Reviewerly/exHarmony huggingface dataset:
Governed by Edtech? Valuing Pedagogical Autonomy in a Platform Society

In this essay, Niels Kerssens and José van Dijck discuss the implications of platformization on the key public value of pedagogical autonomy in K–12 education. They focus on two interconnected concerns: how the integration of education into a global digital infrastructure contests the institutional pedagogical autonomy of schools and how the integration of digital platforms with educational practices in classrooms challenges the professional pedagogical autonomy of teachers. The authors engage with the symposium contributions by Williamson, Gulson, Perrotta &amp; Witzenberger on the Amazon infrastructure and by Pangrazio, Stornaiuolo, Nichols, Garcia &amp; Philip on platform practices at the classroom level. With this dual focus, Kerssens and van Dijck explore how critical research in the emerging field of platform studies in education pertains to both the political-economic level of building educational platform infrastructures and the social-technical level of how teaching and learning are (re)shaped by digital platforms. The essay concludes with a brief discussion of recommendations for the future governance of edtech to serve the pedagogical interest of schools and teachers.
MLFNet: Multi-Level Fusion Network for Real-Time Semantic Segmentation of Autonomous Driving

The tradeoff between speed and accuracy is important in semantic segmentation problems, especially for resource-constrained platforms, such as intelligent vehicles. In this paper, we address this issue by proposing a well-deployed real-time semantic segmentation architecture named MLFNet. Specifically, we first build a lightweight backbone (SEFE) with a larger receptive field and multi-scale contextual representation performance to encode the pixel-level features. For better preserving target boundaries and contours, a spatial compensation branch (SPFE) is designed to gradually reduce the dimension of feature maps and refine the low-level specifics. In the decoding phase, we introduce a well-designed multi-branch fusion extractor (MBFD) for integrating the spatial details into high-level layers. Finally, the outputs from the semantic and spatial branches are fused to predict the final segmentation results. Extensive offline and online experiments have shown that our model has a superior speed and accuracy trade-off. On the Cityscapes test dataset, our model (MLFNet-Res18) achieves 71.0% mIoU with 95.1 FPS for 512 × 1024 inputs, and 72.1% mIoU with 72.2 FPS while inferring on MLFNet-Res34 model. Meanwhile, MLFNet-Res18 can reach 24.5 FPS when deployed to NVIDIA Jetson AGX Xavier and 64.0 FPS with an experimental vehicle.
Bias in algorithms of AI systems developed for COVID-19: A scoping review

To analyze which ethically relevant biases have been identified by academic literature in artificial intelligence (AI) algorithms developed either for patient risk prediction and triage, or for contact tracing to deal with the COVID-19 pandemic. Additionally, to specifically investigate whether the role of social determinants of health (SDOH) have been considered in these AI developments or not. We conducted a scoping review of the literature, which covered publications from March 2020 to April 2021. ​Studies mentioning biases on AI algorithms developed for contact tracing and medical triage or risk prediction regarding COVID-19 were included. From 1054 identified articles, 20 studies were finally included. We propose a typology of biases identified in the literature based on bias, limitations and other ethical issues in both areas of analysis. Results on health disparities and SDOH were classified into five categories: racial disparities, biased data, socio-economic disparities, unequal accessibility and workforce, and information communication. SDOH needs to be considered in the clinical context, where they still seem underestimated. Epidemiological conditions depend on geographic location, so the use of local data in studies to develop international solutions may increase some biases. Gender bias was not specifically addressed in the articles included. The main biases are related to data collection and management. Ethical problems related to privacy, consent, and lack of regulation have been identified in contact tracing while some bias-related health inequalities have been highlighted. There is a need for further research focusing on SDOH and these specific AI apps.
Deep learning models for histologic grading of breast cancer and association with disease prognosis

Histologic grading of breast cancer involves review and scoring of three well-established morphologic features: mitotic count, nuclear pleomorphism, and tubule formation. Taken together, these features form the basis of the Nottingham Grading System which is used to inform breast cancer characterization and prognosis. In this study, we develop deep learning models to perform histologic scoring of all three components using digitized hematoxylin and eosin-stained slides containing invasive breast carcinoma. We first evaluate model performance using pathologist-based reference standards for each component. To complement this typical approach to evaluation, we further evaluate the deep learning models via prognostic analyses. The individual component models perform at or above published benchmarks for algorithm-based grading approaches, achieving high concordance rates with pathologist grading. Further, prognostic performance using deep learning-based grading is on par with that of pathologists performing review of matched slides. By providing scores for each component feature, the deep-learning based approach also provides the potential to identify the grading components contributing most to prognostic value. This may enable optimized prognostic models, opportunities to improve access to consistent grading, and approaches to better understand the links between histologic features and clinical outcomes in breast cancer.
An improved sparrow search algorithm based on quantum computations and multi-strategy enhancement

