Reviewerly/exHarmony huggingface dataset:
Object Classification and Tracking Using Scaled P8 YOLOv4 Lite Model

One of the most difficult tasks in the area of computer vision is object detection, which combines object categorization and object location within a scene. In terms of object detection, Deep Neural Networks have been recently demonstrated to outperform alterna-tive approaches. The issues related deep learning neural network is its complexity and huge computation, so it is not possible to detect and track the objects in image of high resolution in real time. We proposed scaled YOLOv4 lite model as Single Stage Detector Neural Network for object detection, tracking and it is trained using COCO 2017 dataset. To create the YOLOv4-CSP- P5- P6- P7- P8 networks, the Scaled YOLOv4 applied efficient network scaling strategies. The additional layer in YOLOv4 lite model is added as P8 layer which improves accuracy. Cross-stage-partial (CSP) connections and Mish activation are used in improved network design, such as backbone optimization and Neck (PAN). In the case of YOLOv4, however, it can only be trained once for all resolutions. Width and Height activations have been changed, allowing for faster network training. With YOLOv4 lite model, we used CSPDarkNet-53 model as a backbone. The experimental result show our YOLOv4 lite model can detect and track object up to 28 fps when model run with the video input and has an accuracy of 86.09% when tested on real-time video with resolutions 1920 × 1080 (full HD). AP = 50.81%, AP @50 = 63.6%, and AP @75 = 52.5% for CSPDarkNet-53 model backbone.
Guided Depth Completion with Instance Segmentation Fusion in Autonomous Driving Applications

Pixel-level depth information is crucial to many applications, such as autonomous driving, robotics navigation, 3D scene reconstruction, and augmented reality. However, depth information, which is usually acquired by sensors such as LiDAR, is sparse. Depth completion is a process that predicts missing pixels' depth information from a set of sparse depth measurements. Most of the ongoing research applies deep neural networks on the entire sparse depth map and camera scene without utilizing any information about the available objects, which results in more complex and resource-demanding networks. In this work, we propose to use image instance segmentation to detect objects of interest with pixel-level locations, along with sparse depth data, to support depth completion. The framework utilizes a two-branch encoder-decoder deep neural network. It fuses information about scene available objects, such as objects' type and pixel-level location, LiDAR, and RGB camera, to predict dense accurate depth maps. Experimental results on the KITTI dataset showed faster training and improved prediction accuracy. The proposed method reaches a convergence state faster and surpasses the baseline model in all evaluation metrics.
Research on Attitude Detection and Flight Experiment of Coaxial Twin-Rotor UAV

Aiming at the problem that the single sensor of the coaxial UAV cannot accurately measure attitude information, a pose estimation algorithm based on unscented Kalman filter information fusion is proposed. The kinematics and dynamics characteristics of coaxial folding twin-rotor UAV are studied, and a mathematical model is established. The common attitude estimation methods are analyzed, and the extended Kalman filter algorithm and unscented Kalman filter algorithm are established. In order to complete the test of the prototype of a small coaxial twin-rotor UAV, a test platform for the dynamic performance and attitude angle of the semi-physical flight of the UAV was established. The platform can analyze the mechanical vibration, attitude angle and noise of the aircraft. It can also test and analyze the characteristics of the mechanical vibration and noise produced by the UAV at different rotor speeds. Furthermore, the static and time-varying trends of the pitch angle and yaw angle of the Kalman filter attitude estimation algorithm is further analyzed through static and dynamic experiments. The analysis results show that the attitude estimation of the UKF is better than that of the EKF when the throttle is between 0.2σ and 0.9σ. The error of the algorithm is less than 0.6°. The experiment and analysis provide a reference for the optimization of the control parameters and flight control strategy of the coaxial folding dual-rotor aircraft.
Target-specific image clutter metrics for visual search

Researchers working across a variety of domains investigate the effects of visual clutter on performance have developed a handful of measures for characterizing this clutter. Although these measures can provide coarse predictions of search performance, they do not account for the properties of search targets and how these interact with clutter. In particular, they do not consider target-background similarity in quantifying the effect of clutter on search performance. However, when search targets and backgrounds share similar features, performance declines (Semizer & Michel, 2017). Here, we propose two new clutter metrics based on different measures of target-background similarity (i.e., exemplar level and category level) to predict the effect of clutter on search performance. Our metrics compute the similarity between target and background features (i.e., orientation subbands) in images while also accounting for size of a search target. The exemplar-level metric quantified the overlap between features of a specific search target (present in the search image) and features of a search background, while the categorical-level metric quantified the overlap between features of a search target category and features of a search background, where the latter can be used to predict search performance when the target is absent. We tested the predictive power of these metrics, along with that of an existing target-agnostic clutter metric, using a set of search data where the task was to detect and locate categorical targets in a set of natural images. Our results demonstrate that both clutter metrics successfully contributed to explaining search performance as a function of the search target. More importantly, these metrics can predict such differences even for scenes in which the target is absent, suggesting that the categorical representation of the target guides the search.
Representation of Face Shape and Surface Reflectance in Deep Convolutional Neural Networks

