Reviewerly/exHarmony huggingface dataset:
DeepFormableTag

Fiducial markers have been broadly used to identify objects or embed messages that can be detected by a camera. Primarily, existing detection methods assume that markers are printed on ideally planar surfaces. The size of a message or identification code is limited by the spatial resolution of binary patterns in a marker. Markers often fail to be recognized due to various imaging artifacts of optical/perspective distortion and motion blur. To overcome these limitations, we propose a novel deformable fiducial marker system that consists of three main parts: First, a fiducial marker generator creates a set of free-form color patterns to encode significantly large-scale information in unique visual codes. Second, a differentiable image simulator creates a training dataset of photorealistic scene images with the deformed markers, being rendered during optimization in a differentiable manner. The rendered images include realistic shading with specular reflection, optical distortion, defocus and motion blur, color alteration, imaging noise, and shape deformation of markers. Lastly, a trained marker detector seeks the regions of interest and recognizes multiple marker patterns simultaneously via inverse deformation transformation. The deformable marker creator and detector networks are jointly optimized via the differentiable photorealistic renderer in an end-to-end manner, allowing us to robustly recognize a wide range of deformable markers with high accuracy. Our deformable marker system is capable of decoding 36-bit messages successfully at ~29 fps with severe shape deformation. Results validate that our system significantly outperforms the traditional and data-driven marker methods. Our learning-based marker system opens up new interesting applications of fiducial markers, including cost-effective motion capture of the human body, active 3D scanning using our fiducial markers' array as structured light patterns, and robust augmented reality rendering of virtual objects on dynamic surfaces.
A Spoken English Teaching System Based on Speech Recognition and Machine Learning

The learning model and environment are two major constraints on spoken English learning by Chinese learners. The maturity of computer-aided language learning brings a new opportunity to spoken English learners. Based on speech recognition and machine learning, this paper designs a spoken English teaching system, and determines the overall architecture and functional modules of the system according to the system’s functional demand. Specifically, MATLAB was adopted to realize speech recognition, and generate a speech recognition module. Combined with machine learning algorithm, a deep belief network (DBN)-support vector machine (SVM) model was proposed to classify and detect the errors in pronunciation; the module also scores the quality and corrects the errors in pronunciation. This model was extended to a speech evaluation module was created. Next, several experiments were carried out to test multiple attributes of the system, including the accuracy of pronunciation classification and error detection, recognition rates of different environments and vocabularies, and the real-timeliness of recognition. The results show that our system achieved good performance, realized the preset design goals, and satisfied the user demand. This research provides an important theoretical and practical reference to transforming English teaching method, and improving the spoken English of learners.
Subjective Assessment Experiments That Recruit Few Observers With Repetitions (FOWR)

Recent studies have shown that it is possible to characterize subject bias and variance in subjective assessment tests. Apparent differences among subjects can, for the most part, be explained by random factors. Building on that theory, we propose a subjective test design where three to four team members each rate the stimuli multiple times. The results are comparable to a high performing objective metric. This provides a quick and simple way to analyze new technologies and perform pre-tests for subjective assessment.
Pix2Pix-Based Grayscale Image Coloring Method

<p indent=0mm>In this study, a grayscale image coloring method combining the Pix2Pix model is proposed to solve the problem of unclear object boundaries and low image coloring quality in colorization neural networks. First, an improved U-Net structure, using eight down-sampling and up-sampling layers, is adopted to extract features and predict the image color, which improves the network model’s ability to extract deep image features. Second, the coloring image quality is tested under different loss functions, <inline-formula id=inline5><mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML display=inline><mml:mrow><mml:msub><mml:mi>L</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>loss and smooth <inline-formula id=inline6><mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML display=inline><mml:mrow><mml:msub><mml:mi>L</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>loss, to measure the distance between the generated image and ground truth. Finally, gradient penalty is added to improve the network stability of the training process. The gradient of each input data is penalized by constructing a new data distribution between the generated and real image distribution to limit the discriminator gradient. In the same experimental environment, the Pix2Pix model and summer2winter data are utilized for comparative analysis. The experiments demonstrate that the improved U-Net using the smooth <inline-formula id=inline7><mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML display=inline><mml:mrow><mml:msub><mml:mi>L</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>loss as generator loss generates better colored images, whereas the <inline-formula id=inline8><mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML display=inline><mml:mrow><mml:msub><mml:mi>L</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>loss better maintains the structural information of the image. Furthermore, the gradient penalty accelerates the model convergence speed, and improves the model stability and image quality. The proposed image coloring method learns deep image features and reduces the image blurs. The model raises the image quality while effectively maintaining the image structure similarity.
Photon indistinguishability measurements under pulsed and continuous excitation