Aiming at the defects of the sparrow search algorithm (SSA), such as a deficient optimization accuracy and low search efficiency, the sparrow search algorithm based on quantum computations and multi-strategy enhancement (QMESSA) is proposed. Firstly, based on a diversified initial population strategy, an improved circle chaotic mapping theory was proposed, and an initial population with more randomness and diversity was obtained by combining quantum computations with a quantum gate mutation mechanism. Secondly, using an enhanced search strategy, an adaptive T-distribution and a new position update formula were constructed to accelerate the convergence and enhance its variability. Finally, a dynamic evolution formula was designed for the precision elimination mechanism. Based on this, individuals with poor fitness are replaced by new individuals generated using this formula. In addition, a new boundary control strategy was proposed. The convergence of QMESSA was systematically proven, and the proposed algorithm was tested on 24 benchmark functions and CEC 2017 functions. The experiment results as well as the results of a Wilcoxon rank-sum test show that QMESSA achieves a better comprehensive performance than SSA and other advanced optimization algorithms. Finally, the superiority of QMESSA was verified for several classical practical application problems. The source code of QMESSA is available at https://ww2.mathworks.cn/matlabcentral/fileexchange/120013-project1-0.
Unsupervised Adaptive Feature Selection With Binary Hashing

Unsupervised feature selection chooses a subset of discriminative features to reduce feature dimension under the unsupervised learning paradigm. Although lots of efforts have been made so far, existing solutions perform feature selection either without any label guidance or with only single pseudo label guidance. They may cause significant information loss and lead to semantic shortage of the selected features as many real-world data, such as images and videos are generally annotated with multiple labels. In this paper, we propose a new Unsupervised Adaptive Feature Selection with Binary Hashing (UAFS-BH) model, which learns binary hash codes as weakly-supervised multi-labels and simultaneously exploits the learned labels to guide feature selection. Specifically, in order to exploit the discriminative information under the unsupervised scenarios, the weakly-supervised multi-labels are learned automatically by specially imposing binary hash constraints on the spectral embedding process to guide the ultimate feature selection. The number of weakly-supervised multi-labels (the number of “1” in binary hash codes) is adaptively determined according to the specific data content. Further, to enhance the discriminative capability of binary labels, we model the intrinsic data structure by adaptively constructing the dynamic similarity graph. Finally, we extend UAFS-BH to multi-view setting as Multi-view Feature Selection with Binary Hashing (MVFS-BH) to handle the multi-view feature selection problem. An effective binary optimization method based on the Augmented Lagrangian Multiple (ALM) is derived to iteratively solve the formulated problem. Extensive experiments on widely tested benchmarks demonstrate the state-of-the-art performance of the proposed method on both single-view and multi-view feature selection tasks. For the purpose of reproducibility, we provide the source codes and testing datasets at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/shidan0122/UMFS.git</uri> ..
A Survey of AI-Based Anomaly Detection in IoT and Sensor Networks

Machine learning (ML) and deep learning (DL), in particular, are common tools for anomaly detection (AD). With the rapid increase in the number of Internet-connected devices, the growing desire for Internet of Things (IoT) devices in the home, on our person, and in our vehicles, and the transition to smart infrastructure and the Industrial IoT (IIoT), anomaly detection in these devices is critical. This paper is a survey of anomaly detection in sensor networks/the IoT. This paper defines what an anomaly is and surveys multiple sources based on those definitions. The goal of this survey was to highlight how anomaly detection is being performed on the Internet of Things and sensor networks, identify anomaly detection approaches, and outlines gaps in the research in this domain.
Incorporating Secret Door in Teaching Vocabulary for EFL Vocational Secondary School Students in Indonesia

This study seeks to: (1) assess students' vocabulary mastery levels before and after utilizing the Secret Door approach; and (2) ascertain whether employing the Secret Door method enhances students' vocabulary.This supports the idea that using the Secret Door approach improves pupils' vocabularies.Preexperimental One Group Pretest-Posttest Design with a Sample of 16 Students from Class X IPS 2 and Cluster Random Sampling Technique is the design employed in this study.The test served as an instrument for the researcher to gather the data.Data research revealed that applying the Secret Door approach increased students' vocabulary mastery.It can be proven by the ttest value of -27.547 which is less than the t-table value of 2.602, which means that H0 of this research was rejected and H1 was accepted.Therefore, it can be concluded that the use of the Secret Door method improve students' vocabulary at the first year students of Vocational Secondary School in South Sulawesi.
Ternary Weight Networks

