Reviewerly/exHarmony huggingface dataset:
Combining Self-training and Minimal Annotations for Handwritten Word Recognition

Handwritten Text Recognition (HTR) relies on deep learning to achieve high performances. Its success is substantially driven by large annotated training datasets resulting in powerful recognition models. Performances suffer considerably when applied to document collections with a distinctive style that is not well represented by training data. Applying a recognition model to a new data collection poses a tremendous annotation effort, which is often out of scope, for example considering historic collections. To overcome this limitation, we propose a training scheme that combines multiple data sources. Synthetically generated samples are used to train an initial model. Self-training offers the possibility to exploit unlabeled samples. We further investigate the question of how a small number of manually annotated samples can be integrated to achieve maximal performance with limited annotation effort. Therefore, we add labeled samples at different stages of self-training and propose two criteria, namely confidence and diversity, for the selection of samples to annotate. In our experiments, we show that the proposed training scheme is able to considerably close the gap to fully-supervised training on the designated training set with less than ten percent of the labeling demand.
DKS-PKI: A Distributed Key Server Architecture for Public Key Infrastructure

Public key infrastructure (PKI) is the core of authentications performed in secure internet communications. The most popular PKI arrangement is the certificate authority (CA)-based PKI. The traditional security protocols use this CA-based PKI to provide server-side authentications. In this approach, the server-side uses its public key certificate that is the server’s public key signed by a trusted CA. This CA-based PKI is not able to properly address the challenges associated with the growing demand of secure internet communications. We identified two major problems with this CA-based PKI to be used in secure communications. First, the mis-issuance of certificates or the disclosure of any such CAs’ private keys can cause serious problems to the security of these internet communications. Second, revoking an issued public key certificate or any trusted CA’s certificate is not a trivial task. In this paper, we proposed a distributed key server architecture (DKS-PKI) that provides a PKI arrangement that can solve the above mentioned problems. The proposed architecture offers registration/issuance, storage, distribution, and revocation of certificates in an efficient manner. It ensures transparency and accountability of the certificate issuers. All the registered/issued certificates and their sensitive identity information are verified and stored into a permissioned distributed storage system. The key-server nodes are responsible to make these issued certificates publicly accessible by means of their associated 256-bit unique identifiers (UIDs). We presented a thorough security analysis of the proposed architecture.
Poor Posture Detection System of Hug using Depth Camera and Pose Estimation

In this study, we use a software called OpenPose estimate the posture of carrying. A depth camera is used as the imaging device. In this study, we use a photographic device called Realsens. From the front view, we examine three points: the position of the hand supporting the baby's buttocks, the angle of the arm, and the angle of the arm supporting the baby's back. From the side view, the angle of the arm supporting the baby's back and two points of the back muscles are examined. From the space vectors with the depth information corresponding to the coordinates of the feature points, we calculate the values. We will consider a system to visualize the evaluation values from the analysis results in an easy-to-understand manner for users.
Singlish Checker: A Tool for Understanding and Analysing an English Creole Language

As English is a widely used language in many countries of different cultures, variants of English also known as English creoles have also been created. Singlish is one such English creole used by people in Singapore. Nevertheless, unlike English, Singlish is not taught in schools nor encouraged to be used in formal communications. Hence, it remains to be a low resource language with a lack of up-to-date Singlish word dictionary and computational tools to analyse the language. In this paper, we therefore propose Singlish Checker, a tool that is able to help detecting Singlish text, Singlish words and phrases. To develop this tool, we first construct a large set of Singlish words and phrases by identifying different sources of Singlish words and their definitions and integrating them. We later propose a Singlish classifier model based on a BERT model fine-tuned with a large number of classified Singlish sentences. Our experiment show that the BERT-based classifier can achieved very high F1 performance, outperforming the baseline.
ABC-BPNN Based Enhanced Hybrid Strategy for the Prediction Model of 6-DoF FKP

Ususally, Back Propagation Neural Network (BPNN) was used to solve the problem of complex forward kinematics for Gough-Stewart platform. However, BPNN will falls short of the neural network requirements in a hybrid strategy. Then, to improve the prediction ability of neural network model, the ABC-BPNN, a new neural network for employment in hybrid algorithm was proposed in this paper. This paper firstly introduced kinematics model of 6-DoF. Subsequently, improving the prediction ability of neural network model to the pose of the machine by ABC optimization. Lastly, we designed three models and proved that the network model with one hidden layer has the best prediction effect and the lowest metrics by qualitative comparisons.
A Hybrid Approach for Table Detection in Document Images

