Reviewerly/exHarmony huggingface dataset:
A Deep-Learning Framework for Analysing Students’ Review in Higher Education

As part of continuous process improvements to teaching and learning, the management of tertiary institutions requests students to review modules towards the end of each semester. These reviews capture students' perceptions about various aspects of their learning experience. Considering the large volume of textual feedback, it is not feasible to manually analyze all the comments, hence the need for automated approaches. This study presents a framework for analyzing students' qualitative reviews. The framework consists of four distinct components: aspect-term extraction, aspect-category identification, sentiment polarity determination, and grades' prediction. We evaluated the framework with the dataset from the Lilongwe University of Agriculture and Natural Resources (LUANAR). A sample size of 1,111 reviews was used. A microaverage F1-score of 0.67 was achieved using Bi- LSTM-CRF and BIO tagging scheme for aspect-term extraction. Twelve aspect categories were then defined for the education domain and four variants of RNNs models (GRU, LSTM, Bi-LSTM, and Bi-GRU) were compared. A Bi-GRU model was developed for sentiment polarity determination and the model achieved a weighted F1-score of 0.96 for sentiment analysis. Finally, a Bi-LSTM-ANN model which combined textual and numerical features was implemented to predict students' grades based on the reviews. A weighted F1-score of 0.59 was obtained, and out of 29 students with "F" grade, 20 were correctly identified by the model.
Continuous deployment in software-intensive system-of-systems

While continuous deployment is popular among web-based software development organizations, adopting continuous deployment in software-intensive system-of-systems is more challenging. On top of the challenges arising from deploying software to a single software-intensive embedded system, software-intensive system-of-systems (SiSoS) add a layer of complexity as new software undergoes an extensive field validation applied to individual components of the SiSoS, as well as the overall SiSoS, to ensure that both legacy and new functionalities are working as desired. This paper aims to study how SiSoS transitions to continuous deployment by exploring how continuous deployment impacts field testing and validation activities, how continuous deployment can be practiced in SiSoS, and to identify the success factors that companies need to consider when transitioning to continuous deployment. We conducted a case study at Ericsson AB focusing on the embedded software of the Third Generation Radio Access Network (3G RAN). The 3G RAN consists of two large-scale software-intensive embedded systems, representing a simple SiSoS composed of two systems. 3G RAN software was the first to transition to continuous deployment and is used as a reference case for other products within Ericsson AB. Software deployment, in addition to field testing and validation, have transitioned from being a discrete activity performed at the end of software development to a continuous process performed in parallel to software development. Further, our study reveals an orchestrating approach for software deployment, which allows pre/post validation of legacy behavior and new features in a shorter release and deployment cadence. Furthermore, we identified the essential success factors that organizations should consider when transitioning to continuous deployment. Transition to continuous deployment, in addition to field testing and validation, shall be considered and planned carefully. In this paper, we provide a set of success factors and orchestration technique that helps organization when transitioning to continuous deployment in the software-intensive embedded system-of-systems context.
Hessian regularization of deep neural networks: A novel approach based on stochastic estimators of Hessian trace

In this paper, we develop a novel regularization method for deep neural networks by penalizing the trace of Hessian. This regularizer is motivated by a recent guarantee bound of the generalization error. We explain its benefits in finding flat minima and avoiding Lyapunov stability in dynamical systems. We adopt the Hutchinson method as a classical unbiased estimator for the trace of a matrix and further accelerate its calculation using a Dropout scheme. Experiments demonstrate that our method outperforms existing regularizers and data augmentation methods, such as Jacobian, Confidence Penalty, Label Smoothing, Cutout, and Mixup. The code is available at https://github.com/Dean-lyc/Hessian-Regularization.
Gradient-coupled cross-patch attention map for weakly supervised semantic segmentation

