Reviewerly/exHarmony huggingface dataset:
Human Tracking in Top-View Fisheye Images: Analysis of Familiar Similarity Measures via HOG and against Various Color Spaces

The purpose of this paper is to find the best way to track human subjects in fisheye images by considering the most common similarity measures in the function of various color spaces as well as the HOG. To this end, we have relied on videos taken by a fisheye camera wherein multiple human subjects were recorded walking simultaneously, in random directions. Using an existing deep-learning method for the detection of persons in fisheye images, bounding boxes are extracted each containing information related to a single person. Consequently, each bounding box can be described by color features, usually color histograms; with the HOG relying on object shapes and contours. These descriptors do not inform the same features and they need to be evaluated in the context of tracking in top-view fisheye images. With this in perspective, a distance is computed to compare similarities between the detected bounding boxes of two consecutive frames. To do so, we are proposing a rate function (S) in order to compare and evaluate together the six different color spaces and six distances, and with the HOG. This function links inter-distance (i.e., the distance between the images of the same person throughout the frames of the video) with intra-distance (i.e., the distance between images of different people throughout the frames). It enables ascertaining a given feature descriptor (color or HOG) mapped to a corresponding similarity function and hence deciding the most reliable one to compute the similarity or the difference between two segmented persons. All these comparisons lead to some interesting results, as explained in the later part of the article.
Where to Go for the Holidays: Towards Mixed-Type Dialogs for Clarification of User Goals

Most dialog systems posit that users have figured out clear and specific goals before starting an interaction.For example, users have determined the departure, the destination, and the travel time for booking a flight.However, in many scenarios, limited by experience and knowledge, users may know what they need, but still struggle to figure out clear and specific goals by determining all the necessary slots.
Hipertensão arterial sistêmica como fator de risco para a forma grave da covid-19: revisão de escopo

Identify whether systemic arterial hypertension is a risk factor for the severe form of covid-19.This is a scoping review, searches were performed on the Lilacs, PubMed, SciELO and Web of Science databases.Thirteen articles were selected. The studies presented systemic arterial hypertension as the most common chronic disease in subjects diagnosed with covid-19. Hypertensive subjects were older, and men were more likely to develop severe covid-19. Hypertensive subjects without antihypertensive treatment were associated with a higher risk of mortality.subjects with chronic diseases tend to have a different clinical profile. Blood pressure should be controlled in hypertensive subjects that should be continuously monitored during the covid-19 infection.
Distantly Supervised Named Entity Recognition via Confidence-Based Multi-Class Positive and Unlabeled Learning

In this paper, we study the named entity recognition (NER) problem under distant supervision.Due to the incompleteness of the external dictionaries and/or knowledge bases, such distantly annotated training data usually suffer from a high false negative rate.To this end, we formulate the Distantly Supervised NER (DS-NER) problem via Multi-class Positive and Unlabeled (MPU) learning and propose a theoretically and practically novel CONFidence-based MPU (Conf-MPU) approach.To handle the incomplete annotations, Conf-MPU consists of two steps.First, a confidence score is estimated for each token of being an entity token.Then, the proposed Conf-MPU risk estimation is applied to train a multi-class classifier for the NER task.Thorough experiments on two benchmark datasets labeled by various external knowledge demonstrate the superiority of the proposed Conf-MPU over existing DS-NER methods.Our code is available at Github 1 .
Improving Cybersecurity Awareness Among SMEs in the Manufacturing Industry

Small and medium-sized (SME) manufacturing enterprises have been described as a sector that traditionally has not been data-intensive, with low spending on IT and cybersecurity and employees with low cybersecurity awareness. SMEs have also been described as agile and under pressure to adopt new technology and embrace digitalization to gain a competitive advantage. Entering this data-intensive world also comes with new risks, making them extra vulnerable. Not much attention has been directed at how SMEs in the manufacturing sector are working with improving employees’ cybersecurity awareness. Especially not where cybersecurity training programs are in focus. To investigate these aspects, we opted for a set of five SMEs in the manufacturing industry where it was possible to perform in-depth semi-structured interviews with chief information security officers’ (CISO) and employees. The results show several interesting results, for example, regarding the view on contextualization of training material and the relevance of microlearning. The study also presents several practical implications, including recommendations for improving cybersecurity training measures for SMEs in the manufacturing sector.
OBTAINING PREFERENCES FROM A HYBRID LEARNING SYSTEM TO PROMOTE ENGLISH-SPEAKING ABILITY THROUGH FOCUS GROUP DISCUSSION

