Reviewerly/exHarmony huggingface dataset:
COMPARATİVE ANALYSİS OF THE CLASSİFİCATİON OF RECYCLABLE WASTES

The classification of recycling wastes is of great importance both environmentally and economically. Correct classification of recyclable wastes such as packaging wastes increases the efficiency of the recycling process. This classification process can be done according to the raw material type, colour, shape, size and source of the waste. Correct classification of recycling wastes also provides economic benefits by ensuring more efficient use of resources. The traditional waste classification method involves manually sorting waste into different categories. This method requires a lot of labour and is time consuming. The traditional waste classification method is also prone to human error, which can lead to contamination of recyclable materials. Deep neural networks can quickly identify different types of recyclable materials by analysing images of waste materials. Thus, it can increase efficiency and reduce pollution by sorting them appropriately. In this study, an experimental study was carried out on a data set consisting of 6 classes and 2527 images under the name of "Garbage classification". In this study, a comparative analysis was carried out using the Convolutional Neural Network architectures Resnet101, Convnext and Densenet121. As a result of this study, Resnet101 architecture was more successful than other architectures with an accuracy rate of 98.41%.
Implementation of Smartphone Media on Information Search Strategy in PAI Learning Class XI SMA Negeri 1 Pangkalpinang

The observations for this problem were conducted in class XI of SMA Negeri 1 Pangkalpinang. Contemporary educators continue to employ conventional instructional methods in the present day. Due to a lack of alignment between educators' tactics and the information being taught, students encounter difficulties in comprehending the subject matter. This paper addresses two issues: (1) optimizing the utilization of smartphone media for information retrieval strategies in PAI learning among XI grade students at SMA Negeri 1 Pangkalpinang; and (2) identifying the factors that impede the effective use of smartphone media for information retrieval strategies in PAI learning among XI grade students at SMA Negeri 1 Pangkalpinang. This qualitative research methodology entailed conducting interviews with the principal, teachers specializing in PAI, and grade XI students at SMA Negeri 1 Pangkalpinang. The study also investigated the utilization of smartphone media to optimize the implementation of information-seeking strategies in PAI learning for 11th-grade students. The study also employed pertinent data and papers. The findings indicated that utilizing cellphones to optimize knowledge retrieval strategies in PAI learning at SMA Negeri 1 Pangkalpinang proved to be a viable approach for instructors to develop learning strategies that align with students' expectations. Nevertheless, students frequently misuse smartphones, particularly by developing an addiction to gaming and perhaps failing to utilize them appropriately.
Program Peningkatan Kemampuan Bahasa Inggris Dengan Pendekatan Active And Fun Learning Bagi Siswa Sekolah Dasar

English is a foreign language that is learned at all levels of education. Fun learning methods can make students active in learning and communicating in English. The aim of this program is to improve English skills for elementary students with an active and fun learning approach. This program used experimental method with one group pretest-posttest design, where all 13 students in fifth grade of SD N 1 Grogol are assigned to the experimental class. The implementation of this program consists of three stages, namely pretest, teaching and learning activities (treatment), and posttest. The mean score as the results of the pretest was 52.31 and the posttest was 83.46. The paired sample t-test showed a significance value of 0.000 (p &lt; 0.05). These results indicate that there is an effect of using an active and fun learning approach in improving the English skills of fifth grade students at SD N 1 Grogol
Combining Phenomenology and Grounded Theory in Software Engineering: An Experience

Phenomenology and grounded theory are two prominent qualitative methods, particularly used in social sciences research. Phenomenology is carried out to understand the individuals’ actual experience regarding a phenomenon. The method describes "what" individuals experience and "how" they experience it. The focus is on the meaning of the exposures experienced by individuals regarding the phenomenon. Grounded theory on the other hand allows researchers to explore a phenomenon in depth with individuals, by which a theory is then generated. The goal is to go beyond the understanding of phenomenon by producing a theory that describes comprehensively the problem being studied. Although these two methods are initiated by similar motivations, namely to understand a phenomenon, they however employ slightly different approaches during execution. The differences make them fit complementary together to produce a more concrete and holistic outcome. To research that occasionally use qualitative methods such as Software Engineering, these methods bring new and multifaceted experience. Software Engineering research opts for qualitative methods to promote understanding, as many phenomena in the field have yet to be understood by its community. In that respect, grounded theory is becoming quite a norm in Software Engineering research in recent years, phenomenology however is relatively sporadic. This paper shares the experience of employing as well as combining phenomenology and grounded theory in Software Engineering research. The sharing is intended to inspire future research in twofold: more technical fields such as Software Engineering to employ qualitative methods in research; and leveraging the benefits of combining two qualitative methods complementarily in one study.
Problematika Kurangnya Media Pembelajaran Di SD Tanjungsari Yang Berdampak Pada Ketidak Efektifan Pada Proses Penilaian

