Reviewerly/exHarmony huggingface dataset:
CCLSL: Combination of Contrastive Learning and Supervised Learning for Handwritten Mathematical Expression Recognition

Handwritten Mathematical Expressions differ considerably from ordinary linear handwritten texts, due to their two-dimentional structures plus many special symbols and characters. Hence, HMER(Handwritten Mathematical Expression Recognition) is a lot more challenging compared with normal handwriting recognition. At present, the mainstream offline recognition systems are generally built on deep learning methods, but these methods can hardly cope with HEMR due to the lack of training data. In this paper, we propose an encoder-decoder method combining contrastive learning and supervised learning(CCLSL), whose encoder is trained to learn semantic-invariant features between printed and handwritten characters effectively. CCLSL improves the robustness of the model in handwritten styles. Extensive experiments on CROHME benchmark show that without data enhancement, our model achieves an expression accuracy of 58.07% on CROHME2014, 55.88% on CROHME2016 and 59.63% on CROHME2019, which is much better than all previous state-of-the-art methods. Furthermore, our ensemble model added a boost of 2.5% to 3.4% to the accuracy, achieving the state-of-the-art performance on public CROHME datasets for the first time.
Patch Embedding as Local Features: Unifying Deep Local and Global Features via Vision Transformer for Image Retrieval

Image retrieval is the task of finding all images in the data-base that are similar to a query image. Two types of image representations have been studied to address this task: global and local image features. Those features can be extracted separately or jointly in a single model. State-of-the-art methods usually learn them with Convolutional Neural Networks (CNNs) and perform retrieval with multi-scale image representation. This paper's main contribution is to unify global and local features with Vision Transformers (ViTs) and multi-atrous convolutions for high-performing retrieval. We refer to the new model as ViTGaL, standing for Vision Transformer based Global and Local features (ViTGaL). Specifically, we add a multi-atrous convolution to the output of the transformer encoder layer of ViTs to simulate the image pyramid used in standard image retrieval algorithms. We use class attention to aggregate the token embeddings output from the multi-atrous layer to get both global and local features. The entire network can be learned end-to-end, requiring only image-level labels. Extensive experiments show the proposed method outperforms the state-of-the-art methods on the Revisited Oxford and Paris datasets. Our code is available at here
Class Specialized Knowledge Distillation

Knowledge Distillation (KD) is a compression framework that transfers distilled knowledge from a teacher to a smaller student model. KD approaches conventionally address problem domains where the teacher and student network have equal numbers of classes for classification. We provide a knowledge distillation solution tailored for class specialization, where the user requires a compact and performant network specializing in a subset of classes from the class set used to train the teacher model. To this end, we introduce a novel knowledge distillation framework, Class Specialized Knowledge Distillation (CSKD), that combines two loss functions: Renormalized Knowledge Distillation (RKD) and Intra-Class Variance (ICV) to render a computationally-efficient, specialized student network. We report results on several popular architectural benchmarks and tasks. In particular, CSKD consistently demonstrates significant performance improvements over teacher models for highly restrictive specialization tasks (e.g., instances where the number of subclasses or datasets is relatively small), in addition to outperforming other state-of-the-art knowledge distillation approaches for class specialization tasks.
Investigating Fractal Decomposition Based Algorithm on Low-Dimensional Continuous Optimization Problems

This paper analyzes the performance of the Fractal Decomposition Algorithm (FDA) metaheuristic applied to low-dimensional continuous optimization problems. This algorithm was originally developed specifically to deal efficiently with high-dimensional continuous optimization problems by building a fractal-based search tree with a branching factor linearly proportional to the number of dimensions. Here, we aim to answer the question of whether FDA could be equally effective for low-dimensional problems. For this purpose, we evaluate the performance of FDA on the Black Box Optimization Benchmark (BBOB) for dimensions 2, 3, 5, 10, 20, and 40. The experimental results show that overall the FDA in its current form does not perform well enough. Among different function groups, FDA shows its best performance on Misc. moderate and Weak structure functions.
Evaluating the Effects of Chaos in Variable Neighbourhood Search

