Reviewerly/exHarmony huggingface dataset:
Pseudonymization of Text and Image Data to Provide Confidentiality

It is important to maintain secrecy of one’s valuable data when it is loaded in the Internet. Unintended parties can view the data, resulting in breach of confidentiality. Another serious concern is the possibility of misuse of data to the advantage of an unintended party. To overcome these problems, this paper presents an algorithm developed and implemented to make the data that is uploaded to the servers, pseudo anonymous (pseudonymous). Typically, user-sensitive information exists as either texts or images. Hence, such data is used as input to the developed algorithm. From the textual data, the algorithm can detect keywords called tags, such as names, places, financials, and replace the original tag with newly created false tags. Detection of the tags is done by the existing natural language processing (NLP) algorithms. For image input, the algorithm detects face and replaces it with a falsified one. This is done by using generative adversarial networks (GANs). Further, the original or true image is hidden in the falsified image using SteganoGAN. This image will be sent over the network. At the receiver, both anonymized text and image data are de-anonymized as per the developed algorithm.
Neighbourhood Aging Factors for Limited Information Social Network Collaborative Filtering

Businesses benefit by recommender systems since the latter analyse reviews and ratings of products and services, providing useful insight of the buyer perception of them. One of the most popular, successful and easy-to-build recommender system techniques is collaborative filtering. Recommender systems take into account social network information, to achieve more accurate predictions. Unfortunately, however, many applications do not have full access to such “rich” information, so they have to properly manage the limited information, which, in the worst case, is comprised of just the user relationships in the social network. A social network collaborative filtering system combines the two sources of information, in order to formulate rating predictions which will lead to recommendations. However, the vast majority of users change their tastes, as time goes by, a phenomenon termed as concept drift, and in order for a recommender system to be successful, it must effectively face this problem. In this paper, we present a social network collaborative filtering rating prediction algorithm that tunes the weight-importance of each source of information based on the age of the information. The proposed algorithm considerably improves rating prediction accuracy, while it can be easily integrated in social network collaborative filtering recommender systems.
Hidden dangers in complex computational structures: A commentary on Lustick and Tetlock 2021

Data sharing is not applicable to this article as no new data were created or analyzed in this study.
THE DEVELOPMENT OF FUNCTIONAL LITERACY WHEN STUDYING A SCHOOL COURSE IN MATHEMATICS USING INTERSUBJECT COMMUNICATIONS

The article considers the issue of the formation of functional literacy when conducting mathematics lessons in a secondary school, taking into account the use of intrasubject communications. Methodology and research methods. Research is based on a system-structural and activity-based approach. Results. First of all, attention is paid to the consideration of the concept of functional literacy, the question of assessing the level of development of functional literacy and the means to assess this level of development is considered. Examples of specific tasks of an interdisciplinary nature that are used to determine the level of development of the Federal State Educational Standard of Basic General Education are given. Formal and substantive approaches in mathematics, the possibilities of their application in the framework of intrasubject communications as a means of developing functional literacy of high school students are considered. The issues of synchronization of educational material as a key element in the implementation of intrasubject communications are considered. The practical significance of the work. The directions of changing approaches to the implementation of intrasubject relations of mathematics with other areas of knowledge as the basis for achieving meta-subject educational results of the Federal State Standard of Basic General Education are revealed. The question of the connection of meta-subject educational results as the requirements of the Federal State Educational Standard of LLC and the functional literacy of students, as well as means of assessing the level of formation of functional literacy, is considered. Particular attention is paid to mathematical literacy as one of the key components of functional literacy. In this regard, examples of tasks from other subject areas are considered that require the use of mathematical literacy as a component of functional literacy as part of the assessment of subject achievements of students. The main recommendations on the implementation of intrasubject communications to achieve meta-subject educational results are formulated.
METHODS FOR FORMING PROFESSIONAL COMPETENCIES IN FUTURE PROGRAMMERS

The presented article is devoted to the formation of professional competencies in future programmers. The relevance of the research is based on the fact that information technologies are used in all spheres of life of modern society, therefore, specialists who can develop and code algorithms for working with information are in great demand. Algorithms form the basis of any information protection process, labor efficiency calculations, therefore it becomes important to form competencies in future IT specialists related to the ability to program using various programming languages and methods, using the main types and data structures from the everyday practice of programmers. As indicators of the effectiveness of methods for the formation of professional competencies, there are competencies: knowledge of modern trends in the development of tools and software; the presence of theoretical knowledge and practical skills that allow you to build an algorithm, analyze its work with different input data and implement it using modern programming languages; the ability to use high-level PL and professional programming systems, tools for solving professional-applied problems in the information sphere of the enterprise. The article deals with traditional and developed by the author methods of teaching programming
Curriculum Innovation Utilizing High School and University Connection System of National Institute of Technology

