Reviewerly/exHarmony huggingface dataset:
Quantum advantage for noisy channel discrimination

Many quantum mechanical experiments can be viewed as multi-round interactive protocols between known quantum circuits and an unknown quantum process. Fully quantum "coherent" access to the unknown process is known to provide an advantage in many discrimination tasks compared to when only incoherent access is permitted, but it is unclear if this advantage persists when the process is noisy. Here, we show that a quantum advantage can be maintained when distinguishing between two noisy single qubit rotation channels. Numerical and analytical calculations reveal a distinct transition between optimal performance by fully coherent and fully incoherent protocols as a function of noise strength. Moreover, the size of the region of coherent quantum advantage shrinks inverse polynomially in the number of channel uses, and in an intermediate regime an improved strategy is a hybrid of fully-coherent and fully-incoherent subroutines. The fully coherent protocol is based on quantum signal processing, suggesting a generalizable algorithmic framework for the study of quantum advantage in the presence of realistic noise.
Convolution neural network application for first‐break picking for land seismic data

ABSTRACT An automatic and robust algorithm for the first‐break picking is necessary to build the near‐surface velocity model. We propose the algorithm based on a convolution neural network. The introduced first‐break picking strategy and neural network architecture are suited for processing large volumes of seismic exploration data with reasonable computational resources. To develop an optimal neural network topology and architecture, extensive testing was performed. We compared several architectures of neural networks, including one‐ and two‐dimensional approaches. Our tests justify that the one‐dimensional approach (trace‐by‐trace processing) provides the most reliable results in the case of first‐break travel‐time variations typical of complicated near‐surface structures. This study demonstrates that the four‐layered neural network trained on 5,000 traces is enough for robust first‐break picking. The algorithm is evaluated on two land‐acquisition field datasets from West Siberia with a total used size of about 7 million traces. The first dataset is used for training, and the second one is used only for testing. For both datasets, the error between the original and the predicted first breaks is not more than three samples for 95% of traces. The final evaluation is done by a comparison of seismic stacks to prove the benefits of the approach and its robustness for offsets over 600 m. Finally, the influence of choosing the locations for the training dataset is discussed, and a strategy for using the proposed approach in production work is introduced.
Alternative Methodology and Framework for Assessing Differential Privacy Constraints and Consequences From a GDPR Perspective

Nowadays, manipulation with personal data is constant and used in many industries, and while processing such data, questions arise related to the need to minimize or eliminate the risks of personal data disclosure. Meanwhile, various regulatory acts and laws, including GDPR, has been established to regulate the processing and collection of data, which, in practice, may result in unwanted personal data disclosure. However, data anonymization and a clear description of the term and process of anonymization are not clearly formulated in the context of the GDPR legislation, which causes the issue of multiple interpretations and poses additional risks of personal data disclosure. These issues create a valuable need to use different privacy-enhancing technologies such as differential privacy (DP). This study establishes hypotheses about the DP application and its limitation from the GDPR perspective. Furthermore, it considers technical and practical approaches for such integration and implications regarding personal data confidentiality. Finally, these hypotheses are presented in the framework of a future Doctoral study, which, among other things, will examine the significance of the DP concept in the existing legal ecosystem. This position paper proposes a novel approach to investigate the GDPR impact on data subject privacy with respect to the DP concept and focus on the anonymization level.
Neural Network-based Motion Feasibility Checker to Validate Instructions in Rearrangement Tasks before Execution by Robots

In this paper, we address the task of rearranging items with a robot. A rearrangement task is challenging because it requires us to solve the following issues: determine how to pick the items and plan how and where to place the items. In our previous work, we proposed to solve a rearrangement task by combining the symbolic and motion planners using a Motion Feasibility Checker (MFC) and a Monte Carlo Tree Search (MCTS). The MCTS searches for the goal while it collaborates with the MFC to accept or reject instructions. We could solve the rearrangement task, but one drawback is the time it takes to find a solution. In this study, we focus on quickly accepting or rejecting tentative instructions obtained from an MCTS. We propose using a Neural Network-based Motion Feasibility Checker (NN-MFC), a Fully Connected Neural Network trained with data obtained from the MFC. This NN-MFC quickly decides if the instruction is valid or not, reducing the time the MCTS uses to find a solution to the task. The NN-MFC determines the validity of the instruction based on the initial and target poses of the item. Before the final execution of the instructions, we re-validate the instructions with the MFC as a confirmation before execution. We tested the proposed method in a simulation environment by doing an item rearrangement task in a convenience store setup.
Realistic Image Generation from Text by Using BERT-Based Embedding

