Reviewerly/exHarmony huggingface dataset:
Homography Decomposition Networks for Planar Object Tracking

Planar object tracking plays an important role in AI applications, such as robotics, visual servoing, and visual SLAM. Although the previous planar trackers work well in most scenarios, it is still a challenging task due to the rapid motion and large transformation between two consecutive frames. The essential reason behind this problem is that the condition number of such a non-linear system changes unstably when the searching range of the homography parameter space becomes larger. To this end, we propose a novel Homography Decomposition Networks~(HDN) approach that drastically reduces and stabilizes the condition number by decomposing the homography transformation into two groups. Specifically, a similarity transformation estimator is designed to predict the first group robustly by a deep convolution equivariant network. By taking advantage of the scale and rotation estimation with high confidence, a residual transformation is estimated by a simple regression model. Furthermore, the proposed end-to-end network is trained in a semi-supervised fashion. Extensive experiments show that our proposed approach outperforms the state-of-the-art planar tracking methods at a large margin on the challenging POT, UCSB and POIC datasets. Codes and models are available at https://github.com/zhanxinrui/HDN.
The Impact of Educational Practices in Learning Comics and Video Media on Social Science Subjects as Alternatives in a Pandemic Period

This study aimed to see the impact of learning using comic and video media methods on social studies subjects during the Covid-19 pandemic. The research method used is descriptive qualitative. This research was conducted in 5 State Elementary Schools (SDN) Miri District, Sragen Regency, namely: (1) SDN Soko 2; (2) SDN Girimargo 1; (3) SDN Girimargo 3; (4) SDN Gilirejo 2, and (5) SDN Gilirejo. The research subjects were 5th-grade students and school teachers. The data is focused on learning activities with the theme of 7 social studies lessons to answer research. Data were collected utilizing observation, interviews and documentation. Data were analyzed in a fashion way; triangulation consisting of data reduction, data presentation, and concluding. The results showed that using comics media increased students' interest, enthusiasm, and participation in social studies lessons. In addition, comics media are livelier for students' learning atmosphere. Learning using video media also shows students' enthusiasm for learning and parents who accompany online learning. This study concludes that innovation is the key to urgency in the teaching and learning process. Innovations such as using or incorporating learning media are created to overcome the current pandemic conditions without reducing learning effectiveness.
Security Evaluation of Block-based Image Encryption for Vision Transformer against Jigsaw Puzzle Solver Attack

The aim of this paper is to evaluate the security of a block-based image encryption for the vision transformer against jigsaw puzzle solver attacks. The vision transformer, a model for image classification based on the transformer architecture, is carried out by dividing an image into a grid of square patches. Some encryption schemes for the vision transformer have been proposed by applying block-based image encryption such as block scrambling and rotating to patches of the image. On the other hand, the security of encryption scheme for the vision transformer has never evaluated. In this paper, jigsaw puzzle solver attacks are utilized to evaluate the security of encrypted images by regarding the divided patches as pieces of a jigsaw puzzle. In experiments, an image is resized and divided into patches to apply block scrambling-based image encryption, and then the security of encrypted images for the vision transformer against jigsaw puzzle solver attacks is evaluated.
ESP32-CAM as a programmable camera research platform

Experimenting with custom-programming of cameras can be difficult.Most consumer cameras are protected to prevent users from reprogramming them.Industrial cameras can be flexibly controlled by an external computer, but are generally not standalone programmable devices.However, various inexpensive camera modules, designed largely to be used for building IoT (Internet of Things) devices, combine extensive programmability with a camera in a compact, low-power, module.One of the smallest and least expensive, the ESP32-CAM module, combines a 2MP Omnivision OV2640 camera with a dual-core 32-bit processor, 802.11WiFi and BlueTooth as well as wired I/O interfaces, a mi-croSD slot, low power modes, etc., all supported by the Arduino programming environment and a rich collection of open source libraries.Why not use it for programmable camera research?This paper describes how the ESP32-CAM had to be adapted to enable use in a variety of experimental cameras.For example, some of these cameras do not use the lens screwed and glued onto the OV2640, and replacing this lens revealed a number of issues ranging from spectral response to adjustment of lens corrections.There are numerous strange interactions between different functions that end-up sharing the same I/O pins, so work-arounds were needed.It also was necessary to devise ways to handle various higher-level issues such as implementation of a live view and synchronization across cameras.However, the key problems have been resolved with open source software and hardware designs described here.
Adaptive Joint Optimization for 3D Reconstruction With Differentiable Rendering

