Reviewerly/exHarmony huggingface dataset:
Improving the Learning of Regular Verbs through TikTok among Primary School ESL Pupils

In English as a second language (ESL) learning vocabulary items plays a vital role in all language skills (i.e.listening, speaking, reading, and writing).The importance of vocabulary items has been highlighted by experts at the University of Reading.Upper primary ESL learners are facing difficulties when it comes to vocabulary mastery.Therefore, this study aims to identify the effectiveness of TikTok in improving the use of vocabulary mainly focusing on regular verbs with a particular representation by a group of Year 5 pupils.This study was conducted using mixed method which is a combination of collecting qualitative and quantitative data.45 Year 5 pupils were chosen using convenience sampling as the participants of this study.The participants sat for a pre-test and after intervening the lesson with TikTok those participants sat for post-test.The data was then triangulated by interviewing 10 pupils from the same group using semi-structured interview that was adapted by the Technology Acceptance Model (TAM).After analysing the data collected from both the instruments, the findings showed that pupils improved tremendously in constructing a sentence with regular verbs after teaching regular verbs using TikTok.The results further revealed that the pupils really enjoyed the lesson when TikTok was involved, found it effective and very easy to use in making their own verb videos, and most of them were keen in creating educational contents in future.This study concludes that TikTok is indeed an effective teaching tool, especially when it comes to teaching regular verbs and sentence construction.
VC-YOLO: Towards Real-Time Object Detection in Aerial Images

Object detection for aerial images is a crucial and challenging task in the field of computer vision. Previous CNN-based methods face problems related to extreme variation of object scales and the complex background in aerial images, which vary significantly from natural scenes. On the other hand, a great many of existing detectors highly rely on computational performance and cannot handle real-time tasks. To address this problems, we propose a lightweight real-time object detection network which is named VC-YOLO. In the backbone part, we introduce a receptive field extended backbone with limited number of convolution layers to learn the features and context information of various objects. In the detection part, channel attention module and spatial attention module are used to generate discriminative feature representation. To make full use of semantic feature maps in backbone network, we improve the feature pyramid network (FPN) with more lateral connections to reuse the features in each convolution stage. We evaluate VC-YOLO on NWPU VHR-10 and VisDrone benchmark datasets. Experimental results show that VC-YOLO achieves superior detection accuracy with high efficiency compared with the existing methods.
Combining exploratory learning with structured practice educational technologies to foster both conceptual and procedural fractions knowledge

Abstract Educational technologies in mathematics typically focus on fostering either procedural knowledge by means of structured tasks or, less often, conceptual knowledge by means of exploratory tasks. However, both types of knowledge are needed for complete domain knowledge that persists over time and supports subsequent learning. We investigated in two quasi-experimental studies whether a combination of an exploratory learning environment, providing exploratory tasks, and an intelligent tutoring system, providing structured tasks, fosters procedural and conceptual knowledge more than the intelligent tutoring system alone. Participants were 121 students from the UK (aged 8–10 years old) and 151 students from Germany (aged 10–12 years old) who were studying equivalent fractions. Results confirmed that students learning with a combination of exploratory and structured tasks gained more conceptual knowledge and equal procedural knowledge compared to students learning with structured tasks only. This supports the use of different but complementary educational technologies, interleaving exploratory and structured tasks, to achieve a “combination effect” that fosters robust fractions knowledge.
Videofall - A Hierarchical Search Engine for VBS2022

In this paper, we introduce a multi-user hierarchical video search tool called Videofall. Our objective, in the Video Browser Showdown (VBS) 2022, is to explore if Videofall interactive video retrieval under time constraints is a useful approach to take, given the overhead of requiring multiple users. It is our conjecture that combining different skills of normal users can support a master user to retrieve target videos efficiently. The system is designed on top of the CLIP pre-trained model and the video keyframes are embedded into a vector space in which queries would also be encoded to facilitate retrieval.
Time series forecasting by the novel Gaussian process wavelet self-join adjacent-feedback loop reservoir model

For a long time, echo state network (ESN) has been a good application in solving the time series forecasting problem. As an improved ESN, the Self-join Adjacent-feedback Loop Reservoir (SALR) model effectively solves the problem for which ESN is not principled enough and its computational cost is too large. On this basis, this study proposes a novel Gaussian Process Wavelet Self-join Adjacent-feedback Loop Reservoir (GP-WSALR) model. It combines the advantages of SALR and Gaussian process regression, and provides a more reliable alternative than the traditional reservoir calculation model. At the same time, by adjusting the neurons in the reservoir, the problem of poor nonlinear approximation ability and easily fall into singular solutions caused by a single S-type neurons is solved. Specifically, this study proposes to: (1) use Gaussian process regression to replace the classic linear regression method to obtain the output weight of SALR and (2) use wavelet neurons to replace part of the S-type neurons in the GP-SALR reservoir. The novel Gaussian Process Wavelet Self-join Adjacent-feedback Loop Reservoir (GP-WSALR) model is developed by the two new mechanisms. The experimental results show that GP-WSALR obtains superior forecasting accuracy in the electrical load and network traffic forecasting problems.
An adaptive bitonic filtering based edge fusion algorithm for Gaussian denoising

