Reviewerly/exHarmony huggingface dataset:
A Catalog of Broad Morphology of Pan-STARRS Galaxies Based on Deep Learning

Autonomous digital sky surveys such as Pan-STARRS have the ability to image a very large number of galactic and extra-galactic objects, and the large and complex nature of the image data reinforces the use of automation. Here we describe the design and implementation of a data analysis process for automatic broad morphology annotation of galaxies, and applied it to the data of Pan-STARRS DR1. The process is based on filters followed by a two-step convolutional neural network (CNN) classification. Training samples are generated by using an augmented and balanced set of manually classified galaxies. Results are evaluated for accuracy by comparison to the annotation of Pan-STARRS included in a previous broad morphology catalog of SDSS galaxies. Our analysis shows that a CNN combined with several filters is an effective approach for annotating the galaxies and removing unclean images. The catalog contains morphology labels for 1,662,190 galaxies with ~95% accuracy. The accuracy can be further improved by selecting labels above certain confidence thresholds. The catalog is publicly available.
Pedestrian detection based on attention mechanism and feature enhancement with SSD

Some factors such as low resolution of small targets, limited target features, and noise interference affect the effect of pedestrian detection. Therefore, a pedestrian detection algorithm based on attention mechanism and feature enhancement with SSD is presented in this paper. It uses channel feature fusion to fuse non-adjacent convolutional layers to obtain significant edge gradient features and semantic information features.Finally, through the optimization of the attention mechanism CBAM, the channel features and spatial features are coupled under different fusion detection layers to improve the feature weight of the pedestrian's salient region, so as to the detection accuracy of the algorithm has taken a big step forward. The improved algorithm is verified on the fusion dataset and the VOC2007TEST dataset, compared with the SSD algorithm, the detection accuracy of the improved algorithm model on the fusion dataset reaches 60.1%, with an increase of 7.2%, and that on the VOC2007TEST dataset reaches 78%, with an increase of 4.1%. The experimental results show that this method can effectively detect the small target pedestrian in the image, and reduce the error detection and omission detection, thus verifying its feasibility.
Propaganda Identification Using Topic Modelling

This paper presents a method based on topic modelling for identifying texts with propagandistic content. The method is an attempt to incorporate transfer learning idea of obtaining effective vector representation from a large unlabeled or (semi-) automatically labelled dataset, while also attempting to minimize the amount of necessary manual expert labelling by introducing high level labelling (either manual or automatic) on some explicit document property. The proposed method includes four key stages: formation of corpus partitioning, computing a topic model of a united corpus, calculation of corpora imbalance estimates of each topic; extrapolating the results of the imbalance estimation on all documents. The method was cross-validated on a labelled subsample of 1000 news, and achieves high predictive power – ROC AUC 0.73.
Pelee-Text++: A Tiny Neural Network for Scene Text Detection

Scene text detection has become an important field in the computer vision area due to the increasing number of applications. This is a very challenging problem as textual elements are commonly found in “noisy” and complex natural scenes. Another issue refers to the presence of texts encoded into different languages within the same image. State-of-the-art solutions rely on the use of deep neural network approaches or even ensembles of them. However, such solutions are associated with “heavy” models, which are computationally expensive in terms of memory and storage footprints, which hampers their use in real-time mobile applications. In this work, we introduce Pelee-Text++, a lightweight neural network architecture for multi-lingual multi-oriented scene text detection, especially tailored to running on devices with computational restrictions. Additionally, to the best of our knowledge, this is the first work to evaluate the performance of text detection methods in commercial smartphones. Over this scenario, Pelee-Text++ processes 2.94 frames per second and it is the only evaluated approach that did not cause memory issues on smartphones, even using an input image of $1024 × 1024 pixels. Our proposal achieves a promising trade-off between efficiency and effectiveness, with a model size of 27 Megabytes and F-measure of 91.20%, 85.78%, 81.72%, 80.30%, 82.53% and 66.51% on ICDAR 2011, ICDAR 2013, ICDAR 2015, MSRA-TD500, ReCTS 2019 and Multi-lingual 2019 datasets, respectively.
Determinants and Barriers of Artificial Intelligence Adoption – A Literature Review

