Reviewerly/exHarmony huggingface dataset:
Depth as Attention for Face Representation Learning

Face representation learning solutions have recently achieved great success for various applications such as verification and identification. However, face recognition approaches that are based purely on RGB images rely solely on intensity information, and therefore are more sensitive to facial variations, notably pose, occlusions, and environmental changes such as illumination and background. A novel depth-guided attention mechanism is proposed for deep multi-modal face recognition using low-cost RGB-D sensors. Our novel attention mechanism directs the deep network “where to look” for visual features in the RGB image by focusing the attention of the network using depth features extracted by a Convolution Neural Network (CNN). The depth features help the network focus on regions of the face in the RGB image that contain more prominent person-specific information. Our attention mechanism then uses this correlation to generate an attention map for RGB images from the depth features extracted by the CNN. We test our network on four public datasets, showing that the features obtained by our proposed solution yield better results on the Lock3DFace, CurtinFaces, IIIT-D RGB-D, and KaspAROV datasets which include challenging variations in pose, occlusion, illumination, expression, and time lapse. Our solution achieves average (increased) accuracies of 87.3% (+5.0%), 99.1% (+0.9%), 99.7% (+0.6%) and 95.3%(+0.5%) for the four datasets respectively, thereby improving the state-of-the-art. We also perform additional experiments with thermal images, instead of depth images, showing the high generalization ability of our solution when adopting other modalities for guiding the attention mechanism instead of depth information.
Feature Selection and Ensemble Learning Techniques in One-Class Classifiers: An Empirical Study of Two-Class Imbalanced Datasets

Class imbalance learning is an important research problem in data mining and machine learning.Most solutions including data levels, algorithm levels, and cost sensitive approaches are derived using multi-class classifiers, depending on the number of classes to be classified.One-class classification (OCC) techniques, in contrast, have been widely used for anomaly or outlier detection where only normal or positive class training data are available.In this study, we treat every two-class imbalanced dataset as an anomaly detection problem, which contains a larger number of data in the majority class, i.e. normal or positive class, and a very small number of data in the minority class.The research objectives of this paper are to understand the performance of OCC classifiers and examine the level of performance improvement when feature selection is considered for pre-processing the training data in the majority class and ensemble learning is employed to combine multiple OCC classifiers.Based on 55 datasets with different ranges of class imbalance ratios and one-class support vector machine, isolation forest, and local outlier factor as the representative OCC classifiers, we found that the OCC classifiers are good at high imbalance ratio datasets, outperforming the C4.5 baseline.In most cases, though, performing feature selection does not improve the performance of the OCC classifiers in most.However, many homogeneous and heterogeneous OCC classifier ensembles do outperform the single OCC classifiers, with some specific combinations of multiple OCC classifiers, both with and without feature selection, performing similar to or better than the baseline combination of SMOTE and C4.5.
A Lightweight Depth Estimation Network for Wide-Baseline Light Fields

Existing traditional and ConvNet-based methods for light field depth estimation mainly work on the narrow-baseline scenario. This paper explores the feasibility and capability of ConvNets to estimate depth in another promising scenario: wide-baseline light fields. Due to the deficiency of training samples, a large-scale and diverse synthetic wide-baseline dataset with labelled data is introduced for depth prediction tasks. Considering the practical goal for real-world applications, we design an end-to-end trained lightweight convolutional network to infer depths from light fields, called LLF-Net. The proposed LLF-Net is built by incorporating a cost volume which allows variable angular light field inputs and an attention module that enables to recover details at occlusion areas. Evaluations are made on the synthetic and real-world wide-baseline light fields, and experimental results show that the proposed network achieves the best performance when compared to recent state-of-the-art methods. We also evaluate our LLF-Net on narrow-baseline datasets, and it consequently improves the performance of previous methods.
Overhead for simulating a non-local channel with local channels by quasiprobability sampling

