Reviewerly/exHarmony huggingface dataset:
A Novel Gradient-Weighted Voting Approach for Classical and Fuzzy Circular Hough Transforms and Their Application in Medical Image Analysis—Case Study: Colonoscopy

Classical circular Hough transform was proven to be effective for some types of colorectal polyps. However, the polyps are very rarely perfectly circular, so some tolerance is needed, that can be ensured by applying fuzzy Hough transform instead of the classical one. In addition, the edge detection method, which is used as a preprocessing step of the Hough transforms, was changed from the generally used Canny method to Prewitt that detects fewer edge points outside of the polyp contours and also a smaller number of points to be transformed based on statistical data from three colonoscopy databases. According to the statistical study we performed, in the colonoscopy images the polyp contours usually belong to gradient domain of neither too large, nor too small gradients, though they can also have stronger or weaker segments. In order to prioritize the gradient domain typical for the polyps, a relative gradient-based thresholding as well as a gradient-weighted voting was introduced in this paper. For evaluating the improvement of the shape deviation tolerance of the classical and fuzzy Hough transforms, the maximum radial displacement and the average radius were used to characterize the roundness of the objects to be detected. The gradient thresholding proved to decrease the calculation time to less than 50% of the full Hough transforms, and the number of the resulting circles outside the polyp’s environment also decreased, especially for low resolution images.
SPOAHA: Spark Program Optimizer Based on Artificial Hummingbird Algorithm

In this era of the Internet of Things (IoT), a large number of sensor devices collect and generate various sensing data over time. It is very essential to mine fresh information by analyzing large amounts of data, predict the future, and make correct decisions. Therefore, a growing number of data-intensive computing frameworks have been proposed, such as Hadoop, Spark, Flink, etc. Rather than reading and writing files to disks, Spark processes data with a memory-based computing framework to improve the performance, which has attracted more attention from researchers. However, due to a wealth of operators provided by Spark, a certain application can be implemented in various ways, which also show big differences in performance. Therefore, tuning a Spark application is a very error-prone and time-consuming process, and requires developers to have a deep understanding of Spark’s operating principles and characteristics. In this paper, we summarize a series of rules such as operator reordering and operator replacement to design and implement a Spark program optimizer, called SPOAHA, based on the artificial Hummingbird algorithm. Experimental results show that without changing the semantics of the original program, the optimized program dramatically reduces the amount of data involved in the shuffling period, and speeds up the execution time by up to 2.7 $$\times $$ .
Individual Cryptography

We initiate a formal study of individual cryptography. Informally speaking, an algorithm $$\textsf{Alg}$$ is individual if, in every implementation of $$\textsf{Alg}$$ , there always exists an individual user with full knowledge of the cryptographic data S used by $$\textsf{Alg}$$ . In particular, it should be infeasible to design implementations of this algorithm that would hide S by distributing it between a group of parties using an MPC protocol or outsourcing it to a trusted execution environment. We define and construct two primitives in this model. The first one, called proofs of individual knowledge, is a tool for proving that a given message is fully known to a single ("individual") machine on the Internet, i.e., it cannot be shared between a group of parties. The second one, dubbed individual secret sharing, is a scheme for sharing a secret S between a group of parties so that the parties have no knowledge of S as long as they do not reconstruct it. The reconstruction ensures that if the shareholders attempt to collude, one of them will learn the secret entirely. Individual secret sharing has applications for preventing collusion in secret sharing. A central technique for constructing individual cryptographic primitives is the concept of MPC hardness. MPC hardness precludes an adversary from completing a cryptographic task in a distributed fashion within a specific time frame.
Smart Approaches for Evaluating Photosynthetically Active Radiation at Various Stations Based on MSG Prime Satellite Imagery

