Reviewerly/exHarmony huggingface dataset:
Research on K12 Exercise Recommendation Algorithm based on Educational Neuroscience

In order to help primary and middle school students improve their self-efficacy and reduce their workload, a K12 exercise recommendation algorithm based on educational neuroscience is proposed in this paper. Experimental tests show that, under the same number of classroom exercises, the exercises recommended by the algorithm can help the target students to achieve greater ability improvement, and thus achieve the effect of reducing the workload of primary and secondary school students.
Study on the Application of Learning-Style in Junior Middle School English Teaching

This research takes the 8 grade of Z middle school in H Province as the research object, adopts Reid's learning-style tendency questionnaire, analyzes it through SPSS17.0 software, describes and analyzes the learner's style tendency from the aspect of learning-style, and explores the relationship between the perception and learning-style of junior high school student English learning performance.Based on the research results, the author points out that teachers' and students' understanding of perceptual learning-style needs to be improved, and puts forward specific suggestions on how to apply perceptual learning-style in junior high school English teaching.
Bernie Krause i jego dźwiękowe archiwa. Między dziką naturą a ucieleśnioną relacją

Bernie Krause and His Sound Archives. Between Wild Nature and Embodied Relation The paper aims to compare two natural sound archives by Bernie Krause, Wild Sanctuary (being developed since 1968) and Dawn Chorus (established in 2020), from the perspective of ecology paradigms changes that have occurred over the years. The author assumes that both of the abovementioned projects are consistent with each other in respect of the goals formulated by Krause – they are thought to affect their listeners and evoke ecological attentiveness among them – but differ in terms of their structure and presupposed strategies of enhancing human-environment entanglements. The first part of the article is dedicated to the description of analysed projects. Also, the article considerations are being positioned as the amplification of the previously published paper by Renata Tańczuk (who treated Wild Sanctuary as an Anthropocene archive). Secondly, the author investigates the visibility of the archive production process in the forms in which they were publicly shared. Afterwards, the conceptualizations of the Nature underlying both of the archives are described. Finally, the author presents the differences in the affective agencies of Wild Sanctuary and Dawn Chorus, coming to the conclusion that the latter project, not the former one, responds to the contemporary requirements imposed on the pro-environmental artistic and research activities.
Identification and Extraction of Features from Malayalam Poems for Analyzing Syllable Duration Patterns

Text-to-speech (TTS) synthesis is an active area of research to generate synthetic speech from the underlying text. Compared to English and many European languages, TTS is yet to mature in Malayalam, the principal language of the South Indian state of Kerala. A syllable has to be uttered with proper durational and prosodic characteristics to emulate natural speech. When it comes to poems in Malayalam, many of them have an inherent rhythm attached to them. In Malayalam, this property is characterized by the Vruta [ 28 ] in which the poem is written. Vruta decides the meter of narration of the poem. Therefore, it is only consequential that Vruta can give away vital cues about the durational and prosodic characteristics of the poem verses recited. This study intends to identify the features that determine the durational characteristics of a poem written in a particular Vruta and develop an algorithm to extract those features required to build a dataset to model the duration of syllable utterances for tuneful TTS in Malayalam. Poems written in three Vrutas, namely Kakali, Manjari, and Keka, are considered in this study. Nineteen extractible features from the orthographic representation of a poem are identified for this purpose. A standard dataset is built using these extracted features. Later, support vector machine and feed forward neural network based estimators are proposed to model the duration of Malayalam poem syllables for tuneful speech synthesis. The hyperparameters are optimized using the GridsearchCV algorithm from the Scikit-learn machine learning library [ 15 ].
Composing Word Embeddings for Compound Words Using Linguistic Knowledge

In recent years, the use of distributed representations has been a fundamental technology for natural language processing. However, Japanese has multiple compound words, and often we must compare the meanings of a word and a compound word. Moreover, word boundaries in Japanese are unspecific because Japanese does not have delimiters between words, e.g., “ぶどう狩り” (grape picking) is one word according to one dictionary, whereas “ぶどう” and “狩り” are different words according to another dictionary. This study describes an attempt to compose word embeddings of a compound word from its constituent words in Japanese. We used “short unit” and “long unit,” both of which are the units of terms in UniDic—a Japanese dictionary compiled by the National Institute for Japanese Language and Linguistics—for constituent and compound words, respectively. Furthermore, we composed a word embedding of a compound word from the word embeddings of two constituent words using a neural network. The training data for the word embedding of compound words was created using a corpus generated by concatenating the corpora divided by constituent and compound words. We propose using linguistic knowledge for compositing word embedding to demonstrate how it improves the composition performance. We compared cosine similarity between composed and correct word embeddings of compound words to assess models with and without linguistic knowledge. Furthermore, we evaluated our methods by the ranking of synonyms using a thesaurus. We compared several frameworks and algorithms that use three types of linguistic knowledge—semantic patterns, parts of speech patterns, and compositionality score—and then investigated which linguistic knowledge improves the composition performance. The experiments demonstrated that the multitask models with the classification task of the parts of speech patterns and the estimation task of compositionality scores achieved high performances.
Passive Video Quality Assessment Using Lab and Remote Testing

