Reviewerly/exHarmony huggingface dataset:
YOLOD: A Target Detection Method for UAV Aerial Imagery

Target detection based on unmanned aerial vehicle (UAV) images has increasingly become a hot topic with the rapid development of UAVs and related technologies. UAV aerial images often feature a large number of small targets and complex backgrounds due to the UAV’s flying height and shooting angle of view. These characteristics make the advanced YOLOv4 detection method lack outstanding performance in UAV aerial images. In light of the aforementioned problems, this study adjusted YOLOv4 to the image’s characteristics, making the improved method more suitable for target detection in UAV aerial images. Specifically, according to the characteristics of the activation function, different activation functions were used in the shallow network and the deep network, respectively. The loss for the bounding box regression was computed using the EIOU loss function. Improved Efficient Channel Attention (IECA) modules were added to the backbone. At the neck, the Spatial Pyramid Pooling (SPP) module was replaced with a pyramid pooling module. At the end of the model, Adaptive Spatial Feature Fusion (ASFF) modules were added. In addition, a dataset of forklifts based on UAV aerial imagery was also established. On the PASCAL VOC, VEDAI, and forklift datasets, we ran a series of experiments. The experimental results reveal that the proposed method (YOLO-DRONE, YOLOD) has better detection performance than YOLOv4 for the aforementioned three datasets, with the mean average precision (mAP) being improved by 3.06%, 3.75%, and 1.42%, respectively.
A benchmark and comprehensive survey on knowledge graph entity alignment via representation learning

In the last few years, the interest in knowledge bases has grown exponentially in both the research community and the industry due to their essential role in AI applications. Entity alignment is an important task for enriching knowledge bases. This paper provides a comprehensive tutorial-type survey on representative entity alignment techniques that use the new approach of representation learning. We present a framework for capturing the key characteristics of these techniques, propose a benchmark addressing the limitation of existing benchmark datasets, and conduct extensive experiments using our benchmark. The framework gives a clear picture of how various techniques work. The experiments yield important results about the empirical performance of the techniques and how various factors affect the performance. One important observation not stressed by previous work is that techniques making good use of attribute triples and relation predicates as features stand out as winners. We are also the first to investigate the question of how to perform entity alignments on large-scale knowledge graphs such as the full Wikidata and Freebase (in Experiment 5).
Dynamic Dataset Augmentation for Deep Learning-based Oracle Bone Inscriptions Recognition

Oracle bone inscriptions (OBIs) are a kind of hieroglyph, used about 3,600 years ago for divination and the recording of events. The characters on these OBIs are of great interest because they are precursors to the modern Chinese characters widely used across Asia today. However, as the OBIs were only discovered in 1899, there are currently few documents to describe them. Hence, recognizing and unlocking the meaning of OBIs helps to understand the ancient history of China, the evaluation of Chinese characters, and more. Currently, deep learning has made great progress and brought about a revolution in the research field of recognition, and shows good potential to meet the challenges of OBIs recognition. Due to the scarcity of resources, many OBIs contain only a small number of instances, which causes dataset imbalance and limits the accuracy of recognition. This paper attempts to provide a suite of OBIs recognition methods comprising an original OBIs dataset creation, dynamic dataset augmentation, and a novel deep learning-based recognition method. To this end, we create an original OBIs dataset and propose a modified Generative Adversarial Network for augmenting the original OBIs dataset. The augmented data is then dynamically selected for training the deep learning model considering the data imbalance problem. A novel model called C-A Net is proposed for OBIs recognition. The results of evaluation experiments show that the dynamical dataset augmentation can effectively locate a suitable training dataset for the deep learning model and solve the problem of imbalanced OBIs distribution. In addition, the recognition accuracy of C-A Net is 91.10%, which is higher than that of eight state-of-the-art models, and thus effectively suppresses the occurrence of overfitting. We also present an original OBIs dataset named OBI125, which is currently the only rubbing-type OBIs dataset that is open to the public. The code is available at http://www.ihpc.se.ritsumei.ac.jp/obidataset.html.
SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification

The paper describes the SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification (MAMI),which explores the detection of misogynous memes on the web by taking advantage of available texts and images.The task has been organised in two related sub-tasks: the first one is focused on recognising whether a meme is misogynous or not (Sub-task A), while the second one is devoted to recognising types of misogyny (Sub-task B).MAMI has been one of the most popular tasks at SemEval-2022 with more than 400 participants, 65 teams involved in Sub-task A and 41 in Sub-task B from 13 countries.The MAMI challenge received 4214 submitted runs (of which 166 uploaded on the leader-board), denoting an enthusiastic participation for the proposed problem.The collection and annotation is described for the task dataset.The paper provides an overview of the systems proposed for the challenge, reports the results achieved in both sub-tasks and outlines a description of the main errors for a comprehension of the systems capabilities and for detailing future research perspectives.
FedTwin: Blockchain-Enabled Adaptive Asynchronous Federated Learning for Digital Twin Networks

