Reviewerly/exHarmony huggingface dataset:
Hierarchical User Intent Graph Network for Multimedia Recommendation

Understanding user preference on item context is the key to acquire a high-quality multimedia recommendation. Typically, the pre-existing features of items are derived from pre-trained models (e.g. visual features of micro-videos extracted from some neural networks), and then introduced into the recommendation framework (e.g. collaborative filtering) to capture user preference. However, we argue that such a paradigm is insufficient to output satisfactory user representations, which hardly profile personal interests well. The key reason is that present works largely leave user intents untouched, then failing to encode such informative representation of users. In this work, we aim to learn multi-level user intents from the co-interacted patterns of items, so as to obtain high-quality representations of users and items and further enhance the recommendation performance. Towards this end, we develop a novel framework, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hierarchical User Intent Graph Network</i> , which exhibits user intents in a hierarchical graph structure, from the fine-grained to coarse-grained intents. In particular, we get the multi-level user intents by recursively performing two operations: 1) intra-level aggregation, which distills the signal pertinent to user intents from co-interacted item graphs; and 2) inter-level aggregation, which constitutes the supernode in higher levels to model coarser-grained user intents via gathering the nodes’ representations in the lower ones. Then, we refine the user and item representations as a distribution over the discovered intents, instead of simple pre-existing features. To demonstrate the effectiveness of our model, we conducted extensive experiments on three public datasets. Our model achieves significant improvements over the state-of-the-art methods, including MMGCN and DisenGCN. Furthermore, by visualizing the item representations, we provide the semantics of user intents.
Learning Semantic Person Image Generation by Region-Adaptive Normalization

Human pose transfer has received great attention due to its wide applications, yet is still a challenging task that is not well solved. Recent works have achieved great success to transfer the person image from the source to the target pose. However, most of them cannot well capture the semantic appearance, resulting in inconsistent and less realistic textures on the reconstructed results. To address this issue, we propose a new two-stage framework to handle the pose and appearance translation. In the first stage, we predict the target semantic parsing maps to eliminate the difficulties of pose transfer and further benefit the latter translation of per-region appearance style. In the second one, with the predicted target semantic maps, we suggest a new person image generation method by incorporating the region-adaptive normalization, in which it takes the per-region styles to guide the target appearance generation. Extensive experiments show that our proposed SPGNet can generate more semantic, consistent, and photorealistic results and perform favorably against the state of the art methods in terms of quantitative and qualitative evaluation. The source code and model are available at https://github.com/cszy98/SPGNet.git.
NRGNN

Graph Neural Networks (GNNs) have achieved promising results for semi-supervised learning tasks on graphs such as node classification. Despite the great success of GNNs, many real-world graphs are often sparsely and noisily labeled, which could significantly degrade the performance of GNNs, as the noisy information could propagate to unlabeled nodes via graph structure. Thus, it is important to develop a label noise-resistant GNN for semi-supervised node classification. Though extensive studies have been conducted to learn neural networks with noisy labels, they mostly focus on independent and identically distributed data and assume a large number of noisy labels are available, which are not directly applicable for GNNs. Thus, we investigate a novel problem of learning a robust GNN with noisy and limited labels. To alleviate the negative effects of label noise, we propose to link the unlabeled nodes with labeled nodes of high feature similarity to bring more clean label information. Furthermore, accurate pseudo labels could be obtained by this strategy to provide more supervision and further reduce the effects of label noise. Our theoretical and empirical analysis verify the effectiveness of these two strategies under mild conditions. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed method in learning a robust GNN with noisy and limited labels.
Efficient Exact Algorithms for Maximum Balanced Biclique Search in Bipartite Graphs

