Reviewerly/exHarmony huggingface dataset:
Augmented Reality in E-Commerce: The New Era of Online Shopping

New shopping approaches that offer significant advantages over traditional online shopping can aid in the administration of business websites and e-commerce systems. This paper is a dense summary of the most significant components of alternative innovation based on AR partnerships that minimize the use of physical product contact. Online shopping has grown in popularity over time. AR may be the most essential factor in enhancing online purchases. The development of new AR technology can be advantageous if it can mimic the advantages that have enabled web-based shopping the most favoured shopping source in recent times. This paper focuses on the idea anxd core concepts of AR technology, the implementation of augmented reality in e-commerce, statistics of augmented reality in e-commerce, benefits, and applications of augmented reality in e-commerce, and many other topics.
SMC Controller Design for DC Motor Speed Control Applications and Performance Comparison with FLC, PID and PI Controllers

Sliding Mode Control (SMC), which is built on the variable structure control (VSC) algorithm, is a robust and non-linear control method that can provide the desired dynamic behaviour for the system to be controlled despite external and internal disturbances and uncertainties. The SMC method can be successfully implemented in the control of high-dimensional nonlinear systems operating under uncertain conditions due to its high accuracy and simplicity of application. In this MATLAB/Simulink based study; the SMC method is applied to the speed control of a DC motor. For this purpose, firstly, the dynamic model of DC motor and the mathematical model of the SMC have been designed and transferred to the Simulink environment. The performance of the SMC system has been examined under different loading conditions applied to the motor. In addition, the effects of changing the SMC parameters on the sliding surface, chattering and motor dynamic behaviours have been investigated. In order to evaluate the success of the SMC topology in DC motor control application, Fuzzy Logic Control, PID and PI control methods were applied on the same motor and their performances were compared with the SMC method.
Improving Low-Resource Cross-lingual Parsing with Expected Statistic Regularization

Abstract We present Expected Statistic Regulariza tion (ESR), a novel regularization technique that utilizes low-order multi-task structural statistics to shape model distributions for semi- supervised learning on low-resource datasets. We study ESR in the context of cross-lingual transfer for syntactic analysis (POS tagging and labeled dependency parsing) and present several classes of low-order statistic functions that bear on model behavior. Experimentally, we evaluate the proposed statistics with ESR for unsupervised transfer on 5 diverse target languages and show that all statistics, when estimated accurately, yield improvements to both POS and LAS, with the best statistic improving POS by +7.0 and LAS by +8.5 on average. We also present semi-supervised transfer and learning curve experiments that show ESR provides significant gains over strong cross-lingual-transfer-plus-fine-tuning baselines for modest amounts of label data. These results indicate that ESR is a promising and complementary approach to model-transfer approaches for cross-lingual parsing.1
Underwater image illumination estimation via an evolving extreme learning machine by an improved salp swarm algorithm

Underwater images have chromatic aberrations under different light sources and complex underwater scenes, which can lead to the wrong choice when using an underwater robot. To solve this problem, this paper proposes an underwater image illumination estimation model, which we call the modified salp swarm algorithm (SSA) extreme learning machine (MSSA-ELM). It uses the Harris hawks optimization algorithm to generate a high-quality SSA population, and uses a multiverse optimizer algorithm to improve the follower position that makes an individual salp carry out global and local searches with a different scope. Then, the improved SSA is used to iteratively optimize the input weights and hidden layer bias of ELM to form a stable MSSA-ELM illumination estimation model. The experimental results of our underwater image illumination estimations and predictions show that the average accuracy of the MSSA-ELM model is 0.9209. Compared to similar models, the MSSA-ELM model has the best accuracy for underwater image illumination estimation. The analysis results show that the MSSA-ELM model also has high stability and is significantly different from other models.
Single-photon scattering on a qubit: Space-time structure of the scattered field

We study the space-time structure of the scattered field induced by the scattering of a narrow single-photon Gaussian pulse on a qubit embedded in 1D open waveguide. For a weak excitation power we obtain explicit analytical expressions for space and time dependence of reflected and transmitted fields which are, in general, are different from plain travelling waves. The scattered field consists of two parts: a damping part which represent a spontaneous decay of the excited qubit and a coherent, lossless part. We show that for large distance $x$ from qubit and at times $t$ long after the scattering event our theory provides the result which is well known from the stationary photon transport. However, the approach to the stationary limit is very slow. The scattered field decreases as the inverse powers of $x$ and $t$ as both the distance from the qubit and the time after the interaction increase.
Confidently extracting hierarchical taxonomy information from unstructured maintenance records of industrial equipment

