Reviewerly/exHarmony huggingface dataset:
Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks

Semi-supervised node classification on graph-structured data has many applications such as fraud detection, fake account and review detection, user’s private attribute inference in social networks, and community detection. Various methods such as pairwise Markov Random Fields (pMRF) and graph neural networks were developed for semi-supervised node classification. pMRF is more efficient than graph neural networks. However, existing pMRF-based methods are less accurate than graph neural networks, due to a key limitation that they assume a heuristics-based constant edge potential for all edges. In this work, we aim to address the key limitation of existing pMRF-based methods. In particular, we propose to learn edge potentials for pMRF. Our evaluation results on various types of graph datasets show that our optimized pMRF-based method consistently outperforms existing graph neural networks in terms of both accuracy and efficiency. Our results highlight that previous work may have underestimated the power of pMRF for semi-supervised node classification.
Deep Learning Based Object Detection and Recognition of Unmanned Aerial Vehicles

In this study, the methods of deep learning-based detection and recognition of the threats, evaluated in terms of military and defense industry, by unmanned aerial vehicles (UAV) are presented. In the proposed approach, firstly, the training for machine learning on the objects is carried out using convolutional neural networks, which is one of the deep learning algorithms. By choosing the Faster-RCNN and YoloV4 architectures of the deep learning method, it is aimed to compare the achievements of the accuracy in the training process. In order to be used in the training and testing stages of the recommended methods, data sets containing images selected from different weather, land conditions and different time periods of the day are determined. The model for the detection and recognition of the threatening elements is trained, using 2595 images. The method of detecting and recognizing the objects is tested with military operation images and records taken by the UAVs. While an accuracy rate of 93% has been achieved in the Faster-RCNN architecture in object detection and recognition, this rate has been observed as 88% in the YoloV4 architecture.
Comparative analysis of credit card fraud detection in Simulated Annealing trained Artificial Neural Network and Hierarchical Temporal Memory

The problem of misclassification has always been a major concern in detecting online credit card fraud in e-commerce systems. This concern greatly poses a significant challenge to financial institutions and online merchants with regards to financial loss. This paper specifically compares an Artificial Neural Network trained by the Simulated Annealing technique (SA-ANN) with a proposed emerging online learning technology in anomaly detection known as the Hierarchical Temporal Memory based on the Cortical Learning Algorithms (HTM-CLA). Comparisons are also made with a deep recurrent neural technique based on the Long Short-Term Memory ANN (LSTM-ANN). The performances of these systems are investigated on the basis of correctly classifying credit card fraud (CCF) using an average classification performance ratio metric. The results of simulations on two CCF benchmark datasets (the Australian and German CCF data) showed promising competitive performance of the proposed HTM-CLA with the SA-ANN. The HTM-CLA also clearly outperformed the LSTM-ANN in the considered benchmark datasets by a factor of 2:1.
Transmission trend of the COVID-19 pandemic predicted by dendritic neural regression

In 2020, a novel coronavirus disease became a global problem. The disease was called COVID-19, as the first patient was diagnosed in December 2019. The disease spread around the world quickly due to its powerful viral ability. To date, the spread of COVID-19 has been relatively mild in China due to timely control measures. However, in other countries, the pandemic remains severe, and COVID-19 protection and control policies are urgently needed, which has motivated this research. Since the outbreak of the pandemic, many researchers have hoped to identify the mechanism of COVID-19 transmission and predict its spread by using machine learning (ML) methods to supply meaningful reference information to decision-makers in various countries. Since the historical data of COVID-19 is time series data, most researchers have adopted recurrent neural networks (RNNs), which can capture time information, for this problem. However, even with a state-of-the-art RNN, it is still difficult to perfectly capture the temporal information and nonlinear characteristics from the historical data of COVID-19. Therefore, in this study, we develop a novel dendritic neural regression (DNR) method to improve prediction performance. In the DNR, the multiplication operator is used to capture the nonlinear relationships between input feature signals in the dendrite layer. Considering the complex and large landscape of DNR’s weight space, a new scale-free state-of-matter search (SFSMS) algorithm is proposed to optimize the DNR, which combines the state-of-matter search algorithm with a scale-free local search. The SFSMS achieves a better global search ability and thus can effectively reduce the possibility of falling into local minima. In addition, according to Takens’s theorem, phase space reconstruction techniques are used to discover the information hidden in the high-dimensional space of COVID-19 data, which further improves the precision of prediction. The experimental results suggest that the proposed method is more competitive in solving this problem than other prevailing methods.
A singular value decomposition approach for testing the efficiency of Bitcoin and Ethereum markets