In the new normal era, the learning system has changed from online learning to hybrid learning. This learning system is widely applied in many universities. Thus surfacing students and lecturers’ voices is of absolute importance in obtaining preferences for best practices in English language teaching. In the EFL context, speaking fluency is one of the elements of speaking that have many difficulties to be mastered by students. This study was conducted to derive suggestions and preferences for best practices in hybrid learning systems for English-speaking ability through Focus Group Discussion (FGD) method. From the FGD, we draw transcripts, back-to-back translations, field notes and activity artefacts to demonstrate the process of the discussion. The information gathered was examined using thematic analysis and an interpretative description approach. The participants of this study were Non-English majors and English lecturers. The result revealed that college students faced speaking challenges during hybrid learning to hone their speaking fluency. Emerging themes were identified wherein in a hybrid learning system, the lecturer utilized multifaceted learning, digital resources and communication tools to improve students' speaking fluency. As for the implementation, technical support and mental readiness for hybrid learning are the reasons for the possibility of hybrid learning that affects students' speaking fluency.
Robust Graph Neural Networks via Ensemble Learning

Graph neural networks (GNNs) have demonstrated a remarkable ability in the task of semi-supervised node classification. However, most existing GNNs suffer from the nonrobustness issues, which poses a great challenge for applying GNNs into sensitive scenarios. Some researchers concentrate on constructing an ensemble model to mitigate the nonrobustness issues. Nevertheless, these methods ignore the interaction among base models, leading to similar graph representations. Moreover, due to the deterministic propagation applied in most existing GNNs, each node highly relies on its neighbors, leaving the nodes to be sensitive to perturbations. Therefore, in this paper, we propose a novel framework of graph ensemble learning based on knowledge passing (called GEL) to address the above issues. In order to achieve interaction, we consider the predictions of prior models as knowledge to obtain more reliable predictions. Moreover, we design a multilayer DropNode propagation strategy to reduce each node’s dependence on particular neighbors. This strategy also empowers each node to aggregate information from diverse neighbors, alleviating oversmoothing issues. We conduct experiments on three benchmark datasets, including Cora, Citeseer, and Pubmed. GEL outperforms GCN by more than 5% in terms of accuracy across all three datasets and also performs better than other state-of-the-art baselines. Extensive experimental results also show that the GEL alleviates the nonrobustness and oversmoothing issues.
A novel solar radio spectrogram encryption algorithm based on parameter variable chaotic systems and DNA dynamic encoding

Abstract Considering that chaotic systems are highly sensitive to parameters, we design two new parameter variable chaotic systems by constructing parameter perturbation items. These systems are constructed using the state variables of the Liu chaotic system to perturb the parameters of the Lorenz and Chen chaotic systems and are called the Lorenz-Liu chaotic system (LLCS) and Chen-Liu chaotic system (CLCS), respectively. In particular, the parameter perturbation items constructed in this study are not periodic but rather chaotic signals and change in real time. Compared with the original systems, they exhibit more complex randomness and dynamic behaviors. In the proposed cryptosystem, which considers the concept of Deoxyribonucleic Acid (DNA), the solar radio spectrogram is dynamically encoded through the LLCS, and then, the CLCS is used to scramble and diffuse the decoding matrices. In addition, the algorithm uses the 256-bit Secure Hash Algorithm (SHA-256) to generate the initial keys, which enhances the algorithm’s sensitivity to plaintext. Simulation results and security analysis show that the cryptosystem has a large key space and high key sensitivity, and can resist various attacks, such as differential attacks and chosen-plaintext attacks.
Path-aware multi-hop graph towards improving graph learning

Graph Neural Networks (GNNs) have achieved state-of-the-art performance in graph-related tasks. Most of them pass messages between direct neighbors and the deeper GNNs can theoretically capture the more global neighborhood information. However, they often suffer from over-smoothing problems when the GNNs' depth deepens. To eliminate this limitation, we propose a path-aware multi-hop graph framework with hop-level attention (HAPG), which has a larger receptive field than GNNs with the same depth. HAPG generates some multi-path multi-hop graphs by converting the original graph to achieve message passing between multi-hop neighbors in a single layer. HAPG aggregates one-hop and multi-hop neighbors by stacking the original graph and the generated graphs into GNNs. The node embeddings are obtained by combining the multiple outputs of the GNNs with the help of hop-level attention. Comparative experiments with various existing GNNs are conducted on three benchmark datasets, and results show that the HAPG is an effective way for improving these models. Specifically, for semi-supervised node classification tasks, the proposed HAPG-GAT and HAPG-AGNN have achieved state-of-the-art performance.
A prototype of quantum von Neumann architecture

