Reviewerly/exHarmony huggingface dataset:
Language and Era Prediction of Digitized Indian Manuscripts Using Convolutional Neural Networks

With an increasing number of Indian manuscripts being digitized, the subject of their era prediction is readdressed to interpret the socio-economic fabric of different periods. This paper describes a novel approach to estimate the era of Indian manuscripts from their scanned images using convolutional neural networks (CNN). The method primarily uses image processing to harness visual features from small image patches and classifies them based on the difference in writing styles in terms of strokes and letter formation. We follow a two-step approach of language prediction followed by a separate era prediction model for each language to achieve optimal results. For this paper, we restrict consideration to six Indian language manuscripts written between the sixteenth and twentieth centuries. Conclusively, our model outperforms other well-known architectures and gave over 90% and 80% accuracy on the training and validation data, respectively.
Indoor Crowd Density Estimation Based on Sound Field Disturbance Analysis

With the frequent occurrence of large-scale indoor stampedes, people pay more and more attention to the problem of indoor crowd density, and indoor crowd density estimation has become a very important security work. This paper proposes an indoor crowd density estimation method based on acoustic means. This method makes use of the reverberation effect and spatial distribution of sound wave propagation in indoor environment, and realizes the estimation of crowd density by analyzing the disturbance of indoor crowd to sound field. It can be applied to intelligent office, indoor security and other scenes where there is a high demand for crowd density estimation and there are many obstacles, that traditional computer vision methods cannot be applied.
An A*-algorithm for the Unordered Tree Edit Distance with Custom Costs

The unordered tree edit distance is a natural metric to compute distances between trees without intrinsic child order, such as representations of chemical molecules. While the unordered tree edit distance is MAX SNP-hard in principle, it is feasible for small cases, e.g. via an A* algorithm. Unfortunately, current heuristics for the A* algorithm assume unit costs for deletions, insertions, and replacements, which limits our ability to inject domain knowledge. In this paper, we present three novel heuristics for the A* algorithm that work with custom cost functions. In experiments on two chemical data sets, we show that custom costs make the A* computation faster and improve the error of a 5-nearest neighbor regressor, predicting chemical properties. We also show that, on these data, polynomial edit distances can achieve similar results as the unordered tree edit distance.
Understanding and Amplifying Labor among Content Creators with Disabilities

My research combines field observations, interview, and design exploration methods to understand content creation practices involving disabled creators and, at times, their caregivers and collaborators. This analysis investigates the varying ways in which disabled creators approach their labor across different creative modalities and spaces. Further, it reveals how their practices might be shaped by broader social issues around disability such as ableism and marginalization from traditional workplaces. In doing so, I uncover the ways technology can both support and hinder the goals of these creators. Insights from this work can inform future directions and opportunities for research that supports and recognizes the contributions of disabled creators.
Towards a Compulsory Computing Curriculum at Primary and Lower-Secondary Schools: The Case of Czechia

A new national curriculum in informatics for primary and lower secondary education has been in force in the Czech Republic since September 2021. This curriculum introduces a compulsory subject, which systematically focuses on computing from the age of 9. This paper shows the path we have taken, the visions and principles on which the reform has been built, and the milestones that have lined the way from a user-led approach to computers to development of computational thinking and understanding the world of computers as a major educational objective.This paper is structured as follows. We start with an introduction to the Czech educational context and some terminology. In Sect. 2, we describe the initial situation and the first steps towards the reform of school informatics. In Sect. 3, we explain the main concept and describe a project preparing new educational content and the conception of teacher education. An outline of the contents of the curriculum and teachers’ first reactions are shown in Sect. 4.
Confidence Prediction Driven Iterative Data Annotation

Much of the success of deep learning can be attributed to the existence of a huge amount of annotated data. Although many techniques such as pre-training and data augmentation exist, the availability of sufficiently large, task-specific ground-truth datasets plays an important role in the development of successful deep learning-powered solutions. In this paper, we proposed and evaluated two iterative annotation strategies called low confidence and high confidence annotation. They aim to help construct datasets in a more efficient way. Both strategies utilize predicted confidence score from a classifier to select suitable samples for manual annotation. We compared both strategies with a baseline method based on random annotation. All strategies were evaluated based on the three different criteria to assess their strong and weak points. The results show that each strategy has its use in certain situations. We also investigated the possibility to utilize data augmentation in proposed strategies and showed that data augmentation is beneficial for high confidence annotation strategy.
Multi-Domain Dialog State Tracking based on Machine Reading Comprehension

