Reviewerly/exHarmony huggingface dataset:
Penggunaan Desain Pembelajaran dalam Meningkatkan Keterampilan Membaca Siswa SD Kelas III SDN Curug 1

This research was conducted at SDN Curug 1. The purpose of this study was to determine the use of learning designs in improving reading skills of third grade students. The object of this research is a class III teacher at SDN Curug 1. The method used in this research is qualitative research using observation, interview and documentation techniques. Class teachers at SDN Curug 1 use the design method of learning to read aloud and fast and the teacher also uses the existing library in the form of interesting reading books so that students are expected to be able to improve their reading skills.
PERFORMANCE ANALYSIS OF ARTIFICIAL INTELLIGENCE TECHNIQUES APPLIED IN BREAST CANCER DATA SET.

In this work, a comparative study was carried out betweentwo classification methods:  The Multi layer Perceptron Ar- tificial Neural Network (MLP ANN) and the method of clas- sification of the Nearest Neighbors, used in the classification of the diagnosis of breast cancer.  The data used in this work were taken from the UCI Machine Learning Repository and contains numerical data extracted from mammography im- ages.In  addition,  the  results  were  evaluated  based  on  thecross-validation strategy.
Adaptive Iterative Low-Rank for Real Image Denoising

Noise distribution of real images is often unknown, and generally has no regularity, therefore the denoising of real images has always been challenging. Based on the prior knowledge that there are a large number of self-similar patches within real images, this paper considers that the similarity and the number of similar patches in real images are different, and then proposes a similarity function to estimate texture features of patch groups. After clustering similar patches by using Gaussian mixture model (GMM), an efficient adaptive iterative scheme for low-rank denoising is proposed to enhance sparsity of patch group and prevent over-smoothness simultaneously.
Spoken language understanding for Icelandic Airline Reservation System, and translated corpus

Natural language understanding (NLU) or spoken language understanding (SLU) is one of the main components in a spoken dialog system (SDS).The goal of an SLU is to automatically create structured data from the natural text from a spoken utterance.In an airline reservation system (ARS), the SLU’s primary goal is to extract essential information such as the departure location, the destination, dates, and time.For the past three decades, the Airline Travel Information System (ATIS) corpus has been used as a benchmark dataset for English SLU for ARS.We have translated all the ATIS text corpus to Icelandic to create a benchmark SLU dataset for Icelandic ARS.The dataset was used to create a baseline n-gram language model for slot-filling (SF), as well as to create a neural network for intent determination (ID) to tag and classify unseen texts.This work consists of two parts. The first part is to create the ICE-ATIS dataset by translating the samples from the ATIS text corpus to Icelandic and reannotate them accordingly. The second part is to create SF and ID for Icelandic ARS.When creating the n-gram language model for SF, a unigram, bigram, and a trigram models were created and evaluated. We report F1 as high as 88.9% running the models on the test set from the ICE-ATIS dataset. Likewise, for the shallow feedforward neural network (FFNN) used for ID, we report F1 score of 87.2% using bag-of-words embedding (BOW).The ICE-ATIS corpus, code, and models will be made publicly available to strengthen Icelandic language technology further.; Malskilningarkerfi eða talmalsskilningarkerfi er ein af morgum stoðum samraeðukerfa.Megin hlutverk talmalskilningarkerfa er að utbua motuð gogn ut fra natturulegum texta sem fenginn er ut fra talaðri segð.I flugbokunarkerfum sem styðja við samraeðukerfi er hlutverk talmalsskilningarkerfis að draga fram upplýsingar eins og brottfararstað, afangastað og aðrar mikilvaegar upplýsingar eins og dagsetningar og tima.Siðastliðna þrja aratugi hefur ATIS gagnasafnið verið notað sem viðmiðunarmalheild fyrir talmalsskilningarkerfi i flugbokunarkerfum a ensku.Við hofum þýtt ATIS yfir a islensku til þess að getað notað gagnasafnið sem viðmiðunarmalheild fyrir m.a. talmalsskilningarkerfi i islensku flugbokunarkerfi.Verkefnið skiptist i tvo hluta. Fyrsti hluturinn for i að utbua ICE-ATIS, islenska utgafu af ATIS malheildinni með þvi að þýða oll gognin og endurmarka þau. Siðari hlutur verkefnisins for i að bua til n-gram mallikon utfra ICE-ATIS sem voru notuð til að utbua talmalsskilningarkerfi fyrir flugbokunarkerfi a islensku. Malheildin var siðan notuð til að utbua grunnmallikon sem ma nota til viðmiðunar i frekari rannsoknum.Þessi mallikon geta annað hvort verið notuð til þess að marka texta sem ma notast við til að fylla ut i dalka eða til að flokka segðir ut fra aformi segðarinnar.Við utbjuggum þrju n-gram mallikon til að marka texta. Niðurstoður gafu okkur að F1 gilidið naeði allt að 88.9% með þvi að keyra mallikonin a aður oseðum texta. Til auks við mallikonin…
Detection of Quantitatively Spread Movement of Atom on the Oxygen Generator

