Reviewerly/exHarmony huggingface dataset:
Verifying IMU Suitability for Recognition of Freshwater Mussel Behaviors

Freshwater mussels are both an important and threatened part of our ecosystem. They are extremely efficient in cleaning natural water resources, yet surprisingly little is known about the behaviors of these animals, making threat mitigation difficult. As activity recognition based on the use of accelerometers is known to be effective in humans and other animals, this work applies such methods to the study of freshwater mussels in order to aid further scientific study. In this paper, we present results from a laboratory experiment where three inertial measurement units (IMU) were attached to the shell of mussels for approximately 24 to 48 hours. Over this period, we observed several interesting behaviors and verified the sensor arrangement can differentiate active and inactive periods as well as quantify gape frequency, duration, and intensity. Our future work is to develop a complete mussel sensor network where multiple mussels can be monitored in situ for an extended period of time. By better understanding the behavior of these animals, we aim to develop new ways to quantify ecosystem health and more effective conservation strategies.
A Study on Demand Characteristics of Public Libraries: Case of Public Libraries in Seoul

This study attempted to identify variables that affect public library demand. The analysis are was limited to Seoul in consideration of locational conditions. In this study, the dependent variable, library demand, was largely divided into the number of library visitors per year, the number of books borrowed per year, and the number of participants in cultural programs per year to examine the factors affecting each demand. According to the analysis results, as the size of facilities provided by libraries increases, the demand for libraries increases elastically. However, as the number of cultural program courses increases, the number of cultural program participants increases elastically. As the number of public libraries and public sports facilities in the region increases, the demand for libraries decreases, indicating that there is an alternative relationship with the facilities. However, fluctuations in demand were found to be inelastic. On the other hand, in the case of the population in the region, the magnitude of elasticity was relatively large compared to other variables, so it was found that a review related to this was essential in relation to the establishment of a public library in the future in a situation where the population was continuously declining.
The Effects of Test Anxiety and Social Anxiety on Eating Attitudes among Nursing Students

The purpose of this study was to identify the status of test anxiety, social anxiety, and eating attitudes among nursing students and the factors that affect their eating attitudes. The study’s subjects were 160 nursing students in grades 1 to 3 located in Gyeonggi-do, and data collection was conducted from May 27 to June 14, 2022. The collected data were subjected to t-test, ANOVA, Pearson's correlation coefficient, and multiple regression analysis. Eating attitudes showed significant differences according to gender(t=-2.31, p=.022), BMI(F=5.67, p=.004), caffeine intake(t=-3.46, p&lt;.001), and academic satisfaction(F=4.23, p=.016), and were significantly correlated with test anxiety(r=.285, p&lt;.001) and social anxiety(r=.266, p&lt;.001). The predictors of eating attitudes of nursing students were gender (β=.23, p=.002), BMI (18.5kg/m2∼22.9kg/m2, β=.34, p=.001; ≥23kg/m2, β=.43, p&lt;.001), caffeine intake (β=.23, p=.002), test anxiety (β=.18, p=.026), and social anxiety (=β.16, p=.049) and the model’s explanatory power was 26.6%. Based on this study’s results, it is necessary to take a multilateral approach at the school and community levels to form a healthy body image for female nursing students. Furthermore, the development of a psychological support program for nursing students and its application in the educational field should be considered to reduce nursing students’ test anxiety and social anxiety.
Blockchain-Based Smart Integrated Healthcare System

The goal of this research is to implement a smart healthcare application based upon blockchain technology, to construct a theoretical model that is hierarchical for smart healthcare, and to build a smart healthcare development software system based on stakeholder theory. Currently, electronic health records allow for just the automatic updating and exchange of medical data on a single patient inside a single organization or network of organizations. This could be extended if the data was organized in a way such that the blockchain's uppermost layer only contained non-PHI or personally identifiable data (PII). Decentralization, anonymity, tamper resistance, and auditability are all properties of blockchain. It is a key fourth industrial revolution technology. The combination of blockchain and smart healthcare can help to alleviate traditional smart healthcare's pain points in information sharing, data protection, and privacy preservation, as well as optimize user-centered smart healthcare systems and establish a multiparty medical cooperation chain involving government, businesses, and individuals, all of which can imperialize smart healthcare.
Evaluation of Approaches for Documentation in Continuous Software Development

