Reviewerly/exHarmony huggingface dataset:
Logistic-Based Design of Fuzzy Interpretable Classifiers

The paper develops an idea of fuzzy evidential classifiers based on modification of logistic regression model and Dempster–Shafer methodology. The proposed approach is integrating the additional linguistic variable into the classifier. This variable considers different shades of truth for class membership hypotheses and enriches available information for decision-making. It leads to identification of pre-failure states and detecting anomalies, inconsistency, and incorrectness in the initial data. As a result of the research, linguistic log-regression model is shown, and its components are justified. The inference procedure based on the model is illustrated. In the end, a simple example of implementation is also shown.
Scalable supervised online hashing for image retrieval

Abstract Online hashing methods aim to learn compact binary codes of the new data stream, and update the hash function to renew the codes of the existing data. However, the addition of new data streams has a vital impact on the retrieval performance of the entire retrieval system, especially the similarity measurement between new data streams and existing data, which has always been one of the focuses of online retrieval research. In this paper, we present a novel scalable supervised online hashing method, to solve the above problems within a unified framework. Specifically, the similarity matrix is established by the label matrix of the existing data and the new data stream. The projection of the existing data label matrix is then used as an intermediate term to approximate the binary codes of the existing data, which not only realizes the semantic information of the hash codes learning but also effectively alleviates the problem of data imbalance. In addition, an alternate optimization algorithm is proposed to efficiently make the solution of the model. Extensive experiments on three widely used datasets validate its superior performance over several state-of-the-art methods in terms of both accuracy and scalability for online retrieval task.
The indolent lambdification of Java

Abstract As Java 8 introduced functional interfaces and lambda expressions to the Java programming language, the JDK API was changed to introduce support for lambda expressions, thus allowing consumers to define lambda functions when using Java’s collections. While the JDK API allows for a functional paradigm, for API consumers to be able to completely embrace Java’s new functional features, third-party APIs must also support lambda expressions. To understand the current state of the Java ecosystem, we investigate (i) the extent to which third-party Java APIs have changed their interfaces, (ii) why or why not they introduce functional interface support and (iii) in the case the API has changed its interface how it does so. We also investigate the consumers’ perspective, particularly their ease in using lambda expressions in Java with APIs. We perform our investigation by manually analyzing the top 50 popular Java APIs, conducting in-person and email interviews with 23 API producers, and surveying 110 developers. We find that only a minority of the top 50 APIs support functional interfaces, the rest does not support them, predominantly in the interest of backward compatibility. Java 7 support is still greatly desirable due to enterprise projects not migrating to newer versions of Java. This suggests that the Java ecosystem is stagnant and that the introduction of new language features will not be enough to save it from the advent of new languages such as Kotlin (JVM based) and Rust (non-JVM based).
Designing the Quantum Channels Induced by Diagonal Gates

The challenge of quantum computing is to combine error resilience with universal computation. Diagonal gates such as the transversal <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>T</mml:mi></mml:math> gate play an important role in implementing a universal set of quantum operations. This paper introduces a framework that describes the process of preparing a code state, applying a diagonal physical gate, measuring a code syndrome, and applying a Pauli correction that may depend on the measured syndrome (the average logical channel induced by an arbitrary diagonal gate). It focuses on CSS codes, and describes the interaction of code states and physical gates in terms of generator coefficients determined by the induced logical operator. The interaction of code states and diagonal gates depends very strongly on the signs of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Z</mml:mi></mml:math>-stabilizers in the CSS code, and the proposed generator coefficient framework explicitly includes this degree of freedom. The paper derives necessary and sufficient conditions for an arbitrary diagonal gate to preserve the code space of a stabilizer code, and provides an explicit expression of the induced logical operator. When the diagonal gate is a quadratic form diagonal gate (introduced by Rengaswamy et al.), the conditions can be expressed in terms of divisibility of weights in the two classical codes that determine the CSS code. These codes find application in magic state distillation and elsewhere. When all the signs are positive, the paper characterizes all possible CSS codes, invariant under transversal <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Z</mml:mi></mml:math>-rotation through <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>&amp;#x03C0;</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:msup><mml:mn>2</mml:mn><mml:mi>l</mml:mi></mml:msup></mml:math>, that are constructed from classical Reed-Muller codes by deriving the necessary and sufficient constraints on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>l</mml:mi></mml:math>. The generator coefficient framework extends to arbitrary stabilizer codes but there is nothing to be gained by considering the more general class of non-degenerate stabilizer codes.
Video Based Drowning Detection System

