Reviewerly/exHarmony huggingface dataset:
Optimizing Feature Set for Click-Through Rate Prediction

Click-through prediction (CTR) models transform features into latent vectors and enumerate possible feature interactions to improve performance based on the input feature set. Therefore, when selecting an optimal feature set, we should consider the influence of both features and their interaction. However, most previous works focus on either feature field selection or only select feature interaction based on the fixed feature set to produce the feature set. The former restricts search space to the feature field, which is too coarse to determine subtle features. They also do not filter useless feature interactions, leading to higher computation costs and degraded model performance. The latter identifies useful feature interaction from all available features, resulting in many redundant features in the feature set. In this paper, we propose a novel method named OptFS to address these problems. To unify the selection of features and their interaction, we decompose the selection of each feature interaction into the selection of two correlated features. Such a decomposition makes the model end-to-end trainable given various feature interaction operations. By adopting feature-level search space, we set a learnable gate to determine whether each feature should be within the feature set. Because of the large-scale search space, we develop a learning-by-continuation training scheme to learn such gates. Hence, OptFS generates the feature set containing features that improve the final prediction results. Experimentally, we evaluate OptFS on three public datasets, demonstrating OptFS can optimize feature sets which enhance the model performance and further reduce both the storage and computational cost.
Learning like human annotators: Cyberbullying detection in lengthy social media sessions

The inherent characteristic of cyberbullying of being a recurrent attitude calls for the investigation of the problem by looking at social media sessions as a whole, beyond just isolated social media posts. However, the lengthy nature of social media sessions challenges the applicability and performance of session-based cyberbullying detection models. This is especially true when one aims to use state-of-the-art Transformer-based pre-trained language models, which only take inputs of a limited length. In this paper, we address this limitation of transformer models by proposing a conceptually intuitive framework called LS-CB, which enables cyberbullying detection from lengthy social media sessions. LS-CB relies on the intuition that we can effectively aggregate the predictions made by transformer models on smaller sliding windows extracted from lengthy social media sessions, leading to an overall improved performance. Our extensive experiments with six transformer models on two session-based datasets show that LS-CB consistently outperforms three types of competitive baselines including state-of-the-art cyberbullying detection models. In addition, we conduct a set of qualitative analyses to validate the hypotheses that cyberbullying incidents can be detected through aggregated analysis of smaller chunks derived from lengthy social media sessions (H1), and that cyberbullying incidents can occur at different points of the session (H2), hence positing that frequently used text truncation strategies are suboptimal compared to relying on holistic views of sessions. Our research in turn opens an avenue for fine-grained cyberbullying detection within sessions in future work.
Improving Recommendation Fairness via Data Augmentation

Collaborative filtering based recommendation learns users’ preferences from all users’ historical behavior data, and has been popular to facilitate decision making. Recently, the fairness issue of recommendation has become more and more essential. A recommender system is considered unfair when it does not perform equally well for different user groups according to users’ sensitive attributes (e.g., gender, race). Plenty of methods have been proposed to alleviate unfairness by optimizing a predefined fairness goal or changing the distribution of unbalanced training data. However, they either suffered from the specific fairness optimization metrics or relied on redesigning the current recommendation architecture. In this paper, we study how to improve recommendation fairness from the data augmentation perspective. The recommendation model amplifies the inherent unfairness of imbalanced training data. We augment imbalanced training data towards balanced data distribution to improve fairness. Given each real original user-item interaction record, we propose the following hypotheses for augmenting the training data: each user in one group has a similar item preference (click or non-click) as the item preference of any user in the remaining group. With these hypotheses, we generate “fake" interaction behaviors to complement the original training data. After that, we design a bi-level optimization target, with the inner optimization generates better fake data to augment training data with our hypotheses, and the outer one updates the recommendation model parameters based on the augmented training data. The proposed framework is generally applicable to any embedding-based recommendation, and does not need to pre-define a fairness metric. Extensive experiments on two real-world datasets clearly demonstrate the superiority of our proposed framework. We publish the source code at https://github.com/newlei/FDA.
FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures

Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of the world’s web traffic, making a great data source for various machine learning (ML) applications, particularly federated learning (FL) which offers a promising solution to privacy-preserving ML feeding on these data. FL allows edge mobile and WoT devices to train a shared global ML model under the orchestration of a central parameter server. In the real world, due to resource heterogeneity, these edge devices often train different versions of models (e.g., VGG-16 and VGG-19) or different ML models (e.g., VGG and ResNet) for the same ML task (e.g., computer vision and speech recognition). Existing FL schemes have assumed that participating edge devices share a common model architecture, and thus cannot facilitate FL across edge devices with heterogeneous ML model architectures. We explored this architecture heterogeneity challenge and found that FL can and should accommodate these edge devices to improve model accuracy and accelerate model training. This paper presents our findings and FlexiFed, a novel scheme for FL across edge devices with heterogeneous model architectures, and three model aggregation strategies for accommodating architecture heterogeneity under FlexiFed. Experiments with four widely-used ML models on four public datasets demonstrate 1) the usefulness of FlexiFed; and 2) that compared with the state-of-the-art FL scheme, FlexiFed improves model accuracy by 2.6%-9.7% and accelerates model convergence by 1.24 × -4.04 ×.
Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum

Graph anomaly detection (GAD) suffers from heterophily — abnormal nodes are sparse so that they are connected to vast normal nodes. The current solutions upon Graph Neural Networks (GNNs) blindly smooth the representation of neiboring nodes, thus undermining the discriminative information of the anomalies. To alleviate the issue, recent studies identify and discard inter-class edges through estimating and comparing the node-level representation similarity. However, the representation of a single node can be misleading when the prediction error is high, thus hindering the performance of the edge indicator.
Beyond Two-Tower: Attribute Guided Representation Learning for Candidate Retrieval

Candidate retrieval is a key part of the modern search engines whose goal is to find candidate items that are semantically related to the query from a large item pool. The core difference against the later ranking stage is the requirement of low latency. Hence, two-tower structure with two parallel yet independent encoder for both query and item is prevalent in many systems. In these efforts, the semantic information of a query and a candidate item is fed into the corresponding encoder and then use their representations for retrieval. With the popularity of pre-trained semantic models, the state-of-the-art for semantic retrieval tasks has achieved the significant performance gain.
Negative Data in Data Sets for Machine Learning Training

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTNegative Data in Data Sets for Machine Learning TrainingMichael P. MaloneyMichael P. MaloneyDepartment of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United StatesMore by Michael P. Maloneyhttps://orcid.org/0009-0001-3385-7567, Connor W. ColeyConnor W. ColeyDepartment of Chemical Engineering and Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United StatesMore by Connor W. Coleyhttps://orcid.org/0000-0002-8271-8723, Samuel GenhedenSamuel GenhedenMolecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Pepparedsleden 1, SE-431 83 Mölndal, SwedenMore by Samuel Genhedenhttps://orcid.org/0000-0002-7624-7363, Nessa CarsonNessa CarsonEarly Chemical Development, Pharmaceutical Sciences, R&D, AstraZeneca, Macclesfield SK10 2NA, U.K.More by Nessa Carsonhttps://orcid.org/0000-0002-2769-1775, Paul HelquistPaul HelquistDepartment of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United StatesMore by Paul Helquisthttps://orcid.org/0000-0003-4380-9566, Per-Ola NorrbyPer-Ola NorrbyData Science and Modelling, Pharmaceutical Sciences, R&D, AstraZeneca, Gothenburg, Pepparedsleden 1, SE-431 83 Mölndal, SwedenMore by Per-Ola Norrbyhttps://orcid.org/0000-0002-2419-0705, and Olaf Wiest*Olaf WiestDepartment of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States*Email: [email protected]More by Olaf Wiesthttps://orcid.org/0000-0001-9316-7720Cite this: Org. Lett. 2023, 25, 17, 2945–2947Publication Date (Web):April 26, 2023Publication History Received19 April 2023Published online26 April 2023Published inissue 5 May 2023https://doi.org/10.1021/acs.orglett.3c01282Copyright © Published 2023 by American Chemical SocietyRequest reuse permissions This publication is free to access through this site. Learn MoreArticle Views7016Altmetric-Citations2LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (945 KB) Get e-AlertscloseSUBJECTS:Addition reactions,Chemical reactions,Machine learning,Materials,Organic synthesis Get e-Alerts
Deep Learning for Head Pose Estimation: A Survey

