Reviewerly/exHarmony huggingface dataset:
MID: A Novel Mountainous Remote Sensing Imagery Registration Dataset Assessed by a Coarse-to-Fine Unsupervised Cascading Network

The geometric registration of mountainous remote sensing images is always a challenging project, as terrain fluctuations increase the complexity. Deep learning, with its superior computing power and data-driven nature, promises to solve this problem. However, the lack of an appropriate dataset limits the development of deep learning technology for mountainous remote sensing image registration, which it still an unsolved problem in photogrammetry and remote sensing. To remedy this problem, this paper presents a manually constructed imagery dataset of mountainous regions, called the MID (mountainous imagery dataset). To create the MID, we use 38 images from the Gaofen-2 satellite developed by China and generated 4093 pairs of reference and sensed image patches, making this the first real mountainous dataset to our knowledge. Simultaneously, we propose a fully unsupervised, convolutional-network-based iterative registration scheme for the MID. First, the large and global deformation of the reference and sensed images is reduced using an affine registration module, generating the coarse alignment. Then, the local and varied distortions are learned and eliminated progressively using a hybrid dilated convolution (HDC)-based encoder–decoder module with multistep iterations, achieving fine registration results. The HDC aims to increase the receptive field without blocking the artifacts, allowing for the continuous characteristics of the mountainous images of a local region to be represented. We provide a performance analysis of some typical registration algorithms and the developed approach for the MID. The proposed scheme gives the highest registration precision, achieving the subpixel alignment of mountainous remote sensing images. Additionally, the experimental results demonstrate the usability of the MID, which can lay a foundation for the development of deep learning technology in large mountainous remote sensing image registration tasks.
Enhanced Dual-Policy Attribute-Based Encryption for Secure Data Sharing in the Cloud

As a promising service paradigm, cloud computing has attracted lots of enterprises and individuals to outsource big data to public cloud. To facilitate secure data using and sharing, dual-policy attribute-based encryption (DP-ABE) is a suitable solution. It allows two access control mechanisms over encrypted data at the same time: one involves access policies over subjective attributes ascribed to user credentials, and the other involves policies over objective attributes ascribed to data. In this work, we are exploring methods to make DP-ABE more flexible, more efficient, and more secure for deployments in cloud scenes. Our proposal features the following achievements simultaneously: (1) beyond the access control mechanisms of DP-ABE, it also supports two flexible features called encryption and key generation in single-policy modes; (2) most operations of key generation, encryption, and decryption are securely outsourced to cloud servers, leaving extremely low overheads for the PKG, data owners, and users; and (3) it realizes the strongest security notion of public-key encryption schemes, namely, CCA security. We formalize the security definition and formally prove its security in the random oracle model. Moreover, we implement the proposed schemes using the Charm framework. The experiment results demonstrate that our schemes are efficient and practical.
Application of Deep Learning to Reduce the Rate of Malignancy Among BI-RADS 4A Breast Lesions Based on Ultrasonography

The aim of the work described here was to develop an ultrasound (US) image-based deep learning model to reduce the rate of malignancy among breast lesions diagnosed as category 4A of the Breast Imaging-Reporting and Data System (BI-RADS) during the pre-operative US examination. A total of 479 breast lesions diagnosed as BI-RADS 4A in pre-operative US examination were enrolled. There were 362 benign lesions and 117 malignant lesions confirmed by postoperative pathology with a malignancy rate of 24.4%. US images were collected from the database server. They were then randomly divided into training and testing cohorts at a ratio of 4:1. To correctly classify malignant and benign tumors diagnosed as BI-RADS 4A in US, four deep learning models, including MobileNet, DenseNet121, Xception and Inception V3, were developed. The performance of deep learning models was compared using the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Meanwhile, the robustness of the models was evaluated by five-fold cross-validation. Among the four models, the MobileNet model turned to be the optimal model with the best performance in classifying benign and malignant lesions among BI-RADS 4A breast lesions. The AUROC, accuracy, sensitivity, specificity, PPV and NPV of the optimal model in the testing cohort were 0.897, 0.913, 0.926, 0.899, 0.958 and 0.784, respectively. About 14.4% of patients were expected to be upgraded to BI-RADS 4B in US with the assistance of the MobileNet model. The deep learning model MobileNet can help to reduce the rate of malignancy among BI-RADS 4A breast lesions in pre-operative US examinations, which is valuable to clinicians in tailoring treatment for suspicious breast lesions identified on US.
Challenges, threats and advantages of using blockchain technology in the framework of sustainability of the logistics sector