Given a bipartite graph, the maximum balanced biclique (MBB) problem, discovering a mutually connected while disjoint sets of equal size with the maximum cardinality, plays a significant role for mining the bipartite graph and has numerous applications. Despite the NP-hardness of the MBB problem, in this paper, we show that an exact MBB can be discovered extremely fast in bipartite graphs for real applications. We propose two exact algorithms dedicated for small dense and large sparse bipartite graphs respectively. For dense bipartite graphs, an O*(1.3803n) algorithm is proposed. This algorithm in fact can find an MBB very fast for small dense bipartite graphs that are common for applications such as VLSI design. This is because, using our proposed novel techniques, the search can fast converge to sufficiently dense bipartite graphs which we prove to be polynomial-time solvable. For large sparse bipartite graphs typical for applications such as biological data analysis, an O*(1.3803 δ) algorithm is proposed, where δ is only a few hundred for large sparse bipartite graphs with millions of vertices. The indispensible optimization that leads to this time complexity is: we transform a large sparse bipartite graph into a limited number of dense subgraphs such that each of the dense subgraphs has up to δ vertices and then apply our proposed algorithm for dense bipartite graphs on each of the subgraphs. To further speed up this algorithm, tighter upper bounds, faster heuristics and more effective reductions are proposed, allowing an MBB to be discovered within a few seconds for bipartite graphs with millions of vertices. Extensive experiments are conducted on synthetic and real large bipartite graphs to demonstrate the efficiency and effectiveness of our proposed algorithms and techniques.
Overview of the MEDIQA 2021 Shared Task on Summarization in the Medical Domain

The MEDIQA 2021 shared tasks at the BioNLP 2021 workshop addressed three tasks on summarization for medical text: (i) a question summarization task aimed at exploring new approaches to understanding complex real-world consumer health queries, (ii) a multi-answer summarization task that targeted aggregation of multiple relevant answers to a biomedical question into one concise and relevant answer, and (iii) a radiology report summarization task addressing the development of clinically relevant impressions from radiology report findings.Thirty-five teams participated in these shared tasks with sixteen working notes submitted (fifteen accepted) describing a wide variety of models developed and tested on the shared and external datasets.In this paper, we describe the tasks, the datasets, the models and techniques developed by various teams, the results of the evaluation, and a study of correlations among various summarization evaluation measures.We hope that these shared tasks will bring new research and insights in biomedical text summarization and evaluation.
A lightweight 3D convolutional neural network for deepfake detection

The rapid development of DeepFake technologies has brought great challenges to the authenticity of video contents. It is of vital importance to develop DeepFake detection methods, among which three-dimensional (3D) convolution neural networks (CNN) have attracted wide interest and achieved satisfying performances. However, there are few 3D CNNs designed for DeepFake detection and the parameters of them are large, which cause heavy memory and storage consumption. In this paper, a lightweight 3D CNN is proposed for DeepFake detection. Channel transformation module is designed to extract features with much fewer parameters in higher level. Serving as spatial-temporal module, 3D CNNs are adopted to fuse the spatial features in time dimension. To suppress frame content and highlight frame texture, spatial rich model features are extracted from the input frames, which helps the spatial-temporal module achieve better performance. Experimental results show that the number of parameters of the proposed network is much less than those of other networks and the proposed network outperforms other state-of-the-art DeepFake detection methods on mainstream DeepFake data sets.
Multi-stage malaria parasite recognition by deep learning

Abstract Motivation Malaria, a mosquito-borne infectious disease affecting humans and other animals, is widespread in tropical and subtropical regions. Microscopy is the most common method for diagnosing the malaria parasite from stained blood smear samples. However, this technique is time consuming and must be performed by a well-trained professional, yet it remains prone to errors. Distinguishing the multiple growth stages of parasites remains an especially challenging task. Results In this article, we develop a novel deep learning approach for the recognition of malaria parasites of various stages in blood smear images using a deep transfer graph convolutional network (DTGCN). To our knowledge, this is the first application of graph convolutional network (GCN) on multi-stage malaria parasite recognition in such images. The proposed DTGCN model is based on unsupervised learning by transferring knowledge learnt from source images that contain the discriminative morphology characteristics of multi-stage malaria parasites. This transferred information guarantees the effectiveness of the target parasite recognition. This approach first learns the identical representations from the source to establish topological correlations between source class groups and the unlabelled target samples. At this stage, the GCN is implemented to extract graph feature representations for multi-stage malaria parasite recognition. The proposed method showed higher accuracy and effectiveness in publicly available microscopic images of multi-stage malaria parasites compared to a wide range of state-of-the-art approaches. Furthermore, this method is also evaluated on a large-scale dataset of unseen malaria parasites and the Babesia dataset. Availability Code and dataset are available at https://github.com/senli2018/DTGCN_2021 under a MIT license.
Innovation and Creativity for Data Mining Using Computational Statistics

