Reviewerly/exHarmony huggingface dataset:
Learning to Rank Learning Curves

Many automated machine learning methods, such as those for hyperparameter and neural architecture optimization, are computationally expensive because they involve training many different model configurations. In this work, we present a new method that saves computational budget by terminating poor configurations early on in the training. In contrast to existing methods, we consider this task as a ranking and transfer learning problem. We qualitatively show that by optimizing a pairwise ranking loss and leveraging learning curves from other datasets, our model is able to effectively rank learning curves without having to observe many or very long learning curves. We further demonstrate that our method can be used to accelerate a neural architecture search by a factor of up to 100 without a significant performance degradation of the discovered architecture. In further experiments we analyze the quality of ranking, the influence of different model components as well as the predictive behavior of the model.
Implementing Deep Learning Algorithms in Anatomic Pathology Using Open-source Deep Learning Libraries

The application of artificial intelligence technologies to anatomic pathology has the potential to transform the practice of pathology, but, despite this, many pathologists are unfamiliar with how these models are created, trained, and evaluated. In addition, many pathologists may feel that they do not possess the necessary skills to allow them to embark on research into this field. This article aims to act as an introductory tutorial to illustrate how to create, train, and evaluate simple artificial learning models (neural networks) on histopathology data sets in the programming language Python using the popular freely available, open-source libraries Keras , TensorFlow , PyTorch , and Detecto . Furthermore, it aims to introduce pathologists to commonly used terms and concepts used in artificial intelligence.
The Application of Artificial Neural Network Method in Forecasting the Number of Pregnant Woman Visits (K4)

Forecasting is a systematic attempt to predict future events usingpast data, based on scientific and qualitative methods. For the maternal health program, forecasting is important as its process consists of planning, targetting and achievement. Based on data from the Ministry of Health, the quality of antenatal care in Indonesia was still low (87.48 percent) compared to that of the national target (95 percent). This study aims to apply the methods of artificial neural network in predicting the antenatalcare (K4). This applied research used a descriptive method with secondary data in the form of monthly antenatal care visits (K4) from the year of 2012 to2015 obtained from the Provincial Health Office of East Java, with a case study in Bondowoso. The forecasting result in 2016 based onthe 12-4-1 network architecture was 9533.5698, with the value of Mean Square Error (MSE) of 3091.84404. The average percentage of errord based on a comparison with the actual data is 0.1854 or reaching the accuracy of 99.81 percent. The conclusion of this study is that a neural network has a low error value and a high accuracy.Therefore, forecasting results can be used as an input in the planning program.
Automated Conjecturing II: Chomp and Reasoned Game Play

We demonstrate the use of a program that generates conjectures about positions of the combinatorial game Chomp—explanations of why certain moves are bad. These could be used in the design of a Chomp-playing program that gives reasons for its moves. We prove one of these Chomp conjectures—demonstrating that our conjecturing program can produce genuine Chomp knowledge.&#x0D; The conjectures are generated by a general purpose conjecturing program that was previously and successfully used to generate mathematical conjectures. Our program is initialized with Chomp invariants and example game boards—the conjectures take the form of invariant-relation statements interpreted to be true for all board positions of a certain kind. The conjectures describe a theory of Chomp positions.&#x0D; The program uses limited, natural input and suggests how theories generated on-the-fly might be used in a variety of situations where decisions—based on reasons—are required.
Fluorite Specialist Jim Gebel

That really friendly fluorite guy in his signature striped jersey, who exhibits at the Tucson and Cincinnati shows and faithfully attends the Dallas Mineral Symposium, is Jim Gebel (pronounced “gab...
Vivat academia, vivant professores! Challenges of teaching professional communication in foreign languages amidst global pandemic

This paper is dedicated to challenges of transitioning the educational process to online learning. To present an objective analysis the authors have compared the experience of two Russian universities – MGIMO University and Samara National Research University in the field of foreign language teaching, in particular professional English. The focus of the paper is on difficulties faced by the faculty and the ways academics coped with them.The authors highlight the specificity and peculiarities of such a complicated form of education as distance e-learning. Comparative analysis of online platforms, which are mostly used for the organization of e-learning, reveals their benefits and major drawbacks. Furthermore, the authors reflect upon how learning material is blended and assimilated in order to form an e-course. As one of acute pressing problems, the complexity of students’ knowledge assessment is described. The goal of the paper predetermined the necessity to carry out a survey to accumulate and summarize data on teachers’ and students’ individual experiences of the newly formed online educational environment. As the survey indicates, both professors and students consider elearning to be a real breakthrough. On the other hand, the authors conclude that currently it can be only an alternative to the more traditional forms of education, since it can’t be relevantenough to teaching various aspects of professional communication at universities.
Spellcaster Control Agent in StarCraft II Using Deep Reinforcement Learning

