Reviewerly/exHarmony huggingface dataset:
Learning to Adapt With Memory for Probabilistic Few-Shot Learning

Few-shot learning has recently generated increasing popularity in machine learning, which addresses the fundamental yet challenging problem of learning to adapt to new tasks with the limited data. In this paper, we propose a new probabilistic framework that learns to fast adapt with external memory. We model the classifier parameters as distributions that are inferred from the support set and directly applied to the query set for prediction. The model is optimized by formulating as a variational inference problem. The probabilistic modeling enables better handling prediction uncertainty due to the limited data. We impose a discriminative constraint on the feature representations by exploring the class structure, which can improve the classification performance. We further introduce a memory unit to store task-specific information extracted from the support set and used for the query set to achieve explicit adaption to individual tasks. By episodic training, the model learns to acquire the capability of adapting to specific tasks, which guarantees its performance on new related tasks. We conduct extensive experiments on widely-used benchmarks for few-shot recognition. Our method achieves new state-of-the-art performance and largely surpassing previous methods by large margins. The ablation study further demonstrates the effectiveness of the proposed discriminative learning and memory unit.
Breast mass detection in digital mammography based on anchor-free architecture

Accurate detection of breast masses in mammography images is critical to diagnose early breast cancer, which can greatly improve the patients’ survival rate. However, it is still a big challenge due to the heterogeneity of breast masses and the complexity of their surrounding environment. Therefore, how to develop a robust breast mass detection framework in clinical practical applications to improve patient survival is a topic that researchers need to continue to explore. To address these problems, we propose a one-stage object detection architecture, called Breast Mass Detection Network (BMassDNet), based on anchor-free and feature pyramid which makes the detection of breast masses of different sizes well adapted. We introduce a truncation normalization method and combine it with adaptive histogram equalization to enhance the contrast between the breast mass and the surrounding environment. Meanwhile, to solve the overfitting problem caused by small data size, we propose a natural deformation data augmentation method and mend the train data dynamic updating method based on the data complexity to effectively utilize the limited data. Finally, we use transfer learning to assist the training process and to improve the robustness of the model ulteriorly. On the INbreast dataset, each image has an average of 0.495 false positives whilst the recall rate is 0.930; On the DDSM dataset, when each image has 0.599 false positives, the recall rate reaches 0.943. The experimental results on datasets INbreast and DDSM show that the proposed BMassDNet can obtain competitive detection performance over the current top ranked methods.
Real-Time SSDLite Object Detection on FPGA

Deep neural network (DNN)-based object detection has been investigated and applied to various real-time applications. However, it is hard to employ the DNNs in embedded systems due to their high computational complexity and deep-layered structure. Although several field-programmable gate array (FPGA) implementations have been presented recently for real-time object detection, they suffer from either low throughput or low detection accuracy. In this article, we propose an efficient computing system for real-time SSDLite object detection on FPGA devices, which includes novel hardware architecture and system optimization techniques. In the proposed hardware architecture, a neural processing unit (NPU) that consists of heterogeneous units, such as band processing, scaling, and accumulating, and data fetching and formatting units is designed to accelerate the DNNs efficiently. In addition, system optimization techniques are presented to improve the throughput further. A task control unit is employed to balance the workload and increase the utilization of heterogeneous units in the NPU, and the object detection algorithm is refined accordingly. The proposed architecture is realized on an Intel Arria 10 FPGA and enhances the throughput by up to 13.6× compared to the state-of-the-art FPGA implementation.
A financial ticket image intelligent recognition system based on deep learning

Facing rapid growth in the issuance of financial tickets (e.g., bills, invoices), traditional manual invoice reimbursement and financial accounting systems are imposing an increasing burden on financial accountants and consuming excessive manpower. To solve this problem, we propose an iterative self-learning framework of Financial Ticket Intelligent Recognition System (FTIRS), which supports iteratively updating and extensibility of the algorithm model, which are the fundamental requirements for a practical financial accounting system. In addition, we designed a simple yet efficient Financial Ticket Faster Detection Network (FTFDNet) and an intelligent data warehouse of financial tickets to strengthen its efficiency and performance. Currently, the system can recognize 482 types of financial tickets and has an automatic iterative optimization mechanism. Thus, with increased application time, the types of tickets supported by the system will increase, and the accuracy of recognition will improve. Experimental results show that the average recognition accuracy of the system is 97.41%, and the average running time for a single ticket is 173.72 ms. The practical value of the system has been verified in business. It can greatly improve the efficiency of financial accounting and reduce the human cost of accounting staff.
The Role of Information &amp; Communication Technology in Elearning Environments: A Systematic Review

