Reviewerly/exHarmony huggingface dataset:
Toward Standardised Vocabularies for Norwegian Archaeology

The Museum of Cultural History (MCH) at the University of Oslo, Norway, has undertaken a series of infrastructure projects with the aim of improving the standardisation of archaeological data and increasing data integration at both a national and international level. This builds on decades of earlier work and includes a revision of shared National database systems (unimus), integration of previously disparate data types and spatial data (ADED), and more recently the development of a 3D publishing platform (BItFROST). These projects feed into broader aims of large-scale data integration as part of the European-wide ARIADNE Research Infrastructure. This article provides an overview of the history and development of these systems in Norway and takes a look at some of the roads still ahead.
How AI could lead to a better understanding of the brain

Early machine-learning systems were inspired by neural networks — now AI might allow neuroscientists to get to grips with the brain’s unique complexities. Early machine-learning systems were inspired by neural networks — now AI might allow neuroscientists to get to grips with the brain’s unique complexities.
Measurement-Device-Independent Quantum Random-Number Generator With Source Flaws

The measurement-device-independent quantum random number generator (MDI-QRNG) can resist all security loopholes on measurement devices, and it thus seems very promising in practical implementations. In the MDI-QRNG, sources are often assumed to be perfectly prepared, however, there inevitably exist errors in the quantum state preparation process due to imperfections in realistic equipments and devices. In this paper, we do investigations on the MDI-QRNG with source flaws, showing its performance at different state errors. Besides, we also consider the influence of the finite-size effect, and compare the performance of different sources in MDI-QRNGs. This work provides valuable references for practical implementation of MDI-QRNGs
Archiving Rogue Scholar blogs with the Internet Archive

Blogs participating in the Rogue Scholar science blog archive are now archived in the Internet Archive. Starting November 1st, Rogue Scholar is participating in the Internet Archive Archive-It service and all archived blogs can be found here.
AI-based efficiency analysis technique for photovoltaic renewable energy system

Abstract Artificial intelligence (AI) in renewable energy technologies plays a crucial part due to its modeling and performance forecasting. Therefore, a novel AI-based evolving generative adversarial Fuzzy network (EGAFN) has been presented in this paper as a forecasting tool for the efficiency analysis of renewable solar energy for four distinct regions. The power ratings from environmental parameters and solar panels were monitored and recorded for a year. The data pre-processing is primarily applied to improve the system’s function using a data filter. Furthermore, the data’s energy estimation accuracy is enhanced using feature extraction and selection by a multi-objective lionized manta-ray foraging optimizer (MLMRFO). Finally, the hyperparameters of the EGAFN method are optimized by multi-objective optimization. The proposed technique uses an optimized multi-objective algorithm to enhance the energy efficiency of PV systems for solar power production forecasting. The findings show that the suggested technique’s prediction performance is better than earlier methods. Thus, the proposed methodology can assist in increasing energy efficiency and making better use of renewable energy sources.
Scale Composite BaaS Services With AFCL Workflows

Due to various restrictions in serverless computing, developers face significant challenges to pipeline multiple Backend-as-a-Service (BaaS) services, which is restricted by the maximum size of the serverless function’s deployment package, or by throughput and concurrency restrictions for functions and BaaS services.
To Trust or Not: Towards Efficient Uncertainty Quantification for Stochastic Shapley Explanations

Recently, explainable AI (XAI) techniques have gained traction in the field of prognostics and health management (PHM) to enhance the credibility and trustworthiness of data-driven nonlinear models. Post-hoc model explanations have been popularized via algorithms such as SHapley Additive exPlanations (SHAP), but remain impractical for real-time prognostics applications due to the curse of dimensionality. As an alternative to deterministic approaches, stochastically sampled Shapley-based approximations have computational benefits for explaining model predictions. This paper will introduce and examine a new concept of explanation uncertainty through the lens of uncertainty quantification of stochastic Shapley attribution estimates. The proposed algorithm for estimating Shapley explanation uncertainty is efficiently applied for the 2021 PHM Data Challenge problem. The uncertainty in the derived explanation for a single prediction is also illustrated through personalized prediction recipe plots, improving post-hoc model visualization. Finally, important practical considerations for the implementation of Shapley-based XAI for industrial prognostics are provided.
Efficient Real-Time Path Planning with Self-Evolving Particle Swarm Optimization in Dynamic Scenarios

