Reviewerly/exHarmony huggingface dataset:
The relationship between mobile learning and self-regulated learning: A systematic review

A systematic review of 38 primary research peer-reviewed articles, drawn from six databases and spanning from January 2007 to January 2019, was conducted to determine the principle information that they collectively offered on the relationship between mobile learning (m-learning) and self-regulated learning (SRL). In answering the research questions posed, a synthesis of the following 12 key elements was undertaken: (1) research aims, (2) research methodologies, (3) outcomes, (4) education discipline areas, (5) educational levels, (6) educational contexts, (7) geographic location, (8) time frame, (9) type of device, (10) m-learning and SRL definitions, (11) theoretical models, and (12) m-learning, self-regulation (SR), and SRL variable measurement instruments. The frequency of studies on the relationship between m-learning and SRL increased in more recent years, as did the types of devices used in these studies. More than three quarters of the studies concluded that m-learning enhanced SRL, SRL enhanced m-learning, or m-learning and SRL enhanced other learning factors (e.g., health, curriculum development). Moreover, the relationship between m-learning and SRL was dynamic and complex. A primary recommendation was to intentionally integrate m-learning and SRL into formal curricula guided by informed, technologically adept educators who provided appropriate, ever-decreasing support and scaffolding as learners became more self-determined.&#x0D; &#x0D; Implications for practice or policy:&#x0D; &#x0D; M-learning research and practice should be founded upon relevant theory and validated definitions of m-learning that consider ever-advancing technologies and related pedagogies that include participatory activities.&#x0D; M-learning designers should ensure that mobile technologies are used intentionally and selectively, guided by clearly defined learning objectives, and integrated into the curriculum by technologically adept educators who provide appropriate, ever-decreasing support and scaffolding as learners become more self-determined.&#x0D; When designing m-learning, educators should consider digital safety and privacy issues.&#x0D;
A Dynamic Three-Bit Image Steganography Algorithm for Medical and e-Healthcare Systems

Massive advances in internet infrastructure are impacting e-healthcare services compared to conventional means. Therefore, extra care and protection is needed for extremely confidential patient medical records. With this intention, we have proposed an enhanced image steganography method, to improve imperceptibility and data hiding capacity of stego images. The proposed Image Region Decomposition (IRD) method, embeds more secret information with better imperceptibility, in patient's medical images. The algorithm decomposes the grayscale magnetic resonance imaging (MRI) images into three unique regions: low-intensity, medium-intensity, and high-intensity. Each region is made up of k number of pixels, and in each pixel we operate the block of n least significant bits (LSBs), where 1 ≤ n ≤ 3. Four classes of MRI images of different dimensions are used for embedding. Data with different volumes are used to test the images for imperceptibility and verified with quality factors. The proposed IRD algorithm is tested for performance, on the set of brain MRI images using peak signal-to-noise ratio (PSNR), mean square error (MSE) and structural similarity (SSIM) index. The results elucidated that the MRI stego image is imperceptible, like the original cover image by adjusting 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> and 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> LSBs in the low-intensity region. Our proposed steganography technique provides a better average PSNR (49.27), than other similar methods. The empirical results show that the proposed IRD algorithm, significantly improves the imperceptibility and data embedding capacity, compared to the existing state-of-the-art methods.
Generating spatially entangled itinerant photons with waveguide quantum electrodynamics

Realizing a fully connected network of quantum processors requires the ability to distribute quantum entanglement. For distant processing nodes, this can be achieved by generating, routing, and capturing spatially entangled itinerant photons. In this work, we demonstrate the deterministic generation of such photons using superconducting transmon qubits that are directly coupled to a waveguide. In particular, we generate two-photon N00N states and show that the state and spatial entanglement of the emitted photons are tunable via the qubit frequencies. Using quadrature amplitude detection, we reconstruct the moments and correlations of the photonic modes and demonstrate state preparation fidelities of 84%. Our results provide a path toward realizing quantum communication and teleportation protocols using itinerant photons generated by quantum interference within a waveguide quantum electrodynamics architecture.
Like hiking? You probably enjoy nature: Persona-grounded Dialog with Commonsense Expansions

Existing persona-grounded dialog models often fail to capture simple implications of given persona descriptions, something which humans are able to do seamlessly.For example, state-of-the-art models cannot infer that interest in hiking might imply love for nature or longing for a break.In this paper, we propose to expand available persona sentences using existing commonsense knowledge bases and paraphrasing resources to imbue dialog models with access to an expanded and richer set of persona descriptions.Additionally, we introduce fine-grained grounding on personas by encouraging the model to make a discrete choice among persona sentences while synthesizing a dialog response.Since such a choice is not observed in the data, we model it using a discrete latent random variable and use variational learning to sample from hundreds of persona expansions.Our model outperforms competitive baselines on the PERSONA-CHAT dataset in terms of dialog quality and diversity while achieving persona-consistent and controllable dialog generation.
Knowledge Graph Alignment with Entity-Pair Embedding