Photosynthetically active radiation (PAR) is the 400–700 nm portion of the solar radiation spectrum that photoautotrophic organisms including plants, algae, and cyanobacteria use for photosynthesis. PAR is a key variable in global ecosystem and Earth system modeling, playing a prominent role in carbon and water cycling. Alongside air temperature, water availability, and atmospheric CO2 concentration, PAR controls photosynthesis and consequently biomass productivity in general. The management of agricultural and horticultural crops, forests, grasslands, and even grasses at sports venues is a non-exhaustive list of applications for which an accurate knowledge of the PAR resource is desirable. Modern agrivoltaic systems also require a good knowledge of PAR in conjunction with the variables needed to monitor the co-located photovoltaic system. In situ quality-controlled PAR sensors provide high-quality information for specific locations. However, due to associated installation and maintenance costs, such high-quality data are relatively scarce and generally extend over a restricted and sometimes non-continuous period. Numerous studies have already demonstrated the potential offered by surface radiation estimates based on satellite information as reliable alternatives to in situ measurements. The accuracy of these estimations is site-dependent and is related, for example, to the local climate, landscape, and viewing angle of the satellite. To assess the accuracy of PAR satellite models, we inter-compared 11 methods for estimating 30 min surface PAR based on satellite-derived estimations at 33 ground-based station locations over several climate regions in Europe, Africa, and South America. Averaged across stations, the results showed average relative biases (relative to the measurement mean) across methods of 1 to 20%, an average relative standard deviation of 25 to 30%, an average relative root mean square error of 25% to 35% and a correlation coefficient always above 0.95 for all methods. Improved performance was seen for all methods at relatively cloud-free sites, and quality degraded towards the edge of the Meteosat Second Generation viewing area. A good compromise between computational time, memory allocation, and performance was achieved for most locations using the Jacovides coefficient applied to the global horizontal irradiance from HelioClim-3 or the CAMS Radiation Service. In conclusion, satellite estimations can provide a reliable alternative estimation of ground-based PAR for most applications.
A Survey on Cyber Risk Management for the Internet of Things

The Internet of Things (IoT) continues to grow at a rapid pace, becoming integrated into the daily operations of individuals and organisations. IoT systems automate crucial services within daily life that users may rely on, which makes the assurance of security towards entities such as devices and information even more significant. In this paper, we present a comprehensive survey of papers that model cyber risk management processes within the context of IoT, and provide recommendations for further work. Using 39 collected papers, we studied IoT cyber risk management frameworks against four research questions that delve into cyber risk management concepts and human-orientated vulnerabilities. The importance of this work being human-driven is to better understand how individuals can affect risk and the ways that humans can be impacted by attacks within different IoT domains. Through the analysis, we identified open areas for future research and ideas that researchers should consider.
E-FPN: Evidential Feature Pyramid Network for Ship Classification

Ship classification, as an important problem in the field of computer vision, has been the focus of research for various algorithms over the past few decades. In particular, convolutional neural networks (CNNs) have become one of the most popular models for ship classification tasks, especially using deep learning methods. Currently, several classical methods have used single-scale features to tackle ship classification, without paying much attention to the impact of multiscale features. Therefore, this paper proposes a multiscale feature fusion ship classification method based on evidence theory. In this method, multiple scales of features were utilized to fuse the feature maps of three different sizes (40 × 40 × 256, 20 × 20 × 512, and 10 × 10 × 1024), which were used to perform ship classification tasks separately. Finally, the multiscales-based classification results were treated as pieces of evidence and fused at the decision level using evidence theory to obtain the final classification result. Experimental results demonstrate that, compared to classical classification networks, this method can effectively improve classification accuracy.
Automatic Detection of Inconsistencies and Hierarchical Topic Classification for Open-Domain Chatbots

Current State-of-the-Art (SotA) chatbots are able to produce high-quality sentences, handling different conversation topics and larger interaction times. Unfortunately, the generated responses depend greatly on the data on which they have been trained, the specific dialogue history and current turn used for guiding the response, the internal decoding mechanisms, and ranking strategies, among others. Therefore, it may happen that for semantically similar questions asked by users, the chatbot may provide a different answer, which can be considered as a form of hallucination or producing confusion in long-term interactions. In this research paper, we propose a novel methodology consisting of two main phases: (a) hierarchical automatic detection of topics and subtopics in dialogue interactions using a zero-shot learning approach, and (b) detecting inconsistent answers using k-means and the Silhouette coefficient. To evaluate the efficacy of topic and subtopic detection, we use a subset of the DailyDialog dataset and real dialogue interactions gathered during the Alexa Socialbot Grand Challenge 5 (SGC5). The proposed approach enables the detection of up to 18 different topics and 102 subtopics. For the purpose of detecting inconsistencies, we manually generate multiple paraphrased questions and employ several pre-trained SotA chatbot models to generate responses. Our experimental results demonstrate a weighted F-1 value of 0.34 for topic detection, a weighted F-1 value of 0.78 for subtopic detection in DailyDialog, then 81% and 62% accuracy for topic and subtopic classification in SGC5, respectively. Finally, to predict the number of different responses, we obtained a mean squared error (MSE) of 3.4 when testing smaller generative models and 4.9 in recent large language models.
A Framework of a Blockchain-Supported Remanufacturing Trading Platform through Gap Analysis

