Reviewerly/exHarmony huggingface dataset:
Surround-View Fisheye Camera Viewpoint Augmentation for Image Semantic Segmentation

In autonomous vehicles, perception information about the surrounding road environment can be obtained through image semantic segmentation.The fisheye camera commonly used in autonomous vehicle surround view systems offers a wide field of view (FoV), providing comprehensive perception information about the surrounding environment and assisting in understanding complex scenes.However, there is a challenge in model training due to the limited availability of fisheye semantic image datasets, resulting in reduced generalization performance and unreliable results in various test environments.In particular, changes in the position and orientation of the camera result in changes in the camera viewpoint, which can impair the model's segmentation performance.Generally, data scarcity problems are solved using augmentation methods, but existing methods have difficulty reflecting the distortion characteristics of fisheye images.To solve this problem, we propose viewpoint augmentation considering the spatially variant distortion characteristic of fisheye images.First, we use the fisheye camera projection model in reverse to map the captured 2D fisheye image to a point on the surface of a unit sphere in 3D.Then, we change the camera's orientation and position by applying rotation and translation operations to the point.Finally, we reproject the transformed point to the fisheye image to generate a fisheye image with a changed viewpoint.The experimental results show that the proposed augmentation method increases the generalization performance of the model and effectively reduces model performance degradation under changing camera viewpoints, making it suitable for practical applications.
Difficulties of FAIR Principles Implementation in Cross-Domain Research Infrastructures

Continuously increasing complexity of scientific research lead to the growing need for close integration of heterogeneous scientific communities. Such integration can be provided by the development of cross-domain research data infrastructures. In this paper, problems of the effective organization of scientific data in research infrastructures, based on FAIR principles, are considered. Analysis of the representation of various subjects in the existing infrastructures used for highlighting the main difficulties facing researchers, the ways of its overcoming are considered.
Enabling Efficient Real-Time Calibration on Cloud Quantum Machines

Noisy intermediate-scale quantum computers are widely used for quantum computing (QC) from quantum cloud providers. Among them, superconducting quantum computers, with their high scalability and mature processing technology based on traditional silicon-based chips, have become the preferred solution for most commercial companies and research institutions to develop QC. However, superconducting quantum computers suffer from fluctuation due to noisy environments. To maintain reliability for every execution, calibration of the quantum processor is significantly important. During the long procedure to calibrate physical quantum bits (qubits), quantum processors must be turned into offline mode. In this work, we propose a real-time calibration framework (RCF) to execute quantum program tasks and calibrate in-demand qubits simultaneously, without interrupting quantum processors. Across a widely used noisy intermediate-scale quantum (NISQ) evaluation benchmark suite such as QASMBench, RCF achieves up to 18% reliability improvement for applications. For reliability on different physical qubits, RCF achieves an average gain of 15.7% (up to 36.7%). For cloud quantum machines, the throughput can be improved up to 9.5 throughput per minute (6.5 on average) based on baseline calibration time. In conclusion, RCF offers a reliable solution for large-scale, long-serving quantum machines.
Nonlinear and machine learning analyses on high-density EEG data of math experts and novices

Current trend in neurosciences is to use naturalistic stimuli, such as cinema, class-room biology or video gaming, aiming to understand the brain functions during ecologically valid conditions. Naturalistic stimuli recruit complex and overlapping cognitive, emotional and sensory brain processes. Brain oscillations form underlying mechanisms for such processes, and further, these processes can be modified by expertise. Human cortical functions are often analyzed with linear methods despite brain as a biological system is highly nonlinear. This study applies a relatively robust nonlinear method, Higuchi fractal dimension (HFD), to classify cortical functions of math experts and novices when they solve long and complex math demonstrations in an EEG laboratory. Brain imaging data, which is collected over a long time span during naturalistic stimuli, enables the application of data-driven analyses. Therefore, we also explore the neural signature of math expertise with machine learning algorithms. There is a need for novel methodologies in analyzing naturalistic data because formulation of theories of the brain functions in the real world based on reductionist and simplified study designs is both challenging and questionable. Data-driven intelligent approaches may be helpful in developing and testing new theories on complex brain functions. Our results clarify the different neural signature, analyzed by HFD, of math experts and novices during complex math and suggest machine learning as a promising data-driven approach to understand the brain processes in expertise and mathematical cognition.
MEAI: an artificial intelligence platform for predicting distant and lymph node metastases directly from primary breast cancer

