Reviewerly/exHarmony huggingface dataset:
Impact factor analysis affecting the operation of asset lifecycle by using asset information modeling and blockchain technology

Purpose Currently, owners find it difficult to manage their assets throughout their project life cycle. The fact that asset information models (AIMs) are mandatory as deliverables for building information modeling-driven projects makes it a key requirement for the client to understand in detail those factors affecting asset operation. Hence, because the Kingdom of Saudi Arabia is the most significant market in the Middle East, this study aims to investigate those factors where blockchain and AIMs could impact the asset management (AM) life cycle. Design/methodology/approach Researchers used a hypothesis-based approach over a systematic literature review and a workshop (descriptive statistics) to understand the current challenges in AM. Later, a second workshop was run to understand the impact factor analysis affecting the operation of the asset life cycle by using asset information modeling and blockchain technology over a multiquantitative method. Findings Results found that factors affecting the operation of assets could be the improvement of trust and stakeholder’s influences; the availability of handover process products’ accurate data; manufacturers providing detailed product models; increasing the speed of preparing holistic and integrated AM systems; improving collaboration between stakeholders; and returning clients’ investments faster. Originality/value Understanding the factors affecting AM life cycle based on the utilization of AIMs and blockchain then allows investors and their team members to work in a secure and collaborative environment that helps them to pre-identify certain risks and improve decision-making in a more effective way, as is required by ISO55000.
Enhanced Security in Public Key Cryptography: A Novel Approach Combining Gaussian Graceful Labeling and NTRU Public Key Cryptosystem

This research explores an encryption system that combines the Nth-degree Truncated Polynomial Ring Unit (NTRU) public key cryptosystem with Gaussian Graceful Labeling. This process assigns distinct labels to a graph's vertices, resulting in successive Gaussian integers. The NTRU method offers enhanced security and efficient key exchange. The communication encryption process depends on integers P, a, and b, with P being the largest prime number in the vertex labeling. The original receivers are the vertex labeling with the largest prime number coefficient, while all other receivers receive messages from the sender. A polynomial algebraic mixing system and a clustering principle based on the abecedarian probability proposition are used in NTRU encryption and decryption. The choice of relatively prime integers p and q in NTRU plays a role in the construction of polynomial rings used for encryption and decryption, with specific choices and properties designed to ensure scheme security.
How body postures affect gaze control in scene viewing under specific task conditions

Gaze movements during visual exploration of natural scenes are typically investigated with the static picture viewing paradigm in the laboratory. While this paradigm is attractive for its highly controlled conditions, limitations in the generalizability of the resulting findings to more natural viewing behavior have been raised frequently. Here, we address the combined influences of body posture and viewing task on gaze behavior with the static picture viewing paradigm under free viewing as a baseline condition. We recorded gaze data using mobile eye tracking during postural manipulations in scene viewing. Specifically, in Experiment 1, we compared gaze behavior during head-supported sitting and quiet standing under two task conditions. We found that task affects temporal and spatial gaze parameters, while posture produces no effects on temporal and small effects on spatial parameters. In Experiment 2, we further investigated body posture by introducing four conditions (sitting with chin rest, head-free sitting, quiet standing, standing on an unstable platform). Again, we found no effects on temporal and small effects on spatial gaze parameters. In our experiments, gaze behavior is largely unaffected by body posture, while task conditions readily produce effects. We conclude that results from static picture viewing may allow predictions of gaze statistics under more natural viewing conditions, however, viewing tasks should be chosen carefully because of their potential effects on gaze characteristics.
Semantic search as extractive paraphrase span detection

Abstract In this paper, we approach the problem of semantic search by introducing a task of paraphrase span detection, i.e. given a segment of text as a query phrase, the task is to identify its paraphrase in a given document, the same modelling setup as typically used in extractive question answering. While current work in paraphrasing has almost uniquely focused on sentence-level approaches, the novel span detection approach gives a possibility to retrieve a segment of arbitrary length. On the Turku Paraphrase Corpus of 100,000 manually extracted Finnish paraphrase pairs including their original document context, we find that by achieving an exact match of 88.73 our paraphrase span detection approach outperforms widely adopted sentence-level retrieval baselines (lexical similarity as well as BERT and SBERT sentence embeddings) by more than 20pp in terms of exact match, and 11pp in terms of token-level F-score. This demonstrates a strong advantage of modelling the paraphrase retrieval in terms of span extraction rather than commonly used sentence similarity, the sentence-level approaches being clearly suboptimal for applications where the retrieval targets are not guaranteed to be full sentences. Even when limiting the evaluation to sentence-level retrieval targets only, the span detection model still outperforms the sentence-level baselines by more than 4 pp in terms of exact match, and almost 6pp F-score. Additionally, we introduce a method for creating artificial paraphrase data through back-translation, suitable for languages where manually annotated paraphrase resources for training the span detection model are not available.
Differentially private Riemannian optimization

