Reviewerly/exHarmony huggingface dataset:
A Typological Approach to Word Order and Information Processing: English as a SVO Language

The main purpose of this study is to examine a relation between word order and language processing and identify language-specific features that affect the degree of accessibility to information structure in a SVO word order. The findings from this study reveal that subject-prominent mechanisms in English, such as clefting structures, dummy subjects, existential sentences, non-agentive subjects, raising constructions, and passive voices, are well motivated for the purpose of maximizing language-use efficiency. In addition, passive constructions and non-agentive subjects are strategically exploited for the purpose of promoting high accessibility to a rigid SVO sequence and improving textual coherence. Furthermore, the tendency towards short–long ordering in SVO languages like English is predominant, and thus the language-specific maxims, such as ‘end-focus’, ‘end-weight’, and ‘dependency length minimization’, are well observed in the use of left-branching and right-branching modifiers in English for the purpose of increasing the efficiency of information processing. Finally, this study suggests that the language-specific structures, patterns, devices, or principles that are available in SVO word order seem to be optimized for facilitating information processing.
Summarization of User-Generated Videos Fusing Handcrafted and Deep Audiovisual Features

The ever-increasing amount of user-generated audiovisual content has increased the demand for easy navigation across content collections and repositories, necessitating detailed, yet concise content representations. A typical method to this goal is to construct a visual summary, which is significantly more expressive than other alternatives, such as verbal annotations. In this paper, we describe a video summarization technique which is based on the extraction and the fusion of audio and visual data, in order to generate dynamic video summaries, i.e., video summaries that include the most essential video segments from the original video, while maintaining their original temporal sequence. Based on the extracted features, each video segment is classified as being “interesting” or “uninteresting,” and hence included or excluded from the final summary. The originality of our technique is that prior to classification, we employ a transfer learning strategy to extract deep features from pre-trained models as input to the classifiers, making them more intuitive and robust to objectiveness. We evaluate our technique on a large dataset of user-generated videos and demonstrate that the addition of deep features is able to improve classification performance, resulting in more concrete video summaries, compared to the use of only hand-crafted features.
Federal Sectoral Agreement on Higher Education Educational Organizations Needs Improvement

The article, based on the norms of the Labor Code of the Russian Federation, the doctrine of modern labor law, analyzed the Federal Sectoral Agreement on educational organizations of higher education under the jurisdiction of the Ministry of Science and Higher Education of the Russian Federation, identified duplicate and conflict norms, as well as other controversial aspects
In the Middle of Korean Middles

This paper proposes a Distributed Morphology account of middle sentences in Korean. In the middle of Korean middles lie the functional head Voice bearing [-ACT] and the generic operator GEN, interacting with each other to produce the morphosyntactic and semantic properties of Korean middles. VoiceSUB[-ACT]/SUB is responsible for the demotion of an external argument of eventive verbs, characteristic of middle sentences where a theme occupies the grammatical subject position. This functional head also figures prominently in passives, where the same range of verbalizing suffixes of middle sentences also appear. This functional head is responsible for the external argument demotion and the sharing of the same verbal suffixes with passives. The generic operator unselectively binds the event variable e and consequently ascribes a disposition to the theme occupying the grammatical subject position. This binding prevents Existentential Closure from binding this variable which would result in an episodic reading of the event that middles sentence are incapable of yielding. Unlike English middles which employ VoiceSUB[+ACT]/SUB, Korean middles are generic passives.
Comparison of University Students’ Perceptions of Interaction in English Classes on Two Online Platforms: Gather.town vs. Zoom

This study is purported to compare university students’ perceptions of interaction in English classes on two synchronous online platforms: Gather.town, the metaverse vs. Zoom, non-metaverse. Moore’s three types of interaction in online education are utilized: learner-learner, learner-teacher, and learner-content. A total of 22 college students at an English pedagogy class participate in the survey; later, four individual students are interviewed. Non-parametric repeated-measures analysis utilized to see differences between the two platforms reveals the following findings. Firstly, students’ interaction on the metaverse between learners and learners and between learners and the teacher is significantly greater than its counterpart, not for learner-to-content interaction. Secondly, avatar use on the map-based classroom on the metaverse provides students with both multiple ways for interaction and an optimized environment for small group activity. In contrast, student-initiated interaction on Zoom is limited. Thirdly, the metaverse platform has easy ways of communication with the teacher. Finally, for learner-to-content interaction, difficulty in accessing and sharing class files on the metaverse causes inconveniences. Pedagogical implications are discussed for language teachers aspiring to adopt the metaverse for their future classes.
Impact of Viral Marketing Tools on Dealing with Commercial Bank Services - Empirical Study on Commercial Bank Customers in Jordan