Breast cancer patients typically have decent prognoses, with a 5-year survival rate of more than 90%, but when the disease metastases to lymph node or distant, the prognosis drastically declines. Therefore, it is essential for future treatment and patient survival to quickly and accurately identify tumor metastasis in patients. An artificial intelligence system was developed to recognize lymph node and distant tumor metastases on whole-slide images (WSIs) of primary breast cancer.In this study, a total of 832 WSIs from 520 patients without tumor metastases and 312 patients with breast cancer metastases (including lymph node, bone, lung, liver, and other) were gathered. Based on the WSIs were randomly divided into the training and testing cohorts, a brand-new artificial intelligence system called MEAI was built to identify lymph node and distant metastases in primary breast cancer.The final AI system attained an area under the receiver operating characteristic curve of 0.934 in a test set of 187 patients. In addition, the potential for AI system to increase the precision, consistency, and effectiveness of tumor metastasis detection in patients with breast cancer was highlighted by the AI's achievement of an AUROC higher than the average of six board-certified pathologists (AUROC 0.811) in a retrospective pathologist evaluation.The proposed MEAI system can provide a non-invasive approach to assess the metastatic probability of patients with primary breast cancer.
Classification of spreads of Tits quadrangles of order 64

Brown et al. provide a representation of a spread of the Tits quadrangle T2(O), O an oval of PG(2,q), q even, in terms of a certain family of q ovals of PG(2,q). By combining this representation with the Vandendriessche classification of hyperovals in PG(2,64) and the classification of flocks of the quadratic cone in PG(3,64), recently given by the authors, in this paper, we classify all the spreads of T2(O), O an oval of PG(2,64), up to equivalence. These complete the classification of spreads of T2(O) for q≤64.
Quantum Bit Commitment Without Quantum Memory

Abstract Commitment scheme is a fundamental cryptographic primitive that serve as building blocks for many other two-party protocols. In this paper, we propose a novel quantum bit commitment scheme, which is secure and does not require quantum memory. Our scheme processes the quantum information using coherent states and unambiguous state discrimination (USD) measurements, which can be experimentally realized by linear optics and photon detectors. We ensure the unconditionally hiding and binding property by preventing both Alice and Bob from obtaining complete information about the commitment in committing stage, and eliminate the requirement for quantum memory by performing USD measurements and phase shifts immediately after receiving the coherent states.
Classification of Alzheimer's using Deep-learning Methods on Webcam-based Gaze Data

There has been increasing interest in non-invasive predictors of Alzheimer's disease (AD) as an initial screen for this condition. Previously, successful attempts leveraged eye-tracking and language data generated during picture narration and reading tasks. These results were obtained with high-end, expensive eye-trackers. Instead, we explore classification using eye-tracking data collected with a webcam, where our classifiers are built using a deep-learning approach. Our results show that the webcam gaze classifier is not as good as the classifier based on high-end eye-tracking data. However, the webcam-based classifier still beats the majority-class baseline classifier in terms of AU-ROC, indicating that predictive signals can be extracted from webcam gaze tracking. Hence, although our results indicate that there is still a long way to go before webcam gaze tracking can reach practical relevance, they still provide an encouraging proof of concept that this technology should be further explored as an affordable alternative to high-end eye-trackers for the detection of AD.
Landscape properties of the very large-scale and the variable neighborhood search metaheuristics for the multidimensional assignment problem

We study the recent metaheuristic search algorithm for the multidimensional assignment problem (MAP) using fitness landscape theory. The analyzed algorithm performs a very large-scale neighborhood search on a set of feasible solutions to the problem. We derive properties of the landscape graphs that represent these very large-scale search algorithms acting on the solutions of the MAP. In particular, we show that the search graph is a generalization of a hypercube. We extend and generalize the original very large-scale neighborhood search to develop the variable neighborhood search. The new search is capable of searching even larger large-scale neighborhoods. We perform numerical analyses of the search graph structures for various problem instances of the MAP and different neighborhood structures of the MAP algorithm based on a very large-scale search. We also investigate the correlation between fitness (i.e., objective values) and distance (i.e., path lengths) of the local minima (i.e., sinks of the landscape). Our results can be used to design improved search-based metaheuristics for the MAP.
Unconscious Frustration: Dynamically Assessing User Experience using Eye and Mouse Tracking

Eye-tracking has become easier to deploy in user experience (UX) studies to get a sense of where users attend to during interactions. Additionally, mouse tracking grants insights into the cognition driving the user's behaviours and end goals, as can measuring the coordination between the eye and mouse-cursor. We created a menu navigation task based on a popular video game to assess two populations: a local cohort, and a remote cohort. We used two different eye trackers (monitor-mounted hardware, and a webcam-based algorithm; local used both simultaneously, remote used webcam only) with concurrent mouse tracking to detect friction in the UX. We found that both eye trackers had similar performance and revealed a previously undetected friction point. We argue this friction point was only detected because of the use of quantified, coordinated unconscious behaviours (eye and hand movements). The methods demonstrated are easily integrated into current UX studies with minimal cost.
On the girth cycles of the bipartite graph D(k,q)

