Reviewerly/exHarmony huggingface dataset:
Development of distance learning of English for students of technical specialties based on the use of songs technology

Modern methods of education make greater use of the opportunities provided by information technologies. This, in turn, contributes to the emergence of new forms of organizing the learning process. Distance learning allows you to take into account the needs and characteristics of each student in the learning process. For distance learning of a foreign language, high motivation and hard work of the student are necessary. The use of English-language songs in learning English helps to increase the interest of students in the educational process and increases their motivation. Distance education makes it possible to successfully overcome the limitations in opportunities and time. Learning English is important for the training of modern specialists in the field of metallurgy, The specialist's knowledge of foreign languages allows him to increase the level of his competitiveness in the labor market To improve the effectiveness of distance education, it is advisable to develop and implement new methods of organizing the educational process.
A study of neural artistic style transfer models and architectures for Indian art styles

Neural Style Transfer (NST) has been a widely researched topic as of late enabling new forms of image manipulation. Here we perform an extensive study on NST algorithms and extend the existing methods with custom modifications for application to Indian art styles. In this paper, we aim to provide a comprehensive analysis of various methods ranging from the seminal work of Gatys et al which demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style, to the state of the art image-to-image translation models which use Generative Adversarial Networks (GANs) to learn the mapping between two domain of images. We observe and infer based on the results produced by the models on which one could be a more suitable approach for Indian art styles, especially Tanjore paintings which are unique compared to the Western art styles. We then propose the method which is more suitable for the domain of Indian Art style along with custom architecture which includes an enhancement and evaluation module. We then present evaluation methods, both qualitative and quantitative which includes our proposed metric, to evaluate the results produced by the model.
Name Links

A correspondence between the link in hypertext and the sign (both semiotic and linguistic) is well established. Consisting of source and destination, links parallel the signifier and signified of the semiotic and linguistic sign, as they do wider models and approaches to intertextuality. Deeper investigation of the connection between the sign and the link is, however, a currently rather neglected area for hypertext. Better understanding the complexities of the semiotic sign, however, can be beneficial for the epistemology of hypertext and more generally for understanding the complex meaning the link engenders.
Characterizing polariton states in the nondispersive regime of circuit quantum electrodynamics

A superconducting qubit coupled to a readout resonator is currently the building block of multiple quantum computing as well as quantum optics experiments. A typical qubit-resonator system is coupled in the dispersive regime, where the detuning between qubit and resonator is much greater than the coupling between them. In this work, we fabricated and measured a superconducting transmon-resonator system in the nondispersive regime. The dressed states formed by the mixing of the bare qubit and resonator states can be further mixed by applying a drive on the qubit, leading to the formation of polariton states. We report experimental studies of transitions between polariton states at varying driving powers and frequencies and show how the nondispersive coupling of the higher levels of the qubit-resonator system modifies the polariton eigenstates and the corresponding transition frequencies. We also report close agreement with numerical results obtained from a driven Jaynes-Cummings model beyond the dispersive regime.
ContextBot

Crowd-powered conversational systems (CPCS) solicit the wisdom of crowds to quickly respond to on-demand users' needs. The very factors that make this a viable solution ---such as the availability of diverse crowd workers on-demand--- also lead to great challenges. The ever-changing pool of online workers powering conversations with individual users makes it particularly difficult to generate contextually consistent responses from a single user's standpoint. To tackle this, prior work has employed conversational facts extracted by workers to maintain a global memory, albeit with limited success. Through a controlled experiment, we explored if a conversational agent, dubbed ContextBot, can provide workers with the required context on the fly for successful completion of affective support tasks in CPCS, and explore the impact of ContextBot on the response quality of workers and their interaction experience. To this end, we recruited workers (N=351) from the Prolific crowd-sourcing platform and carried out a 3×3 factorial between-subjects study. Experimental conditions varied based on (i) whether or not context was elicited and informed by motivational interviewing techniques (MI-adherent guidance, general guidance, and no guidance), and (ii) different conversational entry points for workers to produce responses (early, middle, and late). Our findings show that: (a) workers who entered the conversation earliest were more likely to produce highly consistent responses after interacting with ContextBot; (b) showed better user experience after they interacted with ContextBot with a long chat history to surf; (c) produced more professional responses as endorsed by psychologists; (d) and that interacting with ContextBot through task completion did not negatively impact workers' cognitive load. Our findings shed light on the implications of building intelligent interfaces for scaffolding strategies to preserve consistency in dialogue in CPCS.
From Trolling to Cyberbullying

