Reviewerly/exHarmony huggingface dataset:
Personalized Automated Driving Decision Based on the Gaussian Mixture Model

Abstract：Under the era of big data, a personalized driving decision system would further improve the safety and comfort of the intelligent vehicle with the help of the research on naturalistic driving data.An improving method for Bézier curve path is proposed to achieve the efficient path planning for intelligent vehicle, and then, the quadratic programming for speed and acceleration of ego vehicle is developed, and a speed predictive model for naturalistic driver is built based on the Gaussian process theory, which lay the foundation for collision avoidance in path planning.A personalized path assessing method is developed based on Gaussian mixture model and combining with path rationality, planning homogeneity and speed fluctuation objectives to filter the personalized and agreeable target path.With the consideration of complete control targets, an optimal controller with quadratic programming is designed, and the reference path and target control parameters for automated driving are obtained simultaneously.The naturalistic driving data could be effectively applied with the proposed methods and the driving performance such as safety, comfort, personality would be improved. Key words：Bézier
Implementation of WB-BL Learning in Improving Caring Ability

Caring is one of the abilities that must be achieved by Bachelor of Nursing students.This ability will be widely used in the application of nursing services through the nursing process.Caring is a skill that is needed in the provision of nursing care, where the maximum implementation of caring will provide a feeling of comfort and calm so that the patient will be open and cooperative in providing nursing care.Until now, the ability of caring in its application in the field of nursing has not yet achieved maximum results.This study aims to analyze the increase in caring ability with WBBL learning.The design in this study was Quasi Experimental using the pre-experimental design approach.Static Group Comparison, in this case the researcher measured the caring ability of the respondent first, then was given the WBBL learning action and after that, the caring ability was measured again.The population in this study were 80 Bachelor of Nursing students.The sample used was part of Bachelor of Nursing students.The sampling technique used was simple random sampling with a sample of forty people.The data collection was carried out using observation sheets.After that the data were processed and analyzed using the Spearman Rho statistical test with ρ ≤ 0.01.If the results are less than α 0.05, the research results are said to be significant.Based on the Spearman Rho statistical test, it was found that ρ = 0.000.This shows that WBBL learning can improve caring abilities in students.Based on the data, the caring ability of students was obtained with an average value of 82.63.Thus, WBBL learning can be used to improve caring abilities.
Information Retrieval Based on the Sequential Hypertext Induced Topic Search in Web Pages

The World Wide Web is widely used as a global standard for collecting information. Millions of Internet pages provide information on the existence of users worldwide. To extract the relevant web page, a search engine application is applied to the online search because of the important information through search queries entered by the user or page rank modules. The size and complexity of the World Wide Web are growing. That’s why it needs to be improved as the pages of information are more important in terms of better user experience who enters a query in a search engine. Website traffic analysis and network users in the development of Internet usage are critical in the world. The way of growth point to extract intelligence information resources knowledge and develop a variety of client and server-side tools. The traditional Web search engines take a long time to browse hundreds of thousands of users to return search results. Online library search engines and other large file libraries (such as customer support database product specification database, News Release Archives, News archive of articles, etc.) become difficult and expensive to file in manually. To overcome the problem, in this work, Sequential Hypertext Induced Topic Search (SHITS) method for web search is proposed and the role of web association is to provide web search engine’s different implementations of relevant results. The proposed method is using web search based on user behavior analysis using the Information Retrieval (IR). The web-based database of semantically related concepts is connected in Uniform Resource Locators (URL). It also uses the SHITS algorithm to extract useful keyword frequency from a huge set of words on each page and keywords. The proposed algorithm is used in many optimizations and the user’s useful results are obtained.
IMPROVING ENGLISH STUDENTS’ SPEAKING SKILLS THROUGH AN ACTION LEARNING STRATEGY

For students of English education, speaking skills are of paramount importance. However, in the context of English students at Universitas Bengkulu, many students seemed to still struggle with speaking skills. Therefore, to cope with this problem, this classroom action research was conducted with the aim of improving the speaking skills of students who took English conversation course by applying action learning strategy (ALS) since this strategy was scientifically promising. Two phases of the class action design were employed in this research project. The data were garnered using observations, interviews, and English-speaking test. It was evident from the first phase of observation that almost all students recommended the ALS. The data from the interview revealed that a significant proportion of students had profited from this approach, and there were few students who spoke in front of the class. The first phase's precise decision how an average score of 74 was attained by the students. At the second phase, the observation and interview data showed that nearly all students actively participated during discussions and had good improvements of English-speaking skills. The result revealed that the implementation of ALS in the conversation course in semester 1 improves S1 students’ speaking skills.
THE CHANGE OF GREETING WORDS IN BESEMAH LANGUAGE AND ITS IMPLICATION FOR LEARNING REGIONAL LANGUAGES

