Reviewerly/exHarmony huggingface dataset:
Quantum Mixed State Compiling

Abstract The task of learning a quantum circuit to prepare a given mixed state is a fundamental quantum subroutine. We present a variational quantum algorithm (VQA) to learn mixed states which is suitable for near-term hardware. Our algorithm represents a generalization of previous VQAs that aimed at learning preparation circuits for pure states. We consider two different ansätze for compiling the target state; the first is based on learning a purification of the state and the second on representing it as a convex combination of pure states. In both cases, the resources required to store and manipulate the compiled state grow with the rank of the approximation. Thus, by learning a lower rank approximation of the target state, our algorithm provides a means of compressing a state for more efficient processing. As a byproduct of our algorithm, one effectively learns the principal components of the target state, and hence our algorithm further provides a new method for principal component analysis. We investigate the efficacy of our algorithm through extensive numerical implementations, showing that typical random states and thermal states of many body systems may be learnt this way. Additionally, we demonstrate on quantum hardware how our algorithm can be used to study hardware noise-induced states.
Fermion-qudit quantum processors for simulating lattice gauge theories with matter

Simulating the real-time dynamics of lattice gauge theories, underlying the Standard Model of particle physics, is a notoriously difficult problem where quantum simulators can provide a practical advantage over classical approaches. In this work, we present a complete Rydberg-based architecture, co-designed to digitally simulate the dynamics of general gauge theories coupled to matter fields in a hardware-efficient manner. Ref. [1] showed how a qudit processor, where non-abelian gauge fields are locally encoded and time-evolved, considerably reduces the required simulation resources compared to standard qubit-based quantum computers. Here we integrate the latter with a recently introduced fermionic quantum processor [2], where fermionic statistics are accounted for at the hardware level, allowing us to construct quantum circuits that preserve the locality of the gauge-matter interactions. We exemplify the flexibility of such a fermion-qudit processor by focusing on two paradigmatic high-energy phenomena. First, we present a resource-efficient protocol to simulate the Abelian-Higgs model, where the dynamics of confinement and string breaking can be investigated. Then, we show how to prepare hadrons made up of fermionic matter constituents bound by non-abelian gauge fields, and show how to extract the corresponding hadronic tensor. In both cases, we estimate the required resources, showing how quantum devices can be used to calculate experimentally-relevant quantities in particle physics.
Ghost-ResNeXt: An Effective Deep Learning Based on Mature and Immature WBC Classification

White blood cells (WBCs) must be evaluated to determine how well the human immune system performs. Abnormal WBC counts may indicate malignancy, tuberculosis, severe anemia, cancer, and other serious diseases. To get an early diagnosis and to check if WBCs are abnormal or normal, one needs to examine the numbers and determine the shape of the WBCs. To address this problem, computer-aided procedures have been developed because hematologists perform this laborious, expensive, and time-consuming process manually. Resultantly, a powerful deep learning model was developed in the present study to categorize WBCs, including immature WBCs, from the images of peripheral blood smears. A network based on W-Net, a CNN-based method for WBC classification, was developed to execute the segmentation of leukocytes. Thereafter, significant feature maps were retrieved using a deep learning framework built on GhostNet. Then, they were categorized using a ResNeXt with a Wildebeest Herd Optimization (WHO)-based method. In addition, Deep Convolutional Generative Adversarial Network (DCGAN)-based data augmentation was implemented to handle the imbalanced data issue. To validate the model performance, the proposed technique was compared with the existing techniques and achieved 99.16%, 99.24%, and 98.61% accuracy levels for Leukocyte Images for Segmentation and Classification (LISC), Blood Cell Count and Detection (BCCD), and the single-cell morphological dataset, respectively. Thus, we can conclude that the proposed approach is valuable and adaptable for blood cell microscopic analysis in clinical settings.
Adaptive ELM-Based Security Control for a Class of Nonlinear-Interconnected Systems With DoS Attacks

This article is concerned with the output feedback security control of a class of high-order nonlinear-interconnected systems with denial-of-service (DoS) attacks, nonlinear dynamics, and exogenous disturbances. First, extreme learning machine (ELM) and adaptive techniques are adopted to approximate the unknown nonlinearities. Then, novel adaptive ELM-based nonlinear state observers with adaptive compensation functions are developed to estimate the unmeasurable states during DoS attacks under the influence of the disturbances. Further, by combining with the backstepping control and filtering techniques, adaptive ELM-based controllers are proposed to achieve uniformly ultimately bounded results based on the observation and adaption control signals under the influence of DoS attacks, nonlinear dynamics, and exogenous disturbances. Comparative studies are carried out to validate the effectiveness of the developed ELM-based adaptive observation and control strategies for two interconnected power systems.
Assisting artificial intelligence adoption drivers in human resources management: a mediation model