As the hardware technology for quantum computing advances, its possible applications are actively searched and developed. However, such applications still suffer from the noise on quantum devices, in particular when using two-qubit gates whose fidelity is relatively low. One way to overcome this difficulty is to substitute such non-local operations by local ones. Such substitution can be performed by decomposing a non-local channel into a linear combination of local channels and simulating the original channel with a quasiprobability-based method. In this work, we first define a quantity that we call channel robustness of non-locality, which quantifies the cost for the decomposition. While this quantity is challenging to calculate for a general non-local channel, we give an upper bound for a general two-qubit unitary channel by providing an explicit decomposition. The decomposition is obtained by generalizing our previous work whose application has been restricted to a certain form of two-qubit unitary. This work develops a framework for a resource reduction suitable for first-generation quantum devices.
Smart Contracts on the Blockchain – A Bibliometric Analysis and Review

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Josephson Microwave Sources Applied to Quantum Information Systems

Quantum computers with thousands or millions of qubits will require a scalable solution for qubit control and readout electronics.Colocating these electronics at millikelvin temperatures has been proposed and demonstrated, but there exist significant challenges with power dissipation, reproducibility, fidelity, and scalability.In this article, we experimentally demonstrate the use of a Josephson arbitrary waveform synthesizer (JAWS) to generate control signals at 4 K and perform spectroscopy of two components of a typical superconducting quantum information system: a linear resonator and a (nonlinear) transmon qubit.By locating the JAWS chip at 4 K and a qubit at 0.1 K, the direct path for quasi-particle poisoning from the JAWS chip to the qubit is broken.We demonstrate the stable, self-calibrated, and reproducible output signal of the JAWS when operated in its quantum locking range, a feature that allows these synthesizers to be replicated and scaled in the cryostat, all with identical on-chip, quantized, outputs.This is a proof-of-concept demonstration to generate signals at 4 K using driven superconducting electronics to control qubits at lower temperatures.
CLeaR: An adaptive continual learning framework for regression tasks

Abstract Catastrophic forgetting means that a trained neural network model gradually forgets the previously learned tasks when being retrained on new tasks. Overcoming the forgetting problem is a major problem in machine learning. Numerous continual learning algorithms are very successful in incremental learning of classification tasks, where new samples with their labels appear frequently. However, there is currently no research that addresses the catastrophic forgetting problem in regression tasks as far as we know. This problem has emerged as one of the primary constraints in some applications, such as renewable energy forecasts. This article clarifies problem-related definitions and proposes a new methodological framework that can forecast targets and update itself by means of continual learning. The framework consists of forecasting neural networks and buffers, which store newly collected data from a non-stationary data stream in an application. The changed probability distribution of the data stream, which the framework has identified, will be learned sequentially. The framework is called CLeaR ( C ontinual Lea rning for R egression Tasks), where components can be flexibly customized for a specific application scenario. We design two sets of experiments to evaluate the CLeaR framework concerning fitting error (training), prediction error (test), and forgetting ratio. The first one is based on an artificial time series to explore how hyperparameters affect the CLeaR framework. The second one is designed with data collected from European wind farms to evaluate the CLeaR framework’s performance in a real-world application. The experimental results demonstrate that the CLeaR framework can continually acquire knowledge in the data stream and improve the prediction accuracy. The article concludes with further research issues arising from requirements to extend the framework.
Automated mesenchymal stem cell segmentation and machine learning-based phenotype classification using morphometric and textural analysis

Purpose: Mesenchymal stem cells (MSCs) have demonstrated clinically relevant therapeutic effects for treatment of trauma and chronic diseases. The proliferative potential, immunomodulatory characteristics, and multipotentiality of MSCs in monolayer culture is reflected by their morphological phenotype. Standard techniques to evaluate culture viability are subjective, destructive, or time-consuming. We present an image analysis approach to objectively determine morphological phenotype of MSCs for prediction of culture efficacy. Approach: The algorithm was trained using phase-contrast micrographs acquired during the early and mid-logarithmic stages of MSC expansion. Cell regions are localized using edge detection, thresholding, and morphological operations, followed by cell marker identification using H-minima transform within each region to differentiate individual cells from cell clusters. Clusters are segmented using marker-controlled watershed to obtain single cells. Morphometric and textural features are extracted to classify cells based on phenotype using machine learning. Results: Algorithm performance was validated using an independent test dataset of 186 MSCs in 36 culture images. Results show 88% sensitivity and 86% precision for overall cell detection and a mean Sorensen-Dice coefficient of 0.849±0.106 for segmentation per image. The algorithm exhibited an area under the curve of 0.816 ( CI95=0.769 to 0.886) and 0.787 ( CI95=0.716 to 0.851) for classifying MSCs according to their phenotype at early and mid-logarithmic expansion, respectively. Conclusions: The proposed method shows potential to segment and classify low and moderately dense MSCs based on phenotype with high accuracy and robustness. It enables quantifiable and consistent morphology-based quality assessment for various culture protocols to facilitate cytotherapy development.
Comparative Assessment of Extractive Summarization: TextRank, TF-IDF and LDA

