Reviewerly/exHarmony huggingface dataset:
Lattice renormalization of quantum simulations

With advances in quantum computing, new opportunities arise to tackle challenging calculations in quantum field theory. We show that trotterized time-evolution operators can be related by analytic continuation to the Euclidean transfer matrix on an anisotropic lattice. In turn, trotterization entails renormalization of the temporal and spatial lattice spacings. Based on the tools of Euclidean lattice field theory, we propose two schemes to determine Minkowski lattice spacings, using Euclidean data and thereby overcoming the demands on quantum resources for scale setting. In addition, we advocate using a fixed-anisotropy approach to the continuum to reduce both circuit depth and number of independent simulations. We demonstrate these methods with Qiskit noiseless simulators for a $2+1$D discrete non-Abelian $D_4$ gauge theory with two spatial plaquettes.
A novel model usability evaluation framework (MUsE) for explainable artificial intelligence

When it comes to complex machine learning models, commonly referred to as black boxes, understanding the underlying decision making process is crucial for domains such as healthcare and financial services, as well as when they are used in connection with safety critical systems such as autonomous vehicles. As a result, interest in explainable artificial intelligence (xAI) tools and techniques has increased in recent years. However, the user experience (UX) effectiveness of existing xAI frameworks, especially concerning algorithms that work with data as opposed to images, is still an open research question. In order to address this gap, we examine the UX effectiveness of the Local Interpretable Model-Agnostic Explanations (LIME) xAI framework, one of the most popular model agnostic frameworks found in the literature, with a specific focus on its performance in terms of making tabular models more interpretable. In particular, we apply several state of the art machine learning algorithms on a tabular dataset, and demonstrate how LIME can be used to supplement conventional performance assessment methods. Based on this experience, we evaluate the understandability of the output produced by LIME both via a usability study, involving participants who are not familiar with LIME, and its overall usability via a custom made assessment framework, called Model Usability Evaluation (MUsE), which is derived from the International Organisation for Standardisation 9241-11:2018 standard.
Enhanced Sparrow Search Algorithm With Mutation Strategy for Global Optimization

In order to improve the performance of the sparrow search algorithm (SSA), in this paper, a novel series of SSA variants is proposed by combining SSA with improved Tent chaos mutation (IT), Lévy flights mutation (LF), elite opposition-based learning mutation (EOBL), variable radius mutation (VR) and the combination of IT, LF, EOBL, and VR, namely, ITSSA, LFSSA, EOBLSSA, VRSSA, and CMSSA, respectively. Initially, the performance of these variants is evaluated on a comprehensive set of 31 benchmark test functions. Moreover, the performance of the best algorithm among these variants is compared with 19 state-of-the-art optimization algorithms to validate its performance on 31 benchmark test functions. The convergence and computational complexity of the best variant are also analyzed to test exploration, exploitation, and local optima avoidance. It is then employed on eight real-world constrained engineering problems to further verify its robustness. The experimental results reveal that the best algorithm of SSA variants outperforms other competitors and is highly effective in solving real-life cases.
GCR: Gradient Coreset based Replay Buffer Selection for Continual Learning

Continual learning (CL) aims to develop techniques by which a single model adapts to an increasing number of tasks encountered sequentially, thereby potentially leveraging learnings across tasks in a resource-efficient manner. A major challenge for CL systems is catastrophic forgetting, where earlier tasks are forgotten while learning a new task. To address this, replay-based CL approaches maintain and repeatedly retrain on a small buffer of data selected across encountered tasks. We propose Gradient Coreset Replay (GCR), a novel strategy for replay buffer selection and update using a carefully designed optimization criterion. Specifically, we select and maintain a ‘coreset’ that closely approximates the gradient of all the data seen so far with respect to current model parameters, and discuss key strategies needed for its effective application to the continual learning setting. We show significant gains (2%-4% absolute) over the state-of-the-art in the well-studied offline continual learning setting. Our findings also effectively transfer to online / streaming CL settings, showing up to 5% gains over existing approaches. Finally, we demonstrate the value of supervised contrastive loss for continual learning, which yields a cumulative gain of up to 5% accuracy when combined with our subset selection strategy.
Silicon-integrated coherent neurons with 32GMAC/sec/axon compute line-rates using EAM-based input and weighting cells

We experimentally demonstrate a coherent SiPho neuron that relies on EAM for both on-chip data generation and weighting. A record-high 32GMAC/s/axon compute rate and an accuracy of 95.91% is reported, when the neuron is deployed as a hidden layer of a MNIST classifier neural network.
A Decade of Research on the Effectiveness of Augmented Reality on Students with Special Disability in Higher Education

