Reviewerly/exHarmony huggingface dataset:
Dynamical System in Chaotic Neurons with Time Delay Self-Feedback and Its Application in Color Image Encryption

The time delay caused by transmission in neurons is often ignored, but it is demonstrated by theories and practices that time delay is unavoidable. A new chaotic neuron model with time delay self-feedback is proposed based on Chen’s chaotic neuron. The bifurcation diagram and Lyapunov exponential diagram are used to analyze the chaotic characteristics of neurons in the model when they receive the output signals at different times. The experimental results exhibit that it has a rich dynamic behavior. In addition, the randomness of chaotic series generated by chaotic neurons with time delay self-feedback under different conditions is verified. In order to investigate the application of this model in image encryption, an image encryption scheme is proposed. The security analysis of the simulation results shows that the encryption algorithm has an excellent anti-attack ability. Therefore, it is necessary and practical to study chaotic neurons with time delay self-feedback.
Reduction of Gaussian noise from Computed Tomography Images using Optimized Bilateral Filter by Enhanced Grasshopper Algorithm

Medical image de-noising is most important pre-processing task in the field of medical science as medical images plays pivotal role to diagnose any abnormality or disease in the human body. Accurate diagnosis requires noise free medical images and with the presence of noise false decision may be taken by the radiologists or doctors. Many filters have been developed to eliminate noise signals from medical images but there is still a chance to get better results. In this paper Bilateral Filter has been used to de-noise the medical images. However the performance of bilateral filter has been improved using Enhanced Grasshopper optimization Algorithm. This technique optimizes the parameters of Bilateral Filter which gives better results in terms of various performance index parameters.
Privacy-preserving bidirectional keyword search over encrypted data for cloud-assisted IIoT

Cloud-assisted industrial Internet of Things (IIoT) technology is increasingly used by related enterprises. To preserve the privacy of sensitive data, IIoT devices must encrypt data before sending them to a cloud server. Public-key encryption with keyword search (PEKS) provides an important search function over cloud-assisted IIoT, allowing users to search for encrypted data without decryption. To increase practical functionality, Zhang et al. recently proposed the concept of public-key encryption with bidirectional keyword search, which supports both sender and receiver searches. However, their scheme provides insufficient security because it cannot resist keyword guessing attacks (KGA). Additionally, their scheme requires time-consuming bilinear pairing operations, resulting in high computational costs. In this study, a novel concept called public-key authenticated encryption with bidirectional keyword search was devised for multi-user settings. The system definition and security requirements are formally defined to ensure that no adversary can overcome the indistinguishability against chosen-keyword attacks or KGA. Furthermore, we propose a pairing-free semi-generic construction, combining a multiparty non-interactive protocol and authenticated functionality, which has proven to be secure under the standard model. The experimental results reveal that, compared with other state-of-the-art schemes, the proposed scheme is more practical, secure, and suitable for use with cloud-assisted IIoT systems.
Improvement of Monitoring Technology for Corrosive Pollution of Marine Environment under Cloud Computing Platform

In view of the increasingly serious problem of marine ecological environmental pollution, the traditional marine environmental corrosive pollution monitoring technology has poor monitoring accuracy and poor monitoring timeliness, and the improvement of the marine environmental corrosive pollution monitoring technology under the cloud computing platform is proposed. The research significance and corrosion influence factors of steel corrosion in the marine environment are described, and the research progress of corrosion mechanism in five different zones of the marine environment is reviewed. Cloud computing parallelizes the processing of corrosive pollution data in the marine environment through virtualization and distributed technology, which greatly improves the efficiency of the algorithm. This paper studies the existing cloud computing platform and ocean monitoring system architecture, uses the distributed architecture to design a cloud computing-oriented ocean monitoring system and meets the design requirements in data collection and data processing. The experimental results show that the precision of marine environmental corrosion pollution monitoring technology proposed in this paper is 96% on average, and the completion rate of monitoring images is 82% on average, which can effectively realize marine environmental corrosion pollution monitoring.
Towards Supporting Adaptive Training of Injection Procedures: Detecting Differences in the Visual Attention of Nursing Students and Experts

A large part of health education involves acquiring operational skills that require continued cooperation from the sense of sight. Traditional skill-training systems heavily rely on post-task evaluation and cannot automatically identify the tacit knowledge (e.g., visual attention allocation) that is used to support the decision-making and tool manipulation process. Recent research has started addressing this issue by identifying learner behaviours to understand learner needs and respond with personalized real-time support. To enable this type of adaptivity when training people in how to give injections, we conducted a study to investigate user's visual attention. We analyzed the eye-tracking data of 8 skilled medical practitioners and 10 student nurses from when they were performing an injection task on a simulated abdominal pad. Differences in the detected visual attention and eye-scan patterns across expertise levels create an opportunity for using overlay models or other diagnosis approaches to provide adaptive support to nurse trainees.
A hybrid 3D object auto-completion approach with self-supervised data augmentation for fragments of archaeological objects