Due to the complexity and lack of transparency of traditional supply chains, the introduction and development of blockchain technology is of great interest to the stakeholders involved in the logistics process to improve logistics processes in the supply chain and make them more sustainable. A detailed analysis has been made for the logistics industry in this study, which is discussed to investigate the undeniable benefits of blockchain technology. Today, when we try to determine our application methods in line with sustainable development purposes, it is seen that the logistics industry gains many advantages when it is integrated with blockchain technology. For this purpose, SWOT analysis has been applied as a result of literature review for logistics management, which is one of the sub-activities of supply chain management that applies blockchain technology, and contributes to the application potential in the logistics industry. At this point, some of the strengths that will require the use of blockchain technology in the logistics sector are decentralized structure, effective information sharing, fast response, robust risk management, interprocess integrity, etc. While its weaknesses are low performance, having a complex structure and high energy consumption can be the problem. In addition, some of the opportunities that the sector will gain are the ability to optimize time, solve problems, gain competition, increase sustainability; on the other hand, external threats, cross-integration adoption, new technology, limited data privacy etc. is happening.
Machine Learning and Lexicon Approach to Texts Processing in the Detection of Degrees of Toxicity in Online Discussions

This article focuses on the problem of detecting toxicity in online discussions. Toxicity is currently a serious problem when people are largely influenced by opinions on social networks. We offer a solution based on classification models using machine learning methods to classify short texts on social networks into multiple degrees of toxicity. The classification models used both classic methods of machine learning, such as naïve Bayes and SVM (support vector machine) as well ensemble methods, such as bagging and RF (random forest). The models were created using text data, which we extracted from social networks in the Slovak language. The labelling of our dataset of short texts into multiple classes—the degrees of toxicity—was provided automatically by our method based on the lexicon approach to texts processing. This lexicon method required creating a dictionary of toxic words in the Slovak language, which is another contribution of the work. Finally, an application was created based on the learned machine learning models, which can be used to detect the degree of toxicity of new social network comments as well as for experimentation with various machine learning methods. We achieved the best results using an SVM—average value of accuracy = 0.89 and F1 = 0.79. This model also outperformed the ensemble learning by the RF and Bagging methods; however, the ensemble learning methods achieved better results than the naïve Bayes method.
Gauging Biases in Various Deep Learning AI Models

With the broader usage of Artificial Intelligence (AI) in all areas of our life, accountability of such systems is one of the most important topics for research. Trustworthiness of AI results require very detailed and careful validation of the applied algorithms, as some errors and biases could reside deeply inside AI components, which might affect inclusiveness, equity, justice and irreversibly influence human lives. It is critical to detect them and to reduce their negative effect on AI users. In this paper, we introduce a new approach to bias detection. Using the Deep Learning (DL) models as examples of a broader scope of AI systems, we make the models self-detective of the underlying defects and biases. Our system looks 'under the hood' of AI-model components layer by layer, treating the neurons as similarity estimators – as we claim the main indicator of hidden defects and bias. In this paper, we report on the result of applying our self-detection approach to a Transformer DL model, and its Detection Transformer object detection (DETR) framework, introduced by Facebook AI Research (FAIR) team in 2020. Our approach automatically measures the weights and biases of transformer encoding layers to identify and eventually mitigate the sources of bias. This paper focuses on the measurement and visualization of the weights and biases of the DETR-model layers. The outcome of this research will be our implementation of a modernistic Bias Testing and Mitigation platform. It will be open to the public to validate AI applications and mitigate their biases before their usage.
An Improved Hierarchical Clustering Algorithm Based on the Idea of Population Reproduction and Fusion

Aiming to resolve the problems of the traditional hierarchical clustering algorithm that cannot find clusters with uneven density, requires a large amount of calculation, and has low efficiency, this paper proposes an improved hierarchical clustering algorithm (referred to as PRI-MFC) based on the idea of population reproduction and fusion. It is divided into two stages: fuzzy pre-clustering and Jaccard fusion clustering. In the fuzzy pre-clustering stage, it determines the center point, uses the product of the neighborhood radius eps and the dispersion degree fog as the benchmark to divide the data, uses the Euclidean distance to determine the similarity of the two data points, and uses the membership grade to record the information of the common points in each cluster. In the Jaccard fusion clustering stage, the clusters with common points are the clusters to be fused, and the clusters whose Jaccard similarity coefficient between the clusters to be fused is greater than the fusion parameter jac are fused. The common points of the clusters whose Jaccard similarity coefficient between clusters is less than the fusion parameter jac are divided into the cluster with the largest membership grade. A variety of experiments are designed from multiple perspectives on artificial datasets and real datasets to demonstrate the superiority of the PRI-MFC algorithm in terms of clustering effect, clustering quality, and time consumption. Experiments are carried out on Chinese household financial survey data, and the clustering results that conform to the actual situation of Chinese households are obtained, which shows the practicability of this algorithm.
Improved Q-Learning Applied to Dynamic Obstacle Avoidance and Path Planning

