Reviewerly/exHarmony huggingface dataset:
Supporting elementary gifted learners during the COVID-19 pandemic: A survey of teaching practices

The present mixed methods study aimed to understand the challenges faced by teachers of the gifted and how elementary gifted learners were supported and taught during the COVID-19 pandemic. A survey was administered to 310 elementary school teachers of the gifted across the United States between March and June 2021, one year after initial school closings. Focus groups were utilized to further explore how the COVID-19 pandemic impacted the work of teachers of the gifted in their schools. Educators shared lessons learned from this experience related to (a) a better understanding of how to utilize remote instruction, (b) reimagining the gifted education classroom, and (c) use of a variety of strategies to provide more student choice, differentiation, and individualized pace. Results from this study provide insights that can guide educators, curriculum specialists, and school administrators to improve online instruction for gifted learners in the future.
CarFree: Hassle-Free Object Detection Dataset Generation Using Carla Autonomous Driving Simulator

For safe autonomous driving, deep neural network (DNN)-based perception systems play essential roles, where a vast amount of driving images should be manually collected and labeled with ground truth (GT) for training and validation purposes. After observing the manual GT generation’s high cost and unavoidable human errors, this study presents an open-source automatic GT generation tool, CarFree, based on the Carla autonomous driving simulator. By that, we aim to democratize the daunting task of (in particular) object detection dataset generation, which was only possible by big companies or institutes due to its high cost. CarFree comprises (i) a data extraction client that automatically collects relevant information from the Carla simulator’s server and (ii) a post-processing software that produces precise 2D bounding boxes of vehicles and pedestrians on the gathered driving images. Our evaluation results show that CarFree can generate a considerable amount of realistic driving images along with their GTs in a reasonable time. Moreover, using the synthesized training images with artificially made unusual weather and lighting conditions, which are difficult to obtain in real-world driving scenarios, CarFree significantly improves the object detection accuracy in the real world, particularly in the case of harsh environments. With CarFree, we expect its users to generate a variety of object detection datasets in hassle-free ways.
Improving the Performance of HDBSCAN on Short Text Clustering by Using Word Embedding and UMAP

Short text is one of the data formats usually generated by people on social media, for instance, tweets on Twitter. They are often used as data to analyze what is trending in the community. However, topic modeling or text clustering algorithms on short text have some unique problems. Namely, sparsity which is caused by too many unique words only appear in few documents, and a lack of word co-occurrences that makes it difficult for the system to find semantic information of words. To overcome those 2 problems, we propose a novel method to use the word embedding technique to represent the document in vector space. FastText and BERT embedding models are chosen not only because of the quality of their text representation but also their ability to handle out of vocabulary words. As for clustering, the HDBSCAN algorithm is used because of its ability to handle noise. However, it has poor performance on clustering high-dimensional data. Because vectors resulting from word embedding are high-dimensional, therefore dimension reduction by UMAP is done on the vectors before feeding it to HDBSCAN. The experimental results prove that our novel method is better than the baseline, which is evaluated on purity and NMI metrics.
Handwriting-Assistant

Pen-based handwriting has become one of the major human-computer interaction methods. Traditional approaches either require writing on the specific supporting device like the touch screen, or limit the way of using the pen to pure rotation or translation. In this paper, we propose Handwriting-Assistant, to capture the free handwriting of ordinary pens on regular planes with mm-level accuracy. By attaching the inertial measurement unit (IMU) to the pen tail, we can infer the handwriting on the notebook, blackboard or other planes. Particularly, we build a generalized writing model to correlate the rotation and translation of IMU with the tip displacement comprehensively, thereby we can infer the tip trace accurately. Further, to display the effective handwriting during the continuous writing process, we leverage the principal component analysis (PCA) based method to detect the candidate writing plane, and then exploit the distance variation of each segment relative to the plane to distinguish on-plane strokes. Moreover, our solution can apply to other rigid bodies, enabling smart devices embedded with IMUs to act as handwriting tools. Experiment results show that our approach can capture the handwriting with high accuracy, e.g., the average tracking error is 1.84mm for letters with the size of about 2cmx1cm, and the average character recognition rate of recovered single letters achieves 98.2% accuracy of the ground-truth recorded by touch screen.
Statistics-Based Outlier Detection and Correction Method for Amazon Customer Reviews