We was constructed of the spread movement with tremor layer point by the tractile-dot structure that was analyzed the squirm quake forms of the perception movement on the atom liquid. Algorithm of squirm quake forms was used to move the spread tremor on the atom state. To detect the tiny signal, we compared the association average value of the squirm quake form on the atom state. Their subject were issued the valuation standard and perception movement for basic atom condition by the spread tremor. We take to detect the tiny scores of average during perception movement side from the spread tremor that magnetic condition get to a variation of the Ma-αAVG and Ma-αMAX-MIN with 6.25±0.35 units, that electric condition get to a variation for the El-αAVG and El-αMAX-MIN with 5.68±0.42 units. The spread tremor was to investigate the capacity of the tremor form, to uptake a spread data of spread tremor level on the CCPL that was denoted the calm-classification form by the spread perception level system. As the squirm quake forms was demanded by the spread tremor signal, max-average values of perception movement were checked the spread position for association average data. We make mention of squirm quake forms for a signal association and a quake data signal of relation system.
High Dimensional Frustum PointNet for 3D Object Detection from Camera, LiDAR, and Radar.

Fusing the raw data from different automotive sensors for real-world environment perception is still challenging due to their different representations and data formats. In this work, we propose a novel method termed High Dimensional Frustum PointNet for 3D object detection in the context of autonomous driving. Motivated by the goals data diversity and lossless processing of the data, our deep learning approach directly and jointly uses the raw data from the camera, LiDAR, and radar. In more detail, given 2D region proposals and classification from camera images, a high dimensional convolution operator captures local features from a point cloud enhanced with color and temporal information. Radars are used as adaptive plug-in sensors to refine object detection performance. As shown by an extensive evaluation on the nuScenes 3D detection benchmark, our network outperforms most of the previous methods.
Effectiveness of Quantum Teaching Model Using Handphone Android Media Toward Student Achievement Motivation On Indonesian Language Learning In The Industrial Revolution 4.0

The purpose of this study is to examine the effectiveness of a quantum teaching model with an android handphone on student achievement motivation in learning Indonesian in the industrial revolution era 4.0. This type of research uses quasy experiment. The data analysis test uses the Independent-Samples-t Test with SPSS. The results showed the sign value obtained was smaller than α = 0.05 (0,000 <0.05) and the average score of the student achievement motivation questionnaire in the class that was taught with a quantum teaching model with an android handphone was 53.23 and the class taught the quantum teaching model without using android handphones has an average score of 50.16, so the achievement motivation of students in Indonesian lectures in classes taught with the quantum teaching model with an android handphone is more effective than classes taught with a quantum teaching model without using an android handphone . The quantum teaching model using the android handphone is effectively used on Indonesian language learning in the era of the industrial revolution 4.0.
The Intersection of Planning and Learning

Intelligent sequential decision making is a key challenge in artificial intelligence. The problem, commonly formalized as a Markov Decision Process, is studied in two different research communities: planning and reinforcement learning. Departing from a fundamentally different assumption about the type of access to the environment, both research fields have developed their own solution approaches and conventions. The combination of both fields, known as model-based reinforcement learning, has recently shown state-of-the-art results, for example defeating human experts in classic board games like Chess and Go. Nevertheless, literature lacks an integrated view on 1) the similarities between planning and learning, and 2) the possible combinations of both. This dissertation aims to fill this gap. The first half of the book presents a conceptual answer to both questions. We first present a framework that disentangles the common algorithmic space of both fields, showing that they essentially face the same algorithmic design decisions. Moreover, we also present an overview of the different ways in which planning and learning can be combined in one algorithm. The second half of the dissertation provides experimental illustration of these ideas. We present several new combinations of planning and learning, such as a flexible method to learn stochastic dynamics models with neural networks, an extension of a successful planning-learning algorithm (AlphaZero) to deal with continuous action spaces, and a study of the empirical trade-off between planning and learning. Finally, we also illustrate the commonalities between both fields, by designing a new algorithm in one field based on inspiration from the other field. We conclude the thesis with an outlook for the planning-learning field as a whole. Altogether, the dissertation provides a broad theoretical and empirical view on the combination of planning and learning, which promises to be an important frontier in artificial intelligence research in the coming years.
Improving the network traffic classification using the Packet Vision approach