Recently, in the field of artificial intelligence, multimodal learning has received a lot of attention due to expectations for the enhancement of AI performance and potential applications. Text-to-image generation, which is one of the multimodal tasks, is a challenging topic in computer vision and natural language processing. The text-to-image generation model based on generative adversarial network (GAN) utilizes a text encoder pre-trained with image-text pairs. However, text encoders pre-trained with image-text pairs cannot obtain rich information about texts not seen during pre-training, thus it is hard to generate an image that semantically matches a given text description. In this paper, we propose a new text-to-image generation model using pre-trained BERT, which is widely used in the field of natural language processing. The pre-trained BERT is used as a text encoder by performing fine-tuning with a large amount of text, so that rich information about the text is obtained and thus suitable for the image generation task. Through experiments using a multimodal benchmark dataset, we show that the proposed method improves the performance over the baseline model both quantitatively and qualitatively.
Bi-GRU Relation Extraction Model Based on Keywords Attention

Abstract Relational extraction plays an important role in the field of natural language processing to predict semantic relationships between entities in a sentence. Currently, most models have typically utilized the natural language processing tools to capture high-level features with an attention mechanism to mitigate the adverse effects of noise in sentences for the prediction results. However, in the task of relational classification, these attention mechanisms do not take full advantage of the semantic information of some keywords which have information on relational expressions in the sentences. Therefore, we propose a novel relation extraction model based on the attention mechanism with keywords, named Relation Extraction Based on Keywords Attention (REKA). In particular, the proposed model makes use of bi-directional GRU (Bi-GRU) to reduce computation, obtain the representation of sentences, and extracts prior knowledge of entity pair without any NLP tools. Besides the calculation of the entity-pair similarity, Keywords attention in the REKA model also utilizes a linear-chain conditional random field (CRF) combining entity-pair features, similarity features between entity-pair features, and its hidden vectors, to obtain the attention weight resulting from the marginal distribution of each word. Experiments demonstrate that the proposed approach can utilize keywords incorporating relational expression semantics in sentences without the assistance of any high-level features and achieve better performance than traditional methods.
Learning Feature Channel Weighting for Real-Time Visual Tracking

Recently, the siamese convolutional neural network plays an important role in the field of visual tracking, which can obtain high tracking accuracy and good real-time performance. However, the requirement of offline training a specific neural network results in the hardware source and time consumption. In order to improve the tracking efficiency and save computation resources, we adopt pre-trained densely connected neural network to extract robust target features. Since the pre-trained model is mainly used for classification task, it is not appropriate to directly adopt these deep features for visual tracking. We design a regression network to measure the importance of each channel to the target, and then propose a weighting fusion strategy to select the suitable features for visual tracking. Besides, we provide deep analysis about the proposed channel weighting method to demonstrate the superiority of this method through visualization of feature heatmaps. Extensive experiments on four classical benckmarks show that compared with state-of-the-art methods, our algorithm achieves the best results on several standard indicators and comparable results on other indicators.
Improving Crowd Density Estimation by Fusing Aerial Images and Radio Signals

A recent line of research focuses on crowd density estimation from RGB images for a variety of applications, for example, surveillance and traffic flow control. The performance drops dramatically for low-quality images, such as occlusion, or poor light conditions. However, people are equipped with various wireless devices, allowing the received signals to be easily collected at the base station. As such, another line of research utilizes received signals for crowd counting. Nevertheless, received signals offer only information regarding the number of people, while an accurate density map cannot be derived. As unmanned aerial vehicles (UAVs) are now treated as flying base stations and equipped with cameras, we make the first attempt to leverage both RGB images and received signals for crowd density estimation on UAVs. Specifically, we propose a novel network to effectively fuse the RGB images and received signal strength (RSS) information. Moreover, we design a new loss function that considers the uncertainty from RSS and makes the prediction consistent with the received signals. Experimental results show that the proposed method successfully helps break the limit of traditional crowd density estimation methods and achieves state-of-the-art performance. The proposed dataset is released as a public download for future research.
The use of automatic text simplification to provide reading assistance to deaf and hard-of-hearing individuals in computing fields