This research described the addressing terms and analyzed the addressing term shifts of Besemah community in Pagar Alam regency. This research also described the implementation of Besemah vernacular language on vernacular langauge lesson. The applied method was descriptive qualitative method and objective appraoch with four research techniques. The data source was the community of Besemah, consisting of five districts in Pagar Alam regency. The results found five hundred and fifty-two addressing terms. These words include addressing terms within family context and community context. Then, the researchers also found the shifting addressing terms. Four of the words were not found at the present days. The result implications have some implications on vernacular language lesson and could be the teaching materials for primary school levels.
Techniques for predicting dark web events focused on the delivery of illicit products and ordered crime

Malicious actors, specially trained professionals operating anonymously on the dark web (DW) platform to conduct cyber fraud, illegal drug supply, online kidnapping orders, CryptoLocker induction, contract hacking, terrorist recruitment portals on the online social network (OSN) platform, and financing are always a possibility in the hyperspace. The amount and variety of unlawful actions are increasing, which has prompted law enforcement (LE) agencies to develop efficient prevention tactics. In the current atmosphere of rapidly expanding cybercrime, conventional crime-solving methods are unable to produce results due to their slowness and inefficiency. The methods for accurately predicting crime before it happens "automated machine" to help police officers ease the burden on personnel while also assisting in preventing offense. To achieve and explain the results of a few cases in which such approaches were applied, we advise combining machine learning (ML) with computer vision (CV) strategies. This study's objective is to present dark web crime statistics and a forecasting model for generating alerts of illegal operations like drug supply, people smuggling, terrorist staffing and radicalization, and deceitful activities that are connected to gangs or organizations showing online presence using ML and CV to help law enforcement organizations identify, and accumulate proactive tactics for solving crimes.
Physical Interaction and Psychological Immersion: Analysis of Digital Technologies in Museum Exhibitions

In contemporary society, digital technologies are increasingly applied in museum exhibitions, in response to both the current digital economy, the experience economy, and the visiting needs.Rather than assessing any technical issues, this study aims to clarify the principles behind digital technologies being used in museum exhibitions through the use of examples and case studies.Based on Csikszentmihalyi's flow experience theory, it will be argued that digital technologies should enhance physical interaction and create psychological immersion.Moreover, the usage of digital technologies is fundamental to improving the intellectual experience for visitors, rather than to show off technology or cater to pleasure.
Data Driven Dimensionality Reduction to Improve Modeling Performance

In a number of applications, data may be anonymized, obfuscated, or highly noisy. In such cases, it is difficult to use domain knowledge or low-dimensional visualizations to engineer the features for tasks such as machine learning, instead, we explore dimensionality reduction (DR) as a data-driven approach for engineering these low-dimensional representations. Through a careful examination of available feature selection and feature extraction techniques, we propose a new class named feature clustering. These new methods could utilize different forms of clustering to help evaluate the relative importance of features and take on properties different from the well-known DR algorithms. To evaluate these algorithms, we develop a parallel computing framework that optimizes their hyperparameters on a sample of application datasets. This framework harnesses the parallel computing power to examine a large number of parameter combinations and enables hyperparameter tuning and model tuning purely based on observed performance. This optimization framework provides mechanism for users to control computational cost and is able to examine many parameter choices in seconds. On a set of building energy data where the key features are known based on domain knowledge, the optimized DR algorithms indeed identify the expected main drivers of building electricity usage: outdoor temperature and solar radiance. This shows the automated optimization procedure is able to find known features. In terms of modeling accuracy, a distance correlation-based feature clustering method outperforms other DR algorithms including the well-known KPCA, LLE, and UMAP on two different tests.
A Comparative Study for Block Chain Applications in the Manet

MANET- Mobile Ad-hoc Networks are famous for their infrastructure-less arrangement for communication. In this network, nodes are self-organized and can act as router. They are battery operated and self-organizing. Block chain is a new concept from 2008 and researchers are trying the possible application of Block chain in many sectors including MANETs. This paper surveys the existing researches done in applying block chain in a MANET environment. Block chain is mainly used in MANETs for improving security while routing packets from one node to another. Some researchers have proposed trust models using block chain. This paper reviews some of the existing approaches where block chain is used in MANETs for routing the packets, creating trust models, and dealing with network partitioning problem and scalability problem. This paper acts as a review paper to study on block chain applications in MANET.
Cryptocurrencies go quantum

