Reviewerly/exHarmony huggingface dataset:
Addressing Confounding Feature Issue for Causal Recommendation

In recommender systems, some features directly affect whether an interaction would happen, making the happened interactions not necessarily indicate user preference. For instance, short videos are objectively easier to finish even though the user may not like the video. We term such feature as confounding feature , and video length is a confounding feature in video recommendation. If we fit a model on such interaction data, just as done by most data-driven recommender systems, the model will be biased to recommend short videos more, and deviate from user actual requirement. This work formulates and addresses the problem from the causal perspective. Assuming there are some factors affecting both the confounding feature and other item features, e.g., the video creator, we find the confounding feature opens a backdoor path behind user-item matching and introduces spurious correlation. To remove the effect of backdoor path, we propose a framework named Deconfounding Causal Recommendation (DCR) , which performs intervened inference with do-calculus . Nevertheless, evaluating do-calculus requires to sum over the prediction on all possible values of confounding feature, significantly increasing the time cost. To address the efficiency challenge, we further propose a mixture-of-experts (MoE) model architecture, modeling each value of confounding feature with a separate expert module. Through this way, we retain the model expressiveness with few additional costs. We demonstrate DCR on the backbone model of neural factorization machine (NFM) , showing that DCR leads to more accurate prediction of user preference with small inference time cost. We release our code at: https://github.com/zyang1580/DCR .
Balanced Spatial Feature Distillation and Pyramid Attention Network for Lightweight Image Super-resolution

Recently, the attention mechanism became the key issue for image super-resolution (SR) because it has the ability to extract different features from the image according to the used attention type. Despite the great success of attention-based methods, the conflict among features of different attention types can affect the SR performance. In this paper, we propose an efficient single image SR model called balanced spatial feature distillation and pyramid attention (BSPAN). The idea of BSPAN is based on the trade-off among the extracted features of different attention types. Also, we propose a balanced spatial feature distillation block (BSFDB) as the backbone of BSPAN so that the network can effectively advantage from the different attention features. Two different attention types, namely spatial attention residual feature distillation (SARFD) and classical attention (CA) are considered in the BSFDB to achieve a leveling between them based on the contents of the low-resolution feature map using the balancing attention block. The BSFDB block is designed to improve the SR performance and has lightweight parameters and low computation complexity. Moreover, to further improve the SR performance, the pyramid attention is introduced in the middle of the BSPAN network for extracting long-range features at a variety of locations and scales. Evaluation based on five benchmark datasets, we concluded that the balancing between features of a variety of attention types can effectively improve the SR performance. So, the proposed BSPAN model achieves significant enhancements in comparison with the state-of-the-art and superior visual quality and reconstruction accuracy.
Exploring Human Pose Estimation and the Usage of Synthetic Data for Elderly Fall Detection in Real-World Surveillance

The world's elderly population continues to grow at an unprecedented rate, creating a need to monitor the safety of an aging population.One of the current problems is accurately classifying elderly physical activities, especially falling down, and delivering prompt assistance to someone in need.With the advent of deep learning, there has been extensive research on vision-based action recognition and fall detection in particular using human pose estimation employing 2D human pose information.Nevertheless, due to a lack of large-scale elderly fall datasets and the continuation of numerous challenges such as varying camera angles, illumination, and occlusion accurately classifying falls has been a problematic.To address these problems, this research first carried out a comprehensive study of the AI Hub dataset collected from real lives of elderly people in order to benchmark the performance of state-of-the-art human pose estimation methods.Secondly, owing to the limited number of real datasets, augmentation with synthetic data was applied and performance improvement was validated based on changes in the degree of accuracy.Third, this study shows that a Transformer network applied to elderly action recognition outperforms LSTM-based networks by a noticeable margin.Lastly, by observing the quantitative and qualitative performances of different networks, this paper proposes an efficient solution for elderly activity recognition and fall detection in the context of surveillance cameras.
From Model-Centric to Data-Centric AI: A Paradigm Shift or Rather a Complementary Approach?

