Reviewerly/exHarmony huggingface dataset:
PENINGKATAN KOMPETENSI GURU SEKOLAH MENENGAH KEJURUAN MELALUI PELATIHAN SOFTWARE ENGINEERING

Increasing teacher competency is a process that cannot be done instantly. Teachers must always be provided with new knowledge that can be applied to their own needs and institutions. Computer-based learning will greatly help educators in the current era of Information and Communication Technology, especially vocational teachers who teach in productive fields (engineering subjects). The problem faced is the inability of teachers to use computers as learning media. Teachers only use computers to present powerpoint media, even in practical subjects. The teacher has not been able to utilize software engineering that will assist in the practical learning process, such as AutoCAD software in learning Drawing Engineering and PLC software in learning control systems. Therefore, it is necessary to conduct integrated and comprehensive training for teachers in utilizing computers as learning media especially in the use of software engineering. All Muhammadiyah 1 Padang Vocational Middle School teachers and SMK Dhuafa Nusantara Padang were invited to take part in engineering training. In the implementation of the activity there were 15 teacher participants. The training material consisted of the introduction and use of AutoCAD software, and PLC software. The results of this training show that more than 90% of participants have been able to use AutoCAD and PLC software. Besides that, there was an increase in motivation and enthusiasm from the teacher to show performance, both in the use of software for classroom learning and in daily life as a professional who always tried to improve his competence.
The New Paradigms In Education and Support of Critical Thinking with Artificial Intelligence (AI) Tools

The growing abundance of information in all spheres of public life, stemming from the rapid take-up of new information and communication technologies, poses new challenges for the development of mankind. The study examines this phenomenon in the field of education, as education has particular importance and complex influence on the whole human life and careers. The need to establish, from an early age, some sort of "filters" in the consciousness of students to help them sift the really important things in the ocean of information and develop their knowledge in a consistent and balanced way, building on sustained learning, is key to their harmonious development as citizens and professionals, regardless of their profession. At the same time, for logical reasons, the increasingly sophisticated systems of AI can and should find an adequate implementation in education, assisting this essential process of filtering and promoting the so-called critical thinking among adolescents.
A READER RESPONSE APPROACH IN COLLABORATIVE READING PROJECTS TO FOSTER CRITICAL THINKING SKILLS

Reading has become a major concern of EFL educators. Reading does not only help students learn foreign languages, but it is also believed that it has a strong link with critical thinking skills. A reader response approach in collaborative works, adapted from literary theory, is believed to be beneficial for the students. Therefore, this study aims at investigating the answers to these two questions: (1) how are the collaborative reader responses implemented in Critical Reading and Writing II? and (2) To what extent does reader response approaches promote students critical thinking skills? With these questions in mind, the researchers collect the data by involving 24 participants from CRW II (Critical Reading and Writing) class. The data gathered from classroom observations, online archives and students reflections are analyzed descriptively, using qualitative case study method. It is hoped that the implementation of this approach can be useful not only to improve students reading skills, but also to provide more opportunity for students to exercise their critical thinking skills.
AN ANALYSIS OF STUDENTS� WRITING SKILLS: FOCUS ON GRAMMATICAL AND DISCOURSE COMPETENCE

This paper presents an analysis of the students writing skills. It is believed that writing is a powerful device to reflect language structure and to promote language learning. However, many students still experience difficulties in writing since it requires the appropriate grammatical and textual structures. Therefore, this study tries to analyse the students writing skills that will help them to improve their communicative competence. Data were obtained through documentation of students texts and interviews. The theories of Systemic Functional Linguistics, explanation text, and communicative competence were employed as the framework of the text analysis. The findings revealed the social function, the generic structure, the linguistic features, and also the grammatical and discourse competence in students explanation texts. It was concluded that the students perceived the importance of the use of correct grammar and textual structure in their writing. Regarding some grammatical mistakes in students explanation texts, it is recommended that teacher give explicit teaching and more exercises to them so that the students communicative competence can be developed.DOI: 10.24071/llt.2019.220206
Developing Assessment Instrument on Writing on Descriptive Paragraph Based on Picture of Junior High School Students