Knowledge Graph (KG) alignment is to match entities in different KGs, which is important to knowledge fusion and integration.Recently, a number of embedding-based approaches for KG alignment have been proposed and achieved promising results.These approaches first embed entities in low-dimensional vector spaces, and then obtain entity alignments by computations on their vector representations.Although continuous improvements have been achieved by recent work, the performances of existing approaches are still not satisfactory.In this work, we present a new approach that directly learns embeddings of entity-pairs for KG alignment.Our approach first generates a pair-wise connectivity graph (PCG) of two KGs, whose nodes are entitypairs and edges correspond to relation-pairs; it then learns node (entity-pair) embeddings of the PCG, which are used to predict equivalent relations of entities.To get desirable embeddings, a convolutional neural network is used to generate similarity features of entity-pairs from their attributes; and a graph neural network is employed to propagate the similarity features and get the final embeddings of entitypairs.Experiments on five real-world datasets show that our approach can achieve the stateof-the-art KG alignment results.
Keep CALM and Explore: Language Models for Action Generation in Text-based Games

Text-based games present a unique challenge for autonomous agents to operate in natural language and handle enormous action spaces.In this paper, we propose the Contextual Action Language Model (CALM) to generate a compact set of action candidates at each game state.Our key insight is to train language models on human gameplay, where people demonstrate linguistic priors and a general game sense for promising actions conditioned on game history.We combine CALM with a reinforcement learning agent which re-ranks the generated action candidates to maximize ingame rewards.We evaluate our approach using the Jericho benchmark (Hausknecht et al., 2019a), on games unseen by CALM during training.Our method obtains a 69% relative improvement in average game score over the previous state-of-the-art model.Surprisingly, on half of these games, CALM is competitive with or better than other models that have access to ground truth admissible actions.
Experimental Demonstration of a Quantum Receiver Beating the Standard Quantum Limit at Telecom Wavelength

Discrimination of coherent states beyond the standard quantum limit (SQL) is an important task not only for quantum information processing but also for optical coherent communication. To optimize long-distance optical fiber networks, it is of practical importance to develop a quantum receiver beating the SQL and approaching the quantum bound at telecom wavelength. In this paper, we experimentally demonstrate a receiver beating the conventional SQL at telecom wavelength. Our receiver is composed of a displacement operation, a single-photon counter, and a real-time adaptive-feedback operation. By using a high-performance single-photon detector operating at the telecom wavelength, we achieve a discrimination error beyond the SQL. The demonstration in the telecom band is an enabler for quantum and classical communication beyond the SQL using a coherent state alphabet, and we envision that the technology can be used for long-distance quantum-key distribution, effective quantum-state preparation, and quantum estimation.
Enhancing Aspect Term Extraction with Soft Prototypes

Aspect term extraction (ATE) aims to extract aspect terms from a review sentence that users have expressed opinions on.Existing studies mostly focus on designing neural sequence taggers to extract linguistic features from the token level.However, since the aspect terms and context words usually exhibit long-tail distributions, these taggers often converge to an inferior state without enough sample exposure.In this paper, we propose to tackle this problem by correlating words with each other through soft prototypes.These prototypes, generated by a soft retrieval process, can introduce global knowledge from internal or external data and serve as the supporting evidence for discovering the aspect terms.Our proposed model is a general framework and can be combined with almost all sequence taggers.Experiments on four SemEval datasets show that our model boosts the performance of three typical ATE methods by a large margin.
Does my multimodal model learn cross-modal interactions? It’s harder to tell than you might think!

Modeling expressive cross-modal interactions seems crucial in multimodal tasks, such as visual question answering.However, sometimes high-performing black-box algorithms turn out to be mostly exploiting unimodal signals in the data.We propose a new diagnostic tool, empirical multimodally-additive function projection (EMAP), for isolating whether or not cross-modal interactions improve performance for a given model on a given task.This function projection modifies model predictions so that cross-modal interactions are eliminated, isolating the additive, unimodal structure.For seven image+text classification tasks (on each of which we set new state-ofthe-art benchmarks), we find that, in many cases, removing cross-modal interactions results in little to no performance degradation.Surprisingly, this holds even when expressive models, with capacity to consider interactions, otherwise outperform less expressive models; thus, performance improvements, even when present, often cannot be attributed to consideration of cross-modal feature interactions.We hence recommend that researchers in multimodal machine learning report the performance not only of unimodal baselines, but also the EMAP of their best-performing model.
Paraphrasing vs Coreferring: Two Sides of the Same Coin

