Reviewerly/exHarmony huggingface dataset:
A Study of Du Fu’s Poetry in the West in Modern Times

Chinese classical literature has attracted much attention in the Western world. As an important part of Chinese classical literature, Du Fu’s poetry, also as an important carrier, plays a great role in the international communication of cultural confidence. In recent years, some domestic research institutions and scholars have also written literature reviews on the research progress of Du Fu, but few works of literature comprehensively review related western research results. With the combination of big data and visualization tools, this study uses the title keyword “Du Fu Poetry” to search the English literature of Google scholar. VOSviewer is used to make visual analysis and draw a knowledge map from the aspects of literature quantity, authors, research institutions, co-citation, keyword clustering, etc., to analyze the research situation and hotspots of the Western study of Du’s poetry. The following conclusions are drawn: 1) In recent decades, western translation of Du Fu’s poetry have emerged constantly, attracting more and more famous international publishing houses to cooperate; 2) The study of Du’s poetry abroad mainly focuses on the English translation of Du’s poetry as well as the interpretation and evaluation of poets' thoughts; 3) The study of Du’s poetry abroad ignores the dynamic study of Du's poetry development, and there are some obvious cultural errors or omissions in translation. In a word, this study objectively reflects the development, theme and hotspots of modern western Du Fu poetry research, which has certain significance for fully understanding the general situation of Du Fu research field in Western countries and help to deepen Du Fu’s research.
Peningkatan Keterampilan Berbicara dengan Pendekatan Komunikatif-Integratif di SMK Nusantara Plus Tangerang Selatan

This study aims to improve students' speaking skills with a communicative-integrative approach at SMP Nusantara Plus Tangerang Selatan with the material of continuing the story. The subjects of this study were students of class 7A who were relevant to 30 people. The design in this research is classroom action research (classroom action research) which is conducted in three cycles. The data aids used are observation, interviews. The results of the study in cycles 1-3 said that the students' ability in speaking through the communicative-integrative approach had a significant increase. This increase can be seen based on the average value obtained when students carry out the teaching and learning process with the material continuing the story. In the first cycle, the number of students who told stories was 30 students with 70.55%. In the second cycle, 37 students who told stories had increased by 73.46%. In the third cycle, the number of students who told stories was 39 students with a percentage of 83.67%increased by 9.89%. The increase in speaking ability also increases with the increase in the average student activity during learning. The average student activity in cycle I was 65.30%. Student activity in cycle II had an increase of 9.48%, which was 75.77%, and activity in cycle III was 89.90%, which was an increase of 11, 65% of the average activity in cycle II. With the results obtained in each cycle, it is clear that they try to express their ideas and ideas when telling stories using communicative language and pay attention to linguistic and non-linguistic elements.
PSO Trained Hybrid Intelligent Classifier Using Wavelet and Statistical Features for Pipeline Leak Classification

One of the main problems in the oil industrial field is the leakage in transporting pipelines due to its effect on human society , environment, and money loss. Therefore, the bottleneck for most researches in this subject is to minimize false alarm rate (FAR) for the adopted leak detection method. Although some recent methods succeed in classifying the existence or absence of the leak as a binary classification problem. But this paper proposed a novel leak detection technique which predicts the leak location and estimates its size within certain pre-defined ranges. In order to simulate the environmental conditions for real-time operating oil pipeline, accurate simulator known as OLGA program creates the oil physical parameters. Various methods for features extraction are considered such as statistical and wavelet techniques which are implemented to get the features from the fluid simulated waveforms. These features are organized and fed to an ANN classifier trained by PSO algorithm to determine the leak class out of 10 suggested classes. The proposed leak detection technique is used to simulate 18 kilometers belonging to Iraqi crude oil pipelines company operated in Baghdad. The achieved results of the true positive rate (TPR) for the proposed applied method for the leak detection and classification of different leak classes in terms of their positions and magnitudes were about 97%.
Speeding Up Search-Based Motion Planning using Expansion Delay Heuristics

