Reviewerly/exHarmony huggingface dataset:
Learning matrix factorization with scalable distance metric and regularizer

Matrix factorization has always been an encouraging field, which attempts to extract discriminative features from high-dimensional data. However, it suffers from negative generalization ability and high computational complexity when handling large-scale data. In this paper, we propose a learnable deep matrix factorization via the projected gradient descent method, which learns multi-layer low-rank factors from scalable metric distances and flexible regularizers. Accordingly, solving a constrained matrix factorization problem is equivalently transformed into training a neural network with an appropriate activation function induced from the projection onto a feasible set. Distinct from other neural networks, the proposed method activates the connected weights not just the hidden layers. As a result, it is proved that the proposed method can learn several existing well-known matrix factorizations, including singular value decomposition, convex, nonnegative and semi-nonnegative matrix factorizations. Finally, comprehensive experiments demonstrate the superiority of the proposed method against other state-of-the-arts.
Text Summarization Method Based on Gated Attention Graph Neural Network

Text summarization is an information compression technology to extract important information from long text, which has become a challenging research direction in the field of natural language processing. At present, the text summary model based on deep learning has shown good results, but how to more effectively model the relationship between words, more accurately extract feature information and eliminate redundant information is still a problem of concern. This paper proposes a graph neural network model GA-GNN based on gated attention, which effectively improves the accuracy and readability of text summarization. First, the words are encoded using a concatenated sentence encoder to generate a deeper vector containing local and global semantic information. Secondly, the ability to extract key information features is improved by using gated attention units to eliminate local irrelevant information. Finally, the loss function is optimized from the three aspects of contrastive learning, confidence calculation of important sentences, and graph feature extraction to improve the robustness of the model. Experimental validation was conducted on a CNN/Daily Mail dataset and MR dataset, and the results showed that the model in this paper outperformed existing methods.
Pandemic sociomaterial bricolage: how vulnerable communities used social media to tackle the COVID-19 crisis

Purpose This research was conducted to understand how vulnerable communities used social media (SM) tools to face the impacts of the COVID-19 pandemic. Affected by the lack of information and the absence of effective public policies, residents from slums in the city of Rio de Janeiro displayed new and unexpected uses to SM tools to tackle the health and socio-economic impacts of the pandemic. Design/methodology/approach The research methodology consisted of a qualitative, exploratory study, combining a series of in-depth interviews with the analysis of various posts, containing videos and texts, extracted from SM during the first six months of the pandemic. The data were collected in the context of 10 different communities in Rio de Janeiro city. Findings In the context of the pandemic, people combined different uses of SM not only to inform themselves and communicate with others but also to articulate and execute fundraising and food donation strategies within vulnerable communities. Accordingly, this SM use is characterized by improvisation, learning by doing and building resilience, which are all constructs related to the concept of bricolage. Users had no specific SM knowledge, and adjusted these technological tools to emergent new activities in practice, which is characteristic of sociomaterial process. In addition to emphasizing the importance of context for the emergence of the phenomenon, this work also highlights reliability, validity and authority as characteristics related to the citizen-led participation approach that was observed. Research limitations/implications Future research can develop approaches based on pandemic sociomaterial bricolage (PSB) aspects, which could guide governments and practitioners on building innovative solutions for the use of SM by the population, especially in emergency situations. Originality/value This study proposes a framework, termed PSB, to represent SM usage promoted by the pandemic context, which emerged from the triangulation of empirical data and an analysis based on the concepts of bricolage and sociomateriality.
Teacher or supervisor? Effective online knowledge distillation via guided collaborative learning

Knowledge distillation is a widely-used and effective technique to boost the performance of a lightweight student network, by having it mimic the behavior of a more powerful teacher network. This paper presents an end-to-end online knowledge distillation strategy, in which several peer students are trained together and their predictions are aggregated into a powerful teacher ensemble via an effective ensembling technique that uses an online supervisor network to determine the optimal way of combining the student logits. Intuitively, this supervisor network learns the area of expertise of each student and assigns a weight to each student accordingly►it has knowledge of the input image, the ground truth data, and the predictions of each individual student, and tries to answer the following question: “how much can we rely on each student’s prediction, given the current input image with this ground truth class?”. The proposed technique can be thought of as an inference optimization mechanism as it improves the overall accuracy over the same number of parameters. The experiments we performed show that the proposed knowledge distillation consistently improves the performance of the knowledge-distilled students vs. the independently trained students.
How can we create a recommender system for tourism? A location centric spatial binning-based methodology using social networks

