Reviewerly/exHarmony huggingface dataset:
In Between 21st Century Skills and Constructivism in ELT: Designing a Model Derived From a Narrative Literature Review

While the 21st-century demands the learners to be technologically competent and self-driven to cope with technological advancement, the educational practitioners find it challenging to prepare the young learners to keep pace with this demand. However, in doing so, to prepare the learners as adaptable to the 21st-century demands, constructivism can be envisaged as the most time-appropriate and updated theory of the teaching-learning process, compared to other approaches. Specifically, because one of the requirements from the learners of the 21st-century is to be self-driven and take ownership of the learning, constructivism is the closest theory to achieve this goal. This position paper aims to build a procedural link within 21st-century expectations and its cooperation with the constructivist learning approach. The paper also offers a design for constructivist teaching in ELT classrooms in the 21st-century and defines the role of teachers and students, as established by the literature.
A Transformer-Based Neural Machine Translation Model for Arabic Dialects That Utilizes Subword Units

Languages that allow free word order, such as Arabic dialects, are of significant difficulty for neural machine translation (NMT) because of many scarce words and the inefficiency of NMT systems to translate these words. Unknown Word (UNK) tokens represent the out-of-vocabulary words for the reason that NMT systems run with vocabulary that has fixed size. Scarce words are encoded completely as sequences of subword pieces employing the Word-Piece Model. This research paper introduces the first Transformer-based neural machine translation model for Arabic vernaculars that employs subword units. The proposed solution is based on the Transformer model that has been presented lately. The use of subword units and shared vocabulary within the Arabic dialect (the source language) and modern standard Arabic (the target language) enhances the behavior of the multi-head attention sublayers for the encoder by obtaining the overall dependencies between words of input sentence for Arabic vernacular. Experiments are carried out from Levantine Arabic vernacular (LEV) to modern standard Arabic (MSA) and Maghrebi Arabic vernacular (MAG) to MSA, Gulf-MSA, Nile-MSA, Iraqi Arabic (IRQ) to MSA translation tasks. Extensive experiments confirm that the suggested model adequately addresses the unknown word issue and boosts the quality of translation from Arabic vernaculars to Modern standard Arabic (MSA).
A Spatial Guided Self-supervised Clustering Network for Medical Image Segmentation

The segmentation of medical images is a fundamental step in automated clinical decision support systems. Existing medical image segmentation methods based on supervised deep learning, however, remain problematic because of their reliance on large amounts of labelled training data. Although medical imaging data repositories continue to expand, there has not been a commensurate increase in the amount of annotated data. Hence, we propose a new spatial guided self-supervised clustering network (SGSCN) for medical image segmentation, where we introduce multiple loss functions designed to aid in grouping image pixels that are spatially connected and have similar feature representations. It iteratively learns feature representations and clustering assignment of each pixel in an end-to-end fashion from a single image. We also propose a context-based consistency loss that better delineates the shape and boundaries of image regions. It enforces all the pixels belonging to a cluster to be spatially close to the cluster centre. We evaluated our method on 2 public medical image datasets and compared it to existing conventional and self-supervised clustering methods. Experimental results show that our method was most accurate for medical image segmentation.
Generating Infinitely Many Coexisting Attractors via a New 3D Cosine System and Its Application in Image Encryption

In this paper, a new third-order chaotic system which has extremely multistability is constructed by introducing the boosted control of cosine function. In comparison with other chaotic systems of extremely multistability, the proposed chaotic system can spontaneously generate the infinitely many coexisting attractors towards two directions of the phase plane. It indicates the proposed system can output more chaotic sequences of different amplitudes at the same time. This peculiar physical phenomena is very interesting and worth studying. Relative to original chaotic system, the chaos characteristic of the proposed system is obviously enhanced, the value of max Lyapunov exponent is increased significantly and the complexity value was higher. In particular, many periodic windows of the original chaotic system become chaos. It means the proposed chaotic system has better chaotic characteristics. If the new system is applied to the field of cryptography, it would be a better system model as a pseudo-random signal generator (PRSG). Then, the new image encryption algorithm is designed based on the proposed discrete system, and its safety performance is tested. The experimental results demonstrate the feasibility of its application in the field of cryptography.
Quantifying Qubit Magic Resource with Gottesman-Kitaev-Preskill Encoding

Quantum resource theories are a powerful framework for characterizing and quantifying relevant quantum phenomena and identifying processes that optimize their use for different tasks. Here, we define a resource measure for magic, the sought-after property in most fault-tolerant quantum computers. In contrast to previous literature, our formulation is based on bosonic codes, well-studied tools in continuous-variable quantum computation. Particularly, we use the Gottesman-Kitaev-Preskill code to represent multiqubit states and consider the resource theory for the Wigner negativity. Our techniques are useful in finding resource lower bounds for different applications as state conversion and gate synthesis. The analytical expression of our magic measure allows us to extend current analysis limited to small dimensions, easily addressing systems of up to 12 qubits.
Longitudinal Self-supervision to Disentangle Inter-patient Variability from Disease Progression