This paper proposes a DRL -based training method for spellcaster units in StarCraft II, one of the most representative Real-Time Strategy (RTS) games. During combat situations in StarCraft II, micro-controlling various combat units is crucial in order to win the game. Among many other combat units, the spellcaster unit is one of the most significant components that greatly influences the combat results. Despite the importance of the spellcaster units in combat, training methods to carefully control spellcasters have not been thoroughly considered in related studies due to the complexity. Therefore, we suggest a training method for spellcaster units in StarCraft II by using the A3C algorithm. The main idea is to train two Protoss spellcaster units under three newly designed minigames, each representing a unique spell usage scenario, to use ‘Force Field’ and ‘Psionic Storm’ effectively. As a result, the trained agents show winning rates of more than 85% in each scenario. We present a new training method for spellcaster units that releases the limitation of StarCraft II AI research. We expect that our training method can be used for training other advanced and tactical units by applying transfer learning in more complex minigame scenarios or full game maps.
Predicted Line-of-Sight Coordinate Approach to Target Tracking for Airborne AESA Radars

This paper deals with the problem of tracking a target using an airborne AESA radar. Unlike the ground-based tracking systems, the angular motion of a platform makes the noise statistics of the converted measurements (CMs) complicated, hence the conventional approaches produce the biased target state estimates. To settle this problem, the predicted line-of-sight Cartesian coordinate system (PLCCS) is introduced. Provided that the a priori state estimation error is small enough, the statistical properties of the CMs in PLCCS can be accurately approximated only using the 1st and 2nd moments of the radar measurement noises. In addition, the resulting debiasing term leads to the satisfactory performance of the proposed filter. Enjoying the diagonal form of the noise covariance matrix, we can decouple the tracking filter into each axis for real-time implementation. Through simulations, the enhanced performance of the suggested method over the previous schemes is demonstrated.
Automatic Synthesis of Generalized Winning Strategies of Impartial Combinatorial Games Using SMT Solvers

Strategy representation and reasoning has recently received much attention in artificial intelligence. Impartial combinatorial games (ICGs) are a type of elementary and fundamental games in game theory. One of the challenging problems of ICGs is to construct winning strategies, particularly, generalized winning strategies for possibly infinitely many instances of ICGs. In this paper, we investigate synthesizing generalized winning strategies for ICGs. To this end, we first propose a logical framework to formalize ICGs based on the linear integer arithmetic fragment of numeric part of PDDL. We then propose an approach to generating the winning formula that exactly captures the states in which the player can force to win. Furthermore, we compute winning strategies for ICGs based on the winning formula. Experimental results on several games demonstrate the effectiveness of our approach.
Semi-supervised learning for facial component-landmark detection

Facial component and landmark detection have many applications in many facial analysis tasks. In this paper, a semisupervised method for this task is proposed to detect facial components and landmarks. Different from other facial detectors algorithms, our model without extra input solve the occlusion problem by detecting the visible facial components. Firstly, we propose a data augmentation method based on the Deep Convolutional Generative Adversarial Network to generate a large amount of semi-supervised training data. Then, a semi-supervised learning model based on Region-based CNN is responsible for multi-task facial component and landmark detection by training on the generated semi-supervised training data. During training, facial component regions and landmarks are used as supervised training data, while unsupervised training data only contains component bounding box. Experimental results illustrate that the proposed model can handle multi-task facial detection, and outperforms the state-of-the-art algorithms.
Multi-sensor images registration based on SIFT and extended phase correlation

To achieve registration of multi-sensor images by utilizing complementary information, this paper proposes an iterative image registration method based on scale invariant feature transformation (SIFT) and extended phase correlation (EPC), named as SIFT_IEPC. The reference image and the sensed image are pre-registered by SIFT and a geometrical outlier removal method. Overlapping regions corresponding to the reference image and the rectified sensed image are partitioned to block image with equal size, and the extended phase correlation is used to estimate the translation parameters between each block pairs, which are used to tune the matched feature point pairs in the block. The tuned feature point sets are used to update the registration parameters between the reference image and the sensed image. Repeat the process of EPC matching and feature tuning until terminate condition is satisfied. Experiments on three pairs including simulated and real remote sensing images are conducted to evaluate the performance of SIFT_IEPC. The comparison experiments demonstrate that SIFT_IEPC can apparently increase the accuracy of image registration.
Annular subaperture stitching interferometry with planar reference wavefront for measurement of spherical and aspherical surfaces