Deep Convolutional Neural Networks (DCNNs) recognize faces over image/appearance variation (e.g. viewpoint, illumination, expression) while retaining information about this variation (Hill et al., 2019). Here, we examined the extent to which DCNNs encode the 3D shape and surface reflectance properties of a face in the presence of challenging image variability. We generated synthetic face images using parametric models of 3D face shape and surface reflectance. The FLAME model uses a linear shape space to generate faces by varying 3D shape and expression parametrically (Li et al., 2017). The reflectance model was based on the Basel Face Model (Paysan et al., 2009). We generated five face shapes and five reflectance maps to create 25 unique faces by combining all possible pairs of shapes and reflectance maps. The stimulus set (n=1,125) consisted of images of these faces rendered at 9 viewpoints (yaw angle: 0°, 30°, and 60°; pitch angle: -30°, 0°, 30°) with 5 expressions. Each image was processed by a DCNN trained for face identification (ArcFace: Deng et al., 2019) to generate a vector representation. Cosine similarity was calculated between all possible pairs of image representations. Across this set of “identities”, shape and reflectance are confounded, with each identity sharing both its shape and reflectance with many other identities. We asked whether the network's reflectance code is tolerant to changes in viewpoint and expression by splitting the cosine similarity scores into distributions of same-reflectance and different-reflectance pairs. Results showed that the DCNN encodes reflectance over viewpoint and expression variation (Area Under the ROC Curve, AUC=0.66). Analogously, the distributions for same-shape and different-shape pairs yielded an AUC of 0.58, indicating that the shape properties of the face are encoded also. Therefore, DCNNs can separately encode the fundamental dimensions of shape and reflectance in the face, despite wide variations in viewpoint and expression.
Teaching in Times of COVID-19: The Effects of Course Standardization as Perceived by Lecturers

The current study reports on a university’s initiative to standardize courses and the effect of this on teaching during the COVID-19 pandemic. Based on the Bologna Process, which aims to harmonize academic programs that improve national and international comparability in European countries, the case institution started initiatives to foster the standardization of course didactics. At course level, the standardization provides a blended-learning approach, a close interlinking of self-study phases, and an application-driven approach during classes. Literature clearly shows the effects of standardized course infrastructure and didactics in general (e.g., comprehensive learning paths) and the impacts of certain didactic elements in particular (e.g., video quality). However, we identified a perceivable gap in finding effects and impacts of standardization during the COVID-19 pandemic and its associated challenges in teaching those academic contents online. Therefore, we conducted a qualitative study aiming to clarify those effects and impacts. Lecturers, teaching in both standardized and non-standardized courses, have been interviewed. Afterwards, the semi-structured interviews were transcribed and analyzed using qualitative content analysis. Preliminary results show inhibiting effects concerning flexibility and agile course handling on the one hand and unequivocally positive effects towards clarity and transparency in teaching standardized courses. To summarize, we were able to derive thoughtful insights into the field of tension between the need to react deftly to profound global interventions and the benefits of having a transparent, standardized, but rather rigid course didactic.
BOOSTING STUDENTS’ SPEED READING THROUGH THE SCANNING AND SKIMMING METHOD AT SMPN 1 PA’JUKUKANG

The speed reading ability of grade VIII E SMPN 1 Pa'jukukang students was still lacking. One of the reasons for the lack of students' ability to read quickly was due to the application of inappropriate learning techniques. In learning, the teacher only uses the lecture method so that students feel bored in the learning process. Therefore, appropriate learning techniques are needed to overcome these problems. Learning to read quickly with scanning and skimming techniques can be used as a way to improve students' speed reading skills.The results showed that there was an increase in the speed reading ability of class VIII E SMPN 1 Pa'jukukang students after participating in speed reading lessons using scanning and skimming techniques. The improvement achieved is from diagnostic test with very not good, and in cycle 2 it becomes good category. The increase in students' speed reading ability can be seen from the results of the reading speed test in the diagnostic test (DT), first cycle, and second cycle. The result of the effective reading speed in the pre cycle was 99 wpm and in the first cycle the average was 132 wpm or an increase of 32.67% from the DT. Then in cycle II the average effective reading speed of students was 172 wpm or an increase of 30.30% from cycle I. The increase in effective reading speed from DT to cycle II was 73.73%. The results obtained indicate that learning to read quickly using scanning and skimming techniques for class VIII E SMPN 1 Pa'jukukang has been successful. Suggestions that can be given include: (1) It is hoped that teachers can apply various kinds of appropriate learning techniques, especially in learning to read quickly so that students' speed reading abilities can be optimal. (2) Skimming and scanning techniques can be an alternative when learning to read quickly because they can improve, facilitate, and motivate students to read quickly.
ANALYZING ENGLISH TEACHERS’ CHALLENGES IN DEALING WITH STUDENTS’ AFFECTIVE COMPETENCE

