Reviewerly/exHarmony huggingface dataset:
Vocabulary Mobile Learning Application in Blended English Language Learning

Mobile devices and applications, which have become an integral part of our lives, are gradually used for different purposes, including learning languages in EFL classrooms. Since vocabulary plays an important role in the process of foreign language learning, the aim of this study was to explore the use of the vocabulary mobile learning application and its usefulness in blended English learning. Quantitative and qualitative approach to research was used, since the integration of both approaches created the possibility to solve complex research problem. The case study was based on the use of the developed mobile application called Angličtina Today the content of which corresponded to the language needs of the target group of students. The quantitative approach used a method of quasi-experiment aiming to achieve the pre-tests and post-test results of the students from the experimental and control groups. The results showed that the students facing blended learning, including mobile application in the process of language learning, achieved better results than the students exposed to the traditional, face-to-face education. In addition, the results revealed students' overall satisfaction with the application. The main reasons for their satisfaction were improved vocabulary knowledge, ease of use, and enhanced motivation. Based on these findings from the current study, it can be argued that the vocabulary mobile learning application proved to be useful in the process of blended English language learning.
Disentangling Content and Fine-Grained Prosody Information Via Hybrid ASR Bottleneck Features for Voice Conversion

Non-parallel data voice conversion (VC) have achieved considerable breakthroughs recently through introducing bottleneck features (BNFs) extracted by the automatic speech recognition(ASR) model. However, selection of BNFs have a significant impact on VC result. For example, when extracting BNFs from ASR trained with Cross Entropy loss (CE-BNFs) and feeding into neural network to train a VC system, the timbre similarity of converted speech is significantly degraded. If BNFs are extracted from ASR trained using Connectionist Temporal Classification loss (CTC-BNFs), the naturalness of the converted speech may decrease. This phenomenon is caused by the difference of information contained in BNFs. In this paper, we proposed an any-to-one VC method using hybrid bottleneck features extracted from CTC-BNFs and CE-BNFs to complement each other advantages. Gradient reversal layer and instance normalization were used to extract prosody information from CE-BNFs and content information from CTC-BNFs. Auto-regressive decoder and Hifi-GAN vocoder were used to generate high-quality waveform. Experimental results show that our proposed method achieves higher similarity, naturalness, quality than baseline method and reveals the differences between the information contained in CE-BNFs and CTC-BNFs as well as the influence they have on the converted speech.
The tree based linear regression model for hierarchical categorical variables

Many real-life applications consider nominal categorical predictor variables that have a hierarchical structure, e.g. economic activity data in Official Statistics. In this paper, we focus on linear regression models built in the presence of this type of nominal categorical predictor variables, and study the consolidation of their categories to have a better tradeoff between interpretability and fit of the model to the data. We propose the so-called Tree based Linear Regression (TLR) model that optimizes both the accuracy of the reduced linear regression model and its complexity, measured as a cost function of the level of granularity of the representation of the hierarchical categorical variables. We show that finding non-dominated outcomes for this problem boils down to solving Mixed Integer Convex Quadratic Problems with Linear Constraints, and small to medium size instances can be tackled using off-the-shelf solvers. We illustrate our approach in two real-world datasets, as well as a synthetic one, where our methodology finds a much less complex model with a very mild worsening of the accuracy.
Self-adaptive opposition-based differential evolution with subpopulation strategy for numerical and engineering optimization problems

Abstract Opposition-based differential evolution (ODE) is a well-known DE variant that employs opposition-based learning (OBL) to accelerate the convergence speed. However, the existing OBL variants are population-based, which causes many shortcomings. The value of the jumping rate is not self-adaptively adjusted, so the algorithm easily traps into local optima. The population-based OBL wastes fitness evaluations when the algorithm converges to sub-optimal. In this paper, we proposed a novel OBL called subpopulation-based OBL (SPOBL) with a self-adaptive parameter control strategy. In SPOBL, the jumping rate acts on the individual, and the subpopulation is selected according to the individual’s jumping rate. In the self-adaptive parameter control strategy, the surviving individual’s jumping rate in each iteration will participate in the self-adaptive process. A generalized Lehmer mean is introduced to achieve an equilibrium between exploration and exploitation. We used DE and advanced DE variants combined with SPOBL to verify performance. The results of performance are evaluated on the CEC 2017 and CEC 2020 test suites. The SPOBL shows better performance compared to other OBL variants in terms of benchmark functions as well as real-world constrained optimization problems.
Azimuth-Sensitive Object Detection of High-Resolution SAR Images in Complex Scenes by Using a Spatial Orientation Attention Enhancement Network