We introduce the new curriculum from 2019 of Kagawa national institute of technology. This is an unprecedented curriculum that takes advantage of the high school and university connection system for national institute of technology. This curriculum emphasizes mathematics education, which is the foundation of specialized subjects, and have physics using calculus. The liberal arts instead of national language and society study for standard high school are introduced in grade 4 or above in order to develop human power. The elective specialized subjects are common to all departments. The engineers can be trained in multiple fields by attending according to the purpose of students. We also consider the introduction of a second major system. All elective courses for grade 4 and 5 are taken as academic credits due to reduce lecture responsibility for staff and induce students to take initiative in learning. In addition, we carry out “pre-research” which is a PBL course for solving regional problems in grade 1-3.
Present Applications of Humanoid Robots and Fuzzy Control

In this chapter, after given short information about humanoid robotsHumanoid robots and fuzzy control, recent applications and research papers of humanoid robots related with fuzzy control are reviewed. Studies about the humanoid robots and fuzzy logic-based control have been grouped under four major topic; the first one is stability and reliability control, the second one is walking patternWalking pattern detection and the third one is navigationNavigation, and the final one is obstacle avoidanceObstacle avoidance. In fact, these four topics are basic components of autonomous humanoid robots as similar as the other type robots. But the implementation of the control approach for each problem is special for humanoid robots in this chapter. As a result, this chapter reveals that various adaptations of the fuzzy logic-based control approach are used effectively to solve the different control problems of humanoid robots. Fuzzy logic-based control approaches are one of the basic control approaches that are widely used in humanoid robots, just like other robotic systems. Studies on this subject in the literature are gathered, grouped and presented in a structure that is easily accessible to the reader within this chapter.
Code analysis of the SDNet model

Previous chapters have introduced the design principles of the network architecture for machine reading comprehension (MRC) models. In practice, there are many important implementation details, such as establishing a word dictionary, build training data in batches, and padding text of unequal lengths. This chapter details these implementation techniques through analysis of the source code of an MRC model, SDNet. Many of these techniques are transferrable to other models. In addition, since SDNet utilizes the pretrained model Bidirectional Encoder Representations from Transformers (BERT), the analysis will showcase how to efficiently use BERT in natural language processing tasks.
Variational Autoencoding Dialogue Sub-Structures Using a Novel Hierarchical Annotation Schema

This work presents a novel method to extract sub-structures in dialogues for the following genres: human-human task driven, human-human chit-chat, human-machine task driven, and human-machine chit-chat dialogues. The model consists of a novel semi-supervised annotation schema of syntactic features, communicative functions, dialogue policy, sequence expansion and sender information. These labels are then transformed into tuples of three, four and five segments, the tuples are used as features and modelled to learn sub-structures in above mentioned genres of dialogues with sequence-to-sequence variational autoencoders. The results analyse the latent space of generic sub-structures decomposed by PCA and ICA, showing an increase in silhouette scores for clustering of the latent space.
Speaker Identification in Spoken Language Mismatch Condition: An Experimental Study

This paper describes the impact of spoken language variation in a multilingual speaker identification (SID) system. The development of speech technology applications in low resource languages (LRL) is challenging due to the unavailability of proper speech corpus. This paper illustrates an experimental study of SID on Eastern and Northeastern (E&NE) Indian languages in language mismatch conditions. For this purpose, several experiments are carried out using the LRL data to build speaker identification models. Here, spectral features are explored for investigating the presence of speaker-specific information. Mel frequency cepstral coefficients (MFCCs) and shifted delta cepstral (SDC) are used for representing the spectral information. Gaussian mixture model (GMM) and support vector machine (SVM)-based models are developed to represent the speaker-specific information captured through the spectral features. Apart from that, to build the modern SID i-vectors, time delay neural networks (TDNN), and recurrent neural network with long short-term memory (LSTM-RNN) have been considered. For the evaluation, equal error rate (EER) has been used as a performance matrix of the SID system. Performances of the developed systems are analyzed with native and non-native corpus in terms of speaker identification (SID) accuracy. The best SID performances are observed to be EER 10.52% after the corpus fusion mechanism.
Sentiment-Oriented Metric Learning for Text-to-Image Retrieval