In this chapter, research about the assessment of video quality for gaming content will be provided. At first, a dataset that was used for the development of the ITU-T Rec. G.1072 will be presented. The dataset was created in a laboratory environment using the passive test paradigm described in Chap. 3 . Next, some results of the collected video quality ratings will be illustrated. While QoE assessment studies traditionally make use of controlled laboratory environments, there are also other possibilities to conduct user studies without a laboratory environment. Especially during the COVID-19 pandemic, which prevented many researchers from performing lab studies, the concept of supervised and unsupervised remote studies got lots of attention. By using such a remote study design, two studies assessing video quality ratings of similar conditions as in the previously mentioned dataset were conducted. These two studies allow to address three research topics that will be the focus of the remainder of this chapter. At first, it will be investigated whether video quality ratings obtained using the remote study design are comparable to those collected in the lab environment. Second, a comparison between video quality ratings collected using a stimulus duration of 20 s instead of 30 s will be performed, which tries to answer whether it is enough to use a shorter stimulus duration as proposed in ITU-T Rec. P.809. Lastly, the differences between using a discrete 5-point ACR scale and the extended continuous 7-point scales will be investigated.
Conclusion and Outlook

In this chapter, at first, the previous chapters’ key insights and research findings are summarized. This includes the state-of-the-art of gaming QoE for cloud gaming systems, the performed steps for the creation of an assessment method covering a broad spectrum of quality features of the cloud gaming taxonomy, studies about passive and interactive subjective tests in different test environments, i.e., in the laboratory, via remote testing, and video crowdsourcing, the development of the Gaming Input Quality Scale (GIPS), research about the importance of game characteristics, and finally a quantitative evaluation of the assessment method and the taxonomy utilizing Structural Equation Modeling (SEM). Next, the five overarching research questions covered in the book will be answered. Afterward, the most valuable contributions of the presented research are summarized. The chapter will close with insights into the limitations of the research findings and provide some thoughts about possible future work.
Exploration of the Emergency Renovation of Sports Venues Resilience Space Based on Green Building Technology Under the Normal Epidemic Situation

The sudden outbreak of COVID-19 has caused a surge in medical demand. It has inspired people to continuously explore how to transform public buildings such as gymnasiums in a fast, low-cost and green way during emergencies. The article studies the feasibility of applying gymnasium to sudden public events, discusses the design methods for the renovation of gymnasium space, water supply and drainage system, ventilation system and intelligent system in emergency situations. The focus is on preventing cross-contamination, preventing backflow contamination, pressure shaving, airflow organization, and system control. Through these design methods, the gymnasium has the characteristics of efficient, adaptable and inclusive epidemic prevention. The application prospect of green building technology in emergency reconstruction was explored, and a reference is put forward for the design and reconstruction of gymnasiums in the post-epidemic era with “combination of epidemic control” and improving the resilience space of gymnasiums.
Introduction

The video gaming industry created the largest entertainment sector in our times that is rapidly growing and impacts the lives of many generations worldwide. However, especially highly complex games demand players to possess powerful hardware. Thus, a new concept called Cloud Gaming evolved in recent years to allow players to remotely control these games on a cloud server. To overcome various challenges posed by this new paradigm, and to ensure customers’ satisfaction, companies have a strong interest in evaluating and predicting the Quality of Experience (QoE) of players. However, the applicability of established and validated assessment methods to measure the highly multidimensional construct of gaming QoE of cloud gaming services is very limited. Following up on these circumstances, in this chapter, the detailed motivation and the scope of the research presented in this book will be explained. This includes the perspective of the considered stakeholders, derived research questions that will be targeted, as well as an overview of the structure and topics of the remaining book chapters.
DESIGNING A HUMAN DIGITAL TWIN AND ITS CLASSES OF TASKS IN THE SUPPLEMENT TO STAFF TRAINING