At its core, an artificial intelligence (AI) system mainly consists of an algorithm (code) that solves a problem by learning prototypical features from voluminous data clouds. Currently, there are two schools of thought on improving the performance of AI systems: model-centric AI and data-centric AI. In model-centric AI, developers of an AI system successively upgrade a devised model (algorithm/code), while holding the amount and type of data collected fixed. Conversely, practitioners of the data-centric AI keep the model fixed, while continuously improving the quality of data. Despite its dominance over the past three decades, model-centric AI has been recently criticised as being limited to businesses and industries where consumer platforms with hundreds of millions of users can freely rely on generalized solutions. This paper, rather than favoring any of the two approaches, supports a &#x2018;both and&#x2019; position. We argue that overcoming the alleged limitation of model-centric AI may well require paying extra attention to the alternative data-centric approach. However, this shouldn&#x0027;t result in reducing interest in model-centric AI. We corroborate our position by the notion that successful &#x2018;problem solving&#x2019; requires considering both the way we act upon things (algorithm) as well as harnessing knowledge of their states and properties (data).
Efficient complementary graph convolutional network without negative sampling for item recommendation

Learning vector representations (a.k.a., embeddings) of users and items lies at the heart of building a modern recommender system. Typically, graph neural networks (GNN) have been a prevalent way to learn user and item embeddings with implicit feedback. Nevertheless, since item popularity in many recommendation scenarios follows a long-tail distribution, we argue that recent GNN-based recommenders are not sufficient to yield satisfactory embeddings, especially for tail items. More importantly, most of GNN-based methods depend on negative sampling in model optimization, which makes them inefficient in the GPU era and highly sensitive to the design of negative sampler. In light of these limitations, we propose a novel recommendation method named ECGN, short for efficient complementary graph convolutional network. Specifically, by means of the complementary relationships amongst items, we introduce a complementary-aware graph convolution module, which better alleviates the long-tail problem. By regarding user behaviors and item relationships as two bipartite graphs, such module could explicitly explore the propagation process of user interest and complementary influence. Furthermore, an item-specific attention mechanism is designed to fuse item representations from different bipartite graphs. To make our solution attractive in large-scale industrial scenarios, we devise an efficient non-sampling learning paradigm to optimize ECGN’s parameters. Experiments on six real-world datasets demonstrate that our method consistently and significantly outperforms several state-of-the-art methods in model effectiveness and training efficiency. Our implementations and datasets are available at https://github.com/wubinzzu/ECGN.
Survey on Attendance System Using Face Recognition

In this framework, face is distinguished and perceived while the video is real time, to recognize the individual amongst difficulties. This recommends comparing people on either pictures or sequential recordings. For video based face acknowledgment, it is tough to accomplish the comparative levels of general execution. There are a few drawbacks in video based face acknowledgment, when face identification depends on the verification with existing face pictures. The drawbacks are indicated by the following: At first, CCTV cameras get pictures which are generally of low norm. Second, picture assurance is generally decreased for video frameworks. Third, face picture varieties, which incorporate light, appearing, stance, obstruction, and development, are more serious in video real time. Therefore this review concentrates on different face position and learning calculations to break down the proficiency in recognition framework.
Program source code comprehension by module clustering using combination of discretized gray wolf and genetic algorithms

Maintenance is a critical and costly phase of software lifecycle. Understanding the structure of software will make it much easier to maintain the software. Clustering the modules of software is regarded as a useful reverse engineering technique for constructing software structural models from source code. Minimizing the connections between produced clusters, maximizing the internal connections within the clusters, and maximizing the clustering quality are the most important objectives in software module clustering. Finding the optimal software clustering model is regarded as an NP-complete problem. The low success rate, limited stability, and poor modularization quality are the main drawbacks of the previous methods. In this paper, a combination of gray wolf optimization algorithm and genetic algorithms is suggested for efficient clustering of software modules. An extensive series of experiments on 14 standard benchmarks have been conducted to evaluated the proposed method. The results illustrate that using the combination of gray wolf and genetic algorithms to the software-module clustering problem increases the quality of clustering. In terms of modularization quality and convergence speed, proposed hybrid method outperforms the other heuristic approaches.
An attention-based deep learning method for solving the cold-start and sparsity issues of recommender systems