This letter revisits the informationally efficiency of the two major cryptocurrencies Bitcoin (2013–2021) and Ethereum (2016–2021). The analysis is based on the computation of the singular value decomposition (SVD) entropy of a matrix formed by lagged vectors of price returns. The computed entropy is compared with a reference obtained from uncorrelated time series to decide whether the rows of the lagged matrix are uncorrelated. The procedure was implemented over a sliding window to assess the time variations of the entropy. The results show that the markets are informationally efficient most of time over different scales, except for some short periods that are linked to the 2016–2017 price-boom period and the 2020 Covid-19 economic downturn.
Towards Efficient Scene Understanding via Squeeze Reasoning

Graph-based convolutional model such as non-local block has shown to be effective for strengthening the context modeling ability in convolutional neural networks (CNNs). However, its pixel-wise computational overhead is prohibitive which renders it unsuitable for high resolution imagery. In this paper, we explore the efficiency of context graph reasoning and propose a novel framework called Squeeze Reasoning. Instead of propagating information on the spatial map, we first learn to squeeze the input feature into a channel-wise global vector and perform reasoning within the single vector where the computation cost can be significantly reduced. Specifically, we build the node graph in the vector where each node represents an abstract semantic concept. The refined feature within the same semantic category results to be consistent, which is thus beneficial for downstream tasks. We show that our approach can be modularized as an end-to-end trained block and can be easily plugged into existing networks. {Despite its simplicity and being lightweight, the proposed strategy allows us to establish the considerable results on different semantic segmentation datasets and shows significant improvements with respect to strong baselines on various other scene understanding tasks including object detection, instance segmentation and panoptic segmentation.} Code is available at \url{https://github.com/lxtGH/SFSegNets}.
SemEval-2021 Task 9: Fact Verification and Evidence Finding for Tabular Data in Scientific Documents (SEM-TAB-FACTS)

Understanding tables is an important and relevant task that involves understanding table structure as well as being able to compare and contrast information within cells.In this paper, we address this challenge by presenting a new dataset and tasks that addresses this goal in a shared task in SemEval 2020 Task 9: Fact Verification and Evidence Finding for Tabular Data in Scientific Documents (SEM-TAB-FACTS).Our dataset contains 981 manuallygenerated tables and an auto-generated dataset of 1980 tables providing over 180K statement and over 16M evidence annotations.SEM-TAB-FACTS featured two sub-tasks.In subtask A, the goal was to determine if a statement is supported, refuted or unknown in relation to a table.In sub-task B, the focus was on identifying the specific cells of a table that provide evidence for the statement.69 teams signed up to participate in the task with 19 successful submissions to subtask A and 12 successful submissions to subtask B. We present our results and main findings from the competition.
WPD-ResNeSt: Substation station level network anomaly traffic detection based on deep transfer learning

With the advancement of new infrastructure, the digitalization of the substation communication network has rapidly increased, and its information security risks have become increasingly prominent. Accurate and reliable substation communication network flow models and flow anomaly detection methods have become an important means to prevent network security problems and identify network anomalies. The existing substation network analyzers and flow anomaly detection algorithms are usually based on threshold determination, which cannot reflect the inherent characteristics of substation automation flow based on IEC 61850 and have low detection accuracy. To effectively detect abnormal traffic, this paper fully explores the substation network traffic rules, extracts the frequency domain features of the station level network, and designs an abnormal traffic identification model based on the ResNeSt convolutional neural network. Transfer learning is used to solve the problem of insufficient abnormal traffic labeled samples in substation. Finally, a new method of abnormal traffic detection in smart substation station level communication network based on deep transfer learning is proposed. The T1-1 substation communication network is constructed on OPNET for abnormal simulation, and the actual network traffic in a 110kV substation is fused with CIC DDoS2019 and KDD99 data sets for algorithm performance test, respectively. The accuracy reached 98.73% and 98.95%, indicating that the detection model proposed in this paper has higher detection accuracy than existing algorithms.
The Challenge of Teacher Training in the 2030 Agenda Framework Using Geotechnologies

