Reviewerly/exHarmony huggingface dataset:
Experimental Performance Comparison of Bidirectional Actuator Configurations for Suspended Aerial Platforms

Suspended payload requires bidirectional thrust actuation to control their motion. Such systems, like the small suspended aerial cliff sampling system considered in this letter, require strong bidirectional actuators capable of fine positioning and fast disturbance rejection to fight wind gusts. This letter presents a detailed experimental comparison of three bidirectional thrust actuator configurations (i.e., reverse thrust, antagonist thrusters, and variable pitch propeller) to understand their characteristics at the scale of small aerial systems. Five tests highlight the strengths and weaknesses of each configuration regarding static thrust capabilities, large and small step response, and bandwidth capabilities. This information is then used to select the best configuration for the suspended aerial cliff sampling system described, which in turn was able to sample rare cliff plants in Kaua'i in winds gusts of up to 37 km/h. The results will also help aerial roboticists make better-informed design decisions.
K-Means Clustering Approach to UAS Classification via Graphical Signal Representation of Radio Frequency Signals for Air Traffic Early Warning

Small Unmanned Aerial Systems (UASs) provide significant benefits to economies across the world on a daily basis but increased usage brings a number of security challenges. For example, identifying small UASs operating with malicious intent in a restricted airspace. Supervised learning techniques applied to radio frequency (RF) signals have been considered for the classification of UAS type with high accuracy but due to labelled data assume the UAS signal is already known. Unsupervised learning algorithms such as K-means clustering provide a potential for identifying small UAS signals which have not been seen before. The use of transfer learning and CNN feature extraction (FE) with spectrogram graphical signal representations have been successfully used in a supervised manner for medical diagnosis and audio classification. This research is the first application of transfer learning and CNN FE as a pre-cursor to an unsupervised learning algorithm. This paper shows that clustering graphical representations of the signal and utilising CNN FE with transfer learning produces the highest v-measure score 0.814 but at a cost of 6s in time. Small UASs can travel at speeds of 45 mph so timely detection is essential in many use cases. A decrease in 0.2 v-measure score using PSD graphical image representations of the RF signal and PCA initialisation allows the clustering time to complete in under 0.3s even in environments with active interference in the same band. This timely result could provide effective early warning with the cueing of a secondary sensor or supervised algorithm with higher classification accuracy.
An Unsupervised Depth-Estimation Model for Monocular Images Based on Perceptual Image Error Assessment

In this paper, we propose a novel unsupervised learning-based model for estimating the depth of monocular images by integrating a simple ResNet-based auto-encoder and some special loss functions. We use only stereo images obtained from binocular cameras as training data without using depth ground-truth data. Our model basically outputs a disparity map that is necessary to warp an input image to an image corresponding to a different viewpoint. When the input image is warped using the output-disparity map, distortions of various patterns inevitably occur in the reconstructed image. During the training process, the occurrence frequency and size of these distortions gradually decrease, while the similarity between the reconstructed and target images increases, which proves that the accuracy of the predicted disparity maps also increases. Therefore, one of the important factors in this type of training is an efficient loss function that accurately measures how much the difference in quality between the reconstructed and target images is and guides the gap to be properly and quickly closed as the training progresses. In recent related studies, the photometric difference was calculated through simple methods such as L1 and L2 loss or by combining one of these with a traditional computer vision-based hand-coded image-quality assessment algorithm such as SSIM. However, these methods have limitations in modeling various patterns at the level of the human visual system. Therefore, the proposed model uses a pre-trained perceptual image-quality assessment model that effectively mimics human-perception mechanisms to measure the quality of distorted images as image-reconstruction loss. In order to highlight the performance of the proposed loss functions, a simple ResNet50-based network is adopted in our model. We trained our model using stereo images of the KITTI 2015 driving dataset to measure the pixel-level depth for 768 × 384 images. Despite the simplicity of the network structure, thanks to the effectiveness of the proposed image-reconstruction loss, our model outperformed other state-of-the-art studies that have been trained in unsupervised methods on a variety of evaluation indicators.
Quantum random access stored-program machines