The indistinguishability of successively generated photons from a single quantum emitter is most commonly measured using two-photon interference at a beam splitter. Whilst for sources excited in the pulsed regime the measured bunching of photons reflects the full wave-packet indistinguishability of the emitted photons, for continuous wave (cw) excitation, the inevitable dependence on detector timing resolution and driving strength obscures the underlying photon interference process. Here we derive a method to extract full photon wave-packet indistinguishability from cw measurements by considering the relevant correlation functions. The equivalence of both methods is experimentally verified through a comparison of cw and pulsed excitation measurements on an archetypal source of photons, a single molecule.
An assessment of cybersecurity awareness level among Northeastern University students in Nigeria

&lt;p&gt;The world economy today has adopted the internet as a medium of transactions, this has made many organizations use the internet for their daily activities. With this, there is an urgent need to have knowledge in cybersecurity and also how to defend critical assets. The objective of this paper is to identify the level of cybersecurity awareness of students in Northeastern Nigeria. A quantitative approach was used for data collection and cyberbully, personal information, internet banking, internet addiction, and Self-protection were the items ask for cybersecurity awareness level identification. Descriptive analysis was performed for initial result findings using SPSS and OriginPro for graphical design. the preliminary result shows of the students have some basic knowledge of cybersecurity in an item like internet banking, while other items like cyberbully, self-protection and, internet addiction result show moderate awareness, the students' participation based on gender, males constitute 77.1% i.e. (N=340) and females constitute 22.9% i.e. (N=101). Future research would concentrate on designing awareness programs that would increase the level of their awareness especially the students in the Northeastern part of Nigeria.&lt;/p&gt;
Comparative Study of VGG16 and MobileNetV2 for Masked Face Recognition

Indonesia is one of the countries affected by the coronavirus pandemic, which has taken too many lives. The coronavirus pandemic forces us to continue to wear masks daily, especially when working to break the chain of the spread of the coronavirus. Before the pandemic, face recognition for attendance used the entire face as input data, so the results were accurate. However, during this pandemic, all employees use masks, including attendance, which can reduce the level of accuracy when using masks. In this research, we use a deep learning technique to recognize masked faces. We propose using transfer learning pre-trained models to perform feature extraction and classification of masked face image data. The use of transfer learning techniques is due to the small amount of data used. We analyzed two transfer learning models, namely VGG16 and MobileNetV2. The parameters of batch size and number of epochs were used to evaluate each model. The best model is obtained with a batch size value of 32 and the number of epochs 50 in each model. The results showed that using the MobileNetV2 model was more accurate than VGG16, with an accuracy value of 95.42%. The results of this study can provide an overview of the use of transfer learning techniques for masked face recognition.
UIUC_BioNLP at SemEval-2021 Task 11: A Cascade of Neural Models for Structuring Scholarly NLP Contributions

We propose a cascade of neural models that performs sentence classification, phrase recognition, and triple extraction to automatically structure the scholarly contributions of NLP publications in English.To identify the most important contribution sentences in a paper, we used a BERT-based classifier with positional features (Subtask 1).A BERT-CRF model was used to recognize and characterize relevant phrases in contribution sentences (Subtask 2).We categorized the triples into several types based on whether and how their elements were expressed in text, and addressed each type using separate BERT-based classifiers as well as rules (Subtask 3).Our system was officially ranked second in Phase 1 evaluation and first in both parts of Phase 2 evaluation.After fixing a submission error in Phase 1, our approach yielded the best results overall.In this paper, in addition to a system description, we also provide further analysis of our results, highlighting its strengths and limitations.We make our code publicly available at
Realistic Evaluation Principles for Cross-document Coreference Resolution

We point out that common evaluation practices for cross-document coreference resolution have been unrealistically permissive in their assumed settings, yielding inflated results.We propose addressing this issue via two evaluation methodology principles.First, as in other tasks, models should be evaluated on predicted mentions rather than on gold mentions.Doing this raises a subtle issue regarding singleton coreference clusters, which we address by decoupling the evaluation of mention detection from that of coreference linking.Second, we argue that models should not exploit the synthetic topic structure of the standard ECB+ dataset, forcing models to confront the lexical ambiguity challenge, as intended by the dataset creators.We demonstrate empirically the drastic impact of our more realistic evaluation principles on a competitive model, yielding a score which is 33 F1 lower compared to evaluating by prior lenient practices. 1
DACBench: A Benchmark Library for Dynamic Algorithm Configuration

