Reviewerly/exHarmony huggingface dataset:
Sequence Stratigraphic Framework of the Upper Jurassic-Cretaceous Section of Khizi-Altyagaj Area (Eastern Part of the Greater Caucasus)

Summary Khizi-Altyagaj area of Azerbaijan is located in the South-Eastern part of Greater Caucasus. This mountain area with river valleys was formed by compressional regime after Miocene creating a system of folds and thrusts. They belong to Tithonian (Upper Jurassic) and Cretaceous ages.
Impact of corona pandemic on educational undertakings and possible breakthrough mechanisms

This impact survey is based on experiential reflections and international sources. The purpose is to forward workable solutions for virtual learning based on review of breakthrough mechanisms so far employed to continue learning during the lockdown due to corona pandemic. Desk-review method was employed for the analysis, and the sources were national and international experiences that focused on the analysis of COVID-19 impacts on teaching and learning. Overall, four issues were addressed in the impact survey which enclosed social, economic, psychological and resource issues. For each case raised as impact on teaching and learning, experience-based recommendations were given.
Students’ English learning motivaton at the Tenth Grade of SMK Karsa Mulya Palangka Raya

This study aimed to find out about the, to that related to students’ learn English motivation in the tenth grade of SMK Karsa Mulya Palangka Raya. The type of research used was a survey in research form of the quantitative method, Quantitative research, is defined as the systematic investigation of phenomena by gathering quantifiable data and performing statistical, mathematical or computational techniques. Quantitative research gathers information from existing and potential customers using  sampling methods  and sending out  online surveys ,  online polls ,  questionnaires etc., the results of which can be depicted in the form of numerical. After careful understanding of these numbers to predict the future of a product or service and make changes accordingly. The instrument used was a questionnaire, adopted from Tomoharu Takahashi there were 34 items, which contained about motivation. The data analysis used SPSS 20.00 software. The research subjects were 135 tenth grade students of Karsa Mulya Palangka Raya Vocational High School. Based on the result of the study, it was found that: 1). Intrinsic motivation in learning English (80.2%) students like to learn English and English it is important to get a job. 2). Extrinsic motivation (74.2%) students learn English, they follow friends and follow school rules or are told by teachers to learn English.
Study of Target Tracking Algorithm using iterative Joint Integrated Probabilistic Data Association in Low SNR Multi-Target Environments

For general target tracking works by receiving a set of measurements from sensor.However, if the SNR(Signal to Noise Ratio) is low due to small RCS(Radar Cross Section), caused by remote small targets, the target's information can be lost during signal processing.TBD(Track Before Detect) is an algorithm that performs target tracking without threshold for detection.That is, all sensor data is sent to the tracking system, which prevents the loss of the target's information by thresholding the signal intensity.On the other hand, using all sensor data inevitably leads to computational problems that can severely limit the application.In this paper, we propose an iterative Joint Integrated Probabilistic Data Association as a practical target tracking technique suitable for a low SNR multi-target environment with real time operation capability, and verify its performance through simulation studies.
In Memoriam: Claren (“Si”) Frazier (1933–2020)

Photo by Elise Skalwold.When news reached me about the passing of Si Frazier, well, all I could do was sigh. When you realize that a well-respected man within the mineral community has departed the...
DISTANCE EDUCATION IN UNIVERSITIES OF MODERN RUSSIA

The article shows that most Russian universities are only at the initial stage of introducing modern distance learning technologies in the educational process, and the main barriers to the development of distance learning technologies in higher education are: the high cost of developing, introducing e-learning, the lack of necessary technical support, a small number of qualified teachers for the introduction of distance learning, the lack of an exact number of universities for e-learning courses.At the same time, an analysis of the foreign experience in the application of these technologies testifies to their demand at universities in the developed countries of the world, which is determined by the comparable quality of foreign distance learning and traditional full-time education.
Standards: Learning, Networking, and Recognition [Standards News]

Reports on IAS society standards activities.
Improvement of the Turajlić Method for the Estimation of Gaussian Noise in Images

Gaussian noise estimation is an important step in some of the more recently developed noise removal methods. This is a difficult task and although several estimation techniques have been proposed recently, they generally do not produce good results. In a previous comparative study, among several noise estimation techniques, a method proposed in 2017 by Turajlić was found to give the best results. Although acceptable, they are still far from ideal. Therefore, several changes to this method are introduced in this paper to improve the estimation. Tests on monochromatic images contaminated with different levels of Gaussian noise showed that the modified method produces a significant improvement in the estimation of Gaussian noise, over 35%, at a slightly higher computational cost.
Question generation for language café interaction between robot and human : NLP applied in a robot

