Reviewerly/exHarmony huggingface dataset:
"And My Screen Wouldn't Share": Student-Teachers' Perceptions of ICT in Online Teaching Practice and Online Teaching

The sudden switch to online teaching enforced by the covid-19 pandemic has impacted teacher education at universities, particularly micro-teachings and teaching practice, as technology has become an inherent part of these processes. The growing body of literature on online teaching and teacher education during lockdown conditions mainly addresses challenges in teacher education and educator perceptions. However, very few studies deal with the perceptions of student- teachers. To fill this gap, a group of teacher educators conducted a research study with 63 students enrolled in a master’s Degree in Teaching efl for Secondary Schools offered at Masaryk University, Czechia. To carry it out, qualitative coding procedures were employed on a dataset of 120 lesson reflections written by students completing their teaching practice via online courses which were ordinarily conducted in person. The purpose was to find out how student-teachers perceived technology use when teaching online. The main findings show that, despite constant comparison between the face-to-face and online classrooms and an initial reliance on the success of technology to determine a lesson’s success, the majority of student-teachers normalized technology as a platform for teaching, using technology-specific language for teaching strategies and classroom events. These findings suggest that online teaching and learning should be seen as an integral part of teacher education.
Plateau Phenomenon in Gradient Descent Training of RELU Networks: Explanation, Quantification, and Avoidance

Plateau Phenomenon in Gradient Descent Training of RELU Networks: Explanation, Quantification, and Avoidance
YOLOv5s-FCG : An Improved YOLOv5 Method for Inspecting Riders’ Helmet Wearing

Abstract Manual inspection of riders’ helmets is time-consuming and labor-intensive, low in efficiency and small in coverage. Aiming at this shortcoming, this paper proposed an improved YOLOv5s-FCG(FourLayers, CBAM attention, GhostBottleneck) helmet wearing detection method based on YOLOv5 (You Only Look Once). Based on the smallest volume of YOLOv5s in YOLOv5 series, the network was improved, the shallow feature detection layer was added, the three-scale feature detection was changed to four-scale feature detection, and the up sampling was increased by four times. Add the CBAM attention module; Use lightweight GhostBottleneck instead of Bottleneck structures. The results in our experiments show that YOLOv5s-FCG raises the average detection accuracy (mAP) by 2.0% compared with YOLOv5s on the ourselves’ riding safety helmet data set and 1.5% on the NWPU-VHR-10 public data set. The proposed algorithm not only ensured the detection rate, volume, computation and number of parameters, but also improved the detection accuracy. And it had good adaptability and generalization ability in complex road environments such as poor light and small targets.
Experimental Realization of Multi-ion Sympathetic Cooling on a Trapped Ion Crystal

Trapped ions are one of the leading platforms in quantum information science. For quantum computing with large circuit depth and quantum simulation with long evolution time, it is of crucial importance to cool large ion crystals at runtime without affecting the internal states of the computational qubits, thus the necessity of sympathetic cooling. Here, we report multi-ion sympathetic cooling on a long ion chain using a narrow cooling beam focused on two adjacent ions, and optimize the choice of the cooling ions according to the collective oscillation modes of the chain. We show that, by cooling a small fraction of ions, cooling effects close to the global Doppler cooling limit can be achieved. This experiment therefore demonstrates an important enabling step for quantum information processing with large ion crystals.
Brain-Like Approaches to Unsupervised Learning of Hidden Representations - A Comparative Study

Unsupervised learning of hidden representations has been one of the most vibrant research directions in machine learning in recent years. In this work we study the brain-like Bayesian Confidence Propagating Neural Network (BCPNN) model, recently extended to extract sparse distributed high-dimensional representations. The usefulness and class-dependent separability of the hidden representations when trained on MNIST and Fashion-MNIST datasets is studied using an external linear classifier and compared with other unsupervised learning methods that include restricted Boltzmann machines and autoencoders.
Hubungan Antara Pola Asuh Orang Tua Dan Prestasi Belajar Siswa