Considering that consumers are more willing to buy products online, companies are increasingly selling remanufactured products online through e-commerce platforms. Notwithstanding the high attention it elicits from researchers and companies, the current study on the remanufacturing trading platform is still in its infancy. Thus, we investigate 20 remanufacturing trading platforms and make a gap analysis among them in terms of (i) business model, (ii) product display, (iii) delivery products, (iv) quality assurance and after-sales service, (v) product review and star rate, and (vi) transaction and payment. On this basis, we analyze features for the development of remanufacturing trading platforms and propose six key applications aimed at filling the identified gaps. The consortium blockchain has the characteristics of security and transparency, high credibility, traceability and unfalsifiability, low cost, and strong scalability, which can provide effective support for the six key applications. Then, we construct the technical framework and the model of a consortium blockchain-supported remanufacturing trading platform. Further, we analyze the coupling mechanism between the consortium blockchain and the remanufacturing trading platform to explain how the remanufacturing trading platform supported by the consortium blockchain achieves the development characteristics. This study provides important guidance for the development, construction, and operation management of remanufacturing trading platforms.
Performance evaluation of TRNG based on Ring Oscillators implemented on FPGA

Nowadays cryptography is increasingly more important, not only to keep communications safe but also to authenticate users and devices alike. A few years ago, most of the algorithms and cryptographic systems available to the public were implemented in software. However currently one of the best ways to tackle this problem is using specific hardware to produce a random output. In this paper we present an investigation to different True Random Number Generator (TRNG) architectures, implemented in a Field Programmable Gate Array (FPGA), based on ring oscillators and the results of a specific set of tests developed to verify the resilience and randomness characteristics of random number generators, NIST SP800-22R. Our objective is to create a dynamic entropy generator based on standard Ring Oscillators using the lowest number of resources possible. The tests ran using a 10 Mbytes sample stored in a .txt file received from the FPGA with the size of 1 kbytes and 7000 bitstreams (number of times the 1 kbytes will be tested). The best performing configuration only uses 160 gates. But it is possible to reduce by almost a third the number of gates and still get on the 5th best performing configuration with a higher efficiency.
Flexible Photonic Integrated Circuits: A New Paradigm to Process Data on-Board Satellites

Photonic Integrated Circuits (PICs) are becoming an enabling technology suitable for a large range of applications such as Space and biomedicine. PICs deal with light signals exploiting photonic basic building blocks. Their attractiveness is mainly related to the capability to manage analog signals without A/D conversion [1].NewSpace Economy sets several constraints mainly in terms of reliability and flexibility of on-board systems, taking into account the need for small size and low weight, low propagation losses, low power consumption, immunity to electromagnetic disturbances, high processing speed, wide bandwidth, and radiation resistance. Reconfigurable PICs are one of the most promising technologies to fulfil all those requirements. In particular, system flexibility could be achieved by means of programmable or tuneable Application-Specific PICs (ASPICs) [2].A tuneable PIC should be tailored according to the targeted applications. It exploits different physical effects, e.g. thermo-optic effect, electro-optic effect, or phase-changing materials, to optimize its functionalities also reconfiguring itself in relation to the user demands or external environment. The main bottlenecks of this approach are the need for specific design and optimization each time, the recursive designing costs, and the lack of multitasking. On the other hand, a programmable PIC exploits the same chip to perform several functions, even targeted to different applications. Although the latter approach ensures larger flexibility, the main issues are the high power consumption related to the tuning of different elements at the same time, and lower performance than ASPIC (e.g. insertion loss, wavelength precision, limited free spectral range) [3] - [5].Here, both approaches for systems flexibility are carefully reviewed, rating their limits and potential developments, also evaluating the tuning mechanisms and technological challenges. The suitability of the aforementioned systems for Space applications is also critically analysed, aiming at highlighting the main functionalities needed to process data on-board.
Quantum Virtual Link Generation via Reinforcement Learning

Quantum networks make use of the quantum entanglement as building block. When two qubits are entangled, their state changes exhibit non-classical correlations used to design new applications not possible with classical communication, such us quantum key distribution or distributed quantum computation. Unfortunately, quantum entanglement is a probabilistic process strongly dependent on the features of involved devices (optical fibers, lasers, quantum memories, …). The management decisions (i.e., the control policy) to set up and keep the entanglement as long as possible with the highest quality constitutes a stochastic control problem. This process can be modelled as Markov Decision Process (MDP) and solved via the Reinforcement Learning (RL) framework (a form of Machine Learning). In this work, we apply this RL framework to learn an entanglement management policy outperforming the State-of-the-Art policy when models characterising precisely the involved quantum devices are not known.
True Random Number Generator Prototype Implemented in an FPGA