An interferometric line sensor in a Michelson configuration without a transmission sphere is employed for annular subaperture stitching interferometry to measure spherical and aspherical surfaces. The misalignments between the subapertures, which are caused by positioning error induced wavefront aberrations, are corrected employing a machine learning based Zernike polynomial fitting in the overlapping regions.
A Centre of Gravity-Based Preprocessing Approach for Feature Selection Using Artificial Bee Colony Algorithm on High-Dimensional Datasets

The process of feature selection has a high impact on data mining tasks such as classification and clustering. Removing irrelevant, noisy and redundant data not only increases the quality of the task but also reduces the computational complexity and execution time. Nature-inspired algorithms have tackled the problem of feature selection efficiently. But when applying on a high-dimensional dataset, the metaheuristic algorithms have difficulty to converge. In this paper, an existing artificial bee colony algorithm for feature selection is modified by incorporating a data preprocessing step to reduce the size of the input dataset. The preprocessing step computes the centre of gravity vectors corresponding to the original dataset to form a smaller dataset. The artificial bee colony algorithm works on this smaller dataset for feature selection. The proposed method generates better results with less time and complexity when compared to the existing algorithms.
Research on the Risk and Supervision Method of Big Data Application in Financial Field

In order to solve the possible risks in the application of big data in the financial field, this paper proposes a new supervision method based on big data. This method is based on big data, constructs the application platform of financial big data, and carries on the real-time mining to the massive big data in the financial field. The results show that this method can not only break the problem of data island, improve the security of personal privacy data, but also promote the standardization of big data.
Network Resource Management Drives Machine Learning: A Survey and Future Research Direction

Network resource management is one of the vibrant factors in the current dynamic technological computing paradigms. This reduces poor resource utilization; network resources include network devices, management networks, management systems, and management support organizations carrying out task planning, resource scheduling, and network managing among many more. It's now been observed within different networks that machine learning has been applied to help in carrying some network tasks. This paper surveys current approaches that have been done in managing resources with a deep focus on energy optimization, auto-scaling methods based on current machine learning approaches. We further present the proposed techniques that are identified by evolving this overview by investigating other state-of-the-art recommendations. This study provides a deep insight into proposed methods, loopholes that will aid researchers to design, model, or create novel frameworks that focus on the better options of resource management in contrast to the existing methods.
Bridging Cross-Tasks Gap for Cognitive Assessment via Fine-Grained Domain Adaptation

Discriminating pathologic cognitive decline from the expected decline of normal aging is an important research topic for elderly care and health monitoring. However, most cognitive assessment methods only work when data distributions of the training set and testing set are consistent. Enabling existing cognitive assessment models to adapt to the data in new cognitive assessment tasks is a significant challenge. In this paper, we propose a novel domain adaptation method, namely the Fine-Grained Adaptation Random Forest (FAT), to bridge the cognitive assessment gap when the data distribution is changed. FAT is composed of two essential parts 1) information gain based model evaluation strategy (IGME) and 2) domain adaptation tree growing mechanism (DATG). IGME is used to evaluate every individual tree, and DATG is used to transfer the source model to the target domain. To evaluate the performance of FAT, we conduct experiments in real clinical environments. Experimental results demonstrate that FAT is significantly more accurate and efficient compared with other state-of-the-art methods.
Convolutional neural network model based on text similarity for customer service

Abstract Customer service is a good way for companies to communicate with customers, high-quality customer service can improve customer satisfaction and dependence on the enterprise. Text matching is a core problem in natural language understanding, and it can be applied to a large number of natural language processing tasks, such as information retrieval, question answering systems, repetition questions, dialogue systems, machine translation. These natural language processing tasks can be approximately abstracted into text matching problems. This paper combines text matching and convolutional neural networks to build an intelligent customer service model, and finally achieves ideal results in F1 value, recall rate and accuracy rate.
Analysis of Question Type Can Help Inform Chat Staffing Decisions