One of the crucial requirements of document analysis is accurate detection of tables and data present in it. It is easier to parse and extract information from tables as it is more structured compared to paragraphs. However, detecting tables in document images is a challenging task due to different layouts. Since image processing and machine learning approaches like Logistic Regression and SVM did not result in good detection, several deep learning architectures along with labeled datasets were experimented resulting in better segmentation performance. But, the deep learning models suffer from a major drawback of lack of explainability of prediction. In this work, we use features computed on word segments in order to segment tables in document images using Logistic Regression and SVM with linear and RBF kernels. All these models are more explainable compared to Deep Learning models. We further augment a recent deep learning model, TableNet, with the features as additional inputs and report the comparison studies on a publicly available dataset Marmot. Performance of all models was compared by running segmentation on 100 test images in Marmot dataset. Among the machine learning models that were trained using word alignment features, SVM with RBF kernel gave the best word-level Dice score of 83.1% with a precision of 91.4% and recall of 76.1%. The augmented deep learning model gave a pixel-wise Dice score of 95.2% with a precision of 99.98% and recall of 91.44%. The same model without augmentation gave a pixel-wise Dice score of 94.8% with a precision of 99.99% and recall of 90.7%. The proposed hybrid model results in a reduction of 0.7% of false positives.
User-Generated Content (UGC)/In-The-Wild Video Content Recognition

According to Cisco, we are facing a three-fold increase in IP traffic in five years, ranging from 2017 to 2022. IP video traffic generated by users is largely related to user-generated content (UGC). Although at the beginning of UGC creation, this content was often characterised by amateur acquisition conditions and unprofessional processing, the development of widely available knowledge and affordable equipment allows one to create UGC of a quality practically indistinguishable from professional content. Since some UGC content is indistinguishable from professional content, we are not interested in all UGC content, but only in the quality that clearly differs from the professional. For this content, we use the term “in the wild” as a concept closely related to the concept of UGC, which is its special case. In this paper, we show that it is possible to deliver the new concept of an objective “in-the-wild” video content recognition model. The value of the F measure in our model is 0.988. The resulting model is trained and tested with the use of video sequence databases containing professional and “in the wild” content. These modelling results are obtained when the random forest learning method is used. However, it should be noted that the use of the more explainable decision tree learning method does not cause a significant decrease in the value of measure F (an F-measure of 0.973).
Toward Updating Belief Functions over Belnap-Dunn Logic

[3] and [7] generalize the notion of probability measures and belief functions to Belnap-Dunn ( $$\textsf{BD}$$ ) logic, respectively. This work aims at providing an alternative way to treat contradictory information by relying on a logic that was introduced to reason about incomplete and contradictory information rather than on classical logic. In this article, we study how to update belief functions over $$\textsf{BD}$$ logic with new pieces of information. We present a first approach via a frame semantics of $$\textsf{BD}$$ logic. This frame semantics relying on sets, we can use Bayesian update and Dempster-Shafer combination rule over powerset algebras to define their corresponding updates within the framework of $$\textsf{BD}$$ logic.
Sergei Vasilievich Vonsovskii, Scientist and Reader

Sergei Vasilievich Vonsovskii (1910–1998), also spelled Vonsovsky, a brilliant Russian scientist in the field of magnetism and solid state physics, organizer and manager of science, Full Member of the USSR / Russian Academy of Sciences, and founder of the Urals’ scientific school in the physics of magnetic phenomena, attached great importance to books. When working on his numerous articles and monographs, Vonsovskii always used all available sources. For him, familiarizing himself with the latest scientific periodicals was a matter of professional competence while fiction gave him opportunities for reflections and was a source of delight. The article describes the range of Vonsovskii’s interests in reading. The sources for our analysis were the documents from “Academician S. V. Vonsovskii’s Personal Fonds” deposited at the Central Scientific Library of the Ural Branch of the Russian Academy of Sciences (CSL UB RAS), and his personal collection of books that comprised two parts. The first of these is a part of the preserved family library assembled by his parents, Sofia Ivanovna and Vasily Semyonovich Vonsovskii. Many books from this collection bear the traces of existence (the autographs and marks made by family members, book shop stamps) and can be regarded as family chronicle of a kind. The second part comprises scientific literature used by Sergei Vasilievich in his work (it was handed over to CSL UB RAS from the scientist’s study). Additional information was drawn from the memories of Vonsovskii himself, his colleagues and his pupils. In addition to his personal library, Vonsovskii also used the literature from CSL UB RAS, as evidenced by several inscriptions in the book circulation cards. Sergei Vasilievich also chaired the Library Board for several decades. As he wrote in his memories, “in my whole life, my scientific and public activities, the book has been playing a huge role, sometimes, probably, a decisive one”.
Path Planning for Collision Avoidance Based on Artificial Potential Field with Vehicle Dimension Constraint