Due to inevitable noises introduced during scanning and quantization, 3D reconstruction via RGB-D sensors suffers from errors both in geometry and texture, leading to artifacts such as camera drifting, mesh distortion, texture ghosting, and blurriness. Given an imperfect reconstructed 3D model, most previous methods have focused on refining either geometry, texture, or camera pose. Consequently, different optimization schemes and objectives for optimizing each component have been used in previous joint optimization methods, forming a complicated system. In this paper, we propose a novel optimization approach based on differentiable rendering, which integrates the optimization of camera pose, geometry, and texture into a unified framework by enforcing consistency between the rendered results and the corresponding RGB-D inputs. Based on the unified framework, we introduce a joint optimization approach to fully exploit the inter-relationships among the three objective components, and describe an adaptive interleaving strategy to improve optimization stability and efficiency. Using differentiable rendering, an image-level adversarial loss is applied to further improve the 3D model, making it more photorealistic. Experiments on synthetic and real data using quantitative and qualitative evaluation demonstrated the superiority of our approach in recovering both fine-scale geometry and high-fidelity texture.
An Ensemble Learning Method of Adaptive Structural Deep Belief Network for AffectNet

Deep Learning is a hierarchical network architecture to express complex abstractions of input patterns of images. A Deep Belief Network (DBN) that builds hierarchical structure of Restricted Boltzmann Machine (RBM) is a well known unsupervised learning method as one of deep learning methods. The adaptive structural learning method of RBM (Adaptive RBM) was developed to find a suitable network structure for the input data set by neuron generation / annihilation algorithm during training. The Adaptive DBN can construct to pile an appropriate number of RBMs up to realize higher classification task. In this paper, our developed model was applied to AffectNet as the facial image data set and showed the better performance of classification rate than the State-of-The-Art CNN models. However, the model outputs incorrect wrong emotion category for some test cases, because the output labels for data set were annotated by two or more human annotators. For the problem, this paper proposes an ensemble learning model of Adaptive DBN, where the ensemble model consists of a parent DBN and some child DBNs. KL divergence is a measure of similarity for the parent and the child to each case. The new neurons are generated at the child to improve the classification according to KL divergence. Moreover, the generated neuron at the child is transferred to the parent to integrate better knowledge. In this paper, the proposed method improved the classification accuracy from 87.4% to 92.5%.
Correction to: Introduction to Homomorphic Encryption and Schemes

The original version of this book has been revised because it was inadvertently published with the following errors which have now been updated.
SerialTrack: ScalE and rotation invariant augmented Lagrangian particle tracking

We present a new particle tracking algorithm for accurately resolving large deformation and rotational motion fields, which takes advantage of both local and global particle tracking algorithms. We call this method ScalE and Rotation Invariant Augmented Lagrangian Particle Tracking (SerialTrack). This method builds an iterative scale and rotation invariant topology-based feature vector for each particle within a multi-scale tracking algorithm. The global kinematic compatibility condition is applied as a global augmented Lagrangian constraint to enhance tracking accuracy. An open source software package implementing this numerical approach to track both 2D and 3D, incremental and cumulative deformation fields is provided.
Signs Taken for Wonders: AI, Art &amp; the Matter of Race

Abstract AI shares with earlier socially transformative technologies a reliance on limiting models of the “human” that embed racialized metrics for human achievement, expression, and progress. Many of these fundamental mindsets about what constitutes humanity have become institutionally codified, continuing to mushroom in design practices and research development of devices, applications, and platforms despite the best efforts of many well-intentioned technologists, scholars, policy-makers, and industries. This essay argues why and how AI needs to be much more deeply integrated with the humanities and arts in order to contribute to human flourishing, particularly with regard to social justice. Informed by decolonial, disability, and gender critical frameworks, some AI artist-technologists of color challenge commercial imperatives of “personalization” and “frictionlessness,” representing race, ethnicity, and gender not as normative self-evident categories nor monetized data points, but as dynamic social processes always indexing political tensions and interests.
Emotion Classification through Product Consumer Reviews