Different theories, models and frameworks have been used to study technology adoption, some explaining the determinants of adoption at the individual level, some at the organizational level and some at both. As Artificial Intelligence (AI) is gaining traction in many sectors, it will be beneficial to understand the determinants and barriers to AI adoption. In this paper, an attempt has been made to review journal articles and other reports pertaining to AI adoption and understand the adoption theories used and the factors that facilitate and those that hinder AI adoption. Articles on adoption studies of autonomous vehicles, big data analytics, robots and cognitive engagement applications dominated the list of journal articles. Diffusion of Innovation, Technology, Organization and Environment Framework and the unified theory of acceptance and use of technology (UTAUT) were some of the dominant theories/frameworks used. Factors influencing adoption at the individual level were related to trust, security, purchase price, intrinsic motivation, social influence, utilitarian benefit whereas at the organizational level, it was related to the technical competencies, strategic road mapping for AI, top management support and the digital maturity of the organization.
Impact for whom? Mapping the users of public research with lexicon-based text mining

Abstract We contribute to the debate on societal impact of SSH by developing a methodology that allows a fine-grained observation of social groups that make use, directly or indirectly, of the results of research. We develop a lexicon of users with 76,857 entries, which saturates the semantic field of social groups of users and allows normalization. We use the lexicon in order to filter text structures in the 6637 impact case studies collected under the Research Excellence Framework in the UK. We then follow the steps recommended by Börner et al. (Annu Rev Inf Sci Technol 37:179–255, 2003) to build up visual maps of science, using co-occurrence of words describing users of research. We explore the properties of this novel kind of maps, in which science is seen from the perspective of research users.
Hybrid-shield

Cache-based Side-Channel Attacks (SCAs) exploit the emerging hardware vulnerabilities to steal secret information by observing cache access patterns of cryptographic applications. To address the challenges introduced by SCAs, existing solutions either rely on detecting them using the profiled hardware-related information of victim and attack programs, or mitigating the cache-based SCAs by focusing on cache partitioning techniques and/or randomization of cache mappings by modifying the underlying cache architecture of the processor. However, the former's detectors highly rely on the knowledge of attack programs that are not always available or could be obfuscated in real-world benign applications resulting in misidentifying attacks. On the other hand, the latter approach though effective, requires modification in the processor hardware which increases the complexity of architecture design and are not applicable to current as well as legacy architectures. To address the drawbacks, we propose Hybrid-Shield, an accurate and efficient cross-layer countermeasure for run-time detection and mitigation of cache-based side-channel attacks. For the detection stage, microarchitectural information of victim under attack and under no attack conditions are collected for training machine learning classifiers. For the mitigation stage, Hybrid-Shield adapts hardware prefetchers and scales processor frequency to increase the noise level in observed cache access pattern attacks to induce secret information. The experimental results indicate that Hybrid-Shield can achieve 100% detection rate with 0% false alarm rate and detected attacks' error rate increases from less than 5% to above 35% with only 15% performance overhead.
Why the new consensus mechanism is needed in blockchain technology?

The Blockchain with PoW consensus was first developed to solve the cryptocurrency double-spending and byzantine fault problem. The PoW consensus solved the problem very effectively. But due to its high computation and specialized hardware need, a lot of questions from society has been raised on the PoW consensus. To solve the limitations of PoW, a lot of other consensuses have been proposed in the literature as an alternative solution of PoW. However, the other consensus has its own benefits and drawbacks. So, to understand the consensus mechanisms our paper first describes the internal architecture of blockchain and then explain five consensuses of the blockchain network with its detailed comparative analysis of internal characteristic. We believe this comparative analysis will be helpful for those researchers who need to understand the major characteristics that a given consensus offer for their work.
Scalable Discrete Matrix Factorization and Semantic Autoencoder for Cross-Media Retrieval

Hashing methods have sparked great attention on multimedia tasks due to their effectiveness and efficiency. However, most existing methods generate binary codes by relaxing the binary constraints, which may cause large quantization error. In addition, most supervised cross-modal approaches preserve the similarity relationship by constructing an n×n large-size similarity matrix, which requires huge computation, making these methods unscalable. To address the above challenges, this article presents a novel algorithm, called scalable discrete matrix factorization and semantic autoencoder method (SDMSA). SDMSA is a two-stage method. In the first stage, the matrix factorization scheme is utilized to learn the latent semantic information, the label matrix is incorporated into the loss function instead of the similarity matrix. Thereafter, the binary codes can be generated by the latent representations. During optimization, we can avoid manipulating a large n×n similarity matrix, and the hash codes can be generated directly. In the second stage, a novel hash function learning scheme based on the autoencoder is proposed. The encoder-decoder paradigm aims to learn projections, the feature vectors are projected to code vectors by encoder, and the code vectors are projected back to the original feature vectors by the decoder. The encoder-decoder scheme ensures the embedding can well preserve both the semantic and feature information. Specifically, two algorithms SDMSA-lin and SDMSA-ker are developed under the SDMSA framework. Owing to the merit of SDMSA, we can get more semantically meaningful binary hash codes. Extensive experiments on several databases show that SDMSA-lin and SDMSA-ker achieve promising performance.
Multi-Branch Networks for Video Super-Resolution With Dynamic Reconstruction Strategy

