Reviewerly/exHarmony huggingface dataset:
Transfer Learning and Deep Metric Learning for Automated Galaxy Morphology Representation

Galaxy morphology characterisation is an important area of study, as the type and formation of galaxies offer insights into the origin and evolution of the universe. Owing to the increased availability of images of galaxies, scientists have turned to crowd-sourcing to automate the process of instance labelling. However, research has shown that using crowd-sourced labels for galaxy classification comes with many pitfalls. An alternative approach to galaxy classification is metric learning. Metric learning allows for improved representations for classification, anomaly detection, information retrieval, clustering and dimensionality reduction. Understanding the implications of this approach regarding crowd-sourced labels is of paramount importance if scientists intend to continue using them. This paper compares metric learning and classification models trained or fine-tuned on both the crowd-sourced Galaxy Zoo 2 (GZ2-H) dataset and expertly labelled EFIGI catalogue. The study uses the Revised Shapley-Ames (RSA) catalogue of bright galaxies, also labelled by experts, as an unseen test set. The RSA catalogue allows for an accurate comparison of the performance of the models at predicting the Hubble types of galaxies. The classification accuracy for the crowd-sourced and expert models indicated that the models are comparable on the surface. However, using alternative metrics, the results show that the models trained on the expert dataset outperformed the model trained on the crowd-sourced data in terms of actual vs predicted labels. Further, the results show that fine-tuning a model pre-trained on crowd-sourced data can outperform the state-of-the-art in galaxy characterisation. The models trained to predict the Hubble types of galaxies are better when fine-tuned using the Proxy-NCA and Normalised-Softmax loss functions than with other pairwise losses. The Normalised-Softmax loss yielded the best overall 9-class models with accuracies at 30.88% (GZ2-H) and 30.05% (EFIGI) and MAP values of 0.3483 (GZ2-H) and 0.3889. The Proxy-NCA loss produced the second-best overall 9-class models with accuracies at 30.33% (GZ2-H) and 20.03% (EFIGI) and MAP values of 0.3577 (GZ2-H) and 0.3917 (EFIGI). Finally, the paper highlights the need for caution when utilising crowd-sourced labels; however, it argues that transfer learning from crowd-sourced labelled data to expert-labelled data can still lead to significant improvements.
Active tag recommendation for interactive entity search: Interaction effectiveness and retrieval performance

We introduce active tag recommendation for interactive entity search, an approach that actively learns to suggest tags from preceding user interactions with the recommended tags. The approach utilizes an online reinforcement learning model and observes user interactions on the recommended tags to reward or penalize the model. Active tag recommendation is implemented as part of a realistic search engine indexing a large collection of movie data. The approach is evaluated in task-based user experiments comparing a complete search system enhanced with active tag recommendation to a control system in which active tag recommendation is not available. In the experiment, participants (N = 45) performed search tasks on the movie domain and the corresponding search interactions, information selections, and entity rankings were logged and analyzed. The results show that active tag recommendation (1) improves the ranking of entities compared to written-query interaction, (2) increases the amount of interaction and effectiveness of interactions to rank entities that end up being selected in a task, and (3) reduces, but does not substitute, the need for written-query interaction (4) without compromising task execution time. The results imply that active learning for search support can help users to interact with entity search systems by reducing the need for writing queries and improve search outcomes without compromising the time used for searching.
Fast Change-Based Alarm Reporting for Evolving Software Systems

Static analysis tools, being scalable, are widely used to detect runtime errors in industry strength software. However, the downside is that these tools generate a large number of false alarms which considerably reduces their effectiveness in detecting the real bugs and fixing them. This shortcoming becomes more pronounced in analysis of evolving software where false alarms reported in an earlier version are re-reported while analysing subsequent versions. Ideally, developers would not like to see the re-reporting of old alarms that are inconsequential to the changes made. To address this problem, static analyzers have been enhanced with techniques like syntactic masking, and several heuristics to decide if an old alarm should be reported again or not. Naturally, however, as they do not take semantics of change into consideration, they are either unsound, or still end up reporting a large number of old alarms. This paper proposes a change-based alarm reporting approach, that reports an alarm only if the alarm point lies on a newly introduced, potentially unsafe, execution path. For this, we intro-duce a novel and effective semantic-aware change-impact analysis (CIA), that helps in detecting presence of such execution paths. In order to make this efficient, especially for development processes involving frequent code commits, our technique incrementally builds the required dataflow analyses and program dependence information. Our experiments, on 124 versions of a core banking application, demonstrate that the proposed approach is i) 66% faster than whole program analysis, ii) leads to 83% reduction in repetitive alarms, and iii) reports 62% lesser alarms as compared to syntactic CIA.
New Breakthroughs and Innovation Modes in English Education in Post-pandemic Era