Social evolution, globalization, and advances in technology are making it increasingly necessary to offer complete and comprehensive teacher training. This training should produce citizens who are concerned about the planet and its future. These values are embedded in the Sustainable Development Goals (SDGs), the breadth of which allows them to be integrated into the secondary school curriculum for most subjects. To construct a complete teacher training model, the following have been considered: previous studies based on qualitative and quantitative methodologies (the Delphi method and questionnaires for ‘expert’ teachers), the teaching experience of the authors, the action research methodology, and validation by other teachers who use technologies and are concerned about sustainability issues. The result is a teacher training model that is in line with UNESCO’s sustainability competencies and based not only on technology, the scientific content of the subject to be taught, and didactics (pedagogy), but also on education in sustainability and the SDGs that need to be integrated. This approach is expected to produce changes in citizens’ attitudes that contribute to the achievement of the SDGs and lead to the teachers feeling positive about their teaching experiences. However, a systematic application of this approach in classrooms and an assessment of its learning results are still pending.
Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images

Algorithms proposed in computational pathology can allow to automatically analyze digitized tissue samples of histopathological images to help diagnosing diseases. Tissue samples are scanned at a high-resolution and usually saved as images with several magnification levels, namely whole slide images (WSIs). Convolutional neural networks (CNNs) represent the state-of-the-art computer vision methods targeting the analysis of histopathology images, aiming for detection, classification and segmentation. However, the development of CNNs that work with multi-scale images such as WSIs is still an open challenge. The image characteristics and the CNN properties impose architecture designs that are not trivial. Therefore, single scale CNN architectures are still often used. This paper presents Multi_Scale_Tools, a library aiming to facilitate exploiting the multi-scale structure of WSIs. Multi_Scale_Tools currently include four components: a pre-processing component, a scale detector, a multi-scale CNN for classification and a multi-scale CNN for segmentation of the images. The pre-processing component includes methods to extract patches at several magnification levels. The scale detector allows to identify the magnification level of images that do not contain this information, such as images from the scientific literature. The multi-scale CNNs are trained combining features and predictions that originate from different magnification levels. The components are developed using private datasets, including colon and breast cancer tissue samples. They are tested on private and public external data sources, such as The Cancer Genome Atlas (TCGA). The results of the library demonstrate its effectiveness and applicability. The scale detector accurately predicts multiple levels of image magnification and generalizes well to independent external data. The multi-scale CNNs outperform the single-magnification CNN for both classification and segmentation tasks. The code is developed in Python and it will be made publicly available upon publication. It aims to be easy to use and easy to be improved with additional functions.
A multimodal attention fusion network with a dynamic vocabulary for TextVQA

Visual question answering (VQA) is a well-known problem in computer vision. Recently, Text-based VQA tasks are getting more and more attention because text information is very important for image understanding. The key to this task is to make good use of text information in the image. In this work, we propose an attention-based encoder-decoder network that combines the multimodal information of visual, linguistic, and location features together. By using the attention mechanism to focus on key features to the question, our multimodal feature fusion can provide more accurate information to improve the performance. Furthermore, we present a decoder with attention map loss, which can not only predict complex answers but also deal with a dynamic vocabulary to reduce the decoding space. Compared with softmax-based cross entropy loss which can only handle a fixed-length vocabulary, the attention map loss significantly improves the accuracy and efficiency. Our method achieved the first place of all three tasks in the ICDAR2019 robust reading challenge on scene text visual question answering (ST-VQA).
BlockHealth: Blockchain-based secure and peer-to-peer health information sharing with data protection and right to be forgotten

To identify health risks in working environments, it is crucial for companies to share personal health data demonstrating to clients and suppliers their employees are healthy, while being compliant with data protection legislation. Based on these considerations, our Blockchain-based BlockHealth solution allows personal health data sharing with tamper proofing and data protection. Traditionally, the Blockchain guarantees data immutability but not confidentiality. On the contrary, BlockHealth stores in the Blockchain only hash values of data. Health data is stored in private databases managed by companies, thus also allowing to delete data in compliance with the right to be forgotten.
HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation

Tables are often created with hierarchies, but existing works on table reasoning mainly focus on flat tables and neglect hierarchical tables.Hierarchical tables challenge table reasoning by complex hierarchical indexing, as well as implicit relationships of calculation and semantics.We present a new dataset, HiTab, to study question answering (QA) and natural language generation (NLG) over hierarchical tables.HiTab is a cross-domain dataset constructed from a wealth of statistical reports and Wikipedia pages, and has unique characteristics: (1) nearly all tables are hierarchical, and (2) questions are not proposed by annotators from scratch, but are revised from real and meaningful sentences authored by analysts.(3) To reveal complex numerical reasoning in analysis, we provide fine-grained annotations of quantity and entity alignment.Experimental results show that HiTab presents a strong challenge for existing baselines and a valuable benchmark for future research.Targeting hierarchical structure, we devise an effective hierarchy-aware logical form for symbolic reasoning over tables.Furthermore, we leverage entity and quantity alignment to explore partially supervised training in QA and conditional generation in NLG, which largely reduces spurious predictions in QA and meaningless descriptions in NLG.
AISHELL-4: An Open Source Dataset for Speech Enhancement, Separation, Recognition and Speaker Diarization in Conference Scenario

