Reviewerly/exHarmony huggingface dataset:
An empirical study on cultivating college students' cross-cultural communicative competence based on the artificial-intelligence English-teaching mode

Artificial intelligence education will be an important part of information technology education in the future. It will enter the classroom and the learning life of students in various forms. The modern teaching media used in artificial intelligence courses at universities include some conventional teaching courseware, multimedia videos, and tool software. Some teaching media are unique to artificial intelligence, such as smart software, smart devices, and smart websites. The existing cross-cultural competence dimensions and evaluation scales were theoretically explained with factor analysis by comparing and selecting comprehensive evaluation methods in the work. Its main advantages were as follows: simple mathematical models and easy operation. The construction of a comprehensive evaluation model for college students' cross-cultural competence included the principles and ideas of model construction, the methods and steps of model construction, and model calculations. The survey results showed that the four-level scores of all samples had a significant positive correlation with foreign cultural knowledge, attitudes, and cross-cultural communication skills at 0.01 level (bilateral) and 0.05 (bilateral), respectively.
Phishing Target Identification Based on Neural Networks Using Category Features and Images

Phishing attack, as a significant security concern in cyberspace, has continuously threatened organizations and Internet users. For organizations, the rise in the number of phishing target brands has instilled distrust and dissatisfaction in legitimate Internet users and even damaged brand equity. Therefore, more fine-grained phishing detection mechanisms are urgently needed. In this study, we propose PTI-NN, an effective model based on neural networks that uses category features and images to identify the target brands of phishing websites. We furthermore contribute a new dataset including 3,500 phishing websites and present thirty phishing category features, which facilitate pertinent phishing detection in the field of cyber security. In the proposed PTI-NN, an embedding-based DNN is constructed to process the category features, a 2D-CNN is constructed to process the images, and finally, a fully connected layer is used to predict the target brand of phishing websites. The experimental results show that our proposed model is able to classify seventy phishing-targeted brands with a high accuracy of 91.10%, which showcases the effectiveness of our method on the identification of phishing target brands.
The genre contrastive analysis of the first 14 weeks of Dr. Mahathir Mohamad in government: a study on two online newspapers under attitude analysis

The study aimed to identify the hidden meaning lying behind the text of two Malaysian Online newspapers i.e. ‘Malaysiakini’ (alternative newspaper) and ‘The New Straits Times’ (mainstream newspaper). The language of the newspaper always depicts its ideology through its stances. To discover the stances of newspapers, Martin and White (2005) ‘Appraisal Analysis’ framework was employed on 5 news reports (3,265 words) from ‘Malaysiakini’ and 3 news reports from ‘The New Straits Times’ (2,456 words) which were based on the performance of social actors during the 17th and 18th August 2018. It has been found that Pakatan Harapan (PH) government (Dr.Mahathir) was represented the most in both newspapers as compared to the Barisan Nasional (BN) as an opposition who lost the election in 2018, the very first time after independence. By ‘Appraisal Analysis’ it is found that both newspapers have represented Dr. Mahathir the most by positive attitudinal results in ‘Appreciation’ resource as ‘Malaysiakini’ has shown 37%, while; ‘NST’ given 56% positive attitudinal instances being a governmental influenced newspaper.
Developing English for Tourism Learning Model Based on Hypermedia

The development of the global tourism industry rapidly makes the importance of English communication skill at tourism workplace. State Polytechnic of Lampung has provided English subjects 1, 2, and 3 for students. Unfortunately, the implementation has not given any significant contribution to improve students’ English communication skills especially English for tourism. English instructional materials did not fulfill students’ needs and stakeholders’ needs and standard English competences in tourism workplace. The limitation of learning source become a learning problem for students due to limitation of time to practice English out site of classroom without teacher’s guidance. Therefore, it is important to develop a kind of learning model which independence resource that gives a solution to solve the learning problems. Developing model of English learning for tourism based on hypermedia was intended to give an alternative independence learning resource to improve competences in English for Tourism based on hypermedia. The developing of English learning model base on hypermedia applied research and development (R&amp;D) model used a combination of Dick and Carrey model, Hannafin peck Model and Allen model. There were three stages in developing model of English for tourism based on hypermedia. The first step was conducting needs analysis; identifying instructional goals, conducting instructional analysis, and analyzing learners and contexts. The second step was designing material for learning based on hypermedia that was electronic module; writing instructional objectives, developing assessment instruments, and developing learning strategies. The third steps were developing learning material and implementation; developing and selecting instructional materials, implementing and conducting formative evaluation and revision. The results of the formative evaluation from subject matter experts (instructional, media and English for tourism), one to one evaluation, showed that model of English learning for tourism based on hypermedia generally was categorized very good (average score: 89,31). One to one evaluation by three learners was also and small group evaluation by eight learners which evaluated on clarity, impact on learners and feasibility showed that design model of English learning for tourism based on hypermedia was categorized very good. Field trial that was conducted to 37 students indicated that model design of English learning based on hypermedia for tourism was good and suitable with learning objective, easy to use and understand, demonstrate learning experiences, sufficient for English materials, useful, assessment measure learning outcome, feedback, interesting and engaging learning, motivating, and collaboration between students. The t-test paired sample test showed that design of English for tourism was effective to be used as learning resources with average pretest score was 58,05 and average post test score was 70,92 with gain score was 12,86, while the significant (2-tailed) was 0,000 &lt; 0,05.
Application of blockchain technology in autonomous electricity transaction and settlement at the end of distribution network