At present, there are swimming pools in every part of the world. Most of the swimming pool accidents or incidents occur due to improper security. Therefore, Accidental deaths in swimming pools are actually increasing. So, Video based drowning detection system is designed in this article. The proposed system structure comprises raspberry pi (Single Board Computer) equipped with a USB camera for taking the live feed from the pool area. The system also covers the alerting phenomena using a buzzer so that necessary actions are taken intermittently without any delay. The working structure starts from the raspberry pi with image processing for video feed intake, deep learning for activity recognition and finally GPIO system for alerting and short message service.
Remote Instruction for Data Visualization Design—A Report From the Trenches

In this paper, we report on our experiences of running visual design workshops within the context of a Master's level data visualization course, in a remote setting.These workshops aim to teach students to explore visual design space for data by creating and discussing hand-drawn sketches.We describe the technical setup employed, the different parts of the workshop, how the actual sessions were run, and to what extent the remote version can substitute for in-person sessions.In general, the visual designs created by the students as well as the feedback provided by them indicate that the setup described here can be a feasible replacement for in-person visual design workshops.
Blockchain and the Digital Credentialing for Education System

A blockchain technique is a sequence of encrypted, peer-to-peer transactions that, together, constitutes a distributed ledger. Starting with the conceptualisation of Bitcoin in 2008, both platforms and their applications are evolving rapidly. Following the launch of the Ethereum platform in mid-2015, “smart contracts” that are secured in a set of blockchain transactions have been proposed for just about anything of value. This includes education qualifications and several universities are now offering digital certificates that are recorded in a blockchain. In parallel with these digital innovations—part of the emerging Web 3.0—the long-established ways of verifying university qualifications are becoming non-viable. Developed for a very different world, where few went into higher education and everyone could be expected to identify every university, the traditional systems of certification are non-scalable, increasingly unintelligible and inefficient. It is also inappropriate for new challenges, such as the widespread loss and destruction of records through contemporary wars and the extent of forced migrancy. At the same time, the combination of the large numbers of those now qualifying in higher education, along with the ever-increasing sophistication of work, means that many people hold qualifications from several universities, along with a cluster of certificates attesting to specialised competencies. Both individuals and employing institutions need a system of verification that provides a secure, immutable profile of a person’s full and constantly developing professional profile. This paper evaluates the fit between the new and evolving opportunities of the blockchain and the requirements for the radical reform of the ways in which higher education qualifications are certified and can be verified.
A lightweight and effective deep learning model for Gaussian noise removal

Presently, Gaussian noise removal is a very attractive research direction, because Gaussian noise can effectively simulate the real world noise when the real noise sources are complex and diverse. Existing traditional denoising methods have high complexity and low efficiency. Means of image Gaussian noise denoising will become more efficient with the development of deep learning. In this paper a novel deep learning model for Gaussian noise removal is proposed. The proposed method combines dilated convolution with skip connection of residual learning, which is trained by our proposed mixed loss function during back propagation. Not only compared with existing traditional methods, but also compared with representative deep learning methods, our proposed model has a better performance. In addition, our proposed model has less layers and training parameters than representative deep learning methods. Through several comparative experiments on test data sets, we can draw a conclusion that our proposed deep learning model can remove Gaussian noise more effectively than some state-of-art methods with quantitative and visual qualitative analyses.
VPN-nonVPN Traffic Classification Using Deep Reinforced Naive Bayes and Fuzzy K-means Clustering

This paper addresses one of the attack methods where threat actors break into secure networks through virtual private networks (VPN) to launch tunneling based encrypted attacks, obfuscated advanced persistent threats and malware. The paper proposes Naive Bayes augmented with deep reinforcement learning (DRL) and fuzzy k-means clustering to classify VPN and non-VPN data. The proposed method is validated on the publicly available UNB-CIC VPN non-VPN dataset and shows an accuracy comparable to other state-of-the-art machine learning algorithms for traffic characterization. The proposed approach for traffic characterization of traffic classes (e.g., FTP and P2P) and application identification (e.g., Netflix and Amazon Prime) is analyzed for traffic-class detection efficiency and accuracy, and the ability to distinguish between VPN and non-VPN network traffic. The experimental results reveal that an optimum network traffic classification is achieved for ‘NB+DRL’ approach, where DRL is used to reinforce the NB classification model through iterative policy evaluation and improvement and achieves recall of 0.96 in traffic categorization and 0.95 in application identification. This is further improved with fuzzy k-means clustering to reduce computational costs for encrypted network traffic classification using ‘NB+DRL+fuzzy k-means clustering’, where the class partitions based on k-means converge to a local minimum based on dissimilarity measures for given packet features. The test set F1 scores of 0.973 and 0.965 are achieved for traffic characterization and application identification, respectively.
Cross‐scale feature fusion connection for a YOLO detector