For integer k≥2 and prime power q, the algebraic bipartite graph D(k,q) proposed by Lazebnik and Ustimenko (1995) is meaningful not only in extremal graph theory but also in coding theory and cryptography. This graph is q-regular, edge-transitive and of girth at least k+4. For its exact girth g=g(D(k,q)), Füredi et al. (1995) conjectured g=k+5 for odd k and q≥4. This conjecture was shown to be valid in 2016 when (k+5)/2 is the product of an arbitrary factor of q−1 and an arbitrary power of the characteristic of Fq. In this paper, we determine all the girth cycles of D(k,q) for 3≤k≤5, q>3, and those for 3≤k≤14, q=3.
The EU MiCA Directive – chances and risks from a compliance perspective

Purpose This study aims to review the current EU approach to regulating crypto assets. It highlights the key points, opportunities and risks of the MiCA regulation, which is designed to provide a comprehensive regulatory framework for digital assets in the EU. Design/methodology/approach To do so, the authors extensively reviewed the literature and reports from several advisory and watchdog bodies and international organizations. Findings Although MiCA is an ambitious piece of legislation, there are still many unresolved issues and questions that the new regulation raises. Controversially several items have also been excluded from the MiCA regulations, including decentralized finance, non-fungible tokens unless they qualify under the existing cryptocurrency categories, as well as central bank digital currencies. Originality/value This study also addresses the Liechtenstein Token Act Regulation, which is considered to have served as a model for the EU MiCA Directive and the regulation of cryptocurrencies at the European level.
Estimating of Photovoltaic Energy Saving Potential and Investment Energy Value in Malaysia based on GIS Technology

Abstract Creating environmentally friendly energy schemes that are sustainable is critical as a solution to the economic, ecological, and societal influences related to the energy sector. It should be noted that buildings utilize up to forty percent of global energy consumption. Furthermore, by 2030, it is anticipated to reach fifty percent. As a result, the world faces a significant dilemma in overcoming such worldwide energy generation issues. Concerning primary energy consumption, Malaysia is among Asia’s largest consumers. Malaysia’s immediate energy use in 2018 was 3.79 quadrillion Btu, growing at a 4.58% annualized rate. This article thoroughly reviewed past studies of global energy usage, particularly in Malaysia, and how the geographical information system (GIS) methodologies were employed for spatial evaluation of energy efficacy. Undoubtedly, effective energy strategies are critical in energy policy, and they can be developed through the application of a variety of methods for energy conservation in building structures. The findings of this study indicate that some GIS methods, such as machine learning, deep learning and multiple criteria decision analysis, are mainly employed for calculating energy consumption, researching renewable energy sources, and analyzing solar radiation. The results also show that the fuzzy_AHP and fuzzy_DS techniques have a higher capability and reliability in identifying the most suitable sites for photovoltaic (PV) farms. Due to the generation of a 3D model exportable to software tools through this technique, the 3D models are deemed efficient for calculating solar radiation. Furthermore, GIS and 3D can assist with various tasks, including access to sunlight in built structures and environments in urban areas, urban growth prediction models, and the habitability of public spaces analysis.
Mapping and litho‐geophysical classification of potential mining areas by spatial remote sensing and helicopter‐borne gamma‐ray spectrometry (Tikirt, Anti‐Atlas, Morocco)

Abstract The primary focus of this study is to explore the potential mineral areas. Advanced Spaceborne Thermal Emission and Reflection Radiometer images and radiometric data were combined in the Tikirt region to map hydrothermal alteration zones associated with mineralized deposits. Advanced Spaceborne Thermal Emission and Reflection Radiometer images were analysed using band ratios, principal component analysis and a fuzzy logic model to discover argillic, phyllic, propylitic and iron oxide alterations and to generate a mineral prospectivity map. In addition, mono‐element maps of radiometric elements (K in %, eU in ppm, eTh in ppm) and their behaviour in the ternary image have been elaborated to determine the concentrations of radiometric elements and the variation of radiometric character along the exposed terrains. The F parameter was calculated to target high potassium concentration areas associated with hydrothermal alteration zones. Combining the two methods highlighted four hydrothermal alteration zones considered very promising from a mining point of view. These zones are generally linked to the magma bodies affected by normal faults and exhibit a clear spatial correlation with mineral occurrences.
Accounting Systems With Smart Contracts

