Reviewerly/exHarmony huggingface dataset:
LARA: an extensible open source platform for learning languages by reading

Learning and Reading Assistant (LARA) is an open source platform that enables conversion of plain texts into an interactive multimedia form designed to support second- and foreign-language (L2) learners. In this workshop, we illustrate the open source aspects using collaborative work carried out during a six-week summer project at the Árni Magnússon Institute for Icelandic Studies. Three undergraduate level students extended the platform in different directions in cooperation with other members of the international LARA team. The three subprojects were respectively concerned with adding automatically generated flashcards, adding multimedia versions of poetic texts in the archaic language Old Norse, and extending LARA to allow the inclusion of sign language content in Icelandic sign language – Íslenskt TáknMál (ÍTM). All three reached successful conclusions.
An EMD-based data hiding scheme for JPEG images

As a commonly used image format, JPEG image is popularly used as the carrier for data hiding. After analyzing the virtue of the exploiting-modification-direction (EMD) scheme, which is designed for the spatial domain, this paper reforms its extraction function and applies it to the non-zero alternating current (AC) coefficients of JPEG images. Besides, a block and coefficient selection strategy is utilised to further improve the image quality and suppress the increase of file size. More specifically, the smooth discrete cosine transform (DCT) blocks with fewer zero AC coefficients are preferentially selected to embed data. The experiments indicate that our scheme achieves a high embedding capacity, meanwhile, the stego-image quality keeps good and the file size preserves well, moreover, our scheme is superior to the state-of-the-art scheme.
API-Prefer: An API Package Recommender System Based on Composition Feature Learning

With the exponential increase in Web Application Programming Interfaces (APIs), selecting appropriate APIs to construct a mashup is a challenging task. When multiple APIs are put together, their overall function is not just a superposition of their individual functions in many cases. Unfortunately, the approaches proposed to date do not sufficiently model the synthetical functions of the combined APIs. In this paper, an API Package recommender system based on composition feature learning (API-Prefer) is proposed. API-Prefer tries to learn the composition features of an API pair. Then the composition features can be used to predict whether this API pair can be adopted by a mashup or not. Specifically, a deep neural network is designed for composition feature learning and adoption probability prediction in API-Prefer. Since there is a large amount of API pairs, API-Prefer applies a strategy to select the potential APIs first, then the API packages can be discovered based on the predicted scores over multiple API pairs. Experiments on a real-world dataset show API-Prefer is significantly better than the comparative methods.
How has the coronavirus (COVID-19) pandemic affected global emoji usage?

Over the past few decades, emojis have emerged as a popular form of non-linguistic expression in computer-mediated communication. Various factors have been known to affect emoji usage patterns (such as age, gender, platform diversity etc.). The aim of the current study is to explore if the onset of the coronavirus pandemic (2019–20) has affected emoji usage patterns across various countries. The present study was conducted on two sets of tweets, collected before (July, 2019) and during the pandemic (March, 2020). The results suggest that although the usage of specific emojis has not changed noticeably (that is, the popular emojis mostly remained the same), the emoji density (average number of emojis per tweet) and the relative popularity of specific emojis have changed. This could potentially point toward a sense of insufficiency of emojis to express the sentiments associated with the pandemic.
Designing arithmetic neural primitive for sub-symbolic aggregation of linguistic assessments

Abstract The very first step towards a challenging goal of creation of monolithic generic neuro-symbolic systems is application of sub-symbolic ideas to particular symbolic algorithms like aggregation of fuzzy linguistic assessments during Linguistic Decision Making. A novel theoretical idea is to express this aggregation as structural manipulations and translate them in a neuroalgorithm. Tensor Product Representation (TPR) methodology provides a generic framework of designing neural networks that do not require training and produce an exact result equivalent to the result of symbolic algorithms. This paper demonstrates design of TPR-based arithmetic as a basic building block for expressing linguistic assessments aggregation on a sub-symbolic level and a neural architecture for the basic arithmetic operation.
Forensic Analysis of Instagram on Android

Abstract The use of social media has become a new lifestyle trend in today’s society. Social media provides a variety of benefits, ranging from building connections, delivering news, and sharing daily activities. On the other hand, social media is often misused to commit crimes, such as cyberbullying, child grooming, buying and selling drugs, spreading hoaxes, etc. Information uploaded on social media can be made into consideration for investigations in finding evidence of crimes. Therefore, efforts in supporting the verification of crimes in social media need to be done using the right forensic techniques. Topic of this research is an analysis of artifacts produced by the Instagram application on Android. This research provides benefits for analysts and practitioners to help in finding digital evidence of Instagram on Android. The results of this study successfully identified artifacts of Instagram application on Android, such as user account information, message exchange documentation, and user-uploaded activity traces.
Long-time protection of correlations and coherence in squeezed thermal bath

