Reviewerly/exHarmony huggingface dataset:
A cascaded approach for page-object detection in scientific papers

In recent years, Page Object Detection (POD) has become a popular document understanding task, proving to be a non-trivial task given the potential complexity of documents. The rise of neural networks facilitated a more general learning approach to this task. However, in the literature, the different objects such as formulae, or figures among others, are generally considered individually. In this paper, we describe the joint localisation of six object classes relevant to scientific papers, namely isolated formulae, embedded formulae, figures, tables, variables and references. Through a qualitative analysis of these object classes, we note a hierarchy among the classes and propose a new localisation approach, using two, cascaded You Only Look Once (YOLO) networks. We also present a new data set consisting of labelled bounding boxes for all six object classes. This data set combines two commonly used data sets in the literature for formulae localisation, adding to the document images in these data sets the labels for figures, tables, variables and references. Using this data set, we achieve an average F1-score of 0.755 across all classes, which is comparable to the state-of-the-art for the object classes when considered individually for localisation.
Development of automatic attendance marking system using facial recognition method

Human beings have been using physical features such as voice, face, etc. to identify each other from centuries. With the latest advancement in technology, biometrics has become arising innovations for identifying humansby using biological traits. The purpose of the system is to make process of attendance very faster and accurate using Face Recognition. The traditional method for marking attendance is by roll number or calling the name of the student to record attendance was time consuming and less accurate process of marking attendance as we know the data written on a paper can be lost or can be less accurate.Students oftenmark each other attendance proxy so as to avoid such issues; we tried to develop an opensource Face Recognition system using Python. This system can be used for various groups of people such as students, employees etc. The software updates and stores the data in actual time.
Robust Combination of Distributed Gradients Under Adversarial Perturbations

We consider distributed (gradient descent-based) learning scenarios where the server combines the gradients of learning objectives gathered from local clients. As individual data collection and learning environments can vary, some clients could transfer erroneous gradients e.g. due to ad-versarial data or gradient perturbations. Further, for data privacy and security, the identities of such affected clients are often unknown to the server. In such cases, naively ag-gregating the resulting gradients can mislead the learning process. We propose a new server-side learning algorithm that robustly combines gradients. Our algorithm embeds the local gradients into the manifold of normalized gradients and refines their combinations via simulating a diffusion process therein. The resulting algorithm is instantiated as a compu-tationally simple and efficient weighted gradient averaging algorithm. In the experiments with five classification and three regression benchmark datasets, our algorithm demon-strated significant performance improvements over existing robust gradient combination algorithms as well as the base-line uniform gradient averaging algorithm.
Automatic Unseen Class Discovery Algorithm Based on Clustering Analysis

Although deep learning has achieved great success in automatic target recognition, the model needs a large number of labeled samples for training. In real life, it is a time-consuming and laborious work to label unlabeled samples, so how to find unknown classes from a large number of unlabeled samples has aroused widespread concern. In this paper, we study how to discover unseen class from an unlabeled image set under the assumption that there are samples related to the unseen class but of different classes as prior knowledge. The Automatic Unseen Class Discovery (AUCD) algorithm is proposed in this paper, which mainly solves the problem of unseen class discovery from two aspects, one is how to actively form clusters according to their classes for unknown class samples, and the other is how to obtain the number of formed clusters. Several experiments based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset prove the effectiveness of the proposed approach in the field of unseen class discovery.
Research on VR Support Writing Learning at Dual Cyclic Theory

There are lack of problems in traditional writing teaching for real context experience and perception, and the virtual reality (VR) to support students support students' participation in enthusiasm and writing literacy. Therefore, the article has passed the use of quasi-experimental research, incorporates the five-cycle teaching concept, and randomly distributes 97 primary school students as experimental groups and control groups, in VR, PPT learning environment, and uses the lag sequence analysis method. Differences in the Interactive Effect of Classroom Teachers and Students in Different Learning Environments. The research results show that the VR-based writing learning environment can promote students' class interaction; the integration of dual cycle teaching concept has promoted VR in writing classroom applications, making teaching models and teaching technologies.
Women’s Clothing Application by Using Waterfall Method in the Form of Website of Rafika Modes UMKM

