Attribute-wise Explainable Fashion Compatibility Modeling

2021 ◽  
Vol 17 (1) ◽  
pp. 1-21
Author(s):  
Xin Yang ◽  
Xuemeng Song ◽  
Fuli Feng ◽  
Haokun Wen ◽  
Ling-Yu Duan ◽  
...  
Keyword(s):  
Interaction Mechanism ◽  
Representation Learning ◽  
Black Box ◽  
Attribute Level ◽  
Research Attention ◽  
Practical Applications ◽  
Item Level ◽  
Interaction Modeling ◽  
Real World Datasets ◽  
Commercial Applications

With the boom of the fashion market and people’s daily needs for beauty, clothing matching has gained increased research attention. In a sense, tackling this problem lies in modeling the human notions of the compatibility between fashion items, i.e., Fashion Compatibility Modeling (FCM), which plays an important role in a wide bunch of commercial applications, including clothing recommendation and dressing assistant. Recent advances in multimedia processing have shown remarkable effectiveness in accurate compatibility evaluation. However, these studies work like a black box and cannot provide appropriate explanations, which are indeed of importance for gaining users’ trust and improving their experience. In fact, fashion experts usually explain the compatibility evaluation through the matching patterns between fashion attributes (e.g., a silk tank top cannot go with a knit dress). Inspired by this, we devise an attribute-wise explainable FCM solution, named ExFCM , which can simultaneously generate the item-level compatibility evaluation for input fashion items and the attribute-level explanations for the evaluation result. In particular, ExFCM consists of two key components: attribute-wise representation learning and attribute interaction modeling. The former works on learning the region-aware attribute representation for each item with the threshold global average pooling. Besides, the latter is responsible for compiling the attribute-level matching signals into the overall compatibility evaluation adaptively with the attentive interaction mechanism. Note that ExFCM is trained without any attribute-level compatibility annotations, which facilitates its practical applications. Extensive experiments on two real-world datasets validate that ExFCM can generate more accurate compatibility evaluations than the existing methods, together with reasonable explanations.

Time-Efficient Ensemble Learning with Sample Exchange for Edge Computing

2021 ◽  
Vol 21 (3) ◽  
pp. 1-17
Author(s):  
Wu Chen ◽  
Yong Yu ◽  
Keke Gai ◽  
Jiamou Liu ◽  
Kim-Kwang Raymond Choo
Keyword(s):  
Ensemble Learning ◽  
Real World ◽  
Interaction Mechanism ◽  
Training Model ◽  
Edge Computing ◽  
Learning Techniques ◽  
Multi Agent ◽  
Real World Datasets ◽  
Entire Dataset ◽  
Exchange Data

In existing ensemble learning algorithms (e.g., random forest), each base learner’s model needs the entire dataset for sampling and training. However, this may not be practical in many real-world applications, and it incurs additional computational costs. To achieve better efficiency, we propose a decentralized framework: Multi-Agent Ensemble. The framework leverages edge computing to facilitate ensemble learning techniques by focusing on the balancing of access restrictions (small sub-dataset) and accuracy enhancement. Specifically, network edge nodes (learners) are utilized to model classifications and predictions in our framework. Data is then distributed to multiple base learners who exchange data via an interaction mechanism to achieve improved prediction. The proposed approach relies on a training model rather than conventional centralized learning. Findings from the experimental evaluations using 20 real-world datasets suggest that Multi-Agent Ensemble outperforms other ensemble approaches in terms of accuracy even though the base learners require fewer samples (i.e., significant reduction in computation costs).

scholarly journals Hydroxyurea—The Good, the Bad and the Ugly

Genes ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 1096
Author(s):  
Marcelina W. Musiałek ◽  
Dorota Rybaczek
Keyword(s):  
Cancer Progression ◽  
Specific Interaction ◽  
Interaction Mechanism ◽  
Myeloproliferative Disorders ◽  
Prolonged Treatment ◽  
Actual State ◽  
Cytotoxic Effects ◽  
Factors Affecting ◽  
Cell Cycles ◽  
Practical Applications

