Reviewerly/exHarmony huggingface dataset:
The Effective Commands Used by the Teacher in English Teaching at SMP Lab Undiksha

This study aimed at identify the types of disruptive behavior, identify the types of commands given by an English teacher, and analyze how the effective commands are given to students with disruptive behavior at SMP Lab UndikshaSingaraja. This study was a case study research. The method used in this study was descriptive qualitative method. The data were collected through filling observation sheet, distributing questionnaire, and conducting interview then analyzed by the researcher so that the researcher was able to draw and integrate ideas to create the conclusion. The result of this study revealed that students’ disruptive behavior that appeared during the teaching and learning process could be categorized as The Immature, The Well to Do, The Mountain Climber and The Game Player, furthermore. In overcoming those disruptive behaviors the teacher used several types of commands such as Others Commands, Don’t Commands, Regular Commands, Question Commands, Indirect Commands, Interrogations Commands, Stop Command, and Negative Commands. From those commands, thirteen times commands that appeared were categorized effective.
Analyzed Lexicon in Legong Mahawidya Dance Movement: A Descriptive Qualitative Study

Language has an important role in preserving culture, because language is the key to communication. The research was conducted at the Pancer Langit Art Studio. The proposed research focus on the lexicons found in the motions and clothing of the Legong Mahawidya dance. Informants in this study are people who can perform this dance and the choreographer. This study used a descriptive qualitative approach. This study aims to identify the lexicon in Mahawidya Legong Dance. The data obtained through interviews with choreographers and dancers as informants in this study. Thirty-three (33) lexicon are found in the movemnet of Legong Mahawidya Dance. Not all lexicons in the movement have cultural meaning. Preserving Balinese culture, especially in language and art, is the responsibility of the Balinese people. By keeping the language, using the language continuously and understanding the language, the Balinese culture will be preserved and not extinct.
A Feature Construction Method That Combines Particle Swarm Optimization and Grammatical Evolution

The problem of data classification or data fitting is widely applicable in a multitude of scientific areas, and for this reason, a number of machine learning models have been developed. However, in many cases, these models present problems of overfitting and cannot generalize satisfactorily to unknown data. Furthermore, in many cases, many of the features of the input data do not contribute to learning, or there may even be hidden correlations between the features of the dataset. The purpose of the proposed method is to significantly reduce data classification or regression errors through the usage of a technique that utilizes the particle swarm optimization method and grammatical evolution. This method is divided into two phases. In the first phase, artificial features are constructed using grammatical evolution, and the progress of the creation of these features is controlled by the particle swarm optimization method. In addition, this new technique utilizes penalty factors to limit the generated features to a range of values to make training machine learning models more efficient. In the second phase of the proposed technique, these features are exploited to transform the original dataset, and then any machine learning method can be applied to this dataset. The performance of the proposed method was measured on some benchmark datasets from the relevant literature. Also, the method was tested against a series of widely used machine learning models. The experiments performed showed a significant improvement of 30% on average in the classification datasets and an even greater improvement of 60% in the data fitting datasets.
The Effective Commands Used by the Teacher in English Teaching at SMA

This study aimed at investigating the types of students’ disruptive behavior, the types of commands used by the English teacher, and analyzing the effective commands used by the English teacher to overcome students’ disruptive behavior. The method used in this study was descriptive method in which the data were described under the reality to answer the purpose of this research in finding section and draw the conclusion. The findings of this study revealed that students’ disruptive behaviors that appeared during the learning process could be categorized as The Immature and Well to Do. In handling both types of students' disruptive behavior, the teacher used several commands; those were Interrogation Commands, Regular Commands, Question Commands, Don’t Commands, Indirect Command, and Other Commands. Several commands that were effective were Interrogation Command, Question Command, Indirect Command, and Other Commands.
A Two-Path Multibehavior Model of User Interaction

Personalized recommendation is an important part of e-commerce platforms. In recommendation systems, a neural network is used to enhance collaborative filtering to accurately capture user preferences, so as to obtain better recommendation performance. Traditional recommendation methods focus on the results of a single user behavior, ignoring the modeling of multiple interaction behaviors of users, such as click, add to cart and purchase. Although many studies have also focused on multibehavior modeling, two important challenges remain: (1) Since the multiple behaviors of the time-evolving trends of context information are ignored, it is still a challenge to identify the multimodal relationships of behaviors; (2) surveillance signals are still sparse. In order to solve these problem, this paper proposes a two-path multibehavior model of user interaction (TP_MB). First, a two-path learning strategy is introduced to maximize the multiple-interaction information of users and items learned by the two paths, which effectively enhances the robustness of the model. Second, a multibehavior dependent encoder is designed. Contextual information is obtained through behavior dependencies in the interaction of different users. In addition, three contrastive learning methods are designed, which not only obtain additional auxiliary supervision signals, but also alleviate the problem of sparse supervision signals. Extensive experiments on two real datasets demonstrate that our method outperforms state-of-the-art multibehavior recommendation methods.
STAB-GCN: A Spatio-Temporal Attention-Based Graph Convolutional Network for Group Activity Recognition