We present a memory and computation efficient ternary weight networks (TWNs) - with weights constrained to +1, 0 and -1. The Euclidian distance between full (float or double) precision weights and the ternary weights along with a scaling factor is minimized in training stage. Besides, a threshold-based ternary function is optimized to get an approximated solution which can be fast and easily computed. TWNs have shown better expressive abilities than binary precision counterparts. Meanwhile, TWNs achieve up to 16× model compression rate and need fewer multiplications compared with the float32 precision counterparts. Extensive experiments on MNIST, CIFAR-10, and ImageNet datasets show that the TWNs achieve much better result than the Binary-Weight-Networks (BWNs) and the classification performance on MNIST and CIFAR-10 is very close to the full precision networks. We also verify our method on object detection task and show that TWNs significantly outperforms BWN by more than 10% mAP on PASCAL VOC dataset. The pytorch version of source code is available at: https://github.com/Thinklab-SJTU/twns.
Introduction to Deep Learning

The deep learning paradigm tackles problems on which shallow architectures (e.g. SVM) are affected by the curse of dimensionality. As part of a two-stage learning scheme involving multiple layers of non-linear processing a set of statistically robust features is automatically extracted from the data. The present tutorial introducing the ESANN deep learning special session details the state-of-the-art models and summarizes the current understanding of this learning approach which is a reference for many difficult classification tasks.
HCBC: A Hierarchical Case-Based Classifier Integrated with Conceptual Clustering

The structured case representation improves case-based reasoning (CBR) by exploring structures in the case base and the relevance of case structures. Recent CBR classifiers have mostly been built upon the attribute-value case representation rather than structured case representation, in which the structural relations embodied in their representation structure are accordingly overlooked in improving the similarity measure. This results in retrieval inefficiency and limitations on the performance of CBR classifiers. This paper proposes a hierarchical case-based classifier, HCBC, which introduces a concept lattice to hierarchically organize cases. By exploiting structural case relations in the concept lattice, a novel dynamic weighting model is proposed to enhance the concept similarity measure. Based on this similarity measure, HCBC retrieves the top-K concepts that are most similar to a new case by using a bottom-up pruning-based recursive retrieval (PRR) algorithm. The concepts extracted in this way are applied to suggest a class label for the case by a weighted majority voting. Experimental results show that HCBC outperforms other classifiers in terms of classification performance and robustness on categorical data, and also works confidently well on numeric datasets. In addition, PRR effectively reduces the search space and greatly improves the retrieval efficiency of HCBC.
ViBr: Visualizing Bipartite Relations at Scale with the Minimum Description Length Principle

Bipartite graphs model the key relations in many large scale real-world data: customers purchasing items, legislators voting for bills, people's affiliation with different social groups, faults occurring in vehicles, etc. However, it is challenging to visualize large scale bipartite graphs with tens of thousands or even more nodes or edges. In this paper, we propose a novel visual summarization technique for bipartite graphs based on the minimum description length (MDL) principle. The method simultaneously groups the two different set of nodes and constructs aggregated bipartite relations with balanced granularity and precision. It addresses the key trade-off that often occurs for visualizing large scale and noisy data: acquiring a clear and uncluttered overview while maximizing the information content in it. We formulate the visual summarization task as a co-clustering problem and propose an efficient algorithm based on locality sensitive hashing (LSH) that can easily scale to large graphs under reasonable interactive time constraints that previous related methods cannot satisfy. The method leads to the opportunity of introducing a visual analytics framework with multiple levels-of-detail to facilitate interactive data exploration. In the framework, we also introduce a compact visual design inspired by adjacency list representation of graphs as the building block for a small multiples display to compare the bipartite relations for different subsets of data. We showcase the applicability and effectiveness of our approach by applying it on synthetic data with ground truth and performing case studies on real-world datasets from two application domains including roll-call vote record analysis and vehicle fault pattern analysis. Interviews with experts in the political science community and the automotive industry further highlight the benefits of our approach.
Light-field compression using a pair of steps and depth estimation

Advanced handheld plenoptic cameras are being rapidly developed to capture information about light fields (LFs) from the 3D world.Rich LF data can be used to develop dense sub-aperture images (SAIs) that can provide a more immersive experience for users.Unlike conventional 2D images, 4D SAIs contain both the positional and directional information of light rays; the practical applications of handheld plenoptic cameras are limited by the huge volume of data required to capture this information.Therefore, an efficient LF compression method is vital for further application of the cameras.To this end, the pair of steps and depth estimation (PoS&DE) method is proposed in this paper, and the multiview video and depth (MVD) coding structure is used to relieve the LF coding burden.More specifically, a precise depth-estimation approach is presented for SAIs based on the cost function, and an SAI-guided depth optimization algorithm is designed to refine the initial depth map based on pixel variation tendency.Meanwhile, to reduce running time, intermediate SAI synthesis quality and coding bitrates, including the key SAIs selected and cost-computation steps, are set via extensive statistical experiments.In this way, only a limited number of optimally selected SAIs and their corresponding depth maps must be encoded.The experimental results demonstrate that our proposed LF compression solution using PoS&DE can obtain a satisfied coding performance.
Applying Deep Learning Models to Mouse Behavior Recognition