Dynamic Algorithm Configuration (DAC) aims to dynamically control a target algorithm's hyperparameters in order to improve its performance. Several theoretical and empirical results have demonstrated the benefits of dynamically controlling hyperparameters in domains like evolutionary computation, AI Planning or deep learning. Replicating these results, as well as studying new methods for DAC, however, is difficult since existing benchmarks are often specialized and incompatible with the same interfaces. To facilitate benchmarking and thus research on DAC, we propose DACBench, a benchmark library that seeks to collect and standardize existing DAC benchmarks from different AI domains, as well as provide a template for new ones. For the design of DACBench, we focused on important desiderata, such as (i) flexibility, (ii) reproducibility, (iii) extensibility and (iv) automatic documentation and visualization. To show the potential, broad applicability and challenges of DAC, we explore how a set of six initial benchmarks compare in several dimensions of difficulty.
An Algebraic Quantum Circuit Compression Algorithm for Hamiltonian Simulation

Quantum computing is a promising technology that harnesses the peculiarities of quantum mechanics to deliver computational speedups for some problems that are intractable to solve on a classical computer.Current generation noisy intermediate-scale quantum (NISQ) computers are severely limited in terms of chip size and error rates.Shallow quantum circuits with uncomplicated topologies are essential for successful applications in the NISQ era.Based on matrix analysis, we derive localized circuit transformations to efficiently compress quantum circuits for simulation of certain spin Hamiltonians known as free fermions.The depth of the compressed circuits is independent of simulation time and grows linearly with the number of spins.The proposed numerical circuit compression algorithm behaves backward stable and scales cubically in the number of spins enabling circuit synthesis beyond O(10 3 ) spins.The resulting quantum circuits have a simple nearest-neighbor topology, which makes them ideally suited for NISQ devices.
Cascade and Fusion: A Deep Learning Approach for Camouflaged Object Sensing

The demand for the sensor-based detection of camouflage objects widely exists in biological research, remote sensing, and military applications. However, the performance of traditional object detection algorithms is limited, as they are incapable of extracting informative parts from low signal-to-noise ratio features. To address this problem, we propose Camouflaged Object Detection with Cascade and Feedback Fusion (CODCEF), a deep learning framework based on an RGB optical sensor that leverages a cascaded structure with Feedback Partial Decoders (FPD) instead of a traditional encoder–decoder structure. Through a selective fusion strategy and feedback loop, FPD reduces the loss of information and the interference of noises in the process of feature interweaving. Furthermore, we introduce Pixel Perception Fusion (PPF) loss, which aims to pay more attention to local pixels that might become the edges of an object. Experimental results on an edge device show that CODCEF achieved competitive results compared with 10 state-of-the-art methods.
Development of an Internet-of-Healthcare System Using Blockchain

The Internet-of-Healthcare-Systems is a highly distributed special emulation of the Internet of Things technology using web agents as front-ends to the many and varied internal patient information systems of the participating hospitals, providing a canonical interface to all those diverse hospital systems.Extending on previously developed and distributed health care apps, blockchain technology has been applied for the storage of sensitive patient data that requires a high degree of security that is imperative for patient data and medical histories.Data sourced from participating hospitals are integrated with a decentralized storage system using blockchain technology, guaranteeing secure access and control, and veracity of that data over time.Web agents, accessed using the Message Queueing Telemetry Transport protocol, efficiently handle potentially thousands of recognised local systems without loss of network timeliness.Mobile device apps have been developed that enable secure direct access to patient data in a central blockchain with download capability to a mobile device, under strict access control using Amazon Web Services under fully managed access control, with permissions controlled by the Key Management System.The Internet-of-Healthcare-Systems model is an exemplar for many different types of secure networks that could be generically called an Internet of Special Things; a network of web agents programmed for a special purpose.Security analysis, and feedback from participating medical staff, indicates security improvements and a high level of satisfaction with all aspects of the system: ease of use, ease of installation, maintenance and update, and, importantly, the security of the system.
Interacting with Explanations through Critiquing