With the development of big data and deep learning technology, the goal of creating an automatic human-machine dialog system is no longer an illusion. However, obtaining correct dialog label is very challenging due to expensive purchase cost and time cost. Through the transfer of knowledge, we proposed RC_DST which enables the dialog state tracking model to infer the state of dialog between different domains accurately and improves the accuracy in a new domain without or with a few of labeled data. In this paper, we utilizes XLNet which performs well in processing long texts to encode the dependency between the dialog context and slot semantics. Meanwhile, we expands machine reading comprehension to non-categorical and categorical slots in different ways. Extensive experiment results show that our method achieves competitive results in a new domain with zero samples compared with exclusive training data in this domain.
The Effectiveness of Module and GeoGebra Based Learning Media to Improve Mathematics Learning Outcomes of Grade IV Students

This study aims to determine the effectiveness of learning media based on modules and GeoGebra to improve mathematics learning outcomes. The population in this study were fourth-grade students of SD Negeri (Public Elementary School) Toso 01 and SD Negeri Bawang with a sample of 40 students. This research method uses a quasi-experimental design with a nonequivalent control group design. This study was divided into an experimental class and a control class. The results of this study include: 1) the media module and GeoGebra are effective for improving mathematics learning outcomes for fourth-grade students; 2) there are differences in mathematics learning outcomes for students who use module learning media and students who use GeoGebra-assisted modules. The use of learning media based on the GeoGebra-assisted module is stated to be more effective in improving mathematics learning outcomes for fourth-grade students.
High-Performance Discriminative Tracking with Target-Aware Feature Embeddings

Discriminative model-based trackers have made remarkable progress recently. However, due to the extreme imbalance of foreground and background samples, the learned model is hard to fit the training samples well in the online tracking. In this paper, to alleviate the negative influence caused by the imbalance issue, we propose a novel construction scheme of target-aware features for online discriminative tracking. Specifically, we design a sub-network to generate target-aware feature embeddings of foregrounds and backgrounds by projecting the learned feature embeddings into the target-aware feature space. Then, a model solver, which is integrated into our networks, is applied to learn the discriminative model. Based on such feature construction, the learned model is able to fit training samples well in the online tracking. Experimental results on four benchmarks, OTB-2015, VOT-2018, NfS, and GOT-10k, show that the proposed target-aware feature construction is effective for visual tracking, leading to the high-performance of our tracker.
The Core Competence Construction of Primary and Middle School Sports Skills Based on Decision Support System

Objectives: With the continuous development of sports, the current learning of motor skills in primary and secondary school students has become universal, so the research on the core literacy learning evaluation of primary and secondary school sports skills based on decision support system is established. Methods: According to the computer aided skills learning in sports, the model is established. Results: By adding the decision support system to the virtual reality motion model, the problems existing in the skills learning of primary and secondary school students are further studied, thus providing a better learning method. Conclusion: In the test for computing decision support technology, the test time and accuracy test for the model shows that proficient mastery of motor skills has a prominent role in improving core literacy.
An Improved Bacterial Foraging Optimization for Global Optimization

This paper presents an Improved Bacterial Foraging Optimization (IBFO) to solve the high computational complexity and less conductive search capability of the original BFO. A single loop implementation structure is adopted to reduce the computational complexity of the original algorithm with a triple-nested implementation structure. We adopt a cuckoo search in chemotaxis operation to increase the randomness of step size and improve search efficiency. Additionally, a new reproduction strategy is explored by employing the Lévy flight strategy to generate new individuals to replace the less conductive ones evaluated and sorted according to the current fitness values rather than accumulated fitness cost. Finally, the candidate mechanism is introduced into reproduction and elimination-dispersal events for comparing and obtaining a better solution. The proposed algorithm's effectiveness is compared with 6 well-known heuristic algorithms on 12 benchmark functions. The results indicate that the proposed IBFO outperforms other algorithms significantly in most cases.
A Blockchain Voting Scheme Based on Data Security Separation

Electronic voting systems are becoming more and more indispensable in real life. However, the traditional ones can hardly protect the voting data and user privacy well. This paper proposes a new electronic voting scheme based on data separator and blockchain technology. The solution uses blockchain to ensure the openness and transparency of all voting links. Besides, a data security separator is designed to separate users' identity data and poll data. Thus the association between voters and polls is eliminated, and the privacy of users during voting is safeguarded against leakage. Compared with the traditional electronic voting schemes, this scheme can promise that voting results are exactly correct, open, and verifiable, and the poll information is anonymous, hardly tampered with.
Autoregressive Pre-training Model-Assisted Low-Resource Neural Machine Translation

