Reviewerly/exHarmony huggingface dataset:
Towards Table-to-Text Generation with Numerical Reasoning

Recent neural text generation models have shown significant improvement in generating descriptive text from structured data such as table formats.One of the remaining important challenges is generating more analytical descriptions that can be inferred from facts in a data source.The use of a template-based generator and a pointer-generator is among the potential alternatives for table-to-text generators.In this paper, we propose a framework consisting of a pre-trained model and a copy mechanism.The pre-trained models are fine-tuned to produce fluent text that is enriched with numerical reasoning.However, it still lacks fidelity to the table contents.The copy mechanism is incorporated in the fine-tuning step by using general placeholders to avoid producing hallucinated phrases that are not supported by a table while preserving high fluency.In summary, our contributions are (1) a new dataset for numerical table-to-text generation using pairs of a table and a paragraph of a table description with richer inference from scientific papers, and (2) a table-to-text generation framework enriched with numerical reasoning.
Modulation leakage vulnerability in continuous-variable quantum key distribution

Flaws in the process of modulation, or encoding of key bits in the quadratures of the electromagnetic light field, can make continuous-variable quantum key distribution systems susceptible to leakage of secret information.Here, we report such a modulation leakage vulnerability in a system that uses an optical in-phase and quadrature modulator to implement a single sideband encoding scheme.The leakage arises from the limited suppression of a quantuminformation-carrying sideband during modulation.Based on the results from a proof-of-concept experiment, we theoretically analyse the impact of this vulnerability.Our results indicate that the leakage reduces the range over which a positive secret key can be obtained, and can even lead to a security breach if not properly taken into account.We also study the effectiveness of additional trusted noise as a countermeasure to this vulnerability.
Modular distance learning modality: Challenges of teachers in teaching amid the Covid-19 pandemic

This study aimed to identify the challenges of teachers in the use of modular distance learning modality amidst pandemic and how teachers cope with these challenges.This study is a qualitative research which employed the phenomenological research design to determine the challenges encountered by teachers in the use of modular distance learning modality.The study was conducted among teachers in different public secondary schools within Tacloban City.Ten (10) professional public secondary teachers were approached to request their voluntary involvement as key participants through convenience sampling.The personal experiences and coping mechanisms of the teachers were gathered through a survey, particularly by using a semi-structured questionnaire with open-ended questions.Colaizzi's method was used in the interpretation of data.The challenges of teachers were identified based on how they plan, prepare and distribute modules, monitor students' learning, check, evaluate outputs, and provide feedback on students' performance.Furthermore, teachers used various ways to cope with the challenges encountered in modular distance learning modality such as time management, innovating teaching strategies, adapting to the changes brought by the new normal trend in education, being flexible, providing alternative plans, being optimistic, patient, and equipping oneself with the necessary skills for the new normal ways of education.Various stakeholders need to work and plan for alternatives on different issues that may arise as they are involved in the teaching-learning process considering all the limitations in these trying times brought by the pandemic.
P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection From Point Clouds

The most recent 3D object detectors for point clouds rely on the coarse voxel-based representation rather than the accurate point-based representation due to a higher box recall in the voxel-based Region Proposal Network (RPN). However, the detection accuracy is severely restricted by the information loss of pose details in the voxels. Different from considering the point cloud as voxel or point representation only, we propose a point-to-voxel feature learning approach to voxelize the point cloud with both the point-wise semantic and local spatial features, which maintains the voxel-wise features to build the high-recall voxel-based RPN and also provides the accurate point-wise features for refining the detection results. Another difficulty in object detection for point cloud is that the visible part varies a lot against the full view of object because of the perspective issues in data acquisition. To address this, we propose an attentive corner aggregation module to attentively aggregate the features of local point cloud surrounding a 3D proposal from the perspectives of eight corners in the proposal 3D bounding box. The experimental results on the competitive KITTI 3D object detection benchmark show that the proposed method achieves state-of-the-art performance.
Which Factors Promote and Inhibit the Technology Acceptance of Immersive Virtual Reality Technology in Teaching-Learning Contexts? Results of an Expert Survey

Although immersive virtual reality (IVR) is now accessible for large-scale use due to rapid technological developments, there appear to be few organizations in the German-speaking countries that are already actively using this technology on a large scale in education and training. Therefore, little is known about the technology acceptance. Questions arise as to how the technology acceptance can be explained and which technology-specific influencing factors can be identified in the field of training. 15 persons from 13 organizations, who are experienced with use of IVR in teaching-learning contexts such as training, were interviewed in an expert survey to identify promoting and inhibiting aspects of the technology acceptance of IVR in teaching-learning contexts. The results provide information about personal, organizational and technology-related promoting and inhibiting aspects for trainers and training participants. Furthermore, general aspects which are decisive for future use in the companies are derived.
Blockchain Application in Banking System