Using personalized explanations to support recommendations has been shown to increase trust and perceived quality. However, to actually obtain better recommendations, there needs to be a means for users to modify the recommendation criteria by interacting with the explanation. We present a novel technique using aspect markers that learns to generate personalized explanations of recommendations from review texts, and we show that human users significantly prefer these explanations over those produced by state-of-the-art techniques. Our work's most important innovation is that it allows users to react to a recommendation by critiquing the textual explanation: removing (symmetrically adding) certain aspects they dislike or that are no longer relevant (symmetrically that are of interest). The system updates its user model and the resulting recommendations according to the critique. This is based on a novel unsupervised critiquing method for single- and multi-step critiquing with textual explanations. Empirical results show that our system achieves good performance in adapting to the preferences expressed in multi-step critiquing and generates consistent explanations.
Unsupervised Progressive Learning and the STAM Architecture

We first pose the Unsupervised Progressive Learning (UPL) problem: an online representation learning problem in which the learner observes a non-stationary and unlabeled data stream, learning a growing number of features that persist over time even though the data is not stored or replayed. To solve the UPL problem we propose the Self-Taught Associative Memory (STAM) architecture. Layered hierarchies of STAM modules learn based on a combination of online clustering, novelty detection, forgetting outliers, and storing only prototypical features rather than specific examples. We evaluate STAM representations using clustering and classification tasks. While there are no existing learning scenarios that are directly comparable to UPL, we compare the STAM architecture with two recent continual learning models, Memory Aware Synapses (MAS) and Gradient Episodic Memories (GEM), after adapting them in the UPL setting.
On the post-hoc explainability of deep echo state networks for time series forecasting, image and video classification

Since their inception, learning techniques under the reservoir computing paradigm have shown a great modeling capability for recurrent systems without the computing overheads required for other approaches, specially deep neural networks. Among them, different flavors of echo state networks have attracted many stares through time, mainly due to the simplicity and computational efficiency of their learning algorithm. However, these advantages do not compensate for the fact that echo state networks remain as black-box models whose decisions cannot be easily explained to the general audience. This issue is even more involved for multi-layered (also referred to as deep) echo state networks, whose more complex hierarchical structure hinders even further the explainability of their internals to users without expertise in machine learning or even computer science. This lack of explainability can jeopardize the widespread adoption of these models in certain domains where accountability and understandability of machine learning models is a must (e.g., medical diagnosis, social politics). This work addresses this issue by conducting an explainability study of echo state networks when applied to learning tasks with time series, image and video data. Among these tasks, we stress on the latter one (video classification) which, to the best of our knowledge, has never been tackled before with echo state networks in the related literature. Specifically, the study proposes three different techniques capable of eliciting understandable information about the knowledge grasped by these recurrent models, namely potential memory, temporal patterns and pixel absence effect. Potential memory addresses questions related to the effect of the reservoir size in the capability of the model to store temporal information, whereas temporal patterns unveil the recurrent relationships captured by the model over time. Finally, pixel absence effect attempts at evaluating the effect of the absence of a given pixel when the echo state network model is used for image and video classification. The benefits of the proposed suite of techniques are showcased over three different domains of applicability: time series modeling, image and, for the first time in the related literature, video classification. The obtained results reveal that the proposed techniques not only allow for an informed understanding of the way these models work, but also serve as diagnostic tools capable of detecting issues inherited from data (e.g., presence of hidden bias).
Deep Learning-Based Automatic Modulation Classification With Blind OFDM Parameter Estimation

Automatic modulation classification (AMC) is an essential factor in dynamic spectrum access to fulfill the spectrum demand of 5G wireless communications for achieving high data rate and low latency. Many deep learning (DL)-based AMC methods have achieved improved accuracy for classifying analog modulation schemes, single-carrier-based modulation schemes, and multi-carrier signals using several DL architectures such as the convolutional neural network (CNN) and long-short term memory (LSTM). However, most conventional DL-based AMC methods have confused the orthogonal frequency multiplexing division (OFDM)-based signals with different OFDM useful symbol lengths. To resolve the issue, we propose a CNN model operating on the fast Fourier transformation window bank (FWB) to extract the useful symbol length in OFDM, which represents the identification of each OFDM-based wireless communication technology. After extracting the OFDM useful symbol length, we propose a DL-based AMC system combined with FWB and in-phase and quadrature-phase signals to classify the OFDM symbol length and single-carrier modulation schemes simultaneously. Furthermore, we explore the constraints of the FWB parameters according to the length and the fast Fourier transformation (FFT) size of the OFDM signal to achieve good classification accuracy through the experiment. We constructed a dataset by generating OFDM signals of different lengths while changing the FFT size in a fixed bandwidth and selecting only quadrature amplitude modulation (QAM) schemes from RadioML2016.10a. Experimental results show the improved classification accuracy by about 30% over conventional classifiers in additive white Gaussian noise, synchronization impairments, and fading environments.