In this paper, a path planning algorithm combined the artificial potential field with the vehicle dimension constraint is proposed for autonomous vehicles to generate a collision-free path. To describe the vehicle dimension constraint, an ellipse inflated model of the obstacle is constructed, and the parameters of model are determined with the minimum initial longitudinal safety distance in the lane changing process. Then, the artificial potential field of the surrounding environment is established, which includes inflated obstacle vehicle, lane line, and virtual target point. Collision avoidance path points are produced in the artificial potential field by the potential gradient descent algorithm, and optimized by a fifth-order polynomial. Simulation results demonstrate that the path generated by this method is continuous, smooth, and responds rapidly to dynamic environments.
Digital currency price formation: A production cost perspective

&lt;abstract&gt;&lt;p&gt;The paper investigates the long-run relationship between bitcoin and its marginal cost between July 2010 and July 2022. We derive Bitcoin's marginal cost of production from a model of Bitcoin mining grounded in the Bitcoin code, and show that its production cost is a function of only two variables, the electricity price and the mining hardware efficiency. We then estimate a time-varying vector error correction model, and also the cointegration between bitcoin's price and Bitcoin network's hash rate, a commonly used production cost proxy. Our results show that the time-varying cointegration between bitcoin's price and its hash rate is permanently in disequilibrium, bar a short time interval between March 2017 and January 2018. Consequently, although bitcoin's price and the hash rate are cointegrated, it is clear that the latter does not function as a stable long-run explanatory variable for bitcoin price dynamics. On the contrary, we found that bitcoin's price and its marginal cost of production have been cointegrated since its inception, and that their time-varying long-run relationship always reverts towards equilibrium - and often &lt;italic&gt;to&lt;/italic&gt; equilibrium- after long periods of divergence. These results contrast with most of the empirical literature that attempted to model the relationship betweeen bitcoin and its fundamentals in a time-invariant framework, but are consistent with recent research showing a significant role for production cost in the determination of bitcoin's price dynamics.&lt;/p&gt;&lt;/abstract&gt;
Apply Neutrosophic AHP Analysis of the Internet of Things (IoT) and the Internet of Behavior (IoB) in Smart Education

The concept of the Internet of Things (IoT) has emerged in recent years to describe our increasingly interconnected physical world. Even if there are now goods on the market that are connected to the Internet of Things, there are still obstacles to be addressed, such as security concerns and a general lack of trust in and familiarity with the technology. The purpose of this research is to examine what goes into making a good Internet of Things (IoT) system for smart education that deals with IoT. In this research, we use neutrosophic theory and the Analytic Hierarchy Process (AHP) to create a decision-support system based on rules for assessing the impactful elements of the Internet of Things in smart education. According to the results of this investigation, security, value, and connectivity are more crucial than intangible qualities (Telepresence and Intelligence). This research suggests that the internet of behavior (IoB) and a sustainable Internet of Things (IoT) ecosystem requires strong IoT security measures to be implemented once smart education has established a reliable IoT connection. Likewise, smart education needs to think about the benefits of IoT security and IoT ethics for the goods they use and the comfort of their staff.
Research on Generative Design of Car Side Colour Rendering Based on Generative Adversarial Networks

The traditional artificial color rendering process often relies on the limited personal design experience of the designer. Therefore, the design results are often uncertain. In order to help designers get inspiration for color rendering, a multimodal generation method for car side color rendering schemes was proposed through the multimodal unsupervised image translation framework MUNIT based on generative adversarial network. Firstly, based on the image crawling technology and image batch collection tools, the car colour rendering inspiration dataset consisting of hand-drawn colour pictures of the car side was constructed. After that, the car side hand-drawn images were processed through the image styling processing technology and deep learning pre-trained models to construct the car color rendering design object dataset. Next, the car color rendering generation experiment was conducted through MUNIT framework. Then, the generated images were evaluated in quantitative and qualitative methods to select the best iterative model. Finally, with the experimental data integrated, the intelligent generative design system Analogist for car color rendering was designed. The results of the research showed that the method we proposed can realize the multimodal generation of color rendering schemes of line drawings of car side through an image-to-image inspired approach. It can be concluded that Analogist can assist designers in stimulating design inspiration and improving design efficiency in colour rendering of car styling.
Custom Coarse Grained Named Entity Recognition for Filipino Storytelling Data Using Uncased Transformer Models