Recently, the rapid development of 2-dimensional (2D) convolutional neural networks (CNNs) has driven single image super-resolution (SISR) into a new era, owing to their powerful ability in modeling spatial relation within one single image. However, few studies focus on video super-resolution (VSR) due to the key challenge that apart from the spatial relation, the temporal dependence among consecutive low-resolution (LR) frames must be taken into consideration for better reconstruction. In this article, unlike most previous methods based on optical flow for motion compensation, 3-dimensional (3D) convolution is utilized to capture the temporal relation. Firstly, in contrast to the conventional 3D convolution which is notorious for the excessively high computational burden, we propose an efficient 3D convolutional block (E3DB) through convolution factorization principle (CFP), which significantly reduces the computing load while maximally maintaining the temporal information. Then, by taking advantage of E3DB, we propose a novel multi-resolution extraction block (MREB) which aggregates the information from multiple resolutions, leading to the stronger high-resolution representation learning and better feature extraction. Besides, based on our 3-branch architecture, instead of the simple addition or concatenation, a dynamic reconstruction strategy (DRS) is proposed to adaptively fuse the optimal information of temporal dependence from each branch. It is therefore termed as dynamic multiple branch network (DMBN). Comprehensive experiments on public benchmark datasets demonstrate the superiority of our DMBN over the current state-of-the-art methods in terms of accuracy and efficiency.
Workflow Integration Alleviates Identity and Access Management in Serverless Computing

As serverless computing continues to revolutionize the design and deployment of web services, it has become an increasingly attractive target to attackers. These adversaries are developing novel tactics for circumventing the ephemeral nature of serverless functions, exploiting container reuse optimizations and achieving lateral movement by "living off the land" provided by legitimate serverless workflows. Unfortunately, the traditional security controls currently offered by cloud providers are inadequate to counter these new threats.
Block-wise focal stack image representation for end-to-end applications

In optical imaging systems, the depth of field (DoF) is generally constricted due to the nature of optical lens. The limited DoF produces partially focused images of the scene. Focal stack images (FoSIs) are a sequence of images that focused on serial depths of a scene. FoSIs are capable of extending DoF of optical systems and provide practical solutions for computational photography, macroscopic and microscopic imaging, interactive and immersive media. However, high volumes of data remains one of the biggest obstacles to the development of end-to-end applications. In order to solve this challenge, we propose a block-wise Gaussian based representation model for FoSIs and utilize this model to solve the problem of coding, reconstruction and rendering for end-to-end applications. Experimental results demonstrate the high efficiency of proposed representation model and the superior performance of proposed schemes.
Secure and Verifiable Inference in Deep Neural Networks

Outsourced inference service has enormously promoted the popularity of deep learning, and helped users to customize a range of personalized applications. However, it also entails a variety of security and privacy issues brought by untrusted service providers. Particularly, a malicious adversary may violate user privacy during the inference process, or worse, return incorrect results to the client through compromising the integrity of the outsourced model. To address these problems, we propose SecureDL to protect the model’s integrity and user’s privacy in Deep Neural Networks (DNNs) inference process. In SecureDL, we first transform complicated non-linear activation functions of DNNs to low-degree polynomials. Then, we give a novel method to generate sensitive-samples, which can verify the integrity of a model’s parameters outsourced to the server with high accuracy. Finally, We exploit Leveled Homomorphic Encryption (LHE) to achieve the privacy-preserving inference. We shown that our sensitive-samples are indeed very sensitive to model changes, such that even a small change in parameters can be reflected in the model outputs. Based on the experiments conducted on real data and different types of attacks, we demonstrate the superior performance of SecureDL in terms of detection accuracy, inference accuracy, computation, and communication overheads.
Predicting State Uncertainty Bounds Using Non-Linear Stochastic Reachability Analysis for Urban GNSS-Based UAS Navigation