This research is motivated by the absence of writing assessment instruments on descriptive material based on picture, especially students of class VIII. The writing assessment instruments on paragraph descriptive material based on the picture used are still holistic. This study aims to develop Writing assessment instruments on Descriptive Material Based on Middle School Class VIII Students. The method used is instrument development research with 9 development steps. Content validation by experts was analyzed using the Aiken formula, the reliability of the experts was analyzed using Two way ANOVA and analyzed using the Hoyt formula. The construct validity was analyzed using Exploratory Factor Analysis (EFA) analysis. The results of the development research show that the validity of writing appraisal content instruments on descriptive material based on picture specially students of class VIII through Expert Judgment produces a reliability coefficient of&gt; 0.3, meaning that it can be said that writing appraisal on descriptive material has high validity. Also, the reliability results of the instrument using interrater with the Hoyt formula showed that 0.89. Analysis of the data in the field trials can be seen in the KMO which shows a KMO value&gt; 0.5 which means that the instrument indicators can be further analyzed. In writing assessment produces component factors that are formed and can represent all indicators. The factors are the main ideas, organization, and mechanic. While the reliability obtained a coefficient&gt; 0.5 which means that the writing assessment has good reliability. Based on the results of the study, it is recommended that all lecturers, teachers and other educators use valid and reliable instruments. Thank you to all the teams especially the lecturers who have given good direction and participation so that this journal can give best contributes in theory and practice.
Periodicity counting in videos with unsupervised learning of cyclic embeddings

We introduce a context-agnostic unsupervised method to count periodicity in videos. Current methods estimate periodicity for a specific type of application (e.g. some repetitive human motion). We propose a novel method that provides a powerful generalisation ability since it is not biased towards specific visual features. It is thus applicable to a range of diverse domains that require no adaptation, by relying on a deep neural network that is trained completely unsupervised. More specifically, it is trained to transform the periodic temporal data into some lower-dimensional latent encoding in such a way that it forms a cyclic path in this latent space. We also introduce a novel algorithm that is able to reliably detect and count periods in complex time series. Despite being unsupervised and facing supervised methods with complex architectures, our experimental results demonstrate that our approach is able to reach state-of-the-art performance for periodicity counting on the challenging QUVA video benchmark.
Cycle-Spinning Convolution for Object Detection

Deep convolutional networks are prominently used in object detection tasks due to their notable performances.These networks typically have pooling layers following the convolution, which effectively subsamples the convolution output, potentially introducing aliasing.An aliased signal emerging in the earlier layers inevitably propagates throughout the network and such distortion potentially prevents getting the best performance out of a network.In this study, we propose integrating the cycle-spinning (CS) into the convolutional layer to have a more robust object detection pipeline.CS makes use of the shift-variant characteristics of pooling, where each shifted version of the image results in a different output.The CS combines these outputs after unshifting them to remove artifacts and alleviate distortion.The proposed method does not introduce any additional trainable parameters and can be straightforwardly integrated into convolution layers.The experimental results show that integrating CS into object detection algorithms in the DOTA dataset results in an up to 5.5% increase in the mAP values.
Metaverse, HCI, and Its Future

With the development of the times, scientists have been looking for a balance between the virtual world and the real world.This means that many things in the real world will also be reflected in the virtual world.In this virtual world, people can interact and socialize with others and can do a series of operations such as shopping and consumption.Metaverse is a large online computing platform composed of large-scale individual users and their devices and different platforms.The achievement of this goal depends to a large extent on the application of human-computer interaction (HCI) in the development of the Metaverse, that is, how to feed the user's actions into the virtual world.HCI is a process of information exchange between a human and a computer in a certain way to accomplish certain tasks.Moreover, since many technology companies such as Microsoft and Facebook have started to develop Metaverse, a living space with a new social system, its existence has brought some questions and controversies, therefore its future is not smooth.This paper briefly discusses the basic principles of Metaverse and HCI, analyzes the application of these two concepts in video games, and analyzes the possible future development direction of Metaverse and its problem.This paper will refer to several articles and reports in recent years and conclude that although the metaverse is an emerging industry with good growth prospects, there are still many critical issues.
The BEE Model with Live Virtual Classroom to Enhancing Creative Works

Online learning is recognized as an effective teaching method and tool, widely integrated into different teaching and learning strategies to provide quality education at different levels. However, the field of design education does not have extensive research into online learning, delivery, and assessment. This study developed an online learning model for design disciplines to improve creative work effectively. This paper describes an empirical study to test the BEE model outcomes developed following previous research. This present study has focused on the intensity of the live virtual classroom in every step to compare differences in the scores of creative works. Therefore, the assumption was that Thai undergraduate design students who learned with the BEE model through live classroom learning (experimental group) would have at least one element of the creative work scores compared to the group learning through the traditional online teaching method (control group). Furthermore, we conducted a multistage randomized sample of 66 participants, divided into an experimental group of 33 participants and a control group of 33 subjects. To compare the scores of creative works, the test of creative works assessment rubric was used to collect the data. Then, they were analyzed using the MANOVA statistical test. The results found at least one element of them, and the average creative work scores of the experimental group showed that they were significantly higher than the control group (p&lt;0.05). Doi: 10.28991/ESJ-2022-SIED-08 Full Text: PDF
AGS-SSD: Attention-Guided Sampling for 3D Single-Stage Detector

