Reviewerly/exHarmony huggingface dataset:
Designing Online Learning Environment: ICT Tools and Teaching Strategies

The authors analyze the capabilities of Information and Communication Technologies (ICT) and e-learning tools applied in the educational process through all levels of Higher Education in Russia: Bachelor’s/Master’s Degree Programmes and Professional Training. The article is based on the data obtained during the study organized at Sevastopol State University, Lomonosov Moscow State University Branch in Sevastopol, V.I. Vernadsky Crimean Federal University, Institute of Foreign Languages (Simferopol) and S.I. Georgievsky Medical Academy in 2019. The participants of the research were undergraduate students, educators and applicants of further Professional Training programmes. The research aimed at the analysis of ICT and e-learning tools used to design online learning environment at university. The study included a survey to identify e-learning tools applied by students and professors in educational process. The authors also specify blended learning peculiarities in Higher Education. The results showed that ICT and e-learning tools are widely used at universities to manage educational process, establish various forms of communication and interaction, to conduct an assessment and evaluate progress, to organize team projects in blended learning. ICT and online educational services are also applied to develop students’ language and professional skills. Keywords: online learning, e-learning tools, blended learning, teaching strategies, autonomy.
Medical Image Captioning Model to Convey More Details: Methodological Comparison of Feature Difference Generation

The steadily increasing number of medical images places a tremendous burden on doctors, who toned to read and write reports.If an image captioning model could generate drafts of the reports from the corresponding images, the workload of doctors would be reduced, thereby saving time and expenses.The aim of this study was to develop a chest x-ray image captioning model that considers the differences between patient images and normal images, and uses hierarchical long short-term memory (LSTM) or a transformer as a decoder to generate reports.We investigated which feature representation method was the most appropriate for capturing the differences.The feature representations differed in terms of whether global average pooling was used for the visual feature vectors and how the feature difference vectors were generated.Experiments were conducted on two datasets using the proposed models and recent captioning models (X-LAN and X-Transformer).BLEU, METEOR, ROUGE-L, and CIDEr were used as evaluation metrics.The best model for most metric scores was the multi-difference non-average-pooling transformer model, which uses the transformer decoder, does not use global average pooling for the visual feature vectors, and applies the element-wise product.The transformer decoder was found to be more suitable than hierarchical LSTM.Furthermore, for models that do not condense features with global average pooling, the element-wise product was observed to be more effective than subtraction in expressing the feature differences.
A Novel Transfer Learning Technique for Detecting Breast Cancer Mammograms Using VGG16 Bottleneck Feature

Breast cancer represents the highest percentage of cancers and the second most common cancer overall that affect women with 87,090 deaths approximately as reported by ICMR, 2018 in India (1). Breast tumours are classified into two types as a) benign, which is not very harmful and would not cause breast cancer and b) malignant, the tumours are extremely dangerous and would form an abnormal cell that may cause cancer. One transfer learning model called VGG16 (Visual Geometry Group (16) has been implemented with a focus on breast cancer classification using mammography images taken from the MIAS dataset. Moreover, at the preprocessing step, all the breast images are enhanced by using CLAHE (Contrast Limited Adaptive Histogram Equalization) technique that specifically maintains adaptive techniques to remove the lines, letters, and other boxes irrelevant to the Breast Image. As the name suggests, this VGG16 has taken 13 convolutional layers and 3 fully connected layers to train the dataset. In the training period, this model could predict whether the breast images contain any type of cancerous cells or not. And finally, the model has successfully implemented and produced Network’s test score of 87.999 percent.
Fast Simulation of Bosonic Qubits via Gaussian Functions in Phase Space

Bosonic qubits are a promising route to building fault-tolerant quantum computers on a variety of physical platforms. Studying the performance of bosonic qubits under realistic gates and measurements is challenging with existing analytical and numerical tools. We present a novel formalism for simulating classes of states that can be represented as linear combinations of Gaussian functions in phase space. This formalism allows us to analyze and simulate a wide class of non-Gaussian states, transformations, and measurements. We demonstrate how useful classes of bosonic qubits—Gottesman-Kitaev-Preskill (GKP), cat, and Fock states—can be simulated using this formalism, opening the door to investigating the behavior of bosonic qubits under Gaussian channels and measurements, non-Gaussian transformations such as those achieved via gate teleportation, and important non-Gaussian measurements such as threshold and photon-number detection. Our formalism enables simulating these situations with levels of accuracy that are not feasible with existing methods. Finally, we use a method informed by our formalism to simulate circuits critical to the study of fault-tolerant quantum computing with bosonic qubits but beyond the reach of existing techniques. Specifically, we examine how finite-energy GKP states transform under realistic qubit phase gates; interface with a continuous-variable cluster state; and transform under non-Clifford t gate teleportation using magic states. We implement our simulation method as a part of the open-source Strawberry Fields python library.11 MoreReceived 31 March 2021Accepted 7 September 2021DOI:https://doi.org/10.1103/PRXQuantum.2.040315Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasOptical quantum information processingQuantum computationQuantum gatesQuantum information architectures & platformsQuantum information processing with continuous variablesQuantum Information
An Improved Dueling Deep Double-Q Network Based on Prioritized Experience Replay for Path Planning of Unmanned Surface Vehicles