It is a common case that archaeological finds are frequently found in fragments and cannot always be reassembled in their entirety. Additionally, the completion of their shape and volume is sometimes costly and occasionally requires a lot of manual work, in particular for exhibition purposes. As a first step, improving the geometry of the fragments could potentially help the archaeological conservators in the assembling process. In this direction, in this paper we propose an end-to-end hybrid approach (i.e. point clouds to benefit from the limited complexity and creating results in mesh benefiting from their visualization capabilities) for the digital auto-completion of fragmented archaeological artifacts with missing parts in combination with a novel self-supervised data augmentation scheme in order to address a significant limitation in the archaeological domain (i.e. limited available observations and absence of the complete object). Our approach uses only 5 fragments of a partly preserved archaeological object for training. As a first step, an object (i.e artifact fragment) in mesh representation is converted to point cloud, from which more fragments of the incomplete original object are artificially generated, with the help of 3D surfaces, to augment the dataset. This dataset is then passed in a GAN-based Cascaded refinement network for generating a complete output, which is, finally, converted back to a mesh. Our experiments showed that the proposed hybrid approach, can be successfully used to complete and restore the geometry of a partly preserved archaeological object without having ever seen the complete form of it. However, due to the complexity of the fragments, such methods in most cases cannot generate their fine decorative and surface details.
How to Ask for Donations? Learning User-Specific Persuasive Dialogue Policies through Online Interactions

Persuasive conversations are more effective when they are custom-tailored for the intended audience. Current persuasive dialogue systems rely heavily on advice-giving or focus on different framing policies in a constrained and less dynamic/flexible manner. In this paper, we argue for a new approach, in which the system can identify optimal persuasive strategies in context and persuade users through online interactions. We study two main questions (1) can a reinforcement-learning-based dialogue framework learn to exercise user-specific communicative strategies for persuading users? (2) How can we leverage the crowd-sourcing platforms to collect data for training, and evaluating such frameworks for human-AI(/machine) conversations? We describe a prototype system that interacts with users with the goal of persuading them to donate to a charity and use experiments with crowd workers and analyses of our learned policies to document that our approach leads to learning context-sensitive persuasive strategies that focus on user's reactions towards donation and contribute to increasing dialogue success.
An Improved Method for Evaluating Image Sharpness Based on Edge Information

In order to improve the subjective and objective consistency of image sharpness evaluation while meeting the requirement of image content irrelevance, this paper proposes an improved sharpness evaluation method without a reference image. First, the positions of the edge points are obtained by a Canny edge detection algorithm based on the activation mechanism. Then, the edge direction detection algorithm based on the grayscale information of the eight neighboring pixels is used to acquire the edge direction of each edge point. Further, the edge width is solved to establish the histogram of edge width. Finally, according to the performance of three distance factors based on the histogram information, the type 3 distance factor is introduced into the weighted average edge width solving model to obtain the sharpness evaluation index. The image sharpness evaluation method proposed in this paper was tested on the LIVE database. The test results were as follows: the Pearson linear correlation coefficient (CC) was 0.9346, the root mean square error (RMSE) was 5.78, the mean absolute error (MAE) was 4.9383, the Spearman rank-order correlation coefficient (ROCC) was 0.9373, and the outlier rate (OR) as 0. In addition, through a comparative analysis with two other methods and a real shooting experiment, the superiority and effectiveness of the proposed method in performance were verified.
Using Shor’s algorithm on near term Quantum computers: a reduced version

Considering its relevance in the field of cryptography, integer factorization is a prominent application where Quantum computers are expected to have a substantial impact. Thanks to Shor’s algorithm, this peculiar problem can be solved in polynomial time. However, both the number of qubits and applied gates detrimentally affect the ability to run a particular quantum circuit on the near term Quantum hardware. In this work, we help addressing both these problems by introducing a reduced version of Shor’s algorithm that proposes a step forward in increasing the range of numbers that can be factorized on noisy Quantum devices. More specifically, the structure of the Shor’s circuit has been modified to reduce the number of gates in the modular arithmetic and the Quantum Fourier Transform. The implementation presented in this work is general and does not use any assumptions on the number to factor. In particular, we have found noteworthy results in most cases, often being able to factor the given number with only one iteration of the proposed algorithm. Finally, comparing the original quantum algorithm with our version on simulator, the outcomes are identical for some of the numbers considered.
Octagon Shell Based Image Steganography for Avoiding Human Visual System with Lower Computational Time

