Reviewerly/exHarmony huggingface dataset:
Technical and Diagnostic Issues in Whole Slide Imaging Published Validation Studies

Digital pathology with whole-slide imaging (WSI) has many potential clinical and non-clinical applications. In the past two decades, despite significant advances in WSI technology adoption remains slow for primary diagnosis. The aim of this study was to identify common pitfalls of WSI reported in validation studies and offer measures to overcome these challenges.A systematic search was conducted in the electronic databases Pubmed-MEDLINE and Embase. Inclusion criteria were all validation studies designed to evaluate the feasibility of WSI for diagnostic clinical use in pathology. Technical and diagnostic problems encountered with WSI in these studies were recorded.A total of 45 studies were identified in which technical issues were reported in 15 (33%), diagnostic issues in 8 (18%), and 22 (49%) reported both. Key technical problems encompassed slide scan failure, prolonged time for pathologists to review cases, and a need for higher image resolution. Diagnostic challenges encountered were concerned with grading dysplasia, reliable assessment of mitoses, identification of microorganisms, and clearly defining the invasive front of tumors.Despite technical advances with WSI technology, some critical concerns remain that need to be addressed to ensure trustworthy clinical diagnostic use. More focus on the quality of the pre-scanning phase and training of pathologists could help reduce the negative impact of WSI technical difficulties. WSI also seems to exacerbate specific diagnostic tasks that are already challenging among pathologists even when examining glass slides with conventional light microscopy.
Digital Forensics Classification Based on a Hybrid Neural Network and the Salp Swarm Algorithm

In recent times, cybercrime has increased significantly and dramatically. This made the need for Digital Forensics (DF) urgent. The main objective of DF is to keep proof in its original state by identifying, collecting, analyzing, and evaluating digital data to rebuild past acts. The proof of cybercrime can be found inside a computer’s system files. This paper investigates the viability of Multilayer perceptron (MLP) in DF application. The proposed method relies on analyzing the file system in a computer to determine if it is tampered by a specific computer program. A dataset describes a set of features of file system activities in a given period. These data are used to train the MLP and build a training model for classification purposes. Identifying the optimal set of MLP parameters (weights and biases) is a challenging matter in training MLPs. Using traditional training algorithms causes stagnation in local minima and slow convergence. This paper proposes a Salp Swarm Algorithm (SSA) as a trainer for MLP using an optimized set of MLP parameters. SSA has proved its applicability in different applications and obtained promising optimization results. This motivated us to apply SSA in the context of DF to train MLP as it was never used for this purpose before. The results are validated by comparisons with other meta-heuristic algorithms. The SSAMLP-DF is the best algorithm because it achieves the highest accuracy results, minimum error rate, and best convergence scale.
Fintech, Female Employment, and Gender Inequality

Fintech, which delivers financial services digitally, promises to promote financial inclusion and close the gender gap.Using a novel fintech dataset for 114 economies worldwide, this paper shows that fintech adoption significantly improves female employment and reduces gender inequality, the effect being more pronounced in firms without traditional financial access.Fintech not only increases the number and ratio of female employees in the workforce, but also mitigates financial constraints of female-headed firms.Digital divide and poor institutions weaken such benefits.Endogeneity is accounted for by a fixed effects identification strategy.We conclude by providing policy recommendations and outlining avenues for future research.
Open Logistics: Blockchain-Enabled Trusted Hyperconnected Logistics Platform

The digitalization and adoption of advanced technologies in supply chain and logistics not only change the business model but also transfer logistics infrastructure to a service-oriented architecture and introduce new avenues concerning supply chain 4.0 (SC4.0). Sharing logistic assets between various businesses leads to improving logistics work, enhancing work productivity, and reducing logistics expenses and environmental impact. However, due to the lack of a secure, trustworthy, and open sharing platform, the companies are not willing to rely on sharing economics. Aiming to improve trust-ability, openness, and interoperability in the SC4.0, this paper presents a blockchain-enabled hyperconnected logistics platform. Firstly, the Open Logistic platform (OL) is proposed, and the key characteristics of this platform are explained. Secondly, the concept of proof of delivery (PoD) based on smart contracts is defined and developed to explore its rule-based management and control among the dynamic assets sharing. Thirdly, the Blockchain asset sharing service is designed and discussed in the context of asset sharing. Fourthly to evaluate the feasibility of the proposed platform, a simulation environment is developed, and OL is implemented based on the case study.
Homophobic and Hate Speech Detection Using Multilingual-BERT Model on Turkish Social Media

