Reviewerly/exHarmony huggingface dataset:
State of the art in telepresence

course Share on State of the art in telepresence Authors: Jason Lawrence Google Research Google ResearchView Profile , Ye Pan Shanghai Jiao Tong University Shanghai Jiao Tong UniversityView Profile , Dan B. Goldman Google Research Google ResearchView Profile , Rachel McDonnell Trinity College Dublin Trinity College DublinView Profile , Julie Robillard UBC UBCView Profile , Carol O'Sullivan Trinity College Dublin Trinity College DublinView Profile , Yaser Sheikh Reality Labs Research Reality Labs ResearchView Profile , Michael Zollhoefer Reality Labs Research Reality Labs ResearchView Profile , Jason Saragih Reality Labs Research Reality Labs ResearchView Profile Authors Info & Claims SIGGRAPH '22: ACM SIGGRAPH 2022 CoursesAugust 2022 Article No.: 15Pages 1–74https://doi.org/10.1145/3532720.3535679Published:02 August 2022Publication History 0citation145DownloadsMetricsTotal Citations0Total Downloads145Last 12 Months145Last 6 weeks28 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 Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
An Evaluation Model for the Design of Virtual Reality Systems

Virtual reality (VR) has sparked a new wave of revolution in the technology industry as gaming, films, social media, sports, medical science, shopping, travel, and education and training embrace the arrival of the “dreaming while awake” era. This research integrates the innovation theory and the product design theory for the establishment of an evaluation model of virtual reality system designs. We have established four principal dimensions and 23 criteria for virtual reality system designs. The four principal dimensions include functional attributes, new product attributes, product styling, and product attractiveness. The questionnaire was distributed to 19 renowned experts of R&D managers, project managers, or top managers in VR industry in Taiwan. We used the Analytic Hierarchy Process (AHP) to analyze the evaluation model of virtual reality system designs to obtain the weight and rank of each dimension and the corresponding criteria. We established the priority ranking for design elements of virtual reality systems. The results of the research show that among the four principal dimensions, the functional attributes are the first priority for design. The design priorities of the criteria for each of the four principal dimensions are as follows. (1) For functional attributes, it is the display technology. (2) For new product attributes, it is the compatibility. (3) For product styling, it is the product size. (4) For product attractiveness, it is the semantic attraction. The results of the research can be used as reference for future development strategies of virtual reality systems.
Data Mining as a Knowledge Extraction Tool and an Application on Decision Tree-Based Algorithms

In today’s information society, especially the knowledge has come to the forefront; changes have occurred in the habits of individuals and societies, in the business models and processes of institutions and organizations. Developing database and data warehouse technologies allows the collection, storage, and management of more and complex data with less cost. This recorded data becomes a great resource in the effort to access knowledge. This huge recorded data must be processed and analyzed with appropriate techniques and converted into knowledge quickly and effectively. Statistical and mathematical based machine learning and data mining methods that enable data analysis have been developed. Data mining is the job of extracting knowledge from the data stack. There are many situations that affect data mining model performance. One of the situations is the imbalanced distribution of the class attribute field values in the data set. Within the scope of this study, it is aimed to observe the performance change of various decision tree-based algorithms used in the data mining process against data with balanced and imbalanced class distribution and to show what the implications can be obtained with a data mining study for enterprises. C4.5, C5.0, C5.0 (boosted), Gini, and Random Forest algorithms have been used to develop the classification model and undersampling to balance the data set. The results show that it is important to eliminate the unbalanced class distribution in order to evaluate the model in real terms.
The Mentality of a Museum on a Library Site: Book Exhibitions and the Representative Function

The development of digital culture has made significant changes in the field of culture and information, in particular in the field of library activities. Digitization of library collections forms a digital resource of knowledge, which requires reflection and identification of its potential, strengths and weaknesses. The strengths of the digital resource include a multiple increase in the availability of the book collections and a qualitative change in the opportunity of their research. The problematic aspects of digitalization include the issue of reliability of the digital sources and authenticity of a book unit translated into digital format. One of the verification solutions to this issue may be creating a digital information block accompanying a digital book to collect, accumulate and preserve information about the paper book unit and the history of its existence. Due to the development of digital technologies, a paper book ceases to be the only medium of information. From an information document, it turns into an object of culture with a memory value, historical and artistic features. The translation of the book into the category of a cultural object brings together the position of libraries and museums as medium socio-communicative institutions that act as a platform for the presentation of their collections, one of the most optimal forms of which are exhibitions. The article considers the issues of representativeness of book exhibits and book exhibitions in the conditions of development of the digital environment and digital technologies. There is examined the representativeness of book exhibitions using the example of thematic and artistic-mythological exposition methods. The article considers the activities of the Museum of Painting Culture (MZHK) and the Higher Art and Technical Workshops (VKHUTEMAS) and their experimental searches of the first third of the 20th century in the field of creating new museum exposition forms and exhibition formats. The author draws parallels between universal methods of working with an art object developed by MZHK and VKHUTEMAS and working with book exhibits when creating an exhibition today. The article briefly outlines the tasks solved by the curatorial and design group to strengthen the representative function of an exhibit-book. There are analyzed modern forms of real and virtual exhibitions, their interrelation and mutual influence, formats of interaction. The article notes the importance of exhibitions for the self-presentation of cultural institutions in the digital space.
Platform-Oriented Event Time Allocation(Extended Abstract)

