Reviewerly/exHarmony huggingface dataset:
Deeper look at question categories, concepts, and context covered: Modified module analysis of quantum mechanics concept assessment

Concept inventories are commonly used tools in physics education research (PER) for evaluating teaching methods. Student responses to concept inventories have been studied using classical test theoretical methods such as factor analysis and network methods such as module analysis. The results of these studies have been used to evaluate the instruments and to find aggregate patterns that may be used to infer how students’ think about the concepts on the inventory. In this work, we apply modified model analysis using partial correlations, a network based method using partial correlations, to evaluate the Quantum Mechanics Concept Assessment (QMCA). The QMCA is designed to measure student understanding of upper-division quantum mechanics in both the spatial wave function and the spins contexts. This represents the first time a network-based analysis has been applied to an upper-division concept inventory. The modules we found are related to the structure of the instrument. There were two broad classes of modules: those that connected responses (either both correct or both incorrect) to isomorphic pairs questions written to probe the same concept in the spatial wave function and the spin contexts. The second class of modules were explain pairs, where the first question asked about a concept and the second question asked students to explain their choice, again either both correct or both incorrect. This structure resembles the structure of the Force Concept Inventory where there are a common correct and a common incorrect pair for many concepts, compared to the Force and Motion Conceptual Evaluation, which has larger and more varied communities reflecting a smaller number of topics.Received 13 April 2021Accepted 29 June 2021DOI:https://doi.org/10.1103/PhysRevPhysEducRes.17.020113Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasAssessmentProfessional TopicsUpper undergraduate studentsPhysics Education Research
Iterative Weighted Transductive Learning for Handwriting Recognition

The established paradigm in handwriting recognition techniques involves supervised learning, where training is performed over fully labelled (transcribed) data. In this paper, we propose a weak supervision technique that involves tuning the trained network to perform better on a specific test set, after having completed its training with the standard training data. The proposed technique is based on the notion of a reference model, to which we refer as the "Oracle", which is used for test data inference and retraining in an iterative manner. As test data that are erroneously labelled will be a hindrance to model retraining, we explore ways to properly weight Oracle labels. The proposed method is shown to improve model performance as much as \(2\%\) for Character Error Rate and \(5\%\) for Word Error Rate. Combined with a competitive convolutional-recurrent architecture, we achieve state-of-the-art recognition results in the IAM and RIMES datasets.
Prior Knowledge Regularized Self-Representation Model for Partial Multilabel Learning

Partial multilabel learning (PML) aims to learn from training data, where each instance is associated with a set of candidate labels, among which only a part is correct. The common strategy to deal with such a problem is disambiguation, that is, identifying the ground-truth labels from the given candidate labels. However, the existing PML approaches always focus on leveraging the instance relationship to disambiguate the given noisy label space, while the potentially useful information in label space is not effectively explored. Meanwhile, the existence of noise and outliers in training data also makes the disambiguation operation less reliable, which inevitably decreases the robustness of the learned model. In this article, we propose a prior label knowledge regularized self-representation PML approach, called PAKS, where the self-representation scheme and prior label knowledge are jointly incorporated into a unified framework. Specifically, we introduce a self-representation model with a low-rank constraint, which aims to learn the subspace representations of distinct instances and explore the high-order underlying correlation among different instances. Meanwhile, we incorporate prior label knowledge into the above self-representation model, where the prior label knowledge is regarded as the complement of features to obtain an accurate self-representation matrix. The core of PAKS is to take advantage of the data membership preference, which is derived from the prior label knowledge, to purify the discovered membership of the data and accordingly obtain more representative feature subspace for model induction. Enormous experiments on both synthetic and real-world datasets show that our proposed approach can achieve superior or comparable performance to state-of-the-art approaches.
Ten Questions in Lifelog Mining and Information Recall

With the advance of science and technology, people are used to recording their daily life events via writing blogs, uploading social media posts, taking photos, or filming videos. Such rich repository personal information is useful for supporting human living assistance, such as information recall service. The main challenges are how to store and manage personal knowledge from various sources, and how to provide support for people who may have difficulty recalling past experiences. In this position paper, we propose a research agenda on personal knowledge mining from various sources of lifelogs, personal knowledge base construction, and information recall for assisting people to recall their experiences. Ten research questions are formulated.
Knowledge Graph Completion by Jointly Learning Structural Features and Soft Logical Rules