The concept of “digital twin” is the digital transformation basis of any modern enterprise. To meet the demands of Industry 4.0, the modern economy is faced with the task of forming a citizen’s image, which cannot be effectively solved without creating its digital twin. These data can help solve such classes of problems as forming staffing for territorial development initiatives, forecasting and planning personnel dynamics in accordance with the organization strategy, and many others. The technology of creating a person’s digital twin is considered, UML models of this process are built, existing technologies and the possibility of their application for each stage are considered. When examining an object, after identifying all the characteristics that are essential for solving the problem of building a digital twin, a mathematical model is made. At the data collection stage, the proposed method for integrated processing of unstructured information can be based on constructing ontology. The stage of a digital design consists of building an engineering model, for which the necessary technical tools and software applications used in the digital twin construction are determined. The stage of developing a digital twin is analyzed on the example of the educational process. The article discusses the existing approaches to applying this technology and the tasks that can be solved with its application to the education system. The article considers the design of an intelligent decision-making system based on processing the collected data of a student’s digital portrait. With its help, it will be possible to predict the student’s life path: his or her career, professional development, health, social activity, etc.
DMSTL: A Deep Multi-Scale Transfer Learning Framework for Unsupervised Cross-Position Human Activity Recognition

Human activity recognition (HAR) based on wearable sensors has been a prosperous research topic in recent years. Considering that the sensor may be worn at diverse body positions, obtaining enough labeled human activity data for each body-worn position is usually expensive and labor-intensive. Furthermore, the variability and diversity of data distribution induced by different body-worn positions make the HAR model trained on data collected from one body position perform poorly for other body-worn positions. To achieve accurate HAR with low labeling cost, which we call unsupervised cross-position HAR, in this article, we propose a deep multiscale transfer learning (DMSTL) model. In the model, we first introduce an unsupervised source selection method to select the most similar source domain for transferring domain knowledge. Then, we develop a multiscale spatial–temporal Net (MSSTNet) to learn comprehensive multimodal representations from multiple feature subspaces. Finally, we design the category-level adaptation module and the domain-level adversarial module for learning domain-invariant features. We conduct extensive experiments on three public HAR data sets and demonstrate the reasonable generalization performance of the DMSTL, which remarkably outperforms other state-of-the-art baselines.
${{\sf PEBA}}$: Enhancing User Privacy and Coverage of Safe Browsing Services

To keep web users away from unsafe websites, modern web browsers enable the embedded feature of safe browsing (SB) by default. In this work, through theoretical analysis and empirical evidence, we reveal two major shortcomings in the current SB infrastructure. First, we derive a feasible tracking technique for industry best practice. We show that the current mitigation techniques cannot eliminate the threat of de-anonymization permanently. Second, we gauge the effectiveness of blacklists provided by major vendors. Our discovery indicates the urge for blacklist integration in order to boost service quality. In light of this, we propose a new three-party paradigm <inline-formula><tex-math notation="LaTeX">${{\sf PEBA}}$</tex-math></inline-formula> with an intermediate third party decoupling the direct interaction of users and proprietary blacklist vendors. To satisfy practical usage requirements, we instantiate our design with trusted hardware, detailing how it can be leveraged to fulfill the requirements of privacy enhancement and broader content coverage at the same time. We also tackle numerous implementation challenges that emerged from this proxy-based and hardware-enabled solution. Extensive evaluation confirms that <inline-formula><tex-math notation="LaTeX">${{\sf PEBA}}$</tex-math></inline-formula> can balance well among desirable goals of security, usability, performance, and elasticity, making it suitable for deployment in practice.
Resúmenes al Español

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Resúmenes al Español. Inclusion 1 September 2022; 10 (3): 253–254. doi: https://doi.org/10.1352/2326-6988-10.3.253 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest Search
Optimization Method of University English Guidance Based on Enhanced Decision Tree Model in the Context of Big Data

The development of economy has propelled the university’s English guidance system to pay attention to students’ development which further impacts the development of the country because students are the future of any nation. The college English function is rather complicated, and thus, reunderstanding and positioning of the university’s English guidance plays a significant role in the field of education. The existing teaching guidance system puts more emphasis on teaching content and teaching mechanism resulting in lack of application of the knowledge gained. Thus, it is extremely important to clarify and highlight the objectives of college English learning and on the basis of these design personalized curriculum which could convert the skilled talents into compound ones. The present paper explores the evaluation model of the University English Guidance effect based on an enhanced decision tree algorithm. The model has the potential to improve accuracy and efficiency of University English Guidance effect evaluation system and meet the requirements of University English Guidance effect evaluation. The framework constructs a multi-index University English Guidance effect evaluation system constituting of teachers and students as the main entities. It considers the University English Guidance effect evaluation index data as the input sample and implements the least squares support vector machine to realize the University English Guidance effect evaluation. The effectiveness of this model is verified by experiments, which lays a foundation for the optimization of University English Guidance.
Improved Few-Shot Learning for Images Classification