Homophobic expressions are a form of insulting the sexual orientation or personality of people. Severe psychological traumas may occur in people who are exposed to this type of communication. It is important to develop automatic classification systems based on language models to examine social media content and distinguish homophobic discourse. This study aims to present a pre-trained Multilingual Bidirectional Encoder Representations from Transformers (M-BERT) model that can successfully detect whether Turkish comments on social media contain homophobic or related hate comments (i.e., sexist, severe humiliation, and defecation expressions). Comments in the Homophobic-Abusive Turkish Comments (HATC) dataset were collected from Instagram to train the detection models. The HATC dataset was manually labeled at the sentence level and combined with the Abusive Turkish Comments (ATC) dataset that has developed in our previous study. The HATC dataset has been balanced using the resampling method and two forms of the dataset (i.e., resHATC and original HATC) were used in the experiments. Afterward, the M-BERT model was compared with DL-based models (i.e., Long-Short Term Memory, Bidirectional Long-Short Term Memory (BiLSTM), Gated Recurrent Unit), Traditional Machine Learning (TML) classifiers (i.e., Support Vector Machine, Naive Bayes, Random Forest) and Ensemble Classifiers (i.e., Adaptive Boosting, eXtreme Gradient Boosting, Gradient Boosting) for the best model selection. The performance of the detection models was evaluated using F1-score, precision, and recall performance metrics. Results showed the best performance (homophobic F1-score: 82.64%, hateful F1-score: 91.75%, neutral F1-score: 96.08%, average F1-score: 90.15%) was achieved with the M-BERT model on the HATC dataset. The M-BERT detection model can increase the effectiveness of filters in detecting Turkish homophobic and related hate speech in social networks. It can be used to detect homophobic and related hate speech for different languages since the M-BERT model has multilingual pre-trained data.
Compact quantum kernel-based binary classifier

Abstract Quantum computing opens exciting opportunities for kernel-based machine learning methods, which have broad applications in data analysis. Recent works show that quantum computers can efficiently construct a model of a classifier by engineering the quantum interference effect to carry out the kernel evaluation in parallel. For practical applications of these quantum machine learning methods, an important issue is to minimize the size of quantum circuits. We present the simplest quantum circuit for constructing a kernel-based binary classifier. This is achieved by generalizing the interference circuit to encode data labels in the relative phases of the quantum state and by introducing compact amplitude encoding, which encodes two training data vectors into one quantum register. When compared to the simplest known quantum binary classifier, the number of qubits is reduced by two and the number of steps is reduced linearly with respect to the number of training data. The two-qubit measurement with post-selection required in the previous method is simplified to single-qubit measurement. Furthermore, the final quantum state has a smaller amount of entanglement than that of the previous method, which advocates the cost-effectiveness of our method. Our design also provides a straightforward way to handle an imbalanced data set, which is often encountered in many machine learning problems.
Performance Evaluation Analysis of Spark Streaming Backpressure for Data-Intensive Pipelines

A significant rise in the adoption of streaming applications has changed the decision-making processes in the last decade. This movement has led to the emergence of several Big Data technologies for in-memory processing, such as the systems Apache Storm, Spark, Heron, Samza, Flink, and others. Spark Streaming, a widespread open-source implementation, processes data-intensive applications that often require large amounts of memory. However, Spark Unified Memory Manager cannot properly manage sudden or intensive data surges and their related in-memory caching needs, resulting in performance and throughput degradation, high latency, a large number of garbage collection operations, out-of-memory issues, and data loss. This work presents a comprehensive performance evaluation of Spark Streaming backpressure to investigate the hypothesis that it could support data-intensive pipelines under specific pressure requirements. The results reveal that backpressure is suitable only for small and medium pipelines for stateless and stateful applications. Furthermore, it points out the Spark Streaming limitations that lead to in-memory-based issues for data-intensive pipelines and stateful applications. In addition, the work indicates potential solutions.
Quantum signal processing for simulating cold plasma waves

Numerical modeling of radio-frequency waves in plasma with sufficiently high spatial and temporal resolution remains challenging even with modern computers. However, such simulations can be sped up using quantum computers in the future. Here, we propose how to do such modeling for cold plasma waves, in particular, for an X wave propagating in an inhomogeneous one-dimensional plasma. The wave system is represented in the form of a vector Schr\"odinger equation with a Hermitian Hamiltonian. Block encoding is used to represent the Hamiltonian through unitary operations that can be implemented on a quantum computer. To perform the modeling, we apply the so-called quantum signal processing algorithm and construct the corresponding circuit. Quantum simulations with this circuit are emulated on a classical computer, and the results show agreement with traditional classical calculations. We also discuss how our quantum circuit scales with the resolution.
Designing for Confidence: The Impact of Visualizing Artificial Intelligence Decisions