With the rapid development and widespread application of Knowledge graphs (KGs) in many artificial intelligence tasks, a large number of efforts have been made to refine them and increase their quality. Knowedge graph embedding (KGE) has become one of the main refinement tasks, which aims to predict missing facts based on existing ones in KGs. However, there are still mainly two difficult unresolved challenges: (i) how to leverage the local structural features of entities and the potential soft logical rules to learn more expressive embedding of entites and relations; and (ii) how to combine these two learning processes into one unified model. To conquer these problems, we propose a novel KGE model named JSSKGE, which can \textbf{J}ointly learn the local \textbf{S}tructural features of entities and \textbf{S}oft logical rules. Firstly, we employ graph attention networks which are specially designed for graph-structured data to aggregate the local structural information of nodes. Then, we utilize soft logical rules implicated in KGs as an expert to further rectify the embeddings of entities and relations. By jointly learning, we can obtain more informative embeddings to predict new facts. With experiments on four commonly used datasets, the JSSKGE obtains better performance than state-of-the-art approaches.
Image Matching Algorithm Based on Improved FAST and RANSAC

Aiming at the problems of large amount of calculation, low matching accuracy and long matching time in image matching in visual positioning system, this paper proposes an image matching algorithm based on improved FAST (MFAST) and RANSAC (P-RANSAC). First, the multi-level FAST algorithm is used to extract the corner points, and the SURF algorithm is used to determine the main direction to generate the feature descriptor; then the fast approximate nearest neighbor algorithm is used to complete the rough matching of the feature points. Use the pre-sampling algorithm to select a new sample set for sampling and test the calculated model and discard the incorrect model parameters. Experimental results show that the proposed algorithm can effectively improve the accuracy and real-time performance of image matching compared with traditional algorithms.
Neural Machine Translation for Harmonized System Codes prediction

The harmonized system codes (HS codes) are used worldwide to categorize products in international shipments. In its basic form HS codes come in 6 digit format, subdivided hierarchically into groups of two digits (chapters, headings and subheadings). When shipping products, it is mandatory to specify a HS code for the purpose of producing a custom declaration. Currently the process is mostly carried out by human experts who take a decision on the HS code to be assigned to a shipment depending on the item description provided by the shipper. As such the process is time consuming and prone to errors due to generic, incomplete or non-interpretable descriptions. The objective of this research is to automate the classification of HS codes in order to increase productivity to cope with extra volume in the custom classification area. For the purpose of testing the developed models, we used an anonymized data set of shipments provided by DHL. The main contribution of this paper is we applied a deep learning model which have not been tried on tackling the HS code classification problem: an attention-based neural machine translation (NMT) model with integration of hierarchical loss. The model can classify around 29% percentage of the dataset where the model's accuracy can reach 85%.
A perspective on industrial quantum networks

The vision is a software-defined quantum network that enables a flexible experimental platform for developing quantum applications for industry. While components of the quantum Internet are under development, the control plane remains undefined. The quantum Internet, like the classical Internet, will be a network of networks. Operation of an industrial quantum network is viewed as a networked control problem, and a time-sensitive network control plane is proposed to enable a quantum software-defined network. Measurement-device-independent quantum key distribution is used as an example implementation since it provides a foundation for a quantum repeater and, by extension, the quantum Internet. Results indicate that a time-sensitive network control plane design is feasible, and its pros and cons are discussed.
ASAR 2021 Competition on Online Arabic Character Recognition: ACRC

The online Arabic handwriting recognition task always presents a challenge due to the existence of some complexity and variability of its writing style. In this paper, we describe an online Arabic handwritten Character recognition competition (ACRC) held at ASAR 2021. The aim is to evaluate the limits of Arabic character recognition systems on collected LMCA database (with and without noising). Four systems are participating in this competition which were tested on an unknown test dataset to all participants. Two metrics; notably Character Error Rate (CER) and speed are used to compare the systems. The achieved results in ACRC 2021 demonstrate the high potential of competitive participating systems which are based on deep learning methods.
Analyzing a Process Core Ontology and Its Usefulness for Different Domains

A well-specified strategy should define and integrate consistently three capabilities: process, method, and common vocabulary specifications. The domain vocabularies of different strategies should be built on common reference terminologies. For example, a process ontology should be a common reference since it considers cross-cutting concerns for different domains. This work specifies and defines the main terms of ProcessCO (Process Core Ontology). This is an ontology placed at the core level in the context of a four-layered ontological architecture. A practical use of an upper-level ontology is to semantically enrich the lowest-level ontologies. For example, ThingFO (an ontology at the foundational level in that architecture) enriches ProcessCO. Since ProcessCO is at the core level, ontologies at the domain level benefit from reusing and extending its concepts. Therefore, ProcessCO can be seen as a reusable resource to semantically enrich domain ontologies. To illustrate its applicability, this work shows the semantic enrichment of two top-domain ontologies. By using ProcessCO (and other core ontologies) as a common terminological reference, the domain ontologies used in the different strategies are conceptually harmonized. Hence, strategies ensure terminological uniformity and consistency, thus facilitating the understanding of process and method specifications.
Improved Automated Classification of Sentences in Data Science Exercises