The rise of social media and other web and mobile applications has transformed how people interact, but it has also created new challenges, such as anti-social behavior like trolling, cyberbullying, and hate speech. This behavior can have severe negative consequences for individuals and communities. This tutorial is intended for researchers and practitioners interested in computational social science and provides an overview of how to use machine learning and social network analysis techniques to detect and examine anti-social behavior in online discourse.
Development of a signature verification model based on a small number of samples

Abstract In this study, an improved AlexNet and transfer learning architecture was used to construct a signature recognition model based on a small number of samples to verify offline signatures. To enhance the features of signatures, drawing boards were used to extract the pen pressure and brush stroke of these signatures. The signatures of eight individuals were used to build the samples and test the generalizability of the model for signatures of different individuals. Visualization technology was first used to adjust the architecture, activation function, and normalization of the model, improving the accuracy from 77.50 to 96.87%. Subsequently, transfer learning was used to solve the problem of the small number of samples, and the number of channels of the model was changed to improve its feature extraction. After all samples were prepared, 560 samples of the source database and 80 samples of the target database were used for transfer learning, which further improved the accuracy to 97.28%. To verify whether the model can recognize newly collected signatures, several original signatures, simple forged signatures, and skilled forged signatures were tested, revealing recognition rates of 91.25%, 95.63%, and 85.63%, respectively. In addition, the overall precision, recall, and F1 score of the model were 90.68%, 91.25%, and 90.97%, respectively, confirming an improvement in the feature extraction capability by extracting brush stroke and that transfer learning can be used with a small number of samples to create a model with excellent recognition rates and generalizability.
Progress on a Memory-Compatible MDI-QKD System over Standard Telecom Infrastructure: a First-Generation Quantum Internet Service

We present the design and function of a long-distance, polarization-encoded MDI-QKD system. Our system is designed for online key streaming and compatibility with quantum memories—a key step toward constructing quantum repeater networks.
3-Qubit Gates in a Microwave-controlled Trapped Ion Quantum Computer Using an Always-On Interaction

Direct implementation of N-qubit gates (N ≥ 3) circumvents decomposition of a quantum algorithm into 2-qubit operations, effectively reducing the required depth of quantum circuits. Using all-to-all coupling in a trapped ion quantum computer, we experimentally realize classical Toffoli and quantum perceptron gates.
Implementing a deep neural network using a single nonlinear element and delayed feedback

Optically implemented neural networks promise faster speeds and lower costs. However, large size networks remain challenging. We derive how to emulate a deep neural network with just a single nonlinear element using delayed feedback signals.
Characterizing the spatiotemporal correlations of bright entangled quantum states of light using classical methods

We classically model parametric down-conversion as a difference-frequency generation of the pump and a stochastic vacuum field. We then experimentally characterize the output fields produced in the high-gain regime using only intensity measurements.
Study of the Role of ZOOM Platform in Collaborative Learning from the Perspective of the Kabul University Lecturers

This paper examines the different qualities of the Zoom platform with a focus on its advantages and disadvantages in a collaborative learning style from Kabul University’s lecturers’ perspective. The study explored the role of both humans and technology in shaping the higher education system. The descriptive methodology is used to collect data through a questionnaire with sixteen questions. The statistical population is Kabul University, and the target population is the professors of Journalism, Computer Science, Psychology, Social Sciences and Agriculture faculties who were selected by cluster sampling method. The findings of this research indicate that the majority of the participants of this study believe that the use of Zoom can facilitate the process of collaborative learning and can increase the interaction between students and professors. Participants of this study considered sharing in discussions easily and cheaply as one of the noteworthy advantages of Zoom technology.
Multi-aspect Understanding with Cooperative Graph Attention Networks for Medical Dialogue Information Extraction

Medical dialogue information extraction is an important but challenging task for Electronic Medical Records. Existing medical information extraction methods ignore the crucial information of sentence and multi-level dependency in dialogue, which limits their effectiveness for capturing essential medical information. To address these issues, we present a novel Multi-aspect Understanding with Cooperative Graph Attention Networks for Medical Dialogue Information Extraction to capture multi-aspect sentence information and multi-level dependency information from the dialogue. First, we propose the multi-aspect sentence encoder to capture various features from different perspectives. Second, we propose double graph attention networks to model the dependency features from intra-window and inter-window, respectively. Extensive experiments on a benchmark dataset have well-validated the effectiveness of the proposed method.
Registration‐enhanced multiple instance learning for cervical cancer whole slide image classification