Group activity recognition is a central theme in many domains, such as sports video analysis, CCTV surveillance, sports tactics, and social scenario understanding. However, there are still challenges in embedding actors’ relations in a multi-person scenario due to occlusion, movement, and light. Current studies mainly focus on collective and individual local features from the spatial and temporal perspectives, which results in inefficiency, low robustness, and low portability. To this end, a Spatio-Temporal Attention-Based Graph Convolution Network (STAB-GCN) model is proposed to effectively embed deep complex relations between actors. Specifically, we leverage the attention mechanism to attentively explore spatio-temporal latent relations between actors. This approach captures spatio-temporal contextual information and improves individual and group embedding. Then, we feed actor relation graphs built from group activity videos into our proposed STAB-GCN for further inference, which selectively attends to the relevant features while ignoring those irrelevant to the relation extraction task. We perform experiments on three available group activity datasets, acquiring better performance than state-of-the-art methods. The results verify the validity of our proposed model and highlight the obstructive impacts of spatio-temporal attention-based graph embedding on group activity recognition.
PCBSNet: A Pure Convolutional Bilateral Segmentation Network for Real-Time Natural Scene Text Detection

Scene text detection is a fundamental research work in the field of image processing and has extensive application value. Segmentation-based methods have time-consuming feature processing, while post-processing algorithms are excellent. Real-time semantic segmentation methods use lightweight backbone networks for feature extraction and aggregation but lack effective post-processing methods. The pure convolutional network improves model performance by changing key components. Combining the advantages of three types of methods, we propose a Pure Convolutional Bilateral Segmentation Network (PCBSNet) for real-time natural scene text detection. First, we constructed a bilateral feature extraction backbone network to significantly improve detection speed. The low extraction detail branch captures spatial information, while the efficient semantic extraction branch accurately captures semantic features through a series of micro designs. Second, we built an efficient attention aggregation module to guide the efficient and adaptive aggregation of features from the two branches. The fused feature map undergoes feature enhancement to obtain more accurate and reliable feature representation. Finally, we used differentiable binarization post-processing to construct text instance boundaries. To evaluate the effectiveness of the proposed model, we compared it with mainstream lightweight models on three datasets: ICDAR2015, MSRA-TD500, and CTW1500. The F-measure scores were 82.9%, 82.8%, and 78.9%, respectively, and the FPS were 59.1, 94.3, and 75.5 frames per second. We also conducted extensive ablation experiments on the ICDAR2015 dataset to validate the rationality of the proposed improvements. The obtained results indicate that the proposed model significantly improves inference speed while enhancing accuracy and demonstrates good competitiveness compared to other advanced detection methods. However, when faced with curved text, the detection performance of PCBSNet needs to be improved.
Spectroscopic characterization of the quantum linear-zigzag transition in trapped ions

Abstract While engineered quantum systems are a general route to the manipulation of multipartite quantum states, access in a physical system to a continuous quantum phase transition under sufficient control offers the possibility of an intrinsic source of entangled states. To this end we realize the quantum version of the linear-zigzag structural transition for arrays of up to five ground state-cooled ions held in a linear Paul trap and we demonstrate several of the control requirements towards entangled-state interferometry near the critical point. Using in situ spectroscopy we probe the energy level structure and occupation of the soft mode associated with the structural transition, and show a stable critical point and majority ground state occupation crossing the transition. We resolve biases arising from trap electrode asymmetries that change the nature of the transition, show that they can be suppressed by varying the ion number, and demonstrate control of the transition bias using optical dipole forces.
Socialization of Bridge Base Online