Online Event-based social networks (EBSNs), such as Meetup and Whova, which provide platforms for users to publish, arrange and participate in events, have become increasingly popular. A major challenge for managing EBSNs is to generate the most satisfactory event arrangement. Existing approaches usually focus on assigning a set of events organized to time intervals, but ignore the competitive relationships among different event organizers, which will lead to event time allocations unacceptable to organizers. Thus, a more intelligent EBSNs platform that allocates social events properly in a global view (i.e. the perspective of platform) is desired. In this work, we first formally define the problem of Platform-oriented Event Time Allocation (PETA), which contains two parts: the prediction of event feasible time period and the event time allocation. We propose a method to calculate event feasible time period based on event time prediction, and design a greedy algorithm and two approximation algorithms to solve the PETA problem. Extensive experiments on both real and synthetic datasets demonstrate that the proposed algorithms have high effectiveness and efficiency.
Learning Multiagent Options for Tabular Reinforcement Learning using Factor Graphs

Covering option discovery has been developed to improve the exploration of reinforcement learning in single-agent scenarios, where only sparse reward signals are available. It aims to connect the most distant states identified through the Fiedler vector of the state transition graph. However, the approach cannot be directly extended to multiagent scenarios, since the joint state space grows exponentially with the number of agents, thus prohibiting efficient option computation. Existing research adopting options in multiagent scenarios still relies on single-agent algorithms and fails to directly discover joint options that can improve the connectivity of the joint state space. In this article, we propose a new algorithm to directly compute multiagent options with collaborative exploratory behaviors while still enjoying the ease of decomposition. Our key idea is to approximate the joint state space as the Kronecker product of individual agents' state spaces, based on which we can directly estimate the Fiedler vector of the joint state space using the Laplacian spectrum of individual agents' transition graphs. This decomposition enables us to efficiently construct multiagent joint options by encouraging agents to connect the subgoal joint states, which are corresponding to the minimum or maximum of the estimated joint Fiedler vector. Evaluation on multiagent collaborative tasks shows that our algorithm can successfully identify multiagent options and significantly outperforms prior works using single-agent options or no options, in terms of both faster exploration and higher cumulative rewards.
„Майсторските класове“ и ролята им за повишаване професионалната подготовка на бъдещите педагози

The article presents and analyzes the conducted training seminars in the form of shared good pedagogical practices; the criteria for selecting a teacher to share his/her pedagogical experience and ideas with the future pedagogues in order to better prepare them for the performance of their professional duties in terms of distance learning, blended learning, in person learning; the innovative moments in the shared procedural innovations and innovations in the services are highlighted; the results of the survey of students' opinions and evaluation positions on the effectiveness and applicability of the proposed ideas are summarized and analyzed.
High Dimensional Data Clustering Using Forward Feature Selection Framework for Medical Data

The advent of the “curse of dimensionality” issue while dealing with medical data as a result of large reduced datasets weakens the power of learning algorithms and requires expensive memory and processing expenses. Feature selection helps improve the performance of machine learning algorithms by decreasing the time it takes to create a learning model and enhancing the accuracy of the learning process. The High Dimensional Forward Feature Selection Clustering (HDFFSC) technique is proposed in this paper, which is a simple yet effective parallel processing method based on Map Reduce. Even though numerous algorithms have been created, they still fall short when dealing with high dimensional data in the medical area. To address the challenges with high dimensional data in the medical area, this paper also provides a Forward Feature Selection (FFS) technique with Artificial Bee Colony (ABC) optimization. Between and between clusters, this architecture enabled both global and local search capabilities. In comparison to standard clustering methods, it also increases clustering performance over chosen UCI medical datasets.
An enterprise entity relation extraction model based on ERNIE