Maintenance records of complex industrial equipment contain a large amount of unstructured data (e.g. technician notes) pertaining to repair actions and associated equipment sub-components, degradation conditions, failure mechanisms, etc. These unstructured data can yield valuable insights to improve the equipment design and maintenance plans, resulting in higher productivity and lower operating costs. Since manual review of information is time-consuming, companies make limited use of the maintenance records. To address this opportunity, we propose a taxonomy-guided method for automatically analysing the unstructured data and inferring critical information, specifically the hierarchy of the equipment's sub-assemblies and constituent parts that malfunctioned or failed during a breakdown event. Our method leverages syntactic (related to word frequency) as well as semantic (related to word co-occurrence and their meaning) knowledge. A novel contribution of our work is that we provide a confidence score for the information inferred by our method. Only the maintenance records which receive a low confidence score will require manual review to confirm the automated method's results, thus ensuring minimal use of human resources. We demonstrate the performance of our method using a real-world data set from equipment used in oil rigs.
Efficient dynamic channel assignment through laser chaos: a multiuser parallel processing learning algorithm

Abstract As laser chaos has been proven to be a robust tool to solve the multi-armed bandit (MAB) problem, this study investigates the problem of multiuser dynamic channel assignment using laser chaos in cognitive radio networks with K -orthogonal channels and M secondary users. A novel dynamic channel assignment algorithm with laser chaos series for multiple users, named parallel processing learning with laser chaos (PPL-LC) algorithm, is proposed to efficiently address two main objectives: stable channel assignment and fuzzy stable channel assignment. The latter objective accounts for the realistic scenario where users have fuzzy preferences and do not necessarily pursue the best preference. The PPL-LC algorithm uses the randomness properties of laser chaos to learn the assignment of channels to multiple users without any limitations on the number of channels, which has not been considered in existing laser chaos algorithms. Moreover, the PPL-LC is equipped with parallel processing channel selections, resulting in higher throughput and stronger adaptability with environmental changes over time than comparison algorithms, such as distributed stable strategy learning and coordinated stable marriage MAB algorithms. Finally, numerical examples are presented to demonstrate the performance of the PPL-LC algorithm.
Intellectual capital of the university in the conditions of transformation &#x0D; of the innovation market

Taking into account the current trends in the development of innovations related to the impact of total economic sanctions against Russia by the United States and Western countries, which represent a ban and significant restrictions on innovative activities, in particular in the education system, the formation and development of intellectual capital comes to the fore, becoming an active measure to counter Western sanctions. The research presents scientific directions for the research of intellectual capital, clarifications of its formulation. The article reveals the essence and content of intellectual capital, presents transformational processes associated with its development, taking into account modern transformational processes in the innovation market. It is shown that the role of intellectual capital has increased even more it is an integral part of innovative products, based on the use of intellectual results: licenses, patents, copyrights. The study presents the prerequisites that cause the transformation of the structure and functions of intellectual capital, its elemental composition is given. It is proved that the structural and functional transformation of the higher education system is carried out within the framework of the Priority 2030 program, which defines the goals and objectives of the development of the education system, presents the paradigm of the development of the intellectual capital of the university. The article reveals the directions of transformational processes in the structure of education and proposes a model of the ecosystem of intellectual capital.
Tracking the trajectory of an object in a noisy environment with unknown statistics: A novel robust Kalman filter residue-based approach

A novel robust Kalman filter (KF)-based controller design approach is proposed to accurately track a specified trajectory under unknown stochastic disturbance, known deterministic disturbance, and measurement noise. The system is nonlinear and is approximated by a piecewise-linear dynamic model. The Box–Jenkins model is an augmented model of the signal and the disturbance, is non-controllable and observable, while the signal model is controllable and observable. An emulator-based two-stage identification is employed to obtain an accurate system model needed to design the robust controller. The system and its KF are identified, and the signal and output errors are estimated. From the identified models, the signal, its KF, the disturbance model, and the whitening filter are all obtained using balanced model reduction techniques. It is shown that the signal model is a transfer matrix description relating the system output and KF residual, and the residual is the whitened output error. The disturbance model is identified by inverse filtering. A new combined feedforward–feedback controller is designed and implemented using an internal model of the reference driven by both the error between the reference and the signal estimate, and by the feedforwarded reference signal. The proposed scheme was successfully evaluated on a simulated autonomously guided drone.
Educational L2 constructs and diagnostic measurement

