Reviewerly/exHarmony huggingface dataset:
Semantic-Relation-First Active Learning for Scene Understanding in Indoor Environments

Traditional object recognition algorithms are based on a common close-set hypothesis where the robot has complete knowledge during the training stage. However, in reality, we only have incomplete knowledge in the training dataset. This paper deals with one case of incomplete knowledge where new object classes appear after deployment. We propose an active learning method based on the semantic-relation-first principle for scene understanding in this situation. Our approach takes the objects with known labels and their physical properties as features. Then, by combining the support relations and uncertainty of existing classifier, we construct a metric function to select key objects in the scene using a semi-supervised learning method. We have following findings from experiments: 1) the proposed semi-supervised active learning approach works well in object recognition with incomplete knowledge; 2) the performace of our approach is similar to the existing approach with less training data, and thus it can significantly reduce the amount of manual annotation costs.
Improved Alternative Average Support Value for Automatic Ingredient Substitute Recommendation in Cooking Recipes

Nowadays, any individual interested in learning how to prepare a new dish or complete meal can consult specialized cooking recipe websites and video sharing platforms. Such online repositories are able to keep hundreds of thousands of entries in their databases, being rich sources of information for users with the most vast degrees of expertise. But sometimes it is hard for a user to find an adequate recipe that fits, simultaneously, his nutritional needs, tastes, dietary restrictions and the set of ingredients at hand, and, for non-expert users, it may be too much complicated, or even impossible, to adapt the recipes returned by such systems into his current state of needs. In this work, we propose a new cooking recipe recommendation and generation system, based on improved alternative Average Support Value (ASV) filters and a data-driven text mining approach, for single ingredient substitution. Three new ASV variants are proposed as mechanisms to aggregate recipe context and ingredient relevance into standard ASV, in an attempt to promote better ingredient substitute recommendations to adapt recipes into new culinary domains. The proposed ASV-based recipe generation systems are tested and evaluated by means of qualitative analysis, and the proposed filters performances are compared with standard ASV, when adapting recipes with no restrictions into different dietary restriction domains, showing promising results.
Finding Electronic Evidence

The process of finding evidence is the first stage of preparation for litigations. As analyzed in previous chapters, the category of electronic evidence is numerous and diverse due to the continuous development of new information technology. Moreover, electronic evidence is easy to be changed, deleted, falsified or manipulated. Therefore, the process of finding electronic evidence is more complex and needs to be paid more attention than the process of finding traditional evidence. Although the process of finding electronic evidence relates more to technical issues, it is still important to carve out with the legal view to ensure the legitimacy of the whole process. This chapter starts by explaining briefly the process of finding electronic evidence in civil and commercial dispute resolution, then the following sections of this chapter shall examine the process of finding electronic evidence from significant electronic sources. Although the process of finding electronic evidence must follow the law, the investigator may obtain electronic evidence illegally which raised several questions about its legality. The following section identifies the notion of illegally obtained electronic evidence and the relationship between the need of the involved parties to prove their arguments and the legal boundaries of finding electronic evidence. One of the major challenges to the process of finding electronic evidence is balancing the effectiveness of finding electronic evidence and the respect for personal privacy protection and data protection, which is discussed in the last section of this chapter.
Federated Resistance Against Adversarial Attacks in Resource-constrained IoT

Federated learning (FL) is a recently evolved distributed learning paradigm that gains increased research attention. To alleviate privacy concerns, FL fundamentally suggests that many entities can cooperatively train the machinedeep learning model by exchanging the learning parameters instead of raw data. Nevertheless, FL still exhibits inherent privacy problems caused by exposing the users’ data based on the training gradients. Besides, the unnoticeable adjustments on inputs done by adversarial attacks pose a critical security threat leading to damaging consequences on FL. To tackle this problem, this study proposes an innovative Federated Deep Resistance (FDR) framework, to provide collaborative resistance against adversarial attacks from various sources in a Fog-assisted IIoT environment. The FDR is designed to enable fog nodes to cooperate to train the FDL model in a way that ensures that contributors have no access to the data of each other, where class probabilities are protected utilizing a private identifier generated for each class. The FDR mainly emphasizes convolutional networks for image recognition from the Food-101 and CIFAR-100 datasets. The empirical results have revealed that FDR outperformed the state-of-the-art adversarial attacks resistance approaches with 5% of accuracy improvements.
Joint Forecasting of Panoptic Segmentations with Difference Attention

