Reviewerly/exHarmony huggingface dataset:
The Effect of Virtual Reality on Learning Outcomes Mediated by Interaction and Learning Experiences

In Indonesia, only a few educational institutions have implemented VR learning technology due to the lack of available content for learning and the insignificant effect for students after using VR. This study aims to examine the effect of VR learning technology (immersion, visualization, interaction) on learning outcomes (perceived learning effectiveness, satisfaction) with interaction experience (perceived usefulness, perceived ease of use) and learning experiences (motivation, interest, active learning) as mediators. 117 questionnaires are distributed to Junior High Schools, Senior High Schools, and Vocational High Schools students which have implemented the VR technology learningbased and implemented their classes with VR content, between the age of 12 until 19 years old. The data is analyzed by using Structural Equations Modelling (SEM) and Multiple Regression Analysis. We find VR learning technology affect student’s motivation and learning activity and also improve student learning outcomes in terms of perceived learning effectiveness dimension. On the other hand, the interaction experience was unperceived because of a lack of learning content characteristics for the immersive dimension that can affect the perceived usefulness and perceived ease of use. This research showed that the characteristics of VR learning technology affect student’s motivation and learning activeness. It improves student’s learning outcomes for perceived learning effectiveness.
Advertising Harassment on Internet. What Is Wrong With So Called ‘Cookie Policy’

With the development of modern technologies, we now can observe a phenomenon, which we call Advertising harassment. Advertising comes on your computer while visiting and using Internet sites in such a volume and repeatedly that you are not only bored but you start to experience mental discomfort. What are the remedies? The article observes as well some aspects of so called Cookie Policy of Internet sites.
Random Selection of Parameters in Asynchronous Pool-Based Evolutionary Algorithms

Synchronous operation is not the most natural, as in biologically inspired, mode to run distributed algorithms. In many grid, cloud or volunteer setups nodes are heterogeneous, or simply are not available at the exact same time; this is a challenge for the researcher if their full performance is going to be actually leveraged. Asynchronous distributed evolutionary algorithms try to solve this by dropping the homogeneity, as well as the synchronicity, assumption. These algorithms share the population between distributed workers which execute the actual evolutionary process by taking samples of the population, and replacing them in the population pool by evolved individuals. The performance of these EAs depends in part on the selection of parameters for the EA running in each worker. In this paper we study how randomly varying parameters in distributed evolutionary algorithms affects performance. Experiments were conducted in the AWS cloud using 2, 6 and 12 virtual machine configurations, with both homogeneous and heterogeneous random settings using five test functions for real-valued optimization and the OneMax binary problem. The results suggest that this method can produce a performance that is competitive with instances of the algorithm using workers with parameters specially tuned for the benchmark.
Next Generation OTT Distribution Architecture Supporting Multicast-Assisted ABR (mABR) and HTTP/3 over QUIC

The potential audience for streaming media services is massive, but satisfying that kind of demand poses a huge technical challenge. Even if existing Content Delivery Networks were capable of distributing such loads, the costs at that scale would remain beyond the monetary capabilities of any broadcaster. By reducing transmission costs associated with content delivery at scale, broadcasters would pay less to the CDNs, making the content cheaper to the end user. — In traditional broadcasting technologies, a majority of commercially available content distribution architectures are based on a paradigm rooted in linear broadcast services, having a very simple unified service composition focusing and delivery interpretable on compatible TV sets with builtin tuners. Whilst this helped transform the industry by facilitating the transition from analogue to digital broadcast content distribution, it now leaves content providers and broadcast network operators wondering how they can remain relevant in the age of Web and Internet streaming. — This article will take its point of departure in evolved content distribution systems and the resulting system architectures for a next generation broadcast OTT live and on-demand streaming. It will focus on a solution for efficient and secure transport of well-known low latency streaming protocols by employing QUIC as a primary transport protocol for the industry leading IP Multicast architectures supporting multicast-assisted adaptive bitrate (mABR) streaming, enabled by novel HTTP/3 Server Push techniques. At scale, the solution could significantly reduce the amount of open streaming sessions, improving the scalability of existing audio-video streaming architectures, and reducing the wastage of scarce bandwidth. Placing physical gateway boxes within users' home network that fully support QUIC and Multicast content reception can reduce the prevalent lack of user agent support for reception of Multicast QUIC content. The report includes state-of-the-art research behind elements of Multicast QUIC featuring Multicast Gateway authorization using OAuth 2.0 with Proof Key for Code Exchange (PKCE) flow, followed by its design, requirements specification and proof-of-concept implementation written in Golang. — This new model with the core transport protocol based on QUIC will be the foundation for unified content distribution architectures, spanning Internet streaming, IPTV, 5G, and the latest ATSC and DVB physical layers allowing for an efficient, IP-based delivery of linear content to thin clients.
On the Utility of Recovering Dropped Words in Chinese Named Entity Recognition

