Reviewerly/exHarmony huggingface dataset:
Detecting Errors with Zero-Shot Learning

Error detection is a critical step in data cleaning. Most traditional error detection methods are based on rules and external information with high cost, especially when dealing with large-scaled data. Recently, with the advances of deep learning, some researchers focus their attention on learning the semantic distribution of data for error detection; however, the low error rate in real datasets makes it hard to collect negative samples for training supervised deep learning models. Most of the existing deep-learning-based error detection algorithms solve the class imbalance problem by data augmentation. Due to the inadequate sampling of negative samples, the features learned by those methods may be biased. In this paper, we propose an AEGAN (Auto-Encoder Generative Adversarial Network)-based deep learning model named SAT-GAN (Self-Attention Generative Adversarial Network) to detect errors in relational datasets. Combining the self-attention mechanism with the pre-trained language model, our model can capture semantic features of the dataset, specifically the functional dependency between attributes, so that no rules or constraints are needed for SAT-GAN to identify inconsistent data. For the lack of negative samples, we propose to train our model via zero-shot learning. As a clean-data tailored model, SAT-GAN tries to recognize error data as outliers by learning the latent features of clean data. In our evaluation, SAT-GAN achieves an average F1-score of 0.95 on five datasets, which yields at least 46.2% F1-score improvement over rule-based methods and outperforms state-of-the-art deep learning approaches in the absence of rules and negative samples.
Generating Repetitions with Appropriate Repeated Words

A repetition is a response that repeats words in the previous speaker's utterance in a dialogue.Repetitions are essential in communication to build trust with others, as investigated in linguistic studies.In this work, we focus on repetition generation.To the best of our knowledge, this is the first neural approach to address repetition generation.We propose Weighted Label Smoothing, a smoothing method for explicitly learning which words to repeat during finetuning, and a repetition scoring method that can output more appropriate repetitions during decoding.We conducted automatic and human evaluations involving applying these methods to the pre-trained language model T5 for generating repetitions.The experimental results indicate that our methods outperformed baselines in both evaluations.
Combating Label Noise in Image Data Using MultiNET Flexible Confident Learning

Deep neural networks (DNNs) have been used successfully for many image classification problems. One of the most important factors that determines the final efficiency of a DNN is the correct construction of the training set. Erroneously labeled training images can degrade the final accuracy and additionally lead to unpredictable model behavior, reducing reliability. In this paper, we propose MultiNET, a novel method for the automatic detection of noisy labels within image datasets. MultiNET is an adaptation of the current state-of-the-art confident learning method. In contrast to the original, our method aggregates the outputs of multiple DNNs and allows for the adjustment of detection sensitivity. We conduct an exhaustive evaluation, incorporating four widely used datasets (CIFAR10, CIFAR100, MNIST, and GTSRB), eight state-of-the-art DNN architectures, and a variety of noise scenarios. Our results demonstrate that MultiNET significantly outperforms the confident learning method.
Robot's Gendering Trouble: A Scoping Review of Gendering Humanoid Robots and its Effects on HRI

The discussion around the problematic practice of gendering humanoid robots has risen to the foreground in the last few years. To lay the basis for a thorough understanding of how robot's "gender" has been understood within the Human-Robot Interaction (HRI) community - i.e., how it has been manipulated, in which contexts, and which effects it has yield on people's perceptions and interactions with robots - we performed a scoping review of the literature. We identified 553 papers relevant for our review retrieved from 5 different databases. The final sample of reviewed papers included 35 papers written between 2005 and 2021, which involved a total of 3902 participants. In this article, we thoroughly summarize these papers by reporting information about their objectives and assumptions on gender (i.e., definitions and reasons to manipulate gender), their manipulation of robot's "gender" (i.e., gender cues and manipulation checks), their experimental designs (e.g., demographics of participants, employed robots), and their results (i.e., main and interaction effects). The review reveals that robot's "gender" does not affect crucial constructs for the HRI, such as likability and acceptance, but rather bears its strongest effect on stereotyping. We leverage our different epistemological backgrounds in Social Robotics and Gender Studies to provide a comprehensive interdisciplinary perspective on the results of the review and suggest ways to move forward in the field of HRI.
Social science theories in software engineering research