In the field of enterprise and economics, there are a lot of unstructured text data of investment, industry and commerce. These data often exist in the form of triples of “entity”, “object” and “relationship”. For the information and data extraction of such problems, this paper proposes a financial entity relationship extraction model based on ERNIE. ERNIE was used for pre-training, BIO mode was used for annotation, and coding sequence was obtained. The triplet containing subject, object and predicate is extracted by the semantic representation of context, and the subject, object and relation are extracted by sigmoid in the prediction layer, and the coding sequence is decoded to obtain the final processing result. Experimental results show that in the data set of manually annotated investment events and industrial and commercial events, the effect of this model is significantly better than the traditional BERT and CNN-Attention based joint entity relation extraction model, and the F1 value used to evaluate the model performance reaches 81.72%.
The Interaction Between Design and Manufacture of Waveguide in Communication Satellite

The function of Communication Satellite is transporting information of Telephone, Telegram, Fax and TV, with the feature of numerous transmission path and high power capacity. The waveguide has the advantages of low insertion loss, high power capacity, simple structure, and being easy to manufacture. It is an ideal transmission tool for Communication Satellite. So It plays an significant role in Satellite Communication. The method of waveguide design is important to the performance of Communication Satellite. The various ways to produce waveguide also raise counterclaim to waveguide design. The thesis will introduce different ways to design and produce waveguide, analyze the interaction between them in real samples, apply a type of reasonable design and manufacture method in Communication Satellite.
Video Abnormal Behavior Detection Based on Human Skeletal Information and GRU

Video abnormal behavior detection is an important research direction in the field of computer vision and has been widely used in surveillance video. This paper proposes an abnormal behavior detection model based on human skeletal structure and recurrent neural network, which uses dynamic skeletal features for abnormal behavior detection. The model in this paper extracts key points from multiple frames through the human pose estimation algorithm, obtains human skeleton information, and inputs it into the established autoencoder recurrent neural network to detect abnormal human behavior from video sequences. Compared with traditional appearance-based models, our method has better anomaly detection performance under multi-scene and multi-view.
DroBoost: An Intelligent Score and Model Boosting Method for Drone Detection

Drone detection is a challenging object detection task where visibility conditions and quality of the images may be unfavorable, and detections might become difficult due to complex backgrounds, small visible objects, and hard to distinguish objects. Both provide high confidence for drone detections, and eliminating false detections requires efficient algorithms and approaches. Our previous work, which uses YOLOv5, uses both real and synthetic data and a Kalman-based tracker to track the detections and increase their confidence using temporal information. Our current work improves on the previous approach by combining several improvements. We used a more diverse dataset combining multiple sources and combined with synthetic samples chosen from a large synthetic dataset based on the error analysis of the base model. Also, to obtain more resilient confidence scores for objects, we introduced a classification component that discriminates whether the object is a drone or not. Finally, we developed a more advanced scoring algorithm for object tracking that we use to adjust localization confidence. Furthermore, the proposed technique won 1st Place in the Drone vs. Bird Challenge (Workshop on Small-Drone Surveillance, Detection and Counteraction Techniques at ICIAP 2021).
Towards the Creation of AI-powered Queries Using Transfer Learning on NLP Model - The THESPIAN-NER Experience

Tools for HEritage Science Processing, Integration, and ANalysis (THESPIAN) is a cloud system that offers multiple web services to the researchers of INFN-CHNet, from storing their raw data to reusing them by following the FAIR principles for establishing integration and interoperability among shared information. The injection in the CHNet cloud database of data and metadata (the latter modelled on a CIDOC-based ontology called CRMhs [20]) is performed by using the cloud service THESPIAN-Mask. THESPIAN-NER is a tool based on a deep neural network for Named Entity Recognition (NER), which will ease the data extraction from the database, enabling users to upload .pdf or .txt files and obtain named entities and keywords to be fetched in the metadata entries of the database. The neural network, on which THESPIAN-NER relies, is based on a set of open-source NLP models; transfer learning was employed to customise the Named Entity Recognition output of the models to match the CRMhs ontology properties. The service is now available in alpha version to researchers on the CHNet cloud.
Recognition of Complex Gestures for Real-Time Emoji Assignment

