Reviewerly/exHarmony huggingface dataset:
Hierarchical Deep Reinforcement Learning Approach for Multi-Objective Scheduling With Varying Queue Sizes

Multi-objective task scheduling (MOTS) combines the task of scheduling with the need to optimize multiple-and possibly contradicting-constraints. A challenging extension of this problem occurs when every individual task is a multiobjective optimization problem by itself. While deep reinforcement learning (DRL) has been successfully applied to complex sequential problems, its application to the MOTS domain has been stymied by two challenges. The first challenge is the inability of the DRL algorithm to ensure that every item is processed identically regardless of its position in the queue. The second challenge is the need to manage large queues, which results in large neural architectures and long training times. In this study we present MERLIN, a robust, modular and near-optimal DRL-based approach for multi-objective task scheduling. Our approach addresses both aforementioned challenges while also being more efficient and easier to train. Extensive evaluation on multiple queue sizes show that MERLIN outperforms multiple well-known scheduling algorithms by a large margin (≥ 22%).
Edge Detection Cascaded with Simplified WDSR of IR Super Resolution

Edge Detection has been one of the techniques that can be used to solve super resolution problems. In this work, we present a research that was based on the WDSR (Wide Activation Super Resolution) model and the combination of three edge detection techniques: Sobel, Kirsch, and Prewitt. For the model portion, we simplified the original WDSR model by reducing the number of convolutional layers in each residual block and decreasing the number of residual blocks to cut down the computational burden of our computer. As for the dataset, we used our own high-resolution IR dataset that contained 7968 IR images for training and 1359 images for validation. These data are used to perform bicubic interpolation to create low-resolution images and the low-resolution images were paired with the original data to observe the result. By the end of our work, we managed to show that the model was capable to successfully reduce the computational time by approximately 11% from the baseline while maintaining the quality of super resolution for IR image within roughly 1% difference.
Classification of Imbalanced leukocytes Dataset using ANN-based Deep Learning

Abstract Nowadays, classification of imbalanced data is a major challenge in the machine learning (ML) algorithms, especially in medical data analysis, In this paper, deep learning algorithm which is the advance artificial neural network (ANN) is used for classifying five white blood cells (WBCs). Different preprocessing image techniques and algorithms are applied to isolate WBCs and segment the nucleus for the cytoplasm. Geometric, statistical and color features are extracted, the principal component analysis technique is applied to select the optimal features. The classification process has been repeated several times to tune the algorithm parameters and to find the best pattrens match through the training data in the learning process until achieve best classification accuracy. Multi-class classification results show high accuracy of more than 94% for the five types of WBCs. We evaluate the classification model using the geometric mean, Cohen’s Kappa, Receiver operating characteristic curve, Root mean squared error, relative absolute error and cross-validation techniques. The algorithm model achieves high accuracy and can conduct a multi-class classification of imbalanced datasets in terms of the above-mentioned metrics.
THE USAGE OF STEAM PROGRAM IN DEVELOPING AND IMPROVING OF STUDENTS' EXPERIMENTAL SKILLS

The research analyzes the usage of STEAM program “Cognition of Energy and Thermal Processes” for students of ninth (1st Gymnasium) classes in order to deepen and broaden the knowledge of natural science education, develop practical abilities of students and their scientific researcher's competence. Students were advised to do five experimental works in this field. The program engages a basic educational strategy – inquiry based learning. The results of the pedagogical experiment and the questionnaire survey are discussed. Summarizing the results of the research, it can be stated that educational experimental activities are necessary and useful for students. By using the experiment, students can be provided with educational material in an attractive form, which stimulates the interest in the subject. Program participants have deepened and expanded their knowledge of energy and thermal processes in nature. Students improved their competence in natural science research. They learned how to plan and perform experiments, acquired the ability to formulate hypotheses, to make assumptions, to analyze and explain results, and to formulate reasoned conclusions. Students acquired practical skills to work properly and safely with devices and tools (computer systems Nova 5000 and Xplorer GLX, temperature, humidity sensors, caliper, scales, etc.) Students liked to be young researchers; they felt the joy of discovery by practically experimenting and independently exploring natural phenomena. Keywords: inquiry-based learning, experimental skills, science learning, STEAM education.
Camera Calibration Method Based on Self-made 3D Target

A camera calibration algorithm based on self-made target is proposed in this paper, which can solve the difficulty of making high precision 3D target. The self-made target consists of two intersecting chess board. With the classic scale method, the 3D coordinates of selected points in the target were derived from the distance matrix. The element in distance matrix is the distance between every two points, which can be obtained by measurement. The spatial location precision of points in the target was ensured by measurement instead of manufacturing, which reduced the production cost and the requirements for the production accuracy greatly. Camera calibration was completed using 3D target based method. It can be further extended to the applications where the target cannot be produced. The experimental results show the validity of this method.
End To End Model For Speaker Identification With Minimal Training Data