Matrix Factorization is a successful approach for generating an effective recommender system. However, most existing matrix factorization methods suffer from the sparsity and cold-start issues of the user-item matrix as the primary information source of recommender systems. Besides, they are not much scalable to apply to large real-world applications. A main idea to overcome the cold-start and sparsity issues is to use additional information sources such as user/item profiles or user reviews on items. In this paper, a novel Attention-based Deep Learning Recommender System, so-called ADLRS, is proposed to employ the information sources in the matrix factorization method framework. The proposed method uses a language model to represent contextual information such that important features are effectively embedded. Moreover, a deep autoencoder reduces the dimensionality of item vectors embedded by the language model. Then, these vectors are used as regularization terms in the matrix factorization framework to form an objective function. Then, an iterative algorithm is designed to solve the objective function and provide a method prediction of unknown rating values. Experimental results show that the proposed method achieves superior performance compared to other state-of-the-art ones in most cases. Moreover, the improvement rate for sparse datasets and cold items proves that the proposed method effectively deals with sparsity, cold start and scalability problems.
Geochemical anomaly identification and uncertainty quantification using a Bayesian convolutional neural network model

Geochemical prospecting plays an important role in mineral exploration. In recent years, deep learning algorithms (DLAs) have been applied in mapping geochemical anomalies associated with mineralization. However, few of them evaluated the effects of data and model uncertainty on geochemical anomaly identification, which can introduce bias and risk in geochemical prospecting. In this study, a Bayesian convolutional neural network model (BCNN), which applies Bayes by Backprop to a convolutional neural network, was employed to extract geochemical anomalies associated with mineralization and quantify the two types of uncertainties, i.e., aleatoric (data-related) and epistemic (model-related) uncertainty. A case study on recognizing geochemical anomalies related to gold polymetallic mineralization in northern Sichuan Province of China was conducted. The results show that the geochemical anomalies extracted by BCNN are highly spatially correlated with known gold deposits. Meanwhile, both aleatoric and epistemic uncertainty linked to the identified geochemical anomalies were quantified. Areas with high uncertainty are mainly distributed at the boundaries of the geochemical high anomalous zones. Therefore, future mineral exploration should focus on regions with high anomalies but low uncertainty. The obtained results provide an important decision basis for the prospecting of gold polymetallic deposits in the study area. • A Bayesian convolutional neural network was used for identifying geochemical anomalies. • Aleatoric (data-related) and epistemic (model-related) uncertainty are quantified. • Future mineral exploration of gold deposits should focus on the high probability area of geochemical anomalies.
Multiple kernel learning for label relation and class imbalance in multi-label learning

There are two common challenges in multi-label learning (MLL), complex label relation and imbalanced class. Few studies have focused on addressing both problems at the same time. In this paper, we propose a multiple kernel learning (MKL) approach to tackle two challenges, named as Multi-Kernel Multi-Label (MKML) method. MKML contains three kernel modules. The first kernel module adopts the traditional kernel function which contains the global information, and the other two kernel modules are designed for the two problems respectively. The second kernel module learns the inter-label relation by adding supervised information. The third kernel module adjusts the imbalanced decision boundary through multi-layer fusion strategy, which is proved to improve the representation ability of kernels in this paper. Finally, the proposed joint optimization method in this MKL framework achieves good generalization ability. We conduct several related experiments using real-world datasets to evaluate the effectiveness of our method. The results demonstrate that MKML outperforms other state-of-the art methods in MLL task.
A blockchain-based fine-grained data sharing scheme for e-healthcare system

The cloud-aided sharing of e-healthcare data has great positive significance for research. However, due to the privacy consideration, these data are usually encrypted before uploading to the cloud server which impedes data sharing between different medical institutions. Conditional proxy re-encryption (CPRE) allows the proxy to converse ciphertext, especially by specifying a condition embed in the re-encryption key to achieve fine-grained access control over the ciphertext. Unfortunately, existing CPRE schemes cannot ensure the privacy of the condition, which may contain some sensitive private information. Furthermore, a malicious proxy server may return part of the results and even false data to save its computation or bandwidth. To solve these problems, we propose a blockchain-based condition invisible proxy re-encryption scheme for the e-healthcare system. The proposed scheme guarantees the confidentiality of the data by hiding the condition in the re-encryption key so that the proxy cannot learn anything about the condition. Moreover, the ciphertext-searching algorithm is leveraged by executing the smart contract in the blockchain which ensures the results are correct and complete. Finally, experiment results demonstrate the practicability of the proposed scheme in applications.
An improved ensemble classification-based secure two stage bagging pruning technique for guaranteeing privacy preservation of DNA sequences in electronic health records