Abstract Histopathological diagnosis is the golden standard for cancer diagnosis and grading. The past decade has witnessed great successes of vision‐based deep learning in computer‐aided cancer diagnosis. However, fully‐supervised methods require collecting large amounts of annotated data, which is a major challenge. In this research, we propose RMIL, a novel registration‐enhanced multiple instance learning pipeline, for cervical cancer biopsy grading of whole slide images as well as patch‐level regions. It requires only slide‐level annotations and a small amount of patch‐level annotations for training. To enhance it, an efficient registration method is implemented to generate more labeled patches with discriminative local features as data augmentations. In addition, attention mechanisms and self‐supervised learning are introduced to enhance feature extraction. Experiments showed that its classification accuracy is increased by 11.11%, compared to the vanilla multiple instance learning methods. The method has great potential for transferring to other scenarios.
Embracing the uncertainty in human–machine collaboration to support clinical decision-making for mental health conditions

Two significant obstacles exist preventing the widespread usage of Deep Learning (DL) models for predicting healthcare outcomes in general and mental health conditions in particular. Firstly, DL models do not quantify the uncertainty in their predictions, so clinicians are unsure of which predictions they can trust. Secondly, DL models do not triage, i.e., separate which cases could be best handled by the human or the model. This paper attempts to address these obstacles using Bayesian Deep Learning (BDL), which extends DL probabilistically and allows us to quantify the model’s uncertainty, which we use to improve human–model collaboration. We implement a range of state-of-the-art DL models for Natural Language Processing and apply a range of BDL methods to these models. Taking a step closer to the real-life scenarios of human–AI collaboration, we propose a Referral Learning methodology for the models that make predictions for certain instances while referring the rest of the instances to a human expert for further assessment. The study demonstrates that models can significantly enhance their performance by seeking human assistance in cases where the model exhibits high uncertainty, which is closely linked to misclassifications. Referral Learning offers two options: (1) supporting humans in cases where the model predicts with certainty, and (2) triaging cases where the model evaluated when it had a better chance of being right than the human by evaluating human disagreement. The latter method combines model uncertainty from BDL and human disagreement from multiple annotations, resulting in improved triaging capabilities.
Contact Tracing App Privacy: What Data is Shared by Non-GAEN Contact Tracing Apps

Abstract We describe the data transmitted to backend servers by the contact tracing apps now deployed in France (TousAntiCovid), Australia (CovidSafe), Singapore (TraceTogether), US/Florida (CombatCovid), Israel (HaMagen), India (Aarogya Setu) with the aim of evaluating the privacy of these contact tracing apps as actually deployed. To the best of our knowledge, the measurements we present are the first public data of this sort for the French, Australian, Israeli, Indian and Florida apps. We find that TousAntiCovid and CovidSafe are generally well-behaved with regard to privacy. TraceTogether and CombatCovid make extensive use of Google Firebase services which means that that there are two main parties involved in handling data transmitted from these apsp, namely Google and the health authority operating the app itself. HaMagen is well-behaved for uninfected users of the app but GPS location data associated with infected is publicly published on the HaMagen server. Aarogya Setu, with &gt;160M users, is found to have a number of serious privacy issues, including silent upload of logged location data.
eAOM: Extended agent‐oriented modeling as an alternative methodology for blockchain enabling application development

Abstract Blockchain applications are gradually receiving wide acceptance as a transformative technology that could potentially bring a reformation in the industry. Blockchain handles sensitive data through cryptography, consensus mechanism, transaction immutability, and peer‐to‐peer network to manage decentralized data features. In addition to ensuring operational efficiency, blockchain development is a complex and challenging process, which warrants further exploration. In an attempt to reduce the complexity of the development, various software methodologies have been introduced to support the development of blockchain applications systematically. It has been found that those methodologies introduced structured processes that are not suitable for software models. Moreover, all models are generic and do not take into blockchain concept as first‐class entities. Hence, there is a gap in extending and transforming the current blockchain modeling and practices when developing a blockchain application. This paper introduces a new insight and innovative methodology for blockchain‐based application development through extended agent‐oriented modeling (eAOM) to discover blockchain opportunities and contribute to the growth of the technology. With eAOM, the blockchain modeling processes start with requirement elicitation, computation independent modeling, early identification of blockchain use case, platform independent modeling, and platform specific model. A walkthrough example of a blockchain application, “win a fortune games” has showcased the feasibility of eAOM in modeling blockchain enabling application development in a systematic manner. A usability analysis is conducted among novice students to test the usability of eAOM versus original AOM in modeling blockchain application development. From the findings, 44% of the students managed to capture more than 80% of blockchain requirements through eAOM. On the other hand, 51% of the students scored between 20% and 60% of the blockchain requirement, and only 5% of the students failed to model blockchain requirements through eAOM. The results reveal the usage of the eAOM to model blockchain‐enabled application comprehensively.
Multi-aspect Graph Contrastive Learning for Review-enhanced Recommendation

