Reviewerly/exHarmony huggingface dataset:
Validation of The Scale on Pre-Service Teachers’ Digital Competence to Assist Students with Functional Diversity

One of the objectives of education is to provide diverse pupils with proper educational opportunities. It is anticipated that the night instructors will have this equipment. In this study, validity, and reliability evaluations of the scale to be used to identify digital competence in prospective instructors to support children with functional diversity were conducted. To conduct the research, education faculty students were selected as the target audience. The data gathered were subjected to exploratory factor analysis. A confirmatory factor analysis was used to validate the obtained results. According to the findings of the investigation, thirty components and five contributing variables were found. It has been proposed that future academics do studies on the validity and reliability of the instruments with a number of different populations.
Deep Learning with Natural Language Processing Enabled Sentimental Analysis on Sarcasm Classification

Sentiment analysis (SA) is the procedure of recognizing the emotions related to the data that exist in social networking.The existence of sarcasm in textual data is a major challenge in the efficiency of the SA.Earlier works on sarcasm detection on text utilize lexical as well as pragmatic cues namely interjection, punctuations, and sentiment shift that are vital indicators of sarcasm.With the advent of deep-learning, recent works, leveraging neural networks in learning lexical and contextual features, removing the need for handcrafted feature.In this aspect, this study designs a deep learning with natural language processing enabled SA (DLNLP-SA) technique for sarcasm classification.The proposed DLNLP-SA technique aims to detect and classify the occurrence of sarcasm in the input data.Besides, the DLNLP-SA technique holds various sub-processes namely preprocessing, feature vector conversion, and classification.Initially, the pre-processing is performed in diverse ways such as single character removal, multi-spaces removal, URL removal, stopword removal, and tokenization.Secondly, the transformation of feature vectors takes place using the N-gram feature vector technique.Finally, mayfly optimization (MFO) with multi-head self-attention based gated recurrent unit (MHSA-GRU) model is employed for the detection and classification of sarcasm.To verify the enhanced outcomes of the DLNLP-SA model, a comprehensive experimental investigation is performed on the News Headlines Dataset from Kaggle Repository and the results signified the supremacy over the existing approaches.
BahiaRT Setplays Collecting Toolkit and BahiaRT Gym

<h2>Abstract</h2> One of the challenges in the research using machine learning is the requirement to build realistic datasets using common sense knowledge from domain experts. The BahiaRT Setplays Collecting Toolkit is a software that supports research where soccer fans or experts can watch robots playing soccer and capture situations where they want to demonstrate a better setplay to that robot team. All demonstrations were gathered in a dataset used to feed a reinforcement learning mechanism that produced a control policy for setplays selection in a robot soccer team. This software impacts many research areas such as autonomous vehicles, unmanned aerial vehicles, dataset organization, reinforcement learning, and learning from demonstration. The BahiaRT Gym is an open-source tool designed to integrate the OpenAI Gym toolkit with the RoboCup Soccer Simulation 3D server(rcssserver3d), in order to make available an easier way to create training environments for the soccer teams while also facilitating the development of other RoboCup leagues's environments as well.
Deep Contrastive Self-Supervised Hashing for Remote Sensing Image Retrieval

Hashing has been widely used for large-scale remote sensing image retrieval due to its outstanding advantages in storage and search speed. Recently, deep hashing methods, which produce discriminative hash codes by building end-to-end deep convolutional networks, have shown promising results. However, training these networks requires numerous labeled images, which are scarce and expensive in remote sensing datasets. In order to solve this problem, we propose a deep unsupervised hashing method, namely deep contrastive self-supervised hashing (DCSH), which uses only unlabeled images to learn accurate hash codes. It eliminates the need for label annotation by maximizing the consistency of different views generated from the same image. More specifically, we assume that the hash codes generated from different views of the same image are similar, and those generated from different images are dissimilar. On the basis of the hypothesis, we can develop a novel loss function containing the temperature-scaled cross-entropy loss and the quantization loss to train the developed deep network end-to-end, resulting in hash codes with semantic similarity preserved. Our proposed network contains four parts. First, each image is transformed into two different views using data augmentation. After that, they are fed into an encoder with the same shared parameters to obtain deep discriminate features. Following this, a hash layer converts the high-dimensional image representations into compact binary codes. Lastly, a novel hash function is introduced to train the proposed network end-to-end and thus guide generated hash codes with semantic similarity. Extensive experiments on two popular benchmark datasets of the UC Merced Land Use Database and the Aerial Image Dataset have demonstrated that our DCSH has significant superiority in remote sensing image retrieval compared with state-of-the-art unsupervised hashing methods.
Intelligent Diagnosis Method of Power Equipment Faults Based on Single‐Stage Infrared Image Target Detection