The role of information and communication technology (ICT) in education continues to serve the development of teaching and learning for most subjects, and the subject of Islamic studies is no exception.However, to date, a systematic literature review (SLR) on the role of ICT for this particular subject is lacking.Therefore, to facilitate the implementation and adoption of ICT, we focus on e-learning for the majority of subjects, including Islamic education and ICT applications, e.g.educational software and the Internet, which are gaining exponential importance with time.This SLR considered research works in the literature that have been published in the temporal range of June 2007 to June 2020.We derived 41 publications from an initial 301 candidate publications, by applying the inclusion and exclusion criteria in two distinct rounds.This rigorous review explored teachers' and students' opinions on the application of technology, and shed light on the challenges to date, as well as the potential opportunities and future research directions.We also performed Tweet analysis in an example of public opinion available on social media, e.g.Twitter, for learning education through ICT-based methods.This study delivers several implications for researchers and practitioners, and provides insight on state-of-the-art.Our findings suggest that ICT-based teaching methods for learning Islamic studies as a research context require more attention.Further empirical investigation is crucial to better understand the impact of ICT practices and use, especially in the context of Islamic studies.
Development of an Explainable Prediction Model of Heart Failure Survival by Using Ensemble Trees

Cardiovascular diseases (CVD) are the leading cause of death globally. Heart failure prediction, one of the CVD manifestations, has become a priority for doctors, however, up to date clinical practice usually has failed to reach high accuracy in such tasks. Machine learning offers advantages not only for clinical prediction but also for feature ranking improving the interpretation of the outputs by clinical professionals. Thus, the concept of eXplainable Artificial Intelligence (XAI) is aimed to cope with the lack of explainability of machine learning models in the healthcare domain, in this case, and provide healthcare professionals with patient-tailored decision-making tools that improve treatments and diagnostics. This paper presents a heart failure survival prediction model development by using ensemble trees machine learning techniques. Extreme Gradient Boosting (XGBoost) is demonstrated as the classifier with most accurate results (83% accuracy with unseen data) over the other ensemble trees options. Moreover, a features selection preprocessing is made in order to assess which relevant features contribute to the model's results. Next, in terms of improving the explainability of the model developed, a study of features importance is carried out showing the "follow up time period" feature as the most relevant. Finally, a quantitative evaluation of the interpretability and fidelity of the model developed is performed obtaining a balanced ratio between these two indicators.
TAERT: Triple-Attentional Explainable Recommendation with Temporal Convolutional Network

Explainable Recommendation aims at not only providing the recommended items to users, but also enabling users to be aware of why these items are recommended. To better understand the recommended results, textual reviews have been playing an increasingly important role in the recommender systems. However, how to learn the latent representation of user preferences and item features, and how to model the interactions between them effectively via specific aspects in the reviews are two crucial problems in the explainable recommendation. To this end, we propose a novel Triple-Attentional Explainable Recommendation with Temporal Convolutional Network, named TAERT, which is to jointly generate recommendation results and explanations. Specifically, we first explore a feature learning method based on Temporal Convolutional Network (TCN) to derive word-aware and review-aware vector representations. Then, we introduce three levels of attention networks to model word contribution, review usefulness and importance of latent factors, respectively. Finally, the predicted rating is inferred by the factor-level attention based prediction layer. Furthermore, the attention mechanism is also conducive to identifying the representative item reviews and highlighting the informative words to generate explanations. Compared with the state-of-the-art methods, comprehensive experiments on six real-world datasets are conducted to verify the effectiveness on both recommendation and explanation.
Cycle consistent network for end-to-end style transfer TTS training