Analyzing the Challenges of English Teachers in Facing Students' Affective Competencies at SMA 1 Muhammadiyah Makassar Thesis Department of English, Faculty of Teacher Training and Education, University of Muhammadiyah Makassar. Supervised by Nunung Anugrawati and Ardiana. This study was conducted to determine the purpose of the study, namely to describe Challenges of English Teachers in Facing Students' Affective Competencies at SMA 1 Muhammadiyah Makassar. The subjects in this study were teachers of SMA 1 Muhammadiyah Makassar. The purpose of this study is to find out what challenges are faced by English teachers in dealing with students' affective competence. The instruments used to collect data are observation, interviews, and documentation. Data analysis in this study is Analysis (QDA), Data Reduction, and Data Display.The results showed that the challenges of the English teacher in dealing with students' affective competence consisted of two factors, namely internal factors and external factors. The strategy used by the English teacher in dealing with students' infective competence is to make learning methods, namely the discussion method and the game method.
Integration of Technology in English Classroom Interaction

Technology has been determined to play a vital role in facilitating students’ learning of English as a foreign language in Indonesia. Thus, this study mainly highlights the need for integrating technology in the classroom, for example, using computer-based learning and networks and communications systems to support learning. In this way, classroom interaction can offer a new way for students to interact and engage with course materials. Furthermore, this study aimed to determine the students’ prior knowledge of technology in learning English, including their interests and usage; our findings also discuss how lecturers integrate technology in the classroom. This study used a descriptive quantitative method, with respondents including 60 students and 8 lecturers. The results of the research describe the students’ prior knowledge, the interest and use of technology in learning English, as well as the integration of technology used by the lecturers in classroom interaction. It can be concluded that most students and lecturers are not that all familiar with the 14 kinds of technology that are commonly used for English learning.
Pengaruh Penerapan Teori Belajar Sibernetik Terhadap Kemampuan Mahasiswa Mengelola Pembelajaran Berbasis Digital

The development of increasingly sophisticated technology creates a learning atmosphere that must adapt. Students adapt to digital-based learning, both subject matter and assignments to be received and sent. Students can improve skills in managing learning and assignments digitally. Not a few students have not been able to professionally manage digital-based learning and assignments. Limited knowledge and infrastructure are important factors in the lack of student’s ability to manage their digital-based assignments. In addition, the lack of involvement of lecturers using digital-based learning also makes one of the weak abilities of students to adapt to managing electronic assignments. Therefore, this study uses a quantitative methodology with the aim of finding out how much influence the application of cybernetic learning theory has on students' ability to manage digital-based assignments. To deal with this problem, the researchers provide a solution by conducting training and technical guidance to students on the use of technology in learning and seeking to procure facilities and infrastructure that support digital-based learning processes.
patchIT: A Multipurpose Patch Creation Tool for Image Processing Applications

Patch-based approaches in image processing are often preferable to working with the entire image. They provide an alternative representation of the image as a set of partial local sub-images (patches) which is a vital preprocessing step in many image processing applications. In this paper, a new software tool called patchIT is presented, providing an integrated framework suitable for the systematic and automatized extraction of patches from images based on user-defined geometrical and spatial criteria. Patches can be extracted in both a sliding and random manner and can be exported either as images, MATLAB .mat files, or raw text files. The proposed tool offers further functionality, including masking operations that act as spatial filters, identifying candidate patch areas, as well as geometric transformations by applying patch value indexing. It also efficiently handles issues that arise in large-scale patch processing scenarios in terms of memory and time requirements. In addition, a use case in cartographic research is presented that utilizes patchIT for map evaluation purposes based on a visual heterogeneity indicator. The tool supports all common image file formats and efficiently processes bitonal, grayscale, color, and multispectral images. PatchIT is freely available to the scientific community under the third version of GNU General Public License (GPL v3) on the GitHub platform.
Pengembangan Media Pembelajaran Berbasis Android pada Mata Kuliah Mesin-Mesin Listrik