Suboptimal search algorithms are a popular way to find solutions to planning problems faster by trading off solution optimality for search time. This is often achieved with the help of inadmissible heuristics. Prior work has explored ways to learn such inadmissible heuristics. However, it has focused on learning the heuristic value as an estimate of the cost to reach a goal. In this paper, we present a different approach that computes inadmissible heuristics by learning Expansion Delay for transitions in the state space. Expansion Delay is defined as the number of states expanded during the search between two consecutive states. Expansion Delay can be used as a measure of the depth of local minima regions i.e., regions where the heuristic(s) are weakly correlated with the true cost-to-goal (Vats, Narayanan and Likhachev 2017). Our key idea is to learn this measure in order to guide the search such that it reduces the total Expansion delay for reaching the goal and hence, avoid local minima regions in the state space. We analyze our method on 3D (x, y, theta) planning and Humanoid footstep planning. We find that the heuristics computed using our technique result in finding feasible plans faster.
Reinforcement Based Learning on Classification Task Yields Better Generalization and Adversarial Accuracy (Student Abstract)

Deep Learning has become interestingly popular in the field of computer vision, mostly attaining near or above human-level performance in various vision tasks. But recent work has also demonstrated that these deep neural networks are very vulnerable to adversarial examples (adversarial examples - inputs to a model which are naturally similar to original data but fools the model in classifying it into a wrong class). In this work, we proposed a novel method to train deep learning models on an image classification task. We used a reward-based optimization function, similar to the vanilla policy gradient method in reinforcement learning to train our model instead of conventional cross-entropy loss. An empirical evaluation on cifar10 dataset showed that our method outperforms the same model architecture trained using cross-entropy loss function (on adversarial training). At the same time, our method generalizes better to the training data with the difference in test accuracy and train accuracy &lt; 2% for most of the time as compared to cross-entropy one, whose difference most of the time remains &gt; 2%.
Early Detection of Sexual Predators in Chats

An important risk that children face today is online grooming, where a so-called sexual predator establishes an emotional connection with a minor online with the objective of sexual abuse.Prior work has sought to automatically identify grooming chats, but only after an incidence has already happened in the context of legal prosecution.In this work, we instead investigate this problem from the point of view of prevention.We define and study the task of early sexual predator detection (eSPD) in chats, where the goal is to analyze a running chat from its beginning and predict grooming attempts as early and as accurately as possible.We survey existing datasets and their limitations regarding eSPD, and create a new dataset called PANC for more realistic evaluations.We present strong baselines built on BERT that also reach state-of-the-art results for conventional SPD.Finally, we consider coping with limited computational resources, as real-life applications require eSPD on mobile devices.
A NOVEL DEEP LEARNING BASED METHOD FOR DETECTION AND COUNTING OF VEHICLES IN URBAN TRAFFIC SURVEILLANCE SYSTEMS

Abstract. In intelligent transportation systems (ITS), it is essential to obtain reliable statistics of the vehicular flow in order to create urban traffic management strategies. These systems have benefited from the increase in computational resources and the improvement of image processing methods, especially in object detection based on deep learning. This paper proposes a method for vehicle counting composed of three stages: object detection, tracking and trajectory processing. In order to select the detection model with the best trade-off between accuracy and speed, the following one-stage detection models were compared: SSD512, CenterNet, Efficiedet-D0 and YOLO family models (v2, v3 and v4). Experimental results conducted on the benchmark dataset show that the best rates among the detection models were obtained using YOLOv4 with mAP = 87% and a processing speed of 18 FPS. On the other hand, the accuracy obtained in the proposed counting method was 94% with a real-time processing rate lower than 1.9.
An Attention Based Multi-view Model for Sarcasm Cause Detection (Student Abstract)

