Reviewerly/exHarmony huggingface dataset:
Using Dynamic Image Analysis as a Method for Discerning Microdebitage from Natural Soils in Archaeological Soil Samples

Unlike larger stone tools and debitage, the analysis of microdebitage (measuring less than 6 mm) allows for identifying likely areas where stone tools were manufactured at prehistoric archaeological sites. The tedious, time-consuming, and costly nature of microdebitage, however, has overshadowed its data potential, making most archaeologists wary of implementing this method. To alleviate these issues, this study introduces an experimental protocol using Dynamic Image Analysis (DIA) as a potential method for differentiating between microdebitage and natural soils within archaeological soil samples. In this study, a particle analyzer was employed to test whether microdebitage can be quantifiably identified within archaeological soil samples without the need for flotation and manual sorting. The results of the Multivariate Analysis of Variance (MANOVA) test demonstrate significant differences (p < 0.001) between the lithic and soil datasets, indicating that DIA is a viable and promising method for differentiating between microdebitage and natural soils within archaeological soil samples.
A Regression Model to Correct for Intra-Hourly Irradiance Variability Bias in Solar Energy Models

Industry-standard solar resource assessment methods assume hourly-resolution modeling, which typically overestimates generation due to irradiance variability within an hour. Depending on PV site location and configuration, the high bias introduced by hourly modeling methods is generally greater than 1.5% and can exceed 4% on the annual AC energy when compared to real-world operations. It is critical that bias corrections be applied to hourly solar energy simulations prior to making binding investment and financing decisions. This study presents a random forest regression model that accurately resolves the modeling bias attributed to intra-hour irradiance variability. The model considers site-specific meteorology and layout design parameters to resolve typical seasonal and diurnal variability patterns. It has been validated using minute-resolution observations from operational solar farms and pre-construction meteorological measurements, with model bias error shown to be -0.1% on annual energy.
Efficient Implementation of Nearest Neighbor Quantum Circuits Using Clustering with Genetic Algorithm

Although quantum computing has made tremendous progress in last couple of years and technologies like NMR, Ion Trap, superconducting qubits have come out as promising platforms to implement quantum computing devices, such technologies are facing several design constraints. One such constraint is the Nearest Neighbor (NN) property, which demands the adjacency of logical qubits. Aiming to contribute to this cause, here we are proposing an improved design approach for transforming quantum circuits for NN-based architectures using genetic algorithms. In this work, our primary objective remains to form efficient NN structures by restricting the SWAP usage. In the design phase, initially, we use the k-means clustering scheme for partitioning the qubits into separate objects and, then, a genetic algorithm is applied that eventually fixes the order of qubits for each individual cluster. In the final phase, all these local solutions are combined and, again, a genetic algorithm is employed to obtain a global solution. We have tested our approach over a large spectrum of benchmarks and improvements are registered over some state-of-the-art design works.
On a classification of the automorphism groups of polarized abelian surfaces over finite fields

We give a classification of maximal elements of the set of finite groups that can be realized as the full automorphism groups of polarized abelian surfaces over finite fields.
Learning shape and texture progression for young child face aging

Face aging (FA) for young faces refers to rendering the aging faces at target age for an individual, generally under 20s, which is an important topic of facial age analysis. Unlike traditional FA for adults, it is challenging to age children with one deep learning-based FA network, since there are deformations of facial shapes and variations of textural details. To alleviate the deficiency, a unified FA framework for young faces is proposed, which consists of two decoupled networks to apply aging image translation. It explicitly models transformations of geometry and appearance using two components: GD-GAN, which simulates the Geometric Deformation using Generative Adversarial Network; TV-GAN, which simulates the Textural Variations guided by the age-related saliency map. Extensive experiments demonstrate that our method has advantages over the state-of-the-art methods in terms of synthesizing visually plausible images for young faces, as well as preserving the personalized features.
Improving entanglement generation rates in trapped-ion quantum networks using nondestructive photon measurement and storage