With the rapid expansion of the scale of the power grid, the efficiency of fault diagnosis has been severely challenged by the large amount of inspection image data generated by intelligent devices such as drones and inspection robots. In order to improve the efficiency of fault diagnosis for power equipment in substations, a new method for intelligently diagnosing different types of faults in power equipment is proposed. For circuit breakers and insulators, YOLOv4 is selected as the target detection model. To improve the detection performance of the YOLOv4 model, this paper improves it: the Cross Stage Partial (CSP) structure is introduced in the Spatial Pyramid Pooling (SPP) module of the neck of the YOLOv4 model. The experimental results show that after using the optimal learning rate decay strategy, the mAP and frames per second (FPS) of the improved YOLOv4 model are better than the original YOLOv4 and PP‐YOLO model. Finally, an intelligent diagnosis terminal system for power equipment faults is developed. Through the target recognition and rapid extraction of equipment temperature, the intelligent diagnosis of thermal faults of equipment is realized. This method is especially suitable for accurate fault diagnosis of more power equipment, and has potential huge applicability in the field of power equipment diagnosis. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Class incremental learning for wildlife biodiversity monitoring in camera trap images

Camera traps have been widely used for wildlife biodiversity monitoring, providing abundant ecological data. Manually classifying such abundant images is time-consuming and labor-intensive. Existing deep learning methods solve this problem for a fixed set of predefined wildlife species. The model trained on such sets cannot be applied to new wildlife species. Retraining models on new wildlife species can lead to catastrophic forgetting. Thus, in this work, we propose a class incremental learning method to identify new wildlife species. Our method employs a novel adaptive exemplar assignment (AEA) strategy with dynamic exemplar amounts to adapt to new species while alleviating the forgetting of old ones. Due to memory constraints, the data imbalance between limited exemplars and new species data can lead to class bias. We mitigate it by performing center vector retrieval (CVR) to classify samples in feature space and bypass the biased linear classifier. In addition, we propose two variants of CVR that incorporate the advantage of the linear classifier to further improve the performance. By using only 4% of old species data, our method achieves 77.09% accuracy at a low computational resource for recognition. Through extensive experiments and ablations, we demonstrate the superiority of our proposed approach over state-of-the-art methods. This method facilitates wildlife monitoring, biodiversity conservation, and ecological assessment.
Semantics Driven Embedding Learning for Effective Entity Alignment

Knowledge-based data service has become an emerging form of service in the world wide web (WWW). To ensure the service quality, a comprehensive knowledge base has to be constructed. Knowledge base integration is often a primary way to improve the completeness. In this paper, we focus on the fundamental problem in knowledge base integration, i.e., entity alignment (EA). EA has been studied for years. Traditional approaches focus on the symbolic features of entities and propose various similarity measures to identify equivalent entities. With recent development in knowledge graph representation learning, embedding-based entity alignment has emerged, which encodes the entities into vectors according to the semantic or structural information and computes the relatedness of entities based on the vector representation. While embedding-based approaches achieve promising results, we identify some important information that are not well exploited in existing works: 1) The neighboring entities contribute differently in the EA process, and should be carefully assigned the importance in learning the relatedness of entities; 2) The attribute values (especially the long texts) contain rich semantics that can build supplementary associations between entities. To this end, we propose SDEA - a Semantics Driven entity embedding method for Entity Alignment. SDEA consists of two modules, namely attribute embedding and relation embedding. The attribute embedding captures the semantic information from attribute values with a pre-trained transformer-based language model. The relation embedding selectively aggregates the semantic information from neighbors using a GRU model equipped with an attention mechanism. Both attribute embedding and relation embedding are driven by semantics, building bridges between entities. Experimental results show that our method significantly outperforms the state-of-the-art approaches on three benchmarks.
Estimating Node Importance Values in Heterogeneous Information Networks