Abstract In this paper, we study the differentially private empirical risk minimization problem where the parameter is constrained to a Riemannian manifold. We introduce a framework for performing differentially private Riemannian optimization by adding noise to the Riemannian gradient on the tangent space. The noise follows a Gaussian distribution intrinsically defined with respect to the Riemannian metric on the tangent space. We adapt the Gaussian mechanism from the Euclidean space to the tangent space compatible to such generalized Gaussian distribution. This approach presents a novel analysis as compared to directly adding noise on the manifold. We further prove privacy guarantees of the proposed differentially private Riemannian (stochastic) gradient descent using an extension of the moments accountant technique. Overall, we provide utility guarantees under geodesic (strongly) convex, general nonconvex objectives as well as under the Riemannian Polyak-Łojasiewicz condition. Empirical results illustrate the versatility and efficacy of the proposed framework in several applications.
Research on multi-image encryption method based on image scaling and ghost imaging

Abstract Optical information processing technology itself is characterized by high speed and parallelism, while the wavelength of light is short and the loading information capacity is large. Therefore, optical encryption is of great significance in the field of image encryption. For multi-image encryption, a multi-image encryption system (ISK-MGI) based on image scaling and ghost imaging is proposed in this paper. In the encryption process, image scaling is used to embed the image information to be encrypted into the artifact image to achieve the first step of artifact encryption; then the ghost imaging encryption is used to encrypt any of the artifact images and the intensity sequence obtained from the barrel detector is used as the cipher text; the integration property of Fourier transform is used to obtain the modulation patterns of other artifact images as the key. The feasibility, security and robustness of the scheme are verified by experiments and simulations. The scheme not only solves the image information crosstalk problem of multi-image encryption, but also has high security. Each image in this method has a primary public key and a secondary private key, which solves the potential risk of sharing public keys and provides a new idea for optical information encryption.
Semantics of Belief Change Operators for Intelligent Agents

Abstract This paper summarises several contributions to the theory of belief change by the authors’ dissertation thesis. First, a relational characterization of belief revision for Tarskian logics is considered, encompassing first-order predicate logic, description logic, modal logics and many monotonic logics with model-theoretic semantics. Those logics where total preorders are the standard semantics for revision are characterized. The second contribution considered is a theory of belief revision that builds upon the idea that agents are limited in what the outcome of a revision is. Furthermore, advancements in principles for iterated belief contraction given in the thesis are outlined.
SSANet: normal-mode interference spectrum extraction via SSA algorithm-unrolled neural network

In ocean acoustic fields, extracting the normal-mode interference spectrum (NMIS) from the received sound intensity spectrum (SIS) plays an important role in waveguide-invariant estimation and underwater source ranging. However, the received SIS often has a low signal-to-noise ratio (SNR) owing to ocean ambient noise and the limitations of the received equipment. This can lead to significant performance degradation for the traditional methods of extracting NMIS at low SNR conditions. To address this issue, a new deep neural network model called SSANet is proposed to obtain NMIS based on unrolling the traditional singular spectrum analysis (SSA) algorithm. First, the steps of embedding and singular value decomposition (SVD) in SSA is achieved by the convolutional network. Second, the grouping step of the SSA is simulated using the matrix multiply weight layer, ReLU layer, point multiply weight layer and matrix multiply weight layer. Third, the diagonal averaging step was implemented using a fully connected network. Simulation results in canonical ocean waveguide environments demonstrate that SSANet outperforms other traditional methods such as Fourier transform (FT), multiple signal classification (MUSIC), and SSA in terms of root mean square error, mean absolute error, and extraction performance.
Novel iterative cubature Kalman filters under maximum correntropy criterion for the robust state estimation

Abstract The cubature Kalman filter (CKF) based on maximum correntropy criterion (MCC) is robust under non‐Gaussian noises, but it may encounter numerical problems when there are large outliers. First, based on the MCC and weighted least square (WLS) methods, a new cost function is introduced to calculate the state and covariance updates, which can better solve numerical problems caused by large outliers. Then, a nonlinear measurement function is used, and the measurement information is updated through the latest iterative values, which can obtain more accurate results under various non‐Gaussian noises. In addition, the Gauss–Newton, Levenberg–Marquardt, and Quasi–Newton methods are used, and three iterative algorithms named GN‐IMCC‐CKF, LM‐IMCC‐CKF, and QN‐IMCC‐CKF are respectively derived for the updating steps. Compared with the existing algorithms, the simulation results of two classical models show that the proposed algorithms have better numerical stability and estimation accuracy under non‐Gaussian systems.
The Bitcoin price and Bitcoin price uncertainty: Evidence of Bitcoin price volatility