As software engineering research becomes more concerned with the psychological, sociological and managerial aspects of software development, relevant theories from reference disciplines are increasingly important for understanding the field's core phenomena of interest. However, the degree to which software engineering research draws on relevant social sciences remains unclear. This study therefore investigates the use of social science theories in five influential software engineering journals over 13 years. It analyzes not only the extent of theory use but also what, how and where these theories are used. While 87 different theories are used, less than two percent of papers use a social science theory, most theories are used in only one paper, most social sciences are ignored, and the theories are rarely tested for applicability to software engineering contexts. Ignoring relevant social science theories may (1) undermine the community's ability to generate, elaborate and maintain a cumulative body of knowledge; and (2) lead to oversimplified models of software engineering phenomena. More attention to theory is needed for software engineering to mature as a scientific discipline.
PENGEMBANGAN MEDIA PEMBELAJARAN POP- UP BOOK PANCA INDRA (POPANDRA) TERHADAP PEMAHAMAN SISWA PADA MATERI PANCA INDRA KELAS 1 DI SD NEGERI 2 SELODOKO

The lack of use of learning media in schools and teacher-centered learning using the lecture method has an impact on low student learning outcomes. This study aims to develop a pop-up book learning media for students' understanding of the five senses material for grade 1 elementary school students. This development research uses the ADDIE research method. The sample taken from this study was a grade 1 student at SD Negeri 2 Selodoko. The final result of this study is a pop-up book learning medium for grade 1 on the material of the five human senses. Based on the results of the study, it shows that (1) pop-up book learning media developed using the ADDIE method go through 5 stages, namely analysis, planning, development, implementation and evaluation. (2) based on the results of the pop-up book learning media validators, it was declared valid with an assessment from media experts of 73%, material experts of 87.5% and Linguists of 63%. (3) based on the results of the pre-test and post-test students also showed an increase from an average of 71 to 91, it shows that pop-up book learning media is effectively used in the material of the five senses of grade 1 at SD Negeri 2 Selodoko.
Self-paced Supervision for Multi-source Domain Adaptation

Multi-source domain adaptation has attracted great attention in machine learning community. Most of these methods focus on weighting the predictions produced by the adaptation networks of different domains. Thus the domain shifts between certain of domains and target domain are not effectively relieved, resulting in that these domains are not fully exploited and even may have a negative influence on multi-source domain adaptation task. To address such challenge, we propose a multi-source domain adaptation method to gradually improve the adaptation ability of each source domain by producing more high-confident pseudo-labels with self-paced learning for conditional distribution alignment. The proposed method first trains several separate domain branch networks with single domains and an ensemble branch network with all domains. Then we obtain some high-confident pseudo-labels with the branch networks and learn the branch specific pseudo-labels with self-paced learning. Each branch network reduces the domain gap by aligning the conditional distribution with its branch specific pseudo-labels and the pseudo-labels provided by all branch networks. Experiments on Office31, Office-Home and DomainNet show that the proposed method outperforms the state-of-the-art methods.
Digital Forensics Triage Classification Model using Hybrid Learning Approaches

The Internet and the accessibility of gadgets with connectivity have resulted in the global spread of cyber threats and cybercrime, posing significant hurdles for digital forensics. Consequently, the volume of information that may need to be investigated is growing, necessitating the development of new forensic technologies and methods. Those now in use are, in fact, old-fashioned, as they are more focused on complete device extraction for case-relevant device identification. A practical approach, a Digital Forensics Triage, tries to quickly collect facts and give essential insight into this circumstance, which could be described as data-rich but information-poor. In time- sensitive scenarios, digital forensics triage approaches can prioritize some electronic gadgets over others based on their significance to the criminal case. The Digital Forensic Laboratories (DFS) make it easier to identify essential gadgets in criminal proceedings when time, significant accumulations, and the accused's confidentiality are critical considerations. Consequently, digital forensics and machine learning techniques allow for the rapid classification of appropriate gadgets despite dipping the quantity of information that has to be adequately studied. This study presents a digital forensic model that may be utilized to build a robotic digital device categorization tool employed in real- world criminal investigations.
The Improvement of Attitudes toward Convergence of Preservice Teachers: Blended Learning versus Online Learning in Science Teaching Method Courses

There has been a growing need as preservice teachers develop competencies regarding convergence. Focused on a discussion of blended learning before the COVID-19 pandemic versus online learning in the epidemic, we aimed to explore whether preservice teachers’ attitudes toward convergence can be influenced by the learning environment. Participants were a total of three hundred preservice teachers who attended the science teaching method courses training their TPACK at a teachers college in South Korea during the 2018 to 2020 academic years (194 in the blended learning group and 106 in the online learning group). Survey data on five subcomponents of attitudes toward convergence were collected at the start and end of the courses and analyzed using ANOVA and ANCOVA. As result, preservice teachers’ responses to the attitudes toward convergence in the pretests have a significant difference, whereas the overall scores in the posttests revealed no significant difference in the modalities of learning environments. Consequently, the preservice teachers engaged in the courses enhanced positive attitudes toward convergence regardless of delivery methods either blended learning or online learning. This paper provides evidence that the two teaching modalities of curriculum studies have the potential to foster preservice teachers’ attitudes toward convergence. This study supports that the blended and online learning formats of the course were feasible to induce short-term improvements in bias affective domains under the learning environments of science teaching method courses.
The Formula Study in Determining the Best Number of Neurons in Neural Network Backpropagation Architecture with Three Hidden Layers