Unmanned Surface Vehicle (USV) has a broad application prospect and autonomous path planning as its crucial technology has developed into a hot research direction in the field of USV research. This paper proposes an Improved Dueling Deep Double-Q Network Based on Prioritized Experience Replay (IPD3QN) to address the slow and unstable convergence of traditional Deep Q Network (DQN) algorithms in autonomous path planning of USV. Firstly, we use the deep double Q-Network to decouple the selection and calculation of the target Q value action to eliminate overestimation. The prioritized experience replay method is adopted to extract experience samples from the experience replay unit, increase the utilization rate of actual samples, and accelerate the training speed of the neural network. Then, the neural network is optimized by introducing a dueling network structure. Finally, the soft update method is used to improve the stability of the algorithm, and the dynamic ϵ-greedy method is used to find the optimal strategy. The experiments are first conducted in the Open AI Gym test platform to pre-validate the algorithm for two classical control problems: the Cart pole and Mountain Car problems. The impact of algorithm hyperparameters on the model performance is analyzed in detail. The algorithm is then validated in the Maze environment. The comparative analysis of simulation experiments shows that IPD3QN has a significant improvement in learning performance regarding convergence speed and convergence stability compared with DQN, D3QN, PD2QN, PDQN, PD3QN. Also, USV can plan the optimal path according to the actual navigation environment with the IPD3QN algorithm.
CRFR: Improving Conversational Recommender Systems via Flexible Fragments Reasoning on Knowledge Graphs

Although paths of user interests shift in knowledge graphs (KGs) can benefit conversational recommender systems (CRS), explicit reasoning on KGs has not been well considered in CRS, due to the complex of high-order and incomplete paths.We propose CRFR, which effectively does explicit multi-hop reasoning on KGs with a conversational context-based reinforcement learning model.Considering the incompleteness of KGs, instead of learning single complete reasoning path, CRFR flexibly learns multiple reasoning fragments which are likely contained in the complete paths of interests shift.A fragments-aware unified model is then designed to fuse the fragments information from item-oriented and concept-oriented KGs to enhance the CRS response with entities and words from the fragments.Extensive experiments demonstrate CRFR's SOTA performance on recommendation, conversation and conversation interpretability.
UniKER: A Unified Framework for Combining Embedding and Definite Horn Rule Reasoning for Knowledge Graph Inference

Knowledge graph inference has been studied extensively due to its wide applications.It has been addressed by two lines of research, i.e., the more traditional logical rule reasoning and the more recent knowledge graph embedding (KGE).Several attempts have been made to combine KGE and logical rules for better knowledge graph inference.Unfortunately, they either simply treat logical rules as additional constraints into KGE loss or use probabilistic models to approximate the exact logical inference (i.e., MAX-SAT).Even worse, both approaches need to sample ground rules to tackle the scalability issue, as the total number of ground rules is intractable in practice, making them less effective in handling logical rules.In this paper, we propose a novel framework UniKER to address these challenges by restricting logical rules to be definite Horn rules, which can fully exploit the knowledge in logical rules and enable the mutual enhancement of logical rule-based reasoning and KGE in an extremely efficient way.Extensive experiments have demonstrated that our approach is superior to existing state-of-the-art algorithms in terms of both efficiency and effectiveness.
Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation

Graph-structured datasets usually have irregular graph sizes and connectivities, rendering the use of recent data augmentation techniques, such as Mixup, difficult. To tackle this challenge, we present the first Mixup-like graph augmentation method called Graph Transplant, which mixes irregular graphs in data space. To be well defined on various scales of the graph, our method identifies the sub-structure as a mix unit that can preserve the local information. Since the mixup-based methods without special consideration of the context are prone to generate noisy samples, our method explicitly employs the node saliency information to select meaningful subgraphs and adaptively determine the labels. We extensively validate our method with diverse GNN architectures on multiple graph classification benchmark datasets from a wide range of graph domains of different sizes. Experimental results show the consistent superiority of our method over other basic data augmentation baselines. We also demonstrate that Graph Transplant enhances the performance in terms of robustness and model calibration.
Simple Conversational Data Augmentation for Semi-supervised Abstractive Dialogue Summarization

Abstractive conversation summarization has received growing attention while most current state-of-the-art summarization models heavily rely on human-annotated summaries.To reduce the dependence on labeled summaries, in this work, we present a simple yet effective set of Conversational Data Augmentation (CODA) methods for semisupervised abstractive conversation summarization, such as random swapping/deletion to perturb the discourse relations inside conversations, dialogue-acts-guided insertion to interrupt the development of conversations, and conditional-generation-based substitution to substitute utterances with their paraphrases generated based on the conversation context.To further utilize unlabeled conversations, we combine CODA with two-stage noisy selftraining where we first pre-train the summarization model on unlabeled conversations with pseudo summaries and then fine-tune it on labeled conversations.Experiments conducted on the recent conversation summarization datasets demonstrate the effectiveness of our methods over several state-of-the-art data augmentation baselines.We have publicly
BabyBERTa: Learning More Grammar With Small-Scale Child-Directed Language

Transformer-based language models have taken the NLP world by storm.However, their potential for addressing important questions in language acquisition research has been largely ignored.In this work, we examined the grammatical knowledge of RoBERTa (Liu et al., 2019) when trained on a 5M word corpus of language acquisition data to simulate the input available to children between the ages 1 and 6.Using the behavioral probing paradigm, we found that a smaller version of RoBERTa-base that never predicts unmasked tokens, which we term BabyBERTa, acquires grammatical knowledge comparable to that of pre-trained RoBERTa-base -and does so with approximately 15X fewer parameters and 6,000X fewer words.We discuss implications for building more efficient models and the learnability of grammar from input available to children.Lastly, to support research on this front, we release our novel grammar test suite that is compatible with the small vocabulary of child-directed input.
Visual programming for the greation of digital shadow play performance using mobile devices in times of Covid-19

The design of a guided research project using an educational scenario based on a visual programming environment using smart mobile devices (smartphone, tablet) aimed at creating a digital shadow play performance. It was implemented in a classroom environment as part of the festive activities of the 4th Primary School of Nea Ionia Volos “Panagiotis Katsirelos” to celebrate the 200 years since the Greek Revolution. Moreover, the digital work that emerged was presented at the 10th Digital Creation Festival (Volos, Magnesia junction). The guided research project was approached interdisciplinary with the collaboration of teachers of Informatics, Theatrical Education, and general education teachers. The educational scenario was addressed to the 6th-grade primary school students. The students were already familiar with internet applications, ICT tools, and Scratch, a visual programming environment. The students used three smart mobile devices to create the final digital artifact with Scratch’s visual programming environment due to special learning conditions caused by the Covid-19 pandemic.
WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER

Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP.In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task.We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER.We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements of up to 6 span-based F 1 -score points over previous state-of-the-art systems for data creation.
A deep learning method for counting white blood cells in bone marrow images

Differentiating and counting various types of white blood cells (WBC) in bone marrow smears allows the detection of infection, anemia, and leukemia or analysis of a process of treatment. However, manually locating, identifying, and counting the different classes of WBC is time-consuming and fatiguing. Classification and counting accuracy depends on the capability and experience of operators.This paper uses a deep learning method to count cells in color bone marrow microscopic images automatically. The proposed method uses a Faster RCNN and a Feature Pyramid Network to construct a system that deals with various illumination levels and accounts for color components' stability. The dataset of The Second Affiliated Hospital of Zhejiang University is used to train and test.The experiments test the effectiveness of the proposed white blood cell classification system using a total of 609 white blood cell images with a resolution of 2560 × 1920. The highest overall correct recognition rate could reach 98.8% accuracy. The experimental results show that the proposed system is comparable to some state-of-art systems. A user interface allows pathologists to operate the system easily.
ASL Trigger Recognition in Mixed Activity/Signing Sequences for RF Sensor-Based User Interfaces