Sarcasm often relates to people’s implicit discontent with certain products and policies. Existing research mainly focus on sarcasm detection, while the deep causal relationships in the full conversation remained unexplored. This paper formulates a novel research question of sarcasm cause detection, and proposes an attention based model that simultaneously captures different semantic associations as well as the inner causal logics in multi-view manner. Experiments on public Reddit dataset prove the efficacy of the proposed model.
Penerapan Model Pembelajaran Discovery Learning Untuk Meningkatkan Keaktifan Belajar Siswa pada Pembelajaran Tematik Kelas V Tema 8 di SD Negeri 1 Selo Kabupaten Grobogan Jawa Tengah

This study aims to apply the Discovery Learning learning model to increase student learning activity in the thematic learning of class V SD Negeri 1 Selo. This research method used classroom action research which is carried out in 3 cycles. The subjects of this study were fifth grade students of SD Negeri 1 Selo, totaling 14 students. The instrument used observation and tests. While the data analysis technique used descriptive quantitative analysis. The results of this study indicate that student learning activity has increased. Learning activity in cycle 1, meeting 1, was 59.77%, followed by meeting 2, which was 60%. Based on the results of cycle 1, it has shown an increase. The increase that occurred in cycle 1 also occurred in cycle 2. In cycle 2, meeting 1, the percentage of active learning was 68.09%. Furthermore, at the second meeting, it was 69.53%. Based on the results of cycle 2, it has also shown an increase. Then it increased in cycle 3 of meeting 1 to 75.48%, and meeting 2 of 79.53%.
Learning a Gradient-free Riemannian Optimizer on Tangent Spaces

A principal way of addressing constrained optimization problems is to model them as problems on Riemannian manifolds. Recently, Riemannian meta-optimization provides a promising way for solving constrained optimization problems by learning optimizers on Riemannian manifolds in a data-driven fashion, making it possible to design task-specific constrained optimizers. A close look at the Riemannian meta-optimization reveals that learning optimizers on Riemannian manifolds needs to differentiate through the nonlinear Riemannian optimization, which is complex and computationally expensive. In this paper, we propose a simple yet efficient Riemannian meta-optimization method that learns to optimize on tangent spaces of manifolds. In doing so, we present a gradient-free optimizer on tangent spaces, which takes parameters of the model along with the training data as inputs, and generates the updated parameters directly. As a result, the constrained optimization is transformed from Riemannian manifolds to tangent spaces where complex Riemannian operations (e.g., retraction operations) are removed from the optimizer, and learning the optimizer does not need to differentiate through the Riemannian optimization. We empirically show that our method brings efficient learning of the optimizer, while enjoying a good optimization trajectory in a data-driven manner.
Python: A Quintessential approach towards Data Science

Python is an interpreted object-oriented programming language that is sustainably procuring vogue in the field of data science and analytics by fabricating complex software applications. Establishing a righteous nexus between developers and data scientists. Python has undoubtedly become paramount for data scientists mindful of cosmic and robust standard libraries which are used for analyzing and visualizing the data. Data scientists have to deal with the exceedingly large amount of data alias as big data. With elementary usage and a vast set of python libraries, Python has doubtlessly become an admired option to handle big data. Python has developed and evolved analytical tools which can help data scientist in developing machine learning models, web services, data mining, data classification, exploratory data analysis, etc. In this paper, we will scrutinize various tools which are used by python programmers for efficient data analytics, their scope with contrast to other programming languages.
Impact of Administrative Empowerment on Organizational Commitment: A Case Study of Taj Sheba Hotel in Yemen