In this era of multimedia Web, text-to-image retrieval is a critical function of search engines and visually-oriented online platforms. Traditionally, the task primarily deals with matching a text query with the most relevant images available in the corpus. To an increasing extent, the Web also features visual expressions of preferences, imbuing images with sentiments that express those preferences. Cases in point include photos in online reviews as well as social media. In this work, we study the effects of sentiment information on text-to-image retrieval. Particularly, we present two approaches for incorporating sentiment orientation into metric learning for cross-modal retrieval. Each model emphasizes a hypothesis on how positive and negative sentiment vectors may be aligned in the metric space that also includes text and visual vectors. Comprehensive experiments and analyses on Visual Sentiment Ontology (VSO) and Yelp.com online reviews datasets show that our models significantly boost the retrieval performance as compared to various sentiment-insensitive baselines.
SOLL Platform and the Adoption of Innovation by Teachers

The Internet of Things (IoT) appears with great educational potential, as it is a network composed of various objects and devices connected to the Internet, allowing the performance of interdisciplinary activities. Thus, we present the SOLL platform: Intelligent Objects Linked to Learning, which, supported by a set of technologies that collect and store data from a greenhouse, allows dynamic, interactive and interdisciplinary learning and a synchronous or asynchronous evaluation. In this article, the platform architecture is exposed and, using a focus groups of teachers, the data obtained show that teachers recognize that they would adopt this innovation, at least that it responds to the structure of the learning com-munity, adopting a different learning model, with exploration and enrichment of educational information.
Machine Learning Techniques in Spam Detection

Spam, a contraction of rotten email (or junk email), is a global and massive phenomenon. And as long as email exists, this real problem will always exist. However, it is possible to significantly limit the effects of spam. To do this, you just have to use various anti-spam technologies wisely. In this chapter, the authors present the definitions of spam and its evolution, its objectives and impacts, as well as the different approaches and techniques used for detecting and filtering it.
Scaffolding self-regulated learning through Android-based mobile media on hormone system

Various studies over the past decades have been exploring the advantage of technology use in 4.0 era where students engage with their learning material more effectively using their own device. This study developed Android-based mobile media on hormone system and investigated students’ self-regulated learning effectivity from a cognitive perspective. Hormone system was chosen since it was the most difficult subject to be learned by students according to the need analysis result. The research methods used were Research and Development followed by one group of pre-test and post-test experimental design. The samples were 62 students of grade XI of science class from one state school in Jakarta, applied with self-study learning method. Data were analyzed for product feasibility and t-test, also normalized gain score for product effectivity. The results showed that the developed media received a score of 83,32% on average. The average pre-test and post-test scores were 63 and 79. The gained score showed 0.4 on average, which placed the media as quite good in enhancing students’ comprehension. Statistical analysis of the dependent t-test resulted in sig of 0.000 (sig < α) which means that the media gave an impact on students’ comprehension. The conclusion of this study indicated that the developed media was very feasible to be used in learning and scaffolding students’ self-regulated learning from the cognitive perspective. The product ought to be applied in learning since it gave benefit for students not only in increasing their comprehension but also fostering independent awareness to learn better in mastering some materials.
Comparing different assay methods for the determination of mineral ore grade

Novel, non-destructive material analysis methods provide a useful alternative to conventional chemical assay techniques, allowing rapid or real-time determination of ore grade with minimal sample preparation. Particularly when used for quantitative applications such as resource evaluation, grade control or metal accounting, it is important to establish whether new and old results can be used interchangeably. The combination of heavily skewed grade distributions and large sampling errors lead to significant biases when conventional metrics, such as correlation coefficients, analysis of variance (ANOVA), regressions and Tukey mean-difference or Bland-Altman plots are used to compare two assay methods. We show how these metrics can be modified to produce unbiased estimates of the quality of agreement between different assay results and introduce a procedural approach for validating new analysis methods. PhotonAssay is a recently introduced method for the measurement of gold in ore samples. We illustrate the application of our new metrics through the comparison of PhotonAssay and fire-assay grade results on a variety of ore types.
Analysis of counting learning difficulties in illiterate adults