This study aims to determine whether there is a relationship between parents' parenting and student achievement MTs An-Nidzom Babadan Kunjang Kediri. Hopotesis posed no relationship between parents' parenting and student achievement MTs An-Nidzom Babadan Kunjang 2016-2017 school year. data collection used questionnaires and documents for the parents' parenting variables and variable student achievement. This research is quantitative. subjects of this study were students Mts An-Nidzom Babadan Kunjang 2016/2017 academic year consisting of 89 students. Data analysis technique used Product Moment Correlation from Karl Person. Further, the process of data analiss performed with SPSS (Statistical Package For Sciences) 16:00 Windows. From the analysis stated that the respondents to the Discipline taught in MTs An-Nidzom Babadan Kunjang is the category was evidenced by the respondents amounted to 89 respondents or 60% chose the final score at the interval 50-63. There is a relationship between parents' parenting and learning achievement Academic Year 2016-2017. Based on the overall hypothesis testing conducted by F test, performed in accordance to the annex ANOVA F column, obtained calculated F value of 24 002 and F table with α = (0.05) df = n - k-1 = 89-1-1 = 87 so that the F table = 3.95. This value indicates that the value of F count> F table (24 002> 3.95). And hipotesin calculation by using analysis of coefficient of determination (R2) This means that there is a connection simultaneously learning Discipline (X) to the dependent variable (Y) that is Achievement for 0602 (60%). Thus, in this study we can conclude that the working hypothesis (Ha) which states: The relationship between parenting parents ddan student achievement MTs An-Nidzom Babadan Kunjang Kediri acceptable . While the null hypothesis (H0) that states: relationship between parenting parents and student achievement MTs An-Nidzom Babadan Kunjang 2016-2017 school year '' rejected '
Development of Proposed Ensemble Model for Spam e-mail Classification

Spam e-mail documents classification is a very challenging task for e-mail users, especially non IT users. Billionsof people using the internet and face the problem of spam e-mails. The automatic identification and classificationof spam e-mails help to reduce the problem of e-mail users in managing a large amount of e-mails. This work aimsto do a significant contribution by building a robust model for classification of spam e-mail documents using datamining techniques. In this paper, we use Enorn1 data set which consists of spam and ham documents collectedfrom Kaggle repository. We propose an Ensemble Model-1 that is an ensemble of Multilayer Perceptron (MLP),Naïve Bayes and Random Forest (RF) to obtain better accuracy for the classification of spam and hame-mail documents.Experimental results reveal that the proposed Ensemble Model-1 outperforms other existing classifiers aswell as other proposed ensemble models in terms of classification accuracy. The suggested and proposed EnsembleModel-1 produces a high accuracy of 97.25% for classification of spam e-mail documents.
An Image Matching Method Based on SIFT Feature Extraction and FLANN Search Algorithm Improvement

In order to solve the problems of less feature information and high mismatching rate in traditional image matching algorithms, this paper proposed to extract and describe features based on the SIFT algorithm. FLANN algorithm was used to pre-match feature points, and RANSAC algorithm was used to optimize the matching results, so as to realize real-time image matching and recognition. Experimental results show that the proposed algorithm has better accuracy and better matching effect than traditional image matching methods.
How are project-specific forums utilized? A study of participation, content, and sentiment in the Eclipse ecosystem

Abstract Although many software development projects have moved their developer discussion forums to generic platforms such as Stack Overflow, Eclipse has been steadfast in hosting their self-supported community forums. While recent studies show forums share similarities to generic communication channels, it is unknown how project-specific forums are utilized. In this paper, we analyze 832,058 forum threads and their linkages to four systems with 2,170 connected contributors to understand the participation, content and sentiment. Results show that Seniors are the most active participants to respond bug and non-bug-related threads in the forums (i.e., 66.1% and 45.5%), and sentiment among developers are inconsistent while knowledge sharing within Eclipse. We recommend the users to identify appropriate topics and ask in a positive procedural way when joining forums. For developers, preparing project-specific forums could be an option to bridge the communication between members. Irrespective of the popularity of Stack Overflow, we argue the benefits of using project-specific forum initiatives, such as GitHub Discussions, are needed to cultivate a community and its ecosystem.
Research on Path Planning of UAV Forest Fire Fighting Based on Improved Ant Colony Algorithm

An improved ant colony algorithm is proposed to solve the problem that the ant colony system algorithm is easy to stagnate in the face of complex problems and can not find the optimal solution.In order to ensure the ant colony algorithm to choose the optimal path and ensure a certain exploration space, the combination of pseudo-random rules and roulette is adopted. Pheromone concentration update is based on the distribution rules of wolf colony algorithm, which gives the strongest feedback to the optimal ant, avoiding the ant colony algorithm falling into the sub optimal solution. The simulation results show that the improved ant colony algorithm can find the optimal path and find the shortest path faster,which proves the feasibility of the improved ant colony algorithm.
Pose-Driven Realistic 2-D Motion Synthesis

A realistic 2-D motion can be treated as a deforming process of an individual appearance texture driven by a sequence of human poses. In this article, we thereby propose to transform the 2-D motion synthesis into a pose conditioned realistic motion image generation task considering the promising performance of pose estimation technology and generative adversarial nets (GANs). However, the problem is that GAN is only suitable to do the region-aligned image translation task while motion synthesis involves a large number of spatial deformations. To avoid this drawback, we design a two-step and multistream network architecture. First, we train a special GAN to generate the body segment images with given poses in step-I. Then in step-II, we input the body segment images as well as the poses into the multistream network so that it only needs to generate the textures in each aligned body region. Besides, we provide a real face as another input of the network to improve the face details of the generated motion image. The synthesized results with realism and sharp details on four training sets demonstrate the effectiveness of the proposed model.
HM-Net: A Regression Network for Object Center Detection and Tracking on Wide Area Motion Imagery

