Reviewerly/exHarmony huggingface dataset:
Unified generation and fast emission of arbitrary single-photon multimode <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>W</mml:mi></mml:math> states

We propose a unified and deterministic scheme to generate arbitrary single-photon multimode $W$ states in circuit QED. A three-level system (qutrit) is driven by a pump-laser pulse and coupled to $N$ spatially separated resonators. The coupling strength for each spatial mode ${g}_{i}$ totally decide the generated single-photon $N$-mode $W$ state $|{W}_{N}\ensuremath{\rangle}=\frac{1}{A}{\ensuremath{\sum}}_{i=1}^{N}{g}_{i}|{0}_{1}{0}_{2}\ensuremath{\cdots}{1}_{i}{0}_{i+1}\ensuremath{\cdots}{0}_{N}\ensuremath{\rangle}$, so arbitrary $|{W}_{N}\ensuremath{\rangle}$ can be generated inside resonators by adjusting ${g}_{i}$ and $N$. Moreover, such $W$ states can be fast emitted in symmetric temporal waveform with a probability reaching $98.9%$. The generation (or emission) time and fidelity can both be the same for arbitrary $|{W}_{N}\ensuremath{\rangle}$, independent of mode numbers. Finally, the time evolution process is easy to control since only the pump pulse is time-dependent.
Echo State Networks for Estimating Exteroceptive Conditions From Proprioceptive States in Quadruped Robots

We propose a methodology based on reservoir computing for mapping local proprioceptive information acquired at the level of the leg joints of a simulated quadruped robot into exteroceptive and global information, including both the ground reaction forces at the level of the different legs and information about the type of terrain traversed by the robot. Both dynamic estimation and terrain classification can be achieved concurrently with the same reservoir computing structure, which serves as a soft sensor device. Simulation results are presented together with preliminary experiments on a real quadruped robot. They demonstrate the suitability of the proposed approach for various terrains and sensory system fault conditions. The strategy, which belongs to the class of data-driven models, is independent of the robotic mechanical design and can easily be generalized to different robotic structures.
Link Prediction of Weighted Triples for Knowledge Graph Completion Within the Scholarly Domain

Knowledge graphs (KGs) are widely used for modeling scholarly communication, performing scientometric analyses, and supporting a variety of intelligent services to explore the literature and predict research dynamics. However, they often suffer from incompleteness (e.g., missing affiliations, references, research topics), leading to a reduced scope and quality of the resulting analyses. This issue is usually tackled by computing knowledge graph embeddings (KGEs) and applying link prediction techniques. However, only a few KGE models are capable of taking weights of facts in the knowledge graph into account. Such weights can have different meanings, e.g. describe the degree of association or the degree of truth of a certain triple. In this paper, we propose the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Weighted Triple Loss</i> , a new loss function for KGE models that takes full advantage of the additional numerical weights on facts and it is even tolerant to incorrect weights. We also extend the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rule Loss</i> , a loss function that is able to exploit a set of logical rules, in order to work with weighted triples. The evaluation of our solutions on several knowledge graphs indicates significant performance improvements with respect to the state of the art. Our main use case is the large-scale AIDA knowledge graph, which describes 21 million research articles. Our approach enables to complete information about affiliation types, countries, and research topics, greatly improving the scope of the resulting scientometrics analyses and providing better support to systems for monitoring and predicting research dynamics.
3D human pose estimation in motion based on multi-stage regression

• The multi-stage regression network is used to solve 3D human pose estimation. • The algorithm uses the heatmap to pay more attention to effective information. • In this paper, 2D to 3D mapping network is used to locate 3D human joint points. • This paper presents an optimization scheme for pose estimation in the next stage. 3D human pose estimation in motion is a hot research direction in the field of computer vision. However, the performance of the algorithm is affected by the complexity of 3D spatial information, self-occlusion of human body, mapping uncertainty and other problems. In this paper, we propose a 3D human joint localization method based on multi-stage regression depth network and 2D to 3D point mapping algorithm. First of all, we use a single RGB image as the input, through the introduction of heatmap and multi-stage regression to constantly optimize the coordinates of human joint points. Then we input the 2D joint points into the mapping network for calculation, and get the coordinates of 3D human body joint points, and then to complete the 3D human body pose estimation task. The MPJPE of the algorithm in Human3.6 M dataset is 40.7. The evaluation of dataset shows that our method has obvious advantages.
N-Singer: A Non-Autoregressive Korean Singing Voice Synthesis System for Pronunciation Enhancement