Using quantum computers to mine cryptocurrencies like bitcoin could slash their huge electricity use, finds Alex Wilkins
mmSign: mmWave-based Few-Shot Online Handwritten Signature Verification

Handwritten signature verification has become one of the most important document authentication methods that are widely used in the financial, legal, and administrative sectors. Compared to offline methods based on static signature images, online handwritten signature verification methods are more reliable because of the temporary dynamic information (e.g., signing velocity, writing force, stroke order) that alleviates the risk of being forged. However, most existing online handwritten signature verification solutions are reliant on specific signing devices (e.g., customized pens or writing pads) and require extensive data collection during the registration phase, resulting in poor adaptability and applicability for new users. In this paper, we propose mmSign, a millimeter wave (mmWave)-based online handwritten signature verification system, which enables accurate sensing of the user’s hand movements when signing through the superior sensing capability of mmWave. mmSign extracts the time-velocity feature maps from the captured mmWave signals by the carefully designed signal processing algorithms, and then exploits a transformer-based verification model for signature verification. In addition, a novel meta-learning strategy with proposed task generation and data augmentation methods is introduced in mmSign to teach the verification model to learn effectively with limited samples, allowing our model to quickly adapt to new users. Extensive experiments show that mmSign is a robust, efficient, and secure handwritten signature verification system, achieving 84.07%, 87.31%, 91.12%, and 96.54% verification accuracy when 1, 3, 5, and 10 labeled signatures are available, respectively, while being resistant to common forgery attacks.
Characterizing the Scalability of Graph Convolutional Networks on Intel<sup>®</sup> PIUMA

Large-scale Graph Convolutional Network (GCN) inference on traditional CPU/GPU systems is challenging due to a large memory footprint, sparse computational patterns, and irregular memory accesses with poor locality. Intel’s Programmable Integrated Unffied Memory Architecture (PIUMA) is designed to address these challenges for graph analytics. In this paper, a detailed characterization of GCNs is presented using the Open-Graph Benchmark (OGB) datasets to determine the viability of PIUMA as a potential solution to GCN scalability. First, the extent of sparse matrix dense matrix multiplication (SpMM) as a performance driver for GCN on CPU and GPU is explored, offering a methodology for predicting GCN behavior as a function of dataset characteristics. Second, an SpMM kernel optimized for PIUMA is described and investigated for sensitivity to system parameters including memory bandwidth, latency, and thread count. SpMM scalability on PIUMA is demonstrated, while the scalability limitations of a Xeon-optimized SpMM implementation are discussed. Finally, GCN performance is compared on PIUMA versus a Xeon CPU system and Ampere GPU system, showing impressive results on PIUMA for largescale datasets.
Learning Semantic-Visual Embeddings with a Priority Queue

The Stochastic Gradient Descent (SGD) algorithm and margin-based loss functions have been the learning workhorse of choice to train deep metric learning networks. Often, the random nature of SGD will lead to the selection of sub-optimal mini-batches, several orders of magnitude smaller than the larger dataset. In this paper, we propose to augment SGD mini-batch with a priority learning queue, i.e., SGD+PQ. While the mini-batch SGD replaces all learning samples in the mini-batch at each iteration, the proposed priority queue replaces samples by removing the less informative ones. This novel idea introduces a sample update strategy that balances two sample removal criterion: (i) removal of stale samples from the PQ that are likely outdated, and (ii) removal of samples that are not contributing to the error, i.e. their sample error is not changing during training. Experimental results demonstrate the success of the proposed approach across three datasets.
EFL STUDENTS’ VOICES ON BLENDED LEARNING: PREFERENCES AND CHALLENGES

The objective of the present research was to know how EFL students perceive blended learning by using LMS with Moodle in speaking class at State Islamic University of Mataram especially in English Language Education Study Program. This research employed mixed method by involving 94 students as the respondents to fill out the questionnaires ítems and were invited to the interview. It is found that students positively perceive the use of blended Learning using LMS in speaking class. Most of the students chose to “agree” and “neutral” on their responses to the questions. Furthermore, through the interview, they mostly view LMS positively. It means that Learning speaking with blended learning using LMS had a good impact on them in learning process. Therefore, Blended Learning can be an alternative way in managing a learning process, especially during online learning. Challenges experienced by the students were also discussed in this study.&#x0D; &#x0D;
Can weight hysteresis in a neural network judge the continuity/discontinuity of a phase transition?