Abstract Head pose estimation (HPE) is an active and popular area of research. Over the years, many approaches have constantly been developed, leading to a progressive improvement in accuracy; nevertheless, head pose estimation remains an open research topic, especially in unconstrained environments. In this paper, we will review the increasing amount of available datasets and the modern methodologies used to estimate orientation, with a special attention to deep learning techniques. We will discuss the evolution of the field by proposing a classification of head pose estimation methods, explaining their advantages and disadvantages, and highlighting the different ways deep learning techniques have been used in the context of HPE. An in-depth performance comparison and discussion is presented at the end of the work. We also highlight the most promising research directions for future investigations on the topic.
Compact interactive dual-branch network for real-time semantic segmentation

Abstract The traditional complete dual-branch structure is effective for semantic segmentation tasks. However, it is redundant in some sense. Moreover, the simple additive fusion of the features from the two branches may not achieve the satisfactory performance. To alleviate these two problems, in this paper we propose an efficient compact interactive dual-branch network (CIDNet) for real-time semantic segmentation. Specifically, we first build a compact interactive dual-branch structure by constructing a compact detail branch and a semantic branch. Furthermore, we build a detail-semantic interactive module to fuse several specific stages of the two branches in the backbone network with the corresponding stages of the detail resolution branch. Finally, we propose a dual-branch contextual attention fusion module to deeply fuse the extracted features and predict the final segmentation result. Extensive experiments on Cityscapes and CamVid dataset demonstrate that the proposed CIDNet achieve satisfactory trade-off between segmentation accuracy and inference speed, and outperforms 20 representative real-time semantic segmentation methods.
Explainable AI for SE: Challenges and Future Directions

In recent years, artificial intelligence/machine learning (AI/ML) have been widely used in software engineering (SE) to improve developer productivity, software quality, and decision making. This includes well-known tools for code completion (for example, GitHub’s Copilot) but also code search; automated task recommendation; automated developer recommendation; automated defect/vulnerability/malware prediction, detection, localization, and repair; and many other purposes.
Collaborative Work Enabled by Immersive Environments

Abstract Digital transformation facilitates new methods for remote collaboration while shaping a new understanding of working together. In this chapter, we consider global collaboration in the context of digital transformation, discuss the role of Collaborative Virtual Environments (CVEs) within the transformation process, present an overview of the state of CVEs and go into more detail on significant challenges in CVEs by providing recent approaches from research.
Performance Analysis of Machine Learning Algorithm on Cloud Platforms: AWS vs Azure vs GCP

The significance of adopting cloud technology in enterprises is accelerating and becoming ubiquitous in business and industry. Due to migrating the on-premises servers and services into cloud, companies can leverage several advantages such as cost optimization, high performance, and flexible system maintenance, to name a few. As the data volume, variety, veracity, and velocity are rising tremendously, adopting machine learning (ML) solutions in the cloud platform bring benefits from ML model building through model evaluation more efficiently and accurately. This study will provide a comparative performance analysis of the three big cloud vendors: Amazon Web Service (AWS), Microsoft Azure and Google Cloud Platform (GCP) by building regression models in each of the platforms. For validation purposes, i.e., training and testing the models, five different standard datasets from the UCI machine learning repository have been employed. This work utilizes the ML services of AWS Sage maker, Azure ML Studio and Google Big Query for conducting the experiments. Model evaluation criteria here include measuring R-squared values for each platform, calculating the error metrics (Mean Squared Error, Mean Absolute Error, Root Mean Squared Error etc.) and comparing the results to determine the best performing cloud provider in terms of ML service. The study concludes with presenting a comparative taxonomy of regression models across the three platforms.
Design and Development of a Short-Term Photovoltaic Power Output Forecasting Method Based on Random Forest, Deep Neural Network and LSTM Using Readily Available Weather Features

Renewable energy sources (RES) are an essential part of building a more sustainable future, with higher diversity of clean energy, reduced emissions and less dependence on finite fossil fuels such as coal, oil and natural gas. The advancements in the renewable energy sources domain bring higher hardware efficiency and lower costs, which improves the likelihood of wider RES adoption. However, integrating renewables such as photovoltaic (PV) systems in the current grid is still a major challenge. The main reason is the volatile, intermittent nature of RES, which increases the complexity of the grid management and maintenance. Having access to accurate PV power output forecasting could reduce the number of power supply disruptions, improve the planning of the available and reserve capacities and decrease the management and operational costs. In this context, this paper explores and evaluates three Artificial Intelligence (AI) methods - random forest (RF), deep neural network (DNN) and long short-term memory network (LSTM), which are applied for the task of short-term PV output power forecasting. Following a statistical forecasting approach, the selected models are trained on weather and PV output data collected in Berlin, Germany. The assembled data set contains predominantly broadly accessible weather features, which makes the proposed approach more cost efficient and easily applicable even for geographic locations without access to specialized hardware or hard-to-obtain input features. The performance achieved by two of the selected algorithms indicates that the RF and the DNN models are able to generate accurate solar power forecasts and are also able to handle sudden changes and shifts in the PV power output.
Domain-invariant information aggregation for domain generalization semantic segmentation

