Reviewerly/exHarmony huggingface dataset:
Visualization of Patient-Generated Health Data: A Scoping Review of Dashboard Designs

Abstract Background Patient-centered clinical decision support (PC CDS) aims to assist with tailoring decisions to an individual patient's needs. Patient-generated health data (PGHD), including physiologic measurements captured frequently by automated devices, provide important information for PC CDS. The volume and availability of such PGHD is increasing, but how PGHD should be presented to clinicians to best aid decision-making is unclear. Objectives Identify best practices in visualizations of physiologic PGHD, for designing a software application as a PC CDS tool. Methods We performed a scoping review of studies of PGHD dashboards that involved clinician users in design or evaluations. We included only studies that used physiologic PGHD from single patients for usage in decision-making. Results We screened 468 titles and abstracts, 63 full-text papers, and identified 15 articles to include in our review. Some research primarily sought user input on PGHD presentation; other studies garnered feedback only as a side effort for other objectives (e.g., integration with electronic health records [EHRs]). Development efforts were often in the domains of chronic diseases and collected a mix of physiologic parameters (e.g., blood pressure and heart rate) and activity data. Users' preferences were for data to be presented with statistical summaries and clinical interpretations, alongside other non-PGHD data. Recurrent themes indicated that users desire longitudinal data display, aggregation of multiple data types on the same screen, actionability, and customization. Speed, simplicity, and availability of data for other purposes (e.g., documentation) were key to dashboard adoption. Evaluations were favorable for visualizations using common graphing or table formats, although best practices for implementation have not yet been established. Conclusion Although the literature identified common themes on data display, measures, and usability, more research is needed as PGHD usage grows. Ensuring that care is tailored to individual needs will be important in future development of clinical decision support.
MDGAN-TPAMDPN-Net: A Self-Sufficient and Calibrated Network for Radar Emitter Identification

This paper develops the MDGAN-TPAMDPN-Net for radar emitter identification. (1) The number of training samples in the database is limited in real world, which leads to the inability to effectively support the training of radar emitter identification model. Thus, generative adversarial network (GAN) is designed to generate new samples to expand the training dataset. (2) Since GAN is prone to generate samples with single pattern, resulting in the poor generalization of the radar emitter identification model. Herein maximum diversity generative adversarial network (MDGAN) is proposed to increase the sample diversity. (3) There are some defects in traditional manual feature selection, such as low timeliness and low discrimination, so dual path network (DPN) is utilized to automatically extract and identify features. (4) In the process of feature extraction, the features may loss due to the deep layer of DPN, so we propose to add an attention module, triple path attention module (TPAM), to calibrate and resample the radar emitter feature maps, and embed it into the DPN to form TPAMDPN. (5) Finally, MDGAN and TPAMDPN are defined as the generative model and discriminative model, respectively to form a complete radar emitter identification network: MDGAN-TPAMDPN-Net. The results show that MDGAN-TPAMDPN-Net can self-generate samples to meet the training requirements under the condition of limited sample number, and improve the accuracy through feature calibration resampling.
Masked face reconstruction and recognition through principle components analysis

Due to the Covid-19 virus infecting humans since 2019, the traditional methods of face recognition are not suitable anymore, for the reason that lots of countries and regions request people to wear masks in public places, whereas the masks will add additional noise in the recognition process. To solve this problem, we suggest a new method named masked face recognition, whose principle is first converting the masked face image to an unmasked reconstruction image by applying Principle Components Analysis. We call it the Reconstruction Process; then using Convolutional Neural Network to exert face recognition on unmasked reconstruction images, we call it the Recognition Process. During the Reconstruction Process, we first detect the unmasked area, then linearly fit the unmasked area with Eigenfaces generated from the PCA process and a set of coefficients, which are the objectives we optimize. After the optimization, we are able to obtain the reconstruction image by the linear combination of the Eigenface with its corresponding coefficient. Experimental results clearly show the high effectiveness of our method, and the accuracy of being recognized correctly is increased from about 80% if we use the original masked images to 96.36% if we use the unmasked reconstruction images.
Combining Linear Classifiers Using Score Function Based on Distance to Decision Boundary

In this work, we addressed the issue of combining linear classifiers in the geometrical space. In other words, it means that linear classifiers are combined via the combination of their decision hyperplanes. For this purpose, an approach based on the potential functions is proposed. The potential function spans a potential field around each decision plane. The potential fields coming from decision planes are superposed, and the resultant decision field is used as an aggregated model of the ensemble classifier. During the experimental study, the proposed approach was applied to an ensemble built on heterogeneous base classifiers, and it was compared to two reference methods – majority voting and soft voting, respectively. The result shows that the proposed method can improve classification performance metrics compared to soft voting.
Optimization of Alpha-Beta pruning based on heuristic algorithm

