Reviewerly/exHarmony huggingface dataset:
Pairwise contrastive learning for sentence semantic equivalence identification with limited supervision

Sentence semantic equivalence identification (SSEI) targets to measure the semantic equivalence between two sentences. To supplement limited supervision, existing methods extensively employ contrastive learning to obtain sentence semantics. Albeit considerable progress, traditional sentence-wise contrastive learning cannot grasp the diverse semantics in the polysemic sentence. To alleviate this multi-vocal issue, a Pairwise Contrastive learning method (named PairContrast) for SSEI is developed in this study to imitate the pairwise and intercompared scenarios. Specifically, two unlabelled sentences in any anchor pair are first augmented with an enhanced augmentation strategy to generate three augmented pairs. To reduce augmentation noise, a pair mix-up strategy is also employed to merge these augmented pairs into an anchor-positive pair, which is further combined with the anchor pair to pretrain the interaction module through contrastive learning. Finally, the pretrained SSEI model is finetuned on limited supervision by the binary cross entropy objective. Experiments on two publicly available SSEI datasets demonstrate the superiority of PairContrast against state-of-the-art baselines. The robustness of PairContrast under different scales of limited supervision is also verified.
Enhancing the radar system simulator tool suite to allow the simulation of sensor networks

The number of human-made objects orbiting the Earth is growing steadily. This trend is further accelerating during recent years due to increasing commercialization in the field of satellite operations and an increasing launch rate. Concerning the area of space surveillance and tracking (SST), this leads to higher demands with respect to object observation capabilities. In order to ensure the safe usability of the different Earth orbits for all spacecraft operators, it is of great importance to continuously observe and catalog as many of the human-made objects as possible. This is especially true for inactive and uncontrollable objects, i.e. space debris. Only by maintaining accurate catalogs of the space debris environment, involuntary collisions can be avoided and risks of losing a spacecraft kept low. While the number of space debris objects can only be expected to further increase, it is not trivial to increase the number of observation sites, such as telescopes and radars, or their capabilities to counter this. One way to increase the observation capabilities without constructing new observation sites is to search for possibilities to improve the efficiency with which the existing sensors observe objects. With the goal to study and analyze single sensors, sensor networks, and how they can be used in the most efficient way, a sensor network simulator tool suite has been developed at the Institute of Space Systems of the Technical University of Braunschweig. Originally, the tool suite was developed as a radar system simulator concentrating on the different radar system configurations, such as reflector antennas or phased arrays that are used in a mono- or bistatic mode. Additionally, the corresponding radar performance model is able to simulate a tracking as well as a scanning mode. Building upon this tool suite, it is now extended to include an optical performance model and the possibility to simulate not only ground-based but also space-based sensors. Within this work, the preliminary results obtained with the newly implemented optical performance model are shown. Using the enhanced version of this sensor network simulator tool suite, thorough analyses can now be made using the radar and optical performance models. Sensor networks consisting of radars and telescopes being both ground- or space-based can be studied in different configurations. Thereby, different aspects, such as observation strategies, sensor tasking, or choosing the right location and sensor to reach a certain goal can be investigated.
Ancient handwriting attribution via spectroscopic benchmarks and machine learning: ‘Clavis Prophetarum’ by Antonio Viera

