Reviewerly/exHarmony huggingface dataset:
A Classical Approach to Handcrafted Feature Extraction Techniques for Bangla Handwritten Digit Recognition

Bangla Handwritten Digit recognition is a significant step forward in the development of Bangla OCR.However, intricate shape, structural likeness and distinctive composition style of Bangla digits makes it relatively challenging to distinguish.Thus, in this paper, we benchmarked four rigorous classifiers to recognize Bangla Handwritten Digit: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Gradient-Boosted Decision Trees (GBDT) based on three handcrafted feature extraction techniques: Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP), and Gabor filter on four publicly available Bangla handwriting digits datasets: NumtaDB, CMARTdb, Ekush and BDRW.Here, handcrafted feature extraction methods are used to extract features from the dataset image, which are then utilized to train machine learning classifiers to identify Bangla handwritten digits.We further fine-tuned the hyperparameters of the classification algorithms in order to acquire the finest Bangla handwritten digits recognition performance from these algorithms, and among all the models we employed, the HOG features combined with SVM model (HOG+SVM) attained the best performance metrics across all datasets.The recognition accuracy of the HOG+SVM method on the NumtaDB, CMARTdb, Ekush and BDRW datasets reached 93.32%, 98.08%, 95.68% and 89.68%, respectively as well as we compared the model performance with recent state-of-art methods.
Experiences and attitudes of Setswana speaking teachers in using an indigenous African language on an online assessment platform

Education in the 21st century must have a vision that will support and empower teachers to face the demands of the digital age. The use of information communication technology (ICT) in education can serve this end but ensuring access to digital resources will not address the digital disparity. Culture and language play an equally important role in exacerbating and maintaining the digital disparity as the traditional factor of access. In the study reported on here we investigated the experiences and attitudes of Setswana-speaking teachers in 3 primary schools in the North West province, South Africa, as they were exposed to online software in Setswana, an indigenous African language. Purposive sampling was used to select 7 teachers for the study. Two research questions were answered using systematic self-observation (SSO) instruments, the participant observation (PO) instrument and the in-depth interview (IDI) instrument to determine the experiences and attitudes of the teachers. Thematic analysis was used to analyse the interview data. It was found that accuracy of translation was key for adopting and using software in an African language. The teachers felt that English was the language of ICT and that African languages were not intellectual languages and did not have the capacity to be used in ICT. It is, therefore, recommended that more must be done to translate software into African languages and an effort must be made to raise the status of African languages in academic and technical fields.
Edge data based trailer inception probabilistic matrix factorization for context-aware movie recommendation

The rapid growth of edge data generated by mobile devices and applications deployed at the edge of the network has exacerbated the problem of information overload. As an effective way to alleviate information overload, recommender system can improve the quality of various services by adding application data generated by users on edge devices, such as visual and textual information, on the basis of sparse rating data. The visual information in the movie trailer is a significant part of the movie recommender system. However, due to the complexity of visual information extraction, data sparsity cannot be remarkably alleviated by merely using the rough visual features to improve the rating prediction accuracy. Fortunately, the convolutional neural network can be used to extract the visual features precisely. Therefore, the end-to-end neural image caption (NIC) model can be utilized to obtain the textual information describing the visual features of movie trailers. This paper proposes a trailer inception probabilistic matrix factorization model called Ti-PMF, which combines NIC, recurrent convolutional neural network, and probabilistic matrix factorization models as the rating prediction model. We implement the proposed Ti-PMF model with extensive experiments on three real-world datasets to validate its effectiveness. The experimental results illustrate that the proposed Ti-PMF outperforms the existing ones.
Human Segmentation and Tracking Survey on Masks for MADS Dataset

Human segmentation and tracking often use the outcome of person detection in the video. Thus, the results of segmentation and tracking depend heavily on human detection results in the video. With the advent of Convolutional Neural Networks (CNNs), there are excellent results in this field. Segmentation and tracking of the person in the video have significant applications in monitoring and estimating human pose in 2D images and 3D space. In this paper, we performed a survey of many studies, methods, datasets, and results for human segmentation and tracking in video. We also touch upon detecting persons as it affects the results of human segmentation and human tracking. The survey is performed in great detail up to source code paths. The MADS (Martial Arts, Dancing and Sports) dataset comprises fast and complex activities. It has been published for the task of estimating human posture. However, before determining the human pose, the person needs to be detected as a segment in the video. Moreover, in the paper, we publish a mask dataset to evaluate the segmentation and tracking of people in the video. In our MASK MADS dataset, we have prepared 28 k mask images. We also evaluated the MADS dataset for segmenting and tracking people in the video with many recently published CNNs methods.
Detection of energy levels of a spin system on a quantum computer by probe spin evolution

