Reviewerly/exHarmony huggingface dataset:
Implementation of Morphological Gradient Algorithm for Edge Detection

Vardhan Rao, Mirupala Aarthi Mukherjee, Debasish Savitha, S.This paper shows the implementation of a morphological gradient in MATLAB and colab platforms to analyze the time consumed on different sizes of grayscale images and structuring elements. A morphological gradient is an edge detecting technique that can be derived from the difference of two morphological operations called dilation and erosion. In order to apply the morphological operations to an image, padding is carried out which involves inserting 0 for dilation operation and 225 for erosion. Padding for the number of rows or columns is based on the size of the structuring element. Further, dilation and erosion are implemented on the image to obtain morphological gradient. Since central processing unit (CPU) implementation follows sequential computing, with the increase in the image size, the time consumption also increases significantly. To analyze the time consumption and to verify the performance across various platforms, the morphological gradient algorithm is implemented in MATLAB and colab. The results demonstrate that colab implementation is ten times faster when constant structuring element with varying image size is used and five times faster when constant image size with varying structuring element size is used than the MATLAB implementation.
Model-Based Reinforcement Learning

The previous chapters discussed model-free methods, and we saw their success in video games and simulated robotics. In model-free methods, the agent updates a policy directly from the feedback that the environment provides on its actions. The environment performs the state transitions and calculates the reward. A disadvantage of deep model-free methods is that they can be slow to train; for stable convergence or low variance, often millions of environment samples are needed before the policy function converges to a high-quality optimum.
Interactive Die as an Educational Tool for Children with Special Educational Needs

The die as we know is a small, throwable object with marked sides that can rest in multiple positions. Although there’s variations, the most common form of die is a cube with dots marked on each side with the aim of generating random numbers in tabletop games. This notion can be extended to the pedagogic field making use of recent technologies in connected embedded systems. This article presents the design and proof of concept of an interactive die as an educational tool for children with SEN. The die consists of a RGB LED matrix for each of its six sides so it can display any kind of low-resolution symbols like numbers, letters, arrows or multicolor patterns presented in the form of animations, additionally includes an IMU to obtain the cube orientation and user tap detection when selecting the presented option on top side, audible and haptic feedback
Sentiment Analysis of Political Post Classification Based on XGBoost

The number of Websites and the volume of posts published on the Internet have increased dramatically in recent years. The ability to automatically assess the political polarity of a post (text) can be useful in a variety of fields including security and academics. The sentiment classification of postings, on the other hand, appears to be more complicated compared to classifying the sentiment of traditional texts. The classification procedure adopted in this research uses XGBoost algorithm and bag of word as feature extraction. To test the accuracy of the approach, the study used the confusion matrix. The proposed approach achieved 95.161 accuracy percentage.
Enhancements on a Pipeline Approach for Abstract Meaning Representation Parsing

Abstract Meaning Representation (AMR) parsing attempts to extract a structured representation of a sentence’s meaning. This paper enhances an existing processing pipeline for AMR parsing, inspired by state-of-the-art solutions in dependency parsing. It enhances the existing Concept Identification module by using Pointer-Generator Networks. A further considerable improvement of this module is brought by the use of embeddings. An alternative approach is provided through Transformers, an architecture which needs large data-sets to accurately predict concepts. For predicting the relations between concepts, the proposed pipeline combines the two Heads Selection and now trainable Arcs Labelling tasks into a joint Relation Identification module, which enhances the overall performance of edge prediction. The improvements made to this AMR parser have resulted in a completely trainable model that can be improved further with end-to-end training.
Applying Latent Dirichlet Allocation Technique to Classify Topics on Sustainability Using Arabic Text

In this paper, we build up on the existing literature pertaining topic modelling and sustainability by exploring Arabic text, mapping the Sustainability Development Goals (SDGs) presented by the United Nation to the tweets published in Arabic. The work utilized the popular Latent Dirichlet Allocation (LDA) technique, to summarize and present subtopics that matter to various sustainability areas, with a focus on 3 of the 17 Sustainability Development Goals. Term Weighting Scheme using TF-IDF and a document term matrix extracted to highlight the most influential keywords that formed the topics. The work presented a unique set of topics and terms that correlate with the certain areas of sustainability. Further exploration of Arabic sources, will inform people concerned with sustainability on the various issues related to sustainable development in the Arab World. The work presented in this paper is a step towards formalizing a framework that will capture and analyze various aspects of unstructured data revolving around sustainability.
Chronology of scientific publication

