Reviewerly/exHarmony huggingface dataset:
KEEFEKTIFAN MEDIA POP UP CANDI BERBANTU MODEL SNOWBALL THROWING TERHADAP PEMBELAJARAN ILMU PENGETAHUAN SOSIAL KELAS V SDN KEMBANGARUM 02 MRANGGEN

The use of pop-up book media-assisted learning model throwing snowball can help teachers in teaching learning material IPS Heritage temple in Indonesia. In addition, this study aims to encourage and increase student learning outcomes V. Prior to this research grade students seem inactive and not in the spirit of learning activities. After doing research using pop-up book media-assisted learning model snowball throwing, student interest in learning began to grow and not feel bored in learning, so that student learning outcomes showed an increase in the average value of students reached the KKM. Then use the pop-up book media-assisted learning model snowball throwing increase the interest and student learning outcomes Class V
The Effectiveness of “Pencasts” as an Instructional Medium

Abstract The Effectiveness of “Pencasts” as an Instructional MediumA pencast is a type of video presentation in which recorded digital ink and audio are replayed insynchronization. To create a pencast, a special digital “Smartpen” is used to record handwrittencontent with voice narration. For example, an instructor can use a Smartpen to write the solutionto a sample problem while explaining each step. When a student views the resulting pencast, thepen strokes and audio are displayed like a movie, with the explanation synchronized to therendering of the strokes.“Pencasts” are becoming a popular instructional tool, but their educational effectiveness has notbeen formally studied. Thus, we present a research study aimed at comparing the educationaleffectiveness of pencasts to that of traditional instructional media, specifically, printeddocuments. The study involved two sessions and two treatments within each session. Eachsession included a pretest problem, a tutorial, and a posttest problem. In one treatment thetutorial was provided as a pencast, while in the other the tutorial was a traditional printeddocument with content identical to that of the pencast. Within each treatment group, theproblems used for pretest and posttest were alternated to control for order effects. Likewise, thestudents who received the pencast in the first session were given the traditional document in thesecond, and vice versa. The study included about 65 participants and was conducted in thecontext of a ten-week undergraduate Statics course. The problems in the first session concernedwedge friction, while those in the second concerned belt friction. Students completed the pre-and posttests using digital pens, enabling us to record and examine the solution process. We willreport performance gains from pre- to posttest for the different treatment conditions, examiningerror patterns and solution time. We will also report results of a survey of students’ preferencesfor pencasts vs. traditional printed instructional materials.
Data Mining: Mining Frequent Patterns, Associations Rules, and Correlations

In this article we provide a survey of frequent pattern mining, a fundamental data mining task that deals with the search of recurring regularities in large data sets. Frequent patterns can take different forms depending on the type of data analyzed, for example, frequent itemsets (set of items), frequent sequences, or frequent sub-graphs. We focus here on frequent itemsets and associations between itemsets. We start with a short overview on data mining and the area of frequent pattern mining, then, after reviewing the basic concepts and definitions underlying the problem of frequent itemsets, we introduce the main interestingness metrics used to evaluate the goodness of a mined association and the most important classical algorithms to tackle this mining task.
AI Meets Chemistry

We argue that chemistry should be the next grand challenge for Artificial Intelligence. The AI research community and humanity would benefit tremendously from focusing AI re- search on chemistry on a regular basis, as a benchmark as well as a real-world application domain. To support our position, we review the importance of chemical compound discovery and synthesis planning and discuss the properties of search spaces in a chemistry problem. Knowledge acquired in domains such as two-player board games or single-player puzzles places the AI community in a good position to solve critical problems in the chemistry domain. Yet, we show that searching in chemistry problems poses significant additional challenges that will have to be addressed. Finally, we envision how several AI areas like Natural Language Processing, Machine Learning, planning and search, are relevant for chemistry.
Towards a new supporting platform for collaboration in industrial diagnosis within an agent-based WEB DSS

The main objective of this study is to provide a professional social network which is integrated within an agent-based WEB DSS. This DSS network will facilitate interaction, discussion, and the sharing of information among production operators, especially to deal with nominal situations of resource faults. The coordination among the agents in our approach is made possible by a dynamic agent coordination protocol. The general architecture is based on agents named: production agent (PA), ontology agent (OA), evaluator agent (EA), and coordinator agent (COA). The coordination protocol that is applied comprised a set of behaviours used to maintain and control the messages exchanges among the agents. Learning features enable these agents to gain more time in calculation and executions. As a representation of the agent's common knowledge, domain ontology has been developed to represent major generic concepts in the industrial domain. Analytical hierarchy process (AHP) methodology is applied to evaluate sorting solutions in respect of human participants' preferences. In this study, we choose as a field of application: ALFATRON electronics industry.
Improving Phoneme Sequence Recognition using Phoneme Duration Information in DNN-HSMM