This research is the existence of problems in the scope of the sales information system. The problem is in the form of inefficient and widespread in marketing the products of this women's clothing UMKM. This is because the owner of UMKM does not understand the current technology. So that the sales results are less able to compete with other UMKM in the society. The existence of this women's clothing sales application makes it easier to market products and expand customer reach. In this paper, the writer uses the waterfall development model which an approach to software development that determines the stages of making a website from software requirements analysis, design, program code making, testing and support or maintenance. The web-based women's clothing sales application can make it easier for UMKM owner Rafika Modes to sell products that have been produced and provide reports for what products are sold. The application is in the form of web, so the user is more flexible to open the application with any devices.
Semi-supervised Learning via Multiple Layer Graph Regularized Perception

Recently, Graph Neural Networks (GNNs) have made remarkable achievements in semi-supervised classification tasks. Nevertheless, GNNs usually rely on a specific graph convolution which has high computational complexity. To overcome this issue, recent works attempt to implicitly use adjacency matrix to guide message propagation in multi-layer perception (MLP) via neighboring contrastive loss. However, existing works accomplish implicit message passing only, without considering multi-order graph topology information. In this paper, we propose a novel method called Multiple Layer Graph Regularized Perception (MLGP). The main advantage of MLGP is to incorporate multi-order neighboring information into MLP. Further, inspired by gated mechanism, we design a linear gating to capture important features of nodes. More discriminant features can be obtained to alleviate over-smoothing. MLGP is more effective and more robust than existing works when dealing with large-scale graph data and noisy adjacency information. The comparative experiment results show that our model achieves better performance and strong robustness.
Blockchain Technology Applied to Health Care Supply Chain

Supply Chain Management (SCM) has grown in the last years due to the changing and evolutionary environment. The SCM has become fundamental for gaining financial and social, among others, benefits. Currently, the traditional SCM mechanisms have some areas of improvement, such as the transparency and lack of information, long waiting times for information retrieval and data fidelity. The past months our daily lives have been changing in a drastic way due to COVID-19 and some industries were affected but with this change there has been some opportunities to improve. In this article we describe the main concepts about the blockchain technology, the smart contracts, and their main use. We talk about different areas that these technologies could help to improve in different ways of their internal and external processes. Our main discussion is the description and improvement in the performance inside a supply chain, how it could be possible to speed up the information sharing, how a smart contract could be applied to a consumer-supplier relationship in the healthcare supply chain and the benefits that it would bring to it due to the quick times that this area demands because of the nature of their transactions and operations that are critical to maintain the needs of the industry.
Preliminaries

In this chapter we introduce the main theoretical framework in which our theory of effective fibrations is embedded. Abstractly put, we are studying and constructing new notions of fibred structure and cofibred structure on a category $$\mathcal {E}$$ . notion of fibred structure
Double Compression Efficiency for Image Data Hiding using Integer Wavelet Transform

It is important to reduce image data in transmit and retrieve this data without distortion for many applications such as medical images, military documents, and legal documents. This paper proposes an algorithm that uses a bit-level arithmetic coding to obtain a lossless compressed system through first-level integer wavelet shunting for detailed sub-images. To reduce the data bits, two successive stages of compression are proposed by the discrete cosine transform of the luminance Y channel. The first uses an 8×8 block with a certain threshold value followed & selected triangle zonal region. The second scheme uses an 8×8 block & a certain threshold value followed with selected square zonal region. The results of the dual-stage compression experiments demonstrated its ability to achieve a high compression ratio exceeds to 93% (bit per pixel equals to 0.5) for Y luminance channel while maintaining the quality of the received color original attacked image (Gaussian, Salt& Peppers, and random noise) by about 20 dB than using only one compression stage (discrete cosine transform 8×8 block).
Unsupervised Source Separation for Multi-Label Classification

This paper exploits blind source separation for the purpose of multi-label classification (MLC). The proposed method addresses the multi-label classification problem in two stages, source separation of data consisting of two or more observed classes followed by a single-label classification. This method is evaluated on two data-sets: the new application to electromagnetic interference (EMI) signals that are used for high-voltage power asset condition monitoring, and overlapping handwritten digits (multi-MNIST) for further model validation. Our approach is compared to other deep neural networks such as variational auto-encoder (VAE) and capsule network (CapsNet). Results show high average precision on both data-sets and outperform in most metrics other deep auto-encoding networks that infer label decision from the latent variables distribution.
A Biomedical Trigger Word Identification Method Based on BERT and CRF