The purpose and ways of presentation of key dates characterizing individual stages of the life cycle of scientific publication are considered. For the author who plans to publish his work in a particular journal, the most significant dates are the dates of the preliminary stages: reviewing, proofreading, layout, etc. The key date, usually reflected in a bibliographic reference to a published work, is the year of its publication. With the advent of the Internet, alive publications have become widespread. The authors of alive publications constantly keep their work up to date, eliminating the noted inaccuracies and, most importantly, reflecting the latest achievements in the field under consideration. For the reader, the main interest here is the date of posting online of the latest, recent version of the publication.
Joint Optimization for DNN Model Compression and Corruption Robustness

Abstract Modern deep neural networks (DNNs) are achieving state-of-the-art results due to their capability to learn a faithful representation of the data they are trained on. In this chapter, we address two insufficiencies of DNNs, namely, the lack of robustness to corruptions in the data, and the lack of real-time deployment capabilities, that need to be addressed to enable their safe and efficient deployment in real-time environments. We introduce hybrid corruption-robustness focused compression (HCRC), an approach that jointly optimizes a neural network for achieving network compression along with improvement in corruption robustness, such as noise and blurring artifacts that are commonly observed. For this study, we primarily consider the task of semantic segmentation for automated driving and focus on the interactions between robustness and compression of the network. HCRC improves the robustness of the network by 8.39% absolute mean performance under corruption (mPC) on the Cityscapes dataset, and by 2.93% absolute mPC on the Sim KI-A dataset, while generalizing even to augmentations not seen by the network in the training process. This is achieved with only minor degradations on undisturbed data. Our approach is evaluated over two strong compression ratios (30% and 50%) and consistently outperforms all considered baseline approaches. Additionally, we perform extensive ablation studies to further leverage and extend existing state-of-the-art methods.
Bearing Fault Detection based on Internet of Things using Convolutional Neural Network

In the age of the industrial revolution, industry and machinery are elements of the utmost importance to the development of human civilization.As industries are dependent on their machines, regular maintenance of these machines is required.However, if the machine is too big for humans to look after, we need a system that will observe these giants.This paper proposes a convolutional neural network-based system that detects faults in industrial machines by diagnosing motor sounds using accelerometers sensors.The sensors collect data from the machines and augment the data into 261756 samples to train (70%) and test (30%) the models for better accuracy.The sensor data are sent to the server through the wireless sensor network and decomposed using discrete wavelet transformation (DWT).This big data is processed to detect faults.The study shows that custom CNN architectures surpass the performance of the transfer learning-based MobileNetV2 fault diagnosis model.The system could successfully detect faults with up to 99.64% accuracy and 99.83% precision with the MobileNetV2 pretrained on the ImageNet Dataset.However, the Convolutional 1D and 2D architectures perform excellently with 100% accuracy and 100 % precision.
Pattern Matching Method for Q&amp;A Information Retrieval System

Current information retrieval systems are more concerned with helping users look up the information they want, but retrieving it accurately is a difficult task. In this paper, we proposed a Q&A Information Retrieval system for computer textbooks based on the Pattern Matching method, called the PM-IR system, which achieves accurate retrieval of textbook information and intelligent Q&A. Firstly, the Term Frequency-Inverse Document Frequency-Inverse Head Frequency algorithm (TF-IDF-IHF) is proposed to assign new weights to the headings and first sentences of paragraphs of textbooks. It is combined with the cosine similarity algorithm to achieve text association. Secondly, pattern matching is used to effectively integrate the potential semantic relationships. The PM-IR system can accurately answer three types of questions, basic questions, statistical questions, and inference questions. The experimental results showed that the F-measure of the retrieval and Q&A modules of the PM-IR system was 85.4% and 86.2%, respectively.
Technology and Local State Capacity: Evidence from Ghana

This paper studies the role of technology in local-government tax collection capacity in the developing world. We first conduct a new census of all local governments in Ghana to document a strong association between technology use and property tax billing, collection and enforcement. We then randomize the use of a new revenue collection technology within one large municipal government. Revenue collectors using the new technology delivered 27 percent more bills and collected 103 percent more tax revenues than control collectors. Collectors using the new technology learned faster about which households in their assigned areas were willing and able to make payments. We reconcile these experimental findings in a simple Beckerian time-use model in which technology allows revenue collectors to better allocate their time towards households that are the most likely to comply with taxpaying duties. The model's predictions are consistent with experimental evidence showing that treatment collectors are more likely to target households with greater liquidity, income, awareness of taxpaying duties, and satisfaction with local public goods provision.
Assessment of Document Similarity Visualisation Methods

