitive data being stolen by cyber-attackers. Sophisticated attackers are increasingly exploiting the Domain Name System (DNS) channel for exfiltrating data as well as maintaining tunneled command and control communications for malware. This is because DNS traffic is usually allowed to pass through enterprise firewalls without deep inspection or state maintenance, thereby providing a covert channel for attackers to encode low volumes of data without fear of detection.This paper develops and evaluates a real-time mechanism for detecting exfiltration and tunneling of data over DNS. Unlike prior solutions that operate off-line or in the network core, ours works in real-time at the enterprise edge. Our first contribution is to develop, tune, and train a machine learning algorithm to detect anomalies in DNS queries using a benign dataset of top rank primary domains from two enterprise networks. Our second contribution is to implement our scheme on live 10 Gbps traffic streams from the network borders of the two organizations, inject more than a million malicious DNS queries generated via an exfiltration tool, and show that our solution is able to identify them with high accuracy. Our tools and datasets are made available to the public for validation and further research.
Graphs are not enough: using interactive visual analytics in storage research

Storage researchers have always been interested in understanding the complex behavior of storage systems with the help of statistics, machine learning, and simple visualization techniques. However, when a system's behavior is affected by hundreds or even thousands of factors, existing approaches break down. Results are often difficult to interpret, and it can be challenging for humans to apply domain knowledge to a complex system. We propose to enhance storage system analysis by applying interactive visual analytics, which can address the aforementioned limitations. We have devised a suitable Interactive Configuration Explorer (ICE), and conducted several case studies on a typical storage system, to demonstrate its benefits for storage system researchers and designers. We found that ICE makes it easy to explore a large parameter space, identify critical parameters, and quickly zero in on optimal parameter settings.
The Artist as Model User

The idea of the 'model user' is well established in Computer Science. However, the usual practice of establishing a model user might not be appropriate within the context of personal data where such models are potentially limiting and exclusory. The domain of self-tracking data art offers an alternative view on the concept of the Quantified Self. The model user then becomes that of the data artist. An 'edge case' where the user's representation of 'self' is determined more by creative skills and less by the tools used. Moreover, it is argued that models of creativity could be useful in order to understand the concept of Quantified Self as an on-going process. The Mace and Ward model is given particular attention and examples of artists work, including the author's own, are discussed in order to highlight the complexity of the issues they face and the ways in which they overcome them.
Adaptive and Personalized Privacy and Security (APPS 2019)

It is our great pleasure to welcome you to the First International Workshop on Adaptive and Personalized Privacy and Security (APPS 2019). APPS 2019 (http://appsworkshop.cs.ucy.ac.cy) is a half-day workshop held on June 09, 2019, in conjunction with the ACM Conference on User Modeling, Adaptation and Personalization (ACM UMAP 2019) in Larnaca, Cyprus. Adaptive and personalized privacy and security aims at supporting privacy- and/or security-related tasks by leveraging on holistic user models which reflect the users' unique sociocultural, physical, physiological and technological context in which interaction takes place. As such, APPS 2019 aims to bring together researchers and practitioners working on diverse topics related to understanding and improving the usability of privacy and systems security, by applying user modeling, adaptation and personalization principles framed by User-Centered Design methods.
Design and Implementation of an Automatic Scanning Tool of SQL Injection Vulnerability Based on Web Crawler

An automatic detection tool for SQL injection vulnerability based on web crawler is designed and implemented. By studying the characteristics of various web application vulnerabilities, the causes and detection methods of SQL injection vulnerabilities are analyzed in detail. In addition, functions such as URL (Uniform Resource Locator) optimization and similarity determination are added to each module’s characteristics, so that the vulnerabilities can be scanned more accurately and quickly. The tool can automatically explore the target based on web crawler framework. After testing, it is proved that the scanning tool can effectively detect potential SQL injection security vulnerabilities in a website.
The Development Of Attention, Relevance, Confidence, And Satisfaction (ARCS) Model Based on Active Learning to Improve Students'learning Motivation

This research and development aim to: (1) develop a valid and practical model so that it can improve students' motivation in learning mathematics, (2) know the effectiveness of the active learning based ARCS model. This research refers to the stages of research and development from Borg &amp; Gall which has been modified by Sukmadinata into 3 stages, namely preliminary studies, product development, and product testing. Data analysis techniques used are descriptive qualitative and quantitative. The research finds that (1) active learning based on ARCS model is valid and practical to improve students' learning motivation in mathematics learning based on the assessment given by education experts of 77.94% (good category), material experts of 87.14% (excellent category), and practitioners of 96.56% (excellent category). (2) The result of the effectiveness test shows that the developed product is effective in increasing students' learning motivation in mathematics learning based on experimental results which shows p &lt;0.05 on learning motivation which means there are significant differences in students who were taught using the ARCS model based on active learning.
Rooftop Solar Photovoltaic Power Forecasting Using Characteristic Generation Profiles