Biomedical trigger word identification is a challenging task in biomedical text mining, which plays a key role in improving biomedical research and disease prevention. The traditional trigger word identification methods rely too much on the establishment of dictionaries or rules, which lead to poor performance. Aiming at the above problems, this paper puts forward a BERT-CRF model. In this model, BERT is used to train the emission matrix in CRF model. By using the transformer architecture of BERT, the language model, is used to train. By using CRF layer, some constraints can be added to ensure that the final prediction results are valid. The experimental results on MLEE data set show that the proposed model achieves state-of-the-art performance with an F value of 82.71%, which indicates that this method is beneficial to improve the trigger word recognition performance.
Literacy Movement in Junior High Schools to Form Students' Reading Habits

This research is a qualitative descriptive study with research data obtained by interviewing thirteen informants who were selected based on predetermined criteria, namely because of their position at school, the literacy team, subject teachers and students who excel in academics. Data collection techniques are interviews, and documentation. Checking the validity of the data is done by using source triangulation.The results of this study found that: The implementation of the school literacy movement in Junior High Schools in Majene Regency has been carried out for 2 years, where the stages include: a) The habituation stage, reading reading books before starting learning is carried out every day. b) The development stage, reading books, accompanied by non-academic bills, such as making summaries of reading results or making written works, and utilizing the literacy environment in the form of reading corners for each class and library.The impact of the implementation of the school literacy movement is that students' reading interest continues to increase. The factors that hinder the implementation of the literacy movement in Junior High Schools in Majene Regency are Distance Learning (Online learning) which makes it difficult for students to control so that the implementation of the school literacy movement is not optimal.
Adversarial Sample Generation for Lithography Hotspot Detection

Lithography hotspot detection is of great significance in chip manufacturing. Hotspots are those patterns that may cause fatal defects in the final tape-out, such as short or open circuits. Therefore, identifying and eliminating hotspots in the early design stage can improve chip yield and reduce manufacturing costs. Traditionally, lithography simulation is used to detect hotspot patterns. But as the feature size shrinks and the design complexity increases, the lithography simulation of the entire chip requires a longer time overhead, which seriously delays the design cycle. Consequently, many deep learning-based methods have been proposed to accelerate hotspot detection. These approaches all show a good performance in the ICCAD 2012 contest benchmarks. However, deep neural networks are vulnerable to adversarial attacks. In this paper, we propose to generate samples by adjusting the critical distance between polygons in the layout based on existing patterns. Layouts are very sensitive to the distance between polygons, the type of a layout may flip by slight modifications in the distances. These adversarial samples are closer to the decision boundary of neural networks than the original ones. Experimental results show that the accuracy of neural network-based hotspot detectors drops significantly in the dataset formed by generated samples. Adding the generated samples to the training dataset improves the robustness and generalization ability of neural networks.
Supervised Contrastive Learning for Cross-Lingual Transfer Learning

Multilingual pre-trained representations are not well-aligned by nature, which harms their performance on cross-lingual tasks. Previous methods propose to post-align the multilingual pre-trained representations by multi-view alignment or contrastive learning. However, we argue that both methods are not suitable for the cross-lingual classification objective, and in this paper we propose a simple yet effective method to better align the pre-trained representations. On the basis of cross-lingual data augmentations, we make a minor modification to the canonical contrastive loss, to remove false-negative examples which should not be contrasted. Augmentations with the same class are brought close to the anchor sample, and augmentations with different class are pushed apart. Experiment results on three cross-lingual tasks from XTREME benchmark show our method could improve the transfer performance by a large margin with no additional resource needed. We also provide in-detail analysis and comparison between different post-alignment strategies.
Parameter Identification of Stochastic Delay Differential Equations Using Differential Evolution

This paper aims to identify the model parameters in the stochastic delay differential equations (SDDE) using the differential evolution (DE) algorithm. The financial datasets of the International Business Machines Corporation (IBM), the Microsoft Corporation (MSFT), and the Standard and Poor's 500 (S&P 500) are used in this paper. All datasets are traded every five minutes on weekdays. The empirical results obtained from the DE algorithm indicate that the estimated average volatility of each stock has a similar pattern to the actual volatility. In addition, the performance of different delays is investigated. It can be concluded that the estimate becomes more accurate when the delay is increased.
Fostering Consumers’ Attitude and Satisfaction Towards Voice Assistants: An Empirical Validation