The article deals with the problem of assessing a visualization of the similarity of documents. A well-known approach for showing the similarity of text documents is a scatter plot generated by projecting text documents into a multidimensional feature space and then reducing the dimensionality to two. The problem stems from the fact that there is a large set of possible document vectorization methods, dimensionality reduction methods and their hyperparameters. Therefore, one can generate many possible charts. To enable a qualitative comparison of different scatter plots, the authors propose a set of metrics that assume that the documents are labeled. Proposed measures quantify how the similarity/dissimilarity of original text documents (described by labels) is maintained within a low-dimensional space. The authors verify the proposed metrics on three corpora, seven different vectorization methods, and three reduction algorithms (PCA, t-SNE, UMAP) with many values of their hyperparameters. The results suggest that t-SNE and fastText trained on the KGR10 dataset is the best solution for visualizing the semantic similarity of text documents in Polish.
Improving Neural Political Statement Classification with Class Hierarchical Information

Many tasks in text-based computational social science (CSS) involve the classification of political statements into categories based on a domain-specific codebook.In order to be useful for CSS analysis, these categories must be fine-grained.The typically skewed distribution of fine-grained categories, however, results in a challenging classification problem on the NLP side.This paper proposes to make use of the hierarchical relations among categories typically present in such codebooks: e.g., markets and taxation are both subcategories of economy, while borders is a subcategory of security.We use these ontological relations as prior knowledge to establish additional constraints on the learned model, thus improving performance overall and in particular for infrequent categories.We evaluate several lightweight variants of this intuition by extending state-of-the-art transformer-based text classifiers on two datasets and multiple languages.We find the most consistent improvement for an approach based on regularization.
BanglaBioMed: A Biomedical Named-Entity Annotated Corpus for Bangla (Bengali)

Recognizing biomedical entities in the text has significance in biomedical and health science research, as it benefits myriad downstream tasks, including entity linking, relation extraction, or entity resolution.While English and a few other widely used languages enjoy ample resources for automatic biomedical entity recognition, it is not the case for Bangla, a low-resource language.On that account, in this paper, we introduce BanglaBioMed, a Bangla biomedical named entity (NE) annotated dataset in standard IOB format, the first of its kind, consisting of over 12000 tokens annotated with the biomedical entities.The corpus is created by collecting Bangla text from a list of health articles and then annotated with four distinct types of entities: Anatomy (AN), Chemical and Drugs (CD), Disease and Symptom (DS), and Medical Procedure (MP).We provide the details of the entire data collection and annotation procedure and illustrate various statistics of the created corpus.Our developed corpus is a much-needed addition to the Bangla NLP resource that will facilitate biomedical NLP research in Bangla.
Compiling Linear Algebra Expressions into Efficient Code

In textbooks, linear algebra expressions often use indices to specify the elements of variables. This index form expressions cannot be directly translated into efficient code, since optimized linear algebra libraries and frameworks require expressions in index-free form. To address this problem, we developed Lina, a tool that automatically converts linear algebra expressions with indices into index-free linear algebra expressions that we map efficiently to NumPy and Eigen code.
Noise Simulation-Based Deep Optical Watermarking

Digital watermarking is an important branch of information hiding, which effectively guarantees the robustness of embedded watermarks in distorted channels. To embed the watermark into the host carrier, traditional watermarking schemes often require the modification of the carrier. However, in some cases, the modification of the carrier is not allowed such as paintings in museums. To address such limitation, we utilize optical watermarking to embed the watermark into the host carrier. Optical watermarking refers to a technique that encodes the watermark into the visible light irradiating the object, where the watermark can be further extracted by the camera photography process. To realize transparency and robustness of the watermark, we propose a color-decomposition-based watermarking pattern generation algorithm which satisfies human visual system (HVS) characteristics, a camera shooting simulation algorithm which accurately produces the dataset for training, and a decoding network which can realize loss-less decoding of the embedded watermark. Various experiments demonstrate the superiority of our method and reveal the broad applicability of the proposed technique.
Multi-Scale Distribution Deep Variational Autoencoder for Explanation Generation