Abstract With the deepening of the concept of low-carbon environmental protection, the use of renewable energy in social production has become more and more popular. More and more consumers in the distribution network have begun to change their identities to become consumers with the continuous reform of the electricity sales side. Electricity trading and settlement methods in the electricity market directly affect the relevant income of market entities, so a fair and reasonable settlement mechanism is an important factor for the normal operation of the electricity market. Electricity trading refers to the buying and selling of electricity commodities and services, including electricity trading, auxiliary service trading, and transmission rights trading. With the expansion of the electricity market, how to deal with the deviation of electricity is also a difficult problem that requires electricity settlement. This paper aimed to study the application of blockchain technology in the autonomous electricity transaction and settlement at the end of the distribution network. It was expected to use the blockchain technology to set up related transaction mechanisms, provide new solutions for electricity transaction and settlement, and improve the efficiency and fairness of electricity settlement. Blockchain technology does not rely on additional third-party management agencies or hardware facilities, and there is no central control. Except for the self-contained blockchain itself, through distributed accounting and storage, each node realizes information self-verification, transmission and management. On the basis of sorting out the principles of electricity balance and electricity price formation, this paper proposed a contract decomposition method with a higher degree of fit, and revealed the settlement principle of deviation electricity in the electricity market environment. Aiming at the application scenario of blockchain in the energy Internet, an energy trading platform based on blockchain was designed, and the test results showed the feasibility of blockchain in electricity settlement scenarios. The experimental results of this paper showed that in the traditional electricity purchase fee settlement, the average transaction price is stable at 0.6 yuan/KWh. In the blockchain settlement process, the electricity purchase is 25 yuan when the electricity purchase is 50 KWh. Therefore, blockchain settlement is used more frequently in the market, and the profits are higher.
DeepLontar dataset for handwritten Balinese character detection and syllable recognition on Lontar manuscript

The digitalization of traditional Palmyra manuscripts, such as Lontar, is the government's main focus in efforts to preserve Balinese culture. Digitization is done by acquiring Lontar manuscripts through photos or scans. To understand Lontar's contents, experts usually carry out transliteration. Automatic transliteration using computer vision is generally carried out in several stages: character detection, character recognition, syllable recognition, and word recognition. Many methods can be used for detection and recognition, but they need data to train and evaluate the resulting model. In compiling the dataset, the data needs to be processed and labelled. This paper presented data collection and building datasets for detection and recognition tasks. Lontar was collected from libraries at universities in Bali. Data generation was carried out to produce 400 augmented images from 200 Lontar original images to increase the variousness of data. Annotations were performed to label each character producing over 100,000 characters in 55 character classes. This dataset can be used to train and evaluate performance in character detection and syllable recognition of new manuscripts.
Impact of QR-Codes as a Disruptive Technology During the Covid-19 Contagion

Introduction In the world economy, QR codes became very popular, and their prominence is expanding rapidly. The QR-codes look a bit like barcodes, but are made up of square patterns. As businesses are increasingly embracing these technologies, QR codes are becoming more popular, QR code readers are being integrated into smartphones. Apple released iOS 11 to search QR codes using the smartphone camera back in 2017 which is now a game-changing marketing strategy for businesses and retailers.&#x0D; Objective: The objective of the paper is to conduct an extensive theoretical review on the growth of QR codes in the digital era and QR codes' reach as contactless payment solutions.&#x0D; Methodology: A bibliometric review by refereeing quality articles published in highly ranked journal.&#x0D; Conclusion: When the QR code reader was integrated into the new Android smartphone camera, it proved to be a key differentiator. Following the global COVID-19 contagion, there has been a nudge for contactless activities and remote resource allocation, such as online work, payments and online classes among others. QR-codes have seen a spectacular increase in usage across all aspects of life.
Towards a deep learning-based outlier detection approach in the context of streaming data