Improving phoneme recognition has attracted the attention of many researchers due to its applications in various fields of speech processing. Recent research achievements show that using deep neural network (DNN) in speech recognition systems significantly improves the performance of these systems. There are two phases in DNN-based phoneme recognition systems including training and testing. Most previous research attempted to improve training phase such as training algorithms, different types of network, network architecture, feature type, etc. But in this study, we focus on test phase which is related to generate phoneme sequence that is also essential to achieve good phoneme recognition accuracy. Past research used Viterbi algorithm on hidden Markov model (HMM) to generate phoneme sequences. We address an important problem associated with this method. To deal with the problem of considering geometric distribution of state duration in HMM, we use real duration probability distribution for each phoneme with the aid of hidden semi-Markov model (HSMM). We also represent each phoneme with only one state to simply use phonemes duration information in HSMM. Furthermore, we investigate the performance of a post-processing method, which corrects the phoneme sequence obtained from the neural network, based on our knowledge about phonemes. The experimental results using the Persian FarsDat corpus show that using extended Viterbi algorithm on HSMM achieves phoneme recognition accuracy improvements of 2.68% and 0.56% over conventional methods using Gaussian mixture model-hidden Markov models (GMM-HMMs) and Viterbi on HMM, respectively. The post-processing method also increases the accuracy compared to before its application.
Modeling a Fragmented Archive: A Missing Data Case Study from Provenance Research

Paper presented at the Alliance of Digital Humanities Organizations annual conference, 2018, Mexico City.Historians grapple with missing information constantly. While there are many statistical tools for gauging the impact of missing source data on quantitative results and conclusions, DH researchers have rarely deployed these tools in their work. This paper presents one implementation of data imputation used in the study of the New York City art dealer M. Knoedler & Co. Demonstrating the significant contribution imputation had on our study and its conclusions, this paper will discuss specific, practical rhetorical strategies, including static and interactive visualization, for explaining this methodology to an audience that does not specialize in quantitative methods.
Classical Chinese Sentence Segmentation for Tomb Biographies of Tang Dynasty.

Tomb biographies of the Tang dynasty provide invaluable information about Chinese history. The original biographies are classical Chinese texts which contain neither word boundaries nor sentence boundaries. Relying on three published books of tomb biographies of the Tang dynasty, we investigated the effectiveness of employing machine-learning methods for algorithmically identifying the pauses and terminals of sentences in the biographies. We consider the segmentation task as a classification problem. Chinese characters that are and are not followed by a punctuation mark are classified into two categories. We applied a machine-learning-based mechanism, the conditional random fields (CRF), to classify the characters (and words) in the texts, and we studied the contributions of selected types of lexical information to the resulting quality of the segmentation recommendations. This proposal presented at the DH 2018 conference discussed some of the basic experiments and their evaluations. By considering the contextual information and employing the heuristics provided by experts of Chinese literature, we achieved F1 measures that were better than 80%. More complex experiments that employ deep neural networks helped us further improve the results in recent work.
General Security Considerations for Cryptoassets Custodians

This document discusses the technical and operational risks ofcryptoassets custodians and its security controls to avoid theunintended transactions for its customers. Discussion Venues Thisnote is to be removed before publishing as an RFC. Source for thisdraft and an issue tracker can be found at https://github.com/cgtf/draft-crypto-assets-security-considerations.
Unsupervised Generation of Free-Form and Parameterized Avatars

We study two problems involving the task of mapping images between different domains. The first problem, transfers an image in one domain to an analog image in another domain. The second problem, extends the previous one by mapping an input image to a tied pair, consisting of a vector of parameters and an image that is created using a graphical engine from this vector of parameters. Similar to the first problem, the mapping's objective is to have the output image as similar as possible to the input image. In both cases, no supervision is given during training in the form of matching inputs and outputs. We compare the two unsupervised learning problems to the problem of unsupervised domain adaptation, define generalization bounds that are based on discrepancy, and employ a GAN to implement network solutions that correspond to these bounds. Experimentally, our methods are shown to solve the problem of automatically creating avatars.
Instance theory predicts information theory: Episodic uncertainty as a determinant of keystroke dynamics.

