Reviewerly/exHarmony huggingface dataset:
Detecting Outliers in Data Streams Based on Minimum Rare Pattern Mining and Pattern Matching

Outliers are the major factors to influence the accuracy of data-based processing, thus, they must be discovered from collected datasets to guarantee data security. With the widely use of sensors and other monitoring equipment, data streams are becoming the main form of data. However, the huge scale of data streams results in the number of mined rare patterns very large, which makes it hard to effectively detect outliers through pattern-based outlier detection methods. Since minimal rare patterns (MRPs) can represent rare patterns and the number is much smaller, therefore, the use of MRPs on outlier detection can short the time consumption. Based on this idea, this paper proposes an outlier detection approach based on minimal rare pattern, called ODMRP, which is composed of pattern mining phase and pattern matching phase. Specifically, in the pattern mining phase, an improved minimal rare pattern mining algorithm, namely MRPM, is proposed to mine the MRPs from data streams; It first constructs two matrix structures to store information of transactions and frequent 2-patterns, and then apply “pattern extension” operations to extend frequent 2-patterns to longer patterns, at the same time, rare patterns are removed to prevent them participating into “pattern extension” operation to reduce meaningless time cost; In the pattern matching phase, we use the IM-Sunday algorithm to match the mined MRPs with the patterns stored in outlier pattern library, to find potential outliers. Extensive experimental studies show that the proposed ODMRP method can accurately detect outliers from data streams in less overhead.
Efficiency and Total Factor Productivity of ICT Industries

This paper measured R&amp;D investments efficiency and total factor productivity(TFP) of ICT industries reflecting the time lag of R&amp;D investments and R&amp;D stock as a input, and decomposed the sources of total factor productivity change into technical change and technical efficiency change by the non-parametric Malmquist productivity index. The results of this study are as follows. First, efficiency scores of ICT industries are 39%, and Manufacture of semiconductor components and bare printed circuit boards and loading of electronic components onto PCB showed highest efficiencies. Second, total factor productivity decreased 6.6% annually. But manufacture of semiconductor components and bare printed circuit boards and loading of electronic components onto PCB, and system and package software publishing showed productivity increase, and technical progress contributed to TFP increase. Third, technical efficiency did much for TFP decrease of ICT industries. Technical progress, however, reduced the extent of TFP decrease.
Effects of Strength Training on the Physical Fitness and Obesity of College Girls

The purpose of this study was to examine the influence of strength training on physical fitness and body composition of college girl's students of District Peshawar.The selected participants were (N=70) girls' students of Govt Frontier College for Women, Peshawar.Eight weeks training schedule was adopted, (five days in a week) which comprises of different physical activities such as 30m race, 400m race, sit ups, skipping, matches of badminton and volley ball.A paired t-test was applied to examine the effect of training on body composition and physical fitness of college girls.Result showed that strength training significantly effects on bicep skin fold (P>0.01),hip girth (P>0.05),chest girth (P>0.04),30m race (P>0.08), and flexibility (P>0.04).It is concluded that strength training increases the physical fitness and reduces body fats of college girls and enhances the general strength of participated college girls.
Werkt De Weddenschap? Opvattingen van (v)mbo-docenten en vmbo-leerlingen over leesbevorderingscampagne De Weddenschap en leescoaches DIO, Famke Louise en Jeangu Macrooy

In 2020, Stichting Lezen (a Dutch institution aimed at reading promotion amongst youngsters) announced that three Dutch celebrities would star in its campaign De Weddenschap (The Bet): rapper DIO, singer/vlogger/model Famke Louise, and singer-songwriter Jeangu Macrooy. This article examines how teachers and pupils in preparatory secondary vocational education evaluate this campaign. First, the results of a questionnaire amongst L1-teachers of Dutch are reported. According to this survey, teachers have little trust in the campaign’s efficacy, but they still seem to believe that pupils might achieve reading satisfaction once they are seduced to read. However, the survey amongst pupils indicates that even a campaign starring celebrities does not convince youngsters in preparatory secondary vocational education to pick up a book. While they responded generally positively towards the efforts of one of the celebrities (Jeangu Macrooy), the large majority nevertheless did not consider participating in the campaign.
Pseudo-Labeling Improves News Identification and Categorization with Few Annotated Data

News articles analysis has been the subject of numerous research papers in recent years. Tasks such as identifying fake news and classifying news into categories have been addressed, but all of them require news as the main source of data. Websites offering news articles also include different kinds of information, such as advertising and personal opinions, which should be avoided when collecting data to create a news corpus. In this paper we propose a method that identifies news and separates them from other documents (non-news), following a semi-supervised approach using NER features corresponding to who, where and when questions, along with a measure of subjectivity. We experimented with different pseudo-labeling methods to improve classifier’s performance and obtained a robust increase of 2% to 3% when adding automatically labeled data on top of manually tagged data, even for small quantities of it (20%). We also explored the use of this semi-supervised method for the task of classifying news by categories (news categorization), obtaining better performance than supervised approaches.
Characteristic Vectors of Reversible Boolean Functions with Threshold Components