Machine learning is a key branch of artificial intelligence that uses models to interpret data patterns. Herein, we demonstrate a novel approach that combines X-ray fluorescence and Fourier transform infrared spectroscopies as well as machine learning to provide insights into the genesis of an ancient manuscript by recognising different inks used for author attribution. The procedure was successfully applied to Clavis Prophetarum, a manuscript authored by Antonio Vieira (1608–1697), considered one of the ‘fathers’ of the Portuguese language. This original manuscript, previously considered lost, was recently discovered at the Historical Archive of the Pontifical Gregorian University in Rome. Unsupervised and supervised techniques such as principal component analysis and singular vector machine were used to distinguish between different inks made with similar recipes using spectroscopic benchmarks. On the one hand, sulphur and iron were identified as elemental benchmarks and on the other hand, Arabic gum, oak gall and iron gall precipitates were identified as the main ingredients via molecular fingerprints, which differ in their proportions. The methodology, which does not require data pre-processing, allows the handwriting (i.e., authorship) attribution of small signs and revisions, which cannot be attributed through standard methods. The supervised model enabled assigning 66% of the previously unassigned handwriting and strokes, identifying four main groups. These results, together with macroscopical observations and historical information, recognised Group 2 as the main handwriting assigned to the first author, Antonio Vieira. Group 1 was attributed to P. C. A. Casnedi, who performed the drafting and revisions in a well-defined period and introduced the first (most ancient) page numbering. Group 4 was attributed to P. A. Bonucci, the last collaborator of Antonio Vieira, who reorganised the numbering of quires, suggesting that this author was the last one who worked on the manuscript. A particular section of the book (Book 3, Chapter 1) was assigned to Group 3 and was attributed to an unknown author. This study is expected to have an impact in the field of authentication, with potential applications to ancient and modern manuscripts and handwriting analysis.
국방통합재정정보시스템의 품질 요인이 사용자 만족과 업무성과에 미치는 영향: 정보시스템 성공모델 관점으로

본 연구는 국가회계의 복식부기 발생주의 회계제도 도입으로 인한, 디지털예산회계시스템 구축에 따라 개발된 국방통합재정정보시스템을 DeLone & McLean의 정보시스템성공모델을 중심으로 분석하고자 한다. 다시 말해, 군에서 사용하는 국방통합재정정보시스템의 품질을 정보품질, 시스템품질, 서비스품질로 분류하고, 이를 토대로 사용자 만족이 업무 성과에 미치는 영향을 분석하고자 한다. 즉, 군에서 재정업무를 수행하는데 필요한 시스템의 품질과 사용자 만족도가 어떤 관계를 가지며, 이를 통해 업무 성과를 향상할 수 있는지를 알아보고자 한다. 본 연구는 공군에서 근무하는 재정 특기 장교, 부사관 및 재정처에서 근무하는 행정직렬의 군무원들을 대상으로 편의 추출법을 이용하여 연구대상을 모집하고 구조방정식을 이용하여 분석한다. 이에 따라 국방통합재정정보시스템의 품질요인 중 사용자 만족에 유의성을 가진 요인을 밝히고, 이러한 사용자 만족이 업무성과에 미치는 영향을 검증하여, 전략적 요소로 활용하고자 한다. 분석결과, 첫째, 정보품질은 사용자 만족에 유의하지 않았으며, 둘째, 시스템품질은 사용자 만족에 영향을 미쳤으며, 셋째, 서비스품질 역시 사용자 만족에 유의하였다. 넷째, 이러한 사용자 만족은 업무성과에 영향을 미치는 것을 확인하여, 시스템 품질과 더불어 서비스품질을 향상시켜 업무성과를 높이기 위한 적극적인 방안을 찾아야 할 것이다. 본 연구는 국방통합 재정정보시스템에 대한 실증 연구로서 그 연구의 가치가 있겠으며, 향후 고도화되는 국방통합재정정보시스템의 개선 및 발전 방향을 제시한다. This study evaluated the qualities of the National Defense Integrated Financial Information System (NDIFIS), which was introduced as the government has taken the double-entry book accounting system. It led to the establishment of the digital budget accounting system. This study conducted structural equation modeling using DeLone & McLean"s information system success model as a research setting. The model hypotheses that information quality, system quality, and service quality affect user satisfaction and that user satisfaction affects work performance. The research responses were recruited using a convenience sampling method. They have professional backgrounds in accounting, namely officers, non-commissioned officers, and civilian workers currently working in the department of accounting and finance in the Republic of Korea Air Force. The result shows that out of the three quality factors, only system quality and service quality had a significant effect on user satisfaction, which affected work performance. The findings imply that actions to enhance service quality and system quality will eventually improve work performance.
Is Service with a Smile Enough to Satisfy Customers? Sincere and Insincere Smiles via Video and Vignette