The fast proliferation of digital twin (DT) establishes a direct connection between the physical entity and its deployed digital representation. As markets shift toward mass customization and new service delivery models, the digital representation has become more adaptive and agile by forming digital twin networks (DTNs). The DTN institutes a real-time single source of truth everywhere. However, there are several issues preventing DTNs from further application, including centralized processing, data falsification, privacy leakage, lack of incentive mechanism, and so on. To make DTN better meet the ever changing demands, we propose a novel block-chain-enabled adaptive asynchronous federated learning (FedTwin) paradigm for privacy-preserving and decentralized DTNs. We design Proof-of-Federalism (PoF), which is a tailor-made consensus algorithm for autonomous DTNs. In each DT's local training phase, generative adversarial network enhanced differential privacy is used to protect the privacy of local model parameters, while a modified Isolation Forest is deployed to filter out the falsified DTs. In the global aggregation phase, an improved Markov decision process is leveraged to select optimal DTs to achieve adaptive asynchronous aggregation while providing a rollback mechanism to redact the falsified global models. With this article, we aim to provide insights to forthcoming researchers and readers in this under-explored domain.
Phishing in Organizations: Findings from a Large-Scale and Long-Term Study

In this paper, we present findings from a largescale and long-term phishing experiment that we conducted in collaboration with a partner company. Our experiment ran for 15 months during which time more than 14,000 study participants (employees of the company) received different simulated phishing emails in their normal working context. We also deployed a reporting button to the company’s email client which allowed the participants to report suspicious emails they received. We measured click rates for phishing emails, dangerous actions such as submitting credentials, and reported suspicious emails. The results of our experiment provide three types of contributions. First, some of our findings support previous literature with improved ecological validity. One example of such results is good effectiveness of warnings on emails. Second, some of our results contradict prior literature and common industry practices. Surprisingly, we find that embedded training during simulated phishing exercises, as commonly deployed in the industry today, does not make employees more resilient to phishing, but instead it can have unexpected side effects that can make employees even more susceptible to phishing. And third, we report new findings. In particular, we are the first to demonstrate that using the employees as a collective phishing detection mechanism is practical in large organizations. Our results show that such crowd-sourcing allows fast detection of new phishing campaigns, the operational load for the organization is acceptable, and the employees remain active over long periods of time.
Universal Atomic Swaps: Secure Exchange of Coins Across All Blockchains

Trading goods lies at the backbone of the modern economy and the recent advent of cryptocurrencies has opened the door for trading decentralized (digital) assets: A large fraction of the value of cryptocurrencies comes from the inter-currency exchange and trading, which has been arguably the most successful application of decentralized money. The security issues observed with centralized, custodial cryptocurrency exchanges have motivated the design of atomic swaps, a protocol for coin exchanges between any two users. Yet, somewhat surprisingly, no atomic swap protocol exists that simultaneously satisfies the following simple but desired properties: (i) non-custodial, departing from a third party trusted holding the coins from users during the exchange; (ii) universal that is, compatible with all (current and future) cryptocurrencies; (iii) multi-asset, supporting the exchange of multiple coins in a single atomic swap.From a theoretical standpoint, in this work we show a generic protocol to securely swap n coins from any (possible multiple) currencies for $\tilde{n}$ coins of any other currencies, for any n and $\tilde{n}$. We do not require any custom scripting language supported by the corresponding blockchains, besides the bare minimum ability to verify signatures on transactions. For the special case when the blockchains use ECDSA or Schnorr signatures, we design a practically efficient protocol based on adaptor signatures and time-lock puzzles. As a byproduct of our approach, atomic swaps transactions no longer include custom scripts and are identical to standard one-to-one transactions. We also show that our protocol naturally generalizes to any cycle of users, i.e., atomic swaps with more than two participants. To demonstrate the practicality of our approach, we have evaluated a prototypical implementation of our protocol for Schnorr/ECDSA signatures and observed that an atomic swap requires below one second on commodity machines. Even on blockchains with expressive smart contract support (e.g., Ethereum), our approach reduces the on-chain cost both in terms of transaction size and gas cost.
Target Object Detection from Unmanned Aerial Vehicle (UAV) Images Based on Improved YOLO Algorithm