People nowadays use the internet to project their assessments, impressions, ideas, and observations about various subjects or products on numerous social networking sites. These sites serve as a great source to gather data for data analytics, sentiment analysis, natural language processing, etc. Conventionally, the true sentiment of a customer review matches its corresponding star rating. There are exceptions when the star rating of a review is opposite to its true nature. These are labeled as the outliers in a dataset in this work. The state-of-the-art methods for anomaly detection involve manual searching, predefined rules, or traditional machine learning techniques to detect such instances. This paper conducts a sentiment analysis and outlier detection case study for Amazon customer reviews, and it proposes a statistics-based outlier detection and correction method (SODCM), which helps identify such reviews and rectify their star ratings to enhance the performance of a sentiment analysis algorithm without any data loss. This paper focuses on performing SODCM in datasets containing customer reviews of various products, which are (a) scraped from Amazon.com and (b) publicly available. The paper also studies the dataset and concludes the effect of SODCM on the performance of a sentiment analysis algorithm. The results exhibit that SODCM achieves higher accuracy and recall percentage than other state-of-the-art anomaly detection algorithms.
Trapezoidal type-2 fuzzy inference system with tensor unfolding structure learning method

In the paper, trapezoidal type-2 fuzzy inference system (TT2FIS) is introduced. To construct the type-2 fuzzy inference system, trapezoidal type-2 fuzzy sets are adopted to construct the antecedent part of the fuzzy rules, the centers of the fuzzy sets are roughly clustered by an evolving autonomous data partitioning algorithm. To further eliminate the effect of the centers that are generated by dataset itself, and it can be easily impacted by the unbalance data, the data clouds that are generated by autonomous data partitioning algorithm are filtered by Hammersley sequence, and symmetrical trapezoidal type-2 fuzzy sets are generated by the generated cluster centers and problem-free standard deviation generating method. To the consequent part, generalized type-2 TSK consequent is formulated by tensor, which is obtained via three constituent parts of the trapezoidal type-2 fuzzy sets (lower membership function, upper membership function and type-reduction set of data samples), the parameters of consequent part are the iterative results of a matrix equation that is unfolded from the tensor. Finally, simulation results are carried out to verify the effectiveness of the proposed trapezoidal type-2 fuzzy inference system. Simulation results show that the generalization of the TT2FIS is better than the coincide type-2 fuzzy inference system or adaptive type-1 fuzzy inference system.
Overcoming the Main Challenges of Knowledge Discovery through Tendency to the Intelligent Data Analysis

Intelligent Data Analysis (IDA) approach proves its ability to deal with small and huge data sets; this ability can be done by generating and developing new methodologies. While; Knowledge Discovery in Database (KDD) aiming to automatic interpretation of large data sets. As a result, we can conclude a strong relationship between two concepts. This study discusses five challenges related to data analytical to making right decision. These challenges include: Missing values; Data scarcity, Data dimensionality reduction, Black box; and Mathematical model. First: One of the important trends in KDD will be the growing importance of data processing. But this point faces problems similar to those of data mining (High dimensional data, missing values imputation and data integration). As explained before, one of the open still problems in estimation missing values methods are how to select the optimal number of nearest neighbors of those values. Second: Data scarcity (insufficient size of dataset) problem is one of the challenges in KDD. Insufficient size of data is very often responsible for the poor performances in learning. Third: The goal of dimension reduction methods is using the correlation structure among the predicator variables to reduction the three main dimensions (features, samples and value of features). But, how we can combination among these three dimensions without lose any important inform is still as one of the challenges in KDD system. Fourth: As we know data mining algorithm are &#x201C;black box character&#x201D; for many reasons explained in chapter two. But how we can convert it to system will behaviors and conclusion that can be explained and analyzed in a comprehensible manner (i.e., white box) is remained one of the main challenges of KDD system.
Leveraging Arabic sentiment classification using an enhanced CNN-LSTM approach and effective Arabic text preparation

The high variety in the forms of the Arabic words creates significant complexity related challenges in Natural Language Processing (NLP) tasks for Arabic text. These challenges can be dealt with by using different techniques for semantic representation, such as word embedding methods. In addition, approaches for reducing the diversity in Arabic morphologies can also be employed, for example using appropriate word normalisation for Arabic texts. Deep learning has proven to be very popular in solving different NLP tasks in recent years as well. This paper proposes an approach that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks to improve sentiment classification, by excluding the max-pooling layer from the CNN. This layer reduces the length of generated feature vectors after convolving the filters on the input data. As such, the LSTM networks will receive well-captured vectors from the feature maps. In addition, the paper investigated different effective approaches for preparing and representing the text features in order to increase the accuracy of Arabic sentiment classification.
Learning the parameters of an outranking-based sorting model with characteristic class profiles from large sets of assignment examples