Automatically generating a shorter version of text documents referred to as text summarization.It is an effective method of finding important details from the documents.There is a massive increment in the data worldwide because of rapid growth rate of the internet.It becomes difficult to manually summarize large documents by human beings.Automatic Text Summarization is an approach of NLP which reduces the time and efforts of the human being to produce a summary.There are various approaches to summarize the data.This paper provides a comparative study over the three approaches namely TF-IDF, TextRank, and Latent Dirichlet Allocation (LDA).The comparison is made by using three different types of datasets like reviews of documents, news articles, legal text, etc.The result shows the best-suited approach for the complexity oriented text inputs.Also, the results are evaluated using ROUGE measures.
Greedy, A-Star, and Dijkstra’s Algorithms in Finding Shortest Path

The problem of finding the shortest path from a path or graph has been quite widely discussed. There are also many algorithms that are the solution to this problem. The purpose of this study is to analyze the Greedy, A-Star, and Dijkstra algorithms in the process of finding the shortest path. The author wants to compare the effectiveness of the three algorithms in the process of finding the shortest path in a path or graph. From the results of the research conducted, the author can conclude that the Greedy, A-Star, and Dijkstra algorithms can be a solution in determining the shortest path in a path or graph with different results. The Greedy algorithm is fast in finding solutions but tends not to find the optimal solution. While the A-Star algorithm tends to be better than the Greedy algorithm, but the path or graph must have complex data. Meanwhile, Dijkstra's algorithm in this case is better than the other two algorithms because it always gets optimal results.
Designing an encoder for StyleGAN image manipulation

Recently, there has been a surge of diverse methods for performing image editing by employing pre-trained unconditional generators. Applying these methods on real images, however, remains a challenge, as it necessarily requires the inversion of the images into their latent space. To successfully invert a real image, one needs to find a latent code that reconstructs the input image accurately, and more importantly, allows for its meaningful manipulation. In this paper, we carefully study the latent space of StyleGAN, the state-of-the-art unconditional generator. We identify and analyze the existence of a distortion-editability tradeoff and a distortion-perception tradeoff within the StyleGAN latent space. We then suggest two principles for designing encoders in a manner that allows one to control the proximity of the inversions to regions that StyleGAN was originally trained on. We present an encoder based on our two principles that is specifically designed for facilitating editing on real images by balancing these tradeoffs. By evaluating its performance qualitatively and quantitatively on numerous challenging domains, including cars and horses, we show that our inversion method, followed by common editing techniques, achieves superior real-image editing quality, with only a small reconstruction accuracy drop.
NEW EDUCATION POLICY, 2020: QUALITATIVE (CONTENTS) ANALYSIS AND TWITTER MINING (SENTIMENT) ANALYSIS

The year 2020 has been an exceptional year for countries across the globe. In India, apart from Covid19, one of the important changes that took place was the development of the New Education Policy (NEP) 2020. Time by time, various committees have recommended increasing the allocation of the budget for education to 6% of the GDP, this has lead to the interests of researchers. This paper aims to identify the concerns and focus of NEP 2020. The authors have utilized qualitative data analysis techniques to understand critical areas of focus of policy documents and computer-assisted qualitative data analysis software to address the issues. The research also uses data from Twitter. The data (policy documents) was downloaded from the Ministry of Human Resource Development (MHRD) website. For a better understanding of the collected data, Word Cloud, TreeMap, Project Map, and The Mind Map, along with the graphical representation of sentiments of stakeholders, is presented in the paper. The paper identifies three crucial aspects of the policy – course, language, and students, for the higher education sector. This paper also discusses the sentiment analysis, related to NEP 2020. It was found that most people consider the policy as a positive and welcoming step.
Challenges and Issues of Data Analytics in Emerging Scenarios for Big Data, Cloud and Image Mining

