Reviewerly/exHarmony huggingface dataset:
Creating Omni-Channel In-Store Shopping Experiences through Augmented-Reality-Based Product Recommending and Comparison

We present a novel approach to the inclusion of online services within a physical retailing scenario through the use of augmented reality (AR) technology and head-mounted displays (HMD). A concept has been designed for providing the innovative combination of product data, comparison support and product recommendations, all directly accessible from the pertinent physical products. Prototypes were implemented and evaluated in several user studies to address questions related to information acquisition, product exploration and user acceptance, including a comparison against a baseline system (i.e., smartphone app without AR capabilities). The results indicate that providing in-store functions via AR HMDs can be on par with a non-AR smartphone approach in terms of practicality, and may provide superior benefits concerning the discovery and exploration of products, and the perception of their differences. However, effort must be dedicated to design AR UIs able to compete against the clearer and more structured information visualization of traditional displays.
Alleviating Class-Imbalance Data of Semiconductor Equipment Anomaly Detection Study

Plasma-based semiconductor processing is highly sensitive, thus even minor changes in the procedure can have serious consequences. The monitoring and classification of these equipment anomalies can be performed using fault detection and classification (FDC). However, class imbalance in semiconductor process data poses a significant obstacle to the introduction of FDC into semiconductor equipment. Overfitting can occur in machine learning due to the diversity and imbalance of datasets for normal and abnormal. In this study, we suggest a suitable preprocessing method to address the issue of class imbalance in semiconductor process data. We compare existing oversampling models to reduce class imbalance, and then we suggest an appropriate sampling strategy. In order to improve the FC performance of plasma-based semiconductor process data, it was confirmed that the SMOTE-based model using an undersampling technique such as Tomek link is effective. SMOTE-TOMEK, which removes multiple classes and makes the boundary clear, is suitable for FDC to classify minute changes in plasma-based semiconductor equipment data.
Evaluating Airfoil Mesh Quality with Transformer

Mesh quality is a major factor affecting the structure of computational fluid dynamics (CFD) calculations. Traditional mesh quality evaluation is based on the geometric factors of the mesh cells and does not effectively take into account the defects caused by the integrity of the mesh. Ensuring the generated meshes are of sufficient quality for numerical simulation requires considerable intervention by CFD professionals. In this paper, a Transformer-based network for automatic mesh quality evaluation (Gridformer), which translates the mesh quality evaluation into an image classification problem, is proposed. By comparing different mesh features, we selected the three features that highly influence mesh quality, providing reliability and interpretability for feature extraction work. To validate the effectiveness of Gridformer, we conduct experiments on the NACA-Market dataset. The experimental results demonstrate that Gridformer can automatically identify mesh integrity quality defects and has advantages in computational efficiency and prediction accuracy compared to widely used neural networks. Furthermore, a complete workflow for automatic generation of high-quality meshes based on Gridformer was established to facilitate automated mesh generation. This workflow can produce a high-quality mesh with a low-quality mesh input through automatic evaluation and optimization cycles. The preliminary implementation of automated mesh generation proves the versatility of Gridformer.
Simulation of Chaotic Operation οf A Damped Driven Pendulum Using Python

In this paper, we are presenting a new pedagogical method for the introduction of the study of nonlinear systems. Our approach is based on the use of open-source software which is publicly available. In response to this motivation, we are using the Python programming language which offers a holistic approach to scientific research. We will present the analysis of the pendulum motion under the influence of an external force. The differential equation governing the system will be presented and solved using numerical methods. Moreover, the phase diagram of the system will be presented for various system parameters. We will describe the transition to a chaotic operation and the key factors of this procedure. The chaotic behaviour is verified by calculating the maximum Lyapunov exponent. This pedagogical approach emerging is based on the physical properties of the system and not on the numerical methods used so that the student can understand the dynamics of the system more comprehensively.
Assessment of high-quality counterfeit stamp impressions generated by inkjet printers via texture analysis and likelihood ratio

High-quality counterfeit stamp impressions made by inkjet printers remain challenging in questioned document examination and forensic analyses. A dataset comprised of various printed stamp impressions, using ten options of conditions and materials, and hand stamped impressions was generated. In this paper, we report printed impressions in pure color and high-quality printing mode are very similar to hand stamped impressions in terms of their microscopic characteristics. These similarities may lead to incorrect conclusions via traditional identification methods. Here, we proposed a method for identifying counterfeit stamp impressions via texture features and image quality parameters extracted from impressions. First, the statistical analysis methods were used to verify a significant difference between the printed and hand stamped impressions. Principal component analysis (PCA) was used to show the variation between the impressions, and the differences between printed and hand stamped impressions were obvious in the three-dimensional plot. After filtering the background of the stamp impressions, image processing analysis was introduced to extract features of gray level co-occurrence matrix (GLCM), segmentation-based fractal texture analysis (SFTA), local binary pattern (LBP), and image quality metrics (IQM), which were used to characterize the stamp impressions. Finally, specific cases were simulated by random selection, based on the dataset of stamp impressions, and an evaluation system for stamp evidence was established to calculate the likelihood ratios (LRs) under two alternative hypotheses. The likelihood ratio interprets calibrated evaluations on the strength of stamp impressions as evidence. We can also balance these LRs against the rates of misleading evidence with a reasonable performance (equal error rate = 0.048). This paper provides a system to differentiate high-quality printed and hand stamped impressions with reasonable performance.
The Effect of Web Augmented Reality on Primary Pupils’ Achievement in English