Abstract Uncommon observations that significantly vary from the norm are referred to as outliers. Outlier detection, which aims to detect unexpected behavior, is a critical topic that has attracted significant attention in a wide range of research areas and application domains, including video surveillance, network intrusion detection, disease outbreak detection, and others. Deep learning-based techniques for outlier detection have currently outperformed machine learning and shallow approaches on streaming data, which are big and complicated datasets. Despite the fact that deep learning has been successfully applied in a variety of application domains, developing an effective and appropriate model is a difficult task due to the dynamic nature and variations of real-world applications and data. Hence, this research proposes a novel deep learning model based on a deep neural network (DNN) to handle the outlier detection problem in the context of streaming data. The proposed DNN model is developed with multiple hidden layers to improve feature abstraction and capabilities. Extensive experiments performed on four real-world outlier benchmark datasets, available at the UCI repository, and comparisons to state-of-the-art approaches are used to evaluate the proposed model's performance. Experiment results demonstrate that it outperforms both machine learning algorithms and deep learning competitors, resulting in significant performance gains. Particularly, when compared to other algorithms, the evaluation results clearly demonstrated the efficacy of the proposed approach, with much higher accuracy, recall and f1-score rates of 99.63%, 99.014% and 99.437%, respectively.
HOMECTRL: An Example of Practice-Based Artistic Research as a Methodology in Surveillance Studies

This essay provides an example of how practice-based artistic research can contribute to surveillance studies. It does so by reflecting on the development of the installation HOMECTRL for The Danish Museum of Science &amp; Technology. The installation offers speculative fabulations regarding how the Internet of Things (IoT) is rearticulating and transforming the home as a site of control, on a spectrum ranging from the mundane everyday use of robot vacuum cleaners to domestic abuse facilitated by IoT technologies. By sharing excerpts from narrative monologues that probe the question of where control is located, and still images that experiment with representing how technologies perceive their surroundings, the essay demonstrates that practice-based methods can open up understandings of the home as a distributed spatial structure permeated by smart technologies that have new abilities to access, control, and manipulate domestic space.
Literacy Development Management in Schools

: This study aims to describe literacy development planning in schools, implementation of school literacy development, monitoring of literacy development in schools, and follow-up of literacy development in schools. The approach used in this research was a phenomenological type qualitative approach with a multi-site study design and constant comparative method. Data was collected using in-depth interviews, participant observation, and documentation techniques. The informants involved were selected using purposive techniques combined with snowball sampling techniques. The data collected through these various techniques was examined, then data reduction, data presentation, and data verification were carried out. Checking the validity of the data in this present study was done using credibility, transferability, dependability, and confirmability. After checking, the collected data was analyzed by cross case analysis. The results obtained from this study indicate that the school has a good literacy culture and cooperates with each other among school members to achieve the desired goals of the school literacy movement. The results of this activity also have a major effect on the achievements produced by teachers and students at school. Based on the findings, it is suggested that planning for the development of literacy in schools should be carried out in meetings among school principals, teachers and staff. The implementation of literacy development is expected to be carried out according to the needs of children. Monitoring literacy development is also expected to be an evaluation for implementing literacy in schools. Meanwhile, follow-up literacy development in schools is expected to be a reference for schools to improve the achievements of both teachers and students.
Naïve Bayes and K-Nearest Neighbor Algorithms Performance Comparison in Diabetes Mellitus Early Diagnosis

Diabetes Mellitus (DM) is a chronic disease that occurs when the body cannot effectively use the insulin it produces. The use of artificial intelligence (AI) can provide a means to diagnose. This study aims to obtain the best classification of the Naïve Bayes (NB) and K-Nearest Neighbors (KNN) methods so that accurate results are obtained in diagnosing DM disease using a dataset originating from The Abdul Moeis Hospital, Samarinda, East Kalimantan, Indonesia. The results showed that the KNN performed better in accuracy, precision, and specificity with an Area Under the Curve (AUC) value 10% higher than NB. Overall, KNN obtained a better recall compared to the NB in order to DM diagnosis.
An Enhanced Fluid Registration for Image Multi-Frame Super Resolution