3D object detection based on LiDAR point cloud has always been challenging. Existing point cloud downsampling approaches often use heuristic algorithms such as farthest point sampling (FPS) to extract the features from a massive raw point cloud. However, FPS has disadvantages such as low operating efficiency and inability to sample key areas. This paper presents an attention-guided downsampling method for point-cloud-based 3D object detection, named AGS-SSD. The method contains two modules: PEA (point external attention) and A-FPS (attention-guided FPS). PEA explores the correlation between the data and uses the external attention mechanism to extract the semantic features in the set abstraction stage. The semantic information, including the relationship between the samples, is sent to the candidate point generation module as context points. A-FPS weighs the point cloud according to the generated attention map and samples the foreground points with rich semantic information as candidate points. The experimental results show that our method achieves significant improvements with novel architectures against the baseline and runs at 24 frames per second for inference.
Parrondo’s Paradox for Discrete-Time Quantum Walks in Momentum Space

We investigate the possibility of implementing a sequence of quantum walks whose probability distributions give an overall positive winning probability, while it is negative for the single walks (Parrondo's paradox). In particular, we have in mind an experimental realisation with a Bose-Einstein condensate in which the walker's space is momentum space. Experimental problems in the precise implementation of the coin operations for our discrete-time quantum walks are analysed in detail. We study time-dependent phase fluctuations of the coins as well as perturbations arising from the finite momentum width of the condensate. We confirm the visibility of Parrondo's paradox for experimentally available time scales of up to a few hundred steps of the walk.
Slurs and Freedom of Speech

ABSTRACT A very common argument against restrictions on hate speech says that since such restrictions curtail freedom of speech, they cause more harm than they prevent. A no less common reply has it that the harms caused by hate speech are sufficiently great to justify legal restrictions on free speech. In ‘Freedom of Expression and Derogatory Words’, West questions a common assumption of both arguments concerning the use of slurs, i.e. that restricting the use of slurs necessarily curtails freedom of speech. According to West, everything that can be said with a slur can be said with its neutral counterpart. Given the psychological and social harms of slurs, this would give us a compelling reason to put legal restrictions on their use. However, in this article, I will argue that, according to the main theories of slurs, slurs can be used to express derogatory information which cannot be expressed by their neutral counterparts. This will leave us with two options when it comes to avoiding the negative effects of slurs: (i) to restrict freedom of speech or (ii) to argue that the derogatory information expressed by slurs is not covered by free speech. Both options will depart significantly from West's argument.
QSAN: A Near-term Achievable Quantum Self-Attention Network

Self-Attention Mechanism (SAM) is good at capturing the internal connections of features and greatly improves the performance of machine learning models, espeacially requiring efficient characterization and feature extraction of high-dimensional data. A novel Quantum Self-Attention Network (QSAN) is proposed for image classification tasks on near-term quantum devices. First, a Quantum Self-Attention Mechanism (QSAM) including Quantum Logic Similarity (QLS) and Quantum Bit Self-Attention Score Matrix (QBSASM) is explored as the theoretical basis of QSAN to enhance the data representation of SAM. QLS is employed to prevent measurements from obtaining inner products to allow QSAN to be fully implemented on quantum computers, and QBSASM as a result of the evolution of QSAN to produce a density matrix that effectively reflects the attention distribution of the output. Then, the framework for one-step realization and quantum circuits of QSAN are designed for fully considering the compression of the measurement times to acquire QBSASM in the intermediate process, in which a quantum coordinate prototype is introduced as well in the quantum circuit for describing the mathematical relation between the output and control bits to facilitate programming. Ultimately, the method comparision and binary classification experiments on MNIST with the pennylane platform demonstrate that QSAN converges about 1.7x and 2.3x faster than hardware-efficient ansatz and QAOA ansatz respevtively with similar parameter configurations and 100% prediction accuracy, which indicates it has a better learning capability. QSAN is quite suitable for fast and in-depth analysis of the primary and secondary relationships of image and other data, which has great potential for applications of quantum computer vision from the perspective of enhancing the information extraction ability of models.
FingerprinTV: Fingerprinting Smart TV Apps