Node importance estimation is a fundamental task in graph data analysis. Extensive studies have focused on this task, and various downstream applications have benefited from it, such as recommendation, resource allocation optimization, and missing value completion. However, existing works either focus on the homogeneous network or only study importance-based ranking. We are the first to consider the node importance values as heterogeneous values in heterogeneous information networks (HINs). A typical HIN is built of several distinguished node types where each type has its own measure of importance value (e.g., in the DBLP network, the importance values of authors and papers can be reflected by their h-index and citation numbers, respectively). This characteristic makes the above problem more challenging than computing the node importance in conventional homogeneous networks. In this paper, we formally introduce the problem of node importance value estimation in HINs; that is, given the importance values of a subset of nodes in an HIN, we aim to estimate the importance values of the remaining nodes. To solve this problem, we propose an effective graph neural network (GNN) model, called HIN Importance Value Estimation Network (HIVEN). HIVEN traces the local information of each node, specifically by utilizing the heterogeneity of the HIN. Furthermore, the meta schema is deployed to alleviate the node type domination issue. Additionally, HIVEN exploits the node similarity within each type to remedy the shortcoming of GNN models in capturing global information. Extensive experiments on real-world HIN datasets demonstrate that HIVEN superiorly outperforms the baseline methods.
SEFrame: An SGX-enhanced Smart Contract Execution Framework for Permissioned Blockchain

The current blockchain system suffers from serious scalability bottleneck, which greatly limits the application in large-scale. Furthermore, with the emergence of high-throughput consensus algorithms in permissioned blockchain, how to efficiently execute smart contracts becomes a critical challenge. To solve this issue, a two-phase concurrent execution mechanism has been adopted recently, where the primary executes a batch of transactions concurrently in the first phase, then the rest replays them in the second phase to achieve consistency. However, these works only enable intra-node concurrency, not inter-node concurrency. This demonstration showcases SEFrame, a novel execution framework for smart contracts of permissioned blockchain to achieve intra- and inter-node concurrency with the confidentiality guarantee of Intel Software Guard Extensions (SGX). We use real-time dashboards containing the output of SEFrame, which allows attendees to interactively explore how SEFrame achieves efficient inter- and intra-node concurrency.
Adaptive RF Fingerprints Fusion via Dual Attention Convolutions

In recent years, with the rise of intelligent hardware technology, the Internet of Things (IoT) has achieved rapid development and has been widely used in smart cities, smart power grids, industrial Internet, and other fields. The resulting security risks of IoT have attracted more and more attention. Being different from the traditional authentication that is based on MAC or security certificate, RF fingerprinting technology extracts fingerprints from the emissions of wireless transmitters. These fingerprints root in the hardware imperfection of the transmitting circuits and can be used for wireless device identification and authentication. RF fingerprinting can effectively enhance the security of the wireless network and has become a research focus in the field of IoT. However, how to learn and fuse multiple RF fingerprints is still an urgent problem to be solved. Inspired by the attention mechanism in the field of computer vision, an adaptive RF fingerprints fusion network (ARFNet) is proposed in this article, which is built on our dual attention convolution (DAConv) layer. This neural network can extract and adaptively fuse multiple RF fingerprints in a data-driven manner to obtain more discriminant features. In addition, a data augmentation method is also designed to effectively enhance the network’s robustness to channel variation and SNR variation. Extensive experimental results show that the proposed ARFNet combined with data augmentation can achieve 99.5% recognition accuracy on five USRP X310, and achieve 95.7% recognition accuracy on 56 ADS-B devices. Our source code has been released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zhangweifeng1218/Adaptive_RF_Fingerprinting</uri> .
Generative adversarial interactive imitation learning for path following of autonomous underwater vehicle

Autonomous underwater vehicle (AUV) is playing a more and more important role in marine scientific research and resource exploration due to its flexibility. Recently, deep reinforcement learning (DRL) has been used to improve the autonomy of AUV. However, it is very time-consuming and even unpractical to define efficient reward functions for DRL to learn control policies in various tasks. In this paper, we implemented the generative adversarial imitation learning (GAIL) algorithm learning from demonstrated trajectories and proposed GA2IL learning from demonstrations and additional human rewards for AUV path following. We evaluated GAIL and our GA2IL method in a straight line following task and a sinusoids curve following task on the Gazebo platform extended to simulated underwater environments with AUV simulator of our lab. Both methods were compared to PPO—a classic traditional deep reinforcement learning from a predefined reward function, and a well-tuned PID controller. In addition, to evaluate the generalization of GAIL and our GA2IL method, we tested the trained control policies of the previous two tasks via GAIL and GA2IL in a new complex comb scan following task and a different sinusoids curve following task respectively. Our simulation results show AUV path following with GA2IL and GAIL can obtain a performance at a similar level to PPO and PID controller in both tasks. Moreover, GA2IL can generalize as well as PPO, adapting better to complex and different tasks than traditional PID controller.
Design of a quantum convolutional neural network on quantum circuits