This study aims at identifying the impact of viral marketing on stimulating customers to buy the services of commercial banks through the four themes of the study (i.e., electronic advertising messages, word of mouth, material and moral motivation, and electronic social media). To achieve this objective of the study, the researcher conducted descriptive and analytical method due to its relevance to the study, where a questionnaire was designed to collect primary data. Before distributing the questionnaire on the study sample, it was sent as a pilot study to many experts and referees to get their feedback. The questionnaire was adjusted according to the comments of referees and experts. The final form of the questionnaire was sent to a sample of 400 customers of commercial banks in Jordan. To avoid sampling bias, 290 questionnaires were valid for the analysis. The research employed the most proper statistical methods via the computer software of SPSS. The main finding of the study was that there is a statistically significant effect of viral marketing independent variables on stimulating customers to buy the services of commercial banks. This effect was clear and strong for all independent variables of the study. Based on the findings, the study recommends the following: First, banking institutions should continue using the advanced strategies of marketing and promoting their banking services through the viral marketing proved to be an effective and efficient method for promoting and stimulating customers to buy their services. Second, banking institutions should overcome the weakness points and enhance the strength points of viral marketing. Last but not least, banking institutions should follow up and catch up with any new idea in viral marketing considering technological openness.
Objects and Events

Objects are the lifeblood of GameMaker. Your game will consist of lots of objects that will interact with each other and make things happen accordingly.
Business Communication Model Recommendation Algorithm of New Media Live Broadcast under Big Data Technology

The development of science and technology has provided a more convenient live broadcast platform for new media live broadcast. At the same time, with the support of big data technology, the business communication mode of new media live broadcast is more diversified. This paper builds a commercial communication recommendation platform for new media live broadcasts with advertisers, live broadcast platforms (hosts) and users as the main body, and introduces a recommendation algorithm to recommend live broadcast content for users, in order to hope that the live broadcast platform can recommend videos for users according to their preferences, to meet the needs of users for live viewing. After analyzing the algorithm's recommendation accuracy and user satisfaction, it is found that the use of collaborative filtering-based recommendation algorithm is conducive to recommending live videos that conform to their preferences, and provides a path for promoting the sustainable development of the live broadcast industry's business communication model.
On Some Issues of Mathematical Modeling of Various Approaches to Learning

The enlarged model of comparison of various concepts of education (culturally appropriate and nature-like) in compulsory secondary school proposed earlier by the author is supplemented by taking into account feedback on the health of students, which makes it possible to move from the appropriate modeling of the school “in the moment” to modeling it in time dynamics.
An Unsupervised Keyword Extraction Method based on Text Semantic Graph

The energy and power industry has a large amount of text data, which has become an important research of big data mining. In order to improve the retrieval efficiency of article topic information, this paper proposes an unsupervised keyword extraction method based on text semantic graph, which focuses on improving the text graph construction and word weight calculation. According to the semantic dependencies of words in sentences, we build the text semantic graph which consist of four type edges, namely concept connection, equivalent membership, function attribute and modification limitation. Thus the parameter setting of the window length required in the traditional graph generation method is omitted. Then a word weight is calculated by combining keyword position information, term frequency-inverse document frequency, concept level and connection strength. Finally, we sort the importance of words and the high-score nodes are selected to form the keyword set of the abstract text. Experiment shows that our algorithm achieves better results in keyword extraction than traditional algorithms.
Risk assessment of power optical cable network based on Tsallis entropy

To address the problems that there are many factors affecting the vulnerability of power fiber optic cable network and it is difficult to assess the network risk, this paper proposes a risk assessment method for power fiber optic cable network based on Tsallis entropy. Firstly, the factors affecting the health of fiber optic cable are fully explored, a diagnostic index system for the health of fiber optic cable is constructed, and the health and operation level of fiber optic cable are calculated. The simulation shows that the risk assessment method proposed in this paper can accurately assess the riskiness of the power optical cable network and effectively improve the reliability and stability of the power optical cable network.
A Brief Survey on Representation Learning based Graph Dimensionality Reduction Techniques