Abstract This study examines the Bitcoin price by taking into account global factors, including the Chicago Board Options Exchange's Market Volatility Index (VIX), the US dollar index, the gold price, the oil price, and Bitcoin price volatility. The analysis is conducted using the structural vector autoregression (SVAR) model. The variance decomposition findings revealed that the influence of the VIX on the Bitcoin price was initially restricted, but progressively intensified over time. Among the indicators, Bitcoin price volatility had the highest explanatory share in both daily and weekly data analysis. The impulse response functions demonstrated a statistically significant inverse relationship between the VIX and the Bitcoin price. Furthermore, the analysis revealed that the Bitcoin price was mostly impacted by its own volatility. This implies that investing in Bitcoin requires a certain level of risk‐taking.
Measuring the similarity between shapes of buildings using graph edit distance

Building data play a crucial role in geographic information science, and building shape similarity can be used for spatial inquiry, cartographic generalization, map updating, data quality assessment, and other spatial data mining applications. However, conventional shape representation and measurement methods use geometric and statistical measures and may not effectively capture the subtle differences among building shapes. Geospatial artificial intelligence methods often overlook nuanced differences in building contours, posing a challenge when precise fine-grained measurements are required. Furthermore, they have low interpretability. This study proposes a graph edit distance-based method for measuring the similarity between building shapes. It uses geometrics characteristics to construct a graph, matches and associates similar regional features between two building contours, and defines edit costs based on geometric and positional differences that align with the Gestalt principle of visual similarity. It converts the optimal edit path problem to the maximum weighted cluster solution problem and utilizes a heuristic approach to achieve the exact optimal solution within a moderately suitable timeframe. The experimental results demonstrated that the proposed method identifies detailed differences between building contours, providing accurate similarity measurement with high interpretability.
Prediction of solar irradiance with machine learning methods using satellite data

Solar energy is used in many domestic and industrial areas. To make maximum use of solar energy, it is important to know the irradiance value per unit area. Irradiance measurements are carried out at many stations. In cases where the irradiance value is not measured, empirical models are generally used. With the widespread use of machine learning methods in prediction problems in many fields, it has gained increasing popularity recently. This study aims to predict the solar irradiance of all cities in the Mediterranean region in Turkiye using statistical method and popular machine learning methods. The performances of these models are compared. Ensemble Learning methods (Gradient Boosting, Extreme Gradient Boosting) which are among the popular machine learning methods, and Artificial Neural Networks were used. The data used belong to 8 cities in the Mediterranean Region, Türkiye. Declination angle, wind speed, ambient temperature, relative humidity, and cloudiness index were chosen as input variables. When the results of the models established for each city are examined, it is seen that the models established with machine learning methods are more successful than statistical methods. The best results were obtained from models established with the Extreme Gradient Boosting method (R2 = 0.9993, MAPE = 0.0119).
EPolar‐UNet: An edge‐attending polar UNet for automatic medical image segmentation with small datasets

Medical image segmentation is one of the most key steps in computer-aided clinical diagnosis, geometric characterization, measurement, image registration, and so forth. Convolutional neural networks especially UNet and its variants have been successfully used in many medical image segmentation tasks. However, the results are limited by the deficiency in extracting high resolution edge information because of the design of the skip connections in UNet and the need for large available datasets.In this paper, we proposed an edge-attending polar UNet (EPolar-UNet), which was trained on the polar coordinate system instead of classic Cartesian coordinate system with an edge-attending construction in skip connection path.EPolar-UNet extracted the location information from an eight-stacked hourglass network as the pole for polar transformation and extracted the boundary cues from an edge-attending UNet, which consisted of a deconvolution layer and a subtraction operation.We evaluated the performance of EPolar-UNet across three imaging modalities for different segmentation tasks: CVC-ClinicDB dataset for polyp, ISIC-2018 dataset for skin lesion, and our private ultrasound dataset for liver tumor segmentation. Our proposed model outperformed state-of-the-art models on all three datasets and needed only 30%-60% of training data compared with the benchmark UNet model to achieve similar performances for medical image segmentation tasks.We proposed an end-to-end EPolar-UNet for automatic medical image segmentation and showed good performance on small datasets, which was critical in the field of medical image segmentation.
Deep Cryogenic Temperature CMOS Circuit and System Design for Quantum Computing Applications