Deep learning has achieved immense universality by outperforming GMM and i-vectors on speaker identification. Neural Network approaches have obtained promising results when fed by raw speech samples directly. Modified Convolutional Neural Network (CNN) architecture called SincNet, based on parameterized sinc functions which offer a very compact way to derive a customized filter bank in the short utterance. This paper proposes attention based Long Short Term Memory (LSTM) architecture that encourages discovering more meaningful speaker-related features with minimal training data. Attention layer built using Neural Networks offers a unique and efficient representation of the speaker characteristics which explore the connection between an aspect and the content of short utterances. The proposed approach converges faster and performs better than the SincNet on the experiments carried out in the speaker identification tasks.
Cyber Risk and Financial Stability: It’s a Small World After All

The ability of attackers to undermine, disrupt and disable information and communication technology systems used by financial institutions is a threat to financial stability and one that requires additional attention.
Learning Models of Cyber-Physical Systems using Automata Learning

In this paper we examine two case studies in which we learn finite state machines from models of CPS using automata learning. We explore how well automata learning is suited as an approach for learning CPS. What challenges and problems exist when trying to learn a model of a CPS using automata learning. Automata learning can reliably learn finite state machines of systems like embedded systems or software systems. CPS pose different challenges, like continuous components, for which different levels of abstractions and considerations have to be used, so the resulting finite state machines are useful representations of the systems. Through the small, yet insightful case studies we show examples of how automata learning can be applied to CPS and what information the resulting automata can represent.
A Cross-Lingual Speech Synthesis System of Malay and Indonesian Based on HMM–DNN

English as a second language is widely used in countries such as Malaysia and Indonesia, and it is common for English words to appear in Malay and Indonesian sentences. Malay and Indonesian have high homology and relatively few electronic language resources. We combine the corpus datasets of these two similar languages to design and implement a HMM–DNN-based cross-lingual speech synthesis system for Malay (including English words) and Indonesian (including English words). The methods used include: sharing synthesis units between Malay, Indonesian, and English, designing unified context attributes and question set in the process of acoustic model training, speaker-adaptive training with speech corpus of these three languages, and synthesizing speech using speaker-dependent Malay and Indonesian acoustic models. Experimental results show that the speech synthesis quality of the system is better than the traditional Hidden Markov model-based cross-lingual speech synthesis system.
Valuable Research on Bibliographic Aspects of Social Memory of the Russian State Library

This is a review of the bibliographic index “Bibliographic Activity of the Russian State Library” released by the publishing house “Pashkov Dom” in 2021. The new index has been drawn up by the experts from the Research Department of Bibliography of the Russian State Library (RSL). This unique book serves as a social memory of one of the largest libraries both in this country and worldwide. We might regard this information resource as a kind of chronicle of the RSL Reference and Bibliographic Service, which celebrated its 100th anniversary in 2018. The structural and substantive analyse of the methodological features of the index as well as the criteria for the selection of material have been carried out. The publication consists of two parts (“Literature on the bibliographic activity of the library” and “Publications of the library on bibliographic theory and practice”) and includes 1709 bibliographic records. The book can be used as an example for creating similar works, and it is a comprehensive toolkit for scientometric analysis of the library’s bibliographic activity.
Human-Machine Interaction: Controlling of a Factory with an Augmented Reality Device

This paper describes an implementation of an augmented reality user interface for a model factory. The focus here is that the user interfaces are exactly where the user expects them to be, be it directly on the devices or that they follow the user.
A Neural N-Gram-Based Classifier for Chinese Clinical Named Entity Recognition

Clinical Named Entity Recognition (CNER) focuses on locating named entities in electronic medical records (EMRs) and the obtained results play an important role in the development of intelligent biomedical systems. In addition to the research in alphabetic languages, the study of non-alphabetic languages has attracted considerable attention as well. In this paper, a neural model is proposed to address the extraction of entities from EMRs written in Chinese. To avoid erroneous noise being caused by the Chinese word segmentation, we employ the character embeddings as the only feature without extra resources. In our model, concatenated n-gram character embeddings are used to represent the context semantics. The self-attention mechanism is then applied to model long-range dependencies of embeddings. The concatenation of the new representations obtained by the attention module is taken as the input to bidirectional long short-term memory (BiLSTM), followed by a conditional random field (CRF) layer to extract entities. The empirical study is conducted on the CCKS-2017 Shared Task 2 dataset to evaluate our method and the experimental results show that our model outperforms other approaches.
Adaptive Advantage Estimation for Actor-Critic Algorithms