How does prior experience shape skilled performance in structured environments? We use skilled typing of natural text to evaluate correspondence between performance (keystroke timing) and structure in the environment (letter uncertainty). We had ∼350 typists copy-type English text. We reproduced Ostry's (1983) analysis of interkeystroke interval as a function of letter position and word length, that showed prominent first-letter and midword slowing effects. We propose a novel account that letter position and word length effects on keystroke dynamics reflect informational uncertainty about letters in those locations, rather than resource limited planning/buffering processes. We computed positional uncertainty for letters in all positions of words from length one to nine using Google's n-gram database. We show that variance in interkeystroke interval by letter position and word length tracks natural variation in letter uncertainty. Finally, we provide a model showing how a general learning and memory process could acquire sensitivity to patterns of letter uncertainty in natural English. In doing so, we draw an equivalence between Logan's (1988) instance theory of automatization and Shannon's measure of entropy (H) from information theory. Instance theory's predictions for automatization as a function of experience follow exactly the uncertainty in the choice set being automatized. As a result, instance theory stands as a general process model explaining how context-specific experiences in a structured environment tune skilled performance. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Detecting and Distinguishing Adaptive and Non-Adaptive Steganography by Image Segmentation

This article describes how blind steganalysis aiming at uncovering the existence of hidden data in digital images remains an open problem. Conventional spatial image steganographic algorithms hide data into pixels spreading evenly in the entire cover image, while the content-adaptive algorithms prefer the textural areas and edge regions. In this article, the impact of image content on blind steganalysis is discussed and a practical and extensible approach to distinguish the different types of steganography and construct blind steganalytic detector is proposed. Through the technique of image segmentation, the images are segmented into sub-images with different levels of texture. The classifier only cares for the sub-images which can help modeling the statistical detectability and is trained on sub-images instead of the entire image. Experimental results show the authors' scheme can recognize the type of steganographic methods reliably. The further steps to improve capacity of blind steganalysis based on image segmentation are also mentioned and achieve better performance than ordinary blind steganalysis.
Crime in the Age of Technology

The serious and organised crime landscape in the EU has changed drastically in the past years - in large part due to advancements in technology. Criminals quickly adopt and integrate new technologies into their modi operandi or build brand-new business models around them. The use of new technologies by organised crime groups (OCGs) has an impact on criminal activities across the spectrum of serious and organised crime. This includes developments online, such as the expansion of online trade and widespread availability of encrypted communication channels, as well as other aspects of technological innovation such as more accessible and cheaper drone technology, automated logistics, and advanced printing technologies. Technology has become a key component of most, if not all, criminal activities carried out by OCGs in the EU and has afforded organised crime with an unprecedented degree of flexibility. The vital role of technology for organised crime is reflected in the data collected and analysed for the SOCTA 2017 presented by Europol in March 2017. Also the Internet Organised Crime Threat Assessment (iOCTA) issued by Europol in September provides updated findings regarding the misuse of technologies and the internet by criminals. What can the EU’s law enforcement community do to tackle the new challenges of crime in the age of technology?
Moderating effects of related experience and firm size: will multinationals opt for acquisitions in high informal institutional settings?

This study examines the influence of informal institutional distance on the establishment mode strategies of foreign multinational enterprises (MNEs) in Ghana with the moderation role of experiential knowledge (related experience) and parent firm size. The empirical analysis is based on a sample of 223 green-field and acquisition market entries over the period 2001-2016, drawn from 20 countries. The study employs binomial logistic regression to establish the relationships among the variables, and the results reveal that high informal institutional distance leads to the preference for acquisitions over green-field investments. The study also finds that high level of experiential knowledge (related experience) and large parent firm size have positive relationship with acquisition establishment as compared to green-field investment. We therefore argue that strong related experience and large parent firm size strengthen the choice for acquisitions in relation to high informal institutional distance.
Solving Nonogram puzzles by using group-based fully probing

Nonogram is a typical two-dimensional logical puzzle game in which each pixel involves two constraints associated to the intersecting row and column. The recently proposed approaches can efficiently solve many puzzles via logical deduction based on the 2-SAT formulas. This paper proposes a set of new logical properties for inferencing the consistent and inverse relations among pixels. We show that the pixels with consistent (or inverse) relation must be painted to a same (or an opposite) color in the solution puzzle. Consequently, the pixels can be aggregated into groups, thereby the space of search tree of the Nonogram backtracking algorithm can be reduced.
Interval Valued Intuitionistic Fuzzy Pairs