The scattering features of objects in synthetic aperture radar (SAR) imagery are highly sensitive to different azimuth angles, and detecting azimuth-sensitive objects in complex scenes becomes a challenging task. To address this issue, we propose a novel framework called the spatial orientation attention enhancement network (SOAEN) by using aircraft detection in complex scenes of SAR imagery as a case study. Taking YOLOX as the basic framework, this framework introduces the inverted pyramid ConvMixer network (IPCN), the spatial-orientation-enhanced path aggregation feature pyramid network (SOEPAFPN), and the anchor-free decoupled head (AFDH) to achieve performance improvement. A spatial orientation attention module is proposed and introduced into the path aggregation feature pyramid network to form a new structure, the SOEPAFPN, for capturing feature transformations in different directions, highlighting object features and suppressing background effects; the IPCN is adapted to replace the backbone network of YOLOX for enhancing the multiscale feature extraction capability and reducing the computational complexity, while the AFDH is used to decouple object localization and classification to improve the efficiency and accuracy of object localization and classification. The experimental results of the multiple real complex scenes on Gaofen-3 1 m images show that the proposed method achieves the highest detection accuracy, with an average detection rate of 91.22% compared with the YOLO series networks.
Inference-aware convolutional neural network pruning

Deep neural networks (DNNs) have become an important tool in solving various problems in numerous disciplines. However, DNNs are also known for their high resource requirement, weight redundancy, and large-scale parameters. As a result, the use of DNNs is restricted for devices that lack the necessary resources required to execute, especially resource-constrained devices such as mobile phones, wearable devices, and other edge devices. In recent years, pruning has emerged as an essential technique to reduce insignificant parameters and accelerate the model performance. However, finding the optimal number of parameters that can be pruned without significantly affecting the model performance is a time-consuming, tedious task and require a lot of manual tuning. This paper represent pruning as an optimization problem with the goal of improving DNN run-time inference performance by pruning low impacting parameters (filters) and their corresponding feature maps. To do this, we present a Bayesian optimization-based method for automatically determining the appropriate number of filters for each convolutional layer. Also, we proposed an objective function incorporating distinct model performance and resource-specific constraints. The proposed method is applied to two different kinds of convolutional network architectures (i.e., VGG16 and deeper network ResNet34) on CIFAR10, CIFAR100, and ImageNet datasets. The large-scale ImageNet experimental findings showed that the floating-point operations of the ResNet34 and VGG16 could be reduced by 35.46 percent and 84.97 percent, respectively, with negligible loss of accuracy. • Automatically identify and prune the less important filters. • Pruned models performed superior over original pretrained models. • Method to improve the inference-time performance of the pretrained models. • The FLOPs of the ResNet34 model reduced by 35.46% with negligible accuracy loss.
MUTR3D: A Multi-camera Tracking Framework via 3D-to-2D Queries

Accurate and consistent 3D tracking from multiple cameras is a key component in a vision-based autonomous driving system. It involves modeling 3D dynamic objects in complex scenes across multiple cameras. This problem is inherently challenging due to depth estimation, visual occlusions, appearance ambiguity, etc. Moreover, objects are not consistently associated across time and cameras. To address that, we propose an end-to-end MUlti-camera TRacking framework called MUTR3D. In contrast to prior works, MUTR3D does not explicitly rely on the spatial and appearance similarity of objects. Instead, our method introduces 3D track query to model spatial and appearance coherent track for each object that appears in multiple cameras and multiple frames. We use camera transformations to link 3D trackers with their observations in 2D images. Each tracker is further refined according to the features that are obtained from camera images. MUTR3D uses a set-to-set loss to measure the difference between the predicted tracking results and the ground truths. Therefore, it does not require any post-processing such as non-maximum suppression and/or bounding box association. MUTR3D outperforms state-of-the-art methods by 5.3 AMOTA on the nuScenes dataset. Code will be released.
Eliciting Best Practices for Collaboration with Computational Notebooks

Despite the widespread adoption of computational notebooks, little is known about best practices for their usage in collaborative contexts. In this paper, we fill this gap by eliciting a catalog of best practices for collaborative data science with computational notebooks. With this aim, we first look for best practices through a multivocal literature review. Then, we conduct interviews with professional data scientists to assess their awareness of these best practices. Finally, we assess the adoption of best practices through the analysis of 1,380 Jupyter notebooks retrieved from the Kaggle platform. Findings reveal that experts are mostly aware of the best practices and tend to adopt them in their daily work. Nonetheless, they do not consistently follow all the recommendations as, depending on specific contexts, some are deemed unfeasible or counterproductive due to the lack of proper tool support. As such, we envision the design of notebook solutions that allow data scientists not to have to prioritize exploration and rapid prototyping over writing code of quality.
Instant Reality: Gaze-Contingent Perceptual Optimization for 3D Virtual Reality Streaming