Rooftop solar photovoltaic generation systems are often subject to location and site-specific factors that affect the shape of the daily generation profile. We propose a novel approach to rooftop solar photovoltaic power forecasting that applies digital filtering to recent historical generation data to determine a site-specific "Characteristic Generation Profile". This profile can subsequently be used on its own, or in combination with any available exogenous variables, to improve the solar power forecasting process. The approach is simple to implement, uniquely customised for every system it is applied to, and computationally efficient.
CSCAC: one constant-size CPABE access control scheme in trusted execution environment

The popularity of versatile mobile devices has been increasing concerns about their security. How to protect the sensitive data is an urgent issue to be solved. Ciphertext-policy attribute-based encryption (CPABE) is a practical method for encrypting data and can utilise user's attributes to encrypt the sensitive data. In this paper, we propose a constant-size CPABE access control (CSCAC) model by using the Trusted Execution Environment to manage the dynamic key generated by attributes. The original data is encrypted by a symmetric storage key, then the storage key is encrypted under an AND-gate access policy. Only the user who possesses a set of attributes that satisfy the access policy can recover the storage key. The security analysis shows the design of this access control scheme reduces the burden and risk in the case of one single authority.
Using Spinoza Log Data to Enhance CS1 Pedagogy

Active learning in Computer Science classrooms often involves having students solve programming problems in class using a web-based interpreter. Spinoza is one such system which captures all of the student attempts and uses it to provide actionable learning analytics for the instructor. In this paper we present several new pedagogical applications of the log data from Spinoza, including two approaches to team formation and an in depth analysis of the Solve-Then-Debug debugging pedagogy in Spinoza. We provide some initial evidence that the Team Formation strategies may be effective methods for forming either diverse or homogeneous teams. The second application we examine in depth is the Solve-Then-Debug pedagogy in which students who correctly solve a Spinoza programming problem are then asked to analyze and debug the most common errors that the class has made so far on that problem. This is a social debugging process and in this paper we provide a detailed explanation of the learning goals for each step of this pedagogy. We also give an example of how students engaged with one particular Solve-Then-Debug problem. This provides initial evidence that the Solve-Then-Debug pedagogy engages students in effective program bug analysis activity.
Tracking Free Information Access

This chapter describes the method of Active Information Search (AIS), whose goal is to investigate spontaneous information acquisition while avoiding the limitations that come with pre-structuring the decision task. The method of AIS was developed to understand information needs in decision making with as little demand effects as possible. The proper pre-testing of an AIS scenario is a prerequisite to achieve adequate reliability and validity. The basic as well as the list version of AIS can be translated into a computer program. The experiment can be administered in the laboratory but also on the internet. For instance, basic AIS may be applied with only little programming effort via the internet with chat or messaging programs. The AIS method helps to understand reasoning, judgment and decision making, or complex problem solving, by providing process measures of the type, quantity, and sequence of information people are interested in.
Full Reference Stereoscopic Video Quality Assessment Based On Spatio-Depth Saliency And Motion Strength

Stereoscopic video quality is a perceptual phenomena that is related to the human visual system (HVS). In this paper, we present a spatio-depth saliency and motion strength based full reference stereoscopic video quality metric (FRSVQA). Initially, we obtain a spatial distortion map on every video frame to estimate spatial quality. The spatial distortion map is then refined by the depth salient maps to estimate depth quality. We also estimate the temporal quality by refining the spatial distortion map with the inter-frame difference map at the locations specified by motion edges. The spatial, depth and temporal qualities are systematically combined and averaged over the frames to estimate the overall stereo video quality metric.
Gaining or Losing Perspective

We study MINLO (mixed-integer nonlinear optimization) formulations of the disjunction $$x\in \{0\}\cup [l,u]$$ , where z is a binary indicator of $$x\in [l,u]$$ , and y “captures” $$x^p$$ , for $$p>1$$ . This model is useful when activities have operating ranges, we pay a fixed cost for carrying out each activity, and costs on the levels of activities are strictly convex. One well-known concrete application (with $$p=2$$ ) is mean-variance optimization (in the style of Markowitz). Using volume as a measure to compare convex bodies, we investigate a family of relaxations for this model, employing the inequality $$yz^q \ge x^p$$ , parameterized by the “lifting exponent” $$q\in [0,p-1]$$ . These models are higher-dimensional-power-cone representable, and hence tractable in theory. We analytically determine the behavior of these relaxations as functions of l, u, p and q. We validate our results computationally, for the case of $$p=2$$ . Furthermore, for $$p=2$$ , we obtain results on asymptotic behavior and on optimal branching-point selection.