As was mentioned in [9], the first researches, related to Intuitionistic Fuzzy Sets (IFSs) started  in 1983 and from the beginning, the concept of Intuitionistic Fuzzy Pair (IFP) had started to be used. As we mentioned in [9], a lot of our colleagues working in the area of the intuitionistic  fuzziness, used it without a special definition in many of their publications, using different names: IFP, intuitionistic fuzzy couple, intuitionistic fuzzy value and others. In the mentioned paper, we  proposed to the researchers in the area of the intuitionistic fuzziness to use only one name for the  concept.
Communicating With Customers Using Artificial Intelligence

Artificial intelligence is everywhere. It helps us to find the next movie to watch and guide us in the city to avoid traffic jams while driving. It is also replacing human operators in call centers in order to help customers in need of assistance. This replacement is a piece of software called a chatbot, which is capable of processing natural language to answer instantly a question of a customer. This industry report tells the story of Easybots, a start-up focused on delivering chatbots for medium to large companies willing to improve the experience of digital customers.
Integration of the bands of ASTER, OLI, MSI remote sensing sensors for detection of hydrothermal alterations in southwestern area of the Ardestan, Isfahan Province, Central Iran

This paper reviews the band's integration of three remote sensing sensors for improving the detecting of hydrothermal alterations. The distinguishing feature of the various minerals has always been the challenge of Multispectral sensors due to the band's limitation. On the other hand, accessibility to Hyperspectral data requires paying a high cost. Also, the narrower bands in such data and the absence of suitable data to atmospheric correction can cause more errors. So, we hope that providing more bands by this method can detect hydrothermal alteration zones correctly. The integration method acts based on the spectral offset correction on the reflectance data of the images in a log–log plot. The data include bands of ASTER, MSI, and OLI sensors in the SWIR spectral region that are converted to reflectance by 6S atmospheric correction. For the spectral method of mineral detection, we have chosen the Spectral Angle Mapper (SAM) technique. This is a powerful classification to represses the influences of shading, solar illumination, and Albedo effects. Also, we benefit from the Concentration-Area(C-A) fractal model for specifying the range of thresholds. This review emphasizes that although the integration band method can lead to the detection of minerals in hydrothermal alterations, more investigation is still needed to achieve better results because it is unsuccessful to the identification of minerals which lacking the specific spectrum peak in the range of central wavelengths of the integrated bands.
Metaphor of Colors in Indonesian and its Equivalence in German

Metaphor is a form of speech or phrase that refers to one peculiar thing by mentioning another thing to get a rhetorical effect. The use of metaphor can explain the hidden similarity between two ideas of thought. This paper analyzes metaphors that use color names in Indonesian and their equivalences in German. Through metaphorical analysis, it can be expressed and compared on habits, implied knowledge, social representation of cultural diversity which at the same time reflects the norms embodied in the language user community in Indonesia and Germany. Data obtained from KBBI (2008) and Wahrig dictionary (2009). The research method is qualitative descriptive. The colors are distinguished in primary colors (red, yellow, and blue) and neutral colors (black and white). The results show that there are color metaphors in all primary and neutral colors in the two languages. Metaphors are used to express feelings (sad, happy, envy, angry), express ingenuity, hope, honesty, courage and so on. On the contrary, through color metaphors we can also criticize dishonesty, bad nature, ignorance evil and so on.
Category Theory-Based Mobile User Interface Pattern Recommendation Method

Although using the mobile user interface pattern (MUIP) can increase the efficiency and quality of mobile interface development, the existing pattern search methods have some limitations to provide appropriate patterns for solving a given design problem. In order to find the appropriate patterns, a category theory-based MUIP recommendation (CTR) method is proposed in this paper. First, the MUIP search is represented with a categorical structure so that the design problem can combine with the hierarchical structural MUIP repository closely. Then, to extract more accurate requirement information from the design problem, we present a question-based information extraction method that combines the general architecture for text engineering framework and category theory to extract the design goal, context, and level from the design problem. Finally, based on the categorical structure, we adopt a corresponding search strategy to find MUIPs according to the type of design problem. Some experiments are conducted to evaluate the performance of CTR. The experimental results show that the CTR can provide a high performance in terms of accuracy and ranking when compared with the existing methods.