We propose a method for detection of energy levels of arbitrary spin system on a quantum computer based on studies of evolution of only one probe spin. On the basis of the proposed method energy levels of spin systems are found on IBM's quantum computer ibmq-bogota, among them are spin chain in magnetic field, triangle spin cluster, Ising model on squared lattice in magnetic field. The results of quantum calculations are in agreement with the theoretical ones. The method is efficient for estimation of the energy levels of many-spin systems and opens a possibility to achieve quantum supremacy in solving eigenvalue problem with development of multi-qubit quantum computers.
Quantum Gaussian filter for exploring ground-state properties

Filter methods realize a projection from a superposed quantum state onto a target state, which can be efficient if two states have sufficient overlap. Here we propose a quantum Gaussian filter (QGF) with the filter operator being a Gaussian function of the system Hamiltonian. A hybrid quantum-classical algorithm feasible on near-term quantum computers is developed, which implements the quantum Gaussian filter as a linear combination of Hamiltonian evolution at various times. Remarkably, the linear combination coefficients are determined classically and can be optimized in the postprocessing procedure. Compared to the existing filter algorithms whose coefficients are given in advance, our method is more flexible in practice under given quantum resources with the help of postprocessing on classical computers. We demonstrate the quantum Gaussian filter algorithm for the quantum Ising model with numeral simulations under noises. We also propose an alternative full quantum approach that implements a QGF with an ancillary continuous-variable mode.
New Binary Quantum Codes Constructed from Quasi-Cyclic Codes

It is well known that quantum codes can be constructed by means of classical symplectic dual-containing codes. This paper considers a family of two-generator quasi-cyclic codes and derives sufficient conditions for these codes to be symplectic dual-containing. Then, a new method for constructing binary quantum codes is proposed. As an application, we construct 8 binary quantum codes that exceed the best-known results. Further, another 36 new binary quantum codes are obtained by propagation rules, all of which improve the lower bound on the minimum distances.
High frequency accuracy and loss data of random neural networks trained on image datasets

Neural Networks (NNs) are increasingly used across scientific domains to extract knowledge from experimental or computational data. An NN is composed of natural or artificial neurons that serve as simple processing units and are interconnected into a model architecture; it acquires knowledge from the environment through a learning process and stores this knowledge in its connections. The learning process is conducted by training. During NN training, the learning process can be tracked by periodically validating the NN and calculating its fitness. The resulting sequence of fitness values (i.e., validation accuracy or validation loss) is called the NN learning curve. The development of tools for NN design requires knowledge of diverse NNs and their complete learning curves. Generally, only final fully-trained fitness values for highly accurate NNs are made available to the community, hampering efforts to develop tools for NN design and leaving unaddressed aspects such as explaining the generation of an NN and reproducing its learning process. Our dataset fills this gap by fully recording the structure, metadata, and complete learning curves for a wide variety of random NNs throughout their training. Our dataset captures the lifespan of 6000 NNs throughout generation, training, and validation stages. It consists of a suite of 6000 tables, each table representing the lifespan of one NN. We generate each NN with randomized parameter values and train it for 40 epochs on one of three diverse image datasets (i.e., CIFAR-100, FashionMNIST, SVHN). We calculate and record each NN’s fitness with high frequency—every half epoch—to capture the evolution of the training and validation process. As a result, for each NN, we record the generated parameter values describing the structure of that NN, the image dataset on which the NN trained, and all loss and accuracy values for the NN every half epoch. We put our dataset to the service of researchers studying NN performance and its evolution throughout training and validation. Statistical methods can be applied to our dataset to analyze the shape of learning curves in diverse NNs, and the relationship between an NN’s structure and its fitness. Additionally, the structural data and metadata that we record enable the reconstruction and reproducibility of the associated NN.
Winnowing Knowledge for Multi-choice Question Answering

We tackle multi-choice question answering.Acquiring related commonsense knowledge to the question and options facilitates the recognition of the correct answer.However, the current reasoning models suffer from the noises in the retrieved knowledge.In this paper, we propose a novel encoding method which is able to conduct interception and soft filtering.This contributes to the harvesting and absorption of representative information with less interference from noises.We experiment on Com-monsenseQA.Experimental results illustrate that our method yields substantial and consistent improvements compared to the strong Bert, RoBERTa and Albert-based baselines.
A New Rule-Based Approach for Classical Arabic in Natural Language Processing