An n-ary reversible Boolean function is any bijective mapping from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\{0,1\}^{n}\rightarrow\{0,1\}^{n}$</tex> . It can be considered as an n-tuple of its component functions, i.e., as an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> -tuple of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> -ary balanced operations over {0, 1}. We single out reversible Boolean functions with threshold component functions and discuss their representation by characteristic vectors. In cases <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n=2,3,4$</tex> , we present characteristic vectors of classes of reversible Boolean functions with threshold components that are closed under negation and permutation of variables, and permutation of component functions.
A study on task-oriented dialogue data of a dialogue system for foreign language tutoring: Focusing on Korean dialogue data.

In order to implement a dialogue system for Korean language tutoring, this paper aimed to describe a method of constructing a large amount of Korean task-oriented dialogue data using the Wizard-of-OZ method and a pedagogical feedback and evaluation method for Korean language errors made by foreign learners. The procedure for constructing a large amount of Korean task-oriented dialogue data consisted of three steps. The first step was the preparation step for the learner’s prior learning. The second step was related to a large Korean dialogue map constructed by Korean native speakers using the Wizard-of-OZ method. The third step was implementation of a Korean dialogue system based on the dialogue map. In educational feedback and evaluation, functions such as paraphrases, recommendation expressions, educational success rate, and error content evaluation by learner level were proposed. By means of the Wizard-of-OZ method, 3,253 Korean task-oriented dialogue data were established in four domains: restaurant, hotel, tourist attraction, and taxi. The dialogue system for Korean language tutoring is currently being implemented based on Korean taskoriented dialogue data. Ultimately, the task-oriented dialogue system for Korean language tutoring aims at an “augmentation of education” that collaborates with human teachers, not “teacher-less education” that replaces human teachers.
MSR43 Extracting Severity Markers from Unstructured Clinical Data of Congestive Heart Failure Patients Using a Pretrained Text-to-Text Transfer Transformer Model

Our objective was to determine if severity measures commonly present in unstructured clinical data of congestive heart failure (CHF) patients could be extracted using a text-to-text transfer transformer (T5) model. Comprehensive clinical notes collected from a large United States medical institution were included if they contained strings suggesting a patient history of CHF. These notes were randomly sampled and analyzed using a pretrained T5 model available in a licensed software package using free-text sentences as the questions (e.g., “What is the ejection fraction?”) and complete clinical notes as the context. For New York Heart Association (NYHA) class, returned answers were marked correct if they were integers or roman numerals between 1 and 4. For ejection fraction (EF), returned answers were marked correct if they were in percentage form, integers between 0 and 100, or decimals between 0 and 1. Ranges for these data elements were also marked as correct. Of approximately 9.6 million notes collected, 440 thousand contained CHF-specific strings, and 200 of these notes were randomly sampled for analysis. Processing time was approximately 0.7 hours on a 128-core, 512 GB RAM CPU processor. Four questions (2 for EF and 2 for NYHA class) were presented to the 200 notes for a total of 800 possible answers. Valid EF and NYHA class values or ranges were detected in 31/400 (7.7%) and 16/400 (4.0%) answers, respectively. These results suggest that T5 models can extract disease-specific knowledge related to CHF severity from unstructured clinical data. Potential applications include characterizing patients for incorporation into health economic models and improving predictive accuracy for CHF readmission and mortality. Further work is required to determine sensitivity and specificity of valid data elements in analyzed notes and to improve performance, yield, and process automation of extracting these disease severity concepts.
P31 Feasibility of Using Oncology Specific Electronic Health Records (EHR) Data to Emulate Clinical Trial Inclusion and Exclusion Criteria

Prior studies have attempted to ascertain the feasibility of using EHR to emulate data elements necessary in clinical trials. However, few studies have focused on assessing the utility of both structured and unstructured (i.e., abstracted) data from oncology specific EHR systems for such purposes. This study examined critical elements of recent oncology clinical trials to assess the degree to which these real-world data (RWD) can be reasonably used to retrospectively replicate the information needed in trials.
Microcontroller based Automatic Sun Tracking Solar Panel

The solar industry is a worth billions of dollars industry. This produces 20 GW of power, which is insufficient for processing. Every day, the Sun brightens the globe by irradiating a massive amount of energy, the bulk which one is lost or not utilized as a result of improper solar panel installation the conversion of solar energy to electrical energy is significantly higher when the solar panel is constantly placed 90 degrees to the sun. To build an automatic solar panel using ATMega328 controller, we'll use two Light sensors (Light dependent resistors) to detect light and a servo motor to spin the solar panel in the position of the sun rays. The benefit of this concept is that the solar panel will constantly track light from the sun and will regularly charge when facing the sun, and offer the maximum amount of power.
Artifacts Analysis and Utilization of Decentralized Web Service ZeroNet for Digital Forensics