In this paper, we propose a cycle consistent network based end-to-end TTS for speaking style transfer, including intra-speaker, inter-speaker, and unseen speaker style transfer for both parallel and unparallel transfers. The proposed approach is built upon a multi-speaker Variational Autoencoder (VAE) TTS model. The model is usually trained in a paired manner, which means the reference speech is totally paired with the output including speaker identity, text, and style. To achieve a better quality for style transfer, which for most cases is in an unpaired manner, we augment the model with an unpaired path with a separated variational style encoder. The unpaired path takes as input an unpaired reference speech and yields an unpaired output. The unpaired output, which lacks direct ground-truth target, is then successfully constrained by a delicately designed cycle consistent network. Specifically, the unpaired output of the forward transfer is fed into the model again as an unpaired reference input, and after the backward transfer yields an output expected to be the same as the original unpaired reference speech. Ablation study shows the effectiveness of the unpaired path, separated style encoders and cycle consistent network in the proposed model. The final evaluation demonstrates the proposed approach significantly outperforms the Global Style Token (GST) and VAE based systems for all the six style transfer categories, in metrics of naturalness, speech quality, similarity of speaker identity, and similarity of speaking style.
Functional deep echo state network improved by a bi-level optimization approach for multivariate time series classification

The multivariate time series (MTS) classification is one of the major tasks of time series data mining. Many methods have been proposed to investigate the MTS classification. Among them, the method based on feature representation is the most popular and widely used one. However, there exist some shortcomings for this method, such as unsatisfactory accuracy, being sensitive to noise and not able to fully make use of time series data attributes. In order to overcome these disadvantages, we propose a new method called functional deep echo state network (FDESN) for MTS classification that utilizes two special operators: temporal aggregation and spatial aggregation. In general, the parameters of the FDESN are determined by random selection, human experience or trial and error. This may increase the complexity of the FDESN or reduce the accuracy of the FDESN. In this study, a novel bi-level optimization approach is proposed to optimize the parameters of the FDESN. The parameter selection problem in the FDESN is transformed into the bi-level optimization problem. The state transition algorithm (STA) is used to solve the bi-level optimization problem. Finally, the experimental results show that the proposed method is superior to other methods. In addition, the proposed method is successfully applied to anode condition identification in aluminum electrolysis. For the aluminum electrolysis datasets, the proposed method improved the average classification accuracy by about 3.5% compared with the other methods. For a specific aluminum electrolysis dataset ACS2504, the classification accuracy significantly increased from 77.92% to 82.69% by using the proposed method.
Developing digital competence of teachers of Humanitarian disciplines in the conditions of COVID-19 quarantine measures

Abstract The relevance of the research is explained by the necessity of developing digital competence of teachers of Humanitarian disciplines at the higher education institutions (HEIs) in the conditions of the quarantine measures to prevent the spread of COVID-19. It is particularly challenging for teachers of Humanitarian disciplines, who are not specialists in the digital sphere as their profession is focused on humans. The purpose of the paper is to define the influence of the quarantine on the work of teachers at HEIs, revealing the level of the development, challenges and peculiarities of enhancing the digital competence of teachers of Humanitarian disciplines, theirs needs in education and giving recommendations how to arrange education and make administrative decisions. The methods of the research are survey, theoretical analysis, others. The results are to define the peculiarities of the influence of the quarantine on the arrangement of distance learning, the peculiarities of developing digital competence of teachers during the quarantine, their needs in education, the challenges in conducting mass distance learning, the determined complex of scientific approaches to developing digital competence. The practical significance of the research refers to developing three internship programs, based on the results of diagnosing teachers’ digital competence.
Comparison between Recurrent Networks and Temporal Convolutional Networks Approaches for Skeleton-Based Action Recognition

Action recognition plays an important role in various applications such as video monitoring, automatic video indexing, crowd analysis, human-machine interaction, smart homes and personal assistive robotics. In this paper, we propose improvements to some methods for human action recognition from videos that work with data represented in the form of skeleton poses. These methods are based on the most widely used techniques for this problem—Graph Convolutional Networks (GCNs), Temporal Convolutional Networks (TCNs) and Recurrent Neural Networks (RNNs). Initially, the paper explores and compares different ways to extract the most relevant spatial and temporal characteristics for a sequence of frames describing an action. Based on this comparative analysis, we show how a TCN type unit can be extended to work even on the characteristics extracted from the spatial domain. To validate our approach, we test it against a benchmark often used for human action recognition problems and we show that our solution obtains comparable results to the state-of-the-art, but with a significant increase in the inference speed.
Global context-aware cervical cell detection with soft scale anchor matching