The massive development of mobile computing and mobile networks attracts developers and researchers to emerge a new direction for augmented reality on the Web. Web augmented reality is inherently considered platform independent, no pre-installation is required, easy to apply, easy to access, and easy to develop. The current study aims to introduce a vision of including Web AR in school instructional material to keep the teaching methods in line with the tremendous technological growth and invest students’ knowledge in this field. The main objective of this study is to develop a QR code-based tracking Web AR application to investigate the effect of Web AR on the achievement of 4th-year primary school pupils in English. The procedure of Web AR application includes two basic steps. Firstly, the convincing Web AR development platform is selected according to three evaluation criteria. Secondly, developing the Web AR with 73 English vocabularies included in the last four units of the Iraqi English pupil’s book. The procedures of the study include; First, a random selection of a sample of pupils and assigning them to experimental and control groups. Second, equalize the elected pupils in the factors that may affect their performance. Then, the control and experimental groups have been taught English for 12 weeks, and finally, three achievement posttests are constructed and applied to the involved groups in order to assess their performance. Having a long period of learning on using Web AR is significant, as the pupils’ tendencies and acceptance of the experiment tools require time and effort.
Key‐aggregate searchable encryption with multi‐user authorization and keyword untraceability for distributed IoT healthcare systems

Abstract Adopting Internet‐of‐Things (IoT) enables pervasive computing by integrating distributed healthcare infrastructures to enhance conventional medical services. Integrated healthcare systems store numerous encrypted electronic health records (EHR) on clouds that multiple users frequently access as per delegated access rights. Sharing any group of selected EHR within any group of users involving different encryption keys potentially heightens security and privacy concerns over inadvertent data leaks. This necessitates a searchable encryption mechanism in distributed IoT requiring secure multi‐user authorization and efficient searchable keys management with compact trapdoors. Contemporary state‐of‐the‐art protocols utilize large numbers of expensive encryption keys and keyword trapdoors while lacking secure multi‐user authorization and keyword untraceability on clouds. This motivates us to design a novel key‐aggregate searchable encryption protocol that jointly achieves multi‐user authorization and keyword untraceability for distributed IoT applications. Key‐aggregation reduces complexity in sharing documents by executing a stand‐alone trapdoor, while token‐based authorization allows keyword search only to legitimate data users. Additionally, secure dynamic indexing achieves keyword untraceability on clouds. Security analysis illustrates correctness and resilience against typical attacks. Performance and empirical analysis demonstrate a relative advantage in storage, communication, and computational costs while incorporating secure multi‐user authorization and keyword untraceability as additional security features.
A hybrid approach based on mathematical modelling and improved online learning algorithm for data classification

This paper proposes a novel hybrid classifier method using a mixed-integer linear programming (MILP) model and an improved online learning algorithm for general fuzzy min–max neural networks (IOL_GFMM). IOL_GFMM is used to generate initial hyper-boxes for the MILP model in order to improve its efficiency. This paper is the first attempt that integrates the fuzzy min–max neural networks and the MILP model. The applicability of the proposed hybrid model is proven on both real and artificial datasets. Performance evaluation parameters of the novel hybrid classifier are classification accuracy, training time, number of iterations, and number of misclassified data. Results show that the proposed hybrid method provides satisfying improvements in performance evaluation parameters compared with the pure MILP model. Our proposed approach achieves to speed the training time up to 76 times and decreases the number of misclassified data by up to 17 times. Results of artificial datasets show another contribution that the proposed hybrid approach is very efficient in order to classify data samples not only when they belong to separate regions but also when there exists significant overlap.
Multi-Level Visual Similarity Based Personalized Tourist Attraction Recommendation Using Geo-Tagged Photos