Named entity recognition (NER) is fundamental in several natural language processing applications. It involves finding and categorizing text into predefined categories such as a person's name, location, and so on. One of the most famous approaches to identify named entity is the rule-based approach. This paper introduces a rule-based NER method that can be used to examine Classical Arabic documents. The proposed method relied on triggers words, patterns, gazetteers, rules, and blacklists generated by the linguistic information about entities named in Arabic. The method operates in three stages, operational stage, preprocessing stage, and processing the rule application stage. The proposed approach was evaluated, and the results indicate that this approach achieved a 90.2% rate of precision, an 89.3% level of recall, and an F-measure of 89.5%. This new approach was introduced to overcome the challenges related to coverage in rule-based NER systems, especially when dealing with Classical Arabic texts. It improved their performance and allowed for automated rule updates. The grammar rules, gazetteers, blacklist, patterns, and trigger words were all integrated into the rule-based system in this way.
Building A Large Collection of Multi-domain Electronic Theses and Dissertations

In this work, we report our progress on building a collection containing over 450k Electronic Theses and Dissertations (ETDs), including full-text and metadata. Our goal is to close the gap of accessibility between long text and short text documents, and to create a new research opportunity for the scholarly community. For that, we developed an ETD Ingestion Framework (EIF) that automatically harvests metadata and PDFs of ETDs from university libraries. We faced multiple challenges and learned many lessons during the process, that led to proposed solutions to overcome/mitigate the limitations of the current data. We also described the data that we have collected. We hope our methods will be useful for building similar collections from university libraries and that the data can be used for research and education.
An Open Integrated Rock Spectral Library (RockSL) for a Global Sharing and Matching Service

Minerals and rocks are important natural resources that are formed over a long period of geological history. Spectroscopy is the basis of the identification and characterisation of rocks and minerals via proximal sensing in the field or remote sensing systems with multi- and hyper-spectral capabilities. However, spectral data is scattered around different institutions worldwide and stored in various formats, resulting in poor data usability and an unnecessary waste of time and information. To improve the usability and performance of mineral spectral data, we developed an integrated open mineral spectral library (Rock Spectral Library, RockSL). Shared spectral data and related information were collected worldwide, and data cleaning measures were performed to retain the qualified spectra and merge all qualified data (raster, vector, and text formats) in a common framework to establish a reliable and comprehensive digital data set for an easy sharing and matching service. A software system was developed for the RockSL to manage, analyse, and apply the spectral data of minerals and rocks. We demonstrate how the information encoded in RockSL can determine the species of unknown rocks and describe specific mineral compositions. We also provide a reference scheme of the work chain and present key technologies for building different spectral libraries in diverse fields using RockSL. New contributions to RockSL are encouraged for this work to be improved to provide a better service and extend the applications of geo-sciences. This article introduces the characteristics of RockSL and demonstrates an experimental application.
Confluence of algebraic rewriting systems

Abstract Convergent rewriting systems on algebraic structures give methods to solve decision problems, to prove coherence results, and to compute homological invariants. These methods are based on higher-dimensional extensions of the critical branching lemma that proves local confluence from confluence of the critical branchings. The analysis of local confluence of rewriting systems on algebraic structures, such as groups or linear algebras, is complicated because of the underlying algebraic axioms. This article introduces the structure of algebraic polygraph modulo that formalizes the interaction between the rules of an algebraic rewriting system and the inherent algebraic axioms, and we show a critical branching lemma for algebraic polygraphs. We deduce a critical branching lemma for rewriting systems on algebraic models whose axioms are specified by convergent modulo rewriting systems. We illustrate our constructions for string, linear, and group rewriting systems.
Reduction of hospital readmissions

In recent years, healthcare spending has risen and become a burden on many governments.There are multiple reasons for this increase such as overtesting, long medical treatment path, ignoring doctors' orders, ineffective use of technologies, medical errors, many hospital readmissions, unnecessary emergency room (ER) visits, and medical treatment acquired side effects and infections.The first part of this editorial presents Healthcare Cost and Utilization Project (HCUP) datasets and their hierarchical partition used to build hierarchically structured personalized recommendation systems in healthcare domain.The second part outlines a simple strategy for reducing the number of readmissions using the concept of action rules to provide recommendations.First, we extract from HCUP datasets all possible procedure paths (course of treatments) for a given initial medical procedure.Then, we cluster patients according to the similarities in their diagnoses in order to increase the predictability of the course of treatment following this initial procedure.Finally, we present a novel algorithm that provides recommendations (actionable knowledge) to the physicians to put patients on a treatment path that would result in optimal reduction of the number of readmissions for these patients.There is not much research done on decreasing the number of readmissions to hospitals after initial procedure and almost none based on action rules.
Seas the day: Co‐designing immersive virtual reality exergames with exercise professionals and people living with dementia