The 21 st Century is all about technology.People are open to accepting new technologies as the need for modernization is increasing every single day.Blockchain is one of those new and revolutionary technologies that will have a significant impact on the market and industry.In layman's terms, Blockchain is a data structure that stores transactional records while also ensuring security, transparency, and decentralization.There is a digital signature on every transaction on a blockchain, which proves the authenticity of the blockchain.In a Blockchain, data is stored which is tamper-proof and cannot be changed as it uses encryption and digital signatures.To change a record on a blockchain, one needs to change several records, and one needs to change the distributed ledger.That's why it's quite impossible to change the data which has already been entered into a Blockchain.The Blockchain is a technology
The automated writing assistance landscape in 2021

Abstract Automated writing assistance – a category that encompasses a variety of computer-based tools that help with writing – has been around in one form or another for 60 years, although it’s always been a relatively minor part of the NLP landscape. But the category has been given a substantial boost from recent advances in deep learning. We review some history, look at where things stand today, and consider where things might be going.
BDD4BNN: A BDD-Based Quantitative Analysis Framework for Binarized Neural Networks

Abstract Verifying and explaining the behavior of neural networks is becoming increasingly important, especially when they are deployed in safety-critical applications. In this paper, we study verification and interpretability problems for Binarized Neural Networks (BNNs), the 1-bit quantization of general real-numbered neural networks. Our approach is to encode BNNs into Binary Decision Diagrams (BDDs), which is done by exploiting the internal structure of the BNNs. In particular, we translate the input-output relation of blocks in BNNs to cardinality constraints which are in turn encoded by BDDs. Based on the encoding, we develop a quantitative framework for BNNs where precise and comprehensive analysis of BNNs can be performed. We demonstrate the application of our framework by providing quantitative robustness analysis and interpretability for BNNs. We implement a prototype tool and carry out extensive experiments, confirming the effectiveness and efficiency of our approach.
Robust Automatic Modulation Recognition Through Joint Contribution of Hand-Crafted and Contextual Features

Automatic modulation recognition (AMR) has become increasingly important in the field of signal processing, especially with the advancements of intelligent communication systems. Deep Learning (DL) technologies have been incorporated into the AMR field and they have shown outstanding performances against conventional AMR methods. The robustness of DL-based AMR methods under varying noise regimes is one of major concerns for the widespread utilization of this technology. Furthermore, most existing works have neglected the contributions of hand-crafted features (HCFs) in boosting the classification performances of DL-based AMR methods. In order to address the aforementioned technical challenges, a novel and robust DL-AMR method is proposed by leveraging the benefits of both contextual features (CFs) and HCFs for a specific range of signal-to-noise ratio (SNR). A novel feature selection algorithm is also proposed to search for the optimal sets of HCFs in order to reduce the dimensions of feature vectors without losing any important and relevant features. Simulation studies are performed to investigate the feasibility of proposed method in classifying 11 types of modulation schemes. Extensive performance analyses revealed the superiority of proposed method over baseline method in terms of the classification performance as well as the excellent capability of proposed feature selection algorithm in determining an optimal subset of HCFs.
Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective

The pervasiveness of abusive content on the internet can lead to severe psychological and physical harm. Significant effort in Natural Language Processing (NLP) research has been devoted to addressing this problem through abusive content detection and related sub-areas, such as the detection of hate speech, toxicity, cyberbullying, etc. Although current technologies achieve high classification performance in research studies, it has been observed that the real-life application of this technology can cause unintended harms, such as the silencing of under-represented groups. We review a large body of NLP research on automatic abuse detection with a new focus on ethical challenges, organized around eight established ethical principles: privacy, accountability, safety and security, transparency and explainability, fairness and non-discrimination, human control of technology, professional responsibility, and promotion of human values. In many cases, these principles relate not only to situational ethical codes, which may be context-dependent, but are in fact connected to universal human rights, such as the right to privacy, freedom from discrimination, and freedom of expression. We highlight the need to examine the broad social impacts of this technology, and to bring ethical and human rights considerations to every stage of the application life-cycle, from task formulation and dataset design, to model training and evaluation, to application deployment. Guided by these principles, we identify several opportunities for rights-respecting, socio-technical solutions to detect and confront online abuse, including ‘nudging’, ‘quarantining’, value sensitive design, counter-narratives, style transfer, and AI-driven public education applications.evaluation, to application deployment. Guided by these principles, we identify several opportunities for rights-respecting, socio-technical solutions to detect and confront online abuse, including 'nudging', 'quarantining', value sensitive design, counter-narratives, style transfer, and AI-driven public education applications.
An Edge – IoT Framework and Prototype based on Blockchain for Smart Healthcare Applications