The researchers conducted data simulation experiments, but they did so unstructured in determining the number of neurons in the hidden layer in the Artificial Neural Network Back-Propagation architecture. The researchers also used a general architecture consisting of one hidden layer. Researchers are still producing minimal research that discusses how to determine the number of neurons when using hidden layers. This article examines the results of experiments by conducting training and testing data using seven recommended formulas including the Hecht-Nelson, Marchandani-Cao, Lawrence &amp; Fredrickson, Berry-Linoff, Boger-Guterman, JingTao-Chew, and Lawrence &amp; Fredrickson modifications. We use rainfall data and temperature data with a 10-day type for the last 10 years (2012-2021) sourced from Lombok International Airport Station, Indonesia. The training and testing data used showed the results that in determining the number of neurons on the hidden-1 screen, it was more appropriate to use the Hecht-Nelson formula and the Lawrence &amp; Fredricson formula which is more suitable for use in the 2nd &amp; 3rd hidden layer. The resulting research was able to provide an accuracy rate of up to 97.79% (temperature data) and 99.94% (rainfall data) with an architecture of 36-73-37-19-1.&#x0D;
Flexible and Efficient Multicast Transfers in Inter-Datacenter Networks

The explosive growth of global distributed services has led to a massive increase in bulk multicast data transfers over the inter-datacenter Wide-Area Network. While many solutions have been proposed to improve the performance of inter-DC bulk data transfers, they are insufficient to optimize multicast transfers because they fail to explore the characteristics of multicast transfers and network topology. This paper presents FlexCast, a flexible and efficient solution to optimize the completion times for multicast transfers. FlexCast takes advantage of topological characteristics to divide network sites into groups, partition receivers into subsets, and construct load-adaptive Steiner trees for receiver partitions to reduce completion time. It also employs a flexible multicast model for parallel transmission. For better performance FlexCast uses multiple scheduling policies to handle offline request submission, and for greater efficiency it adopts a combination of small-scale optimization and fast heuristic to address online request submission quickly. Simulations on real-world topologies show that FlexCast improves the completion time for multicast receivers by up to 80% compared to prior solutions.
Design of On-Grid Photovoltaic System Considering Optimized Sizing of Photovoltaic Modules for Enhancing Output Energy

Photovoltaic (PV) systems are utilized all over the world for clean energy production. Photovoltaic simulation software is used to predict the energy produced by photovoltaic array structures. Due to Pakistan&rsquo;s geographical location in the equatorial region, the prospect of harnessing photovoltaic energy is too high. In the context of this fact, this work conducted extensive research to optimize the photovoltaic energy output by using different PV module sizes. For fixed areas where photovoltaic modules are installed, the output energy would remain more or less the same for any size of PV module with insignificant differences in PV module efficiency or quality. Moreover, in this research, it is found that by using different PV module sizes (i.e., 340 watts to 540 watts) at a time, while keeping all other parameters and conditions constant, a large variation in the output energy of the system can be observed. This difference in output energy with the change of PV module sizes raises fundamental concerns about how to choose the right PV modules size to generate maximum output energy at any given location. This study intends to emphasize the fact that when designing an On-Grid photovoltaic system, relatively little consideration is given to selecting the appropriate type and size of PV modules, which can result in a significant energy loss of the system. In this research, different PV modules of various sizes and power ratings with nearly identical efficiencies were analyzed in two selected locations. HelioScope simulation software is used to simulate all PV systems having a PV modules power rating to analyze their monthly and annual energy generation and system losses. The simulation results show that the appropriate PV modules size must be determined in order to generate the maximum output energy from the proposed PV system.
Light-Weight Classification of Human Actions in Video with Skeleton-Based Features

An approach to human action classification in videos is presented, based on knowledge-aware initial features extracted from human skeleton data and on further processing by convolutional networks. The proposed smart tracking of skeleton joints, approximation of missing joints and normalization of skeleton data are important steps of feature extraction. Three neural network models—based on LSTM, Transformer and CNN—are developed and experimentally verified. The models are trained and tested on the well-known NTU-RGB+D (Shahroudy et al., 2016) dataset in the cross-view mode. The obtained results show a competitive performance with other SOTA methods and verify the efficiency of proposed feature engineering. The network has a five times lower number of trainable parameters than other proposed methods to reach nearly similar performance and twenty times lower number than the currently best performing solutions. Thanks to the lightness of the classifier, the solution only requires relatively small computational resources.
Restricted-Area Adversarial Example Attack for Image Captioning Model