The theme of accounting in blockchain environment has been referenced and theoretically discussed over the last few years in international publications, both of academic and professional nature. However, until today, there are no other known works that pursued a path in order to perform accounting records in blockchain. In the present work the authors go beyond this, as they will demonstrate, also proposing a practical solution for its implementation and use in a generalized way. The authors present two new proofs of concept, which are part of an ongoing work process to obtain the development of the Portuguese accounting system using blockchain. The most recent of the sequential steps was designing and developing the tokenization process and a Dapp that will be used to perform and consult accounting operations through any mobile device, by developing smart contracts. In a near future, this system will allow accounting operations to be carried out, in a simple way, on the blockchain, assuming implications for the way in which accountants, users of financial information, and software houses work.
To Transfer or Not to Transfer and Why? Meta-Transfer Learning for Explainable and Controllable Cross-Individual Activity Recognition

Human activity recognition (HAR) plays a central role in ubiquitous computing applications such as health monitoring. In the real world, it is impractical to perform reliably and consistently over time across a population of individuals due to the cross-individual variation in human behavior. Existing transfer learning algorithms suffer the challenge of “negative transfer”. Moreover, these strategies are entirely black-box. To tackle these issues, we propose X-WRAP (eXplain, Weight and Rank Activity Prediction), a simple but effective approach for cross-individual HAR, which improves the performance, transparency, and ease of control for stakeholders in HAR. X-WRAP works by wrapping transfer learning into a meta-learning loop that identifies the approximately optimal source individuals. The candidate source domains are ranked using a linear scoring function based on interpretable meta-features capturing the properties of the source domains. X-WRAP is optimized using Bayesian optimization. Experiments conducted on a publicly available dataset show that the model can effectively improve the performance of transfer learning models consistently. In addition, X-WRAP can provide interpretable analysis according to the meta-features, making it possible for stakeholders to get a high-level understanding of selective transfer. In addition, an extensive empirical analysis demonstrates the promise of the approach to outperform in data-sparse situations.
Adventures in Cryptology: Exploration-Worthy Project Topics

ABSTRACTIn this paper, several research topics are presented that provide productive avenues of exploration for students in an undergraduate cryptography course. The topics are intended for a variety of skill levels and interests. In some cases, a student might be inspired to try to solve the proposed problem or write computer code. Other topics might motivate them to learn new mathematics or algorithms, or research a historical event or person. In all cases, a short description of the topic is given along with several references that can be used as a starting point for student research. The author also describes some challenges and recommendations when teaching a course where a project is a main component.KEYWORDS: Cryptologyundergraduate researchcryptology teaching resources DISCLOSURE STATEMENTNo potential conflict of interest was reported by the author(s).BIOGRAPHICAL SKETCHCheryl Beaver is a professor in the Mathematics Department at Western Oregon University. Prior to Western, Cheryl worked for 7 years as a cryptographer at Sandia National Laboratories in Albuquerque, New Mexico. Her interests lie in number theory, cryptology and the education of preservice teachers. Cheryl is a co-author of the annual online cryptanlaysis contest for undergraduates, Kryptos.
Low-resource Multilingual Neural Translation Using Linguistic Feature-based Relevance Mechanisms

This article investigates approaches to effectively harness source-side linguistic features for low-resource multilingual neural machine translation (MNMT). Previous works focus on using various features of a word such as lemma, part-of-speech tag, dependency label, and so on, to improve translation quality in a low-resource scenario. However, these studies deal with bilingual translation and do not focus on using features in multilingual training setups. Our work focuses on this particular point and experiments with low-resource multilingual models incorporating source-side linguistic features. Although techniques for integrating features into an NMT model such as concatenation and feature relevance perform quite well in bilingual settings, they do not work well in multilingual settings. To remedy this, we propose the use of dummy features and language indicator features in MNMT models. Experiments are conducted on English to Asian language translation on a multilingual, multi-parallel corpus spanning English and eight Asian languages where for each language pair, the training data size does not exceed 20,000 parallel sentences. After establishing strong bilingual baselines using feature relevance mechanisms and multilingual baselines without any features, we show that our proposed dummy features and language indicator features, in combination with feature relevance mechanisms, yield significant improvements in BLEU points for all language pairs. We then analyze our models from the perspectives of model sizes, the impact of individual linguistic features, validation perplexity computed during training, visualization of the activations of the relevance mechanisms, and exhaustive tuning of hyperparameters. We also report preliminary results for multilingual multi-way models using linguistic features.