Hydroxyurea (HU) is mostly referred to as an inhibitor of ribonucleotide reductase (RNR) and as the agent that is commonly used to arrest cells in the S-phase of the cycle by inducing replication stress. It is a well-known and widely used drug, one which has proved to be effective in treating chronic myeloproliferative disorders and which is considered a staple agent in sickle anemia therapy and—recently—a promising factor in preventing cognitive decline in Alzheimer’s disease. The reversibility of HU-induced replication inhibition also makes it a common laboratory ingredient used to synchronize cell cycles. On the other hand, prolonged treatment or higher dosage of hydroxyurea causes cell death due to accumulation of DNA damage and oxidative stress. Hydroxyurea treatments are also still far from perfect and it has been suggested that it facilitates skin cancer progression. Also, recent studies have shown that hydroxyurea may affect a larger number of enzymes due to its less specific interaction mechanism, which may contribute to further as-yet unspecified factors affecting cell response. In this review, we examine the actual state of knowledge about hydroxyurea and the mechanisms behind its cytotoxic effects. The practical applications of the recent findings may prove to enhance the already existing use of the drug in new and promising ways.

scholarly journals Relation Representation Learning Via Signed Graph Mutual Information Maximization for Trust Prediction

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 115
Author(s):  
Yongjun Jing ◽  
Hao Wang ◽  
Kun Shao ◽  
Xing Huo
Keyword(s):  
Mutual Information ◽  
Representation Learning ◽  
Signed Graph ◽  
Trust Prediction ◽  
Trust Networks ◽  
Trust Relation ◽  
Information Maximization ◽  
Real World Datasets ◽  
Negative Links ◽  
Mutual Information Maximization

Trust prediction is essential to enhancing reliability and reducing risk from the unreliable node, especially for online applications in open network environments. An essential fact in trust prediction is to measure the relation of both the interacting entities accurately. However, most of the existing methods infer the trust relation between interacting entities usually rely on modeling the similarity between nodes on a graph and ignore semantic relation and the influence of negative links (e.g., distrust relation). In this paper, we proposed a relation representation learning via signed graph mutual information maximization (called SGMIM). In SGMIM, we incorporate a translation model and positive point-wise mutual information to enhance the relation representations and adopt Mutual Information Maximization to align the entity and relation semantic spaces. Moreover, we further develop a sign prediction model for making accurate trust predictions. We conduct link sign prediction in trust networks based on learned the relation representation. Extensive experimental results in four real-world datasets on trust prediction task show that SGMIM significantly outperforms state-of-the-art baseline methods.

Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks for Fake News Detection

2021 ◽  
Vol 17 (3) ◽  
pp. 1-23
Author(s):  
Shengsheng Qian ◽  
Jun Hu ◽  
Quan Fang ◽  
Changsheng Xu
Keyword(s):  
Social Media ◽  
Visual Information ◽  
Representation Learning ◽  
Fake News ◽  
Unified Framework ◽  
Model Learning ◽  
Convolutional Network ◽  
Textual Information ◽  
Convolutional Networks ◽  
Real World Datasets

In this article, we focus on fake news detection task and aim to automatically identify the fake news from vast amount of social media posts. To date, many approaches have been proposed to detect fake news, which includes traditional learning methods and deep learning-based models. However, there are three existing challenges: (i) How to represent social media posts effectively, since the post content is various and highly complicated; (ii) how to propose a data-driven method to increase the flexibility of the model to deal with the samples in different contexts and news backgrounds; and (iii) how to fully utilize the additional auxiliary information (the background knowledge and multi-modal information) of posts for better representation learning. To tackle the above challenges, we propose a novel Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks (KMAGCN) to capture the semantic representations by jointly modeling the textual information, knowledge concepts, and visual information into a unified framework for fake news detection. We model posts as graphs and use a knowledge-aware multi-modal adaptive graph learning principal for the effective feature learning. Compared with existing methods, the proposed KMAGCN addresses challenges from three aspects: (1) It models posts as graphs to capture the non-consecutive and long-range semantic relations; (2) it proposes a novel adaptive graph convolutional network to handle the variability of graph data; and (3) it leverages textual information, knowledge concepts and visual information jointly for model learning. We have conducted extensive experiments on three public real-world datasets and superior results demonstrate the effectiveness of KMAGCN compared with other state-of-the-art algorithms.

scholarly journals A Residual-Based Approach to Validate Q-Matrix Specifications

2017 ◽  
Vol 41 (4) ◽  
pp. 277-293 ◽  
Author(s):  
Jinsong Chen
Keyword(s):  
Real Life ◽  
Attribute Level ◽  
Simulation Studies ◽  
Cognitive Diagnosis Models ◽  
Modeling Process ◽  
Response Formats ◽  
Item Level ◽  
Q Matrix ◽  
Matrix Construction ◽  
Test Level