This study investigates the artificial intelligence (AI) adoption drivers and the mediating effects of trust and how the latter influence human resources management (HRM) of the banking sector in Saudi Arabia. A survey-based questionnaire was employed to collect data from 261 practitioners and professionals working in different banks in Saudi Arabia. Partial least squares structural equation modelling (PLS-SEM) was used to analyze data. Significant and positive effects of perceived usefulness and trust on artificial intelligence adoption in human resource management are highlighted by the results. In addition, the indirect effects of trust between perceived usefulness and AI adoption in human resource management were also found to be significant.
Monetary Policy Implications of Central Bank Digital Currencies: Perspectives on Jurisdictions with Conventional and Islamic Banking Systems

Central bank digital currencies (CBDCs) promise many benefits but, if not well designed, they could have undesired consequences, including for monetary policy. Issuing an unremunerated CBDC or a wholesale CBDC does not change the objectives of monetary policy or the operational framework for monetary policy. CBDCs can, however, induce changes in the retail, wholesale and cross border payments that have negative spillover effects on monetary policy, through their effects on money velocity, bank deposit disintermediation, volatility of bank reserves, currency substitution, and capital flows. Countries most vulnerable are those with banking systems dominated by small retail deposits and demand deposits, low levels of digital payments and weak macro fundamentals. Proposed CBDC design features, such as caps on CBDC holdings and unremunerating the CBDC can moderate disintermediation risks, but they are not sufficient. Central banks will need to ensure that unintended macroeconomic risks are comprehensively identified and mitigated.
Achieving organizational performance by integrating industrial Internet of things in the SMEs: a developing country perspective

Purpose This research investigates the adoption of the industrial Internet of things (IIoT) in SMEs to achieve and increase organizational performance. With the latest technology, small and medium-sized enterprises (SMEs) can create a competitive edge in the market and better serve customers. Design/methodology/approach Twelve hypotheses are proposed for this study. This study constructed a questionnaire based on technological, organizational, environmental and human perspectives. A survey is conducted on the SMEs of India using the questionnaire. Findings Eight hypotheses were accepted, and four hypotheses were not supported. The hypotheses rejected are infrastructure, organizational readiness, internal excellence and prior experience. The findings suggested that adopting IIoT in SMEs will increase organizational performance. Research limitations/implications This study will be helpful for the manager, top management and policymakers. This study identified the areas SMEs need to work on to adopt the technologies. Originality/value In the literature, no article considered IIoT adoption in SME firms as a human factor. Therefore, this study is unique, including human, technological, organizational and environmental factors.
Technology integration needs empowered instructional coaches: accompanying in-service teachers in school digitalization

Purpose In digital education curricular initiatives, some teachers are taking on key roles when appointed as instructional coaches (ICs) and become crucial in the sustainability of the initiative by coaching teachers. This study aimed to find out the difficulties ICs are facing when mentoring teachers in the development of the digital skills and to identify the coaching activities the coaches offer. Design/methodology/approach Employing a mixed-methods research design focusing on a qualitative approach, the research team conducted 6 focus groups with 38 ICs from 12 primary schools in Switzerland. Quantitative descriptive data were collected through a survey submitted before and after the focus group. Findings The results indicate that (1) the professional development (PD) activities offered to teachers vary greatly depending on the instructional coach characteristics and the school to which the teachers belong, (2) the in-service training of ICs should include coaching and leadership skills and (3) distributed leadership to ICs could facilitate the adoption of technologies by teachers and support change in the school. Practical implications Some ICs offer activities with a low involvement of the teacher. ICs' training programs should provide strategies to better take advantage of PD activities. The authors suggest training ICs in PD activities, aligned with Gibbons and Cobb's (2017) such as analyzing classroom video or lesson study involving a group of teachers working together with an expert to improve a lesson. District and school leaders should provide ICs with explicit guidance and more resources to achieve systemic change. ICs should also be empowered by being involved in the development of the school's continuing education training plans. Originality/value This study presents a new perspective about understanding and applying in-service teacher PD in the context of a digital education curricular initiative.
Semi-Supervised Learning to Automate Tumor Bud Detection in Cytokeratin-Stained Whole-Slide Images of Colorectal Cancer