We study the potential synergy between two different NLP tasks, both confronting predicate lexical variability: identifying predicate paraphrases, and event coreference resolution.First, we used annotations from an event coreference dataset as distant supervision to re-score heuristically-extracted predicate paraphrases.The new scoring gained more than 18 points in average precision upon their ranking by the original scoring method.Then, we used the same re-ranking features as additional inputs to a state-of-the-art event coreference resolution model, which yielded modest but consistent improvements to the model's performance.The results suggest a promising direction to leverage data and models for each of the tasks to the benefit of the other.
Batch Size Influence on Performance of Graphic and Tensor Processing Units During Training and Inference Phases

The impact of the maximally possible batch size (for the better runtime) on performance of graphic processing units (GPU) and tensor processing units (TPU) during training and inference phases is investigated. The numerous runs of the selected deep neural network (DNN) were performed on the standard MNIST and Fashion-MNIST datasets. The significant speedup was obtained even for extremely low-scale usage of Google TPUv2 units (8 cores only) in comparison to the quite powerful GPU NVIDIA Tesla K80 card with the speedup up to 10x for training stage (without taking into account the overheads) and speedup up to 2x for prediction stage (with and without taking into account overheads). The precise speedup values depend on the utilization level of TPUv2 units and increase with the increase of the data volume under processing, but for the datasets used in this work (MNIST and Fashion-MNIST with images of sizes 28 × 28) the speedup was observed for batch sizes >512 images for training phase and >40 000 images for prediction phase. It should be noted that these results were obtained without detriment to the prediction accuracy and loss that were equal for both GPU and TPU runs up to the 3rd significant digit for MNIST dataset, and up to the 2nd significant digit for Fashion-MNIST dataset.
An automatically created novel bug dataset and its validation in bug prediction

Bugs are inescapable during software development due to frequent code changes, tight deadlines, etc.; therefore, it is important to have tools to find these errors. One way of performing bug identification is to analyze the characteristics of buggy source code elements from the past and predict the present ones based on the same characteristics, using e.g. machine learning models. To support model building tasks, code elements and their characteristics are collected in so-called bug datasets which serve as the input for learning. We present the BugHunter Dataset: a novel kind of automatically constructed and freely available bug dataset containing code elements (files, classes, methods) with a wide set of code metrics and bug information. Other available bug datasets follow the traditional approach of gathering the characteristics of all source code elements (buggy and non-buggy) at only one or more pre-selected release versions of the code. Our approach, on the other hand, captures the buggy and the fixed states of the same source code elements from the narrowest timeframe we can identify for a bug’s presence, regardless of release versions. To show the usefulness of the new dataset, we built and evaluated bug prediction models and achieved F-measure values over 0.74.
Blank Language Models

We propose Blank Language Model (BLM), a model that generates sequences by dynamically creating and filling in blanks.The blanks control which part of the sequence to expand, making BLM ideal for a variety of text editing and rewriting tasks.The model can start from a single blank or partially completed text with blanks at specified locations.It iteratively determines which word to place in a blank and whether to insert new blanks, and stops generating when no blanks are left to fill.BLM can be efficiently trained using a lower bound of the marginal data likelihood.On the task of filling missing text snippets, BLM significantly outperforms all other baselines in terms of both accuracy and fluency.Experiments on style transfer and damaged ancient text restoration demonstrate the potential of this framework for a wide range of applications. 1
Programmable Superconducting Processor with Native Three-Qubit Gates

Higher-dimensional gates involving more than two qubits could play a major role in boosting the performance of quantum processors. While native two-qubit gates are ubiquitous on the superconducting-qubit platform, realizing high-fidelity three-qubit gates is challenging and typically requires multiple two-qubit gates, which leads to error accumulation. The authors utilize a multimodal ``trimon'' circuit to realize $n\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}v\phantom{\rule{0}{0ex}}e$ three-qubit gates that enable efficient implementation of important quantum algorithms, such as Grover's search. These results point to improved processor performance when the trimon is used as a building block for larger systems.
Experimental quantum entanglement and teleportation by tuning remote spatial indistinguishability of independent photons