Q-matrix validation is of increasing concern due to the significance and subjective tendency of Q-matrix construction in the modeling process. This research proposes a residual-based approach to empirically validate Q-matrix specification based on a combination of fit measures. The approach separates Q-matrix validation into four logical steps, including the test-level evaluation, possible distinction between attribute-level and item-level misspecifications, identification of the hit item, and fit information to aid in item adjustment. Through simulation studies and real-life examples, it is shown that the misspecified items can be detected as the hit item and adjusted sequentially when the misspecification occurs at the item level or at random. Adjustment can be based on the maximum reduction of the test-level measures. When adjustment of individual items tends to be useless, attribute-level misspecification is of concern. The approach can accommodate a variety of cognitive diagnosis models (CDMs) and be extended to cover other response formats.

Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graphs

2022 ◽  
Vol 13 (1) ◽  
pp. 1-25
Author(s):  
Yuandong Wang ◽  
Hongzhi Yin ◽  
Tong Chen ◽  
Chunyang Liu ◽  
Ben Wang ◽  
...  
Keyword(s):  
Fundamental Problem ◽  
Route Planning ◽  
Representation Learning ◽  
Data Representation ◽  
Weighted Graphs ◽  
Spatial And Temporal Patterns ◽  
Graph Representations ◽  
Demand Prediction ◽  
Passenger Demand ◽  
Real World Datasets

In recent years, ride-hailing services have been increasingly prevalent, as they provide huge convenience for passengers. As a fundamental problem, the timely prediction of passenger demands in different regions is vital for effective traffic flow control and route planning. As both spatial and temporal patterns are indispensable passenger demand prediction, relevant research has evolved from pure time series to graph-structured data for modeling historical passenger demand data, where a snapshot graph is constructed for each time slot by connecting region nodes via different relational edges (origin-destination relationship, geographical distance, etc.). Consequently, the spatiotemporal passenger demand records naturally carry dynamic patterns in the constructed graphs, where the edges also encode important information about the directions and volume (i.e., weights) of passenger demands between two connected regions. aspects in the graph-structure data. representation for DDW is the key to solve the prediction problem. However, existing graph-based solutions fail to simultaneously consider those three crucial aspects of dynamic, directed, and weighted graphs, leading to limited expressiveness when learning graph representations for passenger demand prediction. Therefore, we propose a novel spatiotemporal graph attention network, namely Gallat ( G raph prediction with all at tention) as a solution. In Gallat, by comprehensively incorporating those three intrinsic properties of dynamic directed and weighted graphs, we build three attention layers to fully capture the spatiotemporal dependencies among different regions across all historical time slots. Moreover, the model employs a subtask to conduct pretraining so that it can obtain accurate results more quickly. We evaluate the proposed model on real-world datasets, and our experimental results demonstrate that Gallat outperforms the state-of-the-art approaches.

scholarly journals Exponential Family Graph Embeddings

2020 ◽  
Vol 34 (04) ◽  
pp. 3357-3364
Author(s):  
Abdulkadir Celikkanat ◽  
Fragkiskos D. Malliaros
Keyword(s):  
Random Walk ◽  
Exponential Family ◽  
Representation Learning ◽  
Learning Problems ◽  
Interaction Patterns ◽  
Network Representation ◽  
Learning Tasks ◽  
Learning Techniques ◽  
Real World Datasets ◽  
Low Dimensional

Representing networks in a low dimensional latent space is a crucial task with many interesting applications in graph learning problems, such as link prediction and node classification. A widely applied network representation learning paradigm is based on the combination of random walks for sampling context nodes and the traditional Skip-Gram model to capture center-context node relationships. In this paper, we emphasize on exponential family distributions to capture rich interaction patterns between nodes in random walk sequences. We introduce the generic exponential family graph embedding model, that generalizes random walk-based network representation learning techniques to exponential family conditional distributions. We study three particular instances of this model, analyzing their properties and showing their relationship to existing unsupervised learning models. Our experimental evaluation on real-world datasets demonstrates that the proposed techniques outperform well-known baseline methods in two downstream machine learning tasks.

scholarly journals Temperature-dependent optical and vibrational properties of PtSe2 thin films

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Desman P. Gulo ◽  
Han Yeh ◽  
Wen-Hao Chang ◽  
Hsiang-Lin Liu
Keyword(s):  
Thin Films ◽  
Band Gap ◽  
Room Temperature ◽  
Near Infrared ◽  
Technological Development ◽  
Phonon Modes ◽  
Research Attention ◽  
Practical Applications ◽  
Infrared Frequency Range ◽  
Indirect Band Gap