Long-range quantum information processing will require the integration of different technologies to form hybrid architectures combining the strengths of multiple quantum systems. In this work, we propose a hybrid networking architecture designed to improve entanglement rates in quantum networks based on trapped ions. Trapped ions are excellent candidates as network nodes, but photon losses make long-distance networking difficult. To overcome some losses and extend the range of trapped-ion-based networks, we propose including neutral-atom-based nondestructive single-photon detection and single-photon storage in between networking nodes, forming a hybrid network. This work builds on recently demonstrated optical frequency conversion of single photons emitted by trapped ions. We derive the average two-node entanglement rate for this proposed network architecture as a function of distance. Using reasonable experimental parameters, we show that this proposed quantum network can generate remote entanglement rates up to a factor of 100 larger than that of an equivalent homogeneous network at both near-IR and C-band wavelengths for distances up to 50 km.
Evolution in E-Commerce with Augmented Reality

Abstract New shopping methods with various attributes compared with traditional internet shopping can improve the administrations of business websites or E-commerce system. This paper is a dense abstract of the broadest parts of elective innovation dependent on AR collaborations that avoids the utilization of physical interaction with the product. The utilization of internet shopping has been developing significantly over some time. Augmented reality (AR) may assume the most significant job in improving the method of web-based shopping. The advancement of new AR innovation can be made valuable if it can imitate the highlights that have made web-based shopping the most favorable shopping source in present times. AR is equipped for working in a gadget utilizing Google’s AR Core or Apple’s AR Kit SDKs. It is a straightforward technique to create the 3D model before an individual that can be effectively equivalent which results in a better impression of the item by recreating in reality. 3D models are being delivered utilizing a game motor with AR SDKs in the client gadget which is controlled through a lot of projects written in an IDE. The execution of AR shopping is changing internet business by helping the web retailers to decrease the developing expense of profits and furnishing their clients with all the more captivating and advantageous approach to shop through their gadgets. Imaginative AR arrangements permit clients to envision their item continuously, from the comfort of their place. And it may play a major role in the industrial revolution with a specialization area.
Computer Aided Diagnosis: Approaches to Automate Hematological Tests

Hematological disorders are disorders pertaining to blood cell anomalies. Diseases varying from anemia, malaria to leukemia are diagnosed via careful scrutiny of peripheral blood smears (PBS) prepared on glass slides. Usually, the tests are performed in laboratory settings by skilled professionals and clinicians to reach a diagnosis. However, human error due to fatigue and observational oversights might lead to a misdiagnosis. Automated systems are therefore developed to assist the diagnostic procedure of clinicians and provide them with meaningful insights. Computer aided diagnosis is mostly concerned with the evaluation of medical images by employing various image processing techniques. The chapter gives an overview of the stages in developing a computer aided diagnosis system. It concentrates toward the common approaches in designing and developing an assisting system to detect abnormalities in blood samples using image processing tools alongside tools like machine learning and deep learning. The chapter addresses steps like pre-processing, image segmentation, feature extraction, pattern recognition and classification while focusing on specific studies on blood disorders.
Research on Image Recognition Algorithm Technology for Power Line detection

Abstract Based on the analysis of power line features in aerial images, this paper proposes a power line detection algorithm for aerial images based on adjustable direction filtering. The thesis first performs directional filtering on the image and calculates its directional energy function and ridge energy function, then combines the image local and global information to fit the line segments in the image, and finally verifies the reliability and real-time performance of the algorithm through experimental methods. The calculation of this algorithm is about half of the ED Lines algorithm. Experimental results show that the algorithm can detect power lines in aerial images better, and the algorithm is not complicated, which is suitable for real-time detection of power lines.
A Study on Identification and Cleaning of Struck-Out Words in Handwritten Documents

Handwritten character recognition (HCR) is a challenging research domain in the area of digital document analysis. Presently, various techniques are well known for the recognition of handwritten English characters. HCR can be categorized into online handwritten text recognition and offline handwritten text recognition. The recognition of texts in both the category is challenging since it depends on the writer writing skills. An ideal handwritten document contains various types of noise such as annotation, strike-out text, underlines, ink bleed, making the recognition task difficult and tedious. This survey outlines the research works that were conducted on the identification and recognition of such noise (strike-out text) present in handwritten documents. Support vector machine and graph-based approach have been used by author, which gives better recognition of strike-out text. It discusses various techniques that are used for the recognition of such noise (strike-out text) in a document and performance of these methods. It further discusses the most suitable area of application where strike-out text processing is applicable and ends giving room for research that needs to be carried out.
Transductive Zero-Shot Learning by Decoupled Feature Generation