Conversational robots can be used in several contexts, one of them being language cafes. This setting demands that the robot in question can converse on many different subjects and move between the ...
LibGuides: Engineering: Accessibility

A comprehensive guide to all of the Library service's resources which are tailored to support you with your studies.
FIFA Player Ranking Based on Tweets Recognizing Named Entity

Nowadays social media play an important role to share opinions on various events.People often share their experiences, opinions and measurements.Social media have become important sources of information to evaluate different types of events and people as well.Twitter has become one of the popular social media platforms to express news in real-time.Opinions shared on social media can create impacts on various events.People often share their opinions and feelings about players.Football is one of the most popular game all around the world.People love to watch football, analyze players and post their opinions on social media.In this thesis, an analysis is performed on FIFA top listed eight players of the current time, based on tweets of different persons.5000 tweets are extracted and by applying the Named Entity Recognition technique tweets on each player are isolated.A sentiment analysis approach is applied on tweets to measure the polarity of each tweet.A ranking system is designed to rank the players.By different computational and statistical means and using the designed ranking system the chosen players are ranked.A comparative analysis among FIFA best eight players is shown as well according to public views on them.
Design Project Management Application Of The Blind Color Test Android Based

IT project management is an activity of resources available from IT solution development projects that can produce a system solution that meets predetermined requirements. Developments in the era of technology are moving forward with the development of companies engaged in technology services. This project planning can be prepared using Work Breakdown Structure (WBS), budget plans and network diagrams (AON). The color blind test application system is an application to facilitate the detection of colors early on in someone and also provides accessibility to the black test. By managing a good project management, it is possible to estimate the time and cost needed in implementing the project, so as to minimize the estimated cost due to additional project delays.
Bengston, J. (2019). <i>Library Web Development: Beyond Tips and Tricks</i>

"Bengston, J. (2019). Library Web Development: Beyond Tips and Tricks." Journal of Web Librarianship, 14(3-4), p. 104
Feature Interaction based Neural Network for Click-Through Rate Prediction

Click-Through Rate (CTR) prediction is one of the most important and challenging in calculating advertisements and recommendation systems. To build a machine learning system with these data, it is important to properly model the interaction among features. However, many current works calculate the feature interactions in a simple way such as inner product and element-wise product. This paper aims to fully utilize the information between features and improve the performance of deep neural networks in the CTR prediction task. In this paper, we propose a Feature Interaction based Neural Network (FINN) which is able to model feature interaction via a 3-dimention relation tensor. FINN provides representations for the feature interactions on the the bottom layer and the non-linearity of neural network in modelling higher-order feature interactions. We evaluate our models on CTR prediction tasks compared with classical baselines and show that our deep FINN model outperforms other state-of-the-art deep models such as PNN and DeepFM. Evaluation results demonstrate that feature interaction contains significant information for better CTR prediction. It also indicates that our models can effectively learn the feature interactions, and achieve better performances in real-world datasets.
I know the xAPI vocabulary well enough....even ...

I know the xAPI vocabulary well enough....even though I am not biggest fan, but still be nice to play with smart people on how I
Semantic-driven Colorization

Recent colorization works implicitly predict the semantic information while learning to colorize black-and-white images. Consequently, the generated color is easier to be overflowed, and the semantic faults are invisible. As a human experience in colorization, our brains first detect and recognize the objects in the photo, then imagine their plausible colors based on many similar objects we have seen in real life, and finally colorize them, as described in the teaser. In this study, we simulate that human-like action to let our network first learn to understand the photo, then colorize it. Thus, our work can provide plausible colors at a semantic level. Plus, the semantic information of the learned model becomes understandable and able to interact. Additionally, we also prove that Instance Normalization is also a missing ingredient for colorization, then re-design the inference flow of U-Net to have two streams of data, providing an appropriate way of normalizing the feature maps from the black-and-white image and its semantic map. As a result, our network can provide plausible colors competitive to the typical colorization works for specific objects.
Multi-channel Image Registration of Cardiac MR Using Supervised Feature Learning with Convolutional Encoder-Decoder Network

It is difficult to register the images involving large deformation and intensity inhomogeneity. In this paper, a new multi-channel registration algorithm using modified multi-feature mutual information (α-MI) based on minimal spanning tree (MST) is presented. First, instead of relying on handcrafted features, a convolutional encoder-decoder network is employed to learn the latent feature representation from cardiac MR images. Second, forward computation and backward propagation are performed in a supervised fashion to make the learned features more discriminative. Finally, local features containing appearance information is extracted and integrated into α-MI for achieving multi-channel registration. The proposed method has been evaluated on cardiac cine-MRI data from 100 patients. The experimental results show that features learned from deep network are more effective than handcrafted features in guiding intra-subject registration of cardiac MR images.
Tight Quantum Time-Space Tradeoffs for Function Inversion