Physical activity (PA) is associated with physical and cognitive benefits among people living with dementia or mild cognitive impairment (PLWD/MCI) and is a meaningful activity that can improve their confidence in everyday life. Exercising in virtual reality environments (VR Exergame) is becoming an increasingly feasible and enjoyable way to promote PA and well-being in PLWD/MCI. Although co-design can significantly improve the design of technology, it is rarely done with PLWD/MCI. This study uses participatory design methods and collaborative approaches to involve key stakeholders to develop and test a VR Exergame "Seas the Day", a novel solution targeting PLWD/MCI well-being.A multi-stage, user-centered co-design approach was used to custom-build VR Exergames tailored to the unique needs and abilities of PLWD/MCI based on a first generation of the prototype that was previously developed and tested with PLWD/MCI. This paper describes the next iteration of the prototype. Processes included concept ideation and brainstorming activities, iterative prototyping, and playtesting/input/feedback sessions with key stakeholders (PLWD/MCI, exercise professionals, engineers, VR game designers, content developers).The multidisciplinary and collaborative design process occurred over 15 months (overlapping with COVID-19 pandemic) with 7 PLWD/MCI (6 females; M=81.3 years) and 9 exercise professionals (7 females; M=38.1 years) to date. The game was designed to target movements identified by exercise professionals and researchers (aerobic exercises, range of motion, seated-balance, quick response to stimuli) and is structured in three exercise stages (warm-up, conditioning, cool-down). To ensure safety of participants while using VR headsets, only seated upper-limb exercises were targeted. Stakeholder feedback regarding game mechanics, aesthetics, and visual/auditory cues were gathered during brainstorming and playtesting sessions and implemented into specific game-related scenarios (tai-chi, rowing, fishing).We presented the process, outcomes, and challenges of adopting a participatory/collaborative approach with multiple stakeholder groups to co-design VR Exergames tailored to PLWD/MCI. Next steps will include a mixed-method evaluation of the VR Exergames among community-dwelling older adults and PLWD/MCI in retirement communities and long-term care to evaluate: i) feasibility and acceptability of use, ii) game user experience, iii) barriers/facilitators to uptake of VR Exergames; and iv) inform/validate VR Exergames gameplay metrics reflective of cognitive and motor performance.
A Dataset for Provident Vehicle Detection at Night

In current object detection, algorithms require the object to be directly visible in order to be detected. As humans, however, we intuitively use visual cues caused by the respective object to already make assumptions about its appearance. In the context of driving, such cues can be shadows during the day and often light reflections at night. In this paper, we study the problem of how to map this intuitive human behavior to computer vision algorithms to detect oncoming vehicles at night just from the light reflections they cause by their headlights. For that, we present an extensive open-source dataset containing 59 746 annotated grayscale images out of 346 different scenes in a rural environment at night. In these images, all oncoming vehicles, their corresponding light objects (e. g., headlamps), and their respective light reflections (e. g., light reflections on guardrails) are labeled. In this context, we discuss the characteristics of the dataset and the challenges in objectively describing visual cues such as light reflections. We provide different metrics for different ways to approach the task and report the results we achieved using state-of-the-art and custom object detection models as a first benchmark. With that, we want to bring attention to a new and so far neglected field in computer vision research, encourage more researchers to tackle the problem, and thereby further close the gap between human performance and computer vision systems.
SSD small object detection algorithm based on feature enhancement and sample selection

SSD has a poor detection effect on small objects. The first reasons is its insufficient feature extraction for small objects. To solve this problem, a feature enhancement module is proposed to make better use of the information around the object to improve the identification ability of small objects. The second reason is that the division of positive and negative samples is unreasonable. The threshold is unfriendly for small objects. To solve this problem, an adaptive training sample selection algorithm is adopted to select the threshold. To improve SSD by the above two methods, and experiments on the PASCAL VOC data set. The mean accuracy precision is increased by 2.6% compared to the SSD algorithm. Compared with the series of SSD improved algorithms such as DSOD, RSSD, DSSD, FSSD, the mAP of our method increased by 2.1%, 1.3%, 1.2%, 1.0%. Our method significantly improved the detection effect of small objects, surpassing SSD and its improved algorithms.