In this paper, we present AISHELL-4, a sizable real-recorded Mandarin speech dataset collected by 8-channel circular microphone array for speech processing in conference scenario. The dataset consists of 211 recorded meeting sessions, each containing 4 to 8 speakers, with a total length of 120 hours. This dataset aims to bridge the advanced research on multi-speaker processing and the practical application scenario in three aspects. With real recorded meetings, AISHELL-4 provides realistic acoustics and rich natural speech characteristics in conversation such as short pause, speech overlap, quick speaker turn, noise, etc. Meanwhile, accurate transcription and speaker voice activity are provided for each meeting in AISHELL-4. This allows the researchers to explore different aspects in meeting processing, ranging from individual tasks such as speech front-end processing, speech recognition and speaker diarization, to multi-modality modeling and joint optimization of relevant tasks. Given most open source dataset for multi-speaker tasks are in English, AISHELL-4 is the only Mandarin dataset for conversation speech, providing additional value for data diversity in speech community. We also release a PyTorch-based training and evaluation framework as baseline system to promote reproducible research in this field.
Deep Features for Training Support Vector Machines

Features play a crucial role in computer vision. Initially designed to detect salient elements by means of handcrafted algorithms, features now are often learned using different layers in convolutional neural networks (CNNs). This paper develops a generic computer vision system based on features extracted from trained CNNs. Multiple learned features are combined into a single structure to work on different image classification tasks. The proposed system was derived by testing several approaches for extracting features from the inner layers of CNNs and using them as inputs to support vector machines that are then combined by sum rule. Several dimensionality reduction techniques were tested for reducing the high dimensionality of the inner layers so that they can work with SVMs. The empirically derived generic vision system based on applying a discrete cosine transform (DCT) separately to each channel is shown to significantly boost the performance of standard CNNs across a large and diverse collection of image data sets. In addition, an ensemble of different topologies taking the same DCT approach and combined with global mean thresholding pooling obtained state-of-the-art results on a benchmark image virus data set.
Entanglement-based quantum key distribution with a blinking-free quantum dot operated at a temperature up to 20 K

Entanglement-based quantum key distribution (QKD) promises enhanced robustness against eavesdropping and compatibility with future quantum networks. Among other sources, semiconductor quantum dots (QDs) can generate polarization-entangled photon pairs with near-unity entanglement fidelity and a multiphoton emission probability close to zero even at maximum brightness. These properties have been demonstrated under resonant two-photon excitation (TPE) and at operation temperatures below 10 K. However, source blinking is often reported under TPE conditions, limiting the maximum achievable photon rate. In addition, operation temperatures reachable with compact cryocoolers could facilitate the widespread deployment of QDs, e.g., in satellite-based quantum communication. We demonstrate blinking-free emission of highly entangled photon pairs from GaAs QDs embedded in a p-i-n diode. High fidelity entanglement persists at temperatures of at least 20 K, which we use to implement fiber-based QKD between two buildings with an average raw key rate of 55 bits / s and a qubit error rate of 8.4%. We are confident that by combining electrical control with already demonstrated photonic and strain engineering, QDs will keep approaching the ideal source of entangled photons for real world applications.
A Comparative Eye Tracking Study of Usability—Towards Sustainable Web Design

Websites are one of the most frequently used communication environments, and creating sustainable web designs should be an objective for all companies. Ensuring high usability is proving to be one of the main contributors to sustainable web design, reducing usage time, eliminating frustration and increasing satisfaction and retention. The present paper studies the usability of different website landing pages, seeking to identify the elements, structures and designs that increase usability. The study analyzed the behavior of 22 participants during their interaction with five different landing pages while they performed three tasks on the webpage and freely viewed each page for one minute. The stimuli were represented by five different banking websites, each of them presenting the task content in a different mode (text, image, symbol, graph, etc.).; the data obtained from the eye tracker (fixations location, order and duration, saccades, revisits of the same element, etc.), together with the data from the applied survey lead to interesting conclusions: the top, center and right sides of the webpage attract the most attention; the use of pictures depicting persons increase visibility; the scanpaths follow a vertical and horizontal direction; numerical data should be presented through graphs or tables. Even if a user's past experience influences their experience on a website, we show that the design of the webpage itself has a greater influence on webpage usability.