The problem in this exploration is the appropriate and fluctuating learning media at Padang State University, with the right student learning outcomes. This study aims to disseminate learning media and form substantial, functional, and interesting media in electronic engineering courses for students majoring in electrical engineering, Padang State University. This study uses Research and Development (R&amp;D) planning, using a 4-D improvement model. 4-D repair techniques are Define, Design, Development and Disseminate. The information evaluation method used is intended to describe the truth and reasonable reasoning behind the educational media. the result obtained from this development research is an Android-oriented learning media. Judging from the consequences of this exploration, it can be said that the media designed is declared valid, from the point of view of using the material from a practical point of view. Media designed very simply for students. Finally, the Android-based learning media designed can be used and easy to develop student learning outcomes.
Restaurant Review Analysis using Natural Language Processing

Abstract: The most effective tool any restaurant can have is the capability to track the daily sales of their food and beverage. Currently, recommendation systems plays an important role in both academia and industry. These are very helpful to manage an overload of information. In this paper, applied machine learning techniques for user reviews were used and valuable information in the reviews were analyzed. For both the customers and the owners, reviews are useful to make data-driven decisions. We built a machine learning model with Natural Language Processing techniques which captures a user’s opinions from user’s reviews. A lot of businesses fail due to the lack of profit and a lack of proper improvement measures. Mostly, restaurant owners face a lot of difficulties to enhance their productivity.
Pedagogical Strategies Based on Socio-affective Scenarios: An Outlook Based on Personalized Teaching in a Virtual Learning Environment

The objective of this article is to present the creation process of Pedagogical Strategies (PS) based on Socio-affective Scenarios mapped in a Virtual Learning Environment (VLE). Every year, enterprises are looking for new technologies that can improve the skills of their collaborators, bringing VLE resources to e-training formations. The PSs are actions carried out by professors or manager in their practice, both for e-learning and e-training. In order to develop a PS, it is important to consider the socio-affective profile. For data inference were used: Social and Affective Map. Using these two tools, 38 Socio-affective Scenarios were mapped and their strategies were developed. This study utilizes an applied qualitative approach. As a result, a total of 228 PSs based on Socio-affective Scenarios were developed by 15 specialist professors. Its main contribution is the creation of PS based on criteria that can be adapted to be applied in the industry context.
Clutter degrades visual search performance for multiple targets in scenes

Often, as we search through a visual scene, we are not just looking for one instance of an object, but multiple. These visual scenes tend to have distractors, and the extent to which these distractors are present increases visual clutter. Many studies have shown that visual clutter impairs performance while detecting and localizing targets in scenes; however, it is still unknown how clutter would impact search when multiple targets are present in a scene. In this study, we investigated the effect of clutter on visual search performance in the presence of multiple targets in scenes. Observers completed a search task that required detecting and localizing multiple targets from an object category within a set of Where’s Waldo images. At the start of each trial, observers were given a word representing the search target (i.e., umbrella). Then, they were presented with an image and asked to look for as many instances of the target they could find in the image and record their response. We manipulated the number of targets as well as measured the amount of clutter in each image. Our preliminary results showed that overall search performance declined with higher levels of clutter. Specifically, search times increased with higher clutter and was longer in target-absent images than in target-present images. Additionally, higher clutter and higher number of targets correlated with lower search accuracy. More importantly, the relationship between search time, accuracy, and the number of targets indicated early termination of search when clutter was lower. Overall, our findings suggest that clutter degrades visual search performance for multiple targets in scenes.
Bayesian Selective Median Filtering for Reduction of Impulse Noise in Digital Color Images

The focus of this paper is impulse noise reduction in digital color images. The most popular noise reduction schemes are the vector median filter and its many variants that operate by minimizing the aggregate distance from one pixel to every other pixel in a chosen window. This minimizing operation determines the most confirmative pixel based on its similarity to the chosen window and replaces the central pixel of the window with the determined one. The peer group filters, unlike the vector median filters, determine a set of pixels that are most confirmative to the window and then perform filtering over the determined set. Using a set of pixels in the filtering process rather than one pixel is more helpful as it takes into account the full information of all the pixels that seemingly contribute to the signal. Hence, the peer group filters are found to be more robust to noise. However, the peer group for each pixel is computed deterministically using thresholding schemes. A wrong choice of the threshold will easily impair the filtering performance. In this paper, we propose a peer group filtering approach using principles of Bayesian probability theory and clustering. Here, we present a method to compute the probability that a pixel value is clean (not corrupted by impulse noise) and then apply clustering on the probability measure to determine the peer group. The key benefit of this proposal is that the need for thresholding in peer group filtering is completely avoided. Simulation results show that the proposed method performs better than the conventional vector median and peer group filtering methods in terms of noise reduction and structural similarity, thus validating the proposed approach.