Dimensionality reduction techniques map data represented on higher dimensions onto lower dimensions with varying degrees of information loss. Graph dimensionality reduction techniques adopt the same principle of providing latent representations of the graph structure with minor adaptations to the output representations along with the input data. There exist several cutting edge techniques that are efficient at generating embeddings from graph data and projecting them onto low dimensional latent spaces. Due to variations in the operational philosophy, the benefits of a particular graph dimensionality reduction technique might not prove advantageous to every scenario or rather every dataset. As a result, some techniques are efficient at representing the relationship between nodes at lower dimensions, while others are good at encapsulating the entire graph structure on low dimensional space. We present this survey to outline the benefits as well as problems associated with the existing graph dimensionality reduction techniques. We also attempted to connect the dots regarding the potential improvements to some of the techniques. This survey could be helpful for upcoming researchers interested in exploring the usage of graph representation learning to effectively produce low-dimensional graph embeddings with varying degrees of granularity.
AR Digital Workspace Using a Mobile Device

In this study, to overcome the issues with the narrow display areas of mobile devices such as smartphones and tablets, we propose an Augmented Reality (AR) technology called “AR Digital Workspace” that augments a workspace in real space by superimposing windows on a plane in real space. In the proposed interface, a plane in real space is detected when a user moves a mobile device, and multiple windows are superimposed on the detected plane. By touching the screen of the device, the user can change the position and size of the windows, and copy and paste text between windows. The user can also copy text in real space using character recognition on document images acquired from the camera. This system enables users to work while displaying a large amount of information in a pseudo spacious workspace. We implemented the proposed interface on a mobile device using ARCore, an AR framework for Android devices.
Application of Smart Contracts in Supply Chain Management

Traditional supply chain management systems can struggle with issues such as traceability, transparency and trust between supply chain participants. In order to mitigate such issues, we propose application of blockchain technology and smart contracts. This paper presents a working supply chain model based on the Ethereum blockchain network. Ganache, a personal Ethereum blockchain, was used to deploy contracts, and TruffleSuite was used as a development environment. Although participants in the supply chain are simplified in comparison to real world scenarios, the presented model allows us to demonstrate how application of blockchain and smart contracts can improve mentioned issues characteristic for traditional supply chains. Detailed process flow of such a supply management system was given, and used system structures were described in detail. Described model proves that it is possible to apply smart contracts in order to solve problems of traceability transparency and trust in a simple and reliable way.
Design of Web-Based Hajj Manasik Supervision Certification Information System

In the discussion of this research, the motivation to design a certification information system at the Regional Office (Kanwil) of the West Java Ministry of Religion seeks to resolve, and reduce the problem of the certification system which is not yet effective and far from efficient. The activities of implementing certification for offline (face-to-face) Hajj ritual supervisors, and processing certification data traditionally affect the results of recording certification and implementing the assessment process. Thus, the proposed system is generated through a website-based platform and is run online. The research method uses the System Development Life Cycle (SDLC) with the Waterfall model approach which consists of 5 stages, namely, a) Analysis b) Design c) Implementation d) Maintenance e) Planning. The programming language used is (PHP), and the database application uses a data processor (Mysql) as the database (RDBMS). In addition, to support this system design tool, it uses the UML (Unifed Modeling Language) method by using two diagrams according to design needs, namely Use Case Diagrams and Activity Diagrams. This research produces a Web-Based Certification Information System design that can simplify the certification implementation process, as well as simplify the presentation of certification data and its processing. The system of certification of Manasik Hajj supervisor have been test with a method. Black box testing is the most method to ensure the application is suitable scenario test. This Web-Based Hajj Manasik Supervisor Certification Information System at the West Java Ministry of Religion Regional Office makes it easier to improve the implementation of certification. The design of this system can reduce in addition to overcoming the obstacles of implementing certification to be more accurate.
An Analysis of Phrase based SMT for English to Manipuri Language

Statistical Machine Translation (SMT) is one ruling approach adopted for developing major translation systems today. Here, we report a phrase-based SMT system from English to Manipuri. The variance in the structure and morphology between English and Manipuri languages and the lack of resources for Manipuri languages pose a significant challenge in developing an MT system for the language pair. In comparison, English has poor morphology and SVO structure and belongs to the Indo-European family. Manipuri language has richer morphology and SOV structure and belongs to the Sino-Tibetan family. Manipuri has two scripts- Bengali script and Meitei script. Here the Bengali script is used for developing the system. Our system uses the Moses toolkit. We train and test the system using the tourism, agriculture and entertainment corpus. Further, we use the BLEU metric to evaluate the systems' performance.
A Study on the Metrics-Based Duplicated Code Type Smell Detection Techniques Relating the Metrics to Its Quality