Particle Swarm Optimization (PSO) has demonstrated efficacy in addressing static path planning problems. Nevertheless, such application on dynamic scenarios has been severely precluded by PSO’s low computational efficiency and premature convergence downsides. To address these limitations, we proposed a Tensor Operation Form (TOF) that converts particle-wise manipulations to tensor operations, thereby enhancing computational efficiency. Harnessing the computational advantage of TOF, a variant of PSO, designated as Self-Evolving Particle Swarm Optimization (SEPSO) was developed. The SEPSO is underpinned by a novel Hierarchical Self-Evolving Framework (HSEF) that enables autonomous optimization of its own hyper-parameters to evade premature convergence. Additionally, a Priori Initialization (PI) mechanism and an Auto Truncation (AT) mechanism that substantially elevates the real-time performance of SEPSO on dynamic path planning problems were introduced. Comprehensive experiments on four widely used benchmark optimization functions have been initially conducted to corroborate the validity of SEPSO. Following this, a dynamic simulation environment that encompasses moving start/target points and dynamic/static obstacles was employed to assess the effectiveness of SEPSO on the dynamic path planning problem. Simulation results exhibit that the proposed SEPSO is capable of generating superior paths with considerably better real-time performance (67 path planning computations per second in a regular desktop computer) in contrast to alternative methods. The code and video of this paper can be accessed here [Code and Video: https://github.com/XinJingHao/Real-time-Path-planning-with-SEPSO ].
Physical-layer key distribution based on commonly-driven laser synchronization with random modulation of drive light

We propose and experimentally demonstrate a physical-layer key distribution scheme using commonly-driven laser synchronization with random modulation of drive light. Two parameter-matched semiconductor lasers injected by a common complex drive light are used as entropy sources for legitimate users. Legitimate users generate their own random signal by randomly time-division multiplexing of two random sequences with a certain duration according to individual control codes, and then independently modulate the drive light. Laser synchronization is achieved during time slots when the modulation sequences of two users are identical, and thus provide highly correlated randomness for extracting random numbers as shared keys. Experimental results show that the random modulation of the drive light reduces the correlation between the drive light and laser outputs. In addition, laser synchronization is sensitive to the modulation delay and then the latter can be used as an additional hardware parameter. These mean that security is enhanced. In addition, the proposed method has a short laser synchronization recovery time of lower than 1.1 ns, meaning a high rate of key distribution. The upper limit of final key rate of 2.55 Gb/s with a criterion of bit error rate of 1.68 × 10 −3 is achieved in experiments. Our results provide a promising candidate for protecting the security of optical fiber communication.
HCVC: A High-Capacity Off-Chain Virtual Channel Scheme Based on Bidirectional Locking Mechanism

As one of the key components of Web 3.0, the security of cryptocurrency is essential to its development and application. A Payment Channel Network (PCN) enhances transaction confirmation speed and throughput by executing blockchain transactions off-chain. However, existing PCN protocols have problems like privacy leaks and offline and challenges in meeting privacy and security demands. Due to such problems, we propose in this work High-Capacity Virtual Channel (HCVC) scheme to achieve payment across PCNs. As a lightweight virtual channel protocol based on the unspent transaction output model, HCVC uses a bidirectional locking mechanism to lock the collateral on payment channels to increase the capacity of the virtual channel. Additionally, a bidirectional punishment mechanism is introduced to deter malicious nodes, maintain balance security, and enable secure peer-to-peer transactions across PCNs, as all phases of HCVC can be performed off-chain without confirming transactions on-chain. Experimental analysis shows that the HCVC performs better in n-path PCNs. Compared to Donner, the communication overhead of HCVC in the open and resolve(undesirable) phases can be reduced by 39% and 37%, respectively, effectively reducing the overhead of transactions and enhancing the transaction throughput of the blockchain.
Bio-inspired altitude changing extension to the 3DVFH* local obstacle avoidance algorithm

Abstract Obstacle avoidance is critical for unmanned aerial vehicles (UAVs) operating autonomously. Obstacle avoidance algorithms either rely on global environment data or local sensor data. Local path planners react to unforeseen objects and plan purely on local sensor information. Similarly, animals need to find feasible paths based on local information about their surroundings. Therefore, their behavior is a valuable source of inspiration for path planning. Bumblebees tend to fly vertically over far-away obstacles and horizontally around close ones, implying two zones for different flight strategies depending on the distance to obstacles. This work enhances the local path planner 3DVFH* with this bio-inspired strategy. The algorithm alters the goal-driven function of the 3DVFH* to climb-preferring if obstacles are far away. Prior experiments with bumblebees led to two definitions of flight zone limits depending on the distance to obstacles, leading to two algorithm variants. Both variants reduce the probability of not reaching the goal of a 3DVFH* implementation in Matlab/Simulink. The best variant, 3DVFH*b-b, reduces this probability from 70.7 to 18.6% in city-like worlds using a strong vertical evasion strategy. Energy consumption is higher, and flight paths are longer compared to the algorithm version with pronounced horizontal evasion tendency. A parameter study analyzes the effect of different weighting factors in the cost function. The best parameter combination shows a failure probability of 6.9% in city-like worlds and reduces energy consumption by 28%. Our findings demonstrate the potential of bio-inspired approaches for improving the performance of local path planning algorithms for UAV.
From hate speech recognition to happiness indexing: critical issues in datafication of emotion in text mining