In higher education settings, students are required to use their own devices to record attendance interact with classes through online systems and other learning-teaching sources such as timetables, virtual learning environment. Real opportunity to use digital technology such as augmented reality AR technology have been used in several fields, uncovering diverse benefits regarding its usage. However, further research is needed to understand exactly how the AR enhance students learning. Focusing on individuals with special needs, AR is being used in higher education for the last few years to enhance physical, cognitive, personal, and social abilities. This systematic review presented an overview of the usage of AR technology in the special needs area in higher education literature published between 2011 and 2020 and focused on research indexed in 8 international databases. 36 studies were included for review. The results indicate that the majority of research showed positive outcomes as the AR technology proved to be effective with students with disabilities. The results also showed that AR technology was mostly used in intellectual disability setting. Finally, the result evidenced that AR assists students in enhancing their social skills, social relationships and their engagement. The results from this systematic review provide valuable information regarding to enhance individual with special needs. Future empirical research should ensure that all research is included, including settings, level of students and data collection methods such as quantitative and qualitative.
Genesis of visible gold in pyrite in the Zhaoxian gold deposit, Jiaodong gold province, China: Constraints from EBSD micro-structural and LA-ICP-MS elemental analyses

Pyrite (FeS2) is one of the most common hosts for gold (Au) in different types of gold deposits, including visible and invisible gold. For visible gold in pyrite, it remains unclear whether it resulted from remobilization of invisible gold in the gold-hosting pyrite or from external input. This paper reports a case study from the Zhaoxian gold deposit, which is the deep extension of the well-known Jiaojia gold deposit, in the Jiaodong gold province. The texture and composition of Au-bearing pyrite from the Zhaoxian deposit were studied with petrographic, Electron Back-Scattered Diffraction (EBSD) mapping, and Laser Ablation – Inductively Coupled Plasma – Mass Spectrometry (LA-ICP-MS) methods. Four types of pyrite (Py-1 to Py-4), corresponding to four generations of veins, i.e., the pre-ore V1, syn-ore V2 and V3, and post-ore V4, were distinguished. Py-2 is volumetrically the most abundant among the four, and is the main host of visible gold. LA-ICP-MS analyses of pyrite indicate that gold contents in most analysis points are below the detection limit, with measured Au contents ranging from 0.02 to 0.17 ppm (median 0.09 ppm) in Py-1, and 0.03 to 2.45 ppm (median 0.07 ppm) in Py-2. EBSD analyses indicate that pyrite (Py-2) display brittle deformation features including microstructures, lattice dislocation, and grain boundary displacement. Visible gold in Py-2 occurs in micro-fractures, cavities, and grain boundaries. The generally low contents of Au in pyrite, including Py-2, and the brittle deformation features, suggest that the visible gold grains did not result from remobilization of invisible gold from pyrite. Rather, the majority of gold was introduced by fluids after the formation of the gold-hosting pyrite. These fluids were derived from the same sources as those precipitating the gold-hosting pyrite, but were more evolved and enriched in Au. Circulation of these auriferous ore-forming fluids in microfractures of pyrite and interstitial space resulted in precipitation of visible gold.
Arena-Rosnav: Towards Deployment of Deep-Reinforcement-Learning-Based Obstacle Avoidance into Conventional Autonomous Navigation Systems

Recently, mobile robots have become important tools in various industries, especially in logistics. Deep reinforcement learning emerged as an alternative planning method to replace overly conservative approaches and promises more efficient and flexible navigation. However, deep reinforcement learning approaches are not suitable for long-range navigation due to their proneness to local minima and lack of long term memory, which hinders its widespread integration into industrial applications of mobile robotics. In this paper, we propose a navigation system incorporating deep-reinforcement-learning- based local planners into conventional navigation stacks for long-range navigation. Therefore, a framework for training and testing the deep reinforcement learning algorithms along with classic approaches is presented. We evaluated our deep-reinforcement-learning-enhanced navigation system against various conventional planners and found that our system outperforms them in terms of safety, efficiency and robustness.
Revolutionary Strategies Analysis and Proposed System for Future Infrastructure in Internet of Things