The problem of building disease progression models with longitudinal data has long been addressed with parametric mixed-effect models. They provide interpretable models at the cost of modeling assumptions on the progression profiles and their variability across subjects. Their deep learning counterparts, on the other hand, strive on flexible data-driven modeling, and additional interpretability - or, as far as generative models are involved, disentanglement of latent variables with respect to generative factors - comes from additional constraints. In this work, we propose a deep longitudinal model designed to disentangle inter-patient variability from an estimated disease progression timeline. We do not seek for an explicit mapping between age and disease stage, but to learn the latter solely from the ordering between visits using a differentiable ranking loss. Furthermore, we encourage inter-patient variability to be encoded in a separate latent space, where for each patient a single representation is learned from its set of visits, with a constraint of invariance under permutation of the visits. The modularity of the network architecture allows us to apply our model on various data types: a synthetic image dataset with known generative factors, cognitive assessments and neuroimaging data. We show that, combined with our patient encoder, the ranking loss for visits helps to exceed models with supervision, in particular in terms of disease staging.
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP

The principle of independent causal mechanisms (ICM) states that generative processes of real world data consist of independent modules which do not influence or inform each other.While this idea has led to fruitful developments in the field of causal inference, it is not widely-known in the NLP community.In this work, we argue that the causal direction of the data collection process bears nontrivial implications that can explain a number of published NLP findings, such as differences in semi-supervised learning (SSL) and domain adaptation (DA) performance across different settings.We categorize common NLP tasks according to their causal direction and empirically assay the validity of the ICM principle for text data using minimum description length.We conduct an extensive meta-analysis of over 100 published SSL and 30 DA studies, and find that the results are consistent with our expectations based on causal insights.This work presents the first attempt to analyze the ICM principle in NLP, and provides constructive suggestions for future modeling choices.
Hash Algorithm In Verification Of Certificate Data Integrity And Security

The hash function is the most important cryptographic primitive function and is an integral part of the blockchain data structure. Hashes are often used in cryptographic protocols, information security applications such as Digital Signatures and message authentication codes (MACs). In the current development of certificate data security, there are 2 (two) types of hashes that are widely applied, namely, MD and SHA. However, when it comes to efficiency, in this study the hash type SHA-256 is used because it can be calculated faster with a better level of security. In the hypothesis, the Merkle-Damgård construction method is also proposed to support data integrity verification. Moreover, a cryptographic hash function is a one-way function that converts input data of arbitrary length and produces output of a fixed length so that it can be used to securely authenticate users without storing passwords locally. Since basically, cryptographic hash functions have many different uses in various situations, this research resulted in the use of hash algorithms in verifying the integrity and authenticity of certificate information.
Non-Clifford gate on optical qubits by nonlinear feedforward

In a continuous-variable optical system, the Gottesman-Kitaev-Preskill (GKP) qubit is a promising candidate for fault-tolerant quantum computation. To implement non-Clifford operations on GKP qubits, non-Gaussian operations are required. In this context, the implementation of a cubic phase gate by combining nonlinear feedforward with ancillary states has been widely researched. Recently, however, it was pointed out that the cubic phase gate is not the most suitable for non-Clifford operations on GKP qubits. In this paper, we show that we can achieve linear optical implementation of non-Clifford operations on GKP qubits with high fidelity by applying the nonlinear feedforward originally developed for the cubic phase gate and using a GKP-encoded ancillary state. Our paper shows the versatility of the nonlinear feedforward technique---important for optical implementation of the fault-tolerant continuous-variable quantum computation.
Adversarial Attacks and Defense in Deep Reinforcement Learning (DRL)-Based Traffic Signal Controllers

Security attacks on intelligent transportation systems (ITS) may result in life-threatening situations. Combining deep neural networks with reinforcement learning (RL) models called DRL shows promising results when applied to urban Traffic Signal Control (TSC) for adaptive adjustment of traffic light schedules. In this paper, first, we explore the security vulnerabilities of DRL-based TSCs in the presence of adversarial attacks. We investigate the impact of the two distinct threat models with two state-of-the-art adversarial attacks using white-box and black-box settings. The attacks are simulated on different DRL-based TSC algorithms in a single intersection and multiple intersections. The results show that the performance of the DRL learning agent decreases in both adversarial attack models with white-box and black-box settings resulting in higher levels of traffic congestion. After analysing the adversarial attack models, we explored several sequential anomaly detection models. While sequential anomaly detection models minimizes the detection delays, it also achieves lower false alarm rates due to cumulative anomaly inspection. We also proposed an ensemble model that works with all the attack models without any model assumption. The results of anomaly detectors indicates that low-cost ensemble model achieves the best anomaly detection performance in all attack models and DRL settings.
3D Face Recognition Based on an Attention Mechanism and Sparse Loss Function

