Reviewerly/exHarmony huggingface dataset:
Toward Useful Quantum Kernels

Abstract Supervised machine learning is a popular approach to the solution of many real‐life problems. This approach is characterized by the use of labeled datasets to train algorithms for classifying data or predicting outcomes accurately. The question of the extent to which quantum computation can help improve existing classical supervised learning methods is the subject of intense research in the area of quantum machine learning. The debate centers on whether an advantage can be achieved already with current noisy quantum computer prototypes or it is strictly dependent on the full power of a fault‐tolerant quantum computer. The current proposals can be classified into methods that can be suitably implemented on near‐term quantum computers but are essentially empirical, and methods that use quantum algorithms with a provable advantage over their classical counterparts but only when implemented on the still unavailable fault‐tolerant quantum computer. It turns out that, for the latter class, the benefit offered by quantum computation can be shown rigorously using quantum kernels, whereas the approach based on near‐term quantum computers is very unlikely to bring any advantage if implemented in the form of hybrid algorithms that delegate the hard part (optimization) to the far more powerful classical computers.
Application of machine learning techniques in population pharmacokinetics/pharmacodynamics modeling

Population pharmacokinetics/pharmacodynamics (pop-PK/PD) consolidates pharmacokinetic and pharmacodynamic data from many subjects to understand inter- and intra-individual variability due to patient backgrounds, including disease state and genetics. The typical workflow in pop-PK/PD analysis involves the determination of the structure model, selection of the error model, analysis based on the base model, covariate modeling, and validation of the final model. Machine learning is gaining considerable attention in the medical and various fields because, in contrast to traditional modeling, which often assumes linear or predefined relationships, machine learning modeling learns directly from data and accommodates complex patterns. Machine learning has demonstrated excellent capabilities for prescreening covariates and developing predictive models. This review introduces various applications of machine learning techniques in pop-PK/PD research.
Question answering systems for health professionals at the point of care—a systematic review

Abstract Objectives Question answering (QA) systems have the potential to improve the quality of clinical care by providing health professionals with the latest and most relevant evidence. However, QA systems have not been widely adopted. This systematic review aims to characterize current medical QA systems, assess their suitability for healthcare, and identify areas of improvement. Materials and methods We searched PubMed, IEEE Xplore, ACM Digital Library, ACL Anthology, and forward and backward citations on February 7, 2023. We included peer-reviewed journal and conference papers describing the design and evaluation of biomedical QA systems. Two reviewers screened titles, abstracts, and full-text articles. We conducted a narrative synthesis and risk of bias assessment for each study. We assessed the utility of biomedical QA systems. Results We included 79 studies and identified themes, including question realism, answer reliability, answer utility, clinical specialism, systems, usability, and evaluation methods. Clinicians’ questions used to train and evaluate QA systems were restricted to certain sources, types and complexity levels. No system communicated confidence levels in the answers or sources. Many studies suffered from high risks of bias and applicability concerns. Only 8 studies completely satisfied any criterion for clinical utility, and only 7 reported user evaluations. Most systems were built with limited input from clinicians. Discussion While machine learning methods have led to increased accuracy, most studies imperfectly reflected real-world healthcare information needs. Key research priorities include developing more realistic healthcare QA datasets and considering the reliability of answer sources, rather than merely focusing on accuracy.
BLOCKBOX: Blockchain based black box designing and modeling

Summary With the development of technology, data has become more accessible. The storage of critical and valuable information is getting harder and harder with the increase of cyber attacks and vulnerabilities of network and internet. The centralized storage of data causes security and privacy problems. As a remedy to these problems, blockchain has occurred. The reliability, transparency, integrity and confidentiality of the data have been increased with the blockchain technology. Blockchain maintains the immutability of the data with cryptographic hash algorithms. The data is kept in distributed structures with the consensus algorithms so that it is not lost. Projects and studies using blockchain technology have shown that data stored in centralized structures is not as reliable as data that is distributed and secured by cryptographic algorithms, smart contracts, and consensus. The data of flights, which is a critical data, has a great place in world transportation. The parameters inside the black boxes guide us on why the aircrafts have crashed: whether the responsibility belongs to the manufacturer, to the pilot, or to the aircraft technicians. In this way, the real reasons of the accident may be determined and measures can be taken for the accidents that may occur afterwards. In addition, the safe delivery of all black box data to the interested parties and its storage in a way that is immutable will not only benefit to the manufacturer's self‐development by processing the data, but also help flight companies to find optimum flight data. Both the flight companies and the manufacturer will be able to read the black box data, thus the suspicion of data manipulation will be eliminated and the data will become more transparent. In this study, a system is proposed in which an aircraft's black box data can be saved safely and securely with the help of blockchain technology.
Angell and McCall Meet Wansing

