Reviewerly/exHarmony huggingface dataset:
Super-resolution Reconstruction and Denoising of 3D Millimetre-wave Images using a Complex-valued Convolutional Neural Network

Imaging systems leveraging millimetre-wave (mmW) frequencies have several advantages, however, such systems suffer from poor resolution images as compared to higher frequency reconstructions such as in optical regime. Also, practical radar systems are susceptible to noise such as clutter, thermal noise, motion blurs, etc. To recover the original mmW image from these poorly resolved noisy images, two individual image processing steps are required, that is, super-resolution and denoising. This paper focuses on using a complex-valued convolutional neural network (CV-CNN) to combine the two individual processing steps into one single algorithm. By designing the CV-CNN to accommodate complex-valued reconstruction data, the phase information content of the input images, along with the magnitude information, is considered in the process. A computational imaging (CI) numerical model, instead of an experimental imaging system, is used to train and test the neural network. By comparing the performance metrics of the final reconstruction images, it is observed that the developed CV-CNN can resolve and de-noise the poorly resolved noisy input mmW images to a high degree of fidelity.
Collective Photon Emission and Photon Propagation in Layered Photonic Environments

We present results of our theoretical research on collective emission of photons (super- and subradiance) and photon propagation in photonic environments.Just like the single-atom emission, collective emission depends on the photonic environment. For quantum emitters in layered media, we study whether surface plasmons can enhance collective emission, especially for emitters at distances smaller than an optical wavelength.A common approximation in quantum optics is the rotating-wave approximation in the light-matter interaction. We show that in general multi-atom emission rates depend on the approximation, but exceptions exist. Interatomic interactions induced by the electromagnetic field are given by the classical Green tensor, but only when not making the rotating-wave approximation.As a final example of quantum electromagnetics, we describe photons propagating through a layered medium with alternating loss and gain, so-called loss-compensated media. There is no analogous compensation of the quantum noise associated with loss and gain media.
Smart University: Project Management of Information Infrastructure Based on Internet of Things (IoT) Technologies

A smart university (SmU) combines many “smart” and classical educational components based on a single information infrastructure. The basic components of building and developing a smart university are elements of artificial intelligence, the Internet of Things (IoT), intelligent systems tools, neural networks, and other technologies of Industry 4.0. The study examines the issues of improving project management for the development of smart-university components based on IoT elements. IoT elements are basic for the information infrastructure of a smart university. IoT solution development projects have a high level of uncertainty due to the lack of infrastructure development for implementation, an incomplete request from stakeholders. The features of the project management organization are also the dual nature of the IoT product itself, since it has both the software and hardware of the devices. This is not just an IT project based on smart components or SmU information infrastructure. All this requires additional elaboration of methods and tools for managing IoT projects. The article also presents a system of criteria for evaluating the features of IoT projects for SmU and shows their characteristics. The novelty of the research lies developing an adaptive model of a flexible approach to IoT project management. The proposed developments can be applied to managing SmU digital infrastructure and developing projects to create smart components based on IoT. Expert and graphical methods were used for modeling.
From Research to Privacy-Preserving Industry Applications

This paper summarizes the contents and presentations held at a workshop at the IFIP Summer School on Privacy and Identity Management 2022, focusing on privacy-preserving industry applications developed within the H2020 CyberSec4Europe project. In this document, we provide a short introduction to the project, and then explain three out of the seven vertical demonstrator cases considered within CyberSec4Europe, focusing on fraud detection within the banking sector, job applications, and smart cities. For each of the selected demonstrators, we motivate the need for privacy and research in the domain, and then summarize the achievements made within the project.
Privacy-Enhancing Technologies and Anonymisation in Light of GDPR and Machine Learning

The use of Privacy-Enhancing Technologies in the field of data anonymisation and pseudonymisation raises a lot of questions with respect to legal compliance under GDPR and current international data protection legislation. Here, especially the use of innovative technologies based on machine learning may increase or decrease risks to data protection. A workshop held at the IFIP Summer School on Privacy and Identity Management showed the complexity of this field and the need for further interdisciplinary research on the basis of an improved joint understanding of legal and technical concepts.
SeCCA: Towards Privacy-Preserving Biclustering Algorithm with Homomorphic Encryptions