Aerial image-based target object detection has several glitches such as low accuracy in multi-scale target detection locations, slow detection, missed targets, and misprediction of targets. To solve this problem, this paper proposes an improved You Only Look Once (YOLO) algorithm from the viewpoint of model efficiency using target box dimension clustering, classification of the pre-trained network, multi-scale detection training, and changing the screening rules of the candidate box. This modified approach has the potential to be better adapted to the positioning task. The aerial image of the unmanned aerial vehicle (UAV) can be positioned to the target area in real-time, and the projection relation can convert the latitude and longitude of the UAV. The results proved to be more effective; notably, the average accuracy of the detection network in the aerial image of the target area detection tasks increased to 79.5%. The aerial images containing the target area are considered to experiment with the flight simulation to verify its network positioning accuracy rate and were found to be greater than 84%. This proposed model can be effectively used for real-time target detection for multi-scale targets with reduced misprediction rate due to its superior accuracy.
Quantum Machine Learning Applications in the Biomedical Domain: A Systematic Review

Quantum technologies have become powerful tools for a wide range of application disciplines, which tend to range from chemistry to agriculture, natural language processing, and healthcare due to exponentially growing computational power and advancement in machine learning algorithms.Furthermore, the processing of classical data and machine learning algorithms in the quantum domain has given rise to an emerging field like quantum machine learning.Recently, quantum machine learning, become a quite challenging field in case of healthcare applications.As a result, quantum machine learning has become a common and effective technique for data processing and classification across a wide range of domains.Consequently, quantum machine learning is the most commonly used application of quantum computing.The main objective of this work is to present a brief overview of current state-of-the-art published articles between 2013 and 2021 to identify, analyze, and classify the different QML algorithms and applications in the biomedical field.Furthermore, the approach adheres to the requirements for conducting systematic literature review techniques such as research questions and quality metrics of the articles.Initially, we discovered 3149 articles, excluded the 2847 papers, and read the 121 full papers.Therefore, this research compiled 30 articles that comply with the quantum machine learning models, and quantum circuits are using biomedical data.Eventually, this article provides a broad overview of quantum machine learning limitations and future prospects.
A3N: Attention-based adversarial autoencoder network for detecting anomalies in video sequence

This paper presents a novel attention-based adversarial autoencoder network (A3N) that consists of a two-stream decoder to detect abnormal events in video sequences. The first stream of the decoder is a reconstructive model responsible for recreating the input frame sequence. However, the second stream is a future predictive model used to predict the future frame sequence through adversarial learning. A global attention mechanism is employed at the decoder side that helps to decode the encoded sequences effectively. The training of A3N is carried out on normal video data. The attention-based reconstructive model is used during the inference stage to compute the anomaly score. A3N delivers a considerable average speed of 0.0227 s (∼44 fps ) for detecting anomalies in the testing phase on used datasets. Several experiments and ablation analyses have been performed on UCSD Pedestrian, CUHK Avenue and ShanghaiTech datasets to validate the efficiency of the proposed model.
Short-term solar irradiance forecasting based on a novel Bayesian optimized deep Long Short-Term Memory neural network

Accurate forecasting is indispensable for improving solar renewables integration and minimizing the effects of solar energy's intermittency. Existing research on time series solar forecasting confronts challenges such as determining the accurate hyperparameters and flexibility in considering meteorological parameters. This study proposes a novel deep learning model, namely an optimized stacked Bi-directional Long Short-Term Memory (BiLSTM)/ Long Short-Term Memory (LSTM) model to forecast univariate and multivariate hourly time series data by integrating stacked LSTM layers, drop out architecture, and LSTM based model. The performance of the model is enhanced by Bayesian optimization with the tuning of six relevant hyperparameters. To evaluate the model, standard Global Horizontal Irradiance (GHI) and observed Plane of Array (POA) irradiance with meteorological real-world solar data from Sweihan Photovoltaic Independent Power project in Abu Dhabi, UAE, and NREL solar data for year-round data are forecasted. Furthermore, the performance of the proposed algorithm is also evaluated under weather uncertainty for different climate types. The forecasting accuracy is evaluated based on various performance metrics and it is observed that the proposed model offered the best R2 values, 0.99 for univariate as well as multivariate models using GHI data and 0.97 using POA data. The findings suggest that the proposed model is a reliable technique for solar prediction due to its comparable performance with both GHI and POA in terms of accuracy.
A BWM approach to determinants of sustainable entrepreneurship in small and medium-sized enterprises