The use of artificial intelligence proved to be useful to automating the grading process, especially when the assessment involves a large number of students. The general problem we are addressing is the automated grading of assignments, which solutions are composed of a list of commands, their outputs, and possible comments. In this paper, we focus on the automated classification of the comments, as “right” or “wrong”. In particular, we investigated the effect of different features (i.e., fastText, BERT, distance-based and custom features), fed to several classifiers (i.e., Logistic Regression, Support Vector Machines, Random Forest, Multi-Layer Perceptron – MLP), to select the best one in terms of best balanced accuracy. In the experiment carried out, the best result was obtained by the MLP classifier using the fastText embeddings. When instead fed with BERT embeddings, MLP obtained a slightly lower accuracy and F1 score, even if it remains the best option with respect to the other classifiers. Furthermore, we tested the classifier with comments given to different assignments (of the same structure), given by different students and evaluated by a different professor. Also in this case, we achieved a relatively good accuracy and F1 score.
A Graph Theoretic Approach for Multi-Objective Budget Constrained Capsule Wardrobe Recommendation

Traditionally, capsule wardrobes are manually designed by expert fashionistas through their creativity and technical prowess. The goal is to curate minimal fashion items that can be assembled into several compatible and versatile outfits. It is usually a cost and time intensive process, and hence lacks scalability. Although there are a few approaches that attempt to automate the process, they tend to ignore the price of items or shopping budget. In this article, we formulate this task as a multi-objective budget constrained capsule wardrobe recommendation ( MOBCCWR ) problem. It is modeled as a bipartite graph having two disjoint vertex sets corresponding to top-wear and bottom-wear items, respectively. An edge represents compatibility between the corresponding item pairs. The objective is to find a 1-neighbor subset of fashion items as a capsule wardrobe that jointly maximize compatibility and versatility scores by considering corresponding user-specified preference weight coefficients and an overall shopping budget as a means of achieving personalization. We study the complexity class of MOBCCWR , show that it is NP-Complete, and propose a greedy algorithm for finding a near-optimal solution in real time. We also analyze the time complexity and approximation bound for our algorithm. Experimental results show the effectiveness of the proposed approach on both real and synthetic datasets.
Enhancing Security of Cloud Data through Encryption with AES and Fernet Algorithm through Convolutional-Neural-Networks (CNN)

Cloud as a storage in the recent technological development had been focused by researchers, since it offers more insight towards meta-data based security and safety along with techniques in encryption and decryption of messages."Data" being a crucial and complicated means-of-information in current technological era, it has been majorly accessed and utilized for varied purposes (example: image storage/access) by people globally through 'cloud computing' via social platforms, personal data-storage, professional data-accumulation, research based studies, etc.Thus to protect data in cloud, especially the images, the current study developed the algorithm by combining 'AES' and 'Fernet' where double-level encryption with CNN Auto-Encoders.Thus by developing the model, the study aims to provide more secured cloud computing model than existing models.The original images as input are processed, encrypted/decrypted, converted into bitmap images as outputs that are decrypted by users with 'key' when needed.The study was a success and found to be effective in image encryption field with high RMSE (0.040206), less MSE-Loss (0.001616) and MAE (0.0266323) scores than estimated scores.
ENF Detection in Audio Recordings via Multi-Harmonic Combining

The detection of the electric network frequency (ENF) in digital recordings is an essential step before the subsequent ENF extraction and forensic analysis. In this letter, we extend the state-of-the-art single-tone time-frequency (TF) domain ENF detector to the multi-tone scenario and propose a multi-harmonic combining (MHC) method, exploiting ENF harmonic components for improved detection performance. To exclude the corrupted components interfering rather than contributing to ENF detection, the proposed detector first performs a pre-screening based on the estimated average subband signal-to-noise ratios (SNRs) to exclude interfering components. Then, with the selected harmonic candidates, a second screening process is applied based on the TF test statistics (TSs), i.e., the variances of observed subband traces. After that, the multi-harmonic components are combined to form the final TS, whose sign determines the final decision. The advantages of the proposed method are illustrated via both synthetic analysis and real-world experimental results using the ENF-WHU dataset.
Soft Warping Based Unsupervised Domain Adaptation for Stereo Matching