Massive amounts of newly generated gene expression data have been used to further enhance personalised health predictions. Machine learning algorithms prepare techniques to explore a group of genes with similar profiles. Biclustering algorithms were proposed to resolve key issues of traditional clustering techniques and are well-adapted to the nature of biological processes. Besides, the concept of genome data access should be socially acceptable for patients since they can then be assured that their data analysis will not be harmful to their privacy and ultimately achieve good outcomes for society [1]. Homomorphic encryption has shown considerable potential in securing complicated machine learning tasks. In this paper, we prove that homomorphic encryption operations can be applied directly on biclustering algorithm (Cheng and Church algorithm) to process gene expression data while keeping private data encrypted. This Secure Cheng and Church algorithm (SeCCA) includes nine steps, each providing encryption for a specific section of the algorithm. Because of the current limitations of homomorphic encryption operations in real applications, only four steps of SeCCA are implemented and tested with adjustable parameters on a real-world data set (yeast cell cycle) and synthetic data collection. As a proof of concept, we compare the result of biclusters from the original Cheng and Church algorithm with SeCCA to clarify the applicability of homomorphic encryption operations in biclustering algorithms. As the first study in this domain, our study demonstrates the feasibility of homomorphic encryption operations in gene expression analysis to achieve privacy-preserving biclustering algorithms.
Valuation of Differential Privacy Budget in Data Trade: A Conjoint Analysis

Differential privacy has been proposed as a rigorous privacy guarantee for computation mechanisms. However, it is still unclear how data collectors can correctly and intuitively configure the value of the privacy budget parameter $$\varepsilon $$ for differential privacy, such that the privacy of involved individuals is protected. In this work, we seek to investigate the trade-offs between differential privacy valuation, scenario properties, and preferred differential privacy level of individuals in a data trade. Using a choice-based conjoint analysis ( $$N = 139)$$ , we mimic the decision-making process of individuals under different data-sharing scenarios. We found that, as hypothesized, individuals required lower payments from a data collector for sharing their data, as more substantial perturbation was applied as part of a differentially private data analysis. Furthermore, respondents selected scenarios with lower $$\varepsilon $$ values (requiring more privacy) for indefinitely-retained data for profit generation than for temporarily-retained data with a non-commercial purpose. Our findings may help data processors better tune the differential privacy budget for their data analysis based on individual privacy valuation and contextual properties.
Exploring the Cybersecurity Spillover Effect: How Customer Data Breaches Affect Supplier Cost Management Strategies

Cybersecurity is a significant concern for businesses worldwide among management, stakeholders, and regulators. In this paper, we explore the spillover effect of customer firms’ data breaches on their upstream supplier firms’ cost management strategies, proxied by cost stickiness, a prevalent resource management strategy that occurs when firms cut costs less when demand falls than they increase for an equivalent demand increase. Our primary analyses suggest that data breaches suffered by customer firms are associated with a decrease in cost stickiness among supplier firms. Furthermore, the reductions in supplier cost stickiness are stronger if suppliers are managed by CEOs from national cultural groups with high uncertainty avoidance, low long-term orientations, and/or low individualism. In additional analyses, we provide evidence that our main findings are not influenced by either supplier product market competition or data breaches experienced by suppliers. To strengthen causality, using the passage of mandatory state-level data breach notification laws as natural experiments that generates plausibly exogenous variation in data breaches, we employ a staggered difference-in-differences approach. The results reveal that the adverse effect of customer data breaches on supplier cost stickiness is diminished after these laws became effective in the states in which the breached customer firms are headquartered. Our inferences also persist when utilizing the predicted probability of data breaches derived from a two-stage regression analysis. Finally, suppliers are found to be more likely to hire cybersecurity experts on the top management team following customer data breaches, even if the suppliers themselves have not encountered any cybersecurity issues. In sum, the findings contribute to both Information Systems (IS) and Operations Management (OM) disciplines in terms of data breach, cost management strategy, and the role of national culture in OM. In particular, the findings can facilitate the management and regulation of data breaches for managers and regulators.
ChatGPT, How to Wire Age 5.0 Mindsets: Industry, Society, Healthcare and Education?

In today’s education, healthcare, and manufacturing sectors, organizations and information societies are discussing new enhancements to corporate structure and process efficiency using digital platforms. These enhancements can be achieved using digital tools. Industry 5.0 and Society 5.0 give several potentials for businesses to enhance the adaptability and efficacy of their industrial processes, paving the door for developing new business models facilitated by digital platforms. Society 5.0 can contribute to a super-intelligent society that includes the healthcare industry. In the past decade, the Internet of Things, Big Data Analytics, Neural Networks, Deep Learning, and Artificial Intelligence (AI) have revolutionized our approach to various job sectors, from manufacturing and finance to consumer products. AI is developing quickly and efficiently. We have heard of the latest artificial intelligence chatbot, ChatGPT. OpenAI created this, which has taken the internet by storm. We tested the effectiveness of a considerable language model referred to as ChatGPT on four critical questions concerning “Society 5.0”, “Healthcare 5.0”, “Industry,” and “Future Education” from the perspectives of Age 5.0.
Using IMU to Determine Velocity of Falling Object and Hence Develop Framework to Optimize Parachute Size for Falling UAV