We address the problem of learning the parameters of the outranking-based multiple criteria sorting model from large sets of assignment examples. We focus on a recently devised method called Electre TRI-rC, incorporating a single characteristic profile to describe each decision class. We introduce four algorithms aimed at the problem. They use different optimization techniques, including an evolutionary algorithm, linear programming combined with a genetic approach, simulated annealing, and a dedicated heuristic. We present the results of the experiments carried out on both artificial and real-world data sets. They reveal an impact of the comparison and veto thresholds, various sorting rules, and ensembles on the classification accuracy of the proposed algorithms. From a broader perspective, we contribute to cross-fertilizing the fields of Multiple Criteria Decision Aiding and Machine Learning for supporting real-world decision-making.
Exploring Key Competencies and Professional Development of Music Teachers in Primary Schools in the Era of Artificial Intelligence

Artificial intelligence (AI) has introduced new era elements to the connotation of key competencies and professional development of music teachers in primary schools. The education management department in the AI era has flawed systems for the professional development of music teachers in primary schools. To assist music teachers in primary schools fulfill the development needs of the development of the times and enhance the quality of music education in primary schools, the status and income of music teachers in primary schools warrant improvement, and the system should be upgraded to promote the implementation of key competencies. Moreover, the related system construction of music teachers in primary schools should be reinforced by rationally arranging teachers, workload and creating a teacher learning community, in order to guide schools to provide a suitable environment for teachers, key competencies and professional development. Furthermore, methods like strengthening teachers, awareness of independent development and augmenting professional identity should be adopted to prompt teachers to comprehensively enhance their key competencies and professional development.
Integrated InP optical unitary converter with compact half-integer multimode interferometers

Integrated optical unitary converters (OUCs) are vital devices for various emerging applications such as mode-multiplexed optical communication, optical neural networks, and quantum computing. In order to realize large-scale OUCs in a limited footprint, the number of elements, as well as the size of each element, is important. In this work, we present a novel type of OUC using half-integer multimode interferometers (MMIs) based on the multi-plane light conversion (MPLC) concept. A half-integer MMI enables unitary coupling among the multiple input and output ports, while requiring only half the length of a conventional uniform MMI. Although the splitting ratio is not uniform across the ports, we show both numerically and experimentally that arbitrary unitary operation can still be achieved with comparable performance. We fabricate 4×4 OUC with half-integer MMIs on the monolithic InP platform and experimentally demonstrate reconfigurable 4-mode sorting and switching with a significantly reduced footprint compared with the conventional OUCs using uniform MMIs.
Blockchain-Based Privacy-Preserving System for Genomic Data Management Using Local Differential Privacy

The advances made in genome technology have resulted in significant amounts of genomic data being generated at an increasing speed. As genomic data contain various privacy-sensitive information, security schemes that protect confidentiality and control access are essential. Many security techniques have been proposed to safeguard healthcare data. However, these techniques are inadequate for genomic data management because of their large size. Additionally, privacy problems due to the sharing of gene data are yet to be addressed. In this study, we propose a secure genomic data management system using blockchain and local differential privacy (LDP). The proposed system employs two types of storage: private storage for internal staff and semi-private storage for external users. In private storage, because encrypted gene data are stored, only internal employees can access the data. Meanwhile, in semi-private storage, gene data are irreversibly modified by LDP. Through LDP, different noises are added to each section of the genomic data. Therefore, even though the third party uses or exposes the shared data, the owner’s privacy is guaranteed. Furthermore, the access control for each storage is ensured by the blockchain, and the gene owner can trace the usage and sharing status using a decentralized application in a mobile device.
Digital Forensics in Cyber Security—Recent Trends, Threats, and Opportunities