The outbreak of COVID-19 has brought drastic changes to English teaching as it has shifted from the offline mode before the pandemic to the online mode during the pandemic. However, in the post-pandemic era, there are still many problems in the effective implementation of the process of English teaching, leading to the inability of achieving better results in the quality and efficiency of English teaching and effective cultivation of students' practical application ability. In recent years, English speaking has attracted the attention of experts and scholars. Therefore, this study constructs an interactive English-speaking practice scene based on a virtual character. A dual-modality emotion recognition method is proposed that mainly recognizes and analyzes facial expressions and physiological signals of students and the virtual character in each scene. Thereafter, the system adjusts the difficulty of the conversation according to the current state of students, toward making the conversation more conducive to the students' understanding and gradually improving their English-speaking ability. The simulation compares nine facial expressions based on the eNTERFACE05 and CAS-PEAL datasets, which shows that the emotion recognition method proposed in this manuscript can effectively recognize students' emotions in interactive English-speaking practice and reduce the recognition time to a great extent. The recognition accuracy of the nine facial expressions was close to 90% for the dual-modality emotion recognition method in the eNTERFACE05 dataset, and the recognition accuracy of the dual-modality emotion recognition method was significantly improved with an average improvement of approximately 5%.
Human Body Pose Estimation and Applications

Human Pose Estimation is one of the challenging yet broadly researched areas. Pose estimation is required in applications that include human activity detection, fall detection, motion capture in AR/VR, etc. Nevertheless, images and videos are required for every application that captures images using a standard RGB camera, without any external devices. This paper presents a real-time approach for sign language detection and recognition in videos using the Holistic pose estimation method of MediaPipe. This Holistic framework detects the movements of multiple modalities-facial expression, hand gesture and body pose, which is the best for the sign language recognition model. The experiment conducted includes five different signers, signing ten distinct words in a natural background. Two signs, &#x201C;blank&#x201D; and &#x201C;sad,&#x201D; were best recognized by the model.
Developing NEO Smart Contract for Weather-Based Insurance

Weather-based insurance has been developed across emerging economies in many countries. In several Southeast Asia (SEA) countries, weather-based insurance has been released to protect fishermen from the natural events or extreme weather. The value of insurance coverage is obtained from an expensive and lengthy process, especially when facing an extreme weather condition such as sea storm. This adds a heavy toll on fisherman's shoulders when facing the natural conditions. To solve these problems, which is to reduce evaluation costs, blockchain-based smart contracts are used ensure easier asymmetric information. We proposed a blockchain-based smart contract framework that applies to weather-based index insurance. We chose Docker Container and Neo blockchain platform for our experiment due to its easy access tokens from neo-python with a private network. In this experiment we developed a smart contract implementation on Neo. We defined 6 (six) functions that can be triggered if certain conditions are met from an external data application program interface (API). In our experiment of blockchain implementation using Neo, we found that Neo is scalable and trusted and as well having a low memory consumption that could be running in a low specification machine. The deployment of a blockchain based system is feasible to be conducted using Neo platform.
Black-Box Testing of Deep Neural Networks

Several test adequacy criteria have been developed for quantifying the the coverage of deep neural networks (DNNs) achieved by a test suite. Being dependent on the structure of the DNN, these can be costly to measure and use, especially given the highly iterative nature of the model training workflow. Further, testing provides higher overall assurance when such implementation dependent measures are used along with implementation independent ones. In this paper, we rigorously define a new black-box coverage criterion that is independent of the DNN model under test. We further describe a few desirable properties and associated evaluation metrics for assessing test coverage criteria and use those to empirically compare and contrast the black-box criterion with several DNN structural coverage criteria. Results indicate that the black-box criterion has comparable effectiveness and provides benefits that complement white-box criteria. The results also reveal a few weaknesses of coverage criteria for DNNs.
TRSAv1: A new benchmark dataset for classifying user reviews on Turkish e-commerce websites

The amount of data produced significantly increased with the development of Internet technologies. Accordingly, the importance of natural language processing studies increased, and this topic became one of the most studied artificial intelligence subjects. Even though it is a popular topic that is widely studied on, not enough studies have been conducted on the Turkish language. Even the studies conducted in Turkey are primarily on English and other natural languages instead of Turkish. The lack of a Turkish dataset is the most crucial reason for the lack of studies. Therefore, to create a solution, user reviews on e-commerce websites were collected and labelled reviews as positive, negative and neutral, and a new and unique dataset consisting of 150,000 reviews was created. This dataset was named TRSAv1, which was publicly shared with the researchers will contribute to the Turkish natural language processing studies; however, the effect of different word representation methods on algorithm performance was examined in detail, and the results were compared.
Super‐resolution reconstruction method of infrared images of composite insulators with abnormal heating based on improved SRGAN