Abstract A modern computer system, based on the von Neumann architecture, is a complicated system with several interactive modular parts. It requires a thorough understanding of the physics of information storage, processing, protection, readout, etc. Quantum computing, as the most generic usage of quantum information, follows a hybrid architecture so far, namely, quantum algorithms are stored and controlled classically, and mainly the executions of them are quantum, leading to the so-called quantum processing units. Such a quantum–classical hybrid is constrained by its classical ingredients, and cannot reveal the computational power of a fully quantum computer system as conceived from the beginning of the field. Recently, the nature of quantum information has been further recognized, such as the no-programming and no-control theorems, and the unifying understandings of quantum algorithms and computing models. As a result, in this work, we propose a model of a universal quantum computer system, the quantum version of the von Neumann architecture. It uses ebits (i.e. Bell states) as elements of the quantum memory unit, and qubits as elements of the quantum control unit and processing unit. As a digital quantum system, its global configurations can be viewed as tensor-network states. Its universality is proved by the capability to execute quantum algorithms based on a program composition scheme via a universal quantum gate teleportation. It is also protected by the uncertainty principle, the fundamental law of quantum information, making it quantum-secure and distinct from the classical case. In particular, we introduce a few variants of quantum circuits, including the tailed, nested, and topological ones, to characterize the roles of quantum memory and control, which could also be of independent interest in other contexts. In all, our primary study demonstrates the manifold power of quantum information and paves the way for the creation of quantum computer systems in the near future.
Imagination-Augmented Natural Language Understanding

Human brains integrate linguistic and perceptual information simultaneously to understand natural language, and hold the critical ability to render imaginations.Such abilities enable us to construct new abstract concepts or concrete objects, and are essential in involving practical knowledge to solve problems in low-resource scenarios.However, most existing methods for Natural Language Understanding (NLU) are mainly focused on textual signals.They do not simulate human visual imagination ability, which hinders models from inferring and learning efficiently from limited data samples.Therefore, we introduce an Imagination-Augmented Cross-modal Encoder (iACE) to solve natural language understanding tasks from a novel learning perspective-imagination-augmented cross-modal understanding.iACE enables visual imagination with external knowledge transferred from the powerful generative and pretrained vision-and-language models.Extensive experiments on GLUE (Wang et al., 2018) and SWAG (Zellers et al., 2018) show that iACE achieves consistent improvement over visuallysupervised pre-trained models.More importantly, results in extreme and normal few-shot settings validate the effectiveness of iACE in low-resource natural language understanding circumstances. 1
Domain Robust Deep Embedding Learning for Speaker Recognition

This paper presents a domain robust deep embedding learning method for speaker verification (SV) tasks. Most recent methods utilize deep neural networks (DNN) to learn compact and discriminative speaker embeddings from large-scale labeled datasets such as VoxCeleb and the NIST SRE corpus. Despite the success of exiting methods, performance may degrade significantly for new target datasets, mainly due to the distribution discrepancy between training and test domains. Moreover, how corpora are collected, and the languages they contain differ, leading to them spanning multiple, perhaps mismatched, latent domains. To address this, a multi-task end-to-end framework is proposed to learn speaker embeddings from both labeled source and unlabeled target datasets. Motivated by label smoothing, a smoothed knowledge distillation (SKD) based self-supervised learning method is designed to exploit latent structural information from the unlabeled target domain. Furthermore, a domain-aware batch normalization (DABN) module aims to reduce the cross-domain distribution discrepancy, while a domain-agnostic instance normalization (DAIN) module aims to learn features that are robust to within-domain variance. Evaluation on NIST SRE16 demonstrates significant performance gains.
Tunable Single-Photon Scattering of a Giant Λ-type Atom in a SQUID-Chain Waveguide

We study single-photon scattering of an artificial giant Λ-type atom coupled to a superconducting quantum interference device (SQUID) chains waveguide at two points. In this circuit, the single-photon scattering rates are dependent on the atom-waveguide coupling strengths and the phase accumulated between two coupling points. By modulating the flux-dependent phase velocity in the SQUID-chain waveguide, the photon’s traveling time is changed, and the accumulating phase is adjusted simultaneously. We investigate the relationship between the scattering effect and the flux bias, which allows us to explore novel phenomena with the tunable traveling time in both Markovian and non-Markovian regimes, and to design novel quantum technologies such as quantum storage.
Training Naturalized Semantic Parsers with Very Little Data