Automatic Text Simplification (ATS) aims to rewrite text in a way that reduces its linguistic complexity while preserving its original meaning. While some prior research has explored using ATS to provide reading assistance to different user groups, relatively little work has investigated its use for Deaf and Hard-of-hearing (DHH) adults or readers in a particular domain. In this project, we investigate the use of ATS-based reading assistance tools for DHH individuals in the computing and information technology (IT) fields, motivated by prior work suggesting that computing professions often require reading about new technologies in order to stay current in the profession. Employing a variety of research methods, we investigate questions including the needs and interests of DHH individuals in the computing and IT fields for ATS-based reading assistance tools and their preferences for different interface parameters of these tools. We also investigate how to evaluate these technologies with this particular user group and how they may benefit from using these tools. This summary presents the motivation for this work, positions it in the context of the related literature, and outlines the proposed solution, our current progress and the project's contributions.
Research on a Safe and Reliable Agricultural Product Traceability System Driven by Permissioned BlockChain Technology

This article analyzes and summarizes the current 5W1H problems faced by the Agricultural Product Traceability System. In order to solve these problems, based on the Permissioned BlockChain technology and Cryptographic technology, a logical framework for the safety and reliable Agricultural Product Traceability System is designed. The framework uses data encryption and flexible access control to protect data privacy and security of participants in the traceability system. A safe and reliable three-dimensional agricultural product traceability mechanism of "Combination of Peacetime and Wartime" + "Criss-Cross" is proposed. With the help of Smart Contracts, the agricultural products traceability data can be shared safely in time and matter. Effectively solve the problem of not being able to obtain real traceability data. It can not only trace the responsible party, but also ascertain the truth of the security incident. The research results of this paper provide theoretical support for the research of Agricultural Product Traceability System.
Network Security Situation Assessment Methods and Tactics Based on Multivariate Spatiotemporal Attack Graph Model

In view of the characteristics of large-scale, coordinated and multi-stage network attacks, a network security situation assessment method based on attack Graph model was proposed. Firstly, the network attack behavior characteristics are constructed by integrating the spatio-temporal characteristics of attack events and multi-source alarm data. Secondly, attack nodes are mapped based on alarm information to associate multi-step attack paths. Thirdly, on the basis of constructing the attack Graph, the transfer probability table of attack nodes is constructed combining with the transfer sequence, and the transfer probability is introduced into the attack graph to infer the attack intention of the attacker. Finally, according to the maximum possible attack path, the security situation of attack nodes with high probability is evaluated, and the security situation of potential attack nodes after network attack is scientifically quantified, providing theoretical support and scientific basis for network security management personnel to do a good job of protection in advance.
Analytical Study on LSB-Based Image Steganography Approach

Steganography has recently turned out to be an excellent field of science that is used to shield data from illegal entry. Steganography is described as the skill of communication which involves concealing knowledge in a different medium, for instance, textual, image, audio, video, or network in order not to provoke any trace, whereas steganalysis is the discipline of destroying the steganographic mechanism to expose the undisclosed communication. The efficiency of a steganography procedure is measured utilizing three key criteria, and they include payload efficiency, image excellence calculation, and degree of protection. This investigation is concentrated on image steganography which is extremely accepted in the steganography field. The least significant bit (LSB) approach is mainly the effectual method employed to entrench the undisclosed communication. The study, therefore, focused on the least significant bit steganography algorithm, thereby increasing its performance by proposing a modified least significant bit steganography called circular shift LSB. The study also elucidated the LSB entrenching procedure and presented the assessment outcomes for LSB text steganography for different cover object formats such as .png, .bmp, and .jpeg. This paper has more comprehensive information on different cover image formats, focused on LSB. A comparative assessment was carried out to establish the usefulness of the suggested approaches. The success of the projected approach was appraised by the computation of mean square error (MSE) and peak signal-to-noise ratio (PSNR). The investigation showed that the LSB methods improved PSNR and MSE. Furthermore, PSNR error metrics were high and MSE was low indicating strong and robust cover image quality which makes the image highly imperceptible to human eyes or intruders.
Exploring the Effect of Personality Traits in VR Interaction: The Emergent Role of Perspective-Taking in Task Performance

In this work we explore the effect of personality traits on user interaction in virtual reality (VR), on the less widely studied aspect of task performance during object manipulation. We conducted an experiment measuring the performance of 39 users interacting with a virtual environment using the virtual hand metaphor to execute a simple selection and positioning task, with or without virtual obstacles. Our findings suggest concrete correlations between user personality traits and behavior data. Perspective-taking, in particular, seems to be strongly affecting task performance, highlighting the need for further research. Besides the wider implications of our results in relation to the effect of personality on how users experience VR, our main contribution lies in identifying specific traits that should be taken into account when designing experiments involving users performing such tasks. The study of these traits may also significantly advance our understanding of personality traits as part of the user model in a wider range of VR applications, including those offering personalization and recommendation functionality.
A Comparative Investigation on the Use of Cloud Computing for Big Data Analytics

