AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders

Collaborative filtering (CF) has been successfully used to provide users with personalized products and services. However, dealing with the increasing sparseness of user-item matrix still remains a challenge. To tackle such issue, hybrid CF such as combining with content based filtering and leveraging side information of users and items has been extensively studied to enhance performance. However, most of these approaches depend on hand-crafted feature engineering, which are usually noise-prone and biased by different feature extraction and selection schemes. In this paper, we propose a new hybrid model by generalizing contractive auto-encoder paradigm into matrix factorization framework with good scalability and computational efficiency, which jointly model content information as representations of effectiveness and compactness, and leverage implicit user feedback to make accurate recommendations. Extensive experiments conducted over three large scale real datasets indicate the proposed approach outperforms the compared methods for item recommendation.Comment: 4 pages, 3 figure

Random Inverse Problems Over Graphs: Decentralized Online Learning

We establish a framework of random inverse problems with real-time observations over graphs, and present a decentralized online learning algorithm based on online data streams, which unifies the distributed parameter estimation in Hilbert space and the least mean square problem in reproducing kernel Hilbert space (RKHS-LMS). We transform the algorithm convergence into the asymptotic stability of randomly time-varying difference equations in Hilbert space with L2-bounded martingale difference terms and develop the L2 -asymptotic stability theory. It is shown that if the network graph is connected and the sequence of forward operators satisfies the infinitedimensional spatio-temporal persistence of excitation condition, then the estimates of all nodes are mean square and almost surely strongly consistent. By equivalently transferring the distributed learning problem in RKHS to the random inverse problem over graphs, we propose a decentralized online learning algorithm in RKHS based on non-stationary and non-independent online data streams, and prove that the algorithm is mean square and almost surely strongly consistent if the operators induced by the random input data satisfy the infinite-dimensional spatio-temporal persistence of excitation condition

Application of the morphological ultimate opening to the detection of microaneurysms on eye fundus images from a clinical database

International audienceDiabetic Retinopathy (DR) is a severe disease which can cause blindness. OPHDIAT is a telemedicine network for DR mass screening, which has gathered thousands of clinical high resolution color eye fundus images. The TELEOPHTA project has been launched in order to develop a computer aided diagnosis system of DR, which aims at performing a preliminary analysis of the OPHDIAT images in order to filter most images corresponding to healthy eyes. Microaneurysms (MAs) are likely to be the lesions present at the earliest stage of the disease. In this paper, a new method of MAs detection, using the recently proposed ultimate opening, is presented. The proposed method does not use any supervised classification, while provides a competitive and efficient way to detect MAs, especially for our clinical database. Further improvements may be brought through the accurate detection of the retinal elements and other retinal diseases, or through the estimation of the image quality

A literature review of the COVID-19 pandemic’s effect on sustainable HRM

The ramifications of the COVID-19 pandemic continue to emerge across all facets of the world of work, including the field of human resource management (HRM). Sustainable HRM, drawing on the triple bottom line elements of the economic, environmental and social pillars of sustainability, provides an ideal basis from which to understand the intersection of the COVID-19 pandemic and HRM. In this systematic literature review, we analyze peer reviewed articles published in the nexus of the pandemic and sustainable HRM, identifying the dimensions and extent of research in this topical area of study. Our CEDEL model—complicator–exposer–disruptor–enabler– legitimizer—conceptualizes our understanding of the role of COVID-19 in sustainable HRM. This paper provides a framework from which future studies can benefit when investigating the impacts of COVID-19, and a comprehensive identification of future research avenues. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Making Python Code Idiomatic by Automatic Refactoring Non-Idiomatic Python Code with Pythonic Idioms

Compared to other programming languages (e.g., Java), Python has more idioms to make Python code concise and efficient. Although pythonic idioms are well accepted in the Python community, Python programmers are often faced with many challenges in using them, for example, being unaware of certain pythonic idioms or do not know how to use them properly. Based on an analysis of 7,638 Python repositories on GitHub, we find that non-idiomatic Python code that can be implemented with pythonic idioms occurs frequently and widely. Unfortunately, there is no tool for automatically refactoring such non-idiomatic code into idiomatic code. In this paper, we design and implement an automatic refactoring tool to make Python code idiomatic. We identify nine pythonic idioms by systematically contrasting the abstract syntax grammar of Python and Java. Then we define the syntactic patterns for detecting non-idiomatic code for each pythonic idiom. Finally, we devise atomic AST-rewriting operations and refactoring steps to refactor non-idiomatic code into idiomatic code. We test and review over 4,115 refactorings applied to 1,065 Python projects from GitHub, and submit 90 pull requests for the 90 randomly sampled refactorings to 84 projects. These evaluations confirm the high-accuracy, practicality and usefulness of our refactoring tool on real-world Python code. Our refactoring tool can be accessed at 47.242.131.128:5000.Comment: 12 pages, accepted to ESEC/FSE'202

A review of research on acoustic detection of heat exchanger tube

Leakage in heat exchanger tubes can result in unreliable products and dangerous situations, which could cause great economic losses. Along with fast development of modern acoustic detection technology, using acoustic signals to detect leakage in heat exchange tube has been gradually accepted and considered with great potential by both industrial and research societies. In order to further advance the development of acoustic signal detection technology and investigate better methods for leakage detection in heat exchange tube, in this paper, firstly, we conduct a short overview of the theory of acoustic signal detection on heat exchanger tube, which had already been continuously developed for a few decades by researchers worldwide. Thereafter, we further expound the advantages and limitations of acoustic signal detection technology on heat exchanger tube in four aspects: 1) principles of acoustic signal detection, 2) characteristics of sound wave propagation in heat exchanger tube, 3) methods of leakage detection, and 4) leakage localization in heat exchanger tube

LabelVizier: Interactive Validation and Relabeling for Technical Text Annotations

With the rapid accumulation of text data produced by data-driven techniques, the task of extracting "data annotations"--concise, high-quality data summaries from unstructured raw text--has become increasingly important. The recent advances in weak supervision and crowd-sourcing techniques provide promising solutions to efficiently create annotations (labels) for large-scale technical text data. However, such annotations may fail in practice because of the change in annotation requirements, application scenarios, and modeling goals, where label validation and relabeling by domain experts are required. To approach this issue, we present LabelVizier, a human-in-the-loop workflow that incorporates domain knowledge and user-specific requirements to reveal actionable insights into annotation flaws, then produce better-quality labels for large-scale multi-label datasets. We implement our workflow as an interactive notebook to facilitate flexible error profiling, in-depth annotation validation for three error types, and efficient annotation relabeling on different data scales. We evaluated the efficiency and generalizability of our workflow with two use cases and four expert reviews. The results indicate that LabelVizier is applicable in various application scenarios and assist domain experts with different knowledge backgrounds to efficiently improve technical text annotation quality.Comment: 10 pages, 5 figure