True Random Number Generators (TRNGs) are one popular solution in hardware-based cryptographic systems, aiding in the security and reliability of data exchange. Their operation is based on physical phenomena and these can be used in applications such as key generation or random challenges. Implementing this type of circuits in FPGA allows for fast prototyping and easy integration.In this paper, we propose a prototype of a TRNG system based on Ring Oscillator (RO) structures that is lightweight, using less than 100 LookUp Tables (LUTs), and does not use post-processing modules. When compared to other state-of-the-art architectures, our prototype adds to a basic entropy source a simple module that uses only basic logic elements, resulting in greater jitter accumulation while keeping the overall architecture small. These characteristics also allow for easy modifications on the circuit to be done and make the design suitable for ASIC platform porting.The proposed architecture has been implemented on a Xilinx Zynq-7000 Zybo Z7-20 FPGA and it is able to pass all the National Institute of Standards and Technology (NIST) statistical tests, showing promise in future applications.
Blockchain Technology for E-Notary in Collaborative Networks

Recent developments on communication technology have given rise to new forms of conducting business in a more efficient way that enables small and medium sized organizations to tackle current problems such as lack of resilience, adaptability to changing economic environments and pressures and innovation. With the aim to mitigate these challenges, the study and application of a tool acting as an electronic notary may provide some aid, since it can enable trust among the collaboration members. With the increased popularity of blockchain technologies new frameworks were developed and more research has been done to find new applications for this technology outside of its primordial appliance of digital currency. In this paper it is presented one interesting application of blockchain technology with its adaptation to an electronic notary for Collaborative Networks with the advantages of the decentralized nature and security.
CV-QKD Design for Network Integration

Continuous-variable quantum key distribution (CV-QKD) has reached a maturity that requires to consider its integrability into optical communication networks. As a key exchange, QKD cannot take the central role in optical network design but has to adapt to the requirements of the network.In this article compact CV-QKD prototypes, which have been demonstrated in operational environments, and several of their key features for facilitating network integration are presented. Among these features are the coverage of typical metro link losses with automatic adaptation to the channel conditions as well as in-channel synchronization, which reduces the spectral needs to a single dense-wavelength-division-multiplexing (DWDM) channel. In many situations, this fully C-band tunable channel can simply be connected to a free multiplexer port of an existing DWDM infrastructure. Any-to-any connectivity between many QKD transmitters and receivers can reduce the number of devices, while dynamic protocol switching allows to address different security requirements and cryptographic tasks.
BB84 decoy-state QKD protocol over long-distance optical fiber

Quantum key distribution represents one of the most mature and well-studied advances in quantum technologies. Many protocols have been implemented, breaking record distances both employing satellites [1] and fiber-based systems [2]. However, there still are open challenges in achieving long-distance links without implementing quantum repeaters. One of the main limiting factors comes from the non-ideality of detection systems, in particular from intrinsic dark counts. As the covered distances increase, the signal decreases in intensity and the intrinsic noise in the detector prevails, hindering the extraction of a secret key. In this work, we propose the use of high-performance state-of-the-art superconducting nanowire single-photon detectors (SNSPD) showing ultra-low dark count rates to reduce the quantum bit error rate (QBER) in the detection system. Overall we achieved a link distance of 55 dB equivalent to 275 km in ultra low-loss fiber.
AI-Assisted Polarization Basis Alignment for Quantum Key Distribution System Receivers

In this work, we propose and experimentally validate a polarization basis alignment method based on a hybrid approach, using the Particle Swarm Optimization (PSO) and a gradient search algorithm. This method uses the Quantum Bit Error Ratio (QBER) as metric to be minimized. At first, the PSO runs one iteration to perform a coarse basis alignment, followed by the gradient search algorithm which performs a fine basis alignment, until the QBER is below the threshold. The polarization basis alignment method here proposed is implemented and tested at the receiver of a Quantum Key Distribution (QKD) system testbed. With this proposed hybrid basis alignment approach, a QBER below 1% can be achieved after, on average, nineteen QBER estimations, when using five particles for the PSO. These nineteen QBER estimations represent a 24% improvement regarding the method mostly used in the state of the art which.
Threshold Quantum State Tomography

Quantum state tomography aims at reconstructing the state of a quantum system. The standard approach is based on a tomographically complete set of observables and a maximum likelihood estimation of the density matrix. However, the number of measurements scales exponentially with the number of qubits or qudits of the system. By leveraging some properties of any physical density matrix, we propose a method in which the number of necessary measurements depends on the state under investigation but without the need for any a priori information. In some cases, this can lead to a significant reduction of the resources necessary for state characterization.