The presence of bad smells in code hampers software’s maintainability, comprehensibility, and extensibility. A type of code smell, which is common in software projects, is “duplicated code” bad smell, also known as code clones. These types of smells generally arise in a software system due to the copy-paste-modify actions of software developers. They can either be exact copies or copies with certain modifications. Different clone detection techniques exist, which can be broadly classified as text-based, token-based, abstract syntax tree-based (AST-based), metrics-based, or program dependence graph-based (PDG-based) approaches based on the amount of preprocessing required on the input source code. Researchers have also built clone detection techniques using a hybrid of two or more approaches described above. In this paper, we did a narrative review of the metrics-based techniques (solo or hybrid) reported in the previously published studies and analyzed them for their quality in terms of run-time efficiency, accuracy values, and the types of clones they detect. This study can be helpful for practitioners to select an appropriate set of metrics, measuring all the code characteristics required for clone detection in a particular scenario.
How vector space models disambiguate adjectives: A perilous but valid enterprise

Abstract The present study is part of a larger research project developing computational tools for large-scale corpus-based semantic analyses. One such tool represents semantic structure with vector space models (VSMs). The paper shows that this tool and the models built require a deeper understanding, especially with a view to how its results relate to cognitive theories of meaning. Although token-based VSMs are increasingly used in corpus-based cognitive semantics, we believe it is insufficiently appreciated how alternative parameter settings deal with a range of semantic issues, such as granularity of meaning, prototypicality of the domain of application and interaction with syntactic patterns. For the purpose of this paper, we will focus on only one of those issues, viz. the prototypicality of the domain of application, presenting the results of three of our case studies on the Dutch adjectives heilzaam , hoekig , hachelijk and geldig . The models presented are built from a 520MW corpus of contemporary Dutch and Flemish newspapers and by varying parameters such as window size, part-of-speech and frequency thresholds in the selection of features. The resulting VSMs are evaluated through visual analytics: although multidimensional, they can be reduced to 2D and represented in scatterplots where more similar tokens appear closer to each other. The color-coding with manual sense tags employed here makes it possible to compare the groupings provided by human annotators with those of the computational models in a way consistent with the cognitive approach to meaning and categorization.
The Training Need Assessment of ICT Technology by Industry in the Digital Transformation Era - Focused on SME Executive’s Awareness in Seoul -

Due to the Fourth Industrial Revolution, companies are facing numerous internal and external changes. Respond to these changes, company should adopt digital transformation by applying ICT technologies for continuous growth and development. However, SMEs that have limitations in purchasing and procuring physical and human resources experience difficulties in responding to these changes. As one of the alternatives to this, the need to strengthen the ICT technology capabilities of SME’s members is proposed. To improve the effectiveness of education to strengthen ICT technology capabilities of SME, the purpose of this study were to do the training need assessment of ICT technology by industry in the Digital Transformation era. For conducting the study, First, collect data from people who have completed ‘Digital Leadership courses for Seoul SME’ by Seoul Business Agency. Second, Based on collected data, conduct t-test, Borich’s educational need equation and the Locus for Focus model. The result of this study founds that ICT technologies belonging to the top priority group were different for each industry. In addition, Artificial Intelligence and Virtual reinforcement Reality was found to be factors that should be considered first in most industries. Also, Big data was found to be a factor that should be considered first in a specific industry. Based on the findings, theoretical and practical implication were discussed to improve the effectiveness for SME’s ICT technology capabilities.
Automated vs. manual case investigation and contact tracing for pandemic surveillance: evidence from a stepped wedge cluster randomized trial

BackgroundCase investigation and contact tracing (CICT) is an important tool for communicable disease control, both to proactively interrupt chains of transmission and to collect information for situational awareness. We run the first randomized trial of COVID-19 CICT to investigate the utility of manual (i.e., call-based) vs. automated (i.e., survey-based) CICT for pandemic surveillance.MethodsBetween December 15, 2021 and February 5, 2022, a stepped wedge cluster randomized trial was run in which Santa Clara County ZIP Codes progressively transitioned from manual to automated CICT. Eleven individual-level data fields on demographics and disease dynamics were observed for non-response. The data contains 106,522 positive cases across 29 ZIP Codes.FindingsAutomated CICT reduced overall collected information by 29 percentage points (SE = 0.08, p < 0.01), as well as the response rate for individual fields. However, we find no evidence of differences in information loss by race or ethnicity.InterpretationsAutomated CICT can serve as a useful alternative to manual CICT, with no substantial evidence of skewing data along racial or ethnic lines, but manual CICT improves completeness of key data for monitoring epidemiologic patterns.FundingThis research was supported in part by the Stanford Office of Community Engagement and the Stanford Institute for Human-Centered Artificial Intelligence.