Recently, various decentralized network web services are emerging. However, the decentralized network web service is not well exposed to general users because it is difficult to access it using a general portal search engine. This point is similar to the deep web, and in the case of Tor (The Onion Routing), a kind of deep web. It has been used for various online crimes such as distribution of child pornography and online drug dealing, and its usage is continuously increasing. Therefore, the decentralized network web service is also likely to be abused for crimes, and forensic investigation techniques are needed to respond to the crime of the decentralized network web service. This paper is about artifact analysis to identify traces of users accessing and actioned on ZeroNet, which is one of the decentralized network web services, and the digital forensic method for applying to forensic investigations for decentralized network web services. As a result, the method of acquiring artifacts for meaningful user access trace analysis and the storage structure of access record trace data were analyzed for a total of five platforms including Windows and macOS. As a result of analyzing the acquired data, it was able to identify users who distributed decentralized network web services through the illegal decentralized network web service address accessed by the user, a list of files downloaded to access the decentralized network web service and BitTorrent. In addition, it constructed a hypothetical scenario and presented a plan to use it from the perspective of forensic investigation. Through this thesis, when ZeroNet, a kind of decentralized network web service, is found on the user’s PC during a forensic investigation, it contributes to the development of forensic investigation techniques by presenting a method to obtain a list of decentralized network web service addresses, downloaded files, and users sharing files.
Model development for the estimation of seasonal urban air temperature patterns using MODIS satellite data, a case study in Szeged

&amp;lt;p&amp;gt;Urban air temperature (T&amp;lt;sub&amp;gt;air&amp;lt;/sub&amp;gt;) is an essential variable for a variety of urban issues, and analyzing the spatial patterns of T&amp;lt;sub&amp;gt;air&amp;lt;/sub&amp;gt; is of great importance for urban planning and management. However, it is difficult to obtain T&amp;lt;sub&amp;gt;air&amp;lt;/sub&amp;gt; data with a high spatial resolution because of the absence of weather stations within a heterogeneous city. In this research, T&amp;lt;sub&amp;gt;air&amp;lt;/sub&amp;gt; predictive models were developed in Szeged by the help of an urban climate monitoring system which is a part of COST FAIRNESS project. Our investigation focused on the whole year instead of just the summer which has been the most studied season for urban heat issues. Two statistical mthods, multiple linear regression and random forest regression, were used for developing T&amp;lt;sub&amp;gt;air &amp;lt;/sub&amp;gt;estimation models. T&amp;lt;sub&amp;gt;air&amp;lt;/sub&amp;gt; data obtained from in situ meteorological stations from 2014 to 2017 were used as the dependent variable for models training. Land surface temperature data from numerous MODIS satellite images and 7 auxiliary variables were used as the independent variables. The auxiliary data are open source, including local climate zone (LCZ) lassification data containing a wide range of urban surface information and atmospheric parameters in the urban boundary layer associated with near-surface urban climate. The atmospheric parameters calculated from the ERA5 reanalysis data. We calculated RMSE based on 10-fold cross-validation to valadite the models. The results indicated that the random forest models performed better than multiple linear models with lower RMSE in four seasons. According to the importance analysis in random forest, both LCZ classification and atmospheric parameters are effective in reducing model errors. LCZ parameters affect the models significantly during the day, while atmospheric parameters affects the models significantly at night. Both effects of these two auxiliary variables showed their maximum in summer. In the end of this research, we used the final models to estimate T&amp;lt;sub&amp;gt;air&amp;lt;/sub&amp;gt; and mapped the seasonal average T&amp;lt;sub&amp;gt;air&amp;lt;/sub&amp;gt; patterns from 2018 to 2019. Our overall aim is to develop a generalized methodology using globally available land surface temperature and auxiliary data capable to estimate the urban air temperature.&amp;lt;/p&amp;gt;
In Situ Based Performance of Satellite- and Reanalysis-derived Hourly Global Horizontal Irradiance Products over West Africa