The main idea of multi-frame super resolution (SR) algorithms is to recover a single high-resolution image from a sequence of low resolution ones of the same object. The success of the SR approaches is often related to a well registration and restoration steps. Therefore, we propose a new approach based on fluid image registration and we use a second order partial differential equation (PDE) to treat both the registration and restoration steps that guarantees the success of SR algorithms. Since the registration step is usually a variational ill-posed model, a mathematical study is needed to check the existence of the solution to the regularized problem. Thus, we prove the existence and uniqueness of the well posed fluid image registration and assure also the existence of the used second order PDE in the restoration step. The results show that the proposed method is competitive with the existing methods.
Convolutional-Based Encoder–Decoder Network for Time Series Anomaly Detection during the Milling of 16MnCr5

Machine learning methods have widely been applied to detect anomalies in machine and cutting tool behavior during lathe or milling. However, detecting anomalies in the workpiece itself have not received the same attention by researchers. In this article, the authors present a publicly available multivariate time series dataset which was recorded during the milling of 16MnCr5. Due to artificially introduced, realistic anomalies in the workpiece, the dataset can be applied for anomaly detection. By using a convolutional autoencoder as a first model, good results in detecting the location of the anomalies in the workpiece were achieved. Furthermore, milling tools with two different diameters where used which led to a dataset eligible for transfer learning. The objective of this article is to provide researchers with a real-world time series dataset of the milling process which is suitable for modern machine learning research topics such as anomaly detection and transfer learning.
Improving Collaborative Learning in Virtual Reality With Facial Expressions

This article presents an approach to improve collaborative learning in terms of performance & satisfaction through the generation of non-verbal behavior of users displayed on their avatar in virtual reality. Various works have focused on the behavioral realism of avatars, which can considerably improve interactions. The purpose of this paper is to investigate the impact of displaying the facial expressions of a user in real time on the performance of the task, the satisfaction and the behavioral changes of the users interacting in a virtual environment. To evaluate this approach, we carried out a study where the users collaborated to build a TV stand in dyad including a novice (the participant) and an expert assistant (the experimenter).
Parameterized Algorithms for String Matching to DAGs: Funnels and Beyond

The problem of String Matching to Labeled Graphs (SMLG) asks to find all the paths in a labeled graph $G = (V, E)$ whose spellings match that of an input string $S \in \Sigma^m$. SMLG can be solved in quadratic $O(m|E|)$ time [Amir et al., JALG], which was proven to be optimal by a recent lower bound conditioned on SETH [Equi et al., ICALP 2019]. The lower bound states that no strongly subquadratic time algorithm exists, even if restricted to directed acyclic graphs (DAGs). In this work we present the first parameterized algorithms for SMLG in DAGs. Our parameters capture the topological structure of $G$. All our results are derived from a generalization of the Knuth-Morris-Pratt algorithm [Park and Kim, CPM 1995] optimized to work in time proportional to the number of prefix-incomparable matches. To obtain the parameterization in the topological structure of $G$, we first study a special class of DAGs called funnels [Millani et al., JCO] and generalize them to $k$-funnels and the class $ST_k$. We present several novel characterizations and algorithmic contributions on both funnels and their generalizations.
Beyond the Metaverse: XV (eXtended meta/uni/Verse)

We propose the term and concept XV (eXtended meta/omni/uni/Verse) as an alternative to, and generalization of, the shared/social virtual reality widely known as ``metaverse''. XV is shared/social XR. We, and many others, use XR (eXtended Reality) as a broad umbrella term and concept to encompass all the other realities, where X is an ``anything'' variable, like in mathematics, to denote any reality, X $\in$ \{physical, virtual, augmented, \ldots \} reality. Therefore XV inherits this generality from XR. We begin with a very simple organized taxonomy of all these realities in terms of two simple building blocks: (1) physical reality (PR) as made of ``atoms'', and (2) virtual reality (VR) as made of ``bits''. Next we introduce XV as combining all these realities with extended society as a three-dimensional space and taxonomy of (1) ``atoms'' (physical reality), (2) ``bits'' (virtuality), and (3) ``genes'' (sociality). Thus those working in the liminal space between Virtual Reality (VR), Augmented Reality (AR), metaverse, and their various extensions, can describe their work and research as existing in the new field of XV. XV includes the metaverse along with extensions of reality itself like shared seeing in the infrared, ultraviolet, and shared seeing of electromagnetic radio waves, sound waves, and electric currents in motors. For example, workers in a mechanical room can look at a pump and see a superimposed time-varying waveform of the actual rotating magnetic field inside its motor, in real time, while sharing this vision across multiple sites. Presented at IEEE Standards Association, Behind and Beyond the Metaverse: XV (eXtended meta/uni/Verse), Thurs. Dec. 8, 2022, 2:15-3:30pm, EST.