Wide Area Motion Imagery (WAMI) yields high resolution images with a large number of extremely small objects.Target objects have large spatial displacements throughout consecutive frames.This nature of WAMI images makes object tracking and detection challenging.In this paper, we present our deep neural network-based combined object detection and tracking model, namely, Heat Map Network (HM-Net).HM-Net is significantly faster than state-of-the-art frame differencing and background subtractionbased methods, without compromising detection and tracking performances.HM-Net follows object centerbased joint detection and tracking paradigm.Simple heat map-based predictions support unlimited number of simultaneous detections.The proposed method uses two consecutive frames and the object detection heat map obtained from the previous frame as input, which helps HM-Net monitor spatio-temporal changes between frames and keep track of previously predicted objects.Although reuse of prior object detection heat map acts as a vital feedback-based memory element, it can lead to unintended surge of false positive detections.To increase robustness of the method against false positives and to eliminate low confidence detections, HM-Net employs novel feedback filters and advanced data augmentations.HM-Net outperforms state-of-the-art WAMI moving object detection and tracking methods on WPAFB dataset with its 96.2% F1 and 94.4% mAP detection scores, while achieving 61.8% mAP tracking score on the same dataset.This performance corresponds to an improvement of 2.1% for F1, 6.1% for mAP scores on detection, and 9.5% for mAP score on tracking over state-of-the-art.
Adaptive instantaneous frequency ridge extraction based on target tracking for frequency-modulated signals

The instantaneous frequency (IF) is an important feature for the analysis of non-stationary signals. However, extracting multiple IF ridges, crossing IF ridges, and discontinuous IF ridges simultaneously is still a challenging task. To solve this problem, an adaptive IF ridge extraction method is proposed. This method regards the IF ridge values at each time point as targets to be tracked. The tracking targets are defined as three states: survival, death, and birth. Firstly, the potential peaks in the time-frequency representation are found to reduce the noise effect. Secondly, an adaptive penalty function is constructed to determine the target state and track the ridge target. Thirdly, the crossing ridges are separated base on the slope information. This method is capable of simultaneously extracting multiple IF ridges, crossing IF ridges, and discontinuous IF ridges. The performance of the proposed method is investigated by a simulated signal and two experimental signals.
Evolution and effectiveness of loss functions in generative adversarial networks

Recently, the evolution of Generative Adversarial Networks (GANs) has embarked on a journey of revolutionizing the field of artificial and computational intelligence. To improve the generating ability of GANs, various loss functions are introduced to measure the degree of similarity between the samples generated by the generator and the real data samples, and the effectiveness of the loss functions in improving the generating ability of GANs. In this paper, we present a detailed survey for the loss functions used in GANs, and provide a critical analysis on the pros and cons of these loss functions. First, the basic theory of GANs along with the training mechanism are introduced. Then, the most commonly used loss functions in GANs are introduced and analyzed. Third, the experimental analyses and comparison of these loss functions are presented in different GAN architectures. Finally, several suggestions on choosing suitable loss functions for image synthesis tasks are given.
Pengaruh Bimbingan Belajar dari Orangtua dalam Peningkatan Prestasi Belajar Pada Siswa: Studi Kasus SMP Swasta YWKA Medan

Seeing the importance of how to educate parents in the family environment, the authors conducted a study with the aim to find out if there is a relationship in the way parents educate in the family environment with children's learning achievement in school. In this study, the authors determined a sample of 35 private junior high school students YWKA Medan who live with parents and both parents are still alive, through random sampling techniques. To collect data, closed questionnaires are used, to obtain data on how parents educate and documentary studies to obtain student learning achievement.  Respondents' answers are given a value by giving a value weight for option a given a value of 4, for option b is given a value of 3, and for option c is given a value of 2, and for option d is given a value of 1. To find a correlation between the way parents educate and learning achievement is used pearson product moment formula as follows:Where: x  is the value of the questionnaire, and  y is the student's learning achievement. To test a hypothesis with a significant level of 5% or by using a belief level of 95% used a table r (correlation table) with a sample number of 35 people. Thus the result of the calculation obtained the value r = -0.08 and the value of r in the correlation table is 0.334.  This means that there is a very low correlation. Thus it can be proven that the hypothesis in this study is rejected.  So the author can conclude that the way parents educate is good can not improve students' learning achievement.
librec-auto: A Tool for Recommender Systems Experimentation