Games have become an important place for testing Artificial Intelligence (AI). Minimax and Alpha-Beta Pruning are two common and basic algorithms implemented in game AIs. However, there are still limitations of the searching time and searching depth. This paper strives to improve the game AI with a Heuristic Algorithm to optimize both the searching time and depth. The experiment consists of three AIs built with Minimax, Alpha-Beta Pruning, and Heuristic Algorithm to evident the improvement. These AIs are built to play a traditional Chinese zero-sum game, Gobang, which can be seen as an enhanced version of tic-tac-toe but more advanced. The data is collected when AIs compete with each other. Comparing the search time of three AIs, there is significant improvement of AI implemented Heuristic Algorithm; the median search time for Heuristic AI is only half of the Alpha-Beta Pruning AI, and only quarter of the Minimax AI. Moreover, because the search time decreases, the searching depth of the Heuristic AI can also be increased. With the larger searching depth, the Heuristic AI also gains a higher winning rate against the other two AIs.
Maze solving problem using q-learning

In the recent years, a number of research initiatives have employed q-learning. Because of its straightforward logic of assigning a corresponding action to each potential state, it is the most widely used reinforcement learning technique. In our research, we found a way to expedite the training process for the agent. A simple environment was used, a frozen lake. The goal for the agent is to reach the destination by avoiding obstacles after a series of training. A reasonable example was showed in this research. The basic environment and agent were created in Python, and the basic form of Q-learning was utilized. We implemented a q-learning algorithm to solve a 4x4 frozen lake and a complex 8x8 frozen lake. The results showed that the training process takes a long time and is different in more complex environments. We assume there is an exploration-exploitation tradeoff that can speed up the training process. In this way, we define a new parameter, epsilon, which is used to balance the agent during the training process. Also, among methods of exploration-exploitation tradeoff, exponential decay performs better than linear decay.
The effect of daycare centers' creative capabilities on teachers' creative teaching and the mediating effects of ideation

본 연구의 목적은 어린이집 교사를 대상으로 어린이집의 창의적 역량과 어린이집 교사의 교수창의성 그리고 교사창의성 간의 상관을 살펴보고, 이들 변인들 간의 영향력을 살펴보고자 한다. 서울과 경기, 충청, 강원, 경상, 전라, 제주 지역에 소재한 국공립, 직장, 민간, 가정, 법인어린이집 교사를 대상으로 설문하여 총 823명의 답변을 분석하였다. 본 연구에서 수집된 자료는 SPSS 프로그램을 사용하여 분석하였다. 본 연구의 결과 어린이집의 창의적 역량은 교수창의성 및 교사창의성과 정적 관계가 있으며, 어린이집의 창의적 역량은 교수창의성 정적인 영향을 주며, 어린이집의 창의적 역량이 교수창의성 간의 관계에서 교사창의성(아이디어내기)이 부분매개를 규명 하였다. 이러한 연구 결과는 어린이집의 창의적 역량이 어린이집 교사의 내적 변인인 교수창의성에 기여할 수 있음을 나타내므로, 교사가 자율성과 다양성을 갖고 근무할 수 있는 환경 조성과 교사 개개인의 교사창의성을 높일 수 있는 여러 지원이 이루어져야 함을 시사한다.The purpose of this study is to examine the correlation between daycare centers' creative capabilities, teachers' teaching creativity, and teacher creativity, and to examine the influence between these variables. A total of 823 teachers were surveyed at national, public, workplace, private, home, and corporate daycare centers located in Seoul, Gyeonggi, Chungcheong, Gangwon, Gyeongsang, Jeolla, and Jeju. The data collected in this study were analyzed using the SPSS program. As a result of this study, daycare centers' creative capabilities have a static relationship with teaching creativity and teacher creativity, daycare centers' creative capabilities have a positive effect on teaching creativity, and teacher creativity (ideation) has partially mediated the relationship between teaching creativity. These findings indicate that daycare centers' creative capabilities can contribute to teachers' teaching creativity, suggesting that various support should be provided to create an environment in which teachers can work with autonomy and diversity.
Initiative transfer in conversational recommender systems