Currently, the world is becoming digitalized. Ecommerce is enamoring strength in this digitalized world through the accessibility of products reachable for customers. The recent work in the field of emotion classification based on consumer feedback reviews is gaining popularity worldwide. However, the research lacks to provide better accuracy in terms of pre-processing and feature extraction. Since, to determine consumer emotions in terms of their choices, feature extraction and pre-processing plays an important role to enhance the accuracy rate to determine their choices for product marketability and to identify consumer behavior in selecting product features resulting in increased product demands. The review of related literature shows that the products accuracy rate can be improved though refining the dataset by executing proper pre-processing techniques like removing null values and meaning less values from the dataset to make it more accurate and examining its feature extraction by applying different algorithms which required a lot of pre-processing of reviewed data to make the features more valuable. This research provides an analysis of cars, hotels and mobile datasets and studies sentiment classification with different machine learning Approaches. First of all, to minimize the noise of the dataset was undergoing with pre-processing steps including punctuation, stop words, null entries and duplication removal. In the next step, extract the feature by two different methods: Count Vectorizer and bag of words. After that bidirectional encoder representation from transformers (BERT) algorithm were applied on three datasets to predict the results. By applying BERT Classifier on cars dataset 98.5% accuracy were founded, by applying same algorithm on hotels dataset 98.3% accuracy were founded, and for Mobile dataset 98.7% accuracy were founded.
Information-Theoretic Bias Reduction via Causal View of Spurious Correlation

We propose an information-theoretic bias measurement technique through a causal interpretation of spurious correlation, which is effective to identify the feature-level algorithmic bias by taking advantage of conditional mutual information. Although several bias measurement methods have been proposed and widely investigated to achieve algorithmic fairness in various tasks such as face recognition, their accuracy- or logit-based metrics are susceptible to leading to trivial prediction score adjustment rather than fundamental bias reduction. Hence, we design a novel debiasing framework against the algorithmic bias, which incorporates a bias regularization loss derived by the proposed information-theoretic bias measurement approach. In addition, we present a simple yet effective unsupervised debiasing technique based on stochastic label noise, which does not require the explicit supervision of bias information. The proposed bias measurement and debiasing approaches are validated in diverse realistic scenarios through extensive experiments on multiple standard benchmarks.
Generative Data Augmentation for Automatic Meter Reading Using CNNs

While smart meters are still not widely installed in many countries, automatic reading of traditional-type meters is useful from the perspective of both cost and safety. Although convolutional neural network (CNN) showed a high potential for automatic meter reading under unconstrained environment, it is facing various challenges. One is the difficulty of collecting a sufficient amount of training dataset since some digits of a meter may take a long time to update. Another challenging issue is how to recognize the transitional state between two consecutive numbers. To solve these problems, we propose a new data augmentation technique that can automatically generate annotated images of numbers, including the transitional states. By taking advantage of the state-of-the-art generative neural network model, the generated numbers resemble the local appearance of those in the original meter images. Evaluation experiments confirm that our proposed generative data augmentation techniques improve the robustness of the recognition model and achieve outstanding results when compared to the previous work.
An empirical study of e-learning post-acceptance after the spread of COVID-19

There are various reasons why vaccine fear has resulted in public rejection. Students have raised concerns about vaccine effectiveness, leading to hesitation when it comes to vaccination. Vaccination apprehension impacts students' perceptions, which has an impact on the acceptability of an e-learning platform. As a result, the goal of this study is to look at the post-acceptance of an e-learning platform using a conceptual model with several factors. Every variable makes a unique contribution to the e-learning platform's post-acceptance. In the current study, TAM variables were combined with additional external factors such as fear of vaccination, perceived routine use, perceived enjoyment, perceived critical mass, and self- efficacy, all of which are directly associated with post-acceptance of an e-learning platform. Here, a hybrid conceptual model was used to evaluate the newly widespread use of e-learning platforms in this area in this study in the UAE. In the past, empirical investigations primarily used Structural Equation Modeling (SEM) analysis; however, this study used a developing hybrid analysis approach that combines SEM with deep learning–based Artificial Neural Networks (ANN). This study also employed the Importance–Performance Map Analysis (IPMA) to determine the significance and performance of each element. Through the findings, it was found that fear of vaccination, perceived ease of use, perceived usefulness, perceived routine use, perceived enjoyment, perceived critical mass, and self-efficiency all had a significant impact on students' behavioral intention to use the e-learning platform for educational purposes. It was also shown in the analysis of ANN as well as IPMA that the perceived ease of use of the e-learning platform is the most important indicator of post-acceptance. The proposed model, in theory, provides appropriate explanations for the elements that influence post-acceptance of the e-learning platform in terms of internet service factors at the individual level. In the practical sense, these findings will help decision-makers and practitioners in higher education institutions identify the factors that should be given extra care and plan their policies accordingly. The ability of the deep ANN architecture to identify the non-linear relationships between the factors involved in the theoretical model has been determined in this research. The implication offers extensive information about taking effective steps to decrease the fear of vaccination among people and increase vaccination confidence among teachers and educators and students, consequently impacting society.
Automatic text summarization of konkani texts using pre-trained word embeddings and deep learning