We investigate the dynamics of correlations in terms of quantum discord, dissonance, entanglement and classical correlation for a system formed by two qubits in a dephasing model. Each qubit is embedded in a squeezed thermal bath evolving independently and initially in a state with maximally mixed marginals via a nondemolition interaction. We reveal how the correlations may be protected during the evolution in weak and strong coupling regimes for various ranges of the temperature. We show the existence of a sudden conversion between the quantum discord and classical correlation occurring at a critical time. By appropriately choosing the model parameters, long-time protection of the quantum discord can be correspondingly achieved without any perturbation from the decoherence effect. On the other hand, the quantum dissonance can be used to foretell the behaviour of the correlations during the evolution. Moreover, we examine the time dependence of the coherence and squeezing entropy for the qubits.
Multi-Scale Video Inverse Tone Mapping with Deformable Alignment

Inverse tone mapping(iTM) is an operation to transform low-dynamic-range (LDR) content to high-dynamic-range (HDR) content, which is an effective technique to improve the visual experience. ITM has developed rapidly with deep learning algorithms in recent years. However, the great majority of deep-learning-based iTM methods are aimed at images and ignore the temporal correlations of consecutive frames in videos. In this paper, we propose a multi-scale video iTM network with deformable alignment, which increases time consistency in videos. We first align the input consecutive LDR frames at the feature level by deformable convolutions and then simultaneously use multi-frame information to generate the HDR frame. Additionally, we adopt a multi-scale iTM architecture with a pyramid pooling module, which enables our network to reconstruct details as well as global features. The proposed network achieves better performance compared to other iTM methods on quantitative metrics and gain a significant visual improvement.
Changing times – High Court of Australia library during COVID-19

Purpose The purpose of this study is to provide information and opinion on how the library dealt with this period and some further discussion on longer term impacts. Design/methodology/approach Observations/commentary covering the operation of the High Court of Australia Library and court/law libraries more generally during the COVID-19 pandemic. Findings The success of remote working will likely see this become more normalised. Originality/value This article is basically commentary and personal observation rather than original research.
Cross-cultural O2O English teaching based on AI emotion recognition and neural network algorithm

Cross-cultural English teaching is limited by the influence of traditional teaching models, resulting in poor teaching results. In order to improve the efficiency of cross-cultural English teaching, with the support of AI emotion recognition and neural network algorithm, this paper builds a cross-cultural O2O English teaching system with intelligent recognition and management. Moreover, this research uses background models to detect and track targets, to realize the full recognition of students’ emotions, and to facilitate teachers to effectively control online teaching. In addition, combined with online and offline teaching, this study uses neural network algorithm to stabilize the system and perform data processing, construct an overall O2O English teaching model according to actual needs, and formulate the corresponding teaching process. In order to verify the performance of the model, this study starts from two aspects: system performance test and system practice effect and uses statistical methods to collect and process data. The test results show that the model constructed in this paper has good performance and meets expectations.
Mind the Gap: On the Relationship Between Automatically Measured and Self-Reported Productivity

Two separate schools of thought to measure software engineers' productivity have coexisted: for a long time: on the one hand, using automated product or process measures and, on the other hand, using self-reported or perceived productivity. In this article, we start to bridge the gap between them with an empirical study of 81 software developers at Microsoft.
Modelling of normal domain evolution after single-photon absorption of a superconducting strip of micron width

Abstract The present paper describes a modelling of normal domain evolution in superconducting strip of micron width using solving differential equations describing the temperature and current changes. The solving results are compared with experimental data. This comparison demonstrates the high accuracy of the model. In future, it is possible to employ this model for improvement of single photon detector based on micron-scale superconducting strips.
FAST: A Fairness Assured Service Recommendation Strategy Considering Service Capacity Constraint

An excessive number of customers often leads to a degradation in service quality. However, the capacity constraints of services are ignored by recommender systems, which may lead to unsatisfactory recommendation. This problem can be solved by limiting the number of users who receive the recommendation for a service, but this may be viewed as unfair. In this paper, we propose a novel metric Top-N Fairness to measure the individual fairness of multi-round recommendations of services with capacity constraints. By considering the fact that users are often only affected by top-ranked items in a recommendation, Top-N Fairness only considers a sub-list consisting of top N services. Based on the metric, we design FAST, a Fairness Assured service recommendation STrategy. FAST adjusts the original recommendation list to provide users with recommendation results that guarantee the long-term fairness of multi-round recommendations. We prove the convergence property of the variance of Top-N Fairness of FAST theoretically. FAST is tested on the Yelp dataset and synthetic datasets. The experimental results show that FAST achieves better recommendation fairness while still maintaining high recommendation quality.
Running Transactional Business Processes with Blockchain's Smart Contracts