Face recognition is one of the essential applications in computer vision, while current face recognition technology is mainly based on 2D images without depth information, which are easily affected by illumination and facial expressions. This paper presents a fast face recognition algorithm combining 3D point cloud face data with deep learning, focusing on key part of face for recognition with an attention mechanism, and reducing the coding space by the sparse loss function. First, an attention mechanism-based convolutional neural network was constructed to extract facial features to avoid expressions and illumination interference. Second, a Siamese network was trained with a sparse loss function to minimize the face coding space and enhance the separability of the face features. With the FRGC face dataset, the experimental results show that the proposed method could achieve the recognition accuracy of 95.33%.
BBQ: A hand-built bias benchmark for question answering

It is well documented that NLP models learn social biases, but little work has been done on how these biases manifest in model outputs for applied tasks like question answering (QA).We introduce the Bias Benchmark for QA (BBQ), a dataset of question sets constructed by the authors that highlight attested social biases against people belonging to protected classes along nine social dimensions relevant for U.S. English-speaking contexts.Our task evaluates model responses at two levels: (i) given an under-informative context, we test how strongly responses refect social biases, and (ii) given an adequately informative context, we test whether the model's biases override a correct answer choice.We fnd that models often rely on stereotypes when the context is under-informative, meaning the model's outputs consistently reproduce harmful biases in this setting.Though models are more accurate when the context provides an informative answer, they still rely on stereotypes and average up to 3.4 percentage points higher accuracy when the correct answer aligns with a social bias than when it conficts, with this difference widening to over 5 points on examples targeting gender for most models tested.
Cirrus: A Long-range Bi-pattern LiDAR Dataset

In this paper, we introduce Cirrus, a new long-range bi-pattern LiDAR public dataset for autonomous driving tasks such as 3D object detection, critical to highway driving and timely decision making. Our platform is equipped with a high-resolution video camera and a pair of LiDAR sensors with a 250-meter effective range, which is significantly longer than existing public datasets. We record paired point clouds simultaneously using both Gaussian and uniform scanning patterns. Point density varies significantly across such a long range, and different scanning patterns further diversify object representation in LiDAR. In Cirrus, eight categories of objects are exhaustively annotated in the LiDAR point clouds for the entire effective range. To illustrate the kind of studies supported by this new dataset, we introduce LiDAR model adaptation across different ranges, scanning patterns, and sensor devices. Promising results show the great potential of this new dataset to the robotics and computer vision communities.
DialFact: A Benchmark for Fact-Checking in Dialogue

Fact-checking is an essential tool to mitigate the spread of misinformation and disinformation.We introduce the task of fact-checking in dialogue, which is a relatively unexplored area.We construct DIALFACT, a testing benchmark dataset of 22,245 annotated conversational claims, paired with pieces of evidence from Wikipedia.There are three sub-tasks in DIALFACT: 1) Verifiable claim detection task distinguishes whether a response carries verifiable factual information; 2) Evidence retrieval task retrieves the most relevant Wikipedia snippets as evidence; 3) Claim verification task predicts a dialogue response to be supported, refuted, or not enough information.We found that existing fact-checking models trained on non-dialogue data like FEVER (Thorne et al., 2018) fail to perform well on our task, and thus, we propose a simple yet data-efficient solution to effectively improve fact-checking performance in dialogue.We point out unique challenges in DIALFACT such as handling the colloquialisms, coreferences and retrieval ambiguities in the error analysis to shed light on future research in this direction 1 .Dialogue Context: I have family in Ireland!Have you ever been there?Evidence: Ireland is an island in the North Atlantic.
Sequential Bayesian experiment design for adaptive Ramsey sequence measurements

The Ramsey sequence is a canonical example of a quantum phase measurement for a spin qubit. In Ramsey measurements, the measurement efficiency can be optimized through careful selection of settings for the phase accumulation time setting, τ. This paper implements a sequential Bayesian experiment design protocol in low-fidelity Ramsey measurements, and its performance is compared to a previously reported adaptive heuristic protocol, a quantum phase estimation algorithm, and random setting choices. A workflow allowing measurements and design calculations to run concurrently largely eliminates computation time from measurement overhead. When precession frequency is the lone parameter to estimate, the Bayesian design is faster by factors of roughly 2 and 4 and 5 relative to the adaptive heuristic, random τ choices and the quantum phase estimation algorithm respectively. When four parameters are to be determined, Bayesian experiment design and random τ choices can converge to roughy equivalent sensitivity, but the Bayesian method converges 4 times faster.
A Novel Deep Learning-based Online Proctoring System using Face Recognition, Eye Blinking, and Object Detection Techniques