It is broadly recognized that conserving the essential geometrical features of an image is crucial while denoising it. To accomplish this aim, various denoising techniques have been represented in the literature. The technique based on dual way edge fusion can efficiently solve the problem of denoising. In this paper, an efficient denoising scheme using an innovative method of calculating the image base and details is being proposed. The noisy image is thresholded to remove extra noise by the bitonic filter. Details of the discontinuities is extracted by subtracting the recovered image from the noisy image. Subsequently, details features are subtracted from the noisy image to extract the base information. After that, image features and noise are simultaneously filtered by rolling guidance filter to remove the remaining noise from the features and the significant edge information from the filtered noise. The two images are fused with maximum coefficient value to enhance the information content and visual quality of denoised image. The proposed Dual Way Residue Noise Thresholding (DWEFD) is a combination of various spatial and transform domain commutations performed parallelly. Extensive experimental results and investigations reveal that the proposed methodology is able to recover feature details of an image thereby reducing information loss along with efficient noise removal.
Chaotic Oscillator Prediction Based on Artificial Neural Network and its Realization on FPGA

Chaotic oscillators are widely used in applications due to their unpredictability. These applications include cryptography, true random number generators, and secure communication. On the other hand, artificial Neural Networks (ANNs) are known to be used in modeling and predicting the output of chaotic oscillators. This paper proposes a single switch Jerk chaotic oscillator prediction based on ANN and implemented on FPGA. One-step ahead prediction is implemented on FPGA using a feedforward neural net-work(FFNN). The ANNs performance is measured using Mean Square Error (MSE) and Root Mean Square Error (RMSE) and reaching 5.50E-6 and 2.3452E-3 for MSE and RMSE respectively in one-step prediction. The FFNN is implemented on FPGA based on a 32-bit fixed-point format and using an approximation hyperbolic tangent function by Piece-wise-Linear (PWL) achieving 88.11 MHz.
Mining the ambient commons: building interdisciplinary connections between environmental knowledge, AI and creative practice research

According to Brooks [2017. “The Big Problem with Self-driving Cars Is People”. IEEE Spectrum: Technology, Engineering, and Science News], artificial intelligence has had a variable track-record of usefulness in situations where context and environmental knowledge are responsible for shaping human interactions. In 2021, providing contextually aware training to supervised machine learning is still a non-trivial task for AI models that involve complex systems. In addition, knowledge held only across distributed members of a community, within culture, or tacitly within the wider environment of the ambient commons [McCullough 2013. Ambient Commons: Attention in the Age of Embodied Information. Cambridge, MA: MIT Press] evades consistent generalizable modelling – even in technical domains such as traffic flow management, atmospheric chemistry, or the prediction of election results. Yet it is precisely these interactions of context, community, culture and environment that also define how music can be created. The creative arts can themselves be thought of as a complex system. Assuming that creativity is non-generalizable, this paper assesses creative processes through a humanities-centric lens of machine learning and robotics, aiming to better understand relationships between context, environment and experimental system in artistic research. These relationships are now themselves significantly digitally mediated, requiring a change in academic discourse away from artefacts which need discrete research justification towards a more holistic, and often non-linear view of networks that require cultural situation. In doing so, issues of creative accountability [Field 2021. “Changing the Vocabulary of Creative Research: The Role of Networks, Risk, and Accountability in Transcending Technical Rationality.” In Sound Work: Composition as Critical Technical Practice, edited by J. Impett, 303–317.Orpheus Institute Series. Leuven: Leuven University Press] and the implications of substituting “creative question” for “research question” are examined within creative research. Early twentieth century ideas related to progressivism which have instrumentalized creative practice, particularly where technology forms part of art making, are challenged by re-thinking change through new models. The Three Horizons change model [Sharpe 2016. “Three Horizons: A Pathways Practice for Transformation.” Ecology and Society 21 (2): 47] originally intended to describe environmental ecosystems, is assessed as a practical tool for designing creative research.
Enhanced sine cosine algorithm with crossover: A comparative study and empirical analysis

