Reviewerly/exHarmony huggingface dataset:
Deep Learning Detection and Recognition of Spot Elevations on Historical Topographic Maps

Some information contained in historical topographic maps has yet to be captured digitally, which limits the ability to automatically query such data. For example, U.S. Geological Survey’s historical topographic map collection (HTMC) displays millions of spot elevations at locations that were carefully chosen to best represent the terrain at the time. Although research has attempted to reproduce these data points, it has proven inadequate to automatically detect and recognize spot elevations in the HTMC. We propose a deep learning workflow pretrained using large benchmark text datasets. To these datasets we add manually crafted training image/label pairs, and test how many are required to improve prediction accuracy. We find that the initial model, pretrained solely with benchmark data, fails to predict any HTMC spot elevations correctly, whereas the addition of just 50 custom image/label pairs increases the predictive ability by ∼50%, and the inclusion of 350 data pairs increased performance by ∼80%. Data augmentation in the form of rotation, scaling, and translation (offset) expanded the size and diversity of the training dataset and vastly improved recognition accuracy up to ∼95%. Visualization methods, such as heat map generation and salient feature detection, can be used to better understand why some predictions fail.
Multi-Class Sentiment Classification for Healthcare Tweets Using Supervised Learning Techniques

Social media has revolutionized the way people disclose their personal health concerns and express opinions on public health issues. In this paper a new approach for multi-class sentiment classification using supervised learning techniques. The aim of this multi-class sentiment classification is to assign the healthcare Tweets automatically into predetermined categories on the basis of their linguistic characteristics, their contents, and some of the words that characterize each category from the others. Briefly, relevant health datasets are collected from Twitter using Twitter API; then, use of the methodology is illustrated and evaluated against one with only three different algorithms was used, to improve the accuracy of Decision Trees, SMO, and K-NN classifiers. Many experiments accomplished to prove the validity and efficiency of the approach using datasets tweets and it accomplished the data reduction process to achieve considerable size reduction with the preservation of significant dataset's attributes
Half-Day Tutorial on Combating Online Hate Speech: The Role of Content, Networks, Psychology, User Behavior, etc.

While the rise in popularity of social media is seen as a hugely positive development, it is also accompanied by a proliferation of hate speech, which has recently become a major concern. On the one hand, hateful content creates an unsafe environment for certain members of society. On the other hand, manual moderation causes distress to content moderators, and the volume of harmful content is far beyond what human moderators can manually flag and react to. Thus, researchers in machine learning, social computing, and other areas have worked on developing tools to help automate the process. While initially studied as a text classification problem, over time, researchers realized that hate speech is multi-faceted and requires analysis of the role of linguistic expressions, context, and network structure, while using inspiration from psychology and user behavior, among others. With this in mind, we provide a holistic view of what the research community has explored so far, and what we believe are promising future research directions.
A GRASP algorithm with Tabu Search improvement for solving the maximum intersection of k-subsets problem

Abstract The selection of individuals with similar characteristics from a given population have always been a matter of interest in several scientific areas: data privacy, genetics, art, among others. This work is focused on the maximum intersection of k -subsets problem ( k MIS). This problem tries to find a subset of k individuals with the maximum number of features in common from a given population and a set of relevant features. The research presents a Greedy Randomized Adaptive Search Procedure (GRASP) where the local improvement is replaced by a complete Tabu Search metaheuristic with the aim of further improving the quality of the obtained solutions. Additionally, a novel representation of the solution is considered to reduce the computational effort. The experimental comparison carefully analyzes the contribution of each part of the algorithm to the final results as well as performs a thorough comparison with the state-of-the-art method. Results, supported by non-parametric statistical tests, confirms the superiority of the proposal.
Concept-to-implementation of New Threshold-based Fall Detection Sensor