Abstract Metaheuristics are problem-solving methods which try to find near-optimal solutions to very hard optimization problems within an acceptable computational timeframe, where classical approaches usually fail, or cannot even been applied. Random mechanisms are an integral part of metaheuristics, given randomness has a role in dealing with algorithmic issues such as parameters tuning, adaptation, and combination of existing optimization techniques. In this paper, it is explored whether deterministic chaos can be suitably used instead of random processes within Variable Neighbourhood Search (VNS), a popular metaheuristic for combinatorial optimization. As a use case, in particular, the paper focuses on labelling graph problems, where VNS has been already used with success. These problems are formulated on an undirected labelled graph and consist on selecting the subset of labels such that the subgraph generated by these labels has, respectively, an optimal spanning tree or forest. The effects of using chaotic sequences in the VNS metaheuristic are investigated during several numerical tests. Different one-dimensional chaotic maps are applied to VNS in order to compare the performance of each map in finding the best solutions for this class of graph problems.
Shape Prior is Not All You Need: Discovering Balance Between Texture and Shape Bias in CNN

As Convolutional Neural Network (CNN) trained under ImageNet is known to be biased in image texture rather than object shapes, recent works proposed that elevating shape awareness of the CNNs makes them similar to human visual recognition. However, beyond the ImageNet-trained CNN, how can we make CNNs similar to human vision in the wild? In this paper, we present a series of analyses to answer this question. First, we propose AdaBA, a novel method of quantitatively illustrating CNN's shape and texture bias by resolving several limits of the prior method. With the proposed AdaBA, we focused on fine-tuned CNN's bias landscape which previous studies have not dealt with. We discover that fine-tuned CNNs are also biased to texture, but their bias strengths differ along with the downstream dataset; thus, we presume a data distribution is a root cause of texture bias exists. To tackle this root cause, we propose a granular labeling scheme, a simple but effective solution that redesigns the label space to pursue a balance between texture and shape biases. We empirically examine that the proposed scheme escalates CNN's classification and OOD detection performance. We expect key findings and proposed methods in the study to elevate understanding of the CNN and yield an effective solution to mitigate this texture bias.
Instance Space Analysis for the Generalized Assignment Problem

In this work, we consider the well-studied Generalized Assignment Problem and investigate the performance of several metaheuristic methods. To obtain insights on strengths and weaknesses of these solution approaches, we perform Instance Space Analysis on the existing instance types and propose a set of features describing the hardness of an instance. This is of interest since the existing benchmark set is dated and rather limited and the known instance generators might not be fully representative. Our analysis for metaheuristic methods reveals that this is indeed the case and finds several gaps, which we fill with newly generated instances thus adding diversity and providing a new benchmark instance set. Furthermore, we analyze the impact of problem features on the performance of the methods used and identify the most important ones.
TCVM: Temporal Contrasting Video Montage Framework for Self-supervised Video Representation Learning

Extracting appropriate temporal differences and ignoring irrelevant backgrounds are two important perspectives on preserving sufficient motion information in video representation, such as driver behavior monitoring and driver fatigue detection. In this paper, we propose a unified contrastive learning framework called Temporal Contrasting Video Montage (TCVM) to learn action-specific motion patterns, which can be implemented in a plug-and-play way. On the one hand, Temporal Contrasting (TC) module is designed to guarantee appropriate temporal difference between frames. It utilizes high-level feature space to capture raveled temporal information. On the other hand, Video Montage (VM) module is devised for alleviating the effect from video background. It demonstrates similar temporal motion variances in different positive samples by implicitly mixing up the backgrounds of different videos. Experimental results show that our TCVM reaches promising performances on both large action recognition dataset (i.e. Something-Somethingv2) and small datasets (i.e. UCF101 and HMDB51).
RGB Road Scene Material Segmentation

We address RGB road scene material segmentation, i.e., per-pixel segmentation of materials in real-world driving views with pure RGB images, by building a new tailored benchmark dataset and model for it. Our new dataset, KITTI-Materials, based on the well-established KITTI dataset, consists of 1000 frames covering 24 different road scenes of urban/suburban landscapes, annotated with one of 20 material categories for every pixel in high quality. It is the first dataset tailored to RGB material segmentation in realistic driving scenes which allows us to train and test any RGB material segmentation model. Based on an analysis on KITTI-Materials, we identify the extraction and fusion of texture and context as the key to robust road scene material appearance. We introduce Road scene Material Segmentation Network (RMSNet), a new Transformer-based framework which will serve as a baseline for this challenging task. RMSNet encodes multi-scale hierarchical features with self-attention. We construct the decoder of RMSNet based on a novel lightweight self-attention model, which we refer to as SAMixer. SAMixer achieves adaptive fusion of informative texture and context cues across multiple feature levels. It also significantly accelerates self-attention for feature fusion with a balanced query-key similarity measure. We also introduce a built-in bottleneck of local statistics to achieve further efficiency and accuracy. Extensive experiments on KITTI-Materials validate the effectiveness of our RMSNet. We believe our work lays a solid foundation for further studies on RGB road scene material segmentation.
The Application of Academic Article Writing Techniques for Content Creation for the Promotion of Buddhist-Based Learning Communities and the Conservation of Buddhism via Online Communities