Critics are applied to estimating policy gradients in the actor-critic framework, an essential part of Reinforcement Learning methods. An appropriate critic is supposed to balance variance from sample returns and bias introduced by parameterized value functions. A typical critic of balancing variance and bias, the generalized advantage estimator (GAE), combines sample returns and value functions with a fixed weight parameter. However, such a parameter is hard to fit, which results in no promised stability for GAE. In this paper, indicators of variance and bias are proposed to get adaptive weight parameters, with which adaptive advantage estimators are obtained. Empirical results on both 2D and 3D simulated robotic locomotion tasks show that the adaptive advantage estimators achieve similar or superior performance compared to GAE.
Research on Weak Current Detection System Based on Embedded System

Abstract In today’s society, the rapid development of science and technology requires the detection of weak current signals in many fields of scientific research. The research of its detection technology plays a very important role in promoting the development of science and technology. Considering that the weak current signal has strong background noise, it is easy to be submerged, the detection is difficult, and the professional requirement is strong. This research proposes a weak current detection system based on an embedded system that can detect nan oampere-level weak currents. The detection system is mainly composed of signal conversion amplifier filter circuit module, signal acquisition circuit, STM32, FPGA, upper computer and so on. Through experimental inspection, the researched detection system has the advantages of high accuracy and good stability.
Is Merkle tree the best option to organize keys?

Merkle tree is considered as a common approach for hash-based post-quantum digital signatures schemes. The tree is used to organise public keys of so called one-time digital signature scheme. The backbone idea of Merkle tree is to iteratively imply hash function. The approach is used in well-known digital signatures schemes such as XMSS and LMS that are standardiseded in IETF. But whether the use of Merkle tree is the only way to organize public keys to build post-quantum digital signature scheme? In the article alternative approaches to organize public keys are considered. The approaches are used to build digital signature schemes. Some features of “new” schemes are pointed out. It is shown that offered approaches have disadvantages that make the “new” schemes impractical.
Hair Editing with Two-Phase Image Reconstruction

How to achieve hair editing has always been a longstanding topic. The task of hair editing expects users to create a draft to customize hairstyle, textures and colors. Then it will translate the hair draft to corresponding authentic hair. However, hair generally has complex feature representations due to delicate textures and rich colors, which means it difficult to retain all the hair features during the generation process. Thus, how to reconstruct hair with visually pleasant textures and diverse colors is a challenge. To address this problem, this paper proposes a novel method that takes Image-to-Image Generative Adversarial Networks as the backbone and induces two-phase training mode to capture different hair features. In the first phase, networks take the hair colour draft as input to reconstruct low-frequency information. In the next phase, the result output in the first phase will be fed into the network again to refine hair. Our extensive experiments show that two-phase training mode can build up convincing hair images and also allows users to customize the style of hair in the textures as well as colors.
Study on Conflict-free path planning for picking robots based on traffic rules

With the rapid development of Electronic Commerce and Logistics in China, it has become an inevitable trend to use robots to pick goods in distribution centers, and multi-robot conflict-free path planning is the key point to this application. Based on the analysis of existing algorithms for multi-robot path planning, this paper combines advantages of search-based methods and rule-based methods, and proposes an algorithm based on traffic rules. Experiment results show that this algorithm is fast and suitable for distribution centers with lots of robots, which can continually plan paths for robots.
The interferometric test bench for the ELT M2 polishing and validation at Sanfran Reosc

In the past 20 years, a new generation of telescopes with large primary mirrors has appeared, based on the segmented mirrors design. The Thirty meter Telescope and the European Southern Observatory (ESO) Extremely Large telescope (ELT) are now under construction. They have, respectively, a 30m aperture and a 39m aperture. To cope with these apertures, they both have a large monolithic secondary mirror. These two convex aspherics are the TMT secondary mirror, a 3.1 m convex hyperboloid, and the ELT secondary mirror, a 4.25 m diameter aspheric with an asphericity of 2 mm. The surface measurement of these mirrors is challenging and auxiliary optics, like Test Plates (TP) and Computer Generated Holograms , are needed. In this paper, we present the Interferometry Test Bench, designed at Safran Reosc to polish and validate the ELT secondary mirror (ELT M2). The test bench is composed of three main components: a temporal phase shift interferometer, a Test Plate and the ELT M2 mirror on its polishing support. We perform interferometry between the concave face of the test plate and the mirror surface, forming an analogue to a Fizeau interferometer. The interferometer, named IRIDE, was designed and manufactured by Safran Reosc. It is a temporal phase shift interferometer, where environmental vibrations are addressed by increasing the interferograms acquisition rate. The interferometer has a low coherence light source, to reduce parasitic fringes. The Test Plate is a ZERODUR® meniscus lens fabricated at Safran Reosc. . Its concave surface is an off-axis isophase portion of the ELT M2 mirror and it is the Fizeau cavity’s reference surface. The ELT M2 mirror blank is mounted on its support for metrology and polishing (SMP). The SMP supports the mirror blank and interfaces it with the turning table. A measurement of the entire ELT M2 clear aperture is composed of 12 sub pupils measurements.
Evaluation Model of Classroom Teaching Quality of Chinese as a Foreign Language Based on Deep Learning