Fall-detection is a growing industry, and one which is increasingly subject to attention from medical professionals and academics. Fall-detection is commonly applicable to aged-care practices, where it is used to detect if a human subject has fallen over as a result of a trip, stumble or misstep. This study has researched, designed, and delivered an accurate and functional fall-detection application for elderly persons. Medical-alert products have been on the market since the 1990s, but most of these systems require user-interaction before respondents are dispatched. The proposed application uses accelerometers to measure the movements of its subject, and runs a unique, threshold-based algorithm to identify the pattern of an actual fall. The algorithm distinguishes falls from daily activities such as sitting, standing, lying down and walking short distances. The main attributes considered to differentiate these activities from actual falls are: peak acceleration, instantaneous jolt, and post-fall movement. The development of this program involved significant experimentation to establish statistical parameters related to fall data. The final application has a sensitivity of 100&#x0025; and a selectivity of 97.5&#x0025;, which offers a significant degree of protection against fall-related injuries. This paper gives insight into pertinent literature, presents the implementation details, and discloses experimental results which signify the validity.
Authoring and Reviewing Bibliographies: Design and Development of a Visual Analytics Online Platform

Researchers are daily engaged in bibliographic tasks concerning literature search and review, both in the role of authors of scientific papers and when they are reviewers or evaluators. Current indexing platforms offer powerful tools for searching bibliographic references, but in general they poorly support the integration, visualization and comparative analysis of metadata coming from subsequent searches. To address these issues, we designed and realized VisualBib <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\mathrm{ (va)}}$ </tex-math></inline-formula> , an online analytics solution, where a visual environment includes analysis control, bibliography exploration, automatic metadata extraction and metrics visualization for real-time scenarios. Starting from an analysis of tasks, associating them to different user <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">roles</i> , this study enabled us to define a taxonomy of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tasks</i> , strictly linked to a set of system <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">requirements</i> for our platform. We introduce and discuss here some relevant functions through two usage scenarios related to the creation and the review of a bibliography. The maturity of the system implementation has allowed us to propose a set of evaluation activities, both carried out by two groups of users with different levels of experience in managing bibliographies, and to collect detailed results based on specific tasks and on general aspects of our framework. VisualBib <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\mathrm{ (va)}}$ </tex-math></inline-formula> proved an effective and efficient system for the authoring and reviewing processes of a bibliography.
Improved EDVR Model for Robust and Efficient Video Super-Resolution

Computer vision technologies are increasingly commonly used in daily life, and video super-resolution is gradually drawing more attention in the computer vision community. In this work, we propose an improved EDVR model to tackle the robustness and efficiency problems of the original EDVR model in video super-resolution. First, to handle the blurring situations and emphasize the effective features, we devise a preprocessing module consisting of rigid convolution sub-modules and feature enhancement sub-modules, which are flexible and effective. Second, we devise a temporal 3D convolutional fusion module, which can extract information in image frames more accurately and rapidly. Third, to better utilize the information in feature maps, we design a new reconstruction block by introducing a new channel attention approach. Moreover, we use multiple programmatic methods to accelerate the model training and inference process, making the model useful for practical applications.
The Construction of Online Course Learning Model of Piano Education from the Perspective of Deep Learning

This exploration aims at solving multiple teaching problems in piano online education course. On the premise of collaborative filtering, the K-means clustering algorithm is employed to apply the time data to the neural collaborative filtering algorithm, and the Improved Neu Matrix Factorization (Improved Neu MF) algorithm model is implemented. After the experiment, the relevant evaluation indexes are selected and the simulation test is operated on the relevant dataset. The test results show that root mean square error (RMSE) reaches 1.251 and mean absolute error (MAE) is 0.625. Indexes are adopted to evaluate the order of the model. The results suggest that the designed algorithm is better than the comparison algorithm, proving that the optimized model has better performance and can be used to construct an online course model. Based on deep learning, using the designed algorithm to build the online learning model of piano education can provide better, dynamic, and personalized online course recommendations for piano education. In this way, it can improve students' learning efficiency, promote the online learning development of piano education, and have vital practical significance for disseminating art and culture.
Experimentally Proven Bilateral Blur on SRCNN for Optimal Convergence

Image super-resolution is a contentious issue in developing computer vision applications such as satellite imaging, graphics industry, medical diagnostics, and real-time scene monitoring for security and safety. CNN-based image super-resolution algorithms are the simplest and most resource-efficient in deep learning. The original SRCNN framework, on the other hand, incorporated Gaussian blur, which takes a long time to converge. This is not only computationally expensive, but it also extends the training time. In light of this, we retrained original SRCNN utilizing widely available blurring techniques and observed that the bilateral filter beats the Gaussian filter at optimal convergence. Our goal is to preserve the original SRCNN features while reducing training time and computational resources and hence, speed up the architecture with optimum number of epochs. This is, to the best of our knowledge, the first study of its kind, and no other study has implemented this alternative blurring training on SRCNN and ranked the best of them for image super-resolution.
The System Design and Robotic Manipulation Skills of Dualarm Autonomous Mobile Robot for Bomb Removing