Abstract Various stakeholders, such as policymakers and educators, require diagnostic feedback and actionable test results. One particular context in which fine-grained test results are of utmost importance is English language proficiency (ELP) assessments as they are used for critical decisions for students. Diagnostic classification models (DCMs) afford finer levels of feedback to improve learning outcomes. This study is part of a research project which implemented DCMs to the reading domain of a K–12 ELP assessment for grades 6–8 to evaluate its feasibility and utility. The research project is U.S.-based. However, the investigation is of relevance to the language testing community broadly. The paper discusses how to address second language reading construct within DCMs. Specifically, it details the identification of attributes underlying the construct and the development and selection of alternative Q -matrices.
Visual Object Tracking Based on Modified LeNet-5 and RCCF

The field of object tracking has recently made significant progress.Particularly, the performance results in both deep learning and correlation filters, based trackers achieved effective tracking performance.Moreover, there are still some difficulties with object tracking for example illumination and deformation (DEF).The precision and accuracy of tracking algorithms suffer from the effects of such occurrences.For this situation, finding a solution is important.This research proposes a new tracking algorithm to handle this problem.The features are extracted by using Modified LeNet-5, and the precision and accuracy are improved by developing the Real-Time Cross-modality Correlation Filtering method (RCCF).In Modified LeNet-5, the visual tracking performance is improved by adjusting the number and size of the convolution kernels in the pooling and convolution layers.The high-level, middle-level, and handcraft features are extracted from the modified LeNet-5 network.The handcraft features are used to determine the specific location of the target because the handcraft features contain more spatial information regarding the visual object.The LeNet features are more suitable for a target appearance change in object tracking.Extensive experiments were conducted by the Object Tracking Benchmarking (OTB) databases like OTB50 and OTB100.The experimental results reveal that the proposed tracker outperforms other state-of-the-art trackers under different problems.The experimental simulation is carried out in python.The overall success rate and precision of the proposed algorithm are 93.8% and 92.5%.The average running frame rate reaches 42 frames per second, which can meet the real-time requirements.
An Improved Encoder-Decoder CNN with Region-Based Filtering for Vibrant Colorization

Colorization is the practice of adding appropriate chromatic values to monochrome photographs or videos.A real-valued luminance image can be mapped to a three-dimensional color image.However, it is a severely ill-defined problem and not has a single solution.In this paper, an encoder-decoder Convolutional Neural Network (CNN) model is used for colorizing gray images where the encoder is a Densely Connected Convolutional Network (DenseNet) and the decoder is a conventional CNN.The DenseNet extracts image features from gray images and the conventional CNN outputs a * b * color channels.Due to a large number of desaturated color components compared to saturated color components in the training images, the saturated color components have a strong tendency towards desaturated color components in the predicted a * b * channel.To solve the problems, we rebalance the predicted a * b * color channel by smoothing every subregion individually using the average filter.2 stage k-means clustering technique is applied to divide the subregions.Then we apply Gamma transformation in the entire a * b * channel to saturate the image.We compare our proposed method with several existing methods.From the experimental results, we see that our proposed method has made some notable improvements over the existing methods and color representation of gray-scale images by our proposed method is more plausible to visualize.Additionally, our suggested approach beats other approaches in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Histogram.
Automated White Blood Cell Disease Recognition Using Lightweight Deep Learning