Due to the complexity of interactive environments, dynamic obstacle avoidance path planning poses a significant challenge to agent mobility. Dynamic path planning is a complex multi-constraint combinatorial optimization problem. Some existing algorithms easily fall into local optimization when solving such problems, leading to defects in convergence speed and accuracy. Reinforcement learning has certain advantages in solving decision sequence problems in complex environments. A Q-learning algorithm is a reinforcement learning method. In order to improve the value evaluation of the algorithm in solving practical problems, this paper introduces the priority weight into the Q-learning algorithm. The improved algorithm is compared with existing algorithms and applied to dynamic obstacle avoidance path planning. Experiments show that the improved algorithm dramatically improves the convergence speed and accuracy and increases the value evaluation. The improved algorithm finds the shortest path of 16 units in 27 seconds.
The Holistic Perspective of the INCISIVE Project—Artificial Intelligence in Screening Mammography

Finding new ways to cost-effectively facilitate population screening and improve cancer diagnoses at an early stage supported by data-driven AI models provides unprecedented opportunities to reduce cancer related mortality. This work presents the INCISIVE project initiative towards enhancing AI solutions for health imaging by unifying, harmonizing, and securely sharing scattered cancer-related data to ensure large datasets which are critically needed to develop and evaluate trustworthy AI models. The adopted solutions of the INCISIVE project have been outlined in terms of data collection, harmonization, data sharing, and federated data storage in compliance with legal, ethical, and FAIR principles. Experiences and examples feature breast cancer data integration and mammography collection, indicating the current progress, challenges, and future directions.
Audio Watermarking Algorithm with a Synchronization Mechanism Based on Spectrum Distribution

In order to solve the problem that it is difficult to extract the watermark when the carried audio is subjected to malicious attacks, audio watermarking algorithm based on spectrum distribution is proposed. An eigenvalue is designed to represent the spectrum distribution of the specified frequency-band, and then the binary watermark is embedded by adjusting the difference of the eigenvalues between two adjacent frequency-bands. The polarity of embedding depth is determined by comparing the values of the eigenvalues, which will minimize the modification about the audio so as to improve transparency. The binary watermark can be extracted blindly by judging the difference of the eigenvalues. The improved synchronization mechanism takes the small frame with the largest energy in the voiced frame as the synchronization mark to search the embedding and extracting location of the watermark, which makes the algorithm have strong robustness. Experimental results show that the proposed algorithm has large payload capacity, good transparency, low complexity, and strong robustness against most attacks.
Comparing infrared and webcam eye tracking in the Visual World Paradigm

Visual World eye tracking is a temporally fine-grained method of monitoring attention, making it a popular tool in the study of online sentence processing. Recently, while infrared eye tracking was mostly unavailable, various web-based experiment platforms have rapidly developed webcam eye tracking functionalities, which are now in urgent need of testing and evaluation. We replicated a recent Visual World study on the incremental processing of verb aspect in English using ‘out of the box’ webcam eye tracking software (jsPsych; de Leeuw, 2015) and crowdsourced participants, and fully replicated both the offline and online results of the original study. We furthermore discuss factors influencing the quality and interpretability of webcam eye tracking data, particularly with regards to temporal and spatial resolution; and conclude that remote webcam eye tracking can serve as an affordable and accessible alternative to lab-based infrared eye tracking, even for questions probing the time-course of language processing.
Radar Emitter Recognition Based on Parameter Set Clustering and Classification

An important task in the Electronic Support Measures (ESM) field is analyzing and recognizing radar signals. Feature extraction is one of the primary key elements of radar emitter recognition algorithms. Current research mainly finds statistical features such as the mean and variance of parameters from pluses as the input features of the classifier. However, data noise in intercepted pulse signals greatly interferes with the accuracy of the extracted statistical features and seriously affects the recognition rate of radar emitters. In this paper, we proposed a method of radar emitter recognition. We first clustered parameter sets to establish a set of frequent items and their corresponding clustering centers. Next, we concatenated the clustering centers of each frequent item into a feature vector associated with the data volume dimensions. Then, we built a decision tree classification model based on the feature vector, and finally we used the learned model for the recognition of unknown radar pulse trains. The simulation results show that the proposed method has better robustness when applied to a variety of data volumes and data noise scenarios compared with long short-term memory (LSTM) and support vector machine (SVM) methods.
Deep Learning Model for Selecting Suitable Requirements Elicitation Techniques