Media streaming, with an edge-cloud setting, has been adopted for a variety of applications such as entertainment, visualization, and design. Unlike video/audio streaming where the content is usually consumed passively, virtual reality applications require 3D assets stored on the edge to facilitate frequent edge-side interactions such as object manipulation and viewpoint movement. Compared to audio and video streaming, 3D asset streaming often requires larger data sizes and yet lower latency to ensure sufficient rendering quality, resolution, and latency for perceptual comfort. Thus, streaming 3D assets faces remarkably additional than streaming audios/videos, and existing solutions often suffer from long loading time or limited quality. To address this challenge, we propose a perceptually-optimized progressive 3D streaming method for spatial quality and temporal consistency in immersive interactions. On the cloud-side, our main idea is to estimate perceptual importance in 2D image space based on user gaze behaviors, including where they are looking and how their eyes move. The estimated importance is then mapped to 3D object space for scheduling the streaming priorities for edge-side rendering. Since this computational pipeline could be heavy, we also develop a simple neural network to accelerate the cloud-side scheduling process. We evaluate our method via subjective studies and objective analysis under varying network conditions (from 3G to 5G) and edge devices (HMD and traditional displays), and demonstrate better visual quality and temporal consistency than alternative solutions.
PredictPTB: an interpretable preterm birth prediction model using attention-based recurrent neural networks

Early identification of pregnant women at risk for preterm birth (PTB), a major cause of infant mortality and morbidity, has a significant potential to improve prenatal care. However, we lack effective predictive models which can accurately forecast PTB and complement these predictions with appropriate interpretations for clinicians. In this work, we introduce a clinical prediction model (PredictPTB) which combines variables (medical codes) readily accessible through electronic health record (EHR) to accurately predict the risk of preterm birth at 1, 3, 6, and 9 months prior to delivery.The architecture of PredictPTB employs recurrent neural networks (RNNs) to model the longitudinal patient's EHR visits and exploits a single code-level attention mechanism to improve the predictive performance, while providing temporal code-level and visit-level explanations for the prediction results. We compare the performance of different combinations of prediction time-points, data modalities, and data windows. We also present a case-study of our model's interpretability illustrating how clinicians can gain some transparency into the predictions.Leveraging a large cohort of 222,436 deliveries, comprising a total of 27,100 unique clinical concepts, our model was able to predict preterm birth with an ROC-AUC of 0.82, 0.79, 0.78, and PR-AUC of 0.40, 0.31, 0.24, at 1, 3, and 6 months prior to delivery, respectively. Results also confirm that observational data modalities (such as diagnoses) are more predictive for preterm birth than interventional data modalities (e.g., medications and procedures).Our results demonstrate that PredictPTB can be utilized to achieve accurate and scalable predictions for preterm birth, complemented by explanations that directly highlight evidence in the patient's EHR timeline.
Image Analysis by Fractional-Order Gaussian-Hermite Moments

Moments and moment invariants are effective feature descriptors. They have widespread applications in the field of image processing. The recent researches show that fractional-order moments have notable image representation ability. Hermite polynomials are defined over the interval from negative infinity to positive one. Such unboundedness prevents us from developing fractional-order Gaussian-Hermite moments via the existing ideas or approaches. In this paper, we propose fractional-order Gaussian-Hermite moments by forcing the definition domain of Hermite polynomials to be a bounded interval, meanwhile, resorting to a value-decreasing standard deviation to maintain the orthogonality. Moreover, we successfully develop contrast, translation and rotation invariants from the proposed moments based on the inherent properties of Hermite polynomials. The reconstructions of different types of images demonstrate that the proposed moments have more superior image representation ability to the most existing popular orthogonal moments. Besides, the salient performance in invariant image recognition, noise robustness and region-of-interest feature extraction reflect that these moments and their invariants possess the stronger discrimination power and the better noise robustness in comparison with the existing orthogonal moments. Furthermore, both complexity analysis and time consumption indicate that the proposed moments and their invariants are easy to implement, they are suitable for practical engineering applications.
An exploration of error-driven learning in simple two-layer networks from a discriminative learning perspective

Error-driven learning algorithms, which iteratively adjust expectations based on prediction error, are the basis for a vast array of computational models in the brain and cognitive sciences that often differ widely in their precise form and application: they range from simple models in psychology and cybernetics to current complex deep learning models dominating discussions in machine learning and artificial intelligence. However, despite the ubiquity of this mechanism, detailed analyses of its basic workings uninfluenced by existing theories or specific research goals are rare in the literature. To address this, we present an exposition of error-driven learning - focusing on its simplest form for clarity - and relate this to the historical development of error-driven learning models in the cognitive sciences. Although historically error-driven models have been thought of as associative, such that learning is thought to combine preexisting elemental representations, our analysis will highlight the discriminative nature of learning in these models and the implications of this for the way how learning is conceptualized. We complement our theoretical introduction to error-driven learning with a practical guide to the application of simple error-driven learning models in which we discuss a number of example simulations, that are also presented in detail in an accompanying tutorial.
Optimizing feature selection methods by removing irrelevant features using sparse least squares