In many animal-related studies, a high-performance animal behavior recognition system can help researchers reduce or get rid of the limitation of human assessments and make the experiments easier to reproduce.Recently, although deep learning models are holding state-of-the-art performances in human action recognition tasks, these models are not well-studied in applying to animal behavior recognition tasks.One reason is the lack of extensive datasets which are required to train these deep models for good performances.In this research, we investigated two current state-of-the-art deep learning models in human action recognition tasks, the I3D model and the R(2 + 1)D model, in solving a mouse behavior recognition task.We compared their performances with other models from previous researches and the results showed that the deep learning models that pre-trained using human action datasets then fine-tuned using the mouse behavior dataset can outperform other models from previous researches.It also shows promises of applying these deep learning models to other animal behavior recognition tasks without any significant modification in the models' architecture, all we need to do is collecting proper datasets for the tasks and fine-tuning the pre-trained models using the collected data.
Blockchain and other Distributed Ledger Technologies in Operations

Blockchain is a form of distributed ledger technology (DLT) that has grown in prominence, although its full potential and possible downsides are not yet fully understood, especially with respect to Operations Management (OM). This manuscript contributes to filling in this gap. We identify three research themes in applying Blockchain technology to OM, illustrated through several applications to OM problems. Elsewhere, in a companion article, (Babich and Hilary (2018)), we provide a conceptual framework for the role of Blockchain and other DLT in OM, along with specific examples of research questions, and we demonstrate how research in economics can inform research in OM on Blockchain applications. Finally, we discuss possible future uses for the technology.
Virtual Reality in Education: Learning a Foreign Language

There are still open questions about the effectiveness of Virtual Reality (VR) in Education when compared to conventional learning methods. This paper studies the feasibility of a VR-based learning tool and the possible differences in knowledge retention across a VR learning method and a conventional audio method, when it comes to learning a foreign language. Also, the students’ sense of presence and satisfaction were studied. For such purpose, a user study was conducted and results revealed that while presence and satisfaction were higher in Virtual Reality, the knowledge retention score remains the same across both experimental conditions.
Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment

An image classification algorithm based on adaptive feature weight updating is proposed to address the low classification accuracy of the current single-feature classification algorithms and simple multifeature fusion algorithms. The MapReduce parallel programming model on the Hadoop platform is used to perform an adaptive fusion of hue, local binary pattern (LBP) and scale-invariant feature transform (SIFT) features extracted from images to derive optimal combinations of weights. The support vector machine (SVM) classifier is then used to perform parallel training to obtain the optimal SVM classification model, which is then tested. The Pascal VOC 2012, Caltech 256 and SUN databases are adopted to build a massive image library. The speedup, classification accuracy and training time are tested in the experiment, and the results show that a linear growth tendency is present in the speedup of the system in a cluster environment. In consideration of the hardware costs, time, performance and accuracy, the algorithm is superior to mainstream classification algorithms, such as the power mean SVM and convolutional neural network (CNN). As the number and types of images both increase, the classification accuracy rate exceeds 95%. When the number of images reaches 80,000, the training time of the proposed algorithm is only 1/5 that of traditional single-node architecture algorithms. This result reflects the effectiveness of the algorithm, which provides a basis for the effective analysis and processing of image big data.
Filter Variable Selection Algorithm Using Risk Ratios for Dimensionality Reduction of Healthcare Data for Classification

This research developed and tested a filter algorithm that serves to reduce the feature space in healthcare datasets. The algorithm binarizes the dataset, and then separately evaluates the risk ratio of each predictor with the response, and outputs ratios that represent the association between a predictor and the class attribute. The value of the association translates to the importance rank of the corresponding predictor in determining the outcome. Using Random Forest and Logistic regression classification, the performance of the developed algorithm was compared against the regsubsets and varImp functions, which are unsupervised methods of variable selection. Equally, the proposed algorithm was compared with the supervised Fisher score and Pearson’s correlation feature selection methods. Different datasets were used for the experiment, and, in the majority of the cases, the predictors selected by the new algorithm outperformed those selected by the existing algorithms. The proposed filter algorithm is therefore a reliable alternative for variable ranking in data mining classification tasks with a dichotomous response.