Abstract In this paper, we introduce a new logic, which we call AM3 . It is a connexive logic that has several interesting properties, among them being strongly connexive and validating the Converse Boethius Thesis . These two properties are rather characteristic of the difference between, on the one hand, Angell and McCall’s CC1 and, on the other, Wansing’s C . We will show that in other aspects, as well, AM3 combines what are, arguably, the strengths of both CC1 and C . It also allows us an interesting look at how connexivity and the intuitionistic understanding of negation relate to each other. However, some problems remain, and we end by pointing to a large family of weaker logics that AM3 invites us to further explore.
What characterizes LIS as a fragmenting discipline?

Purpose The purpose of this paper is to characterize library and information science (LIS) as fragmenting discipline both historically and by applying Whitley’s (1984) theory about the organization of sciences and Fuchs’ (1993) theory about scientific change. Design/methodology/approach The study combines historical source analysis with conceptual and theoretical analysis for characterizing LIS. An attempt is made to empirically validate the distinction between LIS context, L&amp;I services and information seeking as fragmented adhocracies and information retrieval and scientific communication (scientometrics) as technologically integrated bureaucracies. Findings The origin of fragmentation in LIS due the contributions of other disciplines can be traced in the 1960s and 1970s for solving the problems produced by the growth of scientific literature. Computer science and business established academic programs and started research relevant to LIS community focusing on information retrieval and bibliometrics. This has led to differing research interests between LIS and other disciplines concerning research topics and methods. LIS has been characterized as fragmented adhocracy as a whole, but we make a distinction between research topics LIS context, L&amp;I services and information seeking as fragmented adhocracies and information retrieval and scientific communication (scientometrics) as technologically integrated bureaucracies. Originality/value The paper provides an elaborated historical perspective on the fragmentation of LIS in the pressure of other disciplines. It also characterizes LIS as discipline in a fresh way by applying Whitley’s (1984) theory.
The architecture of visual narrative: Can text-to-image algorithms enhance the power of stylistic narrative for architecture

Architecture has always been a means of communicating stories through its design, with its structures and spaces serving as visual narratives. However, recent advancements in technology have created opportunities for architects to enhance their storytelling capabilities through the use of text-to-image algorithms. These algorithms have the potential to improve visual narratives by enabling architects to translate written descriptions into tangible visual representations. This article explores the architecture of visual narrative and how text-to-image algorithms can enhance it in diverse styles. This inquiry aims to help architectural epistemology understand and foresee the potential impact of this technology on the field of architecture. To understand the limits of AI in generating styles to enhance architectural narrative, six distinct styles were chosen for experimentation. The styles were selected based on their unique features, including an architect’s style, movement, or era. These styles include Zaha Hadid, Brutalist, modern-minimalistic, Peter Zumthor, Gothic, and Gaudi. The narrative was kept the same for each style while observing the changes in AI-generated visuals. The results were evaluated by comparing AI’s interpretations in terms of stylistic, environmental, material, form-based, and atmospheric features. While the results showed promise in terms of variations in each category, AI was not successful in implementing all stylistic features while keeping the narrative stable. In particular, after the second environment layer, the modern-minimalistic, Zumthor, and Brutalist styles lost their distinct features, while Gothic and Gaudi-inspired visuals were hardly generated even in the second environment layer. As a result, AI performed well in generating detailed environmental features without any given narrative and creating an atmospheric environment with enlightening the environment for the last layer.
Combining Swin Transformer and Attention-Weighted Fusion for Scene Text Detection