This all-inclusive comprehensive research paper talks about the attitude and inclination of customers towards ‘voice assistants’ which has been a sensation in the information technology, automated response and robotics in past one decade. The paper critically examines and discusses about the aspects such as theory of consumption values (TCV); history, current market and penetration of voice assistants in India. Data was gathered by North Indian respondents using voice assistants. The sample comprised of 332 respondents and data was analyzed using Structural Equation Modelling (SEM). The results confirmed the significant positive impact of all the values in building positive attitude. Convenience value and emotional value emerged as strongest predictors of customer attitude followed by device quality and epistemic value. Moving ahead attitude also had strong impact on customer satisfaction. Using TCV theoretical framework, this research is an initial end-eavor to analyze the enablers of voice assistants which will not only help in their adoption but also provide deep insights to the developers also.
Towards a Complete Multi-agent Pathfinding Algorithm for Large Agents

Multi-agent pathfinding (MAPF) is a challenging problem which is hard to solve optimally even when simplifying assumptions are adopted, e.g. planar graphs (typically – grids), discretized time, uniform duration of move and wait actions etc. On the other hand, MAPF under such restrictive assumptions (also known as the Classical MAPF) is equivalent to the so-called pebble motion problem for which non-optimal polynomial time algorithms do exist. Recently, a body of works emerged that investigated MAPF beyond the basic setting and, in particular, considered agents of arbitrary size and shape. Still, to the best of our knowledge no complete algorithms for such MAPF variant exists. In this work we attempt to narrow this gap by considering MAPF for large agents and suggesting how this problem can be reduced to pebble motion on (general) graphs. The crux of this reduction is the procedure that moves away the agents away from the edge which is needed to perform a move action of the current agent. We consider different variants of how this procedure can be implemented and present a variant of the pebble motion algorithm which incorporates this procedure. Unfortunately, the algorithm is still incomplete, but empirically we show that it is able to solve much more MAPF instances (under the strict time limit) with large agents on arbitrary non-planar graphs (roadmaps) compared to the state-of-the-art MAPF solver – Continous Conflict-Based Search (CCBS).
Unification of Source-Code Re-Use Similarity Measures

Computer programs and their source code are commonly available online, making it relatively easy to create new programs from the resource available. Simple stated, methods for detecting the source code reuse apply individual similarity measures between two source codes (the original and the suspect) to identify a case of reuse based on a manually determined threshold; however, the relevance of each similarity measure and the selection of the correct threshold in this task has been little explored. In this study, we propose an evolutionary approach to automatically obtain the relevance of each individual similarity measure and its threshold. Furthermore, we propose a unified method to combine the strengths of individual similarity measures to increase retrieval accuracy. Our experimental results provided three individual similarity systems that outperform the original ones and a unified system that obtains the best result.
Hyperledger Fabric Consensus Optimization Method Based on Endorsing Nodes

Aiming at a series of security risks caused by the transparency and scarcity of endorsing nodes information in Hyperledger fabric, A non-interactive and verifiable optimization scheme for randomly selecting endorsing nodes is proposed, That is, the fabric model based on endorsement-ordering-verification. By introducing the candidate set of endorsing nodes and using the verifiable random function based on the elliptic curve SM2 algorithm to randomly select endorsing nodes as transactions for endorsement strategy, The model realizes the non-interactive random selection function of endorsing nodes in fabric, which can effectively prevent hacker attacks on the network. Experimental results show that the proposed scheme has higher security than the comparison method and the scheme before optimization.
Embracing AWKWARD! Real-Time Adjustment of Reactive Plans Using Social Norms

This paper presents the AWKWARD architecture for the development of hybrid agents in Multi-Agent Systems. AWKWARD agents can have their plans re-configured in real time to align with social role requirements under changing environmental and social circumstances. The proposed hybrid architecture makes use of Behaviour Oriented Design (BOD) to develop agents with reactive planning and of the well-established OperA framework to provide organisational, social, and interaction definitions in order to validate and adjust agents’ behaviours. Together, OperA and BOD can achieve real-time adjustment of agent plans for evolving social roles, while providing the additional benefit of transparency into the interactions that drive this behavioural change in individual agents. We present this architecture to motivate the bridging between traditional symbolic- and behaviour-based AI communities, where such combined solutions can help MAS researchers in their pursuit of building stronger, more robust intelligent agent teams. We use DOTA2—a game where success is heavily dependent on social interactions—as a medium to demonstrate a sample implementation of our proposed hybrid architecture.