Background and Objective:Computer-aided cervical cancer screening based on an automated recognition of cervical cells has the potential to significantly reduce error rate and increase productivity compared to manual screening. Traditional methods often rely on the success of accurate cell segmentation and discriminative hand-crafted features extraction. Recently, detector based on convolutional neural network is applied to reduce the dependency on hand-crafted features and eliminate the necessary segmentation. However, these methods tend to yield too much false positive predictions. Methods:This paper proposes a global context-aware framework to deal with this problem, which integrates global context information by an image-level classification branch and a weighted loss. And the prediction of this branch is merged into cell detection for filtering false positive predictions. Furthermore, a new ground truth assignment strategy in the feature pyramid called soft scale anchor matching is proposed, which matches ground truths with anchors across scales softly. This strategy searches the most appropriate representation of ground truths in each layer and add more positive samples with different scales, which facilitate the feature learning. Results:Our proposed methods finally get 5.7% increase in mean average precision and 18.5% increase in specificity with sacrifice of 2.6% delay in inference time. Conclusions:Our proposed methods which totally avoid the dependence on segmentation of cervical cells, show the great potential to reduce the workload for pathologists in automation-assisted cervical cancer screening.
Unified integration of many-objective optimization algorithm based on temporary offspring for software defects prediction

Software defects prediction technology related to software products’ security and quality and provides guidance for software testing. To solve both the problem of datasets class imbalance in software defects prediction and support vector machine (SVM) parameter selection synchronously, high dimension software defects prediction model (HD-SDP) based on SVM is proposed. Including four objectives that the false positive rate of defects, probability of detection, F-metric, and Balance value. And a unified integration of many-objective optimization algorithm based on temporary offspring (UIMaOTO) is designed for this model to select the parameters of SVM and non-defective module synchronously. UIMaOTO adopts temporary offspring strategy to generate the formal offspring and then proposes the unified integration strategy to enhance the selection pressure of algorithm. UIMaOTO is compared to other state-of-the-art algorithms, and the experiment results are conducted on well-known DTLZ test suite. The results show that the proposed algorithm has better all-around performance and is competitive for many-objective optimization problems. At the same time, the UIMaOTO algorithm is used to address the HD-SDP model, and the performance is improved by 14.27% compared with other algorithms.
Efficient bi-triangle counting for large bipartite networks

A bipartite network is a network with two disjoint vertex sets and its edges only exist between vertices from different sets. It has received much interest since it can be used to model the relationship between two different sets of objects in many applications (e.g., the relationship between users and items in E-commerce). In this paper, we study the problem of efficient bi-triangle counting for a large bipartite network, where a bi-triangle is a cycle with three vertices from one vertex set and three vertices from another vertex set. Counting bi-triangles has found many real applications such as computing the transitivity coefficient and clustering coefficient for bipartite networks. To enable efficient bi-triangle counting, we first develop a baseline algorithm relying on the observation that each bi-triangle can be considered as the join of three wedges. Then, we propose a more sophisticated algorithm which regards a bi-triangle as the join of two super-wedges, where a wedge is a path with two edges while a super-wedge is a path with three edges. We further optimize the algorithm by ranking vertices according to their degrees. We have performed extensive experiments on both real and synthetic bipartite networks, where the largest one contains more than one billion edges, and the results show that the proposed solutions are up to five orders of magnitude faster than the baseline method.
Using a hybrid content-based and behaviour-based featuring approach in a parallel environment to detect fake reviews

The financial impact of positive reviews has prompted some fraudulent sellers to generate fake product reviews for either promoting their products or discrediting competing products. Many e-commerce portals have implemented measures to detect such fake reviews, and these measures require excellent detectors to be effective. In this work, we propose 133 unique features from the combination of content and behaviour-based features to detect fake reviews using machine learning classifiers. Preliminary results show that these features can provide good results for all datasets tested. Detailed analysis of the results, however, reveals the existence of class imbalance issues for two of the bigger datasets - there is a high imbalance between the accuracies of different classes (e.g., 7.73% for the fake class and 99.3% for the genuine class using a Multilayer Perceptron classifier). We therefore introduce two sampling methods that can improve the accuracy of the fake review class on balanced datasets. The accuracies can be improved to a maximum of 89% for both random under and over-sampling on Convolutional Neural Networks. Additionally, we propose a parallel cross-validation method that can speed up the validation process in a parallel environment.
Some classes of power functions with low c-differential uniformity over finite fields

Functions with low c-differential uniformity have optimal resistance to some types of differential cryptanalysis. In this paper, we investigate the c-differential uniformity of power functions over finite fields of odd characteristic. Based on some known almost perfect nonlinear functions, we present several classes of power functions $$f(x)=x^d$$ with $$_{c}\varDelta _f\le 3$$ . Especially, two new classes of perfect c-nonlinear power functions are proposed.