With the ever-increasing popularity of using unmanned aerial vehicles (UAVs) as a means of delivery, inspection, videography and cinematography, the chances of them falling out of the sky and damaging property and causing injuries has never been greater. To solve this problem, one drone service company has experimented with parachutes. Hence, there is a need for a method to find the optimum size of parachute drones depending on their variables.
Informative Path Planning for Mobile Robot Adaptive Sampling Using DDQN Algorithm

Mobile robots have gradually become indispensable in environmental monitoring. They are more flexible and cost effective than conventional static sensor networks. However, current unmanned mobile robots suffer from limited power supply onboard, which results in short endurance and hinders their applications in large-scale environmental monitoring. One feasible solution is to enable adaptive sampling, in which the essential is to generate effective moving trajectory for mobile robots. This paper presents an informative path planning algorithm for mobile robot adaptive sample using double deep Q-learning network (DDQN). The sampling objective is to efficiently locate the extreme feature in a relatively large region that could not be fully sampled or covered by the robot. Numerical simulation has been carried out to seek the extreme feature described by the Gaussian model. Results indicate feasibility of the proposed path planning algorithm and its superiority to conventional lawn-mower pattern and random sampling.
Deceiving Traffic Sign Recognition with Physical One-Pixel Attacks

Traffic sign recognition (TSR) is an important component of advanced driver-assistance systems, which are increasingly prevalent to minimise human error. Most TSR use convolutional neural networks (CNNs) due to their fast execution and superhuman accuracy. However, CNNs are difficult to analyse and sometimes exhibit counterintuitive behaviour, making them vulnerable to adversarial attacks. One-pixel attacks do not require information from the target model and change merely one-pixel. In this study, we examine three methods of one-pixel attacks—brute-force, differential evolution and a hybrid algorithm—and evaluate their speed and effectiveness in generating adversarial attacks against TSR. We demonstrated that one-pixel attacks remain effective against TSR. We related an attack’s effectiveness to the position of the changed pixel and proposed possible explanations to better understand this attack—the first step in defending against it. We also successfully replicated one-pixel attacks in a physical setting using printed modified road signs. This proves that TSR in vehicles can be attacked by simply adding a small, coloured spot to an existing road sign. The position and colour of this spot are easily generated without access to the target model’s inner information. Thus, we demonstrated a novel method of physical adversarial attack against TSR which is more inconspicuous and easily executed than existing ones.
How to Use Transformers for Transfer Learning?

Transformers are increasing replacing older generation of deep neural networks due to their success in a wide range of application. The dominant approach of using transformers is to pre-train them on a large training dataset and then fine-tune them on a downstream task. However, as transformers becoming larger, the fine-tuning approach is become an infeasible approach for transfer learning. In this short survey, we list a few recent methods that makes using transformers based on transfer learning more efficient.
Multi-modal Natural Man–Machine Interaction Technology and Its Application for Lunar Robot

For the requirements of multi-modal man–machine interaction between astronauts and robots in lunar surface exploration, research on three interaction modes and test system based on speech, body posture and tactile recognition are carried out. An improved speech feature classification and recognition algorithm based on Mel-scale Frequency Cepstral Coefficients (MFCC), a three-stage body posture recognition algorithm based on target detection—node recognition—skeleton action recognition, and a tactile recognition algorithm based on Radial Basis Function network (RBF) are proposed. In the body posture recognition algorithm, a lightweight CVC-Net model is developed based on the classical HourglassNet algorithm. Deep separable convolution, attention mechanism and improved up-sampling method are introduced. A lightweight network and adaptive graph convolution module for skeleton action recognition are designed by introducing the information of human body joints and skeleton vectors. Above algorithm is suitable for the embedded computing platform and can greatly reduce the number of module parameters while ensuring the detection accuracy. Based on the simulated lunar surface environment, lunar robot and simulated space suit, the ground test verification of three modes of command recognition is realized, which can meet the low power consumption, lightweight and real-time requirements of lunar robot for recognition algorithms and the embedded computing platform.
Fast Stroke Lesions Segmentation Based on Parzen Estimation and Non-uniform Bit Allocation in Skull CT Images