Forecasting of a representation is important for safe and effective autonomy. For this, panoptic segmentations have been studied as a compelling representation in recent work. However, recent state-of-the-art on panoptic segmentation forecasting suffers from two issues: first, individual object instances are treated independently of each other; second, individual object instance forecasts are merged in a heuristic manner. To address both issues, we study a new panoptic segmentation forecasting model that jointly forecasts all object instances in a scene using a transformer model based on ‘difference attention.’ It further refines the predictions by taking depth estimates into account. We evaluate the proposed model on the Cityscapes and AIODrive datasets. We find difference attention to be particularly suitable for forecasting because the difference of quantities like locations enables a model to explicitly reason about velocities and acceleration. Because of this, we attain state-of-the-art on panoptic segmentation forecasting metrics.
A real-time Augmented Reality application to increase the learners’ engagement in Classroom

Many emerging applications are created with Augment Reality (AR) is to say it’s the doorway to this present reality, involving innovation as another focal point to glance through in education. As the pandemic closes, understudies chasing after their schooling are made to return to their primary methods of disconnected classes after going to classes on the web. The proposed system is a versatile AR application that will increment understudy commitment in the classroom. The application shows dynamic course content rather than the conventional chalkboard. The application uses the marker-based AR module structure to increase a gateway to the board. Whenever the smart devices focus the application towards the class board, the users can see the appropriate video recordings, web pages, and any other interactive activities. The continuous assessment is recorded to compare the proposed and traditional classroom teaching methods. According to the assessment result of the proposed method, it encourages an appropriate use case of AR in the conventional classroom environment.
Robustness Analysis of Discrete State-Based Reinforcement Learning Models in Traffic Signal Control

With the growing traffic congestion problem, more and more deep reinforcement learning (DRL) methods have been applied in traffic signals control(TSC). But researches show that DRL is very fragile with abnormal data. In this paper, special traffic state abnormal data (TSAD) are simulated, based on which the robustness of DRL is analyzed and improved for traffic signals control. Firstly, the perturbation noise is generated based on the Discrete Carlin&Wagner attack, which is then added to the normal data to simulate the TSAD. Secondly, under different type of TSAD, the robustness of DRL models for traffic signals control is explored, which are demonstrated to have certain vulnerability, especially with high traffic flows. Finally, induction model based on reward detection(IMR) and mask the activation values of decision neurons (MVN) are proposed to effectively improve the robustness of DRL models for traffic signals control.
Optimizing Parameters for Noise Reduction in Fingerprint Images Using Double Density Dual-Tree DWT

Fingerprint recognition is an important way in person’s identification. Noise are always present in fingerprint images and reducing them the accuracy of minutiae detection is increased. Discreet Wavelet Transform is a method widely used for noise reduction. It is important to find the appropriate mother wavelet and its parameters. Setting the appropriate parameters has impact in getting optimal results. This paper analyzes the performance of Standard DWT, Real 2D dual-tree and complex dual-tree wavelet transform. The parameters analyzed here are the threshold point and the number of decomposition levels. The metric used to measure noise reduction is PSNR, MSR and visual inspection.
3D Full-field Deformation Measurement using Stereo Vision

Measuring 3D deformation and strain are crucial parameters in structural engineering applications both at the construction and operational stages. Precise 3D full-field measurements are useful in structural optimization, damage detection and retrofitting. Digital Image Correlation is a non-contact optic-based measurement technique that is proven to be an ideal candidate in this regard. It has the potential to become a cheap, simple, and precise solution for deformation measurement. However, the currently available Digital Image Correlation measuring systems require expensive dedicated software packages and physical resources which are difficult to access. Therefore, there is a need to develop a cost-effective measuring technique to effectively use it in the local context. This research focuses on the development and validation of a precise non-contact-based deformation measurement technique. In the proposed method, 3D full-field deformation of the deforming object is measured by processing stereo photographs taken with commonly available digital cameras using the image processing toolbox available in the MATLAB commercial package. Further, the proposed method is enhanced by developing it as a standalone application, which can be installed and conveniently used by any technician. Capability of using the developed application in common civil engineering laboratory experiments has been demonstrated.
Implementation of a Lightweight CNN for American Sign Language Classification