Abstract PtSe2 has received substantial research attention because of its intriguing physical properties and potential practical applications. In this paper, we investigated the optical properties of bilayer and multilayer PtSe2 thin films through spectroscopic ellipsometry over a spectral range of 0.73–6.42 eV and at temperatures between 4.5 and 500 K. At room temperature, the spectra of refractive index exhibited several anomalous dispersion features below 1000 nm and approached a constant value in the near-infrared frequency range. The thermo-optic coefficients of bilayer and multilayer PtSe2 thin films were (4.31 ± 0.04) × 10−4/K and (–9.20 ± 0.03) × 10−4/K at a wavelength of 1200 nm. Analysis of the optical absorption spectrum at room temperature confirmed that bilayer PtSe2 thin films had an indirect band gap of approximately 0.75 ± 0.01 eV, whereas multilayer PtSe2 thin films exhibited semimetal behavior. The band gap of bilayer PtSe2 thin films increased to 0.83 ± 0.01 eV at 4.5 K because of the suppression of electron–phonon interactions. Furthermore, the frequency shifts of Raman-active Eg and A1g phonon modes of both thin films in the temperature range between 10 and 500 K accorded with the predictions of the anharmonic model. These results provide basic information for the technological development of PtSe2-based optoelectronic and photonic devices at various temperatures.

scholarly journals Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

2020 ◽  
Vol 34 (01) ◽  
pp. 27-34 ◽  
Author(s):  
Lei Chen ◽  
Le Wu ◽  
Richang Hong ◽  
Kun Zhang ◽  
Meng Wang
Keyword(s):  
Collaborative Filtering ◽  
Representation Learning ◽  
Superior Performance ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Proposed Model ◽  
Non Linear ◽  
Efficiency And Effectiveness ◽  
Residual Graph ◽  
Interaction Modeling

Graph Convolutional Networks~(GCNs) are state-of-the-art graph based representation learning models by iteratively stacking multiple layers of convolution aggregation operations and non-linear activation operations. Recently, in Collaborative Filtering~(CF) based Recommender Systems~(RS), by treating the user-item interaction behavior as a bipartite graph, some researchers model higher-layer collaborative signals with GCNs. These GCN based recommender models show superior performance compared to traditional works. However, these models suffer from training difficulty with non-linear activations for large user-item graphs. Besides, most GCN based models could not model deeper layers due to the over smoothing effect with the graph convolution operation. In this paper, we revisit GCN based CF models from two aspects. First, we empirically show that removing non-linearities would enhance recommendation performance, which is consistent with the theories in simple graph convolutional networks. Second, we propose a residual network structure that is specifically designed for CF with user-item interaction modeling, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse user-item interaction data. The proposed model is a linear model and it is easy to train, scale to large datasets, and yield better efficiency and effectiveness on two real datasets. We publish the source code at https://github.com/newlei/LR-GCCF.

scholarly journals Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation

2020 ◽  
Vol 34 (01) ◽  
pp. 19-26 ◽  
Author(s):  
Chong Chen ◽  
Min Zhang ◽  
Yongfeng Zhang ◽  
Weizhi Ma ◽  
Yiqun Liu ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Heterogeneous Data ◽  
Model Parameters ◽  
Online Systems ◽  
Practical Applications ◽  
Real World Datasets ◽  
Primary Type

Recent studies on recommendation have largely focused on exploring state-of-the-art neural networks to improve the expressiveness of models, while typically apply the Negative Sampling (NS) strategy for efficient learning. Despite effectiveness, two important issues have not been well-considered in existing methods: 1) NS suffers from dramatic fluctuation, making sampling-based methods difficult to achieve the optimal ranking performance in practical applications; 2) although heterogeneous feedback (e.g., view, click, and purchase) is widespread in many online systems, most existing methods leverage only one primary type of user feedback such as purchase. In this work, we propose a novel non-sampling transfer learning solution, named Efficient Heterogeneous Collaborative Filtering (EHCF) for Top-N recommendation. It can not only model fine-grained user-item relations, but also efficiently learn model parameters from the whole heterogeneous data (including all unlabeled data) with a rather low time complexity. Extensive experiments on three real-world datasets show that EHCF significantly outperforms state-of-the-art recommendation methods in both traditional (single-behavior) and heterogeneous scenarios. Moreover, EHCF shows significant improvements in training efficiency, making it more applicable to real-world large-scale systems. Our implementation has been released 1 to facilitate further developments on efficient whole-data based neural methods.

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