Abnormal heating of composite insulators on ultra-high voltage (UHV) transmission lines is widespread under high humidity and heat in southern China. The infrared imaging technology can be used of quickly and effectively detect insulators with abnormal heating and determine their defect types. However, it may cause misjudgement when the infrared images are not clear. A super-resolution reconstruction method of infrared images of composite insulators with abnormal heating based on improved super-resolution generative adversarial networks (SRGAN) is proposed in this paper, and the dense residual network in SRGAN is perfected by introducing the residual channel attention (ResCA). This method overcomes the problems that the resolution of images reconstructed by traditional methods is not significantly improved, and images are too smooth, and the edge details are easily lost. Compared with the traditional methods, the five reference-free image quality evaluation indexes of the image reconstructed by the improved SRGAN are increased by −0.73% to 63.84%. Further the effectiveness and superiority of the improved SRGAN are verified by comparing the changes in the distribution of grey values of infrared images before and after reconstruction. Finally, the improved SRGAN is used to perform super-resolution reconstruction of the low-resolution infrared image examples.
Automated Climate Analyses Using Knowledge Graph

The FAIR (Findable, Accessible, Interoperable, Reusable) data principles are fundamental for climate researchers and all stakeholders in the current digital ecosystem. In this paper, we demonstrate how relational climate data can be &#x201C;FAIR&#x201D; and modeled using RDF, in line with Semantic Web technologies and our Climate Analysis ontology. Thus, heterogeneous climate data can be stored in graph databases and offered as Linked Data on the Web. As a result, climate researchers will be able to use the standard SPARQL query language to query these sources directly on the Web. In this paper, we demonstrate the usefulness of our SPARQL endpoint for automated climate analytics. We illustrate two sample use cases that establish the advantage of representing climate data as knowledge graphs.
Bivariate polynomial-based secret sharing schemes with secure secret reconstruction

A (t,n)-threshold scheme with secure secret reconstruction, or a (t,n)-SSR scheme for short, is a (t,n)-threshold scheme against the outside adversary who has no valid share, but can impersonate a participant to take part in the secret reconstruction phase. We point out that previous bivariate polynomial-based (t,n)-SSR schemes, such as those of Harn et al. (Information Sciences 2020), are insecure, which is because the outside adversary may obtain the secret by solving a system of t(t+1)2-ary linear equations. We revise Harn et al. scheme and get a secure (t,n)-SSR scheme based on a symmetric bivariate polynomial for the first time, where t⩽n⩽2t-1. To increase the range of n for a given t, we construct a secure (t,n)-SSR scheme based on an asymmetric bivariate polynomial for the first time, where n⩾t. We find that the share sizes of our schemes are the same or almost the same as other existing insecure (t,n)-SSR schemes based on bivariate polynomials. Moreover, our asymmetric bivariate polynomial-based (t,n)-SSR scheme is more easy to be constructed compared to the Chinese Remainder Theorem-based (t,n)-SSR scheme with the stringent condition on moduli, and their share sizes are almost the same.
Integrating Parallel Computation Technique to State-Space Dynamic Neural Network Modeling of Optical Fiber

In this paper, the previously known state-space dynamic neural network has been formulated such that it can be suitable for parallel computation. Using the parallelization method makes the model training extremely faster. In addition, the final models created using this technique have much smaller evaluation times compared to conventional models present in traditional circuit simulation tools. Finally, the proposed method has been validated through transient modeling of 100KM optical fiber.
The design process of a multi-disciplinary tool for developing interactive textiles

Interactive textiles embed sensing and actuating capabilities in their fibers and structure. Adoption of interactive textiles is complex requiring a combination of competences or collaboration between interaction designers, textile designers and textile engineers. These disciplines are typically organized in separate and unconnected departments, using different vocabularies, and requiring different information. Through focus groups with interaction designers, and interviews with experts from the three domains we developed the Interactive Textile Library (ITL). The ITL is a multi-disciplinary tool combining physical samples and a digital platform showing material characteristics, experiential qualities, interactive technology, manufacturing process and support to create and hack the samples. The ITL was evaluated with professional designers who valued the physical samples and videos. Results provide recommendations for researchers developing interactive textile material libraries that could facilitate incorporation of interactive textiles in design practice and reduce fabrication time of prototypes.
Toward Usable and Accessible Two-Factor Authentication Based on the Piezo-Gyro Channel