Steganography is a method of hiding secret or sensitive information into a cover picture for digital photos. Aside from hiding capacity, the most essential factors for determining the effectiveness of a steganography method are computing time and visual quality. The octagon shell matrix is widely utilised in data concealing at the moment; nonetheless, the majority of octagon shell matrix based information concealing schemes are still time intensive. We developed an improved real-time octagon shell-based data concealing strategy in this research to bypass the human visual system (HVS) while reducing execution time. The suggested techniques work by mapping every original pair of pixel to the octagon matrix to determine its reference set for inserting a private digit. The related set may then be applied to update the cover pixel pair with little distortion. Experiments show that the suggested system has a substantially faster execution time than earlier methods, as well as superior visual quality. As a result, the suggested method has a shorter computing time and a greater visual quality, making it suitable for a variety of real-time applications.
Spectral tailoring of photon pairs from microstructured suspended-core optical fibers with liquid-filled nanochannels

We theoretically study the generation of photon pairs via spontaneous four-wave mixing (SFWM) in a liquid-filled microstructured suspended-core optical fiber. We show that it is possible to control the wavelength, group velocity, and bandwidths of the two-photon states. Our proposed fiber structure shows a large number of degrees of freedom to engineer the two-photon state. Here, we focus on the factorable state, which shows no spectral correlation in the two-photon components of the state, and allows the heralding of a single-photon pure state without the need for spectral post-filtering.
Scrutability of Intelligent Personal Assistants

Intelligent personal assistants (IPAs) have become widely available, yet they remain primarily used for discrete, straightforward tasks. By contrast, both user studies and literature reviews indicate that IPAs of the future are to be personalised, proactive, and capable of performing elaborate undertakings. Such systems would have to be based on complex and dynamic user and context models. We believe that scrutability – i.e. the ability of the user to actively study and modify the models towards tuning personalisation – could emerge as an essential element of such a human-assistant interaction paradigm. Yet, to the best of our knowledge, no work so far has investigated how the principles of scrutability, as presented in [21], relate to the context and novel challenges raised by the proactive IPAs and how scrutability could facilitate effort-efficient control of the assistants. This paper introduces our vision of the confluence of the research fields of IPAs and scrutability, presents a diagram of the proposed interaction structure, and reanalyses data from user studies originally presented in [11, 39] to better understand user expectations regarding scrutability and proactivity of IPAs.
A Gaussian mixture filter with adaptive refinement for nonlinear state estimation

The state estimation of highly nonlinear dynamic systems is difficult because the probability distribution of their states can be highly non-Gaussian. An adaptive Gaussian mixture filter is developed in this work to address this challenge, in which the Gaussian mixture models are refined based on the system's local severity of nonlinearity to attain a high-fidelity estimation of the state distribution. A set of nonlinearity assessment criteria are designed to trigger the splitting of Gaussian components at both the prediction and update stages of Bayesian filtering and the error bound of estimated distribution is established. The new filter has been benchmarked against the existing methods on two challenging problems and it consistently provides among-the-best accuracy with a reasonable computational cost, which proves that it can be used as a reliable state estimator for engineering systems with highly nonlinear dynamics and subject to high magnitudes of uncertainties.
Towards robust, interpretable neural networks via Hebbian/anti-Hebbian learning: A software framework for training with feature-based costs

Conventional deep neural network (DNN) training with an end-to-end cost function is unable to exert control on, or to provide guarantees regarding the features extracted by the layers of a DNN. Thus, despite the pervasive impact of DNNs, there remain significant concerns regarding their (lack of) interpretability and robustness. In this work, we develop a software framework in which end-to-end costs can be supplemented with costs which depend on layer-wise activations, permitting more fine-grained control of features. We apply this framework to include Hebbian/anti-Hebbian (HaH) learning in a discriminative setting, demonstrating promising gains in robustness for CIFAR10 image classification.
Evaluation of Black-Box Web Application Security Scanners in Detecting Injection Vulnerabilities