One prominent application of computational methods is the identification of affectivity and emotions in textual data, commonly known as sentiment analysis. In this chapter, we explore the datafication of affective language by focusing on operationalization and translation involved in the analysis processes behind common methods to identify affectivity or specific emotions in text. We draw examples from popular cases and from our own empirical studies that apply and develop sentiment and hate speech analysis. We suggest that sentiment analysis is a fruitful case for discussing the role of and the tensions involved in applying computational techniques in the automated analysis of meaning-laden phenomena. We highlight that any application of sentiment analysis techniques to investigate emotional expression in texts amounts to an effort of constructing sentiment measurements - a process essentially driven by judgments made by researchers in an attempt to reconcile diverging conventions and conceptions of good/proper research practices.
Analysis of the impact of central bank digital currency on stock markets: Dynamics and implications

The purpose of the study is to explore the influence of central bank digital currency on stock markets. To realize the purpose, the TVP-VAR model was built, which determines the impact of volatility of the CBDC attention index (CBDCAI) on the volatility of stock market indices. The study uses a time-varying vector autoregressive model that analyzes weekly data from the first week of January 2015 to the first week of July 2023. The endogenous vector to be assessed by VAR contains CBDCAI and stock market indices of different countries (France: CAC 40, The United States of America: S&amp;amp;amp;P 500, Germany: DAX 40, United Kingdom: FTSE 100, China: SSEC, The Netherlands AEX 25, Switzerland: SMI 20, Japan: Nikkei 225, India: NIFTY 50, Brazil: BVSP, South Korea: KOSPI). The results of the TVP-VAR model show that compared to stock market indices, CBDCAI appeared to be relatively independent and isolated. Interdependence and mutual influence between the digital currency market of central banks and stock markets were also revealed. In addition, CBDC functions primarily as a volatility absorber rather than a source of volatility. Despite the overall ability of the CBDC market to absorb fluctuations in volatility, it may also change its function with the widespread adoption of central bank digital currencies in many countries.
Probe thermometry with continuous measurements

Abstract Temperature estimation plays a vital role across natural sciences. A standard approach is provided by probe thermometry, where a probe is brought into contact with the sample and examined after a certain amount of time has passed. In situations where, for example, preparation of the probe is non-trivial or total measurement time of the experiment is the main resource that must be optimized, continuously monitoring the probe may be preferred. Here, we consider a minimal model, where the probe is provided by a two-level system coupled to a thermal reservoir. Monitoring thermally activated transitions enables real-time estimation of temperature with increasing accuracy over time. Within this framework we comprehensively investigate thermometry in both bosonic and fermionic environments employing a Bayesian approach. Furthermore, we explore adaptive strategies and find a significant improvement on the precision. Additionally, we examine the impact of noise and find that adaptive strategies may suffer more than non-adaptive ones for short observation times. While our main focus is on thermometry, our results are easily extended to the estimation of other environmental parameters, such as chemical potentials and transition rates.
A novel code generator for graphical user interfaces

Abstract Graphical user interfaces (GUIs) are widely used in human–computer interaction, providing a convenient interface for operation. Automating the conversion of GUI design images into source code can significantly reduce the coding workload for front-end developers. Detecting elements in GUI images is a key challenge in achieving automatic GUI code generation and is crucial for tasks such as GUI automation and testing. However, current state-of-the-art methods do not fully consider the unique characteristics of GUI images and elements, and they lack the required high localization accuracy, resulting in low detection accuracy for GUI element boxes. In this paper, we propose GUICG, an automatic GUI code generator that combines deep neural networks with image processing techniques to efficiently detect GUI elements from GUI images and generate front-end code. We empirically investigate various deep learning approaches and image processing methods for GUI component detection. Based on a comprehensive understanding of their performance and characteristics, we design GUICG by fusing image processing with a deep learning-based target detection model, achieving state-of-the-art performance. GUICG outperforms existing methods in accuracy and F1 score for component detection tasks, while producing human-readable code with a logical structure. Furthermore, we conduct an ablation study to quantitatively assess the impact of each key element in GUICG.
Transfer Learning Using Whisper for Dysarthric Automatic Speech Recognition

Dysarthria is a motor speech disorder that affects an individual’s ability to articulate words, making speech recognition a challenging task. Automatic Speech Recognition (ASR) technologies have the potential to greatly benefit individuals with dysarthria by providing them with a means of communication through computing and portable digital devices. These technologies can serve as an interaction medium, enabling dysarthric patients to communicate with others and computers. In this paper, we propose a transfer learning approach using the Whisper model to develop a dysarthric ASR system. Whisper, Web-scale Supervised Pretraining for Speech Recognition, is a multi-tasking model trained on various speech-related tasks, such as speech transcription on various languages, speech translation, voice activity detection, language identification, etc. on a wide scale of 680,000 h of labeled audio data. Using the proposed Whisper-based approach, we have obtained an word recognition average accuracy of $$59.78\%$$ using 155 words of UA-Speech Corpus, using the Bi-LSTM classifier model.
Revisiting Assessment of Text Complexity: Lexical and Syntactic Parameters Fluctuations