Recently, end-to-end Korean singing voice systems have been designed to generate realistic singing voices. However, these systems still suffer from a lack of robustness in terms of pronunciation accuracy. In this paper, we propose N-Singer, a non-autoregressive Korean singing voice system, to synthesize accurate and pronounced Korean singing voices in parallel. N-Singer consists of a Transformer-based mel-generator, a convolutional network-based postnet, and voicing-aware discriminators. It can contribute in the following ways. First, for accurate pronunciation, N-Singer separately models linguistic and pitch information without other acoustic features. Second, to achieve improved mel-spectrograms, N-Singer uses a combination of Transformer-based modules and convolutional network-based modules. Third, in adversarial training, voicing-aware conditional discriminators are used to capture the harmonic features of voiced segments and noise components of unvoiced segments. The experimental results prove that N-Singer can synthesize a natural singing voice in parallel with a more accurate pronunciation than the baseline model.
Survey: Vulnerability Analysis of Low-Cost ECC-Based RFID Protocols against Wireless and Side-Channel Attacks

The radio frequency identification (RFID) system is one of the most important technologies of the Internet of Things (IoT) that tracks single or multiple objects. This technology is extensively used and attracts the attention of many researchers in various fields, including healthcare, supply chains, logistics, asset tracking, and so on. To reach the required security and confidentiality requirements for data transfer, elliptic curve cryptography (ECC) is a powerful solution, which ensures a tag/reader mutual authentication and guarantees data integrity. In this paper, we first review the most relevant ECC-based RFID authentication protocols, focusing on their security analysis and operational performances. We compare the various lightweight ECC primitive implementations designed for RFID applications in terms of occupied area and power consumption. Then, we highlight the security threats that can be encountered considering both network attacks and side-channel attacks and analyze the security effectiveness of RFID authentication protocols against such types of attacks. For this purpose, we classify the different threats that can target an ECC-based RFID system. After that, we present the most promising ECC-based protocols released during 2014–2021 by underlining their advantages and disadvantages. Finally, we perform a comparative study between the different protocols mentioned regarding network and side-channel attacks, as well as their implementation costs to find the optimal one to use in future works.
Implementation of Cryptographic Algorithm in Dynamic QR Code Payment System and Its Performance

QR code payment plays an indispensable role in the mobile payment market, and the security of scanning codes has always been a problem in the field of information security.Static QR codes are easily copied and replaced, and there are huge security loopholes.The QR code payment in a closed system still faces security challenges.In order to solve the security problem of QR code payment, we have studied dynamic QR code payment system that supports SM2, SM3, and SM4 cryptographic algorithms, which can realize QR code scanning and scanned transactions, UnionPay cloud QuickPass transactions, etc., and generate dynamic QR code information in real time during the transaction process, one order and one code.Through dynamic algorithm distribution, the randomness and uniqueness of QR code generation are guaranteed, and it is suitable for multi-scene application transactions.The algorithm correctness test result shows that the system has achieved the expected effect.The performance test results show that the hardware of the security module implements the algorithm flow and improves the payment performance.Compared with some other algorithms, the processing time is shorter, the running speed is faster, and the system is more secure.
Digital validation of breast biomarkers (ER, PR, AR, and HER2) in cytology specimens using three different scanners

Progression in digital pathology has yielded new opportunities for a remote work environment. We evaluated the utility of digital review of breast cancer immunohistochemical prognostic markers (IHC) using whole slide images (WSI) from formalin fixed paraffin embedded (FFPE) cytology cell block specimens (CB) using three different scanners.CB from 20 patients with breast cancer diagnosis and available IHC were included. Glass slides including 20 Hematoxylin and eosin (H&E), 20 Estrogen Receptor (ER), 20 Progesterone Receptor (PR), 16 Androgen Receptor (AR), and 20 Human Epidermal Growth Factor Receptor 2 (HER2) were scanned on 3 different scanners. Four breast pathologists reviewed the WSI and recorded their semi-quantitative scoring for each marker. Kappa concordance was defined as complete agreement between glass/digital pairs. Discordances between microscopic and digital reads were classified as a major when a clinically relevant change was seen. Minor discordances were defined as differences in scoring percentages/staining pattern that would not have resulted in a clinical implication. Scanner precision was tabulated according to the success rate of each scan on all three scanners.In total, we had 228 paired glass/digital IHC reads on all 3 scanners. There was strong concordance kappa ≥0.85 for all pathologists when comparing paired microscopic/digital reads. Strong concordance (kappa ≥0.86) was also seen when comparing reads between scanners.Twenty-three percent of the WSI required rescanning due to barcode detection failures, 14% due to tissue detection failures, and 2% due to focus issues. Scanner 1 had the best average precision of 92%. HER2 IHC had the lowest intra-scanner precision (64%) among all stains.This study is the first to address the utility of WSI in breast cancer IHC in CB and to validate its reporting using 3 different scanners. Digital images are reliable for breast IHC assessment in CB and offer similar reproducibility to microscope reads.
RockNER: A Simple Method to Create Adversarial Examples for Evaluating the Robustness of Named Entity Recognition Models

