Reviewerly/exHarmony huggingface dataset:
Measurement uncertainty relation for three observables

We establish rigorously in this work a measurement uncertainty relation (MUR) for three unbiased qubit observables, which was previously shown to hold true under some presumptions. The triplet MUR states that the uncertainty, which is quantified by the total statistical distance between the target observables and the jointly implemented observables, is lower bounded by an incompatibility measure that reflects the joint measurement conditions. We derive a necessary and sufficient condition for the triplet MUR to be saturated and the corresponding optimal measurement. To facilitate experimental tests of MURs we propose a straightforward implementation of the optimal joint measurements. Lastly, by a symmetry argument, the exact values of incompatibility measure are analytically calculated for some symmetric triplets. We anticipate that our work may enrich the understanding of quantum incompatibility in terms of MURs and inspire further applications in quantum information science. This work presents a complete theory relevant to a parallel work [Y.-L. Mao et al., Testing Heisenberg-type measurement uncertainty relations of three observables, Phys. Rev. Lett. 131, 150203 (2023)] on experimental tests.
γ-Ray Log Tool for Detecting the Presence of Low-Permeability Lenses in High-Resolution Modelling of Contaminated Sites

In contaminated sites, remediation measures mostly depend on previous high-resolution site characterization (HRSC) results. In the case of industrial sites, where there is a high pollution risk for groundwater, many hydrogeological models are often achieved using stratigraphy results of geological drilling after the monitoring network design. This approach is only sometimes possible when contaminant back diffusion from small low-permeability lenses and layers occurs within a high-permeability aquifer. This framework needs a significant resolution that can be obtained by coupling preliminary stratigraphic data with specific in situ activities. Results from these additional investigations may help to locate low-permeability layers and, consequently, to identify the most vulnerable areas towards which groundwater management must be deepened. The use of gamma rays in combination with the analysis of the pluviometric and hydrometric data and together with the time series of the concentrations of a given analyte can provide indispensable support for site characterization, the development of numerical groundwater contamination models and remediation procedures when back diffusion occurs. In this study, within an industrial site in the province of Benevento (Italy), based on stratigraphy, a 3D hydrogeological model has been set up and coupled with results of natural gamma radioactivity logs to detect the release of Chromium VI from low-permeability lenses.
ChatGPT as a COBUILD lexicographer

Abstract Creating high-quality dictionary entries has been a meticulous and time-consuming process, but current developments in Artificial Intelligence (AI) could help to speed up this process. This study explores the use of ChatGPT to create COBUILD-style English monolingual entries and evaluates its ability to generate accurate and high-quality sense definitions and example sentences on a sample of fifteen verbs of communication. AI-generated entries are produced using ChatGPT Plus, and human experts evaluate them alongside COBUILD entries in a blinded randomized process. The quality of sense definitions, example sentences, and overall entries are assessed using a five-point scale. The results indicate that AI-generated definitions are comparable to those created by human lexicographers, while AI-generated examples and overall entries receive lower ratings. The study also explores the possibility of fine-tuning AI models based on expert feedback to enhance their performance.
Comparison between Two Algorithms for Computing the Weighted Generalized Affinity Coefficient in the Case of Interval Data

From the affinity coefficient between two discrete probability distributions proposed by Matusita, Bacelar-Nicolau introduced the affinity coefficient in a cluster analysis context and extended it to different types of data, including for the case of complex and heterogeneous data within the scope of symbolic data analysis (SDA). In this study, we refer to the most significant partitions obtained using the hierarchical cluster analysis (h.c.a.) of two well-known datasets that were taken from the literature on complex (symbolic) data analysis. h.c.a. is based on the weighted generalized affinity coefficient for the case of interval data and on probabilistic aggregation criteria from a VL parametric family. To calculate the values of this coefficient, two alternative algorithms were used and compared. Both algorithms were able to detect clusters of macrodata (aggregated data into groups of interest) that were consistent and consonant with those reported in the literature, but one performed better than the other in some specific cases. Moreover, both approaches allow for the treatment of large microdatabases (non-aggregated data) after their transformation into macrodata from the huge microdata.
Effects of Infection Control Education Program Using Flipped Learning Based on the ARCS Model for Nursing Students: A Mixed Method