Stereo matching is a practical method to estimate depth information and retrieve 3D world in robot perception and autonomous driving scenarios. With the development of convolution neural networks (CNNs), deep-learning based stereo matching algorithms have significantly improved the accuracy and dominated most of the online benchmarks. However, limited labels in real world, especially in challenging weather conditions, still hinder the technology from practical usage. In this paper, we propose a new unsupervised learning mechanism for stereo matching, utilizing adversarial iterative learning and novel soft warping loss to promote the effectiveness of the networks in unseen environments. The experiments transferring the stereo matching module from synthetic domain to real-world domain demonstrate the superiority of our proposed method. Extensive experiments in challenging weathers further prove that our method shows great practical potential in strait environments.
Deformable registration of chest CT images using a 3D convolutional neural network based on unsupervised learning

The deformable registration of 3D chest computed tomography (CT) images is one of the most important tasks in the field of medical image registration. However, the nonlinear deformation and large-scale displacement of lung tissues caused by respiratory motion cause great challenges in the deformable registration of 3D lung CT images.We proposed an end-to-end fast registration method based on unsupervised learning, optimized the classic U-Net, and added inception modules between skip connections. The inception module attempts to capture and merge information at different spatial scales to generate a high-precision dense displacement vector field. To solve the problem of voxel folding in flexible registration, we put the Jacobian regularization term into the loss function to directly penalize the singularity of the displacement field during training to ensure a smooth displacement vector field. In the stage of data preprocessing, we segmented the lung fields to eliminate the interference of irrelevant information in the network during training. The existing publicly available datasets cannot implement model training. To alleviate the problem of overfitting caused by limited data resources being available, we proposed a data augmentation method based on the 3D-TPS (3D thin plate spline) transform to expand the training data.Compared with the experimental results obtained by using the VoxelMorph deep learning method and registration packages, such as ANTs and Elastix, we achieved a competitive target registration error of 2.09 mm, an optimal Dice score of 0.987, and almost no folding voxels. Additionally, the proposed method was much faster than the traditional methods.In this study, we have shown that the proposed method was efficient in 3D chest image registration. The promising results demonstrated that our method showed strong robustness in the deformable registration of 3D chest CT images.
RSKNet-MTSP: Effective and portable deep architecture for speaker verification

The convolutional neural network (CNN) based approaches have shown great success for speaker verification (SV) tasks, where modeling long temporal context and reducing information loss of speaker characteristics are two important challenges significantly affecting the verification performance. Previous works have introduced dilated convolution and multi-scale aggregation methods to address above challenges. However, such methods are also hard to make full use of some valuable information, which make it difficult to substantially improve the verification performance. To address above issues, we construct a novel CNN-based architecture for SV, called RSKNet-MTSP, where a residual selective kernel block (RSKBlock) and a multiple time-scale statistics pooling (MTSP) module are proposed. The RSKNet-MTSP can capture both long temporal context and neighbouring information, and gather more speaker-discriminative information from multi-scale features. In order to design a portable model for real applications with limited resources, we then present a lightweight version of RSKNet-MTSP, namely RSKNet-MTSP-L, which employs a combination technique associating the depthwise separable convolutions with low-rank factorization of weight matrices. Extensive experiments are conducted on two public SV datasets, VoxCeleb and Speaker in the Wild (SITW). The results demonstrate that 1) RSKNet-MTSP significantly outperforms the state-of-the-art deep embedding architectures on all five test sets; 2) RSKNet-MTSP-L achieves competitive performance with 17%-44% less network parameters compared to baseline models. The ablation experiments further illustrate that our proposed approaches can achieve substantial improvement over prior methods.
Track Coalescence and Repulsion: MHT, JPDA, and BP

Joint probabilistic data association (JPDA) and multiple hypothesis tracking (MHT) introduced in the 70s, are still widely used methods for multitarget tracking (MTT). Extensive studies over the last few decades have revealed undesirable behavior of JPDA and MHT type methods in tracking scenarios with targets in close proximity. In particular, JPDA suffers from the track coalescence effect, i.e., estimated tracks of targets in close proximity tend to merge and can become indistinguishable. Interestingly, in MHT, an opposite effect to track coalescence called track repulsion can be observed. In this paper, we review the estimation strategies of the MHT, JPDA, and the recently introduced belief propagation (BP) framework for MTT and we investigate if BP also suffers from these two detrimental effects. Our numerical results indicate that BP-based MTT can mostly avoid both track repulsion and coalescence.