Quantum computing is a fascinating and rapidly evolving field of technology that promises to revolutionize many areas of science, engineering, and society. The fundamental unit of quantum computing is the quantum bit that can exist in two or more states concurrently, as opposed to a classical bit that can only be either 0 or 1. Any subatomic element, including atoms, electrons, and photons, can be used to implement qubits. The chosen sub-atomic elements should have quantum mechanical properties. Most commonly, photons have been used to implement qubits. Qubits can be manipulated and read by applying external fields or pulses, such as lasers, magnets, or microwaves. Quantum computers are currently suffering from various complications such as size, operating temperature, coherence problems, entanglement, etc. The realization of quantum computing, a novel paradigm that uses quantum mechanical phenomena to do computations that are not possible with classical computers, is made possible, most crucially, by the need for a quantum processor and a quantum SOC. As a result, Cryo-CMOS technology can make it possible to integrate a Quantum system on a chip. Cryo-CMOS devices are electronic circuits that operate at cryogenic temperatures, usually below 77 K (−196 °C).
Exploring the application of dual‐energy <scp>CT</scp> to discriminate sediment facies in a varved sequence

Abstract Dual‐energy X‐ray computed tomography consists of imaging objects using two incident X‐ray beams of different energy to distinguish the different compounds within a sample based on their density (electron density, ρ e ) and elemental composition (effective atomic number, Z eff ). The stoichiometric calibration for dual‐energy X‐ray computed tomography was already successfully implemented to identify single and homogeneous minerals easily and non‐destructively. It is here applied for the first time to a more complex and heterogeneous sample, a varved sediment core with three distinct facies. The output of dual‐energy X‐ray computed tomography was compared against elemental geochemistry obtained at the same resolution using a micro‐XRF core scanner. The three individual facies can be successfully differentiated using dual‐energy X‐ray computed tomography because their range of ρ e and Z eff values allow their discrimination. Correlations with elemental geochemistry are also discussed but are less conclusive, probably because of variations in grain size and porosity, and because these high resolution analyses were not performed at the exact same location. The paper not only eventually discusses the limitations when using dual‐energy X‐ray computed tomography on sediments but also demonstrates its potential to quantitatively study sediment cores in a non‐destructive way.
Overcoming Diverse Undesired Effects in Recommender Systems: A Deontological Approach

In today’s digital landscape, recommender systems have gained ubiquity as a means of directing users towards personalized products, services, and content. However, despite their widespread adoption and a long track of research, these systems are not immune to shortcomings. A significant challenge faced by recommender systems is the presence of biases, which produces various undesirable effects, prominently the popularity bias. This bias hampers the diversity of recommended items, thus restricting users’ exposure to less popular or niche content. Furthermore, this issue is compounded when multiple stakeholders are considered, requiring the balance of multiple, potentially conflicting objectives. In this paper, we present a new approach to address a wide range of undesired consequences in recommender systems that involve various stakeholders. Instead of adopting a consequentialist perspective that aims to mitigate the repercussions of a recommendation policy, we propose a deontological approach centered around a minimal set of ethical principles. More precisely, we introduce two distinct principles aimed at avoiding overconfidence in predictions and accurately modeling the genuine interests of users. The proposed approach circumvents the need for defining a multi-objective system, which has been identified as one of the main limitations when developing complex recommenders. Through extensive experimentation, we show the efficacy of our approach in mitigating the adverse impact of the recommender from both user and item perspectives, ultimately enhancing various beyond accuracy metrics. This study underscores the significance of responsible and equitable recommendations and proposes a strategy that can be easily deployed in real-world scenarios.
Generative machine learning for detector response modeling with a conditional normalizing flow

Abstract In this paper, we explore the potential of generative machine learning models as an alternative to the computationally expensive Monte Carlo (MC) simulations commonly used by the Large Hadron Collider (LHC) experiments. Our objective is to develop a generative model capable of efficiently simulating detector responses for specific particle observables, focusing on the correlations between detector responses of different particles in the same event and accommodating asymmetric detector responses. We present a conditional normalizing flow model (𝒞𝒩ℱ) based on a chain of Masked Autoregressive Flows, which effectively incorporates conditional variables and models high-dimensional density distributions. We assess the performance of the 𝒞𝒩ℱ model using a simulated sample of Higgs boson decaying to diphoton events at the LHC. We create reconstruction-level observables using a smearing technique. We show that conditional normalizing flows can accurately model complex detector responses and their correlation. This method can potentially reduce the computational burden associated with generating large numbers of simulated events while ensuring that the generated events meet the requirements for data analyses. We make our code available at https://github.com/allixu/normalizing_flow_for_detector_response .