The current service trend does not merely involve face-to-face interactions, including smiling (high-contact-service) by service employees, but also using methods such as texting (low-contact service). Sincere and insincere smile studies have used a method involving dynamic (videos) and static stimuli (photographs). However, research into the different types of smiles is still limited to methods using a vignette. This study aims to examine the effect of the type of smile on customer satisfaction using a vignette and video. A total of 139 people participated in 2 (smile: sincere versus insincere) x 2 (media: vignette versus video) groups. The study found that there is an influence from the type of smile on customer satisfaction. A sincere smile using video produces greater customer satisfaction than using a vignette does, because the emotional contagion process through video is more real than it is through a vignette. The interesting finding from this study is that there is a gap in the insincere smile condition between using video and a vignette. The dissatisfaction of customers is greater when served with an insincere smile by text (vignette), compared with a face-to-face interaction by video. Video could only capture the muscle movement on a person’s cheeks (zygomaticus major), but it could not see the muscle wrinkles formed around the eyes (orbicularis oculi), compared to a vignette. This study implies that although the standard operational procedure (SOP) for service demands friendliness, a customer can feel whether a server’s smile is genuine or fake. The customer’s dissatisfaction toward an insincere smile will be higher with a low-contact service using text than a high-contact service with video.
Real Time System for Virtual Keyboard and Mouse

These days, PC vision has advanced to where a PC can perceive its client utilizing an essential picture handling calculation. Individuals are involving this vision in many pieces of day to day existence as of now of improvement, like face acknowledgment, variety identification, programmed vehicles, etc. PC vision is utilized in this examination to make an optical mouse and console that utilizations hand movements. The PC's camera will filter the picture of different developments made by an individual's hand, and the mouse or pointer will move in light of the development of the motions, including doing well and left clicks utilizing particular signals. Essentially, unique motions can be utilized to control the console, for example, a one-finger signal to pick a letters in order and a four figure signal to swipe left and right. Without any wires or different gadgets, it will work as a virtual mouse and console. The venture's just piece of equipment is a webcam, and the coding is finished in Python utilizing the Boa constrictor stage. The Raised body absconds are made first, and afterward a calculation is made by planning the mouse and console capabilities to the blemishes utilizing the imperfection computations. On the off chance that you map ping several them with the mouse and console, the PC will perceive the motion and answer fittingly
Comfortable brightness of watching television in the dark

This study exploits an optimal display brightness for watching television in the dark. Two experiments were carried out in a dark room, and static and moving images were tested in each experiment. In Experiment I, three different, full-screen color images were presented with brightness levels ranging from 20 to 400 nit. A total of 23 college students assessed each stimulus' degree of visual comfort. Experiment II was conducted with 31 participants, and six videos with a range of brightness from 20 to 500 nit were shown. As a result, when watching the static image, the participants' assessments were almost the same among stimuli. However, when watching a video, the characteristics of the stimuli made the evaluation vary. This study provides empirical evidence and estimations of visual comfort based on display luminance that should be considered when designing for more comfortable viewing in a dark environment.
Bounds for sets with few distances distinct modulo a prime ideal

Let 𝒪 K be the ring of integers of an algebraic number field K embedded into ℂ. Let X be a subset of the Euclidean space ℝ d , and D(X) be the set of the squared distances of two distinct points in X. In this paper, we prove that if D(X)⊂𝒪 K and there exist s values a 1 ,...,a s ∈𝒪 K distinct modulo a prime ideal 𝔭 of 𝒪 K such that each a i is not zero modulo 𝔭 and each element of D(X) is congruent to some a i , then |X|≤d+s s+d+s-1 s-1.
Deep learning‐based hybrid reconstruction algorithm for fibre instance segmentation from <scp>3D</scp> x‐ray tomographic images

Abstract 3D x‐ray tomography is a powerful scanning technique used for generating images of complex fibre structures. A novel machine‐learning algorithm to identify and separate individual fibres using 3D images is proposed in this article. The developed four‐step hybrid 3D fibre segmentation algorithm involves deep‐learning aided semantic segmentation that slices 3D images to create 2D images for fibre extraction, elliptical contour estimation combined with the marker‐controlled watershed algorithm for separating fibres from the background area, identifying individual fibres through 3D reconstruction, and, lastly, the 3D object refining approach based on outlier object detection and replacement. The proposed methodology is implemented on a real‐time sample of nylon fibre bundle under compression and its 3D x‐ray image volume to validate the performance. The results show its superior performance compared to off‐the‐shelf image processing algorithms in terms of precision, that is, with a validation accuracy greater than 90%, and efficiency, that is, preventing the need for a huge data set and reducing the complexity.
Track Detection Algorithm Based on Trace Correlation Using Hough Transform