This study was conducted to verify the effect of an infection control education program, using flipped learning based on the ARCS model, for nursing students. The study was a mixed-method study, composed of a nonequivalent control group, a pretest-posttest design, and focus group interviews. The total number of participants was 37, with 18 in the experimental group and 19 in the control group. The mean age of the participants was 24.32 ± 5.60 years, consisting of three males (8.1%) and thirty-four females (91.9%). The collected data were analyzed using a linear mixed-effects method. The data regarding experiences of participation were analyzed using conventional content analysis. The experimental group had a higher degree of learning motivation, self-directed learning ability, and confidence in infection control practice than the control group. In the results of the focus group interviews, the themes were derived from 'Engaging learning experience, although difficult, in new ways of learning'. It was confirmed that the infection control education program, using flipped learning based on the ARCS model, was an effective intervention for improving nursing students' self-directed learning ability. Therefore, it is strongly recommended to use the educational program developed in this study for infection control education for nursing students and new nurses.
Bike computer forensics: An efficient and robust method for FIT file recovery

The popularity of bike computer devices has grown in recent years. These devices generate a wealth of data in the form of Flexible and Interoperable Data Transfer (FIT) files, which can be used to store fitness related data efficiently. However, the recovery of corrupted FIT files remains a significant challenge due to their inherent structure. The format relies on a chain of messages stored sequentially, with each message referencing previous data to parse the subsequent record. As a result, the recovery of data situated between corrupted portions becomes notably challenging. This study introduces an efficient, and robust method for dense recovery of corrupted files. Our approach combines multiple phases of data carving techniques to maximize data recovery. By employing this method, investigators can effectively access crucial information including accident reconstruction, and criminal activities. The proposed methods demonstrate higher recovery rate through the proof-of-concept and real-world experiments, proving its utility and reliability in the field of digital forensics.
SAGAN: Deep semantic-aware generative adversarial network for unsupervised image enhancement

Low-light image enhancement (LLIE) is a common pretext task for computer vision, which aims to adjust the luminance of the low-light image to obtain the normal-light image. At present, unsupervised LLIE has been developed. However, its performance is limited due to the lack of sufficient semantic information and guidance from a strict discriminator. In this work, a semantic-aware generative adversarial network is proposed to alleviate the above limitations. We use the pre-trained VGG model on ImageNet to extract the prior semantic information, which is organically fed into the generator to refine its feature representation, and develop an adaptive image fusion strategy working on the output layer of the generator. Further, to improve the discriminator's capacity of supervising generator, we design the dual-discriminator with dense connection and two image quality-driven priority queues with time-aware. The quantitative and qualitative experiments on four testing datasets demonstrate the competitiveness of the proposed model and the effectiveness of each component. Our code is available at: https://github.com/Shecyy/SAGAN.
Artificial Intelligence Algorithm with ICD Coding Technology Guided by Embedded Electronic Medical Record System in Medical Record Information Management

The study aims to discuss the coding technology of international classification of diseases (ICD) and the application of embedded electronic medical record (EMR) system through clinical diagnosis selection and medical record information management by Artificial Intelligence Algorithm. According to the embedded medical record input interface, the embedded medical record information knowledge system is established, and the data are randomly extracted from the knowledge base. The data are randomly extracted from the EMR database to collect patient medical records and disease diagnosis code data on the first page of 8 clinical departments of endocrinology, oncology, obstetrics and gynecology, ophthalmology, burns, orthopedics, neurosurgery and cardiovascular medicine for statistical analysis. The natural language processing-bidirectional recurrent neural network (NLP-BIRNN) algorithm is used to optimize the medical records. The coders are unfamiliar with the basic rule of the main diagnosis selection, and are unclear about the classification of disease codes. They do not code according to the main diagnosis principles. When there is a complication, the previous disease is mistaken as the main diagnosis. The coding of many kinds of diseases is not detailed and accurate. There is no merge coding for diseases that should be merged. The undiagnosed diseases are coded according to the diagnosed diseases. The diseases are not coded according to different conditions. The diseases are not coded according to the specific classification. The diseases are not coded according to cause. Postoperative complications codes are inaccurate. The filling of the disease diagnosis is incomplete. The name of the disease diagnosis site is inaccurate. Coding selection is too general. The solutions to these problems are as follows. Coders and medical staff should strengthen communication. The training of coders and medical staff in this aspect of knowledge should be strengthened. The corresponding incentive measures should be formulated to increase the importance of coders and medical staff on ICD coding technology. BIRNN is compared with convolutional neural network (CNN) and recurrent neural network (RNN) in accuracy, symptom precision, and symptom recall, and BIRNN proposed has higher values. the errors of coding staff and medical staff are reduced in the main diagnosis and coding, and the pathological language reading under artificial intelligence algorithm provides certain convenience for the diagnosis and treatment of diseases.
Tuning N-ary relation extraction as Machine Reading Comprehension