In function inversion, we are given a function $f: [N] \mapsto [N]$, and want to prepare some advice of size $S$, such that we can efficiently invert any image in time $T$. This is a well studied problem with profound connections to cryptography, data structures, communication complexity, and circuit lower bounds. Investigation of this problem in the quantum setting was initiated by Nayebi, Aaronson, Belovs, and Trevisan (2015), who proved a lower bound of $ST^2 = \tilde\Omega(N)$ for random permutations against classical advice, leaving open an intriguing possibility that Grover's search can be sped up to time $\tilde O(\sqrt{N/S})$. Recent works by Hhan, Xagawa, and Yamakawa (2019), and Chung, Liao, and Qian (2019) extended the argument for random functions and quantum advice, but the lower bound remains $ST^2 = \tilde\Omega(N)$. In this work, we prove that even with quantum advice, $ST + T^2 = \tilde\Omega(N)$ is required for an algorithm to invert random functions. This demonstrates that Grover's search is optimal for $S = \tilde O(\sqrt{N})$, ruling out any substantial speed-up for Grover's search even with quantum advice. Further improvements to our bounds would imply new classical circuit lower bounds, as shown by Corrigan-Gibbs and Kogan (2019). To prove this result, we develop a general framework for establishing quantum time-space lower bounds. We further demonstrate the power of our framework by proving quantum time-space lower bounds for Yao's box problem and salted cryptography.
An Algorithmic Introduction to Clustering

This paper tries to present a more unified view of clustering, by identifying the relationships between five different clustering algorithms. Some of the results are not new, but they are presented in a cleaner, simpler and more concise way. To the best of my knowledge, the interpretation of DBSCAN as a climbing procedure, which introduces a theoretical connection between DBSCAN and Mean shift, is a novel result.
Psychological-Pedagogical and Methodical Aspects of the Formation of the Personality Culture of the Youth of Modern Society

At all ages, people appreciated to the formation of the moral culture for young people. The deep socio-economic changes taking place in today's society, we are forced to think about the future of our country and its youth. Currently, crushed moral guidelines, the younger generation feel soulless, aggressive, accused of unbelief. This determines the need for correction of the traditional approaches to the problem of moral culture puts the task of scientific research of the formation of the culture of these relationships in adolescents. The problem of moral education can be solved on condition of formation of the younger generation of the corresponding system of values ​​based on the restoration of the youth culture.
DETERMINATION OF THE CONTENT OF CHEMICAL ELEMENTS IN SAMPLES OF MINERALS AND ROCKS BY THE FREQUENCY-RESONANCE METHOD OF SATELLITE IMAGES AND PHOTOGRAPHS PROCESSING

Summary The results of experimental investigation using the technology of frequency-resonance processing of satellite images and photo images for study the possibility of its application to determine the presence (absence) of various chemical elements in samples of minerals and rocks are analyzed. During the frequency-resonance processing of a core photograph from wells in a gold ore deposit in Peru, three wells were identified that uncovered rocks with gold mineralization. The results of technique testing on eight quartz samples showed that total number of detected chemical elements in the examined samples is in the range of 21–61. In order to determine the content of chemical elements in cross-section the study were carried out on the area of rare-earth chemical elements accumulation in Pacific Ocean, on the local site of a prospecting well drilling and on Zmeinyy Island in Black Sea, as well as in the area of Ukrainian Antarctic Station on Galindez island. The results of experimental studies indicate possibility of the methods of frequency resonance processing and decoding of satellite images and photo images using to quickly establish the presence of various chemical elements in samples of minerals and rocks, in core samples, in sediments of the cross-section at various depths.
COMPRESSION OF HIGH-RESOLUTION VOXEL PHANTOMS BY MEANS OF B+ TREE