Introduction . Track detection is one of the main tasks to be solved in trajectory processing. This task can be efficiently solved using the Hough Transform. A track is considered detected if the number of position measurements received in a number of consecutive radar scans and falling into the same cell of the parameter space (accumulator) has exceeded the detection threshold. However, the effective practical application of the Hough transform requires a sufficiently long time of measurement. Under a small number of scans given for track detection, measurements are also accumulated in those accumulator cells where their traces intersect. Therefore, in order to detect true tracks, additional processing is required to distinguish measurement clusters from different targets based on their geometric proximity. In addition, a large amount of memory and computational operations for the accumulator maintenance significantly increase the computation load of the trajectory processor. Aim . To design a simple and false-detection resilient algorithm for detecting tracks without the Hough accumulator in the processor memory. Materials and methods . In the proposed algorithm, the construction of measurement traces in the Hough accumulator followed by selection of cells with the largest number of traces passed through them is replaced by computation of the cross correlations of the traces and clustering of measurements based on the maximum similarity of their traces. Results . Mathematical simulation with the scenario parameters selected in the paper confirmed the accuracy of the proposed algorithm in detecting all tracks existing in the radar field of view and its efficiency in conducting error free association of target position measurements. Conclusion . A false-detection resilient algorithm for track detection was created based on the Hough transform. The algorithm does not require the Hough accumulator in the processor memory.
Relation between perceived health behavior and changed daily life of adolescents in the COVID-19 pandemic

본 연구는 제17차(2021년) 청소년건강행태조사 원시자료를 이용한 2차 자료 분석연구로 청소년의 인지된 건강행태가 코로나19로 인한 일상생활변화에 미치는 영향 요인을 확인하는 것이다. 수집된 자료의 분석은 SPSS/WIN 26.0 프로그램을 활용하여 빈도분석과 교차분석, 독립표본 t검정, 다중 로지스틱 회귀분석을 실시하였다. 그 결과 인지된 건강행태와 코로나19로 인한 일상생활변화와의 관계는 성별(p ≤ .001), 학교급(p ≤ .001), 주관적 건강상태(p ≤ .021), 주관적 스트레스인지(p ≤ .023), 주관적 수면 충족(p ≤ .007)이 신체활동, 음주, 흡연, 우울감에 통계적으로 유의하게 영향을 미치는 것으로 나타났다. 따라서 청소년의 코로나19로 인해 변화된 일상생활을 개선하기 위해 주관적 건강상태와 스트레스 관리 및 수면의 질 등을 고려해야 할 것이다. This is a secondary data analysis study using the raw data of the 2021 Youth Health Behavior Survey to identify the factors associated with perceived health behaviors due to alterations in the daily life of adolescents caused by COVID-19. Data were analyzed using frequency analysis, chi-square test, independent sample t-test, and multiple logistic regression analysis, and all analyses were performed using the SPSS/WIN 26.0 program. Our results showed that the changes in daily life due to COVID-19, such as physical activity, alcohol drinking, smoking, and depression, were statistically associated with gender (p ≤.001), school level (p ≤.001), subjective health status (p ≤.021), subjective stress perception (p ≤.023), and subjective sleep satisfaction (p ≤.007). This indicates the need to consider subjective health status, stress management, and sleep quality to help improve adolescents" health behaviors altered due to the COVID-19 pandemic.
End-to-end dialogue structure parsing on multi-floor dialogue based on multi-task learning

A multi-floor dialogue consists of multiple sets of dialogue participants, each conversing within their own floor. In the multi-floor dialogue, at least one multi-communicating member who is a participant of multiple floors and coordinates each to achieve a shared dialogue goal. The structure of such dialogues can be complex, involving intentional structure and relations that are within or across floors. In this study, We proposed a neural dialogue structure parser with an attention mechanism that applies multi-task learning to automatically identify the dialogue structure of multi-floor dialogues in a collaborative robot navigation domain. Furthermore, we propose to use dialogue response prediction as an auxiliary objective of the multi-floor dialogue structure parser to enhance the consistency of the multi-floor dialogue structure parsing. Our experimental results show that our proposed model improved the dialogue structure parsing performance more than conventional models in multi-floor dialogue.
LM2K Model for Hosting an Application Based on Microservices in Multi-Cloud