The rapid technological advancement has led the entire world to shift towards digital domain. However, this transition has also result in the emergence of cybercrimes and security breach incidents that threatens the privacy and security of the users. Therefore, this chapter aimed at examining the use of digital forensics in countering cybercrimes, which has been a critical breakthrough in cybersecurity. The chapter has analyzed the most recent trends in digital forensics, which include cloud forensics, social media forensics, and IoT forensics. These technologies are helping the cybersecurity professionals to use the digital traces left by the data storage and processing to keep data safe, while identifying the cybercriminals. However, the research has also observed specific threats to digital forensics, which include technical, operational and personnel-related challenges. The high complexity of these systems, large volume of data, chain of custody, the integrity of personnel, and the validity and accuracy of digital forensics are major threats to its large-scale use. Nevertheless, the chapter has also observed the use of USB forensics, intrusion detection and artificial intelligence as major opportunities for digital forensics that can make the processes easier, efficient, and safe.
Shhh!: 12 Practices for Secret Management in Infrastructure as Code

Despite being beneficial in automated provisioning of computing infrastructure at scale, infrastructure as code (IaC) scripts are susceptible to containing secrets, such as hard-coded passwords. A derivation of practices related to secret management for IaC can help practitioners to secure their secrets, potentially aiding them to securely develop IaC scripts. The goal of the paper is to help practitioners in secure development of infrastructure as code (IaC) scripts by identifying practices for secret management in IaC. We conduct a grey literature review with 38 Internet artifacts to identify 12 practices. We identify practices that are applicable for all IaC languages, e.g., prioritized encryption, as well as language-specific practices, such as state separation for Terraform. Our findings can be beneficial for (i) practitioners who can apply the identified practices to secure secrets in IaC development, and (ii) researchers who can investigate how the secret management process can be improved to facilitate secure development of IaC scripts.
Promoting Code Quality via Automated Feedback on Student Submissions

This research-to-practice work-in-progress paper presents an automated feedback tool that can be used in many teaching environments by integrating it with a web-based assessment submission system. Each time a student submits their work, they will automatically get feedback about aspects of the code quality. Automated feedback tools have been developed to educate students about code quality. However, integrating such a tool into an existing teaching environment can be challenging as these tools can depend on particular working environments, can be separate from the assessment submission system, or can require historical data. Our initial evaluation shows that the tool can be helpful as students do sometimes neglect to satisfy all code quality requirements. However, some false results are expected for spelling correction as student programs are not written in natural language. According to our quasi-experiments, the tool substantially reduces the number of word misspellings in comments due to their substantial frequency of occurrence.
Deep attentive style transfer for images with wavelet decomposition

To solve the issue of texture preservation in the image style transfer process, this paper presents a novel style transfer method for images that often contain tiny details but are easily noticed by human subjects (e.g., human faces). We aim to achieve content-preserving style transfer via an appropriate trade-off between detail preservation and style transfer. To this end, we utilize wavelet transformation with a deep neural network for decoupled style and detail synthesis. Additionally, style transfer should involve a one-to-one correspondence of semantic structures of scenes and avoid noticeable unnatural-looking style transitions around them. To address the above issue, we leverage an attention mechanism and semantic segmentation for matching and design a novel content loss with local one-to-one correspondence for producing content-preserving stylized results. Finally, we employ wavelet transform to perform feature optimization (FO) to repair some imperfect results. We perform various experiments with Qabf evaluation and a user study to validate our proposed method and show its superiority over state-of-the-art methods for ensemble and texture preservation.
Semi-supervised graph regularized nonnegative matrix factorization with local coordinate for image representation

Nonnegative matrix factorization(NMF) is a powerful image representation algorithm in pattern recognition and data mining. However, the traditional NMF does not utilize any label information, or fail to guarantee the sparse parts-based representation. In this paper, we put forward a semi-supervised local coordinate NMF (SLNMF) algorithm, which incorporate the available label information and the local coordinate constraint into NMF. Particularly, SLNMF makes the learned coefficients sparse by adding the coordinate constraint, and enhance the discriminative ability of different classes by using the label information constraint. Furthermore, in order to extract the geometric structure of the data space, we propose a new semi-supervised graph regularized NMF with local coordinate constraint (SGLNMF) method, which incorporates the graph regularization into SLNMF to enhance the discriminative abilities of data representations. SGLNMF not only reveals the intrinsic geometrical information of the data space, but also takes into account the local coordinate constraint and the label information. Clustering experiments on several standard image datasets demonstrate the effectiveness of our proposed SLNMF and SGLNMF methods compared to the state-of-the-art methods.