Learning media is a means of influencing a teaching and learning process. In the absence of learning media, it is difficult for KBM to be implemented effectively. Without realizing it, learning media has an important role in addition to teachers and students as the main objects in the learning process. In this case it can be concluded that if the learning media is minimal/less then it will hinder the creation of real educational goals. Besides that, the full contribution of the school is also very much needed in creating competent teachers in order to be able to form a conducive and enjoyable student learning enthusiasm that can get encouragement in increasing student motivation in learning. The effectiveness of the assessment is also necessary in the teaching and learning process, because with the assessment the teacher can determine the abilities of each individual/student. Assessment does not only focus on the learning outcomes that have been obtained, but also on the process of how students learn. Assessment also functions as a relationship that influences each other and there is reciprocity between students and teachers, assessment in education also aims to find out the increase in student learning activities, in order to improve and enhance student learning at school and at home. Problems that often occur related to learning media usually occur in schools located in remote areas, which are far from the reach of the government. It can be exemplified by the lack of supply of textbooks and infrastructure such as projectors, etc. that can support success in learning. In this case students have difficulty finding learning resources, so the teacher must contribute to creating learning media to cover these deficiencies, because the teacher has a very big role in changing student learning.
Pengembangan Instrumen Penilaian Tes Tertulis Bentuk Eksposisi Berbasis Hots Pada Pelajaran PKN

Assessment is a series of the most important in learning. Assessment also provides objective feedback on what students have learned and is used to determine the effectiveness of learning. The research method is a guide for researchers about how to carry out research steps that are used to find, prove and various information or results that have been obtained from observations. This study uses qualitative and quantitative research models to analyze the questions. If the questions are analyzed qualitatively, this is done by examining aspects of the validity of the validation form, the aspects of which are: Material, Structure and Language. From the results of research activities at Ketopen Elementary School regarding written tests. The group got very satisfying results, because we got a lot of insight about the written test in detail starting from the definition, function of the test, examples of written tests and even how to make written test questions. From some of the results of group interviews with teachers at SDN Ketompen, the next step is for the group to compose questions in the form of HOTS-based descriptions in science learning for class VI. Written in the form of a description is a question that contains questions, where the questions are answered by expressing the thoughts of the test takers. So the way to make HOTS and LOTS questions is guided by the bloom taxonomy in which there are indicators and division of cognitive levels which are complemented by operational verbs starting from C1 to C6 where levels C1, C2, and C3 are the lost question categories. and starting at level C4, C5 and C6, it means the HOTS question category.
Pemanfaatan Media Pembelajaran Wordwall untuk Meningkatkan Hasil Peserta Didik Materi Organ Pernapasan Hewan Kelas V SDN 1 Kedungkumpul Sukorame Lamongan

From the observation that has been done in Kedungkumpul 1 Elementary School.Learning result of the stundents are low. This matter is caused because lack of learning media and learning model usage during the learning process in the class room. This problem could be overcome by utilize Wordwall Web Application as a media that appropriate with students learning characteristics so it may increase students learning motivation. The purpose of this research is to describe students and teacher activity improvements and also improvement of student learning results that involve cognitive aspects with wordwall web application used as a learning media that used to evaluation questions which are expected to increase the attractiveness of students to learning materials.&#x0D; This research is done using Class Action Research Location of this research is in Kedungkumpul 1 Elementary School. The research subject is the teacher and students Grade Fifth Kedungkumpul 1 Elementary School Sukorame Lamongan that has been done in 2 cycle. Each 1 cycle is consist of 3 stage/step, that is planning stage, implementation stage, and observation and reflection stage. The data collection is using teacher activity observation sheet, students activity observation sheet and students learning results assessment.&#x0D; The results of this research shows that the activity of the teacher is improved by 22%, while students activity is increased by 25%. In students learning results from the three aspects also show improvement, in cognitive aspects is increased 16,3. Based on results of the research that have been obtained, it could be concluded that the Wordwall Application Web as a evaluations media with my residence theme able to increase learning results of Grade V students in Kedungkumpul 1 Sukorame Lamongan
An improved training framework in neural network model fordisruption prediction and its application on EXL-50