This research paper explains the characteristics of academic and content writing for online platforms. At the same time, it aims at presenting guidelines for the implementation of some academic writing techniques on content writing for online learning communities and social services. The researchers used the Fund-raising Project to Establish the Meditation Center at Mahavana Ashram, Nepal and a Facebook Page: Super Richy as a case study for this research. The project and data collection were carried out from October 7, 2021 to March 3, 2022, dividing the execution into 4 time periods to collect 4 statistical data sets before and after the project execution. Prior to the implementation of the project on Facebook Page: Super Richy, data collected from October 7 to November 4, 2021, the Facebook Page reach was -64.2%, Facebook Page visits was -47.2% and New Facebook Page likes was -69.4%. After the implementation, there was a significant increase on all sections of the page; Facebook Page reach increased at + 319.6%, Facebook Page visits at 353.1% and New Facebook Page likes at 285.7%. These results address the question, “how are academic article writing techniques applied to content creation for the promotion of Buddhist-based learning communities on social network?” This research transforms the tacit knowledge of writers into an explicit form by using the art and skill of conducting academic work to identify the guidelines that ensure fitting content creation.
Skin Tone Diagnosis in the Wild: Towards More Robust and Inclusive User Experience Using Oriented Aleatoric Uncertainty

The past decade has seen major advances in deep learning models that are trained to predict a supervised label. However, estimating the uncertainty for a predicted value might provide great information beyond the prediction itself. To address this goal, using a probabilistic loss was proven efficient for aleatoric uncertainty, which aims at capturing noise originating from the observations. For multidimensional predictions, this estimated noise is generally a multivariate normal variable, characterized by a mean value and covariance matrix. While most of literature have focused on isotropic uncertainty, with diagonal covariance matrix, estimating full covariance brings additional information, such as the noise orientation in the output space. We propose in this paper a specific decomposition of the covariance matrix that can be efficiently estimated by the neural network. From our experimental comparison to the existing approaches, our model offers the best trade-off between uncertainty orientation likeliness, model accuracy and computation costs. Our industrial application is skin color estimation based on a selfie picture, which is at the core of an online make-up assistant but is a sensitive topic due to ethics and fairness considerations. Thanks to oriented uncertainty, we can reduce this risk by detecting uncertain cases and proposing a simplified color correction bar, thus making user experience more robust and inclusive.
Visual Explanation Generation Based on Lambda Attention Branch Networks

Explanation generation for transformers enhances accountability for their predictions. However, there have been few studies on generating visual explanations for the transformers that use multidimensional context, such as LambdaNetworks. In this paper, we propose the Lambda Attention Branch Networks, which attend to important regions in detail and generate easily interpretable visual explanations. We also propose the Patch Insertion-Deletion score, an extension of the Insertion-Deletion score, as an effective evaluation metric for images with sparse important regions. Experimental results on two public datasets indicate that the proposed method successfully generates visual explanations.
Implementing the Practically-Oriented Curricular in the Field of Cyber-Physical Systems: A Case Study of the School for Ukrainian Students

The ERASMUS + project “Development of practically-oriented student-centered education in the field of modeling of Cyber-Physical Systems” (CybPhys) focuses on curricula modernizations in close cooperation with potential employers. Since the CybPhys project pursues to develop practically-oriented student-centered curricula, we involved industry representatives in the creation of new courses from the very beginning of the project. In the paper, we describe the curriculum development stages and obtain feedback from the main stakeholders. As part of the practically-oriented education, European partners in Riga Technical University, KU Leuven, and the University of Cyprus should arrange three schools for Ukrainian students. In this research, we focus on implementing practically-oriented and student-centered training methods at the school for Ukrainian students arranged at the RTU in January 2022. We describe novel methods and approaches applied in the students’ school, such as transversal skills, Design Thinking, involvement of industry representatives in the teacher team, etc. The survey of Ukrainian students and teachers revealed the presence or absence of correlations between students’ general perception of the school and teaching/ learning methods, e.g. developments of creative thinking, practical application of theory, etc.
How the Pandemic Helped and Hindered Student-Centered Learning