To audit the robustness of named entity recognition (NER) models, we propose RockNER, a simple yet effective method to create natural adversarial examples.Specifically, at the entity level, we replace target entities with other entities of the same semantic class in Wikidata; at the context level, we use pre-trained language models (e.g., BERT) to generate word substitutions.Together, the two levels of attack produce natural adversarial examples that result in a shifted distribution from the training data on which our target models have been trained.We apply the proposed method to the OntoNotes dataset and create a new benchmark named OntoRock for evaluating the robustness of existing NER models via a systematic evaluation protocol.Our experiments and analysis reveal that even the best model has a significant performance drop, and these models seem to memorize in-domain entity patterns instead of reasoning from the context.Our work also studies the effects of a few simple data augmentation methods to improve the robustness of NER models. 1
Comparing Human Activity Recognition Models Based on Complexity and Resource Usage

Human Activity Recognition (HAR) is a field with many contrasting application domains, from medical applications to ambient assisted living and sports applications. With ever-changing use cases and devices also comes a need for newer and better HAR approaches. Machine learning has long been one of the predominant techniques to recognize activities from extracted features. With the advent of deep learning techniques that push state of the art results in many different domains like natural language processing or computer vision, researchers have also started to build deep neural nets for HAR. With this increase in complexity, there also comes a necessity to compare the newer approaches to the previous state of the art algorithms. Not everything that is new is also better. Therefore, this paper aims to compare typical machine learning models like a Random Forest (RF) or a Support Vector Machine (SVM) to two commonly used deep neural net architectures, Convolutional Neural Nets (CNNs) and Recurrent Neural Nets (RNNs). Not only in regards to performance but also in regards to the complexity of the models. We measure complexity as the memory consumption, the mean prediction time and the number of trainable parameters of the models. To achieve comparable results, the models are all tested on the same publicly available dataset, the UCI HAR Smartphone dataset. With this combination of prediction performance and model complexity, we look for the models achieving the best possible performance/complexity tradeoff and therefore being the most favourable to be used in an application. According to our findings, the best model for a strictly memory limited use case is the Random Forest with an F1-Score of 88.34%, memory consumption of only 0.1 MB and mean prediction time of 0.22 ms. The overall best model in terms of complexity and performance is the SVM with a linear kernel with an F1-Score of 95.62%, memory consumption of 2 MB and a mean prediction time of 0.47 ms. The two deep neural nets are on par in terms of performance, but their increased complexity makes them less favourable to be used.
Effect of Visual Extensions on Natural Language Understanding in Vision-and-Language Models

A method for creating a vision-and-language (V&L) model is to extend a language model through structural modifications and V&L pretraining.Such an extension aims to make a V&L model inherit the capability of natural language understanding (NLU) from the original language model.To see how well this is achieved, we propose to evaluate V&L models using an NLU benchmark (GLUE).We compare five V&L models, including singlestream and dual-stream models, trained with the same pre-training.Dual-stream models, with their higher modality independence achieved by approximately doubling the number of parameters, are expected to preserve the NLU capability better.Our main finding is that the dual-stream scores are not much different than the single-stream scores, contrary to expectation.Further analysis shows that pre-training causes the performance drop in NLU tasks with few exceptions.These results suggest that adopting a single-stream structure and devising the pre-training could be an effective method for improving the maintenance of language knowledge in V&L extensions.
Acceptance of immersive head-mounted display virtual reality in stroke patients