Explainable artificial intelligence aims to bring transparency to artificial intelligence (AI) systems by translating, simplifying, and visualizing its decisions. While society remains skeptical about AI systems, studies show that transparent and explainable AI systems can help improve the Human-AI trust relationship. This manuscript presents two studies that assess three AI decision visualization attribution models that manipulate morphological clarity (MC) and two information presentation-order methods to determine each visualization's impact on the Human-AI trust relationship through increased confidence and cognitive fit (CF). The first study, N = 206 (Avg. age = 37.87 ± 10.51, Male = 123), utilized information presentation methods and visualizations delivered through an online experiment to explore trust in AI by asking participants to complete a visual decision-making task. The second study, N = 19 (24.9 ± 8.3 years old, Male = 10), utilized eye-tracking technology and the same stimuli presentation methods to investigate if cognitive load, inferred through pupillometry measures, mediated the confidence-trust relationship. The results indicate that low MC positively impacts Human-AI trust and that the presentation order of information within an interface in terms of adjacency further influences user trust in AI. We conclude that while adjacency and MC significantly affect cognitive load, cognitive load alone does not mediate the confidence-trust relationship. Our findings interpreted through a combination of CF, situation awareness, and ecological interface design have implications for the design of future AI systems, which may facilitate better collaboration between humans and AI-based decision agents.
Determination of trace metal ions in Gentiana rigescens by inductively coupled plasma-optical emission spectrometry after deep eutectic solvent-based digestion and related pharmacodynamic evaluation

A fast, simple and environmentally friendly method for the digestion of solid samples of Gentiana rigescens Franch. ex Hemsl. (G. rigescens) based on deep eutectic solvent (DES) was established, and inductively coupled plasma-optical emission spectrometry (ICP-OES) was used to quantify Ni, Zn, Co, Cr and Cu in DES digestion solution. Under optimized conditions, limits of detection (LOD) and limits of quantification (LOQ) ranged from 0.4 to 2.5 and 1.2–8.2 μg g-1, respectively. The intra-day and inter-day precision were carried out by relative standard deviation (RSD) ranged from 0.8% to 4.9% and 0.6%–1.4%, and the spiked recoveries ranged from 90.6% to 111.9%, respectively. The results of quantitative analysis by the developed method were in good agreement with those of microwave digestion. At the same time, the contents of the metal ions in the G. rigescens decoction liquid and freeze-dried powder solution of the decoction liquid of G. rigescens (crude extract) were determined by ICP-OES. The results showed that the contents of Ni, Zn, Co and Cu in the decoction liquid and crude extract were significantly lower than the contents in DES digestion liquid, and Cr was not detected. Then, the oxidative damage model of LO2 cells induced by H2O2 was established. The protective effect of crude extract of G. rigescens on damaged liver cells and the effect of metal ions with the same contents of decoction liquid on liver protection of G. rigescens were investigated for the first time. The results show that H2O2 could induce the decrease of LO2 cell viability and damage the cell morphology. GPS and crude extract could inhibit H2O2-induced liver injury by adjusting the antioxidant enzyme system. Metal ions corresponding to the content of the decoction liquid may weaken the hepatoprotective effect of GPS or crude extract by regulating the antioxidant enzyme system.
Developing Communicative Professional Competence in Future Economic Specialists in the Conditions of Postmodernism

A detailed analysis of psycho-pedagogical, methodological and linguistic sources in the article has allowed one to justify and build a holistic model of developing communicative professional competence in economics students. This author’s model includes the interrelated components of communicative professional activities (motivation and goals; orientation and cognition; functions and activities; evaluation and correction), criteria for their assessment and levels of communicative professional competence in economics students (high, average, low). The following pedagogical conditions have been presented in the context of the research: modelling probable professional communicative situations in the educational process; ensuring communicative professional orientation of information and communication technologies; strengthening the subjective position of students during communicative professional training. Their effective use in the educational process contributes to developing the motivational sphere of students, improving their knowledge of languages ​​and other academic courses, enhancing their abilities to apply the obtained knowledge in practice and adequately assess themselves as specialists. The international relevance of the article lies in offering guidelines for educational institutions that strive to enhance the educational processes in a postmodern society.
Feasibility, Appropriateness, and Willingness to Use Virtual Reality as an Adjunct to Counseling among Addictions Counselors