Most advanced weakly supervised semantic segmentation studies have explored various strategies for refining the class activation mapping (CAM) maps and thus producing pixelwise pseudomasks for training segmentation networks. However, CAM-based approaches suffer from the limited receptive fields of convolutional neural networks (CNNs) when exploring long-range feature dependencies, resulting in incomplete object region detection and inferior segmentation performance. On the other hand, the self-attention mechan ism has proven to be effective in modeling global contexts, providing a potential perspective for producing integral activation maps. Nevertheless, the use of self-attention maps to generate pseudomasks faces significant obstacles, as the attention activations are class-agnostic, locally inconsistent and noisy. To address these issues, we propose a novel end-to-end transformer-based framework, namely the gradient-coupled cross-patch attention map (GC-PAM). First, the classification score gradients are backpropagated to recover the semantic knowledge of the attention map, which is then used to retrieve the object-relevant patches and object-irrelevant patches. Next, the corresponding object patch attention maps are aggregated through content-adaptive cross-patch coupling to construct comprehensive activation maps, whereas the object-irrelevant maps are employed to suppress the background noise. Subsequently, the resulting object activation maps are used to generate pseudolabels to supervise the segmentation branch. Extensive experiments are conducted to demonstrate the effectiveness of the proposed GC-PAM. Despite its simplicity and computational efficiency, the GC-PAM with the DeiT-S backbone surpasses the existing state-of-the-art approaches on the PASCAL VOC 2012 benchmark (75.3% val, 74.6% test). The results also demonstrate that the GC-PAM is a feasible alternative to CNN-based architectures.
Climate Adaptation and Disaster Assessment using Deep Learning and Earth Observation

&amp;lt;p&amp;gt;Due to anthropogenic climate change, the frequency and intensity of natural disasters is only increasing. As supercomputing capabilities increase in an era of computational geosciences, artificial intelligence has emerged as a key tool in assessing the progression and impact of these disasters. Recovery from extreme weather events is aided by machine learning-based systems trained on multitemporal satellite imagery data. We work on shifting paradigms by seeking to understand the inner decision-making process (interpretability) of convolutional neural networks (CNNs) for damage assessment in buildings after natural disasters, as these deep learning algorithms are typically black boxes. We compare the efficacy of models trained on different input modalities, including combinations of the pre-disaster image, the post-disaster image, the disaster type, and the ground truth of neighboring buildings. Furthermore, we experiment with different loss functions, and find that ordinal cross entropy loss is the most effective criterion for optimization. Finally, we visualize inputs by creating gradient-weighted class activation mapping (Grad-CAM) on the data, with the end goal of deployment. Earth observation data harnessed by deep learning and computer vision is not only useful for disaster assessment, but also in understanding the other impacts of our changing climate from marine ecology to agriculture in the Global South.&amp;lt;/p&amp;gt;
Spatial-aware topic-driven-based image Chinese caption for disaster news

Automatically generating descriptions for disaster news images could effectively accelerate the spread of disaster message and lighten the burden of news editors from tedious news materials. Image caption algorithms are remarkable for generating captions directly from the content of the image. However, current image caption algorithms trained on existing image caption datasets fail to describe the disaster images with fundamental news elements. In this paper, we developed a large-scale disaster news image Chinese caption dataset (DNICC19k), which collected and annotated enormous news images related to disaster. Furthermore, we proposed a spatial-aware topic driven caption network (STCNet) to encode the interrelationships between these news objects and generate descriptive sentences related to news topics. STCNet firstly constructs a graph representation based on objects feature similarity. The graph reasoning module uses the spatial information to infer the weights of aggregated adjacent nodes according to a learnable Gaussian kernel function. Finally, the generation of news sentences are driven by the spatial-aware graph representations and the news topics distribution. Experimental results demonstrate that STCNet trained on DNICC19k could not only automatically creates descriptive sentences related to news topics for disaster news images, but also outperforms benchmark models such as Bottom-up, NIC, Show attend and AoANet on multiple evaluation metrics, achieving CIDEr/BLEU-4 scores of 60.26 and 17.01, respectively.
A Novel Method of Efficient Max-min Metric for Classification

Abstract Distance metric learning is an important method to study distance metrics that reflect the interaction between features and labels. Because of the high computational complexity and the fact that existing studies on algorithms that measure the similarities with Euclidean distances cannot reflect the real correlations between pairs of samples, learning a suitable distance metric is quite demanding for many data mining tasks. This paper innovatively proposes an extended efficient max-min metric (EMM) that maximizes the total distance between different pairs and minimizes the total distance between similar pairs as much as possible. Simultaneously, the adoption of the local preserving projection framework changes the solution process of the algorithm and improves the speed of the algorithm without losing accuracy. Because traditional EMM only considers pairwise constraints and ignores sample distribution, this study extends EMM based on sample distribution and successfully solves the multi-manifold problem. In the process of data realization, compared with the vector representation method, the use of high-order tensors will make the image representation more accurate and natural. To maintain the structure of higher-order tensors, a tensor-efficient max-min metric (TEMM) is proposed. In order to prove the accuracy and superiority of the research method in this paper, a large number of experiments have been carried out on image processing. The experimental results show that the method proposed in this paper has a good effect.
Application of Deep Wavelet Kernel Extreme Learning Machine in Fault Diagnosis of Tamping Vehicle