Smart Contracts (SC for short) are gaining momentum as a suitable technology for ensuring trusted execution of Business Processes (BP for short) in open environment. Nevertheless, the transactional semantics of SC which follow ACID transactions are not appropriate for BP characteristics. Indeed, it is admitted that ACID transactions are limited to cope with complex control structure and long running execution of BP. Transactional Business Processes (TBP for short) have emerged as an extension to ACID models to overcome these limits. A TBP ensures transactional reliability of advanced transactions having a control structure as complex as for BP. In this paper, we propose an approach that builds on SC and extends them to implement TBP. We extend Caterpillar, an existing BP execution engine developed on top of Ethereum, to implement our approach and support the execution of TBP.
Aided Selection of Sampling Methods for Imbalanced Data Classification

Building an effective classifier for imbalanced data is a challenging task as most of classifier work on the assumption of balanced data. Therefore, several sampling methods have been devised to bridge this gap by re-sampling the imbalanced datasets. Although sampling methods are in abundance, there is no single method that is best suitable for all kinds of datasets and applications. Building classifiers for all the sampling methods and comparing the results using appropriate performance metrics is computationally inefficient. In this work, we propose a framework to find a relation between datasets and sampling methods via a set of meta-features that characterizes the distribution of data. Also, we take into account the effect of probability threshold on the choice of sampling methods. The main objective of this work is to develop an approach that aids the selection of one or more sampling methods together with a probability threshold to be used for building a suitable classifier for a given dataset. It is based on mapping functions learned between classifier performance and datasets after re-sampling. In this work, extensive experiments are performed to validate the framework using synthetic as well as KEEL benchmark datasets.
Point Cloud Quality Assessment: Large-scale Dataset Construction and Learning-based No-Reference Approach

Full-reference (FR) point cloud quality assessment (PCQA) has achieved impressive progress in recent years. However, in many cases, obtaining the reference point cloud is difficult, so the no-reference (NR) methods have become a research hotspot. Since learning-based FR-PCQA methods should be driven by data, few researches about NR objective quality metrics are conducted due to lack of large-scale subjective point cloud dataset. The shortage of point cloud data restricts the development of NR-PCQA techniques. Besides, the distinctive property of point cloud format makes infeasible applying blind image quality assessment (IQA) methods directly to predict the quality scores of point clouds. In this paper, we establish a large-scale PCQA dataset, which includes 104 reference point clouds and more than 24,000 distorted point clouds. In the established dataset, each reference point cloud is augmented with 34 types of impairments (e.g., Gaussian noise, contrast distortion, geometry noise, local loss, and compression loss) at 7 different distortion levels. Besides, inspired by the hierarchical perception system and considering the intrinsic attribute of point clouds, an end-to-end sparse convolutional neural network (CNN) is designed to accurately estimate the subjective quality. We conduct experiments on the new dataset to evaluate the performance of the proposed network. The results demonstrate that the proposed network has reliable performance. The dataset presented in this work will be publicly accessible at this http URL
On Perfect Correctness in (Lockable) Obfuscation.

In a lockable obfuscation scheme [28, 39] a party takes as input a program P, a lock value \(\alpha \), a message \(\mathsf {msg}\) and produces an obfuscated program \(\tilde{P}\). The obfuscated program can be evaluated on an input x to learn the message \(\mathsf {msg}\) if \(P(x)= \alpha \). The security of such schemes states that if \(\alpha \) is randomly chosen (independent of P and \(\mathsf {msg}\)), then one cannot distinguish an obfuscation of P from a “dummy” obfuscation. Existing constructions of lockable obfuscation achieve provable security under the Learning with Errors assumption. One limitation of these constructions is that they achieve only statistical correctness and allow for a possible one sided error where the obfuscated program could output the \(\mathsf {msg}\) on some value x where \(P(x) \ne \alpha \).
Adversarial Learning on the Latent Space for Diverse Dialog Generation