Abstract A neural network model with classical annotation method has been used on EXL-50 &amp;#xD;tokamak to predict the impending disruptions. However, the results revealed issues of&amp;#xD;overfitting and overconfidence in predictions caused by the inaccurate labeling. To mitigate&amp;#xD;these issues, an improved training framework has been proposed. In this approach, soft labels&amp;#xD;from previous training serve as teachers to supervise the further learning process, which has &amp;#xD;demonstrated its significant improvement in predictive model performance. Notably, this&amp;#xD;enhancement is primarily attributed to the coupling effect of the soft labels and correction &amp;#xD;mechanism. This improved training framework introduces an instance-specific label smoothing &amp;#xD;method, which reflects a more nuanced model’s assessment on the likelihood of a disruption. &amp;#xD;It presents a possible solution to effectively address the challenges associated with accurate &amp;#xD;labeling across different machines
Radar Signal Deinterleaving in Electronic Warfare Systems: A Combined Approach

Traditional deinterleaving algorithms first cluster the pulses in frequency, pulse width, and angle of arrival parameter space. Then, they utilize time of arrival analysis techniques. This results in a performance loss, since these processes are not jointly combined; rather, they are sequentially connected. Additionally, they perform poorly in the presence of radars with staggered pulse repetition interval type due to abrupt time difference variations. To alleviate the suboptimality in the design and enhance the detection performance in the staggered type, we propose a combined approach based on the pulse repetition interval transform technique. It does not cluster the pulses as the traditional methods, instead, it combines all pulses by distance weighting. It deals with the detection problem in the staggered type by clustering the phases of the autocorrelation function. We develop an optimal weighting mechanism to fuse the outputs from phase clusters. Numerical results show that our method outperforms the traditional methods in different detection metrics via multiple scenarios, which is also validated by the theoretical findings.
Rule-Ranking-Based Approximate Knowledge Interpolation With Directional Monotonicity

Fuzzy rule interpolation (FRI) empowers fuzzy rule-based systems (FRBSs) with the ability to infer, even when presented with a sparse rule base where no direct rules are applicable to a given observation. The core principle lies in creating an intermediate fuzzy rule—either interpolated or extrapolated—derived from rules neighboring the observation. Conventionally, the selection of these rules hinges upon distance metrics. While this approach is easy to grasp and has been instrumental in the evolution of various FRI methods, it is burdened by the necessity of extensive distance calculations. This becomes particularly cumbersome when swift responses are imperative or when dealing with large datasets. This article introduces a groundbreaking rule-ranking-based FRI method, termed RT-FRI, which overcomes the constraints of the longstanding distance-centric FRI approach. Instead of relying on distances, RT-FRI harnesses ranking scores for rules and unmatched observation. These scores are produced by amalgamating the antecedent attributes using aggregation functions, thus streamlining the rule selection procedure. Recognizing the rigid monotonicity demands of aggregation functions, a variant—DMRT-FRI—has been introduced to ensure directional monotonicity. Experimental results indicate that RT-FRI emerges as a highly efficient technique, with DMRT-FRI exemplifying a notable balance of accuracy and efficiency.
Object Goal Navigation with Recursive Implicit Maps

Object goal navigation aims to navigate an agent to locations of a given object category in unseen environments. Classical methods explicitly build maps of environments and require extensive engineering while lacking semantic information for object-oriented exploration. On the other hand, end-to-end learning methods alleviate manual map design and predict actions using implicit representations. Such methods, however, lack an explicit notion of geometry and may have limited ability to encode navigation history. In this work, we propose an implicit spatial map for object goal navigation. Our implicit map is recursively updated with new observations at each step using a transformer. To encourage spatial reasoning, we introduce auxiliary tasks and train our model to reconstruct explicit maps as well as to predict visual features, semantic labels and actions. Our method significantly outperforms the state of the art on the challenging MP3D dataset and generalizes well to the HM3D dataset. We successfully deploy our model on a real robot and achieve encouraging object goal navigation results in real scenes using only a few real-world demonstrations. Code, trained models and videos are available at https://www.di.ens.fr/willow/research/onav_rim/.
SUIT: Learning Significance-Guided Information for 3D Temporal Detection