Named entity recognition (NER) in natural language processing (NLP) considers the problem of identifying a sequence of words in a sentence text that mentions a predefined type of object (entity), e.g., person, organization, location, or time. NER methods are keys in extracting knowledge from texts as entities are fundamental for attaching entity properties or entity relations. However, NER for texts in Chinese is trickier due to that some auxiliary words maybe dropped in a sentence, which is a common phenomenon in Chinese writing for brevity. A usually dropped Chinese word is ‘的’ (often functions as the word ‘of’ in English). One obvious effect of this kind of omitting is bring difficulty in identifying the sub-entities (or nested named entities) contained in a named entity. Previous works considers the effected of recovering dropped pronouns in the Chinese translation task. Here we proposed a rule-based method to rover the auxiliary word ‘的’ for Chinese text, and study the effect of this recovery on the performance of a state-of-the-art Chinese NER method FLAT. Experimental results on Weibo-NER and MSRA-NER datasets shows that our method improves on FLAT. This study thus highlights the promising of recovering more types of dropped words for Chinese NER problem.
An Adaptive Mountain Clustering based Anomaly Detection for Distributed Wireless Sensor Networks

Data inconsistency is an important issue in Wireless sensor networks (WSN). Due to the environmental change, the sensor nodes which are deployed in unattended area are damaged by various factors. Additionally, some of the sensor nodes may be hacked for stealing and altering its original sensed data, which affects the consistency of data sensed by faulty or hacked sensor nodes. The reason for this data inconsistency is the improper identification of anomalous data in the network. We propose an adaptive mountain clustering technique with new density measure for detecting anomalous data that avoids the unwanted data transmission to the end user and improves the data accuracy for the same. The proposed method successfully detects the anomalies with low false alarm rate.
Sentiment Analysis and Evaluation of Movie Reviews Using Classifiers

The analysis of the opinions and likelihood as well as emotions in any form either in the form of text is called as sentiment analysis. Opinion mining is termed as sentiment analysis. The analysis of the data is very helpful in expressing the likelihood of the options for group of persons or individuals. With the advancement of the Internet, a huge collection data is being generated. Facebook, Twitter, YouTube, LinkedIn, Instagram and other social sites are gaining a lot of popularity as they allow users from different parts of the world to share their views upon various topics through comments, posts, tweets and tags. This paper provides a survey of existing technique for sentimental analysis like machine learning-based approaches like Naïve Bayes, logistic regression and SVM. Among them, decision tree recorded the best performance. Decision tree generated a classification accuracy of 91.6% and a minimum execution time of 1.12 s. Hence, it can be concluded that sentiment analysis with decision tree algorithm is an optimal concept for efficient movie review analysis.
Lyndon Words Formalized in Isabelle/HOL

We present a formalization of Lyndon words and basic relevant results in Isabelle/HOL. We give a short review of Isabelle/HOL and focus on challenges that arise in this formalization. The presented formalization is based on an ongoing larger project of formalization of combinatorics on words.
The Governance in Al-Balqa University from the Viewpoint of the Academic Leaders

This study aimed to reveal the degree of application of governance in Al-Balqa Applied University from academic leaders' viewpoint. The study community consisted of all the academic leaders of Al-Balqa Applied University, that they were (260) academic leaders 2020/2021.  The sample of the study consisted of (130) academic leaders. They were selected by the random stratified method and to achieve the study's objectives. A descriptive survey method was used. A questionnaire was developed for this purpose, consisting of (40) items divided into five domains.The findings reveal that the degree of application of governance in Al-Balqa Applied University, from the academic leaders' point of view, came to a high degree. The field of implementing laws and regulations came first, and the field of empowerment in the last place.To the study's findings, the researcher recommends spreading the culture of governance in Jordanian Universities and including it in academic courses.
Analysis and Research on Complex System of Power Grid Company Assets Management pattern and subsystem under the Optimum of Overall Situation

The complex assets management information system under the global optimum is mainly aimed at the current situation of Power Grid Company and realizes the whole process and lean management of assets from the three aspects of organizational structure, rules and regulations and integrated information platform with the help of the full-service data center. This paper mainly analyzes the complex information system of assets management from two aspects: pattern content and information subsystem.
Acoustic Anthropogenic Noise Reduction In Pixel Priority Based Algorithm