Stroke is the second leading cause of death worldwide. Those who survive usually experience vision loss, speech, paralysis, or confusion. Agile diagnosis proves to be decisive for patient survival. This paper presents an approach for fast stroke lesions segmentation in skull CT images, with 0.5 s, on average, by sample. The segmentation stage uses Parzen window density estimation to classify potentially injured regions. We present a methodology for applying the non-uniform distribution of bits in the image from an evaluation of the quantization laws: $$\mu $$ -law and A-law algorithm. The quantization algorithms modify the distribution of pixels, and the result is an enhancement of the region where the lesion is suspected. The results show that the proposed method has the highest mean of accuracy, reaching 99.85%, and specificity of 99.94%, surpassing classical approaches by 16%. On the other hand, the algorithm presented similarity indexes of 93.39% for the Matthews correlation coefficient and DICE of 93.35%. The proposed methodology also compared the results with four approaches that use deep learning; it proved to be equivalent in accuracy, DICE, and specificity, with superior results in sensitivity up to 8% to one of the approaches based on the recent Detectron2 neural network. The results indicate that the proposed method is competitive concerning the approaches already presented in the literature.
Efficient Twitter Sentiment Analysis System Using Deep Learning Algorithm

Emotion reputation from textual content is an essential process in processing natural language inputs that provides huge blessings to special regions inclusive of synthetic intelligence, human interplay with computer systems etc. Emotions are created and reflected as form of human response to the events around them. Analysis of those feelings by not considering face and voice modulation is often essential and demands an efficient technique for accurate interpretation of feelings. Despite these challenges, it’s important to know that the human feelings represent the use of mistreatment textual content thru social media packages like facebook, instagram, twitter etc. This paper provides a comprehensive analysis of sentimental class of multitude of tweets. Here, strategies to categorize the sentiments of an expression into superb or terrible feelings are discussed. The superb feelings include eagerness, happiness, love, neutral, relief, fun, marvel and terrible feelings include anger, boredom, emptiness, hatred, sadness, worrying. The paper have conducted experiments and evaluated performance of Recurrent Neural Networks (RNN) and proposed Improved Long short-time period memory (LSTM) on three special datasets to expose the way to achieve better performance. ILSTM contains efficient preprocessing techniques in addition to traditional LSTM procedures. The simulation results prove the superiority of proposed method over other existing methods in terms of both accuracy and execution time.
Deep Learning for Enhanced Index Tracking

We develop a novel deep learning method for the enhanced index tracking problem, which aims to outperform an index while effectively controlling the tracking error. We generate a dynamic trading policy from a neural network that accepts a set of features as input. We design four blocks in the neural network architecture to handle different types of features, including regimes of the index and stocks, their short-term characteristics, and the current allocation. Outputs from the blocks are integrated into the final output that changes the portfolio allocation. We test our model on the S&P 500 index in an empirical study based on real market data. Out-of-sample results reveal the importance of different features and demonstrate the ability of our method in obtaining impressive excess returns while effectively controlling the tracking error, downside risk, and transaction costs.
Pre-trained Model Robustness Against GAN-Based Poisoning Attack in Medical Imaging Analysis

Deep learning revolutionizes healthcare, particularly in medical image classification, with its analysis performance aided by public architectures and transfer learning for pre-trained models. However, these models are vulnerable to adversarial attacks as they rely on learned parameters, and their unexplainable nature can make it challenging to identify and fix the root cause of the model after an attack. Given the increasing use of pre-trained models in life-critical domains like healthcare, testing their robustness against attacks is essential. Evasion and poisoning attacks are two primary attack types, with poisoning attacks having a broader range of poison sample-generating methods, making testing model robustness under them more critical than under evasion attacks. Poisoning attacks do not require an attacker to have a complete understanding to corrupt the model, making them more likely to occur in the real world. This paper evaluates the robustness of the famous pre-trained models trained as binary classifiers under poisonous label attack. The attacks use GANs to generate mislabeled fake images and feed poison samples to the model in a black box manner. The amount of performance degradation using classification metrics evaluates the model's robustness. We found that ConvNeXt architecture is the most robust against this type of attack, suggesting that transformer architecture can be used to build a more robust deep-learning model.
Card-Not-Present Fraud Detection: Merchant Category Code Prediction of the Next Purchase

Card-not-present transaction fraud still have damage nowadays to banks even after implementing an advanced fraud detection system, we are talking about 75% in value of all card frauds on average [2]. About 8 million reported cases in 2018 and the number is increasing every year [2], billions of dollars are lost due to the CNP fraud and AI systems are still unable to spot those transactions in real-time, and only rely on customers reporting the fraud case after the damage already happened, in this article, We will identify the harm caused by the CNP transaction and offer a potential artificial intelligence-based solution that can forecast the future purchase based on consumer Merchant Category Code activity, fraudster MCC behavior, and how MCC usage may help avoid a fraud case. First, we will showcase the difficulty of this type of fraud, then we will answer the following question, how can a Merchant category code be used to prevent this kind of fraud? (*) finally, we will present the design of our MCC clustering solution and how it can impact the payment system actors.