Geo-tagged photo-based tourist attraction recommendation can discover users’ travel preferences from their taken photos, so as to recommend suitable tourist attractions to them. However, existing visual content-based methods cannot fully exploit the user and tourist attraction information of photos to extract visual features, and do not differentiate the significance of different photos. In this article, we propose multi-level visual similarity-based personalized tourist attraction recommendation using geo-tagged photos (MEAL). MEAL utilizes the visual contents of photos and interaction behavior data to obtain the final embeddings of users and tourist attractions, which are then used to predict the visit probabilities. Specifically, by crossing the user and tourist attraction information of photos, we define four visual similarity levels and introduce a corresponding quintuplet loss to embed the visual contents of photos. In addition, to capture the significance of different photos, we exploit the self-attention mechanism to obtain the visual representations of users and tourist attractions. We conducted experiments on two datasets crawled from Flickr, and the experimental results proved the advantage of this method.
Algorithm 1037: SuiteSparse:GraphBLAS: Parallel Graph Algorithms in the Language of Sparse Linear Algebra

SuiteSparse:GraphBLAS is a full parallel implementation of the GraphBLAS standard, which defines a set of sparse matrix operations on an extended algebra of semirings using an almost unlimited variety of operators and types. When applied to sparse adjacency matrices, these algebraic operations are equivalent to computations on graphs. A description of the parallel implementation of SuiteSparse:GraphBLAS is given, including its novel parallel algorithms for sparse matrix multiply, addition, element-wise multiply, submatrix extraction and assignment, and the GraphBLAS mask/accumulator operation. Its performance is illustrated by solving the graph problems in the GAP Benchmark and by comparing it with other sparse matrix libraries.
Measuring the Effectiveness of AI-Enabled Chatbots in Customer Service Using AnyLogic Simulation

With the rapid technological advancement and innovation in Industry 4.0/5.0, digitalization has become the mainstream that transforms the paradigm of many industries, businesses, and services. In this regard, AI-enabled chatbots have been increasingly adopted by many companies for providing customer services instead of human agents. On the one hand, the use of AI-enabled chatbots provides opportunities to reduce customers' waiting time, improve efficiency, and minimize costs. On the other hand, it may, however, negatively affect customers' experiences due to several reasons, e.g., unfamiliarity with the IT system, incapability to properly answer individualized questions by chatbots, etc. Several qualitative analyses have been done to investigate the benefits and challenges of using AI-enabled chatbots for companies and also to reveal the customers' perceptions and experiences, but there is still a lack of quantitative research that may help companies to adopt this new technology in their customer service. To fill this gap, in this paper, we develop a discrete event simulation model using the AnyLogic simulation software package to measure the effectiveness of AI-enabled chatbots, which, through scenario analyses, provides managerial implications related to the average time in the system, response rate, satisfaction level, and cost savings. Thus, this method can help companies with a better understanding of the impact of adopting AI-enabled chatbots in their customer service.
Automated detection and delineation of lymph nodes in haematoxylin &amp; eosin stained digitised slides

Treatment of patients with oesophageal and gastric cancer (OeGC) is guided by disease stage, patient performance status and preferences. Lymph node (LN) status is one of the strongest prognostic factors for OeGC patients. However, survival varies between patients with the same disease stage and LN status. We recently showed that LN size from patients with OeGC might also have prognostic value, thus making delineations of LNs essential for size estimation and the extraction of other imaging biomarkers. We hypothesized that a machine learning workflow is able to: (1) find digital H&E stained slides containing LNs, (2) create a scoring system providing degrees of certainty for the results, and (3) delineate LNs in those images. To train and validate the pipeline, we used 1695 H&E slides from the OE02 trial. The dataset was divided into training (80%) and validation (20%). The model was tested on an external dataset of 826 H&E slides from the OE05 trial. U-Net architecture was used to generate prediction maps from which predefined features were extracted. These features were subsequently used to train an XGBoost model to determine if a region truly contained a LN. With our innovative method, the balanced accuracies of the LN detection were 0.93 on the validation dataset (0.83 on the test dataset) compared to 0.81 (0.81) on the validation (test) datasets when using the standard method of thresholding U-Net predictions to arrive at a binary mask. Our method allowed for the creation of an "uncertain" category, and partly limited false-positive predictions on the external dataset. The mean Dice score was 0.73 (0.60) per-image and 0.66 (0.48) per-LN for the validation (test) datasets. Our pipeline detects images with LNs more accurately than conventional methods, and high-throughput delineation of LNs can facilitate future LN content analyses of large datasets.
Securing text using Rabin-p public key cryptosystem and Spritz algorithm in a hybrid cryptosystem: a tutorial

Abstract Security in the process of sending digital messages is very important to avoid being theft by unwanted parties. In order to protect the messages, modern cryptography techniques come into play. Public key cryptosystem based on the integer factorization such as Rabin- p could provide high confidentiality as long as the modulus is of a very large integer. On the contrary, having a longer modulus causes two problems: the longer the size of the ciphertext and the longer the time needed for the decryption process. In order to solve this problem, in this research, we combine Rabin- p cryptosystem and Spritz algorithm in a hybrid cryptosystem. Message is encrypted with Spritz algorithm, and the key of Spritz algorithm is then protected by Rabin-p cryptosystem. In this scheme, the encrypted message can be recovered back to the original message while maintaining small decryption time.
Quantum fitting framework applied to effective field theories