&lt;span lang="EN-US"&gt;Automatic text summarization has gained immense popularity in research. Previously, several methods have been explored for obtaining effective text summarization outcomes. However, most of the work pertains to the most popular languages spoken in the world. Through this paper, we explore the area of extractive automatic text summarization using deep learning approach and apply it to Konkani language, which is a low-resource language as there are limited resources, such as data, tools, speakers and/or experts in Konkani. In the proposed technique, Facebook’s fastText &lt;br /&gt; pre-trained word embeddings are used to get a vector representation for sentences. Thereafter, deep multi-layer perceptron technique is employed, as a supervised binary classification task for auto-generating summaries using the feature vectors. Using pre-trained fastText word embeddings eliminated the requirement of a large training set and reduced training time. The system generated summaries were evaluated against the ‘gold-standard’ human generated summaries with recall-oriented understudy for gisting evaluation (ROUGE) toolkit. The results thus obtained showed that performance of the proposed system matched closely to the performance of the human annotators in generating summaries.&lt;/span&gt;
Learning to Solve Travelling Salesman Problem with Hardness-Adaptive Curriculum

Various neural network models have been proposed to tackle combinatorial optimization problems such as the travelling salesman problem (TSP). Existing learning-based TSP methods adopt a simple setting that the training and testing data are independent and identically distributed. However, the existing literature fails to solve TSP instances when training and testing data have different distributions. Concretely, we find that different training and testing distribution will result in more difficult TSP instances, i.e., the solution obtained by the model has a large gap from the optimal solution. To tackle this problem, in this work, we study learning-based TSP methods when training and testing data have different distributions using adaptive-hardness, i.e., how difficult a TSP instance can be for a solver. This problem is challenging because it is non-trivial to (1) define hardness measurement quantitatively; (2) efficiently and continuously generate sufficiently hard TSP instances upon model training; (3) fully utilize instances with different levels of hardness to learn a more powerful TSP solver. To solve these challenges, we first propose a principled hardness measurement to quantify the hardness of TSP instances. Then, we propose a hardness-adaptive generator to generate instances with different hardness. We further propose a curriculum learner fully utilizing these instances to train the TSP solver. Experiments show that our hardness-adaptive generator can generate instances ten times harder than the existing methods, and our proposed method achieves significant improvement over state-of-the-art models in terms of the optimality gap. The codes are publicly available.
The Influence of Interpersonal Conflict, Job Stress, and Work Life Balance on Employee Turnover Intention

Staff providing services to clients is a common feature of the private sector. To remain competitive, the private sector must maintain high-quality and consistent services. The aim of this study to analyze the influence of interpersonal conflict, job stress, and work life balance on employee turnover intention. Quantitative research was applied to measure the current study. A random sampling method was used to gather data. The researchers distribute 120 questionnaires. 112 questionnaires were received but only 104 questionnaires were valid. The findings revealed that the value B for Interpersonal conflict factor is .611 (.611>0.01) therefore first research hypothesis was supported. The value B for Job stress factor is .599 (.599<0.01) therefore second research hypothesis was supported. The value B for Work life balance factor is .643 (.643>0.01) therefore third research hypothesis was supported. Although the literature shows that turnover has a negative impact on many organizations, this study provides evidence that this relationship has an impact on the pesticide sector in this region.
Inducing Positive Perspectives with Text Reframing

Sentiment transfer is one popular example of a text style transfer task, where the goal is to reverse the sentiment polarity of a text.With a sentiment reversal comes also a reversal in meaning.We introduce a different but related task called positive reframing in which we neutralize a negative point of view and generate a more positive perspective for the author without contradicting the original meaning.Our insistence on meaning preservation makes positive reframing a challenging and semantically rich task.To facilitate rapid progress, we introduce a large-scale benchmark, POSITIVE PSY-CHOLOGY FRAMES, with 8,349 sentence pairs and 12,755 structured annotations to explain positive reframing in terms of six theoreticallymotivated reframing strategies.Then we evaluate a set of state-of-the-art text style transfer models, and conclude by discussing key challenges and directions for future work.To download the data, see https://github.com/GT-SALT/positive-frames