Abstract The existing text detection algorithms based on Convolutional Neural Networks (CNN) commonly have the problems of insufficient receptive fields and inadequate extraction of spatial positional information, which limit their ability to detect large-scale variation text instances, long-distance and wide-spaced text instances as well as effectively distinguish complex background textures. To address the above problems, in this paper, a scene text detection algorithm combining Swin Transformer and attention-weighted fusion is proposed. Firstly, an attention-weighted fusion (AWF) module is proposed, which embeds a modified coordinate attention module (CAM) in the feature pyramid network (FPN). This module learns spatial positional weights of foreground information in different-scale features while suppressing redundant background information. As a result, the fused features are more focused on the text regions, enhancing the localization ability for text regions and boundaries. Secondly, the window-based self-attention mechanism of the Swin Transformer is utilized to achieve global feature perception on the fused features of the pyramid network. This compensates for the insufficient receptive fields of CNN and enhances the representation capability of global contextual features, thereby further improving the performance of text detection. Experimental results demonstrate that the proposed algorithm achieves competitive performance on three public datasets, namely ICDAR2015, MSRA-TD500, and Total-Text, with F-measure reaching 87.9%, 91.4%, and 86.7%, respectively. Code is available at: https://github.com/xgli411/ST-AWFNet .
Escaping chronic poverty: long-term impacts of an agricultural development program

Abstract There is a need for groundbreaking initiatives to come to the rescue of the world’s rain-fed regions, especially in the face of the current climate and food crises and the associated reversal of previous advances in poverty reduction. However, there is little evidence of successful programs, which can inform the development of proactive policies to meet these urgent challenges. Can sustainable agricultural intensification enable marginalized, smallholder farmers to break out of chronic poverty? In this study, we scrutinize the Wadi concept, an integrated farming system that includes water resource development, soil conservation, cultivation of fruit trees, and intercropping on degraded land. We use a mixed-methods approach that involves surveying 1860 households, which were randomly sampled from a population of 188,231 participants across four Indian states, encompassing a variety of geographical regions. This is the first investigation into the effects of integrated farming systems on livelihoods on such a large scale and spanning 23 years. We compare the household participants of the program with the non-participants and run a linear regression analysis, ceteris paribus , comparing farmers who established integrated farming systems at different points in time over the period 1990–2017. Significant progress in several socioeconomic factors can be observed, such as higher incomes (the annual income of a typical participant has increased from INR 30,000 to 100,000 or even more), diversified income sources, positive life changes, higher life satisfaction and better perspectives for children, as well as environmental benefits. The Wadi program’s effectiveness in providing sustainable development pathways implies benefits to be gained from scaling it up. By thoroughly assessing its potential to help farmers escape chronic poverty and build resilience, this study provides evidence of the Wadi concept’s success that can enrich the science-policy dialog on development programs.
The effectiveness of simple heuristic features in sensor orientation and placement problems in human activity recognition using a single smartphone accelerometer

Human activity Recognition (HAR) using smartphone sensors suffers from two major problems: sensor orientation and placement. Sensor orientation and sensor placement problems refer to the variation in sensor signal for a particular activity due to sensors' altering orientation and placement. Extracting orientation and position invariant features from raw sensor signals is a simple solution for tackling these problems. Using few heuristic features rather than numerous time-domain and frequency-domain features offers more simplicity in this approach. The heuristic features are features which have very minimal effects of sensor orientation and placement. In this study, we evaluated the effectiveness of four simple heuristic features in solving the sensor orientation and placement problems using a 1D-CNN-LSTM model for a data set consisting of over 12 million samples.We accumulated data from 42 participants for six common daily activities: Lying, Sitting, Walking, and Running at 3-Metabolic Equivalent of Tasks (METs), 5-METs and 7-METs from a single accelerometer sensor of a smartphone. We conducted our study for three smartphone positions: Pocket, Backpack and Hand. We extracted simple heuristic features from the accelerometer data and used them to train and test a 1D-CNN-LSTM model to evaluate their effectiveness in solving sensor orientation and placement problems.We performed intra-position and inter-position evaluations. In intra-position evaluation, we trained and tested the model using data from the same smartphone position, whereas, in inter-position evaluation, the training and test data was from different smartphone positions. For intra-position evaluation, we acquired 70-73% accuracy; for inter-position cases, the accuracies ranged between 59 and 69%. Moreover, we performed participant-specific and activity-specific analyses.We found that the simple heuristic features are considerably effective in solving orientation problems. With further development, such as fusing the heuristic features with other methods that eliminate placement issues, we can also achieve a better result than the outcome we achieved using the heuristic features for the sensor placement problem. In addition, we found the heuristic features to be more effective in recognizing high-intensity activities.
Is ChatGPT taking over the language classroom?