In this digital world, a set of information about the real-world entities is collected and stored in a common place for extraction. When the information generated has no meaning, it will convert into meaningful information with a set of rules. Those data have to be converted from one form to another form based on the attributes where it was generated. Storing these data with huge volume in one place and retrieving from the repository reveals complications. To overcome the problem of extraction, a set of rules and algorithms was framed by the standards and researchers. Mining the data from the repository by certain principles is called data mining. It has a lot of algorithms and rules for extraction from the data warehouses. But when the data is stored under a common structure on the repository, the values derived from that huge volume are complicated. Computing statistical data using data mining provides the exact information about the real-world applications like population, weather report, and probability of occurrences.
Vehicle Detection through Instance Segmentation using Mask R-CNN for Intelligent Vehicle System

The recent advancement in artificial intelligence approach or deep learning techniques explored the ways to facilitate automation in various sectors. The application of deep learning with computer vision field has resulted in realization of intelligent systems. Vehicle detection plays a key role in Intelligent Vehicle System and Intelligent Transport System as it assists critical components of these systems like road scene classification, detecting obstacle vehicles to find an unhindered pathway, and even preventing accidents. This paper presents an implementation of Mask R-CNN state-of-the-art method using transfer learning technique for vehicle detection via instance wise segmentation which produces bounding box and object mask simultaneously. As the autonomous systems demands precise and flawless identification of the vehicles thus segmentation based approach is adopted. The model performs satisfactorily for occluded and small sized objects as well. This study is accomplished using an online GPU and cloud services provided by Google Colab by using Tensorflow and Keras framework. A mAP of 90.27% and mAR of 92.38% is achieved by using a combination of benchmark datasets.
Multimodal End-to-End Sparse Model for Emotion Recognition

Existing works on multimodal affective computing tasks, such as emotion recognition, generally adopt a two-phase pipeline, first extracting feature representations for each single modality with hand-crafted algorithms and then performing end-to-end learning with the extracted features.However, the extracted features are fixed and cannot be further fine-tuned on different target tasks, and manually finding feature extraction algorithms does not generalize or scale well to different tasks, which can lead to sub-optimal performance.In this paper, we develop a fully end-to-end model that connects the two phases and optimizes them jointly.In addition, we restructure the current datasets to enable the fully end-to-end training.Furthermore, to reduce the computational overhead brought by the end-to-end model, we introduce a sparse cross-modal attention mechanism for the feature extraction.Experimental results show that our fully end-to-end model significantly surpasses the current state-of-theart models based on the two-phase pipeline.Moreover, by adding the sparse cross-modal attention, our model can maintain performance with around half the computation in the feature extraction part.
AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization

State-of-the-art abstractive summarization models generally rely on extensive labeled data, which lowers their generalization ability on domains where such data are not available.In this paper, we present a study of domain adaptation for the abstractive summarization task across six diverse target domains in a low-resource setting.Specifically, we investigate the second phase of pre-training on large-scale generative models under three different settings: 1) source domain pre-training; 2) domain-adaptive pre-training; and 3) taskadaptive pre-training.Experiments show that the effectiveness of pre-training is correlated with the similarity between the pre-training data and the target domain task.Moreover, we find that continuing pre-training could lead to the pre-trained model's catastrophic forgetting, and a learning method with less forgetting can alleviate this issue.Furthermore, results illustrate that a huge gap still exists between the low-resource and high-resource settings, which highlights the need for more advanced domain adaptation methods for the abstractive summarization task.
Models of Blended Learning: Organizational and Didactic Typology

The pandemic period revealed the shortcomings of distance learning, which do not allow it to be used as a basic form of education in the main educational programs. The most effective form of organizing the educational process, in the context of its digital transformation, is seen as blended learning, which involves alternating online and face-to-face learning formats. The subject of this article is the pedagogical aspects of blended learning related to the organization of the process of mixed learning in an educational institution and its implementation in the joint activities of the teacher and students. The analysis of existing approaches to the identification and classification of blended learning models is carried out. The authors justify an approach to the typology of blended learning models, which can be implemented at different levels: curriculum, subject, section or topic (within the framework of the discipline), training session, extracurricular learning technology. The proposed methodology is based on the joint work of the network of experimental sites of the Ranepa Federal Institute for the Development of Education in the direction of "Digital Didactics". The article shows the connection of the proposed typology of organizational and didactic models of blended learning with other typologies and individual models presented in foreign and domestic publications.
A New Energy-Efficient and High Throughput Two-Phase Multi-Bit per Cycle Ring Oscillator-Based True Random Number Generator