A Review of:&#x0D; Meert-Williston, D., &amp; Sandieson, R. (2019). Online Chat Reference: Question Type and the Implication for Staffing in a Large Academic Library. The Reference Librarian, 60(1), 51-61. http://www.tandfonline.com/doi/full/10.1080/02763877.2018.1515688&#x0D; Abstract&#x0D; Objective – Determine the type of online chat questions to help inform staffing decisions for chat reference service considering their library’s service mandate.&#x0D; Design – Content analysis of consortial online chat questions.&#x0D; Setting – Large academic library in Canada.&#x0D; Subjects – Analysis included 2,734 chat question transcripts.&#x0D; Methods – The authors analyzed chat question transcripts from patrons at the institution for the period of time from September 2013 to August 2014. The authors coded transcripts by question type using a coding tool created by the authors. For transcripts that fit more than one question type, the authors chose the most prominent type.&#x0D; Main Results – The authors coded the chat questions as follows: service (51%), reference (25%), citation (9%), technology (7%), and miscellaneous (8%). The majority of service questions were informational, followed by account related questions. Most of the reference chat questions were ready reference with only 16% (4% of the total number of chat questions) being in-depth. After removing miscellaneous questions, those that required a high level of expertise (in-depth reference, instructional, copyright, or citation) equaled 19%.&#x0D; Conclusion – At this institution, one in five chat questions needed a high level of expertise. Library assistants with sufficient expertise could effectively answer circulation and general reference questions. With training they could triage complex questions.&#x0D;
Improved Steganography Using Odd Even Substitution

Image steganography is one of the emerging techniques used for hiding content in digital images. This makes the content more secure and free from information attackers. The proposed image steganography technique implements an LSB technique with an algorithm to hide message bits in the DCT coefficients. The technique has chosen a mid-band frequency area for the bit’s substitution in an odd and even bit selection. The results are better than traditional LSB. The parameters used are PSNR and MSE to understand the blur or noise added to the cover image while adding the secret message.
Abstractive Text Summarization using Seq2seq Model

Knowledge is power and information is liberating.As the quote says, in today's world the information is available in abundance and a lot of new possibilities can be explored from them.Text summarization is one of the main applications of natural language processing.Text summarization is one of the widely used methods to process the text corpus and obtain a precise text that captures the entire context and preserves the important information conveyed through the text.This paper presents an approach of abstractive text summarization using the seq2seq model.The proposed methodology aims at enhancing the efficiency of the summary generated with the help of the data augmentation technique.The summary comprises new words and sentences thereby improving the quality of it.For evaluating the quality of summarization bilingual evaluation understudy (BLEU) score is used.
Study of classification of breast lesions using texture GLCM features obtained from the raw ultrasound signal

Most of the methods of classification of breast lesions in ultrasound (US) images have been tested on B-mode images from the commercial equipment. The new possibility of further analysis of this problem showed up with the availability of a public database containing original raw radio frequency (RF) signals. In particular, it appeared that the original texture might contain diagnostic information which could be modified in the typical image processing and which is more difficult to perceive than the details of lesion shape/contour. In this paper a detailed analysis of the lesion texture is conducted by means of the decision trees and logistic regression. The decision trees turned out useful mainly for selecting texture features to be used in the stepwise logistic regression. The RF signals database of 200 breast lesions was used for testing the performance of the benign vs malignant lesion classifier. The Gray Level Cooccurrence Matrix (GLCM) was calculated with the vertical/horizontal offset of up to five pixels. For each of these matrices six features were calculated resulting in a total of 210 features. Using these features a sufficient number of decision trees were generated to calculate pseudo-Receiver Operating Characteristics (ROCs). The outcome of this process is a collection of generated trees for which the employed features are known. These features were then used for generating generalized linear model by means of stepwise logistic regression. The analyzed regression models included the coefficients of up-to-the second degree terms. The texture features were further completed by a single shape feature, that is tumor circularity (TC). The automatic procedure for finding the exact mask of a lesion is also provided for the conditions when the acoustic shadowing makes it impossible to obtain the entire contour reliably and a half-contour has to be used. The selected logistic regression models gave ROCs with the Area Under Curve (AUC) of up to 0.83 and the 95 \% confidence region (0.63 0.96). Analyzing classification results one comes to the conclusion that the tumor circularity, which is the most informative among shape/contour features, is not essential against the background of textural features. The reported study shows that a relatively straightforward procedure can be employed to obtain benign vs malignant classifier comparable with that originally used for the database of the raw RF signals and based on the more complicated segmentation of the parameter maps of homodyned K distribution.
Bernoulli 3D Frames

The analysis of three dimensional frames is quite similar to the analysis of 2D frames. In the 2-node 3D frame finite element we now consider in each node three displacements and three rotations with respect to the three global cartesian axes. However, the complexity in such structures is due to the orientation of the beam in space other than in 2D plane. Before introducing the stiffness and mass matrices in the global reference system rotation matrices for vectors in 3D space are firstly introduced.