Data structures are chosen to save space and to grant fast access to data by it’s key for a particular structural representation. The data structures surveyed are linear lists, hierarchical structures, graph structures. B+ tree is an expansion of a B tree data structure which allows efficient insertions, deletions and search operations. It is used to store a large amount of data that cannot be stored in the main memory. B+ tree leaf nodes are connected together in the form of a singly linked list to make search queries more efficient and effective. The drawback of binary tree geometry is that the decrease in memory use comes at the expense of more frequent memory access, might slow down simulation in which frequent memory access constitutes a significant part of the execution time. Processing and compression of voxel phantoms without loss of quality. Voxels are often utilized in the visualization and analysis of medical and scientific (logical) information. Voxel phantoms which comprise a set of small volume components appeared towards the end of the 1980s and improved on the first scientific models. These are the models of the human body. These phantoms are an extremely exact representation. Fetching of records in the equal number of disk accesses and to reduce the access time by reducing the height of the tree and increasing the number of branches in the node.
Graph Neural Networks for Graph Search

Graph neural networks (GNNs) have received more and more attention in past several years, due to the wide applications of graphs and networks, and the superiority of their performance compared to traditional heuristics-driven approaches. However, most existing GNNs still focus on node-level applications, such as node classification and link prediction, and many challenging graph tasks are graph-level, such as graph search. In this talk, I will introduce our recent progress on graph-level neural operator development. In particular, we will examine three challenging tasks that are key to the success of graph search: (1) How can we conduct efficient graph similarity search by turning the NP-Complete problems, such as the Graph Edit Distance (GED) and Maximum Common Subgraph (MCS) computation, into a learning problem? We will present SimGNN [1] and GraphSim [3] that are able to provide more efficient and effective results compared to state-of-the-art approximate algorithms. (2) How can we provide a neural operator that can turn any graph into a low dimensional representation vector, which is learnable, inductive, and unsupervised? In this line, we propose UGraphEmd [2] that is able to leverage graph-graph interaction to produce manifold-preserving graph-level embedding. Moreover, GHashing [5] is designed to map each graph to a discrete hash code, which enables a much more efficient search (20 times speed up) to handle large graph database with millions of graphs. And (3) how can we design GNNs that can directly detect the best matched subgraphs of two graphs? A deep reinforcement learning framework RLMCS [4] is then proposed to address this issue, with the goal to learn the best strategy to pick the next matching pair for two graphs. In the end, we will provide some discussions to the open questions in the field.
Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective

Learning controllable and generalizable representation of multivariate data with desired structural properties remains a fundamental problem in machine learning. In this paper, we present a novel framework for learning generative models with various underlying structures in the latent space. We represent the inductive bias in the form of mask variables to model the dependency structure in the graphical model and extend the theory of multivariate information bottleneck to enforce it. Our model provides a principled approach to learn a set of semantically meaningful latent factors that reflect various types of desired structures like capturing correlation or encoding invariance, while also offering the flexibility to automatically estimate the dependency structure from data. We show that our framework unifies many existing generative models and can be applied to a variety of tasks including multi-modal data modeling, algorithmic fairness, and invariant risk minimization.
A program for the full axiom of choice

The theory of classical realizability is a framework for the Curry-Howard correspondence which enables to associate a program with each proof in Zermelo-Fraenkel set theory. But, almost all the applications of mathematics in physics, probability, statistics, etc. use Analysis i.e. the axiom of dependent choice (DC) or even the (full) axiom of choice (AC). It is therefore important to find explicit programs for these axioms. Various solutions have been found for DC, for instance the lambda-term called "bar recursion" or the instruction "quote" of LISP. We present here the first program for AC.
TOXIC PUBLIC SCHOOLS, TOXIC TEACHERS’ UNIONS, AND THEIR HARMFUL IMPACT ON INEQUALITY

The consequences are predictably toxic, especially for students, in both advanced countries such as the US and developing countries such as Mexico. In both groups of countries, public school teachers are the single biggest obstacle to education reform. Good teachers who can effectively communicate knowledge to their students are indispensable for good schools. Top-class computer labs, science labs, gyms, and other facilities all help make learning more enjoyable, but they are simply no substitute for knowledgeable, dedicated teachers who want to teach and who are good at teaching. Online teaching, videoconferencing, and other remote teaching platforms have come a long way but they will never be the same as in-person classroom teaching. Yet ironically teachers stand in the way of better schools and a better education. In the US, for example, politically powerful public school teachers oppose education reforms tooth and nail in order to protect their narrow self-interest…
An Overview of Cyber Risks in Internet of Things (IoT) World

Oftentimes the companies participating in the digital supply chain cannot identify and realise the various risks and threats associated in the upcoming digital IoT technologies.The goal of this paper is to discuss how these cyber risks are being implemented in IoT that can be kept in mind while planning the business framework and also while implementing logistic network.The paper illustrates the concept of IoT world and illustrates the design framework for a established support system for conceptualizing the cyber risks from the IoT flexibly chain in the advanced economy.The techniques used in the research are open and categorical coding and discourse analysis.