Introduction Research suggests that virtual reality (VR) experiences can be helpful as adjunctive tools in psychotherapy for some mental health conditions. VR is a computer-generated experience that produces a feeling of being immersed in a different environment. VR experiences could be useful in the treatment of substance use disorders, and several are currently being tested. However, few psychotherapists report using VR experiences in their practices, even when doing so is well-supported. Understanding key barriers and concerns about using VR among drug/alcohol counselors is important to ultimately encouraging adoption.Methods Licensed counselors (N = 101) who provide treatment to clients with substance use disorders were recruited via email Listservs, professional organizations, and social media. Participants viewed a 15-minute educational video about VR and then completed a survey of their views about using it with their clients.Results Most clinicians (82%) believed they would be likely to use a VR experience in drug/alcohol counseling, and 81% believed it would be appropriate for most of their clients. A minority (19%) noted important concerns, including that their clients may be skeptical of it (15%), cost (14%), and space (10%). Those who had cost and space concerns were less likely to report high use intentions (OR = 0.29 and OR = 031, both p < .05, respectively).Conclusions Findings suggest that addictions counselors are eager to use VR, but key barriers should be addressed. VR developers should incorporate features to encourage trust among users, design experiences for small spaces, and explore ways of supporting the purchase of VR systems for counselors.
Tweets and Reactions: Revealing the Geographies of Cybercrime Perpetrators and the North-South Divide

How do tweets reflect the long-standing disparities between the northern and southern regions of Nigeria? This study presents a qualitative analysis of Twitter users' responses (n = 101,518) to the tweets of the Economic and Financial Crimes Commission (EFCC) regarding the production and prosecution of cybercrime. The article uses postcolonial perspectives to shed light on the legacies of British colonial efforts in Nigeria, such as the amalgamation of the northern and southern protectorates in 1914. The results revealed significant discrepancies between Nigeria's northern and southern regions regarding cybercriminal arrest, conviction, and sentencing. Specifically, the results showed that the EFCC's criminalization of Southerners differs substantially from that of Northerners. The contemporary manifestation of inequalities concerning the production and prosecution of cybercrime on Twitter reflects long-standing contestations (e.g., economic, political, cultural, geological) between the northern and southern parts of Nigerian society. Therefore, since the North-South divide in present-day Nigeria originated from British colonization, colonialism is the base that shaped the superstructure comprising political, religious, historical, geological (e.g., crude oil), and economic factors. In turn, the article spotlights that regional differences in educational attainment, originating from differing experiences of Christianization and colonization, interact with regional disparities in the production of cybercrime. More research is required to better understand how these contextual disparities in society interact with the production and prosecution of cybercrime, given that Nigerian cybercriminals defraud victims all over the world.
Evaluation of Field Applicability of High-Speed 3D Digital Image Correlation for Shock Vibration Measurement in Underground Mining

When combined with high-speed photography technology, the digital image correlation (DIC) method provides an excellent photographic image processing capability that can be used to convert the evolving full-field surface properties of structures to sets of two-dimensional (2D) or three-dimensional (3D) coordinate values. In this study, the applicability of the DIC method and high-speed cameras in underground mining was investigated by measuring the displacement and vibration of rock pillars caused by blasting. This technique is used extensively in engineering and is increasingly being applied to new fields. As a result of comparing the DIC results for blast vibration with the measured values of the contact sensor through field tests, the maximum displacement and vibration speed were found to be 86% and 93% accurate, respectively. In addition, the obtained values appeared similar to those predicted through numerical analysis. Field test results indicate that both methods yielded similar results. Therefore, it is concluded that it is feasible to use the DIC and high-speed camera to measure ground displacements and vibrations from blasting in underground mining. In addition, the system conditions required for blast vibration measurement were considered by comparing the accuracy with the existing measurement methods.
Today’s interdisciplinary quantum information classroom: Themes from a survey of quantum information science instructors

Interdisciplinary introduction to quantum information science (QIS) courses are proliferating at universities across the US, but the experiences of instructors in these courses have remained largely unexplored in the discipline-based education research (DBER) communities. Here, we address this gap by reporting on the findings of a survey of instructors teaching introduction to QIS courses at institutions across the US, primarily at the undergraduate or hybrid undergraduate/graduate level, as well as follow-up focus interviews with six individual instructors. Key themes from this analysis include challenges and opportunities associated with the diversity of instructor and student backgrounds, student difficulties with the mathematical formalism (especially though not exclusively with linear algebra), and the need for better textbooks and curricular materials. We also find that while course topics are ostensibly similar, each course is crafted by its instructor to tell a different story about QIS and to uniquely balance goals such as accessibility and academic rigor, such that no canonical introduction to QIS course emerges from our dataset. We discuss the implications of this finding with regard to the benefits and risks associated with standardization of curricula as QIS coursework matures.