The student-centered learning approach “learning office” has been established in vocational school education under constant evaluation through a team of education researchers. In the past two years, emergency online teaching has affected education world-wide with a severe impact on teacher-centered as well as student-centered education. Our goal is to evaluate teachers’ and students’ perspectives on emergency and systematic online teaching and to get an impression of students’ perceived competence in 21st century skills which the learning office is specifically designed to foster. For this, we reconcile the change in perspectives through comparisons of open text answers from a questionnaire after experiencing emergency online teaching and interviews with students and teachers regarding their perceived change in the way contents are taught. We find diverse aspects which are partially favourable for student-centered learning in times of self-reliant education at home. However, we also identified adversary effects of studying alone at home for the learning office due to its interactive nature. On the one hand, students felt more self-reliant and able to work on materials on their own accord without constant input or supervision of a teacher. On the other hand, students missed the personal interaction with their peers, but even more with their teachers in the online setting. Based on our questionnaire and interviews, we find that student-centered learning indeed helped to develop the skills for self-reliant learning in students. However, the position of the educator as a coach and guiding figure to the students is irreplaceable, especially in student-centered learning.
Work in Progress: Importance of Industry Involvement and Interaction Component and Its Weightage and Impact in Assessment and Evaluation Process of National Board of Accreditation

The paper provides insight of holistic education system which is originated in India which used to impart embedded lifelong learning skills with hands-on sessions in that context of era. It describes the indicative foresights of world economic forum from education perspective. The section two of paper details overview of constituents of interaction avenues of institutes with industries. This third section of papers describes the importance of involvement of industries in terms of parameters of assessment criteria of National Board of Accreditation (ABET –Washington Accord). In the fourth section, author has put forward the impact of weightage on industry involvement in NBA process and has put forward its suggestive rearrangement with urge to establish separate criteria as far as assessing the industry involvement in engineering education is concerned. The author has put forward conclusions in last section which states anticipated impact of modifications suggested in fourth section of the paper.
Project Based Learning Approach to Developing Presentation Skills

Project based learning is widely used for training students to solve a problem for an extended period. This approach aims at developing both hard and soft skills, the combination of which is of great importance for successful career promotion of future engineers. This article presents the project-based approach which was implemented in the Foreign Language Course for students majoring in Chemical Engineering at Kazan National Research Technological University. This approach was applied to students receiving their Bachelor's degrees in Chemical Engineering, and then to the same students who continued their study and got Master’s degrees. To implement this approach to the educational process, we developed the project task in such a way as to combine students’ training in Bachelor’s and Master’s programs. The project task was carried out by students with the support of professors from Foreign Language Department and engineering departments of the University. At the end of the course students presented their results in English both at Bachelor’s and Master’s levels of education. Thus, we could connect logically two levels of the university education for fostering the English language communication skills for students’ professional and academic purposes.
Aadhaar Block: An Authenticated System for Counterfeit Aadhaar Enrolment in Citizen Services Using Blockchain

In the past decades, people had to carry a bunch of documents for acquiring citizen beneficiary services such as opening a bank account, issue of a new sim card, new gas connection, Pradhan Mantri Jan Dhan Yojana etc. In this digital era, Aadhaar is acting as the driving force to gain all citizen services. Unfortunately, lot of security breaches have occurred by compromising the Aadhaar details which are stored in the database. Providing security to Aadhaar data and mitigating the misuse of other’s Aadhaar data to gain citizen services by unauthenticated users is the main concern. Hence, a secure and authenticated system is required to store and use the Aadhaar details securely by authenticated users. In this paper, we are proposing a secure authenticated system which uses IPFS to store fingerprints of the customers and blockchain is used to store the Aadhaar details of the registered citizens. In this system, the biometric is integrated with blockchain to secure Aadhaar details and authenticate the customers before providing any citizen services. The system also generates the Aadhaar card after passing a redundancy check for customers who do not have Aadhaar card to access the citizen services. The system is tested and verified for multiple citizen services.
The Use of Computerized Laboratory and Training Complexes in Engineering and Pedagogical Education

The article gives insight into the problem of reforming the practical professional training of engineer-teachers on the basis of integrated use of modern information and computer technologies. To solve this problem, it is proposed to improve and develop new technologies of practical training, considering modern development of information, computer and production technologies as well as modernization and reorganization of existing infrastructure and material and technical support of practical training of workers as well as engineering and pedagogical personnel. This article shows that the use of modern teaching technologies, especially information and computer technology, makes changes in the structure of teaching environments, which in turn result in additional elements in the form of teaching environment management tools, information and communication tools. It describes an innovative approach to the organization of a teaching environment based on the use of computerized laboratory and training complexes, the composition and functions of these complexes, means for managing components of a teaching environment, and means for information and multimedia communication of students with the teacher on the basis of demonstration and projection equipment as well as networks with an educational content server.