Gesture recognition allows humans to interface and interact naturally with the machine. This paper presents analytical and algebraic methods to recognize specific combinations of facial expressions and hand gestures, including interactions between hands and face. The methodologies for extracting the features for both faces and hands were implemented starting from landmarks identified in real-time by the MediaPipe framework. To benchmark our approach, we selected a large set of emoji and designed a system capable of associating chosen emoji to facial expressions and/or hand gestures recognized. Complex poses and gestures combinations have been selected and assigned to specific emoji to be recognized by the system. Furthermore, the Web Application we created demonstrates that our system is able to quickly recognize facial expressions and complex poses from a video sequence from standard camera. The experimental results show that our proposed methods are generalizable, robust and achieve on average 99,25% of recognition accuracy.
Designing a Flow-based Mechanism for Accessing Electronic Health Records on a Cloud Environment

Electronic health record (EHR) implementation not only to facilitate doctor-patient communication reduces paper consumption but also allows the rapid exchange of medical records, integrating patients’ medical information from different locations. However, the costs of establishing massive and repetitive systems, constructing databases, and maintaining and exchanging data, as well as the energy consumption underscoring such an operation, represent substantial costs for a medical institution. Therefore, it is important to develop a cloud solution for EHRs and to provide a platform where resources are truly shareable. This study investigates the feasibility of cloud EHR services provided by trusted third parties. However, not only do medical records stored in an access environment with multiple users potentially endanger patient privacy, but also, without well-designed access control, such an environment may beget excessive unnecessary data access, which is costly and hinders cloud computing. To address the dual challenge of protecting patient privacy and allowing cloud computing, this study proposes the doctor–patient workflow and implements a web-based system. This mechanism ensures patients’ data security and addresses the demand of EHR cloud sharing, i.e., controlling EHR access authorities. The proposed method can protect the privacy of patients’ medical records on the cloud and grant users with minimum granularity access, thereby creating a system with the advantages of data security and cloud computing. This study proposes the doctor–patient workflow as the access control mechanism of cloud medical records, which minimizes the granularity of access. In addition, the access authority of the workflow dynamically changes with the environment, which ensures patients’ access to their medical records and defines the appropriate timing of cloud data access operations, thereby preventing unnecessary energy consumption. In practice, considerable contributions can be made to the establishment of access control and promotion of the cloud environment for medical records.
ON PROFITABILITY OF STUBBORN MINING

We compute and compare profitabilities of stubborn mining strategies that are variations of selfish mining. These are deviant mining strategies violating Bitcoin's network protocol rules. We apply the foundational set-up from our previous companion article on the profitability of selfish mining, and the new martingale techniques to get a closed-form computation for the revenue ratio, which is the correct benchmark for profitability. Catalan numbers and Catalan distributions appear in the closed-form computations. This marks the first appearance of Catalan numbers in the Mathematics of the Bitcoin protocol.
An Evaluation of Teaching Variation Skills in Increasing Students’ Learning Motivation

This study aims to determine the skills of teachers in carrying out variations in learning, their impact on student motivation, and supporting factors and obstacles during the learning process. This research uses descriptive qualitative research. The data collection technique used is the method of observation, interviews, and documentation. The results of the study indicate that (1) The skills of teachers in conducting variations in the learning process have begun to be developed and are quite good. Teachers have been able to apply a varied learning process in order to provide enthusiasm for student learning, avoiding boredom and boredom in the teaching and learning process; (2) The use of teaching variations on student learning motivation greatly affects student activity in the classroom by making the student learning atmosphere more focused and enthusiastic; and (3) Student responses about the use of teaching variations on student learning motivation received a positive response from students' opinions, because it made the learning atmosphere comfortable, focused, and more enthusiastic by using teaching variations.
Pelatihan Penghitungan dan Pengisian SPT PPh Pasal 21/26 di Pondok Pesantren Muhammadiyah Ahmad Dahlan

Community Service as a unit of Higher Education Tri Dharma College aims to improve the standard of living of the community around the campus, the interests of Islamic boarding school students increase the scientific repertoire of Calculation and Filing of SPT PPh Pasal 21/26 which is accurate and correct as a debriefing to face work readiness in the industrial business world. The descriptive analysis method is a mixture of qualitative and quantitative to describe the characteristics of respondents and the tendency of students toward training from primary and secondary data. The training for calculating and filling PPh Pasal 21 is very unique, interestingly, the students are very enthusiastic about learning taxation and can add to the scientific treasures with the results of the initial test and final test being very unique from 57 respondents, interestingly from 37,34 the predicate changed to 86,97 the predicate was good.
Book review: Jiddu Krishnamurti, J. Krishnamurti on Education

Jiddu Krishnamurti, J. Krishnamurti on Education. Chennai: Krishnamurti Foundation India, 2003, 158 pp., ₹100. ISBN: 81-87236-87-8.