Abstract Bomb removing is a very challenging but valuable task in urban anti-terrorist or military applications. With the rapid development of technologies of intelligent robots in these years, the intelligence and security level of bomb removing process can be largely improved by using the autonomous mobile robotic systems. In this paper, a dual-arm autonomous mobile robot for bomb removing is proposed which is designed by Tsinghua University. The system design is introduced which mainly consists of the structure and multimodal perception module of the designed system. Besides, manipulation performance is one of the most important functional attributes in bomb removing. Therefore, the manipulation skills for the bomb removing applications are analyzed which adopt the idea of primitives, and skill decomposition and synthesis. Moreover, a knowledge graph for bomb removing is established in the paper and a knowledge based method for the manipulation skills learning is presented. Finally, simulations and experiments have been carried out in the platform to validate the proposed method in the paper and designed bomb removing mobile robot.
Advanced Design and Fabrication of Islamic Tile Ceramic Wall Tiles With Indonesian Batik Patterns Using Artistic CAD/CAM and 3D Printing Technology

The ceramic tile wall is a decorative element used inside restaurants, museums, hotels, aristocratic residences, and places of worship. It plays a critical role in the development of a structure with strong personality and unique qualities by providing relief patterns specific to history or era. The demand for distinctive ornamental wall ceramics that can be mass produced with precision and accuracy has been a challenge for numerous ceramic enterprises in Indonesia, which have historically relied on the skills of artistic professionals in a handmaking process. The purpose of this research was therefore to develop and apply a ceramic tile design process that integrates computer design and fabrication methods with traditional ceramic production techniques. Using the ArtCAM 2015R2 software, photos of batik and Islamic themes were effectively converted into a 2.5D computer-aided design model. Additive manufacturing technology was then used in conjunction with Objet30 Pro 3D printer to precisely produce master molds with Islamic nuances such as puzzle and floral patterns, then conventional ceramic fabrication technology was employed to produce ceramic tiles using these molds. By combining computer-aided design and rapid prototyping technologies, a variety of exact and detailed Islamic decorative ceramic wall tiles were produced with dimensional accuracies within 2.00 mm. Furthermore, the proposed approach demonstrated a 90% reduction in design-to-production time from to 10–12 months to roughly 1–1.5 months.
Text-Based Gender Classification of Twitter Data using Naive Bayes and SVM Algorithm

This paper presents the development of the gender classification system on Twitter tweets. Three feature extraction techniques are explored: Bag of words and 2 variations of meta-attributes extraction. Feature sets are fed to Multinomial Naive Bayes and Support Vector Machine, and results were compared to see which algorithm can produce the best results in the classification task. Experiments show that the SVM outperformed the Naïve Bayes algorithm, obtaining a performance of 56.31%.
ABID: Attention-Based Bengali Image Description

Image caption or description generation is a fundamental problem of artificial intelligence. It requires both knowledge, natural language processing, and computer vision together. It automatically produces description(s) from an image. The Bengali language is the fifth language spoken widely in the world. It is considered in many areas of research and development. Various established datasets are available in the literature for image caption generation in English since no such standard dataset exists for Bengali. This paper proposes a model for automatic image captioning in the Bengali language. This study uses a Bengali dataset to train the encoder–decoder neural network model. The proposed model trained with two descriptions per image and achieved a 0.67 BLEU-1 score. We expect that a far better result is obtained with a larger and more diverse dataset.
Bronze inscription recognition with distribution calibration based on few-shot learning

Bronze inscription is one of the earliest well-established writing systems dating back to Shang dynasty in China. The Recognition of Bronze character recognition plays an important role in the identification and interpretation of Bronze inscription which traditionally is a tough and challenging task. To deal with class imbalance of training data in bronze inscription recognition, we propose a method based on few-shot learning. The recognition process consists of three stages. In the first stage, a model is pretrained in a large-scale character dataset with a novel negative margin loss. In the second stage, the pretrained weights of the backbone network is transferred to the target dataset. In the final stage, the distribution of few-shot classes is calibrated and a new classifier is re-trained accordingly. Through qualitative and quantitative experimental analyses, the proposed method exceeds the state-of-the-art on our Bronze Character dataset.
T-copula and Wasserstein distance-based stochastic neighbor embedding