Random access machines (RAMs) and random access stored-program machines (RASPs) are models of computing that are closer to the architecture of real-world computers than Turing machines (TMs). They are also convenient in complexity analysis of algorithms. The relationships between RAMs, RASPs and TMs are well-studied. However, clear relationships between their quantum counterparts are still missing in the literature. We fill in this gap by formally defining the models of quantum random access machines (QRAMs) and quantum random access stored-program machines (QRASPs) and clarifying the relationships between QRAMs, QRASPs and quantum Turing machines (QTMs). In particular, we show that $\textbf{P} \subseteq \textbf{EQRAMP} \subseteq \textbf{EQP} \subseteq \textbf{BQP} = \textbf{BQRAMP}$, where $\textbf{EQRAMP}$ and $\textbf{BQRAMP}$ stand for the sets of problems that can be solved by polynomial-time QRAMs with certainty and bounded-error, respectively. At the heart of our proof, is a standardisation of QTM with an extended halting scheme, which is of independent interest.
Multimodal based attention-pyramid for predicting pedestrian trajectory

The goal of pedestrian trajectory prediction is to predict the future trajectory according to the historical one of pedestrians. Multimodal information in the historical trajectory is conducive to perception and positioning, especially visual information and position coordinates. However, most of the current algorithms ignore the significance of multimodal information in the historical trajectory. We describe pedestrian trajectory prediction as a multimodal problem, in which historical trajectory is divided into an image and coordinate information. Specifically, we apply fully connected long short-term memory (FC-LSTM) and convolutional LSTM (ConvLSTM) to receive and process location coordinates and visual information respectively, and then fuse the information by a multimodal fusion module. Then, the attention pyramid social interaction module is built based on information fusion, to reason complex spatial and social relations between target and neighbors adaptively. The proposed approach is validated on different experimental verification tasks on which it can get better performance in terms of accuracy than other counterparts.
A hierarchical protection scheme for intellectual property of semi-open source datasets based on double watermarking

The high quality datasets are an important guarantee for the rapid development of deep learning, so the protection of datasets should be taken seriously. The semi-open source datasets are the datasets that are free for some users and used for academic communication and are charged for another part of users and generates commercial value. However, most of the existing datasets protection mechanisms are only a single passive copyright verification mechanism, which cannot protect semi-open source datasets well. Based on this situation, this paper proposes a hierarchical protection scheme for the intellectual property of semi-open source datasets based on double watermarking. The scheme adopts different protection schemes for datasets for the above mentioned user groups: internal users and purchased users, to achieve the purpose of hierarchical protection. The passive verification and active protection functions for semi-open source datasets are realized by embedding blind watermark and visual watermark, respectively. Finally, the paper conducts experiments on a number of underlying datasets to verify the imperceptibility, universality, harmlessness and effectiveness of the scheme.
Helmholtz machine with differential privacy

Helmholtz machine(HM) is the classic hierarchical probabilistic model for building the probability distribution of perception data, and the wake-sleep(WS) algorithm has been widely used as a training algorithm. To prevent the attacker from restoring the training set data by using the trained HM model, we introduce a Gaussian mechanism to WS algorithm to propose wake-sleep algorithm based on differential privacy (DP-WS) and use DP-WS to train HM to get HM model with privacy protection, named DP-HM. We provide rigorous proof of the privacy guarantee. In addition, our experiments on MNIST and BioID face datasets show that DP-HM model can be trained under a modest privacy budget and still have acceptable model quality.
Generation and Evaluation of Different Modality of Medical Image Based on GAN

In recent years, computed tomography(CT) image, positron emission tomography(PET) image and anatomical image are often used for medical diagnosis. Each have different pros and cons. However, the diagnosis based on some modality images sometimes forces time and cost. Hence We aim to generate image which has different modality among CT image, PET image and anatomical image. Firstly, We apply Generative Adversarial Network for generate a psudo image. And generate image using DCGAN and CycleGAN. In the experiment about CycleGAN, We try to improve accuracy by changing the dataset. Finally, we evaluate generated image.
TANet: Target Attention Network for Video Bit-Depth Enhancement

Video bit-depth enhancement (VBDE) reconstructs high-bit-depth (HBD) frames from a low-bit-depth (LBD) video sequence. As neighboring frames contain a considerable amount of complementary information related to the center frame, it is vital for VBDE to exploit neighboring frames as much as possible. Conventional VBDE algorithms with explicit alignment across frames attempt to warp each neighboring frame to the center frame with estimated optical flow, taking into account only pairwise correlation. Most spatiotemporal fusion approaches involve direct concatenation or 3D convolution and treat all features equally, failing to focus on information related to the center frame. Therefore, in this paper, we introduce an improved nonlocal block as a global attentive alignment (GAA) module, which takes the whole input video sequence into consideration to capture features that are globally correlated, to perform implicit alignment. Furthermore, given the bulk of features extracted from the center and neighboring frames, we propose target-guided attention (TGA). TGA can exploit more center-frame-related details and facilitate feature fusion. The proposed network (dubbed TANet) is capable of effectively eliminating false contours and recovering the center frame in high quality, as demonstrated by the experimental results. TANet outperforms state-of-the-art models in terms of both PSNR and SSIM with low time consumption.
Forensic Handwritten Signature Identification Using Deep Learning