Recommender systems are complex. They integrate the individual needs of users with the characteristics of particular domains of application which may span items from large and potentially heterogeneous collections. Extensive experimentation is required to understand the multidimensional properties of recommendation algorithms and the fit between algorithm and application. librec-auto is a tool that automates many aspects of off-line batch recommender system experimentation. It has a large library of state-of-the-art and historical recommendation algorithms and a wide variety of evaluation metrics. It further supports the study of diversity and fairness in recommendation through the integration of re-ranking algorithms and fairness-aware metrics. It supports declarative configuration for reproducible experiment management and supports multiple forms of hyper-parameter optimization.
DeepMTD: Moving Target Defense for Deep Visual Sensing against Adversarial Examples

Deep learning-based visual sensing has achieved attractive accuracy but is shown vulnerable to adversarial attacks. Specifically, once the attackers obtain the deep model, they can construct adversarial examples to mislead the model to yield wrong classification results. Deployable adversarial examples such as small stickers pasted on the road signs and lanes have been shown effective in misleading advanced driver-assistance systems. Most existing countermeasures against adversarial examples build their security on the attackers’ ignorance of the defense mechanisms. Thus, they fall short of following Kerckhoffs’s principle and can be subverted once the attackers know the details of the defense. This article applies the strategy of moving target defense (MTD) to generate multiple new deep models after system deployment that will collaboratively detect and thwart adversarial examples. Our MTD design is based on the adversarial examples’ minor transferability across different models. The post-deployment of dynamically generated models significantly increase the bar of successful attacks. We also apply serial data fusion with early stopping to reduce the inference time by a factor of up to 5, as well as exploit hardware inference accelerators’ characteristics to strike better tradeoffs between inference time and power consumption. Evaluation based on three datasets including a road sign dataset and two GPU-equipped embedded computing boards shows the effectiveness and efficiency of our approach in counteracting the attack.
An Effective Approach for Constructing a Class of 4D Multicluster Conservative Chaotic Systems without External Excitation

Compared with dissipative chaos, conservative chaos is more suitable for information encryption since it does not have attractors, therefore, it is important to construct different complicated conservative chaotic systems. This paper reports an effective approach to construct a class of 4D conservative systems based on the formalism of a generalized Hamiltonian system. Based on the construction approach, several example systems including one hyperspatial multicluster chaotic system are proposed, and different cluster-shaped chaotic flows are discovered in these example systems. The only difference of these example systems is their Hamiltonians, whose local minimum points directly determine the number and position of these cluster-shaped conservative chaotic flows. Numerical simulations vividly demonstrate that different cluster-shaped chaotic flows really move around different local centers, i.e. the local minimum points of the Hamiltonian. In addition, an analog circuit is designed and simulated in NI Multisim to confirm the existence of multicluster conservative chaotic flows at the physical level.
Bibliometrics of Machine Learning Research Using Homomorphic Encryption

Since the first fully homomorphic encryption scheme was published in 2009, many papers have been published on fully homomorphic encryption and its applications. Machine learning is one of the most interesting applications and has drawn a lot of attention from researchers. To better represent and understand the field of Homomorphic Encryption in Machine Learning (HEML), this paper utilizes automated citation and topic analysis to characterize the HEML research literature over the years and provide the bibliometrics assessments for this burgeoning field. This is conducted by using a bibliometric statistical analysis approach. We make use of web-based literature databases and automated tools to present the development of HEML. This allows us to target several popular topics for in-depth discussion. To achieve these goals, we have chosen the well-established Scopus literature database and analyzed them through keyword counts and Scopus relevance searches. The results show a relative increase in the number of papers published each year that involve both homomorphic cryptography and machine learning. Using text mining of articles titles, we have found that cloud computing is a popular topic in this field, which also includes neural networks, big data, and the Internet of Things. The analysis results show that China, the US, and India have generated almost half of all the research contributions in HEML. The citation statistics, keyword statistics, and topic analyses give us a quick overview of the development of the field, which can be of great help to new researchers. It is also possible to apply our methodology to other research areas, and we see great value in this approach.
An Imperceptible Watermarking Scheme for Medical Image Tamper Detection

One of the important issues in telemedicine field refers to an advanced secure communication. Digital image watermarking is an ideal solution since it protects the electronic patient information’s from unauthorized access. This paper presents a novel blind fragile-based image watermarking scheme in spatial domain that merges Speed Up Robust Features (SURF) descriptor with the well-known Weber Descriptors (WDs) and Arnold algorithm. It provides a good way for enhancing the image quality and time complexity for medical data integrity. Firstly, the watermark image is shuffled using Arnold chaotic map. Secondly, the SURF technique is practiced to Region of Interest (ROI) of the medical image and then the blocks around the SURF points are selected to insert the watermark. Finally, the watermark is encrusted and extracted using WDs. Experimental results show good image fidelity with the shortest execution time to ensure medical images integrity.