The American sign language is the most popular and widely-accepted sign language for people with hearing difficulties. Computer vision techniques, such as skeleton recognition, depth recognition, 3D model recognition, or deep learning recognition, have helped to develop better systems for sign language classification and detection. Despite the promising results from baseline research efforts, overfitting problems have been detected when the training and testing accuracy are compared. In this work, we propose to exploit the scaling method on EfficientNet, which is a convolutional neural network architecture, in order to uniformly scale all the dimensions of depth, width, and resolution using a compound coefficient. Our results show that the overfitting problem can be solved by incorporating hyperparameter tunning and dropout as a regularization method. We also have the benefit of transfer learning to reduce the training time by reusing the weights of EfficientNet, pre-trained with the ImageNet dataset. Our results are compared with the benchmark paper, proving that our model generalizes better to unseen instances. In the first section of this study, we introduce American sign language meaning, hand gesture recognition, and its related works in the computer vision field. It allows us to mention transfer learning concepts and Efficient Nets architectures. In the second section, we establish the methodology by choosing the B0 model as the architecture selected to test with a Kaggle dataset. Adjusting hyperparameters, we enter in the third section, in the training and testing phase where overfitting problems were solved with high accuracy, finishing talking with contributions and future works.
Learning to Evolve Procedural Content in Games Using Cultural Algorithms

The procedural content of a virtual reality system is a key contributor to its success. However, it is often the case that the content needs to be adjusted. This may take place for a variety of reasons. This research investigates the possibility of the use of machine learning technology to facilitate the modification of a games content. Here, the Deep Dive system was designed originally to predict ancient site locations. Recently it was repurposed to be used as an educational tool to facilitate aspects of STEM education. This required the modification of the content to support this novel use. An evolutionary learning algorithm, Cultural Algorithm, is employed to facilitate the addition of the new content required for the educational application.
Building the Quality of the Nation by Fostering a Literacy Culture for Elementary School Students during the Covid-19 Pandemic

Based on the recently released PISA (Program for International Student Assessment) report, Tuesday, December 3, 2019, Indonesia's reading score was ranked 72 out of 77 countries, mathematics scores were ranked 72 out of 78 countries, and science scores were ranked 70 out of 78 countries. . Data from international research institutions such as the Progress in International Reading Literacy Study (PIRLS), and Trends in International Mathematics and Science Studies (TIMS) show similar findings. In connection with this condition, the author wants to discuss the causal factors and how to improve the literacy culture of students in elementary schools. The approach method in this paper uses literacy studies. From various literatures it is found that there are three factors that cause low interest in reading. First, the lack of reading habits that are instilled from an early age. Second, the lack of book production in Indonesia as a result of the undeveloped regional publishers. Third, the lack of main educational facilities for internet-based information systems, which during the Covid'19 pandemic became the only reliable access to obtain various information, while not everyone has the ability to access the internet. Therefore, efforts are needed to solve the problem of low literacy culture. Various literatures suggest several efforts to improve literacy culture among elementary school students, including: firstly implementing the School Literacy Movement program launched by the Government, secondly holding literacy competitions such as writing competitions, reading poetry, etc., as well as reading corners in each class. , and the third held a one day one page program through assignments to read 1 page every day and summarize the reading results. Through these programs, it is expected to be able to improve the literacy culture of elementary school students and can systemically improve the quality of the nation.&nbsp
Comparing User Rating-Based Similarity to User Behavior-Based Similarity in Movie Recommendation Systems

Implementing similarity functions will affect how well the recommender system performs. The traditional similarity solely considers user rating values to calculate the similarity between users. Some studies have recently developed improved similarity functions involving user behavior values. These algorithms combine similarity depending on the user ratings with similarity considering the user behavior. This study aims to compare the performance of the similarity depending on the user rating and the similarity considering the user behavior in movie recommender systems. The similarity functions applied are Cosine similarity, User score Probability Collaborative Filtering (UPCF), User Profile Correlation-based Similarity (UPCSim), and Clustering-Based UPCSim (CB-UPCSim). The experiment results in MovieLens 100K and MovieLens 1M show that the similarities considering the user behavior (UPCF, UPCSim, and CB-UPCSim) can reduce the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values compared to the similarity only depending on the user rating (Cosine). In addition, the CB-UPCSim showed the fastest running time and the highest F1 score, recall, and precision compared to the UPCF, UPCSim, and Cosine similarity. It indicates that the CB-UPCSim approach performs better than the three other similarity functions.
Usability Evaluation Towards a Cultural Perspective: Design Guidelines for Peruvian Government Websites

Usability currently plays an essential role in the software development process. Companies are increasingly concerned with offering understandable, attractive, and easy-to-use websites to ensure their success in the market. However, based on usability studies, cultural factors can affect how people interact with the systems. Although multiple investigations have been conducted to determine the best design settings for each cultural context, there is no evidence of research that analyses the perspective of Latin American countries. In this study, we have analyzed the Cultural Models to establish guidelines that can guide the design of web usable user interfaces oriented to Peruvian citizens. Including cultural factors with which Peruvians are familiar could increase satisfaction with the website. The proposed guidelines were established and validated through a systematic methodology that involved several structured and well-defined steps. In this process, and using the proper cultural model, prominent design elements were identified as those that must represent the cultural identity and, therefore, must guide the design process of a Peruvian website. The selection of featured design elements was based on analyzing cultural factors and design elements presented on Peruvian websites and from countries with similar cultural characteristics. This work intends to serve as a reference to increase the quality of the software systems offered to citizens in Latin America.
Learning Extremely Lightweight and Robust Model with Differentiable Constraints on Sparsity and Condition Number