With the adoption of values, principles, practices, tools and processes from Agile, Lean, and DevOps, knowledge preservation has become a serious issue because documentation is largely left out.We identify two questions that are relevant for knowledge acquisition and distribution concerning design decisions, rationales, or reasons for code change.The first concerns which knowledge is required upfront to start a project.The second question concerns continuation after initial development and addresses which knowledge is required by those who deploy, use or maintain a software product.We evaluate two relevant approaches for alleviating the issues, which are 'Just enough Upfront' and 'Executable Documentation' with a total of 25 related artifacts.For the evaluation, we conducted a case study supported by a literature review, organizational and project metrics, and a survey.We looked into closed source-code and closed classified source-code.We found two conclusive remarks.First, git commit messages typically contain what has been changed but not why source-code has been changed.Design decisions, rationale, or reasons for code change should be saved as close as possible to the source-code with Git Pull Requests.Second, knowledge about a software product is not only written down in artifacts but is also a social construction between team members.
LED: Lexicon-Enlightened Dense Retriever for Large-Scale Retrieval

Retrieval models based on dense representations in semantic space have become an indispensable branch for first-stage retrieval. These retrievers benefit from surging advances in representation learning towards compressive global sequence-level embeddings. However, they are prone to overlook local salient phrases and entity mentions in texts, which usually play pivot roles in first-stage retrieval. To mitigate this weakness, we propose to make a dense retriever align a well-performing lexicon-aware representation model. The alignment is achieved by weakened knowledge distillations to enlighten the retriever via two aspects – 1) a lexicon-augmented contrastive objective to challenge the dense encoder and 2) a pair-wise rank-consistent regularization to make the dense model’s behavior incline to the other. We evaluate our model on three public benchmarks, which shows that with a comparable lexicon-aware retriever as the teacher, our proposed dense one can bring consistent and significant improvements, and even outdo its teacher. In addition, we show our lexicon-aware distillation strategies are compatible with the standard ranker distillation, which can further lift state-of-the-art performance.1
Bad Apples: Understanding the Centralized Security Risks in Decentralized Ecosystems

The blockchain-powered decentralized applications and systems have been widely deployed in recent years. The decentralization feature promises users anonymity, security, and non-censorship, which is especially welcomed in the areas of decentralized finance and digital assets. From the perspective of most common users, a decentralized ecosystem means every service follows the principle of decentralization. However, we find that the services in a decentralized ecosystem still may contain centralized components or scenarios, like third-party SDKs and privileged operations, which violate the promise of decentralization and may cause a series of centralized security risks. In this work, we systematically study the centralized security risks existing in decentralized ecosystems. Specifically, we identify seven centralized security risks in the deployment of two typical decentralized services – crypto wallets and DApps, such as anonymity loss and overpowered owner. Also, to measure these risks in the wild, we designed an automated detection tool called Naga and carried out large-scale experiments. Based on the measurement of 28 Ethereum crypto wallets (Android version) and 110,506 on-chain smart contracts, the result shows that the centralized security risks are widespread. Up to 96.4% of wallets and 83.5% of contracts exist at least one security risk, including 260 well-known tokens with a total market cap of over $98 billion.
Research Achievements of Korean Ancient History in the 21st Century and Expansion of Research Horizons : New, Detailed, and Crossing Boundaries

A major feature of Korean ancient history research since 2000 is the quantitative increase in research papers, like other Korean history research fields. The reasons are the increase in researchers and academic journals, the development of computer devices and digital media, the establishment of DBs for research data, quantitative research evaluation systems, large research projects of various institutions, and political and diplomatic factors. In general, the proportion of research trends is still high in political history, foreign relations history, and cultural history, but micro-historical research has been increasing rather than large discourse since the 1990s.BR This paper summarizes the research results of Korean ancient history in two aspects, focusing on doctorate thesis and books after the 2000s. The first is the study on the historical materials. It summarized the reassessment on historical documents, research on editions of historical documents, new readings or re-examination of epigraph materials, and research on newly excavated wooden tablets and memorial inscriptions. The second is the expansion and diversification of existing research topics. It summarized the studies on laws, administrative systems, national rituals, ritual system, and capital city focusing on institutional history. In particular, in recent topics that have attracted attention, the need for comparison with the cases of China and Japan or interdisciplinary research has increased.BR Since the 2000s, research results have been accumulated to discover new historical materials or review basic historical materials. On the other hand, according to changes in society, research areas were diversified and new methodologies were developed, and research topics were subdivided and specialized accordingly.BR Nevertheless, there is still a direction to emphasize or move forward. The first is to comprehensively review historical materials by perspectives and methods not only in historical studies but also in various fields on the premise of empirical analysis of literature and archaeological data. On the basis of such research, we can expect the establishment of
Privacy-Preserving Dynamic Multi-Keyword Ranked Search Scheme in Multi-User Settings