Forgery is a type of fraud defined as the act of forging a copy or an imitation of a document, signature, or banknote which is considered a form of illegal criminal activity. In this paper, we are focusing on the identification and detection of handwritten signature forgeries inside documents. The proposed system uses contemporary methods that utilize a deep learning approach of CNNs (Convolutional Neural Networks) for binary image classification and aims to help forensic examiners measure the genuineness of handwritten signatures. We considered using a number of five different classification models of CNN which are, VGG-16, ResNet50, Inception-v3, Xception, and Our CNN model. The purpose for using these different CNN models is to determine and study which model is best at identifying images containing text data containing similar resemblances. Upon comparing these CNN models, we concluded that the ResNet50 model was able to reach the highest score at identifying handwritten signatures with an accuracy of 82.3% and 86% when tested on datasets of 300 images and 140 images respectively. Regarding future work, this is a required step that determines what model to focus on for more in-depth analysis and classification of the characteristics of handwritten signatures.
Aspect-Opinion Correlation Aware and Knowledge-Expansion Few Shot Cross-Domain Sentiment Classification

Cross-domain sentiment analysis has recently attracted significant attention, which can effectively alleviate the problem of lacking large-scale labeled data for deep neural network based methods. However, most of the existing cross-domain sentiment classification models neglect the domain-specific features, which limits their performance especially when the domain discrepancy becomes larger. Meanwhile, the relations between the aspect and opinion terms cannot be effectively modeled and thus the sentiment transfer error problem is suffered in the existing unsupervised domain-adaptation methods. To address these two issues, we propose an aspect-opinion correlation aware and knowledge-expansion few shot cross-domain sentiment classification model. Sentiment classification can be effectively conducted with only a few support instances of the target domain. Extensive experiments are conducted and the experimental results show the effectiveness of our proposed model.
Large Graph Signal Denoising With Application to Differential Privacy

Over the last decade, signal processing on graphs has become a very active area of research. Specifically, the number of applications, for instance in statistical or deep learning, using frames built from graphs, such as wavelets on graphs, has increased significantly. We consider in particular the case of signal denoising on graphs via a data-driven wavelet tight frame methodology. This adaptive approach is based on a threshold calibrated using Stein's unbiased risk estimate adapted to a tight-frame representation. We make it scalable to large graphs using Chebyshev-Jackson polynomial approximations, which allow fast computation of the wavelet coefficients, without the need to compute the Laplacian eigendecomposition. However, the overcomplete nature of the tight-frame, transforms a white noise into a correlated one. As a result, the covariance of the transformed noise appears in the divergence term of the SURE, thus requiring the computation and storage of the frame, which leads to an impractical calculation for large graphs. To estimate such covariance, we develop and analyze a Monte-Carlo strategy, based on the fast transformation of zero mean and unit variance random variables. This new data-driven denoising methodology finds a natural application in differential privacy. A comprehensive performance analysis is carried out on graphs of varying size, from real and simulated data.
Dynamic Image Encryption using Neural Networks for Medical Images

There is an increasing need to secure medical images for safe storage and transmission under the booming field of E-Healthcare and telemedicine. Medical images require utmost privacy due to the high level of sensitive information they carry. Most hospitals use AES - 256 to encrypt data in the current scenario. The key used for encryption is randomly generated, and at times this can pose problems to the strength of the encryption. This work proposes a novel cipher that strengthens the encryption using a dynamic key generated by a neural network for medical images. A novel encryption method in the lines of symmetric block encryption is proposed where the region of interest (ROI) of the given medical image is extracted and used as an input seed for a pseudo-random number generator (PRNG) generated using a generative adversarial network (GAN) and served as the key to the novel encryption algorithm. The National Institute of Standards and Technology (NIST) suite evaluates the GAN-based PRNG and the encryption results are benchmarked against existing fast cipher standards and evaluated with common methods like histogram analysis, NPCR values, information entropy, visual testing and complexity analysis.
Image Encryption using Fractional Singular Chaotic Systems: An Extended Kalman Filtering Approach