Abstract Priority pixel enhancement for removing hindrances in acoustics. In this paper underwater sound speed gradient monitoring systems grieved in various conditions data monitoring. Compress and decompress sound waves of water molecules travel through underwater makes monitoring hindrances to ocean monitoring sensors produce dithered image data retrieving is based on pixel based algorithm to get the original point of data legible to enhance the part of the image in the priority enhancement methods in under water sound monitoring systems.
Credal Calculi, Evidence, and Consistency

This paper defends the use of possibility and necessity models based on the Logics of Formal Inconsistency, taking advantage of their expressivity in terms of the notions of consistency ( $$\circ $$ ) and inconsistency ( $$\bullet $$ ). The present proposal directly generalizes the approach of Besnard and Lang (Proceedings of 10th Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, San Francisco, pp. 69–76 1994), whose main guidelines we borrow here. Some basic properties of possibility and necessity functions over the Logics of Formal Inconsistency are obtained and it is shown, by revisiting a paradigmatic example, how paraconsistent possibility and necessity reasoning can, in general, attain realistic models for artificial judgement. We will call such models credal calculi, emphasizing some of their appealing consequences.
Self-recorded Video Segmentation Algorithm Based on Motion Detection

Content-based video retrieval has been very widely used, which plays an important role in the rapid development of multimedia technology. It also takes effective management and retrieval for large quantities of multimedia data, especially for the increasing number of sports video. This video retrieval has already become hot in the field of multimedia. This paper classifies the state of the self-recorded game video, detects the bottom line by canny operator and separates the zones of service and offensive, detects movements in the corresponding regions. Then, according to the characteristics of difference states of the game, the game station is judged and the video is split. This paper designed the system to achieve the above algorithms. Through the experiments of multiple videos, it comes to an expected result, verifies the algorithm’s feasibility and robustness.
Crowd Count Estimator Application using CSRNet

We deploy a network for Congested Scene Recognition which can also be called as CSRNet. The main purpose of CSRNet is to understand and identify people from a highly congested image and perform accurate analysis, project statistical data for the current session and the density map. Basically, it deploys a deeper CNN (Convolution Neural Network) for capturing high level features and generate high quality density maps. We are using a modified version of the VGG-16 architecture and we have used this because it can extract high level features and project high quality density maps without increasing the network complexity exponentially. We showcase our project and its accuracy on one dataset i.e. ShanghaiTech Dataset.
Aspect-Based Attention LSTM for Aspect-Level Sentiment Analysis

With the rapid development of the mobile Internet, how to efficiently extract information from the massive data in cyberspace and use it has become a key concern of all walks of life. Sentiment analysis aims to dig out the subjective emotional information contained in large-scale texts. It has become a hot research topic in the field of natural language processing and has important application value in many fields. This paper focuses on the aspect-level sentiment analysis problem, which is an important sub-problem in fine-grained sentiment analysis. The purpose of aspect-level sentiment classification is to dig out the sentiment polarity of users' opinions on a specific aspect in the text. This paper proposes a sentiment classification method combining BiLSTM and aspect attention module. BiLSTM has fewer parameters, faster model training, and can effectively extract deep-level information of text; combining the attention mechanism with aspect information can fully extract specific aspects of information. The experimental results show that the F1 score that solves the problem of aspect term extraction and aspect sentiment classification on two data sets at the same time, compared with the existing sentiment analysis method, obtains a better classification effect.
Relationships between construction grammar(s) and genre: Evidence from an analysis of Instagram posts

Following the usage-based model, construction grammar assumes the co-existence of different construction inventories, it allows for constructions beyond clause level, and it embraces many types of meaning including discourse-functional meanings. It is thus well-suited to investigate the relationship between grammar and genre on the one hand and between grammar and possible sub-grammars on the other. In the current paper, we discuss these relationships based on a multimodal analysis of Danish and US department stores’ Instagram strategies. We find that Instagram posts may constitute stable configurations of multimodal choices. Instagram posts may therefore be seen as discourse-grammatical constructions, thus supporting the view that genre may be part of grammar. Nevertheless, considerable variation in the realization of Instagram posts can be observed, which however correlates with the success of the respective posts, measured by the numbers of likes they receive. The immediate and objective feedback available for social media posts may have supported the establishment of particular practices, which seem to be attended to and exploited by experts; at the same time, these choices are functionally motivated, transparent and compositional for non-experts. We discuss whether different grammars for experts (professional producers of Instagram posts) and non-experts (the consumers) should be assumed, the role of exemplars and the functional motivation of the choices made by experts. We conclude that the fact that the expert constructions are encoding idioms speaks against separate grammatical subsystems; instead, in accordance with the usage-based model, we find that both experts and non-experts are likely to make use of conventions, exemplars and functional knowledge.
Implementing the humanitarian potential of mathematics in the training of elementary school teachers