With the rapid development of consumer electronics in Industry 5.0, personalized service supplement based on the cloud infrastructure for the Internet of Things (IoT) has been a promising requirement pushed to consumers. During the massive and frequent IoT data interaction from the consumer-centric in Industry 5.0, efficiently searchable encryption for multiple consumers are indispensable. Existing multi-user searchable encryption is based on attribute and predicate encryption technology to realize one-to-many rather than realistic many-to-many scenarios. Moreover, multi-keyword ranked search, dynamic update, and search pattern hiding have not been implemented in the multi-user scenario. In this paper, to address the above problems for consumer electronics in Industry 5.0, we present a privacy-preserving dynamic multi-keyword ranked search scheme over encrypted cloud data to accomplish pay-as-you-consume cloud data security sharing. Our scheme designs a specific tree-based index structure and a “Greedy Breadth-First Search” algorithm to achieve the sub-linear search. To support dynamic updates in the cloud, a novel secure maximum generation protocol is proposed. Finally, the security analysis and experiment evaluation prove our scheme cannot only preserve the privacy of index and search patterns but also support dynamic update operations under the multi-writer/multi-reader setting at an acceptable cost.
Experimentally characterizing multiparticle entanglement based on measuring quantum Fisher information

Quantum Fisher information plays a vital role in the field of quantum metrology and quantum information, because it not only quantifies the ultimate precision bound of parameter estimation but also provides criteria for entanglement detection. Nevertheless, experimentally extracting quantum Fisher information is intractable. Quantum state tomography is a typical approach to obtaining the complete information about a quantum system and extract quantum Fisher information. However it becomes infeasible for large-scale quantum systems owing to the exponentially growing complexity. In this paper, we present a general relationship between quantum Fisher information and the overlap of quantum states. Specifically, we show that for pure states, the quantum Fisher information can be exactly extracted from the overlap, whereas for mixed states, only the lower bound can be obtained. We also develop a protocol for measuring the overlap of quantum states, which only requires one additional auxiliary qubit and a single measurement for pure state. Our protocol is more efficient and scalable than previous approaches because it requires less time and fewer measurements. We use this protocol to characterize the multiparticle entanglement in a three-body interaction system undergoing adiabatic quantum phase transition, and experimentally demonstrate its feasibility for the first time in a nuclear magnetic resonance quantum system. We conduct our experiment on a 4-qubit nuclear magnetic resonance quantum simulator, three of which are used to simulate the quantum phase transition in a three-body interaction system, and the remaining one is used as the auxiliary qubit to detect the overlap of the quantum state. We use gradient ascent pulse engineering pulses to implement the process of evolution. By measuring the auxiliary qubit, the experimental results of quantum Fisher information are obtained and match well with the theoretical predictions, thus successfully characterizing the multiparticle entanglement in a practical quantum system. We further confirm our results by performing quantum state tomography on some quantum states in the adiabatic process. The experimentally reconstructed quantum states are close to the corresponding instantaneous ground states.
Study on urinary erythocyte images classification based on supervised comparative learning

The number of chronic kidney disease in China has shown a rapid upward trend. The morphological analysis of urinary erythrocyte is the key to diagnose various types of chronic kidney disease. Therefore, this paper studies the classification method of urinary erythrocyte based on supervised contrastive learning. Aiming at the low spatial resolution of urine erythrocyte and the difficulty in feature extraction, this paper proposes a Small Resolution Residual Network (SRRN) structure model based on ResNet-50 model. A multi-contrastive loss function is proposed for the singularity of feature similarity measurement in supervised contrastive learning. Based on supervised contrastive loss function, a feature similarity measurement method based on Euclidean distance is added. Aiming at the low accuracy and recall rate of some categories in urine red blood cell classification, this paper introduces a weight balance mechanism in cross entropy loss and sets higher loss weights for more difficult categories. The accuracy of this method and ResNet-50 network model on the urinary erythrocyte dataset is 92.26% and 90.7% respectively, which shows the effectiveness of this method on urinary red blood cell recognition.
Chinese named entity recognition incorporating syntactic information

At present, Chinese named entity recognition techniques incorporating lexical knowledge have gained good development on graph neural networks. However, since there is still a deficiency in graph neural networks for longer distance semantic information and its pointing and localization information. In this task, we try to extract the missing pointing and locating information by employing relative position encoding techniques; it deals with long-distance dependencies by obtaining lexical information through dependency parsing. It is also able to deal more effectively with long-distribution relations and missing data in terms of direction and location through interactions with words in relative location coding. The experimental results on three NER datasets show that the proposed model is improved compared with other governor models.
Fact relation and keywords fusion abstractive summarization