ChatGPT generated much dialogue on the implications of large language models (LLMs) for language teaching and learning. Since language teachers are uniquely positioned to teach metalinguistic awareness, they can support their learners’ understanding of how LLMs are shaped by language ideologies and how their outputs are indexical of social power. This awareness would help learners be more conscientious in using LLMs, deciding how to interact with them and adapt their outputs for their purposes. This article introduces LLMs as statistical systems that predict linguistic forms. It surfaces two language ideologies that have shaped their development: the belief in the separability of language from its social contexts and the belief in the value of larger text corpora. It also highlights some ideological effects including uneven language performance, text outputs that reflect biases, privacy violations, circulation of copyrighted materials, misinformation, and hallucinations. Some suggestions for mitigating these effects are offered.
Improving the risk management process in quality management systems of higher education

The purpose of this paper is to improve the risk management process in the quality management system of higher education, taking into account the hazardous factors that increase the probability of occurrence and severity of consequences of undesirable events, as well as favorable factors that reduce the probability of occurrence and severity of consequences of hazardous events. The basis of risk management in the quality management systems of higher education institutions is the "Bowtie" method, which involves six main steps of identifying inconsistency, determining the impact of hazardous and favorable factors according to the impact group, ranking hazardous and favorable factors, calculating risk, substantiating precautionary measures and checking calculations. To rank hazardous and favorable factors, the authors used the "Decision Making Trial and Evaluation" method (hereinafter-DE-MATEL), which is based on paired comparison and decision-making tools based on graph theory. An improved process is proposed for risk assessment, which differs from the known ones by the presence of the identification of the cause-and-effect relationship "hazard (inconsistency)-hazardous event-consequences", identification of hazardous and favorable factors of the internal and external environment that affect the probability and/or the degree of severity of a hazardous event-the appearance of an inconsistency, which is carried out after the inconsistency has been determined; determination of causal hazardous and favorable factors by an acceptable method. Registers of inconsistencies (hazards), hazardous and favorable factors have been developed and proposed based on the requirements for accreditation of educational programs and the international standard ISO 9001:2015, which will allow, based on a risk-oriented approach, to provide a basis for setting the goals of a higher education institution under martial law in order to guarantee effective implementation of the mission and strategy. They are proposed for decision-making in the quality management systems of educational organizations on the substantiation of precautionary or corrective measures based on ranking the risks from identified inconsistencies, which are determined taking into account the impact of the entire set of identified significant hazardous and favorable factors. The value of this paper is to improve the quality risk management process in educational organizations, taking into account the impact of hazardous and favorable factors, and to develop an appropriate step-by-step algorithm of actions and a risk assessment form.
Diagnostic Performance of Deep Learning in Video-Based Ultrasonography for Breast Cancer: A Retrospective Multicentre Study

Although ultrasound is a common tool for breast cancer screening, its accuracy is often operator-dependent. In this study, we proposed a new automated deep-learning framework that extracts video-based ultrasound data for breast cancer screening.Our framework incorporates DenseNet121, MobileNet, and Xception as backbones for both video- and image-based models. We used data from 3907 patients to train and evaluate the models, which were tested using video- and image-based methods, as well as reader studies with human experts.This study evaluated 3907 female patients aged 22 to 86 years. The results indicated that the MobileNet video model achieved an AUROC of 0.961 in prospective data testing, surpassing the DenseNet121 video model. In real-world data testing, it demonstrated an accuracy of 92.59%, outperforming both the DenseNet121 and Xception video models, and exceeding the 76.00% to 85.60% accuracy range of human experts. Additionally, the MobileNet video model exceeded the performance of image models and other video models across all evaluation metrics, including accuracy, sensitivity, specificity, F1 score, and AUC. Its exceptional performance, particularly suitable for resource-limited clinical settings, demonstrates its potential for clinical application in breast cancer screening.The level of expertise reached by the video models was greater than that achieved by image-based models. We have developed an artificial intelligence framework based on videos that may be able to aid breast cancer diagnosis and alleviate the shortage of experienced experts.
Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks

A physics-informed neural network (PINN) embedded with the susceptible-infected-removed (SIR) model is devised to understand the temporal evolution dynamics of infectious diseases. Firstly, the effectiveness of this approach is demonstrated on synthetic data as generated from the numerical solution of the susceptible-asymptomatic-infected-recovered-dead (SAIRD) model. Then, the method is applied to COVID-19 data reported for Germany and shows that it can accurately identify and predict virus spread trends. The results indicate that an incomplete physics-informed model can approach more complicated dynamics efficiently. Thus, the present work demonstrates the high potential of using machine learning methods, e.g., PINNs, to study and predict epidemic dynamics in combination with compartmental models.