Few-shot learning is an approach that classify unseen classes with limited labeled samples. We propose improved networks of Relation Network to classify images with small samples. The improved networks is ECA Relation Network (ECA-RNET). The accuracy of ECA-RNET is 52.24% and 67.85% on 5-way 1-shot and 5-way 5-shot of mini-ImageNet dataset, respectively.
The Establishment of organizational Culture in Multinational Cellular Telecommunication Companies

This study aims to determine whether there is a dominant organizational culture in cellular telecommunications companies in the Indonesian capital city using survey methods and class actions. In brief, the results of the study show that the dominant organizational culture with Company A is a rule oriented, Company B is organizational ties or glue, Company C is an innovation oriented while Company D is a result-oriented. In the context of audit and control techniques in the information systems department, students who are prepared as the best graduates placed in the company tend to think creatively when the approaches like jigsaw seem to increase focus and capability accordingly. This study implore survey to respective employee in each company. On the other hand, the authors have the assumption that learning method in specific course might influences the characteristic of the student then creating education culture as well give some portion of organizational culture after they got accepted for work. Therefore, organizational culture might hinders this kind of improvement which most the graduates are trapped in the aspects procedural and clerical activities of audit and control of information systems, conflicted with the undergoing education on campus who preferably use certain type of approaches in learning techniques.
Research on small object detection methods based on deep learning

The efficiency and accuracy of object detection are steadily improving due to the development and widespread application of deep learning. However, small object detection remains a challenge. When employing mainstream object detection algorithms, small objects have low resolution, little feature information, and weak expressiveness, which leads to missed false detection and poor detection accuracy. This paper systematically describes on small object detection methods based on deep learning, divides them into four categories based on small object detection optimization methods, such as data augmentation, multi-scale feature fusion, contextual features, and optimized backbone networks, and analyzes the benefits and drawbacks of each method, and offers a forecast on future research directions.
Research on Anti-sorting Signal Design Technology based on Precise Combination of Stagger Signal PRI

With the increasing informatization and intelligentization of modern war, electronic warfare is becoming the key to win the war. Improving the anti-reconnaissance performance of radar is an important means to enhance radar electronic countermeasures. Aiming at the sequence difference histogram algorithm, which is widely used in the field of signal sorting, and from the point of view of destroying the sorting and pulse retrieval of the irregular signal, reasonable and accurate combination design of the pulse repetition period of the irregular signal can achieve the target of anti-sorting. Finally, the simulation results show that the designed signal can effectively counter the sorting algorithm.
A Study of Computer Aided Tools for Evaluation of Text in English as A Foreign Language

The Automated Essay Score System is a web-based system that uses cloud computing to score and evaluate essay entries. It sends real-time score feedback to encourage revision. These essay scoring have alleviated some of the burden on teachers to improve students' writings, but there are still some issues that are not yet addressed. This research looks at the benefits and drawbacks of automated evaluation systems, as well as whether they will continue to grow in the future. This study explains the existing issues based on feedback from teachers and students who use the online writing system in order to improve its effectiveness in writing. A few research questions are addressed in this study concerning the usefulness of an automated essay grading system. What are the most significant issues with computerized essay scoring systems? Will the automated essay scoring system see a good trend in the not-too-distant future? When assessing writing, what are the best ways to utilize online resources, such as automated essay scoring tools?
ANB-PW: An Adaptive Distinguishing Attack Method based on Naive Bayes

Honeywords are decoy passwords placed in user’s account to detect whether password files are leaked. The distinguishing attack of honeywords plays a very important role in evaluating the strength of honeywords. However, the existing attack methods cannot simulate the ability of the attacker, leading to inaccurate evaluation of honeywords strength. In this paper, we propose an adaptive distinguishing attack method ANB-PW to construct a Naive Bayes classifier based on passwords and honeywords. In addition, ANB-PW uses guessed passwords to adjust its guessing strategy in real time. Our results suggest that compared with Top-PW and PCFG, ANB-PW has better attack ability.