Bridge sport game, in playing the player must go through the process of playing mini bridge first. You have to master how to play, you have to know the High Card Point (HCP), the names of the cards, the bidding sheet, the score sheet, this is the basis for playing bridge. Bridge base online is a way of learning bridge online. This online bridge base is learning that must be applied by teachers or trainers in schools that are in a formal school education environment to apply online training to simplify and shorten how to play bridge can be done independently without involving other people. In conventional games, at least 4 people are required to play bridge. With bridge base online everything can be done alone without involving many people and done anytime and anywhere as long as there is an internet connection
Modulation signal recognition based on feed‐forward attention mechanism

Abstract In recent years, research on modulated signal recognition using deep learning (DL) has achieved remarkable success, allowing automatic modulation recognition (AMR) to play a crucial role in modern communication systems. The emergence of the attention mechanism has then rapidly led to a wide range of applications in image classification and speech recognition, which proves the effectiveness of the attention mechanism. In this paper, the authors propose a network model feed‐forward Attention mechanism with Residuals networks and Long Short‐Term Memory (RLADNN) based on feed‐forward attention mechanism with Residuals networks (Resnet) and Long Short‐Term Memory (LSTM), which takes the advantage that the Attention mechanism can effectively solve some long‐term memory problems in [−20:18] signal‐to‐noise ratio (SNR) for the recognition of 11 modulated signals with different SNR, and effectively improves the recognition rate.
Politeness Strategies Used in the First Trump-Clinton Presidential Debate

The way speaker appreciating and maintaining the condition to the recipient in the communication is called politeness. Being polite is complicated but sometimes interesting thing in any language. This study focused on analyses the types of politeness strategy expressed by the speakers and analyze what factors were influenced the chosen of politeness strategy implemented by the speakers. However the population in this study is all people in who come in this presidential debate and the sample are Hillary Clinton, Donald Trump, and the moderator. The data collection method used was documentation method which applied to get the data. The instrument used was the researcher by hearing, observing, and taking a note during the presidential debate. Then, the data analysis method was descriptive qualitative method by analyzing the transcript of debate. It can be concluded that there were four types of politeness strategy found: bald-on-record strategy, positive politeness strategy, negative politeness strategy, and off-record strategy. The result of study found that there were two factors applied in the debate, which are payoff factors and sociological factors. The implication of this study was politeness strategy by Brown and Levinson was known by readers and also this politeness strategy used could be used in our communication, especially for the people who join the election.
Latent feature reconstruction for unsupervised anomaly detection

Abstract Anomalies (or outliers) indicate a minority of data items that are quite different from the majority (inliers) of a dataset in a certain aspect. Unsupervised anomaly detection (UAD) is an important but not yet extensively studied research topic. Recent deep learning based methods exploit the reconstruction gap between inliers and outliers to discriminate them. However, it is observed that the reconstruction gap often decreases rapidly as the training process goes. And there is no reasonable way to set the training stop point. To support effective UAD, we propose a new UAD framework by introducing a Latent Feature Reconstruction (LFR) layer that can be applied to recent UAD methods. The LFR layer acts as a regularizer to constrain the latent features in a low-rank subspace from which inliers can be reconstructed well while outliers cannot. We develop two new UAD methods by implementing the proposed framework with autoencoder architecture and geometric transformation scheme. Experiments on five benchmarks show that our proposed methods can achieve state-of-the-art performance in most cases.
Structuring meaningful bug‐fixing patches to fix software defect

Abstract Currently, software projects require a significant amount of time, effort and other resources to be invested in software testing to reduce the number of code defects. However, this process decreases the efficiency of software development and leads to a significant waste of workforce and resources. To address this challenge, researchers developed various solutions utilising deep neural networks. However, these solutions are frequently challenged by issues, such as a vast vocabulary, network training difficulties and elongated training processes resulting from the handling of redundant information. To overcome these limitations, the authors proposed a new neural network‐based model named HopFix, designed to detect software defects that may be introduced during the coding process. HopFix consists of four parts: data preprocessing, encoder, decoder and code generation components, which were used for preprocessing data, extracting information about software defects, analysing defect information, generating software patches and controlling the generation process of software patches, respectively. Experimental studies on Bug‐Fix Pairs (BFP) show that HopFix correctly fixed 47.2% ( BFP small datasets) and 25.7% ( BFP medium datasets) of software defects.
Organization of character-building work at university: An attempt at analyzing the formation of the institute of curators (by the example of the Saint Petersburg State Chemical and Pharmaceutical University)