Feature (variable) selection is recognized as an integral part of model construction in machine learning. One can use feature selection to remove redundant and irrelevant features. This in turn can help overcome the curse of dimensionality, reduce overfitting, and come up with interpretable models. In this paper, we propose Sparse Least Squares method (SLS) based on singular value decomposition and least squares to remove irrelevant features. We show that augmenting well-known feature selection methods with SLS significantly reduces the running time while improving or maintaining the prediction accuracy of the model.
Study of the Yahoo-Yahoo Hash-Tag Tweets Using Sentiment Analysis and Opinion Mining Algorithms

Mining opinion on social media microblogs presents opportunities to extract meaningful insight from the public from trending issues like the “yahoo-yahoo” which in Nigeria, is synonymous to cybercrime. In this study, content analysis of selected historical tweets from “yahoo-yahoo” hash-tag was conducted for sentiment and topic modelling. A corpus of 5500 tweets was obtained and pre-processed using a pre-trained tweet tokenizer while Valence Aware Dictionary for Sentiment Reasoning (VADER), Liu Hu method, Latent Dirichlet Allocation (LDA), Latent Semantic Indexing (LSI) and Multidimensional Scaling (MDS) graphs were used for sentiment analysis, topic modelling and topic visualization. Results showed the corpus had 173 unique tweet clusters, 5327 duplicates tweets and a frequency of 9555 for “yahoo”. Further validation using the mean sentiment scores of ten volunteers returned R and R2 of 0.8038 and 0.6402; 0.5994 and 0.3463; 0.5999 and 0.3586 for Human and VADER; Human and Liu Hu; Liu Hu and VADER sentiment scores, respectively. While VADER outperforms Liu Hu in sentiment analysis, LDA and LSI returned similar results in the topic modelling. The study confirms VADER’s performance on unstructured social media data containing non-English slangs, conjunctions, emoticons, etc. and proved that emojis are more representative of sentiments in tweets than the texts.
User Gaze-Driven Adaptation of Omnidirectional Video Delivery Using Spatial Tiling and Scalable Video Encoding

Omnidirectional video is becoming increasingly popular among viewers, but its delivery requires considerable amount of network bandwidth. Today’s streaming services are transmitting the full spatial angle of omnidirectional videos, although most of the transmitted content is not utilized. Due to both limited bandwidth availability and its dynamic fluctuations, adaptive delivery solutions play a key role in supporting high user quality streaming of omnidirectional videos. This paper describes research which extends the MPEG-DASH Spatial Relationship Description by adding scalable video encoding to spatial tiling. It proposes a novel tile-layering based gaze adaptation algorithm for omnidirectional video delivery and employs it in conjunction with multiple tiling schemes. The benefits of the proposed algorithm with diverse tiling schemes are evaluated objectively in terms of bandwidth savings and adaptation latency. The results show a reduction of network bandwidth requirements to about 30% of the original bandwidth value with a low processing latency of 70.87ms.
A Survey on Blockchain Acquainted Software Requirements Engineering: Model, Opportunities, Challenges, and Future Directions

Requirements are the basis of software development practices. Ambiguities in requirements lead a project to a point of failure or penalize it with a high budget and time for defect traceability. The ever-growing demand for advanced computing systems has increased the complexity of Software Requirements Engineering (SRE) practices. Blockchain systems require specialized SRE practices as the issues of Requirement Traceability (RT), developer/client confidentiality, and Requirement Negotiation (RN) typically exist in conventional approaches, which require more improvement. Moreover, blockchain technology incorporates the capacity to function as an infrastructure for the SRE framework providing transparency, security, and reliability. Even though the significance of studying blockchain in the context of SRE is evident, it is still in its infancy. None of the previous studies surveyed this domain to the best of our knowledge. We aim to summarize the scholarly contributions of blockchain acquainted SRE from 2015 to 2021 and to provide academia and practitioners with in-depth knowledge about this domain. In this article, we have provided a novel comprehensive review of the aspects of blockchain-acquainted SRE practices. We have presented SRE-based quality improvement factors and outlined the need for blockchain technology in this domain. Furthermore, we have classified SRE practices based on blockchain engineering. In addition, we have proposed a generic SRE model built on blockchain infrastructure along with its workflows. Similarly, we have provided implementation guidelines for the future development guidance of SRE applications built on blockchain technology. Finally, we have presented the current research challenges and provided future directions based on blockchain acquainted SRE.