Remote spatial indistinguishability of identical subsystems as a direct controllable quantum resource at distant sites has not been yet experimentally proven. We design a setup capable to tune the spatial indistinguishability of two photons by independently adjusting their spatial distribution in two distant regions, which leads to polarization entanglement starting from uncorrelated photons. The amount of entanglement uniquely depends on the degree of remote spatial indistinguishability, quantified by an entropic measure $\mathcal{I}$, which enables teleportation with fidelities above the classical threshold. This experiment realizes a basic nonlocal entangling gate by the inherent nature of identical quantum subsystems.
Attention-Based Fully Gated CNN-BGRU for Russian Handwritten Text

This article considers the task of handwritten text recognition using attention-based encoder–decoder networks trained in the Kazakh and Russian languages. We have developed a novel deep neural network model based on a fully gated CNN, supported by multiple bidirectional gated recurrent unit (BGRU) and attention mechanisms to manipulate sophisticated features that achieve 0.045 Character Error Rate (CER), 0.192 Word Error Rate (WER), and 0.253 Sequence Error Rate (SER) for the first test dataset and 0.064 CER, 0.24 WER and 0.361 SER for the second test dataset. Our proposed model is the first work to handle handwriting recognition models in Kazakh and Russian languages. Our results confirm the importance of our proposed Attention-Gated-CNN-BGRU approach for training handwriting text recognition and indicate that it can lead to statistically significant improvements (p-value &lt; 0.05) in the sensitivity (recall) over the tests dataset. The proposed method’s performance was evaluated using handwritten text databases of three languages: English, Russian, and Kazakh. It demonstrates better results on the Handwritten Kazakh and Russian (HKR) dataset than the other well-known models.
MANGO: A Mask Attention Guided One-Stage Scene Text Spotter

Recently end-to-end scene text spotting has become a popular research topic due to its advantages of global optimization and high maintainability in real applications. Most methods attempt to develop various region of interest (RoI) operations to concatenate the detection part and the sequence recognition part into a two-stage text spotting framework. However, in such framework, the recognition part is highly sensitive to the detected results (e.g., the compactness of text contours). To address this problem, in this paper, we propose a novel Mask AttentioN Guided One-stage text spotting framework named MANGO, in which character sequences can be directly recognized without RoI operation. Concretely, a position-aware mask attention module is developed to generate attention weights on each text instance and its characters. It allows different text instances in an image to be allocated on different feature map channels which are further grouped as a batch of instance features. Finally, a lightweight sequence decoder is applied to generate the character sequences. It is worth noting that MANGO inherently adapts to arbitrary-shaped text spotting and can be trained end-to-end with only coarse position information (e.g., rectangular bounding box) and text annotations. Experimental results show that the proposed method achieves competitive and even new state-of-the-art performance on both regular and irregular text spotting benchmarks, i.e., ICDAR 2013, ICDAR 2015, Total-Text, and SCUT-CTW1500.
Wireless Communications and Mobile Computing Blockchain-Based Trust Management in Distributed Internet of Things

The development of Internet of Things (IoT) and Mobile Edge Computing (MEC) has led to close cooperation between electronic devices. It requires strong reliability and trustworthiness of the devices involved in the communication. However, current trust mechanisms have the following issues: (1) heavily relying on a trusted third party, which may incur severe security issues if it is corrupted, and (2) malicious evaluations on the involved devices which may bias the trustrank of the devices. By introducing the concepts of risk management and blockchain into the trust mechanism, we here propose a blockchain-based trust mechanism for distributed IoT devices in this paper. In the proposed trust mechanism, trustrank is quantified by normative trust and risk measures, and a new storage structure is designed for the domain administration manager to identify and delete the malicious evaluations of the devices. Evidence shows that the proposed trust mechanism can ensure data sharing and integrity, in addition to its resistance against malicious attacks to the IoT devices.
Construction of Double-Precision Wisdom Teaching Framework Based on Blockchain Technology in Cloud Platform

Based on the cloud platform, the concept and importance of double-precision teaching in wisdom teaching is analyzed, and the basic framework of online and offline wisdom teaching based on blockchain technology is constructed in this paper.Based on the characteristics of blockchain such as security and decentralization, an accurate learning resource teaching model based on knowledge representation of learners and teaching resources is proposed.A double-precision collaborative strategy of accurate perception of wisdom teaching demand and accurate supply of resources is proposed.The framework of wisdom teaching is constructed, in which the accurate perception of wisdom teaching demand and the accurate supply of resources based on big data technology is analyzed.Based on blockchain technology, tracking the learning records of learners at each stage of learning is helpful.It effectively promotes the distribution and sharing of learning resources, efficiently bridges teaching resources and learning requirements, and provides more accurate, personalized learning support, and services for learners.It provides a secure data access strategy for both teachers and learners.The precise wisdom teaching framework based on blockchain technology has far-reaching practical value for the efficient development of online and offline teaching.