Distance and online learning (or e-learning) has become a norm in training and education due to a variety of benefits such as efficiency, flexibility, affordability, and usability.Moreover, the COVID-19 pandemic has made online learning the only option due to its physical isolation requirements.However, monitoring of attendees and students during classes, particularly during exams, is a major challenge for online systems due to the lack of physical presence.There is a need to develop methods and technologies that provide robust instruments to detect unfair, unethical, and illegal behaviour during classes and exams.We propose in this paper a novel online proctoring system that uses deep learning to continually proctor physical places without the need for a physical proctor.The system employs biometric approaches such as face recognition using the HOG (Histogram of Oriented Gradients) face detector and the OpenCV face recognition algorithm.Also, the system incorporates eye blinking detection to detect stationary pictures.Moreover, to enforce fairness during exams, the system is able to detect gadgets including mobile phones, laptops, iPads, and books.The system is implemented as a software system and evaluated using the FDDB and LFW datasets.We achieved up to 97% and 99.3% accuracies for face detection and face recognition, respectively.
Return Rate Prediction in Blockchain Financial Products Using Deep Learning

Recently, bitcoin-based blockchain technologies have received significant interest among investors. They have concentrated on the prediction of return and risk rates of the financial product. So, an automated tool to predict the return rate of bitcoin is needed for financial products. The recently designed machine learning and deep learning models pave the way for the return rate prediction process. In this aspect, this study develops an intelligent return rate predictive approach using deep learning for blockchain financial products (RRP-DLBFP). The proposed RRP-DLBFP technique involves designing a long short-term memory (LSTM) model for the predictive analysis of return rate. In addition, Adam optimizer is applied to optimally adjust the LSTM model’s hyperparameters, consequently increasing the predictive performance. The learning rate of the LSTM model is adjusted using the oppositional glowworm swarm optimization (OGSO) algorithm. The design of the OGSO algorithm to optimize the LSTM hyperparameters for bitcoin return rate prediction shows the novelty of the work. To ensure the supreme performance of the RRP-DLBFP technique, the Ethereum (ETH) return rate is chosen as the target, and the simulation results are investigated in different measures. The simulation outcomes highlighted the supremacy of the RRP-DLBFP technique over the current state of art techniques in terms of diverse evaluation parameters. For the MSE, the proposed RRP-DLBFP has 0.0435 and 0.0655 compared to an average of 0.6139 and 0.723 for compared methods in training and testing, respectively.
Application of Apriori Improvement Algorithm in Asthma Case Data Mining

In Chinese medicine, asthma cases contain a large amount of empirical data which are obtained from the clinical diagnosis of doctors throughout the year. Data correlation analysis method is among the common mechanisms which are used to mine association between the (1) prescriptions and prescribers (doctors in this case) and (2) symptoms and medications for a particular disease in the hospitals. In this paper, initially, a thorough analysis of expected performance and shortcomings of the Apriori algorithm in mining of medical case data is presented. Secondly, we propose an extended version of the traditional Apriori algorithm which is primarily based on the fast response of computer to bit-string logic operation. A comparative evaluation of the proposed and existing Apriori algorithms is presented particularly in terms of running time, mining of frequent items set and strong association rules. Both experimental and simulation results have proved that the proposed extended Apriori algorithm has outperformed existing algorithms when it is applied to asthma medication and combined symptom-medication data for the association analysis. Furthermore, the association relationship between mind asthma case data and medication is effective in the analysis of asthma case data with significant application value which is verified by the experimental data and observations.
Autonomous Driving Robot That Drives and Returns along a Planned Route in Underground Mines by Recognizing Road Signs

In this study, an autonomous driving robot that drives and returns along a planned route in an underground mine tunnel was developed using a machine-vision-based road sign recognition algorithm. The robot was designed to recognize road signs at the intersection of a tunnel using a geometric matching algorithm of machine vision, and the autonomous driving mode was switched according to the shape of the road sign to drive the robot according to the planned route. The autonomous driving mode recognized the shape of the tunnel using the distance data from the LiDAR sensor; it was designed to drive while maintaining a fixed distance from the centerline or one wall of the tunnel. A machine-vision-based road sign recognition system and an autonomous driving robot for underground mines were used in a field experiment. The results reveal that all road signs were accurately recognized, and the average matching score was 979.14 out of 1000, confirming stable driving along the planned route.