Reviewerly/exHarmony huggingface dataset:
Deep Learning-Based Sequential Recommender Systems: Concepts, Algorithms, and Evaluations

What is sequential recommendation? What challenges are traditional sequential recommendation models facing? How to address these challenges in sequential recommendation using advanced deep learning (DL) techniques? What factors do affect the performance of a DL-based sequential recommendation system? And how to utilize these factors to improve DL models? In this tutorial, we will carefully answer these questions by combining DL techniques with sequential recommendation, and provide a comprehensive overview of DL-based sequential recommender system. Specifically, we propose a novel classification framework for sequential recommendation tasks, with which we systematically introduce representative DL-based algorithms for different sequential recommendation scenarios. We further summarize the potentially influential factors of DL-based sequential recommendation, and thoroughly demonstrate their effects via a carefully designed experimental framework, which will be of great help to future research.
Local Decision Pitfalls in Interactive Machine Learning

Tools for Interactive Machine Learning (IML) enable end users to update models in a “rapid, focused, and incremental”—yet local—manner. In this work, we study the question of local decision making in an IML context around feature selection for a sentiment classification task. Specifically, we characterize the utility of interactive feature selection through a combination of human-subjects experiments and computational simulations. We find that, in expectation, interactive modification fails to improve model performance and may hamper generalization due to overfitting. We examine how these trends are affected by the dataset, learning algorithm, and the training set size. Across these factors we observe consistent generalization issues. Our results suggest that rapid iterations with IML systems can be dangerous if they encourage local actions divorced from global context, degrading overall model performance. We conclude by discussing the implications of our feature selection results to the broader area of IML systems and research.
Small-Scale Pedestrian Detection Based on Deep Neural Network

Pedestrian detection is a crucial component for intelligent transport system and advanced driver assistance system. In recent years, pedestrian detection methods have achieved higher accuracy. However, the existing algorithms are insufficient for small-scale pedestrian detection that is relatively far from cameras in practical applications. In this paper, we propose a novel deep small-scale sense network (termed SSN) for small-scale pedestrian detection. The proposed architecture could generate some proposal regions which are more effective to detect small-scale pedestrians. Furthermore, we design a novel loss function based on cross entropy loss to increase the loss contribution from hard-to-detect small-scale pedestrians. In addition, a novel evaluation metric is introduced, which can measure the location precision of the pedestrian detection methods. In addition an Asian pedestrian detection dataset named VIP pedestrian dataset is constructed from various road condition data. Our method achieves good detection performance on Caltech pedestrian dataset and our VIP pedestrian dataset.
Automatic recognition of different types of acute leukaemia in peripheral blood by image analysis

Aims Morphological differentiation among different blast cell lineages is a difficult task and there is a lack of automated analysers able to recognise these abnormal cells. This study aims to develop a machine learning approach to predict the diagnosis of acute leukaemia using peripheral blood (PB) images. Methods A set of 442 smears was analysed from 206 patients. It was split into a training set with 75% of these smears and a testing set with the remaining 25%. Colour clustering and mathematical morphology were used to segment cell images, which allowed the extraction of 2,867 geometric, colour and texture features. Several classification techniques were studied to obtain the most accurate classification method. Afterwards, the classifier was assessed with the images of the testing set . The final strategy was to predict the patient’s diagnosis using the PB smear, and the final assessment was done with the cell images of the smears of the testing set . Results The highest classification accuracy was achieved with the selection of 700 features with linear discriminant analysis. The overall classification accuracy for the six groups of cell types was 85.8%, while the overall classification accuracy for individual smears was 94% as compared with the true confirmed diagnosis. Conclusions The proposed method achieves a high diagnostic precision in the recognition of different types of blast cells among other mononuclear cells circulating in blood. It is the first encouraging step towards the idea of being a diagnostic support tool in the future.
Efficient Obstacle Rearrangement for Object Manipulation Tasks in Cluttered Environments