Multi-scale feature fusion is often used to address the issue of scale variations in object detection. However, most of the proposed network architectures only combine the features of two adjacent levels sequentially, so the first fusion nodes in both top-down and bottom-up pathways must be blank nodes that only have one input with no feature fusion. In this work, cross-scale feature fusion connection (CFFC) is proposed which aims to enhance the entire feature hierarchy by propagating the features of each level more efficiently. The proposed method reuses and aggregates all the features of other scales to the blank nodes in both top-down and bottom-up pathways. Furthermore, the authors remove the 1 × 1 convolutional layer and replace the shortcut with concatenation before fusing multiple features. These concatenated feature maps are then supervised by the channel attention block at the fusion nodes. This modification allows the network to learn the important degree of each level in concatenated feature maps along the channel dimension. It is also observed that the proposed method alleviates the inconsistency in feature pyramids with fewer parameters. The performance of a YOLO object detector equipped with the proposed method on the COCO test-dev 2017 is evaluated. The results show that the proposed method outperforms other architectures presented in the literature.
Data Discovery and Reuse in Data Service Practices: A Global Perspective

Abstract The proposed panel will address the issues of the discovery and reuse of publicly available data on the web in the context of data service practices from a global perspective. Thousands of data discovery services have appeared around the world since the promotion of “open science”, reproducible research, and the FAIR (Findable, Accessible, Interoperable and Reusable) data principles in the research sector. However, there is also increasing demand for transparency of search algorithms, and in the design, development, evaluation, and deployment of current data search services; this requires a better understanding of how users approach data discovery and interact with data in search settings. From a global perspective, we will identify and discuss the specific system design issues in data discovery and reuse, drawing on our organization of the NTCIR (NII Testbeds and Community for Information access Research) project of Data Search track, the design and evaluation of the data discovery service of the Australian Research Data Commons (ARDC), and studies examining researchers' practices of data discovery and reuse.
Solving the problem of imbalanced dataset with synthetic image generation for cell classification using deep learning

The low number of annotated training images and class imbalance in the field of machine learning is a common problem that is faced in many applications. With this paper, we focus on a clinical dataset where cells were extracted in a previous research. Class imbalance can be experienced within this dataset since the normal cells are in a great majority in contrast to the abnormal ones. To address both problems, we present our idea of synthetic image generation using a custom variational autoencoder, that also enables the pretraining of the subsequent classifier network. Our method is compared with a performant solution, as well as presented with different modifications. We have experienced a performance increase of 4.52% regarding the classification of the abnormal cells.Clinical Relevance — We extract images from cervical smears, using digitized Pap test. When working with these kinds of smears, a single one can contain more than 10,000 cells. Examination of these is done manually by going over each cell individually. Our main goal is to make a system that can rank these samples by importance, thus making the process easier and more effective. The research that is described in this paper gets us a step closer to achieving our goal.
Improving the Classification Rate of Insulation Defects in Power Cables by Automatic Features Extraction and Sample Augmentation of Partial Discharge Signal

Conventional pattern recognition methods employed for differentiating the types of insulation defects in power cables usually rely on the manual extraction of partial discharge features, which is inefficient and easily affected by subjective uncertainty. This work addresses this problem by proposing a new framework based on the automatic features extraction of partial discharge signal. The method first applies a sliding time window to convert partial discharge signals in the time domain into two-dimensional images that serve directly as the input to the convolutional neural networks (CNNs). Then a nonlinear encoder is employed to automatically extract the features of the partial discharge image data as the input of CNNs for classification. In addition, we address the overfitting problem associated with the few-shot by applying a deep convolutional generative adversarial network (DCGAN) to augment the original training dataset. Experimental results demonstrate the validity of the proposed algorithm; it increases the classification accuracy by 4.18% relative to that achieved with manually extracted features; the overall accuracy of the proposed algorithm training with the augmented dataset is 3.175% higher than that with the original experimental dataset.
Emotion Analysis from Facial Expressions Using Convolutional Neural Networks