The Internet of Things (IoT) has changed the worldwide network of people, smart devices, intelligent things, data, and information as an emergent technology. IoT development is still in its early stages, and numerous interrelated challenges must be addressed. IoT is the unifying idea of embedding everything. The Internet of Things offers a huge opportunity to improve the world’s accessibility, integrity, availability, scalability, confidentiality, and interoperability. However, securing the Internet of Things is a difficult issue. The IoT aims to connect almost everything within the framework of a common infrastructure. This helps in controlling devices and, will allow device status to be updated everywhere and at any time. To develop technology via IoT, several critical scientific studies and inquiries have been carried out. However, many obstacles and problems remain to be tackled in order to reach IoT’s maximum potential. These problems and concerns must be taken into consideration in different areas of the IoT, such as implementation in remote areas, threats to the system, development support, social and environmental impacts, etc. This paper reviews the current state of the art in different IoT architectures, with a focus on current technologies, applications, challenges, IoT protocols, and opportunities. As a result, a detailed taxonomy of IoT is presented here which includes interoperability, scalability, security and energy efficiency, among other things. Moreover, the significance of blockchains and big data as well as their analysis in relation to IoT, is discussed. This article aims to help readers and researchers understand the IoT and its applicability to the real world.
SA-GAN: Stain Acclimation Generative Adversarial Network for Histopathology Image Analysis

Histopathological image analysis is an examination of tissue under a light microscope for cancerous disease diagnosis. Computer-assisted diagnosis (CAD) systems work well by diagnosing cancer from histopathology images. However, stain variability in histopathology images is inevitable due to the use of different staining processes, operator ability, and scanner specifications. These stain variations present in histopathology images affect the accuracy of the CAD systems. Various stain normalization techniques have been developed to cope with inter-variability issues, allowing standardizing the appearance of images. However, in stain normalization, these methods rely on the single reference image rather than incorporate color distributions of the entire dataset. In this paper, we design a novel machine learning-based model that takes advantage of whole dataset distributions as well as color statistics of a single target image instead of relying only on a single target image. The proposed deep model, called stain acclimation generative adversarial network (SA-GAN), consists of one generator and two discriminators. The generator maps the input images from the source domain to the target domain. Among discriminators, the first discriminator forces the generated images to maintain the color patterns as of target domain. While second discriminator forces the generated images to preserve the structure contents as of source domain. The proposed model is trained using a color attribute metric, extracted from a selected template image. Therefore, the designed model not only learns dataset-specific staining properties but also image-specific textural contents. Evaluated results on four different histopathology datasets show the efficacy of SA-GAN to acclimate stain contents and enhance the quality of normalization by obtaining the highest values of performance metrics. Additionally, the proposed method is also evaluated for multiclass cancer type classification task, showing a 6.9% improvement in accuracy on ICIAR 2018 hidden test data.
Phonemizer: Text to Phones Transcription for Multiple Languages in Python

Phones are elementary sounds the speech is made of, on which syllables and words are built.The transcription of texts from their orthographic form into a phonetic alphabet is an important requirement in various applications related to speech and language processing, for instance for text to speech systems.Phonemizer is a Python package addressing precisely this issue: it transcribes a text from its orthographic representation into a phonetic one.The package is user-friendly and exposes a single high-level phonemize function, a lower lovel API, and is also available as a command-line interface.It supports about a hundred different languages and provides end-user functionalities such as punctuation preservation, phones accentuation, tokenization at phone/syllable/word levels, as well as parallel processing of large input texts.
Comprehensive learning Harris hawks-equilibrium optimization with terminal replacement mechanism for constrained optimization problems

Harris hawks optimization (HHO) is a novel metaheuristic algorithm which has strong convergence for unconstrained optimization problems. However, HHO may encounter premature or local stagnation for constrained optimization problems. In this paper, a hybrid HHO algorithm named comprehensive learning harris hawks-equilibrium optimization (CLHHEO) is presented for solving constrained optimization problems, with the help of three operators: comprehensive learning, equilibrium optimizer, and terminal replacement mechanism. In the proposed algorithm, comprehensive learning strategy is incorporated with HHO to make search agents share their knowledge to enhance the convergence capacity. The operator of equilibrium optimizer is utilized to improve the exploration capacity of HHO. Besides, the terminal replacement mechanism is incorporated in the proposed algorithm to avoid local stagnation. The proposed CLHHEO is tested on 15 unconstrained and 10 real-world constrained optimization problems, and compared with 10 state-of-the-art metaheuristic algorithms, including PSO, CLPSO, BBBC, GWO, DA, WOA, SSA, HHO, SOA and AOA. From the experimental results, it is observed that CLHHEO outperforms HHO and other comparing metaheuristic algorithms in terms of solution quality. The results also demonstrate that the ensemble strategies of CLHHEO can enhance the performance of HHO for constrained optimization problems.
Detection of tumour infiltrating lymphocytes in CD3 and CD8 stained histopathological images using a two-phase deep CNN