The network traffic classification allows improving the management, and the network services offer taking into account the kind of application. The future network architectures, mainly mobile networks, foresee intelligent mechanisms in their architectural frameworks to deliver application-aware network requirements. The potential of convolutional neural networks capabilities, widely exploited in several contexts, can be used in network traffic classification. Thus, it is necessary to develop methods based on the content of packets transforming it into a suitable input for CNN technologies. Hence, we implemented and evaluated the Packet Vision, a method capable of building images from packets raw-data, considering both header and payload. Our approach excels those found in state-of-the-art by delivering security and privacy by transforming the raw-data packet into images. Therefore, we built a dataset with four traffic classes evaluating the performance of three CNNs architectures: AlexNet, ResNet-18, and SqueezeNet. Experiments showcase the Packet Vision combined with CNNs applicability and suitability as a promising approach to deliver outstanding performance in classifying network traffic.
Διαχείριση δεδομένων βιβλιοθηκών στην πλατφόρμα WEKA: υποσυστήματα Explorer & Knowledge Flow

In the present work, the Explorer and Knowledge Flow subsystems of the WEKA data mining environment will be presented and compared. The comparison will be made in a theoretical context, but also on an experimental level in order to find a prediction model for the type and age of the users of the San Francisco Public Library in relation to their behavior in the movement of material.The technique used to create the forecast is the classification technique based on the algorithms K-NN, SVM, Random Forest, Decision Tree, and Naive Bayes in a data set from the library itself. According to the theoretical approach, the two environments provide the same set of machine learning algorithms as well as data pre-processing tools. In the experimental tests they also gave the same results. The validation method used is that of cross-validation with 10 folds. The Explorer interface seems to be a user interface that takes a lot of familiarization time but offers more flexibility and features. The Knowledge Flow interface is graphical and works with nodes, interfaces and drag and drop menus. The methods that responded best are Random Forest, Random Tree and K-nn where their results, although acceptable after the f-measure reached 61.7%, are not considered encouraging performance. Ultimately, the original research hypothesis concludes that there is no strong correlation between age and consumer lending behavior, and it depends on other factors.
Using QR Codes for Blended Learning in a Micro-Factory

Tutorials and demonstrative design studio activities are often assigned to teaching assistants who need the same instruction that the undergraduate students need. Because students rely on their mobile devices so much for learning, the application of Quick Response codes can automate this repetitive practice and provide a learning environment that is user-centered and offers point-of-need information on a familiar device in a blended learning experience.
Assessment of soil and water characteristics and land cover changes along the Tigris River in Baghdad

Land cover changes impact the soil and water quality which are critical for environmental and human health. The goal of this study is to evaluate whether the land cover change along the Tigris River, one of the largest rivers in the Middle East, is causing any heavy metal contamination. The objectives of this study were: (1) To analyze the metal concentrations in the water and soil samples along the Tigris River and (2) identify and map the land cover changes of the Baghdad district. A total of nine water and soil samples were collected from three different Tigris River (TR) sampling locations, namely Gherai´at (TR1), Bab Al Moatham Bridge (TR2), and Karada-Masbah (TR3). Surface soil and water samples were collected, and analyzed for various metal concentrations.  Landsat satellite imagery from 1984 and 2018 were analyzed and compared for land cover changes. Our water sample analysis revealed that As, Cd, Cr, Cu, Pb and Zn remained low and are within the permissible limit of WHO standards. Soil samples showed that Cu, and Pb concentrations in TR1 and TR2, respectively were higher compared to other locations. The metal concentrations in both water and soil samples at the sampled locations were at safe levels. Remote sensing analysis revealed that the water surface in the study area increased by about 5.3% while the vegetative surface decreased by 10.3% during the period of 1984 to 2018. Water and vegetative cover increased further in the south of Baghdad, along the Tigris River, compared to the north. The impact of land cover changes and increase in soil metal concentrations are higher on TR2 and TR3 locations. Environmental chemical analysis coupled with geospatial data helps to monitor the impact of land cover changes on water and soil quality by identifying areas vulnerable to change.Key words: Water quality, landsat, soil quality, heavy metal, land cover change.
Formal Verification of a Mandatory Integrity Control Model for the KasperskyOS Operating System

Models of mandatory integrity control in operating systems usually restrict accesses of active components of a system to passive ones and represent the accesses directly. This is suitable in case of monolithic operating systems whose components that provide access to resources are part of the trusted computing base. However, due to the sheer complexity of such components, it is extremely nontrivial to prove such a model to be adequate to the real system. KasperskyOS is a microkernel operating system that organizes access to resources via components that are not necessarily part of the trusted computing base. KasperskyOS implements a specifically developed mandatory integrity control model that takes such components into account. This paper formalizes the model and describes the process of automated proof of the model’s properties. For formalization, we use the Event-B language. We clarify parts specific to Event-B to make our presentation accessible to professionals familiar with discrete mathematics but not necessarily with Event-B. We reflect on the proof experience obtained in the Rodin platform.
Gambaran Pelaksanaan Pembelajaran Keterampilan Menjahit Pakaian di PKBM

