Reviewerly/exHarmony huggingface dataset:
Multi-UAV Distributed Collaborative Coverage for Target Search Using Heuristic Strategy

Collaborative coverage for target search using a group of unmanned aerial vehicles (UAVs) has received increasing attention in recent years. However, the design of distributed control strategy and coordination mechanisms remains a challenge. This paper presents a distributed and online heuristic strategy to solve the problem of multi-UAV collaborative coverage. As a basis, each UAV maintains a probability grid map in the form of a locally stored matrix, without shared memory. Then we design two evaluation functions and related technical strategies to enable UAVs to make state transfer or area transfer decisions in an online self-organizing way. The simulation results show that the algorithm integrates geometric features such as parallel search and internal spiral search, and is not interfered by factors such as sudden failure of UAVs, changes in detection range, and target movement. Compared with other commonly used methods for target search, our strategy has high search efficiency, good robustness, and fault tolerance.
Effect of image binarization thresholds on breast cancer identification in mammography images using OTSU, Niblack, Burnsen, Thepade's SBTC

Mortality rates due to breast cancer in women are very high. Accurate prediction and detection at an early stage of cancer are required for a reduction in death rates. A Computer-Aided Diagnosis (CAD) is very useful for doctors in deciding screening levels. For identifying cancerous tumors, a CAD-based classification of a benign, malignant tumor using Machine learning is one of the approaches. This paper has observed the effect of various thresholding methods like Otsu, Niblack, Bernsen, Thepade's Sorted block Truncation Coding (TSBTC), and feature level fusions of them in the identification of breast cancer at an early stage. Extracted features using thresholding techniques are fed as an input to machine learning algorithms for the classification of Benign, malignant, normal tumors, which leads to the identification of breast cancer. For experimentation purposes, a Mammographic Image Analysis Society (MIAS) dataset for Breast cancer detection is used. The accuracy of these machine learning algorithms is calculated using Weka's open-source machine learning software. Finally, the accuracy of machine learning classifiers along with presented thresholding techniques is evaluated.
A spatial-compositional feature fusion convolutional autoencoder for multivariate geochemical anomaly recognition

The spatial structural features and compositional relationships of multivariate geochemicals are influenced by complex geological processes (e.g., diagenesis and mineralization), and can help identify geochemical anomalies and provide key references for mineral resource exploration. However, previous machine-learning-based models often treat spatial structural features or compositional relationships separately. Based on the multitask stack autoencoder structure, this study proposes a feature fusion convolutional autoencoder (FCAE) to extract and fuse the spatial structural features and compositional relationships of multivariate geochemicals for identifying geochemical anomalies. In addition, a three-stage training (3ST) strategy combining greedy layerwise pretraining and overall fine-tuning is adopted to calibrate the FCAE. To assess the performance, the proposed FCAE was used to identify the anomalies related to the Cu ore in the southwest area of the Wuyishan polymetallic metallogenic belt in China. The results showed that fusing both spatial structural features and compositional relationships effectively improved the accuracy of the anomaly identification. The FCAE outperformed several existing models by achieving an AUC of 0.863, a recall of 0.909, and the highest intersection point of the P-A plot in the experiments. In addition, the FCAE is less sensitive to the size of the convolution window, which makes the method more applicable and reliable for mineral resource exploration.
Secure Heterogeneous Data Deduplication via Fog-Assisted Mobile Crowdsensing in 5G-Enabled IIoT

Mobile crowdsensing provides the data collection and sharing for the 5G-enabled industrial Internet of Things. However, the redundant and duplicated heterogeneous sensing data bring unnecessary heavy storage and communication overhead. In this article, we propose a secure heterogeneous data deduplication scheme, which introduces the privacy-preserving cosine similarity computing to eliminate the replicate sensing data without privacy leakage in mobile crowdsensing. Specifically, we use the proxy re-encryption algorithm to realize secure and accurate task assignment via fog-assisted mobile crowdsensing. Based on lightweight two-party random masking and polynomial aggregation techniques, we achieve the privacy-preserving cosine similarity computing protocol. Finally, we conduct the privacy analysis, and experimental results on real-world datasets show that our approach is practical and effective.
Path-length analysis for grid-based path planning