With the Internet’s meteoric rise in popularity and usage over the years, there has been a significant increase in the number of web applications. Nearly all organisations use them for various purposes, such as e-commerce, e-banking, e-learning, and social networking. More importantly, web applications have become increasingly vulnerable to malicious attack. To find web vulnerabilities before an attacker, security experts use black-box web application vulnerability scanners to check for security vulnerabilities in web applications. Most studies have evaluated these black-box scanners against various vulnerable web applications. However, most tested applications are traditional (non-dynamic) and do not reflect current web. This study evaluates the detection accuracy of five black-box web application vulnerability scanners against one of the most modern and sophisticated insecure web applications, representing a real-life e-commerce. The tested vulnerabilities are injection vulnerabilities, in particular, structured query language (SQLi) injection, not only SQL (NoSQL), and server-side template injection (SSTI). We also tested the black-box scanners in four modes to identify their limitations. The findings show that the black-box scanners overlook most vulnerabilities in almost all modes and some scanners missed all the vulnerabilities.
Shard Systems: Scalable, Robust and Persistent Multi-Agent Path Finding with Performance Guarantees

Modern multi-agent robotic systems increasingly require scalable, robust and persistent Multi-Agent Path Finding (MAPF) with performance guarantees. While many MAPF solvers that provide some of these properties exist, none provides them all. To fill this need, we propose a new MAPF framework, the shard system. A shard system partitions the workspace into geographic regions, called shards, linked by a novel system of buffers. Agents are routed optimally within a shard by a local controller to local goals set by a global controller. The buffer system novelly allows shards to plan with perfect parallelism, providing scalability. A novel global controller algorithm can rapidly generate an inter-shard routing plan for thousands of agents while minimizing the traffic routed through any shard. A novel workspace partitioning algorithm produces shards small enough to replan rapidly. These innovations allow a shard system to adjust its routing plan in real time if an agent is delayed or assigned a new goal, enabling robust, persistent MAPF. A shard system's local optimality and optimized inter-shard routing bring the sum-of-costs of its solutions to single-shot MAPF problems to &lt; 20-60% of optimal on a diversity of workspaces. Its scalability allows it to plan paths for 1000s of agents in seconds. If any of their goals change or move actions fails, a shard system can replan in under a second.
Machines that make and keep promises - Lessons for contract automation from algorithmic trading on financial markets

An important part of the criticism raised against the adoption of advanced contract automation relates to the inflexibility of automated contracts. Drawing on rational choice theory, we explain why inflexibility, when seen as a constraint, can ultimately not only enhance welfare but also enable cooperation on algorithmic markets. This illuminates the need to address the inflexibility of contracting algorithms in a nuanced manner, distinguishing between inflexibility as a potentially beneficial constraint on the level of transactions, and inflexibility as a set of systemic risks and changes arising in markets employing inflexible contracting algorithms. Using algorithmic trading in financial markets as an example, we show how the automation of finance has brought about institutional changes in the form of new regulation to hedge against systemic risks from inflexibility. Analyzing the findings through the lens of new institutional economics, we explain how widespread adoption of contract automation can put pressure on institutions to change. We conclude with possible lessons that algorithmic finance can teach to markets deploying algorithmic contracting.
AWDMC-Net: Classification of Adversarial Weather Degraded Multiclass scenes using a Convolution Neural Network

Computer vision systems in outdoor environments are strongly affected by different atmospheric/weather conditions. Therefore, understanding the actual behavior of outdoor scenes is necessary for effective removal and improvement of the overall performance of computer vision systems. Although the classification of atmospheric/weather conditions has been well explored, reporting on the same in multiclass problem using Convolutional Neural Networks (CNNs) has received very little attention. In response to address this disparity, we propose a new CNN architecture named the “Adversarial Weather Degraded Multi-class scenes Classification Network (AWDMC-Net)” for outdoor scene classification degraded by different atmospheric/weather conditions. The proposed network is based on adopting different combinations of skip connections in building blocks of CNN there after adaptively pruning the least important convolutional kernels from the network. For effective pruning, we proposed a new pruning criterion named “Entropy Guided Mean-l1 Norm” that can adaptively evaluate the importance of convolutional kernels by considering the filters and their corresponding output feature maps. The prediction performance of our proposed model was evaluated on our newly designed E-TUVD (Extended Tripura University Video Dataset) and on publicly available benchmark datasets. Our newly created video dataset, E-TUVD, consists of 147 video clips (approximately 793800 frames) that represent six atmospheric/weather conditions, namely, fog, dust, rain, haze, poor illumination, and clear day conditions. Our proposed model achieves an accuracy of 93.85%, a specificity of 93.79%, and a sensitivity of 94.18% on our dataset, which outperforms the prevailing standard CNN models and recent state-of-the-art methods for atmospheric/weather classification tasks. Furthermore, our network also reduces the time consumption for atmospheric/weather classification tasks, and therefore mostly meets the requirements of practical applications in real-world scenarios.