Unmanned aircraft systems (UAS) generally use Global Navigation Satellite System (GNSS) measurements to estimate their state (position and orientation) for outdoor navigation. However, in urban environments, GNSS pseudorange measurements contain biases due to multipath effects and signal blockages by nearby buildings. For safe navigation in such environments, it is beneficial to predict the state uncertainty while accounting for the effect of measurement biases. Reachability analysis is a commonly used tool to predict the state uncertainty of a system. However, existing works do not account for the effect of measurement biases on state estimation, which consequently affects the predicted state uncertainty. Additionally, majority of the existing literature focuses on linear systems, whereas the dynamics of widely used practical systems are better captured by non-linear models. Thus, in this paper we present a non-linear stochastic reachability analysis to predict bounds on the state uncertainty while accounting for measurement biases. We derive the analysis for a fixed-wing UAS navigating using ranging measurements. In order to evaluate our predicted bounds for GNSS-based navigation, we simulate a 3D urban environment and the pseudorange biases due to multipath effects. We validate the predicted bounds for multiple trajectories in the simulated environment. Finally, we demonstrate the applicability of our predicted bounds towards ensuring safe UAS navigation in a shared airspace.
Cluster Model for Edge Computing

This paper discusses the issues of building a cluster model designed for distributed computing systems, namely, for one of the options for implementing boundary computing - Dew Computing. It involves user interaction with an electronic device, including the emergence of a new concept based on the emergence of an environment in which it is possible to lease the computing power of a subscriber device owned by another user. Performing a step-by-step transformation within a distributed system from the concept of cloud computing, which first turns into foggy, and then into dew calculations, a single communication device acts as” droplets”, containing both a means of user identification and a means of computing and communication. Using the appropriate mathematical apparatus that includes elements of the theory of functions of a complex variable, as well as the clustering problem, the possibility of constructing a cluster for any arbitrary point that characterizes the subscriber and his personal computing device is shown.
Social Issues Sentiment Analysis using Python

This Globally people are using social media platforms to share their ideas and information related to different topics every day. Twitter is one of the most popular social media platforms to send and read posts to communicate with others known as “tweets”. Peoples share their ideas, reviews, experiences, and post their opinions on a particular topic or issue. This paper aims to build a model that performs a sentimental analysis of people's opinions related to the social issues of women that is a very critical issue these days in many countries of the world. A dataset of tweets has been collected from Twitter by using a twitter scraper in python programming and then cleaned the dataset using the nltk library to remove noise from the dataset. To analyze the sentiments of people's Machine Learning tools and techniques are used. To classify each tweet as positive, negative, or neutral using Text blob in Python based on the polarity of sentiments. Datasets were collected on two women's hashtags like #Women and #Metoo. The proposed study shows that which hashtag has more popular or used by peoples to share experiences, opinions, and issues related to women. Different machine learning algorithms were used to train and test the model. The results from these models were tested using various testing parameters like precision, recall, and, f1-score. Moreover, compared to the performance of each model.
Loyalty Program using Blockchain

The traditional loyalty systems usually offer people benefits in a specific sector.The users usually need to stay within the loyalty system for a long time and accumulate points in order to win rewards which may not be very interesting for them most of the time.Additionally, users usually do not prefer to share their personal information to join these loyalty systems due to privacy concerns.It has, therefore, been observed that the number of customers in the loyalty systems is decreasing day by day.To reduce these drawbacks a loyalty program which complies with ERC20 standards was proposed in this study using tokens based on the Ethereum blockchain.Using this new generation loyalty system, users can convert their earned tokens to Ether in the market and they can receive services or products with the accumulated tokens according to their interests from any supplier that has been contracted by the manufacturer.Additionally, users in the designed system do not need to carry many different cards, it is adequate to have only one Ethereum wallet.Furthermore, users do not need to share any personal data to join the loyalty system.Suppliers can also request Ether from the manufacturer for the tokens they have accumulated from the members of the loyalty system.The proposed loyalty system has been implemented and presented in this study.
Perspective Matters: Design Implications for Motion Guidance in Mixed Reality

We investigate how Mixed Reality (MR) can be used to guide human body motions, such as in physiotherapy, dancing, or workout applications. While first MR prototypes have shown promising results, many dimensions of the design space behind such applications remain largely unexplored. To better understand this design space, we approach the topic from different angles by contributing three user studies. In particular, we take a closer look at the influence of the perspective, the characteristics of motions, and visual guidance on different user performance measures. Our results indicate that a first-person perspective performs best for all visible motions, whereas the type of visual instruction plays a minor role. From our results we compile a set of considerations that can guide future work on the design of instructions, evaluations, and the technical setup of MR motion guidance systems.