The aim of dimensionality reduction is to obtain the faithful low-dimensional representations of high-dimensional data by preserving the data quality. It is beneficial to better visualize the high-dimensional data and improve the classification or clustering performance. Many dimensionality reduction methods based on the framework of stochastic neighbor embedding have been developed. However, most of them use the Euclidean distance to describe the dissimilarity of data points in high-dimensional space, which is not suitable for high-dimensional data with non-linear manifold structure. In addition, they usually use the family of normal distributions as their embedding distributions in low-dimensional space. This will incur that they are only suitable to deal with the spherical data. In order to deal with these issues, we present a novel dimensionality reduction method by integrating the Wasserstein distance and t-copula function into the stochastic neighbor embedding model. We first employ the Gaussian distribution equipped with the Wasserstein distance to describe the pairwise similarity in the high-dimensional space. Then, the t-copula function is used to generate a general heavy-tailed distribution for the description of low-dimensional pairwise similarity, which can process different shapes of data and avoid the crowding problem. Furthermore, Kullback–Leibler divergence is employed to measure the difference between the high-dimensional and low-dimensional similarities. Finally, a gradient descent algorithm with adaptive moment estimation is developed to solve the proposed objective function. Extensive experiments are conducted on eight real-world datasets to demonstrate the effectiveness of the proposed method in terms of the dimensional reduction quality, classification and clustering evaluation metrics.
A Holistic Marine Video Dataset

With the rapid development of deep learning, many algorithms based on deep learning have been able to effectively help people solve many practical problems. In the field of marine research, it is very necessary to solve practical problems with the help of deep learning. In order to truly simulate the complex marine environment, we integrate a large amount of data including marine scenes, marine organisms and their actions to produce an marine video dataset named Holistic Marine Video Dataset (HMV) with abundant semantic aspects for marine scientific research. What’s more, we also provide baseline experiments of three tasks (marine organisms detection, marine scenes recognition and marine organism actions recognition) on HMV for reference.
Blockchain Interoperability: Connecting Supply Chains Towards Mass Adoption

The rapid development of blockchain technology has gained traction among institutions due to the potential benefits it may have in supply chain management. Many countries and institutions are participating in pilot projects and live projects in hopes that it may one day enhance global supply chains with regards to security, speed, disintermediation, traceability, and cost reduction. As the technology continues to develop, more projects have developed their own blockchain networks with their own network design, governance structure, consensus mechanisms, and protocols, in attempting to address specific issues that are prevalent in existing supply chains. However, the development process is mostly vertical as they work to improve their own blockchain network capabilities. These blockchain silos are unable to communicate with other blockchain networks, which presents a significant problem in the effort towards supply chain mass adoption of blockchain technology. The capability to communicate between blockchain networks is known as blockchain interoperability. Current supply chains have been working towards supply chain integration and collaboration. Supply chain adoption will be highly unlikely if the blockchain interoperability issue is not resolved. This paper will discuss the importance of blockchain interoperability within global supply chains, while identifying the possible use cases of the technology if interoperability solutions are not yet available.
The Subfield Codes of Some [<i>q</i> + 1, 2, <i>q</i>] MDS Codes

Recently, subfield codes of geometric codes over large finite fields <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {GF}}(q)$ </tex-math></inline-formula> with dimension 3 and 4 were studied and distance-optimal subfield codes over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {GF}}(p)$ </tex-math></inline-formula> were obtained, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q=p^{m}$ </tex-math></inline-formula> . The key idea for obtaining very good subfield codes over small fields is to choose very good linear codes over an extension field with small dimension. This paper first presents a general construction of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[q+1, 2, q]$ </tex-math></inline-formula> MDS codes over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {GF}}(q)$ </tex-math></inline-formula> , and then studies the subfield codes over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {GF}}(p)$ </tex-math></inline-formula> of some of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[q+1, 2,q]$ </tex-math></inline-formula> MDS codes over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {GF}}(q)$ </tex-math></inline-formula> . Two families of dimension-optimal codes over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {GF}}(p)$ </tex-math></inline-formula> are obtained, and several families of nearly optimal codes over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {GF}}(p)$ </tex-math></inline-formula> are produced. Several open problems are also proposed in this paper.