&amp;lt;p&amp;gt;Global horizontal irradiance (GHI) is the primary driver for photovoltaic (PV) technology. For PV system design and monitoring, hourly and sub-hourly GHI from reanalysis and satellite-based data are frequently used, especially in data-poor regions like Sub-Saharan Africa. However, the use of these datasets is uncertain and need to be assessed in detail. In this study, we evaluated the performance of state-of-the-art reanalysis and satellite-based datasets (ERA5, CAMS, MERRA-2 and SARAH-2) with in situ measurements of hourly GHI for the main climatic zones (Guinea, Savannah, and Sahel) in West Africa. The in situ measurements come from novel regional and national meteorological networks consisting of 51 automatic weather stations in Burkina Faso and Ghana. The performance assessment was done for the year 2020 for different weather conditions (cloudy, clear and all sky). The effects of clouds and aerosols were also investigated. Moreover, a new overall performance measure is introduced for joint evaluation of different standard measures, such as the root-mean-square error and the index of agreement. The results show that the data from SARAH-2 performs best under cloudy-sky conditions, while ERA5 performs worse under all atmospheric conditions. The low performance under cloudy skies for all datasets is the result of a large bias observed during the Harmattan period, when the region has a high concentration of aerosols. The average diurnal variation of GHI shows good agreement between the in situ measurements, the satellite and the reanalysis data under clear and all-sky conditions, but an overestimation under cloudy skies at some stations. The new overall performance value clearly indicates hourly GHI from SARAH-2 is the best alternative for assessing solar energy in West Africa.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;
Enhanced solar radiation nowcasting by machine-learning-based blending of data-driven, satellite-images and all-sky-imagers based models

&amp;lt;p&amp;gt;The share of solar energy in the electricity systems of many countries in the world will reach unprecedented values in the coming decades, fostered by the mitigation of climate change and also by the economic competitiveness of this energy. Accurate solar radiation forecasting models are critical for the integration of the increasing solar energy in power systems.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this work, the benefits obtained by blending seven models: four All-sky imagers (ASI)-based, two satellite images based (one using low resolution and other using high-resolution images) and a data-driven model, were analyzed. The use of two blending models (linear and Random Forest (RF)) and two blending approaches (General and Horizon) were explored. The horizon approach constructs a different blending model for each forecast horizon, while the general approach trains a single model valid for all horizons. The study is conducted in southern Spain and blending models provide one-minute resolution 90-minutes ahead GHI and DNI forecasts. Results show the General approach and the RF blending model to perform superior and to provide enhanced forecasts. The relative improvement in rRMSE obtained by model blending was up to 30% for GHI (40% for DNI), being maximum at lead times between 15 and 30 minutes and negligible at lead times greater than 50 minutes. Results also show that blending of just the data-driven model and the two satellite models (low and high resolution), without including the ASI-based models, performs similarly to those blending models that used as input the ASI-based models. Results then indicate that, for point nowcasting, the use of ASI-based forecasting systems can be avoided by using a suitable blending of data-driven, high resolution and low resolution satellite-images-based forecasting models.&amp;lt;/p&amp;gt;
Research on Blended Learning Evaluation Method of Overseas Chinese Education in the Post-epidemic Era

The spread of COVID-19 in the world has changed the way of life, economy, society, learning, and work around the world. Under the background of normalization of epidemic prevention and control, how to carry out overseas Chinese education and how to affect overseas Chinese education is an important issue with scholars in China and abroad and overseas Chinese. How to effectively deal with the international situation after the outbreak of the epidemic and how to determine the future developmental direction of overseas Chinese education is an important goal and direction that researchers need to study in depth. Based on the perspective of teaching effect evaluation of blended learning mode under the background of artificial intelligence, this article takes ASEAN Phuket Thai Chinese Schools as an example, collects relevant research data through a grounded survey method, adopts a quantitative analysis method, comprehensively judges the impact of overseas Chinese education after the outbreak of the epidemic, and puts forward systematic, comprehensive, and scientific suggestions. It is hoped that the decision-making mechanism and method innovation of overseas Chinese education in response to COVID-19 will be effectively promoted, and the effective development of overseas Chinese education will be actively promoted.
Cache Memory Design for Single Bit Architecture for Core ITM Processors

This chapter describes the implementation and analysis of cache memory design for single bit architecture comprised of six transistors static random access memory cell, a circuit of write driver, and differential sense amplifiers such as voltage differential sense amplifier, current differential sense amplifier, and charge transfer differential sense amplifier. Furthermore, different values of resistance consumption of power of all the architectures have been analyzed. The conclusion arises that single bit six transistor static random access memory cell voltage differential sense amplifier architecture consumes 11.34µW of power at R = 42.3KΩ, which is the lowest as compared to others. Apart from it, to consume less power, different kinds of power reduction techniques such as power reduction forced stack technique, power reduction sleep transistor technique, power reduction dual sleep technique, and power reduction sleep stack technique is applied over different blocks of cache memory design for single bit architecture. It concludes that single bit six transistor static random-access memory with power reduction forced stack technique and voltage differential sense amplifier with power reduction dual sleep technique in an architecture consume 9.18µW of power which is lowest with 13.12ηs delay in sensing and 32 number of transistors.