The use of experimental data to constrain the values of the Wilson coefficients of an Effective Field Theory (EFT) involves minimising a $\chi^2$ function that may contain local minima. Classical optimisation algorithms can become trapped in these minima, preventing the determination of the global minimum. The quantum annealing framework has the potential to overcome this limitation and reliably find the global minimum of non-convex functions. We present QFitter, a quantum annealing method to perform EFT fits. Using a state-of-the-art quantum annealer, we show with concrete examples that QFitter can be used to fit sets of at least eight coefficients, including their quadratic contributions. An arbitrary number of observables can be included without changing the required number of qubits. We provide an example in which $\chi^2$ is non-convex and show that QFitter can find the global minimum more accurately than its classical alternatives.
DSPPTD: Dynamic Scheme for Privacy Protection of Trajectory Data in LBS

The current trajectory privacy policies primarily focus on the sequence of location in trajectory and depend extensively on manually crafted procedures. Thus, the initial trajectory details can be easily revealed if the trajectory privacy policy procedure is known anyhow. It is also challenging to keep eye on the trade-off between the efficacy of trajectory privacy security policies and the usefulness for spatial and temporal behavior. Therefore, the aim of this paper is to develop new trajectory privacy protection policies in order to protect the online and offline trajectory data in trajectory publication considering time accessibility and path similarity. The Dynamic Scheme for Privacy Protection of Trajectory Data in LBS (DSPPTD) policy combines the Gaussian mechanism as well as k-path trajectory clustering of deep neural network processed trajectory for creating synthetic trajectory as an alternative to real trajectory and achieves differential privacy before sharing and publishing it.
Graph Representation Learning With Adaptive Metric

Contrastive learning has been widely used in graph representation learning, which extracts node or graph representations by contrasting positive and negative node pairs. It requires node representations (embeddings) to reflect their correlations in topology, increasing the similarities between an anchor node and its positive nodes, or reducing the similarities with its negative nodes in embedding space. However, most existing contrastive models measure similarities through a fixed metric that equally scores all sample pairs in a specific feature space, but ignores the varieties of node attributes and network topologies. Moreover, these fixed metrics are always defined explicitly and manually, which makes them unsuitable for applying to all graphs and networks. To solve these problems, we propose a novel graph representation learning model with an adaptive metric, called GRAM, which produces appropriate similarity scores of node pairs according to the different significance of each dimension in their embedding vectors and adaptive metrics based on data distribution. With these scores, it is better to train a graph encoder and obtain representative embeddings. Experimental results show that GRAM has strong competitiveness in multiple tasks.
Privacy-Enhanced Decentralized Federated Learning at Dynamic Edge

Decentralized Federated Learning (DeFL) plays a critical role in improving effectiveness of training and has been proved to give great scope to the development of edge computing. However, on the one hand, inaccessibility of private data and excessively exploiting the data throughout the learning process have become a public concern, and on the other hand the connections between server-less edge devices are always varying due to the mobility of edge intelligent devices. To address the above issues, we propose a <u>P</u> rivacy- <u>E</u> nhanced - <u>D</u> ynamic - <u>D</u> ecentralized - <u>F</u> ederated - <u>L</u> earning algorithm called PED <inline-formula><tex-math notation="LaTeX">$ ^{2}$</tex-math></inline-formula> FL in a dynamic edge environment. We design the PED <inline-formula><tex-math notation="LaTeX">$ ^{2}$</tex-math></inline-formula> FL under the analog transmission scheme, where mobile edge devices transmit privacy preserving data simultaneously and accomplish efficient information aggregation with doubly-stochastic adjacent matrices. With thorough analysis, it can be demonstrated that PED <inline-formula><tex-math notation="LaTeX">$ ^{2}$</tex-math></inline-formula> FL satisfies <inline-formula><tex-math notation="LaTeX">$(\epsilon,\delta)$</tex-math></inline-formula> -differential privacy while the per-device privacy budget decays exponentially with the number of the neighbors, which greatly improved the data utility compared to the fixed budget in the orthogonal transmission strategy. PED <inline-formula><tex-math notation="LaTeX">$ ^{2}$</tex-math></inline-formula> FL has the same convergence rate <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(\sqrt{\frac{1}{KN}})$</tex-math></inline-formula> as the non-private decentralized learning algorithm D-PSGD without enhanced privacy protection, where <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> are the total iterations and the number of nodes, respectively. Extensive experiments show that algorithm PED <inline-formula><tex-math notation="LaTeX">$ ^{2}$</tex-math></inline-formula> FL also performs well with real-world settings.