Oscillator-based elementary true random number generator (TRNG) uses a slow jittery ring oscillator (RO) to sample a fast RO. The ROs are always on but most of the oscillatory cycles of the fast RO are not sampled into random bits. In this paper, a new lightweight TRNG design is proposed to minimize the power wasted by the superfluous oscillations. Random bits are extracted from both phases of the slow ROs to increase the throughput and the fast RO is activated only during the narrow transition time difference between two symmetrically designed slow ROs. The slow jittery ROs are implemented using current starved inverters biased in the weak inversion region to reduce their power consumption. Their jitter amplitudes are increased by lowering the oscillation frequency and reducing the drain current of the transistors. The narrow jittery pulse generated by the differential pair of slow ROs is quantized by the fastest three-stage RO. Two random bits from each phase of the jittery ROs can be extracted by using a gigahertz dynamic toggled D flip-flop counter to count the number of oscillatory cycles of the fast RO. The proposed TRNG is fabricated in a standard 65 nm 1.2 V CMOS process. Measurement results of the fabricated chips show that the proposed TRNG consumes merely <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$260~\mu \text{W}$ </tex-math></inline-formula> at a bit rate of 52 Mbps. It outperforms the state-of-art on-chip jitter-based TRNGs with the best figure-of-merit of 5 pJ/bit and the smallest footprint of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$366~\mu \text{m}^{2}$ </tex-math></inline-formula> . Its generated bit sequence passes the statistical randomness tests including National Institute of Standards and Technology (NIST) test, Auto Correlation Factor (ACF) test and bias. The mean redundancy of the ten tested chips is measured to be less than 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−5</sup> bit/symbol.
Machine learning in/for blockchain: Future and challenges

Machine learning and blockchain are two of the most notable technologies of recent years. The first is the foundation of artificial intelligence and big data analysis, and the second has significantly disrupted the financial industry. Both technologies are data‐driven, and thus there are rapidly growing interests in integrating both for more secure and efficient data sharing and analysis. In this article, we review existing research on combining machine learning and blockchain technologies and demonstrate that they can collaborate efficiently and effectively. In the end, we point out some future directions and expect more research on deeper integration of these two promising technologies.
Smart <scp>contract‐based</scp> land registry system to reduce frauds and time delay

Abstract In today's scenario, many news related to counterfeit land titles, fraud land registry, delay in ownership transfer, the involvement of government officers in fraudulent activities is frequently being heard. However, this depicts that the existing land registry system is not efficient to provide security and timely settlement of transactions between the seller and buyer. To solve this problem, we proposed a blockchain‐based land registry system in this article. The specialty and popularity of blockchain technology is its transparency and security. Blockchain is being inculcated with the trait of persistence, immutability, decentralization. Its ascent to new opportunity of efficiency and cost saving. It can provide right framing for digital asset, online payment, and transfer of remittance. Additional to this it can check upon black money laundering. Enterprise that can use blockchain technology can gain faith of consumer. In this article, we proposed a decentralized application. In particular, for creating and deploying the smart contract, we used Ethereum network. The deployed contracts are interacted through frontend web pages. React is used for the development of web page. For server and routing purposes, Next.js is used. Finally, the results and analysis show that our proposed model is efficient and viable.
Digital divide and financial development in Africa

This paper analyses the influence of the digital divide on the new IMF financial development index on a panel of 34 African countries over the period 2005–2017. Using the instrumental variables technique, we arrive at the main result that ICT divide is a severe handicap for the financial systems development in Africa. The use of financial development sub-indices relating to financial institutions and financial markets, as well as their dimensions confirms the negative effect of the digital divide. Our result remains stable when we use alternative measures of financial development and ICT indicators. Furthermore, we found that the digital divide between countries is also a severe handicap for the financial development of countries lagging behind. On the other hand, countries with a technological lead or a digital dividend have relatively developed financial systems. Some recommendations have been suggested to promote digital penetration in Africa, as well as to improve the receptivity and flexibility of African financial systems.