During the digital age, business leaders had vast amounts of data accessible. Significant knowledge is referred to as databases not only comprehensive but also broad in size and speed, which renders conventional methods and techniques challenging to use. Solutions must be explored and supplied so that these datasets can manage and derive meaningful information due to the rapid development of such data. Decision-makers will, therefore, be willing, through routine activities to consumer communications and social network data, to extract useful knowledge from these diverse and quickly evolving data. This can be done by implementing the latest statistical methods on Large Data using extensive data analytics. Built-in, distributed, distributed, fault-tolerant, flexible and accessible architectures are being widely used in cloud environments for massive computational applications. The HDFS architecture is planned to identify faults, such as accidents of call-nodes, built-in node failures, and network failures, and route, built-in to further integrate processes.Redundancy offers an essential location for facts when running on large sets of information. A backup procedure ensures that the data is available and accessible. A big intuition challenge occupies the bulk of the insufficiency contained inside the current results.The determination of this paper is to explore different analytical methods and tools which can be applied to Big Data and the benefits given by the use of Big Data Analytics in severaljudgements.
Relation-aware Graph Attention Model with Adaptive Self-adversarial Training

This paper describes an end-to-end solution for the relationship prediction task in heterogeneous, multi-relational graphs. We particularly address two building blocks in the pipeline, namely heterogeneous graph representation learning and negative sampling. Existing message passing-based graph neural networks use edges either for graph traversal and/or selection of message encoding functions. Ignoring the edge semantics could have severe repercussions on the quality of embeddings, especially when dealing with two nodes having multiple relations. Furthermore, the expressivity of the learned representation depends on the quality of negative samples used during training. Although existing hard negative sampling techniques can identify challenging negative relationships for optimization, new techniques are required to control false negatives during training as false negatives could corrupt the learning process. To address these issues, first, we propose RelGNN -- a message passing-based heterogeneous graph attention model. In particular, RelGNN generates the states of different relations and leverages them along with the node states to weigh the messages. RelGNN also adopts a self-attention mechanism to balance the importance of attribute features and topological features for generating the final entity embeddings. Second, we introduce a parameter free negative sampling technique -- adaptive self-adversarial (ASA) negative sampling. ASA reduces the false negative rate by leveraging positive relationships to effectively guide the identification of true negative samples. Our experimental evaluation demonstrates that RelGNN optimized by ASA for relationship prediction improves state-of-the-art performance across established benchmarks as well as on a real industrial dataset.
A Teaching Quality Evaluation Model for Preschool Teachers Based on Deep Learning

Developed countries regard preschool education as an important starting point and foundation for elite training. In recent years, preschool education has also attracted a growing attention in developing countries like China. Considering the significance of the teaching quality of preschool teachers to lifelong academic achievement, this paper designs a teaching quality evaluation model for preschool teachers based on deep learning. Firstly, a progressive system with a hierarchical structure was developed for the relevant evaluation indices. Then, the fuzzy comprehensive evaluation of each index layer and evaluation criterion was determined by the principle of fuzzy relationship synthesis. Finally, an evaluation prediction model was established based on extreme gradient boosting (XGBoost) algorithm and technology services’ ResNet (TS-ResNet), and proved effective and accurate through experiments. The research results provide a reference for the application of the proposed model in other evaluation prediction scenarios.
How bibliographic dataset portrays decreasing number of scientific publication from Indonesia

Indonesia faces decreasing in its number during period of 2018-2019 according to SINTA (Indonesian scientific indexing) database. This situation is alarming amid country’s effort to improve its research and development from which the scientific publication has been used as a measure. This article presents a dataset of bibliometric from all Indonesian institutions (424 institutions as of January 2020) indexed by Scopus database. We collected data on documents per year and document type. Different from other datasets, this article presents longitudinal data from 2014-2019, in which during this period dramatic change occurs from steep rise, exponential, and degradation during 2018-2019. We analyzed publication number from leading institutions at different points in time and compared document types (i.e., number of journal article and conference paper, in which both types are the driving force of Indonesia’s growing scientific publication number). Since this dataset covers all country’s institutions, it is potential for reuse by policy makers in research and development, researchers from each institution, as well as other stakeholders.