Abstract Since it is easy to overfit due to the long training time of the fault diagnosis model for machinery. Introducing the idea of autoencoder (AE) into the wavelet extreme learning machine (WELM) and then stacking to form WELM-AE can convert the underlying fault features to more abstract and advanced ones. And then the adaptive boosting kernel extreme learning machine (Adaboost-KELM) is used as the top-level classifier for fault recognition. The experimental results verify the feasibility of the proposed algorithm in the fault diagnosis of tamping machine with the characteristics of the fast training speed of the extreme learning machine, and a higher accuracy rate than back propagation (BP), support vector machine (SVM), stacked autoencoder (SAE), and convolutional neural networks (CNN).
Use big data as a source of data for health care quality indicators construction

This paper proposes a category- and selection-enabled nearest neighbor join (NNJ) between relation r and relation s, with similarity on T and support for category attributes C and selection predicate θ. Our solution does not suffer from redundant fetches and index false hits, which are the main performance bottlenecks of current nearest neighbor join techniques.A category-enabled NNJ leverages the category attributes C for query evaluation. For example, the categories of relation r can be used to limit relation s accessed at most once. Solutions that are not category-enabled must process each category independently and end up fetching, either from disk or memory, the blocks of the input relations multiple times. A selection-enabled NNJ performs well independent of whether the DBMS optimizer pushes the selection down or evaluates it on the fly. In contrast, index-based solutions suffer from many index false hits or end up in an expensive nested loop.Our solution does not constrain the physical design, and is efficient for row- as well as column-stores. Current solutions for column-stores use late materialization, which is only efficient if the data is clustered on the category attributes C. Our evaluation algorithm finds, for each outer tuple r, the inner tuples that satisfy the equality on the category and have the smallest distance to r with only one scan of both inputs. We experimentally evaluate our solution using a data warehouse that manages analyses of animal feeds.
The Value of Preexisting Structures for Digital Access: Modelling the Resolutions of the Dutch States General

The Resolutions of the Dutch States General (1576–1796) is an archive covering over two centuries of decision making and consists of a heterogeneous series of handwritten and printed documents. The archive, which has recently been digitised, is a rich source for historical research. However, owing to the archive’s heterogeneity and dispersion of information, historians and other researchers find it hard to use the archive for their research. In this article, we describe how we deal with the challenges of structuring and connecting the information in this archive. We focus on identifying the existing structural elements, to turn the archive from a set of pages into a set of meeting dates and individual resolutions, with rich metadata for each resolution. To deal with the challenges of historical language change, spelling variation, and text recognition mistakes, we exploit the repetitive nature of the language of the resolutions and use fuzzy string searching to identify structural elements by the formulaic expressions that signal their boundaries. We also discuss and provide an analysis of the value of extracting different types of entities from the text and argue that the choice of which types of entities to focus on should be made based on how they support relevant research questions and methods. In the resolutions, we choose to prioritise person qualifications such as profession, legal status, or title, over person names. Qualifications allow users to select certain groups of people and to meaningfully combine with other layers of metadata, whereas person names lack contextual information to disambiguate them, making it unclear which and how many persons are referred to by selecting a specific person name. We show how our methodology results in a computational platform that allows users to explore and analyse the archive through many connected layers of metadata.
Dual Optical Frequency Comb Neuron: Co‐Developing Hardware and Algorithm

Previous studies on photonic neural network have demonstrated that algorithm can inspire hardware design. This study seeks to demonstrate that hardware can also inspire algorithm design. To further exploit the advantages of photonic analog computing, the authors develop hardware and algorithm simultaneously for photonic convolutional neural networks. Specifically, this work developed an architecture called dual optical frequency comb neuron (DOFCN) enabled by an integrated microcomb to perform cosinusoidal nonlinear activation and vector convolution without temporal or spatial dispersion and large‐scale modulator arrays. Furthermore, DOFCN‐based composite vector convolutional neural networks (CVCNNs), an optical‐electric hybrid model, are proposed to perform classification and regression tests in signal modulation format identification and optical structure inverse design tasks, respectively. The ablation experiments show that under 4‐bit precision limit, the element‐wise activation CVCNN has 14% higher classification accuracy, 76% lower regression residuals, and 100% higher training efficiency than that of the 32‐bit standard convolutional neural network (CNN). DOFCN exhibits impressive spectral information processing ability to facilitate signal‐processing tasks related to optics and electromagnetics.
Generative models and Bayesian inversion using Laplace approximation