3D object detection from LiDAR point cloud is of critical importance for autonomous driving and robotics. While sequential point cloud has the potential to enhance 3D perception through temporal information, utilizing these temporal features effectively and efficiently remains a challenging problem. Based on the observation that the foreground information is sparsely distributed in LiDAR scenes, we believe sufficient knowledge can be provided by sparse format rather than dense maps. To this end, we propose to learn Significance-gUided Information for 3D Temporal detection (SUIT), which simplifies temporal information as sparse features for information fusion across frames. Specifically, we first introduce a significant sampling mechanism that extracts information-rich yet sparse features based on predicted object centroids. On top of that, we present an explicit geometric transformation learning technique, which learns the object-centric transformations among sparse features across frames. We evaluate our method on large-scale nuScenes and Waymo dataset, where our SUIT not only significantly reduces the memory and computation cost of temporal fusion, but also performs well over the state-of-the-art baselines.
Learning a Single Policy for Diverse Behaviors on a Quadrupedal Robot Using Scalable Motion Imitation

Learning various motor skills for quadrupedal robots is a challenging problem that requires careful design of task-specific mathematical models or reward descriptions. In this work, we propose to learn a single capable policy using deep reinforcement learning by imitating a large number of reference motions, including walking, turning, pacing, jumping, sitting, and lying. On top of the existing motion imitation framework, we first carefully design the observation space, the action space, and the reward function to improve the scalability of the learning as well as the robustness of the final policy. In addition, we adopt a novel adaptive motion sampling (AMS) method, which maintains a balance between successful and unsuccessful behaviors. This technique allows the learning algorithm to focus on challenging motor skills and avoid catastrophic forgetting. We demonstrate that the learned policy can exhibit diverse behaviors in simulation by successfully tracking both the training dataset and out-of-distribution trajectories. We also validate the importance of the proposed learning formulation and the adaptive motion sampling scheme by conducting experiments.
Maintaining Visibility of Dynamic Objects in Cluttered Environments Using Mobile Manipulators and Vector Field Inequalities

Vision-based perception has become prevalent in robotic applications, especially in those where the control loop relies on visual data, such as visual servoing. For those applications, ensuring that the features or target object remain visible to the camera is critical, necessitating visibility-aware control. In this paper, we propose a method to guarantee the visibility of a dynamic object using a constrained kinematic controller and Vector Field Inequalities (VFIs) to include a linear visibility constraint. Unlike existing methods, we introduce constraints into the kinematic controller to ensure the target's visibility without needing a trajectory optimizer or local planner. Our method maintains the target object in the camera field of view (FoV) by representing the FoV with four infinite planes and maintaining the distance between the target object and each plane higher than a predefined distance. We evaluated the proposed approach using a mobile manipulator in two simulations involving cluttered environments: the first scenario involves a stationary target object, whereas the second scenario presents a more challenging workspace involving a moving target. Our results demonstrate that the proposed approach successfully maintains the target within the FoV while avoiding obstacles in the workspace, showing the potential of our method to improve the safety and reliability of visual-servoing-based robotic systems.
Visual Servoing on Wheels: Robust Robot Orientation Estimation in Remote Viewpoint Control

This work proposes a fast deployment pipeline for visually-servoed robots which does not assume anything about either the robot - e.g. sizes, colour or the presence of markers - or the deployment environment. Specifically, we apply a learning based approach to reliably estimate the pose of a robot in the image frame of a 2D camera upon which a visual servoing control system can be deployed. To alleviate the time-consuming process of labelling image data, we propose a weakly supervised pipeline that can produce a vast amount of data in a small amount of time. We evaluate our approach on a dataset of remote camera images captured in various indoor environments demonstrating high tracking performances when integrated into a fully-autonomous pipeline with a simple controller. With this, we then analyse the data requirement of our approach, showing how it is possible to deploy a new robot in a new environment in fewer than 30.00 min.
Self-Supervised Drivable Area Segmentation Using LiDAR's Depth Information for Autonomous Driving

Drivable area segmentation is an essential component of the visual perception system for autonomous driving vehicles. Recent efforts in deep neural networks have sig-nificantly improved semantic segmentation performance for autonomous driving. However, most DNN-based methods need a large amount of data to train the models, and collecting large-scale datasets with manually labeled ground truth is costly, tedious, time consuming and requires the availability of experts, making DNN-based methods often difficult to implement in real world applications. Hence, in this paper, we introduce a novel module named automatic data labeler (ADL), which leverages a deterministic LiDAR-based method for ground plane segmentation and road boundary detection to create large datasets suitable for training DNNs. Furthermore, since the data generated by our ADL module is not as accurate as the manually annotated data, we introduce uncertainty estimation to compensate for the gap between the human labeler and our ADL. Finally, we train the semantic segmentation neural networks using our automatically generated labels on the KITTI dataset [10] and KITTI-CARLA dataset [7]. The experimental results demonstrate that our proposed ADL method not only achieves impressive performance compared to manual labeling but also exhibits more robust and accurate results than both traditional methods and state-of-the-art self-supervised methods.