Semantic parsing is an important NLP problem, particularly for voice assistants such as Alexa and Google Assistant. State-of-the-art (SOTA) semantic parsers are seq2seq architectures based on large language models that have been pretrained on vast amounts of text. To better leverage that pretraining, recent work has explored a reformulation of semantic parsing whereby the output sequences are themselves natural language sentences, but in a controlled fragment of natural language. This approach delivers strong results, particularly for few-shot semantic parsing, which is of key importance in practice and the focus of our paper. We push this line of work forward by introducing an automated methodology that delivers very significant additional improvements by utilizing modest amounts of unannotated data, which is typically easy to obtain. Our method is based on a novel synthesis of four techniques: joint training with auxiliary unsupervised tasks; constrained decoding; self-training; and paraphrasing. We show that this method delivers new SOTA few-shot performance on the Overnight dataset, particularly in very low-resource settings, and very compelling few-shot results on a new semantic parsing dataset.
Poisoning Deep Learning Based Recommender Model in Federated Learning Scenarios

Various attack methods against recommender systems have been proposed in the past years, and the security issues of recommender systems have drawn considerable attention. Traditional attacks attempt to make target items recommended to as many users as possible by poisoning the training data. Benifiting from the feature of protecting users' private data, federated recommendation can effectively defend such attacks. Therefore, quite a few works have devoted themselves to developing federated recommender systems. For proving current federated recommendation is still vulnerable, in this work we probe to design attack approaches targeting deep learning based recommender models in federated learning scenarios. Specifically, our attacks generate poisoned gradients for manipulated malicious users to upload based on two strategies (i.e., random approximation and hard user mining). Extensive experiments show that our well-designed attacks can effectively poison the target models, and the attack effectiveness sets the state-of-the-art.
Application of Information and Communication Technologies in the Study of Natural Disciplines

Socio-cultural reality of the present time is marked by quite significant events. First, the active penetration into society of the latest information and communication technologies (ICT), which arose as a result of the rapid development of electronics. And secondly, the formation and spread of a special type of worldview under the general name of "postmodernism". It is the need for a philosophical understanding of these two events and determined the main idea of this article. The article also shows the role of information technology in the transformation of the cultural reality of postmodernism. Also the main directions of application of information systems and innovative technologies in the field of education and science in general, and at studying of disciplines of a natural cycle in particular are covered. Peculiarities of studying natural sciences by students are revealed, didactic potential of ICT and its realization in the process of studying natural disciplines is considered, methodical analysis of the problem of application of information technologies in natural education is carried out. The principle of integration, multimedia clarity, interactive interaction of participants of educational process at application of new information technologies at studying of natural disciplines is allocated. It is determined that ICT in the teaching of natural sciences can intensify the educational process, accelerate the transfer of knowledge and experience, as well as improve the quality of teaching and education.
A Robust Contrastive Alignment Method for Multi-Domain Text Classification

Multi-domain text classification can automatically classify texts in various scenarios. Due to the diversity of human languages, texts with the same label in different domains may differ greatly, which brings challenges to the multi-domain text classification. Current advanced methods use the private-shared paradigm, capturing domain-shared features by a shared encoder, and training a private encoder for each domain to extract domain-specific features. However, in realistic scenarios, these methods suffer from inefficiency as new domains are constantly emerging. In this paper, we propose a robust contrastive alignment method to align text classification features of various domains in the same feature space by supervised contrastive learning. By this means, we only need two universal feature extractors to achieve multi-domain text classification. Extensive experimental results show that our method performs on par with or sometimes better than the state-of-the-art method, which uses the complex multi-classifier in a private-shared framework.
Critical Analysis of Hypothesis Tests in Federal Information Processing Standard (140-2)

This work presents an analysis of the existing dependencies between the tests of the FIPS 140-2 battery. Two main analytical approaches are utilized, the first being a study of correlations through the Pearson's correlation coefficient that detects linear dependencies, and the second one being a novel application of the mutual information measure that allows detecting possible non-linear relationships. In order to carry out this study, the FIPS 140-2 battery is reimplemented to allow the user to obtain p-values and statistics that are essential for more rigorous end-user analysis of random number generators (RNG).
What Data Visualization Reveals: Elizabeth Palmer Peabody and the Work of Knowledge Production

This essay offers the chronological charts of Elizabeth Palmer Peabody (1804-1894), the 19th-century educator and intellectual, as early examples of how data visualization can reveal a range of forms of knowledge.It challenges the universality of the goals of clarity and efficiency when designing data visualizations, and argues for the value of visualizations that encourage sustained reflection and imaginative response.Drawing from feminist and Black studies scholarship, it confirms how visual knowledge is informed by the social, cultural, and political contexts that surround it, and how an awareness of those contexts can lead to more intentional and more effective visualization design.It concludes with a call to expand the archive of data visualization so that visualization designers, in the present, might be prompted to imagine a wider and more capacious array of visual and interactive forms.