Two-factor authentication (2FA) is crucial for protecting the security of users authenticating to online servers. Despite its importance, users hesitate to use 2FA, due to usability issues. In this report we present a prototype implementation of PiGy, a novel system which improves the usability of existing methods, without compromising on security and compatibility. In PiGy, a one time password is automatically passed from the external token to a smartphone by selectively applying an acoustic stimulus to the phone’s microelectromechanical (MEMS) gyroscope, using a piezoelectric transducer. This scheme is much easier to use, requires no additional hardware support on modern phones, and is fully compliant with the time-based one time password (TOTP) standard. We implement a proof of concept of PiGy, and perform both a functional test and a user study to evaluate it. Through our evaluation we show that this authentication scheme is a viable alternative to existing methods, and that users agree with its usability advantages.
Quantifying Entanglement with Coherence

Quantifying entanglement is a work in progress which is important for the active field of quantum information and computation. A measure of bipartite pure state entanglement is proposed here, named entanglement coherence, which is essentially the normalized coherence of the entangled state in its Schmidt basis. Its value is 1 for maximally entangled states, and 0 for separable states, irrespective of the dimensionality of the Hilbert space. So a maximally entangled state is also the one which is maximally coherent in its Schmidt basis. Quantum entanglement and quantum coherence are thus intimately connected. Entanglement coherence turns out to be closely related to the unified entropy of the reduced state of one of the subsystems. Additionally it is shown that the entanglement coherence is closely connected to the Wigner-Yanase skew information of the reduced density operator of one of the subsystems, in an interesting way.
Smart Knowledge Engineering for Cognitive Systems: A Brief Overview

Cognition in computer sciences refers to the ability of a system to learn at scale, reason with purpose, and naturally interact with humans and other smart systems, such as humans do. To enhance intelligence, as well as to introduce cognitive functions into machines, recent studies have brought humans into the loop, turning the system into a human–AI hybrid. To effectively integrate and manipulate hybrid knowledge, suitable technologies and guidelines are required to sustain the human–AI interface so that communication can occur. However, traditional Knowledge Management (KM) and Knowledge Engineering (KE) approaches encounter problems when dealing with cutting-edge technologies, imposing impediments for the use of traditional methods in cognitive systems (CS). This paper presents a brief overview of the Smart Knowledge Engineering for Cognitive Systems (SKECS), which is based on methods, technologies, and procedures that bring innovations to the fields of KE, KM, and CS. The goal is to bridge the gap in the hybrid cognitive interface by the combination of experience-based knowledge representation with the use of emerging technologies such as deep learning, context-aware indexing/retrieval, active learning with a human-in-the-loop, and stream reasoning. In this work Set of Experience Knowledge Structure (SOEKS) and Decision DNA (DDNA) is extended to the visual domain and utilized for knowledge capture, representation, reuse, and evolution. These technologies are examined throughout the layers of SKECS for applications in knowledge acquisition, formalization, storage/retrieval, learning, and reasoning, with the final goal of achieving knowledge augmentation (wisdom) in CS. Features of the SKECS and their practical implementation is discussed through a case study—the Cognitive Vision Platform for Hazard Control (CVP-HC)—suggesting that methods, techniques and procedures comprising the SKECS are suitable for advancing systems toward augmented cognition.
SBEVNet: End-to-End Deep Stereo Layout Estimation

Accurate layout estimation is crucial for planning and navigation in robotics applications, such as self-driving. In this paper, we introduce the Stereo Bird’s Eye View Network (SBEVNet) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , a novel supervised end-to-end framework for estimation of bird’s eye view layout from a pair of stereo images. Although our network reuses some of the building blocks from the state-of-the-art deep learning networks for disparity estimation, we show that explicit depth estimation is neither sufficient nor necessary. Instead, the learning of a good internal bird’s eye view feature representation is effective for layout estimation. Specifically, we first generate a disparity feature volume using the features of the stereo images and then project it to the bird’s eye view coordinates. This gives us coarse-grained information about the scene structure. We also apply inverse perspective mapping (IPM) to map the input images and their features to the bird’s eye view. This gives us fine-grained texture information. Concatenating IPM features with the projected feature volume creates a rich bird’s eye view representation which is useful for spatial reasoning. We use this representation to estimate the BEV semantic map. Additionally, we show that using the IPM features as a supervisory signal for stereo features can give an improvement in performance. We demonstrate our approach on two datasets: the KITTI [5] dataset and a synthetically generated dataset from the CARLA [4] simulator. For both of these datasets, we establish state-of-the-art performance compared to baseline techniques.