Cloud computing has become a popular delivery model service, offering several advantages. However, there are still challenges that need to be addressed when applying the cloud model to specific scenarios. Two of such challenges involve deploying and executing applications across multiple providers, each comprising several services with similar functionalities and different capabilities. Therefore, dealing with application distributions across various providers can be a complex task for a software architect due to the differing characteristics of the application components. Some works have proposed solutions to address the challenges discussed here, but most of them focus on service providers. To facilitate the decision-making process of software architects, we previously presented PacificClouds, an architecture for managing the deployment and execution of applications based on microservices and distributed in a multi-cloud environment. Therefore, in this work, we focus on the challenges of selecting multiple clouds for PacificClouds and choosing providers that best meet the microservices and software architect requirements. We propose a selection model and three approaches to address various scenarios. We evaluate the performance of the approaches and conduct a comparative analysis of them. The results demonstrate their feasibility regarding performance.
Laminar: Dataflow Programming for Serverless IoT Applications

Serverless computing has increased in popularity as a programming model for "Internet of Things" (IoT) applications that amalgamate IoT devices, edge-deployed computers and systems, and the cloud to interoperate. In this paper, we present Laminar - a dataflow program representation for distributed IoT application programming - and describe its implementation based on a network-transparent, event-driven, serverless computing infrastructure that uses append-only log storage to store all program state. We describe the initial implementation of Laminar, discuss some useful properties we obtained by leveraging log-based data structures and triggered computations of the underlying serverless runtime, and illustrate its performance and reliability characteristics using a set of benchmark applications.
Incremental Learning with Memory Regressors for Motion Prediction in Autonomous Racing

Cyber-physical systems (CPS) with learning-enabled components suffer from reduced performance under distribution shift. In this paper, we consider the problem of motion prediction within an autonomous racing setting. In such a setting, the ability to predict an adversaries' behavior is essential for safe and efficient planning. We propose a method using memories to detect anomalous input and incrementally learn a prediction model online, the ability to quickly adapt to unseen behaviors. In our experiments, we demonstrate the effectiveness of this approach in adapting to various motion prediction data collected in the F1Tenth-Gym environment, a simulator for autonomous racing. Our experiments show promising results, and achieve an improvement of 14% in mean squared error compared to a model without adaptation. In the future we would like to extend this to a more extensive evaluation of the ability of this core component to predict and act in an online racing setting.
Self-Preserving Genetic Algorithms for Safe Learning in Discrete Action Spaces

Self-Preserving Genetic Algorithms (SPGA) combine the evolutionary strategy of a genetic algorithm with safety assurance methods commonly implemented in safe reinforcement learning (SRL), a branch of reinforcement learning (RL) that accounts for safety in the exploration and decision-making process of the agent. Safe learning approaches are especially important in safety-critical environments, where failure to account for the safety of the controlled system could result in the loss of millions of dollars in hardware or bodily harm to people working nearby, as is true of many cyber-physical systems. While SRL is a viable approach to safe learning, there are many challenges that must be taken into consideration when training agents, such as sample efficiency, stability, and exploration---an issue that is easily addressed by the evolutionary strategy of a genetic algorithm. By combining GAs with the safety mechanisms used with SRL, SPGA offers a safe learning alternative that is able to explore large areas of the solution space, addressing SRL's challenge of exploration. This work implements SPGA with both action masking and run time assurance safety strategies to evolve safe controllers for three types of discrete action space environments applicable to cyber physical systems (control, routing, and operations) and under various safety conditions. Training and testing evaluation metrics are compared with results from SRL trained controllers to validate results. SPGA and SRL controllers are trained across 5 random seeds and evaluated on 500 episodes to calculate average wall time to train, average expected return, and percentage of safe action evaluation metrics. SPGA achieves comparable reward and safety performance results with significantly improved training efficiency (55x faster on average), demonstrating the effectiveness of this safe learning approach.