The aim of this study is to analyze the impact of administrative empowerment on the organizational commitment among the employees of Taj Sheba Hotel in the Republic of Yemen. The research is based on the descriptive approach using questionnaire which consists of 40 questions with 5 Likert scale. The data was distributed and collected from 100 employees working at Taj Sheba Hotel. The results indicate that the levels of administrative empowerment and organizational commitment of the employees at Taj Sheba Hotel are high. Also, the results show that there is a statistically significant impact of administrative empowerment dimensions (simultaneously) on organizational commitment of employees. Besides, all dimensions of administrative empowerment (job autonomy – motivation – communication – teamwork – training) used in this study have a significant impact on organizational commitment. Further, the results show that an estimated 64.2% of the variance in overall organizational commitment can be accounted for by the five dimensions of administrative empowerment, in which job autonomy was the most important factor in increasing the organizational commitment (β = 0.350) followed by training in the second rank (β = 0.242), then communication in the third rank (β = 0.211), motivation in fourth rank (β = 0.193) and teamwork in the last rank (β = 0.190). Finally, suggestions may be made to Taj Sheba Hotel administration to pay more attention to administrative empowerment as a new strategy and an effective route for development, improvement of quality and enhancement of organizational commitment.
The Discourse of Remote Teaching During the COVID-19 Pandemic

This paper examines the discourse of remote teaching during the COVID-19 pandemic based on its opposition to distance education. The terminological issues in this debate are identified; the main arguments of the opposition between remote teaching and distance education are addressed; and the common elements between the two educational modalities are analyzed. Based on Andrew Feenberg's Critical Theory of Technology, we argue that technological mediation in both distance and remote education has limitations as well as potentials that must be discussed.
E-Services in Sudan During the Covid-19 Pandemic as a Model: a Case Study of University of Bahri-Sudan

The study aims to explore the barriers of online learning in Sudan. The achievement of the research purpose requires an approach which is not only describes the relations between variables, but also the experience of the stakeholders, therefore, the study adopted mixed mothed approach. To collect data, the study used two sources; secondary, and primary data sources. The secondary sources included peer-review international journals from recognized publishers, books, newspapers and reports. The primary sources included questionnaires and interviews. 132 participants were surveyed. Two professors were interviewed. SPSS was used to analyze the data. The findings indicate that there are five unique barriers to e-learning adoption in Sudan including; poor network infrastructure, lack of bandwidth in rural areas, electricity outages, lack of electricity in rural areas, and stakeholder’s deficiency of e-skills. Furthermore, the study showed that some professors are old, and they resist the adoption of e-learning. Likewise, the study found that 67% of respondents were not able to access the internet in their areas during Covid19 lockdown, 25% of respondents do not possess computers, 35% of respondents did not receive training for gaining E-skills, and 74% of respondents prefer learning in classrooms. The study concluded that, E-learning to be adopted in Sudan, several steps can be taken such as e-skills intensive training for stakeholders, support of stakeholders, electricity and internet networks connection to whole parts of Sudan, poverty combat, and stimulation of students to accept e-learning.
REAM♯: An Enhancement Approach to Reference-based Evaluation Metrics for Open-domain Dialog Generation: An Enhancement Approach to Reference-based Evaluation Metrics for Open-domain Dialog Generation

The lack of reliable automatic evaluation metrics is a major impediment to the development of open-domain dialogue systems.Various reference-based metrics have been proposed to calculate a score between a predicted response and a small set of references.However, these metrics show unsatisfactory correlations with human judgments.For a referencebased metric, its reliability mainly depends on two factors: its ability to measure the similarity between the predicted response and the reference response, as well as the reliability of the given reference set.Yet, there are few discussions on the latter.Our work attempts to fill this vacancy.We first clarify an assumption on reference-based metrics that, if more high-quality references are added into the reference set, the reliability of the metric will increase.Next, we present REAM : an enhancement approach to Reference-based EvAluation Metrics 1 for open-domain dialogue systems.A prediction model is designed to estimate the reliability of the given reference set.We show how its predicted results can be helpful to augment the reference set, and thus improve the reliability of the metric.Experiments validate both the effectiveness of our prediction model and that the reliability of reference-based metrics improves with the augmented reference sets.
Calibration of binocular cameras with non-overlapping fields of view based on planar mirrors