With the wide application of deep learning, the abstractive text summary has become an important research topic in natural language processing. The abstractive text summary has high flexibility and can generate words that have not appeared in the text. However, the generated summary model will have factual errors, which significantly affect the usability of the summary. Therefore, this paper proposes a text summary model based on fact relationships and keyword fusion. We extract the fact relation triplet in the input text and automatically extract the keywords in the text to assist in the generation of the abstract. The fusion of fact relations and keywords can effectively alleviate the problem of factual errors in the abstract. Many experiments show that compared with other baseline models, our model (FRKFS) improves the performance of summaries generated on the data sets CNN/Daily Mail and XSum and alleviates the problem of factual errors.
Improving conditions for recording human gaze data

In artificial intelligence, machines first need to be trained before they can work. Most of the time, they are trained on human data, such as human-made labels, or human physiological data. Data quality from humans becomes an important factor for machine learning. In the field of machine vision, machines usually learn human intentions through human gaze data, which is typically collected using video-based eye movement recording devices. Gaze recording accuracy can vary with pupil size, pupil size can be manipulated by screen background luminance. Looking at the variability of pupil size over various luminance levels may provide insight into possible ways to improve data quality. Aiming at improving the quality from human gaze data, we measured human pupil size changes during gaze at different luminance levels using a video-based eye tracker. Results show that mean pupil size gradually decreases, but is most variable at medium luminance levels, the maximum coefficient of variation difference reaches 7.5%. Therefore, gaze data accuracy may be improved if recorded at low or high luminance levels.
A multi-scale sarcasm sentiment recognition algorithm incorporating sentence hierarchical representation

Sarcasm is a special kind of linguistic sentiment that is widely used in a wide range of social media to express strong emotions in users. Therefore, the task of sarcasm recognition is particularly important for social media analysis. There are few studies on sarcasm sentiment recognition in Chinese, and they often ignore the complex interactions between different syntactic components of a sentence, such as sentiment words, entities, dummy words, and special punctuation that occur in the text. In order to improve the accuracy of Chinese sarcasm recognition, this paper proposes a multi-scale neural network sarcasm recognition algorithm incorporating a hierarchical representation of sentences, taking into account the semantic information of sentences and the relationship features between different syntactic components. The hierarchical syntactic tree is reconstructed to distinguish the key components of the sentence, and the multi-channel convolutional network is used to mine the relational features between syntactic levels and deeply fuse them with semantic information to perform the Chinese sarcastic sentiment recognition task. We have tested the method on a publicly available Chinese sarcastic comment dataset, and the results show that the method can effectively improve the accuracy rate of Chinese sarcastic sentiment recognition.
Entity embedding and relational path on small samples for knowledge graph completion

In the modeling work of Knowledge Graph Completion (KGC), we propose a KGC model considering both entity embeddings and relational paths for small sample data. The relational path information between entities can identify the relative positions of two entities in the knowledge graph, and if the distance is too far from there is no need for link prediction with high probability, so limiting the number of hopes of relational path can reduce the cost of link prediction. There are many link prediction models that only consider relations, but such models are not suitable for small sample datasets because there are too few types of relations in small datasets, and considering only relations is not a good way to characterize entities, so we added entity embeddings to consider relational paths, aggregated entity neighborhoods and relational neighborhoods around entities to target entities, and finally combined entity embeddings with relational paths to perform link prediction tasks. We have tested on three small sample datasets and achieved remarkable results.
Design and implementation of patient monitoring system based on Java

This design first carries on the detailed background analysis to the patient monitoring system and explains the main technology and technical difficulties of the system development. After a deeper analysis of the patient monitoring system, the use of Java as the most important development tool, mainly applied to the development and network retrieval, MySQL as the database of patient monitoring, JSP through the web page for data interaction, complete data access, and then create a web page dynamically. After the preliminary design of the patient monitoring system, the software debugging is carried out to ensure the safety and efficiency of the system. The results show that patients and doctors can use the system to find the information they want.
A method of face mosaic for the target person

For the existing face mosaic scheme, all faces in the picture are coded, which destroys the face information of non-target people in the picture, which is not conducive to the subsequent research of the character information in the picture. This paper proposes a face mosaic processing scheme combining retainface and facenet, which realizes face detection with faster speed and high accuracy, can quickly perform mosaic processing, and uses the conditional judgment function to make the program perform face mosaic processing on and only on the target person.