In this article we share findings on linguistic complexity fluctuations of Russian middle school textbooks. The study partially confirmed the null hypothesis that textbook syntactic complexity grows universally over the course of a single school year. The Research Corpus was compiled of 104 textbooks used in 12 subject domains in Russian middle schools. In accordance with school semesters (Fall-Spring), we divided each textbook into two parts and examined 208 texts for 47 quantitative parameters measured with RuLingva (rulingva.kpfu.ru). After considering dynamics of the values of each parameter for Fall and Spring semesters, we narrowed the parameters list down to syntactic (average word length, average sentence length, Flesch-Kincaid grade level) and lexical (lexical diversity and frequency) clusters. We identified and scrutinized three types of text complexity fluctuations: simultaneous increase of both clusters of parameters, opposite dynamics and independent fluctuations. The majority of textbooks demonstrate lexical and syntactic clusters’ “trade-off’” when the lexical complexity increase triggers the syntactic complexity decrease thus balancing the joint complexity. We also discuss the assumptions that underline the concept of complexity fluctuations and algorithms of its measurements in an effort to put the issue on the agenda of researchers and education authorities. Our findings can be useful for scholars, academicians and education policy makers at the national and regional levels.
Power prediction of photovoltaic power generation based on LSTM model with additive Attention mechanism

Photovoltaic power generation is affected by meteorological factors, characterized by intermittency, readiness and fluctuation, which brings challenges to the stable operation of PV power generation systems. Therefore, accurate prediction of PV power generation is of great significance. In this study, the LSTM (Long Short-Term Memory Network) model based on additive attention mechanism aims to improve the prediction accuracy of PV power. Firstly, the Pearson correlation coefficient method is used to pre-screen the meteorological data and select the meteorological parameters with higher correlation with PV power. Then Principal Component Analysis was applied to reduce the data dimensionality, and the most relevant parameters affecting PV power were successfully mined as inputs to the prediction model. Next, the additive Attention mechanism is introduced into the LSTM model, which is conducive to reducing the influence of irrelevant variables and paying more attention to useful input parameters. It is verified experimentally that the Attention-LSTM-based prediction model has higher accuracy in different weather scenarios when compared with single LSTM model, BP (BackPropagation Neural Network) model, and Convolutional Neural Network (CNN) model. Therefore, the model proposed in this study can effectively improve the prediction of PV power.
A Hybrid Fractional Order Fuzzy PID Controller Using Interval Type- 2 Fuzzy Neural Network

In this paper, a hybrid fractional order fuzzy PID controller structure has been studied for fractional order systems. In addition to the fractional order integral and derivative terms, this structure replaces the proportional part of the fractional order PID (FOPID) structure with an interval type-2 fuzzy neural network. In this structure, the fractional order derivative action is implemented on the process output to avoid sudden changes in the set-point. Moreover, the IT2-fuzzy neural network parameters are adapted online using the gradient descent algorithm. Hence, the structure can be more adaptable to system uncertainties, and it can give an observed improvement in the system's time response. By using two different fractional-order examples, simulation studies via five test cases are performed to assess the effectiveness of the controller structure. Moreover, the stabilization problem of the inverted pendulum system is performed to evaluate the developed structure. Furthermore, comparative results with conventional fractional-order PID controller and fractional order IT2- fuzzy controller structures are conducted.
Accelerating supersingular isogeny Diffie-Hellman (SIDH) cryptosystem for the security of resource-constrained IoT devices with FPGA

It is imperative to note that post-quantum cryptography, such as supersingular isogeny Diffie-Hellman (SIDH), is essential for ensuring that Internet of Things (IoT) devices have a restricted amount of resources. The primary challenges in assuring the security of IoT devices are addressed by this work's analysis of SIDH implementations designed for field programmable gate array (FPGA) architecture. Extensive efforts towards implementing the architecture for a rapid and constant-time FPGA implementation of SIDH. This quantum-resistant cryptographic primitive is a crucial component for adhering to NIST's PQC standardization. Our goal with this paper is to demonstrate how FPGA architectures can enhance the parallelism of SIDH, making it a more practical option for securing resource-limited IoT devices. Our focus is on providing a reliable speed record for SIDH, which is crucial for ensuring the security of IoT devices. The design for isogeny computation was built over p434 in Xilinx Virtex 6 and produced 45ms for public key generation in addition to 35ms for secret key generation.