Modern and complex dynamical modeling requires effective and precise state estimation techniques to make the system modes available for control actions without expensive measurement tools. By proposing a reliable Kalman-type estimation algorithm in this study, we aim to synchronize a chaotic fractional order singular (FOS) system which introduces some important information related to memory effects and interconnection properties. Then, the proposed algorithm is used to a secure communication framework, and an image-based cryptography algorithm is provided, in which the main image is well-masked by chaotic FOS signals and is successfully decrypted from the estimated chaotic states in receiver side. Furthermore, the performance of the suggested encryption scheme are demonstrated using histograms, pixel correlation, and key space analyses.
Quantum cryptography for the benefit of API keys safety

To cope with the unauthorized access to the cloud treasure of resources and services, several cloud service providers implement the API-based access control approach and grant thus a full authentication of any client application. Once called, each cloud API (Application Programming Interface) is required to authenticate through a secret access key, commonly termed API key. A plethora of security risks is associated with these keys whether during their generation, storage, or utilization. The hardware secure element-based proposal aims for an end-to-end security between a cloud service provider and a client application. To ensure a reliable and secure API key exchange, the concerned entities rely on Public Key Infrastructure (PKI). As soon as an adversary acquires an unbounded computing power, it would be easy to intercept the keys and then unlock the gate behind the valuables over the cloud. In this paper, we propose a revised scheme that discards the PKI and installs a quantum mechanism to mutually authenticate the secure element and the cloud service provider by establishing a set of shared keys. A pioneering absolute security of the presented approach is warranted by the principles of quantum physics. To analyze the security of the quantum technology, we establish a formal verification based on PRISM model checking tool. We outstandingly focus on satisfying two prominent properties: (i) both the parties engaged in the quantum protocol are able to detect any disallowed eavesdropping and (ii) the valid amount of information caught by an adversary on the installed key must be negligible.
A Study on Video Analytics and Their Performance Analysis for Various Object Detection Algorithms

Video analytics has gained immense significance from the industry and academics as it provides simultaneous monitoring, analysis, and swift response to unforeseen circumstances. The ability to automatically identify temporal and spatial occurrences in videos and generate important insights is a key element of video analytics. The prime functionality of Video analytics includes Object Detection, Anomaly Detection, and Video annotation. Video analytics is extremely beneficial in the security domain, and video surveillance, an application of video analytics, is used to identify improper behavior or anomalies. Anomaly detection is important in cybersecurity intrusion detection, system health monitoring, surveillance detection in sensor networks, and law enforcement. Analysis of videos can be achieved using Object Detection algorithms like You Only Look Once (YOLO), Region Proposals algorithms (R-CNN, Fast R-CNN and Faster R-CNN), or Single Shot MultiBox Detector (SSD). A comprehensive introduction, network architecture, and internal mechanisms of these algorithms are presented in this paper. Furthermore, a comparative performance analysis of these models was conducted by evaluating the models based on results obtained from the implementation and studying previous research works. We further discuss some of the challenges encountered in video analytics and a set of possible future opportunities and new perspectives on addressing them.
A Data Entry Optical Character Recognition Tool using Convolutional Neural Networks

Almost all institutions and organizations rely substantially on data to run their operations. Data is necessary for making informed decisions, adapting to change, and defining strategic objectives. Data administration has always relied on manual data entry. Manual input is used to entail transferring data from various documents into record books, ledger books, and other such books. Manual data entry, as used in recent years, comprises manually entering particular and predetermined data into a target program, such as customer name, business kind, money amount, and so on, from various sources, such as paper bills, invoices, orders, receipts, and so on. Depending on the sort of business, the target program can be handwritten records, spreadsheets, or computer databases. Several businesses require manual data entry, which has a high rate of mistakes. This is because the manual approach places far too much reliance on the ability of humans tocomprehend handwritten documents. As a result, a method for retrieving and storing information from images, particularly text, is required. OCR (optical character recognition) is a rapidly growing topic of research aimed at creatinga computer system that can automatically extract and interpret text from images. OCR converts any type of text or text-containing documents, such as handwritten text, printed text, or scanned text images, into an editable digital format for deeper and more complex processing. As a result, OCR enables a machine to recognize text in such documents without the need for human intervention. In order to achieve successful automation, a few significant difficulties must be identified and resolved. One of the most pressing issues is the quality of character typefaces in paper documents, as well as image quality. The computer system may not correctly recognize characters as a result of these difficulties. We examine OCR utilizing four different approaches in this research. We begin by laying down all of the possible issues that may arise during the OCR stages. We next go over the pre-processing, segmentation, normalization, feature extraction, classification, and post- processing aspects of an OCR system. As a result, this conversation paints a rather complete picture of the current state of text recognition domain.