Requirement elicitation represents one of the most vital phases in information system (IS) and software development projects. Selecting suitable elicitation techniques is critical for eliciting the correct specification in various projects. Recent studies have revealed that improper novice practices in this phase have increased the failure rate in both IS and software development projects. Previous research has primarily relied on creating procedural systems based on contextual studies of elicitation properties. In contrast, this paper introduces a deep learning model for selecting suitable requirement elicitation. An experiment was conducted wherein a collected dataset of 1684 technique selection attributes were investigate with respect to 14 elicitation techniques. The study adopted seven criteria to evaluate predictive model performance using confusion matrix accuracy, precision, recall, F1 Score, and area under the ROC curve (AUC) and loss curve. The model scored prediction accuracy of 82%, precision score of 0.83, recall score of 0.83, F1 score of 0.82, cross-validation score of 0.82 (± 0.10), One-vs-One ROC AUC score of 0.74, and One-vs-Rest ROC AUC score of 0.75 for each label. Our results indicate the model’s high prediction ability. The model provides a robust decision-making process for delivering correct elicitation techniques and lowering the risk of project failure. The implications of this study can be used to promote the automatization of the elicitation technique selection process, thereby enhancing current required elicitation industry practices.
Quantum Zeno repeaters

Abstract Quantum repeaters pave the way for long-distance quantum communications and quantum Internet, and the idea of quantum repeaters is based on entanglement swapping which requires the implementation of controlled quantum gates. Frequently measuring a quantum system affects its dynamics which is known as the quantum Zeno effect (QZE). Beyond slowing down its evolution, QZE can be used to control the dynamics of a quantum system by introducing a carefully designed set of operations between measurements. Here, we propose an entanglement swapping protocol based on QZE, which achieves almost unit fidelity. Implementation of our protocol requires only simple frequent threshold measurements and single particle rotations. We extend the proposed entanglement swapping protocol to a series of repeater stations for constructing quantum Zeno repeaters which also achieve almost unit fidelity regardless of the number of repeaters. Requiring no controlled gates, our proposal reduces the quantum circuit complexity of quantum repeaters. Our work has potential to contribute to long distance quantum communications and quantum computing via quantum Zeno effect.
A Fusion Multi-Strategy Marine Predator Algorithm for Mobile Robot Path Planning

Path planning is a key technology currently being researched in the field of mobile robotics, but traditional path planning algorithms have complex search spaces and are easily trapped in local minima. To solve the above problems and obtain the global optimal path of the mobile robot, a fusion multi-strategy marine predator algorithm (FMMPA) is proposed in this paper. The algorithm uses a spiral complex path search strategy based on Archimedes’ spiral curve for perturbation to expand the global exploration range, enhance the global search ability of the population and strengthen the steadiness of the algorithm. In addition, nonlinear convex decreasing weights are introduced to balance the ability of the algorithm for global exploration and local exploitation to achieve dynamic updating of the predator and prey population positions. At the same time, the golden sine algorithm idea is combined to update the prey position, narrow the search range of the predator population, and improve the convergence accuracy and speed. Furthermore, the superiority of the proposed FMMPA is verified by comparison with the original MPA and several well-known intelligent algorithms on 16 classical benchmark functions, the Wilcoxon rank sum test and part of the CEC2014 complex test functions. Finally, the feasibility of FMMPA in practical application optimization problems is verified by testing and analyzing the mobile robot path planning application design experiments.
No-Reference Image Quality Assessment by Hallucinating Pristine Features

In this paper, we propose a no-reference (NR) image quality assessment (IQA) method via feature level pseudo-reference (PR) hallucination. The proposed quality assessment framework is rooted in the view that the perceptually meaningful features could be well exploited to characterize the visual quality, and the natural image statistical behaviors are exploited in an effort to deliver the accurate predictions. Herein, the PR features from the distorted images are learned by a mutual learning scheme with the pristine reference as the supervision, and the discriminative characteristics of PR features are further ensured with the triplet constraints. Given a distorted image for quality inference, the feature level disentanglement is performed with an invertible neural layer for final quality prediction, leading to the PR and the corresponding distortion features for comparison. The effectiveness of our proposed method is demonstrated on four popular IQA databases, and superior performance on cross-database evaluation also reveals the high generalization capability of our method. The implementation of our method is publicly available on https://github.com/Baoliang93/FPR.