Review-based recommender systems explore semantic aspects of users’ preferences by incorporating user-generated reviews into rating-based models. Recent works have demonstrated the potential of review information to improve the recommendation capacity. However, most existing studies rely on optimizing review-based representation learning part, thus failing to explicitly capture the fine-grained semantic aspects, and also ignoring the intrinsic correlation between ratings and reviews. To address these problems, we propose a multi-aspect graph contrastive learning framework, named MAGCL, with three distinctive designs: (i) a multi-aspect representation learning module, which projects semantic relations to different subspaces by decoupling review information, and then obtains high-order decoupled representations in each aspect via graph encoder. (ii) the contrastive learning module performs graph contrastive learning to capture the correlation between rating and review patterns, which utilize unlabeled data to generate self-supervised signals and, in turn, relieve the data sparsity problem of supervision signals. (iii) the multi-task learning module conducts joint training to learn high-order structure-aware yet self-discriminative node representations by combining recommendation task and self-supervised task, which helps alleviate the over-smoothing problem. Extensive experiments are conducted on four real-world review datasets and the results show the superiority of the proposed framework MAGCL compared with several state of the arts. We also provide further analysis on multi-aspect representations and graph contrastive learning to verify the advantage of proposed framework.
GNN-based Advanced Feature Integration for ICS Anomaly Detection

Recent adversaries targeting the Industrial Control Systems (ICSs) have started exploiting their sophisticated inherent contextual semantics such as the data associativity among heterogeneous field devices. In light of the subtlety rendered in these semantics, anomalies triggered by such interactions tend to be extremely covert, hence giving rise to extensive challenges in their detection. Driven by the critical demands of securing ICS processes, a Graph-Neural-Network (GNN) based method is presented to tackle these subtle hostilities by leveraging an ICS’s advanced contextual features refined from a universal perspective, rather than exclusively following GNN’s conventional local aggregation paradigm. Specifically, we design and implement the Graph Sample-and-Integrate Network (GSIN), a general chained framework performing node-level anomaly detection via advanced feature integration, which combines a node’s local awareness with the graph’s prominent global properties extracted via process-oriented pooling. The proposed GSIN is evaluated on multiple well-known datasets with different kinds of integration configurations, and results demonstrate its superiority consistently on not only anomaly detection performance (e.g., F1 score and AUPRC) but also runtime efficiency over recent representative baselines.
Evaluation on Network Social Media Named Entity Recognition Model Based on Active Learning

The medical security privacy and named entity recognition (NER) technology under the blockchain technology has been a hot topic in all walks of life. As a typical representative of medical security risks and NER, the NER model of online social media based on active learning has attracted worldwide attention. NER is an important part of natural language processing. Traditional recognition technology usually requires a lot of external information, and through the manual identification of its features, which costs a lot of time and energy. In order to solve the shortcomings of traditional recognition algorithms and the lack of feature extraction in network media NER, a new active learning model was introduced in this paper. In the information age, people are increasingly demanding for a large amount of text information, and NER technology came into being. Its main function is to accurately identify important information from text and provide useful information for high-level work. The initial design of NER system is mainly based on the recognition of rules, so as to realize the recognition of named entities. However, in a complex network environment, it takes a lot of time and energy to establish rules without conflicts, and it has poor mobility. In recent years, with the continuous development of computer technology, the use of machine learning to actively learn the unknown information in the target area reduces the workload of manual annotation, thus realizing the active learning of large amounts of data. The research showed that the recognition accuracy under the traditional NER was low, and the information processing speed was slow; the accuracy rate of NER based on active learning was as high as 97%, and the speed of information processing had also been greatly improved, which had solved many problems under the traditional mode. User satisfaction could be as high as 95%, which showed that the latter had broad prospects. The progress of the new era cannot be separated from the support of new technologies. The research of this article has important guiding significance for medical security privacy and the application of NER under blockchain technology.