We present an algorithm that produces a plan for relocating obstacles in order to grasp a target in clutter by a robotic manipulator without collisions. We consider configurations where objects are densely populated in a constrained and confined space. Thus, there exists no collision-free path for the manipulator without relocating obstacles. Since the problem of planning for object rearrangement has shown to be NP-hard, it is difficult to perform manipulation tasks efficiently which could frequently happen in service domains (e.g., taking out a target from a shelf or a fridge). Our proposed planner employs a collision avoidance scheme which has been widely used in mobile robot navigation. The planner determines an obstacle to be removed quickly in real time. It also can deal with dynamic changes in the configuration (e.g., changes in object poses). Our method is shown to be complete and runs in polynomial time. Experimental results in a realistic simulated environment show that our method improves up to 31% of the execution time compared to other competitors.
Reducing Noise in GAN Training with Variance Reduced Extragradient

We study the effect of the stochastic gradient noise on the training of generative adversarial networks (GANs) and show that it can prevent the convergence of standard game optimization methods, while the batch version converges. We address this issue with a novel stochastic variance-reduced extragradient (SVRE) optimization algorithm, which for a large class of games improves upon the previous convergence rates proposed in the literature. We observe empirically that SVRE performs similarly to a batch method on MNIST while being computationally cheaper, and that SVRE yields more stable GAN training on standard datasets.
Self-Critical Reasoning for Robust Visual Question Answering

Visual Question Answering (VQA) deep-learning systems tend to capture superficial statistical correlations in the training data because of strong language priors and fail to generalize to test data with a significantly different question-answer (QA) distribution. To address this issue, we introduce a self-critical training objective that ensures that visual explanations of correct answers match the most influential image regions more than other competitive answer candidates. The influential regions are either determined from human visual/textual explanations or automatically from just significant words in the question and answer. We evaluate our approach on the VQA generalization task using the VQA-CP dataset, achieving a new state-of-the-art i.e. 49.5\% using textual explanations and 48.5\% using automatically
Sim2real transfer learning for 3D human pose estimation: motion to the rescue

Synthetic visual data can provide practicically infinite diversity and rich labels, while avoiding ethical issues with privacy and bias. However, for many tasks, current models trained on synthetic data generalize poorly to real data. The task of 3D human pose estimation is a particularly interesting example of this sim2real problem, because learning-based approaches perform reasonably well given real training data, yet labeled 3D poses are extremely difficult to obtain in the wild, limiting scalability. In this paper, we show that standard neural-network approaches, which perform poorly when trained on synthetic RGB images, can perform well when the data is pre-processed to extract cues about the person’s motion, notably as optical flow and the motion of 2D keypoints. Therefore, our results suggest that motion can be a simple way to bridge a sim2real gap when video is available. We evaluate on the 3D Poses in the Wild dataset, the most challenging modern benchmark for 3D pose estimation, where we show full 3D mesh recovery that is on par with state-of-the-art methods trained on real 3D sequences, despite training only on synthetic humans from the SURREAL dataset.
Novel positional encodings to enable tree-based transformers

Neural models optimized for tree-based problems are of great value in tasks like SQL query extraction and program synthesis. On sequence-structured data, transformers have been shown to learn relationships across arbitrary pairs of positions more reliably than recurrent models. Motivated by this property, we propose a method to extend transformers to tree-structured data, enabling sequence-to-tree, tree-to-sequence, and tree-to-tree mappings. Our approach abstracts the transformer's sinusoidal positional encodings, allowing us to instead use a novel positional encoding scheme to represent node positions within trees. We evaluated our model in tree-to-tree program translation and sequence-to-tree semantic parsing settings, achieving superior performance over both sequence-to-sequence transformers and state-of-the-art tree-based LSTMs on several datasets. In particular, our results include a 22% absolute increase in accuracy on a JavaScript to CoffeeScript translation dataset.
Mining Twitter Data for Depression Detection

Health care twitter analysis deals with the health related tweets through sentimental analysis by the patients themselves. The application of sentiment analysis has grown enormously. Its application in health care has great potential to analyze and improve the health of a country. In this paper, we try to analyze health tweets for Depression, Anxiety from the mixed tweets by using Multinomial Naive Bayes and Support Vector Regression (SVR) Algorithm as a classifier.
Forecasting of Photovoltaic Solar Power Production Using LSTM Approach