This paper presents a custom Named Entity Recognition (NER) model that is specified in the domain of Filipino storytelling. It utilizes the base architecture of the Bidirectional Encoder Representations from Transformers (BERT) model and then modified the Embedding Layer to include the Part-of-Speech (POS) tag and Character-level Embedding as an additional input layer. The dataset used is from Filipino short story books which consist of 4,523 sentences, 7,118 tokens, and 868 unique tokens. The NER tagset was relative to (Yang, 2020) published technical paper. Several data preprocessing techniques were used such as data cleaning, feature extraction for POS tagging, stemmer, and data annotation. The dataset was trained across four (4) different versions of transformer models namely; BERT, BERT-WWM, ELECTRA, and ROBERTA. The highest-performing model was ROBERTA, resulting in an overall f1 score of 95.5%.
Deep Convolutional Dictionary Learning for Multi-modal Image Denoising

With the development of neural networks, some learning-based image denoising methods have achieved powerful performance. However, they usually use single-modal information. So, their behaviors will be further improved if more believable (guided) modals can be introduced. In this work, we propose a novel architecture (MDCDicL) of deep neural network for multi-modal image denoising. Based on K-SVD, we give the constrained optimization learning model of MDCDicL. Then, with Half Quadratic Splitting, the model is unfolded into a deep convolution neural network. As expected, with the help of the guided modal, MDCDicL exhibits powerful performance. Its effectiveness is preliminarily verified on a standard flash/non-flash dataset.
Research on feeding path planning algorithm of bending robot based on improved artificial potential field method

In order to solve the problems of low efficiency and high collision probabilities of traditional bending, the collision between the sheet metal parts and the bending machine cannot be ignored upon establishing a coordinate system. Considering the factors such as pose calculation when moving parts, an improved Artificial Potential Field method based on discrete geometric calculation of main and auxiliary points is proposed for the shape characteristics of sheet metal parts. The potential field path controls the motion path of the sheet metal part and finds a collision-free bending robot working path. Finally, the improved method is simulated, and the results show that the algorithm can successfully guarantee no collision between the sheet metal part and the bending mechanism, and ensure that the robotic arm runs smoothly to the target position.
Recommendation Algorithm and Platform's Duty of Care----Iqiyi V. Bytedance Copyright Case in China

This article examines a platform’s liability and duty of care in the condition of using a recommendation algorithm to push user-generated contents which are copyright infringements themselves. Three questions are discussed: First, is the recommendation algorithm neutral or pseudo-neutral? Second, is the safe harbor still suitable for the platform to exempt liability? Third, if the platform is liable for the recommendation algorithm results, what elements constitute “Should Know?” All these three questions are based on the first Chinese copyright case caused by a recommendation algorithm between two large online platforms.
The Impact of Academic Information System to Students’ Satisfaction in Higher Education

Academic information systems reflect the quality of management of a college and are one of the efforts of educational institutions to provide quality services to provide convenience and comfort to students. There are obstacles such as the difficulty of controlling the scheduling of courses and information that is less updated on academic information systems. This study aims to determine how much influence the academic information system has on student satisfaction. This study uses a type of research based on a quantitative approach. The research subjects were bachelor and graduate school students from Universitas Negeri Yogyakarta. The data collection method used a questionnaire. Data processing techniques and data analysis using simple linear regression formulas. The results showed that (i) there was a significant influence between the implementation of academic information systems on the level of satisfaction of Universitas Negeri Yogyakarta bachelor and graduate school students amount 41% (ii) supporting and inhibiting factors perceived by students in the implementation of academic information systems found in speed, timeliness, and ease of access. This article has empirical contributions related to academic information systems in higher education and can be used as insights for policy holders in universities in making policies.
Social-Aware Federated Learning: Challenges and Opportunities in Collaborative Data Training

Federated learning (FL) is a promising privacy-preserving solution to build powerful AI models. In many FL scenarios, such as healthcare or smart city monitoring, the user’s devices may lack the required capabilities to collect suitable data, which limits their contributions to the global model. We contribute social-aware federated learning as a solution to boost the contributions of individuals by allowing outsourcing tasks to social connections. We identify key challenges and opportunities, and establish a research roadmap for the path forward. Through a user study with N = 30 participants, we study collaborative incentives for FL showing that social-aware collaborations can significantly boost the number of contributions to a global model provided that the right incentive structures are in place.