Conversational recommender systems (CRS) are increasingly designed to offer mixed-initiative dialogs in which the user and the system can take turns in starting a communicative exchange, for example, by asking questions or stating preferences. However, whether and when users make use of the mixed-initiative capabilities in a CRS and which factors influence their behavior is as yet not well understood. We report an online study investigating user interaction behavior, especially the transfer of initiative between user and system in a real-time online CRS. We assessed the impact of dialog initiative at system start as well as of several psychological user characteristics that may influence their preference for either initiative mode. To collect interaction data, we implemented a chatbot in the domain of smartphones. Two groups of participants on Prolific (total n=143) used the system which started either with a system-initiated or user-initiated dialog. In addition to interaction data, we measured several psychological factors as well as users’ subjective assessment of the system through questionnaires. We found that: 1. Most users tended to take over the initiative from the system or stay in user-initiated mode when this mode was offered initially. 2. Starting the dialog in user-initiated mode CRS led to fewer interactions needed for selecting a product than in system-initiated mode. 3. The user’s initiative transfer was mainly affected by their personal interaction preferences (especially initiative preference). 4. The initial mode of the mixed-initiative CRS did not affect the user experience, but the occurrence of initiative transfers in the dialog negatively affected the degree of user interest and excitement. The results can inform the design and potential personalization of CRS.
Design of Fitness Movement Detection and Counting System Based on MediaPipe

With the rapid advancements in artificial intelligence and computer vision technology, the field of visual-based human pose detection has emerged as a highly sought-after research area in recent years. The identification of human poses has practical applications in diverse domains, ranging from motion-sensing games for human-computer interaction to activity prediction and medical rehabilitation. The present study is focused on the utilization of human pose detection for fitness movement counting. The ultimate aim of the system design is to accurately detect the skeletal key points of each body part in the image and subsequently connect them to form a human pose skeleton, which serves as a vital representation of the characteristics of human motion, particularly in the context of video data, where multiple human poses can be linked to form a certain movement trajectory. By judging the trajectory and angle changes, the system can determine whether people's fitness movements are correct and help them improve their fitness effectiveness. Hence, an increasing number of researchers are investing time and effort in this field. One common approach for human pose detection is OpenPose, but this model has a large and complex structure and low detection accuracy. Therefore, this fitness movement detection and counting system uses a lightweight MediaPipe model and improves it to enhance the algorithm's accuracy and recognition speed. The specific work in this paper includes three main points: (1) a suitable network structure to detect human skeletal points; (2) the appropriate skeletal structure for fitness movements through experiments to obtain accurate results; and (3) a Qt interface for human-computer interaction.
Towards Companion Recommenders Assisting Users’ Long-Term Journeys

Share on Towards Companion Recommenders Assisting Users’ Long-Term Journeys Authors: Konstantina Christakopoulou Google DeepMind, Google, USA Google DeepMind, Google, USA 0000-0002-1650-1796View Profile , Minmin Chen Google DeepMind, Google, USA Google DeepMind, Google, USA 0000-0002-7342-9022View Profile Authors Info & Claims RecSys '23: Proceedings of the 17th ACM Conference on Recommender SystemsSeptember 2023Pages 1039–1041https://doi.org/10.1145/3604915.3610241Published:14 September 2023Publication History 0citation175DownloadsMetricsTotal Citations0Total Downloads175Last 12 Months175Last 6 weeks175 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Efficient Data Representation Learning in Google-scale Systems

"Garbage in, Garbage out" is a familiar maxim to ML practitioners and researchers, because the quality of a learned data representation is highly crucial to the quality of any ML model that consumes it as an input. To handle systems that serve billions of users at millions of queries per second (QPS), we need representation learning algorithms with significantly improved efficiency. At Google, we have dedicated thousands of iterations to develop a set of powerful techniques that efficiently learn high quality data representations. We have thoroughly validated these methods through offline evaluation, online A/B testing, and deployed these in over 50 models across major Google products. In this paper, we consider a generalized data representation learning problem that allows us to identify feature embeddings and crosses as common challenges. We propose two solutions, including: 1. Multi-size Unified Embedding to learn high-quality embeddings; and 2. Deep Cross Network V2 for learning effective feature crosses. We discuss the practical challenges we encountered and solutions we developed during deployment to production systems, compare with SOTA methods, and report offline and online experimental results. This work sheds light on the challenges and opportunities for developing next-gen algorithms for web-scale systems.
Demystifying Recommender Systems: A Multi-faceted Examination of Explanation Generation, Impact, and Perception

extended-abstract Share on Demystifying Recommender Systems: A Multi-faceted Examination of Explanation Generation, Impact, and Perception Author: Giacomo Balloccu Department of Mathematics and Informatics, University of Cagliari, Italy Department of Mathematics and Informatics, University of Cagliari, Italy 0000-0002-6857-7709View Profile Authors Info & Claims RecSys '23: Proceedings of the 17th ACM Conference on Recommender SystemsSeptember 2023Pages 1361–1363https://doi.org/10.1145/3604915.3608887Published:14 September 2023Publication History 0citation60DownloadsMetricsTotal Citations0Total Downloads60Last 12 Months60Last 6 weeks60 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Identifying Controversial Pairs in Item-to-Item Recommendations