In video games and robotics, one often discretizes a continuous 2D environment into a regular grid with blocked and unblocked cells and then finds shortest paths for the agents on the resulting grid graph. Shortest grid paths, of course, are not necessarily true shortest paths in the continuous 2D environment. In this article, we therefore study how much longer a shortest grid path can be than a corresponding true shortest path on all regular grids with blocked and unblocked cells that tessellate continuous 2D environments. We study 5 different vertex connectivities that result from both different tessellations and different definitions of the neighbors of a vertex. Our path-length analysis yields either tight or asymptotically tight worst-case bounds in a unified framework. Our results show that the percentage by which a shortest grid path can be longer than a corresponding true shortest path decreases as the vertex connectivity increases. Our path-length analysis is topical because it determines the largest path-length reduction possible for any-angle path-planning algorithms (and thus their benefit), a class of path-planning algorithms in artificial intelligence and robotics that has become popular.
A Survey on SQL Injection Attack: Detection and Challenges

Web applications are seen to play a vital role in the individual lives and even assist in the economic development of the country. However, many security vulnerabilities have been developed which can attack the web applications. One of the most common security threats which affect the web applications is the Structured Query Language Injection (SQL Injection or SQLi) attacks. The Open Web Application Security Project (OWASP) ranked them at the top out of the 10 most effective vulnerable attacks. This attack has been used for a long time for attacking different websites and webpages, due to which the victims had to suffer severe financial loss. In this study, the researchers investigated the different methods which could be used for detecting the SQLi attacks and their limitations. Additionally, they have also highlighted the direction of research being conducted in this area.
FCN-LectureNet: Extractive Summarization of Whiteboard and Chalkboard Lecture Videos

Recording and sharing of educational or lecture videos has increased in recent years. Within these recordings, we find a large number of math-oriented lectures and tutorials which attract students of all levels. Many of the topics covered by these recordings are better explained using handwritten content on whiteboards or chalkboards. Hence, we find large numbers of lecture videos that feature the instructor writing on a surface. In this work, we propose a novel method for extraction and summarization of the handwritten content found in such videos. Our method is based on a fully convolutional network, FCN-LectureNet, which can extract the handwritten content from the video as binary images. These are further analyzed to identify the unique and stable units of content to produce a spatial-temporal index of handwritten content. A signal which approximates content deletion events is then built using information from the spatial-temporal index. The peaks of this signal are used to create temporal segments of the lecture based on the notion that sub-topics change when large portions of content are deleted. Finally, we use these segments to create an extractive summary of the handwritten content based on key-frames. This will facilitate content-based search and retrieval of these lecture videos. In this work, we also extend the AccessMath dataset to create a novel dataset for benchmarking of lecture video summarization called LectureMath. Our experiments on both datasets show that our novel method can outperform existing methods especially on the larger and more challenging dataset. Our code and data are publicly available.
ANCES: A novel method to repair attribute noise in classification problems

Noise negatively affects the complexity and performance of models built in classification problems. The most common approach to mitigate its consequences is the usage of preprocessing techniques, known as noise filters, which are designed to remove noisy samples from the training data. Nevertheless, they are specifically oriented to deal with errors affecting class labels. Their employment may not always result in an improvement when noise affects attribute values. In these cases, correcting the errors is an interesting alternative to traditional noise filtering that has not been enough studied so far in the specialized literature. This research proposes an attribute noise correction method with the final aim of increasing the performance of the classification algorithms used later. The identification of noisy data is based on an error score assigned to each one of the attribute values in the dataset, which are then passed through an optimization process to correct their potential noise. The validity of the proposed method is studied in an exhaustive experimental study, in which it is compared to several well-known preprocessing methods to deal with noisy datasets. The results obtained show the suitability of attribute noise correction with respect to the other alternatives when data suffer from attribute noise.
Unsupervised Monocular Depth Estimation in Highly Complex Environments

With the development of computational intelligence algorithms, unsupervised monocular depth and pose estimation framework, which is driven by warped photometric consistency, has shown great performance in the day-time scenario. While in some challenging environments, like night and rainy night, the essential photometric consistency hypothesis is untenable because of the complex lighting and reflection, so that the above unsupervised framework cannot be directly applied to these complex scenarios. In this paper, we investigate the problem of unsupervised monocular depth estimation in highly complex scenarios and address this challenging problem by adopting an image transfer-based domain adaptation framework. We adapt the depth model trained on day-time scenarios to be applicable to night-time scenarios, and constraints on both feature space and output space promote the framework to learn the key features for depth decoding. Meanwhile, we further tackle the effects of unstable image transfer quality on domain adaptation, and an image adaptation approach is proposed to evaluate the quality of transferred images and re-weight the corresponding losses, so as to improve the performance of the adapted depth model. Extensive experiments show the effectiveness of the proposed unsupervised framework in estimating the dense depth map from highly complex images.
CS-GAN: Cross-Structure Generative Adversarial Networks for Chinese calligraphy translation