In this paper, we address zero-shot learning (ZSL), the problem of recognizing categories for which no labeled visual data are available during training. We focus on the transductive setting, in which unlabelled visual data from unseen classes is available. State-of-the-art paradigms in ZSL typically exploit generative adversarial networks to synthesize visual features from semantic attributes. We posit that the main limitation of these approaches is to adopt a single model to face two problems: 1) generating realistic visual features, and 2) translating semantic attributes into visual cues. Differently, we propose to decouple such tasks, solving them separately. In particular, we train an unconditional generator to solely capture the complexity of the distribution of visual data and we subsequently pair it with a conditional generator devoted to enrich the prior knowledge of the data distribution with the semantic content of the class embeddings. We present a detailed ablation study to dissect the effect of our proposed decoupling approach, while demonstrating its superiority over the related state-of-the-art.
A Learning Mechanism for BRBES Using Enhanced Belief Rule-Based Adaptive Differential Evolution

Nowadays, belief rule-based expert systems (BRBESs) are widely used in various domains which provides a framework to handle qualitative and quantitative data by addressing several kinds of uncertainty. Learning plays an important role in BRBES to upgrade its knowledge base and parameters values, necessary for the improvement of the prediction accuracy. Different optimal training procedures such as Particle Swarm Optimisation (PSO), Differential Evolution (DE), and Genetic Algorithm (GA) have been used as learning mechanisms. Among these procedures, DE performs comparatively better than others. However, DE's performance depends significantly in assigning near optimal values to its control parameters including cross over and mutation factors. Therefore, the objective of this article is to present a novel optimal training procedure by integrating DE with BRBES. This is named as enhanced belief rule-based adaptive differential evolution (eBRBaDE) algorithm because it has the ability to determine the near-optimal values of both the control parameters while ensuring the balanced exploitation and exploration in the search space. In addition, a new joint optimization learning mechanism by using eBRBaDE is presented where both parameter and structure of BRBES are considered. The reliability of the eBRBaDE has been compared with evolutionary optimization algorithms such as GA, PSO, BAT, DE and L-SHADE. This comparison has been carried out by taking account of both conjunctive and disjunctive BRBESs while predicting the Power Usage Effectiveness (PUE) of a datacentre. The comparison demonstrates that the eBRBaDE provides higher prediction accuracy of PUE than from other evolutionary optimization algorithms. Contribution-An enhanced differential evolution algorithm has been proposed in this paper, which is later used as a novel optimal training procedure for BRBES.
Sample weights determination based on cosine similarity method as an extension to infrared action recognition

The widespread application of infrared human action recognition in intelligent surveillance has attracted significant attention. However, the infrared action recognition dataset is limited, which limits the development of infrared action recognition. Existing methods for infrared action recognition are based on features in the same sample, without paying attention to within-class differences. Motivated by the idea of weighting video information, this paper proposes a novel infrared action recognition framework to reweight the samples of training sets named REWS to solve the problems of limited infrared action data and the large within-class differences in the infrared action recognition dataset. In the proposed framework, we first map infrared action video data to a low-dimensional feature space, and use the cosine similarity between the feature data of the training set and the testing set to determine the weight of the training set samples. Each training set sample has an independent weight. Then, a support vector machine (SVM) is trained by the training sets with weights to recognize the infrared actions. Experimental results demonstrate that our approach can achieve state-of-the-art performance compared with hand-crafted features based methods on the benchmark InfAR dataset.
Detection of driver maneuvers using evolving fuzzy cloud-based system

This paper presents an evolving cloud-based algorithm for detecting driver actions and is actually a continuation of our earlier work. The general idea is to develop a system that is able to detect different maneuvers that drivers perform while driving a car. With that we want to detect not only the type but also the time window when the maneuver was performed. As this paper shows, maneuver detection could be done by analyzing the basic senors and signals normally measured in a car, such as revolutions, speed, steering wheel angle, pedal position and others. Moreover, the proposed method does not require any additional (intelligent) sensors such as cameras, radar, etc. On the basis of these signals we can identify two new maneuvers: U-turn and 3-point turn. For the experiment purposes we have acquired a real data from a car simulator with experienced drivers. The experiments show that the evolving fuzzy cloud-based system efficiently deals with detecting of driver maneuvers. Easily we could adapt the algorithm for online processing, analyzing and detecting the maneuvers in real time. Therefore, the proposed method is suitable for real applications.
A Brief Survey of Challenge–Response Authentication Mechanisms