This paper introduces a quantum convolutional neural network model that is implementable on real quantum circuits. Three basic blocks, including the quantum encoding block, the model design block and the parameter tuning block, are designed to formulate the quantum convolutional neural network framework. Simulation results on the MNIST dataset are presented to demonstrate the representation, learning, and optimization capacity of the proposed model.
Reviewer assignment algorithms for peer review automation: A survey

Assigning paper to suitable reviewers is of great significance to ensure the accuracy and fairness of peer review results. In the past three decades, many researchers have made a wealth of achievements on the reviewer assignment problem (RAP). In this survey, we provide a comprehensive review of the primary research achievements on reviewer assignment algorithm from 1992 to 2022. Specially, this survey first discusses the background and necessity of automatic reviewer assignment, and then systematically summarize the existing research work from three aspects, i.e., construction of candidate reviewer database, computation of matching degree between reviewers and papers, and reviewer assignment optimization algorithm, with objective comments on the advantages and disadvantages of the current algorithms. Afterwards, the evaluation metrics and datasets of reviewer assignment algorithm are summarized. To conclude, we prospect the potential research directions of RAP. Since there are few comprehensive survey papers on reviewer assignment algorithm in the past ten years, this survey can serve as a valuable reference for the related researchers and peer review organizers.
Bounds for Privacy-Utility Trade-off with Non-zero Leakage

The design of privacy mechanisms for two scenarios is studied where the private data is hidden or observable. In the first scenario, an agent observes useful data Y , which is correlated with private data X, and wants to disclose the useful information to a user. A privacy mechanism is employed to generate data U that maximizes the revealed information about Y while satisfying a privacy criterion. In the second scenario, the agent has additionally access to the private data. To this end, the Functional Representation Lemma and Strong Functional Representation Lemma are extended relaxing the independence condition and thereby allowing a certain leakage. Lower bounds on privacy-utility trade-off are derived for the second scenario as well as upper bounds for both scenarios. In particular, for the case where no leakage is allowed, our upper and lower bounds improve previous bounds.
Over-the-Air Ensemble Inference with Model Privacy

We consider distributed inference at the wireless edge, where multiple clients with an ensemble of models, each trained independently on a local dataset, are queried in parallel to make an accurate decision on a new sample. In addition to maximizing inference accuracy, we also want to maximize the privacy of local models. We exploit the superposition property of the air to implement bandwidth-efficient ensemble inference methods. We introduce different over-the-air ensemble methods and show that these schemes perform significantly better than their orthogonal counterparts, while using less resources and providing privacy guarantees. We also provide experimental results verifying the benefits of the proposed over-the-air inference approach, whose source code is shared publicly on Github.
A Privacy-Preserving Blockchain Solution to Support Demand Response in Energy Trading

Demand response consists in the modification of energy consumption by end users to attain specific effects on the electrical grid, namely reduce congestion and optimize the usage of renewable energy. When implemented with blockchain, demand response poses important privacy concerns since it generally needs the publication of sensitive information about users regarding their energy consumption. In this work, we devise a system to preserve privacy in such a scenario by using Secure Multiparty Computation (SMC) algorithms and a blockchain-based architecture with the goal of achieving both accountability and privacy.
A Novel Deep Learning-Based Mitosis Recognition Approach and Dataset for Uterine Leiomyosarcoma Histopathology

Uterine leiomyosarcoma (ULMS) is the most common sarcoma of the uterus, It is aggressive and has poor prognosis. Its diagnosis is sometimes challenging owing to its resemblance by benign smooth muscle neoplasms of the uterus. Pathologists diagnose and grade leiomyosarcoma based on three standard criteria (i.e., mitosis count, necrosis, and nuclear atypia). Among these, mitosis count is the most important and challenging biomarker. In general, pathologists use the traditional manual counting method for the detection and counting of mitosis. This procedure is very time-consuming, tedious, and subjective. To overcome these challenges, artificial intelligence (AI) based methods have been developed that automatically detect mitosis. In this paper, we propose a new ULMS dataset and an AI-based approach for mitosis detection. We collected our dataset from a local medical facility in collaboration with highly trained pathologists. Preprocessing and annotations are performed using standard procedures, and a deep learning-based method is applied to provide baseline accuracies. The experimental results showed 0.7462 precision, 0.8981 recall, and 0.8151 F1-score. For research and development, the code and dataset have been made publicly available.