The complex concept of “the humanitarian potential of mathematics” needs to update the educational environment of elementary schools and universities, as well as the relationship of participants in the educational process. The humanization of natural sciences requires the development of new elective courses, cognitive tasks, various dialogues and search activities of participants in the learning process. In the context of mathematics, this phenomenon has many meanings. The main direction in this research is the paradigm of personality-oriented education aimed at the development of general cultural components and the formation of students’ values-based attitude to the surrounding world. While considering the humanitarian potential of mathematics, it is important to take into account its aesthetic, historical, linguistic, activity-based and applied components. The authors of the article dwell on the linguistic component of humanitarian mathematics. They describe a new teaching method improving the speech of a mathematics teacher of elementary school. In addition, the authors consider the possibilities of using the digital educational platform Yandex.Textbook as a source of modern didactic materials and various representations of mathematical knowledge. By using the content of Yandex.Textbook in extracurricular activities and elective courses in mathematics, it is possible to develop the creative imagination of students and expand their cultural horizons. Despite a large number of mathematics textbooks for elementary school, there is a need to create an educational complex based on the humanization of mathematics education. The same task is solved by the developers of teaching aids and elective courses for students, i.e. future mathematics teachers.
Paper Research on the Application of Virtual Reality Panoramic News Communication Technology in Sports Events

With the rapid development of virtual reality technology and new media technology, virtual reality panoramic news has become an emerging form in news communication. In this paper, by studying the feasibility of virtual reality panoramic news, designing the implementation scheme of virtual reality panoramic news for sports events through Unity3D, establishing a news dissemination effect evaluation system and quantitatively evaluating the sports news in this paper, the results show that the virtual reality panoramic news dissemination technology has a better dissemination impact.
Protocol-Based Smart Contract Generation

The popularity of smart contracts is on the rise, yet breaches in reliability and security linger. Among the many facets of smart contract reliability, we concentrate on faults rooted in out-of-order interactions with contract endpoints. We propose SmartScribble, a protocol language to describe valid patterns of interaction between users and endpoints. SmartScribble not only ensures correct interactive behaviour but also simplifies smart contract coding. From a protocol description, our compiler generates a smart contract that can then be completed by the programmer with the relevant business logic. The generated contracts rely on finite state machines to control endpoint invocations. As a proof of concept, we target Plutus, the contract programming language for the Cardano blockchain. Preliminary evaluation points to a 75% decrease in the size of the code that developers must write, coupled with an increase of reliability by enforcing the specified patterns of interaction.
Enabling Modern Application Development with Swift on the Nao/Pepper Robots

We show the advantages of using Swift as the programming language for behaviours on the Pepper and Nao robots as used with the RoboCup Standard Platform League and the RoboCup@Home - Social Standard Platform. We show that Swift is not only incorporating modern features of object-oriented programming and functional programming, but is also now a stable systems programming language that enables both high-level development as well as fine hardware control. Deterministic memory management makes Swift suitable for real-time, embedded systems, and thus for robotic applications. Moreover, we show in this paper we can apply model-driven software-development by deploying behaviours coded as executable arrangements of logic-labelled finite-state machines (LLFSMs). We also show LLFSMs are not only suitable for reactive architectures, but also for deliberative architectures.
Schrodinger Equation in Cosmological Inertial Frame

Schrodinger equation is a wave equation. Wave function uses as a probability amplitude in quantum mechanics. We make Schrodinger from Klein-Gordon free particle’s wave function in cosmological special theory of relativity.
Evaluation of Performance

Abstract Agents evaluate their performances to assess progress, learn, and improve. In doing so, they refer to criteria of various kinds. Some criteria are deeply encoded in mental models, organizational procedures, or cultural norms and logics, while other evaluative criteria are adaptive and may upregulate or downregulate, depending on the agent’s goals, expectations, and context. Here, too, digitalization is transformative. Artificial agents bring unprecedented power to the evaluation of performance, including the rapid intra-cyclical evaluation of ongoing processes. These mechanisms support feedforward guidance in real time. Therefore, when human and artificial agents combine in the evaluation of augmented performance, they face additional risks. Artificial evaluative processing could be fast and precise, while at the same time, human evaluation may be relatively sluggish and imprecise. Overall evaluations of performance could be distorted and dysfunctional.