Challenge–response authentication mechanism (CRAM) is a procedure to verify the genuineness of a claimant entity and involves the claimant to prove that he/she knows a secret without actually revealing it to the verifier. It involves sending a time-varying challenge by the verifier to the claimant which in turn generates a response by applying a secret-dependent function on the challenge. In this paper, we provide a formal definition of a CRAM and survey research work related to its two types, namely static and dynamic CRAMs. We also analyse the capability of these CRAMs to defend some well-known attacks and also suggest the best practices for the implementation of CRAMs towards the end.
Unpaired Stain Style Transfer Using Invertible Neural Networks Based on Channel Attention and Long-Range Residual

Hematoxylin and eosin (H&E) stained colors is a critical step in the digitized pathological diagnosis of cancer. However, differences in section preparations, staining protocols and scanner specifications may result in the variations of stain colors in pathological images, which can potentially hamper the effectiveness of pathologist's diagnosis and the robustness. To alleviate this problem, several color normalization methods have been proposed. Most previous approaches map color information between images highly dependent on a reference template. However, due to the problem that pathological images are usually unpaired, these methods cannot produce satisfactory results. In this work, we propose an unsupervised color normalization method based on channel attention and long-range residual, using a technology called invertible neural networks (INN) to transfer the stain style while preserving the tissue semantics between different hospitals or centers, resulting in a virtual stained sample in the sense that no actual stains are used. In our method, the expert does not need to choose a template image. More specifically, we have developed a new unsupervised stain style transfer framework based on INN that is different from state-of-the-art methods. Meanwhile, we propose a new generator and a discriminator to further improve the performance. Our approach outperforms state-of-the-art methods both in objective metrics and subjective evaluations, yielding an improvement of 1.0 dB in terms of PSNR. Moreover, the amount of computation of the proposed network has been reduced by 33 %. This indicates that the inference speed is almost one third faster while the performance is better.
Nonlinear signal distortion corrections through quantum sensing

Having accurate gate generation is essential for precise control of a quantum system. The generated gate usually suffers from linear and nonlinear distortion. Previous works have demonstrated how to use a qubit to correct linear frequency distortions but have not commented on how to handle nonlinear distortions. This is an important issue as we show that nonlinear amplitude distortions from the RF electronics can affect Rabi pulses by as much as 10%. We present work that demonstrates how a transmon qubit can be used as a highly sensitive cryogenic detector to characterize these nonlinear amplitude distortions. We show that a correction can drive these errors down to <1% over a 700 MHz range. This correction technique provides a method to minimize the effects of signal distortions and can be easily applied to broadband control pulses to produce higher fidelity arbitrary quantum gates.
A Self-certifying Compilation Framework for WebAssembly

A self-certifying compiler is designed to generate a correctness proof for each optimization performed during compilation. The generated proofs are checked automatically by an independent proof validator. The outcome is formally verified compilation, achieved without formally verifying the compiler. This paper describes the design and implementation of a self-certifying compilation framework for WebAssembly, a new intermediate language supported by all major browsers.
Describing the structure of concepts through different feature levels

It has been suggested that concepts can be recognised based on at least some amount of highly shared features across individuals (i.e. core features). However, the boundaries between those features and less-shared ones remain unclear. Therefore, the main purpose of this paper is to operationalise the different levels of the structure of a concept's meaning and to test the effect of those levels on semantic decision latencies. First, we performed a feature based cued-recall task. Then, we designed two speeded verification tasks in which features were presented before and after the concept (i.e. feature-concept and concept-feature condition, respectively). Results of the cued-recall task allowed us to operationalise core features. Furthermore, we observed that core features were verified faster than partially shared, followed by features, and partially shared features faster than idiosyncratic ones, being features, and that this effect is independent of the order of stimulus presentation.
Compositional Satisfiability Solving in Separation Logic

We introduce a novel decision procedure to the satisfiability problem in array separation logic combined with general inductively defined predicates and arithmetic. Our proposal differentiates itself from existing works by solving satisfiability through compositional reasoning. First, following Fermat's method of infinite descent, it infers for every inductive definition a "base" that precisely characterises the satisfiability. It then utilises the base to derive such a base for any formula where these inductive predicates reside in. Especially, we identify an expressive decidable fragment for the compositionality. We have implemented the proposal in a tool and evaluated it over challenging problems. The experimental results show that the compositional satisfiability solving is efficient and our tool is effective and efficient when compared with existing solvers.