Domain generalization semantic segmentation methods aim to generalize well on out-of-distribution scenes, which is crucial for real-world applications. Recent works focus on learning domain-invariant content information by using normalization, whitening, and domain randomization to remove style information. Although these methods improve the performance on out-of-distribution scenes to some extent, they ignore the learning of edge and semantic layout information. The edge information describes the shape and boundary of an object and the semantic layout information contains the common sense priors (e.g., the spatial position of objects). For one thing, we observe that the shape of the same object with different styles is domain-invariant in the edge map. For another, we observe that the common sense priors in the semantic layout information of different scenes are domain-invariant. Motivated by these observations, a novel approach is proposed for domain generalization semantic segmentation by using the edge and semantic layout information. Specifically, the proposed approach contains the edge reconstruction module (ERM), the semantic layout reconstruction module (SLRM), and the triple information aggregation module (TIAM). The ERM and SLRM aim to explicitly learn the edge and semantic layout information. The TIAM aggregates the edge and semantic layout information to refine the content information. Extensive experiments demonstrate that our approach achieves superior performance over current approaches on domain generalization segmentation tasks. The source code will be released at https://github.com/seabearlmx/DIIA.
Translating AI to Clinical Practice: Overcoming Data Shift with Explainability

To translate artificial intelligence (AI) algorithms into clinical practice requires generalizability of models to real-world data. One of the main obstacles to generalizability is data shift, a data distribution mismatch between model training and real environments. Explainable AI techniques offer tools to detect and mitigate the data shift problem and develop reliable AI for clinical practice. Most medical AI is trained with datasets gathered from limited environments, such as restricted disease populations and center-dependent acquisition conditions. The data shift that commonly exists in the limited training set often causes a significant performance decrease in the deployment environment. To develop a medical application, it is important to detect potential data shift and its impact on clinical translation. During AI training stages, from premodel analysis to in-model and post hoc explanations, explainability can play a key role in detecting model susceptibility to data shift, which is otherwise hidden because the test data have the same biased distribution as the training data. Performance-based model assessments cannot effectively distinguish the model overfitting to training data bias without enriched test sets from external environments. In the absence of such external data, explainability techniques can aid in translating AI to clinical practice as a tool to detect and mitigate potential failures due to data shift. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.
MEMS Devices-Based Hand Gesture Recognition via Wearable Computing

Gesture recognition has found widespread applications in various fields, such as virtual reality, medical diagnosis, and robot interaction. The existing mainstream gesture-recognition methods are primarily divided into two categories: inertial-sensor-based and camera-vision-based methods. However, optical detection still has limitations such as reflection and occlusion. In this paper, we investigate static and dynamic gesture-recognition methods based on miniature inertial sensors. Hand-gesture data are obtained through a data glove and preprocessed using Butterworth low-pass filtering and normalization algorithms. Magnetometer correction is performed using ellipsoidal fitting methods. An auxiliary segmentation algorithm is employed to segment the gesture data, and a gesture dataset is constructed. For static gesture recognition, we focus on four machine learning algorithms, namely support vector machine (SVM), backpropagation neural network (BP), decision tree (DT), and random forest (RF). We evaluate the model prediction performance through cross-validation comparison. For dynamic gesture recognition, we investigate the recognition of 10 dynamic gestures using Hidden Markov Models (HMM) and Attention-Biased Mechanisms for Bidirectional Long- and Short-Term Memory Neural Network Models (Attention-BiLSTM). We analyze the differences in accuracy for complex dynamic gesture recognition with different feature datasets and compare them with the prediction results of the traditional long- and short-term memory neural network model (LSTM). Experimental results demonstrate that the random forest algorithm achieves the highest recognition accuracy and shortest recognition time for static gestures. Moreover, the addition of the attention mechanism significantly improves the recognition accuracy of the LSTM model for dynamic gestures, with a prediction accuracy of 98.3%, based on the original six-axis dataset.