Tumor budding is a histopathological biomarker associated with metastases and adverse survival outcomes in colorectal carcinoma (CRC) patients. It is characterized by the presence of single tumor cells or small clusters of cells within the tumor or at the tumor-invasion front. In order to obtain a tumor budding score for a patient, the region with the highest tumor bud density must first be visually identified by a pathologist, after which buds will be counted in the chosen hotspot field. The automation of this process will expectedly increase efficiency and reproducibility. Here, we present a deep learning convolutional neural network model that automates the above procedure. For model training, we used a semi-supervised learning method, to maximize the detection performance despite the limited amount of labeled training data. The model was tested on an independent dataset in which human- and machine-selected hotspots were mapped in relation to each other and manual and machine detected tumor bud numbers in the manually selected fields were compared. We report the results of the proposed method in comparison with visual assessment by pathologists. We show that the automated tumor bud count achieves a prognostic value comparable with visual estimation, while based on an objective and reproducible quantification. We also explore novel metrics to quantify buds such as density and dispersion and report their prognostic value. We have made the model available for research use on the grand-challenge platform.
Hippocampal spatio-predictive cognitive maps adaptively guide reward generalization

The brain forms cognitive maps of relational knowledge-an organizing principle thought to underlie our ability to generalize and make inferences. However, how can a relevant map be selected in situations where a stimulus is embedded in multiple relational structures? Here, we find that both spatial and predictive cognitive maps influence generalization in a choice task, where spatial location determines reward magnitude. Mirroring behavior, the hippocampus not only builds a map of spatial relationships but also encodes the experienced transition structure. As the task progresses, participants' choices become more influenced by spatial relationships, reflected in a strengthening of the spatial map and a weakening of the predictive map. This change is driven by orbitofrontal cortex, which represents the degree to which an outcome is consistent with the spatial rather than the predictive map and updates hippocampal representations accordingly. Taken together, this demonstrates how hippocampal cognitive maps are used and updated flexibly for inference.
Arabic Sentiment Analysis for Twitter Data: A Systematic Literature Review

Social media platforms have a huge impact on our daily lives. They have succeeded in attracting many people to spend time communicating and expressing themselves. Twitter is a social media platform that could be considered as a source of public opinion about products, services, and events. Sentiment analysis is the art of studying public feelings about certain topics, which may be positive, negative, or neutral. This paper provides a systematic review of Arabic tweet sentiment analysis on papers published from 2012 to 2021 in digital libraries including IEEE Explorer, Science Direct, Springer Link, and Google Scholar. The main aim of this systematic review is to investigate the trends in the topics reported and to highlight potential new research lines. To achieve that, three main stages were implemented: planning, conducting, and reporting the review. Our findings suggest the need for an open-source large Arabic tweet dataset that can be used by researchers. Also, it was found that researchers have used various classification techniques, which led to different results.
Preclinical Application of Augmented Reality in Pediatric Craniofacial Surgery: An Accuracy Study

Augmented reality (AR) allows the overlapping and integration of virtual information with the real environment. The camera of the AR device reads the object and integrates the virtual data. It has been widely applied to medical and surgical sciences in recent years and has the potential to enhance intraoperative navigation.In this study, the authors aim to assess the accuracy of AR guidance when using the commercial HoloLens 2 head-mounted display (HMD) in pediatric craniofacial surgery. The Authors selected fronto-orbital remodeling (FOR) as the procedure to test (specifically, frontal osteotomy and nasal osteotomy were considered). Six people (three surgeons and three engineers) were recruited to perform the osteotomies on a 3D printed stereolithographic model under the guidance of AR. By means of calibrated CAD/CAM cutting guides with different grooves, the authors measured the accuracy of the osteotomies that were performed. We tested accuracy levels of ±1.5 mm, ±1 mm, and ±0.5 mm.With the HoloLens 2, the majority of the individuals involved were able to successfully trace the trajectories of the frontal and nasal osteotomies with an accuracy level of ±1.5 mm. Additionally, 80% were able to achieve an accuracy level of ±1 mm when performing a nasal osteotomy, and 52% were able to achieve an accuracy level of ±1 mm when performing a frontal osteotomy, while 61% were able to achieve an accuracy level of ±0.5 mm when performing a nasal osteotomy, and 33% were able to achieve an accuracy level of ±0.5 mm when performing a frontal osteotomy.despite this being an in vitro study, the authors reported encouraging results for the prospective use of AR on actual patients.
A Bibliometric Review of Large Language Models Research from 2017 to 2023

Large language models (LLMs) are a class of language models that have demonstrated outstanding performance across a range of natural language processing (NLP) tasks and have become a highly sought-after research area, because of their ability to generate human-like language and their potential to revolutionize science and technology. In this study, we conduct bibliometric and discourse analyses of scholarly literature on LLMs. Synthesizing over 5,000 publications, this paper serves as a roadmap for researchers, practitioners, and policymakers to navigate the current landscape of LLMs research. We present the research trends from 2017 to early 2023, identifying patterns in research paradigms and collaborations. We start with analyzing the core algorithm developments and NLP tasks that are fundamental in LLMs research. We then investigate the applications of LLMs in various fields and domains including medicine, engineering, social science, and humanities. Our review also reveals the dynamic, fast-paced evolution of LLMs research. Overall, this paper offers valuable insights into the current state, impact, and potential of LLMs research and its applications.
Hyperchaotic Maps and the Single Neuron Model: A Novel Framework for Chaos-Based Image Encryption