In order to better understand human behavior, the emotional content of human facial expressions needs to be accurately analyzed and interpreted. While the perception of faces and facial expressions is a natural skill for humans, it still poses great challenges for computer systems. These difficulties result from the non-uniformity of the human face and differences in conditions such as lighting, shadows, face pose and orientation. Deep learning models, especially Convolutional Neural Networks (CNNs), have great potential to deal with these challenges due to their powerful automatic feature extraction and computational efficiency. In this study, a CNN model is proposed to classify seven different emotions (angry, disgust, fear, happy, sadness, surprise and neutral) using the FER-2013 dataset. With the proposed model, 70.62% accuracy on the training data and 70% on the test data has been achieved. The loss value was found to be 0.80 at the training stage and 0.86 at the testing stage.
Road Obstacle Object Detection Based on Improved YOLO V4

Abstract In recent years, as one of the important technical tasks in the field of deep learning, object detection has broad prospects and applications in the field of road obstacle detection. However, in the real driving scene, there are many obstacles, serious occlusion, overlap and other problems, so that the existing obstacle detection algorithm can not effectively detect the obstacles on the road, so it can not guarantee the driving safety. In order to solve the above problems, this paper improves on the basis of Yolo V4 algorithm. Firstly, kmeans + + clustering is used to generate a priori box suitable for the data set to enhance the scale adaptability; Then, the ciou is used as the loss function of coordinate prediction to evaluate the coincidence degree of prediction frame and truth value frame more reasonably. Finally, a suitable target detection data set is constructed by preprocessing the public data set cityccaps. The experimental results show that the improved algorithm can achieve more than 90% accuracy for obstacles with large number of targets in the training set. Compared with the original Yolo V4, the average detection accuracy of the improved algorithm is improved by 2.03%.
Inverse Domain Adaptation for Remote Sensing Images Using Wasserstein Distance

In this work, an inverse domain adaptation (IDA) method is proposed to cope with the distributional mismatch between the training images in the source domain and the test images in the target domain in remote sensing. More specifically, a cycleGAN structure using the Wasserstein distance is developed to learn the distribution of the remote sensing images in the source domain before the images in the target domain are transformed into similar distribution while preserving the image details and semantic consistency of the target images via style transfer. Extensive experiments using the GF1 data are performed to confirm the effectiveness of the proposed IDA method.
Enhancement of Productivity Using Chatbots

Chatbots automate and personalize conversations. Similar queries are grouped and resolved without human trafficking. This user-friendly keyword-based texting conversational framework chats with the client, takes the request, and responds to the query. WhatsApp encryption maintains the integrity of the conversation. WhatsApp bot attracts attention and keeps the groups active. This is especially useful during pandemic situation where high social media presence is abundant. Educational institutions can take advantage of automated responses to satisfy an ever-growing demand caused by high levels of social media usage. Manual resources are scarce in pandemic crisis. This provides an economical replacement for manual information transfer and infrastructure. WhatsApp is deployed and is popular in social media for entertainment purposes. Smartbot used in WhatsApp is used to reduce quality chatting time. By using customized keyword, smartbot provides accurate information. Smartbot implementation in WhatsApp with monitored chat and automated response is the novelty of this implementation. This paper proposes a solution to streamline academic queries during pandemic crisis and keeps them connected.
Jaxa's Earth Observation Data Analysis on Covid-19

The COVID-19 pandemic has brought difficulties to our daily lives and caused various changes in socio-economic activities and the natural environment. To monitor and record the changes in socio-economic activities and the environment before and during the COVID-19 pandemic, JAXA has collaborated with NASA and ESA in developing a trilateral dashboard to provide socio-economic activities and natural environmental change information derived from earth observation data. JAXA also has analyzed Earth observation data, for example, ALOS-2, GCOM-C, GOSAT and GOSAT-2 and opened at a special website “JAXA for Earth on COVID-19” in the categories of airports, industry, shipping, agriculture, greenhouse gasses and water quality.
Differences in Skill Requirements for Agile Workers in the German and the US IT Industries

The IT industry is getting more and more agile. Therefore, it is important to know about the required skills for agile workers in this industry. In this study, we analyzed 1000 job advertisements from online job portals to determine the differences in skill requirements for agile workers in the IT industry in Germany versus the U.S. We found that searches for non-technical skills are greater in the U.S. than in Germany. Test and Requirements Management are the most important management concept searches in both countries. JavaScript is searched more often in the U.S. This study contributes to a better understanding of the required skills of agile workers in Germany and the U.S.