In order to improve the classroom teaching quality of Chinese as a foreign language and attract more foreigners to participate in Chinese learning, a model for evaluating the quality of Chinese as a foreign language based on deep learning is proposed. Through the study of macro and micro indicators such as teaching methods, teaching tools, teaching content, curriculum relevance, teaching attitudes, etc., a classroom teaching quality evaluation index system is established to improve the coverage of quality evaluation. Based on the quantification of the processing qualitative indicators of deep learning, the deep learning data flow diagram is drawn according to the depth of different quality nodes in the teaching behavior, and the quality evaluation results is classified to complete the comprehensive evaluation of classroom teaching quality. In addition, by calculating the vector value of classroom teaching quality, planning experiment groups, and setting up comparative experiments, it is verified that the proposed model is more practical in the actual classroom teaching quality evaluation.
Electronic resource management in a post-Plan S world

cOAlition S and research funding policies mean open access content is no longer a ‘trend’ but rather another consideration of content management for librarians and libraries. In 2018, the authors of this article launched a new version of TERMS (Techniques for Electronic Resources Management). TERMS 2.0 envisages a post-Plan S e-resources life cycle blending e-resources and open access content management. This article outlines how open content management can dovetail into current e-resource management tactics across six TERMS: Investigation of material, procurement and licensing of content, implementation, troubleshooting of problems, evaluation and preservation, and sustainability concerns. Lastly, we reflect on the themes growing in libraries in regard to management of online resources.
Analysis Youtube Activities as An Engagement Media (a case study at school of information systems BINUS university)

Now the YouTube is one of the platforms that can help students to learn more easiest related the courses in University. The purpose of this study is to be able to analysis of content from School of Information Systems BINUS University (SIS BINUS) YouTube channel. And the benefit of this research is to increase interesting of students to watch the content related motivation and supporting material of courses in University on the YouTube platform. The method used for this research to get the best practice are to create comparisons via the viazle.com web to analysis secondary data related the content on YouTube of two universities and collect the data also from several existing journals. And to get the insights from student perspective, we did depth interviews with students that have the experience watched the YouTube channel of School of Information Systems. The results of this study are creating YouTube SIS BINUS more understandable students needs related the content that can attracting the student attention.
CBRi2: Imputation of solar radiation time series with case based reasoning

This paper proposes a new technique for the short-gaps imputation of not available (NA) values in solar radiation time series. The proposal technique is an update of CBRi hence its name CBRi2. The fundamental changes with respect to CBRi, consist of the definition of a threshold for the recovery of success cases and the insertion of a factor that approximates the results obtained by Case Base Reasoning (CBR) to the closest neighbors of a NA value, that is to its prior and next values. The results achieved show that for the same solar radiation time series CBRi2 exceeds CBRi, Local Average of Nearest Neighbors (LANN) and Autoregressive Integrated Moving Average (ARIMA).
ReRankMatch: Semi-Supervised Learning with Semantics-Oriented Similarity Representation

This paper proposes integrating semantics-oriented similarity representation into RankingMatch, a recently proposed semi-supervised learning method. Our method, dubbed ReRankMatch, aims to deal with the case in which labeled and unlabeled data share non-overlapping categories. ReRankMatch encourages the model to produce the similar image representations for the samples likely belonging to the same class. We evaluate our method on various datasets such as CIFAR-10, CIFAR-100, SVHN, STL-10, and Tiny ImageNet. We obtain promising results (4.21% error rate on CIFAR-10 with 4000 labels, 22.32% error rate on CIFAR-100 with 10000 labels, and 2.19% error rate on SVHN with 1000 labels) when the amount of labeled data is sufficient to learn semantics-oriented similarity representation. The code is made publicly available at https://github.com/tqtrunghnvn/ReRankMatch.