Deep neural networks provide good performance in the fields of image recognition, speech recognition, and text recognition. For example, recurrent neural networks are used by image captioning models to generate text after an image recognition step, thereby providing captions for the images. The image captioning model first extracts features from the image and generates a representation vector; it then generates the text for the image captions by using the recursive neural network. This model has a weakness, however: it is vulnerable to adversarial examples. In this paper, we propose a method for generating restricted adversarial examples that target image captioning models. By adding a minimal amount of noise just to a specific area of an original sample image, the proposed method creates an adversarial example that remains correctly recognizable to humans yet is misinterpreted by the target model. We evaluated the method’s performance through experiments with the MS COCO dataset and using TensorFlow as the machine learning library. The results show that the proposed method generates a restricted adversarial example that is misinterpreted by the target model while minimizing its distortion from the original sample.
Optimization of Dynamic Obstacle Avoidance Path of Multirotor UAV Based on Ant Colony Algorithm

In this paper, the real-time path avoidance problem of multirotor UAV in the case of sudden obstacles in two-dimensional environment is studied. The principle, model, and application of ant colony algorithm are analyzed. On this basis, the adaptive dynamic window ant colony algorithm is proposed, and the adaptive dynamic window method is designed; the heuristic function of adding obstacle detection factors and the double pheromone update strategy are made to the ant colony algorithm, and the improved ant colony algorithm is used to replan the path within the dynamic window that can be automatically adjusted to achieve the purpose of obstacle avoidance. A real-time simulation experiment of path planning was conducted by constructing an environment map in MATLAB. The simulation results show that with the continuous increase of the number of sudden obstacles, the real-time replacement path of multirotor UAV also gradually increases, and when approaching the obstacles, the replacement path is more dense, indicating that the adaptive window ant colony algorithm can be applied to dynamic path replacement, and the multirotor UAV can realize dynamic obstacle avoidance path optimization under the condition of sudden obstacles in a short time.
Improving the Red-Black tree delete algorithm

Abstract Today, Red-Black trees are becoming a popular data structure typically used to implement dictionaries, associative arrays, symbol tables within some compilers (C++, Java …) and many other systems. In this paper, we present an improvement of the delete algorithm of this kind of binary search tree. The proposed algorithm is very promising since it colors differently the tree while reducing color changes by a factor of about 29%. Moreover, the maintenance operations re-establishing Red-Black tree balance properties are reduced by a factor of about 11%. As a consequence, the proposed algorithm saves about 4 % on running time when insert and delete operations are used together while conserving search performance of the standard algorithm.
Augmented Reality in Education System

Abstract: In today’s classroom delivering education and providing crystal clear explanation is very difficult some concepts student feel difficult to understand because of no virtual experience that how exactly things work which they are studying . Textbooks are the major source of getting knowledge for students but the only problem in textbooks are 2D diagram present in the book , for students it is very difficult to understand the working of any diagram for example human heart that how exactly it looks and function because of no virtual experience. To solve this issue a concept called augmented reality for education system came into the picture. Augmented Reality creates wondaful opportunities for college teachers and even for student also to grasp the abstract concepts. With the help of interaction and experimentation that Augmented Reality technologies offers , faculties can enhance offline experience in the classroom, teach new development skills, enhance students minds develop interest in students minds for studies . As of know the most popular application for augmented reality in education is the use of augmented reality apps directly in the offline classroom, where teacher can explain the subject can provide the visual representation of the material and can help students also . The current state of augmented reality in education is examined in this chapter from a variety of angles, including the accessibility of various technologies, the different types of augmented reality learning experiences, the authoring tools available, the appropriate audiences the subject, and the state of augmented reality research in education.
ADAPTIVE IMAGE SUPER-RESOLUTION ALGORITHM BASED ON FRACTIONAL FOURIER TRANSFORM

Super-resolution imaging is a critical image processing stage that improves visual image quality. Super-resolution imaging has a wide array of use in different fields, such as medical, satellite, and astronomical images. The correct execution of this stage could increase the accuracy and quality of any available processes in different executive fields. Learning methods, especially deep learning, have become much more popular in recent years for performing the super-resolution imaging process. Methods with this approach have high-quality levels but lack appropriate performance times. This study intends to perform super-resolution imaging using an algorithmic approach based on the particle swarm optimization algorithm and the fractional Fourier transform. The test results on a dataset show the 92.16% accuracy of this proposed method.