Solar-based energy is becoming one of the most promising sources for producing power for residential, commercial, and industrial applications.Energy production based on solar photovoltaic (PV) systems has gained much attention from researchers and practitioners recently due to its desirable characteristics.However, the main difficulty in solar energy production is the volatility intermittent of photovoltaic system power generation, which is mainly due to weather conditions.For the large-scale solar farms, the power imbalance of the photovoltaic system may cause a significant loss in their economical profit.Accurate forecasting of the power output of PV systems in a short term is of great importance for daily/hourly efficient management of power grid production, delivery, and storage, as well as for decision-making on the energy market.The aim of this chapter is to provide reliable short-term forecasting of power generation of PV solar systems.Specifically, this chapter presents a long short-term memory (LSTM)-based deep learning approach for forecasting power generation of a PV system.This is motivated by the desirable features of LSTM to describe dependencies in time series data.The performance of the algorithm is evaluated using data from a 9 MWp grid-connected plant.Results show promising power forecasting results of LSTM.
Optimal design of real‐size building structures using quantum‐behaved developed swarm optimizer

Summary In this paper, the quantum‐behaved developed swarm optimizer is proposed for optimal design of real‐size building structures in which the quantum computing is introduced into the standard developed swarm optimizer. In this method, the position‐updating process for the search agents is conducted by simultaneous utilization of the so far best position of all particles, center of mass of all particles, so far best position of each particle, and the mean best position of all particles in which the first two of these aspects satisfy the exploration phase of the algorithm, whereas the other two are utilized for improving the exploitation phase of the proposed method. In order to evaluate the capability of the proposed method in dealing with difficult optimization problems, three real‐size building structures are considered, namely, a 10‐story building with 1,026 structural members, a 20‐story building with 3,860 members, and a 60‐story building with 8,272 members. The overall performance of the proposed quantum‐behaved developed swarm optimizer is compared with that of the standard developed swarm optimizer and other approaches. The obtained results proved that the proposed method is capable of providing better results for the considered examples than are the other algorithms.
Semi-Reference Sonar Image Quality Assessment Based on Task and Visual Perception

In submarine and underwater detection tasks, conventional optical imaging and analysis methods are not universally applicable due to the limited penetration depth of visible light. Instead, sonar imaging has become a preferred alternative. However, the capture and transmission conditions in complicated and dynamic underwater environments inevitably lead to visual quality degradation of sonar images, which might also impede further recognition, analysis and understanding. To measure this quality decrease and provide a solid quality indicator for sonar image enhancement, we propose a task- and perception-oriented sonar image quality assessment (TPSIQA) method, in which a semi-reference (SR) approach is applied to adapt to the limited bandwidth of underwater communication channels. In particular, we exploit reduced visual features that are critical for both human perception of and object recognition in sonar images. The final quality indicator is obtained through ensemble learning, which aggregates an optimal subset of multiple base learners to achieve both high accuracy and a high generalization ability. In this way, we are able to develop a compact but generalized quality metric using a small database of sonar images. Experimental results demonstrate competitive performance, high efficiency, and strong robustness of our method compared to the latest available image quality metrics.
Improved Noise and Attack Robustness for Semantic Segmentation by Using Multi-Task Training with Self-Supervised Depth Estimation

While current approaches for neural network training often aim at improving performance, less focus is put on training methods aiming at robustness towards varying noise conditions or directed attacks by adversarial examples. In this paper, we propose to improve robustness by a multi-task training, which extends supervised semantic segmentation by a self-supervised monocular depth estimation on unlabeled videos. This additional task is only performed during training to improve the semantic segmentation model's robustness at test time under several input perturbations. Moreover, we even find that our joint training approach also improves the performance of the model on the original (supervised) semantic segmentation task. Our evaluation exhibits a particular novelty in that it allows to mutually compare the effect of input noises and adversarial attacks on the robustness of the semantic segmentation. We show the effectiveness of our method on the Cityscapes dataset, where our multi-task training approach consistently outperforms the single-task semantic segmentation baseline in terms of both robustness vs. noise and in terms of adversarial attacks, without the need for depth labels in training.
Learning Sparse Nonparametric DAGs