Point of Interest (POI) recommendation is an important Location-Based Social Network (LBSN) task that has become a hotspot in the past decade. It aims to exploit the user's preferences for venues and recommend the POIs for their next visit. In the past, several works using geographical, temporal, social, and other contextual information have been brought to the forefront. But the data sparsity problem has foiled these attempts, and thereby they could not furnish the required level of accuracy. Here in this paper, we propose Long Short-Term Memory (LSTM) based method for POI recommendation. The crux of the approach is the spatial binning used to group the venues in proximity to the user's previously visited venues. This approach has been evaluated on three real-world databases, i.e., Gowalla, Foursquare NYC, and Foursquare TKY. Its evaluation suggests its efficacy. Further, its high accuracy and less time consumption supersede all the other approaches.
Privacy-preserving authentication scheme based on zero trust architecture

Zero trust architecture is an end-to-end approach for server resources and data security which contains identity authentication, access control, dynamic evaluation, and so on. This work focuses on authentication technology in the zero trust network. In this paper, a Traceable Universal Designated Verifier Signature (TUDVS) is used to construct a privacy-preserving authentication scheme for zero trust architecture. Specifically, when a client requests access to server resources, we want to protect the client's access privacy which means that the server administrator cannot disclose the client's access behavior to any third party. In addition, the security of the proposed scheme is proved and its efficiency is analyzed. Finally, TUDVS is applied to the single packet authorization scenario of the zero trust architecture to prove the practicability of the proposed scheme.
Single MCMC Chain Parallelisation on Decision Trees

Decision trees are highly famous in machine learning and usually acquire state-of-the-art performance. Despite that, well-known variants like CART, ID3, random forest, and boosted trees miss a probabilistic version that encodes prior assumptions about tree structures and shares statistical strength between node parameters. Existing work on Bayesian decision trees depend on Markov Chain Monte Carlo (MCMC), which can be computationally slow, especially on high dimensional data and expensive proposals. In this study, we propose a method to parallelise a single MCMC decision tree chain on an average laptop or personal computer that enables us to reduce its run-time through multi-core processing while the results are statistically identical to conventional sequential implementation. We also calculate the theoretical and practical reduction in run time, which can be obtained utilising our method on multi-processor architectures. Experiments showed that we could achieve 18 times faster running time provided that the serial and the parallel implementation are statistically identical.
Use of DIC in the characterisation of mode II crack propagation in adhesive fatigue testing

Adhesively bonded joints are used in many applications which are dynamically loaded. Thus, excellent fatigue properties of adhesives, especially under fracture mode II, are required. The fracture mode II testing of adhesive is challenging, where the observation of the crack length is complicated. Accordingly, compliance-based effective crack length definitions avoiding direct observation have been used instead. The experimental crack monitoring method using digital image correlation (DIC) is studied in this work. The DIC-based monitoring is compared to the compliance-based method in defining the crack length. The developed DIC-based method is useful to study, especially, the microcracked adhesive region ahead of the distinct open crack tip.
Wide Quantum Circuit Optimization with Topology Aware Synthesis

Unitary synthesis is an optimization technique that can achieve optimal gate counts while mapping quantum circuits to restrictive qubit topologies. Synthesis algorithms are limited in scalability by their exponentially growing run times. Application to wide circuits requires partitioning into smaller components. In this work, we explore methods to reduce depth and multi-qubit gate count of wide, mapped quantum circuits using synthesis. We present TopAS, a topology aware synthesis tool that preconditions quantum circuits before mapping. Partitioned subcircuits are optimized and fitted to sparse subtopologies to balance the opposing demands of synthesis and mapping algorithms. Compared to state of the art wide circuit synthesis algorithms, TopAS is able to reduce depth on average by 35.2% and CNOT count by 11.5% for mesh topologies. Compared to the optimization and mapping algorithms of Qiskit and Tket, TopAS is able to reduce CNOT counts by 30.3% and depth by 38.2% on average.
Fast detection of dam zone boundary based on Otsu thresholding optimized by enhanced harris hawks optimization