The past decade has seen great advancements in speech recognition for control of interactive devices, personal assistants, and computer interfaces. However, deaf and hard-of-hearing (HoH) individuals, whose primary mode of communication is sign language, cannot use voice-controlled interfaces. Although there has been significant work in video-based sign language recognition, video is not effective in the dark and has raised privacy concerns in the deaf community when used in the context of human ambient intelligence. RF sensors have been recently proposed as a new modality that can be effective under the circumstances where video is not. This article considers the problem of recognizing a trigger sign (wake word) in the context of daily living, where gross motor activities are interwoven with signing sequences. The proposed approach exploits multiple RF data domain representations (time-frequency, range-Doppler, and range-angle) for sequential classification of mixed motion data streams. The recognition accuracy of signs with varying kinematic properties is compared and used to make recommendations on appropriate trigger sign selection for RF-sensor-based user interfaces. The proposed approach achieves a trigger sign detection rate of 98.9% and a classification accuracy of 92% for 15 ASL words and three gross motor activities.
A future triple entry accounting framework using blockchain technology

The study adopts a single case study approach to bring into conversation, ideas, and views of several scholars on triple entry accounting (TEA). The development of blockchain technology already drives the conscious move towards the TEA. The TEA is currently not being used in any significant way but is in a greater debate whether it is worth adopting such alternative accounting practices as the TEA. Shifting to the TEA system is challenging and at present, it is just a fascinating mental exercise. With these backdrops, the present study discusses the likelihood cases of future accounting practice namely: (I) Sophisticated Accounting Software based on the double entry accounting (DEA); (II) Combination of Blockchain and TEA; and (III) Combination of disruptive technologies in addition to Blockchain and TEA. Finally, the study concludes with describing the basic architecture of a potential system of triple-entry accounting that could support the TEA system to deliver real-time insights into business operations. Lastly, the study plotted a hype cycle for accounting technologies to help global organizations identify the relevant accounting technologies and applications.
Proposal for practical multidimensional quantum networks

A quantum internet, i.e., a global interconnection of quantum devices, is the long-term goal of quantum communications and has so far been based on two-dimensional systems (qubits). Recent years have seen a significant development of high-dimensional quantum systems (qudits). While qudits present higher photon information efficiency and robustness to noise, their use in quantum networks presents experimental challenges due to the impractical resources required in high-dimensional quantum repeaters. Here, we show that such challenges can be met via the use of standard quantum optical resources, such as weak coherent states or weak squeezed states, and linear optics. We report a concrete design and simulations of an entanglement swapping scheme for three- and four-dimensional systems, showing how the network parameters can be tuned to optimize secret key rates and analyzing the enhanced noise robustness at different dimensions. Our work significantly simplifies the implementation of high-dimensional quantum networks, fostering their development with current technology.
Interoperable Multi-Blockchain Platform Based on Integrated REST APIs for Reliable Tourism Management

The tourism industry can significantly benefit from the blockchain since its implementation can build trust among stakeholders and improve customer satisfaction. However, most of the existing tourism-specified blockchain platforms are single-chains that provide business support for enterprises without guaranteeing transaction information privacy. Besides, these platforms are specified to a single use case and lack interoperability with other platforms to support heterogenous tourism services. This paper aims to address this issue by introducing a multi-chain architecture that utilizes multiple blockchains to enhance processing capability and provide various business services for the tourism industry. The proposed multi-chain architecture improves the interoperability between the activities in different chains by providing functional requirements in practical applications and supports the inter-ledger application. In addition, the private blockchain will be made available to allow users to access the network through central authorization. It also increases the transaction processing capability by distributing multiple tasks across the chains for large-scale applications. To demonstrate the usability and efficiency of the developed approach, a case study on hotel booking is conducted using the blockchain frameworks Winding Tree and Hyperledger Fabric. A comprehensive evaluation experiment is conducted, and the results show the significance of the proposed system.
Do technological knowledge and game-based learning promote students achievement: lesson from Indonesia

Continuous managing the quality of education in the Covid-19 pandemic has been a unique challenge, and the government has acknowledged to shift from conventional to screen adopting technology. This research attempts to examine the relationship between technological understanding, game-based learning, and students' achievements. This work engaged a quantitative approach with SEM-PLS to gain a deeper understanding of the connectivity among variables. The participants were senior high school students from several places in East Java of Indonesia. The findings indicate that technological knowledge, educational competence, computer skills play an essential role in supporting technology-based learning. However, this study notes that game-based learning in distance learning cannot act as a mediator in enhancing the students’ achievement. This study offers policy-makers the use of game-based learning in the learning process during synchronous learning using technology.