The Internet of Things (IoT) and the integration of medical devices perform hand-to-hand solutions and comfort to their users. With the inclusion of IoT under medical devices a hybrid (IoMT) is formulated. This features integrated computation and processing of data via dedicated servers. The IoMT is supported with an edge server to assure the mobility of data and information. The backdrop of IoT is a networking framework and hence, the security of such devices under IoT and IoMT is at risk. In this article, a framework and prototype for secure healthcare application processing via blockchain are proposed. The proposed technique uses an optimized Crow search algorithm for intrusion detection and tampering of data extraction in IoT environment. The technique is processed under deep convolution neural networks for comparative analysis and coordination of data security elements. The technique has successfully extracted the instruction detection from un-peer source with a source validation of 100 IoT nodes under initial intervals of 25 nodes based on block access time, block creation, and IPFS storage layer extraction. The proposed technique has a recorded performance efficiency of 92.3%, comparable to trivial intrusion detection techniques under Deep Neural Networks (DNN) supported algorithms.
Residual dense network for medical magnetic resonance images super-resolution

High-resolution magnetic resonance images (MRI) help experts to localize lesions and diagnose diseases, but it is difficult to obtain high-resolution MRI. Furthermore, image super-resolution technology based on deep learning can effectively improve image resolution. In this work, we propose a medical magnetic resonance (MR) image super-resolution reconstruction method based on residual dense network (MRDN). Firstly, we input the convolutional features of the shallow layer into the residual dense block to obtain global and local features. Secondly, each layer in the residual dense block is directly connected to the previous layer to achieve reuse of features. Finally, we use sub-pixel convolution layer for upsampling and super-resolution reconstruction to get a clear high-resolution image. For the 2 ×, 3 ×, and 4 × enlargement, we propose the MRDN method shows the superiority over the state-of-the-art methods on the Set5, Set14, and Urban100 benchmark datasets, extensive benchmark experiment and analysis show that the superiority of our MRDN algorithm in terms of the peak signal-to-noise ratio (PSNR) and structural similarity index indicators (SSIM). Quantitative experiments are conducted on three public datasets: Set5, Set14 and Urban10, evaluate with commonly used evaluation metrics, and the experimental results show that the method in this paper is more effective. In addition, we reconstruct the public MR datasets, and the reconstructed high-resolution MR image has a clear structure and rich texture details.
Eye tracking based dyslexia detection using a holistic approach

Abstract A new detection method for cognitive impairments is presented utilizing an eye tracking signals in a text reading test. This research enhances published articles that extract combination of various features. It does so by processing entire eye-tracking records either in time or frequency whereas applying only basic signal pre-processing. Such signals were classified as a whole by Convolutional Neural Networks (CNN) that hierarchically extract substantial features scatter either in time or frequency and nonlinearly binds them using machine learning to minimize a detection error. In the experiments we used a 100 fold cross validation and a dataset containing signals of 185 subjects (88 subjects with low risk and 97 subjects with high risk of dyslexia). In a series of experiments it was found that magnitude spectrum based representation of time interpolated eye-tracking signals recorded the best results, i.e. an average accuracy of 96.6% was reached in comparison to 95.6% that is the best published result on the same database. These findings suggest that a holistic approach involving small but complex enough CNNs applied to properly pre-process and expressed signals provides even better results than a combination of meticulously selected well-known features.
A Hybrid Solar Irradiance Nowcasting Approach: Combining All Sky Imager Systems and Persistence Irradiance Models for Increased Accuracy