Small and medium-sized enterprises (SMEs) constitute an extremely large percentage of most nations' businesses. These companies are also expressing growing concerns about sustainability and its strategic integration into operations. However, SMEs have been prevented from making investments in sustainability by the absence of a holistic view of this topic, an overemphasis on an economic perspective, and the consequent devaluation of environmental and social perspectives. In this context, greater importance needs to be given to investigating SME sustainability and building a transparent, holistic, and realistic business model. The present study combines cognitive mapping and the best worst method (BWM) to identify determinants of sustainable entrepreneurship in SMEs. These techniques were applied in two meetings with a panel of specialists with knowledge about and experience in this subject matter. The insights obtained during the two panel meetings allowed the proposed process-oriented approach to assist decision-makers in analyzing a sample of SMEs and selecting the ones that best match the following clusters of decision criteria: (1) entrepreneur profile; (2) firm internal characteristics; (3) economic factors; (4) other external factors; and (5) market. The results were validated by four representatives of the Portuguese Institute for Small and Medium-sized Enterprises and Innovation (Instituto de Apoio às Pequenas e Médias Empresas e à Inovação (IAPMEI) in Portuguese). The study's contributions and limitations are also discussed.
DualGraph: Improving Semi-supervised Graph Classification via Dual Contrastive Learning

In this paper, we study semi-supervised graph classification, a fundamental problem in data mining and machine learning. The problem is typically solved by learning graph neural networks with pseudo-labeling or knowledge distillation to incorporate both labeled and unlabeled graphs. However, these methods usually either suffer from overconfident and biased pseudo-labels or suboptimal distillation caused by the insufficient use of unlabeled data. Inspired by the recent progress of contrastive learning and dual learning, we propose DualGraph, a principled framework to leverage unlabeled graphs more effectively for semi-supervised graph classification. DualGraph consists of a prediction module and a retrieval module to model graphs <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$G$</tex> and their labels <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$y$</tex> from opposite while complementary views (i.e., p(y | G) and p(G | y) respectively). The two modules are jointly trained via posterior regularization, which encourages their inter-module consistency on unlabeled graphs. Moreover, we improve model training for each module with a contrastive learning framework to encourage the intra-module consistency on unlabeled data. Experimental results on a range of publicly accessible datasets reveal the effectiveness of our DualGraph.
An emperor penguin optimizer application for medical diagnostics

Extreme gradient boosting (XGBoost) is a broadly adopted machine learning approach often applied to classification problems. It outperforms many contemporary methods with high classification accuracy and admirable performance. However it presets a large number of parameters that require adequate adjustment in order to ensure proper functionality. To address this, a swarm intelligence (SI) algorithm is applied to optimize parameter selection. The Emperor penguin optimizer (EPO) is a well known nature-inspired algorithm. It mathematically models huddling behaviors that allow emperor penguins to survive the harsh arctic conditions and excels in tackling problems concerning optimization. The proposed model combines the EPO algorithm with the XGBoost methodology adapted to hyperparameter optimization and feature selection. The performance of the resulting EPO-XGBoost algorithm is compared with other contemporary algorithms, and results indicate it outperforms them in terms of classification accuracy and performance when tested on datasets concerning well-known real world medical diagnosis problems of breast cancer and diabetes.
YOLOv5-AC: Attention Mechanism-Based Lightweight YOLOv5 for Track Pedestrian Detection

In response to the dangerous behavior of pedestrians roaming freely on unsupervised train tracks, the real-time detection of pedestrians is urgently required to ensure the safety of trains and people. Aiming to improve the low accuracy of railway pedestrian detection, the high missed-detection rate of target pedestrians, and the poor retention of non-redundant boxes, YOLOv5 is adopted as the baseline to improve the effectiveness of pedestrian detection. First of all, L1 regularization is deployed before the BN layer, and the layers with smaller influence factors are removed through sparse training to achieve the effect of model pruning. In the next moment, the context extraction module is applied to the feature extraction network, and the input features are fully extracted using receptive fields of different sizes. In addition, both the context attention module CxAM and the content attention module CnAM are added to the FPN part to correct the target position deviation in the process of feature extraction so that the accuracy of detection can be improved. What is more, DIoU_NMS is employed to replace NMS as the prediction frame screening algorithm to improve the problem of detection target loss in the case of high target coincidence. Experimental results show that compared with YOLOv5, the AP of our YOLOv5-AC model for pedestrians is 95.14%, the recall is 94.22%, and the counting frame rate is 63.1 FPS. Among them, AP and recall increased by 3.78% and 3.92%, respectively, while the detection speed increased by 57.8%. The experimental results verify that our YOLOv5-AC is an effective and accurate method for pedestrian detection in railways.