Sine cosine algorithm (SCA) is a recently developed and widely used metaheuristic to perform global optimization tasks. Due to its simplicity in structure and reasonable performance, it has been utilized to solve several real-world applications. This paper proposes an alternate version of the SCA by adopting the greedy approach of search, crossover and exponentially decreased transition control parameter to overcome the issues of low exploitation, insufficient diversity and premature convergence. The proposed algorithm, called ECr-SCA, is validated and compared with the original SCA using computational time, diversity, performance index, statistical and convergence analysis on a set of 23 standard benchmark problems. Later, the proposed ECr-SCA is compared with seventeen other algorithms including improved versions of the SCA and state-of-the-art algorithms. Furthermore, the ECr-SCA is used to train multi-layer perceptron and the results are compared with variants of SCA and other metaheuristics. Overall comparison based on several different metrics illustrates the significant improvement in the search strategy of the SCA by the proposal of the ECr-SCA.
Engineering Document Summarization: A Bidirectional Language Model-Based Approach

Abstract In this study, the extractive summarization using sentence embeddings generated by the finetuned Bidirectional Encoder Representations from Transformers (BERT) models and the k-means clustering method has been investigated. To show how the BERT model can capture the knowledge in specific domains like engineering design and what it can produce after being finetuned based on domain-specific data sets, several BERT models are trained, and the sentence embeddings extracted from the finetuned models are used to generate summaries of a set of papers. Different evaluation methods are then applied to measure the quality of summarization results. Both the machine evaluation method Recall-Oriented Understudy for Gisting Evaluation (ROUGE) and a human-based evaluation method are used for the comparison study. The results indicate that the BERT model finetuned with a larger dataset can generate summaries with more domain terminologies than the pretrained BERT model. Moreover, the summaries generated by BERT models have more contents overlapping with original documents than those obtained through other popular non-BERT-based models. The experimental results indicate that the BERT-based method can provide better and more informative summaries to engineers. It has also been demonstrated that the contextualized representations generated by BERT-based models can capture information in text and have better performance in applications like text summarizations after being trained by domain-specific data sets.
26.2 Design Considerations for Superconducting Quantum Systems

A distinguishing feature of quantum system engineering is that it must contend with the nature of “quantum processors” as extended objects. A traditional processor is composed of many elements including instruction decoders, arithmetic logical units, register files, and I/O systems. In quantum processors, these functions are distributed across two elements often separated by many meters: a qubit plane and a control system. The qubit chip is sometimes referred to in isolation as a quantum processing unit, but in reality is merely a quantum memory. Quantum logic operations are actuated through external stimuli that manipulate and measure the quantum state of the qubit chip. The control system assumes all other functions we associate with a processor.
Using machine learning for particle track identification in the CLAS12 detector

Particle track reconstruction is the most computationally intensive process in nuclear physics experiments. Traditional algorithms use a combinatorial approach that exhaustively tests track measurements (“hits”) to identify those that form an actual particle trajectory. In this article, we describe the development of four machine learning (ML) models that assist the tracking algorithm by identifying valid track candidates from the measurements in drift chambers. Several types of machine learning models were tested, including: Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), Extremely Randomized Trees (ERT) and Recurrent Neural Networks (RNN). As a result of this work, an MLP network classifier was implemented as part of the CLAS12 reconstruction software to provide the tracking code with recommended track candidates. The resulting software achieved accuracy of greater than 99% and resulted in an end-to-end speedup of 35% compared to existing algorithms.
Well-Conditioned Linear Minimum Mean Square Error Estimation

Linear minimum mean square error (LMMSE) estimation is often ill-conditioned, suggesting that unconstrained minimization of the mean square error is an inadequate approach to filter design. To address this, we first develop a unifying framework for studying constrained LMMSE estimation problems. Using this framework, we explore an important structural property of constrained LMMSE filters involving a certain prefilter. Optimality is invariant under invertible linear transformations of the prefilter. This parameterizes all optimal filters by equivalence classes of prefilters. We then clarify that merely constraining the rank of the filter does not suitably address the problem of ill-conditioning. Instead, we adopt a constraint that explicitly requires solutions to be well-conditioned in a certain specific sense. We introduce two well-conditioned filters and show that they converge to the unconstrained LMMSE filter as their truncation-power loss goes to zero, at the same rate as the low-rank Wiener filter. We also show extensions to the case of weighted trace and determinant of the error covariance as objective functions. Finally, our quantitative results with historical VIX data demonstrate that our two well-conditioned filters have stable performance while the standard LMMSE filter deteriorates with increasing condition number.
Sequential machine learning in prediction of common cancers