Learning lightweight and robust deep learning models is an enormous challenge for safety-critical devices with limited computing and memory resources, owing to robustness against adversarial attacks being proportional to network capacity. The community has extensively explored the integration of adversarial training and model compression, such as weight pruning. However, lightweight models generated by highly pruned over-parameterized models lead to sharp drops in both robust and natural accuracy. It has been observed that the parameters of these models lie in ill-conditioned weight space, i.e., the condition number of weight matrices tend to be large enough that the model is not robust. In this work, we propose a framework for building extremely lightweight models, which combines tensor product with the differentiable constraints for reducing condition number and promoting sparsity. Moreover, the proposed framework is incorporated into adversarial training with the min-max optimization scheme. We evaluate the proposed approach on VGG-16 and Visual Transformer. Experimental results on datasets such as ImageNet, SVHN, and CIFAR $$-10$$ show that we can achieve an overwhelming advantage at a high compression ratio, e.g., 200 times.
An Improved Concat Module Based on HRNet for 2D Multi-person Pose Estimation

The existing shallow networks cannot solve the problem of insufficient cross between feature information and insufficient semantic information for 2D human pose estimation task, while deepening network will lead to gradient disappearance or over-fitting. In this paper, we propose an Improved Concat Module (ICM) based on the HRNet network to improve the accuracy and robustness of 2D multi-person pose estimation. The proposed ICM module is composed of tandem skip connection units and average pooling operations in parallel, where the input information of each improved skip connection unit performs element-wise product operation with the convolution branch, which improves the crossover between feature information more effectively and can reduce the gradient disappearance or over-fitting. Furthermore, three improved skip connection units are cascaded in this paper to provide rich semantic information for the final regression of human pose key points and enhance the nonlinear expression ability of the network. In this paper, our method achieves 70.1%AP score on the COCO test-dev dataset and 69.2%AP score on the CrowPose test dataset, which is significantly improved over multiple state-of-the-art methods and achieves robust results in the case of multiple people.
Error Accuracy Estimation of 3D Reconstruction and 3D Camera Pose from RGB-D Data

We propose an approach to predict accuracy for three-dimensional reconstruction and camera pose using a generic RGB-D camera on a robotic platform. We initially create a ground truth of 3D points and camera poses using a set of smart markers that we specifically devised and constructed for our approach. Then, we compute actual errors and their accuracy during the motion of our mobile robotic platform. The error modeling is then provided, which is used as input to a deep multi-layer perceptron in order to estimate accuracy as a function of the camera’s distance, velocity, and vibration of the vision system. The network outputs are the root mean squared errors for the 3D reconstruction and the relative pose errors for the camera. Experimental results show that this approach has a prediction accuracy of ±1% for the 3D reconstruction and ±2.5% for camera poses, which shows a better performance in comparison with state-of-the-art methods.
Big Data Analysis of ‘ESG’ News Using Topic Modeling

Recently, the rapidly changing global economy and sustainable development of society have been requiring the practice of ESG management. Therefore, the main purpose of this study was to suggest policy implications for the introduction, spread and implementation of ESG through news big data analysis regarding ESG management. To that end, this study collected news data on ESG management and analyzed key keywords and topics of ESG management by period using the topic modeling method to derive major agendas and implications for ESG management. Through this study, we intend to suggest strategic directions for successful ESG management of companies in the rapidly changing business environment.
3D Surface Reconstruction Method for Microsamples Based on Polarization Modulation Microscopy

A novel microscopic Shape-From-Polarization (SFP) 3D shape reconstruction method is proposed, in which degree of polarization is calculated using Polarized Bidirectional Reflectance Distribution Function (PBRDF) to overcome azimuthal ambiguity for both specular and diffuse polarization microscopic scenes. Based on the calculated degree of polarization, a polarization modulation microscopy oriented variational model is established to estimate surface normal vector and rebuild 3D shape of the sample with “fuzzy guidance” based on atomic force microscopy (AFM). Experiments on different microscopic samples indicate the proposed method can restore the stereoscopic morphology distribution with precision in micrometer.