Earth-rock dams are among the most important and expensive infrastructure projects. A key safety issue is dam zone boundary detection to prevent the intrusion of materials from different zones. However, existing detection methods strongly highly depend on human judgement, which is time consuming and labor intensive. To solve this problem, this work proposes a fast boundary detection method based on the Otsu algorithm optimized by enhanced Harris hawks optimization (HHO). Compared with the original Otsu algorithm, the proposed method has a higher computation speed to meet the time requirements of engineering projects. Particle swarm optimization is adopted to enhance the exploration stage of HHO. In addition, a tangent function and chaotic sine map are used to improve the convergence speed and robustness. The application of the proposed method to a real-life project shows that the calculation time can be reduced to 20 s, which is approximately 18.8% of the original calculation time.
Improved FRQI on superconducting processors and its restrictions in the NISQ era

Abstract In image processing, the amount of data to be processed grows rapidly, in particular when dealing with images of more than two dimensions or time series of images. Thus, efficient processing is a challenge, as data sizes may push even supercomputers to their limits. Quantum image processing promises to encode images with logarithmically less qubits than classical pixels in the image. In theory, this is a huge progress, but so far not many experiments have been conducted in practice, in particular on real backends. Often, the precise conversion of classical data to quantum states, the exact implementation, and the interpretation of the measurements in the classical context are challenging. We investigate these practical questions in this paper. In particular, we study the feasibility of the flexible representation of quantum images (FRQI). Furthermore, we check experimentally the limit in the current noisy intermediate-scale quantum era, i.e., up to which image size an image can be encoded, both on simulators and on real backends. Finally, we propose a method for simplifying the circuits needed for the FRQI. With our alteration, the number of gates can be reduced, especially the one of the error-prone controlled-NOT gates. As a consequence, the size of manageable images increases.
DIGITAL TRANSFORMATION OF EDUCATION: A MODERN VIEW ON SELF-DEVELOPMENT

The current situation of «mass character» of higher education has led to the fact that a large number of graduates significantly exceeds market demands. And most often, the acquired competencies do not advise the requirements of the digital economy. Changes in the labor market affect education at all levels. The digitalization of all spheres of society does not bypass the education system. The requirements for skills and abilities are changing quite quickly, among which preference is given primarily to creative thinking, emotional intelligence, and programming. The emergence of new educational technologies has led to a rapid reduction in the time for mastering professional skills. Education is moving from an industrial model to a more flexible model based on building a career development trajectory. In accordance with this, the main trends in the education of the future are identified. Undoubtedly, the digitalization process determines the technologies used for learning — digital platforms, virtual reality technologies, digital organization of the educational process. Thanks to modern technologies, it has become possible for businesses to monitor the practical developments of not only students, but also schoolchildren in real time, preparing personnel for their needs. The development of individual career development trajectories requires an individual approach to the learning process based on adaptive programs, taking into account cognitive abilities, basic education and motivation. Great importance is given to the analysis of the labor market and the determination of the most demanded professions in the short term based on the use of the Gartner curve. All this requires restructuring the educational process in accordance with modern trends and possible changes in the labor market.
A Novel Approach to Decision-Making on Diagnosing Oncological Diseases Using Machine Learning Classifiers Based on Datasets Combining Known and/or New Generated Features of a Different Nature

This paper deals with the problem of diagnosing oncological diseases based on blood protein markers. The goal of the study is to develop a novel approach in decision-making on diagnosing oncological diseases based on blood protein markers by generating datasets that include various combinations of features: both known features corresponding to blood protein markers and new features generated with the help of mathematical tools, particularly with the involvement of the non-linear dimensionality reduction algorithm UMAP, formulas for various entropies and fractal dimensions. These datasets were used to develop a group of multiclass kNN and SVM classifiers using oversampling algorithms to solve the problem of class imbalance in the dataset, which is typical for medical diagnostics problems. The results of the experimental studies confirmed the feasibility of using the UMAP algorithm and approximation entropy, as well as Katz and Higuchi fractal dimensions to generate new features based on blood protein markers. Various combinations of these features can be used to expand the set of features from the original dataset in order to improve the quality of the received classification solutions for diagnosing oncological diseases. The best kNN and SVM classifiers were developed based on the original dataset augmented respectively with a feature based on the approximation entropy and features based on the UMAP algorithm and the approximation entropy. At the same time, the average values of the metric MacroF1-score used to assess the quality of classifiers during cross-validation increased by 16.138% and 4.219%, respectively, compared to the average values of this metric in the case when the original dataset was used in the development of classifiers of the same name.
Self-Supervised Graph Attention Collaborative Filtering for Recommendation