Generative Adversarial Networks (GANs) have made great progress in cross-domain image translation. In fact, image-to-image translation tasks often encounter structural differences in two domains, such as translation on unpaired Chinese calligraphy dataset. However, existing models can only convert color and texture features and keep the structures unchanged (e.g.: in apples to oranges tasks, these models only convert the color of apples, but maintain the shape of apples). In order to address cross-structure image translation, such as cross-structure translation of Chinese calligraphy, a novel Generative Adversarial Networks (GAN) model, named CS-GAN, is proposed in this paper. In CS-GAN, distribution transform, reparameterization trick and sampling features are used to convert feature maps obtained from domain S to domain T. Then images of domain T are generated through features concatenation. The proposed CS-GAN is verified on three sets of Chinese calligraphic data with structural differences from three famous calligraphers, Yan Zhenqing, Zhao Mengfu and Ouyang Xun. The extensive experimental results show that the proposed CS-GAN successfully transforms the Chinese calligraphy data of different structures and outperforms the state of art models.
Adversarial Multi-Path Residual Network for Image Super-Resolution

Recently, deep convolutional neural networks have demonstrated remarkable progresses on single image super-resolution (SR) problem. However, most of them use more deeper and wider networks to improve SR performance, which is not practical in real-world applications due to large complexity, high computation cost, and low efficiency. In addition, they cannot provide high perception quality and guarantee objective quality simultaneously. To address these limitations, we in this paper propose a novel <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> dversarial <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</u> ulti- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</u> ath <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> esidual <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</u> etwork (AMPRN), which can largely suppress the number of network parameters and achieve a higher SR performance compared with the state-of-the-art methods. More specifically, we propose a multi-path residual block (MPRB) for multi-path residual network (MPRN) with fewer network parameters, which can extract abundant local features by fully using features from different paths generated by channel slices. These hierarchical features from all the MPRBs are then jointly aggregated by global gradual feature fusion. Following MPRN, we construct an adversarial gradient network with a gradient loss to make the gradient distribution of the generated SR images and ground truth image closer. In this way, the generated SR images of our model can provide high perception quality and objective quality. Finally, several experimental results demonstrate that our AMPRN achieves better performance in comparison with fewer parameters than the state-of-the-art methods.
Partition of Unity Methods for Signal Processing on Graphs

Partition of unity methods (PUMs) on graphs are simple and highly adaptive auxiliary tools for graph signal processing. Based on a greedy-type metric clustering and augmentation scheme, we show how a partition of unity can be generated in an efficient way on graphs. We investigate how PUMs can be combined with a local graph basis function (GBF) approximation method in order to obtain low-cost global interpolation or classification schemes. From a theoretical point of view, we study necessary prerequisites for the partition of unity such that global error estimates of the PUM follow from corresponding local ones. Finally, properties of the PUM as cost-efficiency and approximation accuracy are investigated numerically.
Privacy-Preserving Decentralized Aggregation for Federated Learning

In this paper, we develop a privacy-preserving decentralized aggregation protocol for federated learning. We formulate the distributed aggregation protocol with the Alternating Direction Method of Multiplier (ADMM) algorithm and examine its privacy challenges. Unlike prior works that use differential privacy or homomorphic encryption for privacy, we develop a protocol that controls communication among participants in each round of aggregation to minimize privacy leakage. We establish the protocol's privacy guarantee against an honest-but-curious adversary. We also propose an efficient algorithm to construct such a communication pattern, which is inspired by combinatorial block design theory. Our secure aggregation protocol based on the novel group-based communication pattern leads to an efficient algorithm for federated training with privacy guarantees. We evaluate our federated training algorithm on computer vision and natural language processing models over benchmark datasets with 9 and 15 distributed sites. Experimental results demonstrate the privacy-preserving capabilities of our algorithm while maintaining learning performance comparable to the baseline centralized federated learning.
Water meter pointer reading recognition method based on target-key point detection