Generating explanations for recommender systems is essential for improving their transparency, as users often wish to understand the reason for receiving a specified recommendation.Previous methods mainly focus on improving the generation quality, but often produce generic explanations that fail to incorporate specific details of user and item.To resolve this problem, we present Multi-Scale Distribution Deep Variational Autoencoders (MVAE).A deep hierarchical VAE with a prior network that eliminates noise while retaining meaningful signals in the input, coupled with a recognition network serving as the source of information to guide the learning of the prior network.Further, the Multi-scale distribution Learning Framework (MLF) along with a Target Tracking Kullback-Leibler divergence (TKL) mechanism are proposed to employ multiple KL divergences at different scales for more effective learning.Extensive empirical experiments demonstrate that our methods can generate explanations with concrete inputspecific contents.
Algorithm for Automatic Brain-Shift Detection Using the Distance Between Feature Descriptors

In this study, we conducted basic research to accurately detect intraoperative brain shifts using preoperative and postoperative normalized (position, posture, and scale aligned) patient Digital Imaging and Communications in Medicine and aimed to develop a brain model that could be used online along with intraoperative sensor basting. For accurate brain-shift detections, we evaluated the agreement between the local brain shifts detected by several feature-point matching algorithms and their parameters and the artificially produced local brain shifts. The results indicated that the scale-invariant feature transform algorithm proved unsuitable for detecting brain shifts, while the accelerated-KAZE algorithm produced good results. Thus, this study identified a suitable feature-point detection algorithm and its parameters for the detection of brain shifts.
A Novel Pruning Method Based on Correlation Applied in Full-Connection Layer Neurons

DNN (Deep Neural Networks) has achieved great success in various fields, but its deployment on many small devices is limited due to its huge model structure and low computing speed. The neural network faces the problems of large models and slow calculations. Experiments have shown that reasonable pruning methods can be effectively solved. In terms of network pruning, pruning technology can be divided into structured pruning and unstructured pruning. Compared with the limited application of unstructured pruning, structured pruning can greatly compress the model and increase the calculation speed under any framework, and has stronger applicability. In current structured pruning, there is a problem that the accuracy of the model decreases too quickly after a large number of neurons are deleted. To address this problem, we propose a new pruning method based on neuron similarity, which sorts the weights of neurons, according to the principle that neural network parameters will change with training, the integration method is introduced to reconstruct the neuron assignment, and the more relevant neurons are deleted by comparing the current ranking and the cumulative ranking difference of the integration system. Based on the experiments of MLP model, compared with other pruning methods, this method shows its superiority, which can compress the model by 10 times and reduce the accuracy by less than 1%.
Real-Time Image Analysis with Neural Networks on Industrial Controllers for Individualized Production

Manufacturing systems for individualized production require workflows depending on individual objects. Machine learning (ML) offers the possibility to classify different objects by training a neural network. Depending on the output values of the network, decisions for the following production step can then be controlled. The question arises whether it is possible to execute the neural network in real time in coordination with the machine and motion control tasks. In this paper, this question is investigated using a programmable logic controller (PLC) runtime environment on a standard industrial PC. The execution times of different neural network implementation methods are measured and compared. The fastest neural network requires an average execution time of only 54 µs. The characteristics of the different methods with respect to training and implementing the neural networks in the controller are also discussed.
A Bibliometric Analysis on Road Traffic Safety, 2009 to 2022

Road traffic accidents are major social and health issues that have received increased attention in the last decade. The goal of this study is to examine the state of research in road traffic safety from 2009 to 2022 and to discuss future research trends. We used Scopus to find relevant road traffic safety research published between 2009 and 2022. This study presented key bibliometric indicators such as annual publication and citation trends, top authors, journals, institutions, highly cited articles, keyword co-occurrence, and so on. The analysis was carried out using R statistical software and the bibloshiny package. According to the analysis, the United States of America, Canada, Italy, and Australia have conducted the most studies on road traffic safety. These findings can guide the formulation of policies and strategies for reducing road traffic accidents.
Fine-Grained Sentiment Analysis of Multi-domain Online Reviews

We propose a fine-grained Sentiment Analysis application focusing in multiple domains and text types. The present approach concerns sentiment analysis implemented at a fine-grained level, beyond the typical polarity of “positive”, “negative” and “neutral” as evaluations of user input, introducing intermediate sentiments community. Fine-grained sentiment analysis is applied on movie reviews, Tripadvisor reviews and Coursera reviews. The implemented application is accessed by a web graphical user interface.