We develop a framework for learning sparse nonparametric directed acyclic graphs (DAGs) from data. Our approach is based on a recent algebraic characterization of DAGs that led to a fully continuous program for score-based learning of DAG models parametrized by a linear structural equation model (SEM). We extend this algebraic characterization to nonparametric SEM by leveraging nonparametric sparsity based on partial derivatives, resulting in a continuous optimization problem that can be applied to a variety of nonparametric and semiparametric models including GLMs, additive noise models, and index models as special cases. Unlike existing approaches that require specific modeling choices, loss functions, or algorithms, we present a completely general framework that can be applied to general nonlinear models (e.g. without additive noise), general differentiable loss functions, and generic black-box optimization routines. The code is available at this https URL.
Study on short-term photovoltaic power prediction model based on the Stacking ensemble learning

As solar photovoltaic (PV) power generation is very sensitive to environmental changes, with the characteristics of randomness and intermittent, a new PV power prediction model based on Stacking ensemble learning method is proposed in this paper. Firstly, a PV power prediction model based on Stacking of multiple machine learning algorithms is established. It considers the difference of training principles and characteristic contribution analysis of different algorithms, and gives full play to the advantages of each model. Secondly, the single model and Stacking model are trained iteratively using the operating state parameters and meteorological parameters of PV panels provided by the data collection system (DCS) of the PV power station. The model can predict the future PV power generation. The eXtreme Gradient Boosting (XGBoost) with superior performance in single model is selected to compare with Stacking. The prediction results show that root mean square error (RMSE) of Stacking is 0.1007, 1.84% lower than that of XGBoost, and the score is 90.85, 1.49 higher than that of XGBoost. Besides, the PV power prediction model proposed is of great significance to distributed generation grid connection, power grid stability, unit optimization, economic dispatch and supervision.
An Efficient and Effective Framework for Session-based Social Recommendation

In many applications of session-based recommendation, social networks are usually available. Since users' interests are influenced by their friends, recommender systems can leverage social networks to better understand their users' preferences and thus provide more accurate recommendations. However, existing methods for session-based social recommendation are not efficient. To predict the next item of a user's ongoing session, the methods need to process many additional sessions of the user's friends to capture social influences, while non-social-aware methods (i.e., those without using social networks) only need to process one single session. To solve the efficiency issue, we propose an efficient framework for session-based social recommendation. In the framework, first, a heterogeneous graph neural network is used to learn user and item representations that integrate the knowledge from social networks. Then, to generate predictions, only the user and item representations relevant to the current session are passed to a non-social-aware model. During inference, since the user and item representations can be precomputed, the overall model runs as fast as the original non-social-aware model, while it can achieve better performance by leveraging the knowledge from social networks. Apart from being efficient, our framework has two additional advantages. First, the framework is flexible because it is compatible with any existing non-social-aware models and can easily incorporate more knowledge other than social networks. Second, our framework can capture cross-session item transitions while existing methods can only capture intra-session item transitions. Extensive experiments conducted on three public datasets demonstrate the effectiveness and efficiency of the proposed framework. Our code is available at https://github.com/twchen/SEFrame.
Unlocking the Potential of Ordinary Classifier: Class-specific Adversarial Erasing Framework for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation (WSSS) using image-level classification labels usually utilizes the Class Activation Maps (CAMs) to localize objects of interest in images. While pointing out that CAMs only highlight the most discriminative regions of the classes of interest, adversarial erasing (AE) methods have been proposed to further explore the less discriminative regions. In this paper, we review the potential of the pre-trained classifier which is trained on the raw images. We experimentally verify that the ordinary classifier <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> already has the capability to activate the less discriminative regions if the most discriminative regions are erased to some extent. Based on that, we propose a class-specific AE-based framework that fully exploits the potential of an ordinary classifier. Our framework (1) adopts the ordinary classifier to notify the regions to be erased and (2) generates a class-specific mask for erasing by randomly sampling a single specific class to be erased (target class) among the existing classes on the image for obtaining more precise CAMs. Specifically, with the guidance of the ordinary classifier, the proposed CAMs Generation Network (CGNet) is enforced to generate a CAM of the target class while constraining the CAM not to intrude the object regions of the other classes. Along with the pseudo-labels refined from our CAMs, we achieve the state-of-the-art WSSS performance on both PASCAL VOC 2012 and MS-COCO dataset only with image-level supervision. The code is available at https://github.com/KAIST-vilab/OC-CSE.