Abstract We often encounter hysteresis associated with short-term memory stored in our brain when we see continuously varying pictures in back-and-forth directions. We expect a neural network (NN) to also experience hysteresis in recognizing information back and forth depending on its continuity. This study shows that using equilibrium configurations obtained from the well-defined models undergoing the phase transitions as learning data, the weights of an NN can exhibit hysteresis behaviors against sequential learning, increasing (or decreasing) an external field or temperature. Indeed, the weights’ hysteresis clearly shows up when an NN learns back and forth a series of configurations passing through a continuous transition or a crossover, whereas no hysteresis arises for discontinuous transitions. This fascinating finding opens up a new application of machine learning to judge the order of a phase transition from the suitably visualized changes in microscopic configurations without setting a specific model.
Teacher- Student Relationship of University Students in relation to their Academic Achievement

The present correlational study makes an attempt to examine the interpersonal relationship between the university students and their teacher along with its effect on their academic achievement.One hundred twenty post-graduate students (60 girls & 60 boys) were selected by using stratified purposive sampling.The investigator collected data by using self-made questionnaire.The data were analysed by simple percentage, mean, standard deviation, t-test, product-moment correlation, and ANOVA (two-way).The researcher found that 40 % university students have very high relationship with their teachers and the teacher-students relationship have significantly positive effect on their academic achievement.This study also shown that no difference exists in teacher-students relationship by considering gender.Also, the result of two-way ANOVA highlighted that there is a significant difference exist in academic achievement in term of the categories of relationship, but no difference examines by considering the interaction of both gender and categories of relationship.
PEDAGOGICAL DEVICES FOR QUALIFICATIONS IN HIGHER EDUCATION

For the preparation of this document, a documentary review was carried out on the production and publication of research works related to the study of the variables Pedagogical Devices and Degree in Higher Education. The purpose of the bibliometric analysis proposed in this document was to know the main characteristics of the volume of publications registered in the Scopus database during the period 2018-2022, achieving the identification of 58 publications. The information provided by this platform was organized through graphs and figures categorizing the information by Year of Publication, Country of Origin, Area of Knowledge and Type of Publication. Once these characteristics have been described, the position of different authors towards the proposed theme is referenced through a qualitative analysis. Among the main findings made through this research, it is found that Spain, with 13 publications, was the country with the highest scientific production registered in the name of authors affiliated with their institutions . The Area of Knowledge that made the greatest contribution to the construction of bibliographic material referring to Pedagogical Devices and Qualifications in Higher Education were the Social Sciences with 29 published documents, and the Type of Publication most used during the period indicated above was the Journal Article with 42 documents of the total scientific production.
GREEN SERVICE QUALITY AND CUSTOMER SATISFACTION FOR MALAYSIA AIRLINE COMPANIES

Purpose: The purpose of this paper is to propose a framework of airlines customer’s evaluation of green service quality toward customer’s satisfaction provided by Malaysia Airline Companies.&#x0D; &#x0D; Design/Methodology/Approach: This paper reviews the conceptual and empirical literature to develop a model of the various aspects of the service quality – customer satisfaction relationship encompassed by the concept of ‘airline customer satisfaction’ and to assess the advantages and disadvantages of alternative methods of measurement. Six conceptual dimensions are identified: (1) Airline tangibles (2) Terminal tangibles (3) Personnel (4) Empathy (5) Image (6) Green Airport (7) Customer Satisfaction&#x0D; Findings: Integration of these dimensions into a conceptual framework that describes the relationships among the components and how they collectively result in green service quality and customer satisfaction. We also elucidate future research directions for management scholars&#x0D; &#x0D; Research Limitation/Implication: The research examines the relationship between green service quality dimension and customer satisfaction in airline industry. This study focuses on the evaluation of all airline service user regarding airline service quality. The main limitation of this study is that it focusses to customer satisfaction on all Airlines Companies: thus, the result cannot be generalized.&#x0D; &#x0D; Practical Implications: The framework proposed can be applied to future research on green service quality and customer satisfaction. This study will lead to passenger’s satisfaction and encourage the development of long-term relationships with their customers.