Compared with conventional binary relation extraction, n-ary relation extraction is a particularly challenging task due to the presence of multiple entities that span across sentences. Although current methods have achieved remarkable results in this area, they often rely on complex modeling like dependency parsing, which easily suffers from error propagation. To address this predicament, this paper proposes a novel framework for n-ary relation extraction that utilizes Machine Reading Comprehension (MRC) as its foundation. In particular, considering the unnameable relations or sub-relations between multiple entities, we resort to learning continuous prompting questions to make up for the deficiency of natural language questions. Additionally, to alleviate the high semantic similarity between close relation classes, we obtain supplementary prompt messages (i.e., additional knowledge) according to the statistical results for each relation class so as to equip the model with a better capacity to make distinctions. Finally, since the one-turn mechanism of MRC is prone to mistakes, especially in those challenging n-ary tasks, we design a double-check mechanism that generates questions from multi-perspective to ascertain the final relation between all the entities by aggregating the answers to all questions. Our method has demonstrated the most advanced results on n-ary relation extraction datasets through extensive experimentation.
Investigation of Strength of E91 Quantum Key Distribution Protocol

This article is about studying how strong the E91 quantum key distribution protocol is. The protocol uses the ideas of quantum mechanics. Quantum cryptography is a very hopeful field in keeping information safe, and the E91 protocol is seen as one of the most hopeful ways to guarantee complete and certain secure transmission of data. The aim of the study is to find weaknesses in the E91 protocol and understand how well it can withstand different kinds of quantum attacks. This work uses math and computer simulations to carefully study and make sure the protocol is secure. The literature review discusses previous studies on quantum cryptography and protocols for distributing keys. This study will help make better E91 protocols and will be important for keeping information safe in modern communication systems. These discoveries can help make quantum cryptography better and more useful in real-life situations. It can secure data by protecting it from increasing cyber-attacks and make sure that information systems work well.
Interaction with Virtual Objects in VR-Applications

The paper investigates the effectiveness of methods of interaction with virtual objects in a virtual reality environment. We consider the role of various input devices, such as virtual reality gloves and vests, in providing a more realistic and immersive user experience, analyze the advantages and applications of these devices in various industries, including medicine, education, the gaming industry and others. We also describe the development of a VR simulator for modeling emergency scenarios, consider the implementation of realistic physics of objects in the virtual world, taking into account the use of Configurable Joint and Rigidbody.
Momentary assessment of drinking: Past methods, current approaches incorporating biosensors, and future directions

Ecological momentary assessment methodologies are valuable tools for alcohol researchers, allowing for comprehensive real-time assessment of subjective and objective alcohol use in naturalistic, ecologically valid drinking environments. This methodology has long been used in alcohol research, but the wide-spread availability and multiple features of smartphones now provide an accessible and low-cost means to schedule and administer surveys assessing alcohol use, behavioral measures, and other variables of interest as well as to capture movement, location, drinking context, and other relevant aspects of the drinking environment. Coupled with objective measures of alcohol levels in the body, including transdermal alcohol concentration, these methods become powerful research tools. In addition to describing the history of the field, the current status of methods and measurement tools, and the potential integration of various technologies, we also provide a detailed review of our research team's efforts to convert transdermal data into estimates of breath alcohol concentration so that researchers will have a non-invasive, continuous, passive, and easily interpretable measure of alcohol levels in the body. This work includes the development of physics-based deterministic and population-based models coupled with machine learning techniques, creation of software to produced estimated BrAC curves and summary scores, development of internet-based cell phone-optimized assessment techniques and web-based applications, and collection of biosensor and behavioral data during laboratory drinking episodes where participants consume alcohol in various patterns over multiple sessions for testing within and between person- and episode-level variation. We conclude with a brief discussion of future directions for the field.
Garbage In, Garbage Out. Regulating Generative AI Through Copyright Law