Immuno-score, a prognostic measure for cancer, employed in determining tumor grade and type, is generated by counting the number of Tumour-Infiltrating Lymphocytes (TILs) in CD3 and CD8 stained histopathological tissue samples. Significant stain variations and heterogeneity in lymphocytes' spatial distribution and density make automated counting of TILs' a challenging task.This work addresses the aforementioned challenges by developing a pipeline "Two-Phase Deep Convolutional Neural Network based Lymphocyte Counter (TDC-LC)" to detect lymphocytes in CD3 and CD8 stained histology images. The proposed pipeline sequentially works by removing hard negative examples (artifacts) in the first phase using a custom CNN "LSATM-Net" that exploits the idea of a split, asymmetric transform, and merge. Whereas, in the second phase, instance segmentation is performed to detect and generate a lymphocyte count against the remaining samples. Furthermore, the effectiveness of the proposed pipeline is measured by comparing it with the state-of-the-art single- and two-stage detectors. The inference code is available at GitHub Repository https://github.com/m-mohsin-zafar/tdc-lc.The empirical evaluation on samples from LYSTO dataset shows that the proposed LSTAM-Net can learn variations in the images and precisely remove the hard negative stain artifacts with an F-score of 0.74. The detection analysis shows that the proposed TDC-LC outperforms the existing models in identifying and counting lymphocytes with high Recall (0.87) and F-score (0.89). Moreover, the commendable performance of the proposed TDC-LC in different organs suggests a good generalization.The promising performance of the proposed pipeline suggests that it can serve as an automated system for detecting and counting lymphocytes from patches of tissue samples thereby reducing the burden on pathologists.
Graph-Based Class-Imbalance Learning With Label Enhancement

Class imbalance is a common issue in the community of machine learning and data mining. The class-imbalance distribution can make most classical classification algorithms neglect the significance of the minority class and tend toward the majority class. In this article, we propose a label enhancement method to solve the class-imbalance problem in a graph manner, which estimates the numerical label and trains the inductive model simultaneously. It gives a new perspective on the class-imbalance learning based on the numerical label rather than the original logical label. We also present an iterative optimization algorithm and analyze the computation complexity and its convergence. To demonstrate the superiority of the proposed method, several single-label and multilabel datasets are applied in the experiments. The experimental results show that the proposed method achieves a promising performance and outperforms some state-of-the-art single-label and multilabel class-imbalance learning methods.
Computer Vision-Enabled Character Recognition of Hand Gestures for Patients with Hearing and Speaking Disability

Hand gesture recognition is one of the most sought technologies in the field of machine learning and computer vision. There has been an unprecedented demand for applications through which one can detect the hand signs for deaf people and people who use sign language to communicate, thereby detecting hand signs and correspondingly predicting the next word or recommending the word that may be most appropriate, followed by producing the word that the deaf people and people who use sign language to communicate want to say. This article presents an approach to develop such a system by that we can determine the most appropriate character from the sign that is being shown by the user or the person to the system. To enable pattern recognition, various machine learning techniques have been explored and we have used the CNN networks as a reliable solution in our context. The creation of such a system involves several convolution layers through which features have been captured layer by layer. The gathered features from the image are further used for training the model. The trained model efficiently predicts the most appropriate character in response to the sign exposed to the model. Thereafter, the predicted character is used to predict further words from it according to the recommendation system used in this case. The proposed system attains a prediction accuracy of 91.07%.
A comparison of natural user interface and graphical user interface for narrative in HMD-based augmented reality

Over the years, the various mediums available for storytelling have progressively expanded, from spoken to written word, then to film, and now to Virtual Reality (VR) and Augmented Reality (AR). In 2016, the cutting-edge Head-Mounted Display (HMD) AR Microsoft HoloLens was released. However, though it has been several years, the quality of the user experience with narration using HMD-based AR technology has been rarely discussed. The present study explored interactive narrative in HMD-based AR regarding different user interfaces and their influence on users' presence, narrative engagement and reflection. Inspired by an existing exhibition at the National Holocaust Centre and Museum in the U.K., a HoloLens narrative application, entitled The AR Journey, was developed by the authors using two different interaction methods, Natural User Interface (NUI) and Graphical User Interface (GUI), which were used to perform an empirical study. As revealed from the results of the between-subject design experiment, NUI exhibited statistically significant advantages in creating presence for users without 3D Role Playing Game (RPG) experience, and GUI was superior in creating presence and increasing narrative engagement for users with 3D RPG experience. As indicated by the results of the interviews, the overall narrative experience in HMD-based AR was acceptable, and the branching narrative design was engaging. However, HoloLens hardware issues, as well as virtuality and reality mismatch, adversely affected user experience. Design guidelines were proposed according to the qualitative results.The online version contains supplementary material available at 10.1007/s11042-021-11723-0.