Due to the complementary nature of graph neural networks and structured data in recommendations, recommendation systems using graph neural network techniques have become mainstream. However, there are still problems, such as sparse supervised signals and interaction noise, in the recommendation task. Therefore, this paper proposes a self-supervised graph attention collaborative filtering for recommendation (SGACF). The correlation between adjacent nodes is deeply mined using a multi-head graph attention network to obtain accurate node representations. It is worth noting that self-supervised learning is brought in as an auxiliary task in the recommendation, where the supervision task is the main task. It assists model training for supervised tasks. A multi-view of a node is generated by the graph data-augmentation method. We maximize the consistency between its different views compared to the views of the same node and minimize the consistency between its different views compared to the views of other nodes. In this paper, the effectiveness of the method is illustrated by abundant experiments on three public datasets. The results show its significant improvement in the accuracy of the long-tail item recommendation and the robustness of the model.
An Object Classification Approach for Autonomous Vehicles Using Machine Learning Techniques

An intelligent, accurate, and powerful object detection system is required for automated driving systems to keep these vehicles aware of their surrounding objects. Thus, vehicles adapt their speed and operations to avoid crashing with the existing objects and follow the driving rules around the existence of emergency vehicles and installed traffic signs. The objects considered in this work are summarized by regular vehicles, big trucks, emergency vehicles, pedestrians, bicycles, traffic lights, and traffic signs on the roadside. Autonomous vehicles are equipped with high-quality sensors and cameras, LiDAR, radars, and GPS tracking systems that help to detect existing objects, identify them, and determine their exact locations. However, these tools are costly and require regular maintenance. This work aims to develop an intelligent object classification mechanism for autonomous vehicles. The proposed mechanism uses machine learning technology to predict the existence of investigated objects over the road network early. We use different datasets to evaluate the performance of the proposed mechanism. Accuracy, Precision, F1-Score, G-Mean, and Recall are the measures considered in the experiments. Moreover, the proposed object classification mechanism is compared to other selected previous techniques in this field. The results show that grouping the dataset based on their mobility nature before applying the classification task improved the results for most of the algorithms, especially for vehicle detection.
Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition

Skeleton-based action recognition receives increasing attention because skeleton sequences reduce training complexity by eliminating visual information irrelevant to actions. To further improve sample efficiency, meta-learning-based one-shot learning solutions were developed for skeleton-based action recognition. These methods predict by finding the nearest neighbors according to the similarity between instance-level global embedding. However, such measurement holds unstable representativity due to inadequate generalized learning on the averaged local invariant and noisy features, while intuitively, steady and fine-grained recognition relies on determining key local body movements. To address this limitation, we present the Adaptive Local-Component-aware Graph Convolutional Network, which replaces the comparison metric with a focused sum of similarity measurements on aligned local embedding of action-critical spatial/temporal segments. Comprehensive one-shot experiments on the public benchmark of NTURGB+D 120 indicate that our method provides a stronger representation than the global embedding and helps our model reach state-of-the-art.
Treatment Learning Causal Transformer for Noisy Image Classification

Current top-notch deep learning (DL) based vision models are primarily based on exploring and exploiting the inherent correlations between training data samples and their associated labels. However, a known practical challenge is their degraded performance against "noisy" data, induced by different circumstances such as spurious correlations, irrelevant contexts, domain shift, and adversarial attacks. In this work, we incorporate this binary information of "existence of noise" as treatment into image classification tasks to improve prediction accuracy by jointly estimating their treatment effects. Motivated from causal variational inference, we propose a transformer-based architecture, Treatment Learning Causal Transformer (TLT), that uses a latent generative model to estimate robust feature representations from current observational input for noise image classification. Depending on the estimated noise level (modeled as a binary treatment factor), TLT assigns the corresponding inference network trained by the designed causal loss for prediction. We also create new noisy image datasets incorporating a wide range of noise factors (e.g., object masking, style transfer, and adversarial perturbation) for performance benchmarking. The superior performance of TLT in noisy image classification is further validated by several refutation evaluation metrics. As a by-product, TLT also improves visual salience methods for perceiving noisy images.