Multicamera systems are commonly applied in the large field-of-view (FOV) measurements. However, two cameras may have a non-overlapping FOV in some scenes, and traditional binocular camera calibration methods cannot be used to directly calibrate non-overlapping cameras. To solve this problem, our study proposes a calibration method for binocular cameras with non-overlapping FOVs on the basis of planar mirrors. According to the reflection characteristics of an optical planar mirror, the proposed method can use the same target to calibrate non-overlapping cameras. Hence, the method overcomes the limitation of non-overlapping cameras being unable to observe common targets. Experiments results show that the maximum RMS error does not exceed 0.53 mm. Hence, the proposed method is effective, and its measurement technique is simpler and more universal than those of other methods. In addition, it is applicable to a wide range of measurements.
Peran Kemandirian Belajar dalam Memediasi Pengaruh Motivasi Berprestasi terhadap Prestasi Belajar Mahasiswa Selama Pembelajaran Daring

This study aims to analyze and examine the role of independent learning in mediating the effect of achievement motivation on student achievement during online learning. Respondents of this study were 158 active students of the Department of Economic Education, UNESA, batch 2018. Data collection techniques were through questionnaires and documentation, then the data were analyzed using the SEM (Structural Equation Modeling) test. The results of this study indicate that achievement motivation has a direct and significant effect on learning achievement. Achievement motivation has a direct and significant effect on learning independence. Learning independence has no direct and insignificant effect on learning achievement. and achievement motivation has an indirect effect on learning achievement through independent learning with a total value of 20%.
Accelerating abelian random walks with hyperbolic dynamics

Given integers $d \geq 2, n \geq 1$, we consider affine random walks on torii $(\mathbb{Z} / n \mathbb{Z})^{d}$ defined as $X_{t+1} = A X_{t} + B_{t} \mod n$, where $A \in \mathrm{GL}_{d}(\mathbb{Z})$ is an invertible matrix with integer entries and $(B_{t})_{t \geq 0}$ is a sequence of iid random increments on $\mathbb{Z}^{d}$. We show that when $A$ has no eigenvalues of modulus $1$, this random walk mixes in $O(\log n \log \log n)$ steps as $n \rightarrow \infty$, and mixes actually in $O(\log n)$ steps only for almost all $n$. These results generalize those on the so-called Chung-Diaconis-Graham process, which corresponds to the case $d=1$. Our proof is based on the initial arguments of Chung, Diaconis and Graham, and relies extensively on the properties of the dynamical system $x \mapsto A^{\top} x$ on the continuous torus $\mathbb{R}^{d} / \mathbb{Z}^{d}$. Having no eigenvalue of modulus one makes this dynamical system a hyperbolic toral automorphism, a typical example of a chaotic system known to have a rich behaviour. As such our proof sheds new light on the speed-up gained by applying a deterministic map to a Markov chain.
Graph Database Modelling on Malay Architecture IFC Data

The school is the primordial nucleus of society, inscribed in the city, in the neighbourhood.On this scale, taking into account road safety around schools and on home-school journeys is a priority.This problematic was applied to the primary school of "TAREK IBN ZIAD" in Guelma city that is located near the primary roads characterized by dense road traffic and mobility practice which cannot be marginalized.This work aims to improve a feeling of belonging and social security, and also, strengthening mitigation measures or setting up specific programs to improve security.To properly conduct this scientific research, an inventory (diagnosis) must be established around the school and its surroundings.Collection of data was based on: a direct observation, a school survey by questionnaire, a series of interviews involving all the actors concerned as sources of information.The results obtained confirm the marginalized situation of the school surroundings.The vast majority of the participants stressed the need to work in partnership with all stakeholders to find sustainable solutions to this recurring problem.
Advertisers could come for your dreams, researchers warn

Inserting ads into dreams may one day be feasible.