This paper proposes FingerprinTV, a fully automated methodology for extracting fingerprints from the network traffic of smart TV apps and assessing their performance. FingerprinTV (1) installs, repeatedly launches, and collects network traffic from smart TV apps; (2) extracts three different types of network fingerprints for each app, i.e., domain-based fingerprints (DBF), packet-pair-based fingerprints (PBF), and TLS-based fingerprints (TBF); and (3) analyzes the extracted fingerprints in terms of their prevalence, distinctiveness, and sizes. From applying FingerprinTV to the top-1000 apps of the three most popular smart TV platforms, we find that smart TV app network fingerprinting is feasible and effective: even the least prevalent type of fingerprint manifests itself in at least 68% of apps of each platform, and up to 89% of fingerprints uniquely identify a specific app when two fingerprinting techniques are used together. By analyzing apps that exhibit identical fingerprints, we find that these apps often stem from the same developer or “no code” app generation toolkit. Furthermore, we show that many apps that are present on all three platforms exhibit platformspecific fingerprints.
SoK: Assumptions Underlying Cryptocurrency Deanonymizations

In recent years, cryptocurrencies have increasingly been used in cybercrime and have become the key means of payment in darknet marketplaces, partly due to their alleged anonymity. Furthermore, the research attacking the anonymity of even those cryptocurrencies that claim to offer anonymity by design is growing and is being applied by law enforcement agencies in the fight against cybercrime. Their investigative measures require a certain degree of suspicion and it is unclear whether findings resulting from attacks on cryptocurrencies’ anonymity can indeed establish that required degree of suspicion. The reason for this is that these attacks are partly based upon uncertain assumptions which are often not properly addressed in the corresponding papers. To close this gap, we extract the assumptions in papers that are attacking Bitcoin, Monero and Zcash, major cryptocurrencies used in darknet markets which have also received the most attention from researchers. We develop a taxonomy to capture the different nature of those assumptions in order to help investigators to better assess whether the required degree of suspicion for specific investigative measures could be established. We found that assumptions based on user behaviour are in general the most unreliable and thus any findings of attacks based on them might not allow for intense investigative measures such as pre-trial detention. We hope to raise awareness of the problem so that in the future there will be fewer unlawful investigations based upon uncertain assumptions and thus fewer human rights violations.
Sculpting bosonic states with arithmetic subtractions

Abstract Continuous-variable (CV) encoding allows information to be processed compactly and efficiently on quantum processors. Recently developed techniques such as controlled beam-splitter operations and the near deterministic phonon subtractions make trapped ion systems attractive for exploring CV quantum computing. Here we propose a probabilistic scheme based on the boson sculpting technique for generating multipartite highly entangled states of motional modes of trapped ion systems. We also investigate the effects of decoherence on the fidelity of the generated state by performing numerical simulations with realistic noise parameters. Our work is a step towards generating multipartite CV entanglement.
Cluster-based fuzzy regression trees for software cost prediction

The current paper &lt;span lang="EN-US"&gt;proposes a novel type of decision tree, which is never used for software development cost prediction (SDCP) purposes, the cluster-based fuzzy regression tree (CFRT). This model uses the fuzzy k-means (FKM), which deals with data uncertainty and imprecision. The tree expansion is based on the variability measure by choosing the node with the highest value of granulation diversity. This paper outlined an experimental study comparing CFRT with four SDCP methods, notably linear regression, multi-layer perceptron, K-nearest-neighbors, and classification and regression trees (CART), employing eight datasets and the leave-one-out cross-validation (LOOCV). The results show that CFRT is among the best, ranked first in 3 datasets according to four accuracy measures. Also, according to the Pred(25%) values, the proposed CFRT model outperformed all the twelve compared techniques in four datasets: Albrecht, constructive cost model (COCOMO), Desharnais, and The International Software Benchmarking Standards Group (ISBSG) using LOOCV and 30-fold cross-validation technique.&lt;/span&gt;
Modulation Awareness Method for Dual-Hop Cooperative Transmissions over Frequency-Selective Channels

Modulation awareness and cooperative transmissions have individually received a significant amount of research in the scholarly literature. However, a limited number of works are principally concerned with the combination of the two topics, and they are restricted to frequency-flat wireless channels. In this study, we propose a new modulation awareness method applicable to dual-hop amplify-and-forward cooperative broadcasts. The suggested method is built on the creation of theoretical representations of cross-correlation functions of the received signals. We conceptually prove that a family of modulation types generates spikes for certain cross-correlation functions, while others do not. We create a numerous layer hypothesis evaluation for the purpose of making judgments centered on this attribute. The suggested method has a number of benefits, such as the ability to operate on both frequency-flat and frequency-selective channels, as well as the absence of the necessity of channel awareness or noise power. Computer simulations analyze the performance of the proposed method, which delivers adequate awareness performance in a variety of operational scenarios.