Both in the U.S. and in the EU, it is a matter of controversy whether copyrighted content may be used to train generative AI. In the U.S., the discourse circles around the impact which copyright has on innovation and competition. In the EU, practical problems dominate the discussions. These problems have led rights holders to formulate concrete policy demands. The European Parliament wants to address these demands in the draft AI Act (AIA). The rule it proposes (Art. 28(4)(c)) threatens to further diminish the extent to which EU Member States participate in the value creation which the development of generative AI entails. As things are, European copyright law already risks to undermine the AIA’s provisions on product safety. Instead of focusing on practical problems, we should devote our attention to how we want to structure the societal change which generative AI will bring about, and on the role which we want copyright to play in this change.
On new protocols of Noncommutative Cryptography in terms of homomorphism of stable multivariate transformation groups

Noncommutative cryptography is based on applications of algebraic structures like noncommutative groups, semigroups, and noncommutative rings. Its intersection with Multivariate cryptography contains studies of cryptographic applications of subsemigroups and subgroups of affine Cremona semigroups defined over finite commutative rings. Efficiently computed homomorphisms between stable subsemigroups of affine Cremona semigroups can be used in tame homomorphisms protocols schemes and their inverse versions. The implementation scheme with the sequence of subgroups of affine Cremona group that defines the projective limit was already suggested. We present the implementation of another scheme that uses two projective limits which define two different infinite groups and the homomorphism between them. The security of the corresponding algorithm is based on complexity of the decomposition problem for an element of affine Cremona semigroup into a product of given generators. These algorithms may be used in postquantum technologies.
Explainable Image Similarity: Integrating Siamese Networks and Grad-CAM

With the proliferation of image-based applications in various domains, the need for accurate and interpretable image similarity measures has become increasingly critical. Existing image similarity models often lack transparency, making it challenging to understand the reasons why two images are considered similar. In this paper, we propose the concept of explainable image similarity, where the goal is the development of an approach, which is capable of providing similarity scores along with visual factual and counterfactual explanations. Along this line, we present a new framework, which integrates Siamese Networks and Grad-CAM for providing explainable image similarity and discuss the potential benefits and challenges of adopting this approach. In addition, we provide a comprehensive discussion about factual and counterfactual explanations provided by the proposed framework for assisting decision making. The proposed approach has the potential to enhance the interpretability, trustworthiness and user acceptance of image-based systems in real-world image similarity applications.
AFRE-Net: Adaptive Feature Representation Enhancement for Arbitrary Oriented Object Detection

Arbitrary-oriented object detection (AOOD) is a crucial task in aerial image analysis but is also faced with significant challenges. In current AOOD detectors, commonly used multi-scale feature fusion modules fall short in spatial and semantic information complement between scales. Additionally, fixed feature extraction structures are usually used following a fusion model, resulting in the inability of detectors to self-adjust. At the same time, feature fusion and extraction modules are designed in isolation and the internal synergy between them is ignored. The above problems result in feature representation deficiency, thus affecting the overall detection precision. To solve these problems, we first create a fine-grained feature pyramid network (FG-FPN) that not only provides richer spatial and semantic features, but also completes neighbor scale features in a self-learning mode. Subsequently, we propose a novel feature enhancement module (FEM) to fit FG-FPN. FEM authorizes the detection unit to automatically adjust the sensing area and adaptively suppress background interference, thereby generating stronger feature representations. Our proposed solution was tested through extensive experiments on challenging datasets, including DOTA (77.44% mAP), HRSC2016 (97.82% mAP), UCAS-AOD (91.34% mAP), as well as ICDAR2015 (86.27% F-score) and its effectiveness and high applicability are verified on all the above datasets.
A Pipeline for Constructing Reference Genomes for Large Cohort-Specific Metagenome Compression

Metagenomic data compression is very important as metagenomic projects are facing the challenges of larger data volumes per sample and more samples nowadays. Reference-based compression is a promising method to obtain a high compression ratio. However, existing microbial reference genome databases are not suitable to be directly used as references for compression due to their large size and redundancy, and different metagenomic cohorts often have various microbial compositions. We present a novel pipeline that generated simplified and tailored reference genomes for large metagenomic cohorts, enabling the reference-based compression of metagenomic data. We constructed customized reference genomes, ranging from 2.4 to 3.9 GB, for 29 real metagenomic datasets and evaluated their compression performance. Reference-based compression achieved an impressive compression ratio of over 20 for human whole-genome data and up to 33.8 for all samples, demonstrating a remarkable 4.5 times improvement than the standard Gzip compression. Our method provides new insights into reference-based metagenomic data compression and has a broad application potential for faster and cheaper data transfer, storage, and analysis.