Big data is working hand in hand with cloud computing. Cloud computing can enable real-time processing and analysis at a bigger scale and efficiently. This paper aims to understand the use of big data analytics in the cloud infrastructure. The discussion begins with various use cases and applications that are implemented via cloud computing in big data analytics. This research also discusses the organization's challenges during the implementation and utilization of cloud computing. Cloud deployment models, cloud services and feature are also part of the discussion. Finally, a comparison between three big cloud providers is drawn to critically review the services offered by these providers with respect to their offerings for big data analytics.
EAGLE: GUI-Based Penetration Testing Tool for Scanning and Enumeration

Penetration testing is a security exercise that is carried out by penetration testers to identify and exploit vulnerabilities in a system. Many penetration testing tools are available in Command Line Interface (CLI) and this requires familiarization of commands. Beginner penetration testers might struggle to understand and choose the most suitable tools. This paper presents a Graphical User Interface (GUI)-Based Penetration Testing Tool for Scanning and Enumeration named as EAGLE. It is developed to assist beginner penetration testers. EAGLE integrates features from several scanning and enumeration tools such as Nmap, Gobuster, Hydra, Nikto, Enum4Linux and Whatweb. The tool's functions include port scanning, web server enumeration, website screenshot, service brute force and service enumeration. Users or beginner penetration testers are only required to provide an IP Address of the target system as the input. This eliminates the familiarization of extensive commands used in multiple tools. The tool displays the results in a convenient manner with a filtering feature so that it is easier to read and understand the scan results. The novelty of this work is done by comparing the output / results obtained using the CLI-based tools separately with the integrated results produced using EAGLE.
Real-time face mask detection for COVID-19 prevention

Covid-19 is a topic that is currently trending. It has instilled fear and danger in the minds of practically everyone. Coronavirus disease is spread from individual to individual through respiratory droplets breathed during coughing, talking, sneezing, or inhaling. Because droplets can be inhaled from one person to another, it is vital to avoid public meetings and to use masks. Manually detecting face masks takes a lot of time. To detect face masks automatically, a lightweight, cost-effective, durable, and video surveillance system is required. Using a binary classifier based on Convolution Neural Networks, this research provides a robust, lightweight, and cost-effective automatic system for detecting face and face mask classification (CNN). MobileNetV2, a single-shot detector (SSD) based on a binary classifier, was employed in this autonomous system. This work also discusses various face detectors and face mask classifiers, as well as the differences between these models. The F1 Score, as well as accuracy, are used to appraise the autonomous system's accomplishment. The system that is proposed gives an f1 score of 98% and an accuracy of 95.85 %.
Retinal fundus image superresolution generated by optical coherence tomography based on a realistic mixed attention GAN

Optical coherence tomography (OCT) is widely used to diagnose retinal diseases. However, due to the limited resolution of OCT imaging systems, the quality of fundus images displayed is not satisfactory, which hinders the diagnosis of patients by ophthalmologists. This is an inevitable problem of OCT imaging systems, but few people have given attention to it. We attempt to solve this problem through deep learning methods.In this paper, we propose a single-image superresolution (SISR) model that is based on a generative adversarial network (GAN) for restoring low-resolution (LR) OCT fundus images to high-resolution (HR) counterparts. To obtain more realistic images, we craft the training data set by obtaining the real blur kernels of the LR images instead of using the bicubic interpolation kernel. The baseline of our generator is similar to that of an enhanced superresolution generative adversarial network (ESRGAN), but we creatively propose a mixed attention block (MAB). In contrast to other superresolution (SR) tasks, to adapt to the characteristics of OCT imaging systems, our network can reconstruct LR images with different upscaling factors in the height and width directions.The results of qualitative and quantitative experiments prove that our model is capable of reconstructing retinal fundus images clearly and accurately.We propose a new GAN model for enhancing the quality of displayed OCT retinal fundus images and achieve state-of-the-art results.
A new online testing technique for reversible circuits

Any technology offering zero power dissipation must be reversible. A reversible circuit can be envisaged as a cascade of reversible gates only, such as Toffoli gate, which has two components: k control bits and a target bit (k-CNOT), k ≥ 1. Analysing testability issues in a reversible circuit is an important phenomenon. A new online design-for-testability (DFT) technique for reversible circuits is proposed. The authors’ method yields less overhead in terms of quantum cost as compared to the previous online approaches.