The research was based on the success of the sewing clothes’ skill program in PKBM Anarvani Air Tawar Barat, Padang city. The existence of the implementation in sewing clothes skill learning, it could help the participant in developing its skill, can be shown from the result that has been reached. The researcher expected the cause of the success of sewing clothes’ skill was from the learning process. The aim of this research is to show the learning motivation and the atmosphere of study. This research was descriptive quantitative. The population of the research is the entire participant who enrolled in sewing clothes’ program, 20 people as the sample. The technique used in getting the sample was the census method. The technique used in collecting data was using a questionnaire and the equipment to collect the data was using the question’s list. The result of the research is, 1. the motivation of learning that has done by the tutor to push the spirit of the participant is categorized best. 2. the atmosphere of learning study between the tutor and participant is categorized best made the implementation of learning could run well and evoked the comfortableness for the participant.
Unfolding-Model-Based Visualization: Theory, Method and Applications

Multidimensional unfolding methods are widely used for visualizing item response data. Such methods project respondents and items simultaneously onto a low-dimensional Euclidian space, in which respondents and items are represented by ideal points, with personperson, item-item, and person-item similarities being captured by the Euclidian distances between the points. In this paper, we study the visualization of multidimensional unfolding from a statistical perspective. We cast multidimensional unfolding into an estimation problem, where the respondent and item ideal points are treated as parameters to be estimated. An estimator is then proposed for the simultaneous estimation of these parameters. Asymptotic theory is provided for the recovery of the ideal points, shedding lights on the validity of model-based visualization. An alternating projected gradient descent algorithm is proposed for the parameter estimation. We provide two illustrative examples, one on users’ movie rating and the other on senate roll call voting.
Opening new doors for psycholinguistic research: word and sound frequencies of Cantonese

Our study analyzed and compared three Cantonese language corpora (large textual databases) in order to document type (unique units) and token (total units) frequencies of words and the sounds inside them. The frequencies were calculated for each corpus, and the correlations and similarities among these corpora were analyzed. The frequency analyses reveal that while the three corpora are similar in many regards, they are statistically independent and should not be used interchangeably. In turn, we hope this study will help future empirical studies in making an informed choice regarding the basis of investigation. Furthermore, the documentation of word and sound frequencies can be used for a host of natural language applications such as speech errors, which have been shown to be sensitive to the frequencies of words and its component sounds. By pinning down the impact of frequencies on speech errors, we can better understand the nature of normal (non-erroneous) speech processes.
Experience of emergency organization of distance learning for police managers

The article is devoted to the analysis of the experience of organization of distance learning for police leadership in a higher education institution in the emergency situation. The authors examine the advantages and disadvantages of creating a digital educational environment for police managers from the point of view of the students as well as the teaching staff of the Academy of Management of the Ministry of Internal Affairs of Russia. The conclusions arising from these opinions along with various aspects of the emergency arrangement of teaching higher educational programs in a remote access mode are examined. The authors focus attention on the evaluation of the organization and content of the distance learning course, including the analysis of the level of readiness of the teachers of the educational organization to conduct training sessions in a distance mode; the assessment of the students’ abilities for self-organization and e-learning; the quality of the pedagogical control of the current and final academic performance of the students in a remote mode and the objectivity of the assessments made during the intermediate testing and final examination etc. The authors identify positive and negative aspects of the experience of emergency organization of distance learning for police leaders.
Analyze Analisis Penanda Hubungan Elipsis pada Saran Penyajian Produk Makanan

AbstracThe purpose of this research is to describe (1) Identify the writing of a food product presentation suggestion that contains a marker of elpsis relationship. (2) Analyzing the effect of writing the ellipsis marker relationship in the presentation of food products. (3) Implementing the authorship of elliptical marker relationships in learning text procedures in class XI high school. This research uses qualitative research, using descriptive qualitative research design. Data collection techniques using observation and documentation. Data analysis techniques using interactive analysis. The validity of the data in this study uses triangulation. The results obtained are three types of ellipsis contained in the procedure text in the form of noun ellipsis, ellipsis verbs, and clauses. But what is often found by the writer in this research is ellipsis clause, because the number is more dominant. This type also removes clause elements. Ellipse nouns, this type occurs when noun phrases or nouns are removed or assumed. Verbal ellipsis, this type occurs when verb phrases or verbs are omitted and it has two general types, echoing and contrasting aides. In addition, this research will contribute to teachers, students, Indonesian language students and other researchers related to the use of three types of ellipses.Keywords: ellipsis, food packaging, procedure text, high school