Pre-training methods have been proven to significantly improve language understanding ability of the model. However, when dealing with machine translation tasks involving two or more languages, the pre-training method can only handle a single language and prevent further improvement of machine translation performance. Therefore, there are two main methods to improve the quality of machine translation model by using the pre-training model. One is to use the word embedding generated by the pre-training model as the modeling unit. Second is to make the machine translation model learn the probability distribution of the pre-training model through the knowledge distillation method. In addition, the self-attention based pre-training model affects the effect of machine translation due to the “training-fine-tuning” difference and limited by the assumption of conditional independence. For this reason, we proposed a XLNet based pre-training method, that corrects the defects of the general self-encoding based pre-training model, and enhance NMT model for context feature extraction. We conducted experiments on the CCMT2019 Mongolian-Chinese (Mo-Zh), Uyghur-Chinese (Ug-Zh) and Tibetan-Chinese (Ti-Zh) tasks, our method significantly improves the quality compared to the baseline (Transformer), which fully verifies the effectiveness.
HPA-Net: Hierarchical and Parallel Aggregation Network for Context Learning in Stereo Matching

Accurate disparity estimation with regard to rectified stereo image pairs is essential for many computer vision tasks. Current deep learning-based stereo networks generally construct single-scale cost volume to regularize and regress the disparity. However, these methods do not take advantage of multi-scale context information, leading to the limited performance of disparity prediction in ill-posed regions. In this paper, we propose a novel stereo network named HPA-Net, which provides an efficient representation of context information and lower error rates in ill-posed regions. First, we propose a hierarchical aggregation module to fuse context information from multi-scale cost volumes into an integrated cost volume. Then, we apply the integrated cost volume to the proposed parallel aggregation module, which utilizes several 3D dilated convolutions simultaneously to capture global and local clues of context information for disparity regressions. Experimental results show that our proposed HPA-Net achieves state-of-the-art stereo matching performances on KITTI datasets.
Data Selection and Machine Learning Algorithm Application Under the Background of Big Data

At present, machine learning, as an important tool in data mining, is not only the exploration of human cognitive learning process, but also the analysis and processing of data. Facing the challenge of large amounts of data, part of the current research focuses on the improvement and development of machine learning algorithms, and another part of the researchers is devoted to the selection of sample data and the reduction of data sets. These two aspects of research work are parallel. Training sample data selection is a research hotspot in machine learning. Through effective selection of sample data, more informative samples are extracted, redundant samples and noise data are eliminated, so as to improve the quality of training samples and obtain better learning performance. This article aims to study data selection and the application of machine learning algorithms in the context of big data. Based on the analysis of machine learning implementation methods, the construction process of random forests, and random group sampling integration algorithms, the application of random group sampling methods is used to accurately select bases. Compared with the previous algorithms, the RPSE algorithm greatly improves the calculation speed of the data in the classifier and training samples, and ensures that the base classifier performs random calculations on the samples during training. According to the integrated gap spacing, a support vector machine training data can be selected, and the selected data set that needs to be filtered is used as a classifier for the support vector machine for training, so as to obtain the final classification. The experimental results show that compared with the more common traditional data selection algorithms, the RPSE algorithm greatly accelerates the accuracy and speed of data selection, and reduces the accuracy and precision of the support vector computer classification under the necessary conditions.
<scp>RDOF</scp>: An outlier detection algorithm based on relative density

Abstract An outlier has a significant impact on data quality and the efficiency of data mining. The outlier identification algorithm observes only data points that do not follow clearly defined meanings of projected behaviour in a data set. Several techniques for identifying outliers have been presented in recent years, but if outliers are located in areas where neighbourhood density varies substantially, it can result in an imprecise estimate. To address this problem, we provide a ‘Relative Density‐based Outlier Factor (RDOF)’ algorithm based on the concept of mutual proximity between a data point and its neighbours. The proposed approach is divided into two stages: an influential space is created at a test point in the first stage. In the later stage, a test point is assigned an outlier‐ness score. We have conducted experiments on three real‐world data sets, namely the Johns Hopkins University Ionosphere, the Iris Plant, and Wisconsin Breast Cancer data sets. We have investigated three performance metrics for comparison: precision, recall, and rank power. In addition, we have compared our proposed method against a set of relevant baseline methods. The experimental results reveal that our proposed method detected all (i.e., 100%) outlier class objects with higher rank power than baseline approaches over these experimental data sets.
CFHider: Protecting Control Flow Confidentiality with Intel SGX