The research aims to identify the features of the character-building process at the Saint Petersburg State Chemical and Pharmaceutical University by studying the history of the organization of curatorial work. The paper presents the prerequisites for the formation of the institute of curators, reveals its functions and areas of activities, its place and role in the university system of character building and training, characterizes its current state and influence on the creation of the tutoring system. The scientific novelty of the research lies in considering the institute of curators in its development and introducing new facts into scientific use, which made it possible to get a clearer idea of the historical process of determining the responsibilities and content of curators’ work using the example of a particular university. As a result, the historical and logical justification for the existence of the institute of curators and its expediency in the aspect of optimizing the training and character-building process and pedagogical support of students was proved.
The effect of artificial intelligence (AI) robot characteristics and dialectical thinking on <scp>AI</scp> robot adoption intention

Abstract Artificial intelligence (AI) is an essential strategic element for many industries. Practitioners are exploring ways to incorporate AI into their products, services, and processes, as it transforms everyday life. Recent studies have increasingly examined how consumers respond to various forms of AI robots. This study adds to the existing literature by investigating the factors that drive consumers to adopt AI robots and the underlying psychological processes. Through three studies, we show that consumers' perceived congruence in an AI robot plays an important role in determining their adoption intention for the AI robot. We further demonstrate that such a perception of congruence is jointly determined by the AI robot's benefit type and appearance familiarity. The findings suggest that consumers are more inclined to adopt an AI robot when they are designed to provide hedonic benefits and resembles real‐life objects. By contrast, an AI robot designed for utilitarian benefits are more likely to be adopted when it has the appearance of a machine. In addition, we show that perception of fit mediates this joint effect. Furthermore, we show that the effect of congruence perception on adoption intention is attenuated when consumers have a higher level of dialectical thinking. This study discusses both the theoretical and practical implications.
A generalized closed-form solution for 3D registration of two-point sets under isotropic and anisotropic scaling

Fitting of two-point sets under non-uniform scaling involves several applications, including ultrasound-guided surgery, where ultrasound images generally show anisotropy between its lateral and depth resolution. However, the current state-of-the-art only accomplishes the closed-form solution under the assumption of isotropic scaling, and only the iterative form of the solution is available for registration regardless of scaling type. A generalized closed-form solution, including anisotropic scaling, could robust the computation in the applications of multi-modality image fusion and surgical navigation based on ultrasound images. In this paper, a generalized closed-form solution (GCFS) that accounts for isotropic scaling and anisotropic scaling is proposed. GCFS finds the least-squares solution of rotation, translation, and scaling based on the singular value decomposition (SVD). The method was demonstrated to 1) register two 3D point sets under the effect of anisotropic scaling and 2) register two coplanar 3D point sets under the effect of anisotropic scaling. This study evaluates 3D registration between the preoperative model and intraoperative ultrasonic scanning of a femur and 2D registration of ultrasound images to the phantom for ultrasound calibration. In addition, the conditions that affect the transformation estimations like estimating reflection and noise degeneracy were also considered in the evaluation.
Optimal discharge of patients from intensive care via a data-driven policy learning framework

Clinical decision support tools rooted in machine learning and optimization can provide significant value to healthcare providers through better management of intensive care units. In particular, it is important that intensive care unit patient discharge decisions account for the nuanced trade-off between decreasing the length of stay and the risk of readmission or death after discharge of a patient. This work introduces a comprehensive framework (i.e., not geared towards any particular disease or condition) for capturing this trade-off and to recommend optimal discharge timing decisions given the electronic health records of a patient. A data-driven approach is used to derive a parsimonious, discrete state space representation to represent the physiological condition of a given patient. Based on this model and a given cost function, an infinite-horizon discounted Markov decision process is formulated and solved numerically to compute an optimal discharge policy, whose performance is assessed using off-policy evaluation strategies. Extensive numerical experiments are performed to validate the proposed framework using real-life intensive care unit patient data.
Airborne radar forward‐looking image enhancing algorithm based on generative adversarial networks

Abstract Radar forward‐looking imaging is gaining significance in various applications like battlefield reconnaissance, target surveillance, and precision guidance. Although synthetic aperture radar techniques provide high azimuth resolution but faced limitations in forward‐looking area due to the poor Doppler resolution and the “left‐right” ambiguity problem. Recently, generative adversarial networks have been extensively used for image motion blur removal. This letter proposes an end‐to‐end forward‐looking image enhancing network using generative adversarial network to produce high‐resolution images, improving the efficiency, and quality of imaging. Compared to conventional methods such as the deconvolution‐based methods, this algorithm eliminates the need for design and iterative processes of the observation matrix. Simulated and real radar data validate that this approach offers robust recovery and better performance.