With the rapid development of video image technology and fifth-generation mobile network technology, the automatic verification of mechanical water meters has become an increasingly important topic in smart cities. Although much research has been done on this subject, the efficiency and accuracy of existing water meter pointer reading technology can still be improved. This paper proposes a new water meter pointer reading recognition method based on target-key point detection. Our method consists of a target detection module and a key point detection module. The target detection module uses a modified YOLOv4-Tiny network to detect and classify the areas where the dials and pointers are located in water meter images with distinct characteristics. The key point detection module is used to detect the key point of the pointer image. In this module, the structure of the RFB-Net network is improved to introduce multiple layers of low-level feature information, therefore, it can make full use of the information between multi-scale feature layers for key point detection. In addition to, aiming at the problem of dial rotation, a method of establishing a right-angle coordinate system based on key point is proposed to realize pointer reading. The whole method proposed in this paper is compared to the Hough transform feature matching algorithm and traditional machine learning algorithms through experiments which test the detection and recognition of the water meter dial, pointer and key points. The experiment results show that the missed detection rate of the model in this paper is 1.88% and 1.07% for the dial region and the pointer region, respectively. And the accuracy rate reaches 98.68%, the average processing time per image is 0.37 s. This implies that the water meter inspection task is completed quickly and accurately with strong robustness. Thanks to the lightweight algorithm of our approach, the model can also be fully automated and easily deployed on mobile devices.
Illicit Account Detection in the Ethereum Blockchain Using Machine Learning

Blockchain is a platform technology for the cryptocurrency's applications like Bitcoin and Ethereum. The purpose of the blockchain is to eliminate the need for third trusted parties such as banks. In recent years and because of the properties of this technology like immutability and transparency, the technology was extended beyond cryptocurrencies and was exploited by various sectors like education, healthcare, finance, energy, government, and IoT providing more privacy, faster transactions and more security. In this research, we investigated Illicit accounts on Ethereum blockchain and proposed a Fraud detection model using three different machine learning algorithms: decision tree (j48), Random Forest and K-nearest neighbors (KNN). These algorithms were applied on a data set obtained from Kaggle.com containing 42 features. We have used the correlation coefficient to select the most effective features and built a new data set using 6 features only. Our research results show a significant improvement in time measurements using the three algorithms and an improvement in the F measure using the Random Forest algorithm.
A New Split for Evaluating True Zero-Shot Action Recognition

Zero-shot action recognition is the task of classifying action categories that are not available in the training set. In this setting, the standard evaluation protocol is to use existing action recognition datasets (e.g. UCF101) and randomly split the classes into seen and unseen. However, most recent work builds on representations pre-trained on the Kinetics dataset, where classes largely overlap with classes in the zero-shot evaluation datasets. As a result, classes which are supposed to be unseen, are present during supervised pre-training, invalidating the condition of the zero-shot setting. A similar concern was previously noted several years ago for image based zero-shot recognition, but has not been considered by the zero-shot action recognition community. In this paper, we propose a new split for true zero-shot action recognition with no overlap between unseen test classes and training or pre-training classes. We benchmark several recent approaches on the proposed True Zero-Shot (TruZe) Split for UCF101 and HMDB51, with zero-shot and generalized zero-shot evaluation. In our extensive analysis we find that our TruZe splits are significantly harder than comparable random splits as nothing is leaking from pre-training, i.e. unseen performance is consistently lower, up to 8.9% for zero-shot action recognition. In an additional evaluation we also find that similar issues exist in the splits used in few-shot action recognition, here we see differences of up to 17.1%. We publish our splits (Splits can be found at https://github.com/kini5gowda/TruZe) and hope that our benchmark analysis will change how the field is evaluating zero- and few-shot action recognition moving forward.
A deadline aware load balancing strategy for cloud computing

Abstract The load balancing (LB) may be used at different levels to reduce overhead for the decision‐making process. In the past decade, cloud computing has drawn a lot of attention from both the academic and commercial communities to get demanded resources (machines, platforms, data, storage, software, and so forth) as a service on rent economically. Generally, a situation may arise when requests are not meeting their deadlines and the cloud provider wants to finish the running application in minimum time. In this article, a receiver initiated deadline aware LB strategy (RDLBS2) has been proposed which attempts the migration of incoming cloudlets to appropriate virtual machines (VMs) where the deadlines of the cloudlets are met to optimize the turnaround time by exploiting the remaining processing capacities of VMs. A simulation study has been carried out by using Cloud‐Sim as a simulator. A sensitivity analysis has been presented to analyze the effects on performance parameters by varying the number of cloudlets and the number of VMs while keeping the remaining input parameters fixed. The experimental evaluation and analysis suggest that RDLBS2 performs significantly better than its peers on objective parameters almost in all cases under study.