The advent of machine learning in the recent decade has excelled in determining new potential features and non-linear relationships existing between the data derived from the Electronic Health Records (EHR). Machine learning also enhances the process of handling data with maximum predictor variables compared to observations during the data mining process of prediction. The EHR data is often confronted with quality issues that are related to misclassification, missingness and measurement errors. In this context, ensemble classification schemes are determined to be essential for preventing the quality issues of EHR data. Moreover, the data sources like EHR include sensitive information that needs to be protected from disclosure before it is forwarded to the mining process. Further, the sensitive data of EHR must be hidden without modifying the dataset such that it does not influence the prediction accuracy of the incorporated ensemble classification mechanism. In this paper, the process of hiding EHR data is facilitated through Improved Sensitivity Drift based k-Anonymized Data Perturbation Scheme (ISD-k-ADP) that randomly perturbs the data in the dataset by including restricted amount of noise. This controlled amount of included noise is derived carefully from the Sensitivity Drift based depending on the expected privacy level before it is sent to the process of classification. This ISD-k-ADP scheme is reliable such that, it prevents the impact induced by the hidden data during the process of Two Stage Bagging Pruning based Ensemble Classification (TSBP-EC). Furthermore, the TSBP-EC uses the methods of distance and accuracy based pruning that aids in minimizing the size of the ensemble for ensuring effective and efficient classification using machine learning. The simulation results of the proposed ISD-k-ADP-TSBP-EC scheme is determined to be predominant based on Classification Accuracy, Precision, Recall and Kappa Statistic in contrast to the standard schemes.
A Survey on Causal Discovery: Theory and Practice

Understanding the laws that govern a phenomenon is the core of scientific progress. This is especially true when the goal is to model the interplay between different aspects in a causal fashion. Indeed, causal inference itself is specifically designed to quantify the underlying relationships that connect a cause to its effect. Causal discovery is a branch of the broader field of causality in which causal graphs are recovered from data (whenever possible), enabling the identification and estimation of causal effects. In this paper, we explore recent advancements in causal discovery in a unified manner, provide a consistent overview of existing algorithms developed under different settings, report useful tools and data, present real-world applications to understand why and how these methods can be fruitfully exploited.
Automated BI-RADS classification of lesions using pyramid triple deep feature generator technique on breast ultrasound images

Ultrasound (US) is an important imaging modality used to assess breast lesions for malignant features. In the past decade, many machine learning models have been developed for automated discrimination of breast cancer versus normal on US images, but few have classified the images based on the Breast Imaging Reporting and Data System (BI-RADS) classes. This work aimed to develop a model for classifying US breast lesions using a BI-RADS classification framework with a new multi-class US image dataset. We proposed a deep model that combined a novel pyramid triple deep feature generator (PTDFG) with transfer learning based on three pre-trained networks for creating deep features. Bilinear interpolation was applied to decompose the input image into four images of successively smaller dimensions, constituting a four-level pyramid for downstream feature generation with the pre-trained networks. Neighborhood component analysis was applied to the generated features to select each network's 1,000 most informative features, which were fed to support vector machine classifier for automated classification using a ten-fold cross-validation strategy. Our proposed model was validated using a new US image dataset containing 1,038 images divided into eight BI-RADS classes and histopathological results. We defined three classification schemes: Case 1 involved the classification of all images into eight categories; Case 2, classification of breast US images into five BI-RADS classes; and Case 3, classification of BI-RADS 4 lesions into benign versus malignant classes. Our PTDFG-based transfer learning model attained accuracy rates of 79.29%, 80.42%, and 88.67% for Case 1, Case 2, and Case 3, respectively.
Improving the Performance of Sentiment Analysis Using Enhanced Preprocessing Technique and Artificial Neural Network

With the presence of a massive amount of digitally recorded data, an automated computation can be preferable over the manual approach to evaluate sentiments within given textual fragments. Artificial neural network (ANN) is preferred for sentiment analysis (SA) because of its learning ability and adaptive nature towards diverse data. Handling negation in SA is a challenging task, and to address the same, we propose a specific order of preprocessing (PPR) steps to enhance the performance of SA using ANN. Typically, ANN weights are randomly initialized (R-ANN), which may not give the desired performance. As a potential solution, we propose a novel approach named Matching features with output label based Advanced Technique (MAT) to initialize the ANN weights (MAT-ANN). Simulation results conclude the superiority of the proposed approach PPR+MAT-ANN compared to the existing approach EPR+R-ANN i.e., integrating existing preprocessing (EPR) steps with R-ANN. Moreover, PPR+MAT-ANN architecture is significantly simpler than the existing deep learning-based approach named the NeuroSent tool and gives better performance when evaluated upon the Dranziera protocol.