With the explosion of the generation, transmission and sharing of image data over the Internet and other unsecured networks, the need and significance for the development of novel image encryption algorithms is unprecedented. In this research work, we propose a novel framework for image encryption that is based on two hyperchaotic maps, utilized in conjunction with the single neuron model (SNM). The framework entails three successive stages, where in every stage, a substitution box (S-box) is applied, then XORing with an encryption key is carried out. The S-boxes and the encryption keys are generated from the numerical solutions of the hyperchaotic maps and the SNM. The performance of the proposed framework is gauged through a number of metrics, reflecting superior performance and complete asymmetry between the plain images and their encrypted versions. However, the main advantages of this work are: (1) The attained width of the key space; and (2) The achieved efficiency in software implementation. The superior key space of 2^{2551} is the result of employing the two hyperchaotic maps, while the improved efficiency, resulting in an average encryption rate of 8.54 Mbps, is the result of the utilization of the single neuron model, as well as the employment of optimized parallel processing techniques. In addition, the proposed encryption framework is shown to output encrypted images that pass the NIST SP 800 suite.
Automatic identification of self-admitted technical debt from four different sources

Technical debt refers to taking shortcuts to achieve short-term goals while sacrificing the long-term maintainability and evolvability of software systems. A large part of technical debt is explicitly reported by the developers themselves; this is commonly referred to as Self-Admitted Technical Debt or SATD. Previous work has focused on identifying SATD from source code comments and issue trackers. However, there are no approaches available for automatically identifying SATD from other sources such as commit messages and pull requests, or by combining multiple sources. Therefore, we propose and evaluate an approach for automated SATD identification that integrates four sources: source code comments, commit messages, pull requests, and issue tracking systems. Our findings show that our approach outperforms baseline approaches and achieves an average F1-score of 0.611 when detecting four types of SATD (i.e., code/design debt, requirement debt, documentation debt, and test debt) from the four aforementioned sources. Thereafter, we analyze 23.6M code comments, 1.3M commit messages, 3.7M issue sections, and 1.7M pull request sections to characterize SATD in 103 open-source projects. Furthermore, we investigate the SATD keywords and relations between SATD in different sources. The findings indicate, among others, that: 1) SATD is evenly spread among all sources; 2) issues and pull requests are the two most similar sources regarding the number of shared SATD keywords, followed by commit messages, and then followed by code comments; 3) there are four kinds of relations between SATD items in the different sources.
Gold rush optimizer: A new population-based metaheuristic algorithm

Today’s world is characterised by competitive environments, optimal resource utilization, and cost reduction, which has resulted in an increasing role for metaheuristic algorithms in solving complex modern problems. As a result, this paper introduces the gold rush optimizer (GRO), a population-based metaheuristic algorithm that simulates how gold-seekers prospected for gold during the Gold Rush Era using three key concepts of gold prospecting: migration, collaboration, and panning. The GRO algorithm is compared to twelve well-known metaheuristic algorithms on 29 benchmark test cases to assess the proposed approach’s performance. For scientific evaluation, the Friedman and Wilcoxon signed-rank tests are used. In addition to these test cases, the GRO algorithm is evaluated using three real-world engineering problems. The results indicated that the proposed algorithm was more capable than other algorithms in proposing qualitative and competitive solutions.
Inquiring Natural Language Processing Capabilities on Robotic Systems through Virtual Assistants: A Systemic Approach

This paper attempts to approach the interface of a robot from the perspective of virtual assistants. Virtual assistants can also be characterized as the mind of a robot, since they manage communication and action with the rest of the world they exist in. Therefore, virtual assistants can also be described as the brain of a robot and they include a Natural Language Processing (NLP) module for conducting communication in their human-robot interface. This work is focused on inquiring and enhancing the capabilities of this module. The problem is that nothing much is revealed about the nature of the human-robot interface of commercial virtual assistants. Therefore, any new attempt of developing such a capability has to start from scratch. Accordingly, to include corresponding capabilities to a developing NLP system of a virtual assistant, a method of systemic semantic modelling is proposed and applied. For this purpose, the paper briefly reviews the evolution of virtual assistants from the first assistant, in the form of a game, to the latest assistant that has significantly elevated their standards. Then there is a reference to the evolution of their services and their continued offerings, as well as future expectations. The paper presents their structure and the technologies used, according to the data provided by the development companies to the public, while an attempt is made to classify virtual assistants, based on their characteristics and capabilities. Consequently, a robotic NLP interface is being developed, based on the communicative power of a proposed systemic conceptual model that may enhance the NLP capabilities of virtual assistants, being tested through a small natural language dictionary in Greek.