As virtual reality (VR) has become more accessible, it has increasingly come into focus for clinical application. Therapy with VR shows potential as an engaging, effective, and economic way to improve cognitive abilities following a stroke. While application of VR in clinical settings appears promising, its widespread use will crucially depend on acceptance of immersive head mounted display virtual reality (HMD-VR) systems when used in different patient groups. This study aimed to investigate acceptance of HMD-VR in stroke patients compared to a control group of healthy age-matched adults. The attitude towards HMD-VR, as one of the most important predictors of technology acceptance within the technology acceptance model framework, was assessed in 20 stroke patients and 20 age-matched healthy adults. Further HMD-VR acceptance related measures concerned self-reported user experience, computer self-efficacy and cybersickness (see also Huygelier et al., 2019). Additionally, performance measures for memory span and speed were recorded in two VR-tasks. Both groups showed positive attitude above the neutral point of the scale and reported positive user experience in the VR-setting. Self-reported cybersickness was at modest levels and comparable in both groups. Controls had higher and more homogeneous scores in user experience, and performed notably faster in the VR-task while there were no significant differences in memory-span. The study results suggest that treatment provided by immersive HMD-VR is tolerated by older adults, including those who have had strokes. This was the case without prior acquaintance with the VR-device or -software, and it was neither hindered by negative attitudes towards VR, nor cybersickness.
Improving Multilingual Translation by Representation and Gradient Regularization

Multilingual Neural Machine Translation (NMT) enables one model to serve all translation directions, including ones that are unseen during training, i.e. zero-shot translation.Despite being theoretically attractive, current models often produce low quality translations -commonly failing to even produce outputs in the right target language.In this work, we observe that off-target translation is dominant even in strong multilingual systems, trained on massive multilingual corpora.To address this issue, we propose a joint approach to regularize NMT models at both representation-level and gradient-level.At the representation level, we leverage an auxiliary target language prediction task to regularize decoder outputs to retain information about the target language.At the gradient level, we leverage a small amount of direct data (in thousands of sentence pairs) to regularize model gradients.Our results demonstrate that our approach is highly effective in both reducing off-target translation occurrences and improving zero-shot translation performance by +5.59 and +10.38 BLEU on WMT and OPUS datasets respectively.Moreover, experiments show that our method also works well when the small amount of direct data is not available.
On the Transferability of Adversarial Attacks against Neural Text Classifier

Deep neural networks are vulnerable to adversarial attacks, where a small perturbation to an input alters the model prediction.In many cases, malicious inputs intentionally crafted for one model can fool another model.In this paper, we present the first study to systematically investigate the transferability of adversarial examples for text classification models and explore how various factors, including network architecture, tokenization scheme, word embedding, and model capacity, affect the transferability of adversarial examples.Based on these studies, we propose a genetic algorithm to find an ensemble of models that can be used to induce adversarial examples to fool almost all existing models.Such adversarial examples reflect the defects of the learning process and the data bias in the training set.Finally, we derive word replacement rules that can be used for model diagnostics from these adversarial examples.
AfroMT: Pretraining Strategies and Reproducible Benchmarks for Translation of 8 African Languages

Reproducible benchmarks are crucial in driving progress of machine translation research.However, existing machine translation benchmarks have been mostly limited to highresource or well-represented languages.Despite an increasing interest in low-resource machine translation, there are no standardized reproducible benchmarks for many African languages, many of which are used by millions of speakers but have less digitized textual data.To tackle these challenges, we propose AFROMT, a standardized, clean, and reproducible machine translation benchmark for eight widely spoken African languages.We also develop a suite of analysis tools for system diagnosis taking into account unique properties of these languages.Furthermore, we explore the newly considered case of low-resource focused pretraining and develop two novel data augmentation-based strategies, leveraging word-level alignment information and pseudo-monolingual data for pretraining multilingual sequence-to-sequence models.We demonstrate significant improvements when pretraining on 11 languages, with gains of up to 2 BLEU points over strong baselines.We also show gains of up to 12 BLEU points over cross-lingual transfer baselines in data-constrained scenarios.All code and pretrained models will be released as further steps towards larger reproducible benchmarks for African languages. 1
The Use of Reading Speed Strategy in Promoting Reading Comprehension among EFL Students with Learning Disabilities

This study explored the effect of developing reading comprehension among English language students with learning disabilities using the reading speed strategy, identifying the effect of both gender, and teaching method on developing reading comprehension. To achieve the aim of the study, a test was prepared to measure reading comprehension after ensuring its validity and reliability. The study population consisted of all (200) students studying English language in the schools of the Directorate of Education for the Second Irbid Region for the second semester of the academic year 2020/2021. The study was applied to a sample of (80) male and female students, who were selected in the simple method. The participants were distributed into two experimental groups, which received the training, and another two control groups, which studied the same educational material in the traditional method. The test was applied to the study groups before starting the implementation of the study to ensure the equality of groups. The results showed clear differences in the mean scores of the experimental group students and those of the control group students, and in favor of the experimental group. The results also showed that there were no differences between the mean scores of the students' scores attributed to gender.