Based on the socio-economic survey conducted by the Central Bureau of Statistics (2018), there are 3.29 million people or 1.93% of Indonesians who bear illiteracy (read, write, and count). Their average is 45 years old and above, living in rural areas, and most women. They need to be taught to read, write, and count. This study focuses on the analysis of the difficulty of learning to count. The research method used is a case study at PKBM Pratama in Indramayu District. The research subject is 17 learners. Data collection techniques include observations, interviews, and documentation studies, while data analysis is conducted with preliminary study stages, data collection, data reduction, data presentation, confirmation, and withdrawal of conclusions. This study found that the blind students’ difficulty of learning in the PKBM Pratama were the barriers to learning concentration, quickly bored, the vision began to diminish, felt saturated, and feelings of inferiority with the community environment. Especially in counting learning, the difficulty is in understanding the counts' symbols and interpreting the measurement results between theory and practice in daily life. The research recommendation is that educators need to provide examples and simulations realistically in counting learning so that students understand more about the concept of interpretation of measurement results and count symbols in the daily life of learners.
Estimation of peak discharge using a rational method in Kodil Sub-Watershed, Purworejo Regency, Central Java

Abstract The Kodil Sub-Watershed is one of the areas that has a high potential for flooding in Bogowonto Watershed. Flood disasters can cause significant losses to society, both in terms of physical, social and economic. This causes are need for information on the estimated peak discharge in The Kodil Sub-Watershed. Peak discharge estimation uses the rational method and The Cook Method to measure runoff coefficient or C value. Runoff coefficient using The Cook Method uses physical parameters, i.e land use, slope, drainage density, and infiltration. These parameters are extracted using remote sensing technology and Geographic Information System (GIS). Remote sensing data used for extraction of physical land parameters of Sentinel 2A Imagery and DEM ALOS PALSAR imagery. The results of the processing of peak discharge by the method obtained an estimate of the peak discharge of the Kodil Sub-Watershed of 209,659 m 3 /second and an average peak discharge of 135,024 m 3 /second.
Related Statistical Universals

This last chapter of Part II further considers the nature of a vocabulary population in terms of two related properties that have a mathematical relation with Zipf’s law: the density function and the vocabulary growth. Similarly to Zipf’s law, both nearly indicate power-law behavior but are subject to some deviations.
Spatial skew‐normal/independent models for nonrandomly missing clustered data

Clinical studies on periodontal disease (PD) often lead to data collected which are clustered in nature (viz. clinical attachment level, or CAL, measured at tooth-sites and clustered within subjects) that are routinely analyzed under a linear mixed model framework, with underlying normality assumptions of the random effects and random errors. However, a careful look reveals that these data might exhibit skewness and tail behavior, and hence the usual normality assumptions might be questionable. Besides, PD progression is often hypothesized to be spatially associated, that is, a diseased tooth-site may influence the disease status of a set of neighboring sites. Also, the presence/absence of a tooth is informative, as the number and location of missing teeth informs about the periodontal health in that region. In this paper, we develop a (shared) random effects model for site-level CAL and binary presence/absence status of a tooth under a Bayesian paradigm. The random effects are modeled using a spatial skew-normal/independent (S-SNI) distribution, whose dependence structure is conditionally autoregressive (CAR). Our S-SNI density presents an attractive parametric tool to model spatially referenced asymmetric thick-tailed structures. Both simulation studies and application to a clinical dataset recording PD status reveal the advantages of our proposition in providing a significantly improved fit, over models that do not consider these features in a unified way.
Distributed Graph Processing: Techniques and Systems

During the past 10 years, there has been a surging interest in developing distributed graph processing systems. This tutorial provides a comprehensive review of existing distributed graph processing systems. We firstly review the programming models for distributed graph processing and then summarize the common optimization techniques for improving graph execution performance, including graph partitioning methods, communication mechanisms, parallel processing models, hardware-specific optimizations, and incremental graph processing. We also present an emerging hot topic, distributed Graph Neural Networks (GNN) frameworks, and review recent progress on this topic.
AscentX: A System for Crowdsourcing and Annotating with Active Supervision

This paper presents a system, AscentX, for labeled data creation and training with active supervision. The aim of system is that it makes collecting data and training models easier. We integrate semi-supervised learning and active learning as well as crowdsourcing learning paradigms. We design a web-based interface for labeling examples based on Python-Django and incorporate our proposed algorithms into it, including semi-supervised classification algorithms, semi-supervised clustering algorithms and with application to data filtering. Moreover, we provide the interfaces for future development including standard datasets attaching, algorithms and active learning models plunging. The system supports the visualization of labeling the examples and analyzing the results of algorithms, which is user-friendly for human annotator. We experimentally verify that AscentX achieves comparable performance to the state-of-the-art systems, using real-life (crawling web data) and benchmark datasets.