Cancer is one of the most common causes of death in the world. It is characterized by the multi-stage transformation of normal cells into tumor cells. Early cancer detection can significantly reduce its consequences, which was the objective of many machine learning (ML) published studies. However, most of them focused on microarray, gene expression, or publicly available medical datasets. Almost none offered an approach for predicting cancer through analysis of sequential data, such as Electronic Health Record (EHR) data. This paper presents a sequential ML approach to predict the occurrence of lung cancer, breast cancer, cervical cancer, and liver cell cancer using EHR data. The accuracy of sequence learning models based on long short-term memory (LSTM) and bidirectional gated recurrent units (GRU) were compared to traditional ML methods based on multilayer perceptron, random forest, decision tree, and K-nearest neighbor. The models were trained and tested on 50,606 patient hospitalization histories. Unsupervised and supervised data reduction methods (singular value decomposition (SVD) and a neural network embedding layer) were applied to overcome the challenges of high-dimensionality and sparsity of EHR data. The results provided evidence that for this application GRU outperforms alternatives based on accuracy, Area Under the Receiver Operating Characteristic curve (AUROC), sensitivity (recall), specificity, precision, and F1 score. It was the best performing model with accuracy between 81% (breast cancer) and 88% (liver cancer) on balanced out of sample EHRs. Multilayer perceptron and LSTM manifested comparable performances (accuracies between 78% and 87%) among the alternatives, while decision tree was the worst-performing model. The findings of this study could potentially aid medical professionals in cancer diagnostics, treatment, and prevention. In particular, experiments confirmed that GRU could accurately predict cancer by learning from simplified patient representations using an embedding layer or SVD. Therefore, GRU's predictions could be used in early cancer detection, potentially improving patients' survival rates.
ARM3D: Attention-based relation module for indoor 3D object detection

Abstract Relation contexts have been proved to be useful for many challenging vision tasks. In the field of 3D object detection, previous methods have been taking the advantage of context encoding, graph embedding, or explicit relation reasoning to extract relation contexts. However, there exist inevitably redundant relation contexts due to noisy or low-quality proposals. In fact, invalid relation contexts usually indicate underlying scene misunderstanding and ambiguity, which may, on the contrary, reduce the performance in complex scenes. Inspired by recent attention mechanism like Transformer, we propose a novel 3D attention-based relation module (ARM3D). It encompasses object-aware relation reasoning to extract pair-wise relation contexts among qualified proposals and an attention module to distribute attention weights towards different relation contexts. In this way, ARM3D can take full advantage of the useful relation contexts and filter those less relevant or even confusing contexts, which mitigates the ambiguity in detection. We have evaluated the effectiveness of ARM3D by plugging it into several state-of-the-art 3D object detectors and showing more accurate and robust detection results. Extensive experiments show the capability and generalization of ARM3D on 3D object detection. Our source code is available at https://github.com/lanlan96/ARM3D .
Blockchain-Based Model for Intelligent Supply Chain Production and Distribution

Blockchain is a term in the field of information technology. In essence, it is a shared database, and the data or information stored in it has the characteristics of “unforgeable,” “full traces,” “traceable,” “open and transparent,” and “collective maintenance.” Based on these characteristics, blockchain technology has laid a solid “trust” foundation, created a reliable “cooperation” mechanism, and has broad application prospects. This paper is aimed at studying a method in view of the integrate of system analysis and system simulation to solve the problems in the production and distribution (P&amp;D) system in the supply chain, proposing a more realistic optimization plan, and then promoting it to various enterprises. This article proposes the idea of using supply chain management, through system analysis, system simulation, case analysis, and comparative research methods, starting from the strategic level, tactical level, and operation level of supply chain management to optimize the P&amp;D system. Pay attention to the P&amp;D system issues in the supply chain environment, and apply the method of system analysis and system simulation to get a better P&amp;D plan. The experimental results of this paper show that the total cost of the supplier selection strategy is 5.204 million yuan, and the delayed delivery rate is 30%. The new reconstruction strategy makes the total cost as low as 3,809,700 yuan, and the total delayed delivery rate is as low as 7%.
Duality-Induced Regularizer for Semantic Matching Knowledge Graph Embeddings

Semantic matching models-which assume that entities with similar semantics have similar embeddings-have shown great power in knowledge graph embeddings (KGE). Many existing semantic matching models use inner products in embedding spaces to measure the plausibility of triples and quadruples in static and temporal knowledge graphs. However, vectors that have the same inner products with another vector can still be orthogonal to each other, which implies that entities with similar semantics may have dissimilar embeddings. This property of inner products significantly limits the performance of semantic matching models. To address this challenge, we propose a novel regularizer-namely, DUality-induced RegulArizer (DURA)-which effectively encourages the entities with similar semantics to have similar embeddings. The major novelty of DURA is based on the observation that, for an existing semantic matching KGE model (primal), there is often another distance based KGE model (dual) closely associated with it, which can be used as effective constraints for entity embeddings. Experiments demonstrate that DURA consistently and significantly improves the performance of state-of-the-art semantic matching models on both static and temporal knowledge graph benchmarks.