Program control flow reflects the algorithm of that program and may reveal implementation vulnerabilities. Thus its confidentiality needs to be protected, especially in a cloud setting. However, most existing control flow obfuscation methods are software-based, which cannot offer high confidentiality while maintaining low performance overhead. In this paper, we propose CFHider, a hardware-assisted solution. By performing program transformation and leveraging Trusted Execution Environments (Intel SGX), CFHider moves branch statement conditions to an opaque and trusted memory space during the program execution. We proved that by generating Obfuscation Invariants, CFHider is able to provide provable control flow confidentiality protection. Based on the design of CFHider, we also developed a prototype system for Java applications. Our security analysis and experimental results indicate that CFHider is effective in protecting control flow confidentiality and incurs a much reduced performance overhead than existing software-based solutions (by a factor of 18.1).
Selecting a Passive Network Monitoring Solution for Medical Device Cybersecurity Management

Abstract The number of cyberattacks and information system breaches in healthcare have grown exponentially, as well as escalated from accidental incidents to targeted and malicious attacks. With medical devices representing a substantial repository of all the assets in a healthcare system, network security and monitoring are critical to ensuring cyber hygiene of these medical devices. Because of the unique challenges of connected medical devices, a passive network monitoring (PNM) solution is preferred for its overall cybersecurity management. This article is intended to provide guidance on selecting PNM solutions while reinforcing the importance of program assessment, project management, and use of leading practices that facilitate the selection and further implementation of PNM solutions for medical devices. The article provides a detailed introduction to connected medical devices and its role in effective care delivery, an overview of network security types and PNM, an overview of the National Institute of Standards and Technology Cybersecurity Framework and its application for program assessment, essentials of project management for PNM solution selection and implementation, key performance indicators for measuring a solution's ability to meet critical cybersecurity needs for medical devices, and lessons learned from the author's professional experience, selective literature review, and leading practices. Rather than describing a complete list of guidelines for selecting PNM solutions, the current work is intended to provide guidance based on the author's experience and leading practices compiled from successful medical device cybersecurity programs.
Research on the Influencing Factors of Wuhan University Students' Willingness to Stay in Wuhan Based on SEM

There are many universities and research institutes in Wuhan, with hundreds of thousands of university graduates every year. Letting more talents stay is not only related to the economic and social development after the epidemic, but also related to the stamina of Wuhan's urban construction. However, according to relevant data, there is a serious brain drain. The proportion of Wuhan's 2019 undergraduate graduates staying in Wuhan is only 51.11%, of which less than 30% of key university graduates are staying in Wuhan. Based on the Structural Equation Model, this paper studies the influencing factors of Wuhan university students’ willingness to stay in Wuhan, so as to explore the deep-seated reasons for the brain drain in Wuhan and put forward Suggestions: 1)Attracting high-quality companies to settle in, improving salary and treatment in a targeted manner, and pay attention to the contradictory phenomenon of the strong willingness of students from Wuhan's key universities to stay, but the low percentage of graduates actually staying in Wuhan. 2) Improve the environment for entrepreneurship and employment, and increase support for entrepreneurship and employment. 3) Optimize the talent policy and increase people's participation in the talent policy. 4) Increase publicity to increase people's favorability towards Wuhan. 5) Strengthen the construction of urban infrastructure and improve the happiness of residents' lives.
Campus Ontology-Based Correlation Detection Among Heterogeneous Data Sets

For data-driven decision making, it is essential to build a data infrastructure that stores various data. Since various data are accumulated within organizations such as universities, companies, and local governments, integration of data in different contexts and cross-sectional analysis are issues. Knowledge graphs with a graphical structure that can flexibly change the schema are suitable for integrating heterogeneous data. In this study, we focused on a university campus as an example and proposed an ontology for various data such as lectures, buildings, purchasing, bicycle parking, and energy consumption. In particular, it has become easier to extract data across heterogeneous data collected within an organization by semantically linking dimensions with various expressions. Then, the collected unstructured data was accumulated as a knowledge graph based on the ontology, and the data infrastructure was constructed. In addition, we found several correlations through scenario-based experiment using this knowledge graph and showed the possibility that it could be applied to considering approach for university managemsent and improving the campus environment.