Generating relevant responses in a dialog is challenging, and requires not only proper modeling of context in the conversation, but also being able to generate fluent sentences during inference.In this paper, we propose a two-step framework based on generative adversarial nets for generating conditioned responses.Our model first learns a meaningful representation of sentences by autoencoding, and then learns to map an input query to the response representation, which is in turn decoded as a response sentence.Both quantitative and qualitative evaluations show that our model generates more fluent, relevant, and diverse responses than existing state-of-the-art methods.
ALCencryption: A Secure and Efficient Algorithm for Medical Image Encryption

With the rapid development of medical informatization and the popularization of digital imaging equipment, DICOM images contain the personal privacy of patients, and there are security risks in the process of storage and transmission, so it needs to be encrypted. In order to solve the security problem of medical images on mobile devices, a safe and efficient medical image encryption algorithm called ALCencryption is designed. The algorithm first analyzes the medical image and distinguishes the color image from the gray image. For gray images, the improved Arnold map is used to scramble them according to the optimal number of iterations, and then the diffusion is realized by the Logistic and Chebyshev map cross-diffusion algorithm. The color image is encrypted by cross-diffusion algorithm of double chaotic map. Security and efficiency analysis show that the ALCencryption algorithm has the characteristics of small neighboring pixels, large key space, strong key sensitivity, high safety and short encryption time. It is suitable for medical image encryption of mobile devices with high real-time requirements.
966: Continuous Risk of Desaturation Within the Next Hour Prediction Using a Recurrent Neural Network

Ehrlich, Lili1; Ledbetter, David1; Aczon, Melissa1; Laksana, Eugene2; Wetzel, Randall3 Author Information
Welcome to Hydrogen—A New International and Interdisciplinary Open Access Journal of Growing Interest in Our Society

The 2018 Intergovernmental Panel on Climate Change (IPCC) report [...]
Dynamic reconstruction of chaotic system based on exponential weighted online sequential extreme learning machine with kernel

For the dynamic reconstruction of the chaotic dynamical system, a method of identifying an exponential weighted online sequential extreme learning machine with kernel(EW-KOSELM) is proposed. The kernel recursive least square (KRLS) algorithm is directly extended to an online sequential ELM framework, and weakens the effect of old data by introducing a forgetting factor. Meanwhile, the proposed algorithm can deal with the ever-increasing computational difficulties inherent in online kernel learning algorithms based on the ‘fixed-budget’ memory technique. The employed EW-KOSELM identification method is firstly applied to the numerical example of Duffing-Ueda oscillator for chaotic dynamical system based on simulated data, the qualitative and quantitative analysis for various validation tests of the dynamical properties of the original system as well as the identification model are carried out. A set of qualitative validation criteria is implemented by comparing chaotic attractors i.e. embedding trajectories, computing the corresponding Poincare mapping, plotting the bifurcation diagram, and plotting the steady-state trajectory i.e. the limit cycle between the original system and the identification model. Simultaneously, the quantitative validation criterion which includes computing the largest positive Lyapunov exponent and the correlation dimension of the chaotic attractors is also calculated to measure the closeness i.e. the approximation error between the original system and the identification model. The employed method is further applied to a practical implementation example of Chua's circuit based on the experimental data which are generated by sampling and recording the measured voltage across a capacitor, the inductor current from the double-scroll attractor, the measured voltage across a capacitor from the Chua's spiral attractor and an experimental time series from a chaotic circuit. The digital filtering technique is then used as a preprocessing approach, on the basis of wavelet denoising the measured data with lower signal-noise ratio (SNR) which can produce the double-scroll attractor or the spiral attractor, the reconstruction attractor of the identification model is compared with the reconstruction attractor from the experimental data for original system. The above experimental results confirm that the EW-FB-KOSELM identification method has a better performance of dynamic reconstruction, which can produce an accurate nonlinear model of process exhibiting chaotic dynamics. The identification model is dynamically equivalent or system approximation to the original system.
Generating Commonsense Explanation by Extracting Bridge Concepts from Reasoning Paths.

Commonsense explanation generation aims to empower the machine’s sense-making capability by generating plausible explanations to statements against commonsense. While this task is easy to human, the machine still struggles to generate reasonable and informative explanations. In this work, we propose a method that first extracts the underlying concepts which are served as bridges in the reasoning chain and then integrates these concepts to generate the final explanation. To facilitate the reasoning process, we utilize external commonsense knowledge to build the connection between a statement and the bridge concepts by extracting and pruning multi-hop paths to build a subgraph. We design a bridge concept extraction model that first scores the triples, routes the paths in the subgraph, and further selects bridge concepts with weak supervision at both the triple level and the concept level. We conduct experiments on the commonsense explanation generation task and our model outperforms the state-of-the-art baselines in both automatic and human evaluation.