The share of distributed solar power generation is continuously growing. This increase, combined with the intermittent nature of the solar resource creates new challenges for all relevant stakeholders, from generation to transmission and demand. Insufficient consideration of intra‐minute and intra‐hour variabilities might lead to grid instabilities. Therefore, the relevance of nowcasts (shortest‐term forecasts) is steadily increasing. Nowcasts are suitable for fine‐grained control applications to operate solar power plants in a grid‐friendly way and to secure stable operations of electrical grids. In space and time, highly resolved nowcasts can be obtained by all sky imager (ASI) systems. ASI systems create hemispherical sky images. The associated software analyzes the sky conditions and derives solar irradiance nowcasts. Accuracy is the decisive factor for the effective use of nowcasts. Therefore, the goal of this work is to increase the nowcast accuracy by combining ASI nowcasts and persistence nowcasts, which persist with the prevailing irradiance conditions, while maintaining the spatial coverage and resolution obtained by the ASI system. This hybrid approach combines the strengths while reducing the respective weaknesses of both approaches. Results of a validation show reductions of the root mean square deviation of up to 12% due to the hybrid approach.
Joint Denoising and Demosaicking With Green Channel Prior for Real-World Burst Images

Denoising and demosaicking are essential yet correlated steps to reconstruct a full color image from the raw color filter array (CFA) data. By learning a deep convolutional neural network (CNN), significant progress has been achieved to perform denoising and demosaicking jointly. However, most existing CNN-based joint denoising and demosaicking (JDD) methods work on a single image while assuming additive white Gaussian noise, which limits their performance on real-world applications. In this work, we study the JDD problem for real-world burst images, namely JDD-B. Considering the fact that the green channel has twice the sampling rate and better quality than the red and blue channels in CFA raw data, we propose to use this green channel prior (GCP) to build a GCP-Net for the JDD-B task. In GCP-Net, the GCP features extracted from green channels are utilized to guide the feature extraction and feature upsampling of the whole image. To compensate for the shift between frames, the offset is also estimated from GCP features to reduce the impact of noise. Our GCP-Net can preserve more image structures and details than other JDD methods while removing noise. Experiments on synthetic and real-world noisy images demonstrate the effectiveness of GCP-Net quantitatively and qualitatively.
Demonstration of Shor’s factoring algorithm for N $$=$$ 21 on IBM quantum processors

We report a proof-of-concept demonstration of a quantum order-finding algorithm for factoring the integer 21. Our demonstration involves the use of a compiled version of the quantum phase estimation routine, and builds upon a previous demonstration by Mart\'in-L\'{o}pez et al. in Nature Photonics 6, 773 (2012). We go beyond this work by using a configuration of approximate Toffoli gates with residual phase shifts, which preserves the functional correctness and allows us to achieve a complete factoring of N=21. We implemented the algorithm on IBM quantum processors using only 5 qubits and successfully verified the presence of entanglement between the control and work register qubits, which is a necessary condition for the algorithm's speedup in general. The techniques we employ may be useful in carrying out Shor's algorithm for larger integers, or other algorithms in systems with a limited number of noisy qubits.
Cross-chain deals and adversarial commerce

Abstract Modern distributed data management systems face a new challenge: how can autonomous, mutually distrusting parties cooperate safely and effectively? Addressing this challenge brings up familiar questions from classical distributed systems: how to combine multiple steps into a single atomic action, how to recover from failures, and how to synchronize concurrent access to data. Nevertheless, each of these issues requires rethinking when participants are autonomous and potentially adversarial. We propose the notion of a cross-chain deal , a new way to structure complex distributed computations that manage assets in an adversarial setting. Deals are inspired by classical atomic transactions, but are necessarily different, in important ways, to accommodate the decentralized and untrusting nature of the exchange. We describe novel safety and liveness properties, along with two alternative protocols for implementing cross-chain deals in a system of independent blockchain ledgers. One protocol, based on synchronous communication, is fully decentralized, while the other, based on semi-synchronous communication, requires a globally shared ledger. We also prove that some degree of centralization is required in the semi-synchronous communication model.
Investigating Methods to Improve Language Model Integration for Attention-Based Encoder-Decoder ASR Models

Attention-based encoder-decoder (AED) models learn an implicit internal language model (ILM) from the training transcriptions. The integration with an external LM trained on much more unpaired text usually leads to better performance. A Bayesian interpretation as in the hybrid autoregressive transducer (HAT) suggests dividing by the prior of the discriminative acoustic model, which corresponds to this implicit LM, similarly as in the hybrid hidden Markov model approach. The implicit LM cannot be calculated efficiently in general and it is yet unclear what are the best methods to estimate it. In this work, we compare different approaches from the literature and propose several novel methods to estimate the ILM directly from the AED model. Our proposed methods outperform all previous approaches. We also investigate other methods to suppress the ILM mainly by decreasing the capacity of the AED model, limiting the label context, and also by training the AED model together with a pre-existing LM.