White blood cells (WBC) are immune system cells, which is why they are also known as immune cells.They protect the human body from a variety of dangerous diseases and outside invaders.The majority of WBCs come from red bone marrow, although some come from other important organs in the body.Because manual diagnosis of blood disorders is difficult, it is necessary to design a computerized technique.Researchers have introduced various automated strategies in recent years, but they still face several obstacles, such as imbalanced datasets, incorrect feature selection, and incorrect deep model selection.We proposed an automated deep learning approach for classifying white blood disorders in this paper.The data augmentation approach is initially used to increase the size of a dataset.Then, a Darknet-53 pre-trained deep learning model is used and finetuned according to the nature of the chosen dataset.On the fine-tuned model, transfer learning is used, and features engineering is done on the global average pooling layer.The retrieved characteristics are subsequently improved with a specified number of iterations using a hybrid reformed binary grey wolf optimization technique.Following that, machine learning classifiers are used to classify the selected best features for final classification.The experiment was carried out using a dataset of increased blood diseases imaging and resulted in an improved accuracy of over 99%.
An Efficient Text Recognition System from Complex Color Image for Helping the Visually Impaired Persons

The challenge faced by the visually impaired persons in their day-today lives is to interpret text from documents.In this context, to help these people, the objective of this work is to develop an efficient text recognition system that allows the isolation, the extraction, and the recognition of text in the case of documents having a textured background, a degraded aspect of colors, and of poor quality, and to synthesize it into speech.This system basically consists of three algorithms: a text localization and detection algorithm based on mathematical morphology method (MMM); a text extraction algorithm based on the gamma correction method (GCM); and an optical character recognition (OCR) algorithm for text recognition.A detailed complexity study of the different blocks of this text recognition system has been realized.Following this study, an acceleration of the GCM algorithm (AGCM) is proposed.The AGCM algorithm has reduced the complexity in the text recognition system by 70% and kept the same quality of text recognition as that of the original method.To assist visually impaired persons, a graphical interface of the entire text recognition chain has been developed, allowing the capture of images from a camera, rapid and intuitive visualization of the recognized text from this image, and text-to-speech synthesis.Our text recognition system provides an improvement of 6.8% for the recognition rate and 7.6% for the F-measure relative to GCM and AGCM algorithms.
Examining the potential use of augmented reality in construction cost management tools and techniques

Purpose Construction cost management is one of the important processes that should be achieved effectively and accurately for successful project delivery. Modern-day construction cost management demands a high level of spatial skills. Augmented reality (AR) can potentially increase the stakeholders’ spatial skills as a supportive technology to traditional cost management tools and techniques. AR is a breakthrough technology that could considerably ease execution in various industries, but AR applicability in cost management has not been studied extensively. Thus, this study aims to explore the use of AR in construction cost management tools and techniques. Design/methodology/approach Data were collected using a qualitative approach consisting of two rounds of the Delphi technique. A total of 22 experts in the construction and information technology fields were interviewed using a purposive sampling technique. The manual content analysis helped analyse data. Findings The study identified AR features with the potential to increase the usage of cost management tools and techniques. AR can enable spatial skills (abilities, thinking and tasks) in most cost management tools and techniques. However, technical, cultural and technical and cultural barriers obstruct the use of AR in the construction industry. Originality/value The usage of AR in construction cost management tools and techniques has not been examined in detail until now. Thus, the study was developed to meet the industry needs and fill the literature gap to investigate the potential use of AR in construction cost management tools and techniques.
The reuse of DCMI metadata terms in linked open vocabulary

Purpose This study aims to explore the reusing of Dublin core metadata initiative (DCMI) metadata terms on the linked open vocabulary (LOV) platform in the linked data environment to offer a better understanding of the reusing behaviour during the process of vocabulary construction and further explain why DC has become a popular vocabulary. Design/methodology/approach The authors selected LOV, as a typical linked data platform. The SPARQL language was used to acquire and parse data to examine the reuse types of DCMI terms, the reuse distribution of classes and properties in different semantic relation types among vocabularies, the subject and size of the reused vocabularies and the correlation between vocabulary reuse and data set reuse. Findings Results showed that DCMI metadata terms were reused by 83.7% of LOV vocabularies and became the core nodes on the vocabulary-linked network. Among the six relationships between vocabularies and the DCMI metadata terms, the metadata relationship is the most frequently used. DCMI metadata terms are reused by small- and medium-sized vocabularies and are not limited to subject domain. Originality/value This is one of the first studies focussing on the roles of DCMI metadata terms in vocabulary reusing. Furthermore, it provides a systematic view of how these DCMI terms participate in the construction of other vocabularies and in features of reused vocabularies.