Recommendation systems in large-scale online marketplaces are essential to aiding users in discovering new content. However, state-of-the-art systems for item-to-item recommendation tasks are often based on a shallow level of contextual relevance, which can make the system insufficient for tasks where item relationships are more nuanced. Contextually relevant item pairs can sometimes have problematic relationships that are confusing or even controversial to end users, and they could degrade user experiences and brand perception when recommended to users. For example, the recommendation of a book about one sports team to someone reading a book about that team’s biggest rival could be a bad experience, despite the presumed similarities of the books. In this paper, we propose a classifier to identify and prevent such problematic item-to-item recommendations and to enhance overall user experiences. The proposed approach utilizes active learning to sample hard examples effectively across sensitive item categories and employs human raters for data labeling. We also perform offline experiments to demonstrate the efficacy of this system for identifying and filtering problematic recommendations while maintaining recommendation quality.
Detecting Anomalies: A Case Study in Predictive Maintenance of Telescopes

A predictive maintenance (PdM) approach is considered among the best solution for the maintenance, as it introduces a lot of advantages among which there is the increase of reliability performances, the saving in maintenance and inventory holding costs. Although several techniques are available for predictive maintenance, those based on data science feature a widespread use; the reasons are mainly due to the availability of huge sets of telemetric data from the system to which maintenance must be applied, coming from Internet of Things – based sensors. Supervised learning – based approaches are the most used in these cases. Sometimes, data available features the lack of information allowing to discern a state of malfunction of the system from those relevant to the faulty-free functioning. In this case, suitable unsupervised learning – based approaches may be used. The paper aims at presenting an approach to the realization of a predictive maintenance system even in the absence of data labels related to failure information. The approach is described and applied to the real case of study of an astronomical telescope.
Teaching Listening Skill by Using Video (Audio-Visual) to the Eleventh Grade Students at SMA Al-Ikhlas Lubuklinggau

This objective of the research was to prove whether there is significantly effective or not to use video in teaching listening for the eleventh grade students at SMA Al-Ikhlas Lubuklinggau. In this research, the researcher used pre-experimental method with one group pre-test and post-test. In collecting data, the researcher used the written test that was given twice to the students, pre-test and post-test. The researcher analyzed the data through individual score, normality testing, and matched t-test formula. The results showed that were 6 students passed the pre-test and 10 students failed in pre-test. While in the post-test, the students’ score increased, there were 14 students mastered the post-test and 2 students failed the post-test. In pre-test mean score was 66.25 and mean score in post-test was 85.31. The result of matched t-test, tobtained was 7.94 and ttable was 1.7531. it meant that the null hypothesis (Ho) was rejected and alternative hypothesis (Ha) was accepted. So it was significantly effective to teach listening skill by using video media to the eleventh grade students at SMA Al-Ikhlas Lubuklinggau.&#x0D; Keywords: Listening Skill, Teaching, Video Media
Collaborative obstacle avoidance algorithm and simulation of swarm robots with limited viewing angle

Abstract In this paper, the problem of collaborative obstacle avoidance of swarm robots with limited view angles is studied. In the case of limited view angles, the control model of swarm robots system is designed. The swarm robots can safely avoid a single static obstacle when the obstacle is within the robot’s visual angle. The main research result of this paper is to design a collaborative obstacle avoidance control algorithm for swarm robots, and subdivide the overall control algorithm into three sub algorithms: formation, obstacle avoidance and navigation. The condition of limited view angle is added to the collaborative obstacle avoidance control algorithm of swarm robots, and a control model of collaborative obstacle avoidance of swarm robots with limited view angle is constructed. Then based on the Simulink platform, the simulation is carried out in the case of static obstacle to verify the correctness and stability of the algorithm.
Bibliometric Analysis of Game Research Trends in Elementary Learning and Self-Regulated Learning Skill

Self-regulated learning is a skill that is important to be taught to students because this skill is useful in their learning environment management to achieve the best learning outcomes. Thus, teaching this ability as early as possible is better, such as in elementary school. One alternative way to introduce and teach this skill is through games. This study aims to capture the relevant previous research landscape regarding the use of games in elementary school learning and for self-regulated learning from 2020 to 2023 using bibliometric analysis with the help of the VOSviewer application. The Scopus database was used to search for data with the keywords "games", "self-regulated learning", "elementary education," and "primary education," which were then refined to become 196 journal article publications. From the results of this analysis, it was found that the trend of publications about the use of games in learning had appeared in 2020, but about self-regulated learning, game-based learning, and elementary/primary education had only appeared in 2021. The research focus can be divided into four major topics, namely: (1) the use of game and game-based learning in primary education, (2) child development, and (3) the use of technology used in primary school education. Surprisingly, there hasn’t been research on self-regulated learning in elementary education. Thus, further research and development on the topic can be done.