Abstract The Bayesian approach to solving inverse problems relies on the choice of a prior. This critical ingredient allows expert knowledge or physical constraints to be formulated in a probabilistic fashion and plays an important role for the success of the inference. Recently, Bayesian inverse problems were solved using generative models as highly informative priors. Generative models are a popular tool in machine learning to generate data whose properties closely resemble those of a given database. Typically, the generated distribution of data is embedded in a low-dimensional manifold. For the inverse problem, a generative model is trained on a database that reflects the properties of the sought solution, such as typical structures of the tissue in the human brain in magnetic resonance imaging. The inference is carried out in the low-dimensional manifold determined by the generative model that strongly reduces the dimensionality of the inverse problem. However, this procedure produces a posterior that does not admit a Lebesgue density in the actual variables and the accuracy attained can strongly depend on the quality of the generative model. For linear Gaussian models, we explore an alternative Bayesian inference based on probabilistic generative models; this inference is carried out in the original high-dimensional space. A Laplace approximation is employed to analytically derive the prior probability density function required, which is induced by the generative model. Properties of the resulting inference are investigated. Specifically, we show that derived Bayes estimates are consistent, in contrast to the approach in which the low-dimensional manifold of the generative model is employed. The MNIST data set is used to design numerical experiments that confirm our theoretical findings. It is shown that the approach proposed can be advantageous when the information contained in the data is high and a simple heuristic is considered for the detection of this case. Finally, the pros and cons of both approaches are discussed.
Teacher role of learners in a mobile learning environment: practice in higher education

Purpose Available research on mobile learning all stand on the viewpoint of teachers. However, as mobile learning is learner-centric, learners' roles must be understood clearly from the learners' viewpoint, particularly considering its self-directed learning feature. It is well-known learners for self-directed learning must bear some teachers' core responsibilities. The knowledge gap on this important issue inspires the present work which tries to answer two questions: which “teacher roles” can learners play and how will learners play their “teacher roles” in a mobile learning context. This study aims to discuss the aforementioned objectives. Design/methodology/approach A novel research approach integrating an action research model and a teaching presence scale analysis is proposed to answer the questions. The mobile learning courses conducted by the present authors for engineering undergraduate students, during the COVID-19 pandemic, provided the experimental data for this study. Findings The experimental results reveal that the learners could play a number of “teacher roles” actively for mobile learning. Some of this research studies are consistent with available studies but a discrepancy is also observed. Discussion is conducted for such discrepancy. Originality/value The findings will contribute to improve the pedagogy of mobile learning theoretically and practically.
An Approach to Designing Critical Railway Voice Communication

The digitalization of railways allows for improvements in service reliability, safety, security, and predictability. It relies on stable connections and innovative services. This paper presents an approach to designing critical railway voice communication applications, which follows the principles of a service-oriented architecture. Security-related functions, such as user data management and policy management that can be reused by different railway applications, are identified and designed as services. Common features, such as role management and presence, location, invite-a-user messaging, assured voice communications, and multiuser talker control, are also designed as services. The resources relevant to each service are defined and their unique identifiers are organized in tree structures. Moreover, the supported methods are described. Use cases illustrate the use of the services’ Application Programming Interfaces (APIs) in critical voice communication applications. The common application logic is modeled using call-state models, which present the application and network views. The models are formally described and it is proven mathematically that they exhibit equivalent behavior. The service API’s performance is studied through emulation to evaluate the injected latency.
Monocular Depth Estimation of Noncooperative Spacecraft Based on Deep Learning

Estimating position and stereographic geometry information of noncooperative spacecraft through three-dimensional (3-D) reconstruction techniques is of great significance. Depth information acquisition is an important component of 3-D reconstruction. Monocular cameras are cheaper and more widely used than depth sensors. The relative positions of noncooperative spacecraft and our spacecraft can be calculated from depth maps of monocular depth estimation and the camera parameters, providing data support for subsequent tracking and capture missions. This paper proposes a monocular depth estimation network combining the convolutional neural network (CNN) and a vision transformer (VIT) to improve the prediction accuracy of few-shot samples. We extract detail features and global features from the CNN and VIT encoders, respectively, and then fuse deep features and shallow features by a skip-connected upsampling decoder. Compared with the representative depth estimation algorithms in recent years on the NYU-Depth V2 dataset, the proposed network structure combines the advantages of the CNN and VIT as well as estimates the global depth of the scene more accurately while maintaining details. To solve the lack of spacecraft data collection, a new dataset is made from 3-D simulation models. Experiments on the self-made dataset demonstrate the feasibility of this method in aerospace engineering.